diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..366e0c864bf6ece4c401fe827430e07fd7fc4a09 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import os +from io import BytesIO +from typing import IO + +from . import ExifTags, Image, ImageFile + +try: + from . import _avif + + SUPPORTED = True +except ImportError: + SUPPORTED = False + +# Decoder options as module globals, until there is a way to pass parameters +# to Image.open (see https://github.com/python-pillow/Pillow/issues/569) +DECODE_CODEC_CHOICE = "auto" +DEFAULT_MAX_THREADS = 0 + + +def get_codec_version(codec_name: str) -> str | None: + versions = _avif.codec_versions() + for version in versions.split(", "): + if version.split(" [")[0] == codec_name: + return version.split(":")[-1].split(" ")[0] + return None + + +def _accept(prefix: bytes) -> bool | str: + if prefix[4:8] != b"ftyp": + return False + major_brand = prefix[8:12] + if major_brand in ( + # coding brands + b"avif", + b"avis", + # We accept files with AVIF container brands; we can't yet know if + # the ftyp box has the correct compatible brands, but if it doesn't + # then the plugin will raise a SyntaxError which Pillow will catch + # before moving on to the next plugin that accepts the file. + # + # Also, because this file might not actually be an AVIF file, we + # don't raise an error if AVIF support isn't properly compiled. + b"mif1", + b"msf1", + ): + if not SUPPORTED: + return ( + "image file could not be identified because AVIF support not installed" + ) + return True + return False + + +def _get_default_max_threads() -> int: + if DEFAULT_MAX_THREADS: + return DEFAULT_MAX_THREADS + if hasattr(os, "sched_getaffinity"): + return len(os.sched_getaffinity(0)) + else: + return os.cpu_count() or 1 + + +class AvifImageFile(ImageFile.ImageFile): + format = "AVIF" + format_description = "AVIF image" + __frame = -1 + + def _open(self) -> None: + if not SUPPORTED: + msg = "image file could not be opened because AVIF support not installed" + raise SyntaxError(msg) + + if DECODE_CODEC_CHOICE != "auto" and not _avif.decoder_codec_available( + DECODE_CODEC_CHOICE + ): + msg = "Invalid opening codec" + raise ValueError(msg) + self._decoder = _avif.AvifDecoder( + self.fp.read(), + DECODE_CODEC_CHOICE, + _get_default_max_threads(), + ) + + # Get info from decoder + self._size, self.n_frames, self._mode, icc, exif, exif_orientation, xmp = ( + self._decoder.get_info() + ) + self.is_animated = self.n_frames > 1 + + if icc: + self.info["icc_profile"] = icc + if xmp: + self.info["xmp"] = xmp + + if exif_orientation != 1 or exif: + exif_data = Image.Exif() + if exif: + exif_data.load(exif) + original_orientation = exif_data.get(ExifTags.Base.Orientation, 1) + else: + original_orientation = 1 + if exif_orientation != original_orientation: + exif_data[ExifTags.Base.Orientation] = exif_orientation + exif = exif_data.tobytes() + if exif: + self.info["exif"] = exif + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + + # Set tile + self.__frame = frame + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 0, self.mode)] + + def load(self) -> Image.core.PixelAccess | None: + if self.tile: + # We need to load the image data for this frame + data, timescale, pts_in_timescales, duration_in_timescales = ( + self._decoder.get_frame(self.__frame) + ) + self.info["timestamp"] = round(1000 * (pts_in_timescales / timescale)) + self.info["duration"] = round(1000 * (duration_in_timescales / timescale)) + + if self.fp and self._exclusive_fp: + self.fp.close() + self.fp = BytesIO(data) + + return super().load() + + def load_seek(self, pos: int) -> None: + pass + + def tell(self) -> int: + return self.__frame + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + info = im.encoderinfo.copy() + if save_all: + append_images = list(info.get("append_images", [])) + else: + append_images = [] + + total = 0 + for ims in [im] + append_images: + total += getattr(ims, "n_frames", 1) + + quality = info.get("quality", 75) + if not isinstance(quality, int) or quality < 0 or quality > 100: + msg = "Invalid quality setting" + raise ValueError(msg) + + duration = info.get("duration", 0) + subsampling = info.get("subsampling", "4:2:0") + speed = info.get("speed", 6) + max_threads = info.get("max_threads", _get_default_max_threads()) + codec = info.get("codec", "auto") + if codec != "auto" and not _avif.encoder_codec_available(codec): + msg = "Invalid saving codec" + raise ValueError(msg) + range_ = info.get("range", "full") + tile_rows_log2 = info.get("tile_rows", 0) + tile_cols_log2 = info.get("tile_cols", 0) + alpha_premultiplied = bool(info.get("alpha_premultiplied", False)) + autotiling = bool(info.get("autotiling", tile_rows_log2 == tile_cols_log2 == 0)) + + icc_profile = info.get("icc_profile", im.info.get("icc_profile")) + exif_orientation = 1 + if exif := info.get("exif"): + if isinstance(exif, Image.Exif): + exif_data = exif + else: + exif_data = Image.Exif() + exif_data.load(exif) + if ExifTags.Base.Orientation in exif_data: + exif_orientation = exif_data.pop(ExifTags.Base.Orientation) + exif = exif_data.tobytes() if exif_data else b"" + elif isinstance(exif, Image.Exif): + exif = exif_data.tobytes() + + xmp = info.get("xmp") + + if isinstance(xmp, str): + xmp = xmp.encode("utf-8") + + advanced = info.get("advanced") + if advanced is not None: + if isinstance(advanced, dict): + advanced = advanced.items() + try: + advanced = tuple(advanced) + except TypeError: + invalid = True + else: + invalid = any(not isinstance(v, tuple) or len(v) != 2 for v in advanced) + if invalid: + msg = ( + "advanced codec options must be a dict of key-value string " + "pairs or a series of key-value two-tuples" + ) + raise ValueError(msg) + + # Setup the AVIF encoder + enc = _avif.AvifEncoder( + im.size, + subsampling, + quality, + speed, + max_threads, + codec, + range_, + tile_rows_log2, + tile_cols_log2, + alpha_premultiplied, + autotiling, + icc_profile or b"", + exif or b"", + exif_orientation, + xmp or b"", + advanced, + ) + + # Add each frame + frame_idx = 0 + frame_duration = 0 + cur_idx = im.tell() + is_single_frame = total == 1 + try: + for ims in [im] + append_images: + # Get number of frames in this image + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + + # Make sure image mode is supported + frame = ims + rawmode = ims.mode + if ims.mode not in {"RGB", "RGBA"}: + rawmode = "RGBA" if ims.has_transparency_data else "RGB" + frame = ims.convert(rawmode) + + # Update frame duration + if isinstance(duration, (list, tuple)): + frame_duration = duration[frame_idx] + else: + frame_duration = duration + + # Append the frame to the animation encoder + enc.add( + frame.tobytes("raw", rawmode), + frame_duration, + frame.size, + rawmode, + is_single_frame, + ) + + # Update frame index + frame_idx += 1 + + if not save_all: + break + + finally: + im.seek(cur_idx) + + # Get the final output from the encoder + data = enc.finish() + if data is None: + msg = "cannot write file as AVIF (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +Image.register_open(AvifImageFile.format, AvifImageFile, _accept) +if SUPPORTED: + Image.register_save(AvifImageFile.format, _save) + Image.register_save_all(AvifImageFile.format, _save_all) + Image.register_extensions(AvifImageFile.format, [".avif", ".avifs"]) + Image.register_mime(AvifImageFile.format, "image/avif") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..f175e2f4f80b1b232d79f15a6db0667296917c97 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py @@ -0,0 +1,122 @@ +# +# The Python Imaging Library +# $Id$ +# +# bitmap distribution font (bdf) file parser +# +# history: +# 1996-05-16 fl created (as bdf2pil) +# 1997-08-25 fl converted to FontFile driver +# 2001-05-25 fl removed bogus __init__ call +# 2002-11-20 fl robustification (from Kevin Cazabon, Dmitry Vasiliev) +# 2003-04-22 fl more robustification (from Graham Dumpleton) +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1997-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +""" +Parse X Bitmap Distribution Format (BDF) +""" +from __future__ import annotations + +from typing import BinaryIO + +from . import FontFile, Image + + +def bdf_char( + f: BinaryIO, +) -> ( + tuple[ + str, + int, + tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]], + Image.Image, + ] + | None +): + # skip to STARTCHAR + while True: + s = f.readline() + if not s: + return None + if s.startswith(b"STARTCHAR"): + break + id = s[9:].strip().decode("ascii") + + # load symbol properties + props = {} + while True: + s = f.readline() + if not s or s.startswith(b"BITMAP"): + break + i = s.find(b" ") + props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii") + + # load bitmap + bitmap = bytearray() + while True: + s = f.readline() + if not s or s.startswith(b"ENDCHAR"): + break + bitmap += s[:-1] + + # The word BBX + # followed by the width in x (BBw), height in y (BBh), + # and x and y displacement (BBxoff0, BByoff0) + # of the lower left corner from the origin of the character. + width, height, x_disp, y_disp = (int(p) for p in props["BBX"].split()) + + # The word DWIDTH + # followed by the width in x and y of the character in device pixels. + dwx, dwy = (int(p) for p in props["DWIDTH"].split()) + + bbox = ( + (dwx, dwy), + (x_disp, -y_disp - height, width + x_disp, -y_disp), + (0, 0, width, height), + ) + + try: + im = Image.frombytes("1", (width, height), bitmap, "hex", "1") + except ValueError: + # deal with zero-width characters + im = Image.new("1", (width, height)) + + return id, int(props["ENCODING"]), bbox, im + + +class BdfFontFile(FontFile.FontFile): + """Font file plugin for the X11 BDF format.""" + + def __init__(self, fp: BinaryIO) -> None: + super().__init__() + + s = fp.readline() + if not s.startswith(b"STARTFONT 2.1"): + msg = "not a valid BDF file" + raise SyntaxError(msg) + + props = {} + comments = [] + + while True: + s = fp.readline() + if not s or s.startswith(b"ENDPROPERTIES"): + break + i = s.find(b" ") + props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii") + if s[:i] in [b"COMMENT", b"COPYRIGHT"]: + if s.find(b"LogicalFontDescription") < 0: + comments.append(s[i + 1 : -1].decode("ascii")) + + while True: + c = bdf_char(fp) + if not c: + break + id, ch, (xy, dst, src), im = c + if 0 <= ch < len(self.glyph): + self.glyph[ch] = xy, dst, src, im diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f7be7746d84bbc076e0a41124a903a8b2b05ae61 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py @@ -0,0 +1,497 @@ +""" +Blizzard Mipmap Format (.blp) +Jerome Leclanche + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: + https://creativecommons.org/publicdomain/zero/1.0/ + +BLP1 files, used mostly in Warcraft III, are not fully supported. +All types of BLP2 files used in World of Warcraft are supported. + +The BLP file structure consists of a header, up to 16 mipmaps of the +texture + +Texture sizes must be powers of two, though the two dimensions do +not have to be equal; 512x256 is valid, but 512x200 is not. +The first mipmap (mipmap #0) is the full size image; each subsequent +mipmap halves both dimensions. The final mipmap should be 1x1. + +BLP files come in many different flavours: +* JPEG-compressed (type == 0) - only supported for BLP1. +* RAW images (type == 1, encoding == 1). Each mipmap is stored as an + array of 8-bit values, one per pixel, left to right, top to bottom. + Each value is an index to the palette. +* DXT-compressed (type == 1, encoding == 2): +- DXT1 compression is used if alpha_encoding == 0. + - An additional alpha bit is used if alpha_depth == 1. + - DXT3 compression is used if alpha_encoding == 1. + - DXT5 compression is used if alpha_encoding == 7. +""" + +from __future__ import annotations + +import abc +import os +import struct +from enum import IntEnum +from io import BytesIO +from typing import IO + +from . import Image, ImageFile + + +class Format(IntEnum): + JPEG = 0 + + +class Encoding(IntEnum): + UNCOMPRESSED = 1 + DXT = 2 + UNCOMPRESSED_RAW_BGRA = 3 + + +class AlphaEncoding(IntEnum): + DXT1 = 0 + DXT3 = 1 + DXT5 = 7 + + +def unpack_565(i: int) -> tuple[int, int, int]: + return ((i >> 11) & 0x1F) << 3, ((i >> 5) & 0x3F) << 2, (i & 0x1F) << 3 + + +def decode_dxt1( + data: bytes, alpha: bool = False +) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4*width pixels) + """ + + blocks = len(data) // 8 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + # Decode next 8-byte block. + idx = block_index * 8 + color0, color1, bits = struct.unpack_from("> 2 + + a = 0xFF + if control == 0: + r, g, b = r0, g0, b0 + elif control == 1: + r, g, b = r1, g1, b1 + elif control == 2: + if color0 > color1: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + else: + r = (r0 + r1) // 2 + g = (g0 + g1) // 2 + b = (b0 + b1) // 2 + elif control == 3: + if color0 > color1: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + else: + r, g, b, a = 0, 0, 0, 0 + + if alpha: + ret[j].extend([r, g, b, a]) + else: + ret[j].extend([r, g, b]) + + return ret + + +def decode_dxt3(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4*width pixels) + """ + + blocks = len(data) // 16 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + idx = block_index * 16 + block = data[idx : idx + 16] + # Decode next 16-byte block. + bits = struct.unpack_from("<8B", block) + color0, color1 = struct.unpack_from(">= 4 + else: + high = True + a &= 0xF + a *= 17 # We get a value between 0 and 15 + + color_code = (code >> 2 * (4 * j + i)) & 0x03 + + if color_code == 0: + r, g, b = r0, g0, b0 + elif color_code == 1: + r, g, b = r1, g1, b1 + elif color_code == 2: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + elif color_code == 3: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + + ret[j].extend([r, g, b, a]) + + return ret + + +def decode_dxt5(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4 * width pixels) + """ + + blocks = len(data) // 16 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + idx = block_index * 16 + block = data[idx : idx + 16] + # Decode next 16-byte block. + a0, a1 = struct.unpack_from("> alphacode_index) & 0x07 + elif alphacode_index == 15: + alphacode = (alphacode2 >> 15) | ((alphacode1 << 1) & 0x06) + else: # alphacode_index >= 18 and alphacode_index <= 45 + alphacode = (alphacode1 >> (alphacode_index - 16)) & 0x07 + + if alphacode == 0: + a = a0 + elif alphacode == 1: + a = a1 + elif a0 > a1: + a = ((8 - alphacode) * a0 + (alphacode - 1) * a1) // 7 + elif alphacode == 6: + a = 0 + elif alphacode == 7: + a = 255 + else: + a = ((6 - alphacode) * a0 + (alphacode - 1) * a1) // 5 + + color_code = (code >> 2 * (4 * j + i)) & 0x03 + + if color_code == 0: + r, g, b = r0, g0, b0 + elif color_code == 1: + r, g, b = r1, g1, b1 + elif color_code == 2: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + elif color_code == 3: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + + ret[j].extend([r, g, b, a]) + + return ret + + +class BLPFormatError(NotImplementedError): + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"BLP1", b"BLP2")) + + +class BlpImageFile(ImageFile.ImageFile): + """ + Blizzard Mipmap Format + """ + + format = "BLP" + format_description = "Blizzard Mipmap Format" + + def _open(self) -> None: + self.magic = self.fp.read(4) + if not _accept(self.magic): + msg = f"Bad BLP magic {repr(self.magic)}" + raise BLPFormatError(msg) + + compression = struct.unpack(" tuple[int, int]: + try: + self._read_header() + self._load() + except struct.error as e: + msg = "Truncated BLP file" + raise OSError(msg) from e + return -1, 0 + + @abc.abstractmethod + def _load(self) -> None: + pass + + def _read_header(self) -> None: + self._offsets = struct.unpack("<16I", self._safe_read(16 * 4)) + self._lengths = struct.unpack("<16I", self._safe_read(16 * 4)) + + def _safe_read(self, length: int) -> bytes: + assert self.fd is not None + return ImageFile._safe_read(self.fd, length) + + def _read_palette(self) -> list[tuple[int, int, int, int]]: + ret = [] + for i in range(256): + try: + b, g, r, a = struct.unpack("<4B", self._safe_read(4)) + except struct.error: + break + ret.append((b, g, r, a)) + return ret + + def _read_bgra( + self, palette: list[tuple[int, int, int, int]], alpha: bool + ) -> bytearray: + data = bytearray() + _data = BytesIO(self._safe_read(self._lengths[0])) + while True: + try: + (offset,) = struct.unpack(" None: + self._compression, self._encoding, alpha = self.args + + if self._compression == Format.JPEG: + self._decode_jpeg_stream() + + elif self._compression == 1: + if self._encoding in (4, 5): + palette = self._read_palette() + data = self._read_bgra(palette, alpha) + self.set_as_raw(data) + else: + msg = f"Unsupported BLP encoding {repr(self._encoding)}" + raise BLPFormatError(msg) + else: + msg = f"Unsupported BLP compression {repr(self._encoding)}" + raise BLPFormatError(msg) + + def _decode_jpeg_stream(self) -> None: + from .JpegImagePlugin import JpegImageFile + + (jpeg_header_size,) = struct.unpack(" None: + self._compression, self._encoding, alpha, self._alpha_encoding = self.args + + palette = self._read_palette() + + assert self.fd is not None + self.fd.seek(self._offsets[0]) + + if self._compression == 1: + # Uncompressed or DirectX compression + + if self._encoding == Encoding.UNCOMPRESSED: + data = self._read_bgra(palette, alpha) + + elif self._encoding == Encoding.DXT: + data = bytearray() + if self._alpha_encoding == AlphaEncoding.DXT1: + linesize = (self.state.xsize + 3) // 4 * 8 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt1(self._safe_read(linesize), alpha): + data += d + + elif self._alpha_encoding == AlphaEncoding.DXT3: + linesize = (self.state.xsize + 3) // 4 * 16 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt3(self._safe_read(linesize)): + data += d + + elif self._alpha_encoding == AlphaEncoding.DXT5: + linesize = (self.state.xsize + 3) // 4 * 16 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt5(self._safe_read(linesize)): + data += d + else: + msg = f"Unsupported alpha encoding {repr(self._alpha_encoding)}" + raise BLPFormatError(msg) + else: + msg = f"Unknown BLP encoding {repr(self._encoding)}" + raise BLPFormatError(msg) + + else: + msg = f"Unknown BLP compression {repr(self._compression)}" + raise BLPFormatError(msg) + + self.set_as_raw(data) + + +class BLPEncoder(ImageFile.PyEncoder): + _pushes_fd = True + + def _write_palette(self) -> bytes: + data = b"" + assert self.im is not None + palette = self.im.getpalette("RGBA", "RGBA") + for i in range(len(palette) // 4): + r, g, b, a = palette[i * 4 : (i + 1) * 4] + data += struct.pack("<4B", b, g, r, a) + while len(data) < 256 * 4: + data += b"\x00" * 4 + return data + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + palette_data = self._write_palette() + + offset = 20 + 16 * 4 * 2 + len(palette_data) + data = struct.pack("<16I", offset, *((0,) * 15)) + + assert self.im is not None + w, h = self.im.size + data += struct.pack("<16I", w * h, *((0,) * 15)) + + data += palette_data + + for y in range(h): + for x in range(w): + data += struct.pack(" None: + if im.mode != "P": + msg = "Unsupported BLP image mode" + raise ValueError(msg) + + magic = b"BLP1" if im.encoderinfo.get("blp_version") == "BLP1" else b"BLP2" + fp.write(magic) + + assert im.palette is not None + fp.write(struct.pack(" mode, rawmode + 1: ("P", "P;1"), + 4: ("P", "P;4"), + 8: ("P", "P"), + 16: ("RGB", "BGR;15"), + 24: ("RGB", "BGR"), + 32: ("RGB", "BGRX"), +} + +USE_RAW_ALPHA = False + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"BM") + + +def _dib_accept(prefix: bytes) -> bool: + return i32(prefix) in [12, 40, 52, 56, 64, 108, 124] + + +# ============================================================================= +# Image plugin for the Windows BMP format. +# ============================================================================= +class BmpImageFile(ImageFile.ImageFile): + """Image plugin for the Windows Bitmap format (BMP)""" + + # ------------------------------------------------------------- Description + format_description = "Windows Bitmap" + format = "BMP" + + # -------------------------------------------------- BMP Compression values + COMPRESSIONS = {"RAW": 0, "RLE8": 1, "RLE4": 2, "BITFIELDS": 3, "JPEG": 4, "PNG": 5} + for k, v in COMPRESSIONS.items(): + vars()[k] = v + + def _bitmap(self, header: int = 0, offset: int = 0) -> None: + """Read relevant info about the BMP""" + read, seek = self.fp.read, self.fp.seek + if header: + seek(header) + # read bmp header size @offset 14 (this is part of the header size) + file_info: dict[str, bool | int | tuple[int, ...]] = { + "header_size": i32(read(4)), + "direction": -1, + } + + # -------------------- If requested, read header at a specific position + # read the rest of the bmp header, without its size + assert isinstance(file_info["header_size"], int) + header_data = ImageFile._safe_read(self.fp, file_info["header_size"] - 4) + + # ------------------------------- Windows Bitmap v2, IBM OS/2 Bitmap v1 + # ----- This format has different offsets because of width/height types + # 12: BITMAPCOREHEADER/OS21XBITMAPHEADER + if file_info["header_size"] == 12: + file_info["width"] = i16(header_data, 0) + file_info["height"] = i16(header_data, 2) + file_info["planes"] = i16(header_data, 4) + file_info["bits"] = i16(header_data, 6) + file_info["compression"] = self.COMPRESSIONS["RAW"] + file_info["palette_padding"] = 3 + + # --------------------------------------------- Windows Bitmap v3 to v5 + # 40: BITMAPINFOHEADER + # 52: BITMAPV2HEADER + # 56: BITMAPV3HEADER + # 64: BITMAPCOREHEADER2/OS22XBITMAPHEADER + # 108: BITMAPV4HEADER + # 124: BITMAPV5HEADER + elif file_info["header_size"] in (40, 52, 56, 64, 108, 124): + file_info["y_flip"] = header_data[7] == 0xFF + file_info["direction"] = 1 if file_info["y_flip"] else -1 + file_info["width"] = i32(header_data, 0) + file_info["height"] = ( + i32(header_data, 4) + if not file_info["y_flip"] + else 2**32 - i32(header_data, 4) + ) + file_info["planes"] = i16(header_data, 8) + file_info["bits"] = i16(header_data, 10) + file_info["compression"] = i32(header_data, 12) + # byte size of pixel data + file_info["data_size"] = i32(header_data, 16) + file_info["pixels_per_meter"] = ( + i32(header_data, 20), + i32(header_data, 24), + ) + file_info["colors"] = i32(header_data, 28) + file_info["palette_padding"] = 4 + assert isinstance(file_info["pixels_per_meter"], tuple) + self.info["dpi"] = tuple(x / 39.3701 for x in file_info["pixels_per_meter"]) + if file_info["compression"] == self.COMPRESSIONS["BITFIELDS"]: + masks = ["r_mask", "g_mask", "b_mask"] + if len(header_data) >= 48: + if len(header_data) >= 52: + masks.append("a_mask") + else: + file_info["a_mask"] = 0x0 + for idx, mask in enumerate(masks): + file_info[mask] = i32(header_data, 36 + idx * 4) + else: + # 40 byte headers only have the three components in the + # bitfields masks, ref: + # https://msdn.microsoft.com/en-us/library/windows/desktop/dd183376(v=vs.85).aspx + # See also + # https://github.com/python-pillow/Pillow/issues/1293 + # There is a 4th component in the RGBQuad, in the alpha + # location, but it is listed as a reserved component, + # and it is not generally an alpha channel + file_info["a_mask"] = 0x0 + for mask in masks: + file_info[mask] = i32(read(4)) + assert isinstance(file_info["r_mask"], int) + assert isinstance(file_info["g_mask"], int) + assert isinstance(file_info["b_mask"], int) + assert isinstance(file_info["a_mask"], int) + file_info["rgb_mask"] = ( + file_info["r_mask"], + file_info["g_mask"], + file_info["b_mask"], + ) + file_info["rgba_mask"] = ( + file_info["r_mask"], + file_info["g_mask"], + file_info["b_mask"], + file_info["a_mask"], + ) + else: + msg = f"Unsupported BMP header type ({file_info['header_size']})" + raise OSError(msg) + + # ------------------ Special case : header is reported 40, which + # ---------------------- is shorter than real size for bpp >= 16 + assert isinstance(file_info["width"], int) + assert isinstance(file_info["height"], int) + self._size = file_info["width"], file_info["height"] + + # ------- If color count was not found in the header, compute from bits + assert isinstance(file_info["bits"], int) + file_info["colors"] = ( + file_info["colors"] + if file_info.get("colors", 0) + else (1 << file_info["bits"]) + ) + assert isinstance(file_info["colors"], int) + if offset == 14 + file_info["header_size"] and file_info["bits"] <= 8: + offset += 4 * file_info["colors"] + + # ---------------------- Check bit depth for unusual unsupported values + self._mode, raw_mode = BIT2MODE.get(file_info["bits"], ("", "")) + if not self.mode: + msg = f"Unsupported BMP pixel depth ({file_info['bits']})" + raise OSError(msg) + + # ---------------- Process BMP with Bitfields compression (not palette) + decoder_name = "raw" + if file_info["compression"] == self.COMPRESSIONS["BITFIELDS"]: + SUPPORTED: dict[int, list[tuple[int, ...]]] = { + 32: [ + (0xFF0000, 0xFF00, 0xFF, 0x0), + (0xFF000000, 0xFF0000, 0xFF00, 0x0), + (0xFF000000, 0xFF00, 0xFF, 0x0), + (0xFF000000, 0xFF0000, 0xFF00, 0xFF), + (0xFF, 0xFF00, 0xFF0000, 0xFF000000), + (0xFF0000, 0xFF00, 0xFF, 0xFF000000), + (0xFF000000, 0xFF00, 0xFF, 0xFF0000), + (0x0, 0x0, 0x0, 0x0), + ], + 24: [(0xFF0000, 0xFF00, 0xFF)], + 16: [(0xF800, 0x7E0, 0x1F), (0x7C00, 0x3E0, 0x1F)], + } + MASK_MODES = { + (32, (0xFF0000, 0xFF00, 0xFF, 0x0)): "BGRX", + (32, (0xFF000000, 0xFF0000, 0xFF00, 0x0)): "XBGR", + (32, (0xFF000000, 0xFF00, 0xFF, 0x0)): "BGXR", + (32, (0xFF000000, 0xFF0000, 0xFF00, 0xFF)): "ABGR", + (32, (0xFF, 0xFF00, 0xFF0000, 0xFF000000)): "RGBA", + (32, (0xFF0000, 0xFF00, 0xFF, 0xFF000000)): "BGRA", + (32, (0xFF000000, 0xFF00, 0xFF, 0xFF0000)): "BGAR", + (32, (0x0, 0x0, 0x0, 0x0)): "BGRA", + (24, (0xFF0000, 0xFF00, 0xFF)): "BGR", + (16, (0xF800, 0x7E0, 0x1F)): "BGR;16", + (16, (0x7C00, 0x3E0, 0x1F)): "BGR;15", + } + if file_info["bits"] in SUPPORTED: + if ( + file_info["bits"] == 32 + and file_info["rgba_mask"] in SUPPORTED[file_info["bits"]] + ): + assert isinstance(file_info["rgba_mask"], tuple) + raw_mode = MASK_MODES[(file_info["bits"], file_info["rgba_mask"])] + self._mode = "RGBA" if "A" in raw_mode else self.mode + elif ( + file_info["bits"] in (24, 16) + and file_info["rgb_mask"] in SUPPORTED[file_info["bits"]] + ): + assert isinstance(file_info["rgb_mask"], tuple) + raw_mode = MASK_MODES[(file_info["bits"], file_info["rgb_mask"])] + else: + msg = "Unsupported BMP bitfields layout" + raise OSError(msg) + else: + msg = "Unsupported BMP bitfields layout" + raise OSError(msg) + elif file_info["compression"] == self.COMPRESSIONS["RAW"]: + if file_info["bits"] == 32 and ( + header == 22 or USE_RAW_ALPHA # 32-bit .cur offset + ): + raw_mode, self._mode = "BGRA", "RGBA" + elif file_info["compression"] in ( + self.COMPRESSIONS["RLE8"], + self.COMPRESSIONS["RLE4"], + ): + decoder_name = "bmp_rle" + else: + msg = f"Unsupported BMP compression ({file_info['compression']})" + raise OSError(msg) + + # --------------- Once the header is processed, process the palette/LUT + if self.mode == "P": # Paletted for 1, 4 and 8 bit images + # ---------------------------------------------------- 1-bit images + if not (0 < file_info["colors"] <= 65536): + msg = f"Unsupported BMP Palette size ({file_info['colors']})" + raise OSError(msg) + else: + assert isinstance(file_info["palette_padding"], int) + padding = file_info["palette_padding"] + palette = read(padding * file_info["colors"]) + grayscale = True + indices = ( + (0, 255) + if file_info["colors"] == 2 + else list(range(file_info["colors"])) + ) + + # ----------------- Check if grayscale and ignore palette if so + for ind, val in enumerate(indices): + rgb = palette[ind * padding : ind * padding + 3] + if rgb != o8(val) * 3: + grayscale = False + + # ------- If all colors are gray, white or black, ditch palette + if grayscale: + self._mode = "1" if file_info["colors"] == 2 else "L" + raw_mode = self.mode + else: + self._mode = "P" + self.palette = ImagePalette.raw( + "BGRX" if padding == 4 else "BGR", palette + ) + + # ---------------------------- Finally set the tile data for the plugin + self.info["compression"] = file_info["compression"] + args: list[Any] = [raw_mode] + if decoder_name == "bmp_rle": + args.append(file_info["compression"] == self.COMPRESSIONS["RLE4"]) + else: + assert isinstance(file_info["width"], int) + args.append(((file_info["width"] * file_info["bits"] + 31) >> 3) & (~3)) + args.append(file_info["direction"]) + self.tile = [ + ImageFile._Tile( + decoder_name, + (0, 0, file_info["width"], file_info["height"]), + offset or self.fp.tell(), + tuple(args), + ) + ] + + def _open(self) -> None: + """Open file, check magic number and read header""" + # read 14 bytes: magic number, filesize, reserved, header final offset + head_data = self.fp.read(14) + # choke if the file does not have the required magic bytes + if not _accept(head_data): + msg = "Not a BMP file" + raise SyntaxError(msg) + # read the start position of the BMP image data (u32) + offset = i32(head_data, 10) + # load bitmap information (offset=raster info) + self._bitmap(offset=offset) + + +class BmpRleDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + rle4 = self.args[1] + data = bytearray() + x = 0 + dest_length = self.state.xsize * self.state.ysize + while len(data) < dest_length: + pixels = self.fd.read(1) + byte = self.fd.read(1) + if not pixels or not byte: + break + num_pixels = pixels[0] + if num_pixels: + # encoded mode + if x + num_pixels > self.state.xsize: + # Too much data for row + num_pixels = max(0, self.state.xsize - x) + if rle4: + first_pixel = o8(byte[0] >> 4) + second_pixel = o8(byte[0] & 0x0F) + for index in range(num_pixels): + if index % 2 == 0: + data += first_pixel + else: + data += second_pixel + else: + data += byte * num_pixels + x += num_pixels + else: + if byte[0] == 0: + # end of line + while len(data) % self.state.xsize != 0: + data += b"\x00" + x = 0 + elif byte[0] == 1: + # end of bitmap + break + elif byte[0] == 2: + # delta + bytes_read = self.fd.read(2) + if len(bytes_read) < 2: + break + right, up = self.fd.read(2) + data += b"\x00" * (right + up * self.state.xsize) + x = len(data) % self.state.xsize + else: + # absolute mode + if rle4: + # 2 pixels per byte + byte_count = byte[0] // 2 + bytes_read = self.fd.read(byte_count) + for byte_read in bytes_read: + data += o8(byte_read >> 4) + data += o8(byte_read & 0x0F) + else: + byte_count = byte[0] + bytes_read = self.fd.read(byte_count) + data += bytes_read + if len(bytes_read) < byte_count: + break + x += byte[0] + + # align to 16-bit word boundary + if self.fd.tell() % 2 != 0: + self.fd.seek(1, os.SEEK_CUR) + rawmode = "L" if self.mode == "L" else "P" + self.set_as_raw(bytes(data), rawmode, (0, self.args[-1])) + return -1, 0 + + +# ============================================================================= +# Image plugin for the DIB format (BMP alias) +# ============================================================================= +class DibImageFile(BmpImageFile): + format = "DIB" + format_description = "Windows Bitmap" + + def _open(self) -> None: + self._bitmap() + + +# +# -------------------------------------------------------------------- +# Write BMP file + + +SAVE = { + "1": ("1", 1, 2), + "L": ("L", 8, 256), + "P": ("P", 8, 256), + "RGB": ("BGR", 24, 0), + "RGBA": ("BGRA", 32, 0), +} + + +def _dib_save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, False) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, bitmap_header: bool = True +) -> None: + try: + rawmode, bits, colors = SAVE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as BMP" + raise OSError(msg) from e + + info = im.encoderinfo + + dpi = info.get("dpi", (96, 96)) + + # 1 meter == 39.3701 inches + ppm = tuple(int(x * 39.3701 + 0.5) for x in dpi) + + stride = ((im.size[0] * bits + 7) // 8 + 3) & (~3) + header = 40 # or 64 for OS/2 version 2 + image = stride * im.size[1] + + if im.mode == "1": + palette = b"".join(o8(i) * 3 + b"\x00" for i in (0, 255)) + elif im.mode == "L": + palette = b"".join(o8(i) * 3 + b"\x00" for i in range(256)) + elif im.mode == "P": + palette = im.im.getpalette("RGB", "BGRX") + colors = len(palette) // 4 + else: + palette = None + + # bitmap header + if bitmap_header: + offset = 14 + header + colors * 4 + file_size = offset + image + if file_size > 2**32 - 1: + msg = "File size is too large for the BMP format" + raise ValueError(msg) + fp.write( + b"BM" # file type (magic) + + o32(file_size) # file size + + o32(0) # reserved + + o32(offset) # image data offset + ) + + # bitmap info header + fp.write( + o32(header) # info header size + + o32(im.size[0]) # width + + o32(im.size[1]) # height + + o16(1) # planes + + o16(bits) # depth + + o32(0) # compression (0=uncompressed) + + o32(image) # size of bitmap + + o32(ppm[0]) # resolution + + o32(ppm[1]) # resolution + + o32(colors) # colors used + + o32(colors) # colors important + ) + + fp.write(b"\0" * (header - 40)) # padding (for OS/2 format) + + if palette: + fp.write(palette) + + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, stride, -1))] + ) + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(BmpImageFile.format, BmpImageFile, _accept) +Image.register_save(BmpImageFile.format, _save) + +Image.register_extension(BmpImageFile.format, ".bmp") + +Image.register_mime(BmpImageFile.format, "image/bmp") + +Image.register_decoder("bmp_rle", BmpRleDecoder) + +Image.register_open(DibImageFile.format, DibImageFile, _dib_accept) +Image.register_save(DibImageFile.format, _dib_save) + +Image.register_extension(DibImageFile.format, ".dib") + +Image.register_mime(DibImageFile.format, "image/bmp") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5da14f5f6769127e0ed23d36fa8f3dd0b086c2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# BUFR stub adapter +# +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific BUFR image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"BUFR", b"ZCZC")) + + +class BufrStubImageFile(ImageFile.StubImageFile): + format = "BUFR" + format_description = "BUFR" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "Not a BUFR file" + raise SyntaxError(msg) + + self.fp.seek(-4, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "BUFR save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(BufrStubImageFile.format, BufrStubImageFile, _accept) +Image.register_save(BufrStubImageFile.format, _save) + +Image.register_extension(BufrStubImageFile.format, ".bufr") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py new file mode 100644 index 0000000000000000000000000000000000000000..ec9e66c714fbbfec8c597f6127e5d932b0da521f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py @@ -0,0 +1,173 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a class to read from a container file +# +# History: +# 1995-06-18 fl Created +# 1995-09-07 fl Added readline(), readlines() +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1995 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from collections.abc import Iterable +from typing import IO, AnyStr, NoReturn + + +class ContainerIO(IO[AnyStr]): + """ + A file object that provides read access to a part of an existing + file (for example a TAR file). + """ + + def __init__(self, file: IO[AnyStr], offset: int, length: int) -> None: + """ + Create file object. + + :param file: Existing file. + :param offset: Start of region, in bytes. + :param length: Size of region, in bytes. + """ + self.fh: IO[AnyStr] = file + self.pos = 0 + self.offset = offset + self.length = length + self.fh.seek(offset) + + ## + # Always false. + + def isatty(self) -> bool: + return False + + def seekable(self) -> bool: + return True + + def seek(self, offset: int, mode: int = io.SEEK_SET) -> int: + """ + Move file pointer. + + :param offset: Offset in bytes. + :param mode: Starting position. Use 0 for beginning of region, 1 + for current offset, and 2 for end of region. You cannot move + the pointer outside the defined region. + :returns: Offset from start of region, in bytes. + """ + if mode == 1: + self.pos = self.pos + offset + elif mode == 2: + self.pos = self.length + offset + else: + self.pos = offset + # clamp + self.pos = max(0, min(self.pos, self.length)) + self.fh.seek(self.offset + self.pos) + return self.pos + + def tell(self) -> int: + """ + Get current file pointer. + + :returns: Offset from start of region, in bytes. + """ + return self.pos + + def readable(self) -> bool: + return True + + def read(self, n: int = -1) -> AnyStr: + """ + Read data. + + :param n: Number of bytes to read. If omitted, zero or negative, + read until end of region. + :returns: An 8-bit string. + """ + if n > 0: + n = min(n, self.length - self.pos) + else: + n = self.length - self.pos + if n <= 0: # EOF + return b"" if "b" in self.fh.mode else "" # type: ignore[return-value] + self.pos = self.pos + n + return self.fh.read(n) + + def readline(self, n: int = -1) -> AnyStr: + """ + Read a line of text. + + :param n: Number of bytes to read. If omitted, zero or negative, + read until end of line. + :returns: An 8-bit string. + """ + s: AnyStr = b"" if "b" in self.fh.mode else "" # type: ignore[assignment] + newline_character = b"\n" if "b" in self.fh.mode else "\n" + while True: + c = self.read(1) + if not c: + break + s = s + c + if c == newline_character or len(s) == n: + break + return s + + def readlines(self, n: int | None = -1) -> list[AnyStr]: + """ + Read multiple lines of text. + + :param n: Number of lines to read. If omitted, zero, negative or None, + read until end of region. + :returns: A list of 8-bit strings. + """ + lines = [] + while True: + s = self.readline() + if not s: + break + lines.append(s) + if len(lines) == n: + break + return lines + + def writable(self) -> bool: + return False + + def write(self, b: AnyStr) -> NoReturn: + raise NotImplementedError() + + def writelines(self, lines: Iterable[AnyStr]) -> NoReturn: + raise NotImplementedError() + + def truncate(self, size: int | None = None) -> int: + raise NotImplementedError() + + def __enter__(self) -> ContainerIO[AnyStr]: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def __iter__(self) -> ContainerIO[AnyStr]: + return self + + def __next__(self) -> AnyStr: + line = self.readline() + if not line: + msg = "end of region" + raise StopIteration(msg) + return line + + def fileno(self) -> int: + return self.fh.fileno() + + def flush(self) -> None: + self.fh.flush() + + def close(self) -> None: + self.fh.close() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b817dbc87b8c291d4bffd5f0b83d5e63a5e6ecbb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Windows Cursor support for PIL +# +# notes: +# uses BmpImagePlugin.py to read the bitmap data. +# +# history: +# 96-05-27 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import BmpImagePlugin, Image, ImageFile +from ._binary import i16le as i16 +from ._binary import i32le as i32 + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\0\0\2\0") + + +## +# Image plugin for Windows Cursor files. + + +class CurImageFile(BmpImagePlugin.BmpImageFile): + format = "CUR" + format_description = "Windows Cursor" + + def _open(self) -> None: + offset = self.fp.tell() + + # check magic + s = self.fp.read(6) + if not _accept(s): + msg = "not a CUR file" + raise SyntaxError(msg) + + # pick the largest cursor in the file + m = b"" + for i in range(i16(s, 4)): + s = self.fp.read(16) + if not m: + m = s + elif s[0] > m[0] and s[1] > m[1]: + m = s + if not m: + msg = "No cursors were found" + raise TypeError(msg) + + # load as bitmap + self._bitmap(i32(m, 12) + offset) + + # patch up the bitmap height + self._size = self.size[0], self.size[1] // 2 + d, e, o, a = self.tile[0] + self.tile[0] = ImageFile._Tile(d, (0, 0) + self.size, o, a) + + +# +# -------------------------------------------------------------------- + +Image.register_open(CurImageFile.format, CurImageFile, _accept) + +Image.register_extension(CurImageFile.format, ".cur") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..aea661b9cb6eef184696e377678ee69f66c5f772 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py @@ -0,0 +1,83 @@ +# +# The Python Imaging Library. +# $Id$ +# +# DCX file handling +# +# DCX is a container file format defined by Intel, commonly used +# for fax applications. Each DCX file consists of a directory +# (a list of file offsets) followed by a set of (usually 1-bit) +# PCX files. +# +# History: +# 1995-09-09 fl Created +# 1996-03-20 fl Properly derived from PcxImageFile. +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 2002-07-30 fl Fixed file handling +# +# Copyright (c) 1997-98 by Secret Labs AB. +# Copyright (c) 1995-96 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image +from ._binary import i32le as i32 +from ._util import DeferredError +from .PcxImagePlugin import PcxImageFile + +MAGIC = 0x3ADE68B1 # QUIZ: what's this value, then? + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 4 and i32(prefix) == MAGIC + + +## +# Image plugin for the Intel DCX format. + + +class DcxImageFile(PcxImageFile): + format = "DCX" + format_description = "Intel DCX" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # Header + s = self.fp.read(4) + if not _accept(s): + msg = "not a DCX file" + raise SyntaxError(msg) + + # Component directory + self._offset = [] + for i in range(1024): + offset = i32(self.fp.read(4)) + if not offset: + break + self._offset.append(offset) + + self._fp = self.fp + self.frame = -1 + self.n_frames = len(self._offset) + self.is_animated = self.n_frames > 1 + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.frame = frame + self.fp = self._fp + self.fp.seek(self._offset[frame]) + PcxImageFile._open(self) + + def tell(self) -> int: + return self.frame + + +Image.register_open(DcxImageFile.format, DcxImageFile, _accept) + +Image.register_extension(DcxImageFile.format, ".dcx") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ade18f9a1edf431524dd86a238f6b0445e6ab0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py @@ -0,0 +1,624 @@ +""" +A Pillow plugin for .dds files (S3TC-compressed aka DXTC) +Jerome Leclanche + +Documentation: +https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: +https://creativecommons.org/publicdomain/zero/1.0/ +""" + +from __future__ import annotations + +import io +import struct +import sys +from enum import IntEnum, IntFlag +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i32le as i32 +from ._binary import o8 +from ._binary import o32le as o32 + +# Magic ("DDS ") +DDS_MAGIC = 0x20534444 + + +# DDS flags +class DDSD(IntFlag): + CAPS = 0x1 + HEIGHT = 0x2 + WIDTH = 0x4 + PITCH = 0x8 + PIXELFORMAT = 0x1000 + MIPMAPCOUNT = 0x20000 + LINEARSIZE = 0x80000 + DEPTH = 0x800000 + + +# DDS caps +class DDSCAPS(IntFlag): + COMPLEX = 0x8 + TEXTURE = 0x1000 + MIPMAP = 0x400000 + + +class DDSCAPS2(IntFlag): + CUBEMAP = 0x200 + CUBEMAP_POSITIVEX = 0x400 + CUBEMAP_NEGATIVEX = 0x800 + CUBEMAP_POSITIVEY = 0x1000 + CUBEMAP_NEGATIVEY = 0x2000 + CUBEMAP_POSITIVEZ = 0x4000 + CUBEMAP_NEGATIVEZ = 0x8000 + VOLUME = 0x200000 + + +# Pixel Format +class DDPF(IntFlag): + ALPHAPIXELS = 0x1 + ALPHA = 0x2 + FOURCC = 0x4 + PALETTEINDEXED8 = 0x20 + RGB = 0x40 + LUMINANCE = 0x20000 + + +# dxgiformat.h +class DXGI_FORMAT(IntEnum): + UNKNOWN = 0 + R32G32B32A32_TYPELESS = 1 + R32G32B32A32_FLOAT = 2 + R32G32B32A32_UINT = 3 + R32G32B32A32_SINT = 4 + R32G32B32_TYPELESS = 5 + R32G32B32_FLOAT = 6 + R32G32B32_UINT = 7 + R32G32B32_SINT = 8 + R16G16B16A16_TYPELESS = 9 + R16G16B16A16_FLOAT = 10 + R16G16B16A16_UNORM = 11 + R16G16B16A16_UINT = 12 + R16G16B16A16_SNORM = 13 + R16G16B16A16_SINT = 14 + R32G32_TYPELESS = 15 + R32G32_FLOAT = 16 + R32G32_UINT = 17 + R32G32_SINT = 18 + R32G8X24_TYPELESS = 19 + D32_FLOAT_S8X24_UINT = 20 + R32_FLOAT_X8X24_TYPELESS = 21 + X32_TYPELESS_G8X24_UINT = 22 + R10G10B10A2_TYPELESS = 23 + R10G10B10A2_UNORM = 24 + R10G10B10A2_UINT = 25 + R11G11B10_FLOAT = 26 + R8G8B8A8_TYPELESS = 27 + R8G8B8A8_UNORM = 28 + R8G8B8A8_UNORM_SRGB = 29 + R8G8B8A8_UINT = 30 + R8G8B8A8_SNORM = 31 + R8G8B8A8_SINT = 32 + R16G16_TYPELESS = 33 + R16G16_FLOAT = 34 + R16G16_UNORM = 35 + R16G16_UINT = 36 + R16G16_SNORM = 37 + R16G16_SINT = 38 + R32_TYPELESS = 39 + D32_FLOAT = 40 + R32_FLOAT = 41 + R32_UINT = 42 + R32_SINT = 43 + R24G8_TYPELESS = 44 + D24_UNORM_S8_UINT = 45 + R24_UNORM_X8_TYPELESS = 46 + X24_TYPELESS_G8_UINT = 47 + R8G8_TYPELESS = 48 + R8G8_UNORM = 49 + R8G8_UINT = 50 + R8G8_SNORM = 51 + R8G8_SINT = 52 + R16_TYPELESS = 53 + R16_FLOAT = 54 + D16_UNORM = 55 + R16_UNORM = 56 + R16_UINT = 57 + R16_SNORM = 58 + R16_SINT = 59 + R8_TYPELESS = 60 + R8_UNORM = 61 + R8_UINT = 62 + R8_SNORM = 63 + R8_SINT = 64 + A8_UNORM = 65 + R1_UNORM = 66 + R9G9B9E5_SHAREDEXP = 67 + R8G8_B8G8_UNORM = 68 + G8R8_G8B8_UNORM = 69 + BC1_TYPELESS = 70 + BC1_UNORM = 71 + BC1_UNORM_SRGB = 72 + BC2_TYPELESS = 73 + BC2_UNORM = 74 + BC2_UNORM_SRGB = 75 + BC3_TYPELESS = 76 + BC3_UNORM = 77 + BC3_UNORM_SRGB = 78 + BC4_TYPELESS = 79 + BC4_UNORM = 80 + BC4_SNORM = 81 + BC5_TYPELESS = 82 + BC5_UNORM = 83 + BC5_SNORM = 84 + B5G6R5_UNORM = 85 + B5G5R5A1_UNORM = 86 + B8G8R8A8_UNORM = 87 + B8G8R8X8_UNORM = 88 + R10G10B10_XR_BIAS_A2_UNORM = 89 + B8G8R8A8_TYPELESS = 90 + B8G8R8A8_UNORM_SRGB = 91 + B8G8R8X8_TYPELESS = 92 + B8G8R8X8_UNORM_SRGB = 93 + BC6H_TYPELESS = 94 + BC6H_UF16 = 95 + BC6H_SF16 = 96 + BC7_TYPELESS = 97 + BC7_UNORM = 98 + BC7_UNORM_SRGB = 99 + AYUV = 100 + Y410 = 101 + Y416 = 102 + NV12 = 103 + P010 = 104 + P016 = 105 + OPAQUE_420 = 106 + YUY2 = 107 + Y210 = 108 + Y216 = 109 + NV11 = 110 + AI44 = 111 + IA44 = 112 + P8 = 113 + A8P8 = 114 + B4G4R4A4_UNORM = 115 + P208 = 130 + V208 = 131 + V408 = 132 + SAMPLER_FEEDBACK_MIN_MIP_OPAQUE = 189 + SAMPLER_FEEDBACK_MIP_REGION_USED_OPAQUE = 190 + + +class D3DFMT(IntEnum): + UNKNOWN = 0 + R8G8B8 = 20 + A8R8G8B8 = 21 + X8R8G8B8 = 22 + R5G6B5 = 23 + X1R5G5B5 = 24 + A1R5G5B5 = 25 + A4R4G4B4 = 26 + R3G3B2 = 27 + A8 = 28 + A8R3G3B2 = 29 + X4R4G4B4 = 30 + A2B10G10R10 = 31 + A8B8G8R8 = 32 + X8B8G8R8 = 33 + G16R16 = 34 + A2R10G10B10 = 35 + A16B16G16R16 = 36 + A8P8 = 40 + P8 = 41 + L8 = 50 + A8L8 = 51 + A4L4 = 52 + V8U8 = 60 + L6V5U5 = 61 + X8L8V8U8 = 62 + Q8W8V8U8 = 63 + V16U16 = 64 + A2W10V10U10 = 67 + D16_LOCKABLE = 70 + D32 = 71 + D15S1 = 73 + D24S8 = 75 + D24X8 = 77 + D24X4S4 = 79 + D16 = 80 + D32F_LOCKABLE = 82 + D24FS8 = 83 + D32_LOCKABLE = 84 + S8_LOCKABLE = 85 + L16 = 81 + VERTEXDATA = 100 + INDEX16 = 101 + INDEX32 = 102 + Q16W16V16U16 = 110 + R16F = 111 + G16R16F = 112 + A16B16G16R16F = 113 + R32F = 114 + G32R32F = 115 + A32B32G32R32F = 116 + CxV8U8 = 117 + A1 = 118 + A2B10G10R10_XR_BIAS = 119 + BINARYBUFFER = 199 + + UYVY = i32(b"UYVY") + R8G8_B8G8 = i32(b"RGBG") + YUY2 = i32(b"YUY2") + G8R8_G8B8 = i32(b"GRGB") + DXT1 = i32(b"DXT1") + DXT2 = i32(b"DXT2") + DXT3 = i32(b"DXT3") + DXT4 = i32(b"DXT4") + DXT5 = i32(b"DXT5") + DX10 = i32(b"DX10") + BC4S = i32(b"BC4S") + BC4U = i32(b"BC4U") + BC5S = i32(b"BC5S") + BC5U = i32(b"BC5U") + ATI1 = i32(b"ATI1") + ATI2 = i32(b"ATI2") + MULTI2_ARGB8 = i32(b"MET1") + + +# Backward compatibility layer +module = sys.modules[__name__] +for item in DDSD: + assert item.name is not None + setattr(module, f"DDSD_{item.name}", item.value) +for item1 in DDSCAPS: + assert item1.name is not None + setattr(module, f"DDSCAPS_{item1.name}", item1.value) +for item2 in DDSCAPS2: + assert item2.name is not None + setattr(module, f"DDSCAPS2_{item2.name}", item2.value) +for item3 in DDPF: + assert item3.name is not None + setattr(module, f"DDPF_{item3.name}", item3.value) + +DDS_FOURCC = DDPF.FOURCC +DDS_RGB = DDPF.RGB +DDS_RGBA = DDPF.RGB | DDPF.ALPHAPIXELS +DDS_LUMINANCE = DDPF.LUMINANCE +DDS_LUMINANCEA = DDPF.LUMINANCE | DDPF.ALPHAPIXELS +DDS_ALPHA = DDPF.ALPHA +DDS_PAL8 = DDPF.PALETTEINDEXED8 + +DDS_HEADER_FLAGS_TEXTURE = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PIXELFORMAT +DDS_HEADER_FLAGS_MIPMAP = DDSD.MIPMAPCOUNT +DDS_HEADER_FLAGS_VOLUME = DDSD.DEPTH +DDS_HEADER_FLAGS_PITCH = DDSD.PITCH +DDS_HEADER_FLAGS_LINEARSIZE = DDSD.LINEARSIZE + +DDS_HEIGHT = DDSD.HEIGHT +DDS_WIDTH = DDSD.WIDTH + +DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS.TEXTURE +DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS.COMPLEX | DDSCAPS.MIPMAP +DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS.COMPLEX + +DDS_CUBEMAP_POSITIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEX +DDS_CUBEMAP_NEGATIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEX +DDS_CUBEMAP_POSITIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEY +DDS_CUBEMAP_NEGATIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEY +DDS_CUBEMAP_POSITIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEZ +DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEZ + +DXT1_FOURCC = D3DFMT.DXT1 +DXT3_FOURCC = D3DFMT.DXT3 +DXT5_FOURCC = D3DFMT.DXT5 + +DXGI_FORMAT_R8G8B8A8_TYPELESS = DXGI_FORMAT.R8G8B8A8_TYPELESS +DXGI_FORMAT_R8G8B8A8_UNORM = DXGI_FORMAT.R8G8B8A8_UNORM +DXGI_FORMAT_R8G8B8A8_UNORM_SRGB = DXGI_FORMAT.R8G8B8A8_UNORM_SRGB +DXGI_FORMAT_BC5_TYPELESS = DXGI_FORMAT.BC5_TYPELESS +DXGI_FORMAT_BC5_UNORM = DXGI_FORMAT.BC5_UNORM +DXGI_FORMAT_BC5_SNORM = DXGI_FORMAT.BC5_SNORM +DXGI_FORMAT_BC6H_UF16 = DXGI_FORMAT.BC6H_UF16 +DXGI_FORMAT_BC6H_SF16 = DXGI_FORMAT.BC6H_SF16 +DXGI_FORMAT_BC7_TYPELESS = DXGI_FORMAT.BC7_TYPELESS +DXGI_FORMAT_BC7_UNORM = DXGI_FORMAT.BC7_UNORM +DXGI_FORMAT_BC7_UNORM_SRGB = DXGI_FORMAT.BC7_UNORM_SRGB + + +class DdsImageFile(ImageFile.ImageFile): + format = "DDS" + format_description = "DirectDraw Surface" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not a DDS file" + raise SyntaxError(msg) + (header_size,) = struct.unpack(" None: + pass + + +class DdsRgbDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + bitcount, masks = self.args + + # Some masks will be padded with zeros, e.g. R 0b11 G 0b1100 + # Calculate how many zeros each mask is padded with + mask_offsets = [] + # And the maximum value of each channel without the padding + mask_totals = [] + for mask in masks: + offset = 0 + if mask != 0: + while mask >> (offset + 1) << (offset + 1) == mask: + offset += 1 + mask_offsets.append(offset) + mask_totals.append(mask >> offset) + + data = bytearray() + bytecount = bitcount // 8 + dest_length = self.state.xsize * self.state.ysize * len(masks) + while len(data) < dest_length: + value = int.from_bytes(self.fd.read(bytecount), "little") + for i, mask in enumerate(masks): + masked_value = value & mask + # Remove the zero padding, and scale it to 8 bits + data += o8( + int(((masked_value >> mask_offsets[i]) / mask_totals[i]) * 255) + ) + self.set_as_raw(data) + return -1, 0 + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode not in ("RGB", "RGBA", "L", "LA"): + msg = f"cannot write mode {im.mode} as DDS" + raise OSError(msg) + + flags = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PIXELFORMAT + bitcount = len(im.getbands()) * 8 + pixel_format = im.encoderinfo.get("pixel_format") + args: tuple[int] | str + if pixel_format: + codec_name = "bcn" + flags |= DDSD.LINEARSIZE + pitch = (im.width + 3) * 4 + rgba_mask = [0, 0, 0, 0] + pixel_flags = DDPF.FOURCC + if pixel_format == "DXT1": + fourcc = D3DFMT.DXT1 + args = (1,) + elif pixel_format == "DXT3": + fourcc = D3DFMT.DXT3 + args = (2,) + elif pixel_format == "DXT5": + fourcc = D3DFMT.DXT5 + args = (3,) + else: + fourcc = D3DFMT.DX10 + if pixel_format == "BC2": + args = (2,) + dxgi_format = DXGI_FORMAT.BC2_TYPELESS + elif pixel_format == "BC3": + args = (3,) + dxgi_format = DXGI_FORMAT.BC3_TYPELESS + elif pixel_format == "BC5": + args = (5,) + dxgi_format = DXGI_FORMAT.BC5_TYPELESS + if im.mode != "RGB": + msg = "only RGB mode can be written as BC5" + raise OSError(msg) + else: + msg = f"cannot write pixel format {pixel_format}" + raise OSError(msg) + else: + codec_name = "raw" + flags |= DDSD.PITCH + pitch = (im.width * bitcount + 7) // 8 + + alpha = im.mode[-1] == "A" + if im.mode[0] == "L": + pixel_flags = DDPF.LUMINANCE + args = im.mode + if alpha: + rgba_mask = [0x000000FF, 0x000000FF, 0x000000FF] + else: + rgba_mask = [0xFF000000, 0xFF000000, 0xFF000000] + else: + pixel_flags = DDPF.RGB + args = im.mode[::-1] + rgba_mask = [0x00FF0000, 0x0000FF00, 0x000000FF] + + if alpha: + r, g, b, a = im.split() + im = Image.merge("RGBA", (a, r, g, b)) + if alpha: + pixel_flags |= DDPF.ALPHAPIXELS + rgba_mask.append(0xFF000000 if alpha else 0) + + fourcc = D3DFMT.UNKNOWN + fp.write( + o32(DDS_MAGIC) + + struct.pack( + "<7I", + 124, # header size + flags, # flags + im.height, + im.width, + pitch, + 0, # depth + 0, # mipmaps + ) + + struct.pack("11I", *((0,) * 11)) # reserved + # pfsize, pfflags, fourcc, bitcount + + struct.pack("<4I", 32, pixel_flags, fourcc, bitcount) + + struct.pack("<4I", *rgba_mask) # dwRGBABitMask + + struct.pack("<5I", DDSCAPS.TEXTURE, 0, 0, 0, 0) + ) + if fourcc == D3DFMT.DX10: + fp.write( + # dxgi_format, 2D resource, misc, array size, straight alpha + struct.pack("<5I", dxgi_format, 3, 0, 0, 1) + ) + ImageFile._save(im, fp, [ImageFile._Tile(codec_name, (0, 0) + im.size, 0, args)]) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"DDS ") + + +Image.register_open(DdsImageFile.format, DdsImageFile, _accept) +Image.register_decoder("dds_rgb", DdsRgbDecoder) +Image.register_save(DdsImageFile.format, _save) +Image.register_extension(DdsImageFile.format, ".dds") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2ddad99e99565fee795d49c9563f5c9ba5dac1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py @@ -0,0 +1,476 @@ +# +# The Python Imaging Library. +# $Id$ +# +# EPS file handling +# +# History: +# 1995-09-01 fl Created (0.1) +# 1996-05-18 fl Don't choke on "atend" fields, Ghostscript interface (0.2) +# 1996-08-22 fl Don't choke on floating point BoundingBox values +# 1996-08-23 fl Handle files from Macintosh (0.3) +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.4) +# 2003-09-07 fl Check gs.close status (from Federico Di Gregorio) (0.5) +# 2014-05-07 e Handling of EPS with binary preview and fixed resolution +# resizing +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import re +import subprocess +import sys +import tempfile +from typing import IO + +from . import Image, ImageFile +from ._binary import i32le as i32 + +# -------------------------------------------------------------------- + + +split = re.compile(r"^%%([^:]*):[ \t]*(.*)[ \t]*$") +field = re.compile(r"^%[%!\w]([^:]*)[ \t]*$") + +gs_binary: str | bool | None = None +gs_windows_binary = None + + +def has_ghostscript() -> bool: + global gs_binary, gs_windows_binary + if gs_binary is None: + if sys.platform.startswith("win"): + if gs_windows_binary is None: + import shutil + + for binary in ("gswin32c", "gswin64c", "gs"): + if shutil.which(binary) is not None: + gs_windows_binary = binary + break + else: + gs_windows_binary = False + gs_binary = gs_windows_binary + else: + try: + subprocess.check_call(["gs", "--version"], stdout=subprocess.DEVNULL) + gs_binary = "gs" + except OSError: + gs_binary = False + return gs_binary is not False + + +def Ghostscript( + tile: list[ImageFile._Tile], + size: tuple[int, int], + fp: IO[bytes], + scale: int = 1, + transparency: bool = False, +) -> Image.core.ImagingCore: + """Render an image using Ghostscript""" + global gs_binary + if not has_ghostscript(): + msg = "Unable to locate Ghostscript on paths" + raise OSError(msg) + assert isinstance(gs_binary, str) + + # Unpack decoder tile + args = tile[0].args + assert isinstance(args, tuple) + length, bbox = args + + # Hack to support hi-res rendering + scale = int(scale) or 1 + width = size[0] * scale + height = size[1] * scale + # resolution is dependent on bbox and size + res_x = 72.0 * width / (bbox[2] - bbox[0]) + res_y = 72.0 * height / (bbox[3] - bbox[1]) + + out_fd, outfile = tempfile.mkstemp() + os.close(out_fd) + + infile_temp = None + if hasattr(fp, "name") and os.path.exists(fp.name): + infile = fp.name + else: + in_fd, infile_temp = tempfile.mkstemp() + os.close(in_fd) + infile = infile_temp + + # Ignore length and offset! + # Ghostscript can read it + # Copy whole file to read in Ghostscript + with open(infile_temp, "wb") as f: + # fetch length of fp + fp.seek(0, io.SEEK_END) + fsize = fp.tell() + # ensure start position + # go back + fp.seek(0) + lengthfile = fsize + while lengthfile > 0: + s = fp.read(min(lengthfile, 100 * 1024)) + if not s: + break + lengthfile -= len(s) + f.write(s) + + if transparency: + # "RGBA" + device = "pngalpha" + else: + # "pnmraw" automatically chooses between + # PBM ("1"), PGM ("L"), and PPM ("RGB"). + device = "pnmraw" + + # Build Ghostscript command + command = [ + gs_binary, + "-q", # quiet mode + f"-g{width:d}x{height:d}", # set output geometry (pixels) + f"-r{res_x:f}x{res_y:f}", # set input DPI (dots per inch) + "-dBATCH", # exit after processing + "-dNOPAUSE", # don't pause between pages + "-dSAFER", # safe mode + f"-sDEVICE={device}", + f"-sOutputFile={outfile}", # output file + # adjust for image origin + "-c", + f"{-bbox[0]} {-bbox[1]} translate", + "-f", + infile, # input file + # showpage (see https://bugs.ghostscript.com/show_bug.cgi?id=698272) + "-c", + "showpage", + ] + + # push data through Ghostscript + try: + startupinfo = None + if sys.platform.startswith("win"): + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + subprocess.check_call(command, startupinfo=startupinfo) + with Image.open(outfile) as out_im: + out_im.load() + return out_im.im.copy() + finally: + try: + os.unlink(outfile) + if infile_temp: + os.unlink(infile_temp) + except OSError: + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"%!PS") or ( + len(prefix) >= 4 and i32(prefix) == 0xC6D3D0C5 + ) + + +## +# Image plugin for Encapsulated PostScript. This plugin supports only +# a few variants of this format. + + +class EpsImageFile(ImageFile.ImageFile): + """EPS File Parser for the Python Imaging Library""" + + format = "EPS" + format_description = "Encapsulated Postscript" + + mode_map = {1: "L", 2: "LAB", 3: "RGB", 4: "CMYK"} + + def _open(self) -> None: + (length, offset) = self._find_offset(self.fp) + + # go to offset - start of "%!PS" + self.fp.seek(offset) + + self._mode = "RGB" + + # When reading header comments, the first comment is used. + # When reading trailer comments, the last comment is used. + bounding_box: list[int] | None = None + imagedata_size: tuple[int, int] | None = None + + byte_arr = bytearray(255) + bytes_mv = memoryview(byte_arr) + bytes_read = 0 + reading_header_comments = True + reading_trailer_comments = False + trailer_reached = False + + def check_required_header_comments() -> None: + """ + The EPS specification requires that some headers exist. + This should be checked when the header comments formally end, + when image data starts, or when the file ends, whichever comes first. + """ + if "PS-Adobe" not in self.info: + msg = 'EPS header missing "%!PS-Adobe" comment' + raise SyntaxError(msg) + if "BoundingBox" not in self.info: + msg = 'EPS header missing "%%BoundingBox" comment' + raise SyntaxError(msg) + + def read_comment(s: str) -> bool: + nonlocal bounding_box, reading_trailer_comments + try: + m = split.match(s) + except re.error as e: + msg = "not an EPS file" + raise SyntaxError(msg) from e + + if not m: + return False + + k, v = m.group(1, 2) + self.info[k] = v + if k == "BoundingBox": + if v == "(atend)": + reading_trailer_comments = True + elif not bounding_box or (trailer_reached and reading_trailer_comments): + try: + # Note: The DSC spec says that BoundingBox + # fields should be integers, but some drivers + # put floating point values there anyway. + bounding_box = [int(float(i)) for i in v.split()] + except Exception: + pass + return True + + while True: + byte = self.fp.read(1) + if byte == b"": + # if we didn't read a byte we must be at the end of the file + if bytes_read == 0: + if reading_header_comments: + check_required_header_comments() + break + elif byte in b"\r\n": + # if we read a line ending character, ignore it and parse what + # we have already read. if we haven't read any other characters, + # continue reading + if bytes_read == 0: + continue + else: + # ASCII/hexadecimal lines in an EPS file must not exceed + # 255 characters, not including line ending characters + if bytes_read >= 255: + # only enforce this for lines starting with a "%", + # otherwise assume it's binary data + if byte_arr[0] == ord("%"): + msg = "not an EPS file" + raise SyntaxError(msg) + else: + if reading_header_comments: + check_required_header_comments() + reading_header_comments = False + # reset bytes_read so we can keep reading + # data until the end of the line + bytes_read = 0 + byte_arr[bytes_read] = byte[0] + bytes_read += 1 + continue + + if reading_header_comments: + # Load EPS header + + # if this line doesn't start with a "%", + # or does start with "%%EndComments", + # then we've reached the end of the header/comments + if byte_arr[0] != ord("%") or bytes_mv[:13] == b"%%EndComments": + check_required_header_comments() + reading_header_comments = False + continue + + s = str(bytes_mv[:bytes_read], "latin-1") + if not read_comment(s): + m = field.match(s) + if m: + k = m.group(1) + if k.startswith("PS-Adobe"): + self.info["PS-Adobe"] = k[9:] + else: + self.info[k] = "" + elif s[0] == "%": + # handle non-DSC PostScript comments that some + # tools mistakenly put in the Comments section + pass + else: + msg = "bad EPS header" + raise OSError(msg) + elif bytes_mv[:11] == b"%ImageData:": + # Check for an "ImageData" descriptor + # https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577413_pgfId-1035096 + + # If we've already read an "ImageData" descriptor, + # don't read another one. + if imagedata_size: + bytes_read = 0 + continue + + # Values: + # columns + # rows + # bit depth (1 or 8) + # mode (1: L, 2: LAB, 3: RGB, 4: CMYK) + # number of padding channels + # block size (number of bytes per row per channel) + # binary/ascii (1: binary, 2: ascii) + # data start identifier (the image data follows after a single line + # consisting only of this quoted value) + image_data_values = byte_arr[11:bytes_read].split(None, 7) + columns, rows, bit_depth, mode_id = ( + int(value) for value in image_data_values[:4] + ) + + if bit_depth == 1: + self._mode = "1" + elif bit_depth == 8: + try: + self._mode = self.mode_map[mode_id] + except ValueError: + break + else: + break + + # Parse the columns and rows after checking the bit depth and mode + # in case the bit depth and/or mode are invalid. + imagedata_size = columns, rows + elif bytes_mv[:5] == b"%%EOF": + break + elif trailer_reached and reading_trailer_comments: + # Load EPS trailer + s = str(bytes_mv[:bytes_read], "latin-1") + read_comment(s) + elif bytes_mv[:9] == b"%%Trailer": + trailer_reached = True + bytes_read = 0 + + # A "BoundingBox" is always required, + # even if an "ImageData" descriptor size exists. + if not bounding_box: + msg = "cannot determine EPS bounding box" + raise OSError(msg) + + # An "ImageData" size takes precedence over the "BoundingBox". + self._size = imagedata_size or ( + bounding_box[2] - bounding_box[0], + bounding_box[3] - bounding_box[1], + ) + + self.tile = [ + ImageFile._Tile("eps", (0, 0) + self.size, offset, (length, bounding_box)) + ] + + def _find_offset(self, fp: IO[bytes]) -> tuple[int, int]: + s = fp.read(4) + + if s == b"%!PS": + # for HEAD without binary preview + fp.seek(0, io.SEEK_END) + length = fp.tell() + offset = 0 + elif i32(s) == 0xC6D3D0C5: + # FIX for: Some EPS file not handled correctly / issue #302 + # EPS can contain binary data + # or start directly with latin coding + # more info see: + # https://web.archive.org/web/20160528181353/http://partners.adobe.com/public/developer/en/ps/5002.EPSF_Spec.pdf + s = fp.read(8) + offset = i32(s) + length = i32(s, 4) + else: + msg = "not an EPS file" + raise SyntaxError(msg) + + return length, offset + + def load( + self, scale: int = 1, transparency: bool = False + ) -> Image.core.PixelAccess | None: + # Load EPS via Ghostscript + if self.tile: + self.im = Ghostscript(self.tile, self.size, self.fp, scale, transparency) + self._mode = self.im.mode + self._size = self.im.size + self.tile = [] + return Image.Image.load(self) + + def load_seek(self, pos: int) -> None: + # we can't incrementally load, so force ImageFile.parser to + # use our custom load method by defining this method. + pass + + +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes, eps: int = 1) -> None: + """EPS Writer for the Python Imaging Library.""" + + # make sure image data is available + im.load() + + # determine PostScript image mode + if im.mode == "L": + operator = (8, 1, b"image") + elif im.mode == "RGB": + operator = (8, 3, b"false 3 colorimage") + elif im.mode == "CMYK": + operator = (8, 4, b"false 4 colorimage") + else: + msg = "image mode is not supported" + raise ValueError(msg) + + if eps: + # write EPS header + fp.write(b"%!PS-Adobe-3.0 EPSF-3.0\n") + fp.write(b"%%Creator: PIL 0.1 EpsEncode\n") + # fp.write("%%CreationDate: %s"...) + fp.write(b"%%%%BoundingBox: 0 0 %d %d\n" % im.size) + fp.write(b"%%Pages: 1\n") + fp.write(b"%%EndComments\n") + fp.write(b"%%Page: 1 1\n") + fp.write(b"%%ImageData: %d %d " % im.size) + fp.write(b'%d %d 0 1 1 "%s"\n' % operator) + + # image header + fp.write(b"gsave\n") + fp.write(b"10 dict begin\n") + fp.write(b"/buf %d string def\n" % (im.size[0] * operator[1])) + fp.write(b"%d %d scale\n" % im.size) + fp.write(b"%d %d 8\n" % im.size) # <= bits + fp.write(b"[%d 0 0 -%d 0 %d]\n" % (im.size[0], im.size[1], im.size[1])) + fp.write(b"{ currentfile buf readhexstring pop } bind\n") + fp.write(operator[2] + b"\n") + if hasattr(fp, "flush"): + fp.flush() + + ImageFile._save(im, fp, [ImageFile._Tile("eps", (0, 0) + im.size)]) + + fp.write(b"\n%%%%EndBinary\n") + fp.write(b"grestore end\n") + if hasattr(fp, "flush"): + fp.flush() + + +# -------------------------------------------------------------------- + + +Image.register_open(EpsImageFile.format, EpsImageFile, _accept) + +Image.register_save(EpsImageFile.format, _save) + +Image.register_extensions(EpsImageFile.format, [".ps", ".eps"]) + +Image.register_mime(EpsImageFile.format, "application/postscript") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ExifTags.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ExifTags.py new file mode 100644 index 0000000000000000000000000000000000000000..2280d5ce84b9badabe16cfb0db37f739d50d51c6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ExifTags.py @@ -0,0 +1,382 @@ +# +# The Python Imaging Library. +# $Id$ +# +# EXIF tags +# +# Copyright (c) 2003 by Secret Labs AB +# +# See the README file for information on usage and redistribution. +# + +""" +This module provides constants and clear-text names for various +well-known EXIF tags. +""" +from __future__ import annotations + +from enum import IntEnum + + +class Base(IntEnum): + # possibly incomplete + InteropIndex = 0x0001 + ProcessingSoftware = 0x000B + NewSubfileType = 0x00FE + SubfileType = 0x00FF + ImageWidth = 0x0100 + ImageLength = 0x0101 + BitsPerSample = 0x0102 + Compression = 0x0103 + PhotometricInterpretation = 0x0106 + Thresholding = 0x0107 + CellWidth = 0x0108 + CellLength = 0x0109 + FillOrder = 0x010A + DocumentName = 0x010D + ImageDescription = 0x010E + Make = 0x010F + Model = 0x0110 + StripOffsets = 0x0111 + Orientation = 0x0112 + SamplesPerPixel = 0x0115 + RowsPerStrip = 0x0116 + StripByteCounts = 0x0117 + MinSampleValue = 0x0118 + MaxSampleValue = 0x0119 + XResolution = 0x011A + YResolution = 0x011B + PlanarConfiguration = 0x011C + PageName = 0x011D + FreeOffsets = 0x0120 + FreeByteCounts = 0x0121 + GrayResponseUnit = 0x0122 + GrayResponseCurve = 0x0123 + T4Options = 0x0124 + T6Options = 0x0125 + ResolutionUnit = 0x0128 + PageNumber = 0x0129 + TransferFunction = 0x012D + Software = 0x0131 + DateTime = 0x0132 + Artist = 0x013B + HostComputer = 0x013C + Predictor = 0x013D + WhitePoint = 0x013E + PrimaryChromaticities = 0x013F + ColorMap = 0x0140 + HalftoneHints = 0x0141 + TileWidth = 0x0142 + TileLength = 0x0143 + TileOffsets = 0x0144 + TileByteCounts = 0x0145 + SubIFDs = 0x014A + InkSet = 0x014C + InkNames = 0x014D + NumberOfInks = 0x014E + DotRange = 0x0150 + TargetPrinter = 0x0151 + ExtraSamples = 0x0152 + SampleFormat = 0x0153 + SMinSampleValue = 0x0154 + SMaxSampleValue = 0x0155 + TransferRange = 0x0156 + ClipPath = 0x0157 + XClipPathUnits = 0x0158 + YClipPathUnits = 0x0159 + Indexed = 0x015A + JPEGTables = 0x015B + OPIProxy = 0x015F + JPEGProc = 0x0200 + JpegIFOffset = 0x0201 + JpegIFByteCount = 0x0202 + JpegRestartInterval = 0x0203 + JpegLosslessPredictors = 0x0205 + JpegPointTransforms = 0x0206 + JpegQTables = 0x0207 + JpegDCTables = 0x0208 + JpegACTables = 0x0209 + YCbCrCoefficients = 0x0211 + YCbCrSubSampling = 0x0212 + YCbCrPositioning = 0x0213 + ReferenceBlackWhite = 0x0214 + XMLPacket = 0x02BC + RelatedImageFileFormat = 0x1000 + RelatedImageWidth = 0x1001 + RelatedImageLength = 0x1002 + Rating = 0x4746 + RatingPercent = 0x4749 + ImageID = 0x800D + CFARepeatPatternDim = 0x828D + BatteryLevel = 0x828F + Copyright = 0x8298 + ExposureTime = 0x829A + FNumber = 0x829D + IPTCNAA = 0x83BB + ImageResources = 0x8649 + ExifOffset = 0x8769 + InterColorProfile = 0x8773 + ExposureProgram = 0x8822 + SpectralSensitivity = 0x8824 + GPSInfo = 0x8825 + ISOSpeedRatings = 0x8827 + OECF = 0x8828 + Interlace = 0x8829 + TimeZoneOffset = 0x882A + SelfTimerMode = 0x882B + SensitivityType = 0x8830 + StandardOutputSensitivity = 0x8831 + RecommendedExposureIndex = 0x8832 + ISOSpeed = 0x8833 + ISOSpeedLatitudeyyy = 0x8834 + ISOSpeedLatitudezzz = 0x8835 + ExifVersion = 0x9000 + DateTimeOriginal = 0x9003 + DateTimeDigitized = 0x9004 + OffsetTime = 0x9010 + OffsetTimeOriginal = 0x9011 + OffsetTimeDigitized = 0x9012 + ComponentsConfiguration = 0x9101 + CompressedBitsPerPixel = 0x9102 + ShutterSpeedValue = 0x9201 + ApertureValue = 0x9202 + BrightnessValue = 0x9203 + ExposureBiasValue = 0x9204 + MaxApertureValue = 0x9205 + SubjectDistance = 0x9206 + MeteringMode = 0x9207 + LightSource = 0x9208 + Flash = 0x9209 + FocalLength = 0x920A + Noise = 0x920D + ImageNumber = 0x9211 + SecurityClassification = 0x9212 + ImageHistory = 0x9213 + TIFFEPStandardID = 0x9216 + MakerNote = 0x927C + UserComment = 0x9286 + SubsecTime = 0x9290 + SubsecTimeOriginal = 0x9291 + SubsecTimeDigitized = 0x9292 + AmbientTemperature = 0x9400 + Humidity = 0x9401 + Pressure = 0x9402 + WaterDepth = 0x9403 + Acceleration = 0x9404 + CameraElevationAngle = 0x9405 + XPTitle = 0x9C9B + XPComment = 0x9C9C + XPAuthor = 0x9C9D + XPKeywords = 0x9C9E + XPSubject = 0x9C9F + FlashPixVersion = 0xA000 + ColorSpace = 0xA001 + ExifImageWidth = 0xA002 + ExifImageHeight = 0xA003 + RelatedSoundFile = 0xA004 + ExifInteroperabilityOffset = 0xA005 + FlashEnergy = 0xA20B + SpatialFrequencyResponse = 0xA20C + FocalPlaneXResolution = 0xA20E + FocalPlaneYResolution = 0xA20F + FocalPlaneResolutionUnit = 0xA210 + SubjectLocation = 0xA214 + ExposureIndex = 0xA215 + SensingMethod = 0xA217 + FileSource = 0xA300 + SceneType = 0xA301 + CFAPattern = 0xA302 + CustomRendered = 0xA401 + ExposureMode = 0xA402 + WhiteBalance = 0xA403 + DigitalZoomRatio = 0xA404 + FocalLengthIn35mmFilm = 0xA405 + SceneCaptureType = 0xA406 + GainControl = 0xA407 + Contrast = 0xA408 + Saturation = 0xA409 + Sharpness = 0xA40A + DeviceSettingDescription = 0xA40B + SubjectDistanceRange = 0xA40C + ImageUniqueID = 0xA420 + CameraOwnerName = 0xA430 + BodySerialNumber = 0xA431 + LensSpecification = 0xA432 + LensMake = 0xA433 + LensModel = 0xA434 + LensSerialNumber = 0xA435 + CompositeImage = 0xA460 + CompositeImageCount = 0xA461 + CompositeImageExposureTimes = 0xA462 + Gamma = 0xA500 + PrintImageMatching = 0xC4A5 + DNGVersion = 0xC612 + DNGBackwardVersion = 0xC613 + UniqueCameraModel = 0xC614 + LocalizedCameraModel = 0xC615 + CFAPlaneColor = 0xC616 + CFALayout = 0xC617 + LinearizationTable = 0xC618 + BlackLevelRepeatDim = 0xC619 + BlackLevel = 0xC61A + BlackLevelDeltaH = 0xC61B + BlackLevelDeltaV = 0xC61C + WhiteLevel = 0xC61D + DefaultScale = 0xC61E + DefaultCropOrigin = 0xC61F + DefaultCropSize = 0xC620 + ColorMatrix1 = 0xC621 + ColorMatrix2 = 0xC622 + CameraCalibration1 = 0xC623 + CameraCalibration2 = 0xC624 + ReductionMatrix1 = 0xC625 + ReductionMatrix2 = 0xC626 + AnalogBalance = 0xC627 + AsShotNeutral = 0xC628 + AsShotWhiteXY = 0xC629 + BaselineExposure = 0xC62A + BaselineNoise = 0xC62B + BaselineSharpness = 0xC62C + BayerGreenSplit = 0xC62D + LinearResponseLimit = 0xC62E + CameraSerialNumber = 0xC62F + LensInfo = 0xC630 + ChromaBlurRadius = 0xC631 + AntiAliasStrength = 0xC632 + ShadowScale = 0xC633 + DNGPrivateData = 0xC634 + MakerNoteSafety = 0xC635 + CalibrationIlluminant1 = 0xC65A + CalibrationIlluminant2 = 0xC65B + BestQualityScale = 0xC65C + RawDataUniqueID = 0xC65D + OriginalRawFileName = 0xC68B + OriginalRawFileData = 0xC68C + ActiveArea = 0xC68D + MaskedAreas = 0xC68E + AsShotICCProfile = 0xC68F + AsShotPreProfileMatrix = 0xC690 + CurrentICCProfile = 0xC691 + CurrentPreProfileMatrix = 0xC692 + ColorimetricReference = 0xC6BF + CameraCalibrationSignature = 0xC6F3 + ProfileCalibrationSignature = 0xC6F4 + AsShotProfileName = 0xC6F6 + NoiseReductionApplied = 0xC6F7 + ProfileName = 0xC6F8 + ProfileHueSatMapDims = 0xC6F9 + ProfileHueSatMapData1 = 0xC6FA + ProfileHueSatMapData2 = 0xC6FB + ProfileToneCurve = 0xC6FC + ProfileEmbedPolicy = 0xC6FD + ProfileCopyright = 0xC6FE + ForwardMatrix1 = 0xC714 + ForwardMatrix2 = 0xC715 + PreviewApplicationName = 0xC716 + PreviewApplicationVersion = 0xC717 + PreviewSettingsName = 0xC718 + PreviewSettingsDigest = 0xC719 + PreviewColorSpace = 0xC71A + PreviewDateTime = 0xC71B + RawImageDigest = 0xC71C + OriginalRawFileDigest = 0xC71D + SubTileBlockSize = 0xC71E + RowInterleaveFactor = 0xC71F + ProfileLookTableDims = 0xC725 + ProfileLookTableData = 0xC726 + OpcodeList1 = 0xC740 + OpcodeList2 = 0xC741 + OpcodeList3 = 0xC74E + NoiseProfile = 0xC761 + + +"""Maps EXIF tags to tag names.""" +TAGS = { + **{i.value: i.name for i in Base}, + 0x920C: "SpatialFrequencyResponse", + 0x9214: "SubjectLocation", + 0x9215: "ExposureIndex", + 0x828E: "CFAPattern", + 0x920B: "FlashEnergy", + 0x9216: "TIFF/EPStandardID", +} + + +class GPS(IntEnum): + GPSVersionID = 0x00 + GPSLatitudeRef = 0x01 + GPSLatitude = 0x02 + GPSLongitudeRef = 0x03 + GPSLongitude = 0x04 + GPSAltitudeRef = 0x05 + GPSAltitude = 0x06 + GPSTimeStamp = 0x07 + GPSSatellites = 0x08 + GPSStatus = 0x09 + GPSMeasureMode = 0x0A + GPSDOP = 0x0B + GPSSpeedRef = 0x0C + GPSSpeed = 0x0D + GPSTrackRef = 0x0E + GPSTrack = 0x0F + GPSImgDirectionRef = 0x10 + GPSImgDirection = 0x11 + GPSMapDatum = 0x12 + GPSDestLatitudeRef = 0x13 + GPSDestLatitude = 0x14 + GPSDestLongitudeRef = 0x15 + GPSDestLongitude = 0x16 + GPSDestBearingRef = 0x17 + GPSDestBearing = 0x18 + GPSDestDistanceRef = 0x19 + GPSDestDistance = 0x1A + GPSProcessingMethod = 0x1B + GPSAreaInformation = 0x1C + GPSDateStamp = 0x1D + GPSDifferential = 0x1E + GPSHPositioningError = 0x1F + + +"""Maps EXIF GPS tags to tag names.""" +GPSTAGS = {i.value: i.name for i in GPS} + + +class Interop(IntEnum): + InteropIndex = 0x0001 + InteropVersion = 0x0002 + RelatedImageFileFormat = 0x1000 + RelatedImageWidth = 0x1001 + RelatedImageHeight = 0x1002 + + +class IFD(IntEnum): + Exif = 0x8769 + GPSInfo = 0x8825 + MakerNote = 0x927C + Makernote = 0x927C # Deprecated + Interop = 0xA005 + IFD1 = -1 + + +class LightSource(IntEnum): + Unknown = 0x00 + Daylight = 0x01 + Fluorescent = 0x02 + Tungsten = 0x03 + Flash = 0x04 + Fine = 0x09 + Cloudy = 0x0A + Shade = 0x0B + DaylightFluorescent = 0x0C + DayWhiteFluorescent = 0x0D + CoolWhiteFluorescent = 0x0E + WhiteFluorescent = 0x0F + StandardLightA = 0x11 + StandardLightB = 0x12 + StandardLightC = 0x13 + D55 = 0x14 + D65 = 0x15 + D75 = 0x16 + D50 = 0x17 + ISO = 0x18 + Other = 0xFF diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..a3fdc0efeec6f7ec195112ded41d8ff1e248a6a0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py @@ -0,0 +1,152 @@ +# +# The Python Imaging Library +# $Id$ +# +# FITS file handling +# +# Copyright (c) 1998-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import gzip +import math + +from . import Image, ImageFile + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"SIMPLE") + + +class FitsImageFile(ImageFile.ImageFile): + format = "FITS" + format_description = "FITS" + + def _open(self) -> None: + assert self.fp is not None + + headers: dict[bytes, bytes] = {} + header_in_progress = False + decoder_name = "" + while True: + header = self.fp.read(80) + if not header: + msg = "Truncated FITS file" + raise OSError(msg) + keyword = header[:8].strip() + if keyword in (b"SIMPLE", b"XTENSION"): + header_in_progress = True + elif headers and not header_in_progress: + # This is now a data unit + break + elif keyword == b"END": + # Seek to the end of the header unit + self.fp.seek(math.ceil(self.fp.tell() / 2880) * 2880) + if not decoder_name: + decoder_name, offset, args = self._parse_headers(headers) + + header_in_progress = False + continue + + if decoder_name: + # Keep going to read past the headers + continue + + value = header[8:].split(b"/")[0].strip() + if value.startswith(b"="): + value = value[1:].strip() + if not headers and (not _accept(keyword) or value != b"T"): + msg = "Not a FITS file" + raise SyntaxError(msg) + headers[keyword] = value + + if not decoder_name: + msg = "No image data" + raise ValueError(msg) + + offset += self.fp.tell() - 80 + self.tile = [ImageFile._Tile(decoder_name, (0, 0) + self.size, offset, args)] + + def _get_size( + self, headers: dict[bytes, bytes], prefix: bytes + ) -> tuple[int, int] | None: + naxis = int(headers[prefix + b"NAXIS"]) + if naxis == 0: + return None + + if naxis == 1: + return 1, int(headers[prefix + b"NAXIS1"]) + else: + return int(headers[prefix + b"NAXIS1"]), int(headers[prefix + b"NAXIS2"]) + + def _parse_headers( + self, headers: dict[bytes, bytes] + ) -> tuple[str, int, tuple[str | int, ...]]: + prefix = b"" + decoder_name = "raw" + offset = 0 + if ( + headers.get(b"XTENSION") == b"'BINTABLE'" + and headers.get(b"ZIMAGE") == b"T" + and headers[b"ZCMPTYPE"] == b"'GZIP_1 '" + ): + no_prefix_size = self._get_size(headers, prefix) or (0, 0) + number_of_bits = int(headers[b"BITPIX"]) + offset = no_prefix_size[0] * no_prefix_size[1] * (number_of_bits // 8) + + prefix = b"Z" + decoder_name = "fits_gzip" + + size = self._get_size(headers, prefix) + if not size: + return "", 0, () + + self._size = size + + number_of_bits = int(headers[prefix + b"BITPIX"]) + if number_of_bits == 8: + self._mode = "L" + elif number_of_bits == 16: + self._mode = "I;16" + elif number_of_bits == 32: + self._mode = "I" + elif number_of_bits in (-32, -64): + self._mode = "F" + + args: tuple[str | int, ...] + if decoder_name == "raw": + args = (self.mode, 0, -1) + else: + args = (number_of_bits,) + return decoder_name, offset, args + + +class FitsGzipDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + value = gzip.decompress(self.fd.read()) + + rows = [] + offset = 0 + number_of_bits = min(self.args[0] // 8, 4) + for y in range(self.state.ysize): + row = bytearray() + for x in range(self.state.xsize): + row += value[offset + (4 - number_of_bits) : offset + 4] + offset += 4 + rows.append(row) + self.set_as_raw(bytes([pixel for row in rows[::-1] for pixel in row])) + return -1, 0 + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(FitsImageFile.format, FitsImageFile, _accept) +Image.register_decoder("fits_gzip", FitsGzipDecoder) + +Image.register_extensions(FitsImageFile.format, [".fit", ".fits"]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..7c5bfeefa1bf24ee536e13e26ce6ca8aa4ba319d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py @@ -0,0 +1,178 @@ +# +# The Python Imaging Library. +# $Id$ +# +# FLI/FLC file handling. +# +# History: +# 95-09-01 fl Created +# 97-01-03 fl Fixed parser, setup decoder tile +# 98-07-15 fl Renamed offset attribute to avoid name clash +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1995-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import i32le as i32 +from ._binary import o8 +from ._util import DeferredError + +# +# decoder + + +def _accept(prefix: bytes) -> bool: + return ( + len(prefix) >= 6 + and i16(prefix, 4) in [0xAF11, 0xAF12] + and i16(prefix, 14) in [0, 3] # flags + ) + + +## +# Image plugin for the FLI/FLC animation format. Use the seek +# method to load individual frames. + + +class FliImageFile(ImageFile.ImageFile): + format = "FLI" + format_description = "Autodesk FLI/FLC Animation" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # HEAD + s = self.fp.read(128) + if not (_accept(s) and s[20:22] == b"\x00\x00"): + msg = "not an FLI/FLC file" + raise SyntaxError(msg) + + # frames + self.n_frames = i16(s, 6) + self.is_animated = self.n_frames > 1 + + # image characteristics + self._mode = "P" + self._size = i16(s, 8), i16(s, 10) + + # animation speed + duration = i32(s, 16) + magic = i16(s, 4) + if magic == 0xAF11: + duration = (duration * 1000) // 70 + self.info["duration"] = duration + + # look for palette + palette = [(a, a, a) for a in range(256)] + + s = self.fp.read(16) + + self.__offset = 128 + + if i16(s, 4) == 0xF100: + # prefix chunk; ignore it + self.__offset = self.__offset + i32(s) + self.fp.seek(self.__offset) + s = self.fp.read(16) + + if i16(s, 4) == 0xF1FA: + # look for palette chunk + number_of_subchunks = i16(s, 6) + chunk_size: int | None = None + for _ in range(number_of_subchunks): + if chunk_size is not None: + self.fp.seek(chunk_size - 6, os.SEEK_CUR) + s = self.fp.read(6) + chunk_type = i16(s, 4) + if chunk_type in (4, 11): + self._palette(palette, 2 if chunk_type == 11 else 0) + break + chunk_size = i32(s) + if not chunk_size: + break + + self.palette = ImagePalette.raw( + "RGB", b"".join(o8(r) + o8(g) + o8(b) for (r, g, b) in palette) + ) + + # set things up to decode first frame + self.__frame = -1 + self._fp = self.fp + self.__rewind = self.fp.tell() + self.seek(0) + + def _palette(self, palette: list[tuple[int, int, int]], shift: int) -> None: + # load palette + + i = 0 + for e in range(i16(self.fp.read(2))): + s = self.fp.read(2) + i = i + s[0] + n = s[1] + if n == 0: + n = 256 + s = self.fp.read(n * 3) + for n in range(0, len(s), 3): + r = s[n] << shift + g = s[n + 1] << shift + b = s[n + 2] << shift + palette[i] = (r, g, b) + i += 1 + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._seek(0) + + for f in range(self.__frame + 1, frame + 1): + self._seek(f) + + def _seek(self, frame: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + if frame == 0: + self.__frame = -1 + self._fp.seek(self.__rewind) + self.__offset = 128 + else: + # ensure that the previous frame was loaded + self.load() + + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + self.__frame = frame + + # move to next frame + self.fp = self._fp + self.fp.seek(self.__offset) + + s = self.fp.read(4) + if not s: + msg = "missing frame size" + raise EOFError(msg) + + framesize = i32(s) + + self.decodermaxblock = framesize + self.tile = [ImageFile._Tile("fli", (0, 0) + self.size, self.__offset)] + + self.__offset += framesize + + def tell(self) -> int: + return self.__frame + + +# +# registry + +Image.register_open(FliImageFile.format, FliImageFile, _accept) + +Image.register_extensions(FliImageFile.format, [".fli", ".flc"]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FontFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..1e0c1c166b5932a7621e510eba047586465e03d8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FontFile.py @@ -0,0 +1,134 @@ +# +# The Python Imaging Library +# $Id$ +# +# base class for raster font file parsers +# +# history: +# 1997-06-05 fl created +# 1997-08-19 fl restrict image width +# +# Copyright (c) 1997-1998 by Secret Labs AB +# Copyright (c) 1997-1998 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import BinaryIO + +from . import Image, _binary + +WIDTH = 800 + + +def puti16( + fp: BinaryIO, values: tuple[int, int, int, int, int, int, int, int, int, int] +) -> None: + """Write network order (big-endian) 16-bit sequence""" + for v in values: + if v < 0: + v += 65536 + fp.write(_binary.o16be(v)) + + +class FontFile: + """Base class for raster font file handlers.""" + + bitmap: Image.Image | None = None + + def __init__(self) -> None: + self.info: dict[bytes, bytes | int] = {} + self.glyph: list[ + tuple[ + tuple[int, int], + tuple[int, int, int, int], + tuple[int, int, int, int], + Image.Image, + ] + | None + ] = [None] * 256 + + def __getitem__(self, ix: int) -> ( + tuple[ + tuple[int, int], + tuple[int, int, int, int], + tuple[int, int, int, int], + Image.Image, + ] + | None + ): + return self.glyph[ix] + + def compile(self) -> None: + """Create metrics and bitmap""" + + if self.bitmap: + return + + # create bitmap large enough to hold all data + h = w = maxwidth = 0 + lines = 1 + for glyph in self.glyph: + if glyph: + d, dst, src, im = glyph + h = max(h, src[3] - src[1]) + w = w + (src[2] - src[0]) + if w > WIDTH: + lines += 1 + w = src[2] - src[0] + maxwidth = max(maxwidth, w) + + xsize = maxwidth + ysize = lines * h + + if xsize == 0 and ysize == 0: + return + + self.ysize = h + + # paste glyphs into bitmap + self.bitmap = Image.new("1", (xsize, ysize)) + self.metrics: list[ + tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]] + | None + ] = [None] * 256 + x = y = 0 + for i in range(256): + glyph = self[i] + if glyph: + d, dst, src, im = glyph + xx = src[2] - src[0] + x0, y0 = x, y + x = x + xx + if x > WIDTH: + x, y = 0, y + h + x0, y0 = x, y + x = xx + s = src[0] + x0, src[1] + y0, src[2] + x0, src[3] + y0 + self.bitmap.paste(im.crop(src), s) + self.metrics[i] = d, dst, s + + def save(self, filename: str) -> None: + """Save font""" + + self.compile() + + # font data + if not self.bitmap: + msg = "No bitmap created" + raise ValueError(msg) + self.bitmap.save(os.path.splitext(filename)[0] + ".pbm", "PNG") + + # font metrics + with open(os.path.splitext(filename)[0] + ".pil", "wb") as fp: + fp.write(b"PILfont\n") + fp.write(f";;;;;;{self.ysize};\n".encode("ascii")) # HACK!!! + fp.write(b"DATA\n") + for id in range(256): + m = self.metrics[id] + if not m: + puti16(fp, (0,) * 10) + else: + puti16(fp, m[0] + m[1] + m[2]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..fd992cd9e20eb7cf0c6de347ac0a76f928ffc238 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py @@ -0,0 +1,257 @@ +# +# THIS IS WORK IN PROGRESS +# +# The Python Imaging Library. +# $Id$ +# +# FlashPix support for PIL +# +# History: +# 97-01-25 fl Created (reads uncompressed RGB images only) +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import olefile + +from . import Image, ImageFile +from ._binary import i32le as i32 + +# we map from colour field tuples to (mode, rawmode) descriptors +MODES = { + # opacity + (0x00007FFE,): ("A", "L"), + # monochrome + (0x00010000,): ("L", "L"), + (0x00018000, 0x00017FFE): ("RGBA", "LA"), + # photo YCC + (0x00020000, 0x00020001, 0x00020002): ("RGB", "YCC;P"), + (0x00028000, 0x00028001, 0x00028002, 0x00027FFE): ("RGBA", "YCCA;P"), + # standard RGB (NIFRGB) + (0x00030000, 0x00030001, 0x00030002): ("RGB", "RGB"), + (0x00038000, 0x00038001, 0x00038002, 0x00037FFE): ("RGBA", "RGBA"), +} + + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(olefile.MAGIC) + + +## +# Image plugin for the FlashPix images. + + +class FpxImageFile(ImageFile.ImageFile): + format = "FPX" + format_description = "FlashPix" + + def _open(self) -> None: + # + # read the OLE directory and see if this is a likely + # to be a FlashPix file + + try: + self.ole = olefile.OleFileIO(self.fp) + except OSError as e: + msg = "not an FPX file; invalid OLE file" + raise SyntaxError(msg) from e + + root = self.ole.root + if not root or root.clsid != "56616700-C154-11CE-8553-00AA00A1F95B": + msg = "not an FPX file; bad root CLSID" + raise SyntaxError(msg) + + self._open_index(1) + + def _open_index(self, index: int = 1) -> None: + # + # get the Image Contents Property Set + + prop = self.ole.getproperties( + [f"Data Object Store {index:06d}", "\005Image Contents"] + ) + + # size (highest resolution) + + assert isinstance(prop[0x1000002], int) + assert isinstance(prop[0x1000003], int) + self._size = prop[0x1000002], prop[0x1000003] + + size = max(self.size) + i = 1 + while size > 64: + size = size // 2 + i += 1 + self.maxid = i - 1 + + # mode. instead of using a single field for this, flashpix + # requires you to specify the mode for each channel in each + # resolution subimage, and leaves it to the decoder to make + # sure that they all match. for now, we'll cheat and assume + # that this is always the case. + + id = self.maxid << 16 + + s = prop[0x2000002 | id] + + if not isinstance(s, bytes) or (bands := i32(s, 4)) > 4: + msg = "Invalid number of bands" + raise OSError(msg) + + # note: for now, we ignore the "uncalibrated" flag + colors = tuple(i32(s, 8 + i * 4) & 0x7FFFFFFF for i in range(bands)) + + self._mode, self.rawmode = MODES[colors] + + # load JPEG tables, if any + self.jpeg = {} + for i in range(256): + id = 0x3000001 | (i << 16) + if id in prop: + self.jpeg[i] = prop[id] + + self._open_subimage(1, self.maxid) + + def _open_subimage(self, index: int = 1, subimage: int = 0) -> None: + # + # setup tile descriptors for a given subimage + + stream = [ + f"Data Object Store {index:06d}", + f"Resolution {subimage:04d}", + "Subimage 0000 Header", + ] + + fp = self.ole.openstream(stream) + + # skip prefix + fp.read(28) + + # header stream + s = fp.read(36) + + size = i32(s, 4), i32(s, 8) + # tilecount = i32(s, 12) + tilesize = i32(s, 16), i32(s, 20) + # channels = i32(s, 24) + offset = i32(s, 28) + length = i32(s, 32) + + if size != self.size: + msg = "subimage mismatch" + raise OSError(msg) + + # get tile descriptors + fp.seek(28 + offset) + s = fp.read(i32(s, 12) * length) + + x = y = 0 + xsize, ysize = size + xtile, ytile = tilesize + self.tile = [] + + for i in range(0, len(s), length): + x1 = min(xsize, x + xtile) + y1 = min(ysize, y + ytile) + + compression = i32(s, i + 8) + + if compression == 0: + self.tile.append( + ImageFile._Tile( + "raw", + (x, y, x1, y1), + i32(s, i) + 28, + self.rawmode, + ) + ) + + elif compression == 1: + # FIXME: the fill decoder is not implemented + self.tile.append( + ImageFile._Tile( + "fill", + (x, y, x1, y1), + i32(s, i) + 28, + (self.rawmode, s[12:16]), + ) + ) + + elif compression == 2: + internal_color_conversion = s[14] + jpeg_tables = s[15] + rawmode = self.rawmode + + if internal_color_conversion: + # The image is stored as usual (usually YCbCr). + if rawmode == "RGBA": + # For "RGBA", data is stored as YCbCrA based on + # negative RGB. The following trick works around + # this problem : + jpegmode, rawmode = "YCbCrK", "CMYK" + else: + jpegmode = None # let the decoder decide + + else: + # The image is stored as defined by rawmode + jpegmode = rawmode + + self.tile.append( + ImageFile._Tile( + "jpeg", + (x, y, x1, y1), + i32(s, i) + 28, + (rawmode, jpegmode), + ) + ) + + # FIXME: jpeg tables are tile dependent; the prefix + # data must be placed in the tile descriptor itself! + + if jpeg_tables: + self.tile_prefix = self.jpeg[jpeg_tables] + + else: + msg = "unknown/invalid compression" + raise OSError(msg) + + x = x + xtile + if x >= xsize: + x, y = 0, y + ytile + if y >= ysize: + break # isn't really required + + self.stream = stream + self._fp = self.fp + self.fp = None + + def load(self) -> Image.core.PixelAccess | None: + if not self.fp: + self.fp = self.ole.openstream(self.stream[:2] + ["Subimage 0000 Data"]) + + return ImageFile.ImageFile.load(self) + + def close(self) -> None: + self.ole.close() + super().close() + + def __exit__(self, *args: object) -> None: + self.ole.close() + super().__exit__() + + +# +# -------------------------------------------------------------------- + + +Image.register_open(FpxImageFile.format, FpxImageFile, _accept) + +Image.register_extension(FpxImageFile.format, ".fpx") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d60e75bb60bdb5113c7cb3c48840918207ced694 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py @@ -0,0 +1,114 @@ +""" +A Pillow loader for .ftc and .ftu files (FTEX) +Jerome Leclanche + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: + https://creativecommons.org/publicdomain/zero/1.0/ + +Independence War 2: Edge Of Chaos - Texture File Format - 16 October 2001 + +The textures used for 3D objects in Independence War 2: Edge Of Chaos are in a +packed custom format called FTEX. This file format uses file extensions FTC +and FTU. +* FTC files are compressed textures (using standard texture compression). +* FTU files are not compressed. +Texture File Format +The FTC and FTU texture files both use the same format. This +has the following structure: +{header} +{format_directory} +{data} +Where: +{header} = { + u32:magic, + u32:version, + u32:width, + u32:height, + u32:mipmap_count, + u32:format_count +} + +* The "magic" number is "FTEX". +* "width" and "height" are the dimensions of the texture. +* "mipmap_count" is the number of mipmaps in the texture. +* "format_count" is the number of texture formats (different versions of the +same texture) in this file. + +{format_directory} = format_count * { u32:format, u32:where } + +The format value is 0 for DXT1 compressed textures and 1 for 24-bit RGB +uncompressed textures. +The texture data for a format starts at the position "where" in the file. + +Each set of texture data in the file has the following structure: +{data} = format_count * { u32:mipmap_size, mipmap_size * { u8 } } +* "mipmap_size" is the number of bytes in that mip level. For compressed +textures this is the size of the texture data compressed with DXT1. For 24 bit +uncompressed textures, this is 3 * width * height. Following this are the image +bytes for that mipmap level. + +Note: All data is stored in little-Endian (Intel) byte order. +""" + +from __future__ import annotations + +import struct +from enum import IntEnum +from io import BytesIO + +from . import Image, ImageFile + +MAGIC = b"FTEX" + + +class Format(IntEnum): + DXT1 = 0 + UNCOMPRESSED = 1 + + +class FtexImageFile(ImageFile.ImageFile): + format = "FTEX" + format_description = "Texture File Format (IW2:EOC)" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not an FTEX file" + raise SyntaxError(msg) + struct.unpack(" None: + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(MAGIC) + + +Image.register_open(FtexImageFile.format, FtexImageFile, _accept) +Image.register_extensions(FtexImageFile.format, [".ftc", ".ftu"]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f319d7e846e4c7ecb32a43751204bd1fbee168c0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py @@ -0,0 +1,103 @@ +# +# The Python Imaging Library +# +# load a GIMP brush file +# +# History: +# 96-03-14 fl Created +# 16-01-08 es Version 2 +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# Copyright (c) Eric Soroos 2016. +# +# See the README file for information on usage and redistribution. +# +# +# See https://github.com/GNOME/gimp/blob/mainline/devel-docs/gbr.txt for +# format documentation. +# +# This code Interprets version 1 and 2 .gbr files. +# Version 1 files are obsolete, and should not be used for new +# brushes. +# Version 2 files are saved by GIMP v2.8 (at least) +# Version 3 files have a format specifier of 18 for 16bit floats in +# the color depth field. This is currently unsupported by Pillow. +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i32be as i32 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 8 and i32(prefix, 0) >= 20 and i32(prefix, 4) in (1, 2) + + +## +# Image plugin for the GIMP brush format. + + +class GbrImageFile(ImageFile.ImageFile): + format = "GBR" + format_description = "GIMP brush file" + + def _open(self) -> None: + header_size = i32(self.fp.read(4)) + if header_size < 20: + msg = "not a GIMP brush" + raise SyntaxError(msg) + version = i32(self.fp.read(4)) + if version not in (1, 2): + msg = f"Unsupported GIMP brush version: {version}" + raise SyntaxError(msg) + + width = i32(self.fp.read(4)) + height = i32(self.fp.read(4)) + color_depth = i32(self.fp.read(4)) + if width <= 0 or height <= 0: + msg = "not a GIMP brush" + raise SyntaxError(msg) + if color_depth not in (1, 4): + msg = f"Unsupported GIMP brush color depth: {color_depth}" + raise SyntaxError(msg) + + if version == 1: + comment_length = header_size - 20 + else: + comment_length = header_size - 28 + magic_number = self.fp.read(4) + if magic_number != b"GIMP": + msg = "not a GIMP brush, bad magic number" + raise SyntaxError(msg) + self.info["spacing"] = i32(self.fp.read(4)) + + comment = self.fp.read(comment_length)[:-1] + + if color_depth == 1: + self._mode = "L" + else: + self._mode = "RGBA" + + self._size = width, height + + self.info["comment"] = comment + + # Image might not be small + Image._decompression_bomb_check(self.size) + + # Data is an uncompressed block of w * h * bytes/pixel + self._data_size = width * height * color_depth + + def load(self) -> Image.core.PixelAccess | None: + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + self.frombytes(self.fp.read(self._data_size)) + return Image.Image.load(self) + + +# +# registry + + +Image.register_open(GbrImageFile.format, GbrImageFile, _accept) +Image.register_extension(GbrImageFile.format, ".gbr") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..891225ce2fd034a11963bb64212cfa7311190441 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py @@ -0,0 +1,102 @@ +# +# The Python Imaging Library. +# $Id$ +# +# GD file handling +# +# History: +# 1996-04-12 fl Created +# +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + + +""" +.. note:: + This format cannot be automatically recognized, so the + class is not registered for use with :py:func:`PIL.Image.open()`. To open a + gd file, use the :py:func:`PIL.GdImageFile.open()` function instead. + +.. warning:: + THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This + implementation is provided for convenience and demonstrational + purposes only. +""" +from __future__ import annotations + +from typing import IO + +from . import ImageFile, ImagePalette, UnidentifiedImageError +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._typing import StrOrBytesPath + + +class GdImageFile(ImageFile.ImageFile): + """ + Image plugin for the GD uncompressed format. Note that this format + is not supported by the standard :py:func:`PIL.Image.open()` function. To use + this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and + use the :py:func:`PIL.GdImageFile.open()` function. + """ + + format = "GD" + format_description = "GD uncompressed images" + + def _open(self) -> None: + # Header + assert self.fp is not None + + s = self.fp.read(1037) + + if i16(s) not in [65534, 65535]: + msg = "Not a valid GD 2.x .gd file" + raise SyntaxError(msg) + + self._mode = "P" + self._size = i16(s, 2), i16(s, 4) + + true_color = s[6] + true_color_offset = 2 if true_color else 0 + + # transparency index + tindex = i32(s, 7 + true_color_offset) + if tindex < 256: + self.info["transparency"] = tindex + + self.palette = ImagePalette.raw( + "RGBX", s[7 + true_color_offset + 6 : 7 + true_color_offset + 6 + 256 * 4] + ) + + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + 7 + true_color_offset + 6 + 256 * 4, + "L", + ) + ] + + +def open(fp: StrOrBytesPath | IO[bytes], mode: str = "r") -> GdImageFile: + """ + Load texture from a GD image file. + + :param fp: GD file name, or an opened file handle. + :param mode: Optional mode. In this version, if the mode argument + is given, it must be "r". + :returns: An image instance. + :raises OSError: If the image could not be read. + """ + if mode != "r": + msg = "bad mode" + raise ValueError(msg) + + try: + return GdImageFile(fp) + except SyntaxError as e: + msg = "cannot identify this image file" + raise UnidentifiedImageError(msg) from e diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b03aa7f1505e8624b1a50551adbc4488ac3bd1fa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py @@ -0,0 +1,1213 @@ +# +# The Python Imaging Library. +# $Id$ +# +# GIF file handling +# +# History: +# 1995-09-01 fl Created +# 1996-12-14 fl Added interlace support +# 1996-12-30 fl Added animation support +# 1997-01-05 fl Added write support, fixed local colour map bug +# 1997-02-23 fl Make sure to load raster data in getdata() +# 1997-07-05 fl Support external decoder (0.4) +# 1998-07-09 fl Handle all modes when saving (0.5) +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 2001-04-16 fl Added rewind support (seek to frame 0) (0.6) +# 2001-04-17 fl Added palette optimization (0.7) +# 2002-06-06 fl Added transparency support for save (0.8) +# 2004-02-24 fl Disable interlacing for small images +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1995-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import itertools +import math +import os +import subprocess +from enum import IntEnum +from functools import cached_property +from typing import IO, Any, Literal, NamedTuple, Union, cast + +from . import ( + Image, + ImageChops, + ImageFile, + ImageMath, + ImageOps, + ImagePalette, + ImageSequence, +) +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 +from ._util import DeferredError + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + from ._typing import Buffer + + +class LoadingStrategy(IntEnum): + """.. versionadded:: 9.1.0""" + + RGB_AFTER_FIRST = 0 + RGB_AFTER_DIFFERENT_PALETTE_ONLY = 1 + RGB_ALWAYS = 2 + + +#: .. versionadded:: 9.1.0 +LOADING_STRATEGY = LoadingStrategy.RGB_AFTER_FIRST + +# -------------------------------------------------------------------- +# Identify/read GIF files + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"GIF87a", b"GIF89a")) + + +## +# Image plugin for GIF images. This plugin supports both GIF87 and +# GIF89 images. + + +class GifImageFile(ImageFile.ImageFile): + format = "GIF" + format_description = "Compuserve GIF" + _close_exclusive_fp_after_loading = False + + global_palette = None + + def data(self) -> bytes | None: + s = self.fp.read(1) + if s and s[0]: + return self.fp.read(s[0]) + return None + + def _is_palette_needed(self, p: bytes) -> bool: + for i in range(0, len(p), 3): + if not (i // 3 == p[i] == p[i + 1] == p[i + 2]): + return True + return False + + def _open(self) -> None: + # Screen + s = self.fp.read(13) + if not _accept(s): + msg = "not a GIF file" + raise SyntaxError(msg) + + self.info["version"] = s[:6] + self._size = i16(s, 6), i16(s, 8) + flags = s[10] + bits = (flags & 7) + 1 + + if flags & 128: + # get global palette + self.info["background"] = s[11] + # check if palette contains colour indices + p = self.fp.read(3 << bits) + if self._is_palette_needed(p): + p = ImagePalette.raw("RGB", p) + self.global_palette = self.palette = p + + self._fp = self.fp # FIXME: hack + self.__rewind = self.fp.tell() + self._n_frames: int | None = None + self._seek(0) # get ready to read first frame + + @property + def n_frames(self) -> int: + if self._n_frames is None: + current = self.tell() + try: + while True: + self._seek(self.tell() + 1, False) + except EOFError: + self._n_frames = self.tell() + 1 + self.seek(current) + return self._n_frames + + @cached_property + def is_animated(self) -> bool: + if self._n_frames is not None: + return self._n_frames != 1 + + current = self.tell() + if current: + return True + + try: + self._seek(1, False) + is_animated = True + except EOFError: + is_animated = False + + self.seek(current) + return is_animated + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._im = None + self._seek(0) + + last_frame = self.__frame + for f in range(self.__frame + 1, frame + 1): + try: + self._seek(f) + except EOFError as e: + self.seek(last_frame) + msg = "no more images in GIF file" + raise EOFError(msg) from e + + def _seek(self, frame: int, update_image: bool = True) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + if frame == 0: + # rewind + self.__offset = 0 + self.dispose: _imaging.ImagingCore | None = None + self.__frame = -1 + self._fp.seek(self.__rewind) + self.disposal_method = 0 + if "comment" in self.info: + del self.info["comment"] + else: + # ensure that the previous frame was loaded + if self.tile and update_image: + self.load() + + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + + self.fp = self._fp + if self.__offset: + # backup to last frame + self.fp.seek(self.__offset) + while self.data(): + pass + self.__offset = 0 + + s = self.fp.read(1) + if not s or s == b";": + msg = "no more images in GIF file" + raise EOFError(msg) + + palette: ImagePalette.ImagePalette | Literal[False] | None = None + + info: dict[str, Any] = {} + frame_transparency = None + interlace = None + frame_dispose_extent = None + while True: + if not s: + s = self.fp.read(1) + if not s or s == b";": + break + + elif s == b"!": + # + # extensions + # + s = self.fp.read(1) + block = self.data() + if s[0] == 249 and block is not None: + # + # graphic control extension + # + flags = block[0] + if flags & 1: + frame_transparency = block[3] + info["duration"] = i16(block, 1) * 10 + + # disposal method - find the value of bits 4 - 6 + dispose_bits = 0b00011100 & flags + dispose_bits = dispose_bits >> 2 + if dispose_bits: + # only set the dispose if it is not + # unspecified. I'm not sure if this is + # correct, but it seems to prevent the last + # frame from looking odd for some animations + self.disposal_method = dispose_bits + elif s[0] == 254: + # + # comment extension + # + comment = b"" + + # Read this comment block + while block: + comment += block + block = self.data() + + if "comment" in info: + # If multiple comment blocks in frame, separate with \n + info["comment"] += b"\n" + comment + else: + info["comment"] = comment + s = None + continue + elif s[0] == 255 and frame == 0 and block is not None: + # + # application extension + # + info["extension"] = block, self.fp.tell() + if block.startswith(b"NETSCAPE2.0"): + block = self.data() + if block and len(block) >= 3 and block[0] == 1: + self.info["loop"] = i16(block, 1) + while self.data(): + pass + + elif s == b",": + # + # local image + # + s = self.fp.read(9) + + # extent + x0, y0 = i16(s, 0), i16(s, 2) + x1, y1 = x0 + i16(s, 4), y0 + i16(s, 6) + if (x1 > self.size[0] or y1 > self.size[1]) and update_image: + self._size = max(x1, self.size[0]), max(y1, self.size[1]) + Image._decompression_bomb_check(self._size) + frame_dispose_extent = x0, y0, x1, y1 + flags = s[8] + + interlace = (flags & 64) != 0 + + if flags & 128: + bits = (flags & 7) + 1 + p = self.fp.read(3 << bits) + if self._is_palette_needed(p): + palette = ImagePalette.raw("RGB", p) + else: + palette = False + + # image data + bits = self.fp.read(1)[0] + self.__offset = self.fp.tell() + break + s = None + + if interlace is None: + msg = "image not found in GIF frame" + raise EOFError(msg) + + self.__frame = frame + if not update_image: + return + + self.tile = [] + + if self.dispose: + self.im.paste(self.dispose, self.dispose_extent) + + self._frame_palette = palette if palette is not None else self.global_palette + self._frame_transparency = frame_transparency + if frame == 0: + if self._frame_palette: + if LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS: + self._mode = "RGBA" if frame_transparency is not None else "RGB" + else: + self._mode = "P" + else: + self._mode = "L" + + if palette: + self.palette = palette + elif self.global_palette: + from copy import copy + + self.palette = copy(self.global_palette) + else: + self.palette = None + else: + if self.mode == "P": + if ( + LOADING_STRATEGY != LoadingStrategy.RGB_AFTER_DIFFERENT_PALETTE_ONLY + or palette + ): + if "transparency" in self.info: + self.im.putpalettealpha(self.info["transparency"], 0) + self.im = self.im.convert("RGBA", Image.Dither.FLOYDSTEINBERG) + self._mode = "RGBA" + del self.info["transparency"] + else: + self._mode = "RGB" + self.im = self.im.convert("RGB", Image.Dither.FLOYDSTEINBERG) + + def _rgb(color: int) -> tuple[int, int, int]: + if self._frame_palette: + if color * 3 + 3 > len(self._frame_palette.palette): + color = 0 + return cast( + tuple[int, int, int], + tuple(self._frame_palette.palette[color * 3 : color * 3 + 3]), + ) + else: + return (color, color, color) + + self.dispose = None + self.dispose_extent: tuple[int, int, int, int] | None = frame_dispose_extent + if self.dispose_extent and self.disposal_method >= 2: + try: + if self.disposal_method == 2: + # replace with background colour + + # only dispose the extent in this frame + x0, y0, x1, y1 = self.dispose_extent + dispose_size = (x1 - x0, y1 - y0) + + Image._decompression_bomb_check(dispose_size) + + # by convention, attempt to use transparency first + dispose_mode = "P" + color = self.info.get("transparency", frame_transparency) + if color is not None: + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGBA" + color = _rgb(color) + (0,) + else: + color = self.info.get("background", 0) + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGB" + color = _rgb(color) + self.dispose = Image.core.fill(dispose_mode, dispose_size, color) + else: + # replace with previous contents + if self._im is not None: + # only dispose the extent in this frame + self.dispose = self._crop(self.im, self.dispose_extent) + elif frame_transparency is not None: + x0, y0, x1, y1 = self.dispose_extent + dispose_size = (x1 - x0, y1 - y0) + + Image._decompression_bomb_check(dispose_size) + dispose_mode = "P" + color = frame_transparency + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGBA" + color = _rgb(frame_transparency) + (0,) + self.dispose = Image.core.fill( + dispose_mode, dispose_size, color + ) + except AttributeError: + pass + + if interlace is not None: + transparency = -1 + if frame_transparency is not None: + if frame == 0: + if LOADING_STRATEGY != LoadingStrategy.RGB_ALWAYS: + self.info["transparency"] = frame_transparency + elif self.mode not in ("RGB", "RGBA"): + transparency = frame_transparency + self.tile = [ + ImageFile._Tile( + "gif", + (x0, y0, x1, y1), + self.__offset, + (bits, interlace, transparency), + ) + ] + + if info.get("comment"): + self.info["comment"] = info["comment"] + for k in ["duration", "extension"]: + if k in info: + self.info[k] = info[k] + elif k in self.info: + del self.info[k] + + def load_prepare(self) -> None: + temp_mode = "P" if self._frame_palette else "L" + self._prev_im = None + if self.__frame == 0: + if self._frame_transparency is not None: + self.im = Image.core.fill( + temp_mode, self.size, self._frame_transparency + ) + elif self.mode in ("RGB", "RGBA"): + self._prev_im = self.im + if self._frame_palette: + self.im = Image.core.fill("P", self.size, self._frame_transparency or 0) + self.im.putpalette("RGB", *self._frame_palette.getdata()) + else: + self._im = None + if not self._prev_im and self._im is not None and self.size != self.im.size: + expanded_im = Image.core.fill(self.im.mode, self.size) + if self._frame_palette: + expanded_im.putpalette("RGB", *self._frame_palette.getdata()) + expanded_im.paste(self.im, (0, 0) + self.im.size) + + self.im = expanded_im + self._mode = temp_mode + self._frame_palette = None + + super().load_prepare() + + def load_end(self) -> None: + if self.__frame == 0: + if self.mode == "P" and LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS: + if self._frame_transparency is not None: + self.im.putpalettealpha(self._frame_transparency, 0) + self._mode = "RGBA" + else: + self._mode = "RGB" + self.im = self.im.convert(self.mode, Image.Dither.FLOYDSTEINBERG) + return + if not self._prev_im: + return + if self.size != self._prev_im.size: + if self._frame_transparency is not None: + expanded_im = Image.core.fill("RGBA", self.size) + else: + expanded_im = Image.core.fill("P", self.size) + expanded_im.putpalette("RGB", "RGB", self.im.getpalette()) + expanded_im = expanded_im.convert("RGB") + expanded_im.paste(self._prev_im, (0, 0) + self._prev_im.size) + + self._prev_im = expanded_im + assert self._prev_im is not None + if self._frame_transparency is not None: + if self.mode == "L": + frame_im = self.im.convert_transparent("LA", self._frame_transparency) + else: + self.im.putpalettealpha(self._frame_transparency, 0) + frame_im = self.im.convert("RGBA") + else: + frame_im = self.im.convert("RGB") + + assert self.dispose_extent is not None + frame_im = self._crop(frame_im, self.dispose_extent) + + self.im = self._prev_im + self._mode = self.im.mode + if frame_im.mode in ("LA", "RGBA"): + self.im.paste(frame_im, self.dispose_extent, frame_im) + else: + self.im.paste(frame_im, self.dispose_extent) + + def tell(self) -> int: + return self.__frame + + +# -------------------------------------------------------------------- +# Write GIF files + + +RAWMODE = {"1": "L", "L": "L", "P": "P"} + + +def _normalize_mode(im: Image.Image) -> Image.Image: + """ + Takes an image (or frame), returns an image in a mode that is appropriate + for saving in a Gif. + + It may return the original image, or it may return an image converted to + palette or 'L' mode. + + :param im: Image object + :returns: Image object + """ + if im.mode in RAWMODE: + im.load() + return im + if Image.getmodebase(im.mode) == "RGB": + im = im.convert("P", palette=Image.Palette.ADAPTIVE) + assert im.palette is not None + if im.palette.mode == "RGBA": + for rgba in im.palette.colors: + if rgba[3] == 0: + im.info["transparency"] = im.palette.colors[rgba] + break + return im + return im.convert("L") + + +_Palette = Union[bytes, bytearray, list[int], ImagePalette.ImagePalette] + + +def _normalize_palette( + im: Image.Image, palette: _Palette | None, info: dict[str, Any] +) -> Image.Image: + """ + Normalizes the palette for image. + - Sets the palette to the incoming palette, if provided. + - Ensures that there's a palette for L mode images + - Optimizes the palette if necessary/desired. + + :param im: Image object + :param palette: bytes object containing the source palette, or .... + :param info: encoderinfo + :returns: Image object + """ + source_palette = None + if palette: + # a bytes palette + if isinstance(palette, (bytes, bytearray, list)): + source_palette = bytearray(palette[:768]) + if isinstance(palette, ImagePalette.ImagePalette): + source_palette = bytearray(palette.palette) + + if im.mode == "P": + if not source_palette: + im_palette = im.getpalette(None) + assert im_palette is not None + source_palette = bytearray(im_palette) + else: # L-mode + if not source_palette: + source_palette = bytearray(i // 3 for i in range(768)) + im.palette = ImagePalette.ImagePalette("RGB", palette=source_palette) + assert source_palette is not None + + if palette: + used_palette_colors: list[int | None] = [] + assert im.palette is not None + for i in range(0, len(source_palette), 3): + source_color = tuple(source_palette[i : i + 3]) + index = im.palette.colors.get(source_color) + if index in used_palette_colors: + index = None + used_palette_colors.append(index) + for i, index in enumerate(used_palette_colors): + if index is None: + for j in range(len(used_palette_colors)): + if j not in used_palette_colors: + used_palette_colors[i] = j + break + dest_map: list[int] = [] + for index in used_palette_colors: + assert index is not None + dest_map.append(index) + im = im.remap_palette(dest_map) + else: + optimized_palette_colors = _get_optimize(im, info) + if optimized_palette_colors is not None: + im = im.remap_palette(optimized_palette_colors, source_palette) + if "transparency" in info: + try: + info["transparency"] = optimized_palette_colors.index( + info["transparency"] + ) + except ValueError: + del info["transparency"] + return im + + assert im.palette is not None + im.palette.palette = source_palette + return im + + +def _write_single_frame( + im: Image.Image, + fp: IO[bytes], + palette: _Palette | None, +) -> None: + im_out = _normalize_mode(im) + for k, v in im_out.info.items(): + if isinstance(k, str): + im.encoderinfo.setdefault(k, v) + im_out = _normalize_palette(im_out, palette, im.encoderinfo) + + for s in _get_global_header(im_out, im.encoderinfo): + fp.write(s) + + # local image header + flags = 0 + if get_interlace(im): + flags = flags | 64 + _write_local_header(fp, im, (0, 0), flags) + + im_out.encoderconfig = (8, get_interlace(im)) + ImageFile._save( + im_out, fp, [ImageFile._Tile("gif", (0, 0) + im.size, 0, RAWMODE[im_out.mode])] + ) + + fp.write(b"\0") # end of image data + + +def _getbbox( + base_im: Image.Image, im_frame: Image.Image +) -> tuple[Image.Image, tuple[int, int, int, int] | None]: + palette_bytes = [ + bytes(im.palette.palette) if im.palette else b"" for im in (base_im, im_frame) + ] + if palette_bytes[0] != palette_bytes[1]: + im_frame = im_frame.convert("RGBA") + base_im = base_im.convert("RGBA") + delta = ImageChops.subtract_modulo(im_frame, base_im) + return delta, delta.getbbox(alpha_only=False) + + +class _Frame(NamedTuple): + im: Image.Image + bbox: tuple[int, int, int, int] | None + encoderinfo: dict[str, Any] + + +def _write_multiple_frames( + im: Image.Image, fp: IO[bytes], palette: _Palette | None +) -> bool: + duration = im.encoderinfo.get("duration") + disposal = im.encoderinfo.get("disposal", im.info.get("disposal")) + + im_frames: list[_Frame] = [] + previous_im: Image.Image | None = None + frame_count = 0 + background_im = None + for imSequence in itertools.chain([im], im.encoderinfo.get("append_images", [])): + for im_frame in ImageSequence.Iterator(imSequence): + # a copy is required here since seek can still mutate the image + im_frame = _normalize_mode(im_frame.copy()) + if frame_count == 0: + for k, v in im_frame.info.items(): + if k == "transparency": + continue + if isinstance(k, str): + im.encoderinfo.setdefault(k, v) + + encoderinfo = im.encoderinfo.copy() + if "transparency" in im_frame.info: + encoderinfo.setdefault("transparency", im_frame.info["transparency"]) + im_frame = _normalize_palette(im_frame, palette, encoderinfo) + if isinstance(duration, (list, tuple)): + encoderinfo["duration"] = duration[frame_count] + elif duration is None and "duration" in im_frame.info: + encoderinfo["duration"] = im_frame.info["duration"] + if isinstance(disposal, (list, tuple)): + encoderinfo["disposal"] = disposal[frame_count] + frame_count += 1 + + diff_frame = None + if im_frames and previous_im: + # delta frame + delta, bbox = _getbbox(previous_im, im_frame) + if not bbox: + # This frame is identical to the previous frame + if encoderinfo.get("duration"): + im_frames[-1].encoderinfo["duration"] += encoderinfo["duration"] + continue + if im_frames[-1].encoderinfo.get("disposal") == 2: + # To appear correctly in viewers using a convention, + # only consider transparency, and not background color + color = im.encoderinfo.get( + "transparency", im.info.get("transparency") + ) + if color is not None: + if background_im is None: + background = _get_background(im_frame, color) + background_im = Image.new("P", im_frame.size, background) + first_palette = im_frames[0].im.palette + assert first_palette is not None + background_im.putpalette(first_palette, first_palette.mode) + bbox = _getbbox(background_im, im_frame)[1] + else: + bbox = (0, 0) + im_frame.size + elif encoderinfo.get("optimize") and im_frame.mode != "1": + if "transparency" not in encoderinfo: + assert im_frame.palette is not None + try: + encoderinfo["transparency"] = ( + im_frame.palette._new_color_index(im_frame) + ) + except ValueError: + pass + if "transparency" in encoderinfo: + # When the delta is zero, fill the image with transparency + diff_frame = im_frame.copy() + fill = Image.new("P", delta.size, encoderinfo["transparency"]) + if delta.mode == "RGBA": + r, g, b, a = delta.split() + mask = ImageMath.lambda_eval( + lambda args: args["convert"]( + args["max"]( + args["max"]( + args["max"](args["r"], args["g"]), args["b"] + ), + args["a"], + ) + * 255, + "1", + ), + r=r, + g=g, + b=b, + a=a, + ) + else: + if delta.mode == "P": + # Convert to L without considering palette + delta_l = Image.new("L", delta.size) + delta_l.putdata(delta.getdata()) + delta = delta_l + mask = ImageMath.lambda_eval( + lambda args: args["convert"](args["im"] * 255, "1"), + im=delta, + ) + diff_frame.paste(fill, mask=ImageOps.invert(mask)) + else: + bbox = None + previous_im = im_frame + im_frames.append(_Frame(diff_frame or im_frame, bbox, encoderinfo)) + + if len(im_frames) == 1: + if "duration" in im.encoderinfo: + # Since multiple frames will not be written, use the combined duration + im.encoderinfo["duration"] = im_frames[0].encoderinfo["duration"] + return False + + for frame_data in im_frames: + im_frame = frame_data.im + if not frame_data.bbox: + # global header + for s in _get_global_header(im_frame, frame_data.encoderinfo): + fp.write(s) + offset = (0, 0) + else: + # compress difference + if not palette: + frame_data.encoderinfo["include_color_table"] = True + + if frame_data.bbox != (0, 0) + im_frame.size: + im_frame = im_frame.crop(frame_data.bbox) + offset = frame_data.bbox[:2] + _write_frame_data(fp, im_frame, offset, frame_data.encoderinfo) + return True + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + # header + if "palette" in im.encoderinfo or "palette" in im.info: + palette = im.encoderinfo.get("palette", im.info.get("palette")) + else: + palette = None + im.encoderinfo.setdefault("optimize", True) + + if not save_all or not _write_multiple_frames(im, fp, palette): + _write_single_frame(im, fp, palette) + + fp.write(b";") # end of file + + if hasattr(fp, "flush"): + fp.flush() + + +def get_interlace(im: Image.Image) -> int: + interlace = im.encoderinfo.get("interlace", 1) + + # workaround for @PIL153 + if min(im.size) < 16: + interlace = 0 + + return interlace + + +def _write_local_header( + fp: IO[bytes], im: Image.Image, offset: tuple[int, int], flags: int +) -> None: + try: + transparency = im.encoderinfo["transparency"] + except KeyError: + transparency = None + + if "duration" in im.encoderinfo: + duration = int(im.encoderinfo["duration"] / 10) + else: + duration = 0 + + disposal = int(im.encoderinfo.get("disposal", 0)) + + if transparency is not None or duration != 0 or disposal: + packed_flag = 1 if transparency is not None else 0 + packed_flag |= disposal << 2 + + fp.write( + b"!" + + o8(249) # extension intro + + o8(4) # length + + o8(packed_flag) # packed fields + + o16(duration) # duration + + o8(transparency or 0) # transparency index + + o8(0) + ) + + include_color_table = im.encoderinfo.get("include_color_table") + if include_color_table: + palette_bytes = _get_palette_bytes(im) + color_table_size = _get_color_table_size(palette_bytes) + if color_table_size: + flags = flags | 128 # local color table flag + flags = flags | color_table_size + + fp.write( + b"," + + o16(offset[0]) # offset + + o16(offset[1]) + + o16(im.size[0]) # size + + o16(im.size[1]) + + o8(flags) # flags + ) + if include_color_table and color_table_size: + fp.write(_get_header_palette(palette_bytes)) + fp.write(o8(8)) # bits + + +def _save_netpbm(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # Unused by default. + # To use, uncomment the register_save call at the end of the file. + # + # If you need real GIF compression and/or RGB quantization, you + # can use the external NETPBM/PBMPLUS utilities. See comments + # below for information on how to enable this. + tempfile = im._dump() + + try: + with open(filename, "wb") as f: + if im.mode != "RGB": + subprocess.check_call( + ["ppmtogif", tempfile], stdout=f, stderr=subprocess.DEVNULL + ) + else: + # Pipe ppmquant output into ppmtogif + # "ppmquant 256 %s | ppmtogif > %s" % (tempfile, filename) + quant_cmd = ["ppmquant", "256", tempfile] + togif_cmd = ["ppmtogif"] + quant_proc = subprocess.Popen( + quant_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL + ) + togif_proc = subprocess.Popen( + togif_cmd, + stdin=quant_proc.stdout, + stdout=f, + stderr=subprocess.DEVNULL, + ) + + # Allow ppmquant to receive SIGPIPE if ppmtogif exits + assert quant_proc.stdout is not None + quant_proc.stdout.close() + + retcode = quant_proc.wait() + if retcode: + raise subprocess.CalledProcessError(retcode, quant_cmd) + + retcode = togif_proc.wait() + if retcode: + raise subprocess.CalledProcessError(retcode, togif_cmd) + finally: + try: + os.unlink(tempfile) + except OSError: + pass + + +# Force optimization so that we can test performance against +# cases where it took lots of memory and time previously. +_FORCE_OPTIMIZE = False + + +def _get_optimize(im: Image.Image, info: dict[str, Any]) -> list[int] | None: + """ + Palette optimization is a potentially expensive operation. + + This function determines if the palette should be optimized using + some heuristics, then returns the list of palette entries in use. + + :param im: Image object + :param info: encoderinfo + :returns: list of indexes of palette entries in use, or None + """ + if im.mode in ("P", "L") and info and info.get("optimize"): + # Potentially expensive operation. + + # The palette saves 3 bytes per color not used, but palette + # lengths are restricted to 3*(2**N) bytes. Max saving would + # be 768 -> 6 bytes if we went all the way down to 2 colors. + # * If we're over 128 colors, we can't save any space. + # * If there aren't any holes, it's not worth collapsing. + # * If we have a 'large' image, the palette is in the noise. + + # create the new palette if not every color is used + optimise = _FORCE_OPTIMIZE or im.mode == "L" + if optimise or im.width * im.height < 512 * 512: + # check which colors are used + used_palette_colors = [] + for i, count in enumerate(im.histogram()): + if count: + used_palette_colors.append(i) + + if optimise or max(used_palette_colors) >= len(used_palette_colors): + return used_palette_colors + + assert im.palette is not None + num_palette_colors = len(im.palette.palette) // Image.getmodebands( + im.palette.mode + ) + current_palette_size = 1 << (num_palette_colors - 1).bit_length() + if ( + # check that the palette would become smaller when saved + len(used_palette_colors) <= current_palette_size // 2 + # check that the palette is not already the smallest possible size + and current_palette_size > 2 + ): + return used_palette_colors + return None + + +def _get_color_table_size(palette_bytes: bytes) -> int: + # calculate the palette size for the header + if not palette_bytes: + return 0 + elif len(palette_bytes) < 9: + return 1 + else: + return math.ceil(math.log(len(palette_bytes) // 3, 2)) - 1 + + +def _get_header_palette(palette_bytes: bytes) -> bytes: + """ + Returns the palette, null padded to the next power of 2 (*3) bytes + suitable for direct inclusion in the GIF header + + :param palette_bytes: Unpadded palette bytes, in RGBRGB form + :returns: Null padded palette + """ + color_table_size = _get_color_table_size(palette_bytes) + + # add the missing amount of bytes + # the palette has to be 2< 0: + palette_bytes += o8(0) * 3 * actual_target_size_diff + return palette_bytes + + +def _get_palette_bytes(im: Image.Image) -> bytes: + """ + Gets the palette for inclusion in the gif header + + :param im: Image object + :returns: Bytes, len<=768 suitable for inclusion in gif header + """ + if not im.palette: + return b"" + + palette = bytes(im.palette.palette) + if im.palette.mode == "RGBA": + palette = b"".join(palette[i * 4 : i * 4 + 3] for i in range(len(palette) // 3)) + return palette + + +def _get_background( + im: Image.Image, + info_background: int | tuple[int, int, int] | tuple[int, int, int, int] | None, +) -> int: + background = 0 + if info_background: + if isinstance(info_background, tuple): + # WebPImagePlugin stores an RGBA value in info["background"] + # So it must be converted to the same format as GifImagePlugin's + # info["background"] - a global color table index + assert im.palette is not None + try: + background = im.palette.getcolor(info_background, im) + except ValueError as e: + if str(e) not in ( + # If all 256 colors are in use, + # then there is no need for the background color + "cannot allocate more than 256 colors", + # Ignore non-opaque WebP background + "cannot add non-opaque RGBA color to RGB palette", + ): + raise + else: + background = info_background + return background + + +def _get_global_header(im: Image.Image, info: dict[str, Any]) -> list[bytes]: + """Return a list of strings representing a GIF header""" + + # Header Block + # https://www.matthewflickinger.com/lab/whatsinagif/bits_and_bytes.asp + + version = b"87a" + if im.info.get("version") == b"89a" or ( + info + and ( + "transparency" in info + or info.get("loop") is not None + or info.get("duration") + or info.get("comment") + ) + ): + version = b"89a" + + background = _get_background(im, info.get("background")) + + palette_bytes = _get_palette_bytes(im) + color_table_size = _get_color_table_size(palette_bytes) + + header = [ + b"GIF" # signature + + version # version + + o16(im.size[0]) # canvas width + + o16(im.size[1]), # canvas height + # Logical Screen Descriptor + # size of global color table + global color table flag + o8(color_table_size + 128), # packed fields + # background + reserved/aspect + o8(background) + o8(0), + # Global Color Table + _get_header_palette(palette_bytes), + ] + if info.get("loop") is not None: + header.append( + b"!" + + o8(255) # extension intro + + o8(11) + + b"NETSCAPE2.0" + + o8(3) + + o8(1) + + o16(info["loop"]) # number of loops + + o8(0) + ) + if info.get("comment"): + comment_block = b"!" + o8(254) # extension intro + + comment = info["comment"] + if isinstance(comment, str): + comment = comment.encode() + for i in range(0, len(comment), 255): + subblock = comment[i : i + 255] + comment_block += o8(len(subblock)) + subblock + + comment_block += o8(0) + header.append(comment_block) + return header + + +def _write_frame_data( + fp: IO[bytes], + im_frame: Image.Image, + offset: tuple[int, int], + params: dict[str, Any], +) -> None: + try: + im_frame.encoderinfo = params + + # local image header + _write_local_header(fp, im_frame, offset, 0) + + ImageFile._save( + im_frame, + fp, + [ImageFile._Tile("gif", (0, 0) + im_frame.size, 0, RAWMODE[im_frame.mode])], + ) + + fp.write(b"\0") # end of image data + finally: + del im_frame.encoderinfo + + +# -------------------------------------------------------------------- +# Legacy GIF utilities + + +def getheader( + im: Image.Image, palette: _Palette | None = None, info: dict[str, Any] | None = None +) -> tuple[list[bytes], list[int] | None]: + """ + Legacy Method to get Gif data from image. + + Warning:: May modify image data. + + :param im: Image object + :param palette: bytes object containing the source palette, or .... + :param info: encoderinfo + :returns: tuple of(list of header items, optimized palette) + + """ + if info is None: + info = {} + + used_palette_colors = _get_optimize(im, info) + + if "background" not in info and "background" in im.info: + info["background"] = im.info["background"] + + im_mod = _normalize_palette(im, palette, info) + im.palette = im_mod.palette + im.im = im_mod.im + header = _get_global_header(im, info) + + return header, used_palette_colors + + +def getdata( + im: Image.Image, offset: tuple[int, int] = (0, 0), **params: Any +) -> list[bytes]: + """ + Legacy Method + + Return a list of strings representing this image. + The first string is a local image header, the rest contains + encoded image data. + + To specify duration, add the time in milliseconds, + e.g. ``getdata(im_frame, duration=1000)`` + + :param im: Image object + :param offset: Tuple of (x, y) pixels. Defaults to (0, 0) + :param \\**params: e.g. duration or other encoder info parameters + :returns: List of bytes containing GIF encoded frame data + + """ + from io import BytesIO + + class Collector(BytesIO): + data = [] + + def write(self, data: Buffer) -> int: + self.data.append(data) + return len(data) + + im.load() # make sure raster data is available + + fp = Collector() + + _write_frame_data(fp, im, offset, params) + + return fp.data + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(GifImageFile.format, GifImageFile, _accept) +Image.register_save(GifImageFile.format, _save) +Image.register_save_all(GifImageFile.format, _save_all) +Image.register_extension(GifImageFile.format, ".gif") +Image.register_mime(GifImageFile.format, "image/gif") + +# +# Uncomment the following line if you wish to use NETPBM/PBMPLUS +# instead of the built-in "uncompressed" GIF encoder + +# Image.register_save(GifImageFile.format, _save_netpbm) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py new file mode 100644 index 0000000000000000000000000000000000000000..ec62f8e4ebc37d3aef9b171a0d03b7deeab702c4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py @@ -0,0 +1,149 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read (and render) GIMP gradient files +# +# History: +# 97-08-23 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# + +""" +Stuff to translate curve segments to palette values (derived from +the corresponding code in GIMP, written by Federico Mena Quintero. +See the GIMP distribution for more information.) +""" +from __future__ import annotations + +from math import log, pi, sin, sqrt +from typing import IO, Callable + +from ._binary import o8 + +EPSILON = 1e-10 +"""""" # Enable auto-doc for data member + + +def linear(middle: float, pos: float) -> float: + if pos <= middle: + if middle < EPSILON: + return 0.0 + else: + return 0.5 * pos / middle + else: + pos = pos - middle + middle = 1.0 - middle + if middle < EPSILON: + return 1.0 + else: + return 0.5 + 0.5 * pos / middle + + +def curved(middle: float, pos: float) -> float: + return pos ** (log(0.5) / log(max(middle, EPSILON))) + + +def sine(middle: float, pos: float) -> float: + return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0 + + +def sphere_increasing(middle: float, pos: float) -> float: + return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2) + + +def sphere_decreasing(middle: float, pos: float) -> float: + return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2) + + +SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing] +"""""" # Enable auto-doc for data member + + +class GradientFile: + gradient: ( + list[ + tuple[ + float, + float, + float, + list[float], + list[float], + Callable[[float, float], float], + ] + ] + | None + ) = None + + def getpalette(self, entries: int = 256) -> tuple[bytes, str]: + assert self.gradient is not None + palette = [] + + ix = 0 + x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix] + + for i in range(entries): + x = i / (entries - 1) + + while x1 < x: + ix += 1 + x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix] + + w = x1 - x0 + + if w < EPSILON: + scale = segment(0.5, 0.5) + else: + scale = segment((xm - x0) / w, (x - x0) / w) + + # expand to RGBA + r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5)) + g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5)) + b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5)) + a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5)) + + # add to palette + palette.append(r + g + b + a) + + return b"".join(palette), "RGBA" + + +class GimpGradientFile(GradientFile): + """File handler for GIMP's gradient format.""" + + def __init__(self, fp: IO[bytes]) -> None: + if not fp.readline().startswith(b"GIMP Gradient"): + msg = "not a GIMP gradient file" + raise SyntaxError(msg) + + line = fp.readline() + + # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do + if line.startswith(b"Name: "): + line = fp.readline().strip() + + count = int(line) + + self.gradient = [] + + for i in range(count): + s = fp.readline().split() + w = [float(x) for x in s[:11]] + + x0, x1 = w[0], w[2] + xm = w[1] + rgb0 = w[3:7] + rgb1 = w[7:11] + + segment = SEGMENTS[int(s[11])] + cspace = int(s[12]) + + if cspace != 0: + msg = "cannot handle HSV colour space" + raise OSError(msg) + + self.gradient.append((x0, x1, xm, rgb0, rgb1, segment)) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py new file mode 100644 index 0000000000000000000000000000000000000000..379ffd739182c4caaad3bce92e0e8344ced2eef4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py @@ -0,0 +1,72 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read GIMP palette files +# +# History: +# 1997-08-23 fl Created +# 2004-09-07 fl Support GIMP 2.0 palette files. +# +# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. +# Copyright (c) Fredrik Lundh 1997-2004. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from io import BytesIO +from typing import IO + + +class GimpPaletteFile: + """File handler for GIMP's palette format.""" + + rawmode = "RGB" + + def _read(self, fp: IO[bytes], limit: bool = True) -> None: + if not fp.readline().startswith(b"GIMP Palette"): + msg = "not a GIMP palette file" + raise SyntaxError(msg) + + palette: list[int] = [] + i = 0 + while True: + if limit and i == 256 + 3: + break + + i += 1 + s = fp.readline() + if not s: + break + + # skip fields and comment lines + if re.match(rb"\w+:|#", s): + continue + if limit and len(s) > 100: + msg = "bad palette file" + raise SyntaxError(msg) + + v = s.split(maxsplit=3) + if len(v) < 3: + msg = "bad palette entry" + raise ValueError(msg) + + palette += (int(v[i]) for i in range(3)) + if limit and len(palette) == 768: + break + + self.palette = bytes(palette) + + def __init__(self, fp: IO[bytes]) -> None: + self._read(fp) + + @classmethod + def frombytes(cls, data: bytes) -> GimpPaletteFile: + self = cls.__new__(cls) + self._read(BytesIO(data), False) + return self + + def getpalette(self) -> tuple[bytes, str]: + return self.palette, self.rawmode diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..439fc5a3eda8414add95d53660eca8d11bf6ab8f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# GRIB stub adapter +# +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific GRIB image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"GRIB") and prefix[7] == 1 + + +class GribStubImageFile(ImageFile.StubImageFile): + format = "GRIB" + format_description = "GRIB" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "Not a GRIB file" + raise SyntaxError(msg) + + self.fp.seek(-8, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "GRIB save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(GribStubImageFile.format, GribStubImageFile, _accept) +Image.register_save(GribStubImageFile.format, _save) + +Image.register_extension(GribStubImageFile.format, ".grib") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..76e640f15abfe60a56a571380133a0463c104035 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# HDF5 stub adapter +# +# Copyright (c) 2000-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific HDF5 image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x89HDF\r\n\x1a\n") + + +class HDF5StubImageFile(ImageFile.StubImageFile): + format = "HDF5" + format_description = "HDF5" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "Not an HDF file" + raise SyntaxError(msg) + + self.fp.seek(-8, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "HDF5 save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(HDF5StubImageFile.format, HDF5StubImageFile, _accept) +Image.register_save(HDF5StubImageFile.format, _save) + +Image.register_extensions(HDF5StubImageFile.format, [".h5", ".hdf"]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..5a88429e5e4b3be4e57ce85b70fdaa4c7927fe09 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py @@ -0,0 +1,411 @@ +# +# The Python Imaging Library. +# $Id$ +# +# macOS icns file decoder, based on icns.py by Bob Ippolito. +# +# history: +# 2004-10-09 fl Turned into a PIL plugin; removed 2.3 dependencies. +# 2020-04-04 Allow saving on all operating systems. +# +# Copyright (c) 2004 by Bob Ippolito. +# Copyright (c) 2004 by Secret Labs. +# Copyright (c) 2004 by Fredrik Lundh. +# Copyright (c) 2014 by Alastair Houghton. +# Copyright (c) 2020 by Pan Jing. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import struct +import sys +from typing import IO + +from . import Image, ImageFile, PngImagePlugin, features +from ._deprecate import deprecate + +enable_jpeg2k = features.check_codec("jpg_2000") +if enable_jpeg2k: + from . import Jpeg2KImagePlugin + +MAGIC = b"icns" +HEADERSIZE = 8 + + +def nextheader(fobj: IO[bytes]) -> tuple[bytes, int]: + return struct.unpack(">4sI", fobj.read(HEADERSIZE)) + + +def read_32t( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + # The 128x128 icon seems to have an extra header for some reason. + (start, length) = start_length + fobj.seek(start) + sig = fobj.read(4) + if sig != b"\x00\x00\x00\x00": + msg = "Unknown signature, expecting 0x00000000" + raise SyntaxError(msg) + return read_32(fobj, (start + 4, length - 4), size) + + +def read_32( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + """ + Read a 32bit RGB icon resource. Seems to be either uncompressed or + an RLE packbits-like scheme. + """ + (start, length) = start_length + fobj.seek(start) + pixel_size = (size[0] * size[2], size[1] * size[2]) + sizesq = pixel_size[0] * pixel_size[1] + if length == sizesq * 3: + # uncompressed ("RGBRGBGB") + indata = fobj.read(length) + im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1) + else: + # decode image + im = Image.new("RGB", pixel_size, None) + for band_ix in range(3): + data = [] + bytesleft = sizesq + while bytesleft > 0: + byte = fobj.read(1) + if not byte: + break + byte_int = byte[0] + if byte_int & 0x80: + blocksize = byte_int - 125 + byte = fobj.read(1) + for i in range(blocksize): + data.append(byte) + else: + blocksize = byte_int + 1 + data.append(fobj.read(blocksize)) + bytesleft -= blocksize + if bytesleft <= 0: + break + if bytesleft != 0: + msg = f"Error reading channel [{repr(bytesleft)} left]" + raise SyntaxError(msg) + band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1) + im.im.putband(band.im, band_ix) + return {"RGB": im} + + +def read_mk( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + # Alpha masks seem to be uncompressed + start = start_length[0] + fobj.seek(start) + pixel_size = (size[0] * size[2], size[1] * size[2]) + sizesq = pixel_size[0] * pixel_size[1] + band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1) + return {"A": band} + + +def read_png_or_jpeg2000( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + (start, length) = start_length + fobj.seek(start) + sig = fobj.read(12) + + im: Image.Image + if sig.startswith(b"\x89PNG\x0d\x0a\x1a\x0a"): + fobj.seek(start) + im = PngImagePlugin.PngImageFile(fobj) + Image._decompression_bomb_check(im.size) + return {"RGBA": im} + elif ( + sig.startswith((b"\xff\x4f\xff\x51", b"\x0d\x0a\x87\x0a")) + or sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a" + ): + if not enable_jpeg2k: + msg = ( + "Unsupported icon subimage format (rebuild PIL " + "with JPEG 2000 support to fix this)" + ) + raise ValueError(msg) + # j2k, jpc or j2c + fobj.seek(start) + jp2kstream = fobj.read(length) + f = io.BytesIO(jp2kstream) + im = Jpeg2KImagePlugin.Jpeg2KImageFile(f) + Image._decompression_bomb_check(im.size) + if im.mode != "RGBA": + im = im.convert("RGBA") + return {"RGBA": im} + else: + msg = "Unsupported icon subimage format" + raise ValueError(msg) + + +class IcnsFile: + SIZES = { + (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)], + (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)], + (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)], + (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)], + (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)], + (128, 128, 1): [ + (b"ic07", read_png_or_jpeg2000), + (b"it32", read_32t), + (b"t8mk", read_mk), + ], + (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)], + (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)], + (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)], + (32, 32, 1): [ + (b"icp5", read_png_or_jpeg2000), + (b"il32", read_32), + (b"l8mk", read_mk), + ], + (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)], + (16, 16, 1): [ + (b"icp4", read_png_or_jpeg2000), + (b"is32", read_32), + (b"s8mk", read_mk), + ], + } + + def __init__(self, fobj: IO[bytes]) -> None: + """ + fobj is a file-like object as an icns resource + """ + # signature : (start, length) + self.dct = {} + self.fobj = fobj + sig, filesize = nextheader(fobj) + if not _accept(sig): + msg = "not an icns file" + raise SyntaxError(msg) + i = HEADERSIZE + while i < filesize: + sig, blocksize = nextheader(fobj) + if blocksize <= 0: + msg = "invalid block header" + raise SyntaxError(msg) + i += HEADERSIZE + blocksize -= HEADERSIZE + self.dct[sig] = (i, blocksize) + fobj.seek(blocksize, io.SEEK_CUR) + i += blocksize + + def itersizes(self) -> list[tuple[int, int, int]]: + sizes = [] + for size, fmts in self.SIZES.items(): + for fmt, reader in fmts: + if fmt in self.dct: + sizes.append(size) + break + return sizes + + def bestsize(self) -> tuple[int, int, int]: + sizes = self.itersizes() + if not sizes: + msg = "No 32bit icon resources found" + raise SyntaxError(msg) + return max(sizes) + + def dataforsize(self, size: tuple[int, int, int]) -> dict[str, Image.Image]: + """ + Get an icon resource as {channel: array}. Note that + the arrays are bottom-up like windows bitmaps and will likely + need to be flipped or transposed in some way. + """ + dct = {} + for code, reader in self.SIZES[size]: + desc = self.dct.get(code) + if desc is not None: + dct.update(reader(self.fobj, desc, size)) + return dct + + def getimage( + self, size: tuple[int, int] | tuple[int, int, int] | None = None + ) -> Image.Image: + if size is None: + size = self.bestsize() + elif len(size) == 2: + size = (size[0], size[1], 1) + channels = self.dataforsize(size) + + im = channels.get("RGBA") + if im: + return im + + im = channels["RGB"].copy() + try: + im.putalpha(channels["A"]) + except KeyError: + pass + return im + + +## +# Image plugin for Mac OS icons. + + +class IcnsImageFile(ImageFile.ImageFile): + """ + PIL image support for Mac OS .icns files. + Chooses the best resolution, but will possibly load + a different size image if you mutate the size attribute + before calling 'load'. + + The info dictionary has a key 'sizes' that is a list + of sizes that the icns file has. + """ + + format = "ICNS" + format_description = "Mac OS icns resource" + + def _open(self) -> None: + self.icns = IcnsFile(self.fp) + self._mode = "RGBA" + self.info["sizes"] = self.icns.itersizes() + self.best_size = self.icns.bestsize() + self.size = ( + self.best_size[0] * self.best_size[2], + self.best_size[1] * self.best_size[2], + ) + + @property # type: ignore[override] + def size(self) -> tuple[int, int] | tuple[int, int, int]: + return self._size + + @size.setter + def size(self, value: tuple[int, int] | tuple[int, int, int]) -> None: + if len(value) == 3: + deprecate("Setting size to (width, height, scale)", 12, "load(scale)") + if value in self.info["sizes"]: + self._size = value # type: ignore[assignment] + return + else: + # Check that a matching size exists, + # or that there is a scale that would create a size that matches + for size in self.info["sizes"]: + simple_size = size[0] * size[2], size[1] * size[2] + scale = simple_size[0] // value[0] + if simple_size[1] / value[1] == scale: + self._size = value + return + msg = "This is not one of the allowed sizes of this image" + raise ValueError(msg) + + def load(self, scale: int | None = None) -> Image.core.PixelAccess | None: + if scale is not None or len(self.size) == 3: + if scale is None and len(self.size) == 3: + scale = self.size[2] + assert scale is not None + width, height = self.size[:2] + self.size = width * scale, height * scale + self.best_size = width, height, scale + + px = Image.Image.load(self) + if self._im is not None and self.im.size == self.size: + # Already loaded + return px + self.load_prepare() + # This is likely NOT the best way to do it, but whatever. + im = self.icns.getimage(self.best_size) + + # If this is a PNG or JPEG 2000, it won't be loaded yet + px = im.load() + + self.im = im.im + self._mode = im.mode + self.size = im.size + + return px + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + """ + Saves the image as a series of PNG files, + that are then combined into a .icns file. + """ + if hasattr(fp, "flush"): + fp.flush() + + sizes = { + b"ic07": 128, + b"ic08": 256, + b"ic09": 512, + b"ic10": 1024, + b"ic11": 32, + b"ic12": 64, + b"ic13": 256, + b"ic14": 512, + } + provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])} + size_streams = {} + for size in set(sizes.values()): + image = ( + provided_images[size] + if size in provided_images + else im.resize((size, size)) + ) + + temp = io.BytesIO() + image.save(temp, "png") + size_streams[size] = temp.getvalue() + + entries = [] + for type, size in sizes.items(): + stream = size_streams[size] + entries.append((type, HEADERSIZE + len(stream), stream)) + + # Header + fp.write(MAGIC) + file_length = HEADERSIZE # Header + file_length += HEADERSIZE + 8 * len(entries) # TOC + file_length += sum(entry[1] for entry in entries) + fp.write(struct.pack(">i", file_length)) + + # TOC + fp.write(b"TOC ") + fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE)) + for entry in entries: + fp.write(entry[0]) + fp.write(struct.pack(">i", entry[1])) + + # Data + for entry in entries: + fp.write(entry[0]) + fp.write(struct.pack(">i", entry[1])) + fp.write(entry[2]) + + if hasattr(fp, "flush"): + fp.flush() + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(MAGIC) + + +Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept) +Image.register_extension(IcnsImageFile.format, ".icns") + +Image.register_save(IcnsImageFile.format, _save) +Image.register_mime(IcnsImageFile.format, "image/icns") + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 IcnsImagePlugin.py [file]") + sys.exit() + + with open(sys.argv[1], "rb") as fp: + imf = IcnsImageFile(fp) + for size in imf.info["sizes"]: + width, height, scale = imf.size = size + imf.save(f"out-{width}-{height}-{scale}.png") + with Image.open(sys.argv[1]) as im: + im.save("out.png") + if sys.platform == "windows": + os.startfile("out.png") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..bd35ac890e6cf824e9c890404416d871e5b94f7c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py @@ -0,0 +1,381 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Windows Icon support for PIL +# +# History: +# 96-05-27 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# + +# This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis +# . +# https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki +# +# Icon format references: +# * https://en.wikipedia.org/wiki/ICO_(file_format) +# * https://msdn.microsoft.com/en-us/library/ms997538.aspx +from __future__ import annotations + +import warnings +from io import BytesIO +from math import ceil, log +from typing import IO, NamedTuple + +from . import BmpImagePlugin, Image, ImageFile, PngImagePlugin +from ._binary import i16le as i16 +from ._binary import i32le as i32 +from ._binary import o8 +from ._binary import o16le as o16 +from ._binary import o32le as o32 + +# +# -------------------------------------------------------------------- + +_MAGIC = b"\0\0\1\0" + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + fp.write(_MAGIC) # (2+2) + bmp = im.encoderinfo.get("bitmap_format") == "bmp" + sizes = im.encoderinfo.get( + "sizes", + [(16, 16), (24, 24), (32, 32), (48, 48), (64, 64), (128, 128), (256, 256)], + ) + frames = [] + provided_ims = [im] + im.encoderinfo.get("append_images", []) + width, height = im.size + for size in sorted(set(sizes)): + if size[0] > width or size[1] > height or size[0] > 256 or size[1] > 256: + continue + + for provided_im in provided_ims: + if provided_im.size != size: + continue + frames.append(provided_im) + if bmp: + bits = BmpImagePlugin.SAVE[provided_im.mode][1] + bits_used = [bits] + for other_im in provided_ims: + if other_im.size != size: + continue + bits = BmpImagePlugin.SAVE[other_im.mode][1] + if bits not in bits_used: + # Another image has been supplied for this size + # with a different bit depth + frames.append(other_im) + bits_used.append(bits) + break + else: + # TODO: invent a more convenient method for proportional scalings + frame = provided_im.copy() + frame.thumbnail(size, Image.Resampling.LANCZOS, reducing_gap=None) + frames.append(frame) + fp.write(o16(len(frames))) # idCount(2) + offset = fp.tell() + len(frames) * 16 + for frame in frames: + width, height = frame.size + # 0 means 256 + fp.write(o8(width if width < 256 else 0)) # bWidth(1) + fp.write(o8(height if height < 256 else 0)) # bHeight(1) + + bits, colors = BmpImagePlugin.SAVE[frame.mode][1:] if bmp else (32, 0) + fp.write(o8(colors)) # bColorCount(1) + fp.write(b"\0") # bReserved(1) + fp.write(b"\0\0") # wPlanes(2) + fp.write(o16(bits)) # wBitCount(2) + + image_io = BytesIO() + if bmp: + frame.save(image_io, "dib") + + if bits != 32: + and_mask = Image.new("1", size) + ImageFile._save( + and_mask, + image_io, + [ImageFile._Tile("raw", (0, 0) + size, 0, ("1", 0, -1))], + ) + else: + frame.save(image_io, "png") + image_io.seek(0) + image_bytes = image_io.read() + if bmp: + image_bytes = image_bytes[:8] + o32(height * 2) + image_bytes[12:] + bytes_len = len(image_bytes) + fp.write(o32(bytes_len)) # dwBytesInRes(4) + fp.write(o32(offset)) # dwImageOffset(4) + current = fp.tell() + fp.seek(offset) + fp.write(image_bytes) + offset = offset + bytes_len + fp.seek(current) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +class IconHeader(NamedTuple): + width: int + height: int + nb_color: int + reserved: int + planes: int + bpp: int + size: int + offset: int + dim: tuple[int, int] + square: int + color_depth: int + + +class IcoFile: + def __init__(self, buf: IO[bytes]) -> None: + """ + Parse image from file-like object containing ico file data + """ + + # check magic + s = buf.read(6) + if not _accept(s): + msg = "not an ICO file" + raise SyntaxError(msg) + + self.buf = buf + self.entry = [] + + # Number of items in file + self.nb_items = i16(s, 4) + + # Get headers for each item + for i in range(self.nb_items): + s = buf.read(16) + + # See Wikipedia + width = s[0] or 256 + height = s[1] or 256 + + # No. of colors in image (0 if >=8bpp) + nb_color = s[2] + bpp = i16(s, 6) + icon_header = IconHeader( + width=width, + height=height, + nb_color=nb_color, + reserved=s[3], + planes=i16(s, 4), + bpp=i16(s, 6), + size=i32(s, 8), + offset=i32(s, 12), + dim=(width, height), + square=width * height, + # See Wikipedia notes about color depth. + # We need this just to differ images with equal sizes + color_depth=bpp or (nb_color != 0 and ceil(log(nb_color, 2))) or 256, + ) + + self.entry.append(icon_header) + + self.entry = sorted(self.entry, key=lambda x: x.color_depth) + # ICO images are usually squares + self.entry = sorted(self.entry, key=lambda x: x.square, reverse=True) + + def sizes(self) -> set[tuple[int, int]]: + """ + Get a set of all available icon sizes and color depths. + """ + return {(h.width, h.height) for h in self.entry} + + def getentryindex(self, size: tuple[int, int], bpp: int | bool = False) -> int: + for i, h in enumerate(self.entry): + if size == h.dim and (bpp is False or bpp == h.color_depth): + return i + return 0 + + def getimage(self, size: tuple[int, int], bpp: int | bool = False) -> Image.Image: + """ + Get an image from the icon + """ + return self.frame(self.getentryindex(size, bpp)) + + def frame(self, idx: int) -> Image.Image: + """ + Get an image from frame idx + """ + + header = self.entry[idx] + + self.buf.seek(header.offset) + data = self.buf.read(8) + self.buf.seek(header.offset) + + im: Image.Image + if data[:8] == PngImagePlugin._MAGIC: + # png frame + im = PngImagePlugin.PngImageFile(self.buf) + Image._decompression_bomb_check(im.size) + else: + # XOR + AND mask bmp frame + im = BmpImagePlugin.DibImageFile(self.buf) + Image._decompression_bomb_check(im.size) + + # change tile dimension to only encompass XOR image + im._size = (im.size[0], int(im.size[1] / 2)) + d, e, o, a = im.tile[0] + im.tile[0] = ImageFile._Tile(d, (0, 0) + im.size, o, a) + + # figure out where AND mask image starts + if header.bpp == 32: + # 32-bit color depth icon image allows semitransparent areas + # PIL's DIB format ignores transparency bits, recover them. + # The DIB is packed in BGRX byte order where X is the alpha + # channel. + + # Back up to start of bmp data + self.buf.seek(o) + # extract every 4th byte (eg. 3,7,11,15,...) + alpha_bytes = self.buf.read(im.size[0] * im.size[1] * 4)[3::4] + + # convert to an 8bpp grayscale image + try: + mask = Image.frombuffer( + "L", # 8bpp + im.size, # (w, h) + alpha_bytes, # source chars + "raw", # raw decoder + ("L", 0, -1), # 8bpp inverted, unpadded, reversed + ) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + mask = None + else: + raise + else: + # get AND image from end of bitmap + w = im.size[0] + if (w % 32) > 0: + # bitmap row data is aligned to word boundaries + w += 32 - (im.size[0] % 32) + + # the total mask data is + # padded row size * height / bits per char + + total_bytes = int((w * im.size[1]) / 8) + and_mask_offset = header.offset + header.size - total_bytes + + self.buf.seek(and_mask_offset) + mask_data = self.buf.read(total_bytes) + + # convert raw data to image + try: + mask = Image.frombuffer( + "1", # 1 bpp + im.size, # (w, h) + mask_data, # source chars + "raw", # raw decoder + ("1;I", int(w / 8), -1), # 1bpp inverted, padded, reversed + ) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + mask = None + else: + raise + + # now we have two images, im is XOR image and mask is AND image + + # apply mask image as alpha channel + if mask: + im = im.convert("RGBA") + im.putalpha(mask) + + return im + + +## +# Image plugin for Windows Icon files. + + +class IcoImageFile(ImageFile.ImageFile): + """ + PIL read-only image support for Microsoft Windows .ico files. + + By default the largest resolution image in the file will be loaded. This + can be changed by altering the 'size' attribute before calling 'load'. + + The info dictionary has a key 'sizes' that is a list of the sizes available + in the icon file. + + Handles classic, XP and Vista icon formats. + + When saving, PNG compression is used. Support for this was only added in + Windows Vista. If you are unable to view the icon in Windows, convert the + image to "RGBA" mode before saving. + + This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis + . + https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki + """ + + format = "ICO" + format_description = "Windows Icon" + + def _open(self) -> None: + self.ico = IcoFile(self.fp) + self.info["sizes"] = self.ico.sizes() + self.size = self.ico.entry[0].dim + self.load() + + @property + def size(self) -> tuple[int, int]: + return self._size + + @size.setter + def size(self, value: tuple[int, int]) -> None: + if value not in self.info["sizes"]: + msg = "This is not one of the allowed sizes of this image" + raise ValueError(msg) + self._size = value + + def load(self) -> Image.core.PixelAccess | None: + if self._im is not None and self.im.size == self.size: + # Already loaded + return Image.Image.load(self) + im = self.ico.getimage(self.size) + # if tile is PNG, it won't really be loaded yet + im.load() + self.im = im.im + self._mode = im.mode + if im.palette: + self.palette = im.palette + if im.size != self.size: + warnings.warn("Image was not the expected size") + + index = self.ico.getentryindex(self.size) + sizes = list(self.info["sizes"]) + sizes[index] = im.size + self.info["sizes"] = set(sizes) + + self.size = im.size + return Image.Image.load(self) + + def load_seek(self, pos: int) -> None: + # Flag the ImageFile.Parser so that it + # just does all the decode at the end. + pass + + +# +# -------------------------------------------------------------------- + + +Image.register_open(IcoImageFile.format, IcoImageFile, _accept) +Image.register_save(IcoImageFile.format, _save) +Image.register_extension(IcoImageFile.format, ".ico") + +Image.register_mime(IcoImageFile.format, "image/x-icon") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..71b9996780ce8dfc420670b5732216f934a1f677 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py @@ -0,0 +1,389 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IFUNC IM file handling for PIL +# +# history: +# 1995-09-01 fl Created. +# 1997-01-03 fl Save palette images +# 1997-01-08 fl Added sequence support +# 1997-01-23 fl Added P and RGB save support +# 1997-05-31 fl Read floating point images +# 1997-06-22 fl Save floating point images +# 1997-08-27 fl Read and save 1-bit images +# 1998-06-25 fl Added support for RGB+LUT images +# 1998-07-02 fl Added support for YCC images +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 1998-12-29 fl Added I;16 support +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7) +# 2003-09-26 fl Added LA/PA support +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2001 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import re +from typing import IO, Any + +from . import Image, ImageFile, ImagePalette +from ._util import DeferredError + +# -------------------------------------------------------------------- +# Standard tags + +COMMENT = "Comment" +DATE = "Date" +EQUIPMENT = "Digitalization equipment" +FRAMES = "File size (no of images)" +LUT = "Lut" +NAME = "Name" +SCALE = "Scale (x,y)" +SIZE = "Image size (x*y)" +MODE = "Image type" + +TAGS = { + COMMENT: 0, + DATE: 0, + EQUIPMENT: 0, + FRAMES: 0, + LUT: 0, + NAME: 0, + SCALE: 0, + SIZE: 0, + MODE: 0, +} + +OPEN = { + # ifunc93/p3cfunc formats + "0 1 image": ("1", "1"), + "L 1 image": ("1", "1"), + "Greyscale image": ("L", "L"), + "Grayscale image": ("L", "L"), + "RGB image": ("RGB", "RGB;L"), + "RLB image": ("RGB", "RLB"), + "RYB image": ("RGB", "RLB"), + "B1 image": ("1", "1"), + "B2 image": ("P", "P;2"), + "B4 image": ("P", "P;4"), + "X 24 image": ("RGB", "RGB"), + "L 32 S image": ("I", "I;32"), + "L 32 F image": ("F", "F;32"), + # old p3cfunc formats + "RGB3 image": ("RGB", "RGB;T"), + "RYB3 image": ("RGB", "RYB;T"), + # extensions + "LA image": ("LA", "LA;L"), + "PA image": ("LA", "PA;L"), + "RGBA image": ("RGBA", "RGBA;L"), + "RGBX image": ("RGB", "RGBX;L"), + "CMYK image": ("CMYK", "CMYK;L"), + "YCC image": ("YCbCr", "YCbCr;L"), +} + +# ifunc95 extensions +for i in ["8", "8S", "16", "16S", "32", "32F"]: + OPEN[f"L {i} image"] = ("F", f"F;{i}") + OPEN[f"L*{i} image"] = ("F", f"F;{i}") +for i in ["16", "16L", "16B"]: + OPEN[f"L {i} image"] = (f"I;{i}", f"I;{i}") + OPEN[f"L*{i} image"] = (f"I;{i}", f"I;{i}") +for i in ["32S"]: + OPEN[f"L {i} image"] = ("I", f"I;{i}") + OPEN[f"L*{i} image"] = ("I", f"I;{i}") +for j in range(2, 33): + OPEN[f"L*{j} image"] = ("F", f"F;{j}") + + +# -------------------------------------------------------------------- +# Read IM directory + +split = re.compile(rb"^([A-Za-z][^:]*):[ \t]*(.*)[ \t]*$") + + +def number(s: Any) -> float: + try: + return int(s) + except ValueError: + return float(s) + + +## +# Image plugin for the IFUNC IM file format. + + +class ImImageFile(ImageFile.ImageFile): + format = "IM" + format_description = "IFUNC Image Memory" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # Quick rejection: if there's not an LF among the first + # 100 bytes, this is (probably) not a text header. + + if b"\n" not in self.fp.read(100): + msg = "not an IM file" + raise SyntaxError(msg) + self.fp.seek(0) + + n = 0 + + # Default values + self.info[MODE] = "L" + self.info[SIZE] = (512, 512) + self.info[FRAMES] = 1 + + self.rawmode = "L" + + while True: + s = self.fp.read(1) + + # Some versions of IFUNC uses \n\r instead of \r\n... + if s == b"\r": + continue + + if not s or s == b"\0" or s == b"\x1a": + break + + # FIXME: this may read whole file if not a text file + s = s + self.fp.readline() + + if len(s) > 100: + msg = "not an IM file" + raise SyntaxError(msg) + + if s.endswith(b"\r\n"): + s = s[:-2] + elif s.endswith(b"\n"): + s = s[:-1] + + try: + m = split.match(s) + except re.error as e: + msg = "not an IM file" + raise SyntaxError(msg) from e + + if m: + k, v = m.group(1, 2) + + # Don't know if this is the correct encoding, + # but a decent guess (I guess) + k = k.decode("latin-1", "replace") + v = v.decode("latin-1", "replace") + + # Convert value as appropriate + if k in [FRAMES, SCALE, SIZE]: + v = v.replace("*", ",") + v = tuple(map(number, v.split(","))) + if len(v) == 1: + v = v[0] + elif k == MODE and v in OPEN: + v, self.rawmode = OPEN[v] + + # Add to dictionary. Note that COMMENT tags are + # combined into a list of strings. + if k == COMMENT: + if k in self.info: + self.info[k].append(v) + else: + self.info[k] = [v] + else: + self.info[k] = v + + if k in TAGS: + n += 1 + + else: + msg = f"Syntax error in IM header: {s.decode('ascii', 'replace')}" + raise SyntaxError(msg) + + if not n: + msg = "Not an IM file" + raise SyntaxError(msg) + + # Basic attributes + self._size = self.info[SIZE] + self._mode = self.info[MODE] + + # Skip forward to start of image data + while s and not s.startswith(b"\x1a"): + s = self.fp.read(1) + if not s: + msg = "File truncated" + raise SyntaxError(msg) + + if LUT in self.info: + # convert lookup table to palette or lut attribute + palette = self.fp.read(768) + greyscale = 1 # greyscale palette + linear = 1 # linear greyscale palette + for i in range(256): + if palette[i] == palette[i + 256] == palette[i + 512]: + if palette[i] != i: + linear = 0 + else: + greyscale = 0 + if self.mode in ["L", "LA", "P", "PA"]: + if greyscale: + if not linear: + self.lut = list(palette[:256]) + else: + if self.mode in ["L", "P"]: + self._mode = self.rawmode = "P" + elif self.mode in ["LA", "PA"]: + self._mode = "PA" + self.rawmode = "PA;L" + self.palette = ImagePalette.raw("RGB;L", palette) + elif self.mode == "RGB": + if not greyscale or not linear: + self.lut = list(palette) + + self.frame = 0 + + self.__offset = offs = self.fp.tell() + + self._fp = self.fp # FIXME: hack + + if self.rawmode.startswith("F;"): + # ifunc95 formats + try: + # use bit decoder (if necessary) + bits = int(self.rawmode[2:]) + if bits not in [8, 16, 32]: + self.tile = [ + ImageFile._Tile( + "bit", (0, 0) + self.size, offs, (bits, 8, 3, 0, -1) + ) + ] + return + except ValueError: + pass + + if self.rawmode in ["RGB;T", "RYB;T"]: + # Old LabEye/3PC files. Would be very surprised if anyone + # ever stumbled upon such a file ;-) + size = self.size[0] * self.size[1] + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, ("G", 0, -1)), + ImageFile._Tile("raw", (0, 0) + self.size, offs + size, ("R", 0, -1)), + ImageFile._Tile( + "raw", (0, 0) + self.size, offs + 2 * size, ("B", 0, -1) + ), + ] + else: + # LabEye/IFUNC files + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1)) + ] + + @property + def n_frames(self) -> int: + return self.info[FRAMES] + + @property + def is_animated(self) -> bool: + return self.info[FRAMES] > 1 + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + self.frame = frame + + if self.mode == "1": + bits = 1 + else: + bits = 8 * len(self.mode) + + size = ((self.size[0] * bits + 7) // 8) * self.size[1] + offs = self.__offset + frame * size + + self.fp = self._fp + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1)) + ] + + def tell(self) -> int: + return self.frame + + +# +# -------------------------------------------------------------------- +# Save IM files + + +SAVE = { + # mode: (im type, raw mode) + "1": ("0 1", "1"), + "L": ("Greyscale", "L"), + "LA": ("LA", "LA;L"), + "P": ("Greyscale", "P"), + "PA": ("LA", "PA;L"), + "I": ("L 32S", "I;32S"), + "I;16": ("L 16", "I;16"), + "I;16L": ("L 16L", "I;16L"), + "I;16B": ("L 16B", "I;16B"), + "F": ("L 32F", "F;32F"), + "RGB": ("RGB", "RGB;L"), + "RGBA": ("RGBA", "RGBA;L"), + "RGBX": ("RGBX", "RGBX;L"), + "CMYK": ("CMYK", "CMYK;L"), + "YCbCr": ("YCC", "YCbCr;L"), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + image_type, rawmode = SAVE[im.mode] + except KeyError as e: + msg = f"Cannot save {im.mode} images as IM" + raise ValueError(msg) from e + + frames = im.encoderinfo.get("frames", 1) + + fp.write(f"Image type: {image_type} image\r\n".encode("ascii")) + if filename: + # Each line must be 100 characters or less, + # or: SyntaxError("not an IM file") + # 8 characters are used for "Name: " and "\r\n" + # Keep just the filename, ditch the potentially overlong path + if isinstance(filename, bytes): + filename = filename.decode("ascii") + name, ext = os.path.splitext(os.path.basename(filename)) + name = "".join([name[: 92 - len(ext)], ext]) + + fp.write(f"Name: {name}\r\n".encode("ascii")) + fp.write(f"Image size (x*y): {im.size[0]}*{im.size[1]}\r\n".encode("ascii")) + fp.write(f"File size (no of images): {frames}\r\n".encode("ascii")) + if im.mode in ["P", "PA"]: + fp.write(b"Lut: 1\r\n") + fp.write(b"\000" * (511 - fp.tell()) + b"\032") + if im.mode in ["P", "PA"]: + im_palette = im.im.getpalette("RGB", "RGB;L") + colors = len(im_palette) // 3 + palette = b"" + for i in range(3): + palette += im_palette[colors * i : colors * (i + 1)] + palette += b"\x00" * (256 - colors) + fp.write(palette) # 768 bytes + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, -1))] + ) + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(ImImageFile.format, ImImageFile) +Image.register_save(ImImageFile.format, _save) + +Image.register_extension(ImImageFile.format, ".im") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Image.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Image.py new file mode 100644 index 0000000000000000000000000000000000000000..d209405c4c5e3c00179aa82c706730bca7bc7b28 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Image.py @@ -0,0 +1,4245 @@ +# +# The Python Imaging Library. +# $Id$ +# +# the Image class wrapper +# +# partial release history: +# 1995-09-09 fl Created +# 1996-03-11 fl PIL release 0.0 (proof of concept) +# 1996-04-30 fl PIL release 0.1b1 +# 1999-07-28 fl PIL release 1.0 final +# 2000-06-07 fl PIL release 1.1 +# 2000-10-20 fl PIL release 1.1.1 +# 2001-05-07 fl PIL release 1.1.2 +# 2002-03-15 fl PIL release 1.1.3 +# 2003-05-10 fl PIL release 1.1.4 +# 2005-03-28 fl PIL release 1.1.5 +# 2006-12-02 fl PIL release 1.1.6 +# 2009-11-15 fl PIL release 1.1.7 +# +# Copyright (c) 1997-2009 by Secret Labs AB. All rights reserved. +# Copyright (c) 1995-2009 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +import abc +import atexit +import builtins +import io +import logging +import math +import os +import re +import struct +import sys +import tempfile +import warnings +from collections.abc import Callable, Iterator, MutableMapping, Sequence +from enum import IntEnum +from types import ModuleType +from typing import IO, Any, Literal, Protocol, cast + +# VERSION was removed in Pillow 6.0.0. +# PILLOW_VERSION was removed in Pillow 9.0.0. +# Use __version__ instead. +from . import ( + ExifTags, + ImageMode, + TiffTags, + UnidentifiedImageError, + __version__, + _plugins, +) +from ._binary import i32le, o32be, o32le +from ._deprecate import deprecate +from ._util import DeferredError, is_path + +ElementTree: ModuleType | None +try: + from defusedxml import ElementTree +except ImportError: + ElementTree = None + +logger = logging.getLogger(__name__) + + +class DecompressionBombWarning(RuntimeWarning): + pass + + +class DecompressionBombError(Exception): + pass + + +WARN_POSSIBLE_FORMATS: bool = False + +# Limit to around a quarter gigabyte for a 24-bit (3 bpp) image +MAX_IMAGE_PIXELS: int | None = int(1024 * 1024 * 1024 // 4 // 3) + + +try: + # If the _imaging C module is not present, Pillow will not load. + # Note that other modules should not refer to _imaging directly; + # import Image and use the Image.core variable instead. + # Also note that Image.core is not a publicly documented interface, + # and should be considered private and subject to change. + from . import _imaging as core + + if __version__ != getattr(core, "PILLOW_VERSION", None): + msg = ( + "The _imaging extension was built for another version of Pillow or PIL:\n" + f"Core version: {getattr(core, 'PILLOW_VERSION', None)}\n" + f"Pillow version: {__version__}" + ) + raise ImportError(msg) + +except ImportError as v: + core = DeferredError.new(ImportError("The _imaging C module is not installed.")) + # Explanations for ways that we know we might have an import error + if str(v).startswith("Module use of python"): + # The _imaging C module is present, but not compiled for + # the right version (windows only). Print a warning, if + # possible. + warnings.warn( + "The _imaging extension was built for another version of Python.", + RuntimeWarning, + ) + elif str(v).startswith("The _imaging extension"): + warnings.warn(str(v), RuntimeWarning) + # Fail here anyway. Don't let people run with a mostly broken Pillow. + # see docs/porting.rst + raise + + +def isImageType(t: Any) -> TypeGuard[Image]: + """ + Checks if an object is an image object. + + .. warning:: + + This function is for internal use only. + + :param t: object to check if it's an image + :returns: True if the object is an image + """ + deprecate("Image.isImageType(im)", 12, "isinstance(im, Image.Image)") + return hasattr(t, "im") + + +# +# Constants + + +# transpose +class Transpose(IntEnum): + FLIP_LEFT_RIGHT = 0 + FLIP_TOP_BOTTOM = 1 + ROTATE_90 = 2 + ROTATE_180 = 3 + ROTATE_270 = 4 + TRANSPOSE = 5 + TRANSVERSE = 6 + + +# transforms (also defined in Imaging.h) +class Transform(IntEnum): + AFFINE = 0 + EXTENT = 1 + PERSPECTIVE = 2 + QUAD = 3 + MESH = 4 + + +# resampling filters (also defined in Imaging.h) +class Resampling(IntEnum): + NEAREST = 0 + BOX = 4 + BILINEAR = 2 + HAMMING = 5 + BICUBIC = 3 + LANCZOS = 1 + + +_filters_support = { + Resampling.BOX: 0.5, + Resampling.BILINEAR: 1.0, + Resampling.HAMMING: 1.0, + Resampling.BICUBIC: 2.0, + Resampling.LANCZOS: 3.0, +} + + +# dithers +class Dither(IntEnum): + NONE = 0 + ORDERED = 1 # Not yet implemented + RASTERIZE = 2 # Not yet implemented + FLOYDSTEINBERG = 3 # default + + +# palettes/quantizers +class Palette(IntEnum): + WEB = 0 + ADAPTIVE = 1 + + +class Quantize(IntEnum): + MEDIANCUT = 0 + MAXCOVERAGE = 1 + FASTOCTREE = 2 + LIBIMAGEQUANT = 3 + + +module = sys.modules[__name__] +for enum in (Transpose, Transform, Resampling, Dither, Palette, Quantize): + for item in enum: + setattr(module, item.name, item.value) + + +if hasattr(core, "DEFAULT_STRATEGY"): + DEFAULT_STRATEGY = core.DEFAULT_STRATEGY + FILTERED = core.FILTERED + HUFFMAN_ONLY = core.HUFFMAN_ONLY + RLE = core.RLE + FIXED = core.FIXED + + +# -------------------------------------------------------------------- +# Registries + +TYPE_CHECKING = False +if TYPE_CHECKING: + import mmap + from xml.etree.ElementTree import Element + + from IPython.lib.pretty import PrettyPrinter + + from . import ImageFile, ImageFilter, ImagePalette, ImageQt, TiffImagePlugin + from ._typing import CapsuleType, NumpyArray, StrOrBytesPath, TypeGuard +ID: list[str] = [] +OPEN: dict[ + str, + tuple[ + Callable[[IO[bytes], str | bytes], ImageFile.ImageFile], + Callable[[bytes], bool | str] | None, + ], +] = {} +MIME: dict[str, str] = {} +SAVE: dict[str, Callable[[Image, IO[bytes], str | bytes], None]] = {} +SAVE_ALL: dict[str, Callable[[Image, IO[bytes], str | bytes], None]] = {} +EXTENSION: dict[str, str] = {} +DECODERS: dict[str, type[ImageFile.PyDecoder]] = {} +ENCODERS: dict[str, type[ImageFile.PyEncoder]] = {} + +# -------------------------------------------------------------------- +# Modes + +_ENDIAN = "<" if sys.byteorder == "little" else ">" + + +def _conv_type_shape(im: Image) -> tuple[tuple[int, ...], str]: + m = ImageMode.getmode(im.mode) + shape: tuple[int, ...] = (im.height, im.width) + extra = len(m.bands) + if extra != 1: + shape += (extra,) + return shape, m.typestr + + +MODES = [ + "1", + "CMYK", + "F", + "HSV", + "I", + "I;16", + "I;16B", + "I;16L", + "I;16N", + "L", + "LA", + "La", + "LAB", + "P", + "PA", + "RGB", + "RGBA", + "RGBa", + "RGBX", + "YCbCr", +] + +# raw modes that may be memory mapped. NOTE: if you change this, you +# may have to modify the stride calculation in map.c too! +_MAPMODES = ("L", "P", "RGBX", "RGBA", "CMYK", "I;16", "I;16L", "I;16B") + + +def getmodebase(mode: str) -> str: + """ + Gets the "base" mode for given mode. This function returns "L" for + images that contain grayscale data, and "RGB" for images that + contain color data. + + :param mode: Input mode. + :returns: "L" or "RGB". + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).basemode + + +def getmodetype(mode: str) -> str: + """ + Gets the storage type mode. Given a mode, this function returns a + single-layer mode suitable for storing individual bands. + + :param mode: Input mode. + :returns: "L", "I", or "F". + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).basetype + + +def getmodebandnames(mode: str) -> tuple[str, ...]: + """ + Gets a list of individual band names. Given a mode, this function returns + a tuple containing the names of individual bands (use + :py:method:`~PIL.Image.getmodetype` to get the mode used to store each + individual band. + + :param mode: Input mode. + :returns: A tuple containing band names. The length of the tuple + gives the number of bands in an image of the given mode. + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).bands + + +def getmodebands(mode: str) -> int: + """ + Gets the number of individual bands for this mode. + + :param mode: Input mode. + :returns: The number of bands in this mode. + :exception KeyError: If the input mode was not a standard mode. + """ + return len(ImageMode.getmode(mode).bands) + + +# -------------------------------------------------------------------- +# Helpers + +_initialized = 0 + + +def preinit() -> None: + """ + Explicitly loads BMP, GIF, JPEG, PPM and PPM file format drivers. + + It is called when opening or saving images. + """ + + global _initialized + if _initialized >= 1: + return + + try: + from . import BmpImagePlugin + + assert BmpImagePlugin + except ImportError: + pass + try: + from . import GifImagePlugin + + assert GifImagePlugin + except ImportError: + pass + try: + from . import JpegImagePlugin + + assert JpegImagePlugin + except ImportError: + pass + try: + from . import PpmImagePlugin + + assert PpmImagePlugin + except ImportError: + pass + try: + from . import PngImagePlugin + + assert PngImagePlugin + except ImportError: + pass + + _initialized = 1 + + +def init() -> bool: + """ + Explicitly initializes the Python Imaging Library. This function + loads all available file format drivers. + + It is called when opening or saving images if :py:meth:`~preinit()` is + insufficient, and by :py:meth:`~PIL.features.pilinfo`. + """ + + global _initialized + if _initialized >= 2: + return False + + parent_name = __name__.rpartition(".")[0] + for plugin in _plugins: + try: + logger.debug("Importing %s", plugin) + __import__(f"{parent_name}.{plugin}", globals(), locals(), []) + except ImportError as e: + logger.debug("Image: failed to import %s: %s", plugin, e) + + if OPEN or SAVE: + _initialized = 2 + return True + return False + + +# -------------------------------------------------------------------- +# Codec factories (used by tobytes/frombytes and ImageFile.load) + + +def _getdecoder( + mode: str, decoder_name: str, args: Any, extra: tuple[Any, ...] = () +) -> core.ImagingDecoder | ImageFile.PyDecoder: + # tweak arguments + if args is None: + args = () + elif not isinstance(args, tuple): + args = (args,) + + try: + decoder = DECODERS[decoder_name] + except KeyError: + pass + else: + return decoder(mode, *args + extra) + + try: + # get decoder + decoder = getattr(core, f"{decoder_name}_decoder") + except AttributeError as e: + msg = f"decoder {decoder_name} not available" + raise OSError(msg) from e + return decoder(mode, *args + extra) + + +def _getencoder( + mode: str, encoder_name: str, args: Any, extra: tuple[Any, ...] = () +) -> core.ImagingEncoder | ImageFile.PyEncoder: + # tweak arguments + if args is None: + args = () + elif not isinstance(args, tuple): + args = (args,) + + try: + encoder = ENCODERS[encoder_name] + except KeyError: + pass + else: + return encoder(mode, *args + extra) + + try: + # get encoder + encoder = getattr(core, f"{encoder_name}_encoder") + except AttributeError as e: + msg = f"encoder {encoder_name} not available" + raise OSError(msg) from e + return encoder(mode, *args + extra) + + +# -------------------------------------------------------------------- +# Simple expression analyzer + + +class ImagePointTransform: + """ + Used with :py:meth:`~PIL.Image.Image.point` for single band images with more than + 8 bits, this represents an affine transformation, where the value is multiplied by + ``scale`` and ``offset`` is added. + """ + + def __init__(self, scale: float, offset: float) -> None: + self.scale = scale + self.offset = offset + + def __neg__(self) -> ImagePointTransform: + return ImagePointTransform(-self.scale, -self.offset) + + def __add__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return ImagePointTransform( + self.scale + other.scale, self.offset + other.offset + ) + return ImagePointTransform(self.scale, self.offset + other) + + __radd__ = __add__ + + def __sub__(self, other: ImagePointTransform | float) -> ImagePointTransform: + return self + -other + + def __rsub__(self, other: ImagePointTransform | float) -> ImagePointTransform: + return other + -self + + def __mul__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return NotImplemented + return ImagePointTransform(self.scale * other, self.offset * other) + + __rmul__ = __mul__ + + def __truediv__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return NotImplemented + return ImagePointTransform(self.scale / other, self.offset / other) + + +def _getscaleoffset( + expr: Callable[[ImagePointTransform], ImagePointTransform | float], +) -> tuple[float, float]: + a = expr(ImagePointTransform(1, 0)) + return (a.scale, a.offset) if isinstance(a, ImagePointTransform) else (0, a) + + +# -------------------------------------------------------------------- +# Implementation wrapper + + +class SupportsGetData(Protocol): + def getdata( + self, + ) -> tuple[Transform, Sequence[int]]: ... + + +class Image: + """ + This class represents an image object. To create + :py:class:`~PIL.Image.Image` objects, use the appropriate factory + functions. There's hardly ever any reason to call the Image constructor + directly. + + * :py:func:`~PIL.Image.open` + * :py:func:`~PIL.Image.new` + * :py:func:`~PIL.Image.frombytes` + """ + + format: str | None = None + format_description: str | None = None + _close_exclusive_fp_after_loading = True + + def __init__(self) -> None: + # FIXME: take "new" parameters / other image? + self._im: core.ImagingCore | DeferredError | None = None + self._mode = "" + self._size = (0, 0) + self.palette: ImagePalette.ImagePalette | None = None + self.info: dict[str | tuple[int, int], Any] = {} + self.readonly = 0 + self._exif: Exif | None = None + + @property + def im(self) -> core.ImagingCore: + if isinstance(self._im, DeferredError): + raise self._im.ex + assert self._im is not None + return self._im + + @im.setter + def im(self, im: core.ImagingCore) -> None: + self._im = im + + @property + def width(self) -> int: + return self.size[0] + + @property + def height(self) -> int: + return self.size[1] + + @property + def size(self) -> tuple[int, int]: + return self._size + + @property + def mode(self) -> str: + return self._mode + + @property + def readonly(self) -> int: + return (self._im and self._im.readonly) or self._readonly + + @readonly.setter + def readonly(self, readonly: int) -> None: + self._readonly = readonly + + def _new(self, im: core.ImagingCore) -> Image: + new = Image() + new.im = im + new._mode = im.mode + new._size = im.size + if im.mode in ("P", "PA"): + if self.palette: + new.palette = self.palette.copy() + else: + from . import ImagePalette + + new.palette = ImagePalette.ImagePalette() + new.info = self.info.copy() + return new + + # Context manager support + def __enter__(self): + return self + + def __exit__(self, *args): + from . import ImageFile + + if isinstance(self, ImageFile.ImageFile): + if getattr(self, "_exclusive_fp", False): + self._close_fp() + self.fp = None + + def close(self) -> None: + """ + This operation will destroy the image core and release its memory. + The image data will be unusable afterward. + + This function is required to close images that have multiple frames or + have not had their file read and closed by the + :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for + more information. + """ + if getattr(self, "map", None): + if sys.platform == "win32" and hasattr(sys, "pypy_version_info"): + self.map.close() + self.map: mmap.mmap | None = None + + # Instead of simply setting to None, we're setting up a + # deferred error that will better explain that the core image + # object is gone. + self._im = DeferredError(ValueError("Operation on closed image")) + + def _copy(self) -> None: + self.load() + self.im = self.im.copy() + self.readonly = 0 + + def _ensure_mutable(self) -> None: + if self.readonly: + self._copy() + else: + self.load() + + def _dump( + self, file: str | None = None, format: str | None = None, **options: Any + ) -> str: + suffix = "" + if format: + suffix = f".{format}" + + if not file: + f, filename = tempfile.mkstemp(suffix) + os.close(f) + else: + filename = file + if not filename.endswith(suffix): + filename = filename + suffix + + self.load() + + if not format or format == "PPM": + self.im.save_ppm(filename) + else: + self.save(filename, format, **options) + + return filename + + def __eq__(self, other: object) -> bool: + if self.__class__ is not other.__class__: + return False + assert isinstance(other, Image) + return ( + self.mode == other.mode + and self.size == other.size + and self.info == other.info + and self.getpalette() == other.getpalette() + and self.tobytes() == other.tobytes() + ) + + def __repr__(self) -> str: + return ( + f"<{self.__class__.__module__}.{self.__class__.__name__} " + f"image mode={self.mode} size={self.size[0]}x{self.size[1]} " + f"at 0x{id(self):X}>" + ) + + def _repr_pretty_(self, p: PrettyPrinter, cycle: bool) -> None: + """IPython plain text display support""" + + # Same as __repr__ but without unpredictable id(self), + # to keep Jupyter notebook `text/plain` output stable. + p.text( + f"<{self.__class__.__module__}.{self.__class__.__name__} " + f"image mode={self.mode} size={self.size[0]}x{self.size[1]}>" + ) + + def _repr_image(self, image_format: str, **kwargs: Any) -> bytes | None: + """Helper function for iPython display hook. + + :param image_format: Image format. + :returns: image as bytes, saved into the given format. + """ + b = io.BytesIO() + try: + self.save(b, image_format, **kwargs) + except Exception: + return None + return b.getvalue() + + def _repr_png_(self) -> bytes | None: + """iPython display hook support for PNG format. + + :returns: PNG version of the image as bytes + """ + return self._repr_image("PNG", compress_level=1) + + def _repr_jpeg_(self) -> bytes | None: + """iPython display hook support for JPEG format. + + :returns: JPEG version of the image as bytes + """ + return self._repr_image("JPEG") + + @property + def __array_interface__(self) -> dict[str, str | bytes | int | tuple[int, ...]]: + # numpy array interface support + new: dict[str, str | bytes | int | tuple[int, ...]] = {"version": 3} + if self.mode == "1": + # Binary images need to be extended from bits to bytes + # See: https://github.com/python-pillow/Pillow/issues/350 + new["data"] = self.tobytes("raw", "L") + else: + new["data"] = self.tobytes() + new["shape"], new["typestr"] = _conv_type_shape(self) + return new + + def __arrow_c_schema__(self) -> object: + self.load() + return self.im.__arrow_c_schema__() + + def __arrow_c_array__( + self, requested_schema: object | None = None + ) -> tuple[object, object]: + self.load() + return (self.im.__arrow_c_schema__(), self.im.__arrow_c_array__()) + + def __getstate__(self) -> list[Any]: + im_data = self.tobytes() # load image first + return [self.info, self.mode, self.size, self.getpalette(), im_data] + + def __setstate__(self, state: list[Any]) -> None: + Image.__init__(self) + info, mode, size, palette, data = state[:5] + self.info = info + self._mode = mode + self._size = size + self.im = core.new(mode, size) + if mode in ("L", "LA", "P", "PA") and palette: + self.putpalette(palette) + self.frombytes(data) + + def tobytes(self, encoder_name: str = "raw", *args: Any) -> bytes: + """ + Return image as a bytes object. + + .. warning:: + + This method returns raw image data derived from Pillow's internal + storage. For compressed image data (e.g. PNG, JPEG) use + :meth:`~.save`, with a BytesIO parameter for in-memory data. + + :param encoder_name: What encoder to use. + + The default is to use the standard "raw" encoder. + To see how this packs pixel data into the returned + bytes, see :file:`libImaging/Pack.c`. + + A list of C encoders can be seen under codecs + section of the function array in + :file:`_imaging.c`. Python encoders are registered + within the relevant plugins. + :param args: Extra arguments to the encoder. + :returns: A :py:class:`bytes` object. + """ + + encoder_args: Any = args + if len(encoder_args) == 1 and isinstance(encoder_args[0], tuple): + # may pass tuple instead of argument list + encoder_args = encoder_args[0] + + if encoder_name == "raw" and encoder_args == (): + encoder_args = self.mode + + self.load() + + if self.width == 0 or self.height == 0: + return b"" + + # unpack data + e = _getencoder(self.mode, encoder_name, encoder_args) + e.setimage(self.im) + + from . import ImageFile + + bufsize = max(ImageFile.MAXBLOCK, self.size[0] * 4) # see RawEncode.c + + output = [] + while True: + bytes_consumed, errcode, data = e.encode(bufsize) + output.append(data) + if errcode: + break + if errcode < 0: + msg = f"encoder error {errcode} in tobytes" + raise RuntimeError(msg) + + return b"".join(output) + + def tobitmap(self, name: str = "image") -> bytes: + """ + Returns the image converted to an X11 bitmap. + + .. note:: This method only works for mode "1" images. + + :param name: The name prefix to use for the bitmap variables. + :returns: A string containing an X11 bitmap. + :raises ValueError: If the mode is not "1" + """ + + self.load() + if self.mode != "1": + msg = "not a bitmap" + raise ValueError(msg) + data = self.tobytes("xbm") + return b"".join( + [ + f"#define {name}_width {self.size[0]}\n".encode("ascii"), + f"#define {name}_height {self.size[1]}\n".encode("ascii"), + f"static char {name}_bits[] = {{\n".encode("ascii"), + data, + b"};", + ] + ) + + def frombytes( + self, + data: bytes | bytearray | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, + ) -> None: + """ + Loads this image with pixel data from a bytes object. + + This method is similar to the :py:func:`~PIL.Image.frombytes` function, + but loads data into this image instead of creating a new image object. + """ + + if self.width == 0 or self.height == 0: + return + + decoder_args: Any = args + if len(decoder_args) == 1 and isinstance(decoder_args[0], tuple): + # may pass tuple instead of argument list + decoder_args = decoder_args[0] + + # default format + if decoder_name == "raw" and decoder_args == (): + decoder_args = self.mode + + # unpack data + d = _getdecoder(self.mode, decoder_name, decoder_args) + d.setimage(self.im) + s = d.decode(data) + + if s[0] >= 0: + msg = "not enough image data" + raise ValueError(msg) + if s[1] != 0: + msg = "cannot decode image data" + raise ValueError(msg) + + def load(self) -> core.PixelAccess | None: + """ + Allocates storage for the image and loads the pixel data. In + normal cases, you don't need to call this method, since the + Image class automatically loads an opened image when it is + accessed for the first time. + + If the file associated with the image was opened by Pillow, then this + method will close it. The exception to this is if the image has + multiple frames, in which case the file will be left open for seek + operations. See :ref:`file-handling` for more information. + + :returns: An image access object. + :rtype: :py:class:`.PixelAccess` + """ + if self._im is not None and self.palette and self.palette.dirty: + # realize palette + mode, arr = self.palette.getdata() + self.im.putpalette(self.palette.mode, mode, arr) + self.palette.dirty = 0 + self.palette.rawmode = None + if "transparency" in self.info and mode in ("LA", "PA"): + if isinstance(self.info["transparency"], int): + self.im.putpalettealpha(self.info["transparency"], 0) + else: + self.im.putpalettealphas(self.info["transparency"]) + self.palette.mode = "RGBA" + else: + self.palette.palette = self.im.getpalette( + self.palette.mode, self.palette.mode + ) + + if self._im is not None: + return self.im.pixel_access(self.readonly) + return None + + def verify(self) -> None: + """ + Verifies the contents of a file. For data read from a file, this + method attempts to determine if the file is broken, without + actually decoding the image data. If this method finds any + problems, it raises suitable exceptions. If you need to load + the image after using this method, you must reopen the image + file. + """ + pass + + def convert( + self, + mode: str | None = None, + matrix: tuple[float, ...] | None = None, + dither: Dither | None = None, + palette: Palette = Palette.WEB, + colors: int = 256, + ) -> Image: + """ + Returns a converted copy of this image. For the "P" mode, this + method translates pixels through the palette. If mode is + omitted, a mode is chosen so that all information in the image + and the palette can be represented without a palette. + + This supports all possible conversions between "L", "RGB" and "CMYK". The + ``matrix`` argument only supports "L" and "RGB". + + When translating a color image to grayscale (mode "L"), + the library uses the ITU-R 601-2 luma transform:: + + L = R * 299/1000 + G * 587/1000 + B * 114/1000 + + The default method of converting a grayscale ("L") or "RGB" + image into a bilevel (mode "1") image uses Floyd-Steinberg + dither to approximate the original image luminosity levels. If + dither is ``None``, all values larger than 127 are set to 255 (white), + all other values to 0 (black). To use other thresholds, use the + :py:meth:`~PIL.Image.Image.point` method. + + When converting from "RGBA" to "P" without a ``matrix`` argument, + this passes the operation to :py:meth:`~PIL.Image.Image.quantize`, + and ``dither`` and ``palette`` are ignored. + + When converting from "PA", if an "RGBA" palette is present, the alpha + channel from the image will be used instead of the values from the palette. + + :param mode: The requested mode. See: :ref:`concept-modes`. + :param matrix: An optional conversion matrix. If given, this + should be 4- or 12-tuple containing floating point values. + :param dither: Dithering method, used when converting from + mode "RGB" to "P" or from "RGB" or "L" to "1". + Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG` + (default). Note that this is not used when ``matrix`` is supplied. + :param palette: Palette to use when converting from mode "RGB" + to "P". Available palettes are :data:`Palette.WEB` or + :data:`Palette.ADAPTIVE`. + :param colors: Number of colors to use for the :data:`Palette.ADAPTIVE` + palette. Defaults to 256. + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + + self.load() + + has_transparency = "transparency" in self.info + if not mode and self.mode == "P": + # determine default mode + if self.palette: + mode = self.palette.mode + else: + mode = "RGB" + if mode == "RGB" and has_transparency: + mode = "RGBA" + if not mode or (mode == self.mode and not matrix): + return self.copy() + + if matrix: + # matrix conversion + if mode not in ("L", "RGB"): + msg = "illegal conversion" + raise ValueError(msg) + im = self.im.convert_matrix(mode, matrix) + new_im = self._new(im) + if has_transparency and self.im.bands == 3: + transparency = new_im.info["transparency"] + + def convert_transparency( + m: tuple[float, ...], v: tuple[int, int, int] + ) -> int: + value = m[0] * v[0] + m[1] * v[1] + m[2] * v[2] + m[3] * 0.5 + return max(0, min(255, int(value))) + + if mode == "L": + transparency = convert_transparency(matrix, transparency) + elif len(mode) == 3: + transparency = tuple( + convert_transparency(matrix[i * 4 : i * 4 + 4], transparency) + for i in range(len(transparency)) + ) + new_im.info["transparency"] = transparency + return new_im + + if mode == "P" and self.mode == "RGBA": + return self.quantize(colors) + + trns = None + delete_trns = False + # transparency handling + if has_transparency: + if (self.mode in ("1", "L", "I", "I;16") and mode in ("LA", "RGBA")) or ( + self.mode == "RGB" and mode in ("La", "LA", "RGBa", "RGBA") + ): + # Use transparent conversion to promote from transparent + # color to an alpha channel. + new_im = self._new( + self.im.convert_transparent(mode, self.info["transparency"]) + ) + del new_im.info["transparency"] + return new_im + elif self.mode in ("L", "RGB", "P") and mode in ("L", "RGB", "P"): + t = self.info["transparency"] + if isinstance(t, bytes): + # Dragons. This can't be represented by a single color + warnings.warn( + "Palette images with Transparency expressed in bytes should be " + "converted to RGBA images" + ) + delete_trns = True + else: + # get the new transparency color. + # use existing conversions + trns_im = new(self.mode, (1, 1)) + if self.mode == "P": + assert self.palette is not None + trns_im.putpalette(self.palette, self.palette.mode) + if isinstance(t, tuple): + err = "Couldn't allocate a palette color for transparency" + assert trns_im.palette is not None + try: + t = trns_im.palette.getcolor(t, self) + except ValueError as e: + if str(e) == "cannot allocate more than 256 colors": + # If all 256 colors are in use, + # then there is no need for transparency + t = None + else: + raise ValueError(err) from e + if t is None: + trns = None + else: + trns_im.putpixel((0, 0), t) + + if mode in ("L", "RGB"): + trns_im = trns_im.convert(mode) + else: + # can't just retrieve the palette number, got to do it + # after quantization. + trns_im = trns_im.convert("RGB") + trns = trns_im.getpixel((0, 0)) + + elif self.mode == "P" and mode in ("LA", "PA", "RGBA"): + t = self.info["transparency"] + delete_trns = True + + if isinstance(t, bytes): + self.im.putpalettealphas(t) + elif isinstance(t, int): + self.im.putpalettealpha(t, 0) + else: + msg = "Transparency for P mode should be bytes or int" + raise ValueError(msg) + + if mode == "P" and palette == Palette.ADAPTIVE: + im = self.im.quantize(colors) + new_im = self._new(im) + from . import ImagePalette + + new_im.palette = ImagePalette.ImagePalette( + "RGB", new_im.im.getpalette("RGB") + ) + if delete_trns: + # This could possibly happen if we requantize to fewer colors. + # The transparency would be totally off in that case. + del new_im.info["transparency"] + if trns is not None: + try: + new_im.info["transparency"] = new_im.palette.getcolor( + cast(tuple[int, ...], trns), # trns was converted to RGB + new_im, + ) + except Exception: + # if we can't make a transparent color, don't leave the old + # transparency hanging around to mess us up. + del new_im.info["transparency"] + warnings.warn("Couldn't allocate palette entry for transparency") + return new_im + + if "LAB" in (self.mode, mode): + im = self + if mode == "LAB": + if im.mode not in ("RGB", "RGBA", "RGBX"): + im = im.convert("RGBA") + other_mode = im.mode + else: + other_mode = mode + if other_mode in ("RGB", "RGBA", "RGBX"): + from . import ImageCms + + srgb = ImageCms.createProfile("sRGB") + lab = ImageCms.createProfile("LAB") + profiles = [lab, srgb] if im.mode == "LAB" else [srgb, lab] + transform = ImageCms.buildTransform( + profiles[0], profiles[1], im.mode, mode + ) + return transform.apply(im) + + # colorspace conversion + if dither is None: + dither = Dither.FLOYDSTEINBERG + + try: + im = self.im.convert(mode, dither) + except ValueError: + try: + # normalize source image and try again + modebase = getmodebase(self.mode) + if modebase == self.mode: + raise + im = self.im.convert(modebase) + im = im.convert(mode, dither) + except KeyError as e: + msg = "illegal conversion" + raise ValueError(msg) from e + + new_im = self._new(im) + if mode == "P" and palette != Palette.ADAPTIVE: + from . import ImagePalette + + new_im.palette = ImagePalette.ImagePalette("RGB", im.getpalette("RGB")) + if delete_trns: + # crash fail if we leave a bytes transparency in an rgb/l mode. + del new_im.info["transparency"] + if trns is not None: + if new_im.mode == "P" and new_im.palette: + try: + new_im.info["transparency"] = new_im.palette.getcolor( + cast(tuple[int, ...], trns), new_im # trns was converted to RGB + ) + except ValueError as e: + del new_im.info["transparency"] + if str(e) != "cannot allocate more than 256 colors": + # If all 256 colors are in use, + # then there is no need for transparency + warnings.warn( + "Couldn't allocate palette entry for transparency" + ) + else: + new_im.info["transparency"] = trns + return new_im + + def quantize( + self, + colors: int = 256, + method: int | None = None, + kmeans: int = 0, + palette: Image | None = None, + dither: Dither = Dither.FLOYDSTEINBERG, + ) -> Image: + """ + Convert the image to 'P' mode with the specified number + of colors. + + :param colors: The desired number of colors, <= 256 + :param method: :data:`Quantize.MEDIANCUT` (median cut), + :data:`Quantize.MAXCOVERAGE` (maximum coverage), + :data:`Quantize.FASTOCTREE` (fast octree), + :data:`Quantize.LIBIMAGEQUANT` (libimagequant; check support + using :py:func:`PIL.features.check_feature` with + ``feature="libimagequant"``). + + By default, :data:`Quantize.MEDIANCUT` will be used. + + The exception to this is RGBA images. :data:`Quantize.MEDIANCUT` + and :data:`Quantize.MAXCOVERAGE` do not support RGBA images, so + :data:`Quantize.FASTOCTREE` is used by default instead. + :param kmeans: Integer greater than or equal to zero. + :param palette: Quantize to the palette of given + :py:class:`PIL.Image.Image`. + :param dither: Dithering method, used when converting from + mode "RGB" to "P" or from "RGB" or "L" to "1". + Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG` + (default). + :returns: A new image + """ + + self.load() + + if method is None: + # defaults: + method = Quantize.MEDIANCUT + if self.mode == "RGBA": + method = Quantize.FASTOCTREE + + if self.mode == "RGBA" and method not in ( + Quantize.FASTOCTREE, + Quantize.LIBIMAGEQUANT, + ): + # Caller specified an invalid mode. + msg = ( + "Fast Octree (method == 2) and libimagequant (method == 3) " + "are the only valid methods for quantizing RGBA images" + ) + raise ValueError(msg) + + if palette: + # use palette from reference image + palette.load() + if palette.mode != "P": + msg = "bad mode for palette image" + raise ValueError(msg) + if self.mode not in {"RGB", "L"}: + msg = "only RGB or L mode images can be quantized to a palette" + raise ValueError(msg) + im = self.im.convert("P", dither, palette.im) + new_im = self._new(im) + assert palette.palette is not None + new_im.palette = palette.palette.copy() + return new_im + + if kmeans < 0: + msg = "kmeans must not be negative" + raise ValueError(msg) + + im = self._new(self.im.quantize(colors, method, kmeans)) + + from . import ImagePalette + + mode = im.im.getpalettemode() + palette_data = im.im.getpalette(mode, mode)[: colors * len(mode)] + im.palette = ImagePalette.ImagePalette(mode, palette_data) + + return im + + def copy(self) -> Image: + """ + Copies this image. Use this method if you wish to paste things + into an image, but still retain the original. + + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + self.load() + return self._new(self.im.copy()) + + __copy__ = copy + + def crop(self, box: tuple[float, float, float, float] | None = None) -> Image: + """ + Returns a rectangular region from this image. The box is a + 4-tuple defining the left, upper, right, and lower pixel + coordinate. See :ref:`coordinate-system`. + + Note: Prior to Pillow 3.4.0, this was a lazy operation. + + :param box: The crop rectangle, as a (left, upper, right, lower)-tuple. + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if box is None: + return self.copy() + + if box[2] < box[0]: + msg = "Coordinate 'right' is less than 'left'" + raise ValueError(msg) + elif box[3] < box[1]: + msg = "Coordinate 'lower' is less than 'upper'" + raise ValueError(msg) + + self.load() + return self._new(self._crop(self.im, box)) + + def _crop( + self, im: core.ImagingCore, box: tuple[float, float, float, float] + ) -> core.ImagingCore: + """ + Returns a rectangular region from the core image object im. + + This is equivalent to calling im.crop((x0, y0, x1, y1)), but + includes additional sanity checks. + + :param im: a core image object + :param box: The crop rectangle, as a (left, upper, right, lower)-tuple. + :returns: A core image object. + """ + + x0, y0, x1, y1 = map(int, map(round, box)) + + absolute_values = (abs(x1 - x0), abs(y1 - y0)) + + _decompression_bomb_check(absolute_values) + + return im.crop((x0, y0, x1, y1)) + + def draft( + self, mode: str | None, size: tuple[int, int] | None + ) -> tuple[str, tuple[int, int, float, float]] | None: + """ + Configures the image file loader so it returns a version of the + image that as closely as possible matches the given mode and + size. For example, you can use this method to convert a color + JPEG to grayscale while loading it. + + If any changes are made, returns a tuple with the chosen ``mode`` and + ``box`` with coordinates of the original image within the altered one. + + Note that this method modifies the :py:class:`~PIL.Image.Image` object + in place. If the image has already been loaded, this method has no + effect. + + Note: This method is not implemented for most images. It is + currently implemented only for JPEG and MPO images. + + :param mode: The requested mode. + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + """ + pass + + def _expand(self, xmargin: int, ymargin: int | None = None) -> Image: + if ymargin is None: + ymargin = xmargin + self.load() + return self._new(self.im.expand(xmargin, ymargin)) + + def filter(self, filter: ImageFilter.Filter | type[ImageFilter.Filter]) -> Image: + """ + Filters this image using the given filter. For a list of + available filters, see the :py:mod:`~PIL.ImageFilter` module. + + :param filter: Filter kernel. + :returns: An :py:class:`~PIL.Image.Image` object.""" + + from . import ImageFilter + + self.load() + + if callable(filter): + filter = filter() + if not hasattr(filter, "filter"): + msg = "filter argument should be ImageFilter.Filter instance or class" + raise TypeError(msg) + + multiband = isinstance(filter, ImageFilter.MultibandFilter) + if self.im.bands == 1 or multiband: + return self._new(filter.filter(self.im)) + + ims = [ + self._new(filter.filter(self.im.getband(c))) for c in range(self.im.bands) + ] + return merge(self.mode, ims) + + def getbands(self) -> tuple[str, ...]: + """ + Returns a tuple containing the name of each band in this image. + For example, ``getbands`` on an RGB image returns ("R", "G", "B"). + + :returns: A tuple containing band names. + :rtype: tuple + """ + return ImageMode.getmode(self.mode).bands + + def getbbox(self, *, alpha_only: bool = True) -> tuple[int, int, int, int] | None: + """ + Calculates the bounding box of the non-zero regions in the + image. + + :param alpha_only: Optional flag, defaulting to ``True``. + If ``True`` and the image has an alpha channel, trim transparent pixels. + Otherwise, trim pixels when all channels are zero. + Keyword-only argument. + :returns: The bounding box is returned as a 4-tuple defining the + left, upper, right, and lower pixel coordinate. See + :ref:`coordinate-system`. If the image is completely empty, this + method returns None. + + """ + + self.load() + return self.im.getbbox(alpha_only) + + def getcolors( + self, maxcolors: int = 256 + ) -> list[tuple[int, tuple[int, ...]]] | list[tuple[int, float]] | None: + """ + Returns a list of colors used in this image. + + The colors will be in the image's mode. For example, an RGB image will + return a tuple of (red, green, blue) color values, and a P image will + return the index of the color in the palette. + + :param maxcolors: Maximum number of colors. If this number is + exceeded, this method returns None. The default limit is + 256 colors. + :returns: An unsorted list of (count, pixel) values. + """ + + self.load() + if self.mode in ("1", "L", "P"): + h = self.im.histogram() + out: list[tuple[int, float]] = [(h[i], i) for i in range(256) if h[i]] + if len(out) > maxcolors: + return None + return out + return self.im.getcolors(maxcolors) + + def getdata(self, band: int | None = None) -> core.ImagingCore: + """ + Returns the contents of this image as a sequence object + containing pixel values. The sequence object is flattened, so + that values for line one follow directly after the values of + line zero, and so on. + + Note that the sequence object returned by this method is an + internal PIL data type, which only supports certain sequence + operations. To convert it to an ordinary sequence (e.g. for + printing), use ``list(im.getdata())``. + + :param band: What band to return. The default is to return + all bands. To return a single band, pass in the index + value (e.g. 0 to get the "R" band from an "RGB" image). + :returns: A sequence-like object. + """ + + self.load() + if band is not None: + return self.im.getband(band) + return self.im # could be abused + + def getextrema(self) -> tuple[float, float] | tuple[tuple[int, int], ...]: + """ + Gets the minimum and maximum pixel values for each band in + the image. + + :returns: For a single-band image, a 2-tuple containing the + minimum and maximum pixel value. For a multi-band image, + a tuple containing one 2-tuple for each band. + """ + + self.load() + if self.im.bands > 1: + return tuple(self.im.getband(i).getextrema() for i in range(self.im.bands)) + return self.im.getextrema() + + def getxmp(self) -> dict[str, Any]: + """ + Returns a dictionary containing the XMP tags. + Requires defusedxml to be installed. + + :returns: XMP tags in a dictionary. + """ + + def get_name(tag: str) -> str: + return re.sub("^{[^}]+}", "", tag) + + def get_value(element: Element) -> str | dict[str, Any] | None: + value: dict[str, Any] = {get_name(k): v for k, v in element.attrib.items()} + children = list(element) + if children: + for child in children: + name = get_name(child.tag) + child_value = get_value(child) + if name in value: + if not isinstance(value[name], list): + value[name] = [value[name]] + value[name].append(child_value) + else: + value[name] = child_value + elif value: + if element.text: + value["text"] = element.text + else: + return element.text + return value + + if ElementTree is None: + warnings.warn("XMP data cannot be read without defusedxml dependency") + return {} + if "xmp" not in self.info: + return {} + root = ElementTree.fromstring(self.info["xmp"].rstrip(b"\x00 ")) + return {get_name(root.tag): get_value(root)} + + def getexif(self) -> Exif: + """ + Gets EXIF data from the image. + + :returns: an :py:class:`~PIL.Image.Exif` object. + """ + if self._exif is None: + self._exif = Exif() + elif self._exif._loaded: + return self._exif + self._exif._loaded = True + + exif_info = self.info.get("exif") + if exif_info is None: + if "Raw profile type exif" in self.info: + exif_info = bytes.fromhex( + "".join(self.info["Raw profile type exif"].split("\n")[3:]) + ) + elif hasattr(self, "tag_v2"): + self._exif.bigtiff = self.tag_v2._bigtiff + self._exif.endian = self.tag_v2._endian + self._exif.load_from_fp(self.fp, self.tag_v2._offset) + if exif_info is not None: + self._exif.load(exif_info) + + # XMP tags + if ExifTags.Base.Orientation not in self._exif: + xmp_tags = self.info.get("XML:com.adobe.xmp") + pattern: str | bytes = r'tiff:Orientation(="|>)([0-9])' + if not xmp_tags and (xmp_tags := self.info.get("xmp")): + pattern = rb'tiff:Orientation(="|>)([0-9])' + if xmp_tags: + match = re.search(pattern, xmp_tags) + if match: + self._exif[ExifTags.Base.Orientation] = int(match[2]) + + return self._exif + + def _reload_exif(self) -> None: + if self._exif is None or not self._exif._loaded: + return + self._exif._loaded = False + self.getexif() + + def get_child_images(self) -> list[ImageFile.ImageFile]: + from . import ImageFile + + deprecate("Image.Image.get_child_images", 13) + return ImageFile.ImageFile.get_child_images(self) # type: ignore[arg-type] + + def getim(self) -> CapsuleType: + """ + Returns a capsule that points to the internal image memory. + + :returns: A capsule object. + """ + + self.load() + return self.im.ptr + + def getpalette(self, rawmode: str | None = "RGB") -> list[int] | None: + """ + Returns the image palette as a list. + + :param rawmode: The mode in which to return the palette. ``None`` will + return the palette in its current mode. + + .. versionadded:: 9.1.0 + + :returns: A list of color values [r, g, b, ...], or None if the + image has no palette. + """ + + self.load() + try: + mode = self.im.getpalettemode() + except ValueError: + return None # no palette + if rawmode is None: + rawmode = mode + return list(self.im.getpalette(mode, rawmode)) + + @property + def has_transparency_data(self) -> bool: + """ + Determine if an image has transparency data, whether in the form of an + alpha channel, a palette with an alpha channel, or a "transparency" key + in the info dictionary. + + Note the image might still appear solid, if all of the values shown + within are opaque. + + :returns: A boolean. + """ + if ( + self.mode in ("LA", "La", "PA", "RGBA", "RGBa") + or "transparency" in self.info + ): + return True + if self.mode == "P": + assert self.palette is not None + return self.palette.mode.endswith("A") + return False + + def apply_transparency(self) -> None: + """ + If a P mode image has a "transparency" key in the info dictionary, + remove the key and instead apply the transparency to the palette. + Otherwise, the image is unchanged. + """ + if self.mode != "P" or "transparency" not in self.info: + return + + from . import ImagePalette + + palette = self.getpalette("RGBA") + assert palette is not None + transparency = self.info["transparency"] + if isinstance(transparency, bytes): + for i, alpha in enumerate(transparency): + palette[i * 4 + 3] = alpha + else: + palette[transparency * 4 + 3] = 0 + self.palette = ImagePalette.ImagePalette("RGBA", bytes(palette)) + self.palette.dirty = 1 + + del self.info["transparency"] + + def getpixel( + self, xy: tuple[int, int] | list[int] + ) -> float | tuple[int, ...] | None: + """ + Returns the pixel value at a given position. + + :param xy: The coordinate, given as (x, y). See + :ref:`coordinate-system`. + :returns: The pixel value. If the image is a multi-layer image, + this method returns a tuple. + """ + + self.load() + return self.im.getpixel(tuple(xy)) + + def getprojection(self) -> tuple[list[int], list[int]]: + """ + Get projection to x and y axes + + :returns: Two sequences, indicating where there are non-zero + pixels along the X-axis and the Y-axis, respectively. + """ + + self.load() + x, y = self.im.getprojection() + return list(x), list(y) + + def histogram( + self, mask: Image | None = None, extrema: tuple[float, float] | None = None + ) -> list[int]: + """ + Returns a histogram for the image. The histogram is returned as a + list of pixel counts, one for each pixel value in the source + image. Counts are grouped into 256 bins for each band, even if + the image has more than 8 bits per band. If the image has more + than one band, the histograms for all bands are concatenated (for + example, the histogram for an "RGB" image contains 768 values). + + A bilevel image (mode "1") is treated as a grayscale ("L") image + by this method. + + If a mask is provided, the method returns a histogram for those + parts of the image where the mask image is non-zero. The mask + image must have the same size as the image, and be either a + bi-level image (mode "1") or a grayscale image ("L"). + + :param mask: An optional mask. + :param extrema: An optional tuple of manually-specified extrema. + :returns: A list containing pixel counts. + """ + self.load() + if mask: + mask.load() + return self.im.histogram((0, 0), mask.im) + if self.mode in ("I", "F"): + return self.im.histogram( + extrema if extrema is not None else self.getextrema() + ) + return self.im.histogram() + + def entropy( + self, mask: Image | None = None, extrema: tuple[float, float] | None = None + ) -> float: + """ + Calculates and returns the entropy for the image. + + A bilevel image (mode "1") is treated as a grayscale ("L") + image by this method. + + If a mask is provided, the method employs the histogram for + those parts of the image where the mask image is non-zero. + The mask image must have the same size as the image, and be + either a bi-level image (mode "1") or a grayscale image ("L"). + + :param mask: An optional mask. + :param extrema: An optional tuple of manually-specified extrema. + :returns: A float value representing the image entropy + """ + self.load() + if mask: + mask.load() + return self.im.entropy((0, 0), mask.im) + if self.mode in ("I", "F"): + return self.im.entropy( + extrema if extrema is not None else self.getextrema() + ) + return self.im.entropy() + + def paste( + self, + im: Image | str | float | tuple[float, ...], + box: Image | tuple[int, int, int, int] | tuple[int, int] | None = None, + mask: Image | None = None, + ) -> None: + """ + Pastes another image into this image. The box argument is either + a 2-tuple giving the upper left corner, a 4-tuple defining the + left, upper, right, and lower pixel coordinate, or None (same as + (0, 0)). See :ref:`coordinate-system`. If a 4-tuple is given, the size + of the pasted image must match the size of the region. + + If the modes don't match, the pasted image is converted to the mode of + this image (see the :py:meth:`~PIL.Image.Image.convert` method for + details). + + Instead of an image, the source can be a integer or tuple + containing pixel values. The method then fills the region + with the given color. When creating RGB images, you can + also use color strings as supported by the ImageColor module. + + If a mask is given, this method updates only the regions + indicated by the mask. You can use either "1", "L", "LA", "RGBA" + or "RGBa" images (if present, the alpha band is used as mask). + Where the mask is 255, the given image is copied as is. Where + the mask is 0, the current value is preserved. Intermediate + values will mix the two images together, including their alpha + channels if they have them. + + See :py:meth:`~PIL.Image.Image.alpha_composite` if you want to + combine images with respect to their alpha channels. + + :param im: Source image or pixel value (integer, float or tuple). + :param box: An optional 4-tuple giving the region to paste into. + If a 2-tuple is used instead, it's treated as the upper left + corner. If omitted or None, the source is pasted into the + upper left corner. + + If an image is given as the second argument and there is no + third, the box defaults to (0, 0), and the second argument + is interpreted as a mask image. + :param mask: An optional mask image. + """ + + if isinstance(box, Image): + if mask is not None: + msg = "If using second argument as mask, third argument must be None" + raise ValueError(msg) + # abbreviated paste(im, mask) syntax + mask = box + box = None + + if box is None: + box = (0, 0) + + if len(box) == 2: + # upper left corner given; get size from image or mask + if isinstance(im, Image): + size = im.size + elif isinstance(mask, Image): + size = mask.size + else: + # FIXME: use self.size here? + msg = "cannot determine region size; use 4-item box" + raise ValueError(msg) + box += (box[0] + size[0], box[1] + size[1]) + + source: core.ImagingCore | str | float | tuple[float, ...] + if isinstance(im, str): + from . import ImageColor + + source = ImageColor.getcolor(im, self.mode) + elif isinstance(im, Image): + im.load() + if self.mode != im.mode: + if self.mode != "RGB" or im.mode not in ("LA", "RGBA", "RGBa"): + # should use an adapter for this! + im = im.convert(self.mode) + source = im.im + else: + source = im + + self._ensure_mutable() + + if mask: + mask.load() + self.im.paste(source, box, mask.im) + else: + self.im.paste(source, box) + + def alpha_composite( + self, im: Image, dest: Sequence[int] = (0, 0), source: Sequence[int] = (0, 0) + ) -> None: + """'In-place' analog of Image.alpha_composite. Composites an image + onto this image. + + :param im: image to composite over this one + :param dest: Optional 2 tuple (left, top) specifying the upper + left corner in this (destination) image. + :param source: Optional 2 (left, top) tuple for the upper left + corner in the overlay source image, or 4 tuple (left, top, right, + bottom) for the bounds of the source rectangle + + Performance Note: Not currently implemented in-place in the core layer. + """ + + if not isinstance(source, (list, tuple)): + msg = "Source must be a list or tuple" + raise ValueError(msg) + if not isinstance(dest, (list, tuple)): + msg = "Destination must be a list or tuple" + raise ValueError(msg) + + if len(source) == 4: + overlay_crop_box = tuple(source) + elif len(source) == 2: + overlay_crop_box = tuple(source) + im.size + else: + msg = "Source must be a sequence of length 2 or 4" + raise ValueError(msg) + + if not len(dest) == 2: + msg = "Destination must be a sequence of length 2" + raise ValueError(msg) + if min(source) < 0: + msg = "Source must be non-negative" + raise ValueError(msg) + + # over image, crop if it's not the whole image. + if overlay_crop_box == (0, 0) + im.size: + overlay = im + else: + overlay = im.crop(overlay_crop_box) + + # target for the paste + box = tuple(dest) + (dest[0] + overlay.width, dest[1] + overlay.height) + + # destination image. don't copy if we're using the whole image. + if box == (0, 0) + self.size: + background = self + else: + background = self.crop(box) + + result = alpha_composite(background, overlay) + self.paste(result, box) + + def point( + self, + lut: ( + Sequence[float] + | NumpyArray + | Callable[[int], float] + | Callable[[ImagePointTransform], ImagePointTransform | float] + | ImagePointHandler + ), + mode: str | None = None, + ) -> Image: + """ + Maps this image through a lookup table or function. + + :param lut: A lookup table, containing 256 (or 65536 if + self.mode=="I" and mode == "L") values per band in the + image. A function can be used instead, it should take a + single argument. The function is called once for each + possible pixel value, and the resulting table is applied to + all bands of the image. + + It may also be an :py:class:`~PIL.Image.ImagePointHandler` + object:: + + class Example(Image.ImagePointHandler): + def point(self, im: Image) -> Image: + # Return result + :param mode: Output mode (default is same as input). This can only be used if + the source image has mode "L" or "P", and the output has mode "1" or the + source image mode is "I" and the output mode is "L". + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + self.load() + + if isinstance(lut, ImagePointHandler): + return lut.point(self) + + if callable(lut): + # if it isn't a list, it should be a function + if self.mode in ("I", "I;16", "F"): + # check if the function can be used with point_transform + # UNDONE wiredfool -- I think this prevents us from ever doing + # a gamma function point transform on > 8bit images. + scale, offset = _getscaleoffset(lut) # type: ignore[arg-type] + return self._new(self.im.point_transform(scale, offset)) + # for other modes, convert the function to a table + flatLut = [lut(i) for i in range(256)] * self.im.bands # type: ignore[arg-type] + else: + flatLut = lut + + if self.mode == "F": + # FIXME: _imaging returns a confusing error message for this case + msg = "point operation not supported for this mode" + raise ValueError(msg) + + if mode != "F": + flatLut = [round(i) for i in flatLut] + return self._new(self.im.point(flatLut, mode)) + + def putalpha(self, alpha: Image | int) -> None: + """ + Adds or replaces the alpha layer in this image. If the image + does not have an alpha layer, it's converted to "LA" or "RGBA". + The new layer must be either "L" or "1". + + :param alpha: The new alpha layer. This can either be an "L" or "1" + image having the same size as this image, or an integer. + """ + + self._ensure_mutable() + + if self.mode not in ("LA", "PA", "RGBA"): + # attempt to promote self to a matching alpha mode + try: + mode = getmodebase(self.mode) + "A" + try: + self.im.setmode(mode) + except (AttributeError, ValueError) as e: + # do things the hard way + im = self.im.convert(mode) + if im.mode not in ("LA", "PA", "RGBA"): + msg = "alpha channel could not be added" + raise ValueError(msg) from e # sanity check + self.im = im + self._mode = self.im.mode + except KeyError as e: + msg = "illegal image mode" + raise ValueError(msg) from e + + if self.mode in ("LA", "PA"): + band = 1 + else: + band = 3 + + if isinstance(alpha, Image): + # alpha layer + if alpha.mode not in ("1", "L"): + msg = "illegal image mode" + raise ValueError(msg) + alpha.load() + if alpha.mode == "1": + alpha = alpha.convert("L") + else: + # constant alpha + try: + self.im.fillband(band, alpha) + except (AttributeError, ValueError): + # do things the hard way + alpha = new("L", self.size, alpha) + else: + return + + self.im.putband(alpha.im, band) + + def putdata( + self, + data: Sequence[float] | Sequence[Sequence[int]] | core.ImagingCore | NumpyArray, + scale: float = 1.0, + offset: float = 0.0, + ) -> None: + """ + Copies pixel data from a flattened sequence object into the image. The + values should start at the upper left corner (0, 0), continue to the + end of the line, followed directly by the first value of the second + line, and so on. Data will be read until either the image or the + sequence ends. The scale and offset values are used to adjust the + sequence values: **pixel = value*scale + offset**. + + :param data: A flattened sequence object. + :param scale: An optional scale value. The default is 1.0. + :param offset: An optional offset value. The default is 0.0. + """ + + self._ensure_mutable() + + self.im.putdata(data, scale, offset) + + def putpalette( + self, + data: ImagePalette.ImagePalette | bytes | Sequence[int], + rawmode: str = "RGB", + ) -> None: + """ + Attaches a palette to this image. The image must be a "P", "PA", "L" + or "LA" image. + + The palette sequence must contain at most 256 colors, made up of one + integer value for each channel in the raw mode. + For example, if the raw mode is "RGB", then it can contain at most 768 + values, made up of red, green and blue values for the corresponding pixel + index in the 256 colors. + If the raw mode is "RGBA", then it can contain at most 1024 values, + containing red, green, blue and alpha values. + + Alternatively, an 8-bit string may be used instead of an integer sequence. + + :param data: A palette sequence (either a list or a string). + :param rawmode: The raw mode of the palette. Either "RGB", "RGBA", or a mode + that can be transformed to "RGB" or "RGBA" (e.g. "R", "BGR;15", "RGBA;L"). + """ + from . import ImagePalette + + if self.mode not in ("L", "LA", "P", "PA"): + msg = "illegal image mode" + raise ValueError(msg) + if isinstance(data, ImagePalette.ImagePalette): + if data.rawmode is not None: + palette = ImagePalette.raw(data.rawmode, data.palette) + else: + palette = ImagePalette.ImagePalette(palette=data.palette) + palette.dirty = 1 + else: + if not isinstance(data, bytes): + data = bytes(data) + palette = ImagePalette.raw(rawmode, data) + self._mode = "PA" if "A" in self.mode else "P" + self.palette = palette + self.palette.mode = "RGBA" if "A" in rawmode else "RGB" + self.load() # install new palette + + def putpixel( + self, xy: tuple[int, int], value: float | tuple[int, ...] | list[int] + ) -> None: + """ + Modifies the pixel at the given position. The color is given as + a single numerical value for single-band images, and a tuple for + multi-band images. In addition to this, RGB and RGBA tuples are + accepted for P and PA images. + + Note that this method is relatively slow. For more extensive changes, + use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw` + module instead. + + See: + + * :py:meth:`~PIL.Image.Image.paste` + * :py:meth:`~PIL.Image.Image.putdata` + * :py:mod:`~PIL.ImageDraw` + + :param xy: The pixel coordinate, given as (x, y). See + :ref:`coordinate-system`. + :param value: The pixel value. + """ + + if self.readonly: + self._copy() + self.load() + + if ( + self.mode in ("P", "PA") + and isinstance(value, (list, tuple)) + and len(value) in [3, 4] + ): + # RGB or RGBA value for a P or PA image + if self.mode == "PA": + alpha = value[3] if len(value) == 4 else 255 + value = value[:3] + assert self.palette is not None + palette_index = self.palette.getcolor(tuple(value), self) + value = (palette_index, alpha) if self.mode == "PA" else palette_index + return self.im.putpixel(xy, value) + + def remap_palette( + self, dest_map: list[int], source_palette: bytes | bytearray | None = None + ) -> Image: + """ + Rewrites the image to reorder the palette. + + :param dest_map: A list of indexes into the original palette. + e.g. ``[1,0]`` would swap a two item palette, and ``list(range(256))`` + is the identity transform. + :param source_palette: Bytes or None. + :returns: An :py:class:`~PIL.Image.Image` object. + + """ + from . import ImagePalette + + if self.mode not in ("L", "P"): + msg = "illegal image mode" + raise ValueError(msg) + + bands = 3 + palette_mode = "RGB" + if source_palette is None: + if self.mode == "P": + self.load() + palette_mode = self.im.getpalettemode() + if palette_mode == "RGBA": + bands = 4 + source_palette = self.im.getpalette(palette_mode, palette_mode) + else: # L-mode + source_palette = bytearray(i // 3 for i in range(768)) + elif len(source_palette) > 768: + bands = 4 + palette_mode = "RGBA" + + palette_bytes = b"" + new_positions = [0] * 256 + + # pick only the used colors from the palette + for i, oldPosition in enumerate(dest_map): + palette_bytes += source_palette[ + oldPosition * bands : oldPosition * bands + bands + ] + new_positions[oldPosition] = i + + # replace the palette color id of all pixel with the new id + + # Palette images are [0..255], mapped through a 1 or 3 + # byte/color map. We need to remap the whole image + # from palette 1 to palette 2. New_positions is + # an array of indexes into palette 1. Palette 2 is + # palette 1 with any holes removed. + + # We're going to leverage the convert mechanism to use the + # C code to remap the image from palette 1 to palette 2, + # by forcing the source image into 'L' mode and adding a + # mapping 'L' mode palette, then converting back to 'L' + # sans palette thus converting the image bytes, then + # assigning the optimized RGB palette. + + # perf reference, 9500x4000 gif, w/~135 colors + # 14 sec prepatch, 1 sec postpatch with optimization forced. + + mapping_palette = bytearray(new_positions) + + m_im = self.copy() + m_im._mode = "P" + + m_im.palette = ImagePalette.ImagePalette( + palette_mode, palette=mapping_palette * bands + ) + # possibly set palette dirty, then + # m_im.putpalette(mapping_palette, 'L') # converts to 'P' + # or just force it. + # UNDONE -- this is part of the general issue with palettes + m_im.im.putpalette(palette_mode, palette_mode + ";L", m_im.palette.tobytes()) + + m_im = m_im.convert("L") + + m_im.putpalette(palette_bytes, palette_mode) + m_im.palette = ImagePalette.ImagePalette(palette_mode, palette=palette_bytes) + + if "transparency" in self.info: + try: + m_im.info["transparency"] = dest_map.index(self.info["transparency"]) + except ValueError: + if "transparency" in m_im.info: + del m_im.info["transparency"] + + return m_im + + def _get_safe_box( + self, + size: tuple[int, int], + resample: Resampling, + box: tuple[float, float, float, float], + ) -> tuple[int, int, int, int]: + """Expands the box so it includes adjacent pixels + that may be used by resampling with the given resampling filter. + """ + filter_support = _filters_support[resample] - 0.5 + scale_x = (box[2] - box[0]) / size[0] + scale_y = (box[3] - box[1]) / size[1] + support_x = filter_support * scale_x + support_y = filter_support * scale_y + + return ( + max(0, int(box[0] - support_x)), + max(0, int(box[1] - support_y)), + min(self.size[0], math.ceil(box[2] + support_x)), + min(self.size[1], math.ceil(box[3] + support_y)), + ) + + def resize( + self, + size: tuple[int, int] | list[int] | NumpyArray, + resample: int | None = None, + box: tuple[float, float, float, float] | None = None, + reducing_gap: float | None = None, + ) -> Image: + """ + Returns a resized copy of this image. + + :param size: The requested size in pixels, as a tuple or array: + (width, height). + :param resample: An optional resampling filter. This can be + one of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`, + :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`, + :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`. + If the image has mode "1" or "P", it is always set to + :py:data:`Resampling.NEAREST`. If the image mode is "BGR;15", + "BGR;16" or "BGR;24", then the default filter is + :py:data:`Resampling.NEAREST`. Otherwise, the default filter is + :py:data:`Resampling.BICUBIC`. See: :ref:`concept-filters`. + :param box: An optional 4-tuple of floats providing + the source image region to be scaled. + The values must be within (0, 0, width, height) rectangle. + If omitted or None, the entire source is used. + :param reducing_gap: Apply optimization by resizing the image + in two steps. First, reducing the image by integer times + using :py:meth:`~PIL.Image.Image.reduce`. + Second, resizing using regular resampling. The last step + changes size no less than by ``reducing_gap`` times. + ``reducing_gap`` may be None (no first step is performed) + or should be greater than 1.0. The bigger ``reducing_gap``, + the closer the result to the fair resampling. + The smaller ``reducing_gap``, the faster resizing. + With ``reducing_gap`` greater or equal to 3.0, the result is + indistinguishable from fair resampling in most cases. + The default value is None (no optimization). + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if resample is None: + bgr = self.mode.startswith("BGR;") + resample = Resampling.NEAREST if bgr else Resampling.BICUBIC + elif resample not in ( + Resampling.NEAREST, + Resampling.BILINEAR, + Resampling.BICUBIC, + Resampling.LANCZOS, + Resampling.BOX, + Resampling.HAMMING, + ): + msg = f"Unknown resampling filter ({resample})." + + filters = [ + f"{filter[1]} ({filter[0]})" + for filter in ( + (Resampling.NEAREST, "Image.Resampling.NEAREST"), + (Resampling.LANCZOS, "Image.Resampling.LANCZOS"), + (Resampling.BILINEAR, "Image.Resampling.BILINEAR"), + (Resampling.BICUBIC, "Image.Resampling.BICUBIC"), + (Resampling.BOX, "Image.Resampling.BOX"), + (Resampling.HAMMING, "Image.Resampling.HAMMING"), + ) + ] + msg += f" Use {', '.join(filters[:-1])} or {filters[-1]}" + raise ValueError(msg) + + if reducing_gap is not None and reducing_gap < 1.0: + msg = "reducing_gap must be 1.0 or greater" + raise ValueError(msg) + + if box is None: + box = (0, 0) + self.size + + size = tuple(size) + if self.size == size and box == (0, 0) + self.size: + return self.copy() + + if self.mode in ("1", "P"): + resample = Resampling.NEAREST + + if self.mode in ["LA", "RGBA"] and resample != Resampling.NEAREST: + im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + im = im.resize(size, resample, box) + return im.convert(self.mode) + + self.load() + + if reducing_gap is not None and resample != Resampling.NEAREST: + factor_x = int((box[2] - box[0]) / size[0] / reducing_gap) or 1 + factor_y = int((box[3] - box[1]) / size[1] / reducing_gap) or 1 + if factor_x > 1 or factor_y > 1: + reduce_box = self._get_safe_box(size, cast(Resampling, resample), box) + factor = (factor_x, factor_y) + self = ( + self.reduce(factor, box=reduce_box) + if callable(self.reduce) + else Image.reduce(self, factor, box=reduce_box) + ) + box = ( + (box[0] - reduce_box[0]) / factor_x, + (box[1] - reduce_box[1]) / factor_y, + (box[2] - reduce_box[0]) / factor_x, + (box[3] - reduce_box[1]) / factor_y, + ) + + return self._new(self.im.resize(size, resample, box)) + + def reduce( + self, + factor: int | tuple[int, int], + box: tuple[int, int, int, int] | None = None, + ) -> Image: + """ + Returns a copy of the image reduced ``factor`` times. + If the size of the image is not dividable by ``factor``, + the resulting size will be rounded up. + + :param factor: A greater than 0 integer or tuple of two integers + for width and height separately. + :param box: An optional 4-tuple of ints providing + the source image region to be reduced. + The values must be within ``(0, 0, width, height)`` rectangle. + If omitted or ``None``, the entire source is used. + """ + if not isinstance(factor, (list, tuple)): + factor = (factor, factor) + + if box is None: + box = (0, 0) + self.size + + if factor == (1, 1) and box == (0, 0) + self.size: + return self.copy() + + if self.mode in ["LA", "RGBA"]: + im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + im = im.reduce(factor, box) + return im.convert(self.mode) + + self.load() + + return self._new(self.im.reduce(factor, box)) + + def rotate( + self, + angle: float, + resample: Resampling = Resampling.NEAREST, + expand: int | bool = False, + center: tuple[float, float] | None = None, + translate: tuple[int, int] | None = None, + fillcolor: float | tuple[float, ...] | str | None = None, + ) -> Image: + """ + Returns a rotated copy of this image. This method returns a + copy of this image, rotated the given number of degrees counter + clockwise around its centre. + + :param angle: In degrees counter clockwise. + :param resample: An optional resampling filter. This can be + one of :py:data:`Resampling.NEAREST` (use nearest neighbour), + :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2 + environment), or :py:data:`Resampling.BICUBIC` (cubic spline + interpolation in a 4x4 environment). If omitted, or if the image has + mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`. + See :ref:`concept-filters`. + :param expand: Optional expansion flag. If true, expands the output + image to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the + input image. Note that the expand flag assumes rotation around + the center and no translation. + :param center: Optional center of rotation (a 2-tuple). Origin is + the upper left corner. Default is the center of the image. + :param translate: An optional post-rotate translation (a 2-tuple). + :param fillcolor: An optional color for area outside the rotated image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + angle = angle % 360.0 + + # Fast paths regardless of filter, as long as we're not + # translating or changing the center. + if not (center or translate): + if angle == 0: + return self.copy() + if angle == 180: + return self.transpose(Transpose.ROTATE_180) + if angle in (90, 270) and (expand or self.width == self.height): + return self.transpose( + Transpose.ROTATE_90 if angle == 90 else Transpose.ROTATE_270 + ) + + # Calculate the affine matrix. Note that this is the reverse + # transformation (from destination image to source) because we + # want to interpolate the (discrete) destination pixel from + # the local area around the (floating) source pixel. + + # The matrix we actually want (note that it operates from the right): + # (1, 0, tx) (1, 0, cx) ( cos a, sin a, 0) (1, 0, -cx) + # (0, 1, ty) * (0, 1, cy) * (-sin a, cos a, 0) * (0, 1, -cy) + # (0, 0, 1) (0, 0, 1) ( 0, 0, 1) (0, 0, 1) + + # The reverse matrix is thus: + # (1, 0, cx) ( cos -a, sin -a, 0) (1, 0, -cx) (1, 0, -tx) + # (0, 1, cy) * (-sin -a, cos -a, 0) * (0, 1, -cy) * (0, 1, -ty) + # (0, 0, 1) ( 0, 0, 1) (0, 0, 1) (0, 0, 1) + + # In any case, the final translation may be updated at the end to + # compensate for the expand flag. + + w, h = self.size + + if translate is None: + post_trans = (0, 0) + else: + post_trans = translate + if center is None: + center = (w / 2, h / 2) + + angle = -math.radians(angle) + matrix = [ + round(math.cos(angle), 15), + round(math.sin(angle), 15), + 0.0, + round(-math.sin(angle), 15), + round(math.cos(angle), 15), + 0.0, + ] + + def transform(x: float, y: float, matrix: list[float]) -> tuple[float, float]: + (a, b, c, d, e, f) = matrix + return a * x + b * y + c, d * x + e * y + f + + matrix[2], matrix[5] = transform( + -center[0] - post_trans[0], -center[1] - post_trans[1], matrix + ) + matrix[2] += center[0] + matrix[5] += center[1] + + if expand: + # calculate output size + xx = [] + yy = [] + for x, y in ((0, 0), (w, 0), (w, h), (0, h)): + transformed_x, transformed_y = transform(x, y, matrix) + xx.append(transformed_x) + yy.append(transformed_y) + nw = math.ceil(max(xx)) - math.floor(min(xx)) + nh = math.ceil(max(yy)) - math.floor(min(yy)) + + # We multiply a translation matrix from the right. Because of its + # special form, this is the same as taking the image of the + # translation vector as new translation vector. + matrix[2], matrix[5] = transform(-(nw - w) / 2.0, -(nh - h) / 2.0, matrix) + w, h = nw, nh + + return self.transform( + (w, h), Transform.AFFINE, matrix, resample, fillcolor=fillcolor + ) + + def save( + self, fp: StrOrBytesPath | IO[bytes], format: str | None = None, **params: Any + ) -> None: + """ + Saves this image under the given filename. If no format is + specified, the format to use is determined from the filename + extension, if possible. + + Keyword options can be used to provide additional instructions + to the writer. If a writer doesn't recognise an option, it is + silently ignored. The available options are described in the + :doc:`image format documentation + <../handbook/image-file-formats>` for each writer. + + You can use a file object instead of a filename. In this case, + you must always specify the format. The file object must + implement the ``seek``, ``tell``, and ``write`` + methods, and be opened in binary mode. + + :param fp: A filename (string), os.PathLike object or file object. + :param format: Optional format override. If omitted, the + format to use is determined from the filename extension. + If a file object was used instead of a filename, this + parameter should always be used. + :param params: Extra parameters to the image writer. These can also be + set on the image itself through ``encoderinfo``. This is useful when + saving multiple images:: + + # Saving XMP data to a single image + from PIL import Image + red = Image.new("RGB", (1, 1), "#f00") + red.save("out.mpo", xmp=b"test") + + # Saving XMP data to the second frame of an image + from PIL import Image + black = Image.new("RGB", (1, 1)) + red = Image.new("RGB", (1, 1), "#f00") + red.encoderinfo = {"xmp": b"test"} + black.save("out.mpo", save_all=True, append_images=[red]) + :returns: None + :exception ValueError: If the output format could not be determined + from the file name. Use the format option to solve this. + :exception OSError: If the file could not be written. The file + may have been created, and may contain partial data. + """ + + filename: str | bytes = "" + open_fp = False + if is_path(fp): + filename = os.fspath(fp) + open_fp = True + elif fp == sys.stdout: + try: + fp = sys.stdout.buffer + except AttributeError: + pass + if not filename and hasattr(fp, "name") and is_path(fp.name): + # only set the name for metadata purposes + filename = os.fspath(fp.name) + + preinit() + + filename_ext = os.path.splitext(filename)[1].lower() + ext = filename_ext.decode() if isinstance(filename_ext, bytes) else filename_ext + + if not format: + if ext not in EXTENSION: + init() + try: + format = EXTENSION[ext] + except KeyError as e: + msg = f"unknown file extension: {ext}" + raise ValueError(msg) from e + + from . import ImageFile + + # may mutate self! + if isinstance(self, ImageFile.ImageFile) and os.path.abspath( + filename + ) == os.path.abspath(self.filename): + self._ensure_mutable() + else: + self.load() + + save_all = params.pop("save_all", None) + self._default_encoderinfo = params + encoderinfo = getattr(self, "encoderinfo", {}) + self._attach_default_encoderinfo(self) + self.encoderconfig: tuple[Any, ...] = () + + if format.upper() not in SAVE: + init() + if save_all or ( + save_all is None + and params.get("append_images") + and format.upper() in SAVE_ALL + ): + save_handler = SAVE_ALL[format.upper()] + else: + save_handler = SAVE[format.upper()] + + created = False + if open_fp: + created = not os.path.exists(filename) + if params.get("append", False): + # Open also for reading ("+"), because TIFF save_all + # writer needs to go back and edit the written data. + fp = builtins.open(filename, "r+b") + else: + fp = builtins.open(filename, "w+b") + else: + fp = cast(IO[bytes], fp) + + try: + save_handler(self, fp, filename) + except Exception: + if open_fp: + fp.close() + if created: + try: + os.remove(filename) + except PermissionError: + pass + raise + finally: + self.encoderinfo = encoderinfo + if open_fp: + fp.close() + + def _attach_default_encoderinfo(self, im: Image) -> dict[str, Any]: + encoderinfo = getattr(self, "encoderinfo", {}) + self.encoderinfo = {**im._default_encoderinfo, **encoderinfo} + return encoderinfo + + def seek(self, frame: int) -> None: + """ + Seeks to the given frame in this sequence file. If you seek + beyond the end of the sequence, the method raises an + ``EOFError`` exception. When a sequence file is opened, the + library automatically seeks to frame 0. + + See :py:meth:`~PIL.Image.Image.tell`. + + If defined, :attr:`~PIL.Image.Image.n_frames` refers to the + number of available frames. + + :param frame: Frame number, starting at 0. + :exception EOFError: If the call attempts to seek beyond the end + of the sequence. + """ + + # overridden by file handlers + if frame != 0: + msg = "no more images in file" + raise EOFError(msg) + + def show(self, title: str | None = None) -> None: + """ + Displays this image. This method is mainly intended for debugging purposes. + + This method calls :py:func:`PIL.ImageShow.show` internally. You can use + :py:func:`PIL.ImageShow.register` to override its default behaviour. + + The image is first saved to a temporary file. By default, it will be in + PNG format. + + On Unix, the image is then opened using the **xdg-open**, **display**, + **gm**, **eog** or **xv** utility, depending on which one can be found. + + On macOS, the image is opened with the native Preview application. + + On Windows, the image is opened with the standard PNG display utility. + + :param title: Optional title to use for the image window, where possible. + """ + + _show(self, title=title) + + def split(self) -> tuple[Image, ...]: + """ + Split this image into individual bands. This method returns a + tuple of individual image bands from an image. For example, + splitting an "RGB" image creates three new images each + containing a copy of one of the original bands (red, green, + blue). + + If you need only one band, :py:meth:`~PIL.Image.Image.getchannel` + method can be more convenient and faster. + + :returns: A tuple containing bands. + """ + + self.load() + if self.im.bands == 1: + return (self.copy(),) + return tuple(map(self._new, self.im.split())) + + def getchannel(self, channel: int | str) -> Image: + """ + Returns an image containing a single channel of the source image. + + :param channel: What channel to return. Could be index + (0 for "R" channel of "RGB") or channel name + ("A" for alpha channel of "RGBA"). + :returns: An image in "L" mode. + + .. versionadded:: 4.3.0 + """ + self.load() + + if isinstance(channel, str): + try: + channel = self.getbands().index(channel) + except ValueError as e: + msg = f'The image has no channel "{channel}"' + raise ValueError(msg) from e + + return self._new(self.im.getband(channel)) + + def tell(self) -> int: + """ + Returns the current frame number. See :py:meth:`~PIL.Image.Image.seek`. + + If defined, :attr:`~PIL.Image.Image.n_frames` refers to the + number of available frames. + + :returns: Frame number, starting with 0. + """ + return 0 + + def thumbnail( + self, + size: tuple[float, float], + resample: Resampling = Resampling.BICUBIC, + reducing_gap: float | None = 2.0, + ) -> None: + """ + Make this image into a thumbnail. This method modifies the + image to contain a thumbnail version of itself, no larger than + the given size. This method calculates an appropriate thumbnail + size to preserve the aspect of the image, calls the + :py:meth:`~PIL.Image.Image.draft` method to configure the file reader + (where applicable), and finally resizes the image. + + Note that this function modifies the :py:class:`~PIL.Image.Image` + object in place. If you need to use the full resolution image as well, + apply this method to a :py:meth:`~PIL.Image.Image.copy` of the original + image. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param resample: Optional resampling filter. This can be one + of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`, + :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`, + :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`. + If omitted, it defaults to :py:data:`Resampling.BICUBIC`. + (was :py:data:`Resampling.NEAREST` prior to version 2.5.0). + See: :ref:`concept-filters`. + :param reducing_gap: Apply optimization by resizing the image + in two steps. First, reducing the image by integer times + using :py:meth:`~PIL.Image.Image.reduce` or + :py:meth:`~PIL.Image.Image.draft` for JPEG images. + Second, resizing using regular resampling. The last step + changes size no less than by ``reducing_gap`` times. + ``reducing_gap`` may be None (no first step is performed) + or should be greater than 1.0. The bigger ``reducing_gap``, + the closer the result to the fair resampling. + The smaller ``reducing_gap``, the faster resizing. + With ``reducing_gap`` greater or equal to 3.0, the result is + indistinguishable from fair resampling in most cases. + The default value is 2.0 (very close to fair resampling + while still being faster in many cases). + :returns: None + """ + + provided_size = tuple(map(math.floor, size)) + + def preserve_aspect_ratio() -> tuple[int, int] | None: + def round_aspect(number: float, key: Callable[[int], float]) -> int: + return max(min(math.floor(number), math.ceil(number), key=key), 1) + + x, y = provided_size + if x >= self.width and y >= self.height: + return None + + aspect = self.width / self.height + if x / y >= aspect: + x = round_aspect(y * aspect, key=lambda n: abs(aspect - n / y)) + else: + y = round_aspect( + x / aspect, key=lambda n: 0 if n == 0 else abs(aspect - x / n) + ) + return x, y + + preserved_size = preserve_aspect_ratio() + if preserved_size is None: + return + final_size = preserved_size + + box = None + if reducing_gap is not None: + res = self.draft( + None, (int(size[0] * reducing_gap), int(size[1] * reducing_gap)) + ) + if res is not None: + box = res[1] + + if self.size != final_size: + im = self.resize(final_size, resample, box=box, reducing_gap=reducing_gap) + + self.im = im.im + self._size = final_size + self._mode = self.im.mode + + self.readonly = 0 + + # FIXME: the different transform methods need further explanation + # instead of bloating the method docs, add a separate chapter. + def transform( + self, + size: tuple[int, int], + method: Transform | ImageTransformHandler | SupportsGetData, + data: Sequence[Any] | None = None, + resample: int = Resampling.NEAREST, + fill: int = 1, + fillcolor: float | tuple[float, ...] | str | None = None, + ) -> Image: + """ + Transforms this image. This method creates a new image with the + given size, and the same mode as the original, and copies data + to the new image using the given transform. + + :param size: The output size in pixels, as a 2-tuple: + (width, height). + :param method: The transformation method. This is one of + :py:data:`Transform.EXTENT` (cut out a rectangular subregion), + :py:data:`Transform.AFFINE` (affine transform), + :py:data:`Transform.PERSPECTIVE` (perspective transform), + :py:data:`Transform.QUAD` (map a quadrilateral to a rectangle), or + :py:data:`Transform.MESH` (map a number of source quadrilaterals + in one operation). + + It may also be an :py:class:`~PIL.Image.ImageTransformHandler` + object:: + + class Example(Image.ImageTransformHandler): + def transform(self, size, data, resample, fill=1): + # Return result + + Implementations of :py:class:`~PIL.Image.ImageTransformHandler` + for some of the :py:class:`Transform` methods are provided + in :py:mod:`~PIL.ImageTransform`. + + It may also be an object with a ``method.getdata`` method + that returns a tuple supplying new ``method`` and ``data`` values:: + + class Example: + def getdata(self): + method = Image.Transform.EXTENT + data = (0, 0, 100, 100) + return method, data + :param data: Extra data to the transformation method. + :param resample: Optional resampling filter. It can be one of + :py:data:`Resampling.NEAREST` (use nearest neighbour), + :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2 + environment), or :py:data:`Resampling.BICUBIC` (cubic spline + interpolation in a 4x4 environment). If omitted, or if the image + has mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`. + See: :ref:`concept-filters`. + :param fill: If ``method`` is an + :py:class:`~PIL.Image.ImageTransformHandler` object, this is one of + the arguments passed to it. Otherwise, it is unused. + :param fillcolor: Optional fill color for the area outside the + transform in the output image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if self.mode in ("LA", "RGBA") and resample != Resampling.NEAREST: + return ( + self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + .transform(size, method, data, resample, fill, fillcolor) + .convert(self.mode) + ) + + if isinstance(method, ImageTransformHandler): + return method.transform(size, self, resample=resample, fill=fill) + + if hasattr(method, "getdata"): + # compatibility w. old-style transform objects + method, data = method.getdata() + + if data is None: + msg = "missing method data" + raise ValueError(msg) + + im = new(self.mode, size, fillcolor) + if self.mode == "P" and self.palette: + im.palette = self.palette.copy() + im.info = self.info.copy() + if method == Transform.MESH: + # list of quads + for box, quad in data: + im.__transformer( + box, self, Transform.QUAD, quad, resample, fillcolor is None + ) + else: + im.__transformer( + (0, 0) + size, self, method, data, resample, fillcolor is None + ) + + return im + + def __transformer( + self, + box: tuple[int, int, int, int], + image: Image, + method: Transform, + data: Sequence[float], + resample: int = Resampling.NEAREST, + fill: bool = True, + ) -> None: + w = box[2] - box[0] + h = box[3] - box[1] + + if method == Transform.AFFINE: + data = data[:6] + + elif method == Transform.EXTENT: + # convert extent to an affine transform + x0, y0, x1, y1 = data + xs = (x1 - x0) / w + ys = (y1 - y0) / h + method = Transform.AFFINE + data = (xs, 0, x0, 0, ys, y0) + + elif method == Transform.PERSPECTIVE: + data = data[:8] + + elif method == Transform.QUAD: + # quadrilateral warp. data specifies the four corners + # given as NW, SW, SE, and NE. + nw = data[:2] + sw = data[2:4] + se = data[4:6] + ne = data[6:8] + x0, y0 = nw + As = 1.0 / w + At = 1.0 / h + data = ( + x0, + (ne[0] - x0) * As, + (sw[0] - x0) * At, + (se[0] - sw[0] - ne[0] + x0) * As * At, + y0, + (ne[1] - y0) * As, + (sw[1] - y0) * At, + (se[1] - sw[1] - ne[1] + y0) * As * At, + ) + + else: + msg = "unknown transformation method" + raise ValueError(msg) + + if resample not in ( + Resampling.NEAREST, + Resampling.BILINEAR, + Resampling.BICUBIC, + ): + if resample in (Resampling.BOX, Resampling.HAMMING, Resampling.LANCZOS): + unusable: dict[int, str] = { + Resampling.BOX: "Image.Resampling.BOX", + Resampling.HAMMING: "Image.Resampling.HAMMING", + Resampling.LANCZOS: "Image.Resampling.LANCZOS", + } + msg = unusable[resample] + f" ({resample}) cannot be used." + else: + msg = f"Unknown resampling filter ({resample})." + + filters = [ + f"{filter[1]} ({filter[0]})" + for filter in ( + (Resampling.NEAREST, "Image.Resampling.NEAREST"), + (Resampling.BILINEAR, "Image.Resampling.BILINEAR"), + (Resampling.BICUBIC, "Image.Resampling.BICUBIC"), + ) + ] + msg += f" Use {', '.join(filters[:-1])} or {filters[-1]}" + raise ValueError(msg) + + image.load() + + self.load() + + if image.mode in ("1", "P"): + resample = Resampling.NEAREST + + self.im.transform(box, image.im, method, data, resample, fill) + + def transpose(self, method: Transpose) -> Image: + """ + Transpose image (flip or rotate in 90 degree steps) + + :param method: One of :py:data:`Transpose.FLIP_LEFT_RIGHT`, + :py:data:`Transpose.FLIP_TOP_BOTTOM`, :py:data:`Transpose.ROTATE_90`, + :py:data:`Transpose.ROTATE_180`, :py:data:`Transpose.ROTATE_270`, + :py:data:`Transpose.TRANSPOSE` or :py:data:`Transpose.TRANSVERSE`. + :returns: Returns a flipped or rotated copy of this image. + """ + + self.load() + return self._new(self.im.transpose(method)) + + def effect_spread(self, distance: int) -> Image: + """ + Randomly spread pixels in an image. + + :param distance: Distance to spread pixels. + """ + self.load() + return self._new(self.im.effect_spread(distance)) + + def toqimage(self) -> ImageQt.ImageQt: + """Returns a QImage copy of this image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.toqimage(self) + + def toqpixmap(self) -> ImageQt.QPixmap: + """Returns a QPixmap copy of this image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.toqpixmap(self) + + +# -------------------------------------------------------------------- +# Abstract handlers. + + +class ImagePointHandler(abc.ABC): + """ + Used as a mixin by point transforms + (for use with :py:meth:`~PIL.Image.Image.point`) + """ + + @abc.abstractmethod + def point(self, im: Image) -> Image: + pass + + +class ImageTransformHandler(abc.ABC): + """ + Used as a mixin by geometry transforms + (for use with :py:meth:`~PIL.Image.Image.transform`) + """ + + @abc.abstractmethod + def transform( + self, + size: tuple[int, int], + image: Image, + **options: Any, + ) -> Image: + pass + + +# -------------------------------------------------------------------- +# Factories + + +def _check_size(size: Any) -> None: + """ + Common check to enforce type and sanity check on size tuples + + :param size: Should be a 2 tuple of (width, height) + :returns: None, or raises a ValueError + """ + + if not isinstance(size, (list, tuple)): + msg = "Size must be a list or tuple" + raise ValueError(msg) + if len(size) != 2: + msg = "Size must be a sequence of length 2" + raise ValueError(msg) + if size[0] < 0 or size[1] < 0: + msg = "Width and height must be >= 0" + raise ValueError(msg) + + +def new( + mode: str, + size: tuple[int, int] | list[int], + color: float | tuple[float, ...] | str | None = 0, +) -> Image: + """ + Creates a new image with the given mode and size. + + :param mode: The mode to use for the new image. See: + :ref:`concept-modes`. + :param size: A 2-tuple, containing (width, height) in pixels. + :param color: What color to use for the image. Default is black. + If given, this should be a single integer or floating point value + for single-band modes, and a tuple for multi-band modes (one value + per band). When creating RGB or HSV images, you can also use color + strings as supported by the ImageColor module. If the color is + None, the image is not initialised. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + + _check_size(size) + + if color is None: + # don't initialize + return Image()._new(core.new(mode, size)) + + if isinstance(color, str): + # css3-style specifier + + from . import ImageColor + + color = ImageColor.getcolor(color, mode) + + im = Image() + if ( + mode == "P" + and isinstance(color, (list, tuple)) + and all(isinstance(i, int) for i in color) + ): + color_ints: tuple[int, ...] = cast(tuple[int, ...], tuple(color)) + if len(color_ints) == 3 or len(color_ints) == 4: + # RGB or RGBA value for a P image + from . import ImagePalette + + im.palette = ImagePalette.ImagePalette() + color = im.palette.getcolor(color_ints) + return im._new(core.fill(mode, size, color)) + + +def frombytes( + mode: str, + size: tuple[int, int], + data: bytes | bytearray | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, +) -> Image: + """ + Creates a copy of an image memory from pixel data in a buffer. + + In its simplest form, this function takes three arguments + (mode, size, and unpacked pixel data). + + You can also use any pixel decoder supported by PIL. For more + information on available decoders, see the section + :ref:`Writing Your Own File Codec `. + + Note that this function decodes pixel data only, not entire images. + If you have an entire image in a string, wrap it in a + :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load + it. + + :param mode: The image mode. See: :ref:`concept-modes`. + :param size: The image size. + :param data: A byte buffer containing raw data for the given mode. + :param decoder_name: What decoder to use. + :param args: Additional parameters for the given decoder. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + _check_size(size) + + im = new(mode, size) + if im.width != 0 and im.height != 0: + decoder_args: Any = args + if len(decoder_args) == 1 and isinstance(decoder_args[0], tuple): + # may pass tuple instead of argument list + decoder_args = decoder_args[0] + + if decoder_name == "raw" and decoder_args == (): + decoder_args = mode + + im.frombytes(data, decoder_name, decoder_args) + return im + + +def frombuffer( + mode: str, + size: tuple[int, int], + data: bytes | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, +) -> Image: + """ + Creates an image memory referencing pixel data in a byte buffer. + + This function is similar to :py:func:`~PIL.Image.frombytes`, but uses data + in the byte buffer, where possible. This means that changes to the + original buffer object are reflected in this image). Not all modes can + share memory; supported modes include "L", "RGBX", "RGBA", and "CMYK". + + Note that this function decodes pixel data only, not entire images. + If you have an entire image file in a string, wrap it in a + :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load it. + + The default parameters used for the "raw" decoder differs from that used for + :py:func:`~PIL.Image.frombytes`. This is a bug, and will probably be fixed in a + future release. The current release issues a warning if you do this; to disable + the warning, you should provide the full set of parameters. See below for details. + + :param mode: The image mode. See: :ref:`concept-modes`. + :param size: The image size. + :param data: A bytes or other buffer object containing raw + data for the given mode. + :param decoder_name: What decoder to use. + :param args: Additional parameters for the given decoder. For the + default encoder ("raw"), it's recommended that you provide the + full set of parameters:: + + frombuffer(mode, size, data, "raw", mode, 0, 1) + + :returns: An :py:class:`~PIL.Image.Image` object. + + .. versionadded:: 1.1.4 + """ + + _check_size(size) + + # may pass tuple instead of argument list + if len(args) == 1 and isinstance(args[0], tuple): + args = args[0] + + if decoder_name == "raw": + if args == (): + args = mode, 0, 1 + if args[0] in _MAPMODES: + im = new(mode, (0, 0)) + im = im._new(core.map_buffer(data, size, decoder_name, 0, args)) + if mode == "P": + from . import ImagePalette + + im.palette = ImagePalette.ImagePalette("RGB", im.im.getpalette("RGB")) + im.readonly = 1 + return im + + return frombytes(mode, size, data, decoder_name, args) + + +class SupportsArrayInterface(Protocol): + """ + An object that has an ``__array_interface__`` dictionary. + """ + + @property + def __array_interface__(self) -> dict[str, Any]: + raise NotImplementedError() + + +class SupportsArrowArrayInterface(Protocol): + """ + An object that has an ``__arrow_c_array__`` method corresponding to the arrow c + data interface. + """ + + def __arrow_c_array__( + self, requested_schema: "PyCapsule" = None # type: ignore[name-defined] # noqa: F821, UP037 + ) -> tuple["PyCapsule", "PyCapsule"]: # type: ignore[name-defined] # noqa: F821, UP037 + raise NotImplementedError() + + +def fromarray(obj: SupportsArrayInterface, mode: str | None = None) -> Image: + """ + Creates an image memory from an object exporting the array interface + (using the buffer protocol):: + + from PIL import Image + import numpy as np + a = np.zeros((5, 5)) + im = Image.fromarray(a) + + If ``obj`` is not contiguous, then the ``tobytes`` method is called + and :py:func:`~PIL.Image.frombuffer` is used. + + In the case of NumPy, be aware that Pillow modes do not always correspond + to NumPy dtypes. Pillow modes only offer 1-bit pixels, 8-bit pixels, + 32-bit signed integer pixels, and 32-bit floating point pixels. + + Pillow images can also be converted to arrays:: + + from PIL import Image + import numpy as np + im = Image.open("hopper.jpg") + a = np.asarray(im) + + When converting Pillow images to arrays however, only pixel values are + transferred. This means that P and PA mode images will lose their palette. + + :param obj: Object with array interface + :param mode: Optional mode to use when reading ``obj``. Will be determined from + type if ``None``. Deprecated. + + This will not be used to convert the data after reading, but will be used to + change how the data is read:: + + from PIL import Image + import numpy as np + a = np.full((1, 1), 300) + im = Image.fromarray(a, mode="L") + im.getpixel((0, 0)) # 44 + im = Image.fromarray(a, mode="RGB") + im.getpixel((0, 0)) # (44, 1, 0) + + See: :ref:`concept-modes` for general information about modes. + :returns: An image object. + + .. versionadded:: 1.1.6 + """ + arr = obj.__array_interface__ + shape = arr["shape"] + ndim = len(shape) + strides = arr.get("strides", None) + if mode is None: + try: + typekey = (1, 1) + shape[2:], arr["typestr"] + except KeyError as e: + msg = "Cannot handle this data type" + raise TypeError(msg) from e + try: + mode, rawmode = _fromarray_typemap[typekey] + except KeyError as e: + typekey_shape, typestr = typekey + msg = f"Cannot handle this data type: {typekey_shape}, {typestr}" + raise TypeError(msg) from e + else: + deprecate("'mode' parameter", 13) + rawmode = mode + if mode in ["1", "L", "I", "P", "F"]: + ndmax = 2 + elif mode == "RGB": + ndmax = 3 + else: + ndmax = 4 + if ndim > ndmax: + msg = f"Too many dimensions: {ndim} > {ndmax}." + raise ValueError(msg) + + size = 1 if ndim == 1 else shape[1], shape[0] + if strides is not None: + if hasattr(obj, "tobytes"): + obj = obj.tobytes() + elif hasattr(obj, "tostring"): + obj = obj.tostring() + else: + msg = "'strides' requires either tobytes() or tostring()" + raise ValueError(msg) + + return frombuffer(mode, size, obj, "raw", rawmode, 0, 1) + + +def fromarrow( + obj: SupportsArrowArrayInterface, mode: str, size: tuple[int, int] +) -> Image: + """Creates an image with zero-copy shared memory from an object exporting + the arrow_c_array interface protocol:: + + from PIL import Image + import pyarrow as pa + arr = pa.array([0]*(5*5*4), type=pa.uint8()) + im = Image.fromarrow(arr, 'RGBA', (5, 5)) + + If the data representation of the ``obj`` is not compatible with + Pillow internal storage, a ValueError is raised. + + Pillow images can also be converted to Arrow objects:: + + from PIL import Image + import pyarrow as pa + im = Image.open('hopper.jpg') + arr = pa.array(im) + + As with array support, when converting Pillow images to arrays, + only pixel values are transferred. This means that P and PA mode + images will lose their palette. + + :param obj: Object with an arrow_c_array interface + :param mode: Image mode. + :param size: Image size. This must match the storage of the arrow object. + :returns: An Image object + + Note that according to the Arrow spec, both the producer and the + consumer should consider the exported array to be immutable, as + unsynchronized updates will potentially cause inconsistent data. + + See: :ref:`arrow-support` for more detailed information + + .. versionadded:: 11.2.1 + + """ + if not hasattr(obj, "__arrow_c_array__"): + msg = "arrow_c_array interface not found" + raise ValueError(msg) + + (schema_capsule, array_capsule) = obj.__arrow_c_array__() + _im = core.new_arrow(mode, size, schema_capsule, array_capsule) + if _im: + return Image()._new(_im) + + msg = "new_arrow returned None without an exception" + raise ValueError(msg) + + +def fromqimage(im: ImageQt.QImage) -> ImageFile.ImageFile: + """Creates an image instance from a QImage image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.fromqimage(im) + + +def fromqpixmap(im: ImageQt.QPixmap) -> ImageFile.ImageFile: + """Creates an image instance from a QPixmap image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.fromqpixmap(im) + + +_fromarray_typemap = { + # (shape, typestr) => mode, rawmode + # first two members of shape are set to one + ((1, 1), "|b1"): ("1", "1;8"), + ((1, 1), "|u1"): ("L", "L"), + ((1, 1), "|i1"): ("I", "I;8"), + ((1, 1), "u2"): ("I", "I;16B"), + ((1, 1), "i2"): ("I", "I;16BS"), + ((1, 1), "u4"): ("I", "I;32B"), + ((1, 1), "i4"): ("I", "I;32BS"), + ((1, 1), "f4"): ("F", "F;32BF"), + ((1, 1), "f8"): ("F", "F;64BF"), + ((1, 1, 2), "|u1"): ("LA", "LA"), + ((1, 1, 3), "|u1"): ("RGB", "RGB"), + ((1, 1, 4), "|u1"): ("RGBA", "RGBA"), + # shortcuts: + ((1, 1), f"{_ENDIAN}i4"): ("I", "I"), + ((1, 1), f"{_ENDIAN}f4"): ("F", "F"), +} + + +def _decompression_bomb_check(size: tuple[int, int]) -> None: + if MAX_IMAGE_PIXELS is None: + return + + pixels = max(1, size[0]) * max(1, size[1]) + + if pixels > 2 * MAX_IMAGE_PIXELS: + msg = ( + f"Image size ({pixels} pixels) exceeds limit of {2 * MAX_IMAGE_PIXELS} " + "pixels, could be decompression bomb DOS attack." + ) + raise DecompressionBombError(msg) + + if pixels > MAX_IMAGE_PIXELS: + warnings.warn( + f"Image size ({pixels} pixels) exceeds limit of {MAX_IMAGE_PIXELS} pixels, " + "could be decompression bomb DOS attack.", + DecompressionBombWarning, + ) + + +def open( + fp: StrOrBytesPath | IO[bytes], + mode: Literal["r"] = "r", + formats: list[str] | tuple[str, ...] | None = None, +) -> ImageFile.ImageFile: + """ + Opens and identifies the given image file. + + This is a lazy operation; this function identifies the file, but + the file remains open and the actual image data is not read from + the file until you try to process the data (or call the + :py:meth:`~PIL.Image.Image.load` method). See + :py:func:`~PIL.Image.new`. See :ref:`file-handling`. + + :param fp: A filename (string), os.PathLike object or a file object. + The file object must implement ``file.read``, + ``file.seek``, and ``file.tell`` methods, + and be opened in binary mode. The file object will also seek to zero + before reading. + :param mode: The mode. If given, this argument must be "r". + :param formats: A list or tuple of formats to attempt to load the file in. + This can be used to restrict the set of formats checked. + Pass ``None`` to try all supported formats. You can print the set of + available formats by running ``python3 -m PIL`` or using + the :py:func:`PIL.features.pilinfo` function. + :returns: An :py:class:`~PIL.Image.Image` object. + :exception FileNotFoundError: If the file cannot be found. + :exception PIL.UnidentifiedImageError: If the image cannot be opened and + identified. + :exception ValueError: If the ``mode`` is not "r", or if a ``StringIO`` + instance is used for ``fp``. + :exception TypeError: If ``formats`` is not ``None``, a list or a tuple. + """ + + if mode != "r": + msg = f"bad mode {repr(mode)}" # type: ignore[unreachable] + raise ValueError(msg) + elif isinstance(fp, io.StringIO): + msg = ( # type: ignore[unreachable] + "StringIO cannot be used to open an image. " + "Binary data must be used instead." + ) + raise ValueError(msg) + + if formats is None: + formats = ID + elif not isinstance(formats, (list, tuple)): + msg = "formats must be a list or tuple" # type: ignore[unreachable] + raise TypeError(msg) + + exclusive_fp = False + filename: str | bytes = "" + if is_path(fp): + filename = os.fspath(fp) + fp = builtins.open(filename, "rb") + exclusive_fp = True + else: + fp = cast(IO[bytes], fp) + + try: + fp.seek(0) + except (AttributeError, io.UnsupportedOperation): + fp = io.BytesIO(fp.read()) + exclusive_fp = True + + prefix = fp.read(16) + + preinit() + + warning_messages: list[str] = [] + + def _open_core( + fp: IO[bytes], + filename: str | bytes, + prefix: bytes, + formats: list[str] | tuple[str, ...], + ) -> ImageFile.ImageFile | None: + for i in formats: + i = i.upper() + if i not in OPEN: + init() + try: + factory, accept = OPEN[i] + result = not accept or accept(prefix) + if isinstance(result, str): + warning_messages.append(result) + elif result: + fp.seek(0) + im = factory(fp, filename) + _decompression_bomb_check(im.size) + return im + except (SyntaxError, IndexError, TypeError, struct.error) as e: + if WARN_POSSIBLE_FORMATS: + warning_messages.append(i + " opening failed. " + str(e)) + except BaseException: + if exclusive_fp: + fp.close() + raise + return None + + im = _open_core(fp, filename, prefix, formats) + + if im is None and formats is ID: + checked_formats = ID.copy() + if init(): + im = _open_core( + fp, + filename, + prefix, + tuple(format for format in formats if format not in checked_formats), + ) + + if im: + im._exclusive_fp = exclusive_fp + return im + + if exclusive_fp: + fp.close() + for message in warning_messages: + warnings.warn(message) + msg = "cannot identify image file %r" % (filename if filename else fp) + raise UnidentifiedImageError(msg) + + +# +# Image processing. + + +def alpha_composite(im1: Image, im2: Image) -> Image: + """ + Alpha composite im2 over im1. + + :param im1: The first image. Must have mode RGBA. + :param im2: The second image. Must have mode RGBA, and the same size as + the first image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + im1.load() + im2.load() + return im1._new(core.alpha_composite(im1.im, im2.im)) + + +def blend(im1: Image, im2: Image, alpha: float) -> Image: + """ + Creates a new image by interpolating between two input images, using + a constant alpha:: + + out = image1 * (1.0 - alpha) + image2 * alpha + + :param im1: The first image. + :param im2: The second image. Must have the same mode and size as + the first image. + :param alpha: The interpolation alpha factor. If alpha is 0.0, a + copy of the first image is returned. If alpha is 1.0, a copy of + the second image is returned. There are no restrictions on the + alpha value. If necessary, the result is clipped to fit into + the allowed output range. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + im1.load() + im2.load() + return im1._new(core.blend(im1.im, im2.im, alpha)) + + +def composite(image1: Image, image2: Image, mask: Image) -> Image: + """ + Create composite image by blending images using a transparency mask. + + :param image1: The first image. + :param image2: The second image. Must have the same mode and + size as the first image. + :param mask: A mask image. This image can have mode + "1", "L", or "RGBA", and must have the same size as the + other two images. + """ + + image = image2.copy() + image.paste(image1, None, mask) + return image + + +def eval(image: Image, *args: Callable[[int], float]) -> Image: + """ + Applies the function (which should take one argument) to each pixel + in the given image. If the image has more than one band, the same + function is applied to each band. Note that the function is + evaluated once for each possible pixel value, so you cannot use + random components or other generators. + + :param image: The input image. + :param function: A function object, taking one integer argument. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + return image.point(args[0]) + + +def merge(mode: str, bands: Sequence[Image]) -> Image: + """ + Merge a set of single band images into a new multiband image. + + :param mode: The mode to use for the output image. See: + :ref:`concept-modes`. + :param bands: A sequence containing one single-band image for + each band in the output image. All bands must have the + same size. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if getmodebands(mode) != len(bands) or "*" in mode: + msg = "wrong number of bands" + raise ValueError(msg) + for band in bands[1:]: + if band.mode != getmodetype(mode): + msg = "mode mismatch" + raise ValueError(msg) + if band.size != bands[0].size: + msg = "size mismatch" + raise ValueError(msg) + for band in bands: + band.load() + return bands[0]._new(core.merge(mode, *[b.im for b in bands])) + + +# -------------------------------------------------------------------- +# Plugin registry + + +def register_open( + id: str, + factory: ( + Callable[[IO[bytes], str | bytes], ImageFile.ImageFile] + | type[ImageFile.ImageFile] + ), + accept: Callable[[bytes], bool | str] | None = None, +) -> None: + """ + Register an image file plugin. This function should not be used + in application code. + + :param id: An image format identifier. + :param factory: An image file factory method. + :param accept: An optional function that can be used to quickly + reject images having another format. + """ + id = id.upper() + if id not in ID: + ID.append(id) + OPEN[id] = factory, accept + + +def register_mime(id: str, mimetype: str) -> None: + """ + Registers an image MIME type by populating ``Image.MIME``. This function + should not be used in application code. + + ``Image.MIME`` provides a mapping from image format identifiers to mime + formats, but :py:meth:`~PIL.ImageFile.ImageFile.get_format_mimetype` can + provide a different result for specific images. + + :param id: An image format identifier. + :param mimetype: The image MIME type for this format. + """ + MIME[id.upper()] = mimetype + + +def register_save( + id: str, driver: Callable[[Image, IO[bytes], str | bytes], None] +) -> None: + """ + Registers an image save function. This function should not be + used in application code. + + :param id: An image format identifier. + :param driver: A function to save images in this format. + """ + SAVE[id.upper()] = driver + + +def register_save_all( + id: str, driver: Callable[[Image, IO[bytes], str | bytes], None] +) -> None: + """ + Registers an image function to save all the frames + of a multiframe format. This function should not be + used in application code. + + :param id: An image format identifier. + :param driver: A function to save images in this format. + """ + SAVE_ALL[id.upper()] = driver + + +def register_extension(id: str, extension: str) -> None: + """ + Registers an image extension. This function should not be + used in application code. + + :param id: An image format identifier. + :param extension: An extension used for this format. + """ + EXTENSION[extension.lower()] = id.upper() + + +def register_extensions(id: str, extensions: list[str]) -> None: + """ + Registers image extensions. This function should not be + used in application code. + + :param id: An image format identifier. + :param extensions: A list of extensions used for this format. + """ + for extension in extensions: + register_extension(id, extension) + + +def registered_extensions() -> dict[str, str]: + """ + Returns a dictionary containing all file extensions belonging + to registered plugins + """ + init() + return EXTENSION + + +def register_decoder(name: str, decoder: type[ImageFile.PyDecoder]) -> None: + """ + Registers an image decoder. This function should not be + used in application code. + + :param name: The name of the decoder + :param decoder: An ImageFile.PyDecoder object + + .. versionadded:: 4.1.0 + """ + DECODERS[name] = decoder + + +def register_encoder(name: str, encoder: type[ImageFile.PyEncoder]) -> None: + """ + Registers an image encoder. This function should not be + used in application code. + + :param name: The name of the encoder + :param encoder: An ImageFile.PyEncoder object + + .. versionadded:: 4.1.0 + """ + ENCODERS[name] = encoder + + +# -------------------------------------------------------------------- +# Simple display support. + + +def _show(image: Image, **options: Any) -> None: + from . import ImageShow + + ImageShow.show(image, **options) + + +# -------------------------------------------------------------------- +# Effects + + +def effect_mandelbrot( + size: tuple[int, int], extent: tuple[float, float, float, float], quality: int +) -> Image: + """ + Generate a Mandelbrot set covering the given extent. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param extent: The extent to cover, as a 4-tuple: + (x0, y0, x1, y1). + :param quality: Quality. + """ + return Image()._new(core.effect_mandelbrot(size, extent, quality)) + + +def effect_noise(size: tuple[int, int], sigma: float) -> Image: + """ + Generate Gaussian noise centered around 128. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param sigma: Standard deviation of noise. + """ + return Image()._new(core.effect_noise(size, sigma)) + + +def linear_gradient(mode: str) -> Image: + """ + Generate 256x256 linear gradient from black to white, top to bottom. + + :param mode: Input mode. + """ + return Image()._new(core.linear_gradient(mode)) + + +def radial_gradient(mode: str) -> Image: + """ + Generate 256x256 radial gradient from black to white, centre to edge. + + :param mode: Input mode. + """ + return Image()._new(core.radial_gradient(mode)) + + +# -------------------------------------------------------------------- +# Resources + + +def _apply_env_variables(env: dict[str, str] | None = None) -> None: + env_dict = env if env is not None else os.environ + + for var_name, setter in [ + ("PILLOW_ALIGNMENT", core.set_alignment), + ("PILLOW_BLOCK_SIZE", core.set_block_size), + ("PILLOW_BLOCKS_MAX", core.set_blocks_max), + ]: + if var_name not in env_dict: + continue + + var = env_dict[var_name].lower() + + units = 1 + for postfix, mul in [("k", 1024), ("m", 1024 * 1024)]: + if var.endswith(postfix): + units = mul + var = var[: -len(postfix)] + + try: + var_int = int(var) * units + except ValueError: + warnings.warn(f"{var_name} is not int") + continue + + try: + setter(var_int) + except ValueError as e: + warnings.warn(f"{var_name}: {e}") + + +_apply_env_variables() +atexit.register(core.clear_cache) + + +if TYPE_CHECKING: + _ExifBase = MutableMapping[int, Any] +else: + _ExifBase = MutableMapping + + +class Exif(_ExifBase): + """ + This class provides read and write access to EXIF image data:: + + from PIL import Image + im = Image.open("exif.png") + exif = im.getexif() # Returns an instance of this class + + Information can be read and written, iterated over or deleted:: + + print(exif[274]) # 1 + exif[274] = 2 + for k, v in exif.items(): + print("Tag", k, "Value", v) # Tag 274 Value 2 + del exif[274] + + To access information beyond IFD0, :py:meth:`~PIL.Image.Exif.get_ifd` + returns a dictionary:: + + from PIL import ExifTags + im = Image.open("exif_gps.jpg") + exif = im.getexif() + gps_ifd = exif.get_ifd(ExifTags.IFD.GPSInfo) + print(gps_ifd) + + Other IFDs include ``ExifTags.IFD.Exif``, ``ExifTags.IFD.MakerNote``, + ``ExifTags.IFD.Interop`` and ``ExifTags.IFD.IFD1``. + + :py:mod:`~PIL.ExifTags` also has enum classes to provide names for data:: + + print(exif[ExifTags.Base.Software]) # PIL + print(gps_ifd[ExifTags.GPS.GPSDateStamp]) # 1999:99:99 99:99:99 + """ + + endian: str | None = None + bigtiff = False + _loaded = False + + def __init__(self) -> None: + self._data: dict[int, Any] = {} + self._hidden_data: dict[int, Any] = {} + self._ifds: dict[int, dict[int, Any]] = {} + self._info: TiffImagePlugin.ImageFileDirectory_v2 | None = None + self._loaded_exif: bytes | None = None + + def _fixup(self, value: Any) -> Any: + try: + if len(value) == 1 and isinstance(value, tuple): + return value[0] + except Exception: + pass + return value + + def _fixup_dict(self, src_dict: dict[int, Any]) -> dict[int, Any]: + # Helper function + # returns a dict with any single item tuples/lists as individual values + return {k: self._fixup(v) for k, v in src_dict.items()} + + def _get_ifd_dict( + self, offset: int, group: int | None = None + ) -> dict[int, Any] | None: + try: + # an offset pointer to the location of the nested embedded IFD. + # It should be a long, but may be corrupted. + self.fp.seek(offset) + except (KeyError, TypeError): + return None + else: + from . import TiffImagePlugin + + info = TiffImagePlugin.ImageFileDirectory_v2(self.head, group=group) + info.load(self.fp) + return self._fixup_dict(dict(info)) + + def _get_head(self) -> bytes: + version = b"\x2b" if self.bigtiff else b"\x2a" + if self.endian == "<": + head = b"II" + version + b"\x00" + o32le(8) + else: + head = b"MM\x00" + version + o32be(8) + if self.bigtiff: + head += o32le(8) if self.endian == "<" else o32be(8) + head += b"\x00\x00\x00\x00" + return head + + def load(self, data: bytes) -> None: + # Extract EXIF information. This is highly experimental, + # and is likely to be replaced with something better in a future + # version. + + # The EXIF record consists of a TIFF file embedded in a JPEG + # application marker (!). + if data == self._loaded_exif: + return + self._loaded_exif = data + self._data.clear() + self._hidden_data.clear() + self._ifds.clear() + while data and data.startswith(b"Exif\x00\x00"): + data = data[6:] + if not data: + self._info = None + return + + self.fp: IO[bytes] = io.BytesIO(data) + self.head = self.fp.read(8) + # process dictionary + from . import TiffImagePlugin + + self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head) + self.endian = self._info._endian + self.fp.seek(self._info.next) + self._info.load(self.fp) + + def load_from_fp(self, fp: IO[bytes], offset: int | None = None) -> None: + self._loaded_exif = None + self._data.clear() + self._hidden_data.clear() + self._ifds.clear() + + # process dictionary + from . import TiffImagePlugin + + self.fp = fp + if offset is not None: + self.head = self._get_head() + else: + self.head = self.fp.read(8) + self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head) + if self.endian is None: + self.endian = self._info._endian + if offset is None: + offset = self._info.next + self.fp.tell() + self.fp.seek(offset) + self._info.load(self.fp) + + def _get_merged_dict(self) -> dict[int, Any]: + merged_dict = dict(self) + + # get EXIF extension + if ExifTags.IFD.Exif in self: + ifd = self._get_ifd_dict(self[ExifTags.IFD.Exif], ExifTags.IFD.Exif) + if ifd: + merged_dict.update(ifd) + + # GPS + if ExifTags.IFD.GPSInfo in self: + merged_dict[ExifTags.IFD.GPSInfo] = self._get_ifd_dict( + self[ExifTags.IFD.GPSInfo], ExifTags.IFD.GPSInfo + ) + + return merged_dict + + def tobytes(self, offset: int = 8) -> bytes: + from . import TiffImagePlugin + + head = self._get_head() + ifd = TiffImagePlugin.ImageFileDirectory_v2(ifh=head) + for tag, ifd_dict in self._ifds.items(): + if tag not in self: + ifd[tag] = ifd_dict + for tag, value in self.items(): + if tag in [ + ExifTags.IFD.Exif, + ExifTags.IFD.GPSInfo, + ] and not isinstance(value, dict): + value = self.get_ifd(tag) + if ( + tag == ExifTags.IFD.Exif + and ExifTags.IFD.Interop in value + and not isinstance(value[ExifTags.IFD.Interop], dict) + ): + value = value.copy() + value[ExifTags.IFD.Interop] = self.get_ifd(ExifTags.IFD.Interop) + ifd[tag] = value + return b"Exif\x00\x00" + head + ifd.tobytes(offset) + + def get_ifd(self, tag: int) -> dict[int, Any]: + if tag not in self._ifds: + if tag == ExifTags.IFD.IFD1: + if self._info is not None and self._info.next != 0: + ifd = self._get_ifd_dict(self._info.next) + if ifd is not None: + self._ifds[tag] = ifd + elif tag in [ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo]: + offset = self._hidden_data.get(tag, self.get(tag)) + if offset is not None: + ifd = self._get_ifd_dict(offset, tag) + if ifd is not None: + self._ifds[tag] = ifd + elif tag in [ExifTags.IFD.Interop, ExifTags.IFD.MakerNote]: + if ExifTags.IFD.Exif not in self._ifds: + self.get_ifd(ExifTags.IFD.Exif) + tag_data = self._ifds[ExifTags.IFD.Exif][tag] + if tag == ExifTags.IFD.MakerNote: + from .TiffImagePlugin import ImageFileDirectory_v2 + + if tag_data.startswith(b"FUJIFILM"): + ifd_offset = i32le(tag_data, 8) + ifd_data = tag_data[ifd_offset:] + + makernote = {} + for i in range(struct.unpack(" 4: + (offset,) = struct.unpack("H", tag_data[:2])[0]): + ifd_tag, typ, count, data = struct.unpack( + ">HHL4s", tag_data[i * 12 + 2 : (i + 1) * 12 + 2] + ) + if ifd_tag == 0x1101: + # CameraInfo + (offset,) = struct.unpack(">L", data) + self.fp.seek(offset) + + camerainfo: dict[str, int | bytes] = { + "ModelID": self.fp.read(4) + } + + self.fp.read(4) + # Seconds since 2000 + camerainfo["TimeStamp"] = i32le(self.fp.read(12)) + + self.fp.read(4) + camerainfo["InternalSerialNumber"] = self.fp.read(4) + + self.fp.read(12) + parallax = self.fp.read(4) + handler = ImageFileDirectory_v2._load_dispatch[ + TiffTags.FLOAT + ][1] + camerainfo["Parallax"] = handler( + ImageFileDirectory_v2(), parallax, False + )[0] + + self.fp.read(4) + camerainfo["Category"] = self.fp.read(2) + + makernote = {0x1101: camerainfo} + self._ifds[tag] = makernote + else: + # Interop + ifd = self._get_ifd_dict(tag_data, tag) + if ifd is not None: + self._ifds[tag] = ifd + ifd = self._ifds.setdefault(tag, {}) + if tag == ExifTags.IFD.Exif and self._hidden_data: + ifd = { + k: v + for (k, v) in ifd.items() + if k not in (ExifTags.IFD.Interop, ExifTags.IFD.MakerNote) + } + return ifd + + def hide_offsets(self) -> None: + for tag in (ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo): + if tag in self: + self._hidden_data[tag] = self[tag] + del self[tag] + + def __str__(self) -> str: + if self._info is not None: + # Load all keys into self._data + for tag in self._info: + self[tag] + + return str(self._data) + + def __len__(self) -> int: + keys = set(self._data) + if self._info is not None: + keys.update(self._info) + return len(keys) + + def __getitem__(self, tag: int) -> Any: + if self._info is not None and tag not in self._data and tag in self._info: + self._data[tag] = self._fixup(self._info[tag]) + del self._info[tag] + return self._data[tag] + + def __contains__(self, tag: object) -> bool: + return tag in self._data or (self._info is not None and tag in self._info) + + def __setitem__(self, tag: int, value: Any) -> None: + if self._info is not None and tag in self._info: + del self._info[tag] + self._data[tag] = value + + def __delitem__(self, tag: int) -> None: + if self._info is not None and tag in self._info: + del self._info[tag] + else: + del self._data[tag] + + def __iter__(self) -> Iterator[int]: + keys = set(self._data) + if self._info is not None: + keys.update(self._info) + return iter(keys) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageChops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageChops.py new file mode 100644 index 0000000000000000000000000000000000000000..29a5c995fd802c9be16784f80707cfecb88b2002 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageChops.py @@ -0,0 +1,311 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard channel operations +# +# History: +# 1996-03-24 fl Created +# 1996-08-13 fl Added logical operations (for "1" images) +# 2000-10-12 fl Added offset method (from Image.py) +# +# Copyright (c) 1997-2000 by Secret Labs AB +# Copyright (c) 1996-2000 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +from . import Image + + +def constant(image: Image.Image, value: int) -> Image.Image: + """Fill a channel with a given gray level. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.new("L", image.size, value) + + +def duplicate(image: Image.Image) -> Image.Image: + """Copy a channel. Alias for :py:meth:`PIL.Image.Image.copy`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return image.copy() + + +def invert(image: Image.Image) -> Image.Image: + """ + Invert an image (channel). :: + + out = MAX - image + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image.load() + return image._new(image.im.chop_invert()) + + +def lighter(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Compares the two images, pixel by pixel, and returns a new image containing + the lighter values. :: + + out = max(image1, image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_lighter(image2.im)) + + +def darker(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Compares the two images, pixel by pixel, and returns a new image containing + the darker values. :: + + out = min(image1, image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_darker(image2.im)) + + +def difference(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Returns the absolute value of the pixel-by-pixel difference between the two + images. :: + + out = abs(image1 - image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_difference(image2.im)) + + +def multiply(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other. + + If you multiply an image with a solid black image, the result is black. If + you multiply with a solid white image, the image is unaffected. :: + + out = image1 * image2 / MAX + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_multiply(image2.im)) + + +def screen(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two inverted images on top of each other. :: + + out = MAX - ((MAX - image1) * (MAX - image2) / MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_screen(image2.im)) + + +def soft_light(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Soft Light algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_soft_light(image2.im)) + + +def hard_light(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Hard Light algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_hard_light(image2.im)) + + +def overlay(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Overlay algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_overlay(image2.im)) + + +def add( + image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0 +) -> Image.Image: + """ + Adds two images, dividing the result by scale and adding the + offset. If omitted, scale defaults to 1.0, and offset to 0.0. :: + + out = ((image1 + image2) / scale + offset) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_add(image2.im, scale, offset)) + + +def subtract( + image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0 +) -> Image.Image: + """ + Subtracts two images, dividing the result by scale and adding the offset. + If omitted, scale defaults to 1.0, and offset to 0.0. :: + + out = ((image1 - image2) / scale + offset) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_subtract(image2.im, scale, offset)) + + +def add_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Add two images, without clipping the result. :: + + out = ((image1 + image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_add_modulo(image2.im)) + + +def subtract_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Subtract two images, without clipping the result. :: + + out = ((image1 - image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_subtract_modulo(image2.im)) + + +def logical_and(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical AND between two images. + + Both of the images must have mode "1". If you would like to perform a + logical AND on an image with a mode other than "1", try + :py:meth:`~PIL.ImageChops.multiply` instead, using a black-and-white mask + as the second image. :: + + out = ((image1 and image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_and(image2.im)) + + +def logical_or(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical OR between two images. + + Both of the images must have mode "1". :: + + out = ((image1 or image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_or(image2.im)) + + +def logical_xor(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical XOR between two images. + + Both of the images must have mode "1". :: + + out = ((bool(image1) != bool(image2)) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_xor(image2.im)) + + +def blend(image1: Image.Image, image2: Image.Image, alpha: float) -> Image.Image: + """Blend images using constant transparency weight. Alias for + :py:func:`PIL.Image.blend`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.blend(image1, image2, alpha) + + +def composite( + image1: Image.Image, image2: Image.Image, mask: Image.Image +) -> Image.Image: + """Create composite using transparency mask. Alias for + :py:func:`PIL.Image.composite`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.composite(image1, image2, mask) + + +def offset(image: Image.Image, xoffset: int, yoffset: int | None = None) -> Image.Image: + """Returns a copy of the image where data has been offset by the given + distances. Data wraps around the edges. If ``yoffset`` is omitted, it + is assumed to be equal to ``xoffset``. + + :param image: Input image. + :param xoffset: The horizontal distance. + :param yoffset: The vertical distance. If omitted, both + distances are set to the same value. + :rtype: :py:class:`~PIL.Image.Image` + """ + + if yoffset is None: + yoffset = xoffset + image.load() + return image._new(image.im.offset(xoffset, yoffset)) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageCms.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageCms.py new file mode 100644 index 0000000000000000000000000000000000000000..a1584f111d4a6c33a62dcb94ae583a539138f0c3 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageCms.py @@ -0,0 +1,1123 @@ +# The Python Imaging Library. +# $Id$ + +# Optional color management support, based on Kevin Cazabon's PyCMS +# library. + +# Originally released under LGPL. Graciously donated to PIL in +# March 2009, for distribution under the standard PIL license + +# History: + +# 2009-03-08 fl Added to PIL. + +# Copyright (C) 2002-2003 Kevin Cazabon +# Copyright (c) 2009 by Fredrik Lundh +# Copyright (c) 2013 by Eric Soroos + +# See the README file for information on usage and redistribution. See +# below for the original description. +from __future__ import annotations + +import operator +import sys +from enum import IntEnum, IntFlag +from functools import reduce +from typing import Any, Literal, SupportsFloat, SupportsInt, Union + +from . import Image, __version__ +from ._deprecate import deprecate +from ._typing import SupportsRead + +try: + from . import _imagingcms as core + + _CmsProfileCompatible = Union[ + str, SupportsRead[bytes], core.CmsProfile, "ImageCmsProfile" + ] +except ImportError as ex: + # Allow error import for doc purposes, but error out when accessing + # anything in core. + from ._util import DeferredError + + core = DeferredError.new(ex) + +_DESCRIPTION = """ +pyCMS + + a Python / PIL interface to the littleCMS ICC Color Management System + Copyright (C) 2002-2003 Kevin Cazabon + kevin@cazabon.com + https://www.cazabon.com + + pyCMS home page: https://www.cazabon.com/pyCMS + littleCMS home page: https://www.littlecms.com + (littleCMS is Copyright (C) 1998-2001 Marti Maria) + + Originally released under LGPL. Graciously donated to PIL in + March 2009, for distribution under the standard PIL license + + The pyCMS.py module provides a "clean" interface between Python/PIL and + pyCMSdll, taking care of some of the more complex handling of the direct + pyCMSdll functions, as well as error-checking and making sure that all + relevant data is kept together. + + While it is possible to call pyCMSdll functions directly, it's not highly + recommended. + + Version History: + + 1.0.0 pil Oct 2013 Port to LCMS 2. + + 0.1.0 pil mod March 10, 2009 + + Renamed display profile to proof profile. The proof + profile is the profile of the device that is being + simulated, not the profile of the device which is + actually used to display/print the final simulation + (that'd be the output profile) - also see LCMSAPI.txt + input colorspace -> using 'renderingIntent' -> proof + colorspace -> using 'proofRenderingIntent' -> output + colorspace + + Added LCMS FLAGS support. + Added FLAGS["SOFTPROOFING"] as default flag for + buildProofTransform (otherwise the proof profile/intent + would be ignored). + + 0.1.0 pil March 2009 - added to PIL, as PIL.ImageCms + + 0.0.2 alpha Jan 6, 2002 + + Added try/except statements around type() checks of + potential CObjects... Python won't let you use type() + on them, and raises a TypeError (stupid, if you ask + me!) + + Added buildProofTransformFromOpenProfiles() function. + Additional fixes in DLL, see DLL code for details. + + 0.0.1 alpha first public release, Dec. 26, 2002 + + Known to-do list with current version (of Python interface, not pyCMSdll): + + none + +""" + +_VERSION = "1.0.0 pil" + + +def __getattr__(name: str) -> Any: + if name == "DESCRIPTION": + deprecate("PIL.ImageCms.DESCRIPTION", 12) + return _DESCRIPTION + elif name == "VERSION": + deprecate("PIL.ImageCms.VERSION", 12) + return _VERSION + elif name == "FLAGS": + deprecate("PIL.ImageCms.FLAGS", 12, "PIL.ImageCms.Flags") + return _FLAGS + msg = f"module '{__name__}' has no attribute '{name}'" + raise AttributeError(msg) + + +# --------------------------------------------------------------------. + + +# +# intent/direction values + + +class Intent(IntEnum): + PERCEPTUAL = 0 + RELATIVE_COLORIMETRIC = 1 + SATURATION = 2 + ABSOLUTE_COLORIMETRIC = 3 + + +class Direction(IntEnum): + INPUT = 0 + OUTPUT = 1 + PROOF = 2 + + +# +# flags + + +class Flags(IntFlag): + """Flags and documentation are taken from ``lcms2.h``.""" + + NONE = 0 + NOCACHE = 0x0040 + """Inhibit 1-pixel cache""" + NOOPTIMIZE = 0x0100 + """Inhibit optimizations""" + NULLTRANSFORM = 0x0200 + """Don't transform anyway""" + GAMUTCHECK = 0x1000 + """Out of Gamut alarm""" + SOFTPROOFING = 0x4000 + """Do softproofing""" + BLACKPOINTCOMPENSATION = 0x2000 + NOWHITEONWHITEFIXUP = 0x0004 + """Don't fix scum dot""" + HIGHRESPRECALC = 0x0400 + """Use more memory to give better accuracy""" + LOWRESPRECALC = 0x0800 + """Use less memory to minimize resources""" + # this should be 8BITS_DEVICELINK, but that is not a valid name in Python: + USE_8BITS_DEVICELINK = 0x0008 + """Create 8 bits devicelinks""" + GUESSDEVICECLASS = 0x0020 + """Guess device class (for ``transform2devicelink``)""" + KEEP_SEQUENCE = 0x0080 + """Keep profile sequence for devicelink creation""" + FORCE_CLUT = 0x0002 + """Force CLUT optimization""" + CLUT_POST_LINEARIZATION = 0x0001 + """create postlinearization tables if possible""" + CLUT_PRE_LINEARIZATION = 0x0010 + """create prelinearization tables if possible""" + NONEGATIVES = 0x8000 + """Prevent negative numbers in floating point transforms""" + COPY_ALPHA = 0x04000000 + """Alpha channels are copied on ``cmsDoTransform()``""" + NODEFAULTRESOURCEDEF = 0x01000000 + + _GRIDPOINTS_1 = 1 << 16 + _GRIDPOINTS_2 = 2 << 16 + _GRIDPOINTS_4 = 4 << 16 + _GRIDPOINTS_8 = 8 << 16 + _GRIDPOINTS_16 = 16 << 16 + _GRIDPOINTS_32 = 32 << 16 + _GRIDPOINTS_64 = 64 << 16 + _GRIDPOINTS_128 = 128 << 16 + + @staticmethod + def GRIDPOINTS(n: int) -> Flags: + """ + Fine-tune control over number of gridpoints + + :param n: :py:class:`int` in range ``0 <= n <= 255`` + """ + return Flags.NONE | ((n & 0xFF) << 16) + + +_MAX_FLAG = reduce(operator.or_, Flags) + + +_FLAGS = { + "MATRIXINPUT": 1, + "MATRIXOUTPUT": 2, + "MATRIXONLY": (1 | 2), + "NOWHITEONWHITEFIXUP": 4, # Don't hot fix scum dot + # Don't create prelinearization tables on precalculated transforms + # (internal use): + "NOPRELINEARIZATION": 16, + "GUESSDEVICECLASS": 32, # Guess device class (for transform2devicelink) + "NOTCACHE": 64, # Inhibit 1-pixel cache + "NOTPRECALC": 256, + "NULLTRANSFORM": 512, # Don't transform anyway + "HIGHRESPRECALC": 1024, # Use more memory to give better accuracy + "LOWRESPRECALC": 2048, # Use less memory to minimize resources + "WHITEBLACKCOMPENSATION": 8192, + "BLACKPOINTCOMPENSATION": 8192, + "GAMUTCHECK": 4096, # Out of Gamut alarm + "SOFTPROOFING": 16384, # Do softproofing + "PRESERVEBLACK": 32768, # Black preservation + "NODEFAULTRESOURCEDEF": 16777216, # CRD special + "GRIDPOINTS": lambda n: (n & 0xFF) << 16, # Gridpoints +} + + +# --------------------------------------------------------------------. +# Experimental PIL-level API +# --------------------------------------------------------------------. + +## +# Profile. + + +class ImageCmsProfile: + def __init__(self, profile: str | SupportsRead[bytes] | core.CmsProfile) -> None: + """ + :param profile: Either a string representing a filename, + a file like object containing a profile or a + low-level profile object + + """ + self.filename = None + self.product_name = None # profile.product_name + self.product_info = None # profile.product_info + + if isinstance(profile, str): + if sys.platform == "win32": + profile_bytes_path = profile.encode() + try: + profile_bytes_path.decode("ascii") + except UnicodeDecodeError: + with open(profile, "rb") as f: + self.profile = core.profile_frombytes(f.read()) + return + self.filename = profile + self.profile = core.profile_open(profile) + elif hasattr(profile, "read"): + self.profile = core.profile_frombytes(profile.read()) + elif isinstance(profile, core.CmsProfile): + self.profile = profile + else: + msg = "Invalid type for Profile" # type: ignore[unreachable] + raise TypeError(msg) + + def tobytes(self) -> bytes: + """ + Returns the profile in a format suitable for embedding in + saved images. + + :returns: a bytes object containing the ICC profile. + """ + + return core.profile_tobytes(self.profile) + + +class ImageCmsTransform(Image.ImagePointHandler): + """ + Transform. This can be used with the procedural API, or with the standard + :py:func:`~PIL.Image.Image.point` method. + + Will return the output profile in the ``output.info['icc_profile']``. + """ + + def __init__( + self, + input: ImageCmsProfile, + output: ImageCmsProfile, + input_mode: str, + output_mode: str, + intent: Intent = Intent.PERCEPTUAL, + proof: ImageCmsProfile | None = None, + proof_intent: Intent = Intent.ABSOLUTE_COLORIMETRIC, + flags: Flags = Flags.NONE, + ): + supported_modes = ( + "RGB", + "RGBA", + "RGBX", + "CMYK", + "I;16", + "I;16L", + "I;16B", + "YCbCr", + "LAB", + "L", + "1", + ) + for mode in (input_mode, output_mode): + if mode not in supported_modes: + deprecate( + mode, + 12, + { + "L;16": "I;16 or I;16L", + "L:16B": "I;16B", + "YCCA": "YCbCr", + "YCC": "YCbCr", + }.get(mode), + ) + if proof is None: + self.transform = core.buildTransform( + input.profile, output.profile, input_mode, output_mode, intent, flags + ) + else: + self.transform = core.buildProofTransform( + input.profile, + output.profile, + proof.profile, + input_mode, + output_mode, + intent, + proof_intent, + flags, + ) + # Note: inputMode and outputMode are for pyCMS compatibility only + self.input_mode = self.inputMode = input_mode + self.output_mode = self.outputMode = output_mode + + self.output_profile = output + + def point(self, im: Image.Image) -> Image.Image: + return self.apply(im) + + def apply(self, im: Image.Image, imOut: Image.Image | None = None) -> Image.Image: + if imOut is None: + imOut = Image.new(self.output_mode, im.size, None) + self.transform.apply(im.getim(), imOut.getim()) + imOut.info["icc_profile"] = self.output_profile.tobytes() + return imOut + + def apply_in_place(self, im: Image.Image) -> Image.Image: + if im.mode != self.output_mode: + msg = "mode mismatch" + raise ValueError(msg) # wrong output mode + self.transform.apply(im.getim(), im.getim()) + im.info["icc_profile"] = self.output_profile.tobytes() + return im + + +def get_display_profile(handle: SupportsInt | None = None) -> ImageCmsProfile | None: + """ + (experimental) Fetches the profile for the current display device. + + :returns: ``None`` if the profile is not known. + """ + + if sys.platform != "win32": + return None + + from . import ImageWin # type: ignore[unused-ignore, unreachable] + + if isinstance(handle, ImageWin.HDC): + profile = core.get_display_profile_win32(int(handle), 1) + else: + profile = core.get_display_profile_win32(int(handle or 0)) + if profile is None: + return None + return ImageCmsProfile(profile) + + +# --------------------------------------------------------------------. +# pyCMS compatible layer +# --------------------------------------------------------------------. + + +class PyCMSError(Exception): + """(pyCMS) Exception class. + This is used for all errors in the pyCMS API.""" + + pass + + +def profileToProfile( + im: Image.Image, + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + renderingIntent: Intent = Intent.PERCEPTUAL, + outputMode: str | None = None, + inPlace: bool = False, + flags: Flags = Flags.NONE, +) -> Image.Image | None: + """ + (pyCMS) Applies an ICC transformation to a given image, mapping from + ``inputProfile`` to ``outputProfile``. + + If the input or output profiles specified are not valid filenames, a + :exc:`PyCMSError` will be raised. If ``inPlace`` is ``True`` and + ``outputMode != im.mode``, a :exc:`PyCMSError` will be raised. + If an error occurs during application of the profiles, + a :exc:`PyCMSError` will be raised. + If ``outputMode`` is not a mode supported by the ``outputProfile`` (or by pyCMS), + a :exc:`PyCMSError` will be raised. + + This function applies an ICC transformation to im from ``inputProfile``'s + color space to ``outputProfile``'s color space using the specified rendering + intent to decide how to handle out-of-gamut colors. + + ``outputMode`` can be used to specify that a color mode conversion is to + be done using these profiles, but the specified profiles must be able + to handle that mode. I.e., if converting im from RGB to CMYK using + profiles, the input profile must handle RGB data, and the output + profile must handle CMYK data. + + :param im: An open :py:class:`~PIL.Image.Image` object (i.e. Image.new(...) + or Image.open(...), etc.) + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this image, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + profile you wish to use for this image, or a profile object + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param outputMode: A valid PIL mode for the output image (i.e. "RGB", + "CMYK", etc.). Note: if rendering the image "inPlace", outputMode + MUST be the same mode as the input, or omitted completely. If + omitted, the outputMode will be the same as the mode of the input + image (im.mode) + :param inPlace: Boolean. If ``True``, the original image is modified in-place, + and ``None`` is returned. If ``False`` (default), a new + :py:class:`~PIL.Image.Image` object is returned with the transform applied. + :param flags: Integer (0-...) specifying additional flags + :returns: Either None or a new :py:class:`~PIL.Image.Image` object, depending on + the value of ``inPlace`` + :exception PyCMSError: + """ + + if outputMode is None: + outputMode = im.mode + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + transform = ImageCmsTransform( + inputProfile, + outputProfile, + im.mode, + outputMode, + renderingIntent, + flags=flags, + ) + if inPlace: + transform.apply_in_place(im) + imOut = None + else: + imOut = transform.apply(im) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + return imOut + + +def getOpenProfile( + profileFilename: str | SupportsRead[bytes] | core.CmsProfile, +) -> ImageCmsProfile: + """ + (pyCMS) Opens an ICC profile file. + + The PyCMSProfile object can be passed back into pyCMS for use in creating + transforms and such (as in ImageCms.buildTransformFromOpenProfiles()). + + If ``profileFilename`` is not a valid filename for an ICC profile, + a :exc:`PyCMSError` will be raised. + + :param profileFilename: String, as a valid filename path to the ICC profile + you wish to open, or a file-like object. + :returns: A CmsProfile class object. + :exception PyCMSError: + """ + + try: + return ImageCmsProfile(profileFilename) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def buildTransform( + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + inMode: str, + outMode: str, + renderingIntent: Intent = Intent.PERCEPTUAL, + flags: Flags = Flags.NONE, +) -> ImageCmsTransform: + """ + (pyCMS) Builds an ICC transform mapping from the ``inputProfile`` to the + ``outputProfile``. Use applyTransform to apply the transform to a given + image. + + If the input or output profiles specified are not valid filenames, a + :exc:`PyCMSError` will be raised. If an error occurs during creation + of the transform, a :exc:`PyCMSError` will be raised. + + If ``inMode`` or ``outMode`` are not a mode supported by the ``outputProfile`` + (or by pyCMS), a :exc:`PyCMSError` will be raised. + + This function builds and returns an ICC transform from the ``inputProfile`` + to the ``outputProfile`` using the ``renderingIntent`` to determine what to do + with out-of-gamut colors. It will ONLY work for converting images that + are in ``inMode`` to images that are in ``outMode`` color format (PIL mode, + i.e. "RGB", "RGBA", "CMYK", etc.). + + Building the transform is a fair part of the overhead in + ImageCms.profileToProfile(), so if you're planning on converting multiple + images using the same input/output settings, this can save you time. + Once you have a transform object, it can be used with + ImageCms.applyProfile() to convert images without the need to re-compute + the lookup table for the transform. + + The reason pyCMS returns a class object rather than a handle directly + to the transform is that it needs to keep track of the PIL input/output + modes that the transform is meant for. These attributes are stored in + the ``inMode`` and ``outMode`` attributes of the object (which can be + manually overridden if you really want to, but I don't know of any + time that would be of use, or would even work). + + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this transform, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + profile you wish to use for this transform, or a profile object + :param inMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param outMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param flags: Integer (0-...) specifying additional flags + :returns: A CmsTransform class object. + :exception PyCMSError: + """ + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + return ImageCmsTransform( + inputProfile, outputProfile, inMode, outMode, renderingIntent, flags=flags + ) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def buildProofTransform( + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + proofProfile: _CmsProfileCompatible, + inMode: str, + outMode: str, + renderingIntent: Intent = Intent.PERCEPTUAL, + proofRenderingIntent: Intent = Intent.ABSOLUTE_COLORIMETRIC, + flags: Flags = Flags.SOFTPROOFING, +) -> ImageCmsTransform: + """ + (pyCMS) Builds an ICC transform mapping from the ``inputProfile`` to the + ``outputProfile``, but tries to simulate the result that would be + obtained on the ``proofProfile`` device. + + If the input, output, or proof profiles specified are not valid + filenames, a :exc:`PyCMSError` will be raised. + + If an error occurs during creation of the transform, + a :exc:`PyCMSError` will be raised. + + If ``inMode`` or ``outMode`` are not a mode supported by the ``outputProfile`` + (or by pyCMS), a :exc:`PyCMSError` will be raised. + + This function builds and returns an ICC transform from the ``inputProfile`` + to the ``outputProfile``, but tries to simulate the result that would be + obtained on the ``proofProfile`` device using ``renderingIntent`` and + ``proofRenderingIntent`` to determine what to do with out-of-gamut + colors. This is known as "soft-proofing". It will ONLY work for + converting images that are in ``inMode`` to images that are in outMode + color format (PIL mode, i.e. "RGB", "RGBA", "CMYK", etc.). + + Usage of the resulting transform object is exactly the same as with + ImageCms.buildTransform(). + + Proof profiling is generally used when using an output device to get a + good idea of what the final printed/displayed image would look like on + the ``proofProfile`` device when it's quicker and easier to use the + output device for judging color. Generally, this means that the + output device is a monitor, or a dye-sub printer (etc.), and the simulated + device is something more expensive, complicated, or time consuming + (making it difficult to make a real print for color judgement purposes). + + Soft-proofing basically functions by adjusting the colors on the + output device to match the colors of the device being simulated. However, + when the simulated device has a much wider gamut than the output + device, you may obtain marginal results. + + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this transform, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + (monitor, usually) profile you wish to use for this transform, or a + profile object + :param proofProfile: String, as a valid filename path to the ICC proof + profile you wish to use for this transform, or a profile object + :param inMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param outMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the input->proof (simulated) transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param proofRenderingIntent: Integer (0-3) specifying the rendering intent + you wish to use for proof->output transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param flags: Integer (0-...) specifying additional flags + :returns: A CmsTransform class object. + :exception PyCMSError: + """ + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + if not isinstance(proofProfile, ImageCmsProfile): + proofProfile = ImageCmsProfile(proofProfile) + return ImageCmsTransform( + inputProfile, + outputProfile, + inMode, + outMode, + renderingIntent, + proofProfile, + proofRenderingIntent, + flags, + ) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +buildTransformFromOpenProfiles = buildTransform +buildProofTransformFromOpenProfiles = buildProofTransform + + +def applyTransform( + im: Image.Image, transform: ImageCmsTransform, inPlace: bool = False +) -> Image.Image | None: + """ + (pyCMS) Applies a transform to a given image. + + If ``im.mode != transform.input_mode``, a :exc:`PyCMSError` is raised. + + If ``inPlace`` is ``True`` and ``transform.input_mode != transform.output_mode``, a + :exc:`PyCMSError` is raised. + + If ``im.mode``, ``transform.input_mode`` or ``transform.output_mode`` is not + supported by pyCMSdll or the profiles you used for the transform, a + :exc:`PyCMSError` is raised. + + If an error occurs while the transform is being applied, + a :exc:`PyCMSError` is raised. + + This function applies a pre-calculated transform (from + ImageCms.buildTransform() or ImageCms.buildTransformFromOpenProfiles()) + to an image. The transform can be used for multiple images, saving + considerable calculation time if doing the same conversion multiple times. + + If you want to modify im in-place instead of receiving a new image as + the return value, set ``inPlace`` to ``True``. This can only be done if + ``transform.input_mode`` and ``transform.output_mode`` are the same, because we + can't change the mode in-place (the buffer sizes for some modes are + different). The default behavior is to return a new :py:class:`~PIL.Image.Image` + object of the same dimensions in mode ``transform.output_mode``. + + :param im: An :py:class:`~PIL.Image.Image` object, and ``im.mode`` must be the same + as the ``input_mode`` supported by the transform. + :param transform: A valid CmsTransform class object + :param inPlace: Bool. If ``True``, ``im`` is modified in place and ``None`` is + returned, if ``False``, a new :py:class:`~PIL.Image.Image` object with the + transform applied is returned (and ``im`` is not changed). The default is + ``False``. + :returns: Either ``None``, or a new :py:class:`~PIL.Image.Image` object, + depending on the value of ``inPlace``. The profile will be returned in + the image's ``info['icc_profile']``. + :exception PyCMSError: + """ + + try: + if inPlace: + transform.apply_in_place(im) + imOut = None + else: + imOut = transform.apply(im) + except (TypeError, ValueError) as v: + raise PyCMSError(v) from v + + return imOut + + +def createProfile( + colorSpace: Literal["LAB", "XYZ", "sRGB"], colorTemp: SupportsFloat = 0 +) -> core.CmsProfile: + """ + (pyCMS) Creates a profile. + + If colorSpace not in ``["LAB", "XYZ", "sRGB"]``, + a :exc:`PyCMSError` is raised. + + If using LAB and ``colorTemp`` is not a positive integer, + a :exc:`PyCMSError` is raised. + + If an error occurs while creating the profile, + a :exc:`PyCMSError` is raised. + + Use this function to create common profiles on-the-fly instead of + having to supply a profile on disk and knowing the path to it. It + returns a normal CmsProfile object that can be passed to + ImageCms.buildTransformFromOpenProfiles() to create a transform to apply + to images. + + :param colorSpace: String, the color space of the profile you wish to + create. + Currently only "LAB", "XYZ", and "sRGB" are supported. + :param colorTemp: Positive number for the white point for the profile, in + degrees Kelvin (i.e. 5000, 6500, 9600, etc.). The default is for D50 + illuminant if omitted (5000k). colorTemp is ONLY applied to LAB + profiles, and is ignored for XYZ and sRGB. + :returns: A CmsProfile class object + :exception PyCMSError: + """ + + if colorSpace not in ["LAB", "XYZ", "sRGB"]: + msg = ( + f"Color space not supported for on-the-fly profile creation ({colorSpace})" + ) + raise PyCMSError(msg) + + if colorSpace == "LAB": + try: + colorTemp = float(colorTemp) + except (TypeError, ValueError) as e: + msg = f'Color temperature must be numeric, "{colorTemp}" not valid' + raise PyCMSError(msg) from e + + try: + return core.createProfile(colorSpace, colorTemp) + except (TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileName(profile: _CmsProfileCompatible) -> str: + """ + + (pyCMS) Gets the internal product name for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, + a :exc:`PyCMSError` is raised If an error occurs while trying + to obtain the name tag, a :exc:`PyCMSError` is raised. + + Use this function to obtain the INTERNAL name of the profile (stored + in an ICC tag in the profile itself), usually the one used when the + profile was originally created. Sometimes this tag also contains + additional information supplied by the creator. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal name of the profile as stored + in an ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # do it in python, not c. + # // name was "%s - %s" (model, manufacturer) || Description , + # // but if the Model and Manufacturer were the same or the model + # // was long, Just the model, in 1.x + model = profile.profile.model + manufacturer = profile.profile.manufacturer + + if not (model or manufacturer): + return (profile.profile.profile_description or "") + "\n" + if not manufacturer or (model and len(model) > 30): + return f"{model}\n" + return f"{model} - {manufacturer}\n" + + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileInfo(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the internal product information for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, + a :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the info tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + info tag. This often contains details about the profile, and how it + was created, as supplied by the creator. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # add an extra newline to preserve pyCMS compatibility + # Python, not C. the white point bits weren't working well, + # so skipping. + # info was description \r\n\r\n copyright \r\n\r\n K007 tag \r\n\r\n whitepoint + description = profile.profile.profile_description + cpright = profile.profile.copyright + elements = [element for element in (description, cpright) if element] + return "\r\n\r\n".join(elements) + "\r\n\r\n" + + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileCopyright(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the copyright for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the copyright tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + copyright tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.copyright or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileManufacturer(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the manufacturer for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the manufacturer tag, a + :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + manufacturer tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.manufacturer or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileModel(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the model for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the model tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + model tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.model or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileDescription(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the description for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the description tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + description tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in an + ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.profile_description or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getDefaultIntent(profile: _CmsProfileCompatible) -> int: + """ + (pyCMS) Gets the default intent name for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the default intent, a + :exc:`PyCMSError` is raised. + + Use this function to determine the default (and usually best optimized) + rendering intent for this profile. Most profiles support multiple + rendering intents, but are intended mostly for one type of conversion. + If you wish to use a different intent than returned, use + ImageCms.isIntentSupported() to verify it will work first. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: Integer 0-3 specifying the default rendering intent for this + profile. + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return profile.profile.rendering_intent + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def isIntentSupported( + profile: _CmsProfileCompatible, intent: Intent, direction: Direction +) -> Literal[-1, 1]: + """ + (pyCMS) Checks if a given intent is supported. + + Use this function to verify that you can use your desired + ``intent`` with ``profile``, and that ``profile`` can be used for the + input/output/proof profile as you desire. + + Some profiles are created specifically for one "direction", can cannot + be used for others. Some profiles can only be used for certain + rendering intents, so it's best to either verify this before trying + to create a transform with them (using this function), or catch the + potential :exc:`PyCMSError` that will occur if they don't + support the modes you select. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :param intent: Integer (0-3) specifying the rendering intent you wish to + use with this profile + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param direction: Integer specifying if the profile is to be used for + input, output, or proof + + INPUT = 0 (or use ImageCms.Direction.INPUT) + OUTPUT = 1 (or use ImageCms.Direction.OUTPUT) + PROOF = 2 (or use ImageCms.Direction.PROOF) + + :returns: 1 if the intent/direction are supported, -1 if they are not. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # FIXME: I get different results for the same data w. different + # compilers. Bug in LittleCMS or in the binding? + if profile.profile.is_intent_supported(intent, direction): + return 1 + else: + return -1 + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def versions() -> tuple[str, str | None, str, str]: + """ + (pyCMS) Fetches versions. + """ + + deprecate( + "PIL.ImageCms.versions()", + 12, + '(PIL.features.version("littlecms2"), sys.version, PIL.__version__)', + ) + return _VERSION, core.littlecms_version, sys.version.split()[0], __version__ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageColor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageColor.py new file mode 100644 index 0000000000000000000000000000000000000000..9a15a8eb7597998f1bc9a01e8eae3588c087838b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageColor.py @@ -0,0 +1,320 @@ +# +# The Python Imaging Library +# $Id$ +# +# map CSS3-style colour description strings to RGB +# +# History: +# 2002-10-24 fl Added support for CSS-style color strings +# 2002-12-15 fl Added RGBA support +# 2004-03-27 fl Fixed remaining int() problems for Python 1.5.2 +# 2004-07-19 fl Fixed gray/grey spelling issues +# 2009-03-05 fl Fixed rounding error in grayscale calculation +# +# Copyright (c) 2002-2004 by Secret Labs AB +# Copyright (c) 2002-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from functools import lru_cache + +from . import Image + + +@lru_cache +def getrgb(color: str) -> tuple[int, int, int] | tuple[int, int, int, int]: + """ + Convert a color string to an RGB or RGBA tuple. If the string cannot be + parsed, this function raises a :py:exc:`ValueError` exception. + + .. versionadded:: 1.1.4 + + :param color: A color string + :return: ``(red, green, blue[, alpha])`` + """ + if len(color) > 100: + msg = "color specifier is too long" + raise ValueError(msg) + color = color.lower() + + rgb = colormap.get(color, None) + if rgb: + if isinstance(rgb, tuple): + return rgb + rgb_tuple = getrgb(rgb) + assert len(rgb_tuple) == 3 + colormap[color] = rgb_tuple + return rgb_tuple + + # check for known string formats + if re.match("#[a-f0-9]{3}$", color): + return int(color[1] * 2, 16), int(color[2] * 2, 16), int(color[3] * 2, 16) + + if re.match("#[a-f0-9]{4}$", color): + return ( + int(color[1] * 2, 16), + int(color[2] * 2, 16), + int(color[3] * 2, 16), + int(color[4] * 2, 16), + ) + + if re.match("#[a-f0-9]{6}$", color): + return int(color[1:3], 16), int(color[3:5], 16), int(color[5:7], 16) + + if re.match("#[a-f0-9]{8}$", color): + return ( + int(color[1:3], 16), + int(color[3:5], 16), + int(color[5:7], 16), + int(color[7:9], 16), + ) + + m = re.match(r"rgb\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$", color) + if m: + return int(m.group(1)), int(m.group(2)), int(m.group(3)) + + m = re.match(r"rgb\(\s*(\d+)%\s*,\s*(\d+)%\s*,\s*(\d+)%\s*\)$", color) + if m: + return ( + int((int(m.group(1)) * 255) / 100.0 + 0.5), + int((int(m.group(2)) * 255) / 100.0 + 0.5), + int((int(m.group(3)) * 255) / 100.0 + 0.5), + ) + + m = re.match( + r"hsl\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)%\s*,\s*(\d+\.?\d*)%\s*\)$", color + ) + if m: + from colorsys import hls_to_rgb + + rgb_floats = hls_to_rgb( + float(m.group(1)) / 360.0, + float(m.group(3)) / 100.0, + float(m.group(2)) / 100.0, + ) + return ( + int(rgb_floats[0] * 255 + 0.5), + int(rgb_floats[1] * 255 + 0.5), + int(rgb_floats[2] * 255 + 0.5), + ) + + m = re.match( + r"hs[bv]\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)%\s*,\s*(\d+\.?\d*)%\s*\)$", color + ) + if m: + from colorsys import hsv_to_rgb + + rgb_floats = hsv_to_rgb( + float(m.group(1)) / 360.0, + float(m.group(2)) / 100.0, + float(m.group(3)) / 100.0, + ) + return ( + int(rgb_floats[0] * 255 + 0.5), + int(rgb_floats[1] * 255 + 0.5), + int(rgb_floats[2] * 255 + 0.5), + ) + + m = re.match(r"rgba\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$", color) + if m: + return int(m.group(1)), int(m.group(2)), int(m.group(3)), int(m.group(4)) + msg = f"unknown color specifier: {repr(color)}" + raise ValueError(msg) + + +@lru_cache +def getcolor(color: str, mode: str) -> int | tuple[int, ...]: + """ + Same as :py:func:`~PIL.ImageColor.getrgb` for most modes. However, if + ``mode`` is HSV, converts the RGB value to a HSV value, or if ``mode`` is + not color or a palette image, converts the RGB value to a grayscale value. + If the string cannot be parsed, this function raises a :py:exc:`ValueError` + exception. + + .. versionadded:: 1.1.4 + + :param color: A color string + :param mode: Convert result to this mode + :return: ``graylevel, (graylevel, alpha) or (red, green, blue[, alpha])`` + """ + # same as getrgb, but converts the result to the given mode + rgb, alpha = getrgb(color), 255 + if len(rgb) == 4: + alpha = rgb[3] + rgb = rgb[:3] + + if mode == "HSV": + from colorsys import rgb_to_hsv + + r, g, b = rgb + h, s, v = rgb_to_hsv(r / 255, g / 255, b / 255) + return int(h * 255), int(s * 255), int(v * 255) + elif Image.getmodebase(mode) == "L": + r, g, b = rgb + # ITU-R Recommendation 601-2 for nonlinear RGB + # scaled to 24 bits to match the convert's implementation. + graylevel = (r * 19595 + g * 38470 + b * 7471 + 0x8000) >> 16 + if mode[-1] == "A": + return graylevel, alpha + return graylevel + elif mode[-1] == "A": + return rgb + (alpha,) + return rgb + + +colormap: dict[str, str | tuple[int, int, int]] = { + # X11 colour table from https://drafts.csswg.org/css-color-4/, with + # gray/grey spelling issues fixed. This is a superset of HTML 4.0 + # colour names used in CSS 1. + "aliceblue": "#f0f8ff", + "antiquewhite": "#faebd7", + "aqua": "#00ffff", + "aquamarine": "#7fffd4", + "azure": "#f0ffff", + "beige": "#f5f5dc", + "bisque": "#ffe4c4", + "black": "#000000", + "blanchedalmond": "#ffebcd", + "blue": "#0000ff", + "blueviolet": "#8a2be2", + "brown": "#a52a2a", + "burlywood": "#deb887", + "cadetblue": "#5f9ea0", + "chartreuse": "#7fff00", + "chocolate": "#d2691e", + "coral": "#ff7f50", + "cornflowerblue": "#6495ed", + "cornsilk": "#fff8dc", + "crimson": "#dc143c", + "cyan": "#00ffff", + "darkblue": "#00008b", + "darkcyan": "#008b8b", + "darkgoldenrod": "#b8860b", + "darkgray": "#a9a9a9", + "darkgrey": "#a9a9a9", + "darkgreen": "#006400", + "darkkhaki": "#bdb76b", + "darkmagenta": "#8b008b", + "darkolivegreen": "#556b2f", + "darkorange": "#ff8c00", + "darkorchid": "#9932cc", + "darkred": "#8b0000", + "darksalmon": "#e9967a", + "darkseagreen": "#8fbc8f", + "darkslateblue": "#483d8b", + "darkslategray": "#2f4f4f", + "darkslategrey": "#2f4f4f", + "darkturquoise": "#00ced1", + "darkviolet": "#9400d3", + "deeppink": "#ff1493", + "deepskyblue": "#00bfff", + "dimgray": "#696969", + "dimgrey": "#696969", + "dodgerblue": "#1e90ff", + "firebrick": "#b22222", + "floralwhite": "#fffaf0", + "forestgreen": "#228b22", + "fuchsia": "#ff00ff", + "gainsboro": "#dcdcdc", + "ghostwhite": "#f8f8ff", + "gold": "#ffd700", + "goldenrod": "#daa520", + "gray": "#808080", + "grey": "#808080", + "green": "#008000", + "greenyellow": "#adff2f", + "honeydew": "#f0fff0", + "hotpink": "#ff69b4", + "indianred": "#cd5c5c", + "indigo": "#4b0082", + "ivory": "#fffff0", + "khaki": "#f0e68c", + "lavender": "#e6e6fa", + "lavenderblush": "#fff0f5", + "lawngreen": "#7cfc00", + "lemonchiffon": "#fffacd", + "lightblue": "#add8e6", + "lightcoral": "#f08080", + "lightcyan": "#e0ffff", + "lightgoldenrodyellow": "#fafad2", + "lightgreen": "#90ee90", + "lightgray": "#d3d3d3", + "lightgrey": "#d3d3d3", + "lightpink": "#ffb6c1", + "lightsalmon": "#ffa07a", + "lightseagreen": "#20b2aa", + "lightskyblue": "#87cefa", + "lightslategray": "#778899", + "lightslategrey": "#778899", + "lightsteelblue": "#b0c4de", + "lightyellow": "#ffffe0", + "lime": "#00ff00", + "limegreen": "#32cd32", + "linen": "#faf0e6", + "magenta": "#ff00ff", + "maroon": "#800000", + "mediumaquamarine": "#66cdaa", + "mediumblue": "#0000cd", + "mediumorchid": "#ba55d3", + "mediumpurple": "#9370db", + "mediumseagreen": "#3cb371", + "mediumslateblue": "#7b68ee", + "mediumspringgreen": "#00fa9a", + "mediumturquoise": "#48d1cc", + "mediumvioletred": "#c71585", + "midnightblue": "#191970", + "mintcream": "#f5fffa", + "mistyrose": "#ffe4e1", + "moccasin": "#ffe4b5", + "navajowhite": "#ffdead", + "navy": "#000080", + "oldlace": "#fdf5e6", + "olive": "#808000", + "olivedrab": "#6b8e23", + "orange": "#ffa500", + "orangered": "#ff4500", + "orchid": "#da70d6", + "palegoldenrod": "#eee8aa", + "palegreen": "#98fb98", + "paleturquoise": "#afeeee", + "palevioletred": "#db7093", + "papayawhip": "#ffefd5", + "peachpuff": "#ffdab9", + "peru": "#cd853f", + "pink": "#ffc0cb", + "plum": "#dda0dd", + "powderblue": "#b0e0e6", + "purple": "#800080", + "rebeccapurple": "#663399", + "red": "#ff0000", + "rosybrown": "#bc8f8f", + "royalblue": "#4169e1", + "saddlebrown": "#8b4513", + "salmon": "#fa8072", + "sandybrown": "#f4a460", + "seagreen": "#2e8b57", + "seashell": "#fff5ee", + "sienna": "#a0522d", + "silver": "#c0c0c0", + "skyblue": "#87ceeb", + "slateblue": "#6a5acd", + "slategray": "#708090", + "slategrey": "#708090", + "snow": "#fffafa", + "springgreen": "#00ff7f", + "steelblue": "#4682b4", + "tan": "#d2b48c", + "teal": "#008080", + "thistle": "#d8bfd8", + "tomato": "#ff6347", + "turquoise": "#40e0d0", + "violet": "#ee82ee", + "wheat": "#f5deb3", + "white": "#ffffff", + "whitesmoke": "#f5f5f5", + "yellow": "#ffff00", + "yellowgreen": "#9acd32", +} diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py new file mode 100644 index 0000000000000000000000000000000000000000..6cf1ee62659f07b21f257a37565a6f81022d9910 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py @@ -0,0 +1,1232 @@ +# +# The Python Imaging Library +# $Id$ +# +# drawing interface operations +# +# History: +# 1996-04-13 fl Created (experimental) +# 1996-08-07 fl Filled polygons, ellipses. +# 1996-08-13 fl Added text support +# 1998-06-28 fl Handle I and F images +# 1998-12-29 fl Added arc; use arc primitive to draw ellipses +# 1999-01-10 fl Added shape stuff (experimental) +# 1999-02-06 fl Added bitmap support +# 1999-02-11 fl Changed all primitives to take options +# 1999-02-20 fl Fixed backwards compatibility +# 2000-10-12 fl Copy on write, when necessary +# 2001-02-18 fl Use default ink for bitmap/text also in fill mode +# 2002-10-24 fl Added support for CSS-style color strings +# 2002-12-10 fl Added experimental support for RGBA-on-RGB drawing +# 2002-12-11 fl Refactored low-level drawing API (work in progress) +# 2004-08-26 fl Made Draw() a factory function, added getdraw() support +# 2004-09-04 fl Added width support to line primitive +# 2004-09-10 fl Added font mode handling +# 2006-06-19 fl Added font bearing support (getmask2) +# +# Copyright (c) 1997-2006 by Secret Labs AB +# Copyright (c) 1996-2006 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +import struct +from collections.abc import Sequence +from types import ModuleType +from typing import Any, AnyStr, Callable, Union, cast + +from . import Image, ImageColor +from ._deprecate import deprecate +from ._typing import Coords + +# experimental access to the outline API +Outline: Callable[[], Image.core._Outline] = Image.core.outline + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageDraw2, ImageFont + +_Ink = Union[float, tuple[int, ...], str] + +""" +A simple 2D drawing interface for PIL images. +

+Application code should use the Draw factory, instead of +directly. +""" + + +class ImageDraw: + font: ( + ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont | None + ) = None + + def __init__(self, im: Image.Image, mode: str | None = None) -> None: + """ + Create a drawing instance. + + :param im: The image to draw in. + :param mode: Optional mode to use for color values. For RGB + images, this argument can be RGB or RGBA (to blend the + drawing into the image). For all other modes, this argument + must be the same as the image mode. If omitted, the mode + defaults to the mode of the image. + """ + im.load() + if im.readonly: + im._copy() # make it writeable + blend = 0 + if mode is None: + mode = im.mode + if mode != im.mode: + if mode == "RGBA" and im.mode == "RGB": + blend = 1 + else: + msg = "mode mismatch" + raise ValueError(msg) + if mode == "P": + self.palette = im.palette + else: + self.palette = None + self._image = im + self.im = im.im + self.draw = Image.core.draw(self.im, blend) + self.mode = mode + if mode in ("I", "F"): + self.ink = self.draw.draw_ink(1) + else: + self.ink = self.draw.draw_ink(-1) + if mode in ("1", "P", "I", "F"): + # FIXME: fix Fill2 to properly support matte for I+F images + self.fontmode = "1" + else: + self.fontmode = "L" # aliasing is okay for other modes + self.fill = False + + def getfont( + self, + ) -> ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont: + """ + Get the current default font. + + To set the default font for this ImageDraw instance:: + + from PIL import ImageDraw, ImageFont + draw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") + + To set the default font for all future ImageDraw instances:: + + from PIL import ImageDraw, ImageFont + ImageDraw.ImageDraw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") + + If the current default font is ``None``, + it is initialized with ``ImageFont.load_default()``. + + :returns: An image font.""" + if not self.font: + # FIXME: should add a font repository + from . import ImageFont + + self.font = ImageFont.load_default() + return self.font + + def _getfont( + self, font_size: float | None + ) -> ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont: + if font_size is not None: + from . import ImageFont + + return ImageFont.load_default(font_size) + else: + return self.getfont() + + def _getink( + self, ink: _Ink | None, fill: _Ink | None = None + ) -> tuple[int | None, int | None]: + result_ink = None + result_fill = None + if ink is None and fill is None: + if self.fill: + result_fill = self.ink + else: + result_ink = self.ink + else: + if ink is not None: + if isinstance(ink, str): + ink = ImageColor.getcolor(ink, self.mode) + if self.palette and isinstance(ink, tuple): + ink = self.palette.getcolor(ink, self._image) + result_ink = self.draw.draw_ink(ink) + if fill is not None: + if isinstance(fill, str): + fill = ImageColor.getcolor(fill, self.mode) + if self.palette and isinstance(fill, tuple): + fill = self.palette.getcolor(fill, self._image) + result_fill = self.draw.draw_ink(fill) + return result_ink, result_fill + + def arc( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw an arc.""" + ink, fill = self._getink(fill) + if ink is not None: + self.draw.draw_arc(xy, start, end, ink, width) + + def bitmap( + self, xy: Sequence[int], bitmap: Image.Image, fill: _Ink | None = None + ) -> None: + """Draw a bitmap.""" + bitmap.load() + ink, fill = self._getink(fill) + if ink is None: + ink = fill + if ink is not None: + self.draw.draw_bitmap(xy, bitmap.im, ink) + + def chord( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a chord.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_chord(xy, start, end, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_chord(xy, start, end, ink, 0, width) + + def ellipse( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw an ellipse.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_ellipse(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_ellipse(xy, ink, 0, width) + + def circle( + self, + xy: Sequence[float], + radius: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a circle given center coordinates and a radius.""" + ellipse_xy = (xy[0] - radius, xy[1] - radius, xy[0] + radius, xy[1] + radius) + self.ellipse(ellipse_xy, fill, outline, width) + + def line( + self, + xy: Coords, + fill: _Ink | None = None, + width: int = 0, + joint: str | None = None, + ) -> None: + """Draw a line, or a connected sequence of line segments.""" + ink = self._getink(fill)[0] + if ink is not None: + self.draw.draw_lines(xy, ink, width) + if joint == "curve" and width > 4: + points: Sequence[Sequence[float]] + if isinstance(xy[0], (list, tuple)): + points = cast(Sequence[Sequence[float]], xy) + else: + points = [ + cast(Sequence[float], tuple(xy[i : i + 2])) + for i in range(0, len(xy), 2) + ] + for i in range(1, len(points) - 1): + point = points[i] + angles = [ + math.degrees(math.atan2(end[0] - start[0], start[1] - end[1])) + % 360 + for start, end in ( + (points[i - 1], point), + (point, points[i + 1]), + ) + ] + if angles[0] == angles[1]: + # This is a straight line, so no joint is required + continue + + def coord_at_angle( + coord: Sequence[float], angle: float + ) -> tuple[float, ...]: + x, y = coord + angle -= 90 + distance = width / 2 - 1 + return tuple( + p + (math.floor(p_d) if p_d > 0 else math.ceil(p_d)) + for p, p_d in ( + (x, distance * math.cos(math.radians(angle))), + (y, distance * math.sin(math.radians(angle))), + ) + ) + + flipped = ( + angles[1] > angles[0] and angles[1] - 180 > angles[0] + ) or (angles[1] < angles[0] and angles[1] + 180 > angles[0]) + coords = [ + (point[0] - width / 2 + 1, point[1] - width / 2 + 1), + (point[0] + width / 2 - 1, point[1] + width / 2 - 1), + ] + if flipped: + start, end = (angles[1] + 90, angles[0] + 90) + else: + start, end = (angles[0] - 90, angles[1] - 90) + self.pieslice(coords, start - 90, end - 90, fill) + + if width > 8: + # Cover potential gaps between the line and the joint + if flipped: + gap_coords = [ + coord_at_angle(point, angles[0] + 90), + point, + coord_at_angle(point, angles[1] + 90), + ] + else: + gap_coords = [ + coord_at_angle(point, angles[0] - 90), + point, + coord_at_angle(point, angles[1] - 90), + ] + self.line(gap_coords, fill, width=3) + + def shape( + self, + shape: Image.core._Outline, + fill: _Ink | None = None, + outline: _Ink | None = None, + ) -> None: + """(Experimental) Draw a shape.""" + shape.close() + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_outline(shape, fill_ink, 1) + if ink is not None and ink != fill_ink: + self.draw.draw_outline(shape, ink, 0) + + def pieslice( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a pieslice.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_pieslice(xy, start, end, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_pieslice(xy, start, end, ink, 0, width) + + def point(self, xy: Coords, fill: _Ink | None = None) -> None: + """Draw one or more individual pixels.""" + ink, fill = self._getink(fill) + if ink is not None: + self.draw.draw_points(xy, ink) + + def polygon( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a polygon.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_polygon(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + if width == 1: + self.draw.draw_polygon(xy, ink, 0, width) + elif self.im is not None: + # To avoid expanding the polygon outwards, + # use the fill as a mask + mask = Image.new("1", self.im.size) + mask_ink = self._getink(1)[0] + draw = Draw(mask) + draw.draw.draw_polygon(xy, mask_ink, 1) + + self.draw.draw_polygon(xy, ink, 0, width * 2 - 1, mask.im) + + def regular_polygon( + self, + bounding_circle: Sequence[Sequence[float] | float], + n_sides: int, + rotation: float = 0, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a regular polygon.""" + xy = _compute_regular_polygon_vertices(bounding_circle, n_sides, rotation) + self.polygon(xy, fill, outline, width) + + def rectangle( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a rectangle.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_rectangle(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_rectangle(xy, ink, 0, width) + + def rounded_rectangle( + self, + xy: Coords, + radius: float = 0, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + *, + corners: tuple[bool, bool, bool, bool] | None = None, + ) -> None: + """Draw a rounded rectangle.""" + if isinstance(xy[0], (list, tuple)): + (x0, y0), (x1, y1) = cast(Sequence[Sequence[float]], xy) + else: + x0, y0, x1, y1 = cast(Sequence[float], xy) + if x1 < x0: + msg = "x1 must be greater than or equal to x0" + raise ValueError(msg) + if y1 < y0: + msg = "y1 must be greater than or equal to y0" + raise ValueError(msg) + if corners is None: + corners = (True, True, True, True) + + d = radius * 2 + + x0 = round(x0) + y0 = round(y0) + x1 = round(x1) + y1 = round(y1) + full_x, full_y = False, False + if all(corners): + full_x = d >= x1 - x0 - 1 + if full_x: + # The two left and two right corners are joined + d = x1 - x0 + full_y = d >= y1 - y0 - 1 + if full_y: + # The two top and two bottom corners are joined + d = y1 - y0 + if full_x and full_y: + # If all corners are joined, that is a circle + return self.ellipse(xy, fill, outline, width) + + if d == 0 or not any(corners): + # If the corners have no curve, + # or there are no corners, + # that is a rectangle + return self.rectangle(xy, fill, outline, width) + + r = int(d // 2) + ink, fill_ink = self._getink(outline, fill) + + def draw_corners(pieslice: bool) -> None: + parts: tuple[tuple[tuple[float, float, float, float], int, int], ...] + if full_x: + # Draw top and bottom halves + parts = ( + ((x0, y0, x0 + d, y0 + d), 180, 360), + ((x0, y1 - d, x0 + d, y1), 0, 180), + ) + elif full_y: + # Draw left and right halves + parts = ( + ((x0, y0, x0 + d, y0 + d), 90, 270), + ((x1 - d, y0, x1, y0 + d), 270, 90), + ) + else: + # Draw four separate corners + parts = tuple( + part + for i, part in enumerate( + ( + ((x0, y0, x0 + d, y0 + d), 180, 270), + ((x1 - d, y0, x1, y0 + d), 270, 360), + ((x1 - d, y1 - d, x1, y1), 0, 90), + ((x0, y1 - d, x0 + d, y1), 90, 180), + ) + ) + if corners[i] + ) + for part in parts: + if pieslice: + self.draw.draw_pieslice(*(part + (fill_ink, 1))) + else: + self.draw.draw_arc(*(part + (ink, width))) + + if fill_ink is not None: + draw_corners(True) + + if full_x: + self.draw.draw_rectangle((x0, y0 + r + 1, x1, y1 - r - 1), fill_ink, 1) + elif x1 - r - 1 > x0 + r + 1: + self.draw.draw_rectangle((x0 + r + 1, y0, x1 - r - 1, y1), fill_ink, 1) + if not full_x and not full_y: + left = [x0, y0, x0 + r, y1] + if corners[0]: + left[1] += r + 1 + if corners[3]: + left[3] -= r + 1 + self.draw.draw_rectangle(left, fill_ink, 1) + + right = [x1 - r, y0, x1, y1] + if corners[1]: + right[1] += r + 1 + if corners[2]: + right[3] -= r + 1 + self.draw.draw_rectangle(right, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + draw_corners(False) + + if not full_x: + top = [x0, y0, x1, y0 + width - 1] + if corners[0]: + top[0] += r + 1 + if corners[1]: + top[2] -= r + 1 + self.draw.draw_rectangle(top, ink, 1) + + bottom = [x0, y1 - width + 1, x1, y1] + if corners[3]: + bottom[0] += r + 1 + if corners[2]: + bottom[2] -= r + 1 + self.draw.draw_rectangle(bottom, ink, 1) + if not full_y: + left = [x0, y0, x0 + width - 1, y1] + if corners[0]: + left[1] += r + 1 + if corners[3]: + left[3] -= r + 1 + self.draw.draw_rectangle(left, ink, 1) + + right = [x1 - width + 1, y0, x1, y1] + if corners[1]: + right[1] += r + 1 + if corners[2]: + right[3] -= r + 1 + self.draw.draw_rectangle(right, ink, 1) + + def _multiline_check(self, text: AnyStr) -> bool: + split_character = "\n" if isinstance(text, str) else b"\n" + + return split_character in text + + def text( + self, + xy: tuple[float, float], + text: AnyStr, + fill: _Ink | None = None, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + stroke_fill: _Ink | None = None, + embedded_color: bool = False, + *args: Any, + **kwargs: Any, + ) -> None: + """Draw text.""" + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(kwargs.get("font_size")) + + if self._multiline_check(text): + return self.multiline_text( + xy, + text, + fill, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + stroke_fill, + embedded_color, + ) + + def getink(fill: _Ink | None) -> int: + ink, fill_ink = self._getink(fill) + if ink is None: + assert fill_ink is not None + return fill_ink + return ink + + def draw_text(ink: int, stroke_width: float = 0) -> None: + mode = self.fontmode + if stroke_width == 0 and embedded_color: + mode = "RGBA" + coord = [] + for i in range(2): + coord.append(int(xy[i])) + start = (math.modf(xy[0])[0], math.modf(xy[1])[0]) + try: + mask, offset = font.getmask2( # type: ignore[union-attr,misc] + text, + mode, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + stroke_filled=True, + anchor=anchor, + ink=ink, + start=start, + *args, + **kwargs, + ) + coord = [coord[0] + offset[0], coord[1] + offset[1]] + except AttributeError: + try: + mask = font.getmask( # type: ignore[misc] + text, + mode, + direction, + features, + language, + stroke_width, + anchor, + ink, + start=start, + *args, + **kwargs, + ) + except TypeError: + mask = font.getmask(text) + if mode == "RGBA": + # font.getmask2(mode="RGBA") returns color in RGB bands and mask in A + # extract mask and set text alpha + color, mask = mask, mask.getband(3) + ink_alpha = struct.pack("i", ink)[3] + color.fillband(3, ink_alpha) + x, y = coord + if self.im is not None: + self.im.paste( + color, (x, y, x + mask.size[0], y + mask.size[1]), mask + ) + else: + self.draw.draw_bitmap(coord, mask, ink) + + ink = getink(fill) + if ink is not None: + stroke_ink = None + if stroke_width: + stroke_ink = getink(stroke_fill) if stroke_fill is not None else ink + + if stroke_ink is not None: + # Draw stroked text + draw_text(stroke_ink, stroke_width) + + # Draw normal text + if ink != stroke_ink: + draw_text(ink) + else: + # Only draw normal text + draw_text(ink) + + def _prepare_multiline_text( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ), + anchor: str | None, + spacing: float, + align: str, + direction: str | None, + features: list[str] | None, + language: str | None, + stroke_width: float, + embedded_color: bool, + font_size: float | None, + ) -> tuple[ + ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont, + list[tuple[tuple[float, float], str, AnyStr]], + ]: + if anchor is None: + anchor = "lt" if direction == "ttb" else "la" + elif len(anchor) != 2: + msg = "anchor must be a 2 character string" + raise ValueError(msg) + elif anchor[1] in "tb" and direction != "ttb": + msg = "anchor not supported for multiline text" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + + lines = text.split("\n" if isinstance(text, str) else b"\n") + line_spacing = ( + self.textbbox((0, 0), "A", font, stroke_width=stroke_width)[3] + + stroke_width + + spacing + ) + + top = xy[1] + parts = [] + if direction == "ttb": + left = xy[0] + for line in lines: + parts.append(((left, top), anchor, line)) + left += line_spacing + else: + widths = [] + max_width: float = 0 + for line in lines: + line_width = self.textlength( + line, + font, + direction=direction, + features=features, + language=language, + embedded_color=embedded_color, + ) + widths.append(line_width) + max_width = max(max_width, line_width) + + if anchor[1] == "m": + top -= (len(lines) - 1) * line_spacing / 2.0 + elif anchor[1] == "d": + top -= (len(lines) - 1) * line_spacing + + for idx, line in enumerate(lines): + left = xy[0] + width_difference = max_width - widths[idx] + + # align by align parameter + if align in ("left", "justify"): + pass + elif align == "center": + left += width_difference / 2.0 + elif align == "right": + left += width_difference + else: + msg = 'align must be "left", "center", "right" or "justify"' + raise ValueError(msg) + + if ( + align == "justify" + and width_difference != 0 + and idx != len(lines) - 1 + ): + words = line.split(" " if isinstance(text, str) else b" ") + if len(words) > 1: + # align left by anchor + if anchor[0] == "m": + left -= max_width / 2.0 + elif anchor[0] == "r": + left -= max_width + + word_widths = [ + self.textlength( + word, + font, + direction=direction, + features=features, + language=language, + embedded_color=embedded_color, + ) + for word in words + ] + word_anchor = "l" + anchor[1] + width_difference = max_width - sum(word_widths) + for i, word in enumerate(words): + parts.append(((left, top), word_anchor, word)) + left += word_widths[i] + width_difference / (len(words) - 1) + top += line_spacing + continue + + # align left by anchor + if anchor[0] == "m": + left -= width_difference / 2.0 + elif anchor[0] == "r": + left -= width_difference + parts.append(((left, top), anchor, line)) + top += line_spacing + + return font, parts + + def multiline_text( + self, + xy: tuple[float, float], + text: AnyStr, + fill: _Ink | None = None, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + stroke_fill: _Ink | None = None, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> None: + font, lines = self._prepare_multiline_text( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + font_size, + ) + + for xy, anchor, line in lines: + self.text( + xy, + line, + fill, + font, + anchor, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + stroke_fill=stroke_fill, + embedded_color=embedded_color, + ) + + def textlength( + self, + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> float: + """Get the length of a given string, in pixels with 1/64 precision.""" + if self._multiline_check(text): + msg = "can't measure length of multiline text" + raise ValueError(msg) + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + mode = "RGBA" if embedded_color else self.fontmode + return font.getlength(text, mode, direction, features, language) + + def textbbox( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> tuple[float, float, float, float]: + """Get the bounding box of a given string, in pixels.""" + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + + if self._multiline_check(text): + return self.multiline_textbbox( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + ) + + mode = "RGBA" if embedded_color else self.fontmode + bbox = font.getbbox( + text, mode, direction, features, language, stroke_width, anchor + ) + return bbox[0] + xy[0], bbox[1] + xy[1], bbox[2] + xy[0], bbox[3] + xy[1] + + def multiline_textbbox( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> tuple[float, float, float, float]: + font, lines = self._prepare_multiline_text( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + font_size, + ) + + bbox: tuple[float, float, float, float] | None = None + + for xy, anchor, line in lines: + bbox_line = self.textbbox( + xy, + line, + font, + anchor, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + embedded_color=embedded_color, + ) + if bbox is None: + bbox = bbox_line + else: + bbox = ( + min(bbox[0], bbox_line[0]), + min(bbox[1], bbox_line[1]), + max(bbox[2], bbox_line[2]), + max(bbox[3], bbox_line[3]), + ) + + if bbox is None: + return xy[0], xy[1], xy[0], xy[1] + return bbox + + +def Draw(im: Image.Image, mode: str | None = None) -> ImageDraw: + """ + A simple 2D drawing interface for PIL images. + + :param im: The image to draw in. + :param mode: Optional mode to use for color values. For RGB + images, this argument can be RGB or RGBA (to blend the + drawing into the image). For all other modes, this argument + must be the same as the image mode. If omitted, the mode + defaults to the mode of the image. + """ + try: + return getattr(im, "getdraw")(mode) + except AttributeError: + return ImageDraw(im, mode) + + +def getdraw( + im: Image.Image | None = None, hints: list[str] | None = None +) -> tuple[ImageDraw2.Draw | None, ModuleType]: + """ + :param im: The image to draw in. + :param hints: An optional list of hints. Deprecated. + :returns: A (drawing context, drawing resource factory) tuple. + """ + if hints is not None: + deprecate("'hints' parameter", 12) + from . import ImageDraw2 + + draw = ImageDraw2.Draw(im) if im is not None else None + return draw, ImageDraw2 + + +def floodfill( + image: Image.Image, + xy: tuple[int, int], + value: float | tuple[int, ...], + border: float | tuple[int, ...] | None = None, + thresh: float = 0, +) -> None: + """ + .. warning:: This method is experimental. + + Fills a bounded region with a given color. + + :param image: Target image. + :param xy: Seed position (a 2-item coordinate tuple). See + :ref:`coordinate-system`. + :param value: Fill color. + :param border: Optional border value. If given, the region consists of + pixels with a color different from the border color. If not given, + the region consists of pixels having the same color as the seed + pixel. + :param thresh: Optional threshold value which specifies a maximum + tolerable difference of a pixel value from the 'background' in + order for it to be replaced. Useful for filling regions of + non-homogeneous, but similar, colors. + """ + # based on an implementation by Eric S. Raymond + # amended by yo1995 @20180806 + pixel = image.load() + assert pixel is not None + x, y = xy + try: + background = pixel[x, y] + if _color_diff(value, background) <= thresh: + return # seed point already has fill color + pixel[x, y] = value + except (ValueError, IndexError): + return # seed point outside image + edge = {(x, y)} + # use a set to keep record of current and previous edge pixels + # to reduce memory consumption + full_edge = set() + while edge: + new_edge = set() + for x, y in edge: # 4 adjacent method + for s, t in ((x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)): + # If already processed, or if a coordinate is negative, skip + if (s, t) in full_edge or s < 0 or t < 0: + continue + try: + p = pixel[s, t] + except (ValueError, IndexError): + pass + else: + full_edge.add((s, t)) + if border is None: + fill = _color_diff(p, background) <= thresh + else: + fill = p not in (value, border) + if fill: + pixel[s, t] = value + new_edge.add((s, t)) + full_edge = edge # discard pixels processed + edge = new_edge + + +def _compute_regular_polygon_vertices( + bounding_circle: Sequence[Sequence[float] | float], n_sides: int, rotation: float +) -> list[tuple[float, float]]: + """ + Generate a list of vertices for a 2D regular polygon. + + :param bounding_circle: The bounding circle is a sequence defined + by a point and radius. The polygon is inscribed in this circle. + (e.g. ``bounding_circle=(x, y, r)`` or ``((x, y), r)``) + :param n_sides: Number of sides + (e.g. ``n_sides=3`` for a triangle, ``6`` for a hexagon) + :param rotation: Apply an arbitrary rotation to the polygon + (e.g. ``rotation=90``, applies a 90 degree rotation) + :return: List of regular polygon vertices + (e.g. ``[(25, 50), (50, 50), (50, 25), (25, 25)]``) + + How are the vertices computed? + 1. Compute the following variables + - theta: Angle between the apothem & the nearest polygon vertex + - side_length: Length of each polygon edge + - centroid: Center of bounding circle (1st, 2nd elements of bounding_circle) + - polygon_radius: Polygon radius (last element of bounding_circle) + - angles: Location of each polygon vertex in polar grid + (e.g. A square with 0 degree rotation => [225.0, 315.0, 45.0, 135.0]) + + 2. For each angle in angles, get the polygon vertex at that angle + The vertex is computed using the equation below. + X= xcos(φ) + ysin(φ) + Y= −xsin(φ) + ycos(φ) + + Note: + φ = angle in degrees + x = 0 + y = polygon_radius + + The formula above assumes rotation around the origin. + In our case, we are rotating around the centroid. + To account for this, we use the formula below + X = xcos(φ) + ysin(φ) + centroid_x + Y = −xsin(φ) + ycos(φ) + centroid_y + """ + # 1. Error Handling + # 1.1 Check `n_sides` has an appropriate value + if not isinstance(n_sides, int): + msg = "n_sides should be an int" # type: ignore[unreachable] + raise TypeError(msg) + if n_sides < 3: + msg = "n_sides should be an int > 2" + raise ValueError(msg) + + # 1.2 Check `bounding_circle` has an appropriate value + if not isinstance(bounding_circle, (list, tuple)): + msg = "bounding_circle should be a sequence" + raise TypeError(msg) + + if len(bounding_circle) == 3: + if not all(isinstance(i, (int, float)) for i in bounding_circle): + msg = "bounding_circle should only contain numeric data" + raise ValueError(msg) + + *centroid, polygon_radius = cast(list[float], list(bounding_circle)) + elif len(bounding_circle) == 2 and isinstance(bounding_circle[0], (list, tuple)): + if not all( + isinstance(i, (int, float)) for i in bounding_circle[0] + ) or not isinstance(bounding_circle[1], (int, float)): + msg = "bounding_circle should only contain numeric data" + raise ValueError(msg) + + if len(bounding_circle[0]) != 2: + msg = "bounding_circle centre should contain 2D coordinates (e.g. (x, y))" + raise ValueError(msg) + + centroid = cast(list[float], list(bounding_circle[0])) + polygon_radius = cast(float, bounding_circle[1]) + else: + msg = ( + "bounding_circle should contain 2D coordinates " + "and a radius (e.g. (x, y, r) or ((x, y), r) )" + ) + raise ValueError(msg) + + if polygon_radius <= 0: + msg = "bounding_circle radius should be > 0" + raise ValueError(msg) + + # 1.3 Check `rotation` has an appropriate value + if not isinstance(rotation, (int, float)): + msg = "rotation should be an int or float" # type: ignore[unreachable] + raise ValueError(msg) + + # 2. Define Helper Functions + def _apply_rotation(point: list[float], degrees: float) -> tuple[float, float]: + return ( + round( + point[0] * math.cos(math.radians(360 - degrees)) + - point[1] * math.sin(math.radians(360 - degrees)) + + centroid[0], + 2, + ), + round( + point[1] * math.cos(math.radians(360 - degrees)) + + point[0] * math.sin(math.radians(360 - degrees)) + + centroid[1], + 2, + ), + ) + + def _compute_polygon_vertex(angle: float) -> tuple[float, float]: + start_point = [polygon_radius, 0] + return _apply_rotation(start_point, angle) + + def _get_angles(n_sides: int, rotation: float) -> list[float]: + angles = [] + degrees = 360 / n_sides + # Start with the bottom left polygon vertex + current_angle = (270 - 0.5 * degrees) + rotation + for _ in range(n_sides): + angles.append(current_angle) + current_angle += degrees + if current_angle > 360: + current_angle -= 360 + return angles + + # 3. Variable Declarations + angles = _get_angles(n_sides, rotation) + + # 4. Compute Vertices + return [_compute_polygon_vertex(angle) for angle in angles] + + +def _color_diff( + color1: float | tuple[int, ...], color2: float | tuple[int, ...] +) -> float: + """ + Uses 1-norm distance to calculate difference between two values. + """ + first = color1 if isinstance(color1, tuple) else (color1,) + second = color2 if isinstance(color2, tuple) else (color2,) + + return sum(abs(first[i] - second[i]) for i in range(len(second))) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py new file mode 100644 index 0000000000000000000000000000000000000000..3d68658ed5b79a36597e4953b888c41aa82fc7da --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py @@ -0,0 +1,243 @@ +# +# The Python Imaging Library +# $Id$ +# +# WCK-style drawing interface operations +# +# History: +# 2003-12-07 fl created +# 2005-05-15 fl updated; added to PIL as ImageDraw2 +# 2005-05-15 fl added text support +# 2005-05-20 fl added arc/chord/pieslice support +# +# Copyright (c) 2003-2005 by Secret Labs AB +# Copyright (c) 2003-2005 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + + +""" +(Experimental) WCK-style drawing interface operations + +.. seealso:: :py:mod:`PIL.ImageDraw` +""" +from __future__ import annotations + +from typing import Any, AnyStr, BinaryIO + +from . import Image, ImageColor, ImageDraw, ImageFont, ImagePath +from ._typing import Coords, StrOrBytesPath + + +class Pen: + """Stores an outline color and width.""" + + def __init__(self, color: str, width: int = 1, opacity: int = 255) -> None: + self.color = ImageColor.getrgb(color) + self.width = width + + +class Brush: + """Stores a fill color""" + + def __init__(self, color: str, opacity: int = 255) -> None: + self.color = ImageColor.getrgb(color) + + +class Font: + """Stores a TrueType font and color""" + + def __init__( + self, color: str, file: StrOrBytesPath | BinaryIO, size: float = 12 + ) -> None: + # FIXME: add support for bitmap fonts + self.color = ImageColor.getrgb(color) + self.font = ImageFont.truetype(file, size) + + +class Draw: + """ + (Experimental) WCK-style drawing interface + """ + + def __init__( + self, + image: Image.Image | str, + size: tuple[int, int] | list[int] | None = None, + color: float | tuple[float, ...] | str | None = None, + ) -> None: + if isinstance(image, str): + if size is None: + msg = "If image argument is mode string, size must be a list or tuple" + raise ValueError(msg) + image = Image.new(image, size, color) + self.draw = ImageDraw.Draw(image) + self.image = image + self.transform: tuple[float, float, float, float, float, float] | None = None + + def flush(self) -> Image.Image: + return self.image + + def render( + self, + op: str, + xy: Coords, + pen: Pen | Brush | None, + brush: Brush | Pen | None = None, + **kwargs: Any, + ) -> None: + # handle color arguments + outline = fill = None + width = 1 + if isinstance(pen, Pen): + outline = pen.color + width = pen.width + elif isinstance(brush, Pen): + outline = brush.color + width = brush.width + if isinstance(brush, Brush): + fill = brush.color + elif isinstance(pen, Brush): + fill = pen.color + # handle transformation + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + # render the item + if op in ("arc", "line"): + kwargs.setdefault("fill", outline) + else: + kwargs.setdefault("fill", fill) + kwargs.setdefault("outline", outline) + if op == "line": + kwargs.setdefault("width", width) + getattr(self.draw, op)(xy, **kwargs) + + def settransform(self, offset: tuple[float, float]) -> None: + """Sets a transformation offset.""" + (xoffset, yoffset) = offset + self.transform = (1, 0, xoffset, 0, 1, yoffset) + + def arc( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Draws an arc (a portion of a circle outline) between the start and end + angles, inside the given bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.arc` + """ + self.render("arc", xy, pen, *options, start=start, end=end) + + def chord( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Same as :py:meth:`~PIL.ImageDraw2.Draw.arc`, but connects the end points + with a straight line. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.chord` + """ + self.render("chord", xy, pen, *options, start=start, end=end) + + def ellipse(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws an ellipse inside the given bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.ellipse` + """ + self.render("ellipse", xy, pen, *options) + + def line(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a line between the coordinates in the ``xy`` list. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.line` + """ + self.render("line", xy, pen, *options) + + def pieslice( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Same as arc, but also draws straight lines between the end points and the + center of the bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.pieslice` + """ + self.render("pieslice", xy, pen, *options, start=start, end=end) + + def polygon(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a polygon. + + The polygon outline consists of straight lines between the given + coordinates, plus a straight line between the last and the first + coordinate. + + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.polygon` + """ + self.render("polygon", xy, pen, *options) + + def rectangle(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a rectangle. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.rectangle` + """ + self.render("rectangle", xy, pen, *options) + + def text(self, xy: tuple[float, float], text: AnyStr, font: Font) -> None: + """ + Draws the string at the given position. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.text` + """ + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + self.draw.text(xy, text, font=font.font, fill=font.color) + + def textbbox( + self, xy: tuple[float, float], text: AnyStr, font: Font + ) -> tuple[float, float, float, float]: + """ + Returns bounding box (in pixels) of given text. + + :return: ``(left, top, right, bottom)`` bounding box + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.textbbox` + """ + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + return self.draw.textbbox(xy, text, font=font.font) + + def textlength(self, text: AnyStr, font: Font) -> float: + """ + Returns length (in pixels) of given text. + This is the amount by which following text should be offset. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.textlength` + """ + return self.draw.textlength(text, font=font.font) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7e6dd8ae631ad3577bda1d3e823bd2a3227536 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py @@ -0,0 +1,113 @@ +# +# The Python Imaging Library. +# $Id$ +# +# image enhancement classes +# +# For a background, see "Image Processing By Interpolation and +# Extrapolation", Paul Haeberli and Douglas Voorhies. Available +# at http://www.graficaobscura.com/interp/index.html +# +# History: +# 1996-03-23 fl Created +# 2009-06-16 fl Fixed mean calculation +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFilter, ImageStat + + +class _Enhance: + image: Image.Image + degenerate: Image.Image + + def enhance(self, factor: float) -> Image.Image: + """ + Returns an enhanced image. + + :param factor: A floating point value controlling the enhancement. + Factor 1.0 always returns a copy of the original image, + lower factors mean less color (brightness, contrast, + etc), and higher values more. There are no restrictions + on this value. + :rtype: :py:class:`~PIL.Image.Image` + """ + return Image.blend(self.degenerate, self.image, factor) + + +class Color(_Enhance): + """Adjust image color balance. + + This class can be used to adjust the colour balance of an image, in + a manner similar to the controls on a colour TV set. An enhancement + factor of 0.0 gives a black and white image. A factor of 1.0 gives + the original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.intermediate_mode = "L" + if "A" in image.getbands(): + self.intermediate_mode = "LA" + + if self.intermediate_mode != image.mode: + image = image.convert(self.intermediate_mode).convert(image.mode) + self.degenerate = image + + +class Contrast(_Enhance): + """Adjust image contrast. + + This class can be used to control the contrast of an image, similar + to the contrast control on a TV set. An enhancement factor of 0.0 + gives a solid gray image. A factor of 1.0 gives the original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + if image.mode != "L": + image = image.convert("L") + mean = int(ImageStat.Stat(image).mean[0] + 0.5) + self.degenerate = Image.new("L", image.size, mean) + if self.degenerate.mode != self.image.mode: + self.degenerate = self.degenerate.convert(self.image.mode) + + if "A" in self.image.getbands(): + self.degenerate.putalpha(self.image.getchannel("A")) + + +class Brightness(_Enhance): + """Adjust image brightness. + + This class can be used to control the brightness of an image. An + enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the + original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.degenerate = Image.new(image.mode, image.size, 0) + + if "A" in image.getbands(): + self.degenerate.putalpha(image.getchannel("A")) + + +class Sharpness(_Enhance): + """Adjust image sharpness. + + This class can be used to adjust the sharpness of an image. An + enhancement factor of 0.0 gives a blurred image, a factor of 1.0 gives the + original image, and a factor of 2.0 gives a sharpened image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.degenerate = image.filter(ImageFilter.SMOOTH) + + if "A" in image.getbands(): + self.degenerate.putalpha(image.getchannel("A")) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..bf556a2c69036902d4b842cd8f9fa33ee04fa633 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFile.py @@ -0,0 +1,922 @@ +# +# The Python Imaging Library. +# $Id$ +# +# base class for image file handlers +# +# history: +# 1995-09-09 fl Created +# 1996-03-11 fl Fixed load mechanism. +# 1996-04-15 fl Added pcx/xbm decoders. +# 1996-04-30 fl Added encoders. +# 1996-12-14 fl Added load helpers +# 1997-01-11 fl Use encode_to_file where possible +# 1997-08-27 fl Flush output in _save +# 1998-03-05 fl Use memory mapping for some modes +# 1999-02-04 fl Use memory mapping also for "I;16" and "I;16B" +# 1999-05-31 fl Added image parser +# 2000-10-12 fl Set readonly flag on memory-mapped images +# 2002-03-20 fl Use better messages for common decoder errors +# 2003-04-21 fl Fall back on mmap/map_buffer if map is not available +# 2003-10-30 fl Added StubImageFile class +# 2004-02-25 fl Made incremental parser more robust +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1995-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import io +import itertools +import logging +import os +import struct +from typing import IO, Any, NamedTuple, cast + +from . import ExifTags, Image +from ._deprecate import deprecate +from ._util import DeferredError, is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import StrOrBytesPath + +logger = logging.getLogger(__name__) + +MAXBLOCK = 65536 + +SAFEBLOCK = 1024 * 1024 + +LOAD_TRUNCATED_IMAGES = False +"""Whether or not to load truncated image files. User code may change this.""" + +ERRORS = { + -1: "image buffer overrun error", + -2: "decoding error", + -3: "unknown error", + -8: "bad configuration", + -9: "out of memory error", +} +""" +Dict of known error codes returned from :meth:`.PyDecoder.decode`, +:meth:`.PyEncoder.encode` :meth:`.PyEncoder.encode_to_pyfd` and +:meth:`.PyEncoder.encode_to_file`. +""" + + +# +# -------------------------------------------------------------------- +# Helpers + + +def _get_oserror(error: int, *, encoder: bool) -> OSError: + try: + msg = Image.core.getcodecstatus(error) + except AttributeError: + msg = ERRORS.get(error) + if not msg: + msg = f"{'encoder' if encoder else 'decoder'} error {error}" + msg += f" when {'writing' if encoder else 'reading'} image file" + return OSError(msg) + + +def raise_oserror(error: int) -> OSError: + deprecate( + "raise_oserror", + 12, + action="It is only useful for translating error codes returned by a codec's " + "decode() method, which ImageFile already does automatically.", + ) + raise _get_oserror(error, encoder=False) + + +def _tilesort(t: _Tile) -> int: + # sort on offset + return t[2] + + +class _Tile(NamedTuple): + codec_name: str + extents: tuple[int, int, int, int] | None + offset: int = 0 + args: tuple[Any, ...] | str | None = None + + +# +# -------------------------------------------------------------------- +# ImageFile base class + + +class ImageFile(Image.Image): + """Base class for image file format handlers.""" + + def __init__( + self, fp: StrOrBytesPath | IO[bytes], filename: str | bytes | None = None + ) -> None: + super().__init__() + + self._min_frame = 0 + + self.custom_mimetype: str | None = None + + self.tile: list[_Tile] = [] + """ A list of tile descriptors """ + + self.readonly = 1 # until we know better + + self.decoderconfig: tuple[Any, ...] = () + self.decodermaxblock = MAXBLOCK + + if is_path(fp): + # filename + self.fp = open(fp, "rb") + self.filename = os.fspath(fp) + self._exclusive_fp = True + else: + # stream + self.fp = cast(IO[bytes], fp) + self.filename = filename if filename is not None else "" + # can be overridden + self._exclusive_fp = False + + try: + try: + self._open() + except ( + IndexError, # end of data + TypeError, # end of data (ord) + KeyError, # unsupported mode + EOFError, # got header but not the first frame + struct.error, + ) as v: + raise SyntaxError(v) from v + + if not self.mode or self.size[0] <= 0 or self.size[1] <= 0: + msg = "not identified by this driver" + raise SyntaxError(msg) + except BaseException: + # close the file only if we have opened it this constructor + if self._exclusive_fp: + self.fp.close() + raise + + def _open(self) -> None: + pass + + def _close_fp(self): + if getattr(self, "_fp", False) and not isinstance(self._fp, DeferredError): + if self._fp != self.fp: + self._fp.close() + self._fp = DeferredError(ValueError("Operation on closed image")) + if self.fp: + self.fp.close() + + def close(self) -> None: + """ + Closes the file pointer, if possible. + + This operation will destroy the image core and release its memory. + The image data will be unusable afterward. + + This function is required to close images that have multiple frames or + have not had their file read and closed by the + :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for + more information. + """ + try: + self._close_fp() + self.fp = None + except Exception as msg: + logger.debug("Error closing: %s", msg) + + super().close() + + def get_child_images(self) -> list[ImageFile]: + child_images = [] + exif = self.getexif() + ifds = [] + if ExifTags.Base.SubIFDs in exif: + subifd_offsets = exif[ExifTags.Base.SubIFDs] + if subifd_offsets: + if not isinstance(subifd_offsets, tuple): + subifd_offsets = (subifd_offsets,) + for subifd_offset in subifd_offsets: + ifds.append((exif._get_ifd_dict(subifd_offset), subifd_offset)) + ifd1 = exif.get_ifd(ExifTags.IFD.IFD1) + if ifd1 and ifd1.get(ExifTags.Base.JpegIFOffset): + assert exif._info is not None + ifds.append((ifd1, exif._info.next)) + + offset = None + for ifd, ifd_offset in ifds: + assert self.fp is not None + current_offset = self.fp.tell() + if offset is None: + offset = current_offset + + fp = self.fp + if ifd is not None: + thumbnail_offset = ifd.get(ExifTags.Base.JpegIFOffset) + if thumbnail_offset is not None: + thumbnail_offset += getattr(self, "_exif_offset", 0) + self.fp.seek(thumbnail_offset) + + length = ifd.get(ExifTags.Base.JpegIFByteCount) + assert isinstance(length, int) + data = self.fp.read(length) + fp = io.BytesIO(data) + + with Image.open(fp) as im: + from . import TiffImagePlugin + + if thumbnail_offset is None and isinstance( + im, TiffImagePlugin.TiffImageFile + ): + im._frame_pos = [ifd_offset] + im._seek(0) + im.load() + child_images.append(im) + + if offset is not None: + assert self.fp is not None + self.fp.seek(offset) + return child_images + + def get_format_mimetype(self) -> str | None: + if self.custom_mimetype: + return self.custom_mimetype + if self.format is not None: + return Image.MIME.get(self.format.upper()) + return None + + def __getstate__(self) -> list[Any]: + return super().__getstate__() + [self.filename] + + def __setstate__(self, state: list[Any]) -> None: + self.tile = [] + if len(state) > 5: + self.filename = state[5] + super().__setstate__(state) + + def verify(self) -> None: + """Check file integrity""" + + # raise exception if something's wrong. must be called + # directly after open, and closes file when finished. + if self._exclusive_fp: + self.fp.close() + self.fp = None + + def load(self) -> Image.core.PixelAccess | None: + """Load image data based on tile list""" + + if not self.tile and self._im is None: + msg = "cannot load this image" + raise OSError(msg) + + pixel = Image.Image.load(self) + if not self.tile: + return pixel + + self.map: mmap.mmap | None = None + use_mmap = self.filename and len(self.tile) == 1 + + readonly = 0 + + # look for read/seek overrides + if hasattr(self, "load_read"): + read = self.load_read + # don't use mmap if there are custom read/seek functions + use_mmap = False + else: + read = self.fp.read + + if hasattr(self, "load_seek"): + seek = self.load_seek + use_mmap = False + else: + seek = self.fp.seek + + if use_mmap: + # try memory mapping + decoder_name, extents, offset, args = self.tile[0] + if isinstance(args, str): + args = (args, 0, 1) + if ( + decoder_name == "raw" + and isinstance(args, tuple) + and len(args) >= 3 + and args[0] == self.mode + and args[0] in Image._MAPMODES + ): + try: + # use mmap, if possible + import mmap + + with open(self.filename) as fp: + self.map = mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ) + if offset + self.size[1] * args[1] > self.map.size(): + msg = "buffer is not large enough" + raise OSError(msg) + self.im = Image.core.map_buffer( + self.map, self.size, decoder_name, offset, args + ) + readonly = 1 + # After trashing self.im, + # we might need to reload the palette data. + if self.palette: + self.palette.dirty = 1 + except (AttributeError, OSError, ImportError): + self.map = None + + self.load_prepare() + err_code = -3 # initialize to unknown error + if not self.map: + # sort tiles in file order + self.tile.sort(key=_tilesort) + + # FIXME: This is a hack to handle TIFF's JpegTables tag. + prefix = getattr(self, "tile_prefix", b"") + + # Remove consecutive duplicates that only differ by their offset + self.tile = [ + list(tiles)[-1] + for _, tiles in itertools.groupby( + self.tile, lambda tile: (tile[0], tile[1], tile[3]) + ) + ] + for i, (decoder_name, extents, offset, args) in enumerate(self.tile): + seek(offset) + decoder = Image._getdecoder( + self.mode, decoder_name, args, self.decoderconfig + ) + try: + decoder.setimage(self.im, extents) + if decoder.pulls_fd: + decoder.setfd(self.fp) + err_code = decoder.decode(b"")[1] + else: + b = prefix + while True: + read_bytes = self.decodermaxblock + if i + 1 < len(self.tile): + next_offset = self.tile[i + 1].offset + if next_offset > offset: + read_bytes = next_offset - offset + try: + s = read(read_bytes) + except (IndexError, struct.error) as e: + # truncated png/gif + if LOAD_TRUNCATED_IMAGES: + break + else: + msg = "image file is truncated" + raise OSError(msg) from e + + if not s: # truncated jpeg + if LOAD_TRUNCATED_IMAGES: + break + else: + msg = ( + "image file is truncated " + f"({len(b)} bytes not processed)" + ) + raise OSError(msg) + + b = b + s + n, err_code = decoder.decode(b) + if n < 0: + break + b = b[n:] + finally: + # Need to cleanup here to prevent leaks + decoder.cleanup() + + self.tile = [] + self.readonly = readonly + + self.load_end() + + if self._exclusive_fp and self._close_exclusive_fp_after_loading: + self.fp.close() + self.fp = None + + if not self.map and not LOAD_TRUNCATED_IMAGES and err_code < 0: + # still raised if decoder fails to return anything + raise _get_oserror(err_code, encoder=False) + + return Image.Image.load(self) + + def load_prepare(self) -> None: + # create image memory if necessary + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + # create palette (optional) + if self.mode == "P": + Image.Image.load(self) + + def load_end(self) -> None: + # may be overridden + pass + + # may be defined for contained formats + # def load_seek(self, pos: int) -> None: + # pass + + # may be defined for blocked formats (e.g. PNG) + # def load_read(self, read_bytes: int) -> bytes: + # pass + + def _seek_check(self, frame: int) -> bool: + if ( + frame < self._min_frame + # Only check upper limit on frames if additional seek operations + # are not required to do so + or ( + not (hasattr(self, "_n_frames") and self._n_frames is None) + and frame >= getattr(self, "n_frames") + self._min_frame + ) + ): + msg = "attempt to seek outside sequence" + raise EOFError(msg) + + return self.tell() != frame + + +class StubHandler(abc.ABC): + def open(self, im: StubImageFile) -> None: + pass + + @abc.abstractmethod + def load(self, im: StubImageFile) -> Image.Image: + pass + + +class StubImageFile(ImageFile, metaclass=abc.ABCMeta): + """ + Base class for stub image loaders. + + A stub loader is an image loader that can identify files of a + certain format, but relies on external code to load the file. + """ + + @abc.abstractmethod + def _open(self) -> None: + pass + + def load(self) -> Image.core.PixelAccess | None: + loader = self._load() + if loader is None: + msg = f"cannot find loader for this {self.format} file" + raise OSError(msg) + image = loader.load(self) + assert image is not None + # become the other object (!) + self.__class__ = image.__class__ # type: ignore[assignment] + self.__dict__ = image.__dict__ + return image.load() + + @abc.abstractmethod + def _load(self) -> StubHandler | None: + """(Hook) Find actual image loader.""" + pass + + +class Parser: + """ + Incremental image parser. This class implements the standard + feed/close consumer interface. + """ + + incremental = None + image: Image.Image | None = None + data: bytes | None = None + decoder: Image.core.ImagingDecoder | PyDecoder | None = None + offset = 0 + finished = 0 + + def reset(self) -> None: + """ + (Consumer) Reset the parser. Note that you can only call this + method immediately after you've created a parser; parser + instances cannot be reused. + """ + assert self.data is None, "cannot reuse parsers" + + def feed(self, data: bytes) -> None: + """ + (Consumer) Feed data to the parser. + + :param data: A string buffer. + :exception OSError: If the parser failed to parse the image file. + """ + # collect data + + if self.finished: + return + + if self.data is None: + self.data = data + else: + self.data = self.data + data + + # parse what we have + if self.decoder: + if self.offset > 0: + # skip header + skip = min(len(self.data), self.offset) + self.data = self.data[skip:] + self.offset = self.offset - skip + if self.offset > 0 or not self.data: + return + + n, e = self.decoder.decode(self.data) + + if n < 0: + # end of stream + self.data = None + self.finished = 1 + if e < 0: + # decoding error + self.image = None + raise _get_oserror(e, encoder=False) + else: + # end of image + return + self.data = self.data[n:] + + elif self.image: + # if we end up here with no decoder, this file cannot + # be incrementally parsed. wait until we've gotten all + # available data + pass + + else: + # attempt to open this file + try: + with io.BytesIO(self.data) as fp: + im = Image.open(fp) + except OSError: + pass # not enough data + else: + flag = hasattr(im, "load_seek") or hasattr(im, "load_read") + if flag or len(im.tile) != 1: + # custom load code, or multiple tiles + self.decode = None + else: + # initialize decoder + im.load_prepare() + d, e, o, a = im.tile[0] + im.tile = [] + self.decoder = Image._getdecoder(im.mode, d, a, im.decoderconfig) + self.decoder.setimage(im.im, e) + + # calculate decoder offset + self.offset = o + if self.offset <= len(self.data): + self.data = self.data[self.offset :] + self.offset = 0 + + self.image = im + + def __enter__(self) -> Parser: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def close(self) -> Image.Image: + """ + (Consumer) Close the stream. + + :returns: An image object. + :exception OSError: If the parser failed to parse the image file either + because it cannot be identified or cannot be + decoded. + """ + # finish decoding + if self.decoder: + # get rid of what's left in the buffers + self.feed(b"") + self.data = self.decoder = None + if not self.finished: + msg = "image was incomplete" + raise OSError(msg) + if not self.image: + msg = "cannot parse this image" + raise OSError(msg) + if self.data: + # incremental parsing not possible; reopen the file + # not that we have all data + with io.BytesIO(self.data) as fp: + try: + self.image = Image.open(fp) + finally: + self.image.load() + return self.image + + +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], tile: list[_Tile], bufsize: int = 0) -> None: + """Helper to save image based on tile list + + :param im: Image object. + :param fp: File object. + :param tile: Tile list. + :param bufsize: Optional buffer size + """ + + im.load() + if not hasattr(im, "encoderconfig"): + im.encoderconfig = () + tile.sort(key=_tilesort) + # FIXME: make MAXBLOCK a configuration parameter + # It would be great if we could have the encoder specify what it needs + # But, it would need at least the image size in most cases. RawEncode is + # a tricky case. + bufsize = max(MAXBLOCK, bufsize, im.size[0] * 4) # see RawEncode.c + try: + fh = fp.fileno() + fp.flush() + _encode_tile(im, fp, tile, bufsize, fh) + except (AttributeError, io.UnsupportedOperation) as exc: + _encode_tile(im, fp, tile, bufsize, None, exc) + if hasattr(fp, "flush"): + fp.flush() + + +def _encode_tile( + im: Image.Image, + fp: IO[bytes], + tile: list[_Tile], + bufsize: int, + fh: int | None, + exc: BaseException | None = None, +) -> None: + for encoder_name, extents, offset, args in tile: + if offset > 0: + fp.seek(offset) + encoder = Image._getencoder(im.mode, encoder_name, args, im.encoderconfig) + try: + encoder.setimage(im.im, extents) + if encoder.pushes_fd: + encoder.setfd(fp) + errcode = encoder.encode_to_pyfd()[1] + else: + if exc: + # compress to Python file-compatible object + while True: + errcode, data = encoder.encode(bufsize)[1:] + fp.write(data) + if errcode: + break + else: + # slight speedup: compress to real file object + assert fh is not None + errcode = encoder.encode_to_file(fh, bufsize) + if errcode < 0: + raise _get_oserror(errcode, encoder=True) from exc + finally: + encoder.cleanup() + + +def _safe_read(fp: IO[bytes], size: int) -> bytes: + """ + Reads large blocks in a safe way. Unlike fp.read(n), this function + doesn't trust the user. If the requested size is larger than + SAFEBLOCK, the file is read block by block. + + :param fp: File handle. Must implement a read method. + :param size: Number of bytes to read. + :returns: A string containing size bytes of data. + + Raises an OSError if the file is truncated and the read cannot be completed + + """ + if size <= 0: + return b"" + if size <= SAFEBLOCK: + data = fp.read(size) + if len(data) < size: + msg = "Truncated File Read" + raise OSError(msg) + return data + blocks: list[bytes] = [] + remaining_size = size + while remaining_size > 0: + block = fp.read(min(remaining_size, SAFEBLOCK)) + if not block: + break + blocks.append(block) + remaining_size -= len(block) + if sum(len(block) for block in blocks) < size: + msg = "Truncated File Read" + raise OSError(msg) + return b"".join(blocks) + + +class PyCodecState: + def __init__(self) -> None: + self.xsize = 0 + self.ysize = 0 + self.xoff = 0 + self.yoff = 0 + + def extents(self) -> tuple[int, int, int, int]: + return self.xoff, self.yoff, self.xoff + self.xsize, self.yoff + self.ysize + + +class PyCodec: + fd: IO[bytes] | None + + def __init__(self, mode: str, *args: Any) -> None: + self.im: Image.core.ImagingCore | None = None + self.state = PyCodecState() + self.fd = None + self.mode = mode + self.init(args) + + def init(self, args: tuple[Any, ...]) -> None: + """ + Override to perform codec specific initialization + + :param args: Tuple of arg items from the tile entry + :returns: None + """ + self.args = args + + def cleanup(self) -> None: + """ + Override to perform codec specific cleanup + + :returns: None + """ + pass + + def setfd(self, fd: IO[bytes]) -> None: + """ + Called from ImageFile to set the Python file-like object + + :param fd: A Python file-like object + :returns: None + """ + self.fd = fd + + def setimage( + self, + im: Image.core.ImagingCore, + extents: tuple[int, int, int, int] | None = None, + ) -> None: + """ + Called from ImageFile to set the core output image for the codec + + :param im: A core image object + :param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle + for this tile + :returns: None + """ + + # following c code + self.im = im + + if extents: + (x0, y0, x1, y1) = extents + else: + (x0, y0, x1, y1) = (0, 0, 0, 0) + + if x0 == 0 and x1 == 0: + self.state.xsize, self.state.ysize = self.im.size + else: + self.state.xoff = x0 + self.state.yoff = y0 + self.state.xsize = x1 - x0 + self.state.ysize = y1 - y0 + + if self.state.xsize <= 0 or self.state.ysize <= 0: + msg = "Size cannot be negative" + raise ValueError(msg) + + if ( + self.state.xsize + self.state.xoff > self.im.size[0] + or self.state.ysize + self.state.yoff > self.im.size[1] + ): + msg = "Tile cannot extend outside image" + raise ValueError(msg) + + +class PyDecoder(PyCodec): + """ + Python implementation of a format decoder. Override this class and + add the decoding logic in the :meth:`decode` method. + + See :ref:`Writing Your Own File Codec in Python` + """ + + _pulls_fd = False + + @property + def pulls_fd(self) -> bool: + return self._pulls_fd + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + """ + Override to perform the decoding process. + + :param buffer: A bytes object with the data to be decoded. + :returns: A tuple of ``(bytes consumed, errcode)``. + If finished with decoding return -1 for the bytes consumed. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + msg = "unavailable in base decoder" + raise NotImplementedError(msg) + + def set_as_raw( + self, data: bytes, rawmode: str | None = None, extra: tuple[Any, ...] = () + ) -> None: + """ + Convenience method to set the internal image from a stream of raw data + + :param data: Bytes to be set + :param rawmode: The rawmode to be used for the decoder. + If not specified, it will default to the mode of the image + :param extra: Extra arguments for the decoder. + :returns: None + """ + + if not rawmode: + rawmode = self.mode + d = Image._getdecoder(self.mode, "raw", rawmode, extra) + assert self.im is not None + d.setimage(self.im, self.state.extents()) + s = d.decode(data) + + if s[0] >= 0: + msg = "not enough image data" + raise ValueError(msg) + if s[1] != 0: + msg = "cannot decode image data" + raise ValueError(msg) + + +class PyEncoder(PyCodec): + """ + Python implementation of a format encoder. Override this class and + add the decoding logic in the :meth:`encode` method. + + See :ref:`Writing Your Own File Codec in Python` + """ + + _pushes_fd = False + + @property + def pushes_fd(self) -> bool: + return self._pushes_fd + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + """ + Override to perform the encoding process. + + :param bufsize: Buffer size. + :returns: A tuple of ``(bytes encoded, errcode, bytes)``. + If finished with encoding return 1 for the error code. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + msg = "unavailable in base encoder" + raise NotImplementedError(msg) + + def encode_to_pyfd(self) -> tuple[int, int]: + """ + If ``pushes_fd`` is ``True``, then this method will be used, + and ``encode()`` will only be called once. + + :returns: A tuple of ``(bytes consumed, errcode)``. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + if not self.pushes_fd: + return 0, -8 # bad configuration + bytes_consumed, errcode, data = self.encode(0) + if data: + assert self.fd is not None + self.fd.write(data) + return bytes_consumed, errcode + + def encode_to_file(self, fh: int, bufsize: int) -> int: + """ + :param fh: File handle. + :param bufsize: Buffer size. + + :returns: If finished successfully, return 0. + Otherwise, return an error code. Err codes are from + :data:`.ImageFile.ERRORS`. + """ + errcode = 0 + while errcode == 0: + status, errcode, buf = self.encode(bufsize) + if status > 0: + os.write(fh, buf[status:]) + return errcode diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ed54ab20a132aa9d2aec894dd846736923f70e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py @@ -0,0 +1,604 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard filters +# +# History: +# 1995-11-27 fl Created +# 2002-06-08 fl Added rank and mode filters +# 2003-09-15 fl Fixed rank calculation in rank filter; added expand call +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2002 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import functools +from collections.abc import Sequence +from types import ModuleType +from typing import Any, Callable, cast + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + from ._typing import NumpyArray + + +class Filter(abc.ABC): + @abc.abstractmethod + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + pass + + +class MultibandFilter(Filter): + pass + + +class BuiltinFilter(MultibandFilter): + filterargs: tuple[Any, ...] + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + if image.mode == "P": + msg = "cannot filter palette images" + raise ValueError(msg) + return image.filter(*self.filterargs) + + +class Kernel(BuiltinFilter): + """ + Create a convolution kernel. This only supports 3x3 and 5x5 integer and floating + point kernels. + + Kernels can only be applied to "L" and "RGB" images. + + :param size: Kernel size, given as (width, height). This must be (3,3) or (5,5). + :param kernel: A sequence containing kernel weights. The kernel will be flipped + vertically before being applied to the image. + :param scale: Scale factor. If given, the result for each pixel is divided by this + value. The default is the sum of the kernel weights. + :param offset: Offset. If given, this value is added to the result, after it has + been divided by the scale factor. + """ + + name = "Kernel" + + def __init__( + self, + size: tuple[int, int], + kernel: Sequence[float], + scale: float | None = None, + offset: float = 0, + ) -> None: + if scale is None: + # default scale is sum of kernel + scale = functools.reduce(lambda a, b: a + b, kernel) + if size[0] * size[1] != len(kernel): + msg = "not enough coefficients in kernel" + raise ValueError(msg) + self.filterargs = size, scale, offset, kernel + + +class RankFilter(Filter): + """ + Create a rank filter. The rank filter sorts all pixels in + a window of the given size, and returns the ``rank``'th value. + + :param size: The kernel size, in pixels. + :param rank: What pixel value to pick. Use 0 for a min filter, + ``size * size / 2`` for a median filter, ``size * size - 1`` + for a max filter, etc. + """ + + name = "Rank" + + def __init__(self, size: int, rank: int) -> None: + self.size = size + self.rank = rank + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + if image.mode == "P": + msg = "cannot filter palette images" + raise ValueError(msg) + image = image.expand(self.size // 2, self.size // 2) + return image.rankfilter(self.size, self.rank) + + +class MedianFilter(RankFilter): + """ + Create a median filter. Picks the median pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Median" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = size * size // 2 + + +class MinFilter(RankFilter): + """ + Create a min filter. Picks the lowest pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Min" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = 0 + + +class MaxFilter(RankFilter): + """ + Create a max filter. Picks the largest pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Max" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = size * size - 1 + + +class ModeFilter(Filter): + """ + Create a mode filter. Picks the most frequent pixel value in a box with the + given size. Pixel values that occur only once or twice are ignored; if no + pixel value occurs more than twice, the original pixel value is preserved. + + :param size: The kernel size, in pixels. + """ + + name = "Mode" + + def __init__(self, size: int = 3) -> None: + self.size = size + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + return image.modefilter(self.size) + + +class GaussianBlur(MultibandFilter): + """Blurs the image with a sequence of extended box filters, which + approximates a Gaussian kernel. For details on accuracy see + + + :param radius: Standard deviation of the Gaussian kernel. Either a sequence of two + numbers for x and y, or a single number for both. + """ + + name = "GaussianBlur" + + def __init__(self, radius: float | Sequence[float] = 2) -> None: + self.radius = radius + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + xy = self.radius + if isinstance(xy, (int, float)): + xy = (xy, xy) + if xy == (0, 0): + return image.copy() + return image.gaussian_blur(xy) + + +class BoxBlur(MultibandFilter): + """Blurs the image by setting each pixel to the average value of the pixels + in a square box extending radius pixels in each direction. + Supports float radius of arbitrary size. Uses an optimized implementation + which runs in linear time relative to the size of the image + for any radius value. + + :param radius: Size of the box in a direction. Either a sequence of two numbers for + x and y, or a single number for both. + + Radius 0 does not blur, returns an identical image. + Radius 1 takes 1 pixel in each direction, i.e. 9 pixels in total. + """ + + name = "BoxBlur" + + def __init__(self, radius: float | Sequence[float]) -> None: + xy = radius if isinstance(radius, (tuple, list)) else (radius, radius) + if xy[0] < 0 or xy[1] < 0: + msg = "radius must be >= 0" + raise ValueError(msg) + self.radius = radius + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + xy = self.radius + if isinstance(xy, (int, float)): + xy = (xy, xy) + if xy == (0, 0): + return image.copy() + return image.box_blur(xy) + + +class UnsharpMask(MultibandFilter): + """Unsharp mask filter. + + See Wikipedia's entry on `digital unsharp masking`_ for an explanation of + the parameters. + + :param radius: Blur Radius + :param percent: Unsharp strength, in percent + :param threshold: Threshold controls the minimum brightness change that + will be sharpened + + .. _digital unsharp masking: https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking + + """ + + name = "UnsharpMask" + + def __init__( + self, radius: float = 2, percent: int = 150, threshold: int = 3 + ) -> None: + self.radius = radius + self.percent = percent + self.threshold = threshold + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + return image.unsharp_mask(self.radius, self.percent, self.threshold) + + +class BLUR(BuiltinFilter): + name = "Blur" + # fmt: off + filterargs = (5, 5), 16, 0, ( + 1, 1, 1, 1, 1, + 1, 0, 0, 0, 1, + 1, 0, 0, 0, 1, + 1, 0, 0, 0, 1, + 1, 1, 1, 1, 1, + ) + # fmt: on + + +class CONTOUR(BuiltinFilter): + name = "Contour" + # fmt: off + filterargs = (3, 3), 1, 255, ( + -1, -1, -1, + -1, 8, -1, + -1, -1, -1, + ) + # fmt: on + + +class DETAIL(BuiltinFilter): + name = "Detail" + # fmt: off + filterargs = (3, 3), 6, 0, ( + 0, -1, 0, + -1, 10, -1, + 0, -1, 0, + ) + # fmt: on + + +class EDGE_ENHANCE(BuiltinFilter): + name = "Edge-enhance" + # fmt: off + filterargs = (3, 3), 2, 0, ( + -1, -1, -1, + -1, 10, -1, + -1, -1, -1, + ) + # fmt: on + + +class EDGE_ENHANCE_MORE(BuiltinFilter): + name = "Edge-enhance More" + # fmt: off + filterargs = (3, 3), 1, 0, ( + -1, -1, -1, + -1, 9, -1, + -1, -1, -1, + ) + # fmt: on + + +class EMBOSS(BuiltinFilter): + name = "Emboss" + # fmt: off + filterargs = (3, 3), 1, 128, ( + -1, 0, 0, + 0, 1, 0, + 0, 0, 0, + ) + # fmt: on + + +class FIND_EDGES(BuiltinFilter): + name = "Find Edges" + # fmt: off + filterargs = (3, 3), 1, 0, ( + -1, -1, -1, + -1, 8, -1, + -1, -1, -1, + ) + # fmt: on + + +class SHARPEN(BuiltinFilter): + name = "Sharpen" + # fmt: off + filterargs = (3, 3), 16, 0, ( + -2, -2, -2, + -2, 32, -2, + -2, -2, -2, + ) + # fmt: on + + +class SMOOTH(BuiltinFilter): + name = "Smooth" + # fmt: off + filterargs = (3, 3), 13, 0, ( + 1, 1, 1, + 1, 5, 1, + 1, 1, 1, + ) + # fmt: on + + +class SMOOTH_MORE(BuiltinFilter): + name = "Smooth More" + # fmt: off + filterargs = (5, 5), 100, 0, ( + 1, 1, 1, 1, 1, + 1, 5, 5, 5, 1, + 1, 5, 44, 5, 1, + 1, 5, 5, 5, 1, + 1, 1, 1, 1, 1, + ) + # fmt: on + + +class Color3DLUT(MultibandFilter): + """Three-dimensional color lookup table. + + Transforms 3-channel pixels using the values of the channels as coordinates + in the 3D lookup table and interpolating the nearest elements. + + This method allows you to apply almost any color transformation + in constant time by using pre-calculated decimated tables. + + .. versionadded:: 5.2.0 + + :param size: Size of the table. One int or tuple of (int, int, int). + Minimal size in any dimension is 2, maximum is 65. + :param table: Flat lookup table. A list of ``channels * size**3`` + float elements or a list of ``size**3`` channels-sized + tuples with floats. Channels are changed first, + then first dimension, then second, then third. + Value 0.0 corresponds lowest value of output, 1.0 highest. + :param channels: Number of channels in the table. Could be 3 or 4. + Default is 3. + :param target_mode: A mode for the result image. Should have not less + than ``channels`` channels. Default is ``None``, + which means that mode wouldn't be changed. + """ + + name = "Color 3D LUT" + + def __init__( + self, + size: int | tuple[int, int, int], + table: Sequence[float] | Sequence[Sequence[int]] | NumpyArray, + channels: int = 3, + target_mode: str | None = None, + **kwargs: bool, + ) -> None: + if channels not in (3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + self.size = size = self._check_size(size) + self.channels = channels + self.mode = target_mode + + # Hidden flag `_copy_table=False` could be used to avoid extra copying + # of the table if the table is specially made for the constructor. + copy_table = kwargs.get("_copy_table", True) + items = size[0] * size[1] * size[2] + wrong_size = False + + numpy: ModuleType | None = None + if hasattr(table, "shape"): + try: + import numpy + except ImportError: + pass + + if numpy and isinstance(table, numpy.ndarray): + numpy_table: NumpyArray = table + if copy_table: + numpy_table = numpy_table.copy() + + if numpy_table.shape in [ + (items * channels,), + (items, channels), + (size[2], size[1], size[0], channels), + ]: + table = numpy_table.reshape(items * channels) + else: + wrong_size = True + + else: + if copy_table: + table = list(table) + + # Convert to a flat list + if table and isinstance(table[0], (list, tuple)): + raw_table = cast(Sequence[Sequence[int]], table) + flat_table: list[int] = [] + for pixel in raw_table: + if len(pixel) != channels: + msg = ( + "The elements of the table should " + f"have a length of {channels}." + ) + raise ValueError(msg) + flat_table.extend(pixel) + table = flat_table + + if wrong_size or len(table) != items * channels: + msg = ( + "The table should have either channels * size**3 float items " + "or size**3 items of channels-sized tuples with floats. " + f"Table should be: {channels}x{size[0]}x{size[1]}x{size[2]}. " + f"Actual length: {len(table)}" + ) + raise ValueError(msg) + self.table = table + + @staticmethod + def _check_size(size: Any) -> tuple[int, int, int]: + try: + _, _, _ = size + except ValueError as e: + msg = "Size should be either an integer or a tuple of three integers." + raise ValueError(msg) from e + except TypeError: + size = (size, size, size) + size = tuple(int(x) for x in size) + for size_1d in size: + if not 2 <= size_1d <= 65: + msg = "Size should be in [2, 65] range." + raise ValueError(msg) + return size + + @classmethod + def generate( + cls, + size: int | tuple[int, int, int], + callback: Callable[[float, float, float], tuple[float, ...]], + channels: int = 3, + target_mode: str | None = None, + ) -> Color3DLUT: + """Generates new LUT using provided callback. + + :param size: Size of the table. Passed to the constructor. + :param callback: Function with three parameters which correspond + three color channels. Will be called ``size**3`` + times with values from 0.0 to 1.0 and should return + a tuple with ``channels`` elements. + :param channels: The number of channels which should return callback. + :param target_mode: Passed to the constructor of the resulting + lookup table. + """ + size_1d, size_2d, size_3d = cls._check_size(size) + if channels not in (3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + + table: list[float] = [0] * (size_1d * size_2d * size_3d * channels) + idx_out = 0 + for b in range(size_3d): + for g in range(size_2d): + for r in range(size_1d): + table[idx_out : idx_out + channels] = callback( + r / (size_1d - 1), g / (size_2d - 1), b / (size_3d - 1) + ) + idx_out += channels + + return cls( + (size_1d, size_2d, size_3d), + table, + channels=channels, + target_mode=target_mode, + _copy_table=False, + ) + + def transform( + self, + callback: Callable[..., tuple[float, ...]], + with_normals: bool = False, + channels: int | None = None, + target_mode: str | None = None, + ) -> Color3DLUT: + """Transforms the table values using provided callback and returns + a new LUT with altered values. + + :param callback: A function which takes old lookup table values + and returns a new set of values. The number + of arguments which function should take is + ``self.channels`` or ``3 + self.channels`` + if ``with_normals`` flag is set. + Should return a tuple of ``self.channels`` or + ``channels`` elements if it is set. + :param with_normals: If true, ``callback`` will be called with + coordinates in the color cube as the first + three arguments. Otherwise, ``callback`` + will be called only with actual color values. + :param channels: The number of channels in the resulting lookup table. + :param target_mode: Passed to the constructor of the resulting + lookup table. + """ + if channels not in (None, 3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + ch_in = self.channels + ch_out = channels or ch_in + size_1d, size_2d, size_3d = self.size + + table: list[float] = [0] * (size_1d * size_2d * size_3d * ch_out) + idx_in = 0 + idx_out = 0 + for b in range(size_3d): + for g in range(size_2d): + for r in range(size_1d): + values = self.table[idx_in : idx_in + ch_in] + if with_normals: + values = callback( + r / (size_1d - 1), + g / (size_2d - 1), + b / (size_3d - 1), + *values, + ) + else: + values = callback(*values) + table[idx_out : idx_out + ch_out] = values + idx_in += ch_in + idx_out += ch_out + + return type(self)( + self.size, + table, + channels=ch_out, + target_mode=target_mode or self.mode, + _copy_table=False, + ) + + def __repr__(self) -> str: + r = [ + f"{self.__class__.__name__} from {self.table.__class__.__name__}", + "size={:d}x{:d}x{:d}".format(*self.size), + f"channels={self.channels:d}", + ] + if self.mode: + r.append(f"target_mode={self.mode}") + return "<{}>".format(" ".join(r)) + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + from . import Image + + return image.color_lut_3d( + self.mode or image.mode, + Image.Resampling.BILINEAR, + self.channels, + self.size, + self.table, + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFont.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFont.py new file mode 100644 index 0000000000000000000000000000000000000000..329c463ff864191849506bd61f7c0559af671f8f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageFont.py @@ -0,0 +1,1339 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PIL raster font management +# +# History: +# 1996-08-07 fl created (experimental) +# 1997-08-25 fl minor adjustments to handle fonts from pilfont 0.3 +# 1999-02-06 fl rewrote most font management stuff in C +# 1999-03-17 fl take pth files into account in load_path (from Richard Jones) +# 2001-02-17 fl added freetype support +# 2001-05-09 fl added TransposedFont wrapper class +# 2002-03-04 fl make sure we have a "L" or "1" font +# 2002-12-04 fl skip non-directory entries in the system path +# 2003-04-29 fl add embedded default font +# 2003-09-27 fl added support for truetype charmap encodings +# +# Todo: +# Adapt to PILFONT2 format (16-bit fonts, compressed, single file) +# +# Copyright (c) 1997-2003 by Secret Labs AB +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +import base64 +import os +import sys +import warnings +from enum import IntEnum +from io import BytesIO +from types import ModuleType +from typing import IO, Any, BinaryIO, TypedDict, cast + +from . import Image, features +from ._typing import StrOrBytesPath +from ._util import DeferredError, is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageFile + from ._imaging import ImagingFont + from ._imagingft import Font + + +class Axis(TypedDict): + minimum: int | None + default: int | None + maximum: int | None + name: bytes | None + + +class Layout(IntEnum): + BASIC = 0 + RAQM = 1 + + +MAX_STRING_LENGTH = 1_000_000 + + +core: ModuleType | DeferredError +try: + from . import _imagingft as core +except ImportError as ex: + core = DeferredError.new(ex) + + +def _string_length_check(text: str | bytes | bytearray) -> None: + if MAX_STRING_LENGTH is not None and len(text) > MAX_STRING_LENGTH: + msg = "too many characters in string" + raise ValueError(msg) + + +# FIXME: add support for pilfont2 format (see FontFile.py) + +# -------------------------------------------------------------------- +# Font metrics format: +# "PILfont" LF +# fontdescriptor LF +# (optional) key=value... LF +# "DATA" LF +# binary data: 256*10*2 bytes (dx, dy, dstbox, srcbox) +# +# To place a character, cut out srcbox and paste at dstbox, +# relative to the character position. Then move the character +# position according to dx, dy. +# -------------------------------------------------------------------- + + +class ImageFont: + """PIL font wrapper""" + + font: ImagingFont + + def _load_pilfont(self, filename: str) -> None: + with open(filename, "rb") as fp: + image: ImageFile.ImageFile | None = None + root = os.path.splitext(filename)[0] + + for ext in (".png", ".gif", ".pbm"): + if image: + image.close() + try: + fullname = root + ext + image = Image.open(fullname) + except Exception: + pass + else: + if image and image.mode in ("1", "L"): + break + else: + if image: + image.close() + + msg = f"cannot find glyph data file {root}.{{gif|pbm|png}}" + raise OSError(msg) + + self.file = fullname + + self._load_pilfont_data(fp, image) + image.close() + + def _load_pilfont_data(self, file: IO[bytes], image: Image.Image) -> None: + # read PILfont header + if file.readline() != b"PILfont\n": + msg = "Not a PILfont file" + raise SyntaxError(msg) + file.readline().split(b";") + self.info = [] # FIXME: should be a dictionary + while True: + s = file.readline() + if not s or s == b"DATA\n": + break + self.info.append(s) + + # read PILfont metrics + data = file.read(256 * 20) + + # check image + if image.mode not in ("1", "L"): + msg = "invalid font image mode" + raise TypeError(msg) + + image.load() + + self.font = Image.core.font(image.im, data) + + def getmask( + self, text: str | bytes, mode: str = "", *args: Any, **kwargs: Any + ) -> Image.core.ImagingCore: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :return: An internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module. + """ + _string_length_check(text) + Image._decompression_bomb_check(self.font.getsize(text)) + return self.font.getmask(text, mode) + + def getbbox( + self, text: str | bytes | bytearray, *args: Any, **kwargs: Any + ) -> tuple[int, int, int, int]: + """ + Returns bounding box (in pixels) of given text. + + .. versionadded:: 9.2.0 + + :param text: Text to render. + + :return: ``(left, top, right, bottom)`` bounding box + """ + _string_length_check(text) + width, height = self.font.getsize(text) + return 0, 0, width, height + + def getlength( + self, text: str | bytes | bytearray, *args: Any, **kwargs: Any + ) -> int: + """ + Returns length (in pixels) of given text. + This is the amount by which following text should be offset. + + .. versionadded:: 9.2.0 + """ + _string_length_check(text) + width, height = self.font.getsize(text) + return width + + +## +# Wrapper for FreeType fonts. Application code should use the +# truetype factory function to create font objects. + + +class FreeTypeFont: + """FreeType font wrapper (requires _imagingft service)""" + + font: Font + font_bytes: bytes + + def __init__( + self, + font: StrOrBytesPath | BinaryIO, + size: float = 10, + index: int = 0, + encoding: str = "", + layout_engine: Layout | None = None, + ) -> None: + # FIXME: use service provider instead + + if isinstance(core, DeferredError): + raise core.ex + + if size <= 0: + msg = f"font size must be greater than 0, not {size}" + raise ValueError(msg) + + self.path = font + self.size = size + self.index = index + self.encoding = encoding + + try: + from packaging.version import parse as parse_version + except ImportError: + pass + else: + if freetype_version := features.version_module("freetype2"): + if parse_version(freetype_version) < parse_version("2.9.1"): + warnings.warn( + "Support for FreeType 2.9.0 is deprecated and will be removed " + "in Pillow 12 (2025-10-15). Please upgrade to FreeType 2.9.1 " + "or newer, preferably FreeType 2.10.4 which fixes " + "CVE-2020-15999.", + DeprecationWarning, + ) + + if layout_engine not in (Layout.BASIC, Layout.RAQM): + layout_engine = Layout.BASIC + if core.HAVE_RAQM: + layout_engine = Layout.RAQM + elif layout_engine == Layout.RAQM and not core.HAVE_RAQM: + warnings.warn( + "Raqm layout was requested, but Raqm is not available. " + "Falling back to basic layout." + ) + layout_engine = Layout.BASIC + + self.layout_engine = layout_engine + + def load_from_bytes(f: IO[bytes]) -> None: + self.font_bytes = f.read() + self.font = core.getfont( + "", size, index, encoding, self.font_bytes, layout_engine + ) + + if is_path(font): + font = os.fspath(font) + if sys.platform == "win32": + font_bytes_path = font if isinstance(font, bytes) else font.encode() + try: + font_bytes_path.decode("ascii") + except UnicodeDecodeError: + # FreeType cannot load fonts with non-ASCII characters on Windows + # So load it into memory first + with open(font, "rb") as f: + load_from_bytes(f) + return + self.font = core.getfont( + font, size, index, encoding, layout_engine=layout_engine + ) + else: + load_from_bytes(cast(IO[bytes], font)) + + def __getstate__(self) -> list[Any]: + return [self.path, self.size, self.index, self.encoding, self.layout_engine] + + def __setstate__(self, state: list[Any]) -> None: + path, size, index, encoding, layout_engine = state + FreeTypeFont.__init__(self, path, size, index, encoding, layout_engine) + + def getname(self) -> tuple[str | None, str | None]: + """ + :return: A tuple of the font family (e.g. Helvetica) and the font style + (e.g. Bold) + """ + return self.font.family, self.font.style + + def getmetrics(self) -> tuple[int, int]: + """ + :return: A tuple of the font ascent (the distance from the baseline to + the highest outline point) and descent (the distance from the + baseline to the lowest outline point, a negative value) + """ + return self.font.ascent, self.font.descent + + def getlength( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + ) -> float: + """ + Returns length (in pixels with 1/64 precision) of given text when rendered + in font with provided direction, features, and language. + + This is the amount by which following text should be offset. + Text bounding box may extend past the length in some fonts, + e.g. when using italics or accents. + + The result is returned as a float; it is a whole number if using basic layout. + + Note that the sum of two lengths may not equal the length of a concatenated + string due to kerning. If you need to adjust for kerning, include the following + character and subtract its length. + + For example, instead of :: + + hello = font.getlength("Hello") + world = font.getlength("World") + hello_world = hello + world # not adjusted for kerning + assert hello_world == font.getlength("HelloWorld") # may fail + + use :: + + hello = font.getlength("HelloW") - font.getlength("W") # adjusted for kerning + world = font.getlength("World") + hello_world = hello + world # adjusted for kerning + assert hello_world == font.getlength("HelloWorld") # True + + or disable kerning with (requires libraqm) :: + + hello = draw.textlength("Hello", font, features=["-kern"]) + world = draw.textlength("World", font, features=["-kern"]) + hello_world = hello + world # kerning is disabled, no need to adjust + assert hello_world == draw.textlength("HelloWorld", font, features=["-kern"]) + + .. versionadded:: 8.0.0 + + :param text: Text to measure. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + :return: Either width for horizontal text, or height for vertical text. + """ + _string_length_check(text) + return self.font.getlength(text, mode, direction, features, language) / 64 + + def getbbox( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ) -> tuple[float, float, float, float]: + """ + Returns bounding box (in pixels) of given text relative to given anchor + when rendered in font with provided direction, features, and language. + + Use :py:meth:`getlength()` to get the offset of following text with + 1/64 pixel precision. The bounding box includes extra margins for + some fonts, e.g. italics or accents. + + .. versionadded:: 8.0.0 + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + :param stroke_width: The width of the text stroke. + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + :return: ``(left, top, right, bottom)`` bounding box + """ + _string_length_check(text) + size, offset = self.font.getsize( + text, mode, direction, features, language, anchor + ) + left, top = offset[0] - stroke_width, offset[1] - stroke_width + width, height = size[0] + 2 * stroke_width, size[1] + 2 * stroke_width + return left, top, left + width, top + height + + def getmask( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ink: int = 0, + start: tuple[float, float] | None = None, + ) -> Image.core.ImagingCore: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. If the font has embedded color data, the bitmap + should have mode ``RGBA``. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + .. versionadded:: 6.0.0 + + :param stroke_width: The width of the text stroke. + + .. versionadded:: 6.2.0 + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + .. versionadded:: 8.0.0 + + :param ink: Foreground ink for rendering in RGBA mode. + + .. versionadded:: 8.0.0 + + :param start: Tuple of horizontal and vertical offset, as text may render + differently when starting at fractional coordinates. + + .. versionadded:: 9.4.0 + + :return: An internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module. + """ + return self.getmask2( + text, + mode, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + anchor=anchor, + ink=ink, + start=start, + )[0] + + def getmask2( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ink: int = 0, + start: tuple[float, float] | None = None, + *args: Any, + **kwargs: Any, + ) -> tuple[Image.core.ImagingCore, tuple[int, int]]: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. If the font has embedded color data, the bitmap + should have mode ``RGBA``. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + .. versionadded:: 6.0.0 + + :param stroke_width: The width of the text stroke. + + .. versionadded:: 6.2.0 + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + .. versionadded:: 8.0.0 + + :param ink: Foreground ink for rendering in RGBA mode. + + .. versionadded:: 8.0.0 + + :param start: Tuple of horizontal and vertical offset, as text may render + differently when starting at fractional coordinates. + + .. versionadded:: 9.4.0 + + :return: A tuple of an internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module, and the text offset, the + gap between the starting coordinate and the first marking + """ + _string_length_check(text) + if start is None: + start = (0, 0) + + def fill(width: int, height: int) -> Image.core.ImagingCore: + size = (width, height) + Image._decompression_bomb_check(size) + return Image.core.fill("RGBA" if mode == "RGBA" else "L", size) + + return self.font.render( + text, + fill, + mode, + direction, + features, + language, + stroke_width, + kwargs.get("stroke_filled", False), + anchor, + ink, + start, + ) + + def font_variant( + self, + font: StrOrBytesPath | BinaryIO | None = None, + size: float | None = None, + index: int | None = None, + encoding: str | None = None, + layout_engine: Layout | None = None, + ) -> FreeTypeFont: + """ + Create a copy of this FreeTypeFont object, + using any specified arguments to override the settings. + + Parameters are identical to the parameters used to initialize this + object. + + :return: A FreeTypeFont object. + """ + if font is None: + try: + font = BytesIO(self.font_bytes) + except AttributeError: + font = self.path + return FreeTypeFont( + font=font, + size=self.size if size is None else size, + index=self.index if index is None else index, + encoding=self.encoding if encoding is None else encoding, + layout_engine=layout_engine or self.layout_engine, + ) + + def get_variation_names(self) -> list[bytes]: + """ + :returns: A list of the named styles in a variation font. + :exception OSError: If the font is not a variation font. + """ + try: + names = self.font.getvarnames() + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + return [name.replace(b"\x00", b"") for name in names] + + def set_variation_by_name(self, name: str | bytes) -> None: + """ + :param name: The name of the style. + :exception OSError: If the font is not a variation font. + """ + names = self.get_variation_names() + if not isinstance(name, bytes): + name = name.encode() + index = names.index(name) + 1 + + if index == getattr(self, "_last_variation_index", None): + # When the same name is set twice in a row, + # there is an 'unknown freetype error' + # https://savannah.nongnu.org/bugs/?56186 + return + self._last_variation_index = index + + self.font.setvarname(index) + + def get_variation_axes(self) -> list[Axis]: + """ + :returns: A list of the axes in a variation font. + :exception OSError: If the font is not a variation font. + """ + try: + axes = self.font.getvaraxes() + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + for axis in axes: + if axis["name"]: + axis["name"] = axis["name"].replace(b"\x00", b"") + return axes + + def set_variation_by_axes(self, axes: list[float]) -> None: + """ + :param axes: A list of values for each axis. + :exception OSError: If the font is not a variation font. + """ + try: + self.font.setvaraxes(axes) + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + + +class TransposedFont: + """Wrapper for writing rotated or mirrored text""" + + def __init__( + self, font: ImageFont | FreeTypeFont, orientation: Image.Transpose | None = None + ): + """ + Wrapper that creates a transposed font from any existing font + object. + + :param font: A font object. + :param orientation: An optional orientation. If given, this should + be one of Image.Transpose.FLIP_LEFT_RIGHT, Image.Transpose.FLIP_TOP_BOTTOM, + Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_180, or + Image.Transpose.ROTATE_270. + """ + self.font = font + self.orientation = orientation # any 'transpose' argument, or None + + def getmask( + self, text: str | bytes, mode: str = "", *args: Any, **kwargs: Any + ) -> Image.core.ImagingCore: + im = self.font.getmask(text, mode, *args, **kwargs) + if self.orientation is not None: + return im.transpose(self.orientation) + return im + + def getbbox( + self, text: str | bytes, *args: Any, **kwargs: Any + ) -> tuple[int, int, float, float]: + # TransposedFont doesn't support getmask2, move top-left point to (0, 0) + # this has no effect on ImageFont and simulates anchor="lt" for FreeTypeFont + left, top, right, bottom = self.font.getbbox(text, *args, **kwargs) + width = right - left + height = bottom - top + if self.orientation in (Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_270): + return 0, 0, height, width + return 0, 0, width, height + + def getlength(self, text: str | bytes, *args: Any, **kwargs: Any) -> float: + if self.orientation in (Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_270): + msg = "text length is undefined for text rotated by 90 or 270 degrees" + raise ValueError(msg) + return self.font.getlength(text, *args, **kwargs) + + +def load(filename: str) -> ImageFont: + """ + Load a font file. This function loads a font object from the given + bitmap font file, and returns the corresponding font object. For loading TrueType + or OpenType fonts instead, see :py:func:`~PIL.ImageFont.truetype`. + + :param filename: Name of font file. + :return: A font object. + :exception OSError: If the file could not be read. + """ + f = ImageFont() + f._load_pilfont(filename) + return f + + +def truetype( + font: StrOrBytesPath | BinaryIO, + size: float = 10, + index: int = 0, + encoding: str = "", + layout_engine: Layout | None = None, +) -> FreeTypeFont: + """ + Load a TrueType or OpenType font from a file or file-like object, + and create a font object. This function loads a font object from the given + file or file-like object, and creates a font object for a font of the given + size. For loading bitmap fonts instead, see :py:func:`~PIL.ImageFont.load` + and :py:func:`~PIL.ImageFont.load_path`. + + Pillow uses FreeType to open font files. On Windows, be aware that FreeType + will keep the file open as long as the FreeTypeFont object exists. Windows + limits the number of files that can be open in C at once to 512, so if many + fonts are opened simultaneously and that limit is approached, an + ``OSError`` may be thrown, reporting that FreeType "cannot open resource". + A workaround would be to copy the file(s) into memory, and open that instead. + + This function requires the _imagingft service. + + :param font: A filename or file-like object containing a TrueType font. + If the file is not found in this filename, the loader may also + search in other directories, such as: + + * The :file:`fonts/` directory on Windows, + * :file:`/Library/Fonts/`, :file:`/System/Library/Fonts/` + and :file:`~/Library/Fonts/` on macOS. + * :file:`~/.local/share/fonts`, :file:`/usr/local/share/fonts`, + and :file:`/usr/share/fonts` on Linux; or those specified by + the ``XDG_DATA_HOME`` and ``XDG_DATA_DIRS`` environment variables + for user-installed and system-wide fonts, respectively. + + :param size: The requested size, in pixels. + :param index: Which font face to load (default is first available face). + :param encoding: Which font encoding to use (default is Unicode). Possible + encodings include (see the FreeType documentation for more + information): + + * "unic" (Unicode) + * "symb" (Microsoft Symbol) + * "ADOB" (Adobe Standard) + * "ADBE" (Adobe Expert) + * "ADBC" (Adobe Custom) + * "armn" (Apple Roman) + * "sjis" (Shift JIS) + * "gb " (PRC) + * "big5" + * "wans" (Extended Wansung) + * "joha" (Johab) + * "lat1" (Latin-1) + + This specifies the character set to use. It does not alter the + encoding of any text provided in subsequent operations. + :param layout_engine: Which layout engine to use, if available: + :attr:`.ImageFont.Layout.BASIC` or :attr:`.ImageFont.Layout.RAQM`. + If it is available, Raqm layout will be used by default. + Otherwise, basic layout will be used. + + Raqm layout is recommended for all non-English text. If Raqm layout + is not required, basic layout will have better performance. + + You can check support for Raqm layout using + :py:func:`PIL.features.check_feature` with ``feature="raqm"``. + + .. versionadded:: 4.2.0 + :return: A font object. + :exception OSError: If the file could not be read. + :exception ValueError: If the font size is not greater than zero. + """ + + def freetype(font: StrOrBytesPath | BinaryIO) -> FreeTypeFont: + return FreeTypeFont(font, size, index, encoding, layout_engine) + + try: + return freetype(font) + except OSError: + if not is_path(font): + raise + ttf_filename = os.path.basename(font) + + dirs = [] + if sys.platform == "win32": + # check the windows font repository + # NOTE: must use uppercase WINDIR, to work around bugs in + # 1.5.2's os.environ.get() + windir = os.environ.get("WINDIR") + if windir: + dirs.append(os.path.join(windir, "fonts")) + elif sys.platform in ("linux", "linux2"): + data_home = os.environ.get("XDG_DATA_HOME") + if not data_home: + # The freedesktop spec defines the following default directory for + # when XDG_DATA_HOME is unset or empty. This user-level directory + # takes precedence over system-level directories. + data_home = os.path.expanduser("~/.local/share") + xdg_dirs = [data_home] + + data_dirs = os.environ.get("XDG_DATA_DIRS") + if not data_dirs: + # Similarly, defaults are defined for the system-level directories + data_dirs = "/usr/local/share:/usr/share" + xdg_dirs += data_dirs.split(":") + + dirs += [os.path.join(xdg_dir, "fonts") for xdg_dir in xdg_dirs] + elif sys.platform == "darwin": + dirs += [ + "/Library/Fonts", + "/System/Library/Fonts", + os.path.expanduser("~/Library/Fonts"), + ] + + ext = os.path.splitext(ttf_filename)[1] + first_font_with_a_different_extension = None + for directory in dirs: + for walkroot, walkdir, walkfilenames in os.walk(directory): + for walkfilename in walkfilenames: + if ext and walkfilename == ttf_filename: + return freetype(os.path.join(walkroot, walkfilename)) + elif not ext and os.path.splitext(walkfilename)[0] == ttf_filename: + fontpath = os.path.join(walkroot, walkfilename) + if os.path.splitext(fontpath)[1] == ".ttf": + return freetype(fontpath) + if not ext and first_font_with_a_different_extension is None: + first_font_with_a_different_extension = fontpath + if first_font_with_a_different_extension: + return freetype(first_font_with_a_different_extension) + raise + + +def load_path(filename: str | bytes) -> ImageFont: + """ + Load font file. Same as :py:func:`~PIL.ImageFont.load`, but searches for a + bitmap font along the Python path. + + :param filename: Name of font file. + :return: A font object. + :exception OSError: If the file could not be read. + """ + if not isinstance(filename, str): + filename = filename.decode("utf-8") + for directory in sys.path: + try: + return load(os.path.join(directory, filename)) + except OSError: + pass + msg = f'cannot find font file "{filename}" in sys.path' + if os.path.exists(filename): + msg += f', did you mean ImageFont.load("{filename}") instead?' + + raise OSError(msg) + + +def load_default_imagefont() -> ImageFont: + f = ImageFont() + f._load_pilfont_data( + # courB08 + BytesIO( + base64.b64decode( + b""" +UElMZm9udAo7Ozs7OzsxMDsKREFUQQoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA 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+ ), + 10 if size is None else size, + layout_engine=Layout.BASIC, + ) + return load_default_imagefont() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb4507344cd412bed5c113d4c52e39f26cecab2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py @@ -0,0 +1,196 @@ +# +# The Python Imaging Library +# $Id$ +# +# screen grabber +# +# History: +# 2001-04-26 fl created +# 2001-09-17 fl use builtin driver, if present +# 2002-11-19 fl added grabclipboard support +# +# Copyright (c) 2001-2002 by Secret Labs AB +# Copyright (c) 2001-2002 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import shutil +import subprocess +import sys +import tempfile + +from . import Image + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageWin + + +def grab( + bbox: tuple[int, int, int, int] | None = None, + include_layered_windows: bool = False, + all_screens: bool = False, + xdisplay: str | None = None, + window: int | ImageWin.HWND | None = None, +) -> Image.Image: + im: Image.Image + if xdisplay is None: + if sys.platform == "darwin": + fh, filepath = tempfile.mkstemp(".png") + os.close(fh) + args = ["screencapture"] + if bbox: + left, top, right, bottom = bbox + args += ["-R", f"{left},{top},{right-left},{bottom-top}"] + subprocess.call(args + ["-x", filepath]) + im = Image.open(filepath) + im.load() + os.unlink(filepath) + if bbox: + im_resized = im.resize((right - left, bottom - top)) + im.close() + return im_resized + return im + elif sys.platform == "win32": + if window is not None: + all_screens = -1 + offset, size, data = Image.core.grabscreen_win32( + include_layered_windows, + all_screens, + int(window) if window is not None else 0, + ) + im = Image.frombytes( + "RGB", + size, + data, + # RGB, 32-bit line padding, origin lower left corner + "raw", + "BGR", + (size[0] * 3 + 3) & -4, + -1, + ) + if bbox: + x0, y0 = offset + left, top, right, bottom = bbox + im = im.crop((left - x0, top - y0, right - x0, bottom - y0)) + return im + # Cast to Optional[str] needed for Windows and macOS. + display_name: str | None = xdisplay + try: + if not Image.core.HAVE_XCB: + msg = "Pillow was built without XCB support" + raise OSError(msg) + size, data = Image.core.grabscreen_x11(display_name) + except OSError: + if display_name is None and sys.platform not in ("darwin", "win32"): + if shutil.which("gnome-screenshot"): + args = ["gnome-screenshot", "-f"] + elif shutil.which("grim"): + args = ["grim"] + elif shutil.which("spectacle"): + args = ["spectacle", "-n", "-b", "-f", "-o"] + else: + raise + fh, filepath = tempfile.mkstemp(".png") + os.close(fh) + subprocess.call(args + [filepath]) + im = Image.open(filepath) + im.load() + os.unlink(filepath) + if bbox: + im_cropped = im.crop(bbox) + im.close() + return im_cropped + return im + else: + raise + else: + im = Image.frombytes("RGB", size, data, "raw", "BGRX", size[0] * 4, 1) + if bbox: + im = im.crop(bbox) + return im + + +def grabclipboard() -> Image.Image | list[str] | None: + if sys.platform == "darwin": + p = subprocess.run( + ["osascript", "-e", "get the clipboard as «class PNGf»"], + capture_output=True, + ) + if p.returncode != 0: + return None + + import binascii + + data = io.BytesIO(binascii.unhexlify(p.stdout[11:-3])) + return Image.open(data) + elif sys.platform == "win32": + fmt, data = Image.core.grabclipboard_win32() + if fmt == "file": # CF_HDROP + import struct + + o = struct.unpack_from("I", data)[0] + if data[16] == 0: + files = data[o:].decode("mbcs").split("\0") + else: + files = data[o:].decode("utf-16le").split("\0") + return files[: files.index("")] + if isinstance(data, bytes): + data = io.BytesIO(data) + if fmt == "png": + from . import PngImagePlugin + + return PngImagePlugin.PngImageFile(data) + elif fmt == "DIB": + from . import BmpImagePlugin + + return BmpImagePlugin.DibImageFile(data) + return None + else: + if os.getenv("WAYLAND_DISPLAY"): + session_type = "wayland" + elif os.getenv("DISPLAY"): + session_type = "x11" + else: # Session type check failed + session_type = None + + if shutil.which("wl-paste") and session_type in ("wayland", None): + args = ["wl-paste", "-t", "image"] + elif shutil.which("xclip") and session_type in ("x11", None): + args = ["xclip", "-selection", "clipboard", "-t", "image/png", "-o"] + else: + msg = "wl-paste or xclip is required for ImageGrab.grabclipboard() on Linux" + raise NotImplementedError(msg) + + p = subprocess.run(args, capture_output=True) + if p.returncode != 0: + err = p.stderr + for silent_error in [ + # wl-paste, when the clipboard is empty + b"Nothing is copied", + # Ubuntu/Debian wl-paste, when the clipboard is empty + b"No selection", + # Ubuntu/Debian wl-paste, when an image isn't available + b"No suitable type of content copied", + # wl-paste or Ubuntu/Debian xclip, when an image isn't available + b" not available", + # xclip, when an image isn't available + b"cannot convert ", + # xclip, when the clipboard isn't initialized + b"xclip: Error: There is no owner for the ", + ]: + if silent_error in err: + return None + msg = f"{args[0]} error" + if err: + msg += f": {err.strip().decode()}" + raise ChildProcessError(msg) + + data = io.BytesIO(p.stdout) + im = Image.open(data) + im.load() + return im diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMath.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMath.py new file mode 100644 index 0000000000000000000000000000000000000000..c33809ced890e437d10102a6c065d9efe3207685 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMath.py @@ -0,0 +1,368 @@ +# +# The Python Imaging Library +# $Id$ +# +# a simple math add-on for the Python Imaging Library +# +# History: +# 1999-02-15 fl Original PIL Plus release +# 2005-05-05 fl Simplified and cleaned up for PIL 1.1.6 +# 2005-09-12 fl Fixed int() and float() for Python 2.4.1 +# +# Copyright (c) 1999-2005 by Secret Labs AB +# Copyright (c) 2005 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import builtins +from types import CodeType +from typing import Any, Callable + +from . import Image, _imagingmath +from ._deprecate import deprecate + + +class _Operand: + """Wraps an image operand, providing standard operators""" + + def __init__(self, im: Image.Image): + self.im = im + + def __fixup(self, im1: _Operand | float) -> Image.Image: + # convert image to suitable mode + if isinstance(im1, _Operand): + # argument was an image. + if im1.im.mode in ("1", "L"): + return im1.im.convert("I") + elif im1.im.mode in ("I", "F"): + return im1.im + else: + msg = f"unsupported mode: {im1.im.mode}" + raise ValueError(msg) + else: + # argument was a constant + if isinstance(im1, (int, float)) and self.im.mode in ("1", "L", "I"): + return Image.new("I", self.im.size, im1) + else: + return Image.new("F", self.im.size, im1) + + def apply( + self, + op: str, + im1: _Operand | float, + im2: _Operand | float | None = None, + mode: str | None = None, + ) -> _Operand: + im_1 = self.__fixup(im1) + if im2 is None: + # unary operation + out = Image.new(mode or im_1.mode, im_1.size, None) + try: + op = getattr(_imagingmath, f"{op}_{im_1.mode}") + except AttributeError as e: + msg = f"bad operand type for '{op}'" + raise TypeError(msg) from e + _imagingmath.unop(op, out.getim(), im_1.getim()) + else: + # binary operation + im_2 = self.__fixup(im2) + if im_1.mode != im_2.mode: + # convert both arguments to floating point + if im_1.mode != "F": + im_1 = im_1.convert("F") + if im_2.mode != "F": + im_2 = im_2.convert("F") + if im_1.size != im_2.size: + # crop both arguments to a common size + size = ( + min(im_1.size[0], im_2.size[0]), + min(im_1.size[1], im_2.size[1]), + ) + if im_1.size != size: + im_1 = im_1.crop((0, 0) + size) + if im_2.size != size: + im_2 = im_2.crop((0, 0) + size) + out = Image.new(mode or im_1.mode, im_1.size, None) + try: + op = getattr(_imagingmath, f"{op}_{im_1.mode}") + except AttributeError as e: + msg = f"bad operand type for '{op}'" + raise TypeError(msg) from e + _imagingmath.binop(op, out.getim(), im_1.getim(), im_2.getim()) + return _Operand(out) + + # unary operators + def __bool__(self) -> bool: + # an image is "true" if it contains at least one non-zero pixel + return self.im.getbbox() is not None + + def __abs__(self) -> _Operand: + return self.apply("abs", self) + + def __pos__(self) -> _Operand: + return self + + def __neg__(self) -> _Operand: + return self.apply("neg", self) + + # binary operators + def __add__(self, other: _Operand | float) -> _Operand: + return self.apply("add", self, other) + + def __radd__(self, other: _Operand | float) -> _Operand: + return self.apply("add", other, self) + + def __sub__(self, other: _Operand | float) -> _Operand: + return self.apply("sub", self, other) + + def __rsub__(self, other: _Operand | float) -> _Operand: + return self.apply("sub", other, self) + + def __mul__(self, other: _Operand | float) -> _Operand: + return self.apply("mul", self, other) + + def __rmul__(self, other: _Operand | float) -> _Operand: + return self.apply("mul", other, self) + + def __truediv__(self, other: _Operand | float) -> _Operand: + return self.apply("div", self, other) + + def __rtruediv__(self, other: _Operand | float) -> _Operand: + return self.apply("div", other, self) + + def __mod__(self, other: _Operand | float) -> _Operand: + return self.apply("mod", self, other) + + def __rmod__(self, other: _Operand | float) -> _Operand: + return self.apply("mod", other, self) + + def __pow__(self, other: _Operand | float) -> _Operand: + return self.apply("pow", self, other) + + def __rpow__(self, other: _Operand | float) -> _Operand: + return self.apply("pow", other, self) + + # bitwise + def __invert__(self) -> _Operand: + return self.apply("invert", self) + + def __and__(self, other: _Operand | float) -> _Operand: + return self.apply("and", self, other) + + def __rand__(self, other: _Operand | float) -> _Operand: + return self.apply("and", other, self) + + def __or__(self, other: _Operand | float) -> _Operand: + return self.apply("or", self, other) + + def __ror__(self, other: _Operand | float) -> _Operand: + return self.apply("or", other, self) + + def __xor__(self, other: _Operand | float) -> _Operand: + return self.apply("xor", self, other) + + def __rxor__(self, other: _Operand | float) -> _Operand: + return self.apply("xor", other, self) + + def __lshift__(self, other: _Operand | float) -> _Operand: + return self.apply("lshift", self, other) + + def __rshift__(self, other: _Operand | float) -> _Operand: + return self.apply("rshift", self, other) + + # logical + def __eq__(self, other: _Operand | float) -> _Operand: # type: ignore[override] + return self.apply("eq", self, other) + + def __ne__(self, other: _Operand | float) -> _Operand: # type: ignore[override] + return self.apply("ne", self, other) + + def __lt__(self, other: _Operand | float) -> _Operand: + return self.apply("lt", self, other) + + def __le__(self, other: _Operand | float) -> _Operand: + return self.apply("le", self, other) + + def __gt__(self, other: _Operand | float) -> _Operand: + return self.apply("gt", self, other) + + def __ge__(self, other: _Operand | float) -> _Operand: + return self.apply("ge", self, other) + + +# conversions +def imagemath_int(self: _Operand) -> _Operand: + return _Operand(self.im.convert("I")) + + +def imagemath_float(self: _Operand) -> _Operand: + return _Operand(self.im.convert("F")) + + +# logical +def imagemath_equal(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("eq", self, other, mode="I") + + +def imagemath_notequal(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("ne", self, other, mode="I") + + +def imagemath_min(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("min", self, other) + + +def imagemath_max(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("max", self, other) + + +def imagemath_convert(self: _Operand, mode: str) -> _Operand: + return _Operand(self.im.convert(mode)) + + +ops = { + "int": imagemath_int, + "float": imagemath_float, + "equal": imagemath_equal, + "notequal": imagemath_notequal, + "min": imagemath_min, + "max": imagemath_max, + "convert": imagemath_convert, +} + + +def lambda_eval( + expression: Callable[[dict[str, Any]], Any], + options: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Returns the result of an image function. + + :py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band + images, use the :py:meth:`~PIL.Image.Image.split` method or + :py:func:`~PIL.Image.merge` function. + + :param expression: A function that receives a dictionary. + :param options: Values to add to the function's dictionary. Deprecated. + You can instead use one or more keyword arguments. + :param **kw: Values to add to the function's dictionary. + :return: The expression result. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + """ + + if options: + deprecate( + "ImageMath.lambda_eval options", + 12, + "ImageMath.lambda_eval keyword arguments", + ) + + args: dict[str, Any] = ops.copy() + args.update(options) + args.update(kw) + for k, v in args.items(): + if isinstance(v, Image.Image): + args[k] = _Operand(v) + + out = expression(args) + try: + return out.im + except AttributeError: + return out + + +def unsafe_eval( + expression: str, + options: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Evaluates an image expression. This uses Python's ``eval()`` function to process + the expression string, and carries the security risks of doing so. It is not + recommended to process expressions without considering this. + :py:meth:`~lambda_eval` is a more secure alternative. + + :py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band + images, use the :py:meth:`~PIL.Image.Image.split` method or + :py:func:`~PIL.Image.merge` function. + + :param expression: A string containing a Python-style expression. + :param options: Values to add to the evaluation context. Deprecated. + You can instead use one or more keyword arguments. + :param **kw: Values to add to the evaluation context. + :return: The evaluated expression. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + """ + + if options: + deprecate( + "ImageMath.unsafe_eval options", + 12, + "ImageMath.unsafe_eval keyword arguments", + ) + + # build execution namespace + args: dict[str, Any] = ops.copy() + for k in [*options, *kw]: + if "__" in k or hasattr(builtins, k): + msg = f"'{k}' not allowed" + raise ValueError(msg) + + args.update(options) + args.update(kw) + for k, v in args.items(): + if isinstance(v, Image.Image): + args[k] = _Operand(v) + + compiled_code = compile(expression, "", "eval") + + def scan(code: CodeType) -> None: + for const in code.co_consts: + if type(const) is type(compiled_code): + scan(const) + + for name in code.co_names: + if name not in args and name != "abs": + msg = f"'{name}' not allowed" + raise ValueError(msg) + + scan(compiled_code) + out = builtins.eval(expression, {"__builtins": {"abs": abs}}, args) + try: + return out.im + except AttributeError: + return out + + +def eval( + expression: str, + _dict: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Evaluates an image expression. + + Deprecated. Use lambda_eval() or unsafe_eval() instead. + + :param expression: A string containing a Python-style expression. + :param _dict: Values to add to the evaluation context. You + can either use a dictionary, or one or more keyword + arguments. + :return: The evaluated expression. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + + .. deprecated:: 10.3.0 + """ + + deprecate( + "ImageMath.eval", + 12, + "ImageMath.lambda_eval or ImageMath.unsafe_eval", + ) + return unsafe_eval(expression, _dict, **kw) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMode.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMode.py new file mode 100644 index 0000000000000000000000000000000000000000..92a08d2cbcb4f8f2f7e24a265b83cd6c1012b41f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageMode.py @@ -0,0 +1,92 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard mode descriptors +# +# History: +# 2006-03-20 fl Added +# +# Copyright (c) 2006 by Secret Labs AB. +# Copyright (c) 2006 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from functools import lru_cache +from typing import NamedTuple + +from ._deprecate import deprecate + + +class ModeDescriptor(NamedTuple): + """Wrapper for mode strings.""" + + mode: str + bands: tuple[str, ...] + basemode: str + basetype: str + typestr: str + + def __str__(self) -> str: + return self.mode + + +@lru_cache +def getmode(mode: str) -> ModeDescriptor: + """Gets a mode descriptor for the given mode.""" + endian = "<" if sys.byteorder == "little" else ">" + + modes = { + # core modes + # Bits need to be extended to bytes + "1": ("L", "L", ("1",), "|b1"), + "L": ("L", "L", ("L",), "|u1"), + "I": ("L", "I", ("I",), f"{endian}i4"), + "F": ("L", "F", ("F",), f"{endian}f4"), + "P": ("P", "L", ("P",), "|u1"), + "RGB": ("RGB", "L", ("R", "G", "B"), "|u1"), + "RGBX": ("RGB", "L", ("R", "G", "B", "X"), "|u1"), + "RGBA": ("RGB", "L", ("R", "G", "B", "A"), "|u1"), + "CMYK": ("RGB", "L", ("C", "M", "Y", "K"), "|u1"), + "YCbCr": ("RGB", "L", ("Y", "Cb", "Cr"), "|u1"), + # UNDONE - unsigned |u1i1i1 + "LAB": ("RGB", "L", ("L", "A", "B"), "|u1"), + "HSV": ("RGB", "L", ("H", "S", "V"), "|u1"), + # extra experimental modes + "RGBa": ("RGB", "L", ("R", "G", "B", "a"), "|u1"), + "BGR;15": ("RGB", "L", ("B", "G", "R"), "|u1"), + "BGR;16": ("RGB", "L", ("B", "G", "R"), "|u1"), + "BGR;24": ("RGB", "L", ("B", "G", "R"), "|u1"), + "LA": ("L", "L", ("L", "A"), "|u1"), + "La": ("L", "L", ("L", "a"), "|u1"), + "PA": ("RGB", "L", ("P", "A"), "|u1"), + } + if mode in modes: + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + base_mode, base_type, bands, type_str = modes[mode] + return ModeDescriptor(mode, bands, base_mode, base_type, type_str) + + mapping_modes = { + # I;16 == I;16L, and I;32 == I;32L + "I;16": "u2", + "I;16BS": ">i2", + "I;16N": f"{endian}u2", + "I;16NS": f"{endian}i2", + "I;32": "u4", + "I;32L": "i4", + "I;32LS": " +from __future__ import annotations + +import re + +from . import Image, _imagingmorph + +LUT_SIZE = 1 << 9 + +# fmt: off +ROTATION_MATRIX = [ + 6, 3, 0, + 7, 4, 1, + 8, 5, 2, +] +MIRROR_MATRIX = [ + 2, 1, 0, + 5, 4, 3, + 8, 7, 6, +] +# fmt: on + + +class LutBuilder: + """A class for building a MorphLut from a descriptive language + + The input patterns is a list of a strings sequences like these:: + + 4:(... + .1. + 111)->1 + + (whitespaces including linebreaks are ignored). The option 4 + describes a series of symmetry operations (in this case a + 4-rotation), the pattern is described by: + + - . or X - Ignore + - 1 - Pixel is on + - 0 - Pixel is off + + The result of the operation is described after "->" string. + + The default is to return the current pixel value, which is + returned if no other match is found. + + Operations: + + - 4 - 4 way rotation + - N - Negate + - 1 - Dummy op for no other operation (an op must always be given) + - M - Mirroring + + Example:: + + lb = LutBuilder(patterns = ["4:(... .1. 111)->1"]) + lut = lb.build_lut() + + """ + + def __init__( + self, patterns: list[str] | None = None, op_name: str | None = None + ) -> None: + if patterns is not None: + self.patterns = patterns + else: + self.patterns = [] + self.lut: bytearray | None = None + if op_name is not None: + known_patterns = { + "corner": ["1:(... ... ...)->0", "4:(00. 01. ...)->1"], + "dilation4": ["4:(... .0. .1.)->1"], + "dilation8": ["4:(... .0. .1.)->1", "4:(... .0. ..1)->1"], + "erosion4": ["4:(... .1. .0.)->0"], + "erosion8": ["4:(... .1. .0.)->0", "4:(... .1. ..0)->0"], + "edge": [ + "1:(... ... ...)->0", + "4:(.0. .1. ...)->1", + "4:(01. .1. ...)->1", + ], + } + if op_name not in known_patterns: + msg = f"Unknown pattern {op_name}!" + raise Exception(msg) + + self.patterns = known_patterns[op_name] + + def add_patterns(self, patterns: list[str]) -> None: + self.patterns += patterns + + def build_default_lut(self) -> None: + symbols = [0, 1] + m = 1 << 4 # pos of current pixel + self.lut = bytearray(symbols[(i & m) > 0] for i in range(LUT_SIZE)) + + def get_lut(self) -> bytearray | None: + return self.lut + + def _string_permute(self, pattern: str, permutation: list[int]) -> str: + """string_permute takes a pattern and a permutation and returns the + string permuted according to the permutation list. + """ + assert len(permutation) == 9 + return "".join(pattern[p] for p in permutation) + + def _pattern_permute( + self, basic_pattern: str, options: str, basic_result: int + ) -> list[tuple[str, int]]: + """pattern_permute takes a basic pattern and its result and clones + the pattern according to the modifications described in the $options + parameter. It returns a list of all cloned patterns.""" + patterns = [(basic_pattern, basic_result)] + + # rotations + if "4" in options: + res = patterns[-1][1] + for i in range(4): + patterns.append( + (self._string_permute(patterns[-1][0], ROTATION_MATRIX), res) + ) + # mirror + if "M" in options: + n = len(patterns) + for pattern, res in patterns[:n]: + patterns.append((self._string_permute(pattern, MIRROR_MATRIX), res)) + + # negate + if "N" in options: + n = len(patterns) + for pattern, res in patterns[:n]: + # Swap 0 and 1 + pattern = pattern.replace("0", "Z").replace("1", "0").replace("Z", "1") + res = 1 - int(res) + patterns.append((pattern, res)) + + return patterns + + def build_lut(self) -> bytearray: + """Compile all patterns into a morphology lut. + + TBD :Build based on (file) morphlut:modify_lut + """ + self.build_default_lut() + assert self.lut is not None + patterns = [] + + # Parse and create symmetries of the patterns strings + for p in self.patterns: + m = re.search(r"(\w*):?\s*\((.+?)\)\s*->\s*(\d)", p.replace("\n", "")) + if not m: + msg = 'Syntax error in pattern "' + p + '"' + raise Exception(msg) + options = m.group(1) + pattern = m.group(2) + result = int(m.group(3)) + + # Get rid of spaces + pattern = pattern.replace(" ", "").replace("\n", "") + + patterns += self._pattern_permute(pattern, options, result) + + # compile the patterns into regular expressions for speed + compiled_patterns = [] + for pattern in patterns: + p = pattern[0].replace(".", "X").replace("X", "[01]") + compiled_patterns.append((re.compile(p), pattern[1])) + + # Step through table and find patterns that match. + # Note that all the patterns are searched. The last one + # caught overrides + for i in range(LUT_SIZE): + # Build the bit pattern + bitpattern = bin(i)[2:] + bitpattern = ("0" * (9 - len(bitpattern)) + bitpattern)[::-1] + + for pattern, r in compiled_patterns: + if pattern.match(bitpattern): + self.lut[i] = [0, 1][r] + + return self.lut + + +class MorphOp: + """A class for binary morphological operators""" + + def __init__( + self, + lut: bytearray | None = None, + op_name: str | None = None, + patterns: list[str] | None = None, + ) -> None: + """Create a binary morphological operator""" + self.lut = lut + if op_name is not None: + self.lut = LutBuilder(op_name=op_name).build_lut() + elif patterns is not None: + self.lut = LutBuilder(patterns=patterns).build_lut() + + def apply(self, image: Image.Image) -> tuple[int, Image.Image]: + """Run a single morphological operation on an image + + Returns a tuple of the number of changed pixels and the + morphed image""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + outimage = Image.new(image.mode, image.size, None) + count = _imagingmorph.apply(bytes(self.lut), image.getim(), outimage.getim()) + return count, outimage + + def match(self, image: Image.Image) -> list[tuple[int, int]]: + """Get a list of coordinates matching the morphological operation on + an image. + + Returns a list of tuples of (x,y) coordinates + of all matching pixels. See :ref:`coordinate-system`.""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + return _imagingmorph.match(bytes(self.lut), image.getim()) + + def get_on_pixels(self, image: Image.Image) -> list[tuple[int, int]]: + """Get a list of all turned on pixels in a binary image + + Returns a list of tuples of (x,y) coordinates + of all matching pixels. See :ref:`coordinate-system`.""" + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + return _imagingmorph.get_on_pixels(image.getim()) + + def load_lut(self, filename: str) -> None: + """Load an operator from an mrl file""" + with open(filename, "rb") as f: + self.lut = bytearray(f.read()) + + if len(self.lut) != LUT_SIZE: + self.lut = None + msg = "Wrong size operator file!" + raise Exception(msg) + + def save_lut(self, filename: str) -> None: + """Save an operator to an mrl file""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + with open(filename, "wb") as f: + f.write(self.lut) + + def set_lut(self, lut: bytearray | None) -> None: + """Set the lut from an external source""" + self.lut = lut diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageOps.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageOps.py new file mode 100644 index 0000000000000000000000000000000000000000..da28854b5745459cc682beabb53480855092e7a4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageOps.py @@ -0,0 +1,745 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard image operations +# +# History: +# 2001-10-20 fl Created +# 2001-10-23 fl Added autocontrast operator +# 2001-12-18 fl Added Kevin's fit operator +# 2004-03-14 fl Fixed potential division by zero in equalize +# 2005-05-05 fl Fixed equalize for low number of values +# +# Copyright (c) 2001-2004 by Secret Labs AB +# Copyright (c) 2001-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import functools +import operator +import re +from collections.abc import Sequence +from typing import Literal, Protocol, cast, overload + +from . import ExifTags, Image, ImagePalette + +# +# helpers + + +def _border(border: int | tuple[int, ...]) -> tuple[int, int, int, int]: + if isinstance(border, tuple): + if len(border) == 2: + left, top = right, bottom = border + elif len(border) == 4: + left, top, right, bottom = border + else: + left = top = right = bottom = border + return left, top, right, bottom + + +def _color(color: str | int | tuple[int, ...], mode: str) -> int | tuple[int, ...]: + if isinstance(color, str): + from . import ImageColor + + color = ImageColor.getcolor(color, mode) + return color + + +def _lut(image: Image.Image, lut: list[int]) -> Image.Image: + if image.mode == "P": + # FIXME: apply to lookup table, not image data + msg = "mode P support coming soon" + raise NotImplementedError(msg) + elif image.mode in ("L", "RGB"): + if image.mode == "RGB" and len(lut) == 256: + lut = lut + lut + lut + return image.point(lut) + else: + msg = f"not supported for mode {image.mode}" + raise OSError(msg) + + +# +# actions + + +def autocontrast( + image: Image.Image, + cutoff: float | tuple[float, float] = 0, + ignore: int | Sequence[int] | None = None, + mask: Image.Image | None = None, + preserve_tone: bool = False, +) -> Image.Image: + """ + Maximize (normalize) image contrast. This function calculates a + histogram of the input image (or mask region), removes ``cutoff`` percent of the + lightest and darkest pixels from the histogram, and remaps the image + so that the darkest pixel becomes black (0), and the lightest + becomes white (255). + + :param image: The image to process. + :param cutoff: The percent to cut off from the histogram on the low and + high ends. Either a tuple of (low, high), or a single + number for both. + :param ignore: The background pixel value (use None for no background). + :param mask: Histogram used in contrast operation is computed using pixels + within the mask. If no mask is given the entire image is used + for histogram computation. + :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast. + + .. versionadded:: 8.2.0 + + :return: An image. + """ + if preserve_tone: + histogram = image.convert("L").histogram(mask) + else: + histogram = image.histogram(mask) + + lut = [] + for layer in range(0, len(histogram), 256): + h = histogram[layer : layer + 256] + if ignore is not None: + # get rid of outliers + if isinstance(ignore, int): + h[ignore] = 0 + else: + for ix in ignore: + h[ix] = 0 + if cutoff: + # cut off pixels from both ends of the histogram + if not isinstance(cutoff, tuple): + cutoff = (cutoff, cutoff) + # get number of pixels + n = 0 + for ix in range(256): + n = n + h[ix] + # remove cutoff% pixels from the low end + cut = int(n * cutoff[0] // 100) + for lo in range(256): + if cut > h[lo]: + cut = cut - h[lo] + h[lo] = 0 + else: + h[lo] -= cut + cut = 0 + if cut <= 0: + break + # remove cutoff% samples from the high end + cut = int(n * cutoff[1] // 100) + for hi in range(255, -1, -1): + if cut > h[hi]: + cut = cut - h[hi] + h[hi] = 0 + else: + h[hi] -= cut + cut = 0 + if cut <= 0: + break + # find lowest/highest samples after preprocessing + for lo in range(256): + if h[lo]: + break + for hi in range(255, -1, -1): + if h[hi]: + break + if hi <= lo: + # don't bother + lut.extend(list(range(256))) + else: + scale = 255.0 / (hi - lo) + offset = -lo * scale + for ix in range(256): + ix = int(ix * scale + offset) + if ix < 0: + ix = 0 + elif ix > 255: + ix = 255 + lut.append(ix) + return _lut(image, lut) + + +def colorize( + image: Image.Image, + black: str | tuple[int, ...], + white: str | tuple[int, ...], + mid: str | int | tuple[int, ...] | None = None, + blackpoint: int = 0, + whitepoint: int = 255, + midpoint: int = 127, +) -> Image.Image: + """ + Colorize grayscale image. + This function calculates a color wedge which maps all black pixels in + the source image to the first color and all white pixels to the + second color. If ``mid`` is specified, it uses three-color mapping. + The ``black`` and ``white`` arguments should be RGB tuples or color names; + optionally you can use three-color mapping by also specifying ``mid``. + Mapping positions for any of the colors can be specified + (e.g. ``blackpoint``), where these parameters are the integer + value corresponding to where the corresponding color should be mapped. + These parameters must have logical order, such that + ``blackpoint <= midpoint <= whitepoint`` (if ``mid`` is specified). + + :param image: The image to colorize. + :param black: The color to use for black input pixels. + :param white: The color to use for white input pixels. + :param mid: The color to use for midtone input pixels. + :param blackpoint: an int value [0, 255] for the black mapping. + :param whitepoint: an int value [0, 255] for the white mapping. + :param midpoint: an int value [0, 255] for the midtone mapping. + :return: An image. + """ + + # Initial asserts + assert image.mode == "L" + if mid is None: + assert 0 <= blackpoint <= whitepoint <= 255 + else: + assert 0 <= blackpoint <= midpoint <= whitepoint <= 255 + + # Define colors from arguments + rgb_black = cast(Sequence[int], _color(black, "RGB")) + rgb_white = cast(Sequence[int], _color(white, "RGB")) + rgb_mid = cast(Sequence[int], _color(mid, "RGB")) if mid is not None else None + + # Empty lists for the mapping + red = [] + green = [] + blue = [] + + # Create the low-end values + for i in range(blackpoint): + red.append(rgb_black[0]) + green.append(rgb_black[1]) + blue.append(rgb_black[2]) + + # Create the mapping (2-color) + if rgb_mid is None: + range_map = range(whitepoint - blackpoint) + + for i in range_map: + red.append( + rgb_black[0] + i * (rgb_white[0] - rgb_black[0]) // len(range_map) + ) + green.append( + rgb_black[1] + i * (rgb_white[1] - rgb_black[1]) // len(range_map) + ) + blue.append( + rgb_black[2] + i * (rgb_white[2] - rgb_black[2]) // len(range_map) + ) + + # Create the mapping (3-color) + else: + range_map1 = range(midpoint - blackpoint) + range_map2 = range(whitepoint - midpoint) + + for i in range_map1: + red.append( + rgb_black[0] + i * (rgb_mid[0] - rgb_black[0]) // len(range_map1) + ) + green.append( + rgb_black[1] + i * (rgb_mid[1] - rgb_black[1]) // len(range_map1) + ) + blue.append( + rgb_black[2] + i * (rgb_mid[2] - rgb_black[2]) // len(range_map1) + ) + for i in range_map2: + red.append(rgb_mid[0] + i * (rgb_white[0] - rgb_mid[0]) // len(range_map2)) + green.append( + rgb_mid[1] + i * (rgb_white[1] - rgb_mid[1]) // len(range_map2) + ) + blue.append(rgb_mid[2] + i * (rgb_white[2] - rgb_mid[2]) // len(range_map2)) + + # Create the high-end values + for i in range(256 - whitepoint): + red.append(rgb_white[0]) + green.append(rgb_white[1]) + blue.append(rgb_white[2]) + + # Return converted image + image = image.convert("RGB") + return _lut(image, red + green + blue) + + +def contain( + image: Image.Image, size: tuple[int, int], method: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a resized version of the image, set to the maximum width and height + within the requested size, while maintaining the original aspect ratio. + + :param image: The image to resize. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :return: An image. + """ + + im_ratio = image.width / image.height + dest_ratio = size[0] / size[1] + + if im_ratio != dest_ratio: + if im_ratio > dest_ratio: + new_height = round(image.height / image.width * size[0]) + if new_height != size[1]: + size = (size[0], new_height) + else: + new_width = round(image.width / image.height * size[1]) + if new_width != size[0]: + size = (new_width, size[1]) + return image.resize(size, resample=method) + + +def cover( + image: Image.Image, size: tuple[int, int], method: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a resized version of the image, so that the requested size is + covered, while maintaining the original aspect ratio. + + :param image: The image to resize. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :return: An image. + """ + + im_ratio = image.width / image.height + dest_ratio = size[0] / size[1] + + if im_ratio != dest_ratio: + if im_ratio < dest_ratio: + new_height = round(image.height / image.width * size[0]) + if new_height != size[1]: + size = (size[0], new_height) + else: + new_width = round(image.width / image.height * size[1]) + if new_width != size[0]: + size = (new_width, size[1]) + return image.resize(size, resample=method) + + +def pad( + image: Image.Image, + size: tuple[int, int], + method: int = Image.Resampling.BICUBIC, + color: str | int | tuple[int, ...] | None = None, + centering: tuple[float, float] = (0.5, 0.5), +) -> Image.Image: + """ + Returns a resized and padded version of the image, expanded to fill the + requested aspect ratio and size. + + :param image: The image to resize and crop. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :param color: The background color of the padded image. + :param centering: Control the position of the original image within the + padded version. + + (0.5, 0.5) will keep the image centered + (0, 0) will keep the image aligned to the top left + (1, 1) will keep the image aligned to the bottom + right + :return: An image. + """ + + resized = contain(image, size, method) + if resized.size == size: + out = resized + else: + out = Image.new(image.mode, size, color) + if resized.palette: + palette = resized.getpalette() + if palette is not None: + out.putpalette(palette) + if resized.width != size[0]: + x = round((size[0] - resized.width) * max(0, min(centering[0], 1))) + out.paste(resized, (x, 0)) + else: + y = round((size[1] - resized.height) * max(0, min(centering[1], 1))) + out.paste(resized, (0, y)) + return out + + +def crop(image: Image.Image, border: int = 0) -> Image.Image: + """ + Remove border from image. The same amount of pixels are removed + from all four sides. This function works on all image modes. + + .. seealso:: :py:meth:`~PIL.Image.Image.crop` + + :param image: The image to crop. + :param border: The number of pixels to remove. + :return: An image. + """ + left, top, right, bottom = _border(border) + return image.crop((left, top, image.size[0] - right, image.size[1] - bottom)) + + +def scale( + image: Image.Image, factor: float, resample: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a rescaled image by a specific factor given in parameter. + A factor greater than 1 expands the image, between 0 and 1 contracts the + image. + + :param image: The image to rescale. + :param factor: The expansion factor, as a float. + :param resample: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + if factor == 1: + return image.copy() + elif factor <= 0: + msg = "the factor must be greater than 0" + raise ValueError(msg) + else: + size = (round(factor * image.width), round(factor * image.height)) + return image.resize(size, resample) + + +class SupportsGetMesh(Protocol): + """ + An object that supports the ``getmesh`` method, taking an image as an + argument, and returning a list of tuples. Each tuple contains two tuples, + the source box as a tuple of 4 integers, and a tuple of 8 integers for the + final quadrilateral, in order of top left, bottom left, bottom right, top + right. + """ + + def getmesh( + self, image: Image.Image + ) -> list[ + tuple[tuple[int, int, int, int], tuple[int, int, int, int, int, int, int, int]] + ]: ... + + +def deform( + image: Image.Image, + deformer: SupportsGetMesh, + resample: int = Image.Resampling.BILINEAR, +) -> Image.Image: + """ + Deform the image. + + :param image: The image to deform. + :param deformer: A deformer object. Any object that implements a + ``getmesh`` method can be used. + :param resample: An optional resampling filter. Same values possible as + in the PIL.Image.transform function. + :return: An image. + """ + return image.transform( + image.size, Image.Transform.MESH, deformer.getmesh(image), resample + ) + + +def equalize(image: Image.Image, mask: Image.Image | None = None) -> Image.Image: + """ + Equalize the image histogram. This function applies a non-linear + mapping to the input image, in order to create a uniform + distribution of grayscale values in the output image. + + :param image: The image to equalize. + :param mask: An optional mask. If given, only the pixels selected by + the mask are included in the analysis. + :return: An image. + """ + if image.mode == "P": + image = image.convert("RGB") + h = image.histogram(mask) + lut = [] + for b in range(0, len(h), 256): + histo = [_f for _f in h[b : b + 256] if _f] + if len(histo) <= 1: + lut.extend(list(range(256))) + else: + step = (functools.reduce(operator.add, histo) - histo[-1]) // 255 + if not step: + lut.extend(list(range(256))) + else: + n = step // 2 + for i in range(256): + lut.append(n // step) + n = n + h[i + b] + return _lut(image, lut) + + +def expand( + image: Image.Image, + border: int | tuple[int, ...] = 0, + fill: str | int | tuple[int, ...] = 0, +) -> Image.Image: + """ + Add border to the image + + :param image: The image to expand. + :param border: Border width, in pixels. + :param fill: Pixel fill value (a color value). Default is 0 (black). + :return: An image. + """ + left, top, right, bottom = _border(border) + width = left + image.size[0] + right + height = top + image.size[1] + bottom + color = _color(fill, image.mode) + if image.palette: + palette = ImagePalette.ImagePalette(palette=image.getpalette()) + if isinstance(color, tuple) and (len(color) == 3 or len(color) == 4): + color = palette.getcolor(color) + else: + palette = None + out = Image.new(image.mode, (width, height), color) + if palette: + out.putpalette(palette.palette) + out.paste(image, (left, top)) + return out + + +def fit( + image: Image.Image, + size: tuple[int, int], + method: int = Image.Resampling.BICUBIC, + bleed: float = 0.0, + centering: tuple[float, float] = (0.5, 0.5), +) -> Image.Image: + """ + Returns a resized and cropped version of the image, cropped to the + requested aspect ratio and size. + + This function was contributed by Kevin Cazabon. + + :param image: The image to resize and crop. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :param bleed: Remove a border around the outside of the image from all + four edges. The value is a decimal percentage (use 0.01 for + one percent). The default value is 0 (no border). + Cannot be greater than or equal to 0.5. + :param centering: Control the cropping position. Use (0.5, 0.5) for + center cropping (e.g. if cropping the width, take 50% off + of the left side, and therefore 50% off the right side). + (0.0, 0.0) will crop from the top left corner (i.e. if + cropping the width, take all of the crop off of the right + side, and if cropping the height, take all of it off the + bottom). (1.0, 0.0) will crop from the bottom left + corner, etc. (i.e. if cropping the width, take all of the + crop off the left side, and if cropping the height take + none from the top, and therefore all off the bottom). + :return: An image. + """ + + # by Kevin Cazabon, Feb 17/2000 + # kevin@cazabon.com + # https://www.cazabon.com + + centering_x, centering_y = centering + + if not 0.0 <= centering_x <= 1.0: + centering_x = 0.5 + if not 0.0 <= centering_y <= 1.0: + centering_y = 0.5 + + if not 0.0 <= bleed < 0.5: + bleed = 0.0 + + # calculate the area to use for resizing and cropping, subtracting + # the 'bleed' around the edges + + # number of pixels to trim off on Top and Bottom, Left and Right + bleed_pixels = (bleed * image.size[0], bleed * image.size[1]) + + live_size = ( + image.size[0] - bleed_pixels[0] * 2, + image.size[1] - bleed_pixels[1] * 2, + ) + + # calculate the aspect ratio of the live_size + live_size_ratio = live_size[0] / live_size[1] + + # calculate the aspect ratio of the output image + output_ratio = size[0] / size[1] + + # figure out if the sides or top/bottom will be cropped off + if live_size_ratio == output_ratio: + # live_size is already the needed ratio + crop_width = live_size[0] + crop_height = live_size[1] + elif live_size_ratio >= output_ratio: + # live_size is wider than what's needed, crop the sides + crop_width = output_ratio * live_size[1] + crop_height = live_size[1] + else: + # live_size is taller than what's needed, crop the top and bottom + crop_width = live_size[0] + crop_height = live_size[0] / output_ratio + + # make the crop + crop_left = bleed_pixels[0] + (live_size[0] - crop_width) * centering_x + crop_top = bleed_pixels[1] + (live_size[1] - crop_height) * centering_y + + crop = (crop_left, crop_top, crop_left + crop_width, crop_top + crop_height) + + # resize the image and return it + return image.resize(size, method, box=crop) + + +def flip(image: Image.Image) -> Image.Image: + """ + Flip the image vertically (top to bottom). + + :param image: The image to flip. + :return: An image. + """ + return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + + +def grayscale(image: Image.Image) -> Image.Image: + """ + Convert the image to grayscale. + + :param image: The image to convert. + :return: An image. + """ + return image.convert("L") + + +def invert(image: Image.Image) -> Image.Image: + """ + Invert (negate) the image. + + :param image: The image to invert. + :return: An image. + """ + lut = list(range(255, -1, -1)) + return image.point(lut) if image.mode == "1" else _lut(image, lut) + + +def mirror(image: Image.Image) -> Image.Image: + """ + Flip image horizontally (left to right). + + :param image: The image to mirror. + :return: An image. + """ + return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + + +def posterize(image: Image.Image, bits: int) -> Image.Image: + """ + Reduce the number of bits for each color channel. + + :param image: The image to posterize. + :param bits: The number of bits to keep for each channel (1-8). + :return: An image. + """ + mask = ~(2 ** (8 - bits) - 1) + lut = [i & mask for i in range(256)] + return _lut(image, lut) + + +def solarize(image: Image.Image, threshold: int = 128) -> Image.Image: + """ + Invert all pixel values above a threshold. + + :param image: The image to solarize. + :param threshold: All pixels above this grayscale level are inverted. + :return: An image. + """ + lut = [] + for i in range(256): + if i < threshold: + lut.append(i) + else: + lut.append(255 - i) + return _lut(image, lut) + + +@overload +def exif_transpose(image: Image.Image, *, in_place: Literal[True]) -> None: ... + + +@overload +def exif_transpose( + image: Image.Image, *, in_place: Literal[False] = False +) -> Image.Image: ... + + +def exif_transpose(image: Image.Image, *, in_place: bool = False) -> Image.Image | None: + """ + If an image has an EXIF Orientation tag, other than 1, transpose the image + accordingly, and remove the orientation data. + + :param image: The image to transpose. + :param in_place: Boolean. Keyword-only argument. + If ``True``, the original image is modified in-place, and ``None`` is returned. + If ``False`` (default), a new :py:class:`~PIL.Image.Image` object is returned + with the transposition applied. If there is no transposition, a copy of the + image will be returned. + """ + image.load() + image_exif = image.getexif() + orientation = image_exif.get(ExifTags.Base.Orientation, 1) + method = { + 2: Image.Transpose.FLIP_LEFT_RIGHT, + 3: Image.Transpose.ROTATE_180, + 4: Image.Transpose.FLIP_TOP_BOTTOM, + 5: Image.Transpose.TRANSPOSE, + 6: Image.Transpose.ROTATE_270, + 7: Image.Transpose.TRANSVERSE, + 8: Image.Transpose.ROTATE_90, + }.get(orientation) + if method is not None: + if in_place: + image.im = image.im.transpose(method) + image._size = image.im.size + else: + transposed_image = image.transpose(method) + exif_image = image if in_place else transposed_image + + exif = exif_image.getexif() + if ExifTags.Base.Orientation in exif: + del exif[ExifTags.Base.Orientation] + if "exif" in exif_image.info: + exif_image.info["exif"] = exif.tobytes() + elif "Raw profile type exif" in exif_image.info: + exif_image.info["Raw profile type exif"] = exif.tobytes().hex() + for key in ("XML:com.adobe.xmp", "xmp"): + if key in exif_image.info: + for pattern in ( + r'tiff:Orientation="([0-9])"', + r"([0-9])", + ): + value = exif_image.info[key] + if isinstance(value, str): + value = re.sub(pattern, "", value) + elif isinstance(value, tuple): + value = tuple( + re.sub(pattern.encode(), b"", v) for v in value + ) + else: + value = re.sub(pattern.encode(), b"", value) + exif_image.info[key] = value + if not in_place: + return transposed_image + elif not in_place: + return image.copy() + return None diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py new file mode 100644 index 0000000000000000000000000000000000000000..103697117b92a3dca9794fbea5b9c92306b9b198 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py @@ -0,0 +1,286 @@ +# +# The Python Imaging Library. +# $Id$ +# +# image palette object +# +# History: +# 1996-03-11 fl Rewritten. +# 1997-01-03 fl Up and running. +# 1997-08-23 fl Added load hack +# 2001-04-16 fl Fixed randint shadow bug in random() +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import array +from collections.abc import Sequence +from typing import IO + +from . import GimpGradientFile, GimpPaletteFile, ImageColor, PaletteFile + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import Image + + +class ImagePalette: + """ + Color palette for palette mapped images + + :param mode: The mode to use for the palette. See: + :ref:`concept-modes`. Defaults to "RGB" + :param palette: An optional palette. If given, it must be a bytearray, + an array or a list of ints between 0-255. The list must consist of + all channels for one color followed by the next color (e.g. RGBRGBRGB). + Defaults to an empty palette. + """ + + def __init__( + self, + mode: str = "RGB", + palette: Sequence[int] | bytes | bytearray | None = None, + ) -> None: + self.mode = mode + self.rawmode: str | None = None # if set, palette contains raw data + self.palette = palette or bytearray() + self.dirty: int | None = None + + @property + def palette(self) -> Sequence[int] | bytes | bytearray: + return self._palette + + @palette.setter + def palette(self, palette: Sequence[int] | bytes | bytearray) -> None: + self._colors: dict[tuple[int, ...], int] | None = None + self._palette = palette + + @property + def colors(self) -> dict[tuple[int, ...], int]: + if self._colors is None: + mode_len = len(self.mode) + self._colors = {} + for i in range(0, len(self.palette), mode_len): + color = tuple(self.palette[i : i + mode_len]) + if color in self._colors: + continue + self._colors[color] = i // mode_len + return self._colors + + @colors.setter + def colors(self, colors: dict[tuple[int, ...], int]) -> None: + self._colors = colors + + def copy(self) -> ImagePalette: + new = ImagePalette() + + new.mode = self.mode + new.rawmode = self.rawmode + if self.palette is not None: + new.palette = self.palette[:] + new.dirty = self.dirty + + return new + + def getdata(self) -> tuple[str, Sequence[int] | bytes | bytearray]: + """ + Get palette contents in format suitable for the low-level + ``im.putpalette`` primitive. + + .. warning:: This method is experimental. + """ + if self.rawmode: + return self.rawmode, self.palette + return self.mode, self.tobytes() + + def tobytes(self) -> bytes: + """Convert palette to bytes. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(self.palette, bytes): + return self.palette + arr = array.array("B", self.palette) + return arr.tobytes() + + # Declare tostring as an alias for tobytes + tostring = tobytes + + def _new_color_index( + self, image: Image.Image | None = None, e: Exception | None = None + ) -> int: + if not isinstance(self.palette, bytearray): + self._palette = bytearray(self.palette) + index = len(self.palette) // 3 + special_colors: tuple[int | tuple[int, ...] | None, ...] = () + if image: + special_colors = ( + image.info.get("background"), + image.info.get("transparency"), + ) + while index in special_colors: + index += 1 + if index >= 256: + if image: + # Search for an unused index + for i, count in reversed(list(enumerate(image.histogram()))): + if count == 0 and i not in special_colors: + index = i + break + if index >= 256: + msg = "cannot allocate more than 256 colors" + raise ValueError(msg) from e + return index + + def getcolor( + self, + color: tuple[int, ...], + image: Image.Image | None = None, + ) -> int: + """Given an rgb tuple, allocate palette entry. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(color, tuple): + if self.mode == "RGB": + if len(color) == 4: + if color[3] != 255: + msg = "cannot add non-opaque RGBA color to RGB palette" + raise ValueError(msg) + color = color[:3] + elif self.mode == "RGBA": + if len(color) == 3: + color += (255,) + try: + return self.colors[color] + except KeyError as e: + # allocate new color slot + index = self._new_color_index(image, e) + assert isinstance(self._palette, bytearray) + self.colors[color] = index + if index * 3 < len(self.palette): + self._palette = ( + self._palette[: index * 3] + + bytes(color) + + self._palette[index * 3 + 3 :] + ) + else: + self._palette += bytes(color) + self.dirty = 1 + return index + else: + msg = f"unknown color specifier: {repr(color)}" # type: ignore[unreachable] + raise ValueError(msg) + + def save(self, fp: str | IO[str]) -> None: + """Save palette to text file. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(fp, str): + fp = open(fp, "w") + fp.write("# Palette\n") + fp.write(f"# Mode: {self.mode}\n") + for i in range(256): + fp.write(f"{i}") + for j in range(i * len(self.mode), (i + 1) * len(self.mode)): + try: + fp.write(f" {self.palette[j]}") + except IndexError: + fp.write(" 0") + fp.write("\n") + fp.close() + + +# -------------------------------------------------------------------- +# Internal + + +def raw(rawmode: str, data: Sequence[int] | bytes | bytearray) -> ImagePalette: + palette = ImagePalette() + palette.rawmode = rawmode + palette.palette = data + palette.dirty = 1 + return palette + + +# -------------------------------------------------------------------- +# Factories + + +def make_linear_lut(black: int, white: float) -> list[int]: + if black == 0: + return [int(white * i // 255) for i in range(256)] + + msg = "unavailable when black is non-zero" + raise NotImplementedError(msg) # FIXME + + +def make_gamma_lut(exp: float) -> list[int]: + return [int(((i / 255.0) ** exp) * 255.0 + 0.5) for i in range(256)] + + +def negative(mode: str = "RGB") -> ImagePalette: + palette = list(range(256 * len(mode))) + palette.reverse() + return ImagePalette(mode, [i // len(mode) for i in palette]) + + +def random(mode: str = "RGB") -> ImagePalette: + from random import randint + + palette = [randint(0, 255) for _ in range(256 * len(mode))] + return ImagePalette(mode, palette) + + +def sepia(white: str = "#fff0c0") -> ImagePalette: + bands = [make_linear_lut(0, band) for band in ImageColor.getrgb(white)] + return ImagePalette("RGB", [bands[i % 3][i // 3] for i in range(256 * 3)]) + + +def wedge(mode: str = "RGB") -> ImagePalette: + palette = list(range(256 * len(mode))) + return ImagePalette(mode, [i // len(mode) for i in palette]) + + +def load(filename: str) -> tuple[bytes, str]: + # FIXME: supports GIMP gradients only + + with open(filename, "rb") as fp: + paletteHandlers: list[ + type[ + GimpPaletteFile.GimpPaletteFile + | GimpGradientFile.GimpGradientFile + | PaletteFile.PaletteFile + ] + ] = [ + GimpPaletteFile.GimpPaletteFile, + GimpGradientFile.GimpGradientFile, + PaletteFile.PaletteFile, + ] + for paletteHandler in paletteHandlers: + try: + fp.seek(0) + lut = paletteHandler(fp).getpalette() + if lut: + break + except (SyntaxError, ValueError): + pass + else: + msg = "cannot load palette" + raise OSError(msg) + + return lut # data, rawmode diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePath.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePath.py new file mode 100644 index 0000000000000000000000000000000000000000..77e8a609a552ae7d8c6b87e78a36ecbfc1cdce89 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImagePath.py @@ -0,0 +1,20 @@ +# +# The Python Imaging Library +# $Id$ +# +# path interface +# +# History: +# 1996-11-04 fl Created +# 2002-04-14 fl Added documentation stub class +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image + +Path = Image.core.path diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageQt.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageQt.py new file mode 100644 index 0000000000000000000000000000000000000000..df7a57b652cd18b43c33774ca0006546ff4af274 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageQt.py @@ -0,0 +1,220 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a simple Qt image interface. +# +# history: +# 2006-06-03 fl: created +# 2006-06-04 fl: inherit from QImage instead of wrapping it +# 2006-06-05 fl: removed toimage helper; move string support to ImageQt +# 2013-11-13 fl: add support for Qt5 (aurelien.ballier@cyclonit.com) +# +# Copyright (c) 2006 by Secret Labs AB +# Copyright (c) 2006 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from io import BytesIO +from typing import Any, Callable, Union + +from . import Image +from ._util import is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + import PyQt6 + import PySide6 + + from . import ImageFile + + QBuffer: type + QByteArray = Union[PyQt6.QtCore.QByteArray, PySide6.QtCore.QByteArray] + QIODevice = Union[PyQt6.QtCore.QIODevice, PySide6.QtCore.QIODevice] + QImage = Union[PyQt6.QtGui.QImage, PySide6.QtGui.QImage] + QPixmap = Union[PyQt6.QtGui.QPixmap, PySide6.QtGui.QPixmap] + +qt_version: str | None +qt_versions = [ + ["6", "PyQt6"], + ["side6", "PySide6"], +] + +# If a version has already been imported, attempt it first +qt_versions.sort(key=lambda version: version[1] in sys.modules, reverse=True) +for version, qt_module in qt_versions: + try: + qRgba: Callable[[int, int, int, int], int] + if qt_module == "PyQt6": + from PyQt6.QtCore import QBuffer, QIODevice + from PyQt6.QtGui import QImage, QPixmap, qRgba + elif qt_module == "PySide6": + from PySide6.QtCore import QBuffer, QIODevice + from PySide6.QtGui import QImage, QPixmap, qRgba + except (ImportError, RuntimeError): + continue + qt_is_installed = True + qt_version = version + break +else: + qt_is_installed = False + qt_version = None + + +def rgb(r: int, g: int, b: int, a: int = 255) -> int: + """(Internal) Turns an RGB color into a Qt compatible color integer.""" + # use qRgb to pack the colors, and then turn the resulting long + # into a negative integer with the same bitpattern. + return qRgba(r, g, b, a) & 0xFFFFFFFF + + +def fromqimage(im: QImage | QPixmap) -> ImageFile.ImageFile: + """ + :param im: QImage or PIL ImageQt object + """ + buffer = QBuffer() + qt_openmode: object + if qt_version == "6": + try: + qt_openmode = getattr(QIODevice, "OpenModeFlag") + except AttributeError: + qt_openmode = getattr(QIODevice, "OpenMode") + else: + qt_openmode = QIODevice + buffer.open(getattr(qt_openmode, "ReadWrite")) + # preserve alpha channel with png + # otherwise ppm is more friendly with Image.open + if im.hasAlphaChannel(): + im.save(buffer, "png") + else: + im.save(buffer, "ppm") + + b = BytesIO() + b.write(buffer.data()) + buffer.close() + b.seek(0) + + return Image.open(b) + + +def fromqpixmap(im: QPixmap) -> ImageFile.ImageFile: + return fromqimage(im) + + +def align8to32(bytes: bytes, width: int, mode: str) -> bytes: + """ + converts each scanline of data from 8 bit to 32 bit aligned + """ + + bits_per_pixel = {"1": 1, "L": 8, "P": 8, "I;16": 16}[mode] + + # calculate bytes per line and the extra padding if needed + bits_per_line = bits_per_pixel * width + full_bytes_per_line, remaining_bits_per_line = divmod(bits_per_line, 8) + bytes_per_line = full_bytes_per_line + (1 if remaining_bits_per_line else 0) + + extra_padding = -bytes_per_line % 4 + + # already 32 bit aligned by luck + if not extra_padding: + return bytes + + new_data = [ + bytes[i * bytes_per_line : (i + 1) * bytes_per_line] + b"\x00" * extra_padding + for i in range(len(bytes) // bytes_per_line) + ] + + return b"".join(new_data) + + +def _toqclass_helper(im: Image.Image | str | QByteArray) -> dict[str, Any]: + data = None + colortable = None + exclusive_fp = False + + # handle filename, if given instead of image name + if hasattr(im, "toUtf8"): + # FIXME - is this really the best way to do this? + im = str(im.toUtf8(), "utf-8") + if is_path(im): + im = Image.open(im) + exclusive_fp = True + assert isinstance(im, Image.Image) + + qt_format = getattr(QImage, "Format") if qt_version == "6" else QImage + if im.mode == "1": + format = getattr(qt_format, "Format_Mono") + elif im.mode == "L": + format = getattr(qt_format, "Format_Indexed8") + colortable = [rgb(i, i, i) for i in range(256)] + elif im.mode == "P": + format = getattr(qt_format, "Format_Indexed8") + palette = im.getpalette() + assert palette is not None + colortable = [rgb(*palette[i : i + 3]) for i in range(0, len(palette), 3)] + elif im.mode == "RGB": + # Populate the 4th channel with 255 + im = im.convert("RGBA") + + data = im.tobytes("raw", "BGRA") + format = getattr(qt_format, "Format_RGB32") + elif im.mode == "RGBA": + data = im.tobytes("raw", "BGRA") + format = getattr(qt_format, "Format_ARGB32") + elif im.mode == "I;16": + im = im.point(lambda i: i * 256) + + format = getattr(qt_format, "Format_Grayscale16") + else: + if exclusive_fp: + im.close() + msg = f"unsupported image mode {repr(im.mode)}" + raise ValueError(msg) + + size = im.size + __data = data or align8to32(im.tobytes(), size[0], im.mode) + if exclusive_fp: + im.close() + return {"data": __data, "size": size, "format": format, "colortable": colortable} + + +if qt_is_installed: + + class ImageQt(QImage): # type: ignore[misc] + def __init__(self, im: Image.Image | str | QByteArray) -> None: + """ + An PIL image wrapper for Qt. This is a subclass of PyQt's QImage + class. + + :param im: A PIL Image object, or a file name (given either as + Python string or a PyQt string object). + """ + im_data = _toqclass_helper(im) + # must keep a reference, or Qt will crash! + # All QImage constructors that take data operate on an existing + # buffer, so this buffer has to hang on for the life of the image. + # Fixes https://github.com/python-pillow/Pillow/issues/1370 + self.__data = im_data["data"] + super().__init__( + self.__data, + im_data["size"][0], + im_data["size"][1], + im_data["format"], + ) + if im_data["colortable"]: + self.setColorTable(im_data["colortable"]) + + +def toqimage(im: Image.Image | str | QByteArray) -> ImageQt: + return ImageQt(im) + + +def toqpixmap(im: Image.Image | str | QByteArray) -> QPixmap: + qimage = toqimage(im) + pixmap = getattr(QPixmap, "fromImage")(qimage) + if qt_version == "6": + pixmap.detach() + return pixmap diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py new file mode 100644 index 0000000000000000000000000000000000000000..a6fc340d55f5516934349b3fa62c1276a204b03b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py @@ -0,0 +1,86 @@ +# +# The Python Imaging Library. +# $Id$ +# +# sequence support classes +# +# history: +# 1997-02-20 fl Created +# +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1997 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +## +from __future__ import annotations + +from typing import Callable + +from . import Image + + +class Iterator: + """ + This class implements an iterator object that can be used to loop + over an image sequence. + + You can use the ``[]`` operator to access elements by index. This operator + will raise an :py:exc:`IndexError` if you try to access a nonexistent + frame. + + :param im: An image object. + """ + + def __init__(self, im: Image.Image) -> None: + if not hasattr(im, "seek"): + msg = "im must have seek method" + raise AttributeError(msg) + self.im = im + self.position = getattr(self.im, "_min_frame", 0) + + def __getitem__(self, ix: int) -> Image.Image: + try: + self.im.seek(ix) + return self.im + except EOFError as e: + msg = "end of sequence" + raise IndexError(msg) from e + + def __iter__(self) -> Iterator: + return self + + def __next__(self) -> Image.Image: + try: + self.im.seek(self.position) + self.position += 1 + return self.im + except EOFError as e: + msg = "end of sequence" + raise StopIteration(msg) from e + + +def all_frames( + im: Image.Image | list[Image.Image], + func: Callable[[Image.Image], Image.Image] | None = None, +) -> list[Image.Image]: + """ + Applies a given function to all frames in an image or a list of images. + The frames are returned as a list of separate images. + + :param im: An image, or a list of images. + :param func: The function to apply to all of the image frames. + :returns: A list of images. + """ + if not isinstance(im, list): + im = [im] + + ims = [] + for imSequence in im: + current = imSequence.tell() + + ims += [im_frame.copy() for im_frame in Iterator(imSequence)] + + imSequence.seek(current) + return [func(im) for im in ims] if func else ims diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageShow.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageShow.py new file mode 100644 index 0000000000000000000000000000000000000000..7705608e3eccd5e82cfca87daa1264df2c81dacd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageShow.py @@ -0,0 +1,362 @@ +# +# The Python Imaging Library. +# $Id$ +# +# im.show() drivers +# +# History: +# 2008-04-06 fl Created +# +# Copyright (c) Secret Labs AB 2008. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import os +import shutil +import subprocess +import sys +from shlex import quote +from typing import Any + +from . import Image + +_viewers = [] + + +def register(viewer: type[Viewer] | Viewer, order: int = 1) -> None: + """ + The :py:func:`register` function is used to register additional viewers:: + + from PIL import ImageShow + ImageShow.register(MyViewer()) # MyViewer will be used as a last resort + ImageShow.register(MySecondViewer(), 0) # MySecondViewer will be prioritised + ImageShow.register(ImageShow.XVViewer(), 0) # XVViewer will be prioritised + + :param viewer: The viewer to be registered. + :param order: + Zero or a negative integer to prepend this viewer to the list, + a positive integer to append it. + """ + if isinstance(viewer, type) and issubclass(viewer, Viewer): + viewer = viewer() + if order > 0: + _viewers.append(viewer) + else: + _viewers.insert(0, viewer) + + +def show(image: Image.Image, title: str | None = None, **options: Any) -> bool: + r""" + Display a given image. + + :param image: An image object. + :param title: Optional title. Not all viewers can display the title. + :param \**options: Additional viewer options. + :returns: ``True`` if a suitable viewer was found, ``False`` otherwise. + """ + for viewer in _viewers: + if viewer.show(image, title=title, **options): + return True + return False + + +class Viewer: + """Base class for viewers.""" + + # main api + + def show(self, image: Image.Image, **options: Any) -> int: + """ + The main function for displaying an image. + Converts the given image to the target format and displays it. + """ + + if not ( + image.mode in ("1", "RGBA") + or (self.format == "PNG" and image.mode in ("I;16", "LA")) + ): + base = Image.getmodebase(image.mode) + if image.mode != base: + image = image.convert(base) + + return self.show_image(image, **options) + + # hook methods + + format: str | None = None + """The format to convert the image into.""" + options: dict[str, Any] = {} + """Additional options used to convert the image.""" + + def get_format(self, image: Image.Image) -> str | None: + """Return format name, or ``None`` to save as PGM/PPM.""" + return self.format + + def get_command(self, file: str, **options: Any) -> str: + """ + Returns the command used to display the file. + Not implemented in the base class. + """ + msg = "unavailable in base viewer" + raise NotImplementedError(msg) + + def save_image(self, image: Image.Image) -> str: + """Save to temporary file and return filename.""" + return image._dump(format=self.get_format(image), **self.options) + + def show_image(self, image: Image.Image, **options: Any) -> int: + """Display the given image.""" + return self.show_file(self.save_image(image), **options) + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + os.system(self.get_command(path, **options)) # nosec + return 1 + + +# -------------------------------------------------------------------- + + +class WindowsViewer(Viewer): + """The default viewer on Windows is the default system application for PNG files.""" + + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + def get_command(self, file: str, **options: Any) -> str: + return ( + f'start "Pillow" /WAIT "{file}" ' + "&& ping -n 4 127.0.0.1 >NUL " + f'&& del /f "{file}"' + ) + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen( + self.get_command(path, **options), + shell=True, + creationflags=getattr(subprocess, "CREATE_NO_WINDOW"), + ) # nosec + return 1 + + +if sys.platform == "win32": + register(WindowsViewer) + + +class MacViewer(Viewer): + """The default viewer on macOS using ``Preview.app``.""" + + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + def get_command(self, file: str, **options: Any) -> str: + # on darwin open returns immediately resulting in the temp + # file removal while app is opening + command = "open -a Preview.app" + command = f"({command} {quote(file)}; sleep 20; rm -f {quote(file)})&" + return command + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.call(["open", "-a", "Preview.app", path]) + + pyinstaller = getattr(sys, "frozen", False) and hasattr(sys, "_MEIPASS") + executable = (not pyinstaller and sys.executable) or shutil.which("python3") + if executable: + subprocess.Popen( + [ + executable, + "-c", + "import os, sys, time; time.sleep(20); os.remove(sys.argv[1])", + path, + ] + ) + return 1 + + +if sys.platform == "darwin": + register(MacViewer) + + +class UnixViewer(abc.ABC, Viewer): + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + @abc.abstractmethod + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + pass + + def get_command(self, file: str, **options: Any) -> str: + command = self.get_command_ex(file, **options)[0] + return f"{command} {quote(file)}" + + +class XDGViewer(UnixViewer): + """ + The freedesktop.org ``xdg-open`` command. + """ + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + command = executable = "xdg-open" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["xdg-open", path]) + return 1 + + +class DisplayViewer(UnixViewer): + """ + The ImageMagick ``display`` command. + This viewer supports the ``title`` parameter. + """ + + def get_command_ex( + self, file: str, title: str | None = None, **options: Any + ) -> tuple[str, str]: + command = executable = "display" + if title: + command += f" -title {quote(title)}" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + args = ["display"] + title = options.get("title") + if title: + args += ["-title", title] + args.append(path) + + subprocess.Popen(args) + return 1 + + +class GmDisplayViewer(UnixViewer): + """The GraphicsMagick ``gm display`` command.""" + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + executable = "gm" + command = "gm display" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["gm", "display", path]) + return 1 + + +class EogViewer(UnixViewer): + """The GNOME Image Viewer ``eog`` command.""" + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + executable = "eog" + command = "eog -n" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["eog", "-n", path]) + return 1 + + +class XVViewer(UnixViewer): + """ + The X Viewer ``xv`` command. + This viewer supports the ``title`` parameter. + """ + + def get_command_ex( + self, file: str, title: str | None = None, **options: Any + ) -> tuple[str, str]: + # note: xv is pretty outdated. most modern systems have + # imagemagick's display command instead. + command = executable = "xv" + if title: + command += f" -name {quote(title)}" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + args = ["xv"] + title = options.get("title") + if title: + args += ["-name", title] + args.append(path) + + subprocess.Popen(args) + return 1 + + +if sys.platform not in ("win32", "darwin"): # unixoids + if shutil.which("xdg-open"): + register(XDGViewer) + if shutil.which("display"): + register(DisplayViewer) + if shutil.which("gm"): + register(GmDisplayViewer) + if shutil.which("eog"): + register(EogViewer) + if shutil.which("xv"): + register(XVViewer) + + +class IPythonViewer(Viewer): + """The viewer for IPython frontends.""" + + def show_image(self, image: Image.Image, **options: Any) -> int: + ipython_display(image) + return 1 + + +try: + from IPython.display import display as ipython_display +except ImportError: + pass +else: + register(IPythonViewer) + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 ImageShow.py imagefile [title]") + sys.exit() + + with Image.open(sys.argv[1]) as im: + print(show(im, *sys.argv[2:])) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageStat.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageStat.py new file mode 100644 index 0000000000000000000000000000000000000000..8bc504526f0a00cde1229798234d4f0c5db95138 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageStat.py @@ -0,0 +1,160 @@ +# +# The Python Imaging Library. +# $Id$ +# +# global image statistics +# +# History: +# 1996-04-05 fl Created +# 1997-05-21 fl Added mask; added rms, var, stddev attributes +# 1997-08-05 fl Added median +# 1998-07-05 hk Fixed integer overflow error +# +# Notes: +# This class shows how to implement delayed evaluation of attributes. +# To get a certain value, simply access the corresponding attribute. +# The __getattr__ dispatcher takes care of the rest. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +from functools import cached_property + +from . import Image + + +class Stat: + def __init__( + self, image_or_list: Image.Image | list[int], mask: Image.Image | None = None + ) -> None: + """ + Calculate statistics for the given image. If a mask is included, + only the regions covered by that mask are included in the + statistics. You can also pass in a previously calculated histogram. + + :param image: A PIL image, or a precalculated histogram. + + .. note:: + + For a PIL image, calculations rely on the + :py:meth:`~PIL.Image.Image.histogram` method. The pixel counts are + grouped into 256 bins, even if the image has more than 8 bits per + channel. So ``I`` and ``F`` mode images have a maximum ``mean``, + ``median`` and ``rms`` of 255, and cannot have an ``extrema`` maximum + of more than 255. + + :param mask: An optional mask. + """ + if isinstance(image_or_list, Image.Image): + self.h = image_or_list.histogram(mask) + elif isinstance(image_or_list, list): + self.h = image_or_list + else: + msg = "first argument must be image or list" # type: ignore[unreachable] + raise TypeError(msg) + self.bands = list(range(len(self.h) // 256)) + + @cached_property + def extrema(self) -> list[tuple[int, int]]: + """ + Min/max values for each band in the image. + + .. note:: + This relies on the :py:meth:`~PIL.Image.Image.histogram` method, and + simply returns the low and high bins used. This is correct for + images with 8 bits per channel, but fails for other modes such as + ``I`` or ``F``. Instead, use :py:meth:`~PIL.Image.Image.getextrema` to + return per-band extrema for the image. This is more correct and + efficient because, for non-8-bit modes, the histogram method uses + :py:meth:`~PIL.Image.Image.getextrema` to determine the bins used. + """ + + def minmax(histogram: list[int]) -> tuple[int, int]: + res_min, res_max = 255, 0 + for i in range(256): + if histogram[i]: + res_min = i + break + for i in range(255, -1, -1): + if histogram[i]: + res_max = i + break + return res_min, res_max + + return [minmax(self.h[i:]) for i in range(0, len(self.h), 256)] + + @cached_property + def count(self) -> list[int]: + """Total number of pixels for each band in the image.""" + return [sum(self.h[i : i + 256]) for i in range(0, len(self.h), 256)] + + @cached_property + def sum(self) -> list[float]: + """Sum of all pixels for each band in the image.""" + + v = [] + for i in range(0, len(self.h), 256): + layer_sum = 0.0 + for j in range(256): + layer_sum += j * self.h[i + j] + v.append(layer_sum) + return v + + @cached_property + def sum2(self) -> list[float]: + """Squared sum of all pixels for each band in the image.""" + + v = [] + for i in range(0, len(self.h), 256): + sum2 = 0.0 + for j in range(256): + sum2 += (j**2) * float(self.h[i + j]) + v.append(sum2) + return v + + @cached_property + def mean(self) -> list[float]: + """Average (arithmetic mean) pixel level for each band in the image.""" + return [self.sum[i] / self.count[i] for i in self.bands] + + @cached_property + def median(self) -> list[int]: + """Median pixel level for each band in the image.""" + + v = [] + for i in self.bands: + s = 0 + half = self.count[i] // 2 + b = i * 256 + for j in range(256): + s = s + self.h[b + j] + if s > half: + break + v.append(j) + return v + + @cached_property + def rms(self) -> list[float]: + """RMS (root-mean-square) for each band in the image.""" + return [math.sqrt(self.sum2[i] / self.count[i]) for i in self.bands] + + @cached_property + def var(self) -> list[float]: + """Variance for each band in the image.""" + return [ + (self.sum2[i] - (self.sum[i] ** 2.0) / self.count[i]) / self.count[i] + for i in self.bands + ] + + @cached_property + def stddev(self) -> list[float]: + """Standard deviation for each band in the image.""" + return [math.sqrt(self.var[i]) for i in self.bands] + + +Global = Stat # compatibility diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTk.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTk.py new file mode 100644 index 0000000000000000000000000000000000000000..3a4cb81e9ef5ef4abe617d4a364074c2203571ad --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTk.py @@ -0,0 +1,266 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a Tk display interface +# +# History: +# 96-04-08 fl Created +# 96-09-06 fl Added getimage method +# 96-11-01 fl Rewritten, removed image attribute and crop method +# 97-05-09 fl Use PyImagingPaste method instead of image type +# 97-05-12 fl Minor tweaks to match the IFUNC95 interface +# 97-05-17 fl Support the "pilbitmap" booster patch +# 97-06-05 fl Added file= and data= argument to image constructors +# 98-03-09 fl Added width and height methods to Image classes +# 98-07-02 fl Use default mode for "P" images without palette attribute +# 98-07-02 fl Explicitly destroy Tkinter image objects +# 99-07-24 fl Support multiple Tk interpreters (from Greg Couch) +# 99-07-26 fl Automatically hook into Tkinter (if possible) +# 99-08-15 fl Hook uses _imagingtk instead of _imaging +# +# Copyright (c) 1997-1999 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import tkinter +from io import BytesIO +from typing import Any + +from . import Image, ImageFile + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import CapsuleType + +# -------------------------------------------------------------------- +# Check for Tkinter interface hooks + + +def _get_image_from_kw(kw: dict[str, Any]) -> ImageFile.ImageFile | None: + source = None + if "file" in kw: + source = kw.pop("file") + elif "data" in kw: + source = BytesIO(kw.pop("data")) + if not source: + return None + return Image.open(source) + + +def _pyimagingtkcall( + command: str, photo: PhotoImage | tkinter.PhotoImage, ptr: CapsuleType +) -> None: + tk = photo.tk + try: + tk.call(command, photo, repr(ptr)) + except tkinter.TclError: + # activate Tkinter hook + # may raise an error if it cannot attach to Tkinter + from . import _imagingtk + + _imagingtk.tkinit(tk.interpaddr()) + tk.call(command, photo, repr(ptr)) + + +# -------------------------------------------------------------------- +# PhotoImage + + +class PhotoImage: + """ + A Tkinter-compatible photo image. This can be used + everywhere Tkinter expects an image object. If the image is an RGBA + image, pixels having alpha 0 are treated as transparent. + + The constructor takes either a PIL image, or a mode and a size. + Alternatively, you can use the ``file`` or ``data`` options to initialize + the photo image object. + + :param image: Either a PIL image, or a mode string. If a mode string is + used, a size must also be given. + :param size: If the first argument is a mode string, this defines the size + of the image. + :keyword file: A filename to load the image from (using + ``Image.open(file)``). + :keyword data: An 8-bit string containing image data (as loaded from an + image file). + """ + + def __init__( + self, + image: Image.Image | str | None = None, + size: tuple[int, int] | None = None, + **kw: Any, + ) -> None: + # Tk compatibility: file or data + if image is None: + image = _get_image_from_kw(kw) + + if image is None: + msg = "Image is required" + raise ValueError(msg) + elif isinstance(image, str): + mode = image + image = None + + if size is None: + msg = "If first argument is mode, size is required" + raise ValueError(msg) + else: + # got an image instead of a mode + mode = image.mode + if mode == "P": + # palette mapped data + image.apply_transparency() + image.load() + mode = image.palette.mode if image.palette else "RGB" + size = image.size + kw["width"], kw["height"] = size + + if mode not in ["1", "L", "RGB", "RGBA"]: + mode = Image.getmodebase(mode) + + self.__mode = mode + self.__size = size + self.__photo = tkinter.PhotoImage(**kw) + self.tk = self.__photo.tk + if image: + self.paste(image) + + def __del__(self) -> None: + try: + name = self.__photo.name + except AttributeError: + return + self.__photo.name = None + try: + self.__photo.tk.call("image", "delete", name) + except Exception: + pass # ignore internal errors + + def __str__(self) -> str: + """ + Get the Tkinter photo image identifier. This method is automatically + called by Tkinter whenever a PhotoImage object is passed to a Tkinter + method. + + :return: A Tkinter photo image identifier (a string). + """ + return str(self.__photo) + + def width(self) -> int: + """ + Get the width of the image. + + :return: The width, in pixels. + """ + return self.__size[0] + + def height(self) -> int: + """ + Get the height of the image. + + :return: The height, in pixels. + """ + return self.__size[1] + + def paste(self, im: Image.Image) -> None: + """ + Paste a PIL image into the photo image. Note that this can + be very slow if the photo image is displayed. + + :param im: A PIL image. The size must match the target region. If the + mode does not match, the image is converted to the mode of + the bitmap image. + """ + # convert to blittable + ptr = im.getim() + image = im.im + if not image.isblock() or im.mode != self.__mode: + block = Image.core.new_block(self.__mode, im.size) + image.convert2(block, image) # convert directly between buffers + ptr = block.ptr + + _pyimagingtkcall("PyImagingPhoto", self.__photo, ptr) + + +# -------------------------------------------------------------------- +# BitmapImage + + +class BitmapImage: + """ + A Tkinter-compatible bitmap image. This can be used everywhere Tkinter + expects an image object. + + The given image must have mode "1". Pixels having value 0 are treated as + transparent. Options, if any, are passed on to Tkinter. The most commonly + used option is ``foreground``, which is used to specify the color for the + non-transparent parts. See the Tkinter documentation for information on + how to specify colours. + + :param image: A PIL image. + """ + + def __init__(self, image: Image.Image | None = None, **kw: Any) -> None: + # Tk compatibility: file or data + if image is None: + image = _get_image_from_kw(kw) + + if image is None: + msg = "Image is required" + raise ValueError(msg) + self.__mode = image.mode + self.__size = image.size + + self.__photo = tkinter.BitmapImage(data=image.tobitmap(), **kw) + + def __del__(self) -> None: + try: + name = self.__photo.name + except AttributeError: + return + self.__photo.name = None + try: + self.__photo.tk.call("image", "delete", name) + except Exception: + pass # ignore internal errors + + def width(self) -> int: + """ + Get the width of the image. + + :return: The width, in pixels. + """ + return self.__size[0] + + def height(self) -> int: + """ + Get the height of the image. + + :return: The height, in pixels. + """ + return self.__size[1] + + def __str__(self) -> str: + """ + Get the Tkinter bitmap image identifier. This method is automatically + called by Tkinter whenever a BitmapImage object is passed to a Tkinter + method. + + :return: A Tkinter bitmap image identifier (a string). + """ + return str(self.__photo) + + +def getimage(photo: PhotoImage) -> Image.Image: + """Copies the contents of a PhotoImage to a PIL image memory.""" + im = Image.new("RGBA", (photo.width(), photo.height())) + + _pyimagingtkcall("PyImagingPhotoGet", photo, im.getim()) + + return im diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py new file mode 100644 index 0000000000000000000000000000000000000000..fb144ff38a1ee7ff77cc01f3b941756a60b2b4cd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py @@ -0,0 +1,136 @@ +# +# The Python Imaging Library. +# $Id$ +# +# transform wrappers +# +# History: +# 2002-04-08 fl Created +# +# Copyright (c) 2002 by Secret Labs AB +# Copyright (c) 2002 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from . import Image + + +class Transform(Image.ImageTransformHandler): + """Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`.""" + + method: Image.Transform + + def __init__(self, data: Sequence[Any]) -> None: + self.data = data + + def getdata(self) -> tuple[Image.Transform, Sequence[int]]: + return self.method, self.data + + def transform( + self, + size: tuple[int, int], + image: Image.Image, + **options: Any, + ) -> Image.Image: + """Perform the transform. Called from :py:meth:`.Image.transform`.""" + # can be overridden + method, data = self.getdata() + return image.transform(size, method, data, **options) + + +class AffineTransform(Transform): + """ + Define an affine image transform. + + This function takes a 6-tuple (a, b, c, d, e, f) which contain the first + two rows from the inverse of an affine transform matrix. For each pixel + (x, y) in the output image, the new value is taken from a position (a x + + b y + c, d x + e y + f) in the input image, rounded to nearest pixel. + + This function can be used to scale, translate, rotate, and shear the + original image. + + See :py:meth:`.Image.transform` + + :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows + from the inverse of an affine transform matrix. + """ + + method = Image.Transform.AFFINE + + +class PerspectiveTransform(Transform): + """ + Define a perspective image transform. + + This function takes an 8-tuple (a, b, c, d, e, f, g, h). For each pixel + (x, y) in the output image, the new value is taken from a position + ((a x + b y + c) / (g x + h y + 1), (d x + e y + f) / (g x + h y + 1)) in + the input image, rounded to nearest pixel. + + This function can be used to scale, translate, rotate, and shear the + original image. + + See :py:meth:`.Image.transform` + + :param matrix: An 8-tuple (a, b, c, d, e, f, g, h). + """ + + method = Image.Transform.PERSPECTIVE + + +class ExtentTransform(Transform): + """ + Define a transform to extract a subregion from an image. + + Maps a rectangle (defined by two corners) from the image to a rectangle of + the given size. The resulting image will contain data sampled from between + the corners, such that (x0, y0) in the input image will end up at (0,0) in + the output image, and (x1, y1) at size. + + This method can be used to crop, stretch, shrink, or mirror an arbitrary + rectangle in the current image. It is slightly slower than crop, but about + as fast as a corresponding resize operation. + + See :py:meth:`.Image.transform` + + :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the + input image's coordinate system. See :ref:`coordinate-system`. + """ + + method = Image.Transform.EXTENT + + +class QuadTransform(Transform): + """ + Define a quad image transform. + + Maps a quadrilateral (a region defined by four corners) from the image to a + rectangle of the given size. + + See :py:meth:`.Image.transform` + + :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the + upper left, lower left, lower right, and upper right corner of the + source quadrilateral. + """ + + method = Image.Transform.QUAD + + +class MeshTransform(Transform): + """ + Define a mesh image transform. A mesh transform consists of one or more + individual quad transforms. + + See :py:meth:`.Image.transform` + + :param data: A list of (bbox, quad) tuples. + """ + + method = Image.Transform.MESH diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageWin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageWin.py new file mode 100644 index 0000000000000000000000000000000000000000..98c28f29f1dbbb069b68dc9359051b6629148f0d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImageWin.py @@ -0,0 +1,247 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a Windows DIB display interface +# +# History: +# 1996-05-20 fl Created +# 1996-09-20 fl Fixed subregion exposure +# 1997-09-21 fl Added draw primitive (for tzPrint) +# 2003-05-21 fl Added experimental Window/ImageWindow classes +# 2003-09-05 fl Added fromstring/tostring methods +# +# Copyright (c) Secret Labs AB 1997-2003. +# Copyright (c) Fredrik Lundh 1996-2003. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image + + +class HDC: + """ + Wraps an HDC integer. The resulting object can be passed to the + :py:meth:`~PIL.ImageWin.Dib.draw` and :py:meth:`~PIL.ImageWin.Dib.expose` + methods. + """ + + def __init__(self, dc: int) -> None: + self.dc = dc + + def __int__(self) -> int: + return self.dc + + +class HWND: + """ + Wraps an HWND integer. The resulting object can be passed to the + :py:meth:`~PIL.ImageWin.Dib.draw` and :py:meth:`~PIL.ImageWin.Dib.expose` + methods, instead of a DC. + """ + + def __init__(self, wnd: int) -> None: + self.wnd = wnd + + def __int__(self) -> int: + return self.wnd + + +class Dib: + """ + A Windows bitmap with the given mode and size. The mode can be one of "1", + "L", "P", or "RGB". + + If the display requires a palette, this constructor creates a suitable + palette and associates it with the image. For an "L" image, 128 graylevels + are allocated. For an "RGB" image, a 6x6x6 colour cube is used, together + with 20 graylevels. + + To make sure that palettes work properly under Windows, you must call the + ``palette`` method upon certain events from Windows. + + :param image: Either a PIL image, or a mode string. If a mode string is + used, a size must also be given. The mode can be one of "1", + "L", "P", or "RGB". + :param size: If the first argument is a mode string, this + defines the size of the image. + """ + + def __init__( + self, image: Image.Image | str, size: tuple[int, int] | None = None + ) -> None: + if isinstance(image, str): + mode = image + image = "" + if size is None: + msg = "If first argument is mode, size is required" + raise ValueError(msg) + else: + mode = image.mode + size = image.size + if mode not in ["1", "L", "P", "RGB"]: + mode = Image.getmodebase(mode) + self.image = Image.core.display(mode, size) + self.mode = mode + self.size = size + if image: + assert not isinstance(image, str) + self.paste(image) + + def expose(self, handle: int | HDC | HWND) -> None: + """ + Copy the bitmap contents to a device context. + + :param handle: Device context (HDC), cast to a Python integer, or an + HDC or HWND instance. In PythonWin, you can use + ``CDC.GetHandleAttrib()`` to get a suitable handle. + """ + handle_int = int(handle) + if isinstance(handle, HWND): + dc = self.image.getdc(handle_int) + try: + self.image.expose(dc) + finally: + self.image.releasedc(handle_int, dc) + else: + self.image.expose(handle_int) + + def draw( + self, + handle: int | HDC | HWND, + dst: tuple[int, int, int, int], + src: tuple[int, int, int, int] | None = None, + ) -> None: + """ + Same as expose, but allows you to specify where to draw the image, and + what part of it to draw. + + The destination and source areas are given as 4-tuple rectangles. If + the source is omitted, the entire image is copied. If the source and + the destination have different sizes, the image is resized as + necessary. + """ + if src is None: + src = (0, 0) + self.size + handle_int = int(handle) + if isinstance(handle, HWND): + dc = self.image.getdc(handle_int) + try: + self.image.draw(dc, dst, src) + finally: + self.image.releasedc(handle_int, dc) + else: + self.image.draw(handle_int, dst, src) + + def query_palette(self, handle: int | HDC | HWND) -> int: + """ + Installs the palette associated with the image in the given device + context. + + This method should be called upon **QUERYNEWPALETTE** and + **PALETTECHANGED** events from Windows. If this method returns a + non-zero value, one or more display palette entries were changed, and + the image should be redrawn. + + :param handle: Device context (HDC), cast to a Python integer, or an + HDC or HWND instance. + :return: The number of entries that were changed (if one or more entries, + this indicates that the image should be redrawn). + """ + handle_int = int(handle) + if isinstance(handle, HWND): + handle = self.image.getdc(handle_int) + try: + result = self.image.query_palette(handle) + finally: + self.image.releasedc(handle, handle) + else: + result = self.image.query_palette(handle_int) + return result + + def paste( + self, im: Image.Image, box: tuple[int, int, int, int] | None = None + ) -> None: + """ + Paste a PIL image into the bitmap image. + + :param im: A PIL image. The size must match the target region. + If the mode does not match, the image is converted to the + mode of the bitmap image. + :param box: A 4-tuple defining the left, upper, right, and + lower pixel coordinate. See :ref:`coordinate-system`. If + None is given instead of a tuple, all of the image is + assumed. + """ + im.load() + if self.mode != im.mode: + im = im.convert(self.mode) + if box: + self.image.paste(im.im, box) + else: + self.image.paste(im.im) + + def frombytes(self, buffer: bytes) -> None: + """ + Load display memory contents from byte data. + + :param buffer: A buffer containing display data (usually + data returned from :py:func:`~PIL.ImageWin.Dib.tobytes`) + """ + self.image.frombytes(buffer) + + def tobytes(self) -> bytes: + """ + Copy display memory contents to bytes object. + + :return: A bytes object containing display data. + """ + return self.image.tobytes() + + +class Window: + """Create a Window with the given title size.""" + + def __init__( + self, title: str = "PIL", width: int | None = None, height: int | None = None + ) -> None: + self.hwnd = Image.core.createwindow( + title, self.__dispatcher, width or 0, height or 0 + ) + + def __dispatcher(self, action: str, *args: int) -> None: + getattr(self, f"ui_handle_{action}")(*args) + + def ui_handle_clear(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_damage(self, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_destroy(self) -> None: + pass + + def ui_handle_repair(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_resize(self, width: int, height: int) -> None: + pass + + def mainloop(self) -> None: + Image.core.eventloop() + + +class ImageWindow(Window): + """Create an image window which displays the given image.""" + + def __init__(self, image: Image.Image | Dib, title: str = "PIL") -> None: + if not isinstance(image, Dib): + image = Dib(image) + self.image = image + width, height = image.size + super().__init__(title, width=width, height=height) + + def ui_handle_repair(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + self.image.draw(dc, (x0, y0, x1, y1)) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..c4eccee3423dc6c273bdc1ea88eda5ef4e17cf7d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py @@ -0,0 +1,103 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IM Tools support for PIL +# +# history: +# 1996-05-27 fl Created (read 8-bit images only) +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.2) +# +# Copyright (c) Secret Labs AB 1997-2001. +# Copyright (c) Fredrik Lundh 1996-2001. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re + +from . import Image, ImageFile + +# +# -------------------------------------------------------------------- + +field = re.compile(rb"([a-z]*) ([^ \r\n]*)") + + +## +# Image plugin for IM Tools images. + + +class ImtImageFile(ImageFile.ImageFile): + format = "IMT" + format_description = "IM Tools" + + def _open(self) -> None: + # Quick rejection: if there's not a LF among the first + # 100 bytes, this is (probably) not a text header. + + assert self.fp is not None + + buffer = self.fp.read(100) + if b"\n" not in buffer: + msg = "not an IM file" + raise SyntaxError(msg) + + xsize = ysize = 0 + + while True: + if buffer: + s = buffer[:1] + buffer = buffer[1:] + else: + s = self.fp.read(1) + if not s: + break + + if s == b"\x0c": + # image data begins + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + self.fp.tell() - len(buffer), + self.mode, + ) + ] + + break + + else: + # read key/value pair + if b"\n" not in buffer: + buffer += self.fp.read(100) + lines = buffer.split(b"\n") + s += lines.pop(0) + buffer = b"\n".join(lines) + if len(s) == 1 or len(s) > 100: + break + if s[0] == ord(b"*"): + continue # comment + + m = field.match(s) + if not m: + break + k, v = m.group(1, 2) + if k == b"width": + xsize = int(v) + self._size = xsize, ysize + elif k == b"height": + ysize = int(v) + self._size = xsize, ysize + elif k == b"pixel" and v == b"n8": + self._mode = "L" + + +# +# -------------------------------------------------------------------- + +Image.register_open(ImtImageFile.format, ImtImageFile) + +# +# no extension registered (".im" is simply too common) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..fc024d668f2f42c4b0ea4c90c702ccc6d7c528f0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py @@ -0,0 +1,250 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IPTC/NAA file handling +# +# history: +# 1995-10-01 fl Created +# 1998-03-09 fl Cleaned up and added to PIL +# 2002-06-18 fl Added getiptcinfo helper +# +# Copyright (c) Secret Labs AB 1997-2002. +# Copyright (c) Fredrik Lundh 1995. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from collections.abc import Sequence +from io import BytesIO +from typing import cast + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._deprecate import deprecate + +COMPRESSION = {1: "raw", 5: "jpeg"} + + +def __getattr__(name: str) -> bytes: + if name == "PAD": + deprecate("IptcImagePlugin.PAD", 12) + return b"\0\0\0\0" + msg = f"module '{__name__}' has no attribute '{name}'" + raise AttributeError(msg) + + +# +# Helpers + + +def _i(c: bytes) -> int: + return i32((b"\0\0\0\0" + c)[-4:]) + + +def _i8(c: int | bytes) -> int: + return c if isinstance(c, int) else c[0] + + +def i(c: bytes) -> int: + """.. deprecated:: 10.2.0""" + deprecate("IptcImagePlugin.i", 12) + return _i(c) + + +def dump(c: Sequence[int | bytes]) -> None: + """.. deprecated:: 10.2.0""" + deprecate("IptcImagePlugin.dump", 12) + for i in c: + print(f"{_i8(i):02x}", end=" ") + print() + + +## +# Image plugin for IPTC/NAA datastreams. To read IPTC/NAA fields +# from TIFF and JPEG files, use the getiptcinfo function. + + +class IptcImageFile(ImageFile.ImageFile): + format = "IPTC" + format_description = "IPTC/NAA" + + def getint(self, key: tuple[int, int]) -> int: + return _i(self.info[key]) + + def field(self) -> tuple[tuple[int, int] | None, int]: + # + # get a IPTC field header + s = self.fp.read(5) + if not s.strip(b"\x00"): + return None, 0 + + tag = s[1], s[2] + + # syntax + if s[0] != 0x1C or tag[0] not in [1, 2, 3, 4, 5, 6, 7, 8, 9, 240]: + msg = "invalid IPTC/NAA file" + raise SyntaxError(msg) + + # field size + size = s[3] + if size > 132: + msg = "illegal field length in IPTC/NAA file" + raise OSError(msg) + elif size == 128: + size = 0 + elif size > 128: + size = _i(self.fp.read(size - 128)) + else: + size = i16(s, 3) + + return tag, size + + def _open(self) -> None: + # load descriptive fields + while True: + offset = self.fp.tell() + tag, size = self.field() + if not tag or tag == (8, 10): + break + if size: + tagdata = self.fp.read(size) + else: + tagdata = None + if tag in self.info: + if isinstance(self.info[tag], list): + self.info[tag].append(tagdata) + else: + self.info[tag] = [self.info[tag], tagdata] + else: + self.info[tag] = tagdata + + # mode + layers = self.info[(3, 60)][0] + component = self.info[(3, 60)][1] + if (3, 65) in self.info: + id = self.info[(3, 65)][0] - 1 + else: + id = 0 + if layers == 1 and not component: + self._mode = "L" + elif layers == 3 and component: + self._mode = "RGB"[id] + elif layers == 4 and component: + self._mode = "CMYK"[id] + + # size + self._size = self.getint((3, 20)), self.getint((3, 30)) + + # compression + try: + compression = COMPRESSION[self.getint((3, 120))] + except KeyError as e: + msg = "Unknown IPTC image compression" + raise OSError(msg) from e + + # tile + if tag == (8, 10): + self.tile = [ + ImageFile._Tile("iptc", (0, 0) + self.size, offset, compression) + ] + + def load(self) -> Image.core.PixelAccess | None: + if len(self.tile) != 1 or self.tile[0][0] != "iptc": + return ImageFile.ImageFile.load(self) + + offset, compression = self.tile[0][2:] + + self.fp.seek(offset) + + # Copy image data to temporary file + o = BytesIO() + if compression == "raw": + # To simplify access to the extracted file, + # prepend a PPM header + o.write(b"P5\n%d %d\n255\n" % self.size) + while True: + type, size = self.field() + if type != (8, 10): + break + while size > 0: + s = self.fp.read(min(size, 8192)) + if not s: + break + o.write(s) + size -= len(s) + + with Image.open(o) as _im: + _im.load() + self.im = _im.im + self.tile = [] + return Image.Image.load(self) + + +Image.register_open(IptcImageFile.format, IptcImageFile) + +Image.register_extension(IptcImageFile.format, ".iim") + + +def getiptcinfo( + im: ImageFile.ImageFile, +) -> dict[tuple[int, int], bytes | list[bytes]] | None: + """ + Get IPTC information from TIFF, JPEG, or IPTC file. + + :param im: An image containing IPTC data. + :returns: A dictionary containing IPTC information, or None if + no IPTC information block was found. + """ + from . import JpegImagePlugin, TiffImagePlugin + + data = None + + info: dict[tuple[int, int], bytes | list[bytes]] = {} + if isinstance(im, IptcImageFile): + # return info dictionary right away + for k, v in im.info.items(): + if isinstance(k, tuple): + info[k] = v + return info + + elif isinstance(im, JpegImagePlugin.JpegImageFile): + # extract the IPTC/NAA resource + photoshop = im.info.get("photoshop") + if photoshop: + data = photoshop.get(0x0404) + + elif isinstance(im, TiffImagePlugin.TiffImageFile): + # get raw data from the IPTC/NAA tag (PhotoShop tags the data + # as 4-byte integers, so we cannot use the get method...) + try: + data = im.tag_v2._tagdata[TiffImagePlugin.IPTC_NAA_CHUNK] + except KeyError: + pass + + if data is None: + return None # no properties + + # create an IptcImagePlugin object without initializing it + class FakeImage: + pass + + fake_im = FakeImage() + fake_im.__class__ = IptcImageFile # type: ignore[assignment] + iptc_im = cast(IptcImageFile, fake_im) + + # parse the IPTC information chunk + iptc_im.info = {} + iptc_im.fp = BytesIO(data) + + try: + iptc_im._open() + except (IndexError, KeyError): + pass # expected failure + + for k, v in iptc_im.info.items(): + if isinstance(k, tuple): + info[k] = v + return info diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..e0f4ecae595d3f1aef3f529d91efeefa560c5134 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py @@ -0,0 +1,442 @@ +# +# The Python Imaging Library +# $Id$ +# +# JPEG2000 file handling +# +# History: +# 2014-03-12 ajh Created +# 2021-06-30 rogermb Extract dpi information from the 'resc' header box +# +# Copyright (c) 2014 Coriolis Systems Limited +# Copyright (c) 2014 Alastair Houghton +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import struct +from collections.abc import Callable +from typing import IO, cast + +from . import Image, ImageFile, ImagePalette, _binary + + +class BoxReader: + """ + A small helper class to read fields stored in JPEG2000 header boxes + and to easily step into and read sub-boxes. + """ + + def __init__(self, fp: IO[bytes], length: int = -1) -> None: + self.fp = fp + self.has_length = length >= 0 + self.length = length + self.remaining_in_box = -1 + + def _can_read(self, num_bytes: int) -> bool: + if self.has_length and self.fp.tell() + num_bytes > self.length: + # Outside box: ensure we don't read past the known file length + return False + if self.remaining_in_box >= 0: + # Inside box contents: ensure read does not go past box boundaries + return num_bytes <= self.remaining_in_box + else: + return True # No length known, just read + + def _read_bytes(self, num_bytes: int) -> bytes: + if not self._can_read(num_bytes): + msg = "Not enough data in header" + raise SyntaxError(msg) + + data = self.fp.read(num_bytes) + if len(data) < num_bytes: + msg = f"Expected to read {num_bytes} bytes but only got {len(data)}." + raise OSError(msg) + + if self.remaining_in_box > 0: + self.remaining_in_box -= num_bytes + return data + + def read_fields(self, field_format: str) -> tuple[int | bytes, ...]: + size = struct.calcsize(field_format) + data = self._read_bytes(size) + return struct.unpack(field_format, data) + + def read_boxes(self) -> BoxReader: + size = self.remaining_in_box + data = self._read_bytes(size) + return BoxReader(io.BytesIO(data), size) + + def has_next_box(self) -> bool: + if self.has_length: + return self.fp.tell() + self.remaining_in_box < self.length + else: + return True + + def next_box_type(self) -> bytes: + # Skip the rest of the box if it has not been read + if self.remaining_in_box > 0: + self.fp.seek(self.remaining_in_box, os.SEEK_CUR) + self.remaining_in_box = -1 + + # Read the length and type of the next box + lbox, tbox = cast(tuple[int, bytes], self.read_fields(">I4s")) + if lbox == 1: + lbox = cast(int, self.read_fields(">Q")[0]) + hlen = 16 + else: + hlen = 8 + + if lbox < hlen or not self._can_read(lbox - hlen): + msg = "Invalid header length" + raise SyntaxError(msg) + + self.remaining_in_box = lbox - hlen + return tbox + + +def _parse_codestream(fp: IO[bytes]) -> tuple[tuple[int, int], str]: + """Parse the JPEG 2000 codestream to extract the size and component + count from the SIZ marker segment, returning a PIL (size, mode) tuple.""" + + hdr = fp.read(2) + lsiz = _binary.i16be(hdr) + siz = hdr + fp.read(lsiz - 2) + lsiz, rsiz, xsiz, ysiz, xosiz, yosiz, _, _, _, _, csiz = struct.unpack_from( + ">HHIIIIIIIIH", siz + ) + + size = (xsiz - xosiz, ysiz - yosiz) + if csiz == 1: + ssiz = struct.unpack_from(">B", siz, 38) + if (ssiz[0] & 0x7F) + 1 > 8: + mode = "I;16" + else: + mode = "L" + elif csiz == 2: + mode = "LA" + elif csiz == 3: + mode = "RGB" + elif csiz == 4: + mode = "RGBA" + else: + msg = "unable to determine J2K image mode" + raise SyntaxError(msg) + + return size, mode + + +def _res_to_dpi(num: int, denom: int, exp: int) -> float | None: + """Convert JPEG2000's (numerator, denominator, exponent-base-10) resolution, + calculated as (num / denom) * 10^exp and stored in dots per meter, + to floating-point dots per inch.""" + if denom == 0: + return None + return (254 * num * (10**exp)) / (10000 * denom) + + +def _parse_jp2_header( + fp: IO[bytes], +) -> tuple[ + tuple[int, int], + str, + str | None, + tuple[float, float] | None, + ImagePalette.ImagePalette | None, +]: + """Parse the JP2 header box to extract size, component count, + color space information, and optionally DPI information, + returning a (size, mode, mimetype, dpi) tuple.""" + + # Find the JP2 header box + reader = BoxReader(fp) + header = None + mimetype = None + while reader.has_next_box(): + tbox = reader.next_box_type() + + if tbox == b"jp2h": + header = reader.read_boxes() + break + elif tbox == b"ftyp": + if reader.read_fields(">4s")[0] == b"jpx ": + mimetype = "image/jpx" + assert header is not None + + size = None + mode = None + bpc = None + nc = None + dpi = None # 2-tuple of DPI info, or None + palette = None + + while header.has_next_box(): + tbox = header.next_box_type() + + if tbox == b"ihdr": + height, width, nc, bpc = header.read_fields(">IIHB") + assert isinstance(height, int) + assert isinstance(width, int) + assert isinstance(bpc, int) + size = (width, height) + if nc == 1 and (bpc & 0x7F) > 8: + mode = "I;16" + elif nc == 1: + mode = "L" + elif nc == 2: + mode = "LA" + elif nc == 3: + mode = "RGB" + elif nc == 4: + mode = "RGBA" + elif tbox == b"colr" and nc == 4: + meth, _, _, enumcs = header.read_fields(">BBBI") + if meth == 1 and enumcs == 12: + mode = "CMYK" + elif tbox == b"pclr" and mode in ("L", "LA"): + ne, npc = header.read_fields(">HB") + assert isinstance(ne, int) + assert isinstance(npc, int) + max_bitdepth = 0 + for bitdepth in header.read_fields(">" + ("B" * npc)): + assert isinstance(bitdepth, int) + if bitdepth > max_bitdepth: + max_bitdepth = bitdepth + if max_bitdepth <= 8: + palette = ImagePalette.ImagePalette("RGBA" if npc == 4 else "RGB") + for i in range(ne): + color: list[int] = [] + for value in header.read_fields(">" + ("B" * npc)): + assert isinstance(value, int) + color.append(value) + palette.getcolor(tuple(color)) + mode = "P" if mode == "L" else "PA" + elif tbox == b"res ": + res = header.read_boxes() + while res.has_next_box(): + tres = res.next_box_type() + if tres == b"resc": + vrcn, vrcd, hrcn, hrcd, vrce, hrce = res.read_fields(">HHHHBB") + assert isinstance(vrcn, int) + assert isinstance(vrcd, int) + assert isinstance(hrcn, int) + assert isinstance(hrcd, int) + assert isinstance(vrce, int) + assert isinstance(hrce, int) + hres = _res_to_dpi(hrcn, hrcd, hrce) + vres = _res_to_dpi(vrcn, vrcd, vrce) + if hres is not None and vres is not None: + dpi = (hres, vres) + break + + if size is None or mode is None: + msg = "Malformed JP2 header" + raise SyntaxError(msg) + + return size, mode, mimetype, dpi, palette + + +## +# Image plugin for JPEG2000 images. + + +class Jpeg2KImageFile(ImageFile.ImageFile): + format = "JPEG2000" + format_description = "JPEG 2000 (ISO 15444)" + + def _open(self) -> None: + sig = self.fp.read(4) + if sig == b"\xff\x4f\xff\x51": + self.codec = "j2k" + self._size, self._mode = _parse_codestream(self.fp) + self._parse_comment() + else: + sig = sig + self.fp.read(8) + + if sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a": + self.codec = "jp2" + header = _parse_jp2_header(self.fp) + self._size, self._mode, self.custom_mimetype, dpi, self.palette = header + if dpi is not None: + self.info["dpi"] = dpi + if self.fp.read(12).endswith(b"jp2c\xff\x4f\xff\x51"): + hdr = self.fp.read(2) + length = _binary.i16be(hdr) + self.fp.seek(length - 2, os.SEEK_CUR) + self._parse_comment() + else: + msg = "not a JPEG 2000 file" + raise SyntaxError(msg) + + self._reduce = 0 + self.layers = 0 + + fd = -1 + length = -1 + + try: + fd = self.fp.fileno() + length = os.fstat(fd).st_size + except Exception: + fd = -1 + try: + pos = self.fp.tell() + self.fp.seek(0, io.SEEK_END) + length = self.fp.tell() + self.fp.seek(pos) + except Exception: + length = -1 + + self.tile = [ + ImageFile._Tile( + "jpeg2k", + (0, 0) + self.size, + 0, + (self.codec, self._reduce, self.layers, fd, length), + ) + ] + + def _parse_comment(self) -> None: + while True: + marker = self.fp.read(2) + if not marker: + break + typ = marker[1] + if typ in (0x90, 0xD9): + # Start of tile or end of codestream + break + hdr = self.fp.read(2) + length = _binary.i16be(hdr) + if typ == 0x64: + # Comment + self.info["comment"] = self.fp.read(length - 2)[2:] + break + else: + self.fp.seek(length - 2, os.SEEK_CUR) + + @property # type: ignore[override] + def reduce( + self, + ) -> ( + Callable[[int | tuple[int, int], tuple[int, int, int, int] | None], Image.Image] + | int + ): + # https://github.com/python-pillow/Pillow/issues/4343 found that the + # new Image 'reduce' method was shadowed by this plugin's 'reduce' + # property. This attempts to allow for both scenarios + return self._reduce or super().reduce + + @reduce.setter + def reduce(self, value: int) -> None: + self._reduce = value + + def load(self) -> Image.core.PixelAccess | None: + if self.tile and self._reduce: + power = 1 << self._reduce + adjust = power >> 1 + self._size = ( + int((self.size[0] + adjust) / power), + int((self.size[1] + adjust) / power), + ) + + # Update the reduce and layers settings + t = self.tile[0] + assert isinstance(t[3], tuple) + t3 = (t[3][0], self._reduce, self.layers, t[3][3], t[3][4]) + self.tile = [ImageFile._Tile(t[0], (0, 0) + self.size, t[2], t3)] + + return ImageFile.ImageFile.load(self) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith( + (b"\xff\x4f\xff\x51", b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a") + ) + + +# ------------------------------------------------------------ +# Save support + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # Get the keyword arguments + info = im.encoderinfo + + if isinstance(filename, str): + filename = filename.encode() + if filename.endswith(b".j2k") or info.get("no_jp2", False): + kind = "j2k" + else: + kind = "jp2" + + offset = info.get("offset", None) + tile_offset = info.get("tile_offset", None) + tile_size = info.get("tile_size", None) + quality_mode = info.get("quality_mode", "rates") + quality_layers = info.get("quality_layers", None) + if quality_layers is not None and not ( + isinstance(quality_layers, (list, tuple)) + and all( + isinstance(quality_layer, (int, float)) for quality_layer in quality_layers + ) + ): + msg = "quality_layers must be a sequence of numbers" + raise ValueError(msg) + + num_resolutions = info.get("num_resolutions", 0) + cblk_size = info.get("codeblock_size", None) + precinct_size = info.get("precinct_size", None) + irreversible = info.get("irreversible", False) + progression = info.get("progression", "LRCP") + cinema_mode = info.get("cinema_mode", "no") + mct = info.get("mct", 0) + signed = info.get("signed", False) + comment = info.get("comment") + if isinstance(comment, str): + comment = comment.encode() + plt = info.get("plt", False) + + fd = -1 + if hasattr(fp, "fileno"): + try: + fd = fp.fileno() + except Exception: + fd = -1 + + im.encoderconfig = ( + offset, + tile_offset, + tile_size, + quality_mode, + quality_layers, + num_resolutions, + cblk_size, + precinct_size, + irreversible, + progression, + cinema_mode, + mct, + signed, + fd, + comment, + plt, + ) + + ImageFile._save(im, fp, [ImageFile._Tile("jpeg2k", (0, 0) + im.size, 0, kind)]) + + +# ------------------------------------------------------------ +# Registry stuff + + +Image.register_open(Jpeg2KImageFile.format, Jpeg2KImageFile, _accept) +Image.register_save(Jpeg2KImageFile.format, _save) + +Image.register_extensions( + Jpeg2KImageFile.format, [".jp2", ".j2k", ".jpc", ".jpf", ".jpx", ".j2c"] +) + +Image.register_mime(Jpeg2KImageFile.format, "image/jp2") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..defe9f773f9215c9f5f31f918edfdaeda6474a16 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py @@ -0,0 +1,902 @@ +# +# The Python Imaging Library. +# $Id$ +# +# JPEG (JFIF) file handling +# +# See "Digital Compression and Coding of Continuous-Tone Still Images, +# Part 1, Requirements and Guidelines" (CCITT T.81 / ISO 10918-1) +# +# History: +# 1995-09-09 fl Created +# 1995-09-13 fl Added full parser +# 1996-03-25 fl Added hack to use the IJG command line utilities +# 1996-05-05 fl Workaround Photoshop 2.5 CMYK polarity bug +# 1996-05-28 fl Added draft support, JFIF version (0.1) +# 1996-12-30 fl Added encoder options, added progression property (0.2) +# 1997-08-27 fl Save mode 1 images as BW (0.3) +# 1998-07-12 fl Added YCbCr to draft and save methods (0.4) +# 1998-10-19 fl Don't hang on files using 16-bit DQT's (0.4.1) +# 2001-04-16 fl Extract DPI settings from JFIF files (0.4.2) +# 2002-07-01 fl Skip pad bytes before markers; identify Exif files (0.4.3) +# 2003-04-25 fl Added experimental EXIF decoder (0.5) +# 2003-06-06 fl Added experimental EXIF GPSinfo decoder +# 2003-09-13 fl Extract COM markers +# 2009-09-06 fl Added icc_profile support (from Florian Hoech) +# 2009-03-06 fl Changed CMYK handling; always use Adobe polarity (0.6) +# 2009-03-08 fl Added subsampling support (from Justin Huff). +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import array +import io +import math +import os +import struct +import subprocess +import sys +import tempfile +import warnings +from typing import IO, Any + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o16be as o16 +from ._deprecate import deprecate +from .JpegPresets import presets + +TYPE_CHECKING = False +if TYPE_CHECKING: + from .MpoImagePlugin import MpoImageFile + +# +# Parser + + +def Skip(self: JpegImageFile, marker: int) -> None: + n = i16(self.fp.read(2)) - 2 + ImageFile._safe_read(self.fp, n) + + +def APP(self: JpegImageFile, marker: int) -> None: + # + # Application marker. Store these in the APP dictionary. + # Also look for well-known application markers. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + + app = f"APP{marker & 15}" + + self.app[app] = s # compatibility + self.applist.append((app, s)) + + if marker == 0xFFE0 and s.startswith(b"JFIF"): + # extract JFIF information + self.info["jfif"] = version = i16(s, 5) # version + self.info["jfif_version"] = divmod(version, 256) + # extract JFIF properties + try: + jfif_unit = s[7] + jfif_density = i16(s, 8), i16(s, 10) + except Exception: + pass + else: + if jfif_unit == 1: + self.info["dpi"] = jfif_density + elif jfif_unit == 2: # cm + # 1 dpcm = 2.54 dpi + self.info["dpi"] = tuple(d * 2.54 for d in jfif_density) + self.info["jfif_unit"] = jfif_unit + self.info["jfif_density"] = jfif_density + elif marker == 0xFFE1 and s.startswith(b"Exif\0\0"): + # extract EXIF information + if "exif" in self.info: + self.info["exif"] += s[6:] + else: + self.info["exif"] = s + self._exif_offset = self.fp.tell() - n + 6 + elif marker == 0xFFE1 and s.startswith(b"http://ns.adobe.com/xap/1.0/\x00"): + self.info["xmp"] = s.split(b"\x00", 1)[1] + elif marker == 0xFFE2 and s.startswith(b"FPXR\0"): + # extract FlashPix information (incomplete) + self.info["flashpix"] = s # FIXME: value will change + elif marker == 0xFFE2 and s.startswith(b"ICC_PROFILE\0"): + # Since an ICC profile can be larger than the maximum size of + # a JPEG marker (64K), we need provisions to split it into + # multiple markers. The format defined by the ICC specifies + # one or more APP2 markers containing the following data: + # Identifying string ASCII "ICC_PROFILE\0" (12 bytes) + # Marker sequence number 1, 2, etc (1 byte) + # Number of markers Total of APP2's used (1 byte) + # Profile data (remainder of APP2 data) + # Decoders should use the marker sequence numbers to + # reassemble the profile, rather than assuming that the APP2 + # markers appear in the correct sequence. + self.icclist.append(s) + elif marker == 0xFFED and s.startswith(b"Photoshop 3.0\x00"): + # parse the image resource block + offset = 14 + photoshop = self.info.setdefault("photoshop", {}) + while s[offset : offset + 4] == b"8BIM": + try: + offset += 4 + # resource code + code = i16(s, offset) + offset += 2 + # resource name (usually empty) + name_len = s[offset] + # name = s[offset+1:offset+1+name_len] + offset += 1 + name_len + offset += offset & 1 # align + # resource data block + size = i32(s, offset) + offset += 4 + data = s[offset : offset + size] + if code == 0x03ED: # ResolutionInfo + photoshop[code] = { + "XResolution": i32(data, 0) / 65536, + "DisplayedUnitsX": i16(data, 4), + "YResolution": i32(data, 8) / 65536, + "DisplayedUnitsY": i16(data, 12), + } + else: + photoshop[code] = data + offset += size + offset += offset & 1 # align + except struct.error: + break # insufficient data + + elif marker == 0xFFEE and s.startswith(b"Adobe"): + self.info["adobe"] = i16(s, 5) + # extract Adobe custom properties + try: + adobe_transform = s[11] + except IndexError: + pass + else: + self.info["adobe_transform"] = adobe_transform + elif marker == 0xFFE2 and s.startswith(b"MPF\0"): + # extract MPO information + self.info["mp"] = s[4:] + # offset is current location minus buffer size + # plus constant header size + self.info["mpoffset"] = self.fp.tell() - n + 4 + + +def COM(self: JpegImageFile, marker: int) -> None: + # + # Comment marker. Store these in the APP dictionary. + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + + self.info["comment"] = s + self.app["COM"] = s # compatibility + self.applist.append(("COM", s)) + + +def SOF(self: JpegImageFile, marker: int) -> None: + # + # Start of frame marker. Defines the size and mode of the + # image. JPEG is colour blind, so we use some simple + # heuristics to map the number of layers to an appropriate + # mode. Note that this could be made a bit brighter, by + # looking for JFIF and Adobe APP markers. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + self._size = i16(s, 3), i16(s, 1) + + self.bits = s[0] + if self.bits != 8: + msg = f"cannot handle {self.bits}-bit layers" + raise SyntaxError(msg) + + self.layers = s[5] + if self.layers == 1: + self._mode = "L" + elif self.layers == 3: + self._mode = "RGB" + elif self.layers == 4: + self._mode = "CMYK" + else: + msg = f"cannot handle {self.layers}-layer images" + raise SyntaxError(msg) + + if marker in [0xFFC2, 0xFFC6, 0xFFCA, 0xFFCE]: + self.info["progressive"] = self.info["progression"] = 1 + + if self.icclist: + # fixup icc profile + self.icclist.sort() # sort by sequence number + if self.icclist[0][13] == len(self.icclist): + profile = [p[14:] for p in self.icclist] + icc_profile = b"".join(profile) + else: + icc_profile = None # wrong number of fragments + self.info["icc_profile"] = icc_profile + self.icclist = [] + + for i in range(6, len(s), 3): + t = s[i : i + 3] + # 4-tuples: id, vsamp, hsamp, qtable + self.layer.append((t[0], t[1] // 16, t[1] & 15, t[2])) + + +def DQT(self: JpegImageFile, marker: int) -> None: + # + # Define quantization table. Note that there might be more + # than one table in each marker. + + # FIXME: The quantization tables can be used to estimate the + # compression quality. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + while len(s): + v = s[0] + precision = 1 if (v // 16 == 0) else 2 # in bytes + qt_length = 1 + precision * 64 + if len(s) < qt_length: + msg = "bad quantization table marker" + raise SyntaxError(msg) + data = array.array("B" if precision == 1 else "H", s[1:qt_length]) + if sys.byteorder == "little" and precision > 1: + data.byteswap() # the values are always big-endian + self.quantization[v & 15] = [data[i] for i in zigzag_index] + s = s[qt_length:] + + +# +# JPEG marker table + +MARKER = { + 0xFFC0: ("SOF0", "Baseline DCT", SOF), + 0xFFC1: ("SOF1", "Extended Sequential DCT", SOF), + 0xFFC2: ("SOF2", "Progressive DCT", SOF), + 0xFFC3: ("SOF3", "Spatial lossless", SOF), + 0xFFC4: ("DHT", "Define Huffman table", Skip), + 0xFFC5: ("SOF5", "Differential sequential DCT", SOF), + 0xFFC6: ("SOF6", "Differential progressive DCT", SOF), + 0xFFC7: ("SOF7", "Differential spatial", SOF), + 0xFFC8: ("JPG", "Extension", None), + 0xFFC9: ("SOF9", "Extended sequential DCT (AC)", SOF), + 0xFFCA: ("SOF10", "Progressive DCT (AC)", SOF), + 0xFFCB: ("SOF11", "Spatial lossless DCT (AC)", SOF), + 0xFFCC: ("DAC", "Define arithmetic coding conditioning", Skip), + 0xFFCD: ("SOF13", "Differential sequential DCT (AC)", SOF), + 0xFFCE: ("SOF14", "Differential progressive DCT (AC)", SOF), + 0xFFCF: ("SOF15", "Differential spatial (AC)", SOF), + 0xFFD0: ("RST0", "Restart 0", None), + 0xFFD1: ("RST1", "Restart 1", None), + 0xFFD2: ("RST2", "Restart 2", None), + 0xFFD3: ("RST3", "Restart 3", None), + 0xFFD4: ("RST4", "Restart 4", None), + 0xFFD5: ("RST5", "Restart 5", None), + 0xFFD6: ("RST6", "Restart 6", None), + 0xFFD7: ("RST7", "Restart 7", None), + 0xFFD8: ("SOI", "Start of image", None), + 0xFFD9: ("EOI", "End of image", None), + 0xFFDA: ("SOS", "Start of scan", Skip), + 0xFFDB: ("DQT", "Define quantization table", DQT), + 0xFFDC: ("DNL", "Define number of lines", Skip), + 0xFFDD: ("DRI", "Define restart interval", Skip), + 0xFFDE: ("DHP", "Define hierarchical progression", SOF), + 0xFFDF: ("EXP", "Expand reference component", Skip), + 0xFFE0: ("APP0", "Application segment 0", APP), + 0xFFE1: ("APP1", "Application segment 1", APP), + 0xFFE2: ("APP2", "Application segment 2", APP), + 0xFFE3: ("APP3", "Application segment 3", APP), + 0xFFE4: ("APP4", "Application segment 4", APP), + 0xFFE5: ("APP5", "Application segment 5", APP), + 0xFFE6: ("APP6", "Application segment 6", APP), + 0xFFE7: ("APP7", "Application segment 7", APP), + 0xFFE8: ("APP8", "Application segment 8", APP), + 0xFFE9: ("APP9", "Application segment 9", APP), + 0xFFEA: ("APP10", "Application segment 10", APP), + 0xFFEB: ("APP11", "Application segment 11", APP), + 0xFFEC: ("APP12", "Application segment 12", APP), + 0xFFED: ("APP13", "Application segment 13", APP), + 0xFFEE: ("APP14", "Application segment 14", APP), + 0xFFEF: ("APP15", "Application segment 15", APP), + 0xFFF0: ("JPG0", "Extension 0", None), + 0xFFF1: ("JPG1", "Extension 1", None), + 0xFFF2: ("JPG2", "Extension 2", None), + 0xFFF3: ("JPG3", "Extension 3", None), + 0xFFF4: ("JPG4", "Extension 4", None), + 0xFFF5: ("JPG5", "Extension 5", None), + 0xFFF6: ("JPG6", "Extension 6", None), + 0xFFF7: ("JPG7", "Extension 7", None), + 0xFFF8: ("JPG8", "Extension 8", None), + 0xFFF9: ("JPG9", "Extension 9", None), + 0xFFFA: ("JPG10", "Extension 10", None), + 0xFFFB: ("JPG11", "Extension 11", None), + 0xFFFC: ("JPG12", "Extension 12", None), + 0xFFFD: ("JPG13", "Extension 13", None), + 0xFFFE: ("COM", "Comment", COM), +} + + +def _accept(prefix: bytes) -> bool: + # Magic number was taken from https://en.wikipedia.org/wiki/JPEG + return prefix.startswith(b"\xff\xd8\xff") + + +## +# Image plugin for JPEG and JFIF images. + + +class JpegImageFile(ImageFile.ImageFile): + format = "JPEG" + format_description = "JPEG (ISO 10918)" + + def _open(self) -> None: + s = self.fp.read(3) + + if not _accept(s): + msg = "not a JPEG file" + raise SyntaxError(msg) + s = b"\xff" + + # Create attributes + self.bits = self.layers = 0 + self._exif_offset = 0 + + # JPEG specifics (internal) + self.layer: list[tuple[int, int, int, int]] = [] + self._huffman_dc: dict[Any, Any] = {} + self._huffman_ac: dict[Any, Any] = {} + self.quantization: dict[int, list[int]] = {} + self.app: dict[str, bytes] = {} # compatibility + self.applist: list[tuple[str, bytes]] = [] + self.icclist: list[bytes] = [] + + while True: + i = s[0] + if i == 0xFF: + s = s + self.fp.read(1) + i = i16(s) + else: + # Skip non-0xFF junk + s = self.fp.read(1) + continue + + if i in MARKER: + name, description, handler = MARKER[i] + if handler is not None: + handler(self, i) + if i == 0xFFDA: # start of scan + rawmode = self.mode + if self.mode == "CMYK": + rawmode = "CMYK;I" # assume adobe conventions + self.tile = [ + ImageFile._Tile("jpeg", (0, 0) + self.size, 0, (rawmode, "")) + ] + # self.__offset = self.fp.tell() + break + s = self.fp.read(1) + elif i in {0, 0xFFFF}: + # padded marker or junk; move on + s = b"\xff" + elif i == 0xFF00: # Skip extraneous data (escaped 0xFF) + s = self.fp.read(1) + else: + msg = "no marker found" + raise SyntaxError(msg) + + self._read_dpi_from_exif() + + def __getattr__(self, name: str) -> Any: + if name in ("huffman_ac", "huffman_dc"): + deprecate(name, 12) + return getattr(self, "_" + name) + raise AttributeError(name) + + def __getstate__(self) -> list[Any]: + return super().__getstate__() + [self.layers, self.layer] + + def __setstate__(self, state: list[Any]) -> None: + self.layers, self.layer = state[6:] + super().__setstate__(state) + + def load_read(self, read_bytes: int) -> bytes: + """ + internal: read more image data + For premature EOF and LOAD_TRUNCATED_IMAGES adds EOI marker + so libjpeg can finish decoding + """ + s = self.fp.read(read_bytes) + + if not s and ImageFile.LOAD_TRUNCATED_IMAGES and not hasattr(self, "_ended"): + # Premature EOF. + # Pretend file is finished adding EOI marker + self._ended = True + return b"\xff\xd9" + + return s + + def draft( + self, mode: str | None, size: tuple[int, int] | None + ) -> tuple[str, tuple[int, int, float, float]] | None: + if len(self.tile) != 1: + return None + + # Protect from second call + if self.decoderconfig: + return None + + d, e, o, a = self.tile[0] + scale = 1 + original_size = self.size + + assert isinstance(a, tuple) + if a[0] == "RGB" and mode in ["L", "YCbCr"]: + self._mode = mode + a = mode, "" + + if size: + scale = min(self.size[0] // size[0], self.size[1] // size[1]) + for s in [8, 4, 2, 1]: + if scale >= s: + break + assert e is not None + e = ( + e[0], + e[1], + (e[2] - e[0] + s - 1) // s + e[0], + (e[3] - e[1] + s - 1) // s + e[1], + ) + self._size = ((self.size[0] + s - 1) // s, (self.size[1] + s - 1) // s) + scale = s + + self.tile = [ImageFile._Tile(d, e, o, a)] + self.decoderconfig = (scale, 0) + + box = (0, 0, original_size[0] / scale, original_size[1] / scale) + return self.mode, box + + def load_djpeg(self) -> None: + # ALTERNATIVE: handle JPEGs via the IJG command line utilities + + f, path = tempfile.mkstemp() + os.close(f) + if os.path.exists(self.filename): + subprocess.check_call(["djpeg", "-outfile", path, self.filename]) + else: + try: + os.unlink(path) + except OSError: + pass + + msg = "Invalid Filename" + raise ValueError(msg) + + try: + with Image.open(path) as _im: + _im.load() + self.im = _im.im + finally: + try: + os.unlink(path) + except OSError: + pass + + self._mode = self.im.mode + self._size = self.im.size + + self.tile = [] + + def _getexif(self) -> dict[int, Any] | None: + return _getexif(self) + + def _read_dpi_from_exif(self) -> None: + # If DPI isn't in JPEG header, fetch from EXIF + if "dpi" in self.info or "exif" not in self.info: + return + try: + exif = self.getexif() + resolution_unit = exif[0x0128] + x_resolution = exif[0x011A] + try: + dpi = float(x_resolution[0]) / x_resolution[1] + except TypeError: + dpi = x_resolution + if math.isnan(dpi): + msg = "DPI is not a number" + raise ValueError(msg) + if resolution_unit == 3: # cm + # 1 dpcm = 2.54 dpi + dpi *= 2.54 + self.info["dpi"] = dpi, dpi + except ( + struct.error, # truncated EXIF + KeyError, # dpi not included + SyntaxError, # invalid/unreadable EXIF + TypeError, # dpi is an invalid float + ValueError, # dpi is an invalid float + ZeroDivisionError, # invalid dpi rational value + ): + self.info["dpi"] = 72, 72 + + def _getmp(self) -> dict[int, Any] | None: + return _getmp(self) + + +def _getexif(self: JpegImageFile) -> dict[int, Any] | None: + if "exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + +def _getmp(self: JpegImageFile) -> dict[int, Any] | None: + # Extract MP information. This method was inspired by the "highly + # experimental" _getexif version that's been in use for years now, + # itself based on the ImageFileDirectory class in the TIFF plugin. + + # The MP record essentially consists of a TIFF file embedded in a JPEG + # application marker. + try: + data = self.info["mp"] + except KeyError: + return None + file_contents = io.BytesIO(data) + head = file_contents.read(8) + endianness = ">" if head.startswith(b"\x4d\x4d\x00\x2a") else "<" + # process dictionary + from . import TiffImagePlugin + + try: + info = TiffImagePlugin.ImageFileDirectory_v2(head) + file_contents.seek(info.next) + info.load(file_contents) + mp = dict(info) + except Exception as e: + msg = "malformed MP Index (unreadable directory)" + raise SyntaxError(msg) from e + # it's an error not to have a number of images + try: + quant = mp[0xB001] + except KeyError as e: + msg = "malformed MP Index (no number of images)" + raise SyntaxError(msg) from e + # get MP entries + mpentries = [] + try: + rawmpentries = mp[0xB002] + for entrynum in range(quant): + unpackedentry = struct.unpack_from( + f"{endianness}LLLHH", rawmpentries, entrynum * 16 + ) + labels = ("Attribute", "Size", "DataOffset", "EntryNo1", "EntryNo2") + mpentry = dict(zip(labels, unpackedentry)) + mpentryattr = { + "DependentParentImageFlag": bool(mpentry["Attribute"] & (1 << 31)), + "DependentChildImageFlag": bool(mpentry["Attribute"] & (1 << 30)), + "RepresentativeImageFlag": bool(mpentry["Attribute"] & (1 << 29)), + "Reserved": (mpentry["Attribute"] & (3 << 27)) >> 27, + "ImageDataFormat": (mpentry["Attribute"] & (7 << 24)) >> 24, + "MPType": mpentry["Attribute"] & 0x00FFFFFF, + } + if mpentryattr["ImageDataFormat"] == 0: + mpentryattr["ImageDataFormat"] = "JPEG" + else: + msg = "unsupported picture format in MPO" + raise SyntaxError(msg) + mptypemap = { + 0x000000: "Undefined", + 0x010001: "Large Thumbnail (VGA Equivalent)", + 0x010002: "Large Thumbnail (Full HD Equivalent)", + 0x020001: "Multi-Frame Image (Panorama)", + 0x020002: "Multi-Frame Image: (Disparity)", + 0x020003: "Multi-Frame Image: (Multi-Angle)", + 0x030000: "Baseline MP Primary Image", + } + mpentryattr["MPType"] = mptypemap.get(mpentryattr["MPType"], "Unknown") + mpentry["Attribute"] = mpentryattr + mpentries.append(mpentry) + mp[0xB002] = mpentries + except KeyError as e: + msg = "malformed MP Index (bad MP Entry)" + raise SyntaxError(msg) from e + # Next we should try and parse the individual image unique ID list; + # we don't because I've never seen this actually used in a real MPO + # file and so can't test it. + return mp + + +# -------------------------------------------------------------------- +# stuff to save JPEG files + +RAWMODE = { + "1": "L", + "L": "L", + "RGB": "RGB", + "RGBX": "RGB", + "CMYK": "CMYK;I", # assume adobe conventions + "YCbCr": "YCbCr", +} + +# fmt: off +zigzag_index = ( + 0, 1, 5, 6, 14, 15, 27, 28, + 2, 4, 7, 13, 16, 26, 29, 42, + 3, 8, 12, 17, 25, 30, 41, 43, + 9, 11, 18, 24, 31, 40, 44, 53, + 10, 19, 23, 32, 39, 45, 52, 54, + 20, 22, 33, 38, 46, 51, 55, 60, + 21, 34, 37, 47, 50, 56, 59, 61, + 35, 36, 48, 49, 57, 58, 62, 63, +) + +samplings = { + (1, 1, 1, 1, 1, 1): 0, + (2, 1, 1, 1, 1, 1): 1, + (2, 2, 1, 1, 1, 1): 2, +} +# fmt: on + + +def get_sampling(im: Image.Image) -> int: + # There's no subsampling when images have only 1 layer + # (grayscale images) or when they are CMYK (4 layers), + # so set subsampling to the default value. + # + # NOTE: currently Pillow can't encode JPEG to YCCK format. + # If YCCK support is added in the future, subsampling code will have + # to be updated (here and in JpegEncode.c) to deal with 4 layers. + if not isinstance(im, JpegImageFile) or im.layers in (1, 4): + return -1 + sampling = im.layer[0][1:3] + im.layer[1][1:3] + im.layer[2][1:3] + return samplings.get(sampling, -1) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.width == 0 or im.height == 0: + msg = "cannot write empty image as JPEG" + raise ValueError(msg) + + try: + rawmode = RAWMODE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as JPEG" + raise OSError(msg) from e + + info = im.encoderinfo + + dpi = [round(x) for x in info.get("dpi", (0, 0))] + + quality = info.get("quality", -1) + subsampling = info.get("subsampling", -1) + qtables = info.get("qtables") + + if quality == "keep": + quality = -1 + subsampling = "keep" + qtables = "keep" + elif quality in presets: + preset = presets[quality] + quality = -1 + subsampling = preset.get("subsampling", -1) + qtables = preset.get("quantization") + elif not isinstance(quality, int): + msg = "Invalid quality setting" + raise ValueError(msg) + else: + if subsampling in presets: + subsampling = presets[subsampling].get("subsampling", -1) + if isinstance(qtables, str) and qtables in presets: + qtables = presets[qtables].get("quantization") + + if subsampling == "4:4:4": + subsampling = 0 + elif subsampling == "4:2:2": + subsampling = 1 + elif subsampling == "4:2:0": + subsampling = 2 + elif subsampling == "4:1:1": + # For compatibility. Before Pillow 4.3, 4:1:1 actually meant 4:2:0. + # Set 4:2:0 if someone is still using that value. + subsampling = 2 + elif subsampling == "keep": + if im.format != "JPEG": + msg = "Cannot use 'keep' when original image is not a JPEG" + raise ValueError(msg) + subsampling = get_sampling(im) + + def validate_qtables( + qtables: ( + str | tuple[list[int], ...] | list[list[int]] | dict[int, list[int]] | None + ), + ) -> list[list[int]] | None: + if qtables is None: + return qtables + if isinstance(qtables, str): + try: + lines = [ + int(num) + for line in qtables.splitlines() + for num in line.split("#", 1)[0].split() + ] + except ValueError as e: + msg = "Invalid quantization table" + raise ValueError(msg) from e + else: + qtables = [lines[s : s + 64] for s in range(0, len(lines), 64)] + if isinstance(qtables, (tuple, list, dict)): + if isinstance(qtables, dict): + qtables = [ + qtables[key] for key in range(len(qtables)) if key in qtables + ] + elif isinstance(qtables, tuple): + qtables = list(qtables) + if not (0 < len(qtables) < 5): + msg = "None or too many quantization tables" + raise ValueError(msg) + for idx, table in enumerate(qtables): + try: + if len(table) != 64: + msg = "Invalid quantization table" + raise TypeError(msg) + table_array = array.array("H", table) + except TypeError as e: + msg = "Invalid quantization table" + raise ValueError(msg) from e + else: + qtables[idx] = list(table_array) + return qtables + + if qtables == "keep": + if im.format != "JPEG": + msg = "Cannot use 'keep' when original image is not a JPEG" + raise ValueError(msg) + qtables = getattr(im, "quantization", None) + qtables = validate_qtables(qtables) + + extra = info.get("extra", b"") + + MAX_BYTES_IN_MARKER = 65533 + if xmp := info.get("xmp"): + overhead_len = 29 # b"http://ns.adobe.com/xap/1.0/\x00" + max_data_bytes_in_marker = MAX_BYTES_IN_MARKER - overhead_len + if len(xmp) > max_data_bytes_in_marker: + msg = "XMP data is too long" + raise ValueError(msg) + size = o16(2 + overhead_len + len(xmp)) + extra += b"\xff\xe1" + size + b"http://ns.adobe.com/xap/1.0/\x00" + xmp + + if icc_profile := info.get("icc_profile"): + overhead_len = 14 # b"ICC_PROFILE\0" + o8(i) + o8(len(markers)) + max_data_bytes_in_marker = MAX_BYTES_IN_MARKER - overhead_len + markers = [] + while icc_profile: + markers.append(icc_profile[:max_data_bytes_in_marker]) + icc_profile = icc_profile[max_data_bytes_in_marker:] + i = 1 + for marker in markers: + size = o16(2 + overhead_len + len(marker)) + extra += ( + b"\xff\xe2" + + size + + b"ICC_PROFILE\0" + + o8(i) + + o8(len(markers)) + + marker + ) + i += 1 + + comment = info.get("comment", im.info.get("comment")) + + # "progressive" is the official name, but older documentation + # says "progression" + # FIXME: issue a warning if the wrong form is used (post-1.1.7) + progressive = info.get("progressive", False) or info.get("progression", False) + + optimize = info.get("optimize", False) + + exif = info.get("exif", b"") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + if len(exif) > MAX_BYTES_IN_MARKER: + msg = "EXIF data is too long" + raise ValueError(msg) + + # get keyword arguments + im.encoderconfig = ( + quality, + progressive, + info.get("smooth", 0), + optimize, + info.get("keep_rgb", False), + info.get("streamtype", 0), + dpi, + subsampling, + info.get("restart_marker_blocks", 0), + info.get("restart_marker_rows", 0), + qtables, + comment, + extra, + exif, + ) + + # if we optimize, libjpeg needs a buffer big enough to hold the whole image + # in a shot. Guessing on the size, at im.size bytes. (raw pixel size is + # channels*size, this is a value that's been used in a django patch. + # https://github.com/matthewwithanm/django-imagekit/issues/50 + if optimize or progressive: + # CMYK can be bigger + if im.mode == "CMYK": + bufsize = 4 * im.size[0] * im.size[1] + # keep sets quality to -1, but the actual value may be high. + elif quality >= 95 or quality == -1: + bufsize = 2 * im.size[0] * im.size[1] + else: + bufsize = im.size[0] * im.size[1] + if exif: + bufsize += len(exif) + 5 + if extra: + bufsize += len(extra) + 1 + else: + # The EXIF info needs to be written as one block, + APP1, + one spare byte. + # Ensure that our buffer is big enough. Same with the icc_profile block. + bufsize = max(len(exif) + 5, len(extra) + 1) + + ImageFile._save( + im, fp, [ImageFile._Tile("jpeg", (0, 0) + im.size, 0, rawmode)], bufsize + ) + + +def _save_cjpeg(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # ALTERNATIVE: handle JPEGs via the IJG command line utilities. + tempfile = im._dump() + subprocess.check_call(["cjpeg", "-outfile", filename, tempfile]) + try: + os.unlink(tempfile) + except OSError: + pass + + +## +# Factory for making JPEG and MPO instances +def jpeg_factory( + fp: IO[bytes], filename: str | bytes | None = None +) -> JpegImageFile | MpoImageFile: + im = JpegImageFile(fp, filename) + try: + mpheader = im._getmp() + if mpheader is not None and mpheader[45057] > 1: + for segment, content in im.applist: + if segment == "APP1" and b' hdrgm:Version="' in content: + # Ultra HDR images are not yet supported + return im + # It's actually an MPO + from .MpoImagePlugin import MpoImageFile + + # Don't reload everything, just convert it. + im = MpoImageFile.adopt(im, mpheader) + except (TypeError, IndexError): + # It is really a JPEG + pass + except SyntaxError: + warnings.warn( + "Image appears to be a malformed MPO file, it will be " + "interpreted as a base JPEG file" + ) + return im + + +# --------------------------------------------------------------------- +# Registry stuff + +Image.register_open(JpegImageFile.format, jpeg_factory, _accept) +Image.register_save(JpegImageFile.format, _save) + +Image.register_extensions(JpegImageFile.format, [".jfif", ".jpe", ".jpg", ".jpeg"]) + +Image.register_mime(JpegImageFile.format, "image/jpeg") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e64a35ee1b6fe3ac6da792682a3129253993bb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py @@ -0,0 +1,242 @@ +""" +JPEG quality settings equivalent to the Photoshop settings. +Can be used when saving JPEG files. + +The following presets are available by default: +``web_low``, ``web_medium``, ``web_high``, ``web_very_high``, ``web_maximum``, +``low``, ``medium``, ``high``, ``maximum``. +More presets can be added to the :py:data:`presets` dict if needed. + +To apply the preset, specify:: + + quality="preset_name" + +To apply only the quantization table:: + + qtables="preset_name" + +To apply only the subsampling setting:: + + subsampling="preset_name" + +Example:: + + im.save("image_name.jpg", quality="web_high") + +Subsampling +----------- + +Subsampling is the practice of encoding images by implementing less resolution +for chroma information than for luma information. +(ref.: https://en.wikipedia.org/wiki/Chroma_subsampling) + +Possible subsampling values are 0, 1 and 2 that correspond to 4:4:4, 4:2:2 and +4:2:0. + +You can get the subsampling of a JPEG with the +:func:`.JpegImagePlugin.get_sampling` function. + +In JPEG compressed data a JPEG marker is used instead of an EXIF tag. +(ref.: https://exiv2.org/tags.html) + + +Quantization tables +------------------- + +They are values use by the DCT (Discrete cosine transform) to remove +*unnecessary* information from the image (the lossy part of the compression). +(ref.: https://en.wikipedia.org/wiki/Quantization_matrix#Quantization_matrices, +https://en.wikipedia.org/wiki/JPEG#Quantization) + +You can get the quantization tables of a JPEG with:: + + im.quantization + +This will return a dict with a number of lists. You can pass this dict +directly as the qtables argument when saving a JPEG. + +The quantization table format in presets is a list with sublists. These formats +are interchangeable. + +Libjpeg ref.: +https://web.archive.org/web/20120328125543/http://www.jpegcameras.com/libjpeg/libjpeg-3.html + +""" + +from __future__ import annotations + +# fmt: off +presets = { + 'web_low': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [20, 16, 25, 39, 50, 46, 62, 68, + 16, 18, 23, 38, 38, 53, 65, 68, + 25, 23, 31, 38, 53, 65, 68, 68, + 39, 38, 38, 53, 65, 68, 68, 68, + 50, 38, 53, 65, 68, 68, 68, 68, + 46, 53, 65, 68, 68, 68, 68, 68, + 62, 65, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68], + [21, 25, 32, 38, 54, 68, 68, 68, + 25, 28, 24, 38, 54, 68, 68, 68, + 32, 24, 32, 43, 66, 68, 68, 68, + 38, 38, 43, 53, 68, 68, 68, 68, + 54, 54, 66, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68] + ]}, + 'web_medium': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [16, 11, 11, 16, 23, 27, 31, 30, + 11, 12, 12, 15, 20, 23, 23, 30, + 11, 12, 13, 16, 23, 26, 35, 47, + 16, 15, 16, 23, 26, 37, 47, 64, + 23, 20, 23, 26, 39, 51, 64, 64, + 27, 23, 26, 37, 51, 64, 64, 64, + 31, 23, 35, 47, 64, 64, 64, 64, + 30, 30, 47, 64, 64, 64, 64, 64], + [17, 15, 17, 21, 20, 26, 38, 48, + 15, 19, 18, 17, 20, 26, 35, 43, + 17, 18, 20, 22, 26, 30, 46, 53, + 21, 17, 22, 28, 30, 39, 53, 64, + 20, 20, 26, 30, 39, 48, 64, 64, + 26, 26, 30, 39, 48, 63, 64, 64, + 38, 35, 46, 53, 64, 64, 64, 64, + 48, 43, 53, 64, 64, 64, 64, 64] + ]}, + 'web_high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [6, 4, 4, 6, 9, 11, 12, 16, + 4, 5, 5, 6, 8, 10, 12, 12, + 4, 5, 5, 6, 10, 12, 14, 19, + 6, 6, 6, 11, 12, 15, 19, 28, + 9, 8, 10, 12, 16, 20, 27, 31, + 11, 10, 12, 15, 20, 27, 31, 31, + 12, 12, 14, 19, 27, 31, 31, 31, + 16, 12, 19, 28, 31, 31, 31, 31], + [7, 7, 13, 24, 26, 31, 31, 31, + 7, 12, 16, 21, 31, 31, 31, 31, + 13, 16, 17, 31, 31, 31, 31, 31, + 24, 21, 31, 31, 31, 31, 31, 31, + 26, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31] + ]}, + 'web_very_high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 4, 5, 7, 9, + 2, 2, 2, 4, 5, 7, 9, 12, + 3, 3, 4, 5, 8, 10, 12, 12, + 4, 4, 5, 7, 10, 12, 12, 12, + 5, 5, 7, 9, 12, 12, 12, 12, + 6, 6, 9, 12, 12, 12, 12, 12], + [3, 3, 5, 9, 13, 15, 15, 15, + 3, 4, 6, 11, 14, 12, 12, 12, + 5, 6, 9, 14, 12, 12, 12, 12, + 9, 11, 14, 12, 12, 12, 12, 12, + 13, 14, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'web_maximum': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 2, + 1, 1, 1, 1, 1, 1, 2, 2, + 1, 1, 1, 1, 1, 2, 2, 3, + 1, 1, 1, 1, 2, 2, 3, 3, + 1, 1, 1, 2, 2, 3, 3, 3, + 1, 1, 2, 2, 3, 3, 3, 3], + [1, 1, 1, 2, 2, 3, 3, 3, + 1, 1, 1, 2, 3, 3, 3, 3, + 1, 1, 1, 3, 3, 3, 3, 3, + 2, 2, 3, 3, 3, 3, 3, 3, + 2, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3] + ]}, + 'low': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [18, 14, 14, 21, 30, 35, 34, 17, + 14, 16, 16, 19, 26, 23, 12, 12, + 14, 16, 17, 21, 23, 12, 12, 12, + 21, 19, 21, 23, 12, 12, 12, 12, + 30, 26, 23, 12, 12, 12, 12, 12, + 35, 23, 12, 12, 12, 12, 12, 12, + 34, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12], + [20, 19, 22, 27, 20, 20, 17, 17, + 19, 25, 23, 14, 14, 12, 12, 12, + 22, 23, 14, 14, 12, 12, 12, 12, + 27, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'medium': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [12, 8, 8, 12, 17, 21, 24, 17, + 8, 9, 9, 11, 15, 19, 12, 12, + 8, 9, 10, 12, 19, 12, 12, 12, + 12, 11, 12, 21, 12, 12, 12, 12, + 17, 15, 19, 12, 12, 12, 12, 12, + 21, 19, 12, 12, 12, 12, 12, 12, + 24, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12], + [13, 11, 13, 16, 20, 20, 17, 17, + 11, 14, 14, 14, 14, 12, 12, 12, + 13, 14, 14, 14, 12, 12, 12, 12, + 16, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [6, 4, 4, 6, 9, 11, 12, 16, + 4, 5, 5, 6, 8, 10, 12, 12, + 4, 5, 5, 6, 10, 12, 12, 12, + 6, 6, 6, 11, 12, 12, 12, 12, + 9, 8, 10, 12, 12, 12, 12, 12, + 11, 10, 12, 12, 12, 12, 12, 12, + 12, 12, 12, 12, 12, 12, 12, 12, + 16, 12, 12, 12, 12, 12, 12, 12], + [7, 7, 13, 24, 20, 20, 17, 17, + 7, 12, 16, 14, 14, 12, 12, 12, + 13, 16, 14, 14, 12, 12, 12, 12, + 24, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'maximum': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 4, 5, 7, 9, + 2, 2, 2, 4, 5, 7, 9, 12, + 3, 3, 4, 5, 8, 10, 12, 12, + 4, 4, 5, 7, 10, 12, 12, 12, + 5, 5, 7, 9, 12, 12, 12, 12, + 6, 6, 9, 12, 12, 12, 12, 12], + [3, 3, 5, 9, 13, 15, 15, 15, + 3, 4, 6, 10, 14, 12, 12, 12, + 5, 6, 9, 14, 12, 12, 12, 12, + 9, 10, 14, 12, 12, 12, 12, 12, + 13, 14, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12] + ]}, +} +# fmt: on diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..9a47933b69cbdc628faafb67b2fca8de703abfc1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py @@ -0,0 +1,78 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Basic McIdas support for PIL +# +# History: +# 1997-05-05 fl Created (8-bit images only) +# 2009-03-08 fl Added 16/32-bit support. +# +# Thanks to Richard Jones and Craig Swank for specs and samples. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import struct + +from . import Image, ImageFile + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x00\x00\x00\x00\x00\x00\x00\x04") + + +## +# Image plugin for McIdas area images. + + +class McIdasImageFile(ImageFile.ImageFile): + format = "MCIDAS" + format_description = "McIdas area file" + + def _open(self) -> None: + # parse area file directory + assert self.fp is not None + + s = self.fp.read(256) + if not _accept(s) or len(s) != 256: + msg = "not an McIdas area file" + raise SyntaxError(msg) + + self.area_descriptor_raw = s + self.area_descriptor = w = [0, *struct.unpack("!64i", s)] + + # get mode + if w[11] == 1: + mode = rawmode = "L" + elif w[11] == 2: + mode = rawmode = "I;16B" + elif w[11] == 4: + # FIXME: add memory map support + mode = "I" + rawmode = "I;32B" + else: + msg = "unsupported McIdas format" + raise SyntaxError(msg) + + self._mode = mode + self._size = w[10], w[9] + + offset = w[34] + w[15] + stride = w[15] + w[10] * w[11] * w[14] + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offset, (rawmode, stride, 1)) + ] + + +# -------------------------------------------------------------------- +# registry + +Image.register_open(McIdasImageFile.format, McIdasImageFile, _accept) + +# no default extension diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..9ce38c427b6c19be9e0c5092181a54b936a7a2f3 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py @@ -0,0 +1,102 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Microsoft Image Composer support for PIL +# +# Notes: +# uses TiffImagePlugin.py to read the actual image streams +# +# History: +# 97-01-20 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import olefile + +from . import Image, TiffImagePlugin + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(olefile.MAGIC) + + +## +# Image plugin for Microsoft's Image Composer file format. + + +class MicImageFile(TiffImagePlugin.TiffImageFile): + format = "MIC" + format_description = "Microsoft Image Composer" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # read the OLE directory and see if this is a likely + # to be a Microsoft Image Composer file + + try: + self.ole = olefile.OleFileIO(self.fp) + except OSError as e: + msg = "not an MIC file; invalid OLE file" + raise SyntaxError(msg) from e + + # find ACI subfiles with Image members (maybe not the + # best way to identify MIC files, but what the... ;-) + + self.images = [ + path + for path in self.ole.listdir() + if path[1:] and path[0].endswith(".ACI") and path[1] == "Image" + ] + + # if we didn't find any images, this is probably not + # an MIC file. + if not self.images: + msg = "not an MIC file; no image entries" + raise SyntaxError(msg) + + self.frame = -1 + self._n_frames = len(self.images) + self.is_animated = self._n_frames > 1 + + self.__fp = self.fp + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + filename = self.images[frame] + self.fp = self.ole.openstream(filename) + + TiffImagePlugin.TiffImageFile._open(self) + + self.frame = frame + + def tell(self) -> int: + return self.frame + + def close(self) -> None: + self.__fp.close() + self.ole.close() + super().close() + + def __exit__(self, *args: object) -> None: + self.__fp.close() + self.ole.close() + super().__exit__() + + +# +# -------------------------------------------------------------------- + +Image.register_open(MicImageFile.format, MicImageFile, _accept) + +Image.register_extension(MicImageFile.format, ".mic") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..47ebe9d62c4edd3b5e97f760ff7e9b0417e5b5ab --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py @@ -0,0 +1,84 @@ +# +# The Python Imaging Library. +# $Id$ +# +# MPEG file handling +# +# History: +# 95-09-09 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i8 +from ._typing import SupportsRead + +# +# Bitstream parser + + +class BitStream: + def __init__(self, fp: SupportsRead[bytes]) -> None: + self.fp = fp + self.bits = 0 + self.bitbuffer = 0 + + def next(self) -> int: + return i8(self.fp.read(1)) + + def peek(self, bits: int) -> int: + while self.bits < bits: + self.bitbuffer = (self.bitbuffer << 8) + self.next() + self.bits += 8 + return self.bitbuffer >> (self.bits - bits) & (1 << bits) - 1 + + def skip(self, bits: int) -> None: + while self.bits < bits: + self.bitbuffer = (self.bitbuffer << 8) + i8(self.fp.read(1)) + self.bits += 8 + self.bits = self.bits - bits + + def read(self, bits: int) -> int: + v = self.peek(bits) + self.bits = self.bits - bits + return v + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x00\x00\x01\xb3") + + +## +# Image plugin for MPEG streams. This plugin can identify a stream, +# but it cannot read it. + + +class MpegImageFile(ImageFile.ImageFile): + format = "MPEG" + format_description = "MPEG" + + def _open(self) -> None: + assert self.fp is not None + + s = BitStream(self.fp) + if s.read(32) != 0x1B3: + msg = "not an MPEG file" + raise SyntaxError(msg) + + self._mode = "RGB" + self._size = s.read(12), s.read(12) + + +# -------------------------------------------------------------------- +# Registry stuff + +Image.register_open(MpegImageFile.format, MpegImageFile, _accept) + +Image.register_extensions(MpegImageFile.format, [".mpg", ".mpeg"]) + +Image.register_mime(MpegImageFile.format, "video/mpeg") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b1ae07873ac215b7abeeed9fe32d0f17db45d124 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py @@ -0,0 +1,202 @@ +# +# The Python Imaging Library. +# $Id$ +# +# MPO file handling +# +# See "Multi-Picture Format" (CIPA DC-007-Translation 2009, Standard of the +# Camera & Imaging Products Association) +# +# The multi-picture object combines multiple JPEG images (with a modified EXIF +# data format) into a single file. While it can theoretically be used much like +# a GIF animation, it is commonly used to represent 3D photographs and is (as +# of this writing) the most commonly used format by 3D cameras. +# +# History: +# 2014-03-13 Feneric Created +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import struct +from typing import IO, Any, cast + +from . import ( + Image, + ImageFile, + ImageSequence, + JpegImagePlugin, + TiffImagePlugin, +) +from ._binary import o32le +from ._util import DeferredError + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + JpegImagePlugin._save(im, fp, filename) + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + append_images = im.encoderinfo.get("append_images", []) + if not append_images and not getattr(im, "is_animated", False): + _save(im, fp, filename) + return + + mpf_offset = 28 + offsets: list[int] = [] + im_sequences = [im, *append_images] + total = sum(getattr(seq, "n_frames", 1) for seq in im_sequences) + for im_sequence in im_sequences: + for im_frame in ImageSequence.Iterator(im_sequence): + if not offsets: + # APP2 marker + ifd_length = 66 + 16 * total + im_frame.encoderinfo["extra"] = ( + b"\xff\xe2" + + struct.pack(">H", 6 + ifd_length) + + b"MPF\0" + + b" " * ifd_length + ) + exif = im_frame.encoderinfo.get("exif") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + im_frame.encoderinfo["exif"] = exif + if exif: + mpf_offset += 4 + len(exif) + + JpegImagePlugin._save(im_frame, fp, filename) + offsets.append(fp.tell()) + else: + encoderinfo = im_frame._attach_default_encoderinfo(im) + im_frame.save(fp, "JPEG") + im_frame.encoderinfo = encoderinfo + offsets.append(fp.tell() - offsets[-1]) + + ifd = TiffImagePlugin.ImageFileDirectory_v2() + ifd[0xB000] = b"0100" + ifd[0xB001] = len(offsets) + + mpentries = b"" + data_offset = 0 + for i, size in enumerate(offsets): + if i == 0: + mptype = 0x030000 # Baseline MP Primary Image + else: + mptype = 0x000000 # Undefined + mpentries += struct.pack(" None: + self.fp.seek(0) # prep the fp in order to pass the JPEG test + JpegImagePlugin.JpegImageFile._open(self) + self._after_jpeg_open() + + def _after_jpeg_open(self, mpheader: dict[int, Any] | None = None) -> None: + self.mpinfo = mpheader if mpheader is not None else self._getmp() + if self.mpinfo is None: + msg = "Image appears to be a malformed MPO file" + raise ValueError(msg) + self.n_frames = self.mpinfo[0xB001] + self.__mpoffsets = [ + mpent["DataOffset"] + self.info["mpoffset"] for mpent in self.mpinfo[0xB002] + ] + self.__mpoffsets[0] = 0 + # Note that the following assertion will only be invalid if something + # gets broken within JpegImagePlugin. + assert self.n_frames == len(self.__mpoffsets) + del self.info["mpoffset"] # no longer needed + self.is_animated = self.n_frames > 1 + self._fp = self.fp # FIXME: hack + self._fp.seek(self.__mpoffsets[0]) # get ready to read first frame + self.__frame = 0 + self.offset = 0 + # for now we can only handle reading and individual frame extraction + self.readonly = 1 + + def load_seek(self, pos: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self._fp.seek(pos) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.fp = self._fp + self.offset = self.__mpoffsets[frame] + + original_exif = self.info.get("exif") + if "exif" in self.info: + del self.info["exif"] + + self.fp.seek(self.offset + 2) # skip SOI marker + if not self.fp.read(2): + msg = "No data found for frame" + raise ValueError(msg) + self.fp.seek(self.offset) + JpegImagePlugin.JpegImageFile._open(self) + if self.info.get("exif") != original_exif: + self._reload_exif() + + self.tile = [ + ImageFile._Tile("jpeg", (0, 0) + self.size, self.offset, self.tile[0][-1]) + ] + self.__frame = frame + + def tell(self) -> int: + return self.__frame + + @staticmethod + def adopt( + jpeg_instance: JpegImagePlugin.JpegImageFile, + mpheader: dict[int, Any] | None = None, + ) -> MpoImageFile: + """ + Transform the instance of JpegImageFile into + an instance of MpoImageFile. + After the call, the JpegImageFile is extended + to be an MpoImageFile. + + This is essentially useful when opening a JPEG + file that reveals itself as an MPO, to avoid + double call to _open. + """ + jpeg_instance.__class__ = MpoImageFile + mpo_instance = cast(MpoImageFile, jpeg_instance) + mpo_instance._after_jpeg_open(mpheader) + return mpo_instance + + +# --------------------------------------------------------------------- +# Registry stuff + +# Note that since MPO shares a factory with JPEG, we do not need to do a +# separate registration for it here. +# Image.register_open(MpoImageFile.format, +# JpegImagePlugin.jpeg_factory, _accept) +Image.register_save(MpoImageFile.format, _save) +Image.register_save_all(MpoImageFile.format, _save_all) + +Image.register_extension(MpoImageFile.format, ".mpo") + +Image.register_mime(MpoImageFile.format, "image/mpo") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..277087a8677708a3a5fe21a3f6d2c3b27f880d03 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py @@ -0,0 +1,200 @@ +# +# The Python Imaging Library. +# +# MSP file handling +# +# This is the format used by the Paint program in Windows 1 and 2. +# +# History: +# 95-09-05 fl Created +# 97-01-03 fl Read/write MSP images +# 17-02-21 es Fixed RLE interpretation +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995-97. +# Copyright (c) Eric Soroos 2017. +# +# See the README file for information on usage and redistribution. +# +# More info on this format: https://archive.org/details/gg243631 +# Page 313: +# Figure 205. Windows Paint Version 1: "DanM" Format +# Figure 206. Windows Paint Version 2: "LinS" Format. Used in Windows V2.03 +# +# See also: https://www.fileformat.info/format/mspaint/egff.htm +from __future__ import annotations + +import io +import struct +from typing import IO + +from . import Image, ImageFile +from ._binary import i16le as i16 +from ._binary import o16le as o16 + +# +# read MSP files + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"DanM", b"LinS")) + + +## +# Image plugin for Windows MSP images. This plugin supports both +# uncompressed (Windows 1.0). + + +class MspImageFile(ImageFile.ImageFile): + format = "MSP" + format_description = "Windows Paint" + + def _open(self) -> None: + # Header + assert self.fp is not None + + s = self.fp.read(32) + if not _accept(s): + msg = "not an MSP file" + raise SyntaxError(msg) + + # Header checksum + checksum = 0 + for i in range(0, 32, 2): + checksum = checksum ^ i16(s, i) + if checksum != 0: + msg = "bad MSP checksum" + raise SyntaxError(msg) + + self._mode = "1" + self._size = i16(s, 4), i16(s, 6) + + if s.startswith(b"DanM"): + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 32, "1")] + else: + self.tile = [ImageFile._Tile("MSP", (0, 0) + self.size, 32)] + + +class MspDecoder(ImageFile.PyDecoder): + # The algo for the MSP decoder is from + # https://www.fileformat.info/format/mspaint/egff.htm + # cc-by-attribution -- That page references is taken from the + # Encyclopedia of Graphics File Formats and is licensed by + # O'Reilly under the Creative Common/Attribution license + # + # For RLE encoded files, the 32byte header is followed by a scan + # line map, encoded as one 16bit word of encoded byte length per + # line. + # + # NOTE: the encoded length of the line can be 0. This was not + # handled in the previous version of this encoder, and there's no + # mention of how to handle it in the documentation. From the few + # examples I've seen, I've assumed that it is a fill of the + # background color, in this case, white. + # + # + # Pseudocode of the decoder: + # Read a BYTE value as the RunType + # If the RunType value is zero + # Read next byte as the RunCount + # Read the next byte as the RunValue + # Write the RunValue byte RunCount times + # If the RunType value is non-zero + # Use this value as the RunCount + # Read and write the next RunCount bytes literally + # + # e.g.: + # 0x00 03 ff 05 00 01 02 03 04 + # would yield the bytes: + # 0xff ff ff 00 01 02 03 04 + # + # which are then interpreted as a bit packed mode '1' image + + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + img = io.BytesIO() + blank_line = bytearray((0xFF,) * ((self.state.xsize + 7) // 8)) + try: + self.fd.seek(32) + rowmap = struct.unpack_from( + f"<{self.state.ysize}H", self.fd.read(self.state.ysize * 2) + ) + except struct.error as e: + msg = "Truncated MSP file in row map" + raise OSError(msg) from e + + for x, rowlen in enumerate(rowmap): + try: + if rowlen == 0: + img.write(blank_line) + continue + row = self.fd.read(rowlen) + if len(row) != rowlen: + msg = f"Truncated MSP file, expected {rowlen} bytes on row {x}" + raise OSError(msg) + idx = 0 + while idx < rowlen: + runtype = row[idx] + idx += 1 + if runtype == 0: + (runcount, runval) = struct.unpack_from("Bc", row, idx) + img.write(runval * runcount) + idx += 2 + else: + runcount = runtype + img.write(row[idx : idx + runcount]) + idx += runcount + + except struct.error as e: + msg = f"Corrupted MSP file in row {x}" + raise OSError(msg) from e + + self.set_as_raw(img.getvalue(), "1") + + return -1, 0 + + +Image.register_decoder("MSP", MspDecoder) + + +# +# write MSP files (uncompressed only) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "1": + msg = f"cannot write mode {im.mode} as MSP" + raise OSError(msg) + + # create MSP header + header = [0] * 16 + + header[0], header[1] = i16(b"Da"), i16(b"nM") # version 1 + header[2], header[3] = im.size + header[4], header[5] = 1, 1 + header[6], header[7] = 1, 1 + header[8], header[9] = im.size + + checksum = 0 + for h in header: + checksum = checksum ^ h + header[12] = checksum # FIXME: is this the right field? + + # header + for h in header: + fp.write(o16(h)) + + # image body + ImageFile._save(im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 32, "1")]) + + +# +# registry + +Image.register_open(MspImageFile.format, MspImageFile, _accept) +Image.register_save(MspImageFile.format, _save) + +Image.register_extension(MspImageFile.format, ".msp") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PSDraw.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PSDraw.py new file mode 100644 index 0000000000000000000000000000000000000000..7fd4c5c94cfa7ec46332f4da78f3e402fd5b311b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PSDraw.py @@ -0,0 +1,237 @@ +# +# The Python Imaging Library +# $Id$ +# +# Simple PostScript graphics interface +# +# History: +# 1996-04-20 fl Created +# 1999-01-10 fl Added gsave/grestore to image method +# 2005-05-04 fl Fixed floating point issue in image (from Eric Etheridge) +# +# Copyright (c) 1997-2005 by Secret Labs AB. All rights reserved. +# Copyright (c) 1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from typing import IO + +from . import EpsImagePlugin + +TYPE_CHECKING = False + + +## +# Simple PostScript graphics interface. + + +class PSDraw: + """ + Sets up printing to the given file. If ``fp`` is omitted, + ``sys.stdout.buffer`` is assumed. + """ + + def __init__(self, fp: IO[bytes] | None = None) -> None: + if not fp: + fp = sys.stdout.buffer + self.fp = fp + + def begin_document(self, id: str | None = None) -> None: + """Set up printing of a document. (Write PostScript DSC header.)""" + # FIXME: incomplete + self.fp.write( + b"%!PS-Adobe-3.0\n" + b"save\n" + b"/showpage { } def\n" + b"%%EndComments\n" + b"%%BeginDocument\n" + ) + # self.fp.write(ERROR_PS) # debugging! + self.fp.write(EDROFF_PS) + self.fp.write(VDI_PS) + self.fp.write(b"%%EndProlog\n") + self.isofont: dict[bytes, int] = {} + + def end_document(self) -> None: + """Ends printing. (Write PostScript DSC footer.)""" + self.fp.write(b"%%EndDocument\nrestore showpage\n%%End\n") + if hasattr(self.fp, "flush"): + self.fp.flush() + + def setfont(self, font: str, size: int) -> None: + """ + Selects which font to use. + + :param font: A PostScript font name + :param size: Size in points. + """ + font_bytes = bytes(font, "UTF-8") + if font_bytes not in self.isofont: + # reencode font + self.fp.write( + b"/PSDraw-%s ISOLatin1Encoding /%s E\n" % (font_bytes, font_bytes) + ) + self.isofont[font_bytes] = 1 + # rough + self.fp.write(b"/F0 %d /PSDraw-%s F\n" % (size, font_bytes)) + + def line(self, xy0: tuple[int, int], xy1: tuple[int, int]) -> None: + """ + Draws a line between the two points. Coordinates are given in + PostScript point coordinates (72 points per inch, (0, 0) is the lower + left corner of the page). + """ + self.fp.write(b"%d %d %d %d Vl\n" % (*xy0, *xy1)) + + def rectangle(self, box: tuple[int, int, int, int]) -> None: + """ + Draws a rectangle. + + :param box: A tuple of four integers, specifying left, bottom, width and + height. + """ + self.fp.write(b"%d %d M 0 %d %d Vr\n" % box) + + def text(self, xy: tuple[int, int], text: str) -> None: + """ + Draws text at the given position. You must use + :py:meth:`~PIL.PSDraw.PSDraw.setfont` before calling this method. + """ + text_bytes = bytes(text, "UTF-8") + text_bytes = b"\\(".join(text_bytes.split(b"(")) + text_bytes = b"\\)".join(text_bytes.split(b")")) + self.fp.write(b"%d %d M (%s) S\n" % (xy + (text_bytes,))) + + if TYPE_CHECKING: + from . import Image + + def image( + self, box: tuple[int, int, int, int], im: Image.Image, dpi: int | None = None + ) -> None: + """Draw a PIL image, centered in the given box.""" + # default resolution depends on mode + if not dpi: + if im.mode == "1": + dpi = 200 # fax + else: + dpi = 100 # grayscale + # image size (on paper) + x = im.size[0] * 72 / dpi + y = im.size[1] * 72 / dpi + # max allowed size + xmax = float(box[2] - box[0]) + ymax = float(box[3] - box[1]) + if x > xmax: + y = y * xmax / x + x = xmax + if y > ymax: + x = x * ymax / y + y = ymax + dx = (xmax - x) / 2 + box[0] + dy = (ymax - y) / 2 + box[1] + self.fp.write(b"gsave\n%f %f translate\n" % (dx, dy)) + if (x, y) != im.size: + # EpsImagePlugin._save prints the image at (0,0,xsize,ysize) + sx = x / im.size[0] + sy = y / im.size[1] + self.fp.write(b"%f %f scale\n" % (sx, sy)) + EpsImagePlugin._save(im, self.fp, "", 0) + self.fp.write(b"\ngrestore\n") + + +# -------------------------------------------------------------------- +# PostScript driver + +# +# EDROFF.PS -- PostScript driver for Edroff 2 +# +# History: +# 94-01-25 fl: created (edroff 2.04) +# +# Copyright (c) Fredrik Lundh 1994. +# + + +EDROFF_PS = b"""\ +/S { show } bind def +/P { moveto show } bind def +/M { moveto } bind def +/X { 0 rmoveto } bind def +/Y { 0 exch rmoveto } bind def +/E { findfont + dup maxlength dict begin + { + 1 index /FID ne { def } { pop pop } ifelse + } forall + /Encoding exch def + dup /FontName exch def + currentdict end definefont pop +} bind def +/F { findfont exch scalefont dup setfont + [ exch /setfont cvx ] cvx bind def +} bind def +""" + +# +# VDI.PS -- PostScript driver for VDI meta commands +# +# History: +# 94-01-25 fl: created (edroff 2.04) +# +# Copyright (c) Fredrik Lundh 1994. +# + +VDI_PS = b"""\ +/Vm { moveto } bind def +/Va { newpath arcn stroke } bind def +/Vl { moveto lineto stroke } bind def +/Vc { newpath 0 360 arc closepath } bind def +/Vr { exch dup 0 rlineto + exch dup 0 exch rlineto + exch neg 0 rlineto + 0 exch neg rlineto + setgray fill } bind def +/Tm matrix def +/Ve { Tm currentmatrix pop + translate scale newpath 0 0 .5 0 360 arc closepath + Tm setmatrix +} bind def +/Vf { currentgray exch setgray fill setgray } bind def +""" + +# +# ERROR.PS -- Error handler +# +# History: +# 89-11-21 fl: created (pslist 1.10) +# + +ERROR_PS = b"""\ +/landscape false def +/errorBUF 200 string def +/errorNL { currentpoint 10 sub exch pop 72 exch moveto } def +errordict begin /handleerror { + initmatrix /Courier findfont 10 scalefont setfont + newpath 72 720 moveto $error begin /newerror false def + (PostScript Error) show errorNL errorNL + (Error: ) show + /errorname load errorBUF cvs show errorNL errorNL + (Command: ) show + /command load dup type /stringtype ne { errorBUF cvs } if show + errorNL errorNL + (VMstatus: ) show + vmstatus errorBUF cvs show ( bytes available, ) show + errorBUF cvs show ( bytes used at level ) show + errorBUF cvs show errorNL errorNL + (Operand stargck: ) show errorNL /ostargck load { + dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL + } forall errorNL + (Execution stargck: ) show errorNL /estargck load { + dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL + } forall + end showpage +} def end +""" diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py new file mode 100644 index 0000000000000000000000000000000000000000..2a26e5d4e223ba0bc80ad1bfb37b4c3927e222ac --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py @@ -0,0 +1,54 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read simple, teragon-style palette files +# +# History: +# 97-08-23 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from typing import IO + +from ._binary import o8 + + +class PaletteFile: + """File handler for Teragon-style palette files.""" + + rawmode = "RGB" + + def __init__(self, fp: IO[bytes]) -> None: + palette = [o8(i) * 3 for i in range(256)] + + while True: + s = fp.readline() + + if not s: + break + if s.startswith(b"#"): + continue + if len(s) > 100: + msg = "bad palette file" + raise SyntaxError(msg) + + v = [int(x) for x in s.split()] + try: + [i, r, g, b] = v + except ValueError: + [i, r] = v + g = b = r + + if 0 <= i <= 255: + palette[i] = o8(r) + o8(g) + o8(b) + + self.palette = b"".join(palette) + + def getpalette(self) -> tuple[bytes, str]: + return self.palette, self.rawmode diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..15f71290816c5fa6a5178842260a1520eb0b372f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py @@ -0,0 +1,217 @@ +# +# The Python Imaging Library. +# $Id$ +# + +## +# Image plugin for Palm pixmap images (output only). +## +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import o8 +from ._binary import o16be as o16b + +# fmt: off +_Palm8BitColormapValues = ( + (255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255), + (255, 51, 255), (255, 0, 255), (255, 255, 204), (255, 204, 204), + (255, 153, 204), (255, 102, 204), (255, 51, 204), (255, 0, 204), + (255, 255, 153), (255, 204, 153), (255, 153, 153), (255, 102, 153), + (255, 51, 153), (255, 0, 153), (204, 255, 255), (204, 204, 255), + (204, 153, 255), (204, 102, 255), (204, 51, 255), (204, 0, 255), + (204, 255, 204), (204, 204, 204), (204, 153, 204), (204, 102, 204), + (204, 51, 204), (204, 0, 204), (204, 255, 153), (204, 204, 153), + (204, 153, 153), (204, 102, 153), (204, 51, 153), (204, 0, 153), + (153, 255, 255), (153, 204, 255), (153, 153, 255), (153, 102, 255), + (153, 51, 255), (153, 0, 255), (153, 255, 204), (153, 204, 204), + (153, 153, 204), (153, 102, 204), (153, 51, 204), (153, 0, 204), + (153, 255, 153), (153, 204, 153), (153, 153, 153), (153, 102, 153), + (153, 51, 153), (153, 0, 153), (102, 255, 255), (102, 204, 255), + (102, 153, 255), (102, 102, 255), (102, 51, 255), (102, 0, 255), + (102, 255, 204), (102, 204, 204), (102, 153, 204), (102, 102, 204), + (102, 51, 204), (102, 0, 204), (102, 255, 153), (102, 204, 153), + (102, 153, 153), (102, 102, 153), (102, 51, 153), (102, 0, 153), + (51, 255, 255), (51, 204, 255), (51, 153, 255), (51, 102, 255), + (51, 51, 255), (51, 0, 255), (51, 255, 204), (51, 204, 204), + (51, 153, 204), (51, 102, 204), (51, 51, 204), (51, 0, 204), + (51, 255, 153), (51, 204, 153), (51, 153, 153), (51, 102, 153), + (51, 51, 153), (51, 0, 153), (0, 255, 255), (0, 204, 255), + (0, 153, 255), (0, 102, 255), (0, 51, 255), (0, 0, 255), + (0, 255, 204), (0, 204, 204), (0, 153, 204), (0, 102, 204), + (0, 51, 204), (0, 0, 204), (0, 255, 153), (0, 204, 153), + (0, 153, 153), (0, 102, 153), (0, 51, 153), (0, 0, 153), + (255, 255, 102), (255, 204, 102), (255, 153, 102), (255, 102, 102), + (255, 51, 102), (255, 0, 102), (255, 255, 51), (255, 204, 51), + (255, 153, 51), (255, 102, 51), (255, 51, 51), (255, 0, 51), + (255, 255, 0), (255, 204, 0), (255, 153, 0), (255, 102, 0), + (255, 51, 0), (255, 0, 0), (204, 255, 102), (204, 204, 102), + (204, 153, 102), (204, 102, 102), (204, 51, 102), (204, 0, 102), + (204, 255, 51), (204, 204, 51), (204, 153, 51), (204, 102, 51), + (204, 51, 51), (204, 0, 51), (204, 255, 0), (204, 204, 0), + (204, 153, 0), (204, 102, 0), (204, 51, 0), (204, 0, 0), + (153, 255, 102), (153, 204, 102), (153, 153, 102), (153, 102, 102), + (153, 51, 102), (153, 0, 102), (153, 255, 51), (153, 204, 51), + (153, 153, 51), (153, 102, 51), (153, 51, 51), (153, 0, 51), + (153, 255, 0), (153, 204, 0), (153, 153, 0), (153, 102, 0), + (153, 51, 0), (153, 0, 0), (102, 255, 102), (102, 204, 102), + (102, 153, 102), (102, 102, 102), (102, 51, 102), (102, 0, 102), + (102, 255, 51), (102, 204, 51), (102, 153, 51), (102, 102, 51), + (102, 51, 51), (102, 0, 51), (102, 255, 0), (102, 204, 0), + (102, 153, 0), (102, 102, 0), (102, 51, 0), (102, 0, 0), + (51, 255, 102), (51, 204, 102), (51, 153, 102), (51, 102, 102), + (51, 51, 102), (51, 0, 102), (51, 255, 51), (51, 204, 51), + (51, 153, 51), (51, 102, 51), (51, 51, 51), (51, 0, 51), + (51, 255, 0), (51, 204, 0), (51, 153, 0), (51, 102, 0), + (51, 51, 0), (51, 0, 0), (0, 255, 102), (0, 204, 102), + (0, 153, 102), (0, 102, 102), (0, 51, 102), (0, 0, 102), + (0, 255, 51), (0, 204, 51), (0, 153, 51), (0, 102, 51), + (0, 51, 51), (0, 0, 51), (0, 255, 0), (0, 204, 0), + (0, 153, 0), (0, 102, 0), (0, 51, 0), (17, 17, 17), + (34, 34, 34), (68, 68, 68), (85, 85, 85), (119, 119, 119), + (136, 136, 136), (170, 170, 170), (187, 187, 187), (221, 221, 221), + (238, 238, 238), (192, 192, 192), (128, 0, 0), (128, 0, 128), + (0, 128, 0), (0, 128, 128), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0)) +# fmt: on + + +# so build a prototype image to be used for palette resampling +def build_prototype_image() -> Image.Image: + image = Image.new("L", (1, len(_Palm8BitColormapValues))) + image.putdata(list(range(len(_Palm8BitColormapValues)))) + palettedata: tuple[int, ...] = () + for colormapValue in _Palm8BitColormapValues: + palettedata += colormapValue + palettedata += (0, 0, 0) * (256 - len(_Palm8BitColormapValues)) + image.putpalette(palettedata) + return image + + +Palm8BitColormapImage = build_prototype_image() + +# OK, we now have in Palm8BitColormapImage, +# a "P"-mode image with the right palette +# +# -------------------------------------------------------------------- + +_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000} + +_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00} + + +# +# -------------------------------------------------------------------- + +## +# (Internal) Image save plugin for the Palm format. + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "P": + rawmode = "P" + bpp = 8 + version = 1 + + elif im.mode == "L": + if im.encoderinfo.get("bpp") in (1, 2, 4): + # this is 8-bit grayscale, so we shift it to get the high-order bits, + # and invert it because + # Palm does grayscale from white (0) to black (1) + bpp = im.encoderinfo["bpp"] + maxval = (1 << bpp) - 1 + shift = 8 - bpp + im = im.point(lambda x: maxval - (x >> shift)) + elif im.info.get("bpp") in (1, 2, 4): + # here we assume that even though the inherent mode is 8-bit grayscale, + # only the lower bpp bits are significant. + # We invert them to match the Palm. + bpp = im.info["bpp"] + maxval = (1 << bpp) - 1 + im = im.point(lambda x: maxval - (x & maxval)) + else: + msg = f"cannot write mode {im.mode} as Palm" + raise OSError(msg) + + # we ignore the palette here + im._mode = "P" + rawmode = f"P;{bpp}" + version = 1 + + elif im.mode == "1": + # monochrome -- write it inverted, as is the Palm standard + rawmode = "1;I" + bpp = 1 + version = 0 + + else: + msg = f"cannot write mode {im.mode} as Palm" + raise OSError(msg) + + # + # make sure image data is available + im.load() + + # write header + + cols = im.size[0] + rows = im.size[1] + + rowbytes = int((cols + (16 // bpp - 1)) / (16 // bpp)) * 2 + transparent_index = 0 + compression_type = _COMPRESSION_TYPES["none"] + + flags = 0 + if im.mode == "P": + flags |= _FLAGS["custom-colormap"] + colormap = im.im.getpalette() + colors = len(colormap) // 3 + colormapsize = 4 * colors + 2 + else: + colormapsize = 0 + + if "offset" in im.info: + offset = (rowbytes * rows + 16 + 3 + colormapsize) // 4 + else: + offset = 0 + + fp.write(o16b(cols) + o16b(rows) + o16b(rowbytes) + o16b(flags)) + fp.write(o8(bpp)) + fp.write(o8(version)) + fp.write(o16b(offset)) + fp.write(o8(transparent_index)) + fp.write(o8(compression_type)) + fp.write(o16b(0)) # reserved by Palm + + # now write colormap if necessary + + if colormapsize: + fp.write(o16b(colors)) + for i in range(colors): + fp.write(o8(i)) + fp.write(colormap[3 * i : 3 * i + 3]) + + # now convert data to raw form + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, rowbytes, 1))] + ) + + if hasattr(fp, "flush"): + fp.flush() + + +# +# -------------------------------------------------------------------- + +Image.register_save("Palm", _save) + +Image.register_extension("Palm", ".palm") + +Image.register_mime("Palm", "image/palm") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa249988c8b035822ab994866e405990d1cc96e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py @@ -0,0 +1,64 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PCD file handling +# +# History: +# 96-05-10 fl Created +# 96-05-27 fl Added draft mode (128x192, 256x384) +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile + +## +# Image plugin for PhotoCD images. This plugin only reads the 768x512 +# image from the file; higher resolutions are encoded in a proprietary +# encoding. + + +class PcdImageFile(ImageFile.ImageFile): + format = "PCD" + format_description = "Kodak PhotoCD" + + def _open(self) -> None: + # rough + assert self.fp is not None + + self.fp.seek(2048) + s = self.fp.read(2048) + + if not s.startswith(b"PCD_"): + msg = "not a PCD file" + raise SyntaxError(msg) + + orientation = s[1538] & 3 + self.tile_post_rotate = None + if orientation == 1: + self.tile_post_rotate = 90 + elif orientation == 3: + self.tile_post_rotate = -90 + + self._mode = "RGB" + self._size = 768, 512 # FIXME: not correct for rotated images! + self.tile = [ImageFile._Tile("pcd", (0, 0) + self.size, 96 * 2048)] + + def load_end(self) -> None: + if self.tile_post_rotate: + # Handle rotated PCDs + self.im = self.im.rotate(self.tile_post_rotate) + self._size = self.im.size + + +# +# registry + +Image.register_open(PcdImageFile.format, PcdImageFile) + +Image.register_extension(PcdImageFile.format, ".pcd") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..0d1968b140a93fb3d1a026d2fd9186e8696e2d1b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py @@ -0,0 +1,254 @@ +# +# THIS IS WORK IN PROGRESS +# +# The Python Imaging Library +# $Id$ +# +# portable compiled font file parser +# +# history: +# 1997-08-19 fl created +# 2003-09-13 fl fixed loading of unicode fonts +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1997-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from typing import BinaryIO, Callable + +from . import FontFile, Image +from ._binary import i8 +from ._binary import i16be as b16 +from ._binary import i16le as l16 +from ._binary import i32be as b32 +from ._binary import i32le as l32 + +# -------------------------------------------------------------------- +# declarations + +PCF_MAGIC = 0x70636601 # "\x01fcp" + +PCF_PROPERTIES = 1 << 0 +PCF_ACCELERATORS = 1 << 1 +PCF_METRICS = 1 << 2 +PCF_BITMAPS = 1 << 3 +PCF_INK_METRICS = 1 << 4 +PCF_BDF_ENCODINGS = 1 << 5 +PCF_SWIDTHS = 1 << 6 +PCF_GLYPH_NAMES = 1 << 7 +PCF_BDF_ACCELERATORS = 1 << 8 + +BYTES_PER_ROW: list[Callable[[int], int]] = [ + lambda bits: ((bits + 7) >> 3), + lambda bits: ((bits + 15) >> 3) & ~1, + lambda bits: ((bits + 31) >> 3) & ~3, + lambda bits: ((bits + 63) >> 3) & ~7, +] + + +def sz(s: bytes, o: int) -> bytes: + return s[o : s.index(b"\0", o)] + + +class PcfFontFile(FontFile.FontFile): + """Font file plugin for the X11 PCF format.""" + + name = "name" + + def __init__(self, fp: BinaryIO, charset_encoding: str = "iso8859-1"): + self.charset_encoding = charset_encoding + + magic = l32(fp.read(4)) + if magic != PCF_MAGIC: + msg = "not a PCF file" + raise SyntaxError(msg) + + super().__init__() + + count = l32(fp.read(4)) + self.toc = {} + for i in range(count): + type = l32(fp.read(4)) + self.toc[type] = l32(fp.read(4)), l32(fp.read(4)), l32(fp.read(4)) + + self.fp = fp + + self.info = self._load_properties() + + metrics = self._load_metrics() + bitmaps = self._load_bitmaps(metrics) + encoding = self._load_encoding() + + # + # create glyph structure + + for ch, ix in enumerate(encoding): + if ix is not None: + ( + xsize, + ysize, + left, + right, + width, + ascent, + descent, + attributes, + ) = metrics[ix] + self.glyph[ch] = ( + (width, 0), + (left, descent - ysize, xsize + left, descent), + (0, 0, xsize, ysize), + bitmaps[ix], + ) + + def _getformat( + self, tag: int + ) -> tuple[BinaryIO, int, Callable[[bytes], int], Callable[[bytes], int]]: + format, size, offset = self.toc[tag] + + fp = self.fp + fp.seek(offset) + + format = l32(fp.read(4)) + + if format & 4: + i16, i32 = b16, b32 + else: + i16, i32 = l16, l32 + + return fp, format, i16, i32 + + def _load_properties(self) -> dict[bytes, bytes | int]: + # + # font properties + + properties = {} + + fp, format, i16, i32 = self._getformat(PCF_PROPERTIES) + + nprops = i32(fp.read(4)) + + # read property description + p = [(i32(fp.read(4)), i8(fp.read(1)), i32(fp.read(4))) for _ in range(nprops)] + + if nprops & 3: + fp.seek(4 - (nprops & 3), io.SEEK_CUR) # pad + + data = fp.read(i32(fp.read(4))) + + for k, s, v in p: + property_value: bytes | int = sz(data, v) if s else v + properties[sz(data, k)] = property_value + + return properties + + def _load_metrics(self) -> list[tuple[int, int, int, int, int, int, int, int]]: + # + # font metrics + + metrics: list[tuple[int, int, int, int, int, int, int, int]] = [] + + fp, format, i16, i32 = self._getformat(PCF_METRICS) + + append = metrics.append + + if (format & 0xFF00) == 0x100: + # "compressed" metrics + for i in range(i16(fp.read(2))): + left = i8(fp.read(1)) - 128 + right = i8(fp.read(1)) - 128 + width = i8(fp.read(1)) - 128 + ascent = i8(fp.read(1)) - 128 + descent = i8(fp.read(1)) - 128 + xsize = right - left + ysize = ascent + descent + append((xsize, ysize, left, right, width, ascent, descent, 0)) + + else: + # "jumbo" metrics + for i in range(i32(fp.read(4))): + left = i16(fp.read(2)) + right = i16(fp.read(2)) + width = i16(fp.read(2)) + ascent = i16(fp.read(2)) + descent = i16(fp.read(2)) + attributes = i16(fp.read(2)) + xsize = right - left + ysize = ascent + descent + append((xsize, ysize, left, right, width, ascent, descent, attributes)) + + return metrics + + def _load_bitmaps( + self, metrics: list[tuple[int, int, int, int, int, int, int, int]] + ) -> list[Image.Image]: + # + # bitmap data + + fp, format, i16, i32 = self._getformat(PCF_BITMAPS) + + nbitmaps = i32(fp.read(4)) + + if nbitmaps != len(metrics): + msg = "Wrong number of bitmaps" + raise OSError(msg) + + offsets = [i32(fp.read(4)) for _ in range(nbitmaps)] + + bitmap_sizes = [i32(fp.read(4)) for _ in range(4)] + + # byteorder = format & 4 # non-zero => MSB + bitorder = format & 8 # non-zero => MSB + padindex = format & 3 + + bitmapsize = bitmap_sizes[padindex] + offsets.append(bitmapsize) + + data = fp.read(bitmapsize) + + pad = BYTES_PER_ROW[padindex] + mode = "1;R" + if bitorder: + mode = "1" + + bitmaps = [] + for i in range(nbitmaps): + xsize, ysize = metrics[i][:2] + b, e = offsets[i : i + 2] + bitmaps.append( + Image.frombytes("1", (xsize, ysize), data[b:e], "raw", mode, pad(xsize)) + ) + + return bitmaps + + def _load_encoding(self) -> list[int | None]: + fp, format, i16, i32 = self._getformat(PCF_BDF_ENCODINGS) + + first_col, last_col = i16(fp.read(2)), i16(fp.read(2)) + first_row, last_row = i16(fp.read(2)), i16(fp.read(2)) + + i16(fp.read(2)) # default + + nencoding = (last_col - first_col + 1) * (last_row - first_row + 1) + + # map character code to bitmap index + encoding: list[int | None] = [None] * min(256, nencoding) + + encoding_offsets = [i16(fp.read(2)) for _ in range(nencoding)] + + for i in range(first_col, len(encoding)): + try: + encoding_offset = encoding_offsets[ + ord(bytearray([i]).decode(self.charset_encoding)) + ] + if encoding_offset != 0xFFFF: + encoding[i] = encoding_offset + except UnicodeDecodeError: + # character is not supported in selected encoding + pass + + return encoding diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..458d586c463ce5355d807f2a2dd583545ccc8229 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py @@ -0,0 +1,228 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PCX file handling +# +# This format was originally used by ZSoft's popular PaintBrush +# program for the IBM PC. It is also supported by many MS-DOS and +# Windows applications, including the Windows PaintBrush program in +# Windows 3. +# +# history: +# 1995-09-01 fl Created +# 1996-05-20 fl Fixed RGB support +# 1997-01-03 fl Fixed 2-bit and 4-bit support +# 1999-02-03 fl Fixed 8-bit support (broken in 1.0b1) +# 1999-02-07 fl Added write support +# 2002-06-09 fl Made 2-bit and 4-bit support a bit more robust +# 2002-07-30 fl Seek from to current position, not beginning of file +# 2003-06-03 fl Extract DPI settings (info["dpi"]) +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import logging +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 + +logger = logging.getLogger(__name__) + + +def _accept(prefix: bytes) -> bool: + return prefix[0] == 10 and prefix[1] in [0, 2, 3, 5] + + +## +# Image plugin for Paintbrush images. + + +class PcxImageFile(ImageFile.ImageFile): + format = "PCX" + format_description = "Paintbrush" + + def _open(self) -> None: + # header + assert self.fp is not None + + s = self.fp.read(68) + if not _accept(s): + msg = "not a PCX file" + raise SyntaxError(msg) + + # image + bbox = i16(s, 4), i16(s, 6), i16(s, 8) + 1, i16(s, 10) + 1 + if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]: + msg = "bad PCX image size" + raise SyntaxError(msg) + logger.debug("BBox: %s %s %s %s", *bbox) + + offset = self.fp.tell() + 60 + + # format + version = s[1] + bits = s[3] + planes = s[65] + provided_stride = i16(s, 66) + logger.debug( + "PCX version %s, bits %s, planes %s, stride %s", + version, + bits, + planes, + provided_stride, + ) + + self.info["dpi"] = i16(s, 12), i16(s, 14) + + if bits == 1 and planes == 1: + mode = rawmode = "1" + + elif bits == 1 and planes in (2, 4): + mode = "P" + rawmode = f"P;{planes}L" + self.palette = ImagePalette.raw("RGB", s[16:64]) + + elif version == 5 and bits == 8 and planes == 1: + mode = rawmode = "L" + # FIXME: hey, this doesn't work with the incremental loader !!! + self.fp.seek(-769, io.SEEK_END) + s = self.fp.read(769) + if len(s) == 769 and s[0] == 12: + # check if the palette is linear grayscale + for i in range(256): + if s[i * 3 + 1 : i * 3 + 4] != o8(i) * 3: + mode = rawmode = "P" + break + if mode == "P": + self.palette = ImagePalette.raw("RGB", s[1:]) + + elif version == 5 and bits == 8 and planes == 3: + mode = "RGB" + rawmode = "RGB;L" + + else: + msg = "unknown PCX mode" + raise OSError(msg) + + self._mode = mode + self._size = bbox[2] - bbox[0], bbox[3] - bbox[1] + + # Don't trust the passed in stride. + # Calculate the approximate position for ourselves. + # CVE-2020-35653 + stride = (self._size[0] * bits + 7) // 8 + + # While the specification states that this must be even, + # not all images follow this + if provided_stride != stride: + stride += stride % 2 + + bbox = (0, 0) + self.size + logger.debug("size: %sx%s", *self.size) + + self.tile = [ImageFile._Tile("pcx", bbox, offset, (rawmode, planes * stride))] + + +# -------------------------------------------------------------------- +# save PCX files + + +SAVE = { + # mode: (version, bits, planes, raw mode) + "1": (2, 1, 1, "1"), + "L": (5, 8, 1, "L"), + "P": (5, 8, 1, "P"), + "RGB": (5, 8, 3, "RGB;L"), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + version, bits, planes, rawmode = SAVE[im.mode] + except KeyError as e: + msg = f"Cannot save {im.mode} images as PCX" + raise ValueError(msg) from e + + # bytes per plane + stride = (im.size[0] * bits + 7) // 8 + # stride should be even + stride += stride % 2 + # Stride needs to be kept in sync with the PcxEncode.c version. + # Ideally it should be passed in in the state, but the bytes value + # gets overwritten. + + logger.debug( + "PcxImagePlugin._save: xwidth: %d, bits: %d, stride: %d", + im.size[0], + bits, + stride, + ) + + # under windows, we could determine the current screen size with + # "Image.core.display_mode()[1]", but I think that's overkill... + + screen = im.size + + dpi = 100, 100 + + # PCX header + fp.write( + o8(10) + + o8(version) + + o8(1) + + o8(bits) + + o16(0) + + o16(0) + + o16(im.size[0] - 1) + + o16(im.size[1] - 1) + + o16(dpi[0]) + + o16(dpi[1]) + + b"\0" * 24 + + b"\xff" * 24 + + b"\0" + + o8(planes) + + o16(stride) + + o16(1) + + o16(screen[0]) + + o16(screen[1]) + + b"\0" * 54 + ) + + assert fp.tell() == 128 + + ImageFile._save( + im, fp, [ImageFile._Tile("pcx", (0, 0) + im.size, 0, (rawmode, bits * planes))] + ) + + if im.mode == "P": + # colour palette + fp.write(o8(12)) + palette = im.im.getpalette("RGB", "RGB") + palette += b"\x00" * (768 - len(palette)) + fp.write(palette) # 768 bytes + elif im.mode == "L": + # grayscale palette + fp.write(o8(12)) + for i in range(256): + fp.write(o8(i) * 3) + + +# -------------------------------------------------------------------- +# registry + + +Image.register_open(PcxImageFile.format, PcxImageFile, _accept) +Image.register_save(PcxImageFile.format, _save) + +Image.register_extension(PcxImageFile.format, ".pcx") + +Image.register_mime(PcxImageFile.format, "image/x-pcx") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c20ddc159e150676ead01cf314420008f05232 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py @@ -0,0 +1,311 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PDF (Acrobat) file handling +# +# History: +# 1996-07-16 fl Created +# 1997-01-18 fl Fixed header +# 2004-02-21 fl Fixes for 1/L/CMYK images, etc. +# 2004-02-24 fl Fixes for 1 and P images. +# +# Copyright (c) 1997-2004 by Secret Labs AB. All rights reserved. +# Copyright (c) 1996-1997 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +## +# Image plugin for PDF images (output only). +## +from __future__ import annotations + +import io +import math +import os +import time +from typing import IO, Any + +from . import Image, ImageFile, ImageSequence, PdfParser, __version__, features + +# +# -------------------------------------------------------------------- + +# object ids: +# 1. catalogue +# 2. pages +# 3. image +# 4. page +# 5. page contents + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +## +# (Internal) Image save plugin for the PDF format. + + +def _write_image( + im: Image.Image, + filename: str | bytes, + existing_pdf: PdfParser.PdfParser, + image_refs: list[PdfParser.IndirectReference], +) -> tuple[PdfParser.IndirectReference, str]: + # FIXME: Should replace ASCIIHexDecode with RunLengthDecode + # (packbits) or LZWDecode (tiff/lzw compression). Note that + # PDF 1.2 also supports Flatedecode (zip compression). + + params = None + decode = None + + # + # Get image characteristics + + width, height = im.size + + dict_obj: dict[str, Any] = {"BitsPerComponent": 8} + if im.mode == "1": + if features.check("libtiff"): + decode_filter = "CCITTFaxDecode" + dict_obj["BitsPerComponent"] = 1 + params = PdfParser.PdfArray( + [ + PdfParser.PdfDict( + { + "K": -1, + "BlackIs1": True, + "Columns": width, + "Rows": height, + } + ) + ] + ) + else: + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceGray") + procset = "ImageB" # grayscale + elif im.mode == "L": + decode_filter = "DCTDecode" + # params = f"<< /Predictor 15 /Columns {width-2} >>" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceGray") + procset = "ImageB" # grayscale + elif im.mode == "LA": + decode_filter = "JPXDecode" + # params = f"<< /Predictor 15 /Columns {width-2} >>" + procset = "ImageB" # grayscale + dict_obj["SMaskInData"] = 1 + elif im.mode == "P": + decode_filter = "ASCIIHexDecode" + palette = im.getpalette() + assert palette is not None + dict_obj["ColorSpace"] = [ + PdfParser.PdfName("Indexed"), + PdfParser.PdfName("DeviceRGB"), + len(palette) // 3 - 1, + PdfParser.PdfBinary(palette), + ] + procset = "ImageI" # indexed color + + if "transparency" in im.info: + smask = im.convert("LA").getchannel("A") + smask.encoderinfo = {} + + image_ref = _write_image(smask, filename, existing_pdf, image_refs)[0] + dict_obj["SMask"] = image_ref + elif im.mode == "RGB": + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceRGB") + procset = "ImageC" # color images + elif im.mode == "RGBA": + decode_filter = "JPXDecode" + procset = "ImageC" # color images + dict_obj["SMaskInData"] = 1 + elif im.mode == "CMYK": + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceCMYK") + procset = "ImageC" # color images + decode = [1, 0, 1, 0, 1, 0, 1, 0] + else: + msg = f"cannot save mode {im.mode}" + raise ValueError(msg) + + # + # image + + op = io.BytesIO() + + if decode_filter == "ASCIIHexDecode": + ImageFile._save(im, op, [ImageFile._Tile("hex", (0, 0) + im.size, 0, im.mode)]) + elif decode_filter == "CCITTFaxDecode": + im.save( + op, + "TIFF", + compression="group4", + # use a single strip + strip_size=math.ceil(width / 8) * height, + ) + elif decode_filter == "DCTDecode": + Image.SAVE["JPEG"](im, op, filename) + elif decode_filter == "JPXDecode": + del dict_obj["BitsPerComponent"] + Image.SAVE["JPEG2000"](im, op, filename) + else: + msg = f"unsupported PDF filter ({decode_filter})" + raise ValueError(msg) + + stream = op.getvalue() + filter: PdfParser.PdfArray | PdfParser.PdfName + if decode_filter == "CCITTFaxDecode": + stream = stream[8:] + filter = PdfParser.PdfArray([PdfParser.PdfName(decode_filter)]) + else: + filter = PdfParser.PdfName(decode_filter) + + image_ref = image_refs.pop(0) + existing_pdf.write_obj( + image_ref, + stream=stream, + Type=PdfParser.PdfName("XObject"), + Subtype=PdfParser.PdfName("Image"), + Width=width, # * 72.0 / x_resolution, + Height=height, # * 72.0 / y_resolution, + Filter=filter, + Decode=decode, + DecodeParms=params, + **dict_obj, + ) + + return image_ref, procset + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + is_appending = im.encoderinfo.get("append", False) + filename_str = filename.decode() if isinstance(filename, bytes) else filename + if is_appending: + existing_pdf = PdfParser.PdfParser(f=fp, filename=filename_str, mode="r+b") + else: + existing_pdf = PdfParser.PdfParser(f=fp, filename=filename_str, mode="w+b") + + dpi = im.encoderinfo.get("dpi") + if dpi: + x_resolution = dpi[0] + y_resolution = dpi[1] + else: + x_resolution = y_resolution = im.encoderinfo.get("resolution", 72.0) + + info = { + "title": ( + None if is_appending else os.path.splitext(os.path.basename(filename))[0] + ), + "author": None, + "subject": None, + "keywords": None, + "creator": None, + "producer": None, + "creationDate": None if is_appending else time.gmtime(), + "modDate": None if is_appending else time.gmtime(), + } + for k, default in info.items(): + v = im.encoderinfo.get(k) if k in im.encoderinfo else default + if v: + existing_pdf.info[k[0].upper() + k[1:]] = v + + # + # make sure image data is available + im.load() + + existing_pdf.start_writing() + existing_pdf.write_header() + existing_pdf.write_comment(f"created by Pillow {__version__} PDF driver") + + # + # pages + ims = [im] + if save_all: + append_images = im.encoderinfo.get("append_images", []) + for append_im in append_images: + append_im.encoderinfo = im.encoderinfo.copy() + ims.append(append_im) + number_of_pages = 0 + image_refs = [] + page_refs = [] + contents_refs = [] + for im in ims: + im_number_of_pages = 1 + if save_all: + im_number_of_pages = getattr(im, "n_frames", 1) + number_of_pages += im_number_of_pages + for i in range(im_number_of_pages): + image_refs.append(existing_pdf.next_object_id(0)) + if im.mode == "P" and "transparency" in im.info: + image_refs.append(existing_pdf.next_object_id(0)) + + page_refs.append(existing_pdf.next_object_id(0)) + contents_refs.append(existing_pdf.next_object_id(0)) + existing_pdf.pages.append(page_refs[-1]) + + # + # catalog and list of pages + existing_pdf.write_catalog() + + page_number = 0 + for im_sequence in ims: + im_pages: ImageSequence.Iterator | list[Image.Image] = ( + ImageSequence.Iterator(im_sequence) if save_all else [im_sequence] + ) + for im in im_pages: + image_ref, procset = _write_image(im, filename, existing_pdf, image_refs) + + # + # page + + existing_pdf.write_page( + page_refs[page_number], + Resources=PdfParser.PdfDict( + ProcSet=[PdfParser.PdfName("PDF"), PdfParser.PdfName(procset)], + XObject=PdfParser.PdfDict(image=image_ref), + ), + MediaBox=[ + 0, + 0, + im.width * 72.0 / x_resolution, + im.height * 72.0 / y_resolution, + ], + Contents=contents_refs[page_number], + ) + + # + # page contents + + page_contents = b"q %f 0 0 %f 0 0 cm /image Do Q\n" % ( + im.width * 72.0 / x_resolution, + im.height * 72.0 / y_resolution, + ) + + existing_pdf.write_obj(contents_refs[page_number], stream=page_contents) + + page_number += 1 + + # + # trailer + existing_pdf.write_xref_and_trailer() + if hasattr(fp, "flush"): + fp.flush() + existing_pdf.close() + + +# +# -------------------------------------------------------------------- + + +Image.register_save("PDF", _save) +Image.register_save_all("PDF", _save_all) + +Image.register_extension("PDF", ".pdf") + +Image.register_mime("PDF", "application/pdf") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfParser.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfParser.py new file mode 100644 index 0000000000000000000000000000000000000000..73d8c21c023c95304c6fddfc2e5fce6d331401b1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PdfParser.py @@ -0,0 +1,1074 @@ +from __future__ import annotations + +import calendar +import codecs +import collections +import mmap +import os +import re +import time +import zlib +from typing import IO, Any, NamedTuple, Union + + +# see 7.9.2.2 Text String Type on page 86 and D.3 PDFDocEncoding Character Set +# on page 656 +def encode_text(s: str) -> bytes: + return codecs.BOM_UTF16_BE + s.encode("utf_16_be") + + +PDFDocEncoding = { + 0x16: "\u0017", + 0x18: "\u02d8", + 0x19: "\u02c7", + 0x1A: "\u02c6", + 0x1B: "\u02d9", + 0x1C: "\u02dd", + 0x1D: "\u02db", + 0x1E: "\u02da", + 0x1F: "\u02dc", + 0x80: "\u2022", + 0x81: "\u2020", + 0x82: "\u2021", + 0x83: "\u2026", + 0x84: "\u2014", + 0x85: "\u2013", + 0x86: "\u0192", + 0x87: "\u2044", + 0x88: "\u2039", + 0x89: "\u203a", + 0x8A: "\u2212", + 0x8B: "\u2030", + 0x8C: "\u201e", + 0x8D: "\u201c", + 0x8E: "\u201d", + 0x8F: "\u2018", + 0x90: "\u2019", + 0x91: "\u201a", + 0x92: "\u2122", + 0x93: "\ufb01", + 0x94: "\ufb02", + 0x95: "\u0141", + 0x96: "\u0152", + 0x97: "\u0160", + 0x98: "\u0178", + 0x99: "\u017d", + 0x9A: "\u0131", + 0x9B: "\u0142", + 0x9C: "\u0153", + 0x9D: "\u0161", + 0x9E: "\u017e", + 0xA0: "\u20ac", +} + + +def decode_text(b: bytes) -> str: + if b[: len(codecs.BOM_UTF16_BE)] == codecs.BOM_UTF16_BE: + return b[len(codecs.BOM_UTF16_BE) :].decode("utf_16_be") + else: + return "".join(PDFDocEncoding.get(byte, chr(byte)) for byte in b) + + +class PdfFormatError(RuntimeError): + """An error that probably indicates a syntactic or semantic error in the + PDF file structure""" + + pass + + +def check_format_condition(condition: bool, error_message: str) -> None: + if not condition: + raise PdfFormatError(error_message) + + +class IndirectReferenceTuple(NamedTuple): + object_id: int + generation: int + + +class IndirectReference(IndirectReferenceTuple): + def __str__(self) -> str: + return f"{self.object_id} {self.generation} R" + + def __bytes__(self) -> bytes: + return self.__str__().encode("us-ascii") + + def __eq__(self, other: object) -> bool: + if self.__class__ is not other.__class__: + return False + assert isinstance(other, IndirectReference) + return other.object_id == self.object_id and other.generation == self.generation + + def __ne__(self, other: object) -> bool: + return not (self == other) + + def __hash__(self) -> int: + return hash((self.object_id, self.generation)) + + +class IndirectObjectDef(IndirectReference): + def __str__(self) -> str: + return f"{self.object_id} {self.generation} obj" + + +class XrefTable: + def __init__(self) -> None: + self.existing_entries: dict[int, tuple[int, int]] = ( + {} + ) # object ID => (offset, generation) + self.new_entries: dict[int, tuple[int, int]] = ( + {} + ) # object ID => (offset, generation) + self.deleted_entries = {0: 65536} # object ID => generation + self.reading_finished = False + + def __setitem__(self, key: int, value: tuple[int, int]) -> None: + if self.reading_finished: + self.new_entries[key] = value + else: + self.existing_entries[key] = value + if key in self.deleted_entries: + del self.deleted_entries[key] + + def __getitem__(self, key: int) -> tuple[int, int]: + try: + return self.new_entries[key] + except KeyError: + return self.existing_entries[key] + + def __delitem__(self, key: int) -> None: + if key in self.new_entries: + generation = self.new_entries[key][1] + 1 + del self.new_entries[key] + self.deleted_entries[key] = generation + elif key in self.existing_entries: + generation = self.existing_entries[key][1] + 1 + self.deleted_entries[key] = generation + elif key in self.deleted_entries: + generation = self.deleted_entries[key] + else: + msg = f"object ID {key} cannot be deleted because it doesn't exist" + raise IndexError(msg) + + def __contains__(self, key: int) -> bool: + return key in self.existing_entries or key in self.new_entries + + def __len__(self) -> int: + return len( + set(self.existing_entries.keys()) + | set(self.new_entries.keys()) + | set(self.deleted_entries.keys()) + ) + + def keys(self) -> set[int]: + return ( + set(self.existing_entries.keys()) - set(self.deleted_entries.keys()) + ) | set(self.new_entries.keys()) + + def write(self, f: IO[bytes]) -> int: + keys = sorted(set(self.new_entries.keys()) | set(self.deleted_entries.keys())) + deleted_keys = sorted(set(self.deleted_entries.keys())) + startxref = f.tell() + f.write(b"xref\n") + while keys: + # find a contiguous sequence of object IDs + prev: int | None = None + for index, key in enumerate(keys): + if prev is None or prev + 1 == key: + prev = key + else: + contiguous_keys = keys[:index] + keys = keys[index:] + break + else: + contiguous_keys = keys + keys = [] + f.write(b"%d %d\n" % (contiguous_keys[0], len(contiguous_keys))) + for object_id in contiguous_keys: + if object_id in self.new_entries: + f.write(b"%010d %05d n \n" % self.new_entries[object_id]) + else: + this_deleted_object_id = deleted_keys.pop(0) + check_format_condition( + object_id == this_deleted_object_id, + f"expected the next deleted object ID to be {object_id}, " + f"instead found {this_deleted_object_id}", + ) + try: + next_in_linked_list = deleted_keys[0] + except IndexError: + next_in_linked_list = 0 + f.write( + b"%010d %05d f \n" + % (next_in_linked_list, self.deleted_entries[object_id]) + ) + return startxref + + +class PdfName: + name: bytes + + def __init__(self, name: PdfName | bytes | str) -> None: + if isinstance(name, PdfName): + self.name = name.name + elif isinstance(name, bytes): + self.name = name + else: + self.name = name.encode("us-ascii") + + def name_as_str(self) -> str: + return self.name.decode("us-ascii") + + def __eq__(self, other: object) -> bool: + return ( + isinstance(other, PdfName) and other.name == self.name + ) or other == self.name + + def __hash__(self) -> int: + return hash(self.name) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({repr(self.name)})" + + @classmethod + def from_pdf_stream(cls, data: bytes) -> PdfName: + return cls(PdfParser.interpret_name(data)) + + allowed_chars = set(range(33, 127)) - {ord(c) for c in "#%/()<>[]{}"} + + def __bytes__(self) -> bytes: + result = bytearray(b"/") + for b in self.name: + if b in self.allowed_chars: + result.append(b) + else: + result.extend(b"#%02X" % b) + return bytes(result) + + +class PdfArray(list[Any]): + def __bytes__(self) -> bytes: + return b"[ " + b" ".join(pdf_repr(x) for x in self) + b" ]" + + +TYPE_CHECKING = False +if TYPE_CHECKING: + _DictBase = collections.UserDict[Union[str, bytes], Any] +else: + _DictBase = collections.UserDict + + +class PdfDict(_DictBase): + def __setattr__(self, key: str, value: Any) -> None: + if key == "data": + collections.UserDict.__setattr__(self, key, value) + else: + self[key.encode("us-ascii")] = value + + def __getattr__(self, key: str) -> str | time.struct_time: + try: + value = self[key.encode("us-ascii")] + except KeyError as e: + raise AttributeError(key) from e + if isinstance(value, bytes): + value = decode_text(value) + if key.endswith("Date"): + if value.startswith("D:"): + value = value[2:] + + relationship = "Z" + if len(value) > 17: + relationship = value[14] + offset = int(value[15:17]) * 60 + if len(value) > 20: + offset += int(value[18:20]) + + format = "%Y%m%d%H%M%S"[: len(value) - 2] + value = time.strptime(value[: len(format) + 2], format) + if relationship in ["+", "-"]: + offset *= 60 + if relationship == "+": + offset *= -1 + value = time.gmtime(calendar.timegm(value) + offset) + return value + + def __bytes__(self) -> bytes: + out = bytearray(b"<<") + for key, value in self.items(): + if value is None: + continue + value = pdf_repr(value) + out.extend(b"\n") + out.extend(bytes(PdfName(key))) + out.extend(b" ") + out.extend(value) + out.extend(b"\n>>") + return bytes(out) + + +class PdfBinary: + def __init__(self, data: list[int] | bytes) -> None: + self.data = data + + def __bytes__(self) -> bytes: + return b"<%s>" % b"".join(b"%02X" % b for b in self.data) + + +class PdfStream: + def __init__(self, dictionary: PdfDict, buf: bytes) -> None: + self.dictionary = dictionary + self.buf = buf + + def decode(self) -> bytes: + try: + filter = self.dictionary[b"Filter"] + except KeyError: + return self.buf + if filter == b"FlateDecode": + try: + expected_length = self.dictionary[b"DL"] + except KeyError: + expected_length = self.dictionary[b"Length"] + return zlib.decompress(self.buf, bufsize=int(expected_length)) + else: + msg = f"stream filter {repr(filter)} unknown/unsupported" + raise NotImplementedError(msg) + + +def pdf_repr(x: Any) -> bytes: + if x is True: + return b"true" + elif x is False: + return b"false" + elif x is None: + return b"null" + elif isinstance(x, (PdfName, PdfDict, PdfArray, PdfBinary)): + return bytes(x) + elif isinstance(x, (int, float)): + return str(x).encode("us-ascii") + elif isinstance(x, time.struct_time): + return b"(D:" + time.strftime("%Y%m%d%H%M%SZ", x).encode("us-ascii") + b")" + elif isinstance(x, dict): + return bytes(PdfDict(x)) + elif isinstance(x, list): + return bytes(PdfArray(x)) + elif isinstance(x, str): + return pdf_repr(encode_text(x)) + elif isinstance(x, bytes): + # XXX escape more chars? handle binary garbage + x = x.replace(b"\\", b"\\\\") + x = x.replace(b"(", b"\\(") + x = x.replace(b")", b"\\)") + return b"(" + x + b")" + else: + return bytes(x) + + +class PdfParser: + """Based on + https://www.adobe.com/content/dam/acom/en/devnet/acrobat/pdfs/PDF32000_2008.pdf + Supports PDF up to 1.4 + """ + + def __init__( + self, + filename: str | None = None, + f: IO[bytes] | None = None, + buf: bytes | bytearray | None = None, + start_offset: int = 0, + mode: str = "rb", + ) -> None: + if buf and f: + msg = "specify buf or f or filename, but not both buf and f" + raise RuntimeError(msg) + self.filename = filename + self.buf: bytes | bytearray | mmap.mmap | None = buf + self.f = f + self.start_offset = start_offset + self.should_close_buf = False + self.should_close_file = False + if filename is not None and f is None: + self.f = f = open(filename, mode) + self.should_close_file = True + if f is not None: + self.buf = self.get_buf_from_file(f) + self.should_close_buf = True + if not filename and hasattr(f, "name"): + self.filename = f.name + self.cached_objects: dict[IndirectReference, Any] = {} + self.root_ref: IndirectReference | None + self.info_ref: IndirectReference | None + self.pages_ref: IndirectReference | None + self.last_xref_section_offset: int | None + if self.buf: + self.read_pdf_info() + else: + self.file_size_total = self.file_size_this = 0 + self.root = PdfDict() + self.root_ref = None + self.info = PdfDict() + self.info_ref = None + self.page_tree_root = PdfDict() + self.pages: list[IndirectReference] = [] + self.orig_pages: list[IndirectReference] = [] + self.pages_ref = None + self.last_xref_section_offset = None + self.trailer_dict: dict[bytes, Any] = {} + self.xref_table = XrefTable() + self.xref_table.reading_finished = True + if f: + self.seek_end() + + def __enter__(self) -> PdfParser: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def start_writing(self) -> None: + self.close_buf() + self.seek_end() + + def close_buf(self) -> None: + if isinstance(self.buf, mmap.mmap): + self.buf.close() + self.buf = None + + def close(self) -> None: + if self.should_close_buf: + self.close_buf() + if self.f is not None and self.should_close_file: + self.f.close() + self.f = None + + def seek_end(self) -> None: + assert self.f is not None + self.f.seek(0, os.SEEK_END) + + def write_header(self) -> None: + assert self.f is not None + self.f.write(b"%PDF-1.4\n") + + def write_comment(self, s: str) -> None: + assert self.f is not None + self.f.write(f"% {s}\n".encode()) + + def write_catalog(self) -> IndirectReference: + assert self.f is not None + self.del_root() + self.root_ref = self.next_object_id(self.f.tell()) + self.pages_ref = self.next_object_id(0) + self.rewrite_pages() + self.write_obj(self.root_ref, Type=PdfName(b"Catalog"), Pages=self.pages_ref) + self.write_obj( + self.pages_ref, + Type=PdfName(b"Pages"), + Count=len(self.pages), + Kids=self.pages, + ) + return self.root_ref + + def rewrite_pages(self) -> None: + pages_tree_nodes_to_delete = [] + for i, page_ref in enumerate(self.orig_pages): + page_info = self.cached_objects[page_ref] + del self.xref_table[page_ref.object_id] + pages_tree_nodes_to_delete.append(page_info[PdfName(b"Parent")]) + if page_ref not in self.pages: + # the page has been deleted + continue + # make dict keys into strings for passing to write_page + stringified_page_info = {} + for key, value in page_info.items(): + # key should be a PdfName + stringified_page_info[key.name_as_str()] = value + stringified_page_info["Parent"] = self.pages_ref + new_page_ref = self.write_page(None, **stringified_page_info) + for j, cur_page_ref in enumerate(self.pages): + if cur_page_ref == page_ref: + # replace the page reference with the new one + self.pages[j] = new_page_ref + # delete redundant Pages tree nodes from xref table + for pages_tree_node_ref in pages_tree_nodes_to_delete: + while pages_tree_node_ref: + pages_tree_node = self.cached_objects[pages_tree_node_ref] + if pages_tree_node_ref.object_id in self.xref_table: + del self.xref_table[pages_tree_node_ref.object_id] + pages_tree_node_ref = pages_tree_node.get(b"Parent", None) + self.orig_pages = [] + + def write_xref_and_trailer( + self, new_root_ref: IndirectReference | None = None + ) -> None: + assert self.f is not None + if new_root_ref: + self.del_root() + self.root_ref = new_root_ref + if self.info: + self.info_ref = self.write_obj(None, self.info) + start_xref = self.xref_table.write(self.f) + num_entries = len(self.xref_table) + trailer_dict: dict[str | bytes, Any] = { + b"Root": self.root_ref, + b"Size": num_entries, + } + if self.last_xref_section_offset is not None: + trailer_dict[b"Prev"] = self.last_xref_section_offset + if self.info: + trailer_dict[b"Info"] = self.info_ref + self.last_xref_section_offset = start_xref + self.f.write( + b"trailer\n" + + bytes(PdfDict(trailer_dict)) + + b"\nstartxref\n%d\n%%%%EOF" % start_xref + ) + + def write_page( + self, ref: int | IndirectReference | None, *objs: Any, **dict_obj: Any + ) -> IndirectReference: + obj_ref = self.pages[ref] if isinstance(ref, int) else ref + if "Type" not in dict_obj: + dict_obj["Type"] = PdfName(b"Page") + if "Parent" not in dict_obj: + dict_obj["Parent"] = self.pages_ref + return self.write_obj(obj_ref, *objs, **dict_obj) + + def write_obj( + self, ref: IndirectReference | None, *objs: Any, **dict_obj: Any + ) -> IndirectReference: + assert self.f is not None + f = self.f + if ref is None: + ref = self.next_object_id(f.tell()) + else: + self.xref_table[ref.object_id] = (f.tell(), ref.generation) + f.write(bytes(IndirectObjectDef(*ref))) + stream = dict_obj.pop("stream", None) + if stream is not None: + dict_obj["Length"] = len(stream) + if dict_obj: + f.write(pdf_repr(dict_obj)) + for obj in objs: + f.write(pdf_repr(obj)) + if stream is not None: + f.write(b"stream\n") + f.write(stream) + f.write(b"\nendstream\n") + f.write(b"endobj\n") + return ref + + def del_root(self) -> None: + if self.root_ref is None: + return + del self.xref_table[self.root_ref.object_id] + del self.xref_table[self.root[b"Pages"].object_id] + + @staticmethod + def get_buf_from_file(f: IO[bytes]) -> bytes | mmap.mmap: + if hasattr(f, "getbuffer"): + return f.getbuffer() + elif hasattr(f, "getvalue"): + return f.getvalue() + else: + try: + return mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) + except ValueError: # cannot mmap an empty file + return b"" + + def read_pdf_info(self) -> None: + assert self.buf is not None + self.file_size_total = len(self.buf) + self.file_size_this = self.file_size_total - self.start_offset + self.read_trailer() + check_format_condition( + self.trailer_dict.get(b"Root") is not None, "Root is missing" + ) + self.root_ref = self.trailer_dict[b"Root"] + assert self.root_ref is not None + self.info_ref = self.trailer_dict.get(b"Info", None) + self.root = PdfDict(self.read_indirect(self.root_ref)) + if self.info_ref is None: + self.info = PdfDict() + else: + self.info = PdfDict(self.read_indirect(self.info_ref)) + check_format_condition(b"Type" in self.root, "/Type missing in Root") + check_format_condition( + self.root[b"Type"] == b"Catalog", "/Type in Root is not /Catalog" + ) + check_format_condition( + self.root.get(b"Pages") is not None, "/Pages missing in Root" + ) + check_format_condition( + isinstance(self.root[b"Pages"], IndirectReference), + "/Pages in Root is not an indirect reference", + ) + self.pages_ref = self.root[b"Pages"] + assert self.pages_ref is not None + self.page_tree_root = self.read_indirect(self.pages_ref) + self.pages = self.linearize_page_tree(self.page_tree_root) + # save the original list of page references + # in case the user modifies, adds or deletes some pages + # and we need to rewrite the pages and their list + self.orig_pages = self.pages[:] + + def next_object_id(self, offset: int | None = None) -> IndirectReference: + try: + # TODO: support reuse of deleted objects + reference = IndirectReference(max(self.xref_table.keys()) + 1, 0) + except ValueError: + reference = IndirectReference(1, 0) + if offset is not None: + self.xref_table[reference.object_id] = (offset, 0) + return reference + + delimiter = rb"[][()<>{}/%]" + delimiter_or_ws = rb"[][()<>{}/%\000\011\012\014\015\040]" + whitespace = rb"[\000\011\012\014\015\040]" + whitespace_or_hex = rb"[\000\011\012\014\015\0400-9a-fA-F]" + whitespace_optional = whitespace + b"*" + whitespace_mandatory = whitespace + b"+" + # No "\012" aka "\n" or "\015" aka "\r": + whitespace_optional_no_nl = rb"[\000\011\014\040]*" + newline_only = rb"[\r\n]+" + newline = whitespace_optional_no_nl + newline_only + whitespace_optional_no_nl + re_trailer_end = re.compile( + whitespace_mandatory + + rb"trailer" + + whitespace_optional + + rb"<<(.*>>)" + + newline + + rb"startxref" + + newline + + rb"([0-9]+)" + + newline + + rb"%%EOF" + + whitespace_optional + + rb"$", + re.DOTALL, + ) + re_trailer_prev = re.compile( + whitespace_optional + + rb"trailer" + + whitespace_optional + + rb"<<(.*?>>)" + + newline + + rb"startxref" + + newline + + rb"([0-9]+)" + + newline + + rb"%%EOF" + + whitespace_optional, + re.DOTALL, + ) + + def read_trailer(self) -> None: + assert self.buf is not None + search_start_offset = len(self.buf) - 16384 + if search_start_offset < self.start_offset: + search_start_offset = self.start_offset + m = self.re_trailer_end.search(self.buf, search_start_offset) + check_format_condition(m is not None, "trailer end not found") + # make sure we found the LAST trailer + last_match = m + while m: + last_match = m + m = self.re_trailer_end.search(self.buf, m.start() + 16) + if not m: + m = last_match + assert m is not None + trailer_data = m.group(1) + self.last_xref_section_offset = int(m.group(2)) + self.trailer_dict = self.interpret_trailer(trailer_data) + self.xref_table = XrefTable() + self.read_xref_table(xref_section_offset=self.last_xref_section_offset) + if b"Prev" in self.trailer_dict: + self.read_prev_trailer(self.trailer_dict[b"Prev"]) + + def read_prev_trailer(self, xref_section_offset: int) -> None: + assert self.buf is not None + trailer_offset = self.read_xref_table(xref_section_offset=xref_section_offset) + m = self.re_trailer_prev.search( + self.buf[trailer_offset : trailer_offset + 16384] + ) + check_format_condition(m is not None, "previous trailer not found") + assert m is not None + trailer_data = m.group(1) + check_format_condition( + int(m.group(2)) == xref_section_offset, + "xref section offset in previous trailer doesn't match what was expected", + ) + trailer_dict = self.interpret_trailer(trailer_data) + if b"Prev" in trailer_dict: + self.read_prev_trailer(trailer_dict[b"Prev"]) + + re_whitespace_optional = re.compile(whitespace_optional) + re_name = re.compile( + whitespace_optional + + rb"/([!-$&'*-.0-;=?-Z\\^-z|~]+)(?=" + + delimiter_or_ws + + rb")" + ) + re_dict_start = re.compile(whitespace_optional + rb"<<") + re_dict_end = re.compile(whitespace_optional + rb">>" + whitespace_optional) + + @classmethod + def interpret_trailer(cls, trailer_data: bytes) -> dict[bytes, Any]: + trailer = {} + offset = 0 + while True: + m = cls.re_name.match(trailer_data, offset) + if not m: + m = cls.re_dict_end.match(trailer_data, offset) + check_format_condition( + m is not None and m.end() == len(trailer_data), + "name not found in trailer, remaining data: " + + repr(trailer_data[offset:]), + ) + break + key = cls.interpret_name(m.group(1)) + assert isinstance(key, bytes) + value, value_offset = cls.get_value(trailer_data, m.end()) + trailer[key] = value + if value_offset is None: + break + offset = value_offset + check_format_condition( + b"Size" in trailer and isinstance(trailer[b"Size"], int), + "/Size not in trailer or not an integer", + ) + check_format_condition( + b"Root" in trailer and isinstance(trailer[b"Root"], IndirectReference), + "/Root not in trailer or not an indirect reference", + ) + return trailer + + re_hashes_in_name = re.compile(rb"([^#]*)(#([0-9a-fA-F]{2}))?") + + @classmethod + def interpret_name(cls, raw: bytes, as_text: bool = False) -> str | bytes: + name = b"" + for m in cls.re_hashes_in_name.finditer(raw): + if m.group(3): + name += m.group(1) + bytearray.fromhex(m.group(3).decode("us-ascii")) + else: + name += m.group(1) + if as_text: + return name.decode("utf-8") + else: + return bytes(name) + + re_null = re.compile(whitespace_optional + rb"null(?=" + delimiter_or_ws + rb")") + re_true = re.compile(whitespace_optional + rb"true(?=" + delimiter_or_ws + rb")") + re_false = re.compile(whitespace_optional + rb"false(?=" + delimiter_or_ws + rb")") + re_int = re.compile( + whitespace_optional + rb"([-+]?[0-9]+)(?=" + delimiter_or_ws + rb")" + ) + re_real = re.compile( + whitespace_optional + + rb"([-+]?([0-9]+\.[0-9]*|[0-9]*\.[0-9]+))(?=" + + delimiter_or_ws + + rb")" + ) + re_array_start = re.compile(whitespace_optional + rb"\[") + re_array_end = re.compile(whitespace_optional + rb"]") + re_string_hex = re.compile( + whitespace_optional + rb"<(" + whitespace_or_hex + rb"*)>" + ) + re_string_lit = re.compile(whitespace_optional + rb"\(") + re_indirect_reference = re.compile( + whitespace_optional + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"R(?=" + + delimiter_or_ws + + rb")" + ) + re_indirect_def_start = re.compile( + whitespace_optional + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"obj(?=" + + delimiter_or_ws + + rb")" + ) + re_indirect_def_end = re.compile( + whitespace_optional + rb"endobj(?=" + delimiter_or_ws + rb")" + ) + re_comment = re.compile( + rb"(" + whitespace_optional + rb"%[^\r\n]*" + newline + rb")*" + ) + re_stream_start = re.compile(whitespace_optional + rb"stream\r?\n") + re_stream_end = re.compile( + whitespace_optional + rb"endstream(?=" + delimiter_or_ws + rb")" + ) + + @classmethod + def get_value( + cls, + data: bytes | bytearray | mmap.mmap, + offset: int, + expect_indirect: IndirectReference | None = None, + max_nesting: int = -1, + ) -> tuple[Any, int | None]: + if max_nesting == 0: + return None, None + m = cls.re_comment.match(data, offset) + if m: + offset = m.end() + m = cls.re_indirect_def_start.match(data, offset) + if m: + check_format_condition( + int(m.group(1)) > 0, + "indirect object definition: object ID must be greater than 0", + ) + check_format_condition( + int(m.group(2)) >= 0, + "indirect object definition: generation must be non-negative", + ) + check_format_condition( + expect_indirect is None + or expect_indirect + == IndirectReference(int(m.group(1)), int(m.group(2))), + "indirect object definition different than expected", + ) + object, object_offset = cls.get_value( + data, m.end(), max_nesting=max_nesting - 1 + ) + if object_offset is None: + return object, None + m = cls.re_indirect_def_end.match(data, object_offset) + check_format_condition( + m is not None, "indirect object definition end not found" + ) + assert m is not None + return object, m.end() + check_format_condition( + not expect_indirect, "indirect object definition not found" + ) + m = cls.re_indirect_reference.match(data, offset) + if m: + check_format_condition( + int(m.group(1)) > 0, + "indirect object reference: object ID must be greater than 0", + ) + check_format_condition( + int(m.group(2)) >= 0, + "indirect object reference: generation must be non-negative", + ) + return IndirectReference(int(m.group(1)), int(m.group(2))), m.end() + m = cls.re_dict_start.match(data, offset) + if m: + offset = m.end() + result: dict[Any, Any] = {} + m = cls.re_dict_end.match(data, offset) + current_offset: int | None = offset + while not m: + assert current_offset is not None + key, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + if current_offset is None: + return result, None + value, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + result[key] = value + if current_offset is None: + return result, None + m = cls.re_dict_end.match(data, current_offset) + current_offset = m.end() + m = cls.re_stream_start.match(data, current_offset) + if m: + stream_len = result.get(b"Length") + if stream_len is None or not isinstance(stream_len, int): + msg = f"bad or missing Length in stream dict ({stream_len})" + raise PdfFormatError(msg) + stream_data = data[m.end() : m.end() + stream_len] + m = cls.re_stream_end.match(data, m.end() + stream_len) + check_format_condition(m is not None, "stream end not found") + assert m is not None + current_offset = m.end() + return PdfStream(PdfDict(result), stream_data), current_offset + return PdfDict(result), current_offset + m = cls.re_array_start.match(data, offset) + if m: + offset = m.end() + results = [] + m = cls.re_array_end.match(data, offset) + current_offset = offset + while not m: + assert current_offset is not None + value, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + results.append(value) + if current_offset is None: + return results, None + m = cls.re_array_end.match(data, current_offset) + return results, m.end() + m = cls.re_null.match(data, offset) + if m: + return None, m.end() + m = cls.re_true.match(data, offset) + if m: + return True, m.end() + m = cls.re_false.match(data, offset) + if m: + return False, m.end() + m = cls.re_name.match(data, offset) + if m: + return PdfName(cls.interpret_name(m.group(1))), m.end() + m = cls.re_int.match(data, offset) + if m: + return int(m.group(1)), m.end() + m = cls.re_real.match(data, offset) + if m: + # XXX Decimal instead of float??? + return float(m.group(1)), m.end() + m = cls.re_string_hex.match(data, offset) + if m: + # filter out whitespace + hex_string = bytearray( + b for b in m.group(1) if b in b"0123456789abcdefABCDEF" + ) + if len(hex_string) % 2 == 1: + # append a 0 if the length is not even - yes, at the end + hex_string.append(ord(b"0")) + return bytearray.fromhex(hex_string.decode("us-ascii")), m.end() + m = cls.re_string_lit.match(data, offset) + if m: + return cls.get_literal_string(data, m.end()) + # return None, offset # fallback (only for debugging) + msg = f"unrecognized object: {repr(data[offset : offset + 32])}" + raise PdfFormatError(msg) + + re_lit_str_token = re.compile( + rb"(\\[nrtbf()\\])|(\\[0-9]{1,3})|(\\(\r\n|\r|\n))|(\r\n|\r|\n)|(\()|(\))" + ) + escaped_chars = { + b"n": b"\n", + b"r": b"\r", + b"t": b"\t", + b"b": b"\b", + b"f": b"\f", + b"(": b"(", + b")": b")", + b"\\": b"\\", + ord(b"n"): b"\n", + ord(b"r"): b"\r", + ord(b"t"): b"\t", + ord(b"b"): b"\b", + ord(b"f"): b"\f", + ord(b"("): b"(", + ord(b")"): b")", + ord(b"\\"): b"\\", + } + + @classmethod + def get_literal_string( + cls, data: bytes | bytearray | mmap.mmap, offset: int + ) -> tuple[bytes, int]: + nesting_depth = 0 + result = bytearray() + for m in cls.re_lit_str_token.finditer(data, offset): + result.extend(data[offset : m.start()]) + if m.group(1): + result.extend(cls.escaped_chars[m.group(1)[1]]) + elif m.group(2): + result.append(int(m.group(2)[1:], 8)) + elif m.group(3): + pass + elif m.group(5): + result.extend(b"\n") + elif m.group(6): + result.extend(b"(") + nesting_depth += 1 + elif m.group(7): + if nesting_depth == 0: + return bytes(result), m.end() + result.extend(b")") + nesting_depth -= 1 + offset = m.end() + msg = "unfinished literal string" + raise PdfFormatError(msg) + + re_xref_section_start = re.compile(whitespace_optional + rb"xref" + newline) + re_xref_subsection_start = re.compile( + whitespace_optional + + rb"([0-9]+)" + + whitespace_mandatory + + rb"([0-9]+)" + + whitespace_optional + + newline_only + ) + re_xref_entry = re.compile(rb"([0-9]{10}) ([0-9]{5}) ([fn])( \r| \n|\r\n)") + + def read_xref_table(self, xref_section_offset: int) -> int: + assert self.buf is not None + subsection_found = False + m = self.re_xref_section_start.match( + self.buf, xref_section_offset + self.start_offset + ) + check_format_condition(m is not None, "xref section start not found") + assert m is not None + offset = m.end() + while True: + m = self.re_xref_subsection_start.match(self.buf, offset) + if not m: + check_format_condition( + subsection_found, "xref subsection start not found" + ) + break + subsection_found = True + offset = m.end() + first_object = int(m.group(1)) + num_objects = int(m.group(2)) + for i in range(first_object, first_object + num_objects): + m = self.re_xref_entry.match(self.buf, offset) + check_format_condition(m is not None, "xref entry not found") + assert m is not None + offset = m.end() + is_free = m.group(3) == b"f" + if not is_free: + generation = int(m.group(2)) + new_entry = (int(m.group(1)), generation) + if i not in self.xref_table: + self.xref_table[i] = new_entry + return offset + + def read_indirect(self, ref: IndirectReference, max_nesting: int = -1) -> Any: + offset, generation = self.xref_table[ref[0]] + check_format_condition( + generation == ref[1], + f"expected to find generation {ref[1]} for object ID {ref[0]} in xref " + f"table, instead found generation {generation} at offset {offset}", + ) + assert self.buf is not None + value = self.get_value( + self.buf, + offset + self.start_offset, + expect_indirect=IndirectReference(*ref), + max_nesting=max_nesting, + )[0] + self.cached_objects[ref] = value + return value + + def linearize_page_tree( + self, node: PdfDict | None = None + ) -> list[IndirectReference]: + page_node = node if node is not None else self.page_tree_root + check_format_condition( + page_node[b"Type"] == b"Pages", "/Type of page tree node is not /Pages" + ) + pages = [] + for kid in page_node[b"Kids"]: + kid_object = self.read_indirect(kid) + if kid_object[b"Type"] == b"Page": + pages.append(kid) + else: + pages.extend(self.linearize_page_tree(node=kid_object)) + return pages diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d2b6d0a97e4bd230134d4741fc997baca5b4507f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py @@ -0,0 +1,72 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PIXAR raster support for PIL +# +# history: +# 97-01-29 fl Created +# +# notes: +# This is incomplete; it is based on a few samples created with +# Photoshop 2.5 and 3.0, and a summary description provided by +# Greg Coats . Hopefully, "L" and +# "RGBA" support will be added in future versions. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i16le as i16 + +# +# helpers + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\200\350\000\000") + + +## +# Image plugin for PIXAR raster images. + + +class PixarImageFile(ImageFile.ImageFile): + format = "PIXAR" + format_description = "PIXAR raster image" + + def _open(self) -> None: + # assuming a 4-byte magic label + assert self.fp is not None + + s = self.fp.read(4) + if not _accept(s): + msg = "not a PIXAR file" + raise SyntaxError(msg) + + # read rest of header + s = s + self.fp.read(508) + + self._size = i16(s, 418), i16(s, 416) + + # get channel/depth descriptions + mode = i16(s, 424), i16(s, 426) + + if mode == (14, 2): + self._mode = "RGB" + # FIXME: to be continued... + + # create tile descriptor (assuming "dumped") + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 1024, self.mode)] + + +# +# -------------------------------------------------------------------- + +Image.register_open(PixarImageFile.format, PixarImageFile, _accept) + +Image.register_extension(PixarImageFile.format, ".pxr") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1b9a89aef0dd15a292b5e2e55b6e56369dc31b62 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py @@ -0,0 +1,1551 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PNG support code +# +# See "PNG (Portable Network Graphics) Specification, version 1.0; +# W3C Recommendation", 1996-10-01, Thomas Boutell (ed.). +# +# history: +# 1996-05-06 fl Created (couldn't resist it) +# 1996-12-14 fl Upgraded, added read and verify support (0.2) +# 1996-12-15 fl Separate PNG stream parser +# 1996-12-29 fl Added write support, added getchunks +# 1996-12-30 fl Eliminated circular references in decoder (0.3) +# 1998-07-12 fl Read/write 16-bit images as mode I (0.4) +# 2001-02-08 fl Added transparency support (from Zircon) (0.5) +# 2001-04-16 fl Don't close data source in "open" method (0.6) +# 2004-02-24 fl Don't even pretend to support interlaced files (0.7) +# 2004-08-31 fl Do basic sanity check on chunk identifiers (0.8) +# 2004-09-20 fl Added PngInfo chunk container +# 2004-12-18 fl Added DPI read support (based on code by Niki Spahiev) +# 2008-08-13 fl Added tRNS support for RGB images +# 2009-03-06 fl Support for preserving ICC profiles (by Florian Hoech) +# 2009-03-08 fl Added zTXT support (from Lowell Alleman) +# 2009-03-29 fl Read interlaced PNG files (from Conrado Porto Lopes Gouvua) +# +# Copyright (c) 1997-2009 by Secret Labs AB +# Copyright (c) 1996 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import itertools +import logging +import re +import struct +import warnings +import zlib +from collections.abc import Callable +from enum import IntEnum +from typing import IO, Any, NamedTuple, NoReturn, cast + +from . import Image, ImageChops, ImageFile, ImagePalette, ImageSequence +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o16be as o16 +from ._binary import o32be as o32 +from ._deprecate import deprecate +from ._util import DeferredError + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + +logger = logging.getLogger(__name__) + +is_cid = re.compile(rb"\w\w\w\w").match + + +_MAGIC = b"\211PNG\r\n\032\n" + + +_MODES = { + # supported bits/color combinations, and corresponding modes/rawmodes + # Grayscale + (1, 0): ("1", "1"), + (2, 0): ("L", "L;2"), + (4, 0): ("L", "L;4"), + (8, 0): ("L", "L"), + (16, 0): ("I;16", "I;16B"), + # Truecolour + (8, 2): ("RGB", "RGB"), + (16, 2): ("RGB", "RGB;16B"), + # Indexed-colour + (1, 3): ("P", "P;1"), + (2, 3): ("P", "P;2"), + (4, 3): ("P", "P;4"), + (8, 3): ("P", "P"), + # Grayscale with alpha + (8, 4): ("LA", "LA"), + (16, 4): ("RGBA", "LA;16B"), # LA;16B->LA not yet available + # Truecolour with alpha + (8, 6): ("RGBA", "RGBA"), + (16, 6): ("RGBA", "RGBA;16B"), +} + + +_simple_palette = re.compile(b"^\xff*\x00\xff*$") + +MAX_TEXT_CHUNK = ImageFile.SAFEBLOCK +""" +Maximum decompressed size for a iTXt or zTXt chunk. +Eliminates decompression bombs where compressed chunks can expand 1000x. +See :ref:`Text in PNG File Format`. +""" +MAX_TEXT_MEMORY = 64 * MAX_TEXT_CHUNK +""" +Set the maximum total text chunk size. +See :ref:`Text in PNG File Format`. +""" + + +# APNG frame disposal modes +class Disposal(IntEnum): + OP_NONE = 0 + """ + No disposal is done on this frame before rendering the next frame. + See :ref:`Saving APNG sequences`. + """ + OP_BACKGROUND = 1 + """ + This frame’s modified region is cleared to fully transparent black before rendering + the next frame. + See :ref:`Saving APNG sequences`. + """ + OP_PREVIOUS = 2 + """ + This frame’s modified region is reverted to the previous frame’s contents before + rendering the next frame. + See :ref:`Saving APNG sequences`. + """ + + +# APNG frame blend modes +class Blend(IntEnum): + OP_SOURCE = 0 + """ + All color components of this frame, including alpha, overwrite the previous output + image contents. + See :ref:`Saving APNG sequences`. + """ + OP_OVER = 1 + """ + This frame should be alpha composited with the previous output image contents. + See :ref:`Saving APNG sequences`. + """ + + +def _safe_zlib_decompress(s: bytes) -> bytes: + dobj = zlib.decompressobj() + plaintext = dobj.decompress(s, MAX_TEXT_CHUNK) + if dobj.unconsumed_tail: + msg = "Decompressed data too large for PngImagePlugin.MAX_TEXT_CHUNK" + raise ValueError(msg) + return plaintext + + +def _crc32(data: bytes, seed: int = 0) -> int: + return zlib.crc32(data, seed) & 0xFFFFFFFF + + +# -------------------------------------------------------------------- +# Support classes. Suitable for PNG and related formats like MNG etc. + + +class ChunkStream: + def __init__(self, fp: IO[bytes]) -> None: + self.fp: IO[bytes] | None = fp + self.queue: list[tuple[bytes, int, int]] | None = [] + + def read(self) -> tuple[bytes, int, int]: + """Fetch a new chunk. Returns header information.""" + cid = None + + assert self.fp is not None + if self.queue: + cid, pos, length = self.queue.pop() + self.fp.seek(pos) + else: + s = self.fp.read(8) + cid = s[4:] + pos = self.fp.tell() + length = i32(s) + + if not is_cid(cid): + if not ImageFile.LOAD_TRUNCATED_IMAGES: + msg = f"broken PNG file (chunk {repr(cid)})" + raise SyntaxError(msg) + + return cid, pos, length + + def __enter__(self) -> ChunkStream: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def close(self) -> None: + self.queue = self.fp = None + + def push(self, cid: bytes, pos: int, length: int) -> None: + assert self.queue is not None + self.queue.append((cid, pos, length)) + + def call(self, cid: bytes, pos: int, length: int) -> bytes: + """Call the appropriate chunk handler""" + + logger.debug("STREAM %r %s %s", cid, pos, length) + return getattr(self, f"chunk_{cid.decode('ascii')}")(pos, length) + + def crc(self, cid: bytes, data: bytes) -> None: + """Read and verify checksum""" + + # Skip CRC checks for ancillary chunks if allowed to load truncated + # images + # 5th byte of first char is 1 [specs, section 5.4] + if ImageFile.LOAD_TRUNCATED_IMAGES and (cid[0] >> 5 & 1): + self.crc_skip(cid, data) + return + + assert self.fp is not None + try: + crc1 = _crc32(data, _crc32(cid)) + crc2 = i32(self.fp.read(4)) + if crc1 != crc2: + msg = f"broken PNG file (bad header checksum in {repr(cid)})" + raise SyntaxError(msg) + except struct.error as e: + msg = f"broken PNG file (incomplete checksum in {repr(cid)})" + raise SyntaxError(msg) from e + + def crc_skip(self, cid: bytes, data: bytes) -> None: + """Read checksum""" + + assert self.fp is not None + self.fp.read(4) + + def verify(self, endchunk: bytes = b"IEND") -> list[bytes]: + # Simple approach; just calculate checksum for all remaining + # blocks. Must be called directly after open. + + cids = [] + + assert self.fp is not None + while True: + try: + cid, pos, length = self.read() + except struct.error as e: + msg = "truncated PNG file" + raise OSError(msg) from e + + if cid == endchunk: + break + self.crc(cid, ImageFile._safe_read(self.fp, length)) + cids.append(cid) + + return cids + + +class iTXt(str): + """ + Subclass of string to allow iTXt chunks to look like strings while + keeping their extra information + + """ + + lang: str | bytes | None + tkey: str | bytes | None + + @staticmethod + def __new__( + cls, text: str, lang: str | None = None, tkey: str | None = None + ) -> iTXt: + """ + :param cls: the class to use when creating the instance + :param text: value for this key + :param lang: language code + :param tkey: UTF-8 version of the key name + """ + + self = str.__new__(cls, text) + self.lang = lang + self.tkey = tkey + return self + + +class PngInfo: + """ + PNG chunk container (for use with save(pnginfo=)) + + """ + + def __init__(self) -> None: + self.chunks: list[tuple[bytes, bytes, bool]] = [] + + def add(self, cid: bytes, data: bytes, after_idat: bool = False) -> None: + """Appends an arbitrary chunk. Use with caution. + + :param cid: a byte string, 4 bytes long. + :param data: a byte string of the encoded data + :param after_idat: for use with private chunks. Whether the chunk + should be written after IDAT + + """ + + self.chunks.append((cid, data, after_idat)) + + def add_itxt( + self, + key: str | bytes, + value: str | bytes, + lang: str | bytes = "", + tkey: str | bytes = "", + zip: bool = False, + ) -> None: + """Appends an iTXt chunk. + + :param key: latin-1 encodable text key name + :param value: value for this key + :param lang: language code + :param tkey: UTF-8 version of the key name + :param zip: compression flag + + """ + + if not isinstance(key, bytes): + key = key.encode("latin-1", "strict") + if not isinstance(value, bytes): + value = value.encode("utf-8", "strict") + if not isinstance(lang, bytes): + lang = lang.encode("utf-8", "strict") + if not isinstance(tkey, bytes): + tkey = tkey.encode("utf-8", "strict") + + if zip: + self.add( + b"iTXt", + key + b"\0\x01\0" + lang + b"\0" + tkey + b"\0" + zlib.compress(value), + ) + else: + self.add(b"iTXt", key + b"\0\0\0" + lang + b"\0" + tkey + b"\0" + value) + + def add_text( + self, key: str | bytes, value: str | bytes | iTXt, zip: bool = False + ) -> None: + """Appends a text chunk. + + :param key: latin-1 encodable text key name + :param value: value for this key, text or an + :py:class:`PIL.PngImagePlugin.iTXt` instance + :param zip: compression flag + + """ + if isinstance(value, iTXt): + return self.add_itxt( + key, + value, + value.lang if value.lang is not None else b"", + value.tkey if value.tkey is not None else b"", + zip=zip, + ) + + # The tEXt chunk stores latin-1 text + if not isinstance(value, bytes): + try: + value = value.encode("latin-1", "strict") + except UnicodeError: + return self.add_itxt(key, value, zip=zip) + + if not isinstance(key, bytes): + key = key.encode("latin-1", "strict") + + if zip: + self.add(b"zTXt", key + b"\0\0" + zlib.compress(value)) + else: + self.add(b"tEXt", key + b"\0" + value) + + +# -------------------------------------------------------------------- +# PNG image stream (IHDR/IEND) + + +class _RewindState(NamedTuple): + info: dict[str | tuple[int, int], Any] + tile: list[ImageFile._Tile] + seq_num: int | None + + +class PngStream(ChunkStream): + def __init__(self, fp: IO[bytes]) -> None: + super().__init__(fp) + + # local copies of Image attributes + self.im_info: dict[str | tuple[int, int], Any] = {} + self.im_text: dict[str, str | iTXt] = {} + self.im_size = (0, 0) + self.im_mode = "" + self.im_tile: list[ImageFile._Tile] = [] + self.im_palette: tuple[str, bytes] | None = None + self.im_custom_mimetype: str | None = None + self.im_n_frames: int | None = None + self._seq_num: int | None = None + self.rewind_state = _RewindState({}, [], None) + + self.text_memory = 0 + + def check_text_memory(self, chunklen: int) -> None: + self.text_memory += chunklen + if self.text_memory > MAX_TEXT_MEMORY: + msg = ( + "Too much memory used in text chunks: " + f"{self.text_memory}>MAX_TEXT_MEMORY" + ) + raise ValueError(msg) + + def save_rewind(self) -> None: + self.rewind_state = _RewindState( + self.im_info.copy(), + self.im_tile, + self._seq_num, + ) + + def rewind(self) -> None: + self.im_info = self.rewind_state.info.copy() + self.im_tile = self.rewind_state.tile + self._seq_num = self.rewind_state.seq_num + + def chunk_iCCP(self, pos: int, length: int) -> bytes: + # ICC profile + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + # according to PNG spec, the iCCP chunk contains: + # Profile name 1-79 bytes (character string) + # Null separator 1 byte (null character) + # Compression method 1 byte (0) + # Compressed profile n bytes (zlib with deflate compression) + i = s.find(b"\0") + logger.debug("iCCP profile name %r", s[:i]) + comp_method = s[i + 1] + logger.debug("Compression method %s", comp_method) + if comp_method != 0: + msg = f"Unknown compression method {comp_method} in iCCP chunk" + raise SyntaxError(msg) + try: + icc_profile = _safe_zlib_decompress(s[i + 2 :]) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + icc_profile = None + else: + raise + except zlib.error: + icc_profile = None # FIXME + self.im_info["icc_profile"] = icc_profile + return s + + def chunk_IHDR(self, pos: int, length: int) -> bytes: + # image header + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 13: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated IHDR chunk" + raise ValueError(msg) + self.im_size = i32(s, 0), i32(s, 4) + try: + self.im_mode, self.im_rawmode = _MODES[(s[8], s[9])] + except Exception: + pass + if s[12]: + self.im_info["interlace"] = 1 + if s[11]: + msg = "unknown filter category" + raise SyntaxError(msg) + return s + + def chunk_IDAT(self, pos: int, length: int) -> NoReturn: + # image data + if "bbox" in self.im_info: + tile = [ImageFile._Tile("zip", self.im_info["bbox"], pos, self.im_rawmode)] + else: + if self.im_n_frames is not None: + self.im_info["default_image"] = True + tile = [ImageFile._Tile("zip", (0, 0) + self.im_size, pos, self.im_rawmode)] + self.im_tile = tile + self.im_idat = length + msg = "image data found" + raise EOFError(msg) + + def chunk_IEND(self, pos: int, length: int) -> NoReturn: + msg = "end of PNG image" + raise EOFError(msg) + + def chunk_PLTE(self, pos: int, length: int) -> bytes: + # palette + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if self.im_mode == "P": + self.im_palette = "RGB", s + return s + + def chunk_tRNS(self, pos: int, length: int) -> bytes: + # transparency + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if self.im_mode == "P": + if _simple_palette.match(s): + # tRNS contains only one full-transparent entry, + # other entries are full opaque + i = s.find(b"\0") + if i >= 0: + self.im_info["transparency"] = i + else: + # otherwise, we have a byte string with one alpha value + # for each palette entry + self.im_info["transparency"] = s + elif self.im_mode in ("1", "L", "I;16"): + self.im_info["transparency"] = i16(s) + elif self.im_mode == "RGB": + self.im_info["transparency"] = i16(s), i16(s, 2), i16(s, 4) + return s + + def chunk_gAMA(self, pos: int, length: int) -> bytes: + # gamma setting + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + self.im_info["gamma"] = i32(s) / 100000.0 + return s + + def chunk_cHRM(self, pos: int, length: int) -> bytes: + # chromaticity, 8 unsigned ints, actual value is scaled by 100,000 + # WP x,y, Red x,y, Green x,y Blue x,y + + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + raw_vals = struct.unpack(f">{len(s) // 4}I", s) + self.im_info["chromaticity"] = tuple(elt / 100000.0 for elt in raw_vals) + return s + + def chunk_sRGB(self, pos: int, length: int) -> bytes: + # srgb rendering intent, 1 byte + # 0 perceptual + # 1 relative colorimetric + # 2 saturation + # 3 absolute colorimetric + + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 1: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated sRGB chunk" + raise ValueError(msg) + self.im_info["srgb"] = s[0] + return s + + def chunk_pHYs(self, pos: int, length: int) -> bytes: + # pixels per unit + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 9: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated pHYs chunk" + raise ValueError(msg) + px, py = i32(s, 0), i32(s, 4) + unit = s[8] + if unit == 1: # meter + dpi = px * 0.0254, py * 0.0254 + self.im_info["dpi"] = dpi + elif unit == 0: + self.im_info["aspect"] = px, py + return s + + def chunk_tEXt(self, pos: int, length: int) -> bytes: + # text + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + try: + k, v = s.split(b"\0", 1) + except ValueError: + # fallback for broken tEXt tags + k = s + v = b"" + if k: + k_str = k.decode("latin-1", "strict") + v_str = v.decode("latin-1", "replace") + + self.im_info[k_str] = v if k == b"exif" else v_str + self.im_text[k_str] = v_str + self.check_text_memory(len(v_str)) + + return s + + def chunk_zTXt(self, pos: int, length: int) -> bytes: + # compressed text + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + try: + k, v = s.split(b"\0", 1) + except ValueError: + k = s + v = b"" + if v: + comp_method = v[0] + else: + comp_method = 0 + if comp_method != 0: + msg = f"Unknown compression method {comp_method} in zTXt chunk" + raise SyntaxError(msg) + try: + v = _safe_zlib_decompress(v[1:]) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + v = b"" + else: + raise + except zlib.error: + v = b"" + + if k: + k_str = k.decode("latin-1", "strict") + v_str = v.decode("latin-1", "replace") + + self.im_info[k_str] = self.im_text[k_str] = v_str + self.check_text_memory(len(v_str)) + + return s + + def chunk_iTXt(self, pos: int, length: int) -> bytes: + # international text + assert self.fp is not None + r = s = ImageFile._safe_read(self.fp, length) + try: + k, r = r.split(b"\0", 1) + except ValueError: + return s + if len(r) < 2: + return s + cf, cm, r = r[0], r[1], r[2:] + try: + lang, tk, v = r.split(b"\0", 2) + except ValueError: + return s + if cf != 0: + if cm == 0: + try: + v = _safe_zlib_decompress(v) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + else: + raise + except zlib.error: + return s + else: + return s + if k == b"XML:com.adobe.xmp": + self.im_info["xmp"] = v + try: + k_str = k.decode("latin-1", "strict") + lang_str = lang.decode("utf-8", "strict") + tk_str = tk.decode("utf-8", "strict") + v_str = v.decode("utf-8", "strict") + except UnicodeError: + return s + + self.im_info[k_str] = self.im_text[k_str] = iTXt(v_str, lang_str, tk_str) + self.check_text_memory(len(v_str)) + + return s + + def chunk_eXIf(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + self.im_info["exif"] = b"Exif\x00\x00" + s + return s + + # APNG chunks + def chunk_acTL(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 8: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "APNG contains truncated acTL chunk" + raise ValueError(msg) + if self.im_n_frames is not None: + self.im_n_frames = None + warnings.warn("Invalid APNG, will use default PNG image if possible") + return s + n_frames = i32(s) + if n_frames == 0 or n_frames > 0x80000000: + warnings.warn("Invalid APNG, will use default PNG image if possible") + return s + self.im_n_frames = n_frames + self.im_info["loop"] = i32(s, 4) + self.im_custom_mimetype = "image/apng" + return s + + def chunk_fcTL(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 26: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "APNG contains truncated fcTL chunk" + raise ValueError(msg) + seq = i32(s) + if (self._seq_num is None and seq != 0) or ( + self._seq_num is not None and self._seq_num != seq - 1 + ): + msg = "APNG contains frame sequence errors" + raise SyntaxError(msg) + self._seq_num = seq + width, height = i32(s, 4), i32(s, 8) + px, py = i32(s, 12), i32(s, 16) + im_w, im_h = self.im_size + if px + width > im_w or py + height > im_h: + msg = "APNG contains invalid frames" + raise SyntaxError(msg) + self.im_info["bbox"] = (px, py, px + width, py + height) + delay_num, delay_den = i16(s, 20), i16(s, 22) + if delay_den == 0: + delay_den = 100 + self.im_info["duration"] = float(delay_num) / float(delay_den) * 1000 + self.im_info["disposal"] = s[24] + self.im_info["blend"] = s[25] + return s + + def chunk_fdAT(self, pos: int, length: int) -> bytes: + assert self.fp is not None + if length < 4: + if ImageFile.LOAD_TRUNCATED_IMAGES: + s = ImageFile._safe_read(self.fp, length) + return s + msg = "APNG contains truncated fDAT chunk" + raise ValueError(msg) + s = ImageFile._safe_read(self.fp, 4) + seq = i32(s) + if self._seq_num != seq - 1: + msg = "APNG contains frame sequence errors" + raise SyntaxError(msg) + self._seq_num = seq + return self.chunk_IDAT(pos + 4, length - 4) + + +# -------------------------------------------------------------------- +# PNG reader + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +## +# Image plugin for PNG images. + + +class PngImageFile(ImageFile.ImageFile): + format = "PNG" + format_description = "Portable network graphics" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "not a PNG file" + raise SyntaxError(msg) + self._fp = self.fp + self.__frame = 0 + + # + # Parse headers up to the first IDAT or fDAT chunk + + self.private_chunks: list[tuple[bytes, bytes] | tuple[bytes, bytes, bool]] = [] + self.png: PngStream | None = PngStream(self.fp) + + while True: + # + # get next chunk + + cid, pos, length = self.png.read() + + try: + s = self.png.call(cid, pos, length) + except EOFError: + break + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + s = ImageFile._safe_read(self.fp, length) + if cid[1:2].islower(): + self.private_chunks.append((cid, s)) + + self.png.crc(cid, s) + + # + # Copy relevant attributes from the PngStream. An alternative + # would be to let the PngStream class modify these attributes + # directly, but that introduces circular references which are + # difficult to break if things go wrong in the decoder... + # (believe me, I've tried ;-) + + self._mode = self.png.im_mode + self._size = self.png.im_size + self.info = self.png.im_info + self._text: dict[str, str | iTXt] | None = None + self.tile = self.png.im_tile + self.custom_mimetype = self.png.im_custom_mimetype + self.n_frames = self.png.im_n_frames or 1 + self.default_image = self.info.get("default_image", False) + + if self.png.im_palette: + rawmode, data = self.png.im_palette + self.palette = ImagePalette.raw(rawmode, data) + + if cid == b"fdAT": + self.__prepare_idat = length - 4 + else: + self.__prepare_idat = length # used by load_prepare() + + if self.png.im_n_frames is not None: + self._close_exclusive_fp_after_loading = False + self.png.save_rewind() + self.__rewind_idat = self.__prepare_idat + self.__rewind = self._fp.tell() + if self.default_image: + # IDAT chunk contains default image and not first animation frame + self.n_frames += 1 + self._seek(0) + self.is_animated = self.n_frames > 1 + + @property + def text(self) -> dict[str, str | iTXt]: + # experimental + if self._text is None: + # iTxt, tEXt and zTXt chunks may appear at the end of the file + # So load the file to ensure that they are read + if self.is_animated: + frame = self.__frame + # for APNG, seek to the final frame before loading + self.seek(self.n_frames - 1) + self.load() + if self.is_animated: + self.seek(frame) + assert self._text is not None + return self._text + + def verify(self) -> None: + """Verify PNG file""" + + if self.fp is None: + msg = "verify must be called directly after open" + raise RuntimeError(msg) + + # back up to beginning of IDAT block + self.fp.seek(self.tile[0][2] - 8) + + assert self.png is not None + self.png.verify() + self.png.close() + + if self._exclusive_fp: + self.fp.close() + self.fp = None + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._seek(0, True) + + last_frame = self.__frame + for f in range(self.__frame + 1, frame + 1): + try: + self._seek(f) + except EOFError as e: + self.seek(last_frame) + msg = "no more images in APNG file" + raise EOFError(msg) from e + + def _seek(self, frame: int, rewind: bool = False) -> None: + assert self.png is not None + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + self.dispose: _imaging.ImagingCore | None + dispose_extent = None + if frame == 0: + if rewind: + self._fp.seek(self.__rewind) + self.png.rewind() + self.__prepare_idat = self.__rewind_idat + self._im = None + self.info = self.png.im_info + self.tile = self.png.im_tile + self.fp = self._fp + self._prev_im = None + self.dispose = None + self.default_image = self.info.get("default_image", False) + self.dispose_op = self.info.get("disposal") + self.blend_op = self.info.get("blend") + dispose_extent = self.info.get("bbox") + self.__frame = 0 + else: + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + + # ensure previous frame was loaded + self.load() + + if self.dispose: + self.im.paste(self.dispose, self.dispose_extent) + self._prev_im = self.im.copy() + + self.fp = self._fp + + # advance to the next frame + if self.__prepare_idat: + ImageFile._safe_read(self.fp, self.__prepare_idat) + self.__prepare_idat = 0 + frame_start = False + while True: + self.fp.read(4) # CRC + + try: + cid, pos, length = self.png.read() + except (struct.error, SyntaxError): + break + + if cid == b"IEND": + msg = "No more images in APNG file" + raise EOFError(msg) + if cid == b"fcTL": + if frame_start: + # there must be at least one fdAT chunk between fcTL chunks + msg = "APNG missing frame data" + raise SyntaxError(msg) + frame_start = True + + try: + self.png.call(cid, pos, length) + except UnicodeDecodeError: + break + except EOFError: + if cid == b"fdAT": + length -= 4 + if frame_start: + self.__prepare_idat = length + break + ImageFile._safe_read(self.fp, length) + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + ImageFile._safe_read(self.fp, length) + + self.__frame = frame + self.tile = self.png.im_tile + self.dispose_op = self.info.get("disposal") + self.blend_op = self.info.get("blend") + dispose_extent = self.info.get("bbox") + + if not self.tile: + msg = "image not found in APNG frame" + raise EOFError(msg) + if dispose_extent: + self.dispose_extent: tuple[float, float, float, float] = dispose_extent + + # setup frame disposal (actual disposal done when needed in the next _seek()) + if self._prev_im is None and self.dispose_op == Disposal.OP_PREVIOUS: + self.dispose_op = Disposal.OP_BACKGROUND + + self.dispose = None + if self.dispose_op == Disposal.OP_PREVIOUS: + if self._prev_im: + self.dispose = self._prev_im.copy() + self.dispose = self._crop(self.dispose, self.dispose_extent) + elif self.dispose_op == Disposal.OP_BACKGROUND: + self.dispose = Image.core.fill(self.mode, self.size) + self.dispose = self._crop(self.dispose, self.dispose_extent) + + def tell(self) -> int: + return self.__frame + + def load_prepare(self) -> None: + """internal: prepare to read PNG file""" + + if self.info.get("interlace"): + self.decoderconfig = self.decoderconfig + (1,) + + self.__idat = self.__prepare_idat # used by load_read() + ImageFile.ImageFile.load_prepare(self) + + def load_read(self, read_bytes: int) -> bytes: + """internal: read more image data""" + + assert self.png is not None + while self.__idat == 0: + # end of chunk, skip forward to next one + + self.fp.read(4) # CRC + + cid, pos, length = self.png.read() + + if cid not in [b"IDAT", b"DDAT", b"fdAT"]: + self.png.push(cid, pos, length) + return b"" + + if cid == b"fdAT": + try: + self.png.call(cid, pos, length) + except EOFError: + pass + self.__idat = length - 4 # sequence_num has already been read + else: + self.__idat = length # empty chunks are allowed + + # read more data from this chunk + if read_bytes <= 0: + read_bytes = self.__idat + else: + read_bytes = min(read_bytes, self.__idat) + + self.__idat = self.__idat - read_bytes + + return self.fp.read(read_bytes) + + def load_end(self) -> None: + """internal: finished reading image data""" + assert self.png is not None + if self.__idat != 0: + self.fp.read(self.__idat) + while True: + self.fp.read(4) # CRC + + try: + cid, pos, length = self.png.read() + except (struct.error, SyntaxError): + break + + if cid == b"IEND": + break + elif cid == b"fcTL" and self.is_animated: + # start of the next frame, stop reading + self.__prepare_idat = 0 + self.png.push(cid, pos, length) + break + + try: + self.png.call(cid, pos, length) + except UnicodeDecodeError: + break + except EOFError: + if cid == b"fdAT": + length -= 4 + try: + ImageFile._safe_read(self.fp, length) + except OSError as e: + if ImageFile.LOAD_TRUNCATED_IMAGES: + break + else: + raise e + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + s = ImageFile._safe_read(self.fp, length) + if cid[1:2].islower(): + self.private_chunks.append((cid, s, True)) + self._text = self.png.im_text + if not self.is_animated: + self.png.close() + self.png = None + else: + if self._prev_im and self.blend_op == Blend.OP_OVER: + updated = self._crop(self.im, self.dispose_extent) + if self.im.mode == "RGB" and "transparency" in self.info: + mask = updated.convert_transparent( + "RGBA", self.info["transparency"] + ) + else: + if self.im.mode == "P" and "transparency" in self.info: + t = self.info["transparency"] + if isinstance(t, bytes): + updated.putpalettealphas(t) + elif isinstance(t, int): + updated.putpalettealpha(t) + mask = updated.convert("RGBA") + self._prev_im.paste(updated, self.dispose_extent, mask) + self.im = self._prev_im + + def _getexif(self) -> dict[int, Any] | None: + if "exif" not in self.info: + self.load() + if "exif" not in self.info and "Raw profile type exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + def getexif(self) -> Image.Exif: + if "exif" not in self.info: + self.load() + + return super().getexif() + + +# -------------------------------------------------------------------- +# PNG writer + +_OUTMODES = { + # supported PIL modes, and corresponding rawmode, bit depth and color type + "1": ("1", b"\x01", b"\x00"), + "L;1": ("L;1", b"\x01", b"\x00"), + "L;2": ("L;2", b"\x02", b"\x00"), + "L;4": ("L;4", b"\x04", b"\x00"), + "L": ("L", b"\x08", b"\x00"), + "LA": ("LA", b"\x08", b"\x04"), + "I": ("I;16B", b"\x10", b"\x00"), + "I;16": ("I;16B", b"\x10", b"\x00"), + "I;16B": ("I;16B", b"\x10", b"\x00"), + "P;1": ("P;1", b"\x01", b"\x03"), + "P;2": ("P;2", b"\x02", b"\x03"), + "P;4": ("P;4", b"\x04", b"\x03"), + "P": ("P", b"\x08", b"\x03"), + "RGB": ("RGB", b"\x08", b"\x02"), + "RGBA": ("RGBA", b"\x08", b"\x06"), +} + + +def putchunk(fp: IO[bytes], cid: bytes, *data: bytes) -> None: + """Write a PNG chunk (including CRC field)""" + + byte_data = b"".join(data) + + fp.write(o32(len(byte_data)) + cid) + fp.write(byte_data) + crc = _crc32(byte_data, _crc32(cid)) + fp.write(o32(crc)) + + +class _idat: + # wrap output from the encoder in IDAT chunks + + def __init__(self, fp: IO[bytes], chunk: Callable[..., None]) -> None: + self.fp = fp + self.chunk = chunk + + def write(self, data: bytes) -> None: + self.chunk(self.fp, b"IDAT", data) + + +class _fdat: + # wrap encoder output in fdAT chunks + + def __init__(self, fp: IO[bytes], chunk: Callable[..., None], seq_num: int) -> None: + self.fp = fp + self.chunk = chunk + self.seq_num = seq_num + + def write(self, data: bytes) -> None: + self.chunk(self.fp, b"fdAT", o32(self.seq_num), data) + self.seq_num += 1 + + +class _Frame(NamedTuple): + im: Image.Image + bbox: tuple[int, int, int, int] | None + encoderinfo: dict[str, Any] + + +def _write_multiple_frames( + im: Image.Image, + fp: IO[bytes], + chunk: Callable[..., None], + mode: str, + rawmode: str, + default_image: Image.Image | None, + append_images: list[Image.Image], +) -> Image.Image | None: + duration = im.encoderinfo.get("duration") + loop = im.encoderinfo.get("loop", im.info.get("loop", 0)) + disposal = im.encoderinfo.get("disposal", im.info.get("disposal", Disposal.OP_NONE)) + blend = im.encoderinfo.get("blend", im.info.get("blend", Blend.OP_SOURCE)) + + if default_image: + chain = itertools.chain(append_images) + else: + chain = itertools.chain([im], append_images) + + im_frames: list[_Frame] = [] + frame_count = 0 + for im_seq in chain: + for im_frame in ImageSequence.Iterator(im_seq): + if im_frame.mode == mode: + im_frame = im_frame.copy() + else: + im_frame = im_frame.convert(mode) + encoderinfo = im.encoderinfo.copy() + if isinstance(duration, (list, tuple)): + encoderinfo["duration"] = duration[frame_count] + elif duration is None and "duration" in im_frame.info: + encoderinfo["duration"] = im_frame.info["duration"] + if isinstance(disposal, (list, tuple)): + encoderinfo["disposal"] = disposal[frame_count] + if isinstance(blend, (list, tuple)): + encoderinfo["blend"] = blend[frame_count] + frame_count += 1 + + if im_frames: + previous = im_frames[-1] + prev_disposal = previous.encoderinfo.get("disposal") + prev_blend = previous.encoderinfo.get("blend") + if prev_disposal == Disposal.OP_PREVIOUS and len(im_frames) < 2: + prev_disposal = Disposal.OP_BACKGROUND + + if prev_disposal == Disposal.OP_BACKGROUND: + base_im = previous.im.copy() + dispose = Image.core.fill("RGBA", im.size, (0, 0, 0, 0)) + bbox = previous.bbox + if bbox: + dispose = dispose.crop(bbox) + else: + bbox = (0, 0) + im.size + base_im.paste(dispose, bbox) + elif prev_disposal == Disposal.OP_PREVIOUS: + base_im = im_frames[-2].im + else: + base_im = previous.im + delta = ImageChops.subtract_modulo( + im_frame.convert("RGBA"), base_im.convert("RGBA") + ) + bbox = delta.getbbox(alpha_only=False) + if ( + not bbox + and prev_disposal == encoderinfo.get("disposal") + and prev_blend == encoderinfo.get("blend") + and "duration" in encoderinfo + ): + previous.encoderinfo["duration"] += encoderinfo["duration"] + continue + else: + bbox = None + im_frames.append(_Frame(im_frame, bbox, encoderinfo)) + + if len(im_frames) == 1 and not default_image: + return im_frames[0].im + + # animation control + chunk( + fp, + b"acTL", + o32(len(im_frames)), # 0: num_frames + o32(loop), # 4: num_plays + ) + + # default image IDAT (if it exists) + if default_image: + if im.mode != mode: + im = im.convert(mode) + ImageFile._save( + im, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + im.size, 0, rawmode)], + ) + + seq_num = 0 + for frame, frame_data in enumerate(im_frames): + im_frame = frame_data.im + if not frame_data.bbox: + bbox = (0, 0) + im_frame.size + else: + bbox = frame_data.bbox + im_frame = im_frame.crop(bbox) + size = im_frame.size + encoderinfo = frame_data.encoderinfo + frame_duration = int(round(encoderinfo.get("duration", 0))) + frame_disposal = encoderinfo.get("disposal", disposal) + frame_blend = encoderinfo.get("blend", blend) + # frame control + chunk( + fp, + b"fcTL", + o32(seq_num), # sequence_number + o32(size[0]), # width + o32(size[1]), # height + o32(bbox[0]), # x_offset + o32(bbox[1]), # y_offset + o16(frame_duration), # delay_numerator + o16(1000), # delay_denominator + o8(frame_disposal), # dispose_op + o8(frame_blend), # blend_op + ) + seq_num += 1 + # frame data + if frame == 0 and not default_image: + # first frame must be in IDAT chunks for backwards compatibility + ImageFile._save( + im_frame, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + im_frame.size, 0, rawmode)], + ) + else: + fdat_chunks = _fdat(fp, chunk, seq_num) + ImageFile._save( + im_frame, + cast(IO[bytes], fdat_chunks), + [ImageFile._Tile("zip", (0, 0) + im_frame.size, 0, rawmode)], + ) + seq_num = fdat_chunks.seq_num + return None + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, + fp: IO[bytes], + filename: str | bytes, + chunk: Callable[..., None] = putchunk, + save_all: bool = False, +) -> None: + # save an image to disk (called by the save method) + + if save_all: + default_image = im.encoderinfo.get( + "default_image", im.info.get("default_image") + ) + modes = set() + sizes = set() + append_images = im.encoderinfo.get("append_images", []) + for im_seq in itertools.chain([im], append_images): + for im_frame in ImageSequence.Iterator(im_seq): + modes.add(im_frame.mode) + sizes.add(im_frame.size) + for mode in ("RGBA", "RGB", "P"): + if mode in modes: + break + else: + mode = modes.pop() + size = tuple(max(frame_size[i] for frame_size in sizes) for i in range(2)) + else: + size = im.size + mode = im.mode + + outmode = mode + if mode == "P": + # + # attempt to minimize storage requirements for palette images + if "bits" in im.encoderinfo: + # number of bits specified by user + colors = min(1 << im.encoderinfo["bits"], 256) + else: + # check palette contents + if im.palette: + colors = max(min(len(im.palette.getdata()[1]) // 3, 256), 1) + else: + colors = 256 + + if colors <= 16: + if colors <= 2: + bits = 1 + elif colors <= 4: + bits = 2 + else: + bits = 4 + outmode += f";{bits}" + + # encoder options + im.encoderconfig = ( + im.encoderinfo.get("optimize", False), + im.encoderinfo.get("compress_level", -1), + im.encoderinfo.get("compress_type", -1), + im.encoderinfo.get("dictionary", b""), + ) + + # get the corresponding PNG mode + try: + rawmode, bit_depth, color_type = _OUTMODES[outmode] + except KeyError as e: + msg = f"cannot write mode {mode} as PNG" + raise OSError(msg) from e + if outmode == "I": + deprecate("Saving I mode images as PNG", 13, stacklevel=4) + + # + # write minimal PNG file + + fp.write(_MAGIC) + + chunk( + fp, + b"IHDR", + o32(size[0]), # 0: size + o32(size[1]), + bit_depth, + color_type, + b"\0", # 10: compression + b"\0", # 11: filter category + b"\0", # 12: interlace flag + ) + + chunks = [b"cHRM", b"cICP", b"gAMA", b"sBIT", b"sRGB", b"tIME"] + + icc = im.encoderinfo.get("icc_profile", im.info.get("icc_profile")) + if icc: + # ICC profile + # according to PNG spec, the iCCP chunk contains: + # Profile name 1-79 bytes (character string) + # Null separator 1 byte (null character) + # Compression method 1 byte (0) + # Compressed profile n bytes (zlib with deflate compression) + name = b"ICC Profile" + data = name + b"\0\0" + zlib.compress(icc) + chunk(fp, b"iCCP", data) + + # You must either have sRGB or iCCP. + # Disallow sRGB chunks when an iCCP-chunk has been emitted. + chunks.remove(b"sRGB") + + info = im.encoderinfo.get("pnginfo") + if info: + chunks_multiple_allowed = [b"sPLT", b"iTXt", b"tEXt", b"zTXt"] + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid in chunks: + chunks.remove(cid) + chunk(fp, cid, data) + elif cid in chunks_multiple_allowed: + chunk(fp, cid, data) + elif cid[1:2].islower(): + # Private chunk + after_idat = len(info_chunk) == 3 and info_chunk[2] + if not after_idat: + chunk(fp, cid, data) + + if im.mode == "P": + palette_byte_number = colors * 3 + palette_bytes = im.im.getpalette("RGB")[:palette_byte_number] + while len(palette_bytes) < palette_byte_number: + palette_bytes += b"\0" + chunk(fp, b"PLTE", palette_bytes) + + transparency = im.encoderinfo.get("transparency", im.info.get("transparency", None)) + + if transparency or transparency == 0: + if im.mode == "P": + # limit to actual palette size + alpha_bytes = colors + if isinstance(transparency, bytes): + chunk(fp, b"tRNS", transparency[:alpha_bytes]) + else: + transparency = max(0, min(255, transparency)) + alpha = b"\xff" * transparency + b"\0" + chunk(fp, b"tRNS", alpha[:alpha_bytes]) + elif im.mode in ("1", "L", "I", "I;16"): + transparency = max(0, min(65535, transparency)) + chunk(fp, b"tRNS", o16(transparency)) + elif im.mode == "RGB": + red, green, blue = transparency + chunk(fp, b"tRNS", o16(red) + o16(green) + o16(blue)) + else: + if "transparency" in im.encoderinfo: + # don't bother with transparency if it's an RGBA + # and it's in the info dict. It's probably just stale. + msg = "cannot use transparency for this mode" + raise OSError(msg) + else: + if im.mode == "P" and im.im.getpalettemode() == "RGBA": + alpha = im.im.getpalette("RGBA", "A") + alpha_bytes = colors + chunk(fp, b"tRNS", alpha[:alpha_bytes]) + + dpi = im.encoderinfo.get("dpi") + if dpi: + chunk( + fp, + b"pHYs", + o32(int(dpi[0] / 0.0254 + 0.5)), + o32(int(dpi[1] / 0.0254 + 0.5)), + b"\x01", + ) + + if info: + chunks = [b"bKGD", b"hIST"] + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid in chunks: + chunks.remove(cid) + chunk(fp, cid, data) + + exif = im.encoderinfo.get("exif") + if exif: + if isinstance(exif, Image.Exif): + exif = exif.tobytes(8) + if exif.startswith(b"Exif\x00\x00"): + exif = exif[6:] + chunk(fp, b"eXIf", exif) + + single_im: Image.Image | None = im + if save_all: + single_im = _write_multiple_frames( + im, fp, chunk, mode, rawmode, default_image, append_images + ) + if single_im: + ImageFile._save( + single_im, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + single_im.size, 0, rawmode)], + ) + + if info: + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid[1:2].islower(): + # Private chunk + after_idat = len(info_chunk) == 3 and info_chunk[2] + if after_idat: + chunk(fp, cid, data) + + chunk(fp, b"IEND", b"") + + if hasattr(fp, "flush"): + fp.flush() + + +# -------------------------------------------------------------------- +# PNG chunk converter + + +def getchunks(im: Image.Image, **params: Any) -> list[tuple[bytes, bytes, bytes]]: + """Return a list of PNG chunks representing this image.""" + from io import BytesIO + + chunks = [] + + def append(fp: IO[bytes], cid: bytes, *data: bytes) -> None: + byte_data = b"".join(data) + crc = o32(_crc32(byte_data, _crc32(cid))) + chunks.append((cid, byte_data, crc)) + + fp = BytesIO() + + try: + im.encoderinfo = params + _save(im, fp, "", append) + finally: + del im.encoderinfo + + return chunks + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(PngImageFile.format, PngImageFile, _accept) +Image.register_save(PngImageFile.format, _save) +Image.register_save_all(PngImageFile.format, _save_all) + +Image.register_extensions(PngImageFile.format, [".png", ".apng"]) + +Image.register_mime(PngImageFile.format, "image/png") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..db34d107a4f4a9a7a27fe332e211133d88870fce --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py @@ -0,0 +1,375 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PPM support for PIL +# +# History: +# 96-03-24 fl Created +# 98-03-06 fl Write RGBA images (as RGB, that is) +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +from typing import IO + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import o8 +from ._binary import o32le as o32 + +# +# -------------------------------------------------------------------- + +b_whitespace = b"\x20\x09\x0a\x0b\x0c\x0d" + +MODES = { + # standard + b"P1": "1", + b"P2": "L", + b"P3": "RGB", + b"P4": "1", + b"P5": "L", + b"P6": "RGB", + # extensions + b"P0CMYK": "CMYK", + b"Pf": "F", + # PIL extensions (for test purposes only) + b"PyP": "P", + b"PyRGBA": "RGBA", + b"PyCMYK": "CMYK", +} + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"P") and prefix[1] in b"0123456fy" + + +## +# Image plugin for PBM, PGM, and PPM images. + + +class PpmImageFile(ImageFile.ImageFile): + format = "PPM" + format_description = "Pbmplus image" + + def _read_magic(self) -> bytes: + assert self.fp is not None + + magic = b"" + # read until whitespace or longest available magic number + for _ in range(6): + c = self.fp.read(1) + if not c or c in b_whitespace: + break + magic += c + return magic + + def _read_token(self) -> bytes: + assert self.fp is not None + + token = b"" + while len(token) <= 10: # read until next whitespace or limit of 10 characters + c = self.fp.read(1) + if not c: + break + elif c in b_whitespace: # token ended + if not token: + # skip whitespace at start + continue + break + elif c == b"#": + # ignores rest of the line; stops at CR, LF or EOF + while self.fp.read(1) not in b"\r\n": + pass + continue + token += c + if not token: + # Token was not even 1 byte + msg = "Reached EOF while reading header" + raise ValueError(msg) + elif len(token) > 10: + msg_too_long = b"Token too long in file header: %s" % token + raise ValueError(msg_too_long) + return token + + def _open(self) -> None: + assert self.fp is not None + + magic_number = self._read_magic() + try: + mode = MODES[magic_number] + except KeyError: + msg = "not a PPM file" + raise SyntaxError(msg) + self._mode = mode + + if magic_number in (b"P1", b"P4"): + self.custom_mimetype = "image/x-portable-bitmap" + elif magic_number in (b"P2", b"P5"): + self.custom_mimetype = "image/x-portable-graymap" + elif magic_number in (b"P3", b"P6"): + self.custom_mimetype = "image/x-portable-pixmap" + + self._size = int(self._read_token()), int(self._read_token()) + + decoder_name = "raw" + if magic_number in (b"P1", b"P2", b"P3"): + decoder_name = "ppm_plain" + + args: str | tuple[str | int, ...] + if mode == "1": + args = "1;I" + elif mode == "F": + scale = float(self._read_token()) + if scale == 0.0 or not math.isfinite(scale): + msg = "scale must be finite and non-zero" + raise ValueError(msg) + self.info["scale"] = abs(scale) + + rawmode = "F;32F" if scale < 0 else "F;32BF" + args = (rawmode, 0, -1) + else: + maxval = int(self._read_token()) + if not 0 < maxval < 65536: + msg = "maxval must be greater than 0 and less than 65536" + raise ValueError(msg) + if maxval > 255 and mode == "L": + self._mode = "I" + + rawmode = mode + if decoder_name != "ppm_plain": + # If maxval matches a bit depth, use the raw decoder directly + if maxval == 65535 and mode == "L": + rawmode = "I;16B" + elif maxval != 255: + decoder_name = "ppm" + + args = rawmode if decoder_name == "raw" else (rawmode, maxval) + self.tile = [ + ImageFile._Tile(decoder_name, (0, 0) + self.size, self.fp.tell(), args) + ] + + +# +# -------------------------------------------------------------------- + + +class PpmPlainDecoder(ImageFile.PyDecoder): + _pulls_fd = True + _comment_spans: bool + + def _read_block(self) -> bytes: + assert self.fd is not None + + return self.fd.read(ImageFile.SAFEBLOCK) + + def _find_comment_end(self, block: bytes, start: int = 0) -> int: + a = block.find(b"\n", start) + b = block.find(b"\r", start) + return min(a, b) if a * b > 0 else max(a, b) # lowest nonnegative index (or -1) + + def _ignore_comments(self, block: bytes) -> bytes: + if self._comment_spans: + # Finish current comment + while block: + comment_end = self._find_comment_end(block) + if comment_end != -1: + # Comment ends in this block + # Delete tail of comment + block = block[comment_end + 1 :] + break + else: + # Comment spans whole block + # So read the next block, looking for the end + block = self._read_block() + + # Search for any further comments + self._comment_spans = False + while True: + comment_start = block.find(b"#") + if comment_start == -1: + # No comment found + break + comment_end = self._find_comment_end(block, comment_start) + if comment_end != -1: + # Comment ends in this block + # Delete comment + block = block[:comment_start] + block[comment_end + 1 :] + else: + # Comment continues to next block(s) + block = block[:comment_start] + self._comment_spans = True + break + return block + + def _decode_bitonal(self) -> bytearray: + """ + This is a separate method because in the plain PBM format, all data tokens are + exactly one byte, so the inter-token whitespace is optional. + """ + data = bytearray() + total_bytes = self.state.xsize * self.state.ysize + + while len(data) != total_bytes: + block = self._read_block() # read next block + if not block: + # eof + break + + block = self._ignore_comments(block) + + tokens = b"".join(block.split()) + for token in tokens: + if token not in (48, 49): + msg = b"Invalid token for this mode: %s" % bytes([token]) + raise ValueError(msg) + data = (data + tokens)[:total_bytes] + invert = bytes.maketrans(b"01", b"\xff\x00") + return data.translate(invert) + + def _decode_blocks(self, maxval: int) -> bytearray: + data = bytearray() + max_len = 10 + out_byte_count = 4 if self.mode == "I" else 1 + out_max = 65535 if self.mode == "I" else 255 + bands = Image.getmodebands(self.mode) + total_bytes = self.state.xsize * self.state.ysize * bands * out_byte_count + + half_token = b"" + while len(data) != total_bytes: + block = self._read_block() # read next block + if not block: + if half_token: + block = bytearray(b" ") # flush half_token + else: + # eof + break + + block = self._ignore_comments(block) + + if half_token: + block = half_token + block # stitch half_token to new block + half_token = b"" + + tokens = block.split() + + if block and not block[-1:].isspace(): # block might split token + half_token = tokens.pop() # save half token for later + if len(half_token) > max_len: # prevent buildup of half_token + msg = ( + b"Token too long found in data: %s" % half_token[: max_len + 1] + ) + raise ValueError(msg) + + for token in tokens: + if len(token) > max_len: + msg = b"Token too long found in data: %s" % token[: max_len + 1] + raise ValueError(msg) + value = int(token) + if value < 0: + msg_str = f"Channel value is negative: {value}" + raise ValueError(msg_str) + if value > maxval: + msg_str = f"Channel value too large for this mode: {value}" + raise ValueError(msg_str) + value = round(value / maxval * out_max) + data += o32(value) if self.mode == "I" else o8(value) + if len(data) == total_bytes: # finished! + break + return data + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + self._comment_spans = False + if self.mode == "1": + data = self._decode_bitonal() + rawmode = "1;8" + else: + maxval = self.args[-1] + data = self._decode_blocks(maxval) + rawmode = "I;32" if self.mode == "I" else self.mode + self.set_as_raw(bytes(data), rawmode) + return -1, 0 + + +class PpmDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + data = bytearray() + maxval = self.args[-1] + in_byte_count = 1 if maxval < 256 else 2 + out_byte_count = 4 if self.mode == "I" else 1 + out_max = 65535 if self.mode == "I" else 255 + bands = Image.getmodebands(self.mode) + dest_length = self.state.xsize * self.state.ysize * bands * out_byte_count + while len(data) < dest_length: + pixels = self.fd.read(in_byte_count * bands) + if len(pixels) < in_byte_count * bands: + # eof + break + for b in range(bands): + value = ( + pixels[b] if in_byte_count == 1 else i16(pixels, b * in_byte_count) + ) + value = min(out_max, round(value / maxval * out_max)) + data += o32(value) if self.mode == "I" else o8(value) + rawmode = "I;32" if self.mode == "I" else self.mode + self.set_as_raw(bytes(data), rawmode) + return -1, 0 + + +# +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "1": + rawmode, head = "1;I", b"P4" + elif im.mode == "L": + rawmode, head = "L", b"P5" + elif im.mode in ("I", "I;16"): + rawmode, head = "I;16B", b"P5" + elif im.mode in ("RGB", "RGBA"): + rawmode, head = "RGB", b"P6" + elif im.mode == "F": + rawmode, head = "F;32F", b"Pf" + else: + msg = f"cannot write mode {im.mode} as PPM" + raise OSError(msg) + fp.write(head + b"\n%d %d\n" % im.size) + if head == b"P6": + fp.write(b"255\n") + elif head == b"P5": + if rawmode == "L": + fp.write(b"255\n") + else: + fp.write(b"65535\n") + elif head == b"Pf": + fp.write(b"-1.0\n") + row_order = -1 if im.mode == "F" else 1 + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, row_order))] + ) + + +# +# -------------------------------------------------------------------- + + +Image.register_open(PpmImageFile.format, PpmImageFile, _accept) +Image.register_save(PpmImageFile.format, _save) + +Image.register_decoder("ppm", PpmDecoder) +Image.register_decoder("ppm_plain", PpmPlainDecoder) + +Image.register_extensions(PpmImageFile.format, [".pbm", ".pgm", ".ppm", ".pnm", ".pfm"]) + +Image.register_mime(PpmImageFile.format, "image/x-portable-anymap") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f49aaeeb1f55cd2b6b17d9aa10f68876384fd410 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py @@ -0,0 +1,333 @@ +# +# The Python Imaging Library +# $Id$ +# +# Adobe PSD 2.5/3.0 file handling +# +# History: +# 1995-09-01 fl Created +# 1997-01-03 fl Read most PSD images +# 1997-01-18 fl Fixed P and CMYK support +# 2001-10-21 fl Added seek/tell support (for layers) +# +# Copyright (c) 1997-2001 by Secret Labs AB. +# Copyright (c) 1995-2001 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from functools import cached_property +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i8 +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import si16be as si16 +from ._binary import si32be as si32 +from ._util import DeferredError + +MODES = { + # (photoshop mode, bits) -> (pil mode, required channels) + (0, 1): ("1", 1), + (0, 8): ("L", 1), + (1, 8): ("L", 1), + (2, 8): ("P", 1), + (3, 8): ("RGB", 3), + (4, 8): ("CMYK", 4), + (7, 8): ("L", 1), # FIXME: multilayer + (8, 8): ("L", 1), # duotone + (9, 8): ("LAB", 3), +} + + +# --------------------------------------------------------------------. +# read PSD images + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"8BPS") + + +## +# Image plugin for Photoshop images. + + +class PsdImageFile(ImageFile.ImageFile): + format = "PSD" + format_description = "Adobe Photoshop" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + read = self.fp.read + + # + # header + + s = read(26) + if not _accept(s) or i16(s, 4) != 1: + msg = "not a PSD file" + raise SyntaxError(msg) + + psd_bits = i16(s, 22) + psd_channels = i16(s, 12) + psd_mode = i16(s, 24) + + mode, channels = MODES[(psd_mode, psd_bits)] + + if channels > psd_channels: + msg = "not enough channels" + raise OSError(msg) + if mode == "RGB" and psd_channels == 4: + mode = "RGBA" + channels = 4 + + self._mode = mode + self._size = i32(s, 18), i32(s, 14) + + # + # color mode data + + size = i32(read(4)) + if size: + data = read(size) + if mode == "P" and size == 768: + self.palette = ImagePalette.raw("RGB;L", data) + + # + # image resources + + self.resources = [] + + size = i32(read(4)) + if size: + # load resources + end = self.fp.tell() + size + while self.fp.tell() < end: + read(4) # signature + id = i16(read(2)) + name = read(i8(read(1))) + if not (len(name) & 1): + read(1) # padding + data = read(i32(read(4))) + if len(data) & 1: + read(1) # padding + self.resources.append((id, name, data)) + if id == 1039: # ICC profile + self.info["icc_profile"] = data + + # + # layer and mask information + + self._layers_position = None + + size = i32(read(4)) + if size: + end = self.fp.tell() + size + size = i32(read(4)) + if size: + self._layers_position = self.fp.tell() + self._layers_size = size + self.fp.seek(end) + self._n_frames: int | None = None + + # + # image descriptor + + self.tile = _maketile(self.fp, mode, (0, 0) + self.size, channels) + + # keep the file open + self._fp = self.fp + self.frame = 1 + self._min_frame = 1 + + @cached_property + def layers( + self, + ) -> list[tuple[str, str, tuple[int, int, int, int], list[ImageFile._Tile]]]: + layers = [] + if self._layers_position is not None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self._fp.seek(self._layers_position) + _layer_data = io.BytesIO(ImageFile._safe_read(self._fp, self._layers_size)) + layers = _layerinfo(_layer_data, self._layers_size) + self._n_frames = len(layers) + return layers + + @property + def n_frames(self) -> int: + if self._n_frames is None: + self._n_frames = len(self.layers) + return self._n_frames + + @property + def is_animated(self) -> bool: + return len(self.layers) > 1 + + def seek(self, layer: int) -> None: + if not self._seek_check(layer): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + # seek to given layer (1..max) + _, mode, _, tile = self.layers[layer - 1] + self._mode = mode + self.tile = tile + self.frame = layer + self.fp = self._fp + + def tell(self) -> int: + # return layer number (0=image, 1..max=layers) + return self.frame + + +def _layerinfo( + fp: IO[bytes], ct_bytes: int +) -> list[tuple[str, str, tuple[int, int, int, int], list[ImageFile._Tile]]]: + # read layerinfo block + layers = [] + + def read(size: int) -> bytes: + return ImageFile._safe_read(fp, size) + + ct = si16(read(2)) + + # sanity check + if ct_bytes < (abs(ct) * 20): + msg = "Layer block too short for number of layers requested" + raise SyntaxError(msg) + + for _ in range(abs(ct)): + # bounding box + y0 = si32(read(4)) + x0 = si32(read(4)) + y1 = si32(read(4)) + x1 = si32(read(4)) + + # image info + bands = [] + ct_types = i16(read(2)) + if ct_types > 4: + fp.seek(ct_types * 6 + 12, io.SEEK_CUR) + size = i32(read(4)) + fp.seek(size, io.SEEK_CUR) + continue + + for _ in range(ct_types): + type = i16(read(2)) + + if type == 65535: + b = "A" + else: + b = "RGBA"[type] + + bands.append(b) + read(4) # size + + # figure out the image mode + bands.sort() + if bands == ["R"]: + mode = "L" + elif bands == ["B", "G", "R"]: + mode = "RGB" + elif bands == ["A", "B", "G", "R"]: + mode = "RGBA" + else: + mode = "" # unknown + + # skip over blend flags and extra information + read(12) # filler + name = "" + size = i32(read(4)) # length of the extra data field + if size: + data_end = fp.tell() + size + + length = i32(read(4)) + if length: + fp.seek(length - 16, io.SEEK_CUR) + + length = i32(read(4)) + if length: + fp.seek(length, io.SEEK_CUR) + + length = i8(read(1)) + if length: + # Don't know the proper encoding, + # Latin-1 should be a good guess + name = read(length).decode("latin-1", "replace") + + fp.seek(data_end) + layers.append((name, mode, (x0, y0, x1, y1))) + + # get tiles + layerinfo = [] + for i, (name, mode, bbox) in enumerate(layers): + tile = [] + for m in mode: + t = _maketile(fp, m, bbox, 1) + if t: + tile.extend(t) + layerinfo.append((name, mode, bbox, tile)) + + return layerinfo + + +def _maketile( + file: IO[bytes], mode: str, bbox: tuple[int, int, int, int], channels: int +) -> list[ImageFile._Tile]: + tiles = [] + read = file.read + + compression = i16(read(2)) + + xsize = bbox[2] - bbox[0] + ysize = bbox[3] - bbox[1] + + offset = file.tell() + + if compression == 0: + # + # raw compression + for channel in range(channels): + layer = mode[channel] + if mode == "CMYK": + layer += ";I" + tiles.append(ImageFile._Tile("raw", bbox, offset, layer)) + offset = offset + xsize * ysize + + elif compression == 1: + # + # packbits compression + i = 0 + bytecount = read(channels * ysize * 2) + offset = file.tell() + for channel in range(channels): + layer = mode[channel] + if mode == "CMYK": + layer += ";I" + tiles.append(ImageFile._Tile("packbits", bbox, offset, layer)) + for y in range(ysize): + offset = offset + i16(bytecount, i) + i += 2 + + file.seek(offset) + + if offset & 1: + read(1) # padding + + return tiles + + +# -------------------------------------------------------------------- +# registry + + +Image.register_open(PsdImageFile.format, PsdImageFile, _accept) + +Image.register_extension(PsdImageFile.format, ".psd") + +Image.register_mime(PsdImageFile.format, "image/vnd.adobe.photoshop") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..dba5d809fef75e281ac10f92f1868c58b1b4508c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py @@ -0,0 +1,234 @@ +# +# The Python Imaging Library. +# +# QOI support for PIL +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o32be as o32 + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"qoif") + + +class QoiImageFile(ImageFile.ImageFile): + format = "QOI" + format_description = "Quite OK Image" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not a QOI file" + raise SyntaxError(msg) + + self._size = i32(self.fp.read(4)), i32(self.fp.read(4)) + + channels = self.fp.read(1)[0] + self._mode = "RGB" if channels == 3 else "RGBA" + + self.fp.seek(1, os.SEEK_CUR) # colorspace + self.tile = [ImageFile._Tile("qoi", (0, 0) + self._size, self.fp.tell())] + + +class QoiDecoder(ImageFile.PyDecoder): + _pulls_fd = True + _previous_pixel: bytes | bytearray | None = None + _previously_seen_pixels: dict[int, bytes | bytearray] = {} + + def _add_to_previous_pixels(self, value: bytes | bytearray) -> None: + self._previous_pixel = value + + r, g, b, a = value + hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64 + self._previously_seen_pixels[hash_value] = value + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + self._previously_seen_pixels = {} + self._previous_pixel = bytearray((0, 0, 0, 255)) + + data = bytearray() + bands = Image.getmodebands(self.mode) + dest_length = self.state.xsize * self.state.ysize * bands + while len(data) < dest_length: + byte = self.fd.read(1)[0] + value: bytes | bytearray + if byte == 0b11111110 and self._previous_pixel: # QOI_OP_RGB + value = bytearray(self.fd.read(3)) + self._previous_pixel[3:] + elif byte == 0b11111111: # QOI_OP_RGBA + value = self.fd.read(4) + else: + op = byte >> 6 + if op == 0: # QOI_OP_INDEX + op_index = byte & 0b00111111 + value = self._previously_seen_pixels.get( + op_index, bytearray((0, 0, 0, 0)) + ) + elif op == 1 and self._previous_pixel: # QOI_OP_DIFF + value = bytearray( + ( + (self._previous_pixel[0] + ((byte & 0b00110000) >> 4) - 2) + % 256, + (self._previous_pixel[1] + ((byte & 0b00001100) >> 2) - 2) + % 256, + (self._previous_pixel[2] + (byte & 0b00000011) - 2) % 256, + self._previous_pixel[3], + ) + ) + elif op == 2 and self._previous_pixel: # QOI_OP_LUMA + second_byte = self.fd.read(1)[0] + diff_green = (byte & 0b00111111) - 32 + diff_red = ((second_byte & 0b11110000) >> 4) - 8 + diff_blue = (second_byte & 0b00001111) - 8 + + value = bytearray( + tuple( + (self._previous_pixel[i] + diff_green + diff) % 256 + for i, diff in enumerate((diff_red, 0, diff_blue)) + ) + ) + value += self._previous_pixel[3:] + elif op == 3 and self._previous_pixel: # QOI_OP_RUN + run_length = (byte & 0b00111111) + 1 + value = self._previous_pixel + if bands == 3: + value = value[:3] + data += value * run_length + continue + self._add_to_previous_pixels(value) + + if bands == 3: + value = value[:3] + data += value + self.set_as_raw(data) + return -1, 0 + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "RGB": + channels = 3 + elif im.mode == "RGBA": + channels = 4 + else: + msg = "Unsupported QOI image mode" + raise ValueError(msg) + + colorspace = 0 if im.encoderinfo.get("colorspace") == "sRGB" else 1 + + fp.write(b"qoif") + fp.write(o32(im.size[0])) + fp.write(o32(im.size[1])) + fp.write(o8(channels)) + fp.write(o8(colorspace)) + + ImageFile._save(im, fp, [ImageFile._Tile("qoi", (0, 0) + im.size)]) + + +class QoiEncoder(ImageFile.PyEncoder): + _pushes_fd = True + _previous_pixel: tuple[int, int, int, int] | None = None + _previously_seen_pixels: dict[int, tuple[int, int, int, int]] = {} + _run = 0 + + def _write_run(self) -> bytes: + data = o8(0b11000000 | (self._run - 1)) # QOI_OP_RUN + self._run = 0 + return data + + def _delta(self, left: int, right: int) -> int: + result = (left - right) & 255 + if result >= 128: + result -= 256 + return result + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + assert self.im is not None + + self._previously_seen_pixels = {0: (0, 0, 0, 0)} + self._previous_pixel = (0, 0, 0, 255) + + data = bytearray() + w, h = self.im.size + bands = Image.getmodebands(self.mode) + + for y in range(h): + for x in range(w): + pixel = self.im.getpixel((x, y)) + if bands == 3: + pixel = (*pixel, 255) + + if pixel == self._previous_pixel: + self._run += 1 + if self._run == 62: + data += self._write_run() + else: + if self._run: + data += self._write_run() + + r, g, b, a = pixel + hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64 + if self._previously_seen_pixels.get(hash_value) == pixel: + data += o8(hash_value) # QOI_OP_INDEX + elif self._previous_pixel: + self._previously_seen_pixels[hash_value] = pixel + + prev_r, prev_g, prev_b, prev_a = self._previous_pixel + if prev_a == a: + delta_r = self._delta(r, prev_r) + delta_g = self._delta(g, prev_g) + delta_b = self._delta(b, prev_b) + + if ( + -2 <= delta_r < 2 + and -2 <= delta_g < 2 + and -2 <= delta_b < 2 + ): + data += o8( + 0b01000000 + | (delta_r + 2) << 4 + | (delta_g + 2) << 2 + | (delta_b + 2) + ) # QOI_OP_DIFF + else: + delta_gr = self._delta(delta_r, delta_g) + delta_gb = self._delta(delta_b, delta_g) + if ( + -8 <= delta_gr < 8 + and -32 <= delta_g < 32 + and -8 <= delta_gb < 8 + ): + data += o8( + 0b10000000 | (delta_g + 32) + ) # QOI_OP_LUMA + data += o8((delta_gr + 8) << 4 | (delta_gb + 8)) + else: + data += o8(0b11111110) # QOI_OP_RGB + data += bytes(pixel[:3]) + else: + data += o8(0b11111111) # QOI_OP_RGBA + data += bytes(pixel) + + self._previous_pixel = pixel + + if self._run: + data += self._write_run() + data += bytes((0, 0, 0, 0, 0, 0, 0, 1)) # padding + + return len(data), 0, data + + +Image.register_open(QoiImageFile.format, QoiImageFile, _accept) +Image.register_decoder("qoi", QoiDecoder) +Image.register_extension(QoiImageFile.format, ".qoi") + +Image.register_save(QoiImageFile.format, _save) +Image.register_encoder("qoi", QoiEncoder) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..853022150ae849e490378e41e831897050c207a2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py @@ -0,0 +1,231 @@ +# +# The Python Imaging Library. +# $Id$ +# +# SGI image file handling +# +# See "The SGI Image File Format (Draft version 0.97)", Paul Haeberli. +# +# +# +# History: +# 2017-22-07 mb Add RLE decompression +# 2016-16-10 mb Add save method without compression +# 1995-09-10 fl Created +# +# Copyright (c) 2016 by Mickael Bonfill. +# Copyright (c) 2008 by Karsten Hiddemann. +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1995 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import struct +from typing import IO + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import o8 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 2 and i16(prefix) == 474 + + +MODES = { + (1, 1, 1): "L", + (1, 2, 1): "L", + (2, 1, 1): "L;16B", + (2, 2, 1): "L;16B", + (1, 3, 3): "RGB", + (2, 3, 3): "RGB;16B", + (1, 3, 4): "RGBA", + (2, 3, 4): "RGBA;16B", +} + + +## +# Image plugin for SGI images. +class SgiImageFile(ImageFile.ImageFile): + format = "SGI" + format_description = "SGI Image File Format" + + def _open(self) -> None: + # HEAD + assert self.fp is not None + + headlen = 512 + s = self.fp.read(headlen) + + if not _accept(s): + msg = "Not an SGI image file" + raise ValueError(msg) + + # compression : verbatim or RLE + compression = s[2] + + # bpc : 1 or 2 bytes (8bits or 16bits) + bpc = s[3] + + # dimension : 1, 2 or 3 (depending on xsize, ysize and zsize) + dimension = i16(s, 4) + + # xsize : width + xsize = i16(s, 6) + + # ysize : height + ysize = i16(s, 8) + + # zsize : channels count + zsize = i16(s, 10) + + # determine mode from bits/zsize + try: + rawmode = MODES[(bpc, dimension, zsize)] + except KeyError: + msg = "Unsupported SGI image mode" + raise ValueError(msg) + + self._size = xsize, ysize + self._mode = rawmode.split(";")[0] + if self.mode == "RGB": + self.custom_mimetype = "image/rgb" + + # orientation -1 : scanlines begins at the bottom-left corner + orientation = -1 + + # decoder info + if compression == 0: + pagesize = xsize * ysize * bpc + if bpc == 2: + self.tile = [ + ImageFile._Tile( + "SGI16", + (0, 0) + self.size, + headlen, + (self.mode, 0, orientation), + ) + ] + else: + self.tile = [] + offset = headlen + for layer in self.mode: + self.tile.append( + ImageFile._Tile( + "raw", (0, 0) + self.size, offset, (layer, 0, orientation) + ) + ) + offset += pagesize + elif compression == 1: + self.tile = [ + ImageFile._Tile( + "sgi_rle", (0, 0) + self.size, headlen, (rawmode, orientation, bpc) + ) + ] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode not in {"RGB", "RGBA", "L"}: + msg = "Unsupported SGI image mode" + raise ValueError(msg) + + # Get the keyword arguments + info = im.encoderinfo + + # Byte-per-pixel precision, 1 = 8bits per pixel + bpc = info.get("bpc", 1) + + if bpc not in (1, 2): + msg = "Unsupported number of bytes per pixel" + raise ValueError(msg) + + # Flip the image, since the origin of SGI file is the bottom-left corner + orientation = -1 + # Define the file as SGI File Format + magic_number = 474 + # Run-Length Encoding Compression - Unsupported at this time + rle = 0 + + # X Dimension = width / Y Dimension = height + x, y = im.size + # Z Dimension: Number of channels + z = len(im.mode) + # Number of dimensions (x,y,z) + if im.mode == "L": + dimension = 1 if y == 1 else 2 + else: + dimension = 3 + + # Minimum Byte value + pinmin = 0 + # Maximum Byte value (255 = 8bits per pixel) + pinmax = 255 + # Image name (79 characters max, truncated below in write) + img_name = os.path.splitext(os.path.basename(filename))[0] + if isinstance(img_name, str): + img_name = img_name.encode("ascii", "ignore") + # Standard representation of pixel in the file + colormap = 0 + fp.write(struct.pack(">h", magic_number)) + fp.write(o8(rle)) + fp.write(o8(bpc)) + fp.write(struct.pack(">H", dimension)) + fp.write(struct.pack(">H", x)) + fp.write(struct.pack(">H", y)) + fp.write(struct.pack(">H", z)) + fp.write(struct.pack(">l", pinmin)) + fp.write(struct.pack(">l", pinmax)) + fp.write(struct.pack("4s", b"")) # dummy + fp.write(struct.pack("79s", img_name)) # truncates to 79 chars + fp.write(struct.pack("s", b"")) # force null byte after img_name + fp.write(struct.pack(">l", colormap)) + fp.write(struct.pack("404s", b"")) # dummy + + rawmode = "L" + if bpc == 2: + rawmode = "L;16B" + + for channel in im.split(): + fp.write(channel.tobytes("raw", rawmode, 0, orientation)) + + if hasattr(fp, "flush"): + fp.flush() + + +class SGI16Decoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + assert self.im is not None + + rawmode, stride, orientation = self.args + pagesize = self.state.xsize * self.state.ysize + zsize = len(self.mode) + self.fd.seek(512) + + for band in range(zsize): + channel = Image.new("L", (self.state.xsize, self.state.ysize)) + channel.frombytes( + self.fd.read(2 * pagesize), "raw", "L;16B", stride, orientation + ) + self.im.putband(channel.im, band) + + return -1, 0 + + +# +# registry + + +Image.register_decoder("SGI16", SGI16Decoder) +Image.register_open(SgiImageFile.format, SgiImageFile, _accept) +Image.register_save(SgiImageFile.format, _save) +Image.register_mime(SgiImageFile.format, "image/sgi") + +Image.register_extensions(SgiImageFile.format, [".bw", ".rgb", ".rgba", ".sgi"]) + +# End of file diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..868019e80a80cffc5e9f193ddbf96a0ba64ad9ea --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py @@ -0,0 +1,331 @@ +# +# The Python Imaging Library. +# +# SPIDER image file handling +# +# History: +# 2004-08-02 Created BB +# 2006-03-02 added save method +# 2006-03-13 added support for stack images +# +# Copyright (c) 2004 by Health Research Inc. (HRI) RENSSELAER, NY 12144. +# Copyright (c) 2004 by William Baxter. +# Copyright (c) 2004 by Secret Labs AB. +# Copyright (c) 2004 by Fredrik Lundh. +# + +## +# Image plugin for the Spider image format. This format is used +# by the SPIDER software, in processing image data from electron +# microscopy and tomography. +## + +# +# SpiderImagePlugin.py +# +# The Spider image format is used by SPIDER software, in processing +# image data from electron microscopy and tomography. +# +# Spider home page: +# https://spider.wadsworth.org/spider_doc/spider/docs/spider.html +# +# Details about the Spider image format: +# https://spider.wadsworth.org/spider_doc/spider/docs/image_doc.html +# +from __future__ import annotations + +import os +import struct +import sys +from typing import IO, Any, cast + +from . import Image, ImageFile +from ._util import DeferredError + +TYPE_CHECKING = False + + +def isInt(f: Any) -> int: + try: + i = int(f) + if f - i == 0: + return 1 + else: + return 0 + except (ValueError, OverflowError): + return 0 + + +iforms = [1, 3, -11, -12, -21, -22] + + +# There is no magic number to identify Spider files, so just check a +# series of header locations to see if they have reasonable values. +# Returns no. of bytes in the header, if it is a valid Spider header, +# otherwise returns 0 + + +def isSpiderHeader(t: tuple[float, ...]) -> int: + h = (99,) + t # add 1 value so can use spider header index start=1 + # header values 1,2,5,12,13,22,23 should be integers + for i in [1, 2, 5, 12, 13, 22, 23]: + if not isInt(h[i]): + return 0 + # check iform + iform = int(h[5]) + if iform not in iforms: + return 0 + # check other header values + labrec = int(h[13]) # no. records in file header + labbyt = int(h[22]) # total no. of bytes in header + lenbyt = int(h[23]) # record length in bytes + if labbyt != (labrec * lenbyt): + return 0 + # looks like a valid header + return labbyt + + +def isSpiderImage(filename: str) -> int: + with open(filename, "rb") as fp: + f = fp.read(92) # read 23 * 4 bytes + t = struct.unpack(">23f", f) # try big-endian first + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + t = struct.unpack("<23f", f) # little-endian + hdrlen = isSpiderHeader(t) + return hdrlen + + +class SpiderImageFile(ImageFile.ImageFile): + format = "SPIDER" + format_description = "Spider 2D image" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # check header + n = 27 * 4 # read 27 float values + f = self.fp.read(n) + + try: + self.bigendian = 1 + t = struct.unpack(">27f", f) # try big-endian first + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + self.bigendian = 0 + t = struct.unpack("<27f", f) # little-endian + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + msg = "not a valid Spider file" + raise SyntaxError(msg) + except struct.error as e: + msg = "not a valid Spider file" + raise SyntaxError(msg) from e + + h = (99,) + t # add 1 value : spider header index starts at 1 + iform = int(h[5]) + if iform != 1: + msg = "not a Spider 2D image" + raise SyntaxError(msg) + + self._size = int(h[12]), int(h[2]) # size in pixels (width, height) + self.istack = int(h[24]) + self.imgnumber = int(h[27]) + + if self.istack == 0 and self.imgnumber == 0: + # stk=0, img=0: a regular 2D image + offset = hdrlen + self._nimages = 1 + elif self.istack > 0 and self.imgnumber == 0: + # stk>0, img=0: Opening the stack for the first time + self.imgbytes = int(h[12]) * int(h[2]) * 4 + self.hdrlen = hdrlen + self._nimages = int(h[26]) + # Point to the first image in the stack + offset = hdrlen * 2 + self.imgnumber = 1 + elif self.istack == 0 and self.imgnumber > 0: + # stk=0, img>0: an image within the stack + offset = hdrlen + self.stkoffset + self.istack = 2 # So Image knows it's still a stack + else: + msg = "inconsistent stack header values" + raise SyntaxError(msg) + + if self.bigendian: + self.rawmode = "F;32BF" + else: + self.rawmode = "F;32F" + self._mode = "F" + + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, offset, self.rawmode)] + self._fp = self.fp # FIXME: hack + + @property + def n_frames(self) -> int: + return self._nimages + + @property + def is_animated(self) -> bool: + return self._nimages > 1 + + # 1st image index is zero (although SPIDER imgnumber starts at 1) + def tell(self) -> int: + if self.imgnumber < 1: + return 0 + else: + return self.imgnumber - 1 + + def seek(self, frame: int) -> None: + if self.istack == 0: + msg = "attempt to seek in a non-stack file" + raise EOFError(msg) + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.stkoffset = self.hdrlen + frame * (self.hdrlen + self.imgbytes) + self.fp = self._fp + self.fp.seek(self.stkoffset) + self._open() + + # returns a byte image after rescaling to 0..255 + def convert2byte(self, depth: int = 255) -> Image.Image: + extrema = self.getextrema() + assert isinstance(extrema[0], float) + minimum, maximum = cast(tuple[float, float], extrema) + m: float = 1 + if maximum != minimum: + m = depth / (maximum - minimum) + b = -m * minimum + return self.point(lambda i: i * m + b).convert("L") + + if TYPE_CHECKING: + from . import ImageTk + + # returns a ImageTk.PhotoImage object, after rescaling to 0..255 + def tkPhotoImage(self) -> ImageTk.PhotoImage: + from . import ImageTk + + return ImageTk.PhotoImage(self.convert2byte(), palette=256) + + +# -------------------------------------------------------------------- +# Image series + + +# given a list of filenames, return a list of images +def loadImageSeries(filelist: list[str] | None = None) -> list[Image.Image] | None: + """create a list of :py:class:`~PIL.Image.Image` objects for use in a montage""" + if filelist is None or len(filelist) < 1: + return None + + byte_imgs = [] + for img in filelist: + if not os.path.exists(img): + print(f"unable to find {img}") + continue + try: + with Image.open(img) as im: + assert isinstance(im, SpiderImageFile) + byte_im = im.convert2byte() + except Exception: + if not isSpiderImage(img): + print(f"{img} is not a Spider image file") + continue + byte_im.info["filename"] = img + byte_imgs.append(byte_im) + return byte_imgs + + +# -------------------------------------------------------------------- +# For saving images in Spider format + + +def makeSpiderHeader(im: Image.Image) -> list[bytes]: + nsam, nrow = im.size + lenbyt = nsam * 4 # There are labrec records in the header + labrec = int(1024 / lenbyt) + if 1024 % lenbyt != 0: + labrec += 1 + labbyt = labrec * lenbyt + nvalues = int(labbyt / 4) + if nvalues < 23: + return [] + + hdr = [0.0] * nvalues + + # NB these are Fortran indices + hdr[1] = 1.0 # nslice (=1 for an image) + hdr[2] = float(nrow) # number of rows per slice + hdr[3] = float(nrow) # number of records in the image + hdr[5] = 1.0 # iform for 2D image + hdr[12] = float(nsam) # number of pixels per line + hdr[13] = float(labrec) # number of records in file header + hdr[22] = float(labbyt) # total number of bytes in header + hdr[23] = float(lenbyt) # record length in bytes + + # adjust for Fortran indexing + hdr = hdr[1:] + hdr.append(0.0) + # pack binary data into a string + return [struct.pack("f", v) for v in hdr] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "F": + im = im.convert("F") + + hdr = makeSpiderHeader(im) + if len(hdr) < 256: + msg = "Error creating Spider header" + raise OSError(msg) + + # write the SPIDER header + fp.writelines(hdr) + + rawmode = "F;32NF" # 32-bit native floating point + ImageFile._save(im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, rawmode)]) + + +def _save_spider(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # get the filename extension and register it with Image + filename_ext = os.path.splitext(filename)[1] + ext = filename_ext.decode() if isinstance(filename_ext, bytes) else filename_ext + Image.register_extension(SpiderImageFile.format, ext) + _save(im, fp, filename) + + +# -------------------------------------------------------------------- + + +Image.register_open(SpiderImageFile.format, SpiderImageFile) +Image.register_save(SpiderImageFile.format, _save_spider) + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 SpiderImagePlugin.py [infile] [outfile]") + sys.exit() + + filename = sys.argv[1] + if not isSpiderImage(filename): + print("input image must be in Spider format") + sys.exit() + + with Image.open(filename) as im: + print(f"image: {im}") + print(f"format: {im.format}") + print(f"size: {im.size}") + print(f"mode: {im.mode}") + print("max, min: ", end=" ") + print(im.getextrema()) + + if len(sys.argv) > 2: + outfile = sys.argv[2] + + # perform some image operation + im = im.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + print( + f"saving a flipped version of {os.path.basename(filename)} " + f"as {outfile} " + ) + im.save(outfile, SpiderImageFile.format) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..8912379ea3e7801cdac9a557d2bc0c557bce8991 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py @@ -0,0 +1,145 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Sun image file handling +# +# History: +# 1995-09-10 fl Created +# 1996-05-28 fl Fixed 32-bit alignment +# 1998-12-29 fl Import ImagePalette module +# 2001-12-18 fl Fixed palette loading (from Jean-Claude Rimbault) +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1995-1996 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile, ImagePalette +from ._binary import i32be as i32 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 4 and i32(prefix) == 0x59A66A95 + + +## +# Image plugin for Sun raster files. + + +class SunImageFile(ImageFile.ImageFile): + format = "SUN" + format_description = "Sun Raster File" + + def _open(self) -> None: + # The Sun Raster file header is 32 bytes in length + # and has the following format: + + # typedef struct _SunRaster + # { + # DWORD MagicNumber; /* Magic (identification) number */ + # DWORD Width; /* Width of image in pixels */ + # DWORD Height; /* Height of image in pixels */ + # DWORD Depth; /* Number of bits per pixel */ + # DWORD Length; /* Size of image data in bytes */ + # DWORD Type; /* Type of raster file */ + # DWORD ColorMapType; /* Type of color map */ + # DWORD ColorMapLength; /* Size of the color map in bytes */ + # } SUNRASTER; + + assert self.fp is not None + + # HEAD + s = self.fp.read(32) + if not _accept(s): + msg = "not an SUN raster file" + raise SyntaxError(msg) + + offset = 32 + + self._size = i32(s, 4), i32(s, 8) + + depth = i32(s, 12) + # data_length = i32(s, 16) # unreliable, ignore. + file_type = i32(s, 20) + palette_type = i32(s, 24) # 0: None, 1: RGB, 2: Raw/arbitrary + palette_length = i32(s, 28) + + if depth == 1: + self._mode, rawmode = "1", "1;I" + elif depth == 4: + self._mode, rawmode = "L", "L;4" + elif depth == 8: + self._mode = rawmode = "L" + elif depth == 24: + if file_type == 3: + self._mode, rawmode = "RGB", "RGB" + else: + self._mode, rawmode = "RGB", "BGR" + elif depth == 32: + if file_type == 3: + self._mode, rawmode = "RGB", "RGBX" + else: + self._mode, rawmode = "RGB", "BGRX" + else: + msg = "Unsupported Mode/Bit Depth" + raise SyntaxError(msg) + + if palette_length: + if palette_length > 1024: + msg = "Unsupported Color Palette Length" + raise SyntaxError(msg) + + if palette_type != 1: + msg = "Unsupported Palette Type" + raise SyntaxError(msg) + + offset = offset + palette_length + self.palette = ImagePalette.raw("RGB;L", self.fp.read(palette_length)) + if self.mode == "L": + self._mode = "P" + rawmode = rawmode.replace("L", "P") + + # 16 bit boundaries on stride + stride = ((self.size[0] * depth + 15) // 16) * 2 + + # file type: Type is the version (or flavor) of the bitmap + # file. The following values are typically found in the Type + # field: + # 0000h Old + # 0001h Standard + # 0002h Byte-encoded + # 0003h RGB format + # 0004h TIFF format + # 0005h IFF format + # FFFFh Experimental + + # Old and standard are the same, except for the length tag. + # byte-encoded is run-length-encoded + # RGB looks similar to standard, but RGB byte order + # TIFF and IFF mean that they were converted from T/IFF + # Experimental means that it's something else. + # (https://www.fileformat.info/format/sunraster/egff.htm) + + if file_type in (0, 1, 3, 4, 5): + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offset, (rawmode, stride)) + ] + elif file_type == 2: + self.tile = [ + ImageFile._Tile("sun_rle", (0, 0) + self.size, offset, rawmode) + ] + else: + msg = "Unsupported Sun Raster file type" + raise SyntaxError(msg) + + +# +# registry + + +Image.register_open(SunImageFile.format, SunImageFile, _accept) + +Image.register_extension(SunImageFile.format, ".ras") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TarIO.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TarIO.py new file mode 100644 index 0000000000000000000000000000000000000000..86490a496f3f106fcc042c03fb235ed5fb41f3a7 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TarIO.py @@ -0,0 +1,61 @@ +# +# The Python Imaging Library. +# $Id$ +# +# read files from within a tar file +# +# History: +# 95-06-18 fl Created +# 96-05-28 fl Open files in binary mode +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995-96. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io + +from . import ContainerIO + + +class TarIO(ContainerIO.ContainerIO[bytes]): + """A file object that provides read access to a given member of a TAR file.""" + + def __init__(self, tarfile: str, file: str) -> None: + """ + Create file object. + + :param tarfile: Name of TAR file. + :param file: Name of member file. + """ + self.fh = open(tarfile, "rb") + + while True: + s = self.fh.read(512) + if len(s) != 512: + self.fh.close() + + msg = "unexpected end of tar file" + raise OSError(msg) + + name = s[:100].decode("utf-8") + i = name.find("\0") + if i == 0: + self.fh.close() + + msg = "cannot find subfile" + raise OSError(msg) + if i > 0: + name = name[:i] + + size = int(s[124:135], 8) + + if file == name: + break + + self.fh.seek((size + 511) & (~511), io.SEEK_CUR) + + # Open region + super().__init__(self.fh, self.fh.tell(), size) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..90d5b5cf4ee17fc050784bf591adae3247407b56 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py @@ -0,0 +1,264 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TGA file handling +# +# History: +# 95-09-01 fl created (reads 24-bit files only) +# 97-01-04 fl support more TGA versions, including compressed images +# 98-07-04 fl fixed orientation and alpha layer bugs +# 98-09-11 fl fixed orientation for runlength decoder +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1995-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import warnings +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 + +# +# -------------------------------------------------------------------- +# Read RGA file + + +MODES = { + # map imagetype/depth to rawmode + (1, 8): "P", + (3, 1): "1", + (3, 8): "L", + (3, 16): "LA", + (2, 16): "BGRA;15Z", + (2, 24): "BGR", + (2, 32): "BGRA", +} + + +## +# Image plugin for Targa files. + + +class TgaImageFile(ImageFile.ImageFile): + format = "TGA" + format_description = "Targa" + + def _open(self) -> None: + # process header + assert self.fp is not None + + s = self.fp.read(18) + + id_len = s[0] + + colormaptype = s[1] + imagetype = s[2] + + depth = s[16] + + flags = s[17] + + self._size = i16(s, 12), i16(s, 14) + + # validate header fields + if ( + colormaptype not in (0, 1) + or self.size[0] <= 0 + or self.size[1] <= 0 + or depth not in (1, 8, 16, 24, 32) + ): + msg = "not a TGA file" + raise SyntaxError(msg) + + # image mode + if imagetype in (3, 11): + self._mode = "L" + if depth == 1: + self._mode = "1" # ??? + elif depth == 16: + self._mode = "LA" + elif imagetype in (1, 9): + self._mode = "P" if colormaptype else "L" + elif imagetype in (2, 10): + self._mode = "RGB" if depth == 24 else "RGBA" + else: + msg = "unknown TGA mode" + raise SyntaxError(msg) + + # orientation + orientation = flags & 0x30 + self._flip_horizontally = orientation in [0x10, 0x30] + if orientation in [0x20, 0x30]: + orientation = 1 + elif orientation in [0, 0x10]: + orientation = -1 + else: + msg = "unknown TGA orientation" + raise SyntaxError(msg) + + self.info["orientation"] = orientation + + if imagetype & 8: + self.info["compression"] = "tga_rle" + + if id_len: + self.info["id_section"] = self.fp.read(id_len) + + if colormaptype: + # read palette + start, size, mapdepth = i16(s, 3), i16(s, 5), s[7] + if mapdepth == 16: + self.palette = ImagePalette.raw( + "BGRA;15Z", bytes(2 * start) + self.fp.read(2 * size) + ) + self.palette.mode = "RGBA" + elif mapdepth == 24: + self.palette = ImagePalette.raw( + "BGR", bytes(3 * start) + self.fp.read(3 * size) + ) + elif mapdepth == 32: + self.palette = ImagePalette.raw( + "BGRA", bytes(4 * start) + self.fp.read(4 * size) + ) + else: + msg = "unknown TGA map depth" + raise SyntaxError(msg) + + # setup tile descriptor + try: + rawmode = MODES[(imagetype & 7, depth)] + if imagetype & 8: + # compressed + self.tile = [ + ImageFile._Tile( + "tga_rle", + (0, 0) + self.size, + self.fp.tell(), + (rawmode, orientation, depth), + ) + ] + else: + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + self.fp.tell(), + (rawmode, 0, orientation), + ) + ] + except KeyError: + pass # cannot decode + + def load_end(self) -> None: + if self._flip_horizontally: + self.im = self.im.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + + +# +# -------------------------------------------------------------------- +# Write TGA file + + +SAVE = { + "1": ("1", 1, 0, 3), + "L": ("L", 8, 0, 3), + "LA": ("LA", 16, 0, 3), + "P": ("P", 8, 1, 1), + "RGB": ("BGR", 24, 0, 2), + "RGBA": ("BGRA", 32, 0, 2), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + rawmode, bits, colormaptype, imagetype = SAVE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as TGA" + raise OSError(msg) from e + + if "rle" in im.encoderinfo: + rle = im.encoderinfo["rle"] + else: + compression = im.encoderinfo.get("compression", im.info.get("compression")) + rle = compression == "tga_rle" + if rle: + imagetype += 8 + + id_section = im.encoderinfo.get("id_section", im.info.get("id_section", "")) + id_len = len(id_section) + if id_len > 255: + id_len = 255 + id_section = id_section[:255] + warnings.warn("id_section has been trimmed to 255 characters") + + if colormaptype: + palette = im.im.getpalette("RGB", "BGR") + colormaplength, colormapentry = len(palette) // 3, 24 + else: + colormaplength, colormapentry = 0, 0 + + if im.mode in ("LA", "RGBA"): + flags = 8 + else: + flags = 0 + + orientation = im.encoderinfo.get("orientation", im.info.get("orientation", -1)) + if orientation > 0: + flags = flags | 0x20 + + fp.write( + o8(id_len) + + o8(colormaptype) + + o8(imagetype) + + o16(0) # colormapfirst + + o16(colormaplength) + + o8(colormapentry) + + o16(0) + + o16(0) + + o16(im.size[0]) + + o16(im.size[1]) + + o8(bits) + + o8(flags) + ) + + if id_section: + fp.write(id_section) + + if colormaptype: + fp.write(palette) + + if rle: + ImageFile._save( + im, + fp, + [ImageFile._Tile("tga_rle", (0, 0) + im.size, 0, (rawmode, orientation))], + ) + else: + ImageFile._save( + im, + fp, + [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, orientation))], + ) + + # write targa version 2 footer + fp.write(b"\000" * 8 + b"TRUEVISION-XFILE." + b"\000") + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(TgaImageFile.format, TgaImageFile) +Image.register_save(TgaImageFile.format, _save) + +Image.register_extensions(TgaImageFile.format, [".tga", ".icb", ".vda", ".vst"]) + +Image.register_mime(TgaImageFile.format, "image/x-tga") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..daf20f2e899608c28905272de7b5c0620ef5938c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py @@ -0,0 +1,2339 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TIFF file handling +# +# TIFF is a flexible, if somewhat aged, image file format originally +# defined by Aldus. Although TIFF supports a wide variety of pixel +# layouts and compression methods, the name doesn't really stand for +# "thousands of incompatible file formats," it just feels that way. +# +# To read TIFF data from a stream, the stream must be seekable. For +# progressive decoding, make sure to use TIFF files where the tag +# directory is placed first in the file. +# +# History: +# 1995-09-01 fl Created +# 1996-05-04 fl Handle JPEGTABLES tag +# 1996-05-18 fl Fixed COLORMAP support +# 1997-01-05 fl Fixed PREDICTOR support +# 1997-08-27 fl Added support for rational tags (from Perry Stoll) +# 1998-01-10 fl Fixed seek/tell (from Jan Blom) +# 1998-07-15 fl Use private names for internal variables +# 1999-06-13 fl Rewritten for PIL 1.0 (1.0) +# 2000-10-11 fl Additional fixes for Python 2.0 (1.1) +# 2001-04-17 fl Fixed rewind support (seek to frame 0) (1.2) +# 2001-05-12 fl Added write support for more tags (from Greg Couch) (1.3) +# 2001-12-18 fl Added workaround for broken Matrox library +# 2002-01-18 fl Don't mess up if photometric tag is missing (D. Alan Stewart) +# 2003-05-19 fl Check FILLORDER tag +# 2003-09-26 fl Added RGBa support +# 2004-02-24 fl Added DPI support; fixed rational write support +# 2005-02-07 fl Added workaround for broken Corel Draw 10 files +# 2006-01-09 fl Added support for float/double tags (from Russell Nelson) +# +# Copyright (c) 1997-2006 by Secret Labs AB. All rights reserved. +# Copyright (c) 1995-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import itertools +import logging +import math +import os +import struct +import warnings +from collections.abc import Iterator, MutableMapping +from fractions import Fraction +from numbers import Number, Rational +from typing import IO, Any, Callable, NoReturn, cast + +from . import ExifTags, Image, ImageFile, ImageOps, ImagePalette, TiffTags +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._deprecate import deprecate +from ._typing import StrOrBytesPath +from ._util import DeferredError, is_path +from .TiffTags import TYPES + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import Buffer, IntegralLike + +logger = logging.getLogger(__name__) + +# Set these to true to force use of libtiff for reading or writing. +READ_LIBTIFF = False +WRITE_LIBTIFF = False +STRIP_SIZE = 65536 + +II = b"II" # little-endian (Intel style) +MM = b"MM" # big-endian (Motorola style) + +# +# -------------------------------------------------------------------- +# Read TIFF files + +# a few tag names, just to make the code below a bit more readable +OSUBFILETYPE = 255 +IMAGEWIDTH = 256 +IMAGELENGTH = 257 +BITSPERSAMPLE = 258 +COMPRESSION = 259 +PHOTOMETRIC_INTERPRETATION = 262 +FILLORDER = 266 +IMAGEDESCRIPTION = 270 +STRIPOFFSETS = 273 +SAMPLESPERPIXEL = 277 +ROWSPERSTRIP = 278 +STRIPBYTECOUNTS = 279 +X_RESOLUTION = 282 +Y_RESOLUTION = 283 +PLANAR_CONFIGURATION = 284 +RESOLUTION_UNIT = 296 +TRANSFERFUNCTION = 301 +SOFTWARE = 305 +DATE_TIME = 306 +ARTIST = 315 +PREDICTOR = 317 +COLORMAP = 320 +TILEWIDTH = 322 +TILELENGTH = 323 +TILEOFFSETS = 324 +TILEBYTECOUNTS = 325 +SUBIFD = 330 +EXTRASAMPLES = 338 +SAMPLEFORMAT = 339 +JPEGTABLES = 347 +YCBCRSUBSAMPLING = 530 +REFERENCEBLACKWHITE = 532 +COPYRIGHT = 33432 +IPTC_NAA_CHUNK = 33723 # newsphoto properties +PHOTOSHOP_CHUNK = 34377 # photoshop properties +ICCPROFILE = 34675 +EXIFIFD = 34665 +XMP = 700 +JPEGQUALITY = 65537 # pseudo-tag by libtiff + +# https://github.com/imagej/ImageJA/blob/master/src/main/java/ij/io/TiffDecoder.java +IMAGEJ_META_DATA_BYTE_COUNTS = 50838 +IMAGEJ_META_DATA = 50839 + +COMPRESSION_INFO = { + # Compression => pil compression name + 1: "raw", + 2: "tiff_ccitt", + 3: "group3", + 4: "group4", + 5: "tiff_lzw", + 6: "tiff_jpeg", # obsolete + 7: "jpeg", + 8: "tiff_adobe_deflate", + 32771: "tiff_raw_16", # 16-bit padding + 32773: "packbits", + 32809: "tiff_thunderscan", + 32946: "tiff_deflate", + 34676: "tiff_sgilog", + 34677: "tiff_sgilog24", + 34925: "lzma", + 50000: "zstd", + 50001: "webp", +} + +COMPRESSION_INFO_REV = {v: k for k, v in COMPRESSION_INFO.items()} + +OPEN_INFO = { + # (ByteOrder, PhotoInterpretation, SampleFormat, FillOrder, BitsPerSample, + # ExtraSamples) => mode, rawmode + (II, 0, (1,), 1, (1,), ()): ("1", "1;I"), + (MM, 0, (1,), 1, (1,), ()): ("1", "1;I"), + (II, 0, (1,), 2, (1,), ()): ("1", "1;IR"), + (MM, 0, (1,), 2, (1,), ()): ("1", "1;IR"), + (II, 1, (1,), 1, (1,), ()): ("1", "1"), + (MM, 1, (1,), 1, (1,), ()): ("1", "1"), + (II, 1, (1,), 2, (1,), ()): ("1", "1;R"), + (MM, 1, (1,), 2, (1,), ()): ("1", "1;R"), + (II, 0, (1,), 1, (2,), ()): ("L", "L;2I"), + (MM, 0, (1,), 1, (2,), ()): ("L", "L;2I"), + (II, 0, (1,), 2, (2,), ()): ("L", "L;2IR"), + (MM, 0, (1,), 2, (2,), ()): ("L", "L;2IR"), + (II, 1, (1,), 1, (2,), ()): ("L", "L;2"), + (MM, 1, (1,), 1, (2,), ()): ("L", "L;2"), + (II, 1, (1,), 2, (2,), ()): ("L", "L;2R"), + (MM, 1, (1,), 2, (2,), ()): ("L", "L;2R"), + (II, 0, (1,), 1, (4,), ()): ("L", "L;4I"), + (MM, 0, (1,), 1, (4,), ()): ("L", "L;4I"), + (II, 0, (1,), 2, (4,), ()): ("L", "L;4IR"), + (MM, 0, (1,), 2, (4,), ()): ("L", "L;4IR"), + (II, 1, (1,), 1, (4,), ()): ("L", "L;4"), + (MM, 1, (1,), 1, (4,), ()): ("L", "L;4"), + (II, 1, (1,), 2, (4,), ()): ("L", "L;4R"), + (MM, 1, (1,), 2, (4,), ()): ("L", "L;4R"), + (II, 0, (1,), 1, (8,), ()): ("L", "L;I"), + (MM, 0, (1,), 1, (8,), ()): ("L", "L;I"), + (II, 0, (1,), 2, (8,), ()): ("L", "L;IR"), + (MM, 0, (1,), 2, (8,), ()): ("L", "L;IR"), + (II, 1, (1,), 1, (8,), ()): ("L", "L"), + (MM, 1, (1,), 1, (8,), ()): ("L", "L"), + (II, 1, (2,), 1, (8,), ()): ("L", "L"), + (MM, 1, (2,), 1, (8,), ()): ("L", "L"), + (II, 1, (1,), 2, (8,), ()): ("L", "L;R"), + (MM, 1, (1,), 2, (8,), ()): ("L", "L;R"), + (II, 1, (1,), 1, (12,), ()): ("I;16", "I;12"), + (II, 0, (1,), 1, (16,), ()): ("I;16", "I;16"), + (II, 1, (1,), 1, (16,), ()): ("I;16", "I;16"), + (MM, 1, (1,), 1, (16,), ()): ("I;16B", "I;16B"), + (II, 1, (1,), 2, (16,), ()): ("I;16", "I;16R"), + (II, 1, (2,), 1, (16,), ()): ("I", "I;16S"), + (MM, 1, (2,), 1, (16,), ()): ("I", "I;16BS"), + (II, 0, (3,), 1, (32,), ()): ("F", "F;32F"), + (MM, 0, (3,), 1, (32,), ()): ("F", "F;32BF"), + (II, 1, (1,), 1, (32,), ()): ("I", "I;32N"), + (II, 1, (2,), 1, (32,), ()): ("I", "I;32S"), + (MM, 1, (2,), 1, (32,), ()): ("I", "I;32BS"), + (II, 1, (3,), 1, (32,), ()): ("F", "F;32F"), + (MM, 1, (3,), 1, (32,), ()): ("F", "F;32BF"), + (II, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"), + (MM, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"), + (II, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"), + (MM, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"), + (II, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"), + (MM, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"), + (II, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"), # missing ExtraSamples + (MM, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"), # missing ExtraSamples + (II, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGB", "RGBX"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGB", "RGBX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGB", "RGBXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGB", "RGBXX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGB", "RGBXXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGB", "RGBXXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"), # Corel Draw 10 + (MM, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"), # Corel Draw 10 + (II, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16L"), + (MM, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGB", "RGBX;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGB", "RGBX;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16B"), + (II, 3, (1,), 1, (1,), ()): ("P", "P;1"), + (MM, 3, (1,), 1, (1,), ()): ("P", "P;1"), + (II, 3, (1,), 2, (1,), ()): ("P", "P;1R"), + (MM, 3, (1,), 2, (1,), ()): ("P", "P;1R"), + (II, 3, (1,), 1, (2,), ()): ("P", "P;2"), + (MM, 3, (1,), 1, (2,), ()): ("P", "P;2"), + (II, 3, (1,), 2, (2,), ()): ("P", "P;2R"), + (MM, 3, (1,), 2, (2,), ()): ("P", "P;2R"), + (II, 3, (1,), 1, (4,), ()): ("P", "P;4"), + (MM, 3, (1,), 1, (4,), ()): ("P", "P;4"), + (II, 3, (1,), 2, (4,), ()): ("P", "P;4R"), + (MM, 3, (1,), 2, (4,), ()): ("P", "P;4R"), + (II, 3, (1,), 1, (8,), ()): ("P", "P"), + (MM, 3, (1,), 1, (8,), ()): ("P", "P"), + (II, 3, (1,), 1, (8, 8), (0,)): ("P", "PX"), + (II, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"), + (MM, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"), + (II, 3, (1,), 2, (8,), ()): ("P", "P;R"), + (MM, 3, (1,), 2, (8,), ()): ("P", "P;R"), + (II, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"), + (MM, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"), + (II, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"), + (MM, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"), + (II, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"), + (MM, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"), + (II, 5, (1,), 1, (16, 16, 16, 16), ()): ("CMYK", "CMYK;16L"), + (MM, 5, (1,), 1, (16, 16, 16, 16), ()): ("CMYK", "CMYK;16B"), + (II, 6, (1,), 1, (8,), ()): ("L", "L"), + (MM, 6, (1,), 1, (8,), ()): ("L", "L"), + # JPEG compressed images handled by LibTiff and auto-converted to RGBX + # Minimal Baseline TIFF requires YCbCr images to have 3 SamplesPerPixel + (II, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"), + (MM, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"), + (II, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"), + (MM, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"), +} + +MAX_SAMPLESPERPIXEL = max(len(key_tp[4]) for key_tp in OPEN_INFO) + +PREFIXES = [ + b"MM\x00\x2a", # Valid TIFF header with big-endian byte order + b"II\x2a\x00", # Valid TIFF header with little-endian byte order + b"MM\x2a\x00", # Invalid TIFF header, assume big-endian + b"II\x00\x2a", # Invalid TIFF header, assume little-endian + b"MM\x00\x2b", # BigTIFF with big-endian byte order + b"II\x2b\x00", # BigTIFF with little-endian byte order +] + +if not getattr(Image.core, "libtiff_support_custom_tags", True): + deprecate("Support for LibTIFF earlier than version 4", 12) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(tuple(PREFIXES)) + + +def _limit_rational( + val: float | Fraction | IFDRational, max_val: int +) -> tuple[IntegralLike, IntegralLike]: + inv = abs(val) > 1 + n_d = IFDRational(1 / val if inv else val).limit_rational(max_val) + return n_d[::-1] if inv else n_d + + +def _limit_signed_rational( + val: IFDRational, max_val: int, min_val: int +) -> tuple[IntegralLike, IntegralLike]: + frac = Fraction(val) + n_d: tuple[IntegralLike, IntegralLike] = frac.numerator, frac.denominator + + if min(float(i) for i in n_d) < min_val: + n_d = _limit_rational(val, abs(min_val)) + + n_d_float = tuple(float(i) for i in n_d) + if max(n_d_float) > max_val: + n_d = _limit_rational(n_d_float[0] / n_d_float[1], max_val) + + return n_d + + +## +# Wrapper for TIFF IFDs. + +_load_dispatch = {} +_write_dispatch = {} + + +def _delegate(op: str) -> Any: + def delegate( + self: IFDRational, *args: tuple[float, ...] + ) -> bool | float | Fraction: + return getattr(self._val, op)(*args) + + return delegate + + +class IFDRational(Rational): + """Implements a rational class where 0/0 is a legal value to match + the in the wild use of exif rationals. + + e.g., DigitalZoomRatio - 0.00/0.00 indicates that no digital zoom was used + """ + + """ If the denominator is 0, store this as a float('nan'), otherwise store + as a fractions.Fraction(). Delegate as appropriate + + """ + + __slots__ = ("_numerator", "_denominator", "_val") + + def __init__( + self, value: float | Fraction | IFDRational, denominator: int = 1 + ) -> None: + """ + :param value: either an integer numerator, a + float/rational/other number, or an IFDRational + :param denominator: Optional integer denominator + """ + self._val: Fraction | float + if isinstance(value, IFDRational): + self._numerator = value.numerator + self._denominator = value.denominator + self._val = value._val + return + + if isinstance(value, Fraction): + self._numerator = value.numerator + self._denominator = value.denominator + else: + if TYPE_CHECKING: + self._numerator = cast(IntegralLike, value) + else: + self._numerator = value + self._denominator = denominator + + if denominator == 0: + self._val = float("nan") + elif denominator == 1: + self._val = Fraction(value) + elif int(value) == value: + self._val = Fraction(int(value), denominator) + else: + self._val = Fraction(value / denominator) + + @property + def numerator(self) -> IntegralLike: + return self._numerator + + @property + def denominator(self) -> int: + return self._denominator + + def limit_rational(self, max_denominator: int) -> tuple[IntegralLike, int]: + """ + + :param max_denominator: Integer, the maximum denominator value + :returns: Tuple of (numerator, denominator) + """ + + if self.denominator == 0: + return self.numerator, self.denominator + + assert isinstance(self._val, Fraction) + f = self._val.limit_denominator(max_denominator) + return f.numerator, f.denominator + + def __repr__(self) -> str: + return str(float(self._val)) + + def __hash__(self) -> int: # type: ignore[override] + return self._val.__hash__() + + def __eq__(self, other: object) -> bool: + val = self._val + if isinstance(other, IFDRational): + other = other._val + if isinstance(other, float): + val = float(val) + return val == other + + def __getstate__(self) -> list[float | Fraction | IntegralLike]: + return [self._val, self._numerator, self._denominator] + + def __setstate__(self, state: list[float | Fraction | IntegralLike]) -> None: + IFDRational.__init__(self, 0) + _val, _numerator, _denominator = state + assert isinstance(_val, (float, Fraction)) + self._val = _val + if TYPE_CHECKING: + self._numerator = cast(IntegralLike, _numerator) + else: + self._numerator = _numerator + assert isinstance(_denominator, int) + self._denominator = _denominator + + """ a = ['add','radd', 'sub', 'rsub', 'mul', 'rmul', + 'truediv', 'rtruediv', 'floordiv', 'rfloordiv', + 'mod','rmod', 'pow','rpow', 'pos', 'neg', + 'abs', 'trunc', 'lt', 'gt', 'le', 'ge', 'bool', + 'ceil', 'floor', 'round'] + print("\n".join("__%s__ = _delegate('__%s__')" % (s,s) for s in a)) + """ + + __add__ = _delegate("__add__") + __radd__ = _delegate("__radd__") + __sub__ = _delegate("__sub__") + __rsub__ = _delegate("__rsub__") + __mul__ = _delegate("__mul__") + __rmul__ = _delegate("__rmul__") + __truediv__ = _delegate("__truediv__") + __rtruediv__ = _delegate("__rtruediv__") + __floordiv__ = _delegate("__floordiv__") + __rfloordiv__ = _delegate("__rfloordiv__") + __mod__ = _delegate("__mod__") + __rmod__ = _delegate("__rmod__") + __pow__ = _delegate("__pow__") + __rpow__ = _delegate("__rpow__") + __pos__ = _delegate("__pos__") + __neg__ = _delegate("__neg__") + __abs__ = _delegate("__abs__") + __trunc__ = _delegate("__trunc__") + __lt__ = _delegate("__lt__") + __gt__ = _delegate("__gt__") + __le__ = _delegate("__le__") + __ge__ = _delegate("__ge__") + __bool__ = _delegate("__bool__") + __ceil__ = _delegate("__ceil__") + __floor__ = _delegate("__floor__") + __round__ = _delegate("__round__") + # Python >= 3.11 + if hasattr(Fraction, "__int__"): + __int__ = _delegate("__int__") + + +_LoaderFunc = Callable[["ImageFileDirectory_v2", bytes, bool], Any] + + +def _register_loader(idx: int, size: int) -> Callable[[_LoaderFunc], _LoaderFunc]: + def decorator(func: _LoaderFunc) -> _LoaderFunc: + from .TiffTags import TYPES + + if func.__name__.startswith("load_"): + TYPES[idx] = func.__name__[5:].replace("_", " ") + _load_dispatch[idx] = size, func # noqa: F821 + return func + + return decorator + + +def _register_writer(idx: int) -> Callable[[Callable[..., Any]], Callable[..., Any]]: + def decorator(func: Callable[..., Any]) -> Callable[..., Any]: + _write_dispatch[idx] = func # noqa: F821 + return func + + return decorator + + +def _register_basic(idx_fmt_name: tuple[int, str, str]) -> None: + from .TiffTags import TYPES + + idx, fmt, name = idx_fmt_name + TYPES[idx] = name + size = struct.calcsize(f"={fmt}") + + def basic_handler( + self: ImageFileDirectory_v2, data: bytes, legacy_api: bool = True + ) -> tuple[Any, ...]: + return self._unpack(f"{len(data) // size}{fmt}", data) + + _load_dispatch[idx] = size, basic_handler # noqa: F821 + _write_dispatch[idx] = lambda self, *values: ( # noqa: F821 + b"".join(self._pack(fmt, value) for value in values) + ) + + +if TYPE_CHECKING: + _IFDv2Base = MutableMapping[int, Any] +else: + _IFDv2Base = MutableMapping + + +class ImageFileDirectory_v2(_IFDv2Base): + """This class represents a TIFF tag directory. To speed things up, we + don't decode tags unless they're asked for. + + Exposes a dictionary interface of the tags in the directory:: + + ifd = ImageFileDirectory_v2() + ifd[key] = 'Some Data' + ifd.tagtype[key] = TiffTags.ASCII + print(ifd[key]) + 'Some Data' + + Individual values are returned as the strings or numbers, sequences are + returned as tuples of the values. + + The tiff metadata type of each item is stored in a dictionary of + tag types in + :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v2.tagtype`. The types + are read from a tiff file, guessed from the type added, or added + manually. + + Data Structures: + + * ``self.tagtype = {}`` + + * Key: numerical TIFF tag number + * Value: integer corresponding to the data type from + :py:data:`.TiffTags.TYPES` + + .. versionadded:: 3.0.0 + + 'Internal' data structures: + + * ``self._tags_v2 = {}`` + + * Key: numerical TIFF tag number + * Value: decoded data, as tuple for multiple values + + * ``self._tagdata = {}`` + + * Key: numerical TIFF tag number + * Value: undecoded byte string from file + + * ``self._tags_v1 = {}`` + + * Key: numerical TIFF tag number + * Value: decoded data in the v1 format + + Tags will be found in the private attributes ``self._tagdata``, and in + ``self._tags_v2`` once decoded. + + ``self.legacy_api`` is a value for internal use, and shouldn't be changed + from outside code. In cooperation with + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`, if ``legacy_api`` + is true, then decoded tags will be populated into both ``_tags_v1`` and + ``_tags_v2``. ``_tags_v2`` will be used if this IFD is used in the TIFF + save routine. Tags should be read from ``_tags_v1`` if + ``legacy_api == true``. + + """ + + _load_dispatch: dict[int, tuple[int, _LoaderFunc]] = {} + _write_dispatch: dict[int, Callable[..., Any]] = {} + + def __init__( + self, + ifh: bytes = b"II\x2a\x00\x00\x00\x00\x00", + prefix: bytes | None = None, + group: int | None = None, + ) -> None: + """Initialize an ImageFileDirectory. + + To construct an ImageFileDirectory from a real file, pass the 8-byte + magic header to the constructor. To only set the endianness, pass it + as the 'prefix' keyword argument. + + :param ifh: One of the accepted magic headers (cf. PREFIXES); also sets + endianness. + :param prefix: Override the endianness of the file. + """ + if not _accept(ifh): + msg = f"not a TIFF file (header {repr(ifh)} not valid)" + raise SyntaxError(msg) + self._prefix = prefix if prefix is not None else ifh[:2] + if self._prefix == MM: + self._endian = ">" + elif self._prefix == II: + self._endian = "<" + else: + msg = "not a TIFF IFD" + raise SyntaxError(msg) + self._bigtiff = ifh[2] == 43 + self.group = group + self.tagtype: dict[int, int] = {} + """ Dictionary of tag types """ + self.reset() + self.next = ( + self._unpack("Q", ifh[8:])[0] + if self._bigtiff + else self._unpack("L", ifh[4:])[0] + ) + self._legacy_api = False + + prefix = property(lambda self: self._prefix) + offset = property(lambda self: self._offset) + + @property + def legacy_api(self) -> bool: + return self._legacy_api + + @legacy_api.setter + def legacy_api(self, value: bool) -> NoReturn: + msg = "Not allowing setting of legacy api" + raise Exception(msg) + + def reset(self) -> None: + self._tags_v1: dict[int, Any] = {} # will remain empty if legacy_api is false + self._tags_v2: dict[int, Any] = {} # main tag storage + self._tagdata: dict[int, bytes] = {} + self.tagtype = {} # added 2008-06-05 by Florian Hoech + self._next = None + self._offset: int | None = None + + def __str__(self) -> str: + return str(dict(self)) + + def named(self) -> dict[str, Any]: + """ + :returns: dict of name|key: value + + Returns the complete tag dictionary, with named tags where possible. + """ + return { + TiffTags.lookup(code, self.group).name: value + for code, value in self.items() + } + + def __len__(self) -> int: + return len(set(self._tagdata) | set(self._tags_v2)) + + def __getitem__(self, tag: int) -> Any: + if tag not in self._tags_v2: # unpack on the fly + data = self._tagdata[tag] + typ = self.tagtype[tag] + size, handler = self._load_dispatch[typ] + self[tag] = handler(self, data, self.legacy_api) # check type + val = self._tags_v2[tag] + if self.legacy_api and not isinstance(val, (tuple, bytes)): + val = (val,) + return val + + def __contains__(self, tag: object) -> bool: + return tag in self._tags_v2 or tag in self._tagdata + + def __setitem__(self, tag: int, value: Any) -> None: + self._setitem(tag, value, self.legacy_api) + + def _setitem(self, tag: int, value: Any, legacy_api: bool) -> None: + basetypes = (Number, bytes, str) + + info = TiffTags.lookup(tag, self.group) + values = [value] if isinstance(value, basetypes) else value + + if tag not in self.tagtype: + if info.type: + self.tagtype[tag] = info.type + else: + self.tagtype[tag] = TiffTags.UNDEFINED + if all(isinstance(v, IFDRational) for v in values): + for v in values: + assert isinstance(v, IFDRational) + if v < 0: + self.tagtype[tag] = TiffTags.SIGNED_RATIONAL + break + else: + self.tagtype[tag] = TiffTags.RATIONAL + elif all(isinstance(v, int) for v in values): + short = True + signed_short = True + long = True + for v in values: + assert isinstance(v, int) + if short and not (0 <= v < 2**16): + short = False + if signed_short and not (-(2**15) < v < 2**15): + signed_short = False + if long and v < 0: + long = False + if short: + self.tagtype[tag] = TiffTags.SHORT + elif signed_short: + self.tagtype[tag] = TiffTags.SIGNED_SHORT + elif long: + self.tagtype[tag] = TiffTags.LONG + else: + self.tagtype[tag] = TiffTags.SIGNED_LONG + elif all(isinstance(v, float) for v in values): + self.tagtype[tag] = TiffTags.DOUBLE + elif all(isinstance(v, str) for v in values): + self.tagtype[tag] = TiffTags.ASCII + elif all(isinstance(v, bytes) for v in values): + self.tagtype[tag] = TiffTags.BYTE + + if self.tagtype[tag] == TiffTags.UNDEFINED: + values = [ + v.encode("ascii", "replace") if isinstance(v, str) else v + for v in values + ] + elif self.tagtype[tag] == TiffTags.RATIONAL: + values = [float(v) if isinstance(v, int) else v for v in values] + + is_ifd = self.tagtype[tag] == TiffTags.LONG and isinstance(values, dict) + if not is_ifd: + values = tuple( + info.cvt_enum(value) if isinstance(value, str) else value + for value in values + ) + + dest = self._tags_v1 if legacy_api else self._tags_v2 + + # Three branches: + # Spec'd length == 1, Actual length 1, store as element + # Spec'd length == 1, Actual > 1, Warn and truncate. Formerly barfed. + # No Spec, Actual length 1, Formerly (<4.2) returned a 1 element tuple. + # Don't mess with the legacy api, since it's frozen. + if not is_ifd and ( + (info.length == 1) + or self.tagtype[tag] == TiffTags.BYTE + or (info.length is None and len(values) == 1 and not legacy_api) + ): + # Don't mess with the legacy api, since it's frozen. + if legacy_api and self.tagtype[tag] in [ + TiffTags.RATIONAL, + TiffTags.SIGNED_RATIONAL, + ]: # rationals + values = (values,) + try: + (dest[tag],) = values + except ValueError: + # We've got a builtin tag with 1 expected entry + warnings.warn( + f"Metadata Warning, tag {tag} had too many entries: " + f"{len(values)}, expected 1" + ) + dest[tag] = values[0] + + else: + # Spec'd length > 1 or undefined + # Unspec'd, and length > 1 + dest[tag] = values + + def __delitem__(self, tag: int) -> None: + self._tags_v2.pop(tag, None) + self._tags_v1.pop(tag, None) + self._tagdata.pop(tag, None) + + def __iter__(self) -> Iterator[int]: + return iter(set(self._tagdata) | set(self._tags_v2)) + + def _unpack(self, fmt: str, data: bytes) -> tuple[Any, ...]: + return struct.unpack(self._endian + fmt, data) + + def _pack(self, fmt: str, *values: Any) -> bytes: + return struct.pack(self._endian + fmt, *values) + + list( + map( + _register_basic, + [ + (TiffTags.SHORT, "H", "short"), + (TiffTags.LONG, "L", "long"), + (TiffTags.SIGNED_BYTE, "b", "signed byte"), + (TiffTags.SIGNED_SHORT, "h", "signed short"), + (TiffTags.SIGNED_LONG, "l", "signed long"), + (TiffTags.FLOAT, "f", "float"), + (TiffTags.DOUBLE, "d", "double"), + (TiffTags.IFD, "L", "long"), + (TiffTags.LONG8, "Q", "long8"), + ], + ) + ) + + @_register_loader(1, 1) # Basic type, except for the legacy API. + def load_byte(self, data: bytes, legacy_api: bool = True) -> bytes: + return data + + @_register_writer(1) # Basic type, except for the legacy API. + def write_byte(self, data: bytes | int | IFDRational) -> bytes: + if isinstance(data, IFDRational): + data = int(data) + if isinstance(data, int): + data = bytes((data,)) + return data + + @_register_loader(2, 1) + def load_string(self, data: bytes, legacy_api: bool = True) -> str: + if data.endswith(b"\0"): + data = data[:-1] + return data.decode("latin-1", "replace") + + @_register_writer(2) + def write_string(self, value: str | bytes | int) -> bytes: + # remerge of https://github.com/python-pillow/Pillow/pull/1416 + if isinstance(value, int): + value = str(value) + if not isinstance(value, bytes): + value = value.encode("ascii", "replace") + return value + b"\0" + + @_register_loader(5, 8) + def load_rational( + self, data: bytes, legacy_api: bool = True + ) -> tuple[tuple[int, int] | IFDRational, ...]: + vals = self._unpack(f"{len(data) // 4}L", data) + + def combine(a: int, b: int) -> tuple[int, int] | IFDRational: + return (a, b) if legacy_api else IFDRational(a, b) + + return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2])) + + @_register_writer(5) + def write_rational(self, *values: IFDRational) -> bytes: + return b"".join( + self._pack("2L", *_limit_rational(frac, 2**32 - 1)) for frac in values + ) + + @_register_loader(7, 1) + def load_undefined(self, data: bytes, legacy_api: bool = True) -> bytes: + return data + + @_register_writer(7) + def write_undefined(self, value: bytes | int | IFDRational) -> bytes: + if isinstance(value, IFDRational): + value = int(value) + if isinstance(value, int): + value = str(value).encode("ascii", "replace") + return value + + @_register_loader(10, 8) + def load_signed_rational( + self, data: bytes, legacy_api: bool = True + ) -> tuple[tuple[int, int] | IFDRational, ...]: + vals = self._unpack(f"{len(data) // 4}l", data) + + def combine(a: int, b: int) -> tuple[int, int] | IFDRational: + return (a, b) if legacy_api else IFDRational(a, b) + + return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2])) + + @_register_writer(10) + def write_signed_rational(self, *values: IFDRational) -> bytes: + return b"".join( + self._pack("2l", *_limit_signed_rational(frac, 2**31 - 1, -(2**31))) + for frac in values + ) + + def _ensure_read(self, fp: IO[bytes], size: int) -> bytes: + ret = fp.read(size) + if len(ret) != size: + msg = ( + "Corrupt EXIF data. " + f"Expecting to read {size} bytes but only got {len(ret)}. " + ) + raise OSError(msg) + return ret + + def load(self, fp: IO[bytes]) -> None: + self.reset() + self._offset = fp.tell() + + try: + tag_count = ( + self._unpack("Q", self._ensure_read(fp, 8)) + if self._bigtiff + else self._unpack("H", self._ensure_read(fp, 2)) + )[0] + for i in range(tag_count): + tag, typ, count, data = ( + self._unpack("HHQ8s", self._ensure_read(fp, 20)) + if self._bigtiff + else self._unpack("HHL4s", self._ensure_read(fp, 12)) + ) + + tagname = TiffTags.lookup(tag, self.group).name + typname = TYPES.get(typ, "unknown") + msg = f"tag: {tagname} ({tag}) - type: {typname} ({typ})" + + try: + unit_size, handler = self._load_dispatch[typ] + except KeyError: + logger.debug("%s - unsupported type %s", msg, typ) + continue # ignore unsupported type + size = count * unit_size + if size > (8 if self._bigtiff else 4): + here = fp.tell() + (offset,) = self._unpack("Q" if self._bigtiff else "L", data) + msg += f" Tag Location: {here} - Data Location: {offset}" + fp.seek(offset) + data = ImageFile._safe_read(fp, size) + fp.seek(here) + else: + data = data[:size] + + if len(data) != size: + warnings.warn( + "Possibly corrupt EXIF data. " + f"Expecting to read {size} bytes but only got {len(data)}." + f" Skipping tag {tag}" + ) + logger.debug(msg) + continue + + if not data: + logger.debug(msg) + continue + + self._tagdata[tag] = data + self.tagtype[tag] = typ + + msg += " - value: " + msg += f"" if size > 32 else repr(data) + + logger.debug(msg) + + (self.next,) = ( + self._unpack("Q", self._ensure_read(fp, 8)) + if self._bigtiff + else self._unpack("L", self._ensure_read(fp, 4)) + ) + except OSError as msg: + warnings.warn(str(msg)) + return + + def _get_ifh(self) -> bytes: + ifh = self._prefix + self._pack("H", 43 if self._bigtiff else 42) + if self._bigtiff: + ifh += self._pack("HH", 8, 0) + ifh += self._pack("Q", 16) if self._bigtiff else self._pack("L", 8) + + return ifh + + def tobytes(self, offset: int = 0) -> bytes: + # FIXME What about tagdata? + result = self._pack("Q" if self._bigtiff else "H", len(self._tags_v2)) + + entries: list[tuple[int, int, int, bytes, bytes]] = [] + + fmt = "Q" if self._bigtiff else "L" + fmt_size = 8 if self._bigtiff else 4 + offset += ( + len(result) + len(self._tags_v2) * (20 if self._bigtiff else 12) + fmt_size + ) + stripoffsets = None + + # pass 1: convert tags to binary format + # always write tags in ascending order + for tag, value in sorted(self._tags_v2.items()): + if tag == STRIPOFFSETS: + stripoffsets = len(entries) + typ = self.tagtype[tag] + logger.debug("Tag %s, Type: %s, Value: %s", tag, typ, repr(value)) + is_ifd = typ == TiffTags.LONG and isinstance(value, dict) + if is_ifd: + ifd = ImageFileDirectory_v2(self._get_ifh(), group=tag) + values = self._tags_v2[tag] + for ifd_tag, ifd_value in values.items(): + ifd[ifd_tag] = ifd_value + data = ifd.tobytes(offset) + else: + values = value if isinstance(value, tuple) else (value,) + data = self._write_dispatch[typ](self, *values) + + tagname = TiffTags.lookup(tag, self.group).name + typname = "ifd" if is_ifd else TYPES.get(typ, "unknown") + msg = f"save: {tagname} ({tag}) - type: {typname} ({typ}) - value: " + msg += f"" if len(data) >= 16 else str(values) + logger.debug(msg) + + # count is sum of lengths for string and arbitrary data + if is_ifd: + count = 1 + elif typ in [TiffTags.BYTE, TiffTags.ASCII, TiffTags.UNDEFINED]: + count = len(data) + else: + count = len(values) + # figure out if data fits into the entry + if len(data) <= fmt_size: + entries.append((tag, typ, count, data.ljust(fmt_size, b"\0"), b"")) + else: + entries.append((tag, typ, count, self._pack(fmt, offset), data)) + offset += (len(data) + 1) // 2 * 2 # pad to word + + # update strip offset data to point beyond auxiliary data + if stripoffsets is not None: + tag, typ, count, value, data = entries[stripoffsets] + if data: + size, handler = self._load_dispatch[typ] + values = [val + offset for val in handler(self, data, self.legacy_api)] + data = self._write_dispatch[typ](self, *values) + else: + value = self._pack(fmt, self._unpack(fmt, value)[0] + offset) + entries[stripoffsets] = tag, typ, count, value, data + + # pass 2: write entries to file + for tag, typ, count, value, data in entries: + logger.debug("%s %s %s %s %s", tag, typ, count, repr(value), repr(data)) + result += self._pack( + "HHQ8s" if self._bigtiff else "HHL4s", tag, typ, count, value + ) + + # -- overwrite here for multi-page -- + result += self._pack(fmt, 0) # end of entries + + # pass 3: write auxiliary data to file + for tag, typ, count, value, data in entries: + result += data + if len(data) & 1: + result += b"\0" + + return result + + def save(self, fp: IO[bytes]) -> int: + if fp.tell() == 0: # skip TIFF header on subsequent pages + fp.write(self._get_ifh()) + + offset = fp.tell() + result = self.tobytes(offset) + fp.write(result) + return offset + len(result) + + +ImageFileDirectory_v2._load_dispatch = _load_dispatch +ImageFileDirectory_v2._write_dispatch = _write_dispatch +for idx, name in TYPES.items(): + name = name.replace(" ", "_") + setattr(ImageFileDirectory_v2, f"load_{name}", _load_dispatch[idx][1]) + setattr(ImageFileDirectory_v2, f"write_{name}", _write_dispatch[idx]) +del _load_dispatch, _write_dispatch, idx, name + + +# Legacy ImageFileDirectory support. +class ImageFileDirectory_v1(ImageFileDirectory_v2): + """This class represents the **legacy** interface to a TIFF tag directory. + + Exposes a dictionary interface of the tags in the directory:: + + ifd = ImageFileDirectory_v1() + ifd[key] = 'Some Data' + ifd.tagtype[key] = TiffTags.ASCII + print(ifd[key]) + ('Some Data',) + + Also contains a dictionary of tag types as read from the tiff image file, + :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v1.tagtype`. + + Values are returned as a tuple. + + .. deprecated:: 3.0.0 + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self._legacy_api = True + + tags = property(lambda self: self._tags_v1) + tagdata = property(lambda self: self._tagdata) + + # defined in ImageFileDirectory_v2 + tagtype: dict[int, int] + """Dictionary of tag types""" + + @classmethod + def from_v2(cls, original: ImageFileDirectory_v2) -> ImageFileDirectory_v1: + """Returns an + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + instance with the same data as is contained in the original + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + instance. + + :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + + """ + + ifd = cls(prefix=original.prefix) + ifd._tagdata = original._tagdata + ifd.tagtype = original.tagtype + ifd.next = original.next # an indicator for multipage tiffs + return ifd + + def to_v2(self) -> ImageFileDirectory_v2: + """Returns an + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + instance with the same data as is contained in the original + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + instance. + + :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + + """ + + ifd = ImageFileDirectory_v2(prefix=self.prefix) + ifd._tagdata = dict(self._tagdata) + ifd.tagtype = dict(self.tagtype) + ifd._tags_v2 = dict(self._tags_v2) + return ifd + + def __contains__(self, tag: object) -> bool: + return tag in self._tags_v1 or tag in self._tagdata + + def __len__(self) -> int: + return len(set(self._tagdata) | set(self._tags_v1)) + + def __iter__(self) -> Iterator[int]: + return iter(set(self._tagdata) | set(self._tags_v1)) + + def __setitem__(self, tag: int, value: Any) -> None: + for legacy_api in (False, True): + self._setitem(tag, value, legacy_api) + + def __getitem__(self, tag: int) -> Any: + if tag not in self._tags_v1: # unpack on the fly + data = self._tagdata[tag] + typ = self.tagtype[tag] + size, handler = self._load_dispatch[typ] + for legacy in (False, True): + self._setitem(tag, handler(self, data, legacy), legacy) + val = self._tags_v1[tag] + if not isinstance(val, (tuple, bytes)): + val = (val,) + return val + + +# undone -- switch this pointer +ImageFileDirectory = ImageFileDirectory_v1 + + +## +# Image plugin for TIFF files. + + +class TiffImageFile(ImageFile.ImageFile): + format = "TIFF" + format_description = "Adobe TIFF" + _close_exclusive_fp_after_loading = False + + def __init__( + self, + fp: StrOrBytesPath | IO[bytes], + filename: str | bytes | None = None, + ) -> None: + self.tag_v2: ImageFileDirectory_v2 + """ Image file directory (tag dictionary) """ + + self.tag: ImageFileDirectory_v1 + """ Legacy tag entries """ + + super().__init__(fp, filename) + + def _open(self) -> None: + """Open the first image in a TIFF file""" + + # Header + ifh = self.fp.read(8) + if ifh[2] == 43: + ifh += self.fp.read(8) + + self.tag_v2 = ImageFileDirectory_v2(ifh) + + # setup frame pointers + self.__first = self.__next = self.tag_v2.next + self.__frame = -1 + self._fp = self.fp + self._frame_pos: list[int] = [] + self._n_frames: int | None = None + + logger.debug("*** TiffImageFile._open ***") + logger.debug("- __first: %s", self.__first) + logger.debug("- ifh: %s", repr(ifh)) # Use repr to avoid str(bytes) + + # and load the first frame + self._seek(0) + + @property + def n_frames(self) -> int: + current_n_frames = self._n_frames + if current_n_frames is None: + current = self.tell() + self._seek(len(self._frame_pos)) + while self._n_frames is None: + self._seek(self.tell() + 1) + self.seek(current) + assert self._n_frames is not None + return self._n_frames + + def seek(self, frame: int) -> None: + """Select a given frame as current image""" + if not self._seek_check(frame): + return + self._seek(frame) + if self._im is not None and ( + self.im.size != self._tile_size + or self.im.mode != self.mode + or self.readonly + ): + self._im = None + + def _seek(self, frame: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.fp = self._fp + + while len(self._frame_pos) <= frame: + if not self.__next: + msg = "no more images in TIFF file" + raise EOFError(msg) + logger.debug( + "Seeking to frame %s, on frame %s, __next %s, location: %s", + frame, + self.__frame, + self.__next, + self.fp.tell(), + ) + if self.__next >= 2**63: + msg = "Unable to seek to frame" + raise ValueError(msg) + self.fp.seek(self.__next) + self._frame_pos.append(self.__next) + logger.debug("Loading tags, location: %s", self.fp.tell()) + self.tag_v2.load(self.fp) + if self.tag_v2.next in self._frame_pos: + # This IFD has already been processed + # Declare this to be the end of the image + self.__next = 0 + else: + self.__next = self.tag_v2.next + if self.__next == 0: + self._n_frames = frame + 1 + if len(self._frame_pos) == 1: + self.is_animated = self.__next != 0 + self.__frame += 1 + self.fp.seek(self._frame_pos[frame]) + self.tag_v2.load(self.fp) + if XMP in self.tag_v2: + xmp = self.tag_v2[XMP] + if isinstance(xmp, tuple) and len(xmp) == 1: + xmp = xmp[0] + self.info["xmp"] = xmp + elif "xmp" in self.info: + del self.info["xmp"] + self._reload_exif() + # fill the legacy tag/ifd entries + self.tag = self.ifd = ImageFileDirectory_v1.from_v2(self.tag_v2) + self.__frame = frame + self._setup() + + def tell(self) -> int: + """Return the current frame number""" + return self.__frame + + def get_photoshop_blocks(self) -> dict[int, dict[str, bytes]]: + """ + Returns a dictionary of Photoshop "Image Resource Blocks". + The keys are the image resource ID. For more information, see + https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577409_pgfId-1037727 + + :returns: Photoshop "Image Resource Blocks" in a dictionary. + """ + blocks = {} + val = self.tag_v2.get(ExifTags.Base.ImageResources) + if val: + while val.startswith(b"8BIM"): + id = i16(val[4:6]) + n = math.ceil((val[6] + 1) / 2) * 2 + size = i32(val[6 + n : 10 + n]) + data = val[10 + n : 10 + n + size] + blocks[id] = {"data": data} + + val = val[math.ceil((10 + n + size) / 2) * 2 :] + return blocks + + def load(self) -> Image.core.PixelAccess | None: + if self.tile and self.use_load_libtiff: + return self._load_libtiff() + return super().load() + + def load_prepare(self) -> None: + if self._im is None: + Image._decompression_bomb_check(self._tile_size) + self.im = Image.core.new(self.mode, self._tile_size) + ImageFile.ImageFile.load_prepare(self) + + def load_end(self) -> None: + # allow closing if we're on the first frame, there's no next + # This is the ImageFile.load path only, libtiff specific below. + if not self.is_animated: + self._close_exclusive_fp_after_loading = True + + # load IFD data from fp before it is closed + exif = self.getexif() + for key in TiffTags.TAGS_V2_GROUPS: + if key not in exif: + continue + exif.get_ifd(key) + + ImageOps.exif_transpose(self, in_place=True) + if ExifTags.Base.Orientation in self.tag_v2: + del self.tag_v2[ExifTags.Base.Orientation] + + def _load_libtiff(self) -> Image.core.PixelAccess | None: + """Overload method triggered when we detect a compressed tiff + Calls out to libtiff""" + + Image.Image.load(self) + + self.load_prepare() + + if not len(self.tile) == 1: + msg = "Not exactly one tile" + raise OSError(msg) + + # (self._compression, (extents tuple), + # 0, (rawmode, self._compression, fp)) + extents = self.tile[0][1] + args = self.tile[0][3] + + # To be nice on memory footprint, if there's a + # file descriptor, use that instead of reading + # into a string in python. + try: + fp = hasattr(self.fp, "fileno") and self.fp.fileno() + # flush the file descriptor, prevents error on pypy 2.4+ + # should also eliminate the need for fp.tell + # in _seek + if hasattr(self.fp, "flush"): + self.fp.flush() + except OSError: + # io.BytesIO have a fileno, but returns an OSError if + # it doesn't use a file descriptor. + fp = False + + if fp: + assert isinstance(args, tuple) + args_list = list(args) + args_list[2] = fp + args = tuple(args_list) + + decoder = Image._getdecoder(self.mode, "libtiff", args, self.decoderconfig) + try: + decoder.setimage(self.im, extents) + except ValueError as e: + msg = "Couldn't set the image" + raise OSError(msg) from e + + close_self_fp = self._exclusive_fp and not self.is_animated + if hasattr(self.fp, "getvalue"): + # We've got a stringio like thing passed in. Yay for all in memory. + # The decoder needs the entire file in one shot, so there's not + # a lot we can do here other than give it the entire file. + # unless we could do something like get the address of the + # underlying string for stringio. + # + # Rearranging for supporting byteio items, since they have a fileno + # that returns an OSError if there's no underlying fp. Easier to + # deal with here by reordering. + logger.debug("have getvalue. just sending in a string from getvalue") + n, err = decoder.decode(self.fp.getvalue()) + elif fp: + # we've got a actual file on disk, pass in the fp. + logger.debug("have fileno, calling fileno version of the decoder.") + if not close_self_fp: + self.fp.seek(0) + # Save and restore the file position, because libtiff will move it + # outside of the Python runtime, and that will confuse + # io.BufferedReader and possible others. + # NOTE: This must use os.lseek(), and not fp.tell()/fp.seek(), + # because the buffer read head already may not equal the actual + # file position, and fp.seek() may just adjust it's internal + # pointer and not actually seek the OS file handle. + pos = os.lseek(fp, 0, os.SEEK_CUR) + # 4 bytes, otherwise the trace might error out + n, err = decoder.decode(b"fpfp") + os.lseek(fp, pos, os.SEEK_SET) + else: + # we have something else. + logger.debug("don't have fileno or getvalue. just reading") + self.fp.seek(0) + # UNDONE -- so much for that buffer size thing. + n, err = decoder.decode(self.fp.read()) + + self.tile = [] + self.readonly = 0 + + self.load_end() + + if close_self_fp: + self.fp.close() + self.fp = None # might be shared + + if err < 0: + msg = f"decoder error {err}" + raise OSError(msg) + + return Image.Image.load(self) + + def _setup(self) -> None: + """Setup this image object based on current tags""" + + if 0xBC01 in self.tag_v2: + msg = "Windows Media Photo files not yet supported" + raise OSError(msg) + + # extract relevant tags + self._compression = COMPRESSION_INFO[self.tag_v2.get(COMPRESSION, 1)] + self._planar_configuration = self.tag_v2.get(PLANAR_CONFIGURATION, 1) + + # photometric is a required tag, but not everyone is reading + # the specification + photo = self.tag_v2.get(PHOTOMETRIC_INTERPRETATION, 0) + + # old style jpeg compression images most certainly are YCbCr + if self._compression == "tiff_jpeg": + photo = 6 + + fillorder = self.tag_v2.get(FILLORDER, 1) + + logger.debug("*** Summary ***") + logger.debug("- compression: %s", self._compression) + logger.debug("- photometric_interpretation: %s", photo) + logger.debug("- planar_configuration: %s", self._planar_configuration) + logger.debug("- fill_order: %s", fillorder) + logger.debug("- YCbCr subsampling: %s", self.tag_v2.get(YCBCRSUBSAMPLING)) + + # size + try: + xsize = self.tag_v2[IMAGEWIDTH] + ysize = self.tag_v2[IMAGELENGTH] + except KeyError as e: + msg = "Missing dimensions" + raise TypeError(msg) from e + if not isinstance(xsize, int) or not isinstance(ysize, int): + msg = "Invalid dimensions" + raise ValueError(msg) + self._tile_size = xsize, ysize + orientation = self.tag_v2.get(ExifTags.Base.Orientation) + if orientation in (5, 6, 7, 8): + self._size = ysize, xsize + else: + self._size = xsize, ysize + + logger.debug("- size: %s", self.size) + + sample_format = self.tag_v2.get(SAMPLEFORMAT, (1,)) + if len(sample_format) > 1 and max(sample_format) == min(sample_format) == 1: + # SAMPLEFORMAT is properly per band, so an RGB image will + # be (1,1,1). But, we don't support per band pixel types, + # and anything more than one band is a uint8. So, just + # take the first element. Revisit this if adding support + # for more exotic images. + sample_format = (1,) + + bps_tuple = self.tag_v2.get(BITSPERSAMPLE, (1,)) + extra_tuple = self.tag_v2.get(EXTRASAMPLES, ()) + if photo in (2, 6, 8): # RGB, YCbCr, LAB + bps_count = 3 + elif photo == 5: # CMYK + bps_count = 4 + else: + bps_count = 1 + bps_count += len(extra_tuple) + bps_actual_count = len(bps_tuple) + samples_per_pixel = self.tag_v2.get( + SAMPLESPERPIXEL, + 3 if self._compression == "tiff_jpeg" and photo in (2, 6) else 1, + ) + + if samples_per_pixel > MAX_SAMPLESPERPIXEL: + # DOS check, samples_per_pixel can be a Long, and we extend the tuple below + logger.error( + "More samples per pixel than can be decoded: %s", samples_per_pixel + ) + msg = "Invalid value for samples per pixel" + raise SyntaxError(msg) + + if samples_per_pixel < bps_actual_count: + # If a file has more values in bps_tuple than expected, + # remove the excess. + bps_tuple = bps_tuple[:samples_per_pixel] + elif samples_per_pixel > bps_actual_count and bps_actual_count == 1: + # If a file has only one value in bps_tuple, when it should have more, + # presume it is the same number of bits for all of the samples. + bps_tuple = bps_tuple * samples_per_pixel + + if len(bps_tuple) != samples_per_pixel: + msg = "unknown data organization" + raise SyntaxError(msg) + + # mode: check photometric interpretation and bits per pixel + key = ( + self.tag_v2.prefix, + photo, + sample_format, + fillorder, + bps_tuple, + extra_tuple, + ) + logger.debug("format key: %s", key) + try: + self._mode, rawmode = OPEN_INFO[key] + except KeyError as e: + logger.debug("- unsupported format") + msg = "unknown pixel mode" + raise SyntaxError(msg) from e + + logger.debug("- raw mode: %s", rawmode) + logger.debug("- pil mode: %s", self.mode) + + self.info["compression"] = self._compression + + xres = self.tag_v2.get(X_RESOLUTION, 1) + yres = self.tag_v2.get(Y_RESOLUTION, 1) + + if xres and yres: + resunit = self.tag_v2.get(RESOLUTION_UNIT) + if resunit == 2: # dots per inch + self.info["dpi"] = (xres, yres) + elif resunit == 3: # dots per centimeter. convert to dpi + self.info["dpi"] = (xres * 2.54, yres * 2.54) + elif resunit is None: # used to default to 1, but now 2) + self.info["dpi"] = (xres, yres) + # For backward compatibility, + # we also preserve the old behavior + self.info["resolution"] = xres, yres + else: # No absolute unit of measurement + self.info["resolution"] = xres, yres + + # build tile descriptors + x = y = layer = 0 + self.tile = [] + self.use_load_libtiff = READ_LIBTIFF or self._compression != "raw" + if self.use_load_libtiff: + # Decoder expects entire file as one tile. + # There's a buffer size limit in load (64k) + # so large g4 images will fail if we use that + # function. + # + # Setup the one tile for the whole image, then + # use the _load_libtiff function. + + # libtiff handles the fillmode for us, so 1;IR should + # actually be 1;I. Including the R double reverses the + # bits, so stripes of the image are reversed. See + # https://github.com/python-pillow/Pillow/issues/279 + if fillorder == 2: + # Replace fillorder with fillorder=1 + key = key[:3] + (1,) + key[4:] + logger.debug("format key: %s", key) + # this should always work, since all the + # fillorder==2 modes have a corresponding + # fillorder=1 mode + self._mode, rawmode = OPEN_INFO[key] + # YCbCr images with new jpeg compression with pixels in one plane + # unpacked straight into RGB values + if ( + photo == 6 + and self._compression == "jpeg" + and self._planar_configuration == 1 + ): + rawmode = "RGB" + # libtiff always returns the bytes in native order. + # we're expecting image byte order. So, if the rawmode + # contains I;16, we need to convert from native to image + # byte order. + elif rawmode == "I;16": + rawmode = "I;16N" + elif rawmode.endswith((";16B", ";16L")): + rawmode = rawmode[:-1] + "N" + + # Offset in the tile tuple is 0, we go from 0,0 to + # w,h, and we only do this once -- eds + a = (rawmode, self._compression, False, self.tag_v2.offset) + self.tile.append(ImageFile._Tile("libtiff", (0, 0, xsize, ysize), 0, a)) + + elif STRIPOFFSETS in self.tag_v2 or TILEOFFSETS in self.tag_v2: + # striped image + if STRIPOFFSETS in self.tag_v2: + offsets = self.tag_v2[STRIPOFFSETS] + h = self.tag_v2.get(ROWSPERSTRIP, ysize) + w = xsize + else: + # tiled image + offsets = self.tag_v2[TILEOFFSETS] + tilewidth = self.tag_v2.get(TILEWIDTH) + h = self.tag_v2.get(TILELENGTH) + if not isinstance(tilewidth, int) or not isinstance(h, int): + msg = "Invalid tile dimensions" + raise ValueError(msg) + w = tilewidth + + if w == xsize and h == ysize and self._planar_configuration != 2: + # Every tile covers the image. Only use the last offset + offsets = offsets[-1:] + + for offset in offsets: + if x + w > xsize: + stride = w * sum(bps_tuple) / 8 # bytes per line + else: + stride = 0 + + tile_rawmode = rawmode + if self._planar_configuration == 2: + # each band on it's own layer + tile_rawmode = rawmode[layer] + # adjust stride width accordingly + stride /= bps_count + + args = (tile_rawmode, int(stride), 1) + self.tile.append( + ImageFile._Tile( + self._compression, + (x, y, min(x + w, xsize), min(y + h, ysize)), + offset, + args, + ) + ) + x += w + if x >= xsize: + x, y = 0, y + h + if y >= ysize: + y = 0 + layer += 1 + else: + logger.debug("- unsupported data organization") + msg = "unknown data organization" + raise SyntaxError(msg) + + # Fix up info. + if ICCPROFILE in self.tag_v2: + self.info["icc_profile"] = self.tag_v2[ICCPROFILE] + + # fixup palette descriptor + + if self.mode in ["P", "PA"]: + palette = [o8(b // 256) for b in self.tag_v2[COLORMAP]] + self.palette = ImagePalette.raw("RGB;L", b"".join(palette)) + + +# +# -------------------------------------------------------------------- +# Write TIFF files + +# little endian is default except for image modes with +# explicit big endian byte-order + +SAVE_INFO = { + # mode => rawmode, byteorder, photometrics, + # sampleformat, bitspersample, extra + "1": ("1", II, 1, 1, (1,), None), + "L": ("L", II, 1, 1, (8,), None), + "LA": ("LA", II, 1, 1, (8, 8), 2), + "P": ("P", II, 3, 1, (8,), None), + "PA": ("PA", II, 3, 1, (8, 8), 2), + "I": ("I;32S", II, 1, 2, (32,), None), + "I;16": ("I;16", II, 1, 1, (16,), None), + "I;16L": ("I;16L", II, 1, 1, (16,), None), + "F": ("F;32F", II, 1, 3, (32,), None), + "RGB": ("RGB", II, 2, 1, (8, 8, 8), None), + "RGBX": ("RGBX", II, 2, 1, (8, 8, 8, 8), 0), + "RGBA": ("RGBA", II, 2, 1, (8, 8, 8, 8), 2), + "CMYK": ("CMYK", II, 5, 1, (8, 8, 8, 8), None), + "YCbCr": ("YCbCr", II, 6, 1, (8, 8, 8), None), + "LAB": ("LAB", II, 8, 1, (8, 8, 8), None), + "I;16B": ("I;16B", MM, 1, 1, (16,), None), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + rawmode, prefix, photo, format, bits, extra = SAVE_INFO[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as TIFF" + raise OSError(msg) from e + + encoderinfo = im.encoderinfo + encoderconfig = im.encoderconfig + + ifd = ImageFileDirectory_v2(prefix=prefix) + if encoderinfo.get("big_tiff"): + ifd._bigtiff = True + + try: + compression = encoderinfo["compression"] + except KeyError: + compression = im.info.get("compression") + if isinstance(compression, int): + # compression value may be from BMP. Ignore it + compression = None + if compression is None: + compression = "raw" + elif compression == "tiff_jpeg": + # OJPEG is obsolete, so use new-style JPEG compression instead + compression = "jpeg" + elif compression == "tiff_deflate": + compression = "tiff_adobe_deflate" + + libtiff = WRITE_LIBTIFF or compression != "raw" + + # required for color libtiff images + ifd[PLANAR_CONFIGURATION] = 1 + + ifd[IMAGEWIDTH] = im.size[0] + ifd[IMAGELENGTH] = im.size[1] + + # write any arbitrary tags passed in as an ImageFileDirectory + if "tiffinfo" in encoderinfo: + info = encoderinfo["tiffinfo"] + elif "exif" in encoderinfo: + info = encoderinfo["exif"] + if isinstance(info, bytes): + exif = Image.Exif() + exif.load(info) + info = exif + else: + info = {} + logger.debug("Tiffinfo Keys: %s", list(info)) + if isinstance(info, ImageFileDirectory_v1): + info = info.to_v2() + for key in info: + if isinstance(info, Image.Exif) and key in TiffTags.TAGS_V2_GROUPS: + ifd[key] = info.get_ifd(key) + else: + ifd[key] = info.get(key) + try: + ifd.tagtype[key] = info.tagtype[key] + except Exception: + pass # might not be an IFD. Might not have populated type + + legacy_ifd = {} + if hasattr(im, "tag"): + legacy_ifd = im.tag.to_v2() + + supplied_tags = {**legacy_ifd, **getattr(im, "tag_v2", {})} + for tag in ( + # IFD offset that may not be correct in the saved image + EXIFIFD, + # Determined by the image format and should not be copied from legacy_ifd. + SAMPLEFORMAT, + ): + if tag in supplied_tags: + del supplied_tags[tag] + + # additions written by Greg Couch, gregc@cgl.ucsf.edu + # inspired by image-sig posting from Kevin Cazabon, kcazabon@home.com + if hasattr(im, "tag_v2"): + # preserve tags from original TIFF image file + for key in ( + RESOLUTION_UNIT, + X_RESOLUTION, + Y_RESOLUTION, + IPTC_NAA_CHUNK, + PHOTOSHOP_CHUNK, + XMP, + ): + if key in im.tag_v2: + if key == IPTC_NAA_CHUNK and im.tag_v2.tagtype[key] not in ( + TiffTags.BYTE, + TiffTags.UNDEFINED, + ): + del supplied_tags[key] + else: + ifd[key] = im.tag_v2[key] + ifd.tagtype[key] = im.tag_v2.tagtype[key] + + # preserve ICC profile (should also work when saving other formats + # which support profiles as TIFF) -- 2008-06-06 Florian Hoech + icc = encoderinfo.get("icc_profile", im.info.get("icc_profile")) + if icc: + ifd[ICCPROFILE] = icc + + for key, name in [ + (IMAGEDESCRIPTION, "description"), + (X_RESOLUTION, "resolution"), + (Y_RESOLUTION, "resolution"), + (X_RESOLUTION, "x_resolution"), + (Y_RESOLUTION, "y_resolution"), + (RESOLUTION_UNIT, "resolution_unit"), + (SOFTWARE, "software"), + (DATE_TIME, "date_time"), + (ARTIST, "artist"), + (COPYRIGHT, "copyright"), + ]: + if name in encoderinfo: + ifd[key] = encoderinfo[name] + + dpi = encoderinfo.get("dpi") + if dpi: + ifd[RESOLUTION_UNIT] = 2 + ifd[X_RESOLUTION] = dpi[0] + ifd[Y_RESOLUTION] = dpi[1] + + if bits != (1,): + ifd[BITSPERSAMPLE] = bits + if len(bits) != 1: + ifd[SAMPLESPERPIXEL] = len(bits) + if extra is not None: + ifd[EXTRASAMPLES] = extra + if format != 1: + ifd[SAMPLEFORMAT] = format + + if PHOTOMETRIC_INTERPRETATION not in ifd: + ifd[PHOTOMETRIC_INTERPRETATION] = photo + elif im.mode in ("1", "L") and ifd[PHOTOMETRIC_INTERPRETATION] == 0: + if im.mode == "1": + inverted_im = im.copy() + px = inverted_im.load() + if px is not None: + for y in range(inverted_im.height): + for x in range(inverted_im.width): + px[x, y] = 0 if px[x, y] == 255 else 255 + im = inverted_im + else: + im = ImageOps.invert(im) + + if im.mode in ["P", "PA"]: + lut = im.im.getpalette("RGB", "RGB;L") + colormap = [] + colors = len(lut) // 3 + for i in range(3): + colormap += [v * 256 for v in lut[colors * i : colors * (i + 1)]] + colormap += [0] * (256 - colors) + ifd[COLORMAP] = colormap + # data orientation + w, h = ifd[IMAGEWIDTH], ifd[IMAGELENGTH] + stride = len(bits) * ((w * bits[0] + 7) // 8) + if ROWSPERSTRIP not in ifd: + # aim for given strip size (64 KB by default) when using libtiff writer + if libtiff: + im_strip_size = encoderinfo.get("strip_size", STRIP_SIZE) + rows_per_strip = 1 if stride == 0 else min(im_strip_size // stride, h) + # JPEG encoder expects multiple of 8 rows + if compression == "jpeg": + rows_per_strip = min(((rows_per_strip + 7) // 8) * 8, h) + else: + rows_per_strip = h + if rows_per_strip == 0: + rows_per_strip = 1 + ifd[ROWSPERSTRIP] = rows_per_strip + strip_byte_counts = 1 if stride == 0 else stride * ifd[ROWSPERSTRIP] + strips_per_image = (h + ifd[ROWSPERSTRIP] - 1) // ifd[ROWSPERSTRIP] + if strip_byte_counts >= 2**16: + ifd.tagtype[STRIPBYTECOUNTS] = TiffTags.LONG + ifd[STRIPBYTECOUNTS] = (strip_byte_counts,) * (strips_per_image - 1) + ( + stride * h - strip_byte_counts * (strips_per_image - 1), + ) + ifd[STRIPOFFSETS] = tuple( + range(0, strip_byte_counts * strips_per_image, strip_byte_counts) + ) # this is adjusted by IFD writer + # no compression by default: + ifd[COMPRESSION] = COMPRESSION_INFO_REV.get(compression, 1) + + if im.mode == "YCbCr": + for tag, default_value in { + YCBCRSUBSAMPLING: (1, 1), + REFERENCEBLACKWHITE: (0, 255, 128, 255, 128, 255), + }.items(): + ifd.setdefault(tag, default_value) + + blocklist = [TILEWIDTH, TILELENGTH, TILEOFFSETS, TILEBYTECOUNTS] + if libtiff: + if "quality" in encoderinfo: + quality = encoderinfo["quality"] + if not isinstance(quality, int) or quality < 0 or quality > 100: + msg = "Invalid quality setting" + raise ValueError(msg) + if compression != "jpeg": + msg = "quality setting only supported for 'jpeg' compression" + raise ValueError(msg) + ifd[JPEGQUALITY] = quality + + logger.debug("Saving using libtiff encoder") + logger.debug("Items: %s", sorted(ifd.items())) + _fp = 0 + if hasattr(fp, "fileno"): + try: + fp.seek(0) + _fp = fp.fileno() + except io.UnsupportedOperation: + pass + + # optional types for non core tags + types = {} + # STRIPOFFSETS and STRIPBYTECOUNTS are added by the library + # based on the data in the strip. + # OSUBFILETYPE is deprecated. + # The other tags expect arrays with a certain length (fixed or depending on + # BITSPERSAMPLE, etc), passing arrays with a different length will result in + # segfaults. Block these tags until we add extra validation. + # SUBIFD may also cause a segfault. + blocklist += [ + OSUBFILETYPE, + REFERENCEBLACKWHITE, + STRIPBYTECOUNTS, + STRIPOFFSETS, + TRANSFERFUNCTION, + SUBIFD, + ] + + # bits per sample is a single short in the tiff directory, not a list. + atts: dict[int, Any] = {BITSPERSAMPLE: bits[0]} + # Merge the ones that we have with (optional) more bits from + # the original file, e.g x,y resolution so that we can + # save(load('')) == original file. + for tag, value in itertools.chain(ifd.items(), supplied_tags.items()): + # Libtiff can only process certain core items without adding + # them to the custom dictionary. + # Custom items are supported for int, float, unicode, string and byte + # values. Other types and tuples require a tagtype. + if tag not in TiffTags.LIBTIFF_CORE: + if not getattr(Image.core, "libtiff_support_custom_tags", False): + continue + + if tag in TiffTags.TAGS_V2_GROUPS: + types[tag] = TiffTags.LONG8 + elif tag in ifd.tagtype: + types[tag] = ifd.tagtype[tag] + elif not (isinstance(value, (int, float, str, bytes))): + continue + else: + type = TiffTags.lookup(tag).type + if type: + types[tag] = type + if tag not in atts and tag not in blocklist: + if isinstance(value, str): + atts[tag] = value.encode("ascii", "replace") + b"\0" + elif isinstance(value, IFDRational): + atts[tag] = float(value) + else: + atts[tag] = value + + if SAMPLEFORMAT in atts and len(atts[SAMPLEFORMAT]) == 1: + atts[SAMPLEFORMAT] = atts[SAMPLEFORMAT][0] + + logger.debug("Converted items: %s", sorted(atts.items())) + + # libtiff always expects the bytes in native order. + # we're storing image byte order. So, if the rawmode + # contains I;16, we need to convert from native to image + # byte order. + if im.mode in ("I;16", "I;16B", "I;16L"): + rawmode = "I;16N" + + # Pass tags as sorted list so that the tags are set in a fixed order. + # This is required by libtiff for some tags. For example, the JPEGQUALITY + # pseudo tag requires that the COMPRESS tag was already set. + tags = list(atts.items()) + tags.sort() + a = (rawmode, compression, _fp, filename, tags, types) + encoder = Image._getencoder(im.mode, "libtiff", a, encoderconfig) + encoder.setimage(im.im, (0, 0) + im.size) + while True: + errcode, data = encoder.encode(ImageFile.MAXBLOCK)[1:] + if not _fp: + fp.write(data) + if errcode: + break + if errcode < 0: + msg = f"encoder error {errcode} when writing image file" + raise OSError(msg) + + else: + for tag in blocklist: + del ifd[tag] + offset = ifd.save(fp) + + ImageFile._save( + im, + fp, + [ImageFile._Tile("raw", (0, 0) + im.size, offset, (rawmode, stride, 1))], + ) + + # -- helper for multi-page save -- + if "_debug_multipage" in encoderinfo: + # just to access o32 and o16 (using correct byte order) + setattr(im, "_debug_multipage", ifd) + + +class AppendingTiffWriter(io.BytesIO): + fieldSizes = [ + 0, # None + 1, # byte + 1, # ascii + 2, # short + 4, # long + 8, # rational + 1, # sbyte + 1, # undefined + 2, # sshort + 4, # slong + 8, # srational + 4, # float + 8, # double + 4, # ifd + 2, # unicode + 4, # complex + 8, # long8 + ] + + Tags = { + 273, # StripOffsets + 288, # FreeOffsets + 324, # TileOffsets + 519, # JPEGQTables + 520, # JPEGDCTables + 521, # JPEGACTables + } + + def __init__(self, fn: StrOrBytesPath | IO[bytes], new: bool = False) -> None: + self.f: IO[bytes] + if is_path(fn): + self.name = fn + self.close_fp = True + try: + self.f = open(fn, "w+b" if new else "r+b") + except OSError: + self.f = open(fn, "w+b") + else: + self.f = cast(IO[bytes], fn) + self.close_fp = False + self.beginning = self.f.tell() + self.setup() + + def setup(self) -> None: + # Reset everything. + self.f.seek(self.beginning, os.SEEK_SET) + + self.whereToWriteNewIFDOffset: int | None = None + self.offsetOfNewPage = 0 + + self.IIMM = iimm = self.f.read(4) + self._bigtiff = b"\x2b" in iimm + if not iimm: + # empty file - first page + self.isFirst = True + return + + self.isFirst = False + if iimm not in PREFIXES: + msg = "Invalid TIFF file header" + raise RuntimeError(msg) + + self.setEndian("<" if iimm.startswith(II) else ">") + + if self._bigtiff: + self.f.seek(4, os.SEEK_CUR) + self.skipIFDs() + self.goToEnd() + + def finalize(self) -> None: + if self.isFirst: + return + + # fix offsets + self.f.seek(self.offsetOfNewPage) + + iimm = self.f.read(4) + if not iimm: + # Make it easy to finish a frame without committing to a new one. + return + + if iimm != self.IIMM: + msg = "IIMM of new page doesn't match IIMM of first page" + raise RuntimeError(msg) + + if self._bigtiff: + self.f.seek(4, os.SEEK_CUR) + ifd_offset = self._read(8 if self._bigtiff else 4) + ifd_offset += self.offsetOfNewPage + assert self.whereToWriteNewIFDOffset is not None + self.f.seek(self.whereToWriteNewIFDOffset) + self._write(ifd_offset, 8 if self._bigtiff else 4) + self.f.seek(ifd_offset) + self.fixIFD() + + def newFrame(self) -> None: + # Call this to finish a frame. + self.finalize() + self.setup() + + def __enter__(self) -> AppendingTiffWriter: + return self + + def __exit__(self, *args: object) -> None: + if self.close_fp: + self.close() + + def tell(self) -> int: + return self.f.tell() - self.offsetOfNewPage + + def seek(self, offset: int, whence: int = io.SEEK_SET) -> int: + """ + :param offset: Distance to seek. + :param whence: Whether the distance is relative to the start, + end or current position. + :returns: The resulting position, relative to the start. + """ + if whence == os.SEEK_SET: + offset += self.offsetOfNewPage + + self.f.seek(offset, whence) + return self.tell() + + def goToEnd(self) -> None: + self.f.seek(0, os.SEEK_END) + pos = self.f.tell() + + # pad to 16 byte boundary + pad_bytes = 16 - pos % 16 + if 0 < pad_bytes < 16: + self.f.write(bytes(pad_bytes)) + self.offsetOfNewPage = self.f.tell() + + def setEndian(self, endian: str) -> None: + self.endian = endian + self.longFmt = f"{self.endian}L" + self.shortFmt = f"{self.endian}H" + self.tagFormat = f"{self.endian}HH" + ("Q" if self._bigtiff else "L") + + def skipIFDs(self) -> None: + while True: + ifd_offset = self._read(8 if self._bigtiff else 4) + if ifd_offset == 0: + self.whereToWriteNewIFDOffset = self.f.tell() - ( + 8 if self._bigtiff else 4 + ) + break + + self.f.seek(ifd_offset) + num_tags = self._read(8 if self._bigtiff else 2) + self.f.seek(num_tags * (20 if self._bigtiff else 12), os.SEEK_CUR) + + def write(self, data: Buffer, /) -> int: + return self.f.write(data) + + def _fmt(self, field_size: int) -> str: + try: + return {2: "H", 4: "L", 8: "Q"}[field_size] + except KeyError: + msg = "offset is not supported" + raise RuntimeError(msg) + + def _read(self, field_size: int) -> int: + (value,) = struct.unpack( + self.endian + self._fmt(field_size), self.f.read(field_size) + ) + return value + + def readShort(self) -> int: + return self._read(2) + + def readLong(self) -> int: + return self._read(4) + + @staticmethod + def _verify_bytes_written(bytes_written: int | None, expected: int) -> None: + if bytes_written is not None and bytes_written != expected: + msg = f"wrote only {bytes_written} bytes but wanted {expected}" + raise RuntimeError(msg) + + def _rewriteLast( + self, value: int, field_size: int, new_field_size: int = 0 + ) -> None: + self.f.seek(-field_size, os.SEEK_CUR) + if not new_field_size: + new_field_size = field_size + bytes_written = self.f.write( + struct.pack(self.endian + self._fmt(new_field_size), value) + ) + self._verify_bytes_written(bytes_written, new_field_size) + + def rewriteLastShortToLong(self, value: int) -> None: + self._rewriteLast(value, 2, 4) + + def rewriteLastShort(self, value: int) -> None: + return self._rewriteLast(value, 2) + + def rewriteLastLong(self, value: int) -> None: + return self._rewriteLast(value, 4) + + def _write(self, value: int, field_size: int) -> None: + bytes_written = self.f.write( + struct.pack(self.endian + self._fmt(field_size), value) + ) + self._verify_bytes_written(bytes_written, field_size) + + def writeShort(self, value: int) -> None: + self._write(value, 2) + + def writeLong(self, value: int) -> None: + self._write(value, 4) + + def close(self) -> None: + self.finalize() + if self.close_fp: + self.f.close() + + def fixIFD(self) -> None: + num_tags = self._read(8 if self._bigtiff else 2) + + for i in range(num_tags): + tag, field_type, count = struct.unpack( + self.tagFormat, self.f.read(12 if self._bigtiff else 8) + ) + + field_size = self.fieldSizes[field_type] + total_size = field_size * count + fmt_size = 8 if self._bigtiff else 4 + is_local = total_size <= fmt_size + if not is_local: + offset = self._read(fmt_size) + self.offsetOfNewPage + self._rewriteLast(offset, fmt_size) + + if tag in self.Tags: + cur_pos = self.f.tell() + + logger.debug( + "fixIFD: %s (%d) - type: %s (%d) - type size: %d - count: %d", + TiffTags.lookup(tag).name, + tag, + TYPES.get(field_type, "unknown"), + field_type, + field_size, + count, + ) + + if is_local: + self._fixOffsets(count, field_size) + self.f.seek(cur_pos + fmt_size) + else: + self.f.seek(offset) + self._fixOffsets(count, field_size) + self.f.seek(cur_pos) + + elif is_local: + # skip the locally stored value that is not an offset + self.f.seek(fmt_size, os.SEEK_CUR) + + def _fixOffsets(self, count: int, field_size: int) -> None: + for i in range(count): + offset = self._read(field_size) + offset += self.offsetOfNewPage + + new_field_size = 0 + if self._bigtiff and field_size in (2, 4) and offset >= 2**32: + # offset is now too large - we must convert long to long8 + new_field_size = 8 + elif field_size == 2 and offset >= 2**16: + # offset is now too large - we must convert short to long + new_field_size = 4 + if new_field_size: + if count != 1: + msg = "not implemented" + raise RuntimeError(msg) # XXX TODO + + # simple case - the offset is just one and therefore it is + # local (not referenced with another offset) + self._rewriteLast(offset, field_size, new_field_size) + # Move back past the new offset, past 'count', and before 'field_type' + rewind = -new_field_size - 4 - 2 + self.f.seek(rewind, os.SEEK_CUR) + self.writeShort(new_field_size) # rewrite the type + self.f.seek(2 - rewind, os.SEEK_CUR) + else: + self._rewriteLast(offset, field_size) + + def fixOffsets( + self, count: int, isShort: bool = False, isLong: bool = False + ) -> None: + if isShort: + field_size = 2 + elif isLong: + field_size = 4 + else: + field_size = 0 + return self._fixOffsets(count, field_size) + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + append_images = list(im.encoderinfo.get("append_images", [])) + if not hasattr(im, "n_frames") and not append_images: + return _save(im, fp, filename) + + cur_idx = im.tell() + try: + with AppendingTiffWriter(fp) as tf: + for ims in [im] + append_images: + encoderinfo = ims._attach_default_encoderinfo(im) + if not hasattr(ims, "encoderconfig"): + ims.encoderconfig = () + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + ims.load() + _save(ims, tf, filename) + tf.newFrame() + ims.encoderinfo = encoderinfo + finally: + im.seek(cur_idx) + + +# +# -------------------------------------------------------------------- +# Register + +Image.register_open(TiffImageFile.format, TiffImageFile, _accept) +Image.register_save(TiffImageFile.format, _save) +Image.register_save_all(TiffImageFile.format, _save_all) + +Image.register_extensions(TiffImageFile.format, [".tif", ".tiff"]) + +Image.register_mime(TiffImageFile.format, "image/tiff") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffTags.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffTags.py new file mode 100644 index 0000000000000000000000000000000000000000..86adaa45857338f9fa8864296077393a3dc3350f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/TiffTags.py @@ -0,0 +1,562 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TIFF tags +# +# This module provides clear-text names for various well-known +# TIFF tags. the TIFF codec works just fine without it. +# +# Copyright (c) Secret Labs AB 1999. +# +# See the README file for information on usage and redistribution. +# + +## +# This module provides constants and clear-text names for various +# well-known TIFF tags. +## +from __future__ import annotations + +from typing import NamedTuple + + +class _TagInfo(NamedTuple): + value: int | None + name: str + type: int | None + length: int | None + enum: dict[str, int] + + +class TagInfo(_TagInfo): + __slots__: list[str] = [] + + def __new__( + cls, + value: int | None = None, + name: str = "unknown", + type: int | None = None, + length: int | None = None, + enum: dict[str, int] | None = None, + ) -> TagInfo: + return super().__new__(cls, value, name, type, length, enum or {}) + + def cvt_enum(self, value: str) -> int | str: + # Using get will call hash(value), which can be expensive + # for some types (e.g. Fraction). Since self.enum is rarely + # used, it's usually better to test it first. + return self.enum.get(value, value) if self.enum else value + + +def lookup(tag: int, group: int | None = None) -> TagInfo: + """ + :param tag: Integer tag number + :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in + + .. versionadded:: 8.3.0 + + :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible, + otherwise just populating the value and name from ``TAGS``. + If the tag is not recognized, "unknown" is returned for the name + + """ + + if group is not None: + info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None + else: + info = TAGS_V2.get(tag) + return info or TagInfo(tag, TAGS.get(tag, "unknown")) + + +## +# Map tag numbers to tag info. +# +# id: (Name, Type, Length[, enum_values]) +# +# The length here differs from the length in the tiff spec. For +# numbers, the tiff spec is for the number of fields returned. We +# agree here. For string-like types, the tiff spec uses the length of +# field in bytes. In Pillow, we are using the number of expected +# fields, in general 1 for string-like types. + + +BYTE = 1 +ASCII = 2 +SHORT = 3 +LONG = 4 +RATIONAL = 5 +SIGNED_BYTE = 6 +UNDEFINED = 7 +SIGNED_SHORT = 8 +SIGNED_LONG = 9 +SIGNED_RATIONAL = 10 +FLOAT = 11 +DOUBLE = 12 +IFD = 13 +LONG8 = 16 + +_tags_v2: dict[int, tuple[str, int, int] | tuple[str, int, int, dict[str, int]]] = { + 254: ("NewSubfileType", LONG, 1), + 255: ("SubfileType", SHORT, 1), + 256: ("ImageWidth", LONG, 1), + 257: ("ImageLength", LONG, 1), + 258: ("BitsPerSample", SHORT, 0), + 259: ( + "Compression", + SHORT, + 1, + { + "Uncompressed": 1, + "CCITT 1d": 2, + "Group 3 Fax": 3, + "Group 4 Fax": 4, + "LZW": 5, + "JPEG": 6, + "PackBits": 32773, + }, + ), + 262: ( + "PhotometricInterpretation", + SHORT, + 1, + { + "WhiteIsZero": 0, + "BlackIsZero": 1, + "RGB": 2, + "RGB Palette": 3, + "Transparency Mask": 4, + "CMYK": 5, + "YCbCr": 6, + "CieLAB": 8, + "CFA": 32803, # TIFF/EP, Adobe DNG + "LinearRaw": 32892, # Adobe DNG + }, + ), + 263: ("Threshholding", SHORT, 1), + 264: ("CellWidth", SHORT, 1), + 265: ("CellLength", SHORT, 1), + 266: ("FillOrder", SHORT, 1), + 269: ("DocumentName", ASCII, 1), + 270: ("ImageDescription", ASCII, 1), + 271: ("Make", ASCII, 1), + 272: ("Model", ASCII, 1), + 273: ("StripOffsets", LONG, 0), + 274: ("Orientation", SHORT, 1), + 277: ("SamplesPerPixel", SHORT, 1), + 278: ("RowsPerStrip", LONG, 1), + 279: ("StripByteCounts", LONG, 0), + 280: ("MinSampleValue", SHORT, 0), + 281: ("MaxSampleValue", SHORT, 0), + 282: ("XResolution", RATIONAL, 1), + 283: ("YResolution", RATIONAL, 1), + 284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}), + 285: ("PageName", ASCII, 1), + 286: ("XPosition", RATIONAL, 1), + 287: ("YPosition", RATIONAL, 1), + 288: ("FreeOffsets", LONG, 1), + 289: ("FreeByteCounts", LONG, 1), + 290: ("GrayResponseUnit", SHORT, 1), + 291: ("GrayResponseCurve", SHORT, 0), + 292: ("T4Options", LONG, 1), + 293: ("T6Options", LONG, 1), + 296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}), + 297: ("PageNumber", SHORT, 2), + 301: ("TransferFunction", SHORT, 0), + 305: ("Software", ASCII, 1), + 306: ("DateTime", ASCII, 1), + 315: ("Artist", ASCII, 1), + 316: ("HostComputer", ASCII, 1), + 317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}), + 318: ("WhitePoint", RATIONAL, 2), + 319: ("PrimaryChromaticities", RATIONAL, 6), + 320: ("ColorMap", SHORT, 0), + 321: ("HalftoneHints", SHORT, 2), + 322: ("TileWidth", LONG, 1), + 323: ("TileLength", LONG, 1), + 324: ("TileOffsets", LONG, 0), + 325: ("TileByteCounts", LONG, 0), + 330: ("SubIFDs", LONG, 0), + 332: ("InkSet", SHORT, 1), + 333: ("InkNames", ASCII, 1), + 334: ("NumberOfInks", SHORT, 1), + 336: ("DotRange", SHORT, 0), + 337: ("TargetPrinter", ASCII, 1), + 338: ("ExtraSamples", SHORT, 0), + 339: ("SampleFormat", SHORT, 0), + 340: ("SMinSampleValue", DOUBLE, 0), + 341: ("SMaxSampleValue", DOUBLE, 0), + 342: ("TransferRange", SHORT, 6), + 347: ("JPEGTables", UNDEFINED, 1), + # obsolete JPEG tags + 512: ("JPEGProc", SHORT, 1), + 513: ("JPEGInterchangeFormat", LONG, 1), + 514: ("JPEGInterchangeFormatLength", LONG, 1), + 515: ("JPEGRestartInterval", SHORT, 1), + 517: ("JPEGLosslessPredictors", SHORT, 0), + 518: ("JPEGPointTransforms", SHORT, 0), + 519: ("JPEGQTables", LONG, 0), + 520: ("JPEGDCTables", LONG, 0), + 521: ("JPEGACTables", LONG, 0), + 529: ("YCbCrCoefficients", RATIONAL, 3), + 530: ("YCbCrSubSampling", SHORT, 2), + 531: ("YCbCrPositioning", SHORT, 1), + 532: ("ReferenceBlackWhite", RATIONAL, 6), + 700: ("XMP", BYTE, 0), + 33432: ("Copyright", ASCII, 1), + 33723: ("IptcNaaInfo", UNDEFINED, 1), + 34377: ("PhotoshopInfo", BYTE, 0), + # FIXME add more tags here + 34665: ("ExifIFD", LONG, 1), + 34675: ("ICCProfile", UNDEFINED, 1), + 34853: ("GPSInfoIFD", LONG, 1), + 36864: ("ExifVersion", UNDEFINED, 1), + 37724: ("ImageSourceData", UNDEFINED, 1), + 40965: ("InteroperabilityIFD", LONG, 1), + 41730: ("CFAPattern", UNDEFINED, 1), + # MPInfo + 45056: ("MPFVersion", UNDEFINED, 1), + 45057: ("NumberOfImages", LONG, 1), + 45058: ("MPEntry", UNDEFINED, 1), + 45059: ("ImageUIDList", UNDEFINED, 0), # UNDONE, check + 45060: ("TotalFrames", LONG, 1), + 45313: ("MPIndividualNum", LONG, 1), + 45569: ("PanOrientation", LONG, 1), + 45570: ("PanOverlap_H", RATIONAL, 1), + 45571: ("PanOverlap_V", RATIONAL, 1), + 45572: ("BaseViewpointNum", LONG, 1), + 45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1), + 45574: ("BaselineLength", RATIONAL, 1), + 45575: ("VerticalDivergence", SIGNED_RATIONAL, 1), + 45576: ("AxisDistance_X", SIGNED_RATIONAL, 1), + 45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1), + 45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1), + 45579: ("YawAngle", SIGNED_RATIONAL, 1), + 45580: ("PitchAngle", SIGNED_RATIONAL, 1), + 45581: ("RollAngle", SIGNED_RATIONAL, 1), + 40960: ("FlashPixVersion", UNDEFINED, 1), + 50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}), + 50780: ("BestQualityScale", RATIONAL, 1), + 50838: ("ImageJMetaDataByteCounts", LONG, 0), # Can be more than one + 50839: ("ImageJMetaData", UNDEFINED, 1), # see Issue #2006 +} +_tags_v2_groups = { + # ExifIFD + 34665: { + 36864: ("ExifVersion", UNDEFINED, 1), + 40960: ("FlashPixVersion", UNDEFINED, 1), + 40965: ("InteroperabilityIFD", LONG, 1), + 41730: ("CFAPattern", UNDEFINED, 1), + }, + # GPSInfoIFD + 34853: { + 0: ("GPSVersionID", BYTE, 4), + 1: ("GPSLatitudeRef", ASCII, 2), + 2: ("GPSLatitude", RATIONAL, 3), + 3: ("GPSLongitudeRef", ASCII, 2), + 4: ("GPSLongitude", RATIONAL, 3), + 5: ("GPSAltitudeRef", BYTE, 1), + 6: ("GPSAltitude", RATIONAL, 1), + 7: ("GPSTimeStamp", RATIONAL, 3), + 8: ("GPSSatellites", ASCII, 0), + 9: ("GPSStatus", ASCII, 2), + 10: ("GPSMeasureMode", ASCII, 2), + 11: ("GPSDOP", RATIONAL, 1), + 12: ("GPSSpeedRef", ASCII, 2), + 13: ("GPSSpeed", RATIONAL, 1), + 14: ("GPSTrackRef", ASCII, 2), + 15: ("GPSTrack", RATIONAL, 1), + 16: ("GPSImgDirectionRef", ASCII, 2), + 17: ("GPSImgDirection", RATIONAL, 1), + 18: ("GPSMapDatum", ASCII, 0), + 19: ("GPSDestLatitudeRef", ASCII, 2), + 20: ("GPSDestLatitude", RATIONAL, 3), + 21: ("GPSDestLongitudeRef", ASCII, 2), + 22: ("GPSDestLongitude", RATIONAL, 3), + 23: ("GPSDestBearingRef", ASCII, 2), + 24: ("GPSDestBearing", RATIONAL, 1), + 25: ("GPSDestDistanceRef", ASCII, 2), + 26: ("GPSDestDistance", RATIONAL, 1), + 27: ("GPSProcessingMethod", UNDEFINED, 0), + 28: ("GPSAreaInformation", UNDEFINED, 0), + 29: ("GPSDateStamp", ASCII, 11), + 30: ("GPSDifferential", SHORT, 1), + }, + # InteroperabilityIFD + 40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)}, +} + +# Legacy Tags structure +# these tags aren't included above, but were in the previous versions +TAGS: dict[int | tuple[int, int], str] = { + 347: "JPEGTables", + 700: "XMP", + # Additional Exif Info + 32932: "Wang Annotation", + 33434: "ExposureTime", + 33437: "FNumber", + 33445: "MD FileTag", + 33446: "MD ScalePixel", + 33447: "MD ColorTable", + 33448: "MD LabName", + 33449: "MD SampleInfo", + 33450: "MD PrepDate", + 33451: "MD PrepTime", + 33452: "MD FileUnits", + 33550: "ModelPixelScaleTag", + 33723: "IptcNaaInfo", + 33918: "INGR Packet Data Tag", + 33919: "INGR Flag Registers", + 33920: "IrasB Transformation Matrix", + 33922: "ModelTiepointTag", + 34264: "ModelTransformationTag", + 34377: "PhotoshopInfo", + 34735: "GeoKeyDirectoryTag", + 34736: "GeoDoubleParamsTag", + 34737: "GeoAsciiParamsTag", + 34850: "ExposureProgram", + 34852: "SpectralSensitivity", + 34855: "ISOSpeedRatings", + 34856: "OECF", + 34864: "SensitivityType", + 34865: "StandardOutputSensitivity", + 34866: "RecommendedExposureIndex", + 34867: "ISOSpeed", + 34868: "ISOSpeedLatitudeyyy", + 34869: "ISOSpeedLatitudezzz", + 34908: "HylaFAX FaxRecvParams", + 34909: "HylaFAX FaxSubAddress", + 34910: "HylaFAX FaxRecvTime", + 36864: "ExifVersion", + 36867: "DateTimeOriginal", + 36868: "DateTimeDigitized", + 37121: "ComponentsConfiguration", + 37122: "CompressedBitsPerPixel", + 37724: "ImageSourceData", + 37377: "ShutterSpeedValue", + 37378: "ApertureValue", + 37379: "BrightnessValue", + 37380: "ExposureBiasValue", + 37381: "MaxApertureValue", + 37382: "SubjectDistance", + 37383: "MeteringMode", + 37384: "LightSource", + 37385: "Flash", + 37386: "FocalLength", + 37396: "SubjectArea", + 37500: "MakerNote", + 37510: "UserComment", + 37520: "SubSec", + 37521: "SubSecTimeOriginal", + 37522: "SubsecTimeDigitized", + 40960: "FlashPixVersion", + 40961: "ColorSpace", + 40962: "PixelXDimension", + 40963: "PixelYDimension", + 40964: "RelatedSoundFile", + 40965: "InteroperabilityIFD", + 41483: "FlashEnergy", + 41484: "SpatialFrequencyResponse", + 41486: "FocalPlaneXResolution", + 41487: "FocalPlaneYResolution", + 41488: "FocalPlaneResolutionUnit", + 41492: "SubjectLocation", + 41493: "ExposureIndex", + 41495: "SensingMethod", + 41728: "FileSource", + 41729: "SceneType", + 41730: "CFAPattern", + 41985: "CustomRendered", + 41986: "ExposureMode", + 41987: "WhiteBalance", + 41988: "DigitalZoomRatio", + 41989: "FocalLengthIn35mmFilm", + 41990: "SceneCaptureType", + 41991: "GainControl", + 41992: "Contrast", + 41993: "Saturation", + 41994: "Sharpness", + 41995: "DeviceSettingDescription", + 41996: "SubjectDistanceRange", + 42016: "ImageUniqueID", + 42032: "CameraOwnerName", + 42033: "BodySerialNumber", + 42034: "LensSpecification", + 42035: "LensMake", + 42036: "LensModel", + 42037: "LensSerialNumber", + 42112: "GDAL_METADATA", + 42113: "GDAL_NODATA", + 42240: "Gamma", + 50215: "Oce Scanjob Description", + 50216: "Oce Application Selector", + 50217: "Oce Identification Number", + 50218: "Oce ImageLogic Characteristics", + # Adobe DNG + 50706: "DNGVersion", + 50707: "DNGBackwardVersion", + 50708: "UniqueCameraModel", + 50709: "LocalizedCameraModel", + 50710: "CFAPlaneColor", + 50711: "CFALayout", + 50712: "LinearizationTable", + 50713: "BlackLevelRepeatDim", + 50714: "BlackLevel", + 50715: "BlackLevelDeltaH", + 50716: "BlackLevelDeltaV", + 50717: "WhiteLevel", + 50718: "DefaultScale", + 50719: "DefaultCropOrigin", + 50720: "DefaultCropSize", + 50721: "ColorMatrix1", + 50722: "ColorMatrix2", + 50723: "CameraCalibration1", + 50724: "CameraCalibration2", + 50725: "ReductionMatrix1", + 50726: "ReductionMatrix2", + 50727: "AnalogBalance", + 50728: "AsShotNeutral", + 50729: "AsShotWhiteXY", + 50730: "BaselineExposure", + 50731: "BaselineNoise", + 50732: "BaselineSharpness", + 50733: "BayerGreenSplit", + 50734: "LinearResponseLimit", + 50735: "CameraSerialNumber", + 50736: "LensInfo", + 50737: "ChromaBlurRadius", + 50738: "AntiAliasStrength", + 50740: "DNGPrivateData", + 50778: "CalibrationIlluminant1", + 50779: "CalibrationIlluminant2", + 50784: "Alias Layer Metadata", +} + +TAGS_V2: dict[int, TagInfo] = {} +TAGS_V2_GROUPS: dict[int, dict[int, TagInfo]] = {} + + +def _populate() -> None: + for k, v in _tags_v2.items(): + # Populate legacy structure. + TAGS[k] = v[0] + if len(v) == 4: + for sk, sv in v[3].items(): + TAGS[(k, sv)] = sk + + TAGS_V2[k] = TagInfo(k, *v) + + for group, tags in _tags_v2_groups.items(): + TAGS_V2_GROUPS[group] = {k: TagInfo(k, *v) for k, v in tags.items()} + + +_populate() +## +# Map type numbers to type names -- defined in ImageFileDirectory. + +TYPES: dict[int, str] = {} + +# +# These tags are handled by default in libtiff, without +# adding to the custom dictionary. From tif_dir.c, searching for +# case TIFFTAG in the _TIFFVSetField function: +# Line: item. +# 148: case TIFFTAG_SUBFILETYPE: +# 151: case TIFFTAG_IMAGEWIDTH: +# 154: case TIFFTAG_IMAGELENGTH: +# 157: case TIFFTAG_BITSPERSAMPLE: +# 181: case TIFFTAG_COMPRESSION: +# 202: case TIFFTAG_PHOTOMETRIC: +# 205: case TIFFTAG_THRESHHOLDING: +# 208: case TIFFTAG_FILLORDER: +# 214: case TIFFTAG_ORIENTATION: +# 221: case TIFFTAG_SAMPLESPERPIXEL: +# 228: case TIFFTAG_ROWSPERSTRIP: +# 238: case TIFFTAG_MINSAMPLEVALUE: +# 241: case TIFFTAG_MAXSAMPLEVALUE: +# 244: case TIFFTAG_SMINSAMPLEVALUE: +# 247: case TIFFTAG_SMAXSAMPLEVALUE: +# 250: case TIFFTAG_XRESOLUTION: +# 256: case TIFFTAG_YRESOLUTION: +# 262: case TIFFTAG_PLANARCONFIG: +# 268: case TIFFTAG_XPOSITION: +# 271: case TIFFTAG_YPOSITION: +# 274: case TIFFTAG_RESOLUTIONUNIT: +# 280: case TIFFTAG_PAGENUMBER: +# 284: case TIFFTAG_HALFTONEHINTS: +# 288: case TIFFTAG_COLORMAP: +# 294: case TIFFTAG_EXTRASAMPLES: +# 298: case TIFFTAG_MATTEING: +# 305: case TIFFTAG_TILEWIDTH: +# 316: case TIFFTAG_TILELENGTH: +# 327: case TIFFTAG_TILEDEPTH: +# 333: case TIFFTAG_DATATYPE: +# 344: case TIFFTAG_SAMPLEFORMAT: +# 361: case TIFFTAG_IMAGEDEPTH: +# 364: case TIFFTAG_SUBIFD: +# 376: case TIFFTAG_YCBCRPOSITIONING: +# 379: case TIFFTAG_YCBCRSUBSAMPLING: +# 383: case TIFFTAG_TRANSFERFUNCTION: +# 389: case TIFFTAG_REFERENCEBLACKWHITE: +# 393: case TIFFTAG_INKNAMES: + +# Following pseudo-tags are also handled by default in libtiff: +# TIFFTAG_JPEGQUALITY 65537 + +# some of these are not in our TAGS_V2 dict and were included from tiff.h + +# This list also exists in encode.c +LIBTIFF_CORE = { + 255, + 256, + 257, + 258, + 259, + 262, + 263, + 266, + 274, + 277, + 278, + 280, + 281, + 340, + 341, + 282, + 283, + 284, + 286, + 287, + 296, + 297, + 321, + 320, + 338, + 32995, + 322, + 323, + 32998, + 32996, + 339, + 32997, + 330, + 531, + 530, + 301, + 532, + 333, + # as above + 269, # this has been in our tests forever, and works + 65537, +} + +LIBTIFF_CORE.remove(255) # We don't have support for subfiletypes +LIBTIFF_CORE.remove(322) # We don't have support for writing tiled images with libtiff +LIBTIFF_CORE.remove(323) # Tiled images +LIBTIFF_CORE.remove(333) # Ink Names either + +# Note to advanced users: There may be combinations of these +# parameters and values that when added properly, will work and +# produce valid tiff images that may work in your application. +# It is safe to add and remove tags from this set from Pillow's point +# of view so long as you test against libtiff. diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..87e32878b1970876913e88fd5e8480d6813392c4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py @@ -0,0 +1,127 @@ +# +# The Python Imaging Library. +# $Id$ +# +# WAL file handling +# +# History: +# 2003-04-23 fl created +# +# Copyright (c) 2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +""" +This reader is based on the specification available from: +https://www.flipcode.com/archives/Quake_2_BSP_File_Format.shtml +and has been tested with a few sample files found using google. + +.. note:: + This format cannot be automatically recognized, so the reader + is not registered for use with :py:func:`PIL.Image.open()`. + To open a WAL file, use the :py:func:`PIL.WalImageFile.open()` function instead. +""" +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import i32le as i32 +from ._typing import StrOrBytesPath + + +class WalImageFile(ImageFile.ImageFile): + format = "WAL" + format_description = "Quake2 Texture" + + def _open(self) -> None: + self._mode = "P" + + # read header fields + header = self.fp.read(32 + 24 + 32 + 12) + self._size = i32(header, 32), i32(header, 36) + Image._decompression_bomb_check(self.size) + + # load pixel data + offset = i32(header, 40) + self.fp.seek(offset) + + # strings are null-terminated + self.info["name"] = header[:32].split(b"\0", 1)[0] + next_name = header[56 : 56 + 32].split(b"\0", 1)[0] + if next_name: + self.info["next_name"] = next_name + + def load(self) -> Image.core.PixelAccess | None: + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + self.frombytes(self.fp.read(self.size[0] * self.size[1])) + self.putpalette(quake2palette) + return Image.Image.load(self) + + +def open(filename: StrOrBytesPath | IO[bytes]) -> WalImageFile: + """ + Load texture from a Quake2 WAL texture file. + + By default, a Quake2 standard palette is attached to the texture. + To override the palette, use the :py:func:`PIL.Image.Image.putpalette()` method. + + :param filename: WAL file name, or an opened file handle. + :returns: An image instance. + """ + return WalImageFile(filename) + + +quake2palette = ( + # default palette taken from piffo 0.93 by Hans Häggström + b"\x01\x01\x01\x0b\x0b\x0b\x12\x12\x12\x17\x17\x17\x1b\x1b\x1b\x1e" + b"\x1e\x1e\x22\x22\x22\x26\x26\x26\x29\x29\x29\x2c\x2c\x2c\x2f\x2f" + b"\x2f\x32\x32\x32\x35\x35\x35\x37\x37\x37\x3a\x3a\x3a\x3c\x3c\x3c" + b"\x24\x1e\x13\x22\x1c\x12\x20\x1b\x12\x1f\x1a\x10\x1d\x19\x10\x1b" + b"\x17\x0f\x1a\x16\x0f\x18\x14\x0d\x17\x13\x0d\x16\x12\x0d\x14\x10" + b"\x0b\x13\x0f\x0b\x10\x0d\x0a\x0f\x0b\x0a\x0d\x0b\x07\x0b\x0a\x07" + b"\x23\x23\x26\x22\x22\x25\x22\x20\x23\x21\x1f\x22\x20\x1e\x20\x1f" + b"\x1d\x1e\x1d\x1b\x1c\x1b\x1a\x1a\x1a\x19\x19\x18\x17\x17\x17\x16" + b"\x16\x14\x14\x14\x13\x13\x13\x10\x10\x10\x0f\x0f\x0f\x0d\x0d\x0d" + b"\x2d\x28\x20\x29\x24\x1c\x27\x22\x1a\x25\x1f\x17\x38\x2e\x1e\x31" + b"\x29\x1a\x2c\x25\x17\x26\x20\x14\x3c\x30\x14\x37\x2c\x13\x33\x28" + b"\x12\x2d\x24\x10\x28\x1f\x0f\x22\x1a\x0b\x1b\x14\x0a\x13\x0f\x07" + b"\x31\x1a\x16\x30\x17\x13\x2e\x16\x10\x2c\x14\x0d\x2a\x12\x0b\x27" + b"\x0f\x0a\x25\x0f\x07\x21\x0d\x01\x1e\x0b\x01\x1c\x0b\x01\x1a\x0b" + b"\x01\x18\x0a\x01\x16\x0a\x01\x13\x0a\x01\x10\x07\x01\x0d\x07\x01" + b"\x29\x23\x1e\x27\x21\x1c\x26\x20\x1b\x25\x1f\x1a\x23\x1d\x19\x21" + b"\x1c\x18\x20\x1b\x17\x1e\x19\x16\x1c\x18\x14\x1b\x17\x13\x19\x14" + b"\x10\x17\x13\x0f\x14\x10\x0d\x12\x0f\x0b\x0f\x0b\x0a\x0b\x0a\x07" + b"\x26\x1a\x0f\x23\x19\x0f\x20\x17\x0f\x1c\x16\x0f\x19\x13\x0d\x14" + b"\x10\x0b\x10\x0d\x0a\x0b\x0a\x07\x33\x22\x1f\x35\x29\x26\x37\x2f" + b"\x2d\x39\x35\x34\x37\x39\x3a\x33\x37\x39\x30\x34\x36\x2b\x31\x34" + b"\x27\x2e\x31\x22\x2b\x2f\x1d\x28\x2c\x17\x25\x2a\x0f\x20\x26\x0d" + b"\x1e\x25\x0b\x1c\x22\x0a\x1b\x20\x07\x19\x1e\x07\x17\x1b\x07\x14" + b"\x18\x01\x12\x16\x01\x0f\x12\x01\x0b\x0d\x01\x07\x0a\x01\x01\x01" + b"\x2c\x21\x21\x2a\x1f\x1f\x29\x1d\x1d\x27\x1c\x1c\x26\x1a\x1a\x24" + b"\x18\x18\x22\x17\x17\x21\x16\x16\x1e\x13\x13\x1b\x12\x12\x18\x10" + b"\x10\x16\x0d\x0d\x12\x0b\x0b\x0d\x0a\x0a\x0a\x07\x07\x01\x01\x01" + b"\x2e\x30\x29\x2d\x2e\x27\x2b\x2c\x26\x2a\x2a\x24\x28\x29\x23\x27" + b"\x27\x21\x26\x26\x1f\x24\x24\x1d\x22\x22\x1c\x1f\x1f\x1a\x1c\x1c" + b"\x18\x19\x19\x16\x17\x17\x13\x13\x13\x10\x0f\x0f\x0d\x0b\x0b\x0a" + b"\x30\x1e\x1b\x2d\x1c\x19\x2c\x1a\x17\x2a\x19\x14\x28\x17\x13\x26" + b"\x16\x10\x24\x13\x0f\x21\x12\x0d\x1f\x10\x0b\x1c\x0f\x0a\x19\x0d" + b"\x0a\x16\x0b\x07\x12\x0a\x07\x0f\x07\x01\x0a\x01\x01\x01\x01\x01" + b"\x28\x29\x38\x26\x27\x36\x25\x26\x34\x24\x24\x31\x22\x22\x2f\x20" + b"\x21\x2d\x1e\x1f\x2a\x1d\x1d\x27\x1b\x1b\x25\x19\x19\x21\x17\x17" + b"\x1e\x14\x14\x1b\x13\x12\x17\x10\x0f\x13\x0d\x0b\x0f\x0a\x07\x07" + b"\x2f\x32\x29\x2d\x30\x26\x2b\x2e\x24\x29\x2c\x21\x27\x2a\x1e\x25" + b"\x28\x1c\x23\x26\x1a\x21\x25\x18\x1e\x22\x14\x1b\x1f\x10\x19\x1c" + b"\x0d\x17\x1a\x0a\x13\x17\x07\x10\x13\x01\x0d\x0f\x01\x0a\x0b\x01" + b"\x01\x3f\x01\x13\x3c\x0b\x1b\x39\x10\x20\x35\x14\x23\x31\x17\x23" + b"\x2d\x18\x23\x29\x18\x3f\x3f\x3f\x3f\x3f\x39\x3f\x3f\x31\x3f\x3f" + b"\x2a\x3f\x3f\x20\x3f\x3f\x14\x3f\x3c\x12\x3f\x39\x0f\x3f\x35\x0b" + b"\x3f\x32\x07\x3f\x2d\x01\x3d\x2a\x01\x3b\x26\x01\x39\x21\x01\x37" + b"\x1d\x01\x34\x1a\x01\x32\x16\x01\x2f\x12\x01\x2d\x0f\x01\x2a\x0b" + b"\x01\x27\x07\x01\x23\x01\x01\x1d\x01\x01\x17\x01\x01\x10\x01\x01" + b"\x3d\x01\x01\x19\x19\x3f\x3f\x01\x01\x01\x01\x3f\x16\x16\x13\x10" + b"\x10\x0f\x0d\x0d\x0b\x3c\x2e\x2a\x36\x27\x20\x30\x21\x18\x29\x1b" + b"\x10\x3c\x39\x37\x37\x32\x2f\x31\x2c\x28\x2b\x26\x21\x30\x22\x20" +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1716a18ccda48ea47c6176c22914ab1f8b035e7c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py @@ -0,0 +1,320 @@ +from __future__ import annotations + +from io import BytesIO +from typing import IO, Any + +from . import Image, ImageFile + +try: + from . import _webp + + SUPPORTED = True +except ImportError: + SUPPORTED = False + + +_VP8_MODES_BY_IDENTIFIER = { + b"VP8 ": "RGB", + b"VP8X": "RGBA", + b"VP8L": "RGBA", # lossless +} + + +def _accept(prefix: bytes) -> bool | str: + is_riff_file_format = prefix.startswith(b"RIFF") + is_webp_file = prefix[8:12] == b"WEBP" + is_valid_vp8_mode = prefix[12:16] in _VP8_MODES_BY_IDENTIFIER + + if is_riff_file_format and is_webp_file and is_valid_vp8_mode: + if not SUPPORTED: + return ( + "image file could not be identified because WEBP support not installed" + ) + return True + return False + + +class WebPImageFile(ImageFile.ImageFile): + format = "WEBP" + format_description = "WebP image" + __loaded = 0 + __logical_frame = 0 + + def _open(self) -> None: + # Use the newer AnimDecoder API to parse the (possibly) animated file, + # and access muxed chunks like ICC/EXIF/XMP. + self._decoder = _webp.WebPAnimDecoder(self.fp.read()) + + # Get info from decoder + self._size, loop_count, bgcolor, frame_count, mode = self._decoder.get_info() + self.info["loop"] = loop_count + bg_a, bg_r, bg_g, bg_b = ( + (bgcolor >> 24) & 0xFF, + (bgcolor >> 16) & 0xFF, + (bgcolor >> 8) & 0xFF, + bgcolor & 0xFF, + ) + self.info["background"] = (bg_r, bg_g, bg_b, bg_a) + self.n_frames = frame_count + self.is_animated = self.n_frames > 1 + self._mode = "RGB" if mode == "RGBX" else mode + self.rawmode = mode + + # Attempt to read ICC / EXIF / XMP chunks from file + icc_profile = self._decoder.get_chunk("ICCP") + exif = self._decoder.get_chunk("EXIF") + xmp = self._decoder.get_chunk("XMP ") + if icc_profile: + self.info["icc_profile"] = icc_profile + if exif: + self.info["exif"] = exif + if xmp: + self.info["xmp"] = xmp + + # Initialize seek state + self._reset(reset=False) + + def _getexif(self) -> dict[int, Any] | None: + if "exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + + # Set logical frame to requested position + self.__logical_frame = frame + + def _reset(self, reset: bool = True) -> None: + if reset: + self._decoder.reset() + self.__physical_frame = 0 + self.__loaded = -1 + self.__timestamp = 0 + + def _get_next(self) -> tuple[bytes, int, int]: + # Get next frame + ret = self._decoder.get_next() + self.__physical_frame += 1 + + # Check if an error occurred + if ret is None: + self._reset() # Reset just to be safe + self.seek(0) + msg = "failed to decode next frame in WebP file" + raise EOFError(msg) + + # Compute duration + data, timestamp = ret + duration = timestamp - self.__timestamp + self.__timestamp = timestamp + + # libwebp gives frame end, adjust to start of frame + timestamp -= duration + return data, timestamp, duration + + def _seek(self, frame: int) -> None: + if self.__physical_frame == frame: + return # Nothing to do + if frame < self.__physical_frame: + self._reset() # Rewind to beginning + while self.__physical_frame < frame: + self._get_next() # Advance to the requested frame + + def load(self) -> Image.core.PixelAccess | None: + if self.__loaded != self.__logical_frame: + self._seek(self.__logical_frame) + + # We need to load the image data for this frame + data, timestamp, duration = self._get_next() + self.info["timestamp"] = timestamp + self.info["duration"] = duration + self.__loaded = self.__logical_frame + + # Set tile + if self.fp and self._exclusive_fp: + self.fp.close() + self.fp = BytesIO(data) + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 0, self.rawmode)] + + return super().load() + + def load_seek(self, pos: int) -> None: + pass + + def tell(self) -> int: + return self.__logical_frame + + +def _convert_frame(im: Image.Image) -> Image.Image: + # Make sure image mode is supported + if im.mode not in ("RGBX", "RGBA", "RGB"): + im = im.convert("RGBA" if im.has_transparency_data else "RGB") + return im + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + encoderinfo = im.encoderinfo.copy() + append_images = list(encoderinfo.get("append_images", [])) + + # If total frame count is 1, then save using the legacy API, which + # will preserve non-alpha modes + total = 0 + for ims in [im] + append_images: + total += getattr(ims, "n_frames", 1) + if total == 1: + _save(im, fp, filename) + return + + background: int | tuple[int, ...] = (0, 0, 0, 0) + if "background" in encoderinfo: + background = encoderinfo["background"] + elif "background" in im.info: + background = im.info["background"] + if isinstance(background, int): + # GifImagePlugin stores a global color table index in + # info["background"]. So it must be converted to an RGBA value + palette = im.getpalette() + if palette: + r, g, b = palette[background * 3 : (background + 1) * 3] + background = (r, g, b, 255) + else: + background = (background, background, background, 255) + + duration = im.encoderinfo.get("duration", im.info.get("duration", 0)) + loop = im.encoderinfo.get("loop", 0) + minimize_size = im.encoderinfo.get("minimize_size", False) + kmin = im.encoderinfo.get("kmin", None) + kmax = im.encoderinfo.get("kmax", None) + allow_mixed = im.encoderinfo.get("allow_mixed", False) + verbose = False + lossless = im.encoderinfo.get("lossless", False) + quality = im.encoderinfo.get("quality", 80) + alpha_quality = im.encoderinfo.get("alpha_quality", 100) + method = im.encoderinfo.get("method", 0) + icc_profile = im.encoderinfo.get("icc_profile") or "" + exif = im.encoderinfo.get("exif", "") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + xmp = im.encoderinfo.get("xmp", "") + if allow_mixed: + lossless = False + + # Sensible keyframe defaults are from gif2webp.c script + if kmin is None: + kmin = 9 if lossless else 3 + if kmax is None: + kmax = 17 if lossless else 5 + + # Validate background color + if ( + not isinstance(background, (list, tuple)) + or len(background) != 4 + or not all(0 <= v < 256 for v in background) + ): + msg = f"Background color is not an RGBA tuple clamped to (0-255): {background}" + raise OSError(msg) + + # Convert to packed uint + bg_r, bg_g, bg_b, bg_a = background + background = (bg_a << 24) | (bg_r << 16) | (bg_g << 8) | (bg_b << 0) + + # Setup the WebP animation encoder + enc = _webp.WebPAnimEncoder( + im.size, + background, + loop, + minimize_size, + kmin, + kmax, + allow_mixed, + verbose, + ) + + # Add each frame + frame_idx = 0 + timestamp = 0 + cur_idx = im.tell() + try: + for ims in [im] + append_images: + # Get number of frames in this image + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + + frame = _convert_frame(ims) + + # Append the frame to the animation encoder + enc.add( + frame.getim(), + round(timestamp), + lossless, + quality, + alpha_quality, + method, + ) + + # Update timestamp and frame index + if isinstance(duration, (list, tuple)): + timestamp += duration[frame_idx] + else: + timestamp += duration + frame_idx += 1 + + finally: + im.seek(cur_idx) + + # Force encoder to flush frames + enc.add(None, round(timestamp), lossless, quality, alpha_quality, 0) + + # Get the final output from the encoder + data = enc.assemble(icc_profile, exif, xmp) + if data is None: + msg = "cannot write file as WebP (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + lossless = im.encoderinfo.get("lossless", False) + quality = im.encoderinfo.get("quality", 80) + alpha_quality = im.encoderinfo.get("alpha_quality", 100) + icc_profile = im.encoderinfo.get("icc_profile") or "" + exif = im.encoderinfo.get("exif", b"") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + if exif.startswith(b"Exif\x00\x00"): + exif = exif[6:] + xmp = im.encoderinfo.get("xmp", "") + method = im.encoderinfo.get("method", 4) + exact = 1 if im.encoderinfo.get("exact") else 0 + + im = _convert_frame(im) + + data = _webp.WebPEncode( + im.getim(), + lossless, + float(quality), + float(alpha_quality), + icc_profile, + method, + exact, + exif, + xmp, + ) + if data is None: + msg = "cannot write file as WebP (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +Image.register_open(WebPImageFile.format, WebPImageFile, _accept) +if SUPPORTED: + Image.register_save(WebPImageFile.format, _save) + Image.register_save_all(WebPImageFile.format, _save_all) + Image.register_extension(WebPImageFile.format, ".webp") + Image.register_mime(WebPImageFile.format, "image/webp") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d569cb4b819db35966c67fbe75518c719973ef59 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py @@ -0,0 +1,186 @@ +# +# The Python Imaging Library +# $Id$ +# +# WMF stub codec +# +# history: +# 1996-12-14 fl Created +# 2004-02-22 fl Turned into a stub driver +# 2004-02-23 fl Added EMF support +# +# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +# WMF/EMF reference documentation: +# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf +# http://wvware.sourceforge.net/caolan/index.html +# http://wvware.sourceforge.net/caolan/ora-wmf.html +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import i16le as word +from ._binary import si16le as short +from ._binary import si32le as _long + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific WMF image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +if hasattr(Image.core, "drawwmf"): + # install default handler (windows only) + + class WmfHandler(ImageFile.StubHandler): + def open(self, im: ImageFile.StubImageFile) -> None: + im._mode = "RGB" + self.bbox = im.info["wmf_bbox"] + + def load(self, im: ImageFile.StubImageFile) -> Image.Image: + im.fp.seek(0) # rewind + return Image.frombytes( + "RGB", + im.size, + Image.core.drawwmf(im.fp.read(), im.size, self.bbox), + "raw", + "BGR", + (im.size[0] * 3 + 3) & -4, + -1, + ) + + register_handler(WmfHandler()) + +# +# -------------------------------------------------------------------- +# Read WMF file + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"\xd7\xcd\xc6\x9a\x00\x00", b"\x01\x00\x00\x00")) + + +## +# Image plugin for Windows metafiles. + + +class WmfStubImageFile(ImageFile.StubImageFile): + format = "WMF" + format_description = "Windows Metafile" + + def _open(self) -> None: + # check placable header + s = self.fp.read(44) + + if s.startswith(b"\xd7\xcd\xc6\x9a\x00\x00"): + # placeable windows metafile + + # get units per inch + inch = word(s, 14) + if inch == 0: + msg = "Invalid inch" + raise ValueError(msg) + self._inch: tuple[float, float] = inch, inch + + # get bounding box + x0 = short(s, 6) + y0 = short(s, 8) + x1 = short(s, 10) + y1 = short(s, 12) + + # normalize size to 72 dots per inch + self.info["dpi"] = 72 + size = ( + (x1 - x0) * self.info["dpi"] // inch, + (y1 - y0) * self.info["dpi"] // inch, + ) + + self.info["wmf_bbox"] = x0, y0, x1, y1 + + # sanity check (standard metafile header) + if s[22:26] != b"\x01\x00\t\x00": + msg = "Unsupported WMF file format" + raise SyntaxError(msg) + + elif s.startswith(b"\x01\x00\x00\x00") and s[40:44] == b" EMF": + # enhanced metafile + + # get bounding box + x0 = _long(s, 8) + y0 = _long(s, 12) + x1 = _long(s, 16) + y1 = _long(s, 20) + + # get frame (in 0.01 millimeter units) + frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36) + + size = x1 - x0, y1 - y0 + + # calculate dots per inch from bbox and frame + xdpi = 2540.0 * (x1 - x0) / (frame[2] - frame[0]) + ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1]) + + self.info["wmf_bbox"] = x0, y0, x1, y1 + + if xdpi == ydpi: + self.info["dpi"] = xdpi + else: + self.info["dpi"] = xdpi, ydpi + self._inch = xdpi, ydpi + + else: + msg = "Unsupported file format" + raise SyntaxError(msg) + + self._mode = "RGB" + self._size = size + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + def load( + self, dpi: float | tuple[float, float] | None = None + ) -> Image.core.PixelAccess | None: + if dpi is not None: + self.info["dpi"] = dpi + x0, y0, x1, y1 = self.info["wmf_bbox"] + if not isinstance(dpi, tuple): + dpi = dpi, dpi + self._size = ( + int((x1 - x0) * dpi[0] / self._inch[0]), + int((y1 - y0) * dpi[1] / self._inch[1]), + ) + return super().load() + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "WMF save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# +# -------------------------------------------------------------------- +# Registry stuff + + +Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept) +Image.register_save(WmfStubImageFile.format, _save) + +Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"]) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..cde28388ff0535262770dd0336ee2b48db2178ba --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py @@ -0,0 +1,83 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XV Thumbnail file handler by Charles E. "Gene" Cash +# (gcash@magicnet.net) +# +# see xvcolor.c and xvbrowse.c in the sources to John Bradley's XV, +# available from ftp://ftp.cis.upenn.edu/pub/xv/ +# +# history: +# 98-08-15 cec created (b/w only) +# 98-12-09 cec added color palette +# 98-12-28 fl added to PIL (with only a few very minor modifications) +# +# To do: +# FIXME: make save work (this requires quantization support) +# +from __future__ import annotations + +from . import Image, ImageFile, ImagePalette +from ._binary import o8 + +_MAGIC = b"P7 332" + +# standard color palette for thumbnails (RGB332) +PALETTE = b"" +for r in range(8): + for g in range(8): + for b in range(4): + PALETTE = PALETTE + ( + o8((r * 255) // 7) + o8((g * 255) // 7) + o8((b * 255) // 3) + ) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +## +# Image plugin for XV thumbnail images. + + +class XVThumbImageFile(ImageFile.ImageFile): + format = "XVThumb" + format_description = "XV thumbnail image" + + def _open(self) -> None: + # check magic + assert self.fp is not None + + if not _accept(self.fp.read(6)): + msg = "not an XV thumbnail file" + raise SyntaxError(msg) + + # Skip to beginning of next line + self.fp.readline() + + # skip info comments + while True: + s = self.fp.readline() + if not s: + msg = "Unexpected EOF reading XV thumbnail file" + raise SyntaxError(msg) + if s[0] != 35: # ie. when not a comment: '#' + break + + # parse header line (already read) + s = s.strip().split() + + self._mode = "P" + self._size = int(s[0]), int(s[1]) + + self.palette = ImagePalette.raw("RGB", PALETTE) + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, self.fp.tell(), self.mode) + ] + + +# -------------------------------------------------------------------- + +Image.register_open(XVThumbImageFile.format, XVThumbImageFile, _accept) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1e57aa162ea4f8618dac66cf042352f73d2199c8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py @@ -0,0 +1,98 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XBM File handling +# +# History: +# 1995-09-08 fl Created +# 1996-11-01 fl Added save support +# 1997-07-07 fl Made header parser more tolerant +# 1997-07-22 fl Fixed yet another parser bug +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.4) +# 2001-05-13 fl Added hotspot handling (based on code from Bernhard Herzog) +# 2004-02-24 fl Allow some whitespace before first #define +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from typing import IO + +from . import Image, ImageFile + +# XBM header +xbm_head = re.compile( + rb"\s*#define[ \t]+.*_width[ \t]+(?P[0-9]+)[\r\n]+" + b"#define[ \t]+.*_height[ \t]+(?P[0-9]+)[\r\n]+" + b"(?P" + b"#define[ \t]+[^_]*_x_hot[ \t]+(?P[0-9]+)[\r\n]+" + b"#define[ \t]+[^_]*_y_hot[ \t]+(?P[0-9]+)[\r\n]+" + b")?" + rb"[\000-\377]*_bits\[]" +) + + +def _accept(prefix: bytes) -> bool: + return prefix.lstrip().startswith(b"#define") + + +## +# Image plugin for X11 bitmaps. + + +class XbmImageFile(ImageFile.ImageFile): + format = "XBM" + format_description = "X11 Bitmap" + + def _open(self) -> None: + assert self.fp is not None + + m = xbm_head.match(self.fp.read(512)) + + if not m: + msg = "not a XBM file" + raise SyntaxError(msg) + + xsize = int(m.group("width")) + ysize = int(m.group("height")) + + if m.group("hotspot"): + self.info["hotspot"] = (int(m.group("xhot")), int(m.group("yhot"))) + + self._mode = "1" + self._size = xsize, ysize + + self.tile = [ImageFile._Tile("xbm", (0, 0) + self.size, m.end())] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "1": + msg = f"cannot write mode {im.mode} as XBM" + raise OSError(msg) + + fp.write(f"#define im_width {im.size[0]}\n".encode("ascii")) + fp.write(f"#define im_height {im.size[1]}\n".encode("ascii")) + + hotspot = im.encoderinfo.get("hotspot") + if hotspot: + fp.write(f"#define im_x_hot {hotspot[0]}\n".encode("ascii")) + fp.write(f"#define im_y_hot {hotspot[1]}\n".encode("ascii")) + + fp.write(b"static char im_bits[] = {\n") + + ImageFile._save(im, fp, [ImageFile._Tile("xbm", (0, 0) + im.size)]) + + fp.write(b"};\n") + + +Image.register_open(XbmImageFile.format, XbmImageFile, _accept) +Image.register_save(XbmImageFile.format, _save) + +Image.register_extension(XbmImageFile.format, ".xbm") + +Image.register_mime(XbmImageFile.format, "image/xbm") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..3be240fbc1aeb7660de46fbd4f99f309ce9915dd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py @@ -0,0 +1,157 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XPM File handling +# +# History: +# 1996-12-29 fl Created +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7) +# +# Copyright (c) Secret Labs AB 1997-2001. +# Copyright (c) Fredrik Lundh 1996-2001. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re + +from . import Image, ImageFile, ImagePalette +from ._binary import o8 + +# XPM header +xpm_head = re.compile(b'"([0-9]*) ([0-9]*) ([0-9]*) ([0-9]*)') + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"/* XPM */") + + +## +# Image plugin for X11 pixel maps. + + +class XpmImageFile(ImageFile.ImageFile): + format = "XPM" + format_description = "X11 Pixel Map" + + def _open(self) -> None: + assert self.fp is not None + if not _accept(self.fp.read(9)): + msg = "not an XPM file" + raise SyntaxError(msg) + + # skip forward to next string + while True: + line = self.fp.readline() + if not line: + msg = "broken XPM file" + raise SyntaxError(msg) + m = xpm_head.match(line) + if m: + break + + self._size = int(m.group(1)), int(m.group(2)) + + palette_length = int(m.group(3)) + bpp = int(m.group(4)) + + # + # load palette description + + palette = {} + + for _ in range(palette_length): + line = self.fp.readline().rstrip() + + c = line[1 : bpp + 1] + s = line[bpp + 1 : -2].split() + + for i in range(0, len(s), 2): + if s[i] == b"c": + # process colour key + rgb = s[i + 1] + if rgb == b"None": + self.info["transparency"] = c + elif rgb.startswith(b"#"): + rgb_int = int(rgb[1:], 16) + palette[c] = ( + o8((rgb_int >> 16) & 255) + + o8((rgb_int >> 8) & 255) + + o8(rgb_int & 255) + ) + else: + # unknown colour + msg = "cannot read this XPM file" + raise ValueError(msg) + break + + else: + # missing colour key + msg = "cannot read this XPM file" + raise ValueError(msg) + + args: tuple[int, dict[bytes, bytes] | tuple[bytes, ...]] + if palette_length > 256: + self._mode = "RGB" + args = (bpp, palette) + else: + self._mode = "P" + self.palette = ImagePalette.raw("RGB", b"".join(palette.values())) + args = (bpp, tuple(palette.keys())) + + self.tile = [ImageFile._Tile("xpm", (0, 0) + self.size, self.fp.tell(), args)] + + def load_read(self, read_bytes: int) -> bytes: + # + # load all image data in one chunk + + xsize, ysize = self.size + + assert self.fp is not None + s = [self.fp.readline()[1 : xsize + 1].ljust(xsize) for i in range(ysize)] + + return b"".join(s) + + +class XpmDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + data = bytearray() + bpp, palette = self.args + dest_length = self.state.xsize * self.state.ysize + if self.mode == "RGB": + dest_length *= 3 + pixel_header = False + while len(data) < dest_length: + line = self.fd.readline() + if not line: + break + if line.rstrip() == b"/* pixels */" and not pixel_header: + pixel_header = True + continue + line = b'"'.join(line.split(b'"')[1:-1]) + for i in range(0, len(line), bpp): + key = line[i : i + bpp] + if self.mode == "RGB": + data += palette[key] + else: + data += o8(palette.index(key)) + self.set_as_raw(bytes(data)) + return -1, 0 + + +# +# Registry + + +Image.register_open(XpmImageFile.format, XpmImageFile, _accept) +Image.register_decoder("xpm", XpmDecoder) + +Image.register_extension(XpmImageFile.format, ".xpm") + +Image.register_mime(XpmImageFile.format, "image/xpm") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6e4c23f897f83ef72fc10070bd22e9dc70614cf9 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__init__.py @@ -0,0 +1,87 @@ +"""Pillow (Fork of the Python Imaging Library) + +Pillow is the friendly PIL fork by Jeffrey A. Clark and contributors. + https://github.com/python-pillow/Pillow/ + +Pillow is forked from PIL 1.1.7. + +PIL is the Python Imaging Library by Fredrik Lundh and contributors. +Copyright (c) 1999 by Secret Labs AB. + +Use PIL.__version__ for this Pillow version. + +;-) +""" + +from __future__ import annotations + +from . import _version + +# VERSION was removed in Pillow 6.0.0. +# PILLOW_VERSION was removed in Pillow 9.0.0. +# Use __version__ instead. +__version__ = _version.__version__ +del _version + + +_plugins = [ + "AvifImagePlugin", + "BlpImagePlugin", + "BmpImagePlugin", + "BufrStubImagePlugin", + "CurImagePlugin", + "DcxImagePlugin", + "DdsImagePlugin", + "EpsImagePlugin", + "FitsImagePlugin", + "FliImagePlugin", + "FpxImagePlugin", + "FtexImagePlugin", + "GbrImagePlugin", + "GifImagePlugin", + "GribStubImagePlugin", + "Hdf5StubImagePlugin", + "IcnsImagePlugin", + "IcoImagePlugin", + "ImImagePlugin", + "ImtImagePlugin", + "IptcImagePlugin", + "JpegImagePlugin", + "Jpeg2KImagePlugin", + "McIdasImagePlugin", + "MicImagePlugin", + "MpegImagePlugin", + "MpoImagePlugin", + "MspImagePlugin", + "PalmImagePlugin", + "PcdImagePlugin", + "PcxImagePlugin", + "PdfImagePlugin", + "PixarImagePlugin", + "PngImagePlugin", + "PpmImagePlugin", + "PsdImagePlugin", + "QoiImagePlugin", + "SgiImagePlugin", + "SpiderImagePlugin", + "SunImagePlugin", + "TgaImagePlugin", + "TiffImagePlugin", + "WebPImagePlugin", + "WmfImagePlugin", + "XbmImagePlugin", + "XpmImagePlugin", + "XVThumbImagePlugin", +] + + +class UnidentifiedImageError(OSError): + """ + Raised in :py:meth:`PIL.Image.open` if an image cannot be opened and identified. + + If a PNG image raises this error, setting :data:`.ImageFile.LOAD_TRUNCATED_IMAGES` + to true may allow the image to be opened after all. The setting will ignore missing + data and checksum failures. + """ + + pass diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__main__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..043156e892dadc4fb1222b33f5eda33251cd15aa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__main__.py @@ -0,0 +1,7 @@ +from __future__ import annotations + +import sys + +from .features import pilinfo + +pilinfo(supported_formats="--report" not in sys.argv) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__pycache__/BmpImagePlugin.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/__pycache__/BmpImagePlugin.cpython-310.pyc new file mode 100644 index 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a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..79bbb6114866c3badeece31eef5a4614c0061b3c Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_avif.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_binary.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_binary.py new file mode 100644 index 0000000000000000000000000000000000000000..4594ccce361168cf77e630cb88ffb09bb4362831 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_binary.py @@ -0,0 +1,112 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Binary input/output support routines. +# +# Copyright (c) 1997-2003 by Secret Labs AB +# Copyright (c) 1995-2003 by Fredrik Lundh +# Copyright (c) 2012 by Brian Crowell +# +# See the README file for information on usage and redistribution. +# + + +"""Binary input/output support routines.""" +from __future__ import annotations + +from struct import pack, unpack_from + + +def i8(c: bytes) -> int: + return c[0] + + +def o8(i: int) -> bytes: + return bytes((i & 255,)) + + +# Input, le = little endian, be = big endian +def i16le(c: bytes, o: int = 0) -> int: + """ + Converts a 2-bytes (16 bits) string to an unsigned integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 2-bytes (16 bits) string to a signed integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 2-bytes (16 bits) string to a signed integer, big endian. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(">h", c, o)[0] + + +def i32le(c: bytes, o: int = 0) -> int: + """ + Converts a 4-bytes (32 bits) string to an unsigned integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 4-bytes (32 bits) string to a signed integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 4-bytes (32 bits) string to a signed integer, big endian. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(">i", c, o)[0] + + +def i16be(c: bytes, o: int = 0) -> int: + return unpack_from(">H", c, o)[0] + + +def i32be(c: bytes, o: int = 0) -> int: + return unpack_from(">I", c, o)[0] + + +# Output, le = little endian, be = big endian +def o16le(i: int) -> bytes: + return pack(" bytes: + return pack(" bytes: + return pack(">H", i) + + +def o32be(i: int) -> bytes: + return pack(">I", i) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_deprecate.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_deprecate.py new file mode 100644 index 0000000000000000000000000000000000000000..170d444904996cac753bf33deedcd13e442c9027 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_deprecate.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import warnings + +from . import __version__ + + +def deprecate( + deprecated: str, + when: int | None, + replacement: str | None = None, + *, + action: str | None = None, + plural: bool = False, + stacklevel: int = 3, +) -> None: + """ + Deprecations helper. + + :param deprecated: Name of thing to be deprecated. + :param when: Pillow major version to be removed in. + :param replacement: Name of replacement. + :param action: Instead of "replacement", give a custom call to action + e.g. "Upgrade to new thing". + :param plural: if the deprecated thing is plural, needing "are" instead of "is". + + Usually of the form: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd). + Use [replacement] instead." + + You can leave out the replacement sentence: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd)" + + Or with another call to action: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd). + [action]." + """ + + is_ = "are" if plural else "is" + + if when is None: + removed = "a future version" + elif when <= int(__version__.split(".")[0]): + msg = f"{deprecated} {is_} deprecated and should be removed." + raise RuntimeError(msg) + elif when == 12: + removed = "Pillow 12 (2025-10-15)" + elif when == 13: + removed = "Pillow 13 (2026-10-15)" + else: + msg = f"Unknown removal version: {when}. Update {__name__}?" + raise ValueError(msg) + + if replacement and action: + msg = "Use only one of 'replacement' and 'action'" + raise ValueError(msg) + + if replacement: + action = f". Use {replacement} instead." + elif action: + action = f". {action.rstrip('.')}." + else: + action = "" + + warnings.warn( + f"{deprecated} {is_} deprecated and will be removed in {removed}{action}", + DeprecationWarning, + stacklevel=stacklevel, + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi new file mode 100644 index 0000000000000000000000000000000000000000..998bc52eb8a73b5ee5868cd2c8e5c87c4e6d3037 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi @@ -0,0 +1,31 @@ +from typing import Any + +class ImagingCore: + def __getitem__(self, index: int) -> float: ... + def __getattr__(self, name: str) -> Any: ... + +class ImagingFont: + def __getattr__(self, name: str) -> Any: ... + +class ImagingDraw: + def __getattr__(self, name: str) -> Any: ... + +class PixelAccess: + def __getitem__(self, xy: tuple[int, int]) -> float | tuple[int, ...]: ... + def __setitem__( + self, xy: tuple[int, int], color: float | tuple[int, ...] + ) -> None: ... + +class ImagingDecoder: + def __getattr__(self, name: str) -> Any: ... + +class ImagingEncoder: + def __getattr__(self, name: str) -> Any: ... + +class _Outline: + def close(self) -> None: ... + def __getattr__(self, name: str) -> Any: ... + +def font(image: ImagingCore, glyphdata: bytes) -> ImagingFont: ... +def outline() -> _Outline: ... +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ddcf93ab1ebd51947f900ad2dba1aee9392338bb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi @@ -0,0 +1,143 @@ +import datetime +import sys +from typing import Literal, SupportsFloat, TypedDict + +from ._typing import CapsuleType + +littlecms_version: str | None + +_Tuple3f = tuple[float, float, float] +_Tuple2x3f = tuple[_Tuple3f, _Tuple3f] +_Tuple3x3f = tuple[_Tuple3f, _Tuple3f, _Tuple3f] + +class _IccMeasurementCondition(TypedDict): + observer: int + backing: _Tuple3f + geo: str + flare: float + illuminant_type: str + +class _IccViewingCondition(TypedDict): + illuminant: _Tuple3f + surround: _Tuple3f + illuminant_type: str + +class CmsProfile: + @property + def rendering_intent(self) -> int: ... + @property + def creation_date(self) -> datetime.datetime | None: ... + @property + def copyright(self) -> str | None: ... + @property + def target(self) -> str | None: ... + @property + def manufacturer(self) -> str | None: ... + @property + def model(self) -> str | None: ... + @property + def profile_description(self) -> str | None: ... + @property + def screening_description(self) -> str | None: ... + @property + def viewing_condition(self) -> str | None: ... + @property + def version(self) -> float: ... + @property + def icc_version(self) -> int: ... + @property + def attributes(self) -> int: ... + @property + def header_flags(self) -> int: ... + @property + def header_manufacturer(self) -> str: ... + @property + def header_model(self) -> str: ... + @property + def device_class(self) -> str: ... + @property + def connection_space(self) -> str: ... + @property + def xcolor_space(self) -> str: ... + @property + def profile_id(self) -> bytes: ... + @property + def is_matrix_shaper(self) -> bool: ... + @property + def technology(self) -> str | None: ... + @property + def colorimetric_intent(self) -> str | None: ... + @property + def perceptual_rendering_intent_gamut(self) -> str | None: ... + @property + def saturation_rendering_intent_gamut(self) -> str | None: ... + @property + def red_colorant(self) -> _Tuple2x3f | None: ... + @property + def green_colorant(self) -> _Tuple2x3f | None: ... + @property + def blue_colorant(self) -> _Tuple2x3f | None: ... + @property + def red_primary(self) -> _Tuple2x3f | None: ... + @property + def green_primary(self) -> _Tuple2x3f | None: ... + @property + def blue_primary(self) -> _Tuple2x3f | None: ... + @property + def media_white_point_temperature(self) -> float | None: ... + @property + def media_white_point(self) -> _Tuple2x3f | None: ... + @property + def media_black_point(self) -> _Tuple2x3f | None: ... + @property + def luminance(self) -> _Tuple2x3f | None: ... + @property + def chromatic_adaptation(self) -> tuple[_Tuple3x3f, _Tuple3x3f] | None: ... + @property + def chromaticity(self) -> _Tuple3x3f | None: ... + @property + def colorant_table(self) -> list[str] | None: ... + @property + def colorant_table_out(self) -> list[str] | None: ... + @property + def intent_supported(self) -> dict[int, tuple[bool, bool, bool]] | None: ... + @property + def clut(self) -> dict[int, tuple[bool, bool, bool]] | None: ... + @property + def icc_measurement_condition(self) -> _IccMeasurementCondition | None: ... + @property + def icc_viewing_condition(self) -> _IccViewingCondition | None: ... + def is_intent_supported(self, intent: int, direction: int, /) -> int: ... + +class CmsTransform: + def apply(self, id_in: CapsuleType, id_out: CapsuleType) -> int: ... + +def profile_open(profile: str, /) -> CmsProfile: ... +def profile_frombytes(profile: bytes, /) -> CmsProfile: ... +def profile_tobytes(profile: CmsProfile, /) -> bytes: ... +def buildTransform( + input_profile: CmsProfile, + output_profile: CmsProfile, + in_mode: str, + out_mode: str, + rendering_intent: int = 0, + cms_flags: int = 0, + /, +) -> CmsTransform: ... +def buildProofTransform( + input_profile: CmsProfile, + output_profile: CmsProfile, + proof_profile: CmsProfile, + in_mode: str, + out_mode: str, + rendering_intent: int = 0, + proof_intent: int = 0, + cms_flags: int = 0, + /, +) -> CmsTransform: ... +def createProfile( + color_space: Literal["LAB", "XYZ", "sRGB"], color_temp: SupportsFloat = 0.0, / +) -> CmsProfile: ... + +if sys.platform == "win32": + def get_display_profile_win32(handle: int = 0, is_dc: int = 0, /) -> str | None: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi new file mode 100644 index 0000000000000000000000000000000000000000..1cb1429d6cf6f29432fbd7f56d3aa0e45f0722f3 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi @@ -0,0 +1,69 @@ +from typing import Any, Callable + +from . import ImageFont, _imaging + +class Font: + @property + def family(self) -> str | None: ... + @property + def style(self) -> str | None: ... + @property + def ascent(self) -> int: ... + @property + def descent(self) -> int: ... + @property + def height(self) -> int: ... + @property + def x_ppem(self) -> int: ... + @property + def y_ppem(self) -> int: ... + @property + def glyphs(self) -> int: ... + def render( + self, + string: str | bytes, + fill: Callable[[int, int], _imaging.ImagingCore], + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + stroke_width: float, + stroke_filled: bool, + anchor: str | None, + foreground_ink_long: int, + start: tuple[float, float], + /, + ) -> tuple[_imaging.ImagingCore, tuple[int, int]]: ... + def getsize( + self, + string: str | bytes | bytearray, + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + anchor: str | None, + /, + ) -> tuple[tuple[int, int], tuple[int, int]]: ... + def getlength( + self, + string: str | bytes, + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + /, + ) -> float: ... + def getvarnames(self) -> list[bytes]: ... + def getvaraxes(self) -> list[ImageFont.Axis]: ... + def setvarname(self, instance_index: int, /) -> None: ... + def setvaraxes(self, axes: list[float], /) -> None: ... + +def getfont( + filename: str | bytes, + size: float, + index: int, + encoding: str, + font_bytes: bytes, + layout_engine: int, +) -> Font: ... +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..ea41a9e27b3914e7a4f192da35e3be1cc5b8ada9 Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..d4302f4bc93d136d0af56fe4797beb93dd5f9556 Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..9c0143003a7320dd475cfcd168168b82e4f64964 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py @@ -0,0 +1,20 @@ +"""Find compiled module linking to Tcl / Tk libraries""" + +from __future__ import annotations + +import sys +import tkinter + +tk = getattr(tkinter, "_tkinter") + +try: + if hasattr(sys, "pypy_find_executable"): + TKINTER_LIB = tk.tklib_cffi.__file__ + else: + TKINTER_LIB = tk.__file__ +except AttributeError: + # _tkinter may be compiled directly into Python, in which case __file__ is + # not available. load_tkinter_funcs will check the binary first in any case. + TKINTER_LIB = None + +tk_version = str(tkinter.TkVersion) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..373938e71e0331ed5d9ce2517dcf5f5098ffca72 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_typing.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +import os +import sys +from collections.abc import Sequence +from typing import Any, Protocol, TypeVar, Union + +TYPE_CHECKING = False +if TYPE_CHECKING: + from numbers import _IntegralLike as IntegralLike + + try: + import numpy.typing as npt + + NumpyArray = npt.NDArray[Any] # requires numpy>=1.21 + except (ImportError, AttributeError): + pass + +if sys.version_info >= (3, 13): + from types import CapsuleType +else: + CapsuleType = object + +if sys.version_info >= (3, 12): + from collections.abc import Buffer +else: + Buffer = Any + +if sys.version_info >= (3, 10): + from typing import TypeGuard +else: + try: + from typing_extensions import TypeGuard + except ImportError: + + class TypeGuard: # type: ignore[no-redef] + def __class_getitem__(cls, item: Any) -> type[bool]: + return bool + + +Coords = Union[Sequence[float], Sequence[Sequence[float]]] + + +_T_co = TypeVar("_T_co", covariant=True) + + +class SupportsRead(Protocol[_T_co]): + def read(self, length: int = ..., /) -> _T_co: ... + + +StrOrBytesPath = Union[str, bytes, os.PathLike[str], os.PathLike[bytes]] + + +__all__ = ["Buffer", "IntegralLike", "StrOrBytesPath", "SupportsRead", "TypeGuard"] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_util.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef0d36f7545a85e57829e036818bc4fa2eb72c7 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_util.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +import os +from typing import Any, NoReturn + +from ._typing import StrOrBytesPath, TypeGuard + + +def is_path(f: Any) -> TypeGuard[StrOrBytesPath]: + return isinstance(f, (bytes, str, os.PathLike)) + + +class DeferredError: + def __init__(self, ex: BaseException): + self.ex = ex + + def __getattr__(self, elt: str) -> NoReturn: + raise self.ex + + @staticmethod + def new(ex: BaseException) -> Any: + """ + Creates an object that raises the wrapped exception ``ex`` when used, + and casts it to :py:obj:`~typing.Any` type. + """ + return DeferredError(ex) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..74e63356c95519ff405d0fdd29ce997d3dd5c8f5 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_version.py @@ -0,0 +1,4 @@ +# Master version for Pillow +from __future__ import annotations + +__version__ = "11.3.0" diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..6e1e542a9ae8d05efb85a3344a27a527490af79a Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/_webp.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/features.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/features.py new file mode 100644 index 0000000000000000000000000000000000000000..573f1d41256012ccf1a021055222c1f5c3c23339 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/features.py @@ -0,0 +1,361 @@ +from __future__ import annotations + +import collections +import os +import sys +import warnings +from typing import IO + +import PIL + +from . import Image +from ._deprecate import deprecate + +modules = { + "pil": ("PIL._imaging", "PILLOW_VERSION"), + "tkinter": ("PIL._tkinter_finder", "tk_version"), + "freetype2": ("PIL._imagingft", "freetype2_version"), + "littlecms2": ("PIL._imagingcms", "littlecms_version"), + "webp": ("PIL._webp", "webpdecoder_version"), + "avif": ("PIL._avif", "libavif_version"), +} + + +def check_module(feature: str) -> bool: + """ + Checks if a module is available. + + :param feature: The module to check for. + :returns: ``True`` if available, ``False`` otherwise. + :raises ValueError: If the module is not defined in this version of Pillow. + """ + if feature not in modules: + msg = f"Unknown module {feature}" + raise ValueError(msg) + + module, ver = modules[feature] + + try: + __import__(module) + return True + except ModuleNotFoundError: + return False + except ImportError as ex: + warnings.warn(str(ex)) + return False + + +def version_module(feature: str) -> str | None: + """ + :param feature: The module to check for. + :returns: + The loaded version number as a string, or ``None`` if unknown or not available. + :raises ValueError: If the module is not defined in this version of Pillow. + """ + if not check_module(feature): + return None + + module, ver = modules[feature] + + return getattr(__import__(module, fromlist=[ver]), ver) + + +def get_supported_modules() -> list[str]: + """ + :returns: A list of all supported modules. + """ + return [f for f in modules if check_module(f)] + + +codecs = { + "jpg": ("jpeg", "jpeglib"), + "jpg_2000": ("jpeg2k", "jp2klib"), + "zlib": ("zip", "zlib"), + "libtiff": ("libtiff", "libtiff"), +} + + +def check_codec(feature: str) -> bool: + """ + Checks if a codec is available. + + :param feature: The codec to check for. + :returns: ``True`` if available, ``False`` otherwise. + :raises ValueError: If the codec is not defined in this version of Pillow. + """ + if feature not in codecs: + msg = f"Unknown codec {feature}" + raise ValueError(msg) + + codec, lib = codecs[feature] + + return f"{codec}_encoder" in dir(Image.core) + + +def version_codec(feature: str) -> str | None: + """ + :param feature: The codec to check for. + :returns: + The version number as a string, or ``None`` if not available. + Checked at compile time for ``jpg``, run-time otherwise. + :raises ValueError: If the codec is not defined in this version of Pillow. + """ + if not check_codec(feature): + return None + + codec, lib = codecs[feature] + + version = getattr(Image.core, f"{lib}_version") + + if feature == "libtiff": + return version.split("\n")[0].split("Version ")[1] + + return version + + +def get_supported_codecs() -> list[str]: + """ + :returns: A list of all supported codecs. + """ + return [f for f in codecs if check_codec(f)] + + +features: dict[str, tuple[str, str | bool, str | None]] = { + "webp_anim": ("PIL._webp", True, None), + "webp_mux": ("PIL._webp", True, None), + "transp_webp": ("PIL._webp", True, None), + "raqm": ("PIL._imagingft", "HAVE_RAQM", "raqm_version"), + "fribidi": ("PIL._imagingft", "HAVE_FRIBIDI", "fribidi_version"), + "harfbuzz": ("PIL._imagingft", "HAVE_HARFBUZZ", "harfbuzz_version"), + "libjpeg_turbo": ("PIL._imaging", "HAVE_LIBJPEGTURBO", "libjpeg_turbo_version"), + "mozjpeg": ("PIL._imaging", "HAVE_MOZJPEG", "libjpeg_turbo_version"), + "zlib_ng": ("PIL._imaging", "HAVE_ZLIBNG", "zlib_ng_version"), + "libimagequant": ("PIL._imaging", "HAVE_LIBIMAGEQUANT", "imagequant_version"), + "xcb": ("PIL._imaging", "HAVE_XCB", None), +} + + +def check_feature(feature: str) -> bool | None: + """ + Checks if a feature is available. + + :param feature: The feature to check for. + :returns: ``True`` if available, ``False`` if unavailable, ``None`` if unknown. + :raises ValueError: If the feature is not defined in this version of Pillow. + """ + if feature not in features: + msg = f"Unknown feature {feature}" + raise ValueError(msg) + + module, flag, ver = features[feature] + + if isinstance(flag, bool): + deprecate(f'check_feature("{feature}")', 12) + try: + imported_module = __import__(module, fromlist=["PIL"]) + if isinstance(flag, bool): + return flag + return getattr(imported_module, flag) + except ModuleNotFoundError: + return None + except ImportError as ex: + warnings.warn(str(ex)) + return None + + +def version_feature(feature: str) -> str | None: + """ + :param feature: The feature to check for. + :returns: The version number as a string, or ``None`` if not available. + :raises ValueError: If the feature is not defined in this version of Pillow. + """ + if not check_feature(feature): + return None + + module, flag, ver = features[feature] + + if ver is None: + return None + + return getattr(__import__(module, fromlist=[ver]), ver) + + +def get_supported_features() -> list[str]: + """ + :returns: A list of all supported features. + """ + supported_features = [] + for f, (module, flag, _) in features.items(): + if flag is True: + for feature, (feature_module, _) in modules.items(): + if feature_module == module: + if check_module(feature): + supported_features.append(f) + break + elif check_feature(f): + supported_features.append(f) + return supported_features + + +def check(feature: str) -> bool | None: + """ + :param feature: A module, codec, or feature name. + :returns: + ``True`` if the module, codec, or feature is available, + ``False`` or ``None`` otherwise. + """ + + if feature in modules: + return check_module(feature) + if feature in codecs: + return check_codec(feature) + if feature in features: + return check_feature(feature) + warnings.warn(f"Unknown feature '{feature}'.", stacklevel=2) + return False + + +def version(feature: str) -> str | None: + """ + :param feature: + The module, codec, or feature to check for. + :returns: + The version number as a string, or ``None`` if unknown or not available. + """ + if feature in modules: + return version_module(feature) + if feature in codecs: + return version_codec(feature) + if feature in features: + return version_feature(feature) + return None + + +def get_supported() -> list[str]: + """ + :returns: A list of all supported modules, features, and codecs. + """ + + ret = get_supported_modules() + ret.extend(get_supported_features()) + ret.extend(get_supported_codecs()) + return ret + + +def pilinfo(out: IO[str] | None = None, supported_formats: bool = True) -> None: + """ + Prints information about this installation of Pillow. + This function can be called with ``python3 -m PIL``. + It can also be called with ``python3 -m PIL.report`` or ``python3 -m PIL --report`` + to have "supported_formats" set to ``False``, omitting the list of all supported + image file formats. + + :param out: + The output stream to print to. Defaults to ``sys.stdout`` if ``None``. + :param supported_formats: + If ``True``, a list of all supported image file formats will be printed. + """ + + if out is None: + out = sys.stdout + + Image.init() + + print("-" * 68, file=out) + print(f"Pillow {PIL.__version__}", file=out) + py_version_lines = sys.version.splitlines() + print(f"Python {py_version_lines[0].strip()}", file=out) + for py_version in py_version_lines[1:]: + print(f" {py_version.strip()}", file=out) + print("-" * 68, file=out) + print(f"Python executable is {sys.executable or 'unknown'}", file=out) + if sys.prefix != sys.base_prefix: + print(f"Environment Python files loaded from {sys.prefix}", file=out) + print(f"System Python files loaded from {sys.base_prefix}", file=out) + print("-" * 68, file=out) + print( + f"Python Pillow modules loaded from {os.path.dirname(Image.__file__)}", + file=out, + ) + print( + f"Binary Pillow modules loaded from {os.path.dirname(Image.core.__file__)}", + file=out, + ) + print("-" * 68, file=out) + + for name, feature in [ + ("pil", "PIL CORE"), + ("tkinter", "TKINTER"), + ("freetype2", "FREETYPE2"), + ("littlecms2", "LITTLECMS2"), + ("webp", "WEBP"), + ("avif", "AVIF"), + ("jpg", "JPEG"), + ("jpg_2000", "OPENJPEG (JPEG2000)"), + ("zlib", "ZLIB (PNG/ZIP)"), + ("libtiff", "LIBTIFF"), + ("raqm", "RAQM (Bidirectional Text)"), + ("libimagequant", "LIBIMAGEQUANT (Quantization method)"), + ("xcb", "XCB (X protocol)"), + ]: + if check(name): + v: str | None = None + if name == "jpg": + libjpeg_turbo_version = version_feature("libjpeg_turbo") + if libjpeg_turbo_version is not None: + v = "mozjpeg" if check_feature("mozjpeg") else "libjpeg-turbo" + v += " " + libjpeg_turbo_version + if v is None: + v = version(name) + if v is not None: + version_static = name in ("pil", "jpg") + if name == "littlecms2": + # this check is also in src/_imagingcms.c:setup_module() + version_static = tuple(int(x) for x in v.split(".")) < (2, 7) + t = "compiled for" if version_static else "loaded" + if name == "zlib": + zlib_ng_version = version_feature("zlib_ng") + if zlib_ng_version is not None: + v += ", compiled for zlib-ng " + zlib_ng_version + elif name == "raqm": + for f in ("fribidi", "harfbuzz"): + v2 = version_feature(f) + if v2 is not None: + v += f", {f} {v2}" + print("---", feature, "support ok,", t, v, file=out) + else: + print("---", feature, "support ok", file=out) + else: + print("***", feature, "support not installed", file=out) + print("-" * 68, file=out) + + if supported_formats: + extensions = collections.defaultdict(list) + for ext, i in Image.EXTENSION.items(): + extensions[i].append(ext) + + for i in sorted(Image.ID): + line = f"{i}" + if i in Image.MIME: + line = f"{line} {Image.MIME[i]}" + print(line, file=out) + + if i in extensions: + print( + "Extensions: {}".format(", ".join(sorted(extensions[i]))), file=out + ) + + features = [] + if i in Image.OPEN: + features.append("open") + if i in Image.SAVE: + features.append("save") + if i in Image.SAVE_ALL: + features.append("save_all") + if i in Image.DECODERS: + features.append("decode") + if i in Image.ENCODERS: + features.append("encode") + + print("Features: {}".format(", ".join(features)), file=out) + print("-" * 68, file=out) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/py.typed b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/report.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/report.py new file mode 100644 index 0000000000000000000000000000000000000000..d2815e8455e2ead803de4417314987ce7e9b7598 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/PIL/report.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from .features import pilinfo + +pilinfo(supported_formats=False) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0aef38c8a9bb84a9833c4c2c9c34ad528d564b32 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/__init__.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import torch +from torch._functorch.deprecated import ( + combine_state_for_ensemble, + functionalize, + grad, + grad_and_value, + hessian, + jacfwd, + jacrev, + jvp, + make_functional, + make_functional_with_buffers, + vjp, + vmap, +) + +# utilities. Maybe these should go in their own namespace in the future? +from torch._functorch.make_functional import ( + FunctionalModule, + FunctionalModuleWithBuffers, +) + +# Was never documented +from torch._functorch.python_key import make_fx + + +# Top-level APIs. Please think carefully before adding something to the +# top-level namespace: +# - private helper functions should go into torch._functorch +# - very experimental things should go into functorch.experimental +# - compilation related things should go into functorch.compile + + +__version__ = torch.__version__ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/aot_autograd/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/aot_autograd/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94f258df84ba8730208768fc44222bee4b3ebc33 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/aot_autograd/__init__.py @@ -0,0 +1,8 @@ +# This file has moved to under torch/_functorch. It is not public API. +# If you are not a PyTorch developer and you are relying on the following +# imports, please file an issue. +from torch._functorch.aot_autograd import ( + aot_autograd_decompositions, + KNOWN_TYPES, + PytreeThunk, +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/eager_transforms/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/eager_transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6052b5548f4af3dbc6d9d45b0ffe72a8d5013d41 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/eager_transforms/__init__.py @@ -0,0 +1,7 @@ +# This file has moved to under torch/_functorch. It is not public API. +# If you are not a PyTorch developer and you are relying on the following +# imports, please file an issue. +from torch._functorch.eager_transforms import ( + _assert_wrapped_functional, + _unwrap_functional_tensor, +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3de7787df0c3304207b42b51e9fb62da9d33c7d0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/make_functional/__init__.py @@ -0,0 +1,4 @@ +# This file has moved to under torch/_functorch. It is not public API. +# If you are not a PyTorch developer and you are relying on the following +# imports, please file an issue. +from torch._functorch.make_functional import _swap_state diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/vmap/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/vmap/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc90517753e50f92362ba954248e31f69f7cfcd5 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/_src/vmap/__init__.py @@ -0,0 +1,16 @@ +# This file has moved to under torch/_functorch. It is not public API. +# If you are not a PyTorch developer and you are relying on the following +# imports, please file an issue. +from torch._functorch.vmap import ( + _add_batch_dim, + _broadcast_to_and_flatten, + _create_batched_inputs, + _get_name, + _process_batched_inputs, + _remove_batch_dim, + _unwrap_batched, + _validate_and_get_batch_size, + Tensor, + tree_flatten, + tree_unflatten, +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/compile/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/compile/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7548a5ff6b91bae4fa561f0de7ad5d3492eda05 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/compile/__init__.py @@ -0,0 +1,30 @@ +from torch._functorch import config +from torch._functorch.aot_autograd import ( + aot_function, + aot_module, + aot_module_simplified, + compiled_function, + compiled_module, + get_aot_compilation_context, + get_aot_graph_name, + get_graph_being_compiled, + make_boxed_compiler, + make_boxed_func, +) +from torch._functorch.compilers import ( + debug_compile, + default_decompositions, + draw_graph_compile, + memory_efficient_fusion, + nnc_jit, + nop, + print_compile, + ts_compile, +) +from torch._functorch.fx_minifier import minifier +from torch._functorch.partitioners import ( + default_partition, + draw_graph, + min_cut_rematerialization_partition, +) +from torch._functorch.python_key import pythonkey_decompose diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..691b1b984f8d55f1bcf8f5a916b091ea8b7f7710 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/__init__.py @@ -0,0 +1,177 @@ +import functorch._C +import torch +from functorch._C import dim as _C + +from .tree_map import tree_flatten, tree_map +from .wrap_type import wrap_type + + +_C._patch_tensor_class() +dims, DimList, dimlists = _C.dims, _C.DimList, _C.dimlists + + +class DimensionMismatchError(Exception): + pass + + +class DimensionBindError(Exception): + pass + + +from . import op_properties + + +# use dict to avoid writing C++ bindings for set +pointwise = dict.fromkeys(op_properties.pointwise, True) + +use_c = True +if not use_c: + from . import reference + + +class _Tensor: + # fast path around slow wrapping/unwrapping logic for simply queries used + # by the implementation... + + @property + def dims(self): + return tuple(d for d in self._levels if isinstance(d, Dim)) + + def dim(self): + return self.ndim + + if use_c: + __torch_function__ = classmethod(_C.__torch_function__) + expand = _C._instancemethod(_C.expand) + else: + __torch_function__ = reference.__torch_function__ + expand = reference.expand + + index = _C._instancemethod(_C.index) + + def __repr__(self): + tensor, levels, ndim = self._tensor, self._levels, self.ndim + return f"{tensor}\nwith dims={tuple(l + ndim if isinstance(l, int) else l for l in levels)} sizes={tuple(tensor.size())}" + + +TensorLike = (_Tensor, torch.Tensor) + + +class Dim(_C.Dim, _Tensor): + # note that _C.Dim comes before tensor because we want the Dim API for things like size to take precendence. + # Tensor defines format, but we want to print Dims with special formatting + __format__ = object.__format__ + + +class Tensor(_Tensor, _C.Tensor): + if not use_c: + from_batched = staticmethod(_C.Tensor_from_batched) + from_positional = staticmethod(_C.Tensor_from_positional) + sum = _C._instancemethod(_C.Tensor_sum) + + +def cat(tensors, dim, new_dim): + n = dims() + return stack(tensors, n, dim).index([n, dim], new_dim) + + +if use_c: + _wrap = _C._wrap + + def _def(name, *args, **kwargs): + orig = getattr(torch.Tensor, name) + setattr(_Tensor, name, _C._instancemethod(_wrap(orig, *args, **kwargs))) + + t__getitem__ = _C._instancemethod(_C.__getitem__) + stack = _C.stack + split = _C._instancemethod(_C.split) +else: + _wrap, _def = reference._wrap, reference._def + t__getitem__ = reference.t__getitem__ + stack = reference.stack + split = reference.split + +# note: there is no python reference +t__setitem__ = _C._instancemethod(_C.__setitem__) +# this is patched in the C API because otherwise torch.Tensor will +# no longer be considered a sequence and things will break +# torch.Tensor.__getitem__ = t__getitem__ + +_Tensor.__getitem__ = t__getitem__ +# torch.Tensor.__setitem__ = t__setitem__ +_Tensor.__setitem__ = t__setitem__ + +torch.Tensor.split = split +_Tensor.split = split +torch.Tensor.expand = _C._instancemethod(_C.expand) +torch.Tensor.index = _C._instancemethod(_C.index) +wrap_type(use_c, _Tensor, torch.Tensor, _Tensor.__torch_function__) +del _Tensor.ndim + +if use_c: + _Tensor.order = _C._instancemethod(_C.order) +else: + _Tensor.order = reference.positional + +_def("mean") +_def("sum") +_def("all") +_def("amax") +_def("amin") +_def("aminmax") +_def("any") +_def("count_nonzero") +_def("logsumexp") +_def("nanmean") +_def("nansum") +_def("prod") +_def("std", keepdim_offset=2) +_def("var", keepdim_offset=2) +_def("max", single_dim=True) +_def("min", single_dim=True) +_def("argmax", single_dim=True) +_def("argmin", single_dim=True) +_def("kthvalue", single_dim=True) +_def("median", single_dim=True) +_def("nanmedian", single_dim=True) +_def("mode", single_dim=True) +_def("sort", reduce=False) +_def("argsort", reduce=False) +_def("unbind", single_dim=True) +_def("chunk", dim_offset=1, reduce=False) +_def("cummax", single_dim=True, reduce=False) +_def("cummin", single_dim=True, reduce=False) +_def("cumprod", single_dim=True, reduce=False) +_def("cumprod_", single_dim=True, reduce=False) +_def("cumsum", single_dim=True, reduce=False) +_def("cumsum_", single_dim=True, reduce=False) +_def("logcumsumexp", single_dim=True, reduce=False) +_def("renorm", dim_offset=1, single_dim=True, reduce=False) +_def("softmax", single_dim=True, reduce=False) +softmax = _wrap(torch.nn.functional.softmax, single_dim=True, reduce=False) + +# stuff to handle in the future, because they require special +# binding logic for dims +# cross +# diag_embed +# diagonal +# diagonal_scatter +# diff +# nanquantile +# quantile +# roll +# rot90 +# topk (new dimes on output) +# should these all be subsumed by inplace indexing? +# index_add_ +# index_add +# index_copy +# index_copy_ +# index_fill +# index_fill_ +# index_select +# scatter +# scatter_ +# scatter_add +# scatter_add_ +# scatter_reduce diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/batch_tensor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/batch_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..dae9b270896e988151741409b666a82ba1aba5a4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/batch_tensor.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from contextlib import contextmanager + +from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers + + +_enabled = False + + +@contextmanager +def _enable_layers(dims): + global _enabled + assert not _enabled + input = sorted((d._level, d.size) for d in dims if not isinstance(d, int)) + n = len(input) + try: + _vmap_add_layers(input) + _enabled = True + yield + finally: + _enabled = False + _vmap_remove_layers(n) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/delayed_mul_tensor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/delayed_mul_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..3c136cfe1247dbd7dcc38777e6326b360619941b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/delayed_mul_tensor.py @@ -0,0 +1,76 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import torch + +from . import _Tensor, Tensor +from .reference import _dims, _enable_layers, llist, ltuple + + +class DelayedMulTensor(_Tensor): + def __init__(self, lhs, rhs): + self._lhs, self._rhs = lhs, rhs + self._data = None + self._levels_data = None + self._has_device = lhs._has_device or rhs._has_device + self._batchtensor_data = None + self._tensor_data = None + + @property + def _levels(self): + if self._levels_data is None: + levels = llist(self._lhs._levels) + for l in self._rhs._levels: + if l not in levels: + levels.append(l) + self._levels_data = ltuple(levels) + return self._levels_data + + @property + def _batchtensor(self): + if self._batchtensor_data is None: + with _enable_layers(self._levels): + print("bt multiply fallback") + self._batchtensor_data = self._lhs._batchtensor * self._rhs._batchtensor + return self._batchtensor_data + + @property + def _tensor(self): + if self._tensor_data is None: + self._tensor_data = Tensor.from_batched( + self._batchtensor, self._has_device + )._tensor + return self._tensor_data + + @property + def ndim(self): + return self._batchtensor.ndim + + @property + def dims(self): + return ltuple(super().dims) + + def sum(self, dim): + dims = _dims(dim, 0, False, False) + n = ord("a") + all_levels = self._levels + + def to_char(d): + return chr(n + all_levels.index(d)) + + plhs, levelslhs = self._lhs._tensor, self._lhs._levels + prhs, levelsrhs = self._rhs._tensor, self._rhs._levels + new_levels = [l for l in self._levels if l not in dims] + fmt = "".join( + [ + *(to_char(d) for d in levelslhs), + ",", + *(to_char(d) for d in levelsrhs), + "->", + *(to_char(d) for d in new_levels), + ] + ) + result_data = torch.einsum(fmt, (plhs, prhs)) + return Tensor.from_positional(result_data, new_levels, True) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/dim.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/dim.py new file mode 100644 index 0000000000000000000000000000000000000000..9a4b56866484905cc0bd693c865d03c937aa1b75 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/dim.py @@ -0,0 +1,120 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import dis +import inspect +from dataclasses import dataclass +from typing import Union + +from . import DimList + + +_vmap_levels = [] + + +@dataclass +class LevelInfo: + level: int + alive: bool = True + + +class Dim: + def __init__(self, name: str, size: Union[None, int] = None): + self.name = name + self._size = None + self._vmap_level = None + if size is not None: + self.size = size + + def __del__(self): + if self._vmap_level is not None: + _vmap_active_levels[self._vmap_stack].alive = False # noqa: F821 + while ( + not _vmap_levels[-1].alive and current_level() == _vmap_levels[-1].level # noqa: F821 + ): + _vmap_decrement_nesting() # noqa: F821 + _vmap_levels.pop() + + @property + def size(self): + assert self.is_bound + return self._size + + @size.setter + def size(self, size: int): + from . import DimensionBindError + + if self._size is None: + self._size = size + self._vmap_level = _vmap_increment_nesting(size, "same") # noqa: F821 + self._vmap_stack = len(_vmap_levels) + _vmap_levels.append(LevelInfo(self._vmap_level)) + + elif self._size != size: + raise DimensionBindError( + f"Dim '{self}' previously bound to a dimension of size {self._size} cannot bind to a dimension of size {size}" + ) + + @property + def is_bound(self): + return self._size is not None + + def __repr__(self): + return self.name + + +def extract_name(inst): + assert inst.opname == "STORE_FAST" or inst.opname == "STORE_NAME" + return inst.argval + + +_cache = {} + + +def dims(lists=0): + frame = inspect.currentframe() + assert frame is not None + calling_frame = frame.f_back + assert calling_frame is not None + code, lasti = calling_frame.f_code, calling_frame.f_lasti + key = (code, lasti) + if key not in _cache: + first = lasti // 2 + 1 + instructions = list(dis.get_instructions(calling_frame.f_code)) + unpack = instructions[first] + + if unpack.opname == "STORE_FAST" or unpack.opname == "STORE_NAME": + # just a single dim, not a list + name = unpack.argval + ctor = Dim if lists == 0 else DimList + _cache[key] = lambda: ctor(name=name) + else: + assert unpack.opname == "UNPACK_SEQUENCE" + ndims = unpack.argval + names = tuple( + extract_name(instructions[first + 1 + i]) for i in range(ndims) + ) + first_list = len(names) - lists + _cache[key] = lambda: tuple( + Dim(n) if i < first_list else DimList(name=n) + for i, n in enumerate(names) + ) + return _cache[key]() + + +def _dim_set(positional, arg): + def convert(a): + if isinstance(a, Dim): + return a + else: + assert isinstance(a, int) + return positional[a] + + if arg is None: + return positional + elif not isinstance(arg, (Dim, int)): + return tuple(convert(a) for a in arg) + else: + return (convert(arg),) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/magic_trace.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/magic_trace.py new file mode 100644 index 0000000000000000000000000000000000000000..5c962a898ca79cfe3d8af7432aacc3802d4f4ade --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/magic_trace.py @@ -0,0 +1,42 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import os +import signal +import subprocess +from contextlib import contextmanager + + +@contextmanager +def magic_trace(output="trace.fxt", magic_trace_cache="/tmp/magic-trace"): + pid = os.getpid() + if not os.path.exists(magic_trace_cache): + print(f"Downloading magic_trace to: {magic_trace_cache}") + subprocess.run( + [ + "wget", + "-O", + magic_trace_cache, + "-q", + "https://github.com/janestreet/magic-trace/releases/download/v1.0.2/magic-trace", + ] + ) + subprocess.run(["chmod", "+x", magic_trace_cache]) + args = [magic_trace_cache, "attach", "-pid", str(pid), "-o", output] + p = subprocess.Popen(args, stderr=subprocess.PIPE, encoding="utf-8") + while True: + x = p.stderr.readline() + print(x) + if "Attached" in x: + break + try: + yield + finally: + p.send_signal(signal.SIGINT) + r = p.wait() + print(p.stderr.read()) + p.stderr.close() + if r != 0: + raise ValueError(f"magic_trace exited abnormally: {r}") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/op_properties.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/op_properties.py new file mode 100644 index 0000000000000000000000000000000000000000..01313f71f030d58ce76c15c7f8516c4a0bdcf48a --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/op_properties.py @@ -0,0 +1,312 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import torch + + +# pointwise operators can go through a faster pathway + +tensor_magic_methods = ["add", ""] +pointwise_magic_methods_with_reverse = ( + "add", + "sub", + "mul", + "floordiv", + "div", + "truediv", + "mod", + "pow", + "lshift", + "rshift", + "and", + "or", + "xor", +) +pointwise_magic_methods = ( + *(x for m in pointwise_magic_methods_with_reverse for x in (m, "r" + m)), + "eq", + "gt", + "le", + "lt", + "ge", + "gt", + "ne", + "neg", + "pos", + "abs", + "invert", + "iadd", + "isub", + "imul", + "ifloordiv", + "idiv", + "itruediv", + "imod", + "ipow", + "ilshift", + "irshift", + "iand", + "ior", + "ixor", + "int", + "long", + "float", + "complex", +) + +pointwise_methods = (*(f"__{m}__" for m in pointwise_magic_methods),) + +pointwise = ( + *(getattr(torch.Tensor, m) for m in pointwise_methods), + torch.nn.functional.dropout, + torch.where, + torch.Tensor.abs, + torch.abs, + torch.Tensor.acos, + torch.acos, + torch.Tensor.acosh, + torch.acosh, + torch.Tensor.add, + torch.add, + torch.Tensor.addcdiv, + torch.addcdiv, + torch.Tensor.addcmul, + torch.addcmul, + torch.Tensor.addr, + torch.addr, + torch.Tensor.angle, + torch.angle, + torch.Tensor.asin, + torch.asin, + torch.Tensor.asinh, + torch.asinh, + torch.Tensor.atan, + torch.atan, + torch.Tensor.atan2, + torch.atan2, + torch.Tensor.atanh, + torch.atanh, + torch.Tensor.bitwise_and, + torch.bitwise_and, + torch.Tensor.bitwise_left_shift, + torch.bitwise_left_shift, + torch.Tensor.bitwise_not, + torch.bitwise_not, + torch.Tensor.bitwise_or, + torch.bitwise_or, + torch.Tensor.bitwise_right_shift, + torch.bitwise_right_shift, + torch.Tensor.bitwise_xor, + torch.bitwise_xor, + torch.Tensor.ceil, + torch.ceil, + torch.celu, + torch.nn.functional.celu, + torch.Tensor.clamp, + torch.clamp, + torch.Tensor.clamp_max, + torch.clamp_max, + torch.Tensor.clamp_min, + torch.clamp_min, + torch.Tensor.copysign, + torch.copysign, + torch.Tensor.cos, + torch.cos, + torch.Tensor.cosh, + torch.cosh, + torch.Tensor.deg2rad, + torch.deg2rad, + torch.Tensor.digamma, + torch.digamma, + torch.Tensor.div, + torch.div, + torch.dropout, + torch.nn.functional.dropout, + torch.nn.functional.elu, + torch.Tensor.eq, + torch.eq, + torch.Tensor.erf, + torch.erf, + torch.Tensor.erfc, + torch.erfc, + torch.Tensor.erfinv, + torch.erfinv, + torch.Tensor.exp, + torch.exp, + torch.Tensor.exp2, + torch.exp2, + torch.Tensor.expm1, + torch.expm1, + torch.feature_dropout, + torch.Tensor.float_power, + torch.float_power, + torch.Tensor.floor, + torch.floor, + torch.Tensor.floor_divide, + torch.floor_divide, + torch.Tensor.fmod, + torch.fmod, + torch.Tensor.frac, + torch.frac, + torch.Tensor.frexp, + torch.frexp, + torch.Tensor.gcd, + torch.gcd, + torch.Tensor.ge, + torch.ge, + torch.nn.functional.gelu, + torch.nn.functional.glu, + torch.Tensor.gt, + torch.gt, + torch.Tensor.hardshrink, + torch.hardshrink, + torch.nn.functional.hardshrink, + torch.nn.functional.hardsigmoid, + torch.nn.functional.hardswish, + torch.nn.functional.hardtanh, + torch.Tensor.heaviside, + torch.heaviside, + torch.Tensor.hypot, + torch.hypot, + torch.Tensor.i0, + torch.i0, + torch.Tensor.igamma, + torch.igamma, + torch.Tensor.igammac, + torch.igammac, + torch.Tensor.isclose, + torch.isclose, + torch.Tensor.isfinite, + torch.isfinite, + torch.Tensor.isinf, + torch.isinf, + torch.Tensor.isnan, + torch.isnan, + torch.Tensor.isneginf, + torch.isneginf, + torch.Tensor.isposinf, + torch.isposinf, + torch.Tensor.isreal, + torch.isreal, + torch.Tensor.kron, + torch.kron, + torch.Tensor.lcm, + torch.lcm, + torch.Tensor.ldexp, + torch.ldexp, + torch.Tensor.le, + torch.le, + torch.nn.functional.leaky_relu, + torch.Tensor.lerp, + torch.lerp, + torch.Tensor.lgamma, + torch.lgamma, + torch.Tensor.log, + torch.log, + torch.Tensor.log10, + torch.log10, + torch.Tensor.log1p, + torch.log1p, + torch.Tensor.log2, + torch.log2, + torch.nn.functional.logsigmoid, + torch.Tensor.logical_and, + torch.logical_and, + torch.Tensor.logical_not, + torch.logical_not, + torch.Tensor.logical_or, + torch.logical_or, + torch.Tensor.logical_xor, + torch.logical_xor, + torch.Tensor.logit, + torch.logit, + torch.Tensor.lt, + torch.lt, + torch.Tensor.maximum, + torch.maximum, + torch.Tensor.minimum, + torch.minimum, + torch.nn.functional.mish, + torch.Tensor.mvlgamma, + torch.mvlgamma, + torch.Tensor.nan_to_num, + torch.nan_to_num, + torch.Tensor.ne, + torch.ne, + torch.Tensor.neg, + torch.neg, + torch.Tensor.nextafter, + torch.nextafter, + torch.Tensor.outer, + torch.outer, + torch.polar, + torch.Tensor.polygamma, + torch.polygamma, + torch.Tensor.positive, + torch.positive, + torch.Tensor.pow, + torch.pow, + torch.Tensor.prelu, + torch.prelu, + torch.nn.functional.prelu, + torch.Tensor.rad2deg, + torch.rad2deg, + torch.Tensor.reciprocal, + torch.reciprocal, + torch.Tensor.relu, + torch.relu, + torch.nn.functional.relu, + torch.nn.functional.relu6, + torch.Tensor.remainder, + torch.remainder, + torch.Tensor.round, + torch.round, + torch.rrelu, + torch.nn.functional.rrelu, + torch.Tensor.rsqrt, + torch.rsqrt, + torch.rsub, + torch.selu, + torch.nn.functional.selu, + torch.Tensor.sgn, + torch.sgn, + torch.Tensor.sigmoid, + torch.sigmoid, + torch.nn.functional.sigmoid, + torch.Tensor.sign, + torch.sign, + torch.Tensor.signbit, + torch.signbit, + torch.nn.functional.silu, + torch.Tensor.sin, + torch.sin, + torch.Tensor.sinc, + torch.sinc, + torch.Tensor.sinh, + torch.sinh, + torch.nn.functional.softplus, + torch.nn.functional.softshrink, + torch.Tensor.sqrt, + torch.sqrt, + torch.Tensor.square, + torch.square, + torch.Tensor.sub, + torch.sub, + torch.Tensor.tan, + torch.tan, + torch.Tensor.tanh, + torch.tanh, + torch.nn.functional.tanh, + torch.threshold, + torch.nn.functional.threshold, + torch.trapz, + torch.Tensor.true_divide, + torch.true_divide, + torch.Tensor.trunc, + torch.trunc, + torch.Tensor.xlogy, + torch.xlogy, + torch.rand_like, +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/reference.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/reference.py new file mode 100644 index 0000000000000000000000000000000000000000..5c6178c0981c756636b35e754f1bfb2f07698381 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/reference.py @@ -0,0 +1,645 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# reference python implementations for C ops +import torch +from functorch._C import dim as _C + +from . import op_properties +from .batch_tensor import _enable_layers +from .tree_map import tree_flatten, tree_map + + +DimList = _C.DimList +import operator +from functools import reduce + + +# use dict to avoid writing C++ bindings for set +pointwise = set(op_properties.pointwise) + + +def prod(x): + return reduce(operator.mul, x, 1) + + +def _wrap_dim(d, N, keepdim): + from . import Dim + + if isinstance(d, Dim): + assert not keepdim, "cannot preserve first-class dimensions with keepdim=True" + return d + elif d >= 0: + return d - N + else: + return d + + +def _dims(d, N, keepdim, single_dim): + from . import Dim + + if isinstance(d, (Dim, int)): + return ltuple((_wrap_dim(d, N, keepdim),)) + assert not single_dim, f"expected a single dimension or int but found: {d}" + return ltuple(_wrap_dim(x, N, keepdim) for x in d) + + +def _bind_dims_to_size(lhs_size, rhs, lhs_debug): + from . import DimensionMismatchError + + not_bound = tuple((i, r) for i, r in enumerate(rhs) if not r.is_bound) + if len(not_bound) == 1: + idx, d = not_bound[0] + rhs_so_far = prod(r.size for r in rhs if r.is_bound) + if lhs_size % rhs_so_far != 0: + rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs) + raise DimensionMismatchError( + f"inferred dimension does not evenly fit into larger dimension: {lhs_size} vs {rhs_s}" + ) + new_size = lhs_size // rhs_so_far + d.size = new_size + elif len(not_bound) > 1: + rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs) + raise DimensionMismatchError( + f"cannot infer the size of two dimensions at once: {rhs} with sizes {rhs_s}" + ) + else: + rhs_size = prod(r.size for r in rhs) + if lhs_size != rhs_size: + raise DimensionMismatchError( + f"Dimension sizes to do not match ({lhs_size} != {rhs_size}) when matching {lhs_debug} to {rhs}" + ) + + +def _tensor_levels(inp): + from . import _Tensor + + if isinstance(inp, _Tensor): + return inp._tensor, llist(inp._levels), inp._has_device + else: + return inp, llist(range(-inp.ndim, 0)), True + + +def _match_levels(v, from_levels, to_levels): + view = [] + permute = [] + requires_view = False + size = v.size() + for t in to_levels: + try: + idx = from_levels.index(t) + permute.append(idx) + view.append(size[idx]) + except ValueError: + view.append(1) + requires_view = True + if permute != list(range(len(permute))): + v = v.permute(*permute) + if requires_view: + v = v.view(*view) + return v + + +# make a single dimension positional but do not permute it, +# used to do multi-tensor operators where the dim being acted on +# should not physically move if possible +def _positional_no_permute(self, dim, expand_dim=False): + from . import Tensor + + ptensor, levels = self._tensor, llist(self._levels) + try: + idx = levels.index(dim) + except ValueError: + if not expand_dim: + raise + idx = 0 + ptensor = ptensor.expand(dim.size, *ptensor.size()) + levels.insert(0, 0) + idx_batched = 0 + for i in range(idx): + if isinstance(levels[i], int): + levels[i] -= 1 + idx_batched += 1 + levels[idx] = -idx_batched - 1 + return Tensor.from_positional(ptensor, levels, self._has_device), idx_batched + + +def seq(a, b): + from . import Dim + + if isinstance(a, Dim) != isinstance(b, Dim): + return False + if isinstance(a, Dim): + return a is b + else: + return a == b + + +class isin: + __slots__ = () + + def __contains__(self, item): + for x in self: + if seq(item, x): + return True + return False + + def index(self, item): + for i, x in enumerate(self): + if seq(item, x): + return i + raise ValueError + + +class llist(isin, list): + __slots__ = () + + +class ltuple(isin, tuple): + __slots__ = () + + +empty_dict = {} + + +@classmethod +def __torch_function__(self, orig, cls, args, kwargs=empty_dict): + from . import _Tensor, Tensor, TensorLike + from .delayed_mul_tensor import DelayedMulTensor + + if orig is torch.Tensor.__mul__: + lhs, rhs = args + if ( + isinstance(lhs, _Tensor) + and isinstance(rhs, _Tensor) + and lhs.ndim == 0 + and rhs.ndim == 0 + ): + return DelayedMulTensor(lhs, rhs) + all_dims = llist() + flat_args, unflatten = tree_flatten((args, kwargs)) + device_holding_tensor = None + for f in flat_args: + if isinstance(f, _Tensor): + if f._has_device: + device_holding_tensor = f._batchtensor + for d in f.dims: + if d not in all_dims: + all_dims.append(d) + + def unwrap(t): + if isinstance(t, _Tensor): + r = t._batchtensor + if device_holding_tensor is not None and not t._has_device: + r = r.to(device=device_holding_tensor.device) + return r + return t + + if orig in pointwise: + result_levels = llist() + to_expand = [] + for i, f in enumerate(flat_args): + if isinstance(f, TensorLike): + ptensor, levels, _ = _tensor_levels(f) + if ( + isinstance(f, _Tensor) + and not f._has_device + and device_holding_tensor is not None + ): + ptensor = ptensor.to(device=device_holding_tensor.device) + flat_args[i] = ptensor + for l in levels: + if l not in result_levels: + result_levels.append(l) + to_expand.append((i, levels)) + + for i, levels in to_expand: + flat_args[i] = _match_levels(flat_args[i], levels, result_levels) + args, kwargs = unflatten(flat_args) + result = orig(*args, **kwargs) + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_positional( + t, result_levels, device_holding_tensor is not None + ) + return t + + return tree_map(wrap, result) + else: + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_batched(t, device_holding_tensor is not None) + return t + + with _enable_layers(all_dims): + print(f"batch_tensor for {orig}") + args, kwargs = unflatten(unwrap(f) for f in flat_args) + result = orig(*args, **kwargs) + # print("END", orig) + return tree_map(wrap, result) + + +def positional(self, *dims): + from . import Dim, DimensionBindError, Tensor + + ptensor, levels = self._tensor, llist(self._levels) + flat_dims = llist() + view = [] + needs_view = False + ndim = self.ndim + for d in dims: + if isinstance(d, DimList): + flat_dims.extend(d) + view.extend(e.size for e in d) + elif isinstance(d, Dim): + flat_dims.append(d) + view.append(d.size) + elif isinstance(d, int): + d = _wrap_dim(d, ndim, False) + flat_dims.append(d) + view.append(ptensor.size(d)) + else: + flat_dims.extend(d) + view.append(prod(e.size for e in d)) + needs_view = True + + permute = list(range(len(levels))) + for i, d in enumerate(flat_dims): + try: + idx = levels.index(d) + except ValueError as e: + raise DimensionBindError( + f"tensor of dimensions {self.dims} does not contain dim {d}" + ) from e + p = permute[idx] + del levels[idx] + del permute[idx] + levels.insert(i, 0) + permute.insert(i, p) + ptensor = ptensor.permute(*permute) + seen = 0 + for i in range(len(levels) - 1, -1, -1): + if isinstance(levels[i], int): + seen += 1 + levels[i] = -seen + result = Tensor.from_positional(ptensor, levels, self._has_device) + if needs_view: + result = result.reshape(*view, *result.size()[len(flat_dims) :]) + return result + + +def _contains_dim(input): + from . import Dim + + for i in input: + if isinstance(i, Dim): + return True + + +def expand(self, *sizes): + if not _contains_dim(sizes): + return self.__torch_function__(torch.Tensor.expand, None, (self, *sizes)) + dims = sizes + sizes = [d.size for d in dims] + [-1] * self.ndim + self = self.expand(*sizes) + return self[dims] + + +_not_present = object() + + +def _getarg(name, offset, args, kwargs, default): + if len(args) > offset: + return args[offset] + return kwargs.get(name, default) + + +def _patcharg(name, offset, args, kwargs, value): + if len(args) > offset: + args[offset] = value + else: + kwargs[name] = value + + +def _wrap( + orig, dim_offset=0, keepdim_offset=1, dim_name="dim", single_dim=False, reduce=True +): + from . import Dim, Tensor, TensorLike + + def fn(self, *args, **kwargs): + dim = _getarg(dim_name, dim_offset, args, kwargs, _not_present) + if dim is _not_present or (single_dim and not isinstance(dim, Dim)): + with _enable_layers(self.dims): + print(f"dim fallback batch_tensor for {orig}") + return Tensor.from_batched( + orig(self._batchtensor, *args, **kwargs), self._has_device + ) + keepdim = ( + _getarg("keepdim", keepdim_offset, args, kwargs, False) if reduce else False + ) + t, levels = self._tensor, llist(self._levels) + dims = _dims(dim, self._batchtensor.ndim, keepdim, single_dim) + dim_indices = tuple(levels.index(d) for d in dims) + if reduce and not keepdim: + new_levels = [l for i, l in enumerate(levels) if i not in dim_indices] + else: + new_levels = levels + + if len(dim_indices) == 1: + dim_indices = dim_indices[ + 0 + ] # so that dims that really only take a single argument work... + args = list(args) + _patcharg(dim_name, dim_offset, args, kwargs, dim_indices) + + def wrap(t): + if isinstance(t, TensorLike): + return Tensor.from_positional(t, new_levels, self._has_device) + return t + + with _enable_layers(new_levels): + print(f"dim used batch_tensor for {orig}") + r = orig(t, *args, **kwargs) + return tree_map(wrap, r) + + return fn + + +def _def(name, *args, **kwargs): + from . import _Tensor + + orig = getattr(torch.Tensor, name) + setattr(_Tensor, name, _wrap(orig, *args, **kwargs)) + + +no_slice = slice(None) + +_orig_getitem = torch.Tensor.__getitem__ + + +class dim_tracker: + def __init__(self) -> None: + self.dims = llist() + self.count = [] + + def record(self, d): + if d not in self.dims: + self.dims.append(d) + self.count.append(1) + + def __getitem__(self, d): + return self.count[self.dims.index(d)] + + +def t__getitem__(self, input): + from . import _Tensor, Dim, DimensionBindError, DimList, Tensor, TensorLike + + # * bail to original example if we have a single non-Dim tensor, or a non-tensor + # * locate ... or an unbound tensor list, and determine its size, bind dim list + # (remember that None does not count to the total dim count) + # * bind simple dims and dim-packs to their sizes, count the number of uses of each dim, + # produce the re-view if needed + # * for each single-use dim index, replace with no_slice and mark that it will be added + # (keep track of whether we have to call super) + # * call super if needed + # * if we have dims to bind, bind them (it will help if we eliminated ... and None before) + # this handles bool indexing handling, as well as some other simple cases. + + is_simple = ( + not isinstance(input, Dim) + and not isinstance(input, (tuple, list)) + and + # WAR for functorch bug where zero time tensors in getitem are not handled correctly. + not (isinstance(input, TensorLike) and input.ndim == 0) + ) + + if is_simple: + if isinstance(self, _Tensor): + return _Tensor.__torch_function__(_orig_getitem, None, (self, input)) + else: + return _orig_getitem(self, input) + + # can further optimize this case + if not isinstance(input, tuple): + input = [input] + else: + input = list(input) + + dims_indexed = 0 + expanding_object = None + dimlists = [] + for i, s in enumerate(input): + if s is ... or isinstance(s, DimList) and not s.is_bound: + if expanding_object is not None: + msg = ( + "at most one ... or unbound dimension list can exist in indexing list but" + f" found 2 at offsets {i} and {expanding_object}" + ) + raise DimensionBindError(msg) + expanding_object = i + + if isinstance(s, DimList): + dims_indexed += len(s) if s.is_bound else 0 + dimlists.append(i) + elif s is not None and s is not ...: + dims_indexed += 1 + + ndim = self.ndim + if dims_indexed > ndim: + raise IndexError( + f"at least {dims_indexed} indices were supplied but the tensor only has {ndim} dimensions." + ) + if expanding_object is not None: + expanding_ndims = ndim - dims_indexed + obj = input[expanding_object] + if obj is ...: + input[expanding_object : expanding_object + 1] = [ + no_slice + ] * expanding_ndims + else: + obj.bind_len(expanding_ndims) + # flatten the dimslists into the indexing + for i in reversed(dimlists): + input[i : i + 1] = input[i] + dims_indexed = 0 + requires_view = False + size = self.size() + view_sizes = [] + dims_seen = dim_tracker() + + def add_dims(t): + if not isinstance(t, _Tensor): + return + for d in t.dims: + dims_seen.record(d) + + add_dims(self) + dim_packs = [] + for i, idx in enumerate(input): + if idx is None: + input[i] = no_slice + view_sizes.append(1) + requires_view = True + else: + sz = size[dims_indexed] + if isinstance(idx, Dim): + idx.size = sz + dims_seen.record(idx) + view_sizes.append(sz) + elif isinstance(idx, (tuple, list)) and idx and isinstance(idx[0], Dim): + for d in idx: + dims_seen.record(idx) + _bind_dims_to_size(sz, idx, f"offset {i}") + view_sizes.extend(d.size for d in idx) + requires_view = True + dim_packs.append(i) + else: + add_dims(idx) + view_sizes.append(sz) + dims_indexed += 1 + if requires_view: + self = self.view(*view_sizes) + for i in reversed(dim_packs): + input[i : i + 1] = input[i] + + # currenty: + # input is flat, containing either Dim, or Tensor, or something valid for standard indexing + # self may have first-class dims as well. + + # to index: + # drop the first class dims from self, they just become direct indices of their positions + + # figure out the dimensions of the indexing tensors: union of all the dims in the tensors in the index. + # these dimensions will appear and need to be bound at the first place tensor occures + + if isinstance(self, _Tensor): + ptensor_self, levels = self._tensor, list(self._levels) + # indices to ptensor rather than self which has first-class dimensions + input_it = iter(input) + flat_inputs = [next(input_it) if isinstance(l, int) else l for l in levels] + has_device = self._has_device + to_pad = 0 + else: + ptensor_self, flat_inputs = self, input + to_pad = ptensor_self.ndim - len(flat_inputs) + has_device = True + + result_levels = [] + index_levels = [] + tensor_insert_point = None + to_expand = {} + requires_getindex = False + for i, inp in enumerate(flat_inputs): + if isinstance(inp, Dim) and dims_seen[inp] == 1: + flat_inputs[i] = no_slice + result_levels.append(inp) + elif isinstance(inp, TensorLike): + requires_getindex = True + if tensor_insert_point is None: + tensor_insert_point = len(result_levels) + ptensor, levels, _ = _tensor_levels(inp) + to_expand[i] = levels + flat_inputs[i] = ptensor + for l in levels: + if l not in index_levels: + index_levels.append(l) + else: + requires_getindex = True + result_levels.append(0) + + if tensor_insert_point is not None: + result_levels[tensor_insert_point:tensor_insert_point] = index_levels + + for i, levels in to_expand.items(): + flat_inputs[i] = _match_levels(flat_inputs[i], levels, index_levels) + + if requires_getindex: + result = _orig_getitem(ptensor_self, flat_inputs) + else: + result = ptensor_self + + next_positional = -1 + if to_pad > 0: + result_levels.extend([0] * to_pad) + for i, r in enumerate(reversed(result_levels)): + if isinstance(r, int): + result_levels[-1 - i] = next_positional + next_positional -= 1 + + return Tensor.from_positional(result, result_levels, has_device) + + +# XXX - dim is optional and can be the outer-most dimension... +def stack(tensors, new_dim, dim=0, out=None): + if isinstance(dim, int): + return torch.stack(tensors, dim, out).index(dim, new_dim) + index = None + if out is not None: + out, index = _positional_no_permute(out, dim, expand_dim=True) + ptensors = [] + for t in tensors: + pt, pi = _positional_no_permute(t, dim, expand_dim=True) + if index is not None and pi != index: + pt = pt.move_dim(pi, index) + else: + index = pi + ptensors.append(pt) + pr = torch.stack(ptensors, index, out=out) + return pr.index((index, index + 1), (new_dim, dim)) + + +_orig_split = torch.Tensor.split + + +def split(self, split_size_or_sections, dim=0): + from . import _Tensor, Dim + + if isinstance(split_size_or_sections, int) or any( + isinstance(t, int) for t in split_size_or_sections + ): + if isinstance(dim, Dim): + raise ValueError( + "when dim is specified as a Dim object, split sizes must also be dimensions." + ) + return _orig_split(self, split_size_or_sections, dim=dim) + + if isinstance(dim, Dim): + assert isinstance(self, _Tensor), f"Tensor does not have dimension {dim}" + self, dim = _positional_no_permute(self, dim) + + size = self.size(dim) + total_bound_size = 0 + unbound = [] + sizes = [] + for i, d in enumerate(split_size_or_sections): + if d.is_bound: + sizes.append(d.size) + total_bound_size += d.size + else: + sizes.append(0) + unbound.append(i) + + if unbound: + assert total_bound_size <= size, ( + f"result dimensions are larger than original: {total_bound_size} vs {size} ({split_size_or_sections})" + ) + remaining_size = size - total_bound_size + chunk_size = -(-remaining_size // len(unbound)) + for u in unbound: + sz = min(chunk_size, remaining_size) + split_size_or_sections[u].size = sz + sizes[u] = sz + remaining_size -= sz + else: + assert total_bound_size == size, ( + f"result dimensions do not match original: {total_bound_size} vs {size} ({split_size_or_sections})" + ) + return tuple( + t.index(dim, d) + for d, t in zip(split_size_or_sections, _orig_split(self, sizes, dim=dim)) + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/tree_map.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/tree_map.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2eae0582c85619e0d28e648af58cbd847eee97 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/tree_map.py @@ -0,0 +1,15 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from functorch._C import dim + + +tree_flatten = dim.tree_flatten + + +def tree_map(fn, tree): + vs, unflatten = tree_flatten(tree) + return unflatten(fn(v) for v in vs) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/wrap_type.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/wrap_type.py new file mode 100644 index 0000000000000000000000000000000000000000..aae543b91a896e2d5cef3b03d725c3d91ce1305d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/dim/wrap_type.py @@ -0,0 +1,72 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from types import ( + BuiltinMethodType, + FunctionType, + GetSetDescriptorType, + MethodDescriptorType, + WrapperDescriptorType, +) + +from functorch._C import dim as _C + + +_wrap_method = _C._wrap_method + +FUNC_TYPES = ( + FunctionType, + MethodDescriptorType, + BuiltinMethodType, + WrapperDescriptorType, +) +PROPERTY_TYPES = (GetSetDescriptorType, property) + + +def _py_wrap_method(orig, __torch_function__): + def impl(*args, **kwargs): + return __torch_function__(orig, None, args, kwargs) + + return impl + + +def wrap_type(use_c, to_patch, pattern, __torch_function__): + if use_c: + wrap_method = _wrap_method + else: + wrap_method = _py_wrap_method + + all = {} + for t in reversed(pattern.mro()[:-1]): # skip object + all.update(t.__dict__) + + def wrap_attr(orig): + return property(wrap_method(orig.__get__, __torch_function__)) + + for name, obj in all.items(): + if name in ( + "__dict__", + "__new__", + "__init__", + "__repr__", + "__weakref__", + "__doc__", + "__module__", + "__dir__", + ): + continue + + # skip things that have been overloaded + # things that come from object like `__eq__` still need to be patched, however. + if hasattr(to_patch, name) and getattr(to_patch, name) is not getattr( + object, name, None + ): + continue + + if isinstance(obj, FUNC_TYPES): + setattr(to_patch, name, wrap_method(obj, __torch_function__)) + elif isinstance(obj, PROPERTY_TYPES): + setattr(to_patch, name, wrap_attr(obj)) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d7ac34f7a3722010fc0fde97fd1cd72e76fa88b7 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/__init__.py @@ -0,0 +1,4 @@ +from .rearrange import rearrange + + +__all__ = ["rearrange"] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/_parsing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/_parsing.py new file mode 100644 index 0000000000000000000000000000000000000000..2352ea932426271fdc16f660abb4308ea9b3c924 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/_parsing.py @@ -0,0 +1,308 @@ +"""Adapted from https://github.com/arogozhnikov/einops/blob/36c7bb16e57d6e57f8f3050f9e07abdf3f00469f/einops/parsing.py. + +MIT License + +Copyright (c) 2018 Alex Rogozhnikov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + +from __future__ import annotations + +import keyword +import warnings +from typing import Optional, TYPE_CHECKING, Union + + +if TYPE_CHECKING: + from collections.abc import Collection, Mapping + + +_ellipsis: str = "\u2026" # NB, this is a single unicode symbol. String is used as it is not a list, but can be iterated + + +class AnonymousAxis: + """Used by `ParsedExpression` to represent an axis with a size (> 1), but no associated identifier. + + Note: Different instances of this class are not equal to each other, even if they have the same value. + """ + + def __init__(self, value: str) -> None: + self.value = int(value) + if self.value < 1: + raise ValueError( + f"Anonymous axis should have positive length, not {self.value}" + ) + + def __repr__(self) -> str: + return f"{self.value}-axis" + + +class ParsedExpression: + """Structure containing information about one side of an `einops`-style pattern (e.g. 'b c (h w)').""" + + def __init__( + self, + expression: str, + *, + allow_underscore: bool = False, + allow_duplicates: bool = False, + ) -> None: + """Parse the expression and store relevant metadata. + + Args: + expression (str): the `einops`-pattern to parse + allow_underscore (bool): whether to allow axis identifier names to begin with an underscore + allow_duplicates (bool): whether to allow an identifier to appear more than once in the expression + """ + self.has_ellipsis: bool = False + self.has_ellipsis_parenthesized: Optional[bool] = None + self.identifiers: set[Union[str, AnonymousAxis]] = set() + # that's axes like 2, 3, 4 or 5. Axes with size 1 are exceptional and replaced with empty composition + self.has_non_unitary_anonymous_axes: bool = False + # composition keeps structure of composite axes, see how different corner cases are handled in tests + self.composition: list[Union[list[Union[str, AnonymousAxis]], str]] = [] + if "." in expression: + if "..." not in expression: + raise ValueError( + "Expression may contain dots only inside ellipsis (...)" + ) + if str.count(expression, "...") != 1 or str.count(expression, ".") != 3: + raise ValueError( + "Expression may contain dots only inside ellipsis (...); only one ellipsis for tensor " + ) + expression = expression.replace("...", _ellipsis) + self.has_ellipsis = True + + bracket_group: Optional[list[Union[str, AnonymousAxis]]] = None + + def add_axis_name(x: str) -> None: + if x in self.identifiers: + if not (allow_underscore and x == "_") and not allow_duplicates: + raise ValueError( + f"Indexing expression contains duplicate dimension '{x}'" + ) + if x == _ellipsis: + self.identifiers.add(_ellipsis) + if bracket_group is None: + self.composition.append(_ellipsis) + self.has_ellipsis_parenthesized = False + else: + bracket_group.append(_ellipsis) + self.has_ellipsis_parenthesized = True + else: + is_number = str.isdecimal(x) + if is_number and int(x) == 1: + # handling the case of anonymous axis of length 1 + if bracket_group is None: + self.composition.append([]) + else: + pass # no need to think about 1s inside parenthesis + return + is_axis_name, reason = self.check_axis_name_return_reason( + x, allow_underscore=allow_underscore + ) + if not (is_number or is_axis_name): + raise ValueError(f"Invalid axis identifier: {x}\n{reason}") + axis_name: Union[str, AnonymousAxis] = ( + AnonymousAxis(x) if is_number else x + ) + self.identifiers.add(axis_name) + if is_number: + self.has_non_unitary_anonymous_axes = True + if bracket_group is None: + self.composition.append([axis_name]) + else: + bracket_group.append(axis_name) + + current_identifier = None + for char in expression: + if char in "() ": + if current_identifier is not None: + add_axis_name(current_identifier) + current_identifier = None + if char == "(": + if bracket_group is not None: + raise ValueError( + "Axis composition is one-level (brackets inside brackets not allowed)" + ) + bracket_group = [] + elif char == ")": + if bracket_group is None: + raise ValueError("Brackets are not balanced") + self.composition.append(bracket_group) + bracket_group = None + elif str.isalnum(char) or char in ["_", _ellipsis]: + if current_identifier is None: + current_identifier = char + else: + current_identifier += char + else: + raise ValueError(f"Unknown character '{char}'") + + if bracket_group is not None: + raise ValueError(f"Imbalanced parentheses in expression: '{expression}'") + if current_identifier is not None: + add_axis_name(current_identifier) + + @staticmethod + def check_axis_name_return_reason( + name: str, allow_underscore: bool = False + ) -> tuple[bool, str]: + """Check if the given axis name is valid, and a message explaining why if not. + + Valid axes names are python identifiers except keywords, and should not start or end with an underscore. + + Args: + name (str): the axis name to check + allow_underscore (bool): whether axis names are allowed to start with an underscore + + Returns: + tuple[bool, str]: whether the axis name is valid, a message explaining why if not + """ + if not str.isidentifier(name): + return False, "not a valid python identifier" + elif name[0] == "_" or name[-1] == "_": + if name == "_" and allow_underscore: + return True, "" + return False, "axis name should should not start or end with underscore" + else: + if keyword.iskeyword(name): + warnings.warn( + f"It is discouraged to use axes names that are keywords: {name}", + RuntimeWarning, + ) + if name in ["axis"]: + warnings.warn( + "It is discouraged to use 'axis' as an axis name and will raise an error in future", + FutureWarning, + ) + return True, "" + + @staticmethod + def check_axis_name(name: str) -> bool: + """Check if the name is a valid axis name. + + Args: + name (str): the axis name to check + + Returns: + bool: whether the axis name is valid + """ + is_valid, _ = ParsedExpression.check_axis_name_return_reason(name) + return is_valid + + +def parse_pattern( + pattern: str, axes_lengths: Mapping[str, int] +) -> tuple[ParsedExpression, ParsedExpression]: + """Parse an `einops`-style pattern into a left-hand side and right-hand side `ParsedExpression` object. + + Args: + pattern (str): the `einops`-style rearrangement pattern + axes_lengths (Mapping[str, int]): any additional length specifications for dimensions + + Returns: + tuple[ParsedExpression, ParsedExpression]: a tuple containing the left-hand side and right-hand side expressions + """ + # adapted from einops.einops._prepare_transformation_recipe + # https://github.com/arogozhnikov/einops/blob/230ac1526c1f42c9e1f7373912c7f8047496df11/einops/einops.py + try: + left_str, right_str = pattern.split("->") + except ValueError: + raise ValueError("Pattern must contain a single '->' separator") from None + + if _ellipsis in axes_lengths: + raise ValueError(f"'{_ellipsis}' is not an allowed axis identifier") + + left = ParsedExpression(left_str) + right = ParsedExpression(right_str) + + if not left.has_ellipsis and right.has_ellipsis: + raise ValueError( + f"Ellipsis found in right side, but not left side of a pattern {pattern}" + ) + if left.has_ellipsis and left.has_ellipsis_parenthesized: + raise ValueError( + f"Ellipsis is parenthesis in the left side is not allowed: {pattern}" + ) + + return left, right + + +def validate_rearrange_expressions( + left: ParsedExpression, right: ParsedExpression, axes_lengths: Mapping[str, int] +) -> None: + """Perform expression validations that are specific to the `rearrange` operation. + + Args: + left (ParsedExpression): left-hand side expression + right (ParsedExpression): right-hand side expression + axes_lengths (Mapping[str, int]): any additional length specifications for dimensions + """ + for length in axes_lengths.values(): + if (length_type := type(length)) is not int: + raise TypeError( + f"rearrange axis lengths must be integers, got: {length_type}" + ) + + if left.has_non_unitary_anonymous_axes or right.has_non_unitary_anonymous_axes: + raise ValueError("rearrange only supports unnamed axes of size 1") + + difference = set.symmetric_difference(left.identifiers, right.identifiers) + if len(difference) > 0: + raise ValueError( + f"Identifiers only on one side of rearrange expression (should be on both): {difference}" + ) + + unmatched_axes = axes_lengths.keys() - left.identifiers + if len(unmatched_axes) > 0: + raise ValueError( + f"Identifiers not found in rearrange expression: {unmatched_axes}" + ) + + +def comma_separate(collection: Collection[Union[str, Collection[str]]]) -> str: + """Convert a collection of strings representing first class dims into a comma-separated string. + + Args: + collection (Collection[Union[str, Collection[str]]]): the collection of strings to convert + + Returns: + str: the comma-separated string + + Examples: + >>> comma_separate(("d0",)) + 'd0' + + >>> comma_separate(("d0", "d1", "d2", "d3")) + 'd0, d1, d2, d3' + + >>> comma_separate([("d1", "d4")]) + '(d1, d4)' + + >>> comma_separate([("d0",), (), ("d1",), ("d2",), ("d3", "d4")]) + '(d0,), (), (d1,), (d2,), (d3, d4)' + """ + return ", ".join( + item + if isinstance(item, str) + else f"({comma_separate(item)}{',' if len(item) == 1 else ''})" + for item in collection + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/rearrange.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/rearrange.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d71f5103f94b1a3667e7c02106a4418157fa2e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/einops/rearrange.py @@ -0,0 +1,211 @@ +from __future__ import annotations + +import functools +from typing import Callable, TYPE_CHECKING, Union + +import torch +from functorch._C import dim as _C + +from ._parsing import ( + _ellipsis, + AnonymousAxis, + comma_separate, + parse_pattern, + validate_rearrange_expressions, +) + + +if TYPE_CHECKING: + from collections.abc import Sequence + +__all__ = ["rearrange"] + +dims = _C.dims + + +@functools.lru_cache(256) +def _create_rearrange_callable( + tensor_ndim: int, pattern: str, **axes_lengths: int +) -> Callable[[torch.Tensor], torch.Tensor]: + r"""Translate an `einops`-style pattern into a callable that performs the rearrange using first-class dimensions. + + Since the an equivalent result is computed for tensors with the same number of dimensions, with the same pattern and + specified axes lengths, this function can be memoized. + + Args: + tensor_ndim (int): the number of dimensions in the tensor to rearrange + pattern (str): the `einops`-style rearrangement pattern + axes_lengths (int): any additional length specifications for dimensions + + Returns: + Callable[[torch.Tensor], torch.Tensor]: a callable that performs the rearrangement + """ + left, right = parse_pattern(pattern, axes_lengths) + validate_rearrange_expressions(left, right, axes_lengths) + + n_anon_dims = sum(not dim for dim in left.composition) + if left.has_ellipsis: + n_ellipsis_dims = tensor_ndim - (len(left.composition) - 1) + n_named_dims = len(left.identifiers) - 1 + + if (pattern_ndim := n_anon_dims + n_named_dims) > tensor_ndim: + raise ValueError( + f"Number of dimensions in pattern ({pattern_ndim}) must be less than or equal to the number of " + f"dimensions in the tensor ({tensor_ndim})" + ) + else: + n_ellipsis_dims = 0 + n_named_dims = len(left.identifiers) + + if (pattern_ndim := len(left.composition)) != tensor_ndim: + raise ValueError( + f"Number of dimensions in pattern ({pattern_ndim}) must be equal to the number of dimensions in " + f"the tensor ({tensor_ndim})" + ) + n_dims = n_named_dims + n_ellipsis_dims + n_anon_dims + + if n_dims == 0: + # an identity rearrangement on a 0-dimension tensor + return lambda tensor: tensor + + first_class_dims: tuple[str, ...] = tuple(f"d{i}" for i in range(n_dims)) + identifier_dim_map: dict[Union[str, AnonymousAxis], tuple[str, ...]] = {} + anon_axes: list[AnonymousAxis] = [] + + # map the left-hand side identifiers to strings representing first class dims + dims_i = 0 + for dimension in left.composition: + if isinstance(dimension, list): + for identifier in dimension: + # non-unitary anon axes are not allowed in rearrange & unitary anon axes are represented as empty lists + assert isinstance(identifier, str) + identifier_dim_map[identifier] = (first_class_dims[dims_i],) + dims_i += 1 + if not dimension: + # unitary anonymous axis + anon_axis = AnonymousAxis("1") + identifier_dim_map[anon_axis] = (first_class_dims[dims_i],) + anon_axes.append(anon_axis) + dimension.append(anon_axis) + dims_i += 1 + elif dimension == _ellipsis: + identifier = _ellipsis + identifier_dim_map[identifier] = tuple( + first_class_dims[dims_i + j] for j in range(n_ellipsis_dims) + ) + dims_i += n_ellipsis_dims + else: + raise ValueError(f"Unexpected dimension: {dimension}") + + def composition_to_dims( + composition: Sequence[Union[list[Union[str, AnonymousAxis]], str]], + ) -> list[Union[str, tuple[str, ...]]]: + """Convert a `ParsedExpression.composition` into a `Tensor.__getitem__` index of strings representing first + class dims.""" + dim_composition: list[Union[str, tuple[str, ...]]] = [] + for dimension in composition: + if isinstance(dimension, list): + dim_composition.append( + tuple( + dim + for identifier in dimension + for dim in identifier_dim_map[identifier] + ) + ) + elif dimension == _ellipsis: + dim_composition.extend(identifier_dim_map[_ellipsis]) + else: + raise ValueError(f"Unexpected dimension: {dimension}") + return dim_composition + + left_dims = composition_to_dims(left.composition) + right_dims = composition_to_dims(right.composition) + anon_dims = tuple(identifier_dim_map[axis][0] for axis in anon_axes) + specified_lengths = tuple( + (identifier_dim_map[axis][0], length) for axis, length in axes_lengths.items() + ) + + custom_rearrange_callable_name = "do_rearrange" + custom_rearrange_callable_code = ( + ( + f"def {custom_rearrange_callable_name}(tensor):\n" + f" {comma_separate(first_class_dims)} = dims({n_dims})\n" + ) + + ( + "".join( + f" {dim}.size = {length}\n" for (dim, length) in specified_lengths + ) + if specified_lengths + else "" + ) + + f" tensor = tensor[{comma_separate(left_dims)}].order({comma_separate(right_dims)})\n" + + ( + f" return tensor.sum({comma_separate([anon_dims])}, keepdim=False)\n" + if anon_dims + else " return tensor\n" + ) + ) + + exec(custom_rearrange_callable_code) + return locals()[custom_rearrange_callable_name] + + +def rearrange( + tensor: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor, ...]], + pattern: str, + **axes_lengths: int, +) -> torch.Tensor: + r"""A native implementation of `einops.rearrange`, a reader-friendly smart element reordering for multidimensional + tensors. This operation includes functionality of transpose (axes permutation), reshape (view), squeeze, unsqueeze, + stack, concatenate and other operations. + + See: https://einops.rocks/api/rearrange/ + + Args: + tensor (Tensor or sequence of Tensor): the tensor(s) to rearrange + pattern (str): the rearrangement pattern + axes_lengths (int): any additional length specifications for dimensions + + Returns: + Tensor: the rearranged tensor + + Examples: + >>> # suppose we have a set of 32 images in "h w c" format (height-width-channel) + >>> images = torch.randn((32, 30, 40, 3)) + + >>> # stack along first (batch) axis, output is a single array + >>> rearrange(images, "b h w c -> b h w c").shape + torch.Size([32, 30, 40, 3]) + + >>> # concatenate images along height (vertical axis), 960 = 32 * 30 + >>> rearrange(images, "b h w c -> (b h) w c").shape + torch.Size([960, 40, 3]) + + >>> # concatenated images along horizontal axis, 1280 = 32 * 40 + >>> rearrange(images, "b h w c -> h (b w) c").shape + torch.Size([30, 1280, 3]) + + >>> # reordered axes to "b c h w" format for deep learning + >>> rearrange(images, "b h w c -> b c h w").shape + torch.Size([32, 3, 30, 40]) + + >>> # flattened each image into a vector, 3600 = 30 * 40 * 3 + >>> rearrange(images, "b h w c -> b (c h w)").shape + torch.Size([32, 3600]) + + >>> # split each image into 4 smaller (top-left, top-right, bottom-left, bottom-right), 128 = 32 * 2 * 2 + >>> rearrange(images, "b (h1 h) (w1 w) c -> (b h1 w1) h w c", h1=2, w1=2).shape + torch.Size([128, 15, 20, 3]) + + >>> # space-to-depth operation + >>> rearrange(images, "b (h h1) (w w1) c -> b h w (c h1 w1)", h1=2, w1=2).shape + torch.Size([32, 15, 20, 12]) + """ + if not isinstance(tensor, torch.Tensor): + tensor = torch.stack(tensor) + + rearrange_callable = _create_rearrange_callable( + tensor.ndim, pattern, **axes_lengths + ) + + return rearrange_callable(tensor) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3941f6d96e1f6df532966d06de4f72a952a58465 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/__init__.py @@ -0,0 +1,5 @@ +# PyTorch forward-mode is not mature yet +from functorch import functionalize +from torch._functorch.apis import chunk_vmap +from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_ +from torch._functorch.eager_transforms import hessian, jacfwd, jvp diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/control_flow.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/control_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..cbfd76d184cc224042a1ee19454d6957c2221b3b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/control_flow.py @@ -0,0 +1,7 @@ +from torch import cond # noqa: F401 +from torch._higher_order_ops.cond import UnsupportedAliasMutationException # noqa: F401 +from torch._higher_order_ops.map import ( # noqa: F401 + _stack_pytree, + _unstack_pytree, + map, +) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..7a502ef2b002cd824e7b67d08fccac872b313110 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/functorch/experimental/ops.py @@ -0,0 +1 @@ +from torch._ops import HigherOrderOperator # noqa: F401 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c55302485e3fd570f55a255314318c274adc9098 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py @@ -0,0 +1,10 @@ +from . import axes_size as Size +from .axes_divider import Divider, SubplotDivider, make_axes_locatable +from .axes_grid import AxesGrid, Grid, ImageGrid + +from .parasite_axes import host_subplot, host_axes + +__all__ = ["Size", + "Divider", "SubplotDivider", "make_axes_locatable", + "AxesGrid", "Grid", "ImageGrid", + "host_subplot", "host_axes"] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py new file mode 100644 index 0000000000000000000000000000000000000000..214b15843ebfaf2e2ee26e100a2194dfe092382d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py @@ -0,0 +1,414 @@ +from matplotlib import transforms +from matplotlib.offsetbox import (AnchoredOffsetbox, AuxTransformBox, + DrawingArea, TextArea, VPacker) +from matplotlib.patches import (Rectangle, ArrowStyle, + FancyArrowPatch, PathPatch) +from matplotlib.text import TextPath + +__all__ = ['AnchoredDrawingArea', 'AnchoredAuxTransformBox', + 'AnchoredSizeBar', 'AnchoredDirectionArrows'] + + +class AnchoredDrawingArea(AnchoredOffsetbox): + def __init__(self, width, height, xdescent, ydescent, + loc, pad=0.4, borderpad=0.5, prop=None, frameon=True, + **kwargs): + """ + An anchored container with a fixed size and fillable `.DrawingArea`. + + Artists added to the *drawing_area* will have their coordinates + interpreted as pixels. Any transformations set on the artists will be + overridden. + + Parameters + ---------- + width, height : float + Width and height of the container, in pixels. + xdescent, ydescent : float + Descent of the container in the x- and y- direction, in pixels. + loc : str + Location of this artist. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.4 + Padding around the child objects, in fraction of the font size. + borderpad : float, default: 0.5 + Border padding, in fraction of the font size. + prop : `~matplotlib.font_manager.FontProperties`, optional + Font property used as a reference for paddings. + frameon : bool, default: True + If True, draw a box around this artist. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + drawing_area : `~matplotlib.offsetbox.DrawingArea` + A container for artists to display. + + Examples + -------- + To display blue and red circles of different sizes in the upper right + of an Axes *ax*: + + >>> ada = AnchoredDrawingArea(20, 20, 0, 0, + ... loc='upper right', frameon=False) + >>> ada.drawing_area.add_artist(Circle((10, 10), 10, fc="b")) + >>> ada.drawing_area.add_artist(Circle((30, 10), 5, fc="r")) + >>> ax.add_artist(ada) + """ + self.da = DrawingArea(width, height, xdescent, ydescent) + self.drawing_area = self.da + + super().__init__( + loc, pad=pad, borderpad=borderpad, child=self.da, prop=None, + frameon=frameon, **kwargs + ) + + +class AnchoredAuxTransformBox(AnchoredOffsetbox): + def __init__(self, transform, loc, + pad=0.4, borderpad=0.5, prop=None, frameon=True, **kwargs): + """ + An anchored container with transformed coordinates. + + Artists added to the *drawing_area* are scaled according to the + coordinates of the transformation used. The dimensions of this artist + will scale to contain the artists added. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transData`. + loc : str + Location of this artist. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.4 + Padding around the child objects, in fraction of the font size. + borderpad : float, default: 0.5 + Border padding, in fraction of the font size. + prop : `~matplotlib.font_manager.FontProperties`, optional + Font property used as a reference for paddings. + frameon : bool, default: True + If True, draw a box around this artist. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + drawing_area : `~matplotlib.offsetbox.AuxTransformBox` + A container for artists to display. + + Examples + -------- + To display an ellipse in the upper left, with a width of 0.1 and + height of 0.4 in data coordinates: + + >>> box = AnchoredAuxTransformBox(ax.transData, loc='upper left') + >>> el = Ellipse((0, 0), width=0.1, height=0.4, angle=30) + >>> box.drawing_area.add_artist(el) + >>> ax.add_artist(box) + """ + self.drawing_area = AuxTransformBox(transform) + + super().__init__(loc, pad=pad, borderpad=borderpad, + child=self.drawing_area, prop=prop, frameon=frameon, + **kwargs) + + +class AnchoredSizeBar(AnchoredOffsetbox): + def __init__(self, transform, size, label, loc, + pad=0.1, borderpad=0.1, sep=2, + frameon=True, size_vertical=0, color='black', + label_top=False, fontproperties=None, fill_bar=None, + **kwargs): + """ + Draw a horizontal scale bar with a center-aligned label underneath. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transData`. + size : float + Horizontal length of the size bar, given in coordinates of + *transform*. + label : str + Label to display. + loc : str + Location of the size bar. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.1 + Padding around the label and size bar, in fraction of the font + size. + borderpad : float, default: 0.1 + Border padding, in fraction of the font size. + sep : float, default: 2 + Separation between the label and the size bar, in points. + frameon : bool, default: True + If True, draw a box around the horizontal bar and label. + size_vertical : float, default: 0 + Vertical length of the size bar, given in coordinates of + *transform*. + color : str, default: 'black' + Color for the size bar and label. + label_top : bool, default: False + If True, the label will be over the size bar. + fontproperties : `~matplotlib.font_manager.FontProperties`, optional + Font properties for the label text. + fill_bar : bool, optional + If True and if *size_vertical* is nonzero, the size bar will + be filled in with the color specified by the size bar. + Defaults to True if *size_vertical* is greater than + zero and False otherwise. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + size_bar : `~matplotlib.offsetbox.AuxTransformBox` + Container for the size bar. + txt_label : `~matplotlib.offsetbox.TextArea` + Container for the label of the size bar. + + Notes + ----- + If *prop* is passed as a keyword argument, but *fontproperties* is + not, then *prop* is assumed to be the intended *fontproperties*. + Using both *prop* and *fontproperties* is not supported. + + Examples + -------- + >>> import matplotlib.pyplot as plt + >>> import numpy as np + >>> from mpl_toolkits.axes_grid1.anchored_artists import ( + ... AnchoredSizeBar) + >>> fig, ax = plt.subplots() + >>> ax.imshow(np.random.random((10, 10))) + >>> bar = AnchoredSizeBar(ax.transData, 3, '3 data units', 4) + >>> ax.add_artist(bar) + >>> fig.show() + + Using all the optional parameters + + >>> import matplotlib.font_manager as fm + >>> fontprops = fm.FontProperties(size=14, family='monospace') + >>> bar = AnchoredSizeBar(ax.transData, 3, '3 units', 4, pad=0.5, + ... sep=5, borderpad=0.5, frameon=False, + ... size_vertical=0.5, color='white', + ... fontproperties=fontprops) + """ + if fill_bar is None: + fill_bar = size_vertical > 0 + + self.size_bar = AuxTransformBox(transform) + self.size_bar.add_artist(Rectangle((0, 0), size, size_vertical, + fill=fill_bar, facecolor=color, + edgecolor=color)) + + if fontproperties is None and 'prop' in kwargs: + fontproperties = kwargs.pop('prop') + + if fontproperties is None: + textprops = {'color': color} + else: + textprops = {'color': color, 'fontproperties': fontproperties} + + self.txt_label = TextArea(label, textprops=textprops) + + if label_top: + _box_children = [self.txt_label, self.size_bar] + else: + _box_children = [self.size_bar, self.txt_label] + + self._box = VPacker(children=_box_children, + align="center", + pad=0, sep=sep) + + super().__init__(loc, pad=pad, borderpad=borderpad, child=self._box, + prop=fontproperties, frameon=frameon, **kwargs) + + +class AnchoredDirectionArrows(AnchoredOffsetbox): + def __init__(self, transform, label_x, label_y, length=0.15, + fontsize=0.08, loc='upper left', angle=0, aspect_ratio=1, + pad=0.4, borderpad=0.4, frameon=False, color='w', alpha=1, + sep_x=0.01, sep_y=0, fontproperties=None, back_length=0.15, + head_width=10, head_length=15, tail_width=2, + text_props=None, arrow_props=None, + **kwargs): + """ + Draw two perpendicular arrows to indicate directions. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transAxes`. + label_x, label_y : str + Label text for the x and y arrows + length : float, default: 0.15 + Length of the arrow, given in coordinates of *transform*. + fontsize : float, default: 0.08 + Size of label strings, given in coordinates of *transform*. + loc : str, default: 'upper left' + Location of the arrow. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + angle : float, default: 0 + The angle of the arrows in degrees. + aspect_ratio : float, default: 1 + The ratio of the length of arrow_x and arrow_y. + Negative numbers can be used to change the direction. + pad : float, default: 0.4 + Padding around the labels and arrows, in fraction of the font size. + borderpad : float, default: 0.4 + Border padding, in fraction of the font size. + frameon : bool, default: False + If True, draw a box around the arrows and labels. + color : str, default: 'white' + Color for the arrows and labels. + alpha : float, default: 1 + Alpha values of the arrows and labels + sep_x, sep_y : float, default: 0.01 and 0 respectively + Separation between the arrows and labels in coordinates of + *transform*. + fontproperties : `~matplotlib.font_manager.FontProperties`, optional + Font properties for the label text. + back_length : float, default: 0.15 + Fraction of the arrow behind the arrow crossing. + head_width : float, default: 10 + Width of arrow head, sent to `.ArrowStyle`. + head_length : float, default: 15 + Length of arrow head, sent to `.ArrowStyle`. + tail_width : float, default: 2 + Width of arrow tail, sent to `.ArrowStyle`. + text_props, arrow_props : dict + Properties of the text and arrows, passed to `.TextPath` and + `.FancyArrowPatch`. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + arrow_x, arrow_y : `~matplotlib.patches.FancyArrowPatch` + Arrow x and y + text_path_x, text_path_y : `~matplotlib.text.TextPath` + Path for arrow labels + p_x, p_y : `~matplotlib.patches.PathPatch` + Patch for arrow labels + box : `~matplotlib.offsetbox.AuxTransformBox` + Container for the arrows and labels. + + Notes + ----- + If *prop* is passed as a keyword argument, but *fontproperties* is + not, then *prop* is assumed to be the intended *fontproperties*. + Using both *prop* and *fontproperties* is not supported. + + Examples + -------- + >>> import matplotlib.pyplot as plt + >>> import numpy as np + >>> from mpl_toolkits.axes_grid1.anchored_artists import ( + ... AnchoredDirectionArrows) + >>> fig, ax = plt.subplots() + >>> ax.imshow(np.random.random((10, 10))) + >>> arrows = AnchoredDirectionArrows(ax.transAxes, '111', '110') + >>> ax.add_artist(arrows) + >>> fig.show() + + Using several of the optional parameters, creating downward pointing + arrow and high contrast text labels. + + >>> import matplotlib.font_manager as fm + >>> fontprops = fm.FontProperties(family='monospace') + >>> arrows = AnchoredDirectionArrows(ax.transAxes, 'East', 'South', + ... loc='lower left', color='k', + ... aspect_ratio=-1, sep_x=0.02, + ... sep_y=-0.01, + ... text_props={'ec':'w', 'fc':'k'}, + ... fontproperties=fontprops) + """ + if arrow_props is None: + arrow_props = {} + + if text_props is None: + text_props = {} + + arrowstyle = ArrowStyle("Simple", + head_width=head_width, + head_length=head_length, + tail_width=tail_width) + + if fontproperties is None and 'prop' in kwargs: + fontproperties = kwargs.pop('prop') + + if 'color' not in arrow_props: + arrow_props['color'] = color + + if 'alpha' not in arrow_props: + arrow_props['alpha'] = alpha + + if 'color' not in text_props: + text_props['color'] = color + + if 'alpha' not in text_props: + text_props['alpha'] = alpha + + t_start = transform + t_end = t_start + transforms.Affine2D().rotate_deg(angle) + + self.box = AuxTransformBox(t_end) + + length_x = length + length_y = length*aspect_ratio + + self.arrow_x = FancyArrowPatch( + (0, back_length*length_y), + (length_x, back_length*length_y), + arrowstyle=arrowstyle, + shrinkA=0.0, + shrinkB=0.0, + **arrow_props) + + self.arrow_y = FancyArrowPatch( + (back_length*length_x, 0), + (back_length*length_x, length_y), + arrowstyle=arrowstyle, + shrinkA=0.0, + shrinkB=0.0, + **arrow_props) + + self.box.add_artist(self.arrow_x) + self.box.add_artist(self.arrow_y) + + text_path_x = TextPath(( + length_x+sep_x, back_length*length_y+sep_y), label_x, + size=fontsize, prop=fontproperties) + self.p_x = PathPatch(text_path_x, transform=t_start, **text_props) + self.box.add_artist(self.p_x) + + text_path_y = TextPath(( + length_x*back_length+sep_x, length_y*(1-back_length)+sep_y), + label_y, size=fontsize, prop=fontproperties) + self.p_y = PathPatch(text_path_y, **text_props) + self.box.add_artist(self.p_y) + + super().__init__(loc, pad=pad, borderpad=borderpad, child=self.box, + frameon=frameon, **kwargs) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py new file mode 100644 index 0000000000000000000000000000000000000000..50365f482b725d3be435ddd14d9729d30b343124 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py @@ -0,0 +1,618 @@ +""" +Helper classes to adjust the positions of multiple axes at drawing time. +""" + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +from matplotlib.gridspec import SubplotSpec +import matplotlib.transforms as mtransforms +from . import axes_size as Size + + +class Divider: + """ + An Axes positioning class. + + The divider is initialized with lists of horizontal and vertical sizes + (:mod:`mpl_toolkits.axes_grid1.axes_size`) based on which a given + rectangular area will be divided. + + The `new_locator` method then creates a callable object + that can be used as the *axes_locator* of the axes. + """ + + def __init__(self, fig, pos, horizontal, vertical, + aspect=None, anchor="C"): + """ + Parameters + ---------- + fig : Figure + pos : tuple of 4 floats + Position of the rectangle that will be divided. + horizontal : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + Sizes for horizontal division. + vertical : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + Sizes for vertical division. + aspect : bool, optional + Whether overall rectangular area is reduced so that the relative + part of the horizontal and vertical scales have the same scale. + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'}, default: 'C' + Placement of the reduced rectangle, when *aspect* is True. + """ + + self._fig = fig + self._pos = pos + self._horizontal = horizontal + self._vertical = vertical + self._anchor = anchor + self.set_anchor(anchor) + self._aspect = aspect + self._xrefindex = 0 + self._yrefindex = 0 + self._locator = None + + def get_horizontal_sizes(self, renderer): + return np.array([s.get_size(renderer) for s in self.get_horizontal()]) + + def get_vertical_sizes(self, renderer): + return np.array([s.get_size(renderer) for s in self.get_vertical()]) + + def set_position(self, pos): + """ + Set the position of the rectangle. + + Parameters + ---------- + pos : tuple of 4 floats + position of the rectangle that will be divided + """ + self._pos = pos + + def get_position(self): + """Return the position of the rectangle.""" + return self._pos + + def set_anchor(self, anchor): + """ + Parameters + ---------- + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'} + Either an (*x*, *y*) pair of relative coordinates (0 is left or + bottom, 1 is right or top), 'C' (center), or a cardinal direction + ('SW', southwest, is bottom left, etc.). + + See Also + -------- + .Axes.set_anchor + """ + if isinstance(anchor, str): + _api.check_in_list(mtransforms.Bbox.coefs, anchor=anchor) + elif not isinstance(anchor, (tuple, list)) or len(anchor) != 2: + raise TypeError("anchor must be str or 2-tuple") + self._anchor = anchor + + def get_anchor(self): + """Return the anchor.""" + return self._anchor + + def get_subplotspec(self): + return None + + def set_horizontal(self, h): + """ + Parameters + ---------- + h : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + sizes for horizontal division + """ + self._horizontal = h + + def get_horizontal(self): + """Return horizontal sizes.""" + return self._horizontal + + def set_vertical(self, v): + """ + Parameters + ---------- + v : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + sizes for vertical division + """ + self._vertical = v + + def get_vertical(self): + """Return vertical sizes.""" + return self._vertical + + def set_aspect(self, aspect=False): + """ + Parameters + ---------- + aspect : bool + """ + self._aspect = aspect + + def get_aspect(self): + """Return aspect.""" + return self._aspect + + def set_locator(self, _locator): + self._locator = _locator + + def get_locator(self): + return self._locator + + def get_position_runtime(self, ax, renderer): + if self._locator is None: + return self.get_position() + else: + return self._locator(ax, renderer).bounds + + @staticmethod + def _calc_k(sizes, total): + # sizes is a (n, 2) array of (rel_size, abs_size); this method finds + # the k factor such that sum(rel_size * k + abs_size) == total. + rel_sum, abs_sum = sizes.sum(0) + return (total - abs_sum) / rel_sum if rel_sum else 0 + + @staticmethod + def _calc_offsets(sizes, k): + # Apply k factors to (n, 2) sizes array of (rel_size, abs_size); return + # the resulting cumulative offset positions. + return np.cumsum([0, *(sizes @ [k, 1])]) + + def new_locator(self, nx, ny, nx1=None, ny1=None): + """ + Return an axes locator callable for the specified cell. + + Parameters + ---------- + nx, nx1 : int + Integers specifying the column-position of the + cell. When *nx1* is None, a single *nx*-th column is + specified. Otherwise, location of columns spanning between *nx* + to *nx1* (but excluding *nx1*-th column) is specified. + ny, ny1 : int + Same as *nx* and *nx1*, but for row positions. + """ + if nx1 is None: + nx1 = nx + 1 + if ny1 is None: + ny1 = ny + 1 + # append_size("left") adds a new size at the beginning of the + # horizontal size lists; this shift transforms e.g. + # new_locator(nx=2, ...) into effectively new_locator(nx=3, ...). To + # take that into account, instead of recording nx, we record + # nx-self._xrefindex, where _xrefindex is shifted by 1 by each + # append_size("left"), and re-add self._xrefindex back to nx in + # _locate, when the actual axes position is computed. Ditto for y. + xref = self._xrefindex + yref = self._yrefindex + locator = functools.partial( + self._locate, nx - xref, ny - yref, nx1 - xref, ny1 - yref) + locator.get_subplotspec = self.get_subplotspec + return locator + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + """ + Implementation of ``divider.new_locator().__call__``. + + The axes locator callable returned by ``new_locator()`` is created as + a `functools.partial` of this method with *nx*, *ny*, *nx1*, and *ny1* + specifying the requested cell. + """ + nx += self._xrefindex + nx1 += self._xrefindex + ny += self._yrefindex + ny1 += self._yrefindex + + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + + hsizes = self.get_horizontal_sizes(renderer) + vsizes = self.get_vertical_sizes(renderer) + k_h = self._calc_k(hsizes, fig_w * w) + k_v = self._calc_k(vsizes, fig_h * h) + + if self.get_aspect(): + k = min(k_h, k_v) + ox = self._calc_offsets(hsizes, k) + oy = self._calc_offsets(vsizes, k) + + ww = (ox[-1] - ox[0]) / fig_w + hh = (oy[-1] - oy[0]) / fig_h + pb = mtransforms.Bbox.from_bounds(x, y, w, h) + pb1 = mtransforms.Bbox.from_bounds(x, y, ww, hh) + x0, y0 = pb1.anchored(self.get_anchor(), pb).p0 + + else: + ox = self._calc_offsets(hsizes, k_h) + oy = self._calc_offsets(vsizes, k_v) + x0, y0 = x, y + + if nx1 is None: + nx1 = -1 + if ny1 is None: + ny1 = -1 + + x1, w1 = x0 + ox[nx] / fig_w, (ox[nx1] - ox[nx]) / fig_w + y1, h1 = y0 + oy[ny] / fig_h, (oy[ny1] - oy[ny]) / fig_h + + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + def append_size(self, position, size): + _api.check_in_list(["left", "right", "bottom", "top"], + position=position) + if position == "left": + self._horizontal.insert(0, size) + self._xrefindex += 1 + elif position == "right": + self._horizontal.append(size) + elif position == "bottom": + self._vertical.insert(0, size) + self._yrefindex += 1 + else: # 'top' + self._vertical.append(size) + + def add_auto_adjustable_area(self, use_axes, pad=0.1, adjust_dirs=None): + """ + Add auto-adjustable padding around *use_axes* to take their decorations + (title, labels, ticks, ticklabels) into account during layout. + + Parameters + ---------- + use_axes : `~matplotlib.axes.Axes` or list of `~matplotlib.axes.Axes` + The Axes whose decorations are taken into account. + pad : float, default: 0.1 + Additional padding in inches. + adjust_dirs : list of {"left", "right", "bottom", "top"}, optional + The sides where padding is added; defaults to all four sides. + """ + if adjust_dirs is None: + adjust_dirs = ["left", "right", "bottom", "top"] + for d in adjust_dirs: + self.append_size(d, Size._AxesDecorationsSize(use_axes, d) + pad) + + +class SubplotDivider(Divider): + """ + The Divider class whose rectangle area is specified as a subplot geometry. + """ + + def __init__(self, fig, *args, horizontal=None, vertical=None, + aspect=None, anchor='C'): + """ + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + + *args : tuple (*nrows*, *ncols*, *index*) or int + The array of subplots in the figure has dimensions ``(nrows, + ncols)``, and *index* is the index of the subplot being created. + *index* starts at 1 in the upper left corner and increases to the + right. + + If *nrows*, *ncols*, and *index* are all single digit numbers, then + *args* can be passed as a single 3-digit number (e.g. 234 for + (2, 3, 4)). + horizontal : list of :mod:`~mpl_toolkits.axes_grid1.axes_size`, optional + Sizes for horizontal division. + vertical : list of :mod:`~mpl_toolkits.axes_grid1.axes_size`, optional + Sizes for vertical division. + aspect : bool, optional + Whether overall rectangular area is reduced so that the relative + part of the horizontal and vertical scales have the same scale. + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'}, default: 'C' + Placement of the reduced rectangle, when *aspect* is True. + """ + self.figure = fig + super().__init__(fig, [0, 0, 1, 1], + horizontal=horizontal or [], vertical=vertical or [], + aspect=aspect, anchor=anchor) + self.set_subplotspec(SubplotSpec._from_subplot_args(fig, args)) + + def get_position(self): + """Return the bounds of the subplot box.""" + return self.get_subplotspec().get_position(self.figure).bounds + + def get_subplotspec(self): + """Get the SubplotSpec instance.""" + return self._subplotspec + + def set_subplotspec(self, subplotspec): + """Set the SubplotSpec instance.""" + self._subplotspec = subplotspec + self.set_position(subplotspec.get_position(self.figure)) + + +class AxesDivider(Divider): + """ + Divider based on the preexisting axes. + """ + + def __init__(self, axes, xref=None, yref=None): + """ + Parameters + ---------- + axes : :class:`~matplotlib.axes.Axes` + xref + yref + """ + self._axes = axes + if xref is None: + self._xref = Size.AxesX(axes) + else: + self._xref = xref + if yref is None: + self._yref = Size.AxesY(axes) + else: + self._yref = yref + + super().__init__(fig=axes.get_figure(), pos=None, + horizontal=[self._xref], vertical=[self._yref], + aspect=None, anchor="C") + + def _get_new_axes(self, *, axes_class=None, **kwargs): + axes = self._axes + if axes_class is None: + axes_class = type(axes) + return axes_class(axes.get_figure(), axes.get_position(original=True), + **kwargs) + + def new_horizontal(self, size, pad=None, pack_start=False, **kwargs): + """ + Helper method for ``append_axes("left")`` and ``append_axes("right")``. + + See the documentation of `append_axes` for more details. + + :meta private: + """ + if pad is None: + pad = mpl.rcParams["figure.subplot.wspace"] * self._xref + pos = "left" if pack_start else "right" + if pad: + if not isinstance(pad, Size._Base): + pad = Size.from_any(pad, fraction_ref=self._xref) + self.append_size(pos, pad) + if not isinstance(size, Size._Base): + size = Size.from_any(size, fraction_ref=self._xref) + self.append_size(pos, size) + locator = self.new_locator( + nx=0 if pack_start else len(self._horizontal) - 1, + ny=self._yrefindex) + ax = self._get_new_axes(**kwargs) + ax.set_axes_locator(locator) + return ax + + def new_vertical(self, size, pad=None, pack_start=False, **kwargs): + """ + Helper method for ``append_axes("top")`` and ``append_axes("bottom")``. + + See the documentation of `append_axes` for more details. + + :meta private: + """ + if pad is None: + pad = mpl.rcParams["figure.subplot.hspace"] * self._yref + pos = "bottom" if pack_start else "top" + if pad: + if not isinstance(pad, Size._Base): + pad = Size.from_any(pad, fraction_ref=self._yref) + self.append_size(pos, pad) + if not isinstance(size, Size._Base): + size = Size.from_any(size, fraction_ref=self._yref) + self.append_size(pos, size) + locator = self.new_locator( + nx=self._xrefindex, + ny=0 if pack_start else len(self._vertical) - 1) + ax = self._get_new_axes(**kwargs) + ax.set_axes_locator(locator) + return ax + + def append_axes(self, position, size, pad=None, *, axes_class=None, + **kwargs): + """ + Add a new axes on a given side of the main axes. + + Parameters + ---------- + position : {"left", "right", "bottom", "top"} + Where the new axes is positioned relative to the main axes. + size : :mod:`~mpl_toolkits.axes_grid1.axes_size` or float or str + The axes width or height. float or str arguments are interpreted + as ``axes_size.from_any(size, AxesX())`` for left or + right axes, and likewise with ``AxesY`` for bottom or top axes. + pad : :mod:`~mpl_toolkits.axes_grid1.axes_size` or float or str + Padding between the axes. float or str arguments are interpreted + as for *size*. Defaults to :rc:`figure.subplot.wspace` times the + main Axes width (left or right axes) or :rc:`figure.subplot.hspace` + times the main Axes height (bottom or top axes). + axes_class : subclass type of `~.axes.Axes`, optional + The type of the new axes. Defaults to the type of the main axes. + **kwargs + All extra keywords arguments are passed to the created axes. + """ + create_axes, pack_start = _api.check_getitem({ + "left": (self.new_horizontal, True), + "right": (self.new_horizontal, False), + "bottom": (self.new_vertical, True), + "top": (self.new_vertical, False), + }, position=position) + ax = create_axes( + size, pad, pack_start=pack_start, axes_class=axes_class, **kwargs) + self._fig.add_axes(ax) + return ax + + def get_aspect(self): + if self._aspect is None: + aspect = self._axes.get_aspect() + if aspect == "auto": + return False + else: + return True + else: + return self._aspect + + def get_position(self): + if self._pos is None: + bbox = self._axes.get_position(original=True) + return bbox.bounds + else: + return self._pos + + def get_anchor(self): + if self._anchor is None: + return self._axes.get_anchor() + else: + return self._anchor + + def get_subplotspec(self): + return self._axes.get_subplotspec() + + +# Helper for HBoxDivider/VBoxDivider. +# The variable names are written for a horizontal layout, but the calculations +# work identically for vertical layouts. +def _locate(x, y, w, h, summed_widths, equal_heights, fig_w, fig_h, anchor): + + total_width = fig_w * w + max_height = fig_h * h + + # Determine the k factors. + n = len(equal_heights) + eq_rels, eq_abss = equal_heights.T + sm_rels, sm_abss = summed_widths.T + A = np.diag([*eq_rels, 0]) + A[:n, -1] = -1 + A[-1, :-1] = sm_rels + B = [*(-eq_abss), total_width - sm_abss.sum()] + # A @ K = B: This finds factors {k_0, ..., k_{N-1}, H} so that + # eq_rel_i * k_i + eq_abs_i = H for all i: all axes have the same height + # sum(sm_rel_i * k_i + sm_abs_i) = total_width: fixed total width + # (foo_rel_i * k_i + foo_abs_i will end up being the size of foo.) + *karray, height = np.linalg.solve(A, B) + if height > max_height: # Additionally, upper-bound the height. + karray = (max_height - eq_abss) / eq_rels + + # Compute the offsets corresponding to these factors. + ox = np.cumsum([0, *(sm_rels * karray + sm_abss)]) + ww = (ox[-1] - ox[0]) / fig_w + h0_rel, h0_abs = equal_heights[0] + hh = (karray[0]*h0_rel + h0_abs) / fig_h + pb = mtransforms.Bbox.from_bounds(x, y, w, h) + pb1 = mtransforms.Bbox.from_bounds(x, y, ww, hh) + x0, y0 = pb1.anchored(anchor, pb).p0 + + return x0, y0, ox, hh + + +class HBoxDivider(SubplotDivider): + """ + A `.SubplotDivider` for laying out axes horizontally, while ensuring that + they have equal heights. + + Examples + -------- + .. plot:: gallery/axes_grid1/demo_axes_hbox_divider.py + """ + + def new_locator(self, nx, nx1=None): + """ + Create an axes locator callable for the specified cell. + + Parameters + ---------- + nx, nx1 : int + Integers specifying the column-position of the + cell. When *nx1* is None, a single *nx*-th column is + specified. Otherwise, location of columns spanning between *nx* + to *nx1* (but excluding *nx1*-th column) is specified. + """ + return super().new_locator(nx, 0, nx1, 0) + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + # docstring inherited + nx += self._xrefindex + nx1 += self._xrefindex + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + summed_ws = self.get_horizontal_sizes(renderer) + equal_hs = self.get_vertical_sizes(renderer) + x0, y0, ox, hh = _locate( + x, y, w, h, summed_ws, equal_hs, fig_w, fig_h, self.get_anchor()) + if nx1 is None: + nx1 = -1 + x1, w1 = x0 + ox[nx] / fig_w, (ox[nx1] - ox[nx]) / fig_w + y1, h1 = y0, hh + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + +class VBoxDivider(SubplotDivider): + """ + A `.SubplotDivider` for laying out axes vertically, while ensuring that + they have equal widths. + """ + + def new_locator(self, ny, ny1=None): + """ + Create an axes locator callable for the specified cell. + + Parameters + ---------- + ny, ny1 : int + Integers specifying the row-position of the + cell. When *ny1* is None, a single *ny*-th row is + specified. Otherwise, location of rows spanning between *ny* + to *ny1* (but excluding *ny1*-th row) is specified. + """ + return super().new_locator(0, ny, 0, ny1) + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + # docstring inherited + ny += self._yrefindex + ny1 += self._yrefindex + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + summed_hs = self.get_vertical_sizes(renderer) + equal_ws = self.get_horizontal_sizes(renderer) + y0, x0, oy, ww = _locate( + y, x, h, w, summed_hs, equal_ws, fig_h, fig_w, self.get_anchor()) + if ny1 is None: + ny1 = -1 + x1, w1 = x0, ww + y1, h1 = y0 + oy[ny] / fig_h, (oy[ny1] - oy[ny]) / fig_h + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + +def make_axes_locatable(axes): + divider = AxesDivider(axes) + locator = divider.new_locator(nx=0, ny=0) + axes.set_axes_locator(locator) + + return divider + + +def make_axes_area_auto_adjustable( + ax, use_axes=None, pad=0.1, adjust_dirs=None): + """ + Add auto-adjustable padding around *ax* to take its decorations (title, + labels, ticks, ticklabels) into account during layout, using + `.Divider.add_auto_adjustable_area`. + + By default, padding is determined from the decorations of *ax*. + Pass *use_axes* to consider the decorations of other Axes instead. + """ + if adjust_dirs is None: + adjust_dirs = ["left", "right", "bottom", "top"] + divider = make_axes_locatable(ax) + if use_axes is None: + use_axes = ax + divider.add_auto_adjustable_area(use_axes=use_axes, pad=pad, + adjust_dirs=adjust_dirs) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..20abf18ea79c32ccdfe678d81ad975e83fe4bc0b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py @@ -0,0 +1,563 @@ +from numbers import Number +import functools +from types import MethodType + +import numpy as np + +from matplotlib import _api, cbook +from matplotlib.gridspec import SubplotSpec + +from .axes_divider import Size, SubplotDivider, Divider +from .mpl_axes import Axes, SimpleAxisArtist + + +class CbarAxesBase: + def __init__(self, *args, orientation, **kwargs): + self.orientation = orientation + super().__init__(*args, **kwargs) + + def colorbar(self, mappable, **kwargs): + return self.get_figure(root=False).colorbar( + mappable, cax=self, location=self.orientation, **kwargs) + + +_cbaraxes_class_factory = cbook._make_class_factory(CbarAxesBase, "Cbar{}") + + +class Grid: + """ + A grid of Axes. + + In Matplotlib, the Axes location (and size) is specified in normalized + figure coordinates. This may not be ideal for images that needs to be + displayed with a given aspect ratio; for example, it is difficult to + display multiple images of a same size with some fixed padding between + them. AxesGrid can be used in such case. + + Attributes + ---------- + axes_all : list of Axes + A flat list of Axes. Note that you can also access this directly + from the grid. The following is equivalent :: + + grid[i] == grid.axes_all[i] + len(grid) == len(grid.axes_all) + + axes_column : list of list of Axes + A 2D list of Axes where the first index is the column. This results + in the usage pattern ``grid.axes_column[col][row]``. + axes_row : list of list of Axes + A 2D list of Axes where the first index is the row. This results + in the usage pattern ``grid.axes_row[row][col]``. + axes_llc : Axes + The Axes in the lower left corner. + ngrids : int + Number of Axes in the grid. + """ + + _defaultAxesClass = Axes + + def __init__(self, fig, + rect, + nrows_ncols, + ngrids=None, + direction="row", + axes_pad=0.02, + *, + share_all=False, + share_x=True, + share_y=True, + label_mode="L", + axes_class=None, + aspect=False, + ): + """ + Parameters + ---------- + fig : `.Figure` + The parent figure. + rect : (float, float, float, float), (int, int, int), int, or \ + `~.SubplotSpec` + The axes position, as a ``(left, bottom, width, height)`` tuple, + as a three-digit subplot position code (e.g., ``(1, 2, 1)`` or + ``121``), or as a `~.SubplotSpec`. + nrows_ncols : (int, int) + Number of rows and columns in the grid. + ngrids : int or None, default: None + If not None, only the first *ngrids* axes in the grid are created. + direction : {"row", "column"}, default: "row" + Whether axes are created in row-major ("row by row") or + column-major order ("column by column"). This also affects the + order in which axes are accessed using indexing (``grid[index]``). + axes_pad : float or (float, float), default: 0.02 + Padding or (horizontal padding, vertical padding) between axes, in + inches. + share_all : bool, default: False + Whether all axes share their x- and y-axis. Overrides *share_x* + and *share_y*. + share_x : bool, default: True + Whether all axes of a column share their x-axis. + share_y : bool, default: True + Whether all axes of a row share their y-axis. + label_mode : {"L", "1", "all", "keep"}, default: "L" + Determines which axes will get tick labels: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": All axes are labelled. + - "keep": Do not do anything. + + axes_class : subclass of `matplotlib.axes.Axes`, default: `.mpl_axes.Axes` + The type of Axes to create. + aspect : bool, default: False + Whether the axes aspect ratio follows the aspect ratio of the data + limits. + """ + self._nrows, self._ncols = nrows_ncols + + if ngrids is None: + ngrids = self._nrows * self._ncols + else: + if not 0 < ngrids <= self._nrows * self._ncols: + raise ValueError( + "ngrids must be positive and not larger than nrows*ncols") + + self.ngrids = ngrids + + self._horiz_pad_size, self._vert_pad_size = map( + Size.Fixed, np.broadcast_to(axes_pad, 2)) + + _api.check_in_list(["column", "row"], direction=direction) + self._direction = direction + + if axes_class is None: + axes_class = self._defaultAxesClass + elif isinstance(axes_class, (list, tuple)): + cls, kwargs = axes_class + axes_class = functools.partial(cls, **kwargs) + + kw = dict(horizontal=[], vertical=[], aspect=aspect) + if isinstance(rect, (Number, SubplotSpec)): + self._divider = SubplotDivider(fig, rect, **kw) + elif len(rect) == 3: + self._divider = SubplotDivider(fig, *rect, **kw) + elif len(rect) == 4: + self._divider = Divider(fig, rect, **kw) + else: + raise TypeError("Incorrect rect format") + + rect = self._divider.get_position() + + axes_array = np.full((self._nrows, self._ncols), None, dtype=object) + for i in range(self.ngrids): + col, row = self._get_col_row(i) + if share_all: + sharex = sharey = axes_array[0, 0] + else: + sharex = axes_array[0, col] if share_x else None + sharey = axes_array[row, 0] if share_y else None + axes_array[row, col] = axes_class( + fig, rect, sharex=sharex, sharey=sharey) + self.axes_all = axes_array.ravel( + order="C" if self._direction == "row" else "F").tolist() + self.axes_column = axes_array.T.tolist() + self.axes_row = axes_array.tolist() + self.axes_llc = self.axes_column[0][-1] + + self._init_locators() + + for ax in self.axes_all: + fig.add_axes(ax) + + self.set_label_mode(label_mode) + + def _init_locators(self): + self._divider.set_horizontal( + [Size.Scaled(1), self._horiz_pad_size] * (self._ncols-1) + [Size.Scaled(1)]) + self._divider.set_vertical( + [Size.Scaled(1), self._vert_pad_size] * (self._nrows-1) + [Size.Scaled(1)]) + for i in range(self.ngrids): + col, row = self._get_col_row(i) + self.axes_all[i].set_axes_locator( + self._divider.new_locator(nx=2 * col, ny=2 * (self._nrows - 1 - row))) + + def _get_col_row(self, n): + if self._direction == "column": + col, row = divmod(n, self._nrows) + else: + row, col = divmod(n, self._ncols) + + return col, row + + # Good to propagate __len__ if we have __getitem__ + def __len__(self): + return len(self.axes_all) + + def __getitem__(self, i): + return self.axes_all[i] + + def get_geometry(self): + """ + Return the number of rows and columns of the grid as (nrows, ncols). + """ + return self._nrows, self._ncols + + def set_axes_pad(self, axes_pad): + """ + Set the padding between the axes. + + Parameters + ---------- + axes_pad : (float, float) + The padding (horizontal pad, vertical pad) in inches. + """ + self._horiz_pad_size.fixed_size = axes_pad[0] + self._vert_pad_size.fixed_size = axes_pad[1] + + def get_axes_pad(self): + """ + Return the axes padding. + + Returns + ------- + hpad, vpad + Padding (horizontal pad, vertical pad) in inches. + """ + return (self._horiz_pad_size.fixed_size, + self._vert_pad_size.fixed_size) + + def set_aspect(self, aspect): + """Set the aspect of the SubplotDivider.""" + self._divider.set_aspect(aspect) + + def get_aspect(self): + """Return the aspect of the SubplotDivider.""" + return self._divider.get_aspect() + + def set_label_mode(self, mode): + """ + Define which axes have tick labels. + + Parameters + ---------- + mode : {"L", "1", "all", "keep"} + The label mode: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": All axes are labelled. + - "keep": Do not do anything. + """ + _api.check_in_list(["all", "L", "1", "keep"], mode=mode) + is_last_row, is_first_col = ( + np.mgrid[:self._nrows, :self._ncols] == [[[self._nrows - 1]], [[0]]]) + if mode == "all": + bottom = left = np.full((self._nrows, self._ncols), True) + elif mode == "L": + bottom = is_last_row + left = is_first_col + elif mode == "1": + bottom = left = is_last_row & is_first_col + else: + return + for i in range(self._nrows): + for j in range(self._ncols): + ax = self.axes_row[i][j] + if isinstance(ax.axis, MethodType): + bottom_axis = SimpleAxisArtist(ax.xaxis, 1, ax.spines["bottom"]) + left_axis = SimpleAxisArtist(ax.yaxis, 1, ax.spines["left"]) + else: + bottom_axis = ax.axis["bottom"] + left_axis = ax.axis["left"] + bottom_axis.toggle(ticklabels=bottom[i, j], label=bottom[i, j]) + left_axis.toggle(ticklabels=left[i, j], label=left[i, j]) + + def get_divider(self): + return self._divider + + def set_axes_locator(self, locator): + self._divider.set_locator(locator) + + def get_axes_locator(self): + return self._divider.get_locator() + + +class ImageGrid(Grid): + """ + A grid of Axes for Image display. + + This class is a specialization of `~.axes_grid1.axes_grid.Grid` for displaying a + grid of images. In particular, it forces all axes in a column to share their x-axis + and all axes in a row to share their y-axis. It further provides helpers to add + colorbars to some or all axes. + """ + + def __init__(self, fig, + rect, + nrows_ncols, + ngrids=None, + direction="row", + axes_pad=0.02, + *, + share_all=False, + aspect=True, + label_mode="L", + cbar_mode=None, + cbar_location="right", + cbar_pad=None, + cbar_size="5%", + cbar_set_cax=True, + axes_class=None, + ): + """ + Parameters + ---------- + fig : `.Figure` + The parent figure. + rect : (float, float, float, float) or int + The axes position, as a ``(left, bottom, width, height)`` tuple or + as a three-digit subplot position code (e.g., "121"). + nrows_ncols : (int, int) + Number of rows and columns in the grid. + ngrids : int or None, default: None + If not None, only the first *ngrids* axes in the grid are created. + direction : {"row", "column"}, default: "row" + Whether axes are created in row-major ("row by row") or + column-major order ("column by column"). This also affects the + order in which axes are accessed using indexing (``grid[index]``). + axes_pad : float or (float, float), default: 0.02in + Padding or (horizontal padding, vertical padding) between axes, in + inches. + share_all : bool, default: False + Whether all axes share their x- and y-axis. Note that in any case, + all axes in a column share their x-axis and all axes in a row share + their y-axis. + aspect : bool, default: True + Whether the axes aspect ratio follows the aspect ratio of the data + limits. + label_mode : {"L", "1", "all"}, default: "L" + Determines which axes will get tick labels: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": all axes are labelled. + + cbar_mode : {"each", "single", "edge", None}, default: None + Whether to create a colorbar for "each" axes, a "single" colorbar + for the entire grid, colorbars only for axes on the "edge" + determined by *cbar_location*, or no colorbars. The colorbars are + stored in the :attr:`cbar_axes` attribute. + cbar_location : {"left", "right", "bottom", "top"}, default: "right" + cbar_pad : float, default: None + Padding between the image axes and the colorbar axes. + + .. versionchanged:: 3.10 + ``cbar_mode="single"`` no longer adds *axes_pad* between the axes + and the colorbar if the *cbar_location* is "left" or "bottom". + + cbar_size : size specification (see `.Size.from_any`), default: "5%" + Colorbar size. + cbar_set_cax : bool, default: True + If True, each axes in the grid has a *cax* attribute that is bound + to associated *cbar_axes*. + axes_class : subclass of `matplotlib.axes.Axes`, default: None + """ + _api.check_in_list(["each", "single", "edge", None], + cbar_mode=cbar_mode) + _api.check_in_list(["left", "right", "bottom", "top"], + cbar_location=cbar_location) + self._colorbar_mode = cbar_mode + self._colorbar_location = cbar_location + self._colorbar_pad = cbar_pad + self._colorbar_size = cbar_size + # The colorbar axes are created in _init_locators(). + + super().__init__( + fig, rect, nrows_ncols, ngrids, + direction=direction, axes_pad=axes_pad, + share_all=share_all, share_x=True, share_y=True, aspect=aspect, + label_mode=label_mode, axes_class=axes_class) + + for ax in self.cbar_axes: + fig.add_axes(ax) + + if cbar_set_cax: + if self._colorbar_mode == "single": + for ax in self.axes_all: + ax.cax = self.cbar_axes[0] + elif self._colorbar_mode == "edge": + for index, ax in enumerate(self.axes_all): + col, row = self._get_col_row(index) + if self._colorbar_location in ("left", "right"): + ax.cax = self.cbar_axes[row] + else: + ax.cax = self.cbar_axes[col] + else: + for ax, cax in zip(self.axes_all, self.cbar_axes): + ax.cax = cax + + def _init_locators(self): + # Slightly abusing this method to inject colorbar creation into init. + + if self._colorbar_pad is None: + # horizontal or vertical arrangement? + if self._colorbar_location in ("left", "right"): + self._colorbar_pad = self._horiz_pad_size.fixed_size + else: + self._colorbar_pad = self._vert_pad_size.fixed_size + self.cbar_axes = [ + _cbaraxes_class_factory(self._defaultAxesClass)( + self.axes_all[0].get_figure(root=False), self._divider.get_position(), + orientation=self._colorbar_location) + for _ in range(self.ngrids)] + + cb_mode = self._colorbar_mode + cb_location = self._colorbar_location + + h = [] + v = [] + + h_ax_pos = [] + h_cb_pos = [] + if cb_mode == "single" and cb_location in ("left", "bottom"): + if cb_location == "left": + sz = self._nrows * Size.AxesX(self.axes_llc) + h.append(Size.from_any(self._colorbar_size, sz)) + h.append(Size.from_any(self._colorbar_pad, sz)) + locator = self._divider.new_locator(nx=0, ny=0, ny1=-1) + elif cb_location == "bottom": + sz = self._ncols * Size.AxesY(self.axes_llc) + v.append(Size.from_any(self._colorbar_size, sz)) + v.append(Size.from_any(self._colorbar_pad, sz)) + locator = self._divider.new_locator(nx=0, nx1=-1, ny=0) + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[0].set_axes_locator(locator) + self.cbar_axes[0].set_visible(True) + + for col, ax in enumerate(self.axes_row[0]): + if col != 0: + h.append(self._horiz_pad_size) + + if ax: + sz = Size.AxesX(ax, aspect="axes", ref_ax=self.axes_all[0]) + else: + sz = Size.AxesX(self.axes_all[0], + aspect="axes", ref_ax=self.axes_all[0]) + + if (cb_location == "left" + and (cb_mode == "each" + or (cb_mode == "edge" and col == 0))): + h_cb_pos.append(len(h)) + h.append(Size.from_any(self._colorbar_size, sz)) + h.append(Size.from_any(self._colorbar_pad, sz)) + + h_ax_pos.append(len(h)) + h.append(sz) + + if (cb_location == "right" + and (cb_mode == "each" + or (cb_mode == "edge" and col == self._ncols - 1))): + h.append(Size.from_any(self._colorbar_pad, sz)) + h_cb_pos.append(len(h)) + h.append(Size.from_any(self._colorbar_size, sz)) + + v_ax_pos = [] + v_cb_pos = [] + for row, ax in enumerate(self.axes_column[0][::-1]): + if row != 0: + v.append(self._vert_pad_size) + + if ax: + sz = Size.AxesY(ax, aspect="axes", ref_ax=self.axes_all[0]) + else: + sz = Size.AxesY(self.axes_all[0], + aspect="axes", ref_ax=self.axes_all[0]) + + if (cb_location == "bottom" + and (cb_mode == "each" + or (cb_mode == "edge" and row == 0))): + v_cb_pos.append(len(v)) + v.append(Size.from_any(self._colorbar_size, sz)) + v.append(Size.from_any(self._colorbar_pad, sz)) + + v_ax_pos.append(len(v)) + v.append(sz) + + if (cb_location == "top" + and (cb_mode == "each" + or (cb_mode == "edge" and row == self._nrows - 1))): + v.append(Size.from_any(self._colorbar_pad, sz)) + v_cb_pos.append(len(v)) + v.append(Size.from_any(self._colorbar_size, sz)) + + for i in range(self.ngrids): + col, row = self._get_col_row(i) + locator = self._divider.new_locator(nx=h_ax_pos[col], + ny=v_ax_pos[self._nrows-1-row]) + self.axes_all[i].set_axes_locator(locator) + + if cb_mode == "each": + if cb_location in ("right", "left"): + locator = self._divider.new_locator( + nx=h_cb_pos[col], ny=v_ax_pos[self._nrows - 1 - row]) + + elif cb_location in ("top", "bottom"): + locator = self._divider.new_locator( + nx=h_ax_pos[col], ny=v_cb_pos[self._nrows - 1 - row]) + + self.cbar_axes[i].set_axes_locator(locator) + elif cb_mode == "edge": + if (cb_location == "left" and col == 0 + or cb_location == "right" and col == self._ncols - 1): + locator = self._divider.new_locator( + nx=h_cb_pos[0], ny=v_ax_pos[self._nrows - 1 - row]) + self.cbar_axes[row].set_axes_locator(locator) + elif (cb_location == "bottom" and row == self._nrows - 1 + or cb_location == "top" and row == 0): + locator = self._divider.new_locator(nx=h_ax_pos[col], + ny=v_cb_pos[0]) + self.cbar_axes[col].set_axes_locator(locator) + + if cb_mode == "single": + if cb_location == "right": + sz = self._nrows * Size.AxesX(self.axes_llc) + h.append(Size.from_any(self._colorbar_pad, sz)) + h.append(Size.from_any(self._colorbar_size, sz)) + locator = self._divider.new_locator(nx=-2, ny=0, ny1=-1) + elif cb_location == "top": + sz = self._ncols * Size.AxesY(self.axes_llc) + v.append(Size.from_any(self._colorbar_pad, sz)) + v.append(Size.from_any(self._colorbar_size, sz)) + locator = self._divider.new_locator(nx=0, nx1=-1, ny=-2) + if cb_location in ("right", "top"): + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[0].set_axes_locator(locator) + self.cbar_axes[0].set_visible(True) + elif cb_mode == "each": + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(True) + elif cb_mode == "edge": + if cb_location in ("right", "left"): + count = self._nrows + else: + count = self._ncols + for i in range(count): + self.cbar_axes[i].set_visible(True) + for j in range(i + 1, self.ngrids): + self.cbar_axes[j].set_visible(False) + else: + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[i].set_position([1., 1., 0.001, 0.001], + which="active") + + self._divider.set_horizontal(h) + self._divider.set_vertical(v) + + +AxesGrid = ImageGrid diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py new file mode 100644 index 0000000000000000000000000000000000000000..52fd707e87043eb17929a84c53da2741722ce002 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py @@ -0,0 +1,157 @@ +from types import MethodType + +import numpy as np + +from .axes_divider import make_axes_locatable, Size +from .mpl_axes import Axes, SimpleAxisArtist + + +def make_rgb_axes(ax, pad=0.01, axes_class=None, **kwargs): + """ + Parameters + ---------- + ax : `~matplotlib.axes.Axes` + Axes instance to create the RGB Axes in. + pad : float, optional + Fraction of the Axes height to pad. + axes_class : `matplotlib.axes.Axes` or None, optional + Axes class to use for the R, G, and B Axes. If None, use + the same class as *ax*. + **kwargs + Forwarded to *axes_class* init for the R, G, and B Axes. + """ + + divider = make_axes_locatable(ax) + + pad_size = pad * Size.AxesY(ax) + + xsize = ((1-2*pad)/3) * Size.AxesX(ax) + ysize = ((1-2*pad)/3) * Size.AxesY(ax) + + divider.set_horizontal([Size.AxesX(ax), pad_size, xsize]) + divider.set_vertical([ysize, pad_size, ysize, pad_size, ysize]) + + ax.set_axes_locator(divider.new_locator(0, 0, ny1=-1)) + + ax_rgb = [] + if axes_class is None: + axes_class = type(ax) + + for ny in [4, 2, 0]: + ax1 = axes_class(ax.get_figure(), ax.get_position(original=True), + sharex=ax, sharey=ax, **kwargs) + locator = divider.new_locator(nx=2, ny=ny) + ax1.set_axes_locator(locator) + for t in ax1.yaxis.get_ticklabels() + ax1.xaxis.get_ticklabels(): + t.set_visible(False) + try: + for axis in ax1.axis.values(): + axis.major_ticklabels.set_visible(False) + except AttributeError: + pass + + ax_rgb.append(ax1) + + fig = ax.get_figure() + for ax1 in ax_rgb: + fig.add_axes(ax1) + + return ax_rgb + + +class RGBAxes: + """ + 4-panel `~.Axes.imshow` (RGB, R, G, B). + + Layout:: + + ┌───────────────┬─────┐ + │ │ R │ + │ ├─────┤ + │ RGB │ G │ + │ ├─────┤ + │ │ B │ + └───────────────┴─────┘ + + Subclasses can override the ``_defaultAxesClass`` attribute. + By default RGBAxes uses `.mpl_axes.Axes`. + + Attributes + ---------- + RGB : ``_defaultAxesClass`` + The Axes object for the three-channel `~.Axes.imshow`. + R : ``_defaultAxesClass`` + The Axes object for the red channel `~.Axes.imshow`. + G : ``_defaultAxesClass`` + The Axes object for the green channel `~.Axes.imshow`. + B : ``_defaultAxesClass`` + The Axes object for the blue channel `~.Axes.imshow`. + """ + + _defaultAxesClass = Axes + + def __init__(self, *args, pad=0, **kwargs): + """ + Parameters + ---------- + pad : float, default: 0 + Fraction of the Axes height to put as padding. + axes_class : `~matplotlib.axes.Axes` + Axes class to use. If not provided, ``_defaultAxesClass`` is used. + *args + Forwarded to *axes_class* init for the RGB Axes + **kwargs + Forwarded to *axes_class* init for the RGB, R, G, and B Axes + """ + axes_class = kwargs.pop("axes_class", self._defaultAxesClass) + self.RGB = ax = axes_class(*args, **kwargs) + ax.get_figure().add_axes(ax) + self.R, self.G, self.B = make_rgb_axes( + ax, pad=pad, axes_class=axes_class, **kwargs) + # Set the line color and ticks for the axes. + for ax1 in [self.RGB, self.R, self.G, self.B]: + if isinstance(ax1.axis, MethodType): + ad = Axes.AxisDict(self) + ad.update( + bottom=SimpleAxisArtist(ax1.xaxis, 1, ax1.spines["bottom"]), + top=SimpleAxisArtist(ax1.xaxis, 2, ax1.spines["top"]), + left=SimpleAxisArtist(ax1.yaxis, 1, ax1.spines["left"]), + right=SimpleAxisArtist(ax1.yaxis, 2, ax1.spines["right"])) + else: + ad = ax1.axis + ad[:].line.set_color("w") + ad[:].major_ticks.set_markeredgecolor("w") + + def imshow_rgb(self, r, g, b, **kwargs): + """ + Create the four images {rgb, r, g, b}. + + Parameters + ---------- + r, g, b : array-like + The red, green, and blue arrays. + **kwargs + Forwarded to `~.Axes.imshow` calls for the four images. + + Returns + ------- + rgb : `~matplotlib.image.AxesImage` + r : `~matplotlib.image.AxesImage` + g : `~matplotlib.image.AxesImage` + b : `~matplotlib.image.AxesImage` + """ + if not (r.shape == g.shape == b.shape): + raise ValueError( + f'Input shapes ({r.shape}, {g.shape}, {b.shape}) do not match') + RGB = np.dstack([r, g, b]) + R = np.zeros_like(RGB) + R[:, :, 0] = r + G = np.zeros_like(RGB) + G[:, :, 1] = g + B = np.zeros_like(RGB) + B[:, :, 2] = b + im_rgb = self.RGB.imshow(RGB, **kwargs) + im_r = self.R.imshow(R, **kwargs) + im_g = self.G.imshow(G, **kwargs) + im_b = self.B.imshow(B, **kwargs) + return im_rgb, im_r, im_g, im_b diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py new file mode 100644 index 0000000000000000000000000000000000000000..86e5f70d9824392a8c49bce9a18aaf45cf6f0232 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py @@ -0,0 +1,271 @@ +""" +Provides classes of simple units that will be used with `.AxesDivider` +class (or others) to determine the size of each Axes. The unit +classes define `get_size` method that returns a tuple of two floats, +meaning relative and absolute sizes, respectively. + +Note that this class is nothing more than a simple tuple of two +floats. Take a look at the Divider class to see how these two +values are used. + +Once created, the unit classes can be modified by simple arithmetic +operations: addition /subtraction with another unit type or a real number and scaling +(multiplication or division) by a real number. +""" + +from numbers import Real + +from matplotlib import _api +from matplotlib.axes import Axes + + +class _Base: + def __rmul__(self, other): + return self * other + + def __mul__(self, other): + if not isinstance(other, Real): + return NotImplemented + return Fraction(other, self) + + def __div__(self, other): + return (1 / other) * self + + def __add__(self, other): + if isinstance(other, _Base): + return Add(self, other) + else: + return Add(self, Fixed(other)) + + def __neg__(self): + return -1 * self + + def __radd__(self, other): + # other cannot be a _Base instance, because A + B would trigger + # A.__add__(B) first. + return Add(self, Fixed(other)) + + def __sub__(self, other): + return self + (-other) + + def get_size(self, renderer): + """ + Return two-float tuple with relative and absolute sizes. + """ + raise NotImplementedError("Subclasses must implement") + + +class Add(_Base): + """ + Sum of two sizes. + """ + + def __init__(self, a, b): + self._a = a + self._b = b + + def get_size(self, renderer): + a_rel_size, a_abs_size = self._a.get_size(renderer) + b_rel_size, b_abs_size = self._b.get_size(renderer) + return a_rel_size + b_rel_size, a_abs_size + b_abs_size + + +class Fixed(_Base): + """ + Simple fixed size with absolute part = *fixed_size* and relative part = 0. + """ + + def __init__(self, fixed_size): + _api.check_isinstance(Real, fixed_size=fixed_size) + self.fixed_size = fixed_size + + def get_size(self, renderer): + rel_size = 0. + abs_size = self.fixed_size + return rel_size, abs_size + + +class Scaled(_Base): + """ + Simple scaled(?) size with absolute part = 0 and + relative part = *scalable_size*. + """ + + def __init__(self, scalable_size): + self._scalable_size = scalable_size + + def get_size(self, renderer): + rel_size = self._scalable_size + abs_size = 0. + return rel_size, abs_size + +Scalable = Scaled + + +def _get_axes_aspect(ax): + aspect = ax.get_aspect() + if aspect == "auto": + aspect = 1. + return aspect + + +class AxesX(_Base): + """ + Scaled size whose relative part corresponds to the data width + of the *axes* multiplied by the *aspect*. + """ + + def __init__(self, axes, aspect=1., ref_ax=None): + self._axes = axes + self._aspect = aspect + if aspect == "axes" and ref_ax is None: + raise ValueError("ref_ax must be set when aspect='axes'") + self._ref_ax = ref_ax + + def get_size(self, renderer): + l1, l2 = self._axes.get_xlim() + if self._aspect == "axes": + ref_aspect = _get_axes_aspect(self._ref_ax) + aspect = ref_aspect / _get_axes_aspect(self._axes) + else: + aspect = self._aspect + + rel_size = abs(l2-l1)*aspect + abs_size = 0. + return rel_size, abs_size + + +class AxesY(_Base): + """ + Scaled size whose relative part corresponds to the data height + of the *axes* multiplied by the *aspect*. + """ + + def __init__(self, axes, aspect=1., ref_ax=None): + self._axes = axes + self._aspect = aspect + if aspect == "axes" and ref_ax is None: + raise ValueError("ref_ax must be set when aspect='axes'") + self._ref_ax = ref_ax + + def get_size(self, renderer): + l1, l2 = self._axes.get_ylim() + + if self._aspect == "axes": + ref_aspect = _get_axes_aspect(self._ref_ax) + aspect = _get_axes_aspect(self._axes) + else: + aspect = self._aspect + + rel_size = abs(l2-l1)*aspect + abs_size = 0. + return rel_size, abs_size + + +class MaxExtent(_Base): + """ + Size whose absolute part is either the largest width or the largest height + of the given *artist_list*. + """ + + def __init__(self, artist_list, w_or_h): + self._artist_list = artist_list + _api.check_in_list(["width", "height"], w_or_h=w_or_h) + self._w_or_h = w_or_h + + def add_artist(self, a): + self._artist_list.append(a) + + def get_size(self, renderer): + rel_size = 0. + extent_list = [ + getattr(a.get_window_extent(renderer), self._w_or_h) / a.figure.dpi + for a in self._artist_list] + abs_size = max(extent_list, default=0) + return rel_size, abs_size + + +class MaxWidth(MaxExtent): + """ + Size whose absolute part is the largest width of the given *artist_list*. + """ + + def __init__(self, artist_list): + super().__init__(artist_list, "width") + + +class MaxHeight(MaxExtent): + """ + Size whose absolute part is the largest height of the given *artist_list*. + """ + + def __init__(self, artist_list): + super().__init__(artist_list, "height") + + +class Fraction(_Base): + """ + An instance whose size is a *fraction* of the *ref_size*. + + >>> s = Fraction(0.3, AxesX(ax)) + """ + + def __init__(self, fraction, ref_size): + _api.check_isinstance(Real, fraction=fraction) + self._fraction_ref = ref_size + self._fraction = fraction + + def get_size(self, renderer): + if self._fraction_ref is None: + return self._fraction, 0. + else: + r, a = self._fraction_ref.get_size(renderer) + rel_size = r*self._fraction + abs_size = a*self._fraction + return rel_size, abs_size + + +def from_any(size, fraction_ref=None): + """ + Create a Fixed unit when the first argument is a float, or a + Fraction unit if that is a string that ends with %. The second + argument is only meaningful when Fraction unit is created. + + >>> from mpl_toolkits.axes_grid1.axes_size import from_any + >>> a = from_any(1.2) # => Fixed(1.2) + >>> from_any("50%", a) # => Fraction(0.5, a) + """ + if isinstance(size, Real): + return Fixed(size) + elif isinstance(size, str): + if size[-1] == "%": + return Fraction(float(size[:-1]) / 100, fraction_ref) + raise ValueError("Unknown format") + + +class _AxesDecorationsSize(_Base): + """ + Fixed size, corresponding to the size of decorations on a given Axes side. + """ + + _get_size_map = { + "left": lambda tight_bb, axes_bb: axes_bb.xmin - tight_bb.xmin, + "right": lambda tight_bb, axes_bb: tight_bb.xmax - axes_bb.xmax, + "bottom": lambda tight_bb, axes_bb: axes_bb.ymin - tight_bb.ymin, + "top": lambda tight_bb, axes_bb: tight_bb.ymax - axes_bb.ymax, + } + + def __init__(self, ax, direction): + _api.check_in_list(self._get_size_map, direction=direction) + self._direction = direction + self._ax_list = [ax] if isinstance(ax, Axes) else ax + + def get_size(self, renderer): + sz = max([ + self._get_size_map[self._direction]( + ax.get_tightbbox(renderer, call_axes_locator=False), ax.bbox) + for ax in self._ax_list]) + dpi = renderer.points_to_pixels(72) + abs_size = sz / dpi + rel_size = 0 + return rel_size, abs_size diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py new file mode 100644 index 0000000000000000000000000000000000000000..52fe6efc061856bd33939c31efe404f619faf033 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py @@ -0,0 +1,519 @@ +""" +A collection of functions and objects for creating or placing inset axes. +""" + +from matplotlib import _api, _docstring +from matplotlib.offsetbox import AnchoredOffsetbox +from matplotlib.patches import Patch, Rectangle +from matplotlib.path import Path +from matplotlib.transforms import Bbox +from matplotlib.transforms import IdentityTransform, TransformedBbox + +from . import axes_size as Size +from .parasite_axes import HostAxes + + +class AnchoredLocatorBase(AnchoredOffsetbox): + def __init__(self, bbox_to_anchor, offsetbox, loc, + borderpad=0.5, bbox_transform=None): + super().__init__( + loc, pad=0., child=None, borderpad=borderpad, + bbox_to_anchor=bbox_to_anchor, bbox_transform=bbox_transform + ) + + def draw(self, renderer): + raise RuntimeError("No draw method should be called") + + def __call__(self, ax, renderer): + fig = ax.get_figure(root=False) + if renderer is None: + renderer = fig._get_renderer() + self.axes = ax + bbox = self.get_window_extent(renderer) + px, py = self.get_offset(bbox.width, bbox.height, 0, 0, renderer) + bbox_canvas = Bbox.from_bounds(px, py, bbox.width, bbox.height) + tr = fig.transSubfigure.inverted() + return TransformedBbox(bbox_canvas, tr) + + +class AnchoredSizeLocator(AnchoredLocatorBase): + def __init__(self, bbox_to_anchor, x_size, y_size, loc, + borderpad=0.5, bbox_transform=None): + super().__init__( + bbox_to_anchor, None, loc, + borderpad=borderpad, bbox_transform=bbox_transform + ) + + self.x_size = Size.from_any(x_size) + self.y_size = Size.from_any(y_size) + + def get_bbox(self, renderer): + bbox = self.get_bbox_to_anchor() + dpi = renderer.points_to_pixels(72.) + + r, a = self.x_size.get_size(renderer) + width = bbox.width * r + a * dpi + r, a = self.y_size.get_size(renderer) + height = bbox.height * r + a * dpi + + fontsize = renderer.points_to_pixels(self.prop.get_size_in_points()) + pad = self.pad * fontsize + + return Bbox.from_bounds(0, 0, width, height).padded(pad) + + +class AnchoredZoomLocator(AnchoredLocatorBase): + def __init__(self, parent_axes, zoom, loc, + borderpad=0.5, + bbox_to_anchor=None, + bbox_transform=None): + self.parent_axes = parent_axes + self.zoom = zoom + if bbox_to_anchor is None: + bbox_to_anchor = parent_axes.bbox + super().__init__( + bbox_to_anchor, None, loc, borderpad=borderpad, + bbox_transform=bbox_transform) + + def get_bbox(self, renderer): + bb = self.parent_axes.transData.transform_bbox(self.axes.viewLim) + fontsize = renderer.points_to_pixels(self.prop.get_size_in_points()) + pad = self.pad * fontsize + return ( + Bbox.from_bounds( + 0, 0, abs(bb.width * self.zoom), abs(bb.height * self.zoom)) + .padded(pad)) + + +class BboxPatch(Patch): + @_docstring.interpd + def __init__(self, bbox, **kwargs): + """ + Patch showing the shape bounded by a Bbox. + + Parameters + ---------- + bbox : `~matplotlib.transforms.Bbox` + Bbox to use for the extents of this patch. + + **kwargs + Patch properties. Valid arguments include: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + + kwargs["transform"] = IdentityTransform() + super().__init__(**kwargs) + self.bbox = bbox + + def get_path(self): + # docstring inherited + x0, y0, x1, y1 = self.bbox.extents + return Path._create_closed([(x0, y0), (x1, y0), (x1, y1), (x0, y1)]) + + +class BboxConnector(Patch): + @staticmethod + def get_bbox_edge_pos(bbox, loc): + """ + Return the ``(x, y)`` coordinates of corner *loc* of *bbox*; parameters + behave as documented for the `.BboxConnector` constructor. + """ + x0, y0, x1, y1 = bbox.extents + if loc == 1: + return x1, y1 + elif loc == 2: + return x0, y1 + elif loc == 3: + return x0, y0 + elif loc == 4: + return x1, y0 + + @staticmethod + def connect_bbox(bbox1, bbox2, loc1, loc2=None): + """ + Construct a `.Path` connecting corner *loc1* of *bbox1* to corner + *loc2* of *bbox2*, where parameters behave as documented as for the + `.BboxConnector` constructor. + """ + if isinstance(bbox1, Rectangle): + bbox1 = TransformedBbox(Bbox.unit(), bbox1.get_transform()) + if isinstance(bbox2, Rectangle): + bbox2 = TransformedBbox(Bbox.unit(), bbox2.get_transform()) + if loc2 is None: + loc2 = loc1 + x1, y1 = BboxConnector.get_bbox_edge_pos(bbox1, loc1) + x2, y2 = BboxConnector.get_bbox_edge_pos(bbox2, loc2) + return Path([[x1, y1], [x2, y2]]) + + @_docstring.interpd + def __init__(self, bbox1, bbox2, loc1, loc2=None, **kwargs): + """ + Connect two bboxes with a straight line. + + Parameters + ---------- + bbox1, bbox2 : `~matplotlib.transforms.Bbox` + Bounding boxes to connect. + + loc1, loc2 : {1, 2, 3, 4} + Corner of *bbox1* and *bbox2* to draw the line. Valid values are:: + + 'upper right' : 1, + 'upper left' : 2, + 'lower left' : 3, + 'lower right' : 4 + + *loc2* is optional and defaults to *loc1*. + + **kwargs + Patch properties for the line drawn. Valid arguments include: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + + kwargs["transform"] = IdentityTransform() + kwargs.setdefault( + "fill", bool({'fc', 'facecolor', 'color'}.intersection(kwargs))) + super().__init__(**kwargs) + self.bbox1 = bbox1 + self.bbox2 = bbox2 + self.loc1 = loc1 + self.loc2 = loc2 + + def get_path(self): + # docstring inherited + return self.connect_bbox(self.bbox1, self.bbox2, + self.loc1, self.loc2) + + +class BboxConnectorPatch(BboxConnector): + @_docstring.interpd + def __init__(self, bbox1, bbox2, loc1a, loc2a, loc1b, loc2b, **kwargs): + """ + Connect two bboxes with a quadrilateral. + + The quadrilateral is specified by two lines that start and end at + corners of the bboxes. The four sides of the quadrilateral are defined + by the two lines given, the line between the two corners specified in + *bbox1* and the line between the two corners specified in *bbox2*. + + Parameters + ---------- + bbox1, bbox2 : `~matplotlib.transforms.Bbox` + Bounding boxes to connect. + + loc1a, loc2a, loc1b, loc2b : {1, 2, 3, 4} + The first line connects corners *loc1a* of *bbox1* and *loc2a* of + *bbox2*; the second line connects corners *loc1b* of *bbox1* and + *loc2b* of *bbox2*. Valid values are:: + + 'upper right' : 1, + 'upper left' : 2, + 'lower left' : 3, + 'lower right' : 4 + + **kwargs + Patch properties for the line drawn: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + super().__init__(bbox1, bbox2, loc1a, loc2a, **kwargs) + self.loc1b = loc1b + self.loc2b = loc2b + + def get_path(self): + # docstring inherited + path1 = self.connect_bbox(self.bbox1, self.bbox2, self.loc1, self.loc2) + path2 = self.connect_bbox(self.bbox2, self.bbox1, + self.loc2b, self.loc1b) + path_merged = [*path1.vertices, *path2.vertices, path1.vertices[0]] + return Path(path_merged) + + +def _add_inset_axes(parent_axes, axes_class, axes_kwargs, axes_locator): + """Helper function to add an inset axes and disable navigation in it.""" + if axes_class is None: + axes_class = HostAxes + if axes_kwargs is None: + axes_kwargs = {} + fig = parent_axes.get_figure(root=False) + inset_axes = axes_class( + fig, parent_axes.get_position(), + **{"navigate": False, **axes_kwargs, "axes_locator": axes_locator}) + return fig.add_axes(inset_axes) + + +@_docstring.interpd +def inset_axes(parent_axes, width, height, loc='upper right', + bbox_to_anchor=None, bbox_transform=None, + axes_class=None, axes_kwargs=None, + borderpad=0.5): + """ + Create an inset axes with a given width and height. + + Both sizes used can be specified either in inches or percentage. + For example,:: + + inset_axes(parent_axes, width='40%%', height='30%%', loc='lower left') + + creates in inset axes in the lower left corner of *parent_axes* which spans + over 30%% in height and 40%% in width of the *parent_axes*. Since the usage + of `.inset_axes` may become slightly tricky when exceeding such standard + cases, it is recommended to read :doc:`the examples + `. + + Notes + ----- + The meaning of *bbox_to_anchor* and *bbox_to_transform* is interpreted + differently from that of legend. The value of bbox_to_anchor + (or the return value of its get_points method; the default is + *parent_axes.bbox*) is transformed by the bbox_transform (the default + is Identity transform) and then interpreted as points in the pixel + coordinate (which is dpi dependent). + + Thus, following three calls are identical and creates an inset axes + with respect to the *parent_axes*:: + + axins = inset_axes(parent_axes, "30%%", "40%%") + axins = inset_axes(parent_axes, "30%%", "40%%", + bbox_to_anchor=parent_axes.bbox) + axins = inset_axes(parent_axes, "30%%", "40%%", + bbox_to_anchor=(0, 0, 1, 1), + bbox_transform=parent_axes.transAxes) + + Parameters + ---------- + parent_axes : `matplotlib.axes.Axes` + Axes to place the inset axes. + + width, height : float or str + Size of the inset axes to create. If a float is provided, it is + the size in inches, e.g. *width=1.3*. If a string is provided, it is + the size in relative units, e.g. *width='40%%'*. By default, i.e. if + neither *bbox_to_anchor* nor *bbox_transform* are specified, those + are relative to the parent_axes. Otherwise, they are to be understood + relative to the bounding box provided via *bbox_to_anchor*. + + loc : str, default: 'upper right' + Location to place the inset axes. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + + bbox_to_anchor : tuple or `~matplotlib.transforms.BboxBase`, optional + Bbox that the inset axes will be anchored to. If None, + a tuple of (0, 0, 1, 1) is used if *bbox_transform* is set + to *parent_axes.transAxes* or *parent_axes.figure.transFigure*. + Otherwise, *parent_axes.bbox* is used. If a tuple, can be either + [left, bottom, width, height], or [left, bottom]. + If the kwargs *width* and/or *height* are specified in relative units, + the 2-tuple [left, bottom] cannot be used. Note that, + unless *bbox_transform* is set, the units of the bounding box + are interpreted in the pixel coordinate. When using *bbox_to_anchor* + with tuple, it almost always makes sense to also specify + a *bbox_transform*. This might often be the axes transform + *parent_axes.transAxes*. + + bbox_transform : `~matplotlib.transforms.Transform`, optional + Transformation for the bbox that contains the inset axes. + If None, a `.transforms.IdentityTransform` is used. The value + of *bbox_to_anchor* (or the return value of its get_points method) + is transformed by the *bbox_transform* and then interpreted + as points in the pixel coordinate (which is dpi dependent). + You may provide *bbox_to_anchor* in some normalized coordinate, + and give an appropriate transform (e.g., *parent_axes.transAxes*). + + axes_class : `~matplotlib.axes.Axes` type, default: `.HostAxes` + The type of the newly created inset axes. + + axes_kwargs : dict, optional + Keyword arguments to pass to the constructor of the inset axes. + Valid arguments include: + + %(Axes:kwdoc)s + + borderpad : float, default: 0.5 + Padding between inset axes and the bbox_to_anchor. + The units are axes font size, i.e. for a default font size of 10 points + *borderpad = 0.5* is equivalent to a padding of 5 points. + + Returns + ------- + inset_axes : *axes_class* + Inset axes object created. + """ + + if (bbox_transform in [parent_axes.transAxes, + parent_axes.get_figure(root=False).transFigure] + and bbox_to_anchor is None): + _api.warn_external("Using the axes or figure transform requires a " + "bounding box in the respective coordinates. " + "Using bbox_to_anchor=(0, 0, 1, 1) now.") + bbox_to_anchor = (0, 0, 1, 1) + if bbox_to_anchor is None: + bbox_to_anchor = parent_axes.bbox + if (isinstance(bbox_to_anchor, tuple) and + (isinstance(width, str) or isinstance(height, str))): + if len(bbox_to_anchor) != 4: + raise ValueError("Using relative units for width or height " + "requires to provide a 4-tuple or a " + "`Bbox` instance to `bbox_to_anchor.") + return _add_inset_axes( + parent_axes, axes_class, axes_kwargs, + AnchoredSizeLocator( + bbox_to_anchor, width, height, loc=loc, + bbox_transform=bbox_transform, borderpad=borderpad)) + + +@_docstring.interpd +def zoomed_inset_axes(parent_axes, zoom, loc='upper right', + bbox_to_anchor=None, bbox_transform=None, + axes_class=None, axes_kwargs=None, + borderpad=0.5): + """ + Create an anchored inset axes by scaling a parent axes. For usage, also see + :doc:`the examples `. + + Parameters + ---------- + parent_axes : `~matplotlib.axes.Axes` + Axes to place the inset axes. + + zoom : float + Scaling factor of the data axes. *zoom* > 1 will enlarge the + coordinates (i.e., "zoomed in"), while *zoom* < 1 will shrink the + coordinates (i.e., "zoomed out"). + + loc : str, default: 'upper right' + Location to place the inset axes. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + + bbox_to_anchor : tuple or `~matplotlib.transforms.BboxBase`, optional + Bbox that the inset axes will be anchored to. If None, + *parent_axes.bbox* is used. If a tuple, can be either + [left, bottom, width, height], or [left, bottom]. + If the kwargs *width* and/or *height* are specified in relative units, + the 2-tuple [left, bottom] cannot be used. Note that + the units of the bounding box are determined through the transform + in use. When using *bbox_to_anchor* it almost always makes sense to + also specify a *bbox_transform*. This might often be the axes transform + *parent_axes.transAxes*. + + bbox_transform : `~matplotlib.transforms.Transform`, optional + Transformation for the bbox that contains the inset axes. + If None, a `.transforms.IdentityTransform` is used (i.e. pixel + coordinates). This is useful when not providing any argument to + *bbox_to_anchor*. When using *bbox_to_anchor* it almost always makes + sense to also specify a *bbox_transform*. This might often be the + axes transform *parent_axes.transAxes*. Inversely, when specifying + the axes- or figure-transform here, be aware that not specifying + *bbox_to_anchor* will use *parent_axes.bbox*, the units of which are + in display (pixel) coordinates. + + axes_class : `~matplotlib.axes.Axes` type, default: `.HostAxes` + The type of the newly created inset axes. + + axes_kwargs : dict, optional + Keyword arguments to pass to the constructor of the inset axes. + Valid arguments include: + + %(Axes:kwdoc)s + + borderpad : float, default: 0.5 + Padding between inset axes and the bbox_to_anchor. + The units are axes font size, i.e. for a default font size of 10 points + *borderpad = 0.5* is equivalent to a padding of 5 points. + + Returns + ------- + inset_axes : *axes_class* + Inset axes object created. + """ + + return _add_inset_axes( + parent_axes, axes_class, axes_kwargs, + AnchoredZoomLocator( + parent_axes, zoom=zoom, loc=loc, + bbox_to_anchor=bbox_to_anchor, bbox_transform=bbox_transform, + borderpad=borderpad)) + + +class _TransformedBboxWithCallback(TransformedBbox): + """ + Variant of `.TransformBbox` which calls *callback* before returning points. + + Used by `.mark_inset` to unstale the parent axes' viewlim as needed. + """ + + def __init__(self, *args, callback, **kwargs): + super().__init__(*args, **kwargs) + self._callback = callback + + def get_points(self): + self._callback() + return super().get_points() + + +@_docstring.interpd +def mark_inset(parent_axes, inset_axes, loc1, loc2, **kwargs): + """ + Draw a box to mark the location of an area represented by an inset axes. + + This function draws a box in *parent_axes* at the bounding box of + *inset_axes*, and shows a connection with the inset axes by drawing lines + at the corners, giving a "zoomed in" effect. + + Parameters + ---------- + parent_axes : `~matplotlib.axes.Axes` + Axes which contains the area of the inset axes. + + inset_axes : `~matplotlib.axes.Axes` + The inset axes. + + loc1, loc2 : {1, 2, 3, 4} + Corners to use for connecting the inset axes and the area in the + parent axes. + + **kwargs + Patch properties for the lines and box drawn: + + %(Patch:kwdoc)s + + Returns + ------- + pp : `~matplotlib.patches.Patch` + The patch drawn to represent the area of the inset axes. + + p1, p2 : `~matplotlib.patches.Patch` + The patches connecting two corners of the inset axes and its area. + """ + rect = _TransformedBboxWithCallback( + inset_axes.viewLim, parent_axes.transData, + callback=parent_axes._unstale_viewLim) + + kwargs.setdefault("fill", bool({'fc', 'facecolor', 'color'}.intersection(kwargs))) + pp = BboxPatch(rect, **kwargs) + parent_axes.add_patch(pp) + + p1 = BboxConnector(inset_axes.bbox, rect, loc1=loc1, **kwargs) + inset_axes.add_patch(p1) + p1.set_clip_on(False) + p2 = BboxConnector(inset_axes.bbox, rect, loc1=loc2, **kwargs) + inset_axes.add_patch(p2) + p2.set_clip_on(False) + + return pp, p1, p2 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..51c8748758cb6da3052f0e8b05ceba427d77a3f6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py @@ -0,0 +1,128 @@ +import matplotlib.axes as maxes +from matplotlib.artist import Artist +from matplotlib.axis import XAxis, YAxis + + +class SimpleChainedObjects: + def __init__(self, objects): + self._objects = objects + + def __getattr__(self, k): + _a = SimpleChainedObjects([getattr(a, k) for a in self._objects]) + return _a + + def __call__(self, *args, **kwargs): + for m in self._objects: + m(*args, **kwargs) + + +class Axes(maxes.Axes): + + class AxisDict(dict): + def __init__(self, axes): + self.axes = axes + super().__init__() + + def __getitem__(self, k): + if isinstance(k, tuple): + r = SimpleChainedObjects( + # super() within a list comprehension needs explicit args. + [super(Axes.AxisDict, self).__getitem__(k1) for k1 in k]) + return r + elif isinstance(k, slice): + if k.start is None and k.stop is None and k.step is None: + return SimpleChainedObjects(list(self.values())) + else: + raise ValueError("Unsupported slice") + else: + return dict.__getitem__(self, k) + + def __call__(self, *v, **kwargs): + return maxes.Axes.axis(self.axes, *v, **kwargs) + + @property + def axis(self): + return self._axislines + + def clear(self): + # docstring inherited + super().clear() + # Init axis artists. + self._axislines = self.AxisDict(self) + self._axislines.update( + bottom=SimpleAxisArtist(self.xaxis, 1, self.spines["bottom"]), + top=SimpleAxisArtist(self.xaxis, 2, self.spines["top"]), + left=SimpleAxisArtist(self.yaxis, 1, self.spines["left"]), + right=SimpleAxisArtist(self.yaxis, 2, self.spines["right"])) + + +class SimpleAxisArtist(Artist): + def __init__(self, axis, axisnum, spine): + self._axis = axis + self._axisnum = axisnum + self.line = spine + + if isinstance(axis, XAxis): + self._axis_direction = ["bottom", "top"][axisnum-1] + elif isinstance(axis, YAxis): + self._axis_direction = ["left", "right"][axisnum-1] + else: + raise ValueError( + f"axis must be instance of XAxis or YAxis, but got {axis}") + super().__init__() + + @property + def major_ticks(self): + tickline = "tick%dline" % self._axisnum + return SimpleChainedObjects([getattr(tick, tickline) + for tick in self._axis.get_major_ticks()]) + + @property + def major_ticklabels(self): + label = "label%d" % self._axisnum + return SimpleChainedObjects([getattr(tick, label) + for tick in self._axis.get_major_ticks()]) + + @property + def label(self): + return self._axis.label + + def set_visible(self, b): + self.toggle(all=b) + self.line.set_visible(b) + self._axis.set_visible(True) + super().set_visible(b) + + def set_label(self, txt): + self._axis.set_label_text(txt) + + def toggle(self, all=None, ticks=None, ticklabels=None, label=None): + + if all: + _ticks, _ticklabels, _label = True, True, True + elif all is not None: + _ticks, _ticklabels, _label = False, False, False + else: + _ticks, _ticklabels, _label = None, None, None + + if ticks is not None: + _ticks = ticks + if ticklabels is not None: + _ticklabels = ticklabels + if label is not None: + _label = label + + if _ticks is not None: + tickparam = {f"tick{self._axisnum}On": _ticks} + self._axis.set_tick_params(**tickparam) + if _ticklabels is not None: + tickparam = {f"label{self._axisnum}On": _ticklabels} + self._axis.set_tick_params(**tickparam) + + if _label is not None: + pos = self._axis.get_label_position() + if (pos == self._axis_direction) and not _label: + self._axis.label.set_visible(False) + elif _label: + self._axis.label.set_visible(True) + self._axis.set_label_position(self._axis_direction) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..f7bc2df6d7e01141254114065316a70df5f35e33 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py @@ -0,0 +1,257 @@ +from matplotlib import _api, cbook +import matplotlib.artist as martist +import matplotlib.transforms as mtransforms +from matplotlib.transforms import Bbox +from .mpl_axes import Axes + + +class ParasiteAxesBase: + + def __init__(self, parent_axes, aux_transform=None, + *, viewlim_mode=None, **kwargs): + self._parent_axes = parent_axes + self.transAux = aux_transform + self.set_viewlim_mode(viewlim_mode) + kwargs["frameon"] = False + super().__init__(parent_axes.get_figure(root=False), + parent_axes._position, **kwargs) + + def clear(self): + super().clear() + martist.setp(self.get_children(), visible=False) + self._get_lines = self._parent_axes._get_lines + self._parent_axes.callbacks._connect_picklable( + "xlim_changed", self._sync_lims) + self._parent_axes.callbacks._connect_picklable( + "ylim_changed", self._sync_lims) + + def pick(self, mouseevent): + # This most likely goes to Artist.pick (depending on axes_class given + # to the factory), which only handles pick events registered on the + # axes associated with each child: + super().pick(mouseevent) + # But parasite axes are additionally given pick events from their host + # axes (cf. HostAxesBase.pick), which we handle here: + for a in self.get_children(): + if (hasattr(mouseevent.inaxes, "parasites") + and self in mouseevent.inaxes.parasites): + a.pick(mouseevent) + + # aux_transform support + + def _set_lim_and_transforms(self): + if self.transAux is not None: + self.transAxes = self._parent_axes.transAxes + self.transData = self.transAux + self._parent_axes.transData + self._xaxis_transform = mtransforms.blended_transform_factory( + self.transData, self.transAxes) + self._yaxis_transform = mtransforms.blended_transform_factory( + self.transAxes, self.transData) + else: + super()._set_lim_and_transforms() + + def set_viewlim_mode(self, mode): + _api.check_in_list([None, "equal", "transform"], mode=mode) + self._viewlim_mode = mode + + def get_viewlim_mode(self): + return self._viewlim_mode + + def _sync_lims(self, parent): + viewlim = parent.viewLim.frozen() + mode = self.get_viewlim_mode() + if mode is None: + pass + elif mode == "equal": + self.viewLim.set(viewlim) + elif mode == "transform": + self.viewLim.set(viewlim.transformed(self.transAux.inverted())) + else: + _api.check_in_list([None, "equal", "transform"], mode=mode) + + # end of aux_transform support + + +parasite_axes_class_factory = cbook._make_class_factory( + ParasiteAxesBase, "{}Parasite") +ParasiteAxes = parasite_axes_class_factory(Axes) + + +class HostAxesBase: + def __init__(self, *args, **kwargs): + self.parasites = [] + super().__init__(*args, **kwargs) + + def get_aux_axes( + self, tr=None, viewlim_mode="equal", axes_class=None, **kwargs): + """ + Add a parasite axes to this host. + + Despite this method's name, this should actually be thought of as an + ``add_parasite_axes`` method. + + .. versionchanged:: 3.7 + Defaults to same base axes class as host axes. + + Parameters + ---------- + tr : `~matplotlib.transforms.Transform` or None, default: None + If a `.Transform`, the following relation will hold: + ``parasite.transData = tr + host.transData``. + If None, the parasite's and the host's ``transData`` are unrelated. + viewlim_mode : {"equal", "transform", None}, default: "equal" + How the parasite's view limits are set: directly equal to the + parent axes ("equal"), equal after application of *tr* + ("transform"), or independently (None). + axes_class : subclass type of `~matplotlib.axes.Axes`, optional + The `~.axes.Axes` subclass that is instantiated. If None, the base + class of the host axes is used. + **kwargs + Other parameters are forwarded to the parasite axes constructor. + """ + if axes_class is None: + axes_class = self._base_axes_class + parasite_axes_class = parasite_axes_class_factory(axes_class) + ax2 = parasite_axes_class( + self, tr, viewlim_mode=viewlim_mode, **kwargs) + # note that ax2.transData == tr + ax1.transData + # Anything you draw in ax2 will match the ticks and grids of ax1. + self.parasites.append(ax2) + ax2._remove_method = self.parasites.remove + return ax2 + + def draw(self, renderer): + orig_children_len = len(self._children) + + locator = self.get_axes_locator() + if locator: + pos = locator(self, renderer) + self.set_position(pos, which="active") + self.apply_aspect(pos) + else: + self.apply_aspect() + + rect = self.get_position() + for ax in self.parasites: + ax.apply_aspect(rect) + self._children.extend(ax.get_children()) + + super().draw(renderer) + del self._children[orig_children_len:] + + def clear(self): + super().clear() + for ax in self.parasites: + ax.clear() + + def pick(self, mouseevent): + super().pick(mouseevent) + # Also pass pick events on to parasite axes and, in turn, their + # children (cf. ParasiteAxesBase.pick) + for a in self.parasites: + a.pick(mouseevent) + + def twinx(self, axes_class=None): + """ + Create a twin of Axes with a shared x-axis but independent y-axis. + + The y-axis of self will have ticks on the left and the returned axes + will have ticks on the right. + """ + ax = self._add_twin_axes(axes_class, sharex=self) + self.axis["right"].set_visible(False) + ax.axis["right"].set_visible(True) + ax.axis["left", "top", "bottom"].set_visible(False) + return ax + + def twiny(self, axes_class=None): + """ + Create a twin of Axes with a shared y-axis but independent x-axis. + + The x-axis of self will have ticks on the bottom and the returned axes + will have ticks on the top. + """ + ax = self._add_twin_axes(axes_class, sharey=self) + self.axis["top"].set_visible(False) + ax.axis["top"].set_visible(True) + ax.axis["left", "right", "bottom"].set_visible(False) + return ax + + def twin(self, aux_trans=None, axes_class=None): + """ + Create a twin of Axes with no shared axis. + + While self will have ticks on the left and bottom axis, the returned + axes will have ticks on the top and right axis. + """ + if aux_trans is None: + aux_trans = mtransforms.IdentityTransform() + ax = self._add_twin_axes( + axes_class, aux_transform=aux_trans, viewlim_mode="transform") + self.axis["top", "right"].set_visible(False) + ax.axis["top", "right"].set_visible(True) + ax.axis["left", "bottom"].set_visible(False) + return ax + + def _add_twin_axes(self, axes_class, **kwargs): + """ + Helper for `.twinx`/`.twiny`/`.twin`. + + *kwargs* are forwarded to the parasite axes constructor. + """ + if axes_class is None: + axes_class = self._base_axes_class + ax = parasite_axes_class_factory(axes_class)(self, **kwargs) + self.parasites.append(ax) + ax._remove_method = self._remove_any_twin + return ax + + def _remove_any_twin(self, ax): + self.parasites.remove(ax) + restore = ["top", "right"] + if ax._sharex: + restore.remove("top") + if ax._sharey: + restore.remove("right") + self.axis[tuple(restore)].set_visible(True) + self.axis[tuple(restore)].toggle(ticklabels=False, label=False) + + def get_tightbbox(self, renderer=None, *, call_axes_locator=True, + bbox_extra_artists=None): + bbs = [ + *[ax.get_tightbbox(renderer, call_axes_locator=call_axes_locator) + for ax in self.parasites], + super().get_tightbbox(renderer, + call_axes_locator=call_axes_locator, + bbox_extra_artists=bbox_extra_artists)] + return Bbox.union([b for b in bbs if b.width != 0 or b.height != 0]) + + +host_axes_class_factory = host_subplot_class_factory = \ + cbook._make_class_factory(HostAxesBase, "{}HostAxes", "_base_axes_class") +HostAxes = SubplotHost = host_axes_class_factory(Axes) + + +def host_axes(*args, axes_class=Axes, figure=None, **kwargs): + """ + Create axes that can act as a hosts to parasitic axes. + + Parameters + ---------- + figure : `~matplotlib.figure.Figure` + Figure to which the axes will be added. Defaults to the current figure + `.pyplot.gcf()`. + + *args, **kwargs + Will be passed on to the underlying `~.axes.Axes` object creation. + """ + import matplotlib.pyplot as plt + host_axes_class = host_axes_class_factory(axes_class) + if figure is None: + figure = plt.gcf() + ax = host_axes_class(figure, *args, **kwargs) + figure.add_axes(ax) + return ax + + +host_subplot = host_axes diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py new file mode 100644 index 0000000000000000000000000000000000000000..b045e8dc87569b0ea5653cf8b0c6307135b69c75 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py @@ -0,0 +1,782 @@ +from itertools import product +import io +import platform + +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +from matplotlib import cbook +from matplotlib.backend_bases import MouseEvent +from matplotlib.colors import LogNorm +from matplotlib.patches import Circle, Ellipse +from matplotlib.transforms import Bbox, TransformedBbox +from matplotlib.testing.decorators import ( + check_figures_equal, image_comparison, remove_ticks_and_titles) + +from mpl_toolkits.axes_grid1 import ( + axes_size as Size, + host_subplot, make_axes_locatable, + Grid, AxesGrid, ImageGrid) +from mpl_toolkits.axes_grid1.anchored_artists import ( + AnchoredAuxTransformBox, AnchoredDrawingArea, + AnchoredDirectionArrows, AnchoredSizeBar) +from mpl_toolkits.axes_grid1.axes_divider import ( + Divider, HBoxDivider, make_axes_area_auto_adjustable, SubplotDivider, + VBoxDivider) +from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes +from mpl_toolkits.axes_grid1.inset_locator import ( + zoomed_inset_axes, mark_inset, inset_axes, BboxConnectorPatch) +import mpl_toolkits.axes_grid1.mpl_axes +import pytest + +import numpy as np +from numpy.testing import assert_array_equal, assert_array_almost_equal + + +def test_divider_append_axes(): + fig, ax = plt.subplots() + divider = make_axes_locatable(ax) + axs = { + "main": ax, + "top": divider.append_axes("top", 1.2, pad=0.1, sharex=ax), + "bottom": divider.append_axes("bottom", 1.2, pad=0.1, sharex=ax), + "left": divider.append_axes("left", 1.2, pad=0.1, sharey=ax), + "right": divider.append_axes("right", 1.2, pad=0.1, sharey=ax), + } + fig.canvas.draw() + bboxes = {k: axs[k].get_window_extent() for k in axs} + dpi = fig.dpi + assert bboxes["top"].height == pytest.approx(1.2 * dpi) + assert bboxes["bottom"].height == pytest.approx(1.2 * dpi) + assert bboxes["left"].width == pytest.approx(1.2 * dpi) + assert bboxes["right"].width == pytest.approx(1.2 * dpi) + assert bboxes["top"].y0 - bboxes["main"].y1 == pytest.approx(0.1 * dpi) + assert bboxes["main"].y0 - bboxes["bottom"].y1 == pytest.approx(0.1 * dpi) + assert bboxes["main"].x0 - bboxes["left"].x1 == pytest.approx(0.1 * dpi) + assert bboxes["right"].x0 - bboxes["main"].x1 == pytest.approx(0.1 * dpi) + assert bboxes["left"].y0 == bboxes["main"].y0 == bboxes["right"].y0 + assert bboxes["left"].y1 == bboxes["main"].y1 == bboxes["right"].y1 + assert bboxes["top"].x0 == bboxes["main"].x0 == bboxes["bottom"].x0 + assert bboxes["top"].x1 == bboxes["main"].x1 == bboxes["bottom"].x1 + + +# Update style when regenerating the test image +@image_comparison(['twin_axes_empty_and_removed'], extensions=["png"], tol=1, + style=('classic', '_classic_test_patch')) +def test_twin_axes_empty_and_removed(): + # Purely cosmetic font changes (avoid overlap) + mpl.rcParams.update( + {"font.size": 8, "xtick.labelsize": 8, "ytick.labelsize": 8}) + generators = ["twinx", "twiny", "twin"] + modifiers = ["", "host invisible", "twin removed", "twin invisible", + "twin removed\nhost invisible"] + # Unmodified host subplot at the beginning for reference + h = host_subplot(len(modifiers)+1, len(generators), 2) + h.text(0.5, 0.5, "host_subplot", + horizontalalignment="center", verticalalignment="center") + # Host subplots with various modifications (twin*, visibility) applied + for i, (mod, gen) in enumerate(product(modifiers, generators), + len(generators) + 1): + h = host_subplot(len(modifiers)+1, len(generators), i) + t = getattr(h, gen)() + if "twin invisible" in mod: + t.axis[:].set_visible(False) + if "twin removed" in mod: + t.remove() + if "host invisible" in mod: + h.axis[:].set_visible(False) + h.text(0.5, 0.5, gen + ("\n" + mod if mod else ""), + horizontalalignment="center", verticalalignment="center") + plt.subplots_adjust(wspace=0.5, hspace=1) + + +def test_twin_axes_both_with_units(): + host = host_subplot(111) + with pytest.warns(mpl.MatplotlibDeprecationWarning): + host.plot_date([0, 1, 2], [0, 1, 2], xdate=False, ydate=True) + twin = host.twinx() + twin.plot(["a", "b", "c"]) + assert host.get_yticklabels()[0].get_text() == "00:00:00" + assert twin.get_yticklabels()[0].get_text() == "a" + + +def test_axesgrid_colorbar_log_smoketest(): + fig = plt.figure() + grid = AxesGrid(fig, 111, # modified to be only subplot + nrows_ncols=(1, 1), + ngrids=1, + label_mode="L", + cbar_location="top", + cbar_mode="single", + ) + + Z = 10000 * np.random.rand(10, 10) + im = grid[0].imshow(Z, interpolation="nearest", norm=LogNorm()) + + grid.cbar_axes[0].colorbar(im) + + +def test_inset_colorbar_tight_layout_smoketest(): + fig, ax = plt.subplots(1, 1) + pts = ax.scatter([0, 1], [0, 1], c=[1, 5]) + + cax = inset_axes(ax, width="3%", height="70%") + plt.colorbar(pts, cax=cax) + + with pytest.warns(UserWarning, match="This figure includes Axes"): + # Will warn, but not raise an error + plt.tight_layout() + + +@image_comparison(['inset_locator.png'], style='default', remove_text=True) +def test_inset_locator(): + fig, ax = plt.subplots(figsize=[5, 4]) + + # prepare the demo image + # Z is a 15x15 array + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + Z2 = np.zeros((150, 150)) + ny, nx = Z.shape + Z2[30:30+ny, 30:30+nx] = Z + + ax.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + + axins = zoomed_inset_axes(ax, zoom=6, loc='upper right') + axins.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + axins.yaxis.get_major_locator().set_params(nbins=7) + axins.xaxis.get_major_locator().set_params(nbins=7) + # sub region of the original image + x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 + axins.set_xlim(x1, x2) + axins.set_ylim(y1, y2) + + plt.xticks(visible=False) + plt.yticks(visible=False) + + # draw a bbox of the region of the inset axes in the parent axes and + # connecting lines between the bbox and the inset axes area + mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") + + asb = AnchoredSizeBar(ax.transData, + 0.5, + '0.5', + loc='lower center', + pad=0.1, borderpad=0.5, sep=5, + frameon=False) + ax.add_artist(asb) + + +@image_comparison(['inset_axes.png'], style='default', remove_text=True) +def test_inset_axes(): + fig, ax = plt.subplots(figsize=[5, 4]) + + # prepare the demo image + # Z is a 15x15 array + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + Z2 = np.zeros((150, 150)) + ny, nx = Z.shape + Z2[30:30+ny, 30:30+nx] = Z + + ax.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + + # creating our inset axes with a bbox_transform parameter + axins = inset_axes(ax, width=1., height=1., bbox_to_anchor=(1, 1), + bbox_transform=ax.transAxes) + + axins.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + axins.yaxis.get_major_locator().set_params(nbins=7) + axins.xaxis.get_major_locator().set_params(nbins=7) + # sub region of the original image + x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 + axins.set_xlim(x1, x2) + axins.set_ylim(y1, y2) + + plt.xticks(visible=False) + plt.yticks(visible=False) + + # draw a bbox of the region of the inset axes in the parent axes and + # connecting lines between the bbox and the inset axes area + mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") + + asb = AnchoredSizeBar(ax.transData, + 0.5, + '0.5', + loc='lower center', + pad=0.1, borderpad=0.5, sep=5, + frameon=False) + ax.add_artist(asb) + + +def test_inset_axes_complete(): + dpi = 100 + figsize = (6, 5) + fig, ax = plt.subplots(figsize=figsize, dpi=dpi) + fig.subplots_adjust(.1, .1, .9, .9) + + ins = inset_axes(ax, width=2., height=2., borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, + [(0.9*figsize[0]-2.)/figsize[0], (0.9*figsize[1]-2.)/figsize[1], + 0.9, 0.9]) + + ins = inset_axes(ax, width="40%", height="30%", borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, [.9-.8*.4, .9-.8*.3, 0.9, 0.9]) + + ins = inset_axes(ax, width=1., height=1.2, bbox_to_anchor=(200, 100), + loc=3, borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, + [200/dpi/figsize[0], 100/dpi/figsize[1], + (200/dpi+1)/figsize[0], (100/dpi+1.2)/figsize[1]]) + + ins1 = inset_axes(ax, width="35%", height="60%", loc=3, borderpad=1) + ins2 = inset_axes(ax, width="100%", height="100%", + bbox_to_anchor=(0, 0, .35, .60), + bbox_transform=ax.transAxes, loc=3, borderpad=1) + fig.canvas.draw() + assert_array_equal(ins1.get_position().extents, + ins2.get_position().extents) + + with pytest.raises(ValueError): + ins = inset_axes(ax, width="40%", height="30%", + bbox_to_anchor=(0.4, 0.5)) + + with pytest.warns(UserWarning): + ins = inset_axes(ax, width="40%", height="30%", + bbox_transform=ax.transAxes) + + +def test_inset_axes_tight(): + # gh-26287 found that inset_axes raised with bbox_inches=tight + fig, ax = plt.subplots() + inset_axes(ax, width=1.3, height=0.9) + + f = io.BytesIO() + fig.savefig(f, bbox_inches="tight") + + +@image_comparison(['fill_facecolor.png'], remove_text=True, style='mpl20') +def test_fill_facecolor(): + fig, ax = plt.subplots(1, 5) + fig.set_size_inches(5, 5) + for i in range(1, 4): + ax[i].yaxis.set_visible(False) + ax[4].yaxis.tick_right() + bbox = Bbox.from_extents(0, 0.4, 1, 0.6) + + # fill with blue by setting 'fc' field + bbox1 = TransformedBbox(bbox, ax[0].transData) + bbox2 = TransformedBbox(bbox, ax[1].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", fc="b") + p.set_clip_on(False) + ax[0].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[0], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5") + + # fill with yellow by setting 'facecolor' field + bbox3 = TransformedBbox(bbox, ax[1].transData) + bbox4 = TransformedBbox(bbox, ax[2].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", facecolor="y") + p.set_clip_on(False) + ax[1].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[1], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5") + + # fill with green by setting 'color' field + bbox5 = TransformedBbox(bbox, ax[2].transData) + bbox6 = TransformedBbox(bbox, ax[3].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", color="g") + p.set_clip_on(False) + ax[2].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[2], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5") + + # fill with green but color won't show if set fill to False + bbox7 = TransformedBbox(bbox, ax[3].transData) + bbox8 = TransformedBbox(bbox, ax[4].transData) + # BboxConnectorPatch won't show green + p = BboxConnectorPatch( + bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", fc="g", fill=False) + p.set_clip_on(False) + ax[3].add_patch(p) + # marked area won't show green + axins = zoomed_inset_axes(ax[3], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + axins.xaxis.set_ticks([]) + axins.yaxis.set_ticks([]) + mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False) + + +# Update style when regenerating the test image +@image_comparison(['zoomed_axes.png', 'inverted_zoomed_axes.png'], + style=('classic', '_classic_test_patch'), + tol=0 if platform.machine() == 'x86_64' else 0.02) +def test_zooming_with_inverted_axes(): + fig, ax = plt.subplots() + ax.plot([1, 2, 3], [1, 2, 3]) + ax.axis([1, 3, 1, 3]) + inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') + inset_ax.axis([1.1, 1.4, 1.1, 1.4]) + + fig, ax = plt.subplots() + ax.plot([1, 2, 3], [1, 2, 3]) + ax.axis([3, 1, 3, 1]) + inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') + inset_ax.axis([1.4, 1.1, 1.4, 1.1]) + + +# Update style when regenerating the test image +@image_comparison(['anchored_direction_arrows.png'], + tol=0 if platform.machine() == 'x86_64' else 0.01, + style=('classic', '_classic_test_patch')) +def test_anchored_direction_arrows(): + fig, ax = plt.subplots() + ax.imshow(np.zeros((10, 10)), interpolation='nearest') + + simple_arrow = AnchoredDirectionArrows(ax.transAxes, 'X', 'Y') + ax.add_artist(simple_arrow) + + +# Update style when regenerating the test image +@image_comparison(['anchored_direction_arrows_many_args.png'], + style=('classic', '_classic_test_patch')) +def test_anchored_direction_arrows_many_args(): + fig, ax = plt.subplots() + ax.imshow(np.ones((10, 10))) + + direction_arrows = AnchoredDirectionArrows( + ax.transAxes, 'A', 'B', loc='upper right', color='red', + aspect_ratio=-0.5, pad=0.6, borderpad=2, frameon=True, alpha=0.7, + sep_x=-0.06, sep_y=-0.08, back_length=0.1, head_width=9, + head_length=10, tail_width=5) + ax.add_artist(direction_arrows) + + +def test_axes_locatable_position(): + fig, ax = plt.subplots() + divider = make_axes_locatable(ax) + with mpl.rc_context({"figure.subplot.wspace": 0.02}): + cax = divider.append_axes('right', size='5%') + fig.canvas.draw() + assert np.isclose(cax.get_position(original=False).width, + 0.03621495327102808) + + +@image_comparison(['image_grid_each_left_label_mode_all.png'], style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid_each_left_label_mode_all(): + imdata = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (3, 3)) + grid = ImageGrid(fig, (1, 1, 1), nrows_ncols=(3, 2), axes_pad=(0.5, 0.3), + cbar_mode="each", cbar_location="left", cbar_size="15%", + label_mode="all") + # 3-tuple rect => SubplotDivider + assert isinstance(grid.get_divider(), SubplotDivider) + assert grid.get_axes_pad() == (0.5, 0.3) + assert grid.get_aspect() # True by default for ImageGrid + for ax, cax in zip(grid, grid.cbar_axes): + im = ax.imshow(imdata, interpolation='none') + cax.colorbar(im) + + +@image_comparison(['image_grid_single_bottom_label_mode_1.png'], style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid_single_bottom(): + imdata = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (2.5, 1.5)) + grid = ImageGrid(fig, (0, 0, 1, 1), nrows_ncols=(1, 3), + axes_pad=(0.2, 0.15), cbar_mode="single", cbar_pad=0.3, + cbar_location="bottom", cbar_size="10%", label_mode="1") + # 4-tuple rect => Divider, isinstance will give True for SubplotDivider + assert type(grid.get_divider()) is Divider + for i in range(3): + im = grid[i].imshow(imdata, interpolation='none') + grid.cbar_axes[0].colorbar(im) + + +def test_image_grid_label_mode_invalid(): + fig = plt.figure() + with pytest.raises(ValueError, match="'foo' is not a valid value for mode"): + ImageGrid(fig, (0, 0, 1, 1), (2, 1), label_mode="foo") + + +@image_comparison(['image_grid.png'], + remove_text=True, style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid(): + # test that image grid works with bbox_inches=tight. + im = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (4, 4)) + grid = ImageGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.1) + assert grid.get_axes_pad() == (0.1, 0.1) + for i in range(4): + grid[i].imshow(im, interpolation='nearest') + + +def test_gettightbbox(): + fig, ax = plt.subplots(figsize=(8, 6)) + + l, = ax.plot([1, 2, 3], [0, 1, 0]) + + ax_zoom = zoomed_inset_axes(ax, 4) + ax_zoom.plot([1, 2, 3], [0, 1, 0]) + + mark_inset(ax, ax_zoom, loc1=1, loc2=3, fc="none", ec='0.3') + + remove_ticks_and_titles(fig) + bbox = fig.get_tightbbox(fig.canvas.get_renderer()) + np.testing.assert_array_almost_equal(bbox.extents, + [-17.7, -13.9, 7.2, 5.4]) + + +@pytest.mark.parametrize("click_on", ["big", "small"]) +@pytest.mark.parametrize("big_on_axes,small_on_axes", [ + ("gca", "gca"), + ("host", "host"), + ("host", "parasite"), + ("parasite", "host"), + ("parasite", "parasite") +]) +def test_picking_callbacks_overlap(big_on_axes, small_on_axes, click_on): + """Test pick events on normal, host or parasite axes.""" + # Two rectangles are drawn and "clicked on", a small one and a big one + # enclosing the small one. The axis on which they are drawn as well as the + # rectangle that is clicked on are varied. + # In each case we expect that both rectangles are picked if we click on the + # small one and only the big one is picked if we click on the big one. + # Also tests picking on normal axes ("gca") as a control. + big = plt.Rectangle((0.25, 0.25), 0.5, 0.5, picker=5) + small = plt.Rectangle((0.4, 0.4), 0.2, 0.2, facecolor="r", picker=5) + # Machinery for "receiving" events + received_events = [] + def on_pick(event): + received_events.append(event) + plt.gcf().canvas.mpl_connect('pick_event', on_pick) + # Shortcut + rectangles_on_axes = (big_on_axes, small_on_axes) + # Axes setup + axes = {"gca": None, "host": None, "parasite": None} + if "gca" in rectangles_on_axes: + axes["gca"] = plt.gca() + if "host" in rectangles_on_axes or "parasite" in rectangles_on_axes: + axes["host"] = host_subplot(111) + axes["parasite"] = axes["host"].twin() + # Add rectangles to axes + axes[big_on_axes].add_patch(big) + axes[small_on_axes].add_patch(small) + # Simulate picking with click mouse event + if click_on == "big": + click_axes = axes[big_on_axes] + axes_coords = (0.3, 0.3) + else: + click_axes = axes[small_on_axes] + axes_coords = (0.5, 0.5) + # In reality mouse events never happen on parasite axes, only host axes + if click_axes is axes["parasite"]: + click_axes = axes["host"] + (x, y) = click_axes.transAxes.transform(axes_coords) + m = MouseEvent("button_press_event", click_axes.get_figure(root=True).canvas, x, y, + button=1) + click_axes.pick(m) + # Checks + expected_n_events = 2 if click_on == "small" else 1 + assert len(received_events) == expected_n_events + event_rects = [event.artist for event in received_events] + assert big in event_rects + if click_on == "small": + assert small in event_rects + + +@image_comparison(['anchored_artists.png'], remove_text=True, style='mpl20') +def test_anchored_artists(): + fig, ax = plt.subplots(figsize=(3, 3)) + ada = AnchoredDrawingArea(40, 20, 0, 0, loc='upper right', pad=0., + frameon=False) + p1 = Circle((10, 10), 10) + ada.drawing_area.add_artist(p1) + p2 = Circle((30, 10), 5, fc="r") + ada.drawing_area.add_artist(p2) + ax.add_artist(ada) + + box = AnchoredAuxTransformBox(ax.transData, loc='upper left') + el = Ellipse((0, 0), width=0.1, height=0.4, angle=30, color='cyan') + box.drawing_area.add_artist(el) + ax.add_artist(box) + + # This block used to test the AnchoredEllipse class, but that was removed. The block + # remains, though it duplicates the above ellipse, so that the test image doesn't + # need to be regenerated. + box = AnchoredAuxTransformBox(ax.transData, loc='lower left', frameon=True, + pad=0.5, borderpad=0.4) + el = Ellipse((0, 0), width=0.1, height=0.25, angle=-60) + box.drawing_area.add_artist(el) + ax.add_artist(box) + + asb = AnchoredSizeBar(ax.transData, 0.2, r"0.2 units", loc='lower right', + pad=0.3, borderpad=0.4, sep=4, fill_bar=True, + frameon=False, label_top=True, prop={'size': 20}, + size_vertical=0.05, color='green') + ax.add_artist(asb) + + +def test_hbox_divider(): + arr1 = np.arange(20).reshape((4, 5)) + arr2 = np.arange(20).reshape((5, 4)) + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(arr1) + ax2.imshow(arr2) + + pad = 0.5 # inches. + divider = HBoxDivider( + fig, 111, # Position of combined axes. + horizontal=[Size.AxesX(ax1), Size.Fixed(pad), Size.AxesX(ax2)], + vertical=[Size.AxesY(ax1), Size.Scaled(1), Size.AxesY(ax2)]) + ax1.set_axes_locator(divider.new_locator(0)) + ax2.set_axes_locator(divider.new_locator(2)) + + fig.canvas.draw() + p1 = ax1.get_position() + p2 = ax2.get_position() + assert p1.height == p2.height + assert p2.width / p1.width == pytest.approx((4 / 5) ** 2) + + +def test_vbox_divider(): + arr1 = np.arange(20).reshape((4, 5)) + arr2 = np.arange(20).reshape((5, 4)) + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(arr1) + ax2.imshow(arr2) + + pad = 0.5 # inches. + divider = VBoxDivider( + fig, 111, # Position of combined axes. + horizontal=[Size.AxesX(ax1), Size.Scaled(1), Size.AxesX(ax2)], + vertical=[Size.AxesY(ax1), Size.Fixed(pad), Size.AxesY(ax2)]) + ax1.set_axes_locator(divider.new_locator(0)) + ax2.set_axes_locator(divider.new_locator(2)) + + fig.canvas.draw() + p1 = ax1.get_position() + p2 = ax2.get_position() + assert p1.width == p2.width + assert p1.height / p2.height == pytest.approx((4 / 5) ** 2) + + +def test_axes_class_tuple(): + fig = plt.figure() + axes_class = (mpl_toolkits.axes_grid1.mpl_axes.Axes, {}) + gr = AxesGrid(fig, 111, nrows_ncols=(1, 1), axes_class=axes_class) + + +def test_grid_axes_lists(): + """Test Grid axes_all, axes_row and axes_column relationship.""" + fig = plt.figure() + grid = Grid(fig, 111, (2, 3), direction="row") + assert_array_equal(grid, grid.axes_all) + assert_array_equal(grid.axes_row, np.transpose(grid.axes_column)) + assert_array_equal(grid, np.ravel(grid.axes_row), "row") + assert grid.get_geometry() == (2, 3) + grid = Grid(fig, 111, (2, 3), direction="column") + assert_array_equal(grid, np.ravel(grid.axes_column), "column") + + +@pytest.mark.parametrize('direction', ('row', 'column')) +def test_grid_axes_position(direction): + """Test positioning of the axes in Grid.""" + fig = plt.figure() + grid = Grid(fig, 111, (2, 2), direction=direction) + loc = [ax.get_axes_locator() for ax in np.ravel(grid.axes_row)] + # Test nx. + assert loc[1].args[0] > loc[0].args[0] + assert loc[0].args[0] == loc[2].args[0] + assert loc[3].args[0] == loc[1].args[0] + # Test ny. + assert loc[2].args[1] < loc[0].args[1] + assert loc[0].args[1] == loc[1].args[1] + assert loc[3].args[1] == loc[2].args[1] + + +@pytest.mark.parametrize('rect, ngrids, error, message', ( + ((1, 1), None, TypeError, "Incorrect rect format"), + (111, -1, ValueError, "ngrids must be positive"), + (111, 7, ValueError, "ngrids must be positive"), +)) +def test_grid_errors(rect, ngrids, error, message): + fig = plt.figure() + with pytest.raises(error, match=message): + Grid(fig, rect, (2, 3), ngrids=ngrids) + + +@pytest.mark.parametrize('anchor, error, message', ( + (None, TypeError, "anchor must be str"), + ("CC", ValueError, "'CC' is not a valid value for anchor"), + ((1, 1, 1), TypeError, "anchor must be str"), +)) +def test_divider_errors(anchor, error, message): + fig = plt.figure() + with pytest.raises(error, match=message): + Divider(fig, [0, 0, 1, 1], [Size.Fixed(1)], [Size.Fixed(1)], + anchor=anchor) + + +@check_figures_equal(extensions=["png"]) +def test_mark_inset_unstales_viewlim(fig_test, fig_ref): + inset, full = fig_test.subplots(1, 2) + full.plot([0, 5], [0, 5]) + inset.set(xlim=(1, 2), ylim=(1, 2)) + # Check that mark_inset unstales full's viewLim before drawing the marks. + mark_inset(full, inset, 1, 4) + + inset, full = fig_ref.subplots(1, 2) + full.plot([0, 5], [0, 5]) + inset.set(xlim=(1, 2), ylim=(1, 2)) + mark_inset(full, inset, 1, 4) + # Manually unstale the full's viewLim. + fig_ref.canvas.draw() + + +def test_auto_adjustable(): + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1]) + pad = 0.1 + make_axes_area_auto_adjustable(ax, pad=pad) + fig.canvas.draw() + tbb = ax.get_tightbbox() + assert tbb.x0 == pytest.approx(pad * fig.dpi) + assert tbb.x1 == pytest.approx(fig.bbox.width - pad * fig.dpi) + assert tbb.y0 == pytest.approx(pad * fig.dpi) + assert tbb.y1 == pytest.approx(fig.bbox.height - pad * fig.dpi) + + +# Update style when regenerating the test image +@image_comparison(['rgb_axes.png'], remove_text=True, + style=('classic', '_classic_test_patch')) +def test_rgb_axes(): + fig = plt.figure() + ax = RGBAxes(fig, (0.1, 0.1, 0.8, 0.8), pad=0.1) + rng = np.random.default_rng(19680801) + r = rng.random((5, 5)) + g = rng.random((5, 5)) + b = rng.random((5, 5)) + ax.imshow_rgb(r, g, b, interpolation='none') + + +# The original version of this test relied on mpl_toolkits's slightly different +# colorbar implementation; moving to matplotlib's own colorbar implementation +# caused the small image comparison error. +@image_comparison(['imagegrid_cbar_mode.png'], + remove_text=True, style='mpl20', tol=0.3) +def test_imagegrid_cbar_mode_edge(): + arr = np.arange(16).reshape((4, 4)) + + fig = plt.figure(figsize=(18, 9)) + + positions = (241, 242, 243, 244, 245, 246, 247, 248) + directions = ['row']*4 + ['column']*4 + cbar_locations = ['left', 'right', 'top', 'bottom']*2 + + for position, direction, location in zip( + positions, directions, cbar_locations): + grid = ImageGrid(fig, position, + nrows_ncols=(2, 2), + direction=direction, + cbar_location=location, + cbar_size='20%', + cbar_mode='edge') + ax1, ax2, ax3, ax4 = grid + + ax1.imshow(arr, cmap='nipy_spectral') + ax2.imshow(arr.T, cmap='hot') + ax3.imshow(np.hypot(arr, arr.T), cmap='jet') + ax4.imshow(np.arctan2(arr, arr.T), cmap='hsv') + + # In each row/column, the "first" colorbars must be overwritten by the + # "second" ones. To achieve this, clear out the axes first. + for ax in grid: + ax.cax.cla() + cb = ax.cax.colorbar(ax.images[0]) + + +def test_imagegrid(): + fig = plt.figure() + grid = ImageGrid(fig, 111, nrows_ncols=(1, 1)) + ax = grid[0] + im = ax.imshow([[1, 2]], norm=mpl.colors.LogNorm()) + cb = ax.cax.colorbar(im) + assert isinstance(cb.locator, mticker.LogLocator) + + +def test_removal(): + import matplotlib.pyplot as plt + import mpl_toolkits.axisartist as AA + fig = plt.figure() + ax = host_subplot(111, axes_class=AA.Axes, figure=fig) + col = ax.fill_between(range(5), 0, range(5)) + fig.canvas.draw() + col.remove() + fig.canvas.draw() + + +@image_comparison(['anchored_locator_base_call.png'], style="mpl20") +def test_anchored_locator_base_call(): + fig = plt.figure(figsize=(3, 3)) + fig1, fig2 = fig.subfigures(nrows=2, ncols=1) + + ax = fig1.subplots() + ax.set(aspect=1, xlim=(-15, 15), ylim=(-20, 5)) + ax.set(xticks=[], yticks=[]) + + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + + axins = zoomed_inset_axes(ax, zoom=2, loc="upper left") + axins.set(xticks=[], yticks=[]) + + axins.imshow(Z, extent=extent, origin="lower") + + +def test_grid_with_axes_class_not_overriding_axis(): + Grid(plt.figure(), 111, (2, 2), axes_class=mpl.axes.Axes) + RGBAxes(plt.figure(), 111, axes_class=mpl.axes.Axes) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7b8d8c08ac226d105a5a54c5f21040cd25107ae6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py @@ -0,0 +1,14 @@ +from .axislines import Axes +from .axislines import ( # noqa: F401 + AxesZero, AxisArtistHelper, AxisArtistHelperRectlinear, + GridHelperBase, GridHelperRectlinear, Subplot, SubplotZero) +from .axis_artist import AxisArtist, GridlinesCollection # noqa: F401 +from .grid_helper_curvelinear import GridHelperCurveLinear # noqa: F401 +from .floating_axes import FloatingAxes, FloatingSubplot # noqa: F401 +from mpl_toolkits.axes_grid1.parasite_axes import ( + host_axes_class_factory, parasite_axes_class_factory) + + +ParasiteAxes = parasite_axes_class_factory(Axes) +HostAxes = host_axes_class_factory(Axes) +SubplotHost = HostAxes diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..1786cd70bcdb297de6c17374353cc2a49dfd0ae1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py @@ -0,0 +1,394 @@ +import numpy as np +import math + +from mpl_toolkits.axisartist.grid_finder import ExtremeFinderSimple + + +def select_step_degree(dv): + + degree_limits_ = [1.5, 3, 7, 13, 20, 40, 70, 120, 270, 520] + degree_steps_ = [1, 2, 5, 10, 15, 30, 45, 90, 180, 360] + degree_factors = [1.] * len(degree_steps_) + + minsec_limits_ = [1.5, 2.5, 3.5, 8, 11, 18, 25, 45] + minsec_steps_ = [1, 2, 3, 5, 10, 15, 20, 30] + + minute_limits_ = np.array(minsec_limits_) / 60 + minute_factors = [60.] * len(minute_limits_) + + second_limits_ = np.array(minsec_limits_) / 3600 + second_factors = [3600.] * len(second_limits_) + + degree_limits = [*second_limits_, *minute_limits_, *degree_limits_] + degree_steps = [*minsec_steps_, *minsec_steps_, *degree_steps_] + degree_factors = [*second_factors, *minute_factors, *degree_factors] + + n = np.searchsorted(degree_limits, dv) + step = degree_steps[n] + factor = degree_factors[n] + + return step, factor + + +def select_step_hour(dv): + + hour_limits_ = [1.5, 2.5, 3.5, 5, 7, 10, 15, 21, 36] + hour_steps_ = [1, 2, 3, 4, 6, 8, 12, 18, 24] + hour_factors = [1.] * len(hour_steps_) + + minsec_limits_ = [1.5, 2.5, 3.5, 4.5, 5.5, 8, 11, 14, 18, 25, 45] + minsec_steps_ = [1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30] + + minute_limits_ = np.array(minsec_limits_) / 60 + minute_factors = [60.] * len(minute_limits_) + + second_limits_ = np.array(minsec_limits_) / 3600 + second_factors = [3600.] * len(second_limits_) + + hour_limits = [*second_limits_, *minute_limits_, *hour_limits_] + hour_steps = [*minsec_steps_, *minsec_steps_, *hour_steps_] + hour_factors = [*second_factors, *minute_factors, *hour_factors] + + n = np.searchsorted(hour_limits, dv) + step = hour_steps[n] + factor = hour_factors[n] + + return step, factor + + +def select_step_sub(dv): + + # subarcsec or degree + tmp = 10.**(int(math.log10(dv))-1.) + + factor = 1./tmp + + if 1.5*tmp >= dv: + step = 1 + elif 3.*tmp >= dv: + step = 2 + elif 7.*tmp >= dv: + step = 5 + else: + step = 1 + factor = 0.1*factor + + return step, factor + + +def select_step(v1, v2, nv, hour=False, include_last=True, + threshold_factor=3600.): + + if v1 > v2: + v1, v2 = v2, v1 + + dv = (v2 - v1) / nv + + if hour: + _select_step = select_step_hour + cycle = 24. + else: + _select_step = select_step_degree + cycle = 360. + + # for degree + if dv > 1 / threshold_factor: + step, factor = _select_step(dv) + else: + step, factor = select_step_sub(dv*threshold_factor) + + factor = factor * threshold_factor + + levs = np.arange(np.floor(v1 * factor / step), + np.ceil(v2 * factor / step) + 0.5, + dtype=int) * step + + # n : number of valid levels. If there is a cycle, e.g., [0, 90, 180, + # 270, 360], the grid line needs to be extended from 0 to 360, so + # we need to return the whole array. However, the last level (360) + # needs to be ignored often. In this case, so we return n=4. + + n = len(levs) + + # we need to check the range of values + # for example, -90 to 90, 0 to 360, + + if factor == 1. and levs[-1] >= levs[0] + cycle: # check for cycle + nv = int(cycle / step) + if include_last: + levs = levs[0] + np.arange(0, nv+1, 1) * step + else: + levs = levs[0] + np.arange(0, nv, 1) * step + + n = len(levs) + + return np.array(levs), n, factor + + +def select_step24(v1, v2, nv, include_last=True, threshold_factor=3600): + v1, v2 = v1 / 15, v2 / 15 + levs, n, factor = select_step(v1, v2, nv, hour=True, + include_last=include_last, + threshold_factor=threshold_factor) + return levs * 15, n, factor + + +def select_step360(v1, v2, nv, include_last=True, threshold_factor=3600): + return select_step(v1, v2, nv, hour=False, + include_last=include_last, + threshold_factor=threshold_factor) + + +class LocatorBase: + def __init__(self, nbins, include_last=True): + self.nbins = nbins + self._include_last = include_last + + def set_params(self, nbins=None): + if nbins is not None: + self.nbins = int(nbins) + + +class LocatorHMS(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last) + + +class LocatorHM(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last, + threshold_factor=60) + + +class LocatorH(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last, + threshold_factor=1) + + +class LocatorDMS(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last) + + +class LocatorDM(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last, + threshold_factor=60) + + +class LocatorD(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last, + threshold_factor=1) + + +class FormatterDMS: + deg_mark = r"^{\circ}" + min_mark = r"^{\prime}" + sec_mark = r"^{\prime\prime}" + + fmt_d = "$%d" + deg_mark + "$" + fmt_ds = r"$%d.%s" + deg_mark + "$" + + # %s for sign + fmt_d_m = r"$%s%d" + deg_mark + r"\,%02d" + min_mark + "$" + fmt_d_ms = r"$%s%d" + deg_mark + r"\,%02d.%s" + min_mark + "$" + + fmt_d_m_partial = "$%s%d" + deg_mark + r"\,%02d" + min_mark + r"\," + fmt_s_partial = "%02d" + sec_mark + "$" + fmt_ss_partial = "%02d.%s" + sec_mark + "$" + + def _get_number_fraction(self, factor): + ## check for fractional numbers + number_fraction = None + # check for 60 + + for threshold in [1, 60, 3600]: + if factor <= threshold: + break + + d = factor // threshold + int_log_d = int(np.floor(np.log10(d))) + if 10**int_log_d == d and d != 1: + number_fraction = int_log_d + factor = factor // 10**int_log_d + return factor, number_fraction + + return factor, number_fraction + + def __call__(self, direction, factor, values): + if len(values) == 0: + return [] + + ss = np.sign(values) + signs = ["-" if v < 0 else "" for v in values] + + factor, number_fraction = self._get_number_fraction(factor) + + values = np.abs(values) + + if number_fraction is not None: + values, frac_part = divmod(values, 10 ** number_fraction) + frac_fmt = "%%0%dd" % (number_fraction,) + frac_str = [frac_fmt % (f1,) for f1 in frac_part] + + if factor == 1: + if number_fraction is None: + return [self.fmt_d % (s * int(v),) for s, v in zip(ss, values)] + else: + return [self.fmt_ds % (s * int(v), f1) + for s, v, f1 in zip(ss, values, frac_str)] + elif factor == 60: + deg_part, min_part = divmod(values, 60) + if number_fraction is None: + return [self.fmt_d_m % (s1, d1, m1) + for s1, d1, m1 in zip(signs, deg_part, min_part)] + else: + return [self.fmt_d_ms % (s, d1, m1, f1) + for s, d1, m1, f1 + in zip(signs, deg_part, min_part, frac_str)] + + elif factor == 3600: + if ss[-1] == -1: + inverse_order = True + values = values[::-1] + signs = signs[::-1] + else: + inverse_order = False + + l_hm_old = "" + r = [] + + deg_part, min_part_ = divmod(values, 3600) + min_part, sec_part = divmod(min_part_, 60) + + if number_fraction is None: + sec_str = [self.fmt_s_partial % (s1,) for s1 in sec_part] + else: + sec_str = [self.fmt_ss_partial % (s1, f1) + for s1, f1 in zip(sec_part, frac_str)] + + for s, d1, m1, s1 in zip(signs, deg_part, min_part, sec_str): + l_hm = self.fmt_d_m_partial % (s, d1, m1) + if l_hm != l_hm_old: + l_hm_old = l_hm + l = l_hm + s1 + else: + l = "$" + s + s1 + r.append(l) + + if inverse_order: + return r[::-1] + else: + return r + + else: # factor > 3600. + return [r"$%s^{\circ}$" % v for v in ss*values] + + +class FormatterHMS(FormatterDMS): + deg_mark = r"^\mathrm{h}" + min_mark = r"^\mathrm{m}" + sec_mark = r"^\mathrm{s}" + + fmt_d = "$%d" + deg_mark + "$" + fmt_ds = r"$%d.%s" + deg_mark + "$" + + # %s for sign + fmt_d_m = r"$%s%d" + deg_mark + r"\,%02d" + min_mark+"$" + fmt_d_ms = r"$%s%d" + deg_mark + r"\,%02d.%s" + min_mark+"$" + + fmt_d_m_partial = "$%s%d" + deg_mark + r"\,%02d" + min_mark + r"\," + fmt_s_partial = "%02d" + sec_mark + "$" + fmt_ss_partial = "%02d.%s" + sec_mark + "$" + + def __call__(self, direction, factor, values): # hour + return super().__call__(direction, factor, np.asarray(values) / 15) + + +class ExtremeFinderCycle(ExtremeFinderSimple): + # docstring inherited + + def __init__(self, nx, ny, + lon_cycle=360., lat_cycle=None, + lon_minmax=None, lat_minmax=(-90, 90)): + """ + This subclass handles the case where one or both coordinates should be + taken modulo 360, or be restricted to not exceed a specific range. + + Parameters + ---------- + nx, ny : int + The number of samples in each direction. + + lon_cycle, lat_cycle : 360 or None + If not None, values in the corresponding direction are taken modulo + *lon_cycle* or *lat_cycle*; in theory this can be any number but + the implementation actually assumes that it is 360 (if not None); + other values give nonsensical results. + + This is done by "unwrapping" the transformed grid coordinates so + that jumps are less than a half-cycle; then normalizing the span to + no more than a full cycle. + + For example, if values are in the union of the [0, 2] and + [358, 360] intervals (typically, angles measured modulo 360), the + values in the second interval are normalized to [-2, 0] instead so + that the values now cover [-2, 2]. If values are in a range of + [5, 1000], this gets normalized to [5, 365]. + + lon_minmax, lat_minmax : (float, float) or None + If not None, the computed bounding box is clipped to the given + range in the corresponding direction. + """ + self.nx, self.ny = nx, ny + self.lon_cycle, self.lat_cycle = lon_cycle, lat_cycle + self.lon_minmax = lon_minmax + self.lat_minmax = lat_minmax + + def __call__(self, transform_xy, x1, y1, x2, y2): + # docstring inherited + x, y = np.meshgrid( + np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) + lon, lat = transform_xy(np.ravel(x), np.ravel(y)) + + # iron out jumps, but algorithm should be improved. + # This is just naive way of doing and my fail for some cases. + # Consider replacing this with numpy.unwrap + # We are ignoring invalid warnings. They are triggered when + # comparing arrays with NaNs using > We are already handling + # that correctly using np.nanmin and np.nanmax + with np.errstate(invalid='ignore'): + if self.lon_cycle is not None: + lon0 = np.nanmin(lon) + lon -= 360. * ((lon - lon0) > 180.) + if self.lat_cycle is not None: + lat0 = np.nanmin(lat) + lat -= 360. * ((lat - lat0) > 180.) + + lon_min, lon_max = np.nanmin(lon), np.nanmax(lon) + lat_min, lat_max = np.nanmin(lat), np.nanmax(lat) + + lon_min, lon_max, lat_min, lat_max = \ + self._add_pad(lon_min, lon_max, lat_min, lat_max) + + # check cycle + if self.lon_cycle: + lon_max = min(lon_max, lon_min + self.lon_cycle) + if self.lat_cycle: + lat_max = min(lat_max, lat_min + self.lat_cycle) + + if self.lon_minmax is not None: + min0 = self.lon_minmax[0] + lon_min = max(min0, lon_min) + max0 = self.lon_minmax[1] + lon_max = min(max0, lon_max) + + if self.lat_minmax is not None: + min0 = self.lat_minmax[0] + lat_min = max(min0, lat_min) + max0 = self.lat_minmax[1] + lat_max = min(max0, lat_max) + + return lon_min, lon_max, lat_min, lat_max diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py new file mode 100644 index 0000000000000000000000000000000000000000..d0392be782d9c06404c3d83c2b8ca271bbb8fa72 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py @@ -0,0 +1,2 @@ +from mpl_toolkits.axes_grid1.axes_divider import ( # noqa + Divider, SubplotDivider, AxesDivider, make_axes_locatable) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py new file mode 100644 index 0000000000000000000000000000000000000000..725c665d818ade7772b427114975a0b1bfcec3b0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py @@ -0,0 +1,1116 @@ +""" +The :mod:`.axis_artist` module implements custom artists to draw axis elements +(axis lines and labels, tick lines and labels, grid lines). + +Axis lines and labels and tick lines and labels are managed by the `AxisArtist` +class; grid lines are managed by the `GridlinesCollection` class. + +There is one `AxisArtist` per Axis; it can be accessed through +the ``axis`` dictionary of the parent Axes (which should be a +`mpl_toolkits.axislines.Axes`), e.g. ``ax.axis["bottom"]``. + +Children of the AxisArtist are accessed as attributes: ``.line`` and ``.label`` +for the axis line and label, ``.major_ticks``, ``.major_ticklabels``, +``.minor_ticks``, ``.minor_ticklabels`` for the tick lines and labels (e.g. +``ax.axis["bottom"].line``). + +Children properties (colors, fonts, line widths, etc.) can be set using +setters, e.g. :: + + # Make the major ticks of the bottom axis red. + ax.axis["bottom"].major_ticks.set_color("red") + +However, things like the locations of ticks, and their ticklabels need to be +changed from the side of the grid_helper. + +axis_direction +-------------- + +`AxisArtist`, `AxisLabel`, `TickLabels` have an *axis_direction* attribute, +which adjusts the location, angle, etc. The *axis_direction* must be one of +"left", "right", "bottom", "top", and follows the Matplotlib convention for +rectangular axis. + +For example, for the *bottom* axis (the left and right is relative to the +direction of the increasing coordinate), + +* ticklabels and axislabel are on the right +* ticklabels and axislabel have text angle of 0 +* ticklabels are baseline, center-aligned +* axislabel is top, center-aligned + +The text angles are actually relative to (90 + angle of the direction to the +ticklabel), which gives 0 for bottom axis. + +=================== ====== ======== ====== ======== +Property left bottom right top +=================== ====== ======== ====== ======== +ticklabel location left right right left +axislabel location left right right left +ticklabel angle 90 0 -90 180 +axislabel angle 180 0 0 180 +ticklabel va center baseline center baseline +axislabel va center top center bottom +ticklabel ha right center right center +axislabel ha right center right center +=================== ====== ======== ====== ======== + +Ticks are by default direct opposite side of the ticklabels. To make ticks to +the same side of the ticklabels, :: + + ax.axis["bottom"].major_ticks.set_tick_out(True) + +The following attributes can be customized (use the ``set_xxx`` methods): + +* `Ticks`: ticksize, tick_out +* `TickLabels`: pad +* `AxisLabel`: pad +""" + +# FIXME : +# angles are given in data coordinate - need to convert it to canvas coordinate + + +from operator import methodcaller + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook +import matplotlib.artist as martist +import matplotlib.colors as mcolors +import matplotlib.text as mtext +from matplotlib.collections import LineCollection +from matplotlib.lines import Line2D +from matplotlib.patches import PathPatch +from matplotlib.path import Path +from matplotlib.transforms import ( + Affine2D, Bbox, IdentityTransform, ScaledTranslation) + +from .axisline_style import AxislineStyle + + +class AttributeCopier: + def get_ref_artist(self): + """ + Return the underlying artist that actually defines some properties + (e.g., color) of this artist. + """ + raise RuntimeError("get_ref_artist must overridden") + + def get_attribute_from_ref_artist(self, attr_name): + getter = methodcaller("get_" + attr_name) + prop = getter(super()) + return getter(self.get_ref_artist()) if prop == "auto" else prop + + +class Ticks(AttributeCopier, Line2D): + """ + Ticks are derived from `.Line2D`, and note that ticks themselves + are markers. Thus, you should use set_mec, set_mew, etc. + + To change the tick size (length), you need to use + `set_ticksize`. To change the direction of the ticks (ticks are + in opposite direction of ticklabels by default), use + ``set_tick_out(False)`` + """ + + def __init__(self, ticksize, tick_out=False, *, axis=None, **kwargs): + self._ticksize = ticksize + self.locs_angles_labels = [] + + self.set_tick_out(tick_out) + + self._axis = axis + if self._axis is not None: + if "color" not in kwargs: + kwargs["color"] = "auto" + if "mew" not in kwargs and "markeredgewidth" not in kwargs: + kwargs["markeredgewidth"] = "auto" + + Line2D.__init__(self, [0.], [0.], **kwargs) + self.set_snap(True) + + def get_ref_artist(self): + # docstring inherited + return self._axis.majorTicks[0].tick1line + + def set_color(self, color): + # docstring inherited + # Unlike the base Line2D.set_color, this also supports "auto". + if not cbook._str_equal(color, "auto"): + mcolors._check_color_like(color=color) + self._color = color + self.stale = True + + def get_color(self): + return self.get_attribute_from_ref_artist("color") + + def get_markeredgecolor(self): + return self.get_attribute_from_ref_artist("markeredgecolor") + + def get_markeredgewidth(self): + return self.get_attribute_from_ref_artist("markeredgewidth") + + def set_tick_out(self, b): + """Set whether ticks are drawn inside or outside the axes.""" + self._tick_out = b + + def get_tick_out(self): + """Return whether ticks are drawn inside or outside the axes.""" + return self._tick_out + + def set_ticksize(self, ticksize): + """Set length of the ticks in points.""" + self._ticksize = ticksize + + def get_ticksize(self): + """Return length of the ticks in points.""" + return self._ticksize + + def set_locs_angles(self, locs_angles): + self.locs_angles = locs_angles + + _tickvert_path = Path([[0., 0.], [1., 0.]]) + + def draw(self, renderer): + if not self.get_visible(): + return + + gc = renderer.new_gc() + gc.set_foreground(self.get_markeredgecolor()) + gc.set_linewidth(self.get_markeredgewidth()) + gc.set_alpha(self._alpha) + + path_trans = self.get_transform() + marker_transform = (Affine2D() + .scale(renderer.points_to_pixels(self._ticksize))) + if self.get_tick_out(): + marker_transform.rotate_deg(180) + + for loc, angle in self.locs_angles: + locs = path_trans.transform_non_affine(np.array([loc])) + if self.axes and not self.axes.viewLim.contains(*locs[0]): + continue + renderer.draw_markers( + gc, self._tickvert_path, + marker_transform + Affine2D().rotate_deg(angle), + Path(locs), path_trans.get_affine()) + + gc.restore() + + +class LabelBase(mtext.Text): + """ + A base class for `.AxisLabel` and `.TickLabels`. The position and + angle of the text are calculated by the offset_ref_angle, + text_ref_angle, and offset_radius attributes. + """ + + def __init__(self, *args, **kwargs): + self.locs_angles_labels = [] + self._ref_angle = 0 + self._offset_radius = 0. + + super().__init__(*args, **kwargs) + + self.set_rotation_mode("anchor") + self._text_follow_ref_angle = True + + @property + def _text_ref_angle(self): + if self._text_follow_ref_angle: + return self._ref_angle + 90 + else: + return 0 + + @property + def _offset_ref_angle(self): + return self._ref_angle + + _get_opposite_direction = {"left": "right", + "right": "left", + "top": "bottom", + "bottom": "top"}.__getitem__ + + def draw(self, renderer): + if not self.get_visible(): + return + + # save original and adjust some properties + tr = self.get_transform() + angle_orig = self.get_rotation() + theta = np.deg2rad(self._offset_ref_angle) + dd = self._offset_radius + dx, dy = dd * np.cos(theta), dd * np.sin(theta) + + self.set_transform(tr + Affine2D().translate(dx, dy)) + self.set_rotation(self._text_ref_angle + angle_orig) + super().draw(renderer) + # restore original properties + self.set_transform(tr) + self.set_rotation(angle_orig) + + def get_window_extent(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + # save original and adjust some properties + tr = self.get_transform() + angle_orig = self.get_rotation() + theta = np.deg2rad(self._offset_ref_angle) + dd = self._offset_radius + dx, dy = dd * np.cos(theta), dd * np.sin(theta) + + self.set_transform(tr + Affine2D().translate(dx, dy)) + self.set_rotation(self._text_ref_angle + angle_orig) + bbox = super().get_window_extent(renderer).frozen() + # restore original properties + self.set_transform(tr) + self.set_rotation(angle_orig) + + return bbox + + +class AxisLabel(AttributeCopier, LabelBase): + """ + Axis label. Derived from `.Text`. The position of the text is updated + in the fly, so changing text position has no effect. Otherwise, the + properties can be changed as a normal `.Text`. + + To change the pad between tick labels and axis label, use `set_pad`. + """ + + def __init__(self, *args, axis_direction="bottom", axis=None, **kwargs): + self._axis = axis + self._pad = 5 + self._external_pad = 0 # in pixels + LabelBase.__init__(self, *args, **kwargs) + self.set_axis_direction(axis_direction) + + def set_pad(self, pad): + """ + Set the internal pad in points. + + The actual pad will be the sum of the internal pad and the + external pad (the latter is set automatically by the `.AxisArtist`). + + Parameters + ---------- + pad : float + The internal pad in points. + """ + self._pad = pad + + def get_pad(self): + """ + Return the internal pad in points. + + See `.set_pad` for more details. + """ + return self._pad + + def get_ref_artist(self): + # docstring inherited + return self._axis.label + + def get_text(self): + # docstring inherited + t = super().get_text() + if t == "__from_axes__": + return self._axis.label.get_text() + return self._text + + _default_alignments = dict(left=("bottom", "center"), + right=("top", "center"), + bottom=("top", "center"), + top=("bottom", "center")) + + def set_default_alignment(self, d): + """ + Set the default alignment. See `set_axis_direction` for details. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + va, ha = _api.check_getitem(self._default_alignments, d=d) + self.set_va(va) + self.set_ha(ha) + + _default_angles = dict(left=180, + right=0, + bottom=0, + top=180) + + def set_default_angle(self, d): + """ + Set the default angle. See `set_axis_direction` for details. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + self.set_rotation(_api.check_getitem(self._default_angles, d=d)) + + def set_axis_direction(self, d): + """ + Adjust the text angle and text alignment of axis label + according to the matplotlib convention. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + axislabel angle 180 0 0 180 + axislabel va center top center bottom + axislabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the text angles are actually relative to (90 + angle + of the direction to the ticklabel), which gives 0 for bottom + axis. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + self.set_default_alignment(d) + self.set_default_angle(d) + + def get_color(self): + return self.get_attribute_from_ref_artist("color") + + def draw(self, renderer): + if not self.get_visible(): + return + + self._offset_radius = \ + self._external_pad + renderer.points_to_pixels(self.get_pad()) + + super().draw(renderer) + + def get_window_extent(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + if not self.get_visible(): + return + + r = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + + bb = super().get_window_extent(renderer) + + return bb + + +class TickLabels(AxisLabel): # mtext.Text + """ + Tick labels. While derived from `.Text`, this single artist draws all + ticklabels. As in `.AxisLabel`, the position of the text is updated + in the fly, so changing text position has no effect. Otherwise, + the properties can be changed as a normal `.Text`. Unlike the + ticklabels of the mainline Matplotlib, properties of a single + ticklabel alone cannot be modified. + + To change the pad between ticks and ticklabels, use `~.AxisLabel.set_pad`. + """ + + def __init__(self, *, axis_direction="bottom", **kwargs): + super().__init__(**kwargs) + self.set_axis_direction(axis_direction) + self._axislabel_pad = 0 + + def get_ref_artist(self): + # docstring inherited + return self._axis.get_ticklabels()[0] + + def set_axis_direction(self, label_direction): + """ + Adjust the text angle and text alignment of ticklabels + according to the Matplotlib convention. + + The *label_direction* must be one of [left, right, bottom, top]. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + ticklabel angle 90 0 -90 180 + ticklabel va center baseline center baseline + ticklabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the text angles are actually relative to (90 + angle + of the direction to the ticklabel), which gives 0 for bottom + axis. + + Parameters + ---------- + label_direction : {"left", "bottom", "right", "top"} + + """ + self.set_default_alignment(label_direction) + self.set_default_angle(label_direction) + self._axis_direction = label_direction + + def invert_axis_direction(self): + label_direction = self._get_opposite_direction(self._axis_direction) + self.set_axis_direction(label_direction) + + def _get_ticklabels_offsets(self, renderer, label_direction): + """ + Calculate the ticklabel offsets from the tick and their total heights. + + The offset only takes account the offset due to the vertical alignment + of the ticklabels: if axis direction is bottom and va is 'top', it will + return 0; if va is 'baseline', it will return (height-descent). + """ + whd_list = self.get_texts_widths_heights_descents(renderer) + + if not whd_list: + return 0, 0 + + r = 0 + va, ha = self.get_va(), self.get_ha() + + if label_direction == "left": + pad = max(w for w, h, d in whd_list) + if ha == "left": + r = pad + elif ha == "center": + r = .5 * pad + elif label_direction == "right": + pad = max(w for w, h, d in whd_list) + if ha == "right": + r = pad + elif ha == "center": + r = .5 * pad + elif label_direction == "bottom": + pad = max(h for w, h, d in whd_list) + if va == "bottom": + r = pad + elif va == "center": + r = .5 * pad + elif va == "baseline": + max_ascent = max(h - d for w, h, d in whd_list) + max_descent = max(d for w, h, d in whd_list) + r = max_ascent + pad = max_ascent + max_descent + elif label_direction == "top": + pad = max(h for w, h, d in whd_list) + if va == "top": + r = pad + elif va == "center": + r = .5 * pad + elif va == "baseline": + max_ascent = max(h - d for w, h, d in whd_list) + max_descent = max(d for w, h, d in whd_list) + r = max_descent + pad = max_ascent + max_descent + + # r : offset + # pad : total height of the ticklabels. This will be used to + # calculate the pad for the axislabel. + return r, pad + + _default_alignments = dict(left=("center", "right"), + right=("center", "left"), + bottom=("baseline", "center"), + top=("baseline", "center")) + + _default_angles = dict(left=90, + right=-90, + bottom=0, + top=180) + + def draw(self, renderer): + if not self.get_visible(): + self._axislabel_pad = self._external_pad + return + + r, total_width = self._get_ticklabels_offsets(renderer, + self._axis_direction) + + pad = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + pad + + for (x, y), a, l in self._locs_angles_labels: + if not l.strip(): + continue + self._ref_angle = a + self.set_x(x) + self.set_y(y) + self.set_text(l) + LabelBase.draw(self, renderer) + + # the value saved will be used to draw axislabel. + self._axislabel_pad = total_width + pad + + def set_locs_angles_labels(self, locs_angles_labels): + self._locs_angles_labels = locs_angles_labels + + def get_window_extents(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + if not self.get_visible(): + self._axislabel_pad = self._external_pad + return [] + + bboxes = [] + + r, total_width = self._get_ticklabels_offsets(renderer, + self._axis_direction) + + pad = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + pad + + for (x, y), a, l in self._locs_angles_labels: + self._ref_angle = a + self.set_x(x) + self.set_y(y) + self.set_text(l) + bb = LabelBase.get_window_extent(self, renderer) + bboxes.append(bb) + + # the value saved will be used to draw axislabel. + self._axislabel_pad = total_width + pad + + return bboxes + + def get_texts_widths_heights_descents(self, renderer): + """ + Return a list of ``(width, height, descent)`` tuples for ticklabels. + + Empty labels are left out. + """ + whd_list = [] + for _loc, _angle, label in self._locs_angles_labels: + if not label.strip(): + continue + clean_line, ismath = self._preprocess_math(label) + whd = mtext._get_text_metrics_with_cache( + renderer, clean_line, self._fontproperties, ismath=ismath, + dpi=self.get_figure(root=True).dpi) + whd_list.append(whd) + return whd_list + + +class GridlinesCollection(LineCollection): + def __init__(self, *args, which="major", axis="both", **kwargs): + """ + Collection of grid lines. + + Parameters + ---------- + which : {"major", "minor"} + Which grid to consider. + axis : {"both", "x", "y"} + Which axis to consider. + *args, **kwargs + Passed to `.LineCollection`. + """ + self._which = which + self._axis = axis + super().__init__(*args, **kwargs) + self.set_grid_helper(None) + + def set_which(self, which): + """ + Select major or minor grid lines. + + Parameters + ---------- + which : {"major", "minor"} + """ + self._which = which + + def set_axis(self, axis): + """ + Select axis. + + Parameters + ---------- + axis : {"both", "x", "y"} + """ + self._axis = axis + + def set_grid_helper(self, grid_helper): + """ + Set grid helper. + + Parameters + ---------- + grid_helper : `.GridHelperBase` subclass + """ + self._grid_helper = grid_helper + + def draw(self, renderer): + if self._grid_helper is not None: + self._grid_helper.update_lim(self.axes) + gl = self._grid_helper.get_gridlines(self._which, self._axis) + self.set_segments([np.transpose(l) for l in gl]) + super().draw(renderer) + + +class AxisArtist(martist.Artist): + """ + An artist which draws axis (a line along which the n-th axes coord + is constant) line, ticks, tick labels, and axis label. + """ + + zorder = 2.5 + + @property + def LABELPAD(self): + return self.label.get_pad() + + @LABELPAD.setter + def LABELPAD(self, v): + self.label.set_pad(v) + + def __init__(self, axes, + helper, + offset=None, + axis_direction="bottom", + **kwargs): + """ + Parameters + ---------- + axes : `mpl_toolkits.axisartist.axislines.Axes` + helper : `~mpl_toolkits.axisartist.axislines.AxisArtistHelper` + """ + # axes is also used to follow the axis attribute (tick color, etc). + + super().__init__(**kwargs) + + self.axes = axes + + self._axis_artist_helper = helper + + if offset is None: + offset = (0, 0) + self.offset_transform = ScaledTranslation( + *offset, + Affine2D().scale(1 / 72) # points to inches. + + self.axes.get_figure(root=False).dpi_scale_trans) + + if axis_direction in ["left", "right"]: + self.axis = axes.yaxis + else: + self.axis = axes.xaxis + + self._axisline_style = None + self._axis_direction = axis_direction + + self._init_line() + self._init_ticks(**kwargs) + self._init_offsetText(axis_direction) + self._init_label() + + # axis direction + self._ticklabel_add_angle = 0. + self._axislabel_add_angle = 0. + self.set_axis_direction(axis_direction) + + # axis direction + + def set_axis_direction(self, axis_direction): + """ + Adjust the direction, text angle, and text alignment of tick labels + and axis labels following the Matplotlib convention for the rectangle + axes. + + The *axis_direction* must be one of [left, right, bottom, top]. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + ticklabel direction "-" "+" "+" "-" + axislabel direction "-" "+" "+" "-" + ticklabel angle 90 0 -90 180 + ticklabel va center baseline center baseline + ticklabel ha right center right center + axislabel angle 180 0 0 180 + axislabel va center top center bottom + axislabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the direction "+" and "-" are relative to the direction of + the increasing coordinate. Also, the text angles are actually + relative to (90 + angle of the direction to the ticklabel), + which gives 0 for bottom axis. + + Parameters + ---------- + axis_direction : {"left", "bottom", "right", "top"} + """ + self.major_ticklabels.set_axis_direction(axis_direction) + self.label.set_axis_direction(axis_direction) + self._axis_direction = axis_direction + if axis_direction in ["left", "top"]: + self.set_ticklabel_direction("-") + self.set_axislabel_direction("-") + else: + self.set_ticklabel_direction("+") + self.set_axislabel_direction("+") + + def set_ticklabel_direction(self, tick_direction): + r""" + Adjust the direction of the tick labels. + + Note that the *tick_direction*\s '+' and '-' are relative to the + direction of the increasing coordinate. + + Parameters + ---------- + tick_direction : {"+", "-"} + """ + self._ticklabel_add_angle = _api.check_getitem( + {"+": 0, "-": 180}, tick_direction=tick_direction) + + def invert_ticklabel_direction(self): + self._ticklabel_add_angle = (self._ticklabel_add_angle + 180) % 360 + self.major_ticklabels.invert_axis_direction() + self.minor_ticklabels.invert_axis_direction() + + def set_axislabel_direction(self, label_direction): + r""" + Adjust the direction of the axis label. + + Note that the *label_direction*\s '+' and '-' are relative to the + direction of the increasing coordinate. + + Parameters + ---------- + label_direction : {"+", "-"} + """ + self._axislabel_add_angle = _api.check_getitem( + {"+": 0, "-": 180}, label_direction=label_direction) + + def get_transform(self): + return self.axes.transAxes + self.offset_transform + + def get_helper(self): + """ + Return axis artist helper instance. + """ + return self._axis_artist_helper + + def set_axisline_style(self, axisline_style=None, **kwargs): + """ + Set the axisline style. + + The new style is completely defined by the passed attributes. Existing + style attributes are forgotten. + + Parameters + ---------- + axisline_style : str or None + The line style, e.g. '->', optionally followed by a comma-separated + list of attributes. Alternatively, the attributes can be provided + as keywords. + + If *None* this returns a string containing the available styles. + + Examples + -------- + The following two commands are equal: + + >>> set_axisline_style("->,size=1.5") + >>> set_axisline_style("->", size=1.5) + """ + if axisline_style is None: + return AxislineStyle.pprint_styles() + + if isinstance(axisline_style, AxislineStyle._Base): + self._axisline_style = axisline_style + else: + self._axisline_style = AxislineStyle(axisline_style, **kwargs) + + self._init_line() + + def get_axisline_style(self): + """Return the current axisline style.""" + return self._axisline_style + + def _init_line(self): + """ + Initialize the *line* artist that is responsible to draw the axis line. + """ + tran = (self._axis_artist_helper.get_line_transform(self.axes) + + self.offset_transform) + + axisline_style = self.get_axisline_style() + if axisline_style is None: + self.line = PathPatch( + self._axis_artist_helper.get_line(self.axes), + color=mpl.rcParams['axes.edgecolor'], + fill=False, + linewidth=mpl.rcParams['axes.linewidth'], + capstyle=mpl.rcParams['lines.solid_capstyle'], + joinstyle=mpl.rcParams['lines.solid_joinstyle'], + transform=tran) + else: + self.line = axisline_style(self, transform=tran) + + def _draw_line(self, renderer): + self.line.set_path(self._axis_artist_helper.get_line(self.axes)) + if self.get_axisline_style() is not None: + self.line.set_line_mutation_scale(self.major_ticklabels.get_size()) + self.line.draw(renderer) + + def _init_ticks(self, **kwargs): + axis_name = self.axis.axis_name + + trans = (self._axis_artist_helper.get_tick_transform(self.axes) + + self.offset_transform) + + self.major_ticks = Ticks( + kwargs.get( + "major_tick_size", + mpl.rcParams[f"{axis_name}tick.major.size"]), + axis=self.axis, transform=trans) + self.minor_ticks = Ticks( + kwargs.get( + "minor_tick_size", + mpl.rcParams[f"{axis_name}tick.minor.size"]), + axis=self.axis, transform=trans) + + size = mpl.rcParams[f"{axis_name}tick.labelsize"] + self.major_ticklabels = TickLabels( + axis=self.axis, + axis_direction=self._axis_direction, + figure=self.axes.get_figure(root=False), + transform=trans, + fontsize=size, + pad=kwargs.get( + "major_tick_pad", mpl.rcParams[f"{axis_name}tick.major.pad"]), + ) + self.minor_ticklabels = TickLabels( + axis=self.axis, + axis_direction=self._axis_direction, + figure=self.axes.get_figure(root=False), + transform=trans, + fontsize=size, + pad=kwargs.get( + "minor_tick_pad", mpl.rcParams[f"{axis_name}tick.minor.pad"]), + ) + + def _get_tick_info(self, tick_iter): + """ + Return a pair of: + + - list of locs and angles for ticks + - list of locs, angles and labels for ticklabels. + """ + ticks_loc_angle = [] + ticklabels_loc_angle_label = [] + + ticklabel_add_angle = self._ticklabel_add_angle + + for loc, angle_normal, angle_tangent, label in tick_iter: + angle_label = angle_tangent - 90 + ticklabel_add_angle + angle_tick = (angle_normal + if 90 <= (angle_label - angle_normal) % 360 <= 270 + else angle_normal + 180) + ticks_loc_angle.append([loc, angle_tick]) + ticklabels_loc_angle_label.append([loc, angle_label, label]) + + return ticks_loc_angle, ticklabels_loc_angle_label + + def _update_ticks(self, renderer=None): + # set extra pad for major and minor ticklabels: use ticksize of + # majorticks even for minor ticks. not clear what is best. + + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + dpi_cor = renderer.points_to_pixels(1.) + if self.major_ticks.get_visible() and self.major_ticks.get_tick_out(): + ticklabel_pad = self.major_ticks._ticksize * dpi_cor + self.major_ticklabels._external_pad = ticklabel_pad + self.minor_ticklabels._external_pad = ticklabel_pad + else: + self.major_ticklabels._external_pad = 0 + self.minor_ticklabels._external_pad = 0 + + majortick_iter, minortick_iter = \ + self._axis_artist_helper.get_tick_iterators(self.axes) + + tick_loc_angle, ticklabel_loc_angle_label = \ + self._get_tick_info(majortick_iter) + self.major_ticks.set_locs_angles(tick_loc_angle) + self.major_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label) + + tick_loc_angle, ticklabel_loc_angle_label = \ + self._get_tick_info(minortick_iter) + self.minor_ticks.set_locs_angles(tick_loc_angle) + self.minor_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label) + + def _draw_ticks(self, renderer): + self._update_ticks(renderer) + self.major_ticks.draw(renderer) + self.major_ticklabels.draw(renderer) + self.minor_ticks.draw(renderer) + self.minor_ticklabels.draw(renderer) + if (self.major_ticklabels.get_visible() + or self.minor_ticklabels.get_visible()): + self._draw_offsetText(renderer) + + _offsetText_pos = dict(left=(0, 1, "bottom", "right"), + right=(1, 1, "bottom", "left"), + bottom=(1, 0, "top", "right"), + top=(1, 1, "bottom", "right")) + + def _init_offsetText(self, direction): + x, y, va, ha = self._offsetText_pos[direction] + self.offsetText = mtext.Annotation( + "", + xy=(x, y), xycoords="axes fraction", + xytext=(0, 0), textcoords="offset points", + color=mpl.rcParams['xtick.color'], + horizontalalignment=ha, verticalalignment=va, + ) + self.offsetText.set_transform(IdentityTransform()) + self.axes._set_artist_props(self.offsetText) + + def _update_offsetText(self): + self.offsetText.set_text(self.axis.major.formatter.get_offset()) + self.offsetText.set_size(self.major_ticklabels.get_size()) + offset = (self.major_ticklabels.get_pad() + + self.major_ticklabels.get_size() + + 2) + self.offsetText.xyann = (0, offset) + + def _draw_offsetText(self, renderer): + self._update_offsetText() + self.offsetText.draw(renderer) + + def _init_label(self, **kwargs): + tr = (self._axis_artist_helper.get_axislabel_transform(self.axes) + + self.offset_transform) + self.label = AxisLabel( + 0, 0, "__from_axes__", + color="auto", + fontsize=kwargs.get("labelsize", mpl.rcParams['axes.labelsize']), + fontweight=mpl.rcParams['axes.labelweight'], + axis=self.axis, + transform=tr, + axis_direction=self._axis_direction, + ) + self.label.set_figure(self.axes.get_figure(root=False)) + labelpad = kwargs.get("labelpad", 5) + self.label.set_pad(labelpad) + + def _update_label(self, renderer): + if not self.label.get_visible(): + return + + if self._ticklabel_add_angle != self._axislabel_add_angle: + if ((self.major_ticks.get_visible() + and not self.major_ticks.get_tick_out()) + or (self.minor_ticks.get_visible() + and not self.major_ticks.get_tick_out())): + axislabel_pad = self.major_ticks._ticksize + else: + axislabel_pad = 0 + else: + axislabel_pad = max(self.major_ticklabels._axislabel_pad, + self.minor_ticklabels._axislabel_pad) + + self.label._external_pad = axislabel_pad + + xy, angle_tangent = \ + self._axis_artist_helper.get_axislabel_pos_angle(self.axes) + if xy is None: + return + + angle_label = angle_tangent - 90 + + x, y = xy + self.label._ref_angle = angle_label + self._axislabel_add_angle + self.label.set(x=x, y=y) + + def _draw_label(self, renderer): + self._update_label(renderer) + self.label.draw(renderer) + + def set_label(self, s): + # docstring inherited + self.label.set_text(s) + + def get_tightbbox(self, renderer=None): + if not self.get_visible(): + return + self._axis_artist_helper.update_lim(self.axes) + self._update_ticks(renderer) + self._update_label(renderer) + + self.line.set_path(self._axis_artist_helper.get_line(self.axes)) + if self.get_axisline_style() is not None: + self.line.set_line_mutation_scale(self.major_ticklabels.get_size()) + + bb = [ + *self.major_ticklabels.get_window_extents(renderer), + *self.minor_ticklabels.get_window_extents(renderer), + self.label.get_window_extent(renderer), + self.offsetText.get_window_extent(renderer), + self.line.get_window_extent(renderer), + ] + bb = [b for b in bb if b and (b.width != 0 or b.height != 0)] + if bb: + _bbox = Bbox.union(bb) + return _bbox + else: + return None + + @martist.allow_rasterization + def draw(self, renderer): + # docstring inherited + if not self.get_visible(): + return + renderer.open_group(__name__, gid=self.get_gid()) + self._axis_artist_helper.update_lim(self.axes) + self._draw_ticks(renderer) + self._draw_line(renderer) + self._draw_label(renderer) + renderer.close_group(__name__) + + def toggle(self, all=None, ticks=None, ticklabels=None, label=None): + """ + Toggle visibility of ticks, ticklabels, and (axis) label. + To turn all off, :: + + axis.toggle(all=False) + + To turn all off but ticks on :: + + axis.toggle(all=False, ticks=True) + + To turn all on but (axis) label off :: + + axis.toggle(all=True, label=False) + + """ + if all: + _ticks, _ticklabels, _label = True, True, True + elif all is not None: + _ticks, _ticklabels, _label = False, False, False + else: + _ticks, _ticklabels, _label = None, None, None + + if ticks is not None: + _ticks = ticks + if ticklabels is not None: + _ticklabels = ticklabels + if label is not None: + _label = label + + if _ticks is not None: + self.major_ticks.set_visible(_ticks) + self.minor_ticks.set_visible(_ticks) + if _ticklabels is not None: + self.major_ticklabels.set_visible(_ticklabels) + self.minor_ticklabels.set_visible(_ticklabels) + if _label is not None: + self.label.set_visible(_label) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py new file mode 100644 index 0000000000000000000000000000000000000000..7f25b98082ef52b9f162542a3eafaec3b9750fd0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py @@ -0,0 +1,193 @@ +""" +Provides classes to style the axis lines. +""" +import math + +import numpy as np + +import matplotlib as mpl +from matplotlib.patches import _Style, FancyArrowPatch +from matplotlib.path import Path +from matplotlib.transforms import IdentityTransform + + +class _FancyAxislineStyle: + class SimpleArrow(FancyArrowPatch): + """The artist class that will be returned for SimpleArrow style.""" + _ARROW_STYLE = "->" + + def __init__(self, axis_artist, line_path, transform, + line_mutation_scale): + self._axis_artist = axis_artist + self._line_transform = transform + self._line_path = line_path + self._line_mutation_scale = line_mutation_scale + + FancyArrowPatch.__init__(self, + path=self._line_path, + arrowstyle=self._ARROW_STYLE, + patchA=None, + patchB=None, + shrinkA=0., + shrinkB=0., + mutation_scale=line_mutation_scale, + mutation_aspect=None, + transform=IdentityTransform(), + ) + + def set_line_mutation_scale(self, scale): + self.set_mutation_scale(scale*self._line_mutation_scale) + + def _extend_path(self, path, mutation_size=10): + """ + Extend the path to make a room for drawing arrow. + """ + (x0, y0), (x1, y1) = path.vertices[-2:] + theta = math.atan2(y1 - y0, x1 - x0) + x2 = x1 + math.cos(theta) * mutation_size + y2 = y1 + math.sin(theta) * mutation_size + if path.codes is None: + return Path(np.concatenate([path.vertices, [[x2, y2]]])) + else: + return Path(np.concatenate([path.vertices, [[x2, y2]]]), + np.concatenate([path.codes, [Path.LINETO]])) + + def set_path(self, path): + self._line_path = path + + def draw(self, renderer): + """ + Draw the axis line. + 1) Transform the path to the display coordinate. + 2) Extend the path to make a room for arrow. + 3) Update the path of the FancyArrowPatch. + 4) Draw. + """ + path_in_disp = self._line_transform.transform_path(self._line_path) + mutation_size = self.get_mutation_scale() # line_mutation_scale() + extended_path = self._extend_path(path_in_disp, + mutation_size=mutation_size) + self._path_original = extended_path + FancyArrowPatch.draw(self, renderer) + + def get_window_extent(self, renderer=None): + + path_in_disp = self._line_transform.transform_path(self._line_path) + mutation_size = self.get_mutation_scale() # line_mutation_scale() + extended_path = self._extend_path(path_in_disp, + mutation_size=mutation_size) + self._path_original = extended_path + return FancyArrowPatch.get_window_extent(self, renderer) + + class FilledArrow(SimpleArrow): + """The artist class that will be returned for FilledArrow style.""" + _ARROW_STYLE = "-|>" + + def __init__(self, axis_artist, line_path, transform, + line_mutation_scale, facecolor): + super().__init__(axis_artist, line_path, transform, + line_mutation_scale) + self.set_facecolor(facecolor) + + +class AxislineStyle(_Style): + """ + A container class which defines style classes for AxisArtists. + + An instance of any axisline style class is a callable object, + whose call signature is :: + + __call__(self, axis_artist, path, transform) + + When called, this should return an `.Artist` with the following methods:: + + def set_path(self, path): + # set the path for axisline. + + def set_line_mutation_scale(self, scale): + # set the scale + + def draw(self, renderer): + # draw + """ + + _style_list = {} + + class _Base: + # The derived classes are required to be able to be initialized + # w/o arguments, i.e., all its argument (except self) must have + # the default values. + + def __init__(self): + """ + initialization. + """ + super().__init__() + + def __call__(self, axis_artist, transform): + """ + Given the AxisArtist instance, and transform for the path (set_path + method), return the Matplotlib artist for drawing the axis line. + """ + return self.new_line(axis_artist, transform) + + class SimpleArrow(_Base): + """ + A simple arrow. + """ + + ArrowAxisClass = _FancyAxislineStyle.SimpleArrow + + def __init__(self, size=1): + """ + Parameters + ---------- + size : float + Size of the arrow as a fraction of the ticklabel size. + """ + + self.size = size + super().__init__() + + def new_line(self, axis_artist, transform): + + linepath = Path([(0, 0), (0, 1)]) + axisline = self.ArrowAxisClass(axis_artist, linepath, transform, + line_mutation_scale=self.size) + return axisline + + _style_list["->"] = SimpleArrow + + class FilledArrow(SimpleArrow): + """ + An arrow with a filled head. + """ + + ArrowAxisClass = _FancyAxislineStyle.FilledArrow + + def __init__(self, size=1, facecolor=None): + """ + Parameters + ---------- + size : float + Size of the arrow as a fraction of the ticklabel size. + facecolor : :mpltype:`color`, default: :rc:`axes.edgecolor` + Fill color. + + .. versionadded:: 3.7 + """ + + if facecolor is None: + facecolor = mpl.rcParams['axes.edgecolor'] + self.size = size + self._facecolor = facecolor + super().__init__(size=size) + + def new_line(self, axis_artist, transform): + linepath = Path([(0, 0), (0, 1)]) + axisline = self.ArrowAxisClass(axis_artist, linepath, transform, + line_mutation_scale=self.size, + facecolor=self._facecolor) + return axisline + + _style_list["-|>"] = FilledArrow diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py new file mode 100644 index 0000000000000000000000000000000000000000..8d06cb236269d00ed20a27297b59fa816338e79a --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py @@ -0,0 +1,479 @@ +""" +Axislines includes modified implementation of the Axes class. The +biggest difference is that the artists responsible for drawing the axis spine, +ticks, ticklabels and axis labels are separated out from Matplotlib's Axis +class. Originally, this change was motivated to support curvilinear +grid. Here are a few reasons that I came up with a new axes class: + +* "top" and "bottom" x-axis (or "left" and "right" y-axis) can have + different ticks (tick locations and labels). This is not possible + with the current Matplotlib, although some twin axes trick can help. + +* Curvilinear grid. + +* angled ticks. + +In the new axes class, xaxis and yaxis is set to not visible by +default, and new set of artist (AxisArtist) are defined to draw axis +line, ticks, ticklabels and axis label. Axes.axis attribute serves as +a dictionary of these artists, i.e., ax.axis["left"] is a AxisArtist +instance responsible to draw left y-axis. The default Axes.axis contains +"bottom", "left", "top" and "right". + +AxisArtist can be considered as a container artist and has the following +children artists which will draw ticks, labels, etc. + +* line +* major_ticks, major_ticklabels +* minor_ticks, minor_ticklabels +* offsetText +* label + +Note that these are separate artists from `matplotlib.axis.Axis`, thus most +tick-related functions in Matplotlib won't work. For example, color and +markerwidth of the ``ax.axis["bottom"].major_ticks`` will follow those of +Axes.xaxis unless explicitly specified. + +In addition to AxisArtist, the Axes will have *gridlines* attribute, +which obviously draws grid lines. The gridlines needs to be separated +from the axis as some gridlines can never pass any axis. +""" + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +import matplotlib.axes as maxes +from matplotlib.path import Path +from mpl_toolkits.axes_grid1 import mpl_axes +from .axisline_style import AxislineStyle # noqa +from .axis_artist import AxisArtist, GridlinesCollection + + +class _AxisArtistHelperBase: + """ + Base class for axis helper. + + Subclasses should define the methods listed below. The *axes* + argument will be the ``.axes`` attribute of the caller artist. :: + + # Construct the spine. + + def get_line_transform(self, axes): + return transform + + def get_line(self, axes): + return path + + # Construct the label. + + def get_axislabel_transform(self, axes): + return transform + + def get_axislabel_pos_angle(self, axes): + return (x, y), angle + + # Construct the ticks. + + def get_tick_transform(self, axes): + return transform + + def get_tick_iterators(self, axes): + # A pair of iterables (one for major ticks, one for minor ticks) + # that yield (tick_position, tick_angle, tick_label). + return iter_major, iter_minor + """ + + def __init__(self, nth_coord): + self.nth_coord = nth_coord + + def update_lim(self, axes): + pass + + def get_nth_coord(self): + return self.nth_coord + + def _to_xy(self, values, const): + """ + Create a (*values.shape, 2)-shape array representing (x, y) pairs. + + The other coordinate is filled with the constant *const*. + + Example:: + + >>> self.nth_coord = 0 + >>> self._to_xy([1, 2, 3], const=0) + array([[1, 0], + [2, 0], + [3, 0]]) + """ + if self.nth_coord == 0: + return np.stack(np.broadcast_arrays(values, const), axis=-1) + elif self.nth_coord == 1: + return np.stack(np.broadcast_arrays(const, values), axis=-1) + else: + raise ValueError("Unexpected nth_coord") + + +class _FixedAxisArtistHelperBase(_AxisArtistHelperBase): + """Helper class for a fixed (in the axes coordinate) axis.""" + + @_api.delete_parameter("3.9", "nth_coord") + def __init__(self, loc, nth_coord=None): + """``nth_coord = 0``: x-axis; ``nth_coord = 1``: y-axis.""" + super().__init__(_api.check_getitem( + {"bottom": 0, "top": 0, "left": 1, "right": 1}, loc=loc)) + self._loc = loc + self._pos = {"bottom": 0, "top": 1, "left": 0, "right": 1}[loc] + # axis line in transAxes + self._path = Path(self._to_xy((0, 1), const=self._pos)) + + # LINE + + def get_line(self, axes): + return self._path + + def get_line_transform(self, axes): + return axes.transAxes + + # LABEL + + def get_axislabel_transform(self, axes): + return axes.transAxes + + def get_axislabel_pos_angle(self, axes): + """ + Return the label reference position in transAxes. + + get_label_transform() returns a transform of (transAxes+offset) + """ + return dict(left=((0., 0.5), 90), # (position, angle_tangent) + right=((1., 0.5), 90), + bottom=((0.5, 0.), 0), + top=((0.5, 1.), 0))[self._loc] + + # TICK + + def get_tick_transform(self, axes): + return [axes.get_xaxis_transform(), axes.get_yaxis_transform()][self.nth_coord] + + +class _FloatingAxisArtistHelperBase(_AxisArtistHelperBase): + def __init__(self, nth_coord, value): + self._value = value + super().__init__(nth_coord) + + def get_line(self, axes): + raise RuntimeError("get_line method should be defined by the derived class") + + +class FixedAxisArtistHelperRectilinear(_FixedAxisArtistHelperBase): + + @_api.delete_parameter("3.9", "nth_coord") + def __init__(self, axes, loc, nth_coord=None): + """ + nth_coord = along which coordinate value varies + in 2D, nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + super().__init__(loc) + self.axis = [axes.xaxis, axes.yaxis][self.nth_coord] + + # TICK + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + angle_normal, angle_tangent = {0: (90, 0), 1: (0, 90)}[self.nth_coord] + + major = self.axis.major + major_locs = major.locator() + major_labels = major.formatter.format_ticks(major_locs) + + minor = self.axis.minor + minor_locs = minor.locator() + minor_labels = minor.formatter.format_ticks(minor_locs) + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + + def _f(locs, labels): + for loc, label in zip(locs, labels): + c = self._to_xy(loc, const=self._pos) + # check if the tick point is inside axes + c2 = tick_to_axes.transform(c) + if mpl.transforms._interval_contains_close((0, 1), c2[self.nth_coord]): + yield c, angle_normal, angle_tangent, label + + return _f(major_locs, major_labels), _f(minor_locs, minor_labels) + + +class FloatingAxisArtistHelperRectilinear(_FloatingAxisArtistHelperBase): + + def __init__(self, axes, nth_coord, + passingthrough_point, axis_direction="bottom"): + super().__init__(nth_coord, passingthrough_point) + self._axis_direction = axis_direction + self.axis = [axes.xaxis, axes.yaxis][self.nth_coord] + + def get_line(self, axes): + fixed_coord = 1 - self.nth_coord + data_to_axes = axes.transData - axes.transAxes + p = data_to_axes.transform([self._value, self._value]) + return Path(self._to_xy((0, 1), const=p[fixed_coord])) + + def get_line_transform(self, axes): + return axes.transAxes + + def get_axislabel_transform(self, axes): + return axes.transAxes + + def get_axislabel_pos_angle(self, axes): + """ + Return the label reference position in transAxes. + + get_label_transform() returns a transform of (transAxes+offset) + """ + angle = [0, 90][self.nth_coord] + fixed_coord = 1 - self.nth_coord + data_to_axes = axes.transData - axes.transAxes + p = data_to_axes.transform([self._value, self._value]) + verts = self._to_xy(0.5, const=p[fixed_coord]) + return (verts, angle) if 0 <= verts[fixed_coord] <= 1 else (None, None) + + def get_tick_transform(self, axes): + return axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + angle_normal, angle_tangent = {0: (90, 0), 1: (0, 90)}[self.nth_coord] + + major = self.axis.major + major_locs = major.locator() + major_labels = major.formatter.format_ticks(major_locs) + + minor = self.axis.minor + minor_locs = minor.locator() + minor_labels = minor.formatter.format_ticks(minor_locs) + + data_to_axes = axes.transData - axes.transAxes + + def _f(locs, labels): + for loc, label in zip(locs, labels): + c = self._to_xy(loc, const=self._value) + c1, c2 = data_to_axes.transform(c) + if 0 <= c1 <= 1 and 0 <= c2 <= 1: + yield c, angle_normal, angle_tangent, label + + return _f(major_locs, major_labels), _f(minor_locs, minor_labels) + + +class AxisArtistHelper: # Backcompat. + Fixed = _FixedAxisArtistHelperBase + Floating = _FloatingAxisArtistHelperBase + + +class AxisArtistHelperRectlinear: # Backcompat. + Fixed = FixedAxisArtistHelperRectilinear + Floating = FloatingAxisArtistHelperRectilinear + + +class GridHelperBase: + + def __init__(self): + self._old_limits = None + super().__init__() + + def update_lim(self, axes): + x1, x2 = axes.get_xlim() + y1, y2 = axes.get_ylim() + if self._old_limits != (x1, x2, y1, y2): + self._update_grid(x1, y1, x2, y2) + self._old_limits = (x1, x2, y1, y2) + + def _update_grid(self, x1, y1, x2, y2): + """Cache relevant computations when the axes limits have changed.""" + + def get_gridlines(self, which, axis): + """ + Return list of grid lines as a list of paths (list of points). + + Parameters + ---------- + which : {"both", "major", "minor"} + axis : {"both", "x", "y"} + """ + return [] + + +class GridHelperRectlinear(GridHelperBase): + + def __init__(self, axes): + super().__init__() + self.axes = axes + + @_api.delete_parameter( + "3.9", "nth_coord", addendum="'nth_coord' is now inferred from 'loc'.") + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + _api.warn_external( + "'new_fixed_axis' explicitly requires the axes keyword.") + axes = self.axes + if axis_direction is None: + axis_direction = loc + return AxisArtist(axes, FixedAxisArtistHelperRectilinear(axes, loc), + offset=offset, axis_direction=axis_direction) + + def new_floating_axis(self, nth_coord, value, axis_direction="bottom", axes=None): + if axes is None: + _api.warn_external( + "'new_floating_axis' explicitly requires the axes keyword.") + axes = self.axes + helper = FloatingAxisArtistHelperRectilinear( + axes, nth_coord, value, axis_direction) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + return axisline + + def get_gridlines(self, which="major", axis="both"): + """ + Return list of gridline coordinates in data coordinates. + + Parameters + ---------- + which : {"both", "major", "minor"} + axis : {"both", "x", "y"} + """ + _api.check_in_list(["both", "major", "minor"], which=which) + _api.check_in_list(["both", "x", "y"], axis=axis) + gridlines = [] + + if axis in ("both", "x"): + locs = [] + y1, y2 = self.axes.get_ylim() + if which in ("both", "major"): + locs.extend(self.axes.xaxis.major.locator()) + if which in ("both", "minor"): + locs.extend(self.axes.xaxis.minor.locator()) + gridlines.extend([[x, x], [y1, y2]] for x in locs) + + if axis in ("both", "y"): + x1, x2 = self.axes.get_xlim() + locs = [] + if self.axes.yaxis._major_tick_kw["gridOn"]: + locs.extend(self.axes.yaxis.major.locator()) + if self.axes.yaxis._minor_tick_kw["gridOn"]: + locs.extend(self.axes.yaxis.minor.locator()) + gridlines.extend([[x1, x2], [y, y]] for y in locs) + + return gridlines + + +class Axes(maxes.Axes): + + def __init__(self, *args, grid_helper=None, **kwargs): + self._axisline_on = True + self._grid_helper = grid_helper if grid_helper else GridHelperRectlinear(self) + super().__init__(*args, **kwargs) + self.toggle_axisline(True) + + def toggle_axisline(self, b=None): + if b is None: + b = not self._axisline_on + if b: + self._axisline_on = True + self.spines[:].set_visible(False) + self.xaxis.set_visible(False) + self.yaxis.set_visible(False) + else: + self._axisline_on = False + self.spines[:].set_visible(True) + self.xaxis.set_visible(True) + self.yaxis.set_visible(True) + + @property + def axis(self): + return self._axislines + + def clear(self): + # docstring inherited + + # Init gridlines before clear() as clear() calls grid(). + self.gridlines = gridlines = GridlinesCollection( + [], + colors=mpl.rcParams['grid.color'], + linestyles=mpl.rcParams['grid.linestyle'], + linewidths=mpl.rcParams['grid.linewidth']) + self._set_artist_props(gridlines) + gridlines.set_grid_helper(self.get_grid_helper()) + + super().clear() + + # clip_path is set after Axes.clear(): that's when a patch is created. + gridlines.set_clip_path(self.axes.patch) + + # Init axis artists. + self._axislines = mpl_axes.Axes.AxisDict(self) + new_fixed_axis = self.get_grid_helper().new_fixed_axis + self._axislines.update({ + loc: new_fixed_axis(loc=loc, axes=self, axis_direction=loc) + for loc in ["bottom", "top", "left", "right"]}) + for axisline in [self._axislines["top"], self._axislines["right"]]: + axisline.label.set_visible(False) + axisline.major_ticklabels.set_visible(False) + axisline.minor_ticklabels.set_visible(False) + + def get_grid_helper(self): + return self._grid_helper + + def grid(self, visible=None, which='major', axis="both", **kwargs): + """ + Toggle the gridlines, and optionally set the properties of the lines. + """ + # There are some discrepancies in the behavior of grid() between + # axes_grid and Matplotlib, because axes_grid explicitly sets the + # visibility of the gridlines. + super().grid(visible, which=which, axis=axis, **kwargs) + if not self._axisline_on: + return + if visible is None: + visible = (self.axes.xaxis._minor_tick_kw["gridOn"] + or self.axes.xaxis._major_tick_kw["gridOn"] + or self.axes.yaxis._minor_tick_kw["gridOn"] + or self.axes.yaxis._major_tick_kw["gridOn"]) + self.gridlines.set(which=which, axis=axis, visible=visible) + self.gridlines.set(**kwargs) + + def get_children(self): + if self._axisline_on: + children = [*self._axislines.values(), self.gridlines] + else: + children = [] + children.extend(super().get_children()) + return children + + def new_fixed_axis(self, loc, offset=None): + return self.get_grid_helper().new_fixed_axis(loc, offset=offset, axes=self) + + def new_floating_axis(self, nth_coord, value, axis_direction="bottom"): + return self.get_grid_helper().new_floating_axis( + nth_coord, value, axis_direction=axis_direction, axes=self) + + +class AxesZero(Axes): + + def clear(self): + super().clear() + new_floating_axis = self.get_grid_helper().new_floating_axis + self._axislines.update( + xzero=new_floating_axis( + nth_coord=0, value=0., axis_direction="bottom", axes=self), + yzero=new_floating_axis( + nth_coord=1, value=0., axis_direction="left", axes=self), + ) + for k in ["xzero", "yzero"]: + self._axislines[k].line.set_clip_path(self.patch) + self._axislines[k].set_visible(False) + + +Subplot = Axes +SubplotZero = AxesZero diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..74e4c941879babca194cec7d60fe949e648cb4cf --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py @@ -0,0 +1,275 @@ +""" +An experimental support for curvilinear grid. +""" + +# TODO : +# see if tick_iterator method can be simplified by reusing the parent method. + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook +import matplotlib.patches as mpatches +from matplotlib.path import Path + +from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory + +from . import axislines, grid_helper_curvelinear +from .axis_artist import AxisArtist +from .grid_finder import ExtremeFinderSimple + + +class FloatingAxisArtistHelper( + grid_helper_curvelinear.FloatingAxisArtistHelper): + pass + + +class FixedAxisArtistHelper(grid_helper_curvelinear.FloatingAxisArtistHelper): + + def __init__(self, grid_helper, side, nth_coord_ticks=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + lon1, lon2, lat1, lat2 = grid_helper.grid_finder.extreme_finder(*[None] * 5) + value, nth_coord = _api.check_getitem( + dict(left=(lon1, 0), right=(lon2, 0), bottom=(lat1, 1), top=(lat2, 1)), + side=side) + super().__init__(grid_helper, nth_coord, value, axis_direction=side) + if nth_coord_ticks is None: + nth_coord_ticks = nth_coord + self.nth_coord_ticks = nth_coord_ticks + + self.value = value + self.grid_helper = grid_helper + self._side = side + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + self._grid_info = self.grid_helper._grid_info + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label, (optionally) tick_label""" + + grid_finder = self.grid_helper.grid_finder + + lat_levs, lat_n, lat_factor = self._grid_info["lat_info"] + yy0 = lat_levs / lat_factor + + lon_levs, lon_n, lon_factor = self._grid_info["lon_info"] + xx0 = lon_levs / lon_factor + + extremes = self.grid_helper.grid_finder.extreme_finder(*[None] * 5) + xmin, xmax = sorted(extremes[:2]) + ymin, ymax = sorted(extremes[2:]) + + def trf_xy(x, y): + trf = grid_finder.get_transform() + axes.transData + return trf.transform(np.column_stack(np.broadcast_arrays(x, y))).T + + if self.nth_coord == 0: + mask = (ymin <= yy0) & (yy0 <= ymax) + (xx1, yy1), (dxx1, dyy1), (dxx2, dyy2) = \ + grid_helper_curvelinear._value_and_jacobian( + trf_xy, self.value, yy0[mask], (xmin, xmax), (ymin, ymax)) + labels = self._grid_info["lat_labels"] + + elif self.nth_coord == 1: + mask = (xmin <= xx0) & (xx0 <= xmax) + (xx1, yy1), (dxx2, dyy2), (dxx1, dyy1) = \ + grid_helper_curvelinear._value_and_jacobian( + trf_xy, xx0[mask], self.value, (xmin, xmax), (ymin, ymax)) + labels = self._grid_info["lon_labels"] + + labels = [l for l, m in zip(labels, mask) if m] + + angle_normal = np.arctan2(dyy1, dxx1) + angle_tangent = np.arctan2(dyy2, dxx2) + mm = (dyy1 == 0) & (dxx1 == 0) # points with degenerate normal + angle_normal[mm] = angle_tangent[mm] + np.pi / 2 + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + in_01 = functools.partial( + mpl.transforms._interval_contains_close, (0, 1)) + + def f1(): + for x, y, normal, tangent, lab \ + in zip(xx1, yy1, angle_normal, angle_tangent, labels): + c2 = tick_to_axes.transform((x, y)) + if in_01(c2[0]) and in_01(c2[1]): + yield [x, y], *np.rad2deg([normal, tangent]), lab + + return f1(), iter([]) + + def get_line(self, axes): + self.update_lim(axes) + k, v = dict(left=("lon_lines0", 0), + right=("lon_lines0", 1), + bottom=("lat_lines0", 0), + top=("lat_lines0", 1))[self._side] + xx, yy = self._grid_info[k][v] + return Path(np.column_stack([xx, yy])) + + +class ExtremeFinderFixed(ExtremeFinderSimple): + # docstring inherited + + def __init__(self, extremes): + """ + This subclass always returns the same bounding box. + + Parameters + ---------- + extremes : (float, float, float, float) + The bounding box that this helper always returns. + """ + self._extremes = extremes + + def __call__(self, transform_xy, x1, y1, x2, y2): + # docstring inherited + return self._extremes + + +class GridHelperCurveLinear(grid_helper_curvelinear.GridHelperCurveLinear): + + def __init__(self, aux_trans, extremes, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + # docstring inherited + super().__init__(aux_trans, + extreme_finder=ExtremeFinderFixed(extremes), + grid_locator1=grid_locator1, + grid_locator2=grid_locator2, + tick_formatter1=tick_formatter1, + tick_formatter2=tick_formatter2) + + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + axes = self.axes + if axis_direction is None: + axis_direction = loc + # This is not the same as the FixedAxisArtistHelper class used by + # grid_helper_curvelinear.GridHelperCurveLinear.new_fixed_axis! + helper = FixedAxisArtistHelper( + self, loc, nth_coord_ticks=nth_coord) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + # Perhaps should be moved to the base class? + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + return axisline + + # new_floating_axis will inherit the grid_helper's extremes. + + # def new_floating_axis(self, nth_coord, value, axes=None, axis_direction="bottom"): + # axis = super(GridHelperCurveLinear, + # self).new_floating_axis(nth_coord, + # value, axes=axes, + # axis_direction=axis_direction) + # # set extreme values of the axis helper + # if nth_coord == 1: + # axis.get_helper().set_extremes(*self._extremes[:2]) + # elif nth_coord == 0: + # axis.get_helper().set_extremes(*self._extremes[2:]) + # return axis + + def _update_grid(self, x1, y1, x2, y2): + if self._grid_info is None: + self._grid_info = dict() + + grid_info = self._grid_info + + grid_finder = self.grid_finder + extremes = grid_finder.extreme_finder(grid_finder.inv_transform_xy, + x1, y1, x2, y2) + + lon_min, lon_max = sorted(extremes[:2]) + lat_min, lat_max = sorted(extremes[2:]) + grid_info["extremes"] = lon_min, lon_max, lat_min, lat_max # extremes + + lon_levs, lon_n, lon_factor = \ + grid_finder.grid_locator1(lon_min, lon_max) + lon_levs = np.asarray(lon_levs) + lat_levs, lat_n, lat_factor = \ + grid_finder.grid_locator2(lat_min, lat_max) + lat_levs = np.asarray(lat_levs) + + grid_info["lon_info"] = lon_levs, lon_n, lon_factor + grid_info["lat_info"] = lat_levs, lat_n, lat_factor + + grid_info["lon_labels"] = grid_finder._format_ticks( + 1, "bottom", lon_factor, lon_levs) + grid_info["lat_labels"] = grid_finder._format_ticks( + 2, "bottom", lat_factor, lat_levs) + + lon_values = lon_levs[:lon_n] / lon_factor + lat_values = lat_levs[:lat_n] / lat_factor + + lon_lines, lat_lines = grid_finder._get_raw_grid_lines( + lon_values[(lon_min < lon_values) & (lon_values < lon_max)], + lat_values[(lat_min < lat_values) & (lat_values < lat_max)], + lon_min, lon_max, lat_min, lat_max) + + grid_info["lon_lines"] = lon_lines + grid_info["lat_lines"] = lat_lines + + lon_lines, lat_lines = grid_finder._get_raw_grid_lines( + # lon_min, lon_max, lat_min, lat_max) + extremes[:2], extremes[2:], *extremes) + + grid_info["lon_lines0"] = lon_lines + grid_info["lat_lines0"] = lat_lines + + def get_gridlines(self, which="major", axis="both"): + grid_lines = [] + if axis in ["both", "x"]: + grid_lines.extend(self._grid_info["lon_lines"]) + if axis in ["both", "y"]: + grid_lines.extend(self._grid_info["lat_lines"]) + return grid_lines + + +class FloatingAxesBase: + + def __init__(self, *args, grid_helper, **kwargs): + _api.check_isinstance(GridHelperCurveLinear, grid_helper=grid_helper) + super().__init__(*args, grid_helper=grid_helper, **kwargs) + self.set_aspect(1.) + + def _gen_axes_patch(self): + # docstring inherited + x0, x1, y0, y1 = self.get_grid_helper().grid_finder.extreme_finder(*[None] * 5) + patch = mpatches.Polygon([(x0, y0), (x1, y0), (x1, y1), (x0, y1)]) + patch.get_path()._interpolation_steps = 100 + return patch + + def clear(self): + super().clear() + self.patch.set_transform( + self.get_grid_helper().grid_finder.get_transform() + + self.transData) + # The original patch is not in the draw tree; it is only used for + # clipping purposes. + orig_patch = super()._gen_axes_patch() + orig_patch.set_figure(self.get_figure(root=False)) + orig_patch.set_transform(self.transAxes) + self.patch.set_clip_path(orig_patch) + self.gridlines.set_clip_path(orig_patch) + self.adjust_axes_lim() + + def adjust_axes_lim(self): + bbox = self.patch.get_path().get_extents( + # First transform to pixel coords, then to parent data coords. + self.patch.get_transform() - self.transData) + bbox = bbox.expanded(1.02, 1.02) + self.set_xlim(bbox.xmin, bbox.xmax) + self.set_ylim(bbox.ymin, bbox.ymax) + + +floatingaxes_class_factory = cbook._make_class_factory(FloatingAxesBase, "Floating{}") +FloatingAxes = floatingaxes_class_factory(host_axes_class_factory(axislines.Axes)) +FloatingSubplot = FloatingAxes diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..ff67aa6e872044bb74e8f2e3882a4d3719cbd55d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py @@ -0,0 +1,326 @@ +import numpy as np + +from matplotlib import ticker as mticker, _api +from matplotlib.transforms import Bbox, Transform + + +def _find_line_box_crossings(xys, bbox): + """ + Find the points where a polyline crosses a bbox, and the crossing angles. + + Parameters + ---------- + xys : (N, 2) array + The polyline coordinates. + bbox : `.Bbox` + The bounding box. + + Returns + ------- + list of ((float, float), float) + Four separate lists of crossings, for the left, right, bottom, and top + sides of the bbox, respectively. For each list, the entries are the + ``((x, y), ccw_angle_in_degrees)`` of the crossing, where an angle of 0 + means that the polyline is moving to the right at the crossing point. + + The entries are computed by linearly interpolating at each crossing + between the nearest points on either side of the bbox edges. + """ + crossings = [] + dxys = xys[1:] - xys[:-1] + for sl in [slice(None), slice(None, None, -1)]: + us, vs = xys.T[sl] # "this" coord, "other" coord + dus, dvs = dxys.T[sl] + umin, vmin = bbox.min[sl] + umax, vmax = bbox.max[sl] + for u0, inside in [(umin, us > umin), (umax, us < umax)]: + cross = [] + idxs, = (inside[:-1] ^ inside[1:]).nonzero() + for idx in idxs: + v = vs[idx] + (u0 - us[idx]) * dvs[idx] / dus[idx] + if not vmin <= v <= vmax: + continue + crossing = (u0, v)[sl] + theta = np.degrees(np.arctan2(*dxys[idx][::-1])) + cross.append((crossing, theta)) + crossings.append(cross) + return crossings + + +class ExtremeFinderSimple: + """ + A helper class to figure out the range of grid lines that need to be drawn. + """ + + def __init__(self, nx, ny): + """ + Parameters + ---------- + nx, ny : int + The number of samples in each direction. + """ + self.nx = nx + self.ny = ny + + def __call__(self, transform_xy, x1, y1, x2, y2): + """ + Compute an approximation of the bounding box obtained by applying + *transform_xy* to the box delimited by ``(x1, y1, x2, y2)``. + + The intended use is to have ``(x1, y1, x2, y2)`` in axes coordinates, + and have *transform_xy* be the transform from axes coordinates to data + coordinates; this method then returns the range of data coordinates + that span the actual axes. + + The computation is done by sampling ``nx * ny`` equispaced points in + the ``(x1, y1, x2, y2)`` box and finding the resulting points with + extremal coordinates; then adding some padding to take into account the + finite sampling. + + As each sampling step covers a relative range of *1/nx* or *1/ny*, + the padding is computed by expanding the span covered by the extremal + coordinates by these fractions. + """ + x, y = np.meshgrid( + np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) + xt, yt = transform_xy(np.ravel(x), np.ravel(y)) + return self._add_pad(xt.min(), xt.max(), yt.min(), yt.max()) + + def _add_pad(self, x_min, x_max, y_min, y_max): + """Perform the padding mentioned in `__call__`.""" + dx = (x_max - x_min) / self.nx + dy = (y_max - y_min) / self.ny + return x_min - dx, x_max + dx, y_min - dy, y_max + dy + + +class _User2DTransform(Transform): + """A transform defined by two user-set functions.""" + + input_dims = output_dims = 2 + + def __init__(self, forward, backward): + """ + Parameters + ---------- + forward, backward : callable + The forward and backward transforms, taking ``x`` and ``y`` as + separate arguments and returning ``(tr_x, tr_y)``. + """ + # The normal Matplotlib convention would be to take and return an + # (N, 2) array but axisartist uses the transposed version. + super().__init__() + self._forward = forward + self._backward = backward + + def transform_non_affine(self, values): + # docstring inherited + return np.transpose(self._forward(*np.transpose(values))) + + def inverted(self): + # docstring inherited + return type(self)(self._backward, self._forward) + + +class GridFinder: + """ + Internal helper for `~.grid_helper_curvelinear.GridHelperCurveLinear`, with + the same constructor parameters; should not be directly instantiated. + """ + + def __init__(self, + transform, + extreme_finder=None, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + if extreme_finder is None: + extreme_finder = ExtremeFinderSimple(20, 20) + if grid_locator1 is None: + grid_locator1 = MaxNLocator() + if grid_locator2 is None: + grid_locator2 = MaxNLocator() + if tick_formatter1 is None: + tick_formatter1 = FormatterPrettyPrint() + if tick_formatter2 is None: + tick_formatter2 = FormatterPrettyPrint() + self.extreme_finder = extreme_finder + self.grid_locator1 = grid_locator1 + self.grid_locator2 = grid_locator2 + self.tick_formatter1 = tick_formatter1 + self.tick_formatter2 = tick_formatter2 + self.set_transform(transform) + + def _format_ticks(self, idx, direction, factor, levels): + """ + Helper to support both standard formatters (inheriting from + `.mticker.Formatter`) and axisartist-specific ones; should be called instead of + directly calling ``self.tick_formatter1`` and ``self.tick_formatter2``. This + method should be considered as a temporary workaround which will be removed in + the future at the same time as axisartist-specific formatters. + """ + fmt = _api.check_getitem( + {1: self.tick_formatter1, 2: self.tick_formatter2}, idx=idx) + return (fmt.format_ticks(levels) if isinstance(fmt, mticker.Formatter) + else fmt(direction, factor, levels)) + + def get_grid_info(self, x1, y1, x2, y2): + """ + lon_values, lat_values : list of grid values. if integer is given, + rough number of grids in each direction. + """ + + extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2) + + # min & max rage of lat (or lon) for each grid line will be drawn. + # i.e., gridline of lon=0 will be drawn from lat_min to lat_max. + + lon_min, lon_max, lat_min, lat_max = extremes + lon_levs, lon_n, lon_factor = self.grid_locator1(lon_min, lon_max) + lon_levs = np.asarray(lon_levs) + lat_levs, lat_n, lat_factor = self.grid_locator2(lat_min, lat_max) + lat_levs = np.asarray(lat_levs) + + lon_values = lon_levs[:lon_n] / lon_factor + lat_values = lat_levs[:lat_n] / lat_factor + + lon_lines, lat_lines = self._get_raw_grid_lines(lon_values, + lat_values, + lon_min, lon_max, + lat_min, lat_max) + + bb = Bbox.from_extents(x1, y1, x2, y2).expanded(1 + 2e-10, 1 + 2e-10) + + grid_info = { + "extremes": extremes, + # "lon", "lat", filled below. + } + + for idx, lon_or_lat, levs, factor, values, lines in [ + (1, "lon", lon_levs, lon_factor, lon_values, lon_lines), + (2, "lat", lat_levs, lat_factor, lat_values, lat_lines), + ]: + grid_info[lon_or_lat] = gi = { + "lines": [[l] for l in lines], + "ticks": {"left": [], "right": [], "bottom": [], "top": []}, + } + for (lx, ly), v, level in zip(lines, values, levs): + all_crossings = _find_line_box_crossings(np.column_stack([lx, ly]), bb) + for side, crossings in zip( + ["left", "right", "bottom", "top"], all_crossings): + for crossing in crossings: + gi["ticks"][side].append({"level": level, "loc": crossing}) + for side in gi["ticks"]: + levs = [tick["level"] for tick in gi["ticks"][side]] + labels = self._format_ticks(idx, side, factor, levs) + for tick, label in zip(gi["ticks"][side], labels): + tick["label"] = label + + return grid_info + + def _get_raw_grid_lines(self, + lon_values, lat_values, + lon_min, lon_max, lat_min, lat_max): + + lons_i = np.linspace(lon_min, lon_max, 100) # for interpolation + lats_i = np.linspace(lat_min, lat_max, 100) + + lon_lines = [self.transform_xy(np.full_like(lats_i, lon), lats_i) + for lon in lon_values] + lat_lines = [self.transform_xy(lons_i, np.full_like(lons_i, lat)) + for lat in lat_values] + + return lon_lines, lat_lines + + def set_transform(self, aux_trans): + if isinstance(aux_trans, Transform): + self._aux_transform = aux_trans + elif len(aux_trans) == 2 and all(map(callable, aux_trans)): + self._aux_transform = _User2DTransform(*aux_trans) + else: + raise TypeError("'aux_trans' must be either a Transform " + "instance or a pair of callables") + + def get_transform(self): + return self._aux_transform + + update_transform = set_transform # backcompat alias. + + def transform_xy(self, x, y): + return self._aux_transform.transform(np.column_stack([x, y])).T + + def inv_transform_xy(self, x, y): + return self._aux_transform.inverted().transform( + np.column_stack([x, y])).T + + def update(self, **kwargs): + for k, v in kwargs.items(): + if k in ["extreme_finder", + "grid_locator1", + "grid_locator2", + "tick_formatter1", + "tick_formatter2"]: + setattr(self, k, v) + else: + raise ValueError(f"Unknown update property {k!r}") + + +class MaxNLocator(mticker.MaxNLocator): + def __init__(self, nbins=10, steps=None, + trim=True, + integer=False, + symmetric=False, + prune=None): + # trim argument has no effect. It has been left for API compatibility + super().__init__(nbins, steps=steps, integer=integer, + symmetric=symmetric, prune=prune) + self.create_dummy_axis() + + def __call__(self, v1, v2): + locs = super().tick_values(v1, v2) + return np.array(locs), len(locs), 1 # 1: factor (see angle_helper) + + +class FixedLocator: + def __init__(self, locs): + self._locs = locs + + def __call__(self, v1, v2): + v1, v2 = sorted([v1, v2]) + locs = np.array([l for l in self._locs if v1 <= l <= v2]) + return locs, len(locs), 1 # 1: factor (see angle_helper) + + +# Tick Formatter + +class FormatterPrettyPrint: + def __init__(self, useMathText=True): + self._fmt = mticker.ScalarFormatter( + useMathText=useMathText, useOffset=False) + self._fmt.create_dummy_axis() + + def __call__(self, direction, factor, values): + return self._fmt.format_ticks(values) + + +class DictFormatter: + def __init__(self, format_dict, formatter=None): + """ + format_dict : dictionary for format strings to be used. + formatter : fall-back formatter + """ + super().__init__() + self._format_dict = format_dict + self._fallback_formatter = formatter + + def __call__(self, direction, factor, values): + """ + factor is ignored if value is found in the dictionary + """ + if self._fallback_formatter: + fallback_strings = self._fallback_formatter( + direction, factor, values) + else: + fallback_strings = [""] * len(values) + return [self._format_dict.get(k, v) + for k, v in zip(values, fallback_strings)] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py new file mode 100644 index 0000000000000000000000000000000000000000..a7eb9d5cfe2108c3691e72f527e98b7b73e95a65 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py @@ -0,0 +1,328 @@ +""" +An experimental support for curvilinear grid. +""" + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +from matplotlib.path import Path +from matplotlib.transforms import Affine2D, IdentityTransform +from .axislines import ( + _FixedAxisArtistHelperBase, _FloatingAxisArtistHelperBase, GridHelperBase) +from .axis_artist import AxisArtist +from .grid_finder import GridFinder + + +def _value_and_jacobian(func, xs, ys, xlims, ylims): + """ + Compute *func* and its derivatives along x and y at positions *xs*, *ys*, + while ensuring that finite difference calculations don't try to evaluate + values outside of *xlims*, *ylims*. + """ + eps = np.finfo(float).eps ** (1/2) # see e.g. scipy.optimize.approx_fprime + val = func(xs, ys) + # Take the finite difference step in the direction where the bound is the + # furthest; the step size is min of epsilon and distance to that bound. + xlo, xhi = sorted(xlims) + dxlo = xs - xlo + dxhi = xhi - xs + xeps = (np.take([-1, 1], dxhi >= dxlo) + * np.minimum(eps, np.maximum(dxlo, dxhi))) + val_dx = func(xs + xeps, ys) + ylo, yhi = sorted(ylims) + dylo = ys - ylo + dyhi = yhi - ys + yeps = (np.take([-1, 1], dyhi >= dylo) + * np.minimum(eps, np.maximum(dylo, dyhi))) + val_dy = func(xs, ys + yeps) + return (val, (val_dx - val) / xeps, (val_dy - val) / yeps) + + +class FixedAxisArtistHelper(_FixedAxisArtistHelperBase): + """ + Helper class for a fixed axis. + """ + + def __init__(self, grid_helper, side, nth_coord_ticks=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + + super().__init__(loc=side) + + self.grid_helper = grid_helper + if nth_coord_ticks is None: + nth_coord_ticks = self.nth_coord + self.nth_coord_ticks = nth_coord_ticks + + self.side = side + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + + def get_tick_transform(self, axes): + return axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + v1, v2 = axes.get_ylim() if self.nth_coord == 0 else axes.get_xlim() + if v1 > v2: # Inverted limits. + side = {"left": "right", "right": "left", + "top": "bottom", "bottom": "top"}[self.side] + else: + side = self.side + + angle_tangent = dict(left=90, right=90, bottom=0, top=0)[side] + + def iter_major(): + for nth_coord, show_labels in [ + (self.nth_coord_ticks, True), (1 - self.nth_coord_ticks, False)]: + gi = self.grid_helper._grid_info[["lon", "lat"][nth_coord]] + for tick in gi["ticks"][side]: + yield (*tick["loc"], angle_tangent, + (tick["label"] if show_labels else "")) + + return iter_major(), iter([]) + + +class FloatingAxisArtistHelper(_FloatingAxisArtistHelperBase): + + def __init__(self, grid_helper, nth_coord, value, axis_direction=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + super().__init__(nth_coord, value) + self.value = value + self.grid_helper = grid_helper + self._extremes = -np.inf, np.inf + self._line_num_points = 100 # number of points to create a line + + def set_extremes(self, e1, e2): + if e1 is None: + e1 = -np.inf + if e2 is None: + e2 = np.inf + self._extremes = e1, e2 + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + + x1, x2 = axes.get_xlim() + y1, y2 = axes.get_ylim() + grid_finder = self.grid_helper.grid_finder + extremes = grid_finder.extreme_finder(grid_finder.inv_transform_xy, + x1, y1, x2, y2) + + lon_min, lon_max, lat_min, lat_max = extremes + e_min, e_max = self._extremes # ranges of other coordinates + if self.nth_coord == 0: + lat_min = max(e_min, lat_min) + lat_max = min(e_max, lat_max) + elif self.nth_coord == 1: + lon_min = max(e_min, lon_min) + lon_max = min(e_max, lon_max) + + lon_levs, lon_n, lon_factor = \ + grid_finder.grid_locator1(lon_min, lon_max) + lat_levs, lat_n, lat_factor = \ + grid_finder.grid_locator2(lat_min, lat_max) + + if self.nth_coord == 0: + xx0 = np.full(self._line_num_points, self.value) + yy0 = np.linspace(lat_min, lat_max, self._line_num_points) + xx, yy = grid_finder.transform_xy(xx0, yy0) + elif self.nth_coord == 1: + xx0 = np.linspace(lon_min, lon_max, self._line_num_points) + yy0 = np.full(self._line_num_points, self.value) + xx, yy = grid_finder.transform_xy(xx0, yy0) + + self._grid_info = { + "extremes": (lon_min, lon_max, lat_min, lat_max), + "lon_info": (lon_levs, lon_n, np.asarray(lon_factor)), + "lat_info": (lat_levs, lat_n, np.asarray(lat_factor)), + "lon_labels": grid_finder._format_ticks( + 1, "bottom", lon_factor, lon_levs), + "lat_labels": grid_finder._format_ticks( + 2, "bottom", lat_factor, lat_levs), + "line_xy": (xx, yy), + } + + def get_axislabel_transform(self, axes): + return Affine2D() # axes.transData + + def get_axislabel_pos_angle(self, axes): + def trf_xy(x, y): + trf = self.grid_helper.grid_finder.get_transform() + axes.transData + return trf.transform([x, y]).T + + xmin, xmax, ymin, ymax = self._grid_info["extremes"] + if self.nth_coord == 0: + xx0 = self.value + yy0 = (ymin + ymax) / 2 + elif self.nth_coord == 1: + xx0 = (xmin + xmax) / 2 + yy0 = self.value + xy1, dxy1_dx, dxy1_dy = _value_and_jacobian( + trf_xy, xx0, yy0, (xmin, xmax), (ymin, ymax)) + p = axes.transAxes.inverted().transform(xy1) + if 0 <= p[0] <= 1 and 0 <= p[1] <= 1: + d = [dxy1_dy, dxy1_dx][self.nth_coord] + return xy1, np.rad2deg(np.arctan2(*d[::-1])) + else: + return None, None + + def get_tick_transform(self, axes): + return IdentityTransform() # axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label, (optionally) tick_label""" + + lat_levs, lat_n, lat_factor = self._grid_info["lat_info"] + yy0 = lat_levs / lat_factor + + lon_levs, lon_n, lon_factor = self._grid_info["lon_info"] + xx0 = lon_levs / lon_factor + + e0, e1 = self._extremes + + def trf_xy(x, y): + trf = self.grid_helper.grid_finder.get_transform() + axes.transData + return trf.transform(np.column_stack(np.broadcast_arrays(x, y))).T + + # find angles + if self.nth_coord == 0: + mask = (e0 <= yy0) & (yy0 <= e1) + (xx1, yy1), (dxx1, dyy1), (dxx2, dyy2) = _value_and_jacobian( + trf_xy, self.value, yy0[mask], (-np.inf, np.inf), (e0, e1)) + labels = self._grid_info["lat_labels"] + + elif self.nth_coord == 1: + mask = (e0 <= xx0) & (xx0 <= e1) + (xx1, yy1), (dxx2, dyy2), (dxx1, dyy1) = _value_and_jacobian( + trf_xy, xx0[mask], self.value, (-np.inf, np.inf), (e0, e1)) + labels = self._grid_info["lon_labels"] + + labels = [l for l, m in zip(labels, mask) if m] + + angle_normal = np.arctan2(dyy1, dxx1) + angle_tangent = np.arctan2(dyy2, dxx2) + mm = (dyy1 == 0) & (dxx1 == 0) # points with degenerate normal + angle_normal[mm] = angle_tangent[mm] + np.pi / 2 + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + in_01 = functools.partial( + mpl.transforms._interval_contains_close, (0, 1)) + + def iter_major(): + for x, y, normal, tangent, lab \ + in zip(xx1, yy1, angle_normal, angle_tangent, labels): + c2 = tick_to_axes.transform((x, y)) + if in_01(c2[0]) and in_01(c2[1]): + yield [x, y], *np.rad2deg([normal, tangent]), lab + + return iter_major(), iter([]) + + def get_line_transform(self, axes): + return axes.transData + + def get_line(self, axes): + self.update_lim(axes) + x, y = self._grid_info["line_xy"] + return Path(np.column_stack([x, y])) + + +class GridHelperCurveLinear(GridHelperBase): + def __init__(self, aux_trans, + extreme_finder=None, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + """ + Parameters + ---------- + aux_trans : `.Transform` or tuple[Callable, Callable] + The transform from curved coordinates to rectilinear coordinate: + either a `.Transform` instance (which provides also its inverse), + or a pair of callables ``(trans, inv_trans)`` that define the + transform and its inverse. The callables should have signature:: + + x_rect, y_rect = trans(x_curved, y_curved) + x_curved, y_curved = inv_trans(x_rect, y_rect) + + extreme_finder + + grid_locator1, grid_locator2 + Grid locators for each axis. + + tick_formatter1, tick_formatter2 + Tick formatters for each axis. + """ + super().__init__() + self._grid_info = None + self.grid_finder = GridFinder(aux_trans, + extreme_finder, + grid_locator1, + grid_locator2, + tick_formatter1, + tick_formatter2) + + def update_grid_finder(self, aux_trans=None, **kwargs): + if aux_trans is not None: + self.grid_finder.update_transform(aux_trans) + self.grid_finder.update(**kwargs) + self._old_limits = None # Force revalidation. + + @_api.make_keyword_only("3.9", "nth_coord") + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + axes = self.axes + if axis_direction is None: + axis_direction = loc + helper = FixedAxisArtistHelper(self, loc, nth_coord_ticks=nth_coord) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + # Why is clip not set on axisline, unlike in new_floating_axis or in + # the floating_axig.GridHelperCurveLinear subclass? + return axisline + + def new_floating_axis(self, nth_coord, value, axes=None, axis_direction="bottom"): + if axes is None: + axes = self.axes + helper = FloatingAxisArtistHelper( + self, nth_coord, value, axis_direction) + axisline = AxisArtist(axes, helper) + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + # axisline.major_ticklabels.set_visible(True) + # axisline.minor_ticklabels.set_visible(False) + return axisline + + def _update_grid(self, x1, y1, x2, y2): + self._grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2) + + def get_gridlines(self, which="major", axis="both"): + grid_lines = [] + if axis in ["both", "x"]: + for gl in self._grid_info["lon"]["lines"]: + grid_lines.extend(gl) + if axis in ["both", "y"]: + for gl in self._grid_info["lat"]["lines"]: + grid_lines.extend(gl) + return grid_lines + + @_api.deprecated("3.9") + def get_tick_iterator(self, nth_coord, axis_side, minor=False): + angle_tangent = dict(left=90, right=90, bottom=0, top=0)[axis_side] + lon_or_lat = ["lon", "lat"][nth_coord] + if not minor: # major ticks + for tick in self._grid_info[lon_or_lat]["ticks"][axis_side]: + yield *tick["loc"], angle_tangent, tick["label"] + else: + for tick in self._grid_info[lon_or_lat]["ticks"][axis_side]: + yield *tick["loc"], angle_tangent, "" diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..4ebd6acc03be2dbb0f5c3360ede2a6a36a3be01b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py @@ -0,0 +1,7 @@ +from mpl_toolkits.axes_grid1.parasite_axes import ( + host_axes_class_factory, parasite_axes_class_factory) +from .axislines import Axes + + +ParasiteAxes = parasite_axes_class_factory(Axes) +HostAxes = SubplotHost = host_axes_class_factory(Axes) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..3156b33b69c4f59418f2dc78f700ba105547155a --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py @@ -0,0 +1,141 @@ +import re + +import numpy as np +import pytest + +from mpl_toolkits.axisartist.angle_helper import ( + FormatterDMS, FormatterHMS, select_step, select_step24, select_step360) + + +_MS_RE = ( + r'''\$ # Mathtext + ( + # The sign sometimes appears on a 0 when a fraction is shown. + # Check later that there's only one. + (?P-)? + (?P[0-9.]+) # Degrees value + {degree} # Degree symbol (to be replaced by format.) + )? + ( + (?(degree)\\,) # Separator if degrees are also visible. + (?P-)? + (?P[0-9.]+) # Minutes value + {minute} # Minute symbol (to be replaced by format.) + )? + ( + (?(minute)\\,) # Separator if minutes are also visible. + (?P-)? + (?P[0-9.]+) # Seconds value + {second} # Second symbol (to be replaced by format.) + )? + \$ # Mathtext + ''' +) +DMS_RE = re.compile(_MS_RE.format(degree=re.escape(FormatterDMS.deg_mark), + minute=re.escape(FormatterDMS.min_mark), + second=re.escape(FormatterDMS.sec_mark)), + re.VERBOSE) +HMS_RE = re.compile(_MS_RE.format(degree=re.escape(FormatterHMS.deg_mark), + minute=re.escape(FormatterHMS.min_mark), + second=re.escape(FormatterHMS.sec_mark)), + re.VERBOSE) + + +def dms2float(degrees, minutes=0, seconds=0): + return degrees + minutes / 60.0 + seconds / 3600.0 + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((-180, 180, 10), {'hour': False}, np.arange(-180, 181, 30), 1.0), + ((-12, 12, 10), {'hour': True}, np.arange(-12, 13, 2), 1.0) +]) +def test_select_step(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((-180, 180, 10), {}, np.arange(-180, 181, 30), 1.0), + ((-12, 12, 10), {}, np.arange(-750, 751, 150), 60.0) +]) +def test_select_step24(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step24(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((dms2float(20, 21.2), dms2float(21, 33.3), 5), {}, + np.arange(1215, 1306, 15), 60.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=33.3), 5), {}, + np.arange(73820, 73835, 2), 3600.0), + ((dms2float(20, 21.2), dms2float(20, 53.3), 5), {}, + np.arange(1220, 1256, 5), 60.0), + ((21.2, 33.3, 5), {}, + np.arange(20, 35, 2), 1.0), + ((dms2float(20, 21.2), dms2float(21, 33.3), 5), {}, + np.arange(1215, 1306, 15), 60.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=33.3), 5), {}, + np.arange(73820, 73835, 2), 3600.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=21.4), 5), {}, + np.arange(7382120, 7382141, 5), 360000.0), + # test threshold factor + ((dms2float(20.5, seconds=11.2), dms2float(20.5, seconds=53.3), 5), + {'threshold_factor': 60}, np.arange(12301, 12310), 600.0), + ((dms2float(20.5, seconds=11.2), dms2float(20.5, seconds=53.3), 5), + {'threshold_factor': 1}, np.arange(20502, 20517, 2), 1000.0), +]) +def test_select_step360(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step360(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('Formatter, regex', + [(FormatterDMS, DMS_RE), + (FormatterHMS, HMS_RE)], + ids=['Degree/Minute/Second', 'Hour/Minute/Second']) +@pytest.mark.parametrize('direction, factor, values', [ + ("left", 60, [0, -30, -60]), + ("left", 600, [12301, 12302, 12303]), + ("left", 3600, [0, -30, -60]), + ("left", 36000, [738210, 738215, 738220]), + ("left", 360000, [7382120, 7382125, 7382130]), + ("left", 1., [45, 46, 47]), + ("left", 10., [452, 453, 454]), +]) +def test_formatters(Formatter, regex, direction, factor, values): + fmt = Formatter() + result = fmt(direction, factor, values) + + prev_degree = prev_minute = prev_second = None + for tick, value in zip(result, values): + m = regex.match(tick) + assert m is not None, f'{tick!r} is not an expected tick format.' + + sign = sum(m.group(sign + '_sign') is not None + for sign in ('degree', 'minute', 'second')) + assert sign <= 1, f'Only one element of tick {tick!r} may have a sign.' + sign = 1 if sign == 0 else -1 + + degree = float(m.group('degree') or prev_degree or 0) + minute = float(m.group('minute') or prev_minute or 0) + second = float(m.group('second') or prev_second or 0) + if Formatter == FormatterHMS: + # 360 degrees as plot range -> 24 hours as labelled range + expected_value = pytest.approx((value // 15) / factor) + else: + expected_value = pytest.approx(value / factor) + assert sign * dms2float(degree, minute, second) == expected_value, \ + f'{tick!r} does not match expected tick value.' + + prev_degree = degree + prev_minute = minute + prev_second = second diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py new file mode 100644 index 0000000000000000000000000000000000000000..d44a61b6dd4aebebd14f0e240c130e390b0bc70b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py @@ -0,0 +1,99 @@ +import matplotlib.pyplot as plt +from matplotlib.testing.decorators import image_comparison + +from mpl_toolkits.axisartist import AxisArtistHelperRectlinear +from mpl_toolkits.axisartist.axis_artist import (AxisArtist, AxisLabel, + LabelBase, Ticks, TickLabels) + + +@image_comparison(['axis_artist_ticks.png'], style='default') +def test_ticks(): + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + locs_angles = [((i / 10, 0.0), i * 30) for i in range(-1, 12)] + + ticks_in = Ticks(ticksize=10, axis=ax.xaxis) + ticks_in.set_locs_angles(locs_angles) + ax.add_artist(ticks_in) + + ticks_out = Ticks(ticksize=10, tick_out=True, color='C3', axis=ax.xaxis) + ticks_out.set_locs_angles(locs_angles) + ax.add_artist(ticks_out) + + +@image_comparison(['axis_artist_labelbase.png'], style='default') +def test_labelbase(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.plot([0.5], [0.5], "o") + + label = LabelBase(0.5, 0.5, "Test") + label._ref_angle = -90 + label._offset_radius = 50 + label.set_rotation(-90) + label.set(ha="center", va="top") + ax.add_artist(label) + + +@image_comparison(['axis_artist_ticklabels.png'], style='default') +def test_ticklabels(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + ax.plot([0.2, 0.4], [0.5, 0.5], "o") + + ticks = Ticks(ticksize=10, axis=ax.xaxis) + ax.add_artist(ticks) + locs_angles_labels = [((0.2, 0.5), -90, "0.2"), + ((0.4, 0.5), -120, "0.4")] + tick_locs_angles = [(xy, a + 180) for xy, a, l in locs_angles_labels] + ticks.set_locs_angles(tick_locs_angles) + + ticklabels = TickLabels(axis_direction="left") + ticklabels._locs_angles_labels = locs_angles_labels + ticklabels.set_pad(10) + ax.add_artist(ticklabels) + + ax.plot([0.5], [0.5], "s") + axislabel = AxisLabel(0.5, 0.5, "Test") + axislabel._offset_radius = 20 + axislabel._ref_angle = 0 + axislabel.set_axis_direction("bottom") + ax.add_artist(axislabel) + + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + + +@image_comparison(['axis_artist.png'], style='default') +def test_axis_artist(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + for loc in ('left', 'right', 'bottom'): + helper = AxisArtistHelperRectlinear.Fixed(ax, loc=loc) + axisline = AxisArtist(ax, helper, offset=None, axis_direction=loc) + ax.add_artist(axisline) + + # Settings for bottom AxisArtist. + axisline.set_label("TTT") + axisline.major_ticks.set_tick_out(False) + axisline.label.set_pad(5) + + ax.set_ylabel("Test") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py new file mode 100644 index 0000000000000000000000000000000000000000..8bc3707421b61fed84a5ba7fc3958cac7de8dc85 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py @@ -0,0 +1,145 @@ +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.testing.decorators import image_comparison +from matplotlib.transforms import IdentityTransform + +from mpl_toolkits.axisartist.axislines import AxesZero, SubplotZero, Subplot +from mpl_toolkits.axisartist import Axes, SubplotHost + + +@image_comparison(['SubplotZero.png'], style='default') +def test_SubplotZero(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure() + + ax = SubplotZero(fig, 1, 1, 1) + fig.add_subplot(ax) + + ax.axis["xzero"].set_visible(True) + ax.axis["xzero"].label.set_text("Axis Zero") + + for n in ["top", "right"]: + ax.axis[n].set_visible(False) + + xx = np.arange(0, 2 * np.pi, 0.01) + ax.plot(xx, np.sin(xx)) + ax.set_ylabel("Test") + + +@image_comparison(['Subplot.png'], style='default') +def test_Subplot(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure() + + ax = Subplot(fig, 1, 1, 1) + fig.add_subplot(ax) + + xx = np.arange(0, 2 * np.pi, 0.01) + ax.plot(xx, np.sin(xx)) + ax.set_ylabel("Test") + + ax.axis["top"].major_ticks.set_tick_out(True) + ax.axis["bottom"].major_ticks.set_tick_out(True) + + ax.axis["bottom"].set_label("Tk0") + + +def test_Axes(): + fig = plt.figure() + ax = Axes(fig, [0.15, 0.1, 0.65, 0.8]) + fig.add_axes(ax) + ax.plot([1, 2, 3], [0, 1, 2]) + ax.set_xscale('log') + fig.canvas.draw() + + +@image_comparison(['ParasiteAxesAuxTrans_meshplot.png'], + remove_text=True, style='default', tol=0.075) +def test_ParasiteAxesAuxTrans(): + data = np.ones((6, 6)) + data[2, 2] = 2 + data[0, :] = 0 + data[-2, :] = 0 + data[:, 0] = 0 + data[:, -2] = 0 + x = np.arange(6) + y = np.arange(6) + xx, yy = np.meshgrid(x, y) + + funcnames = ['pcolor', 'pcolormesh', 'contourf'] + + fig = plt.figure() + for i, name in enumerate(funcnames): + + ax1 = SubplotHost(fig, 1, 3, i+1) + fig.add_subplot(ax1) + + ax2 = ax1.get_aux_axes(IdentityTransform(), viewlim_mode=None) + if name.startswith('pcolor'): + getattr(ax2, name)(xx, yy, data[:-1, :-1]) + else: + getattr(ax2, name)(xx, yy, data) + ax1.set_xlim((0, 5)) + ax1.set_ylim((0, 5)) + + ax2.contour(xx, yy, data, colors='k') + + +@image_comparison(['axisline_style.png'], remove_text=True, style='mpl20') +def test_axisline_style(): + fig = plt.figure(figsize=(2, 2)) + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>") + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['axisline_style_size_color.png'], remove_text=True, + style='mpl20') +def test_axisline_style_size_color(): + fig = plt.figure(figsize=(2, 2)) + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>", size=2.0, facecolor='r') + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->, size=1.5") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['axisline_style_tight.png'], remove_text=True, + style='mpl20') +def test_axisline_style_tight(): + fig = plt.figure(figsize=(2, 2), layout='tight') + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>", size=5, facecolor='g') + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->, size=8") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['subplotzero_ylabel.png'], style='mpl20') +def test_subplotzero_ylabel(): + fig = plt.figure() + ax = fig.add_subplot(111, axes_class=SubplotZero) + + ax.set(xlim=(-3, 7), ylim=(-3, 7), xlabel="x", ylabel="y") + + zero_axis = ax.axis["xzero", "yzero"] + zero_axis.set_visible(True) # they are hidden by default + + ax.axis["left", "right", "bottom", "top"].set_visible(False) + + zero_axis.set_axisline_style("->") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..7644fea1696599273f6214d7735bb919b500108c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py @@ -0,0 +1,115 @@ +import numpy as np + +import matplotlib.pyplot as plt +import matplotlib.projections as mprojections +import matplotlib.transforms as mtransforms +from matplotlib.testing.decorators import image_comparison +from mpl_toolkits.axisartist.axislines import Subplot +from mpl_toolkits.axisartist.floating_axes import ( + FloatingAxes, GridHelperCurveLinear) +from mpl_toolkits.axisartist.grid_finder import FixedLocator +from mpl_toolkits.axisartist import angle_helper + + +def test_subplot(): + fig = plt.figure(figsize=(5, 5)) + ax = Subplot(fig, 111) + fig.add_subplot(ax) + + +# Rather high tolerance to allow ongoing work with floating axes internals; +# remove when image is regenerated. +@image_comparison(['curvelinear3.png'], style='default', tol=5) +def test_curvelinear3(): + fig = plt.figure(figsize=(5, 5)) + + tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) + + mprojections.PolarAxes.PolarTransform(apply_theta_transforms=False)) + grid_helper = GridHelperCurveLinear( + tr, + extremes=(0, 360, 10, 3), + grid_locator1=angle_helper.LocatorDMS(15), + grid_locator2=FixedLocator([2, 4, 6, 8, 10]), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=None) + ax1 = fig.add_subplot(axes_class=FloatingAxes, grid_helper=grid_helper) + + r_scale = 10 + tr2 = mtransforms.Affine2D().scale(1, 1 / r_scale) + tr + grid_helper2 = GridHelperCurveLinear( + tr2, + extremes=(0, 360, 10 * r_scale, 3 * r_scale), + grid_locator2=FixedLocator([30, 60, 90])) + + ax1.axis["right"] = axis = grid_helper2.new_fixed_axis("right", axes=ax1) + + ax1.axis["left"].label.set_text("Test 1") + ax1.axis["right"].label.set_text("Test 2") + ax1.axis["left", "right"].set_visible(False) + + axis = grid_helper.new_floating_axis(1, 7, axes=ax1, + axis_direction="bottom") + ax1.axis["z"] = axis + axis.toggle(all=True, label=True) + axis.label.set_text("z = ?") + axis.label.set_visible(True) + axis.line.set_color("0.5") + + ax2 = ax1.get_aux_axes(tr) + + xx, yy = [67, 90, 75, 30], [2, 5, 8, 4] + ax2.scatter(xx, yy) + l, = ax2.plot(xx, yy, "k-") + l.set_clip_path(ax1.patch) + + +# Rather high tolerance to allow ongoing work with floating axes internals; +# remove when image is regenerated. +@image_comparison(['curvelinear4.png'], style='default', tol=0.9) +def test_curvelinear4(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure(figsize=(5, 5)) + + tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) + + mprojections.PolarAxes.PolarTransform(apply_theta_transforms=False)) + grid_helper = GridHelperCurveLinear( + tr, + extremes=(120, 30, 10, 0), + grid_locator1=angle_helper.LocatorDMS(5), + grid_locator2=FixedLocator([2, 4, 6, 8, 10]), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=None) + ax1 = fig.add_subplot(axes_class=FloatingAxes, grid_helper=grid_helper) + ax1.clear() # Check that clear() also restores the correct limits on ax1. + + ax1.axis["left"].label.set_text("Test 1") + ax1.axis["right"].label.set_text("Test 2") + ax1.axis["top"].set_visible(False) + + axis = grid_helper.new_floating_axis(1, 70, axes=ax1, + axis_direction="bottom") + ax1.axis["z"] = axis + axis.toggle(all=True, label=True) + axis.label.set_axis_direction("top") + axis.label.set_text("z = ?") + axis.label.set_visible(True) + axis.line.set_color("0.5") + + ax2 = ax1.get_aux_axes(tr) + + xx, yy = [67, 90, 75, 30], [2, 5, 8, 4] + ax2.scatter(xx, yy) + l, = ax2.plot(xx, yy, "k-") + l.set_clip_path(ax1.patch) + + +def test_axis_direction(): + # Check that axis direction is propagated on a floating axis + fig = plt.figure() + ax = Subplot(fig, 111) + fig.add_subplot(ax) + ax.axis['y'] = ax.new_floating_axis(nth_coord=1, value=0, + axis_direction='left') + assert ax.axis['y']._axis_direction == 'left' diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..6b397675ee10098a93545cc712a4b3c07384cbc2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py @@ -0,0 +1,34 @@ +import numpy as np +import pytest + +from matplotlib.transforms import Bbox +from mpl_toolkits.axisartist.grid_finder import ( + _find_line_box_crossings, FormatterPrettyPrint, MaxNLocator) + + +def test_find_line_box_crossings(): + x = np.array([-3, -2, -1, 0., 1, 2, 3, 2, 1, 0, -1, -2, -3, 5]) + y = np.arange(len(x)) + bbox = Bbox.from_extents(-2, 3, 2, 12.5) + left, right, bottom, top = _find_line_box_crossings( + np.column_stack([x, y]), bbox) + ((lx0, ly0), la0), ((lx1, ly1), la1), = left + ((rx0, ry0), ra0), ((rx1, ry1), ra1), = right + ((bx0, by0), ba0), = bottom + ((tx0, ty0), ta0), = top + assert (lx0, ly0, la0) == (-2, 11, 135) + assert (lx1, ly1, la1) == pytest.approx((-2., 12.125, 7.125016)) + assert (rx0, ry0, ra0) == (2, 5, 45) + assert (rx1, ry1, ra1) == (2, 7, 135) + assert (bx0, by0, ba0) == (0, 3, 45) + assert (tx0, ty0, ta0) == pytest.approx((1., 12.5, 7.125016)) + + +def test_pretty_print_format(): + locator = MaxNLocator() + locs, nloc, factor = locator(0, 100) + + fmt = FormatterPrettyPrint() + + assert fmt("left", None, locs) == \ + [r'$\mathdefault{%d}$' % (l, ) for l in locs] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py new file mode 100644 index 0000000000000000000000000000000000000000..1b266044bdd06d995a1df86779dac97dae8cb13e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py @@ -0,0 +1,207 @@ +import numpy as np + +import matplotlib.pyplot as plt +from matplotlib.path import Path +from matplotlib.projections import PolarAxes +from matplotlib.ticker import FuncFormatter +from matplotlib.transforms import Affine2D, Transform +from matplotlib.testing.decorators import image_comparison + +from mpl_toolkits.axisartist import SubplotHost +from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory +from mpl_toolkits.axisartist import angle_helper +from mpl_toolkits.axisartist.axislines import Axes +from mpl_toolkits.axisartist.grid_helper_curvelinear import \ + GridHelperCurveLinear + + +@image_comparison(['custom_transform.png'], style='default', tol=0.2) +def test_custom_transform(): + class MyTransform(Transform): + input_dims = output_dims = 2 + + def __init__(self, resolution): + """ + Resolution is the number of steps to interpolate between each input + line segment to approximate its path in transformed space. + """ + Transform.__init__(self) + self._resolution = resolution + + def transform(self, ll): + x, y = ll.T + return np.column_stack([x, y - x]) + + transform_non_affine = transform + + def transform_path(self, path): + ipath = path.interpolated(self._resolution) + return Path(self.transform(ipath.vertices), ipath.codes) + + transform_path_non_affine = transform_path + + def inverted(self): + return MyTransformInv(self._resolution) + + class MyTransformInv(Transform): + input_dims = output_dims = 2 + + def __init__(self, resolution): + Transform.__init__(self) + self._resolution = resolution + + def transform(self, ll): + x, y = ll.T + return np.column_stack([x, y + x]) + + def inverted(self): + return MyTransform(self._resolution) + + fig = plt.figure() + + SubplotHost = host_axes_class_factory(Axes) + + tr = MyTransform(1) + grid_helper = GridHelperCurveLinear(tr) + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + fig.add_subplot(ax1) + + ax2 = ax1.get_aux_axes(tr, viewlim_mode="equal") + ax2.plot([3, 6], [5.0, 10.]) + + ax1.set_aspect(1.) + ax1.set_xlim(0, 10) + ax1.set_ylim(0, 10) + + ax1.grid(True) + + +@image_comparison(['polar_box.png'], style='default', tol=0.04) +def test_polar_box(): + fig = plt.figure(figsize=(5, 5)) + + # PolarAxes.PolarTransform takes radian. However, we want our coordinate + # system in degree + tr = (Affine2D().scale(np.pi / 180., 1.) + + PolarAxes.PolarTransform(apply_theta_transforms=False)) + + # polar projection, which involves cycle, and also has limits in + # its coordinates, needs a special method to find the extremes + # (min, max of the coordinate within the view). + extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, + lon_cycle=360, + lat_cycle=None, + lon_minmax=None, + lat_minmax=(0, np.inf)) + + grid_helper = GridHelperCurveLinear( + tr, + extreme_finder=extreme_finder, + grid_locator1=angle_helper.LocatorDMS(12), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=FuncFormatter(lambda x, p: "eight" if x == 8 else f"{int(x)}"), + ) + + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + + ax1.axis["right"].major_ticklabels.set_visible(True) + ax1.axis["top"].major_ticklabels.set_visible(True) + + # let right axis shows ticklabels for 1st coordinate (angle) + ax1.axis["right"].get_helper().nth_coord_ticks = 0 + # let bottom axis shows ticklabels for 2nd coordinate (radius) + ax1.axis["bottom"].get_helper().nth_coord_ticks = 1 + + fig.add_subplot(ax1) + + ax1.axis["lat"] = axis = grid_helper.new_floating_axis(0, 45, axes=ax1) + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(2, 12) + + ax1.axis["lon"] = axis = grid_helper.new_floating_axis(1, 6, axes=ax1) + axis.label.set_text("Test 2") + axis.get_helper().set_extremes(-180, 90) + + # A parasite axes with given transform + ax2 = ax1.get_aux_axes(tr, viewlim_mode="equal") + assert ax2.transData == tr + ax1.transData + # Anything you draw in ax2 will match the ticks and grids of ax1. + ax2.plot(np.linspace(0, 30, 50), np.linspace(10, 10, 50)) + + ax1.set_aspect(1.) + ax1.set_xlim(-5, 12) + ax1.set_ylim(-5, 10) + + ax1.grid(True) + + +# Remove tol & kerning_factor when this test image is regenerated. +@image_comparison(['axis_direction.png'], style='default', tol=0.13) +def test_axis_direction(): + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure(figsize=(5, 5)) + + # PolarAxes.PolarTransform takes radian. However, we want our coordinate + # system in degree + tr = (Affine2D().scale(np.pi / 180., 1.) + + PolarAxes.PolarTransform(apply_theta_transforms=False)) + + # polar projection, which involves cycle, and also has limits in + # its coordinates, needs a special method to find the extremes + # (min, max of the coordinate within the view). + + # 20, 20 : number of sampling points along x, y direction + extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, + lon_cycle=360, + lat_cycle=None, + lon_minmax=None, + lat_minmax=(0, np.inf), + ) + + grid_locator1 = angle_helper.LocatorDMS(12) + tick_formatter1 = angle_helper.FormatterDMS() + + grid_helper = GridHelperCurveLinear(tr, + extreme_finder=extreme_finder, + grid_locator1=grid_locator1, + tick_formatter1=tick_formatter1) + + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + + for axis in ax1.axis.values(): + axis.set_visible(False) + + fig.add_subplot(ax1) + + ax1.axis["lat1"] = axis = grid_helper.new_floating_axis( + 0, 130, + axes=ax1, axis_direction="left") + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(0.001, 10) + + ax1.axis["lat2"] = axis = grid_helper.new_floating_axis( + 0, 50, + axes=ax1, axis_direction="right") + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(0.001, 10) + + ax1.axis["lon"] = axis = grid_helper.new_floating_axis( + 1, 10, + axes=ax1, axis_direction="bottom") + axis.label.set_text("Test 2") + axis.get_helper().set_extremes(50, 130) + axis.major_ticklabels.set_axis_direction("top") + axis.label.set_axis_direction("top") + + grid_helper.grid_finder.grid_locator1.set_params(nbins=5) + grid_helper.grid_finder.grid_locator2.set_params(nbins=5) + + ax1.set_aspect(1.) + ax1.set_xlim(-8, 8) + ax1.set_ylim(-4, 12) + + ax1.grid(True) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__init__.py 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0000000000000000000000000000000000000000..deb0ca34302c03cda08e57b28a82cb3654e45aaa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/art3d.py @@ -0,0 +1,1427 @@ +# art3d.py, original mplot3d version by John Porter +# Parts rewritten by Reinier Heeres +# Minor additions by Ben Axelrod + +""" +Module containing 3D artist code and functions to convert 2D +artists into 3D versions which can be added to an Axes3D. +""" + +import math + +import numpy as np + +from contextlib import contextmanager + +from matplotlib import ( + _api, artist, cbook, colors as mcolors, lines, text as mtext, + path as mpath) +from matplotlib.collections import ( + Collection, LineCollection, PolyCollection, PatchCollection, PathCollection) +from matplotlib.colors import Normalize +from matplotlib.patches import Patch +from . import proj3d + + +def _norm_angle(a): + """Return the given angle normalized to -180 < *a* <= 180 degrees.""" + a = (a + 360) % 360 + if a > 180: + a = a - 360 + return a + + +def _norm_text_angle(a): + """Return the given angle normalized to -90 < *a* <= 90 degrees.""" + a = (a + 180) % 180 + if a > 90: + a = a - 180 + return a + + +def get_dir_vector(zdir): + """ + Return a direction vector. + + Parameters + ---------- + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction. Possible values are: + + - 'x': equivalent to (1, 0, 0) + - 'y': equivalent to (0, 1, 0) + - 'z': equivalent to (0, 0, 1) + - *None*: equivalent to (0, 0, 0) + - an iterable (x, y, z) is converted to an array + + Returns + ------- + x, y, z : array + The direction vector. + """ + if zdir == 'x': + return np.array((1, 0, 0)) + elif zdir == 'y': + return np.array((0, 1, 0)) + elif zdir == 'z': + return np.array((0, 0, 1)) + elif zdir is None: + return np.array((0, 0, 0)) + elif np.iterable(zdir) and len(zdir) == 3: + return np.array(zdir) + else: + raise ValueError("'x', 'y', 'z', None or vector of length 3 expected") + + +def _viewlim_mask(xs, ys, zs, axes): + """ + Return original points with points outside the axes view limits masked. + + Parameters + ---------- + xs, ys, zs : array-like + The points to mask. + axes : Axes3D + The axes to use for the view limits. + + Returns + ------- + xs_masked, ys_masked, zs_masked : np.ma.array + The masked points. + """ + mask = np.logical_or.reduce((xs < axes.xy_viewLim.xmin, + xs > axes.xy_viewLim.xmax, + ys < axes.xy_viewLim.ymin, + ys > axes.xy_viewLim.ymax, + zs < axes.zz_viewLim.xmin, + zs > axes.zz_viewLim.xmax)) + xs_masked = np.ma.array(xs, mask=mask) + ys_masked = np.ma.array(ys, mask=mask) + zs_masked = np.ma.array(zs, mask=mask) + return xs_masked, ys_masked, zs_masked + + +class Text3D(mtext.Text): + """ + Text object with 3D position and direction. + + Parameters + ---------- + x, y, z : float + The position of the text. + text : str + The text string to display. + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction of the text. See `.get_dir_vector` for a description of + the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + + Other Parameters + ---------------- + **kwargs + All other parameters are passed on to `~matplotlib.text.Text`. + """ + + def __init__(self, x=0, y=0, z=0, text='', zdir='z', axlim_clip=False, + **kwargs): + mtext.Text.__init__(self, x, y, text, **kwargs) + self.set_3d_properties(z, zdir, axlim_clip) + + def get_position_3d(self): + """Return the (x, y, z) position of the text.""" + return self._x, self._y, self._z + + def set_position_3d(self, xyz, zdir=None): + """ + Set the (*x*, *y*, *z*) position of the text. + + Parameters + ---------- + xyz : (float, float, float) + The position in 3D space. + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction of the text. If unspecified, the *zdir* will not be + changed. See `.get_dir_vector` for a description of the values. + """ + super().set_position(xyz[:2]) + self.set_z(xyz[2]) + if zdir is not None: + self._dir_vec = get_dir_vector(zdir) + + def set_z(self, z): + """ + Set the *z* position of the text. + + Parameters + ---------- + z : float + """ + self._z = z + self.stale = True + + def set_3d_properties(self, z=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the text. + + Parameters + ---------- + z : float + The z-position in 3D space. + zdir : {'x', 'y', 'z', 3-tuple} + The direction of the text. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + """ + self._z = z + self._dir_vec = get_dir_vector(zdir) + self._axlim_clip = axlim_clip + self.stale = True + + @artist.allow_rasterization + def draw(self, renderer): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(self._x, self._y, self._z, self.axes) + position3d = np.ma.row_stack((xs, ys, zs)).ravel().filled(np.nan) + else: + xs, ys, zs = self._x, self._y, self._z + position3d = np.asanyarray([xs, ys, zs]) + + proj = proj3d._proj_trans_points( + [position3d, position3d + self._dir_vec], self.axes.M) + dx = proj[0][1] - proj[0][0] + dy = proj[1][1] - proj[1][0] + angle = math.degrees(math.atan2(dy, dx)) + with cbook._setattr_cm(self, _x=proj[0][0], _y=proj[1][0], + _rotation=_norm_text_angle(angle)): + mtext.Text.draw(self, renderer) + self.stale = False + + def get_tightbbox(self, renderer=None): + # Overwriting the 2d Text behavior which is not valid for 3d. + # For now, just return None to exclude from layout calculation. + return None + + +def text_2d_to_3d(obj, z=0, zdir='z', axlim_clip=False): + """ + Convert a `.Text` to a `.Text3D` object. + + Parameters + ---------- + z : float + The z-position in 3D space. + zdir : {'x', 'y', 'z', 3-tuple} + The direction of the text. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + """ + obj.__class__ = Text3D + obj.set_3d_properties(z, zdir, axlim_clip) + + +class Line3D(lines.Line2D): + """ + 3D line object. + + .. note:: Use `get_data_3d` to obtain the data associated with the line. + `~.Line2D.get_data`, `~.Line2D.get_xdata`, and `~.Line2D.get_ydata` return + the x- and y-coordinates of the projected 2D-line, not the x- and y-data of + the 3D-line. Similarly, use `set_data_3d` to set the data, not + `~.Line2D.set_data`, `~.Line2D.set_xdata`, and `~.Line2D.set_ydata`. + """ + + def __init__(self, xs, ys, zs, *args, axlim_clip=False, **kwargs): + """ + + Parameters + ---------- + xs : array-like + The x-data to be plotted. + ys : array-like + The y-data to be plotted. + zs : array-like + The z-data to be plotted. + *args, **kwargs + Additional arguments are passed to `~matplotlib.lines.Line2D`. + """ + super().__init__([], [], *args, **kwargs) + self.set_data_3d(xs, ys, zs) + self._axlim_clip = axlim_clip + + def set_3d_properties(self, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the line. + + Parameters + ---------- + zs : float or array of floats + The location along the *zdir* axis in 3D space to position the + line. + zdir : {'x', 'y', 'z'} + Plane to plot line orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide lines with an endpoint outside the axes view limits. + """ + xs = self.get_xdata() + ys = self.get_ydata() + zs = cbook._to_unmasked_float_array(zs).ravel() + zs = np.broadcast_to(zs, len(xs)) + self._verts3d = juggle_axes(xs, ys, zs, zdir) + self._axlim_clip = axlim_clip + self.stale = True + + def set_data_3d(self, *args): + """ + Set the x, y and z data + + Parameters + ---------- + x : array-like + The x-data to be plotted. + y : array-like + The y-data to be plotted. + z : array-like + The z-data to be plotted. + + Notes + ----- + Accepts x, y, z arguments or a single array-like (x, y, z) + """ + if len(args) == 1: + args = args[0] + for name, xyz in zip('xyz', args): + if not np.iterable(xyz): + raise RuntimeError(f'{name} must be a sequence') + self._verts3d = args + self.stale = True + + def get_data_3d(self): + """ + Get the current data + + Returns + ------- + verts3d : length-3 tuple or array-like + The current data as a tuple or array-like. + """ + return self._verts3d + + @artist.allow_rasterization + def draw(self, renderer): + if self._axlim_clip: + xs3d, ys3d, zs3d = _viewlim_mask(*self._verts3d, self.axes) + else: + xs3d, ys3d, zs3d = self._verts3d + xs, ys, zs, tis = proj3d._proj_transform_clip(xs3d, ys3d, zs3d, + self.axes.M, + self.axes._focal_length) + self.set_data(xs, ys) + super().draw(renderer) + self.stale = False + + +def line_2d_to_3d(line, zs=0, zdir='z', axlim_clip=False): + """ + Convert a `.Line2D` to a `.Line3D` object. + + Parameters + ---------- + zs : float + The location along the *zdir* axis in 3D space to position the line. + zdir : {'x', 'y', 'z'} + Plane to plot line orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide lines with an endpoint outside the axes view limits. + """ + + line.__class__ = Line3D + line.set_3d_properties(zs, zdir, axlim_clip) + + +def _path_to_3d_segment(path, zs=0, zdir='z'): + """Convert a path to a 3D segment.""" + + zs = np.broadcast_to(zs, len(path)) + pathsegs = path.iter_segments(simplify=False, curves=False) + seg = [(x, y, z) for (((x, y), code), z) in zip(pathsegs, zs)] + seg3d = [juggle_axes(x, y, z, zdir) for (x, y, z) in seg] + return seg3d + + +def _paths_to_3d_segments(paths, zs=0, zdir='z'): + """Convert paths from a collection object to 3D segments.""" + + if not np.iterable(zs): + zs = np.broadcast_to(zs, len(paths)) + else: + if len(zs) != len(paths): + raise ValueError('Number of z-coordinates does not match paths.') + + segs = [_path_to_3d_segment(path, pathz, zdir) + for path, pathz in zip(paths, zs)] + return segs + + +def _path_to_3d_segment_with_codes(path, zs=0, zdir='z'): + """Convert a path to a 3D segment with path codes.""" + + zs = np.broadcast_to(zs, len(path)) + pathsegs = path.iter_segments(simplify=False, curves=False) + seg_codes = [((x, y, z), code) for ((x, y), code), z in zip(pathsegs, zs)] + if seg_codes: + seg, codes = zip(*seg_codes) + seg3d = [juggle_axes(x, y, z, zdir) for (x, y, z) in seg] + else: + seg3d = [] + codes = [] + return seg3d, list(codes) + + +def _paths_to_3d_segments_with_codes(paths, zs=0, zdir='z'): + """ + Convert paths from a collection object to 3D segments with path codes. + """ + + zs = np.broadcast_to(zs, len(paths)) + segments_codes = [_path_to_3d_segment_with_codes(path, pathz, zdir) + for path, pathz in zip(paths, zs)] + if segments_codes: + segments, codes = zip(*segments_codes) + else: + segments, codes = [], [] + return list(segments), list(codes) + + +class Collection3D(Collection): + """A collection of 3D paths.""" + + def do_3d_projection(self): + """Project the points according to renderer matrix.""" + vs_list = [vs for vs, _ in self._3dverts_codes] + if self._axlim_clip: + vs_list = [np.ma.row_stack(_viewlim_mask(*vs.T, self.axes)).T + for vs in vs_list] + xyzs_list = [proj3d.proj_transform(*vs.T, self.axes.M) for vs in vs_list] + self._paths = [mpath.Path(np.ma.column_stack([xs, ys]), cs) + for (xs, ys, _), (_, cs) in zip(xyzs_list, self._3dverts_codes)] + zs = np.concatenate([zs for _, _, zs in xyzs_list]) + return zs.min() if len(zs) else 1e9 + + +def collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """Convert a `.Collection` to a `.Collection3D` object.""" + zs = np.broadcast_to(zs, len(col.get_paths())) + col._3dverts_codes = [ + (np.column_stack(juggle_axes( + *np.column_stack([p.vertices, np.broadcast_to(z, len(p.vertices))]).T, + zdir)), + p.codes) + for p, z in zip(col.get_paths(), zs)] + col.__class__ = cbook._make_class_factory(Collection3D, "{}3D")(type(col)) + col._axlim_clip = axlim_clip + + +class Line3DCollection(LineCollection): + """ + A collection of 3D lines. + """ + def __init__(self, lines, axlim_clip=False, **kwargs): + super().__init__(lines, **kwargs) + self._axlim_clip = axlim_clip + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_segments(self, segments): + """ + Set 3D segments. + """ + self._segments3d = segments + super().set_segments([]) + + def do_3d_projection(self): + """ + Project the points according to renderer matrix. + """ + segments = self._segments3d + if self._axlim_clip: + all_points = np.ma.vstack(segments) + masked_points = np.ma.column_stack([*_viewlim_mask(*all_points.T, + self.axes)]) + segment_lengths = [np.shape(segment)[0] for segment in segments] + segments = np.split(masked_points, np.cumsum(segment_lengths[:-1])) + xyslist = [proj3d._proj_trans_points(points, self.axes.M) + for points in segments] + segments_2d = [np.ma.column_stack([xs, ys]) for xs, ys, zs in xyslist] + LineCollection.set_segments(self, segments_2d) + + # FIXME + minz = 1e9 + for xs, ys, zs in xyslist: + minz = min(minz, min(zs)) + return minz + + +def line_collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """Convert a `.LineCollection` to a `.Line3DCollection` object.""" + segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir) + col.__class__ = Line3DCollection + col.set_segments(segments3d) + col._axlim_clip = axlim_clip + + +class Patch3D(Patch): + """ + 3D patch object. + """ + + def __init__(self, *args, zs=(), zdir='z', axlim_clip=False, **kwargs): + """ + Parameters + ---------- + verts : + zs : float + The location along the *zdir* axis in 3D space to position the + patch. + zdir : {'x', 'y', 'z'} + Plane to plot patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + + def set_3d_properties(self, verts, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the patch. + + Parameters + ---------- + verts : + zs : float + The location along the *zdir* axis in 3D space to position the + patch. + zdir : {'x', 'y', 'z'} + Plane to plot patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + zs = np.broadcast_to(zs, len(verts)) + self._segment3d = [juggle_axes(x, y, z, zdir) + for ((x, y), z) in zip(verts, zs)] + self._axlim_clip = axlim_clip + + def get_path(self): + # docstring inherited + # self._path2d is not initialized until do_3d_projection + if not hasattr(self, '_path2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return self._path2d + + def do_3d_projection(self): + s = self._segment3d + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*zip(*s), self.axes) + else: + xs, ys, zs = zip(*s) + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._path2d = mpath.Path(np.ma.column_stack([vxs, vys])) + return min(vzs) + + +class PathPatch3D(Patch3D): + """ + 3D PathPatch object. + """ + + def __init__(self, path, *, zs=(), zdir='z', axlim_clip=False, **kwargs): + """ + Parameters + ---------- + path : + zs : float + The location along the *zdir* axis in 3D space to position the + path patch. + zdir : {'x', 'y', 'z', 3-tuple} + Plane to plot path patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide path patches with a point outside the axes view limits. + """ + # Not super().__init__! + Patch.__init__(self, **kwargs) + self.set_3d_properties(path, zs, zdir, axlim_clip) + + def set_3d_properties(self, path, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the path patch. + + Parameters + ---------- + path : + zs : float + The location along the *zdir* axis in 3D space to position the + path patch. + zdir : {'x', 'y', 'z', 3-tuple} + Plane to plot path patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide path patches with a point outside the axes view limits. + """ + Patch3D.set_3d_properties(self, path.vertices, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + self._code3d = path.codes + + def do_3d_projection(self): + s = self._segment3d + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*zip(*s), self.axes) + else: + xs, ys, zs = zip(*s) + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._path2d = mpath.Path(np.ma.column_stack([vxs, vys]), self._code3d) + return min(vzs) + + +def _get_patch_verts(patch): + """Return a list of vertices for the path of a patch.""" + trans = patch.get_patch_transform() + path = patch.get_path() + polygons = path.to_polygons(trans) + return polygons[0] if len(polygons) else np.array([]) + + +def patch_2d_to_3d(patch, z=0, zdir='z', axlim_clip=False): + """Convert a `.Patch` to a `.Patch3D` object.""" + verts = _get_patch_verts(patch) + patch.__class__ = Patch3D + patch.set_3d_properties(verts, z, zdir, axlim_clip) + + +def pathpatch_2d_to_3d(pathpatch, z=0, zdir='z'): + """Convert a `.PathPatch` to a `.PathPatch3D` object.""" + path = pathpatch.get_path() + trans = pathpatch.get_patch_transform() + + mpath = trans.transform_path(path) + pathpatch.__class__ = PathPatch3D + pathpatch.set_3d_properties(mpath, z, zdir) + + +class Patch3DCollection(PatchCollection): + """ + A collection of 3D patches. + """ + + def __init__(self, *args, + zs=0, zdir='z', depthshade=True, axlim_clip=False, **kwargs): + """ + Create a collection of flat 3D patches with its normal vector + pointed in *zdir* direction, and located at *zs* on the *zdir* + axis. 'zs' can be a scalar or an array-like of the same length as + the number of patches in the collection. + + Constructor arguments are the same as for + :class:`~matplotlib.collections.PatchCollection`. In addition, + keywords *zs=0* and *zdir='z'* are available. + + Also, the keyword argument *depthshade* is available to indicate + whether to shade the patches in order to give the appearance of depth + (default is *True*). This is typically desired in scatter plots. + """ + self._depthshade = depthshade + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + + def get_depthshade(self): + return self._depthshade + + def set_depthshade(self, depthshade): + """ + Set whether depth shading is performed on collection members. + + Parameters + ---------- + depthshade : bool + Whether to shade the patches in order to give the appearance of + depth. + """ + self._depthshade = depthshade + self.stale = True + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_3d_properties(self, zs, zdir, axlim_clip=False): + """ + Set the *z* positions and direction of the patches. + + Parameters + ---------- + zs : float or array of floats + The location or locations to place the patches in the collection + along the *zdir* axis. + zdir : {'x', 'y', 'z'} + Plane to plot patches orthogonal to. + All patches must have the same direction. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + offsets = self.get_offsets() + if len(offsets) > 0: + xs, ys = offsets.T + else: + xs = [] + ys = [] + self._offsets3d = juggle_axes(xs, ys, np.atleast_1d(zs), zdir) + self._z_markers_idx = slice(-1) + self._vzs = None + self._axlim_clip = axlim_clip + self.stale = True + + def do_3d_projection(self): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._offsets3d, self.axes) + else: + xs, ys, zs = self._offsets3d + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._vzs = vzs + super().set_offsets(np.ma.column_stack([vxs, vys])) + + if vzs.size > 0: + return min(vzs) + else: + return np.nan + + def _maybe_depth_shade_and_sort_colors(self, color_array): + color_array = ( + _zalpha(color_array, self._vzs) + if self._vzs is not None and self._depthshade + else color_array + ) + if len(color_array) > 1: + color_array = color_array[self._z_markers_idx] + return mcolors.to_rgba_array(color_array, self._alpha) + + def get_facecolor(self): + return self._maybe_depth_shade_and_sort_colors(super().get_facecolor()) + + def get_edgecolor(self): + # We need this check here to make sure we do not double-apply the depth + # based alpha shading when the edge color is "face" which means the + # edge colour should be identical to the face colour. + if cbook._str_equal(self._edgecolors, 'face'): + return self.get_facecolor() + return self._maybe_depth_shade_and_sort_colors(super().get_edgecolor()) + + +class Path3DCollection(PathCollection): + """ + A collection of 3D paths. + """ + + def __init__(self, *args, + zs=0, zdir='z', depthshade=True, axlim_clip=False, **kwargs): + """ + Create a collection of flat 3D paths with its normal vector + pointed in *zdir* direction, and located at *zs* on the *zdir* + axis. 'zs' can be a scalar or an array-like of the same length as + the number of paths in the collection. + + Constructor arguments are the same as for + :class:`~matplotlib.collections.PathCollection`. In addition, + keywords *zs=0* and *zdir='z'* are available. + + Also, the keyword argument *depthshade* is available to indicate + whether to shade the patches in order to give the appearance of depth + (default is *True*). This is typically desired in scatter plots. + """ + self._depthshade = depthshade + self._in_draw = False + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + self._offset_zordered = None + + def draw(self, renderer): + with self._use_zordered_offset(): + with cbook._setattr_cm(self, _in_draw=True): + super().draw(renderer) + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_3d_properties(self, zs, zdir, axlim_clip=False): + """ + Set the *z* positions and direction of the paths. + + Parameters + ---------- + zs : float or array of floats + The location or locations to place the paths in the collection + along the *zdir* axis. + zdir : {'x', 'y', 'z'} + Plane to plot paths orthogonal to. + All paths must have the same direction. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide paths with a vertex outside the axes view limits. + """ + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + offsets = self.get_offsets() + if len(offsets) > 0: + xs, ys = offsets.T + else: + xs = [] + ys = [] + self._zdir = zdir + self._offsets3d = juggle_axes(xs, ys, np.atleast_1d(zs), zdir) + # In the base draw methods we access the attributes directly which + # means we cannot resolve the shuffling in the getter methods like + # we do for the edge and face colors. + # + # This means we need to carry around a cache of the unsorted sizes and + # widths (postfixed with 3d) and in `do_3d_projection` set the + # depth-sorted version of that data into the private state used by the + # base collection class in its draw method. + # + # Grab the current sizes and linewidths to preserve them. + self._sizes3d = self._sizes + self._linewidths3d = np.array(self._linewidths) + xs, ys, zs = self._offsets3d + + # Sort the points based on z coordinates + # Performance optimization: Create a sorted index array and reorder + # points and point properties according to the index array + self._z_markers_idx = slice(-1) + self._vzs = None + + self._axlim_clip = axlim_clip + self.stale = True + + def set_sizes(self, sizes, dpi=72.0): + super().set_sizes(sizes, dpi) + if not self._in_draw: + self._sizes3d = sizes + + def set_linewidth(self, lw): + super().set_linewidth(lw) + if not self._in_draw: + self._linewidths3d = np.array(self._linewidths) + + def get_depthshade(self): + return self._depthshade + + def set_depthshade(self, depthshade): + """ + Set whether depth shading is performed on collection members. + + Parameters + ---------- + depthshade : bool + Whether to shade the patches in order to give the appearance of + depth. + """ + self._depthshade = depthshade + self.stale = True + + def do_3d_projection(self): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._offsets3d, self.axes) + else: + xs, ys, zs = self._offsets3d + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + # Sort the points based on z coordinates + # Performance optimization: Create a sorted index array and reorder + # points and point properties according to the index array + z_markers_idx = self._z_markers_idx = np.ma.argsort(vzs)[::-1] + self._vzs = vzs + + # we have to special case the sizes because of code in collections.py + # as the draw method does + # self.set_sizes(self._sizes, self.figure.dpi) + # so we cannot rely on doing the sorting on the way out via get_* + + if len(self._sizes3d) > 1: + self._sizes = self._sizes3d[z_markers_idx] + + if len(self._linewidths3d) > 1: + self._linewidths = self._linewidths3d[z_markers_idx] + + PathCollection.set_offsets(self, np.ma.column_stack((vxs, vys))) + + # Re-order items + vzs = vzs[z_markers_idx] + vxs = vxs[z_markers_idx] + vys = vys[z_markers_idx] + + # Store ordered offset for drawing purpose + self._offset_zordered = np.ma.column_stack((vxs, vys)) + + return np.min(vzs) if vzs.size else np.nan + + @contextmanager + def _use_zordered_offset(self): + if self._offset_zordered is None: + # Do nothing + yield + else: + # Swap offset with z-ordered offset + old_offset = self._offsets + super().set_offsets(self._offset_zordered) + try: + yield + finally: + self._offsets = old_offset + + def _maybe_depth_shade_and_sort_colors(self, color_array): + color_array = ( + _zalpha(color_array, self._vzs) + if self._vzs is not None and self._depthshade + else color_array + ) + if len(color_array) > 1: + color_array = color_array[self._z_markers_idx] + return mcolors.to_rgba_array(color_array, self._alpha) + + def get_facecolor(self): + return self._maybe_depth_shade_and_sort_colors(super().get_facecolor()) + + def get_edgecolor(self): + # We need this check here to make sure we do not double-apply the depth + # based alpha shading when the edge color is "face" which means the + # edge colour should be identical to the face colour. + if cbook._str_equal(self._edgecolors, 'face'): + return self.get_facecolor() + return self._maybe_depth_shade_and_sort_colors(super().get_edgecolor()) + + +def patch_collection_2d_to_3d(col, zs=0, zdir='z', depthshade=True, axlim_clip=False): + """ + Convert a `.PatchCollection` into a `.Patch3DCollection` object + (or a `.PathCollection` into a `.Path3DCollection` object). + + Parameters + ---------- + col : `~matplotlib.collections.PatchCollection` or \ +`~matplotlib.collections.PathCollection` + The collection to convert. + zs : float or array of floats + The location or locations to place the patches in the collection along + the *zdir* axis. Default: 0. + zdir : {'x', 'y', 'z'} + The axis in which to place the patches. Default: "z". + See `.get_dir_vector` for a description of the values. + depthshade : bool, default: True + Whether to shade the patches to give a sense of depth. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + if isinstance(col, PathCollection): + col.__class__ = Path3DCollection + col._offset_zordered = None + elif isinstance(col, PatchCollection): + col.__class__ = Patch3DCollection + col._depthshade = depthshade + col._in_draw = False + col.set_3d_properties(zs, zdir, axlim_clip) + + +class Poly3DCollection(PolyCollection): + """ + A collection of 3D polygons. + + .. note:: + **Filling of 3D polygons** + + There is no simple definition of the enclosed surface of a 3D polygon + unless the polygon is planar. + + In practice, Matplotlib fills the 2D projection of the polygon. This + gives a correct filling appearance only for planar polygons. For all + other polygons, you'll find orientations in which the edges of the + polygon intersect in the projection. This will lead to an incorrect + visualization of the 3D area. + + If you need filled areas, it is recommended to create them via + `~mpl_toolkits.mplot3d.axes3d.Axes3D.plot_trisurf`, which creates a + triangulation and thus generates consistent surfaces. + """ + + def __init__(self, verts, *args, zsort='average', shade=False, + lightsource=None, axlim_clip=False, **kwargs): + """ + Parameters + ---------- + verts : list of (N, 3) array-like + The sequence of polygons [*verts0*, *verts1*, ...] where each + element *verts_i* defines the vertices of polygon *i* as a 2D + array-like of shape (N, 3). + zsort : {'average', 'min', 'max'}, default: 'average' + The calculation method for the z-order. + See `~.Poly3DCollection.set_zsort` for details. + shade : bool, default: False + Whether to shade *facecolors* and *edgecolors*. When activating + *shade*, *facecolors* and/or *edgecolors* must be provided. + + .. versionadded:: 3.7 + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + .. versionadded:: 3.7 + + axlim_clip : bool, default: False + Whether to hide polygons with a vertex outside the view limits. + + *args, **kwargs + All other parameters are forwarded to `.PolyCollection`. + + Notes + ----- + Note that this class does a bit of magic with the _facecolors + and _edgecolors properties. + """ + if shade: + normals = _generate_normals(verts) + facecolors = kwargs.get('facecolors', None) + if facecolors is not None: + kwargs['facecolors'] = _shade_colors( + facecolors, normals, lightsource + ) + + edgecolors = kwargs.get('edgecolors', None) + if edgecolors is not None: + kwargs['edgecolors'] = _shade_colors( + edgecolors, normals, lightsource + ) + if facecolors is None and edgecolors is None: + raise ValueError( + "You must provide facecolors, edgecolors, or both for " + "shade to work.") + super().__init__(verts, *args, **kwargs) + if isinstance(verts, np.ndarray): + if verts.ndim != 3: + raise ValueError('verts must be a list of (N, 3) array-like') + else: + if any(len(np.shape(vert)) != 2 for vert in verts): + raise ValueError('verts must be a list of (N, 3) array-like') + self.set_zsort(zsort) + self._codes3d = None + self._axlim_clip = axlim_clip + + _zsort_functions = { + 'average': np.average, + 'min': np.min, + 'max': np.max, + } + + def set_zsort(self, zsort): + """ + Set the calculation method for the z-order. + + Parameters + ---------- + zsort : {'average', 'min', 'max'} + The function applied on the z-coordinates of the vertices in the + viewer's coordinate system, to determine the z-order. + """ + self._zsortfunc = self._zsort_functions[zsort] + self._sort_zpos = None + self.stale = True + + @_api.deprecated("3.10") + def get_vector(self, segments3d): + return self._get_vector(segments3d) + + def _get_vector(self, segments3d): + """Optimize points for projection.""" + if len(segments3d): + xs, ys, zs = np.vstack(segments3d).T + else: # vstack can't stack zero arrays. + xs, ys, zs = [], [], [] + ones = np.ones(len(xs)) + self._vec = np.array([xs, ys, zs, ones]) + + indices = [0, *np.cumsum([len(segment) for segment in segments3d])] + self._segslices = [*map(slice, indices[:-1], indices[1:])] + + def set_verts(self, verts, closed=True): + """ + Set 3D vertices. + + Parameters + ---------- + verts : list of (N, 3) array-like + The sequence of polygons [*verts0*, *verts1*, ...] where each + element *verts_i* defines the vertices of polygon *i* as a 2D + array-like of shape (N, 3). + closed : bool, default: True + Whether the polygon should be closed by adding a CLOSEPOLY + connection at the end. + """ + self._get_vector(verts) + # 2D verts will be updated at draw time + super().set_verts([], False) + self._closed = closed + + def set_verts_and_codes(self, verts, codes): + """Set 3D vertices with path codes.""" + # set vertices with closed=False to prevent PolyCollection from + # setting path codes + self.set_verts(verts, closed=False) + # and set our own codes instead. + self._codes3d = codes + + def set_3d_properties(self, axlim_clip=False): + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + self._sort_zpos = None + self.set_zsort('average') + self._facecolor3d = PolyCollection.get_facecolor(self) + self._edgecolor3d = PolyCollection.get_edgecolor(self) + self._alpha3d = PolyCollection.get_alpha(self) + self.stale = True + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def do_3d_projection(self): + """ + Perform the 3D projection for this object. + """ + if self._A is not None: + # force update of color mapping because we re-order them + # below. If we do not do this here, the 2D draw will call + # this, but we will never port the color mapped values back + # to the 3D versions. + # + # We hold the 3D versions in a fixed order (the order the user + # passed in) and sort the 2D version by view depth. + self.update_scalarmappable() + if self._face_is_mapped: + self._facecolor3d = self._facecolors + if self._edge_is_mapped: + self._edgecolor3d = self._edgecolors + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._vec[0:3], self.axes) + if self._vec.shape[0] == 4: # Will be 3 (xyz) or 4 (xyzw) + w_masked = np.ma.masked_where(zs.mask, self._vec[3]) + vec = np.ma.array([xs, ys, zs, w_masked]) + else: + vec = np.ma.array([xs, ys, zs]) + else: + vec = self._vec + txs, tys, tzs = proj3d._proj_transform_vec(vec, self.axes.M) + xyzlist = [(txs[sl], tys[sl], tzs[sl]) for sl in self._segslices] + + # This extra fuss is to re-order face / edge colors + cface = self._facecolor3d + cedge = self._edgecolor3d + if len(cface) != len(xyzlist): + cface = cface.repeat(len(xyzlist), axis=0) + if len(cedge) != len(xyzlist): + if len(cedge) == 0: + cedge = cface + else: + cedge = cedge.repeat(len(xyzlist), axis=0) + + if xyzlist: + # sort by depth (furthest drawn first) + z_segments_2d = sorted( + ((self._zsortfunc(zs.data), np.ma.column_stack([xs, ys]), fc, ec, idx) + for idx, ((xs, ys, zs), fc, ec) + in enumerate(zip(xyzlist, cface, cedge))), + key=lambda x: x[0], reverse=True) + + _, segments_2d, self._facecolors2d, self._edgecolors2d, idxs = \ + zip(*z_segments_2d) + else: + segments_2d = [] + self._facecolors2d = np.empty((0, 4)) + self._edgecolors2d = np.empty((0, 4)) + idxs = [] + + if self._codes3d is not None: + codes = [self._codes3d[idx] for idx in idxs] + PolyCollection.set_verts_and_codes(self, segments_2d, codes) + else: + PolyCollection.set_verts(self, segments_2d, self._closed) + + if len(self._edgecolor3d) != len(cface): + self._edgecolors2d = self._edgecolor3d + + # Return zorder value + if self._sort_zpos is not None: + zvec = np.array([[0], [0], [self._sort_zpos], [1]]) + ztrans = proj3d._proj_transform_vec(zvec, self.axes.M) + return ztrans[2][0] + elif tzs.size > 0: + # FIXME: Some results still don't look quite right. + # In particular, examine contourf3d_demo2.py + # with az = -54 and elev = -45. + return np.min(tzs) + else: + return np.nan + + def set_facecolor(self, colors): + # docstring inherited + super().set_facecolor(colors) + self._facecolor3d = PolyCollection.get_facecolor(self) + + def set_edgecolor(self, colors): + # docstring inherited + super().set_edgecolor(colors) + self._edgecolor3d = PolyCollection.get_edgecolor(self) + + def set_alpha(self, alpha): + # docstring inherited + artist.Artist.set_alpha(self, alpha) + try: + self._facecolor3d = mcolors.to_rgba_array( + self._facecolor3d, self._alpha) + except (AttributeError, TypeError, IndexError): + pass + try: + self._edgecolors = mcolors.to_rgba_array( + self._edgecolor3d, self._alpha) + except (AttributeError, TypeError, IndexError): + pass + self.stale = True + + def get_facecolor(self): + # docstring inherited + # self._facecolors2d is not initialized until do_3d_projection + if not hasattr(self, '_facecolors2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return np.asarray(self._facecolors2d) + + def get_edgecolor(self): + # docstring inherited + # self._edgecolors2d is not initialized until do_3d_projection + if not hasattr(self, '_edgecolors2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return np.asarray(self._edgecolors2d) + + +def poly_collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """ + Convert a `.PolyCollection` into a `.Poly3DCollection` object. + + Parameters + ---------- + col : `~matplotlib.collections.PolyCollection` + The collection to convert. + zs : float or array of floats + The location or locations to place the polygons in the collection along + the *zdir* axis. Default: 0. + zdir : {'x', 'y', 'z'} + The axis in which to place the patches. Default: 'z'. + See `.get_dir_vector` for a description of the values. + """ + segments_3d, codes = _paths_to_3d_segments_with_codes( + col.get_paths(), zs, zdir) + col.__class__ = Poly3DCollection + col.set_verts_and_codes(segments_3d, codes) + col.set_3d_properties() + col._axlim_clip = axlim_clip + + +def juggle_axes(xs, ys, zs, zdir): + """ + Reorder coordinates so that 2D *xs*, *ys* can be plotted in the plane + orthogonal to *zdir*. *zdir* is normally 'x', 'y' or 'z'. However, if + *zdir* starts with a '-' it is interpreted as a compensation for + `rotate_axes`. + """ + if zdir == 'x': + return zs, xs, ys + elif zdir == 'y': + return xs, zs, ys + elif zdir[0] == '-': + return rotate_axes(xs, ys, zs, zdir) + else: + return xs, ys, zs + + +def rotate_axes(xs, ys, zs, zdir): + """ + Reorder coordinates so that the axes are rotated with *zdir* along + the original z axis. Prepending the axis with a '-' does the + inverse transform, so *zdir* can be 'x', '-x', 'y', '-y', 'z' or '-z'. + """ + if zdir in ('x', '-y'): + return ys, zs, xs + elif zdir in ('-x', 'y'): + return zs, xs, ys + else: + return xs, ys, zs + + +def _zalpha(colors, zs): + """Modify the alphas of the color list according to depth.""" + # FIXME: This only works well if the points for *zs* are well-spaced + # in all three dimensions. Otherwise, at certain orientations, + # the min and max zs are very close together. + # Should really normalize against the viewing depth. + if len(colors) == 0 or len(zs) == 0: + return np.zeros((0, 4)) + norm = Normalize(min(zs), max(zs)) + sats = 1 - norm(zs) * 0.7 + rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4)) + return np.column_stack([rgba[:, :3], rgba[:, 3] * sats]) + + +def _all_points_on_plane(xs, ys, zs, atol=1e-8): + """ + Check if all points are on the same plane. Note that NaN values are + ignored. + + Parameters + ---------- + xs, ys, zs : array-like + The x, y, and z coordinates of the points. + atol : float, default: 1e-8 + The tolerance for the equality check. + """ + xs, ys, zs = np.asarray(xs), np.asarray(ys), np.asarray(zs) + points = np.column_stack([xs, ys, zs]) + points = points[~np.isnan(points).any(axis=1)] + # Check for the case where we have less than 3 unique points + points = np.unique(points, axis=0) + if len(points) <= 3: + return True + # Calculate the vectors from the first point to all other points + vs = (points - points[0])[1:] + vs = vs / np.linalg.norm(vs, axis=1)[:, np.newaxis] + # Filter out parallel vectors + vs = np.unique(vs, axis=0) + if len(vs) <= 2: + return True + # Filter out parallel and antiparallel vectors to the first vector + cross_norms = np.linalg.norm(np.cross(vs[0], vs[1:]), axis=1) + zero_cross_norms = np.where(np.isclose(cross_norms, 0, atol=atol))[0] + 1 + vs = np.delete(vs, zero_cross_norms, axis=0) + if len(vs) <= 2: + return True + # Calculate the normal vector from the first three points + n = np.cross(vs[0], vs[1]) + n = n / np.linalg.norm(n) + # If the dot product of the normal vector and all other vectors is zero, + # all points are on the same plane + dots = np.dot(n, vs.transpose()) + return np.allclose(dots, 0, atol=atol) + + +def _generate_normals(polygons): + """ + Compute the normals of a list of polygons, one normal per polygon. + + Normals point towards the viewer for a face with its vertices in + counterclockwise order, following the right hand rule. + + Uses three points equally spaced around the polygon. This method assumes + that the points are in a plane. Otherwise, more than one shade is required, + which is not supported. + + Parameters + ---------- + polygons : list of (M_i, 3) array-like, or (..., M, 3) array-like + A sequence of polygons to compute normals for, which can have + varying numbers of vertices. If the polygons all have the same + number of vertices and array is passed, then the operation will + be vectorized. + + Returns + ------- + normals : (..., 3) array + A normal vector estimated for the polygon. + """ + if isinstance(polygons, np.ndarray): + # optimization: polygons all have the same number of points, so can + # vectorize + n = polygons.shape[-2] + i1, i2, i3 = 0, n//3, 2*n//3 + v1 = polygons[..., i1, :] - polygons[..., i2, :] + v2 = polygons[..., i2, :] - polygons[..., i3, :] + else: + # The subtraction doesn't vectorize because polygons is jagged. + v1 = np.empty((len(polygons), 3)) + v2 = np.empty((len(polygons), 3)) + for poly_i, ps in enumerate(polygons): + n = len(ps) + ps = np.asarray(ps) + i1, i2, i3 = 0, n//3, 2*n//3 + v1[poly_i, :] = ps[i1, :] - ps[i2, :] + v2[poly_i, :] = ps[i2, :] - ps[i3, :] + return np.cross(v1, v2) + + +def _shade_colors(color, normals, lightsource=None): + """ + Shade *color* using normal vectors given by *normals*, + assuming a *lightsource* (using default position if not given). + *color* can also be an array of the same length as *normals*. + """ + if lightsource is None: + # chosen for backwards-compatibility + lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712) + + with np.errstate(invalid="ignore"): + shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True)) + @ lightsource.direction) + mask = ~np.isnan(shade) + + if mask.any(): + # convert dot product to allowed shading fractions + in_norm = mcolors.Normalize(-1, 1) + out_norm = mcolors.Normalize(0.3, 1).inverse + + def norm(x): + return out_norm(in_norm(x)) + + shade[~mask] = 0 + + color = mcolors.to_rgba_array(color) + # shape of color should be (M, 4) (where M is number of faces) + # shape of shade should be (M,) + # colors should have final shape of (M, 4) + alpha = color[:, 3] + colors = norm(shade)[:, np.newaxis] * color + colors[:, 3] = alpha + else: + colors = np.asanyarray(color).copy() + + return colors diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py new file mode 100644 index 0000000000000000000000000000000000000000..d0ba360c314bbaa39efd5e64936a07e6cd0ec0f6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py @@ -0,0 +1,4162 @@ +""" +axes3d.py, original mplot3d version by John Porter +Created: 23 Sep 2005 + +Parts fixed by Reinier Heeres +Minor additions by Ben Axelrod +Significant updates and revisions by Ben Root + +Module containing Axes3D, an object which can plot 3D objects on a +2D matplotlib figure. +""" + +from collections import defaultdict +import itertools +import math +import textwrap +import warnings + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook, _docstring, _preprocess_data +import matplotlib.artist as martist +import matplotlib.collections as mcoll +import matplotlib.colors as mcolors +import matplotlib.image as mimage +import matplotlib.lines as mlines +import matplotlib.patches as mpatches +import matplotlib.container as mcontainer +import matplotlib.transforms as mtransforms +from matplotlib.axes import Axes +from matplotlib.axes._base import _axis_method_wrapper, _process_plot_format +from matplotlib.transforms import Bbox +from matplotlib.tri._triangulation import Triangulation + +from . import art3d +from . import proj3d +from . import axis3d + + +@_docstring.interpd +@_api.define_aliases({ + "xlim": ["xlim3d"], "ylim": ["ylim3d"], "zlim": ["zlim3d"]}) +class Axes3D(Axes): + """ + 3D Axes object. + + .. note:: + + As a user, you do not instantiate Axes directly, but use Axes creation + methods instead; e.g. from `.pyplot` or `.Figure`: + `~.pyplot.subplots`, `~.pyplot.subplot_mosaic` or `.Figure.add_axes`. + """ + name = '3d' + + _axis_names = ("x", "y", "z") + Axes._shared_axes["z"] = cbook.Grouper() + Axes._shared_axes["view"] = cbook.Grouper() + + def __init__( + self, fig, rect=None, *args, + elev=30, azim=-60, roll=0, shareview=None, sharez=None, + proj_type='persp', focal_length=None, + box_aspect=None, + computed_zorder=True, + **kwargs, + ): + """ + Parameters + ---------- + fig : Figure + The parent figure. + rect : tuple (left, bottom, width, height), default: None. + The ``(left, bottom, width, height)`` Axes position. + elev : float, default: 30 + The elevation angle in degrees rotates the camera above and below + the x-y plane, with a positive angle corresponding to a location + above the plane. + azim : float, default: -60 + The azimuthal angle in degrees rotates the camera about the z axis, + with a positive angle corresponding to a right-handed rotation. In + other words, a positive azimuth rotates the camera about the origin + from its location along the +x axis towards the +y axis. + roll : float, default: 0 + The roll angle in degrees rotates the camera about the viewing + axis. A positive angle spins the camera clockwise, causing the + scene to rotate counter-clockwise. + shareview : Axes3D, optional + Other Axes to share view angles with. Note that it is not possible + to unshare axes. + sharez : Axes3D, optional + Other Axes to share z-limits with. Note that it is not possible to + unshare axes. + proj_type : {'persp', 'ortho'} + The projection type, default 'persp'. + focal_length : float, default: None + For a projection type of 'persp', the focal length of the virtual + camera. Must be > 0. If None, defaults to 1. + For a projection type of 'ortho', must be set to either None + or infinity (numpy.inf). If None, defaults to infinity. + The focal length can be computed from a desired Field Of View via + the equation: focal_length = 1/tan(FOV/2) + box_aspect : 3-tuple of floats, default: None + Changes the physical dimensions of the Axes3D, such that the ratio + of the axis lengths in display units is x:y:z. + If None, defaults to 4:4:3 + computed_zorder : bool, default: True + If True, the draw order is computed based on the average position + of the `.Artist`\\s along the view direction. + Set to False if you want to manually control the order in which + Artists are drawn on top of each other using their *zorder* + attribute. This can be used for fine-tuning if the automatic order + does not produce the desired result. Note however, that a manual + zorder will only be correct for a limited view angle. If the figure + is rotated by the user, it will look wrong from certain angles. + + **kwargs + Other optional keyword arguments: + + %(Axes3D:kwdoc)s + """ + + if rect is None: + rect = [0.0, 0.0, 1.0, 1.0] + + self.initial_azim = azim + self.initial_elev = elev + self.initial_roll = roll + self.set_proj_type(proj_type, focal_length) + self.computed_zorder = computed_zorder + + self.xy_viewLim = Bbox.unit() + self.zz_viewLim = Bbox.unit() + xymargin = 0.05 * 10/11 # match mpl3.8 appearance + self.xy_dataLim = Bbox([[xymargin, xymargin], + [1 - xymargin, 1 - xymargin]]) + # z-limits are encoded in the x-component of the Bbox, y is un-used + self.zz_dataLim = Bbox.unit() + + # inhibit autoscale_view until the axes are defined + # they can't be defined until Axes.__init__ has been called + self.view_init(self.initial_elev, self.initial_azim, self.initial_roll) + + self._sharez = sharez + if sharez is not None: + self._shared_axes["z"].join(self, sharez) + self._adjustable = 'datalim' + + self._shareview = shareview + if shareview is not None: + self._shared_axes["view"].join(self, shareview) + + if kwargs.pop('auto_add_to_figure', False): + raise AttributeError( + 'auto_add_to_figure is no longer supported for Axes3D. ' + 'Use fig.add_axes(ax) instead.' + ) + + super().__init__( + fig, rect, frameon=True, box_aspect=box_aspect, *args, **kwargs + ) + # Disable drawing of axes by base class + super().set_axis_off() + # Enable drawing of axes by Axes3D class + self.set_axis_on() + self.M = None + self.invM = None + + self._view_margin = 1/48 # default value to match mpl3.8 + self.autoscale_view() + + # func used to format z -- fall back on major formatters + self.fmt_zdata = None + + self.mouse_init() + fig = self.get_figure(root=True) + fig.canvas.callbacks._connect_picklable( + 'motion_notify_event', self._on_move) + fig.canvas.callbacks._connect_picklable( + 'button_press_event', self._button_press) + fig.canvas.callbacks._connect_picklable( + 'button_release_event', self._button_release) + self.set_top_view() + + self.patch.set_linewidth(0) + # Calculate the pseudo-data width and height + pseudo_bbox = self.transLimits.inverted().transform([(0, 0), (1, 1)]) + self._pseudo_w, self._pseudo_h = pseudo_bbox[1] - pseudo_bbox[0] + + # mplot3d currently manages its own spines and needs these turned off + # for bounding box calculations + self.spines[:].set_visible(False) + + def set_axis_off(self): + self._axis3don = False + self.stale = True + + def set_axis_on(self): + self._axis3don = True + self.stale = True + + def convert_zunits(self, z): + """ + For artists in an Axes, if the zaxis has units support, + convert *z* using zaxis unit type + """ + return self.zaxis.convert_units(z) + + def set_top_view(self): + # this happens to be the right view for the viewing coordinates + # moved up and to the left slightly to fit labels and axes + xdwl = 0.95 / self._dist + xdw = 0.9 / self._dist + ydwl = 0.95 / self._dist + ydw = 0.9 / self._dist + # Set the viewing pane. + self.viewLim.intervalx = (-xdwl, xdw) + self.viewLim.intervaly = (-ydwl, ydw) + self.stale = True + + def _init_axis(self): + """Init 3D Axes; overrides creation of regular X/Y Axes.""" + self.xaxis = axis3d.XAxis(self) + self.yaxis = axis3d.YAxis(self) + self.zaxis = axis3d.ZAxis(self) + + def get_zaxis(self): + """Return the ``ZAxis`` (`~.axis3d.Axis`) instance.""" + return self.zaxis + + get_zgridlines = _axis_method_wrapper("zaxis", "get_gridlines") + get_zticklines = _axis_method_wrapper("zaxis", "get_ticklines") + + def _transformed_cube(self, vals): + """Return cube with limits from *vals* transformed by self.M.""" + minx, maxx, miny, maxy, minz, maxz = vals + xyzs = [(minx, miny, minz), + (maxx, miny, minz), + (maxx, maxy, minz), + (minx, maxy, minz), + (minx, miny, maxz), + (maxx, miny, maxz), + (maxx, maxy, maxz), + (minx, maxy, maxz)] + return proj3d._proj_points(xyzs, self.M) + + def set_aspect(self, aspect, adjustable=None, anchor=None, share=False): + """ + Set the aspect ratios. + + Parameters + ---------- + aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'} + Possible values: + + ========= ================================================== + value description + ========= ================================================== + 'auto' automatic; fill the position rectangle with data. + 'equal' adapt all the axes to have equal aspect ratios. + 'equalxy' adapt the x and y axes to have equal aspect ratios. + 'equalxz' adapt the x and z axes to have equal aspect ratios. + 'equalyz' adapt the y and z axes to have equal aspect ratios. + ========= ================================================== + + adjustable : None or {'box', 'datalim'}, optional + If not *None*, this defines which parameter will be adjusted to + meet the required aspect. See `.set_adjustable` for further + details. + + anchor : None or str or 2-tuple of float, optional + If not *None*, this defines where the Axes will be drawn if there + is extra space due to aspect constraints. The most common way to + specify the anchor are abbreviations of cardinal directions: + + ===== ===================== + value description + ===== ===================== + 'C' centered + 'SW' lower left corner + 'S' middle of bottom edge + 'SE' lower right corner + etc. + ===== ===================== + + See `~.Axes.set_anchor` for further details. + + share : bool, default: False + If ``True``, apply the settings to all shared Axes. + + See Also + -------- + mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect + """ + _api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'), + aspect=aspect) + super().set_aspect( + aspect='auto', adjustable=adjustable, anchor=anchor, share=share) + self._aspect = aspect + + if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'): + ax_indices = self._equal_aspect_axis_indices(aspect) + + view_intervals = np.array([self.xaxis.get_view_interval(), + self.yaxis.get_view_interval(), + self.zaxis.get_view_interval()]) + ptp = np.ptp(view_intervals, axis=1) + if self._adjustable == 'datalim': + mean = np.mean(view_intervals, axis=1) + scale = max(ptp[ax_indices] / self._box_aspect[ax_indices]) + deltas = scale * self._box_aspect + + for i, set_lim in enumerate((self.set_xlim3d, + self.set_ylim3d, + self.set_zlim3d)): + if i in ax_indices: + set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2., + auto=True, view_margin=None) + else: # 'box' + # Change the box aspect such that the ratio of the length of + # the unmodified axis to the length of the diagonal + # perpendicular to it remains unchanged. + box_aspect = np.array(self._box_aspect) + box_aspect[ax_indices] = ptp[ax_indices] + remaining_ax_indices = {0, 1, 2}.difference(ax_indices) + if remaining_ax_indices: + remaining = remaining_ax_indices.pop() + old_diag = np.linalg.norm(self._box_aspect[ax_indices]) + new_diag = np.linalg.norm(box_aspect[ax_indices]) + box_aspect[remaining] *= new_diag / old_diag + self.set_box_aspect(box_aspect) + + def _equal_aspect_axis_indices(self, aspect): + """ + Get the indices for which of the x, y, z axes are constrained to have + equal aspect ratios. + + Parameters + ---------- + aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'} + See descriptions in docstring for `.set_aspect()`. + """ + ax_indices = [] # aspect == 'auto' + if aspect == 'equal': + ax_indices = [0, 1, 2] + elif aspect == 'equalxy': + ax_indices = [0, 1] + elif aspect == 'equalxz': + ax_indices = [0, 2] + elif aspect == 'equalyz': + ax_indices = [1, 2] + return ax_indices + + def set_box_aspect(self, aspect, *, zoom=1): + """ + Set the Axes box aspect. + + The box aspect is the ratio of height to width in display + units for each face of the box when viewed perpendicular to + that face. This is not to be confused with the data aspect (see + `~.Axes3D.set_aspect`). The default ratios are 4:4:3 (x:y:z). + + To simulate having equal aspect in data space, set the box + aspect to match your data range in each dimension. + + *zoom* controls the overall size of the Axes3D in the figure. + + Parameters + ---------- + aspect : 3-tuple of floats or None + Changes the physical dimensions of the Axes3D, such that the ratio + of the axis lengths in display units is x:y:z. + If None, defaults to (4, 4, 3). + + zoom : float, default: 1 + Control overall size of the Axes3D in the figure. Must be > 0. + """ + if zoom <= 0: + raise ValueError(f'Argument zoom = {zoom} must be > 0') + + if aspect is None: + aspect = np.asarray((4, 4, 3), dtype=float) + else: + aspect = np.asarray(aspect, dtype=float) + _api.check_shape((3,), aspect=aspect) + # The scale 1.8294640721620434 is tuned to match the mpl3.2 appearance. + # The 25/24 factor is to compensate for the change in automargin + # behavior in mpl3.9. This comes from the padding of 1/48 on both sides + # of the axes in mpl3.8. + aspect *= 1.8294640721620434 * 25/24 * zoom / np.linalg.norm(aspect) + + self._box_aspect = self._roll_to_vertical(aspect, reverse=True) + self.stale = True + + def apply_aspect(self, position=None): + if position is None: + position = self.get_position(original=True) + + # in the superclass, we would go through and actually deal with axis + # scales and box/datalim. Those are all irrelevant - all we need to do + # is make sure our coordinate system is square. + trans = self.get_figure().transSubfigure + bb = mtransforms.Bbox.unit().transformed(trans) + # this is the physical aspect of the panel (or figure): + fig_aspect = bb.height / bb.width + + box_aspect = 1 + pb = position.frozen() + pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect) + self._set_position(pb1.anchored(self.get_anchor(), pb), 'active') + + @martist.allow_rasterization + def draw(self, renderer): + if not self.get_visible(): + return + self._unstale_viewLim() + + # draw the background patch + self.patch.draw(renderer) + self._frameon = False + + # first, set the aspect + # this is duplicated from `axes._base._AxesBase.draw` + # but must be called before any of the artist are drawn as + # it adjusts the view limits and the size of the bounding box + # of the Axes + locator = self.get_axes_locator() + self.apply_aspect(locator(self, renderer) if locator else None) + + # add the projection matrix to the renderer + self.M = self.get_proj() + self.invM = np.linalg.inv(self.M) + + collections_and_patches = ( + artist for artist in self._children + if isinstance(artist, (mcoll.Collection, mpatches.Patch)) + and artist.get_visible()) + if self.computed_zorder: + # Calculate projection of collections and patches and zorder + # them. Make sure they are drawn above the grids. + zorder_offset = max(axis.get_zorder() + for axis in self._axis_map.values()) + 1 + collection_zorder = patch_zorder = zorder_offset + + for artist in sorted(collections_and_patches, + key=lambda artist: artist.do_3d_projection(), + reverse=True): + if isinstance(artist, mcoll.Collection): + artist.zorder = collection_zorder + collection_zorder += 1 + elif isinstance(artist, mpatches.Patch): + artist.zorder = patch_zorder + patch_zorder += 1 + else: + for artist in collections_and_patches: + artist.do_3d_projection() + + if self._axis3don: + # Draw panes first + for axis in self._axis_map.values(): + axis.draw_pane(renderer) + # Then gridlines + for axis in self._axis_map.values(): + axis.draw_grid(renderer) + # Then axes, labels, text, and ticks + for axis in self._axis_map.values(): + axis.draw(renderer) + + # Then rest + super().draw(renderer) + + def get_axis_position(self): + tc = self._transformed_cube(self.get_w_lims()) + xhigh = tc[1][2] > tc[2][2] + yhigh = tc[3][2] > tc[2][2] + zhigh = tc[0][2] > tc[2][2] + return xhigh, yhigh, zhigh + + def update_datalim(self, xys, **kwargs): + """ + Not implemented in `~mpl_toolkits.mplot3d.axes3d.Axes3D`. + """ + pass + + get_autoscalez_on = _axis_method_wrapper("zaxis", "_get_autoscale_on") + set_autoscalez_on = _axis_method_wrapper("zaxis", "_set_autoscale_on") + + def get_zmargin(self): + """ + Retrieve autoscaling margin of the z-axis. + + .. versionadded:: 3.9 + + Returns + ------- + zmargin : float + + See Also + -------- + mpl_toolkits.mplot3d.axes3d.Axes3D.set_zmargin + """ + return self._zmargin + + def set_zmargin(self, m): + """ + Set padding of Z data limits prior to autoscaling. + + *m* times the data interval will be added to each end of that interval + before it is used in autoscaling. If *m* is negative, this will clip + the data range instead of expanding it. + + For example, if your data is in the range [0, 2], a margin of 0.1 will + result in a range [-0.2, 2.2]; a margin of -0.1 will result in a range + of [0.2, 1.8]. + + Parameters + ---------- + m : float greater than -0.5 + """ + if m <= -0.5: + raise ValueError("margin must be greater than -0.5") + self._zmargin = m + self._request_autoscale_view("z") + self.stale = True + + def margins(self, *margins, x=None, y=None, z=None, tight=True): + """ + Set or retrieve autoscaling margins. + + See `.Axes.margins` for full documentation. Because this function + applies to 3D Axes, it also takes a *z* argument, and returns + ``(xmargin, ymargin, zmargin)``. + """ + if margins and (x is not None or y is not None or z is not None): + raise TypeError('Cannot pass both positional and keyword ' + 'arguments for x, y, and/or z.') + elif len(margins) == 1: + x = y = z = margins[0] + elif len(margins) == 3: + x, y, z = margins + elif margins: + raise TypeError('Must pass a single positional argument for all ' + 'margins, or one for each margin (x, y, z).') + + if x is None and y is None and z is None: + if tight is not True: + _api.warn_external(f'ignoring tight={tight!r} in get mode') + return self._xmargin, self._ymargin, self._zmargin + + if x is not None: + self.set_xmargin(x) + if y is not None: + self.set_ymargin(y) + if z is not None: + self.set_zmargin(z) + + self.autoscale_view( + tight=tight, scalex=(x is not None), scaley=(y is not None), + scalez=(z is not None) + ) + + def autoscale(self, enable=True, axis='both', tight=None): + """ + Convenience method for simple axis view autoscaling. + + See `.Axes.autoscale` for full documentation. Because this function + applies to 3D Axes, *axis* can also be set to 'z', and setting *axis* + to 'both' autoscales all three axes. + """ + if enable is None: + scalex = True + scaley = True + scalez = True + else: + if axis in ['x', 'both']: + self.set_autoscalex_on(enable) + scalex = self.get_autoscalex_on() + else: + scalex = False + if axis in ['y', 'both']: + self.set_autoscaley_on(enable) + scaley = self.get_autoscaley_on() + else: + scaley = False + if axis in ['z', 'both']: + self.set_autoscalez_on(enable) + scalez = self.get_autoscalez_on() + else: + scalez = False + if scalex: + self._request_autoscale_view("x", tight=tight) + if scaley: + self._request_autoscale_view("y", tight=tight) + if scalez: + self._request_autoscale_view("z", tight=tight) + + def auto_scale_xyz(self, X, Y, Z=None, had_data=None): + # This updates the bounding boxes as to keep a record as to what the + # minimum sized rectangular volume holds the data. + if np.shape(X) == np.shape(Y): + self.xy_dataLim.update_from_data_xy( + np.column_stack([np.ravel(X), np.ravel(Y)]), not had_data) + else: + self.xy_dataLim.update_from_data_x(X, not had_data) + self.xy_dataLim.update_from_data_y(Y, not had_data) + if Z is not None: + self.zz_dataLim.update_from_data_x(Z, not had_data) + # Let autoscale_view figure out how to use this data. + self.autoscale_view() + + def autoscale_view(self, tight=None, + scalex=True, scaley=True, scalez=True): + """ + Autoscale the view limits using the data limits. + + See `.Axes.autoscale_view` for full documentation. Because this + function applies to 3D Axes, it also takes a *scalez* argument. + """ + # This method looks at the rectangular volume (see above) + # of data and decides how to scale the view portal to fit it. + if tight is None: + _tight = self._tight + if not _tight: + # if image data only just use the datalim + for artist in self._children: + if isinstance(artist, mimage.AxesImage): + _tight = True + elif isinstance(artist, (mlines.Line2D, mpatches.Patch)): + _tight = False + break + else: + _tight = self._tight = bool(tight) + + if scalex and self.get_autoscalex_on(): + x0, x1 = self.xy_dataLim.intervalx + xlocator = self.xaxis.get_major_locator() + x0, x1 = xlocator.nonsingular(x0, x1) + if self._xmargin > 0: + delta = (x1 - x0) * self._xmargin + x0 -= delta + x1 += delta + if not _tight: + x0, x1 = xlocator.view_limits(x0, x1) + self.set_xbound(x0, x1, self._view_margin) + + if scaley and self.get_autoscaley_on(): + y0, y1 = self.xy_dataLim.intervaly + ylocator = self.yaxis.get_major_locator() + y0, y1 = ylocator.nonsingular(y0, y1) + if self._ymargin > 0: + delta = (y1 - y0) * self._ymargin + y0 -= delta + y1 += delta + if not _tight: + y0, y1 = ylocator.view_limits(y0, y1) + self.set_ybound(y0, y1, self._view_margin) + + if scalez and self.get_autoscalez_on(): + z0, z1 = self.zz_dataLim.intervalx + zlocator = self.zaxis.get_major_locator() + z0, z1 = zlocator.nonsingular(z0, z1) + if self._zmargin > 0: + delta = (z1 - z0) * self._zmargin + z0 -= delta + z1 += delta + if not _tight: + z0, z1 = zlocator.view_limits(z0, z1) + self.set_zbound(z0, z1, self._view_margin) + + def get_w_lims(self): + """Get 3D world limits.""" + minx, maxx = self.get_xlim3d() + miny, maxy = self.get_ylim3d() + minz, maxz = self.get_zlim3d() + return minx, maxx, miny, maxy, minz, maxz + + def _set_bound3d(self, get_bound, set_lim, axis_inverted, + lower=None, upper=None, view_margin=None): + """ + Set 3D axis bounds. + """ + if upper is None and np.iterable(lower): + lower, upper = lower + + old_lower, old_upper = get_bound() + if lower is None: + lower = old_lower + if upper is None: + upper = old_upper + + set_lim(sorted((lower, upper), reverse=bool(axis_inverted())), + auto=None, view_margin=view_margin) + + def set_xbound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the x-axis. + + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscalex_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_xlim`. + + See Also + -------- + get_xbound + get_xlim, set_xlim + invert_xaxis, xaxis_inverted + """ + self._set_bound3d(self.get_xbound, self.set_xlim, self.xaxis_inverted, + lower, upper, view_margin) + + def set_ybound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the y-axis. + + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscaley_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_ylim`. + + See Also + -------- + get_ybound + get_ylim, set_ylim + invert_yaxis, yaxis_inverted + """ + self._set_bound3d(self.get_ybound, self.set_ylim, self.yaxis_inverted, + lower, upper, view_margin) + + def set_zbound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the z-axis. + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscaley_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_zlim`. + + See Also + -------- + get_zbound + get_zlim, set_zlim + invert_zaxis, zaxis_inverted + """ + self._set_bound3d(self.get_zbound, self.set_zlim, self.zaxis_inverted, + lower, upper, view_margin) + + def _set_lim3d(self, axis, lower=None, upper=None, *, emit=True, + auto=False, view_margin=None, axmin=None, axmax=None): + """ + Set 3D axis limits. + """ + if upper is None: + if np.iterable(lower): + lower, upper = lower + elif axmax is None: + upper = axis.get_view_interval()[1] + if lower is None and axmin is None: + lower = axis.get_view_interval()[0] + if axmin is not None: + if lower is not None: + raise TypeError("Cannot pass both 'lower' and 'min'") + lower = axmin + if axmax is not None: + if upper is not None: + raise TypeError("Cannot pass both 'upper' and 'max'") + upper = axmax + if np.isinf(lower) or np.isinf(upper): + raise ValueError(f"Axis limits {lower}, {upper} cannot be infinite") + if view_margin is None: + if mpl.rcParams['axes3d.automargin']: + view_margin = self._view_margin + else: + view_margin = 0 + delta = (upper - lower) * view_margin + lower -= delta + upper += delta + return axis._set_lim(lower, upper, emit=emit, auto=auto) + + def set_xlim(self, left=None, right=None, *, emit=True, auto=False, + view_margin=None, xmin=None, xmax=None): + """ + Set the 3D x-axis view limits. + + Parameters + ---------- + left : float, optional + The left xlim in data coordinates. Passing *None* leaves the + limit unchanged. + + The left and right xlims may also be passed as the tuple + (*left*, *right*) as the first positional argument (or as + the *left* keyword argument). + + .. ACCEPTS: (left: float, right: float) + + right : float, optional + The right xlim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the x-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + xmin, xmax : float, optional + They are equivalent to left and right respectively, and it is an + error to pass both *xmin* and *left* or *xmax* and *right*. + + Returns + ------- + left, right : (float, float) + The new x-axis limits in data coordinates. + + See Also + -------- + get_xlim + set_xbound, get_xbound + invert_xaxis, xaxis_inverted + + Notes + ----- + The *left* value may be greater than the *right* value, in which + case the x-axis values will decrease from *left* to *right*. + + Examples + -------- + >>> set_xlim(left, right) + >>> set_xlim((left, right)) + >>> left, right = set_xlim(left, right) + + One limit may be left unchanged. + + >>> set_xlim(right=right_lim) + + Limits may be passed in reverse order to flip the direction of + the x-axis. For example, suppose ``x`` represents depth of the + ocean in m. The x-axis limits might be set like the following + so 5000 m depth is at the left of the plot and the surface, + 0 m, is at the right. + + >>> set_xlim(5000, 0) + """ + return self._set_lim3d(self.xaxis, left, right, emit=emit, auto=auto, + view_margin=view_margin, axmin=xmin, axmax=xmax) + + def set_ylim(self, bottom=None, top=None, *, emit=True, auto=False, + view_margin=None, ymin=None, ymax=None): + """ + Set the 3D y-axis view limits. + + Parameters + ---------- + bottom : float, optional + The bottom ylim in data coordinates. Passing *None* leaves the + limit unchanged. + + The bottom and top ylims may also be passed as the tuple + (*bottom*, *top*) as the first positional argument (or as + the *bottom* keyword argument). + + .. ACCEPTS: (bottom: float, top: float) + + top : float, optional + The top ylim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the y-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + ymin, ymax : float, optional + They are equivalent to bottom and top respectively, and it is an + error to pass both *ymin* and *bottom* or *ymax* and *top*. + + Returns + ------- + bottom, top : (float, float) + The new y-axis limits in data coordinates. + + See Also + -------- + get_ylim + set_ybound, get_ybound + invert_yaxis, yaxis_inverted + + Notes + ----- + The *bottom* value may be greater than the *top* value, in which + case the y-axis values will decrease from *bottom* to *top*. + + Examples + -------- + >>> set_ylim(bottom, top) + >>> set_ylim((bottom, top)) + >>> bottom, top = set_ylim(bottom, top) + + One limit may be left unchanged. + + >>> set_ylim(top=top_lim) + + Limits may be passed in reverse order to flip the direction of + the y-axis. For example, suppose ``y`` represents depth of the + ocean in m. The y-axis limits might be set like the following + so 5000 m depth is at the bottom of the plot and the surface, + 0 m, is at the top. + + >>> set_ylim(5000, 0) + """ + return self._set_lim3d(self.yaxis, bottom, top, emit=emit, auto=auto, + view_margin=view_margin, axmin=ymin, axmax=ymax) + + def set_zlim(self, bottom=None, top=None, *, emit=True, auto=False, + view_margin=None, zmin=None, zmax=None): + """ + Set the 3D z-axis view limits. + + Parameters + ---------- + bottom : float, optional + The bottom zlim in data coordinates. Passing *None* leaves the + limit unchanged. + + The bottom and top zlims may also be passed as the tuple + (*bottom*, *top*) as the first positional argument (or as + the *bottom* keyword argument). + + .. ACCEPTS: (bottom: float, top: float) + + top : float, optional + The top zlim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the z-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + zmin, zmax : float, optional + They are equivalent to bottom and top respectively, and it is an + error to pass both *zmin* and *bottom* or *zmax* and *top*. + + Returns + ------- + bottom, top : (float, float) + The new z-axis limits in data coordinates. + + See Also + -------- + get_zlim + set_zbound, get_zbound + invert_zaxis, zaxis_inverted + + Notes + ----- + The *bottom* value may be greater than the *top* value, in which + case the z-axis values will decrease from *bottom* to *top*. + + Examples + -------- + >>> set_zlim(bottom, top) + >>> set_zlim((bottom, top)) + >>> bottom, top = set_zlim(bottom, top) + + One limit may be left unchanged. + + >>> set_zlim(top=top_lim) + + Limits may be passed in reverse order to flip the direction of + the z-axis. For example, suppose ``z`` represents depth of the + ocean in m. The z-axis limits might be set like the following + so 5000 m depth is at the bottom of the plot and the surface, + 0 m, is at the top. + + >>> set_zlim(5000, 0) + """ + return self._set_lim3d(self.zaxis, bottom, top, emit=emit, auto=auto, + view_margin=view_margin, axmin=zmin, axmax=zmax) + + set_xlim3d = set_xlim + set_ylim3d = set_ylim + set_zlim3d = set_zlim + + def get_xlim(self): + # docstring inherited + return tuple(self.xy_viewLim.intervalx) + + def get_ylim(self): + # docstring inherited + return tuple(self.xy_viewLim.intervaly) + + def get_zlim(self): + """ + Return the 3D z-axis view limits. + + Returns + ------- + left, right : (float, float) + The current z-axis limits in data coordinates. + + See Also + -------- + set_zlim + set_zbound, get_zbound + invert_zaxis, zaxis_inverted + + Notes + ----- + The z-axis may be inverted, in which case the *left* value will + be greater than the *right* value. + """ + return tuple(self.zz_viewLim.intervalx) + + get_zscale = _axis_method_wrapper("zaxis", "get_scale") + + # Redefine all three methods to overwrite their docstrings. + set_xscale = _axis_method_wrapper("xaxis", "_set_axes_scale") + set_yscale = _axis_method_wrapper("yaxis", "_set_axes_scale") + set_zscale = _axis_method_wrapper("zaxis", "_set_axes_scale") + set_xscale.__doc__, set_yscale.__doc__, set_zscale.__doc__ = map( + """ + Set the {}-axis scale. + + Parameters + ---------- + value : {{"linear"}} + The axis scale type to apply. 3D Axes currently only support + linear scales; other scales yield nonsensical results. + + **kwargs + Keyword arguments are nominally forwarded to the scale class, but + none of them is applicable for linear scales. + """.format, + ["x", "y", "z"]) + + get_zticks = _axis_method_wrapper("zaxis", "get_ticklocs") + set_zticks = _axis_method_wrapper("zaxis", "set_ticks") + get_zmajorticklabels = _axis_method_wrapper("zaxis", "get_majorticklabels") + get_zminorticklabels = _axis_method_wrapper("zaxis", "get_minorticklabels") + get_zticklabels = _axis_method_wrapper("zaxis", "get_ticklabels") + set_zticklabels = _axis_method_wrapper( + "zaxis", "set_ticklabels", + doc_sub={"Axis.set_ticks": "Axes3D.set_zticks"}) + + zaxis_date = _axis_method_wrapper("zaxis", "axis_date") + if zaxis_date.__doc__: + zaxis_date.__doc__ += textwrap.dedent(""" + + Notes + ----- + This function is merely provided for completeness, but 3D Axes do not + support dates for ticks, and so this may not work as expected. + """) + + def clabel(self, *args, **kwargs): + """Currently not implemented for 3D Axes, and returns *None*.""" + return None + + def view_init(self, elev=None, azim=None, roll=None, vertical_axis="z", + share=False): + """ + Set the elevation and azimuth of the Axes in degrees (not radians). + + This can be used to rotate the Axes programmatically. + + To look normal to the primary planes, the following elevation and + azimuth angles can be used. A roll angle of 0, 90, 180, or 270 deg + will rotate these views while keeping the axes at right angles. + + ========== ==== ==== + view plane elev azim + ========== ==== ==== + XY 90 -90 + XZ 0 -90 + YZ 0 0 + -XY -90 90 + -XZ 0 90 + -YZ 0 180 + ========== ==== ==== + + Parameters + ---------- + elev : float, default: None + The elevation angle in degrees rotates the camera above the plane + pierced by the vertical axis, with a positive angle corresponding + to a location above that plane. For example, with the default + vertical axis of 'z', the elevation defines the angle of the camera + location above the x-y plane. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + azim : float, default: None + The azimuthal angle in degrees rotates the camera about the + vertical axis, with a positive angle corresponding to a + right-handed rotation. For example, with the default vertical axis + of 'z', a positive azimuth rotates the camera about the origin from + its location along the +x axis towards the +y axis. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + roll : float, default: None + The roll angle in degrees rotates the camera about the viewing + axis. A positive angle spins the camera clockwise, causing the + scene to rotate counter-clockwise. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + vertical_axis : {"z", "x", "y"}, default: "z" + The axis to align vertically. *azim* rotates about this axis. + share : bool, default: False + If ``True``, apply the settings to all Axes with shared views. + """ + + self._dist = 10 # The camera distance from origin. Behaves like zoom + + if elev is None: + elev = self.initial_elev + if azim is None: + azim = self.initial_azim + if roll is None: + roll = self.initial_roll + vertical_axis = _api.check_getitem( + {name: idx for idx, name in enumerate(self._axis_names)}, + vertical_axis=vertical_axis, + ) + + if share: + axes = {sibling for sibling + in self._shared_axes['view'].get_siblings(self)} + else: + axes = [self] + + for ax in axes: + ax.elev = elev + ax.azim = azim + ax.roll = roll + ax._vertical_axis = vertical_axis + + def set_proj_type(self, proj_type, focal_length=None): + """ + Set the projection type. + + Parameters + ---------- + proj_type : {'persp', 'ortho'} + The projection type. + focal_length : float, default: None + For a projection type of 'persp', the focal length of the virtual + camera. Must be > 0. If None, defaults to 1. + The focal length can be computed from a desired Field Of View via + the equation: focal_length = 1/tan(FOV/2) + """ + _api.check_in_list(['persp', 'ortho'], proj_type=proj_type) + if proj_type == 'persp': + if focal_length is None: + focal_length = 1 + elif focal_length <= 0: + raise ValueError(f"focal_length = {focal_length} must be " + "greater than 0") + self._focal_length = focal_length + else: # 'ortho': + if focal_length not in (None, np.inf): + raise ValueError(f"focal_length = {focal_length} must be " + f"None for proj_type = {proj_type}") + self._focal_length = np.inf + + def _roll_to_vertical( + self, arr: "np.typing.ArrayLike", reverse: bool = False + ) -> np.ndarray: + """ + Roll arrays to match the different vertical axis. + + Parameters + ---------- + arr : ArrayLike + Array to roll. + reverse : bool, default: False + Reverse the direction of the roll. + """ + if reverse: + return np.roll(arr, (self._vertical_axis - 2) * -1) + else: + return np.roll(arr, (self._vertical_axis - 2)) + + def get_proj(self): + """Create the projection matrix from the current viewing position.""" + + # Transform to uniform world coordinates 0-1, 0-1, 0-1 + box_aspect = self._roll_to_vertical(self._box_aspect) + worldM = proj3d.world_transformation( + *self.get_xlim3d(), + *self.get_ylim3d(), + *self.get_zlim3d(), + pb_aspect=box_aspect, + ) + + # Look into the middle of the world coordinates: + R = 0.5 * box_aspect + + # elev: elevation angle in the z plane. + # azim: azimuth angle in the xy plane. + # Coordinates for a point that rotates around the box of data. + # p0, p1 corresponds to rotating the box only around the vertical axis. + # p2 corresponds to rotating the box only around the horizontal axis. + elev_rad = np.deg2rad(self.elev) + azim_rad = np.deg2rad(self.azim) + p0 = np.cos(elev_rad) * np.cos(azim_rad) + p1 = np.cos(elev_rad) * np.sin(azim_rad) + p2 = np.sin(elev_rad) + + # When changing vertical axis the coordinates changes as well. + # Roll the values to get the same behaviour as the default: + ps = self._roll_to_vertical([p0, p1, p2]) + + # The coordinates for the eye viewing point. The eye is looking + # towards the middle of the box of data from a distance: + eye = R + self._dist * ps + + # Calculate the viewing axes for the eye position + u, v, w = self._calc_view_axes(eye) + self._view_u = u # _view_u is towards the right of the screen + self._view_v = v # _view_v is towards the top of the screen + self._view_w = w # _view_w is out of the screen + + # Generate the view and projection transformation matrices + if self._focal_length == np.inf: + # Orthographic projection + viewM = proj3d._view_transformation_uvw(u, v, w, eye) + projM = proj3d._ortho_transformation(-self._dist, self._dist) + else: + # Perspective projection + # Scale the eye dist to compensate for the focal length zoom effect + eye_focal = R + self._dist * ps * self._focal_length + viewM = proj3d._view_transformation_uvw(u, v, w, eye_focal) + projM = proj3d._persp_transformation(-self._dist, + self._dist, + self._focal_length) + + # Combine all the transformation matrices to get the final projection + M0 = np.dot(viewM, worldM) + M = np.dot(projM, M0) + return M + + def mouse_init(self, rotate_btn=1, pan_btn=2, zoom_btn=3): + """ + Set the mouse buttons for 3D rotation and zooming. + + Parameters + ---------- + rotate_btn : int or list of int, default: 1 + The mouse button or buttons to use for 3D rotation of the Axes. + pan_btn : int or list of int, default: 2 + The mouse button or buttons to use to pan the 3D Axes. + zoom_btn : int or list of int, default: 3 + The mouse button or buttons to use to zoom the 3D Axes. + """ + self.button_pressed = None + # coerce scalars into array-like, then convert into + # a regular list to avoid comparisons against None + # which breaks in recent versions of numpy. + self._rotate_btn = np.atleast_1d(rotate_btn).tolist() + self._pan_btn = np.atleast_1d(pan_btn).tolist() + self._zoom_btn = np.atleast_1d(zoom_btn).tolist() + + def disable_mouse_rotation(self): + """Disable mouse buttons for 3D rotation, panning, and zooming.""" + self.mouse_init(rotate_btn=[], pan_btn=[], zoom_btn=[]) + + def can_zoom(self): + # doc-string inherited + return True + + def can_pan(self): + # doc-string inherited + return True + + def sharez(self, other): + """ + Share the z-axis with *other*. + + This is equivalent to passing ``sharez=other`` when constructing the + Axes, and cannot be used if the z-axis is already being shared with + another Axes. Note that it is not possible to unshare axes. + """ + _api.check_isinstance(Axes3D, other=other) + if self._sharez is not None and other is not self._sharez: + raise ValueError("z-axis is already shared") + self._shared_axes["z"].join(self, other) + self._sharez = other + self.zaxis.major = other.zaxis.major # Ticker instances holding + self.zaxis.minor = other.zaxis.minor # locator and formatter. + z0, z1 = other.get_zlim() + self.set_zlim(z0, z1, emit=False, auto=other.get_autoscalez_on()) + self.zaxis._scale = other.zaxis._scale + + def shareview(self, other): + """ + Share the view angles with *other*. + + This is equivalent to passing ``shareview=other`` when constructing the + Axes, and cannot be used if the view angles are already being shared + with another Axes. Note that it is not possible to unshare axes. + """ + _api.check_isinstance(Axes3D, other=other) + if self._shareview is not None and other is not self._shareview: + raise ValueError("view angles are already shared") + self._shared_axes["view"].join(self, other) + self._shareview = other + vertical_axis = self._axis_names[other._vertical_axis] + self.view_init(elev=other.elev, azim=other.azim, roll=other.roll, + vertical_axis=vertical_axis, share=True) + + def clear(self): + # docstring inherited. + super().clear() + if self._focal_length == np.inf: + self._zmargin = mpl.rcParams['axes.zmargin'] + else: + self._zmargin = 0. + + xymargin = 0.05 * 10/11 # match mpl3.8 appearance + self.xy_dataLim = Bbox([[xymargin, xymargin], + [1 - xymargin, 1 - xymargin]]) + # z-limits are encoded in the x-component of the Bbox, y is un-used + self.zz_dataLim = Bbox.unit() + self._view_margin = 1/48 # default value to match mpl3.8 + self.autoscale_view() + + self.grid(mpl.rcParams['axes3d.grid']) + + def _button_press(self, event): + if event.inaxes == self: + self.button_pressed = event.button + self._sx, self._sy = event.xdata, event.ydata + toolbar = self.get_figure(root=True).canvas.toolbar + if toolbar and toolbar._nav_stack() is None: + toolbar.push_current() + if toolbar: + toolbar.set_message(toolbar._mouse_event_to_message(event)) + + def _button_release(self, event): + self.button_pressed = None + toolbar = self.get_figure(root=True).canvas.toolbar + # backend_bases.release_zoom and backend_bases.release_pan call + # push_current, so check the navigation mode so we don't call it twice + if toolbar and self.get_navigate_mode() is None: + toolbar.push_current() + if toolbar: + toolbar.set_message(toolbar._mouse_event_to_message(event)) + + def _get_view(self): + # docstring inherited + return { + "xlim": self.get_xlim(), "autoscalex_on": self.get_autoscalex_on(), + "ylim": self.get_ylim(), "autoscaley_on": self.get_autoscaley_on(), + "zlim": self.get_zlim(), "autoscalez_on": self.get_autoscalez_on(), + }, (self.elev, self.azim, self.roll) + + def _set_view(self, view): + # docstring inherited + props, (elev, azim, roll) = view + self.set(**props) + self.elev = elev + self.azim = azim + self.roll = roll + + def format_zdata(self, z): + """ + Return *z* string formatted. This function will use the + :attr:`fmt_zdata` attribute if it is callable, else will fall + back on the zaxis major formatter + """ + try: + return self.fmt_zdata(z) + except (AttributeError, TypeError): + func = self.zaxis.get_major_formatter().format_data_short + val = func(z) + return val + + def format_coord(self, xv, yv, renderer=None): + """ + Return a string giving the current view rotation angles, or the x, y, z + coordinates of the point on the nearest axis pane underneath the mouse + cursor, depending on the mouse button pressed. + """ + coords = '' + + if self.button_pressed in self._rotate_btn: + # ignore xv and yv and display angles instead + coords = self._rotation_coords() + + elif self.M is not None: + coords = self._location_coords(xv, yv, renderer) + + return coords + + def _rotation_coords(self): + """ + Return the rotation angles as a string. + """ + norm_elev = art3d._norm_angle(self.elev) + norm_azim = art3d._norm_angle(self.azim) + norm_roll = art3d._norm_angle(self.roll) + coords = (f"elevation={norm_elev:.0f}\N{DEGREE SIGN}, " + f"azimuth={norm_azim:.0f}\N{DEGREE SIGN}, " + f"roll={norm_roll:.0f}\N{DEGREE SIGN}" + ).replace("-", "\N{MINUS SIGN}") + return coords + + def _location_coords(self, xv, yv, renderer): + """ + Return the location on the axis pane underneath the cursor as a string. + """ + p1, pane_idx = self._calc_coord(xv, yv, renderer) + xs = self.format_xdata(p1[0]) + ys = self.format_ydata(p1[1]) + zs = self.format_zdata(p1[2]) + if pane_idx == 0: + coords = f'x pane={xs}, y={ys}, z={zs}' + elif pane_idx == 1: + coords = f'x={xs}, y pane={ys}, z={zs}' + elif pane_idx == 2: + coords = f'x={xs}, y={ys}, z pane={zs}' + return coords + + def _get_camera_loc(self): + """ + Returns the current camera location in data coordinates. + """ + cx, cy, cz, dx, dy, dz = self._get_w_centers_ranges() + c = np.array([cx, cy, cz]) + r = np.array([dx, dy, dz]) + + if self._focal_length == np.inf: # orthographic projection + focal_length = 1e9 # large enough to be effectively infinite + else: # perspective projection + focal_length = self._focal_length + eye = c + self._view_w * self._dist * r / self._box_aspect * focal_length + return eye + + def _calc_coord(self, xv, yv, renderer=None): + """ + Given the 2D view coordinates, find the point on the nearest axis pane + that lies directly below those coordinates. Returns a 3D point in data + coordinates. + """ + if self._focal_length == np.inf: # orthographic projection + zv = 1 + else: # perspective projection + zv = -1 / self._focal_length + + # Convert point on view plane to data coordinates + p1 = np.array(proj3d.inv_transform(xv, yv, zv, self.invM)).ravel() + + # Get the vector from the camera to the point on the view plane + vec = self._get_camera_loc() - p1 + + # Get the pane locations for each of the axes + pane_locs = [] + for axis in self._axis_map.values(): + xys, loc = axis.active_pane() + pane_locs.append(loc) + + # Find the distance to the nearest pane by projecting the view vector + scales = np.zeros(3) + for i in range(3): + if vec[i] == 0: + scales[i] = np.inf + else: + scales[i] = (p1[i] - pane_locs[i]) / vec[i] + pane_idx = np.argmin(abs(scales)) + scale = scales[pane_idx] + + # Calculate the point on the closest pane + p2 = p1 - scale*vec + return p2, pane_idx + + def _arcball(self, x: float, y: float) -> np.ndarray: + """ + Convert a point (x, y) to a point on a virtual trackball. + + This is Ken Shoemake's arcball (a sphere), modified + to soften the abrupt edge (optionally). + See: Ken Shoemake, "ARCBALL: A user interface for specifying + three-dimensional rotation using a mouse." in + Proceedings of Graphics Interface '92, 1992, pp. 151-156, + https://doi.org/10.20380/GI1992.18 + The smoothing of the edge is inspired by Gavin Bell's arcball + (a sphere combined with a hyperbola), but here, the sphere + is combined with a section of a cylinder, so it has finite support. + """ + s = mpl.rcParams['axes3d.trackballsize'] / 2 + b = mpl.rcParams['axes3d.trackballborder'] / s + x /= s + y /= s + r2 = x*x + y*y + r = np.sqrt(r2) + ra = 1 + b + a = b * (1 + b/2) + ri = 2/(ra + 1/ra) + if r < ri: + p = np.array([np.sqrt(1 - r2), x, y]) + elif r < ra: + dr = ra - r + p = np.array([a - np.sqrt((a + dr) * (a - dr)), x, y]) + p /= np.linalg.norm(p) + else: + p = np.array([0, x/r, y/r]) + return p + + def _on_move(self, event): + """ + Mouse moving. + + By default, button-1 rotates, button-2 pans, and button-3 zooms; + these buttons can be modified via `mouse_init`. + """ + + if not self.button_pressed: + return + + if self.get_navigate_mode() is not None: + # we don't want to rotate if we are zooming/panning + # from the toolbar + return + + if self.M is None: + return + + x, y = event.xdata, event.ydata + # In case the mouse is out of bounds. + if x is None or event.inaxes != self: + return + + dx, dy = x - self._sx, y - self._sy + w = self._pseudo_w + h = self._pseudo_h + + # Rotation + if self.button_pressed in self._rotate_btn: + # rotate viewing point + # get the x and y pixel coords + if dx == 0 and dy == 0: + return + + style = mpl.rcParams['axes3d.mouserotationstyle'] + if style == 'azel': + roll = np.deg2rad(self.roll) + delev = -(dy/h)*180*np.cos(roll) + (dx/w)*180*np.sin(roll) + dazim = -(dy/h)*180*np.sin(roll) - (dx/w)*180*np.cos(roll) + elev = self.elev + delev + azim = self.azim + dazim + roll = self.roll + else: + q = _Quaternion.from_cardan_angles( + *np.deg2rad((self.elev, self.azim, self.roll))) + + if style == 'trackball': + k = np.array([0, -dy/h, dx/w]) + nk = np.linalg.norm(k) + th = nk / mpl.rcParams['axes3d.trackballsize'] + dq = _Quaternion(np.cos(th), k*np.sin(th)/nk) + else: # 'sphere', 'arcball' + current_vec = self._arcball(self._sx/w, self._sy/h) + new_vec = self._arcball(x/w, y/h) + if style == 'sphere': + dq = _Quaternion.rotate_from_to(current_vec, new_vec) + else: # 'arcball' + dq = _Quaternion(0, new_vec) * _Quaternion(0, -current_vec) + + q = dq * q + elev, azim, roll = np.rad2deg(q.as_cardan_angles()) + + # update view + vertical_axis = self._axis_names[self._vertical_axis] + self.view_init( + elev=elev, + azim=azim, + roll=roll, + vertical_axis=vertical_axis, + share=True, + ) + self.stale = True + + # Pan + elif self.button_pressed in self._pan_btn: + # Start the pan event with pixel coordinates + px, py = self.transData.transform([self._sx, self._sy]) + self.start_pan(px, py, 2) + # pan view (takes pixel coordinate input) + self.drag_pan(2, None, event.x, event.y) + self.end_pan() + + # Zoom + elif self.button_pressed in self._zoom_btn: + # zoom view (dragging down zooms in) + scale = h/(h - dy) + self._scale_axis_limits(scale, scale, scale) + + # Store the event coordinates for the next time through. + self._sx, self._sy = x, y + # Always request a draw update at the end of interaction + self.get_figure(root=True).canvas.draw_idle() + + def drag_pan(self, button, key, x, y): + # docstring inherited + + # Get the coordinates from the move event + p = self._pan_start + (xdata, ydata), (xdata_start, ydata_start) = p.trans_inverse.transform( + [(x, y), (p.x, p.y)]) + self._sx, self._sy = xdata, ydata + # Calling start_pan() to set the x/y of this event as the starting + # move location for the next event + self.start_pan(x, y, button) + du, dv = xdata - xdata_start, ydata - ydata_start + dw = 0 + if key == 'x': + dv = 0 + elif key == 'y': + du = 0 + if du == 0 and dv == 0: + return + + # Transform the pan from the view axes to the data axes + R = np.array([self._view_u, self._view_v, self._view_w]) + R = -R / self._box_aspect * self._dist + duvw_projected = R.T @ np.array([du, dv, dw]) + + # Calculate pan distance + minx, maxx, miny, maxy, minz, maxz = self.get_w_lims() + dx = (maxx - minx) * duvw_projected[0] + dy = (maxy - miny) * duvw_projected[1] + dz = (maxz - minz) * duvw_projected[2] + + # Set the new axis limits + self.set_xlim3d(minx + dx, maxx + dx, auto=None) + self.set_ylim3d(miny + dy, maxy + dy, auto=None) + self.set_zlim3d(minz + dz, maxz + dz, auto=None) + + def _calc_view_axes(self, eye): + """ + Get the unit vectors for the viewing axes in data coordinates. + `u` is towards the right of the screen + `v` is towards the top of the screen + `w` is out of the screen + """ + elev_rad = np.deg2rad(art3d._norm_angle(self.elev)) + roll_rad = np.deg2rad(art3d._norm_angle(self.roll)) + + # Look into the middle of the world coordinates + R = 0.5 * self._roll_to_vertical(self._box_aspect) + + # Define which axis should be vertical. A negative value + # indicates the plot is upside down and therefore the values + # have been reversed: + V = np.zeros(3) + V[self._vertical_axis] = -1 if abs(elev_rad) > np.pi/2 else 1 + + u, v, w = proj3d._view_axes(eye, R, V, roll_rad) + return u, v, w + + def _set_view_from_bbox(self, bbox, direction='in', + mode=None, twinx=False, twiny=False): + """ + Zoom in or out of the bounding box. + + Will center the view in the center of the bounding box, and zoom by + the ratio of the size of the bounding box to the size of the Axes3D. + """ + (start_x, start_y, stop_x, stop_y) = bbox + if mode == 'x': + start_y = self.bbox.min[1] + stop_y = self.bbox.max[1] + elif mode == 'y': + start_x = self.bbox.min[0] + stop_x = self.bbox.max[0] + + # Clip to bounding box limits + start_x, stop_x = np.clip(sorted([start_x, stop_x]), + self.bbox.min[0], self.bbox.max[0]) + start_y, stop_y = np.clip(sorted([start_y, stop_y]), + self.bbox.min[1], self.bbox.max[1]) + + # Move the center of the view to the center of the bbox + zoom_center_x = (start_x + stop_x)/2 + zoom_center_y = (start_y + stop_y)/2 + + ax_center_x = (self.bbox.max[0] + self.bbox.min[0])/2 + ax_center_y = (self.bbox.max[1] + self.bbox.min[1])/2 + + self.start_pan(zoom_center_x, zoom_center_y, 2) + self.drag_pan(2, None, ax_center_x, ax_center_y) + self.end_pan() + + # Calculate zoom level + dx = abs(start_x - stop_x) + dy = abs(start_y - stop_y) + scale_u = dx / (self.bbox.max[0] - self.bbox.min[0]) + scale_v = dy / (self.bbox.max[1] - self.bbox.min[1]) + + # Keep aspect ratios equal + scale = max(scale_u, scale_v) + + # Zoom out + if direction == 'out': + scale = 1 / scale + + self._zoom_data_limits(scale, scale, scale) + + def _zoom_data_limits(self, scale_u, scale_v, scale_w): + """ + Zoom in or out of a 3D plot. + + Will scale the data limits by the scale factors. These will be + transformed to the x, y, z data axes based on the current view angles. + A scale factor > 1 zooms out and a scale factor < 1 zooms in. + + For an Axes that has had its aspect ratio set to 'equal', 'equalxy', + 'equalyz', or 'equalxz', the relevant axes are constrained to zoom + equally. + + Parameters + ---------- + scale_u : float + Scale factor for the u view axis (view screen horizontal). + scale_v : float + Scale factor for the v view axis (view screen vertical). + scale_w : float + Scale factor for the w view axis (view screen depth). + """ + scale = np.array([scale_u, scale_v, scale_w]) + + # Only perform frame conversion if unequal scale factors + if not np.allclose(scale, scale_u): + # Convert the scale factors from the view frame to the data frame + R = np.array([self._view_u, self._view_v, self._view_w]) + S = scale * np.eye(3) + scale = np.linalg.norm(R.T @ S, axis=1) + + # Set the constrained scale factors to the factor closest to 1 + if self._aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'): + ax_idxs = self._equal_aspect_axis_indices(self._aspect) + min_ax_idxs = np.argmin(np.abs(scale[ax_idxs] - 1)) + scale[ax_idxs] = scale[ax_idxs][min_ax_idxs] + + self._scale_axis_limits(scale[0], scale[1], scale[2]) + + def _scale_axis_limits(self, scale_x, scale_y, scale_z): + """ + Keeping the center of the x, y, and z data axes fixed, scale their + limits by scale factors. A scale factor > 1 zooms out and a scale + factor < 1 zooms in. + + Parameters + ---------- + scale_x : float + Scale factor for the x data axis. + scale_y : float + Scale factor for the y data axis. + scale_z : float + Scale factor for the z data axis. + """ + # Get the axis centers and ranges + cx, cy, cz, dx, dy, dz = self._get_w_centers_ranges() + + # Set the scaled axis limits + self.set_xlim3d(cx - dx*scale_x/2, cx + dx*scale_x/2, auto=None) + self.set_ylim3d(cy - dy*scale_y/2, cy + dy*scale_y/2, auto=None) + self.set_zlim3d(cz - dz*scale_z/2, cz + dz*scale_z/2, auto=None) + + def _get_w_centers_ranges(self): + """Get 3D world centers and axis ranges.""" + # Calculate center of axis limits + minx, maxx, miny, maxy, minz, maxz = self.get_w_lims() + cx = (maxx + minx)/2 + cy = (maxy + miny)/2 + cz = (maxz + minz)/2 + + # Calculate range of axis limits + dx = (maxx - minx) + dy = (maxy - miny) + dz = (maxz - minz) + return cx, cy, cz, dx, dy, dz + + def set_zlabel(self, zlabel, fontdict=None, labelpad=None, **kwargs): + """ + Set zlabel. See doc for `.set_ylabel` for description. + """ + if labelpad is not None: + self.zaxis.labelpad = labelpad + return self.zaxis.set_label_text(zlabel, fontdict, **kwargs) + + def get_zlabel(self): + """ + Get the z-label text string. + """ + label = self.zaxis.label + return label.get_text() + + # Axes rectangle characteristics + + # The frame_on methods are not available for 3D axes. + # Python will raise a TypeError if they are called. + get_frame_on = None + set_frame_on = None + + def grid(self, visible=True, **kwargs): + """ + Set / unset 3D grid. + + .. note:: + + Currently, this function does not behave the same as + `.axes.Axes.grid`, but it is intended to eventually support that + behavior. + """ + # TODO: Operate on each axes separately + if len(kwargs): + visible = True + self._draw_grid = visible + self.stale = True + + def tick_params(self, axis='both', **kwargs): + """ + Convenience method for changing the appearance of ticks and + tick labels. + + See `.Axes.tick_params` for full documentation. Because this function + applies to 3D Axes, *axis* can also be set to 'z', and setting *axis* + to 'both' autoscales all three axes. + + Also, because of how Axes3D objects are drawn very differently + from regular 2D Axes, some of these settings may have + ambiguous meaning. For simplicity, the 'z' axis will + accept settings as if it was like the 'y' axis. + + .. note:: + Axes3D currently ignores some of these settings. + """ + _api.check_in_list(['x', 'y', 'z', 'both'], axis=axis) + if axis in ['x', 'y', 'both']: + super().tick_params(axis, **kwargs) + if axis in ['z', 'both']: + zkw = dict(kwargs) + zkw.pop('top', None) + zkw.pop('bottom', None) + zkw.pop('labeltop', None) + zkw.pop('labelbottom', None) + self.zaxis.set_tick_params(**zkw) + + # data limits, ticks, tick labels, and formatting + + def invert_zaxis(self): + """ + Invert the z-axis. + + See Also + -------- + zaxis_inverted + get_zlim, set_zlim + get_zbound, set_zbound + """ + bottom, top = self.get_zlim() + self.set_zlim(top, bottom, auto=None) + + zaxis_inverted = _axis_method_wrapper("zaxis", "get_inverted") + + def get_zbound(self): + """ + Return the lower and upper z-axis bounds, in increasing order. + + See Also + -------- + set_zbound + get_zlim, set_zlim + invert_zaxis, zaxis_inverted + """ + lower, upper = self.get_zlim() + if lower < upper: + return lower, upper + else: + return upper, lower + + def text(self, x, y, z, s, zdir=None, *, axlim_clip=False, **kwargs): + """ + Add the text *s* to the 3D Axes at location *x*, *y*, *z* in data coordinates. + + Parameters + ---------- + x, y, z : float + The position to place the text. + s : str + The text. + zdir : {'x', 'y', 'z', 3-tuple}, optional + The direction to be used as the z-direction. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text that is outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.text`. + + Returns + ------- + `.Text3D` + The created `.Text3D` instance. + """ + text = super().text(x, y, s, **kwargs) + art3d.text_2d_to_3d(text, z, zdir, axlim_clip) + return text + + text3D = text + text2D = Axes.text + + def plot(self, xs, ys, *args, zdir='z', axlim_clip=False, **kwargs): + """ + Plot 2D or 3D data. + + Parameters + ---------- + xs : 1D array-like + x coordinates of vertices. + ys : 1D array-like + y coordinates of vertices. + zs : float or 1D array-like + z coordinates of vertices; either one for all points or one for + each point. + zdir : {'x', 'y', 'z'}, default: 'z' + When plotting 2D data, the direction to use as z. + axlim_clip : bool, default: False + Whether to hide data that is outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.plot`. + """ + had_data = self.has_data() + + # `zs` can be passed positionally or as keyword; checking whether + # args[0] is a string matches the behavior of 2D `plot` (via + # `_process_plot_var_args`). + if args and not isinstance(args[0], str): + zs, *args = args + if 'zs' in kwargs: + raise TypeError("plot() for multiple values for argument 'zs'") + else: + zs = kwargs.pop('zs', 0) + + xs, ys, zs = cbook._broadcast_with_masks(xs, ys, zs) + + lines = super().plot(xs, ys, *args, **kwargs) + for line in lines: + art3d.line_2d_to_3d(line, zs=zs, zdir=zdir, axlim_clip=axlim_clip) + + xs, ys, zs = art3d.juggle_axes(xs, ys, zs, zdir) + self.auto_scale_xyz(xs, ys, zs, had_data) + return lines + + plot3D = plot + + def fill_between(self, x1, y1, z1, x2, y2, z2, *, + where=None, mode='auto', facecolors=None, shade=None, + axlim_clip=False, **kwargs): + """ + Fill the area between two 3D curves. + + The curves are defined by the points (*x1*, *y1*, *z1*) and + (*x2*, *y2*, *z2*). This creates one or multiple quadrangle + polygons that are filled. All points must be the same length N, or a + single value to be used for all points. + + Parameters + ---------- + x1, y1, z1 : float or 1D array-like + x, y, and z coordinates of vertices for 1st line. + + x2, y2, z2 : float or 1D array-like + x, y, and z coordinates of vertices for 2nd line. + + where : array of bool (length N), optional + Define *where* to exclude some regions from being filled. The + filled regions are defined by the coordinates ``pts[where]``, + for all x, y, and z pts. More precisely, fill between ``pts[i]`` + and ``pts[i+1]`` if ``where[i] and where[i+1]``. Note that this + definition implies that an isolated *True* value between two + *False* values in *where* will not result in filling. Both sides of + the *True* position remain unfilled due to the adjacent *False* + values. + + mode : {'quad', 'polygon', 'auto'}, default: 'auto' + The fill mode. One of: + + - 'quad': A separate quadrilateral polygon is created for each + pair of subsequent points in the two lines. + - 'polygon': The two lines are connected to form a single polygon. + This is faster and can render more cleanly for simple shapes + (e.g. for filling between two lines that lie within a plane). + - 'auto': If the points all lie on the same 3D plane, 'polygon' is + used. Otherwise, 'quad' is used. + + facecolors : list of :mpltype:`color`, default: None + Colors of each individual patch, or a single color to be used for + all patches. + + shade : bool, default: None + Whether to shade the facecolors. If *None*, then defaults to *True* + for 'quad' mode and *False* for 'polygon' mode. + + axlim_clip : bool, default: False + Whether to hide data that is outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + All other keyword arguments are passed on to `.Poly3DCollection`. + + Returns + ------- + `.Poly3DCollection` + A `.Poly3DCollection` containing the plotted polygons. + + """ + _api.check_in_list(['auto', 'quad', 'polygon'], mode=mode) + + had_data = self.has_data() + x1, y1, z1, x2, y2, z2 = cbook._broadcast_with_masks(x1, y1, z1, x2, y2, z2) + + if facecolors is None: + facecolors = [self._get_patches_for_fill.get_next_color()] + facecolors = list(mcolors.to_rgba_array(facecolors)) + + if where is None: + where = True + else: + where = np.asarray(where, dtype=bool) + if where.size != x1.size: + raise ValueError(f"where size ({where.size}) does not match " + f"size ({x1.size})") + where = where & ~np.isnan(x1) # NaNs were broadcast in _broadcast_with_masks + + if mode == 'auto': + if art3d._all_points_on_plane(np.concatenate((x1[where], x2[where])), + np.concatenate((y1[where], y2[where])), + np.concatenate((z1[where], z2[where])), + atol=1e-12): + mode = 'polygon' + else: + mode = 'quad' + + if shade is None: + if mode == 'quad': + shade = True + else: + shade = False + + polys = [] + for idx0, idx1 in cbook.contiguous_regions(where): + x1i = x1[idx0:idx1] + y1i = y1[idx0:idx1] + z1i = z1[idx0:idx1] + x2i = x2[idx0:idx1] + y2i = y2[idx0:idx1] + z2i = z2[idx0:idx1] + + if not len(x1i): + continue + + if mode == 'quad': + # Preallocate the array for the region's vertices, and fill it in + n_polys_i = len(x1i) - 1 + polys_i = np.empty((n_polys_i, 4, 3)) + polys_i[:, 0, :] = np.column_stack((x1i[:-1], y1i[:-1], z1i[:-1])) + polys_i[:, 1, :] = np.column_stack((x1i[1:], y1i[1:], z1i[1:])) + polys_i[:, 2, :] = np.column_stack((x2i[1:], y2i[1:], z2i[1:])) + polys_i[:, 3, :] = np.column_stack((x2i[:-1], y2i[:-1], z2i[:-1])) + polys = polys + [*polys_i] + elif mode == 'polygon': + line1 = np.column_stack((x1i, y1i, z1i)) + line2 = np.column_stack((x2i[::-1], y2i[::-1], z2i[::-1])) + poly = np.concatenate((line1, line2), axis=0) + polys.append(poly) + + polyc = art3d.Poly3DCollection(polys, facecolors=facecolors, shade=shade, + axlim_clip=axlim_clip, **kwargs) + self.add_collection(polyc) + + self.auto_scale_xyz([x1, x2], [y1, y2], [z1, z2], had_data) + return polyc + + def plot_surface(self, X, Y, Z, *, norm=None, vmin=None, + vmax=None, lightsource=None, axlim_clip=False, **kwargs): + """ + Create a surface plot. + + By default, it will be colored in shades of a solid color, but it also + supports colormapping by supplying the *cmap* argument. + + .. note:: + + The *rcount* and *ccount* kwargs, which both default to 50, + determine the maximum number of samples used in each direction. If + the input data is larger, it will be downsampled (by slicing) to + these numbers of points. + + .. note:: + + To maximize rendering speed consider setting *rstride* and *cstride* + to divisors of the number of rows minus 1 and columns minus 1 + respectively. For example, given 51 rows rstride can be any of the + divisors of 50. + + Similarly, a setting of *rstride* and *cstride* equal to 1 (or + *rcount* and *ccount* equal the number of rows and columns) can use + the optimized path. + + Parameters + ---------- + X, Y, Z : 2D arrays + Data values. + + rcount, ccount : int + Maximum number of samples used in each direction. If the input + data is larger, it will be downsampled (by slicing) to these + numbers of points. Defaults to 50. + + rstride, cstride : int + Downsampling stride in each direction. These arguments are + mutually exclusive with *rcount* and *ccount*. If only one of + *rstride* or *cstride* is set, the other defaults to 10. + + 'classic' mode uses a default of ``rstride = cstride = 10`` instead + of the new default of ``rcount = ccount = 50``. + + color : :mpltype:`color` + Color of the surface patches. + + cmap : Colormap, optional + Colormap of the surface patches. + + facecolors : list of :mpltype:`color` + Colors of each individual patch. + + norm : `~matplotlib.colors.Normalize`, optional + Normalization for the colormap. + + vmin, vmax : float, optional + Bounds for the normalization. + + shade : bool, default: True + Whether to shade the facecolors. Shading is always disabled when + *cmap* is specified. + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + Other keyword arguments are forwarded to `.Poly3DCollection`. + """ + + had_data = self.has_data() + + if Z.ndim != 2: + raise ValueError("Argument Z must be 2-dimensional.") + + Z = cbook._to_unmasked_float_array(Z) + X, Y, Z = np.broadcast_arrays(X, Y, Z) + rows, cols = Z.shape + + has_stride = 'rstride' in kwargs or 'cstride' in kwargs + has_count = 'rcount' in kwargs or 'ccount' in kwargs + + if has_stride and has_count: + raise ValueError("Cannot specify both stride and count arguments") + + rstride = kwargs.pop('rstride', 10) + cstride = kwargs.pop('cstride', 10) + rcount = kwargs.pop('rcount', 50) + ccount = kwargs.pop('ccount', 50) + + if mpl.rcParams['_internal.classic_mode']: + # Strides have priority over counts in classic mode. + # So, only compute strides from counts + # if counts were explicitly given + compute_strides = has_count + else: + # If the strides are provided then it has priority. + # Otherwise, compute the strides from the counts. + compute_strides = not has_stride + + if compute_strides: + rstride = int(max(np.ceil(rows / rcount), 1)) + cstride = int(max(np.ceil(cols / ccount), 1)) + + fcolors = kwargs.pop('facecolors', None) + + cmap = kwargs.get('cmap', None) + shade = kwargs.pop('shade', cmap is None) + if shade is None: + raise ValueError("shade cannot be None.") + + colset = [] # the sampled facecolor + if (rows - 1) % rstride == 0 and \ + (cols - 1) % cstride == 0 and \ + fcolors is None: + polys = np.stack( + [cbook._array_patch_perimeters(a, rstride, cstride) + for a in (X, Y, Z)], + axis=-1) + else: + # evenly spaced, and including both endpoints + row_inds = list(range(0, rows-1, rstride)) + [rows-1] + col_inds = list(range(0, cols-1, cstride)) + [cols-1] + + polys = [] + for rs, rs_next in itertools.pairwise(row_inds): + for cs, cs_next in itertools.pairwise(col_inds): + ps = [ + # +1 ensures we share edges between polygons + cbook._array_perimeter(a[rs:rs_next+1, cs:cs_next+1]) + for a in (X, Y, Z) + ] + # ps = np.stack(ps, axis=-1) + ps = np.array(ps).T + polys.append(ps) + + if fcolors is not None: + colset.append(fcolors[rs][cs]) + + # In cases where there are non-finite values in the data (possibly NaNs from + # masked arrays), artifacts can be introduced. Here check whether such values + # are present and remove them. + if not isinstance(polys, np.ndarray) or not np.isfinite(polys).all(): + new_polys = [] + new_colset = [] + + # Depending on fcolors, colset is either an empty list or has as + # many elements as polys. In the former case new_colset results in + # a list with None entries, that is discarded later. + for p, col in itertools.zip_longest(polys, colset): + new_poly = np.array(p)[np.isfinite(p).all(axis=1)] + if len(new_poly): + new_polys.append(new_poly) + new_colset.append(col) + + # Replace previous polys and, if fcolors is not None, colset + polys = new_polys + if fcolors is not None: + colset = new_colset + + # note that the striding causes some polygons to have more coordinates + # than others + + if fcolors is not None: + polyc = art3d.Poly3DCollection( + polys, edgecolors=colset, facecolors=colset, shade=shade, + lightsource=lightsource, axlim_clip=axlim_clip, **kwargs) + elif cmap: + polyc = art3d.Poly3DCollection(polys, axlim_clip=axlim_clip, **kwargs) + # can't always vectorize, because polys might be jagged + if isinstance(polys, np.ndarray): + avg_z = polys[..., 2].mean(axis=-1) + else: + avg_z = np.array([ps[:, 2].mean() for ps in polys]) + polyc.set_array(avg_z) + if vmin is not None or vmax is not None: + polyc.set_clim(vmin, vmax) + if norm is not None: + polyc.set_norm(norm) + else: + color = kwargs.pop('color', None) + if color is None: + color = self._get_lines.get_next_color() + color = np.array(mcolors.to_rgba(color)) + + polyc = art3d.Poly3DCollection( + polys, facecolors=color, shade=shade, lightsource=lightsource, + axlim_clip=axlim_clip, **kwargs) + + self.add_collection(polyc) + self.auto_scale_xyz(X, Y, Z, had_data) + + return polyc + + def plot_wireframe(self, X, Y, Z, *, axlim_clip=False, **kwargs): + """ + Plot a 3D wireframe. + + .. note:: + + The *rcount* and *ccount* kwargs, which both default to 50, + determine the maximum number of samples used in each direction. If + the input data is larger, it will be downsampled (by slicing) to + these numbers of points. + + Parameters + ---------- + X, Y, Z : 2D arrays + Data values. + + axlim_clip : bool, default: False + Whether to hide lines and patches with vertices outside the axes + view limits. + + .. versionadded:: 3.10 + + rcount, ccount : int + Maximum number of samples used in each direction. If the input + data is larger, it will be downsampled (by slicing) to these + numbers of points. Setting a count to zero causes the data to be + not sampled in the corresponding direction, producing a 3D line + plot rather than a wireframe plot. Defaults to 50. + + rstride, cstride : int + Downsampling stride in each direction. These arguments are + mutually exclusive with *rcount* and *ccount*. If only one of + *rstride* or *cstride* is set, the other defaults to 1. Setting a + stride to zero causes the data to be not sampled in the + corresponding direction, producing a 3D line plot rather than a + wireframe plot. + + 'classic' mode uses a default of ``rstride = cstride = 1`` instead + of the new default of ``rcount = ccount = 50``. + + **kwargs + Other keyword arguments are forwarded to `.Line3DCollection`. + """ + + had_data = self.has_data() + if Z.ndim != 2: + raise ValueError("Argument Z must be 2-dimensional.") + # FIXME: Support masked arrays + X, Y, Z = np.broadcast_arrays(X, Y, Z) + rows, cols = Z.shape + + has_stride = 'rstride' in kwargs or 'cstride' in kwargs + has_count = 'rcount' in kwargs or 'ccount' in kwargs + + if has_stride and has_count: + raise ValueError("Cannot specify both stride and count arguments") + + rstride = kwargs.pop('rstride', 1) + cstride = kwargs.pop('cstride', 1) + rcount = kwargs.pop('rcount', 50) + ccount = kwargs.pop('ccount', 50) + + if mpl.rcParams['_internal.classic_mode']: + # Strides have priority over counts in classic mode. + # So, only compute strides from counts + # if counts were explicitly given + if has_count: + rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 + cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 + else: + # If the strides are provided then it has priority. + # Otherwise, compute the strides from the counts. + if not has_stride: + rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 + cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 + + # We want two sets of lines, one running along the "rows" of + # Z and another set of lines running along the "columns" of Z. + # This transpose will make it easy to obtain the columns. + tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z) + + if rstride: + rii = list(range(0, rows, rstride)) + # Add the last index only if needed + if rows > 0 and rii[-1] != (rows - 1): + rii += [rows-1] + else: + rii = [] + if cstride: + cii = list(range(0, cols, cstride)) + # Add the last index only if needed + if cols > 0 and cii[-1] != (cols - 1): + cii += [cols-1] + else: + cii = [] + + if rstride == 0 and cstride == 0: + raise ValueError("Either rstride or cstride must be non zero") + + # If the inputs were empty, then just + # reset everything. + if Z.size == 0: + rii = [] + cii = [] + + xlines = [X[i] for i in rii] + ylines = [Y[i] for i in rii] + zlines = [Z[i] for i in rii] + + txlines = [tX[i] for i in cii] + tylines = [tY[i] for i in cii] + tzlines = [tZ[i] for i in cii] + + lines = ([list(zip(xl, yl, zl)) + for xl, yl, zl in zip(xlines, ylines, zlines)] + + [list(zip(xl, yl, zl)) + for xl, yl, zl in zip(txlines, tylines, tzlines)]) + + linec = art3d.Line3DCollection(lines, axlim_clip=axlim_clip, **kwargs) + self.add_collection(linec) + self.auto_scale_xyz(X, Y, Z, had_data) + + return linec + + def plot_trisurf(self, *args, color=None, norm=None, vmin=None, vmax=None, + lightsource=None, axlim_clip=False, **kwargs): + """ + Plot a triangulated surface. + + The (optional) triangulation can be specified in one of two ways; + either:: + + plot_trisurf(triangulation, ...) + + where triangulation is a `~matplotlib.tri.Triangulation` object, or:: + + plot_trisurf(X, Y, ...) + plot_trisurf(X, Y, triangles, ...) + plot_trisurf(X, Y, triangles=triangles, ...) + + in which case a Triangulation object will be created. See + `.Triangulation` for an explanation of these possibilities. + + The remaining arguments are:: + + plot_trisurf(..., Z) + + where *Z* is the array of values to contour, one per point + in the triangulation. + + Parameters + ---------- + X, Y, Z : array-like + Data values as 1D arrays. + color + Color of the surface patches. + cmap + A colormap for the surface patches. + norm : `~matplotlib.colors.Normalize`, optional + An instance of Normalize to map values to colors. + vmin, vmax : float, optional + Minimum and maximum value to map. + shade : bool, default: True + Whether to shade the facecolors. Shading is always disabled when + *cmap* is specified. + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + All other keyword arguments are passed on to + :class:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection` + + Examples + -------- + .. plot:: gallery/mplot3d/trisurf3d.py + .. plot:: gallery/mplot3d/trisurf3d_2.py + """ + + had_data = self.has_data() + + # TODO: Support custom face colours + if color is None: + color = self._get_lines.get_next_color() + color = np.array(mcolors.to_rgba(color)) + + cmap = kwargs.get('cmap', None) + shade = kwargs.pop('shade', cmap is None) + + tri, args, kwargs = \ + Triangulation.get_from_args_and_kwargs(*args, **kwargs) + try: + z = kwargs.pop('Z') + except KeyError: + # We do this so Z doesn't get passed as an arg to PolyCollection + z, *args = args + z = np.asarray(z) + + triangles = tri.get_masked_triangles() + xt = tri.x[triangles] + yt = tri.y[triangles] + zt = z[triangles] + verts = np.stack((xt, yt, zt), axis=-1) + + if cmap: + polyc = art3d.Poly3DCollection(verts, *args, + axlim_clip=axlim_clip, **kwargs) + # average over the three points of each triangle + avg_z = verts[:, :, 2].mean(axis=1) + polyc.set_array(avg_z) + if vmin is not None or vmax is not None: + polyc.set_clim(vmin, vmax) + if norm is not None: + polyc.set_norm(norm) + else: + polyc = art3d.Poly3DCollection( + verts, *args, shade=shade, lightsource=lightsource, + facecolors=color, axlim_clip=axlim_clip, **kwargs) + + self.add_collection(polyc) + self.auto_scale_xyz(tri.x, tri.y, z, had_data) + + return polyc + + def _3d_extend_contour(self, cset, stride=5): + """ + Extend a contour in 3D by creating + """ + + dz = (cset.levels[1] - cset.levels[0]) / 2 + polyverts = [] + colors = [] + for idx, level in enumerate(cset.levels): + path = cset.get_paths()[idx] + subpaths = [*path._iter_connected_components()] + color = cset.get_edgecolor()[idx] + top = art3d._paths_to_3d_segments(subpaths, level - dz) + bot = art3d._paths_to_3d_segments(subpaths, level + dz) + if not len(top[0]): + continue + nsteps = max(round(len(top[0]) / stride), 2) + stepsize = (len(top[0]) - 1) / (nsteps - 1) + polyverts.extend([ + (top[0][round(i * stepsize)], top[0][round((i + 1) * stepsize)], + bot[0][round((i + 1) * stepsize)], bot[0][round(i * stepsize)]) + for i in range(round(nsteps) - 1)]) + colors.extend([color] * (round(nsteps) - 1)) + self.add_collection3d(art3d.Poly3DCollection( + np.array(polyverts), # All polygons have 4 vertices, so vectorize. + facecolors=colors, edgecolors=colors, shade=True)) + cset.remove() + + def add_contour_set( + self, cset, extend3d=False, stride=5, zdir='z', offset=None, + axlim_clip=False): + zdir = '-' + zdir + if extend3d: + self._3d_extend_contour(cset, stride) + else: + art3d.collection_2d_to_3d( + cset, zs=offset if offset is not None else cset.levels, zdir=zdir, + axlim_clip=axlim_clip) + + def add_contourf_set(self, cset, zdir='z', offset=None, *, axlim_clip=False): + self._add_contourf_set(cset, zdir=zdir, offset=offset, + axlim_clip=axlim_clip) + + def _add_contourf_set(self, cset, zdir='z', offset=None, axlim_clip=False): + """ + Returns + ------- + levels : `numpy.ndarray` + Levels at which the filled contours are added. + """ + zdir = '-' + zdir + + midpoints = cset.levels[:-1] + np.diff(cset.levels) / 2 + # Linearly interpolate to get levels for any extensions + if cset._extend_min: + min_level = cset.levels[0] - np.diff(cset.levels[:2]) / 2 + midpoints = np.insert(midpoints, 0, min_level) + if cset._extend_max: + max_level = cset.levels[-1] + np.diff(cset.levels[-2:]) / 2 + midpoints = np.append(midpoints, max_level) + + art3d.collection_2d_to_3d( + cset, zs=offset if offset is not None else midpoints, zdir=zdir, + axlim_clip=axlim_clip) + return midpoints + + @_preprocess_data() + def contour(self, X, Y, Z, *args, + extend3d=False, stride=5, zdir='z', offset=None, axlim_clip=False, + **kwargs): + """ + Create a 3D contour plot. + + Parameters + ---------- + X, Y, Z : array-like, + Input data. See `.Axes.contour` for supported data shapes. + extend3d : bool, default: False + Whether to extend contour in 3D. + stride : int, default: 5 + Step size for extending contour. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.contour`. + + Returns + ------- + matplotlib.contour.QuadContourSet + """ + had_data = self.has_data() + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + cset = super().contour(jX, jY, jZ, *args, **kwargs) + self.add_contour_set(cset, extend3d, stride, zdir, offset, axlim_clip) + + self.auto_scale_xyz(X, Y, Z, had_data) + return cset + + contour3D = contour + + @_preprocess_data() + def tricontour(self, *args, + extend3d=False, stride=5, zdir='z', offset=None, axlim_clip=False, + **kwargs): + """ + Create a 3D contour plot. + + .. note:: + This method currently produces incorrect output due to a + longstanding bug in 3D PolyCollection rendering. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.tricontour` for supported data shapes. + extend3d : bool, default: False + Whether to extend contour in 3D. + stride : int, default: 5 + Step size for extending contour. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.tricontour`. + + Returns + ------- + matplotlib.tri._tricontour.TriContourSet + """ + had_data = self.has_data() + + tri, args, kwargs = Triangulation.get_from_args_and_kwargs( + *args, **kwargs) + X = tri.x + Y = tri.y + if 'Z' in kwargs: + Z = kwargs.pop('Z') + else: + # We do this so Z doesn't get passed as an arg to Axes.tricontour + Z, *args = args + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + tri = Triangulation(jX, jY, tri.triangles, tri.mask) + + cset = super().tricontour(tri, jZ, *args, **kwargs) + self.add_contour_set(cset, extend3d, stride, zdir, offset, axlim_clip) + + self.auto_scale_xyz(X, Y, Z, had_data) + return cset + + def _auto_scale_contourf(self, X, Y, Z, zdir, levels, had_data): + # Autoscale in the zdir based on the levels added, which are + # different from data range if any contour extensions are present + dim_vals = {'x': X, 'y': Y, 'z': Z, zdir: levels} + # Input data and levels have different sizes, but auto_scale_xyz + # expected same-size input, so manually take min/max limits + limits = [(np.nanmin(dim_vals[dim]), np.nanmax(dim_vals[dim])) + for dim in ['x', 'y', 'z']] + self.auto_scale_xyz(*limits, had_data) + + @_preprocess_data() + def contourf(self, X, Y, Z, *args, + zdir='z', offset=None, axlim_clip=False, **kwargs): + """ + Create a 3D filled contour plot. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.contourf` for supported data shapes. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.contourf`. + + Returns + ------- + matplotlib.contour.QuadContourSet + """ + had_data = self.has_data() + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + cset = super().contourf(jX, jY, jZ, *args, **kwargs) + levels = self._add_contourf_set(cset, zdir, offset, axlim_clip) + + self._auto_scale_contourf(X, Y, Z, zdir, levels, had_data) + return cset + + contourf3D = contourf + + @_preprocess_data() + def tricontourf(self, *args, zdir='z', offset=None, axlim_clip=False, **kwargs): + """ + Create a 3D filled contour plot. + + .. note:: + This method currently produces incorrect output due to a + longstanding bug in 3D PolyCollection rendering. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.tricontourf` for supported data shapes. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to zdir. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to + `matplotlib.axes.Axes.tricontourf`. + + Returns + ------- + matplotlib.tri._tricontour.TriContourSet + """ + had_data = self.has_data() + + tri, args, kwargs = Triangulation.get_from_args_and_kwargs( + *args, **kwargs) + X = tri.x + Y = tri.y + if 'Z' in kwargs: + Z = kwargs.pop('Z') + else: + # We do this so Z doesn't get passed as an arg to Axes.tricontourf + Z, *args = args + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + tri = Triangulation(jX, jY, tri.triangles, tri.mask) + + cset = super().tricontourf(tri, jZ, *args, **kwargs) + levels = self._add_contourf_set(cset, zdir, offset, axlim_clip) + + self._auto_scale_contourf(X, Y, Z, zdir, levels, had_data) + return cset + + def add_collection3d(self, col, zs=0, zdir='z', autolim=True, *, + axlim_clip=False): + """ + Add a 3D collection object to the plot. + + 2D collection types are converted to a 3D version by + modifying the object and adding z coordinate information, + *zs* and *zdir*. + + Supported 2D collection types are: + + - `.PolyCollection` + - `.LineCollection` + - `.PatchCollection` (currently not supporting *autolim*) + + Parameters + ---------- + col : `.Collection` + A 2D collection object. + zs : float or array-like, default: 0 + The z-positions to be used for the 2D objects. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use for the z-positions. + autolim : bool, default: True + Whether to update the data limits. + axlim_clip : bool, default: False + Whether to hide the scatter points outside the axes view limits. + + .. versionadded:: 3.10 + """ + had_data = self.has_data() + + zvals = np.atleast_1d(zs) + zsortval = (np.min(zvals) if zvals.size + else 0) # FIXME: arbitrary default + + # FIXME: use issubclass() (although, then a 3D collection + # object would also pass.) Maybe have a collection3d + # abstract class to test for and exclude? + if type(col) is mcoll.PolyCollection: + art3d.poly_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + elif type(col) is mcoll.LineCollection: + art3d.line_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + elif type(col) is mcoll.PatchCollection: + art3d.patch_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + + if autolim: + if isinstance(col, art3d.Line3DCollection): + self.auto_scale_xyz(*np.array(col._segments3d).transpose(), + had_data=had_data) + elif isinstance(col, art3d.Poly3DCollection): + self.auto_scale_xyz(*col._vec[:-1], had_data=had_data) + elif isinstance(col, art3d.Patch3DCollection): + pass + # FIXME: Implement auto-scaling function for Patch3DCollection + # Currently unable to do so due to issues with Patch3DCollection + # See https://github.com/matplotlib/matplotlib/issues/14298 for details + + collection = super().add_collection(col) + return collection + + @_preprocess_data(replace_names=["xs", "ys", "zs", "s", + "edgecolors", "c", "facecolor", + "facecolors", "color"]) + def scatter(self, xs, ys, + zs=0, zdir='z', s=20, c=None, depthshade=True, *args, + axlim_clip=False, **kwargs): + """ + Create a scatter plot. + + Parameters + ---------- + xs, ys : array-like + The data positions. + zs : float or array-like, default: 0 + The z-positions. Either an array of the same length as *xs* and + *ys* or a single value to place all points in the same plane. + zdir : {'x', 'y', 'z', '-x', '-y', '-z'}, default: 'z' + The axis direction for the *zs*. This is useful when plotting 2D + data on a 3D Axes. The data must be passed as *xs*, *ys*. Setting + *zdir* to 'y' then plots the data to the x-z-plane. + + See also :doc:`/gallery/mplot3d/2dcollections3d`. + + s : float or array-like, default: 20 + The marker size in points**2. Either an array of the same length + as *xs* and *ys* or a single value to make all markers the same + size. + c : :mpltype:`color`, sequence, or sequence of colors, optional + The marker color. Possible values: + + - A single color format string. + - A sequence of colors of length n. + - A sequence of n numbers to be mapped to colors using *cmap* and + *norm*. + - A 2D array in which the rows are RGB or RGBA. + + For more details see the *c* argument of `~.axes.Axes.scatter`. + depthshade : bool, default: True + Whether to shade the scatter markers to give the appearance of + depth. Each call to ``scatter()`` will perform its depthshading + independently. + axlim_clip : bool, default: False + Whether to hide the scatter points outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + **kwargs + All other keyword arguments are passed on to `~.axes.Axes.scatter`. + + Returns + ------- + paths : `~matplotlib.collections.PathCollection` + """ + + had_data = self.has_data() + zs_orig = zs + + xs, ys, zs = cbook._broadcast_with_masks(xs, ys, zs) + s = np.ma.ravel(s) # This doesn't have to match x, y in size. + + xs, ys, zs, s, c, color = cbook.delete_masked_points( + xs, ys, zs, s, c, kwargs.get('color', None) + ) + if kwargs.get("color") is not None: + kwargs['color'] = color + + # For xs and ys, 2D scatter() will do the copying. + if np.may_share_memory(zs_orig, zs): # Avoid unnecessary copies. + zs = zs.copy() + + patches = super().scatter(xs, ys, s=s, c=c, *args, **kwargs) + art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir, + depthshade=depthshade, + axlim_clip=axlim_clip) + + if self._zmargin < 0.05 and xs.size > 0: + self.set_zmargin(0.05) + + self.auto_scale_xyz(xs, ys, zs, had_data) + + return patches + + scatter3D = scatter + + @_preprocess_data() + def bar(self, left, height, zs=0, zdir='z', *args, + axlim_clip=False, **kwargs): + """ + Add 2D bar(s). + + Parameters + ---------- + left : 1D array-like + The x coordinates of the left sides of the bars. + height : 1D array-like + The height of the bars. + zs : float or 1D array-like, default: 0 + Z coordinate of bars; if a single value is specified, it will be + used for all bars. + zdir : {'x', 'y', 'z'}, default: 'z' + When plotting 2D data, the direction to use as z ('x', 'y' or 'z'). + axlim_clip : bool, default: False + Whether to hide bars with points outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + **kwargs + Other keyword arguments are forwarded to + `matplotlib.axes.Axes.bar`. + + Returns + ------- + mpl_toolkits.mplot3d.art3d.Patch3DCollection + """ + had_data = self.has_data() + + patches = super().bar(left, height, *args, **kwargs) + + zs = np.broadcast_to(zs, len(left), subok=True) + + verts = [] + verts_zs = [] + for p, z in zip(patches, zs): + vs = art3d._get_patch_verts(p) + verts += vs.tolist() + verts_zs += [z] * len(vs) + art3d.patch_2d_to_3d(p, z, zdir, axlim_clip) + if 'alpha' in kwargs: + p.set_alpha(kwargs['alpha']) + + if len(verts) > 0: + # the following has to be skipped if verts is empty + # NOTE: Bugs could still occur if len(verts) > 0, + # but the "2nd dimension" is empty. + xs, ys = zip(*verts) + else: + xs, ys = [], [] + + xs, ys, verts_zs = art3d.juggle_axes(xs, ys, verts_zs, zdir) + self.auto_scale_xyz(xs, ys, verts_zs, had_data) + + return patches + + @_preprocess_data() + def bar3d(self, x, y, z, dx, dy, dz, color=None, + zsort='average', shade=True, lightsource=None, *args, + axlim_clip=False, **kwargs): + """ + Generate a 3D barplot. + + This method creates three-dimensional barplot where the width, + depth, height, and color of the bars can all be uniquely set. + + Parameters + ---------- + x, y, z : array-like + The coordinates of the anchor point of the bars. + + dx, dy, dz : float or array-like + The width, depth, and height of the bars, respectively. + + color : sequence of colors, optional + The color of the bars can be specified globally or + individually. This parameter can be: + + - A single color, to color all bars the same color. + - An array of colors of length N bars, to color each bar + independently. + - An array of colors of length 6, to color the faces of the + bars similarly. + - An array of colors of length 6 * N bars, to color each face + independently. + + When coloring the faces of the boxes specifically, this is + the order of the coloring: + + 1. -Z (bottom of box) + 2. +Z (top of box) + 3. -Y + 4. +Y + 5. -X + 6. +X + + zsort : {'average', 'min', 'max'}, default: 'average' + The z-axis sorting scheme passed onto `~.art3d.Poly3DCollection` + + shade : bool, default: True + When true, this shades the dark sides of the bars (relative + to the plot's source of light). + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide the bars with points outside the axes view limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + Any additional keyword arguments are passed onto + `~.art3d.Poly3DCollection`. + + Returns + ------- + collection : `~.art3d.Poly3DCollection` + A collection of three-dimensional polygons representing the bars. + """ + + had_data = self.has_data() + + x, y, z, dx, dy, dz = np.broadcast_arrays( + np.atleast_1d(x), y, z, dx, dy, dz) + minx = np.min(x) + maxx = np.max(x + dx) + miny = np.min(y) + maxy = np.max(y + dy) + minz = np.min(z) + maxz = np.max(z + dz) + + # shape (6, 4, 3) + # All faces are oriented facing outwards - when viewed from the + # outside, their vertices are in a counterclockwise ordering. + cuboid = np.array([ + # -z + ( + (0, 0, 0), + (0, 1, 0), + (1, 1, 0), + (1, 0, 0), + ), + # +z + ( + (0, 0, 1), + (1, 0, 1), + (1, 1, 1), + (0, 1, 1), + ), + # -y + ( + (0, 0, 0), + (1, 0, 0), + (1, 0, 1), + (0, 0, 1), + ), + # +y + ( + (0, 1, 0), + (0, 1, 1), + (1, 1, 1), + (1, 1, 0), + ), + # -x + ( + (0, 0, 0), + (0, 0, 1), + (0, 1, 1), + (0, 1, 0), + ), + # +x + ( + (1, 0, 0), + (1, 1, 0), + (1, 1, 1), + (1, 0, 1), + ), + ]) + + # indexed by [bar, face, vertex, coord] + polys = np.empty(x.shape + cuboid.shape) + + # handle each coordinate separately + for i, p, dp in [(0, x, dx), (1, y, dy), (2, z, dz)]: + p = p[..., np.newaxis, np.newaxis] + dp = dp[..., np.newaxis, np.newaxis] + polys[..., i] = p + dp * cuboid[..., i] + + # collapse the first two axes + polys = polys.reshape((-1,) + polys.shape[2:]) + + facecolors = [] + if color is None: + color = [self._get_patches_for_fill.get_next_color()] + + color = list(mcolors.to_rgba_array(color)) + + if len(color) == len(x): + # bar colors specified, need to expand to number of faces + for c in color: + facecolors.extend([c] * 6) + else: + # a single color specified, or face colors specified explicitly + facecolors = color + if len(facecolors) < len(x): + facecolors *= (6 * len(x)) + + col = art3d.Poly3DCollection(polys, + zsort=zsort, + facecolors=facecolors, + shade=shade, + lightsource=lightsource, + axlim_clip=axlim_clip, + *args, **kwargs) + self.add_collection(col) + + self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data) + + return col + + def set_title(self, label, fontdict=None, loc='center', **kwargs): + # docstring inherited + ret = super().set_title(label, fontdict=fontdict, loc=loc, **kwargs) + (x, y) = self.title.get_position() + self.title.set_y(0.92 * y) + return ret + + @_preprocess_data() + def quiver(self, X, Y, Z, U, V, W, *, + length=1, arrow_length_ratio=.3, pivot='tail', normalize=False, + axlim_clip=False, **kwargs): + """ + Plot a 3D field of arrows. + + The arguments can be array-like or scalars, so long as they can be + broadcast together. The arguments can also be masked arrays. If an + element in any of argument is masked, then that corresponding quiver + element will not be plotted. + + Parameters + ---------- + X, Y, Z : array-like + The x, y and z coordinates of the arrow locations (default is + tail of arrow; see *pivot* kwarg). + + U, V, W : array-like + The x, y and z components of the arrow vectors. + + length : float, default: 1 + The length of each quiver. + + arrow_length_ratio : float, default: 0.3 + The ratio of the arrow head with respect to the quiver. + + pivot : {'tail', 'middle', 'tip'}, default: 'tail' + The part of the arrow that is at the grid point; the arrow + rotates about this point, hence the name *pivot*. + + normalize : bool, default: False + Whether all arrows are normalized to have the same length, or keep + the lengths defined by *u*, *v*, and *w*. + + axlim_clip : bool, default: False + Whether to hide arrows with points outside the axes view limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + Any additional keyword arguments are delegated to + :class:`.Line3DCollection` + """ + + def calc_arrows(UVW): + # get unit direction vector perpendicular to (u, v, w) + x = UVW[:, 0] + y = UVW[:, 1] + norm = np.linalg.norm(UVW[:, :2], axis=1) + x_p = np.divide(y, norm, where=norm != 0, out=np.zeros_like(x)) + y_p = np.divide(-x, norm, where=norm != 0, out=np.ones_like(x)) + # compute the two arrowhead direction unit vectors + rangle = math.radians(15) + c = math.cos(rangle) + s = math.sin(rangle) + # construct the rotation matrices of shape (3, 3, n) + r13 = y_p * s + r32 = x_p * s + r12 = x_p * y_p * (1 - c) + Rpos = np.array( + [[c + (x_p ** 2) * (1 - c), r12, r13], + [r12, c + (y_p ** 2) * (1 - c), -r32], + [-r13, r32, np.full_like(x_p, c)]]) + # opposite rotation negates all the sin terms + Rneg = Rpos.copy() + Rneg[[0, 1, 2, 2], [2, 2, 0, 1]] *= -1 + # Batch n (3, 3) x (3) matrix multiplications ((3, 3, n) x (n, 3)). + Rpos_vecs = np.einsum("ij...,...j->...i", Rpos, UVW) + Rneg_vecs = np.einsum("ij...,...j->...i", Rneg, UVW) + # Stack into (n, 2, 3) result. + return np.stack([Rpos_vecs, Rneg_vecs], axis=1) + + had_data = self.has_data() + + input_args = cbook._broadcast_with_masks(X, Y, Z, U, V, W, + compress=True) + + if any(len(v) == 0 for v in input_args): + # No quivers, so just make an empty collection and return early + linec = art3d.Line3DCollection([], **kwargs) + self.add_collection(linec) + return linec + + shaft_dt = np.array([0., length], dtype=float) + arrow_dt = shaft_dt * arrow_length_ratio + + _api.check_in_list(['tail', 'middle', 'tip'], pivot=pivot) + if pivot == 'tail': + shaft_dt -= length + elif pivot == 'middle': + shaft_dt -= length / 2 + + XYZ = np.column_stack(input_args[:3]) + UVW = np.column_stack(input_args[3:]).astype(float) + + # Normalize rows of UVW + if normalize: + norm = np.linalg.norm(UVW, axis=1) + norm[norm == 0] = 1 + UVW = UVW / norm.reshape((-1, 1)) + + if len(XYZ) > 0: + # compute the shaft lines all at once with an outer product + shafts = (XYZ - np.multiply.outer(shaft_dt, UVW)).swapaxes(0, 1) + # compute head direction vectors, n heads x 2 sides x 3 dimensions + head_dirs = calc_arrows(UVW) + # compute all head lines at once, starting from the shaft ends + heads = shafts[:, :1] - np.multiply.outer(arrow_dt, head_dirs) + # stack left and right head lines together + heads = heads.reshape((len(arrow_dt), -1, 3)) + # transpose to get a list of lines + heads = heads.swapaxes(0, 1) + + lines = [*shafts, *heads[::2], *heads[1::2]] + else: + lines = [] + + linec = art3d.Line3DCollection(lines, axlim_clip=axlim_clip, **kwargs) + self.add_collection(linec) + + self.auto_scale_xyz(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], had_data) + + return linec + + quiver3D = quiver + + def voxels(self, *args, facecolors=None, edgecolors=None, shade=True, + lightsource=None, axlim_clip=False, **kwargs): + """ + ax.voxels([x, y, z,] /, filled, facecolors=None, edgecolors=None, \ +**kwargs) + + Plot a set of filled voxels + + All voxels are plotted as 1x1x1 cubes on the axis, with + ``filled[0, 0, 0]`` placed with its lower corner at the origin. + Occluded faces are not plotted. + + Parameters + ---------- + filled : 3D np.array of bool + A 3D array of values, with truthy values indicating which voxels + to fill + + x, y, z : 3D np.array, optional + The coordinates of the corners of the voxels. This should broadcast + to a shape one larger in every dimension than the shape of + *filled*. These can be used to plot non-cubic voxels. + + If not specified, defaults to increasing integers along each axis, + like those returned by :func:`~numpy.indices`. + As indicated by the ``/`` in the function signature, these + arguments can only be passed positionally. + + facecolors, edgecolors : array-like, optional + The color to draw the faces and edges of the voxels. Can only be + passed as keyword arguments. + These parameters can be: + + - A single color value, to color all voxels the same color. This + can be either a string, or a 1D RGB/RGBA array + - ``None``, the default, to use a single color for the faces, and + the style default for the edges. + - A 3D `~numpy.ndarray` of color names, with each item the color + for the corresponding voxel. The size must match the voxels. + - A 4D `~numpy.ndarray` of RGB/RGBA data, with the components + along the last axis. + + shade : bool, default: True + Whether to shade the facecolors. + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide voxels with points outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + Additional keyword arguments to pass onto + `~mpl_toolkits.mplot3d.art3d.Poly3DCollection`. + + Returns + ------- + faces : dict + A dictionary indexed by coordinate, where ``faces[i, j, k]`` is a + `.Poly3DCollection` of the faces drawn for the voxel + ``filled[i, j, k]``. If no faces were drawn for a given voxel, + either because it was not asked to be drawn, or it is fully + occluded, then ``(i, j, k) not in faces``. + + Examples + -------- + .. plot:: gallery/mplot3d/voxels.py + .. plot:: gallery/mplot3d/voxels_rgb.py + .. plot:: gallery/mplot3d/voxels_torus.py + .. plot:: gallery/mplot3d/voxels_numpy_logo.py + """ + + # work out which signature we should be using, and use it to parse + # the arguments. Name must be voxels for the correct error message + if len(args) >= 3: + # underscores indicate position only + def voxels(__x, __y, __z, filled, **kwargs): + return (__x, __y, __z), filled, kwargs + else: + def voxels(filled, **kwargs): + return None, filled, kwargs + + xyz, filled, kwargs = voxels(*args, **kwargs) + + # check dimensions + if filled.ndim != 3: + raise ValueError("Argument filled must be 3-dimensional") + size = np.array(filled.shape, dtype=np.intp) + + # check xyz coordinates, which are one larger than the filled shape + coord_shape = tuple(size + 1) + if xyz is None: + x, y, z = np.indices(coord_shape) + else: + x, y, z = (np.broadcast_to(c, coord_shape) for c in xyz) + + def _broadcast_color_arg(color, name): + if np.ndim(color) in (0, 1): + # single color, like "red" or [1, 0, 0] + return np.broadcast_to(color, filled.shape + np.shape(color)) + elif np.ndim(color) in (3, 4): + # 3D array of strings, or 4D array with last axis rgb + if np.shape(color)[:3] != filled.shape: + raise ValueError( + f"When multidimensional, {name} must match the shape " + "of filled") + return color + else: + raise ValueError(f"Invalid {name} argument") + + # broadcast and default on facecolors + if facecolors is None: + facecolors = self._get_patches_for_fill.get_next_color() + facecolors = _broadcast_color_arg(facecolors, 'facecolors') + + # broadcast but no default on edgecolors + edgecolors = _broadcast_color_arg(edgecolors, 'edgecolors') + + # scale to the full array, even if the data is only in the center + self.auto_scale_xyz(x, y, z) + + # points lying on corners of a square + square = np.array([ + [0, 0, 0], + [1, 0, 0], + [1, 1, 0], + [0, 1, 0], + ], dtype=np.intp) + + voxel_faces = defaultdict(list) + + def permutation_matrices(n): + """Generate cyclic permutation matrices.""" + mat = np.eye(n, dtype=np.intp) + for i in range(n): + yield mat + mat = np.roll(mat, 1, axis=0) + + # iterate over each of the YZ, ZX, and XY orientations, finding faces + # to render + for permute in permutation_matrices(3): + # find the set of ranges to iterate over + pc, qc, rc = permute.T.dot(size) + pinds = np.arange(pc) + qinds = np.arange(qc) + rinds = np.arange(rc) + + square_rot_pos = square.dot(permute.T) + square_rot_neg = square_rot_pos[::-1] + + # iterate within the current plane + for p in pinds: + for q in qinds: + # iterate perpendicularly to the current plane, handling + # boundaries. We only draw faces between a voxel and an + # empty space, to avoid drawing internal faces. + + # draw lower faces + p0 = permute.dot([p, q, 0]) + i0 = tuple(p0) + if filled[i0]: + voxel_faces[i0].append(p0 + square_rot_neg) + + # draw middle faces + for r1, r2 in itertools.pairwise(rinds): + p1 = permute.dot([p, q, r1]) + p2 = permute.dot([p, q, r2]) + + i1 = tuple(p1) + i2 = tuple(p2) + + if filled[i1] and not filled[i2]: + voxel_faces[i1].append(p2 + square_rot_pos) + elif not filled[i1] and filled[i2]: + voxel_faces[i2].append(p2 + square_rot_neg) + + # draw upper faces + pk = permute.dot([p, q, rc-1]) + pk2 = permute.dot([p, q, rc]) + ik = tuple(pk) + if filled[ik]: + voxel_faces[ik].append(pk2 + square_rot_pos) + + # iterate over the faces, and generate a Poly3DCollection for each + # voxel + polygons = {} + for coord, faces_inds in voxel_faces.items(): + # convert indices into 3D positions + if xyz is None: + faces = faces_inds + else: + faces = [] + for face_inds in faces_inds: + ind = face_inds[:, 0], face_inds[:, 1], face_inds[:, 2] + face = np.empty(face_inds.shape) + face[:, 0] = x[ind] + face[:, 1] = y[ind] + face[:, 2] = z[ind] + faces.append(face) + + # shade the faces + facecolor = facecolors[coord] + edgecolor = edgecolors[coord] + + poly = art3d.Poly3DCollection( + faces, facecolors=facecolor, edgecolors=edgecolor, + shade=shade, lightsource=lightsource, axlim_clip=axlim_clip, + **kwargs) + self.add_collection3d(poly) + polygons[coord] = poly + + return polygons + + @_preprocess_data(replace_names=["x", "y", "z", "xerr", "yerr", "zerr"]) + def errorbar(self, x, y, z, zerr=None, yerr=None, xerr=None, fmt='', + barsabove=False, errorevery=1, ecolor=None, elinewidth=None, + capsize=None, capthick=None, xlolims=False, xuplims=False, + ylolims=False, yuplims=False, zlolims=False, zuplims=False, + axlim_clip=False, + **kwargs): + """ + Plot lines and/or markers with errorbars around them. + + *x*/*y*/*z* define the data locations, and *xerr*/*yerr*/*zerr* define + the errorbar sizes. By default, this draws the data markers/lines as + well the errorbars. Use fmt='none' to draw errorbars only. + + Parameters + ---------- + x, y, z : float or array-like + The data positions. + + xerr, yerr, zerr : float or array-like, shape (N,) or (2, N), optional + The errorbar sizes: + + - scalar: Symmetric +/- values for all data points. + - shape(N,): Symmetric +/-values for each data point. + - shape(2, N): Separate - and + values for each bar. First row + contains the lower errors, the second row contains the upper + errors. + - *None*: No errorbar. + + Note that all error arrays should have *positive* values. + + fmt : str, default: '' + The format for the data points / data lines. See `.plot` for + details. + + Use 'none' (case-insensitive) to plot errorbars without any data + markers. + + ecolor : :mpltype:`color`, default: None + The color of the errorbar lines. If None, use the color of the + line connecting the markers. + + elinewidth : float, default: None + The linewidth of the errorbar lines. If None, the linewidth of + the current style is used. + + capsize : float, default: :rc:`errorbar.capsize` + The length of the error bar caps in points. + + capthick : float, default: None + An alias to the keyword argument *markeredgewidth* (a.k.a. *mew*). + This setting is a more sensible name for the property that + controls the thickness of the error bar cap in points. For + backwards compatibility, if *mew* or *markeredgewidth* are given, + then they will over-ride *capthick*. This may change in future + releases. + + barsabove : bool, default: False + If True, will plot the errorbars above the plot + symbols. Default is below. + + xlolims, ylolims, zlolims : bool, default: False + These arguments can be used to indicate that a value gives only + lower limits. In that case a caret symbol is used to indicate + this. *lims*-arguments may be scalars, or array-likes of the same + length as the errors. To use limits with inverted axes, + `~.set_xlim`, `~.set_ylim`, or `~.set_zlim` must be + called before `errorbar`. Note the tricky parameter names: setting + e.g. *ylolims* to True means that the y-value is a *lower* limit of + the True value, so, only an *upward*-pointing arrow will be drawn! + + xuplims, yuplims, zuplims : bool, default: False + Same as above, but for controlling the upper limits. + + errorevery : int or (int, int), default: 1 + draws error bars on a subset of the data. *errorevery* =N draws + error bars on the points (x[::N], y[::N], z[::N]). + *errorevery* =(start, N) draws error bars on the points + (x[start::N], y[start::N], z[start::N]). e.g. *errorevery* =(6, 3) + adds error bars to the data at (x[6], x[9], x[12], x[15], ...). + Used to avoid overlapping error bars when two series share x-axis + values. + + axlim_clip : bool, default: False + Whether to hide error bars that are outside the axes limits. + + .. versionadded:: 3.10 + + Returns + ------- + errlines : list + List of `~mpl_toolkits.mplot3d.art3d.Line3DCollection` instances + each containing an errorbar line. + caplines : list + List of `~mpl_toolkits.mplot3d.art3d.Line3D` instances each + containing a capline object. + limmarks : list + List of `~mpl_toolkits.mplot3d.art3d.Line3D` instances each + containing a marker with an upper or lower limit. + + Other Parameters + ---------------- + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + All other keyword arguments for styling errorbar lines are passed + `~mpl_toolkits.mplot3d.art3d.Line3DCollection`. + + Examples + -------- + .. plot:: gallery/mplot3d/errorbar3d.py + """ + had_data = self.has_data() + + kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) + # Drop anything that comes in as None to use the default instead. + kwargs = {k: v for k, v in kwargs.items() if v is not None} + kwargs.setdefault('zorder', 2) + + self._process_unit_info([("x", x), ("y", y), ("z", z)], kwargs, + convert=False) + + # make sure all the args are iterable; use lists not arrays to + # preserve units + x = x if np.iterable(x) else [x] + y = y if np.iterable(y) else [y] + z = z if np.iterable(z) else [z] + + if not len(x) == len(y) == len(z): + raise ValueError("'x', 'y', and 'z' must have the same size") + + everymask = self._errorevery_to_mask(x, errorevery) + + label = kwargs.pop("label", None) + kwargs['label'] = '_nolegend_' + + # Create the main line and determine overall kwargs for child artists. + # We avoid calling self.plot() directly, or self._get_lines(), because + # that would call self._process_unit_info again, and do other indirect + # data processing. + (data_line, base_style), = self._get_lines._plot_args( + self, (x, y) if fmt == '' else (x, y, fmt), kwargs, return_kwargs=True) + art3d.line_2d_to_3d(data_line, zs=z, axlim_clip=axlim_clip) + + # Do this after creating `data_line` to avoid modifying `base_style`. + if barsabove: + data_line.set_zorder(kwargs['zorder'] - .1) + else: + data_line.set_zorder(kwargs['zorder'] + .1) + + # Add line to plot, or throw it away and use it to determine kwargs. + if fmt.lower() != 'none': + self.add_line(data_line) + else: + data_line = None + # Remove alpha=0 color that _process_plot_format returns. + base_style.pop('color') + + if 'color' not in base_style: + base_style['color'] = 'C0' + if ecolor is None: + ecolor = base_style['color'] + + # Eject any line-specific information from format string, as it's not + # needed for bars or caps. + for key in ['marker', 'markersize', 'markerfacecolor', + 'markeredgewidth', 'markeredgecolor', 'markevery', + 'linestyle', 'fillstyle', 'drawstyle', 'dash_capstyle', + 'dash_joinstyle', 'solid_capstyle', 'solid_joinstyle']: + base_style.pop(key, None) + + # Make the style dict for the line collections (the bars). + eb_lines_style = {**base_style, 'color': ecolor} + + if elinewidth: + eb_lines_style['linewidth'] = elinewidth + elif 'linewidth' in kwargs: + eb_lines_style['linewidth'] = kwargs['linewidth'] + + for key in ('transform', 'alpha', 'zorder', 'rasterized'): + if key in kwargs: + eb_lines_style[key] = kwargs[key] + + # Make the style dict for caps (the "hats"). + eb_cap_style = {**base_style, 'linestyle': 'None'} + if capsize is None: + capsize = mpl.rcParams["errorbar.capsize"] + if capsize > 0: + eb_cap_style['markersize'] = 2. * capsize + if capthick is not None: + eb_cap_style['markeredgewidth'] = capthick + eb_cap_style['color'] = ecolor + + def _apply_mask(arrays, mask): + # Return, for each array in *arrays*, the elements for which *mask* + # is True, without using fancy indexing. + return [[*itertools.compress(array, mask)] for array in arrays] + + def _extract_errs(err, data, lomask, himask): + # For separate +/- error values we need to unpack err + if len(err.shape) == 2: + low_err, high_err = err + else: + low_err, high_err = err, err + + lows = np.where(lomask | ~everymask, data, data - low_err) + highs = np.where(himask | ~everymask, data, data + high_err) + + return lows, highs + + # collect drawn items while looping over the three coordinates + errlines, caplines, limmarks = [], [], [] + + # list of endpoint coordinates, used for auto-scaling + coorderrs = [] + + # define the markers used for errorbar caps and limits below + # the dictionary key is mapped by the `i_xyz` helper dictionary + capmarker = {0: '|', 1: '|', 2: '_'} + i_xyz = {'x': 0, 'y': 1, 'z': 2} + + # Calculate marker size from points to quiver length. Because these are + # not markers, and 3D Axes do not use the normal transform stack, this + # is a bit involved. Since the quiver arrows will change size as the + # scene is rotated, they are given a standard size based on viewing + # them directly in planar form. + quiversize = eb_cap_style.get('markersize', + mpl.rcParams['lines.markersize']) ** 2 + quiversize *= self.get_figure(root=True).dpi / 72 + quiversize = self.transAxes.inverted().transform([ + (0, 0), (quiversize, quiversize)]) + quiversize = np.mean(np.diff(quiversize, axis=0)) + # quiversize is now in Axes coordinates, and to convert back to data + # coordinates, we need to run it through the inverse 3D transform. For + # consistency, this uses a fixed elevation, azimuth, and roll. + with cbook._setattr_cm(self, elev=0, azim=0, roll=0): + invM = np.linalg.inv(self.get_proj()) + # elev=azim=roll=0 produces the Y-Z plane, so quiversize in 2D 'x' is + # 'y' in 3D, hence the 1 index. + quiversize = np.dot(invM, [quiversize, 0, 0, 0])[1] + # Quivers use a fixed 15-degree arrow head, so scale up the length so + # that the size corresponds to the base. In other words, this constant + # corresponds to the equation tan(15) = (base / 2) / (arrow length). + quiversize *= 1.8660254037844388 + eb_quiver_style = {**eb_cap_style, + 'length': quiversize, 'arrow_length_ratio': 1} + eb_quiver_style.pop('markersize', None) + + # loop over x-, y-, and z-direction and draw relevant elements + for zdir, data, err, lolims, uplims in zip( + ['x', 'y', 'z'], [x, y, z], [xerr, yerr, zerr], + [xlolims, ylolims, zlolims], [xuplims, yuplims, zuplims]): + + dir_vector = art3d.get_dir_vector(zdir) + i_zdir = i_xyz[zdir] + + if err is None: + continue + + if not np.iterable(err): + err = [err] * len(data) + + err = np.atleast_1d(err) + + # arrays fine here, they are booleans and hence not units + lolims = np.broadcast_to(lolims, len(data)).astype(bool) + uplims = np.broadcast_to(uplims, len(data)).astype(bool) + + # a nested list structure that expands to (xl,xh),(yl,yh),(zl,zh), + # where x/y/z and l/h correspond to dimensions and low/high + # positions of errorbars in a dimension we're looping over + coorderr = [ + _extract_errs(err * dir_vector[i], coord, lolims, uplims) + for i, coord in enumerate([x, y, z])] + (xl, xh), (yl, yh), (zl, zh) = coorderr + + # draws capmarkers - flat caps orthogonal to the error bars + nolims = ~(lolims | uplims) + if nolims.any() and capsize > 0: + lo_caps_xyz = _apply_mask([xl, yl, zl], nolims & everymask) + hi_caps_xyz = _apply_mask([xh, yh, zh], nolims & everymask) + + # setting '_' for z-caps and '|' for x- and y-caps; + # these markers will rotate as the viewing angle changes + cap_lo = art3d.Line3D(*lo_caps_xyz, ls='', + marker=capmarker[i_zdir], + axlim_clip=axlim_clip, + **eb_cap_style) + cap_hi = art3d.Line3D(*hi_caps_xyz, ls='', + marker=capmarker[i_zdir], + axlim_clip=axlim_clip, + **eb_cap_style) + self.add_line(cap_lo) + self.add_line(cap_hi) + caplines.append(cap_lo) + caplines.append(cap_hi) + + if lolims.any(): + xh0, yh0, zh0 = _apply_mask([xh, yh, zh], lolims & everymask) + self.quiver(xh0, yh0, zh0, *dir_vector, **eb_quiver_style) + if uplims.any(): + xl0, yl0, zl0 = _apply_mask([xl, yl, zl], uplims & everymask) + self.quiver(xl0, yl0, zl0, *-dir_vector, **eb_quiver_style) + + errline = art3d.Line3DCollection(np.array(coorderr).T, + axlim_clip=axlim_clip, + **eb_lines_style) + self.add_collection(errline) + errlines.append(errline) + coorderrs.append(coorderr) + + coorderrs = np.array(coorderrs) + + def _digout_minmax(err_arr, coord_label): + return (np.nanmin(err_arr[:, i_xyz[coord_label], :, :]), + np.nanmax(err_arr[:, i_xyz[coord_label], :, :])) + + minx, maxx = _digout_minmax(coorderrs, 'x') + miny, maxy = _digout_minmax(coorderrs, 'y') + minz, maxz = _digout_minmax(coorderrs, 'z') + self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data) + + # Adapting errorbar containers for 3d case, assuming z-axis points "up" + errorbar_container = mcontainer.ErrorbarContainer( + (data_line, tuple(caplines), tuple(errlines)), + has_xerr=(xerr is not None or yerr is not None), + has_yerr=(zerr is not None), + label=label) + self.containers.append(errorbar_container) + + return errlines, caplines, limmarks + + def get_tightbbox(self, renderer=None, *, call_axes_locator=True, + bbox_extra_artists=None, for_layout_only=False): + ret = super().get_tightbbox(renderer, + call_axes_locator=call_axes_locator, + bbox_extra_artists=bbox_extra_artists, + for_layout_only=for_layout_only) + batch = [ret] + if self._axis3don: + for axis in self._axis_map.values(): + if axis.get_visible(): + axis_bb = martist._get_tightbbox_for_layout_only( + axis, renderer) + if axis_bb: + batch.append(axis_bb) + return mtransforms.Bbox.union(batch) + + @_preprocess_data() + def stem(self, x, y, z, *, linefmt='C0-', markerfmt='C0o', basefmt='C3-', + bottom=0, label=None, orientation='z', axlim_clip=False): + """ + Create a 3D stem plot. + + A stem plot draws lines perpendicular to a baseline, and places markers + at the heads. By default, the baseline is defined by *x* and *y*, and + stems are drawn vertically from *bottom* to *z*. + + Parameters + ---------- + x, y, z : array-like + The positions of the heads of the stems. The stems are drawn along + the *orientation*-direction from the baseline at *bottom* (in the + *orientation*-coordinate) to the heads. By default, the *x* and *y* + positions are used for the baseline and *z* for the head position, + but this can be changed by *orientation*. + + linefmt : str, default: 'C0-' + A string defining the properties of the vertical lines. Usually, + this will be a color or a color and a linestyle: + + ========= ============= + Character Line Style + ========= ============= + ``'-'`` solid line + ``'--'`` dashed line + ``'-.'`` dash-dot line + ``':'`` dotted line + ========= ============= + + Note: While it is technically possible to specify valid formats + other than color or color and linestyle (e.g. 'rx' or '-.'), this + is beyond the intention of the method and will most likely not + result in a reasonable plot. + + markerfmt : str, default: 'C0o' + A string defining the properties of the markers at the stem heads. + + basefmt : str, default: 'C3-' + A format string defining the properties of the baseline. + + bottom : float, default: 0 + The position of the baseline, in *orientation*-coordinates. + + label : str, optional + The label to use for the stems in legends. + + orientation : {'x', 'y', 'z'}, default: 'z' + The direction along which stems are drawn. + + axlim_clip : bool, default: False + Whether to hide stems that are outside the axes limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + Returns + ------- + `.StemContainer` + The container may be treated like a tuple + (*markerline*, *stemlines*, *baseline*) + + Examples + -------- + .. plot:: gallery/mplot3d/stem3d_demo.py + """ + + from matplotlib.container import StemContainer + + had_data = self.has_data() + + _api.check_in_list(['x', 'y', 'z'], orientation=orientation) + + xlim = (np.min(x), np.max(x)) + ylim = (np.min(y), np.max(y)) + zlim = (np.min(z), np.max(z)) + + # Determine the appropriate plane for the baseline and the direction of + # stemlines based on the value of orientation. + if orientation == 'x': + basex, basexlim = y, ylim + basey, baseylim = z, zlim + lines = [[(bottom, thisy, thisz), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + elif orientation == 'y': + basex, basexlim = x, xlim + basey, baseylim = z, zlim + lines = [[(thisx, bottom, thisz), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + else: + basex, basexlim = x, xlim + basey, baseylim = y, ylim + lines = [[(thisx, thisy, bottom), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + + # Determine style for stem lines. + linestyle, linemarker, linecolor = _process_plot_format(linefmt) + if linestyle is None: + linestyle = mpl.rcParams['lines.linestyle'] + + # Plot everything in required order. + baseline, = self.plot(basex, basey, basefmt, zs=bottom, + zdir=orientation, label='_nolegend_') + stemlines = art3d.Line3DCollection( + lines, linestyles=linestyle, colors=linecolor, label='_nolegend_', + axlim_clip=axlim_clip) + self.add_collection(stemlines) + markerline, = self.plot(x, y, z, markerfmt, label='_nolegend_') + + stem_container = StemContainer((markerline, stemlines, baseline), + label=label) + self.add_container(stem_container) + + jx, jy, jz = art3d.juggle_axes(basexlim, baseylim, [bottom, bottom], + orientation) + self.auto_scale_xyz([*jx, *xlim], [*jy, *ylim], [*jz, *zlim], had_data) + + return stem_container + + stem3D = stem + + +def get_test_data(delta=0.05): + """Return a tuple X, Y, Z with a test data set.""" + x = y = np.arange(-3.0, 3.0, delta) + X, Y = np.meshgrid(x, y) + + Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi) + Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) / + (2 * np.pi * 0.5 * 1.5)) + Z = Z2 - Z1 + + X = X * 10 + Y = Y * 10 + Z = Z * 500 + return X, Y, Z + + +class _Quaternion: + """ + Quaternions + consisting of scalar, along 1, and vector, with components along i, j, k + """ + + def __init__(self, scalar, vector): + self.scalar = scalar + self.vector = np.array(vector) + + def __neg__(self): + return self.__class__(-self.scalar, -self.vector) + + def __mul__(self, other): + """ + Product of two quaternions + i*i = j*j = k*k = i*j*k = -1 + Quaternion multiplication can be expressed concisely + using scalar and vector parts, + see + """ + return self.__class__( + self.scalar*other.scalar - np.dot(self.vector, other.vector), + self.scalar*other.vector + self.vector*other.scalar + + np.cross(self.vector, other.vector)) + + def conjugate(self): + """The conjugate quaternion -(1/2)*(q+i*q*i+j*q*j+k*q*k)""" + return self.__class__(self.scalar, -self.vector) + + @property + def norm(self): + """The 2-norm, q*q', a scalar""" + return self.scalar*self.scalar + np.dot(self.vector, self.vector) + + def normalize(self): + """Scaling such that norm equals 1""" + n = np.sqrt(self.norm) + return self.__class__(self.scalar/n, self.vector/n) + + def reciprocal(self): + """The reciprocal, 1/q = q'/(q*q') = q' / norm(q)""" + n = self.norm + return self.__class__(self.scalar/n, -self.vector/n) + + def __div__(self, other): + return self*other.reciprocal() + + __truediv__ = __div__ + + def rotate(self, v): + # Rotate the vector v by the quaternion q, i.e., + # calculate (the vector part of) q*v/q + v = self.__class__(0, v) + v = self*v/self + return v.vector + + def __eq__(self, other): + return (self.scalar == other.scalar) and (self.vector == other.vector).all + + def __repr__(self): + return "_Quaternion({}, {})".format(repr(self.scalar), repr(self.vector)) + + @classmethod + def rotate_from_to(cls, r1, r2): + """ + The quaternion for the shortest rotation from vector r1 to vector r2 + i.e., q = sqrt(r2*r1'), normalized. + If r1 and r2 are antiparallel, then the result is ambiguous; + a normal vector will be returned, and a warning will be issued. + """ + k = np.cross(r1, r2) + nk = np.linalg.norm(k) + th = np.arctan2(nk, np.dot(r1, r2)) + th /= 2 + if nk == 0: # r1 and r2 are parallel or anti-parallel + if np.dot(r1, r2) < 0: + warnings.warn("Rotation defined by anti-parallel vectors is ambiguous") + k = np.zeros(3) + k[np.argmin(r1*r1)] = 1 # basis vector most perpendicular to r1-r2 + k = np.cross(r1, k) + k = k / np.linalg.norm(k) # unit vector normal to r1-r2 + q = cls(0, k) + else: + q = cls(1, [0, 0, 0]) # = 1, no rotation + else: + q = cls(np.cos(th), k*np.sin(th)/nk) + return q + + @classmethod + def from_cardan_angles(cls, elev, azim, roll): + """ + Converts the angles to a quaternion + q = exp((roll/2)*e_x)*exp((elev/2)*e_y)*exp((-azim/2)*e_z) + i.e., the angles are a kind of Tait-Bryan angles, -z,y',x". + The angles should be given in radians, not degrees. + """ + ca, sa = np.cos(azim/2), np.sin(azim/2) + ce, se = np.cos(elev/2), np.sin(elev/2) + cr, sr = np.cos(roll/2), np.sin(roll/2) + + qw = ca*ce*cr + sa*se*sr + qx = ca*ce*sr - sa*se*cr + qy = ca*se*cr + sa*ce*sr + qz = ca*se*sr - sa*ce*cr + return cls(qw, [qx, qy, qz]) + + def as_cardan_angles(self): + """ + The inverse of `from_cardan_angles()`. + Note that the angles returned are in radians, not degrees. + The angles are not sensitive to the quaternion's norm(). + """ + qw = self.scalar + qx, qy, qz = self.vector[..., :] + azim = np.arctan2(2*(-qw*qz+qx*qy), qw*qw+qx*qx-qy*qy-qz*qz) + elev = np.arcsin(np.clip(2*(qw*qy+qz*qx)/(qw*qw+qx*qx+qy*qy+qz*qz), -1, 1)) + roll = np.arctan2(2*(qw*qx-qy*qz), qw*qw-qx*qx-qy*qy+qz*qz) + return elev, azim, roll diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py new file mode 100644 index 0000000000000000000000000000000000000000..4da5031b990ca742eb0d303003af5c6a821c4db9 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py @@ -0,0 +1,750 @@ +# axis3d.py, original mplot3d version by John Porter +# Created: 23 Sep 2005 +# Parts rewritten by Reinier Heeres + +import inspect + +import numpy as np + +import matplotlib as mpl +from matplotlib import ( + _api, artist, lines as mlines, axis as maxis, patches as mpatches, + transforms as mtransforms, colors as mcolors) +from . import art3d, proj3d + + +def _move_from_center(coord, centers, deltas, axmask=(True, True, True)): + """ + For each coordinate where *axmask* is True, move *coord* away from + *centers* by *deltas*. + """ + coord = np.asarray(coord) + return coord + axmask * np.copysign(1, coord - centers) * deltas + + +def _tick_update_position(tick, tickxs, tickys, labelpos): + """Update tick line and label position and style.""" + + tick.label1.set_position(labelpos) + tick.label2.set_position(labelpos) + tick.tick1line.set_visible(True) + tick.tick2line.set_visible(False) + tick.tick1line.set_linestyle('-') + tick.tick1line.set_marker('') + tick.tick1line.set_data(tickxs, tickys) + tick.gridline.set_data([0], [0]) + + +class Axis(maxis.XAxis): + """An Axis class for the 3D plots.""" + # These points from the unit cube make up the x, y and z-planes + _PLANES = ( + (0, 3, 7, 4), (1, 2, 6, 5), # yz planes + (0, 1, 5, 4), (3, 2, 6, 7), # xz planes + (0, 1, 2, 3), (4, 5, 6, 7), # xy planes + ) + + # Some properties for the axes + _AXINFO = { + 'x': {'i': 0, 'tickdir': 1, 'juggled': (1, 0, 2)}, + 'y': {'i': 1, 'tickdir': 0, 'juggled': (0, 1, 2)}, + 'z': {'i': 2, 'tickdir': 0, 'juggled': (0, 2, 1)}, + } + + def _old_init(self, adir, v_intervalx, d_intervalx, axes, *args, + rotate_label=None, **kwargs): + return locals() + + def _new_init(self, axes, *, rotate_label=None, **kwargs): + return locals() + + def __init__(self, *args, **kwargs): + params = _api.select_matching_signature( + [self._old_init, self._new_init], *args, **kwargs) + if "adir" in params: + _api.warn_deprecated( + "3.6", message=f"The signature of 3D Axis constructors has " + f"changed in %(since)s; the new signature is " + f"{inspect.signature(type(self).__init__)}", pending=True) + if params["adir"] != self.axis_name: + raise ValueError(f"Cannot instantiate {type(self).__name__} " + f"with adir={params['adir']!r}") + axes = params["axes"] + rotate_label = params["rotate_label"] + args = params.get("args", ()) + kwargs = params["kwargs"] + + name = self.axis_name + + self._label_position = 'default' + self._tick_position = 'default' + + # This is a temporary member variable. + # Do not depend on this existing in future releases! + self._axinfo = self._AXINFO[name].copy() + # Common parts + self._axinfo.update({ + 'label': {'va': 'center', 'ha': 'center', + 'rotation_mode': 'anchor'}, + 'color': mpl.rcParams[f'axes3d.{name}axis.panecolor'], + 'tick': { + 'inward_factor': 0.2, + 'outward_factor': 0.1, + }, + }) + + if mpl.rcParams['_internal.classic_mode']: + self._axinfo.update({ + 'axisline': {'linewidth': 0.75, 'color': (0, 0, 0, 1)}, + 'grid': { + 'color': (0.9, 0.9, 0.9, 1), + 'linewidth': 1.0, + 'linestyle': '-', + }, + }) + self._axinfo['tick'].update({ + 'linewidth': { + True: mpl.rcParams['lines.linewidth'], # major + False: mpl.rcParams['lines.linewidth'], # minor + } + }) + else: + self._axinfo.update({ + 'axisline': { + 'linewidth': mpl.rcParams['axes.linewidth'], + 'color': mpl.rcParams['axes.edgecolor'], + }, + 'grid': { + 'color': mpl.rcParams['grid.color'], + 'linewidth': mpl.rcParams['grid.linewidth'], + 'linestyle': mpl.rcParams['grid.linestyle'], + }, + }) + self._axinfo['tick'].update({ + 'linewidth': { + True: ( # major + mpl.rcParams['xtick.major.width'] if name in 'xz' + else mpl.rcParams['ytick.major.width']), + False: ( # minor + mpl.rcParams['xtick.minor.width'] if name in 'xz' + else mpl.rcParams['ytick.minor.width']), + } + }) + + super().__init__(axes, *args, **kwargs) + + # data and viewing intervals for this direction + if "d_intervalx" in params: + self.set_data_interval(*params["d_intervalx"]) + if "v_intervalx" in params: + self.set_view_interval(*params["v_intervalx"]) + self.set_rotate_label(rotate_label) + self._init3d() # Inline after init3d deprecation elapses. + + __init__.__signature__ = inspect.signature(_new_init) + adir = _api.deprecated("3.6", pending=True)( + property(lambda self: self.axis_name)) + + def _init3d(self): + self.line = mlines.Line2D( + xdata=(0, 0), ydata=(0, 0), + linewidth=self._axinfo['axisline']['linewidth'], + color=self._axinfo['axisline']['color'], + antialiased=True) + + # Store dummy data in Polygon object + self.pane = mpatches.Polygon([[0, 0], [0, 1]], closed=False) + self.set_pane_color(self._axinfo['color']) + + self.axes._set_artist_props(self.line) + self.axes._set_artist_props(self.pane) + self.gridlines = art3d.Line3DCollection([]) + self.axes._set_artist_props(self.gridlines) + self.axes._set_artist_props(self.label) + self.axes._set_artist_props(self.offsetText) + # Need to be able to place the label at the correct location + self.label._transform = self.axes.transData + self.offsetText._transform = self.axes.transData + + @_api.deprecated("3.6", pending=True) + def init3d(self): # After deprecation elapses, inline _init3d to __init__. + self._init3d() + + def get_major_ticks(self, numticks=None): + ticks = super().get_major_ticks(numticks) + for t in ticks: + for obj in [ + t.tick1line, t.tick2line, t.gridline, t.label1, t.label2]: + obj.set_transform(self.axes.transData) + return ticks + + def get_minor_ticks(self, numticks=None): + ticks = super().get_minor_ticks(numticks) + for t in ticks: + for obj in [ + t.tick1line, t.tick2line, t.gridline, t.label1, t.label2]: + obj.set_transform(self.axes.transData) + return ticks + + def set_ticks_position(self, position): + """ + Set the ticks position. + + Parameters + ---------- + position : {'lower', 'upper', 'both', 'default', 'none'} + The position of the bolded axis lines, ticks, and tick labels. + """ + _api.check_in_list(['lower', 'upper', 'both', 'default', 'none'], + position=position) + self._tick_position = position + + def get_ticks_position(self): + """ + Get the ticks position. + + Returns + ------- + str : {'lower', 'upper', 'both', 'default', 'none'} + The position of the bolded axis lines, ticks, and tick labels. + """ + return self._tick_position + + def set_label_position(self, position): + """ + Set the label position. + + Parameters + ---------- + position : {'lower', 'upper', 'both', 'default', 'none'} + The position of the axis label. + """ + _api.check_in_list(['lower', 'upper', 'both', 'default', 'none'], + position=position) + self._label_position = position + + def get_label_position(self): + """ + Get the label position. + + Returns + ------- + str : {'lower', 'upper', 'both', 'default', 'none'} + The position of the axis label. + """ + return self._label_position + + def set_pane_color(self, color, alpha=None): + """ + Set pane color. + + Parameters + ---------- + color : :mpltype:`color` + Color for axis pane. + alpha : float, optional + Alpha value for axis pane. If None, base it on *color*. + """ + color = mcolors.to_rgba(color, alpha) + self._axinfo['color'] = color + self.pane.set_edgecolor(color) + self.pane.set_facecolor(color) + self.pane.set_alpha(color[-1]) + self.stale = True + + def set_rotate_label(self, val): + """ + Whether to rotate the axis label: True, False or None. + If set to None the label will be rotated if longer than 4 chars. + """ + self._rotate_label = val + self.stale = True + + def get_rotate_label(self, text): + if self._rotate_label is not None: + return self._rotate_label + else: + return len(text) > 4 + + def _get_coord_info(self): + mins, maxs = np.array([ + self.axes.get_xbound(), + self.axes.get_ybound(), + self.axes.get_zbound(), + ]).T + + # Project the bounds along the current position of the cube: + bounds = mins[0], maxs[0], mins[1], maxs[1], mins[2], maxs[2] + bounds_proj = self.axes._transformed_cube(bounds) + + # Determine which one of the parallel planes are higher up: + means_z0 = np.zeros(3) + means_z1 = np.zeros(3) + for i in range(3): + means_z0[i] = np.mean(bounds_proj[self._PLANES[2 * i], 2]) + means_z1[i] = np.mean(bounds_proj[self._PLANES[2 * i + 1], 2]) + highs = means_z0 < means_z1 + + # Special handling for edge-on views + equals = np.abs(means_z0 - means_z1) <= np.finfo(float).eps + if np.sum(equals) == 2: + vertical = np.where(~equals)[0][0] + if vertical == 2: # looking at XY plane + highs = np.array([True, True, highs[2]]) + elif vertical == 1: # looking at XZ plane + highs = np.array([True, highs[1], False]) + elif vertical == 0: # looking at YZ plane + highs = np.array([highs[0], False, False]) + + return mins, maxs, bounds_proj, highs + + def _calc_centers_deltas(self, maxs, mins): + centers = 0.5 * (maxs + mins) + # In mpl3.8, the scale factor was 1/12. mpl3.9 changes this to + # 1/12 * 24/25 = 0.08 to compensate for the change in automargin + # behavior and keep appearance the same. The 24/25 factor is from the + # 1/48 padding added to each side of the axis in mpl3.8. + scale = 0.08 + deltas = (maxs - mins) * scale + return centers, deltas + + def _get_axis_line_edge_points(self, minmax, maxmin, position=None): + """Get the edge points for the black bolded axis line.""" + # When changing vertical axis some of the axes has to be + # moved to the other plane so it looks the same as if the z-axis + # was the vertical axis. + mb = [minmax, maxmin] # line from origin to nearest corner to camera + mb_rev = mb[::-1] + mm = [[mb, mb_rev, mb_rev], [mb_rev, mb_rev, mb], [mb, mb, mb]] + mm = mm[self.axes._vertical_axis][self._axinfo["i"]] + + juggled = self._axinfo["juggled"] + edge_point_0 = mm[0].copy() # origin point + + if ((position == 'lower' and mm[1][juggled[-1]] < mm[0][juggled[-1]]) or + (position == 'upper' and mm[1][juggled[-1]] > mm[0][juggled[-1]])): + edge_point_0[juggled[-1]] = mm[1][juggled[-1]] + else: + edge_point_0[juggled[0]] = mm[1][juggled[0]] + + edge_point_1 = edge_point_0.copy() + edge_point_1[juggled[1]] = mm[1][juggled[1]] + + return edge_point_0, edge_point_1 + + def _get_all_axis_line_edge_points(self, minmax, maxmin, axis_position=None): + # Determine edge points for the axis lines + edgep1s = [] + edgep2s = [] + position = [] + if axis_position in (None, 'default'): + edgep1, edgep2 = self._get_axis_line_edge_points(minmax, maxmin) + edgep1s = [edgep1] + edgep2s = [edgep2] + position = ['default'] + else: + edgep1_l, edgep2_l = self._get_axis_line_edge_points(minmax, maxmin, + position='lower') + edgep1_u, edgep2_u = self._get_axis_line_edge_points(minmax, maxmin, + position='upper') + if axis_position in ('lower', 'both'): + edgep1s.append(edgep1_l) + edgep2s.append(edgep2_l) + position.append('lower') + if axis_position in ('upper', 'both'): + edgep1s.append(edgep1_u) + edgep2s.append(edgep2_u) + position.append('upper') + return edgep1s, edgep2s, position + + def _get_tickdir(self, position): + """ + Get the direction of the tick. + + Parameters + ---------- + position : str, optional : {'upper', 'lower', 'default'} + The position of the axis. + + Returns + ------- + tickdir : int + Index which indicates which coordinate the tick line will + align with. + """ + _api.check_in_list(('upper', 'lower', 'default'), position=position) + + # TODO: Move somewhere else where it's triggered less: + tickdirs_base = [v["tickdir"] for v in self._AXINFO.values()] # default + elev_mod = np.mod(self.axes.elev + 180, 360) - 180 + azim_mod = np.mod(self.axes.azim, 360) + if position == 'upper': + if elev_mod >= 0: + tickdirs_base = [2, 2, 0] + else: + tickdirs_base = [1, 0, 0] + if 0 <= azim_mod < 180: + tickdirs_base[2] = 1 + elif position == 'lower': + if elev_mod >= 0: + tickdirs_base = [1, 0, 1] + else: + tickdirs_base = [2, 2, 1] + if 0 <= azim_mod < 180: + tickdirs_base[2] = 0 + info_i = [v["i"] for v in self._AXINFO.values()] + + i = self._axinfo["i"] + vert_ax = self.axes._vertical_axis + j = vert_ax - 2 + # default: tickdir = [[1, 2, 1], [2, 2, 0], [1, 0, 0]][vert_ax][i] + tickdir = np.roll(info_i, -j)[np.roll(tickdirs_base, j)][i] + return tickdir + + def active_pane(self): + mins, maxs, tc, highs = self._get_coord_info() + info = self._axinfo + index = info['i'] + if not highs[index]: + loc = mins[index] + plane = self._PLANES[2 * index] + else: + loc = maxs[index] + plane = self._PLANES[2 * index + 1] + xys = np.array([tc[p] for p in plane]) + return xys, loc + + def draw_pane(self, renderer): + """ + Draw pane. + + Parameters + ---------- + renderer : `~matplotlib.backend_bases.RendererBase` subclass + """ + renderer.open_group('pane3d', gid=self.get_gid()) + xys, loc = self.active_pane() + self.pane.xy = xys[:, :2] + self.pane.draw(renderer) + renderer.close_group('pane3d') + + def _axmask(self): + axmask = [True, True, True] + axmask[self._axinfo["i"]] = False + return axmask + + def _draw_ticks(self, renderer, edgep1, centers, deltas, highs, + deltas_per_point, pos): + ticks = self._update_ticks() + info = self._axinfo + index = info["i"] + juggled = info["juggled"] + + mins, maxs, tc, highs = self._get_coord_info() + centers, deltas = self._calc_centers_deltas(maxs, mins) + + # Draw ticks: + tickdir = self._get_tickdir(pos) + tickdelta = deltas[tickdir] if highs[tickdir] else -deltas[tickdir] + + tick_info = info['tick'] + tick_out = tick_info['outward_factor'] * tickdelta + tick_in = tick_info['inward_factor'] * tickdelta + tick_lw = tick_info['linewidth'] + edgep1_tickdir = edgep1[tickdir] + out_tickdir = edgep1_tickdir + tick_out + in_tickdir = edgep1_tickdir - tick_in + + default_label_offset = 8. # A rough estimate + points = deltas_per_point * deltas + for tick in ticks: + # Get tick line positions + pos = edgep1.copy() + pos[index] = tick.get_loc() + pos[tickdir] = out_tickdir + x1, y1, z1 = proj3d.proj_transform(*pos, self.axes.M) + pos[tickdir] = in_tickdir + x2, y2, z2 = proj3d.proj_transform(*pos, self.axes.M) + + # Get position of label + labeldeltas = (tick.get_pad() + default_label_offset) * points + + pos[tickdir] = edgep1_tickdir + pos = _move_from_center(pos, centers, labeldeltas, self._axmask()) + lx, ly, lz = proj3d.proj_transform(*pos, self.axes.M) + + _tick_update_position(tick, (x1, x2), (y1, y2), (lx, ly)) + tick.tick1line.set_linewidth(tick_lw[tick._major]) + tick.draw(renderer) + + def _draw_offset_text(self, renderer, edgep1, edgep2, labeldeltas, centers, + highs, pep, dx, dy): + # Get general axis information: + info = self._axinfo + index = info["i"] + juggled = info["juggled"] + tickdir = info["tickdir"] + + # Which of the two edge points do we want to + # use for locating the offset text? + if juggled[2] == 2: + outeredgep = edgep1 + outerindex = 0 + else: + outeredgep = edgep2 + outerindex = 1 + + pos = _move_from_center(outeredgep, centers, labeldeltas, + self._axmask()) + olx, oly, olz = proj3d.proj_transform(*pos, self.axes.M) + self.offsetText.set_text(self.major.formatter.get_offset()) + self.offsetText.set_position((olx, oly)) + angle = art3d._norm_text_angle(np.rad2deg(np.arctan2(dy, dx))) + self.offsetText.set_rotation(angle) + # Must set rotation mode to "anchor" so that + # the alignment point is used as the "fulcrum" for rotation. + self.offsetText.set_rotation_mode('anchor') + + # ---------------------------------------------------------------------- + # Note: the following statement for determining the proper alignment of + # the offset text. This was determined entirely by trial-and-error + # and should not be in any way considered as "the way". There are + # still some edge cases where alignment is not quite right, but this + # seems to be more of a geometry issue (in other words, I might be + # using the wrong reference points). + # + # (TT, FF, TF, FT) are the shorthand for the tuple of + # (centpt[tickdir] <= pep[tickdir, outerindex], + # centpt[index] <= pep[index, outerindex]) + # + # Three-letters (e.g., TFT, FTT) are short-hand for the array of bools + # from the variable 'highs'. + # --------------------------------------------------------------------- + centpt = proj3d.proj_transform(*centers, self.axes.M) + if centpt[tickdir] > pep[tickdir, outerindex]: + # if FT and if highs has an even number of Trues + if (centpt[index] <= pep[index, outerindex] + and np.count_nonzero(highs) % 2 == 0): + # Usually, this means align right, except for the FTT case, + # in which offset for axis 1 and 2 are aligned left. + if highs.tolist() == [False, True, True] and index in (1, 2): + align = 'left' + else: + align = 'right' + else: + # The FF case + align = 'left' + else: + # if TF and if highs has an even number of Trues + if (centpt[index] > pep[index, outerindex] + and np.count_nonzero(highs) % 2 == 0): + # Usually mean align left, except if it is axis 2 + align = 'right' if index == 2 else 'left' + else: + # The TT case + align = 'right' + + self.offsetText.set_va('center') + self.offsetText.set_ha(align) + self.offsetText.draw(renderer) + + def _draw_labels(self, renderer, edgep1, edgep2, labeldeltas, centers, dx, dy): + label = self._axinfo["label"] + + # Draw labels + lxyz = 0.5 * (edgep1 + edgep2) + lxyz = _move_from_center(lxyz, centers, labeldeltas, self._axmask()) + tlx, tly, tlz = proj3d.proj_transform(*lxyz, self.axes.M) + self.label.set_position((tlx, tly)) + if self.get_rotate_label(self.label.get_text()): + angle = art3d._norm_text_angle(np.rad2deg(np.arctan2(dy, dx))) + self.label.set_rotation(angle) + self.label.set_va(label['va']) + self.label.set_ha(label['ha']) + self.label.set_rotation_mode(label['rotation_mode']) + self.label.draw(renderer) + + @artist.allow_rasterization + def draw(self, renderer): + self.label._transform = self.axes.transData + self.offsetText._transform = self.axes.transData + renderer.open_group("axis3d", gid=self.get_gid()) + + # Get general axis information: + mins, maxs, tc, highs = self._get_coord_info() + centers, deltas = self._calc_centers_deltas(maxs, mins) + + # Calculate offset distances + # A rough estimate; points are ambiguous since 3D plots rotate + reltoinches = self.get_figure(root=False).dpi_scale_trans.inverted() + ax_inches = reltoinches.transform(self.axes.bbox.size) + ax_points_estimate = sum(72. * ax_inches) + deltas_per_point = 48 / ax_points_estimate + default_offset = 21. + labeldeltas = (self.labelpad + default_offset) * deltas_per_point * deltas + + # Determine edge points for the axis lines + minmax = np.where(highs, maxs, mins) # "origin" point + maxmin = np.where(~highs, maxs, mins) # "opposite" corner near camera + + for edgep1, edgep2, pos in zip(*self._get_all_axis_line_edge_points( + minmax, maxmin, self._tick_position)): + # Project the edge points along the current position + pep = proj3d._proj_trans_points([edgep1, edgep2], self.axes.M) + pep = np.asarray(pep) + + # The transAxes transform is used because the Text object + # rotates the text relative to the display coordinate system. + # Therefore, if we want the labels to remain parallel to the + # axis regardless of the aspect ratio, we need to convert the + # edge points of the plane to display coordinates and calculate + # an angle from that. + # TODO: Maybe Text objects should handle this themselves? + dx, dy = (self.axes.transAxes.transform([pep[0:2, 1]]) - + self.axes.transAxes.transform([pep[0:2, 0]]))[0] + + # Draw the lines + self.line.set_data(pep[0], pep[1]) + self.line.draw(renderer) + + # Draw ticks + self._draw_ticks(renderer, edgep1, centers, deltas, highs, + deltas_per_point, pos) + + # Draw Offset text + self._draw_offset_text(renderer, edgep1, edgep2, labeldeltas, + centers, highs, pep, dx, dy) + + for edgep1, edgep2, pos in zip(*self._get_all_axis_line_edge_points( + minmax, maxmin, self._label_position)): + # See comments above + pep = proj3d._proj_trans_points([edgep1, edgep2], self.axes.M) + pep = np.asarray(pep) + dx, dy = (self.axes.transAxes.transform([pep[0:2, 1]]) - + self.axes.transAxes.transform([pep[0:2, 0]]))[0] + + # Draw labels + self._draw_labels(renderer, edgep1, edgep2, labeldeltas, centers, dx, dy) + + renderer.close_group('axis3d') + self.stale = False + + @artist.allow_rasterization + def draw_grid(self, renderer): + if not self.axes._draw_grid: + return + + renderer.open_group("grid3d", gid=self.get_gid()) + + ticks = self._update_ticks() + if len(ticks): + # Get general axis information: + info = self._axinfo + index = info["i"] + + mins, maxs, tc, highs = self._get_coord_info() + + minmax = np.where(highs, maxs, mins) + maxmin = np.where(~highs, maxs, mins) + + # Grid points where the planes meet + xyz0 = np.tile(minmax, (len(ticks), 1)) + xyz0[:, index] = [tick.get_loc() for tick in ticks] + + # Grid lines go from the end of one plane through the plane + # intersection (at xyz0) to the end of the other plane. The first + # point (0) differs along dimension index-2 and the last (2) along + # dimension index-1. + lines = np.stack([xyz0, xyz0, xyz0], axis=1) + lines[:, 0, index - 2] = maxmin[index - 2] + lines[:, 2, index - 1] = maxmin[index - 1] + self.gridlines.set_segments(lines) + gridinfo = info['grid'] + self.gridlines.set_color(gridinfo['color']) + self.gridlines.set_linewidth(gridinfo['linewidth']) + self.gridlines.set_linestyle(gridinfo['linestyle']) + self.gridlines.do_3d_projection() + self.gridlines.draw(renderer) + + renderer.close_group('grid3d') + + # TODO: Get this to work (more) properly when mplot3d supports the + # transforms framework. + def get_tightbbox(self, renderer=None, *, for_layout_only=False): + # docstring inherited + if not self.get_visible(): + return + # We have to directly access the internal data structures + # (and hope they are up to date) because at draw time we + # shift the ticks and their labels around in (x, y) space + # based on the projection, the current view port, and their + # position in 3D space. If we extend the transforms framework + # into 3D we would not need to do this different book keeping + # than we do in the normal axis + major_locs = self.get_majorticklocs() + minor_locs = self.get_minorticklocs() + + ticks = [*self.get_minor_ticks(len(minor_locs)), + *self.get_major_ticks(len(major_locs))] + view_low, view_high = self.get_view_interval() + if view_low > view_high: + view_low, view_high = view_high, view_low + interval_t = self.get_transform().transform([view_low, view_high]) + + ticks_to_draw = [] + for tick in ticks: + try: + loc_t = self.get_transform().transform(tick.get_loc()) + except AssertionError: + # Transform.transform doesn't allow masked values but + # some scales might make them, so we need this try/except. + pass + else: + if mtransforms._interval_contains_close(interval_t, loc_t): + ticks_to_draw.append(tick) + + ticks = ticks_to_draw + + bb_1, bb_2 = self._get_ticklabel_bboxes(ticks, renderer) + other = [] + + if self.line.get_visible(): + other.append(self.line.get_window_extent(renderer)) + if (self.label.get_visible() and not for_layout_only and + self.label.get_text()): + other.append(self.label.get_window_extent(renderer)) + + return mtransforms.Bbox.union([*bb_1, *bb_2, *other]) + + d_interval = _api.deprecated( + "3.6", alternative="get_data_interval", pending=True)( + property(lambda self: self.get_data_interval(), + lambda self, minmax: self.set_data_interval(*minmax))) + v_interval = _api.deprecated( + "3.6", alternative="get_view_interval", pending=True)( + property(lambda self: self.get_view_interval(), + lambda self, minmax: self.set_view_interval(*minmax))) + + +class XAxis(Axis): + axis_name = "x" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "xy_viewLim", "intervalx") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "xy_dataLim", "intervalx") + + +class YAxis(Axis): + axis_name = "y" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "xy_viewLim", "intervaly") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "xy_dataLim", "intervaly") + + +class ZAxis(Axis): + axis_name = "z" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "zz_viewLim", "intervalx") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "zz_dataLim", "intervalx") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py new file mode 100644 index 0000000000000000000000000000000000000000..923bd32c9ce032ad27379f8d8c6436564ec16d75 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py @@ -0,0 +1,219 @@ +""" +Various transforms used for by the 3D code +""" + +import numpy as np + +from matplotlib import _api + + +def world_transformation(xmin, xmax, + ymin, ymax, + zmin, zmax, pb_aspect=None): + """ + Produce a matrix that scales homogeneous coords in the specified ranges + to [0, 1], or [0, pb_aspect[i]] if the plotbox aspect ratio is specified. + """ + dx = xmax - xmin + dy = ymax - ymin + dz = zmax - zmin + if pb_aspect is not None: + ax, ay, az = pb_aspect + dx /= ax + dy /= ay + dz /= az + + return np.array([[1/dx, 0, 0, -xmin/dx], + [ 0, 1/dy, 0, -ymin/dy], + [ 0, 0, 1/dz, -zmin/dz], + [ 0, 0, 0, 1]]) + + +def _rotation_about_vector(v, angle): + """ + Produce a rotation matrix for an angle in radians about a vector. + """ + vx, vy, vz = v / np.linalg.norm(v) + s = np.sin(angle) + c = np.cos(angle) + t = 2*np.sin(angle/2)**2 # more numerically stable than t = 1-c + + R = np.array([ + [t*vx*vx + c, t*vx*vy - vz*s, t*vx*vz + vy*s], + [t*vy*vx + vz*s, t*vy*vy + c, t*vy*vz - vx*s], + [t*vz*vx - vy*s, t*vz*vy + vx*s, t*vz*vz + c]]) + + return R + + +def _view_axes(E, R, V, roll): + """ + Get the unit viewing axes in data coordinates. + + Parameters + ---------- + E : 3-element numpy array + The coordinates of the eye/camera. + R : 3-element numpy array + The coordinates of the center of the view box. + V : 3-element numpy array + Unit vector in the direction of the vertical axis. + roll : float + The roll angle in radians. + + Returns + ------- + u : 3-element numpy array + Unit vector pointing towards the right of the screen. + v : 3-element numpy array + Unit vector pointing towards the top of the screen. + w : 3-element numpy array + Unit vector pointing out of the screen. + """ + w = (E - R) + w = w/np.linalg.norm(w) + u = np.cross(V, w) + u = u/np.linalg.norm(u) + v = np.cross(w, u) # Will be a unit vector + + # Save some computation for the default roll=0 + if roll != 0: + # A positive rotation of the camera is a negative rotation of the world + Rroll = _rotation_about_vector(w, -roll) + u = np.dot(Rroll, u) + v = np.dot(Rroll, v) + return u, v, w + + +def _view_transformation_uvw(u, v, w, E): + """ + Return the view transformation matrix. + + Parameters + ---------- + u : 3-element numpy array + Unit vector pointing towards the right of the screen. + v : 3-element numpy array + Unit vector pointing towards the top of the screen. + w : 3-element numpy array + Unit vector pointing out of the screen. + E : 3-element numpy array + The coordinates of the eye/camera. + """ + Mr = np.eye(4) + Mt = np.eye(4) + Mr[:3, :3] = [u, v, w] + Mt[:3, -1] = -E + M = np.dot(Mr, Mt) + return M + + +def _persp_transformation(zfront, zback, focal_length): + e = focal_length + a = 1 # aspect ratio + b = (zfront+zback)/(zfront-zback) + c = -2*(zfront*zback)/(zfront-zback) + proj_matrix = np.array([[e, 0, 0, 0], + [0, e/a, 0, 0], + [0, 0, b, c], + [0, 0, -1, 0]]) + return proj_matrix + + +def _ortho_transformation(zfront, zback): + # note: w component in the resulting vector will be (zback-zfront), not 1 + a = -(zfront + zback) + b = -(zfront - zback) + proj_matrix = np.array([[2, 0, 0, 0], + [0, 2, 0, 0], + [0, 0, -2, 0], + [0, 0, a, b]]) + return proj_matrix + + +def _proj_transform_vec(vec, M): + vecw = np.dot(M, vec.data) + w = vecw[3] + txs, tys, tzs = vecw[0]/w, vecw[1]/w, vecw[2]/w + if np.ma.isMA(vec[0]): # we check each to protect for scalars + txs = np.ma.array(txs, mask=vec[0].mask) + if np.ma.isMA(vec[1]): + tys = np.ma.array(tys, mask=vec[1].mask) + if np.ma.isMA(vec[2]): + tzs = np.ma.array(tzs, mask=vec[2].mask) + return txs, tys, tzs + + +def _proj_transform_vec_clip(vec, M, focal_length): + vecw = np.dot(M, vec.data) + w = vecw[3] + txs, tys, tzs = vecw[0] / w, vecw[1] / w, vecw[2] / w + if np.isinf(focal_length): # don't clip orthographic projection + tis = np.ones(txs.shape, dtype=bool) + else: + tis = (-1 <= txs) & (txs <= 1) & (-1 <= tys) & (tys <= 1) & (tzs <= 0) + if np.ma.isMA(vec[0]): + tis = tis & ~vec[0].mask + if np.ma.isMA(vec[1]): + tis = tis & ~vec[1].mask + if np.ma.isMA(vec[2]): + tis = tis & ~vec[2].mask + + txs = np.ma.masked_array(txs, ~tis) + tys = np.ma.masked_array(tys, ~tis) + tzs = np.ma.masked_array(tzs, ~tis) + return txs, tys, tzs, tis + + +def inv_transform(xs, ys, zs, invM): + """ + Transform the points by the inverse of the projection matrix, *invM*. + """ + vec = _vec_pad_ones(xs, ys, zs) + vecr = np.dot(invM, vec) + if vecr.shape == (4,): + vecr = vecr.reshape((4, 1)) + for i in range(vecr.shape[1]): + if vecr[3][i] != 0: + vecr[:, i] = vecr[:, i] / vecr[3][i] + return vecr[0], vecr[1], vecr[2] + + +def _vec_pad_ones(xs, ys, zs): + if np.ma.isMA(xs) or np.ma.isMA(ys) or np.ma.isMA(zs): + return np.ma.array([xs, ys, zs, np.ones_like(xs)]) + else: + return np.array([xs, ys, zs, np.ones_like(xs)]) + + +def proj_transform(xs, ys, zs, M): + """ + Transform the points by the projection matrix *M*. + """ + vec = _vec_pad_ones(xs, ys, zs) + return _proj_transform_vec(vec, M) + + +@_api.deprecated("3.10") +def proj_transform_clip(xs, ys, zs, M): + return _proj_transform_clip(xs, ys, zs, M, focal_length=np.inf) + + +def _proj_transform_clip(xs, ys, zs, M, focal_length): + """ + Transform the points by the projection matrix + and return the clipping result + returns txs, tys, tzs, tis + """ + vec = _vec_pad_ones(xs, ys, zs) + return _proj_transform_vec_clip(vec, M, focal_length) + + +def _proj_points(points, M): + return np.column_stack(_proj_trans_points(points, M)) + + +def _proj_trans_points(points, M): + points = np.asanyarray(points) + xs, ys, zs = points[:, 0], points[:, 1], points[:, 2] + return proj_transform(xs, ys, zs, M) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py new file mode 100644 index 0000000000000000000000000000000000000000..174c12608ae933c1b918f45e57abfe6a2fb552bd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py @@ -0,0 +1,102 @@ +import numpy as np + +import matplotlib.pyplot as plt + +from matplotlib.backend_bases import MouseEvent +from mpl_toolkits.mplot3d.art3d import ( + Line3DCollection, + Poly3DCollection, + _all_points_on_plane, +) + + +def test_scatter_3d_projection_conservation(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + # fix axes3d projection + ax.roll = 0 + ax.elev = 0 + ax.azim = -45 + ax.stale = True + + x = [0, 1, 2, 3, 4] + scatter_collection = ax.scatter(x, x, x) + fig.canvas.draw_idle() + + # Get scatter location on canvas and freeze the data + scatter_offset = scatter_collection.get_offsets() + scatter_location = ax.transData.transform(scatter_offset) + + # Yaw -44 and -46 are enough to produce two set of scatter + # with opposite z-order without moving points too far + for azim in (-44, -46): + ax.azim = azim + ax.stale = True + fig.canvas.draw_idle() + + for i in range(5): + # Create a mouse event used to locate and to get index + # from each dots + event = MouseEvent("button_press_event", fig.canvas, + *scatter_location[i, :]) + contains, ind = scatter_collection.contains(event) + assert contains is True + assert len(ind["ind"]) == 1 + assert ind["ind"][0] == i + + +def test_zordered_error(): + # Smoke test for https://github.com/matplotlib/matplotlib/issues/26497 + lc = [(np.fromiter([0.0, 0.0, 0.0], dtype="float"), + np.fromiter([1.0, 1.0, 1.0], dtype="float"))] + pc = [np.fromiter([0.0, 0.0], dtype="float"), + np.fromiter([0.0, 1.0], dtype="float"), + np.fromiter([1.0, 1.0], dtype="float")] + + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + ax.add_collection(Line3DCollection(lc)) + ax.scatter(*pc, visible=False) + plt.draw() + + +def test_all_points_on_plane(): + # Non-coplanar points + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) + assert not _all_points_on_plane(*points.T) + + # Duplicate points + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # NaN values + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, np.nan]]) + assert _all_points_on_plane(*points.T) + + # Less than 3 unique points + points = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on a line + points = np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on two lines, with antiparallel vectors + points = np.array([[-2, 2, 0], [-1, 1, 0], [1, -1, 0], + [0, 0, 0], [2, 0, 0], [1, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on a plane + points = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 2, 0]]) + assert _all_points_on_plane(*points.T) + + +def test_generate_normals(): + # Smoke test for https://github.com/matplotlib/matplotlib/issues/29156 + vertices = ((0, 0, 0), (0, 5, 0), (5, 5, 0), (5, 0, 0)) + shape = Poly3DCollection([vertices], edgecolors='r', shade=True) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.add_collection3d(shape) + plt.draw() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py new file mode 100644 index 0000000000000000000000000000000000000000..b30c2556b105e70e3e08fa3877c712f78f09d95c --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py @@ -0,0 +1,2688 @@ +import functools +import itertools +import platform +import sys + +import pytest + +from mpl_toolkits.mplot3d import Axes3D, axes3d, proj3d, art3d +from mpl_toolkits.mplot3d.axes3d import _Quaternion as Quaternion +import matplotlib as mpl +from matplotlib.backend_bases import (MouseButton, MouseEvent, + NavigationToolbar2) +from matplotlib import cm +from matplotlib import colors as mcolors, patches as mpatch +from matplotlib.testing.decorators import image_comparison, check_figures_equal +from matplotlib.testing.widgets import mock_event +from matplotlib.collections import LineCollection, PolyCollection +from matplotlib.patches import Circle, PathPatch +from matplotlib.path import Path +from matplotlib.text import Text + +import matplotlib.pyplot as plt +import numpy as np + + +mpl3d_image_comparison = functools.partial( + image_comparison, remove_text=True, style='default') + + +def plot_cuboid(ax, scale): + # plot a rectangular cuboid with side lengths given by scale (x, y, z) + r = [0, 1] + pts = itertools.combinations(np.array(list(itertools.product(r, r, r))), 2) + for start, end in pts: + if np.sum(np.abs(start - end)) == r[1] - r[0]: + ax.plot3D(*zip(start*np.array(scale), end*np.array(scale))) + + +@check_figures_equal(extensions=["png"]) +def test_invisible_axes(fig_test, fig_ref): + ax = fig_test.subplots(subplot_kw=dict(projection='3d')) + ax.set_visible(False) + + +@mpl3d_image_comparison(['grid_off.png'], style='mpl20') +def test_grid_off(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.grid(False) + + +@mpl3d_image_comparison(['invisible_ticks_axis.png'], style='mpl20') +def test_invisible_ticks_axis(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_zticks([]) + for axis in [ax.xaxis, ax.yaxis, ax.zaxis]: + axis.line.set_visible(False) + + +@mpl3d_image_comparison(['axis_positions.png'], remove_text=False, style='mpl20') +def test_axis_positions(): + positions = ['upper', 'lower', 'both', 'none'] + fig, axs = plt.subplots(2, 2, subplot_kw={'projection': '3d'}) + for ax, pos in zip(axs.flatten(), positions): + for axis in ax.xaxis, ax.yaxis, ax.zaxis: + axis.set_label_position(pos) + axis.set_ticks_position(pos) + title = f'{pos}' + ax.set(xlabel='x', ylabel='y', zlabel='z', title=title) + + +@mpl3d_image_comparison(['aspects.png'], remove_text=False, style='mpl20') +def test_aspects(): + aspects = ('auto', 'equal', 'equalxy', 'equalyz', 'equalxz', 'equal') + _, axs = plt.subplots(2, 3, subplot_kw={'projection': '3d'}) + + for ax in axs.flatten()[0:-1]: + plot_cuboid(ax, scale=[1, 1, 5]) + # plot a cube as well to cover github #25443 + plot_cuboid(axs[1][2], scale=[1, 1, 1]) + + for i, ax in enumerate(axs.flatten()): + ax.set_title(aspects[i]) + ax.set_box_aspect((3, 4, 5)) + ax.set_aspect(aspects[i], adjustable='datalim') + axs[1][2].set_title('equal (cube)') + + +@mpl3d_image_comparison(['aspects_adjust_box.png'], + remove_text=False, style='mpl20') +def test_aspects_adjust_box(): + aspects = ('auto', 'equal', 'equalxy', 'equalyz', 'equalxz') + fig, axs = plt.subplots(1, len(aspects), subplot_kw={'projection': '3d'}, + figsize=(11, 3)) + + for i, ax in enumerate(axs): + plot_cuboid(ax, scale=[4, 3, 5]) + ax.set_title(aspects[i]) + ax.set_aspect(aspects[i], adjustable='box') + + +def test_axes3d_repr(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_label('label') + ax.set_title('title') + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + assert repr(ax) == ( + "") + + +@mpl3d_image_comparison(['axes3d_primary_views.png'], style='mpl20', + tol=0.05 if sys.platform == "darwin" else 0) +def test_axes3d_primary_views(): + # (elev, azim, roll) + views = [(90, -90, 0), # XY + (0, -90, 0), # XZ + (0, 0, 0), # YZ + (-90, 90, 0), # -XY + (0, 90, 0), # -XZ + (0, 180, 0)] # -YZ + # When viewing primary planes, draw the two visible axes so they intersect + # at their low values + fig, axs = plt.subplots(2, 3, subplot_kw={'projection': '3d'}) + for i, ax in enumerate(axs.flat): + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + ax.set_proj_type('ortho') + ax.view_init(elev=views[i][0], azim=views[i][1], roll=views[i][2]) + plt.tight_layout() + + +@mpl3d_image_comparison(['bar3d.png'], style='mpl20') +def test_bar3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]): + xs = np.arange(20) + ys = np.arange(20) + cs = [c] * len(xs) + cs[0] = 'c' + ax.bar(xs, ys, zs=z, zdir='y', align='edge', color=cs, alpha=0.8) + + +def test_bar3d_colors(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + for c in ['red', 'green', 'blue', 'yellow']: + xs = np.arange(len(c)) + ys = np.zeros_like(xs) + zs = np.zeros_like(ys) + # Color names with same length as xs/ys/zs should not be split into + # individual letters. + ax.bar3d(xs, ys, zs, 1, 1, 1, color=c) + + +@mpl3d_image_comparison(['bar3d_shaded.png'], style='mpl20') +def test_bar3d_shaded(): + x = np.arange(4) + y = np.arange(5) + x2d, y2d = np.meshgrid(x, y) + x2d, y2d = x2d.ravel(), y2d.ravel() + z = x2d + y2d + 1 # Avoid triggering bug with zero-depth boxes. + + views = [(30, -60, 0), (30, 30, 30), (-30, 30, -90), (300, -30, 0)] + fig = plt.figure(figsize=plt.figaspect(1 / len(views))) + axs = fig.subplots( + 1, len(views), + subplot_kw=dict(projection='3d') + ) + for ax, (elev, azim, roll) in zip(axs, views): + ax.bar3d(x2d, y2d, x2d * 0, 1, 1, z, shade=True) + ax.view_init(elev=elev, azim=azim, roll=roll) + fig.canvas.draw() + + +@mpl3d_image_comparison(['bar3d_notshaded.png'], style='mpl20') +def test_bar3d_notshaded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(4) + y = np.arange(5) + x2d, y2d = np.meshgrid(x, y) + x2d, y2d = x2d.ravel(), y2d.ravel() + z = x2d + y2d + ax.bar3d(x2d, y2d, x2d * 0, 1, 1, z, shade=False) + fig.canvas.draw() + + +def test_bar3d_lightsource(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection="3d") + + ls = mcolors.LightSource(azdeg=0, altdeg=90) + + length, width = 3, 4 + area = length * width + + x, y = np.meshgrid(np.arange(length), np.arange(width)) + x = x.ravel() + y = y.ravel() + dz = x + y + + color = [cm.coolwarm(i/area) for i in range(area)] + + collection = ax.bar3d(x=x, y=y, z=0, + dx=1, dy=1, dz=dz, + color=color, shade=True, lightsource=ls) + + # Testing that the custom 90° lightsource produces different shading on + # the top facecolors compared to the default, and that those colors are + # precisely (within floating point rounding errors of 4 ULP) the colors + # from the colormap, due to the illumination parallel to the z-axis. + np.testing.assert_array_max_ulp(color, collection._facecolor3d[1::6], 4) + + +@mpl3d_image_comparison(['contour3d.png'], style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.002) +def test_contour3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm) + ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm) + ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm) + ax.axis(xmin=-40, xmax=40, ymin=-40, ymax=40, zmin=-100, zmax=100) + + +@mpl3d_image_comparison(['contour3d_extend3d.png'], style='mpl20') +def test_contour3d_extend3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm, extend3d=True) + ax.set_xlim(-30, 30) + ax.set_ylim(-20, 40) + ax.set_zlim(-80, 80) + + +@mpl3d_image_comparison(['contourf3d.png'], style='mpl20') +def test_contourf3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm) + ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm) + ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm) + ax.set_xlim(-40, 40) + ax.set_ylim(-40, 40) + ax.set_zlim(-100, 100) + + +@mpl3d_image_comparison(['contourf3d_fill.png'], style='mpl20') +def test_contourf3d_fill(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y = np.meshgrid(np.arange(-2, 2, 0.25), np.arange(-2, 2, 0.25)) + Z = X.clip(0, 0) + # This produces holes in the z=0 surface that causes rendering errors if + # the Poly3DCollection is not aware of path code information (issue #4784) + Z[::5, ::5] = 0.1 + ax.contourf(X, Y, Z, offset=0, levels=[-0.1, 0], cmap=cm.coolwarm) + ax.set_xlim(-2, 2) + ax.set_ylim(-2, 2) + ax.set_zlim(-1, 1) + + +@pytest.mark.parametrize('extend, levels', [['both', [2, 4, 6]], + ['min', [2, 4, 6, 8]], + ['max', [0, 2, 4, 6]]]) +@check_figures_equal(extensions=["png"]) +def test_contourf3d_extend(fig_test, fig_ref, extend, levels): + X, Y = np.meshgrid(np.arange(-2, 2, 0.25), np.arange(-2, 2, 0.25)) + # Z is in the range [0, 8] + Z = X**2 + Y**2 + + # Manually set the over/under colors to be the end of the colormap + cmap = mpl.colormaps['viridis'].copy() + cmap.set_under(cmap(0)) + cmap.set_over(cmap(255)) + # Set vmin/max to be the min/max values plotted on the reference image + kwargs = {'vmin': 1, 'vmax': 7, 'cmap': cmap} + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.contourf(X, Y, Z, levels=[0, 2, 4, 6, 8], **kwargs) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.contourf(X, Y, Z, levels, extend=extend, **kwargs) + + for ax in [ax_ref, ax_test]: + ax.set_xlim(-2, 2) + ax.set_ylim(-2, 2) + ax.set_zlim(-10, 10) + + +@mpl3d_image_comparison(['tricontour.png'], tol=0.02, style='mpl20') +def test_tricontour(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + + np.random.seed(19680801) + x = np.random.rand(1000) - 0.5 + y = np.random.rand(1000) - 0.5 + z = -(x**2 + y**2) + + ax = fig.add_subplot(1, 2, 1, projection='3d') + ax.tricontour(x, y, z) + ax = fig.add_subplot(1, 2, 2, projection='3d') + ax.tricontourf(x, y, z) + + +def test_contour3d_1d_input(): + # Check that 1D sequences of different length for {x, y} doesn't error + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + nx, ny = 30, 20 + x = np.linspace(-10, 10, nx) + y = np.linspace(-10, 10, ny) + z = np.random.randint(0, 2, [ny, nx]) + ax.contour(x, y, z, [0.5]) + + +@mpl3d_image_comparison(['lines3d.png'], style='mpl20') +def test_lines3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) + z = np.linspace(-2, 2, 100) + r = z ** 2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + ax.plot(x, y, z) + + +@check_figures_equal(extensions=["png"]) +def test_plot_scalar(fig_test, fig_ref): + ax1 = fig_test.add_subplot(projection='3d') + ax1.plot([1], [1], "o") + ax2 = fig_ref.add_subplot(projection='3d') + ax2.plot(1, 1, "o") + + +def test_invalid_line_data(): + with pytest.raises(RuntimeError, match='x must be'): + art3d.Line3D(0, [], []) + with pytest.raises(RuntimeError, match='y must be'): + art3d.Line3D([], 0, []) + with pytest.raises(RuntimeError, match='z must be'): + art3d.Line3D([], [], 0) + + line = art3d.Line3D([], [], []) + with pytest.raises(RuntimeError, match='x must be'): + line.set_data_3d(0, [], []) + with pytest.raises(RuntimeError, match='y must be'): + line.set_data_3d([], 0, []) + with pytest.raises(RuntimeError, match='z must be'): + line.set_data_3d([], [], 0) + + +@mpl3d_image_comparison(['mixedsubplot.png'], style='mpl20') +def test_mixedsubplots(): + def f(t): + return np.cos(2*np.pi*t) * np.exp(-t) + + t1 = np.arange(0.0, 5.0, 0.1) + t2 = np.arange(0.0, 5.0, 0.02) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure(figsize=plt.figaspect(2.)) + ax = fig.add_subplot(2, 1, 1) + ax.plot(t1, f(t1), 'bo', t2, f(t2), 'k--', markerfacecolor='green') + ax.grid(True) + + ax = fig.add_subplot(2, 1, 2, projection='3d') + X, Y = np.meshgrid(np.arange(-5, 5, 0.25), np.arange(-5, 5, 0.25)) + R = np.hypot(X, Y) + Z = np.sin(R) + + ax.plot_surface(X, Y, Z, rcount=40, ccount=40, + linewidth=0, antialiased=False) + + ax.set_zlim3d(-1, 1) + + +@check_figures_equal(extensions=['png']) +def test_tight_layout_text(fig_test, fig_ref): + # text is currently ignored in tight layout. So the order of text() and + # tight_layout() calls should not influence the result. + ax1 = fig_test.add_subplot(projection='3d') + ax1.text(.5, .5, .5, s='some string') + fig_test.tight_layout() + + ax2 = fig_ref.add_subplot(projection='3d') + fig_ref.tight_layout() + ax2.text(.5, .5, .5, s='some string') + + +@mpl3d_image_comparison(['scatter3d.png'], style='mpl20') +def test_scatter3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + c='r', marker='o') + x = y = z = np.arange(10, 20) + ax.scatter(x, y, z, c='b', marker='^') + z[-1] = 0 # Check that scatter() copies the data. + # Ensure empty scatters do not break. + ax.scatter([], [], [], c='r', marker='X') + + +@mpl3d_image_comparison(['scatter3d_color.png'], style='mpl20') +def test_scatter3d_color(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Check that 'none' color works; these two should overlay to produce the + # same as setting just `color`. + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + facecolor='r', edgecolor='none', marker='o') + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + facecolor='none', edgecolor='r', marker='o') + + ax.scatter(np.arange(10, 20), np.arange(10, 20), np.arange(10, 20), + color='b', marker='s') + + +@mpl3d_image_comparison(['scatter3d_linewidth.png'], style='mpl20') +def test_scatter3d_linewidth(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Check that array-like linewidth can be set + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o', linewidth=np.arange(10)) + + +@check_figures_equal(extensions=['png']) +def test_scatter3d_linewidth_modification(fig_ref, fig_test): + # Changing Path3DCollection linewidths with array-like post-creation + # should work correctly. + ax_test = fig_test.add_subplot(projection='3d') + c = ax_test.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o') + c.set_linewidths(np.arange(10)) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(np.arange(10), np.arange(10), np.arange(10), marker='o', + linewidths=np.arange(10)) + + +@check_figures_equal(extensions=['png']) +def test_scatter3d_modification(fig_ref, fig_test): + # Changing Path3DCollection properties post-creation should work correctly. + ax_test = fig_test.add_subplot(projection='3d') + c = ax_test.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o') + c.set_facecolor('C1') + c.set_edgecolor('C2') + c.set_alpha([0.3, 0.7] * 5) + assert c.get_depthshade() + c.set_depthshade(False) + assert not c.get_depthshade() + c.set_sizes(np.full(10, 75)) + c.set_linewidths(3) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(np.arange(10), np.arange(10), np.arange(10), marker='o', + facecolor='C1', edgecolor='C2', alpha=[0.3, 0.7] * 5, + depthshade=False, s=75, linewidths=3) + + +@pytest.mark.parametrize('depthshade', [True, False]) +@check_figures_equal(extensions=['png']) +def test_scatter3d_sorting(fig_ref, fig_test, depthshade): + """Test that marker properties are correctly sorted.""" + + y, x = np.mgrid[:10, :10] + z = np.arange(x.size).reshape(x.shape) + + sizes = np.full(z.shape, 25) + sizes[0::2, 0::2] = 100 + sizes[1::2, 1::2] = 100 + + facecolors = np.full(z.shape, 'C0') + facecolors[:5, :5] = 'C1' + facecolors[6:, :4] = 'C2' + facecolors[6:, 6:] = 'C3' + + edgecolors = np.full(z.shape, 'C4') + edgecolors[1:5, 1:5] = 'C5' + edgecolors[5:9, 1:5] = 'C6' + edgecolors[5:9, 5:9] = 'C7' + + linewidths = np.full(z.shape, 2) + linewidths[0::2, 0::2] = 5 + linewidths[1::2, 1::2] = 5 + + x, y, z, sizes, facecolors, edgecolors, linewidths = ( + a.flatten() + for a in [x, y, z, sizes, facecolors, edgecolors, linewidths] + ) + + ax_ref = fig_ref.add_subplot(projection='3d') + sets = (np.unique(a) for a in [sizes, facecolors, edgecolors, linewidths]) + for s, fc, ec, lw in itertools.product(*sets): + subset = ( + (sizes != s) | + (facecolors != fc) | + (edgecolors != ec) | + (linewidths != lw) + ) + subset = np.ma.masked_array(z, subset, dtype=float) + + # When depth shading is disabled, the colors are passed through as + # single-item lists; this triggers single path optimization. The + # following reshaping is a hack to disable that, since the optimization + # would not occur for the full scatter which has multiple colors. + fc = np.repeat(fc, sum(~subset.mask)) + + ax_ref.scatter(x, y, subset, s=s, fc=fc, ec=ec, lw=lw, alpha=1, + depthshade=depthshade) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.scatter(x, y, z, s=sizes, fc=facecolors, ec=edgecolors, + lw=linewidths, alpha=1, depthshade=depthshade) + + +@pytest.mark.parametrize('azim', [-50, 130]) # yellow first, blue first +@check_figures_equal(extensions=['png']) +def test_marker_draw_order_data_reversed(fig_test, fig_ref, azim): + """ + Test that the draw order does not depend on the data point order. + + For the given viewing angle at azim=-50, the yellow marker should be in + front. For azim=130, the blue marker should be in front. + """ + x = [-1, 1] + y = [1, -1] + z = [0, 0] + color = ['b', 'y'] + ax = fig_test.add_subplot(projection='3d') + ax.scatter(x, y, z, s=3500, c=color) + ax.view_init(elev=0, azim=azim, roll=0) + ax = fig_ref.add_subplot(projection='3d') + ax.scatter(x[::-1], y[::-1], z[::-1], s=3500, c=color[::-1]) + ax.view_init(elev=0, azim=azim, roll=0) + + +@check_figures_equal(extensions=['png']) +def test_marker_draw_order_view_rotated(fig_test, fig_ref): + """ + Test that the draw order changes with the direction. + + If we rotate *azim* by 180 degrees and exchange the colors, the plot + plot should look the same again. + """ + azim = 130 + x = [-1, 1] + y = [1, -1] + z = [0, 0] + color = ['b', 'y'] + ax = fig_test.add_subplot(projection='3d') + # axis are not exactly invariant under 180 degree rotation -> deactivate + ax.set_axis_off() + ax.scatter(x, y, z, s=3500, c=color) + ax.view_init(elev=0, azim=azim, roll=0) + ax = fig_ref.add_subplot(projection='3d') + ax.set_axis_off() + ax.scatter(x, y, z, s=3500, c=color[::-1]) # color reversed + ax.view_init(elev=0, azim=azim - 180, roll=0) # view rotated by 180 deg + + +@mpl3d_image_comparison(['plot_3d_from_2d.png'], tol=0.019, style='mpl20') +def test_plot_3d_from_2d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + xs = np.arange(0, 5) + ys = np.arange(5, 10) + ax.plot(xs, ys, zs=0, zdir='x') + ax.plot(xs, ys, zs=0, zdir='y') + + +@mpl3d_image_comparison(['fill_between_quad.png'], style='mpl20') +def test_fill_between_quad(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + theta = np.linspace(0, 2*np.pi, 50) + + x1 = np.cos(theta) + y1 = np.sin(theta) + z1 = 0.1 * np.sin(6 * theta) + + x2 = 0.6 * np.cos(theta) + y2 = 0.6 * np.sin(theta) + z2 = 2 + + where = (theta < np.pi/2) | (theta > 3*np.pi/2) + + # Since none of x1 == x2, y1 == y2, or z1 == z2 is True, the fill_between + # mode will map to 'quad' + ax.fill_between(x1, y1, z1, x2, y2, z2, + where=where, mode='auto', alpha=0.5, edgecolor='k') + + +@mpl3d_image_comparison(['fill_between_polygon.png'], style='mpl20') +def test_fill_between_polygon(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + theta = np.linspace(0, 2*np.pi, 50) + + x1 = x2 = theta + y1 = y2 = 0 + z1 = np.cos(theta) + z2 = z1 + 1 + + where = (theta < np.pi/2) | (theta > 3*np.pi/2) + + # Since x1 == x2 and y1 == y2, the fill_between mode will be 'polygon' + ax.fill_between(x1, y1, z1, x2, y2, z2, + where=where, mode='auto', edgecolor='k') + + +@mpl3d_image_comparison(['surface3d.png'], style='mpl20') +def test_surface3d(): + # Remove this line when this test image is regenerated. + plt.rcParams['pcolormesh.snap'] = False + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X = np.arange(-5, 5, 0.25) + Y = np.arange(-5, 5, 0.25) + X, Y = np.meshgrid(X, Y) + R = np.hypot(X, Y) + Z = np.sin(R) + surf = ax.plot_surface(X, Y, Z, rcount=40, ccount=40, cmap=cm.coolwarm, + lw=0, antialiased=False) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_zlim(-1.01, 1.01) + fig.colorbar(surf, shrink=0.5, aspect=5) + + +@image_comparison(['surface3d_label_offset_tick_position.png'], style='mpl20') +def test_surface3d_label_offset_tick_position(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax = plt.figure().add_subplot(projection="3d") + + x, y = np.mgrid[0:6 * np.pi:0.25, 0:4 * np.pi:0.25] + z = np.sqrt(np.abs(np.cos(x) + np.cos(y))) + + ax.plot_surface(x * 1e5, y * 1e6, z * 1e8, cmap='autumn', cstride=2, rstride=2) + ax.set_xlabel("X label") + ax.set_ylabel("Y label") + ax.set_zlabel("Z label") + + +@mpl3d_image_comparison(['surface3d_shaded.png'], style='mpl20') +def test_surface3d_shaded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X = np.arange(-5, 5, 0.25) + Y = np.arange(-5, 5, 0.25) + X, Y = np.meshgrid(X, Y) + R = np.sqrt(X ** 2 + Y ** 2) + Z = np.sin(R) + ax.plot_surface(X, Y, Z, rstride=5, cstride=5, + color=[0.25, 1, 0.25], lw=1, antialiased=False) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_zlim(-1.01, 1.01) + + +@mpl3d_image_comparison(['surface3d_masked.png'], style='mpl20') +def test_surface3d_masked(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + y = [1, 2, 3, 4, 5, 6, 7, 8] + + x, y = np.meshgrid(x, y) + matrix = np.array( + [ + [-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [-1, 1, 2, 3, 4, 4, 4, 3, 2, 1, 1], + [-1, -1., 4, 5, 6, 8, 6, 5, 4, 3, -1.], + [-1, -1., 7, 8, 11, 12, 11, 8, 7, -1., -1.], + [-1, -1., 8, 9, 10, 16, 10, 9, 10, 7, -1.], + [-1, -1., -1., 12, 16, 20, 16, 12, 11, -1., -1.], + [-1, -1., -1., -1., 22, 24, 22, 20, 18, -1., -1.], + [-1, -1., -1., -1., -1., 28, 26, 25, -1., -1., -1.], + ] + ) + z = np.ma.masked_less(matrix, 0) + norm = mcolors.Normalize(vmax=z.max(), vmin=z.min()) + colors = mpl.colormaps["plasma"](norm(z)) + ax.plot_surface(x, y, z, facecolors=colors) + ax.view_init(30, -80, 0) + + +@check_figures_equal(extensions=["png"]) +def test_plot_scatter_masks(fig_test, fig_ref): + x = np.linspace(0, 10, 100) + y = np.linspace(0, 10, 100) + z = np.sin(x) * np.cos(y) + mask = z > 0 + + z_masked = np.ma.array(z, mask=mask) + ax_test = fig_test.add_subplot(projection='3d') + ax_test.scatter(x, y, z_masked) + ax_test.plot(x, y, z_masked) + + x[mask] = y[mask] = z[mask] = np.nan + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(x, y, z) + ax_ref.plot(x, y, z) + + +@check_figures_equal(extensions=["png"]) +def test_plot_surface_None_arg(fig_test, fig_ref): + x, y = np.meshgrid(np.arange(5), np.arange(5)) + z = x + y + ax_test = fig_test.add_subplot(projection='3d') + ax_test.plot_surface(x, y, z, facecolors=None) + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.plot_surface(x, y, z) + + +@mpl3d_image_comparison(['surface3d_masked_strides.png'], style='mpl20') +def test_surface3d_masked_strides(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x, y = np.mgrid[-6:6.1:1, -6:6.1:1] + z = np.ma.masked_less(x * y, 2) + + ax.plot_surface(x, y, z, rstride=4, cstride=4) + ax.view_init(60, -45, 0) + + +@mpl3d_image_comparison(['text3d.png'], remove_text=False, style='mpl20') +def test_text3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + zdirs = (None, 'x', 'y', 'z', (1, 1, 0), (1, 1, 1)) + xs = (2, 6, 4, 9, 7, 2) + ys = (6, 4, 8, 7, 2, 2) + zs = (4, 2, 5, 6, 1, 7) + + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + label = '(%d, %d, %d), dir=%s' % (x, y, z, zdir) + ax.text(x, y, z, label, zdir) + + ax.text(1, 1, 1, "red", color='red') + ax.text2D(0.05, 0.95, "2D Text", transform=ax.transAxes) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim3d(0, 10) + ax.set_ylim3d(0, 10) + ax.set_zlim3d(0, 10) + ax.set_xlabel('X axis') + ax.set_ylabel('Y axis') + ax.set_zlabel('Z axis') + + +@check_figures_equal(extensions=['png']) +def test_text3d_modification(fig_ref, fig_test): + # Modifying the Text position after the fact should work the same as + # setting it directly. + zdirs = (None, 'x', 'y', 'z', (1, 1, 0), (1, 1, 1)) + xs = (2, 6, 4, 9, 7, 2) + ys = (6, 4, 8, 7, 2, 2) + zs = (4, 2, 5, 6, 1, 7) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.set_xlim3d(0, 10) + ax_test.set_ylim3d(0, 10) + ax_test.set_zlim3d(0, 10) + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + t = ax_test.text(0, 0, 0, f'({x}, {y}, {z}), dir={zdir}') + t.set_position_3d((x, y, z), zdir=zdir) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.set_xlim3d(0, 10) + ax_ref.set_ylim3d(0, 10) + ax_ref.set_zlim3d(0, 10) + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + ax_ref.text(x, y, z, f'({x}, {y}, {z}), dir={zdir}', zdir=zdir) + + +@mpl3d_image_comparison(['trisurf3d.png'], tol=0.061, style='mpl20') +def test_trisurf3d(): + n_angles = 36 + n_radii = 8 + radii = np.linspace(0.125, 1.0, n_radii) + angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) + angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) + angles[:, 1::2] += np.pi/n_angles + + x = np.append(0, (radii*np.cos(angles)).flatten()) + y = np.append(0, (radii*np.sin(angles)).flatten()) + z = np.sin(-x*y) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2) + + +@mpl3d_image_comparison(['trisurf3d_shaded.png'], tol=0.03, style='mpl20') +def test_trisurf3d_shaded(): + n_angles = 36 + n_radii = 8 + radii = np.linspace(0.125, 1.0, n_radii) + angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) + angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) + angles[:, 1::2] += np.pi/n_angles + + x = np.append(0, (radii*np.cos(angles)).flatten()) + y = np.append(0, (radii*np.sin(angles)).flatten()) + z = np.sin(-x*y) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot_trisurf(x, y, z, color=[1, 0.5, 0], linewidth=0.2) + + +@mpl3d_image_comparison(['wireframe3d.png'], style='mpl20') +def test_wireframe3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rcount=13, ccount=13) + + +@mpl3d_image_comparison(['wireframe3dzerocstride.png'], style='mpl20') +def test_wireframe3dzerocstride(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rcount=13, ccount=0) + + +@mpl3d_image_comparison(['wireframe3dzerorstride.png'], style='mpl20') +def test_wireframe3dzerorstride(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rstride=0, cstride=10) + + +def test_wireframe3dzerostrideraises(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + with pytest.raises(ValueError): + ax.plot_wireframe(X, Y, Z, rstride=0, cstride=0) + + +def test_mixedsamplesraises(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + with pytest.raises(ValueError): + ax.plot_wireframe(X, Y, Z, rstride=10, ccount=50) + with pytest.raises(ValueError): + ax.plot_surface(X, Y, Z, cstride=50, rcount=10) + + +# remove tolerance when regenerating the test image +@mpl3d_image_comparison(['quiver3d.png'], style='mpl20', tol=0.003) +def test_quiver3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + pivots = ['tip', 'middle', 'tail'] + colors = ['tab:blue', 'tab:orange', 'tab:green'] + for i, (pivot, color) in enumerate(zip(pivots, colors)): + x, y, z = np.meshgrid([-0.5, 0.5], [-0.5, 0.5], [-0.5, 0.5]) + u = -x + v = -y + w = -z + # Offset each set in z direction + z += 2 * i + ax.quiver(x, y, z, u, v, w, length=1, pivot=pivot, color=color) + ax.scatter(x, y, z, color=color) + + ax.set_xlim(-3, 3) + ax.set_ylim(-3, 3) + ax.set_zlim(-1, 5) + + +@check_figures_equal(extensions=["png"]) +def test_quiver3d_empty(fig_test, fig_ref): + fig_ref.add_subplot(projection='3d') + x = y = z = u = v = w = [] + ax = fig_test.add_subplot(projection='3d') + ax.quiver(x, y, z, u, v, w, length=0.1, pivot='tip', normalize=True) + + +@mpl3d_image_comparison(['quiver3d_masked.png'], style='mpl20') +def test_quiver3d_masked(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Using mgrid here instead of ogrid because masked_where doesn't + # seem to like broadcasting very much... + x, y, z = np.mgrid[-1:0.8:10j, -1:0.8:10j, -1:0.6:3j] + + u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z) + v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z) + w = (2/3)**0.5 * np.cos(np.pi * x) * np.cos(np.pi * y) * np.sin(np.pi * z) + u = np.ma.masked_where((-0.4 < x) & (x < 0.1), u, copy=False) + v = np.ma.masked_where((0.1 < y) & (y < 0.7), v, copy=False) + + ax.quiver(x, y, z, u, v, w, length=0.1, pivot='tip', normalize=True) + + +@mpl3d_image_comparison(['quiver3d_colorcoded.png'], style='mpl20') +def test_quiver3d_colorcoded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x = y = dx = dz = np.zeros(10) + z = dy = np.arange(10.) + + color = plt.cm.Reds(dy/dy.max()) + ax.quiver(x, y, z, dx, dy, dz, colors=color) + ax.set_ylim(0, 10) + + +def test_patch_modification(): + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + circle = Circle((0, 0)) + ax.add_patch(circle) + art3d.patch_2d_to_3d(circle) + circle.set_facecolor((1.0, 0.0, 0.0, 1)) + + assert mcolors.same_color(circle.get_facecolor(), (1, 0, 0, 1)) + fig.canvas.draw() + assert mcolors.same_color(circle.get_facecolor(), (1, 0, 0, 1)) + + +@check_figures_equal(extensions=['png']) +def test_patch_collection_modification(fig_test, fig_ref): + # Test that modifying Patch3DCollection properties after creation works. + patch1 = Circle((0, 0), 0.05) + patch2 = Circle((0.1, 0.1), 0.03) + facecolors = np.array([[0., 0.5, 0., 1.], [0.5, 0., 0., 0.5]]) + c = art3d.Patch3DCollection([patch1, patch2], linewidths=3) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.add_collection3d(c) + c.set_edgecolor('C2') + c.set_facecolor(facecolors) + c.set_alpha(0.7) + assert c.get_depthshade() + c.set_depthshade(False) + assert not c.get_depthshade() + + patch1 = Circle((0, 0), 0.05) + patch2 = Circle((0.1, 0.1), 0.03) + facecolors = np.array([[0., 0.5, 0., 1.], [0.5, 0., 0., 0.5]]) + c = art3d.Patch3DCollection([patch1, patch2], linewidths=3, + edgecolor='C2', facecolor=facecolors, + alpha=0.7, depthshade=False) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.add_collection3d(c) + + +def test_poly3dcollection_verts_validation(): + poly = [[0, 0, 1], [0, 1, 1], [0, 1, 0], [0, 0, 0]] + with pytest.raises(ValueError, match=r'list of \(N, 3\) array-like'): + art3d.Poly3DCollection(poly) # should be Poly3DCollection([poly]) + + poly = np.array(poly, dtype=float) + with pytest.raises(ValueError, match=r'list of \(N, 3\) array-like'): + art3d.Poly3DCollection(poly) # should be Poly3DCollection([poly]) + + +@mpl3d_image_comparison(['poly3dcollection_closed.png'], style='mpl20') +def test_poly3dcollection_closed(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + poly1 = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + poly2 = np.array([[0, 1, 1], [1, 1, 1], [1, 1, 0]], float) + c1 = art3d.Poly3DCollection([poly1], linewidths=3, edgecolor='k', + facecolor=(0.5, 0.5, 1, 0.5), closed=True) + c2 = art3d.Poly3DCollection([poly2], linewidths=3, edgecolor='k', + facecolor=(1, 0.5, 0.5, 0.5), closed=False) + ax.add_collection3d(c1, autolim=False) + ax.add_collection3d(c2, autolim=False) + + +def test_poly_collection_2d_to_3d_empty(): + poly = PolyCollection([]) + art3d.poly_collection_2d_to_3d(poly) + assert isinstance(poly, art3d.Poly3DCollection) + assert poly.get_paths() == [] + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.add_artist(poly) + minz = poly.do_3d_projection() + assert np.isnan(minz) + + # Ensure drawing actually works. + fig.canvas.draw() + + +@mpl3d_image_comparison(['poly3dcollection_alpha.png'], style='mpl20') +def test_poly3dcollection_alpha(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + poly1 = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + poly2 = np.array([[0, 1, 1], [1, 1, 1], [1, 1, 0]], float) + c1 = art3d.Poly3DCollection([poly1], linewidths=3, edgecolor='k', + facecolor=(0.5, 0.5, 1), closed=True) + c1.set_alpha(0.5) + c2 = art3d.Poly3DCollection([poly2], linewidths=3, closed=False) + # Post-creation modification should work. + c2.set_facecolor((1, 0.5, 0.5)) + c2.set_edgecolor('k') + c2.set_alpha(0.5) + ax.add_collection3d(c1, autolim=False) + ax.add_collection3d(c2, autolim=False) + + +@mpl3d_image_comparison(['add_collection3d_zs_array.png'], style='mpl20') +def test_add_collection3d_zs_array(): + theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) + z = np.linspace(-2, 2, 100) + r = z**2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + + points = np.column_stack([x, y, z]).reshape(-1, 1, 3) + segments = np.concatenate([points[:-1], points[1:]], axis=1) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + norm = plt.Normalize(0, 2*np.pi) + # 2D LineCollection from x & y values + lc = LineCollection(segments[:, :, :2], cmap='twilight', norm=norm) + lc.set_array(np.mod(theta, 2*np.pi)) + # Add 2D collection at z values to ax + line = ax.add_collection3d(lc, zs=segments[:, :, 2]) + + assert line is not None + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-5, 5) + ax.set_ylim(-4, 6) + ax.set_zlim(-2, 2) + + +@mpl3d_image_comparison(['add_collection3d_zs_scalar.png'], style='mpl20') +def test_add_collection3d_zs_scalar(): + theta = np.linspace(0, 2 * np.pi, 100) + z = 1 + r = z**2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + + points = np.column_stack([x, y]).reshape(-1, 1, 2) + segments = np.concatenate([points[:-1], points[1:]], axis=1) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + norm = plt.Normalize(0, 2*np.pi) + lc = LineCollection(segments, cmap='twilight', norm=norm) + lc.set_array(theta) + line = ax.add_collection3d(lc, zs=z) + + assert line is not None + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-5, 5) + ax.set_ylim(-4, 6) + ax.set_zlim(0, 2) + + +def test_line3dCollection_autoscaling(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + lines = [[(0, 0, 0), (1, 4, 2)], + [(1, 1, 3), (2, 0, 2)], + [(1, 0, 4), (1, 4, 5)]] + + lc = art3d.Line3DCollection(lines) + ax.add_collection3d(lc) + assert np.allclose(ax.get_xlim3d(), (-0.041666666666666664, 2.0416666666666665)) + assert np.allclose(ax.get_ylim3d(), (-0.08333333333333333, 4.083333333333333)) + assert np.allclose(ax.get_zlim3d(), (-0.10416666666666666, 5.104166666666667)) + + +def test_poly3dCollection_autoscaling(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + poly = np.array([[0, 0, 0], [1, 1, 3], [1, 0, 4]]) + col = art3d.Poly3DCollection([poly]) + ax.add_collection3d(col) + assert np.allclose(ax.get_xlim3d(), (-0.020833333333333332, 1.0208333333333333)) + assert np.allclose(ax.get_ylim3d(), (-0.020833333333333332, 1.0208333333333333)) + assert np.allclose(ax.get_zlim3d(), (-0.0833333333333333, 4.083333333333333)) + + +@mpl3d_image_comparison(['axes3d_labelpad.png'], + remove_text=False, style='mpl20') +def test_axes3d_labelpad(): + fig = plt.figure() + ax = fig.add_axes(Axes3D(fig)) + # labelpad respects rcParams + assert ax.xaxis.labelpad == mpl.rcParams['axes.labelpad'] + # labelpad can be set in set_label + ax.set_xlabel('X LABEL', labelpad=10) + assert ax.xaxis.labelpad == 10 + ax.set_ylabel('Y LABEL') + ax.set_zlabel('Z LABEL', labelpad=20) + assert ax.zaxis.labelpad == 20 + assert ax.get_zlabel() == 'Z LABEL' + # or manually + ax.yaxis.labelpad = 20 + ax.zaxis.labelpad = -40 + + # Tick labels also respect tick.pad (also from rcParams) + for i, tick in enumerate(ax.yaxis.get_major_ticks()): + tick.set_pad(tick.get_pad() + 5 - i * 5) + + +@mpl3d_image_comparison(['axes3d_cla.png'], remove_text=False, style='mpl20') +def test_axes3d_cla(): + # fixed in pull request 4553 + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.set_axis_off() + ax.cla() # make sure the axis displayed is 3D (not 2D) + + +@mpl3d_image_comparison(['axes3d_rotated.png'], + remove_text=False, style='mpl20') +def test_axes3d_rotated(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.view_init(90, 45, 0) # look down, rotated. Should be square + + +def test_plotsurface_1d_raises(): + x = np.linspace(0.5, 10, num=100) + y = np.linspace(0.5, 10, num=100) + X, Y = np.meshgrid(x, y) + z = np.random.randn(100) + + fig = plt.figure(figsize=(14, 6)) + ax = fig.add_subplot(1, 2, 1, projection='3d') + with pytest.raises(ValueError): + ax.plot_surface(X, Y, z) + + +def _test_proj_make_M(): + # eye point + E = np.array([1000, -1000, 2000]) + R = np.array([100, 100, 100]) + V = np.array([0, 0, 1]) + roll = 0 + u, v, w = proj3d._view_axes(E, R, V, roll) + viewM = proj3d._view_transformation_uvw(u, v, w, E) + perspM = proj3d._persp_transformation(100, -100, 1) + M = np.dot(perspM, viewM) + return M + + +def test_proj_transform(): + M = _test_proj_make_M() + invM = np.linalg.inv(M) + + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 300.0 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 300.0 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 300.0 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + ixs, iys, izs = proj3d.inv_transform(txs, tys, tzs, invM) + + np.testing.assert_almost_equal(ixs, xs) + np.testing.assert_almost_equal(iys, ys) + np.testing.assert_almost_equal(izs, zs) + + +def _test_proj_draw_axes(M, s=1, *args, **kwargs): + xs = [0, s, 0, 0] + ys = [0, 0, s, 0] + zs = [0, 0, 0, s] + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + o, ax, ay, az = zip(txs, tys) + lines = [(o, ax), (o, ay), (o, az)] + + fig, ax = plt.subplots(*args, **kwargs) + linec = LineCollection(lines) + ax.add_collection(linec) + for x, y, t in zip(txs, tys, ['o', 'x', 'y', 'z']): + ax.text(x, y, t) + + return fig, ax + + +@mpl3d_image_comparison(['proj3d_axes_cube.png'], style='mpl20') +def test_proj_axes_cube(): + M = _test_proj_make_M() + + ts = '0 1 2 3 0 4 5 6 7 4'.split() + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 300.0 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 300.0 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 300.0 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + + fig, ax = _test_proj_draw_axes(M, s=400) + + ax.scatter(txs, tys, c=tzs) + ax.plot(txs, tys, c='r') + for x, y, t in zip(txs, tys, ts): + ax.text(x, y, t) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-0.2, 0.2) + ax.set_ylim(-0.2, 0.2) + + +@mpl3d_image_comparison(['proj3d_axes_cube_ortho.png'], style='mpl20') +def test_proj_axes_cube_ortho(): + E = np.array([200, 100, 100]) + R = np.array([0, 0, 0]) + V = np.array([0, 0, 1]) + roll = 0 + u, v, w = proj3d._view_axes(E, R, V, roll) + viewM = proj3d._view_transformation_uvw(u, v, w, E) + orthoM = proj3d._ortho_transformation(-1, 1) + M = np.dot(orthoM, viewM) + + ts = '0 1 2 3 0 4 5 6 7 4'.split() + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 100 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 100 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 100 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + + fig, ax = _test_proj_draw_axes(M, s=150) + + ax.scatter(txs, tys, s=300-tzs) + ax.plot(txs, tys, c='r') + for x, y, t in zip(txs, tys, ts): + ax.text(x, y, t) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-200, 200) + ax.set_ylim(-200, 200) + + +def test_world(): + xmin, xmax = 100, 120 + ymin, ymax = -100, 100 + zmin, zmax = 0.1, 0.2 + M = proj3d.world_transformation(xmin, xmax, ymin, ymax, zmin, zmax) + np.testing.assert_allclose(M, + [[5e-2, 0, 0, -5], + [0, 5e-3, 0, 5e-1], + [0, 0, 1e1, -1], + [0, 0, 0, 1]]) + + +def test_autoscale(): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + assert ax.get_zscale() == 'linear' + ax._view_margin = 0 + ax.margins(x=0, y=.1, z=.2) + ax.plot([0, 1], [0, 1], [0, 1]) + assert ax.get_w_lims() == (0, 1, -.1, 1.1, -.2, 1.2) + ax.autoscale(False) + ax.set_autoscalez_on(True) + ax.plot([0, 2], [0, 2], [0, 2]) + assert ax.get_w_lims() == (0, 1, -.1, 1.1, -.4, 2.4) + ax.autoscale(axis='x') + ax.plot([0, 2], [0, 2], [0, 2]) + assert ax.get_w_lims() == (0, 2, -.1, 1.1, -.4, 2.4) + + +@pytest.mark.parametrize('axis', ('x', 'y', 'z')) +@pytest.mark.parametrize('auto', (True, False, None)) +def test_unautoscale(axis, auto): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x = np.arange(100) + y = np.linspace(-0.1, 0.1, 100) + ax.scatter(x, y) + + get_autoscale_on = getattr(ax, f'get_autoscale{axis}_on') + set_lim = getattr(ax, f'set_{axis}lim') + get_lim = getattr(ax, f'get_{axis}lim') + + post_auto = get_autoscale_on() if auto is None else auto + + set_lim((-0.5, 0.5), auto=auto) + assert post_auto == get_autoscale_on() + fig.canvas.draw() + np.testing.assert_array_equal(get_lim(), (-0.5, 0.5)) + + +@check_figures_equal(extensions=["png"]) +def test_culling(fig_test, fig_ref): + xmins = (-100, -50) + for fig, xmin in zip((fig_test, fig_ref), xmins): + ax = fig.add_subplot(projection='3d') + n = abs(xmin) + 1 + xs = np.linspace(0, xmin, n) + ys = np.ones(n) + zs = np.zeros(n) + ax.plot(xs, ys, zs, 'k') + + ax.set(xlim=(-5, 5), ylim=(-5, 5), zlim=(-5, 5)) + ax.view_init(5, 180, 0) + + +def test_axes3d_focal_length_checks(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + with pytest.raises(ValueError): + ax.set_proj_type('persp', focal_length=0) + with pytest.raises(ValueError): + ax.set_proj_type('ortho', focal_length=1) + + +@mpl3d_image_comparison(['axes3d_focal_length.png'], + remove_text=False, style='mpl20') +def test_axes3d_focal_length(): + fig, axs = plt.subplots(1, 2, subplot_kw={'projection': '3d'}) + axs[0].set_proj_type('persp', focal_length=np.inf) + axs[1].set_proj_type('persp', focal_length=0.15) + + +@mpl3d_image_comparison(['axes3d_ortho.png'], remove_text=False, style='mpl20') +def test_axes3d_ortho(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_proj_type('ortho') + + +@mpl3d_image_comparison(['axes3d_isometric.png'], style='mpl20') +def test_axes3d_isometric(): + from itertools import combinations, product + fig, ax = plt.subplots(subplot_kw=dict( + projection='3d', + proj_type='ortho', + box_aspect=(4, 4, 4) + )) + r = (-1, 1) # stackoverflow.com/a/11156353 + for s, e in combinations(np.array(list(product(r, r, r))), 2): + if abs(s - e).sum() == r[1] - r[0]: + ax.plot3D(*zip(s, e), c='k') + ax.view_init(elev=np.degrees(np.arctan(1. / np.sqrt(2))), azim=-45, roll=0) + ax.grid(True) + + +@check_figures_equal(extensions=["png"]) +def test_axlim_clip(fig_test, fig_ref): + # With axlim clipping + ax = fig_test.add_subplot(projection="3d") + x = np.linspace(0, 1, 11) + y = np.linspace(0, 1, 11) + X, Y = np.meshgrid(x, y) + Z = X + Y + ax.plot_surface(X, Y, Z, facecolor='C1', edgecolors=None, + rcount=50, ccount=50, axlim_clip=True) + # This ax.plot is to cover the extra surface edge which is not clipped out + ax.plot([0.5, 0.5], [0, 1], [0.5, 1.5], + color='k', linewidth=3, zorder=5, axlim_clip=True) + ax.scatter(X.ravel(), Y.ravel(), Z.ravel() + 1, axlim_clip=True) + ax.quiver(X.ravel(), Y.ravel(), Z.ravel() + 2, + 0*X.ravel(), 0*Y.ravel(), 0*Z.ravel() + 1, + arrow_length_ratio=0, axlim_clip=True) + ax.plot(X[0], Y[0], Z[0] + 3, color='C2', axlim_clip=True) + ax.text(1.1, 0.5, 4, 'test', axlim_clip=True) # won't be visible + ax.set(xlim=(0, 0.5), ylim=(0, 1), zlim=(0, 5)) + + # With manual clipping + ax = fig_ref.add_subplot(projection="3d") + idx = (X <= 0.5) + X = X[idx].reshape(11, 6) + Y = Y[idx].reshape(11, 6) + Z = Z[idx].reshape(11, 6) + ax.plot_surface(X, Y, Z, facecolor='C1', edgecolors=None, + rcount=50, ccount=50, axlim_clip=False) + ax.plot([0.5, 0.5], [0, 1], [0.5, 1.5], + color='k', linewidth=3, zorder=5, axlim_clip=False) + ax.scatter(X.ravel(), Y.ravel(), Z.ravel() + 1, axlim_clip=False) + ax.quiver(X.ravel(), Y.ravel(), Z.ravel() + 2, + 0*X.ravel(), 0*Y.ravel(), 0*Z.ravel() + 1, + arrow_length_ratio=0, axlim_clip=False) + ax.plot(X[0], Y[0], Z[0] + 3, color='C2', axlim_clip=False) + ax.set(xlim=(0, 0.5), ylim=(0, 1), zlim=(0, 5)) + + +@pytest.mark.parametrize('value', [np.inf, np.nan]) +@pytest.mark.parametrize(('setter', 'side'), [ + ('set_xlim3d', 'left'), + ('set_xlim3d', 'right'), + ('set_ylim3d', 'bottom'), + ('set_ylim3d', 'top'), + ('set_zlim3d', 'bottom'), + ('set_zlim3d', 'top'), +]) +def test_invalid_axes_limits(setter, side, value): + limit = {side: value} + fig = plt.figure() + obj = fig.add_subplot(projection='3d') + with pytest.raises(ValueError): + getattr(obj, setter)(**limit) + + +class TestVoxels: + @mpl3d_image_comparison(['voxels-simple.png'], style='mpl20') + def test_simple(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((5, 4, 3)) + voxels = (x == y) | (y == z) + ax.voxels(voxels) + + @mpl3d_image_comparison(['voxels-edge-style.png'], style='mpl20') + def test_edge_style(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((5, 5, 4)) + voxels = ((x - 2)**2 + (y - 2)**2 + (z-1.5)**2) < 2.2**2 + v = ax.voxels(voxels, linewidths=3, edgecolor='C1') + + # change the edge color of one voxel + v[max(v.keys())].set_edgecolor('C2') + + @mpl3d_image_comparison(['voxels-named-colors.png'], style='mpl20') + def test_named_colors(self): + """Test with colors set to a 3D object array of strings.""" + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + voxels = (x == y) | (y == z) + voxels = voxels & ~(x * y * z < 1) + colors = np.full((10, 10, 10), 'C0', dtype=np.object_) + colors[(x < 5) & (y < 5)] = '0.25' + colors[(x + z) < 10] = 'cyan' + ax.voxels(voxels, facecolors=colors) + + @mpl3d_image_comparison(['voxels-rgb-data.png'], style='mpl20') + def test_rgb_data(self): + """Test with colors set to a 4d float array of rgb data.""" + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + voxels = (x == y) | (y == z) + colors = np.zeros((10, 10, 10, 3)) + colors[..., 0] = x / 9 + colors[..., 1] = y / 9 + colors[..., 2] = z / 9 + ax.voxels(voxels, facecolors=colors) + + @mpl3d_image_comparison(['voxels-alpha.png'], style='mpl20') + def test_alpha(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + v1 = x == y + v2 = np.abs(x - y) < 2 + voxels = v1 | v2 + colors = np.zeros((10, 10, 10, 4)) + colors[v2] = [1, 0, 0, 0.5] + colors[v1] = [0, 1, 0, 0.5] + v = ax.voxels(voxels, facecolors=colors) + + assert type(v) is dict + for coord, poly in v.items(): + assert voxels[coord], "faces returned for absent voxel" + assert isinstance(poly, art3d.Poly3DCollection) + + @mpl3d_image_comparison(['voxels-xyz.png'], + tol=0.01, remove_text=False, style='mpl20') + def test_xyz(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + def midpoints(x): + sl = () + for i in range(x.ndim): + x = (x[sl + np.index_exp[:-1]] + + x[sl + np.index_exp[1:]]) / 2.0 + sl += np.index_exp[:] + return x + + # prepare some coordinates, and attach rgb values to each + r, g, b = np.indices((17, 17, 17)) / 16.0 + rc = midpoints(r) + gc = midpoints(g) + bc = midpoints(b) + + # define a sphere about [0.5, 0.5, 0.5] + sphere = (rc - 0.5)**2 + (gc - 0.5)**2 + (bc - 0.5)**2 < 0.5**2 + + # combine the color components + colors = np.zeros(sphere.shape + (3,)) + colors[..., 0] = rc + colors[..., 1] = gc + colors[..., 2] = bc + + # and plot everything + ax.voxels(r, g, b, sphere, + facecolors=colors, + edgecolors=np.clip(2*colors - 0.5, 0, 1), # brighter + linewidth=0.5) + + def test_calling_conventions(self): + x, y, z = np.indices((3, 4, 5)) + filled = np.ones((2, 3, 4)) + + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + # all the valid calling conventions + for kw in (dict(), dict(edgecolor='k')): + ax.voxels(filled, **kw) + ax.voxels(filled=filled, **kw) + ax.voxels(x, y, z, filled, **kw) + ax.voxels(x, y, z, filled=filled, **kw) + + # duplicate argument + with pytest.raises(TypeError, match='voxels'): + ax.voxels(x, y, z, filled, filled=filled) + # missing arguments + with pytest.raises(TypeError, match='voxels'): + ax.voxels(x, y) + # x, y, z are positional only - this passes them on as attributes of + # Poly3DCollection + with pytest.raises(AttributeError, match="keyword argument 'x'") as exec_info: + ax.voxels(filled=filled, x=x, y=y, z=z) + assert exec_info.value.name == 'x' + + +def test_line3d_set_get_data_3d(): + x, y, z = [0, 1], [2, 3], [4, 5] + x2, y2, z2 = [6, 7], [8, 9], [10, 11] + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + lines = ax.plot(x, y, z) + line = lines[0] + np.testing.assert_array_equal((x, y, z), line.get_data_3d()) + line.set_data_3d(x2, y2, z2) + np.testing.assert_array_equal((x2, y2, z2), line.get_data_3d()) + line.set_xdata(x) + line.set_ydata(y) + line.set_3d_properties(zs=z, zdir='z') + np.testing.assert_array_equal((x, y, z), line.get_data_3d()) + line.set_3d_properties(zs=0, zdir='z') + np.testing.assert_array_equal((x, y, np.zeros_like(z)), line.get_data_3d()) + + +@check_figures_equal(extensions=["png"]) +def test_inverted(fig_test, fig_ref): + # Plot then invert. + ax = fig_test.add_subplot(projection="3d") + ax.plot([1, 1, 10, 10], [1, 10, 10, 10], [1, 1, 1, 10]) + ax.invert_yaxis() + # Invert then plot. + ax = fig_ref.add_subplot(projection="3d") + ax.invert_yaxis() + ax.plot([1, 1, 10, 10], [1, 10, 10, 10], [1, 1, 1, 10]) + + +def test_inverted_cla(): + # GitHub PR #5450. Setting autoscale should reset + # axes to be non-inverted. + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + # 1. test that a new axis is not inverted per default + assert not ax.xaxis_inverted() + assert not ax.yaxis_inverted() + assert not ax.zaxis_inverted() + ax.set_xlim(1, 0) + ax.set_ylim(1, 0) + ax.set_zlim(1, 0) + assert ax.xaxis_inverted() + assert ax.yaxis_inverted() + assert ax.zaxis_inverted() + ax.cla() + assert not ax.xaxis_inverted() + assert not ax.yaxis_inverted() + assert not ax.zaxis_inverted() + + +def test_ax3d_tickcolour(): + fig = plt.figure() + ax = Axes3D(fig) + + ax.tick_params(axis='x', colors='red') + ax.tick_params(axis='y', colors='red') + ax.tick_params(axis='z', colors='red') + fig.canvas.draw() + + for tick in ax.xaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + for tick in ax.yaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + for tick in ax.zaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + + +@check_figures_equal(extensions=["png"]) +def test_ticklabel_format(fig_test, fig_ref): + axs = fig_test.subplots(4, 5, subplot_kw={"projection": "3d"}) + for ax in axs.flat: + ax.set_xlim(1e7, 1e7 + 10) + for row, name in zip(axs, ["x", "y", "z", "both"]): + row[0].ticklabel_format( + axis=name, style="plain") + row[1].ticklabel_format( + axis=name, scilimits=(-2, 2)) + row[2].ticklabel_format( + axis=name, useOffset=not mpl.rcParams["axes.formatter.useoffset"]) + row[3].ticklabel_format( + axis=name, useLocale=not mpl.rcParams["axes.formatter.use_locale"]) + row[4].ticklabel_format( + axis=name, + useMathText=not mpl.rcParams["axes.formatter.use_mathtext"]) + + def get_formatters(ax, names): + return [getattr(ax, name).get_major_formatter() for name in names] + + axs = fig_ref.subplots(4, 5, subplot_kw={"projection": "3d"}) + for ax in axs.flat: + ax.set_xlim(1e7, 1e7 + 10) + for row, names in zip( + axs, [["xaxis"], ["yaxis"], ["zaxis"], ["xaxis", "yaxis", "zaxis"]] + ): + for fmt in get_formatters(row[0], names): + fmt.set_scientific(False) + for fmt in get_formatters(row[1], names): + fmt.set_powerlimits((-2, 2)) + for fmt in get_formatters(row[2], names): + fmt.set_useOffset(not mpl.rcParams["axes.formatter.useoffset"]) + for fmt in get_formatters(row[3], names): + fmt.set_useLocale(not mpl.rcParams["axes.formatter.use_locale"]) + for fmt in get_formatters(row[4], names): + fmt.set_useMathText( + not mpl.rcParams["axes.formatter.use_mathtext"]) + + +@check_figures_equal(extensions=["png"]) +def test_quiver3D_smoke(fig_test, fig_ref): + pivot = "middle" + # Make the grid + x, y, z = np.meshgrid( + np.arange(-0.8, 1, 0.2), + np.arange(-0.8, 1, 0.2), + np.arange(-0.8, 1, 0.8) + ) + u = v = w = np.ones_like(x) + + for fig, length in zip((fig_ref, fig_test), (1, 1.0)): + ax = fig.add_subplot(projection="3d") + ax.quiver(x, y, z, u, v, w, length=length, pivot=pivot) + + +@image_comparison(["minor_ticks.png"], style="mpl20") +def test_minor_ticks(): + ax = plt.figure().add_subplot(projection="3d") + ax.set_xticks([0.25], minor=True) + ax.set_xticklabels(["quarter"], minor=True) + ax.set_yticks([0.33], minor=True) + ax.set_yticklabels(["third"], minor=True) + ax.set_zticks([0.50], minor=True) + ax.set_zticklabels(["half"], minor=True) + + +# remove tolerance when regenerating the test image +@mpl3d_image_comparison(['errorbar3d_errorevery.png'], style='mpl20', tol=0.003) +def test_errorbar3d_errorevery(): + """Tests errorevery functionality for 3D errorbars.""" + t = np.arange(0, 2*np.pi+.1, 0.01) + x, y, z = np.sin(t), np.cos(3*t), np.sin(5*t) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + estep = 15 + i = np.arange(t.size) + zuplims = (i % estep == 0) & (i // estep % 3 == 0) + zlolims = (i % estep == 0) & (i // estep % 3 == 2) + + ax.errorbar(x, y, z, 0.2, zuplims=zuplims, zlolims=zlolims, + errorevery=estep) + + +@mpl3d_image_comparison(['errorbar3d.png'], style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.02) +def test_errorbar3d(): + """Tests limits, color styling, and legend for 3D errorbars.""" + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + d = [1, 2, 3, 4, 5] + e = [.5, .5, .5, .5, .5] + ax.errorbar(x=d, y=d, z=d, xerr=e, yerr=e, zerr=e, capsize=3, + zuplims=[False, True, False, True, True], + zlolims=[True, False, False, True, False], + yuplims=True, + ecolor='purple', label='Error lines') + ax.legend() + + +@image_comparison(['stem3d.png'], style='mpl20', tol=0.009) +def test_stem3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig, axs = plt.subplots(2, 3, figsize=(8, 6), + constrained_layout=True, + subplot_kw={'projection': '3d'}) + + theta = np.linspace(0, 2*np.pi) + x = np.cos(theta - np.pi/2) + y = np.sin(theta - np.pi/2) + z = theta + + for ax, zdir in zip(axs[0], ['x', 'y', 'z']): + ax.stem(x, y, z, orientation=zdir) + ax.set_title(f'orientation={zdir}') + + x = np.linspace(-np.pi/2, np.pi/2, 20) + y = np.ones_like(x) + z = np.cos(x) + + for ax, zdir in zip(axs[1], ['x', 'y', 'z']): + markerline, stemlines, baseline = ax.stem( + x, y, z, + linefmt='C4-.', markerfmt='C1D', basefmt='C2', + orientation=zdir) + ax.set_title(f'orientation={zdir}') + markerline.set(markerfacecolor='none', markeredgewidth=2) + baseline.set_linewidth(3) + + +@image_comparison(["equal_box_aspect.png"], style="mpl20") +def test_equal_box_aspect(): + from itertools import product, combinations + + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + + # Make data + u = np.linspace(0, 2 * np.pi, 100) + v = np.linspace(0, np.pi, 100) + x = np.outer(np.cos(u), np.sin(v)) + y = np.outer(np.sin(u), np.sin(v)) + z = np.outer(np.ones_like(u), np.cos(v)) + + # Plot the surface + ax.plot_surface(x, y, z) + + # draw cube + r = [-1, 1] + for s, e in combinations(np.array(list(product(r, r, r))), 2): + if np.sum(np.abs(s - e)) == r[1] - r[0]: + ax.plot3D(*zip(s, e), color="b") + + # Make axes limits + xyzlim = np.column_stack( + [ax.get_xlim3d(), ax.get_ylim3d(), ax.get_zlim3d()] + ) + XYZlim = [min(xyzlim[0]), max(xyzlim[1])] + ax.set_xlim3d(XYZlim) + ax.set_ylim3d(XYZlim) + ax.set_zlim3d(XYZlim) + ax.axis('off') + ax.set_box_aspect((1, 1, 1)) + + with pytest.raises(ValueError, match="Argument zoom ="): + ax.set_box_aspect((1, 1, 1), zoom=-1) + + +def test_colorbar_pos(): + num_plots = 2 + fig, axs = plt.subplots(1, num_plots, figsize=(4, 5), + constrained_layout=True, + subplot_kw={'projection': '3d'}) + for ax in axs: + p_tri = ax.plot_trisurf(np.random.randn(5), np.random.randn(5), + np.random.randn(5)) + + cbar = plt.colorbar(p_tri, ax=axs, orientation='horizontal') + + fig.canvas.draw() + # check that actually on the bottom + assert cbar.ax.get_position().extents[1] < 0.2 + + +def test_inverted_zaxis(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_zlim(0, 1) + assert not ax.zaxis_inverted() + assert ax.get_zlim() == (0, 1) + assert ax.get_zbound() == (0, 1) + + # Change bound + ax.set_zbound((0, 2)) + assert not ax.zaxis_inverted() + assert ax.get_zlim() == (0, 2) + assert ax.get_zbound() == (0, 2) + + # Change invert + ax.invert_zaxis() + assert ax.zaxis_inverted() + assert ax.get_zlim() == (2, 0) + assert ax.get_zbound() == (0, 2) + + # Set upper bound + ax.set_zbound(upper=1) + assert ax.zaxis_inverted() + assert ax.get_zlim() == (1, 0) + assert ax.get_zbound() == (0, 1) + + # Set lower bound + ax.set_zbound(lower=2) + assert ax.zaxis_inverted() + assert ax.get_zlim() == (2, 1) + assert ax.get_zbound() == (1, 2) + + +def test_set_zlim(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + assert np.allclose(ax.get_zlim(), (-1/48, 49/48)) + ax.set_zlim(zmax=2) + assert np.allclose(ax.get_zlim(), (-1/48, 2)) + ax.set_zlim(zmin=1) + assert ax.get_zlim() == (1, 2) + + with pytest.raises( + TypeError, match="Cannot pass both 'lower' and 'min'"): + ax.set_zlim(bottom=0, zmin=1) + with pytest.raises( + TypeError, match="Cannot pass both 'upper' and 'max'"): + ax.set_zlim(top=0, zmax=1) + + +@check_figures_equal(extensions=["png"]) +def test_shared_view(fig_test, fig_ref): + elev, azim, roll = 5, 20, 30 + ax1 = fig_test.add_subplot(131, projection="3d") + ax2 = fig_test.add_subplot(132, projection="3d", shareview=ax1) + ax3 = fig_test.add_subplot(133, projection="3d") + ax3.shareview(ax1) + ax2.view_init(elev=elev, azim=azim, roll=roll, share=True) + + for subplot_num in (131, 132, 133): + ax = fig_ref.add_subplot(subplot_num, projection="3d") + ax.view_init(elev=elev, azim=azim, roll=roll) + + +def test_shared_axes_retick(): + fig = plt.figure() + ax1 = fig.add_subplot(211, projection="3d") + ax2 = fig.add_subplot(212, projection="3d", sharez=ax1) + ax1.plot([0, 1], [0, 1], [0, 2]) + ax2.plot([0, 1], [0, 1], [0, 2]) + ax1.set_zticks([-0.5, 0, 2, 2.5]) + # check that setting ticks on a shared axis is synchronized + assert ax1.get_zlim() == (-0.5, 2.5) + assert ax2.get_zlim() == (-0.5, 2.5) + + +def test_quaternion(): + # 1: + q1 = Quaternion(1, [0, 0, 0]) + assert q1.scalar == 1 + assert (q1.vector == [0, 0, 0]).all + # __neg__: + assert (-q1).scalar == -1 + assert ((-q1).vector == [0, 0, 0]).all + # i, j, k: + qi = Quaternion(0, [1, 0, 0]) + assert qi.scalar == 0 + assert (qi.vector == [1, 0, 0]).all + qj = Quaternion(0, [0, 1, 0]) + assert qj.scalar == 0 + assert (qj.vector == [0, 1, 0]).all + qk = Quaternion(0, [0, 0, 1]) + assert qk.scalar == 0 + assert (qk.vector == [0, 0, 1]).all + # i^2 = j^2 = k^2 = -1: + assert qi*qi == -q1 + assert qj*qj == -q1 + assert qk*qk == -q1 + # identity: + assert q1*qi == qi + assert q1*qj == qj + assert q1*qk == qk + # i*j=k, j*k=i, k*i=j: + assert qi*qj == qk + assert qj*qk == qi + assert qk*qi == qj + assert qj*qi == -qk + assert qk*qj == -qi + assert qi*qk == -qj + # __mul__: + assert (Quaternion(2, [3, 4, 5]) * Quaternion(6, [7, 8, 9]) + == Quaternion(-86, [28, 48, 44])) + # conjugate(): + for q in [q1, qi, qj, qk]: + assert q.conjugate().scalar == q.scalar + assert (q.conjugate().vector == -q.vector).all + assert q.conjugate().conjugate() == q + assert ((q*q.conjugate()).vector == 0).all + # norm: + q0 = Quaternion(0, [0, 0, 0]) + assert q0.norm == 0 + assert q1.norm == 1 + assert qi.norm == 1 + assert qj.norm == 1 + assert qk.norm == 1 + for q in [q0, q1, qi, qj, qk]: + assert q.norm == (q*q.conjugate()).scalar + # normalize(): + for q in [ + Quaternion(2, [0, 0, 0]), + Quaternion(0, [3, 0, 0]), + Quaternion(0, [0, 4, 0]), + Quaternion(0, [0, 0, 5]), + Quaternion(6, [7, 8, 9]) + ]: + assert q.normalize().norm == 1 + # reciprocal(): + for q in [q1, qi, qj, qk]: + assert q*q.reciprocal() == q1 + assert q.reciprocal()*q == q1 + # rotate(): + assert (qi.rotate([1, 2, 3]) == np.array([1, -2, -3])).all + # rotate_from_to(): + for r1, r2, q in [ + ([1, 0, 0], [0, 1, 0], Quaternion(np.sqrt(1/2), [0, 0, np.sqrt(1/2)])), + ([1, 0, 0], [0, 0, 1], Quaternion(np.sqrt(1/2), [0, -np.sqrt(1/2), 0])), + ([1, 0, 0], [1, 0, 0], Quaternion(1, [0, 0, 0])) + ]: + assert Quaternion.rotate_from_to(r1, r2) == q + # rotate_from_to(), special case: + for r1 in [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]]: + r1 = np.array(r1) + with pytest.warns(UserWarning): + q = Quaternion.rotate_from_to(r1, -r1) + assert np.isclose(q.norm, 1) + assert np.dot(q.vector, r1) == 0 + # from_cardan_angles(), as_cardan_angles(): + for elev, azim, roll in [(0, 0, 0), + (90, 0, 0), (0, 90, 0), (0, 0, 90), + (0, 30, 30), (30, 0, 30), (30, 30, 0), + (47, 11, -24)]: + for mag in [1, 2]: + q = Quaternion.from_cardan_angles( + np.deg2rad(elev), np.deg2rad(azim), np.deg2rad(roll)) + assert np.isclose(q.norm, 1) + q = Quaternion(mag * q.scalar, mag * q.vector) + np.testing.assert_allclose(np.rad2deg(Quaternion.as_cardan_angles(q)), + (elev, azim, roll), atol=1e-6) + + +@pytest.mark.parametrize('style', + ('azel', 'trackball', 'sphere', 'arcball')) +def test_rotate(style): + """Test rotating using the left mouse button.""" + if style == 'azel': + s = 0.5 + else: + s = mpl.rcParams['axes3d.trackballsize'] / 2 + s *= 0.5 + mpl.rcParams['axes3d.trackballborder'] = 0 + with mpl.rc_context({'axes3d.mouserotationstyle': style}): + for roll, dx, dy in [ + [0, 1, 0], + [30, 1, 0], + [0, 0, 1], + [30, 0, 1], + [0, 0.5, np.sqrt(3)/2], + [30, 0.5, np.sqrt(3)/2], + [0, 2, 0]]: + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.view_init(0, 0, roll) + ax.figure.canvas.draw() + + # drag mouse to change orientation + ax._button_press( + mock_event(ax, button=MouseButton.LEFT, xdata=0, ydata=0)) + ax._on_move( + mock_event(ax, button=MouseButton.LEFT, + xdata=s*dx*ax._pseudo_w, ydata=s*dy*ax._pseudo_h)) + ax.figure.canvas.draw() + + c = np.sqrt(3)/2 + expectations = { + ('azel', 0, 1, 0): (0, -45, 0), + ('azel', 0, 0, 1): (-45, 0, 0), + ('azel', 0, 0.5, c): (-38.971143, -22.5, 0), + ('azel', 0, 2, 0): (0, -90, 0), + ('azel', 30, 1, 0): (22.5, -38.971143, 30), + ('azel', 30, 0, 1): (-38.971143, -22.5, 30), + ('azel', 30, 0.5, c): (-22.5, -38.971143, 30), + + ('trackball', 0, 1, 0): (0, -28.64789, 0), + ('trackball', 0, 0, 1): (-28.64789, 0, 0), + ('trackball', 0, 0.5, c): (-24.531578, -15.277726, 3.340403), + ('trackball', 0, 2, 0): (0, -180/np.pi, 0), + ('trackball', 30, 1, 0): (13.869588, -25.319385, 26.87008), + ('trackball', 30, 0, 1): (-24.531578, -15.277726, 33.340403), + ('trackball', 30, 0.5, c): (-13.869588, -25.319385, 33.129920), + + ('sphere', 0, 1, 0): (0, -30, 0), + ('sphere', 0, 0, 1): (-30, 0, 0), + ('sphere', 0, 0.5, c): (-25.658906, -16.102114, 3.690068), + ('sphere', 0, 2, 0): (0, -90, 0), + ('sphere', 30, 1, 0): (14.477512, -26.565051, 26.565051), + ('sphere', 30, 0, 1): (-25.658906, -16.102114, 33.690068), + ('sphere', 30, 0.5, c): (-14.477512, -26.565051, 33.434949), + + ('arcball', 0, 1, 0): (0, -60, 0), + ('arcball', 0, 0, 1): (-60, 0, 0), + ('arcball', 0, 0.5, c): (-48.590378, -40.893395, 19.106605), + ('arcball', 0, 2, 0): (0, 180, 0), + ('arcball', 30, 1, 0): (25.658906, -56.309932, 16.102114), + ('arcball', 30, 0, 1): (-48.590378, -40.893395, 49.106605), + ('arcball', 30, 0.5, c): (-25.658906, -56.309932, 43.897886)} + new_elev, new_azim, new_roll = expectations[(style, roll, dx, dy)] + np.testing.assert_allclose((ax.elev, ax.azim, ax.roll), + (new_elev, new_azim, new_roll), atol=1e-6) + + +def test_pan(): + """Test mouse panning using the middle mouse button.""" + + def convert_lim(dmin, dmax): + """Convert min/max limits to center and range.""" + center = (dmin + dmax) / 2 + range_ = dmax - dmin + return center, range_ + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(0, 0, 0) + fig.canvas.draw() + + x_center0, x_range0 = convert_lim(*ax.get_xlim3d()) + y_center0, y_range0 = convert_lim(*ax.get_ylim3d()) + z_center0, z_range0 = convert_lim(*ax.get_zlim3d()) + + # move mouse diagonally to pan along all axis. + ax._button_press( + mock_event(ax, button=MouseButton.MIDDLE, xdata=0, ydata=0)) + ax._on_move( + mock_event(ax, button=MouseButton.MIDDLE, xdata=1, ydata=1)) + + x_center, x_range = convert_lim(*ax.get_xlim3d()) + y_center, y_range = convert_lim(*ax.get_ylim3d()) + z_center, z_range = convert_lim(*ax.get_zlim3d()) + + # Ranges have not changed + assert x_range == pytest.approx(x_range0) + assert y_range == pytest.approx(y_range0) + assert z_range == pytest.approx(z_range0) + + # But center positions have + assert x_center != pytest.approx(x_center0) + assert y_center != pytest.approx(y_center0) + assert z_center != pytest.approx(z_center0) + + +@pytest.mark.parametrize("tool,button,key,expected", + [("zoom", MouseButton.LEFT, None, # zoom in + ((0.00, 0.06), (0.01, 0.07), (0.02, 0.08))), + ("zoom", MouseButton.LEFT, 'x', # zoom in + ((-0.01, 0.10), (-0.03, 0.08), (-0.06, 0.06))), + ("zoom", MouseButton.LEFT, 'y', # zoom in + ((-0.07, 0.05), (-0.04, 0.08), (0.00, 0.12))), + ("zoom", MouseButton.RIGHT, None, # zoom out + ((-0.09, 0.15), (-0.08, 0.17), (-0.07, 0.18))), + ("pan", MouseButton.LEFT, None, + ((-0.70, -0.58), (-1.04, -0.91), (-1.27, -1.15))), + ("pan", MouseButton.LEFT, 'x', + ((-0.97, -0.84), (-0.58, -0.46), (-0.06, 0.06))), + ("pan", MouseButton.LEFT, 'y', + ((0.20, 0.32), (-0.51, -0.39), (-1.27, -1.15)))]) +def test_toolbar_zoom_pan(tool, button, key, expected): + # NOTE: The expected zoom values are rough ballparks of moving in the view + # to make sure we are getting the right direction of motion. + # The specific values can and should change if the zoom movement + # scaling factor gets updated. + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(0, 0, 0) + fig.canvas.draw() + xlim0, ylim0, zlim0 = ax.get_xlim3d(), ax.get_ylim3d(), ax.get_zlim3d() + + # Mouse from (0, 0) to (1, 1) + d0 = (0, 0) + d1 = (1, 1) + # Convert to screen coordinates ("s"). Events are defined only with pixel + # precision, so round the pixel values, and below, check against the + # corresponding xdata/ydata, which are close but not equal to d0/d1. + s0 = ax.transData.transform(d0).astype(int) + s1 = ax.transData.transform(d1).astype(int) + + # Set up the mouse movements + start_event = MouseEvent( + "button_press_event", fig.canvas, *s0, button, key=key) + stop_event = MouseEvent( + "button_release_event", fig.canvas, *s1, button, key=key) + + tb = NavigationToolbar2(fig.canvas) + if tool == "zoom": + tb.zoom() + tb.press_zoom(start_event) + tb.drag_zoom(stop_event) + tb.release_zoom(stop_event) + else: + tb.pan() + tb.press_pan(start_event) + tb.drag_pan(stop_event) + tb.release_pan(stop_event) + + # Should be close, but won't be exact due to screen integer resolution + xlim, ylim, zlim = expected + assert ax.get_xlim3d() == pytest.approx(xlim, abs=0.01) + assert ax.get_ylim3d() == pytest.approx(ylim, abs=0.01) + assert ax.get_zlim3d() == pytest.approx(zlim, abs=0.01) + + # Ensure that back, forward, and home buttons work + tb.back() + assert ax.get_xlim3d() == pytest.approx(xlim0) + assert ax.get_ylim3d() == pytest.approx(ylim0) + assert ax.get_zlim3d() == pytest.approx(zlim0) + + tb.forward() + assert ax.get_xlim3d() == pytest.approx(xlim, abs=0.01) + assert ax.get_ylim3d() == pytest.approx(ylim, abs=0.01) + assert ax.get_zlim3d() == pytest.approx(zlim, abs=0.01) + + tb.home() + assert ax.get_xlim3d() == pytest.approx(xlim0) + assert ax.get_ylim3d() == pytest.approx(ylim0) + assert ax.get_zlim3d() == pytest.approx(zlim0) + + +@mpl.style.context('default') +@check_figures_equal(extensions=["png"]) +def test_scalarmap_update(fig_test, fig_ref): + + x, y, z = np.array(list(itertools.product(*[np.arange(0, 5, 1), + np.arange(0, 5, 1), + np.arange(0, 5, 1)]))).T + c = x + y + + # test + ax_test = fig_test.add_subplot(111, projection='3d') + sc_test = ax_test.scatter(x, y, z, c=c, s=40, cmap='viridis') + # force a draw + fig_test.canvas.draw() + # mark it as "stale" + sc_test.changed() + + # ref + ax_ref = fig_ref.add_subplot(111, projection='3d') + sc_ref = ax_ref.scatter(x, y, z, c=c, s=40, cmap='viridis') + + +def test_subfigure_simple(): + # smoketest that subfigures can work... + fig = plt.figure() + sf = fig.subfigures(1, 2) + ax = sf[0].add_subplot(1, 1, 1, projection='3d') + ax = sf[1].add_subplot(1, 1, 1, projection='3d', label='other') + + +# Update style when regenerating the test image +@image_comparison(baseline_images=['computed_zorder'], remove_text=True, + extensions=['png'], style=('mpl20')) +def test_computed_zorder(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax1 = fig.add_subplot(221, projection='3d') + ax2 = fig.add_subplot(222, projection='3d') + ax2.computed_zorder = False + + # create a horizontal plane + corners = ((0, 0, 0), (0, 5, 0), (5, 5, 0), (5, 0, 0)) + for ax in (ax1, ax2): + tri = art3d.Poly3DCollection([corners], + facecolors='white', + edgecolors='black', + zorder=1) + ax.add_collection3d(tri) + + # plot a vector + ax.plot((2, 2), (2, 2), (0, 4), c='red', zorder=2) + + # plot some points + ax.scatter((3, 3), (1, 3), (1, 3), c='red', zorder=10) + + ax.set_xlim((0, 5.0)) + ax.set_ylim((0, 5.0)) + ax.set_zlim((0, 2.5)) + + ax3 = fig.add_subplot(223, projection='3d') + ax4 = fig.add_subplot(224, projection='3d') + ax4.computed_zorder = False + + dim = 10 + X, Y = np.meshgrid((-dim, dim), (-dim, dim)) + Z = np.zeros((2, 2)) + + angle = 0.5 + X2, Y2 = np.meshgrid((-dim, dim), (0, dim)) + Z2 = Y2 * angle + X3, Y3 = np.meshgrid((-dim, dim), (-dim, 0)) + Z3 = Y3 * angle + + r = 7 + M = 1000 + th = np.linspace(0, 2 * np.pi, M) + x, y, z = r * np.cos(th), r * np.sin(th), angle * r * np.sin(th) + for ax in (ax3, ax4): + ax.plot_surface(X2, Y3, Z3, + color='blue', + alpha=0.5, + linewidth=0, + zorder=-1) + ax.plot(x[y < 0], y[y < 0], z[y < 0], + lw=5, + linestyle='--', + color='green', + zorder=0) + + ax.plot_surface(X, Y, Z, + color='red', + alpha=0.5, + linewidth=0, + zorder=1) + + ax.plot(r * np.sin(th), r * np.cos(th), np.zeros(M), + lw=5, + linestyle='--', + color='black', + zorder=2) + + ax.plot_surface(X2, Y2, Z2, + color='blue', + alpha=0.5, + linewidth=0, + zorder=3) + + ax.plot(x[y > 0], y[y > 0], z[y > 0], lw=5, + linestyle='--', + color='green', + zorder=4) + ax.view_init(elev=20, azim=-20, roll=0) + ax.axis('off') + + +def test_format_coord(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(10) + ax.plot(x, np.sin(x)) + xv = 0.1 + yv = 0.1 + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.5227, y pane=1.0417, z=0.1444' + + # Modify parameters + ax.view_init(roll=30, vertical_axis="y") + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x pane=9.1875, y=0.9761, z=0.1291' + + # Reset parameters + ax.view_init() + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.5227, y pane=1.0417, z=0.1444' + + # Check orthographic projection + ax.set_proj_type('ortho') + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.8869, y pane=1.0417, z=0.1528' + + # Check non-default perspective projection + ax.set_proj_type('persp', focal_length=0.1) + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=9.0620, y pane=1.0417, z=0.1110' + + +def test_get_axis_position(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(10) + ax.plot(x, np.sin(x)) + fig.canvas.draw() + assert ax.get_axis_position() == (False, True, False) + + +def test_margins(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(0.2) + assert ax.margins() == (0.2, 0.2, 0.2) + ax.margins(0.1, 0.2, 0.3) + assert ax.margins() == (0.1, 0.2, 0.3) + ax.margins(x=0) + assert ax.margins() == (0, 0.2, 0.3) + ax.margins(y=0.1) + assert ax.margins() == (0, 0.1, 0.3) + ax.margins(z=0) + assert ax.margins() == (0, 0.1, 0) + + +def test_margin_getters(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(0.1, 0.2, 0.3) + assert ax.get_xmargin() == 0.1 + assert ax.get_ymargin() == 0.2 + assert ax.get_zmargin() == 0.3 + + +@pytest.mark.parametrize('err, args, kwargs, match', ( + (ValueError, (-1,), {}, r'margin must be greater than -0\.5'), + (ValueError, (1, -1, 1), {}, r'margin must be greater than -0\.5'), + (ValueError, (1, 1, -1), {}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'x': -1}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'y': -1}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'z': -1}, r'margin must be greater than -0\.5'), + (TypeError, (1, ), {'x': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, ), {'x': 1, 'y': 1, 'z': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, ), {'x': 1, 'y': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, 1), {}, 'Must pass a single positional argument for'), +)) +def test_margins_errors(err, args, kwargs, match): + with pytest.raises(err, match=match): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(*args, **kwargs) + + +@check_figures_equal(extensions=["png"]) +def test_text_3d(fig_test, fig_ref): + ax = fig_ref.add_subplot(projection="3d") + txt = Text(0.5, 0.5, r'Foo bar $\int$') + art3d.text_2d_to_3d(txt, z=1) + ax.add_artist(txt) + assert txt.get_position_3d() == (0.5, 0.5, 1) + + ax = fig_test.add_subplot(projection="3d") + t3d = art3d.Text3D(0.5, 0.5, 1, r'Foo bar $\int$') + ax.add_artist(t3d) + assert t3d.get_position_3d() == (0.5, 0.5, 1) + + +def test_draw_single_lines_from_Nx1(): + # Smoke test for GH#23459 + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot([[0], [1]], [[0], [1]], [[0], [1]]) + + +@check_figures_equal(extensions=["png"]) +def test_pathpatch_3d(fig_test, fig_ref): + ax = fig_ref.add_subplot(projection="3d") + path = Path.unit_rectangle() + patch = PathPatch(path) + art3d.pathpatch_2d_to_3d(patch, z=(0, 0.5, 0.7, 1, 0), zdir='y') + ax.add_artist(patch) + + ax = fig_test.add_subplot(projection="3d") + pp3d = art3d.PathPatch3D(path, zs=(0, 0.5, 0.7, 1, 0), zdir='y') + ax.add_artist(pp3d) + + +@image_comparison(baseline_images=['scatter_spiral.png'], + remove_text=True, + style='mpl20') +def test_scatter_spiral(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + th = np.linspace(0, 2 * np.pi * 6, 256) + sc = ax.scatter(np.sin(th), np.cos(th), th, s=(1 + th * 5), c=th ** 2) + + # force at least 1 draw! + fig.canvas.draw() + + +def test_Poly3DCollection_get_path(): + # Smoke test to see that get_path does not raise + # See GH#27361 + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + p = Circle((0, 0), 1.0) + ax.add_patch(p) + art3d.pathpatch_2d_to_3d(p) + p.get_path() + + +def test_Poly3DCollection_get_facecolor(): + # Smoke test to see that get_facecolor does not raise + # See GH#4067 + y, x = np.ogrid[1:10:100j, 1:10:100j] + z2 = np.cos(x) ** 3 - np.sin(y) ** 2 + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + r = ax.plot_surface(x, y, z2, cmap='hot') + r.get_facecolor() + + +def test_Poly3DCollection_get_edgecolor(): + # Smoke test to see that get_edgecolor does not raise + # See GH#4067 + y, x = np.ogrid[1:10:100j, 1:10:100j] + z2 = np.cos(x) ** 3 - np.sin(y) ** 2 + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + r = ax.plot_surface(x, y, z2, cmap='hot') + r.get_edgecolor() + + +@pytest.mark.parametrize( + "vertical_axis, proj_expected, axis_lines_expected, tickdirs_expected", + [ + ( + "z", + [ + [0.0, 1.142857, 0.0, -0.571429], + [0.0, 0.0, 0.857143, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [-1.142857, 0.0, 0.0, 10.571429], + ], + [ + ([0.05617978, 0.06329114], [-0.04213483, -0.04746835]), + ([-0.06329114, 0.06329114], [-0.04746835, -0.04746835]), + ([-0.06329114, -0.06329114], [-0.04746835, 0.04746835]), + ], + [1, 0, 0], + ), + ( + "y", + [ + [1.142857, 0.0, 0.0, -0.571429], + [0.0, 0.857143, 0.0, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [0.0, 0.0, -1.142857, 10.571429], + ], + [ + ([-0.06329114, 0.06329114], [0.04746835, 0.04746835]), + ([0.06329114, 0.06329114], [-0.04746835, 0.04746835]), + ([-0.05617978, -0.06329114], [0.04213483, 0.04746835]), + ], + [2, 2, 0], + ), + ( + "x", + [ + [0.0, 0.0, 1.142857, -0.571429], + [0.857143, 0.0, 0.0, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [0.0, -1.142857, 0.0, 10.571429], + ], + [ + ([-0.06329114, -0.06329114], [0.04746835, -0.04746835]), + ([0.06329114, 0.05617978], [0.04746835, 0.04213483]), + ([0.06329114, -0.06329114], [0.04746835, 0.04746835]), + ], + [1, 2, 1], + ), + ], +) +def test_view_init_vertical_axis( + vertical_axis, proj_expected, axis_lines_expected, tickdirs_expected +): + """ + Test the actual projection, axis lines and ticks matches expected values. + + Parameters + ---------- + vertical_axis : str + Axis to align vertically. + proj_expected : ndarray + Expected values from ax.get_proj(). + axis_lines_expected : tuple of arrays + Edgepoints of the axis line. Expected values retrieved according + to ``ax.get_[xyz]axis().line.get_data()``. + tickdirs_expected : list of int + indexes indicating which axis to create a tick line along. + """ + rtol = 2e-06 + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + # Assert the projection matrix: + proj_actual = ax.get_proj() + np.testing.assert_allclose(proj_expected, proj_actual, rtol=rtol) + + for i, axis in enumerate([ax.get_xaxis(), ax.get_yaxis(), ax.get_zaxis()]): + # Assert black lines are correctly aligned: + axis_line_expected = axis_lines_expected[i] + axis_line_actual = axis.line.get_data() + np.testing.assert_allclose(axis_line_expected, axis_line_actual, + rtol=rtol) + + # Assert ticks are correctly aligned: + tickdir_expected = tickdirs_expected[i] + tickdir_actual = axis._get_tickdir('default') + np.testing.assert_array_equal(tickdir_expected, tickdir_actual) + + +@pytest.mark.parametrize("vertical_axis", ["x", "y", "z"]) +def test_on_move_vertical_axis(vertical_axis: str) -> None: + """ + Test vertical axis is respected when rotating the plot interactively. + """ + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + proj_before = ax.get_proj() + event_click = mock_event(ax, button=MouseButton.LEFT, xdata=0, ydata=1) + ax._button_press(event_click) + + event_move = mock_event(ax, button=MouseButton.LEFT, xdata=0.5, ydata=0.8) + ax._on_move(event_move) + + assert ax._axis_names.index(vertical_axis) == ax._vertical_axis + + # Make sure plot has actually moved: + proj_after = ax.get_proj() + np.testing.assert_raises( + AssertionError, np.testing.assert_allclose, proj_before, proj_after + ) + + +@pytest.mark.parametrize( + "vertical_axis, aspect_expected", + [ + ("x", [1.190476, 0.892857, 1.190476]), + ("y", [0.892857, 1.190476, 1.190476]), + ("z", [1.190476, 1.190476, 0.892857]), + ], +) +def test_set_box_aspect_vertical_axis(vertical_axis, aspect_expected): + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + ax.set_box_aspect(None) + + np.testing.assert_allclose(aspect_expected, ax._box_aspect, rtol=1e-6) + + +@image_comparison(baseline_images=['arc_pathpatch.png'], + remove_text=True, + style='mpl20') +def test_arc_pathpatch(): + ax = plt.subplot(1, 1, 1, projection="3d") + a = mpatch.Arc((0.5, 0.5), width=0.5, height=0.9, + angle=20, theta1=10, theta2=130) + ax.add_patch(a) + art3d.pathpatch_2d_to_3d(a, z=0, zdir='z') + + +@image_comparison(baseline_images=['panecolor_rcparams.png'], + remove_text=True, + style='mpl20') +def test_panecolor_rcparams(): + with plt.rc_context({'axes3d.xaxis.panecolor': 'r', + 'axes3d.yaxis.panecolor': 'g', + 'axes3d.zaxis.panecolor': 'b'}): + fig = plt.figure(figsize=(1, 1)) + fig.add_subplot(projection='3d') + + +@check_figures_equal(extensions=["png"]) +def test_mutating_input_arrays_y_and_z(fig_test, fig_ref): + """ + Test to see if the `z` axis does not get mutated + after a call to `Axes3D.plot` + + test cases came from GH#8990 + """ + ax1 = fig_test.add_subplot(111, projection='3d') + x = [1, 2, 3] + y = [0.0, 0.0, 0.0] + z = [0.0, 0.0, 0.0] + ax1.plot(x, y, z, 'o-') + + # mutate y,z to get a nontrivial line + y[:] = [1, 2, 3] + z[:] = [1, 2, 3] + + # draw the same plot without mutating x and y + ax2 = fig_ref.add_subplot(111, projection='3d') + x = [1, 2, 3] + y = [0.0, 0.0, 0.0] + z = [0.0, 0.0, 0.0] + ax2.plot(x, y, z, 'o-') + + +def test_scatter_masked_color(): + """ + Test color parameter usage with non-finite coordinate arrays. + + GH#26236 + """ + + x = [np.nan, 1, 2, 1] + y = [0, np.inf, 2, 1] + z = [0, 1, -np.inf, 1] + colors = [ + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1] + ] + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + path3d = ax.scatter(x, y, z, color=colors) + + # Assert sizes' equality + assert len(path3d.get_offsets()) ==\ + len(super(type(path3d), path3d).get_facecolors()) + + +@mpl3d_image_comparison(['surface3d_zsort_inf.png'], style='mpl20') +def test_surface3d_zsort_inf(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x, y = np.mgrid[-2:2:0.1, -2:2:0.1] + z = np.sin(x)**2 + np.cos(y)**2 + z[x.shape[0] // 2:, x.shape[1] // 2:] = np.inf + + ax.plot_surface(x, y, z, cmap='jet') + ax.view_init(elev=45, azim=145) + + +def test_Poly3DCollection_init_value_error(): + # smoke test to ensure the input check works + # GH#26420 + with pytest.raises(ValueError, + match='You must provide facecolors, edgecolors, ' + 'or both for shade to work.'): + poly = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + c = art3d.Poly3DCollection([poly], shade=True) + + +def test_ndarray_color_kwargs_value_error(): + # smoke test + # ensures ndarray can be passed to color in kwargs for 3d projection plot + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.scatter(1, 0, 0, color=np.array([0, 0, 0, 1])) + fig.canvas.draw() diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py new file mode 100644 index 0000000000000000000000000000000000000000..7fd676df1e31591f343a4a1bd27c27b98d564949 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py @@ -0,0 +1,117 @@ +import platform + +import numpy as np + +import matplotlib as mpl +from matplotlib.colors import same_color +from matplotlib.testing.decorators import image_comparison +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import art3d + + +@image_comparison(['legend_plot.png'], remove_text=True, style='mpl20') +def test_legend_plot(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + x = np.arange(10) + ax.plot(x, 5 - x, 'o', zdir='y', label='z=1') + ax.plot(x, x - 5, 'o', zdir='y', label='z=-1') + ax.legend() + + +@image_comparison(['legend_bar.png'], remove_text=True, style='mpl20') +def test_legend_bar(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + x = np.arange(10) + b1 = ax.bar(x, x, zdir='y', align='edge', color='m') + b2 = ax.bar(x, x[::-1], zdir='x', align='edge', color='g') + ax.legend([b1[0], b2[0]], ['up', 'down']) + + +@image_comparison(['fancy.png'], remove_text=True, style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.011) +def test_fancy(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.plot(np.arange(10), np.full(10, 5), np.full(10, 5), 'o--', label='line') + ax.scatter(np.arange(10), np.arange(10, 0, -1), label='scatter') + ax.errorbar(np.full(10, 5), np.arange(10), np.full(10, 10), + xerr=0.5, zerr=0.5, label='errorbar') + ax.legend(loc='lower left', ncols=2, title='My legend', numpoints=1) + + +def test_linecollection_scaled_dashes(): + lines1 = [[(0, .5), (.5, 1)], [(.3, .6), (.2, .2)]] + lines2 = [[[0.7, .2], [.8, .4]], [[.5, .7], [.6, .1]]] + lines3 = [[[0.6, .2], [.8, .4]], [[.5, .7], [.1, .1]]] + lc1 = art3d.Line3DCollection(lines1, linestyles="--", lw=3) + lc2 = art3d.Line3DCollection(lines2, linestyles="-.") + lc3 = art3d.Line3DCollection(lines3, linestyles=":", lw=.5) + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.add_collection(lc1) + ax.add_collection(lc2) + ax.add_collection(lc3) + + leg = ax.legend([lc1, lc2, lc3], ['line1', 'line2', 'line 3']) + h1, h2, h3 = leg.legend_handles + + for oh, lh in zip((lc1, lc2, lc3), (h1, h2, h3)): + assert oh.get_linestyles()[0] == lh._dash_pattern + + +def test_handlerline3d(): + # Test marker consistency for monolithic Line3D legend handler. + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.scatter([0, 1], [0, 1], marker="v") + handles = [art3d.Line3D([0], [0], [0], marker="v")] + leg = ax.legend(handles, ["Aardvark"], numpoints=1) + assert handles[0].get_marker() == leg.legend_handles[0].get_marker() + + +def test_contour_legend_elements(): + x, y = np.mgrid[1:10, 1:10] + h = x * y + colors = ['blue', '#00FF00', 'red'] + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + cs = ax.contour(x, y, h, levels=[10, 30, 50], colors=colors, extend='both') + + artists, labels = cs.legend_elements() + assert labels == ['$x = 10.0$', '$x = 30.0$', '$x = 50.0$'] + assert all(isinstance(a, mpl.lines.Line2D) for a in artists) + assert all(same_color(a.get_color(), c) + for a, c in zip(artists, colors)) + + +def test_contourf_legend_elements(): + x, y = np.mgrid[1:10, 1:10] + h = x * y + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + cs = ax.contourf(x, y, h, levels=[10, 30, 50], + colors=['#FFFF00', '#FF00FF', '#00FFFF'], + extend='both') + cs.cmap.set_over('red') + cs.cmap.set_under('blue') + cs.changed() + artists, labels = cs.legend_elements() + assert labels == ['$x \\leq -1e+250s$', + '$10.0 < x \\leq 30.0$', + '$30.0 < x \\leq 50.0$', + '$x > 1e+250s$'] + expected_colors = ('blue', '#FFFF00', '#FF00FF', 'red') + assert all(isinstance(a, mpl.patches.Rectangle) for a in artists) + assert all(same_color(a.get_facecolor(), c) + for a, c in zip(artists, expected_colors)) + + +def test_legend_Poly3dCollection(): + + verts = np.asarray([[0, 0, 0], [0, 1, 1], [1, 0, 1]]) + mesh = art3d.Poly3DCollection([verts], label="surface") + + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + mesh.set_edgecolor('k') + handle = ax.add_collection3d(mesh) + leg = ax.legend() + assert (leg.legend_handles[0].get_facecolor() + == handle.get_facecolor()).all() diff --git 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0000000000000000000000000000000000000000..c1a152704fa18c79f928b8fa6644f362579383f1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__multiarray_api.c @@ -0,0 +1,376 @@ + +/* These pointers will be stored in the C-object for use in other + extension modules +*/ + +void *PyArray_API[] = { + (void *) PyArray_GetNDArrayCVersion, + NULL, + (void *) &PyArray_Type, + (void *) &PyArrayDescr_Type, + NULL, + (void *) &PyArrayIter_Type, + (void *) &PyArrayMultiIter_Type, + (int *) &NPY_NUMUSERTYPES, + (void *) &PyBoolArrType_Type, + (void *) &_PyArrayScalar_BoolValues, + (void *) &PyGenericArrType_Type, + (void *) &PyNumberArrType_Type, + (void *) &PyIntegerArrType_Type, + (void *) &PySignedIntegerArrType_Type, + (void *) &PyUnsignedIntegerArrType_Type, + (void *) &PyInexactArrType_Type, + (void *) &PyFloatingArrType_Type, + (void *) &PyComplexFloatingArrType_Type, + (void *) &PyFlexibleArrType_Type, + (void *) &PyCharacterArrType_Type, + (void *) &PyByteArrType_Type, + (void *) &PyShortArrType_Type, + (void *) &PyIntArrType_Type, + (void *) &PyLongArrType_Type, + (void *) &PyLongLongArrType_Type, + (void *) &PyUByteArrType_Type, + (void *) &PyUShortArrType_Type, + (void *) &PyUIntArrType_Type, + (void *) &PyULongArrType_Type, + (void *) &PyULongLongArrType_Type, + (void *) &PyFloatArrType_Type, + (void *) &PyDoubleArrType_Type, + (void *) &PyLongDoubleArrType_Type, + (void *) &PyCFloatArrType_Type, + (void *) &PyCDoubleArrType_Type, + (void *) &PyCLongDoubleArrType_Type, + (void *) &PyObjectArrType_Type, + (void *) &PyStringArrType_Type, + (void *) &PyUnicodeArrType_Type, + (void *) &PyVoidArrType_Type, + NULL, + NULL, + (void *) PyArray_INCREF, + (void *) PyArray_XDECREF, + (void *) PyArray_SetStringFunction, + (void *) PyArray_DescrFromType, + (void *) PyArray_TypeObjectFromType, + (void *) PyArray_Zero, + (void *) PyArray_One, + (void *) PyArray_CastToType, + (void *) PyArray_CopyInto, + (void *) PyArray_CopyAnyInto, + (void *) PyArray_CanCastSafely, + (void *) PyArray_CanCastTo, + (void *) PyArray_ObjectType, + (void *) PyArray_DescrFromObject, + (void *) PyArray_ConvertToCommonType, + (void *) PyArray_DescrFromScalar, + (void *) PyArray_DescrFromTypeObject, + (void *) PyArray_Size, + (void *) PyArray_Scalar, + (void *) PyArray_FromScalar, + (void *) PyArray_ScalarAsCtype, + (void *) PyArray_CastScalarToCtype, + (void *) PyArray_CastScalarDirect, + (void *) PyArray_Pack, + NULL, + NULL, + NULL, + (void *) PyArray_FromAny, + (void *) PyArray_EnsureArray, + (void *) PyArray_EnsureAnyArray, + (void *) PyArray_FromFile, + (void *) PyArray_FromString, + (void *) PyArray_FromBuffer, + (void *) PyArray_FromIter, + (void *) PyArray_Return, + (void *) PyArray_GetField, + (void *) PyArray_SetField, + (void *) PyArray_Byteswap, + (void *) PyArray_Resize, + NULL, + NULL, + NULL, + (void *) PyArray_CopyObject, + (void *) PyArray_NewCopy, + (void *) PyArray_ToList, + (void *) PyArray_ToString, + (void *) PyArray_ToFile, + (void *) PyArray_Dump, + (void *) PyArray_Dumps, + (void *) PyArray_ValidType, + (void *) PyArray_UpdateFlags, + (void *) PyArray_New, + (void *) PyArray_NewFromDescr, + (void *) PyArray_DescrNew, + (void *) PyArray_DescrNewFromType, + (void *) PyArray_GetPriority, + (void *) PyArray_IterNew, + (void *) PyArray_MultiIterNew, + (void *) PyArray_PyIntAsInt, + (void *) PyArray_PyIntAsIntp, + (void *) PyArray_Broadcast, + NULL, + (void *) PyArray_FillWithScalar, + (void *) PyArray_CheckStrides, + (void *) PyArray_DescrNewByteorder, + (void *) PyArray_IterAllButAxis, + (void *) PyArray_CheckFromAny, + (void *) PyArray_FromArray, + (void *) PyArray_FromInterface, + (void *) PyArray_FromStructInterface, + (void *) PyArray_FromArrayAttr, + (void *) PyArray_ScalarKind, + (void *) PyArray_CanCoerceScalar, + NULL, + (void *) PyArray_CanCastScalar, + NULL, + (void *) PyArray_RemoveSmallest, + (void *) PyArray_ElementStrides, + (void *) PyArray_Item_INCREF, + (void *) PyArray_Item_XDECREF, + NULL, + (void *) PyArray_Transpose, + (void *) PyArray_TakeFrom, + (void *) PyArray_PutTo, + (void *) PyArray_PutMask, + (void *) PyArray_Repeat, + (void *) PyArray_Choose, + (void *) PyArray_Sort, + (void *) PyArray_ArgSort, + (void *) PyArray_SearchSorted, + (void *) PyArray_ArgMax, + (void *) PyArray_ArgMin, + (void *) PyArray_Reshape, + (void *) PyArray_Newshape, + (void *) PyArray_Squeeze, + (void *) PyArray_View, + (void *) PyArray_SwapAxes, + (void *) PyArray_Max, + (void *) PyArray_Min, + (void *) PyArray_Ptp, + (void *) PyArray_Mean, + (void *) PyArray_Trace, + (void *) PyArray_Diagonal, + (void *) PyArray_Clip, + (void *) PyArray_Conjugate, + (void *) PyArray_Nonzero, + (void *) PyArray_Std, + (void *) PyArray_Sum, + (void *) PyArray_CumSum, + (void *) PyArray_Prod, + (void *) PyArray_CumProd, + (void *) PyArray_All, + (void *) PyArray_Any, + (void *) PyArray_Compress, + (void *) PyArray_Flatten, + (void *) PyArray_Ravel, + (void *) PyArray_MultiplyList, + (void *) PyArray_MultiplyIntList, + (void *) PyArray_GetPtr, + (void *) PyArray_CompareLists, + (void *) PyArray_AsCArray, + NULL, + NULL, + (void *) PyArray_Free, + (void *) PyArray_Converter, + (void *) PyArray_IntpFromSequence, + (void *) PyArray_Concatenate, + (void *) PyArray_InnerProduct, + (void *) PyArray_MatrixProduct, + NULL, + (void *) PyArray_Correlate, + NULL, + (void *) PyArray_DescrConverter, + (void *) PyArray_DescrConverter2, + (void *) PyArray_IntpConverter, + (void *) PyArray_BufferConverter, + (void *) PyArray_AxisConverter, + (void *) PyArray_BoolConverter, + (void *) PyArray_ByteorderConverter, + (void *) PyArray_OrderConverter, + (void *) PyArray_EquivTypes, + (void *) PyArray_Zeros, + (void *) PyArray_Empty, + (void *) PyArray_Where, + (void *) PyArray_Arange, + (void *) PyArray_ArangeObj, + (void *) PyArray_SortkindConverter, + (void *) PyArray_LexSort, + (void *) PyArray_Round, + (void *) PyArray_EquivTypenums, + (void *) PyArray_RegisterDataType, + (void *) PyArray_RegisterCastFunc, + (void *) PyArray_RegisterCanCast, + (void *) PyArray_InitArrFuncs, + (void *) PyArray_IntTupleFromIntp, + NULL, + (void *) PyArray_ClipmodeConverter, + (void *) PyArray_OutputConverter, + (void *) PyArray_BroadcastToShape, + NULL, + NULL, + (void *) PyArray_DescrAlignConverter, + (void *) PyArray_DescrAlignConverter2, + (void *) PyArray_SearchsideConverter, + (void *) PyArray_CheckAxis, + (void *) PyArray_OverflowMultiplyList, + NULL, + (void *) PyArray_MultiIterFromObjects, + (void *) PyArray_GetEndianness, + (void *) PyArray_GetNDArrayCFeatureVersion, + (void *) PyArray_Correlate2, + (void *) PyArray_NeighborhoodIterNew, + (void *) &PyTimeIntegerArrType_Type, + (void *) &PyDatetimeArrType_Type, + (void *) &PyTimedeltaArrType_Type, + (void *) &PyHalfArrType_Type, + (void *) &NpyIter_Type, + NULL, + NULL, + NULL, + NULL, + NULL, + (void *) NpyIter_New, + (void *) NpyIter_MultiNew, + (void *) NpyIter_AdvancedNew, + (void *) NpyIter_Copy, + (void *) NpyIter_Deallocate, + (void *) NpyIter_HasDelayedBufAlloc, + (void *) NpyIter_HasExternalLoop, + (void *) NpyIter_EnableExternalLoop, + (void *) NpyIter_GetInnerStrideArray, + (void *) NpyIter_GetInnerLoopSizePtr, + (void *) NpyIter_Reset, + (void *) NpyIter_ResetBasePointers, + (void *) NpyIter_ResetToIterIndexRange, + (void *) NpyIter_GetNDim, + (void *) NpyIter_GetNOp, + (void *) NpyIter_GetIterNext, + (void *) NpyIter_GetIterSize, + (void *) NpyIter_GetIterIndexRange, + (void *) NpyIter_GetIterIndex, + (void *) NpyIter_GotoIterIndex, + (void *) NpyIter_HasMultiIndex, + (void *) NpyIter_GetShape, + (void *) NpyIter_GetGetMultiIndex, + (void *) NpyIter_GotoMultiIndex, + (void *) NpyIter_RemoveMultiIndex, + (void *) NpyIter_HasIndex, + (void *) NpyIter_IsBuffered, + (void *) NpyIter_IsGrowInner, + (void *) NpyIter_GetBufferSize, + (void *) NpyIter_GetIndexPtr, + (void *) NpyIter_GotoIndex, + (void *) NpyIter_GetDataPtrArray, + (void *) NpyIter_GetDescrArray, + (void *) NpyIter_GetOperandArray, + (void *) NpyIter_GetIterView, + (void *) NpyIter_GetReadFlags, + (void *) NpyIter_GetWriteFlags, + (void *) NpyIter_DebugPrint, + (void *) NpyIter_IterationNeedsAPI, + (void *) NpyIter_GetInnerFixedStrideArray, + (void *) NpyIter_RemoveAxis, + (void *) NpyIter_GetAxisStrideArray, + (void *) NpyIter_RequiresBuffering, + (void *) NpyIter_GetInitialDataPtrArray, + (void *) NpyIter_CreateCompatibleStrides, + (void *) PyArray_CastingConverter, + (void *) PyArray_CountNonzero, + (void *) PyArray_PromoteTypes, + (void *) PyArray_MinScalarType, + (void *) PyArray_ResultType, + (void *) PyArray_CanCastArrayTo, + (void *) PyArray_CanCastTypeTo, + (void *) PyArray_EinsteinSum, + (void *) PyArray_NewLikeArray, + NULL, + (void *) PyArray_ConvertClipmodeSequence, + (void *) PyArray_MatrixProduct2, + (void *) NpyIter_IsFirstVisit, + (void *) PyArray_SetBaseObject, + (void *) PyArray_CreateSortedStridePerm, + (void *) PyArray_RemoveAxesInPlace, + (void *) PyArray_DebugPrint, + (void *) PyArray_FailUnlessWriteable, + (void *) PyArray_SetUpdateIfCopyBase, + (void *) PyDataMem_NEW, + (void *) PyDataMem_FREE, + (void *) PyDataMem_RENEW, + NULL, + (NPY_CASTING *) &NPY_DEFAULT_ASSIGN_CASTING, + NULL, + NULL, + NULL, + (void *) PyArray_Partition, + (void *) PyArray_ArgPartition, + (void *) PyArray_SelectkindConverter, + (void *) PyDataMem_NEW_ZEROED, + (void *) PyArray_CheckAnyScalarExact, + NULL, + (void *) PyArray_ResolveWritebackIfCopy, + (void *) PyArray_SetWritebackIfCopyBase, + (void *) PyDataMem_SetHandler, + (void *) PyDataMem_GetHandler, + (PyObject* *) &PyDataMem_DefaultHandler, + (void *) NpyDatetime_ConvertDatetime64ToDatetimeStruct, + (void *) NpyDatetime_ConvertDatetimeStructToDatetime64, + (void *) NpyDatetime_ConvertPyDateTimeToDatetimeStruct, + (void *) NpyDatetime_GetDatetimeISO8601StrLen, + (void *) NpyDatetime_MakeISO8601Datetime, + (void *) NpyDatetime_ParseISO8601Datetime, + (void *) NpyString_load, + (void *) NpyString_pack, + (void *) NpyString_pack_null, + (void *) NpyString_acquire_allocator, + (void *) NpyString_acquire_allocators, + (void *) NpyString_release_allocator, + (void *) NpyString_release_allocators, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + NULL, + (void *) PyArray_GetDefaultDescr, + (void *) PyArrayInitDTypeMeta_FromSpec, + (void *) PyArray_CommonDType, + (void *) PyArray_PromoteDTypeSequence, + (void *) _PyDataType_GetArrFuncs, + NULL, + NULL, + NULL +}; diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__multiarray_api.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__multiarray_api.h new file mode 100644 index 0000000000000000000000000000000000000000..cfc3628aa53e41f22d81877df31d40020519bebf --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__multiarray_api.h @@ -0,0 +1,1613 @@ + +#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE) + +typedef struct { + PyObject_HEAD + npy_bool obval; +} PyBoolScalarObject; + +extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type; +extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2]; + +NPY_NO_EXPORT unsigned int PyArray_GetNDArrayCVersion \ + (void); +extern NPY_NO_EXPORT PyTypeObject PyArray_Type; + +extern NPY_NO_EXPORT PyArray_DTypeMeta PyArrayDescr_TypeFull; +#define PyArrayDescr_Type (*(PyTypeObject *)(&PyArrayDescr_TypeFull)) + +extern NPY_NO_EXPORT PyTypeObject PyArrayIter_Type; + +extern NPY_NO_EXPORT PyTypeObject PyArrayMultiIter_Type; + +extern NPY_NO_EXPORT int NPY_NUMUSERTYPES; + +extern NPY_NO_EXPORT PyTypeObject PyBoolArrType_Type; + +extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2]; + +extern NPY_NO_EXPORT PyTypeObject PyGenericArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyNumberArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyIntegerArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PySignedIntegerArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUnsignedIntegerArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyInexactArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyFloatingArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyComplexFloatingArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyFlexibleArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyCharacterArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyByteArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyShortArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyIntArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyLongArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyLongLongArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUByteArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUShortArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUIntArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyULongArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyULongLongArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyFloatArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyDoubleArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyLongDoubleArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyCFloatArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyCDoubleArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyCLongDoubleArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyObjectArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyStringArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUnicodeArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyVoidArrType_Type; + +NPY_NO_EXPORT int PyArray_INCREF \ + (PyArrayObject *); +NPY_NO_EXPORT int PyArray_XDECREF \ + (PyArrayObject *); +NPY_NO_EXPORT void PyArray_SetStringFunction \ + (PyObject *, int); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromType \ + (int); +NPY_NO_EXPORT PyObject * PyArray_TypeObjectFromType \ + (int); +NPY_NO_EXPORT char * PyArray_Zero \ + (PyArrayObject *); +NPY_NO_EXPORT char * PyArray_One \ + (PyArrayObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CastToType \ + (PyArrayObject *, PyArray_Descr *, int); +NPY_NO_EXPORT int PyArray_CopyInto \ + (PyArrayObject *, PyArrayObject *); +NPY_NO_EXPORT int PyArray_CopyAnyInto \ + (PyArrayObject *, PyArrayObject *); +NPY_NO_EXPORT int PyArray_CanCastSafely \ + (int, int); +NPY_NO_EXPORT npy_bool PyArray_CanCastTo \ + (PyArray_Descr *, PyArray_Descr *); +NPY_NO_EXPORT int PyArray_ObjectType \ + (PyObject *, int); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromObject \ + (PyObject *, PyArray_Descr *); +NPY_NO_EXPORT PyArrayObject ** PyArray_ConvertToCommonType \ + (PyObject *, int *); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromScalar \ + (PyObject *); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromTypeObject \ + (PyObject *); +NPY_NO_EXPORT npy_intp PyArray_Size \ + (PyObject *); +NPY_NO_EXPORT PyObject * PyArray_Scalar \ + (void *, PyArray_Descr *, PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromScalar \ + (PyObject *, PyArray_Descr *); +NPY_NO_EXPORT void PyArray_ScalarAsCtype \ + (PyObject *, void *); +NPY_NO_EXPORT int PyArray_CastScalarToCtype \ + (PyObject *, void *, PyArray_Descr *); +NPY_NO_EXPORT int PyArray_CastScalarDirect \ + (PyObject *, PyArray_Descr *, void *, int); +NPY_NO_EXPORT int PyArray_Pack \ + (PyArray_Descr *, void *, PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromAny \ + (PyObject *, PyArray_Descr *, int, int, int, PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureArray \ + (PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureAnyArray \ + (PyObject *); +NPY_NO_EXPORT PyObject * PyArray_FromFile \ + (FILE *, PyArray_Descr *, npy_intp, char *); +NPY_NO_EXPORT PyObject * PyArray_FromString \ + (char *, npy_intp, PyArray_Descr *, npy_intp, char *); +NPY_NO_EXPORT PyObject * PyArray_FromBuffer \ + (PyObject *, PyArray_Descr *, npy_intp, npy_intp); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromIter \ + (PyObject *, PyArray_Descr *, npy_intp); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_Return \ + (PyArrayObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_GetField \ + (PyArrayObject *, PyArray_Descr *, int); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetField \ + (PyArrayObject *, PyArray_Descr *, int, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_Byteswap \ + (PyArrayObject *, npy_bool); +NPY_NO_EXPORT PyObject * PyArray_Resize \ + (PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order)); +NPY_NO_EXPORT int PyArray_CopyObject \ + (PyArrayObject *, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_NewCopy \ + (PyArrayObject *, NPY_ORDER); +NPY_NO_EXPORT PyObject * PyArray_ToList \ + (PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_ToString \ + (PyArrayObject *, NPY_ORDER); +NPY_NO_EXPORT int PyArray_ToFile \ + (PyArrayObject *, FILE *, char *, char *); +NPY_NO_EXPORT int PyArray_Dump \ + (PyObject *, PyObject *, int); +NPY_NO_EXPORT PyObject * PyArray_Dumps \ + (PyObject *, int); +NPY_NO_EXPORT int PyArray_ValidType \ + (int); +NPY_NO_EXPORT void PyArray_UpdateFlags \ + (PyArrayObject *, int); +NPY_NO_EXPORT PyObject * PyArray_New \ + (PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_NewFromDescr \ + (PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNew \ + (PyArray_Descr *); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewFromType \ + (int); +NPY_NO_EXPORT double PyArray_GetPriority \ + (PyObject *, double); +NPY_NO_EXPORT PyObject * PyArray_IterNew \ + (PyObject *); +NPY_NO_EXPORT PyObject* PyArray_MultiIterNew \ + (int, ...); +NPY_NO_EXPORT int PyArray_PyIntAsInt \ + (PyObject *); +NPY_NO_EXPORT npy_intp PyArray_PyIntAsIntp \ + (PyObject *); +NPY_NO_EXPORT int PyArray_Broadcast \ + (PyArrayMultiIterObject *); +NPY_NO_EXPORT int PyArray_FillWithScalar \ + (PyArrayObject *, PyObject *); +NPY_NO_EXPORT npy_bool PyArray_CheckStrides \ + (int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *); +NPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewByteorder \ + (PyArray_Descr *, char); +NPY_NO_EXPORT PyObject * PyArray_IterAllButAxis \ + (PyObject *, int *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CheckFromAny \ + (PyObject *, PyArray_Descr *, int, int, int, PyObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromArray \ + (PyArrayObject *, PyArray_Descr *, int); +NPY_NO_EXPORT PyObject * PyArray_FromInterface \ + (PyObject *); +NPY_NO_EXPORT PyObject * PyArray_FromStructInterface \ + (PyObject *); +NPY_NO_EXPORT PyObject * PyArray_FromArrayAttr \ + (PyObject *, PyArray_Descr *, PyObject *); +NPY_NO_EXPORT NPY_SCALARKIND PyArray_ScalarKind \ + (int, PyArrayObject **); +NPY_NO_EXPORT int PyArray_CanCoerceScalar \ + (int, int, NPY_SCALARKIND); +NPY_NO_EXPORT npy_bool PyArray_CanCastScalar \ + (PyTypeObject *, PyTypeObject *); +NPY_NO_EXPORT int PyArray_RemoveSmallest \ + (PyArrayMultiIterObject *); +NPY_NO_EXPORT int PyArray_ElementStrides \ + (PyObject *); +NPY_NO_EXPORT void PyArray_Item_INCREF \ + (char *, PyArray_Descr *); +NPY_NO_EXPORT void PyArray_Item_XDECREF \ + (char *, PyArray_Descr *); +NPY_NO_EXPORT PyObject * PyArray_Transpose \ + (PyArrayObject *, PyArray_Dims *); +NPY_NO_EXPORT PyObject * PyArray_TakeFrom \ + (PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE); +NPY_NO_EXPORT PyObject * PyArray_PutTo \ + (PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE); +NPY_NO_EXPORT PyObject * PyArray_PutMask \ + (PyArrayObject *, PyObject*, PyObject*); +NPY_NO_EXPORT PyObject * PyArray_Repeat \ + (PyArrayObject *, PyObject *, int); +NPY_NO_EXPORT PyObject * PyArray_Choose \ + (PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE); +NPY_NO_EXPORT int PyArray_Sort \ + (PyArrayObject *, int, NPY_SORTKIND); +NPY_NO_EXPORT PyObject * PyArray_ArgSort \ + (PyArrayObject *, int, NPY_SORTKIND); +NPY_NO_EXPORT PyObject * PyArray_SearchSorted \ + (PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_ArgMax \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_ArgMin \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Reshape \ + (PyArrayObject *, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_Newshape \ + (PyArrayObject *, PyArray_Dims *, NPY_ORDER); +NPY_NO_EXPORT PyObject * PyArray_Squeeze \ + (PyArrayObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_View \ + (PyArrayObject *, PyArray_Descr *, PyTypeObject *); +NPY_NO_EXPORT PyObject * PyArray_SwapAxes \ + (PyArrayObject *, int, int); +NPY_NO_EXPORT PyObject * PyArray_Max \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Min \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Ptp \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Mean \ + (PyArrayObject *, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Trace \ + (PyArrayObject *, int, int, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Diagonal \ + (PyArrayObject *, int, int, int); +NPY_NO_EXPORT PyObject * PyArray_Clip \ + (PyArrayObject *, PyObject *, PyObject *, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Conjugate \ + (PyArrayObject *, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Nonzero \ + (PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Std \ + (PyArrayObject *, int, int, PyArrayObject *, int); +NPY_NO_EXPORT PyObject * PyArray_Sum \ + (PyArrayObject *, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_CumSum \ + (PyArrayObject *, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Prod \ + (PyArrayObject *, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_CumProd \ + (PyArrayObject *, int, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_All \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Any \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Compress \ + (PyArrayObject *, PyObject *, int, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyArray_Flatten \ + (PyArrayObject *, NPY_ORDER); +NPY_NO_EXPORT PyObject * PyArray_Ravel \ + (PyArrayObject *, NPY_ORDER); +NPY_NO_EXPORT npy_intp PyArray_MultiplyList \ + (npy_intp const *, int); +NPY_NO_EXPORT int PyArray_MultiplyIntList \ + (int const *, int); +NPY_NO_EXPORT void * PyArray_GetPtr \ + (PyArrayObject *, npy_intp const*); +NPY_NO_EXPORT int PyArray_CompareLists \ + (npy_intp const *, npy_intp const *, int); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(5) int PyArray_AsCArray \ + (PyObject **, void *, npy_intp *, int, PyArray_Descr*); +NPY_NO_EXPORT int PyArray_Free \ + (PyObject *, void *); +NPY_NO_EXPORT int PyArray_Converter \ + (PyObject *, PyObject **); +NPY_NO_EXPORT int PyArray_IntpFromSequence \ + (PyObject *, npy_intp *, int); +NPY_NO_EXPORT PyObject * PyArray_Concatenate \ + (PyObject *, int); +NPY_NO_EXPORT PyObject * PyArray_InnerProduct \ + (PyObject *, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_MatrixProduct \ + (PyObject *, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_Correlate \ + (PyObject *, PyObject *, int); +NPY_NO_EXPORT int PyArray_DescrConverter \ + (PyObject *, PyArray_Descr **); +NPY_NO_EXPORT int PyArray_DescrConverter2 \ + (PyObject *, PyArray_Descr **); +NPY_NO_EXPORT int PyArray_IntpConverter \ + (PyObject *, PyArray_Dims *); +NPY_NO_EXPORT int PyArray_BufferConverter \ + (PyObject *, PyArray_Chunk *); +NPY_NO_EXPORT int PyArray_AxisConverter \ + (PyObject *, int *); +NPY_NO_EXPORT int PyArray_BoolConverter \ + (PyObject *, npy_bool *); +NPY_NO_EXPORT int PyArray_ByteorderConverter \ + (PyObject *, char *); +NPY_NO_EXPORT int PyArray_OrderConverter \ + (PyObject *, NPY_ORDER *); +NPY_NO_EXPORT unsigned char PyArray_EquivTypes \ + (PyArray_Descr *, PyArray_Descr *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Zeros \ + (int, npy_intp const *, PyArray_Descr *, int); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Empty \ + (int, npy_intp const *, PyArray_Descr *, int); +NPY_NO_EXPORT PyObject * PyArray_Where \ + (PyObject *, PyObject *, PyObject *); +NPY_NO_EXPORT PyObject * PyArray_Arange \ + (double, double, double, int); +NPY_NO_EXPORT PyObject * PyArray_ArangeObj \ + (PyObject *, PyObject *, PyObject *, PyArray_Descr *); +NPY_NO_EXPORT int PyArray_SortkindConverter \ + (PyObject *, NPY_SORTKIND *); +NPY_NO_EXPORT PyObject * PyArray_LexSort \ + (PyObject *, int); +NPY_NO_EXPORT PyObject * PyArray_Round \ + (PyArrayObject *, int, PyArrayObject *); +NPY_NO_EXPORT unsigned char PyArray_EquivTypenums \ + (int, int); +NPY_NO_EXPORT int PyArray_RegisterDataType \ + (PyArray_DescrProto *); +NPY_NO_EXPORT int PyArray_RegisterCastFunc \ + (PyArray_Descr *, int, PyArray_VectorUnaryFunc *); +NPY_NO_EXPORT int PyArray_RegisterCanCast \ + (PyArray_Descr *, int, NPY_SCALARKIND); +NPY_NO_EXPORT void PyArray_InitArrFuncs \ + (PyArray_ArrFuncs *); +NPY_NO_EXPORT PyObject * PyArray_IntTupleFromIntp \ + (int, npy_intp const *); +NPY_NO_EXPORT int PyArray_ClipmodeConverter \ + (PyObject *, NPY_CLIPMODE *); +NPY_NO_EXPORT int PyArray_OutputConverter \ + (PyObject *, PyArrayObject **); +NPY_NO_EXPORT PyObject * PyArray_BroadcastToShape \ + (PyObject *, npy_intp *, int); +NPY_NO_EXPORT int PyArray_DescrAlignConverter \ + (PyObject *, PyArray_Descr **); +NPY_NO_EXPORT int PyArray_DescrAlignConverter2 \ + (PyObject *, PyArray_Descr **); +NPY_NO_EXPORT int PyArray_SearchsideConverter \ + (PyObject *, void *); +NPY_NO_EXPORT PyObject * PyArray_CheckAxis \ + (PyArrayObject *, int *, int); +NPY_NO_EXPORT npy_intp PyArray_OverflowMultiplyList \ + (npy_intp const *, int); +NPY_NO_EXPORT PyObject* PyArray_MultiIterFromObjects \ + (PyObject **, int, int, ...); +NPY_NO_EXPORT int PyArray_GetEndianness \ + (void); +NPY_NO_EXPORT unsigned int PyArray_GetNDArrayCFeatureVersion \ + (void); +NPY_NO_EXPORT PyObject * PyArray_Correlate2 \ + (PyObject *, PyObject *, int); +NPY_NO_EXPORT PyObject* PyArray_NeighborhoodIterNew \ + (PyArrayIterObject *, const npy_intp *, int, PyArrayObject*); +extern NPY_NO_EXPORT PyTypeObject PyTimeIntegerArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyDatetimeArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyTimedeltaArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject PyHalfArrType_Type; + +extern NPY_NO_EXPORT PyTypeObject NpyIter_Type; + +NPY_NO_EXPORT NpyIter * NpyIter_New \ + (PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*); +NPY_NO_EXPORT NpyIter * NpyIter_MultiNew \ + (int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **); +NPY_NO_EXPORT NpyIter * NpyIter_AdvancedNew \ + (int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp); +NPY_NO_EXPORT NpyIter * NpyIter_Copy \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_Deallocate \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_HasDelayedBufAlloc \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_HasExternalLoop \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_EnableExternalLoop \ + (NpyIter *); +NPY_NO_EXPORT npy_intp * NpyIter_GetInnerStrideArray \ + (NpyIter *); +NPY_NO_EXPORT npy_intp * NpyIter_GetInnerLoopSizePtr \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_Reset \ + (NpyIter *, char **); +NPY_NO_EXPORT int NpyIter_ResetBasePointers \ + (NpyIter *, char **, char **); +NPY_NO_EXPORT int NpyIter_ResetToIterIndexRange \ + (NpyIter *, npy_intp, npy_intp, char **); +NPY_NO_EXPORT int NpyIter_GetNDim \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_GetNOp \ + (NpyIter *); +NPY_NO_EXPORT NpyIter_IterNextFunc * NpyIter_GetIterNext \ + (NpyIter *, char **); +NPY_NO_EXPORT npy_intp NpyIter_GetIterSize \ + (NpyIter *); +NPY_NO_EXPORT void NpyIter_GetIterIndexRange \ + (NpyIter *, npy_intp *, npy_intp *); +NPY_NO_EXPORT npy_intp NpyIter_GetIterIndex \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_GotoIterIndex \ + (NpyIter *, npy_intp); +NPY_NO_EXPORT npy_bool NpyIter_HasMultiIndex \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_GetShape \ + (NpyIter *, npy_intp *); +NPY_NO_EXPORT NpyIter_GetMultiIndexFunc * NpyIter_GetGetMultiIndex \ + (NpyIter *, char **); +NPY_NO_EXPORT int NpyIter_GotoMultiIndex \ + (NpyIter *, npy_intp const *); +NPY_NO_EXPORT int NpyIter_RemoveMultiIndex \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_HasIndex \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_IsBuffered \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_IsGrowInner \ + (NpyIter *); +NPY_NO_EXPORT npy_intp NpyIter_GetBufferSize \ + (NpyIter *); +NPY_NO_EXPORT npy_intp * NpyIter_GetIndexPtr \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_GotoIndex \ + (NpyIter *, npy_intp); +NPY_NO_EXPORT char ** NpyIter_GetDataPtrArray \ + (NpyIter *); +NPY_NO_EXPORT PyArray_Descr ** NpyIter_GetDescrArray \ + (NpyIter *); +NPY_NO_EXPORT PyArrayObject ** NpyIter_GetOperandArray \ + (NpyIter *); +NPY_NO_EXPORT PyArrayObject * NpyIter_GetIterView \ + (NpyIter *, npy_intp); +NPY_NO_EXPORT void NpyIter_GetReadFlags \ + (NpyIter *, char *); +NPY_NO_EXPORT void NpyIter_GetWriteFlags \ + (NpyIter *, char *); +NPY_NO_EXPORT void NpyIter_DebugPrint \ + (NpyIter *); +NPY_NO_EXPORT npy_bool NpyIter_IterationNeedsAPI \ + (NpyIter *); +NPY_NO_EXPORT void NpyIter_GetInnerFixedStrideArray \ + (NpyIter *, npy_intp *); +NPY_NO_EXPORT int NpyIter_RemoveAxis \ + (NpyIter *, int); +NPY_NO_EXPORT npy_intp * NpyIter_GetAxisStrideArray \ + (NpyIter *, int); +NPY_NO_EXPORT npy_bool NpyIter_RequiresBuffering \ + (NpyIter *); +NPY_NO_EXPORT char ** NpyIter_GetInitialDataPtrArray \ + (NpyIter *); +NPY_NO_EXPORT int NpyIter_CreateCompatibleStrides \ + (NpyIter *, npy_intp, npy_intp *); +NPY_NO_EXPORT int PyArray_CastingConverter \ + (PyObject *, NPY_CASTING *); +NPY_NO_EXPORT npy_intp PyArray_CountNonzero \ + (PyArrayObject *); +NPY_NO_EXPORT PyArray_Descr * PyArray_PromoteTypes \ + (PyArray_Descr *, PyArray_Descr *); +NPY_NO_EXPORT PyArray_Descr * PyArray_MinScalarType \ + (PyArrayObject *); +NPY_NO_EXPORT PyArray_Descr * PyArray_ResultType \ + (npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[]); +NPY_NO_EXPORT npy_bool PyArray_CanCastArrayTo \ + (PyArrayObject *, PyArray_Descr *, NPY_CASTING); +NPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo \ + (PyArray_Descr *, PyArray_Descr *, NPY_CASTING); +NPY_NO_EXPORT PyArrayObject * PyArray_EinsteinSum \ + (char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_NewLikeArray \ + (PyArrayObject *, NPY_ORDER, PyArray_Descr *, int); +NPY_NO_EXPORT int PyArray_ConvertClipmodeSequence \ + (PyObject *, NPY_CLIPMODE *, int); +NPY_NO_EXPORT PyObject * PyArray_MatrixProduct2 \ + (PyObject *, PyObject *, PyArrayObject*); +NPY_NO_EXPORT npy_bool NpyIter_IsFirstVisit \ + (NpyIter *, int); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetBaseObject \ + (PyArrayObject *, PyObject *); +NPY_NO_EXPORT void PyArray_CreateSortedStridePerm \ + (int, npy_intp const *, npy_stride_sort_item *); +NPY_NO_EXPORT void PyArray_RemoveAxesInPlace \ + (PyArrayObject *, const npy_bool *); +NPY_NO_EXPORT void PyArray_DebugPrint \ + (PyArrayObject *); +NPY_NO_EXPORT int PyArray_FailUnlessWriteable \ + (PyArrayObject *, const char *); +NPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetUpdateIfCopyBase \ + (PyArrayObject *, PyArrayObject *); +NPY_NO_EXPORT void * PyDataMem_NEW \ + (size_t); +NPY_NO_EXPORT void PyDataMem_FREE \ + (void *); +NPY_NO_EXPORT void * PyDataMem_RENEW \ + (void *, size_t); +extern NPY_NO_EXPORT NPY_CASTING NPY_DEFAULT_ASSIGN_CASTING; + +NPY_NO_EXPORT int PyArray_Partition \ + (PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND); +NPY_NO_EXPORT PyObject * PyArray_ArgPartition \ + (PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND); +NPY_NO_EXPORT int PyArray_SelectkindConverter \ + (PyObject *, NPY_SELECTKIND *); +NPY_NO_EXPORT void * PyDataMem_NEW_ZEROED \ + (size_t, size_t); +NPY_NO_EXPORT int PyArray_CheckAnyScalarExact \ + (PyObject *); +NPY_NO_EXPORT int PyArray_ResolveWritebackIfCopy \ + (PyArrayObject *); +NPY_NO_EXPORT int PyArray_SetWritebackIfCopyBase \ + (PyArrayObject *, PyArrayObject *); +NPY_NO_EXPORT PyObject * PyDataMem_SetHandler \ + (PyObject *); +NPY_NO_EXPORT PyObject * PyDataMem_GetHandler \ + (void); +extern NPY_NO_EXPORT PyObject* PyDataMem_DefaultHandler; + +NPY_NO_EXPORT int NpyDatetime_ConvertDatetime64ToDatetimeStruct \ + (PyArray_DatetimeMetaData *, npy_datetime, npy_datetimestruct *); +NPY_NO_EXPORT int NpyDatetime_ConvertDatetimeStructToDatetime64 \ + (PyArray_DatetimeMetaData *, const npy_datetimestruct *, npy_datetime *); +NPY_NO_EXPORT int NpyDatetime_ConvertPyDateTimeToDatetimeStruct \ + (PyObject *, npy_datetimestruct *, NPY_DATETIMEUNIT *, int); +NPY_NO_EXPORT int NpyDatetime_GetDatetimeISO8601StrLen \ + (int, NPY_DATETIMEUNIT); +NPY_NO_EXPORT int NpyDatetime_MakeISO8601Datetime \ + (npy_datetimestruct *, char *, npy_intp, int, int, NPY_DATETIMEUNIT, int, NPY_CASTING); +NPY_NO_EXPORT int NpyDatetime_ParseISO8601Datetime \ + (char const *, Py_ssize_t, NPY_DATETIMEUNIT, NPY_CASTING, npy_datetimestruct *, NPY_DATETIMEUNIT *, npy_bool *); +NPY_NO_EXPORT int NpyString_load \ + (npy_string_allocator *, const npy_packed_static_string *, npy_static_string *); +NPY_NO_EXPORT int NpyString_pack \ + (npy_string_allocator *, npy_packed_static_string *, const char *, size_t); +NPY_NO_EXPORT int NpyString_pack_null \ + (npy_string_allocator *, npy_packed_static_string *); +NPY_NO_EXPORT npy_string_allocator * NpyString_acquire_allocator \ + (const PyArray_StringDTypeObject *); +NPY_NO_EXPORT void NpyString_acquire_allocators \ + (size_t, PyArray_Descr *const descrs[], npy_string_allocator *allocators[]); +NPY_NO_EXPORT void NpyString_release_allocator \ + (npy_string_allocator *); +NPY_NO_EXPORT void NpyString_release_allocators \ + (size_t, npy_string_allocator *allocators[]); +NPY_NO_EXPORT PyArray_Descr * PyArray_GetDefaultDescr \ + (PyArray_DTypeMeta *); +NPY_NO_EXPORT int PyArrayInitDTypeMeta_FromSpec \ + (PyArray_DTypeMeta *, PyArrayDTypeMeta_Spec *); +NPY_NO_EXPORT PyArray_DTypeMeta * PyArray_CommonDType \ + (PyArray_DTypeMeta *, PyArray_DTypeMeta *); +NPY_NO_EXPORT PyArray_DTypeMeta * PyArray_PromoteDTypeSequence \ + (npy_intp, PyArray_DTypeMeta **); +NPY_NO_EXPORT PyArray_ArrFuncs * _PyDataType_GetArrFuncs \ + (const PyArray_Descr *); + +#else + +#if defined(PY_ARRAY_UNIQUE_SYMBOL) + #define PyArray_API PY_ARRAY_UNIQUE_SYMBOL + #define _NPY_VERSION_CONCAT_HELPER2(x, y) x ## y + #define _NPY_VERSION_CONCAT_HELPER(arg) \ + _NPY_VERSION_CONCAT_HELPER2(arg, PyArray_RUNTIME_VERSION) + #define PyArray_RUNTIME_VERSION \ + _NPY_VERSION_CONCAT_HELPER(PY_ARRAY_UNIQUE_SYMBOL) +#endif + +/* By default do not export API in an .so (was never the case on windows) */ +#ifndef NPY_API_SYMBOL_ATTRIBUTE + #define NPY_API_SYMBOL_ATTRIBUTE NPY_VISIBILITY_HIDDEN +#endif + +#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY) +extern NPY_API_SYMBOL_ATTRIBUTE void **PyArray_API; +extern NPY_API_SYMBOL_ATTRIBUTE int PyArray_RUNTIME_VERSION; +#else +#if defined(PY_ARRAY_UNIQUE_SYMBOL) +NPY_API_SYMBOL_ATTRIBUTE void **PyArray_API; +NPY_API_SYMBOL_ATTRIBUTE int PyArray_RUNTIME_VERSION; +#else +static void **PyArray_API = NULL; +static int PyArray_RUNTIME_VERSION = 0; +#endif +#endif + +#define PyArray_GetNDArrayCVersion \ + (*(unsigned int (*)(void)) \ + PyArray_API[0]) +#define PyArray_Type (*(PyTypeObject *)PyArray_API[2]) +#define PyArrayDescr_Type (*(PyTypeObject *)PyArray_API[3]) +#define PyArrayIter_Type (*(PyTypeObject *)PyArray_API[5]) +#define PyArrayMultiIter_Type (*(PyTypeObject *)PyArray_API[6]) +#define NPY_NUMUSERTYPES (*(int *)PyArray_API[7]) +#define PyBoolArrType_Type (*(PyTypeObject *)PyArray_API[8]) +#define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[9]) +#define PyGenericArrType_Type (*(PyTypeObject *)PyArray_API[10]) +#define PyNumberArrType_Type (*(PyTypeObject *)PyArray_API[11]) +#define PyIntegerArrType_Type (*(PyTypeObject *)PyArray_API[12]) +#define PySignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[13]) +#define PyUnsignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[14]) +#define PyInexactArrType_Type (*(PyTypeObject *)PyArray_API[15]) +#define PyFloatingArrType_Type (*(PyTypeObject *)PyArray_API[16]) +#define PyComplexFloatingArrType_Type (*(PyTypeObject *)PyArray_API[17]) +#define PyFlexibleArrType_Type (*(PyTypeObject *)PyArray_API[18]) +#define PyCharacterArrType_Type (*(PyTypeObject *)PyArray_API[19]) +#define PyByteArrType_Type (*(PyTypeObject *)PyArray_API[20]) +#define PyShortArrType_Type (*(PyTypeObject *)PyArray_API[21]) +#define PyIntArrType_Type (*(PyTypeObject *)PyArray_API[22]) +#define PyLongArrType_Type (*(PyTypeObject *)PyArray_API[23]) +#define PyLongLongArrType_Type (*(PyTypeObject *)PyArray_API[24]) +#define PyUByteArrType_Type (*(PyTypeObject *)PyArray_API[25]) +#define PyUShortArrType_Type (*(PyTypeObject *)PyArray_API[26]) +#define PyUIntArrType_Type (*(PyTypeObject *)PyArray_API[27]) +#define PyULongArrType_Type (*(PyTypeObject *)PyArray_API[28]) +#define PyULongLongArrType_Type (*(PyTypeObject *)PyArray_API[29]) +#define PyFloatArrType_Type (*(PyTypeObject *)PyArray_API[30]) +#define PyDoubleArrType_Type (*(PyTypeObject *)PyArray_API[31]) +#define PyLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[32]) +#define PyCFloatArrType_Type (*(PyTypeObject *)PyArray_API[33]) +#define PyCDoubleArrType_Type (*(PyTypeObject *)PyArray_API[34]) +#define PyCLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[35]) +#define PyObjectArrType_Type (*(PyTypeObject *)PyArray_API[36]) +#define PyStringArrType_Type (*(PyTypeObject *)PyArray_API[37]) +#define PyUnicodeArrType_Type (*(PyTypeObject *)PyArray_API[38]) +#define PyVoidArrType_Type (*(PyTypeObject *)PyArray_API[39]) +#define PyArray_INCREF \ + (*(int (*)(PyArrayObject *)) \ + PyArray_API[42]) +#define PyArray_XDECREF \ + (*(int (*)(PyArrayObject *)) \ + PyArray_API[43]) +#define PyArray_SetStringFunction \ + (*(void (*)(PyObject *, int)) \ + PyArray_API[44]) +#define PyArray_DescrFromType \ + (*(PyArray_Descr * (*)(int)) \ + PyArray_API[45]) +#define PyArray_TypeObjectFromType \ + (*(PyObject * (*)(int)) \ + PyArray_API[46]) +#define PyArray_Zero \ + (*(char * (*)(PyArrayObject *)) \ + PyArray_API[47]) +#define PyArray_One \ + (*(char * (*)(PyArrayObject *)) \ + PyArray_API[48]) +#define PyArray_CastToType \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \ + PyArray_API[49]) +#define PyArray_CopyInto \ + (*(int (*)(PyArrayObject *, PyArrayObject *)) \ + PyArray_API[50]) +#define PyArray_CopyAnyInto \ + (*(int (*)(PyArrayObject *, PyArrayObject *)) \ + PyArray_API[51]) +#define PyArray_CanCastSafely \ + (*(int (*)(int, int)) \ + PyArray_API[52]) +#define PyArray_CanCastTo \ + (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *)) \ + PyArray_API[53]) +#define PyArray_ObjectType \ + (*(int (*)(PyObject *, int)) \ + PyArray_API[54]) +#define PyArray_DescrFromObject \ + (*(PyArray_Descr * (*)(PyObject *, PyArray_Descr *)) \ + PyArray_API[55]) +#define PyArray_ConvertToCommonType \ + (*(PyArrayObject ** (*)(PyObject *, int *)) \ + PyArray_API[56]) +#define PyArray_DescrFromScalar \ + (*(PyArray_Descr * (*)(PyObject *)) \ + PyArray_API[57]) +#define PyArray_DescrFromTypeObject \ + (*(PyArray_Descr * (*)(PyObject *)) \ + PyArray_API[58]) +#define PyArray_Size \ + (*(npy_intp (*)(PyObject *)) \ + PyArray_API[59]) +#define PyArray_Scalar \ + (*(PyObject * (*)(void *, PyArray_Descr *, PyObject *)) \ + PyArray_API[60]) +#define PyArray_FromScalar \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *)) \ + PyArray_API[61]) +#define PyArray_ScalarAsCtype \ + (*(void (*)(PyObject *, void *)) \ + PyArray_API[62]) +#define PyArray_CastScalarToCtype \ + (*(int (*)(PyObject *, void *, PyArray_Descr *)) \ + PyArray_API[63]) +#define PyArray_CastScalarDirect \ + (*(int (*)(PyObject *, PyArray_Descr *, void *, int)) \ + PyArray_API[64]) + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyArray_Pack \ + (*(int (*)(PyArray_Descr *, void *, PyObject *)) \ + PyArray_API[65]) +#endif +#define PyArray_FromAny \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \ + PyArray_API[69]) +#define PyArray_EnsureArray \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[70]) +#define PyArray_EnsureAnyArray \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[71]) +#define PyArray_FromFile \ + (*(PyObject * (*)(FILE *, PyArray_Descr *, npy_intp, char *)) \ + PyArray_API[72]) +#define PyArray_FromString \ + (*(PyObject * (*)(char *, npy_intp, PyArray_Descr *, npy_intp, char *)) \ + PyArray_API[73]) +#define PyArray_FromBuffer \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp, npy_intp)) \ + PyArray_API[74]) +#define PyArray_FromIter \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp)) \ + PyArray_API[75]) +#define PyArray_Return \ + (*(PyObject * (*)(PyArrayObject *)) \ + PyArray_API[76]) +#define PyArray_GetField \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \ + PyArray_API[77]) +#define PyArray_SetField \ + (*(int (*)(PyArrayObject *, PyArray_Descr *, int, PyObject *)) \ + PyArray_API[78]) +#define PyArray_Byteswap \ + (*(PyObject * (*)(PyArrayObject *, npy_bool)) \ + PyArray_API[79]) +#define PyArray_Resize \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order))) \ + PyArray_API[80]) +#define PyArray_CopyObject \ + (*(int (*)(PyArrayObject *, PyObject *)) \ + PyArray_API[84]) +#define PyArray_NewCopy \ + (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \ + PyArray_API[85]) +#define PyArray_ToList \ + (*(PyObject * (*)(PyArrayObject *)) \ + PyArray_API[86]) +#define PyArray_ToString \ + (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \ + PyArray_API[87]) +#define PyArray_ToFile \ + (*(int (*)(PyArrayObject *, FILE *, char *, char *)) \ + PyArray_API[88]) +#define PyArray_Dump \ + (*(int (*)(PyObject *, PyObject *, int)) \ + PyArray_API[89]) +#define PyArray_Dumps \ + (*(PyObject * (*)(PyObject *, int)) \ + PyArray_API[90]) +#define PyArray_ValidType \ + (*(int (*)(int)) \ + PyArray_API[91]) +#define PyArray_UpdateFlags \ + (*(void (*)(PyArrayObject *, int)) \ + PyArray_API[92]) +#define PyArray_New \ + (*(PyObject * (*)(PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *)) \ + PyArray_API[93]) +#define PyArray_NewFromDescr \ + (*(PyObject * (*)(PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *)) \ + PyArray_API[94]) +#define PyArray_DescrNew \ + (*(PyArray_Descr * (*)(PyArray_Descr *)) \ + PyArray_API[95]) +#define PyArray_DescrNewFromType \ + (*(PyArray_Descr * (*)(int)) \ + PyArray_API[96]) +#define PyArray_GetPriority \ + (*(double (*)(PyObject *, double)) \ + PyArray_API[97]) +#define PyArray_IterNew \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[98]) +#define PyArray_MultiIterNew \ + (*(PyObject* (*)(int, ...)) \ + PyArray_API[99]) +#define PyArray_PyIntAsInt \ + (*(int (*)(PyObject *)) \ + PyArray_API[100]) +#define PyArray_PyIntAsIntp \ + (*(npy_intp (*)(PyObject *)) \ + PyArray_API[101]) +#define PyArray_Broadcast \ + (*(int (*)(PyArrayMultiIterObject *)) \ + PyArray_API[102]) +#define PyArray_FillWithScalar \ + (*(int (*)(PyArrayObject *, PyObject *)) \ + PyArray_API[104]) +#define PyArray_CheckStrides \ + (*(npy_bool (*)(int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *)) \ + PyArray_API[105]) +#define PyArray_DescrNewByteorder \ + (*(PyArray_Descr * (*)(PyArray_Descr *, char)) \ + PyArray_API[106]) +#define PyArray_IterAllButAxis \ + (*(PyObject * (*)(PyObject *, int *)) \ + PyArray_API[107]) +#define PyArray_CheckFromAny \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \ + PyArray_API[108]) +#define PyArray_FromArray \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \ + PyArray_API[109]) +#define PyArray_FromInterface \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[110]) +#define PyArray_FromStructInterface \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[111]) +#define PyArray_FromArrayAttr \ + (*(PyObject * (*)(PyObject *, PyArray_Descr *, PyObject *)) \ + PyArray_API[112]) +#define PyArray_ScalarKind \ + (*(NPY_SCALARKIND (*)(int, PyArrayObject **)) \ + PyArray_API[113]) +#define PyArray_CanCoerceScalar \ + (*(int (*)(int, int, NPY_SCALARKIND)) \ + PyArray_API[114]) +#define PyArray_CanCastScalar \ + (*(npy_bool (*)(PyTypeObject *, PyTypeObject *)) \ + PyArray_API[116]) +#define PyArray_RemoveSmallest \ + (*(int (*)(PyArrayMultiIterObject *)) \ + PyArray_API[118]) +#define PyArray_ElementStrides \ + (*(int (*)(PyObject *)) \ + PyArray_API[119]) +#define PyArray_Item_INCREF \ + (*(void (*)(char *, PyArray_Descr *)) \ + PyArray_API[120]) +#define PyArray_Item_XDECREF \ + (*(void (*)(char *, PyArray_Descr *)) \ + PyArray_API[121]) +#define PyArray_Transpose \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *)) \ + PyArray_API[123]) +#define PyArray_TakeFrom \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE)) \ + PyArray_API[124]) +#define PyArray_PutTo \ + (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE)) \ + PyArray_API[125]) +#define PyArray_PutMask \ + (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject*)) \ + PyArray_API[126]) +#define PyArray_Repeat \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, int)) \ + PyArray_API[127]) +#define PyArray_Choose \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE)) \ + PyArray_API[128]) +#define PyArray_Sort \ + (*(int (*)(PyArrayObject *, int, NPY_SORTKIND)) \ + PyArray_API[129]) +#define PyArray_ArgSort \ + (*(PyObject * (*)(PyArrayObject *, int, NPY_SORTKIND)) \ + PyArray_API[130]) +#define PyArray_SearchSorted \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *)) \ + PyArray_API[131]) +#define PyArray_ArgMax \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[132]) +#define PyArray_ArgMin \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[133]) +#define PyArray_Reshape \ + (*(PyObject * (*)(PyArrayObject *, PyObject *)) \ + PyArray_API[134]) +#define PyArray_Newshape \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, NPY_ORDER)) \ + PyArray_API[135]) +#define PyArray_Squeeze \ + (*(PyObject * (*)(PyArrayObject *)) \ + PyArray_API[136]) +#define PyArray_View \ + (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, PyTypeObject *)) \ + PyArray_API[137]) +#define PyArray_SwapAxes \ + (*(PyObject * (*)(PyArrayObject *, int, int)) \ + PyArray_API[138]) +#define PyArray_Max \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[139]) +#define PyArray_Min \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[140]) +#define PyArray_Ptp \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[141]) +#define PyArray_Mean \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \ + PyArray_API[142]) +#define PyArray_Trace \ + (*(PyObject * (*)(PyArrayObject *, int, int, int, int, PyArrayObject *)) \ + PyArray_API[143]) +#define PyArray_Diagonal \ + (*(PyObject * (*)(PyArrayObject *, int, int, int)) \ + PyArray_API[144]) +#define PyArray_Clip \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, PyObject *, PyArrayObject *)) \ + PyArray_API[145]) +#define PyArray_Conjugate \ + (*(PyObject * (*)(PyArrayObject *, PyArrayObject *)) \ + PyArray_API[146]) +#define PyArray_Nonzero \ + (*(PyObject * (*)(PyArrayObject *)) \ + PyArray_API[147]) +#define PyArray_Std \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *, int)) \ + PyArray_API[148]) +#define PyArray_Sum \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \ + PyArray_API[149]) +#define PyArray_CumSum \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \ + PyArray_API[150]) +#define PyArray_Prod \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \ + PyArray_API[151]) +#define PyArray_CumProd \ + (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \ + PyArray_API[152]) +#define PyArray_All \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[153]) +#define PyArray_Any \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[154]) +#define PyArray_Compress \ + (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \ + PyArray_API[155]) +#define PyArray_Flatten \ + (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \ + PyArray_API[156]) +#define PyArray_Ravel \ + (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \ + PyArray_API[157]) +#define PyArray_MultiplyList \ + (*(npy_intp (*)(npy_intp const *, int)) \ + PyArray_API[158]) +#define PyArray_MultiplyIntList \ + (*(int (*)(int const *, int)) \ + PyArray_API[159]) +#define PyArray_GetPtr \ + (*(void * (*)(PyArrayObject *, npy_intp const*)) \ + PyArray_API[160]) +#define PyArray_CompareLists \ + (*(int (*)(npy_intp const *, npy_intp const *, int)) \ + PyArray_API[161]) +#define PyArray_AsCArray \ + (*(int (*)(PyObject **, void *, npy_intp *, int, PyArray_Descr*)) \ + PyArray_API[162]) +#define PyArray_Free \ + (*(int (*)(PyObject *, void *)) \ + PyArray_API[165]) +#define PyArray_Converter \ + (*(int (*)(PyObject *, PyObject **)) \ + PyArray_API[166]) +#define PyArray_IntpFromSequence \ + (*(int (*)(PyObject *, npy_intp *, int)) \ + PyArray_API[167]) +#define PyArray_Concatenate \ + (*(PyObject * (*)(PyObject *, int)) \ + PyArray_API[168]) +#define PyArray_InnerProduct \ + (*(PyObject * (*)(PyObject *, PyObject *)) \ + PyArray_API[169]) +#define PyArray_MatrixProduct \ + (*(PyObject * (*)(PyObject *, PyObject *)) \ + PyArray_API[170]) +#define PyArray_Correlate \ + (*(PyObject * (*)(PyObject *, PyObject *, int)) \ + PyArray_API[172]) +#define PyArray_DescrConverter \ + (*(int (*)(PyObject *, PyArray_Descr **)) \ + PyArray_API[174]) +#define PyArray_DescrConverter2 \ + (*(int (*)(PyObject *, PyArray_Descr **)) \ + PyArray_API[175]) +#define PyArray_IntpConverter \ + (*(int (*)(PyObject *, PyArray_Dims *)) \ + PyArray_API[176]) +#define PyArray_BufferConverter \ + (*(int (*)(PyObject *, PyArray_Chunk *)) \ + PyArray_API[177]) +#define PyArray_AxisConverter \ + (*(int (*)(PyObject *, int *)) \ + PyArray_API[178]) +#define PyArray_BoolConverter \ + (*(int (*)(PyObject *, npy_bool *)) \ + PyArray_API[179]) +#define PyArray_ByteorderConverter \ + (*(int (*)(PyObject *, char *)) \ + PyArray_API[180]) +#define PyArray_OrderConverter \ + (*(int (*)(PyObject *, NPY_ORDER *)) \ + PyArray_API[181]) +#define PyArray_EquivTypes \ + (*(unsigned char (*)(PyArray_Descr *, PyArray_Descr *)) \ + PyArray_API[182]) +#define PyArray_Zeros \ + (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \ + PyArray_API[183]) +#define PyArray_Empty \ + (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \ + PyArray_API[184]) +#define PyArray_Where \ + (*(PyObject * (*)(PyObject *, PyObject *, PyObject *)) \ + PyArray_API[185]) +#define PyArray_Arange \ + (*(PyObject * (*)(double, double, double, int)) \ + PyArray_API[186]) +#define PyArray_ArangeObj \ + (*(PyObject * (*)(PyObject *, PyObject *, PyObject *, PyArray_Descr *)) \ + PyArray_API[187]) +#define PyArray_SortkindConverter \ + (*(int (*)(PyObject *, NPY_SORTKIND *)) \ + PyArray_API[188]) +#define PyArray_LexSort \ + (*(PyObject * (*)(PyObject *, int)) \ + PyArray_API[189]) +#define PyArray_Round \ + (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \ + PyArray_API[190]) +#define PyArray_EquivTypenums \ + (*(unsigned char (*)(int, int)) \ + PyArray_API[191]) +#define PyArray_RegisterDataType \ + (*(int (*)(PyArray_DescrProto *)) \ + PyArray_API[192]) +#define PyArray_RegisterCastFunc \ + (*(int (*)(PyArray_Descr *, int, PyArray_VectorUnaryFunc *)) \ + PyArray_API[193]) +#define PyArray_RegisterCanCast \ + (*(int (*)(PyArray_Descr *, int, NPY_SCALARKIND)) \ + PyArray_API[194]) +#define PyArray_InitArrFuncs \ + (*(void (*)(PyArray_ArrFuncs *)) \ + PyArray_API[195]) +#define PyArray_IntTupleFromIntp \ + (*(PyObject * (*)(int, npy_intp const *)) \ + PyArray_API[196]) +#define PyArray_ClipmodeConverter \ + (*(int (*)(PyObject *, NPY_CLIPMODE *)) \ + PyArray_API[198]) +#define PyArray_OutputConverter \ + (*(int (*)(PyObject *, PyArrayObject **)) \ + PyArray_API[199]) +#define PyArray_BroadcastToShape \ + (*(PyObject * (*)(PyObject *, npy_intp *, int)) \ + PyArray_API[200]) +#define PyArray_DescrAlignConverter \ + (*(int (*)(PyObject *, PyArray_Descr **)) \ + PyArray_API[203]) +#define PyArray_DescrAlignConverter2 \ + (*(int (*)(PyObject *, PyArray_Descr **)) \ + PyArray_API[204]) +#define PyArray_SearchsideConverter \ + (*(int (*)(PyObject *, void *)) \ + PyArray_API[205]) +#define PyArray_CheckAxis \ + (*(PyObject * (*)(PyArrayObject *, int *, int)) \ + PyArray_API[206]) +#define PyArray_OverflowMultiplyList \ + (*(npy_intp (*)(npy_intp const *, int)) \ + PyArray_API[207]) +#define PyArray_MultiIterFromObjects \ + (*(PyObject* (*)(PyObject **, int, int, ...)) \ + PyArray_API[209]) +#define PyArray_GetEndianness \ + (*(int (*)(void)) \ + PyArray_API[210]) +#define PyArray_GetNDArrayCFeatureVersion \ + (*(unsigned int (*)(void)) \ + PyArray_API[211]) +#define PyArray_Correlate2 \ + (*(PyObject * (*)(PyObject *, PyObject *, int)) \ + PyArray_API[212]) +#define PyArray_NeighborhoodIterNew \ + (*(PyObject* (*)(PyArrayIterObject *, const npy_intp *, int, PyArrayObject*)) \ + PyArray_API[213]) +#define PyTimeIntegerArrType_Type (*(PyTypeObject *)PyArray_API[214]) +#define PyDatetimeArrType_Type (*(PyTypeObject *)PyArray_API[215]) +#define PyTimedeltaArrType_Type (*(PyTypeObject *)PyArray_API[216]) +#define PyHalfArrType_Type (*(PyTypeObject *)PyArray_API[217]) +#define NpyIter_Type (*(PyTypeObject *)PyArray_API[218]) +#define NpyIter_New \ + (*(NpyIter * (*)(PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*)) \ + PyArray_API[224]) +#define NpyIter_MultiNew \ + (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **)) \ + PyArray_API[225]) +#define NpyIter_AdvancedNew \ + (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp)) \ + PyArray_API[226]) +#define NpyIter_Copy \ + (*(NpyIter * (*)(NpyIter *)) \ + PyArray_API[227]) +#define NpyIter_Deallocate \ + (*(int (*)(NpyIter *)) \ + PyArray_API[228]) +#define NpyIter_HasDelayedBufAlloc \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[229]) +#define NpyIter_HasExternalLoop \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[230]) +#define NpyIter_EnableExternalLoop \ + (*(int (*)(NpyIter *)) \ + PyArray_API[231]) +#define NpyIter_GetInnerStrideArray \ + (*(npy_intp * (*)(NpyIter *)) \ + PyArray_API[232]) +#define NpyIter_GetInnerLoopSizePtr \ + (*(npy_intp * (*)(NpyIter *)) \ + PyArray_API[233]) +#define NpyIter_Reset \ + (*(int (*)(NpyIter *, char **)) \ + PyArray_API[234]) +#define NpyIter_ResetBasePointers \ + (*(int (*)(NpyIter *, char **, char **)) \ + PyArray_API[235]) +#define NpyIter_ResetToIterIndexRange \ + (*(int (*)(NpyIter *, npy_intp, npy_intp, char **)) \ + PyArray_API[236]) +#define NpyIter_GetNDim \ + (*(int (*)(NpyIter *)) \ + PyArray_API[237]) +#define NpyIter_GetNOp \ + (*(int (*)(NpyIter *)) \ + PyArray_API[238]) +#define NpyIter_GetIterNext \ + (*(NpyIter_IterNextFunc * (*)(NpyIter *, char **)) \ + PyArray_API[239]) +#define NpyIter_GetIterSize \ + (*(npy_intp (*)(NpyIter *)) \ + PyArray_API[240]) +#define NpyIter_GetIterIndexRange \ + (*(void (*)(NpyIter *, npy_intp *, npy_intp *)) \ + PyArray_API[241]) +#define NpyIter_GetIterIndex \ + (*(npy_intp (*)(NpyIter *)) \ + PyArray_API[242]) +#define NpyIter_GotoIterIndex \ + (*(int (*)(NpyIter *, npy_intp)) \ + PyArray_API[243]) +#define NpyIter_HasMultiIndex \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[244]) +#define NpyIter_GetShape \ + (*(int (*)(NpyIter *, npy_intp *)) \ + PyArray_API[245]) +#define NpyIter_GetGetMultiIndex \ + (*(NpyIter_GetMultiIndexFunc * (*)(NpyIter *, char **)) \ + PyArray_API[246]) +#define NpyIter_GotoMultiIndex \ + (*(int (*)(NpyIter *, npy_intp const *)) \ + PyArray_API[247]) +#define NpyIter_RemoveMultiIndex \ + (*(int (*)(NpyIter *)) \ + PyArray_API[248]) +#define NpyIter_HasIndex \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[249]) +#define NpyIter_IsBuffered \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[250]) +#define NpyIter_IsGrowInner \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[251]) +#define NpyIter_GetBufferSize \ + (*(npy_intp (*)(NpyIter *)) \ + PyArray_API[252]) +#define NpyIter_GetIndexPtr \ + (*(npy_intp * (*)(NpyIter *)) \ + PyArray_API[253]) +#define NpyIter_GotoIndex \ + (*(int (*)(NpyIter *, npy_intp)) \ + PyArray_API[254]) +#define NpyIter_GetDataPtrArray \ + (*(char ** (*)(NpyIter *)) \ + PyArray_API[255]) +#define NpyIter_GetDescrArray \ + (*(PyArray_Descr ** (*)(NpyIter *)) \ + PyArray_API[256]) +#define NpyIter_GetOperandArray \ + (*(PyArrayObject ** (*)(NpyIter *)) \ + PyArray_API[257]) +#define NpyIter_GetIterView \ + (*(PyArrayObject * (*)(NpyIter *, npy_intp)) \ + PyArray_API[258]) +#define NpyIter_GetReadFlags \ + (*(void (*)(NpyIter *, char *)) \ + PyArray_API[259]) +#define NpyIter_GetWriteFlags \ + (*(void (*)(NpyIter *, char *)) \ + PyArray_API[260]) +#define NpyIter_DebugPrint \ + (*(void (*)(NpyIter *)) \ + PyArray_API[261]) +#define NpyIter_IterationNeedsAPI \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[262]) +#define NpyIter_GetInnerFixedStrideArray \ + (*(void (*)(NpyIter *, npy_intp *)) \ + PyArray_API[263]) +#define NpyIter_RemoveAxis \ + (*(int (*)(NpyIter *, int)) \ + PyArray_API[264]) +#define NpyIter_GetAxisStrideArray \ + (*(npy_intp * (*)(NpyIter *, int)) \ + PyArray_API[265]) +#define NpyIter_RequiresBuffering \ + (*(npy_bool (*)(NpyIter *)) \ + PyArray_API[266]) +#define NpyIter_GetInitialDataPtrArray \ + (*(char ** (*)(NpyIter *)) \ + PyArray_API[267]) +#define NpyIter_CreateCompatibleStrides \ + (*(int (*)(NpyIter *, npy_intp, npy_intp *)) \ + PyArray_API[268]) +#define PyArray_CastingConverter \ + (*(int (*)(PyObject *, NPY_CASTING *)) \ + PyArray_API[269]) +#define PyArray_CountNonzero \ + (*(npy_intp (*)(PyArrayObject *)) \ + PyArray_API[270]) +#define PyArray_PromoteTypes \ + (*(PyArray_Descr * (*)(PyArray_Descr *, PyArray_Descr *)) \ + PyArray_API[271]) +#define PyArray_MinScalarType \ + (*(PyArray_Descr * (*)(PyArrayObject *)) \ + PyArray_API[272]) +#define PyArray_ResultType \ + (*(PyArray_Descr * (*)(npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[])) \ + PyArray_API[273]) +#define PyArray_CanCastArrayTo \ + (*(npy_bool (*)(PyArrayObject *, PyArray_Descr *, NPY_CASTING)) \ + PyArray_API[274]) +#define PyArray_CanCastTypeTo \ + (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *, NPY_CASTING)) \ + PyArray_API[275]) +#define PyArray_EinsteinSum \ + (*(PyArrayObject * (*)(char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *)) \ + PyArray_API[276]) +#define PyArray_NewLikeArray \ + (*(PyObject * (*)(PyArrayObject *, NPY_ORDER, PyArray_Descr *, int)) \ + PyArray_API[277]) +#define PyArray_ConvertClipmodeSequence \ + (*(int (*)(PyObject *, NPY_CLIPMODE *, int)) \ + PyArray_API[279]) +#define PyArray_MatrixProduct2 \ + (*(PyObject * (*)(PyObject *, PyObject *, PyArrayObject*)) \ + PyArray_API[280]) +#define NpyIter_IsFirstVisit \ + (*(npy_bool (*)(NpyIter *, int)) \ + PyArray_API[281]) +#define PyArray_SetBaseObject \ + (*(int (*)(PyArrayObject *, PyObject *)) \ + PyArray_API[282]) +#define PyArray_CreateSortedStridePerm \ + (*(void (*)(int, npy_intp const *, npy_stride_sort_item *)) \ + PyArray_API[283]) +#define PyArray_RemoveAxesInPlace \ + (*(void (*)(PyArrayObject *, const npy_bool *)) \ + PyArray_API[284]) +#define PyArray_DebugPrint \ + (*(void (*)(PyArrayObject *)) \ + PyArray_API[285]) +#define PyArray_FailUnlessWriteable \ + (*(int (*)(PyArrayObject *, const char *)) \ + PyArray_API[286]) +#define PyArray_SetUpdateIfCopyBase \ + (*(int (*)(PyArrayObject *, PyArrayObject *)) \ + PyArray_API[287]) +#define PyDataMem_NEW \ + (*(void * (*)(size_t)) \ + PyArray_API[288]) +#define PyDataMem_FREE \ + (*(void (*)(void *)) \ + PyArray_API[289]) +#define PyDataMem_RENEW \ + (*(void * (*)(void *, size_t)) \ + PyArray_API[290]) +#define NPY_DEFAULT_ASSIGN_CASTING (*(NPY_CASTING *)PyArray_API[292]) +#define PyArray_Partition \ + (*(int (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \ + PyArray_API[296]) +#define PyArray_ArgPartition \ + (*(PyObject * (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \ + PyArray_API[297]) +#define PyArray_SelectkindConverter \ + (*(int (*)(PyObject *, NPY_SELECTKIND *)) \ + PyArray_API[298]) +#define PyDataMem_NEW_ZEROED \ + (*(void * (*)(size_t, size_t)) \ + PyArray_API[299]) +#define PyArray_CheckAnyScalarExact \ + (*(int (*)(PyObject *)) \ + PyArray_API[300]) +#define PyArray_ResolveWritebackIfCopy \ + (*(int (*)(PyArrayObject *)) \ + PyArray_API[302]) +#define PyArray_SetWritebackIfCopyBase \ + (*(int (*)(PyArrayObject *, PyArrayObject *)) \ + PyArray_API[303]) + +#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION +#define PyDataMem_SetHandler \ + (*(PyObject * (*)(PyObject *)) \ + PyArray_API[304]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION +#define PyDataMem_GetHandler \ + (*(PyObject * (*)(void)) \ + PyArray_API[305]) +#endif +#define PyDataMem_DefaultHandler (*(PyObject* *)PyArray_API[306]) + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_ConvertDatetime64ToDatetimeStruct \ + (*(int (*)(PyArray_DatetimeMetaData *, npy_datetime, npy_datetimestruct *)) \ + PyArray_API[307]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_ConvertDatetimeStructToDatetime64 \ + (*(int (*)(PyArray_DatetimeMetaData *, const npy_datetimestruct *, npy_datetime *)) \ + PyArray_API[308]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_ConvertPyDateTimeToDatetimeStruct \ + (*(int (*)(PyObject *, npy_datetimestruct *, NPY_DATETIMEUNIT *, int)) \ + PyArray_API[309]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_GetDatetimeISO8601StrLen \ + (*(int (*)(int, NPY_DATETIMEUNIT)) \ + PyArray_API[310]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_MakeISO8601Datetime \ + (*(int (*)(npy_datetimestruct *, char *, npy_intp, int, int, NPY_DATETIMEUNIT, int, NPY_CASTING)) \ + PyArray_API[311]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyDatetime_ParseISO8601Datetime \ + (*(int (*)(char const *, Py_ssize_t, NPY_DATETIMEUNIT, NPY_CASTING, npy_datetimestruct *, NPY_DATETIMEUNIT *, npy_bool *)) \ + PyArray_API[312]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_load \ + (*(int (*)(npy_string_allocator *, const npy_packed_static_string *, npy_static_string *)) \ + PyArray_API[313]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_pack \ + (*(int (*)(npy_string_allocator *, npy_packed_static_string *, const char *, size_t)) \ + PyArray_API[314]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_pack_null \ + (*(int (*)(npy_string_allocator *, npy_packed_static_string *)) \ + PyArray_API[315]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_acquire_allocator \ + (*(npy_string_allocator * (*)(const PyArray_StringDTypeObject *)) \ + PyArray_API[316]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_acquire_allocators \ + (*(void (*)(size_t, PyArray_Descr *const descrs[], npy_string_allocator *allocators[])) \ + PyArray_API[317]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_release_allocator \ + (*(void (*)(npy_string_allocator *)) \ + PyArray_API[318]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define NpyString_release_allocators \ + (*(void (*)(size_t, npy_string_allocator *allocators[])) \ + PyArray_API[319]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyArray_GetDefaultDescr \ + (*(PyArray_Descr * (*)(PyArray_DTypeMeta *)) \ + PyArray_API[361]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyArrayInitDTypeMeta_FromSpec \ + (*(int (*)(PyArray_DTypeMeta *, PyArrayDTypeMeta_Spec *)) \ + PyArray_API[362]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyArray_CommonDType \ + (*(PyArray_DTypeMeta * (*)(PyArray_DTypeMeta *, PyArray_DTypeMeta *)) \ + PyArray_API[363]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyArray_PromoteDTypeSequence \ + (*(PyArray_DTypeMeta * (*)(npy_intp, PyArray_DTypeMeta **)) \ + PyArray_API[364]) +#endif +#define _PyDataType_GetArrFuncs \ + (*(PyArray_ArrFuncs * (*)(const PyArray_Descr *)) \ + PyArray_API[365]) + +/* + * The DType classes are inconvenient for the Python generation so exposed + * manually in the header below (may be moved). + */ +#include "numpy/_public_dtype_api_table.h" + +#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT) +static int +_import_array(void) +{ + int st; + PyObject *numpy = PyImport_ImportModule("numpy._core._multiarray_umath"); + if (numpy == NULL && PyErr_ExceptionMatches(PyExc_ModuleNotFoundError)) { + PyErr_Clear(); + numpy = PyImport_ImportModule("numpy.core._multiarray_umath"); + } + + if (numpy == NULL) { + return -1; + } + + PyObject *c_api = PyObject_GetAttrString(numpy, "_ARRAY_API"); + Py_DECREF(numpy); + if (c_api == NULL) { + return -1; + } + + if (!PyCapsule_CheckExact(c_api)) { + PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object"); + Py_DECREF(c_api); + return -1; + } + PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL); + Py_DECREF(c_api); + if (PyArray_API == NULL) { + PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer"); + return -1; + } + + /* + * On exceedingly few platforms these sizes may not match, in which case + * We do not support older NumPy versions at all. + */ + if (sizeof(Py_ssize_t) != sizeof(Py_intptr_t) && + PyArray_RUNTIME_VERSION < NPY_2_0_API_VERSION) { + PyErr_Format(PyExc_RuntimeError, + "module compiled against NumPy 2.0 but running on NumPy 1.x. " + "Unfortunately, this is not supported on niche platforms where " + "`sizeof(size_t) != sizeof(inptr_t)`."); + } + /* + * Perform runtime check of C API version. As of now NumPy 2.0 is ABI + * backwards compatible (in the exposed feature subset!) for all practical + * purposes. + */ + if (NPY_VERSION < PyArray_GetNDArrayCVersion()) { + PyErr_Format(PyExc_RuntimeError, "module compiled against "\ + "ABI version 0x%x but this version of numpy is 0x%x", \ + (int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion()); + return -1; + } + PyArray_RUNTIME_VERSION = (int)PyArray_GetNDArrayCFeatureVersion(); + if (NPY_FEATURE_VERSION > PyArray_RUNTIME_VERSION) { + PyErr_Format(PyExc_RuntimeError, + "module was compiled against NumPy C-API version 0x%x " + "(NumPy " NPY_FEATURE_VERSION_STRING ") " + "but the running NumPy has C-API version 0x%x. " + "Check the section C-API incompatibility at the " + "Troubleshooting ImportError section at " + "https://numpy.org/devdocs/user/troubleshooting-importerror.html" + "#c-api-incompatibility " + "for indications on how to solve this problem.", + (int)NPY_FEATURE_VERSION, PyArray_RUNTIME_VERSION); + return -1; + } + + /* + * Perform runtime check of endianness and check it matches the one set by + * the headers (npy_endian.h) as a safeguard + */ + st = PyArray_GetEndianness(); + if (st == NPY_CPU_UNKNOWN_ENDIAN) { + PyErr_SetString(PyExc_RuntimeError, + "FATAL: module compiled as unknown endian"); + return -1; + } +#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN + if (st != NPY_CPU_BIG) { + PyErr_SetString(PyExc_RuntimeError, + "FATAL: module compiled as big endian, but " + "detected different endianness at runtime"); + return -1; + } +#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN + if (st != NPY_CPU_LITTLE) { + PyErr_SetString(PyExc_RuntimeError, + "FATAL: module compiled as little endian, but " + "detected different endianness at runtime"); + return -1; + } +#endif + + return 0; +} + +#define import_array() { \ + if (_import_array() < 0) { \ + PyErr_Print(); \ + PyErr_SetString( \ + PyExc_ImportError, \ + "numpy._core.multiarray failed to import" \ + ); \ + return NULL; \ + } \ +} + +#define import_array1(ret) { \ + if (_import_array() < 0) { \ + PyErr_Print(); \ + PyErr_SetString( \ + PyExc_ImportError, \ + "numpy._core.multiarray failed to import" \ + ); \ + return ret; \ + } \ +} + +#define import_array2(msg, ret) { \ + if (_import_array() < 0) { \ + PyErr_Print(); \ + PyErr_SetString(PyExc_ImportError, msg); \ + return ret; \ + } \ +} + +#endif + +#endif diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.c new file mode 100644 index 0000000000000000000000000000000000000000..10fcbc4553989057667a90ce9d587deefc13f5f1 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.c @@ -0,0 +1,54 @@ + +/* These pointers will be stored in the C-object for use in other + extension modules +*/ + +void *PyUFunc_API[] = { + (void *) &PyUFunc_Type, + (void *) PyUFunc_FromFuncAndData, + (void *) PyUFunc_RegisterLoopForType, + NULL, + (void *) PyUFunc_f_f_As_d_d, + (void *) PyUFunc_d_d, + (void *) PyUFunc_f_f, + (void *) PyUFunc_g_g, + (void *) PyUFunc_F_F_As_D_D, + (void *) PyUFunc_F_F, + (void *) PyUFunc_D_D, + (void *) PyUFunc_G_G, + (void *) PyUFunc_O_O, + (void *) PyUFunc_ff_f_As_dd_d, + (void *) PyUFunc_ff_f, + (void *) PyUFunc_dd_d, + (void *) PyUFunc_gg_g, + (void *) PyUFunc_FF_F_As_DD_D, + (void *) PyUFunc_DD_D, + (void *) PyUFunc_FF_F, + (void *) PyUFunc_GG_G, + (void *) PyUFunc_OO_O, + (void *) PyUFunc_O_O_method, + (void *) PyUFunc_OO_O_method, + (void *) PyUFunc_On_Om, + NULL, + NULL, + (void *) PyUFunc_clearfperr, + (void *) PyUFunc_getfperr, + NULL, + (void *) PyUFunc_ReplaceLoopBySignature, + (void *) PyUFunc_FromFuncAndDataAndSignature, + NULL, + (void *) PyUFunc_e_e, + (void *) PyUFunc_e_e_As_f_f, + (void *) PyUFunc_e_e_As_d_d, + (void *) PyUFunc_ee_e, + (void *) PyUFunc_ee_e_As_ff_f, + (void *) PyUFunc_ee_e_As_dd_d, + (void *) PyUFunc_DefaultTypeResolver, + (void *) PyUFunc_ValidateCasting, + (void *) PyUFunc_RegisterLoopForDescr, + (void *) PyUFunc_FromFuncAndDataAndSignatureAndIdentity, + (void *) PyUFunc_AddLoopFromSpec, + (void *) PyUFunc_AddPromoter, + (void *) PyUFunc_AddWrappingLoop, + (void *) PyUFunc_GiveFloatingpointErrors +}; diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.h new file mode 100644 index 0000000000000000000000000000000000000000..df7ded10b548d495338dc368d727cf77ff70740a --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/__ufunc_api.h @@ -0,0 +1,340 @@ + +#ifdef _UMATHMODULE + +extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type; + +extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type; + +NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndData \ + (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int); +NPY_NO_EXPORT int PyUFunc_RegisterLoopForType \ + (PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *); +NPY_NO_EXPORT void PyUFunc_f_f_As_d_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_d_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_f_f \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_g_g \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_F_F_As_D_D \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_F_F \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_D_D \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_G_G \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_O_O \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_ff_f_As_dd_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_ff_f \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_dd_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_gg_g \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_FF_F_As_DD_D \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_DD_D \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_FF_F \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_GG_G \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_OO_O \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_O_O_method \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_OO_O_method \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_On_Om \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_clearfperr \ + (void); +NPY_NO_EXPORT int PyUFunc_getfperr \ + (void); +NPY_NO_EXPORT int PyUFunc_ReplaceLoopBySignature \ + (PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *); +NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignature \ + (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int, const char *); +NPY_NO_EXPORT void PyUFunc_e_e \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_e_e_As_f_f \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_e_e_As_d_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_ee_e \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_ee_e_As_ff_f \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT void PyUFunc_ee_e_As_dd_d \ + (char **, npy_intp const *, npy_intp const *, void *); +NPY_NO_EXPORT int PyUFunc_DefaultTypeResolver \ + (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **); +NPY_NO_EXPORT int PyUFunc_ValidateCasting \ + (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr *const *); +NPY_NO_EXPORT int PyUFunc_RegisterLoopForDescr \ + (PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *); +NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignatureAndIdentity \ + (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *); +NPY_NO_EXPORT int PyUFunc_AddLoopFromSpec \ + (PyObject *, PyArrayMethod_Spec *); +NPY_NO_EXPORT int PyUFunc_AddPromoter \ + (PyObject *, PyObject *, PyObject *); +NPY_NO_EXPORT int PyUFunc_AddWrappingLoop \ + (PyObject *, PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[], PyArrayMethod_TranslateGivenDescriptors *, PyArrayMethod_TranslateLoopDescriptors *); +NPY_NO_EXPORT int PyUFunc_GiveFloatingpointErrors \ + (const char *, int); + +#else + +#if defined(PY_UFUNC_UNIQUE_SYMBOL) +#define PyUFunc_API PY_UFUNC_UNIQUE_SYMBOL +#endif + +/* By default do not export API in an .so (was never the case on windows) */ +#ifndef NPY_API_SYMBOL_ATTRIBUTE + #define NPY_API_SYMBOL_ATTRIBUTE NPY_VISIBILITY_HIDDEN +#endif + +#if defined(NO_IMPORT) || defined(NO_IMPORT_UFUNC) +extern NPY_API_SYMBOL_ATTRIBUTE void **PyUFunc_API; +#else +#if defined(PY_UFUNC_UNIQUE_SYMBOL) +NPY_API_SYMBOL_ATTRIBUTE void **PyUFunc_API; +#else +static void **PyUFunc_API=NULL; +#endif +#endif + +#define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0]) +#define PyUFunc_FromFuncAndData \ + (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int)) \ + PyUFunc_API[1]) +#define PyUFunc_RegisterLoopForType \ + (*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \ + PyUFunc_API[2]) +#define PyUFunc_f_f_As_d_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[4]) +#define PyUFunc_d_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[5]) +#define PyUFunc_f_f \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[6]) +#define PyUFunc_g_g \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[7]) +#define PyUFunc_F_F_As_D_D \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[8]) +#define PyUFunc_F_F \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[9]) +#define PyUFunc_D_D \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[10]) +#define PyUFunc_G_G \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[11]) +#define PyUFunc_O_O \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[12]) +#define PyUFunc_ff_f_As_dd_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[13]) +#define PyUFunc_ff_f \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[14]) +#define PyUFunc_dd_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[15]) +#define PyUFunc_gg_g \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[16]) +#define PyUFunc_FF_F_As_DD_D \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[17]) +#define PyUFunc_DD_D \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[18]) +#define PyUFunc_FF_F \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[19]) +#define PyUFunc_GG_G \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[20]) +#define PyUFunc_OO_O \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[21]) +#define PyUFunc_O_O_method \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[22]) +#define PyUFunc_OO_O_method \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[23]) +#define PyUFunc_On_Om \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[24]) +#define PyUFunc_clearfperr \ + (*(void (*)(void)) \ + PyUFunc_API[27]) +#define PyUFunc_getfperr \ + (*(int (*)(void)) \ + PyUFunc_API[28]) +#define PyUFunc_ReplaceLoopBySignature \ + (*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \ + PyUFunc_API[30]) +#define PyUFunc_FromFuncAndDataAndSignature \ + (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int, const char *)) \ + PyUFunc_API[31]) +#define PyUFunc_e_e \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[33]) +#define PyUFunc_e_e_As_f_f \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[34]) +#define PyUFunc_e_e_As_d_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[35]) +#define PyUFunc_ee_e \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[36]) +#define PyUFunc_ee_e_As_ff_f \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[37]) +#define PyUFunc_ee_e_As_dd_d \ + (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \ + PyUFunc_API[38]) +#define PyUFunc_DefaultTypeResolver \ + (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \ + PyUFunc_API[39]) +#define PyUFunc_ValidateCasting \ + (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr *const *)) \ + PyUFunc_API[40]) +#define PyUFunc_RegisterLoopForDescr \ + (*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \ + PyUFunc_API[41]) + +#if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION +#define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \ + (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \ + PyUFunc_API[42]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyUFunc_AddLoopFromSpec \ + (*(int (*)(PyObject *, PyArrayMethod_Spec *)) \ + PyUFunc_API[43]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyUFunc_AddPromoter \ + (*(int (*)(PyObject *, PyObject *, PyObject *)) \ + PyUFunc_API[44]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyUFunc_AddWrappingLoop \ + (*(int (*)(PyObject *, PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[], PyArrayMethod_TranslateGivenDescriptors *, PyArrayMethod_TranslateLoopDescriptors *)) \ + PyUFunc_API[45]) +#endif + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +#define PyUFunc_GiveFloatingpointErrors \ + (*(int (*)(const char *, int)) \ + PyUFunc_API[46]) +#endif + +static inline int +_import_umath(void) +{ + PyObject *numpy = PyImport_ImportModule("numpy._core._multiarray_umath"); + if (numpy == NULL && PyErr_ExceptionMatches(PyExc_ModuleNotFoundError)) { + PyErr_Clear(); + numpy = PyImport_ImportModule("numpy.core._multiarray_umath"); + } + + if (numpy == NULL) { + PyErr_SetString(PyExc_ImportError, + "_multiarray_umath failed to import"); + return -1; + } + + PyObject *c_api = PyObject_GetAttrString(numpy, "_UFUNC_API"); + Py_DECREF(numpy); + if (c_api == NULL) { + PyErr_SetString(PyExc_AttributeError, "_UFUNC_API not found"); + return -1; + } + + if (!PyCapsule_CheckExact(c_api)) { + PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCapsule object"); + Py_DECREF(c_api); + return -1; + } + PyUFunc_API = (void **)PyCapsule_GetPointer(c_api, NULL); + Py_DECREF(c_api); + if (PyUFunc_API == NULL) { + PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is NULL pointer"); + return -1; + } + return 0; +} + +#define import_umath() \ + do {\ + UFUNC_NOFPE\ + if (_import_umath() < 0) {\ + PyErr_Print();\ + PyErr_SetString(PyExc_ImportError,\ + "numpy._core.umath failed to import");\ + return NULL;\ + }\ + } while(0) + +#define import_umath1(ret) \ + do {\ + UFUNC_NOFPE\ + if (_import_umath() < 0) {\ + PyErr_Print();\ + PyErr_SetString(PyExc_ImportError,\ + "numpy._core.umath failed to import");\ + return ret;\ + }\ + } while(0) + +#define import_umath2(ret, msg) \ + do {\ + UFUNC_NOFPE\ + if (_import_umath() < 0) {\ + PyErr_Print();\ + PyErr_SetString(PyExc_ImportError, msg);\ + return ret;\ + }\ + } while(0) + +#define import_ufunc() \ + do {\ + UFUNC_NOFPE\ + if (_import_umath() < 0) {\ + PyErr_Print();\ + PyErr_SetString(PyExc_ImportError,\ + "numpy._core.umath failed to import");\ + }\ + } while(0) + + +static inline int +PyUFunc_ImportUFuncAPI() +{ + if (NPY_UNLIKELY(PyUFunc_API == NULL)) { + import_umath1(-1); + } + return 0; +} + +#endif diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_neighborhood_iterator_imp.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_neighborhood_iterator_imp.h new file mode 100644 index 0000000000000000000000000000000000000000..b365cb50854f381f1a399b7aea2adab846490366 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_neighborhood_iterator_imp.h @@ -0,0 +1,90 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ +#error You should not include this header directly +#endif +/* + * Private API (here for inline) + */ +static inline int +_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter); + +/* + * Update to next item of the iterator + * + * Note: this simply increment the coordinates vector, last dimension + * incremented first , i.e, for dimension 3 + * ... + * -1, -1, -1 + * -1, -1, 0 + * -1, -1, 1 + * .... + * -1, 0, -1 + * -1, 0, 0 + * .... + * 0, -1, -1 + * 0, -1, 0 + * .... + */ +#define _UPDATE_COORD_ITER(c) \ + wb = iter->coordinates[c] < iter->bounds[c][1]; \ + if (wb) { \ + iter->coordinates[c] += 1; \ + return 0; \ + } \ + else { \ + iter->coordinates[c] = iter->bounds[c][0]; \ + } + +static inline int +_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter) +{ + npy_intp i, wb; + + for (i = iter->nd - 1; i >= 0; --i) { + _UPDATE_COORD_ITER(i) + } + + return 0; +} + +/* + * Version optimized for 2d arrays, manual loop unrolling + */ +static inline int +_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter) +{ + npy_intp wb; + + _UPDATE_COORD_ITER(1) + _UPDATE_COORD_ITER(0) + + return 0; +} +#undef _UPDATE_COORD_ITER + +/* + * Advance to the next neighbour + */ +static inline int +PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter) +{ + _PyArrayNeighborhoodIter_IncrCoord (iter); + iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates); + + return 0; +} + +/* + * Reset functions + */ +static inline int +PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter) +{ + npy_intp i; + + for (i = 0; i < iter->nd; ++i) { + iter->coordinates[i] = iter->bounds[i][0]; + } + iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates); + + return 0; +} diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_numpyconfig.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_numpyconfig.h new file mode 100644 index 0000000000000000000000000000000000000000..6e50e16505bbc9ad5f859ff63dd16978c7cd3ecb --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_numpyconfig.h @@ -0,0 +1,33 @@ +#define NPY_HAVE_ENDIAN_H 1 + +#define NPY_SIZEOF_SHORT 2 +#define NPY_SIZEOF_INT 4 +#define NPY_SIZEOF_LONG 8 +#define NPY_SIZEOF_FLOAT 4 +#define NPY_SIZEOF_COMPLEX_FLOAT 8 +#define NPY_SIZEOF_DOUBLE 8 +#define NPY_SIZEOF_COMPLEX_DOUBLE 16 +#define NPY_SIZEOF_LONGDOUBLE 16 +#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 +#define NPY_SIZEOF_PY_INTPTR_T 8 +#define NPY_SIZEOF_INTP 8 +#define NPY_SIZEOF_UINTP 8 +#define NPY_SIZEOF_WCHAR_T 4 +#define NPY_SIZEOF_OFF_T 8 +#define NPY_SIZEOF_PY_LONG_LONG 8 +#define NPY_SIZEOF_LONGLONG 8 + +/* + * Defined to 1 or 0. Note that Pyodide hardcodes NPY_NO_SMP (and other defines + * in this header) for better cross-compilation, so don't rename them without a + * good reason. + */ +#define NPY_NO_SMP 0 + +#define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden"))) +#define NPY_ABI_VERSION 0x02000000 +#define NPY_API_VERSION 0x00000013 + +#ifndef __STDC_FORMAT_MACROS +#define __STDC_FORMAT_MACROS 1 +#endif diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_public_dtype_api_table.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_public_dtype_api_table.h new file mode 100644 index 0000000000000000000000000000000000000000..51f39054062762c39b3df4f689b74060e97036c0 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/_public_dtype_api_table.h @@ -0,0 +1,86 @@ +/* + * Public exposure of the DType Classes. These are tricky to expose + * via the Python API, so they are exposed through this header for now. + * + * These definitions are only relevant for the public API and we reserve + * the slots 320-360 in the API table generation for this (currently). + * + * TODO: This file should be consolidated with the API table generation + * (although not sure the current generation is worth preserving). + */ +#ifndef NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_ +#define NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_ + +#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD) + +/* All of these require NumPy 2.0 support */ +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + +/* + * The type of the DType metaclass + */ +#define PyArrayDTypeMeta_Type (*(PyTypeObject *)(PyArray_API + 320)[0]) +/* + * NumPy's builtin DTypes: + */ +#define PyArray_BoolDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[1]) +/* Integers */ +#define PyArray_ByteDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[2]) +#define PyArray_UByteDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[3]) +#define PyArray_ShortDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[4]) +#define PyArray_UShortDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[5]) +#define PyArray_IntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[6]) +#define PyArray_UIntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[7]) +#define PyArray_LongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[8]) +#define PyArray_ULongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[9]) +#define PyArray_LongLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[10]) +#define PyArray_ULongLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[11]) +/* Integer aliases */ +#define PyArray_Int8DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[12]) +#define PyArray_UInt8DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[13]) +#define PyArray_Int16DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[14]) +#define PyArray_UInt16DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[15]) +#define PyArray_Int32DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[16]) +#define PyArray_UInt32DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[17]) +#define PyArray_Int64DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[18]) +#define PyArray_UInt64DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[19]) +#define PyArray_IntpDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[20]) +#define PyArray_UIntpDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[21]) +/* Floats */ +#define PyArray_HalfDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[22]) +#define PyArray_FloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[23]) +#define PyArray_DoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[24]) +#define PyArray_LongDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[25]) +/* Complex */ +#define PyArray_CFloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[26]) +#define PyArray_CDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[27]) +#define PyArray_CLongDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[28]) +/* String/Bytes */ +#define PyArray_BytesDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[29]) +#define PyArray_UnicodeDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[30]) +/* Datetime/Timedelta */ +#define PyArray_DatetimeDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[31]) +#define PyArray_TimedeltaDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[32]) +/* Object/Void */ +#define PyArray_ObjectDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[33]) +#define PyArray_VoidDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[34]) +/* Python types (used as markers for scalars) */ +#define PyArray_PyLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[35]) +#define PyArray_PyFloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[36]) +#define PyArray_PyComplexDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[37]) +/* Default integer type */ +#define PyArray_DefaultIntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[38]) +/* New non-legacy DTypes follow in the order they were added */ +#define PyArray_StringDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[39]) + +/* NOTE: offset 40 is free */ + +/* Need to start with a larger offset again for the abstract classes: */ +#define PyArray_IntAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[366]) +#define PyArray_FloatAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[367]) +#define PyArray_ComplexAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[368]) + +#endif /* NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION */ + +#endif /* NPY_INTERNAL_BUILD */ +#endif /* NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayobject.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayobject.h new file mode 100644 index 0000000000000000000000000000000000000000..97d93590e401fc878b72281d938170623f01da97 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayobject.h @@ -0,0 +1,7 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ +#define Py_ARRAYOBJECT_H + +#include "ndarrayobject.h" + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayscalars.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayscalars.h new file mode 100644 index 0000000000000000000000000000000000000000..ff048061f70abda097606b6247e32234d4eacf29 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/arrayscalars.h @@ -0,0 +1,196 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ + +#ifndef _MULTIARRAYMODULE +typedef struct { + PyObject_HEAD + npy_bool obval; +} PyBoolScalarObject; +#endif + + +typedef struct { + PyObject_HEAD + signed char obval; +} PyByteScalarObject; + + +typedef struct { + PyObject_HEAD + short obval; +} PyShortScalarObject; + + +typedef struct { + PyObject_HEAD + int obval; +} PyIntScalarObject; + + +typedef struct { + PyObject_HEAD + long obval; +} PyLongScalarObject; + + +typedef struct { + PyObject_HEAD + npy_longlong obval; +} PyLongLongScalarObject; + + +typedef struct { + PyObject_HEAD + unsigned char obval; +} PyUByteScalarObject; + + +typedef struct { + PyObject_HEAD + unsigned short obval; +} PyUShortScalarObject; + + +typedef struct { + PyObject_HEAD + unsigned int obval; +} PyUIntScalarObject; + + +typedef struct { + PyObject_HEAD + unsigned long obval; +} PyULongScalarObject; + + +typedef struct { + PyObject_HEAD + npy_ulonglong obval; +} PyULongLongScalarObject; + + +typedef struct { + PyObject_HEAD + npy_half obval; +} PyHalfScalarObject; + + +typedef struct { + PyObject_HEAD + float obval; +} PyFloatScalarObject; + + +typedef struct { + PyObject_HEAD + double obval; +} PyDoubleScalarObject; + + +typedef struct { + PyObject_HEAD + npy_longdouble obval; +} PyLongDoubleScalarObject; + + +typedef struct { + PyObject_HEAD + npy_cfloat obval; +} PyCFloatScalarObject; + + +typedef struct { + PyObject_HEAD + npy_cdouble obval; +} PyCDoubleScalarObject; + + +typedef struct { + PyObject_HEAD + npy_clongdouble obval; +} PyCLongDoubleScalarObject; + + +typedef struct { + PyObject_HEAD + PyObject * obval; +} PyObjectScalarObject; + +typedef struct { + PyObject_HEAD + npy_datetime obval; + PyArray_DatetimeMetaData obmeta; +} PyDatetimeScalarObject; + +typedef struct { + PyObject_HEAD + npy_timedelta obval; + PyArray_DatetimeMetaData obmeta; +} PyTimedeltaScalarObject; + + +typedef struct { + PyObject_HEAD + char obval; +} PyScalarObject; + +#define PyStringScalarObject PyBytesObject +#ifndef Py_LIMITED_API +typedef struct { + /* note that the PyObject_HEAD macro lives right here */ + PyUnicodeObject base; + Py_UCS4 *obval; + #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION + char *buffer_fmt; + #endif +} PyUnicodeScalarObject; +#endif + + +typedef struct { + PyObject_VAR_HEAD + char *obval; +#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD + /* Internally use the subclass to allow accessing names/fields */ + _PyArray_LegacyDescr *descr; +#else + PyArray_Descr *descr; +#endif + int flags; + PyObject *base; + #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION + void *_buffer_info; /* private buffer info, tagged to allow warning */ + #endif +} PyVoidScalarObject; + +/* Macros + PyScalarObject + PyArrType_Type + are defined in ndarrayobject.h +*/ + +#define PyArrayScalar_False ((PyObject *)(&(_PyArrayScalar_BoolValues[0]))) +#define PyArrayScalar_True ((PyObject *)(&(_PyArrayScalar_BoolValues[1]))) +#define PyArrayScalar_FromLong(i) \ + ((PyObject *)(&(_PyArrayScalar_BoolValues[((i)!=0)]))) +#define PyArrayScalar_RETURN_BOOL_FROM_LONG(i) \ + return Py_INCREF(PyArrayScalar_FromLong(i)), \ + PyArrayScalar_FromLong(i) +#define PyArrayScalar_RETURN_FALSE \ + return Py_INCREF(PyArrayScalar_False), \ + PyArrayScalar_False +#define PyArrayScalar_RETURN_TRUE \ + return Py_INCREF(PyArrayScalar_True), \ + PyArrayScalar_True + +#define PyArrayScalar_New(cls) \ + Py##cls##ArrType_Type.tp_alloc(&Py##cls##ArrType_Type, 0) +#ifndef Py_LIMITED_API +/* For the limited API, use PyArray_ScalarAsCtype instead */ +#define PyArrayScalar_VAL(obj, cls) \ + ((Py##cls##ScalarObject *)obj)->obval +#define PyArrayScalar_ASSIGN(obj, cls, val) \ + PyArrayScalar_VAL(obj, cls) = val +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYSCALARS_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/dtype_api.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/dtype_api.h new file mode 100644 index 0000000000000000000000000000000000000000..b37c9fbb68213e8ff7817093bc4c1f495d84b79d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/dtype_api.h @@ -0,0 +1,480 @@ +/* + * The public DType API + */ + +#ifndef NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_ +#define NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_ + +struct PyArrayMethodObject_tag; + +/* + * Largely opaque struct for DType classes (i.e. metaclass instances). + * The internal definition is currently in `ndarraytypes.h` (export is a bit + * more complex because `PyArray_Descr` is a DTypeMeta internally but not + * externally). + */ +#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD) + +#ifndef Py_LIMITED_API + + typedef struct PyArray_DTypeMeta_tag { + PyHeapTypeObject super; + + /* + * Most DTypes will have a singleton default instance, for the + * parametric legacy DTypes (bytes, string, void, datetime) this + * may be a pointer to the *prototype* instance? + */ + PyArray_Descr *singleton; + /* Copy of the legacy DTypes type number, usually invalid. */ + int type_num; + + /* The type object of the scalar instances (may be NULL?) */ + PyTypeObject *scalar_type; + /* + * DType flags to signal legacy, parametric, or + * abstract. But plenty of space for additional information/flags. + */ + npy_uint64 flags; + + /* + * Use indirection in order to allow a fixed size for this struct. + * A stable ABI size makes creating a static DType less painful + * while also ensuring flexibility for all opaque API (with one + * indirection due the pointer lookup). + */ + void *dt_slots; + /* Allow growing (at the moment also beyond this) */ + void *reserved[3]; + } PyArray_DTypeMeta; + +#else + +typedef PyTypeObject PyArray_DTypeMeta; + +#endif /* Py_LIMITED_API */ + +#endif /* not internal build */ + +/* + * ****************************************************** + * ArrayMethod API (Casting and UFuncs) + * ****************************************************** + */ + + +typedef enum { + /* Flag for whether the GIL is required */ + NPY_METH_REQUIRES_PYAPI = 1 << 0, + /* + * Some functions cannot set floating point error flags, this flag + * gives us the option (not requirement) to skip floating point error + * setup/check. No function should set error flags and ignore them + * since it would interfere with chaining operations (e.g. casting). + */ + NPY_METH_NO_FLOATINGPOINT_ERRORS = 1 << 1, + /* Whether the method supports unaligned access (not runtime) */ + NPY_METH_SUPPORTS_UNALIGNED = 1 << 2, + /* + * Used for reductions to allow reordering the operation. At this point + * assume that if set, it also applies to normal operations though! + */ + NPY_METH_IS_REORDERABLE = 1 << 3, + /* + * Private flag for now for *logic* functions. The logical functions + * `logical_or` and `logical_and` can always cast the inputs to booleans + * "safely" (because that is how the cast to bool is defined). + * @seberg: I am not sure this is the best way to handle this, so its + * private for now (also it is very limited anyway). + * There is one "exception". NA aware dtypes cannot cast to bool + * (hopefully), so the `??->?` loop should error even with this flag. + * But a second NA fallback loop will be necessary. + */ + _NPY_METH_FORCE_CAST_INPUTS = 1 << 17, + + /* All flags which can change at runtime */ + NPY_METH_RUNTIME_FLAGS = ( + NPY_METH_REQUIRES_PYAPI | + NPY_METH_NO_FLOATINGPOINT_ERRORS), +} NPY_ARRAYMETHOD_FLAGS; + + +typedef struct PyArrayMethod_Context_tag { + /* The caller, which is typically the original ufunc. May be NULL */ + PyObject *caller; + /* The method "self". Currently an opaque object. */ + struct PyArrayMethodObject_tag *method; + + /* Operand descriptors, filled in by resolve_descriptors */ + PyArray_Descr *const *descriptors; + /* Structure may grow (this is harmless for DType authors) */ +} PyArrayMethod_Context; + + +/* + * The main object for creating a new ArrayMethod. We use the typical `slots` + * mechanism used by the Python limited API (see below for the slot defs). + */ +typedef struct { + const char *name; + int nin, nout; + NPY_CASTING casting; + NPY_ARRAYMETHOD_FLAGS flags; + PyArray_DTypeMeta **dtypes; + PyType_Slot *slots; +} PyArrayMethod_Spec; + + +/* + * ArrayMethod slots + * ----------------- + * + * SLOTS IDs For the ArrayMethod creation, once fully public, IDs are fixed + * but can be deprecated and arbitrarily extended. + */ +#define _NPY_METH_resolve_descriptors_with_scalars 1 +#define NPY_METH_resolve_descriptors 2 +#define NPY_METH_get_loop 3 +#define NPY_METH_get_reduction_initial 4 +/* specific loops for constructions/default get_loop: */ +#define NPY_METH_strided_loop 5 +#define NPY_METH_contiguous_loop 6 +#define NPY_METH_unaligned_strided_loop 7 +#define NPY_METH_unaligned_contiguous_loop 8 +#define NPY_METH_contiguous_indexed_loop 9 +#define _NPY_METH_static_data 10 + + +/* + * The resolve descriptors function, must be able to handle NULL values for + * all output (but not input) `given_descrs` and fill `loop_descrs`. + * Return -1 on error or 0 if the operation is not possible without an error + * set. (This may still be in flux.) + * Otherwise must return the "casting safety", for normal functions, this is + * almost always "safe" (or even "equivalent"?). + * + * `resolve_descriptors` is optional if all output DTypes are non-parametric. + */ +typedef NPY_CASTING (PyArrayMethod_ResolveDescriptors)( + /* "method" is currently opaque (necessary e.g. to wrap Python) */ + struct PyArrayMethodObject_tag *method, + /* DTypes the method was created for */ + PyArray_DTypeMeta *const *dtypes, + /* Input descriptors (instances). Outputs may be NULL. */ + PyArray_Descr *const *given_descrs, + /* Exact loop descriptors to use, must not hold references on error */ + PyArray_Descr **loop_descrs, + npy_intp *view_offset); + + +/* + * Rarely needed, slightly more powerful version of `resolve_descriptors`. + * See also `PyArrayMethod_ResolveDescriptors` for details on shared arguments. + * + * NOTE: This function is private now as it is unclear how and what to pass + * exactly as additional information to allow dealing with the scalars. + * See also gh-24915. + */ +typedef NPY_CASTING (PyArrayMethod_ResolveDescriptorsWithScalar)( + struct PyArrayMethodObject_tag *method, + PyArray_DTypeMeta *const *dtypes, + /* Unlike above, these can have any DType and we may allow NULL. */ + PyArray_Descr *const *given_descrs, + /* + * Input scalars or NULL. Only ever passed for python scalars. + * WARNING: In some cases, a loop may be explicitly selected and the + * value passed is not available (NULL) or does not have the + * expected type. + */ + PyObject *const *input_scalars, + PyArray_Descr **loop_descrs, + npy_intp *view_offset); + + + +typedef int (PyArrayMethod_StridedLoop)(PyArrayMethod_Context *context, + char *const *data, const npy_intp *dimensions, const npy_intp *strides, + NpyAuxData *transferdata); + + +typedef int (PyArrayMethod_GetLoop)( + PyArrayMethod_Context *context, + int aligned, int move_references, + const npy_intp *strides, + PyArrayMethod_StridedLoop **out_loop, + NpyAuxData **out_transferdata, + NPY_ARRAYMETHOD_FLAGS *flags); + +/** + * Query an ArrayMethod for the initial value for use in reduction. + * + * @param context The arraymethod context, mainly to access the descriptors. + * @param reduction_is_empty Whether the reduction is empty. When it is, the + * value returned may differ. In this case it is a "default" value that + * may differ from the "identity" value normally used. For example: + * - `0.0` is the default for `sum([])`. But `-0.0` is the correct + * identity otherwise as it preserves the sign for `sum([-0.0])`. + * - We use no identity for object, but return the default of `0` and `1` + * for the empty `sum([], dtype=object)` and `prod([], dtype=object)`. + * This allows `np.sum(np.array(["a", "b"], dtype=object))` to work. + * - `-inf` or `INT_MIN` for `max` is an identity, but at least `INT_MIN` + * not a good *default* when there are no items. + * @param initial Pointer to initial data to be filled (if possible) + * + * @returns -1, 0, or 1 indicating error, no initial value, and initial being + * successfully filled. Errors must not be given where 0 is correct, NumPy + * may call this even when not strictly necessary. + */ +typedef int (PyArrayMethod_GetReductionInitial)( + PyArrayMethod_Context *context, npy_bool reduction_is_empty, + void *initial); + +/* + * The following functions are only used by the wrapping array method defined + * in umath/wrapping_array_method.c + */ + + +/* + * The function to convert the given descriptors (passed in to + * `resolve_descriptors`) and translates them for the wrapped loop. + * The new descriptors MUST be viewable with the old ones, `NULL` must be + * supported (for outputs) and should normally be forwarded. + * + * The function must clean up on error. + * + * NOTE: We currently assume that this translation gives "viewable" results. + * I.e. there is no additional casting related to the wrapping process. + * In principle that could be supported, but not sure it is useful. + * This currently also means that e.g. alignment must apply identically + * to the new dtypes. + * + * TODO: Due to the fact that `resolve_descriptors` is also used for `can_cast` + * there is no way to "pass out" the result of this function. This means + * it will be called twice for every ufunc call. + * (I am considering including `auxdata` as an "optional" parameter to + * `resolve_descriptors`, so that it can be filled there if not NULL.) + */ +typedef int (PyArrayMethod_TranslateGivenDescriptors)(int nin, int nout, + PyArray_DTypeMeta *const wrapped_dtypes[], + PyArray_Descr *const given_descrs[], PyArray_Descr *new_descrs[]); + +/** + * The function to convert the actual loop descriptors (as returned by the + * original `resolve_descriptors` function) to the ones the output array + * should use. + * This function must return "viewable" types, it must not mutate them in any + * form that would break the inner-loop logic. Does not need to support NULL. + * + * The function must clean up on error. + * + * @param nin Number of input arguments + * @param nout Number of output arguments + * @param new_dtypes The DTypes of the output (usually probably not needed) + * @param given_descrs Original given_descrs to the resolver, necessary to + * fetch any information related to the new dtypes from the original. + * @param original_descrs The `loop_descrs` returned by the wrapped loop. + * @param loop_descrs The output descriptors, compatible to `original_descrs`. + * + * @returns 0 on success, -1 on failure. + */ +typedef int (PyArrayMethod_TranslateLoopDescriptors)(int nin, int nout, + PyArray_DTypeMeta *const new_dtypes[], PyArray_Descr *const given_descrs[], + PyArray_Descr *original_descrs[], PyArray_Descr *loop_descrs[]); + + + +/* + * A traverse loop working on a single array. This is similar to the general + * strided-loop function. This is designed for loops that need to visit every + * element of a single array. + * + * Currently this is used for array clearing, via the NPY_DT_get_clear_loop + * API hook, and zero-filling, via the NPY_DT_get_fill_zero_loop API hook. + * These are most useful for handling arrays storing embedded references to + * python objects or heap-allocated data. + * + * The `void *traverse_context` is passed in because we may need to pass in + * Interpreter state or similar in the future, but we don't want to pass in + * a full context (with pointers to dtypes, method, caller which all make + * no sense for a traverse function). + * + * We assume for now that this context can be just passed through in the + * the future (for structured dtypes). + * + */ +typedef int (PyArrayMethod_TraverseLoop)( + void *traverse_context, const PyArray_Descr *descr, char *data, + npy_intp size, npy_intp stride, NpyAuxData *auxdata); + + +/* + * Simplified get_loop function specific to dtype traversal + * + * It should set the flags needed for the traversal loop and set out_loop to the + * loop function, which must be a valid PyArrayMethod_TraverseLoop + * pointer. Currently this is used for zero-filling and clearing arrays storing + * embedded references. + * + */ +typedef int (PyArrayMethod_GetTraverseLoop)( + void *traverse_context, const PyArray_Descr *descr, + int aligned, npy_intp fixed_stride, + PyArrayMethod_TraverseLoop **out_loop, NpyAuxData **out_auxdata, + NPY_ARRAYMETHOD_FLAGS *flags); + + +/* + * Type of the C promoter function, which must be wrapped into a + * PyCapsule with name "numpy._ufunc_promoter". + * + * Note that currently the output dtypes are always NULL unless they are + * also part of the signature. This is an implementation detail and could + * change in the future. However, in general promoters should not have a + * need for output dtypes. + * (There are potential use-cases, these are currently unsupported.) + */ +typedef int (PyArrayMethod_PromoterFunction)(PyObject *ufunc, + PyArray_DTypeMeta *const op_dtypes[], PyArray_DTypeMeta *const signature[], + PyArray_DTypeMeta *new_op_dtypes[]); + +/* + * **************************** + * DTYPE API + * **************************** + */ + +#define NPY_DT_ABSTRACT 1 << 1 +#define NPY_DT_PARAMETRIC 1 << 2 +#define NPY_DT_NUMERIC 1 << 3 + +/* + * These correspond to slots in the NPY_DType_Slots struct and must + * be in the same order as the members of that struct. If new slots + * get added or old slots get removed NPY_NUM_DTYPE_SLOTS must also + * be updated + */ + +#define NPY_DT_discover_descr_from_pyobject 1 +// this slot is considered private because its API hasn't been decided +#define _NPY_DT_is_known_scalar_type 2 +#define NPY_DT_default_descr 3 +#define NPY_DT_common_dtype 4 +#define NPY_DT_common_instance 5 +#define NPY_DT_ensure_canonical 6 +#define NPY_DT_setitem 7 +#define NPY_DT_getitem 8 +#define NPY_DT_get_clear_loop 9 +#define NPY_DT_get_fill_zero_loop 10 +#define NPY_DT_finalize_descr 11 + +// These PyArray_ArrFunc slots will be deprecated and replaced eventually +// getitem and setitem can be defined as a performance optimization; +// by default the user dtypes call `legacy_getitem_using_DType` and +// `legacy_setitem_using_DType`, respectively. This functionality is +// only supported for basic NumPy DTypes. + + +// used to separate dtype slots from arrfuncs slots +// intended only for internal use but defined here for clarity +#define _NPY_DT_ARRFUNCS_OFFSET (1 << 10) + +// Cast is disabled +// #define NPY_DT_PyArray_ArrFuncs_cast 0 + _NPY_DT_ARRFUNCS_OFFSET + +#define NPY_DT_PyArray_ArrFuncs_getitem 1 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_setitem 2 + _NPY_DT_ARRFUNCS_OFFSET + +// Copyswap is disabled +// #define NPY_DT_PyArray_ArrFuncs_copyswapn 3 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_copyswap 4 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_compare 5 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_argmax 6 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_dotfunc 7 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_scanfunc 8 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_fromstr 9 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_nonzero 10 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_fill 11 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_fillwithscalar 12 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_sort 13 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_argsort 14 + _NPY_DT_ARRFUNCS_OFFSET + +// Casting related slots are disabled. See +// https://github.com/numpy/numpy/pull/23173#discussion_r1101098163 +// #define NPY_DT_PyArray_ArrFuncs_castdict 15 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_scalarkind 16 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_cancastscalarkindto 17 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_cancastto 18 + _NPY_DT_ARRFUNCS_OFFSET + +// These are deprecated in NumPy 1.19, so are disabled here. +// #define NPY_DT_PyArray_ArrFuncs_fastclip 19 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_fastputmask 20 + _NPY_DT_ARRFUNCS_OFFSET +// #define NPY_DT_PyArray_ArrFuncs_fasttake 21 + _NPY_DT_ARRFUNCS_OFFSET +#define NPY_DT_PyArray_ArrFuncs_argmin 22 + _NPY_DT_ARRFUNCS_OFFSET + + +// TODO: These slots probably still need some thought, and/or a way to "grow"? +typedef struct { + PyTypeObject *typeobj; /* type of python scalar or NULL */ + int flags; /* flags, including parametric and abstract */ + /* NULL terminated cast definitions. Use NULL for the newly created DType */ + PyArrayMethod_Spec **casts; + PyType_Slot *slots; + /* Baseclass or NULL (will always subclass `np.dtype`) */ + PyTypeObject *baseclass; +} PyArrayDTypeMeta_Spec; + + +typedef PyArray_Descr *(PyArrayDTypeMeta_DiscoverDescrFromPyobject)( + PyArray_DTypeMeta *cls, PyObject *obj); + +/* + * Before making this public, we should decide whether it should pass + * the type, or allow looking at the object. A possible use-case: + * `np.array(np.array([0]), dtype=np.ndarray)` + * Could consider arrays that are not `dtype=ndarray` "scalars". + */ +typedef int (PyArrayDTypeMeta_IsKnownScalarType)( + PyArray_DTypeMeta *cls, PyTypeObject *obj); + +typedef PyArray_Descr *(PyArrayDTypeMeta_DefaultDescriptor)(PyArray_DTypeMeta *cls); +typedef PyArray_DTypeMeta *(PyArrayDTypeMeta_CommonDType)( + PyArray_DTypeMeta *dtype1, PyArray_DTypeMeta *dtype2); + + +/* + * Convenience utility for getting a reference to the DType metaclass associated + * with a dtype instance. + */ +#define NPY_DTYPE(descr) ((PyArray_DTypeMeta *)Py_TYPE(descr)) + +static inline PyArray_DTypeMeta * +NPY_DT_NewRef(PyArray_DTypeMeta *o) { + Py_INCREF((PyObject *)o); + return o; +} + + +typedef PyArray_Descr *(PyArrayDTypeMeta_CommonInstance)( + PyArray_Descr *dtype1, PyArray_Descr *dtype2); +typedef PyArray_Descr *(PyArrayDTypeMeta_EnsureCanonical)(PyArray_Descr *dtype); +/* + * Returns either a new reference to *dtype* or a new descriptor instance + * initialized with the same parameters as *dtype*. The caller cannot know + * which choice a dtype will make. This function is called just before the + * array buffer is created for a newly created array, it is not called for + * views and the descriptor returned by this function is attached to the array. + */ +typedef PyArray_Descr *(PyArrayDTypeMeta_FinalizeDescriptor)(PyArray_Descr *dtype); + +/* + * TODO: These two functions are currently only used for experimental DType + * API support. Their relation should be "reversed": NumPy should + * always use them internally. + * There are open points about "casting safety" though, e.g. setting + * elements is currently always unsafe. + */ +typedef int(PyArrayDTypeMeta_SetItem)(PyArray_Descr *, PyObject *, char *); +typedef PyObject *(PyArrayDTypeMeta_GetItem)(PyArray_Descr *, char *); + +#endif /* NUMPY_CORE_INCLUDE_NUMPY___DTYPE_API_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/halffloat.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/halffloat.h new file mode 100644 index 0000000000000000000000000000000000000000..950401664e101d17ce5461ecb21c7d3bdf824f42 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/halffloat.h @@ -0,0 +1,70 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * Half-precision routines + */ + +/* Conversions */ +float npy_half_to_float(npy_half h); +double npy_half_to_double(npy_half h); +npy_half npy_float_to_half(float f); +npy_half npy_double_to_half(double d); +/* Comparisons */ +int npy_half_eq(npy_half h1, npy_half h2); +int npy_half_ne(npy_half h1, npy_half h2); +int npy_half_le(npy_half h1, npy_half h2); +int npy_half_lt(npy_half h1, npy_half h2); +int npy_half_ge(npy_half h1, npy_half h2); +int npy_half_gt(npy_half h1, npy_half h2); +/* faster *_nonan variants for when you know h1 and h2 are not NaN */ +int npy_half_eq_nonan(npy_half h1, npy_half h2); +int npy_half_lt_nonan(npy_half h1, npy_half h2); +int npy_half_le_nonan(npy_half h1, npy_half h2); +/* Miscellaneous functions */ +int npy_half_iszero(npy_half h); +int npy_half_isnan(npy_half h); +int npy_half_isinf(npy_half h); +int npy_half_isfinite(npy_half h); +int npy_half_signbit(npy_half h); +npy_half npy_half_copysign(npy_half x, npy_half y); +npy_half npy_half_spacing(npy_half h); +npy_half npy_half_nextafter(npy_half x, npy_half y); +npy_half npy_half_divmod(npy_half x, npy_half y, npy_half *modulus); + +/* + * Half-precision constants + */ + +#define NPY_HALF_ZERO (0x0000u) +#define NPY_HALF_PZERO (0x0000u) +#define NPY_HALF_NZERO (0x8000u) +#define NPY_HALF_ONE (0x3c00u) +#define NPY_HALF_NEGONE (0xbc00u) +#define NPY_HALF_PINF (0x7c00u) +#define NPY_HALF_NINF (0xfc00u) +#define NPY_HALF_NAN (0x7e00u) + +#define NPY_MAX_HALF (0x7bffu) + +/* + * Bit-level conversions + */ + +npy_uint16 npy_floatbits_to_halfbits(npy_uint32 f); +npy_uint16 npy_doublebits_to_halfbits(npy_uint64 d); +npy_uint32 npy_halfbits_to_floatbits(npy_uint16 h); +npy_uint64 npy_halfbits_to_doublebits(npy_uint16 h); + +#ifdef __cplusplus +} +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_HALFFLOAT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarrayobject.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarrayobject.h new file mode 100644 index 0000000000000000000000000000000000000000..f06bafe5b52a4ab930c2aad0c45d95d0b6d8104b --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarrayobject.h @@ -0,0 +1,304 @@ +/* + * DON'T INCLUDE THIS DIRECTLY. + */ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ + +#ifdef __cplusplus +extern "C" { +#endif + +#include +#include "ndarraytypes.h" +#include "dtype_api.h" + +/* Includes the "function" C-API -- these are all stored in a + list of pointers --- one for each file + The two lists are concatenated into one in multiarray. + + They are available as import_array() +*/ + +#include "__multiarray_api.h" + +/* + * Include any definitions which are defined differently for 1.x and 2.x + * (Symbols only available on 2.x are not there, but rather guarded.) + */ +#include "npy_2_compat.h" + +/* C-API that requires previous API to be defined */ + +#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type) + +#define PyArray_Check(op) PyObject_TypeCheck(op, &PyArray_Type) +#define PyArray_CheckExact(op) (((PyObject*)(op))->ob_type == &PyArray_Type) + +#define PyArray_HasArrayInterfaceType(op, type, context, out) \ + ((((out)=PyArray_FromStructInterface(op)) != Py_NotImplemented) || \ + (((out)=PyArray_FromInterface(op)) != Py_NotImplemented) || \ + (((out)=PyArray_FromArrayAttr(op, type, context)) != \ + Py_NotImplemented)) + +#define PyArray_HasArrayInterface(op, out) \ + PyArray_HasArrayInterfaceType(op, NULL, NULL, out) + +#define PyArray_IsZeroDim(op) (PyArray_Check(op) && \ + (PyArray_NDIM((PyArrayObject *)op) == 0)) + +#define PyArray_IsScalar(obj, cls) \ + (PyObject_TypeCheck(obj, &Py##cls##ArrType_Type)) + +#define PyArray_CheckScalar(m) (PyArray_IsScalar(m, Generic) || \ + PyArray_IsZeroDim(m)) +#define PyArray_IsPythonNumber(obj) \ + (PyFloat_Check(obj) || PyComplex_Check(obj) || \ + PyLong_Check(obj) || PyBool_Check(obj)) +#define PyArray_IsIntegerScalar(obj) (PyLong_Check(obj) \ + || PyArray_IsScalar((obj), Integer)) +#define PyArray_IsPythonScalar(obj) \ + (PyArray_IsPythonNumber(obj) || PyBytes_Check(obj) || \ + PyUnicode_Check(obj)) + +#define PyArray_IsAnyScalar(obj) \ + (PyArray_IsScalar(obj, Generic) || PyArray_IsPythonScalar(obj)) + +#define PyArray_CheckAnyScalar(obj) (PyArray_IsPythonScalar(obj) || \ + PyArray_CheckScalar(obj)) + + +#define PyArray_GETCONTIGUOUS(m) (PyArray_ISCONTIGUOUS(m) ? \ + Py_INCREF(m), (m) : \ + (PyArrayObject *)(PyArray_Copy(m))) + +#define PyArray_SAMESHAPE(a1,a2) ((PyArray_NDIM(a1) == PyArray_NDIM(a2)) && \ + PyArray_CompareLists(PyArray_DIMS(a1), \ + PyArray_DIMS(a2), \ + PyArray_NDIM(a1))) + +#define PyArray_SIZE(m) PyArray_MultiplyList(PyArray_DIMS(m), PyArray_NDIM(m)) +#define PyArray_NBYTES(m) (PyArray_ITEMSIZE(m) * PyArray_SIZE(m)) +#define PyArray_FROM_O(m) PyArray_FromAny(m, NULL, 0, 0, 0, NULL) + +#define PyArray_FROM_OF(m,flags) PyArray_CheckFromAny(m, NULL, 0, 0, flags, \ + NULL) + +#define PyArray_FROM_OT(m,type) PyArray_FromAny(m, \ + PyArray_DescrFromType(type), 0, 0, 0, NULL) + +#define PyArray_FROM_OTF(m, type, flags) \ + PyArray_FromAny(m, PyArray_DescrFromType(type), 0, 0, \ + (((flags) & NPY_ARRAY_ENSURECOPY) ? \ + ((flags) | NPY_ARRAY_DEFAULT) : (flags)), NULL) + +#define PyArray_FROMANY(m, type, min, max, flags) \ + PyArray_FromAny(m, PyArray_DescrFromType(type), min, max, \ + (((flags) & NPY_ARRAY_ENSURECOPY) ? \ + (flags) | NPY_ARRAY_DEFAULT : (flags)), NULL) + +#define PyArray_ZEROS(m, dims, type, is_f_order) \ + PyArray_Zeros(m, dims, PyArray_DescrFromType(type), is_f_order) + +#define PyArray_EMPTY(m, dims, type, is_f_order) \ + PyArray_Empty(m, dims, PyArray_DescrFromType(type), is_f_order) + +#define PyArray_FILLWBYTE(obj, val) memset(PyArray_DATA(obj), val, \ + PyArray_NBYTES(obj)) + +#define PyArray_ContiguousFromAny(op, type, min_depth, max_depth) \ + PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \ + max_depth, NPY_ARRAY_DEFAULT, NULL) + +#define PyArray_EquivArrTypes(a1, a2) \ + PyArray_EquivTypes(PyArray_DESCR(a1), PyArray_DESCR(a2)) + +#define PyArray_EquivByteorders(b1, b2) \ + (((b1) == (b2)) || (PyArray_ISNBO(b1) == PyArray_ISNBO(b2))) + +#define PyArray_SimpleNew(nd, dims, typenum) \ + PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, NULL, 0, 0, NULL) + +#define PyArray_SimpleNewFromData(nd, dims, typenum, data) \ + PyArray_New(&PyArray_Type, nd, dims, typenum, NULL, \ + data, 0, NPY_ARRAY_CARRAY, NULL) + +#define PyArray_SimpleNewFromDescr(nd, dims, descr) \ + PyArray_NewFromDescr(&PyArray_Type, descr, nd, dims, \ + NULL, NULL, 0, NULL) + +#define PyArray_ToScalar(data, arr) \ + PyArray_Scalar(data, PyArray_DESCR(arr), (PyObject *)arr) + + +/* These might be faster without the dereferencing of obj + going on inside -- of course an optimizing compiler should + inline the constants inside a for loop making it a moot point +*/ + +#define PyArray_GETPTR1(obj, i) ((void *)(PyArray_BYTES(obj) + \ + (i)*PyArray_STRIDES(obj)[0])) + +#define PyArray_GETPTR2(obj, i, j) ((void *)(PyArray_BYTES(obj) + \ + (i)*PyArray_STRIDES(obj)[0] + \ + (j)*PyArray_STRIDES(obj)[1])) + +#define PyArray_GETPTR3(obj, i, j, k) ((void *)(PyArray_BYTES(obj) + \ + (i)*PyArray_STRIDES(obj)[0] + \ + (j)*PyArray_STRIDES(obj)[1] + \ + (k)*PyArray_STRIDES(obj)[2])) + +#define PyArray_GETPTR4(obj, i, j, k, l) ((void *)(PyArray_BYTES(obj) + \ + (i)*PyArray_STRIDES(obj)[0] + \ + (j)*PyArray_STRIDES(obj)[1] + \ + (k)*PyArray_STRIDES(obj)[2] + \ + (l)*PyArray_STRIDES(obj)[3])) + +static inline void +PyArray_DiscardWritebackIfCopy(PyArrayObject *arr) +{ + PyArrayObject_fields *fa = (PyArrayObject_fields *)arr; + if (fa && fa->base) { + if (fa->flags & NPY_ARRAY_WRITEBACKIFCOPY) { + PyArray_ENABLEFLAGS((PyArrayObject*)fa->base, NPY_ARRAY_WRITEABLE); + Py_DECREF(fa->base); + fa->base = NULL; + PyArray_CLEARFLAGS(arr, NPY_ARRAY_WRITEBACKIFCOPY); + } + } +} + +#define PyArray_DESCR_REPLACE(descr) do { \ + PyArray_Descr *_new_; \ + _new_ = PyArray_DescrNew(descr); \ + Py_XDECREF(descr); \ + descr = _new_; \ + } while(0) + +/* Copy should always return contiguous array */ +#define PyArray_Copy(obj) PyArray_NewCopy(obj, NPY_CORDER) + +#define PyArray_FromObject(op, type, min_depth, max_depth) \ + PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \ + max_depth, NPY_ARRAY_BEHAVED | \ + NPY_ARRAY_ENSUREARRAY, NULL) + +#define PyArray_ContiguousFromObject(op, type, min_depth, max_depth) \ + PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \ + max_depth, NPY_ARRAY_DEFAULT | \ + NPY_ARRAY_ENSUREARRAY, NULL) + +#define PyArray_CopyFromObject(op, type, min_depth, max_depth) \ + PyArray_FromAny(op, PyArray_DescrFromType(type), min_depth, \ + max_depth, NPY_ARRAY_ENSURECOPY | \ + NPY_ARRAY_DEFAULT | \ + NPY_ARRAY_ENSUREARRAY, NULL) + +#define PyArray_Cast(mp, type_num) \ + PyArray_CastToType(mp, PyArray_DescrFromType(type_num), 0) + +#define PyArray_Take(ap, items, axis) \ + PyArray_TakeFrom(ap, items, axis, NULL, NPY_RAISE) + +#define PyArray_Put(ap, items, values) \ + PyArray_PutTo(ap, items, values, NPY_RAISE) + + +/* + Check to see if this key in the dictionary is the "title" + entry of the tuple (i.e. a duplicate dictionary entry in the fields + dict). +*/ + +static inline int +NPY_TITLE_KEY_check(PyObject *key, PyObject *value) +{ + PyObject *title; + if (PyTuple_Size(value) != 3) { + return 0; + } + title = PyTuple_GetItem(value, 2); + if (key == title) { + return 1; + } +#ifdef PYPY_VERSION + /* + * On PyPy, dictionary keys do not always preserve object identity. + * Fall back to comparison by value. + */ + if (PyUnicode_Check(title) && PyUnicode_Check(key)) { + return PyUnicode_Compare(title, key) == 0 ? 1 : 0; + } +#endif + return 0; +} + +/* Macro, for backward compat with "if NPY_TITLE_KEY(key, value) { ..." */ +#define NPY_TITLE_KEY(key, value) (NPY_TITLE_KEY_check((key), (value))) + +#define DEPRECATE(msg) PyErr_WarnEx(PyExc_DeprecationWarning,msg,1) +#define DEPRECATE_FUTUREWARNING(msg) PyErr_WarnEx(PyExc_FutureWarning,msg,1) + + +/* + * These macros and functions unfortunately require runtime version checks + * that are only defined in `npy_2_compat.h`. For that reasons they cannot be + * part of `ndarraytypes.h` which tries to be self contained. + */ + +static inline npy_intp +PyArray_ITEMSIZE(const PyArrayObject *arr) +{ + return PyDataType_ELSIZE(((PyArrayObject_fields *)arr)->descr); +} + +#define PyDataType_HASFIELDS(obj) (PyDataType_ISLEGACY((PyArray_Descr*)(obj)) && PyDataType_NAMES((PyArray_Descr*)(obj)) != NULL) +#define PyDataType_HASSUBARRAY(dtype) (PyDataType_ISLEGACY(dtype) && PyDataType_SUBARRAY(dtype) != NULL) +#define PyDataType_ISUNSIZED(dtype) ((dtype)->elsize == 0 && \ + !PyDataType_HASFIELDS(dtype)) + +#define PyDataType_FLAGCHK(dtype, flag) \ + ((PyDataType_FLAGS(dtype) & (flag)) == (flag)) + +#define PyDataType_REFCHK(dtype) \ + PyDataType_FLAGCHK(dtype, NPY_ITEM_REFCOUNT) + +#define NPY_BEGIN_THREADS_DESCR(dtype) \ + do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \ + NPY_BEGIN_THREADS;} while (0); + +#define NPY_END_THREADS_DESCR(dtype) \ + do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \ + NPY_END_THREADS; } while (0); + +#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD) +/* The internal copy of this is now defined in `dtypemeta.h` */ +/* + * `PyArray_Scalar` is the same as this function but converts will convert + * most NumPy types to Python scalars. + */ +static inline PyObject * +PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr) +{ + return PyDataType_GetArrFuncs(((PyArrayObject_fields *)arr)->descr)->getitem( + (void *)itemptr, (PyArrayObject *)arr); +} + +/* + * SETITEM should only be used if it is known that the value is a scalar + * and of a type understood by the arrays dtype. + * Use `PyArray_Pack` if the value may be of a different dtype. + */ +static inline int +PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v) +{ + return PyDataType_GetArrFuncs(((PyArrayObject_fields *)arr)->descr)->setitem(v, itemptr, arr); +} +#endif /* not internal */ + + +#ifdef __cplusplus +} +#endif + + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYOBJECT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarraytypes.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarraytypes.h new file mode 100644 index 0000000000000000000000000000000000000000..37788a74557f4361f78d611470ffe9318f3c8752 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ndarraytypes.h @@ -0,0 +1,1933 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ + +#include "npy_common.h" +#include "npy_endian.h" +#include "npy_cpu.h" +#include "utils.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN + +/* Always allow threading unless it was explicitly disabled at build time */ +#if !NPY_NO_SMP + #define NPY_ALLOW_THREADS 1 +#else + #define NPY_ALLOW_THREADS 0 +#endif + +#ifndef __has_extension +#define __has_extension(x) 0 +#endif + +/* + * There are several places in the code where an array of dimensions + * is allocated statically. This is the size of that static + * allocation. + * + * The array creation itself could have arbitrary dimensions but all + * the places where static allocation is used would need to be changed + * to dynamic (including inside of several structures) + * + * As of NumPy 2.0, we strongly discourage the downstream use of NPY_MAXDIMS, + * but since auditing everything seems a big ask, define it as 64. + * A future version could: + * - Increase or remove the limit and require recompilation (like 2.0 did) + * - Deprecate or remove the macro but keep the limit (at basically any time) + */ +#define NPY_MAXDIMS 64 +/* We cannot change this as it would break ABI: */ +#define NPY_MAXDIMS_LEGACY_ITERS 32 +/* NPY_MAXARGS is version dependent and defined in npy_2_compat.h */ + +/* Used for Converter Functions "O&" code in ParseTuple */ +#define NPY_FAIL 0 +#define NPY_SUCCEED 1 + + +enum NPY_TYPES { NPY_BOOL=0, + NPY_BYTE, NPY_UBYTE, + NPY_SHORT, NPY_USHORT, + NPY_INT, NPY_UINT, + NPY_LONG, NPY_ULONG, + NPY_LONGLONG, NPY_ULONGLONG, + NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE, + NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE, + NPY_OBJECT=17, + NPY_STRING, NPY_UNICODE, + NPY_VOID, + /* + * New 1.6 types appended, may be integrated + * into the above in 2.0. + */ + NPY_DATETIME, NPY_TIMEDELTA, NPY_HALF, + + NPY_CHAR, /* Deprecated, will raise if used */ + + /* The number of *legacy* dtypes */ + NPY_NTYPES_LEGACY=24, + + /* assign a high value to avoid changing this in the + future when new dtypes are added */ + NPY_NOTYPE=25, + + NPY_USERDEF=256, /* leave room for characters */ + + /* The number of types not including the new 1.6 types */ + NPY_NTYPES_ABI_COMPATIBLE=21, + + /* + * New DTypes which do not share the legacy layout + * (added after NumPy 2.0). VSTRING is the first of these + * we may open up a block for user-defined dtypes in the + * future. + */ + NPY_VSTRING=2056, +}; + + +/* basetype array priority */ +#define NPY_PRIORITY 0.0 + +/* default subtype priority */ +#define NPY_SUBTYPE_PRIORITY 1.0 + +/* default scalar priority */ +#define NPY_SCALAR_PRIORITY -1000000.0 + +/* How many floating point types are there (excluding half) */ +#define NPY_NUM_FLOATTYPE 3 + +/* + * These characters correspond to the array type and the struct + * module + */ + +enum NPY_TYPECHAR { + NPY_BOOLLTR = '?', + NPY_BYTELTR = 'b', + NPY_UBYTELTR = 'B', + NPY_SHORTLTR = 'h', + NPY_USHORTLTR = 'H', + NPY_INTLTR = 'i', + NPY_UINTLTR = 'I', + NPY_LONGLTR = 'l', + NPY_ULONGLTR = 'L', + NPY_LONGLONGLTR = 'q', + NPY_ULONGLONGLTR = 'Q', + NPY_HALFLTR = 'e', + NPY_FLOATLTR = 'f', + NPY_DOUBLELTR = 'd', + NPY_LONGDOUBLELTR = 'g', + NPY_CFLOATLTR = 'F', + NPY_CDOUBLELTR = 'D', + NPY_CLONGDOUBLELTR = 'G', + NPY_OBJECTLTR = 'O', + NPY_STRINGLTR = 'S', + NPY_DEPRECATED_STRINGLTR2 = 'a', + NPY_UNICODELTR = 'U', + NPY_VOIDLTR = 'V', + NPY_DATETIMELTR = 'M', + NPY_TIMEDELTALTR = 'm', + NPY_CHARLTR = 'c', + + /* + * New non-legacy DTypes + */ + NPY_VSTRINGLTR = 'T', + + /* + * Note, we removed `NPY_INTPLTR` due to changing its definition + * to 'n', rather than 'p'. On any typical platform this is the + * same integer. 'n' should be used for the `np.intp` with the same + * size as `size_t` while 'p' remains pointer sized. + * + * 'p', 'P', 'n', and 'N' are valid and defined explicitly + * in `arraytypes.c.src`. + */ + + /* + * These are for dtype 'kinds', not dtype 'typecodes' + * as the above are for. + */ + NPY_GENBOOLLTR ='b', + NPY_SIGNEDLTR = 'i', + NPY_UNSIGNEDLTR = 'u', + NPY_FLOATINGLTR = 'f', + NPY_COMPLEXLTR = 'c', + +}; + +/* + * Changing this may break Numpy API compatibility + * due to changing offsets in PyArray_ArrFuncs, so be + * careful. Here we have reused the mergesort slot for + * any kind of stable sort, the actual implementation will + * depend on the data type. + */ +typedef enum { + _NPY_SORT_UNDEFINED=-1, + NPY_QUICKSORT=0, + NPY_HEAPSORT=1, + NPY_MERGESORT=2, + NPY_STABLESORT=2, +} NPY_SORTKIND; +#define NPY_NSORTS (NPY_STABLESORT + 1) + + +typedef enum { + NPY_INTROSELECT=0 +} NPY_SELECTKIND; +#define NPY_NSELECTS (NPY_INTROSELECT + 1) + + +typedef enum { + NPY_SEARCHLEFT=0, + NPY_SEARCHRIGHT=1 +} NPY_SEARCHSIDE; +#define NPY_NSEARCHSIDES (NPY_SEARCHRIGHT + 1) + + +typedef enum { + NPY_NOSCALAR=-1, + NPY_BOOL_SCALAR, + NPY_INTPOS_SCALAR, + NPY_INTNEG_SCALAR, + NPY_FLOAT_SCALAR, + NPY_COMPLEX_SCALAR, + NPY_OBJECT_SCALAR +} NPY_SCALARKIND; +#define NPY_NSCALARKINDS (NPY_OBJECT_SCALAR + 1) + +/* For specifying array memory layout or iteration order */ +typedef enum { + /* Fortran order if inputs are all Fortran, C otherwise */ + NPY_ANYORDER=-1, + /* C order */ + NPY_CORDER=0, + /* Fortran order */ + NPY_FORTRANORDER=1, + /* An order as close to the inputs as possible */ + NPY_KEEPORDER=2 +} NPY_ORDER; + +/* For specifying allowed casting in operations which support it */ +typedef enum { + _NPY_ERROR_OCCURRED_IN_CAST = -1, + /* Only allow identical types */ + NPY_NO_CASTING=0, + /* Allow identical and byte swapped types */ + NPY_EQUIV_CASTING=1, + /* Only allow safe casts */ + NPY_SAFE_CASTING=2, + /* Allow safe casts or casts within the same kind */ + NPY_SAME_KIND_CASTING=3, + /* Allow any casts */ + NPY_UNSAFE_CASTING=4, +} NPY_CASTING; + +typedef enum { + NPY_CLIP=0, + NPY_WRAP=1, + NPY_RAISE=2 +} NPY_CLIPMODE; + +typedef enum { + NPY_VALID=0, + NPY_SAME=1, + NPY_FULL=2 +} NPY_CORRELATEMODE; + +/* The special not-a-time (NaT) value */ +#define NPY_DATETIME_NAT NPY_MIN_INT64 + +/* + * Upper bound on the length of a DATETIME ISO 8601 string + * YEAR: 21 (64-bit year) + * MONTH: 3 + * DAY: 3 + * HOURS: 3 + * MINUTES: 3 + * SECONDS: 3 + * ATTOSECONDS: 1 + 3*6 + * TIMEZONE: 5 + * NULL TERMINATOR: 1 + */ +#define NPY_DATETIME_MAX_ISO8601_STRLEN (21 + 3*5 + 1 + 3*6 + 6 + 1) + +/* The FR in the unit names stands for frequency */ +typedef enum { + /* Force signed enum type, must be -1 for code compatibility */ + NPY_FR_ERROR = -1, /* error or undetermined */ + + /* Start of valid units */ + NPY_FR_Y = 0, /* Years */ + NPY_FR_M = 1, /* Months */ + NPY_FR_W = 2, /* Weeks */ + /* Gap where 1.6 NPY_FR_B (value 3) was */ + NPY_FR_D = 4, /* Days */ + NPY_FR_h = 5, /* hours */ + NPY_FR_m = 6, /* minutes */ + NPY_FR_s = 7, /* seconds */ + NPY_FR_ms = 8, /* milliseconds */ + NPY_FR_us = 9, /* microseconds */ + NPY_FR_ns = 10, /* nanoseconds */ + NPY_FR_ps = 11, /* picoseconds */ + NPY_FR_fs = 12, /* femtoseconds */ + NPY_FR_as = 13, /* attoseconds */ + NPY_FR_GENERIC = 14 /* unbound units, can convert to anything */ +} NPY_DATETIMEUNIT; + +/* + * NOTE: With the NPY_FR_B gap for 1.6 ABI compatibility, NPY_DATETIME_NUMUNITS + * is technically one more than the actual number of units. + */ +#define NPY_DATETIME_NUMUNITS (NPY_FR_GENERIC + 1) +#define NPY_DATETIME_DEFAULTUNIT NPY_FR_GENERIC + +/* + * Business day conventions for mapping invalid business + * days to valid business days. + */ +typedef enum { + /* Go forward in time to the following business day. */ + NPY_BUSDAY_FORWARD, + NPY_BUSDAY_FOLLOWING = NPY_BUSDAY_FORWARD, + /* Go backward in time to the preceding business day. */ + NPY_BUSDAY_BACKWARD, + NPY_BUSDAY_PRECEDING = NPY_BUSDAY_BACKWARD, + /* + * Go forward in time to the following business day, unless it + * crosses a month boundary, in which case go backward + */ + NPY_BUSDAY_MODIFIEDFOLLOWING, + /* + * Go backward in time to the preceding business day, unless it + * crosses a month boundary, in which case go forward. + */ + NPY_BUSDAY_MODIFIEDPRECEDING, + /* Produce a NaT for non-business days. */ + NPY_BUSDAY_NAT, + /* Raise an exception for non-business days. */ + NPY_BUSDAY_RAISE +} NPY_BUSDAY_ROLL; + + +/************************************************************ + * NumPy Auxiliary Data for inner loops, sort functions, etc. + ************************************************************/ + +/* + * When creating an auxiliary data struct, this should always appear + * as the first member, like this: + * + * typedef struct { + * NpyAuxData base; + * double constant; + * } constant_multiplier_aux_data; + */ +typedef struct NpyAuxData_tag NpyAuxData; + +/* Function pointers for freeing or cloning auxiliary data */ +typedef void (NpyAuxData_FreeFunc) (NpyAuxData *); +typedef NpyAuxData *(NpyAuxData_CloneFunc) (NpyAuxData *); + +struct NpyAuxData_tag { + NpyAuxData_FreeFunc *free; + NpyAuxData_CloneFunc *clone; + /* To allow for a bit of expansion without breaking the ABI */ + void *reserved[2]; +}; + +/* Macros to use for freeing and cloning auxiliary data */ +#define NPY_AUXDATA_FREE(auxdata) \ + do { \ + if ((auxdata) != NULL) { \ + (auxdata)->free(auxdata); \ + } \ + } while(0) +#define NPY_AUXDATA_CLONE(auxdata) \ + ((auxdata)->clone(auxdata)) + +#define NPY_ERR(str) fprintf(stderr, #str); fflush(stderr); +#define NPY_ERR2(str) fprintf(stderr, str); fflush(stderr); + +/* +* Macros to define how array, and dimension/strides data is +* allocated. These should be made private +*/ + +#define NPY_USE_PYMEM 1 + + +#if NPY_USE_PYMEM == 1 +/* use the Raw versions which are safe to call with the GIL released */ +#define PyArray_malloc PyMem_RawMalloc +#define PyArray_free PyMem_RawFree +#define PyArray_realloc PyMem_RawRealloc +#else +#define PyArray_malloc malloc +#define PyArray_free free +#define PyArray_realloc realloc +#endif + +/* Dimensions and strides */ +#define PyDimMem_NEW(size) \ + ((npy_intp *)PyArray_malloc(size*sizeof(npy_intp))) + +#define PyDimMem_FREE(ptr) PyArray_free(ptr) + +#define PyDimMem_RENEW(ptr,size) \ + ((npy_intp *)PyArray_realloc(ptr,size*sizeof(npy_intp))) + +/* forward declaration */ +struct _PyArray_Descr; + +/* These must deal with unaligned and swapped data if necessary */ +typedef PyObject * (PyArray_GetItemFunc) (void *, void *); +typedef int (PyArray_SetItemFunc)(PyObject *, void *, void *); + +typedef void (PyArray_CopySwapNFunc)(void *, npy_intp, void *, npy_intp, + npy_intp, int, void *); + +typedef void (PyArray_CopySwapFunc)(void *, void *, int, void *); +typedef npy_bool (PyArray_NonzeroFunc)(void *, void *); + + +/* + * These assume aligned and notswapped data -- a buffer will be used + * before or contiguous data will be obtained + */ + +typedef int (PyArray_CompareFunc)(const void *, const void *, void *); +typedef int (PyArray_ArgFunc)(void*, npy_intp, npy_intp*, void *); + +typedef void (PyArray_DotFunc)(void *, npy_intp, void *, npy_intp, void *, + npy_intp, void *); + +typedef void (PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *, + void *); + +/* + * XXX the ignore argument should be removed next time the API version + * is bumped. It used to be the separator. + */ +typedef int (PyArray_ScanFunc)(FILE *fp, void *dptr, + char *ignore, struct _PyArray_Descr *); +typedef int (PyArray_FromStrFunc)(char *s, void *dptr, char **endptr, + struct _PyArray_Descr *); + +typedef int (PyArray_FillFunc)(void *, npy_intp, void *); + +typedef int (PyArray_SortFunc)(void *, npy_intp, void *); +typedef int (PyArray_ArgSortFunc)(void *, npy_intp *, npy_intp, void *); + +typedef int (PyArray_FillWithScalarFunc)(void *, npy_intp, void *, void *); + +typedef int (PyArray_ScalarKindFunc)(void *); + +typedef struct { + npy_intp *ptr; + int len; +} PyArray_Dims; + +typedef struct { + /* + * Functions to cast to most other standard types + * Can have some NULL entries. The types + * DATETIME, TIMEDELTA, and HALF go into the castdict + * even though they are built-in. + */ + PyArray_VectorUnaryFunc *cast[NPY_NTYPES_ABI_COMPATIBLE]; + + /* The next four functions *cannot* be NULL */ + + /* + * Functions to get and set items with standard Python types + * -- not array scalars + */ + PyArray_GetItemFunc *getitem; + PyArray_SetItemFunc *setitem; + + /* + * Copy and/or swap data. Memory areas may not overlap + * Use memmove first if they might + */ + PyArray_CopySwapNFunc *copyswapn; + PyArray_CopySwapFunc *copyswap; + + /* + * Function to compare items + * Can be NULL + */ + PyArray_CompareFunc *compare; + + /* + * Function to select largest + * Can be NULL + */ + PyArray_ArgFunc *argmax; + + /* + * Function to compute dot product + * Can be NULL + */ + PyArray_DotFunc *dotfunc; + + /* + * Function to scan an ASCII file and + * place a single value plus possible separator + * Can be NULL + */ + PyArray_ScanFunc *scanfunc; + + /* + * Function to read a single value from a string + * and adjust the pointer; Can be NULL + */ + PyArray_FromStrFunc *fromstr; + + /* + * Function to determine if data is zero or not + * If NULL a default version is + * used at Registration time. + */ + PyArray_NonzeroFunc *nonzero; + + /* + * Used for arange. Should return 0 on success + * and -1 on failure. + * Can be NULL. + */ + PyArray_FillFunc *fill; + + /* + * Function to fill arrays with scalar values + * Can be NULL + */ + PyArray_FillWithScalarFunc *fillwithscalar; + + /* + * Sorting functions + * Can be NULL + */ + PyArray_SortFunc *sort[NPY_NSORTS]; + PyArray_ArgSortFunc *argsort[NPY_NSORTS]; + + /* + * Dictionary of additional casting functions + * PyArray_VectorUnaryFuncs + * which can be populated to support casting + * to other registered types. Can be NULL + */ + PyObject *castdict; + + /* + * Functions useful for generalizing + * the casting rules. + * Can be NULL; + */ + PyArray_ScalarKindFunc *scalarkind; + int **cancastscalarkindto; + int *cancastto; + + void *_unused1; + void *_unused2; + void *_unused3; + + /* + * Function to select smallest + * Can be NULL + */ + PyArray_ArgFunc *argmin; + +} PyArray_ArrFuncs; + + +/* The item must be reference counted when it is inserted or extracted. */ +#define NPY_ITEM_REFCOUNT 0x01 +/* Same as needing REFCOUNT */ +#define NPY_ITEM_HASOBJECT 0x01 +/* Convert to list for pickling */ +#define NPY_LIST_PICKLE 0x02 +/* The item is a POINTER */ +#define NPY_ITEM_IS_POINTER 0x04 +/* memory needs to be initialized for this data-type */ +#define NPY_NEEDS_INIT 0x08 +/* operations need Python C-API so don't give-up thread. */ +#define NPY_NEEDS_PYAPI 0x10 +/* Use f.getitem when extracting elements of this data-type */ +#define NPY_USE_GETITEM 0x20 +/* Use f.setitem when setting creating 0-d array from this data-type.*/ +#define NPY_USE_SETITEM 0x40 +/* A sticky flag specifically for structured arrays */ +#define NPY_ALIGNED_STRUCT 0x80 + +/* + *These are inherited for global data-type if any data-types in the + * field have them + */ +#define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \ + NPY_ITEM_REFCOUNT | NPY_NEEDS_PYAPI) + +#define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \ + NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT | \ + NPY_NEEDS_INIT | NPY_NEEDS_PYAPI) + +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION +/* + * Public version of the Descriptor struct as of 2.x + */ +typedef struct _PyArray_Descr { + PyObject_HEAD + /* + * the type object representing an + * instance of this type -- should not + * be two type_numbers with the same type + * object. + */ + PyTypeObject *typeobj; + /* kind for this type */ + char kind; + /* unique-character representing this type */ + char type; + /* + * '>' (big), '<' (little), '|' + * (not-applicable), or '=' (native). + */ + char byteorder; + /* Former flags flags space (unused) to ensure type_num is stable. */ + char _former_flags; + /* number representing this type */ + int type_num; + /* Space for dtype instance specific flags. */ + npy_uint64 flags; + /* element size (itemsize) for this type */ + npy_intp elsize; + /* alignment needed for this type */ + npy_intp alignment; + /* metadata dict or NULL */ + PyObject *metadata; + /* Cached hash value (-1 if not yet computed). */ + npy_hash_t hash; + /* Unused slot (must be initialized to NULL) for future use */ + void *reserved_null[2]; +} PyArray_Descr; + +#else /* 1.x and 2.x compatible version (only shared fields): */ + +typedef struct _PyArray_Descr { + PyObject_HEAD + PyTypeObject *typeobj; + char kind; + char type; + char byteorder; + char _former_flags; + int type_num; +} PyArray_Descr; + +/* To access modified fields, define the full 2.0 struct: */ +typedef struct { + PyObject_HEAD + PyTypeObject *typeobj; + char kind; + char type; + char byteorder; + char _former_flags; + int type_num; + npy_uint64 flags; + npy_intp elsize; + npy_intp alignment; + PyObject *metadata; + npy_hash_t hash; + void *reserved_null[2]; +} _PyArray_DescrNumPy2; + +#endif /* 1.x and 2.x compatible version */ + +/* + * Semi-private struct with additional field of legacy descriptors (must + * check NPY_DT_is_legacy before casting/accessing). The struct is also not + * valid when running on 1.x (i.e. in public API use). + */ +typedef struct { + PyObject_HEAD + PyTypeObject *typeobj; + char kind; + char type; + char byteorder; + char _former_flags; + int type_num; + npy_uint64 flags; + npy_intp elsize; + npy_intp alignment; + PyObject *metadata; + npy_hash_t hash; + void *reserved_null[2]; + struct _arr_descr *subarray; + PyObject *fields; + PyObject *names; + NpyAuxData *c_metadata; +} _PyArray_LegacyDescr; + + +/* + * Umodified PyArray_Descr struct identical to NumPy 1.x. This struct is + * used as a prototype for registering a new legacy DType. + * It is also used to access the fields in user code running on 1.x. + */ +typedef struct { + PyObject_HEAD + PyTypeObject *typeobj; + char kind; + char type; + char byteorder; + char flags; + int type_num; + int elsize; + int alignment; + struct _arr_descr *subarray; + PyObject *fields; + PyObject *names; + PyArray_ArrFuncs *f; + PyObject *metadata; + NpyAuxData *c_metadata; + npy_hash_t hash; +} PyArray_DescrProto; + + +typedef struct _arr_descr { + PyArray_Descr *base; + PyObject *shape; /* a tuple */ +} PyArray_ArrayDescr; + +/* + * Memory handler structure for array data. + */ +/* The declaration of free differs from PyMemAllocatorEx */ +typedef struct { + void *ctx; + void* (*malloc) (void *ctx, size_t size); + void* (*calloc) (void *ctx, size_t nelem, size_t elsize); + void* (*realloc) (void *ctx, void *ptr, size_t new_size); + void (*free) (void *ctx, void *ptr, size_t size); + /* + * This is the end of the version=1 struct. Only add new fields after + * this line + */ +} PyDataMemAllocator; + +typedef struct { + char name[127]; /* multiple of 64 to keep the struct aligned */ + uint8_t version; /* currently 1 */ + PyDataMemAllocator allocator; +} PyDataMem_Handler; + + +/* + * The main array object structure. + * + * It has been recommended to use the inline functions defined below + * (PyArray_DATA and friends) to access fields here for a number of + * releases. Direct access to the members themselves is deprecated. + * To ensure that your code does not use deprecated access, + * #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + * (or NPY_1_8_API_VERSION or higher as required). + */ +/* This struct will be moved to a private header in a future release */ +typedef struct tagPyArrayObject_fields { + PyObject_HEAD + /* Pointer to the raw data buffer */ + char *data; + /* The number of dimensions, also called 'ndim' */ + int nd; + /* The size in each dimension, also called 'shape' */ + npy_intp *dimensions; + /* + * Number of bytes to jump to get to the + * next element in each dimension + */ + npy_intp *strides; + /* + * This object is decref'd upon + * deletion of array. Except in the + * case of WRITEBACKIFCOPY which has + * special handling. + * + * For views it points to the original + * array, collapsed so no chains of + * views occur. + * + * For creation from buffer object it + * points to an object that should be + * decref'd on deletion + * + * For WRITEBACKIFCOPY flag this is an + * array to-be-updated upon calling + * PyArray_ResolveWritebackIfCopy + */ + PyObject *base; + /* Pointer to type structure */ + PyArray_Descr *descr; + /* Flags describing array -- see below */ + int flags; + /* For weak references */ + PyObject *weakreflist; +#if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION + void *_buffer_info; /* private buffer info, tagged to allow warning */ +#endif + /* + * For malloc/calloc/realloc/free per object + */ +#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION + PyObject *mem_handler; +#endif +} PyArrayObject_fields; + +/* + * To hide the implementation details, we only expose + * the Python struct HEAD. + */ +#if !defined(NPY_NO_DEPRECATED_API) || \ + (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION) +/* + * Can't put this in npy_deprecated_api.h like the others. + * PyArrayObject field access is deprecated as of NumPy 1.7. + */ +typedef PyArrayObject_fields PyArrayObject; +#else +typedef struct tagPyArrayObject { + PyObject_HEAD +} PyArrayObject; +#endif + +/* + * Removed 2020-Nov-25, NumPy 1.20 + * #define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields)) + * + * The above macro was removed as it gave a false sense of a stable ABI + * with respect to the structures size. If you require a runtime constant, + * you can use `PyArray_Type.tp_basicsize` instead. Otherwise, please + * see the PyArrayObject documentation or ask the NumPy developers for + * information on how to correctly replace the macro in a way that is + * compatible with multiple NumPy versions. + */ + +/* Mirrors buffer object to ptr */ + +typedef struct { + PyObject_HEAD + PyObject *base; + void *ptr; + npy_intp len; + int flags; +} PyArray_Chunk; + +typedef struct { + NPY_DATETIMEUNIT base; + int num; +} PyArray_DatetimeMetaData; + +typedef struct { + NpyAuxData base; + PyArray_DatetimeMetaData meta; +} PyArray_DatetimeDTypeMetaData; + +/* + * This structure contains an exploded view of a date-time value. + * NaT is represented by year == NPY_DATETIME_NAT. + */ +typedef struct { + npy_int64 year; + npy_int32 month, day, hour, min, sec, us, ps, as; +} npy_datetimestruct; + +/* This structure contains an exploded view of a timedelta value */ +typedef struct { + npy_int64 day; + npy_int32 sec, us, ps, as; +} npy_timedeltastruct; + +typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *); + +/* + * Means c-style contiguous (last index varies the fastest). The data + * elements right after each other. + * + * This flag may be requested in constructor functions. + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_C_CONTIGUOUS 0x0001 + +/* + * Set if array is a contiguous Fortran array: the first index varies + * the fastest in memory (strides array is reverse of C-contiguous + * array) + * + * This flag may be requested in constructor functions. + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_F_CONTIGUOUS 0x0002 + +/* + * Note: all 0-d arrays are C_CONTIGUOUS and F_CONTIGUOUS. If a + * 1-d array is C_CONTIGUOUS it is also F_CONTIGUOUS. Arrays with + * more then one dimension can be C_CONTIGUOUS and F_CONTIGUOUS + * at the same time if they have either zero or one element. + * A higher dimensional array always has the same contiguity flags as + * `array.squeeze()`; dimensions with `array.shape[dimension] == 1` are + * effectively ignored when checking for contiguity. + */ + +/* + * If set, the array owns the data: it will be free'd when the array + * is deleted. + * + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_OWNDATA 0x0004 + +/* + * An array never has the next four set; they're only used as parameter + * flags to the various FromAny functions + * + * This flag may be requested in constructor functions. + */ + +/* Cause a cast to occur regardless of whether or not it is safe. */ +#define NPY_ARRAY_FORCECAST 0x0010 + +/* + * Always copy the array. Returned arrays are always CONTIGUOUS, + * ALIGNED, and WRITEABLE. See also: NPY_ARRAY_ENSURENOCOPY = 0x4000. + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_ENSURECOPY 0x0020 + +/* + * Make sure the returned array is a base-class ndarray + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_ENSUREARRAY 0x0040 + +/* + * Make sure that the strides are in units of the element size Needed + * for some operations with record-arrays. + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_ELEMENTSTRIDES 0x0080 + +/* + * Array data is aligned on the appropriate memory address for the type + * stored according to how the compiler would align things (e.g., an + * array of integers (4 bytes each) starts on a memory address that's + * a multiple of 4) + * + * This flag may be requested in constructor functions. + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_ALIGNED 0x0100 + +/* + * Array data has the native endianness + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_NOTSWAPPED 0x0200 + +/* + * Array data is writeable + * + * This flag may be requested in constructor functions. + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_WRITEABLE 0x0400 + +/* + * If this flag is set, then base contains a pointer to an array of + * the same size that should be updated with the current contents of + * this array when PyArray_ResolveWritebackIfCopy is called. + * + * This flag may be requested in constructor functions. + * This flag may be tested for in PyArray_FLAGS(arr). + */ +#define NPY_ARRAY_WRITEBACKIFCOPY 0x2000 + +/* + * No copy may be made while converting from an object/array (result is a view) + * + * This flag may be requested in constructor functions. + */ +#define NPY_ARRAY_ENSURENOCOPY 0x4000 + +/* + * NOTE: there are also internal flags defined in multiarray/arrayobject.h, + * which start at bit 31 and work down. + */ + +#define NPY_ARRAY_BEHAVED (NPY_ARRAY_ALIGNED | \ + NPY_ARRAY_WRITEABLE) +#define NPY_ARRAY_BEHAVED_NS (NPY_ARRAY_ALIGNED | \ + NPY_ARRAY_WRITEABLE | \ + NPY_ARRAY_NOTSWAPPED) +#define NPY_ARRAY_CARRAY (NPY_ARRAY_C_CONTIGUOUS | \ + NPY_ARRAY_BEHAVED) +#define NPY_ARRAY_CARRAY_RO (NPY_ARRAY_C_CONTIGUOUS | \ + NPY_ARRAY_ALIGNED) +#define NPY_ARRAY_FARRAY (NPY_ARRAY_F_CONTIGUOUS | \ + NPY_ARRAY_BEHAVED) +#define NPY_ARRAY_FARRAY_RO (NPY_ARRAY_F_CONTIGUOUS | \ + NPY_ARRAY_ALIGNED) +#define NPY_ARRAY_DEFAULT (NPY_ARRAY_CARRAY) +#define NPY_ARRAY_IN_ARRAY (NPY_ARRAY_CARRAY_RO) +#define NPY_ARRAY_OUT_ARRAY (NPY_ARRAY_CARRAY) +#define NPY_ARRAY_INOUT_ARRAY (NPY_ARRAY_CARRAY) +#define NPY_ARRAY_INOUT_ARRAY2 (NPY_ARRAY_CARRAY | \ + NPY_ARRAY_WRITEBACKIFCOPY) +#define NPY_ARRAY_IN_FARRAY (NPY_ARRAY_FARRAY_RO) +#define NPY_ARRAY_OUT_FARRAY (NPY_ARRAY_FARRAY) +#define NPY_ARRAY_INOUT_FARRAY (NPY_ARRAY_FARRAY) +#define NPY_ARRAY_INOUT_FARRAY2 (NPY_ARRAY_FARRAY | \ + NPY_ARRAY_WRITEBACKIFCOPY) + +#define NPY_ARRAY_UPDATE_ALL (NPY_ARRAY_C_CONTIGUOUS | \ + NPY_ARRAY_F_CONTIGUOUS | \ + NPY_ARRAY_ALIGNED) + +/* This flag is for the array interface, not PyArrayObject */ +#define NPY_ARR_HAS_DESCR 0x0800 + + + + +/* + * Size of internal buffers used for alignment Make BUFSIZE a multiple + * of sizeof(npy_cdouble) -- usually 16 so that ufunc buffers are aligned + */ +#define NPY_MIN_BUFSIZE ((int)sizeof(npy_cdouble)) +#define NPY_MAX_BUFSIZE (((int)sizeof(npy_cdouble))*1000000) +#define NPY_BUFSIZE 8192 +/* buffer stress test size: */ +/*#define NPY_BUFSIZE 17*/ + +/* + * C API: consists of Macros and functions. The MACROS are defined + * here. + */ + + +#define PyArray_ISCONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS) +#define PyArray_ISWRITEABLE(m) PyArray_CHKFLAGS((m), NPY_ARRAY_WRITEABLE) +#define PyArray_ISALIGNED(m) PyArray_CHKFLAGS((m), NPY_ARRAY_ALIGNED) + +#define PyArray_IS_C_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS) +#define PyArray_IS_F_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_F_CONTIGUOUS) + +/* the variable is used in some places, so always define it */ +#define NPY_BEGIN_THREADS_DEF PyThreadState *_save=NULL; +#if NPY_ALLOW_THREADS +#define NPY_BEGIN_ALLOW_THREADS Py_BEGIN_ALLOW_THREADS +#define NPY_END_ALLOW_THREADS Py_END_ALLOW_THREADS +#define NPY_BEGIN_THREADS do {_save = PyEval_SaveThread();} while (0); +#define NPY_END_THREADS do { if (_save) \ + { PyEval_RestoreThread(_save); _save = NULL;} } while (0); +#define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) do { if ((loop_size) > 500) \ + { _save = PyEval_SaveThread();} } while (0); + + +#define NPY_ALLOW_C_API_DEF PyGILState_STATE __save__; +#define NPY_ALLOW_C_API do {__save__ = PyGILState_Ensure();} while (0); +#define NPY_DISABLE_C_API do {PyGILState_Release(__save__);} while (0); +#else +#define NPY_BEGIN_ALLOW_THREADS +#define NPY_END_ALLOW_THREADS +#define NPY_BEGIN_THREADS +#define NPY_END_THREADS +#define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) +#define NPY_BEGIN_THREADS_DESCR(dtype) +#define NPY_END_THREADS_DESCR(dtype) +#define NPY_ALLOW_C_API_DEF +#define NPY_ALLOW_C_API +#define NPY_DISABLE_C_API +#endif + +/********************************** + * The nditer object, added in 1.6 + **********************************/ + +/* The actual structure of the iterator is an internal detail */ +typedef struct NpyIter_InternalOnly NpyIter; + +/* Iterator function pointers that may be specialized */ +typedef int (NpyIter_IterNextFunc)(NpyIter *iter); +typedef void (NpyIter_GetMultiIndexFunc)(NpyIter *iter, + npy_intp *outcoords); + +/*** Global flags that may be passed to the iterator constructors ***/ + +/* Track an index representing C order */ +#define NPY_ITER_C_INDEX 0x00000001 +/* Track an index representing Fortran order */ +#define NPY_ITER_F_INDEX 0x00000002 +/* Track a multi-index */ +#define NPY_ITER_MULTI_INDEX 0x00000004 +/* User code external to the iterator does the 1-dimensional innermost loop */ +#define NPY_ITER_EXTERNAL_LOOP 0x00000008 +/* Convert all the operands to a common data type */ +#define NPY_ITER_COMMON_DTYPE 0x00000010 +/* Operands may hold references, requiring API access during iteration */ +#define NPY_ITER_REFS_OK 0x00000020 +/* Zero-sized operands should be permitted, iteration checks IterSize for 0 */ +#define NPY_ITER_ZEROSIZE_OK 0x00000040 +/* Permits reductions (size-0 stride with dimension size > 1) */ +#define NPY_ITER_REDUCE_OK 0x00000080 +/* Enables sub-range iteration */ +#define NPY_ITER_RANGED 0x00000100 +/* Enables buffering */ +#define NPY_ITER_BUFFERED 0x00000200 +/* When buffering is enabled, grows the inner loop if possible */ +#define NPY_ITER_GROWINNER 0x00000400 +/* Delay allocation of buffers until first Reset* call */ +#define NPY_ITER_DELAY_BUFALLOC 0x00000800 +/* When NPY_KEEPORDER is specified, disable reversing negative-stride axes */ +#define NPY_ITER_DONT_NEGATE_STRIDES 0x00001000 +/* + * If output operands overlap with other operands (based on heuristics that + * has false positives but no false negatives), make temporary copies to + * eliminate overlap. + */ +#define NPY_ITER_COPY_IF_OVERLAP 0x00002000 + +/*** Per-operand flags that may be passed to the iterator constructors ***/ + +/* The operand will be read from and written to */ +#define NPY_ITER_READWRITE 0x00010000 +/* The operand will only be read from */ +#define NPY_ITER_READONLY 0x00020000 +/* The operand will only be written to */ +#define NPY_ITER_WRITEONLY 0x00040000 +/* The operand's data must be in native byte order */ +#define NPY_ITER_NBO 0x00080000 +/* The operand's data must be aligned */ +#define NPY_ITER_ALIGNED 0x00100000 +/* The operand's data must be contiguous (within the inner loop) */ +#define NPY_ITER_CONTIG 0x00200000 +/* The operand may be copied to satisfy requirements */ +#define NPY_ITER_COPY 0x00400000 +/* The operand may be copied with WRITEBACKIFCOPY to satisfy requirements */ +#define NPY_ITER_UPDATEIFCOPY 0x00800000 +/* Allocate the operand if it is NULL */ +#define NPY_ITER_ALLOCATE 0x01000000 +/* If an operand is allocated, don't use any subtype */ +#define NPY_ITER_NO_SUBTYPE 0x02000000 +/* This is a virtual array slot, operand is NULL but temporary data is there */ +#define NPY_ITER_VIRTUAL 0x04000000 +/* Require that the dimension match the iterator dimensions exactly */ +#define NPY_ITER_NO_BROADCAST 0x08000000 +/* A mask is being used on this array, affects buffer -> array copy */ +#define NPY_ITER_WRITEMASKED 0x10000000 +/* This array is the mask for all WRITEMASKED operands */ +#define NPY_ITER_ARRAYMASK 0x20000000 +/* Assume iterator order data access for COPY_IF_OVERLAP */ +#define NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE 0x40000000 + +#define NPY_ITER_GLOBAL_FLAGS 0x0000ffff +#define NPY_ITER_PER_OP_FLAGS 0xffff0000 + + +/***************************** + * Basic iterator object + *****************************/ + +/* FWD declaration */ +typedef struct PyArrayIterObject_tag PyArrayIterObject; + +/* + * type of the function which translates a set of coordinates to a + * pointer to the data + */ +typedef char* (*npy_iter_get_dataptr_t)( + PyArrayIterObject* iter, const npy_intp*); + +struct PyArrayIterObject_tag { + PyObject_HEAD + int nd_m1; /* number of dimensions - 1 */ + npy_intp index, size; + npy_intp coordinates[NPY_MAXDIMS_LEGACY_ITERS];/* N-dimensional loop */ + npy_intp dims_m1[NPY_MAXDIMS_LEGACY_ITERS]; /* ao->dimensions - 1 */ + npy_intp strides[NPY_MAXDIMS_LEGACY_ITERS]; /* ao->strides or fake */ + npy_intp backstrides[NPY_MAXDIMS_LEGACY_ITERS];/* how far to jump back */ + npy_intp factors[NPY_MAXDIMS_LEGACY_ITERS]; /* shape factors */ + PyArrayObject *ao; + char *dataptr; /* pointer to current item*/ + npy_bool contiguous; + + npy_intp bounds[NPY_MAXDIMS_LEGACY_ITERS][2]; + npy_intp limits[NPY_MAXDIMS_LEGACY_ITERS][2]; + npy_intp limits_sizes[NPY_MAXDIMS_LEGACY_ITERS]; + npy_iter_get_dataptr_t translate; +} ; + + +/* Iterator API */ +#define PyArrayIter_Check(op) PyObject_TypeCheck((op), &PyArrayIter_Type) + +#define _PyAIT(it) ((PyArrayIterObject *)(it)) +#define PyArray_ITER_RESET(it) do { \ + _PyAIT(it)->index = 0; \ + _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ + memset(_PyAIT(it)->coordinates, 0, \ + (_PyAIT(it)->nd_m1+1)*sizeof(npy_intp)); \ +} while (0) + +#define _PyArray_ITER_NEXT1(it) do { \ + (it)->dataptr += _PyAIT(it)->strides[0]; \ + (it)->coordinates[0]++; \ +} while (0) + +#define _PyArray_ITER_NEXT2(it) do { \ + if ((it)->coordinates[1] < (it)->dims_m1[1]) { \ + (it)->coordinates[1]++; \ + (it)->dataptr += (it)->strides[1]; \ + } \ + else { \ + (it)->coordinates[1] = 0; \ + (it)->coordinates[0]++; \ + (it)->dataptr += (it)->strides[0] - \ + (it)->backstrides[1]; \ + } \ +} while (0) + +#define PyArray_ITER_NEXT(it) do { \ + _PyAIT(it)->index++; \ + if (_PyAIT(it)->nd_m1 == 0) { \ + _PyArray_ITER_NEXT1(_PyAIT(it)); \ + } \ + else if (_PyAIT(it)->contiguous) \ + _PyAIT(it)->dataptr += PyArray_ITEMSIZE(_PyAIT(it)->ao); \ + else if (_PyAIT(it)->nd_m1 == 1) { \ + _PyArray_ITER_NEXT2(_PyAIT(it)); \ + } \ + else { \ + int __npy_i; \ + for (__npy_i=_PyAIT(it)->nd_m1; __npy_i >= 0; __npy_i--) { \ + if (_PyAIT(it)->coordinates[__npy_i] < \ + _PyAIT(it)->dims_m1[__npy_i]) { \ + _PyAIT(it)->coordinates[__npy_i]++; \ + _PyAIT(it)->dataptr += \ + _PyAIT(it)->strides[__npy_i]; \ + break; \ + } \ + else { \ + _PyAIT(it)->coordinates[__npy_i] = 0; \ + _PyAIT(it)->dataptr -= \ + _PyAIT(it)->backstrides[__npy_i]; \ + } \ + } \ + } \ +} while (0) + +#define PyArray_ITER_GOTO(it, destination) do { \ + int __npy_i; \ + _PyAIT(it)->index = 0; \ + _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ + for (__npy_i = _PyAIT(it)->nd_m1; __npy_i>=0; __npy_i--) { \ + if (destination[__npy_i] < 0) { \ + destination[__npy_i] += \ + _PyAIT(it)->dims_m1[__npy_i]+1; \ + } \ + _PyAIT(it)->dataptr += destination[__npy_i] * \ + _PyAIT(it)->strides[__npy_i]; \ + _PyAIT(it)->coordinates[__npy_i] = \ + destination[__npy_i]; \ + _PyAIT(it)->index += destination[__npy_i] * \ + ( __npy_i==_PyAIT(it)->nd_m1 ? 1 : \ + _PyAIT(it)->dims_m1[__npy_i+1]+1) ; \ + } \ +} while (0) + +#define PyArray_ITER_GOTO1D(it, ind) do { \ + int __npy_i; \ + npy_intp __npy_ind = (npy_intp)(ind); \ + if (__npy_ind < 0) __npy_ind += _PyAIT(it)->size; \ + _PyAIT(it)->index = __npy_ind; \ + if (_PyAIT(it)->nd_m1 == 0) { \ + _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \ + __npy_ind * _PyAIT(it)->strides[0]; \ + } \ + else if (_PyAIT(it)->contiguous) \ + _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \ + __npy_ind * PyArray_ITEMSIZE(_PyAIT(it)->ao); \ + else { \ + _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ + for (__npy_i = 0; __npy_i<=_PyAIT(it)->nd_m1; \ + __npy_i++) { \ + _PyAIT(it)->coordinates[__npy_i] = \ + (__npy_ind / _PyAIT(it)->factors[__npy_i]); \ + _PyAIT(it)->dataptr += \ + (__npy_ind / _PyAIT(it)->factors[__npy_i]) \ + * _PyAIT(it)->strides[__npy_i]; \ + __npy_ind %= _PyAIT(it)->factors[__npy_i]; \ + } \ + } \ +} while (0) + +#define PyArray_ITER_DATA(it) ((void *)(_PyAIT(it)->dataptr)) + +#define PyArray_ITER_NOTDONE(it) (_PyAIT(it)->index < _PyAIT(it)->size) + + +/* + * Any object passed to PyArray_Broadcast must be binary compatible + * with this structure. + */ + +typedef struct { + PyObject_HEAD + int numiter; /* number of iters */ + npy_intp size; /* broadcasted size */ + npy_intp index; /* current index */ + int nd; /* number of dims */ + npy_intp dimensions[NPY_MAXDIMS_LEGACY_ITERS]; /* dimensions */ + /* + * Space for the individual iterators, do not specify size publicly + * to allow changing it more easily. + * One reason is that Cython uses this for checks and only allows + * growing structs (as of Cython 3.0.6). It also allows NPY_MAXARGS + * to be runtime dependent. + */ +#if (defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD) + PyArrayIterObject *iters[64]; +#elif defined(__cplusplus) + /* + * C++ doesn't strictly support flexible members and gives compilers + * warnings (pedantic only), so we lie. We can't make it 64 because + * then Cython is unhappy (larger struct at runtime is OK smaller not). + */ + PyArrayIterObject *iters[32]; +#else + PyArrayIterObject *iters[]; +#endif +} PyArrayMultiIterObject; + +#define _PyMIT(m) ((PyArrayMultiIterObject *)(m)) +#define PyArray_MultiIter_RESET(multi) do { \ + int __npy_mi; \ + _PyMIT(multi)->index = 0; \ + for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ + PyArray_ITER_RESET(_PyMIT(multi)->iters[__npy_mi]); \ + } \ +} while (0) + +#define PyArray_MultiIter_NEXT(multi) do { \ + int __npy_mi; \ + _PyMIT(multi)->index++; \ + for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ + PyArray_ITER_NEXT(_PyMIT(multi)->iters[__npy_mi]); \ + } \ +} while (0) + +#define PyArray_MultiIter_GOTO(multi, dest) do { \ + int __npy_mi; \ + for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ + PyArray_ITER_GOTO(_PyMIT(multi)->iters[__npy_mi], dest); \ + } \ + _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \ +} while (0) + +#define PyArray_MultiIter_GOTO1D(multi, ind) do { \ + int __npy_mi; \ + for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ + PyArray_ITER_GOTO1D(_PyMIT(multi)->iters[__npy_mi], ind); \ + } \ + _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \ +} while (0) + +#define PyArray_MultiIter_DATA(multi, i) \ + ((void *)(_PyMIT(multi)->iters[i]->dataptr)) + +#define PyArray_MultiIter_NEXTi(multi, i) \ + PyArray_ITER_NEXT(_PyMIT(multi)->iters[i]) + +#define PyArray_MultiIter_NOTDONE(multi) \ + (_PyMIT(multi)->index < _PyMIT(multi)->size) + + +static NPY_INLINE int +PyArray_MultiIter_NUMITER(PyArrayMultiIterObject *multi) +{ + return multi->numiter; +} + + +static NPY_INLINE npy_intp +PyArray_MultiIter_SIZE(PyArrayMultiIterObject *multi) +{ + return multi->size; +} + + +static NPY_INLINE npy_intp +PyArray_MultiIter_INDEX(PyArrayMultiIterObject *multi) +{ + return multi->index; +} + + +static NPY_INLINE int +PyArray_MultiIter_NDIM(PyArrayMultiIterObject *multi) +{ + return multi->nd; +} + + +static NPY_INLINE npy_intp * +PyArray_MultiIter_DIMS(PyArrayMultiIterObject *multi) +{ + return multi->dimensions; +} + + +static NPY_INLINE void ** +PyArray_MultiIter_ITERS(PyArrayMultiIterObject *multi) +{ + return (void**)multi->iters; +} + + +enum { + NPY_NEIGHBORHOOD_ITER_ZERO_PADDING, + NPY_NEIGHBORHOOD_ITER_ONE_PADDING, + NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING, + NPY_NEIGHBORHOOD_ITER_CIRCULAR_PADDING, + NPY_NEIGHBORHOOD_ITER_MIRROR_PADDING +}; + +typedef struct { + PyObject_HEAD + + /* + * PyArrayIterObject part: keep this in this exact order + */ + int nd_m1; /* number of dimensions - 1 */ + npy_intp index, size; + npy_intp coordinates[NPY_MAXDIMS_LEGACY_ITERS];/* N-dimensional loop */ + npy_intp dims_m1[NPY_MAXDIMS_LEGACY_ITERS]; /* ao->dimensions - 1 */ + npy_intp strides[NPY_MAXDIMS_LEGACY_ITERS]; /* ao->strides or fake */ + npy_intp backstrides[NPY_MAXDIMS_LEGACY_ITERS];/* how far to jump back */ + npy_intp factors[NPY_MAXDIMS_LEGACY_ITERS]; /* shape factors */ + PyArrayObject *ao; + char *dataptr; /* pointer to current item*/ + npy_bool contiguous; + + npy_intp bounds[NPY_MAXDIMS_LEGACY_ITERS][2]; + npy_intp limits[NPY_MAXDIMS_LEGACY_ITERS][2]; + npy_intp limits_sizes[NPY_MAXDIMS_LEGACY_ITERS]; + npy_iter_get_dataptr_t translate; + + /* + * New members + */ + npy_intp nd; + + /* Dimensions is the dimension of the array */ + npy_intp dimensions[NPY_MAXDIMS_LEGACY_ITERS]; + + /* + * Neighborhood points coordinates are computed relatively to the + * point pointed by _internal_iter + */ + PyArrayIterObject* _internal_iter; + /* + * To keep a reference to the representation of the constant value + * for constant padding + */ + char* constant; + + int mode; +} PyArrayNeighborhoodIterObject; + +/* + * Neighborhood iterator API + */ + +/* General: those work for any mode */ +static inline int +PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter); +static inline int +PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter); +#if 0 +static inline int +PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter); +#endif + +/* + * Include inline implementations - functions defined there are not + * considered public API + */ +#define NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ +#include "_neighborhood_iterator_imp.h" +#undef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ + + + +/* The default array type */ +#define NPY_DEFAULT_TYPE NPY_DOUBLE +/* default integer type defined in npy_2_compat header */ + +/* + * All sorts of useful ways to look into a PyArrayObject. It is recommended + * to use PyArrayObject * objects instead of always casting from PyObject *, + * for improved type checking. + * + * In many cases here the macro versions of the accessors are deprecated, + * but can't be immediately changed to inline functions because the + * preexisting macros accept PyObject * and do automatic casts. Inline + * functions accepting PyArrayObject * provides for some compile-time + * checking of correctness when working with these objects in C. + */ + +#define PyArray_ISONESEGMENT(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS) || \ + PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS)) + +#define PyArray_ISFORTRAN(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) && \ + (!PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS))) + +#define PyArray_FORTRAN_IF(m) ((PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) ? \ + NPY_ARRAY_F_CONTIGUOUS : 0)) + +static inline int +PyArray_NDIM(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->nd; +} + +static inline void * +PyArray_DATA(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->data; +} + +static inline char * +PyArray_BYTES(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->data; +} + +static inline npy_intp * +PyArray_DIMS(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->dimensions; +} + +static inline npy_intp * +PyArray_STRIDES(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->strides; +} + +static inline npy_intp +PyArray_DIM(const PyArrayObject *arr, int idim) +{ + return ((PyArrayObject_fields *)arr)->dimensions[idim]; +} + +static inline npy_intp +PyArray_STRIDE(const PyArrayObject *arr, int istride) +{ + return ((PyArrayObject_fields *)arr)->strides[istride]; +} + +static inline NPY_RETURNS_BORROWED_REF PyObject * +PyArray_BASE(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->base; +} + +static inline NPY_RETURNS_BORROWED_REF PyArray_Descr * +PyArray_DESCR(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->descr; +} + +static inline int +PyArray_FLAGS(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->flags; +} + + +static inline int +PyArray_TYPE(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->descr->type_num; +} + +static inline int +PyArray_CHKFLAGS(const PyArrayObject *arr, int flags) +{ + return (PyArray_FLAGS(arr) & flags) == flags; +} + +static inline PyArray_Descr * +PyArray_DTYPE(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->descr; +} + +static inline npy_intp * +PyArray_SHAPE(const PyArrayObject *arr) +{ + return ((PyArrayObject_fields *)arr)->dimensions; +} + +/* + * Enables the specified array flags. Does no checking, + * assumes you know what you're doing. + */ +static inline void +PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags) +{ + ((PyArrayObject_fields *)arr)->flags |= flags; +} + +/* + * Clears the specified array flags. Does no checking, + * assumes you know what you're doing. + */ +static inline void +PyArray_CLEARFLAGS(PyArrayObject *arr, int flags) +{ + ((PyArrayObject_fields *)arr)->flags &= ~flags; +} + +#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION + static inline NPY_RETURNS_BORROWED_REF PyObject * + PyArray_HANDLER(PyArrayObject *arr) + { + return ((PyArrayObject_fields *)arr)->mem_handler; + } +#endif + +#define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL) + +#define PyTypeNum_ISUNSIGNED(type) (((type) == NPY_UBYTE) || \ + ((type) == NPY_USHORT) || \ + ((type) == NPY_UINT) || \ + ((type) == NPY_ULONG) || \ + ((type) == NPY_ULONGLONG)) + +#define PyTypeNum_ISSIGNED(type) (((type) == NPY_BYTE) || \ + ((type) == NPY_SHORT) || \ + ((type) == NPY_INT) || \ + ((type) == NPY_LONG) || \ + ((type) == NPY_LONGLONG)) + +#define PyTypeNum_ISINTEGER(type) (((type) >= NPY_BYTE) && \ + ((type) <= NPY_ULONGLONG)) + +#define PyTypeNum_ISFLOAT(type) ((((type) >= NPY_FLOAT) && \ + ((type) <= NPY_LONGDOUBLE)) || \ + ((type) == NPY_HALF)) + +#define PyTypeNum_ISNUMBER(type) (((type) <= NPY_CLONGDOUBLE) || \ + ((type) == NPY_HALF)) + +#define PyTypeNum_ISSTRING(type) (((type) == NPY_STRING) || \ + ((type) == NPY_UNICODE)) + +#define PyTypeNum_ISCOMPLEX(type) (((type) >= NPY_CFLOAT) && \ + ((type) <= NPY_CLONGDOUBLE)) + +#define PyTypeNum_ISFLEXIBLE(type) (((type) >=NPY_STRING) && \ + ((type) <=NPY_VOID)) + +#define PyTypeNum_ISDATETIME(type) (((type) >=NPY_DATETIME) && \ + ((type) <=NPY_TIMEDELTA)) + +#define PyTypeNum_ISUSERDEF(type) (((type) >= NPY_USERDEF) && \ + ((type) < NPY_USERDEF+ \ + NPY_NUMUSERTYPES)) + +#define PyTypeNum_ISEXTENDED(type) (PyTypeNum_ISFLEXIBLE(type) || \ + PyTypeNum_ISUSERDEF(type)) + +#define PyTypeNum_ISOBJECT(type) ((type) == NPY_OBJECT) + + +#define PyDataType_ISLEGACY(dtype) ((dtype)->type_num < NPY_VSTRING && ((dtype)->type_num >= 0)) +#define PyDataType_ISBOOL(obj) PyTypeNum_ISBOOL(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISSIGNED(obj) PyTypeNum_ISSIGNED(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISINTEGER(obj) PyTypeNum_ISINTEGER(((PyArray_Descr*)(obj))->type_num ) +#define PyDataType_ISFLOAT(obj) PyTypeNum_ISFLOAT(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISNUMBER(obj) PyTypeNum_ISNUMBER(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISSTRING(obj) PyTypeNum_ISSTRING(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISDATETIME(obj) PyTypeNum_ISDATETIME(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_ISOBJECT(obj) PyTypeNum_ISOBJECT(((PyArray_Descr*)(obj))->type_num) +#define PyDataType_MAKEUNSIZED(dtype) ((dtype)->elsize = 0) +/* + * PyDataType_* FLAGS, FLACHK, REFCHK, HASFIELDS, HASSUBARRAY, UNSIZED, + * SUBARRAY, NAMES, FIELDS, C_METADATA, and METADATA require version specific + * lookup and are defined in npy_2_compat.h. + */ + + +#define PyArray_ISBOOL(obj) PyTypeNum_ISBOOL(PyArray_TYPE(obj)) +#define PyArray_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(PyArray_TYPE(obj)) +#define PyArray_ISSIGNED(obj) PyTypeNum_ISSIGNED(PyArray_TYPE(obj)) +#define PyArray_ISINTEGER(obj) PyTypeNum_ISINTEGER(PyArray_TYPE(obj)) +#define PyArray_ISFLOAT(obj) PyTypeNum_ISFLOAT(PyArray_TYPE(obj)) +#define PyArray_ISNUMBER(obj) PyTypeNum_ISNUMBER(PyArray_TYPE(obj)) +#define PyArray_ISSTRING(obj) PyTypeNum_ISSTRING(PyArray_TYPE(obj)) +#define PyArray_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(PyArray_TYPE(obj)) +#define PyArray_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj)) +#define PyArray_ISDATETIME(obj) PyTypeNum_ISDATETIME(PyArray_TYPE(obj)) +#define PyArray_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(PyArray_TYPE(obj)) +#define PyArray_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(PyArray_TYPE(obj)) +#define PyArray_ISOBJECT(obj) PyTypeNum_ISOBJECT(PyArray_TYPE(obj)) +#define PyArray_HASFIELDS(obj) PyDataType_HASFIELDS(PyArray_DESCR(obj)) + + /* + * FIXME: This should check for a flag on the data-type that + * states whether or not it is variable length. Because the + * ISFLEXIBLE check is hard-coded to the built-in data-types. + */ +#define PyArray_ISVARIABLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj)) + +#define PyArray_SAFEALIGNEDCOPY(obj) (PyArray_ISALIGNED(obj) && !PyArray_ISVARIABLE(obj)) + + +#define NPY_LITTLE '<' +#define NPY_BIG '>' +#define NPY_NATIVE '=' +#define NPY_SWAP 's' +#define NPY_IGNORE '|' + +#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN +#define NPY_NATBYTE NPY_BIG +#define NPY_OPPBYTE NPY_LITTLE +#else +#define NPY_NATBYTE NPY_LITTLE +#define NPY_OPPBYTE NPY_BIG +#endif + +#define PyArray_ISNBO(arg) ((arg) != NPY_OPPBYTE) +#define PyArray_IsNativeByteOrder PyArray_ISNBO +#define PyArray_ISNOTSWAPPED(m) PyArray_ISNBO(PyArray_DESCR(m)->byteorder) +#define PyArray_ISBYTESWAPPED(m) (!PyArray_ISNOTSWAPPED(m)) + +#define PyArray_FLAGSWAP(m, flags) (PyArray_CHKFLAGS(m, flags) && \ + PyArray_ISNOTSWAPPED(m)) + +#define PyArray_ISCARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY) +#define PyArray_ISCARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY_RO) +#define PyArray_ISFARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY) +#define PyArray_ISFARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY_RO) +#define PyArray_ISBEHAVED(m) PyArray_FLAGSWAP(m, NPY_ARRAY_BEHAVED) +#define PyArray_ISBEHAVED_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_ALIGNED) + + +#define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder) +#define PyDataType_ISBYTESWAPPED(d) (!PyDataType_ISNOTSWAPPED(d)) + +/************************************************************ + * A struct used by PyArray_CreateSortedStridePerm, new in 1.7. + ************************************************************/ + +typedef struct { + npy_intp perm, stride; +} npy_stride_sort_item; + +/************************************************************ + * This is the form of the struct that's stored in the + * PyCapsule returned by an array's __array_struct__ attribute. See + * https://docs.scipy.org/doc/numpy/reference/arrays.interface.html for the full + * documentation. + ************************************************************/ +typedef struct { + int two; /* + * contains the integer 2 as a sanity + * check + */ + + int nd; /* number of dimensions */ + + char typekind; /* + * kind in array --- character code of + * typestr + */ + + int itemsize; /* size of each element */ + + int flags; /* + * how should be data interpreted. Valid + * flags are CONTIGUOUS (1), F_CONTIGUOUS (2), + * ALIGNED (0x100), NOTSWAPPED (0x200), and + * WRITEABLE (0x400). ARR_HAS_DESCR (0x800) + * states that arrdescr field is present in + * structure + */ + + npy_intp *shape; /* + * A length-nd array of shape + * information + */ + + npy_intp *strides; /* A length-nd array of stride information */ + + void *data; /* A pointer to the first element of the array */ + + PyObject *descr; /* + * A list of fields or NULL (ignored if flags + * does not have ARR_HAS_DESCR flag set) + */ +} PyArrayInterface; + + +/**************************************** + * NpyString + * + * Types used by the NpyString API. + ****************************************/ + +/* + * A "packed" encoded string. The string data must be accessed by first unpacking the string. + */ +typedef struct npy_packed_static_string npy_packed_static_string; + +/* + * An unpacked read-only view onto the data in a packed string + */ +typedef struct npy_unpacked_static_string { + size_t size; + const char *buf; +} npy_static_string; + +/* + * Handles heap allocations for static strings. + */ +typedef struct npy_string_allocator npy_string_allocator; + +typedef struct { + PyArray_Descr base; + // The object representing a null value + PyObject *na_object; + // Flag indicating whether or not to coerce arbitrary objects to strings + char coerce; + // Flag indicating the na object is NaN-like + char has_nan_na; + // Flag indicating the na object is a string + char has_string_na; + // If nonzero, indicates that this instance is owned by an array already + char array_owned; + // The string data to use when a default string is needed + npy_static_string default_string; + // The name of the missing data object, if any + npy_static_string na_name; + // the allocator should only be directly accessed after + // acquiring the allocator_lock and the lock should + // be released immediately after the allocator is + // no longer needed + npy_string_allocator *allocator; +} PyArray_StringDTypeObject; + +/* + * PyArray_DTypeMeta related definitions. + * + * As of now, this API is preliminary and will be extended as necessary. + */ +#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD + /* + * The Structures defined in this block are currently considered + * private API and may change without warning! + * Part of this (at least the size) is expected to be public API without + * further modifications. + */ + /* TODO: Make this definition public in the API, as soon as its settled */ + NPY_NO_EXPORT extern PyTypeObject PyArrayDTypeMeta_Type; + + /* + * While NumPy DTypes would not need to be heap types the plan is to + * make DTypes available in Python at which point they will be heap types. + * Since we also wish to add fields to the DType class, this looks like + * a typical instance definition, but with PyHeapTypeObject instead of + * only the PyObject_HEAD. + * This must only be exposed very extremely careful consideration, since + * it is a fairly complex construct which may be better to allow + * refactoring of. + */ + typedef struct { + PyHeapTypeObject super; + + /* + * Most DTypes will have a singleton default instance, for the + * parametric legacy DTypes (bytes, string, void, datetime) this + * may be a pointer to the *prototype* instance? + */ + PyArray_Descr *singleton; + /* Copy of the legacy DTypes type number, usually invalid. */ + int type_num; + + /* The type object of the scalar instances (may be NULL?) */ + PyTypeObject *scalar_type; + /* + * DType flags to signal legacy, parametric, or + * abstract. But plenty of space for additional information/flags. + */ + npy_uint64 flags; + + /* + * Use indirection in order to allow a fixed size for this struct. + * A stable ABI size makes creating a static DType less painful + * while also ensuring flexibility for all opaque API (with one + * indirection due the pointer lookup). + */ + void *dt_slots; + void *reserved[3]; + } PyArray_DTypeMeta; + +#endif /* NPY_INTERNAL_BUILD */ + + +/* + * Use the keyword NPY_DEPRECATED_INCLUDES to ensure that the header files + * npy_*_*_deprecated_api.h are only included from here and nowhere else. + */ +#ifdef NPY_DEPRECATED_INCLUDES +#error "Do not use the reserved keyword NPY_DEPRECATED_INCLUDES." +#endif +#define NPY_DEPRECATED_INCLUDES +#if !defined(NPY_NO_DEPRECATED_API) || \ + (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION) +#include "npy_1_7_deprecated_api.h" +#endif +/* + * There is no file npy_1_8_deprecated_api.h since there are no additional + * deprecated API features in NumPy 1.8. + * + * Note to maintainers: insert code like the following in future NumPy + * versions. + * + * #if !defined(NPY_NO_DEPRECATED_API) || \ + * (NPY_NO_DEPRECATED_API < NPY_1_9_API_VERSION) + * #include "npy_1_9_deprecated_api.h" + * #endif + */ +#undef NPY_DEPRECATED_INCLUDES + +#ifdef __cplusplus +} +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_1_7_deprecated_api.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_1_7_deprecated_api.h new file mode 100644 index 0000000000000000000000000000000000000000..be53cded488dd373072ab798fd7c25a5041e20ad --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_1_7_deprecated_api.h @@ -0,0 +1,112 @@ +#ifndef NPY_DEPRECATED_INCLUDES +#error "Should never include npy_*_*_deprecated_api directly." +#endif + +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ + +/* Emit a warning if the user did not specifically request the old API */ +#ifndef NPY_NO_DEPRECATED_API +#if defined(_WIN32) +#define _WARN___STR2__(x) #x +#define _WARN___STR1__(x) _WARN___STR2__(x) +#define _WARN___LOC__ __FILE__ "(" _WARN___STR1__(__LINE__) ") : Warning Msg: " +#pragma message(_WARN___LOC__"Using deprecated NumPy API, disable it with " \ + "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION") +#else +#warning "Using deprecated NumPy API, disable it with " \ + "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" +#endif +#endif + +/* + * This header exists to collect all dangerous/deprecated NumPy API + * as of NumPy 1.7. + * + * This is an attempt to remove bad API, the proliferation of macros, + * and namespace pollution currently produced by the NumPy headers. + */ + +/* These array flags are deprecated as of NumPy 1.7 */ +#define NPY_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS +#define NPY_FORTRAN NPY_ARRAY_F_CONTIGUOUS + +/* + * The consistent NPY_ARRAY_* names which don't pollute the NPY_* + * namespace were added in NumPy 1.7. + * + * These versions of the carray flags are deprecated, but + * probably should only be removed after two releases instead of one. + */ +#define NPY_C_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS +#define NPY_F_CONTIGUOUS NPY_ARRAY_F_CONTIGUOUS +#define NPY_OWNDATA NPY_ARRAY_OWNDATA +#define NPY_FORCECAST NPY_ARRAY_FORCECAST +#define NPY_ENSURECOPY NPY_ARRAY_ENSURECOPY +#define NPY_ENSUREARRAY NPY_ARRAY_ENSUREARRAY +#define NPY_ELEMENTSTRIDES NPY_ARRAY_ELEMENTSTRIDES +#define NPY_ALIGNED NPY_ARRAY_ALIGNED +#define NPY_NOTSWAPPED NPY_ARRAY_NOTSWAPPED +#define NPY_WRITEABLE NPY_ARRAY_WRITEABLE +#define NPY_BEHAVED NPY_ARRAY_BEHAVED +#define NPY_BEHAVED_NS NPY_ARRAY_BEHAVED_NS +#define NPY_CARRAY NPY_ARRAY_CARRAY +#define NPY_CARRAY_RO NPY_ARRAY_CARRAY_RO +#define NPY_FARRAY NPY_ARRAY_FARRAY +#define NPY_FARRAY_RO NPY_ARRAY_FARRAY_RO +#define NPY_DEFAULT NPY_ARRAY_DEFAULT +#define NPY_IN_ARRAY NPY_ARRAY_IN_ARRAY +#define NPY_OUT_ARRAY NPY_ARRAY_OUT_ARRAY +#define NPY_INOUT_ARRAY NPY_ARRAY_INOUT_ARRAY +#define NPY_IN_FARRAY NPY_ARRAY_IN_FARRAY +#define NPY_OUT_FARRAY NPY_ARRAY_OUT_FARRAY +#define NPY_INOUT_FARRAY NPY_ARRAY_INOUT_FARRAY +#define NPY_UPDATE_ALL NPY_ARRAY_UPDATE_ALL + +/* This way of accessing the default type is deprecated as of NumPy 1.7 */ +#define PyArray_DEFAULT NPY_DEFAULT_TYPE + +/* + * Deprecated as of NumPy 1.7, this kind of shortcut doesn't + * belong in the public API. + */ +#define NPY_AO PyArrayObject + +/* + * Deprecated as of NumPy 1.7, an all-lowercase macro doesn't + * belong in the public API. + */ +#define fortran fortran_ + +/* + * Deprecated as of NumPy 1.7, as it is a namespace-polluting + * macro. + */ +#define FORTRAN_IF PyArray_FORTRAN_IF + +/* Deprecated as of NumPy 1.7, datetime64 uses c_metadata instead */ +#define NPY_METADATA_DTSTR "__timeunit__" + +/* + * Deprecated as of NumPy 1.7. + * The reasoning: + * - These are for datetime, but there's no datetime "namespace". + * - They just turn NPY_STR_ into "", which is just + * making something simple be indirected. + */ +#define NPY_STR_Y "Y" +#define NPY_STR_M "M" +#define NPY_STR_W "W" +#define NPY_STR_D "D" +#define NPY_STR_h "h" +#define NPY_STR_m "m" +#define NPY_STR_s "s" +#define NPY_STR_ms "ms" +#define NPY_STR_us "us" +#define NPY_STR_ns "ns" +#define NPY_STR_ps "ps" +#define NPY_STR_fs "fs" +#define NPY_STR_as "as" + + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_compat.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_compat.h new file mode 100644 index 0000000000000000000000000000000000000000..e39e65aedea7dc95a1130ea94518bb778d1238cd --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_compat.h @@ -0,0 +1,249 @@ +/* + * This header file defines relevant features which: + * - Require runtime inspection depending on the NumPy version. + * - May be needed when compiling with an older version of NumPy to allow + * a smooth transition. + * + * As such, it is shipped with NumPy 2.0, but designed to be vendored in full + * or parts by downstream projects. + * + * It must be included after any other includes. `import_array()` must have + * been called in the scope or version dependency will misbehave, even when + * only `PyUFunc_` API is used. + * + * If required complicated defs (with inline functions) should be written as: + * + * #if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + * Simple definition when NumPy 2.0 API is guaranteed. + * #else + * static inline definition of a 1.x compatibility shim + * #if NPY_ABI_VERSION < 0x02000000 + * Make 1.x compatibility shim the public API (1.x only branch) + * #else + * Runtime dispatched version (1.x or 2.x) + * #endif + * #endif + * + * An internal build always passes NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + */ + +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_2_COMPAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_2_COMPAT_H_ + +/* + * New macros for accessing real and complex part of a complex number can be + * found in "npy_2_complexcompat.h". + */ + + +/* + * This header is meant to be included by downstream directly for 1.x compat. + * In that case we need to ensure that users first included the full headers + * and not just `ndarraytypes.h`. + */ + +#ifndef NPY_FEATURE_VERSION + #error "The NumPy 2 compat header requires `import_array()` for which " \ + "the `ndarraytypes.h` header include is not sufficient. Please " \ + "include it after `numpy/ndarrayobject.h` or similar.\n" \ + "To simplify inclusion, you may use `PyArray_ImportNumPy()` " \ + "which is defined in the compat header and is lightweight (can be)." +#endif + +#if NPY_ABI_VERSION < 0x02000000 + /* + * Define 2.0 feature version as it is needed below to decide whether we + * compile for both 1.x and 2.x (defining it guarantees 1.x only). + */ + #define NPY_2_0_API_VERSION 0x00000012 + /* + * If we are compiling with NumPy 1.x, PyArray_RUNTIME_VERSION so we + * pretend the `PyArray_RUNTIME_VERSION` is `NPY_FEATURE_VERSION`. + * This allows downstream to use `PyArray_RUNTIME_VERSION` if they need to. + */ + #define PyArray_RUNTIME_VERSION NPY_FEATURE_VERSION + /* Compiling on NumPy 1.x where these are the same: */ + #define PyArray_DescrProto PyArray_Descr +#endif + + +/* + * Define a better way to call `_import_array()` to simplify backporting as + * we now require imports more often (necessary to make ABI flexible). + */ +#ifdef import_array1 + +static inline int +PyArray_ImportNumPyAPI(void) +{ + if (NPY_UNLIKELY(PyArray_API == NULL)) { + import_array1(-1); + } + return 0; +} + +#endif /* import_array1 */ + + +/* + * NPY_DEFAULT_INT + * + * The default integer has changed, `NPY_DEFAULT_INT` is available at runtime + * for use as type number, e.g. `PyArray_DescrFromType(NPY_DEFAULT_INT)`. + * + * NPY_RAVEL_AXIS + * + * This was introduced in NumPy 2.0 to allow indicating that an axis should be + * raveled in an operation. Before NumPy 2.0, NPY_MAXDIMS was used for this purpose. + * + * NPY_MAXDIMS + * + * A constant indicating the maximum number dimensions allowed when creating + * an ndarray. + * + * NPY_NTYPES_LEGACY + * + * The number of built-in NumPy dtypes. + */ +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + #define NPY_DEFAULT_INT NPY_INTP + #define NPY_RAVEL_AXIS NPY_MIN_INT + #define NPY_MAXARGS 64 + +#elif NPY_ABI_VERSION < 0x02000000 + #define NPY_DEFAULT_INT NPY_LONG + #define NPY_RAVEL_AXIS 32 + #define NPY_MAXARGS 32 + + /* Aliases of 2.x names to 1.x only equivalent names */ + #define NPY_NTYPES NPY_NTYPES_LEGACY + #define PyArray_DescrProto PyArray_Descr + #define _PyArray_LegacyDescr PyArray_Descr + /* NumPy 2 definition always works, but add it for 1.x only */ + #define PyDataType_ISLEGACY(dtype) (1) +#else + #define NPY_DEFAULT_INT \ + (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION ? NPY_INTP : NPY_LONG) + #define NPY_RAVEL_AXIS \ + (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION ? NPY_MIN_INT : 32) + #define NPY_MAXARGS \ + (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION ? 64 : 32) +#endif + + +/* + * Access inline functions for descriptor fields. Except for the first + * few fields, these needed to be moved (elsize, alignment) for + * additional space. Or they are descriptor specific and are not generally + * available anymore (metadata, c_metadata, subarray, names, fields). + * + * Most of these are defined via the `DESCR_ACCESSOR` macro helper. + */ +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION || NPY_ABI_VERSION < 0x02000000 + /* Compiling for 1.x or 2.x only, direct field access is OK: */ + + static inline void + PyDataType_SET_ELSIZE(PyArray_Descr *dtype, npy_intp size) + { + dtype->elsize = size; + } + + static inline npy_uint64 + PyDataType_FLAGS(const PyArray_Descr *dtype) + { + #if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + return dtype->flags; + #else + return (unsigned char)dtype->flags; /* Need unsigned cast on 1.x */ + #endif + } + + #define DESCR_ACCESSOR(FIELD, field, type, legacy_only) \ + static inline type \ + PyDataType_##FIELD(const PyArray_Descr *dtype) { \ + if (legacy_only && !PyDataType_ISLEGACY(dtype)) { \ + return (type)0; \ + } \ + return ((_PyArray_LegacyDescr *)dtype)->field; \ + } +#else /* compiling for both 1.x and 2.x */ + + static inline void + PyDataType_SET_ELSIZE(PyArray_Descr *dtype, npy_intp size) + { + if (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION) { + ((_PyArray_DescrNumPy2 *)dtype)->elsize = size; + } + else { + ((PyArray_DescrProto *)dtype)->elsize = (int)size; + } + } + + static inline npy_uint64 + PyDataType_FLAGS(const PyArray_Descr *dtype) + { + if (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION) { + return ((_PyArray_DescrNumPy2 *)dtype)->flags; + } + else { + return (unsigned char)((PyArray_DescrProto *)dtype)->flags; + } + } + + /* Cast to LegacyDescr always fine but needed when `legacy_only` */ + #define DESCR_ACCESSOR(FIELD, field, type, legacy_only) \ + static inline type \ + PyDataType_##FIELD(const PyArray_Descr *dtype) { \ + if (legacy_only && !PyDataType_ISLEGACY(dtype)) { \ + return (type)0; \ + } \ + if (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION) { \ + return ((_PyArray_LegacyDescr *)dtype)->field; \ + } \ + else { \ + return ((PyArray_DescrProto *)dtype)->field; \ + } \ + } +#endif + +DESCR_ACCESSOR(ELSIZE, elsize, npy_intp, 0) +DESCR_ACCESSOR(ALIGNMENT, alignment, npy_intp, 0) +DESCR_ACCESSOR(METADATA, metadata, PyObject *, 1) +DESCR_ACCESSOR(SUBARRAY, subarray, PyArray_ArrayDescr *, 1) +DESCR_ACCESSOR(NAMES, names, PyObject *, 1) +DESCR_ACCESSOR(FIELDS, fields, PyObject *, 1) +DESCR_ACCESSOR(C_METADATA, c_metadata, NpyAuxData *, 1) + +#undef DESCR_ACCESSOR + + +#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD) +#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION + static inline PyArray_ArrFuncs * + PyDataType_GetArrFuncs(const PyArray_Descr *descr) + { + return _PyDataType_GetArrFuncs(descr); + } +#elif NPY_ABI_VERSION < 0x02000000 + static inline PyArray_ArrFuncs * + PyDataType_GetArrFuncs(const PyArray_Descr *descr) + { + return descr->f; + } +#else + static inline PyArray_ArrFuncs * + PyDataType_GetArrFuncs(const PyArray_Descr *descr) + { + if (PyArray_RUNTIME_VERSION >= NPY_2_0_API_VERSION) { + return _PyDataType_GetArrFuncs(descr); + } + else { + return ((PyArray_DescrProto *)descr)->f; + } + } +#endif + + +#endif /* not internal build */ + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_2_COMPAT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_complexcompat.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_complexcompat.h new file mode 100644 index 0000000000000000000000000000000000000000..0b509011b2803b978bd913191816a9bbd6635d35 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_2_complexcompat.h @@ -0,0 +1,28 @@ +/* This header is designed to be copy-pasted into downstream packages, since it provides + a compatibility layer between the old C struct complex types and the new native C99 + complex types. The new macros are in numpy/npy_math.h, which is why it is included here. */ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_2_COMPLEXCOMPAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_2_COMPLEXCOMPAT_H_ + +#include + +#ifndef NPY_CSETREALF +#define NPY_CSETREALF(c, r) (c)->real = (r) +#endif +#ifndef NPY_CSETIMAGF +#define NPY_CSETIMAGF(c, i) (c)->imag = (i) +#endif +#ifndef NPY_CSETREAL +#define NPY_CSETREAL(c, r) (c)->real = (r) +#endif +#ifndef NPY_CSETIMAG +#define NPY_CSETIMAG(c, i) (c)->imag = (i) +#endif +#ifndef NPY_CSETREALL +#define NPY_CSETREALL(c, r) (c)->real = (r) +#endif +#ifndef NPY_CSETIMAGL +#define NPY_CSETIMAGL(c, i) (c)->imag = (i) +#endif + +#endif diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_3kcompat.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_3kcompat.h new file mode 100644 index 0000000000000000000000000000000000000000..c2bf74faf09d9df0df07f23e7d8b8550fe79ba5f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_3kcompat.h @@ -0,0 +1,374 @@ +/* + * This is a convenience header file providing compatibility utilities + * for supporting different minor versions of Python 3. + * It was originally used to support the transition from Python 2, + * hence the "3k" naming. + * + * If you want to use this for your own projects, it's recommended to make a + * copy of it. We don't provide backwards compatibility guarantees. + */ + +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ + +#include +#include + +#include "npy_common.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/* Python13 removes _PyLong_AsInt */ +static inline int +Npy__PyLong_AsInt(PyObject *obj) +{ + int overflow; + long result = PyLong_AsLongAndOverflow(obj, &overflow); + + /* INT_MAX and INT_MIN are defined in Python.h */ + if (overflow || result > INT_MAX || result < INT_MIN) { + /* XXX: could be cute and give a different + message for overflow == -1 */ + PyErr_SetString(PyExc_OverflowError, + "Python int too large to convert to C int"); + return -1; + } + return (int)result; +} + +#if defined _MSC_VER && _MSC_VER >= 1900 + +#include + +/* + * Macros to protect CRT calls against instant termination when passed an + * invalid parameter (https://bugs.python.org/issue23524). + */ +extern _invalid_parameter_handler _Py_silent_invalid_parameter_handler; +#define NPY_BEGIN_SUPPRESS_IPH { _invalid_parameter_handler _Py_old_handler = \ + _set_thread_local_invalid_parameter_handler(_Py_silent_invalid_parameter_handler); +#define NPY_END_SUPPRESS_IPH _set_thread_local_invalid_parameter_handler(_Py_old_handler); } + +#else + +#define NPY_BEGIN_SUPPRESS_IPH +#define NPY_END_SUPPRESS_IPH + +#endif /* _MSC_VER >= 1900 */ + +/* + * PyFile_* compatibility + */ + +/* + * Get a FILE* handle to the file represented by the Python object + */ +static inline FILE* +npy_PyFile_Dup2(PyObject *file, char *mode, npy_off_t *orig_pos) +{ + int fd, fd2, unbuf; + Py_ssize_t fd2_tmp; + PyObject *ret, *os, *io, *io_raw; + npy_off_t pos; + FILE *handle; + + /* Flush first to ensure things end up in the file in the correct order */ + ret = PyObject_CallMethod(file, "flush", ""); + if (ret == NULL) { + return NULL; + } + Py_DECREF(ret); + fd = PyObject_AsFileDescriptor(file); + if (fd == -1) { + return NULL; + } + + /* + * The handle needs to be dup'd because we have to call fclose + * at the end + */ + os = PyImport_ImportModule("os"); + if (os == NULL) { + return NULL; + } + ret = PyObject_CallMethod(os, "dup", "i", fd); + Py_DECREF(os); + if (ret == NULL) { + return NULL; + } + fd2_tmp = PyNumber_AsSsize_t(ret, PyExc_IOError); + Py_DECREF(ret); + if (fd2_tmp == -1 && PyErr_Occurred()) { + return NULL; + } + if (fd2_tmp < INT_MIN || fd2_tmp > INT_MAX) { + PyErr_SetString(PyExc_IOError, + "Getting an 'int' from os.dup() failed"); + return NULL; + } + fd2 = (int)fd2_tmp; + + /* Convert to FILE* handle */ +#ifdef _WIN32 + NPY_BEGIN_SUPPRESS_IPH + handle = _fdopen(fd2, mode); + NPY_END_SUPPRESS_IPH +#else + handle = fdopen(fd2, mode); +#endif + if (handle == NULL) { + PyErr_SetString(PyExc_IOError, + "Getting a FILE* from a Python file object via " + "_fdopen failed. If you built NumPy, you probably " + "linked with the wrong debug/release runtime"); + return NULL; + } + + /* Record the original raw file handle position */ + *orig_pos = npy_ftell(handle); + if (*orig_pos == -1) { + /* The io module is needed to determine if buffering is used */ + io = PyImport_ImportModule("io"); + if (io == NULL) { + fclose(handle); + return NULL; + } + /* File object instances of RawIOBase are unbuffered */ + io_raw = PyObject_GetAttrString(io, "RawIOBase"); + Py_DECREF(io); + if (io_raw == NULL) { + fclose(handle); + return NULL; + } + unbuf = PyObject_IsInstance(file, io_raw); + Py_DECREF(io_raw); + if (unbuf == 1) { + /* Succeed if the IO is unbuffered */ + return handle; + } + else { + PyErr_SetString(PyExc_IOError, "obtaining file position failed"); + fclose(handle); + return NULL; + } + } + + /* Seek raw handle to the Python-side position */ + ret = PyObject_CallMethod(file, "tell", ""); + if (ret == NULL) { + fclose(handle); + return NULL; + } + pos = PyLong_AsLongLong(ret); + Py_DECREF(ret); + if (PyErr_Occurred()) { + fclose(handle); + return NULL; + } + if (npy_fseek(handle, pos, SEEK_SET) == -1) { + PyErr_SetString(PyExc_IOError, "seeking file failed"); + fclose(handle); + return NULL; + } + return handle; +} + +/* + * Close the dup-ed file handle, and seek the Python one to the current position + */ +static inline int +npy_PyFile_DupClose2(PyObject *file, FILE* handle, npy_off_t orig_pos) +{ + int fd, unbuf; + PyObject *ret, *io, *io_raw; + npy_off_t position; + + position = npy_ftell(handle); + + /* Close the FILE* handle */ + fclose(handle); + + /* + * Restore original file handle position, in order to not confuse + * Python-side data structures + */ + fd = PyObject_AsFileDescriptor(file); + if (fd == -1) { + return -1; + } + + if (npy_lseek(fd, orig_pos, SEEK_SET) == -1) { + + /* The io module is needed to determine if buffering is used */ + io = PyImport_ImportModule("io"); + if (io == NULL) { + return -1; + } + /* File object instances of RawIOBase are unbuffered */ + io_raw = PyObject_GetAttrString(io, "RawIOBase"); + Py_DECREF(io); + if (io_raw == NULL) { + return -1; + } + unbuf = PyObject_IsInstance(file, io_raw); + Py_DECREF(io_raw); + if (unbuf == 1) { + /* Succeed if the IO is unbuffered */ + return 0; + } + else { + PyErr_SetString(PyExc_IOError, "seeking file failed"); + return -1; + } + } + + if (position == -1) { + PyErr_SetString(PyExc_IOError, "obtaining file position failed"); + return -1; + } + + /* Seek Python-side handle to the FILE* handle position */ + ret = PyObject_CallMethod(file, "seek", NPY_OFF_T_PYFMT "i", position, 0); + if (ret == NULL) { + return -1; + } + Py_DECREF(ret); + return 0; +} + +static inline PyObject* +npy_PyFile_OpenFile(PyObject *filename, const char *mode) +{ + PyObject *open; + open = PyDict_GetItemString(PyEval_GetBuiltins(), "open"); + if (open == NULL) { + return NULL; + } + return PyObject_CallFunction(open, "Os", filename, mode); +} + +static inline int +npy_PyFile_CloseFile(PyObject *file) +{ + PyObject *ret; + + ret = PyObject_CallMethod(file, "close", NULL); + if (ret == NULL) { + return -1; + } + Py_DECREF(ret); + return 0; +} + +/* This is a copy of _PyErr_ChainExceptions, which + * is no longer exported from Python3.12 + */ +static inline void +npy_PyErr_ChainExceptions(PyObject *exc, PyObject *val, PyObject *tb) +{ + if (exc == NULL) + return; + + if (PyErr_Occurred()) { + PyObject *exc2, *val2, *tb2; + PyErr_Fetch(&exc2, &val2, &tb2); + PyErr_NormalizeException(&exc, &val, &tb); + if (tb != NULL) { + PyException_SetTraceback(val, tb); + Py_DECREF(tb); + } + Py_DECREF(exc); + PyErr_NormalizeException(&exc2, &val2, &tb2); + PyException_SetContext(val2, val); + PyErr_Restore(exc2, val2, tb2); + } + else { + PyErr_Restore(exc, val, tb); + } +} + +/* This is a copy of _PyErr_ChainExceptions, with: + * __cause__ used instead of __context__ + */ +static inline void +npy_PyErr_ChainExceptionsCause(PyObject *exc, PyObject *val, PyObject *tb) +{ + if (exc == NULL) + return; + + if (PyErr_Occurred()) { + PyObject *exc2, *val2, *tb2; + PyErr_Fetch(&exc2, &val2, &tb2); + PyErr_NormalizeException(&exc, &val, &tb); + if (tb != NULL) { + PyException_SetTraceback(val, tb); + Py_DECREF(tb); + } + Py_DECREF(exc); + PyErr_NormalizeException(&exc2, &val2, &tb2); + PyException_SetCause(val2, val); + PyErr_Restore(exc2, val2, tb2); + } + else { + PyErr_Restore(exc, val, tb); + } +} + +/* + * PyCObject functions adapted to PyCapsules. + * + * The main job here is to get rid of the improved error handling + * of PyCapsules. It's a shame... + */ +static inline PyObject * +NpyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *)) +{ + PyObject *ret = PyCapsule_New(ptr, NULL, dtor); + if (ret == NULL) { + PyErr_Clear(); + } + return ret; +} + +static inline PyObject * +NpyCapsule_FromVoidPtrAndDesc(void *ptr, void* context, void (*dtor)(PyObject *)) +{ + PyObject *ret = NpyCapsule_FromVoidPtr(ptr, dtor); + if (ret != NULL && PyCapsule_SetContext(ret, context) != 0) { + PyErr_Clear(); + Py_DECREF(ret); + ret = NULL; + } + return ret; +} + +static inline void * +NpyCapsule_AsVoidPtr(PyObject *obj) +{ + void *ret = PyCapsule_GetPointer(obj, NULL); + if (ret == NULL) { + PyErr_Clear(); + } + return ret; +} + +static inline void * +NpyCapsule_GetDesc(PyObject *obj) +{ + return PyCapsule_GetContext(obj); +} + +static inline int +NpyCapsule_Check(PyObject *ptr) +{ + return PyCapsule_CheckExact(ptr); +} + +#ifdef __cplusplus +} +#endif + + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_3KCOMPAT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_common.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_common.h new file mode 100644 index 0000000000000000000000000000000000000000..79ad8ad78cb2bc68867b3002b091e5d7e7b6bc9a --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_common.h @@ -0,0 +1,1070 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ + +/* need Python.h for npy_intp, npy_uintp */ +#include + +/* numpconfig.h is auto-generated */ +#include "numpyconfig.h" +#ifdef HAVE_NPY_CONFIG_H +#include +#endif + +/* + * using static inline modifiers when defining npy_math functions + * allows the compiler to make optimizations when possible + */ +#ifndef NPY_INLINE_MATH +#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD + #define NPY_INLINE_MATH 1 +#else + #define NPY_INLINE_MATH 0 +#endif +#endif + +/* + * gcc does not unroll even with -O3 + * use with care, unrolling on modern cpus rarely speeds things up + */ +#ifdef HAVE_ATTRIBUTE_OPTIMIZE_UNROLL_LOOPS +#define NPY_GCC_UNROLL_LOOPS \ + __attribute__((optimize("unroll-loops"))) +#else +#define NPY_GCC_UNROLL_LOOPS +#endif + +/* highest gcc optimization level, enabled autovectorizer */ +#ifdef HAVE_ATTRIBUTE_OPTIMIZE_OPT_3 +#define NPY_GCC_OPT_3 __attribute__((optimize("O3"))) +#else +#define NPY_GCC_OPT_3 +#endif + +/* + * mark an argument (starting from 1) that must not be NULL and is not checked + * DO NOT USE IF FUNCTION CHECKS FOR NULL!! the compiler will remove the check + */ +#ifdef HAVE_ATTRIBUTE_NONNULL +#define NPY_GCC_NONNULL(n) __attribute__((nonnull(n))) +#else +#define NPY_GCC_NONNULL(n) +#endif + +/* + * give a hint to the compiler which branch is more likely or unlikely + * to occur, e.g. rare error cases: + * + * if (NPY_UNLIKELY(failure == 0)) + * return NULL; + * + * the double !! is to cast the expression (e.g. NULL) to a boolean required by + * the intrinsic + */ +#ifdef HAVE___BUILTIN_EXPECT +#define NPY_LIKELY(x) __builtin_expect(!!(x), 1) +#define NPY_UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define NPY_LIKELY(x) (x) +#define NPY_UNLIKELY(x) (x) +#endif + +#ifdef HAVE___BUILTIN_PREFETCH +/* unlike _mm_prefetch also works on non-x86 */ +#define NPY_PREFETCH(x, rw, loc) __builtin_prefetch((x), (rw), (loc)) +#else +#ifdef NPY_HAVE_SSE +/* _MM_HINT_ET[01] (rw = 1) unsupported, only available in gcc >= 4.9 */ +#define NPY_PREFETCH(x, rw, loc) _mm_prefetch((x), loc == 0 ? _MM_HINT_NTA : \ + (loc == 1 ? _MM_HINT_T2 : \ + (loc == 2 ? _MM_HINT_T1 : \ + (loc == 3 ? _MM_HINT_T0 : -1)))) +#else +#define NPY_PREFETCH(x, rw,loc) +#endif +#endif + +/* `NPY_INLINE` kept for backwards compatibility; use `inline` instead */ +#if defined(_MSC_VER) && !defined(__clang__) + #define NPY_INLINE __inline +/* clang included here to handle clang-cl on Windows */ +#elif defined(__GNUC__) || defined(__clang__) + #if defined(__STRICT_ANSI__) + #define NPY_INLINE __inline__ + #else + #define NPY_INLINE inline + #endif +#else + #define NPY_INLINE +#endif + +#ifdef _MSC_VER + #define NPY_FINLINE static __forceinline +#elif defined(__GNUC__) + #define NPY_FINLINE static inline __attribute__((always_inline)) +#else + #define NPY_FINLINE static +#endif + +#if defined(_MSC_VER) + #define NPY_NOINLINE static __declspec(noinline) +#elif defined(__GNUC__) || defined(__clang__) + #define NPY_NOINLINE static __attribute__((noinline)) +#else + #define NPY_NOINLINE static +#endif + +#ifdef __cplusplus + #define NPY_TLS thread_local +#elif defined(HAVE_THREAD_LOCAL) + #define NPY_TLS thread_local +#elif defined(HAVE__THREAD_LOCAL) + #define NPY_TLS _Thread_local +#elif defined(HAVE___THREAD) + #define NPY_TLS __thread +#elif defined(HAVE___DECLSPEC_THREAD_) + #define NPY_TLS __declspec(thread) +#else + #define NPY_TLS +#endif + +#ifdef WITH_CPYCHECKER_RETURNS_BORROWED_REF_ATTRIBUTE + #define NPY_RETURNS_BORROWED_REF \ + __attribute__((cpychecker_returns_borrowed_ref)) +#else + #define NPY_RETURNS_BORROWED_REF +#endif + +#ifdef WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE + #define NPY_STEALS_REF_TO_ARG(n) \ + __attribute__((cpychecker_steals_reference_to_arg(n))) +#else + #define NPY_STEALS_REF_TO_ARG(n) +#endif + +/* 64 bit file position support, also on win-amd64. Issue gh-2256 */ +#if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) || \ + defined(__MINGW32__) || defined(__MINGW64__) + #include + + #define npy_fseek _fseeki64 + #define npy_ftell _ftelli64 + #define npy_lseek _lseeki64 + #define npy_off_t npy_int64 + + #if NPY_SIZEOF_INT == 8 + #define NPY_OFF_T_PYFMT "i" + #elif NPY_SIZEOF_LONG == 8 + #define NPY_OFF_T_PYFMT "l" + #elif NPY_SIZEOF_LONGLONG == 8 + #define NPY_OFF_T_PYFMT "L" + #else + #error Unsupported size for type off_t + #endif +#else +#ifdef HAVE_FSEEKO + #define npy_fseek fseeko +#else + #define npy_fseek fseek +#endif +#ifdef HAVE_FTELLO + #define npy_ftell ftello +#else + #define npy_ftell ftell +#endif + #include + #ifndef _WIN32 + #include + #endif + #define npy_lseek lseek + #define npy_off_t off_t + + #if NPY_SIZEOF_OFF_T == NPY_SIZEOF_SHORT + #define NPY_OFF_T_PYFMT "h" + #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_INT + #define NPY_OFF_T_PYFMT "i" + #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONG + #define NPY_OFF_T_PYFMT "l" + #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONGLONG + #define NPY_OFF_T_PYFMT "L" + #else + #error Unsupported size for type off_t + #endif +#endif + +/* enums for detected endianness */ +enum { + NPY_CPU_UNKNOWN_ENDIAN, + NPY_CPU_LITTLE, + NPY_CPU_BIG +}; + +/* + * This is to typedef npy_intp to the appropriate size for Py_ssize_t. + * (Before NumPy 2.0 we used Py_intptr_t and Py_uintptr_t from `pyport.h`.) + */ +typedef Py_ssize_t npy_intp; +typedef size_t npy_uintp; + +/* + * Define sizes that were not defined in numpyconfig.h. + */ +#define NPY_SIZEOF_CHAR 1 +#define NPY_SIZEOF_BYTE 1 +#define NPY_SIZEOF_DATETIME 8 +#define NPY_SIZEOF_TIMEDELTA 8 +#define NPY_SIZEOF_HALF 2 +#define NPY_SIZEOF_CFLOAT NPY_SIZEOF_COMPLEX_FLOAT +#define NPY_SIZEOF_CDOUBLE NPY_SIZEOF_COMPLEX_DOUBLE +#define NPY_SIZEOF_CLONGDOUBLE NPY_SIZEOF_COMPLEX_LONGDOUBLE + +#ifdef constchar +#undef constchar +#endif + +#define NPY_SSIZE_T_PYFMT "n" +#define constchar char + +/* NPY_INTP_FMT Note: + * Unlike the other NPY_*_FMT macros, which are used with PyOS_snprintf, + * NPY_INTP_FMT is used with PyErr_Format and PyUnicode_FromFormat. Those + * functions use different formatting codes that are portably specified + * according to the Python documentation. See issue gh-2388. + */ +#if NPY_SIZEOF_INTP == NPY_SIZEOF_LONG + #define NPY_INTP NPY_LONG + #define NPY_UINTP NPY_ULONG + #define PyIntpArrType_Type PyLongArrType_Type + #define PyUIntpArrType_Type PyULongArrType_Type + #define NPY_MAX_INTP NPY_MAX_LONG + #define NPY_MIN_INTP NPY_MIN_LONG + #define NPY_MAX_UINTP NPY_MAX_ULONG + #define NPY_INTP_FMT "ld" +#elif NPY_SIZEOF_INTP == NPY_SIZEOF_INT + #define NPY_INTP NPY_INT + #define NPY_UINTP NPY_UINT + #define PyIntpArrType_Type PyIntArrType_Type + #define PyUIntpArrType_Type PyUIntArrType_Type + #define NPY_MAX_INTP NPY_MAX_INT + #define NPY_MIN_INTP NPY_MIN_INT + #define NPY_MAX_UINTP NPY_MAX_UINT + #define NPY_INTP_FMT "d" +#elif defined(PY_LONG_LONG) && (NPY_SIZEOF_INTP == NPY_SIZEOF_LONGLONG) + #define NPY_INTP NPY_LONGLONG + #define NPY_UINTP NPY_ULONGLONG + #define PyIntpArrType_Type PyLongLongArrType_Type + #define PyUIntpArrType_Type PyULongLongArrType_Type + #define NPY_MAX_INTP NPY_MAX_LONGLONG + #define NPY_MIN_INTP NPY_MIN_LONGLONG + #define NPY_MAX_UINTP NPY_MAX_ULONGLONG + #define NPY_INTP_FMT "lld" +#else + #error "Failed to correctly define NPY_INTP and NPY_UINTP" +#endif + + +/* + * Some platforms don't define bool, long long, or long double. + * Handle that here. + */ +#define NPY_BYTE_FMT "hhd" +#define NPY_UBYTE_FMT "hhu" +#define NPY_SHORT_FMT "hd" +#define NPY_USHORT_FMT "hu" +#define NPY_INT_FMT "d" +#define NPY_UINT_FMT "u" +#define NPY_LONG_FMT "ld" +#define NPY_ULONG_FMT "lu" +#define NPY_HALF_FMT "g" +#define NPY_FLOAT_FMT "g" +#define NPY_DOUBLE_FMT "g" + + +#ifdef PY_LONG_LONG +typedef PY_LONG_LONG npy_longlong; +typedef unsigned PY_LONG_LONG npy_ulonglong; +# ifdef _MSC_VER +# define NPY_LONGLONG_FMT "I64d" +# define NPY_ULONGLONG_FMT "I64u" +# else +# define NPY_LONGLONG_FMT "lld" +# define NPY_ULONGLONG_FMT "llu" +# endif +# ifdef _MSC_VER +# define NPY_LONGLONG_SUFFIX(x) (x##i64) +# define NPY_ULONGLONG_SUFFIX(x) (x##Ui64) +# else +# define NPY_LONGLONG_SUFFIX(x) (x##LL) +# define NPY_ULONGLONG_SUFFIX(x) (x##ULL) +# endif +#else +typedef long npy_longlong; +typedef unsigned long npy_ulonglong; +# define NPY_LONGLONG_SUFFIX(x) (x##L) +# define NPY_ULONGLONG_SUFFIX(x) (x##UL) +#endif + + +typedef unsigned char npy_bool; +#define NPY_FALSE 0 +#define NPY_TRUE 1 +/* + * `NPY_SIZEOF_LONGDOUBLE` isn't usually equal to sizeof(long double). + * In some certain cases, it may forced to be equal to sizeof(double) + * even against the compiler implementation and the same goes for + * `complex long double`. + * + * Therefore, avoid `long double`, use `npy_longdouble` instead, + * and when it comes to standard math functions make sure of using + * the double version when `NPY_SIZEOF_LONGDOUBLE` == `NPY_SIZEOF_DOUBLE`. + * For example: + * npy_longdouble *ptr, x; + * #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE + * npy_longdouble r = modf(x, ptr); + * #else + * npy_longdouble r = modfl(x, ptr); + * #endif + * + * See https://github.com/numpy/numpy/issues/20348 + */ +#if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE + #define NPY_LONGDOUBLE_FMT "g" + #define longdouble_t double + typedef double npy_longdouble; +#else + #define NPY_LONGDOUBLE_FMT "Lg" + #define longdouble_t long double + typedef long double npy_longdouble; +#endif + +#ifndef Py_USING_UNICODE +#error Must use Python with unicode enabled. +#endif + + +typedef signed char npy_byte; +typedef unsigned char npy_ubyte; +typedef unsigned short npy_ushort; +typedef unsigned int npy_uint; +typedef unsigned long npy_ulong; + +/* These are for completeness */ +typedef char npy_char; +typedef short npy_short; +typedef int npy_int; +typedef long npy_long; +typedef float npy_float; +typedef double npy_double; + +typedef Py_hash_t npy_hash_t; +#define NPY_SIZEOF_HASH_T NPY_SIZEOF_INTP + +#if defined(__cplusplus) + +typedef struct +{ + double _Val[2]; +} npy_cdouble; + +typedef struct +{ + float _Val[2]; +} npy_cfloat; + +typedef struct +{ + long double _Val[2]; +} npy_clongdouble; + +#else + +#include + + +#if defined(_MSC_VER) && !defined(__INTEL_COMPILER) +typedef _Dcomplex npy_cdouble; +typedef _Fcomplex npy_cfloat; +typedef _Lcomplex npy_clongdouble; +#else /* !defined(_MSC_VER) || defined(__INTEL_COMPILER) */ +typedef double _Complex npy_cdouble; +typedef float _Complex npy_cfloat; +typedef longdouble_t _Complex npy_clongdouble; +#endif + +#endif + +/* + * numarray-style bit-width typedefs + */ +#define NPY_MAX_INT8 127 +#define NPY_MIN_INT8 -128 +#define NPY_MAX_UINT8 255 +#define NPY_MAX_INT16 32767 +#define NPY_MIN_INT16 -32768 +#define NPY_MAX_UINT16 65535 +#define NPY_MAX_INT32 2147483647 +#define NPY_MIN_INT32 (-NPY_MAX_INT32 - 1) +#define NPY_MAX_UINT32 4294967295U +#define NPY_MAX_INT64 NPY_LONGLONG_SUFFIX(9223372036854775807) +#define NPY_MIN_INT64 (-NPY_MAX_INT64 - NPY_LONGLONG_SUFFIX(1)) +#define NPY_MAX_UINT64 NPY_ULONGLONG_SUFFIX(18446744073709551615) +#define NPY_MAX_INT128 NPY_LONGLONG_SUFFIX(85070591730234615865843651857942052864) +#define NPY_MIN_INT128 (-NPY_MAX_INT128 - NPY_LONGLONG_SUFFIX(1)) +#define NPY_MAX_UINT128 NPY_ULONGLONG_SUFFIX(170141183460469231731687303715884105728) +#define NPY_MAX_INT256 NPY_LONGLONG_SUFFIX(57896044618658097711785492504343953926634992332820282019728792003956564819967) +#define NPY_MIN_INT256 (-NPY_MAX_INT256 - NPY_LONGLONG_SUFFIX(1)) +#define NPY_MAX_UINT256 NPY_ULONGLONG_SUFFIX(115792089237316195423570985008687907853269984665640564039457584007913129639935) +#define NPY_MIN_DATETIME NPY_MIN_INT64 +#define NPY_MAX_DATETIME NPY_MAX_INT64 +#define NPY_MIN_TIMEDELTA NPY_MIN_INT64 +#define NPY_MAX_TIMEDELTA NPY_MAX_INT64 + + /* Need to find the number of bits for each type and + make definitions accordingly. + + C states that sizeof(char) == 1 by definition + + So, just using the sizeof keyword won't help. + + It also looks like Python itself uses sizeof(char) quite a + bit, which by definition should be 1 all the time. + + Idea: Make Use of CHAR_BIT which should tell us how many + BITS per CHARACTER + */ + + /* Include platform definitions -- These are in the C89/90 standard */ +#include +#define NPY_MAX_BYTE SCHAR_MAX +#define NPY_MIN_BYTE SCHAR_MIN +#define NPY_MAX_UBYTE UCHAR_MAX +#define NPY_MAX_SHORT SHRT_MAX +#define NPY_MIN_SHORT SHRT_MIN +#define NPY_MAX_USHORT USHRT_MAX +#define NPY_MAX_INT INT_MAX +#ifndef INT_MIN +#define INT_MIN (-INT_MAX - 1) +#endif +#define NPY_MIN_INT INT_MIN +#define NPY_MAX_UINT UINT_MAX +#define NPY_MAX_LONG LONG_MAX +#define NPY_MIN_LONG LONG_MIN +#define NPY_MAX_ULONG ULONG_MAX + +#define NPY_BITSOF_BOOL (sizeof(npy_bool) * CHAR_BIT) +#define NPY_BITSOF_CHAR CHAR_BIT +#define NPY_BITSOF_BYTE (NPY_SIZEOF_BYTE * CHAR_BIT) +#define NPY_BITSOF_SHORT (NPY_SIZEOF_SHORT * CHAR_BIT) +#define NPY_BITSOF_INT (NPY_SIZEOF_INT * CHAR_BIT) +#define NPY_BITSOF_LONG (NPY_SIZEOF_LONG * CHAR_BIT) +#define NPY_BITSOF_LONGLONG (NPY_SIZEOF_LONGLONG * CHAR_BIT) +#define NPY_BITSOF_INTP (NPY_SIZEOF_INTP * CHAR_BIT) +#define NPY_BITSOF_HALF (NPY_SIZEOF_HALF * CHAR_BIT) +#define NPY_BITSOF_FLOAT (NPY_SIZEOF_FLOAT * CHAR_BIT) +#define NPY_BITSOF_DOUBLE (NPY_SIZEOF_DOUBLE * CHAR_BIT) +#define NPY_BITSOF_LONGDOUBLE (NPY_SIZEOF_LONGDOUBLE * CHAR_BIT) +#define NPY_BITSOF_CFLOAT (NPY_SIZEOF_CFLOAT * CHAR_BIT) +#define NPY_BITSOF_CDOUBLE (NPY_SIZEOF_CDOUBLE * CHAR_BIT) +#define NPY_BITSOF_CLONGDOUBLE (NPY_SIZEOF_CLONGDOUBLE * CHAR_BIT) +#define NPY_BITSOF_DATETIME (NPY_SIZEOF_DATETIME * CHAR_BIT) +#define NPY_BITSOF_TIMEDELTA (NPY_SIZEOF_TIMEDELTA * CHAR_BIT) + +#if NPY_BITSOF_LONG == 8 +#define NPY_INT8 NPY_LONG +#define NPY_UINT8 NPY_ULONG + typedef long npy_int8; + typedef unsigned long npy_uint8; +#define PyInt8ScalarObject PyLongScalarObject +#define PyInt8ArrType_Type PyLongArrType_Type +#define PyUInt8ScalarObject PyULongScalarObject +#define PyUInt8ArrType_Type PyULongArrType_Type +#define NPY_INT8_FMT NPY_LONG_FMT +#define NPY_UINT8_FMT NPY_ULONG_FMT +#elif NPY_BITSOF_LONG == 16 +#define NPY_INT16 NPY_LONG +#define NPY_UINT16 NPY_ULONG + typedef long npy_int16; + typedef unsigned long npy_uint16; +#define PyInt16ScalarObject PyLongScalarObject +#define PyInt16ArrType_Type PyLongArrType_Type +#define PyUInt16ScalarObject PyULongScalarObject +#define PyUInt16ArrType_Type PyULongArrType_Type +#define NPY_INT16_FMT NPY_LONG_FMT +#define NPY_UINT16_FMT NPY_ULONG_FMT +#elif NPY_BITSOF_LONG == 32 +#define NPY_INT32 NPY_LONG +#define NPY_UINT32 NPY_ULONG + typedef long npy_int32; + typedef unsigned long npy_uint32; + typedef unsigned long npy_ucs4; +#define PyInt32ScalarObject PyLongScalarObject +#define PyInt32ArrType_Type PyLongArrType_Type +#define PyUInt32ScalarObject PyULongScalarObject +#define PyUInt32ArrType_Type PyULongArrType_Type +#define NPY_INT32_FMT NPY_LONG_FMT +#define NPY_UINT32_FMT NPY_ULONG_FMT +#elif NPY_BITSOF_LONG == 64 +#define NPY_INT64 NPY_LONG +#define NPY_UINT64 NPY_ULONG + typedef long npy_int64; + typedef unsigned long npy_uint64; +#define PyInt64ScalarObject PyLongScalarObject +#define PyInt64ArrType_Type PyLongArrType_Type +#define PyUInt64ScalarObject PyULongScalarObject +#define PyUInt64ArrType_Type PyULongArrType_Type +#define NPY_INT64_FMT NPY_LONG_FMT +#define NPY_UINT64_FMT NPY_ULONG_FMT +#define MyPyLong_FromInt64 PyLong_FromLong +#define MyPyLong_AsInt64 PyLong_AsLong +#elif NPY_BITSOF_LONG == 128 +#define NPY_INT128 NPY_LONG +#define NPY_UINT128 NPY_ULONG + typedef long npy_int128; + typedef unsigned long npy_uint128; +#define PyInt128ScalarObject PyLongScalarObject +#define PyInt128ArrType_Type PyLongArrType_Type +#define PyUInt128ScalarObject PyULongScalarObject +#define PyUInt128ArrType_Type PyULongArrType_Type +#define NPY_INT128_FMT NPY_LONG_FMT +#define NPY_UINT128_FMT NPY_ULONG_FMT +#endif + +#if NPY_BITSOF_LONGLONG == 8 +# ifndef NPY_INT8 +# define NPY_INT8 NPY_LONGLONG +# define NPY_UINT8 NPY_ULONGLONG + typedef npy_longlong npy_int8; + typedef npy_ulonglong npy_uint8; +# define PyInt8ScalarObject PyLongLongScalarObject +# define PyInt8ArrType_Type PyLongLongArrType_Type +# define PyUInt8ScalarObject PyULongLongScalarObject +# define PyUInt8ArrType_Type PyULongLongArrType_Type +#define NPY_INT8_FMT NPY_LONGLONG_FMT +#define NPY_UINT8_FMT NPY_ULONGLONG_FMT +# endif +# define NPY_MAX_LONGLONG NPY_MAX_INT8 +# define NPY_MIN_LONGLONG NPY_MIN_INT8 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT8 +#elif NPY_BITSOF_LONGLONG == 16 +# ifndef NPY_INT16 +# define NPY_INT16 NPY_LONGLONG +# define NPY_UINT16 NPY_ULONGLONG + typedef npy_longlong npy_int16; + typedef npy_ulonglong npy_uint16; +# define PyInt16ScalarObject PyLongLongScalarObject +# define PyInt16ArrType_Type PyLongLongArrType_Type +# define PyUInt16ScalarObject PyULongLongScalarObject +# define PyUInt16ArrType_Type PyULongLongArrType_Type +#define NPY_INT16_FMT NPY_LONGLONG_FMT +#define NPY_UINT16_FMT NPY_ULONGLONG_FMT +# endif +# define NPY_MAX_LONGLONG NPY_MAX_INT16 +# define NPY_MIN_LONGLONG NPY_MIN_INT16 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT16 +#elif NPY_BITSOF_LONGLONG == 32 +# ifndef NPY_INT32 +# define NPY_INT32 NPY_LONGLONG +# define NPY_UINT32 NPY_ULONGLONG + typedef npy_longlong npy_int32; + typedef npy_ulonglong npy_uint32; + typedef npy_ulonglong npy_ucs4; +# define PyInt32ScalarObject PyLongLongScalarObject +# define PyInt32ArrType_Type PyLongLongArrType_Type +# define PyUInt32ScalarObject PyULongLongScalarObject +# define PyUInt32ArrType_Type PyULongLongArrType_Type +#define NPY_INT32_FMT NPY_LONGLONG_FMT +#define NPY_UINT32_FMT NPY_ULONGLONG_FMT +# endif +# define NPY_MAX_LONGLONG NPY_MAX_INT32 +# define NPY_MIN_LONGLONG NPY_MIN_INT32 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT32 +#elif NPY_BITSOF_LONGLONG == 64 +# ifndef NPY_INT64 +# define NPY_INT64 NPY_LONGLONG +# define NPY_UINT64 NPY_ULONGLONG + typedef npy_longlong npy_int64; + typedef npy_ulonglong npy_uint64; +# define PyInt64ScalarObject PyLongLongScalarObject +# define PyInt64ArrType_Type PyLongLongArrType_Type +# define PyUInt64ScalarObject PyULongLongScalarObject +# define PyUInt64ArrType_Type PyULongLongArrType_Type +#define NPY_INT64_FMT NPY_LONGLONG_FMT +#define NPY_UINT64_FMT NPY_ULONGLONG_FMT +# define MyPyLong_FromInt64 PyLong_FromLongLong +# define MyPyLong_AsInt64 PyLong_AsLongLong +# endif +# define NPY_MAX_LONGLONG NPY_MAX_INT64 +# define NPY_MIN_LONGLONG NPY_MIN_INT64 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT64 +#elif NPY_BITSOF_LONGLONG == 128 +# ifndef NPY_INT128 +# define NPY_INT128 NPY_LONGLONG +# define NPY_UINT128 NPY_ULONGLONG + typedef npy_longlong npy_int128; + typedef npy_ulonglong npy_uint128; +# define PyInt128ScalarObject PyLongLongScalarObject +# define PyInt128ArrType_Type PyLongLongArrType_Type +# define PyUInt128ScalarObject PyULongLongScalarObject +# define PyUInt128ArrType_Type PyULongLongArrType_Type +#define NPY_INT128_FMT NPY_LONGLONG_FMT +#define NPY_UINT128_FMT NPY_ULONGLONG_FMT +# endif +# define NPY_MAX_LONGLONG NPY_MAX_INT128 +# define NPY_MIN_LONGLONG NPY_MIN_INT128 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT128 +#elif NPY_BITSOF_LONGLONG == 256 +# define NPY_INT256 NPY_LONGLONG +# define NPY_UINT256 NPY_ULONGLONG + typedef npy_longlong npy_int256; + typedef npy_ulonglong npy_uint256; +# define PyInt256ScalarObject PyLongLongScalarObject +# define PyInt256ArrType_Type PyLongLongArrType_Type +# define PyUInt256ScalarObject PyULongLongScalarObject +# define PyUInt256ArrType_Type PyULongLongArrType_Type +#define NPY_INT256_FMT NPY_LONGLONG_FMT +#define NPY_UINT256_FMT NPY_ULONGLONG_FMT +# define NPY_MAX_LONGLONG NPY_MAX_INT256 +# define NPY_MIN_LONGLONG NPY_MIN_INT256 +# define NPY_MAX_ULONGLONG NPY_MAX_UINT256 +#endif + +#if NPY_BITSOF_INT == 8 +#ifndef NPY_INT8 +#define NPY_INT8 NPY_INT +#define NPY_UINT8 NPY_UINT + typedef int npy_int8; + typedef unsigned int npy_uint8; +# define PyInt8ScalarObject PyIntScalarObject +# define PyInt8ArrType_Type PyIntArrType_Type +# define PyUInt8ScalarObject PyUIntScalarObject +# define PyUInt8ArrType_Type PyUIntArrType_Type +#define NPY_INT8_FMT NPY_INT_FMT +#define NPY_UINT8_FMT NPY_UINT_FMT +#endif +#elif NPY_BITSOF_INT == 16 +#ifndef NPY_INT16 +#define NPY_INT16 NPY_INT +#define NPY_UINT16 NPY_UINT + typedef int npy_int16; + typedef unsigned int npy_uint16; +# define PyInt16ScalarObject PyIntScalarObject +# define PyInt16ArrType_Type PyIntArrType_Type +# define PyUInt16ScalarObject PyIntUScalarObject +# define PyUInt16ArrType_Type PyIntUArrType_Type +#define NPY_INT16_FMT NPY_INT_FMT +#define NPY_UINT16_FMT NPY_UINT_FMT +#endif +#elif NPY_BITSOF_INT == 32 +#ifndef NPY_INT32 +#define NPY_INT32 NPY_INT +#define NPY_UINT32 NPY_UINT + typedef int npy_int32; + typedef unsigned int npy_uint32; + typedef unsigned int npy_ucs4; +# define PyInt32ScalarObject PyIntScalarObject +# define PyInt32ArrType_Type PyIntArrType_Type +# define PyUInt32ScalarObject PyUIntScalarObject +# define PyUInt32ArrType_Type PyUIntArrType_Type +#define NPY_INT32_FMT NPY_INT_FMT +#define NPY_UINT32_FMT NPY_UINT_FMT +#endif +#elif NPY_BITSOF_INT == 64 +#ifndef NPY_INT64 +#define NPY_INT64 NPY_INT +#define NPY_UINT64 NPY_UINT + typedef int npy_int64; + typedef unsigned int npy_uint64; +# define PyInt64ScalarObject PyIntScalarObject +# define PyInt64ArrType_Type PyIntArrType_Type +# define PyUInt64ScalarObject PyUIntScalarObject +# define PyUInt64ArrType_Type PyUIntArrType_Type +#define NPY_INT64_FMT NPY_INT_FMT +#define NPY_UINT64_FMT NPY_UINT_FMT +# define MyPyLong_FromInt64 PyLong_FromLong +# define MyPyLong_AsInt64 PyLong_AsLong +#endif +#elif NPY_BITSOF_INT == 128 +#ifndef NPY_INT128 +#define NPY_INT128 NPY_INT +#define NPY_UINT128 NPY_UINT + typedef int npy_int128; + typedef unsigned int npy_uint128; +# define PyInt128ScalarObject PyIntScalarObject +# define PyInt128ArrType_Type PyIntArrType_Type +# define PyUInt128ScalarObject PyUIntScalarObject +# define PyUInt128ArrType_Type PyUIntArrType_Type +#define NPY_INT128_FMT NPY_INT_FMT +#define NPY_UINT128_FMT NPY_UINT_FMT +#endif +#endif + +#if NPY_BITSOF_SHORT == 8 +#ifndef NPY_INT8 +#define NPY_INT8 NPY_SHORT +#define NPY_UINT8 NPY_USHORT + typedef short npy_int8; + typedef unsigned short npy_uint8; +# define PyInt8ScalarObject PyShortScalarObject +# define PyInt8ArrType_Type PyShortArrType_Type +# define PyUInt8ScalarObject PyUShortScalarObject +# define PyUInt8ArrType_Type PyUShortArrType_Type +#define NPY_INT8_FMT NPY_SHORT_FMT +#define NPY_UINT8_FMT NPY_USHORT_FMT +#endif +#elif NPY_BITSOF_SHORT == 16 +#ifndef NPY_INT16 +#define NPY_INT16 NPY_SHORT +#define NPY_UINT16 NPY_USHORT + typedef short npy_int16; + typedef unsigned short npy_uint16; +# define PyInt16ScalarObject PyShortScalarObject +# define PyInt16ArrType_Type PyShortArrType_Type +# define PyUInt16ScalarObject PyUShortScalarObject +# define PyUInt16ArrType_Type PyUShortArrType_Type +#define NPY_INT16_FMT NPY_SHORT_FMT +#define NPY_UINT16_FMT NPY_USHORT_FMT +#endif +#elif NPY_BITSOF_SHORT == 32 +#ifndef NPY_INT32 +#define NPY_INT32 NPY_SHORT +#define NPY_UINT32 NPY_USHORT + typedef short npy_int32; + typedef unsigned short npy_uint32; + typedef unsigned short npy_ucs4; +# define PyInt32ScalarObject PyShortScalarObject +# define PyInt32ArrType_Type PyShortArrType_Type +# define PyUInt32ScalarObject PyUShortScalarObject +# define PyUInt32ArrType_Type PyUShortArrType_Type +#define NPY_INT32_FMT NPY_SHORT_FMT +#define NPY_UINT32_FMT NPY_USHORT_FMT +#endif +#elif NPY_BITSOF_SHORT == 64 +#ifndef NPY_INT64 +#define NPY_INT64 NPY_SHORT +#define NPY_UINT64 NPY_USHORT + typedef short npy_int64; + typedef unsigned short npy_uint64; +# define PyInt64ScalarObject PyShortScalarObject +# define PyInt64ArrType_Type PyShortArrType_Type +# define PyUInt64ScalarObject PyUShortScalarObject +# define PyUInt64ArrType_Type PyUShortArrType_Type +#define NPY_INT64_FMT NPY_SHORT_FMT +#define NPY_UINT64_FMT NPY_USHORT_FMT +# define MyPyLong_FromInt64 PyLong_FromLong +# define MyPyLong_AsInt64 PyLong_AsLong +#endif +#elif NPY_BITSOF_SHORT == 128 +#ifndef NPY_INT128 +#define NPY_INT128 NPY_SHORT +#define NPY_UINT128 NPY_USHORT + typedef short npy_int128; + typedef unsigned short npy_uint128; +# define PyInt128ScalarObject PyShortScalarObject +# define PyInt128ArrType_Type PyShortArrType_Type +# define PyUInt128ScalarObject PyUShortScalarObject +# define PyUInt128ArrType_Type PyUShortArrType_Type +#define NPY_INT128_FMT NPY_SHORT_FMT +#define NPY_UINT128_FMT NPY_USHORT_FMT +#endif +#endif + + +#if NPY_BITSOF_CHAR == 8 +#ifndef NPY_INT8 +#define NPY_INT8 NPY_BYTE +#define NPY_UINT8 NPY_UBYTE + typedef signed char npy_int8; + typedef unsigned char npy_uint8; +# define PyInt8ScalarObject PyByteScalarObject +# define PyInt8ArrType_Type PyByteArrType_Type +# define PyUInt8ScalarObject PyUByteScalarObject +# define PyUInt8ArrType_Type PyUByteArrType_Type +#define NPY_INT8_FMT NPY_BYTE_FMT +#define NPY_UINT8_FMT NPY_UBYTE_FMT +#endif +#elif NPY_BITSOF_CHAR == 16 +#ifndef NPY_INT16 +#define NPY_INT16 NPY_BYTE +#define NPY_UINT16 NPY_UBYTE + typedef signed char npy_int16; + typedef unsigned char npy_uint16; +# define PyInt16ScalarObject PyByteScalarObject +# define PyInt16ArrType_Type PyByteArrType_Type +# define PyUInt16ScalarObject PyUByteScalarObject +# define PyUInt16ArrType_Type PyUByteArrType_Type +#define NPY_INT16_FMT NPY_BYTE_FMT +#define NPY_UINT16_FMT NPY_UBYTE_FMT +#endif +#elif NPY_BITSOF_CHAR == 32 +#ifndef NPY_INT32 +#define NPY_INT32 NPY_BYTE +#define NPY_UINT32 NPY_UBYTE + typedef signed char npy_int32; + typedef unsigned char npy_uint32; + typedef unsigned char npy_ucs4; +# define PyInt32ScalarObject PyByteScalarObject +# define PyInt32ArrType_Type PyByteArrType_Type +# define PyUInt32ScalarObject PyUByteScalarObject +# define PyUInt32ArrType_Type PyUByteArrType_Type +#define NPY_INT32_FMT NPY_BYTE_FMT +#define NPY_UINT32_FMT NPY_UBYTE_FMT +#endif +#elif NPY_BITSOF_CHAR == 64 +#ifndef NPY_INT64 +#define NPY_INT64 NPY_BYTE +#define NPY_UINT64 NPY_UBYTE + typedef signed char npy_int64; + typedef unsigned char npy_uint64; +# define PyInt64ScalarObject PyByteScalarObject +# define PyInt64ArrType_Type PyByteArrType_Type +# define PyUInt64ScalarObject PyUByteScalarObject +# define PyUInt64ArrType_Type PyUByteArrType_Type +#define NPY_INT64_FMT NPY_BYTE_FMT +#define NPY_UINT64_FMT NPY_UBYTE_FMT +# define MyPyLong_FromInt64 PyLong_FromLong +# define MyPyLong_AsInt64 PyLong_AsLong +#endif +#elif NPY_BITSOF_CHAR == 128 +#ifndef NPY_INT128 +#define NPY_INT128 NPY_BYTE +#define NPY_UINT128 NPY_UBYTE + typedef signed char npy_int128; + typedef unsigned char npy_uint128; +# define PyInt128ScalarObject PyByteScalarObject +# define PyInt128ArrType_Type PyByteArrType_Type +# define PyUInt128ScalarObject PyUByteScalarObject +# define PyUInt128ArrType_Type PyUByteArrType_Type +#define NPY_INT128_FMT NPY_BYTE_FMT +#define NPY_UINT128_FMT NPY_UBYTE_FMT +#endif +#endif + + + +#if NPY_BITSOF_DOUBLE == 32 +#ifndef NPY_FLOAT32 +#define NPY_FLOAT32 NPY_DOUBLE +#define NPY_COMPLEX64 NPY_CDOUBLE + typedef double npy_float32; + typedef npy_cdouble npy_complex64; +# define PyFloat32ScalarObject PyDoubleScalarObject +# define PyComplex64ScalarObject PyCDoubleScalarObject +# define PyFloat32ArrType_Type PyDoubleArrType_Type +# define PyComplex64ArrType_Type PyCDoubleArrType_Type +#define NPY_FLOAT32_FMT NPY_DOUBLE_FMT +#define NPY_COMPLEX64_FMT NPY_CDOUBLE_FMT +#endif +#elif NPY_BITSOF_DOUBLE == 64 +#ifndef NPY_FLOAT64 +#define NPY_FLOAT64 NPY_DOUBLE +#define NPY_COMPLEX128 NPY_CDOUBLE + typedef double npy_float64; + typedef npy_cdouble npy_complex128; +# define PyFloat64ScalarObject PyDoubleScalarObject +# define PyComplex128ScalarObject PyCDoubleScalarObject +# define PyFloat64ArrType_Type PyDoubleArrType_Type +# define PyComplex128ArrType_Type PyCDoubleArrType_Type +#define NPY_FLOAT64_FMT NPY_DOUBLE_FMT +#define NPY_COMPLEX128_FMT NPY_CDOUBLE_FMT +#endif +#elif NPY_BITSOF_DOUBLE == 80 +#ifndef NPY_FLOAT80 +#define NPY_FLOAT80 NPY_DOUBLE +#define NPY_COMPLEX160 NPY_CDOUBLE + typedef double npy_float80; + typedef npy_cdouble npy_complex160; +# define PyFloat80ScalarObject PyDoubleScalarObject +# define PyComplex160ScalarObject PyCDoubleScalarObject +# define PyFloat80ArrType_Type PyDoubleArrType_Type +# define PyComplex160ArrType_Type PyCDoubleArrType_Type +#define NPY_FLOAT80_FMT NPY_DOUBLE_FMT +#define NPY_COMPLEX160_FMT NPY_CDOUBLE_FMT +#endif +#elif NPY_BITSOF_DOUBLE == 96 +#ifndef NPY_FLOAT96 +#define NPY_FLOAT96 NPY_DOUBLE +#define NPY_COMPLEX192 NPY_CDOUBLE + typedef double npy_float96; + typedef npy_cdouble npy_complex192; +# define PyFloat96ScalarObject PyDoubleScalarObject +# define PyComplex192ScalarObject PyCDoubleScalarObject +# define PyFloat96ArrType_Type PyDoubleArrType_Type +# define PyComplex192ArrType_Type PyCDoubleArrType_Type +#define NPY_FLOAT96_FMT NPY_DOUBLE_FMT +#define NPY_COMPLEX192_FMT NPY_CDOUBLE_FMT +#endif +#elif NPY_BITSOF_DOUBLE == 128 +#ifndef NPY_FLOAT128 +#define NPY_FLOAT128 NPY_DOUBLE +#define NPY_COMPLEX256 NPY_CDOUBLE + typedef double npy_float128; + typedef npy_cdouble npy_complex256; +# define PyFloat128ScalarObject PyDoubleScalarObject +# define PyComplex256ScalarObject PyCDoubleScalarObject +# define PyFloat128ArrType_Type PyDoubleArrType_Type +# define PyComplex256ArrType_Type PyCDoubleArrType_Type +#define NPY_FLOAT128_FMT NPY_DOUBLE_FMT +#define NPY_COMPLEX256_FMT NPY_CDOUBLE_FMT +#endif +#endif + + + +#if NPY_BITSOF_FLOAT == 32 +#ifndef NPY_FLOAT32 +#define NPY_FLOAT32 NPY_FLOAT +#define NPY_COMPLEX64 NPY_CFLOAT + typedef float npy_float32; + typedef npy_cfloat npy_complex64; +# define PyFloat32ScalarObject PyFloatScalarObject +# define PyComplex64ScalarObject PyCFloatScalarObject +# define PyFloat32ArrType_Type PyFloatArrType_Type +# define PyComplex64ArrType_Type PyCFloatArrType_Type +#define NPY_FLOAT32_FMT NPY_FLOAT_FMT +#define NPY_COMPLEX64_FMT NPY_CFLOAT_FMT +#endif +#elif NPY_BITSOF_FLOAT == 64 +#ifndef NPY_FLOAT64 +#define NPY_FLOAT64 NPY_FLOAT +#define NPY_COMPLEX128 NPY_CFLOAT + typedef float npy_float64; + typedef npy_cfloat npy_complex128; +# define PyFloat64ScalarObject PyFloatScalarObject +# define PyComplex128ScalarObject PyCFloatScalarObject +# define PyFloat64ArrType_Type PyFloatArrType_Type +# define PyComplex128ArrType_Type PyCFloatArrType_Type +#define NPY_FLOAT64_FMT NPY_FLOAT_FMT +#define NPY_COMPLEX128_FMT NPY_CFLOAT_FMT +#endif +#elif NPY_BITSOF_FLOAT == 80 +#ifndef NPY_FLOAT80 +#define NPY_FLOAT80 NPY_FLOAT +#define NPY_COMPLEX160 NPY_CFLOAT + typedef float npy_float80; + typedef npy_cfloat npy_complex160; +# define PyFloat80ScalarObject PyFloatScalarObject +# define PyComplex160ScalarObject PyCFloatScalarObject +# define PyFloat80ArrType_Type PyFloatArrType_Type +# define PyComplex160ArrType_Type PyCFloatArrType_Type +#define NPY_FLOAT80_FMT NPY_FLOAT_FMT +#define NPY_COMPLEX160_FMT NPY_CFLOAT_FMT +#endif +#elif NPY_BITSOF_FLOAT == 96 +#ifndef NPY_FLOAT96 +#define NPY_FLOAT96 NPY_FLOAT +#define NPY_COMPLEX192 NPY_CFLOAT + typedef float npy_float96; + typedef npy_cfloat npy_complex192; +# define PyFloat96ScalarObject PyFloatScalarObject +# define PyComplex192ScalarObject PyCFloatScalarObject +# define PyFloat96ArrType_Type PyFloatArrType_Type +# define PyComplex192ArrType_Type PyCFloatArrType_Type +#define NPY_FLOAT96_FMT NPY_FLOAT_FMT +#define NPY_COMPLEX192_FMT NPY_CFLOAT_FMT +#endif +#elif NPY_BITSOF_FLOAT == 128 +#ifndef NPY_FLOAT128 +#define NPY_FLOAT128 NPY_FLOAT +#define NPY_COMPLEX256 NPY_CFLOAT + typedef float npy_float128; + typedef npy_cfloat npy_complex256; +# define PyFloat128ScalarObject PyFloatScalarObject +# define PyComplex256ScalarObject PyCFloatScalarObject +# define PyFloat128ArrType_Type PyFloatArrType_Type +# define PyComplex256ArrType_Type PyCFloatArrType_Type +#define NPY_FLOAT128_FMT NPY_FLOAT_FMT +#define NPY_COMPLEX256_FMT NPY_CFLOAT_FMT +#endif +#endif + +/* half/float16 isn't a floating-point type in C */ +#define NPY_FLOAT16 NPY_HALF +typedef npy_uint16 npy_half; +typedef npy_half npy_float16; + +#if NPY_BITSOF_LONGDOUBLE == 32 +#ifndef NPY_FLOAT32 +#define NPY_FLOAT32 NPY_LONGDOUBLE +#define NPY_COMPLEX64 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float32; + typedef npy_clongdouble npy_complex64; +# define PyFloat32ScalarObject PyLongDoubleScalarObject +# define PyComplex64ScalarObject PyCLongDoubleScalarObject +# define PyFloat32ArrType_Type PyLongDoubleArrType_Type +# define PyComplex64ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT32_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX64_FMT NPY_CLONGDOUBLE_FMT +#endif +#elif NPY_BITSOF_LONGDOUBLE == 64 +#ifndef NPY_FLOAT64 +#define NPY_FLOAT64 NPY_LONGDOUBLE +#define NPY_COMPLEX128 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float64; + typedef npy_clongdouble npy_complex128; +# define PyFloat64ScalarObject PyLongDoubleScalarObject +# define PyComplex128ScalarObject PyCLongDoubleScalarObject +# define PyFloat64ArrType_Type PyLongDoubleArrType_Type +# define PyComplex128ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT64_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX128_FMT NPY_CLONGDOUBLE_FMT +#endif +#elif NPY_BITSOF_LONGDOUBLE == 80 +#ifndef NPY_FLOAT80 +#define NPY_FLOAT80 NPY_LONGDOUBLE +#define NPY_COMPLEX160 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float80; + typedef npy_clongdouble npy_complex160; +# define PyFloat80ScalarObject PyLongDoubleScalarObject +# define PyComplex160ScalarObject PyCLongDoubleScalarObject +# define PyFloat80ArrType_Type PyLongDoubleArrType_Type +# define PyComplex160ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT80_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX160_FMT NPY_CLONGDOUBLE_FMT +#endif +#elif NPY_BITSOF_LONGDOUBLE == 96 +#ifndef NPY_FLOAT96 +#define NPY_FLOAT96 NPY_LONGDOUBLE +#define NPY_COMPLEX192 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float96; + typedef npy_clongdouble npy_complex192; +# define PyFloat96ScalarObject PyLongDoubleScalarObject +# define PyComplex192ScalarObject PyCLongDoubleScalarObject +# define PyFloat96ArrType_Type PyLongDoubleArrType_Type +# define PyComplex192ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT96_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX192_FMT NPY_CLONGDOUBLE_FMT +#endif +#elif NPY_BITSOF_LONGDOUBLE == 128 +#ifndef NPY_FLOAT128 +#define NPY_FLOAT128 NPY_LONGDOUBLE +#define NPY_COMPLEX256 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float128; + typedef npy_clongdouble npy_complex256; +# define PyFloat128ScalarObject PyLongDoubleScalarObject +# define PyComplex256ScalarObject PyCLongDoubleScalarObject +# define PyFloat128ArrType_Type PyLongDoubleArrType_Type +# define PyComplex256ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT128_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX256_FMT NPY_CLONGDOUBLE_FMT +#endif +#elif NPY_BITSOF_LONGDOUBLE == 256 +#define NPY_FLOAT256 NPY_LONGDOUBLE +#define NPY_COMPLEX512 NPY_CLONGDOUBLE + typedef npy_longdouble npy_float256; + typedef npy_clongdouble npy_complex512; +# define PyFloat256ScalarObject PyLongDoubleScalarObject +# define PyComplex512ScalarObject PyCLongDoubleScalarObject +# define PyFloat256ArrType_Type PyLongDoubleArrType_Type +# define PyComplex512ArrType_Type PyCLongDoubleArrType_Type +#define NPY_FLOAT256_FMT NPY_LONGDOUBLE_FMT +#define NPY_COMPLEX512_FMT NPY_CLONGDOUBLE_FMT +#endif + +/* datetime typedefs */ +typedef npy_int64 npy_timedelta; +typedef npy_int64 npy_datetime; +#define NPY_DATETIME_FMT NPY_INT64_FMT +#define NPY_TIMEDELTA_FMT NPY_INT64_FMT + +/* End of typedefs for numarray style bit-width names */ + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_cpu.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_cpu.h new file mode 100644 index 0000000000000000000000000000000000000000..15f9f12931c82f0be8bfe6e32903754c5915485d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_cpu.h @@ -0,0 +1,134 @@ +/* + * This set (target) cpu specific macros: + * - Possible values: + * NPY_CPU_X86 + * NPY_CPU_AMD64 + * NPY_CPU_PPC + * NPY_CPU_PPC64 + * NPY_CPU_PPC64LE + * NPY_CPU_SPARC + * NPY_CPU_S390 + * NPY_CPU_IA64 + * NPY_CPU_HPPA + * NPY_CPU_ALPHA + * NPY_CPU_ARMEL + * NPY_CPU_ARMEB + * NPY_CPU_SH_LE + * NPY_CPU_SH_BE + * NPY_CPU_ARCEL + * NPY_CPU_ARCEB + * NPY_CPU_RISCV64 + * NPY_CPU_RISCV32 + * NPY_CPU_LOONGARCH + * NPY_CPU_WASM + */ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ + +#include "numpyconfig.h" + +#if defined( __i386__ ) || defined(i386) || defined(_M_IX86) + /* + * __i386__ is defined by gcc and Intel compiler on Linux, + * _M_IX86 by VS compiler, + * i386 by Sun compilers on opensolaris at least + */ + #define NPY_CPU_X86 +#elif defined(__x86_64__) || defined(__amd64__) || defined(__x86_64) || defined(_M_AMD64) + /* + * both __x86_64__ and __amd64__ are defined by gcc + * __x86_64 defined by sun compiler on opensolaris at least + * _M_AMD64 defined by MS compiler + */ + #define NPY_CPU_AMD64 +#elif defined(__powerpc64__) && defined(__LITTLE_ENDIAN__) + #define NPY_CPU_PPC64LE +#elif defined(__powerpc64__) && defined(__BIG_ENDIAN__) + #define NPY_CPU_PPC64 +#elif defined(__ppc__) || defined(__powerpc__) || defined(_ARCH_PPC) + /* + * __ppc__ is defined by gcc, I remember having seen __powerpc__ once, + * but can't find it ATM + * _ARCH_PPC is used by at least gcc on AIX + * As __powerpc__ and _ARCH_PPC are also defined by PPC64 check + * for those specifically first before defaulting to ppc + */ + #define NPY_CPU_PPC +#elif defined(__sparc__) || defined(__sparc) + /* __sparc__ is defined by gcc and Forte (e.g. Sun) compilers */ + #define NPY_CPU_SPARC +#elif defined(__s390__) + #define NPY_CPU_S390 +#elif defined(__ia64) + #define NPY_CPU_IA64 +#elif defined(__hppa) + #define NPY_CPU_HPPA +#elif defined(__alpha__) + #define NPY_CPU_ALPHA +#elif defined(__arm__) || defined(__aarch64__) || defined(_M_ARM64) + /* _M_ARM64 is defined in MSVC for ARM64 compilation on Windows */ + #if defined(__ARMEB__) || defined(__AARCH64EB__) + #if defined(__ARM_32BIT_STATE) + #define NPY_CPU_ARMEB_AARCH32 + #elif defined(__ARM_64BIT_STATE) + #define NPY_CPU_ARMEB_AARCH64 + #else + #define NPY_CPU_ARMEB + #endif + #elif defined(__ARMEL__) || defined(__AARCH64EL__) || defined(_M_ARM64) + #if defined(__ARM_32BIT_STATE) + #define NPY_CPU_ARMEL_AARCH32 + #elif defined(__ARM_64BIT_STATE) || defined(_M_ARM64) || defined(__AARCH64EL__) + #define NPY_CPU_ARMEL_AARCH64 + #else + #define NPY_CPU_ARMEL + #endif + #else + # error Unknown ARM CPU, please report this to numpy maintainers with \ + information about your platform (OS, CPU and compiler) + #endif +#elif defined(__sh__) && defined(__LITTLE_ENDIAN__) + #define NPY_CPU_SH_LE +#elif defined(__sh__) && defined(__BIG_ENDIAN__) + #define NPY_CPU_SH_BE +#elif defined(__MIPSEL__) + #define NPY_CPU_MIPSEL +#elif defined(__MIPSEB__) + #define NPY_CPU_MIPSEB +#elif defined(__or1k__) + #define NPY_CPU_OR1K +#elif defined(__mc68000__) + #define NPY_CPU_M68K +#elif defined(__arc__) && defined(__LITTLE_ENDIAN__) + #define NPY_CPU_ARCEL +#elif defined(__arc__) && defined(__BIG_ENDIAN__) + #define NPY_CPU_ARCEB +#elif defined(__riscv) + #if __riscv_xlen == 64 + #define NPY_CPU_RISCV64 + #elif __riscv_xlen == 32 + #define NPY_CPU_RISCV32 + #endif +#elif defined(__loongarch__) + #define NPY_CPU_LOONGARCH +#elif defined(__EMSCRIPTEN__) + /* __EMSCRIPTEN__ is defined by emscripten: an LLVM-to-Web compiler */ + #define NPY_CPU_WASM +#else + #error Unknown CPU, please report this to numpy maintainers with \ + information about your platform (OS, CPU and compiler) +#endif + +/* + * Except for the following architectures, memory access is limited to the natural + * alignment of data types otherwise it may lead to bus error or performance regression. + * For more details about unaligned access, see https://www.kernel.org/doc/Documentation/unaligned-memory-access.txt. +*/ +#if defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64) || defined(__aarch64__) || defined(__powerpc64__) + #define NPY_ALIGNMENT_REQUIRED 0 +#endif +#ifndef NPY_ALIGNMENT_REQUIRED + #define NPY_ALIGNMENT_REQUIRED 1 +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_CPU_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_endian.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_endian.h new file mode 100644 index 0000000000000000000000000000000000000000..09262120bf82c5065d1f914a5825ce2de0063d4d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_endian.h @@ -0,0 +1,78 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ + +/* + * NPY_BYTE_ORDER is set to the same value as BYTE_ORDER set by glibc in + * endian.h + */ + +#if defined(NPY_HAVE_ENDIAN_H) || defined(NPY_HAVE_SYS_ENDIAN_H) + /* Use endian.h if available */ + + #if defined(NPY_HAVE_ENDIAN_H) + #include + #elif defined(NPY_HAVE_SYS_ENDIAN_H) + #include + #endif + + #if defined(BYTE_ORDER) && defined(BIG_ENDIAN) && defined(LITTLE_ENDIAN) + #define NPY_BYTE_ORDER BYTE_ORDER + #define NPY_LITTLE_ENDIAN LITTLE_ENDIAN + #define NPY_BIG_ENDIAN BIG_ENDIAN + #elif defined(_BYTE_ORDER) && defined(_BIG_ENDIAN) && defined(_LITTLE_ENDIAN) + #define NPY_BYTE_ORDER _BYTE_ORDER + #define NPY_LITTLE_ENDIAN _LITTLE_ENDIAN + #define NPY_BIG_ENDIAN _BIG_ENDIAN + #elif defined(__BYTE_ORDER) && defined(__BIG_ENDIAN) && defined(__LITTLE_ENDIAN) + #define NPY_BYTE_ORDER __BYTE_ORDER + #define NPY_LITTLE_ENDIAN __LITTLE_ENDIAN + #define NPY_BIG_ENDIAN __BIG_ENDIAN + #endif +#endif + +#ifndef NPY_BYTE_ORDER + /* Set endianness info using target CPU */ + #include "npy_cpu.h" + + #define NPY_LITTLE_ENDIAN 1234 + #define NPY_BIG_ENDIAN 4321 + + #if defined(NPY_CPU_X86) \ + || defined(NPY_CPU_AMD64) \ + || defined(NPY_CPU_IA64) \ + || defined(NPY_CPU_ALPHA) \ + || defined(NPY_CPU_ARMEL) \ + || defined(NPY_CPU_ARMEL_AARCH32) \ + || defined(NPY_CPU_ARMEL_AARCH64) \ + || defined(NPY_CPU_SH_LE) \ + || defined(NPY_CPU_MIPSEL) \ + || defined(NPY_CPU_PPC64LE) \ + || defined(NPY_CPU_ARCEL) \ + || defined(NPY_CPU_RISCV64) \ + || defined(NPY_CPU_RISCV32) \ + || defined(NPY_CPU_LOONGARCH) \ + || defined(NPY_CPU_WASM) + #define NPY_BYTE_ORDER NPY_LITTLE_ENDIAN + + #elif defined(NPY_CPU_PPC) \ + || defined(NPY_CPU_SPARC) \ + || defined(NPY_CPU_S390) \ + || defined(NPY_CPU_HPPA) \ + || defined(NPY_CPU_PPC64) \ + || defined(NPY_CPU_ARMEB) \ + || defined(NPY_CPU_ARMEB_AARCH32) \ + || defined(NPY_CPU_ARMEB_AARCH64) \ + || defined(NPY_CPU_SH_BE) \ + || defined(NPY_CPU_MIPSEB) \ + || defined(NPY_CPU_OR1K) \ + || defined(NPY_CPU_M68K) \ + || defined(NPY_CPU_ARCEB) + #define NPY_BYTE_ORDER NPY_BIG_ENDIAN + + #else + #error Unknown CPU: can not set endianness + #endif + +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_ENDIAN_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_math.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_math.h new file mode 100644 index 0000000000000000000000000000000000000000..abc784bc686c18d162a4730a7c60393622023aae --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_math.h @@ -0,0 +1,602 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ + +#include + +#include + +/* By adding static inline specifiers to npy_math function definitions when + appropriate, compiler is given the opportunity to optimize */ +#if NPY_INLINE_MATH +#define NPY_INPLACE static inline +#else +#define NPY_INPLACE +#endif + + +#ifdef __cplusplus +extern "C" { +#endif + +#define PyArray_MAX(a,b) (((a)>(b))?(a):(b)) +#define PyArray_MIN(a,b) (((a)<(b))?(a):(b)) + +/* + * NAN and INFINITY like macros (same behavior as glibc for NAN, same as C99 + * for INFINITY) + * + * XXX: I should test whether INFINITY and NAN are available on the platform + */ +static inline float __npy_inff(void) +{ + const union { npy_uint32 __i; float __f;} __bint = {0x7f800000UL}; + return __bint.__f; +} + +static inline float __npy_nanf(void) +{ + const union { npy_uint32 __i; float __f;} __bint = {0x7fc00000UL}; + return __bint.__f; +} + +static inline float __npy_pzerof(void) +{ + const union { npy_uint32 __i; float __f;} __bint = {0x00000000UL}; + return __bint.__f; +} + +static inline float __npy_nzerof(void) +{ + const union { npy_uint32 __i; float __f;} __bint = {0x80000000UL}; + return __bint.__f; +} + +#define NPY_INFINITYF __npy_inff() +#define NPY_NANF __npy_nanf() +#define NPY_PZEROF __npy_pzerof() +#define NPY_NZEROF __npy_nzerof() + +#define NPY_INFINITY ((npy_double)NPY_INFINITYF) +#define NPY_NAN ((npy_double)NPY_NANF) +#define NPY_PZERO ((npy_double)NPY_PZEROF) +#define NPY_NZERO ((npy_double)NPY_NZEROF) + +#define NPY_INFINITYL ((npy_longdouble)NPY_INFINITYF) +#define NPY_NANL ((npy_longdouble)NPY_NANF) +#define NPY_PZEROL ((npy_longdouble)NPY_PZEROF) +#define NPY_NZEROL ((npy_longdouble)NPY_NZEROF) + +/* + * Useful constants + */ +#define NPY_E 2.718281828459045235360287471352662498 /* e */ +#define NPY_LOG2E 1.442695040888963407359924681001892137 /* log_2 e */ +#define NPY_LOG10E 0.434294481903251827651128918916605082 /* log_10 e */ +#define NPY_LOGE2 0.693147180559945309417232121458176568 /* log_e 2 */ +#define NPY_LOGE10 2.302585092994045684017991454684364208 /* log_e 10 */ +#define NPY_PI 3.141592653589793238462643383279502884 /* pi */ +#define NPY_PI_2 1.570796326794896619231321691639751442 /* pi/2 */ +#define NPY_PI_4 0.785398163397448309615660845819875721 /* pi/4 */ +#define NPY_1_PI 0.318309886183790671537767526745028724 /* 1/pi */ +#define NPY_2_PI 0.636619772367581343075535053490057448 /* 2/pi */ +#define NPY_EULER 0.577215664901532860606512090082402431 /* Euler constant */ +#define NPY_SQRT2 1.414213562373095048801688724209698079 /* sqrt(2) */ +#define NPY_SQRT1_2 0.707106781186547524400844362104849039 /* 1/sqrt(2) */ + +#define NPY_Ef 2.718281828459045235360287471352662498F /* e */ +#define NPY_LOG2Ef 1.442695040888963407359924681001892137F /* log_2 e */ +#define NPY_LOG10Ef 0.434294481903251827651128918916605082F /* log_10 e */ +#define NPY_LOGE2f 0.693147180559945309417232121458176568F /* log_e 2 */ +#define NPY_LOGE10f 2.302585092994045684017991454684364208F /* log_e 10 */ +#define NPY_PIf 3.141592653589793238462643383279502884F /* pi */ +#define NPY_PI_2f 1.570796326794896619231321691639751442F /* pi/2 */ +#define NPY_PI_4f 0.785398163397448309615660845819875721F /* pi/4 */ +#define NPY_1_PIf 0.318309886183790671537767526745028724F /* 1/pi */ +#define NPY_2_PIf 0.636619772367581343075535053490057448F /* 2/pi */ +#define NPY_EULERf 0.577215664901532860606512090082402431F /* Euler constant */ +#define NPY_SQRT2f 1.414213562373095048801688724209698079F /* sqrt(2) */ +#define NPY_SQRT1_2f 0.707106781186547524400844362104849039F /* 1/sqrt(2) */ + +#define NPY_El 2.718281828459045235360287471352662498L /* e */ +#define NPY_LOG2El 1.442695040888963407359924681001892137L /* log_2 e */ +#define NPY_LOG10El 0.434294481903251827651128918916605082L /* log_10 e */ +#define NPY_LOGE2l 0.693147180559945309417232121458176568L /* log_e 2 */ +#define NPY_LOGE10l 2.302585092994045684017991454684364208L /* log_e 10 */ +#define NPY_PIl 3.141592653589793238462643383279502884L /* pi */ +#define NPY_PI_2l 1.570796326794896619231321691639751442L /* pi/2 */ +#define NPY_PI_4l 0.785398163397448309615660845819875721L /* pi/4 */ +#define NPY_1_PIl 0.318309886183790671537767526745028724L /* 1/pi */ +#define NPY_2_PIl 0.636619772367581343075535053490057448L /* 2/pi */ +#define NPY_EULERl 0.577215664901532860606512090082402431L /* Euler constant */ +#define NPY_SQRT2l 1.414213562373095048801688724209698079L /* sqrt(2) */ +#define NPY_SQRT1_2l 0.707106781186547524400844362104849039L /* 1/sqrt(2) */ + +/* + * Integer functions. + */ +NPY_INPLACE npy_uint npy_gcdu(npy_uint a, npy_uint b); +NPY_INPLACE npy_uint npy_lcmu(npy_uint a, npy_uint b); +NPY_INPLACE npy_ulong npy_gcdul(npy_ulong a, npy_ulong b); +NPY_INPLACE npy_ulong npy_lcmul(npy_ulong a, npy_ulong b); +NPY_INPLACE npy_ulonglong npy_gcdull(npy_ulonglong a, npy_ulonglong b); +NPY_INPLACE npy_ulonglong npy_lcmull(npy_ulonglong a, npy_ulonglong b); + +NPY_INPLACE npy_int npy_gcd(npy_int a, npy_int b); +NPY_INPLACE npy_int npy_lcm(npy_int a, npy_int b); +NPY_INPLACE npy_long npy_gcdl(npy_long a, npy_long b); +NPY_INPLACE npy_long npy_lcml(npy_long a, npy_long b); +NPY_INPLACE npy_longlong npy_gcdll(npy_longlong a, npy_longlong b); +NPY_INPLACE npy_longlong npy_lcmll(npy_longlong a, npy_longlong b); + +NPY_INPLACE npy_ubyte npy_rshiftuhh(npy_ubyte a, npy_ubyte b); +NPY_INPLACE npy_ubyte npy_lshiftuhh(npy_ubyte a, npy_ubyte b); +NPY_INPLACE npy_ushort npy_rshiftuh(npy_ushort a, npy_ushort b); +NPY_INPLACE npy_ushort npy_lshiftuh(npy_ushort a, npy_ushort b); +NPY_INPLACE npy_uint npy_rshiftu(npy_uint a, npy_uint b); +NPY_INPLACE npy_uint npy_lshiftu(npy_uint a, npy_uint b); +NPY_INPLACE npy_ulong npy_rshiftul(npy_ulong a, npy_ulong b); +NPY_INPLACE npy_ulong npy_lshiftul(npy_ulong a, npy_ulong b); +NPY_INPLACE npy_ulonglong npy_rshiftull(npy_ulonglong a, npy_ulonglong b); +NPY_INPLACE npy_ulonglong npy_lshiftull(npy_ulonglong a, npy_ulonglong b); + +NPY_INPLACE npy_byte npy_rshifthh(npy_byte a, npy_byte b); +NPY_INPLACE npy_byte npy_lshifthh(npy_byte a, npy_byte b); +NPY_INPLACE npy_short npy_rshifth(npy_short a, npy_short b); +NPY_INPLACE npy_short npy_lshifth(npy_short a, npy_short b); +NPY_INPLACE npy_int npy_rshift(npy_int a, npy_int b); +NPY_INPLACE npy_int npy_lshift(npy_int a, npy_int b); +NPY_INPLACE npy_long npy_rshiftl(npy_long a, npy_long b); +NPY_INPLACE npy_long npy_lshiftl(npy_long a, npy_long b); +NPY_INPLACE npy_longlong npy_rshiftll(npy_longlong a, npy_longlong b); +NPY_INPLACE npy_longlong npy_lshiftll(npy_longlong a, npy_longlong b); + +NPY_INPLACE uint8_t npy_popcountuhh(npy_ubyte a); +NPY_INPLACE uint8_t npy_popcountuh(npy_ushort a); +NPY_INPLACE uint8_t npy_popcountu(npy_uint a); +NPY_INPLACE uint8_t npy_popcountul(npy_ulong a); +NPY_INPLACE uint8_t npy_popcountull(npy_ulonglong a); +NPY_INPLACE uint8_t npy_popcounthh(npy_byte a); +NPY_INPLACE uint8_t npy_popcounth(npy_short a); +NPY_INPLACE uint8_t npy_popcount(npy_int a); +NPY_INPLACE uint8_t npy_popcountl(npy_long a); +NPY_INPLACE uint8_t npy_popcountll(npy_longlong a); + +/* + * C99 double math funcs that need fixups or are blocklist-able + */ +NPY_INPLACE double npy_sin(double x); +NPY_INPLACE double npy_cos(double x); +NPY_INPLACE double npy_tan(double x); +NPY_INPLACE double npy_hypot(double x, double y); +NPY_INPLACE double npy_log2(double x); +NPY_INPLACE double npy_atan2(double x, double y); + +/* Mandatory C99 double math funcs, no blocklisting or fixups */ +/* defined for legacy reasons, should be deprecated at some point */ +#define npy_sinh sinh +#define npy_cosh cosh +#define npy_tanh tanh +#define npy_asin asin +#define npy_acos acos +#define npy_atan atan +#define npy_log log +#define npy_log10 log10 +#define npy_cbrt cbrt +#define npy_fabs fabs +#define npy_ceil ceil +#define npy_fmod fmod +#define npy_floor floor +#define npy_expm1 expm1 +#define npy_log1p log1p +#define npy_acosh acosh +#define npy_asinh asinh +#define npy_atanh atanh +#define npy_rint rint +#define npy_trunc trunc +#define npy_exp2 exp2 +#define npy_frexp frexp +#define npy_ldexp ldexp +#define npy_copysign copysign +#define npy_exp exp +#define npy_sqrt sqrt +#define npy_pow pow +#define npy_modf modf +#define npy_nextafter nextafter + +double npy_spacing(double x); + +/* + * IEEE 754 fpu handling + */ + +/* use builtins to avoid function calls in tight loops + * only available if npy_config.h is available (= numpys own build) */ +#ifdef HAVE___BUILTIN_ISNAN + #define npy_isnan(x) __builtin_isnan(x) +#else + #define npy_isnan(x) isnan(x) +#endif + + +/* only available if npy_config.h is available (= numpys own build) */ +#ifdef HAVE___BUILTIN_ISFINITE + #define npy_isfinite(x) __builtin_isfinite(x) +#else + #define npy_isfinite(x) isfinite((x)) +#endif + +/* only available if npy_config.h is available (= numpys own build) */ +#ifdef HAVE___BUILTIN_ISINF + #define npy_isinf(x) __builtin_isinf(x) +#else + #define npy_isinf(x) isinf((x)) +#endif + +#define npy_signbit(x) signbit((x)) + +/* + * float C99 math funcs that need fixups or are blocklist-able + */ +NPY_INPLACE float npy_sinf(float x); +NPY_INPLACE float npy_cosf(float x); +NPY_INPLACE float npy_tanf(float x); +NPY_INPLACE float npy_expf(float x); +NPY_INPLACE float npy_sqrtf(float x); +NPY_INPLACE float npy_hypotf(float x, float y); +NPY_INPLACE float npy_log2f(float x); +NPY_INPLACE float npy_atan2f(float x, float y); +NPY_INPLACE float npy_powf(float x, float y); +NPY_INPLACE float npy_modff(float x, float* y); + +/* Mandatory C99 float math funcs, no blocklisting or fixups */ +/* defined for legacy reasons, should be deprecated at some point */ + +#define npy_sinhf sinhf +#define npy_coshf coshf +#define npy_tanhf tanhf +#define npy_asinf asinf +#define npy_acosf acosf +#define npy_atanf atanf +#define npy_logf logf +#define npy_log10f log10f +#define npy_cbrtf cbrtf +#define npy_fabsf fabsf +#define npy_ceilf ceilf +#define npy_fmodf fmodf +#define npy_floorf floorf +#define npy_expm1f expm1f +#define npy_log1pf log1pf +#define npy_asinhf asinhf +#define npy_acoshf acoshf +#define npy_atanhf atanhf +#define npy_rintf rintf +#define npy_truncf truncf +#define npy_exp2f exp2f +#define npy_frexpf frexpf +#define npy_ldexpf ldexpf +#define npy_copysignf copysignf +#define npy_nextafterf nextafterf + +float npy_spacingf(float x); + +/* + * long double C99 double math funcs that need fixups or are blocklist-able + */ +NPY_INPLACE npy_longdouble npy_sinl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_cosl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_tanl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_expl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_sqrtl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_hypotl(npy_longdouble x, npy_longdouble y); +NPY_INPLACE npy_longdouble npy_log2l(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_atan2l(npy_longdouble x, npy_longdouble y); +NPY_INPLACE npy_longdouble npy_powl(npy_longdouble x, npy_longdouble y); +NPY_INPLACE npy_longdouble npy_modfl(npy_longdouble x, npy_longdouble* y); + +/* Mandatory C99 double math funcs, no blocklisting or fixups */ +/* defined for legacy reasons, should be deprecated at some point */ +#define npy_sinhl sinhl +#define npy_coshl coshl +#define npy_tanhl tanhl +#define npy_fabsl fabsl +#define npy_floorl floorl +#define npy_ceill ceill +#define npy_rintl rintl +#define npy_truncl truncl +#define npy_cbrtl cbrtl +#define npy_log10l log10l +#define npy_logl logl +#define npy_expm1l expm1l +#define npy_asinl asinl +#define npy_acosl acosl +#define npy_atanl atanl +#define npy_asinhl asinhl +#define npy_acoshl acoshl +#define npy_atanhl atanhl +#define npy_log1pl log1pl +#define npy_exp2l exp2l +#define npy_fmodl fmodl +#define npy_frexpl frexpl +#define npy_ldexpl ldexpl +#define npy_copysignl copysignl +#define npy_nextafterl nextafterl + +npy_longdouble npy_spacingl(npy_longdouble x); + +/* + * Non standard functions + */ +NPY_INPLACE double npy_deg2rad(double x); +NPY_INPLACE double npy_rad2deg(double x); +NPY_INPLACE double npy_logaddexp(double x, double y); +NPY_INPLACE double npy_logaddexp2(double x, double y); +NPY_INPLACE double npy_divmod(double x, double y, double *modulus); +NPY_INPLACE double npy_heaviside(double x, double h0); + +NPY_INPLACE float npy_deg2radf(float x); +NPY_INPLACE float npy_rad2degf(float x); +NPY_INPLACE float npy_logaddexpf(float x, float y); +NPY_INPLACE float npy_logaddexp2f(float x, float y); +NPY_INPLACE float npy_divmodf(float x, float y, float *modulus); +NPY_INPLACE float npy_heavisidef(float x, float h0); + +NPY_INPLACE npy_longdouble npy_deg2radl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_rad2degl(npy_longdouble x); +NPY_INPLACE npy_longdouble npy_logaddexpl(npy_longdouble x, npy_longdouble y); +NPY_INPLACE npy_longdouble npy_logaddexp2l(npy_longdouble x, npy_longdouble y); +NPY_INPLACE npy_longdouble npy_divmodl(npy_longdouble x, npy_longdouble y, + npy_longdouble *modulus); +NPY_INPLACE npy_longdouble npy_heavisidel(npy_longdouble x, npy_longdouble h0); + +#define npy_degrees npy_rad2deg +#define npy_degreesf npy_rad2degf +#define npy_degreesl npy_rad2degl + +#define npy_radians npy_deg2rad +#define npy_radiansf npy_deg2radf +#define npy_radiansl npy_deg2radl + +/* + * Complex declarations + */ + +static inline double npy_creal(const npy_cdouble z) +{ +#if defined(__cplusplus) + return z._Val[0]; +#else + return creal(z); +#endif +} + +static inline void npy_csetreal(npy_cdouble *z, const double r) +{ + ((double *) z)[0] = r; +} + +static inline double npy_cimag(const npy_cdouble z) +{ +#if defined(__cplusplus) + return z._Val[1]; +#else + return cimag(z); +#endif +} + +static inline void npy_csetimag(npy_cdouble *z, const double i) +{ + ((double *) z)[1] = i; +} + +static inline float npy_crealf(const npy_cfloat z) +{ +#if defined(__cplusplus) + return z._Val[0]; +#else + return crealf(z); +#endif +} + +static inline void npy_csetrealf(npy_cfloat *z, const float r) +{ + ((float *) z)[0] = r; +} + +static inline float npy_cimagf(const npy_cfloat z) +{ +#if defined(__cplusplus) + return z._Val[1]; +#else + return cimagf(z); +#endif +} + +static inline void npy_csetimagf(npy_cfloat *z, const float i) +{ + ((float *) z)[1] = i; +} + +static inline npy_longdouble npy_creall(const npy_clongdouble z) +{ +#if defined(__cplusplus) + return (npy_longdouble)z._Val[0]; +#else + return creall(z); +#endif +} + +static inline void npy_csetreall(npy_clongdouble *z, const longdouble_t r) +{ + ((longdouble_t *) z)[0] = r; +} + +static inline npy_longdouble npy_cimagl(const npy_clongdouble z) +{ +#if defined(__cplusplus) + return (npy_longdouble)z._Val[1]; +#else + return cimagl(z); +#endif +} + +static inline void npy_csetimagl(npy_clongdouble *z, const longdouble_t i) +{ + ((longdouble_t *) z)[1] = i; +} + +#define NPY_CSETREAL(z, r) npy_csetreal(z, r) +#define NPY_CSETIMAG(z, i) npy_csetimag(z, i) +#define NPY_CSETREALF(z, r) npy_csetrealf(z, r) +#define NPY_CSETIMAGF(z, i) npy_csetimagf(z, i) +#define NPY_CSETREALL(z, r) npy_csetreall(z, r) +#define NPY_CSETIMAGL(z, i) npy_csetimagl(z, i) + +static inline npy_cdouble npy_cpack(double x, double y) +{ + npy_cdouble z; + npy_csetreal(&z, x); + npy_csetimag(&z, y); + return z; +} + +static inline npy_cfloat npy_cpackf(float x, float y) +{ + npy_cfloat z; + npy_csetrealf(&z, x); + npy_csetimagf(&z, y); + return z; +} + +static inline npy_clongdouble npy_cpackl(npy_longdouble x, npy_longdouble y) +{ + npy_clongdouble z; + npy_csetreall(&z, x); + npy_csetimagl(&z, y); + return z; +} + +/* + * Double precision complex functions + */ +double npy_cabs(npy_cdouble z); +double npy_carg(npy_cdouble z); + +npy_cdouble npy_cexp(npy_cdouble z); +npy_cdouble npy_clog(npy_cdouble z); +npy_cdouble npy_cpow(npy_cdouble x, npy_cdouble y); + +npy_cdouble npy_csqrt(npy_cdouble z); + +npy_cdouble npy_ccos(npy_cdouble z); +npy_cdouble npy_csin(npy_cdouble z); +npy_cdouble npy_ctan(npy_cdouble z); + +npy_cdouble npy_ccosh(npy_cdouble z); +npy_cdouble npy_csinh(npy_cdouble z); +npy_cdouble npy_ctanh(npy_cdouble z); + +npy_cdouble npy_cacos(npy_cdouble z); +npy_cdouble npy_casin(npy_cdouble z); +npy_cdouble npy_catan(npy_cdouble z); + +npy_cdouble npy_cacosh(npy_cdouble z); +npy_cdouble npy_casinh(npy_cdouble z); +npy_cdouble npy_catanh(npy_cdouble z); + +/* + * Single precision complex functions + */ +float npy_cabsf(npy_cfloat z); +float npy_cargf(npy_cfloat z); + +npy_cfloat npy_cexpf(npy_cfloat z); +npy_cfloat npy_clogf(npy_cfloat z); +npy_cfloat npy_cpowf(npy_cfloat x, npy_cfloat y); + +npy_cfloat npy_csqrtf(npy_cfloat z); + +npy_cfloat npy_ccosf(npy_cfloat z); +npy_cfloat npy_csinf(npy_cfloat z); +npy_cfloat npy_ctanf(npy_cfloat z); + +npy_cfloat npy_ccoshf(npy_cfloat z); +npy_cfloat npy_csinhf(npy_cfloat z); +npy_cfloat npy_ctanhf(npy_cfloat z); + +npy_cfloat npy_cacosf(npy_cfloat z); +npy_cfloat npy_casinf(npy_cfloat z); +npy_cfloat npy_catanf(npy_cfloat z); + +npy_cfloat npy_cacoshf(npy_cfloat z); +npy_cfloat npy_casinhf(npy_cfloat z); +npy_cfloat npy_catanhf(npy_cfloat z); + + +/* + * Extended precision complex functions + */ +npy_longdouble npy_cabsl(npy_clongdouble z); +npy_longdouble npy_cargl(npy_clongdouble z); + +npy_clongdouble npy_cexpl(npy_clongdouble z); +npy_clongdouble npy_clogl(npy_clongdouble z); +npy_clongdouble npy_cpowl(npy_clongdouble x, npy_clongdouble y); + +npy_clongdouble npy_csqrtl(npy_clongdouble z); + +npy_clongdouble npy_ccosl(npy_clongdouble z); +npy_clongdouble npy_csinl(npy_clongdouble z); +npy_clongdouble npy_ctanl(npy_clongdouble z); + +npy_clongdouble npy_ccoshl(npy_clongdouble z); +npy_clongdouble npy_csinhl(npy_clongdouble z); +npy_clongdouble npy_ctanhl(npy_clongdouble z); + +npy_clongdouble npy_cacosl(npy_clongdouble z); +npy_clongdouble npy_casinl(npy_clongdouble z); +npy_clongdouble npy_catanl(npy_clongdouble z); + +npy_clongdouble npy_cacoshl(npy_clongdouble z); +npy_clongdouble npy_casinhl(npy_clongdouble z); +npy_clongdouble npy_catanhl(npy_clongdouble z); + + +/* + * Functions that set the floating point error + * status word. + */ + +/* + * platform-dependent code translates floating point + * status to an integer sum of these values + */ +#define NPY_FPE_DIVIDEBYZERO 1 +#define NPY_FPE_OVERFLOW 2 +#define NPY_FPE_UNDERFLOW 4 +#define NPY_FPE_INVALID 8 + +int npy_clear_floatstatus_barrier(char*); +int npy_get_floatstatus_barrier(char*); +/* + * use caution with these - clang and gcc8.1 are known to reorder calls + * to this form of the function which can defeat the check. The _barrier + * form of the call is preferable, where the argument is + * (char*)&local_variable + */ +int npy_clear_floatstatus(void); +int npy_get_floatstatus(void); + +void npy_set_floatstatus_divbyzero(void); +void npy_set_floatstatus_overflow(void); +void npy_set_floatstatus_underflow(void); +void npy_set_floatstatus_invalid(void); + +#ifdef __cplusplus +} +#endif + +#if NPY_INLINE_MATH +#include "npy_math_internal.h" +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_MATH_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_no_deprecated_api.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_no_deprecated_api.h new file mode 100644 index 0000000000000000000000000000000000000000..39658c0bd2d61aacd25f75439e81ea16c3e33db8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_no_deprecated_api.h @@ -0,0 +1,20 @@ +/* + * This include file is provided for inclusion in Cython *.pyd files where + * one would like to define the NPY_NO_DEPRECATED_API macro. It can be + * included by + * + * cdef extern from "npy_no_deprecated_api.h": pass + * + */ +#ifndef NPY_NO_DEPRECATED_API + +/* put this check here since there may be multiple includes in C extensions. */ +#if defined(NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_) || \ + defined(NUMPY_CORE_INCLUDE_NUMPY_NPY_DEPRECATED_API_H) || \ + defined(NUMPY_CORE_INCLUDE_NUMPY_OLD_DEFINES_H_) +#error "npy_no_deprecated_api.h" must be first among numpy includes. +#else +#define NPY_NO_DEPRECATED_API NPY_API_VERSION +#endif + +#endif /* NPY_NO_DEPRECATED_API */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_os.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_os.h new file mode 100644 index 0000000000000000000000000000000000000000..0ce5d78b42c0e53c660654e297446d7811901aa2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/npy_os.h @@ -0,0 +1,42 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ + +#if defined(linux) || defined(__linux) || defined(__linux__) + #define NPY_OS_LINUX +#elif defined(__FreeBSD__) || defined(__NetBSD__) || \ + defined(__OpenBSD__) || defined(__DragonFly__) + #define NPY_OS_BSD + #ifdef __FreeBSD__ + #define NPY_OS_FREEBSD + #elif defined(__NetBSD__) + #define NPY_OS_NETBSD + #elif defined(__OpenBSD__) + #define NPY_OS_OPENBSD + #elif defined(__DragonFly__) + #define NPY_OS_DRAGONFLY + #endif +#elif defined(sun) || defined(__sun) + #define NPY_OS_SOLARIS +#elif defined(__CYGWIN__) + #define NPY_OS_CYGWIN +/* We are on Windows.*/ +#elif defined(_WIN32) + /* We are using MinGW (64-bit or 32-bit)*/ + #if defined(__MINGW32__) || defined(__MINGW64__) + #define NPY_OS_MINGW + /* Otherwise, if _WIN64 is defined, we are targeting 64-bit Windows*/ + #elif defined(_WIN64) + #define NPY_OS_WIN64 + /* Otherwise assume we are targeting 32-bit Windows*/ + #else + #define NPY_OS_WIN32 + #endif +#elif defined(__APPLE__) + #define NPY_OS_DARWIN +#elif defined(__HAIKU__) + #define NPY_OS_HAIKU +#else + #define NPY_OS_UNKNOWN +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_OS_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/numpyconfig.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/numpyconfig.h new file mode 100644 index 0000000000000000000000000000000000000000..46ecade41ada08fad4c1ae389cc4e9b6e902de24 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/numpyconfig.h @@ -0,0 +1,178 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ + +#include "_numpyconfig.h" + +/* + * On Mac OS X, because there is only one configuration stage for all the archs + * in universal builds, any macro which depends on the arch needs to be + * hardcoded. + * + * Note that distutils/pip will attempt a universal2 build when Python itself + * is built as universal2, hence this hardcoding is needed even if we do not + * support universal2 wheels anymore (see gh-22796). + * This code block can be removed after we have dropped the setup.py based + * build completely. + */ +#ifdef __APPLE__ + #undef NPY_SIZEOF_LONG + + #ifdef __LP64__ + #define NPY_SIZEOF_LONG 8 + #else + #define NPY_SIZEOF_LONG 4 + #endif + + #undef NPY_SIZEOF_LONGDOUBLE + #undef NPY_SIZEOF_COMPLEX_LONGDOUBLE + #ifdef HAVE_LDOUBLE_IEEE_DOUBLE_LE + #undef HAVE_LDOUBLE_IEEE_DOUBLE_LE + #endif + #ifdef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE + #undef HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE + #endif + + #if defined(__arm64__) + #define NPY_SIZEOF_LONGDOUBLE 8 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16 + #define HAVE_LDOUBLE_IEEE_DOUBLE_LE 1 + #elif defined(__x86_64) + #define NPY_SIZEOF_LONGDOUBLE 16 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 + #define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE 1 + #elif defined (__i386) + #define NPY_SIZEOF_LONGDOUBLE 12 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 24 + #elif defined(__ppc__) || defined (__ppc64__) + #define NPY_SIZEOF_LONGDOUBLE 16 + #define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32 + #else + #error "unknown architecture" + #endif +#endif + + +/** + * To help with both NPY_TARGET_VERSION and the NPY_NO_DEPRECATED_API macro, + * we include API version numbers for specific versions of NumPy. + * To exclude all API that was deprecated as of 1.7, add the following before + * #including any NumPy headers: + * #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION + * The same is true for NPY_TARGET_VERSION, although NumPy will default to + * a backwards compatible build anyway. + */ +#define NPY_1_7_API_VERSION 0x00000007 +#define NPY_1_8_API_VERSION 0x00000008 +#define NPY_1_9_API_VERSION 0x00000009 +#define NPY_1_10_API_VERSION 0x0000000a +#define NPY_1_11_API_VERSION 0x0000000a +#define NPY_1_12_API_VERSION 0x0000000a +#define NPY_1_13_API_VERSION 0x0000000b +#define NPY_1_14_API_VERSION 0x0000000c +#define NPY_1_15_API_VERSION 0x0000000c +#define NPY_1_16_API_VERSION 0x0000000d +#define NPY_1_17_API_VERSION 0x0000000d +#define NPY_1_18_API_VERSION 0x0000000d +#define NPY_1_19_API_VERSION 0x0000000d +#define NPY_1_20_API_VERSION 0x0000000e +#define NPY_1_21_API_VERSION 0x0000000e +#define NPY_1_22_API_VERSION 0x0000000f +#define NPY_1_23_API_VERSION 0x00000010 +#define NPY_1_24_API_VERSION 0x00000010 +#define NPY_1_25_API_VERSION 0x00000011 +#define NPY_2_0_API_VERSION 0x00000012 +#define NPY_2_1_API_VERSION 0x00000013 + + +/* + * Binary compatibility version number. This number is increased + * whenever the C-API is changed such that binary compatibility is + * broken, i.e. whenever a recompile of extension modules is needed. + */ +#define NPY_VERSION NPY_ABI_VERSION + +/* + * Minor API version we are compiling to be compatible with. The version + * Number is always increased when the API changes via: `NPY_API_VERSION` + * (and should maybe just track the NumPy version). + * + * If we have an internal build, we always target the current version of + * course. + * + * For downstream users, we default to an older version to provide them with + * maximum compatibility by default. Downstream can choose to extend that + * default, or narrow it down if they wish to use newer API. If you adjust + * this, consider the Python version support (example for 1.25.x): + * + * NumPy 1.25.x supports Python: 3.9 3.10 3.11 (3.12) + * NumPy 1.19.x supports Python: 3.6 3.7 3.8 3.9 + * NumPy 1.17.x supports Python: 3.5 3.6 3.7 3.8 + * NumPy 1.15.x supports Python: ... 3.6 3.7 + * + * Users of the stable ABI may wish to target the last Python that is not + * end of life. This would be 3.8 at NumPy 1.25 release time. + * 1.17 as default was the choice of oldest-support-numpy at the time and + * has in practice no limit (compared to 1.19). Even earlier becomes legacy. + */ +#if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD + /* NumPy internal build, always use current version. */ + #define NPY_FEATURE_VERSION NPY_API_VERSION +#elif defined(NPY_TARGET_VERSION) && NPY_TARGET_VERSION + /* user provided a target version, use it */ + #define NPY_FEATURE_VERSION NPY_TARGET_VERSION +#else + /* Use the default (increase when dropping Python 3.10 support) */ + #define NPY_FEATURE_VERSION NPY_1_21_API_VERSION +#endif + +/* Sanity check the (requested) feature version */ +#if NPY_FEATURE_VERSION > NPY_API_VERSION + #error "NPY_TARGET_VERSION higher than NumPy headers!" +#elif NPY_FEATURE_VERSION < NPY_1_15_API_VERSION + /* No support for irrelevant old targets, no need for error, but warn. */ + #ifndef _MSC_VER + #warning "Requested NumPy target lower than supported NumPy 1.15." + #else + #define _WARN___STR2__(x) #x + #define _WARN___STR1__(x) _WARN___STR2__(x) + #define _WARN___LOC__ __FILE__ "(" _WARN___STR1__(__LINE__) ") : Warning Msg: " + #pragma message(_WARN___LOC__"Requested NumPy target lower than supported NumPy 1.15.") + #endif +#endif + +/* + * We define a human readable translation to the Python version of NumPy + * for error messages (and also to allow grepping the binaries for conda). + */ +#if NPY_FEATURE_VERSION == NPY_1_7_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.7" +#elif NPY_FEATURE_VERSION == NPY_1_8_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.8" +#elif NPY_FEATURE_VERSION == NPY_1_9_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.9" +#elif NPY_FEATURE_VERSION == NPY_1_10_API_VERSION /* also 1.11, 1.12 */ + #define NPY_FEATURE_VERSION_STRING "1.10" +#elif NPY_FEATURE_VERSION == NPY_1_13_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.13" +#elif NPY_FEATURE_VERSION == NPY_1_14_API_VERSION /* also 1.15 */ + #define NPY_FEATURE_VERSION_STRING "1.14" +#elif NPY_FEATURE_VERSION == NPY_1_16_API_VERSION /* also 1.17, 1.18, 1.19 */ + #define NPY_FEATURE_VERSION_STRING "1.16" +#elif NPY_FEATURE_VERSION == NPY_1_20_API_VERSION /* also 1.21 */ + #define NPY_FEATURE_VERSION_STRING "1.20" +#elif NPY_FEATURE_VERSION == NPY_1_22_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.22" +#elif NPY_FEATURE_VERSION == NPY_1_23_API_VERSION /* also 1.24 */ + #define NPY_FEATURE_VERSION_STRING "1.23" +#elif NPY_FEATURE_VERSION == NPY_1_25_API_VERSION + #define NPY_FEATURE_VERSION_STRING "1.25" +#elif NPY_FEATURE_VERSION == NPY_2_0_API_VERSION + #define NPY_FEATURE_VERSION_STRING "2.0" +#elif NPY_FEATURE_VERSION == NPY_2_1_API_VERSION + #define NPY_FEATURE_VERSION_STRING "2.1" +#else + #error "Missing version string define for new NumPy version." +#endif + + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_NUMPYCONFIG_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/LICENSE.txt b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..d72a7c388d406191f2b3113efa5f89916a39d9b2 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/LICENSE.txt @@ -0,0 +1,21 @@ + zlib License + ------------ + + Copyright (C) 2010 - 2019 ridiculous_fish, + Copyright (C) 2016 - 2019 Kim Walisch, + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/bitgen.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/bitgen.h new file mode 100644 index 0000000000000000000000000000000000000000..162dd5c5753079eb1d76efa7fc8a3847c2ad6602 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/bitgen.h @@ -0,0 +1,20 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ + +#pragma once +#include +#include +#include + +/* Must match the declaration in numpy/random/.pxd */ + +typedef struct bitgen { + void *state; + uint64_t (*next_uint64)(void *st); + uint32_t (*next_uint32)(void *st); + double (*next_double)(void *st); + uint64_t (*next_raw)(void *st); +} bitgen_t; + + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/distributions.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/distributions.h new file mode 100644 index 0000000000000000000000000000000000000000..e7fa4bd00d43430eb1da23bd577688d8733bb6e8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/distributions.h @@ -0,0 +1,209 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ + +#ifdef __cplusplus +extern "C" { +#endif + +#include +#include "numpy/npy_common.h" +#include +#include +#include + +#include "numpy/npy_math.h" +#include "numpy/random/bitgen.h" + +/* + * RAND_INT_TYPE is used to share integer generators with RandomState which + * used long in place of int64_t. If changing a distribution that uses + * RAND_INT_TYPE, then the original unmodified copy must be retained for + * use in RandomState by copying to the legacy distributions source file. + */ +#ifdef NP_RANDOM_LEGACY +#define RAND_INT_TYPE long +#define RAND_INT_MAX LONG_MAX +#else +#define RAND_INT_TYPE int64_t +#define RAND_INT_MAX INT64_MAX +#endif + +#ifdef _MSC_VER +#define DECLDIR __declspec(dllexport) +#else +#define DECLDIR extern +#endif + +#ifndef MIN +#define MIN(x, y) (((x) < (y)) ? x : y) +#define MAX(x, y) (((x) > (y)) ? x : y) +#endif + +#ifndef M_PI +#define M_PI 3.14159265358979323846264338328 +#endif + +typedef struct s_binomial_t { + int has_binomial; /* !=0: following parameters initialized for binomial */ + double psave; + RAND_INT_TYPE nsave; + double r; + double q; + double fm; + RAND_INT_TYPE m; + double p1; + double xm; + double xl; + double xr; + double c; + double laml; + double lamr; + double p2; + double p3; + double p4; +} binomial_t; + +DECLDIR float random_standard_uniform_f(bitgen_t *bitgen_state); +DECLDIR double random_standard_uniform(bitgen_t *bitgen_state); +DECLDIR void random_standard_uniform_fill(bitgen_t *, npy_intp, double *); +DECLDIR void random_standard_uniform_fill_f(bitgen_t *, npy_intp, float *); + +DECLDIR int64_t random_positive_int64(bitgen_t *bitgen_state); +DECLDIR int32_t random_positive_int32(bitgen_t *bitgen_state); +DECLDIR int64_t random_positive_int(bitgen_t *bitgen_state); +DECLDIR uint64_t random_uint(bitgen_t *bitgen_state); + +DECLDIR double random_standard_exponential(bitgen_t *bitgen_state); +DECLDIR float random_standard_exponential_f(bitgen_t *bitgen_state); +DECLDIR void random_standard_exponential_fill(bitgen_t *, npy_intp, double *); +DECLDIR void random_standard_exponential_fill_f(bitgen_t *, npy_intp, float *); +DECLDIR void random_standard_exponential_inv_fill(bitgen_t *, npy_intp, double *); +DECLDIR void random_standard_exponential_inv_fill_f(bitgen_t *, npy_intp, float *); + +DECLDIR double random_standard_normal(bitgen_t *bitgen_state); +DECLDIR float random_standard_normal_f(bitgen_t *bitgen_state); +DECLDIR void random_standard_normal_fill(bitgen_t *, npy_intp, double *); +DECLDIR void random_standard_normal_fill_f(bitgen_t *, npy_intp, float *); +DECLDIR double random_standard_gamma(bitgen_t *bitgen_state, double shape); +DECLDIR float random_standard_gamma_f(bitgen_t *bitgen_state, float shape); + +DECLDIR double random_normal(bitgen_t *bitgen_state, double loc, double scale); + +DECLDIR double random_gamma(bitgen_t *bitgen_state, double shape, double scale); +DECLDIR float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale); + +DECLDIR double random_exponential(bitgen_t *bitgen_state, double scale); +DECLDIR double random_uniform(bitgen_t *bitgen_state, double lower, double range); +DECLDIR double random_beta(bitgen_t *bitgen_state, double a, double b); +DECLDIR double random_chisquare(bitgen_t *bitgen_state, double df); +DECLDIR double random_f(bitgen_t *bitgen_state, double dfnum, double dfden); +DECLDIR double random_standard_cauchy(bitgen_t *bitgen_state); +DECLDIR double random_pareto(bitgen_t *bitgen_state, double a); +DECLDIR double random_weibull(bitgen_t *bitgen_state, double a); +DECLDIR double random_power(bitgen_t *bitgen_state, double a); +DECLDIR double random_laplace(bitgen_t *bitgen_state, double loc, double scale); +DECLDIR double random_gumbel(bitgen_t *bitgen_state, double loc, double scale); +DECLDIR double random_logistic(bitgen_t *bitgen_state, double loc, double scale); +DECLDIR double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma); +DECLDIR double random_rayleigh(bitgen_t *bitgen_state, double mode); +DECLDIR double random_standard_t(bitgen_t *bitgen_state, double df); +DECLDIR double random_noncentral_chisquare(bitgen_t *bitgen_state, double df, + double nonc); +DECLDIR double random_noncentral_f(bitgen_t *bitgen_state, double dfnum, + double dfden, double nonc); +DECLDIR double random_wald(bitgen_t *bitgen_state, double mean, double scale); +DECLDIR double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa); +DECLDIR double random_triangular(bitgen_t *bitgen_state, double left, double mode, + double right); + +DECLDIR RAND_INT_TYPE random_poisson(bitgen_t *bitgen_state, double lam); +DECLDIR RAND_INT_TYPE random_negative_binomial(bitgen_t *bitgen_state, double n, + double p); + +DECLDIR int64_t random_binomial(bitgen_t *bitgen_state, double p, + int64_t n, binomial_t *binomial); + +DECLDIR int64_t random_logseries(bitgen_t *bitgen_state, double p); +DECLDIR int64_t random_geometric(bitgen_t *bitgen_state, double p); +DECLDIR RAND_INT_TYPE random_geometric_search(bitgen_t *bitgen_state, double p); +DECLDIR RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a); +DECLDIR int64_t random_hypergeometric(bitgen_t *bitgen_state, + int64_t good, int64_t bad, int64_t sample); +DECLDIR uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max); + +/* Generate random uint64 numbers in closed interval [off, off + rng]. */ +DECLDIR uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off, + uint64_t rng, uint64_t mask, + bool use_masked); + +/* Generate random uint32 numbers in closed interval [off, off + rng]. */ +DECLDIR uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state, + uint32_t off, uint32_t rng, + uint32_t mask, bool use_masked, + int *bcnt, uint32_t *buf); +DECLDIR uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state, + uint16_t off, uint16_t rng, + uint16_t mask, bool use_masked, + int *bcnt, uint32_t *buf); +DECLDIR uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state, uint8_t off, + uint8_t rng, uint8_t mask, + bool use_masked, int *bcnt, + uint32_t *buf); +DECLDIR npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state, npy_bool off, + npy_bool rng, npy_bool mask, + bool use_masked, int *bcnt, + uint32_t *buf); + +DECLDIR void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off, + uint64_t rng, npy_intp cnt, + bool use_masked, uint64_t *out); +DECLDIR void random_bounded_uint32_fill(bitgen_t *bitgen_state, uint32_t off, + uint32_t rng, npy_intp cnt, + bool use_masked, uint32_t *out); +DECLDIR void random_bounded_uint16_fill(bitgen_t *bitgen_state, uint16_t off, + uint16_t rng, npy_intp cnt, + bool use_masked, uint16_t *out); +DECLDIR void random_bounded_uint8_fill(bitgen_t *bitgen_state, uint8_t off, + uint8_t rng, npy_intp cnt, + bool use_masked, uint8_t *out); +DECLDIR void random_bounded_bool_fill(bitgen_t *bitgen_state, npy_bool off, + npy_bool rng, npy_intp cnt, + bool use_masked, npy_bool *out); + +DECLDIR void random_multinomial(bitgen_t *bitgen_state, RAND_INT_TYPE n, RAND_INT_TYPE *mnix, + double *pix, npy_intp d, binomial_t *binomial); + +/* multivariate hypergeometric, "count" method */ +DECLDIR int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state, + int64_t total, + size_t num_colors, int64_t *colors, + int64_t nsample, + size_t num_variates, int64_t *variates); + +/* multivariate hypergeometric, "marginals" method */ +DECLDIR void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state, + int64_t total, + size_t num_colors, int64_t *colors, + int64_t nsample, + size_t num_variates, int64_t *variates); + +/* Common to legacy-distributions.c and distributions.c but not exported */ + +RAND_INT_TYPE random_binomial_btpe(bitgen_t *bitgen_state, + RAND_INT_TYPE n, + double p, + binomial_t *binomial); +RAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state, + RAND_INT_TYPE n, + double p, + binomial_t *binomial); +double random_loggam(double x); +static inline double next_double(bitgen_t *bitgen_state) { + return bitgen_state->next_double(bitgen_state->state); +} + +#ifdef __cplusplus +} +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/libdivide.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/libdivide.h new file mode 100644 index 0000000000000000000000000000000000000000..f4eb8039b50c21c0977a38ee5f47c8f89307d482 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/random/libdivide.h @@ -0,0 +1,2079 @@ +// libdivide.h - Optimized integer division +// https://libdivide.com +// +// Copyright (C) 2010 - 2019 ridiculous_fish, +// Copyright (C) 2016 - 2019 Kim Walisch, +// +// libdivide is dual-licensed under the Boost or zlib licenses. +// You may use libdivide under the terms of either of these. +// See LICENSE.txt for more details. + +#ifndef NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ + +#define LIBDIVIDE_VERSION "3.0" +#define LIBDIVIDE_VERSION_MAJOR 3 +#define LIBDIVIDE_VERSION_MINOR 0 + +#include + +#if defined(__cplusplus) + #include + #include + #include +#else + #include + #include +#endif + +#if defined(LIBDIVIDE_AVX512) + #include +#elif defined(LIBDIVIDE_AVX2) + #include +#elif defined(LIBDIVIDE_SSE2) + #include +#endif + +#if defined(_MSC_VER) + #include + // disable warning C4146: unary minus operator applied + // to unsigned type, result still unsigned + #pragma warning(disable: 4146) + #define LIBDIVIDE_VC +#endif + +#if !defined(__has_builtin) + #define __has_builtin(x) 0 +#endif + +#if defined(__SIZEOF_INT128__) + #define HAS_INT128_T + // clang-cl on Windows does not yet support 128-bit division + #if !(defined(__clang__) && defined(LIBDIVIDE_VC)) + #define HAS_INT128_DIV + #endif +#endif + +#if defined(__x86_64__) || defined(_M_X64) + #define LIBDIVIDE_X86_64 +#endif + +#if defined(__i386__) + #define LIBDIVIDE_i386 +#endif + +#if defined(__GNUC__) || defined(__clang__) + #define LIBDIVIDE_GCC_STYLE_ASM +#endif + +#if defined(__cplusplus) || defined(LIBDIVIDE_VC) + #define LIBDIVIDE_FUNCTION __FUNCTION__ +#else + #define LIBDIVIDE_FUNCTION __func__ +#endif + +#define LIBDIVIDE_ERROR(msg) \ + do { \ + fprintf(stderr, "libdivide.h:%d: %s(): Error: %s\n", \ + __LINE__, LIBDIVIDE_FUNCTION, msg); \ + abort(); \ + } while (0) + +#if defined(LIBDIVIDE_ASSERTIONS_ON) + #define LIBDIVIDE_ASSERT(x) \ + do { \ + if (!(x)) { \ + fprintf(stderr, "libdivide.h:%d: %s(): Assertion failed: %s\n", \ + __LINE__, LIBDIVIDE_FUNCTION, #x); \ + abort(); \ + } \ + } while (0) +#else + #define LIBDIVIDE_ASSERT(x) +#endif + +#ifdef __cplusplus +namespace libdivide { +#endif + +// pack divider structs to prevent compilers from padding. +// This reduces memory usage by up to 43% when using a large +// array of libdivide dividers and improves performance +// by up to 10% because of reduced memory bandwidth. +#pragma pack(push, 1) + +struct libdivide_u32_t { + uint32_t magic; + uint8_t more; +}; + +struct libdivide_s32_t { + int32_t magic; + uint8_t more; +}; + +struct libdivide_u64_t { + uint64_t magic; + uint8_t more; +}; + +struct libdivide_s64_t { + int64_t magic; + uint8_t more; +}; + +struct libdivide_u32_branchfree_t { + uint32_t magic; + uint8_t more; +}; + +struct libdivide_s32_branchfree_t { + int32_t magic; + uint8_t more; +}; + +struct libdivide_u64_branchfree_t { + uint64_t magic; + uint8_t more; +}; + +struct libdivide_s64_branchfree_t { + int64_t magic; + uint8_t more; +}; + +#pragma pack(pop) + +// Explanation of the "more" field: +// +// * Bits 0-5 is the shift value (for shift path or mult path). +// * Bit 6 is the add indicator for mult path. +// * Bit 7 is set if the divisor is negative. We use bit 7 as the negative +// divisor indicator so that we can efficiently use sign extension to +// create a bitmask with all bits set to 1 (if the divisor is negative) +// or 0 (if the divisor is positive). +// +// u32: [0-4] shift value +// [5] ignored +// [6] add indicator +// magic number of 0 indicates shift path +// +// s32: [0-4] shift value +// [5] ignored +// [6] add indicator +// [7] indicates negative divisor +// magic number of 0 indicates shift path +// +// u64: [0-5] shift value +// [6] add indicator +// magic number of 0 indicates shift path +// +// s64: [0-5] shift value +// [6] add indicator +// [7] indicates negative divisor +// magic number of 0 indicates shift path +// +// In s32 and s64 branchfree modes, the magic number is negated according to +// whether the divisor is negated. In branchfree strategy, it is not negated. + +enum { + LIBDIVIDE_32_SHIFT_MASK = 0x1F, + LIBDIVIDE_64_SHIFT_MASK = 0x3F, + LIBDIVIDE_ADD_MARKER = 0x40, + LIBDIVIDE_NEGATIVE_DIVISOR = 0x80 +}; + +static inline struct libdivide_s32_t libdivide_s32_gen(int32_t d); +static inline struct libdivide_u32_t libdivide_u32_gen(uint32_t d); +static inline struct libdivide_s64_t libdivide_s64_gen(int64_t d); +static inline struct libdivide_u64_t libdivide_u64_gen(uint64_t d); + +static inline struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d); +static inline struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d); +static inline struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d); +static inline struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d); + +static inline int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom); +static inline uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom); +static inline int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom); +static inline uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom); + +static inline int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom); +static inline uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom); +static inline int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom); +static inline uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom); + +static inline int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom); +static inline uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom); +static inline int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom); +static inline uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom); + +static inline int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom); +static inline uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom); +static inline int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom); +static inline uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom); + +//////// Internal Utility Functions + +static inline uint32_t libdivide_mullhi_u32(uint32_t x, uint32_t y) { + uint64_t xl = x, yl = y; + uint64_t rl = xl * yl; + return (uint32_t)(rl >> 32); +} + +static inline int32_t libdivide_mullhi_s32(int32_t x, int32_t y) { + int64_t xl = x, yl = y; + int64_t rl = xl * yl; + // needs to be arithmetic shift + return (int32_t)(rl >> 32); +} + +static inline uint64_t libdivide_mullhi_u64(uint64_t x, uint64_t y) { +#if defined(LIBDIVIDE_VC) && \ + defined(LIBDIVIDE_X86_64) + return __umulh(x, y); +#elif defined(HAS_INT128_T) + __uint128_t xl = x, yl = y; + __uint128_t rl = xl * yl; + return (uint64_t)(rl >> 64); +#else + // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64) + uint32_t mask = 0xFFFFFFFF; + uint32_t x0 = (uint32_t)(x & mask); + uint32_t x1 = (uint32_t)(x >> 32); + uint32_t y0 = (uint32_t)(y & mask); + uint32_t y1 = (uint32_t)(y >> 32); + uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0); + uint64_t x0y1 = x0 * (uint64_t)y1; + uint64_t x1y0 = x1 * (uint64_t)y0; + uint64_t x1y1 = x1 * (uint64_t)y1; + uint64_t temp = x1y0 + x0y0_hi; + uint64_t temp_lo = temp & mask; + uint64_t temp_hi = temp >> 32; + + return x1y1 + temp_hi + ((temp_lo + x0y1) >> 32); +#endif +} + +static inline int64_t libdivide_mullhi_s64(int64_t x, int64_t y) { +#if defined(LIBDIVIDE_VC) && \ + defined(LIBDIVIDE_X86_64) + return __mulh(x, y); +#elif defined(HAS_INT128_T) + __int128_t xl = x, yl = y; + __int128_t rl = xl * yl; + return (int64_t)(rl >> 64); +#else + // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64) + uint32_t mask = 0xFFFFFFFF; + uint32_t x0 = (uint32_t)(x & mask); + uint32_t y0 = (uint32_t)(y & mask); + int32_t x1 = (int32_t)(x >> 32); + int32_t y1 = (int32_t)(y >> 32); + uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0); + int64_t t = x1 * (int64_t)y0 + x0y0_hi; + int64_t w1 = x0 * (int64_t)y1 + (t & mask); + + return x1 * (int64_t)y1 + (t >> 32) + (w1 >> 32); +#endif +} + +static inline int32_t libdivide_count_leading_zeros32(uint32_t val) { +#if defined(__GNUC__) || \ + __has_builtin(__builtin_clz) + // Fast way to count leading zeros + return __builtin_clz(val); +#elif defined(LIBDIVIDE_VC) + unsigned long result; + if (_BitScanReverse(&result, val)) { + return 31 - result; + } + return 0; +#else + if (val == 0) + return 32; + int32_t result = 8; + uint32_t hi = 0xFFU << 24; + while ((val & hi) == 0) { + hi >>= 8; + result += 8; + } + while (val & hi) { + result -= 1; + hi <<= 1; + } + return result; +#endif +} + +static inline int32_t libdivide_count_leading_zeros64(uint64_t val) { +#if defined(__GNUC__) || \ + __has_builtin(__builtin_clzll) + // Fast way to count leading zeros + return __builtin_clzll(val); +#elif defined(LIBDIVIDE_VC) && defined(_WIN64) + unsigned long result; + if (_BitScanReverse64(&result, val)) { + return 63 - result; + } + return 0; +#else + uint32_t hi = val >> 32; + uint32_t lo = val & 0xFFFFFFFF; + if (hi != 0) return libdivide_count_leading_zeros32(hi); + return 32 + libdivide_count_leading_zeros32(lo); +#endif +} + +// libdivide_64_div_32_to_32: divides a 64-bit uint {u1, u0} by a 32-bit +// uint {v}. The result must fit in 32 bits. +// Returns the quotient directly and the remainder in *r +static inline uint32_t libdivide_64_div_32_to_32(uint32_t u1, uint32_t u0, uint32_t v, uint32_t *r) { +#if (defined(LIBDIVIDE_i386) || defined(LIBDIVIDE_X86_64)) && \ + defined(LIBDIVIDE_GCC_STYLE_ASM) + uint32_t result; + __asm__("divl %[v]" + : "=a"(result), "=d"(*r) + : [v] "r"(v), "a"(u0), "d"(u1) + ); + return result; +#else + uint64_t n = ((uint64_t)u1 << 32) | u0; + uint32_t result = (uint32_t)(n / v); + *r = (uint32_t)(n - result * (uint64_t)v); + return result; +#endif +} + +// libdivide_128_div_64_to_64: divides a 128-bit uint {u1, u0} by a 64-bit +// uint {v}. The result must fit in 64 bits. +// Returns the quotient directly and the remainder in *r +static uint64_t libdivide_128_div_64_to_64(uint64_t u1, uint64_t u0, uint64_t v, uint64_t *r) { +#if defined(LIBDIVIDE_X86_64) && \ + defined(LIBDIVIDE_GCC_STYLE_ASM) + uint64_t result; + __asm__("divq %[v]" + : "=a"(result), "=d"(*r) + : [v] "r"(v), "a"(u0), "d"(u1) + ); + return result; +#elif defined(HAS_INT128_T) && \ + defined(HAS_INT128_DIV) + __uint128_t n = ((__uint128_t)u1 << 64) | u0; + uint64_t result = (uint64_t)(n / v); + *r = (uint64_t)(n - result * (__uint128_t)v); + return result; +#else + // Code taken from Hacker's Delight: + // http://www.hackersdelight.org/HDcode/divlu.c. + // License permits inclusion here per: + // http://www.hackersdelight.org/permissions.htm + + const uint64_t b = (1ULL << 32); // Number base (32 bits) + uint64_t un1, un0; // Norm. dividend LSD's + uint64_t vn1, vn0; // Norm. divisor digits + uint64_t q1, q0; // Quotient digits + uint64_t un64, un21, un10; // Dividend digit pairs + uint64_t rhat; // A remainder + int32_t s; // Shift amount for norm + + // If overflow, set rem. to an impossible value, + // and return the largest possible quotient + if (u1 >= v) { + *r = (uint64_t) -1; + return (uint64_t) -1; + } + + // count leading zeros + s = libdivide_count_leading_zeros64(v); + if (s > 0) { + // Normalize divisor + v = v << s; + un64 = (u1 << s) | (u0 >> (64 - s)); + un10 = u0 << s; // Shift dividend left + } else { + // Avoid undefined behavior of (u0 >> 64). + // The behavior is undefined if the right operand is + // negative, or greater than or equal to the length + // in bits of the promoted left operand. + un64 = u1; + un10 = u0; + } + + // Break divisor up into two 32-bit digits + vn1 = v >> 32; + vn0 = v & 0xFFFFFFFF; + + // Break right half of dividend into two digits + un1 = un10 >> 32; + un0 = un10 & 0xFFFFFFFF; + + // Compute the first quotient digit, q1 + q1 = un64 / vn1; + rhat = un64 - q1 * vn1; + + while (q1 >= b || q1 * vn0 > b * rhat + un1) { + q1 = q1 - 1; + rhat = rhat + vn1; + if (rhat >= b) + break; + } + + // Multiply and subtract + un21 = un64 * b + un1 - q1 * v; + + // Compute the second quotient digit + q0 = un21 / vn1; + rhat = un21 - q0 * vn1; + + while (q0 >= b || q0 * vn0 > b * rhat + un0) { + q0 = q0 - 1; + rhat = rhat + vn1; + if (rhat >= b) + break; + } + + *r = (un21 * b + un0 - q0 * v) >> s; + return q1 * b + q0; +#endif +} + +// Bitshift a u128 in place, left (signed_shift > 0) or right (signed_shift < 0) +static inline void libdivide_u128_shift(uint64_t *u1, uint64_t *u0, int32_t signed_shift) { + if (signed_shift > 0) { + uint32_t shift = signed_shift; + *u1 <<= shift; + *u1 |= *u0 >> (64 - shift); + *u0 <<= shift; + } + else if (signed_shift < 0) { + uint32_t shift = -signed_shift; + *u0 >>= shift; + *u0 |= *u1 << (64 - shift); + *u1 >>= shift; + } +} + +// Computes a 128 / 128 -> 64 bit division, with a 128 bit remainder. +static uint64_t libdivide_128_div_128_to_64(uint64_t u_hi, uint64_t u_lo, uint64_t v_hi, uint64_t v_lo, uint64_t *r_hi, uint64_t *r_lo) { +#if defined(HAS_INT128_T) && \ + defined(HAS_INT128_DIV) + __uint128_t ufull = u_hi; + __uint128_t vfull = v_hi; + ufull = (ufull << 64) | u_lo; + vfull = (vfull << 64) | v_lo; + uint64_t res = (uint64_t)(ufull / vfull); + __uint128_t remainder = ufull - (vfull * res); + *r_lo = (uint64_t)remainder; + *r_hi = (uint64_t)(remainder >> 64); + return res; +#else + // Adapted from "Unsigned Doubleword Division" in Hacker's Delight + // We want to compute u / v + typedef struct { uint64_t hi; uint64_t lo; } u128_t; + u128_t u = {u_hi, u_lo}; + u128_t v = {v_hi, v_lo}; + + if (v.hi == 0) { + // divisor v is a 64 bit value, so we just need one 128/64 division + // Note that we are simpler than Hacker's Delight here, because we know + // the quotient fits in 64 bits whereas Hacker's Delight demands a full + // 128 bit quotient + *r_hi = 0; + return libdivide_128_div_64_to_64(u.hi, u.lo, v.lo, r_lo); + } + // Here v >= 2**64 + // We know that v.hi != 0, so count leading zeros is OK + // We have 0 <= n <= 63 + uint32_t n = libdivide_count_leading_zeros64(v.hi); + + // Normalize the divisor so its MSB is 1 + u128_t v1t = v; + libdivide_u128_shift(&v1t.hi, &v1t.lo, n); + uint64_t v1 = v1t.hi; // i.e. v1 = v1t >> 64 + + // To ensure no overflow + u128_t u1 = u; + libdivide_u128_shift(&u1.hi, &u1.lo, -1); + + // Get quotient from divide unsigned insn. + uint64_t rem_ignored; + uint64_t q1 = libdivide_128_div_64_to_64(u1.hi, u1.lo, v1, &rem_ignored); + + // Undo normalization and division of u by 2. + u128_t q0 = {0, q1}; + libdivide_u128_shift(&q0.hi, &q0.lo, n); + libdivide_u128_shift(&q0.hi, &q0.lo, -63); + + // Make q0 correct or too small by 1 + // Equivalent to `if (q0 != 0) q0 = q0 - 1;` + if (q0.hi != 0 || q0.lo != 0) { + q0.hi -= (q0.lo == 0); // borrow + q0.lo -= 1; + } + + // Now q0 is correct. + // Compute q0 * v as q0v + // = (q0.hi << 64 + q0.lo) * (v.hi << 64 + v.lo) + // = (q0.hi * v.hi << 128) + (q0.hi * v.lo << 64) + + // (q0.lo * v.hi << 64) + q0.lo * v.lo) + // Each term is 128 bit + // High half of full product (upper 128 bits!) are dropped + u128_t q0v = {0, 0}; + q0v.hi = q0.hi*v.lo + q0.lo*v.hi + libdivide_mullhi_u64(q0.lo, v.lo); + q0v.lo = q0.lo*v.lo; + + // Compute u - q0v as u_q0v + // This is the remainder + u128_t u_q0v = u; + u_q0v.hi -= q0v.hi + (u.lo < q0v.lo); // second term is borrow + u_q0v.lo -= q0v.lo; + + // Check if u_q0v >= v + // This checks if our remainder is larger than the divisor + if ((u_q0v.hi > v.hi) || + (u_q0v.hi == v.hi && u_q0v.lo >= v.lo)) { + // Increment q0 + q0.lo += 1; + q0.hi += (q0.lo == 0); // carry + + // Subtract v from remainder + u_q0v.hi -= v.hi + (u_q0v.lo < v.lo); + u_q0v.lo -= v.lo; + } + + *r_hi = u_q0v.hi; + *r_lo = u_q0v.lo; + + LIBDIVIDE_ASSERT(q0.hi == 0); + return q0.lo; +#endif +} + +////////// UINT32 + +static inline struct libdivide_u32_t libdivide_internal_u32_gen(uint32_t d, int branchfree) { + if (d == 0) { + LIBDIVIDE_ERROR("divider must be != 0"); + } + + struct libdivide_u32_t result; + uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(d); + + // Power of 2 + if ((d & (d - 1)) == 0) { + // We need to subtract 1 from the shift value in case of an unsigned + // branchfree divider because there is a hardcoded right shift by 1 + // in its division algorithm. Because of this we also need to add back + // 1 in its recovery algorithm. + result.magic = 0; + result.more = (uint8_t)(floor_log_2_d - (branchfree != 0)); + } else { + uint8_t more; + uint32_t rem, proposed_m; + proposed_m = libdivide_64_div_32_to_32(1U << floor_log_2_d, 0, d, &rem); + + LIBDIVIDE_ASSERT(rem > 0 && rem < d); + const uint32_t e = d - rem; + + // This power works if e < 2**floor_log_2_d. + if (!branchfree && (e < (1U << floor_log_2_d))) { + // This power works + more = floor_log_2_d; + } else { + // We have to use the general 33-bit algorithm. We need to compute + // (2**power) / d. However, we already have (2**(power-1))/d and + // its remainder. By doubling both, and then correcting the + // remainder, we can compute the larger division. + // don't care about overflow here - in fact, we expect it + proposed_m += proposed_m; + const uint32_t twice_rem = rem + rem; + if (twice_rem >= d || twice_rem < rem) proposed_m += 1; + more = floor_log_2_d | LIBDIVIDE_ADD_MARKER; + } + result.magic = 1 + proposed_m; + result.more = more; + // result.more's shift should in general be ceil_log_2_d. But if we + // used the smaller power, we subtract one from the shift because we're + // using the smaller power. If we're using the larger power, we + // subtract one from the shift because it's taken care of by the add + // indicator. So floor_log_2_d happens to be correct in both cases. + } + return result; +} + +struct libdivide_u32_t libdivide_u32_gen(uint32_t d) { + return libdivide_internal_u32_gen(d, 0); +} + +struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d) { + if (d == 1) { + LIBDIVIDE_ERROR("branchfree divider must be != 1"); + } + struct libdivide_u32_t tmp = libdivide_internal_u32_gen(d, 1); + struct libdivide_u32_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_32_SHIFT_MASK)}; + return ret; +} + +uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return numer >> more; + } + else { + uint32_t q = libdivide_mullhi_u32(denom->magic, numer); + if (more & LIBDIVIDE_ADD_MARKER) { + uint32_t t = ((numer - q) >> 1) + q; + return t >> (more & LIBDIVIDE_32_SHIFT_MASK); + } + else { + // All upper bits are 0, + // don't need to mask them off. + return q >> more; + } + } +} + +uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom) { + uint32_t q = libdivide_mullhi_u32(denom->magic, numer); + uint32_t t = ((numer - q) >> 1) + q; + return t >> denom->more; +} + +uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + + if (!denom->magic) { + return 1U << shift; + } else if (!(more & LIBDIVIDE_ADD_MARKER)) { + // We compute q = n/d = n*m / 2^(32 + shift) + // Therefore we have d = 2^(32 + shift) / m + // We need to ceil it. + // We know d is not a power of 2, so m is not a power of 2, + // so we can just add 1 to the floor + uint32_t hi_dividend = 1U << shift; + uint32_t rem_ignored; + return 1 + libdivide_64_div_32_to_32(hi_dividend, 0, denom->magic, &rem_ignored); + } else { + // Here we wish to compute d = 2^(32+shift+1)/(m+2^32). + // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now + // Also note that shift may be as high as 31, so shift + 1 will + // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and + // then double the quotient and remainder. + uint64_t half_n = 1ULL << (32 + shift); + uint64_t d = (1ULL << 32) | denom->magic; + // Note that the quotient is guaranteed <= 32 bits, but the remainder + // may need 33! + uint32_t half_q = (uint32_t)(half_n / d); + uint64_t rem = half_n % d; + // We computed 2^(32+shift)/(m+2^32) + // Need to double it, and then add 1 to the quotient if doubling th + // remainder would increase the quotient. + // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits + uint32_t full_q = half_q + half_q + ((rem<<1) >= d); + + // We rounded down in gen (hence +1) + return full_q + 1; + } +} + +uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + + if (!denom->magic) { + return 1U << (shift + 1); + } else { + // Here we wish to compute d = 2^(32+shift+1)/(m+2^32). + // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now + // Also note that shift may be as high as 31, so shift + 1 will + // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and + // then double the quotient and remainder. + uint64_t half_n = 1ULL << (32 + shift); + uint64_t d = (1ULL << 32) | denom->magic; + // Note that the quotient is guaranteed <= 32 bits, but the remainder + // may need 33! + uint32_t half_q = (uint32_t)(half_n / d); + uint64_t rem = half_n % d; + // We computed 2^(32+shift)/(m+2^32) + // Need to double it, and then add 1 to the quotient if doubling th + // remainder would increase the quotient. + // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits + uint32_t full_q = half_q + half_q + ((rem<<1) >= d); + + // We rounded down in gen (hence +1) + return full_q + 1; + } +} + +/////////// UINT64 + +static inline struct libdivide_u64_t libdivide_internal_u64_gen(uint64_t d, int branchfree) { + if (d == 0) { + LIBDIVIDE_ERROR("divider must be != 0"); + } + + struct libdivide_u64_t result; + uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(d); + + // Power of 2 + if ((d & (d - 1)) == 0) { + // We need to subtract 1 from the shift value in case of an unsigned + // branchfree divider because there is a hardcoded right shift by 1 + // in its division algorithm. Because of this we also need to add back + // 1 in its recovery algorithm. + result.magic = 0; + result.more = (uint8_t)(floor_log_2_d - (branchfree != 0)); + } else { + uint64_t proposed_m, rem; + uint8_t more; + // (1 << (64 + floor_log_2_d)) / d + proposed_m = libdivide_128_div_64_to_64(1ULL << floor_log_2_d, 0, d, &rem); + + LIBDIVIDE_ASSERT(rem > 0 && rem < d); + const uint64_t e = d - rem; + + // This power works if e < 2**floor_log_2_d. + if (!branchfree && e < (1ULL << floor_log_2_d)) { + // This power works + more = floor_log_2_d; + } else { + // We have to use the general 65-bit algorithm. We need to compute + // (2**power) / d. However, we already have (2**(power-1))/d and + // its remainder. By doubling both, and then correcting the + // remainder, we can compute the larger division. + // don't care about overflow here - in fact, we expect it + proposed_m += proposed_m; + const uint64_t twice_rem = rem + rem; + if (twice_rem >= d || twice_rem < rem) proposed_m += 1; + more = floor_log_2_d | LIBDIVIDE_ADD_MARKER; + } + result.magic = 1 + proposed_m; + result.more = more; + // result.more's shift should in general be ceil_log_2_d. But if we + // used the smaller power, we subtract one from the shift because we're + // using the smaller power. If we're using the larger power, we + // subtract one from the shift because it's taken care of by the add + // indicator. So floor_log_2_d happens to be correct in both cases, + // which is why we do it outside of the if statement. + } + return result; +} + +struct libdivide_u64_t libdivide_u64_gen(uint64_t d) { + return libdivide_internal_u64_gen(d, 0); +} + +struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d) { + if (d == 1) { + LIBDIVIDE_ERROR("branchfree divider must be != 1"); + } + struct libdivide_u64_t tmp = libdivide_internal_u64_gen(d, 1); + struct libdivide_u64_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_64_SHIFT_MASK)}; + return ret; +} + +uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return numer >> more; + } + else { + uint64_t q = libdivide_mullhi_u64(denom->magic, numer); + if (more & LIBDIVIDE_ADD_MARKER) { + uint64_t t = ((numer - q) >> 1) + q; + return t >> (more & LIBDIVIDE_64_SHIFT_MASK); + } + else { + // All upper bits are 0, + // don't need to mask them off. + return q >> more; + } + } +} + +uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom) { + uint64_t q = libdivide_mullhi_u64(denom->magic, numer); + uint64_t t = ((numer - q) >> 1) + q; + return t >> denom->more; +} + +uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + + if (!denom->magic) { + return 1ULL << shift; + } else if (!(more & LIBDIVIDE_ADD_MARKER)) { + // We compute q = n/d = n*m / 2^(64 + shift) + // Therefore we have d = 2^(64 + shift) / m + // We need to ceil it. + // We know d is not a power of 2, so m is not a power of 2, + // so we can just add 1 to the floor + uint64_t hi_dividend = 1ULL << shift; + uint64_t rem_ignored; + return 1 + libdivide_128_div_64_to_64(hi_dividend, 0, denom->magic, &rem_ignored); + } else { + // Here we wish to compute d = 2^(64+shift+1)/(m+2^64). + // Notice (m + 2^64) is a 65 bit number. This gets hairy. See + // libdivide_u32_recover for more on what we do here. + // TODO: do something better than 128 bit math + + // Full n is a (potentially) 129 bit value + // half_n is a 128 bit value + // Compute the hi half of half_n. Low half is 0. + uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0; + // d is a 65 bit value. The high bit is always set to 1. + const uint64_t d_hi = 1, d_lo = denom->magic; + // Note that the quotient is guaranteed <= 64 bits, + // but the remainder may need 65! + uint64_t r_hi, r_lo; + uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo); + // We computed 2^(64+shift)/(m+2^64) + // Double the remainder ('dr') and check if that is larger than d + // Note that d is a 65 bit value, so r1 is small and so r1 + r1 + // cannot overflow + uint64_t dr_lo = r_lo + r_lo; + uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry + int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo); + uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0); + return full_q + 1; + } +} + +uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + + if (!denom->magic) { + return 1ULL << (shift + 1); + } else { + // Here we wish to compute d = 2^(64+shift+1)/(m+2^64). + // Notice (m + 2^64) is a 65 bit number. This gets hairy. See + // libdivide_u32_recover for more on what we do here. + // TODO: do something better than 128 bit math + + // Full n is a (potentially) 129 bit value + // half_n is a 128 bit value + // Compute the hi half of half_n. Low half is 0. + uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0; + // d is a 65 bit value. The high bit is always set to 1. + const uint64_t d_hi = 1, d_lo = denom->magic; + // Note that the quotient is guaranteed <= 64 bits, + // but the remainder may need 65! + uint64_t r_hi, r_lo; + uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo); + // We computed 2^(64+shift)/(m+2^64) + // Double the remainder ('dr') and check if that is larger than d + // Note that d is a 65 bit value, so r1 is small and so r1 + r1 + // cannot overflow + uint64_t dr_lo = r_lo + r_lo; + uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry + int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo); + uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0); + return full_q + 1; + } +} + +/////////// SINT32 + +static inline struct libdivide_s32_t libdivide_internal_s32_gen(int32_t d, int branchfree) { + if (d == 0) { + LIBDIVIDE_ERROR("divider must be != 0"); + } + + struct libdivide_s32_t result; + + // If d is a power of 2, or negative a power of 2, we have to use a shift. + // This is especially important because the magic algorithm fails for -1. + // To check if d is a power of 2 or its inverse, it suffices to check + // whether its absolute value has exactly one bit set. This works even for + // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set + // and is a power of 2. + uint32_t ud = (uint32_t)d; + uint32_t absD = (d < 0) ? -ud : ud; + uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(absD); + // check if exactly one bit is set, + // don't care if absD is 0 since that's divide by zero + if ((absD & (absD - 1)) == 0) { + // Branchfree and normal paths are exactly the same + result.magic = 0; + result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0); + } else { + LIBDIVIDE_ASSERT(floor_log_2_d >= 1); + + uint8_t more; + // the dividend here is 2**(floor_log_2_d + 31), so the low 32 bit word + // is 0 and the high word is floor_log_2_d - 1 + uint32_t rem, proposed_m; + proposed_m = libdivide_64_div_32_to_32(1U << (floor_log_2_d - 1), 0, absD, &rem); + const uint32_t e = absD - rem; + + // We are going to start with a power of floor_log_2_d - 1. + // This works if works if e < 2**floor_log_2_d. + if (!branchfree && e < (1U << floor_log_2_d)) { + // This power works + more = floor_log_2_d - 1; + } else { + // We need to go one higher. This should not make proposed_m + // overflow, but it will make it negative when interpreted as an + // int32_t. + proposed_m += proposed_m; + const uint32_t twice_rem = rem + rem; + if (twice_rem >= absD || twice_rem < rem) proposed_m += 1; + more = floor_log_2_d | LIBDIVIDE_ADD_MARKER; + } + + proposed_m += 1; + int32_t magic = (int32_t)proposed_m; + + // Mark if we are negative. Note we only negate the magic number in the + // branchfull case. + if (d < 0) { + more |= LIBDIVIDE_NEGATIVE_DIVISOR; + if (!branchfree) { + magic = -magic; + } + } + + result.more = more; + result.magic = magic; + } + return result; +} + +struct libdivide_s32_t libdivide_s32_gen(int32_t d) { + return libdivide_internal_s32_gen(d, 0); +} + +struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d) { + struct libdivide_s32_t tmp = libdivide_internal_s32_gen(d, 1); + struct libdivide_s32_branchfree_t result = {tmp.magic, tmp.more}; + return result; +} + +int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + + if (!denom->magic) { + uint32_t sign = (int8_t)more >> 7; + uint32_t mask = (1U << shift) - 1; + uint32_t uq = numer + ((numer >> 31) & mask); + int32_t q = (int32_t)uq; + q >>= shift; + q = (q ^ sign) - sign; + return q; + } else { + uint32_t uq = (uint32_t)libdivide_mullhi_s32(denom->magic, numer); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift and then sign extend + int32_t sign = (int8_t)more >> 7; + // q += (more < 0 ? -numer : numer) + // cast required to avoid UB + uq += ((uint32_t)numer ^ sign) - sign; + } + int32_t q = (int32_t)uq; + q >>= shift; + q += (q < 0); + return q; + } +} + +int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + // must be arithmetic shift and then sign extend + int32_t sign = (int8_t)more >> 7; + int32_t magic = denom->magic; + int32_t q = libdivide_mullhi_s32(magic, numer); + q += numer; + + // If q is non-negative, we have nothing to do + // If q is negative, we want to add either (2**shift)-1 if d is a power of + // 2, or (2**shift) if it is not a power of 2 + uint32_t is_power_of_2 = (magic == 0); + uint32_t q_sign = (uint32_t)(q >> 31); + q += q_sign & ((1U << shift) - is_power_of_2); + + // Now arithmetic right shift + q >>= shift; + // Negate if needed + q = (q ^ sign) - sign; + + return q; +} + +int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + if (!denom->magic) { + uint32_t absD = 1U << shift; + if (more & LIBDIVIDE_NEGATIVE_DIVISOR) { + absD = -absD; + } + return (int32_t)absD; + } else { + // Unsigned math is much easier + // We negate the magic number only in the branchfull case, and we don't + // know which case we're in. However we have enough information to + // determine the correct sign of the magic number. The divisor was + // negative if LIBDIVIDE_NEGATIVE_DIVISOR is set. If ADD_MARKER is set, + // the magic number's sign is opposite that of the divisor. + // We want to compute the positive magic number. + int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR); + int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER) + ? denom->magic > 0 : denom->magic < 0; + + // Handle the power of 2 case (including branchfree) + if (denom->magic == 0) { + int32_t result = 1U << shift; + return negative_divisor ? -result : result; + } + + uint32_t d = (uint32_t)(magic_was_negated ? -denom->magic : denom->magic); + uint64_t n = 1ULL << (32 + shift); // this shift cannot exceed 30 + uint32_t q = (uint32_t)(n / d); + int32_t result = (int32_t)q; + result += 1; + return negative_divisor ? -result : result; + } +} + +int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom) { + return libdivide_s32_recover((const struct libdivide_s32_t *)denom); +} + +///////////// SINT64 + +static inline struct libdivide_s64_t libdivide_internal_s64_gen(int64_t d, int branchfree) { + if (d == 0) { + LIBDIVIDE_ERROR("divider must be != 0"); + } + + struct libdivide_s64_t result; + + // If d is a power of 2, or negative a power of 2, we have to use a shift. + // This is especially important because the magic algorithm fails for -1. + // To check if d is a power of 2 or its inverse, it suffices to check + // whether its absolute value has exactly one bit set. This works even for + // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set + // and is a power of 2. + uint64_t ud = (uint64_t)d; + uint64_t absD = (d < 0) ? -ud : ud; + uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(absD); + // check if exactly one bit is set, + // don't care if absD is 0 since that's divide by zero + if ((absD & (absD - 1)) == 0) { + // Branchfree and non-branchfree cases are the same + result.magic = 0; + result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0); + } else { + // the dividend here is 2**(floor_log_2_d + 63), so the low 64 bit word + // is 0 and the high word is floor_log_2_d - 1 + uint8_t more; + uint64_t rem, proposed_m; + proposed_m = libdivide_128_div_64_to_64(1ULL << (floor_log_2_d - 1), 0, absD, &rem); + const uint64_t e = absD - rem; + + // We are going to start with a power of floor_log_2_d - 1. + // This works if works if e < 2**floor_log_2_d. + if (!branchfree && e < (1ULL << floor_log_2_d)) { + // This power works + more = floor_log_2_d - 1; + } else { + // We need to go one higher. This should not make proposed_m + // overflow, but it will make it negative when interpreted as an + // int32_t. + proposed_m += proposed_m; + const uint64_t twice_rem = rem + rem; + if (twice_rem >= absD || twice_rem < rem) proposed_m += 1; + // note that we only set the LIBDIVIDE_NEGATIVE_DIVISOR bit if we + // also set ADD_MARKER this is an annoying optimization that + // enables algorithm #4 to avoid the mask. However we always set it + // in the branchfree case + more = floor_log_2_d | LIBDIVIDE_ADD_MARKER; + } + proposed_m += 1; + int64_t magic = (int64_t)proposed_m; + + // Mark if we are negative + if (d < 0) { + more |= LIBDIVIDE_NEGATIVE_DIVISOR; + if (!branchfree) { + magic = -magic; + } + } + + result.more = more; + result.magic = magic; + } + return result; +} + +struct libdivide_s64_t libdivide_s64_gen(int64_t d) { + return libdivide_internal_s64_gen(d, 0); +} + +struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d) { + struct libdivide_s64_t tmp = libdivide_internal_s64_gen(d, 1); + struct libdivide_s64_branchfree_t ret = {tmp.magic, tmp.more}; + return ret; +} + +int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + + if (!denom->magic) { // shift path + uint64_t mask = (1ULL << shift) - 1; + uint64_t uq = numer + ((numer >> 63) & mask); + int64_t q = (int64_t)uq; + q >>= shift; + // must be arithmetic shift and then sign-extend + int64_t sign = (int8_t)more >> 7; + q = (q ^ sign) - sign; + return q; + } else { + uint64_t uq = (uint64_t)libdivide_mullhi_s64(denom->magic, numer); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift and then sign extend + int64_t sign = (int8_t)more >> 7; + // q += (more < 0 ? -numer : numer) + // cast required to avoid UB + uq += ((uint64_t)numer ^ sign) - sign; + } + int64_t q = (int64_t)uq; + q >>= shift; + q += (q < 0); + return q; + } +} + +int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + // must be arithmetic shift and then sign extend + int64_t sign = (int8_t)more >> 7; + int64_t magic = denom->magic; + int64_t q = libdivide_mullhi_s64(magic, numer); + q += numer; + + // If q is non-negative, we have nothing to do. + // If q is negative, we want to add either (2**shift)-1 if d is a power of + // 2, or (2**shift) if it is not a power of 2. + uint64_t is_power_of_2 = (magic == 0); + uint64_t q_sign = (uint64_t)(q >> 63); + q += q_sign & ((1ULL << shift) - is_power_of_2); + + // Arithmetic right shift + q >>= shift; + // Negate if needed + q = (q ^ sign) - sign; + + return q; +} + +int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom) { + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + if (denom->magic == 0) { // shift path + uint64_t absD = 1ULL << shift; + if (more & LIBDIVIDE_NEGATIVE_DIVISOR) { + absD = -absD; + } + return (int64_t)absD; + } else { + // Unsigned math is much easier + int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR); + int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER) + ? denom->magic > 0 : denom->magic < 0; + + uint64_t d = (uint64_t)(magic_was_negated ? -denom->magic : denom->magic); + uint64_t n_hi = 1ULL << shift, n_lo = 0; + uint64_t rem_ignored; + uint64_t q = libdivide_128_div_64_to_64(n_hi, n_lo, d, &rem_ignored); + int64_t result = (int64_t)(q + 1); + if (negative_divisor) { + result = -result; + } + return result; + } +} + +int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom) { + return libdivide_s64_recover((const struct libdivide_s64_t *)denom); +} + +#if defined(LIBDIVIDE_AVX512) + +static inline __m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom); +static inline __m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom); +static inline __m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom); +static inline __m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom); + +static inline __m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom); +static inline __m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom); +static inline __m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom); +static inline __m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom); + +//////// Internal Utility Functions + +static inline __m512i libdivide_s64_signbits(__m512i v) {; + return _mm512_srai_epi64(v, 63); +} + +static inline __m512i libdivide_s64_shift_right_vector(__m512i v, int amt) { + return _mm512_srai_epi64(v, amt); +} + +// Here, b is assumed to contain one 32-bit value repeated. +static inline __m512i libdivide_mullhi_u32_vector(__m512i a, __m512i b) { + __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epu32(a, b), 32); + __m512i a1X3X = _mm512_srli_epi64(a, 32); + __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0); + __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epu32(a1X3X, b), mask); + return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3); +} + +// b is one 32-bit value repeated. +static inline __m512i libdivide_mullhi_s32_vector(__m512i a, __m512i b) { + __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epi32(a, b), 32); + __m512i a1X3X = _mm512_srli_epi64(a, 32); + __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0); + __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epi32(a1X3X, b), mask); + return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3); +} + +// Here, y is assumed to contain one 64-bit value repeated. +// https://stackoverflow.com/a/28827013 +static inline __m512i libdivide_mullhi_u64_vector(__m512i x, __m512i y) { + __m512i lomask = _mm512_set1_epi64(0xffffffff); + __m512i xh = _mm512_shuffle_epi32(x, (_MM_PERM_ENUM) 0xB1); + __m512i yh = _mm512_shuffle_epi32(y, (_MM_PERM_ENUM) 0xB1); + __m512i w0 = _mm512_mul_epu32(x, y); + __m512i w1 = _mm512_mul_epu32(x, yh); + __m512i w2 = _mm512_mul_epu32(xh, y); + __m512i w3 = _mm512_mul_epu32(xh, yh); + __m512i w0h = _mm512_srli_epi64(w0, 32); + __m512i s1 = _mm512_add_epi64(w1, w0h); + __m512i s1l = _mm512_and_si512(s1, lomask); + __m512i s1h = _mm512_srli_epi64(s1, 32); + __m512i s2 = _mm512_add_epi64(w2, s1l); + __m512i s2h = _mm512_srli_epi64(s2, 32); + __m512i hi = _mm512_add_epi64(w3, s1h); + hi = _mm512_add_epi64(hi, s2h); + + return hi; +} + +// y is one 64-bit value repeated. +static inline __m512i libdivide_mullhi_s64_vector(__m512i x, __m512i y) { + __m512i p = libdivide_mullhi_u64_vector(x, y); + __m512i t1 = _mm512_and_si512(libdivide_s64_signbits(x), y); + __m512i t2 = _mm512_and_si512(libdivide_s64_signbits(y), x); + p = _mm512_sub_epi64(p, t1); + p = _mm512_sub_epi64(p, t2); + return p; +} + +////////// UINT32 + +__m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm512_srli_epi32(numers, more); + } + else { + __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q); + return _mm512_srli_epi32(t, shift); + } + else { + return _mm512_srli_epi32(q, more); + } + } +} + +__m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom) { + __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic)); + __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q); + return _mm512_srli_epi32(t, denom->more); +} + +////////// UINT64 + +__m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm512_srli_epi64(numers, more); + } + else { + __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q); + return _mm512_srli_epi64(t, shift); + } + else { + return _mm512_srli_epi64(q, more); + } + } +} + +__m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom) { + __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic)); + __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q); + return _mm512_srli_epi64(t, denom->more); +} + +////////// SINT32 + +__m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + uint32_t mask = (1U << shift) - 1; + __m512i roundToZeroTweak = _mm512_set1_epi32(mask); + // q = numer + ((numer >> 31) & roundToZeroTweak); + __m512i q = _mm512_add_epi32(numers, _mm512_and_si512(_mm512_srai_epi32(numers, 31), roundToZeroTweak)); + q = _mm512_srai_epi32(q, shift); + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign); + return q; + } + else { + __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm512_add_epi32(q, _mm512_sub_epi32(_mm512_xor_si512(numers, sign), sign)); + } + // q >>= shift + q = _mm512_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK); + q = _mm512_add_epi32(q, _mm512_srli_epi32(q, 31)); // q += (q < 0) + return q; + } +} + +__m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom) { + int32_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + // must be arithmetic shift + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(magic)); + q = _mm512_add_epi32(q, numers); // q += numers + + // If q is non-negative, we have nothing to do + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2 + uint32_t is_power_of_2 = (magic == 0); + __m512i q_sign = _mm512_srai_epi32(q, 31); // q_sign = q >> 31 + __m512i mask = _mm512_set1_epi32((1U << shift) - is_power_of_2); + q = _mm512_add_epi32(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask) + q = _mm512_srai_epi32(q, shift); // q >>= shift + q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +////////// SINT64 + +__m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom) { + uint8_t more = denom->more; + int64_t magic = denom->magic; + if (magic == 0) { // shift path + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + uint64_t mask = (1ULL << shift) - 1; + __m512i roundToZeroTweak = _mm512_set1_epi64(mask); + // q = numer + ((numer >> 63) & roundToZeroTweak); + __m512i q = _mm512_add_epi64(numers, _mm512_and_si512(libdivide_s64_signbits(numers), roundToZeroTweak)); + q = libdivide_s64_shift_right_vector(q, shift); + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign); + return q; + } + else { + __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm512_add_epi64(q, _mm512_sub_epi64(_mm512_xor_si512(numers, sign), sign)); + } + // q >>= denom->mult_path.shift + q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK); + q = _mm512_add_epi64(q, _mm512_srli_epi64(q, 63)); // q += (q < 0) + return q; + } +} + +__m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom) { + int64_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + // must be arithmetic shift + __m512i sign = _mm512_set1_epi32((int8_t)more >> 7); + + // libdivide_mullhi_s64(numers, magic); + __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic)); + q = _mm512_add_epi64(q, numers); // q += numers + + // If q is non-negative, we have nothing to do. + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2. + uint32_t is_power_of_2 = (magic == 0); + __m512i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63 + __m512i mask = _mm512_set1_epi64((1ULL << shift) - is_power_of_2); + q = _mm512_add_epi64(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask) + q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift + q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +#elif defined(LIBDIVIDE_AVX2) + +static inline __m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom); +static inline __m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom); +static inline __m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom); +static inline __m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom); + +static inline __m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom); +static inline __m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom); +static inline __m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom); +static inline __m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom); + +//////// Internal Utility Functions + +// Implementation of _mm256_srai_epi64(v, 63) (from AVX512). +static inline __m256i libdivide_s64_signbits(__m256i v) { + __m256i hiBitsDuped = _mm256_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1)); + __m256i signBits = _mm256_srai_epi32(hiBitsDuped, 31); + return signBits; +} + +// Implementation of _mm256_srai_epi64 (from AVX512). +static inline __m256i libdivide_s64_shift_right_vector(__m256i v, int amt) { + const int b = 64 - amt; + __m256i m = _mm256_set1_epi64x(1ULL << (b - 1)); + __m256i x = _mm256_srli_epi64(v, amt); + __m256i result = _mm256_sub_epi64(_mm256_xor_si256(x, m), m); + return result; +} + +// Here, b is assumed to contain one 32-bit value repeated. +static inline __m256i libdivide_mullhi_u32_vector(__m256i a, __m256i b) { + __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epu32(a, b), 32); + __m256i a1X3X = _mm256_srli_epi64(a, 32); + __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0); + __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epu32(a1X3X, b), mask); + return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3); +} + +// b is one 32-bit value repeated. +static inline __m256i libdivide_mullhi_s32_vector(__m256i a, __m256i b) { + __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epi32(a, b), 32); + __m256i a1X3X = _mm256_srli_epi64(a, 32); + __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0); + __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epi32(a1X3X, b), mask); + return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3); +} + +// Here, y is assumed to contain one 64-bit value repeated. +// https://stackoverflow.com/a/28827013 +static inline __m256i libdivide_mullhi_u64_vector(__m256i x, __m256i y) { + __m256i lomask = _mm256_set1_epi64x(0xffffffff); + __m256i xh = _mm256_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h + __m256i yh = _mm256_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h + __m256i w0 = _mm256_mul_epu32(x, y); // x0l*y0l, x1l*y1l + __m256i w1 = _mm256_mul_epu32(x, yh); // x0l*y0h, x1l*y1h + __m256i w2 = _mm256_mul_epu32(xh, y); // x0h*y0l, x1h*y0l + __m256i w3 = _mm256_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h + __m256i w0h = _mm256_srli_epi64(w0, 32); + __m256i s1 = _mm256_add_epi64(w1, w0h); + __m256i s1l = _mm256_and_si256(s1, lomask); + __m256i s1h = _mm256_srli_epi64(s1, 32); + __m256i s2 = _mm256_add_epi64(w2, s1l); + __m256i s2h = _mm256_srli_epi64(s2, 32); + __m256i hi = _mm256_add_epi64(w3, s1h); + hi = _mm256_add_epi64(hi, s2h); + + return hi; +} + +// y is one 64-bit value repeated. +static inline __m256i libdivide_mullhi_s64_vector(__m256i x, __m256i y) { + __m256i p = libdivide_mullhi_u64_vector(x, y); + __m256i t1 = _mm256_and_si256(libdivide_s64_signbits(x), y); + __m256i t2 = _mm256_and_si256(libdivide_s64_signbits(y), x); + p = _mm256_sub_epi64(p, t1); + p = _mm256_sub_epi64(p, t2); + return p; +} + +////////// UINT32 + +__m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm256_srli_epi32(numers, more); + } + else { + __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q); + return _mm256_srli_epi32(t, shift); + } + else { + return _mm256_srli_epi32(q, more); + } + } +} + +__m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom) { + __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic)); + __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q); + return _mm256_srli_epi32(t, denom->more); +} + +////////// UINT64 + +__m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm256_srli_epi64(numers, more); + } + else { + __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q); + return _mm256_srli_epi64(t, shift); + } + else { + return _mm256_srli_epi64(q, more); + } + } +} + +__m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom) { + __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic)); + __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q); + return _mm256_srli_epi64(t, denom->more); +} + +////////// SINT32 + +__m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + uint32_t mask = (1U << shift) - 1; + __m256i roundToZeroTweak = _mm256_set1_epi32(mask); + // q = numer + ((numer >> 31) & roundToZeroTweak); + __m256i q = _mm256_add_epi32(numers, _mm256_and_si256(_mm256_srai_epi32(numers, 31), roundToZeroTweak)); + q = _mm256_srai_epi32(q, shift); + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign); + return q; + } + else { + __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm256_add_epi32(q, _mm256_sub_epi32(_mm256_xor_si256(numers, sign), sign)); + } + // q >>= shift + q = _mm256_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK); + q = _mm256_add_epi32(q, _mm256_srli_epi32(q, 31)); // q += (q < 0) + return q; + } +} + +__m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom) { + int32_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + // must be arithmetic shift + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(magic)); + q = _mm256_add_epi32(q, numers); // q += numers + + // If q is non-negative, we have nothing to do + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2 + uint32_t is_power_of_2 = (magic == 0); + __m256i q_sign = _mm256_srai_epi32(q, 31); // q_sign = q >> 31 + __m256i mask = _mm256_set1_epi32((1U << shift) - is_power_of_2); + q = _mm256_add_epi32(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask) + q = _mm256_srai_epi32(q, shift); // q >>= shift + q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +////////// SINT64 + +__m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom) { + uint8_t more = denom->more; + int64_t magic = denom->magic; + if (magic == 0) { // shift path + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + uint64_t mask = (1ULL << shift) - 1; + __m256i roundToZeroTweak = _mm256_set1_epi64x(mask); + // q = numer + ((numer >> 63) & roundToZeroTweak); + __m256i q = _mm256_add_epi64(numers, _mm256_and_si256(libdivide_s64_signbits(numers), roundToZeroTweak)); + q = libdivide_s64_shift_right_vector(q, shift); + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign); + return q; + } + else { + __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm256_add_epi64(q, _mm256_sub_epi64(_mm256_xor_si256(numers, sign), sign)); + } + // q >>= denom->mult_path.shift + q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK); + q = _mm256_add_epi64(q, _mm256_srli_epi64(q, 63)); // q += (q < 0) + return q; + } +} + +__m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom) { + int64_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + // must be arithmetic shift + __m256i sign = _mm256_set1_epi32((int8_t)more >> 7); + + // libdivide_mullhi_s64(numers, magic); + __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic)); + q = _mm256_add_epi64(q, numers); // q += numers + + // If q is non-negative, we have nothing to do. + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2. + uint32_t is_power_of_2 = (magic == 0); + __m256i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63 + __m256i mask = _mm256_set1_epi64x((1ULL << shift) - is_power_of_2); + q = _mm256_add_epi64(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask) + q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift + q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +#elif defined(LIBDIVIDE_SSE2) + +static inline __m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom); +static inline __m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom); +static inline __m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom); +static inline __m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom); + +static inline __m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom); +static inline __m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom); +static inline __m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom); +static inline __m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom); + +//////// Internal Utility Functions + +// Implementation of _mm_srai_epi64(v, 63) (from AVX512). +static inline __m128i libdivide_s64_signbits(__m128i v) { + __m128i hiBitsDuped = _mm_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1)); + __m128i signBits = _mm_srai_epi32(hiBitsDuped, 31); + return signBits; +} + +// Implementation of _mm_srai_epi64 (from AVX512). +static inline __m128i libdivide_s64_shift_right_vector(__m128i v, int amt) { + const int b = 64 - amt; + __m128i m = _mm_set1_epi64x(1ULL << (b - 1)); + __m128i x = _mm_srli_epi64(v, amt); + __m128i result = _mm_sub_epi64(_mm_xor_si128(x, m), m); + return result; +} + +// Here, b is assumed to contain one 32-bit value repeated. +static inline __m128i libdivide_mullhi_u32_vector(__m128i a, __m128i b) { + __m128i hi_product_0Z2Z = _mm_srli_epi64(_mm_mul_epu32(a, b), 32); + __m128i a1X3X = _mm_srli_epi64(a, 32); + __m128i mask = _mm_set_epi32(-1, 0, -1, 0); + __m128i hi_product_Z1Z3 = _mm_and_si128(_mm_mul_epu32(a1X3X, b), mask); + return _mm_or_si128(hi_product_0Z2Z, hi_product_Z1Z3); +} + +// SSE2 does not have a signed multiplication instruction, but we can convert +// unsigned to signed pretty efficiently. Again, b is just a 32 bit value +// repeated four times. +static inline __m128i libdivide_mullhi_s32_vector(__m128i a, __m128i b) { + __m128i p = libdivide_mullhi_u32_vector(a, b); + // t1 = (a >> 31) & y, arithmetic shift + __m128i t1 = _mm_and_si128(_mm_srai_epi32(a, 31), b); + __m128i t2 = _mm_and_si128(_mm_srai_epi32(b, 31), a); + p = _mm_sub_epi32(p, t1); + p = _mm_sub_epi32(p, t2); + return p; +} + +// Here, y is assumed to contain one 64-bit value repeated. +// https://stackoverflow.com/a/28827013 +static inline __m128i libdivide_mullhi_u64_vector(__m128i x, __m128i y) { + __m128i lomask = _mm_set1_epi64x(0xffffffff); + __m128i xh = _mm_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h + __m128i yh = _mm_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h + __m128i w0 = _mm_mul_epu32(x, y); // x0l*y0l, x1l*y1l + __m128i w1 = _mm_mul_epu32(x, yh); // x0l*y0h, x1l*y1h + __m128i w2 = _mm_mul_epu32(xh, y); // x0h*y0l, x1h*y0l + __m128i w3 = _mm_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h + __m128i w0h = _mm_srli_epi64(w0, 32); + __m128i s1 = _mm_add_epi64(w1, w0h); + __m128i s1l = _mm_and_si128(s1, lomask); + __m128i s1h = _mm_srli_epi64(s1, 32); + __m128i s2 = _mm_add_epi64(w2, s1l); + __m128i s2h = _mm_srli_epi64(s2, 32); + __m128i hi = _mm_add_epi64(w3, s1h); + hi = _mm_add_epi64(hi, s2h); + + return hi; +} + +// y is one 64-bit value repeated. +static inline __m128i libdivide_mullhi_s64_vector(__m128i x, __m128i y) { + __m128i p = libdivide_mullhi_u64_vector(x, y); + __m128i t1 = _mm_and_si128(libdivide_s64_signbits(x), y); + __m128i t2 = _mm_and_si128(libdivide_s64_signbits(y), x); + p = _mm_sub_epi64(p, t1); + p = _mm_sub_epi64(p, t2); + return p; +} + +////////// UINT32 + +__m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm_srli_epi32(numers, more); + } + else { + __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q); + return _mm_srli_epi32(t, shift); + } + else { + return _mm_srli_epi32(q, more); + } + } +} + +__m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom) { + __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic)); + __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q); + return _mm_srli_epi32(t, denom->more); +} + +////////// UINT64 + +__m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + return _mm_srli_epi64(numers, more); + } + else { + __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // uint32_t t = ((numer - q) >> 1) + q; + // return t >> denom->shift; + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q); + return _mm_srli_epi64(t, shift); + } + else { + return _mm_srli_epi64(q, more); + } + } +} + +__m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom) { + __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic)); + __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q); + return _mm_srli_epi64(t, denom->more); +} + +////////// SINT32 + +__m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom) { + uint8_t more = denom->more; + if (!denom->magic) { + uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + uint32_t mask = (1U << shift) - 1; + __m128i roundToZeroTweak = _mm_set1_epi32(mask); + // q = numer + ((numer >> 31) & roundToZeroTweak); + __m128i q = _mm_add_epi32(numers, _mm_and_si128(_mm_srai_epi32(numers, 31), roundToZeroTweak)); + q = _mm_srai_epi32(q, shift); + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign); + return q; + } + else { + __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(denom->magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm_add_epi32(q, _mm_sub_epi32(_mm_xor_si128(numers, sign), sign)); + } + // q >>= shift + q = _mm_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK); + q = _mm_add_epi32(q, _mm_srli_epi32(q, 31)); // q += (q < 0) + return q; + } +} + +__m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom) { + int32_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK; + // must be arithmetic shift + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(magic)); + q = _mm_add_epi32(q, numers); // q += numers + + // If q is non-negative, we have nothing to do + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2 + uint32_t is_power_of_2 = (magic == 0); + __m128i q_sign = _mm_srai_epi32(q, 31); // q_sign = q >> 31 + __m128i mask = _mm_set1_epi32((1U << shift) - is_power_of_2); + q = _mm_add_epi32(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask) + q = _mm_srai_epi32(q, shift); // q >>= shift + q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +////////// SINT64 + +__m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom) { + uint8_t more = denom->more; + int64_t magic = denom->magic; + if (magic == 0) { // shift path + uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + uint64_t mask = (1ULL << shift) - 1; + __m128i roundToZeroTweak = _mm_set1_epi64x(mask); + // q = numer + ((numer >> 63) & roundToZeroTweak); + __m128i q = _mm_add_epi64(numers, _mm_and_si128(libdivide_s64_signbits(numers), roundToZeroTweak)); + q = libdivide_s64_shift_right_vector(q, shift); + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + // q = (q ^ sign) - sign; + q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign); + return q; + } + else { + __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic)); + if (more & LIBDIVIDE_ADD_MARKER) { + // must be arithmetic shift + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + // q += ((numer ^ sign) - sign); + q = _mm_add_epi64(q, _mm_sub_epi64(_mm_xor_si128(numers, sign), sign)); + } + // q >>= denom->mult_path.shift + q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK); + q = _mm_add_epi64(q, _mm_srli_epi64(q, 63)); // q += (q < 0) + return q; + } +} + +__m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom) { + int64_t magic = denom->magic; + uint8_t more = denom->more; + uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK; + // must be arithmetic shift + __m128i sign = _mm_set1_epi32((int8_t)more >> 7); + + // libdivide_mullhi_s64(numers, magic); + __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic)); + q = _mm_add_epi64(q, numers); // q += numers + + // If q is non-negative, we have nothing to do. + // If q is negative, we want to add either (2**shift)-1 if d is + // a power of 2, or (2**shift) if it is not a power of 2. + uint32_t is_power_of_2 = (magic == 0); + __m128i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63 + __m128i mask = _mm_set1_epi64x((1ULL << shift) - is_power_of_2); + q = _mm_add_epi64(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask) + q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift + q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign + return q; +} + +#endif + +/////////// C++ stuff + +#ifdef __cplusplus + +// The C++ divider class is templated on both an integer type +// (like uint64_t) and an algorithm type. +// * BRANCHFULL is the default algorithm type. +// * BRANCHFREE is the branchfree algorithm type. +enum { + BRANCHFULL, + BRANCHFREE +}; + +#if defined(LIBDIVIDE_AVX512) + #define LIBDIVIDE_VECTOR_TYPE __m512i +#elif defined(LIBDIVIDE_AVX2) + #define LIBDIVIDE_VECTOR_TYPE __m256i +#elif defined(LIBDIVIDE_SSE2) + #define LIBDIVIDE_VECTOR_TYPE __m128i +#endif + +#if !defined(LIBDIVIDE_VECTOR_TYPE) + #define LIBDIVIDE_DIVIDE_VECTOR(ALGO) +#else + #define LIBDIVIDE_DIVIDE_VECTOR(ALGO) \ + LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const { \ + return libdivide_##ALGO##_do_vector(n, &denom); \ + } +#endif + +// The DISPATCHER_GEN() macro generates C++ methods (for the given integer +// and algorithm types) that redirect to libdivide's C API. +#define DISPATCHER_GEN(T, ALGO) \ + libdivide_##ALGO##_t denom; \ + dispatcher() { } \ + dispatcher(T d) \ + : denom(libdivide_##ALGO##_gen(d)) \ + { } \ + T divide(T n) const { \ + return libdivide_##ALGO##_do(n, &denom); \ + } \ + LIBDIVIDE_DIVIDE_VECTOR(ALGO) \ + T recover() const { \ + return libdivide_##ALGO##_recover(&denom); \ + } + +// The dispatcher selects a specific division algorithm for a given +// type and ALGO using partial template specialization. +template struct dispatcher { }; + +template<> struct dispatcher { DISPATCHER_GEN(int32_t, s32) }; +template<> struct dispatcher { DISPATCHER_GEN(int32_t, s32_branchfree) }; +template<> struct dispatcher { DISPATCHER_GEN(uint32_t, u32) }; +template<> struct dispatcher { DISPATCHER_GEN(uint32_t, u32_branchfree) }; +template<> struct dispatcher { DISPATCHER_GEN(int64_t, s64) }; +template<> struct dispatcher { DISPATCHER_GEN(int64_t, s64_branchfree) }; +template<> struct dispatcher { DISPATCHER_GEN(uint64_t, u64) }; +template<> struct dispatcher { DISPATCHER_GEN(uint64_t, u64_branchfree) }; + +// This is the main divider class for use by the user (C++ API). +// The actual division algorithm is selected using the dispatcher struct +// based on the integer and algorithm template parameters. +template +class divider { +public: + // We leave the default constructor empty so that creating + // an array of dividers and then initializing them + // later doesn't slow us down. + divider() { } + + // Constructor that takes the divisor as a parameter + divider(T d) : div(d) { } + + // Divides n by the divisor + T divide(T n) const { + return div.divide(n); + } + + // Recovers the divisor, returns the value that was + // used to initialize this divider object. + T recover() const { + return div.recover(); + } + + bool operator==(const divider& other) const { + return div.denom.magic == other.denom.magic && + div.denom.more == other.denom.more; + } + + bool operator!=(const divider& other) const { + return !(*this == other); + } + +#if defined(LIBDIVIDE_VECTOR_TYPE) + // Treats the vector as packed integer values with the same type as + // the divider (e.g. s32, u32, s64, u64) and divides each of + // them by the divider, returning the packed quotients. + LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const { + return div.divide(n); + } +#endif + +private: + // Storage for the actual divisor + dispatcher::value, + std::is_signed::value, sizeof(T), ALGO> div; +}; + +// Overload of operator / for scalar division +template +T operator/(T n, const divider& div) { + return div.divide(n); +} + +// Overload of operator /= for scalar division +template +T& operator/=(T& n, const divider& div) { + n = div.divide(n); + return n; +} + +#if defined(LIBDIVIDE_VECTOR_TYPE) + // Overload of operator / for vector division + template + LIBDIVIDE_VECTOR_TYPE operator/(LIBDIVIDE_VECTOR_TYPE n, const divider& div) { + return div.divide(n); + } + // Overload of operator /= for vector division + template + LIBDIVIDE_VECTOR_TYPE& operator/=(LIBDIVIDE_VECTOR_TYPE& n, const divider& div) { + n = div.divide(n); + return n; + } +#endif + +// libdivdie::branchfree_divider +template +using branchfree_divider = divider; + +} // namespace libdivide + +#endif // __cplusplus + +#endif // NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ufuncobject.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ufuncobject.h new file mode 100644 index 0000000000000000000000000000000000000000..169a93eb5597777b67733cb5806bca7a2d38cc56 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/ufuncobject.h @@ -0,0 +1,347 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * The legacy generic inner loop for a standard element-wise or + * generalized ufunc. + */ +typedef void (*PyUFuncGenericFunction) + (char **args, + npy_intp const *dimensions, + npy_intp const *strides, + void *innerloopdata); + +/* + * The most generic one-dimensional inner loop for + * a masked standard element-wise ufunc. "Masked" here means that it skips + * doing calculations on any items for which the maskptr array has a true + * value. + */ +typedef void (PyUFunc_MaskedStridedInnerLoopFunc)( + char **dataptrs, npy_intp *strides, + char *maskptr, npy_intp mask_stride, + npy_intp count, + NpyAuxData *innerloopdata); + +/* Forward declaration for the type resolver and loop selector typedefs */ +struct _tagPyUFuncObject; + +/* + * Given the operands for calling a ufunc, should determine the + * calculation input and output data types and return an inner loop function. + * This function should validate that the casting rule is being followed, + * and fail if it is not. + * + * For backwards compatibility, the regular type resolution function does not + * support auxiliary data with object semantics. The type resolution call + * which returns a masked generic function returns a standard NpyAuxData + * object, for which the NPY_AUXDATA_FREE and NPY_AUXDATA_CLONE macros + * work. + * + * ufunc: The ufunc object. + * casting: The 'casting' parameter provided to the ufunc. + * operands: An array of length (ufunc->nin + ufunc->nout), + * with the output parameters possibly NULL. + * type_tup: Either NULL, or the type_tup passed to the ufunc. + * out_dtypes: An array which should be populated with new + * references to (ufunc->nin + ufunc->nout) new + * dtypes, one for each input and output. These + * dtypes should all be in native-endian format. + * + * Should return 0 on success, -1 on failure (with exception set), + * or -2 if Py_NotImplemented should be returned. + */ +typedef int (PyUFunc_TypeResolutionFunc)( + struct _tagPyUFuncObject *ufunc, + NPY_CASTING casting, + PyArrayObject **operands, + PyObject *type_tup, + PyArray_Descr **out_dtypes); + +/* + * This is the signature for the functions that may be assigned to the + * `process_core_dims_func` field of the PyUFuncObject structure. + * Implementation of this function is optional. This function is only used + * by generalized ufuncs (i.e. those with the field `core_enabled` set to 1). + * The function is called by the ufunc during the processing of the arguments + * of a call of the ufunc. The function can check the core dimensions of the + * input and output arrays and return -1 with an exception set if any + * requirements are not satisfied. If the caller of the ufunc didn't provide + * output arrays, the core dimensions associated with the output arrays (i.e. + * those that are not also used in input arrays) will have the value -1 in + * `core_dim_sizes`. This function can replace any output core dimensions + * that are -1 with a value that is appropriate for the ufunc. + * + * Parameter Description + * --------------- ------------------------------------------------------ + * ufunc The ufunc object + * core_dim_sizes An array with length `ufunc->core_num_dim_ix`. + * The core dimensions of the arrays passed to the ufunc + * will have been set. If the caller of the ufunc didn't + * provide the output array(s), the output-only core + * dimensions will have the value -1. + * + * The function must not change any element in `core_dim_sizes` that is + * not -1 on input. Doing so will result in incorrect output from the + * ufunc, and could result in a crash of the Python interpreter. + * + * The function must return 0 on success, -1 on failure (with an exception + * set). + */ +typedef int (PyUFunc_ProcessCoreDimsFunc)( + struct _tagPyUFuncObject *ufunc, + npy_intp *core_dim_sizes); + +typedef struct _tagPyUFuncObject { + PyObject_HEAD + /* + * nin: Number of inputs + * nout: Number of outputs + * nargs: Always nin + nout (Why is it stored?) + */ + int nin, nout, nargs; + + /* + * Identity for reduction, any of PyUFunc_One, PyUFunc_Zero + * PyUFunc_MinusOne, PyUFunc_None, PyUFunc_ReorderableNone, + * PyUFunc_IdentityValue. + */ + int identity; + + /* Array of one-dimensional core loops */ + PyUFuncGenericFunction *functions; + /* Array of funcdata that gets passed into the functions */ + void *const *data; + /* The number of elements in 'functions' and 'data' */ + int ntypes; + + /* Used to be unused field 'check_return' */ + int reserved1; + + /* The name of the ufunc */ + const char *name; + + /* Array of type numbers, of size ('nargs' * 'ntypes') */ + const char *types; + + /* Documentation string */ + const char *doc; + + void *ptr; + PyObject *obj; + PyObject *userloops; + + /* generalized ufunc parameters */ + + /* 0 for scalar ufunc; 1 for generalized ufunc */ + int core_enabled; + /* number of distinct dimension names in signature */ + int core_num_dim_ix; + + /* + * dimension indices of input/output argument k are stored in + * core_dim_ixs[core_offsets[k]..core_offsets[k]+core_num_dims[k]-1] + */ + + /* numbers of core dimensions of each argument */ + int *core_num_dims; + /* + * dimension indices in a flatted form; indices + * are in the range of [0,core_num_dim_ix) + */ + int *core_dim_ixs; + /* + * positions of 1st core dimensions of each + * argument in core_dim_ixs, equivalent to cumsum(core_num_dims) + */ + int *core_offsets; + /* signature string for printing purpose */ + char *core_signature; + + /* + * A function which resolves the types and fills an array + * with the dtypes for the inputs and outputs. + */ + PyUFunc_TypeResolutionFunc *type_resolver; + + /* A dictionary to monkeypatch ufuncs */ + PyObject *dict; + + /* + * This was blocked off to be the "new" inner loop selector in 1.7, + * but this was never implemented. (This is also why the above + * selector is called the "legacy" selector.) + */ + #ifndef Py_LIMITED_API + vectorcallfunc vectorcall; + #else + void *vectorcall; + #endif + + /* Was previously the `PyUFunc_MaskedInnerLoopSelectionFunc` */ + void *reserved3; + + /* + * List of flags for each operand when ufunc is called by nditer object. + * These flags will be used in addition to the default flags for each + * operand set by nditer object. + */ + npy_uint32 *op_flags; + + /* + * List of global flags used when ufunc is called by nditer object. + * These flags will be used in addition to the default global flags + * set by nditer object. + */ + npy_uint32 iter_flags; + + /* New in NPY_API_VERSION 0x0000000D and above */ + #if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION + /* + * for each core_num_dim_ix distinct dimension names, + * the possible "frozen" size (-1 if not frozen). + */ + npy_intp *core_dim_sizes; + + /* + * for each distinct core dimension, a set of UFUNC_CORE_DIM* flags + */ + npy_uint32 *core_dim_flags; + + /* Identity for reduction, when identity == PyUFunc_IdentityValue */ + PyObject *identity_value; + #endif /* NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION */ + + /* New in NPY_API_VERSION 0x0000000F and above */ + #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION + /* New private fields related to dispatching */ + void *_dispatch_cache; + /* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */ + PyObject *_loops; + #endif + #if NPY_FEATURE_VERSION >= NPY_2_1_API_VERSION + /* + * Optional function to process core dimensions of a gufunc. + */ + PyUFunc_ProcessCoreDimsFunc *process_core_dims_func; + #endif +} PyUFuncObject; + +#include "arrayobject.h" +/* Generalized ufunc; 0x0001 reserved for possible use as CORE_ENABLED */ +/* the core dimension's size will be determined by the operands. */ +#define UFUNC_CORE_DIM_SIZE_INFERRED 0x0002 +/* the core dimension may be absent */ +#define UFUNC_CORE_DIM_CAN_IGNORE 0x0004 +/* flags inferred during execution */ +#define UFUNC_CORE_DIM_MISSING 0x00040000 + + +#define UFUNC_OBJ_ISOBJECT 1 +#define UFUNC_OBJ_NEEDS_API 2 + + +#if NPY_ALLOW_THREADS +#define NPY_LOOP_BEGIN_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) _save = PyEval_SaveThread();} while (0); +#define NPY_LOOP_END_THREADS do {if (!(loop->obj & UFUNC_OBJ_NEEDS_API)) PyEval_RestoreThread(_save);} while (0); +#else +#define NPY_LOOP_BEGIN_THREADS +#define NPY_LOOP_END_THREADS +#endif + +/* + * UFunc has unit of 0, and the order of operations can be reordered + * This case allows reduction with multiple axes at once. + */ +#define PyUFunc_Zero 0 +/* + * UFunc has unit of 1, and the order of operations can be reordered + * This case allows reduction with multiple axes at once. + */ +#define PyUFunc_One 1 +/* + * UFunc has unit of -1, and the order of operations can be reordered + * This case allows reduction with multiple axes at once. Intended for + * bitwise_and reduction. + */ +#define PyUFunc_MinusOne 2 +/* + * UFunc has no unit, and the order of operations cannot be reordered. + * This case does not allow reduction with multiple axes at once. + */ +#define PyUFunc_None -1 +/* + * UFunc has no unit, and the order of operations can be reordered + * This case allows reduction with multiple axes at once. + */ +#define PyUFunc_ReorderableNone -2 +/* + * UFunc unit is an identity_value, and the order of operations can be reordered + * This case allows reduction with multiple axes at once. + */ +#define PyUFunc_IdentityValue -3 + + +#define UFUNC_REDUCE 0 +#define UFUNC_ACCUMULATE 1 +#define UFUNC_REDUCEAT 2 +#define UFUNC_OUTER 3 + + +typedef struct { + int nin; + int nout; + PyObject *callable; +} PyUFunc_PyFuncData; + +/* A linked-list of function information for + user-defined 1-d loops. + */ +typedef struct _loop1d_info { + PyUFuncGenericFunction func; + void *data; + int *arg_types; + struct _loop1d_info *next; + int nargs; + PyArray_Descr **arg_dtypes; +} PyUFunc_Loop1d; + + +#define UFUNC_PYVALS_NAME "UFUNC_PYVALS" + +/* + * THESE MACROS ARE DEPRECATED. + * Use npy_set_floatstatus_* in the npymath library. + */ +#define UFUNC_FPE_DIVIDEBYZERO NPY_FPE_DIVIDEBYZERO +#define UFUNC_FPE_OVERFLOW NPY_FPE_OVERFLOW +#define UFUNC_FPE_UNDERFLOW NPY_FPE_UNDERFLOW +#define UFUNC_FPE_INVALID NPY_FPE_INVALID + +#define generate_divbyzero_error() npy_set_floatstatus_divbyzero() +#define generate_overflow_error() npy_set_floatstatus_overflow() + + /* Make sure it gets defined if it isn't already */ +#ifndef UFUNC_NOFPE +/* Clear the floating point exception default of Borland C++ */ +#if defined(__BORLANDC__) +#define UFUNC_NOFPE _control87(MCW_EM, MCW_EM); +#else +#define UFUNC_NOFPE +#endif +#endif + +#include "__ufunc_api.h" + +#ifdef __cplusplus +} +#endif + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_UFUNCOBJECT_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/utils.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/utils.h new file mode 100644 index 0000000000000000000000000000000000000000..97f06092e54050baf3c2fc4372429cbd110429e8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/include/numpy/utils.h @@ -0,0 +1,37 @@ +#ifndef NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ +#define NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ + +#ifndef __COMP_NPY_UNUSED + #if defined(__GNUC__) + #define __COMP_NPY_UNUSED __attribute__ ((__unused__)) + #elif defined(__ICC) + #define __COMP_NPY_UNUSED __attribute__ ((__unused__)) + #elif defined(__clang__) + #define __COMP_NPY_UNUSED __attribute__ ((unused)) + #else + #define __COMP_NPY_UNUSED + #endif +#endif + +#if defined(__GNUC__) || defined(__ICC) || defined(__clang__) + #define NPY_DECL_ALIGNED(x) __attribute__ ((aligned (x))) +#elif defined(_MSC_VER) + #define NPY_DECL_ALIGNED(x) __declspec(align(x)) +#else + #define NPY_DECL_ALIGNED(x) +#endif + +/* Use this to tag a variable as not used. It will remove unused variable + * warning on support platforms (see __COM_NPY_UNUSED) and mangle the variable + * to avoid accidental use */ +#define NPY_UNUSED(x) __NPY_UNUSED_TAGGED ## x __COMP_NPY_UNUSED +#define NPY_EXPAND(x) x + +#define NPY_STRINGIFY(x) #x +#define NPY_TOSTRING(x) NPY_STRINGIFY(x) + +#define NPY_CAT__(a, b) a ## b +#define NPY_CAT_(a, b) NPY_CAT__(a, b) +#define NPY_CAT(a, b) NPY_CAT_(a, b) + +#endif /* NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ */ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/mlib.ini b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/mlib.ini new file mode 100644 index 0000000000000000000000000000000000000000..5840f5e1bc167f50ebc9fc98d60b60ee21ecbeec --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/mlib.ini @@ -0,0 +1,12 @@ +[meta] +Name = mlib +Description = Math library used with this version of numpy +Version = 1.0 + +[default] +Libs=-lm +Cflags= + +[msvc] +Libs=m.lib +Cflags= diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/npymath.ini b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/npymath.ini new file mode 100644 index 0000000000000000000000000000000000000000..8d879e3fb772ef04afe28bf6670a5d233acfe710 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/npy-pkg-config/npymath.ini @@ -0,0 +1,20 @@ +[meta] +Name=npymath +Description=Portable, core math library implementing C99 standard +Version=0.1 + +[variables] +pkgname=numpy._core +prefix=${pkgdir} +libdir=${prefix}/lib +includedir=${prefix}/include + +[default] +Libs=-L${libdir} -lnpymath +Cflags=-I${includedir} +Requires=mlib + +[msvc] +Libs=/LIBPATH:${libdir} npymath.lib +Cflags=/INCLUDE:${includedir} +Requires=mlib diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/pkgconfig/numpy.pc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/pkgconfig/numpy.pc new file mode 100644 index 0000000000000000000000000000000000000000..d41fe0ae095ce21f36fd2491e21ccc3eabf1e18f --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/lib/pkgconfig/numpy.pc @@ -0,0 +1,7 @@ +prefix=${pcfiledir}/../.. +includedir=${prefix}/include + +Name: numpy +Description: NumPy is the fundamental package for scientific computing with Python. +Version: 2.2.6 +Cflags: -I${includedir} diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/generate_umath_validation_data.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/generate_umath_validation_data.cpp new file mode 100644 index 0000000000000000000000000000000000000000..88ff45e161f8a37dd8aa9ee9f624db07eb42e743 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/generate_umath_validation_data.cpp @@ -0,0 +1,170 @@ +#include +#include +#include +#include +#include +#include +#include +#include + +struct ufunc { + std::string name; + double (*f32func)(double); + long double (*f64func)(long double); + float f32ulp; + float f64ulp; +}; + +template +T +RandomFloat(T a, T b) +{ + T random = ((T)rand()) / (T)RAND_MAX; + T diff = b - a; + T r = random * diff; + return a + r; +} + +template +void +append_random_array(std::vector &arr, T min, T max, size_t N) +{ + for (size_t ii = 0; ii < N; ++ii) + arr.emplace_back(RandomFloat(min, max)); +} + +template +std::vector +computeTrueVal(const std::vector &in, T2 (*mathfunc)(T2)) +{ + std::vector out; + for (T1 elem : in) { + T2 elem_d = (T2)elem; + T1 out_elem = (T1)mathfunc(elem_d); + out.emplace_back(out_elem); + } + return out; +} + +/* + * FP range: + * [-inf, -maxflt, -1., -minflt, -minden, 0., minden, minflt, 1., maxflt, inf] + */ + +#define MINDEN std::numeric_limits::denorm_min() +#define MINFLT std::numeric_limits::min() +#define MAXFLT std::numeric_limits::max() +#define INF std::numeric_limits::infinity() +#define qNAN std::numeric_limits::quiet_NaN() +#define sNAN std::numeric_limits::signaling_NaN() + +template +std::vector +generate_input_vector(std::string func) +{ + std::vector input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT, + -MAXFLT, INF, -INF, qNAN, sNAN, + -1.0, 1.0, 0.0, -0.0}; + + // [-1.0, 1.0] + if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) { + append_random_array(input, -1.0, 1.0, 700); + } + // (0.0, INF] + else if ((func == "log2") || (func == "log10")) { + append_random_array(input, 0.0, 1.0, 200); + append_random_array(input, MINDEN, MINFLT, 200); + append_random_array(input, MINFLT, 1.0, 200); + append_random_array(input, 1.0, MAXFLT, 200); + } + // (-1.0, INF] + else if (func == "log1p") { + append_random_array(input, -1.0, 1.0, 200); + append_random_array(input, -MINFLT, -MINDEN, 100); + append_random_array(input, -1.0, -MINFLT, 100); + append_random_array(input, MINDEN, MINFLT, 100); + append_random_array(input, MINFLT, 1.0, 100); + append_random_array(input, 1.0, MAXFLT, 100); + } + // [1.0, INF] + else if (func == "arccosh") { + append_random_array(input, 1.0, 2.0, 400); + append_random_array(input, 2.0, MAXFLT, 300); + } + // [-INF, INF] + else { + append_random_array(input, -1.0, 1.0, 100); + append_random_array(input, MINDEN, MINFLT, 100); + append_random_array(input, -MINFLT, -MINDEN, 100); + append_random_array(input, MINFLT, 1.0, 100); + append_random_array(input, -1.0, -MINFLT, 100); + append_random_array(input, 1.0, MAXFLT, 100); + append_random_array(input, -MAXFLT, -100.0, 100); + } + + std::random_shuffle(input.begin(), input.end()); + return input; +} + +int +main() +{ + srand(42); + std::vector umathfunc = { + {"sin", sin, sin, 1.49, 1.00}, + {"cos", cos, cos, 1.49, 1.00}, + {"tan", tan, tan, 3.91, 1.00}, + {"arcsin", asin, asin, 3.12, 1.00}, + {"arccos", acos, acos, 2.1, 1.00}, + {"arctan", atan, atan, 2.3, 1.00}, + {"sinh", sinh, sinh, 1.55, 1.00}, + {"cosh", cosh, cosh, 2.48, 1.00}, + {"tanh", tanh, tanh, 1.38, 2.00}, + {"arcsinh", asinh, asinh, 1.01, 1.00}, + {"arccosh", acosh, acosh, 1.16, 1.00}, + {"arctanh", atanh, atanh, 1.45, 1.00}, + {"cbrt", cbrt, cbrt, 1.94, 2.00}, + //{"exp",exp,exp,3.76,1.00}, + {"exp2", exp2, exp2, 1.01, 1.00}, + {"expm1", expm1, expm1, 2.62, 1.00}, + //{"log",log,log,1.84,1.00}, + {"log10", log10, log10, 3.5, 1.00}, + {"log1p", log1p, log1p, 1.96, 1.0}, + {"log2", log2, log2, 2.12, 1.00}, + }; + + for (int ii = 0; ii < umathfunc.size(); ++ii) { + // ignore sin/cos + if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) { + std::string fileName = + "umath-validation-set-" + umathfunc[ii].name + ".csv"; + std::ofstream txtOut; + txtOut.open(fileName, std::ofstream::trunc); + txtOut << "dtype,input,output,ulperrortol" << std::endl; + + // Single Precision + auto f32in = generate_input_vector(umathfunc[ii].name); + auto f32out = computeTrueVal(f32in, + umathfunc[ii].f32func); + for (int jj = 0; jj < f32in.size(); ++jj) { + txtOut << "np.float32" << std::hex << ",0x" + << *reinterpret_cast(&f32in[jj]) << ",0x" + << *reinterpret_cast(&f32out[jj]) << "," + << ceil(umathfunc[ii].f32ulp) << std::endl; + } + + // Double Precision + auto f64in = generate_input_vector(umathfunc[ii].name); + auto f64out = computeTrueVal( + f64in, umathfunc[ii].f64func); + for (int jj = 0; jj < f64in.size(); ++jj) { + txtOut << "np.float64" << std::hex << ",0x" + << *reinterpret_cast(&f64in[jj]) << ",0x" + << *reinterpret_cast(&f64out[jj]) << "," + << ceil(umathfunc[ii].f64ulp) << std::endl; + } + txtOut.close(); + } + } + return 0; +} diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-README.txt b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-README.txt new file mode 100644 index 0000000000000000000000000000000000000000..cfc9e4145d10276a4d296f3e189debeb581db780 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-README.txt @@ -0,0 +1,15 @@ +Steps to validate transcendental functions: +1) Add a file 'umath-validation-set-.txt', where ufuncname is name of + the function in NumPy you want to validate +2) The file should contain 4 columns: dtype,input,expected output,ulperror + a. dtype: one of np.float16, np.float32, np.float64 + b. input: floating point input to ufunc in hex. Example: 0x414570a4 + represents 12.340000152587890625 + c. expected output: floating point output for the corresponding input in hex. + This should be computed using a high(er) precision library and then rounded to + same format as the input. + d. ulperror: expected maximum ulp error of the function. This + should be same across all rows of the same dtype. Otherwise, the function is + tested for the maximum ulp error among all entries of that dtype. +3) Add file umath-validation-set-.txt to the test file test_umath_accuracy.py + which will then validate your ufunc. diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-arctan.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-arctan.csv new file mode 100644 index 0000000000000000000000000000000000000000..c03e144a994afceb4b43622381bd07134dec1e9e --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-arctan.csv @@ -0,0 +1,1429 @@ +dtype,input,output,ulperrortol +np.float32,0x3f338252,0x3f1c8d9c,3 +np.float32,0x7e569df2,0x3fc90fdb,3 +np.float32,0xbf347e25,0xbf1d361f,3 +np.float32,0xbf0a654e,0xbefdbfd2,3 +np.float32,0x8070968e,0x8070968e,3 +np.float32,0x803cfb27,0x803cfb27,3 +np.float32,0x8024362e,0x8024362e,3 +np.float32,0xfd55dca0,0xbfc90fdb,3 +np.float32,0x592b82,0x592b82,3 +np.float32,0x802eb8e1,0x802eb8e1,3 +np.float32,0xbc5fef40,0xbc5febae,3 +np.float32,0x3f1f6ce8,0x3f0e967c,3 +np.float32,0x20bedc,0x20bedc,3 +np.float32,0xbf058860,0xbef629c7,3 +np.float32,0x311504,0x311504,3 +np.float32,0xbd23f560,0xbd23defa,3 +np.float32,0x800ff4e8,0x800ff4e8,3 +np.float32,0x355009,0x355009,3 +np.float32,0x3f7be42e,0x3f46fdb3,3 +np.float32,0xbf225f7c,0xbf10b364,3 +np.float32,0x8074fa9e,0x8074fa9e,3 +np.float32,0xbea4b418,0xbe9f59ce,3 +np.float32,0xbe909c14,0xbe8cf045,3 +np.float32,0x80026bee,0x80026bee,3 +np.float32,0x3d789c20,0x3d784e25,3 +np.float32,0x7f56a4ba,0x3fc90fdb,3 +np.float32,0xbf70d141,0xbf413db7,3 +np.float32,0xbf2c4886,0xbf17a505,3 +np.float32,0x7e2993bf,0x3fc90fdb,3 +np.float32,0xbe2c8a30,0xbe2aef28,3 +np.float32,0x803f82d9,0x803f82d9,3 +np.float32,0x3f062fbc,0x3ef730a1,3 +np.float32,0x3f349ee0,0x3f1d4bfa,3 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+np.float64,0x3fc0b3d8872167b1,0x3fe03b178d354f8d,2 +np.float64,0x5ee8b0acbdd17,0x2a9cf69f2e317729,2 +np.float64,0x8006ef0407adde09,0xaa9e82888f3dd83e,2 +np.float64,0x7fdbb08a07b76113,0x553e7e4e35b938b9,2 +np.float64,0x49663f9c92cc9,0x2a9a95e0affe5108,2 +np.float64,0x7fd9b87e79b370fc,0x553dc0b5cff3dc7d,2 +np.float64,0xbfd86ae657b0d5cc,0xbfe73584d02bdd2b,2 +np.float64,0x3fd4d4a13729a942,0x3fe6030a962aaaf8,2 +np.float64,0x7fcc246bcb3848d7,0x5538557309449bba,2 +np.float64,0xbfdc86a7d5b90d50,0xbfe871a2983c2a29,2 +np.float64,0xd2a6e995a54dd,0x2aa2e3e9c0fdd6c0,2 +np.float64,0x3f92eb447825d680,0x3fd0eb4fd2ba16d2,2 +np.float64,0x800d4001697a8003,0xaaa2ee358661b75c,2 +np.float64,0x3fd3705fd1a6e0c0,0x3fe582a6f321d7d6,2 +np.float64,0xbfcfdf51533fbea4,0xbfe421c3bdd9f2a3,2 +np.float64,0x3fe268e87964d1d1,0x3fea9d47e08aad8a,2 +np.float64,0x24b8901e49713,0x2a951adeefe7b31b,2 +np.float64,0x3fedb35d687b66bb,0x3fef36e440850bf8,2 +np.float64,0x3fb7ab5cbe2f56c0,0x3fdcf097380721c6,2 +np.float64,0x3f8c4eaa10389d54,0x3fceb7ecb605b73b,2 +np.float64,0xbfed831ed6fb063e,0xbfef25f462a336f1,2 +np.float64,0x7fd8c52112318a41,0x553d61b0ee609f58,2 +np.float64,0xbfe71c4ff76e38a0,0xbfecb5d32e789771,2 +np.float64,0xbfe35fb7b166bf70,0xbfeb12328e75ee6b,2 +np.float64,0x458e1a3a8b1c4,0x2a9a1cebadc81342,2 +np.float64,0x8003c1b3ad478368,0xaa98df5ed060b28c,2 +np.float64,0x7ff4000000000000,0x7ffc000000000000,2 +np.float64,0x7fe17098c162e131,0x5540775a9a3a104f,2 +np.float64,0xbfd95cb71732b96e,0xbfe7812acf7ea511,2 +np.float64,0x8000000000000001,0xa990000000000000,2 +np.float64,0xbfde0e7d9ebc1cfc,0xbfe8df9ca9e49a5b,2 +np.float64,0xffef4f67143e9ecd,0xd5440348a6a2f231,2 +np.float64,0x7fe37d23c826fa47,0x5541165de17caa03,2 +np.float64,0xbfcc0e5f85381cc0,0xbfe34b44b0deefe9,2 +np.float64,0x3fe858f1c470b1e4,0x3fed36ab90557d89,2 +np.float64,0x800e857278fd0ae5,0xaaa3847d13220545,2 +np.float64,0x3febd31a66f7a635,0x3fee8af90e66b043,2 +np.float64,0x7fd3fde1b127fbc2,0x553b5b186a49b968,2 +np.float64,0x3fd3dabb8b27b577,0x3fe5a99b446bed26,2 +np.float64,0xffeb4500f1768a01,0xd5431cab828e254a,2 +np.float64,0xffccca8fc6399520,0xd53884f8b505e79e,2 +np.float64,0xffeee9406b7dd280,0xd543ed6d27a1a899,2 +np.float64,0xffecdde0f0f9bbc1,0xd5437a6258b14092,2 +np.float64,0xe6b54005cd6a8,0x2aa378c25938dfda,2 +np.float64,0x7fe610f1022c21e1,0x5541cf460b972925,2 +np.float64,0xbfe5a170ec6b42e2,0xbfec1576081e3232,2 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp.csv new file mode 100644 index 0000000000000000000000000000000000000000..7c5ef3b334fbd8a63bb1a0e6ad72577f767dbaf6 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp.csv @@ -0,0 +1,412 @@ +dtype,input,output,ulperrortol +## +ve denormals ## +np.float32,0x004b4716,0x3f800000,3 +np.float32,0x007b2490,0x3f800000,3 +np.float32,0x007c99fa,0x3f800000,3 +np.float32,0x00734a0c,0x3f800000,3 +np.float32,0x0070de24,0x3f800000,3 +np.float32,0x00495d65,0x3f800000,3 +np.float32,0x006894f6,0x3f800000,3 +np.float32,0x00555a76,0x3f800000,3 +np.float32,0x004e1fb8,0x3f800000,3 +np.float32,0x00687de9,0x3f800000,3 +## -ve denormals ## +np.float32,0x805b59af,0x3f800000,3 +np.float32,0x807ed8ed,0x3f800000,3 +np.float32,0x807142ad,0x3f800000,3 +np.float32,0x80772002,0x3f800000,3 +np.float32,0x8062abcb,0x3f800000,3 +np.float32,0x8045e31c,0x3f800000,3 +np.float32,0x805f01c2,0x3f800000,3 +np.float32,0x80506432,0x3f800000,3 +np.float32,0x8060089d,0x3f800000,3 +np.float32,0x8071292f,0x3f800000,3 +## floats that output a denormal ## +np.float32,0xc2cf3fc1,0x00000001,3 +np.float32,0xc2c79726,0x00000021,3 +np.float32,0xc2cb295d,0x00000005,3 +np.float32,0xc2b49e6b,0x00068c4c,3 +np.float32,0xc2ca8116,0x00000008,3 +np.float32,0xc2c23f82,0x000001d7,3 +np.float32,0xc2cb69c0,0x00000005,3 +np.float32,0xc2cc1f4d,0x00000003,3 +np.float32,0xc2ae094e,0x00affc4c,3 +np.float32,0xc2c86c44,0x00000015,3 +## random floats between -87.0f and 88.0f ## +np.float32,0x4030d7e0,0x417d9a05,3 +np.float32,0x426f60e8,0x6aa1be2c,3 +np.float32,0x41a1b220,0x4e0efc11,3 +np.float32,0xc20cc722,0x26159da7,3 +np.float32,0x41c492bc,0x512ec79d,3 +np.float32,0x40980210,0x42e73a0e,3 +np.float32,0xbf1f7b80,0x3f094de3,3 +np.float32,0x42a678a4,0x7b87a383,3 +np.float32,0xc20f3cfd,0x25a1c304,3 +np.float32,0x423ff34c,0x6216467f,3 +np.float32,0x00000000,0x3f800000,3 +## floats that cause an overflow ## +np.float32,0x7f06d8c1,0x7f800000,3 +np.float32,0x7f451912,0x7f800000,3 +np.float32,0x7ecceac3,0x7f800000,3 +np.float32,0x7f643b45,0x7f800000,3 +np.float32,0x7e910ea0,0x7f800000,3 +np.float32,0x7eb4756b,0x7f800000,3 +np.float32,0x7f4ec708,0x7f800000,3 +np.float32,0x7f6b4551,0x7f800000,3 +np.float32,0x7d8edbda,0x7f800000,3 +np.float32,0x7f730718,0x7f800000,3 +np.float32,0x42b17217,0x7f7fff84,3 +np.float32,0x42b17218,0x7f800000,3 +np.float32,0x42b17219,0x7f800000,3 +np.float32,0xfef2b0bc,0x00000000,3 +np.float32,0xff69f83e,0x00000000,3 +np.float32,0xff4ecb12,0x00000000,3 +np.float32,0xfeac6d86,0x00000000,3 +np.float32,0xfde0cdb8,0x00000000,3 +np.float32,0xff26aef4,0x00000000,3 +np.float32,0xff6f9277,0x00000000,3 +np.float32,0xff7adfc4,0x00000000,3 +np.float32,0xff0ad40e,0x00000000,3 +np.float32,0xff6fd8f3,0x00000000,3 +np.float32,0xc2cff1b4,0x00000001,3 +np.float32,0xc2cff1b5,0x00000000,3 +np.float32,0xc2cff1b6,0x00000000,3 +np.float32,0x7f800000,0x7f800000,3 +np.float32,0xff800000,0x00000000,3 +np.float32,0x4292f27c,0x7480000a,3 +np.float32,0x42a920be,0x7c7fff94,3 +np.float32,0x41c214c9,0x50ffffd9,3 +np.float32,0x41abe686,0x4effffd9,3 +np.float32,0x4287db5a,0x707fffd3,3 +np.float32,0x41902cbb,0x4c800078,3 +np.float32,0x42609466,0x67ffffeb,3 +np.float32,0x41a65af5,0x4e7fffd1,3 +np.float32,0x417f13ff,0x4affffc9,3 +np.float32,0x426d0e6c,0x6a3504f2,3 +np.float32,0x41bc8934,0x507fff51,3 +np.float32,0x42a7bdde,0x7c0000d6,3 +np.float32,0x4120cf66,0x46b504f6,3 +np.float32,0x4244da8f,0x62ffff1a,3 +np.float32,0x41a0cf69,0x4e000034,3 +np.float32,0x41cd2bec,0x52000005,3 +np.float32,0x42893e41,0x7100009e,3 +np.float32,0x41b437e1,0x4fb50502,3 +np.float32,0x41d8430f,0x5300001d,3 +np.float32,0x4244da92,0x62ffffda,3 +np.float32,0x41a0cf63,0x4dffffa9,3 +np.float32,0x3eb17218,0x3fb504f3,3 +np.float32,0x428729e8,0x703504dc,3 +np.float32,0x41a0cf67,0x4e000014,3 +np.float32,0x4252b77d,0x65800011,3 +np.float32,0x41902cb9,0x4c800058,3 +np.float32,0x42a0cf67,0x79800052,3 +np.float32,0x4152b77b,0x48ffffe9,3 +np.float32,0x41265af3,0x46ffffc8,3 +np.float32,0x42187e0b,0x5affff9a,3 +np.float32,0xc0d2b77c,0x3ab504f6,3 +np.float32,0xc283b2ac,0x10000072,3 +np.float32,0xc1cff1b4,0x2cb504f5,3 +np.float32,0xc05dce9e,0x3d000000,3 +np.float32,0xc28ec9d2,0x0bfffea5,3 +np.float32,0xc23c893a,0x1d7fffde,3 +np.float32,0xc2a920c0,0x027fff6c,3 +np.float32,0xc1f9886f,0x2900002b,3 +np.float32,0xc2c42920,0x000000b5,3 +np.float32,0xc2893e41,0x0dfffec5,3 +np.float32,0xc2c4da93,0x00000080,3 +np.float32,0xc17f1401,0x3400000c,3 +np.float32,0xc1902cb6,0x327fffaf,3 +np.float32,0xc27c4e3b,0x11ffffc5,3 +np.float32,0xc268e5c5,0x157ffe9d,3 +np.float32,0xc2b4e953,0x0005a826,3 +np.float32,0xc287db5a,0x0e800016,3 +np.float32,0xc207db5a,0x2700000b,3 +np.float32,0xc2b2d4fe,0x000ffff1,3 +np.float32,0xc268e5c0,0x157fffdd,3 +np.float32,0xc22920bd,0x2100003b,3 +np.float32,0xc2902caf,0x0b80011e,3 +np.float32,0xc1902cba,0x327fff2f,3 +np.float32,0xc2ca6625,0x00000008,3 +np.float32,0xc280ece8,0x10fffeb5,3 +np.float32,0xc2918f94,0x0b0000ea,3 +np.float32,0xc29b43d5,0x077ffffc,3 +np.float32,0xc1e61ff7,0x2ab504f5,3 +np.float32,0xc2867878,0x0effff15,3 +np.float32,0xc2a2324a,0x04fffff4,3 +#float64 +## near zero ## +np.float64,0x8000000000000000,0x3ff0000000000000,1 +np.float64,0x8010000000000000,0x3ff0000000000000,1 +np.float64,0x8000000000000001,0x3ff0000000000000,1 +np.float64,0x8360000000000000,0x3ff0000000000000,1 +np.float64,0x9a70000000000000,0x3ff0000000000000,1 +np.float64,0xb9b0000000000000,0x3ff0000000000000,1 +np.float64,0xb810000000000000,0x3ff0000000000000,1 +np.float64,0xbc30000000000000,0x3ff0000000000000,1 +np.float64,0xb6a0000000000000,0x3ff0000000000000,1 +np.float64,0x0000000000000000,0x3ff0000000000000,1 +np.float64,0x0010000000000000,0x3ff0000000000000,1 +np.float64,0x0000000000000001,0x3ff0000000000000,1 +np.float64,0x0360000000000000,0x3ff0000000000000,1 +np.float64,0x1a70000000000000,0x3ff0000000000000,1 +np.float64,0x3c30000000000000,0x3ff0000000000000,1 +np.float64,0x36a0000000000000,0x3ff0000000000000,1 +np.float64,0x39b0000000000000,0x3ff0000000000000,1 +np.float64,0x3810000000000000,0x3ff0000000000000,1 +## underflow ## +np.float64,0xc0c6276800000000,0x0000000000000000,1 +np.float64,0xc0c62d918ce2421d,0x0000000000000000,1 +np.float64,0xc0c62d918ce2421e,0x0000000000000000,1 +np.float64,0xc0c62d91a0000000,0x0000000000000000,1 +np.float64,0xc0c62d9180000000,0x0000000000000000,1 +np.float64,0xc0c62dea45ee3e06,0x0000000000000000,1 +np.float64,0xc0c62dea45ee3e07,0x0000000000000000,1 +np.float64,0xc0c62dea40000000,0x0000000000000000,1 +np.float64,0xc0c62dea60000000,0x0000000000000000,1 +np.float64,0xc0875f1120000000,0x0000000000000000,1 +np.float64,0xc0875f113c30b1c8,0x0000000000000000,1 +np.float64,0xc0875f1140000000,0x0000000000000000,1 +np.float64,0xc093480000000000,0x0000000000000000,1 +np.float64,0xffefffffffffffff,0x0000000000000000,1 +np.float64,0xc7efffffe0000000,0x0000000000000000,1 +## overflow ## +np.float64,0x40862e52fefa39ef,0x7ff0000000000000,1 +np.float64,0x40872e42fefa39ef,0x7ff0000000000000,1 +## +/- INF, +/- NAN ## +np.float64,0x7ff0000000000000,0x7ff0000000000000,1 +np.float64,0xfff0000000000000,0x0000000000000000,1 +np.float64,0x7ff8000000000000,0x7ff8000000000000,1 +np.float64,0xfff8000000000000,0xfff8000000000000,1 +## output denormal ## +np.float64,0xc087438520000000,0x0000000000000001,1 +np.float64,0xc08743853f2f4461,0x0000000000000001,1 +np.float64,0xc08743853f2f4460,0x0000000000000001,1 +np.float64,0xc087438540000000,0x0000000000000001,1 +## between -745.13321910 and 709.78271289 ## +np.float64,0xbff760cd14774bd9,0x3fcdb14ced00ceb6,1 +np.float64,0xbff760cd20000000,0x3fcdb14cd7993879,1 +np.float64,0xbff760cd00000000,0x3fcdb14d12fbd264,1 +np.float64,0xc07f1cf360000000,0x130c1b369af14fda,1 +np.float64,0xbeb0000000000000,0x3feffffe00001000,1 +np.float64,0xbd70000000000000,0x3fefffffffffe000,1 +np.float64,0xc084fd46e5c84952,0x0360000000000139,1 +np.float64,0xc084fd46e5c84953,0x035ffffffffffe71,1 +np.float64,0xc084fd46e0000000,0x0360000b9096d32c,1 +np.float64,0xc084fd4700000000,0x035fff9721d12104,1 +np.float64,0xc086232bc0000000,0x0010003af5e64635,1 +np.float64,0xc086232bdd7abcd2,0x001000000000007c,1 +np.float64,0xc086232bdd7abcd3,0x000ffffffffffe7c,1 +np.float64,0xc086232be0000000,0x000ffffaf57a6fc9,1 +np.float64,0xc086233920000000,0x000fe590e3b45eb0,1 +np.float64,0xc086233938000000,0x000fe56133493c57,1 +np.float64,0xc086233940000000,0x000fe5514deffbbc,1 +np.float64,0xc086234c98000000,0x000fbf1024c32ccb,1 +np.float64,0xc086234ca0000000,0x000fbf0065bae78d,1 +np.float64,0xc086234c80000000,0x000fbf3f623a7724,1 +np.float64,0xc086234ec0000000,0x000fbad237c846f9,1 +np.float64,0xc086234ec8000000,0x000fbac27cfdec97,1 +np.float64,0xc086234ee0000000,0x000fba934cfd3dc2,1 +np.float64,0xc086234ef0000000,0x000fba73d7f618d9,1 +np.float64,0xc086234f00000000,0x000fba54632dddc0,1 +np.float64,0xc0862356e0000000,0x000faae0945b761a,1 +np.float64,0xc0862356f0000000,0x000faac13eb9a310,1 +np.float64,0xc086235700000000,0x000faaa1e9567b0a,1 +np.float64,0xc086236020000000,0x000f98cd75c11ed7,1 +np.float64,0xc086236ca0000000,0x000f8081b4d93f89,1 +np.float64,0xc086236cb0000000,0x000f8062b3f4d6c5,1 +np.float64,0xc086236cc0000000,0x000f8043b34e6f8c,1 +np.float64,0xc086238d98000000,0x000f41220d9b0d2c,1 +np.float64,0xc086238da0000000,0x000f4112cc80a01f,1 +np.float64,0xc086238d80000000,0x000f414fd145db5b,1 +np.float64,0xc08624fd00000000,0x000cbfce8ea1e6c4,1 +np.float64,0xc086256080000000,0x000c250747fcd46e,1 +np.float64,0xc08626c480000000,0x000a34f4bd975193,1 +np.float64,0xbf50000000000000,0x3feff800ffeaac00,1 +np.float64,0xbe10000000000000,0x3fefffffff800000,1 +np.float64,0xbcd0000000000000,0x3feffffffffffff8,1 +np.float64,0xc055d589e0000000,0x38100004bf94f63e,1 +np.float64,0xc055d58a00000000,0x380ffff97f292ce8,1 +np.float64,0xbfd962d900000000,0x3fe585a4b00110e1,1 +np.float64,0x3ff4bed280000000,0x400d411e7a58a303,1 +np.float64,0x3fff0b3620000000,0x401bd7737ffffcf3,1 +np.float64,0x3ff0000000000000,0x4005bf0a8b145769,1 +np.float64,0x3eb0000000000000,0x3ff0000100000800,1 +np.float64,0x3d70000000000000,0x3ff0000000001000,1 +np.float64,0x40862e42e0000000,0x7fefff841808287f,1 +np.float64,0x40862e42fefa39ef,0x7fefffffffffff2a,1 +np.float64,0x40862e0000000000,0x7feef85a11e73f2d,1 +np.float64,0x4000000000000000,0x401d8e64b8d4ddae,1 +np.float64,0x4009242920000000,0x40372a52c383a488,1 +np.float64,0x4049000000000000,0x44719103e4080b45,1 +np.float64,0x4008000000000000,0x403415e5bf6fb106,1 +np.float64,0x3f50000000000000,0x3ff00400800aab55,1 +np.float64,0x3e10000000000000,0x3ff0000000400000,1 +np.float64,0x3cd0000000000000,0x3ff0000000000004,1 +np.float64,0x40562e40a0000000,0x47effed088821c3f,1 +np.float64,0x40562e42e0000000,0x47effff082e6c7ff,1 +np.float64,0x40562e4300000000,0x47f00000417184b8,1 +np.float64,0x3fe8000000000000,0x4000ef9db467dcf8,1 +np.float64,0x402b12e8d4f33589,0x412718f68c71a6fe,1 +np.float64,0x402b12e8d4f3358a,0x412718f68c71a70a,1 +np.float64,0x402b12e8c0000000,0x412718f59a7f472e,1 +np.float64,0x402b12e8e0000000,0x412718f70c0eac62,1 +##use 1th entry +np.float64,0x40631659AE147CB4,0x4db3a95025a4890f,1 +np.float64,0xC061B87D2E85A4E2,0x332640c8e2de2c51,1 +np.float64,0x405A4A50BE243AF4,0x496a45e4b7f0339a,1 +np.float64,0xC0839898B98EC5C6,0x0764027828830df4,1 +#use 2th entry +np.float64,0xC072428C44B6537C,0x2596ade838b96f3e,1 +np.float64,0xC053057C5E1AE9BF,0x3912c8fad18fdadf,1 +np.float64,0x407E89C78328BAA3,0x6bfe35d5b9a1a194,1 +np.float64,0x4083501B6DD87112,0x77a855503a38924e,1 +#use 3th entry +np.float64,0x40832C6195F24540,0x7741e73c80e5eb2f,1 +np.float64,0xC083D4CD557C2EC9,0x06b61727c2d2508e,1 +np.float64,0x400C48F5F67C99BD,0x404128820f02b92e,1 +np.float64,0x4056E36D9B2DF26A,0x4830f52ff34a8242,1 +#use 4th entry +np.float64,0x4080FF700D8CBD06,0x70fa70df9bc30f20,1 +np.float64,0x406C276D39E53328,0x543eb8e20a8f4741,1 +np.float64,0xC070D6159BBD8716,0x27a4a0548c904a75,1 +np.float64,0xC052EBCF8ED61F83,0x391c0e92368d15e4,1 +#use 5th entry +np.float64,0xC061F892A8AC5FBE,0x32f807a89efd3869,1 +np.float64,0x4021D885D2DBA085,0x40bd4dc86d3e3270,1 +np.float64,0x40767AEEEE7D4FCF,0x605e22851ee2afb7,1 +np.float64,0xC0757C5D75D08C80,0x20f0751599b992a2,1 +#use 6th entry +np.float64,0x405ACF7A284C4CE3,0x499a4e0b7a27027c,1 +np.float64,0xC085A6C9E80D7AF5,0x0175914009d62ec2,1 +np.float64,0xC07E4C02F86F1DAE,0x1439269b29a9231e,1 +np.float64,0x4080D80F9691CC87,0x7088a6cdafb041de,1 +#use 7th entry +np.float64,0x407FDFD84FBA0AC1,0x6deb1ae6f9bc4767,1 +np.float64,0x40630C06A1A2213D,0x4dac7a9d51a838b7,1 +np.float64,0x40685FDB30BB8B4F,0x5183f5cc2cac9e79,1 +np.float64,0x408045A2208F77F4,0x6ee299e08e2aa2f0,1 +#use 8th entry +np.float64,0xC08104E391F5078B,0x0ed397b7cbfbd230,1 +np.float64,0xC031501CAEFAE395,0x3e6040fd1ea35085,1 +np.float64,0xC079229124F6247C,0x1babf4f923306b1e,1 +np.float64,0x407FB65F44600435,0x6db03beaf2512b8a,1 +#use 9th entry +np.float64,0xC07EDEE8E8E8A5AC,0x136536cec9cbef48,1 +np.float64,0x4072BB4086099A14,0x5af4d3c3008b56cc,1 +np.float64,0x4050442A2EC42CB4,0x45cd393bd8fad357,1 +np.float64,0xC06AC28FB3D419B4,0x2ca1b9d3437df85f,1 +#use 10th entry +np.float64,0x40567FC6F0A68076,0x480c977fd5f3122e,1 +np.float64,0x40620A2F7EDA59BB,0x4cf278e96f4ce4d7,1 +np.float64,0xC085044707CD557C,0x034aad6c968a045a,1 +np.float64,0xC07374EA5AC516AA,0x23dd6afdc03e83d5,1 +#use 11th entry +np.float64,0x4073CC95332619C1,0x5c804b1498bbaa54,1 +np.float64,0xC0799FEBBE257F31,0x1af6a954c43b87d2,1 +np.float64,0x408159F19EA424F6,0x7200858efcbfc84d,1 +np.float64,0x404A81F6F24C0792,0x44b664a07ce5bbfa,1 +#use 12th entry +np.float64,0x40295FF1EFB9A741,0x4113c0e74c52d7b0,1 +np.float64,0x4073975F4CC411DA,0x5c32be40b4fec2c1,1 +np.float64,0x406E9DE52E82A77E,0x56049c9a3f1ae089,1 +np.float64,0x40748C2F52560ED9,0x5d93bc14fd4cd23b,1 +#use 13th entry +np.float64,0x4062A553CDC4D04C,0x4d6266bfde301318,1 +np.float64,0xC079EC1D63598AB7,0x1a88cb184dab224c,1 +np.float64,0xC0725C1CB3167427,0x25725b46f8a081f6,1 +np.float64,0x407888771D9B45F9,0x6353b1ec6bd7ce80,1 +#use 14th entry +np.float64,0xC082CBA03AA89807,0x09b383723831ce56,1 +np.float64,0xC083A8961BB67DD7,0x0735b118d5275552,1 +np.float64,0xC076BC6ECA12E7E3,0x1f2222679eaef615,1 +np.float64,0xC072752503AA1A5B,0x254eb832242c77e1,1 +#use 15th entry +np.float64,0xC058800792125DEC,0x371882372a0b48d4,1 +np.float64,0x4082909FD863E81C,0x7580d5f386920142,1 +np.float64,0xC071616F8FB534F9,0x26dbe20ef64a412b,1 +np.float64,0x406D1AB571CAA747,0x54ee0d55cb38ac20,1 +#use 16th entry +np.float64,0x406956428B7DAD09,0x52358682c271237f,1 +np.float64,0xC07EFC2D9D17B621,0x133b3e77c27a4d45,1 +np.float64,0xC08469BAC5BA3CCA,0x050863e5f42cc52f,1 +np.float64,0x407189D9626386A5,0x593cb1c0b3b5c1d3,1 +#use 17th entry +np.float64,0x4077E652E3DEB8C6,0x6269a10dcbd3c752,1 +np.float64,0x407674C97DB06878,0x605485dcc2426ec2,1 +np.float64,0xC07CE9969CF4268D,0x16386cf8996669f2,1 +np.float64,0x40780EE32D5847C4,0x62a436bd1abe108d,1 +#use 18th entry +np.float64,0x4076C3AA5E1E8DA1,0x60c62f56a5e72e24,1 +np.float64,0xC0730AFC7239B9BE,0x24758ead095cec1e,1 +np.float64,0xC085CC2B9C420DDB,0x0109cdaa2e5694c1,1 +np.float64,0x406D0765CB6D7AA4,0x54e06f8dd91bd945,1 +#use 19th entry +np.float64,0xC082D011F3B495E7,0x09a6647661d279c2,1 +np.float64,0xC072826AF8F6AFBC,0x253acd3cd224507e,1 +np.float64,0x404EB9C4810CEA09,0x457933dbf07e8133,1 +np.float64,0x408284FBC97C58CE,0x755f6eb234aa4b98,1 +#use 20th entry +np.float64,0x40856008CF6EDC63,0x7d9c0b3c03f4f73c,1 +np.float64,0xC077CB2E9F013B17,0x1d9b3d3a166a55db,1 +np.float64,0xC0479CA3C20AD057,0x3bad40e081555b99,1 +np.float64,0x40844CD31107332A,0x7a821d70aea478e2,1 +#use 21th entry +np.float64,0xC07C8FCC0BFCC844,0x16ba1cc8c539d19b,1 +np.float64,0xC085C4E9A3ABA488,0x011ff675ba1a2217,1 +np.float64,0x4074D538B32966E5,0x5dfd9d78043c6ad9,1 +np.float64,0xC0630CA16902AD46,0x3231a446074cede6,1 +#use 22th entry +np.float64,0xC06C826733D7D0B7,0x2b5f1078314d41e1,1 +np.float64,0xC0520DF55B2B907F,0x396c13a6ce8e833e,1 +np.float64,0xC080712072B0F437,0x107eae02d11d98ea,1 +np.float64,0x40528A6150E19EFB,0x469fdabda02228c5,1 +#use 23th entry +np.float64,0xC07B1D74B6586451,0x18d1253883ae3b48,1 +np.float64,0x4045AFD7867DAEC0,0x43d7d634fc4c5d98,1 +np.float64,0xC07A08B91F9ED3E2,0x1a60973e6397fc37,1 +np.float64,0x407B3ECF0AE21C8C,0x673e03e9d98d7235,1 +#use 24th entry +np.float64,0xC078AEB6F30CEABF,0x1c530b93ab54a1b3,1 +np.float64,0x4084495006A41672,0x7a775b6dc7e63064,1 +np.float64,0x40830B1C0EBF95DD,0x76e1e6eed77cfb89,1 +np.float64,0x407D93E8F33D8470,0x6a9adbc9e1e4f1e5,1 +#use 25th entry +np.float64,0x4066B11A09EFD9E8,0x504dd528065c28a7,1 +np.float64,0x408545823723AEEB,0x7d504a9b1844f594,1 +np.float64,0xC068C711F2CA3362,0x2e104f3496ea118e,1 +np.float64,0x407F317FCC3CA873,0x6cf0732c9948ebf4,1 +#use 26th entry +np.float64,0x407AFB3EBA2ED50F,0x66dc28a129c868d5,1 +np.float64,0xC075377037708ADE,0x21531a329f3d793e,1 +np.float64,0xC07C30066A1F3246,0x174448baa16ded2b,1 +np.float64,0xC06689A75DE2ABD3,0x2fad70662fae230b,1 +#use 27th entry +np.float64,0x4081514E9FCCF1E0,0x71e673b9efd15f44,1 +np.float64,0xC0762C710AF68460,0x1ff1ed7d8947fe43,1 +np.float64,0xC0468102FF70D9C4,0x3be0c3a8ff3419a3,1 +np.float64,0xC07EA4CEEF02A83E,0x13b908f085102c61,1 +#use 28th entry +np.float64,0xC06290B04AE823C4,0x328a83da3c2e3351,1 +np.float64,0xC0770EB1D1C395FB,0x1eab281c1f1db5fe,1 +np.float64,0xC06F5D4D838A5BAE,0x29500ea32fb474ea,1 +np.float64,0x40723B3133B54C5D,0x5a3c82c7c3a2b848,1 +#use 29th entry +np.float64,0x4085E6454CE3B4AA,0x7f20319b9638d06a,1 +np.float64,0x408389F2A0585D4B,0x7850667c58aab3d0,1 +np.float64,0xC0382798F9C8AE69,0x3dc1c79fe8739d6d,1 +np.float64,0xC08299D827608418,0x0a4335f76cdbaeb5,1 +#use 30th entry +np.float64,0xC06F3DED43301BF1,0x2965670ae46750a8,1 +np.float64,0xC070CAF6BDD577D9,0x27b4aa4ffdd29981,1 +np.float64,0x4078529AD4B2D9F2,0x6305c12755d5e0a6,1 +np.float64,0xC055B14E75A31B96,0x381c2eda6d111e5d,1 +#use 31th entry +np.float64,0x407B13EE414FA931,0x6700772c7544564d,1 +np.float64,0x407EAFDE9DE3EC54,0x6c346a0e49724a3c,1 +np.float64,0xC08362F398B9530D,0x07ffeddbadf980cb,1 +np.float64,0x407E865CDD9EEB86,0x6bf866cac5e0d126,1 +#use 32th entry +np.float64,0x407FB62DBC794C86,0x6db009f708ac62cb,1 +np.float64,0xC063D0BAA68CDDDE,0x31a3b2a51ce50430,1 +np.float64,0xC05E7706A2231394,0x34f24bead6fab5c9,1 +np.float64,0x4083E3A06FDE444E,0x79527b7a386d1937,1 diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp2.csv new file mode 100644 index 0000000000000000000000000000000000000000..4e0a63e8e2c5061d6a158f9347a6085e5e8c4ffa --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-exp2.csv @@ -0,0 +1,1429 @@ +dtype,input,output,ulperrortol +np.float32,0xbdfe94b0,0x3f6adda6,2 +np.float32,0x3f20f8f8,0x3fc5ec69,2 +np.float32,0x7040b5,0x3f800000,2 +np.float32,0x30ec5,0x3f800000,2 +np.float32,0x3eb63070,0x3fa3ce29,2 +np.float32,0xff4dda3d,0x0,2 +np.float32,0x805b832f,0x3f800000,2 +np.float32,0x3e883fb7,0x3f99ed8c,2 +np.float32,0x3f14d71f,0x3fbf8708,2 +np.float32,0xff7b1e55,0x0,2 +np.float32,0xbf691ac6,0x3f082fa2,2 +np.float32,0x7ee3e6ab,0x7f800000,2 +np.float32,0xbec6e2b4,0x3f439248,2 +np.float32,0xbf5f5ec2,0x3f0bd2c0,2 +np.float32,0x8025cc2c,0x3f800000,2 +np.float32,0x7e0d7672,0x7f800000,2 +np.float32,0xff4bbc5c,0x0,2 +np.float32,0xbd94fb30,0x3f73696b,2 +np.float32,0x6cc079,0x3f800000,2 +np.float32,0x803cf080,0x3f800000,2 +np.float32,0x71d418,0x3f800000,2 +np.float32,0xbf24a442,0x3f23ec1e,2 +np.float32,0xbe6c9510,0x3f5a1e1d,2 +np.float32,0xbe8fb284,0x3f52be38,2 +np.float32,0x7ea64754,0x7f800000,2 +np.float32,0x7fc00000,0x7fc00000,2 +np.float32,0x80620cfd,0x3f800000,2 +np.float32,0x3f3e20e8,0x3fd62e72,2 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a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-sin.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-sin.csv new file mode 100644 index 0000000000000000000000000000000000000000..03e76ffc2c222772e872908e147d4347effc1626 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_core/tests/data/umath-validation-set-sin.csv @@ -0,0 +1,1370 @@ +dtype,input,output,ulperrortol +## +ve denormals ## +np.float32,0x004b4716,0x004b4716,2 +np.float32,0x007b2490,0x007b2490,2 +np.float32,0x007c99fa,0x007c99fa,2 +np.float32,0x00734a0c,0x00734a0c,2 +np.float32,0x0070de24,0x0070de24,2 +np.float32,0x007fffff,0x007fffff,2 +np.float32,0x00000001,0x00000001,2 +## -ve denormals ## +np.float32,0x80495d65,0x80495d65,2 +np.float32,0x806894f6,0x806894f6,2 +np.float32,0x80555a76,0x80555a76,2 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a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..84f3626b43d572c58d48374a5e6b18e19b9075e5 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.py @@ -0,0 +1,36 @@ +"""This hook should collect all binary files and any hidden modules that numpy +needs. + +Our (some-what inadequate) docs for writing PyInstaller hooks are kept here: +https://pyinstaller.readthedocs.io/en/stable/hooks.html + +""" +from PyInstaller.compat import is_conda, is_pure_conda +from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies + +# Collect all DLLs inside numpy's installation folder, dump them into built +# app's root. +binaries = collect_dynamic_libs("numpy", ".") + +# If using Conda without any non-conda virtual environment manager: +if is_pure_conda: + # Assume running the NumPy from Conda-forge and collect it's DLLs from the + # communal Conda bin directory. DLLs from NumPy's dependencies must also be + # collected to capture MKL, OpenBlas, OpenMP, etc. + from PyInstaller.utils.hooks import conda_support + datas = conda_support.collect_dynamic_libs("numpy", dependencies=True) + +# Submodules PyInstaller cannot detect. `_dtype_ctypes` is only imported +# from C and `_multiarray_tests` is used in tests (which are not packed). +hiddenimports = ['numpy._core._dtype_ctypes', 'numpy._core._multiarray_tests'] + +# Remove testing and building code and packages that are referenced throughout +# NumPy but are not really dependencies. +excludedimports = [ + "scipy", + "pytest", + "f2py", + "setuptools", + "distutils", + "numpy.distutils", +] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.pyi new file mode 100644 index 0000000000000000000000000000000000000000..2642996dad7e5f68b63d66ac59858ec0bc630fa9 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/hook-numpy.pyi @@ -0,0 +1,13 @@ +from typing import Final + +# from `PyInstaller.compat` +is_conda: Final[bool] +is_pure_conda: Final[bool] + +# from `PyInstaller.utils.hooks` +def is_module_satisfies(requirements: str, version: None = None, version_attr: None = None) -> bool: ... + +binaries: Final[list[tuple[str, str]]] + +hiddenimports: Final[list[str]] +excludedimports: Final[list[str]] diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f7c033bcf5037339a2f5387880d7035eec5746ce --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/__init__.py @@ -0,0 +1,16 @@ +from numpy.testing import IS_WASM, IS_EDITABLE +import pytest + + +if IS_WASM: + pytest.skip( + "WASM/Pyodide does not use or support Fortran", + allow_module_level=True + ) + + +if IS_EDITABLE: + pytest.skip( + "Editable install doesn't support tests with a compile step", + allow_module_level=True + ) diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..eb28070e38baf80223fe0178ac0a7c0f5732a2c8 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py @@ -0,0 +1,32 @@ +"""A crude *bit of everything* smoke test to verify PyInstaller compatibility. + +PyInstaller typically goes wrong by forgetting to package modules, extension +modules or shared libraries. This script should aim to touch as many of those +as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure +due to an uncollected resource. Missing resources are unlikely to lead to +arithmetic errors so there's generally no need to verify any calculation's +output - merely that it made it to the end OK. This script should not +explicitly import any of numpy's submodules as that gives PyInstaller undue +hints that those submodules exist and should be collected (accessing implicitly +loaded submodules is OK). + +""" +import numpy as np + +a = np.arange(1., 10.).reshape((3, 3)) % 5 +np.linalg.det(a) +a @ a +a @ a.T +np.linalg.inv(a) +np.sin(np.exp(a)) +np.linalg.svd(a) +np.linalg.eigh(a) + +np.unique(np.random.randint(0, 10, 100)) +np.sort(np.random.uniform(0, 10, 100)) + +np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8)) +np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum() +np.polynomial.Legendre([7, 8, 9]).roots() + +print("I made it!") diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py new file mode 100644 index 0000000000000000000000000000000000000000..a9061da19b88c4243a3fd28bf05fd2986292d836 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py @@ -0,0 +1,35 @@ +import subprocess +from pathlib import Path + +import pytest + + +# PyInstaller has been very unproactive about replacing 'imp' with 'importlib'. +@pytest.mark.filterwarnings('ignore::DeprecationWarning') +# It also leaks io.BytesIO()s. +@pytest.mark.filterwarnings('ignore::ResourceWarning') +@pytest.mark.parametrize("mode", ["--onedir", "--onefile"]) +@pytest.mark.slow +def test_pyinstaller(mode, tmp_path): + """Compile and run pyinstaller-smoke.py using PyInstaller.""" + + pyinstaller_cli = pytest.importorskip("PyInstaller.__main__").run + + source = Path(__file__).with_name("pyinstaller-smoke.py").resolve() + args = [ + # Place all generated files in ``tmp_path``. + '--workpath', str(tmp_path / "build"), + '--distpath', str(tmp_path / "dist"), + '--specpath', str(tmp_path), + mode, + str(source), + ] + pyinstaller_cli(args) + + if mode == "--onefile": + exe = tmp_path / "dist" / source.stem + else: + exe = tmp_path / "dist" / source.stem / source.stem + + p = subprocess.run([str(exe)], check=True, stdout=subprocess.PIPE) + assert p.stdout.strip() == b"I made it!" diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca3aacd84d5b7a884910eec177b9312fb8de1837 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.py @@ -0,0 +1,88 @@ +""" +This is a module for defining private helpers which do not depend on the +rest of NumPy. + +Everything in here must be self-contained so that it can be +imported anywhere else without creating circular imports. +If a utility requires the import of NumPy, it probably belongs +in ``numpy._core``. +""" + +import functools +import warnings +from ._convertions import asunicode, asbytes + + +def set_module(module): + """Private decorator for overriding __module__ on a function or class. + + Example usage:: + + @set_module('numpy') + def example(): + pass + + assert example.__module__ == 'numpy' + """ + def decorator(func): + if module is not None: + func.__module__ = module + return func + return decorator + + +def _rename_parameter(old_names, new_names, dep_version=None): + """ + Generate decorator for backward-compatible keyword renaming. + + Apply the decorator generated by `_rename_parameter` to functions with a + renamed parameter to maintain backward-compatibility. + + After decoration, the function behaves as follows: + If only the new parameter is passed into the function, behave as usual. + If only the old parameter is passed into the function (as a keyword), raise + a DeprecationWarning if `dep_version` is provided, and behave as usual + otherwise. + If both old and new parameters are passed into the function, raise a + DeprecationWarning if `dep_version` is provided, and raise the appropriate + TypeError (function got multiple values for argument). + + Parameters + ---------- + old_names : list of str + Old names of parameters + new_name : list of str + New names of parameters + dep_version : str, optional + Version of NumPy in which old parameter was deprecated in the format + 'X.Y.Z'. If supplied, the deprecation message will indicate that + support for the old parameter will be removed in version 'X.Y+2.Z' + + Notes + ----- + Untested with functions that accept *args. Probably won't work as written. + + """ + def decorator(fun): + @functools.wraps(fun) + def wrapper(*args, **kwargs): + __tracebackhide__ = True # Hide traceback for py.test + for old_name, new_name in zip(old_names, new_names): + if old_name in kwargs: + if dep_version: + end_version = dep_version.split('.') + end_version[1] = str(int(end_version[1]) + 2) + end_version = '.'.join(end_version) + msg = (f"Use of keyword argument `{old_name}` is " + f"deprecated and replaced by `{new_name}`. " + f"Support for `{old_name}` will be removed " + f"in NumPy {end_version}.") + warnings.warn(msg, DeprecationWarning, stacklevel=2) + if new_name in kwargs: + msg = (f"{fun.__name__}() got multiple values for " + f"argument now known as `{new_name}`") + raise TypeError(msg) + kwargs[new_name] = kwargs.pop(old_name) + return fun(*args, **kwargs) + return wrapper + return decorator diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ced45bfdeb44f8f7a26241ab4fb890f22a684e24 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__init__.pyi @@ -0,0 +1,31 @@ +from collections.abc import Callable, Iterable +from typing import Protocol, overload, type_check_only + +from _typeshed import IdentityFunction +from typing_extensions import TypeVar + +from ._convertions import asbytes as asbytes +from ._convertions import asunicode as asunicode + +### + +_T = TypeVar("_T") +_HasModuleT = TypeVar("_HasModuleT", bound=_HasModule) + +@type_check_only +class _HasModule(Protocol): + __module__: str + +### + +@overload +def set_module(module: None) -> IdentityFunction: ... +@overload +def set_module(module: _HasModuleT) -> _HasModuleT: ... + +# +def _rename_parameter( + old_names: Iterable[str], + new_names: Iterable[str], + dep_version: str | None = None, +) -> Callable[[Callable[..., _T]], Callable[..., _T]]: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a9b88cb5a680aeb8aeb39a2b7fdc584b108e9847 Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc differ diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.py new file mode 100644 index 0000000000000000000000000000000000000000..ab15a8ba019f1b6a40ac3f562897fa4581323efc --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.py @@ -0,0 +1,18 @@ +""" +A set of methods retained from np.compat module that +are still used across codebase. +""" + +__all__ = ["asunicode", "asbytes"] + + +def asunicode(s): + if isinstance(s, bytes): + return s.decode('latin1') + return str(s) + + +def asbytes(s): + if isinstance(s, bytes): + return s + return str(s).encode('latin1') diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..6cc599acc94f97f026c3f81a538c3d1766d450d3 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_convertions.pyi @@ -0,0 +1,4 @@ +__all__ = ["asbytes", "asunicode"] + +def asunicode(s: bytes | str) -> str: ... +def asbytes(s: bytes | str) -> str: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.py new file mode 100644 index 0000000000000000000000000000000000000000..c8805dddc014ff87ea5a4b8712c8449e00204c6d --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.py @@ -0,0 +1,191 @@ +"""Subset of inspect module from upstream python + +We use this instead of upstream because upstream inspect is slow to import, and +significantly contributes to numpy import times. Importing this copy has almost +no overhead. + +""" +import types + +__all__ = ['getargspec', 'formatargspec'] + +# ----------------------------------------------------------- type-checking +def ismethod(object): + """Return true if the object is an instance method. + + Instance method objects provide these attributes: + __doc__ documentation string + __name__ name with which this method was defined + im_class class object in which this method belongs + im_func function object containing implementation of method + im_self instance to which this method is bound, or None + + """ + return isinstance(object, types.MethodType) + +def isfunction(object): + """Return true if the object is a user-defined function. + + Function objects provide these attributes: + __doc__ documentation string + __name__ name with which this function was defined + func_code code object containing compiled function bytecode + func_defaults tuple of any default values for arguments + func_doc (same as __doc__) + func_globals global namespace in which this function was defined + func_name (same as __name__) + + """ + return isinstance(object, types.FunctionType) + +def iscode(object): + """Return true if the object is a code object. + + Code objects provide these attributes: + co_argcount number of arguments (not including * or ** args) + co_code string of raw compiled bytecode + co_consts tuple of constants used in the bytecode + co_filename name of file in which this code object was created + co_firstlineno number of first line in Python source code + co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg + co_lnotab encoded mapping of line numbers to bytecode indices + co_name name with which this code object was defined + co_names tuple of names of local variables + co_nlocals number of local variables + co_stacksize virtual machine stack space required + co_varnames tuple of names of arguments and local variables + + """ + return isinstance(object, types.CodeType) + +# ------------------------------------------------ argument list extraction +# These constants are from Python's compile.h. +CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8 + +def getargs(co): + """Get information about the arguments accepted by a code object. + + Three things are returned: (args, varargs, varkw), where 'args' is + a list of argument names (possibly containing nested lists), and + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + + """ + + if not iscode(co): + raise TypeError('arg is not a code object') + + nargs = co.co_argcount + names = co.co_varnames + args = list(names[:nargs]) + + # The following acrobatics are for anonymous (tuple) arguments. + # Which we do not need to support, so remove to avoid importing + # the dis module. + for i in range(nargs): + if args[i][:1] in ['', '.']: + raise TypeError("tuple function arguments are not supported") + varargs = None + if co.co_flags & CO_VARARGS: + varargs = co.co_varnames[nargs] + nargs = nargs + 1 + varkw = None + if co.co_flags & CO_VARKEYWORDS: + varkw = co.co_varnames[nargs] + return args, varargs, varkw + +def getargspec(func): + """Get the names and default values of a function's arguments. + + A tuple of four things is returned: (args, varargs, varkw, defaults). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'defaults' is an n-tuple of the default values of the last n arguments. + + """ + + if ismethod(func): + func = func.__func__ + if not isfunction(func): + raise TypeError('arg is not a Python function') + args, varargs, varkw = getargs(func.__code__) + return args, varargs, varkw, func.__defaults__ + +def getargvalues(frame): + """Get information about arguments passed into a particular frame. + + A tuple of four things is returned: (args, varargs, varkw, locals). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'locals' is the locals dictionary of the given frame. + + """ + args, varargs, varkw = getargs(frame.f_code) + return args, varargs, varkw, frame.f_locals + +def joinseq(seq): + if len(seq) == 1: + return '(' + seq[0] + ',)' + else: + return '(' + ', '.join(seq) + ')' + +def strseq(object, convert, join=joinseq): + """Recursively walk a sequence, stringifying each element. + + """ + if type(object) in [list, tuple]: + return join([strseq(_o, convert, join) for _o in object]) + else: + return convert(object) + +def formatargspec(args, varargs=None, varkw=None, defaults=None, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargspec. + + The first four arguments are (args, varargs, varkw, defaults). The + other four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + specs = [] + if defaults: + firstdefault = len(args) - len(defaults) + for i in range(len(args)): + spec = strseq(args[i], formatarg, join) + if defaults and i >= firstdefault: + spec = spec + formatvalue(defaults[i - firstdefault]) + specs.append(spec) + if varargs is not None: + specs.append(formatvarargs(varargs)) + if varkw is not None: + specs.append(formatvarkw(varkw)) + return '(' + ', '.join(specs) + ')' + +def formatargvalues(args, varargs, varkw, locals, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargvalues. + + The first four arguments are (args, varargs, varkw, locals). The + next four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + def convert(name, locals=locals, + formatarg=formatarg, formatvalue=formatvalue): + return formatarg(name) + formatvalue(locals[name]) + specs = [strseq(arg, convert, join) for arg in args] + + if varargs: + specs.append(formatvarargs(varargs) + formatvalue(locals[varargs])) + if varkw: + specs.append(formatvarkw(varkw) + formatvalue(locals[varkw])) + return '(' + ', '.join(specs) + ')' diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ba0260d3a593d31521fd768d0bfc16c5b394c188 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_inspect.pyi @@ -0,0 +1,71 @@ +import types +from collections.abc import Callable, Mapping +from typing import Any, Final, TypeAlias, overload + +from _typeshed import SupportsLenAndGetItem +from typing_extensions import TypeIs, TypeVar + +__all__ = ["formatargspec", "getargspec"] + +### + +_T = TypeVar("_T") +_RT = TypeVar("_RT") + +_StrSeq: TypeAlias = SupportsLenAndGetItem[str] +_NestedSeq: TypeAlias = list[_T | _NestedSeq[_T]] | tuple[_T | _NestedSeq[_T], ...] + +_JoinFunc: TypeAlias = Callable[[list[_T]], _T] +_FormatFunc: TypeAlias = Callable[[_T], str] + +### + +CO_OPTIMIZED: Final = 1 +CO_NEWLOCALS: Final = 2 +CO_VARARGS: Final = 4 +CO_VARKEYWORDS: Final = 8 + +### + +def ismethod(object: object) -> TypeIs[types.MethodType]: ... +def isfunction(object: object) -> TypeIs[types.FunctionType]: ... +def iscode(object: object) -> TypeIs[types.CodeType]: ... + +### + +def getargs(co: types.CodeType) -> tuple[list[str], str | None, str | None]: ... +def getargspec(func: types.MethodType | types.FunctionType) -> tuple[list[str], str | None, str | None, tuple[Any, ...]]: ... +def getargvalues(frame: types.FrameType) -> tuple[list[str], str | None, str | None, dict[str, Any]]: ... + +# +def joinseq(seq: _StrSeq) -> str: ... + +# +@overload +def strseq(object: _NestedSeq[str], convert: Callable[[Any], Any], join: _JoinFunc[str] = ...) -> str: ... +@overload +def strseq(object: _NestedSeq[_T], convert: Callable[[_T], _RT], join: _JoinFunc[_RT]) -> _RT: ... + +# +def formatargspec( + args: _StrSeq, + varargs: str | None = None, + varkw: str | None = None, + defaults: SupportsLenAndGetItem[object] | None = None, + formatarg: _FormatFunc[str] = ..., # str + formatvarargs: _FormatFunc[str] = ..., # "*{}".format + formatvarkw: _FormatFunc[str] = ..., # "**{}".format + formatvalue: _FormatFunc[object] = ..., # "={!r}".format + join: _JoinFunc[str] = ..., # joinseq +) -> str: ... +def formatargvalues( + args: _StrSeq, + varargs: str | None, + varkw: str | None, + locals: Mapping[str, object] | None, + formatarg: _FormatFunc[str] = ..., # str + formatvarargs: _FormatFunc[str] = ..., # "*{}".format + formatvarkw: _FormatFunc[str] = ..., # "**{}".format + formatvalue: _FormatFunc[object] = ..., # "={!r}".format + join: _JoinFunc[str] = ..., # joinseq +) -> str: ... diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.py new file mode 100644 index 0000000000000000000000000000000000000000..73d0afb5e95f099f8b04253177e8a3ab3d80d0c4 --- /dev/null +++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.py @@ -0,0 +1,487 @@ +"""Utility to compare pep440 compatible version strings. + +The LooseVersion and StrictVersion classes that distutils provides don't +work; they don't recognize anything like alpha/beta/rc/dev versions. +""" + +# Copyright (c) Donald Stufft and individual contributors. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import collections +import itertools +import re + + +__all__ = [ + "parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN", +] + + +# BEGIN packaging/_structures.py + + +class Infinity: + def __repr__(self): + return "Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return False + + def __le__(self, other): + return False + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return True + + def __ge__(self, other): + return True + + def __neg__(self): + return NegativeInfinity + + +Infinity = Infinity() + + +class NegativeInfinity: + def __repr__(self): + return "-Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return True + + def __le__(self, other): + return True + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return False + + def __ge__(self, other): + return False + + def __neg__(self): + return Infinity + + +# BEGIN packaging/version.py + + +NegativeInfinity = NegativeInfinity() + +_Version = collections.namedtuple( + "_Version", + ["epoch", "release", "dev", "pre", "post", "local"], +) + + +def parse(version): + """ + Parse the given version string and return either a :class:`Version` object + or a :class:`LegacyVersion` object depending on if the given version is + a valid PEP 440 version or a legacy version. + """ + try: + return Version(version) + except InvalidVersion: + return LegacyVersion(version) + + +class InvalidVersion(ValueError): + """ + An invalid version was found, users should refer to PEP 440. + """ + + +class _BaseVersion: + + def __hash__(self): + return hash(self._key) + + def __lt__(self, other): + return self._compare(other, lambda s, o: s < o) + + def __le__(self, other): + return self._compare(other, lambda s, o: s <= o) + + def __eq__(self, other): + return self._compare(other, lambda s, o: s == o) + + def __ge__(self, other): + return self._compare(other, lambda s, o: s >= o) + + def __gt__(self, other): + return self._compare(other, lambda s, o: s > o) + + def __ne__(self, other): + return self._compare(other, lambda s, o: s != o) + + def _compare(self, other, method): + if not isinstance(other, _BaseVersion): + return NotImplemented + + return method(self._key, other._key) + + +class LegacyVersion(_BaseVersion): + + def __init__(self, version): + self._version = str(version) + self._key = _legacy_cmpkey(self._version) + + def __str__(self): + return self._version + + def __repr__(self): + return "".format(repr(str(self))) + + @property + def public(self): + return self._version + + @property + def base_version(self): + return self._version + + @property + def local(self): + return None + + @property + def is_prerelease(self): + return False + + @property + def is_postrelease(self): + return False + + +_legacy_version_component_re = re.compile( + r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE, +) + +_legacy_version_replacement_map = { + "pre": "c", "preview": "c", "-": "final-", "rc": "c", "dev": "@", +} + + +def _parse_version_parts(s): + for part in _legacy_version_component_re.split(s): + part = _legacy_version_replacement_map.get(part, part) + + if not part or part == ".": + continue + + if part[:1] in "0123456789": + # pad for numeric comparison + yield part.zfill(8) + else: + yield "*" + part + + # ensure that alpha/beta/candidate are before final + yield "*final" + + +def _legacy_cmpkey(version): + # We hardcode an epoch of -1 here. A PEP 440 version can only have an epoch + # greater than or equal to 0. This will effectively put the LegacyVersion, + # which uses the defacto standard originally implemented by setuptools, + # as before all PEP 440 versions. + epoch = -1 + + # This scheme is taken from pkg_resources.parse_version setuptools prior to + # its adoption of the packaging library. + parts = [] + for part in _parse_version_parts(version.lower()): + if part.startswith("*"): + # remove "-" before a prerelease tag + if part < "*final": + while parts and parts[-1] == "*final-": + parts.pop() + + # remove trailing zeros from each series of numeric parts + while parts and parts[-1] == "00000000": + parts.pop() + + parts.append(part) + parts = tuple(parts) + + return epoch, parts + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P

                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(
+        r"^\s*" + VERSION_PATTERN + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    def __init__(self, version):
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion("Invalid version: '{0}'".format(version))
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(
+                match.group("pre_l"),
+                match.group("pre_n"),
+            ),
+            post=_parse_letter_version(
+                match.group("post_l"),
+                match.group("post_n1") or match.group("post_n2"),
+            ),
+            dev=_parse_letter_version(
+                match.group("dev_l"),
+                match.group("dev_n"),
+            ),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self):
+        return "".format(repr(str(self)))
+
+    def __str__(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        # Pre-release
+        if self._version.pre is not None:
+            parts.append("".join(str(x) for x in self._version.pre))
+
+        # Post-release
+        if self._version.post is not None:
+            parts.append(".post{0}".format(self._version.post[1]))
+
+        # Development release
+        if self._version.dev is not None:
+            parts.append(".dev{0}".format(self._version.dev[1]))
+
+        # Local version segment
+        if self._version.local is not None:
+            parts.append(
+                "+{0}".format(".".join(str(x) for x in self._version.local))
+            )
+
+        return "".join(parts)
+
+    @property
+    def public(self):
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        return "".join(parts)
+
+    @property
+    def local(self):
+        version_string = str(self)
+        if "+" in version_string:
+            return version_string.split("+", 1)[1]
+
+    @property
+    def is_prerelease(self):
+        return bool(self._version.dev or self._version.pre)
+
+    @property
+    def is_postrelease(self):
+        return bool(self._version.post)
+
+
+def _parse_letter_version(letter, number):
+    if letter:
+        # We assume there is an implicit 0 in a pre-release if there is
+        # no numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower-case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume that if we are given a number but not given a letter,
+        # then this is using the implicit post release syntax (e.g., 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+
+_local_version_seperators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local):
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_seperators.split(local)
+        )
+
+
+def _cmpkey(epoch, release, pre, post, dev, local):
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non-zero, then take the rest,
+    # re-reverse it back into the correct order, and make it a tuple and use
+    # that for our sorting key.
+    release = tuple(
+        reversed(list(
+            itertools.dropwhile(
+                lambda x: x == 0,
+                reversed(release),
+            )
+        ))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre-segment, but we _only_ want to do this
+    # if there is no pre- or a post-segment. If we have one of those, then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        pre = -Infinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        pre = Infinity
+
+    # Versions without a post-segment should sort before those with one.
+    if post is None:
+        post = -Infinity
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        dev = Infinity
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        local = -Infinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alphanumeric segments sort before numeric segments
+        # - Alphanumeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        local = tuple(
+            (i, "") if isinstance(i, int) else (-Infinity, i)
+            for i in local
+        )
+
+    return epoch, release, pre, post, dev, local
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..29dd4c912aa99760858a30718256f5bf4b02955a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/_utils/_pep440.pyi
@@ -0,0 +1,121 @@
+import re
+from collections.abc import Callable
+from typing import (
+    Any,
+    ClassVar,
+    Final,
+    Generic,
+    NamedTuple,
+    TypeVar,
+    final,
+    type_check_only,
+)
+from typing import (
+    Literal as L,
+)
+
+from typing_extensions import TypeIs
+
+__all__ = ["VERSION_PATTERN", "InvalidVersion", "LegacyVersion", "Version", "parse"]
+
+###
+
+_CmpKeyT = TypeVar("_CmpKeyT", bound=tuple[object, ...])
+_CmpKeyT_co = TypeVar("_CmpKeyT_co", bound=tuple[object, ...], default=tuple[Any, ...], covariant=True)
+
+###
+
+VERSION_PATTERN: Final[str] = ...
+
+class InvalidVersion(ValueError): ...
+
+@type_check_only
+@final
+class _InfinityType:
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: object, /) -> TypeIs[_InfinityType]: ...
+    def __ne__(self, other: object, /) -> bool: ...
+    def __lt__(self, other: object, /) -> L[False]: ...
+    def __le__(self, other: object, /) -> L[False]: ...
+    def __gt__(self, other: object, /) -> L[True]: ...
+    def __ge__(self, other: object, /) -> L[True]: ...
+    def __neg__(self) -> _NegativeInfinityType: ...
+
+Infinity: Final[_InfinityType] = ...
+
+@type_check_only
+@final
+class _NegativeInfinityType:
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: object, /) -> TypeIs[_NegativeInfinityType]: ...
+    def __ne__(self, other: object, /) -> bool: ...
+    def __lt__(self, other: object, /) -> L[True]: ...
+    def __le__(self, other: object, /) -> L[True]: ...
+    def __gt__(self, other: object, /) -> L[False]: ...
+    def __ge__(self, other: object, /) -> L[False]: ...
+    def __neg__(self) -> _InfinityType: ...
+
+NegativeInfinity: Final[_NegativeInfinityType] = ...
+
+class _Version(NamedTuple):
+    epoch: int
+    release: tuple[int, ...]
+    dev: tuple[str, int] | None
+    pre: tuple[str, int] | None
+    post: tuple[str, int] | None
+    local: tuple[str | int, ...] | None
+
+class _BaseVersion(Generic[_CmpKeyT_co]):
+    _key: _CmpKeyT_co
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: _BaseVersion, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __ne__(self, other: _BaseVersion, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __lt__(self, other: _BaseVersion, /) -> bool: ...
+    def __le__(self, other: _BaseVersion, /) -> bool: ...
+    def __ge__(self, other: _BaseVersion, /) -> bool: ...
+    def __gt__(self, other: _BaseVersion, /) -> bool: ...
+    def _compare(self, /, other: _BaseVersion[_CmpKeyT], method: Callable[[_CmpKeyT_co, _CmpKeyT], bool]) -> bool: ...
+
+class LegacyVersion(_BaseVersion[tuple[L[-1], tuple[str, ...]]]):
+    _version: Final[str]
+    def __init__(self, /, version: str) -> None: ...
+    @property
+    def public(self) -> str: ...
+    @property
+    def base_version(self) -> str: ...
+    @property
+    def local(self) -> None: ...
+    @property
+    def is_prerelease(self) -> L[False]: ...
+    @property
+    def is_postrelease(self) -> L[False]: ...
+
+class Version(
+    _BaseVersion[
+        tuple[
+            int,  # epoch
+            tuple[int, ...],  # release
+            tuple[str, int] | _InfinityType | _NegativeInfinityType,  # pre
+            tuple[str, int] | _NegativeInfinityType,  # post
+            tuple[str, int] | _InfinityType,  # dev
+            tuple[tuple[int, L[""]] | tuple[_NegativeInfinityType, str], ...] | _NegativeInfinityType,  # local
+        ],
+    ],
+):
+    _regex: ClassVar[re.Pattern[str]] = ...
+    _version: Final[str]
+
+    def __init__(self, /, version: str) -> None: ...
+    @property
+    def public(self) -> str: ...
+    @property
+    def base_version(self) -> str: ...
+    @property
+    def local(self) -> str | None: ...
+    @property
+    def is_prerelease(self) -> bool: ...
+    @property
+    def is_postrelease(self) -> bool: ...
+
+#
+def parse(version: str) -> Version | LegacyVersion: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9eb66c180f59a4ede79f4c02b439c1f01eaa96b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.py
@@ -0,0 +1,2 @@
+from numpy._core.defchararray import __all__, __doc__
+from numpy._core.defchararray import *
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2abf86d305f86289e2b300d604e84d9f6eff1234
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__init__.pyi
@@ -0,0 +1,111 @@
+from numpy._core.defchararray import (
+    equal,
+    not_equal,
+    greater_equal,
+    less_equal,
+    greater,
+    less,
+    str_len,
+    add,
+    multiply,
+    mod,
+    capitalize,
+    center,
+    count,
+    decode,
+    encode,
+    endswith,
+    expandtabs,
+    find,
+    index,
+    isalnum,
+    isalpha,
+    isdigit,
+    islower,
+    isspace,
+    istitle,
+    isupper,
+    join,
+    ljust,
+    lower,
+    lstrip,
+    partition,
+    replace,
+    rfind,
+    rindex,
+    rjust,
+    rpartition,
+    rsplit,
+    rstrip,
+    split,
+    splitlines,
+    startswith,
+    strip,
+    swapcase,
+    title,
+    translate,
+    upper,
+    zfill,
+    isnumeric,
+    isdecimal,
+    array,
+    asarray,
+    compare_chararrays,
+    chararray
+)
+
+__all__ = [
+    "equal",
+    "not_equal",
+    "greater_equal",
+    "less_equal",
+    "greater",
+    "less",
+    "str_len",
+    "add",
+    "multiply",
+    "mod",
+    "capitalize",
+    "center",
+    "count",
+    "decode",
+    "encode",
+    "endswith",
+    "expandtabs",
+    "find",
+    "index",
+    "isalnum",
+    "isalpha",
+    "isdigit",
+    "islower",
+    "isspace",
+    "istitle",
+    "isupper",
+    "join",
+    "ljust",
+    "lower",
+    "lstrip",
+    "partition",
+    "replace",
+    "rfind",
+    "rindex",
+    "rjust",
+    "rpartition",
+    "rsplit",
+    "rstrip",
+    "split",
+    "splitlines",
+    "startswith",
+    "strip",
+    "swapcase",
+    "title",
+    "translate",
+    "upper",
+    "zfill",
+    "isnumeric",
+    "isdecimal",
+    "array",
+    "asarray",
+    "compare_chararrays",
+    "chararray",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c12c53392d29b12a6ae01696267ad0ff20202dd
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/char/__pycache__/__init__.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/compat/py3k.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/compat/py3k.py
new file mode 100644
index 0000000000000000000000000000000000000000..74870e8ad9541ab6a54dbc673b9375a124d0f9ac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/compat/py3k.py
@@ -0,0 +1,143 @@
+"""
+Python 3.X compatibility tools.
+
+While this file was originally intended for Python 2 -> 3 transition,
+it is now used to create a compatibility layer between different
+minor versions of Python 3.
+
+While the active version of numpy may not support a given version of python, we
+allow downstream libraries to continue to use these shims for forward
+compatibility with numpy while they transition their code to newer versions of
+Python.
+"""
+__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
+           'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
+           'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
+           'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path',
+           'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike']
+
+import sys
+import os
+from pathlib import Path
+import io
+try:
+    import pickle5 as pickle
+except ImportError:
+    import pickle
+
+long = int
+integer_types = (int,)
+basestring = str
+unicode = str
+bytes = bytes
+
+def asunicode(s):
+    if isinstance(s, bytes):
+        return s.decode('latin1')
+    return str(s)
+
+def asbytes(s):
+    if isinstance(s, bytes):
+        return s
+    return str(s).encode('latin1')
+
+def asstr(s):
+    if isinstance(s, bytes):
+        return s.decode('latin1')
+    return str(s)
+
+def isfileobj(f):
+    if not isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter)):
+        return False
+    try:
+        # BufferedReader/Writer may raise OSError when
+        # fetching `fileno()` (e.g. when wrapping BytesIO).
+        f.fileno()
+        return True
+    except OSError:
+        return False
+
+def open_latin1(filename, mode='r'):
+    return open(filename, mode=mode, encoding='iso-8859-1')
+
+def sixu(s):
+    return s
+
+strchar = 'U'
+
+def getexception():
+    return sys.exc_info()[1]
+
+def asbytes_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
+        return [asbytes_nested(y) for y in x]
+    else:
+        return asbytes(x)
+
+def asunicode_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
+        return [asunicode_nested(y) for y in x]
+    else:
+        return asunicode(x)
+
+def is_pathlib_path(obj):
+    """
+    Check whether obj is a `pathlib.Path` object.
+
+    Prefer using ``isinstance(obj, os.PathLike)`` instead of this function.
+    """
+    return isinstance(obj, Path)
+
+# from Python 3.7
+class contextlib_nullcontext:
+    """Context manager that does no additional processing.
+
+    Used as a stand-in for a normal context manager, when a particular
+    block of code is only sometimes used with a normal context manager:
+
+    cm = optional_cm if condition else nullcontext()
+    with cm:
+        # Perform operation, using optional_cm if condition is True
+
+    .. note::
+        Prefer using `contextlib.nullcontext` instead of this context manager.
+    """
+
+    def __init__(self, enter_result=None):
+        self.enter_result = enter_result
+
+    def __enter__(self):
+        return self.enter_result
+
+    def __exit__(self, *excinfo):
+        pass
+
+
+def npy_load_module(name, fn, info=None):
+    """
+    Load a module. Uses ``load_module`` which will be deprecated in python
+    3.12. An alternative that uses ``exec_module`` is in
+    numpy.distutils.misc_util.exec_mod_from_location
+
+    Parameters
+    ----------
+    name : str
+        Full module name.
+    fn : str
+        Path to module file.
+    info : tuple, optional
+        Only here for backward compatibility with Python 2.*.
+
+    Returns
+    -------
+    mod : module
+
+    """
+    # Explicitly lazy import this to avoid paying the cost
+    # of importing importlib at startup
+    from importlib.machinery import SourceFileLoader
+    return SourceFileLoader(name, fn).load_module()
+
+
+os_fspath = os.fspath
+os_PathLike = os.PathLike
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f74ed4d3f6dbed79dd9cd8284ebd596853204398
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.py
@@ -0,0 +1,64 @@
+"""
+An enhanced distutils, providing support for Fortran compilers, for BLAS,
+LAPACK and other common libraries for numerical computing, and more.
+
+Public submodules are::
+
+    misc_util
+    system_info
+    cpu_info
+    log
+    exec_command
+
+For details, please see the *Packaging* and *NumPy Distutils User Guide*
+sections of the NumPy Reference Guide.
+
+For configuring the preference for and location of libraries like BLAS and
+LAPACK, and for setting include paths and similar build options, please see
+``site.cfg.example`` in the root of the NumPy repository or sdist.
+
+"""
+
+import warnings
+
+# Must import local ccompiler ASAP in order to get
+# customized CCompiler.spawn effective.
+from . import ccompiler
+from . import unixccompiler
+
+from .npy_pkg_config import *
+
+warnings.warn("\n\n"
+    "  `numpy.distutils` is deprecated since NumPy 1.23.0, as a result\n"
+    "  of the deprecation of `distutils` itself. It will be removed for\n"
+    "  Python >= 3.12. For older Python versions it will remain present.\n"
+    "  It is recommended to use `setuptools < 60.0` for those Python versions.\n"
+    "  For more details, see:\n"
+    "    https://numpy.org/devdocs/reference/distutils_status_migration.html \n\n",
+    DeprecationWarning, stacklevel=2
+)
+del warnings
+
+# If numpy is installed, add distutils.test()
+try:
+    from . import __config__
+    # Normally numpy is installed if the above import works, but an interrupted
+    # in-place build could also have left a __config__.py.  In that case the
+    # next import may still fail, so keep it inside the try block.
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+except ImportError:
+    pass
+
+
+def customized_fcompiler(plat=None, compiler=None):
+    from numpy.distutils.fcompiler import new_fcompiler
+    c = new_fcompiler(plat=plat, compiler=compiler)
+    c.customize()
+    return c
+
+def customized_ccompiler(plat=None, compiler=None, verbose=1):
+    c = ccompiler.new_compiler(plat=plat, compiler=compiler, verbose=verbose)
+    c.customize('')
+    return c
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3938d68de14c3f83f9278b5d6b6a151a28549a0d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__init__.pyi
@@ -0,0 +1,4 @@
+from typing import Any
+
+# TODO: remove when the full numpy namespace is defined
+def __getattr__(name: str) -> Any: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__pycache__/conv_template.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__pycache__/conv_template.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c969212425204d0d357522706b57a81373d4019d
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/__pycache__/conv_template.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/_shell_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/_shell_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a1c8ce718c936cbb59d3c232cfd75d354445091
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/_shell_utils.py
@@ -0,0 +1,87 @@
+"""
+Helper functions for interacting with the shell, and consuming shell-style
+parameters provided in config files.
+"""
+import os
+import shlex
+import subprocess
+
+__all__ = ['WindowsParser', 'PosixParser', 'NativeParser']
+
+
+class CommandLineParser:
+    """
+    An object that knows how to split and join command-line arguments.
+
+    It must be true that ``argv == split(join(argv))`` for all ``argv``.
+    The reverse neednt be true - `join(split(cmd))` may result in the addition
+    or removal of unnecessary escaping.
+    """
+    @staticmethod
+    def join(argv):
+        """ Join a list of arguments into a command line string """
+        raise NotImplementedError
+
+    @staticmethod
+    def split(cmd):
+        """ Split a command line string into a list of arguments """
+        raise NotImplementedError
+
+
+class WindowsParser:
+    """
+    The parsing behavior used by `subprocess.call("string")` on Windows, which
+    matches the Microsoft C/C++ runtime.
+
+    Note that this is _not_ the behavior of cmd.
+    """
+    @staticmethod
+    def join(argv):
+        # note that list2cmdline is specific to the windows syntax
+        return subprocess.list2cmdline(argv)
+
+    @staticmethod
+    def split(cmd):
+        import ctypes  # guarded import for systems without ctypes
+        try:
+            ctypes.windll
+        except AttributeError:
+            raise NotImplementedError
+
+        # Windows has special parsing rules for the executable (no quotes),
+        # that we do not care about - insert a dummy element
+        if not cmd:
+            return []
+        cmd = 'dummy ' + cmd
+
+        CommandLineToArgvW = ctypes.windll.shell32.CommandLineToArgvW
+        CommandLineToArgvW.restype = ctypes.POINTER(ctypes.c_wchar_p)
+        CommandLineToArgvW.argtypes = (ctypes.c_wchar_p, ctypes.POINTER(ctypes.c_int))
+
+        nargs = ctypes.c_int()
+        lpargs = CommandLineToArgvW(cmd, ctypes.byref(nargs))
+        args = [lpargs[i] for i in range(nargs.value)]
+        assert not ctypes.windll.kernel32.LocalFree(lpargs)
+
+        # strip the element we inserted
+        assert args[0] == "dummy"
+        return args[1:]
+
+
+class PosixParser:
+    """
+    The parsing behavior used by `subprocess.call("string", shell=True)` on Posix.
+    """
+    @staticmethod
+    def join(argv):
+        return ' '.join(shlex.quote(arg) for arg in argv)
+
+    @staticmethod
+    def split(cmd):
+        return shlex.split(cmd, posix=True)
+
+
+if os.name == 'nt':
+    NativeParser = WindowsParser
+elif os.name == 'posix':
+    NativeParser = PosixParser
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/armccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/armccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..afba7eb3b3529835e59a52b42f7b143225faf465
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/armccompiler.py
@@ -0,0 +1,26 @@
+from distutils.unixccompiler import UnixCCompiler                              
+
+class ArmCCompiler(UnixCCompiler):
+
+    """
+    Arm compiler.
+    """
+
+    compiler_type = 'arm'
+    cc_exe = 'armclang'
+    cxx_exe = 'armclang++'
+
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__(self, verbose, dry_run, force)
+        cc_compiler = self.cc_exe
+        cxx_compiler = self.cxx_exe
+        self.set_executables(compiler=cc_compiler +
+                                      ' -O3 -fPIC',
+                             compiler_so=cc_compiler +
+                                         ' -O3 -fPIC',
+                             compiler_cxx=cxx_compiler +
+                                          ' -O3 -fPIC',
+                             linker_exe=cc_compiler +
+                                        ' -lamath',
+                             linker_so=cc_compiler +
+                                       ' -lamath -shared')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..99f336af158466dd98faeaaf9be8560d6c0ea4f6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler.py
@@ -0,0 +1,826 @@
+import os
+import re
+import sys
+import platform
+import shlex
+import time
+import subprocess
+from copy import copy
+from pathlib import Path
+from distutils import ccompiler
+from distutils.ccompiler import (
+    compiler_class, gen_lib_options, get_default_compiler, new_compiler,
+    CCompiler
+)
+from distutils.errors import (
+    DistutilsExecError, DistutilsModuleError, DistutilsPlatformError,
+    CompileError, UnknownFileError
+)
+from distutils.sysconfig import customize_compiler
+from distutils.version import LooseVersion
+
+from numpy.distutils import log
+from numpy.distutils.exec_command import (
+    filepath_from_subprocess_output, forward_bytes_to_stdout
+)
+from numpy.distutils.misc_util import cyg2win32, is_sequence, mingw32, \
+                                      get_num_build_jobs, \
+                                      _commandline_dep_string, \
+                                      sanitize_cxx_flags
+
+# globals for parallel build management
+import threading
+
+_job_semaphore = None
+_global_lock = threading.Lock()
+_processing_files = set()
+
+
+def _needs_build(obj, cc_args, extra_postargs, pp_opts):
+    """
+    Check if an objects needs to be rebuild based on its dependencies
+
+    Parameters
+    ----------
+    obj : str
+        object file
+
+    Returns
+    -------
+    bool
+    """
+    # defined in unixcompiler.py
+    dep_file = obj + '.d'
+    if not os.path.exists(dep_file):
+        return True
+
+    # dep_file is a makefile containing 'object: dependencies'
+    # formatted like posix shell (spaces escaped, \ line continuations)
+    # the last line contains the compiler commandline arguments as some
+    # projects may compile an extension multiple times with different
+    # arguments
+    with open(dep_file) as f:
+        lines = f.readlines()
+
+    cmdline =_commandline_dep_string(cc_args, extra_postargs, pp_opts)
+    last_cmdline = lines[-1]
+    if last_cmdline != cmdline:
+        return True
+
+    contents = ''.join(lines[:-1])
+    deps = [x for x in shlex.split(contents, posix=True)
+            if x != "\n" and not x.endswith(":")]
+
+    try:
+        t_obj = os.stat(obj).st_mtime
+
+        # check if any of the dependencies is newer than the object
+        # the dependencies includes the source used to create the object
+        for f in deps:
+            if os.stat(f).st_mtime > t_obj:
+                return True
+    except OSError:
+        # no object counts as newer (shouldn't happen if dep_file exists)
+        return True
+
+    return False
+
+
+def replace_method(klass, method_name, func):
+    # Py3k does not have unbound method anymore, MethodType does not work
+    m = lambda self, *args, **kw: func(self, *args, **kw)
+    setattr(klass, method_name, m)
+
+
+######################################################################
+## Method that subclasses may redefine. But don't call this method,
+## it i private to CCompiler class and may return unexpected
+## results if used elsewhere. So, you have been warned..
+
+def CCompiler_find_executables(self):
+    """
+    Does nothing here, but is called by the get_version method and can be
+    overridden by subclasses. In particular it is redefined in the `FCompiler`
+    class where more documentation can be found.
+
+    """
+    pass
+
+
+replace_method(CCompiler, 'find_executables', CCompiler_find_executables)
+
+
+# Using customized CCompiler.spawn.
+def CCompiler_spawn(self, cmd, display=None, env=None):
+    """
+    Execute a command in a sub-process.
+
+    Parameters
+    ----------
+    cmd : str
+        The command to execute.
+    display : str or sequence of str, optional
+        The text to add to the log file kept by `numpy.distutils`.
+        If not given, `display` is equal to `cmd`.
+    env : a dictionary for environment variables, optional
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    DistutilsExecError
+        If the command failed, i.e. the exit status was not 0.
+
+    """
+    env = env if env is not None else dict(os.environ)
+    if display is None:
+        display = cmd
+        if is_sequence(display):
+            display = ' '.join(list(display))
+    log.info(display)
+    try:
+        if self.verbose:
+            subprocess.check_output(cmd, env=env)
+        else:
+            subprocess.check_output(cmd, stderr=subprocess.STDOUT, env=env)
+    except subprocess.CalledProcessError as exc:
+        o = exc.output
+        s = exc.returncode
+    except OSError as e:
+        # OSError doesn't have the same hooks for the exception
+        # output, but exec_command() historically would use an
+        # empty string for EnvironmentError (base class for
+        # OSError)
+        # o = b''
+        # still that would make the end-user lost in translation!
+        o = f"\n\n{e}\n\n\n"
+        try:
+            o = o.encode(sys.stdout.encoding)
+        except AttributeError:
+            o = o.encode('utf8')
+        # status previously used by exec_command() for parent
+        # of OSError
+        s = 127
+    else:
+        # use a convenience return here so that any kind of
+        # caught exception will execute the default code after the
+        # try / except block, which handles various exceptions
+        return None
+
+    if is_sequence(cmd):
+        cmd = ' '.join(list(cmd))
+
+    if self.verbose:
+        forward_bytes_to_stdout(o)
+
+    if re.search(b'Too many open files', o):
+        msg = '\nTry rerunning setup command until build succeeds.'
+    else:
+        msg = ''
+    raise DistutilsExecError('Command "%s" failed with exit status %d%s' %
+                            (cmd, s, msg))
+
+replace_method(CCompiler, 'spawn', CCompiler_spawn)
+
+def CCompiler_object_filenames(self, source_filenames, strip_dir=0, output_dir=''):
+    """
+    Return the name of the object files for the given source files.
+
+    Parameters
+    ----------
+    source_filenames : list of str
+        The list of paths to source files. Paths can be either relative or
+        absolute, this is handled transparently.
+    strip_dir : bool, optional
+        Whether to strip the directory from the returned paths. If True,
+        the file name prepended by `output_dir` is returned. Default is False.
+    output_dir : str, optional
+        If given, this path is prepended to the returned paths to the
+        object files.
+
+    Returns
+    -------
+    obj_names : list of str
+        The list of paths to the object files corresponding to the source
+        files in `source_filenames`.
+
+    """
+    if output_dir is None:
+        output_dir = ''
+    obj_names = []
+    for src_name in source_filenames:
+        base, ext = os.path.splitext(os.path.normpath(src_name))
+        base = os.path.splitdrive(base)[1] # Chop off the drive
+        base = base[os.path.isabs(base):]  # If abs, chop off leading /
+        if base.startswith('..'):
+            # Resolve starting relative path components, middle ones
+            # (if any) have been handled by os.path.normpath above.
+            i = base.rfind('..')+2
+            d = base[:i]
+            d = os.path.basename(os.path.abspath(d))
+            base = d + base[i:]
+        if ext not in self.src_extensions:
+            raise UnknownFileError("unknown file type '%s' (from '%s')" % (ext, src_name))
+        if strip_dir:
+            base = os.path.basename(base)
+        obj_name = os.path.join(output_dir, base + self.obj_extension)
+        obj_names.append(obj_name)
+    return obj_names
+
+replace_method(CCompiler, 'object_filenames', CCompiler_object_filenames)
+
+def CCompiler_compile(self, sources, output_dir=None, macros=None,
+                      include_dirs=None, debug=0, extra_preargs=None,
+                      extra_postargs=None, depends=None):
+    """
+    Compile one or more source files.
+
+    Please refer to the Python distutils API reference for more details.
+
+    Parameters
+    ----------
+    sources : list of str
+        A list of filenames
+    output_dir : str, optional
+        Path to the output directory.
+    macros : list of tuples
+        A list of macro definitions.
+    include_dirs : list of str, optional
+        The directories to add to the default include file search path for
+        this compilation only.
+    debug : bool, optional
+        Whether or not to output debug symbols in or alongside the object
+        file(s).
+    extra_preargs, extra_postargs : ?
+        Extra pre- and post-arguments.
+    depends : list of str, optional
+        A list of file names that all targets depend on.
+
+    Returns
+    -------
+    objects : list of str
+        A list of object file names, one per source file `sources`.
+
+    Raises
+    ------
+    CompileError
+        If compilation fails.
+
+    """
+    global _job_semaphore
+
+    jobs = get_num_build_jobs()
+
+    # setup semaphore to not exceed number of compile jobs when parallelized at
+    # extension level (python >= 3.5)
+    with _global_lock:
+        if _job_semaphore is None:
+            _job_semaphore = threading.Semaphore(jobs)
+
+    if not sources:
+        return []
+    from numpy.distutils.fcompiler import (FCompiler,
+                                           FORTRAN_COMMON_FIXED_EXTENSIONS,
+                                           has_f90_header)
+    if isinstance(self, FCompiler):
+        display = []
+        for fc in ['f77', 'f90', 'fix']:
+            fcomp = getattr(self, 'compiler_'+fc)
+            if fcomp is None:
+                continue
+            display.append("Fortran %s compiler: %s" % (fc, ' '.join(fcomp)))
+        display = '\n'.join(display)
+    else:
+        ccomp = self.compiler_so
+        display = "C compiler: %s\n" % (' '.join(ccomp),)
+    log.info(display)
+    macros, objects, extra_postargs, pp_opts, build = \
+            self._setup_compile(output_dir, macros, include_dirs, sources,
+                                depends, extra_postargs)
+    cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
+    display = "compile options: '%s'" % (' '.join(cc_args))
+    if extra_postargs:
+        display += "\nextra options: '%s'" % (' '.join(extra_postargs))
+    log.info(display)
+
+    def single_compile(args):
+        obj, (src, ext) = args
+        if not _needs_build(obj, cc_args, extra_postargs, pp_opts):
+            return
+
+        # check if we are currently already processing the same object
+        # happens when using the same source in multiple extensions
+        while True:
+            # need explicit lock as there is no atomic check and add with GIL
+            with _global_lock:
+                # file not being worked on, start working
+                if obj not in _processing_files:
+                    _processing_files.add(obj)
+                    break
+            # wait for the processing to end
+            time.sleep(0.1)
+
+        try:
+            # retrieve slot from our #job semaphore and build
+            with _job_semaphore:
+                self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
+        finally:
+            # register being done processing
+            with _global_lock:
+                _processing_files.remove(obj)
+
+
+    if isinstance(self, FCompiler):
+        objects_to_build = list(build.keys())
+        f77_objects, other_objects = [], []
+        for obj in objects:
+            if obj in objects_to_build:
+                src, ext = build[obj]
+                if self.compiler_type=='absoft':
+                    obj = cyg2win32(obj)
+                    src = cyg2win32(src)
+                if Path(src).suffix.lower() in FORTRAN_COMMON_FIXED_EXTENSIONS \
+                   and not has_f90_header(src):
+                    f77_objects.append((obj, (src, ext)))
+                else:
+                    other_objects.append((obj, (src, ext)))
+
+        # f77 objects can be built in parallel
+        build_items = f77_objects
+        # build f90 modules serial, module files are generated during
+        # compilation and may be used by files later in the list so the
+        # ordering is important
+        for o in other_objects:
+            single_compile(o)
+    else:
+        build_items = build.items()
+
+    if len(build) > 1 and jobs > 1:
+        # build parallel
+        from concurrent.futures import ThreadPoolExecutor
+        with ThreadPoolExecutor(jobs) as pool:
+            res = pool.map(single_compile, build_items)
+        list(res)  # access result to raise errors
+    else:
+        # build serial
+        for o in build_items:
+            single_compile(o)
+
+    # Return *all* object filenames, not just the ones we just built.
+    return objects
+
+replace_method(CCompiler, 'compile', CCompiler_compile)
+
+def CCompiler_customize_cmd(self, cmd, ignore=()):
+    """
+    Customize compiler using distutils command.
+
+    Parameters
+    ----------
+    cmd : class instance
+        An instance inheriting from ``distutils.cmd.Command``.
+    ignore : sequence of str, optional
+        List of ``distutils.ccompiler.CCompiler`` commands (without ``'set_'``) that should not be
+        altered. Strings that are checked for are:
+        ``('include_dirs', 'define', 'undef', 'libraries', 'library_dirs',
+        'rpath', 'link_objects')``.
+
+    Returns
+    -------
+    None
+
+    """
+    log.info('customize %s using %s' % (self.__class__.__name__,
+                                        cmd.__class__.__name__))
+
+    if (
+        hasattr(self, 'compiler') and
+        'clang' in self.compiler[0] and
+        not (platform.machine() == 'arm64' and sys.platform == 'darwin')
+    ):
+        # clang defaults to a non-strict floating error point model.
+        # However, '-ftrapping-math' is not currently supported (2023-04-08)
+        # for macosx_arm64.
+        # Since NumPy and most Python libs give warnings for these, override:
+        self.compiler.append('-ftrapping-math')
+        self.compiler_so.append('-ftrapping-math')
+
+    def allow(attr):
+        return getattr(cmd, attr, None) is not None and attr not in ignore
+
+    if allow('include_dirs'):
+        self.set_include_dirs(cmd.include_dirs)
+    if allow('define'):
+        for (name, value) in cmd.define:
+            self.define_macro(name, value)
+    if allow('undef'):
+        for macro in cmd.undef:
+            self.undefine_macro(macro)
+    if allow('libraries'):
+        self.set_libraries(self.libraries + cmd.libraries)
+    if allow('library_dirs'):
+        self.set_library_dirs(self.library_dirs + cmd.library_dirs)
+    if allow('rpath'):
+        self.set_runtime_library_dirs(cmd.rpath)
+    if allow('link_objects'):
+        self.set_link_objects(cmd.link_objects)
+
+replace_method(CCompiler, 'customize_cmd', CCompiler_customize_cmd)
+
+def _compiler_to_string(compiler):
+    props = []
+    mx = 0
+    keys = list(compiler.executables.keys())
+    for key in ['version', 'libraries', 'library_dirs',
+                'object_switch', 'compile_switch',
+                'include_dirs', 'define', 'undef', 'rpath', 'link_objects']:
+        if key not in keys:
+            keys.append(key)
+    for key in keys:
+        if hasattr(compiler, key):
+            v = getattr(compiler, key)
+            mx = max(mx, len(key))
+            props.append((key, repr(v)))
+    fmt = '%-' + repr(mx+1) + 's = %s'
+    lines = [fmt % prop for prop in props]
+    return '\n'.join(lines)
+
+def CCompiler_show_customization(self):
+    """
+    Print the compiler customizations to stdout.
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    None
+
+    Notes
+    -----
+    Printing is only done if the distutils log threshold is < 2.
+
+    """
+    try:
+        self.get_version()
+    except Exception:
+        pass
+    if log._global_log.threshold<2:
+        print('*'*80)
+        print(self.__class__)
+        print(_compiler_to_string(self))
+        print('*'*80)
+
+replace_method(CCompiler, 'show_customization', CCompiler_show_customization)
+
+def CCompiler_customize(self, dist, need_cxx=0):
+    """
+    Do any platform-specific customization of a compiler instance.
+
+    This method calls ``distutils.sysconfig.customize_compiler`` for
+    platform-specific customization, as well as optionally remove a flag
+    to suppress spurious warnings in case C++ code is being compiled.
+
+    Parameters
+    ----------
+    dist : object
+        This parameter is not used for anything.
+    need_cxx : bool, optional
+        Whether or not C++ has to be compiled. If so (True), the
+        ``"-Wstrict-prototypes"`` option is removed to prevent spurious
+        warnings. Default is False.
+
+    Returns
+    -------
+    None
+
+    Notes
+    -----
+    All the default options used by distutils can be extracted with::
+
+      from distutils import sysconfig
+      sysconfig.get_config_vars('CC', 'CXX', 'OPT', 'BASECFLAGS',
+                                'CCSHARED', 'LDSHARED', 'SO')
+
+    """
+    # See FCompiler.customize for suggested usage.
+    log.info('customize %s' % (self.__class__.__name__))
+    customize_compiler(self)
+    if need_cxx:
+        # In general, distutils uses -Wstrict-prototypes, but this option is
+        # not valid for C++ code, only for C.  Remove it if it's there to
+        # avoid a spurious warning on every compilation.
+        try:
+            self.compiler_so.remove('-Wstrict-prototypes')
+        except (AttributeError, ValueError):
+            pass
+
+        if hasattr(self, 'compiler') and 'cc' in self.compiler[0]:
+            if not self.compiler_cxx:
+                if self.compiler[0].startswith('gcc'):
+                    a, b = 'gcc', 'g++'
+                else:
+                    a, b = 'cc', 'c++'
+                self.compiler_cxx = [self.compiler[0].replace(a, b)]\
+                                    + self.compiler[1:]
+        else:
+            if hasattr(self, 'compiler'):
+                log.warn("#### %s #######" % (self.compiler,))
+            if not hasattr(self, 'compiler_cxx'):
+                log.warn('Missing compiler_cxx fix for ' + self.__class__.__name__)
+
+
+    # check if compiler supports gcc style automatic dependencies
+    # run on every extension so skip for known good compilers
+    if hasattr(self, 'compiler') and ('gcc' in self.compiler[0] or
+                                      'g++' in self.compiler[0] or
+                                      'clang' in self.compiler[0]):
+        self._auto_depends = True
+    elif os.name == 'posix':
+        import tempfile
+        import shutil
+        tmpdir = tempfile.mkdtemp()
+        try:
+            fn = os.path.join(tmpdir, "file.c")
+            with open(fn, "w") as f:
+                f.write("int a;\n")
+            self.compile([fn], output_dir=tmpdir,
+                         extra_preargs=['-MMD', '-MF', fn + '.d'])
+            self._auto_depends = True
+        except CompileError:
+            self._auto_depends = False
+        finally:
+            shutil.rmtree(tmpdir)
+
+    return
+
+replace_method(CCompiler, 'customize', CCompiler_customize)
+
+def simple_version_match(pat=r'[-.\d]+', ignore='', start=''):
+    """
+    Simple matching of version numbers, for use in CCompiler and FCompiler.
+
+    Parameters
+    ----------
+    pat : str, optional
+        A regular expression matching version numbers.
+        Default is ``r'[-.\\d]+'``.
+    ignore : str, optional
+        A regular expression matching patterns to skip.
+        Default is ``''``, in which case nothing is skipped.
+    start : str, optional
+        A regular expression matching the start of where to start looking
+        for version numbers.
+        Default is ``''``, in which case searching is started at the
+        beginning of the version string given to `matcher`.
+
+    Returns
+    -------
+    matcher : callable
+        A function that is appropriate to use as the ``.version_match``
+        attribute of a ``distutils.ccompiler.CCompiler`` class. `matcher` takes a single parameter,
+        a version string.
+
+    """
+    def matcher(self, version_string):
+        # version string may appear in the second line, so getting rid
+        # of new lines:
+        version_string = version_string.replace('\n', ' ')
+        pos = 0
+        if start:
+            m = re.match(start, version_string)
+            if not m:
+                return None
+            pos = m.end()
+        while True:
+            m = re.search(pat, version_string[pos:])
+            if not m:
+                return None
+            if ignore and re.match(ignore, m.group(0)):
+                pos = m.end()
+                continue
+            break
+        return m.group(0)
+    return matcher
+
+def CCompiler_get_version(self, force=False, ok_status=[0]):
+    """
+    Return compiler version, or None if compiler is not available.
+
+    Parameters
+    ----------
+    force : bool, optional
+        If True, force a new determination of the version, even if the
+        compiler already has a version attribute. Default is False.
+    ok_status : list of int, optional
+        The list of status values returned by the version look-up process
+        for which a version string is returned. If the status value is not
+        in `ok_status`, None is returned. Default is ``[0]``.
+
+    Returns
+    -------
+    version : str or None
+        Version string, in the format of ``distutils.version.LooseVersion``.
+
+    """
+    if not force and hasattr(self, 'version'):
+        return self.version
+    self.find_executables()
+    try:
+        version_cmd = self.version_cmd
+    except AttributeError:
+        return None
+    if not version_cmd or not version_cmd[0]:
+        return None
+    try:
+        matcher = self.version_match
+    except AttributeError:
+        try:
+            pat = self.version_pattern
+        except AttributeError:
+            return None
+        def matcher(version_string):
+            m = re.match(pat, version_string)
+            if not m:
+                return None
+            version = m.group('version')
+            return version
+
+    try:
+        output = subprocess.check_output(version_cmd, stderr=subprocess.STDOUT)
+    except subprocess.CalledProcessError as exc:
+        output = exc.output
+        status = exc.returncode
+    except OSError:
+        # match the historical returns for a parent
+        # exception class caught by exec_command()
+        status = 127
+        output = b''
+    else:
+        # output isn't actually a filepath but we do this
+        # for now to match previous distutils behavior
+        output = filepath_from_subprocess_output(output)
+        status = 0
+
+    version = None
+    if status in ok_status:
+        version = matcher(output)
+        if version:
+            version = LooseVersion(version)
+    self.version = version
+    return version
+
+replace_method(CCompiler, 'get_version', CCompiler_get_version)
+
+def CCompiler_cxx_compiler(self):
+    """
+    Return the C++ compiler.
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    cxx : class instance
+        The C++ compiler, as a ``distutils.ccompiler.CCompiler`` instance.
+
+    """
+    if self.compiler_type in ('msvc', 'intelw', 'intelemw'):
+        return self
+
+    cxx = copy(self)
+    cxx.compiler_cxx = cxx.compiler_cxx
+    cxx.compiler_so = [cxx.compiler_cxx[0]] + \
+                      sanitize_cxx_flags(cxx.compiler_so[1:])
+    if (sys.platform.startswith(('aix', 'os400')) and
+            'ld_so_aix' in cxx.linker_so[0]):
+        # AIX needs the ld_so_aix script included with Python
+        cxx.linker_so = [cxx.linker_so[0], cxx.compiler_cxx[0]] \
+                        + cxx.linker_so[2:]
+    if sys.platform.startswith('os400'):
+        #This is required by i 7.4 and prievous for PRId64 in printf() call.
+        cxx.compiler_so.append('-D__STDC_FORMAT_MACROS')
+        #This a bug of gcc10.3, which failed to handle the TLS init.
+        cxx.compiler_so.append('-fno-extern-tls-init')
+        cxx.linker_so.append('-fno-extern-tls-init')
+    else:
+        cxx.linker_so = [cxx.compiler_cxx[0]] + cxx.linker_so[1:]
+    return cxx
+
+replace_method(CCompiler, 'cxx_compiler', CCompiler_cxx_compiler)
+
+compiler_class['intel'] = ('intelccompiler', 'IntelCCompiler',
+                           "Intel C Compiler for 32-bit applications")
+compiler_class['intele'] = ('intelccompiler', 'IntelItaniumCCompiler',
+                            "Intel C Itanium Compiler for Itanium-based applications")
+compiler_class['intelem'] = ('intelccompiler', 'IntelEM64TCCompiler',
+                             "Intel C Compiler for 64-bit applications")
+compiler_class['intelw'] = ('intelccompiler', 'IntelCCompilerW',
+                            "Intel C Compiler for 32-bit applications on Windows")
+compiler_class['intelemw'] = ('intelccompiler', 'IntelEM64TCCompilerW',
+                              "Intel C Compiler for 64-bit applications on Windows")
+compiler_class['pathcc'] = ('pathccompiler', 'PathScaleCCompiler',
+                            "PathScale Compiler for SiCortex-based applications")
+compiler_class['arm'] = ('armccompiler', 'ArmCCompiler',
+                            "Arm C Compiler")
+compiler_class['fujitsu'] = ('fujitsuccompiler', 'FujitsuCCompiler',
+                            "Fujitsu C Compiler")
+
+ccompiler._default_compilers += (('linux.*', 'intel'),
+                                 ('linux.*', 'intele'),
+                                 ('linux.*', 'intelem'),
+                                 ('linux.*', 'pathcc'),
+                                 ('nt', 'intelw'),
+                                 ('nt', 'intelemw'))
+
+if sys.platform == 'win32':
+    compiler_class['mingw32'] = ('mingw32ccompiler', 'Mingw32CCompiler',
+                                 "Mingw32 port of GNU C Compiler for Win32"\
+                                 "(for MSC built Python)")
+    if mingw32():
+        # On windows platforms, we want to default to mingw32 (gcc)
+        # because msvc can't build blitz stuff.
+        log.info('Setting mingw32 as default compiler for nt.')
+        ccompiler._default_compilers = (('nt', 'mingw32'),) \
+                                       + ccompiler._default_compilers
+
+
+_distutils_new_compiler = new_compiler
+def new_compiler (plat=None,
+                  compiler=None,
+                  verbose=None,
+                  dry_run=0,
+                  force=0):
+    # Try first C compilers from numpy.distutils.
+    if verbose is None:
+        verbose = log.get_threshold() <= log.INFO
+    if plat is None:
+        plat = os.name
+    try:
+        if compiler is None:
+            compiler = get_default_compiler(plat)
+        (module_name, class_name, long_description) = compiler_class[compiler]
+    except KeyError:
+        msg = "don't know how to compile C/C++ code on platform '%s'" % plat
+        if compiler is not None:
+            msg = msg + " with '%s' compiler" % compiler
+        raise DistutilsPlatformError(msg)
+    module_name = "numpy.distutils." + module_name
+    try:
+        __import__ (module_name)
+    except ImportError as e:
+        msg = str(e)
+        log.info('%s in numpy.distutils; trying from distutils',
+                 str(msg))
+        module_name = module_name[6:]
+        try:
+            __import__(module_name)
+        except ImportError as e:
+            msg = str(e)
+            raise DistutilsModuleError("can't compile C/C++ code: unable to load module '%s'" % \
+                  module_name)
+    try:
+        module = sys.modules[module_name]
+        klass = vars(module)[class_name]
+    except KeyError:
+        raise DistutilsModuleError(("can't compile C/C++ code: unable to find class '%s' " +
+               "in module '%s'") % (class_name, module_name))
+    compiler = klass(None, dry_run, force)
+    compiler.verbose = verbose
+    log.debug('new_compiler returns %s' % (klass))
+    return compiler
+
+ccompiler.new_compiler = new_compiler
+
+_distutils_gen_lib_options = gen_lib_options
+def gen_lib_options(compiler, library_dirs, runtime_library_dirs, libraries):
+    # the version of this function provided by CPython allows the following
+    # to return lists, which are unpacked automatically:
+    # - compiler.runtime_library_dir_option
+    # our version extends the behavior to:
+    # - compiler.library_dir_option
+    # - compiler.library_option
+    # - compiler.find_library_file
+    r = _distutils_gen_lib_options(compiler, library_dirs,
+                                   runtime_library_dirs, libraries)
+    lib_opts = []
+    for i in r:
+        if is_sequence(i):
+            lib_opts.extend(list(i))
+        else:
+            lib_opts.append(i)
+    return lib_opts
+ccompiler.gen_lib_options = gen_lib_options
+
+# Also fix up the various compiler modules, which do
+# from distutils.ccompiler import gen_lib_options
+# Don't bother with mwerks, as we don't support Classic Mac.
+for _cc in ['msvc9', 'msvc', '_msvc', 'bcpp', 'cygwinc', 'emxc', 'unixc']:
+    _m = sys.modules.get('distutils.' + _cc + 'compiler')
+    if _m is not None:
+        setattr(_m, 'gen_lib_options', gen_lib_options)
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler_opt.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1a6fa36061c7c70132b248da51d25d348b3a311
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/ccompiler_opt.py
@@ -0,0 +1,2668 @@
+"""Provides the `CCompilerOpt` class, used for handling the CPU/hardware
+optimization, starting from parsing the command arguments, to managing the
+relation between the CPU baseline and dispatch-able features,
+also generating the required C headers and ending with compiling
+the sources with proper compiler's flags.
+
+`CCompilerOpt` doesn't provide runtime detection for the CPU features,
+instead only focuses on the compiler side, but it creates abstract C headers
+that can be used later for the final runtime dispatching process."""
+
+import atexit
+import inspect
+import os
+import pprint
+import re
+import subprocess
+import textwrap
+
+class _Config:
+    """An abstract class holds all configurable attributes of `CCompilerOpt`,
+    these class attributes can be used to change the default behavior
+    of `CCompilerOpt` in order to fit other requirements.
+
+    Attributes
+    ----------
+    conf_nocache : bool
+        Set True to disable memory and file cache.
+        Default is False.
+
+    conf_noopt : bool
+        Set True to forces the optimization to be disabled,
+        in this case `CCompilerOpt` tends to generate all
+        expected headers in order to 'not' break the build.
+        Default is False.
+
+    conf_cache_factors : list
+        Add extra factors to the primary caching factors. The caching factors
+        are utilized to determine if there are changes had happened that
+        requires to discard the cache and re-updating it. The primary factors
+        are the arguments of `CCompilerOpt` and `CCompiler`'s properties(type, flags, etc).
+        Default is list of two items, containing the time of last modification
+        of `ccompiler_opt` and value of attribute "conf_noopt"
+
+    conf_tmp_path : str,
+        The path of temporary directory. Default is auto-created
+        temporary directory via ``tempfile.mkdtemp()``.
+
+    conf_check_path : str
+        The path of testing files. Each added CPU feature must have a
+        **C** source file contains at least one intrinsic or instruction that
+        related to this feature, so it can be tested against the compiler.
+        Default is ``./distutils/checks``.
+
+    conf_target_groups : dict
+        Extra tokens that can be reached from dispatch-able sources through
+        the special mark ``@targets``. Default is an empty dictionary.
+
+        **Notes**:
+            - case-insensitive for tokens and group names
+            - sign '#' must stick in the begin of group name and only within ``@targets``
+
+        **Example**:
+            .. code-block:: console
+
+                $ "@targets #avx_group other_tokens" > group_inside.c
+
+            >>> CCompilerOpt.conf_target_groups["avx_group"] = \\
+            "$werror $maxopt avx2 avx512f avx512_skx"
+            >>> cco = CCompilerOpt(cc_instance)
+            >>> cco.try_dispatch(["group_inside.c"])
+
+    conf_c_prefix : str
+        The prefix of public C definitions. Default is ``"NPY_"``.
+
+    conf_c_prefix_ : str
+        The prefix of internal C definitions. Default is ``"NPY__"``.
+
+    conf_cc_flags : dict
+        Nested dictionaries defining several compiler flags
+        that linked to some major functions, the main key
+        represent the compiler name and sub-keys represent
+        flags names. Default is already covers all supported
+        **C** compilers.
+
+        Sub-keys explained as follows:
+
+        "native": str or None
+            used by argument option `native`, to detect the current
+            machine support via the compiler.
+        "werror": str or None
+            utilized to treat warning as errors during testing CPU features
+            against the compiler and also for target's policy `$werror`
+            via dispatch-able sources.
+        "maxopt": str or None
+            utilized for target's policy '$maxopt' and the value should
+            contains the maximum acceptable optimization by the compiler.
+            e.g. in gcc ``'-O3'``
+
+        **Notes**:
+            * case-sensitive for compiler names and flags
+            * use space to separate multiple flags
+            * any flag will tested against the compiler and it will skipped
+              if it's not applicable.
+
+    conf_min_features : dict
+        A dictionary defines the used CPU features for
+        argument option ``'min'``, the key represent the CPU architecture
+        name e.g. ``'x86'``. Default values provide the best effort
+        on wide range of users platforms.
+
+        **Note**: case-sensitive for architecture names.
+
+    conf_features : dict
+        Nested dictionaries used for identifying the CPU features.
+        the primary key is represented as a feature name or group name
+        that gathers several features. Default values covers all
+        supported features but without the major options like "flags",
+        these undefined options handle it by method `conf_features_partial()`.
+        Default value is covers almost all CPU features for *X86*, *IBM/Power64*
+        and *ARM 7/8*.
+
+        Sub-keys explained as follows:
+
+        "implies" : str or list, optional,
+            List of CPU feature names to be implied by it,
+            the feature name must be defined within `conf_features`.
+            Default is None.
+
+        "flags": str or list, optional
+            List of compiler flags. Default is None.
+
+        "detect": str or list, optional
+            List of CPU feature names that required to be detected
+            in runtime. By default, its the feature name or features
+            in "group" if its specified.
+
+        "implies_detect": bool, optional
+            If True, all "detect" of implied features will be combined.
+            Default is True. see `feature_detect()`.
+
+        "group": str or list, optional
+            Same as "implies" but doesn't require the feature name to be
+            defined within `conf_features`.
+
+        "interest": int, required
+            a key for sorting CPU features
+
+        "headers": str or list, optional
+            intrinsics C header file
+
+        "disable": str, optional
+            force disable feature, the string value should contains the
+            reason of disabling.
+
+        "autovec": bool or None, optional
+            True or False to declare that CPU feature can be auto-vectorized
+            by the compiler.
+            By default(None), treated as True if the feature contains at
+            least one applicable flag. see `feature_can_autovec()`
+
+        "extra_checks": str or list, optional
+            Extra test case names for the CPU feature that need to be tested
+            against the compiler.
+
+            Each test case must have a C file named ``extra_xxxx.c``, where
+            ``xxxx`` is the case name in lower case, under 'conf_check_path'.
+            It should contain at least one intrinsic or function related to the test case.
+
+            If the compiler able to successfully compile the C file then `CCompilerOpt`
+            will add a C ``#define`` for it into the main dispatch header, e.g.
+            ``#define {conf_c_prefix}_XXXX`` where ``XXXX`` is the case name in upper case.
+
+        **NOTES**:
+            * space can be used as separator with options that supports "str or list"
+            * case-sensitive for all values and feature name must be in upper-case.
+            * if flags aren't applicable, its will skipped rather than disable the
+              CPU feature
+            * the CPU feature will disabled if the compiler fail to compile
+              the test file
+    """
+    conf_nocache = False
+    conf_noopt = False
+    conf_cache_factors = None
+    conf_tmp_path = None
+    conf_check_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "checks"
+    )
+    conf_target_groups = {}
+    conf_c_prefix = 'NPY_'
+    conf_c_prefix_ = 'NPY__'
+    conf_cc_flags = dict(
+        gcc = dict(
+            # native should always fail on arm and ppc64,
+            # native usually works only with x86
+            native = '-march=native',
+            opt = '-O3',
+            werror = '-Werror',
+        ),
+        clang = dict(
+            native = '-march=native',
+            opt = "-O3",
+            # One of the following flags needs to be applicable for Clang to
+            # guarantee the sanity of the testing process, however in certain
+            # cases `-Werror` gets skipped during the availability test due to
+            # "unused arguments" warnings.
+            # see https://github.com/numpy/numpy/issues/19624
+            werror = '-Werror=switch -Werror',
+        ),
+        icc = dict(
+            native = '-xHost',
+            opt = '-O3',
+            werror = '-Werror',
+        ),
+        iccw = dict(
+            native = '/QxHost',
+            opt = '/O3',
+            werror = '/Werror',
+        ),
+        msvc = dict(
+            native = None,
+            opt = '/O2',
+            werror = '/WX',
+        ),
+        fcc = dict(
+            native = '-mcpu=a64fx',
+            opt = None,
+            werror = None,
+        )
+    )
+    conf_min_features = dict(
+        x86 = "SSE SSE2",
+        x64 = "SSE SSE2 SSE3",
+        ppc64 = '', # play it safe
+        ppc64le = "VSX VSX2",
+        s390x = '',
+        armhf = '', # play it safe
+        aarch64 = "NEON NEON_FP16 NEON_VFPV4 ASIMD"
+    )
+    conf_features = dict(
+        # X86
+        SSE = dict(
+            interest=1, headers="xmmintrin.h",
+            # enabling SSE without SSE2 is useless also
+            # it's non-optional for x86_64
+            implies="SSE2"
+        ),
+        SSE2   = dict(interest=2, implies="SSE", headers="emmintrin.h"),
+        SSE3   = dict(interest=3, implies="SSE2", headers="pmmintrin.h"),
+        SSSE3  = dict(interest=4, implies="SSE3", headers="tmmintrin.h"),
+        SSE41  = dict(interest=5, implies="SSSE3", headers="smmintrin.h"),
+        POPCNT = dict(interest=6, implies="SSE41", headers="popcntintrin.h"),
+        SSE42  = dict(interest=7, implies="POPCNT"),
+        AVX    = dict(
+            interest=8, implies="SSE42", headers="immintrin.h",
+            implies_detect=False
+        ),
+        XOP    = dict(interest=9, implies="AVX", headers="x86intrin.h"),
+        FMA4   = dict(interest=10, implies="AVX", headers="x86intrin.h"),
+        F16C   = dict(interest=11, implies="AVX"),
+        FMA3   = dict(interest=12, implies="F16C"),
+        AVX2   = dict(interest=13, implies="F16C"),
+        AVX512F = dict(
+            interest=20, implies="FMA3 AVX2", implies_detect=False,
+            extra_checks="AVX512F_REDUCE"
+        ),
+        AVX512CD = dict(interest=21, implies="AVX512F"),
+        AVX512_KNL = dict(
+            interest=40, implies="AVX512CD", group="AVX512ER AVX512PF",
+            detect="AVX512_KNL", implies_detect=False
+        ),
+        AVX512_KNM = dict(
+            interest=41, implies="AVX512_KNL",
+            group="AVX5124FMAPS AVX5124VNNIW AVX512VPOPCNTDQ",
+            detect="AVX512_KNM", implies_detect=False
+        ),
+        AVX512_SKX = dict(
+            interest=42, implies="AVX512CD", group="AVX512VL AVX512BW AVX512DQ",
+            detect="AVX512_SKX", implies_detect=False,
+            extra_checks="AVX512BW_MASK AVX512DQ_MASK"
+        ),
+        AVX512_CLX = dict(
+            interest=43, implies="AVX512_SKX", group="AVX512VNNI",
+            detect="AVX512_CLX"
+        ),
+        AVX512_CNL = dict(
+            interest=44, implies="AVX512_SKX", group="AVX512IFMA AVX512VBMI",
+            detect="AVX512_CNL", implies_detect=False
+        ),
+        AVX512_ICL = dict(
+            interest=45, implies="AVX512_CLX AVX512_CNL",
+            group="AVX512VBMI2 AVX512BITALG AVX512VPOPCNTDQ",
+            detect="AVX512_ICL", implies_detect=False
+        ),
+        AVX512_SPR = dict(
+            interest=46, implies="AVX512_ICL", group="AVX512FP16",
+            detect="AVX512_SPR", implies_detect=False
+        ),
+        # IBM/Power
+        ## Power7/ISA 2.06
+        VSX = dict(interest=1, headers="altivec.h", extra_checks="VSX_ASM"),
+        ## Power8/ISA 2.07
+        VSX2 = dict(interest=2, implies="VSX", implies_detect=False),
+        ## Power9/ISA 3.00
+        VSX3 = dict(interest=3, implies="VSX2", implies_detect=False,
+                    extra_checks="VSX3_HALF_DOUBLE"),
+        ## Power10/ISA 3.1
+        VSX4 = dict(interest=4, implies="VSX3", implies_detect=False,
+                    extra_checks="VSX4_MMA"),
+        # IBM/Z
+        ## VX(z13) support
+        VX = dict(interest=1, headers="vecintrin.h"),
+        ## Vector-Enhancements Facility
+        VXE = dict(interest=2, implies="VX", implies_detect=False),
+        ## Vector-Enhancements Facility 2
+        VXE2 = dict(interest=3, implies="VXE", implies_detect=False),
+        # ARM
+        NEON  = dict(interest=1, headers="arm_neon.h"),
+        NEON_FP16 = dict(interest=2, implies="NEON"),
+        ## FMA
+        NEON_VFPV4 = dict(interest=3, implies="NEON_FP16"),
+        ## Advanced SIMD
+        ASIMD = dict(interest=4, implies="NEON_FP16 NEON_VFPV4", implies_detect=False),
+        ## ARMv8.2 half-precision & vector arithm
+        ASIMDHP = dict(interest=5, implies="ASIMD"),
+        ## ARMv8.2 dot product
+        ASIMDDP = dict(interest=6, implies="ASIMD"),
+        ## ARMv8.2 Single & half-precision Multiply
+        ASIMDFHM = dict(interest=7, implies="ASIMDHP"),
+    )
+    def conf_features_partial(self):
+        """Return a dictionary of supported CPU features by the platform,
+        and accumulate the rest of undefined options in `conf_features`,
+        the returned dict has same rules and notes in
+        class attribute `conf_features`, also its override
+        any options that been set in 'conf_features'.
+        """
+        if self.cc_noopt:
+            # optimization is disabled
+            return {}
+
+        on_x86 = self.cc_on_x86 or self.cc_on_x64
+        is_unix = self.cc_is_gcc or self.cc_is_clang or self.cc_is_fcc
+
+        if on_x86 and is_unix: return dict(
+            SSE    = dict(flags="-msse"),
+            SSE2   = dict(flags="-msse2"),
+            SSE3   = dict(flags="-msse3"),
+            SSSE3  = dict(flags="-mssse3"),
+            SSE41  = dict(flags="-msse4.1"),
+            POPCNT = dict(flags="-mpopcnt"),
+            SSE42  = dict(flags="-msse4.2"),
+            AVX    = dict(flags="-mavx"),
+            F16C   = dict(flags="-mf16c"),
+            XOP    = dict(flags="-mxop"),
+            FMA4   = dict(flags="-mfma4"),
+            FMA3   = dict(flags="-mfma"),
+            AVX2   = dict(flags="-mavx2"),
+            AVX512F = dict(flags="-mavx512f -mno-mmx"),
+            AVX512CD = dict(flags="-mavx512cd"),
+            AVX512_KNL = dict(flags="-mavx512er -mavx512pf"),
+            AVX512_KNM = dict(
+                flags="-mavx5124fmaps -mavx5124vnniw -mavx512vpopcntdq"
+            ),
+            AVX512_SKX = dict(flags="-mavx512vl -mavx512bw -mavx512dq"),
+            AVX512_CLX = dict(flags="-mavx512vnni"),
+            AVX512_CNL = dict(flags="-mavx512ifma -mavx512vbmi"),
+            AVX512_ICL = dict(
+                flags="-mavx512vbmi2 -mavx512bitalg -mavx512vpopcntdq"
+            ),
+            AVX512_SPR = dict(flags="-mavx512fp16"),
+        )
+        if on_x86 and self.cc_is_icc: return dict(
+            SSE    = dict(flags="-msse"),
+            SSE2   = dict(flags="-msse2"),
+            SSE3   = dict(flags="-msse3"),
+            SSSE3  = dict(flags="-mssse3"),
+            SSE41  = dict(flags="-msse4.1"),
+            POPCNT = {},
+            SSE42  = dict(flags="-msse4.2"),
+            AVX    = dict(flags="-mavx"),
+            F16C   = {},
+            XOP    = dict(disable="Intel Compiler doesn't support it"),
+            FMA4   = dict(disable="Intel Compiler doesn't support it"),
+            # Intel Compiler doesn't support AVX2 or FMA3 independently
+            FMA3 = dict(
+                implies="F16C AVX2", flags="-march=core-avx2"
+            ),
+            AVX2 = dict(implies="FMA3", flags="-march=core-avx2"),
+            # Intel Compiler doesn't support AVX512F or AVX512CD independently
+            AVX512F = dict(
+                implies="AVX2 AVX512CD", flags="-march=common-avx512"
+            ),
+            AVX512CD = dict(
+                implies="AVX2 AVX512F", flags="-march=common-avx512"
+            ),
+            AVX512_KNL = dict(flags="-xKNL"),
+            AVX512_KNM = dict(flags="-xKNM"),
+            AVX512_SKX = dict(flags="-xSKYLAKE-AVX512"),
+            AVX512_CLX = dict(flags="-xCASCADELAKE"),
+            AVX512_CNL = dict(flags="-xCANNONLAKE"),
+            AVX512_ICL = dict(flags="-xICELAKE-CLIENT"),
+            AVX512_SPR = dict(disable="Not supported yet")
+        )
+        if on_x86 and self.cc_is_iccw: return dict(
+            SSE    = dict(flags="/arch:SSE"),
+            SSE2   = dict(flags="/arch:SSE2"),
+            SSE3   = dict(flags="/arch:SSE3"),
+            SSSE3  = dict(flags="/arch:SSSE3"),
+            SSE41  = dict(flags="/arch:SSE4.1"),
+            POPCNT = {},
+            SSE42  = dict(flags="/arch:SSE4.2"),
+            AVX    = dict(flags="/arch:AVX"),
+            F16C   = {},
+            XOP    = dict(disable="Intel Compiler doesn't support it"),
+            FMA4   = dict(disable="Intel Compiler doesn't support it"),
+            # Intel Compiler doesn't support FMA3 or AVX2 independently
+            FMA3 = dict(
+                implies="F16C AVX2", flags="/arch:CORE-AVX2"
+            ),
+            AVX2 = dict(
+                implies="FMA3", flags="/arch:CORE-AVX2"
+            ),
+            # Intel Compiler doesn't support AVX512F or AVX512CD independently
+            AVX512F = dict(
+                implies="AVX2 AVX512CD", flags="/Qx:COMMON-AVX512"
+            ),
+            AVX512CD = dict(
+                implies="AVX2 AVX512F", flags="/Qx:COMMON-AVX512"
+            ),
+            AVX512_KNL = dict(flags="/Qx:KNL"),
+            AVX512_KNM = dict(flags="/Qx:KNM"),
+            AVX512_SKX = dict(flags="/Qx:SKYLAKE-AVX512"),
+            AVX512_CLX = dict(flags="/Qx:CASCADELAKE"),
+            AVX512_CNL = dict(flags="/Qx:CANNONLAKE"),
+            AVX512_ICL = dict(flags="/Qx:ICELAKE-CLIENT"),
+            AVX512_SPR = dict(disable="Not supported yet")
+        )
+        if on_x86 and self.cc_is_msvc: return dict(
+            SSE = dict(flags="/arch:SSE") if self.cc_on_x86 else {},
+            SSE2 = dict(flags="/arch:SSE2") if self.cc_on_x86 else {},
+            SSE3   = {},
+            SSSE3  = {},
+            SSE41  = {},
+            POPCNT = dict(headers="nmmintrin.h"),
+            SSE42  = {},
+            AVX    = dict(flags="/arch:AVX"),
+            F16C   = {},
+            XOP    = dict(headers="ammintrin.h"),
+            FMA4   = dict(headers="ammintrin.h"),
+            # MSVC doesn't support FMA3 or AVX2 independently
+            FMA3 = dict(
+                implies="F16C AVX2", flags="/arch:AVX2"
+            ),
+            AVX2 = dict(
+                implies="F16C FMA3", flags="/arch:AVX2"
+            ),
+            # MSVC doesn't support AVX512F or AVX512CD independently,
+            # always generate instructions belong to (VL/VW/DQ)
+            AVX512F = dict(
+                implies="AVX2 AVX512CD AVX512_SKX", flags="/arch:AVX512"
+            ),
+            AVX512CD = dict(
+                implies="AVX512F AVX512_SKX", flags="/arch:AVX512"
+            ),
+            AVX512_KNL = dict(
+                disable="MSVC compiler doesn't support it"
+            ),
+            AVX512_KNM = dict(
+                disable="MSVC compiler doesn't support it"
+            ),
+            AVX512_SKX = dict(flags="/arch:AVX512"),
+            AVX512_CLX = {},
+            AVX512_CNL = {},
+            AVX512_ICL = {},
+            AVX512_SPR= dict(
+                disable="MSVC compiler doesn't support it"
+            )
+        )
+
+        on_power = self.cc_on_ppc64le or self.cc_on_ppc64
+        if on_power:
+            partial = dict(
+                VSX = dict(
+                    implies=("VSX2" if self.cc_on_ppc64le else ""),
+                    flags="-mvsx"
+                ),
+                VSX2 = dict(
+                    flags="-mcpu=power8", implies_detect=False
+                ),
+                VSX3 = dict(
+                    flags="-mcpu=power9 -mtune=power9", implies_detect=False
+                ),
+                VSX4 = dict(
+                    flags="-mcpu=power10 -mtune=power10", implies_detect=False
+                )
+            )
+            if self.cc_is_clang:
+                partial["VSX"]["flags"]  = "-maltivec -mvsx"
+                partial["VSX2"]["flags"] = "-mcpu=power8"
+                partial["VSX3"]["flags"] = "-mcpu=power9"
+                partial["VSX4"]["flags"] = "-mcpu=power10"
+
+            return partial
+
+        on_zarch = self.cc_on_s390x
+        if on_zarch:
+            partial = dict(
+                VX = dict(
+                    flags="-march=arch11 -mzvector"
+                ),
+                VXE = dict(
+                    flags="-march=arch12", implies_detect=False
+                ),
+                VXE2 = dict(
+                    flags="-march=arch13", implies_detect=False
+                )
+            )
+
+            return partial
+
+
+        if self.cc_on_aarch64 and is_unix: return dict(
+            NEON = dict(
+                implies="NEON_FP16 NEON_VFPV4 ASIMD", autovec=True
+            ),
+            NEON_FP16 = dict(
+                implies="NEON NEON_VFPV4 ASIMD", autovec=True
+            ),
+            NEON_VFPV4 = dict(
+                implies="NEON NEON_FP16 ASIMD", autovec=True
+            ),
+            ASIMD = dict(
+                implies="NEON NEON_FP16 NEON_VFPV4", autovec=True
+            ),
+            ASIMDHP = dict(
+                flags="-march=armv8.2-a+fp16"
+            ),
+            ASIMDDP = dict(
+                flags="-march=armv8.2-a+dotprod"
+            ),
+            ASIMDFHM = dict(
+                flags="-march=armv8.2-a+fp16fml"
+            ),
+        )
+        if self.cc_on_armhf and is_unix: return dict(
+            NEON = dict(
+                flags="-mfpu=neon"
+            ),
+            NEON_FP16 = dict(
+                flags="-mfpu=neon-fp16 -mfp16-format=ieee"
+            ),
+            NEON_VFPV4 = dict(
+                flags="-mfpu=neon-vfpv4",
+            ),
+            ASIMD = dict(
+                flags="-mfpu=neon-fp-armv8 -march=armv8-a+simd",
+            ),
+            ASIMDHP = dict(
+                flags="-march=armv8.2-a+fp16"
+            ),
+            ASIMDDP = dict(
+                flags="-march=armv8.2-a+dotprod",
+            ),
+            ASIMDFHM = dict(
+                flags="-march=armv8.2-a+fp16fml"
+            )
+        )
+        # TODO: ARM MSVC
+        return {}
+
+    def __init__(self):
+        if self.conf_tmp_path is None:
+            import shutil
+            import tempfile
+            tmp = tempfile.mkdtemp()
+            def rm_temp():
+                try:
+                    shutil.rmtree(tmp)
+                except OSError:
+                    pass
+            atexit.register(rm_temp)
+            self.conf_tmp_path = tmp
+
+        if self.conf_cache_factors is None:
+            self.conf_cache_factors = [
+                os.path.getmtime(__file__),
+                self.conf_nocache
+            ]
+
+class _Distutils:
+    """A helper class that provides a collection of fundamental methods
+    implemented in a top of Python and NumPy Distutils.
+
+    The idea behind this class is to gather all methods that it may
+    need to override in case of reuse 'CCompilerOpt' in environment
+    different than of what NumPy has.
+
+    Parameters
+    ----------
+    ccompiler : `CCompiler`
+        The generate instance that returned from `distutils.ccompiler.new_compiler()`.
+    """
+    def __init__(self, ccompiler):
+        self._ccompiler = ccompiler
+
+    def dist_compile(self, sources, flags, ccompiler=None, **kwargs):
+        """Wrap CCompiler.compile()"""
+        assert(isinstance(sources, list))
+        assert(isinstance(flags, list))
+        flags = kwargs.pop("extra_postargs", []) + flags
+        if not ccompiler:
+            ccompiler = self._ccompiler
+
+        return ccompiler.compile(sources, extra_postargs=flags, **kwargs)
+
+    def dist_test(self, source, flags, macros=[]):
+        """Return True if 'CCompiler.compile()' able to compile
+        a source file with certain flags.
+        """
+        assert(isinstance(source, str))
+        from distutils.errors import CompileError
+        cc = self._ccompiler;
+        bk_spawn = getattr(cc, 'spawn', None)
+        if bk_spawn:
+            cc_type = getattr(self._ccompiler, "compiler_type", "")
+            if cc_type in ("msvc",):
+                setattr(cc, 'spawn', self._dist_test_spawn_paths)
+            else:
+                setattr(cc, 'spawn', self._dist_test_spawn)
+        test = False
+        try:
+            self.dist_compile(
+                [source], flags, macros=macros, output_dir=self.conf_tmp_path
+            )
+            test = True
+        except CompileError as e:
+            self.dist_log(str(e), stderr=True)
+        if bk_spawn:
+            setattr(cc, 'spawn', bk_spawn)
+        return test
+
+    def dist_info(self):
+        """
+        Return a tuple containing info about (platform, compiler, extra_args),
+        required by the abstract class '_CCompiler' for discovering the
+        platform environment. This is also used as a cache factor in order
+        to detect any changes happening from outside.
+        """
+        if hasattr(self, "_dist_info"):
+            return self._dist_info
+
+        cc_type = getattr(self._ccompiler, "compiler_type", '')
+        if cc_type in ("intelem", "intelemw"):
+            platform = "x86_64"
+        elif cc_type in ("intel", "intelw", "intele"):
+            platform = "x86"
+        else:
+            from distutils.util import get_platform
+            platform = get_platform()
+
+        cc_info = getattr(self._ccompiler, "compiler", getattr(self._ccompiler, "compiler_so", ''))
+        if not cc_type or cc_type == "unix":
+            if hasattr(cc_info, "__iter__"):
+                compiler = cc_info[0]
+            else:
+                compiler = str(cc_info)
+        else:
+            compiler = cc_type
+
+        if hasattr(cc_info, "__iter__") and len(cc_info) > 1:
+            extra_args = ' '.join(cc_info[1:])
+        else:
+            extra_args  = os.environ.get("CFLAGS", "")
+            extra_args += os.environ.get("CPPFLAGS", "")
+
+        self._dist_info = (platform, compiler, extra_args)
+        return self._dist_info
+
+    @staticmethod
+    def dist_error(*args):
+        """Raise a compiler error"""
+        from distutils.errors import CompileError
+        raise CompileError(_Distutils._dist_str(*args))
+
+    @staticmethod
+    def dist_fatal(*args):
+        """Raise a distutils error"""
+        from distutils.errors import DistutilsError
+        raise DistutilsError(_Distutils._dist_str(*args))
+
+    @staticmethod
+    def dist_log(*args, stderr=False):
+        """Print a console message"""
+        from numpy.distutils import log
+        out = _Distutils._dist_str(*args)
+        if stderr:
+            log.warn(out)
+        else:
+            log.info(out)
+
+    @staticmethod
+    def dist_load_module(name, path):
+        """Load a module from file, required by the abstract class '_Cache'."""
+        from .misc_util import exec_mod_from_location
+        try:
+            return exec_mod_from_location(name, path)
+        except Exception as e:
+            _Distutils.dist_log(e, stderr=True)
+        return None
+
+    @staticmethod
+    def _dist_str(*args):
+        """Return a string to print by log and errors."""
+        def to_str(arg):
+            if not isinstance(arg, str) and hasattr(arg, '__iter__'):
+                ret = []
+                for a in arg:
+                    ret.append(to_str(a))
+                return '('+ ' '.join(ret) + ')'
+            return str(arg)
+
+        stack = inspect.stack()[2]
+        start = "CCompilerOpt.%s[%d] : " % (stack.function, stack.lineno)
+        out = ' '.join([
+            to_str(a)
+            for a in (*args,)
+        ])
+        return start + out
+
+    def _dist_test_spawn_paths(self, cmd, display=None):
+        """
+        Fix msvc SDK ENV path same as distutils do
+        without it we get c1: fatal error C1356: unable to find mspdbcore.dll
+        """
+        if not hasattr(self._ccompiler, "_paths"):
+            self._dist_test_spawn(cmd)
+            return
+        old_path = os.getenv("path")
+        try:
+            os.environ["path"] = self._ccompiler._paths
+            self._dist_test_spawn(cmd)
+        finally:
+            os.environ["path"] = old_path
+
+    _dist_warn_regex = re.compile(
+        # intel and msvc compilers don't raise
+        # fatal errors when flags are wrong or unsupported
+        ".*("
+        "warning D9002|"  # msvc, it should be work with any language.
+        "invalid argument for option" # intel
+        ").*"
+    )
+    @staticmethod
+    def _dist_test_spawn(cmd, display=None):
+        try:
+            o = subprocess.check_output(cmd, stderr=subprocess.STDOUT,
+                                        text=True)
+            if o and re.match(_Distutils._dist_warn_regex, o):
+                _Distutils.dist_error(
+                    "Flags in command", cmd ,"aren't supported by the compiler"
+                    ", output -> \n%s" % o
+                )
+        except subprocess.CalledProcessError as exc:
+            o = exc.output
+            s = exc.returncode
+        except OSError as e:
+            o = e
+            s = 127
+        else:
+            return None
+        _Distutils.dist_error(
+            "Command", cmd, "failed with exit status %d output -> \n%s" % (
+            s, o
+        ))
+
+_share_cache = {}
+class _Cache:
+    """An abstract class handles caching functionality, provides two
+    levels of caching, in-memory by share instances attributes among
+    each other and by store attributes into files.
+
+    **Note**:
+        any attributes that start with ``_`` or ``conf_`` will be ignored.
+
+    Parameters
+    ----------
+    cache_path : str or None
+        The path of cache file, if None then cache in file will disabled.
+
+    *factors :
+        The caching factors that need to utilize next to `conf_cache_factors`.
+
+    Attributes
+    ----------
+    cache_private : set
+        Hold the attributes that need be skipped from "in-memory cache".
+
+    cache_infile : bool
+        Utilized during initializing this class, to determine if the cache was able
+        to loaded from the specified cache path in 'cache_path'.
+    """
+
+    # skip attributes from cache
+    _cache_ignore = re.compile("^(_|conf_)")
+
+    def __init__(self, cache_path=None, *factors):
+        self.cache_me = {}
+        self.cache_private = set()
+        self.cache_infile = False
+        self._cache_path = None
+
+        if self.conf_nocache:
+            self.dist_log("cache is disabled by `Config`")
+            return
+
+        self._cache_hash = self.cache_hash(*factors, *self.conf_cache_factors)
+        self._cache_path = cache_path
+        if cache_path:
+            if os.path.exists(cache_path):
+                self.dist_log("load cache from file ->", cache_path)
+                cache_mod = self.dist_load_module("cache", cache_path)
+                if not cache_mod:
+                    self.dist_log(
+                        "unable to load the cache file as a module",
+                        stderr=True
+                    )
+                elif not hasattr(cache_mod, "hash") or \
+                     not hasattr(cache_mod, "data"):
+                    self.dist_log("invalid cache file", stderr=True)
+                elif self._cache_hash == cache_mod.hash:
+                    self.dist_log("hit the file cache")
+                    for attr, val in cache_mod.data.items():
+                        setattr(self, attr, val)
+                    self.cache_infile = True
+                else:
+                    self.dist_log("miss the file cache")
+
+        if not self.cache_infile:
+            other_cache = _share_cache.get(self._cache_hash)
+            if other_cache:
+                self.dist_log("hit the memory cache")
+                for attr, val in other_cache.__dict__.items():
+                    if attr in other_cache.cache_private or \
+                               re.match(self._cache_ignore, attr):
+                        continue
+                    setattr(self, attr, val)
+
+        _share_cache[self._cache_hash] = self
+        atexit.register(self.cache_flush)
+
+    def __del__(self):
+        for h, o in _share_cache.items():
+            if o == self:
+                _share_cache.pop(h)
+                break
+
+    def cache_flush(self):
+        """
+        Force update the cache.
+        """
+        if not self._cache_path:
+            return
+        # TODO: don't write if the cache doesn't change
+        self.dist_log("write cache to path ->", self._cache_path)
+        cdict = self.__dict__.copy()
+        for attr in self.__dict__.keys():
+            if re.match(self._cache_ignore, attr):
+                cdict.pop(attr)
+
+        d = os.path.dirname(self._cache_path)
+        if not os.path.exists(d):
+            os.makedirs(d)
+
+        repr_dict = pprint.pformat(cdict, compact=True)
+        with open(self._cache_path, "w") as f:
+            f.write(textwrap.dedent("""\
+            # AUTOGENERATED DON'T EDIT
+            # Please make changes to the code generator \
+            (distutils/ccompiler_opt.py)
+            hash = {}
+            data = \\
+            """).format(self._cache_hash))
+            f.write(repr_dict)
+
+    def cache_hash(self, *factors):
+        # is there a built-in non-crypto hash?
+        # sdbm
+        chash = 0
+        for f in factors:
+            for char in str(f):
+                chash  = ord(char) + (chash << 6) + (chash << 16) - chash
+                chash &= 0xFFFFFFFF
+        return chash
+
+    @staticmethod
+    def me(cb):
+        """
+        A static method that can be treated as a decorator to
+        dynamically cache certain methods.
+        """
+        def cache_wrap_me(self, *args, **kwargs):
+            # good for normal args
+            cache_key = str((
+                cb.__name__, *args, *kwargs.keys(), *kwargs.values()
+            ))
+            if cache_key in self.cache_me:
+                return self.cache_me[cache_key]
+            ccb = cb(self, *args, **kwargs)
+            self.cache_me[cache_key] = ccb
+            return ccb
+        return cache_wrap_me
+
+class _CCompiler:
+    """A helper class for `CCompilerOpt` containing all utilities that
+    related to the fundamental compiler's functions.
+
+    Attributes
+    ----------
+    cc_on_x86 : bool
+        True when the target architecture is 32-bit x86
+    cc_on_x64 : bool
+        True when the target architecture is 64-bit x86
+    cc_on_ppc64 : bool
+        True when the target architecture is 64-bit big-endian powerpc
+    cc_on_ppc64le : bool
+        True when the target architecture is 64-bit litle-endian powerpc
+    cc_on_s390x : bool
+        True when the target architecture is IBM/ZARCH on linux
+    cc_on_armhf : bool
+        True when the target architecture is 32-bit ARMv7+
+    cc_on_aarch64 : bool
+        True when the target architecture is 64-bit Armv8-a+
+    cc_on_noarch : bool
+        True when the target architecture is unknown or not supported
+    cc_is_gcc : bool
+        True if the compiler is GNU or
+        if the compiler is unknown
+    cc_is_clang : bool
+        True if the compiler is Clang
+    cc_is_icc : bool
+        True if the compiler is Intel compiler (unix like)
+    cc_is_iccw : bool
+        True if the compiler is Intel compiler (msvc like)
+    cc_is_nocc : bool
+        True if the compiler isn't supported directly,
+        Note: that cause a fail-back to gcc
+    cc_has_debug : bool
+        True if the compiler has debug flags
+    cc_has_native : bool
+        True if the compiler has native flags
+    cc_noopt : bool
+        True if the compiler has definition 'DISABLE_OPT*',
+        or 'cc_on_noarch' is True
+    cc_march : str
+        The target architecture name, or "unknown" if
+        the architecture isn't supported
+    cc_name : str
+        The compiler name, or "unknown" if the compiler isn't supported
+    cc_flags : dict
+        Dictionary containing the initialized flags of `_Config.conf_cc_flags`
+    """
+    def __init__(self):
+        if hasattr(self, "cc_is_cached"):
+            return
+        #      attr            regex        compiler-expression
+        detect_arch = (
+            ("cc_on_x64",      ".*(x|x86_|amd)64.*", ""),
+            ("cc_on_x86",      ".*(win32|x86|i386|i686).*", ""),
+            ("cc_on_ppc64le",  ".*(powerpc|ppc)64(el|le).*|.*powerpc.*",
+                                          "defined(__powerpc64__) && "
+                                          "defined(__LITTLE_ENDIAN__)"),
+            ("cc_on_ppc64",    ".*(powerpc|ppc).*|.*powerpc.*",
+                                          "defined(__powerpc64__) && "
+                                          "defined(__BIG_ENDIAN__)"),
+            ("cc_on_aarch64",  ".*(aarch64|arm64).*", ""),
+            ("cc_on_armhf",    ".*arm.*", "defined(__ARM_ARCH_7__) || "
+                                          "defined(__ARM_ARCH_7A__)"),
+            ("cc_on_s390x",    ".*s390x.*", ""),
+            # undefined platform
+            ("cc_on_noarch",   "", ""),
+        )
+        detect_compiler = (
+            ("cc_is_gcc",     r".*(gcc|gnu\-g).*", ""),
+            ("cc_is_clang",    ".*clang.*", ""),
+            # intel msvc like
+            ("cc_is_iccw",     ".*(intelw|intelemw|iccw).*", ""),
+            ("cc_is_icc",      ".*(intel|icc).*", ""),  # intel unix like
+            ("cc_is_msvc",     ".*msvc.*", ""),
+            ("cc_is_fcc",     ".*fcc.*", ""),
+            # undefined compiler will be treat it as gcc
+            ("cc_is_nocc",     "", ""),
+        )
+        detect_args = (
+           ("cc_has_debug",  ".*(O0|Od|ggdb|coverage|debug:full).*", ""),
+           ("cc_has_native",
+                ".*(-march=native|-xHost|/QxHost|-mcpu=a64fx).*", ""),
+           # in case if the class run with -DNPY_DISABLE_OPTIMIZATION
+           ("cc_noopt", ".*DISABLE_OPT.*", ""),
+        )
+
+        dist_info = self.dist_info()
+        platform, compiler_info, extra_args = dist_info
+        # set False to all attrs
+        for section in (detect_arch, detect_compiler, detect_args):
+            for attr, rgex, cexpr in section:
+                setattr(self, attr, False)
+
+        for detect, searchin in ((detect_arch, platform), (detect_compiler, compiler_info)):
+            for attr, rgex, cexpr in detect:
+                if rgex and not re.match(rgex, searchin, re.IGNORECASE):
+                    continue
+                if cexpr and not self.cc_test_cexpr(cexpr):
+                    continue
+                setattr(self, attr, True)
+                break
+
+        for attr, rgex, cexpr in detect_args:
+            if rgex and not re.match(rgex, extra_args, re.IGNORECASE):
+                continue
+            if cexpr and not self.cc_test_cexpr(cexpr):
+                continue
+            setattr(self, attr, True)
+
+        if self.cc_on_noarch:
+            self.dist_log(
+                "unable to detect CPU architecture which lead to disable the optimization. "
+                f"check dist_info:<<\n{dist_info}\n>>",
+                stderr=True
+            )
+            self.cc_noopt = True
+
+        if self.conf_noopt:
+            self.dist_log("Optimization is disabled by the Config", stderr=True)
+            self.cc_noopt = True
+
+        if self.cc_is_nocc:
+            """
+            mingw can be treated as a gcc, and also xlc even if it based on clang,
+            but still has the same gcc optimization flags.
+            """
+            self.dist_log(
+                "unable to detect compiler type which leads to treating it as GCC. "
+                "this is a normal behavior if you're using gcc-like compiler such as MinGW or IBM/XLC."
+                f"check dist_info:<<\n{dist_info}\n>>",
+                stderr=True
+            )
+            self.cc_is_gcc = True
+
+        self.cc_march = "unknown"
+        for arch in ("x86", "x64", "ppc64", "ppc64le",
+                     "armhf", "aarch64", "s390x"):
+            if getattr(self, "cc_on_" + arch):
+                self.cc_march = arch
+                break
+
+        self.cc_name = "unknown"
+        for name in ("gcc", "clang", "iccw", "icc", "msvc", "fcc"):
+            if getattr(self, "cc_is_" + name):
+                self.cc_name = name
+                break
+
+        self.cc_flags = {}
+        compiler_flags = self.conf_cc_flags.get(self.cc_name)
+        if compiler_flags is None:
+            self.dist_fatal(
+                "undefined flag for compiler '%s', "
+                "leave an empty dict instead" % self.cc_name
+            )
+        for name, flags in compiler_flags.items():
+            self.cc_flags[name] = nflags = []
+            if flags:
+                assert(isinstance(flags, str))
+                flags = flags.split()
+                for f in flags:
+                    if self.cc_test_flags([f]):
+                        nflags.append(f)
+
+        self.cc_is_cached = True
+
+    @_Cache.me
+    def cc_test_flags(self, flags):
+        """
+        Returns True if the compiler supports 'flags'.
+        """
+        assert(isinstance(flags, list))
+        self.dist_log("testing flags", flags)
+        test_path = os.path.join(self.conf_check_path, "test_flags.c")
+        test = self.dist_test(test_path, flags)
+        if not test:
+            self.dist_log("testing failed", stderr=True)
+        return test
+
+    @_Cache.me
+    def cc_test_cexpr(self, cexpr, flags=[]):
+        """
+        Same as the above but supports compile-time expressions.
+        """
+        self.dist_log("testing compiler expression", cexpr)
+        test_path = os.path.join(self.conf_tmp_path, "npy_dist_test_cexpr.c")
+        with open(test_path, "w") as fd:
+            fd.write(textwrap.dedent(f"""\
+               #if !({cexpr})
+                   #error "unsupported expression"
+               #endif
+               int dummy;
+            """))
+        test = self.dist_test(test_path, flags)
+        if not test:
+            self.dist_log("testing failed", stderr=True)
+        return test
+
+    def cc_normalize_flags(self, flags):
+        """
+        Remove the conflicts that caused due gathering implied features flags.
+
+        Parameters
+        ----------
+        'flags' list, compiler flags
+            flags should be sorted from the lowest to the highest interest.
+
+        Returns
+        -------
+        list, filtered from any conflicts.
+
+        Examples
+        --------
+        >>> self.cc_normalize_flags(['-march=armv8.2-a+fp16', '-march=armv8.2-a+dotprod'])
+        ['armv8.2-a+fp16+dotprod']
+
+        >>> self.cc_normalize_flags(
+            ['-msse', '-msse2', '-msse3', '-mssse3', '-msse4.1', '-msse4.2', '-mavx', '-march=core-avx2']
+        )
+        ['-march=core-avx2']
+        """
+        assert(isinstance(flags, list))
+        if self.cc_is_gcc or self.cc_is_clang or self.cc_is_icc:
+            return self._cc_normalize_unix(flags)
+
+        if self.cc_is_msvc or self.cc_is_iccw:
+            return self._cc_normalize_win(flags)
+        return flags
+
+    _cc_normalize_unix_mrgx = re.compile(
+        # 1- to check the highest of
+        r"^(-mcpu=|-march=|-x[A-Z0-9\-])"
+    )
+    _cc_normalize_unix_frgx = re.compile(
+        # 2- to remove any flags starts with
+        # -march, -mcpu, -x(INTEL) and '-m' without '='
+        r"^(?!(-mcpu=|-march=|-x[A-Z0-9\-]|-m[a-z0-9\-\.]*.$))|"
+        # exclude:
+        r"(?:-mzvector)"
+    )
+    _cc_normalize_unix_krgx = re.compile(
+        # 3- keep only the highest of
+        r"^(-mfpu|-mtune)"
+    )
+    _cc_normalize_arch_ver = re.compile(
+        r"[0-9.]"
+    )
+    def _cc_normalize_unix(self, flags):
+        def ver_flags(f):
+            #        arch ver  subflag
+            # -march=armv8.2-a+fp16fml
+            tokens = f.split('+')
+            ver = float('0' + ''.join(
+                re.findall(self._cc_normalize_arch_ver, tokens[0])
+            ))
+            return ver, tokens[0], tokens[1:]
+
+        if len(flags) <= 1:
+            return flags
+        # get the highest matched flag
+        for i, cur_flag in enumerate(reversed(flags)):
+            if not re.match(self._cc_normalize_unix_mrgx, cur_flag):
+                continue
+            lower_flags = flags[:-(i+1)]
+            upper_flags = flags[-i:]
+            filtered = list(filter(
+                self._cc_normalize_unix_frgx.search, lower_flags
+            ))
+            # gather subflags
+            ver, arch, subflags = ver_flags(cur_flag)
+            if ver > 0 and len(subflags) > 0:
+                for xflag in lower_flags:
+                    xver, _, xsubflags = ver_flags(xflag)
+                    if ver == xver:
+                        subflags = xsubflags + subflags
+                cur_flag = arch + '+' + '+'.join(subflags)
+
+            flags = filtered + [cur_flag]
+            if i > 0:
+                flags += upper_flags
+            break
+
+        # to remove overridable flags
+        final_flags = []
+        matched = set()
+        for f in reversed(flags):
+            match = re.match(self._cc_normalize_unix_krgx, f)
+            if not match:
+                pass
+            elif match[0] in matched:
+                continue
+            else:
+                matched.add(match[0])
+            final_flags.insert(0, f)
+        return final_flags
+
+    _cc_normalize_win_frgx = re.compile(
+        r"^(?!(/arch\:|/Qx\:))"
+    )
+    _cc_normalize_win_mrgx = re.compile(
+        r"^(/arch|/Qx:)"
+    )
+    def _cc_normalize_win(self, flags):
+        for i, f in enumerate(reversed(flags)):
+            if not re.match(self._cc_normalize_win_mrgx, f):
+                continue
+            i += 1
+            return list(filter(
+                self._cc_normalize_win_frgx.search, flags[:-i]
+            )) + flags[-i:]
+        return flags
+
+class _Feature:
+    """A helper class for `CCompilerOpt` that managing CPU features.
+
+    Attributes
+    ----------
+    feature_supported : dict
+        Dictionary containing all CPU features that supported
+        by the platform, according to the specified values in attribute
+        `_Config.conf_features` and `_Config.conf_features_partial()`
+
+    feature_min : set
+        The minimum support of CPU features, according to
+        the specified values in attribute `_Config.conf_min_features`.
+    """
+    def __init__(self):
+        if hasattr(self, "feature_is_cached"):
+            return
+        self.feature_supported = pfeatures = self.conf_features_partial()
+        for feature_name in list(pfeatures.keys()):
+            feature  = pfeatures[feature_name]
+            cfeature = self.conf_features[feature_name]
+            feature.update({
+                k:v for k,v in cfeature.items() if k not in feature
+            })
+            disabled = feature.get("disable")
+            if disabled is not None:
+                pfeatures.pop(feature_name)
+                self.dist_log(
+                    "feature '%s' is disabled," % feature_name,
+                    disabled, stderr=True
+                )
+                continue
+            # list is used internally for these options
+            for option in (
+                "implies", "group", "detect", "headers", "flags", "extra_checks"
+            ) :
+                oval = feature.get(option)
+                if isinstance(oval, str):
+                    feature[option] = oval.split()
+
+        self.feature_min = set()
+        min_f = self.conf_min_features.get(self.cc_march, "")
+        for F in min_f.upper().split():
+            if F in self.feature_supported:
+                self.feature_min.add(F)
+
+        self.feature_is_cached = True
+
+    def feature_names(self, names=None, force_flags=None, macros=[]):
+        """
+        Returns a set of CPU feature names that supported by platform and the **C** compiler.
+
+        Parameters
+        ----------
+        names : sequence or None, optional
+            Specify certain CPU features to test it against the **C** compiler.
+            if None(default), it will test all current supported features.
+            **Note**: feature names must be in upper-case.
+
+        force_flags : list or None, optional
+            If None(default), default compiler flags for every CPU feature will
+            be used during the test.
+
+        macros : list of tuples, optional
+            A list of C macro definitions.
+        """
+        assert(
+            names is None or (
+                not isinstance(names, str) and
+                hasattr(names, "__iter__")
+            )
+        )
+        assert(force_flags is None or isinstance(force_flags, list))
+        if names is None:
+            names = self.feature_supported.keys()
+        supported_names = set()
+        for f in names:
+            if self.feature_is_supported(
+                f, force_flags=force_flags, macros=macros
+            ):
+                supported_names.add(f)
+        return supported_names
+
+    def feature_is_exist(self, name):
+        """
+        Returns True if a certain feature is exist and covered within
+        ``_Config.conf_features``.
+
+        Parameters
+        ----------
+        'name': str
+            feature name in uppercase.
+        """
+        assert(name.isupper())
+        return name in self.conf_features
+
+    def feature_sorted(self, names, reverse=False):
+        """
+        Sort a list of CPU features ordered by the lowest interest.
+
+        Parameters
+        ----------
+        'names': sequence
+            sequence of supported feature names in uppercase.
+        'reverse': bool, optional
+            If true, the sorted features is reversed. (highest interest)
+
+        Returns
+        -------
+        list, sorted CPU features
+        """
+        def sort_cb(k):
+            if isinstance(k, str):
+                return self.feature_supported[k]["interest"]
+            # multiple features
+            rank = max([self.feature_supported[f]["interest"] for f in k])
+            # FIXME: that's not a safe way to increase the rank for
+            # multi targets
+            rank += len(k) -1
+            return rank
+        return sorted(names, reverse=reverse, key=sort_cb)
+
+    def feature_implies(self, names, keep_origins=False):
+        """
+        Return a set of CPU features that implied by 'names'
+
+        Parameters
+        ----------
+        names : str or sequence of str
+            CPU feature name(s) in uppercase.
+
+        keep_origins : bool
+            if False(default) then the returned set will not contain any
+            features from 'names'. This case happens only when two features
+            imply each other.
+
+        Examples
+        --------
+        >>> self.feature_implies("SSE3")
+        {'SSE', 'SSE2'}
+        >>> self.feature_implies("SSE2")
+        {'SSE'}
+        >>> self.feature_implies("SSE2", keep_origins=True)
+        # 'SSE2' found here since 'SSE' and 'SSE2' imply each other
+        {'SSE', 'SSE2'}
+        """
+        def get_implies(name, _caller=set()):
+            implies = set()
+            d = self.feature_supported[name]
+            for i in d.get("implies", []):
+                implies.add(i)
+                if i in _caller:
+                    # infinity recursive guard since
+                    # features can imply each other
+                    continue
+                _caller.add(name)
+                implies = implies.union(get_implies(i, _caller))
+            return implies
+
+        if isinstance(names, str):
+            implies = get_implies(names)
+            names = [names]
+        else:
+            assert(hasattr(names, "__iter__"))
+            implies = set()
+            for n in names:
+                implies = implies.union(get_implies(n))
+        if not keep_origins:
+            implies.difference_update(names)
+        return implies
+
+    def feature_implies_c(self, names):
+        """same as feature_implies() but combining 'names'"""
+        if isinstance(names, str):
+            names = set((names,))
+        else:
+            names = set(names)
+        return names.union(self.feature_implies(names))
+
+    def feature_ahead(self, names):
+        """
+        Return list of features in 'names' after remove any
+        implied features and keep the origins.
+
+        Parameters
+        ----------
+        'names': sequence
+            sequence of CPU feature names in uppercase.
+
+        Returns
+        -------
+        list of CPU features sorted as-is 'names'
+
+        Examples
+        --------
+        >>> self.feature_ahead(["SSE2", "SSE3", "SSE41"])
+        ["SSE41"]
+        # assume AVX2 and FMA3 implies each other and AVX2
+        # is the highest interest
+        >>> self.feature_ahead(["SSE2", "SSE3", "SSE41", "AVX2", "FMA3"])
+        ["AVX2"]
+        # assume AVX2 and FMA3 don't implies each other
+        >>> self.feature_ahead(["SSE2", "SSE3", "SSE41", "AVX2", "FMA3"])
+        ["AVX2", "FMA3"]
+        """
+        assert(
+            not isinstance(names, str)
+            and hasattr(names, '__iter__')
+        )
+        implies = self.feature_implies(names, keep_origins=True)
+        ahead = [n for n in names if n not in implies]
+        if len(ahead) == 0:
+            # return the highest interested feature
+            # if all features imply each other
+            ahead = self.feature_sorted(names, reverse=True)[:1]
+        return ahead
+
+    def feature_untied(self, names):
+        """
+        same as 'feature_ahead()' but if both features implied each other
+        and keep the highest interest.
+
+        Parameters
+        ----------
+        'names': sequence
+            sequence of CPU feature names in uppercase.
+
+        Returns
+        -------
+        list of CPU features sorted as-is 'names'
+
+        Examples
+        --------
+        >>> self.feature_untied(["SSE2", "SSE3", "SSE41"])
+        ["SSE2", "SSE3", "SSE41"]
+        # assume AVX2 and FMA3 implies each other
+        >>> self.feature_untied(["SSE2", "SSE3", "SSE41", "FMA3", "AVX2"])
+        ["SSE2", "SSE3", "SSE41", "AVX2"]
+        """
+        assert(
+            not isinstance(names, str)
+            and hasattr(names, '__iter__')
+        )
+        final = []
+        for n in names:
+            implies = self.feature_implies(n)
+            tied = [
+                nn for nn in final
+                if nn in implies and n in self.feature_implies(nn)
+            ]
+            if tied:
+                tied = self.feature_sorted(tied + [n])
+                if n not in tied[1:]:
+                    continue
+                final.remove(tied[:1][0])
+            final.append(n)
+        return final
+
+    def feature_get_til(self, names, keyisfalse):
+        """
+        same as `feature_implies_c()` but stop collecting implied
+        features when feature's option that provided through
+        parameter 'keyisfalse' is False, also sorting the returned
+        features.
+        """
+        def til(tnames):
+            # sort from highest to lowest interest then cut if "key" is False
+            tnames = self.feature_implies_c(tnames)
+            tnames = self.feature_sorted(tnames, reverse=True)
+            for i, n in enumerate(tnames):
+                if not self.feature_supported[n].get(keyisfalse, True):
+                    tnames = tnames[:i+1]
+                    break
+            return tnames
+
+        if isinstance(names, str) or len(names) <= 1:
+            names = til(names)
+            # normalize the sort
+            names.reverse()
+            return names
+
+        names = self.feature_ahead(names)
+        names = {t for n in names for t in til(n)}
+        return self.feature_sorted(names)
+
+    def feature_detect(self, names):
+        """
+        Return a list of CPU features that required to be detected
+        sorted from the lowest to highest interest.
+        """
+        names = self.feature_get_til(names, "implies_detect")
+        detect = []
+        for n in names:
+            d = self.feature_supported[n]
+            detect += d.get("detect", d.get("group", [n]))
+        return detect
+
+    @_Cache.me
+    def feature_flags(self, names):
+        """
+        Return a list of CPU features flags sorted from the lowest
+        to highest interest.
+        """
+        names = self.feature_sorted(self.feature_implies_c(names))
+        flags = []
+        for n in names:
+            d = self.feature_supported[n]
+            f = d.get("flags", [])
+            if not f or not self.cc_test_flags(f):
+                continue
+            flags += f
+        return self.cc_normalize_flags(flags)
+
+    @_Cache.me
+    def feature_test(self, name, force_flags=None, macros=[]):
+        """
+        Test a certain CPU feature against the compiler through its own
+        check file.
+
+        Parameters
+        ----------
+        name : str
+            Supported CPU feature name.
+
+        force_flags : list or None, optional
+            If None(default), the returned flags from `feature_flags()`
+            will be used.
+
+        macros : list of tuples, optional
+            A list of C macro definitions.
+        """
+        if force_flags is None:
+            force_flags = self.feature_flags(name)
+
+        self.dist_log(
+            "testing feature '%s' with flags (%s)" % (
+            name, ' '.join(force_flags)
+        ))
+        # Each CPU feature must have C source code contains at
+        # least one intrinsic or instruction related to this feature.
+        test_path = os.path.join(
+            self.conf_check_path, "cpu_%s.c" % name.lower()
+        )
+        if not os.path.exists(test_path):
+            self.dist_fatal("feature test file is not exist", test_path)
+
+        test = self.dist_test(
+            test_path, force_flags + self.cc_flags["werror"], macros=macros
+        )
+        if not test:
+            self.dist_log("testing failed", stderr=True)
+        return test
+
+    @_Cache.me
+    def feature_is_supported(self, name, force_flags=None, macros=[]):
+        """
+        Check if a certain CPU feature is supported by the platform and compiler.
+
+        Parameters
+        ----------
+        name : str
+            CPU feature name in uppercase.
+
+        force_flags : list or None, optional
+            If None(default), default compiler flags for every CPU feature will
+            be used during test.
+
+        macros : list of tuples, optional
+            A list of C macro definitions.
+        """
+        assert(name.isupper())
+        assert(force_flags is None or isinstance(force_flags, list))
+
+        supported = name in self.feature_supported
+        if supported:
+            for impl in self.feature_implies(name):
+                if not self.feature_test(impl, force_flags, macros=macros):
+                    return False
+            if not self.feature_test(name, force_flags, macros=macros):
+                return False
+        return supported
+
+    @_Cache.me
+    def feature_can_autovec(self, name):
+        """
+        check if the feature can be auto-vectorized by the compiler
+        """
+        assert(isinstance(name, str))
+        d = self.feature_supported[name]
+        can = d.get("autovec", None)
+        if can is None:
+            valid_flags = [
+                self.cc_test_flags([f]) for f in d.get("flags", [])
+            ]
+            can = valid_flags and any(valid_flags)
+        return can
+
+    @_Cache.me
+    def feature_extra_checks(self, name):
+        """
+        Return a list of supported extra checks after testing them against
+        the compiler.
+
+        Parameters
+        ----------
+        names : str
+            CPU feature name in uppercase.
+        """
+        assert isinstance(name, str)
+        d = self.feature_supported[name]
+        extra_checks = d.get("extra_checks", [])
+        if not extra_checks:
+            return []
+
+        self.dist_log("Testing extra checks for feature '%s'" % name, extra_checks)
+        flags = self.feature_flags(name)
+        available = []
+        not_available = []
+        for chk in extra_checks:
+            test_path = os.path.join(
+                self.conf_check_path, "extra_%s.c" % chk.lower()
+            )
+            if not os.path.exists(test_path):
+                self.dist_fatal("extra check file does not exist", test_path)
+
+            is_supported = self.dist_test(test_path, flags + self.cc_flags["werror"])
+            if is_supported:
+                available.append(chk)
+            else:
+                not_available.append(chk)
+
+        if not_available:
+            self.dist_log("testing failed for checks", not_available, stderr=True)
+        return available
+
+
+    def feature_c_preprocessor(self, feature_name, tabs=0):
+        """
+        Generate C preprocessor definitions and include headers of a CPU feature.
+
+        Parameters
+        ----------
+        'feature_name': str
+            CPU feature name in uppercase.
+        'tabs': int
+            if > 0, align the generated strings to the right depend on number of tabs.
+
+        Returns
+        -------
+        str, generated C preprocessor
+
+        Examples
+        --------
+        >>> self.feature_c_preprocessor("SSE3")
+        /** SSE3 **/
+        #define NPY_HAVE_SSE3 1
+        #include 
+        """
+        assert(feature_name.isupper())
+        feature = self.feature_supported.get(feature_name)
+        assert(feature is not None)
+
+        prepr = [
+            "/** %s **/" % feature_name,
+            "#define %sHAVE_%s 1" % (self.conf_c_prefix, feature_name)
+        ]
+        prepr += [
+            "#include <%s>" % h for h in feature.get("headers", [])
+        ]
+
+        extra_defs = feature.get("group", [])
+        extra_defs += self.feature_extra_checks(feature_name)
+        for edef in extra_defs:
+            # Guard extra definitions in case of duplicate with
+            # another feature
+            prepr += [
+                "#ifndef %sHAVE_%s" % (self.conf_c_prefix, edef),
+                "\t#define %sHAVE_%s 1" % (self.conf_c_prefix, edef),
+                "#endif",
+            ]
+
+        if tabs > 0:
+            prepr = [('\t'*tabs) + l for l in prepr]
+        return '\n'.join(prepr)
+
+class _Parse:
+    """A helper class that parsing main arguments of `CCompilerOpt`,
+    also parsing configuration statements in dispatch-able sources.
+
+    Parameters
+    ----------
+    cpu_baseline : str or None
+        minimal set of required CPU features or special options.
+
+    cpu_dispatch : str or None
+        dispatched set of additional CPU features or special options.
+
+    Special options can be:
+        - **MIN**: Enables the minimum CPU features that utilized via `_Config.conf_min_features`
+        - **MAX**: Enables all supported CPU features by the Compiler and platform.
+        - **NATIVE**: Enables all CPU features that supported by the current machine.
+        - **NONE**: Enables nothing
+        - **Operand +/-**: remove or add features, useful with options **MAX**, **MIN** and **NATIVE**.
+            NOTE: operand + is only added for nominal reason.
+
+    NOTES:
+        - Case-insensitive among all CPU features and special options.
+        - Comma or space can be used as a separator.
+        - If the CPU feature is not supported by the user platform or compiler,
+          it will be skipped rather than raising a fatal error.
+        - Any specified CPU features to 'cpu_dispatch' will be skipped if its part of CPU baseline features
+        - 'cpu_baseline' force enables implied features.
+
+    Attributes
+    ----------
+    parse_baseline_names : list
+        Final CPU baseline's feature names(sorted from low to high)
+    parse_baseline_flags : list
+        Compiler flags of baseline features
+    parse_dispatch_names : list
+        Final CPU dispatch-able feature names(sorted from low to high)
+    parse_target_groups : dict
+        Dictionary containing initialized target groups that configured
+        through class attribute `conf_target_groups`.
+
+        The key is represent the group name and value is a tuple
+        contains three items :
+            - bool, True if group has the 'baseline' option.
+            - list, list of CPU features.
+            - list, list of extra compiler flags.
+
+    """
+    def __init__(self, cpu_baseline, cpu_dispatch):
+        self._parse_policies = dict(
+            # POLICY NAME, (HAVE, NOT HAVE, [DEB])
+            KEEP_BASELINE = (
+                None, self._parse_policy_not_keepbase,
+                []
+            ),
+            KEEP_SORT = (
+                self._parse_policy_keepsort,
+                self._parse_policy_not_keepsort,
+                []
+            ),
+            MAXOPT = (
+                self._parse_policy_maxopt, None,
+                []
+            ),
+            WERROR = (
+                self._parse_policy_werror, None,
+                []
+            ),
+            AUTOVEC = (
+                self._parse_policy_autovec, None,
+                ["MAXOPT"]
+            )
+        )
+        if hasattr(self, "parse_is_cached"):
+            return
+
+        self.parse_baseline_names = []
+        self.parse_baseline_flags = []
+        self.parse_dispatch_names = []
+        self.parse_target_groups = {}
+
+        if self.cc_noopt:
+            # skip parsing baseline and dispatch args and keep parsing target groups
+            cpu_baseline = cpu_dispatch = None
+
+        self.dist_log("check requested baseline")
+        if cpu_baseline is not None:
+            cpu_baseline = self._parse_arg_features("cpu_baseline", cpu_baseline)
+            baseline_names = self.feature_names(cpu_baseline)
+            self.parse_baseline_flags = self.feature_flags(baseline_names)
+            self.parse_baseline_names = self.feature_sorted(
+                self.feature_implies_c(baseline_names)
+            )
+
+        self.dist_log("check requested dispatch-able features")
+        if cpu_dispatch is not None:
+            cpu_dispatch_ = self._parse_arg_features("cpu_dispatch", cpu_dispatch)
+            cpu_dispatch = {
+                f for f in cpu_dispatch_
+                if f not in self.parse_baseline_names
+            }
+            conflict_baseline = cpu_dispatch_.difference(cpu_dispatch)
+            self.parse_dispatch_names = self.feature_sorted(
+                self.feature_names(cpu_dispatch)
+            )
+            if len(conflict_baseline) > 0:
+                self.dist_log(
+                    "skip features", conflict_baseline, "since its part of baseline"
+                )
+
+        self.dist_log("initialize targets groups")
+        for group_name, tokens in self.conf_target_groups.items():
+            self.dist_log("parse target group", group_name)
+            GROUP_NAME = group_name.upper()
+            if not tokens or not tokens.strip():
+                # allow empty groups, useful in case if there's a need
+                # to disable certain group since '_parse_target_tokens()'
+                # requires at least one valid target
+                self.parse_target_groups[GROUP_NAME] = (
+                    False, [], []
+                )
+                continue
+            has_baseline, features, extra_flags = \
+                self._parse_target_tokens(tokens)
+            self.parse_target_groups[GROUP_NAME] = (
+                has_baseline, features, extra_flags
+            )
+
+        self.parse_is_cached = True
+
+    def parse_targets(self, source):
+        """
+        Fetch and parse configuration statements that required for
+        defining the targeted CPU features, statements should be declared
+        in the top of source in between **C** comment and start
+        with a special mark **@targets**.
+
+        Configuration statements are sort of keywords representing
+        CPU features names, group of statements and policies, combined
+        together to determine the required optimization.
+
+        Parameters
+        ----------
+        source : str
+            the path of **C** source file.
+
+        Returns
+        -------
+        - bool, True if group has the 'baseline' option
+        - list, list of CPU features
+        - list, list of extra compiler flags
+        """
+        self.dist_log("looking for '@targets' inside -> ", source)
+        # get lines between /*@targets and */
+        with open(source) as fd:
+            tokens = ""
+            max_to_reach = 1000 # good enough, isn't?
+            start_with = "@targets"
+            start_pos = -1
+            end_with = "*/"
+            end_pos = -1
+            for current_line, line in enumerate(fd):
+                if current_line == max_to_reach:
+                    self.dist_fatal("reached the max of lines")
+                    break
+                if start_pos == -1:
+                    start_pos = line.find(start_with)
+                    if start_pos == -1:
+                        continue
+                    start_pos += len(start_with)
+                tokens += line
+                end_pos = line.find(end_with)
+                if end_pos != -1:
+                    end_pos += len(tokens) - len(line)
+                    break
+
+        if start_pos == -1:
+            self.dist_fatal("expected to find '%s' within a C comment" % start_with)
+        if end_pos == -1:
+            self.dist_fatal("expected to end with '%s'" % end_with)
+
+        tokens = tokens[start_pos:end_pos]
+        return self._parse_target_tokens(tokens)
+
+    _parse_regex_arg = re.compile(r'\s|,|([+-])')
+    def _parse_arg_features(self, arg_name, req_features):
+        if not isinstance(req_features, str):
+            self.dist_fatal("expected a string in '%s'" % arg_name)
+
+        final_features = set()
+        # space and comma can be used as a separator
+        tokens = list(filter(None, re.split(self._parse_regex_arg, req_features)))
+        append = True # append is the default
+        for tok in tokens:
+            if tok[0] in ("#", "$"):
+                self.dist_fatal(
+                    arg_name, "target groups and policies "
+                    "aren't allowed from arguments, "
+                    "only from dispatch-able sources"
+                )
+            if tok == '+':
+                append = True
+                continue
+            if tok == '-':
+                append = False
+                continue
+
+            TOK = tok.upper() # we use upper-case internally
+            features_to = set()
+            if TOK == "NONE":
+                pass
+            elif TOK == "NATIVE":
+                native = self.cc_flags["native"]
+                if not native:
+                    self.dist_fatal(arg_name,
+                        "native option isn't supported by the compiler"
+                    )
+                features_to = self.feature_names(
+                    force_flags=native, macros=[("DETECT_FEATURES", 1)]
+                )
+            elif TOK == "MAX":
+                features_to = self.feature_supported.keys()
+            elif TOK == "MIN":
+                features_to = self.feature_min
+            else:
+                if TOK in self.feature_supported:
+                    features_to.add(TOK)
+                else:
+                    if not self.feature_is_exist(TOK):
+                        self.dist_fatal(arg_name,
+                            ", '%s' isn't a known feature or option" % tok
+                        )
+            if append:
+                final_features = final_features.union(features_to)
+            else:
+                final_features = final_features.difference(features_to)
+
+            append = True # back to default
+
+        return final_features
+
+    _parse_regex_target = re.compile(r'\s|[*,/]|([()])')
+    def _parse_target_tokens(self, tokens):
+        assert(isinstance(tokens, str))
+        final_targets = [] # to keep it sorted as specified
+        extra_flags = []
+        has_baseline = False
+
+        skipped  = set()
+        policies = set()
+        multi_target = None
+
+        tokens = list(filter(None, re.split(self._parse_regex_target, tokens)))
+        if not tokens:
+            self.dist_fatal("expected one token at least")
+
+        for tok in tokens:
+            TOK = tok.upper()
+            ch = tok[0]
+            if ch in ('+', '-'):
+                self.dist_fatal(
+                    "+/- are 'not' allowed from target's groups or @targets, "
+                    "only from cpu_baseline and cpu_dispatch parms"
+                )
+            elif ch == '$':
+                if multi_target is not None:
+                    self.dist_fatal(
+                        "policies aren't allowed inside multi-target '()'"
+                        ", only CPU features"
+                    )
+                policies.add(self._parse_token_policy(TOK))
+            elif ch == '#':
+                if multi_target is not None:
+                    self.dist_fatal(
+                        "target groups aren't allowed inside multi-target '()'"
+                        ", only CPU features"
+                    )
+                has_baseline, final_targets, extra_flags = \
+                self._parse_token_group(TOK, has_baseline, final_targets, extra_flags)
+            elif ch == '(':
+                if multi_target is not None:
+                    self.dist_fatal("unclosed multi-target, missing ')'")
+                multi_target = set()
+            elif ch == ')':
+                if multi_target is None:
+                    self.dist_fatal("multi-target opener '(' wasn't found")
+                targets = self._parse_multi_target(multi_target)
+                if targets is None:
+                    skipped.add(tuple(multi_target))
+                else:
+                    if len(targets) == 1:
+                        targets = targets[0]
+                    if targets and targets not in final_targets:
+                        final_targets.append(targets)
+                multi_target = None # back to default
+            else:
+                if TOK == "BASELINE":
+                    if multi_target is not None:
+                        self.dist_fatal("baseline isn't allowed inside multi-target '()'")
+                    has_baseline = True
+                    continue
+
+                if multi_target is not None:
+                    multi_target.add(TOK)
+                    continue
+
+                if not self.feature_is_exist(TOK):
+                    self.dist_fatal("invalid target name '%s'" % TOK)
+
+                is_enabled = (
+                    TOK in self.parse_baseline_names or
+                    TOK in self.parse_dispatch_names
+                )
+                if  is_enabled:
+                    if TOK not in final_targets:
+                        final_targets.append(TOK)
+                    continue
+
+                skipped.add(TOK)
+
+        if multi_target is not None:
+            self.dist_fatal("unclosed multi-target, missing ')'")
+        if skipped:
+            self.dist_log(
+                "skip targets", skipped,
+                "not part of baseline or dispatch-able features"
+            )
+
+        final_targets = self.feature_untied(final_targets)
+
+        # add polices dependencies
+        for p in list(policies):
+            _, _, deps = self._parse_policies[p]
+            for d in deps:
+                if d in policies:
+                    continue
+                self.dist_log(
+                    "policy '%s' force enables '%s'" % (
+                    p, d
+                ))
+                policies.add(d)
+
+        # release policies filtrations
+        for p, (have, nhave, _) in self._parse_policies.items():
+            func = None
+            if p in policies:
+                func = have
+                self.dist_log("policy '%s' is ON" % p)
+            else:
+                func = nhave
+            if not func:
+                continue
+            has_baseline, final_targets, extra_flags = func(
+                has_baseline, final_targets, extra_flags
+            )
+
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_token_policy(self, token):
+        """validate policy token"""
+        if len(token) <= 1 or token[-1:] == token[0]:
+            self.dist_fatal("'$' must stuck in the begin of policy name")
+        token = token[1:]
+        if token not in self._parse_policies:
+            self.dist_fatal(
+                "'%s' is an invalid policy name, available policies are" % token,
+                self._parse_policies.keys()
+            )
+        return token
+
+    def _parse_token_group(self, token, has_baseline, final_targets, extra_flags):
+        """validate group token"""
+        if len(token) <= 1 or token[-1:] == token[0]:
+            self.dist_fatal("'#' must stuck in the begin of group name")
+
+        token = token[1:]
+        ghas_baseline, gtargets, gextra_flags = self.parse_target_groups.get(
+            token, (False, None, [])
+        )
+        if gtargets is None:
+            self.dist_fatal(
+                "'%s' is an invalid target group name, " % token + \
+                "available target groups are",
+                self.parse_target_groups.keys()
+            )
+        if ghas_baseline:
+            has_baseline = True
+        # always keep sorting as specified
+        final_targets += [f for f in gtargets if f not in final_targets]
+        extra_flags += [f for f in gextra_flags if f not in extra_flags]
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_multi_target(self, targets):
+        """validate multi targets that defined between parentheses()"""
+        # remove any implied features and keep the origins
+        if not targets:
+            self.dist_fatal("empty multi-target '()'")
+        if not all([
+            self.feature_is_exist(tar) for tar in targets
+        ]) :
+            self.dist_fatal("invalid target name in multi-target", targets)
+        if not all([
+            (
+                tar in self.parse_baseline_names or
+                tar in self.parse_dispatch_names
+            )
+            for tar in targets
+        ]) :
+            return None
+        targets = self.feature_ahead(targets)
+        if not targets:
+            return None
+        # force sort multi targets, so it can be comparable
+        targets = self.feature_sorted(targets)
+        targets = tuple(targets) # hashable
+        return targets
+
+    def _parse_policy_not_keepbase(self, has_baseline, final_targets, extra_flags):
+        """skip all baseline features"""
+        skipped = []
+        for tar in final_targets[:]:
+            is_base = False
+            if isinstance(tar, str):
+                is_base = tar in self.parse_baseline_names
+            else:
+                # multi targets
+                is_base = all([
+                    f in self.parse_baseline_names
+                    for f in tar
+                ])
+            if is_base:
+                skipped.append(tar)
+                final_targets.remove(tar)
+
+        if skipped:
+            self.dist_log("skip baseline features", skipped)
+
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_policy_keepsort(self, has_baseline, final_targets, extra_flags):
+        """leave a notice that $keep_sort is on"""
+        self.dist_log(
+            "policy 'keep_sort' is on, dispatch-able targets", final_targets, "\n"
+            "are 'not' sorted depend on the highest interest but"
+            "as specified in the dispatch-able source or the extra group"
+        )
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_policy_not_keepsort(self, has_baseline, final_targets, extra_flags):
+        """sorted depend on the highest interest"""
+        final_targets = self.feature_sorted(final_targets, reverse=True)
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_policy_maxopt(self, has_baseline, final_targets, extra_flags):
+        """append the compiler optimization flags"""
+        if self.cc_has_debug:
+            self.dist_log("debug mode is detected, policy 'maxopt' is skipped.")
+        elif self.cc_noopt:
+            self.dist_log("optimization is disabled, policy 'maxopt' is skipped.")
+        else:
+            flags = self.cc_flags["opt"]
+            if not flags:
+                self.dist_log(
+                    "current compiler doesn't support optimization flags, "
+                    "policy 'maxopt' is skipped", stderr=True
+                )
+            else:
+                extra_flags += flags
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_policy_werror(self, has_baseline, final_targets, extra_flags):
+        """force warnings to treated as errors"""
+        flags = self.cc_flags["werror"]
+        if not flags:
+            self.dist_log(
+                "current compiler doesn't support werror flags, "
+                "warnings will 'not' treated as errors", stderr=True
+            )
+        else:
+            self.dist_log("compiler warnings are treated as errors")
+            extra_flags += flags
+        return has_baseline, final_targets, extra_flags
+
+    def _parse_policy_autovec(self, has_baseline, final_targets, extra_flags):
+        """skip features that has no auto-vectorized support by compiler"""
+        skipped = []
+        for tar in final_targets[:]:
+            if isinstance(tar, str):
+                can = self.feature_can_autovec(tar)
+            else: # multiple target
+                can = all([
+                    self.feature_can_autovec(t)
+                    for t in tar
+                ])
+            if not can:
+                final_targets.remove(tar)
+                skipped.append(tar)
+
+        if skipped:
+            self.dist_log("skip non auto-vectorized features", skipped)
+
+        return has_baseline, final_targets, extra_flags
+
+class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse):
+    """
+    A helper class for `CCompiler` aims to provide extra build options
+    to effectively control of compiler optimizations that are directly
+    related to CPU features.
+    """
+    def __init__(self, ccompiler, cpu_baseline="min", cpu_dispatch="max", cache_path=None):
+        _Config.__init__(self)
+        _Distutils.__init__(self, ccompiler)
+        _Cache.__init__(self, cache_path, self.dist_info(), cpu_baseline, cpu_dispatch)
+        _CCompiler.__init__(self)
+        _Feature.__init__(self)
+        if not self.cc_noopt and self.cc_has_native:
+            self.dist_log(
+                "native flag is specified through environment variables. "
+                "force cpu-baseline='native'"
+            )
+            cpu_baseline = "native"
+        _Parse.__init__(self, cpu_baseline, cpu_dispatch)
+        # keep the requested features untouched, need it later for report
+        # and trace purposes
+        self._requested_baseline = cpu_baseline
+        self._requested_dispatch = cpu_dispatch
+        # key is the dispatch-able source and value is a tuple
+        # contains two items (has_baseline[boolean], dispatched-features[list])
+        self.sources_status = getattr(self, "sources_status", {})
+        # every instance should has a separate one
+        self.cache_private.add("sources_status")
+        # set it at the end to make sure the cache writing was done after init
+        # this class
+        self.hit_cache = hasattr(self, "hit_cache")
+
+    def is_cached(self):
+        """
+        Returns True if the class loaded from the cache file
+        """
+        return self.cache_infile and self.hit_cache
+
+    def cpu_baseline_flags(self):
+        """
+        Returns a list of final CPU baseline compiler flags
+        """
+        return self.parse_baseline_flags
+
+    def cpu_baseline_names(self):
+        """
+        return a list of final CPU baseline feature names
+        """
+        return self.parse_baseline_names
+
+    def cpu_dispatch_names(self):
+        """
+        return a list of final CPU dispatch feature names
+        """
+        return self.parse_dispatch_names
+
+    def try_dispatch(self, sources, src_dir=None, ccompiler=None, **kwargs):
+        """
+        Compile one or more dispatch-able sources and generates object files,
+        also generates abstract C config headers and macros that
+        used later for the final runtime dispatching process.
+
+        The mechanism behind it is to takes each source file that specified
+        in 'sources' and branching it into several files depend on
+        special configuration statements that must be declared in the
+        top of each source which contains targeted CPU features,
+        then it compiles every branched source with the proper compiler flags.
+
+        Parameters
+        ----------
+        sources : list
+            Must be a list of dispatch-able sources file paths,
+            and configuration statements must be declared inside
+            each file.
+
+        src_dir : str
+            Path of parent directory for the generated headers and wrapped sources.
+            If None(default) the files will generated in-place.
+
+        ccompiler : CCompiler
+            Distutils `CCompiler` instance to be used for compilation.
+            If None (default), the provided instance during the initialization
+            will be used instead.
+
+        **kwargs : any
+            Arguments to pass on to the `CCompiler.compile()`
+
+        Returns
+        -------
+        list : generated object files
+
+        Raises
+        ------
+        CompileError
+            Raises by `CCompiler.compile()` on compiling failure.
+        DistutilsError
+            Some errors during checking the sanity of configuration statements.
+
+        See Also
+        --------
+        parse_targets :
+            Parsing the configuration statements of dispatch-able sources.
+        """
+        to_compile = {}
+        baseline_flags = self.cpu_baseline_flags()
+        include_dirs = kwargs.setdefault("include_dirs", [])
+
+        for src in sources:
+            output_dir = os.path.dirname(src)
+            if src_dir:
+                if not output_dir.startswith(src_dir):
+                    output_dir = os.path.join(src_dir, output_dir)
+                if output_dir not in include_dirs:
+                    # To allow including the generated config header(*.dispatch.h)
+                    # by the dispatch-able sources
+                    include_dirs.append(output_dir)
+
+            has_baseline, targets, extra_flags = self.parse_targets(src)
+            nochange = self._generate_config(output_dir, src, targets, has_baseline)
+            for tar in targets:
+                tar_src = self._wrap_target(output_dir, src, tar, nochange=nochange)
+                flags = tuple(extra_flags + self.feature_flags(tar))
+                to_compile.setdefault(flags, []).append(tar_src)
+
+            if has_baseline:
+                flags = tuple(extra_flags + baseline_flags)
+                to_compile.setdefault(flags, []).append(src)
+
+            self.sources_status[src] = (has_baseline, targets)
+
+        # For these reasons, the sources are compiled in a separate loop:
+        # - Gathering all sources with the same flags to benefit from
+        #   the parallel compiling as much as possible.
+        # - To generate all config headers of the dispatchable sources,
+        #   before the compilation in case if there are dependency relationships
+        #   among them.
+        objects = []
+        for flags, srcs in to_compile.items():
+            objects += self.dist_compile(
+                srcs, list(flags), ccompiler=ccompiler, **kwargs
+            )
+        return objects
+
+    def generate_dispatch_header(self, header_path):
+        """
+        Generate the dispatch header which contains the #definitions and headers
+        for platform-specific instruction-sets for the enabled CPU baseline and
+        dispatch-able features.
+
+        Its highly recommended to take a look at the generated header
+        also the generated source files via `try_dispatch()`
+        in order to get the full picture.
+        """
+        self.dist_log("generate CPU dispatch header: (%s)" % header_path)
+
+        baseline_names = self.cpu_baseline_names()
+        dispatch_names = self.cpu_dispatch_names()
+        baseline_len = len(baseline_names)
+        dispatch_len = len(dispatch_names)
+
+        header_dir = os.path.dirname(header_path)
+        if not os.path.exists(header_dir):
+            self.dist_log(
+                f"dispatch header dir {header_dir} does not exist, creating it",
+                stderr=True
+            )
+            os.makedirs(header_dir)
+
+        with open(header_path, 'w') as f:
+            baseline_calls = ' \\\n'.join([
+                (
+                    "\t%sWITH_CPU_EXPAND_(MACRO_TO_CALL(%s, __VA_ARGS__))"
+                ) % (self.conf_c_prefix, f)
+                for f in baseline_names
+            ])
+            dispatch_calls = ' \\\n'.join([
+                (
+                    "\t%sWITH_CPU_EXPAND_(MACRO_TO_CALL(%s, __VA_ARGS__))"
+                ) % (self.conf_c_prefix, f)
+                for f in dispatch_names
+            ])
+            f.write(textwrap.dedent("""\
+                /*
+                 * AUTOGENERATED DON'T EDIT
+                 * Please make changes to the code generator (distutils/ccompiler_opt.py)
+                */
+                #define {pfx}WITH_CPU_BASELINE  "{baseline_str}"
+                #define {pfx}WITH_CPU_DISPATCH  "{dispatch_str}"
+                #define {pfx}WITH_CPU_BASELINE_N {baseline_len}
+                #define {pfx}WITH_CPU_DISPATCH_N {dispatch_len}
+                #define {pfx}WITH_CPU_EXPAND_(X) X
+                #define {pfx}WITH_CPU_BASELINE_CALL(MACRO_TO_CALL, ...) \\
+                {baseline_calls}
+                #define {pfx}WITH_CPU_DISPATCH_CALL(MACRO_TO_CALL, ...) \\
+                {dispatch_calls}
+            """).format(
+                pfx=self.conf_c_prefix, baseline_str=" ".join(baseline_names),
+                dispatch_str=" ".join(dispatch_names), baseline_len=baseline_len,
+                dispatch_len=dispatch_len, baseline_calls=baseline_calls,
+                dispatch_calls=dispatch_calls
+            ))
+            baseline_pre = ''
+            for name in baseline_names:
+                baseline_pre += self.feature_c_preprocessor(name, tabs=1) + '\n'
+
+            dispatch_pre = ''
+            for name in dispatch_names:
+                dispatch_pre += textwrap.dedent("""\
+                #ifdef {pfx}CPU_TARGET_{name}
+                {pre}
+                #endif /*{pfx}CPU_TARGET_{name}*/
+                """).format(
+                    pfx=self.conf_c_prefix_, name=name, pre=self.feature_c_preprocessor(
+                    name, tabs=1
+                ))
+
+            f.write(textwrap.dedent("""\
+            /******* baseline features *******/
+            {baseline_pre}
+            /******* dispatch features *******/
+            {dispatch_pre}
+            """).format(
+                pfx=self.conf_c_prefix_, baseline_pre=baseline_pre,
+                dispatch_pre=dispatch_pre
+            ))
+
+    def report(self, full=False):
+        report = []
+        platform_rows = []
+        baseline_rows = []
+        dispatch_rows = []
+        report.append(("Platform", platform_rows))
+        report.append(("", ""))
+        report.append(("CPU baseline", baseline_rows))
+        report.append(("", ""))
+        report.append(("CPU dispatch", dispatch_rows))
+
+        ########## platform ##########
+        platform_rows.append(("Architecture", (
+            "unsupported" if self.cc_on_noarch else self.cc_march)
+        ))
+        platform_rows.append(("Compiler", (
+            "unix-like"   if self.cc_is_nocc   else self.cc_name)
+        ))
+        ########## baseline ##########
+        if self.cc_noopt:
+            baseline_rows.append(("Requested", "optimization disabled"))
+        else:
+            baseline_rows.append(("Requested", repr(self._requested_baseline)))
+
+        baseline_names = self.cpu_baseline_names()
+        baseline_rows.append((
+            "Enabled", (' '.join(baseline_names) if baseline_names else "none")
+        ))
+        baseline_flags = self.cpu_baseline_flags()
+        baseline_rows.append((
+            "Flags", (' '.join(baseline_flags) if baseline_flags else "none")
+        ))
+        extra_checks = []
+        for name in baseline_names:
+            extra_checks += self.feature_extra_checks(name)
+        baseline_rows.append((
+            "Extra checks", (' '.join(extra_checks) if extra_checks else "none")
+        ))
+
+        ########## dispatch ##########
+        if self.cc_noopt:
+            baseline_rows.append(("Requested", "optimization disabled"))
+        else:
+            dispatch_rows.append(("Requested", repr(self._requested_dispatch)))
+
+        dispatch_names = self.cpu_dispatch_names()
+        dispatch_rows.append((
+            "Enabled", (' '.join(dispatch_names) if dispatch_names else "none")
+        ))
+        ########## Generated ##########
+        # TODO:
+        # - collect object names from 'try_dispatch()'
+        #   then get size of each object and printed
+        # - give more details about the features that not
+        #   generated due compiler support
+        # - find a better output's design.
+        #
+        target_sources = {}
+        for source, (_, targets) in self.sources_status.items():
+            for tar in targets:
+                target_sources.setdefault(tar, []).append(source)
+
+        if not full or not target_sources:
+            generated = ""
+            for tar in self.feature_sorted(target_sources):
+                sources = target_sources[tar]
+                name = tar if isinstance(tar, str) else '(%s)' % ' '.join(tar)
+                generated += name + "[%d] " % len(sources)
+            dispatch_rows.append(("Generated", generated[:-1] if generated else "none"))
+        else:
+            dispatch_rows.append(("Generated", ''))
+            for tar in self.feature_sorted(target_sources):
+                sources = target_sources[tar]
+                pretty_name = tar if isinstance(tar, str) else '(%s)' % ' '.join(tar)
+                flags = ' '.join(self.feature_flags(tar))
+                implies = ' '.join(self.feature_sorted(self.feature_implies(tar)))
+                detect = ' '.join(self.feature_detect(tar))
+                extra_checks = []
+                for name in ((tar,) if isinstance(tar, str) else tar):
+                    extra_checks += self.feature_extra_checks(name)
+                extra_checks = (' '.join(extra_checks) if extra_checks else "none")
+
+                dispatch_rows.append(('', ''))
+                dispatch_rows.append((pretty_name, implies))
+                dispatch_rows.append(("Flags", flags))
+                dispatch_rows.append(("Extra checks", extra_checks))
+                dispatch_rows.append(("Detect", detect))
+                for src in sources:
+                    dispatch_rows.append(("", src))
+
+        ###############################
+        # TODO: add support for 'markdown' format
+        text = []
+        secs_len = [len(secs) for secs, _ in report]
+        cols_len = [len(col) for _, rows in report for col, _ in rows]
+        tab = ' ' * 2
+        pad =  max(max(secs_len), max(cols_len))
+        for sec, rows in report:
+            if not sec:
+                text.append("") # empty line
+                continue
+            sec += ' ' * (pad - len(sec))
+            text.append(sec + tab + ': ')
+            for col, val in rows:
+                col += ' ' * (pad - len(col))
+                text.append(tab + col + ': ' + val)
+
+        return '\n'.join(text)
+
+    def _wrap_target(self, output_dir, dispatch_src, target, nochange=False):
+        assert(isinstance(target, (str, tuple)))
+        if isinstance(target, str):
+            ext_name = target_name = target
+        else:
+            # multi-target
+            ext_name = '.'.join(target)
+            target_name = '__'.join(target)
+
+        wrap_path = os.path.join(output_dir, os.path.basename(dispatch_src))
+        wrap_path = "{0}.{2}{1}".format(*os.path.splitext(wrap_path), ext_name.lower())
+        if nochange and os.path.exists(wrap_path):
+            return wrap_path
+
+        self.dist_log("wrap dispatch-able target -> ", wrap_path)
+        # sorting for readability
+        features = self.feature_sorted(self.feature_implies_c(target))
+        target_join = "#define %sCPU_TARGET_" % self.conf_c_prefix_
+        target_defs = [target_join + f for f in features]
+        target_defs = '\n'.join(target_defs)
+
+        with open(wrap_path, "w") as fd:
+            fd.write(textwrap.dedent("""\
+            /**
+             * AUTOGENERATED DON'T EDIT
+             * Please make changes to the code generator \
+             (distutils/ccompiler_opt.py)
+             */
+            #define {pfx}CPU_TARGET_MODE
+            #define {pfx}CPU_TARGET_CURRENT {target_name}
+            {target_defs}
+            #include "{path}"
+            """).format(
+                pfx=self.conf_c_prefix_, target_name=target_name,
+                path=os.path.abspath(dispatch_src), target_defs=target_defs
+            ))
+        return wrap_path
+
+    def _generate_config(self, output_dir, dispatch_src, targets, has_baseline=False):
+        config_path = os.path.basename(dispatch_src)
+        config_path = os.path.splitext(config_path)[0] + '.h'
+        config_path = os.path.join(output_dir, config_path)
+        # check if targets didn't change to avoid recompiling
+        cache_hash = self.cache_hash(targets, has_baseline)
+        try:
+            with open(config_path) as f:
+                last_hash = f.readline().split("cache_hash:")
+                if len(last_hash) == 2 and int(last_hash[1]) == cache_hash:
+                    return True
+        except OSError:
+            pass
+
+        os.makedirs(os.path.dirname(config_path), exist_ok=True)
+
+        self.dist_log("generate dispatched config -> ", config_path)
+        dispatch_calls = []
+        for tar in targets:
+            if isinstance(tar, str):
+                target_name = tar
+            else: # multi target
+                target_name = '__'.join([t for t in tar])
+            req_detect = self.feature_detect(tar)
+            req_detect = '&&'.join([
+                "CHK(%s)" % f for f in req_detect
+            ])
+            dispatch_calls.append(
+                "\t%sCPU_DISPATCH_EXPAND_(CB((%s), %s, __VA_ARGS__))" % (
+                self.conf_c_prefix_, req_detect, target_name
+            ))
+        dispatch_calls = ' \\\n'.join(dispatch_calls)
+
+        if has_baseline:
+            baseline_calls = (
+                "\t%sCPU_DISPATCH_EXPAND_(CB(__VA_ARGS__))"
+            ) % self.conf_c_prefix_
+        else:
+            baseline_calls = ''
+
+        with open(config_path, "w") as fd:
+            fd.write(textwrap.dedent("""\
+            // cache_hash:{cache_hash}
+            /**
+             * AUTOGENERATED DON'T EDIT
+             * Please make changes to the code generator (distutils/ccompiler_opt.py)
+             */
+            #ifndef {pfx}CPU_DISPATCH_EXPAND_
+                #define {pfx}CPU_DISPATCH_EXPAND_(X) X
+            #endif
+            #undef {pfx}CPU_DISPATCH_BASELINE_CALL
+            #undef {pfx}CPU_DISPATCH_CALL
+            #define {pfx}CPU_DISPATCH_BASELINE_CALL(CB, ...) \\
+            {baseline_calls}
+            #define {pfx}CPU_DISPATCH_CALL(CHK, CB, ...) \\
+            {dispatch_calls}
+            """).format(
+                pfx=self.conf_c_prefix_, baseline_calls=baseline_calls,
+                dispatch_calls=dispatch_calls, cache_hash=cache_hash
+            ))
+        return False
+
+def new_ccompiler_opt(compiler, dispatch_hpath, **kwargs):
+    """
+    Create a new instance of 'CCompilerOpt' and generate the dispatch header
+    which contains the #definitions and headers of platform-specific instruction-sets for
+    the enabled CPU baseline and dispatch-able features.
+
+    Parameters
+    ----------
+    compiler : CCompiler instance
+    dispatch_hpath : str
+        path of the dispatch header
+
+    **kwargs: passed as-is to `CCompilerOpt(...)`
+    Returns
+    -------
+    new instance of CCompilerOpt
+    """
+    opt = CCompilerOpt(compiler, **kwargs)
+    if not os.path.exists(dispatch_hpath) or not opt.is_cached():
+        opt.generate_dispatch_header(dispatch_hpath)
+    return opt
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimd.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimd.c
new file mode 100644
index 0000000000000000000000000000000000000000..6bc9022a58d3cd087d167d354224ded89be91884
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimd.c
@@ -0,0 +1,27 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    float *src = (float*)argv[argc-1];
+    float32x4_t v1 = vdupq_n_f32(src[0]), v2 = vdupq_n_f32(src[1]);
+    /* MAXMIN */
+    int ret  = (int)vgetq_lane_f32(vmaxnmq_f32(v1, v2), 0);
+        ret += (int)vgetq_lane_f32(vminnmq_f32(v1, v2), 0);
+    /* ROUNDING */
+    ret += (int)vgetq_lane_f32(vrndq_f32(v1), 0);
+#ifdef __aarch64__
+    {
+        double *src2 = (double*)argv[argc-1];
+        float64x2_t vd1 = vdupq_n_f64(src2[0]), vd2 = vdupq_n_f64(src2[1]);
+        /* MAXMIN */
+        ret += (int)vgetq_lane_f64(vmaxnmq_f64(vd1, vd2), 0);
+        ret += (int)vgetq_lane_f64(vminnmq_f64(vd1, vd2), 0);
+        /* ROUNDING */
+        ret += (int)vgetq_lane_f64(vrndq_f64(vd1), 0);
+    }
+#endif
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimddp.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimddp.c
new file mode 100644
index 0000000000000000000000000000000000000000..e7068ce02e19856349873f40d03caff438efb6fe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimddp.c
@@ -0,0 +1,16 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    unsigned char *src = (unsigned char*)argv[argc-1];
+    uint8x16_t v1 = vdupq_n_u8(src[0]), v2 = vdupq_n_u8(src[1]);
+    uint32x4_t va = vdupq_n_u32(3);
+    int ret = (int)vgetq_lane_u32(vdotq_u32(va, v1, v2), 0);
+#ifdef __aarch64__
+    ret += (int)vgetq_lane_u32(vdotq_laneq_u32(va, v1, v2, 0), 0);
+#endif
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdfhm.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdfhm.c
new file mode 100644
index 0000000000000000000000000000000000000000..54e328098d17b57445024c9859cd4992492c348a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdfhm.c
@@ -0,0 +1,19 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    float16_t *src = (float16_t*)argv[argc-1];
+    float *src2 = (float*)argv[argc-2];
+    float16x8_t vhp  = vdupq_n_f16(src[0]);
+    float16x4_t vlhp = vdup_n_f16(src[1]);
+    float32x4_t vf   = vdupq_n_f32(src2[0]);
+    float32x2_t vlf  = vdup_n_f32(src2[1]);
+
+    int ret  = (int)vget_lane_f32(vfmlal_low_f16(vlf, vlhp, vlhp), 0);
+        ret += (int)vgetq_lane_f32(vfmlslq_high_f16(vf, vhp, vhp), 0);
+
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdhp.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdhp.c
new file mode 100644
index 0000000000000000000000000000000000000000..e2de0306e0acaeda3b861756e598a132f8e1ca9f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_asimdhp.c
@@ -0,0 +1,15 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    float16_t *src = (float16_t*)argv[argc-1];
+    float16x8_t vhp  = vdupq_n_f16(src[0]);
+    float16x4_t vlhp = vdup_n_f16(src[1]);
+
+    int ret  =  (int)vgetq_lane_f16(vabdq_f16(vhp, vhp), 0);
+        ret  += (int)vget_lane_f16(vabd_f16(vlhp, vlhp), 0);
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx.c
new file mode 100644
index 0000000000000000000000000000000000000000..26ae18466740b230f9b964ebb4c72c54f13c73ee
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __AVX__
+        #error "HOST/ARCH doesn't support AVX"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m256 a = _mm256_add_ps(_mm256_loadu_ps((const float*)argv[argc-1]), _mm256_loadu_ps((const float*)argv[1]));
+    return (int)_mm_cvtss_f32(_mm256_castps256_ps128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx2.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx2.c
new file mode 100644
index 0000000000000000000000000000000000000000..ddde868f1b586c7b066c2284556b65ec5fef834e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx2.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __AVX2__
+        #error "HOST/ARCH doesn't support AVX2"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m256i a = _mm256_abs_epi16(_mm256_loadu_si256((const __m256i*)argv[argc-1]));
+    return _mm_cvtsi128_si32(_mm256_castsi256_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_clx.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_clx.c
new file mode 100644
index 0000000000000000000000000000000000000000..81edcd06700518269420f0cf6192e552581c17d8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_clx.c
@@ -0,0 +1,22 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __AVX512VNNI__
+        #error "HOST/ARCH doesn't support CascadeLake AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    /* VNNI */
+    __m512i a = _mm512_loadu_si512((const __m512i*)argv[argc-1]);
+            a = _mm512_dpbusd_epi32(a, _mm512_setzero_si512(), a);
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_cnl.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_cnl.c
new file mode 100644
index 0000000000000000000000000000000000000000..5799f122b511420eb16d066c31dc218bc4fae110
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_cnl.c
@@ -0,0 +1,24 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512VBMI__) || !defined(__AVX512IFMA__)
+        #error "HOST/ARCH doesn't support CannonLake AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i a = _mm512_loadu_si512((const __m512i*)argv[argc-1]);
+    /* IFMA */
+    a = _mm512_madd52hi_epu64(a, a, _mm512_setzero_si512());
+    /* VMBI */
+    a = _mm512_permutex2var_epi8(a, _mm512_setzero_si512(), a);
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_icl.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_icl.c
new file mode 100644
index 0000000000000000000000000000000000000000..3cf44d73164b6a80eca5f23f699bd00dba1f623e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_icl.c
@@ -0,0 +1,26 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512VPOPCNTDQ__) || !defined(__AVX512BITALG__) || !defined(__AVX512VPOPCNTDQ__)
+        #error "HOST/ARCH doesn't support IceLake AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i a = _mm512_loadu_si512((const __m512i*)argv[argc-1]);
+    /* VBMI2 */
+    a = _mm512_shrdv_epi64(a, a, _mm512_setzero_si512());
+    /* BITLAG */
+    a = _mm512_popcnt_epi8(a);
+    /* VPOPCNTDQ */
+    a = _mm512_popcnt_epi64(a);
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knl.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knl.c
new file mode 100644
index 0000000000000000000000000000000000000000..cb55e57aa220ebc8e1b638f7bfb470cff6725ea2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knl.c
@@ -0,0 +1,25 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512ER__) || !defined(__AVX512PF__)
+        #error "HOST/ARCH doesn't support Knights Landing AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    int base[128]={};
+    __m512d ad = _mm512_loadu_pd((const __m512d*)argv[argc-1]);
+    /* ER */
+    __m512i a = _mm512_castpd_si512(_mm512_exp2a23_pd(ad));
+    /* PF */
+    _mm512_mask_prefetch_i64scatter_pd(base, _mm512_cmpeq_epi64_mask(a, a), a, 1, _MM_HINT_T1);
+    return base[0];
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knm.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knm.c
new file mode 100644
index 0000000000000000000000000000000000000000..2c426462bd34e00f9a0b04e01fb124784c2afb7b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_knm.c
@@ -0,0 +1,30 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX5124FMAPS__) || !defined(__AVX5124VNNIW__) || !defined(__AVX512VPOPCNTDQ__)
+        #error "HOST/ARCH doesn't support Knights Mill AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i a = _mm512_loadu_si512((const __m512i*)argv[argc-1]);
+    __m512 b = _mm512_loadu_ps((const __m512*)argv[argc-2]);
+
+    /* 4FMAPS */
+    b = _mm512_4fmadd_ps(b, b, b, b, b, NULL);
+    /* 4VNNIW */
+    a = _mm512_4dpwssd_epi32(a, a, a, a, a, NULL);
+    /* VPOPCNTDQ */
+    a = _mm512_popcnt_epi64(a);
+
+    a = _mm512_add_epi32(a, _mm512_castps_si512(b));
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_skx.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_skx.c
new file mode 100644
index 0000000000000000000000000000000000000000..8840efb7e5eefcb762b69bf8d40b79406f6798a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_skx.c
@@ -0,0 +1,26 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512VL__) || !defined(__AVX512BW__) || !defined(__AVX512DQ__)
+        #error "HOST/ARCH doesn't support SkyLake AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i aa = _mm512_abs_epi32(_mm512_loadu_si512((const __m512i*)argv[argc-1]));
+    /* VL */
+    __m256i a = _mm256_abs_epi64(_mm512_extracti64x4_epi64(aa, 1));
+    /* DQ */
+    __m512i b = _mm512_broadcast_i32x8(a);
+    /* BW */
+    b = _mm512_abs_epi16(b);
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(b));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_spr.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_spr.c
new file mode 100644
index 0000000000000000000000000000000000000000..9710d0b2fe2f2ac1fc9e19c1c9b4688807efd6d7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512_spr.c
@@ -0,0 +1,26 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512FP16__)
+        #error "HOST/ARCH doesn't support Sapphire Rapids AVX512FP16 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+/* clang has a bug regarding our spr coode, see gh-23730. */
+#if __clang__
+#error
+#endif
+    __m512h a = _mm512_loadu_ph((void*)argv[argc-1]);
+    __m512h temp = _mm512_fmadd_ph(a, a, a);
+    _mm512_storeu_ph((void*)(argv[argc-1]), temp);
+    return 0;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512cd.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512cd.c
new file mode 100644
index 0000000000000000000000000000000000000000..5e29c79e34a73bdfbbcc2571333bfdd28007e07f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512cd.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __AVX512CD__
+        #error "HOST/ARCH doesn't support AVX512CD"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i a = _mm512_lzcnt_epi32(_mm512_loadu_si512((const __m512i*)argv[argc-1]));
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512f.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512f.c
new file mode 100644
index 0000000000000000000000000000000000000000..d0eb7b1ad5c63995a995c8fe80f59fd8131538d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_avx512f.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __AVX512F__
+        #error "HOST/ARCH doesn't support AVX512F"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    __m512i a = _mm512_abs_epi32(_mm512_loadu_si512((const __m512i*)argv[argc-1]));
+    return _mm_cvtsi128_si32(_mm512_castsi512_si128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_f16c.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_f16c.c
new file mode 100644
index 0000000000000000000000000000000000000000..fdf36cec580ce9c24fbb9d2a60fdfcaa824b3f11
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_f16c.c
@@ -0,0 +1,22 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __F16C__
+        #error "HOST/ARCH doesn't support F16C"
+    #endif
+#endif
+
+#include 
+#include 
+
+int main(int argc, char **argv)
+{
+    __m128 a  = _mm_cvtph_ps(_mm_loadu_si128((const __m128i*)argv[argc-1]));
+    __m256 a8 = _mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)argv[argc-2]));
+    return (int)(_mm_cvtss_f32(a) + _mm_cvtss_f32(_mm256_castps256_ps128(a8)));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma3.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma3.c
new file mode 100644
index 0000000000000000000000000000000000000000..bfeef22b5f0e86becd6b9f7a8b5b0f4bdea73202
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma3.c
@@ -0,0 +1,22 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__FMA__) && !defined(__AVX2__)
+        #error "HOST/ARCH doesn't support FMA3"
+    #endif
+#endif
+
+#include 
+#include 
+
+int main(int argc, char **argv)
+{
+    __m256 a = _mm256_loadu_ps((const float*)argv[argc-1]);
+           a = _mm256_fmadd_ps(a, a, a);
+    return (int)_mm_cvtss_f32(_mm256_castps256_ps128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma4.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma4.c
new file mode 100644
index 0000000000000000000000000000000000000000..0ff17a483385bec07f9aef023b16fc331e66fb6f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_fma4.c
@@ -0,0 +1,13 @@
+#include 
+#ifdef _MSC_VER
+    #include 
+#else
+    #include 
+#endif
+
+int main(int argc, char **argv)
+{
+    __m256 a = _mm256_loadu_ps((const float*)argv[argc-1]);
+           a = _mm256_macc_ps(a, a, a);
+    return (int)_mm_cvtss_f32(_mm256_castps256_ps128(a));
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon.c
new file mode 100644
index 0000000000000000000000000000000000000000..8c64f864dea63cb9c4ee60249e52b1ad528751c7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon.c
@@ -0,0 +1,19 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    // passing from untraced pointers to avoid optimizing out any constants
+    // so we can test against the linker.
+    float *src = (float*)argv[argc-1];
+    float32x4_t v1 = vdupq_n_f32(src[0]), v2 = vdupq_n_f32(src[1]);
+    int ret = (int)vgetq_lane_f32(vmulq_f32(v1, v2), 0);
+#ifdef __aarch64__
+    double *src2 = (double*)argv[argc-2];
+    float64x2_t vd1 = vdupq_n_f64(src2[0]), vd2 = vdupq_n_f64(src2[1]);
+    ret += (int)vgetq_lane_f64(vmulq_f64(vd1, vd2), 0);
+#endif
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_fp16.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_fp16.c
new file mode 100644
index 0000000000000000000000000000000000000000..f3b949770db66a03a6221a230e75e87f67359759
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_fp16.c
@@ -0,0 +1,11 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    short *src = (short*)argv[argc-1];
+    float32x4_t v_z4 = vcvt_f32_f16((float16x4_t)vld1_s16(src));
+    return (int)vgetq_lane_f32(v_z4, 0);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_vfpv4.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_vfpv4.c
new file mode 100644
index 0000000000000000000000000000000000000000..a039159ddeed006d62f07250a3a1dbb5abfcb6ac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_neon_vfpv4.c
@@ -0,0 +1,21 @@
+#ifdef _MSC_VER
+    #include 
+#endif
+#include 
+
+int main(int argc, char **argv)
+{
+    float *src = (float*)argv[argc-1];
+    float32x4_t v1 = vdupq_n_f32(src[0]);
+    float32x4_t v2 = vdupq_n_f32(src[1]);
+    float32x4_t v3 = vdupq_n_f32(src[2]);
+    int ret = (int)vgetq_lane_f32(vfmaq_f32(v1, v2, v3), 0);
+#ifdef __aarch64__
+    double *src2 = (double*)argv[argc-2];
+    float64x2_t vd1 = vdupq_n_f64(src2[0]);
+    float64x2_t vd2 = vdupq_n_f64(src2[1]);
+    float64x2_t vd3 = vdupq_n_f64(src2[2]);
+    ret += (int)vgetq_lane_f64(vfmaq_f64(vd1, vd2, vd3), 0);
+#endif
+    return ret;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_popcnt.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_popcnt.c
new file mode 100644
index 0000000000000000000000000000000000000000..813c461f05b36b52c855f31d621a23ab7ee0c642
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_popcnt.c
@@ -0,0 +1,32 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env vr `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__SSE4_2__) && !defined(__POPCNT__)
+        #error "HOST/ARCH doesn't support POPCNT"
+    #endif
+#endif
+
+#ifdef _MSC_VER
+    #include 
+#else
+    #include 
+#endif
+
+int main(int argc, char **argv)
+{
+    // To make sure popcnt instructions are generated
+    // and been tested against the assembler
+    unsigned long long a = *((unsigned long long*)argv[argc-1]);
+    unsigned int b = *((unsigned int*)argv[argc-2]);
+
+#if defined(_M_X64) || defined(__x86_64__)
+    a = _mm_popcnt_u64(a);
+#endif
+    b = _mm_popcnt_u32(b);
+    return (int)a + b;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_rvv.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_rvv.c
new file mode 100644
index 0000000000000000000000000000000000000000..45545d88dcd1f996308c266635a81ab1ad062901
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_rvv.c
@@ -0,0 +1,13 @@
+#ifndef __riscv_vector
+  #error RVV not supported
+#endif
+
+#include 
+
+int main(void)
+{
+    size_t vlmax = __riscv_vsetvlmax_e32m1();
+    vuint32m1_t a = __riscv_vmv_v_x_u32m1(0, vlmax);
+    vuint32m1_t b = __riscv_vadd_vv_u32m1(a, a, vlmax);
+    return __riscv_vmv_x_s_u32m1_u32(b);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse.c
new file mode 100644
index 0000000000000000000000000000000000000000..602b74e7bc437ee4fdfbc375280f423700caa49e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE__
+        #error "HOST/ARCH doesn't support SSE"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128 a = _mm_add_ps(_mm_setzero_ps(), _mm_setzero_ps());
+    return (int)_mm_cvtss_f32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse2.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse2.c
new file mode 100644
index 0000000000000000000000000000000000000000..33826a9ed1a53ef27e9c686d870d31d4b12f1736
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse2.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE2__
+        #error "HOST/ARCH doesn't support SSE2"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128i a = _mm_add_epi16(_mm_setzero_si128(), _mm_setzero_si128());
+    return _mm_cvtsi128_si32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse3.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse3.c
new file mode 100644
index 0000000000000000000000000000000000000000..d47c20f74be1f83afd1962917438507c609e5413
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse3.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE3__
+        #error "HOST/ARCH doesn't support SSE3"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128 a = _mm_hadd_ps(_mm_setzero_ps(), _mm_setzero_ps());
+    return (int)_mm_cvtss_f32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse41.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse41.c
new file mode 100644
index 0000000000000000000000000000000000000000..7c80238a3bc1809cdec133c057b1bf0ff46ce64e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse41.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE4_1__
+        #error "HOST/ARCH doesn't support SSE41"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128 a = _mm_floor_ps(_mm_setzero_ps());
+    return (int)_mm_cvtss_f32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse42.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse42.c
new file mode 100644
index 0000000000000000000000000000000000000000..f60e18f3c4f13d58bc9e8ac84752612b5ad11830
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sse42.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE4_2__
+        #error "HOST/ARCH doesn't support SSE42"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128 a = _mm_hadd_ps(_mm_setzero_ps(), _mm_setzero_ps());
+    return (int)_mm_cvtss_f32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_ssse3.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_ssse3.c
new file mode 100644
index 0000000000000000000000000000000000000000..fde390d6a37d3e2c929b7a6841efa42e618742e5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_ssse3.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSSE3__
+        #error "HOST/ARCH doesn't support SSSE3"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128i a = _mm_hadd_epi16(_mm_setzero_si128(), _mm_setzero_si128());
+    return (int)_mm_cvtsi128_si32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sve.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sve.c
new file mode 100644
index 0000000000000000000000000000000000000000..b113b8193d289386f08c904e5e095922f2070b34
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_sve.c
@@ -0,0 +1,14 @@
+#include 
+
+int accumulate(svint64_t a, svint64_t b) {
+    svbool_t p = svptrue_b64();
+    return svaddv(p, svmla_z(p, a, a, b));
+}
+
+int main(void)
+{
+    svbool_t p = svptrue_b64();
+    svint64_t a = svdup_s64(1);
+    svint64_t b = svdup_s64(2);
+    return accumulate(a, b);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx.c
new file mode 100644
index 0000000000000000000000000000000000000000..0b3f30d6a1f43ff32d5c6545560ef3aa41c828fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx.c
@@ -0,0 +1,21 @@
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+#if (defined(__GNUC__) && !defined(vec_xl)) || (defined(__clang__) && !defined(__IBMC__))
+    #define vsx_ld  vec_vsx_ld
+    #define vsx_st  vec_vsx_st
+#else
+    #define vsx_ld  vec_xl
+    #define vsx_st  vec_xst
+#endif
+
+int main(void)
+{
+    unsigned int zout[4];
+    unsigned int z4[] = {0, 0, 0, 0};
+    __vector unsigned int v_z4 = vsx_ld(0, z4);
+    vsx_st(v_z4, 0, zout);
+    return zout[0];
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx2.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx2.c
new file mode 100644
index 0000000000000000000000000000000000000000..410fb29d6db5abab4c6b2a99308f99cce07c10b2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx2.c
@@ -0,0 +1,13 @@
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+typedef __vector unsigned long long v_uint64x2;
+
+int main(void)
+{
+    v_uint64x2 z2 = (v_uint64x2){0, 0};
+    z2 = (v_uint64x2)vec_cmpeq(z2, z2);
+    return (int)vec_extract(z2, 0);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx3.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx3.c
new file mode 100644
index 0000000000000000000000000000000000000000..857526535aa8ff728d8ccd055d766bf4581c6eed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx3.c
@@ -0,0 +1,13 @@
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+typedef __vector unsigned int v_uint32x4;
+
+int main(void)
+{
+    v_uint32x4 z4 = (v_uint32x4){0, 0, 0, 0};
+    z4 = vec_absd(z4, z4);
+    return (int)vec_extract(z4, 0);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx4.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx4.c
new file mode 100644
index 0000000000000000000000000000000000000000..a6acc7384dd95f7ef51d17c85492342dde353d0d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vsx4.c
@@ -0,0 +1,14 @@
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+typedef __vector unsigned int v_uint32x4;
+
+int main(void)
+{
+    v_uint32x4 v1 = (v_uint32x4){2, 4, 8, 16};
+    v_uint32x4 v2 = (v_uint32x4){2, 2, 2, 2};
+    v_uint32x4 v3 = vec_mod(v1, v2);
+    return (int)vec_extractm(v3);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vx.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vx.c
new file mode 100644
index 0000000000000000000000000000000000000000..18fb7ef94a248d0de890bafa9cae67a5559e47f9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vx.c
@@ -0,0 +1,16 @@
+#if (__VEC__ < 10301) || (__ARCH__ < 11)
+    #error VX not supported
+#endif
+
+#include 
+int main(int argc, char **argv)
+{
+    __vector double x = vec_abs(vec_xl(argc, (double*)argv));
+    __vector double y = vec_load_len((double*)argv, (unsigned int)argc);
+
+    x = vec_round(vec_ceil(x) + vec_floor(y));
+    __vector bool long long m = vec_cmpge(x, y);
+    __vector long long i = vec_signed(vec_sel(x, y, m));
+
+    return (int)vec_extract(i, 0);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe.c
new file mode 100644
index 0000000000000000000000000000000000000000..e6933adce3d014d159e93d05166e5b67a0104efe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe.c
@@ -0,0 +1,25 @@
+#if (__VEC__ < 10302) || (__ARCH__ < 12)
+    #error VXE not supported
+#endif
+
+#include 
+int main(int argc, char **argv)
+{
+    __vector float x = vec_nabs(vec_xl(argc, (float*)argv));
+    __vector float y = vec_load_len((float*)argv, (unsigned int)argc);
+    
+    x = vec_round(vec_ceil(x) + vec_floor(y));
+    __vector bool int m = vec_cmpge(x, y);
+    x = vec_sel(x, y, m);
+
+    // need to test the existence of intrin "vflls" since vec_doublee
+    // is vec_doublee maps to wrong intrin "vfll".
+    // see https://gcc.gnu.org/bugzilla/show_bug.cgi?id=100871
+#if defined(__GNUC__) && !defined(__clang__)
+    __vector long long i = vec_signed(__builtin_s390_vflls(x));
+#else
+    __vector long long i = vec_signed(vec_doublee(x));
+#endif
+
+    return (int)vec_extract(i, 0);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe2.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe2.c
new file mode 100644
index 0000000000000000000000000000000000000000..f36d57129af67f111fa9dccca55f76dc52e6001d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_vxe2.c
@@ -0,0 +1,21 @@
+#if (__VEC__ < 10303) || (__ARCH__ < 13)
+    #error VXE2 not supported
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    int val;
+    __vector signed short large = { 'a', 'b', 'c', 'a', 'g', 'h', 'g', 'o' };
+    __vector signed short search = { 'g', 'h', 'g', 'o' };
+    __vector unsigned char len = { 0 };
+    __vector unsigned char res = vec_search_string_cc(large, search, len, &val);
+    __vector float x = vec_xl(argc, (float*)argv);
+    __vector int i = vec_signed(x);
+
+    i = vec_srdb(vec_sldb(i, i, 2), i, 3);
+    val += (int)vec_extract(res, 1);
+    val += vec_extract(i, 0);
+    return val;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_xop.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_xop.c
new file mode 100644
index 0000000000000000000000000000000000000000..51d70cf2b6d85eae5be6bd08625dbff865530f84
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/cpu_xop.c
@@ -0,0 +1,12 @@
+#include 
+#ifdef _MSC_VER
+    #include 
+#else
+    #include 
+#endif
+
+int main(void)
+{
+    __m128i a = _mm_comge_epu32(_mm_setzero_si128(), _mm_setzero_si128());
+    return _mm_cvtsi128_si32(a);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c
new file mode 100644
index 0000000000000000000000000000000000000000..9cfd0c2a57f355cea353abd24d21343543017191
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512bw_mask.c
@@ -0,0 +1,18 @@
+#include 
+/**
+ * Test BW mask operations due to:
+ *  - MSVC has supported it since vs2019 see,
+ *    https://developercommunity.visualstudio.com/content/problem/518298/missing-avx512bw-mask-intrinsics.html
+ *  - Clang >= v8.0
+ *  - GCC >= v7.1
+ */
+int main(void)
+{
+    __mmask64 m64 = _mm512_cmpeq_epi8_mask(_mm512_set1_epi8((char)1), _mm512_set1_epi8((char)1));
+    m64 = _kor_mask64(m64, m64);
+    m64 = _kxor_mask64(m64, m64);
+    m64 = _cvtu64_mask64(_cvtmask64_u64(m64));
+    m64 = _mm512_kunpackd(m64, m64);
+    m64 = (__mmask64)_mm512_kunpackw((__mmask32)m64, (__mmask32)m64);
+    return (int)_cvtmask64_u64(m64);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512dq_mask.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512dq_mask.c
new file mode 100644
index 0000000000000000000000000000000000000000..f0dc88bdd3724189dcc7cb91402db6067f8330be
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512dq_mask.c
@@ -0,0 +1,16 @@
+#include 
+/**
+ * Test DQ mask operations due to:
+ *  - MSVC has supported it since vs2019 see,
+ *    https://developercommunity.visualstudio.com/content/problem/518298/missing-avx512bw-mask-intrinsics.html
+ *  - Clang >= v8.0
+ *  - GCC >= v7.1
+ */
+int main(void)
+{
+    __mmask8 m8 = _mm512_cmpeq_epi64_mask(_mm512_set1_epi64(1), _mm512_set1_epi64(1));
+    m8 = _kor_mask8(m8, m8);
+    m8 = _kxor_mask8(m8, m8);
+    m8 = _cvtu32_mask8(_cvtmask8_u32(m8));
+    return (int)_cvtmask8_u32(m8);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512f_reduce.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512f_reduce.c
new file mode 100644
index 0000000000000000000000000000000000000000..db01aaeef40570139d5df0f2f2a9e91e26f97f74
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_avx512f_reduce.c
@@ -0,0 +1,41 @@
+#include 
+/**
+ * The following intrinsics don't have direct native support but compilers
+ * tend to emulate them.
+ * They're usually supported by gcc >= 7.1, clang >= 4 and icc >= 19
+ */
+int main(void)
+{
+    __m512  one_ps = _mm512_set1_ps(1.0f);
+    __m512d one_pd = _mm512_set1_pd(1.0);
+    __m512i one_i64 = _mm512_set1_epi64(1);
+    // add
+    float sum_ps  = _mm512_reduce_add_ps(one_ps);
+    double sum_pd = _mm512_reduce_add_pd(one_pd);
+    int sum_int   = (int)_mm512_reduce_add_epi64(one_i64);
+        sum_int  += (int)_mm512_reduce_add_epi32(one_i64);
+    // mul
+    sum_ps  += _mm512_reduce_mul_ps(one_ps);
+    sum_pd  += _mm512_reduce_mul_pd(one_pd);
+    sum_int += (int)_mm512_reduce_mul_epi64(one_i64);
+    sum_int += (int)_mm512_reduce_mul_epi32(one_i64);
+    // min
+    sum_ps  += _mm512_reduce_min_ps(one_ps);
+    sum_pd  += _mm512_reduce_min_pd(one_pd);
+    sum_int += (int)_mm512_reduce_min_epi32(one_i64);
+    sum_int += (int)_mm512_reduce_min_epu32(one_i64);
+    sum_int += (int)_mm512_reduce_min_epi64(one_i64);
+    // max
+    sum_ps  += _mm512_reduce_max_ps(one_ps);
+    sum_pd  += _mm512_reduce_max_pd(one_pd);
+    sum_int += (int)_mm512_reduce_max_epi32(one_i64);
+    sum_int += (int)_mm512_reduce_max_epu32(one_i64);
+    sum_int += (int)_mm512_reduce_max_epi64(one_i64);
+    // and
+    sum_int += (int)_mm512_reduce_and_epi32(one_i64);
+    sum_int += (int)_mm512_reduce_and_epi64(one_i64);
+    // or
+    sum_int += (int)_mm512_reduce_or_epi32(one_i64);
+    sum_int += (int)_mm512_reduce_or_epi64(one_i64);
+    return (int)sum_ps + (int)sum_pd + sum_int;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx3_half_double.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx3_half_double.c
new file mode 100644
index 0000000000000000000000000000000000000000..514a2b18f96cb089bb3c96f6420356c892adefdf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx3_half_double.c
@@ -0,0 +1,12 @@
+/**
+ * Assembler may not fully support the following VSX3 scalar
+ * instructions, even though compilers report VSX3 support.
+ */
+int main(void)
+{
+    unsigned short bits = 0xFF;
+    double f;
+    __asm__ __volatile__("xscvhpdp %x0,%x1" : "=wa"(f) : "wa"(bits));
+    __asm__ __volatile__ ("xscvdphp %x0,%x1" : "=wa" (bits) : "wa" (f));
+    return bits;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx4_mma.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx4_mma.c
new file mode 100644
index 0000000000000000000000000000000000000000..a70b2a9f6f95408eb7cfe59c056f114cc363869b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx4_mma.c
@@ -0,0 +1,21 @@
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+typedef __vector float fv4sf_t;
+typedef __vector unsigned char vec_t;
+
+int main(void)
+{
+    __vector_quad acc0;
+    float a[4] = {0,1,2,3};
+    float b[4] = {0,1,2,3};
+    vec_t *va = (vec_t *) a;
+    vec_t *vb = (vec_t *) b;
+    __builtin_mma_xvf32ger(&acc0, va[0], vb[0]);
+    fv4sf_t result[4];
+    __builtin_mma_disassemble_acc((void *)result, &acc0);
+    fv4sf_t c0 = result[0];
+    return (int)((float*)&c0)[0];
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx_asm.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx_asm.c
new file mode 100644
index 0000000000000000000000000000000000000000..b73a6f43808eeb5af2bd212ee88b6c1002a29901
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/extra_vsx_asm.c
@@ -0,0 +1,36 @@
+/**
+ * Testing ASM VSX register number fixer '%x'
+ *
+ * old versions of CLANG doesn't support %x in the inline asm template
+ * which fixes register number when using any of the register constraints wa, wd, wf.
+ *
+ * xref:
+ * - https://bugs.llvm.org/show_bug.cgi?id=31837
+ * - https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html
+ */
+#ifndef __VSX__
+    #error "VSX is not supported"
+#endif
+#include 
+
+#if (defined(__GNUC__) && !defined(vec_xl)) || (defined(__clang__) && !defined(__IBMC__))
+    #define vsx_ld  vec_vsx_ld
+    #define vsx_st  vec_vsx_st
+#else
+    #define vsx_ld  vec_xl
+    #define vsx_st  vec_xst
+#endif
+
+int main(void)
+{
+    float z4[] = {0, 0, 0, 0};
+    signed int zout[] = {0, 0, 0, 0};
+
+    __vector float vz4 = vsx_ld(0, z4);
+    __vector signed int asm_ret = vsx_ld(0, zout);
+
+    __asm__ ("xvcvspsxws %x0,%x1" : "=wa" (vz4) : "wa" (asm_ret));
+
+    vsx_st(asm_ret, 0, zout);
+    return zout[0];
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/test_flags.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/test_flags.c
new file mode 100644
index 0000000000000000000000000000000000000000..4cd09d42a6503780087632aae9ea5b458671fa57
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/checks/test_flags.c
@@ -0,0 +1 @@
+int test_flags;
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ba501de03b6d17da62f03b7cf66f07232679533
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/__init__.py
@@ -0,0 +1,41 @@
+"""distutils.command
+
+Package containing implementation of all the standard Distutils
+commands.
+
+"""
+def test_na_writable_attributes_deletion():
+    a = np.NA(2)
+    attr =  ['payload', 'dtype']
+    for s in attr:
+        assert_raises(AttributeError, delattr, a, s)
+
+
+__revision__ = "$Id: __init__.py,v 1.3 2005/05/16 11:08:49 pearu Exp $"
+
+distutils_all = [  #'build_py',
+                   'clean',
+                   'install_clib',
+                   'install_scripts',
+                   'bdist',
+                   'bdist_dumb',
+                   'bdist_wininst',
+                ]
+
+__import__('distutils.command', globals(), locals(), distutils_all)
+
+__all__ = ['build',
+           'config_compiler',
+           'config',
+           'build_src',
+           'build_py',
+           'build_ext',
+           'build_clib',
+           'build_scripts',
+           'install',
+           'install_data',
+           'install_headers',
+           'install_lib',
+           'bdist_rpm',
+           'sdist',
+          ] + distutils_all
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/autodist.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/autodist.py
new file mode 100644
index 0000000000000000000000000000000000000000..b72d0cab1a7da140c504a254a30a772f89be9f8d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/autodist.py
@@ -0,0 +1,148 @@
+"""This module implements additional tests ala autoconf which can be useful.
+
+"""
+import textwrap
+
+# We put them here since they could be easily reused outside numpy.distutils
+
+def check_inline(cmd):
+    """Return the inline identifier (may be empty)."""
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        #ifndef __cplusplus
+        static %(inline)s int static_func (void)
+        {
+            return 0;
+        }
+        %(inline)s int nostatic_func (void)
+        {
+            return 0;
+        }
+        #endif""")
+
+    for kw in ['inline', '__inline__', '__inline']:
+        st = cmd.try_compile(body % {'inline': kw}, None, None)
+        if st:
+            return kw
+
+    return ''
+
+
+def check_restrict(cmd):
+    """Return the restrict identifier (may be empty)."""
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        static int static_func (char * %(restrict)s a)
+        {
+            return 0;
+        }
+        """)
+
+    for kw in ['restrict', '__restrict__', '__restrict']:
+        st = cmd.try_compile(body % {'restrict': kw}, None, None)
+        if st:
+            return kw
+
+    return ''
+
+
+def check_compiler_gcc(cmd):
+    """Check if the compiler is GCC."""
+
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        int
+        main()
+        {
+        #if (! defined __GNUC__)
+        #error gcc required
+        #endif
+            return 0;
+        }
+        """)
+    return cmd.try_compile(body, None, None)
+
+
+def check_gcc_version_at_least(cmd, major, minor=0, patchlevel=0):
+    """
+    Check that the gcc version is at least the specified version."""
+
+    cmd._check_compiler()
+    version = '.'.join([str(major), str(minor), str(patchlevel)])
+    body = textwrap.dedent("""
+        int
+        main()
+        {
+        #if (! defined __GNUC__) || (__GNUC__ < %(major)d) || \\
+                (__GNUC_MINOR__ < %(minor)d) || \\
+                (__GNUC_PATCHLEVEL__ < %(patchlevel)d)
+        #error gcc >= %(version)s required
+        #endif
+            return 0;
+        }
+        """)
+    kw = {'version': version, 'major': major, 'minor': minor,
+          'patchlevel': patchlevel}
+
+    return cmd.try_compile(body % kw, None, None)
+
+
+def check_gcc_function_attribute(cmd, attribute, name):
+    """Return True if the given function attribute is supported."""
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        #pragma GCC diagnostic error "-Wattributes"
+        #pragma clang diagnostic error "-Wattributes"
+
+        int %s %s(void* unused)
+        {
+            return 0;
+        }
+
+        int
+        main()
+        {
+            return 0;
+        }
+        """) % (attribute, name)
+    return cmd.try_compile(body, None, None) != 0
+
+
+def check_gcc_function_attribute_with_intrinsics(cmd, attribute, name, code,
+                                                include):
+    """Return True if the given function attribute is supported with
+    intrinsics."""
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        #include<%s>
+        int %s %s(void)
+        {
+            %s;
+            return 0;
+        }
+
+        int
+        main()
+        {
+            return 0;
+        }
+        """) % (include, attribute, name, code)
+    return cmd.try_compile(body, None, None) != 0
+
+
+def check_gcc_variable_attribute(cmd, attribute):
+    """Return True if the given variable attribute is supported."""
+    cmd._check_compiler()
+    body = textwrap.dedent("""
+        #pragma GCC diagnostic error "-Wattributes"
+        #pragma clang diagnostic error "-Wattributes"
+
+        int %s foo;
+
+        int
+        main()
+        {
+            return 0;
+        }
+        """) % (attribute, )
+    return cmd.try_compile(body, None, None) != 0
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/bdist_rpm.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/bdist_rpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..682e7a8eb8e2b8cdd922fe77ed13992c5a7a1252
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/bdist_rpm.py
@@ -0,0 +1,22 @@
+import os
+import sys
+if 'setuptools' in sys.modules:
+    from setuptools.command.bdist_rpm import bdist_rpm as old_bdist_rpm
+else:
+    from distutils.command.bdist_rpm import bdist_rpm as old_bdist_rpm
+
+class bdist_rpm(old_bdist_rpm):
+
+    def _make_spec_file(self):
+        spec_file = old_bdist_rpm._make_spec_file(self)
+
+        # Replace hardcoded setup.py script name
+        # with the real setup script name.
+        setup_py = os.path.basename(sys.argv[0])
+        if setup_py == 'setup.py':
+            return spec_file
+        new_spec_file = []
+        for line in spec_file:
+            line = line.replace('setup.py', setup_py)
+            new_spec_file.append(line)
+        return new_spec_file
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..80830d559c61dde4b46dacc7d24e387486476349
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build.py
@@ -0,0 +1,62 @@
+import os
+import sys
+from distutils.command.build import build as old_build
+from distutils.util import get_platform
+from numpy.distutils.command.config_compiler import show_fortran_compilers
+
+class build(old_build):
+
+    sub_commands = [('config_cc',     lambda *args: True),
+                    ('config_fc',     lambda *args: True),
+                    ('build_src',     old_build.has_ext_modules),
+                    ] + old_build.sub_commands
+
+    user_options = old_build.user_options + [
+        ('fcompiler=', None,
+         "specify the Fortran compiler type"),
+        ('warn-error', None,
+         "turn all warnings into errors (-Werror)"),
+        ('cpu-baseline=', None,
+         "specify a list of enabled baseline CPU optimizations"),
+        ('cpu-dispatch=', None,
+         "specify a list of dispatched CPU optimizations"),
+        ('disable-optimization', None,
+         "disable CPU optimized code(dispatch,simd,fast...)"),
+        ('simd-test=', None,
+         "specify a list of CPU optimizations to be tested against NumPy SIMD interface"),
+        ]
+
+    help_options = old_build.help_options + [
+        ('help-fcompiler', None, "list available Fortran compilers",
+         show_fortran_compilers),
+        ]
+
+    def initialize_options(self):
+        old_build.initialize_options(self)
+        self.fcompiler = None
+        self.warn_error = False
+        self.cpu_baseline = "min"
+        self.cpu_dispatch = "max -xop -fma4" # drop AMD legacy features by default
+        self.disable_optimization = False
+        """
+        the '_simd' module is a very large. Adding more dispatched features
+        will increase binary size and compile time. By default we minimize
+        the targeted features to those most commonly used by the NumPy SIMD interface(NPYV),
+        NOTE: any specified features will be ignored if they're:
+            - part of the baseline(--cpu-baseline)
+            - not part of dispatch-able features(--cpu-dispatch)
+            - not supported by compiler or platform
+        """
+        self.simd_test = "BASELINE SSE2 SSE42 XOP FMA4 (FMA3 AVX2) AVX512F " \
+                         "AVX512_SKX VSX VSX2 VSX3 VSX4 NEON ASIMD VX VXE VXE2"
+
+    def finalize_options(self):
+        build_scripts = self.build_scripts
+        old_build.finalize_options(self)
+        plat_specifier = ".{}-{}.{}".format(get_platform(), *sys.version_info[:2])
+        if build_scripts is None:
+            self.build_scripts = os.path.join(self.build_base,
+                                              'scripts' + plat_specifier)
+
+    def run(self):
+        old_build.run(self)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_clib.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cd2f3e7eecaf3b8518ba40973aab4da326fb669
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_clib.py
@@ -0,0 +1,469 @@
+""" Modified version of build_clib that handles fortran source files.
+"""
+import os
+from glob import glob
+import shutil
+from distutils.command.build_clib import build_clib as old_build_clib
+from distutils.errors import DistutilsSetupError, DistutilsError, \
+    DistutilsFileError
+
+from numpy.distutils import log
+from distutils.dep_util import newer_group
+from numpy.distutils.misc_util import (
+    filter_sources, get_lib_source_files, get_numpy_include_dirs,
+    has_cxx_sources, has_f_sources, is_sequence
+)
+from numpy.distutils.ccompiler_opt import new_ccompiler_opt
+
+# Fix Python distutils bug sf #1718574:
+_l = old_build_clib.user_options
+for _i in range(len(_l)):
+    if _l[_i][0] in ['build-clib', 'build-temp']:
+        _l[_i] = (_l[_i][0] + '=',) + _l[_i][1:]
+#
+
+
+class build_clib(old_build_clib):
+
+    description = "build C/C++/F libraries used by Python extensions"
+
+    user_options = old_build_clib.user_options + [
+        ('fcompiler=', None,
+         "specify the Fortran compiler type"),
+        ('inplace', 'i', 'Build in-place'),
+        ('parallel=', 'j',
+         "number of parallel jobs"),
+        ('warn-error', None,
+         "turn all warnings into errors (-Werror)"),
+        ('cpu-baseline=', None,
+         "specify a list of enabled baseline CPU optimizations"),
+        ('cpu-dispatch=', None,
+         "specify a list of dispatched CPU optimizations"),
+        ('disable-optimization', None,
+         "disable CPU optimized code(dispatch,simd,fast...)"),
+    ]
+
+    boolean_options = old_build_clib.boolean_options + \
+    ['inplace', 'warn-error', 'disable-optimization']
+
+    def initialize_options(self):
+        old_build_clib.initialize_options(self)
+        self.fcompiler = None
+        self.inplace = 0
+        self.parallel = None
+        self.warn_error = None
+        self.cpu_baseline = None
+        self.cpu_dispatch = None
+        self.disable_optimization = None
+
+
+    def finalize_options(self):
+        if self.parallel:
+            try:
+                self.parallel = int(self.parallel)
+            except ValueError as e:
+                raise ValueError("--parallel/-j argument must be an integer") from e
+        old_build_clib.finalize_options(self)
+        self.set_undefined_options('build',
+                                        ('parallel', 'parallel'),
+                                        ('warn_error', 'warn_error'),
+                                        ('cpu_baseline', 'cpu_baseline'),
+                                        ('cpu_dispatch', 'cpu_dispatch'),
+                                        ('disable_optimization', 'disable_optimization')
+                                  )
+
+    def have_f_sources(self):
+        for (lib_name, build_info) in self.libraries:
+            if has_f_sources(build_info.get('sources', [])):
+                return True
+        return False
+
+    def have_cxx_sources(self):
+        for (lib_name, build_info) in self.libraries:
+            if has_cxx_sources(build_info.get('sources', [])):
+                return True
+        return False
+
+    def run(self):
+        if not self.libraries:
+            return
+
+        # Make sure that library sources are complete.
+        languages = []
+
+        # Make sure that extension sources are complete.
+        self.run_command('build_src')
+
+        for (lib_name, build_info) in self.libraries:
+            l = build_info.get('language', None)
+            if l and l not in languages:
+                languages.append(l)
+
+        from distutils.ccompiler import new_compiler
+        self.compiler = new_compiler(compiler=self.compiler,
+                                     dry_run=self.dry_run,
+                                     force=self.force)
+        self.compiler.customize(self.distribution,
+                                need_cxx=self.have_cxx_sources())
+
+        if self.warn_error:
+            self.compiler.compiler.append('-Werror')
+            self.compiler.compiler_so.append('-Werror')
+
+        libraries = self.libraries
+        self.libraries = None
+        self.compiler.customize_cmd(self)
+        self.libraries = libraries
+
+        self.compiler.show_customization()
+
+        if not self.disable_optimization:
+            dispatch_hpath = os.path.join("numpy", "distutils", "include", "npy_cpu_dispatch_config.h")
+            dispatch_hpath = os.path.join(self.get_finalized_command("build_src").build_src, dispatch_hpath)
+            opt_cache_path = os.path.abspath(
+                os.path.join(self.build_temp, 'ccompiler_opt_cache_clib.py')
+            )
+            if hasattr(self, "compiler_opt"):
+                # By default `CCompilerOpt` update the cache at the exit of
+                # the process, which may lead to duplicate building
+                # (see build_extension()/force_rebuild) if run() called
+                # multiple times within the same os process/thread without
+                # giving the chance the previous instances of `CCompilerOpt`
+                # to update the cache.
+                self.compiler_opt.cache_flush()
+
+            self.compiler_opt = new_ccompiler_opt(
+                compiler=self.compiler, dispatch_hpath=dispatch_hpath,
+                cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch,
+                cache_path=opt_cache_path
+            )
+            def report(copt):
+                log.info("\n########### CLIB COMPILER OPTIMIZATION ###########")
+                log.info(copt.report(full=True))
+
+            import atexit
+            atexit.register(report, self.compiler_opt)
+
+        if self.have_f_sources():
+            from numpy.distutils.fcompiler import new_fcompiler
+            self._f_compiler = new_fcompiler(compiler=self.fcompiler,
+                                             verbose=self.verbose,
+                                             dry_run=self.dry_run,
+                                             force=self.force,
+                                             requiref90='f90' in languages,
+                                             c_compiler=self.compiler)
+            if self._f_compiler is not None:
+                self._f_compiler.customize(self.distribution)
+
+                libraries = self.libraries
+                self.libraries = None
+                self._f_compiler.customize_cmd(self)
+                self.libraries = libraries
+
+                self._f_compiler.show_customization()
+        else:
+            self._f_compiler = None
+
+        self.build_libraries(self.libraries)
+
+        if self.inplace:
+            for l in self.distribution.installed_libraries:
+                libname = self.compiler.library_filename(l.name)
+                source = os.path.join(self.build_clib, libname)
+                target = os.path.join(l.target_dir, libname)
+                self.mkpath(l.target_dir)
+                shutil.copy(source, target)
+
+    def get_source_files(self):
+        self.check_library_list(self.libraries)
+        filenames = []
+        for lib in self.libraries:
+            filenames.extend(get_lib_source_files(lib))
+        return filenames
+
+    def build_libraries(self, libraries):
+        for (lib_name, build_info) in libraries:
+            self.build_a_library(build_info, lib_name, libraries)
+
+    def assemble_flags(self, in_flags):
+        """ Assemble flags from flag list
+
+        Parameters
+        ----------
+        in_flags : None or sequence
+            None corresponds to empty list.  Sequence elements can be strings
+            or callables that return lists of strings. Callable takes `self` as
+            single parameter.
+
+        Returns
+        -------
+        out_flags : list
+        """
+        if in_flags is None:
+            return []
+        out_flags = []
+        for in_flag in in_flags:
+            if callable(in_flag):
+                out_flags += in_flag(self)
+            else:
+                out_flags.append(in_flag)
+        return out_flags
+
+    def build_a_library(self, build_info, lib_name, libraries):
+        # default compilers
+        compiler = self.compiler
+        fcompiler = self._f_compiler
+
+        sources = build_info.get('sources')
+        if sources is None or not is_sequence(sources):
+            raise DistutilsSetupError(("in 'libraries' option (library '%s'), " +
+                                       "'sources' must be present and must be " +
+                                       "a list of source filenames") % lib_name)
+        sources = list(sources)
+
+        c_sources, cxx_sources, f_sources, fmodule_sources \
+            = filter_sources(sources)
+        requiref90 = not not fmodule_sources or \
+            build_info.get('language', 'c') == 'f90'
+
+        # save source type information so that build_ext can use it.
+        source_languages = []
+        if c_sources:
+            source_languages.append('c')
+        if cxx_sources:
+            source_languages.append('c++')
+        if requiref90:
+            source_languages.append('f90')
+        elif f_sources:
+            source_languages.append('f77')
+        build_info['source_languages'] = source_languages
+
+        lib_file = compiler.library_filename(lib_name,
+                                             output_dir=self.build_clib)
+        depends = sources + build_info.get('depends', [])
+
+        force_rebuild = self.force
+        if not self.disable_optimization and not self.compiler_opt.is_cached():
+            log.debug("Detected changes on compiler optimizations")
+            force_rebuild = True
+        if not (force_rebuild or newer_group(depends, lib_file, 'newer')):
+            log.debug("skipping '%s' library (up-to-date)", lib_name)
+            return
+        else:
+            log.info("building '%s' library", lib_name)
+
+        config_fc = build_info.get('config_fc', {})
+        if fcompiler is not None and config_fc:
+            log.info('using additional config_fc from setup script '
+                     'for fortran compiler: %s'
+                     % (config_fc,))
+            from numpy.distutils.fcompiler import new_fcompiler
+            fcompiler = new_fcompiler(compiler=fcompiler.compiler_type,
+                                      verbose=self.verbose,
+                                      dry_run=self.dry_run,
+                                      force=self.force,
+                                      requiref90=requiref90,
+                                      c_compiler=self.compiler)
+            if fcompiler is not None:
+                dist = self.distribution
+                base_config_fc = dist.get_option_dict('config_fc').copy()
+                base_config_fc.update(config_fc)
+                fcompiler.customize(base_config_fc)
+
+        # check availability of Fortran compilers
+        if (f_sources or fmodule_sources) and fcompiler is None:
+            raise DistutilsError("library %s has Fortran sources"
+                                 " but no Fortran compiler found" % (lib_name))
+
+        if fcompiler is not None:
+            fcompiler.extra_f77_compile_args = build_info.get(
+                'extra_f77_compile_args') or []
+            fcompiler.extra_f90_compile_args = build_info.get(
+                'extra_f90_compile_args') or []
+
+        macros = build_info.get('macros')
+        if macros is None:
+            macros = []
+        include_dirs = build_info.get('include_dirs')
+        if include_dirs is None:
+            include_dirs = []
+        # Flags can be strings, or callables that return a list of strings.
+        extra_postargs = self.assemble_flags(
+            build_info.get('extra_compiler_args'))
+        extra_cflags = self.assemble_flags(
+            build_info.get('extra_cflags'))
+        extra_cxxflags = self.assemble_flags(
+            build_info.get('extra_cxxflags'))
+
+        include_dirs.extend(get_numpy_include_dirs())
+        # where compiled F90 module files are:
+        module_dirs = build_info.get('module_dirs') or []
+        module_build_dir = os.path.dirname(lib_file)
+        if requiref90:
+            self.mkpath(module_build_dir)
+
+        if compiler.compiler_type == 'msvc':
+            # this hack works around the msvc compiler attributes
+            # problem, msvc uses its own convention :(
+            c_sources += cxx_sources
+            cxx_sources = []
+            extra_cflags += extra_cxxflags
+
+        # filtering C dispatch-table sources when optimization is not disabled,
+        # otherwise treated as normal sources.
+        copt_c_sources = []
+        copt_cxx_sources = []
+        copt_baseline_flags = []
+        copt_macros = []
+        if not self.disable_optimization:
+            bsrc_dir = self.get_finalized_command("build_src").build_src
+            dispatch_hpath = os.path.join("numpy", "distutils", "include")
+            dispatch_hpath = os.path.join(bsrc_dir, dispatch_hpath)
+            include_dirs.append(dispatch_hpath)
+            # copt_build_src = None if self.inplace else bsrc_dir
+            copt_build_src = bsrc_dir
+            for _srcs, _dst, _ext in (
+                ((c_sources,), copt_c_sources, ('.dispatch.c',)),
+                ((c_sources, cxx_sources), copt_cxx_sources,
+                    ('.dispatch.cpp', '.dispatch.cxx'))
+            ):
+                for _src in _srcs:
+                    _dst += [
+                        _src.pop(_src.index(s))
+                        for s in _src[:] if s.endswith(_ext)
+                    ]
+            copt_baseline_flags = self.compiler_opt.cpu_baseline_flags()
+        else:
+            copt_macros.append(("NPY_DISABLE_OPTIMIZATION", 1))
+
+        objects = []
+        if copt_cxx_sources:
+            log.info("compiling C++ dispatch-able sources")
+            objects += self.compiler_opt.try_dispatch(
+                copt_c_sources,
+                output_dir=self.build_temp,
+                src_dir=copt_build_src,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=extra_postargs + extra_cxxflags,
+                ccompiler=cxx_compiler
+            )
+
+        if copt_c_sources:
+            log.info("compiling C dispatch-able sources")
+            objects += self.compiler_opt.try_dispatch(
+                copt_c_sources,
+                output_dir=self.build_temp,
+                src_dir=copt_build_src,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=extra_postargs + extra_cflags)
+
+        if c_sources:
+            log.info("compiling C sources")
+            objects += compiler.compile(
+                c_sources,
+                output_dir=self.build_temp,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=(extra_postargs +
+                                copt_baseline_flags +
+                                extra_cflags))
+
+        if cxx_sources:
+            log.info("compiling C++ sources")
+            cxx_compiler = compiler.cxx_compiler()
+            cxx_objects = cxx_compiler.compile(
+                cxx_sources,
+                output_dir=self.build_temp,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=(extra_postargs +
+                                copt_baseline_flags +
+                                extra_cxxflags))
+            objects.extend(cxx_objects)
+
+        if f_sources or fmodule_sources:
+            extra_postargs = []
+            f_objects = []
+
+            if requiref90:
+                if fcompiler.module_dir_switch is None:
+                    existing_modules = glob('*.mod')
+                extra_postargs += fcompiler.module_options(
+                    module_dirs, module_build_dir)
+
+            if fmodule_sources:
+                log.info("compiling Fortran 90 module sources")
+                f_objects += fcompiler.compile(fmodule_sources,
+                                               output_dir=self.build_temp,
+                                               macros=macros,
+                                               include_dirs=include_dirs,
+                                               debug=self.debug,
+                                               extra_postargs=extra_postargs)
+
+            if requiref90 and self._f_compiler.module_dir_switch is None:
+                # move new compiled F90 module files to module_build_dir
+                for f in glob('*.mod'):
+                    if f in existing_modules:
+                        continue
+                    t = os.path.join(module_build_dir, f)
+                    if os.path.abspath(f) == os.path.abspath(t):
+                        continue
+                    if os.path.isfile(t):
+                        os.remove(t)
+                    try:
+                        self.move_file(f, module_build_dir)
+                    except DistutilsFileError:
+                        log.warn('failed to move %r to %r'
+                                 % (f, module_build_dir))
+
+            if f_sources:
+                log.info("compiling Fortran sources")
+                f_objects += fcompiler.compile(f_sources,
+                                               output_dir=self.build_temp,
+                                               macros=macros,
+                                               include_dirs=include_dirs,
+                                               debug=self.debug,
+                                               extra_postargs=extra_postargs)
+        else:
+            f_objects = []
+
+        if f_objects and not fcompiler.can_ccompiler_link(compiler):
+            # Default linker cannot link Fortran object files, and results
+            # need to be wrapped later. Instead of creating a real static
+            # library, just keep track of the object files.
+            listfn = os.path.join(self.build_clib,
+                                  lib_name + '.fobjects')
+            with open(listfn, 'w') as f:
+                f.write("\n".join(os.path.abspath(obj) for obj in f_objects))
+
+            listfn = os.path.join(self.build_clib,
+                                  lib_name + '.cobjects')
+            with open(listfn, 'w') as f:
+                f.write("\n".join(os.path.abspath(obj) for obj in objects))
+
+            # create empty "library" file for dependency tracking
+            lib_fname = os.path.join(self.build_clib,
+                                     lib_name + compiler.static_lib_extension)
+            with open(lib_fname, 'wb') as f:
+                pass
+        else:
+            # assume that default linker is suitable for
+            # linking Fortran object files
+            objects.extend(f_objects)
+            compiler.create_static_lib(objects, lib_name,
+                                       output_dir=self.build_clib,
+                                       debug=self.debug)
+
+        # fix library dependencies
+        clib_libraries = build_info.get('libraries', [])
+        for lname, binfo in libraries:
+            if lname in clib_libraries:
+                clib_libraries.extend(binfo.get('libraries', []))
+        if clib_libraries:
+            build_info['libraries'] = clib_libraries
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_ext.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c62d90c5768ee8fa0f5542531b188c3446237f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_ext.py
@@ -0,0 +1,752 @@
+""" Modified version of build_ext that handles fortran source files.
+
+"""
+import os
+import subprocess
+from glob import glob
+
+from distutils.dep_util import newer_group
+from distutils.command.build_ext import build_ext as old_build_ext
+from distutils.errors import DistutilsFileError, DistutilsSetupError,\
+    DistutilsError
+from distutils.file_util import copy_file
+
+from numpy.distutils import log
+from numpy.distutils.exec_command import filepath_from_subprocess_output
+from numpy.distutils.system_info import combine_paths
+from numpy.distutils.misc_util import (
+    filter_sources, get_ext_source_files, get_numpy_include_dirs,
+    has_cxx_sources, has_f_sources, is_sequence
+)
+from numpy.distutils.command.config_compiler import show_fortran_compilers
+from numpy.distutils.ccompiler_opt import new_ccompiler_opt, CCompilerOpt
+
+class build_ext (old_build_ext):
+
+    description = "build C/C++/F extensions (compile/link to build directory)"
+
+    user_options = old_build_ext.user_options + [
+        ('fcompiler=', None,
+         "specify the Fortran compiler type"),
+        ('parallel=', 'j',
+         "number of parallel jobs"),
+        ('warn-error', None,
+         "turn all warnings into errors (-Werror)"),
+        ('cpu-baseline=', None,
+         "specify a list of enabled baseline CPU optimizations"),
+        ('cpu-dispatch=', None,
+         "specify a list of dispatched CPU optimizations"),
+        ('disable-optimization', None,
+         "disable CPU optimized code(dispatch,simd,fast...)"),
+        ('simd-test=', None,
+         "specify a list of CPU optimizations to be tested against NumPy SIMD interface"),
+    ]
+
+    help_options = old_build_ext.help_options + [
+        ('help-fcompiler', None, "list available Fortran compilers",
+         show_fortran_compilers),
+    ]
+
+    boolean_options = old_build_ext.boolean_options + ['warn-error', 'disable-optimization']
+
+    def initialize_options(self):
+        old_build_ext.initialize_options(self)
+        self.fcompiler = None
+        self.parallel = None
+        self.warn_error = None
+        self.cpu_baseline = None
+        self.cpu_dispatch = None
+        self.disable_optimization = None
+        self.simd_test = None
+
+    def finalize_options(self):
+        if self.parallel:
+            try:
+                self.parallel = int(self.parallel)
+            except ValueError as e:
+                raise ValueError("--parallel/-j argument must be an integer") from e
+
+        # Ensure that self.include_dirs and self.distribution.include_dirs
+        # refer to the same list object. finalize_options will modify
+        # self.include_dirs, but self.distribution.include_dirs is used
+        # during the actual build.
+        # self.include_dirs is None unless paths are specified with
+        # --include-dirs.
+        # The include paths will be passed to the compiler in the order:
+        # numpy paths, --include-dirs paths, Python include path.
+        if isinstance(self.include_dirs, str):
+            self.include_dirs = self.include_dirs.split(os.pathsep)
+        incl_dirs = self.include_dirs or []
+        if self.distribution.include_dirs is None:
+            self.distribution.include_dirs = []
+        self.include_dirs = self.distribution.include_dirs
+        self.include_dirs.extend(incl_dirs)
+
+        old_build_ext.finalize_options(self)
+        self.set_undefined_options('build',
+                                        ('parallel', 'parallel'),
+                                        ('warn_error', 'warn_error'),
+                                        ('cpu_baseline', 'cpu_baseline'),
+                                        ('cpu_dispatch', 'cpu_dispatch'),
+                                        ('disable_optimization', 'disable_optimization'),
+                                        ('simd_test', 'simd_test')
+                                  )
+        CCompilerOpt.conf_target_groups["simd_test"] = self.simd_test
+
+    def run(self):
+        if not self.extensions:
+            return
+
+        # Make sure that extension sources are complete.
+        self.run_command('build_src')
+
+        if self.distribution.has_c_libraries():
+            if self.inplace:
+                if self.distribution.have_run.get('build_clib'):
+                    log.warn('build_clib already run, it is too late to '
+                             'ensure in-place build of build_clib')
+                    build_clib = self.distribution.get_command_obj(
+                        'build_clib')
+                else:
+                    build_clib = self.distribution.get_command_obj(
+                        'build_clib')
+                    build_clib.inplace = 1
+                    build_clib.ensure_finalized()
+                    build_clib.run()
+                    self.distribution.have_run['build_clib'] = 1
+
+            else:
+                self.run_command('build_clib')
+                build_clib = self.get_finalized_command('build_clib')
+            self.library_dirs.append(build_clib.build_clib)
+        else:
+            build_clib = None
+
+        # Not including C libraries to the list of
+        # extension libraries automatically to prevent
+        # bogus linking commands. Extensions must
+        # explicitly specify the C libraries that they use.
+
+        from distutils.ccompiler import new_compiler
+        from numpy.distutils.fcompiler import new_fcompiler
+
+        compiler_type = self.compiler
+        # Initialize C compiler:
+        self.compiler = new_compiler(compiler=compiler_type,
+                                     verbose=self.verbose,
+                                     dry_run=self.dry_run,
+                                     force=self.force)
+        self.compiler.customize(self.distribution)
+        self.compiler.customize_cmd(self)
+
+        if self.warn_error:
+            self.compiler.compiler.append('-Werror')
+            self.compiler.compiler_so.append('-Werror')
+
+        self.compiler.show_customization()
+
+        if not self.disable_optimization:
+            dispatch_hpath = os.path.join("numpy", "distutils", "include", "npy_cpu_dispatch_config.h")
+            dispatch_hpath = os.path.join(self.get_finalized_command("build_src").build_src, dispatch_hpath)
+            opt_cache_path = os.path.abspath(
+                os.path.join(self.build_temp, 'ccompiler_opt_cache_ext.py')
+            )
+            if hasattr(self, "compiler_opt"):
+                # By default `CCompilerOpt` update the cache at the exit of
+                # the process, which may lead to duplicate building
+                # (see build_extension()/force_rebuild) if run() called
+                # multiple times within the same os process/thread without
+                # giving the chance the previous instances of `CCompilerOpt`
+                # to update the cache.
+                self.compiler_opt.cache_flush()
+
+            self.compiler_opt = new_ccompiler_opt(
+                compiler=self.compiler, dispatch_hpath=dispatch_hpath,
+                cpu_baseline=self.cpu_baseline, cpu_dispatch=self.cpu_dispatch,
+                cache_path=opt_cache_path
+            )
+            def report(copt):
+                log.info("\n########### EXT COMPILER OPTIMIZATION ###########")
+                log.info(copt.report(full=True))
+
+            import atexit
+            atexit.register(report, self.compiler_opt)
+
+        # Setup directory for storing generated extra DLL files on Windows
+        self.extra_dll_dir = os.path.join(self.build_temp, '.libs')
+        if not os.path.isdir(self.extra_dll_dir):
+            os.makedirs(self.extra_dll_dir)
+
+        # Create mapping of libraries built by build_clib:
+        clibs = {}
+        if build_clib is not None:
+            for libname, build_info in build_clib.libraries or []:
+                if libname in clibs and clibs[libname] != build_info:
+                    log.warn('library %r defined more than once,'
+                             ' overwriting build_info\n%s... \nwith\n%s...'
+                             % (libname, repr(clibs[libname])[:300], repr(build_info)[:300]))
+                clibs[libname] = build_info
+        # .. and distribution libraries:
+        for libname, build_info in self.distribution.libraries or []:
+            if libname in clibs:
+                # build_clib libraries have a precedence before distribution ones
+                continue
+            clibs[libname] = build_info
+
+        # Determine if C++/Fortran 77/Fortran 90 compilers are needed.
+        # Update extension libraries, library_dirs, and macros.
+        all_languages = set()
+        for ext in self.extensions:
+            ext_languages = set()
+            c_libs = []
+            c_lib_dirs = []
+            macros = []
+            for libname in ext.libraries:
+                if libname in clibs:
+                    binfo = clibs[libname]
+                    c_libs += binfo.get('libraries', [])
+                    c_lib_dirs += binfo.get('library_dirs', [])
+                    for m in binfo.get('macros', []):
+                        if m not in macros:
+                            macros.append(m)
+
+                for l in clibs.get(libname, {}).get('source_languages', []):
+                    ext_languages.add(l)
+            if c_libs:
+                new_c_libs = ext.libraries + c_libs
+                log.info('updating extension %r libraries from %r to %r'
+                         % (ext.name, ext.libraries, new_c_libs))
+                ext.libraries = new_c_libs
+                ext.library_dirs = ext.library_dirs + c_lib_dirs
+            if macros:
+                log.info('extending extension %r defined_macros with %r'
+                         % (ext.name, macros))
+                ext.define_macros = ext.define_macros + macros
+
+            # determine extension languages
+            if has_f_sources(ext.sources):
+                ext_languages.add('f77')
+            if has_cxx_sources(ext.sources):
+                ext_languages.add('c++')
+            l = ext.language or self.compiler.detect_language(ext.sources)
+            if l:
+                ext_languages.add(l)
+
+            # reset language attribute for choosing proper linker
+            #
+            # When we build extensions with multiple languages, we have to
+            # choose a linker. The rules here are:
+            #   1. if there is Fortran code, always prefer the Fortran linker,
+            #   2. otherwise prefer C++ over C,
+            #   3. Users can force a particular linker by using
+            #          `language='c'`  # or 'c++', 'f90', 'f77'
+            #      in their config.add_extension() calls.
+            if 'c++' in ext_languages:
+                ext_language = 'c++'
+            else:
+                ext_language = 'c'  # default
+
+            has_fortran = False
+            if 'f90' in ext_languages:
+                ext_language = 'f90'
+                has_fortran = True
+            elif 'f77' in ext_languages:
+                ext_language = 'f77'
+                has_fortran = True
+
+            if not ext.language or has_fortran:
+                if l and l != ext_language and ext.language:
+                    log.warn('resetting extension %r language from %r to %r.' %
+                             (ext.name, l, ext_language))
+
+            ext.language = ext_language
+
+            # global language
+            all_languages.update(ext_languages)
+
+        need_f90_compiler = 'f90' in all_languages
+        need_f77_compiler = 'f77' in all_languages
+        need_cxx_compiler = 'c++' in all_languages
+
+        # Initialize C++ compiler:
+        if need_cxx_compiler:
+            self._cxx_compiler = new_compiler(compiler=compiler_type,
+                                              verbose=self.verbose,
+                                              dry_run=self.dry_run,
+                                              force=self.force)
+            compiler = self._cxx_compiler
+            compiler.customize(self.distribution, need_cxx=need_cxx_compiler)
+            compiler.customize_cmd(self)
+            compiler.show_customization()
+            self._cxx_compiler = compiler.cxx_compiler()
+        else:
+            self._cxx_compiler = None
+
+        # Initialize Fortran 77 compiler:
+        if need_f77_compiler:
+            ctype = self.fcompiler
+            self._f77_compiler = new_fcompiler(compiler=self.fcompiler,
+                                               verbose=self.verbose,
+                                               dry_run=self.dry_run,
+                                               force=self.force,
+                                               requiref90=False,
+                                               c_compiler=self.compiler)
+            fcompiler = self._f77_compiler
+            if fcompiler:
+                ctype = fcompiler.compiler_type
+                fcompiler.customize(self.distribution)
+            if fcompiler and fcompiler.get_version():
+                fcompiler.customize_cmd(self)
+                fcompiler.show_customization()
+            else:
+                self.warn('f77_compiler=%s is not available.' %
+                          (ctype))
+                self._f77_compiler = None
+        else:
+            self._f77_compiler = None
+
+        # Initialize Fortran 90 compiler:
+        if need_f90_compiler:
+            ctype = self.fcompiler
+            self._f90_compiler = new_fcompiler(compiler=self.fcompiler,
+                                               verbose=self.verbose,
+                                               dry_run=self.dry_run,
+                                               force=self.force,
+                                               requiref90=True,
+                                               c_compiler=self.compiler)
+            fcompiler = self._f90_compiler
+            if fcompiler:
+                ctype = fcompiler.compiler_type
+                fcompiler.customize(self.distribution)
+            if fcompiler and fcompiler.get_version():
+                fcompiler.customize_cmd(self)
+                fcompiler.show_customization()
+            else:
+                self.warn('f90_compiler=%s is not available.' %
+                          (ctype))
+                self._f90_compiler = None
+        else:
+            self._f90_compiler = None
+
+        # Build extensions
+        self.build_extensions()
+
+        # Copy over any extra DLL files
+        # FIXME: In the case where there are more than two packages,
+        # we blindly assume that both packages need all of the libraries,
+        # resulting in a larger wheel than is required. This should be fixed,
+        # but it's so rare that I won't bother to handle it.
+        pkg_roots = {
+            self.get_ext_fullname(ext.name).split('.')[0]
+            for ext in self.extensions
+        }
+        for pkg_root in pkg_roots:
+            shared_lib_dir = os.path.join(pkg_root, '.libs')
+            if not self.inplace:
+                shared_lib_dir = os.path.join(self.build_lib, shared_lib_dir)
+            for fn in os.listdir(self.extra_dll_dir):
+                if not os.path.isdir(shared_lib_dir):
+                    os.makedirs(shared_lib_dir)
+                if not fn.lower().endswith('.dll'):
+                    continue
+                runtime_lib = os.path.join(self.extra_dll_dir, fn)
+                copy_file(runtime_lib, shared_lib_dir)
+
+    def swig_sources(self, sources, extensions=None):
+        # Do nothing. Swig sources have been handled in build_src command.
+        return sources
+
+    def build_extension(self, ext):
+        sources = ext.sources
+        if sources is None or not is_sequence(sources):
+            raise DistutilsSetupError(
+                ("in 'ext_modules' option (extension '%s'), " +
+                 "'sources' must be present and must be " +
+                 "a list of source filenames") % ext.name)
+        sources = list(sources)
+
+        if not sources:
+            return
+
+        fullname = self.get_ext_fullname(ext.name)
+        if self.inplace:
+            modpath = fullname.split('.')
+            package = '.'.join(modpath[0:-1])
+            base = modpath[-1]
+            build_py = self.get_finalized_command('build_py')
+            package_dir = build_py.get_package_dir(package)
+            ext_filename = os.path.join(package_dir,
+                                        self.get_ext_filename(base))
+        else:
+            ext_filename = os.path.join(self.build_lib,
+                                        self.get_ext_filename(fullname))
+        depends = sources + ext.depends
+
+        force_rebuild = self.force
+        if not self.disable_optimization and not self.compiler_opt.is_cached():
+            log.debug("Detected changes on compiler optimizations")
+            force_rebuild = True
+        if not (force_rebuild or newer_group(depends, ext_filename, 'newer')):
+            log.debug("skipping '%s' extension (up-to-date)", ext.name)
+            return
+        else:
+            log.info("building '%s' extension", ext.name)
+
+        extra_args = ext.extra_compile_args or []
+        extra_cflags = getattr(ext, 'extra_c_compile_args', None) or []
+        extra_cxxflags = getattr(ext, 'extra_cxx_compile_args', None) or []
+
+        macros = ext.define_macros[:]
+        for undef in ext.undef_macros:
+            macros.append((undef,))
+
+        c_sources, cxx_sources, f_sources, fmodule_sources = \
+            filter_sources(ext.sources)
+
+        if self.compiler.compiler_type == 'msvc':
+            if cxx_sources:
+                # Needed to compile kiva.agg._agg extension.
+                extra_args.append('/Zm1000')
+                extra_cflags += extra_cxxflags
+            # this hack works around the msvc compiler attributes
+            # problem, msvc uses its own convention :(
+            c_sources += cxx_sources
+            cxx_sources = []
+
+        # Set Fortran/C++ compilers for compilation and linking.
+        if ext.language == 'f90':
+            fcompiler = self._f90_compiler
+        elif ext.language == 'f77':
+            fcompiler = self._f77_compiler
+        else:  # in case ext.language is c++, for instance
+            fcompiler = self._f90_compiler or self._f77_compiler
+        if fcompiler is not None:
+            fcompiler.extra_f77_compile_args = (ext.extra_f77_compile_args or []) if hasattr(
+                ext, 'extra_f77_compile_args') else []
+            fcompiler.extra_f90_compile_args = (ext.extra_f90_compile_args or []) if hasattr(
+                ext, 'extra_f90_compile_args') else []
+        cxx_compiler = self._cxx_compiler
+
+        # check for the availability of required compilers
+        if cxx_sources and cxx_compiler is None:
+            raise DistutilsError("extension %r has C++ sources"
+                                 "but no C++ compiler found" % (ext.name))
+        if (f_sources or fmodule_sources) and fcompiler is None:
+            raise DistutilsError("extension %r has Fortran sources "
+                                 "but no Fortran compiler found" % (ext.name))
+        if ext.language in ['f77', 'f90'] and fcompiler is None:
+            self.warn("extension %r has Fortran libraries "
+                      "but no Fortran linker found, using default linker" % (ext.name))
+        if ext.language == 'c++' and cxx_compiler is None:
+            self.warn("extension %r has C++ libraries "
+                      "but no C++ linker found, using default linker" % (ext.name))
+
+        kws = {'depends': ext.depends}
+        output_dir = self.build_temp
+
+        include_dirs = ext.include_dirs + get_numpy_include_dirs()
+
+        # filtering C dispatch-table sources when optimization is not disabled,
+        # otherwise treated as normal sources.
+        copt_c_sources = []
+        copt_cxx_sources = []
+        copt_baseline_flags = []
+        copt_macros = []
+        if not self.disable_optimization:
+            bsrc_dir = self.get_finalized_command("build_src").build_src
+            dispatch_hpath = os.path.join("numpy", "distutils", "include")
+            dispatch_hpath = os.path.join(bsrc_dir, dispatch_hpath)
+            include_dirs.append(dispatch_hpath)
+
+            # copt_build_src = None if self.inplace else bsrc_dir
+            # Always generate the generated config files and
+            # dispatch-able sources inside the build directory,
+            # even if the build option `inplace` is enabled.
+            # This approach prevents conflicts with Meson-generated
+            # config headers. Since `spin build --clean` will not remove
+            # these headers, they might overwrite the generated Meson headers,
+            # causing compatibility issues. Maintaining separate directories
+            # ensures compatibility between distutils dispatch config headers
+            # and Meson headers, avoiding build disruptions.
+            # See gh-24450 for more details.
+            copt_build_src = bsrc_dir
+            for _srcs, _dst, _ext in (
+                ((c_sources,), copt_c_sources, ('.dispatch.c',)),
+                ((c_sources, cxx_sources), copt_cxx_sources,
+                    ('.dispatch.cpp', '.dispatch.cxx'))
+            ):
+                for _src in _srcs:
+                    _dst += [
+                        _src.pop(_src.index(s))
+                        for s in _src[:] if s.endswith(_ext)
+                    ]
+            copt_baseline_flags = self.compiler_opt.cpu_baseline_flags()
+        else:
+            copt_macros.append(("NPY_DISABLE_OPTIMIZATION", 1))
+
+        c_objects = []
+        if copt_cxx_sources:
+            log.info("compiling C++ dispatch-able sources")
+            c_objects += self.compiler_opt.try_dispatch(
+                copt_cxx_sources,
+                output_dir=output_dir,
+                src_dir=copt_build_src,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=extra_args + extra_cxxflags,
+                ccompiler=cxx_compiler,
+                **kws
+            )
+        if copt_c_sources:
+            log.info("compiling C dispatch-able sources")
+            c_objects += self.compiler_opt.try_dispatch(
+                copt_c_sources,
+                output_dir=output_dir,
+                src_dir=copt_build_src,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=extra_args + extra_cflags,
+                **kws)
+        if c_sources:
+            log.info("compiling C sources")
+            c_objects += self.compiler.compile(
+                c_sources,
+                output_dir=output_dir,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=(extra_args + copt_baseline_flags +
+                                extra_cflags),
+                **kws)
+        if cxx_sources:
+            log.info("compiling C++ sources")
+            c_objects += cxx_compiler.compile(
+                cxx_sources,
+                output_dir=output_dir,
+                macros=macros + copt_macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+                extra_postargs=(extra_args + copt_baseline_flags +
+                                extra_cxxflags),
+                **kws)
+
+        extra_postargs = []
+        f_objects = []
+        if fmodule_sources:
+            log.info("compiling Fortran 90 module sources")
+            module_dirs = ext.module_dirs[:]
+            module_build_dir = os.path.join(
+                self.build_temp, os.path.dirname(
+                    self.get_ext_filename(fullname)))
+
+            self.mkpath(module_build_dir)
+            if fcompiler.module_dir_switch is None:
+                existing_modules = glob('*.mod')
+            extra_postargs += fcompiler.module_options(
+                module_dirs, module_build_dir)
+            f_objects += fcompiler.compile(fmodule_sources,
+                                           output_dir=self.build_temp,
+                                           macros=macros,
+                                           include_dirs=include_dirs,
+                                           debug=self.debug,
+                                           extra_postargs=extra_postargs,
+                                           depends=ext.depends)
+
+            if fcompiler.module_dir_switch is None:
+                for f in glob('*.mod'):
+                    if f in existing_modules:
+                        continue
+                    t = os.path.join(module_build_dir, f)
+                    if os.path.abspath(f) == os.path.abspath(t):
+                        continue
+                    if os.path.isfile(t):
+                        os.remove(t)
+                    try:
+                        self.move_file(f, module_build_dir)
+                    except DistutilsFileError:
+                        log.warn('failed to move %r to %r' %
+                                 (f, module_build_dir))
+        if f_sources:
+            log.info("compiling Fortran sources")
+            f_objects += fcompiler.compile(f_sources,
+                                           output_dir=self.build_temp,
+                                           macros=macros,
+                                           include_dirs=include_dirs,
+                                           debug=self.debug,
+                                           extra_postargs=extra_postargs,
+                                           depends=ext.depends)
+
+        if f_objects and not fcompiler.can_ccompiler_link(self.compiler):
+            unlinkable_fobjects = f_objects
+            objects = c_objects
+        else:
+            unlinkable_fobjects = []
+            objects = c_objects + f_objects
+
+        if ext.extra_objects:
+            objects.extend(ext.extra_objects)
+        extra_args = ext.extra_link_args or []
+        libraries = self.get_libraries(ext)[:]
+        library_dirs = ext.library_dirs[:]
+
+        linker = self.compiler.link_shared_object
+        # Always use system linker when using MSVC compiler.
+        if self.compiler.compiler_type in ('msvc', 'intelw', 'intelemw'):
+            # expand libraries with fcompiler libraries as we are
+            # not using fcompiler linker
+            self._libs_with_msvc_and_fortran(
+                fcompiler, libraries, library_dirs)
+            if ext.runtime_library_dirs:
+                # gcc adds RPATH to the link. On windows, copy the dll into
+                # self.extra_dll_dir instead.
+                for d in ext.runtime_library_dirs:
+                    for f in glob(d + '/*.dll'):
+                        copy_file(f, self.extra_dll_dir)
+                ext.runtime_library_dirs = []
+
+        elif ext.language in ['f77', 'f90'] and fcompiler is not None:
+            linker = fcompiler.link_shared_object
+        if ext.language == 'c++' and cxx_compiler is not None:
+            linker = cxx_compiler.link_shared_object
+
+        if fcompiler is not None:
+            objects, libraries = self._process_unlinkable_fobjects(
+                    objects, libraries,
+                    fcompiler, library_dirs,
+                    unlinkable_fobjects)
+
+        linker(objects, ext_filename,
+               libraries=libraries,
+               library_dirs=library_dirs,
+               runtime_library_dirs=ext.runtime_library_dirs,
+               extra_postargs=extra_args,
+               export_symbols=self.get_export_symbols(ext),
+               debug=self.debug,
+               build_temp=self.build_temp,
+               target_lang=ext.language)
+
+    def _add_dummy_mingwex_sym(self, c_sources):
+        build_src = self.get_finalized_command("build_src").build_src
+        build_clib = self.get_finalized_command("build_clib").build_clib
+        objects = self.compiler.compile([os.path.join(build_src,
+                                                      "gfortran_vs2003_hack.c")],
+                                        output_dir=self.build_temp)
+        self.compiler.create_static_lib(
+            objects, "_gfortran_workaround", output_dir=build_clib, debug=self.debug)
+
+    def _process_unlinkable_fobjects(self, objects, libraries,
+                                     fcompiler, library_dirs,
+                                     unlinkable_fobjects):
+        libraries = list(libraries)
+        objects = list(objects)
+        unlinkable_fobjects = list(unlinkable_fobjects)
+
+        # Expand possible fake static libraries to objects;
+        # make sure to iterate over a copy of the list as
+        # "fake" libraries will be removed as they are
+        # encountered
+        for lib in libraries[:]:
+            for libdir in library_dirs:
+                fake_lib = os.path.join(libdir, lib + '.fobjects')
+                if os.path.isfile(fake_lib):
+                    # Replace fake static library
+                    libraries.remove(lib)
+                    with open(fake_lib) as f:
+                        unlinkable_fobjects.extend(f.read().splitlines())
+
+                    # Expand C objects
+                    c_lib = os.path.join(libdir, lib + '.cobjects')
+                    with open(c_lib) as f:
+                        objects.extend(f.read().splitlines())
+
+        # Wrap unlinkable objects to a linkable one
+        if unlinkable_fobjects:
+            fobjects = [os.path.abspath(obj) for obj in unlinkable_fobjects]
+            wrapped = fcompiler.wrap_unlinkable_objects(
+                    fobjects, output_dir=self.build_temp,
+                    extra_dll_dir=self.extra_dll_dir)
+            objects.extend(wrapped)
+
+        return objects, libraries
+
+    def _libs_with_msvc_and_fortran(self, fcompiler, c_libraries,
+                                    c_library_dirs):
+        if fcompiler is None:
+            return
+
+        for libname in c_libraries:
+            if libname.startswith('msvc'):
+                continue
+            fileexists = False
+            for libdir in c_library_dirs or []:
+                libfile = os.path.join(libdir, '%s.lib' % (libname))
+                if os.path.isfile(libfile):
+                    fileexists = True
+                    break
+            if fileexists:
+                continue
+            # make g77-compiled static libs available to MSVC
+            fileexists = False
+            for libdir in c_library_dirs:
+                libfile = os.path.join(libdir, 'lib%s.a' % (libname))
+                if os.path.isfile(libfile):
+                    # copy libname.a file to name.lib so that MSVC linker
+                    # can find it
+                    libfile2 = os.path.join(self.build_temp, libname + '.lib')
+                    copy_file(libfile, libfile2)
+                    if self.build_temp not in c_library_dirs:
+                        c_library_dirs.append(self.build_temp)
+                    fileexists = True
+                    break
+            if fileexists:
+                continue
+            log.warn('could not find library %r in directories %s'
+                     % (libname, c_library_dirs))
+
+        # Always use system linker when using MSVC compiler.
+        f_lib_dirs = []
+        for dir in fcompiler.library_dirs:
+            # correct path when compiling in Cygwin but with normal Win
+            # Python
+            if dir.startswith('/usr/lib'):
+                try:
+                    dir = subprocess.check_output(['cygpath', '-w', dir])
+                except (OSError, subprocess.CalledProcessError):
+                    pass
+                else:
+                    dir = filepath_from_subprocess_output(dir)
+            f_lib_dirs.append(dir)
+        c_library_dirs.extend(f_lib_dirs)
+
+        # make g77-compiled static libs available to MSVC
+        for lib in fcompiler.libraries:
+            if not lib.startswith('msvc'):
+                c_libraries.append(lib)
+                p = combine_paths(f_lib_dirs, 'lib' + lib + '.a')
+                if p:
+                    dst_name = os.path.join(self.build_temp, lib + '.lib')
+                    if not os.path.isfile(dst_name):
+                        copy_file(p[0], dst_name)
+                    if self.build_temp not in c_library_dirs:
+                        c_library_dirs.append(self.build_temp)
+
+    def get_source_files(self):
+        self.check_extensions_list(self.extensions)
+        filenames = []
+        for ext in self.extensions:
+            filenames.extend(get_ext_source_files(ext))
+        return filenames
+
+    def get_outputs(self):
+        self.check_extensions_list(self.extensions)
+
+        outputs = []
+        for ext in self.extensions:
+            if not ext.sources:
+                continue
+            fullname = self.get_ext_fullname(ext.name)
+            outputs.append(os.path.join(self.build_lib,
+                                        self.get_ext_filename(fullname)))
+        return outputs
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_py.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..d30dc5bf42d806e03b055627b7f813f4b772d2f5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_py.py
@@ -0,0 +1,31 @@
+from distutils.command.build_py import build_py as old_build_py
+from numpy.distutils.misc_util import is_string
+
+class build_py(old_build_py):
+
+    def run(self):
+        build_src = self.get_finalized_command('build_src')
+        if build_src.py_modules_dict and self.packages is None:
+            self.packages = list(build_src.py_modules_dict.keys ())
+        old_build_py.run(self)
+
+    def find_package_modules(self, package, package_dir):
+        modules = old_build_py.find_package_modules(self, package, package_dir)
+
+        # Find build_src generated *.py files.
+        build_src = self.get_finalized_command('build_src')
+        modules += build_src.py_modules_dict.get(package, [])
+
+        return modules
+
+    def find_modules(self):
+        old_py_modules = self.py_modules[:]
+        new_py_modules = [_m for _m in self.py_modules if is_string(_m)]
+        self.py_modules[:] = new_py_modules
+        modules = old_build_py.find_modules(self)
+        self.py_modules[:] = old_py_modules
+
+        return modules
+
+    # XXX: Fix find_source_files for item in py_modules such that item is 3-tuple
+    # and item[2] is source file.
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_scripts.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5cadb2745fefccf2efe646afa986452461e48a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_scripts.py
@@ -0,0 +1,49 @@
+""" Modified version of build_scripts that handles building scripts from functions.
+
+"""
+from distutils.command.build_scripts import build_scripts as old_build_scripts
+from numpy.distutils import log
+from numpy.distutils.misc_util import is_string
+
+class build_scripts(old_build_scripts):
+
+    def generate_scripts(self, scripts):
+        new_scripts = []
+        func_scripts = []
+        for script in scripts:
+            if is_string(script):
+                new_scripts.append(script)
+            else:
+                func_scripts.append(script)
+        if not func_scripts:
+            return new_scripts
+
+        build_dir = self.build_dir
+        self.mkpath(build_dir)
+        for func in func_scripts:
+            script = func(build_dir)
+            if not script:
+                continue
+            if is_string(script):
+                log.info("  adding '%s' to scripts" % (script,))
+                new_scripts.append(script)
+            else:
+                [log.info("  adding '%s' to scripts" % (s,)) for s in script]
+                new_scripts.extend(list(script))
+        return new_scripts
+
+    def run (self):
+        if not self.scripts:
+            return
+
+        self.scripts = self.generate_scripts(self.scripts)
+        # Now make sure that the distribution object has this list of scripts.
+        # setuptools' develop command requires that this be a list of filenames,
+        # not functions.
+        self.distribution.scripts = self.scripts
+
+        return old_build_scripts.run(self)
+
+    def get_source_files(self):
+        from numpy.distutils.misc_util import get_script_files
+        return get_script_files(self.scripts)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_src.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_src.py
new file mode 100644
index 0000000000000000000000000000000000000000..7303db124cc831d1a09bc29322e8d62a7f256031
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/build_src.py
@@ -0,0 +1,773 @@
+""" Build swig and f2py sources.
+"""
+import os
+import re
+import sys
+import shlex
+import copy
+
+from distutils.command import build_ext
+from distutils.dep_util import newer_group, newer
+from distutils.util import get_platform
+from distutils.errors import DistutilsError, DistutilsSetupError
+
+
+# this import can't be done here, as it uses numpy stuff only available
+# after it's installed
+#import numpy.f2py
+from numpy.distutils import log
+from numpy.distutils.misc_util import (
+    fortran_ext_match, appendpath, is_string, is_sequence, get_cmd
+    )
+from numpy.distutils.from_template import process_file as process_f_file
+from numpy.distutils.conv_template import process_file as process_c_file
+
+def subst_vars(target, source, d):
+    """Substitute any occurrence of @foo@ by d['foo'] from source file into
+    target."""
+    var = re.compile('@([a-zA-Z_]+)@')
+    with open(source, 'r') as fs:
+        with open(target, 'w') as ft:
+            for l in fs:
+                m = var.search(l)
+                if m:
+                    ft.write(l.replace('@%s@' % m.group(1), d[m.group(1)]))
+                else:
+                    ft.write(l)
+
+class build_src(build_ext.build_ext):
+
+    description = "build sources from SWIG, F2PY files or a function"
+
+    user_options = [
+        ('build-src=', 'd', "directory to \"build\" sources to"),
+        ('f2py-opts=', None, "list of f2py command line options"),
+        ('swig=', None, "path to the SWIG executable"),
+        ('swig-opts=', None, "list of SWIG command line options"),
+        ('swig-cpp', None, "make SWIG create C++ files (default is autodetected from sources)"),
+        ('f2pyflags=', None, "additional flags to f2py (use --f2py-opts= instead)"), # obsolete
+        ('swigflags=', None, "additional flags to swig (use --swig-opts= instead)"), # obsolete
+        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
+        ('inplace', 'i',
+         "ignore build-lib and put compiled extensions into the source " +
+         "directory alongside your pure Python modules"),
+        ('verbose-cfg', None,
+         "change logging level from WARN to INFO which will show all " +
+         "compiler output")
+        ]
+
+    boolean_options = ['force', 'inplace', 'verbose-cfg']
+
+    help_options = []
+
+    def initialize_options(self):
+        self.extensions = None
+        self.package = None
+        self.py_modules = None
+        self.py_modules_dict = None
+        self.build_src = None
+        self.build_lib = None
+        self.build_base = None
+        self.force = None
+        self.inplace = None
+        self.package_dir = None
+        self.f2pyflags = None # obsolete
+        self.f2py_opts = None
+        self.swigflags = None # obsolete
+        self.swig_opts = None
+        self.swig_cpp = None
+        self.swig = None
+        self.verbose_cfg = None
+
+    def finalize_options(self):
+        self.set_undefined_options('build',
+                                   ('build_base', 'build_base'),
+                                   ('build_lib', 'build_lib'),
+                                   ('force', 'force'))
+        if self.package is None:
+            self.package = self.distribution.ext_package
+        self.extensions = self.distribution.ext_modules
+        self.libraries = self.distribution.libraries or []
+        self.py_modules = self.distribution.py_modules or []
+        self.data_files = self.distribution.data_files or []
+
+        if self.build_src is None:
+            plat_specifier = ".{}-{}.{}".format(get_platform(), *sys.version_info[:2])
+            self.build_src = os.path.join(self.build_base, 'src'+plat_specifier)
+
+        # py_modules_dict is used in build_py.find_package_modules
+        self.py_modules_dict = {}
+
+        if self.f2pyflags:
+            if self.f2py_opts:
+                log.warn('ignoring --f2pyflags as --f2py-opts already used')
+            else:
+                self.f2py_opts = self.f2pyflags
+            self.f2pyflags = None
+        if self.f2py_opts is None:
+            self.f2py_opts = []
+        else:
+            self.f2py_opts = shlex.split(self.f2py_opts)
+
+        if self.swigflags:
+            if self.swig_opts:
+                log.warn('ignoring --swigflags as --swig-opts already used')
+            else:
+                self.swig_opts = self.swigflags
+            self.swigflags = None
+
+        if self.swig_opts is None:
+            self.swig_opts = []
+        else:
+            self.swig_opts = shlex.split(self.swig_opts)
+
+        # use options from build_ext command
+        build_ext = self.get_finalized_command('build_ext')
+        if self.inplace is None:
+            self.inplace = build_ext.inplace
+        if self.swig_cpp is None:
+            self.swig_cpp = build_ext.swig_cpp
+        for c in ['swig', 'swig_opt']:
+            o = '--'+c.replace('_', '-')
+            v = getattr(build_ext, c, None)
+            if v:
+                if getattr(self, c):
+                    log.warn('both build_src and build_ext define %s option' % (o))
+                else:
+                    log.info('using "%s=%s" option from build_ext command' % (o, v))
+                    setattr(self, c, v)
+
+    def run(self):
+        log.info("build_src")
+        if not (self.extensions or self.libraries):
+            return
+        self.build_sources()
+
+    def build_sources(self):
+
+        if self.inplace:
+            self.get_package_dir = \
+                     self.get_finalized_command('build_py').get_package_dir
+
+        self.build_py_modules_sources()
+
+        for libname_info in self.libraries:
+            self.build_library_sources(*libname_info)
+
+        if self.extensions:
+            self.check_extensions_list(self.extensions)
+
+            for ext in self.extensions:
+                self.build_extension_sources(ext)
+
+        self.build_data_files_sources()
+        self.build_npy_pkg_config()
+
+    def build_data_files_sources(self):
+        if not self.data_files:
+            return
+        log.info('building data_files sources')
+        from numpy.distutils.misc_util import get_data_files
+        new_data_files = []
+        for data in self.data_files:
+            if isinstance(data, str):
+                new_data_files.append(data)
+            elif isinstance(data, tuple):
+                d, files = data
+                if self.inplace:
+                    build_dir = self.get_package_dir('.'.join(d.split(os.sep)))
+                else:
+                    build_dir = os.path.join(self.build_src, d)
+                funcs = [f for f in files if hasattr(f, '__call__')]
+                files = [f for f in files if not hasattr(f, '__call__')]
+                for f in funcs:
+                    if f.__code__.co_argcount==1:
+                        s = f(build_dir)
+                    else:
+                        s = f()
+                    if s is not None:
+                        if isinstance(s, list):
+                            files.extend(s)
+                        elif isinstance(s, str):
+                            files.append(s)
+                        else:
+                            raise TypeError(repr(s))
+                filenames = get_data_files((d, files))
+                new_data_files.append((d, filenames))
+            else:
+                raise TypeError(repr(data))
+        self.data_files[:] = new_data_files
+
+
+    def _build_npy_pkg_config(self, info, gd):
+        template, install_dir, subst_dict = info
+        template_dir = os.path.dirname(template)
+        for k, v in gd.items():
+            subst_dict[k] = v
+
+        if self.inplace == 1:
+            generated_dir = os.path.join(template_dir, install_dir)
+        else:
+            generated_dir = os.path.join(self.build_src, template_dir,
+                    install_dir)
+        generated = os.path.basename(os.path.splitext(template)[0])
+        generated_path = os.path.join(generated_dir, generated)
+        if not os.path.exists(generated_dir):
+            os.makedirs(generated_dir)
+
+        subst_vars(generated_path, template, subst_dict)
+
+        # Where to install relatively to install prefix
+        full_install_dir = os.path.join(template_dir, install_dir)
+        return full_install_dir, generated_path
+
+    def build_npy_pkg_config(self):
+        log.info('build_src: building npy-pkg config files')
+
+        # XXX: another ugly workaround to circumvent distutils brain damage. We
+        # need the install prefix here, but finalizing the options of the
+        # install command when only building sources cause error. Instead, we
+        # copy the install command instance, and finalize the copy so that it
+        # does not disrupt how distutils want to do things when with the
+        # original install command instance.
+        install_cmd = copy.copy(get_cmd('install'))
+        if not install_cmd.finalized == 1:
+            install_cmd.finalize_options()
+        build_npkg = False
+        if self.inplace == 1:
+            top_prefix = '.'
+            build_npkg = True
+        elif hasattr(install_cmd, 'install_libbase'):
+            top_prefix = install_cmd.install_libbase
+            build_npkg = True
+
+        if build_npkg:
+            for pkg, infos in self.distribution.installed_pkg_config.items():
+                pkg_path = self.distribution.package_dir[pkg]
+                prefix = os.path.join(os.path.abspath(top_prefix), pkg_path)
+                d = {'prefix': prefix}
+                for info in infos:
+                    install_dir, generated = self._build_npy_pkg_config(info, d)
+                    self.distribution.data_files.append((install_dir,
+                        [generated]))
+
+    def build_py_modules_sources(self):
+        if not self.py_modules:
+            return
+        log.info('building py_modules sources')
+        new_py_modules = []
+        for source in self.py_modules:
+            if is_sequence(source) and len(source)==3:
+                package, module_base, source = source
+                if self.inplace:
+                    build_dir = self.get_package_dir(package)
+                else:
+                    build_dir = os.path.join(self.build_src,
+                                             os.path.join(*package.split('.')))
+                if hasattr(source, '__call__'):
+                    target = os.path.join(build_dir, module_base + '.py')
+                    source = source(target)
+                if source is None:
+                    continue
+                modules = [(package, module_base, source)]
+                if package not in self.py_modules_dict:
+                    self.py_modules_dict[package] = []
+                self.py_modules_dict[package] += modules
+            else:
+                new_py_modules.append(source)
+        self.py_modules[:] = new_py_modules
+
+    def build_library_sources(self, lib_name, build_info):
+        sources = list(build_info.get('sources', []))
+
+        if not sources:
+            return
+
+        log.info('building library "%s" sources' % (lib_name))
+
+        sources = self.generate_sources(sources, (lib_name, build_info))
+
+        sources = self.template_sources(sources, (lib_name, build_info))
+
+        sources, h_files = self.filter_h_files(sources)
+
+        if h_files:
+            log.info('%s - nothing done with h_files = %s',
+                     self.package, h_files)
+
+        #for f in h_files:
+        #    self.distribution.headers.append((lib_name,f))
+
+        build_info['sources'] = sources
+        return
+
+    def build_extension_sources(self, ext):
+
+        sources = list(ext.sources)
+
+        log.info('building extension "%s" sources' % (ext.name))
+
+        fullname = self.get_ext_fullname(ext.name)
+
+        modpath = fullname.split('.')
+        package = '.'.join(modpath[0:-1])
+
+        if self.inplace:
+            self.ext_target_dir = self.get_package_dir(package)
+
+        sources = self.generate_sources(sources, ext)
+        sources = self.template_sources(sources, ext)
+        sources = self.swig_sources(sources, ext)
+        sources = self.f2py_sources(sources, ext)
+        sources = self.pyrex_sources(sources, ext)
+
+        sources, py_files = self.filter_py_files(sources)
+
+        if package not in self.py_modules_dict:
+            self.py_modules_dict[package] = []
+        modules = []
+        for f in py_files:
+            module = os.path.splitext(os.path.basename(f))[0]
+            modules.append((package, module, f))
+        self.py_modules_dict[package] += modules
+
+        sources, h_files = self.filter_h_files(sources)
+
+        if h_files:
+            log.info('%s - nothing done with h_files = %s',
+                     package, h_files)
+        #for f in h_files:
+        #    self.distribution.headers.append((package,f))
+
+        ext.sources = sources
+
+    def generate_sources(self, sources, extension):
+        new_sources = []
+        func_sources = []
+        for source in sources:
+            if is_string(source):
+                new_sources.append(source)
+            else:
+                func_sources.append(source)
+        if not func_sources:
+            return new_sources
+        if self.inplace and not is_sequence(extension):
+            build_dir = self.ext_target_dir
+        else:
+            if is_sequence(extension):
+                name = extension[0]
+            #    if 'include_dirs' not in extension[1]:
+            #        extension[1]['include_dirs'] = []
+            #    incl_dirs = extension[1]['include_dirs']
+            else:
+                name = extension.name
+            #    incl_dirs = extension.include_dirs
+            #if self.build_src not in incl_dirs:
+            #    incl_dirs.append(self.build_src)
+            build_dir = os.path.join(*([self.build_src]
+                                       +name.split('.')[:-1]))
+        self.mkpath(build_dir)
+
+        if self.verbose_cfg:
+            new_level = log.INFO
+        else:
+            new_level = log.WARN
+        old_level = log.set_threshold(new_level)
+
+        for func in func_sources:
+            source = func(extension, build_dir)
+            if not source:
+                continue
+            if is_sequence(source):
+                [log.info("  adding '%s' to sources." % (s,)) for s in source]
+                new_sources.extend(source)
+            else:
+                log.info("  adding '%s' to sources." % (source,))
+                new_sources.append(source)
+        log.set_threshold(old_level)
+        return new_sources
+
+    def filter_py_files(self, sources):
+        return self.filter_files(sources, ['.py'])
+
+    def filter_h_files(self, sources):
+        return self.filter_files(sources, ['.h', '.hpp', '.inc'])
+
+    def filter_files(self, sources, exts = []):
+        new_sources = []
+        files = []
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext in exts:
+                files.append(source)
+            else:
+                new_sources.append(source)
+        return new_sources, files
+
+    def template_sources(self, sources, extension):
+        new_sources = []
+        if is_sequence(extension):
+            depends = extension[1].get('depends')
+            include_dirs = extension[1].get('include_dirs')
+        else:
+            depends = extension.depends
+            include_dirs = extension.include_dirs
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext == '.src':  # Template file
+                if self.inplace:
+                    target_dir = os.path.dirname(base)
+                else:
+                    target_dir = appendpath(self.build_src, os.path.dirname(base))
+                self.mkpath(target_dir)
+                target_file = os.path.join(target_dir, os.path.basename(base))
+                if (self.force or newer_group([source] + depends, target_file)):
+                    if _f_pyf_ext_match(base):
+                        log.info("from_template:> %s" % (target_file))
+                        outstr = process_f_file(source)
+                    else:
+                        log.info("conv_template:> %s" % (target_file))
+                        outstr = process_c_file(source)
+                    with open(target_file, 'w') as fid:
+                        fid.write(outstr)
+                if _header_ext_match(target_file):
+                    d = os.path.dirname(target_file)
+                    if d not in include_dirs:
+                        log.info("  adding '%s' to include_dirs." % (d))
+                        include_dirs.append(d)
+                new_sources.append(target_file)
+            else:
+                new_sources.append(source)
+        return new_sources
+
+    def pyrex_sources(self, sources, extension):
+        """Pyrex not supported; this remains for Cython support (see below)"""
+        new_sources = []
+        ext_name = extension.name.split('.')[-1]
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext == '.pyx':
+                target_file = self.generate_a_pyrex_source(base, ext_name,
+                                                           source,
+                                                           extension)
+                new_sources.append(target_file)
+            else:
+                new_sources.append(source)
+        return new_sources
+
+    def generate_a_pyrex_source(self, base, ext_name, source, extension):
+        """Pyrex is not supported, but some projects monkeypatch this method.
+
+        That allows compiling Cython code, see gh-6955.
+        This method will remain here for compatibility reasons.
+        """
+        return []
+
+    def f2py_sources(self, sources, extension):
+        new_sources = []
+        f2py_sources = []
+        f_sources = []
+        f2py_targets = {}
+        target_dirs = []
+        ext_name = extension.name.split('.')[-1]
+        skip_f2py = 0
+
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext == '.pyf': # F2PY interface file
+                if self.inplace:
+                    target_dir = os.path.dirname(base)
+                else:
+                    target_dir = appendpath(self.build_src, os.path.dirname(base))
+                if os.path.isfile(source):
+                    name = get_f2py_modulename(source)
+                    if name != ext_name:
+                        raise DistutilsSetupError('mismatch of extension names: %s '
+                                                  'provides %r but expected %r' % (
+                            source, name, ext_name))
+                    target_file = os.path.join(target_dir, name+'module.c')
+                else:
+                    log.debug('  source %s does not exist: skipping f2py\'ing.' \
+                              % (source))
+                    name = ext_name
+                    skip_f2py = 1
+                    target_file = os.path.join(target_dir, name+'module.c')
+                    if not os.path.isfile(target_file):
+                        log.warn('  target %s does not exist:\n   '\
+                                 'Assuming %smodule.c was generated with '\
+                                 '"build_src --inplace" command.' \
+                                 % (target_file, name))
+                        target_dir = os.path.dirname(base)
+                        target_file = os.path.join(target_dir, name+'module.c')
+                        if not os.path.isfile(target_file):
+                            raise DistutilsSetupError("%r missing" % (target_file,))
+                        log.info('   Yes! Using %r as up-to-date target.' \
+                                 % (target_file))
+                target_dirs.append(target_dir)
+                f2py_sources.append(source)
+                f2py_targets[source] = target_file
+                new_sources.append(target_file)
+            elif fortran_ext_match(ext):
+                f_sources.append(source)
+            else:
+                new_sources.append(source)
+
+        if not (f2py_sources or f_sources):
+            return new_sources
+
+        for d in target_dirs:
+            self.mkpath(d)
+
+        f2py_options = extension.f2py_options + self.f2py_opts
+
+        if self.distribution.libraries:
+            for name, build_info in self.distribution.libraries:
+                if name in extension.libraries:
+                    f2py_options.extend(build_info.get('f2py_options', []))
+
+        log.info("f2py options: %s" % (f2py_options))
+
+        if f2py_sources:
+            if len(f2py_sources) != 1:
+                raise DistutilsSetupError(
+                    'only one .pyf file is allowed per extension module but got'\
+                    ' more: %r' % (f2py_sources,))
+            source = f2py_sources[0]
+            target_file = f2py_targets[source]
+            target_dir = os.path.dirname(target_file) or '.'
+            depends = [source] + extension.depends
+            if (self.force or newer_group(depends, target_file, 'newer')) \
+                   and not skip_f2py:
+                log.info("f2py: %s" % (source))
+                from numpy.f2py import f2py2e
+                f2py2e.run_main(f2py_options
+                                    + ['--build-dir', target_dir, source])
+            else:
+                log.debug("  skipping '%s' f2py interface (up-to-date)" % (source))
+        else:
+            #XXX TODO: --inplace support for sdist command
+            if is_sequence(extension):
+                name = extension[0]
+            else: name = extension.name
+            target_dir = os.path.join(*([self.build_src]
+                                        +name.split('.')[:-1]))
+            target_file = os.path.join(target_dir, ext_name + 'module.c')
+            new_sources.append(target_file)
+            depends = f_sources + extension.depends
+            if (self.force or newer_group(depends, target_file, 'newer')) \
+                   and not skip_f2py:
+                log.info("f2py:> %s" % (target_file))
+                self.mkpath(target_dir)
+                from numpy.f2py import f2py2e
+                f2py2e.run_main(f2py_options + ['--lower',
+                                                '--build-dir', target_dir]+\
+                                ['-m', ext_name]+f_sources)
+            else:
+                log.debug("  skipping f2py fortran files for '%s' (up-to-date)"\
+                          % (target_file))
+
+        if not os.path.isfile(target_file):
+            raise DistutilsError("f2py target file %r not generated" % (target_file,))
+
+        build_dir = os.path.join(self.build_src, target_dir)
+        target_c = os.path.join(build_dir, 'fortranobject.c')
+        target_h = os.path.join(build_dir, 'fortranobject.h')
+        log.info("  adding '%s' to sources." % (target_c))
+        new_sources.append(target_c)
+        if build_dir not in extension.include_dirs:
+            log.info("  adding '%s' to include_dirs." % (build_dir))
+            extension.include_dirs.append(build_dir)
+
+        if not skip_f2py:
+            import numpy.f2py
+            d = os.path.dirname(numpy.f2py.__file__)
+            source_c = os.path.join(d, 'src', 'fortranobject.c')
+            source_h = os.path.join(d, 'src', 'fortranobject.h')
+            if newer(source_c, target_c) or newer(source_h, target_h):
+                self.mkpath(os.path.dirname(target_c))
+                self.copy_file(source_c, target_c)
+                self.copy_file(source_h, target_h)
+        else:
+            if not os.path.isfile(target_c):
+                raise DistutilsSetupError("f2py target_c file %r not found" % (target_c,))
+            if not os.path.isfile(target_h):
+                raise DistutilsSetupError("f2py target_h file %r not found" % (target_h,))
+
+        for name_ext in ['-f2pywrappers.f', '-f2pywrappers2.f90']:
+            filename = os.path.join(target_dir, ext_name + name_ext)
+            if os.path.isfile(filename):
+                log.info("  adding '%s' to sources." % (filename))
+                f_sources.append(filename)
+
+        return new_sources + f_sources
+
+    def swig_sources(self, sources, extension):
+        # Assuming SWIG 1.3.14 or later. See compatibility note in
+        #   http://www.swig.org/Doc1.3/Python.html#Python_nn6
+
+        new_sources = []
+        swig_sources = []
+        swig_targets = {}
+        target_dirs = []
+        py_files = []     # swig generated .py files
+        target_ext = '.c'
+        if '-c++' in extension.swig_opts:
+            typ = 'c++'
+            is_cpp = True
+            extension.swig_opts.remove('-c++')
+        elif self.swig_cpp:
+            typ = 'c++'
+            is_cpp = True
+        else:
+            typ = None
+            is_cpp = False
+        skip_swig = 0
+        ext_name = extension.name.split('.')[-1]
+
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext == '.i': # SWIG interface file
+                # the code below assumes that the sources list
+                # contains not more than one .i SWIG interface file
+                if self.inplace:
+                    target_dir = os.path.dirname(base)
+                    py_target_dir = self.ext_target_dir
+                else:
+                    target_dir = appendpath(self.build_src, os.path.dirname(base))
+                    py_target_dir = target_dir
+                if os.path.isfile(source):
+                    name = get_swig_modulename(source)
+                    if name != ext_name[1:]:
+                        raise DistutilsSetupError(
+                            'mismatch of extension names: %s provides %r'
+                            ' but expected %r' % (source, name, ext_name[1:]))
+                    if typ is None:
+                        typ = get_swig_target(source)
+                        is_cpp = typ=='c++'
+                    else:
+                        typ2 = get_swig_target(source)
+                        if typ2 is None:
+                            log.warn('source %r does not define swig target, assuming %s swig target' \
+                                     % (source, typ))
+                        elif typ!=typ2:
+                            log.warn('expected %r but source %r defines %r swig target' \
+                                     % (typ, source, typ2))
+                            if typ2=='c++':
+                                log.warn('resetting swig target to c++ (some targets may have .c extension)')
+                                is_cpp = True
+                            else:
+                                log.warn('assuming that %r has c++ swig target' % (source))
+                    if is_cpp:
+                        target_ext = '.cpp'
+                    target_file = os.path.join(target_dir, '%s_wrap%s' \
+                                               % (name, target_ext))
+                else:
+                    log.warn('  source %s does not exist: skipping swig\'ing.' \
+                             % (source))
+                    name = ext_name[1:]
+                    skip_swig = 1
+                    target_file = _find_swig_target(target_dir, name)
+                    if not os.path.isfile(target_file):
+                        log.warn('  target %s does not exist:\n   '\
+                                 'Assuming %s_wrap.{c,cpp} was generated with '\
+                                 '"build_src --inplace" command.' \
+                                 % (target_file, name))
+                        target_dir = os.path.dirname(base)
+                        target_file = _find_swig_target(target_dir, name)
+                        if not os.path.isfile(target_file):
+                            raise DistutilsSetupError("%r missing" % (target_file,))
+                        log.warn('   Yes! Using %r as up-to-date target.' \
+                                 % (target_file))
+                target_dirs.append(target_dir)
+                new_sources.append(target_file)
+                py_files.append(os.path.join(py_target_dir, name+'.py'))
+                swig_sources.append(source)
+                swig_targets[source] = new_sources[-1]
+            else:
+                new_sources.append(source)
+
+        if not swig_sources:
+            return new_sources
+
+        if skip_swig:
+            return new_sources + py_files
+
+        for d in target_dirs:
+            self.mkpath(d)
+
+        swig = self.swig or self.find_swig()
+        swig_cmd = [swig, "-python"] + extension.swig_opts
+        if is_cpp:
+            swig_cmd.append('-c++')
+        for d in extension.include_dirs:
+            swig_cmd.append('-I'+d)
+        for source in swig_sources:
+            target = swig_targets[source]
+            depends = [source] + extension.depends
+            if self.force or newer_group(depends, target, 'newer'):
+                log.info("%s: %s" % (os.path.basename(swig) \
+                                     + (is_cpp and '++' or ''), source))
+                self.spawn(swig_cmd + self.swig_opts \
+                           + ["-o", target, '-outdir', py_target_dir, source])
+            else:
+                log.debug("  skipping '%s' swig interface (up-to-date)" \
+                         % (source))
+
+        return new_sources + py_files
+
+_f_pyf_ext_match = re.compile(r'.*\.(f90|f95|f77|for|ftn|f|pyf)\Z', re.I).match
+_header_ext_match = re.compile(r'.*\.(inc|h|hpp)\Z', re.I).match
+
+#### SWIG related auxiliary functions ####
+_swig_module_name_match = re.compile(r'\s*%module\s*(.*\(\s*package\s*=\s*"(?P[\w_]+)".*\)|)\s*(?P[\w_]+)',
+                                     re.I).match
+_has_c_header = re.compile(r'-\*-\s*c\s*-\*-', re.I).search
+_has_cpp_header = re.compile(r'-\*-\s*c\+\+\s*-\*-', re.I).search
+
+def get_swig_target(source):
+    with open(source) as f:
+        result = None
+        line = f.readline()
+        if _has_cpp_header(line):
+            result = 'c++'
+        if _has_c_header(line):
+            result = 'c'
+    return result
+
+def get_swig_modulename(source):
+    with open(source) as f:
+        name = None
+        for line in f:
+            m = _swig_module_name_match(line)
+            if m:
+                name = m.group('name')
+                break
+    return name
+
+def _find_swig_target(target_dir, name):
+    for ext in ['.cpp', '.c']:
+        target = os.path.join(target_dir, '%s_wrap%s' % (name, ext))
+        if os.path.isfile(target):
+            break
+    return target
+
+#### F2PY related auxiliary functions ####
+
+_f2py_module_name_match = re.compile(r'\s*python\s*module\s*(?P[\w_]+)',
+                                     re.I).match
+_f2py_user_module_name_match = re.compile(r'\s*python\s*module\s*(?P[\w_]*?'
+                                          r'__user__[\w_]*)', re.I).match
+
+def get_f2py_modulename(source):
+    name = None
+    with open(source) as f:
+        for line in f:
+            m = _f2py_module_name_match(line)
+            if m:
+                if _f2py_user_module_name_match(line): # skip *__user__* names
+                    continue
+                name = m.group('name')
+                break
+    return name
+
+##########################################
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bdfb7ec582394e557786c07c773662952fcd2fc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config.py
@@ -0,0 +1,516 @@
+# Added Fortran compiler support to config. Currently useful only for
+# try_compile call. try_run works but is untested for most of Fortran
+# compilers (they must define linker_exe first).
+# Pearu Peterson
+import os
+import signal
+import subprocess
+import sys
+import textwrap
+import warnings
+
+from distutils.command.config import config as old_config
+from distutils.command.config import LANG_EXT
+from distutils import log
+from distutils.file_util import copy_file
+from distutils.ccompiler import CompileError, LinkError
+import distutils
+from numpy.distutils.exec_command import filepath_from_subprocess_output
+from numpy.distutils.mingw32ccompiler import generate_manifest
+from numpy.distutils.command.autodist import (check_gcc_function_attribute,
+                                              check_gcc_function_attribute_with_intrinsics,
+                                              check_gcc_variable_attribute,
+                                              check_gcc_version_at_least,
+                                              check_inline,
+                                              check_restrict,
+                                              check_compiler_gcc)
+
+LANG_EXT['f77'] = '.f'
+LANG_EXT['f90'] = '.f90'
+
+class config(old_config):
+    old_config.user_options += [
+        ('fcompiler=', None, "specify the Fortran compiler type"),
+        ]
+
+    def initialize_options(self):
+        self.fcompiler = None
+        old_config.initialize_options(self)
+
+    def _check_compiler (self):
+        old_config._check_compiler(self)
+        from numpy.distutils.fcompiler import FCompiler, new_fcompiler
+
+        if sys.platform == 'win32' and (self.compiler.compiler_type in
+                                        ('msvc', 'intelw', 'intelemw')):
+            # XXX: hack to circumvent a python 2.6 bug with msvc9compiler:
+            # initialize call query_vcvarsall, which throws an OSError, and
+            # causes an error along the way without much information. We try to
+            # catch it here, hoping it is early enough, and print a helpful
+            # message instead of Error: None.
+            if not self.compiler.initialized:
+                try:
+                    self.compiler.initialize()
+                except OSError as e:
+                    msg = textwrap.dedent("""\
+                        Could not initialize compiler instance: do you have Visual Studio
+                        installed?  If you are trying to build with MinGW, please use "python setup.py
+                        build -c mingw32" instead.  If you have Visual Studio installed, check it is
+                        correctly installed, and the right version (VS 2015 as of this writing).
+
+                        Original exception was: %s, and the Compiler class was %s
+                        ============================================================================""") \
+                        % (e, self.compiler.__class__.__name__)
+                    print(textwrap.dedent("""\
+                        ============================================================================"""))
+                    raise distutils.errors.DistutilsPlatformError(msg) from e
+
+            # After MSVC is initialized, add an explicit /MANIFEST to linker
+            # flags.  See issues gh-4245 and gh-4101 for details.  Also
+            # relevant are issues 4431 and 16296 on the Python bug tracker.
+            from distutils import msvc9compiler
+            if msvc9compiler.get_build_version() >= 10:
+                for ldflags in [self.compiler.ldflags_shared,
+                                self.compiler.ldflags_shared_debug]:
+                    if '/MANIFEST' not in ldflags:
+                        ldflags.append('/MANIFEST')
+
+        if not isinstance(self.fcompiler, FCompiler):
+            self.fcompiler = new_fcompiler(compiler=self.fcompiler,
+                                           dry_run=self.dry_run, force=1,
+                                           c_compiler=self.compiler)
+            if self.fcompiler is not None:
+                self.fcompiler.customize(self.distribution)
+                if self.fcompiler.get_version():
+                    self.fcompiler.customize_cmd(self)
+                    self.fcompiler.show_customization()
+
+    def _wrap_method(self, mth, lang, args):
+        from distutils.ccompiler import CompileError
+        from distutils.errors import DistutilsExecError
+        save_compiler = self.compiler
+        if lang in ['f77', 'f90']:
+            self.compiler = self.fcompiler
+        if self.compiler is None:
+            raise CompileError('%s compiler is not set' % (lang,))
+        try:
+            ret = mth(*((self,)+args))
+        except (DistutilsExecError, CompileError) as e:
+            self.compiler = save_compiler
+            raise CompileError from e
+        self.compiler = save_compiler
+        return ret
+
+    def _compile (self, body, headers, include_dirs, lang):
+        src, obj = self._wrap_method(old_config._compile, lang,
+                                     (body, headers, include_dirs, lang))
+        # _compile in unixcompiler.py sometimes creates .d dependency files.
+        # Clean them up.
+        self.temp_files.append(obj + '.d')
+        return src, obj
+
+    def _link (self, body,
+               headers, include_dirs,
+               libraries, library_dirs, lang):
+        if self.compiler.compiler_type=='msvc':
+            libraries = (libraries or [])[:]
+            library_dirs = (library_dirs or [])[:]
+            if lang in ['f77', 'f90']:
+                lang = 'c' # always use system linker when using MSVC compiler
+                if self.fcompiler:
+                    for d in self.fcompiler.library_dirs or []:
+                        # correct path when compiling in Cygwin but with
+                        # normal Win Python
+                        if d.startswith('/usr/lib'):
+                            try:
+                                d = subprocess.check_output(['cygpath',
+                                                             '-w', d])
+                            except (OSError, subprocess.CalledProcessError):
+                                pass
+                            else:
+                                d = filepath_from_subprocess_output(d)
+                        library_dirs.append(d)
+                    for libname in self.fcompiler.libraries or []:
+                        if libname not in libraries:
+                            libraries.append(libname)
+            for libname in libraries:
+                if libname.startswith('msvc'): continue
+                fileexists = False
+                for libdir in library_dirs or []:
+                    libfile = os.path.join(libdir, '%s.lib' % (libname))
+                    if os.path.isfile(libfile):
+                        fileexists = True
+                        break
+                if fileexists: continue
+                # make g77-compiled static libs available to MSVC
+                fileexists = False
+                for libdir in library_dirs:
+                    libfile = os.path.join(libdir, 'lib%s.a' % (libname))
+                    if os.path.isfile(libfile):
+                        # copy libname.a file to name.lib so that MSVC linker
+                        # can find it
+                        libfile2 = os.path.join(libdir, '%s.lib' % (libname))
+                        copy_file(libfile, libfile2)
+                        self.temp_files.append(libfile2)
+                        fileexists = True
+                        break
+                if fileexists: continue
+                log.warn('could not find library %r in directories %s' \
+                         % (libname, library_dirs))
+        elif self.compiler.compiler_type == 'mingw32':
+            generate_manifest(self)
+        return self._wrap_method(old_config._link, lang,
+                                 (body, headers, include_dirs,
+                                  libraries, library_dirs, lang))
+
+    def check_header(self, header, include_dirs=None, library_dirs=None, lang='c'):
+        self._check_compiler()
+        return self.try_compile(
+                "/* we need a dummy line to make distutils happy */",
+                [header], include_dirs)
+
+    def check_decl(self, symbol,
+                   headers=None, include_dirs=None):
+        self._check_compiler()
+        body = textwrap.dedent("""
+            int main(void)
+            {
+            #ifndef %s
+                (void) %s;
+            #endif
+                ;
+                return 0;
+            }""") % (symbol, symbol)
+
+        return self.try_compile(body, headers, include_dirs)
+
+    def check_macro_true(self, symbol,
+                         headers=None, include_dirs=None):
+        self._check_compiler()
+        body = textwrap.dedent("""
+            int main(void)
+            {
+            #if %s
+            #else
+            #error false or undefined macro
+            #endif
+                ;
+                return 0;
+            }""") % (symbol,)
+
+        return self.try_compile(body, headers, include_dirs)
+
+    def check_type(self, type_name, headers=None, include_dirs=None,
+            library_dirs=None):
+        """Check type availability. Return True if the type can be compiled,
+        False otherwise"""
+        self._check_compiler()
+
+        # First check the type can be compiled
+        body = textwrap.dedent(r"""
+            int main(void) {
+              if ((%(name)s *) 0)
+                return 0;
+              if (sizeof (%(name)s))
+                return 0;
+            }
+            """) % {'name': type_name}
+
+        st = False
+        try:
+            try:
+                self._compile(body % {'type': type_name},
+                        headers, include_dirs, 'c')
+                st = True
+            except distutils.errors.CompileError:
+                st = False
+        finally:
+            self._clean()
+
+        return st
+
+    def check_type_size(self, type_name, headers=None, include_dirs=None, library_dirs=None, expected=None):
+        """Check size of a given type."""
+        self._check_compiler()
+
+        # First check the type can be compiled
+        body = textwrap.dedent(r"""
+            typedef %(type)s npy_check_sizeof_type;
+            int main (void)
+            {
+                static int test_array [1 - 2 * !(((long) (sizeof (npy_check_sizeof_type))) >= 0)];
+                test_array [0] = 0
+
+                ;
+                return 0;
+            }
+            """)
+        self._compile(body % {'type': type_name},
+                headers, include_dirs, 'c')
+        self._clean()
+
+        if expected:
+            body = textwrap.dedent(r"""
+                typedef %(type)s npy_check_sizeof_type;
+                int main (void)
+                {
+                    static int test_array [1 - 2 * !(((long) (sizeof (npy_check_sizeof_type))) == %(size)s)];
+                    test_array [0] = 0
+
+                    ;
+                    return 0;
+                }
+                """)
+            for size in expected:
+                try:
+                    self._compile(body % {'type': type_name, 'size': size},
+                            headers, include_dirs, 'c')
+                    self._clean()
+                    return size
+                except CompileError:
+                    pass
+
+        # this fails to *compile* if size > sizeof(type)
+        body = textwrap.dedent(r"""
+            typedef %(type)s npy_check_sizeof_type;
+            int main (void)
+            {
+                static int test_array [1 - 2 * !(((long) (sizeof (npy_check_sizeof_type))) <= %(size)s)];
+                test_array [0] = 0
+
+                ;
+                return 0;
+            }
+            """)
+
+        # The principle is simple: we first find low and high bounds of size
+        # for the type, where low/high are looked up on a log scale. Then, we
+        # do a binary search to find the exact size between low and high
+        low = 0
+        mid = 0
+        while True:
+            try:
+                self._compile(body % {'type': type_name, 'size': mid},
+                        headers, include_dirs, 'c')
+                self._clean()
+                break
+            except CompileError:
+                #log.info("failure to test for bound %d" % mid)
+                low = mid + 1
+                mid = 2 * mid + 1
+
+        high = mid
+        # Binary search:
+        while low != high:
+            mid = (high - low) // 2 + low
+            try:
+                self._compile(body % {'type': type_name, 'size': mid},
+                        headers, include_dirs, 'c')
+                self._clean()
+                high = mid
+            except CompileError:
+                low = mid + 1
+        return low
+
+    def check_func(self, func,
+                   headers=None, include_dirs=None,
+                   libraries=None, library_dirs=None,
+                   decl=False, call=False, call_args=None):
+        # clean up distutils's config a bit: add void to main(), and
+        # return a value.
+        self._check_compiler()
+        body = []
+        if decl:
+            if type(decl) == str:
+                body.append(decl)
+            else:
+                body.append("int %s (void);" % func)
+        # Handle MSVC intrinsics: force MS compiler to make a function call.
+        # Useful to test for some functions when built with optimization on, to
+        # avoid build error because the intrinsic and our 'fake' test
+        # declaration do not match.
+        body.append("#ifdef _MSC_VER")
+        body.append("#pragma function(%s)" % func)
+        body.append("#endif")
+        body.append("int main (void) {")
+        if call:
+            if call_args is None:
+                call_args = ''
+            body.append("  %s(%s);" % (func, call_args))
+        else:
+            body.append("  %s;" % func)
+        body.append("  return 0;")
+        body.append("}")
+        body = '\n'.join(body) + "\n"
+
+        return self.try_link(body, headers, include_dirs,
+                             libraries, library_dirs)
+
+    def check_funcs_once(self, funcs,
+                   headers=None, include_dirs=None,
+                   libraries=None, library_dirs=None,
+                   decl=False, call=False, call_args=None):
+        """Check a list of functions at once.
+
+        This is useful to speed up things, since all the functions in the funcs
+        list will be put in one compilation unit.
+
+        Arguments
+        ---------
+        funcs : seq
+            list of functions to test
+        include_dirs : seq
+            list of header paths
+        libraries : seq
+            list of libraries to link the code snippet to
+        library_dirs : seq
+            list of library paths
+        decl : dict
+            for every (key, value), the declaration in the value will be
+            used for function in key. If a function is not in the
+            dictionary, no declaration will be used.
+        call : dict
+            for every item (f, value), if the value is True, a call will be
+            done to the function f.
+        """
+        self._check_compiler()
+        body = []
+        if decl:
+            for f, v in decl.items():
+                if v:
+                    body.append("int %s (void);" % f)
+
+        # Handle MS intrinsics. See check_func for more info.
+        body.append("#ifdef _MSC_VER")
+        for func in funcs:
+            body.append("#pragma function(%s)" % func)
+        body.append("#endif")
+
+        body.append("int main (void) {")
+        if call:
+            for f in funcs:
+                if f in call and call[f]:
+                    if not (call_args and f in call_args and call_args[f]):
+                        args = ''
+                    else:
+                        args = call_args[f]
+                    body.append("  %s(%s);" % (f, args))
+                else:
+                    body.append("  %s;" % f)
+        else:
+            for f in funcs:
+                body.append("  %s;" % f)
+        body.append("  return 0;")
+        body.append("}")
+        body = '\n'.join(body) + "\n"
+
+        return self.try_link(body, headers, include_dirs,
+                             libraries, library_dirs)
+
+    def check_inline(self):
+        """Return the inline keyword recognized by the compiler, empty string
+        otherwise."""
+        return check_inline(self)
+
+    def check_restrict(self):
+        """Return the restrict keyword recognized by the compiler, empty string
+        otherwise."""
+        return check_restrict(self)
+
+    def check_compiler_gcc(self):
+        """Return True if the C compiler is gcc"""
+        return check_compiler_gcc(self)
+
+    def check_gcc_function_attribute(self, attribute, name):
+        return check_gcc_function_attribute(self, attribute, name)
+
+    def check_gcc_function_attribute_with_intrinsics(self, attribute, name,
+                                                     code, include):
+        return check_gcc_function_attribute_with_intrinsics(self, attribute,
+                                                            name, code, include)
+
+    def check_gcc_variable_attribute(self, attribute):
+        return check_gcc_variable_attribute(self, attribute)
+
+    def check_gcc_version_at_least(self, major, minor=0, patchlevel=0):
+        """Return True if the GCC version is greater than or equal to the
+        specified version."""
+        return check_gcc_version_at_least(self, major, minor, patchlevel)
+
+    def get_output(self, body, headers=None, include_dirs=None,
+                   libraries=None, library_dirs=None,
+                   lang="c", use_tee=None):
+        """Try to compile, link to an executable, and run a program
+        built from 'body' and 'headers'. Returns the exit status code
+        of the program and its output.
+        """
+        # 2008-11-16, RemoveMe
+        warnings.warn("\n+++++++++++++++++++++++++++++++++++++++++++++++++\n"
+                      "Usage of get_output is deprecated: please do not \n"
+                      "use it anymore, and avoid configuration checks \n"
+                      "involving running executable on the target machine.\n"
+                      "+++++++++++++++++++++++++++++++++++++++++++++++++\n",
+                      DeprecationWarning, stacklevel=2)
+        self._check_compiler()
+        exitcode, output = 255, ''
+        try:
+            grabber = GrabStdout()
+            try:
+                src, obj, exe = self._link(body, headers, include_dirs,
+                                           libraries, library_dirs, lang)
+                grabber.restore()
+            except Exception:
+                output = grabber.data
+                grabber.restore()
+                raise
+            exe = os.path.join('.', exe)
+            try:
+                # specify cwd arg for consistency with
+                # historic usage pattern of exec_command()
+                # also, note that exe appears to be a string,
+                # which exec_command() handled, but we now
+                # use a list for check_output() -- this assumes
+                # that exe is always a single command
+                output = subprocess.check_output([exe], cwd='.')
+            except subprocess.CalledProcessError as exc:
+                exitstatus = exc.returncode
+                output = ''
+            except OSError:
+                # preserve the EnvironmentError exit status
+                # used historically in exec_command()
+                exitstatus = 127
+                output = ''
+            else:
+                output = filepath_from_subprocess_output(output)
+            if hasattr(os, 'WEXITSTATUS'):
+                exitcode = os.WEXITSTATUS(exitstatus)
+                if os.WIFSIGNALED(exitstatus):
+                    sig = os.WTERMSIG(exitstatus)
+                    log.error('subprocess exited with signal %d' % (sig,))
+                    if sig == signal.SIGINT:
+                        # control-C
+                        raise KeyboardInterrupt
+            else:
+                exitcode = exitstatus
+            log.info("success!")
+        except (CompileError, LinkError):
+            log.info("failure.")
+        self._clean()
+        return exitcode, output
+
+class GrabStdout:
+
+    def __init__(self):
+        self.sys_stdout = sys.stdout
+        self.data = ''
+        sys.stdout = self
+
+    def write (self, data):
+        self.sys_stdout.write(data)
+        self.data += data
+
+    def flush (self):
+        self.sys_stdout.flush()
+
+    def restore(self):
+        sys.stdout = self.sys_stdout
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config_compiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config_compiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca4099886d8c67e4c3a02454a746c2e388dfdbf1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/config_compiler.py
@@ -0,0 +1,126 @@
+from distutils.core import Command
+from numpy.distutils import log
+
+#XXX: Linker flags
+
+def show_fortran_compilers(_cache=None):
+    # Using cache to prevent infinite recursion.
+    if _cache:
+        return
+    elif _cache is None:
+        _cache = []
+    _cache.append(1)
+    from numpy.distutils.fcompiler import show_fcompilers
+    import distutils.core
+    dist = distutils.core._setup_distribution
+    show_fcompilers(dist)
+
+class config_fc(Command):
+    """ Distutils command to hold user specified options
+    to Fortran compilers.
+
+    config_fc command is used by the FCompiler.customize() method.
+    """
+
+    description = "specify Fortran 77/Fortran 90 compiler information"
+
+    user_options = [
+        ('fcompiler=', None, "specify Fortran compiler type"),
+        ('f77exec=', None, "specify F77 compiler command"),
+        ('f90exec=', None, "specify F90 compiler command"),
+        ('f77flags=', None, "specify F77 compiler flags"),
+        ('f90flags=', None, "specify F90 compiler flags"),
+        ('opt=', None, "specify optimization flags"),
+        ('arch=', None, "specify architecture specific optimization flags"),
+        ('debug', 'g', "compile with debugging information"),
+        ('noopt', None, "compile without optimization"),
+        ('noarch', None, "compile without arch-dependent optimization"),
+        ]
+
+    help_options = [
+        ('help-fcompiler', None, "list available Fortran compilers",
+         show_fortran_compilers),
+        ]
+
+    boolean_options = ['debug', 'noopt', 'noarch']
+
+    def initialize_options(self):
+        self.fcompiler = None
+        self.f77exec = None
+        self.f90exec = None
+        self.f77flags = None
+        self.f90flags = None
+        self.opt = None
+        self.arch = None
+        self.debug = None
+        self.noopt = None
+        self.noarch = None
+
+    def finalize_options(self):
+        log.info('unifying config_fc, config, build_clib, build_ext, build commands --fcompiler options')
+        build_clib = self.get_finalized_command('build_clib')
+        build_ext = self.get_finalized_command('build_ext')
+        config = self.get_finalized_command('config')
+        build = self.get_finalized_command('build')
+        cmd_list = [self, config, build_clib, build_ext, build]
+        for a in ['fcompiler']:
+            l = []
+            for c in cmd_list:
+                v = getattr(c, a)
+                if v is not None:
+                    if not isinstance(v, str): v = v.compiler_type
+                    if v not in l: l.append(v)
+            if not l: v1 = None
+            else: v1 = l[0]
+            if len(l)>1:
+                log.warn('  commands have different --%s options: %s'\
+                         ', using first in list as default' % (a, l))
+            if v1:
+                for c in cmd_list:
+                    if getattr(c, a) is None: setattr(c, a, v1)
+
+    def run(self):
+        # Do nothing.
+        return
+
+class config_cc(Command):
+    """ Distutils command to hold user specified options
+    to C/C++ compilers.
+    """
+
+    description = "specify C/C++ compiler information"
+
+    user_options = [
+        ('compiler=', None, "specify C/C++ compiler type"),
+        ]
+
+    def initialize_options(self):
+        self.compiler = None
+
+    def finalize_options(self):
+        log.info('unifying config_cc, config, build_clib, build_ext, build commands --compiler options')
+        build_clib = self.get_finalized_command('build_clib')
+        build_ext = self.get_finalized_command('build_ext')
+        config = self.get_finalized_command('config')
+        build = self.get_finalized_command('build')
+        cmd_list = [self, config, build_clib, build_ext, build]
+        for a in ['compiler']:
+            l = []
+            for c in cmd_list:
+                v = getattr(c, a)
+                if v is not None:
+                    if not isinstance(v, str): v = v.compiler_type
+                    if v not in l: l.append(v)
+            if not l: v1 = None
+            else: v1 = l[0]
+            if len(l)>1:
+                log.warn('  commands have different --%s options: %s'\
+                         ', using first in list as default' % (a, l))
+            if v1:
+                for c in cmd_list:
+                    if getattr(c, a) is None: setattr(c, a, v1)
+        return
+
+    def run(self):
+        # Do nothing.
+        return
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/develop.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/develop.py
new file mode 100644
index 0000000000000000000000000000000000000000..af24baf2e7e1e7ff715788cb5acfcbefaafebd48
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/develop.py
@@ -0,0 +1,15 @@
+""" Override the develop command from setuptools so we can ensure that our
+generated files (from build_src or build_scripts) are properly converted to real
+files with filenames.
+
+"""
+from setuptools.command.develop import develop as old_develop
+
+class develop(old_develop):
+    __doc__ = old_develop.__doc__
+    def install_for_development(self):
+        # Build sources in-place, too.
+        self.reinitialize_command('build_src', inplace=1)
+        # Make sure scripts are built.
+        self.run_command('build_scripts')
+        old_develop.install_for_development(self)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/egg_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/egg_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..14c62b4d1b905ec71d8ab97dfdb3f18638d707f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/egg_info.py
@@ -0,0 +1,25 @@
+import sys
+
+from setuptools.command.egg_info import egg_info as _egg_info
+
+class egg_info(_egg_info):
+    def run(self):
+        if 'sdist' in sys.argv:
+            import warnings
+            import textwrap
+            msg = textwrap.dedent("""
+                `build_src` is being run, this may lead to missing
+                files in your sdist!  You want to use distutils.sdist
+                instead of the setuptools version:
+
+                    from distutils.command.sdist import sdist
+                    cmdclass={'sdist': sdist}"
+
+                See numpy's setup.py or gh-7131 for details.""")
+            warnings.warn(msg, UserWarning, stacklevel=2)
+
+        # We need to ensure that build_src has been executed in order to give
+        # setuptools' egg_info command real filenames instead of functions which
+        # generate files.
+        self.run_command("build_src")
+        _egg_info.run(self)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install.py
new file mode 100644
index 0000000000000000000000000000000000000000..efa9b4740fc4b02e9419b7e5d7ccc7cec61cd781
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install.py
@@ -0,0 +1,79 @@
+import sys
+if 'setuptools' in sys.modules:
+    import setuptools.command.install as old_install_mod
+    have_setuptools = True
+else:
+    import distutils.command.install as old_install_mod
+    have_setuptools = False
+from distutils.file_util import write_file
+
+old_install = old_install_mod.install
+
+class install(old_install):
+
+    # Always run install_clib - the command is cheap, so no need to bypass it;
+    # but it's not run by setuptools -- so it's run again in install_data
+    sub_commands = old_install.sub_commands + [
+        ('install_clib', lambda x: True)
+    ]
+
+    def finalize_options (self):
+        old_install.finalize_options(self)
+        self.install_lib = self.install_libbase
+
+    def setuptools_run(self):
+        """ The setuptools version of the .run() method.
+
+        We must pull in the entire code so we can override the level used in the
+        _getframe() call since we wrap this call by one more level.
+        """
+        from distutils.command.install import install as distutils_install
+
+        # Explicit request for old-style install?  Just do it
+        if self.old_and_unmanageable or self.single_version_externally_managed:
+            return distutils_install.run(self)
+
+        # Attempt to detect whether we were called from setup() or by another
+        # command.  If we were called by setup(), our caller will be the
+        # 'run_command' method in 'distutils.dist', and *its* caller will be
+        # the 'run_commands' method.  If we were called any other way, our
+        # immediate caller *might* be 'run_command', but it won't have been
+        # called by 'run_commands'.  This is slightly kludgy, but seems to
+        # work.
+        #
+        caller = sys._getframe(3)
+        caller_module = caller.f_globals.get('__name__', '')
+        caller_name = caller.f_code.co_name
+
+        if caller_module != 'distutils.dist' or caller_name!='run_commands':
+            # We weren't called from the command line or setup(), so we
+            # should run in backward-compatibility mode to support bdist_*
+            # commands.
+            distutils_install.run(self)
+        else:
+            self.do_egg_install()
+
+    def run(self):
+        if not have_setuptools:
+            r = old_install.run(self)
+        else:
+            r = self.setuptools_run()
+        if self.record:
+            # bdist_rpm fails when INSTALLED_FILES contains
+            # paths with spaces. Such paths must be enclosed
+            # with double-quotes.
+            with open(self.record) as f:
+                lines = []
+                need_rewrite = False
+                for l in f:
+                    l = l.rstrip()
+                    if ' ' in l:
+                        need_rewrite = True
+                        l = '"%s"' % (l)
+                    lines.append(l)
+            if need_rewrite:
+                self.execute(write_file,
+                             (self.record, lines),
+                             "re-writing list of installed files to '%s'" %
+                             self.record)
+        return r
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_clib.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2e5594c3c2e87434f7db1e0ffb7b8aa12f7fce
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_clib.py
@@ -0,0 +1,40 @@
+import os
+from distutils.core import Command
+from distutils.ccompiler import new_compiler
+from numpy.distutils.misc_util import get_cmd
+
+class install_clib(Command):
+    description = "Command to install installable C libraries"
+
+    user_options = []
+
+    def initialize_options(self):
+        self.install_dir = None
+        self.outfiles = []
+
+    def finalize_options(self):
+        self.set_undefined_options('install', ('install_lib', 'install_dir'))
+
+    def run (self):
+        build_clib_cmd = get_cmd("build_clib")
+        if not build_clib_cmd.build_clib:
+            # can happen if the user specified `--skip-build`
+            build_clib_cmd.finalize_options()
+        build_dir = build_clib_cmd.build_clib
+
+        # We need the compiler to get the library name -> filename association
+        if not build_clib_cmd.compiler:
+            compiler = new_compiler(compiler=None)
+            compiler.customize(self.distribution)
+        else:
+            compiler = build_clib_cmd.compiler
+
+        for l in self.distribution.installed_libraries:
+            target_dir = os.path.join(self.install_dir, l.target_dir)
+            name = compiler.library_filename(l.name)
+            source = os.path.join(build_dir, name)
+            self.mkpath(target_dir)
+            self.outfiles.append(self.copy_file(source, target_dir)[0])
+
+    def get_outputs(self):
+        return self.outfiles
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_data.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a2e68ae192ae95422ae47136054d394bb45d2f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_data.py
@@ -0,0 +1,24 @@
+import sys
+have_setuptools = ('setuptools' in sys.modules)
+
+from distutils.command.install_data import install_data as old_install_data
+
+#data installer with improved intelligence over distutils
+#data files are copied into the project directory instead
+#of willy-nilly
+class install_data (old_install_data):
+
+    def run(self):
+        old_install_data.run(self)
+
+        if have_setuptools:
+            # Run install_clib again, since setuptools does not run sub-commands
+            # of install automatically
+            self.run_command('install_clib')
+
+    def finalize_options (self):
+        self.set_undefined_options('install',
+                                   ('install_lib', 'install_dir'),
+                                   ('root', 'root'),
+                                   ('force', 'force'),
+                                  )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_headers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..91eba6f17c29e56dcfc8048d3de5544c77ad51f9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/install_headers.py
@@ -0,0 +1,25 @@
+import os
+from distutils.command.install_headers import install_headers as old_install_headers
+
+class install_headers (old_install_headers):
+
+    def run (self):
+        headers = self.distribution.headers
+        if not headers:
+            return
+
+        prefix = os.path.dirname(self.install_dir)
+        for header in headers:
+            if isinstance(header, tuple):
+                # Kind of a hack, but I don't know where else to change this...
+                if header[0] == 'numpy._core':
+                    header = ('numpy', header[1])
+                    if os.path.splitext(header[1])[1] == '.inc':
+                        continue
+                d = os.path.join(*([prefix]+header[0].split('.')))
+                header = header[1]
+            else:
+                d = self.install_dir
+            self.mkpath(d)
+            (out, _) = self.copy_file(header, d)
+            self.outfiles.append(out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/sdist.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..e34193883dea739b09792a86bfc3d4c03b42cb5a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/command/sdist.py
@@ -0,0 +1,27 @@
+import sys
+if 'setuptools' in sys.modules:
+    from setuptools.command.sdist import sdist as old_sdist
+else:
+    from distutils.command.sdist import sdist as old_sdist
+
+from numpy.distutils.misc_util import get_data_files
+
+class sdist(old_sdist):
+
+    def add_defaults (self):
+        old_sdist.add_defaults(self)
+
+        dist = self.distribution
+
+        if dist.has_data_files():
+            for data in dist.data_files:
+                self.filelist.extend(get_data_files(data))
+
+        if dist.has_headers():
+            headers = []
+            for h in dist.headers:
+                if isinstance(h, str): headers.append(h)
+                else: headers.append(h[1])
+            self.filelist.extend(headers)
+
+        return
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/conv_template.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/conv_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8933d1d42865f745bb985f7f9068a96985997f7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/conv_template.py
@@ -0,0 +1,329 @@
+#!/usr/bin/env python3
+"""
+takes templated file .xxx.src and produces .xxx file  where .xxx is
+.i or .c or .h, using the following template rules
+
+/**begin repeat  -- on a line by itself marks the start of a repeated code
+                    segment
+/**end repeat**/ -- on a line by itself marks it's end
+
+After the /**begin repeat and before the */, all the named templates are placed
+these should all have the same number of replacements
+
+Repeat blocks can be nested, with each nested block labeled with its depth,
+i.e.
+/**begin repeat1
+ *....
+ */
+/**end repeat1**/
+
+When using nested loops, you can optionally exclude particular
+combinations of the variables using (inside the comment portion of the inner loop):
+
+ :exclude: var1=value1, var2=value2, ...
+
+This will exclude the pattern where var1 is value1 and var2 is value2 when
+the result is being generated.
+
+
+In the main body each replace will use one entry from the list of named replacements
+
+ Note that all #..# forms in a block must have the same number of
+   comma-separated entries.
+
+Example:
+
+    An input file containing
+
+        /**begin repeat
+         * #a = 1,2,3#
+         * #b = 1,2,3#
+         */
+
+        /**begin repeat1
+         * #c = ted, jim#
+         */
+        @a@, @b@, @c@
+        /**end repeat1**/
+
+        /**end repeat**/
+
+    produces
+
+        line 1 "template.c.src"
+
+        /*
+         *********************************************************************
+         **       This file was autogenerated from a template  DO NOT EDIT!!**
+         **       Changes should be made to the original source (.src) file **
+         *********************************************************************
+         */
+
+        #line 9
+        1, 1, ted
+
+        #line 9
+        1, 1, jim
+
+        #line 9
+        2, 2, ted
+
+        #line 9
+        2, 2, jim
+
+        #line 9
+        3, 3, ted
+
+        #line 9
+        3, 3, jim
+
+"""
+
+__all__ = ['process_str', 'process_file']
+
+import os
+import sys
+import re
+
+# names for replacement that are already global.
+global_names = {}
+
+# header placed at the front of head processed file
+header =\
+"""
+/*
+ *****************************************************************************
+ **       This file was autogenerated from a template  DO NOT EDIT!!!!      **
+ **       Changes should be made to the original source (.src) file         **
+ *****************************************************************************
+ */
+
+"""
+# Parse string for repeat loops
+def parse_structure(astr, level):
+    """
+    The returned line number is from the beginning of the string, starting
+    at zero. Returns an empty list if no loops found.
+
+    """
+    if level == 0 :
+        loopbeg = "/**begin repeat"
+        loopend = "/**end repeat**/"
+    else :
+        loopbeg = "/**begin repeat%d" % level
+        loopend = "/**end repeat%d**/" % level
+
+    ind = 0
+    line = 0
+    spanlist = []
+    while True:
+        start = astr.find(loopbeg, ind)
+        if start == -1:
+            break
+        start2 = astr.find("*/", start)
+        start2 = astr.find("\n", start2)
+        fini1 = astr.find(loopend, start2)
+        fini2 = astr.find("\n", fini1)
+        line += astr.count("\n", ind, start2+1)
+        spanlist.append((start, start2+1, fini1, fini2+1, line))
+        line += astr.count("\n", start2+1, fini2)
+        ind = fini2
+    spanlist.sort()
+    return spanlist
+
+
+def paren_repl(obj):
+    torep = obj.group(1)
+    numrep = obj.group(2)
+    return ','.join([torep]*int(numrep))
+
+parenrep = re.compile(r"\(([^)]*)\)\*(\d+)")
+plainrep = re.compile(r"([^*]+)\*(\d+)")
+def parse_values(astr):
+    # replaces all occurrences of '(a,b,c)*4' in astr
+    # with 'a,b,c,a,b,c,a,b,c,a,b,c'. Empty braces generate
+    # empty values, i.e., ()*4 yields ',,,'. The result is
+    # split at ',' and a list of values returned.
+    astr = parenrep.sub(paren_repl, astr)
+    # replaces occurrences of xxx*3 with xxx, xxx, xxx
+    astr = ','.join([plainrep.sub(paren_repl, x.strip())
+                     for x in astr.split(',')])
+    return astr.split(',')
+
+
+stripast = re.compile(r"\n\s*\*?")
+named_re = re.compile(r"#\s*(\w*)\s*=([^#]*)#")
+exclude_vars_re = re.compile(r"(\w*)=(\w*)")
+exclude_re = re.compile(":exclude:")
+def parse_loop_header(loophead) :
+    """Find all named replacements in the header
+
+    Returns a list of dictionaries, one for each loop iteration,
+    where each key is a name to be substituted and the corresponding
+    value is the replacement string.
+
+    Also return a list of exclusions.  The exclusions are dictionaries
+     of key value pairs. There can be more than one exclusion.
+     [{'var1':'value1', 'var2', 'value2'[,...]}, ...]
+
+    """
+    # Strip out '\n' and leading '*', if any, in continuation lines.
+    # This should not effect code previous to this change as
+    # continuation lines were not allowed.
+    loophead = stripast.sub("", loophead)
+    # parse out the names and lists of values
+    names = []
+    reps = named_re.findall(loophead)
+    nsub = None
+    for rep in reps:
+        name = rep[0]
+        vals = parse_values(rep[1])
+        size = len(vals)
+        if nsub is None :
+            nsub = size
+        elif nsub != size :
+            msg = "Mismatch in number of values, %d != %d\n%s = %s"
+            raise ValueError(msg % (nsub, size, name, vals))
+        names.append((name, vals))
+
+
+    # Find any exclude variables
+    excludes = []
+
+    for obj in exclude_re.finditer(loophead):
+        span = obj.span()
+        # find next newline
+        endline = loophead.find('\n', span[1])
+        substr = loophead[span[1]:endline]
+        ex_names = exclude_vars_re.findall(substr)
+        excludes.append(dict(ex_names))
+
+    # generate list of dictionaries, one for each template iteration
+    dlist = []
+    if nsub is None :
+        raise ValueError("No substitution variables found")
+    for i in range(nsub):
+        tmp = {name: vals[i] for name, vals in names}
+        dlist.append(tmp)
+    return dlist
+
+replace_re = re.compile(r"@(\w+)@")
+def parse_string(astr, env, level, line) :
+    lineno = "#line %d\n" % line
+
+    # local function for string replacement, uses env
+    def replace(match):
+        name = match.group(1)
+        try :
+            val = env[name]
+        except KeyError:
+            msg = 'line %d: no definition of key "%s"'%(line, name)
+            raise ValueError(msg) from None
+        return val
+
+    code = [lineno]
+    struct = parse_structure(astr, level)
+    if struct :
+        # recurse over inner loops
+        oldend = 0
+        newlevel = level + 1
+        for sub in struct:
+            pref = astr[oldend:sub[0]]
+            head = astr[sub[0]:sub[1]]
+            text = astr[sub[1]:sub[2]]
+            oldend = sub[3]
+            newline = line + sub[4]
+            code.append(replace_re.sub(replace, pref))
+            try :
+                envlist = parse_loop_header(head)
+            except ValueError as e:
+                msg = "line %d: %s" % (newline, e)
+                raise ValueError(msg)
+            for newenv in envlist :
+                newenv.update(env)
+                newcode = parse_string(text, newenv, newlevel, newline)
+                code.extend(newcode)
+        suff = astr[oldend:]
+        code.append(replace_re.sub(replace, suff))
+    else :
+        # replace keys
+        code.append(replace_re.sub(replace, astr))
+    code.append('\n')
+    return ''.join(code)
+
+def process_str(astr):
+    code = [header]
+    code.extend(parse_string(astr, global_names, 0, 1))
+    return ''.join(code)
+
+
+include_src_re = re.compile(r"(\n|\A)#include\s*['\"]"
+                            r"(?P[\w\d./\\]+[.]src)['\"]", re.I)
+
+def resolve_includes(source):
+    d = os.path.dirname(source)
+    with open(source) as fid:
+        lines = []
+        for line in fid:
+            m = include_src_re.match(line)
+            if m:
+                fn = m.group('name')
+                if not os.path.isabs(fn):
+                    fn = os.path.join(d, fn)
+                if os.path.isfile(fn):
+                    lines.extend(resolve_includes(fn))
+                else:
+                    lines.append(line)
+            else:
+                lines.append(line)
+    return lines
+
+def process_file(source):
+    lines = resolve_includes(source)
+    sourcefile = os.path.normcase(source).replace("\\", "\\\\")
+    try:
+        code = process_str(''.join(lines))
+    except ValueError as e:
+        raise ValueError('In "%s" loop at %s' % (sourcefile, e)) from None
+    return '#line 1 "%s"\n%s' % (sourcefile, code)
+
+
+def unique_key(adict):
+    # this obtains a unique key given a dictionary
+    # currently it works by appending together n of the letters of the
+    #   current keys and increasing n until a unique key is found
+    # -- not particularly quick
+    allkeys = list(adict.keys())
+    done = False
+    n = 1
+    while not done:
+        newkey = "".join([x[:n] for x in allkeys])
+        if newkey in allkeys:
+            n += 1
+        else:
+            done = True
+    return newkey
+
+
+def main():
+    try:
+        file = sys.argv[1]
+    except IndexError:
+        fid = sys.stdin
+        outfile = sys.stdout
+    else:
+        fid = open(file, 'r')
+        (base, ext) = os.path.splitext(file)
+        newname = base
+        outfile = open(newname, 'w')
+
+    allstr = fid.read()
+    try:
+        writestr = process_str(allstr)
+    except ValueError as e:
+        raise ValueError("In %s loop at %s" % (file, e)) from None
+
+    outfile.write(writestr)
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4a14e59901f90afe1fc65dfb9679bd307e8f66e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/core.py
@@ -0,0 +1,215 @@
+import sys
+from distutils.core import Distribution
+
+if 'setuptools' in sys.modules:
+    have_setuptools = True
+    from setuptools import setup as old_setup
+    # easy_install imports math, it may be picked up from cwd
+    from setuptools.command import easy_install
+    try:
+        # very old versions of setuptools don't have this
+        from setuptools.command import bdist_egg
+    except ImportError:
+        have_setuptools = False
+else:
+    from distutils.core import setup as old_setup
+    have_setuptools = False
+
+import warnings
+import distutils.core
+import distutils.dist
+
+from numpy.distutils.extension import Extension  # noqa: F401
+from numpy.distutils.numpy_distribution import NumpyDistribution
+from numpy.distutils.command import config, config_compiler, \
+     build, build_py, build_ext, build_clib, build_src, build_scripts, \
+     sdist, install_data, install_headers, install, bdist_rpm, \
+     install_clib
+from numpy.distutils.misc_util import is_sequence, is_string
+
+numpy_cmdclass = {'build':            build.build,
+                  'build_src':        build_src.build_src,
+                  'build_scripts':    build_scripts.build_scripts,
+                  'config_cc':        config_compiler.config_cc,
+                  'config_fc':        config_compiler.config_fc,
+                  'config':           config.config,
+                  'build_ext':        build_ext.build_ext,
+                  'build_py':         build_py.build_py,
+                  'build_clib':       build_clib.build_clib,
+                  'sdist':            sdist.sdist,
+                  'install_data':     install_data.install_data,
+                  'install_headers':  install_headers.install_headers,
+                  'install_clib':     install_clib.install_clib,
+                  'install':          install.install,
+                  'bdist_rpm':        bdist_rpm.bdist_rpm,
+                  }
+if have_setuptools:
+    # Use our own versions of develop and egg_info to ensure that build_src is
+    # handled appropriately.
+    from numpy.distutils.command import develop, egg_info
+    numpy_cmdclass['bdist_egg'] = bdist_egg.bdist_egg
+    numpy_cmdclass['develop'] = develop.develop
+    numpy_cmdclass['easy_install'] = easy_install.easy_install
+    numpy_cmdclass['egg_info'] = egg_info.egg_info
+
+def _dict_append(d, **kws):
+    for k, v in kws.items():
+        if k not in d:
+            d[k] = v
+            continue
+        dv = d[k]
+        if isinstance(dv, tuple):
+            d[k] = dv + tuple(v)
+        elif isinstance(dv, list):
+            d[k] = dv + list(v)
+        elif isinstance(dv, dict):
+            _dict_append(dv, **v)
+        elif is_string(dv):
+            d[k] = dv + v
+        else:
+            raise TypeError(repr(type(dv)))
+
+def _command_line_ok(_cache=None):
+    """ Return True if command line does not contain any
+    help or display requests.
+    """
+    if _cache:
+        return _cache[0]
+    elif _cache is None:
+        _cache = []
+    ok = True
+    display_opts = ['--'+n for n in Distribution.display_option_names]
+    for o in Distribution.display_options:
+        if o[1]:
+            display_opts.append('-'+o[1])
+    for arg in sys.argv:
+        if arg.startswith('--help') or arg=='-h' or arg in display_opts:
+            ok = False
+            break
+    _cache.append(ok)
+    return ok
+
+def get_distribution(always=False):
+    dist = distutils.core._setup_distribution
+    # XXX Hack to get numpy installable with easy_install.
+    # The problem is easy_install runs it's own setup(), which
+    # sets up distutils.core._setup_distribution. However,
+    # when our setup() runs, that gets overwritten and lost.
+    # We can't use isinstance, as the DistributionWithoutHelpCommands
+    # class is local to a function in setuptools.command.easy_install
+    if dist is not None and \
+            'DistributionWithoutHelpCommands' in repr(dist):
+        dist = None
+    if always and dist is None:
+        dist = NumpyDistribution()
+    return dist
+
+def setup(**attr):
+
+    cmdclass = numpy_cmdclass.copy()
+
+    new_attr = attr.copy()
+    if 'cmdclass' in new_attr:
+        cmdclass.update(new_attr['cmdclass'])
+    new_attr['cmdclass'] = cmdclass
+
+    if 'configuration' in new_attr:
+        # To avoid calling configuration if there are any errors
+        # or help request in command in the line.
+        configuration = new_attr.pop('configuration')
+
+        old_dist = distutils.core._setup_distribution
+        old_stop = distutils.core._setup_stop_after
+        distutils.core._setup_distribution = None
+        distutils.core._setup_stop_after = "commandline"
+        try:
+            dist = setup(**new_attr)
+        finally:
+            distutils.core._setup_distribution = old_dist
+            distutils.core._setup_stop_after = old_stop
+        if dist.help or not _command_line_ok():
+            # probably displayed help, skip running any commands
+            return dist
+
+        # create setup dictionary and append to new_attr
+        config = configuration()
+        if hasattr(config, 'todict'):
+            config = config.todict()
+        _dict_append(new_attr, **config)
+
+    # Move extension source libraries to libraries
+    libraries = []
+    for ext in new_attr.get('ext_modules', []):
+        new_libraries = []
+        for item in ext.libraries:
+            if is_sequence(item):
+                lib_name, build_info = item
+                _check_append_ext_library(libraries, lib_name, build_info)
+                new_libraries.append(lib_name)
+            elif is_string(item):
+                new_libraries.append(item)
+            else:
+                raise TypeError("invalid description of extension module "
+                                "library %r" % (item,))
+        ext.libraries = new_libraries
+    if libraries:
+        if 'libraries' not in new_attr:
+            new_attr['libraries'] = []
+        for item in libraries:
+            _check_append_library(new_attr['libraries'], item)
+
+    # sources in ext_modules or libraries may contain header files
+    if ('ext_modules' in new_attr or 'libraries' in new_attr) \
+       and 'headers' not in new_attr:
+        new_attr['headers'] = []
+
+    # Use our custom NumpyDistribution class instead of distutils' one
+    new_attr['distclass'] = NumpyDistribution
+
+    return old_setup(**new_attr)
+
+def _check_append_library(libraries, item):
+    for libitem in libraries:
+        if is_sequence(libitem):
+            if is_sequence(item):
+                if item[0]==libitem[0]:
+                    if item[1] is libitem[1]:
+                        return
+                    warnings.warn("[0] libraries list contains %r with"
+                                  " different build_info" % (item[0],),
+                                  stacklevel=2)
+                    break
+            else:
+                if item==libitem[0]:
+                    warnings.warn("[1] libraries list contains %r with"
+                                  " no build_info" % (item[0],),
+                                  stacklevel=2)
+                    break
+        else:
+            if is_sequence(item):
+                if item[0]==libitem:
+                    warnings.warn("[2] libraries list contains %r with"
+                                  " no build_info" % (item[0],),
+                                  stacklevel=2)
+                    break
+            else:
+                if item==libitem:
+                    return
+    libraries.append(item)
+
+def _check_append_ext_library(libraries, lib_name, build_info):
+    for item in libraries:
+        if is_sequence(item):
+            if item[0]==lib_name:
+                if item[1] is build_info:
+                    return
+                warnings.warn("[3] libraries list contains %r with"
+                              " different build_info" % (lib_name,),
+                              stacklevel=2)
+                break
+        elif item==lib_name:
+            warnings.warn("[4] libraries list contains %r with"
+                          " no build_info" % (lib_name,),
+                          stacklevel=2)
+            break
+    libraries.append((lib_name, build_info))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/cpuinfo.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/cpuinfo.py
new file mode 100644
index 0000000000000000000000000000000000000000..77620210981dd1e97d87a078344b3735c3cc6e1d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/cpuinfo.py
@@ -0,0 +1,683 @@
+#!/usr/bin/env python3
+"""
+cpuinfo
+
+Copyright 2002 Pearu Peterson all rights reserved,
+Pearu Peterson 
+Permission to use, modify, and distribute this software is given under the
+terms of the NumPy (BSD style) license.  See LICENSE.txt that came with
+this distribution for specifics.
+
+NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
+Pearu Peterson
+
+"""
+__all__ = ['cpu']
+
+import os
+import platform
+import re
+import sys
+import types
+import warnings
+
+from subprocess import getstatusoutput
+
+
+def getoutput(cmd, successful_status=(0,), stacklevel=1):
+    try:
+        status, output = getstatusoutput(cmd)
+    except OSError as e:
+        warnings.warn(str(e), UserWarning, stacklevel=stacklevel)
+        return False, ""
+    if os.WIFEXITED(status) and os.WEXITSTATUS(status) in successful_status:
+        return True, output
+    return False, output
+
+def command_info(successful_status=(0,), stacklevel=1, **kw):
+    info = {}
+    for key in kw:
+        ok, output = getoutput(kw[key], successful_status=successful_status,
+                               stacklevel=stacklevel+1)
+        if ok:
+            info[key] = output.strip()
+    return info
+
+def command_by_line(cmd, successful_status=(0,), stacklevel=1):
+    ok, output = getoutput(cmd, successful_status=successful_status,
+                           stacklevel=stacklevel+1)
+    if not ok:
+        return
+    for line in output.splitlines():
+        yield line.strip()
+
+def key_value_from_command(cmd, sep, successful_status=(0,),
+                           stacklevel=1):
+    d = {}
+    for line in command_by_line(cmd, successful_status=successful_status,
+                                stacklevel=stacklevel+1):
+        l = [s.strip() for s in line.split(sep, 1)]
+        if len(l) == 2:
+            d[l[0]] = l[1]
+    return d
+
+class CPUInfoBase:
+    """Holds CPU information and provides methods for requiring
+    the availability of various CPU features.
+    """
+
+    def _try_call(self, func):
+        try:
+            return func()
+        except Exception:
+            pass
+
+    def __getattr__(self, name):
+        if not name.startswith('_'):
+            if hasattr(self, '_'+name):
+                attr = getattr(self, '_'+name)
+                if isinstance(attr, types.MethodType):
+                    return lambda func=self._try_call,attr=attr : func(attr)
+            else:
+                return lambda : None
+        raise AttributeError(name)
+
+    def _getNCPUs(self):
+        return 1
+
+    def __get_nbits(self):
+        abits = platform.architecture()[0]
+        nbits = re.compile(r'(\d+)bit').search(abits).group(1)
+        return nbits
+
+    def _is_32bit(self):
+        return self.__get_nbits() == '32'
+
+    def _is_64bit(self):
+        return self.__get_nbits() == '64'
+
+class LinuxCPUInfo(CPUInfoBase):
+
+    info = None
+
+    def __init__(self):
+        if self.info is not None:
+            return
+        info = [ {} ]
+        ok, output = getoutput('uname -m')
+        if ok:
+            info[0]['uname_m'] = output.strip()
+        try:
+            fo = open('/proc/cpuinfo')
+        except OSError as e:
+            warnings.warn(str(e), UserWarning, stacklevel=2)
+        else:
+            for line in fo:
+                name_value = [s.strip() for s in line.split(':', 1)]
+                if len(name_value) != 2:
+                    continue
+                name, value = name_value
+                if not info or name in info[-1]: # next processor
+                    info.append({})
+                info[-1][name] = value
+            fo.close()
+        self.__class__.info = info
+
+    def _not_impl(self): pass
+
+    # Athlon
+
+    def _is_AMD(self):
+        return self.info[0]['vendor_id']=='AuthenticAMD'
+
+    def _is_AthlonK6_2(self):
+        return self._is_AMD() and self.info[0]['model'] == '2'
+
+    def _is_AthlonK6_3(self):
+        return self._is_AMD() and self.info[0]['model'] == '3'
+
+    def _is_AthlonK6(self):
+        return re.match(r'.*?AMD-K6', self.info[0]['model name']) is not None
+
+    def _is_AthlonK7(self):
+        return re.match(r'.*?AMD-K7', self.info[0]['model name']) is not None
+
+    def _is_AthlonMP(self):
+        return re.match(r'.*?Athlon\(tm\) MP\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_AMD64(self):
+        return self.is_AMD() and self.info[0]['family'] == '15'
+
+    def _is_Athlon64(self):
+        return re.match(r'.*?Athlon\(tm\) 64\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_AthlonHX(self):
+        return re.match(r'.*?Athlon HX\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_Opteron(self):
+        return re.match(r'.*?Opteron\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_Hammer(self):
+        return re.match(r'.*?Hammer\b',
+                        self.info[0]['model name']) is not None
+
+    # Alpha
+
+    def _is_Alpha(self):
+        return self.info[0]['cpu']=='Alpha'
+
+    def _is_EV4(self):
+        return self.is_Alpha() and self.info[0]['cpu model'] == 'EV4'
+
+    def _is_EV5(self):
+        return self.is_Alpha() and self.info[0]['cpu model'] == 'EV5'
+
+    def _is_EV56(self):
+        return self.is_Alpha() and self.info[0]['cpu model'] == 'EV56'
+
+    def _is_PCA56(self):
+        return self.is_Alpha() and self.info[0]['cpu model'] == 'PCA56'
+
+    # Intel
+
+    #XXX
+    _is_i386 = _not_impl
+
+    def _is_Intel(self):
+        return self.info[0]['vendor_id']=='GenuineIntel'
+
+    def _is_i486(self):
+        return self.info[0]['cpu']=='i486'
+
+    def _is_i586(self):
+        return self.is_Intel() and self.info[0]['cpu family'] == '5'
+
+    def _is_i686(self):
+        return self.is_Intel() and self.info[0]['cpu family'] == '6'
+
+    def _is_Celeron(self):
+        return re.match(r'.*?Celeron',
+                        self.info[0]['model name']) is not None
+
+    def _is_Pentium(self):
+        return re.match(r'.*?Pentium',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumII(self):
+        return re.match(r'.*?Pentium.*?II\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumPro(self):
+        return re.match(r'.*?PentiumPro\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumMMX(self):
+        return re.match(r'.*?Pentium.*?MMX\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumIII(self):
+        return re.match(r'.*?Pentium.*?III\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumIV(self):
+        return re.match(r'.*?Pentium.*?(IV|4)\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_PentiumM(self):
+        return re.match(r'.*?Pentium.*?M\b',
+                        self.info[0]['model name']) is not None
+
+    def _is_Prescott(self):
+        return self.is_PentiumIV() and self.has_sse3()
+
+    def _is_Nocona(self):
+        return (self.is_Intel()
+                and (self.info[0]['cpu family'] == '6'
+                     or self.info[0]['cpu family'] == '15')
+                and (self.has_sse3() and not self.has_ssse3())
+                and re.match(r'.*?\blm\b', self.info[0]['flags']) is not None)
+
+    def _is_Core2(self):
+        return (self.is_64bit() and self.is_Intel() and
+                re.match(r'.*?Core\(TM\)2\b',
+                         self.info[0]['model name']) is not None)
+
+    def _is_Itanium(self):
+        return re.match(r'.*?Itanium\b',
+                        self.info[0]['family']) is not None
+
+    def _is_XEON(self):
+        return re.match(r'.*?XEON\b',
+                        self.info[0]['model name'], re.IGNORECASE) is not None
+
+    _is_Xeon = _is_XEON
+
+    # Varia
+
+    def _is_singleCPU(self):
+        return len(self.info) == 1
+
+    def _getNCPUs(self):
+        return len(self.info)
+
+    def _has_fdiv_bug(self):
+        return self.info[0]['fdiv_bug']=='yes'
+
+    def _has_f00f_bug(self):
+        return self.info[0]['f00f_bug']=='yes'
+
+    def _has_mmx(self):
+        return re.match(r'.*?\bmmx\b', self.info[0]['flags']) is not None
+
+    def _has_sse(self):
+        return re.match(r'.*?\bsse\b', self.info[0]['flags']) is not None
+
+    def _has_sse2(self):
+        return re.match(r'.*?\bsse2\b', self.info[0]['flags']) is not None
+
+    def _has_sse3(self):
+        return re.match(r'.*?\bpni\b', self.info[0]['flags']) is not None
+
+    def _has_ssse3(self):
+        return re.match(r'.*?\bssse3\b', self.info[0]['flags']) is not None
+
+    def _has_3dnow(self):
+        return re.match(r'.*?\b3dnow\b', self.info[0]['flags']) is not None
+
+    def _has_3dnowext(self):
+        return re.match(r'.*?\b3dnowext\b', self.info[0]['flags']) is not None
+
+class IRIXCPUInfo(CPUInfoBase):
+    info = None
+
+    def __init__(self):
+        if self.info is not None:
+            return
+        info = key_value_from_command('sysconf', sep=' ',
+                                      successful_status=(0, 1))
+        self.__class__.info = info
+
+    def _not_impl(self): pass
+
+    def _is_singleCPU(self):
+        return self.info.get('NUM_PROCESSORS') == '1'
+
+    def _getNCPUs(self):
+        return int(self.info.get('NUM_PROCESSORS', 1))
+
+    def __cputype(self, n):
+        return self.info.get('PROCESSORS').split()[0].lower() == 'r%s' % (n)
+    def _is_r2000(self): return self.__cputype(2000)
+    def _is_r3000(self): return self.__cputype(3000)
+    def _is_r3900(self): return self.__cputype(3900)
+    def _is_r4000(self): return self.__cputype(4000)
+    def _is_r4100(self): return self.__cputype(4100)
+    def _is_r4300(self): return self.__cputype(4300)
+    def _is_r4400(self): return self.__cputype(4400)
+    def _is_r4600(self): return self.__cputype(4600)
+    def _is_r4650(self): return self.__cputype(4650)
+    def _is_r5000(self): return self.__cputype(5000)
+    def _is_r6000(self): return self.__cputype(6000)
+    def _is_r8000(self): return self.__cputype(8000)
+    def _is_r10000(self): return self.__cputype(10000)
+    def _is_r12000(self): return self.__cputype(12000)
+    def _is_rorion(self): return self.__cputype('orion')
+
+    def get_ip(self):
+        try: return self.info.get('MACHINE')
+        except Exception: pass
+    def __machine(self, n):
+        return self.info.get('MACHINE').lower() == 'ip%s' % (n)
+    def _is_IP19(self): return self.__machine(19)
+    def _is_IP20(self): return self.__machine(20)
+    def _is_IP21(self): return self.__machine(21)
+    def _is_IP22(self): return self.__machine(22)
+    def _is_IP22_4k(self): return self.__machine(22) and self._is_r4000()
+    def _is_IP22_5k(self): return self.__machine(22)  and self._is_r5000()
+    def _is_IP24(self): return self.__machine(24)
+    def _is_IP25(self): return self.__machine(25)
+    def _is_IP26(self): return self.__machine(26)
+    def _is_IP27(self): return self.__machine(27)
+    def _is_IP28(self): return self.__machine(28)
+    def _is_IP30(self): return self.__machine(30)
+    def _is_IP32(self): return self.__machine(32)
+    def _is_IP32_5k(self): return self.__machine(32) and self._is_r5000()
+    def _is_IP32_10k(self): return self.__machine(32) and self._is_r10000()
+
+
+class DarwinCPUInfo(CPUInfoBase):
+    info = None
+
+    def __init__(self):
+        if self.info is not None:
+            return
+        info = command_info(arch='arch',
+                            machine='machine')
+        info['sysctl_hw'] = key_value_from_command('sysctl hw', sep='=')
+        self.__class__.info = info
+
+    def _not_impl(self): pass
+
+    def _getNCPUs(self):
+        return int(self.info['sysctl_hw'].get('hw.ncpu', 1))
+
+    def _is_Power_Macintosh(self):
+        return self.info['sysctl_hw']['hw.machine']=='Power Macintosh'
+
+    def _is_i386(self):
+        return self.info['arch']=='i386'
+    def _is_ppc(self):
+        return self.info['arch']=='ppc'
+
+    def __machine(self, n):
+        return self.info['machine'] == 'ppc%s'%n
+    def _is_ppc601(self): return self.__machine(601)
+    def _is_ppc602(self): return self.__machine(602)
+    def _is_ppc603(self): return self.__machine(603)
+    def _is_ppc603e(self): return self.__machine('603e')
+    def _is_ppc604(self): return self.__machine(604)
+    def _is_ppc604e(self): return self.__machine('604e')
+    def _is_ppc620(self): return self.__machine(620)
+    def _is_ppc630(self): return self.__machine(630)
+    def _is_ppc740(self): return self.__machine(740)
+    def _is_ppc7400(self): return self.__machine(7400)
+    def _is_ppc7450(self): return self.__machine(7450)
+    def _is_ppc750(self): return self.__machine(750)
+    def _is_ppc403(self): return self.__machine(403)
+    def _is_ppc505(self): return self.__machine(505)
+    def _is_ppc801(self): return self.__machine(801)
+    def _is_ppc821(self): return self.__machine(821)
+    def _is_ppc823(self): return self.__machine(823)
+    def _is_ppc860(self): return self.__machine(860)
+
+
+class SunOSCPUInfo(CPUInfoBase):
+
+    info = None
+
+    def __init__(self):
+        if self.info is not None:
+            return
+        info = command_info(arch='arch',
+                            mach='mach',
+                            uname_i='uname_i',
+                            isainfo_b='isainfo -b',
+                            isainfo_n='isainfo -n',
+                            )
+        info['uname_X'] = key_value_from_command('uname -X', sep='=')
+        for line in command_by_line('psrinfo -v 0'):
+            m = re.match(r'\s*The (?P
[\w\d]+) processor operates at', line)
+            if m:
+                info['processor'] = m.group('p')
+                break
+        self.__class__.info = info
+
+    def _not_impl(self): pass
+
+    def _is_i386(self):
+        return self.info['isainfo_n']=='i386'
+    def _is_sparc(self):
+        return self.info['isainfo_n']=='sparc'
+    def _is_sparcv9(self):
+        return self.info['isainfo_n']=='sparcv9'
+
+    def _getNCPUs(self):
+        return int(self.info['uname_X'].get('NumCPU', 1))
+
+    def _is_sun4(self):
+        return self.info['arch']=='sun4'
+
+    def _is_SUNW(self):
+        return re.match(r'SUNW', self.info['uname_i']) is not None
+    def _is_sparcstation5(self):
+        return re.match(r'.*SPARCstation-5', self.info['uname_i']) is not None
+    def _is_ultra1(self):
+        return re.match(r'.*Ultra-1', self.info['uname_i']) is not None
+    def _is_ultra250(self):
+        return re.match(r'.*Ultra-250', self.info['uname_i']) is not None
+    def _is_ultra2(self):
+        return re.match(r'.*Ultra-2', self.info['uname_i']) is not None
+    def _is_ultra30(self):
+        return re.match(r'.*Ultra-30', self.info['uname_i']) is not None
+    def _is_ultra4(self):
+        return re.match(r'.*Ultra-4', self.info['uname_i']) is not None
+    def _is_ultra5_10(self):
+        return re.match(r'.*Ultra-5_10', self.info['uname_i']) is not None
+    def _is_ultra5(self):
+        return re.match(r'.*Ultra-5', self.info['uname_i']) is not None
+    def _is_ultra60(self):
+        return re.match(r'.*Ultra-60', self.info['uname_i']) is not None
+    def _is_ultra80(self):
+        return re.match(r'.*Ultra-80', self.info['uname_i']) is not None
+    def _is_ultraenterprice(self):
+        return re.match(r'.*Ultra-Enterprise', self.info['uname_i']) is not None
+    def _is_ultraenterprice10k(self):
+        return re.match(r'.*Ultra-Enterprise-10000', self.info['uname_i']) is not None
+    def _is_sunfire(self):
+        return re.match(r'.*Sun-Fire', self.info['uname_i']) is not None
+    def _is_ultra(self):
+        return re.match(r'.*Ultra', self.info['uname_i']) is not None
+
+    def _is_cpusparcv7(self):
+        return self.info['processor']=='sparcv7'
+    def _is_cpusparcv8(self):
+        return self.info['processor']=='sparcv8'
+    def _is_cpusparcv9(self):
+        return self.info['processor']=='sparcv9'
+
+class Win32CPUInfo(CPUInfoBase):
+
+    info = None
+    pkey = r"HARDWARE\DESCRIPTION\System\CentralProcessor"
+    # XXX: what does the value of
+    #   HKEY_LOCAL_MACHINE\HARDWARE\DESCRIPTION\System\CentralProcessor\0
+    # mean?
+
+    def __init__(self):
+        if self.info is not None:
+            return
+        info = []
+        try:
+            #XXX: Bad style to use so long `try:...except:...`. Fix it!
+            import winreg
+
+            prgx = re.compile(r"family\s+(?P\d+)\s+model\s+(?P\d+)"
+                              r"\s+stepping\s+(?P\d+)", re.IGNORECASE)
+            chnd=winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, self.pkey)
+            pnum=0
+            while True:
+                try:
+                    proc=winreg.EnumKey(chnd, pnum)
+                except winreg.error:
+                    break
+                else:
+                    pnum+=1
+                    info.append({"Processor":proc})
+                    phnd=winreg.OpenKey(chnd, proc)
+                    pidx=0
+                    while True:
+                        try:
+                            name, value, vtpe=winreg.EnumValue(phnd, pidx)
+                        except winreg.error:
+                            break
+                        else:
+                            pidx=pidx+1
+                            info[-1][name]=value
+                            if name=="Identifier":
+                                srch=prgx.search(value)
+                                if srch:
+                                    info[-1]["Family"]=int(srch.group("FML"))
+                                    info[-1]["Model"]=int(srch.group("MDL"))
+                                    info[-1]["Stepping"]=int(srch.group("STP"))
+        except Exception as e:
+            print(e, '(ignoring)')
+        self.__class__.info = info
+
+    def _not_impl(self): pass
+
+    # Athlon
+
+    def _is_AMD(self):
+        return self.info[0]['VendorIdentifier']=='AuthenticAMD'
+
+    def _is_Am486(self):
+        return self.is_AMD() and self.info[0]['Family']==4
+
+    def _is_Am5x86(self):
+        return self.is_AMD() and self.info[0]['Family']==4
+
+    def _is_AMDK5(self):
+        return self.is_AMD() and self.info[0]['Family']==5 \
+               and self.info[0]['Model'] in [0, 1, 2, 3]
+
+    def _is_AMDK6(self):
+        return self.is_AMD() and self.info[0]['Family']==5 \
+               and self.info[0]['Model'] in [6, 7]
+
+    def _is_AMDK6_2(self):
+        return self.is_AMD() and self.info[0]['Family']==5 \
+               and self.info[0]['Model']==8
+
+    def _is_AMDK6_3(self):
+        return self.is_AMD() and self.info[0]['Family']==5 \
+               and self.info[0]['Model']==9
+
+    def _is_AMDK7(self):
+        return self.is_AMD() and self.info[0]['Family'] == 6
+
+    # To reliably distinguish between the different types of AMD64 chips
+    # (Athlon64, Operton, Athlon64 X2, Semperon, Turion 64, etc.) would
+    # require looking at the 'brand' from cpuid
+
+    def _is_AMD64(self):
+        return self.is_AMD() and self.info[0]['Family'] == 15
+
+    # Intel
+
+    def _is_Intel(self):
+        return self.info[0]['VendorIdentifier']=='GenuineIntel'
+
+    def _is_i386(self):
+        return self.info[0]['Family']==3
+
+    def _is_i486(self):
+        return self.info[0]['Family']==4
+
+    def _is_i586(self):
+        return self.is_Intel() and self.info[0]['Family']==5
+
+    def _is_i686(self):
+        return self.is_Intel() and self.info[0]['Family']==6
+
+    def _is_Pentium(self):
+        return self.is_Intel() and self.info[0]['Family']==5
+
+    def _is_PentiumMMX(self):
+        return self.is_Intel() and self.info[0]['Family']==5 \
+               and self.info[0]['Model']==4
+
+    def _is_PentiumPro(self):
+        return self.is_Intel() and self.info[0]['Family']==6 \
+               and self.info[0]['Model']==1
+
+    def _is_PentiumII(self):
+        return self.is_Intel() and self.info[0]['Family']==6 \
+               and self.info[0]['Model'] in [3, 5, 6]
+
+    def _is_PentiumIII(self):
+        return self.is_Intel() and self.info[0]['Family']==6 \
+               and self.info[0]['Model'] in [7, 8, 9, 10, 11]
+
+    def _is_PentiumIV(self):
+        return self.is_Intel() and self.info[0]['Family']==15
+
+    def _is_PentiumM(self):
+        return self.is_Intel() and self.info[0]['Family'] == 6 \
+               and self.info[0]['Model'] in [9, 13, 14]
+
+    def _is_Core2(self):
+        return self.is_Intel() and self.info[0]['Family'] == 6 \
+               and self.info[0]['Model'] in [15, 16, 17]
+
+    # Varia
+
+    def _is_singleCPU(self):
+        return len(self.info) == 1
+
+    def _getNCPUs(self):
+        return len(self.info)
+
+    def _has_mmx(self):
+        if self.is_Intel():
+            return (self.info[0]['Family']==5 and self.info[0]['Model']==4) \
+                   or (self.info[0]['Family'] in [6, 15])
+        elif self.is_AMD():
+            return self.info[0]['Family'] in [5, 6, 15]
+        else:
+            return False
+
+    def _has_sse(self):
+        if self.is_Intel():
+            return ((self.info[0]['Family']==6 and
+                     self.info[0]['Model'] in [7, 8, 9, 10, 11])
+                     or self.info[0]['Family']==15)
+        elif self.is_AMD():
+            return ((self.info[0]['Family']==6 and
+                     self.info[0]['Model'] in [6, 7, 8, 10])
+                     or self.info[0]['Family']==15)
+        else:
+            return False
+
+    def _has_sse2(self):
+        if self.is_Intel():
+            return self.is_Pentium4() or self.is_PentiumM() \
+                   or self.is_Core2()
+        elif self.is_AMD():
+            return self.is_AMD64()
+        else:
+            return False
+
+    def _has_3dnow(self):
+        return self.is_AMD() and self.info[0]['Family'] in [5, 6, 15]
+
+    def _has_3dnowext(self):
+        return self.is_AMD() and self.info[0]['Family'] in [6, 15]
+
+if sys.platform.startswith('linux'): # variations: linux2,linux-i386 (any others?)
+    cpuinfo = LinuxCPUInfo
+elif sys.platform.startswith('irix'):
+    cpuinfo = IRIXCPUInfo
+elif sys.platform == 'darwin':
+    cpuinfo = DarwinCPUInfo
+elif sys.platform.startswith('sunos'):
+    cpuinfo = SunOSCPUInfo
+elif sys.platform.startswith('win32'):
+    cpuinfo = Win32CPUInfo
+elif sys.platform.startswith('cygwin'):
+    cpuinfo = LinuxCPUInfo
+#XXX: other OS's. Eg. use _winreg on Win32. Or os.uname on unices.
+else:
+    cpuinfo = CPUInfoBase
+
+cpu = cpuinfo()
+
+#if __name__ == "__main__":
+#
+#    cpu.is_blaa()
+#    cpu.is_Intel()
+#    cpu.is_Alpha()
+#
+#    print('CPU information:'),
+#    for name in dir(cpuinfo):
+#        if name[0]=='_' and name[1]!='_':
+#            r = getattr(cpu,name[1:])()
+#            if r:
+#                if r!=1:
+#                    print('%s=%s' %(name[1:],r))
+#                else:
+#                    print(name[1:]),
+#    print()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/exec_command.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/exec_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d06585a1497ceadc1d7f11079f17208504ee183
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/exec_command.py
@@ -0,0 +1,315 @@
+"""
+exec_command
+
+Implements exec_command function that is (almost) equivalent to
+commands.getstatusoutput function but on NT, DOS systems the
+returned status is actually correct (though, the returned status
+values may be different by a factor). In addition, exec_command
+takes keyword arguments for (re-)defining environment variables.
+
+Provides functions:
+
+  exec_command  --- execute command in a specified directory and
+                    in the modified environment.
+  find_executable --- locate a command using info from environment
+                    variable PATH. Equivalent to posix `which`
+                    command.
+
+Author: Pearu Peterson 
+Created: 11 January 2003
+
+Requires: Python 2.x
+
+Successfully tested on:
+
+========  ============  =================================================
+os.name   sys.platform  comments
+========  ============  =================================================
+posix     linux2        Debian (sid) Linux, Python 2.1.3+, 2.2.3+, 2.3.3
+                        PyCrust 0.9.3, Idle 1.0.2
+posix     linux2        Red Hat 9 Linux, Python 2.1.3, 2.2.2, 2.3.2
+posix     sunos5        SunOS 5.9, Python 2.2, 2.3.2
+posix     darwin        Darwin 7.2.0, Python 2.3
+nt        win32         Windows Me
+                        Python 2.3(EE), Idle 1.0, PyCrust 0.7.2
+                        Python 2.1.1 Idle 0.8
+nt        win32         Windows 98, Python 2.1.1. Idle 0.8
+nt        win32         Cygwin 98-4.10, Python 2.1.1(MSC) - echo tests
+                        fail i.e. redefining environment variables may
+                        not work. FIXED: don't use cygwin echo!
+                        Comment: also `cmd /c echo` will not work
+                        but redefining environment variables do work.
+posix     cygwin        Cygwin 98-4.10, Python 2.3.3(cygming special)
+nt        win32         Windows XP, Python 2.3.3
+========  ============  =================================================
+
+Known bugs:
+
+* Tests, that send messages to stderr, fail when executed from MSYS prompt
+  because the messages are lost at some point.
+
+"""
+__all__ = ['exec_command', 'find_executable']
+
+import os
+import sys
+import subprocess
+import locale
+import warnings
+
+from numpy.distutils.misc_util import is_sequence, make_temp_file
+from numpy.distutils import log
+
+def filepath_from_subprocess_output(output):
+    """
+    Convert `bytes` in the encoding used by a subprocess into a filesystem-appropriate `str`.
+
+    Inherited from `exec_command`, and possibly incorrect.
+    """
+    mylocale = locale.getpreferredencoding(False)
+    if mylocale is None:
+        mylocale = 'ascii'
+    output = output.decode(mylocale, errors='replace')
+    output = output.replace('\r\n', '\n')
+    # Another historical oddity
+    if output[-1:] == '\n':
+        output = output[:-1]
+    return output
+
+
+def forward_bytes_to_stdout(val):
+    """
+    Forward bytes from a subprocess call to the console, without attempting to
+    decode them.
+
+    The assumption is that the subprocess call already returned bytes in
+    a suitable encoding.
+    """
+    if hasattr(sys.stdout, 'buffer'):
+        # use the underlying binary output if there is one
+        sys.stdout.buffer.write(val)
+    elif hasattr(sys.stdout, 'encoding'):
+        # round-trip the encoding if necessary
+        sys.stdout.write(val.decode(sys.stdout.encoding))
+    else:
+        # make a best-guess at the encoding
+        sys.stdout.write(val.decode('utf8', errors='replace'))
+
+
+def temp_file_name():
+    # 2019-01-30, 1.17
+    warnings.warn('temp_file_name is deprecated since NumPy v1.17, use '
+                  'tempfile.mkstemp instead', DeprecationWarning, stacklevel=1)
+    fo, name = make_temp_file()
+    fo.close()
+    return name
+
+def get_pythonexe():
+    pythonexe = sys.executable
+    if os.name in ['nt', 'dos']:
+        fdir, fn = os.path.split(pythonexe)
+        fn = fn.upper().replace('PYTHONW', 'PYTHON')
+        pythonexe = os.path.join(fdir, fn)
+        assert os.path.isfile(pythonexe), '%r is not a file' % (pythonexe,)
+    return pythonexe
+
+def find_executable(exe, path=None, _cache={}):
+    """Return full path of a executable or None.
+
+    Symbolic links are not followed.
+    """
+    key = exe, path
+    try:
+        return _cache[key]
+    except KeyError:
+        pass
+    log.debug('find_executable(%r)' % exe)
+    orig_exe = exe
+
+    if path is None:
+        path = os.environ.get('PATH', os.defpath)
+    if os.name=='posix':
+        realpath = os.path.realpath
+    else:
+        realpath = lambda a:a
+
+    if exe.startswith('"'):
+        exe = exe[1:-1]
+
+    suffixes = ['']
+    if os.name in ['nt', 'dos', 'os2']:
+        fn, ext = os.path.splitext(exe)
+        extra_suffixes = ['.exe', '.com', '.bat']
+        if ext.lower() not in extra_suffixes:
+            suffixes = extra_suffixes
+
+    if os.path.isabs(exe):
+        paths = ['']
+    else:
+        paths = [ os.path.abspath(p) for p in path.split(os.pathsep) ]
+
+    for path in paths:
+        fn = os.path.join(path, exe)
+        for s in suffixes:
+            f_ext = fn+s
+            if not os.path.islink(f_ext):
+                f_ext = realpath(f_ext)
+            if os.path.isfile(f_ext) and os.access(f_ext, os.X_OK):
+                log.info('Found executable %s' % f_ext)
+                _cache[key] = f_ext
+                return f_ext
+
+    log.warn('Could not locate executable %s' % orig_exe)
+    return None
+
+############################################################
+
+def _preserve_environment( names ):
+    log.debug('_preserve_environment(%r)' % (names))
+    env = {name: os.environ.get(name) for name in names}
+    return env
+
+def _update_environment( **env ):
+    log.debug('_update_environment(...)')
+    for name, value in env.items():
+        os.environ[name] = value or ''
+
+def exec_command(command, execute_in='', use_shell=None, use_tee=None,
+                 _with_python = 1, **env ):
+    """
+    Return (status,output) of executed command.
+
+    .. deprecated:: 1.17
+        Use subprocess.Popen instead
+
+    Parameters
+    ----------
+    command : str
+        A concatenated string of executable and arguments.
+    execute_in : str
+        Before running command ``cd execute_in`` and after ``cd -``.
+    use_shell : {bool, None}, optional
+        If True, execute ``sh -c command``. Default None (True)
+    use_tee : {bool, None}, optional
+        If True use tee. Default None (True)
+
+
+    Returns
+    -------
+    res : str
+        Both stdout and stderr messages.
+
+    Notes
+    -----
+    On NT, DOS systems the returned status is correct for external commands.
+    Wild cards will not work for non-posix systems or when use_shell=0.
+
+    """
+    # 2019-01-30, 1.17
+    warnings.warn('exec_command is deprecated since NumPy v1.17, use '
+                  'subprocess.Popen instead', DeprecationWarning, stacklevel=1)
+    log.debug('exec_command(%r,%s)' % (command,
+         ','.join(['%s=%r'%kv for kv in env.items()])))
+
+    if use_tee is None:
+        use_tee = os.name=='posix'
+    if use_shell is None:
+        use_shell = os.name=='posix'
+    execute_in = os.path.abspath(execute_in)
+    oldcwd = os.path.abspath(os.getcwd())
+
+    if __name__[-12:] == 'exec_command':
+        exec_dir = os.path.dirname(os.path.abspath(__file__))
+    elif os.path.isfile('exec_command.py'):
+        exec_dir = os.path.abspath('.')
+    else:
+        exec_dir = os.path.abspath(sys.argv[0])
+        if os.path.isfile(exec_dir):
+            exec_dir = os.path.dirname(exec_dir)
+
+    if oldcwd!=execute_in:
+        os.chdir(execute_in)
+        log.debug('New cwd: %s' % execute_in)
+    else:
+        log.debug('Retaining cwd: %s' % oldcwd)
+
+    oldenv = _preserve_environment( list(env.keys()) )
+    _update_environment( **env )
+
+    try:
+        st = _exec_command(command,
+                           use_shell=use_shell,
+                           use_tee=use_tee,
+                           **env)
+    finally:
+        if oldcwd!=execute_in:
+            os.chdir(oldcwd)
+            log.debug('Restored cwd to %s' % oldcwd)
+        _update_environment(**oldenv)
+
+    return st
+
+
+def _exec_command(command, use_shell=None, use_tee = None, **env):
+    """
+    Internal workhorse for exec_command().
+    """
+    if use_shell is None:
+        use_shell = os.name=='posix'
+    if use_tee is None:
+        use_tee = os.name=='posix'
+
+    if os.name == 'posix' and use_shell:
+        # On POSIX, subprocess always uses /bin/sh, override
+        sh = os.environ.get('SHELL', '/bin/sh')
+        if is_sequence(command):
+            command = [sh, '-c', ' '.join(command)]
+        else:
+            command = [sh, '-c', command]
+        use_shell = False
+
+    elif os.name == 'nt' and is_sequence(command):
+        # On Windows, join the string for CreateProcess() ourselves as
+        # subprocess does it a bit differently
+        command = ' '.join(_quote_arg(arg) for arg in command)
+
+    # Inherit environment by default
+    env = env or None
+    try:
+        # text is set to False so that communicate()
+        # will return bytes. We need to decode the output ourselves
+        # so that Python will not raise a UnicodeDecodeError when
+        # it encounters an invalid character; rather, we simply replace it
+        proc = subprocess.Popen(command, shell=use_shell, env=env, text=False,
+                                stdout=subprocess.PIPE,
+                                stderr=subprocess.STDOUT)
+    except OSError:
+        # Return 127, as os.spawn*() and /bin/sh do
+        return 127, ''
+
+    text, err = proc.communicate()
+    mylocale = locale.getpreferredencoding(False)
+    if mylocale is None:
+        mylocale = 'ascii'
+    text = text.decode(mylocale, errors='replace')
+    text = text.replace('\r\n', '\n')
+    # Another historical oddity
+    if text[-1:] == '\n':
+        text = text[:-1]
+
+    if use_tee and text:
+        print(text)
+    return proc.returncode, text
+
+
+def _quote_arg(arg):
+    """
+    Quote the argument for safe use in a shell command line.
+    """
+    # If there is a quote in the string, assume relevant parts of the
+    # string are already quoted (e.g. '-I"C:\\Program Files\\..."')
+    if '"' not in arg and ' ' in arg:
+        return '"%s"' % arg
+    return arg
+
+############################################################
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/extension.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..06e6441e65df7ed516c4560aed832793fbfc1f4c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/extension.py
@@ -0,0 +1,101 @@
+"""distutils.extension
+
+Provides the Extension class, used to describe C/C++ extension
+modules in setup scripts.
+
+Overridden to support f2py.
+
+"""
+import re
+from distutils.extension import Extension as old_Extension
+
+
+cxx_ext_re = re.compile(r'.*\.(cpp|cxx|cc)\Z', re.I).match
+fortran_pyf_ext_re = re.compile(r'.*\.(f90|f95|f77|for|ftn|f|pyf)\Z', re.I).match
+
+
+class Extension(old_Extension):
+    """
+    Parameters
+    ----------
+    name : str
+        Extension name.
+    sources : list of str
+        List of source file locations relative to the top directory of
+        the package.
+    extra_compile_args : list of str
+        Extra command line arguments to pass to the compiler.
+    extra_f77_compile_args : list of str
+        Extra command line arguments to pass to the fortran77 compiler.
+    extra_f90_compile_args : list of str
+        Extra command line arguments to pass to the fortran90 compiler.
+    """
+    def __init__(
+            self, name, sources,
+            include_dirs=None,
+            define_macros=None,
+            undef_macros=None,
+            library_dirs=None,
+            libraries=None,
+            runtime_library_dirs=None,
+            extra_objects=None,
+            extra_compile_args=None,
+            extra_link_args=None,
+            export_symbols=None,
+            swig_opts=None,
+            depends=None,
+            language=None,
+            f2py_options=None,
+            module_dirs=None,
+            extra_c_compile_args=None,
+            extra_cxx_compile_args=None,
+            extra_f77_compile_args=None,
+            extra_f90_compile_args=None,):
+
+        old_Extension.__init__(
+                self, name, [],
+                include_dirs=include_dirs,
+                define_macros=define_macros,
+                undef_macros=undef_macros,
+                library_dirs=library_dirs,
+                libraries=libraries,
+                runtime_library_dirs=runtime_library_dirs,
+                extra_objects=extra_objects,
+                extra_compile_args=extra_compile_args,
+                extra_link_args=extra_link_args,
+                export_symbols=export_symbols)
+
+        # Avoid assert statements checking that sources contains strings:
+        self.sources = sources
+
+        # Python 2.4 distutils new features
+        self.swig_opts = swig_opts or []
+        # swig_opts is assumed to be a list. Here we handle the case where it
+        # is specified as a string instead.
+        if isinstance(self.swig_opts, str):
+            import warnings
+            msg = "swig_opts is specified as a string instead of a list"
+            warnings.warn(msg, SyntaxWarning, stacklevel=2)
+            self.swig_opts = self.swig_opts.split()
+
+        # Python 2.3 distutils new features
+        self.depends = depends or []
+        self.language = language
+
+        # numpy_distutils features
+        self.f2py_options = f2py_options or []
+        self.module_dirs = module_dirs or []
+        self.extra_c_compile_args = extra_c_compile_args or []
+        self.extra_cxx_compile_args = extra_cxx_compile_args or []
+        self.extra_f77_compile_args = extra_f77_compile_args or []
+        self.extra_f90_compile_args = extra_f90_compile_args or []
+
+        return
+
+    def has_cxx_sources(self):
+        return any(cxx_ext_re(str(source)) for source in self.sources)
+
+    def has_f2py_sources(self):
+        return any(fortran_pyf_ext_re(source) for source in self.sources)
+
+# class Extension
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5160e2abf54f9c72f9b63901eb2417a21aba90dc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/__init__.py
@@ -0,0 +1,1035 @@
+"""numpy.distutils.fcompiler
+
+Contains FCompiler, an abstract base class that defines the interface
+for the numpy.distutils Fortran compiler abstraction model.
+
+Terminology:
+
+To be consistent, where the term 'executable' is used, it means the single
+file, like 'gcc', that is executed, and should be a string. In contrast,
+'command' means the entire command line, like ['gcc', '-c', 'file.c'], and
+should be a list.
+
+But note that FCompiler.executables is actually a dictionary of commands.
+
+"""
+__all__ = ['FCompiler', 'new_fcompiler', 'show_fcompilers',
+           'dummy_fortran_file']
+
+import os
+import sys
+import re
+from pathlib import Path
+
+from distutils.sysconfig import get_python_lib
+from distutils.fancy_getopt import FancyGetopt
+from distutils.errors import DistutilsModuleError, \
+     DistutilsExecError, CompileError, LinkError, DistutilsPlatformError
+from distutils.util import split_quoted, strtobool
+
+from numpy.distutils.ccompiler import CCompiler, gen_lib_options
+from numpy.distutils import log
+from numpy.distutils.misc_util import is_string, all_strings, is_sequence, \
+    make_temp_file, get_shared_lib_extension
+from numpy.distutils.exec_command import find_executable
+from numpy.distutils import _shell_utils
+
+from .environment import EnvironmentConfig
+
+__metaclass__ = type
+
+
+FORTRAN_COMMON_FIXED_EXTENSIONS = ['.for', '.ftn', '.f77', '.f']
+
+
+class CompilerNotFound(Exception):
+    pass
+
+def flaglist(s):
+    if is_string(s):
+        return split_quoted(s)
+    else:
+        return s
+
+def str2bool(s):
+    if is_string(s):
+        return strtobool(s)
+    return bool(s)
+
+def is_sequence_of_strings(seq):
+    return is_sequence(seq) and all_strings(seq)
+
+class FCompiler(CCompiler):
+    """Abstract base class to define the interface that must be implemented
+    by real Fortran compiler classes.
+
+    Methods that subclasses may redefine:
+
+        update_executables(), find_executables(), get_version()
+        get_flags(), get_flags_opt(), get_flags_arch(), get_flags_debug()
+        get_flags_f77(), get_flags_opt_f77(), get_flags_arch_f77(),
+        get_flags_debug_f77(), get_flags_f90(), get_flags_opt_f90(),
+        get_flags_arch_f90(), get_flags_debug_f90(),
+        get_flags_fix(), get_flags_linker_so()
+
+    DON'T call these methods (except get_version) after
+    constructing a compiler instance or inside any other method.
+    All methods, except update_executables() and find_executables(),
+    may call the get_version() method.
+
+    After constructing a compiler instance, always call customize(dist=None)
+    method that finalizes compiler construction and makes the following
+    attributes available:
+      compiler_f77
+      compiler_f90
+      compiler_fix
+      linker_so
+      archiver
+      ranlib
+      libraries
+      library_dirs
+    """
+
+    # These are the environment variables and distutils keys used.
+    # Each configuration description is
+    # (, , , , )
+    # The hook names are handled by the self._environment_hook method.
+    #  - names starting with 'self.' call methods in this class
+    #  - names starting with 'exe.' return the key in the executables dict
+    #  - names like 'flags.YYY' return self.get_flag_YYY()
+    # convert is either None or a function to convert a string to the
+    # appropriate type used.
+
+    distutils_vars = EnvironmentConfig(
+        distutils_section='config_fc',
+        noopt = (None, None, 'noopt', str2bool, False),
+        noarch = (None, None, 'noarch', str2bool, False),
+        debug = (None, None, 'debug', str2bool, False),
+        verbose = (None, None, 'verbose', str2bool, False),
+    )
+
+    command_vars = EnvironmentConfig(
+        distutils_section='config_fc',
+        compiler_f77 = ('exe.compiler_f77', 'F77', 'f77exec', None, False),
+        compiler_f90 = ('exe.compiler_f90', 'F90', 'f90exec', None, False),
+        compiler_fix = ('exe.compiler_fix', 'F90', 'f90exec', None, False),
+        version_cmd = ('exe.version_cmd', None, None, None, False),
+        linker_so = ('exe.linker_so', 'LDSHARED', 'ldshared', None, False),
+        linker_exe = ('exe.linker_exe', 'LD', 'ld', None, False),
+        archiver = (None, 'AR', 'ar', None, False),
+        ranlib = (None, 'RANLIB', 'ranlib', None, False),
+    )
+
+    flag_vars = EnvironmentConfig(
+        distutils_section='config_fc',
+        f77 = ('flags.f77', 'F77FLAGS', 'f77flags', flaglist, True),
+        f90 = ('flags.f90', 'F90FLAGS', 'f90flags', flaglist, True),
+        free = ('flags.free', 'FREEFLAGS', 'freeflags', flaglist, True),
+        fix = ('flags.fix', None, None, flaglist, False),
+        opt = ('flags.opt', 'FOPT', 'opt', flaglist, True),
+        opt_f77 = ('flags.opt_f77', None, None, flaglist, False),
+        opt_f90 = ('flags.opt_f90', None, None, flaglist, False),
+        arch = ('flags.arch', 'FARCH', 'arch', flaglist, False),
+        arch_f77 = ('flags.arch_f77', None, None, flaglist, False),
+        arch_f90 = ('flags.arch_f90', None, None, flaglist, False),
+        debug = ('flags.debug', 'FDEBUG', 'fdebug', flaglist, True),
+        debug_f77 = ('flags.debug_f77', None, None, flaglist, False),
+        debug_f90 = ('flags.debug_f90', None, None, flaglist, False),
+        flags = ('self.get_flags', 'FFLAGS', 'fflags', flaglist, True),
+        linker_so = ('flags.linker_so', 'LDFLAGS', 'ldflags', flaglist, True),
+        linker_exe = ('flags.linker_exe', 'LDFLAGS', 'ldflags', flaglist, True),
+        ar = ('flags.ar', 'ARFLAGS', 'arflags', flaglist, True),
+    )
+
+    language_map = {'.f': 'f77',
+                    '.for': 'f77',
+                    '.F': 'f77',    # XXX: needs preprocessor
+                    '.ftn': 'f77',
+                    '.f77': 'f77',
+                    '.f90': 'f90',
+                    '.F90': 'f90',  # XXX: needs preprocessor
+                    '.f95': 'f90',
+                    }
+    language_order = ['f90', 'f77']
+
+
+    # These will be set by the subclass
+
+    compiler_type = None
+    compiler_aliases = ()
+    version_pattern = None
+
+    possible_executables = []
+    executables = {
+        'version_cmd': ["f77", "-v"],
+        'compiler_f77': ["f77"],
+        'compiler_f90': ["f90"],
+        'compiler_fix': ["f90", "-fixed"],
+        'linker_so': ["f90", "-shared"],
+        'linker_exe': ["f90"],
+        'archiver': ["ar", "-cr"],
+        'ranlib': None,
+        }
+
+    # If compiler does not support compiling Fortran 90 then it can
+    # suggest using another compiler. For example, gnu would suggest
+    # gnu95 compiler type when there are F90 sources.
+    suggested_f90_compiler = None
+
+    compile_switch = "-c"
+    object_switch = "-o "   # Ending space matters! It will be stripped
+                            # but if it is missing then object_switch
+                            # will be prefixed to object file name by
+                            # string concatenation.
+    library_switch = "-o "  # Ditto!
+
+    # Switch to specify where module files are created and searched
+    # for USE statement.  Normally it is a string and also here ending
+    # space matters. See above.
+    module_dir_switch = None
+
+    # Switch to specify where module files are searched for USE statement.
+    module_include_switch = '-I'
+
+    pic_flags = []           # Flags to create position-independent code
+
+    src_extensions = ['.for', '.ftn', '.f77', '.f', '.f90', '.f95', '.F', '.F90', '.FOR']
+    obj_extension = ".o"
+
+    shared_lib_extension = get_shared_lib_extension()
+    static_lib_extension = ".a"  # or .lib
+    static_lib_format = "lib%s%s" # or %s%s
+    shared_lib_format = "%s%s"
+    exe_extension = ""
+
+    _exe_cache = {}
+
+    _executable_keys = ['version_cmd', 'compiler_f77', 'compiler_f90',
+                        'compiler_fix', 'linker_so', 'linker_exe', 'archiver',
+                        'ranlib']
+
+    # This will be set by new_fcompiler when called in
+    # command/{build_ext.py, build_clib.py, config.py} files.
+    c_compiler = None
+
+    # extra_{f77,f90}_compile_args are set by build_ext.build_extension method
+    extra_f77_compile_args = []
+    extra_f90_compile_args = []
+
+    def __init__(self, *args, **kw):
+        CCompiler.__init__(self, *args, **kw)
+        self.distutils_vars = self.distutils_vars.clone(self._environment_hook)
+        self.command_vars = self.command_vars.clone(self._environment_hook)
+        self.flag_vars = self.flag_vars.clone(self._environment_hook)
+        self.executables = self.executables.copy()
+        for e in self._executable_keys:
+            if e not in self.executables:
+                self.executables[e] = None
+
+        # Some methods depend on .customize() being called first, so
+        # this keeps track of whether that's happened yet.
+        self._is_customised = False
+
+    def __copy__(self):
+        obj = self.__new__(self.__class__)
+        obj.__dict__.update(self.__dict__)
+        obj.distutils_vars = obj.distutils_vars.clone(obj._environment_hook)
+        obj.command_vars = obj.command_vars.clone(obj._environment_hook)
+        obj.flag_vars = obj.flag_vars.clone(obj._environment_hook)
+        obj.executables = obj.executables.copy()
+        return obj
+
+    def copy(self):
+        return self.__copy__()
+
+    # Use properties for the attributes used by CCompiler. Setting them
+    # as attributes from the self.executables dictionary is error-prone,
+    # so we get them from there each time.
+    def _command_property(key):
+        def fget(self):
+            assert self._is_customised
+            return self.executables[key]
+        return property(fget=fget)
+    version_cmd = _command_property('version_cmd')
+    compiler_f77 = _command_property('compiler_f77')
+    compiler_f90 = _command_property('compiler_f90')
+    compiler_fix = _command_property('compiler_fix')
+    linker_so = _command_property('linker_so')
+    linker_exe = _command_property('linker_exe')
+    archiver = _command_property('archiver')
+    ranlib = _command_property('ranlib')
+
+    # Make our terminology consistent.
+    def set_executable(self, key, value):
+        self.set_command(key, value)
+
+    def set_commands(self, **kw):
+        for k, v in kw.items():
+            self.set_command(k, v)
+
+    def set_command(self, key, value):
+        if not key in self._executable_keys:
+            raise ValueError(
+                "unknown executable '%s' for class %s" %
+                (key, self.__class__.__name__))
+        if is_string(value):
+            value = split_quoted(value)
+        assert value is None or is_sequence_of_strings(value[1:]), (key, value)
+        self.executables[key] = value
+
+    ######################################################################
+    ## Methods that subclasses may redefine. But don't call these methods!
+    ## They are private to FCompiler class and may return unexpected
+    ## results if used elsewhere. So, you have been warned..
+
+    def find_executables(self):
+        """Go through the self.executables dictionary, and attempt to
+        find and assign appropriate executables.
+
+        Executable names are looked for in the environment (environment
+        variables, the distutils.cfg, and command line), the 0th-element of
+        the command list, and the self.possible_executables list.
+
+        Also, if the 0th element is "" or "", the Fortran 77
+        or the Fortran 90 compiler executable is used, unless overridden
+        by an environment setting.
+
+        Subclasses should call this if overridden.
+        """
+        assert self._is_customised
+        exe_cache = self._exe_cache
+        def cached_find_executable(exe):
+            if exe in exe_cache:
+                return exe_cache[exe]
+            fc_exe = find_executable(exe)
+            exe_cache[exe] = exe_cache[fc_exe] = fc_exe
+            return fc_exe
+        def verify_command_form(name, value):
+            if value is not None and not is_sequence_of_strings(value):
+                raise ValueError(
+                    "%s value %r is invalid in class %s" %
+                    (name, value, self.__class__.__name__))
+        def set_exe(exe_key, f77=None, f90=None):
+            cmd = self.executables.get(exe_key, None)
+            if not cmd:
+                return None
+            # Note that we get cmd[0] here if the environment doesn't
+            # have anything set
+            exe_from_environ = getattr(self.command_vars, exe_key)
+            if not exe_from_environ:
+                possibles = [f90, f77] + self.possible_executables
+            else:
+                possibles = [exe_from_environ] + self.possible_executables
+
+            seen = set()
+            unique_possibles = []
+            for e in possibles:
+                if e == '':
+                    e = f77
+                elif e == '':
+                    e = f90
+                if not e or e in seen:
+                    continue
+                seen.add(e)
+                unique_possibles.append(e)
+
+            for exe in unique_possibles:
+                fc_exe = cached_find_executable(exe)
+                if fc_exe:
+                    cmd[0] = fc_exe
+                    return fc_exe
+            self.set_command(exe_key, None)
+            return None
+
+        ctype = self.compiler_type
+        f90 = set_exe('compiler_f90')
+        if not f90:
+            f77 = set_exe('compiler_f77')
+            if f77:
+                log.warn('%s: no Fortran 90 compiler found' % ctype)
+            else:
+                raise CompilerNotFound('%s: f90 nor f77' % ctype)
+        else:
+            f77 = set_exe('compiler_f77', f90=f90)
+            if not f77:
+                log.warn('%s: no Fortran 77 compiler found' % ctype)
+            set_exe('compiler_fix', f90=f90)
+
+        set_exe('linker_so', f77=f77, f90=f90)
+        set_exe('linker_exe', f77=f77, f90=f90)
+        set_exe('version_cmd', f77=f77, f90=f90)
+        set_exe('archiver')
+        set_exe('ranlib')
+
+    def update_executables(self):
+        """Called at the beginning of customisation. Subclasses should
+        override this if they need to set up the executables dictionary.
+
+        Note that self.find_executables() is run afterwards, so the
+        self.executables dictionary values can contain  or  as
+        the command, which will be replaced by the found F77 or F90
+        compiler.
+        """
+        pass
+
+    def get_flags(self):
+        """List of flags common to all compiler types."""
+        return [] + self.pic_flags
+
+    def _get_command_flags(self, key):
+        cmd = self.executables.get(key, None)
+        if cmd is None:
+            return []
+        return cmd[1:]
+
+    def get_flags_f77(self):
+        """List of Fortran 77 specific flags."""
+        return self._get_command_flags('compiler_f77')
+    def get_flags_f90(self):
+        """List of Fortran 90 specific flags."""
+        return self._get_command_flags('compiler_f90')
+    def get_flags_free(self):
+        """List of Fortran 90 free format specific flags."""
+        return []
+    def get_flags_fix(self):
+        """List of Fortran 90 fixed format specific flags."""
+        return self._get_command_flags('compiler_fix')
+    def get_flags_linker_so(self):
+        """List of linker flags to build a shared library."""
+        return self._get_command_flags('linker_so')
+    def get_flags_linker_exe(self):
+        """List of linker flags to build an executable."""
+        return self._get_command_flags('linker_exe')
+    def get_flags_ar(self):
+        """List of archiver flags. """
+        return self._get_command_flags('archiver')
+    def get_flags_opt(self):
+        """List of architecture independent compiler flags."""
+        return []
+    def get_flags_arch(self):
+        """List of architecture dependent compiler flags."""
+        return []
+    def get_flags_debug(self):
+        """List of compiler flags to compile with debugging information."""
+        return []
+
+    get_flags_opt_f77 = get_flags_opt_f90 = get_flags_opt
+    get_flags_arch_f77 = get_flags_arch_f90 = get_flags_arch
+    get_flags_debug_f77 = get_flags_debug_f90 = get_flags_debug
+
+    def get_libraries(self):
+        """List of compiler libraries."""
+        return self.libraries[:]
+    def get_library_dirs(self):
+        """List of compiler library directories."""
+        return self.library_dirs[:]
+
+    def get_version(self, force=False, ok_status=[0]):
+        assert self._is_customised
+        version = CCompiler.get_version(self, force=force, ok_status=ok_status)
+        if version is None:
+            raise CompilerNotFound()
+        return version
+
+
+    ############################################################
+
+    ## Public methods:
+
+    def customize(self, dist = None):
+        """Customize Fortran compiler.
+
+        This method gets Fortran compiler specific information from
+        (i) class definition, (ii) environment, (iii) distutils config
+        files, and (iv) command line (later overrides earlier).
+
+        This method should be always called after constructing a
+        compiler instance. But not in __init__ because Distribution
+        instance is needed for (iii) and (iv).
+        """
+        log.info('customize %s' % (self.__class__.__name__))
+
+        self._is_customised = True
+
+        self.distutils_vars.use_distribution(dist)
+        self.command_vars.use_distribution(dist)
+        self.flag_vars.use_distribution(dist)
+
+        self.update_executables()
+
+        # find_executables takes care of setting the compiler commands,
+        # version_cmd, linker_so, linker_exe, ar, and ranlib
+        self.find_executables()
+
+        noopt = self.distutils_vars.get('noopt', False)
+        noarch = self.distutils_vars.get('noarch', noopt)
+        debug = self.distutils_vars.get('debug', False)
+
+        f77 = self.command_vars.compiler_f77
+        f90 = self.command_vars.compiler_f90
+
+        f77flags = []
+        f90flags = []
+        freeflags = []
+        fixflags = []
+
+        if f77:
+            f77 = _shell_utils.NativeParser.split(f77)
+            f77flags = self.flag_vars.f77
+        if f90:
+            f90 = _shell_utils.NativeParser.split(f90)
+            f90flags = self.flag_vars.f90
+            freeflags = self.flag_vars.free
+        # XXX Assuming that free format is default for f90 compiler.
+        fix = self.command_vars.compiler_fix
+        # NOTE: this and similar examples are probably just
+        # excluding --coverage flag when F90 = gfortran --coverage
+        # instead of putting that flag somewhere more appropriate
+        # this and similar examples where a Fortran compiler
+        # environment variable has been customized by CI or a user
+        # should perhaps eventually be more thoroughly tested and more
+        # robustly handled
+        if fix:
+            fix = _shell_utils.NativeParser.split(fix)
+            fixflags = self.flag_vars.fix + f90flags
+
+        oflags, aflags, dflags = [], [], []
+        # examine get_flags__ for extra flags
+        # only add them if the method is different from get_flags_
+        def get_flags(tag, flags):
+            # note that self.flag_vars. calls self.get_flags_()
+            flags.extend(getattr(self.flag_vars, tag))
+            this_get = getattr(self, 'get_flags_' + tag)
+            for name, c, flagvar in [('f77', f77, f77flags),
+                                     ('f90', f90, f90flags),
+                                     ('f90', fix, fixflags)]:
+                t = '%s_%s' % (tag, name)
+                if c and this_get is not getattr(self, 'get_flags_' + t):
+                    flagvar.extend(getattr(self.flag_vars, t))
+        if not noopt:
+            get_flags('opt', oflags)
+            if not noarch:
+                get_flags('arch', aflags)
+        if debug:
+            get_flags('debug', dflags)
+
+        fflags = self.flag_vars.flags + dflags + oflags + aflags
+
+        if f77:
+            self.set_commands(compiler_f77=f77+f77flags+fflags)
+        if f90:
+            self.set_commands(compiler_f90=f90+freeflags+f90flags+fflags)
+        if fix:
+            self.set_commands(compiler_fix=fix+fixflags+fflags)
+
+
+        #XXX: Do we need LDSHARED->SOSHARED, LDFLAGS->SOFLAGS
+        linker_so = self.linker_so
+        if linker_so:
+            linker_so_flags = self.flag_vars.linker_so
+            if sys.platform.startswith('aix'):
+                python_lib = get_python_lib(standard_lib=1)
+                ld_so_aix = os.path.join(python_lib, 'config', 'ld_so_aix')
+                python_exp = os.path.join(python_lib, 'config', 'python.exp')
+                linker_so = [ld_so_aix] + linker_so + ['-bI:'+python_exp]
+            if sys.platform.startswith('os400'):
+                from distutils.sysconfig import get_config_var
+                python_config = get_config_var('LIBPL')
+                ld_so_aix = os.path.join(python_config, 'ld_so_aix')
+                python_exp = os.path.join(python_config, 'python.exp')
+                linker_so = [ld_so_aix] + linker_so + ['-bI:'+python_exp]
+            self.set_commands(linker_so=linker_so+linker_so_flags)
+
+        linker_exe = self.linker_exe
+        if linker_exe:
+            linker_exe_flags = self.flag_vars.linker_exe
+            self.set_commands(linker_exe=linker_exe+linker_exe_flags)
+
+        ar = self.command_vars.archiver
+        if ar:
+            arflags = self.flag_vars.ar
+            self.set_commands(archiver=[ar]+arflags)
+
+        self.set_library_dirs(self.get_library_dirs())
+        self.set_libraries(self.get_libraries())
+
+    def dump_properties(self):
+        """Print out the attributes of a compiler instance."""
+        props = []
+        for key in list(self.executables.keys()) + \
+                ['version', 'libraries', 'library_dirs',
+                 'object_switch', 'compile_switch']:
+            if hasattr(self, key):
+                v = getattr(self, key)
+                props.append((key, None, '= '+repr(v)))
+        props.sort()
+
+        pretty_printer = FancyGetopt(props)
+        for l in pretty_printer.generate_help("%s instance properties:" \
+                                              % (self.__class__.__name__)):
+            if l[:4]=='  --':
+                l = '  ' + l[4:]
+            print(l)
+
+    ###################
+
+    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+        """Compile 'src' to product 'obj'."""
+        src_flags = {}
+        if Path(src).suffix.lower() in FORTRAN_COMMON_FIXED_EXTENSIONS \
+           and not has_f90_header(src):
+            flavor = ':f77'
+            compiler = self.compiler_f77
+            src_flags = get_f77flags(src)
+            extra_compile_args = self.extra_f77_compile_args or []
+        elif is_free_format(src):
+            flavor = ':f90'
+            compiler = self.compiler_f90
+            if compiler is None:
+                raise DistutilsExecError('f90 not supported by %s needed for %s'\
+                      % (self.__class__.__name__, src))
+            extra_compile_args = self.extra_f90_compile_args or []
+        else:
+            flavor = ':fix'
+            compiler = self.compiler_fix
+            if compiler is None:
+                raise DistutilsExecError('f90 (fixed) not supported by %s needed for %s'\
+                      % (self.__class__.__name__, src))
+            extra_compile_args = self.extra_f90_compile_args or []
+        if self.object_switch[-1]==' ':
+            o_args = [self.object_switch.strip(), obj]
+        else:
+            o_args = [self.object_switch.strip()+obj]
+
+        assert self.compile_switch.strip()
+        s_args = [self.compile_switch, src]
+
+        if extra_compile_args:
+            log.info('extra %s options: %r' \
+                     % (flavor[1:], ' '.join(extra_compile_args)))
+
+        extra_flags = src_flags.get(self.compiler_type, [])
+        if extra_flags:
+            log.info('using compile options from source: %r' \
+                     % ' '.join(extra_flags))
+
+        command = compiler + cc_args + extra_flags + s_args + o_args \
+                  + extra_postargs + extra_compile_args
+
+        display = '%s: %s' % (os.path.basename(compiler[0]) + flavor,
+                              src)
+        try:
+            self.spawn(command, display=display)
+        except DistutilsExecError as e:
+            msg = str(e)
+            raise CompileError(msg) from None
+
+    def module_options(self, module_dirs, module_build_dir):
+        options = []
+        if self.module_dir_switch is not None:
+            if self.module_dir_switch[-1]==' ':
+                options.extend([self.module_dir_switch.strip(), module_build_dir])
+            else:
+                options.append(self.module_dir_switch.strip()+module_build_dir)
+        else:
+            print('XXX: module_build_dir=%r option ignored' % (module_build_dir))
+            print('XXX: Fix module_dir_switch for ', self.__class__.__name__)
+        if self.module_include_switch is not None:
+            for d in [module_build_dir]+module_dirs:
+                options.append('%s%s' % (self.module_include_switch, d))
+        else:
+            print('XXX: module_dirs=%r option ignored' % (module_dirs))
+            print('XXX: Fix module_include_switch for ', self.__class__.__name__)
+        return options
+
+    def library_option(self, lib):
+        return "-l" + lib
+    def library_dir_option(self, dir):
+        return "-L" + dir
+
+    def link(self, target_desc, objects,
+             output_filename, output_dir=None, libraries=None,
+             library_dirs=None, runtime_library_dirs=None,
+             export_symbols=None, debug=0, extra_preargs=None,
+             extra_postargs=None, build_temp=None, target_lang=None):
+        objects, output_dir = self._fix_object_args(objects, output_dir)
+        libraries, library_dirs, runtime_library_dirs = \
+            self._fix_lib_args(libraries, library_dirs, runtime_library_dirs)
+
+        lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs,
+                                   libraries)
+        if is_string(output_dir):
+            output_filename = os.path.join(output_dir, output_filename)
+        elif output_dir is not None:
+            raise TypeError("'output_dir' must be a string or None")
+
+        if self._need_link(objects, output_filename):
+            if self.library_switch[-1]==' ':
+                o_args = [self.library_switch.strip(), output_filename]
+            else:
+                o_args = [self.library_switch.strip()+output_filename]
+
+            if is_string(self.objects):
+                ld_args = objects + [self.objects]
+            else:
+                ld_args = objects + self.objects
+            ld_args = ld_args + lib_opts + o_args
+            if debug:
+                ld_args[:0] = ['-g']
+            if extra_preargs:
+                ld_args[:0] = extra_preargs
+            if extra_postargs:
+                ld_args.extend(extra_postargs)
+            self.mkpath(os.path.dirname(output_filename))
+            if target_desc == CCompiler.EXECUTABLE:
+                linker = self.linker_exe[:]
+            else:
+                linker = self.linker_so[:]
+            command = linker + ld_args
+            try:
+                self.spawn(command)
+            except DistutilsExecError as e:
+                msg = str(e)
+                raise LinkError(msg) from None
+        else:
+            log.debug("skipping %s (up-to-date)", output_filename)
+
+    def _environment_hook(self, name, hook_name):
+        if hook_name is None:
+            return None
+        if is_string(hook_name):
+            if hook_name.startswith('self.'):
+                hook_name = hook_name[5:]
+                hook = getattr(self, hook_name)
+                return hook()
+            elif hook_name.startswith('exe.'):
+                hook_name = hook_name[4:]
+                var = self.executables[hook_name]
+                if var:
+                    return var[0]
+                else:
+                    return None
+            elif hook_name.startswith('flags.'):
+                hook_name = hook_name[6:]
+                hook = getattr(self, 'get_flags_' + hook_name)
+                return hook()
+        else:
+            return hook_name()
+
+    def can_ccompiler_link(self, ccompiler):
+        """
+        Check if the given C compiler can link objects produced by
+        this compiler.
+        """
+        return True
+
+    def wrap_unlinkable_objects(self, objects, output_dir, extra_dll_dir):
+        """
+        Convert a set of object files that are not compatible with the default
+        linker, to a file that is compatible.
+
+        Parameters
+        ----------
+        objects : list
+            List of object files to include.
+        output_dir : str
+            Output directory to place generated object files.
+        extra_dll_dir : str
+            Output directory to place extra DLL files that need to be
+            included on Windows.
+
+        Returns
+        -------
+        converted_objects : list of str
+             List of converted object files.
+             Note that the number of output files is not necessarily
+             the same as inputs.
+
+        """
+        raise NotImplementedError()
+
+    ## class FCompiler
+
+_default_compilers = (
+    # sys.platform mappings
+    ('win32', ('gnu', 'intelv', 'absoft', 'compaqv', 'intelev', 'gnu95', 'g95',
+               'intelvem', 'intelem', 'flang')),
+    ('cygwin.*', ('gnu', 'intelv', 'absoft', 'compaqv', 'intelev', 'gnu95', 'g95')),
+    ('linux.*', ('arm', 'gnu95', 'intel', 'lahey', 'pg', 'nv', 'absoft', 'nag',
+                 'vast', 'compaq', 'intele', 'intelem', 'gnu', 'g95', 
+                 'pathf95', 'nagfor', 'fujitsu')),
+    ('darwin.*', ('gnu95', 'nag', 'nagfor', 'absoft', 'ibm', 'intel', 'gnu',
+                 'g95', 'pg')),
+    ('sunos.*', ('sun', 'gnu', 'gnu95', 'g95')),
+    ('irix.*', ('mips', 'gnu', 'gnu95',)),
+    ('aix.*', ('ibm', 'gnu', 'gnu95',)),
+    # os.name mappings
+    ('posix', ('gnu', 'gnu95',)),
+    ('nt', ('gnu', 'gnu95',)),
+    ('mac', ('gnu95', 'gnu', 'pg')),
+    )
+
+fcompiler_class = None
+fcompiler_aliases = None
+
+def load_all_fcompiler_classes():
+    """Cache all the FCompiler classes found in modules in the
+    numpy.distutils.fcompiler package.
+    """
+    from glob import glob
+    global fcompiler_class, fcompiler_aliases
+    if fcompiler_class is not None:
+        return
+    pys = os.path.join(os.path.dirname(__file__), '*.py')
+    fcompiler_class = {}
+    fcompiler_aliases = {}
+    for fname in glob(pys):
+        module_name, ext = os.path.splitext(os.path.basename(fname))
+        module_name = 'numpy.distutils.fcompiler.' + module_name
+        __import__ (module_name)
+        module = sys.modules[module_name]
+        if hasattr(module, 'compilers'):
+            for cname in module.compilers:
+                klass = getattr(module, cname)
+                desc = (klass.compiler_type, klass, klass.description)
+                fcompiler_class[klass.compiler_type] = desc
+                for alias in klass.compiler_aliases:
+                    if alias in fcompiler_aliases:
+                        raise ValueError("alias %r defined for both %s and %s"
+                                         % (alias, klass.__name__,
+                                            fcompiler_aliases[alias][1].__name__))
+                    fcompiler_aliases[alias] = desc
+
+def _find_existing_fcompiler(compiler_types,
+                             osname=None, platform=None,
+                             requiref90=False,
+                             c_compiler=None):
+    from numpy.distutils.core import get_distribution
+    dist = get_distribution(always=True)
+    for compiler_type in compiler_types:
+        v = None
+        try:
+            c = new_fcompiler(plat=platform, compiler=compiler_type,
+                              c_compiler=c_compiler)
+            c.customize(dist)
+            v = c.get_version()
+            if requiref90 and c.compiler_f90 is None:
+                v = None
+                new_compiler = c.suggested_f90_compiler
+                if new_compiler:
+                    log.warn('Trying %r compiler as suggested by %r '
+                             'compiler for f90 support.' % (compiler_type,
+                                                            new_compiler))
+                    c = new_fcompiler(plat=platform, compiler=new_compiler,
+                                      c_compiler=c_compiler)
+                    c.customize(dist)
+                    v = c.get_version()
+                    if v is not None:
+                        compiler_type = new_compiler
+            if requiref90 and c.compiler_f90 is None:
+                raise ValueError('%s does not support compiling f90 codes, '
+                                 'skipping.' % (c.__class__.__name__))
+        except DistutilsModuleError:
+            log.debug("_find_existing_fcompiler: compiler_type='%s' raised DistutilsModuleError", compiler_type)
+        except CompilerNotFound:
+            log.debug("_find_existing_fcompiler: compiler_type='%s' not found", compiler_type)
+        if v is not None:
+            return compiler_type
+    return None
+
+def available_fcompilers_for_platform(osname=None, platform=None):
+    if osname is None:
+        osname = os.name
+    if platform is None:
+        platform = sys.platform
+    matching_compiler_types = []
+    for pattern, compiler_type in _default_compilers:
+        if re.match(pattern, platform) or re.match(pattern, osname):
+            for ct in compiler_type:
+                if ct not in matching_compiler_types:
+                    matching_compiler_types.append(ct)
+    if not matching_compiler_types:
+        matching_compiler_types.append('gnu')
+    return matching_compiler_types
+
+def get_default_fcompiler(osname=None, platform=None, requiref90=False,
+                          c_compiler=None):
+    """Determine the default Fortran compiler to use for the given
+    platform."""
+    matching_compiler_types = available_fcompilers_for_platform(osname,
+                                                                platform)
+    log.info("get_default_fcompiler: matching types: '%s'",
+             matching_compiler_types)
+    compiler_type =  _find_existing_fcompiler(matching_compiler_types,
+                                              osname=osname,
+                                              platform=platform,
+                                              requiref90=requiref90,
+                                              c_compiler=c_compiler)
+    return compiler_type
+
+# Flag to avoid rechecking for Fortran compiler every time
+failed_fcompilers = set()
+
+def new_fcompiler(plat=None,
+                  compiler=None,
+                  verbose=0,
+                  dry_run=0,
+                  force=0,
+                  requiref90=False,
+                  c_compiler = None):
+    """Generate an instance of some FCompiler subclass for the supplied
+    platform/compiler combination.
+    """
+    global failed_fcompilers
+    fcompiler_key = (plat, compiler)
+    if fcompiler_key in failed_fcompilers:
+        return None
+
+    load_all_fcompiler_classes()
+    if plat is None:
+        plat = os.name
+    if compiler is None:
+        compiler = get_default_fcompiler(plat, requiref90=requiref90,
+                                         c_compiler=c_compiler)
+    if compiler in fcompiler_class:
+        module_name, klass, long_description = fcompiler_class[compiler]
+    elif compiler in fcompiler_aliases:
+        module_name, klass, long_description = fcompiler_aliases[compiler]
+    else:
+        msg = "don't know how to compile Fortran code on platform '%s'" % plat
+        if compiler is not None:
+            msg = msg + " with '%s' compiler." % compiler
+            msg = msg + " Supported compilers are: %s)" \
+                  % (','.join(fcompiler_class.keys()))
+        log.warn(msg)
+        failed_fcompilers.add(fcompiler_key)
+        return None
+
+    compiler = klass(verbose=verbose, dry_run=dry_run, force=force)
+    compiler.c_compiler = c_compiler
+    return compiler
+
+def show_fcompilers(dist=None):
+    """Print list of available compilers (used by the "--help-fcompiler"
+    option to "config_fc").
+    """
+    if dist is None:
+        from distutils.dist import Distribution
+        from numpy.distutils.command.config_compiler import config_fc
+        dist = Distribution()
+        dist.script_name = os.path.basename(sys.argv[0])
+        dist.script_args = ['config_fc'] + sys.argv[1:]
+        try:
+            dist.script_args.remove('--help-fcompiler')
+        except ValueError:
+            pass
+        dist.cmdclass['config_fc'] = config_fc
+        dist.parse_config_files()
+        dist.parse_command_line()
+    compilers = []
+    compilers_na = []
+    compilers_ni = []
+    if not fcompiler_class:
+        load_all_fcompiler_classes()
+    platform_compilers = available_fcompilers_for_platform()
+    for compiler in platform_compilers:
+        v = None
+        log.set_verbosity(-2)
+        try:
+            c = new_fcompiler(compiler=compiler, verbose=dist.verbose)
+            c.customize(dist)
+            v = c.get_version()
+        except (DistutilsModuleError, CompilerNotFound) as e:
+            log.debug("show_fcompilers: %s not found" % (compiler,))
+            log.debug(repr(e))
+
+        if v is None:
+            compilers_na.append(("fcompiler="+compiler, None,
+                              fcompiler_class[compiler][2]))
+        else:
+            c.dump_properties()
+            compilers.append(("fcompiler="+compiler, None,
+                              fcompiler_class[compiler][2] + ' (%s)' % v))
+
+    compilers_ni = list(set(fcompiler_class.keys()) - set(platform_compilers))
+    compilers_ni = [("fcompiler="+fc, None, fcompiler_class[fc][2])
+                    for fc in compilers_ni]
+
+    compilers.sort()
+    compilers_na.sort()
+    compilers_ni.sort()
+    pretty_printer = FancyGetopt(compilers)
+    pretty_printer.print_help("Fortran compilers found:")
+    pretty_printer = FancyGetopt(compilers_na)
+    pretty_printer.print_help("Compilers available for this "
+                              "platform, but not found:")
+    if compilers_ni:
+        pretty_printer = FancyGetopt(compilers_ni)
+        pretty_printer.print_help("Compilers not available on this platform:")
+    print("For compiler details, run 'config_fc --verbose' setup command.")
+
+
+def dummy_fortran_file():
+    fo, name = make_temp_file(suffix='.f')
+    fo.write("      subroutine dummy()\n      end\n")
+    fo.close()
+    return name[:-2]
+
+
+_has_f_header = re.compile(r'-\*-\s*fortran\s*-\*-', re.I).search
+_has_f90_header = re.compile(r'-\*-\s*f90\s*-\*-', re.I).search
+_has_fix_header = re.compile(r'-\*-\s*fix\s*-\*-', re.I).search
+_free_f90_start = re.compile(r'[^c*!]\s*[^\s\d\t]', re.I).match
+
+def is_free_format(file):
+    """Check if file is in free format Fortran."""
+    # f90 allows both fixed and free format, assuming fixed unless
+    # signs of free format are detected.
+    result = 0
+    with open(file, encoding='latin1') as f:
+        line = f.readline()
+        n = 10000 # the number of non-comment lines to scan for hints
+        if _has_f_header(line) or _has_fix_header(line):
+            n = 0
+        elif _has_f90_header(line):
+            n = 0
+            result = 1
+        while n>0 and line:
+            line = line.rstrip()
+            if line and line[0]!='!':
+                n -= 1
+                if (line[0]!='\t' and _free_f90_start(line[:5])) or line[-1:]=='&':
+                    result = 1
+                    break
+            line = f.readline()
+    return result
+
+def has_f90_header(src):
+    with open(src, encoding='latin1') as f:
+        line = f.readline()
+    return _has_f90_header(line) or _has_fix_header(line)
+
+_f77flags_re = re.compile(r'(c|)f77flags\s*\(\s*(?P\w+)\s*\)\s*=\s*(?P.*)', re.I)
+def get_f77flags(src):
+    """
+    Search the first 20 lines of fortran 77 code for line pattern
+      `CF77FLAGS()=`
+    Return a dictionary {:}.
+    """
+    flags = {}
+    with open(src, encoding='latin1') as f:
+        i = 0
+        for line in f:
+            i += 1
+            if i>20: break
+            m = _f77flags_re.match(line)
+            if not m: continue
+            fcname = m.group('fcname').strip()
+            fflags = m.group('fflags').strip()
+            flags[fcname] = split_quoted(fflags)
+    return flags
+
+# TODO: implement get_f90flags and use it in _compile similarly to get_f77flags
+
+if __name__ == '__main__':
+    show_fcompilers()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/absoft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/absoft.py
new file mode 100644
index 0000000000000000000000000000000000000000..68f516b92751fd12343d0f3c9375b3e43e587247
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/absoft.py
@@ -0,0 +1,156 @@
+
+# Absoft Corporation ceased operations on 12/31/2022.
+# Thus, all links to  are invalid.
+
+# Notes:
+# - when using -g77 then use -DUNDERSCORE_G77 to compile f2py
+#   generated extension modules (works for f2py v2.45.241_1936 and up)
+import os
+
+from numpy.distutils.cpuinfo import cpu
+from numpy.distutils.fcompiler import FCompiler, dummy_fortran_file
+from numpy.distutils.misc_util import cyg2win32
+
+compilers = ['AbsoftFCompiler']
+
+class AbsoftFCompiler(FCompiler):
+
+    compiler_type = 'absoft'
+    description = 'Absoft Corp Fortran Compiler'
+    #version_pattern = r'FORTRAN 77 Compiler (?P[^\s*,]*).*?Absoft Corp'
+    version_pattern = r'(f90:.*?(Absoft Pro FORTRAN Version|FORTRAN 77 Compiler|Absoft Fortran Compiler Version|Copyright Absoft Corporation.*?Version))'+\
+                       r' (?P[^\s*,]*)(.*?Absoft Corp|)'
+
+    # on windows: f90 -V -c dummy.f
+    # f90: Copyright Absoft Corporation 1994-1998 mV2; Cray Research, Inc. 1994-1996 CF90 (2.x.x.x  f36t87) Version 2.3 Wed Apr 19, 2006  13:05:16
+
+    # samt5735(8)$ f90 -V -c dummy.f
+    # f90: Copyright Absoft Corporation 1994-2002; Absoft Pro FORTRAN Version 8.0
+    # Note that fink installs g77 as f77, so need to use f90 for detection.
+
+    executables = {
+        'version_cmd'  : None,          # set by update_executables
+        'compiler_f77' : ["f77"],
+        'compiler_fix' : ["f90"],
+        'compiler_f90' : ["f90"],
+        'linker_so'    : [""],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    if os.name=='nt':
+        library_switch = '/out:'      #No space after /out:!
+
+    module_dir_switch = None
+    module_include_switch = '-p'
+
+    def update_executables(self):
+        f = cyg2win32(dummy_fortran_file())
+        self.executables['version_cmd'] = ['', '-V', '-c',
+                                           f+'.f', '-o', f+'.o']
+
+    def get_flags_linker_so(self):
+        if os.name=='nt':
+            opt = ['/dll']
+        # The "-K shared" switches are being left in for pre-9.0 versions
+        # of Absoft though I don't think versions earlier than 9 can
+        # actually be used to build shared libraries.  In fact, version
+        # 8 of Absoft doesn't recognize "-K shared" and will fail.
+        elif self.get_version() >= '9.0':
+            opt = ['-shared']
+        else:
+            opt = ["-K", "shared"]
+        return opt
+
+    def library_dir_option(self, dir):
+        if os.name=='nt':
+            return ['-link', '/PATH:%s' % (dir)]
+        return "-L" + dir
+
+    def library_option(self, lib):
+        if os.name=='nt':
+            return '%s.lib' % (lib)
+        return "-l" + lib
+
+    def get_library_dirs(self):
+        opt = FCompiler.get_library_dirs(self)
+        d = os.environ.get('ABSOFT')
+        if d:
+            if self.get_version() >= '10.0':
+                # use shared libraries, the static libraries were not compiled -fPIC
+                prefix = 'sh'
+            else:
+                prefix = ''
+            if cpu.is_64bit():
+                suffix = '64'
+            else:
+                suffix = ''
+            opt.append(os.path.join(d, '%slib%s' % (prefix, suffix)))
+        return opt
+
+    def get_libraries(self):
+        opt = FCompiler.get_libraries(self)
+        if self.get_version() >= '11.0':
+            opt.extend(['af90math', 'afio', 'af77math', 'amisc'])
+        elif self.get_version() >= '10.0':
+            opt.extend(['af90math', 'afio', 'af77math', 'U77'])
+        elif self.get_version() >= '8.0':
+            opt.extend(['f90math', 'fio', 'f77math', 'U77'])
+        else:
+            opt.extend(['fio', 'f90math', 'fmath', 'U77'])
+        if os.name =='nt':
+            opt.append('COMDLG32')
+        return opt
+
+    def get_flags(self):
+        opt = FCompiler.get_flags(self)
+        if os.name != 'nt':
+            opt.extend(['-s'])
+            if self.get_version():
+                if self.get_version()>='8.2':
+                    opt.append('-fpic')
+        return opt
+
+    def get_flags_f77(self):
+        opt = FCompiler.get_flags_f77(self)
+        opt.extend(['-N22', '-N90', '-N110'])
+        v = self.get_version()
+        if os.name == 'nt':
+            if v and v>='8.0':
+                opt.extend(['-f', '-N15'])
+        else:
+            opt.append('-f')
+            if v:
+                if v<='4.6':
+                    opt.append('-B108')
+                else:
+                    # Though -N15 is undocumented, it works with
+                    # Absoft 8.0 on Linux
+                    opt.append('-N15')
+        return opt
+
+    def get_flags_f90(self):
+        opt = FCompiler.get_flags_f90(self)
+        opt.extend(["-YCFRL=1", "-YCOM_NAMES=LCS", "-YCOM_PFX", "-YEXT_PFX",
+                    "-YCOM_SFX=_", "-YEXT_SFX=_", "-YEXT_NAMES=LCS"])
+        if self.get_version():
+            if self.get_version()>'4.6':
+                opt.extend(["-YDEALLOC=ALL"])
+        return opt
+
+    def get_flags_fix(self):
+        opt = FCompiler.get_flags_fix(self)
+        opt.extend(["-YCFRL=1", "-YCOM_NAMES=LCS", "-YCOM_PFX", "-YEXT_PFX",
+                    "-YCOM_SFX=_", "-YEXT_SFX=_", "-YEXT_NAMES=LCS"])
+        opt.extend(["-f", "fixed"])
+        return opt
+
+    def get_flags_opt(self):
+        opt = ['-O']
+        return opt
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='absoft').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/arm.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/arm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eb7e9af9c8ce3c5834f9e4d7283746961ea0981
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/arm.py
@@ -0,0 +1,71 @@
+import sys                                                                     
+                                                                               
+from numpy.distutils.fcompiler import FCompiler, dummy_fortran_file
+from sys import platform                                                       
+from os.path import join, dirname, normpath
+
+compilers = ['ArmFlangCompiler']
+
+import functools
+
+class ArmFlangCompiler(FCompiler):
+    compiler_type = 'arm'
+    description = 'Arm Compiler'
+    version_pattern = r'\s*Arm.*version (?P[\d.-]+).*'
+
+    ar_exe = 'lib.exe'
+    possible_executables = ['armflang']
+
+    executables = {
+        'version_cmd': ["", "--version"],
+        'compiler_f77': ["armflang", "-fPIC"],
+        'compiler_fix': ["armflang", "-fPIC", "-ffixed-form"],
+        'compiler_f90': ["armflang", "-fPIC"],
+        'linker_so': ["armflang", "-fPIC", "-shared"],
+        'archiver': ["ar", "-cr"],
+        'ranlib':  None
+    }
+
+    pic_flags = ["-fPIC", "-DPIC"]
+    c_compiler = 'arm'
+    module_dir_switch = '-module '  # Don't remove ending space!
+
+    def get_libraries(self):
+        opt = FCompiler.get_libraries(self)
+        opt.extend(['flang', 'flangrti', 'ompstub'])
+        return opt
+
+    @functools.lru_cache(maxsize=128)
+    def get_library_dirs(self):
+        """List of compiler library directories."""
+        opt = FCompiler.get_library_dirs(self)
+        flang_dir = dirname(self.executables['compiler_f77'][0])
+        opt.append(normpath(join(flang_dir, '..', 'lib')))
+
+        return opt
+
+    def get_flags(self):
+        return []
+
+    def get_flags_free(self):
+        return []
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_opt(self):
+        return ['-O3']
+
+    def get_flags_arch(self):
+        return []
+
+    def runtime_library_dir_option(self, dir):
+        return '-Wl,-rpath=%s' % dir
+
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='armflang').get_version())
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/compaq.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/compaq.py
new file mode 100644
index 0000000000000000000000000000000000000000..01314c136acff7171298dc2819db4b50d7eec091
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/compaq.py
@@ -0,0 +1,120 @@
+
+#http://www.compaq.com/fortran/docs/
+import os
+import sys
+
+from numpy.distutils.fcompiler import FCompiler
+from distutils.errors import DistutilsPlatformError
+
+compilers = ['CompaqFCompiler']
+if os.name != 'posix' or sys.platform[:6] == 'cygwin' :
+    # Otherwise we'd get a false positive on posix systems with
+    # case-insensitive filesystems (like darwin), because we'll pick
+    # up /bin/df
+    compilers.append('CompaqVisualFCompiler')
+
+class CompaqFCompiler(FCompiler):
+
+    compiler_type = 'compaq'
+    description = 'Compaq Fortran Compiler'
+    version_pattern = r'Compaq Fortran (?P[^\s]*).*'
+
+    if sys.platform[:5]=='linux':
+        fc_exe = 'fort'
+    else:
+        fc_exe = 'f90'
+
+    executables = {
+        'version_cmd'  : ['', "-version"],
+        'compiler_f77' : [fc_exe, "-f77rtl", "-fixed"],
+        'compiler_fix' : [fc_exe, "-fixed"],
+        'compiler_f90' : [fc_exe],
+        'linker_so'    : [''],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    module_dir_switch = '-module ' # not tested
+    module_include_switch = '-I'
+
+    def get_flags(self):
+        return ['-assume no2underscore', '-nomixed_str_len_arg']
+    def get_flags_debug(self):
+        return ['-g', '-check bounds']
+    def get_flags_opt(self):
+        return ['-O4', '-align dcommons', '-assume bigarrays',
+                '-assume nozsize', '-math_library fast']
+    def get_flags_arch(self):
+        return ['-arch host', '-tune host']
+    def get_flags_linker_so(self):
+        if sys.platform[:5]=='linux':
+            return ['-shared']
+        return ['-shared', '-Wl,-expect_unresolved,*']
+
+class CompaqVisualFCompiler(FCompiler):
+
+    compiler_type = 'compaqv'
+    description = 'DIGITAL or Compaq Visual Fortran Compiler'
+    version_pattern = (r'(DIGITAL|Compaq) Visual Fortran Optimizing Compiler'
+                       r' Version (?P[^\s]*).*')
+
+    compile_switch = '/compile_only'
+    object_switch = '/object:'
+    library_switch = '/OUT:'      #No space after /OUT:!
+
+    static_lib_extension = ".lib"
+    static_lib_format = "%s%s"
+    module_dir_switch = '/module:'
+    module_include_switch = '/I'
+
+    ar_exe = 'lib.exe'
+    fc_exe = 'DF'
+
+    if sys.platform=='win32':
+        from numpy.distutils.msvccompiler import MSVCCompiler
+
+        try:
+            m = MSVCCompiler()
+            m.initialize()
+            ar_exe = m.lib
+        except DistutilsPlatformError:
+            pass
+        except AttributeError as e:
+            if '_MSVCCompiler__root' in str(e):
+                print('Ignoring "%s" (I think it is msvccompiler.py bug)' % (e))
+            else:
+                raise
+        except OSError as e:
+            if not "vcvarsall.bat" in str(e):
+                print("Unexpected OSError in", __file__)
+                raise
+        except ValueError as e:
+            if not "'path'" in str(e):
+                print("Unexpected ValueError in", __file__)
+                raise
+
+    executables = {
+        'version_cmd'  : ['', "/what"],
+        'compiler_f77' : [fc_exe, "/f77rtl", "/fixed"],
+        'compiler_fix' : [fc_exe, "/fixed"],
+        'compiler_f90' : [fc_exe],
+        'linker_so'    : [''],
+        'archiver'     : [ar_exe, "/OUT:"],
+        'ranlib'       : None
+        }
+
+    def get_flags(self):
+        return ['/nologo', '/MD', '/WX', '/iface=(cref,nomixed_str_len_arg)',
+                '/names:lowercase', '/assume:underscore']
+    def get_flags_opt(self):
+        return ['/Ox', '/fast', '/optimize:5', '/unroll:0', '/math_library:fast']
+    def get_flags_arch(self):
+        return ['/threads']
+    def get_flags_debug(self):
+        return ['/debug']
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='compaq').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/environment.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecd4d998927961f185dd0ddb498136a4f3581d0e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/environment.py
@@ -0,0 +1,88 @@
+import os
+from distutils.dist import Distribution
+
+__metaclass__ = type
+
+class EnvironmentConfig:
+    def __init__(self, distutils_section='ALL', **kw):
+        self._distutils_section = distutils_section
+        self._conf_keys = kw
+        self._conf = None
+        self._hook_handler = None
+
+    def dump_variable(self, name):
+        conf_desc = self._conf_keys[name]
+        hook, envvar, confvar, convert, append = conf_desc
+        if not convert:
+            convert = lambda x : x
+        print('%s.%s:' % (self._distutils_section, name))
+        v = self._hook_handler(name, hook)
+        print('  hook   : %s' % (convert(v),))
+        if envvar:
+            v = os.environ.get(envvar, None)
+            print('  environ: %s' % (convert(v),))
+        if confvar and self._conf:
+            v = self._conf.get(confvar, (None, None))[1]
+            print('  config : %s' % (convert(v),))
+
+    def dump_variables(self):
+        for name in self._conf_keys:
+            self.dump_variable(name)
+
+    def __getattr__(self, name):
+        try:
+            conf_desc = self._conf_keys[name]
+        except KeyError:
+            raise AttributeError(
+                f"'EnvironmentConfig' object has no attribute '{name}'"
+            ) from None
+
+        return self._get_var(name, conf_desc)
+
+    def get(self, name, default=None):
+        try:
+            conf_desc = self._conf_keys[name]
+        except KeyError:
+            return default
+        var = self._get_var(name, conf_desc)
+        if var is None:
+            var = default
+        return var
+
+    def _get_var(self, name, conf_desc):
+        hook, envvar, confvar, convert, append = conf_desc
+        if convert is None:
+            convert = lambda x: x
+        var = self._hook_handler(name, hook)
+        if envvar is not None:
+            envvar_contents = os.environ.get(envvar)
+            if envvar_contents is not None:
+                envvar_contents = convert(envvar_contents)
+                if var and append:
+                    if os.environ.get('NPY_DISTUTILS_APPEND_FLAGS', '1') == '1':
+                        var.extend(envvar_contents)
+                    else:
+                        # NPY_DISTUTILS_APPEND_FLAGS was explicitly set to 0
+                        # to keep old (overwrite flags rather than append to
+                        # them) behavior
+                        var = envvar_contents
+                else:
+                    var = envvar_contents
+        if confvar is not None and self._conf:
+            if confvar in self._conf:
+                source, confvar_contents = self._conf[confvar]
+                var = convert(confvar_contents)
+        return var
+
+
+    def clone(self, hook_handler):
+        ec = self.__class__(distutils_section=self._distutils_section,
+                            **self._conf_keys)
+        ec._hook_handler = hook_handler
+        return ec
+
+    def use_distribution(self, dist):
+        if isinstance(dist, Distribution):
+            self._conf = dist.get_option_dict(self._distutils_section)
+        else:
+            self._conf = dist
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/fujitsu.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/fujitsu.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddce67456d181e4e8b19a5f5387572b4a9e5b29a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/fujitsu.py
@@ -0,0 +1,46 @@
+"""
+fujitsu
+
+Supports Fujitsu compiler function.
+This compiler is developed by Fujitsu and is used in A64FX on Fugaku.
+"""
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['FujitsuFCompiler']
+
+class FujitsuFCompiler(FCompiler):
+    compiler_type = 'fujitsu'
+    description = 'Fujitsu Fortran Compiler'
+
+    possible_executables = ['frt']
+    version_pattern = r'frt \(FRT\) (?P[a-z\d.]+)'
+    # $ frt --version
+    # frt (FRT) x.x.x yyyymmdd
+
+    executables = {
+        'version_cmd'  : ["", "--version"],
+        'compiler_f77' : ["frt", "-Fixed"],
+        'compiler_fix' : ["frt", "-Fixed"],
+        'compiler_f90' : ["frt"],
+        'linker_so'    : ["frt", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+    pic_flags = ['-KPIC']
+    module_dir_switch = '-M'
+    module_include_switch = '-I'
+
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_flags_debug(self):
+        return ['-g']
+    def runtime_library_dir_option(self, dir):
+        return f'-Wl,-rpath={dir}'
+    def get_libraries(self):
+        return ['fj90f', 'fj90i', 'fjsrcinfo']
+
+if __name__ == '__main__':
+    from distutils import log
+    from numpy.distutils import customized_fcompiler
+    log.set_verbosity(2)
+    print(customized_fcompiler('fujitsu').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/g95.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/g95.py
new file mode 100644
index 0000000000000000000000000000000000000000..e109a972a8729ba886f86b16530b2ed315dcce8b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/g95.py
@@ -0,0 +1,42 @@
+# http://g95.sourceforge.net/
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['G95FCompiler']
+
+class G95FCompiler(FCompiler):
+    compiler_type = 'g95'
+    description = 'G95 Fortran Compiler'
+
+#    version_pattern = r'G95 \((GCC (?P[\d.]+)|.*?) \(g95!\) (?P.*)\).*'
+    # $ g95 --version
+    # G95 (GCC 4.0.3 (g95!) May 22 2006)
+
+    version_pattern = r'G95 \((GCC (?P[\d.]+)|.*?) \(g95 (?P.*)!\) (?P.*)\).*'
+    # $ g95 --version
+    # G95 (GCC 4.0.3 (g95 0.90!) Aug 22 2006)
+
+    executables = {
+        'version_cmd'  : ["", "--version"],
+        'compiler_f77' : ["g95", "-ffixed-form"],
+        'compiler_fix' : ["g95", "-ffixed-form"],
+        'compiler_f90' : ["g95"],
+        'linker_so'    : ["", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+    pic_flags = ['-fpic']
+    module_dir_switch = '-fmod='
+    module_include_switch = '-I'
+
+    def get_flags(self):
+        return ['-fno-second-underscore']
+    def get_flags_opt(self):
+        return ['-O']
+    def get_flags_debug(self):
+        return ['-g']
+
+if __name__ == '__main__':
+    from distutils import log
+    from numpy.distutils import customized_fcompiler
+    log.set_verbosity(2)
+    print(customized_fcompiler('g95').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/gnu.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/gnu.py
new file mode 100644
index 0000000000000000000000000000000000000000..3472b5d4c0951cf4501436614a28375bea2a8cef
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/gnu.py
@@ -0,0 +1,555 @@
+import re
+import os
+import sys
+import warnings
+import platform
+import tempfile
+import hashlib
+import base64
+import subprocess
+from subprocess import Popen, PIPE, STDOUT
+from numpy.distutils.exec_command import filepath_from_subprocess_output
+from numpy.distutils.fcompiler import FCompiler
+from distutils.version import LooseVersion
+
+compilers = ['GnuFCompiler', 'Gnu95FCompiler']
+
+TARGET_R = re.compile(r"Target: ([a-zA-Z0-9_\-]*)")
+
+# XXX: handle cross compilation
+
+
+def is_win64():
+    return sys.platform == "win32" and platform.architecture()[0] == "64bit"
+
+
+class GnuFCompiler(FCompiler):
+    compiler_type = 'gnu'
+    compiler_aliases = ('g77', )
+    description = 'GNU Fortran 77 compiler'
+
+    def gnu_version_match(self, version_string):
+        """Handle the different versions of GNU fortran compilers"""
+        # Strip warning(s) that may be emitted by gfortran
+        while version_string.startswith('gfortran: warning'):
+            version_string =\
+                version_string[version_string.find('\n') + 1:].strip()
+
+        # Gfortran versions from after 2010 will output a simple string
+        # (usually "x.y", "x.y.z" or "x.y.z-q") for ``-dumpversion``; older
+        # gfortrans may still return long version strings (``-dumpversion`` was
+        # an alias for ``--version``)
+        if len(version_string) <= 20:
+            # Try to find a valid version string
+            m = re.search(r'([0-9.]+)', version_string)
+            if m:
+                # g77 provides a longer version string that starts with GNU
+                # Fortran
+                if version_string.startswith('GNU Fortran'):
+                    return ('g77', m.group(1))
+
+                # gfortran only outputs a version string such as #.#.#, so check
+                # if the match is at the start of the string
+                elif m.start() == 0:
+                    return ('gfortran', m.group(1))
+        else:
+            # Output probably from --version, try harder:
+            m = re.search(r'GNU Fortran\s+95.*?([0-9-.]+)', version_string)
+            if m:
+                return ('gfortran', m.group(1))
+            m = re.search(
+                r'GNU Fortran.*?\-?([0-9-.]+\.[0-9-.]+)', version_string)
+            if m:
+                v = m.group(1)
+                if v.startswith('0') or v.startswith('2') or v.startswith('3'):
+                    # the '0' is for early g77's
+                    return ('g77', v)
+                else:
+                    # at some point in the 4.x series, the ' 95' was dropped
+                    # from the version string
+                    return ('gfortran', v)
+
+        # If still nothing, raise an error to make the problem easy to find.
+        err = 'A valid Fortran version was not found in this string:\n'
+        raise ValueError(err + version_string)
+
+    def version_match(self, version_string):
+        v = self.gnu_version_match(version_string)
+        if not v or v[0] != 'g77':
+            return None
+        return v[1]
+
+    possible_executables = ['g77', 'f77']
+    executables = {
+        'version_cmd'  : [None, "-dumpversion"],
+        'compiler_f77' : [None, "-g", "-Wall", "-fno-second-underscore"],
+        'compiler_f90' : None,  # Use --fcompiler=gnu95 for f90 codes
+        'compiler_fix' : None,
+        'linker_so'    : [None, "-g", "-Wall"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"],
+        'linker_exe'   : [None, "-g", "-Wall"]
+    }
+    module_dir_switch = None
+    module_include_switch = None
+
+    # Cygwin: f771: warning: -fPIC ignored for target (all code is
+    # position independent)
+    if os.name != 'nt' and sys.platform != 'cygwin':
+        pic_flags = ['-fPIC']
+
+    # use -mno-cygwin for g77 when Python is not Cygwin-Python
+    if sys.platform == 'win32':
+        for key in ['version_cmd', 'compiler_f77', 'linker_so', 'linker_exe']:
+            executables[key].append('-mno-cygwin')
+
+    g2c = 'g2c'
+    suggested_f90_compiler = 'gnu95'
+
+    def get_flags_linker_so(self):
+        opt = self.linker_so[1:]
+        if sys.platform == 'darwin':
+            target = os.environ.get('MACOSX_DEPLOYMENT_TARGET', None)
+            # If MACOSX_DEPLOYMENT_TARGET is set, we simply trust the value
+            # and leave it alone.  But, distutils will complain if the
+            # environment's value is different from the one in the Python
+            # Makefile used to build Python.  We let distutils handle this
+            # error checking.
+            if not target:
+                # If MACOSX_DEPLOYMENT_TARGET is not set in the environment,
+                # we try to get it first from sysconfig and then
+                # fall back to setting it to 10.9 This is a reasonable default
+                # even when using the official Python dist and those derived
+                # from it.
+                import sysconfig
+                target = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET')
+                if not target:
+                    target = '10.9'
+                    s = f'Env. variable MACOSX_DEPLOYMENT_TARGET set to {target}'
+                    warnings.warn(s, stacklevel=2)
+                os.environ['MACOSX_DEPLOYMENT_TARGET'] = str(target)
+            opt.extend(['-undefined', 'dynamic_lookup', '-bundle'])
+        else:
+            opt.append("-shared")
+        if sys.platform.startswith('sunos'):
+            # SunOS often has dynamically loaded symbols defined in the
+            # static library libg2c.a  The linker doesn't like this.  To
+            # ignore the problem, use the -mimpure-text flag.  It isn't
+            # the safest thing, but seems to work. 'man gcc' says:
+            # ".. Instead of using -mimpure-text, you should compile all
+            #  source code with -fpic or -fPIC."
+            opt.append('-mimpure-text')
+        return opt
+
+    def get_libgcc_dir(self):
+        try:
+            output = subprocess.check_output(self.compiler_f77 +
+                                            ['-print-libgcc-file-name'])
+        except (OSError, subprocess.CalledProcessError):
+            pass
+        else:
+            output = filepath_from_subprocess_output(output)
+            return os.path.dirname(output)
+        return None
+
+    def get_libgfortran_dir(self):
+        if sys.platform[:5] == 'linux':
+            libgfortran_name = 'libgfortran.so'
+        elif sys.platform == 'darwin':
+            libgfortran_name = 'libgfortran.dylib'
+        else:
+            libgfortran_name = None
+
+        libgfortran_dir = None
+        if libgfortran_name:
+            find_lib_arg = ['-print-file-name={0}'.format(libgfortran_name)]
+            try:
+                output = subprocess.check_output(
+                                       self.compiler_f77 + find_lib_arg)
+            except (OSError, subprocess.CalledProcessError):
+                pass
+            else:
+                output = filepath_from_subprocess_output(output)
+                libgfortran_dir = os.path.dirname(output)
+        return libgfortran_dir
+
+    def get_library_dirs(self):
+        opt = []
+        if sys.platform[:5] != 'linux':
+            d = self.get_libgcc_dir()
+            if d:
+                # if windows and not cygwin, libg2c lies in a different folder
+                if sys.platform == 'win32' and not d.startswith('/usr/lib'):
+                    d = os.path.normpath(d)
+                    path = os.path.join(d, "lib%s.a" % self.g2c)
+                    if not os.path.exists(path):
+                        root = os.path.join(d, *((os.pardir, ) * 4))
+                        d2 = os.path.abspath(os.path.join(root, 'lib'))
+                        path = os.path.join(d2, "lib%s.a" % self.g2c)
+                        if os.path.exists(path):
+                            opt.append(d2)
+                opt.append(d)
+        # For Macports / Linux, libgfortran and libgcc are not co-located
+        lib_gfortran_dir = self.get_libgfortran_dir()
+        if lib_gfortran_dir:
+            opt.append(lib_gfortran_dir)
+        return opt
+
+    def get_libraries(self):
+        opt = []
+        d = self.get_libgcc_dir()
+        if d is not None:
+            g2c = self.g2c + '-pic'
+            f = self.static_lib_format % (g2c, self.static_lib_extension)
+            if not os.path.isfile(os.path.join(d, f)):
+                g2c = self.g2c
+        else:
+            g2c = self.g2c
+
+        if g2c is not None:
+            opt.append(g2c)
+        c_compiler = self.c_compiler
+        if sys.platform == 'win32' and c_compiler and \
+                c_compiler.compiler_type == 'msvc':
+            opt.append('gcc')
+        if sys.platform == 'darwin':
+            opt.append('cc_dynamic')
+        return opt
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_opt(self):
+        v = self.get_version()
+        if v and v <= '3.3.3':
+            # With this compiler version building Fortran BLAS/LAPACK
+            # with -O3 caused failures in lib.lapack heevr,syevr tests.
+            opt = ['-O2']
+        else:
+            opt = ['-O3']
+        opt.append('-funroll-loops')
+        return opt
+
+    def _c_arch_flags(self):
+        """ Return detected arch flags from CFLAGS """
+        import sysconfig
+        try:
+            cflags = sysconfig.get_config_vars()['CFLAGS']
+        except KeyError:
+            return []
+        arch_re = re.compile(r"-arch\s+(\w+)")
+        arch_flags = []
+        for arch in arch_re.findall(cflags):
+            arch_flags += ['-arch', arch]
+        return arch_flags
+
+    def get_flags_arch(self):
+        return []
+
+    def runtime_library_dir_option(self, dir):
+        if sys.platform == 'win32' or sys.platform == 'cygwin':
+            # Linux/Solaris/Unix support RPATH, Windows does not
+            raise NotImplementedError
+
+        # TODO: could use -Xlinker here, if it's supported
+        assert "," not in dir
+
+        if sys.platform == 'darwin':
+            return f'-Wl,-rpath,{dir}'
+        elif sys.platform.startswith(('aix', 'os400')):
+            # AIX RPATH is called LIBPATH
+            return f'-Wl,-blibpath:{dir}'
+        else:
+            return f'-Wl,-rpath={dir}'
+
+
+class Gnu95FCompiler(GnuFCompiler):
+    compiler_type = 'gnu95'
+    compiler_aliases = ('gfortran', )
+    description = 'GNU Fortran 95 compiler'
+
+    def version_match(self, version_string):
+        v = self.gnu_version_match(version_string)
+        if not v or v[0] != 'gfortran':
+            return None
+        v = v[1]
+        if LooseVersion(v) >= "4":
+            # gcc-4 series releases do not support -mno-cygwin option
+            pass
+        else:
+            # use -mno-cygwin flag for gfortran when Python is not
+            # Cygwin-Python
+            if sys.platform == 'win32':
+                for key in [
+                        'version_cmd', 'compiler_f77', 'compiler_f90',
+                        'compiler_fix', 'linker_so', 'linker_exe'
+                ]:
+                    self.executables[key].append('-mno-cygwin')
+        return v
+
+    possible_executables = ['gfortran', 'f95']
+    executables = {
+        'version_cmd'  : ["", "-dumpversion"],
+        'compiler_f77' : [None, "-Wall", "-g", "-ffixed-form",
+                          "-fno-second-underscore"],
+        'compiler_f90' : [None, "-Wall", "-g",
+                          "-fno-second-underscore"],
+        'compiler_fix' : [None, "-Wall",  "-g","-ffixed-form",
+                          "-fno-second-underscore"],
+        'linker_so'    : ["", "-Wall", "-g"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"],
+        'linker_exe'   : [None, "-Wall"]
+    }
+
+    module_dir_switch = '-J'
+    module_include_switch = '-I'
+
+    if sys.platform.startswith(('aix', 'os400')):
+        executables['linker_so'].append('-lpthread')
+        if platform.architecture()[0][:2] == '64':
+            for key in ['compiler_f77', 'compiler_f90','compiler_fix','linker_so', 'linker_exe']:
+                executables[key].append('-maix64')
+
+    g2c = 'gfortran'
+
+    def _universal_flags(self, cmd):
+        """Return a list of -arch flags for every supported architecture."""
+        if not sys.platform == 'darwin':
+            return []
+        arch_flags = []
+        # get arches the C compiler gets.
+        c_archs = self._c_arch_flags()
+        if "i386" in c_archs:
+            c_archs[c_archs.index("i386")] = "i686"
+        # check the arches the Fortran compiler supports, and compare with
+        # arch flags from C compiler
+        for arch in ["ppc", "i686", "x86_64", "ppc64", "s390x"]:
+            if _can_target(cmd, arch) and arch in c_archs:
+                arch_flags.extend(["-arch", arch])
+        return arch_flags
+
+    def get_flags(self):
+        flags = GnuFCompiler.get_flags(self)
+        arch_flags = self._universal_flags(self.compiler_f90)
+        if arch_flags:
+            flags[:0] = arch_flags
+        return flags
+
+    def get_flags_linker_so(self):
+        flags = GnuFCompiler.get_flags_linker_so(self)
+        arch_flags = self._universal_flags(self.linker_so)
+        if arch_flags:
+            flags[:0] = arch_flags
+        return flags
+
+    def get_library_dirs(self):
+        opt = GnuFCompiler.get_library_dirs(self)
+        if sys.platform == 'win32':
+            c_compiler = self.c_compiler
+            if c_compiler and c_compiler.compiler_type == "msvc":
+                target = self.get_target()
+                if target:
+                    d = os.path.normpath(self.get_libgcc_dir())
+                    root = os.path.join(d, *((os.pardir, ) * 4))
+                    path = os.path.join(root, "lib")
+                    mingwdir = os.path.normpath(path)
+                    if os.path.exists(os.path.join(mingwdir, "libmingwex.a")):
+                        opt.append(mingwdir)
+        # For Macports / Linux, libgfortran and libgcc are not co-located
+        lib_gfortran_dir = self.get_libgfortran_dir()
+        if lib_gfortran_dir:
+            opt.append(lib_gfortran_dir)
+        return opt
+
+    def get_libraries(self):
+        opt = GnuFCompiler.get_libraries(self)
+        if sys.platform == 'darwin':
+            opt.remove('cc_dynamic')
+        if sys.platform == 'win32':
+            c_compiler = self.c_compiler
+            if c_compiler and c_compiler.compiler_type == "msvc":
+                if "gcc" in opt:
+                    i = opt.index("gcc")
+                    opt.insert(i + 1, "mingwex")
+                    opt.insert(i + 1, "mingw32")
+            c_compiler = self.c_compiler
+            if c_compiler and c_compiler.compiler_type == "msvc":
+                return []
+            else:
+                pass
+        return opt
+
+    def get_target(self):
+        try:
+            p = subprocess.Popen(
+                self.compiler_f77 + ['-v'],
+                stdin=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+            stdout, stderr = p.communicate()
+            output = (stdout or b"") + (stderr or b"")
+        except (OSError, subprocess.CalledProcessError):
+            pass
+        else:
+            output = filepath_from_subprocess_output(output)
+            m = TARGET_R.search(output)
+            if m:
+                return m.group(1)
+        return ""
+
+    def _hash_files(self, filenames):
+        h = hashlib.sha1()
+        for fn in filenames:
+            with open(fn, 'rb') as f:
+                while True:
+                    block = f.read(131072)
+                    if not block:
+                        break
+                    h.update(block)
+        text = base64.b32encode(h.digest())
+        text = text.decode('ascii')
+        return text.rstrip('=')
+
+    def _link_wrapper_lib(self, objects, output_dir, extra_dll_dir,
+                          chained_dlls, is_archive):
+        """Create a wrapper shared library for the given objects
+
+        Return an MSVC-compatible lib
+        """
+
+        c_compiler = self.c_compiler
+        if c_compiler.compiler_type != "msvc":
+            raise ValueError("This method only supports MSVC")
+
+        object_hash = self._hash_files(list(objects) + list(chained_dlls))
+
+        if is_win64():
+            tag = 'win_amd64'
+        else:
+            tag = 'win32'
+
+        basename = 'lib' + os.path.splitext(
+            os.path.basename(objects[0]))[0][:8]
+        root_name = basename + '.' + object_hash + '.gfortran-' + tag
+        dll_name = root_name + '.dll'
+        def_name = root_name + '.def'
+        lib_name = root_name + '.lib'
+        dll_path = os.path.join(extra_dll_dir, dll_name)
+        def_path = os.path.join(output_dir, def_name)
+        lib_path = os.path.join(output_dir, lib_name)
+
+        if os.path.isfile(lib_path):
+            # Nothing to do
+            return lib_path, dll_path
+
+        if is_archive:
+            objects = (["-Wl,--whole-archive"] + list(objects) +
+                       ["-Wl,--no-whole-archive"])
+        self.link_shared_object(
+            objects,
+            dll_name,
+            output_dir=extra_dll_dir,
+            extra_postargs=list(chained_dlls) + [
+                '-Wl,--allow-multiple-definition',
+                '-Wl,--output-def,' + def_path,
+                '-Wl,--export-all-symbols',
+                '-Wl,--enable-auto-import',
+                '-static',
+                '-mlong-double-64',
+            ])
+
+        # No PowerPC!
+        if is_win64():
+            specifier = '/MACHINE:X64'
+        else:
+            specifier = '/MACHINE:X86'
+
+        # MSVC specific code
+        lib_args = ['/def:' + def_path, '/OUT:' + lib_path, specifier]
+        if not c_compiler.initialized:
+            c_compiler.initialize()
+        c_compiler.spawn([c_compiler.lib] + lib_args)
+
+        return lib_path, dll_path
+
+    def can_ccompiler_link(self, compiler):
+        # MSVC cannot link objects compiled by GNU fortran
+        return compiler.compiler_type not in ("msvc", )
+
+    def wrap_unlinkable_objects(self, objects, output_dir, extra_dll_dir):
+        """
+        Convert a set of object files that are not compatible with the default
+        linker, to a file that is compatible.
+        """
+        if self.c_compiler.compiler_type == "msvc":
+            # Compile a DLL and return the lib for the DLL as
+            # the object. Also keep track of previous DLLs that
+            # we have compiled so that we can link against them.
+
+            # If there are .a archives, assume they are self-contained
+            # static libraries, and build separate DLLs for each
+            archives = []
+            plain_objects = []
+            for obj in objects:
+                if obj.lower().endswith('.a'):
+                    archives.append(obj)
+                else:
+                    plain_objects.append(obj)
+
+            chained_libs = []
+            chained_dlls = []
+            for archive in archives[::-1]:
+                lib, dll = self._link_wrapper_lib(
+                    [archive],
+                    output_dir,
+                    extra_dll_dir,
+                    chained_dlls=chained_dlls,
+                    is_archive=True)
+                chained_libs.insert(0, lib)
+                chained_dlls.insert(0, dll)
+
+            if not plain_objects:
+                return chained_libs
+
+            lib, dll = self._link_wrapper_lib(
+                plain_objects,
+                output_dir,
+                extra_dll_dir,
+                chained_dlls=chained_dlls,
+                is_archive=False)
+            return [lib] + chained_libs
+        else:
+            raise ValueError("Unsupported C compiler")
+
+
+def _can_target(cmd, arch):
+    """Return true if the architecture supports the -arch flag"""
+    newcmd = cmd[:]
+    fid, filename = tempfile.mkstemp(suffix=".f")
+    os.close(fid)
+    try:
+        d = os.path.dirname(filename)
+        output = os.path.splitext(filename)[0] + ".o"
+        try:
+            newcmd.extend(["-arch", arch, "-c", filename])
+            p = Popen(newcmd, stderr=STDOUT, stdout=PIPE, cwd=d)
+            p.communicate()
+            return p.returncode == 0
+        finally:
+            if os.path.exists(output):
+                os.remove(output)
+    finally:
+        os.remove(filename)
+
+
+if __name__ == '__main__':
+    from distutils import log
+    from numpy.distutils import customized_fcompiler
+    log.set_verbosity(2)
+
+    print(customized_fcompiler('gnu').get_version())
+    try:
+        print(customized_fcompiler('g95').get_version())
+    except Exception as e:
+        print(e)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/hpux.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/hpux.py
new file mode 100644
index 0000000000000000000000000000000000000000..09e6483bf5adb89fee267a153c82ef76ccb1e12a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/hpux.py
@@ -0,0 +1,41 @@
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['HPUXFCompiler']
+
+class HPUXFCompiler(FCompiler):
+
+    compiler_type = 'hpux'
+    description = 'HP Fortran 90 Compiler'
+    version_pattern =  r'HP F90 (?P[^\s*,]*)'
+
+    executables = {
+        'version_cmd'  : ["f90", "+version"],
+        'compiler_f77' : ["f90"],
+        'compiler_fix' : ["f90"],
+        'compiler_f90' : ["f90"],
+        'linker_so'    : ["ld", "-b"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+    module_dir_switch = None #XXX: fix me
+    module_include_switch = None #XXX: fix me
+    pic_flags = ['+Z']
+    def get_flags(self):
+        return self.pic_flags + ['+ppu', '+DD64']
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_libraries(self):
+        return ['m']
+    def get_library_dirs(self):
+        opt = ['/usr/lib/hpux64']
+        return opt
+    def get_version(self, force=0, ok_status=[256, 0, 1]):
+        # XXX status==256 may indicate 'unrecognized option' or
+        #     'no input file'. So, version_cmd needs more work.
+        return FCompiler.get_version(self, force, ok_status)
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(10)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='hpux').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/ibm.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/ibm.py
new file mode 100644
index 0000000000000000000000000000000000000000..29927518c703581d7c4bf0aecd06fe2ea0904ed8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/ibm.py
@@ -0,0 +1,97 @@
+import os
+import re
+import sys
+import subprocess
+
+from numpy.distutils.fcompiler import FCompiler
+from numpy.distutils.exec_command import find_executable
+from numpy.distutils.misc_util import make_temp_file
+from distutils import log
+
+compilers = ['IBMFCompiler']
+
+class IBMFCompiler(FCompiler):
+    compiler_type = 'ibm'
+    description = 'IBM XL Fortran Compiler'
+    version_pattern =  r'(xlf\(1\)\s*|)IBM XL Fortran ((Advanced Edition |)Version |Enterprise Edition V|for AIX, V)(?P[^\s*]*)'
+    #IBM XL Fortran Enterprise Edition V10.1 for AIX \nVersion: 10.01.0000.0004
+
+    executables = {
+        'version_cmd'  : ["", "-qversion"],
+        'compiler_f77' : ["xlf"],
+        'compiler_fix' : ["xlf90", "-qfixed"],
+        'compiler_f90' : ["xlf90"],
+        'linker_so'    : ["xlf95"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    def get_version(self,*args,**kwds):
+        version = FCompiler.get_version(self,*args,**kwds)
+
+        if version is None and sys.platform.startswith('aix'):
+            # use lslpp to find out xlf version
+            lslpp = find_executable('lslpp')
+            xlf = find_executable('xlf')
+            if os.path.exists(xlf) and os.path.exists(lslpp):
+                try:
+                    o = subprocess.check_output([lslpp, '-Lc', 'xlfcmp'])
+                except (OSError, subprocess.CalledProcessError):
+                    pass
+                else:
+                    m = re.search(r'xlfcmp:(?P\d+([.]\d+)+)', o)
+                    if m: version = m.group('version')
+
+        xlf_dir = '/etc/opt/ibmcmp/xlf'
+        if version is None and os.path.isdir(xlf_dir):
+            # linux:
+            # If the output of xlf does not contain version info
+            # (that's the case with xlf 8.1, for instance) then
+            # let's try another method:
+            l = sorted(os.listdir(xlf_dir))
+            l.reverse()
+            l = [d for d in l if os.path.isfile(os.path.join(xlf_dir, d, 'xlf.cfg'))]
+            if l:
+                from distutils.version import LooseVersion
+                self.version = version = LooseVersion(l[0])
+        return version
+
+    def get_flags(self):
+        return ['-qextname']
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_linker_so(self):
+        opt = []
+        if sys.platform=='darwin':
+            opt.append('-Wl,-bundle,-flat_namespace,-undefined,suppress')
+        else:
+            opt.append('-bshared')
+        version = self.get_version(ok_status=[0, 40])
+        if version is not None:
+            if sys.platform.startswith('aix'):
+                xlf_cfg = '/etc/xlf.cfg'
+            else:
+                xlf_cfg = '/etc/opt/ibmcmp/xlf/%s/xlf.cfg' % version
+            fo, new_cfg = make_temp_file(suffix='_xlf.cfg')
+            log.info('Creating '+new_cfg)
+            with open(xlf_cfg) as fi:
+                crt1_match = re.compile(r'\s*crt\s*=\s*(?P.*)/crt1.o').match
+                for line in fi:
+                    m = crt1_match(line)
+                    if m:
+                        fo.write('crt = %s/bundle1.o\n' % (m.group('path')))
+                    else:
+                        fo.write(line)
+            fo.close()
+            opt.append('-F'+new_cfg)
+        return opt
+
+    def get_flags_opt(self):
+        return ['-O3']
+
+if __name__ == '__main__':
+    from numpy.distutils import customized_fcompiler
+    log.set_verbosity(2)
+    print(customized_fcompiler(compiler='ibm').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/intel.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/intel.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d606590411048e9bebb2dc04d28e56be89783b3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/intel.py
@@ -0,0 +1,211 @@
+# http://developer.intel.com/software/products/compilers/flin/
+import sys
+
+from numpy.distutils.ccompiler import simple_version_match
+from numpy.distutils.fcompiler import FCompiler, dummy_fortran_file
+
+compilers = ['IntelFCompiler', 'IntelVisualFCompiler',
+             'IntelItaniumFCompiler', 'IntelItaniumVisualFCompiler',
+             'IntelEM64VisualFCompiler', 'IntelEM64TFCompiler']
+
+
+def intel_version_match(type):
+    # Match against the important stuff in the version string
+    return simple_version_match(start=r'Intel.*?Fortran.*?(?:%s).*?Version' % (type,))
+
+
+class BaseIntelFCompiler(FCompiler):
+    def update_executables(self):
+        f = dummy_fortran_file()
+        self.executables['version_cmd'] = ['', '-FI', '-V', '-c',
+                                           f + '.f', '-o', f + '.o']
+
+    def runtime_library_dir_option(self, dir):
+        # TODO: could use -Xlinker here, if it's supported
+        assert "," not in dir
+
+        return '-Wl,-rpath=%s' % dir
+
+
+class IntelFCompiler(BaseIntelFCompiler):
+
+    compiler_type = 'intel'
+    compiler_aliases = ('ifort',)
+    description = 'Intel Fortran Compiler for 32-bit apps'
+    version_match = intel_version_match('32-bit|IA-32')
+
+    possible_executables = ['ifort', 'ifc']
+
+    executables = {
+        'version_cmd'  : None,          # set by update_executables
+        'compiler_f77' : [None, "-72", "-w90", "-w95"],
+        'compiler_f90' : [None],
+        'compiler_fix' : [None, "-FI"],
+        'linker_so'    : ["", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    pic_flags = ['-fPIC']
+    module_dir_switch = '-module '  # Don't remove ending space!
+    module_include_switch = '-I'
+
+    def get_flags_free(self):
+        return ['-FR']
+
+    def get_flags(self):
+        return ['-fPIC']
+
+    def get_flags_opt(self):  # Scipy test failures with -O2
+        v = self.get_version()
+        mpopt = 'openmp' if v and v < '15' else 'qopenmp'
+        return ['-fp-model', 'strict', '-O1',
+                '-assume', 'minus0', '-{}'.format(mpopt)]
+
+    def get_flags_arch(self):
+        return []
+
+    def get_flags_linker_so(self):
+        opt = FCompiler.get_flags_linker_so(self)
+        v = self.get_version()
+        if v and v >= '8.0':
+            opt.append('-nofor_main')
+        if sys.platform == 'darwin':
+            # Here, it's -dynamiclib
+            try:
+                idx = opt.index('-shared')
+                opt.remove('-shared')
+            except ValueError:
+                idx = 0
+            opt[idx:idx] = ['-dynamiclib', '-Wl,-undefined,dynamic_lookup']
+        return opt
+
+
+class IntelItaniumFCompiler(IntelFCompiler):
+    compiler_type = 'intele'
+    compiler_aliases = ()
+    description = 'Intel Fortran Compiler for Itanium apps'
+
+    version_match = intel_version_match('Itanium|IA-64')
+
+    possible_executables = ['ifort', 'efort', 'efc']
+
+    executables = {
+        'version_cmd'  : None,
+        'compiler_f77' : [None, "-FI", "-w90", "-w95"],
+        'compiler_fix' : [None, "-FI"],
+        'compiler_f90' : [None],
+        'linker_so'    : ['', "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+
+class IntelEM64TFCompiler(IntelFCompiler):
+    compiler_type = 'intelem'
+    compiler_aliases = ()
+    description = 'Intel Fortran Compiler for 64-bit apps'
+
+    version_match = intel_version_match('EM64T-based|Intel\\(R\\) 64|64|IA-64|64-bit')
+
+    possible_executables = ['ifort', 'efort', 'efc']
+
+    executables = {
+        'version_cmd'  : None,
+        'compiler_f77' : [None, "-FI"],
+        'compiler_fix' : [None, "-FI"],
+        'compiler_f90' : [None],
+        'linker_so'    : ['', "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+# Is there no difference in the version string between the above compilers
+# and the Visual compilers?
+
+
+class IntelVisualFCompiler(BaseIntelFCompiler):
+    compiler_type = 'intelv'
+    description = 'Intel Visual Fortran Compiler for 32-bit apps'
+    version_match = intel_version_match('32-bit|IA-32')
+
+    def update_executables(self):
+        f = dummy_fortran_file()
+        self.executables['version_cmd'] = ['', '/FI', '/c',
+                                           f + '.f', '/o', f + '.o']
+
+    ar_exe = 'lib.exe'
+    possible_executables = ['ifort', 'ifl']
+
+    executables = {
+        'version_cmd'  : None,
+        'compiler_f77' : [None],
+        'compiler_fix' : [None],
+        'compiler_f90' : [None],
+        'linker_so'    : [None],
+        'archiver'     : [ar_exe, "/verbose", "/OUT:"],
+        'ranlib'       : None
+        }
+
+    compile_switch = '/c '
+    object_switch = '/Fo'     # No space after /Fo!
+    library_switch = '/OUT:'  # No space after /OUT:!
+    module_dir_switch = '/module:'  # No space after /module:
+    module_include_switch = '/I'
+
+    def get_flags(self):
+        opt = ['/nologo', '/MD', '/nbs', '/names:lowercase', 
+               '/assume:underscore', '/fpp']
+        return opt
+
+    def get_flags_free(self):
+        return []
+
+    def get_flags_debug(self):
+        return ['/4Yb', '/d2']
+
+    def get_flags_opt(self):
+        return ['/O1', '/assume:minus0']  # Scipy test failures with /O2
+
+    def get_flags_arch(self):
+        return ["/arch:IA32", "/QaxSSE3"]
+
+    def runtime_library_dir_option(self, dir):
+        raise NotImplementedError
+
+
+class IntelItaniumVisualFCompiler(IntelVisualFCompiler):
+    compiler_type = 'intelev'
+    description = 'Intel Visual Fortran Compiler for Itanium apps'
+
+    version_match = intel_version_match('Itanium')
+
+    possible_executables = ['efl']  # XXX this is a wild guess
+    ar_exe = IntelVisualFCompiler.ar_exe
+
+    executables = {
+        'version_cmd'  : None,
+        'compiler_f77' : [None, "-FI", "-w90", "-w95"],
+        'compiler_fix' : [None, "-FI", "-4L72", "-w"],
+        'compiler_f90' : [None],
+        'linker_so'    : ['', "-shared"],
+        'archiver'     : [ar_exe, "/verbose", "/OUT:"],
+        'ranlib'       : None
+        }
+
+
+class IntelEM64VisualFCompiler(IntelVisualFCompiler):
+    compiler_type = 'intelvem'
+    description = 'Intel Visual Fortran Compiler for 64-bit apps'
+
+    version_match = simple_version_match(start=r'Intel\(R\).*?64,')
+
+    def get_flags_arch(self):
+        return []
+
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='intel').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/lahey.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/lahey.py
new file mode 100644
index 0000000000000000000000000000000000000000..e925838268b82d9c26d94e811717cdc58e269a12
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/lahey.py
@@ -0,0 +1,45 @@
+import os
+
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['LaheyFCompiler']
+
+class LaheyFCompiler(FCompiler):
+
+    compiler_type = 'lahey'
+    description = 'Lahey/Fujitsu Fortran 95 Compiler'
+    version_pattern =  r'Lahey/Fujitsu Fortran 95 Compiler Release (?P[^\s*]*)'
+
+    executables = {
+        'version_cmd'  : ["", "--version"],
+        'compiler_f77' : ["lf95", "--fix"],
+        'compiler_fix' : ["lf95", "--fix"],
+        'compiler_f90' : ["lf95"],
+        'linker_so'    : ["lf95", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    module_dir_switch = None  #XXX Fix me
+    module_include_switch = None #XXX Fix me
+
+    def get_flags_opt(self):
+        return ['-O']
+    def get_flags_debug(self):
+        return ['-g', '--chk', '--chkglobal']
+    def get_library_dirs(self):
+        opt = []
+        d = os.environ.get('LAHEY')
+        if d:
+            opt.append(os.path.join(d, 'lib'))
+        return opt
+    def get_libraries(self):
+        opt = []
+        opt.extend(['fj9f6', 'fj9i6', 'fj9ipp', 'fj9e6'])
+        return opt
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='lahey').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/mips.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/mips.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0973804571b1404400e0749533a001d0833f905
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/mips.py
@@ -0,0 +1,54 @@
+from numpy.distutils.cpuinfo import cpu
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['MIPSFCompiler']
+
+class MIPSFCompiler(FCompiler):
+
+    compiler_type = 'mips'
+    description = 'MIPSpro Fortran Compiler'
+    version_pattern =  r'MIPSpro Compilers: Version (?P[^\s*,]*)'
+
+    executables = {
+        'version_cmd'  : ["", "-version"],
+        'compiler_f77' : ["f77", "-f77"],
+        'compiler_fix' : ["f90", "-fixedform"],
+        'compiler_f90' : ["f90"],
+        'linker_so'    : ["f90", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : None
+        }
+    module_dir_switch = None #XXX: fix me
+    module_include_switch = None #XXX: fix me
+    pic_flags = ['-KPIC']
+
+    def get_flags(self):
+        return self.pic_flags + ['-n32']
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_flags_arch(self):
+        opt = []
+        for a in '19 20 21 22_4k 22_5k 24 25 26 27 28 30 32_5k 32_10k'.split():
+            if getattr(cpu, 'is_IP%s'%a)():
+                opt.append('-TARG:platform=IP%s' % a)
+                break
+        return opt
+    def get_flags_arch_f77(self):
+        r = None
+        if cpu.is_r10000(): r = 10000
+        elif cpu.is_r12000(): r = 12000
+        elif cpu.is_r8000(): r = 8000
+        elif cpu.is_r5000(): r = 5000
+        elif cpu.is_r4000(): r = 4000
+        if r is not None:
+            return ['r%s' % (r)]
+        return []
+    def get_flags_arch_f90(self):
+        r = self.get_flags_arch_f77()
+        if r:
+            r[0] = '-' + r[0]
+        return r
+
+if __name__ == '__main__':
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='mips').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nag.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nag.py
new file mode 100644
index 0000000000000000000000000000000000000000..939201f44e024de7b9f3d3858284a1dfce1d1a11
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nag.py
@@ -0,0 +1,87 @@
+import sys
+import re
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['NAGFCompiler', 'NAGFORCompiler']
+
+class BaseNAGFCompiler(FCompiler):
+    version_pattern = r'NAG.* Release (?P[^(\s]*)'
+
+    def version_match(self, version_string):
+        m = re.search(self.version_pattern, version_string)
+        if m:
+            return m.group('version')
+        else:
+            return None
+
+    def get_flags_linker_so(self):
+        return ["-Wl,-shared"]
+    def get_flags_opt(self):
+        return ['-O4']
+    def get_flags_arch(self):
+        return []
+
+class NAGFCompiler(BaseNAGFCompiler):
+
+    compiler_type = 'nag'
+    description = 'NAGWare Fortran 95 Compiler'
+
+    executables = {
+        'version_cmd'  : ["", "-V"],
+        'compiler_f77' : ["f95", "-fixed"],
+        'compiler_fix' : ["f95", "-fixed"],
+        'compiler_f90' : ["f95"],
+        'linker_so'    : [""],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    def get_flags_linker_so(self):
+        if sys.platform == 'darwin':
+            return ['-unsharedf95', '-Wl,-bundle,-flat_namespace,-undefined,suppress']
+        return BaseNAGFCompiler.get_flags_linker_so(self)
+    def get_flags_arch(self):
+        version = self.get_version()
+        if version and version < '5.1':
+            return ['-target=native']
+        else:
+            return BaseNAGFCompiler.get_flags_arch(self)
+    def get_flags_debug(self):
+        return ['-g', '-gline', '-g90', '-nan', '-C']
+
+class NAGFORCompiler(BaseNAGFCompiler):
+
+    compiler_type = 'nagfor'
+    description = 'NAG Fortran Compiler'
+
+    executables = {
+        'version_cmd'  : ["nagfor", "-V"],
+        'compiler_f77' : ["nagfor", "-fixed"],
+        'compiler_fix' : ["nagfor", "-fixed"],
+        'compiler_f90' : ["nagfor"],
+        'linker_so'    : ["nagfor"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+
+    def get_flags_linker_so(self):
+        if sys.platform == 'darwin':
+            return ['-unsharedrts',
+                    '-Wl,-bundle,-flat_namespace,-undefined,suppress']
+        return BaseNAGFCompiler.get_flags_linker_so(self)
+    def get_flags_debug(self):
+        version = self.get_version()
+        if version and version > '6.1':
+            return ['-g', '-u', '-nan', '-C=all', '-thread_safe',
+                    '-kind=unique', '-Warn=allocation', '-Warn=subnormal']
+        else:
+            return ['-g', '-nan', '-C=all', '-u', '-thread_safe']
+
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    compiler = customized_fcompiler(compiler='nagfor')
+    print(compiler.get_version())
+    print(compiler.get_flags_debug())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/none.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/none.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef411fffc7cb7e1d8fac4872eb31292b9fc5d7bb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/none.py
@@ -0,0 +1,28 @@
+from numpy.distutils.fcompiler import FCompiler
+from numpy.distutils import customized_fcompiler
+
+compilers = ['NoneFCompiler']
+
+class NoneFCompiler(FCompiler):
+
+    compiler_type = 'none'
+    description = 'Fake Fortran compiler'
+
+    executables = {'compiler_f77': None,
+                   'compiler_f90': None,
+                   'compiler_fix': None,
+                   'linker_so': None,
+                   'linker_exe': None,
+                   'archiver': None,
+                   'ranlib': None,
+                   'version_cmd': None,
+                   }
+
+    def find_executables(self):
+        pass
+
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    print(customized_fcompiler(compiler='none').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nv.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nv.py
new file mode 100644
index 0000000000000000000000000000000000000000..f518c8b0027a9eb73c853ba5334ff945e07be6fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/nv.py
@@ -0,0 +1,53 @@
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['NVHPCFCompiler']
+
+class NVHPCFCompiler(FCompiler):
+    """ NVIDIA High Performance Computing (HPC) SDK Fortran Compiler
+   
+    https://developer.nvidia.com/hpc-sdk
+   
+    Since august 2020 the NVIDIA HPC SDK includes the compilers formerly known as The Portland Group compilers,
+    https://www.pgroup.com/index.htm.
+    See also `numpy.distutils.fcompiler.pg`.
+    """
+
+    compiler_type = 'nv'
+    description = 'NVIDIA HPC SDK'
+    version_pattern = r'\s*(nvfortran|.+ \(aka nvfortran\)) (?P[\d.-]+).*'
+
+    executables = {
+        'version_cmd': ["", "-V"],
+        'compiler_f77': ["nvfortran"],
+        'compiler_fix': ["nvfortran", "-Mfixed"],
+        'compiler_f90': ["nvfortran"],
+        'linker_so': [""],
+        'archiver': ["ar", "-cr"],
+        'ranlib': ["ranlib"]
+    }
+    pic_flags = ['-fpic']
+
+    module_dir_switch = '-module '
+    module_include_switch = '-I'
+
+    def get_flags(self):
+        opt = ['-Minform=inform', '-Mnosecond_underscore']
+        return self.pic_flags + opt
+
+    def get_flags_opt(self):
+        return ['-fast']
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_linker_so(self):
+        return ["-shared", '-fpic']
+
+    def runtime_library_dir_option(self, dir):
+        return '-R%s' % dir
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='nv').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pathf95.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pathf95.py
new file mode 100644
index 0000000000000000000000000000000000000000..0768cb12e87a54aa7fc0d10a04d97953eaa8aa41
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pathf95.py
@@ -0,0 +1,33 @@
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['PathScaleFCompiler']
+
+class PathScaleFCompiler(FCompiler):
+
+    compiler_type = 'pathf95'
+    description = 'PathScale Fortran Compiler'
+    version_pattern =  r'PathScale\(TM\) Compiler Suite: Version (?P[\d.]+)'
+
+    executables = {
+        'version_cmd'  : ["pathf95", "-version"],
+        'compiler_f77' : ["pathf95", "-fixedform"],
+        'compiler_fix' : ["pathf95", "-fixedform"],
+        'compiler_f90' : ["pathf95"],
+        'linker_so'    : ["pathf95", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+    }
+    pic_flags = ['-fPIC']
+    module_dir_switch = '-module ' # Don't remove ending space!
+    module_include_switch = '-I'
+
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_flags_debug(self):
+        return ['-g']
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='pathf95').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pg.py
new file mode 100644
index 0000000000000000000000000000000000000000..72442c4fec61dc20079231dedd233b6791907867
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/pg.py
@@ -0,0 +1,128 @@
+# http://www.pgroup.com
+import sys
+
+from numpy.distutils.fcompiler import FCompiler
+from sys import platform
+from os.path import join, dirname, normpath
+
+compilers = ['PGroupFCompiler', 'PGroupFlangCompiler']
+
+
+class PGroupFCompiler(FCompiler):
+
+    compiler_type = 'pg'
+    description = 'Portland Group Fortran Compiler'
+    version_pattern = r'\s*pg(f77|f90|hpf|fortran) (?P[\d.-]+).*'
+
+    if platform == 'darwin':
+        executables = {
+            'version_cmd': ["", "-V"],
+            'compiler_f77': ["pgfortran", "-dynamiclib"],
+            'compiler_fix': ["pgfortran", "-Mfixed", "-dynamiclib"],
+            'compiler_f90': ["pgfortran", "-dynamiclib"],
+            'linker_so': ["libtool"],
+            'archiver': ["ar", "-cr"],
+            'ranlib': ["ranlib"]
+        }
+        pic_flags = ['']
+    else:
+        executables = {
+            'version_cmd': ["", "-V"],
+            'compiler_f77': ["pgfortran"],
+            'compiler_fix': ["pgfortran", "-Mfixed"],
+            'compiler_f90': ["pgfortran"],
+            'linker_so': [""],
+            'archiver': ["ar", "-cr"],
+            'ranlib': ["ranlib"]
+        }
+        pic_flags = ['-fpic']
+
+    module_dir_switch = '-module '
+    module_include_switch = '-I'
+
+    def get_flags(self):
+        opt = ['-Minform=inform', '-Mnosecond_underscore']
+        return self.pic_flags + opt
+
+    def get_flags_opt(self):
+        return ['-fast']
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    if platform == 'darwin':
+        def get_flags_linker_so(self):
+            return ["-dynamic", '-undefined', 'dynamic_lookup']
+
+    else:
+        def get_flags_linker_so(self):
+            return ["-shared", '-fpic']
+
+    def runtime_library_dir_option(self, dir):
+        return '-R%s' % dir
+
+
+import functools
+
+class PGroupFlangCompiler(FCompiler):
+    compiler_type = 'flang'
+    description = 'Portland Group Fortran LLVM Compiler'
+    version_pattern = r'\s*(flang|clang) version (?P[\d.-]+).*'
+
+    ar_exe = 'lib.exe'
+    possible_executables = ['flang']
+
+    executables = {
+        'version_cmd': ["", "--version"],
+        'compiler_f77': ["flang"],
+        'compiler_fix': ["flang"],
+        'compiler_f90': ["flang"],
+        'linker_so': [None],
+        'archiver': [ar_exe, "/verbose", "/OUT:"],
+        'ranlib': None
+    }
+
+    library_switch = '/OUT:'  # No space after /OUT:!
+    module_dir_switch = '-module '  # Don't remove ending space!
+
+    def get_libraries(self):
+        opt = FCompiler.get_libraries(self)
+        opt.extend(['flang', 'flangrti', 'ompstub'])
+        return opt
+
+    @functools.lru_cache(maxsize=128)
+    def get_library_dirs(self):
+        """List of compiler library directories."""
+        opt = FCompiler.get_library_dirs(self)
+        flang_dir = dirname(self.executables['compiler_f77'][0])
+        opt.append(normpath(join(flang_dir, '..', 'lib')))
+
+        return opt
+
+    def get_flags(self):
+        return []
+
+    def get_flags_free(self):
+        return []
+
+    def get_flags_debug(self):
+        return ['-g']
+
+    def get_flags_opt(self):
+        return ['-O3']
+
+    def get_flags_arch(self):
+        return []
+
+    def runtime_library_dir_option(self, dir):
+        raise NotImplementedError
+
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    if 'flang' in sys.argv:
+        print(customized_fcompiler(compiler='flang').get_version())
+    else:
+        print(customized_fcompiler(compiler='pg').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/sun.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/sun.py
new file mode 100644
index 0000000000000000000000000000000000000000..d039f0b25705afc915da5266958f0d0ba1145763
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/sun.py
@@ -0,0 +1,51 @@
+from numpy.distutils.ccompiler import simple_version_match
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['SunFCompiler']
+
+class SunFCompiler(FCompiler):
+
+    compiler_type = 'sun'
+    description = 'Sun or Forte Fortran 95 Compiler'
+    # ex:
+    # f90: Sun WorkShop 6 update 2 Fortran 95 6.2 Patch 111690-10 2003/08/28
+    version_match = simple_version_match(
+                      start=r'f9[05]: (Sun|Forte|WorkShop).*Fortran 95')
+
+    executables = {
+        'version_cmd'  : ["", "-V"],
+        'compiler_f77' : ["f90"],
+        'compiler_fix' : ["f90", "-fixed"],
+        'compiler_f90' : ["f90"],
+        'linker_so'    : ["", "-Bdynamic", "-G"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+    module_dir_switch = '-moddir='
+    module_include_switch = '-M'
+    pic_flags = ['-xcode=pic32']
+
+    def get_flags_f77(self):
+        ret = ["-ftrap=%none"]
+        if (self.get_version() or '') >= '7':
+            ret.append("-f77")
+        else:
+            ret.append("-fixed")
+        return ret
+    def get_opt(self):
+        return ['-fast', '-dalign']
+    def get_arch(self):
+        return ['-xtarget=generic']
+    def get_libraries(self):
+        opt = []
+        opt.extend(['fsu', 'sunmath', 'mvec'])
+        return opt
+
+    def runtime_library_dir_option(self, dir):
+        return '-R%s' % dir
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='sun').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/vast.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/vast.py
new file mode 100644
index 0000000000000000000000000000000000000000..92a1647ba43708084ce85e0b986cb9d71329b842
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fcompiler/vast.py
@@ -0,0 +1,52 @@
+import os
+
+from numpy.distutils.fcompiler.gnu import GnuFCompiler
+
+compilers = ['VastFCompiler']
+
+class VastFCompiler(GnuFCompiler):
+    compiler_type = 'vast'
+    compiler_aliases = ()
+    description = 'Pacific-Sierra Research Fortran 90 Compiler'
+    version_pattern = (r'\s*Pacific-Sierra Research vf90 '
+                       r'(Personal|Professional)\s+(?P[^\s]*)')
+
+    # VAST f90 does not support -o with -c. So, object files are created
+    # to the current directory and then moved to build directory
+    object_switch = ' && function _mvfile { mv -v `basename $1` $1 ; } && _mvfile '
+
+    executables = {
+        'version_cmd'  : ["vf90", "-v"],
+        'compiler_f77' : ["g77"],
+        'compiler_fix' : ["f90", "-Wv,-ya"],
+        'compiler_f90' : ["f90"],
+        'linker_so'    : [""],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+        }
+    module_dir_switch = None  #XXX Fix me
+    module_include_switch = None #XXX Fix me
+
+    def find_executables(self):
+        pass
+
+    def get_version_cmd(self):
+        f90 = self.compiler_f90[0]
+        d, b = os.path.split(f90)
+        vf90 = os.path.join(d, 'v'+b)
+        return vf90
+
+    def get_flags_arch(self):
+        vast_version = self.get_version()
+        gnu = GnuFCompiler()
+        gnu.customize(None)
+        self.version = gnu.get_version()
+        opt = GnuFCompiler.get_flags_arch(self)
+        self.version = vast_version
+        return opt
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='vast').get_version())
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/from_template.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/from_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..90d1f4c384c7807c621eada8ed7685e5845c5c56
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/from_template.py
@@ -0,0 +1,261 @@
+#!/usr/bin/env python3
+"""
+
+process_file(filename)
+
+  takes templated file .xxx.src and produces .xxx file where .xxx
+  is .pyf .f90 or .f using the following template rules:
+
+  '<..>' denotes a template.
+
+  All function and subroutine blocks in a source file with names that
+  contain '<..>' will be replicated according to the rules in '<..>'.
+
+  The number of comma-separated words in '<..>' will determine the number of
+  replicates.
+
+  '<..>' may have two different forms, named and short. For example,
+
+  named:
+    where anywhere inside a block '
' will be replaced with
+   'd', 's', 'z', and 'c' for each replicate of the block.
+
+   <_c>  is already defined: <_c=s,d,c,z>
+   <_t>  is already defined: <_t=real,double precision,complex,double complex>
+
+  short:
+   , a short form of the named, useful when no 
 appears inside
+   a block.
+
+  In general, '<..>' contains a comma separated list of arbitrary
+  expressions. If these expression must contain a comma|leftarrow|rightarrow,
+  then prepend the comma|leftarrow|rightarrow with a backslash.
+
+  If an expression matches '\\' then it will be replaced
+  by -th expression.
+
+  Note that all '<..>' forms in a block must have the same number of
+  comma-separated entries.
+
+ Predefined named template rules:
+  
+  
+  
+  
+  
+
+"""
+__all__ = ['process_str', 'process_file']
+
+import os
+import sys
+import re
+
+routine_start_re = re.compile(r'(\n|\A)((     (\$|\*))|)\s*(subroutine|function)\b', re.I)
+routine_end_re = re.compile(r'\n\s*end\s*(subroutine|function)\b.*(\n|\Z)', re.I)
+function_start_re = re.compile(r'\n     (\$|\*)\s*function\b', re.I)
+
+def parse_structure(astr):
+    """ Return a list of tuples for each function or subroutine each
+    tuple is the start and end of a subroutine or function to be
+    expanded.
+    """
+
+    spanlist = []
+    ind = 0
+    while True:
+        m = routine_start_re.search(astr, ind)
+        if m is None:
+            break
+        start = m.start()
+        if function_start_re.match(astr, start, m.end()):
+            while True:
+                i = astr.rfind('\n', ind, start)
+                if i==-1:
+                    break
+                start = i
+                if astr[i:i+7]!='\n     $':
+                    break
+        start += 1
+        m = routine_end_re.search(astr, m.end())
+        ind = end = m and m.end()-1 or len(astr)
+        spanlist.append((start, end))
+    return spanlist
+
+template_re = re.compile(r"<\s*(\w[\w\d]*)\s*>")
+named_re = re.compile(r"<\s*(\w[\w\d]*)\s*=\s*(.*?)\s*>")
+list_re = re.compile(r"<\s*((.*?))\s*>")
+
+def find_repl_patterns(astr):
+    reps = named_re.findall(astr)
+    names = {}
+    for rep in reps:
+        name = rep[0].strip() or unique_key(names)
+        repl = rep[1].replace(r'\,', '@comma@')
+        thelist = conv(repl)
+        names[name] = thelist
+    return names
+
+def find_and_remove_repl_patterns(astr):
+    names = find_repl_patterns(astr)
+    astr = re.subn(named_re, '', astr)[0]
+    return astr, names
+
+item_re = re.compile(r"\A\\(?P\d+)\Z")
+def conv(astr):
+    b = astr.split(',')
+    l = [x.strip() for x in b]
+    for i in range(len(l)):
+        m = item_re.match(l[i])
+        if m:
+            j = int(m.group('index'))
+            l[i] = l[j]
+    return ','.join(l)
+
+def unique_key(adict):
+    """ Obtain a unique key given a dictionary."""
+    allkeys = list(adict.keys())
+    done = False
+    n = 1
+    while not done:
+        newkey = '__l%s' % (n)
+        if newkey in allkeys:
+            n += 1
+        else:
+            done = True
+    return newkey
+
+
+template_name_re = re.compile(r'\A\s*(\w[\w\d]*)\s*\Z')
+def expand_sub(substr, names):
+    substr = substr.replace(r'\>', '@rightarrow@')
+    substr = substr.replace(r'\<', '@leftarrow@')
+    lnames = find_repl_patterns(substr)
+    substr = named_re.sub(r"<\1>", substr)  # get rid of definition templates
+
+    def listrepl(mobj):
+        thelist = conv(mobj.group(1).replace(r'\,', '@comma@'))
+        if template_name_re.match(thelist):
+            return "<%s>" % (thelist)
+        name = None
+        for key in lnames.keys():    # see if list is already in dictionary
+            if lnames[key] == thelist:
+                name = key
+        if name is None:      # this list is not in the dictionary yet
+            name = unique_key(lnames)
+            lnames[name] = thelist
+        return "<%s>" % name
+
+    substr = list_re.sub(listrepl, substr) # convert all lists to named templates
+                                           # newnames are constructed as needed
+
+    numsubs = None
+    base_rule = None
+    rules = {}
+    for r in template_re.findall(substr):
+        if r not in rules:
+            thelist = lnames.get(r, names.get(r, None))
+            if thelist is None:
+                raise ValueError('No replicates found for <%s>' % (r))
+            if r not in names and not thelist.startswith('_'):
+                names[r] = thelist
+            rule = [i.replace('@comma@', ',') for i in thelist.split(',')]
+            num = len(rule)
+
+            if numsubs is None:
+                numsubs = num
+                rules[r] = rule
+                base_rule = r
+            elif num == numsubs:
+                rules[r] = rule
+            else:
+                print("Mismatch in number of replacements (base <%s=%s>)"
+                      " for <%s=%s>. Ignoring." %
+                      (base_rule, ','.join(rules[base_rule]), r, thelist))
+    if not rules:
+        return substr
+
+    def namerepl(mobj):
+        name = mobj.group(1)
+        return rules.get(name, (k+1)*[name])[k]
+
+    newstr = ''
+    for k in range(numsubs):
+        newstr += template_re.sub(namerepl, substr) + '\n\n'
+
+    newstr = newstr.replace('@rightarrow@', '>')
+    newstr = newstr.replace('@leftarrow@', '<')
+    return newstr
+
+def process_str(allstr):
+    newstr = allstr
+    writestr = ''
+
+    struct = parse_structure(newstr)
+
+    oldend = 0
+    names = {}
+    names.update(_special_names)
+    for sub in struct:
+        cleanedstr, defs = find_and_remove_repl_patterns(newstr[oldend:sub[0]])
+        writestr += cleanedstr
+        names.update(defs)
+        writestr += expand_sub(newstr[sub[0]:sub[1]], names)
+        oldend =  sub[1]
+    writestr += newstr[oldend:]
+
+    return writestr
+
+include_src_re = re.compile(r"(\n|\A)\s*include\s*['\"](?P[\w\d./\\]+\.src)['\"]", re.I)
+
+def resolve_includes(source):
+    d = os.path.dirname(source)
+    with open(source) as fid:
+        lines = []
+        for line in fid:
+            m = include_src_re.match(line)
+            if m:
+                fn = m.group('name')
+                if not os.path.isabs(fn):
+                    fn = os.path.join(d, fn)
+                if os.path.isfile(fn):
+                    lines.extend(resolve_includes(fn))
+                else:
+                    lines.append(line)
+            else:
+                lines.append(line)
+    return lines
+
+def process_file(source):
+    lines = resolve_includes(source)
+    return process_str(''.join(lines))
+
+_special_names = find_repl_patterns('''
+<_c=s,d,c,z>
+<_t=real,double precision,complex,double complex>
+
+
+
+
+
+''')
+
+def main():
+    try:
+        file = sys.argv[1]
+    except IndexError:
+        fid = sys.stdin
+        outfile = sys.stdout
+    else:
+        fid = open(file, 'r')
+        (base, ext) = os.path.splitext(file)
+        newname = base
+        outfile = open(newname, 'w')
+
+    allstr = fid.read()
+    writestr = process_str(allstr)
+    outfile.write(writestr)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fujitsuccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fujitsuccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..c25900b34f1ddad3274d9eca1fb4369b39f7437a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/fujitsuccompiler.py
@@ -0,0 +1,28 @@
+from distutils.unixccompiler import UnixCCompiler
+
+class FujitsuCCompiler(UnixCCompiler):
+
+    """
+    Fujitsu compiler.
+    """
+
+    compiler_type = 'fujitsu'
+    cc_exe = 'fcc'
+    cxx_exe = 'FCC'
+
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__(self, verbose, dry_run, force)
+        cc_compiler = self.cc_exe
+        cxx_compiler = self.cxx_exe
+        self.set_executables(
+            compiler=cc_compiler +
+            ' -O3 -Nclang -fPIC',
+            compiler_so=cc_compiler +
+            ' -O3 -Nclang -fPIC',
+            compiler_cxx=cxx_compiler +
+            ' -O3 -Nclang -fPIC',
+            linker_exe=cc_compiler +
+            ' -lfj90i -lfj90f -lfjsrcinfo -lelf -shared',
+            linker_so=cc_compiler +
+            ' -lfj90i -lfj90f -lfjsrcinfo -lelf -shared'
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/intelccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/intelccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..77fb39889a29256dbf25612d4acaf249e03eba35
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/intelccompiler.py
@@ -0,0 +1,106 @@
+import platform
+
+from distutils.unixccompiler import UnixCCompiler
+from numpy.distutils.exec_command import find_executable
+from numpy.distutils.ccompiler import simple_version_match
+if platform.system() == 'Windows':
+    from numpy.distutils.msvc9compiler import MSVCCompiler
+
+
+class IntelCCompiler(UnixCCompiler):
+    """A modified Intel compiler compatible with a GCC-built Python."""
+    compiler_type = 'intel'
+    cc_exe = 'icc'
+    cc_args = 'fPIC'
+
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__(self, verbose, dry_run, force)
+
+        v = self.get_version()
+        mpopt = 'openmp' if v and v < '15' else 'qopenmp'
+        self.cc_exe = ('icc -fPIC -fp-model strict -O3 '
+                       '-fomit-frame-pointer -{}').format(mpopt)
+        compiler = self.cc_exe
+
+        if platform.system() == 'Darwin':
+            shared_flag = '-Wl,-undefined,dynamic_lookup'
+        else:
+            shared_flag = '-shared'
+        self.set_executables(compiler=compiler,
+                             compiler_so=compiler,
+                             compiler_cxx=compiler,
+                             archiver='xiar' + ' cru',
+                             linker_exe=compiler + ' -shared-intel',
+                             linker_so=compiler + ' ' + shared_flag +
+                             ' -shared-intel')
+
+
+class IntelItaniumCCompiler(IntelCCompiler):
+    compiler_type = 'intele'
+    cc_exe = 'icc'
+
+
+class IntelEM64TCCompiler(UnixCCompiler):
+    """
+    A modified Intel x86_64 compiler compatible with a 64bit GCC-built Python.
+    """
+    compiler_type = 'intelem'
+    cc_exe = 'icc -m64'
+    cc_args = '-fPIC'
+
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__(self, verbose, dry_run, force)
+
+        v = self.get_version()
+        mpopt = 'openmp' if v and v < '15' else 'qopenmp'
+        self.cc_exe = ('icc -std=c99 -m64 -fPIC -fp-model strict -O3 '
+                       '-fomit-frame-pointer -{}').format(mpopt)
+        compiler = self.cc_exe
+
+        if platform.system() == 'Darwin':
+            shared_flag = '-Wl,-undefined,dynamic_lookup'
+        else:
+            shared_flag = '-shared'
+        self.set_executables(compiler=compiler,
+                             compiler_so=compiler,
+                             compiler_cxx=compiler,
+                             archiver='xiar' + ' cru',
+                             linker_exe=compiler + ' -shared-intel',
+                             linker_so=compiler + ' ' + shared_flag +
+                             ' -shared-intel')
+
+
+if platform.system() == 'Windows':
+    class IntelCCompilerW(MSVCCompiler):
+        """
+        A modified Intel compiler compatible with an MSVC-built Python.
+        """
+        compiler_type = 'intelw'
+        compiler_cxx = 'icl'
+
+        def __init__(self, verbose=0, dry_run=0, force=0):
+            MSVCCompiler.__init__(self, verbose, dry_run, force)
+            version_match = simple_version_match(start=r'Intel\(R\).*?32,')
+            self.__version = version_match
+
+        def initialize(self, plat_name=None):
+            MSVCCompiler.initialize(self, plat_name)
+            self.cc = self.find_exe('icl.exe')
+            self.lib = self.find_exe('xilib')
+            self.linker = self.find_exe('xilink')
+            self.compile_options = ['/nologo', '/O3', '/MD', '/W3',
+                                    '/Qstd=c99']
+            self.compile_options_debug = ['/nologo', '/Od', '/MDd', '/W3',
+                                          '/Qstd=c99', '/Z7', '/D_DEBUG']
+
+    class IntelEM64TCCompilerW(IntelCCompilerW):
+        """
+        A modified Intel x86_64 compiler compatible with
+        a 64bit MSVC-built Python.
+        """
+        compiler_type = 'intelemw'
+
+        def __init__(self, verbose=0, dry_run=0, force=0):
+            MSVCCompiler.__init__(self, verbose, dry_run, force)
+            version_match = simple_version_match(start=r'Intel\(R\).*?64,')
+            self.__version = version_match
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/lib2def.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/lib2def.py
new file mode 100644
index 0000000000000000000000000000000000000000..851682c633109e4d8644d80bb501e5cafcd39d04
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/lib2def.py
@@ -0,0 +1,116 @@
+import re
+import sys
+import subprocess
+
+__doc__ = """This module generates a DEF file from the symbols in
+an MSVC-compiled DLL import library.  It correctly discriminates between
+data and functions.  The data is collected from the output of the program
+nm(1).
+
+Usage:
+    python lib2def.py [libname.lib] [output.def]
+or
+    python lib2def.py [libname.lib] > output.def
+
+libname.lib defaults to python.lib and output.def defaults to stdout
+
+Author: Robert Kern 
+Last Update: April 30, 1999
+"""
+
+__version__ = '0.1a'
+
+py_ver = "%d%d" % tuple(sys.version_info[:2])
+
+DEFAULT_NM = ['nm', '-Cs']
+
+DEF_HEADER = """LIBRARY         python%s.dll
+;CODE           PRELOAD MOVEABLE DISCARDABLE
+;DATA           PRELOAD SINGLE
+
+EXPORTS
+""" % py_ver
+# the header of the DEF file
+
+FUNC_RE = re.compile(r"^(.*) in python%s\.dll" % py_ver, re.MULTILINE)
+DATA_RE = re.compile(r"^_imp__(.*) in python%s\.dll" % py_ver, re.MULTILINE)
+
+def parse_cmd():
+    """Parses the command-line arguments.
+
+libfile, deffile = parse_cmd()"""
+    if len(sys.argv) == 3:
+        if sys.argv[1][-4:] == '.lib' and sys.argv[2][-4:] == '.def':
+            libfile, deffile = sys.argv[1:]
+        elif sys.argv[1][-4:] == '.def' and sys.argv[2][-4:] == '.lib':
+            deffile, libfile = sys.argv[1:]
+        else:
+            print("I'm assuming that your first argument is the library")
+            print("and the second is the DEF file.")
+    elif len(sys.argv) == 2:
+        if sys.argv[1][-4:] == '.def':
+            deffile = sys.argv[1]
+            libfile = 'python%s.lib' % py_ver
+        elif sys.argv[1][-4:] == '.lib':
+            deffile = None
+            libfile = sys.argv[1]
+    else:
+        libfile = 'python%s.lib' % py_ver
+        deffile = None
+    return libfile, deffile
+
+def getnm(nm_cmd=['nm', '-Cs', 'python%s.lib' % py_ver], shell=True):
+    """Returns the output of nm_cmd via a pipe.
+
+nm_output = getnm(nm_cmd = 'nm -Cs py_lib')"""
+    p = subprocess.Popen(nm_cmd, shell=shell, stdout=subprocess.PIPE,
+                         stderr=subprocess.PIPE, text=True)
+    nm_output, nm_err = p.communicate()
+    if p.returncode != 0:
+        raise RuntimeError('failed to run "%s": "%s"' % (
+                                     ' '.join(nm_cmd), nm_err))
+    return nm_output
+
+def parse_nm(nm_output):
+    """Returns a tuple of lists: dlist for the list of data
+symbols and flist for the list of function symbols.
+
+dlist, flist = parse_nm(nm_output)"""
+    data = DATA_RE.findall(nm_output)
+    func = FUNC_RE.findall(nm_output)
+
+    flist = []
+    for sym in data:
+        if sym in func and (sym[:2] == 'Py' or sym[:3] == '_Py' or sym[:4] == 'init'):
+            flist.append(sym)
+
+    dlist = []
+    for sym in data:
+        if sym not in flist and (sym[:2] == 'Py' or sym[:3] == '_Py'):
+            dlist.append(sym)
+
+    dlist.sort()
+    flist.sort()
+    return dlist, flist
+
+def output_def(dlist, flist, header, file = sys.stdout):
+    """Outputs the final DEF file to a file defaulting to stdout.
+
+output_def(dlist, flist, header, file = sys.stdout)"""
+    for data_sym in dlist:
+        header = header + '\t%s DATA\n' % data_sym
+    header = header + '\n' # blank line
+    for func_sym in flist:
+        header = header + '\t%s\n' % func_sym
+    file.write(header)
+
+if __name__ == '__main__':
+    libfile, deffile = parse_cmd()
+    if deffile is None:
+        deffile = sys.stdout
+    else:
+        deffile = open(deffile, 'w')
+    nm_cmd = DEFAULT_NM + [str(libfile)]
+    nm_output = getnm(nm_cmd, shell=False)
+    dlist, flist = parse_nm(nm_output)
+    output_def(dlist, flist, DEF_HEADER, deffile)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/line_endings.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/line_endings.py
new file mode 100644
index 0000000000000000000000000000000000000000..686e5ebd937fff16d5aa7f154d5c823ed17d9e0a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/line_endings.py
@@ -0,0 +1,77 @@
+""" Functions for converting from DOS to UNIX line endings
+
+"""
+import os
+import re
+import sys
+
+
+def dos2unix(file):
+    "Replace CRLF with LF in argument files.  Print names of changed files."
+    if os.path.isdir(file):
+        print(file, "Directory!")
+        return
+
+    with open(file, "rb") as fp:
+        data = fp.read()
+    if '\0' in data:
+        print(file, "Binary!")
+        return
+
+    newdata = re.sub("\r\n", "\n", data)
+    if newdata != data:
+        print('dos2unix:', file)
+        with open(file, "wb") as f:
+            f.write(newdata)
+        return file
+    else:
+        print(file, 'ok')
+
+def dos2unix_one_dir(modified_files, dir_name, file_names):
+    for file in file_names:
+        full_path = os.path.join(dir_name, file)
+        file = dos2unix(full_path)
+        if file is not None:
+            modified_files.append(file)
+
+def dos2unix_dir(dir_name):
+    modified_files = []
+    os.path.walk(dir_name, dos2unix_one_dir, modified_files)
+    return modified_files
+#----------------------------------
+
+def unix2dos(file):
+    "Replace LF with CRLF in argument files.  Print names of changed files."
+    if os.path.isdir(file):
+        print(file, "Directory!")
+        return
+
+    with open(file, "rb") as fp:
+        data = fp.read()
+    if '\0' in data:
+        print(file, "Binary!")
+        return
+    newdata = re.sub("\r\n", "\n", data)
+    newdata = re.sub("\n", "\r\n", newdata)
+    if newdata != data:
+        print('unix2dos:', file)
+        with open(file, "wb") as f:
+            f.write(newdata)
+        return file
+    else:
+        print(file, 'ok')
+
+def unix2dos_one_dir(modified_files, dir_name, file_names):
+    for file in file_names:
+        full_path = os.path.join(dir_name, file)
+        unix2dos(full_path)
+        if file is not None:
+            modified_files.append(file)
+
+def unix2dos_dir(dir_name):
+    modified_files = []
+    os.path.walk(dir_name, unix2dos_one_dir, modified_files)
+    return modified_files
+
+if __name__ == "__main__":
+    dos2unix_dir(sys.argv[1])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/log.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..3347f56d6fe95ebe5388de8d740ef4ddf8db317d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/log.py
@@ -0,0 +1,111 @@
+# Colored log
+import sys
+from distutils.log import *  # noqa: F403
+from distutils.log import Log as old_Log
+from distutils.log import _global_log
+
+from numpy.distutils.misc_util import (red_text, default_text, cyan_text,
+        green_text, is_sequence, is_string)
+
+
+def _fix_args(args,flag=1):
+    if is_string(args):
+        return args.replace('%', '%%')
+    if flag and is_sequence(args):
+        return tuple([_fix_args(a, flag=0) for a in args])
+    return args
+
+
+class Log(old_Log):
+    def _log(self, level, msg, args):
+        if level >= self.threshold:
+            if args:
+                msg = msg % _fix_args(args)
+            if 0:
+                if msg.startswith('copying ') and msg.find(' -> ') != -1:
+                    return
+                if msg.startswith('byte-compiling '):
+                    return
+            print(_global_color_map[level](msg))
+            sys.stdout.flush()
+
+    def good(self, msg, *args):
+        """
+        If we log WARN messages, log this message as a 'nice' anti-warn
+        message.
+
+        """
+        if WARN >= self.threshold:
+            if args:
+                print(green_text(msg % _fix_args(args)))
+            else:
+                print(green_text(msg))
+            sys.stdout.flush()
+
+
+_global_log.__class__ = Log
+
+good = _global_log.good
+
+def set_threshold(level, force=False):
+    prev_level = _global_log.threshold
+    if prev_level > DEBUG or force:
+        # If we're running at DEBUG, don't change the threshold, as there's
+        # likely a good reason why we're running at this level.
+        _global_log.threshold = level
+        if level <= DEBUG:
+            info('set_threshold: setting threshold to DEBUG level,'
+                    ' it can be changed only with force argument')
+    else:
+        info('set_threshold: not changing threshold from DEBUG level'
+                ' %s to %s' % (prev_level, level))
+    return prev_level
+
+def get_threshold():
+	return _global_log.threshold
+
+def set_verbosity(v, force=False):
+    prev_level = _global_log.threshold
+    if v < 0:
+        set_threshold(ERROR, force)
+    elif v == 0:
+        set_threshold(WARN, force)
+    elif v == 1:
+        set_threshold(INFO, force)
+    elif v >= 2:
+        set_threshold(DEBUG, force)
+    return {FATAL:-2,ERROR:-1,WARN:0,INFO:1,DEBUG:2}.get(prev_level, 1)
+
+
+_global_color_map = {
+    DEBUG:cyan_text,
+    INFO:default_text,
+    WARN:red_text,
+    ERROR:red_text,
+    FATAL:red_text
+}
+
+# don't use INFO,.. flags in set_verbosity, these flags are for set_threshold.
+set_verbosity(0, force=True)
+
+
+_error = error
+_warn = warn
+_info = info
+_debug = debug
+
+
+def error(msg, *a, **kw):
+    _error(f"ERROR: {msg}", *a, **kw)
+
+
+def warn(msg, *a, **kw):
+    _warn(f"WARN: {msg}", *a, **kw)
+
+
+def info(msg, *a, **kw):
+    _info(f"INFO: {msg}", *a, **kw)
+
+
+def debug(msg, *a, **kw):
+    _debug(f"DEBUG: {msg}", *a, **kw)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c
new file mode 100644
index 0000000000000000000000000000000000000000..485a675d8a1fb80bc4927fe236ba3fe550f5a0c9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c
@@ -0,0 +1,6 @@
+int _get_output_format(void)
+{
+    return 0;
+}
+
+int _imp____lc_codepage = 0;
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw32ccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw32ccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2599a9e9a8077359c602e0f963accfc98b3a0037
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/mingw32ccompiler.py
@@ -0,0 +1,597 @@
+"""
+Support code for building Python extensions on Windows.
+
+    # NT stuff
+    # 1. Make sure libpython.a exists for gcc.  If not, build it.
+    # 2. Force windows to use gcc (we're struggling with MSVC and g77 support)
+    # 3. Force windows to use g77
+
+"""
+import os
+import sys
+import subprocess
+import re
+import textwrap
+
+# Overwrite certain distutils.ccompiler functions:
+import numpy.distutils.ccompiler  # noqa: F401
+from numpy.distutils import log
+# NT stuff
+# 1. Make sure libpython.a exists for gcc.  If not, build it.
+# 2. Force windows to use gcc (we're struggling with MSVC and g77 support)
+#    --> this is done in numpy/distutils/ccompiler.py
+# 3. Force windows to use g77
+
+import distutils.cygwinccompiler
+from distutils.unixccompiler import UnixCCompiler
+
+try:
+    from distutils.msvccompiler import get_build_version as get_build_msvc_version
+except ImportError:
+    def get_build_msvc_version():
+        return None
+
+from distutils.errors import UnknownFileError
+from numpy.distutils.misc_util import (msvc_runtime_library,
+                                       msvc_runtime_version,
+                                       msvc_runtime_major,
+                                       get_build_architecture)
+
+def get_msvcr_replacement():
+    """Replacement for outdated version of get_msvcr from cygwinccompiler"""
+    msvcr = msvc_runtime_library()
+    return [] if msvcr is None else [msvcr]
+
+
+# Useful to generate table of symbols from a dll
+_START = re.compile(r'\[Ordinal/Name Pointer\] Table')
+_TABLE = re.compile(r'^\s+\[([\s*[0-9]*)\] ([a-zA-Z0-9_]*)')
+
+# the same as cygwin plus some additional parameters
+class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler):
+    """ A modified MingW32 compiler compatible with an MSVC built Python.
+
+    """
+
+    compiler_type = 'mingw32'
+
+    def __init__ (self,
+                  verbose=0,
+                  dry_run=0,
+                  force=0):
+
+        distutils.cygwinccompiler.CygwinCCompiler.__init__ (self, verbose,
+                                                            dry_run, force)
+
+        # **changes: eric jones 4/11/01
+        # 1. Check for import library on Windows.  Build if it doesn't exist.
+
+        build_import_library()
+
+        # Check for custom msvc runtime library on Windows. Build if it doesn't exist.
+        msvcr_success = build_msvcr_library()
+        msvcr_dbg_success = build_msvcr_library(debug=True)
+        if msvcr_success or msvcr_dbg_success:
+            # add preprocessor statement for using customized msvcr lib
+            self.define_macro('NPY_MINGW_USE_CUSTOM_MSVCR')
+
+        # Define the MSVC version as hint for MinGW
+        msvcr_version = msvc_runtime_version()
+        if msvcr_version:
+            self.define_macro('__MSVCRT_VERSION__', '0x%04i' % msvcr_version)
+
+        # MS_WIN64 should be defined when building for amd64 on windows,
+        # but python headers define it only for MS compilers, which has all
+        # kind of bad consequences, like using Py_ModuleInit4 instead of
+        # Py_ModuleInit4_64, etc... So we add it here
+        if get_build_architecture() == 'AMD64':
+            self.set_executables(
+                compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall',
+                compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall '
+                            '-Wstrict-prototypes',
+                linker_exe='gcc -g',
+                linker_so='gcc -g -shared')
+        else:
+            self.set_executables(
+                compiler='gcc -O2 -Wall',
+                compiler_so='gcc -O2 -Wall -Wstrict-prototypes',
+                linker_exe='g++ ',
+                linker_so='g++ -shared')
+        # added for python2.3 support
+        # we can't pass it through set_executables because pre 2.2 would fail
+        self.compiler_cxx = ['g++']
+
+        # Maybe we should also append -mthreads, but then the finished dlls
+        # need another dll (mingwm10.dll see Mingw32 docs) (-mthreads: Support
+        # thread-safe exception handling on `Mingw32')
+
+        # no additional libraries needed
+        #self.dll_libraries=[]
+        return
+
+    # __init__ ()
+
+    def link(self,
+             target_desc,
+             objects,
+             output_filename,
+             output_dir,
+             libraries,
+             library_dirs,
+             runtime_library_dirs,
+             export_symbols = None,
+             debug=0,
+             extra_preargs=None,
+             extra_postargs=None,
+             build_temp=None,
+             target_lang=None):
+        # Include the appropriate MSVC runtime library if Python was built
+        # with MSVC >= 7.0 (MinGW standard is msvcrt)
+        runtime_library = msvc_runtime_library()
+        if runtime_library:
+            if not libraries:
+                libraries = []
+            libraries.append(runtime_library)
+        args = (self,
+                target_desc,
+                objects,
+                output_filename,
+                output_dir,
+                libraries,
+                library_dirs,
+                runtime_library_dirs,
+                None, #export_symbols, we do this in our def-file
+                debug,
+                extra_preargs,
+                extra_postargs,
+                build_temp,
+                target_lang)
+        func = UnixCCompiler.link
+        func(*args[:func.__code__.co_argcount])
+        return
+
+    def object_filenames (self,
+                          source_filenames,
+                          strip_dir=0,
+                          output_dir=''):
+        if output_dir is None: output_dir = ''
+        obj_names = []
+        for src_name in source_filenames:
+            # use normcase to make sure '.rc' is really '.rc' and not '.RC'
+            (base, ext) = os.path.splitext (os.path.normcase(src_name))
+
+            # added these lines to strip off windows drive letters
+            # without it, .o files are placed next to .c files
+            # instead of the build directory
+            drv, base = os.path.splitdrive(base)
+            if drv:
+                base = base[1:]
+
+            if ext not in (self.src_extensions + ['.rc', '.res']):
+                raise UnknownFileError(
+                      "unknown file type '%s' (from '%s')" % \
+                      (ext, src_name))
+            if strip_dir:
+                base = os.path.basename (base)
+            if ext == '.res' or ext == '.rc':
+                # these need to be compiled to object files
+                obj_names.append (os.path.join (output_dir,
+                                                base + ext + self.obj_extension))
+            else:
+                obj_names.append (os.path.join (output_dir,
+                                                base + self.obj_extension))
+        return obj_names
+
+    # object_filenames ()
+
+
+def find_python_dll():
+    # We can't do much here:
+    # - find it in the virtualenv (sys.prefix)
+    # - find it in python main dir (sys.base_prefix, if in a virtualenv)
+    # - in system32,
+    # - otherwise (Sxs), I don't know how to get it.
+    stems = [sys.prefix]
+    if sys.base_prefix != sys.prefix:
+        stems.append(sys.base_prefix)
+
+    sub_dirs = ['', 'lib', 'bin']
+    # generate possible combinations of directory trees and sub-directories
+    lib_dirs = []
+    for stem in stems:
+        for folder in sub_dirs:
+            lib_dirs.append(os.path.join(stem, folder))
+
+    # add system directory as well
+    if 'SYSTEMROOT' in os.environ:
+        lib_dirs.append(os.path.join(os.environ['SYSTEMROOT'], 'System32'))
+
+    # search in the file system for possible candidates
+    major_version, minor_version = tuple(sys.version_info[:2])
+    implementation = sys.implementation.name
+    if implementation == 'cpython':
+        dllname = f'python{major_version}{minor_version}.dll'
+    elif implementation == 'pypy':
+        dllname = f'libpypy{major_version}.{minor_version}-c.dll'
+    else:
+        dllname = f'Unknown platform {implementation}' 
+    print("Looking for %s" % dllname)
+    for folder in lib_dirs:
+        dll = os.path.join(folder, dllname)
+        if os.path.exists(dll):
+            return dll
+ 
+    raise ValueError("%s not found in %s" % (dllname, lib_dirs))
+
+def dump_table(dll):
+    st = subprocess.check_output(["objdump.exe", "-p", dll])
+    return st.split(b'\n')
+
+def generate_def(dll, dfile):
+    """Given a dll file location,  get all its exported symbols and dump them
+    into the given def file.
+
+    The .def file will be overwritten"""
+    dump = dump_table(dll)
+    for i in range(len(dump)):
+        if _START.match(dump[i].decode()):
+            break
+    else:
+        raise ValueError("Symbol table not found")
+
+    syms = []
+    for j in range(i+1, len(dump)):
+        m = _TABLE.match(dump[j].decode())
+        if m:
+            syms.append((int(m.group(1).strip()), m.group(2)))
+        else:
+            break
+
+    if len(syms) == 0:
+        log.warn('No symbols found in %s' % dll)
+
+    with open(dfile, 'w') as d:
+        d.write('LIBRARY        %s\n' % os.path.basename(dll))
+        d.write(';CODE          PRELOAD MOVEABLE DISCARDABLE\n')
+        d.write(';DATA          PRELOAD SINGLE\n')
+        d.write('\nEXPORTS\n')
+        for s in syms:
+            #d.write('@%d    %s\n' % (s[0], s[1]))
+            d.write('%s\n' % s[1])
+
+def find_dll(dll_name):
+
+    arch = {'AMD64' : 'amd64',
+            'Intel' : 'x86'}[get_build_architecture()]
+
+    def _find_dll_in_winsxs(dll_name):
+        # Walk through the WinSxS directory to find the dll.
+        winsxs_path = os.path.join(os.environ.get('WINDIR', r'C:\WINDOWS'),
+                                   'winsxs')
+        if not os.path.exists(winsxs_path):
+            return None
+        for root, dirs, files in os.walk(winsxs_path):
+            if dll_name in files and arch in root:
+                return os.path.join(root, dll_name)
+        return None
+
+    def _find_dll_in_path(dll_name):
+        # First, look in the Python directory, then scan PATH for
+        # the given dll name.
+        for path in [sys.prefix] + os.environ['PATH'].split(';'):
+            filepath = os.path.join(path, dll_name)
+            if os.path.exists(filepath):
+                return os.path.abspath(filepath)
+
+    return _find_dll_in_winsxs(dll_name) or _find_dll_in_path(dll_name)
+
+def build_msvcr_library(debug=False):
+    if os.name != 'nt':
+        return False
+
+    # If the version number is None, then we couldn't find the MSVC runtime at
+    # all, because we are running on a Python distribution which is customed
+    # compiled; trust that the compiler is the same as the one available to us
+    # now, and that it is capable of linking with the correct runtime without
+    # any extra options.
+    msvcr_ver = msvc_runtime_major()
+    if msvcr_ver is None:
+        log.debug('Skip building import library: '
+                  'Runtime is not compiled with MSVC')
+        return False
+
+    # Skip using a custom library for versions < MSVC 8.0
+    if msvcr_ver < 80:
+        log.debug('Skip building msvcr library:'
+                  ' custom functionality not present')
+        return False
+
+    msvcr_name = msvc_runtime_library()
+    if debug:
+        msvcr_name += 'd'
+
+    # Skip if custom library already exists
+    out_name = "lib%s.a" % msvcr_name
+    out_file = os.path.join(sys.prefix, 'libs', out_name)
+    if os.path.isfile(out_file):
+        log.debug('Skip building msvcr library: "%s" exists' %
+                  (out_file,))
+        return True
+
+    # Find the msvcr dll
+    msvcr_dll_name = msvcr_name + '.dll'
+    dll_file = find_dll(msvcr_dll_name)
+    if not dll_file:
+        log.warn('Cannot build msvcr library: "%s" not found' %
+                 msvcr_dll_name)
+        return False
+
+    def_name = "lib%s.def" % msvcr_name
+    def_file = os.path.join(sys.prefix, 'libs', def_name)
+
+    log.info('Building msvcr library: "%s" (from %s)' \
+             % (out_file, dll_file))
+
+    # Generate a symbol definition file from the msvcr dll
+    generate_def(dll_file, def_file)
+
+    # Create a custom mingw library for the given symbol definitions
+    cmd = ['dlltool', '-d', def_file, '-l', out_file]
+    retcode = subprocess.call(cmd)
+
+    # Clean up symbol definitions
+    os.remove(def_file)
+
+    return (not retcode)
+
+def build_import_library():
+    if os.name != 'nt':
+        return
+
+    arch = get_build_architecture()
+    if arch == 'AMD64':
+        return _build_import_library_amd64()
+    elif arch == 'Intel':
+        return _build_import_library_x86()
+    else:
+        raise ValueError("Unhandled arch %s" % arch)
+
+def _check_for_import_lib():
+    """Check if an import library for the Python runtime already exists."""
+    major_version, minor_version = tuple(sys.version_info[:2])
+
+    # patterns for the file name of the library itself
+    patterns = ['libpython%d%d.a',
+                'libpython%d%d.dll.a',
+                'libpython%d.%d.dll.a']
+
+    # directory trees that may contain the library
+    stems = [sys.prefix]
+    if hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix:
+        stems.append(sys.base_prefix)
+    elif hasattr(sys, 'real_prefix') and sys.real_prefix != sys.prefix:
+        stems.append(sys.real_prefix)
+
+    # possible subdirectories within those trees where it is placed
+    sub_dirs = ['libs', 'lib']
+
+    # generate a list of candidate locations
+    candidates = []
+    for pat in patterns:
+        filename = pat % (major_version, minor_version)
+        for stem_dir in stems:
+            for folder in sub_dirs:
+                candidates.append(os.path.join(stem_dir, folder, filename))
+
+    # test the filesystem to see if we can find any of these
+    for fullname in candidates:
+        if os.path.isfile(fullname):
+            # already exists, in location given
+            return (True, fullname)
+
+    # needs to be built, preferred location given first
+    return (False, candidates[0])
+
+def _build_import_library_amd64():
+    out_exists, out_file = _check_for_import_lib()
+    if out_exists:
+        log.debug('Skip building import library: "%s" exists', out_file)
+        return
+
+    # get the runtime dll for which we are building import library
+    dll_file = find_python_dll()
+    log.info('Building import library (arch=AMD64): "%s" (from %s)' %
+             (out_file, dll_file))
+
+    # generate symbol list from this library
+    def_name = "python%d%d.def" % tuple(sys.version_info[:2])
+    def_file = os.path.join(sys.prefix, 'libs', def_name)
+    generate_def(dll_file, def_file)
+
+    # generate import library from this symbol list
+    cmd = ['dlltool', '-d', def_file, '-l', out_file]
+    subprocess.check_call(cmd)
+
+def _build_import_library_x86():
+    """ Build the import libraries for Mingw32-gcc on Windows
+    """
+    out_exists, out_file = _check_for_import_lib()
+    if out_exists:
+        log.debug('Skip building import library: "%s" exists', out_file)
+        return
+
+    lib_name = "python%d%d.lib" % tuple(sys.version_info[:2])
+    lib_file = os.path.join(sys.prefix, 'libs', lib_name)
+    if not os.path.isfile(lib_file):
+        # didn't find library file in virtualenv, try base distribution, too,
+        # and use that instead if found there. for Python 2.7 venvs, the base
+        # directory is in attribute real_prefix instead of base_prefix.
+        if hasattr(sys, 'base_prefix'):
+            base_lib = os.path.join(sys.base_prefix, 'libs', lib_name)
+        elif hasattr(sys, 'real_prefix'):
+            base_lib = os.path.join(sys.real_prefix, 'libs', lib_name)
+        else:
+            base_lib = ''  # os.path.isfile('') == False
+
+        if os.path.isfile(base_lib):
+            lib_file = base_lib
+        else:
+            log.warn('Cannot build import library: "%s" not found', lib_file)
+            return
+    log.info('Building import library (ARCH=x86): "%s"', out_file)
+
+    from numpy.distutils import lib2def
+
+    def_name = "python%d%d.def" % tuple(sys.version_info[:2])
+    def_file = os.path.join(sys.prefix, 'libs', def_name)
+    nm_output = lib2def.getnm(
+            lib2def.DEFAULT_NM + [lib_file], shell=False)
+    dlist, flist = lib2def.parse_nm(nm_output)
+    with open(def_file, 'w') as fid:
+        lib2def.output_def(dlist, flist, lib2def.DEF_HEADER, fid)
+
+    dll_name = find_python_dll ()
+
+    cmd = ["dlltool",
+           "--dllname", dll_name,
+           "--def", def_file,
+           "--output-lib", out_file]
+    status = subprocess.check_output(cmd)
+    if status:
+        log.warn('Failed to build import library for gcc. Linking will fail.')
+    return
+
+#=====================================
+# Dealing with Visual Studio MANIFESTS
+#=====================================
+
+# Functions to deal with visual studio manifests. Manifest are a mechanism to
+# enforce strong DLL versioning on windows, and has nothing to do with
+# distutils MANIFEST. manifests are XML files with version info, and used by
+# the OS loader; they are necessary when linking against a DLL not in the
+# system path; in particular, official python 2.6 binary is built against the
+# MS runtime 9 (the one from VS 2008), which is not available on most windows
+# systems; python 2.6 installer does install it in the Win SxS (Side by side)
+# directory, but this requires the manifest for this to work. This is a big
+# mess, thanks MS for a wonderful system.
+
+# XXX: ideally, we should use exactly the same version as used by python. I
+# submitted a patch to get this version, but it was only included for python
+# 2.6.1 and above. So for versions below, we use a "best guess".
+_MSVCRVER_TO_FULLVER = {}
+if sys.platform == 'win32':
+    try:
+        import msvcrt
+        # I took one version in my SxS directory: no idea if it is the good
+        # one, and we can't retrieve it from python
+        _MSVCRVER_TO_FULLVER['80'] = "8.0.50727.42"
+        _MSVCRVER_TO_FULLVER['90'] = "9.0.21022.8"
+        # Value from msvcrt.CRT_ASSEMBLY_VERSION under Python 3.3.0
+        # on Windows XP:
+        _MSVCRVER_TO_FULLVER['100'] = "10.0.30319.460"
+        crt_ver = getattr(msvcrt, 'CRT_ASSEMBLY_VERSION', None)
+        if crt_ver is not None:  # Available at least back to Python 3.3
+            maj, min = re.match(r'(\d+)\.(\d)', crt_ver).groups()
+            _MSVCRVER_TO_FULLVER[maj + min] = crt_ver
+            del maj, min
+        del crt_ver
+    except ImportError:
+        # If we are here, means python was not built with MSVC. Not sure what
+        # to do in that case: manifest building will fail, but it should not be
+        # used in that case anyway
+        log.warn('Cannot import msvcrt: using manifest will not be possible')
+
+def msvc_manifest_xml(maj, min):
+    """Given a major and minor version of the MSVCR, returns the
+    corresponding XML file."""
+    try:
+        fullver = _MSVCRVER_TO_FULLVER[str(maj * 10 + min)]
+    except KeyError:
+        raise ValueError("Version %d,%d of MSVCRT not supported yet" %
+                         (maj, min)) from None
+    # Don't be fooled, it looks like an XML, but it is not. In particular, it
+    # should not have any space before starting, and its size should be
+    # divisible by 4, most likely for alignment constraints when the xml is
+    # embedded in the binary...
+    # This template was copied directly from the python 2.6 binary (using
+    # strings.exe from mingw on python.exe).
+    template = textwrap.dedent("""\
+        
+          
+            
+              
+                
+              
+            
+          
+          
+            
+              
+            
+          
+        """)
+
+    return template % {'fullver': fullver, 'maj': maj, 'min': min}
+
+def manifest_rc(name, type='dll'):
+    """Return the rc file used to generate the res file which will be embedded
+    as manifest for given manifest file name, of given type ('dll' or
+    'exe').
+
+    Parameters
+    ----------
+    name : str
+            name of the manifest file to embed
+    type : str {'dll', 'exe'}
+            type of the binary which will embed the manifest
+
+    """
+    if type == 'dll':
+        rctype = 2
+    elif type == 'exe':
+        rctype = 1
+    else:
+        raise ValueError("Type %s not supported" % type)
+
+    return """\
+#include "winuser.h"
+%d RT_MANIFEST %s""" % (rctype, name)
+
+def check_embedded_msvcr_match_linked(msver):
+    """msver is the ms runtime version used for the MANIFEST."""
+    # check msvcr major version are the same for linking and
+    # embedding
+    maj = msvc_runtime_major()
+    if maj:
+        if not maj == int(msver):
+            raise ValueError(
+                  "Discrepancy between linked msvcr " \
+                  "(%d) and the one about to be embedded " \
+                  "(%d)" % (int(msver), maj))
+
+def configtest_name(config):
+    base = os.path.basename(config._gen_temp_sourcefile("yo", [], "c"))
+    return os.path.splitext(base)[0]
+
+def manifest_name(config):
+    # Get configest name (including suffix)
+    root = configtest_name(config)
+    exext = config.compiler.exe_extension
+    return root + exext + ".manifest"
+
+def rc_name(config):
+    # Get configtest name (including suffix)
+    root = configtest_name(config)
+    return root + ".rc"
+
+def generate_manifest(config):
+    msver = get_build_msvc_version()
+    if msver is not None:
+        if msver >= 8:
+            check_embedded_msvcr_match_linked(msver)
+            ma_str, mi_str = str(msver).split('.')
+            # Write the manifest file
+            manxml = msvc_manifest_xml(int(ma_str), int(mi_str))
+            with open(manifest_name(config), "w") as man:
+                config.temp_files.append(manifest_name(config))
+                man.write(manxml)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/misc_util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/misc_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..09145e1ddf5279f05800ed747356b4f4a03f795b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/misc_util.py
@@ -0,0 +1,2484 @@
+import os
+import re
+import sys
+import copy
+import glob
+import atexit
+import tempfile
+import subprocess
+import shutil
+import multiprocessing
+import textwrap
+import importlib.util
+from threading import local as tlocal
+from functools import reduce
+
+import distutils
+from distutils.errors import DistutilsError
+
+# stores temporary directory of each thread to only create one per thread
+_tdata = tlocal()
+
+# store all created temporary directories so they can be deleted on exit
+_tmpdirs = []
+def clean_up_temporary_directory():
+    if _tmpdirs is not None:
+        for d in _tmpdirs:
+            try:
+                shutil.rmtree(d)
+            except OSError:
+                pass
+
+atexit.register(clean_up_temporary_directory)
+
+__all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict',
+           'dict_append', 'appendpath', 'generate_config_py',
+           'get_cmd', 'allpath', 'get_mathlibs',
+           'terminal_has_colors', 'red_text', 'green_text', 'yellow_text',
+           'blue_text', 'cyan_text', 'cyg2win32', 'mingw32', 'all_strings',
+           'has_f_sources', 'has_cxx_sources', 'filter_sources',
+           'get_dependencies', 'is_local_src_dir', 'get_ext_source_files',
+           'get_script_files', 'get_lib_source_files', 'get_data_files',
+           'dot_join', 'get_frame', 'minrelpath', 'njoin',
+           'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language',
+           'get_build_architecture', 'get_info', 'get_pkg_info',
+           'get_num_build_jobs', 'sanitize_cxx_flags',
+           'exec_mod_from_location']
+
+class InstallableLib:
+    """
+    Container to hold information on an installable library.
+
+    Parameters
+    ----------
+    name : str
+        Name of the installed library.
+    build_info : dict
+        Dictionary holding build information.
+    target_dir : str
+        Absolute path specifying where to install the library.
+
+    See Also
+    --------
+    Configuration.add_installed_library
+
+    Notes
+    -----
+    The three parameters are stored as attributes with the same names.
+
+    """
+    def __init__(self, name, build_info, target_dir):
+        self.name = name
+        self.build_info = build_info
+        self.target_dir = target_dir
+
+
+def get_num_build_jobs():
+    """
+    Get number of parallel build jobs set by the --parallel command line
+    argument of setup.py
+    If the command did not receive a setting the environment variable
+    NPY_NUM_BUILD_JOBS is checked. If that is unset, return the number of
+    processors on the system, with a maximum of 8 (to prevent
+    overloading the system if there a lot of CPUs).
+
+    Returns
+    -------
+    out : int
+        number of parallel jobs that can be run
+
+    """
+    from numpy.distutils.core import get_distribution
+    try:
+        cpu_count = len(os.sched_getaffinity(0))
+    except AttributeError:
+        cpu_count = multiprocessing.cpu_count()
+    cpu_count = min(cpu_count, 8)
+    envjobs = int(os.environ.get("NPY_NUM_BUILD_JOBS", cpu_count))
+    dist = get_distribution()
+    # may be None during configuration
+    if dist is None:
+        return envjobs
+
+    # any of these three may have the job set, take the largest
+    cmdattr = (getattr(dist.get_command_obj('build'), 'parallel', None),
+               getattr(dist.get_command_obj('build_ext'), 'parallel', None),
+               getattr(dist.get_command_obj('build_clib'), 'parallel', None))
+    if all(x is None for x in cmdattr):
+        return envjobs
+    else:
+        return max(x for x in cmdattr if x is not None)
+
+def quote_args(args):
+    """Quote list of arguments.
+
+    .. deprecated:: 1.22.
+    """
+    import warnings
+    warnings.warn('"quote_args" is deprecated.',
+                  DeprecationWarning, stacklevel=2)
+    # don't used _nt_quote_args as it does not check if
+    # args items already have quotes or not.
+    args = list(args)
+    for i in range(len(args)):
+        a = args[i]
+        if ' ' in a and a[0] not in '"\'':
+            args[i] = '"%s"' % (a)
+    return args
+
+def allpath(name):
+    "Convert a /-separated pathname to one using the OS's path separator."
+    split = name.split('/')
+    return os.path.join(*split)
+
+def rel_path(path, parent_path):
+    """Return path relative to parent_path."""
+    # Use realpath to avoid issues with symlinked dirs (see gh-7707)
+    pd = os.path.realpath(os.path.abspath(parent_path))
+    apath = os.path.realpath(os.path.abspath(path))
+    if len(apath) < len(pd):
+        return path
+    if apath == pd:
+        return ''
+    if pd == apath[:len(pd)]:
+        assert apath[len(pd)] in [os.sep], repr((path, apath[len(pd)]))
+        path = apath[len(pd)+1:]
+    return path
+
+def get_path_from_frame(frame, parent_path=None):
+    """Return path of the module given a frame object from the call stack.
+
+    Returned path is relative to parent_path when given,
+    otherwise it is absolute path.
+    """
+
+    # First, try to find if the file name is in the frame.
+    try:
+        caller_file = eval('__file__', frame.f_globals, frame.f_locals)
+        d = os.path.dirname(os.path.abspath(caller_file))
+    except NameError:
+        # __file__ is not defined, so let's try __name__. We try this second
+        # because setuptools spoofs __name__ to be '__main__' even though
+        # sys.modules['__main__'] might be something else, like easy_install(1).
+        caller_name = eval('__name__', frame.f_globals, frame.f_locals)
+        __import__(caller_name)
+        mod = sys.modules[caller_name]
+        if hasattr(mod, '__file__'):
+            d = os.path.dirname(os.path.abspath(mod.__file__))
+        else:
+            # we're probably running setup.py as execfile("setup.py")
+            # (likely we're building an egg)
+            d = os.path.abspath('.')
+
+    if parent_path is not None:
+        d = rel_path(d, parent_path)
+
+    return d or '.'
+
+def njoin(*path):
+    """Join two or more pathname components +
+    - convert a /-separated pathname to one using the OS's path separator.
+    - resolve `..` and `.` from path.
+
+    Either passing n arguments as in njoin('a','b'), or a sequence
+    of n names as in njoin(['a','b']) is handled, or a mixture of such arguments.
+    """
+    paths = []
+    for p in path:
+        if is_sequence(p):
+            # njoin(['a', 'b'], 'c')
+            paths.append(njoin(*p))
+        else:
+            assert is_string(p)
+            paths.append(p)
+    path = paths
+    if not path:
+        # njoin()
+        joined = ''
+    else:
+        # njoin('a', 'b')
+        joined = os.path.join(*path)
+    if os.path.sep != '/':
+        joined = joined.replace('/', os.path.sep)
+    return minrelpath(joined)
+
+def get_mathlibs(path=None):
+    """Return the MATHLIB line from numpyconfig.h
+    """
+    if path is not None:
+        config_file = os.path.join(path, '_numpyconfig.h')
+    else:
+        # Look for the file in each of the numpy include directories.
+        dirs = get_numpy_include_dirs()
+        for path in dirs:
+            fn = os.path.join(path, '_numpyconfig.h')
+            if os.path.exists(fn):
+                config_file = fn
+                break
+        else:
+            raise DistutilsError('_numpyconfig.h not found in numpy include '
+                'dirs %r' % (dirs,))
+
+    with open(config_file) as fid:
+        mathlibs = []
+        s = '#define MATHLIB'
+        for line in fid:
+            if line.startswith(s):
+                value = line[len(s):].strip()
+                if value:
+                    mathlibs.extend(value.split(','))
+    return mathlibs
+
+def minrelpath(path):
+    """Resolve `..` and '.' from path.
+    """
+    if not is_string(path):
+        return path
+    if '.' not in path:
+        return path
+    l = path.split(os.sep)
+    while l:
+        try:
+            i = l.index('.', 1)
+        except ValueError:
+            break
+        del l[i]
+    j = 1
+    while l:
+        try:
+            i = l.index('..', j)
+        except ValueError:
+            break
+        if l[i-1]=='..':
+            j += 1
+        else:
+            del l[i], l[i-1]
+            j = 1
+    if not l:
+        return ''
+    return os.sep.join(l)
+
+def sorted_glob(fileglob):
+    """sorts output of python glob for https://bugs.python.org/issue30461
+    to allow extensions to have reproducible build results"""
+    return sorted(glob.glob(fileglob))
+
+def _fix_paths(paths, local_path, include_non_existing):
+    assert is_sequence(paths), repr(type(paths))
+    new_paths = []
+    assert not is_string(paths), repr(paths)
+    for n in paths:
+        if is_string(n):
+            if '*' in n or '?' in n:
+                p = sorted_glob(n)
+                p2 = sorted_glob(njoin(local_path, n))
+                if p2:
+                    new_paths.extend(p2)
+                elif p:
+                    new_paths.extend(p)
+                else:
+                    if include_non_existing:
+                        new_paths.append(n)
+                    print('could not resolve pattern in %r: %r' %
+                            (local_path, n))
+            else:
+                n2 = njoin(local_path, n)
+                if os.path.exists(n2):
+                    new_paths.append(n2)
+                else:
+                    if os.path.exists(n):
+                        new_paths.append(n)
+                    elif include_non_existing:
+                        new_paths.append(n)
+                    if not os.path.exists(n):
+                        print('non-existing path in %r: %r' %
+                                (local_path, n))
+
+        elif is_sequence(n):
+            new_paths.extend(_fix_paths(n, local_path, include_non_existing))
+        else:
+            new_paths.append(n)
+    return [minrelpath(p) for p in new_paths]
+
+def gpaths(paths, local_path='', include_non_existing=True):
+    """Apply glob to paths and prepend local_path if needed.
+    """
+    if is_string(paths):
+        paths = (paths,)
+    return _fix_paths(paths, local_path, include_non_existing)
+
+def make_temp_file(suffix='', prefix='', text=True):
+    if not hasattr(_tdata, 'tempdir'):
+        _tdata.tempdir = tempfile.mkdtemp()
+        _tmpdirs.append(_tdata.tempdir)
+    fid, name = tempfile.mkstemp(suffix=suffix,
+                                 prefix=prefix,
+                                 dir=_tdata.tempdir,
+                                 text=text)
+    fo = os.fdopen(fid, 'w')
+    return fo, name
+
+# Hooks for colored terminal output.
+# See also https://web.archive.org/web/20100314204946/http://www.livinglogic.de/Python/ansistyle
+def terminal_has_colors():
+    if sys.platform=='cygwin' and 'USE_COLOR' not in os.environ:
+        # Avoid importing curses that causes illegal operation
+        # with a message:
+        #  PYTHON2 caused an invalid page fault in
+        #  module CYGNURSES7.DLL as 015f:18bbfc28
+        # Details: Python 2.3.3 [GCC 3.3.1 (cygming special)]
+        #          ssh to Win32 machine from debian
+        #          curses.version is 2.2
+        #          CYGWIN_98-4.10, release 1.5.7(0.109/3/2))
+        return 0
+    if hasattr(sys.stdout, 'isatty') and sys.stdout.isatty():
+        try:
+            import curses
+            curses.setupterm()
+            if (curses.tigetnum("colors") >= 0
+                and curses.tigetnum("pairs") >= 0
+                and ((curses.tigetstr("setf") is not None
+                      and curses.tigetstr("setb") is not None)
+                     or (curses.tigetstr("setaf") is not None
+                         and curses.tigetstr("setab") is not None)
+                     or curses.tigetstr("scp") is not None)):
+                return 1
+        except Exception:
+            pass
+    return 0
+
+if terminal_has_colors():
+    _colour_codes = dict(black=0, red=1, green=2, yellow=3,
+                         blue=4, magenta=5, cyan=6, white=7, default=9)
+    def colour_text(s, fg=None, bg=None, bold=False):
+        seq = []
+        if bold:
+            seq.append('1')
+        if fg:
+            fgcode = 30 + _colour_codes.get(fg.lower(), 0)
+            seq.append(str(fgcode))
+        if bg:
+            bgcode = 40 + _colour_codes.get(bg.lower(), 7)
+            seq.append(str(bgcode))
+        if seq:
+            return '\x1b[%sm%s\x1b[0m' % (';'.join(seq), s)
+        else:
+            return s
+else:
+    def colour_text(s, fg=None, bg=None):
+        return s
+
+def default_text(s):
+    return colour_text(s, 'default')
+def red_text(s):
+    return colour_text(s, 'red')
+def green_text(s):
+    return colour_text(s, 'green')
+def yellow_text(s):
+    return colour_text(s, 'yellow')
+def cyan_text(s):
+    return colour_text(s, 'cyan')
+def blue_text(s):
+    return colour_text(s, 'blue')
+
+#########################
+
+def cyg2win32(path: str) -> str:
+    """Convert a path from Cygwin-native to Windows-native.
+
+    Uses the cygpath utility (part of the Base install) to do the
+    actual conversion.  Falls back to returning the original path if
+    this fails.
+
+    Handles the default ``/cygdrive`` mount prefix as well as the
+    ``/proc/cygdrive`` portable prefix, custom cygdrive prefixes such
+    as ``/`` or ``/mnt``, and absolute paths such as ``/usr/src/`` or
+    ``/home/username``
+
+    Parameters
+    ----------
+    path : str
+       The path to convert
+
+    Returns
+    -------
+    converted_path : str
+        The converted path
+
+    Notes
+    -----
+    Documentation for cygpath utility:
+    https://cygwin.com/cygwin-ug-net/cygpath.html
+    Documentation for the C function it wraps:
+    https://cygwin.com/cygwin-api/func-cygwin-conv-path.html
+
+    """
+    if sys.platform != "cygwin":
+        return path
+    return subprocess.check_output(
+        ["/usr/bin/cygpath", "--windows", path], text=True
+    )
+
+
+def mingw32():
+    """Return true when using mingw32 environment.
+    """
+    if sys.platform=='win32':
+        if os.environ.get('OSTYPE', '')=='msys':
+            return True
+        if os.environ.get('MSYSTEM', '')=='MINGW32':
+            return True
+    return False
+
+def msvc_runtime_version():
+    "Return version of MSVC runtime library, as defined by __MSC_VER__ macro"
+    msc_pos = sys.version.find('MSC v.')
+    if msc_pos != -1:
+        msc_ver = int(sys.version[msc_pos+6:msc_pos+10])
+    else:
+        msc_ver = None
+    return msc_ver
+
+def msvc_runtime_library():
+    "Return name of MSVC runtime library if Python was built with MSVC >= 7"
+    ver = msvc_runtime_major ()
+    if ver:
+        if ver < 140:
+            return "msvcr%i" % ver
+        else:
+            return "vcruntime%i" % ver
+    else:
+        return None
+
+def msvc_runtime_major():
+    "Return major version of MSVC runtime coded like get_build_msvc_version"
+    major = {1300:  70,  # MSVC 7.0
+             1310:  71,  # MSVC 7.1
+             1400:  80,  # MSVC 8
+             1500:  90,  # MSVC 9  (aka 2008)
+             1600: 100,  # MSVC 10 (aka 2010)
+             1900: 140,  # MSVC 14 (aka 2015)
+    }.get(msvc_runtime_version(), None)
+    return major
+
+#########################
+
+#XXX need support for .C that is also C++
+cxx_ext_match = re.compile(r'.*\.(cpp|cxx|cc)\Z', re.I).match
+fortran_ext_match = re.compile(r'.*\.(f90|f95|f77|for|ftn|f)\Z', re.I).match
+f90_ext_match = re.compile(r'.*\.(f90|f95)\Z', re.I).match
+f90_module_name_match = re.compile(r'\s*module\s*(?P[\w_]+)', re.I).match
+def _get_f90_modules(source):
+    """Return a list of Fortran f90 module names that
+    given source file defines.
+    """
+    if not f90_ext_match(source):
+        return []
+    modules = []
+    with open(source) as f:
+        for line in f:
+            m = f90_module_name_match(line)
+            if m:
+                name = m.group('name')
+                modules.append(name)
+                # break  # XXX can we assume that there is one module per file?
+    return modules
+
+def is_string(s):
+    return isinstance(s, str)
+
+def all_strings(lst):
+    """Return True if all items in lst are string objects. """
+    return all(is_string(item) for item in lst)
+
+def is_sequence(seq):
+    if is_string(seq):
+        return False
+    try:
+        len(seq)
+    except Exception:
+        return False
+    return True
+
+def is_glob_pattern(s):
+    return is_string(s) and ('*' in s or '?' in s)
+
+def as_list(seq):
+    if is_sequence(seq):
+        return list(seq)
+    else:
+        return [seq]
+
+def get_language(sources):
+    # not used in numpy/scipy packages, use build_ext.detect_language instead
+    """Determine language value (c,f77,f90) from sources """
+    language = None
+    for source in sources:
+        if isinstance(source, str):
+            if f90_ext_match(source):
+                language = 'f90'
+                break
+            elif fortran_ext_match(source):
+                language = 'f77'
+    return language
+
+def has_f_sources(sources):
+    """Return True if sources contains Fortran files """
+    return any(fortran_ext_match(source) for source in sources)
+
+def has_cxx_sources(sources):
+    """Return True if sources contains C++ files """
+    return any(cxx_ext_match(source) for source in sources)
+
+def filter_sources(sources):
+    """Return four lists of filenames containing
+    C, C++, Fortran, and Fortran 90 module sources,
+    respectively.
+    """
+    c_sources = []
+    cxx_sources = []
+    f_sources = []
+    fmodule_sources = []
+    for source in sources:
+        if fortran_ext_match(source):
+            modules = _get_f90_modules(source)
+            if modules:
+                fmodule_sources.append(source)
+            else:
+                f_sources.append(source)
+        elif cxx_ext_match(source):
+            cxx_sources.append(source)
+        else:
+            c_sources.append(source)
+    return c_sources, cxx_sources, f_sources, fmodule_sources
+
+
+def _get_headers(directory_list):
+    # get *.h files from list of directories
+    headers = []
+    for d in directory_list:
+        head = sorted_glob(os.path.join(d, "*.h")) #XXX: *.hpp files??
+        headers.extend(head)
+    return headers
+
+def _get_directories(list_of_sources):
+    # get unique directories from list of sources.
+    direcs = []
+    for f in list_of_sources:
+        d = os.path.split(f)
+        if d[0] != '' and not d[0] in direcs:
+            direcs.append(d[0])
+    return direcs
+
+def _commandline_dep_string(cc_args, extra_postargs, pp_opts):
+    """
+    Return commandline representation used to determine if a file needs
+    to be recompiled
+    """
+    cmdline = 'commandline: '
+    cmdline += ' '.join(cc_args)
+    cmdline += ' '.join(extra_postargs)
+    cmdline += ' '.join(pp_opts) + '\n'
+    return cmdline
+
+
+def get_dependencies(sources):
+    #XXX scan sources for include statements
+    return _get_headers(_get_directories(sources))
+
+def is_local_src_dir(directory):
+    """Return true if directory is local directory.
+    """
+    if not is_string(directory):
+        return False
+    abs_dir = os.path.abspath(directory)
+    c = os.path.commonprefix([os.getcwd(), abs_dir])
+    new_dir = abs_dir[len(c):].split(os.sep)
+    if new_dir and not new_dir[0]:
+        new_dir = new_dir[1:]
+    if new_dir and new_dir[0]=='build':
+        return False
+    new_dir = os.sep.join(new_dir)
+    return os.path.isdir(new_dir)
+
+def general_source_files(top_path):
+    pruned_directories = {'CVS':1, '.svn':1, 'build':1}
+    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
+    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
+        pruned = [ d for d in dirnames if d not in pruned_directories ]
+        dirnames[:] = pruned
+        for f in filenames:
+            if not prune_file_pat.search(f):
+                yield os.path.join(dirpath, f)
+
+def general_source_directories_files(top_path):
+    """Return a directory name relative to top_path and
+    files contained.
+    """
+    pruned_directories = ['CVS', '.svn', 'build']
+    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
+    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
+        pruned = [ d for d in dirnames if d not in pruned_directories ]
+        dirnames[:] = pruned
+        for d in dirnames:
+            dpath = os.path.join(dirpath, d)
+            rpath = rel_path(dpath, top_path)
+            files = []
+            for f in os.listdir(dpath):
+                fn = os.path.join(dpath, f)
+                if os.path.isfile(fn) and not prune_file_pat.search(fn):
+                    files.append(fn)
+            yield rpath, files
+    dpath = top_path
+    rpath = rel_path(dpath, top_path)
+    filenames = [os.path.join(dpath, f) for f in os.listdir(dpath) \
+                 if not prune_file_pat.search(f)]
+    files = [f for f in filenames if os.path.isfile(f)]
+    yield rpath, files
+
+
+def get_ext_source_files(ext):
+    # Get sources and any include files in the same directory.
+    filenames = []
+    sources = [_m for _m in ext.sources if is_string(_m)]
+    filenames.extend(sources)
+    filenames.extend(get_dependencies(sources))
+    for d in ext.depends:
+        if is_local_src_dir(d):
+            filenames.extend(list(general_source_files(d)))
+        elif os.path.isfile(d):
+            filenames.append(d)
+    return filenames
+
+def get_script_files(scripts):
+    scripts = [_m for _m in scripts if is_string(_m)]
+    return scripts
+
+def get_lib_source_files(lib):
+    filenames = []
+    sources = lib[1].get('sources', [])
+    sources = [_m for _m in sources if is_string(_m)]
+    filenames.extend(sources)
+    filenames.extend(get_dependencies(sources))
+    depends = lib[1].get('depends', [])
+    for d in depends:
+        if is_local_src_dir(d):
+            filenames.extend(list(general_source_files(d)))
+        elif os.path.isfile(d):
+            filenames.append(d)
+    return filenames
+
+def get_shared_lib_extension(is_python_ext=False):
+    """Return the correct file extension for shared libraries.
+
+    Parameters
+    ----------
+    is_python_ext : bool, optional
+        Whether the shared library is a Python extension.  Default is False.
+
+    Returns
+    -------
+    so_ext : str
+        The shared library extension.
+
+    Notes
+    -----
+    For Python shared libs, `so_ext` will typically be '.so' on Linux and OS X,
+    and '.pyd' on Windows.  For Python >= 3.2 `so_ext` has a tag prepended on
+    POSIX systems according to PEP 3149.
+
+    """
+    confvars = distutils.sysconfig.get_config_vars()
+    so_ext = confvars.get('EXT_SUFFIX', '')
+
+    if not is_python_ext:
+        # hardcode known values, config vars (including SHLIB_SUFFIX) are
+        # unreliable (see #3182)
+        # darwin, windows and debug linux are wrong in 3.3.1 and older
+        if (sys.platform.startswith('linux') or
+            sys.platform.startswith('gnukfreebsd')):
+            so_ext = '.so'
+        elif sys.platform.startswith('darwin'):
+            so_ext = '.dylib'
+        elif sys.platform.startswith('win'):
+            so_ext = '.dll'
+        else:
+            # fall back to config vars for unknown platforms
+            # fix long extension for Python >=3.2, see PEP 3149.
+            if 'SOABI' in confvars:
+                # Does nothing unless SOABI config var exists
+                so_ext = so_ext.replace('.' + confvars.get('SOABI'), '', 1)
+
+    return so_ext
+
+def get_data_files(data):
+    if is_string(data):
+        return [data]
+    sources = data[1]
+    filenames = []
+    for s in sources:
+        if hasattr(s, '__call__'):
+            continue
+        if is_local_src_dir(s):
+            filenames.extend(list(general_source_files(s)))
+        elif is_string(s):
+            if os.path.isfile(s):
+                filenames.append(s)
+            else:
+                print('Not existing data file:', s)
+        else:
+            raise TypeError(repr(s))
+    return filenames
+
+def dot_join(*args):
+    return '.'.join([a for a in args if a])
+
+def get_frame(level=0):
+    """Return frame object from call stack with given level.
+    """
+    try:
+        return sys._getframe(level+1)
+    except AttributeError:
+        frame = sys.exc_info()[2].tb_frame
+        for _ in range(level+1):
+            frame = frame.f_back
+        return frame
+
+
+######################
+
+class Configuration:
+
+    _list_keys = ['packages', 'ext_modules', 'data_files', 'include_dirs',
+                  'libraries', 'headers', 'scripts', 'py_modules',
+                  'installed_libraries', 'define_macros']
+    _dict_keys = ['package_dir', 'installed_pkg_config']
+    _extra_keys = ['name', 'version']
+
+    numpy_include_dirs = []
+
+    def __init__(self,
+                 package_name=None,
+                 parent_name=None,
+                 top_path=None,
+                 package_path=None,
+                 caller_level=1,
+                 setup_name='setup.py',
+                 **attrs):
+        """Construct configuration instance of a package.
+
+        package_name -- name of the package
+                        Ex.: 'distutils'
+        parent_name  -- name of the parent package
+                        Ex.: 'numpy'
+        top_path     -- directory of the toplevel package
+                        Ex.: the directory where the numpy package source sits
+        package_path -- directory of package. Will be computed by magic from the
+                        directory of the caller module if not specified
+                        Ex.: the directory where numpy.distutils is
+        caller_level -- frame level to caller namespace, internal parameter.
+        """
+        self.name = dot_join(parent_name, package_name)
+        self.version = None
+
+        caller_frame = get_frame(caller_level)
+        self.local_path = get_path_from_frame(caller_frame, top_path)
+        # local_path -- directory of a file (usually setup.py) that
+        #               defines a configuration() function.
+        # local_path -- directory of a file (usually setup.py) that
+        #               defines a configuration() function.
+        if top_path is None:
+            top_path = self.local_path
+            self.local_path = ''
+        if package_path is None:
+            package_path = self.local_path
+        elif os.path.isdir(njoin(self.local_path, package_path)):
+            package_path = njoin(self.local_path, package_path)
+        if not os.path.isdir(package_path or '.'):
+            raise ValueError("%r is not a directory" % (package_path,))
+        self.top_path = top_path
+        self.package_path = package_path
+        # this is the relative path in the installed package
+        self.path_in_package = os.path.join(*self.name.split('.'))
+
+        self.list_keys = self._list_keys[:]
+        self.dict_keys = self._dict_keys[:]
+
+        for n in self.list_keys:
+            v = copy.copy(attrs.get(n, []))
+            setattr(self, n, as_list(v))
+
+        for n in self.dict_keys:
+            v = copy.copy(attrs.get(n, {}))
+            setattr(self, n, v)
+
+        known_keys = self.list_keys + self.dict_keys
+        self.extra_keys = self._extra_keys[:]
+        for n in attrs.keys():
+            if n in known_keys:
+                continue
+            a = attrs[n]
+            setattr(self, n, a)
+            if isinstance(a, list):
+                self.list_keys.append(n)
+            elif isinstance(a, dict):
+                self.dict_keys.append(n)
+            else:
+                self.extra_keys.append(n)
+
+        if os.path.exists(njoin(package_path, '__init__.py')):
+            self.packages.append(self.name)
+            self.package_dir[self.name] = package_path
+
+        self.options = dict(
+            ignore_setup_xxx_py = False,
+            assume_default_configuration = False,
+            delegate_options_to_subpackages = False,
+            quiet = False,
+            )
+
+        caller_instance = None
+        for i in range(1, 3):
+            try:
+                f = get_frame(i)
+            except ValueError:
+                break
+            try:
+                caller_instance = eval('self', f.f_globals, f.f_locals)
+                break
+            except NameError:
+                pass
+        if isinstance(caller_instance, self.__class__):
+            if caller_instance.options['delegate_options_to_subpackages']:
+                self.set_options(**caller_instance.options)
+
+        self.setup_name = setup_name
+
+    def todict(self):
+        """
+        Return a dictionary compatible with the keyword arguments of distutils
+        setup function.
+
+        Examples
+        --------
+        >>> setup(**config.todict())                           #doctest: +SKIP
+        """
+
+        self._optimize_data_files()
+        d = {}
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        for n in known_keys:
+            a = getattr(self, n)
+            if a:
+                d[n] = a
+        return d
+
+    def info(self, message):
+        if not self.options['quiet']:
+            print(message)
+
+    def warn(self, message):
+        sys.stderr.write('Warning: %s\n' % (message,))
+
+    def set_options(self, **options):
+        """
+        Configure Configuration instance.
+
+        The following options are available:
+         - ignore_setup_xxx_py
+         - assume_default_configuration
+         - delegate_options_to_subpackages
+         - quiet
+
+        """
+        for key, value in options.items():
+            if key in self.options:
+                self.options[key] = value
+            else:
+                raise ValueError('Unknown option: '+key)
+
+    def get_distribution(self):
+        """Return the distutils distribution object for self."""
+        from numpy.distutils.core import get_distribution
+        return get_distribution()
+
+    def _wildcard_get_subpackage(self, subpackage_name,
+                                 parent_name,
+                                 caller_level = 1):
+        l = subpackage_name.split('.')
+        subpackage_path = njoin([self.local_path]+l)
+        dirs = [_m for _m in sorted_glob(subpackage_path) if os.path.isdir(_m)]
+        config_list = []
+        for d in dirs:
+            if not os.path.isfile(njoin(d, '__init__.py')):
+                continue
+            if 'build' in d.split(os.sep):
+                continue
+            n = '.'.join(d.split(os.sep)[-len(l):])
+            c = self.get_subpackage(n,
+                                    parent_name = parent_name,
+                                    caller_level = caller_level+1)
+            config_list.extend(c)
+        return config_list
+
+    def _get_configuration_from_setup_py(self, setup_py,
+                                         subpackage_name,
+                                         subpackage_path,
+                                         parent_name,
+                                         caller_level = 1):
+        # In case setup_py imports local modules:
+        sys.path.insert(0, os.path.dirname(setup_py))
+        try:
+            setup_name = os.path.splitext(os.path.basename(setup_py))[0]
+            n = dot_join(self.name, subpackage_name, setup_name)
+            setup_module = exec_mod_from_location(
+                                '_'.join(n.split('.')), setup_py)
+            if not hasattr(setup_module, 'configuration'):
+                if not self.options['assume_default_configuration']:
+                    self.warn('Assuming default configuration '\
+                              '(%s does not define configuration())'\
+                              % (setup_module))
+                config = Configuration(subpackage_name, parent_name,
+                                       self.top_path, subpackage_path,
+                                       caller_level = caller_level + 1)
+            else:
+                pn = dot_join(*([parent_name] + subpackage_name.split('.')[:-1]))
+                args = (pn,)
+                if setup_module.configuration.__code__.co_argcount > 1:
+                    args = args + (self.top_path,)
+                config = setup_module.configuration(*args)
+            if config.name!=dot_join(parent_name, subpackage_name):
+                self.warn('Subpackage %r configuration returned as %r' % \
+                          (dot_join(parent_name, subpackage_name), config.name))
+        finally:
+            del sys.path[0]
+        return config
+
+    def get_subpackage(self,subpackage_name,
+                       subpackage_path=None,
+                       parent_name=None,
+                       caller_level = 1):
+        """Return list of subpackage configurations.
+
+        Parameters
+        ----------
+        subpackage_name : str or None
+            Name of the subpackage to get the configuration. '*' in
+            subpackage_name is handled as a wildcard.
+        subpackage_path : str
+            If None, then the path is assumed to be the local path plus the
+            subpackage_name. If a setup.py file is not found in the
+            subpackage_path, then a default configuration is used.
+        parent_name : str
+            Parent name.
+        """
+        if subpackage_name is None:
+            if subpackage_path is None:
+                raise ValueError(
+                    "either subpackage_name or subpackage_path must be specified")
+            subpackage_name = os.path.basename(subpackage_path)
+
+        # handle wildcards
+        l = subpackage_name.split('.')
+        if subpackage_path is None and '*' in subpackage_name:
+            return self._wildcard_get_subpackage(subpackage_name,
+                                                 parent_name,
+                                                 caller_level = caller_level+1)
+        assert '*' not in subpackage_name, repr((subpackage_name, subpackage_path, parent_name))
+        if subpackage_path is None:
+            subpackage_path = njoin([self.local_path] + l)
+        else:
+            subpackage_path = njoin([subpackage_path] + l[:-1])
+            subpackage_path = self.paths([subpackage_path])[0]
+        setup_py = njoin(subpackage_path, self.setup_name)
+        if not self.options['ignore_setup_xxx_py']:
+            if not os.path.isfile(setup_py):
+                setup_py = njoin(subpackage_path,
+                                 'setup_%s.py' % (subpackage_name))
+        if not os.path.isfile(setup_py):
+            if not self.options['assume_default_configuration']:
+                self.warn('Assuming default configuration '\
+                          '(%s/{setup_%s,setup}.py was not found)' \
+                          % (os.path.dirname(setup_py), subpackage_name))
+            config = Configuration(subpackage_name, parent_name,
+                                   self.top_path, subpackage_path,
+                                   caller_level = caller_level+1)
+        else:
+            config = self._get_configuration_from_setup_py(
+                setup_py,
+                subpackage_name,
+                subpackage_path,
+                parent_name,
+                caller_level = caller_level + 1)
+        if config:
+            return [config]
+        else:
+            return []
+
+    def add_subpackage(self,subpackage_name,
+                       subpackage_path=None,
+                       standalone = False):
+        """Add a sub-package to the current Configuration instance.
+
+        This is useful in a setup.py script for adding sub-packages to a
+        package.
+
+        Parameters
+        ----------
+        subpackage_name : str
+            name of the subpackage
+        subpackage_path : str
+            if given, the subpackage path such as the subpackage is in
+            subpackage_path / subpackage_name. If None,the subpackage is
+            assumed to be located in the local path / subpackage_name.
+        standalone : bool
+        """
+
+        if standalone:
+            parent_name = None
+        else:
+            parent_name = self.name
+        config_list = self.get_subpackage(subpackage_name, subpackage_path,
+                                          parent_name = parent_name,
+                                          caller_level = 2)
+        if not config_list:
+            self.warn('No configuration returned, assuming unavailable.')
+        for config in config_list:
+            d = config
+            if isinstance(config, Configuration):
+                d = config.todict()
+            assert isinstance(d, dict), repr(type(d))
+
+            self.info('Appending %s configuration to %s' \
+                      % (d.get('name'), self.name))
+            self.dict_append(**d)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add a subpackage '+ subpackage_name)
+
+    def add_data_dir(self, data_path):
+        """Recursively add files under data_path to data_files list.
+
+        Recursively add files under data_path to the list of data_files to be
+        installed (and distributed). The data_path can be either a relative
+        path-name, or an absolute path-name, or a 2-tuple where the first
+        argument shows where in the install directory the data directory
+        should be installed to.
+
+        Parameters
+        ----------
+        data_path : seq or str
+            Argument can be either
+
+                * 2-sequence (, )
+                * path to data directory where python datadir suffix defaults
+                  to package dir.
+
+        Notes
+        -----
+        Rules for installation paths::
+
+            foo/bar -> (foo/bar, foo/bar) -> parent/foo/bar
+            (gun, foo/bar) -> parent/gun
+            foo/* -> (foo/a, foo/a), (foo/b, foo/b) -> parent/foo/a, parent/foo/b
+            (gun, foo/*) -> (gun, foo/a), (gun, foo/b) -> gun
+            (gun/*, foo/*) -> parent/gun/a, parent/gun/b
+            /foo/bar -> (bar, /foo/bar) -> parent/bar
+            (gun, /foo/bar) -> parent/gun
+            (fun/*/gun/*, sun/foo/bar) -> parent/fun/foo/gun/bar
+
+        Examples
+        --------
+        For example suppose the source directory contains fun/foo.dat and
+        fun/bar/car.dat:
+
+        >>> self.add_data_dir('fun')                       #doctest: +SKIP
+        >>> self.add_data_dir(('sun', 'fun'))              #doctest: +SKIP
+        >>> self.add_data_dir(('gun', '/full/path/to/fun'))#doctest: +SKIP
+
+        Will install data-files to the locations::
+
+            /
+              fun/
+                foo.dat
+                bar/
+                  car.dat
+              sun/
+                foo.dat
+                bar/
+                  car.dat
+              gun/
+                foo.dat
+                car.dat
+
+        """
+        if is_sequence(data_path):
+            d, data_path = data_path
+        else:
+            d = None
+        if is_sequence(data_path):
+            [self.add_data_dir((d, p)) for p in data_path]
+            return
+        if not is_string(data_path):
+            raise TypeError("not a string: %r" % (data_path,))
+        if d is None:
+            if os.path.isabs(data_path):
+                return self.add_data_dir((os.path.basename(data_path), data_path))
+            return self.add_data_dir((data_path, data_path))
+        paths = self.paths(data_path, include_non_existing=False)
+        if is_glob_pattern(data_path):
+            if is_glob_pattern(d):
+                pattern_list = allpath(d).split(os.sep)
+                pattern_list.reverse()
+                # /a/*//b/ -> /a/*/b
+                rl = list(range(len(pattern_list)-1)); rl.reverse()
+                for i in rl:
+                    if not pattern_list[i]:
+                        del pattern_list[i]
+                #
+                for path in paths:
+                    if not os.path.isdir(path):
+                        print('Not a directory, skipping', path)
+                        continue
+                    rpath = rel_path(path, self.local_path)
+                    path_list = rpath.split(os.sep)
+                    path_list.reverse()
+                    target_list = []
+                    i = 0
+                    for s in pattern_list:
+                        if is_glob_pattern(s):
+                            if i>=len(path_list):
+                                raise ValueError('cannot fill pattern %r with %r' \
+                                      % (d, path))
+                            target_list.append(path_list[i])
+                        else:
+                            assert s==path_list[i], repr((s, path_list[i], data_path, d, path, rpath))
+                            target_list.append(s)
+                        i += 1
+                    if path_list[i:]:
+                        self.warn('mismatch of pattern_list=%s and path_list=%s'\
+                                  % (pattern_list, path_list))
+                    target_list.reverse()
+                    self.add_data_dir((os.sep.join(target_list), path))
+            else:
+                for path in paths:
+                    self.add_data_dir((d, path))
+            return
+        assert not is_glob_pattern(d), repr(d)
+
+        dist = self.get_distribution()
+        if dist is not None and dist.data_files is not None:
+            data_files = dist.data_files
+        else:
+            data_files = self.data_files
+
+        for path in paths:
+            for d1, f in list(general_source_directories_files(path)):
+                target_path = os.path.join(self.path_in_package, d, d1)
+                data_files.append((target_path, f))
+
+    def _optimize_data_files(self):
+        data_dict = {}
+        for p, files in self.data_files:
+            if p not in data_dict:
+                data_dict[p] = set()
+            for f in files:
+                data_dict[p].add(f)
+        self.data_files[:] = [(p, list(files)) for p, files in data_dict.items()]
+
+    def add_data_files(self,*files):
+        """Add data files to configuration data_files.
+
+        Parameters
+        ----------
+        files : sequence
+            Argument(s) can be either
+
+                * 2-sequence (,)
+                * paths to data files where python datadir prefix defaults
+                  to package dir.
+
+        Notes
+        -----
+        The form of each element of the files sequence is very flexible
+        allowing many combinations of where to get the files from the package
+        and where they should ultimately be installed on the system. The most
+        basic usage is for an element of the files argument sequence to be a
+        simple filename. This will cause that file from the local path to be
+        installed to the installation path of the self.name package (package
+        path). The file argument can also be a relative path in which case the
+        entire relative path will be installed into the package directory.
+        Finally, the file can be an absolute path name in which case the file
+        will be found at the absolute path name but installed to the package
+        path.
+
+        This basic behavior can be augmented by passing a 2-tuple in as the
+        file argument. The first element of the tuple should specify the
+        relative path (under the package install directory) where the
+        remaining sequence of files should be installed to (it has nothing to
+        do with the file-names in the source distribution). The second element
+        of the tuple is the sequence of files that should be installed. The
+        files in this sequence can be filenames, relative paths, or absolute
+        paths. For absolute paths the file will be installed in the top-level
+        package installation directory (regardless of the first argument).
+        Filenames and relative path names will be installed in the package
+        install directory under the path name given as the first element of
+        the tuple.
+
+        Rules for installation paths:
+
+          #. file.txt -> (., file.txt)-> parent/file.txt
+          #. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt
+          #. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt
+          #. ``*``.txt -> parent/a.txt, parent/b.txt
+          #. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt
+          #. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt
+          #. (sun, file.txt) -> parent/sun/file.txt
+          #. (sun, bar/file.txt) -> parent/sun/file.txt
+          #. (sun, /foo/bar/file.txt) -> parent/sun/file.txt
+          #. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+          #. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
+          #. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt
+
+        An additional feature is that the path to a data-file can actually be
+        a function that takes no arguments and returns the actual path(s) to
+        the data-files. This is useful when the data files are generated while
+        building the package.
+
+        Examples
+        --------
+        Add files to the list of data_files to be included with the package.
+
+            >>> self.add_data_files('foo.dat',
+            ...     ('fun', ['gun.dat', 'nun/pun.dat', '/tmp/sun.dat']),
+            ...     'bar/cat.dat',
+            ...     '/full/path/to/can.dat')                   #doctest: +SKIP
+
+        will install these data files to::
+
+            /
+             foo.dat
+             fun/
+               gun.dat
+               nun/
+                 pun.dat
+             sun.dat
+             bar/
+               car.dat
+             can.dat
+
+        where  is the package (or sub-package)
+        directory such as '/usr/lib/python2.4/site-packages/mypackage' ('C:
+        \\Python2.4 \\Lib \\site-packages \\mypackage') or
+        '/usr/lib/python2.4/site- packages/mypackage/mysubpackage' ('C:
+        \\Python2.4 \\Lib \\site-packages \\mypackage \\mysubpackage').
+        """
+
+        if len(files)>1:
+            for f in files:
+                self.add_data_files(f)
+            return
+        assert len(files)==1
+        if is_sequence(files[0]):
+            d, files = files[0]
+        else:
+            d = None
+        if is_string(files):
+            filepat = files
+        elif is_sequence(files):
+            if len(files)==1:
+                filepat = files[0]
+            else:
+                for f in files:
+                    self.add_data_files((d, f))
+                return
+        else:
+            raise TypeError(repr(type(files)))
+
+        if d is None:
+            if hasattr(filepat, '__call__'):
+                d = ''
+            elif os.path.isabs(filepat):
+                d = ''
+            else:
+                d = os.path.dirname(filepat)
+            self.add_data_files((d, files))
+            return
+
+        paths = self.paths(filepat, include_non_existing=False)
+        if is_glob_pattern(filepat):
+            if is_glob_pattern(d):
+                pattern_list = d.split(os.sep)
+                pattern_list.reverse()
+                for path in paths:
+                    path_list = path.split(os.sep)
+                    path_list.reverse()
+                    path_list.pop() # filename
+                    target_list = []
+                    i = 0
+                    for s in pattern_list:
+                        if is_glob_pattern(s):
+                            target_list.append(path_list[i])
+                            i += 1
+                        else:
+                            target_list.append(s)
+                    target_list.reverse()
+                    self.add_data_files((os.sep.join(target_list), path))
+            else:
+                self.add_data_files((d, paths))
+            return
+        assert not is_glob_pattern(d), repr((d, filepat))
+
+        dist = self.get_distribution()
+        if dist is not None and dist.data_files is not None:
+            data_files = dist.data_files
+        else:
+            data_files = self.data_files
+
+        data_files.append((os.path.join(self.path_in_package, d), paths))
+
+    ### XXX Implement add_py_modules
+
+    def add_define_macros(self, macros):
+        """Add define macros to configuration
+
+        Add the given sequence of macro name and value duples to the beginning
+        of the define_macros list This list will be visible to all extension
+        modules of the current package.
+        """
+        dist = self.get_distribution()
+        if dist is not None:
+            if not hasattr(dist, 'define_macros'):
+                dist.define_macros = []
+            dist.define_macros.extend(macros)
+        else:
+            self.define_macros.extend(macros)
+
+
+    def add_include_dirs(self,*paths):
+        """Add paths to configuration include directories.
+
+        Add the given sequence of paths to the beginning of the include_dirs
+        list. This list will be visible to all extension modules of the
+        current package.
+        """
+        include_dirs = self.paths(paths)
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.include_dirs is None:
+                dist.include_dirs = []
+            dist.include_dirs.extend(include_dirs)
+        else:
+            self.include_dirs.extend(include_dirs)
+
+    def add_headers(self,*files):
+        """Add installable headers to configuration.
+
+        Add the given sequence of files to the beginning of the headers list.
+        By default, headers will be installed under // directory. If an item of files
+        is a tuple, then its first argument specifies the actual installation
+        location relative to the  path.
+
+        Parameters
+        ----------
+        files : str or seq
+            Argument(s) can be either:
+
+                * 2-sequence (,)
+                * path(s) to header file(s) where python includedir suffix will
+                  default to package name.
+        """
+        headers = []
+        for path in files:
+            if is_string(path):
+                [headers.append((self.name, p)) for p in self.paths(path)]
+            else:
+                if not isinstance(path, (tuple, list)) or len(path) != 2:
+                    raise TypeError(repr(path))
+                [headers.append((path[0], p)) for p in self.paths(path[1])]
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.headers is None:
+                dist.headers = []
+            dist.headers.extend(headers)
+        else:
+            self.headers.extend(headers)
+
+    def paths(self,*paths,**kws):
+        """Apply glob to paths and prepend local_path if needed.
+
+        Applies glob.glob(...) to each path in the sequence (if needed) and
+        prepends the local_path if needed. Because this is called on all
+        source lists, this allows wildcard characters to be specified in lists
+        of sources for extension modules and libraries and scripts and allows
+        path-names be relative to the source directory.
+
+        """
+        include_non_existing = kws.get('include_non_existing', True)
+        return gpaths(paths,
+                      local_path = self.local_path,
+                      include_non_existing=include_non_existing)
+
+    def _fix_paths_dict(self, kw):
+        for k in kw.keys():
+            v = kw[k]
+            if k in ['sources', 'depends', 'include_dirs', 'library_dirs',
+                     'module_dirs', 'extra_objects']:
+                new_v = self.paths(v)
+                kw[k] = new_v
+
+    def add_extension(self,name,sources,**kw):
+        """Add extension to configuration.
+
+        Create and add an Extension instance to the ext_modules list. This
+        method also takes the following optional keyword arguments that are
+        passed on to the Extension constructor.
+
+        Parameters
+        ----------
+        name : str
+            name of the extension
+        sources : seq
+            list of the sources. The list of sources may contain functions
+            (called source generators) which must take an extension instance
+            and a build directory as inputs and return a source file or list of
+            source files or None. If None is returned then no sources are
+            generated. If the Extension instance has no sources after
+            processing all source generators, then no extension module is
+            built.
+        include_dirs :
+        define_macros :
+        undef_macros :
+        library_dirs :
+        libraries :
+        runtime_library_dirs :
+        extra_objects :
+        extra_compile_args :
+        extra_link_args :
+        extra_f77_compile_args :
+        extra_f90_compile_args :
+        export_symbols :
+        swig_opts :
+        depends :
+            The depends list contains paths to files or directories that the
+            sources of the extension module depend on. If any path in the
+            depends list is newer than the extension module, then the module
+            will be rebuilt.
+        language :
+        f2py_options :
+        module_dirs :
+        extra_info : dict or list
+            dict or list of dict of keywords to be appended to keywords.
+
+        Notes
+        -----
+        The self.paths(...) method is applied to all lists that may contain
+        paths.
+        """
+        ext_args = copy.copy(kw)
+        ext_args['name'] = dot_join(self.name, name)
+        ext_args['sources'] = sources
+
+        if 'extra_info' in ext_args:
+            extra_info = ext_args['extra_info']
+            del ext_args['extra_info']
+            if isinstance(extra_info, dict):
+                extra_info = [extra_info]
+            for info in extra_info:
+                assert isinstance(info, dict), repr(info)
+                dict_append(ext_args,**info)
+
+        self._fix_paths_dict(ext_args)
+
+        # Resolve out-of-tree dependencies
+        libraries = ext_args.get('libraries', [])
+        libnames = []
+        ext_args['libraries'] = []
+        for libname in libraries:
+            if isinstance(libname, tuple):
+                self._fix_paths_dict(libname[1])
+
+            # Handle library names of the form libname@relative/path/to/library
+            if '@' in libname:
+                lname, lpath = libname.split('@', 1)
+                lpath = os.path.abspath(njoin(self.local_path, lpath))
+                if os.path.isdir(lpath):
+                    c = self.get_subpackage(None, lpath,
+                                            caller_level = 2)
+                    if isinstance(c, Configuration):
+                        c = c.todict()
+                    for l in [l[0] for l in c.get('libraries', [])]:
+                        llname = l.split('__OF__', 1)[0]
+                        if llname == lname:
+                            c.pop('name', None)
+                            dict_append(ext_args,**c)
+                            break
+                    continue
+            libnames.append(libname)
+
+        ext_args['libraries'] = libnames + ext_args['libraries']
+        ext_args['define_macros'] = \
+            self.define_macros + ext_args.get('define_macros', [])
+
+        from numpy.distutils.core import Extension
+        ext = Extension(**ext_args)
+        self.ext_modules.append(ext)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add an extension '+name)
+        return ext
+
+    def add_library(self,name,sources,**build_info):
+        """
+        Add library to configuration.
+
+        Parameters
+        ----------
+        name : str
+            Name of the extension.
+        sources : sequence
+            List of the sources. The list of sources may contain functions
+            (called source generators) which must take an extension instance
+            and a build directory as inputs and return a source file or list of
+            source files or None. If None is returned then no sources are
+            generated. If the Extension instance has no sources after
+            processing all source generators, then no extension module is
+            built.
+        build_info : dict, optional
+            The following keys are allowed:
+
+                * depends
+                * macros
+                * include_dirs
+                * extra_compiler_args
+                * extra_f77_compile_args
+                * extra_f90_compile_args
+                * f2py_options
+                * language
+
+        """
+        self._add_library(name, sources, None, build_info)
+
+        dist = self.get_distribution()
+        if dist is not None:
+            self.warn('distutils distribution has been initialized,'\
+                      ' it may be too late to add a library '+ name)
+
+    def _add_library(self, name, sources, install_dir, build_info):
+        """Common implementation for add_library and add_installed_library. Do
+        not use directly"""
+        build_info = copy.copy(build_info)
+        build_info['sources'] = sources
+
+        # Sometimes, depends is not set up to an empty list by default, and if
+        # depends is not given to add_library, distutils barfs (#1134)
+        if not 'depends' in build_info:
+            build_info['depends'] = []
+
+        self._fix_paths_dict(build_info)
+
+        # Add to libraries list so that it is build with build_clib
+        self.libraries.append((name, build_info))
+
+    def add_installed_library(self, name, sources, install_dir, build_info=None):
+        """
+        Similar to add_library, but the specified library is installed.
+
+        Most C libraries used with ``distutils`` are only used to build python
+        extensions, but libraries built through this method will be installed
+        so that they can be reused by third-party packages.
+
+        Parameters
+        ----------
+        name : str
+            Name of the installed library.
+        sources : sequence
+            List of the library's source files. See `add_library` for details.
+        install_dir : str
+            Path to install the library, relative to the current sub-package.
+        build_info : dict, optional
+            The following keys are allowed:
+
+                * depends
+                * macros
+                * include_dirs
+                * extra_compiler_args
+                * extra_f77_compile_args
+                * extra_f90_compile_args
+                * f2py_options
+                * language
+
+        Returns
+        -------
+        None
+
+        See Also
+        --------
+        add_library, add_npy_pkg_config, get_info
+
+        Notes
+        -----
+        The best way to encode the options required to link against the specified
+        C libraries is to use a "libname.ini" file, and use `get_info` to
+        retrieve the required options (see `add_npy_pkg_config` for more
+        information).
+
+        """
+        if not build_info:
+            build_info = {}
+
+        install_dir = os.path.join(self.package_path, install_dir)
+        self._add_library(name, sources, install_dir, build_info)
+        self.installed_libraries.append(InstallableLib(name, build_info, install_dir))
+
+    def add_npy_pkg_config(self, template, install_dir, subst_dict=None):
+        """
+        Generate and install a npy-pkg config file from a template.
+
+        The config file generated from `template` is installed in the
+        given install directory, using `subst_dict` for variable substitution.
+
+        Parameters
+        ----------
+        template : str
+            The path of the template, relatively to the current package path.
+        install_dir : str
+            Where to install the npy-pkg config file, relatively to the current
+            package path.
+        subst_dict : dict, optional
+            If given, any string of the form ``@key@`` will be replaced by
+            ``subst_dict[key]`` in the template file when installed. The install
+            prefix is always available through the variable ``@prefix@``, since the
+            install prefix is not easy to get reliably from setup.py.
+
+        See also
+        --------
+        add_installed_library, get_info
+
+        Notes
+        -----
+        This works for both standard installs and in-place builds, i.e. the
+        ``@prefix@`` refer to the source directory for in-place builds.
+
+        Examples
+        --------
+        ::
+
+            config.add_npy_pkg_config('foo.ini.in', 'lib', {'foo': bar})
+
+        Assuming the foo.ini.in file has the following content::
+
+            [meta]
+            Name=@foo@
+            Version=1.0
+            Description=dummy description
+
+            [default]
+            Cflags=-I@prefix@/include
+            Libs=
+
+        The generated file will have the following content::
+
+            [meta]
+            Name=bar
+            Version=1.0
+            Description=dummy description
+
+            [default]
+            Cflags=-Iprefix_dir/include
+            Libs=
+
+        and will be installed as foo.ini in the 'lib' subpath.
+
+        When cross-compiling with numpy distutils, it might be necessary to
+        use modified npy-pkg-config files.  Using the default/generated files
+        will link with the host libraries (i.e. libnpymath.a).  For
+        cross-compilation you of-course need to link with target libraries,
+        while using the host Python installation.
+
+        You can copy out the numpy/_core/lib/npy-pkg-config directory, add a
+        pkgdir value to the .ini files and set NPY_PKG_CONFIG_PATH environment
+        variable to point to the directory with the modified npy-pkg-config
+        files.
+
+        Example npymath.ini modified for cross-compilation::
+
+            [meta]
+            Name=npymath
+            Description=Portable, core math library implementing C99 standard
+            Version=0.1
+
+            [variables]
+            pkgname=numpy._core
+            pkgdir=/build/arm-linux-gnueabi/sysroot/usr/lib/python3.7/site-packages/numpy/_core
+            prefix=${pkgdir}
+            libdir=${prefix}/lib
+            includedir=${prefix}/include
+
+            [default]
+            Libs=-L${libdir} -lnpymath
+            Cflags=-I${includedir}
+            Requires=mlib
+
+            [msvc]
+            Libs=/LIBPATH:${libdir} npymath.lib
+            Cflags=/INCLUDE:${includedir}
+            Requires=mlib
+
+        """
+        if subst_dict is None:
+            subst_dict = {}
+        template = os.path.join(self.package_path, template)
+
+        if self.name in self.installed_pkg_config:
+            self.installed_pkg_config[self.name].append((template, install_dir,
+                subst_dict))
+        else:
+            self.installed_pkg_config[self.name] = [(template, install_dir,
+                subst_dict)]
+
+
+    def add_scripts(self,*files):
+        """Add scripts to configuration.
+
+        Add the sequence of files to the beginning of the scripts list.
+        Scripts will be installed under the /bin/ directory.
+
+        """
+        scripts = self.paths(files)
+        dist = self.get_distribution()
+        if dist is not None:
+            if dist.scripts is None:
+                dist.scripts = []
+            dist.scripts.extend(scripts)
+        else:
+            self.scripts.extend(scripts)
+
+    def dict_append(self,**dict):
+        for key in self.list_keys:
+            a = getattr(self, key)
+            a.extend(dict.get(key, []))
+        for key in self.dict_keys:
+            a = getattr(self, key)
+            a.update(dict.get(key, {}))
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        for key in dict.keys():
+            if key not in known_keys:
+                a = getattr(self, key, None)
+                if a and a==dict[key]: continue
+                self.warn('Inheriting attribute %r=%r from %r' \
+                          % (key, dict[key], dict.get('name', '?')))
+                setattr(self, key, dict[key])
+                self.extra_keys.append(key)
+            elif key in self.extra_keys:
+                self.info('Ignoring attempt to set %r (from %r to %r)' \
+                          % (key, getattr(self, key), dict[key]))
+            elif key in known_keys:
+                # key is already processed above
+                pass
+            else:
+                raise ValueError("Don't know about key=%r" % (key))
+
+    def __str__(self):
+        from pprint import pformat
+        known_keys = self.list_keys + self.dict_keys + self.extra_keys
+        s = '<'+5*'-' + '\n'
+        s += 'Configuration of '+self.name+':\n'
+        known_keys.sort()
+        for k in known_keys:
+            a = getattr(self, k, None)
+            if a:
+                s += '%s = %s\n' % (k, pformat(a))
+        s += 5*'-' + '>'
+        return s
+
+    def get_config_cmd(self):
+        """
+        Returns the numpy.distutils config command instance.
+        """
+        cmd = get_cmd('config')
+        cmd.ensure_finalized()
+        cmd.dump_source = 0
+        cmd.noisy = 0
+        old_path = os.environ.get('PATH')
+        if old_path:
+            path = os.pathsep.join(['.', old_path])
+            os.environ['PATH'] = path
+        return cmd
+
+    def get_build_temp_dir(self):
+        """
+        Return a path to a temporary directory where temporary files should be
+        placed.
+        """
+        cmd = get_cmd('build')
+        cmd.ensure_finalized()
+        return cmd.build_temp
+
+    def have_f77c(self):
+        """Check for availability of Fortran 77 compiler.
+
+        Use it inside source generating function to ensure that
+        setup distribution instance has been initialized.
+
+        Notes
+        -----
+        True if a Fortran 77 compiler is available (because a simple Fortran 77
+        code was able to be compiled successfully).
+        """
+        simple_fortran_subroutine = '''
+        subroutine simple
+        end
+        '''
+        config_cmd = self.get_config_cmd()
+        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f77')
+        return flag
+
+    def have_f90c(self):
+        """Check for availability of Fortran 90 compiler.
+
+        Use it inside source generating function to ensure that
+        setup distribution instance has been initialized.
+
+        Notes
+        -----
+        True if a Fortran 90 compiler is available (because a simple Fortran
+        90 code was able to be compiled successfully)
+        """
+        simple_fortran_subroutine = '''
+        subroutine simple
+        end
+        '''
+        config_cmd = self.get_config_cmd()
+        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f90')
+        return flag
+
+    def append_to(self, extlib):
+        """Append libraries, include_dirs to extension or library item.
+        """
+        if is_sequence(extlib):
+            lib_name, build_info = extlib
+            dict_append(build_info,
+                        libraries=self.libraries,
+                        include_dirs=self.include_dirs)
+        else:
+            from numpy.distutils.core import Extension
+            assert isinstance(extlib, Extension), repr(extlib)
+            extlib.libraries.extend(self.libraries)
+            extlib.include_dirs.extend(self.include_dirs)
+
+    def _get_svn_revision(self, path):
+        """Return path's SVN revision number.
+        """
+        try:
+            output = subprocess.check_output(['svnversion'], cwd=path)
+        except (subprocess.CalledProcessError, OSError):
+            pass
+        else:
+            m = re.match(rb'(?P\d+)', output)
+            if m:
+                return int(m.group('revision'))
+
+        if sys.platform=='win32' and os.environ.get('SVN_ASP_DOT_NET_HACK', None):
+            entries = njoin(path, '_svn', 'entries')
+        else:
+            entries = njoin(path, '.svn', 'entries')
+        if os.path.isfile(entries):
+            with open(entries) as f:
+                fstr = f.read()
+            if fstr[:5] == '\d+)"', fstr)
+                if m:
+                    return int(m.group('revision'))
+            else:  # non-xml entries file --- check to be sure that
+                m = re.search(r'dir[\n\r]+(?P\d+)', fstr)
+                if m:
+                    return int(m.group('revision'))
+        return None
+
+    def _get_hg_revision(self, path):
+        """Return path's Mercurial revision number.
+        """
+        try:
+            output = subprocess.check_output(
+                ['hg', 'identify', '--num'], cwd=path)
+        except (subprocess.CalledProcessError, OSError):
+            pass
+        else:
+            m = re.match(rb'(?P\d+)', output)
+            if m:
+                return int(m.group('revision'))
+
+        branch_fn = njoin(path, '.hg', 'branch')
+        branch_cache_fn = njoin(path, '.hg', 'branch.cache')
+
+        if os.path.isfile(branch_fn):
+            branch0 = None
+            with open(branch_fn) as f:
+                revision0 = f.read().strip()
+
+            branch_map = {}
+            with open(branch_cache_fn) as f:
+                for line in f:
+                    branch1, revision1  = line.split()[:2]
+                    if revision1==revision0:
+                        branch0 = branch1
+                    try:
+                        revision1 = int(revision1)
+                    except ValueError:
+                        continue
+                    branch_map[branch1] = revision1
+
+            return branch_map.get(branch0)
+
+        return None
+
+
+    def get_version(self, version_file=None, version_variable=None):
+        """Try to get version string of a package.
+
+        Return a version string of the current package or None if the version
+        information could not be detected.
+
+        Notes
+        -----
+        This method scans files named
+        __version__.py, _version.py, version.py, and
+        __svn_version__.py for string variables version, __version__, and
+        _version, until a version number is found.
+        """
+        version = getattr(self, 'version', None)
+        if version is not None:
+            return version
+
+        # Get version from version file.
+        if version_file is None:
+            files = ['__version__.py',
+                     self.name.split('.')[-1]+'_version.py',
+                     'version.py',
+                     '__svn_version__.py',
+                     '__hg_version__.py']
+        else:
+            files = [version_file]
+        if version_variable is None:
+            version_vars = ['version',
+                            '__version__',
+                            self.name.split('.')[-1]+'_version']
+        else:
+            version_vars = [version_variable]
+        for f in files:
+            fn = njoin(self.local_path, f)
+            if os.path.isfile(fn):
+                info = ('.py', 'U', 1)
+                name = os.path.splitext(os.path.basename(fn))[0]
+                n = dot_join(self.name, name)
+                try:
+                    version_module = exec_mod_from_location(
+                                        '_'.join(n.split('.')), fn)
+                except ImportError as e:
+                    self.warn(str(e))
+                    version_module = None
+                if version_module is None:
+                    continue
+
+                for a in version_vars:
+                    version = getattr(version_module, a, None)
+                    if version is not None:
+                        break
+
+                # Try if versioneer module
+                try:
+                    version = version_module.get_versions()['version']
+                except AttributeError:
+                    pass
+
+                if version is not None:
+                    break
+
+        if version is not None:
+            self.version = version
+            return version
+
+        # Get version as SVN or Mercurial revision number
+        revision = self._get_svn_revision(self.local_path)
+        if revision is None:
+            revision = self._get_hg_revision(self.local_path)
+
+        if revision is not None:
+            version = str(revision)
+            self.version = version
+
+        return version
+
+    def make_svn_version_py(self, delete=True):
+        """Appends a data function to the data_files list that will generate
+        __svn_version__.py file to the current package directory.
+
+        Generate package __svn_version__.py file from SVN revision number,
+        it will be removed after python exits but will be available
+        when sdist, etc commands are executed.
+
+        Notes
+        -----
+        If __svn_version__.py existed before, nothing is done.
+
+        This is
+        intended for working with source directories that are in an SVN
+        repository.
+        """
+        target = njoin(self.local_path, '__svn_version__.py')
+        revision = self._get_svn_revision(self.local_path)
+        if os.path.isfile(target) or revision is None:
+            return
+        else:
+            def generate_svn_version_py():
+                if not os.path.isfile(target):
+                    version = str(revision)
+                    self.info('Creating %s (version=%r)' % (target, version))
+                    with open(target, 'w') as f:
+                        f.write('version = %r\n' % (version))
+
+                def rm_file(f=target,p=self.info):
+                    if delete:
+                        try: os.remove(f); p('removed '+f)
+                        except OSError: pass
+                        try: os.remove(f+'c'); p('removed '+f+'c')
+                        except OSError: pass
+
+                atexit.register(rm_file)
+
+                return target
+
+            self.add_data_files(('', generate_svn_version_py()))
+
+    def make_hg_version_py(self, delete=True):
+        """Appends a data function to the data_files list that will generate
+        __hg_version__.py file to the current package directory.
+
+        Generate package __hg_version__.py file from Mercurial revision,
+        it will be removed after python exits but will be available
+        when sdist, etc commands are executed.
+
+        Notes
+        -----
+        If __hg_version__.py existed before, nothing is done.
+
+        This is intended for working with source directories that are
+        in an Mercurial repository.
+        """
+        target = njoin(self.local_path, '__hg_version__.py')
+        revision = self._get_hg_revision(self.local_path)
+        if os.path.isfile(target) or revision is None:
+            return
+        else:
+            def generate_hg_version_py():
+                if not os.path.isfile(target):
+                    version = str(revision)
+                    self.info('Creating %s (version=%r)' % (target, version))
+                    with open(target, 'w') as f:
+                        f.write('version = %r\n' % (version))
+
+                def rm_file(f=target,p=self.info):
+                    if delete:
+                        try: os.remove(f); p('removed '+f)
+                        except OSError: pass
+                        try: os.remove(f+'c'); p('removed '+f+'c')
+                        except OSError: pass
+
+                atexit.register(rm_file)
+
+                return target
+
+            self.add_data_files(('', generate_hg_version_py()))
+
+    def make_config_py(self,name='__config__'):
+        """Generate package __config__.py file containing system_info
+        information used during building the package.
+
+        This file is installed to the
+        package installation directory.
+
+        """
+        self.py_modules.append((self.name, name, generate_config_py))
+
+    def get_info(self,*names):
+        """Get resources information.
+
+        Return information (from system_info.get_info) for all of the names in
+        the argument list in a single dictionary.
+        """
+        from .system_info import get_info, dict_append
+        info_dict = {}
+        for a in names:
+            dict_append(info_dict,**get_info(a))
+        return info_dict
+
+
+def get_cmd(cmdname, _cache={}):
+    if cmdname not in _cache:
+        import distutils.core
+        dist = distutils.core._setup_distribution
+        if dist is None:
+            from distutils.errors import DistutilsInternalError
+            raise DistutilsInternalError(
+                  'setup distribution instance not initialized')
+        cmd = dist.get_command_obj(cmdname)
+        _cache[cmdname] = cmd
+    return _cache[cmdname]
+
+def get_numpy_include_dirs():
+    # numpy_include_dirs are set by numpy/_core/setup.py, otherwise []
+    include_dirs = Configuration.numpy_include_dirs[:]
+    if not include_dirs:
+        import numpy
+        include_dirs = [ numpy.get_include() ]
+    # else running numpy/_core/setup.py
+    return include_dirs
+
+def get_npy_pkg_dir():
+    """Return the path where to find the npy-pkg-config directory.
+
+    If the NPY_PKG_CONFIG_PATH environment variable is set, the value of that
+    is returned.  Otherwise, a path inside the location of the numpy module is
+    returned.
+
+    The NPY_PKG_CONFIG_PATH can be useful when cross-compiling, maintaining
+    customized npy-pkg-config .ini files for the cross-compilation
+    environment, and using them when cross-compiling.
+
+    """
+    d = os.environ.get('NPY_PKG_CONFIG_PATH')
+    if d is not None:
+        return d
+    spec = importlib.util.find_spec('numpy')
+    d = os.path.join(os.path.dirname(spec.origin),
+            '_core', 'lib', 'npy-pkg-config')
+    return d
+
+def get_pkg_info(pkgname, dirs=None):
+    """
+    Return library info for the given package.
+
+    Parameters
+    ----------
+    pkgname : str
+        Name of the package (should match the name of the .ini file, without
+        the extension, e.g. foo for the file foo.ini).
+    dirs : sequence, optional
+        If given, should be a sequence of additional directories where to look
+        for npy-pkg-config files. Those directories are searched prior to the
+        NumPy directory.
+
+    Returns
+    -------
+    pkginfo : class instance
+        The `LibraryInfo` instance containing the build information.
+
+    Raises
+    ------
+    PkgNotFound
+        If the package is not found.
+
+    See Also
+    --------
+    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
+    get_info
+
+    """
+    from numpy.distutils.npy_pkg_config import read_config
+
+    if dirs:
+        dirs.append(get_npy_pkg_dir())
+    else:
+        dirs = [get_npy_pkg_dir()]
+    return read_config(pkgname, dirs)
+
+def get_info(pkgname, dirs=None):
+    """
+    Return an info dict for a given C library.
+
+    The info dict contains the necessary options to use the C library.
+
+    Parameters
+    ----------
+    pkgname : str
+        Name of the package (should match the name of the .ini file, without
+        the extension, e.g. foo for the file foo.ini).
+    dirs : sequence, optional
+        If given, should be a sequence of additional directories where to look
+        for npy-pkg-config files. Those directories are searched prior to the
+        NumPy directory.
+
+    Returns
+    -------
+    info : dict
+        The dictionary with build information.
+
+    Raises
+    ------
+    PkgNotFound
+        If the package is not found.
+
+    See Also
+    --------
+    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
+    get_pkg_info
+
+    Examples
+    --------
+    To get the necessary information for the npymath library from NumPy:
+
+    >>> npymath_info = np.distutils.misc_util.get_info('npymath')
+    >>> npymath_info                                    #doctest: +SKIP
+    {'define_macros': [], 'libraries': ['npymath'], 'library_dirs':
+    ['.../numpy/_core/lib'], 'include_dirs': ['.../numpy/_core/include']}
+
+    This info dict can then be used as input to a `Configuration` instance::
+
+      config.add_extension('foo', sources=['foo.c'], extra_info=npymath_info)
+
+    """
+    from numpy.distutils.npy_pkg_config import parse_flags
+    pkg_info = get_pkg_info(pkgname, dirs)
+
+    # Translate LibraryInfo instance into a build_info dict
+    info = parse_flags(pkg_info.cflags())
+    for k, v in parse_flags(pkg_info.libs()).items():
+        info[k].extend(v)
+
+    # add_extension extra_info argument is ANAL
+    info['define_macros'] = info['macros']
+    del info['macros']
+    del info['ignored']
+
+    return info
+
+def is_bootstrapping():
+    import builtins
+
+    try:
+        builtins.__NUMPY_SETUP__
+        return True
+    except AttributeError:
+        return False
+
+
+#########################
+
+def default_config_dict(name = None, parent_name = None, local_path=None):
+    """Return a configuration dictionary for usage in
+    configuration() function defined in file setup_.py.
+    """
+    import warnings
+    warnings.warn('Use Configuration(%r,%r,top_path=%r) instead of '\
+                  'deprecated default_config_dict(%r,%r,%r)'
+                  % (name, parent_name, local_path,
+                     name, parent_name, local_path,
+                     ), stacklevel=2)
+    c = Configuration(name, parent_name, local_path)
+    return c.todict()
+
+
+def dict_append(d, **kws):
+    for k, v in kws.items():
+        if k in d:
+            ov = d[k]
+            if isinstance(ov, str):
+                d[k] = v
+            else:
+                d[k].extend(v)
+        else:
+            d[k] = v
+
+def appendpath(prefix, path):
+    if os.path.sep != '/':
+        prefix = prefix.replace('/', os.path.sep)
+        path = path.replace('/', os.path.sep)
+    drive = ''
+    if os.path.isabs(path):
+        drive = os.path.splitdrive(prefix)[0]
+        absprefix = os.path.splitdrive(os.path.abspath(prefix))[1]
+        pathdrive, path = os.path.splitdrive(path)
+        d = os.path.commonprefix([absprefix, path])
+        if os.path.join(absprefix[:len(d)], absprefix[len(d):]) != absprefix \
+           or os.path.join(path[:len(d)], path[len(d):]) != path:
+            # Handle invalid paths
+            d = os.path.dirname(d)
+        subpath = path[len(d):]
+        if os.path.isabs(subpath):
+            subpath = subpath[1:]
+    else:
+        subpath = path
+    return os.path.normpath(njoin(drive + prefix, subpath))
+
+def generate_config_py(target):
+    """Generate config.py file containing system_info information
+    used during building the package.
+
+    Usage:
+        config['py_modules'].append((packagename, '__config__',generate_config_py))
+    """
+    from numpy.distutils.system_info import system_info
+    from distutils.dir_util import mkpath
+    mkpath(os.path.dirname(target))
+    with open(target, 'w') as f:
+        f.write('# This file is generated by numpy\'s %s\n' % (os.path.basename(sys.argv[0])))
+        f.write('# It contains system_info results at the time of building this package.\n')
+        f.write('__all__ = ["get_info","show"]\n\n')
+
+        # For gfortran+msvc combination, extra shared libraries may exist
+        f.write(textwrap.dedent("""
+            import os
+            import sys
+
+            extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
+
+            if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
+                os.add_dll_directory(extra_dll_dir)
+
+            """))
+
+        for k, i in system_info.saved_results.items():
+            f.write('%s=%r\n' % (k, i))
+        f.write(textwrap.dedent(r'''
+            def get_info(name):
+                g = globals()
+                return g.get(name, g.get(name + "_info", {}))
+
+            def show():
+                """
+                Show libraries in the system on which NumPy was built.
+
+                Print information about various resources (libraries, library
+                directories, include directories, etc.) in the system on which
+                NumPy was built.
+
+                See Also
+                --------
+                get_include : Returns the directory containing NumPy C
+                              header files.
+
+                Notes
+                -----
+                1. Classes specifying the information to be printed are defined
+                   in the `numpy.distutils.system_info` module.
+
+                   Information may include:
+
+                   * ``language``: language used to write the libraries (mostly
+                     C or f77)
+                   * ``libraries``: names of libraries found in the system
+                   * ``library_dirs``: directories containing the libraries
+                   * ``include_dirs``: directories containing library header files
+                   * ``src_dirs``: directories containing library source files
+                   * ``define_macros``: preprocessor macros used by
+                     ``distutils.setup``
+                   * ``baseline``: minimum CPU features required
+                   * ``found``: dispatched features supported in the system
+                   * ``not found``: dispatched features that are not supported
+                     in the system
+
+                2. NumPy BLAS/LAPACK Installation Notes
+
+                   Installing a numpy wheel (``pip install numpy`` or force it
+                   via ``pip install numpy --only-binary :numpy: numpy``) includes
+                   an OpenBLAS implementation of the BLAS and LAPACK linear algebra
+                   APIs. In this case, ``library_dirs`` reports the original build
+                   time configuration as compiled with gcc/gfortran; at run time
+                   the OpenBLAS library is in
+                   ``site-packages/numpy.libs/`` (linux), or
+                   ``site-packages/numpy/.dylibs/`` (macOS), or
+                   ``site-packages/numpy/.libs/`` (windows).
+
+                   Installing numpy from source
+                   (``pip install numpy --no-binary numpy``) searches for BLAS and
+                   LAPACK dynamic link libraries at build time as influenced by
+                   environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
+                   NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
+                   or the optional file ``~/.numpy-site.cfg``.
+                   NumPy remembers those locations and expects to load the same
+                   libraries at run-time.
+                   In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
+                   library) is in the default build-time search order after
+                   'openblas'.
+
+                Examples
+                --------
+                >>> import numpy as np
+                >>> np.show_config()
+                blas_opt_info:
+                    language = c
+                    define_macros = [('HAVE_CBLAS', None)]
+                    libraries = ['openblas', 'openblas']
+                    library_dirs = ['/usr/local/lib']
+                """
+                from numpy._core._multiarray_umath import (
+                    __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+                )
+                for name,info_dict in globals().items():
+                    if name[0] == "_" or type(info_dict) is not type({}): continue
+                    print(name + ":")
+                    if not info_dict:
+                        print("  NOT AVAILABLE")
+                    for k,v in info_dict.items():
+                        v = str(v)
+                        if k == "sources" and len(v) > 200:
+                            v = v[:60] + " ...\n... " + v[-60:]
+                        print("    %s = %s" % (k,v))
+
+                features_found, features_not_found = [], []
+                for feature in __cpu_dispatch__:
+                    if __cpu_features__[feature]:
+                        features_found.append(feature)
+                    else:
+                        features_not_found.append(feature)
+
+                print("Supported SIMD extensions in this NumPy install:")
+                print("    baseline = %s" % (','.join(__cpu_baseline__)))
+                print("    found = %s" % (','.join(features_found)))
+                print("    not found = %s" % (','.join(features_not_found)))
+
+                    '''))
+
+    return target
+
+def msvc_version(compiler):
+    """Return version major and minor of compiler instance if it is
+    MSVC, raise an exception otherwise."""
+    if not compiler.compiler_type == "msvc":
+        raise ValueError("Compiler instance is not msvc (%s)"\
+                         % compiler.compiler_type)
+    return compiler._MSVCCompiler__version
+
+def get_build_architecture():
+    # Importing distutils.msvccompiler triggers a warning on non-Windows
+    # systems, so delay the import to here.
+    from distutils.msvccompiler import get_build_architecture
+    return get_build_architecture()
+
+
+_cxx_ignore_flags = {'-Werror=implicit-function-declaration', '-std=c99'}
+
+
+def sanitize_cxx_flags(cxxflags):
+    '''
+    Some flags are valid for C but not C++. Prune them.
+    '''
+    return [flag for flag in cxxflags if flag not in _cxx_ignore_flags]
+
+
+def exec_mod_from_location(modname, modfile):
+    '''
+    Use importlib machinery to import a module `modname` from the file
+    `modfile`. Depending on the `spec.loader`, the module may not be
+    registered in sys.modules.
+    '''
+    spec = importlib.util.spec_from_file_location(modname, modfile)
+    foo = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(foo)
+    return foo
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvc9compiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvc9compiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..68239495d6c72b70257e51d7ec3ddb35611940a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvc9compiler.py
@@ -0,0 +1,63 @@
+import os
+from distutils.msvc9compiler import MSVCCompiler as _MSVCCompiler
+
+from .system_info import platform_bits
+
+
+def _merge(old, new):
+    """Concatenate two environment paths avoiding repeats.
+
+    Here `old` is the environment string before the base class initialize
+    function is called and `new` is the string after the call. The new string
+    will be a fixed string if it is not obtained from the current environment,
+    or the same as the old string if obtained from the same environment. The aim
+    here is not to append the new string if it is already contained in the old
+    string so as to limit the growth of the environment string.
+
+    Parameters
+    ----------
+    old : string
+        Previous environment string.
+    new : string
+        New environment string.
+
+    Returns
+    -------
+    ret : string
+        Updated environment string.
+
+    """
+    if not old:
+        return new
+    if new in old:
+        return old
+
+    # Neither new nor old is empty. Give old priority.
+    return ';'.join([old, new])
+
+
+class MSVCCompiler(_MSVCCompiler):
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        _MSVCCompiler.__init__(self, verbose, dry_run, force)
+
+    def initialize(self, plat_name=None):
+        # The 'lib' and 'include' variables may be overwritten
+        # by MSVCCompiler.initialize, so save them for later merge.
+        environ_lib = os.getenv('lib')
+        environ_include = os.getenv('include')
+        _MSVCCompiler.initialize(self, plat_name)
+
+        # Merge current and previous values of 'lib' and 'include'
+        os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
+        os.environ['include'] = _merge(environ_include, os.environ['include'])
+
+        # msvc9 building for 32 bits requires SSE2 to work around a
+        # compiler bug.
+        if platform_bits == 32:
+            self.compile_options += ['/arch:SSE2']
+            self.compile_options_debug += ['/arch:SSE2']
+
+    def manifest_setup_ldargs(self, output_filename, build_temp, ld_args):
+        ld_args.append('/MANIFEST')
+        _MSVCCompiler.manifest_setup_ldargs(self, output_filename,
+                                            build_temp, ld_args)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b93221baac8b122a1cca97278db3748159b780b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/msvccompiler.py
@@ -0,0 +1,76 @@
+import os
+from distutils.msvccompiler import MSVCCompiler as _MSVCCompiler
+
+from .system_info import platform_bits
+
+
+def _merge(old, new):
+    """Concatenate two environment paths avoiding repeats.
+
+    Here `old` is the environment string before the base class initialize
+    function is called and `new` is the string after the call. The new string
+    will be a fixed string if it is not obtained from the current environment,
+    or the same as the old string if obtained from the same environment. The aim
+    here is not to append the new string if it is already contained in the old
+    string so as to limit the growth of the environment string.
+
+    Parameters
+    ----------
+    old : string
+        Previous environment string.
+    new : string
+        New environment string.
+
+    Returns
+    -------
+    ret : string
+        Updated environment string.
+
+    """
+    if new in old:
+        return old
+    if not old:
+        return new
+
+    # Neither new nor old is empty. Give old priority.
+    return ';'.join([old, new])
+
+
+class MSVCCompiler(_MSVCCompiler):
+    def __init__(self, verbose=0, dry_run=0, force=0):
+        _MSVCCompiler.__init__(self, verbose, dry_run, force)
+
+    def initialize(self):
+        # The 'lib' and 'include' variables may be overwritten
+        # by MSVCCompiler.initialize, so save them for later merge.
+        environ_lib = os.getenv('lib', '')
+        environ_include = os.getenv('include', '')
+        _MSVCCompiler.initialize(self)
+
+        # Merge current and previous values of 'lib' and 'include'
+        os.environ['lib'] = _merge(environ_lib, os.environ['lib'])
+        os.environ['include'] = _merge(environ_include, os.environ['include'])
+
+        # msvc9 building for 32 bits requires SSE2 to work around a
+        # compiler bug.
+        if platform_bits == 32:
+            self.compile_options += ['/arch:SSE2']
+            self.compile_options_debug += ['/arch:SSE2']
+
+
+def lib_opts_if_msvc(build_cmd):
+    """ Add flags if we are using MSVC compiler
+
+    We can't see `build_cmd` in our scope, because we have not initialized
+    the distutils build command, so use this deferred calculation to run
+    when we are building the library.
+    """
+    if build_cmd.compiler.compiler_type != 'msvc':
+        return []
+    # Explicitly disable whole-program optimization.
+    flags = ['/GL-']
+    # Disable voltbl section for vc142 to allow link using mingw-w64; see:
+    # https://github.com/matthew-brett/dll_investigation/issues/1#issuecomment-1100468171
+    if build_cmd.compiler_opt.cc_test_flags(['-d2VolatileMetadata-']):
+        flags.append('-d2VolatileMetadata-')
+    return flags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/npy_pkg_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/npy_pkg_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..14e8791b14cda2c0b63bd1f468777fa31c4c7cc9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/npy_pkg_config.py
@@ -0,0 +1,441 @@
+import sys
+import re
+import os
+
+from configparser import RawConfigParser
+
+__all__ = ['FormatError', 'PkgNotFound', 'LibraryInfo', 'VariableSet',
+        'read_config', 'parse_flags']
+
+_VAR = re.compile(r'\$\{([a-zA-Z0-9_-]+)\}')
+
+class FormatError(OSError):
+    """
+    Exception thrown when there is a problem parsing a configuration file.
+
+    """
+    def __init__(self, msg):
+        self.msg = msg
+
+    def __str__(self):
+        return self.msg
+
+class PkgNotFound(OSError):
+    """Exception raised when a package can not be located."""
+    def __init__(self, msg):
+        self.msg = msg
+
+    def __str__(self):
+        return self.msg
+
+def parse_flags(line):
+    """
+    Parse a line from a config file containing compile flags.
+
+    Parameters
+    ----------
+    line : str
+        A single line containing one or more compile flags.
+
+    Returns
+    -------
+    d : dict
+        Dictionary of parsed flags, split into relevant categories.
+        These categories are the keys of `d`:
+
+        * 'include_dirs'
+        * 'library_dirs'
+        * 'libraries'
+        * 'macros'
+        * 'ignored'
+
+    """
+    d = {'include_dirs': [], 'library_dirs': [], 'libraries': [],
+         'macros': [], 'ignored': []}
+
+    flags = (' ' + line).split(' -')
+    for flag in flags:
+        flag = '-' + flag
+        if len(flag) > 0:
+            if flag.startswith('-I'):
+                d['include_dirs'].append(flag[2:].strip())
+            elif flag.startswith('-L'):
+                d['library_dirs'].append(flag[2:].strip())
+            elif flag.startswith('-l'):
+                d['libraries'].append(flag[2:].strip())
+            elif flag.startswith('-D'):
+                d['macros'].append(flag[2:].strip())
+            else:
+                d['ignored'].append(flag)
+
+    return d
+
+def _escape_backslash(val):
+    return val.replace('\\', '\\\\')
+
+class LibraryInfo:
+    """
+    Object containing build information about a library.
+
+    Parameters
+    ----------
+    name : str
+        The library name.
+    description : str
+        Description of the library.
+    version : str
+        Version string.
+    sections : dict
+        The sections of the configuration file for the library. The keys are
+        the section headers, the values the text under each header.
+    vars : class instance
+        A `VariableSet` instance, which contains ``(name, value)`` pairs for
+        variables defined in the configuration file for the library.
+    requires : sequence, optional
+        The required libraries for the library to be installed.
+
+    Notes
+    -----
+    All input parameters (except "sections" which is a method) are available as
+    attributes of the same name.
+
+    """
+    def __init__(self, name, description, version, sections, vars, requires=None):
+        self.name = name
+        self.description = description
+        if requires:
+            self.requires = requires
+        else:
+            self.requires = []
+        self.version = version
+        self._sections = sections
+        self.vars = vars
+
+    def sections(self):
+        """
+        Return the section headers of the config file.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        keys : list of str
+            The list of section headers.
+
+        """
+        return list(self._sections.keys())
+
+    def cflags(self, section="default"):
+        val = self.vars.interpolate(self._sections[section]['cflags'])
+        return _escape_backslash(val)
+
+    def libs(self, section="default"):
+        val = self.vars.interpolate(self._sections[section]['libs'])
+        return _escape_backslash(val)
+
+    def __str__(self):
+        m = ['Name: %s' % self.name, 'Description: %s' % self.description]
+        if self.requires:
+            m.append('Requires:')
+        else:
+            m.append('Requires: %s' % ",".join(self.requires))
+        m.append('Version: %s' % self.version)
+
+        return "\n".join(m)
+
+class VariableSet:
+    """
+    Container object for the variables defined in a config file.
+
+    `VariableSet` can be used as a plain dictionary, with the variable names
+    as keys.
+
+    Parameters
+    ----------
+    d : dict
+        Dict of items in the "variables" section of the configuration file.
+
+    """
+    def __init__(self, d):
+        self._raw_data = dict([(k, v) for k, v in d.items()])
+
+        self._re = {}
+        self._re_sub = {}
+
+        self._init_parse()
+
+    def _init_parse(self):
+        for k, v in self._raw_data.items():
+            self._init_parse_var(k, v)
+
+    def _init_parse_var(self, name, value):
+        self._re[name] = re.compile(r'\$\{%s\}' % name)
+        self._re_sub[name] = value
+
+    def interpolate(self, value):
+        # Brute force: we keep interpolating until there is no '${var}' anymore
+        # or until interpolated string is equal to input string
+        def _interpolate(value):
+            for k in self._re.keys():
+                value = self._re[k].sub(self._re_sub[k], value)
+            return value
+        while _VAR.search(value):
+            nvalue = _interpolate(value)
+            if nvalue == value:
+                break
+            value = nvalue
+
+        return value
+
+    def variables(self):
+        """
+        Return the list of variable names.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        names : list of str
+            The names of all variables in the `VariableSet` instance.
+
+        """
+        return list(self._raw_data.keys())
+
+    # Emulate a dict to set/get variables values
+    def __getitem__(self, name):
+        return self._raw_data[name]
+
+    def __setitem__(self, name, value):
+        self._raw_data[name] = value
+        self._init_parse_var(name, value)
+
+def parse_meta(config):
+    if not config.has_section('meta'):
+        raise FormatError("No meta section found !")
+
+    d = dict(config.items('meta'))
+
+    for k in ['name', 'description', 'version']:
+        if not k in d:
+            raise FormatError("Option %s (section [meta]) is mandatory, "
+                "but not found" % k)
+
+    if not 'requires' in d:
+        d['requires'] = []
+
+    return d
+
+def parse_variables(config):
+    if not config.has_section('variables'):
+        raise FormatError("No variables section found !")
+
+    d = {}
+
+    for name, value in config.items("variables"):
+        d[name] = value
+
+    return VariableSet(d)
+
+def parse_sections(config):
+    return meta_d, r
+
+def pkg_to_filename(pkg_name):
+    return "%s.ini" % pkg_name
+
+def parse_config(filename, dirs=None):
+    if dirs:
+        filenames = [os.path.join(d, filename) for d in dirs]
+    else:
+        filenames = [filename]
+
+    config = RawConfigParser()
+
+    n = config.read(filenames)
+    if not len(n) >= 1:
+        raise PkgNotFound("Could not find file(s) %s" % str(filenames))
+
+    # Parse meta and variables sections
+    meta = parse_meta(config)
+
+    vars = {}
+    if config.has_section('variables'):
+        for name, value in config.items("variables"):
+            vars[name] = _escape_backslash(value)
+
+    # Parse "normal" sections
+    secs = [s for s in config.sections() if not s in ['meta', 'variables']]
+    sections = {}
+
+    requires = {}
+    for s in secs:
+        d = {}
+        if config.has_option(s, "requires"):
+            requires[s] = config.get(s, 'requires')
+
+        for name, value in config.items(s):
+            d[name] = value
+        sections[s] = d
+
+    return meta, vars, sections, requires
+
+def _read_config_imp(filenames, dirs=None):
+    def _read_config(f):
+        meta, vars, sections, reqs = parse_config(f, dirs)
+        # recursively add sections and variables of required libraries
+        for rname, rvalue in reqs.items():
+            nmeta, nvars, nsections, nreqs = _read_config(pkg_to_filename(rvalue))
+
+            # Update var dict for variables not in 'top' config file
+            for k, v in nvars.items():
+                if not k in vars:
+                    vars[k] = v
+
+            # Update sec dict
+            for oname, ovalue in nsections[rname].items():
+                if ovalue:
+                    sections[rname][oname] += ' %s' % ovalue
+
+        return meta, vars, sections, reqs
+
+    meta, vars, sections, reqs = _read_config(filenames)
+
+    # FIXME: document this. If pkgname is defined in the variables section, and
+    # there is no pkgdir variable defined, pkgdir is automatically defined to
+    # the path of pkgname. This requires the package to be imported to work
+    if not 'pkgdir' in vars and "pkgname" in vars:
+        pkgname = vars["pkgname"]
+        if not pkgname in sys.modules:
+            raise ValueError("You should import %s to get information on %s" %
+                             (pkgname, meta["name"]))
+
+        mod = sys.modules[pkgname]
+        vars["pkgdir"] = _escape_backslash(os.path.dirname(mod.__file__))
+
+    return LibraryInfo(name=meta["name"], description=meta["description"],
+            version=meta["version"], sections=sections, vars=VariableSet(vars))
+
+# Trivial cache to cache LibraryInfo instances creation. To be really
+# efficient, the cache should be handled in read_config, since a same file can
+# be parsed many time outside LibraryInfo creation, but I doubt this will be a
+# problem in practice
+_CACHE = {}
+def read_config(pkgname, dirs=None):
+    """
+    Return library info for a package from its configuration file.
+
+    Parameters
+    ----------
+    pkgname : str
+        Name of the package (should match the name of the .ini file, without
+        the extension, e.g. foo for the file foo.ini).
+    dirs : sequence, optional
+        If given, should be a sequence of directories - usually including
+        the NumPy base directory - where to look for npy-pkg-config files.
+
+    Returns
+    -------
+    pkginfo : class instance
+        The `LibraryInfo` instance containing the build information.
+
+    Raises
+    ------
+    PkgNotFound
+        If the package is not found.
+
+    See Also
+    --------
+    misc_util.get_info, misc_util.get_pkg_info
+
+    Examples
+    --------
+    >>> npymath_info = np.distutils.npy_pkg_config.read_config('npymath')
+    >>> type(npymath_info)
+    
+    >>> print(npymath_info)
+    Name: npymath
+    Description: Portable, core math library implementing C99 standard
+    Requires:
+    Version: 0.1  #random
+
+    """
+    try:
+        return _CACHE[pkgname]
+    except KeyError:
+        v = _read_config_imp(pkg_to_filename(pkgname), dirs)
+        _CACHE[pkgname] = v
+        return v
+
+# TODO:
+#   - implements version comparison (modversion + atleast)
+
+# pkg-config simple emulator - useful for debugging, and maybe later to query
+# the system
+if __name__ == '__main__':
+    from optparse import OptionParser
+    import glob
+
+    parser = OptionParser()
+    parser.add_option("--cflags", dest="cflags", action="store_true",
+                      help="output all preprocessor and compiler flags")
+    parser.add_option("--libs", dest="libs", action="store_true",
+                      help="output all linker flags")
+    parser.add_option("--use-section", dest="section",
+                      help="use this section instead of default for options")
+    parser.add_option("--version", dest="version", action="store_true",
+                      help="output version")
+    parser.add_option("--atleast-version", dest="min_version",
+                      help="Minimal version")
+    parser.add_option("--list-all", dest="list_all", action="store_true",
+                      help="Minimal version")
+    parser.add_option("--define-variable", dest="define_variable",
+                      help="Replace variable with the given value")
+
+    (options, args) = parser.parse_args(sys.argv)
+
+    if len(args) < 2:
+        raise ValueError("Expect package name on the command line:")
+
+    if options.list_all:
+        files = glob.glob("*.ini")
+        for f in files:
+            info = read_config(f)
+            print("%s\t%s - %s" % (info.name, info.name, info.description))
+
+    pkg_name = args[1]
+    d = os.environ.get('NPY_PKG_CONFIG_PATH')
+    if d:
+        info = read_config(
+            pkg_name, ['numpy/_core/lib/npy-pkg-config', '.', d]
+        )
+    else:
+        info = read_config(
+            pkg_name, ['numpy/_core/lib/npy-pkg-config', '.']
+        )
+
+    if options.section:
+        section = options.section
+    else:
+        section = "default"
+
+    if options.define_variable:
+        m = re.search(r'([\S]+)=([\S]+)', options.define_variable)
+        if not m:
+            raise ValueError("--define-variable option should be of "
+                             "the form --define-variable=foo=bar")
+        else:
+            name = m.group(1)
+            value = m.group(2)
+        info.vars[name] = value
+
+    if options.cflags:
+        print(info.cflags(section))
+    if options.libs:
+        print(info.libs(section))
+    if options.version:
+        print(info.version)
+    if options.min_version:
+        print(info.version >= options.min_version)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/numpy_distribution.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/numpy_distribution.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea8182659cb1af718879de305798b62c23bf3346
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/numpy_distribution.py
@@ -0,0 +1,17 @@
+# XXX: Handle setuptools ?
+from distutils.core import Distribution
+
+# This class is used because we add new files (sconscripts, and so on) with the
+# scons command
+class NumpyDistribution(Distribution):
+    def __init__(self, attrs = None):
+        # A list of (sconscripts, pre_hook, post_hook, src, parent_names)
+        self.scons_data = []
+        # A list of installable libraries
+        self.installed_libraries = []
+        # A dict of pkg_config files to generate/install
+        self.installed_pkg_config = {}
+        Distribution.__init__(self, attrs)
+
+    def has_scons_scripts(self):
+        return bool(self.scons_data)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/pathccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/pathccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..48051810ee218fb037cc15ccec05293e5ae9bb6b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/pathccompiler.py
@@ -0,0 +1,21 @@
+from distutils.unixccompiler import UnixCCompiler
+
+class PathScaleCCompiler(UnixCCompiler):
+
+    """
+    PathScale compiler compatible with an gcc built Python.
+    """
+
+    compiler_type = 'pathcc'
+    cc_exe = 'pathcc'
+    cxx_exe = 'pathCC'
+
+    def __init__ (self, verbose=0, dry_run=0, force=0):
+        UnixCCompiler.__init__ (self, verbose, dry_run, force)
+        cc_compiler = self.cc_exe
+        cxx_compiler = self.cxx_exe
+        self.set_executables(compiler=cc_compiler,
+                             compiler_so=cc_compiler,
+                             compiler_cxx=cxx_compiler,
+                             linker_exe=cc_compiler,
+                             linker_so=cc_compiler + ' -shared')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/system_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/system_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..64785481b6172dd3a1fed151516557fb6a512744
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/system_info.py
@@ -0,0 +1,3267 @@
+"""
+This file defines a set of system_info classes for getting
+information about various resources (libraries, library directories,
+include directories, etc.) in the system. Usage:
+    info_dict = get_info()
+  where  is a string 'atlas','x11','fftw','lapack','blas',
+  'lapack_src', 'blas_src', etc. For a complete list of allowed names,
+  see the definition of get_info() function below.
+
+  Returned info_dict is a dictionary which is compatible with
+  distutils.setup keyword arguments. If info_dict == {}, then the
+  asked resource is not available (system_info could not find it).
+
+  Several *_info classes specify an environment variable to specify
+  the locations of software. When setting the corresponding environment
+  variable to 'None' then the software will be ignored, even when it
+  is available in system.
+
+Global parameters:
+  system_info.search_static_first - search static libraries (.a)
+             in precedence to shared ones (.so, .sl) if enabled.
+  system_info.verbosity - output the results to stdout if enabled.
+
+The file 'site.cfg' is looked for in
+
+1) Directory of main setup.py file being run.
+2) Home directory of user running the setup.py file as ~/.numpy-site.cfg
+3) System wide directory (location of this file...)
+
+The first one found is used to get system configuration options The
+format is that used by ConfigParser (i.e., Windows .INI style). The
+section ALL is not intended for general use.
+
+Appropriate defaults are used if nothing is specified.
+
+The order of finding the locations of resources is the following:
+ 1. environment variable
+ 2. section in site.cfg
+ 3. DEFAULT section in site.cfg
+ 4. System default search paths (see ``default_*`` variables below).
+Only the first complete match is returned.
+
+Currently, the following classes are available, along with their section names:
+
+    Numeric_info:Numeric
+    _numpy_info:Numeric
+    _pkg_config_info:None
+    accelerate_info:accelerate
+    accelerate_lapack_info:accelerate
+    agg2_info:agg2
+    amd_info:amd
+    atlas_3_10_blas_info:atlas
+    atlas_3_10_blas_threads_info:atlas
+    atlas_3_10_info:atlas
+    atlas_3_10_threads_info:atlas
+    atlas_blas_info:atlas
+    atlas_blas_threads_info:atlas
+    atlas_info:atlas
+    atlas_threads_info:atlas
+    blas64__opt_info:ALL               # usage recommended (general ILP64 BLAS, 64_ symbol suffix)
+    blas_ilp64_opt_info:ALL            # usage recommended (general ILP64 BLAS)
+    blas_ilp64_plain_opt_info:ALL      # usage recommended (general ILP64 BLAS, no symbol suffix)
+    blas_info:blas
+    blas_mkl_info:mkl
+    blas_ssl2_info:ssl2
+    blas_opt_info:ALL                  # usage recommended
+    blas_src_info:blas_src
+    blis_info:blis
+    boost_python_info:boost_python
+    dfftw_info:fftw
+    dfftw_threads_info:fftw
+    djbfft_info:djbfft
+    f2py_info:ALL
+    fft_opt_info:ALL
+    fftw2_info:fftw
+    fftw3_info:fftw3
+    fftw_info:fftw
+    fftw_threads_info:fftw
+    flame_info:flame
+    freetype2_info:freetype2
+    gdk_2_info:gdk_2
+    gdk_info:gdk
+    gdk_pixbuf_2_info:gdk_pixbuf_2
+    gdk_pixbuf_xlib_2_info:gdk_pixbuf_xlib_2
+    gdk_x11_2_info:gdk_x11_2
+    gtkp_2_info:gtkp_2
+    gtkp_x11_2_info:gtkp_x11_2
+    lapack64__opt_info:ALL             # usage recommended (general ILP64 LAPACK, 64_ symbol suffix)
+    lapack_atlas_3_10_info:atlas
+    lapack_atlas_3_10_threads_info:atlas
+    lapack_atlas_info:atlas
+    lapack_atlas_threads_info:atlas
+    lapack_ilp64_opt_info:ALL          # usage recommended (general ILP64 LAPACK)
+    lapack_ilp64_plain_opt_info:ALL    # usage recommended (general ILP64 LAPACK, no symbol suffix)
+    lapack_info:lapack
+    lapack_mkl_info:mkl
+    lapack_ssl2_info:ssl2
+    lapack_opt_info:ALL                # usage recommended
+    lapack_src_info:lapack_src
+    mkl_info:mkl
+    ssl2_info:ssl2
+    numarray_info:numarray
+    numerix_info:numerix
+    numpy_info:numpy
+    openblas64__info:openblas64_
+    openblas64__lapack_info:openblas64_
+    openblas_clapack_info:openblas
+    openblas_ilp64_info:openblas_ilp64
+    openblas_ilp64_lapack_info:openblas_ilp64
+    openblas_info:openblas
+    openblas_lapack_info:openblas
+    sfftw_info:fftw
+    sfftw_threads_info:fftw
+    system_info:ALL
+    umfpack_info:umfpack
+    wx_info:wx
+    x11_info:x11
+    xft_info:xft
+
+Note that blas_opt_info and lapack_opt_info honor the NPY_BLAS_ORDER
+and NPY_LAPACK_ORDER environment variables to determine the order in which
+specific BLAS and LAPACK libraries are searched for.
+
+This search (or autodetection) can be bypassed by defining the environment
+variables NPY_BLAS_LIBS and NPY_LAPACK_LIBS, which should then contain the
+exact linker flags to use (language will be set to F77). Building against
+Netlib BLAS/LAPACK or stub files, in order to be able to switch BLAS and LAPACK
+implementations at runtime. If using this to build NumPy itself, it is
+recommended to also define NPY_CBLAS_LIBS (assuming your BLAS library has a
+CBLAS interface) to enable CBLAS usage for matrix multiplication (unoptimized
+otherwise).
+
+Example:
+----------
+[DEFAULT]
+# default section
+library_dirs = /usr/lib:/usr/local/lib:/opt/lib
+include_dirs = /usr/include:/usr/local/include:/opt/include
+src_dirs = /usr/local/src:/opt/src
+# search static libraries (.a) in preference to shared ones (.so)
+search_static_first = 0
+
+[fftw]
+libraries = rfftw, fftw
+
+[atlas]
+library_dirs = /usr/lib/3dnow:/usr/lib/3dnow/atlas
+# for overriding the names of the atlas libraries
+libraries = lapack, f77blas, cblas, atlas
+
+[x11]
+library_dirs = /usr/X11R6/lib
+include_dirs = /usr/X11R6/include
+----------
+
+Note that the ``libraries`` key is the default setting for libraries.
+
+Authors:
+  Pearu Peterson , February 2002
+  David M. Cooke , April 2002
+
+Copyright 2002 Pearu Peterson all rights reserved,
+Pearu Peterson 
+Permission to use, modify, and distribute this software is given under the
+terms of the NumPy (BSD style) license.  See LICENSE.txt that came with
+this distribution for specifics.
+
+NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
+
+"""
+import sys
+import os
+import re
+import copy
+import warnings
+import subprocess
+import textwrap
+
+from glob import glob
+from functools import reduce
+from configparser import NoOptionError
+from configparser import RawConfigParser as ConfigParser
+# It seems that some people are importing ConfigParser from here so is
+# good to keep its class name. Use of RawConfigParser is needed in
+# order to be able to load path names with percent in them, like
+# `feature%2Fcool` which is common on git flow branch names.
+
+from distutils.errors import DistutilsError
+from distutils.dist import Distribution
+import sysconfig
+from numpy.distutils import log
+from distutils.util import get_platform
+
+from numpy.distutils.exec_command import (
+    find_executable, filepath_from_subprocess_output,
+    )
+from numpy.distutils.misc_util import (is_sequence, is_string,
+                                       get_shared_lib_extension)
+from numpy.distutils.command.config import config as cmd_config
+from numpy.distutils import customized_ccompiler as _customized_ccompiler
+from numpy.distutils import _shell_utils
+import distutils.ccompiler
+import tempfile
+import shutil
+
+__all__ = ['system_info']
+
+# Determine number of bits
+import platform
+_bits = {'32bit': 32, '64bit': 64}
+platform_bits = _bits[platform.architecture()[0]]
+
+
+global_compiler = None
+
+def customized_ccompiler():
+    global global_compiler
+    if not global_compiler:
+        global_compiler = _customized_ccompiler()
+    return global_compiler
+
+
+def _c_string_literal(s):
+    """
+    Convert a python string into a literal suitable for inclusion into C code
+    """
+    # only these three characters are forbidden in C strings
+    s = s.replace('\\', r'\\')
+    s = s.replace('"',  r'\"')
+    s = s.replace('\n', r'\n')
+    return '"{}"'.format(s)
+
+
+def libpaths(paths, bits):
+    """Return a list of library paths valid on 32 or 64 bit systems.
+
+    Inputs:
+      paths : sequence
+        A sequence of strings (typically paths)
+      bits : int
+        An integer, the only valid values are 32 or 64.  A ValueError exception
+      is raised otherwise.
+
+    Examples:
+
+    Consider a list of directories
+    >>> paths = ['/usr/X11R6/lib','/usr/X11/lib','/usr/lib']
+
+    For a 32-bit platform, this is already valid:
+    >>> np.distutils.system_info.libpaths(paths,32)
+    ['/usr/X11R6/lib', '/usr/X11/lib', '/usr/lib']
+
+    On 64 bits, we prepend the '64' postfix
+    >>> np.distutils.system_info.libpaths(paths,64)
+    ['/usr/X11R6/lib64', '/usr/X11R6/lib', '/usr/X11/lib64', '/usr/X11/lib',
+    '/usr/lib64', '/usr/lib']
+    """
+    if bits not in (32, 64):
+        raise ValueError("Invalid bit size in libpaths: 32 or 64 only")
+
+    # Handle 32bit case
+    if bits == 32:
+        return paths
+
+    # Handle 64bit case
+    out = []
+    for p in paths:
+        out.extend([p + '64', p])
+
+    return out
+
+
+if sys.platform == 'win32':
+    default_lib_dirs = ['C:\\',
+                        os.path.join(sysconfig.get_config_var('exec_prefix'),
+                                     'libs')]
+    default_runtime_dirs = []
+    default_include_dirs = []
+    default_src_dirs = ['.']
+    default_x11_lib_dirs = []
+    default_x11_include_dirs = []
+    _include_dirs = [
+        'include',
+        'include/suitesparse',
+    ]
+    _lib_dirs = [
+        'lib',
+    ]
+
+    _include_dirs = [d.replace('/', os.sep) for d in _include_dirs]
+    _lib_dirs = [d.replace('/', os.sep) for d in _lib_dirs]
+    def add_system_root(library_root):
+        """Add a package manager root to the include directories"""
+        global default_lib_dirs
+        global default_include_dirs
+
+        library_root = os.path.normpath(library_root)
+
+        default_lib_dirs.extend(
+            os.path.join(library_root, d) for d in _lib_dirs)
+        default_include_dirs.extend(
+            os.path.join(library_root, d) for d in _include_dirs)
+
+    # VCpkg is the de-facto package manager on windows for C/C++
+    # libraries. If it is on the PATH, then we append its paths here.
+    vcpkg = shutil.which('vcpkg')
+    if vcpkg:
+        vcpkg_dir = os.path.dirname(vcpkg)
+        if platform.architecture()[0] == '32bit':
+            specifier = 'x86'
+        else:
+            specifier = 'x64'
+
+        vcpkg_installed = os.path.join(vcpkg_dir, 'installed')
+        for vcpkg_root in [
+            os.path.join(vcpkg_installed, specifier + '-windows'),
+            os.path.join(vcpkg_installed, specifier + '-windows-static'),
+        ]:
+            add_system_root(vcpkg_root)
+
+    # Conda is another popular package manager that provides libraries
+    conda = shutil.which('conda')
+    if conda:
+        conda_dir = os.path.dirname(conda)
+        add_system_root(os.path.join(conda_dir, '..', 'Library'))
+        add_system_root(os.path.join(conda_dir, 'Library'))
+
+else:
+    default_lib_dirs = libpaths(['/usr/local/lib', '/opt/lib', '/usr/lib',
+                                 '/opt/local/lib', '/sw/lib'], platform_bits)
+    default_runtime_dirs = []
+    default_include_dirs = ['/usr/local/include',
+                            '/opt/include',
+                            # path of umfpack under macports
+                            '/opt/local/include/ufsparse',
+                            '/opt/local/include', '/sw/include',
+                            '/usr/include/suitesparse']
+    default_src_dirs = ['.', '/usr/local/src', '/opt/src', '/sw/src']
+
+    default_x11_lib_dirs = libpaths(['/usr/X11R6/lib', '/usr/X11/lib',
+                                     '/usr/lib'], platform_bits)
+    default_x11_include_dirs = ['/usr/X11R6/include', '/usr/X11/include']
+
+    if os.path.exists('/usr/lib/X11'):
+        globbed_x11_dir = glob('/usr/lib/*/libX11.so')
+        if globbed_x11_dir:
+            x11_so_dir = os.path.split(globbed_x11_dir[0])[0]
+            default_x11_lib_dirs.extend([x11_so_dir, '/usr/lib/X11'])
+            default_x11_include_dirs.extend(['/usr/lib/X11/include',
+                                             '/usr/include/X11'])
+
+    with open(os.devnull, 'w') as tmp:
+        try:
+            p = subprocess.Popen(["gcc", "-print-multiarch"], stdout=subprocess.PIPE,
+                         stderr=tmp)
+        except (OSError, DistutilsError):
+            # OSError if gcc is not installed, or SandboxViolation (DistutilsError
+            # subclass) if an old setuptools bug is triggered (see gh-3160).
+            pass
+        else:
+            triplet = str(p.communicate()[0].decode().strip())
+            if p.returncode == 0:
+                # gcc supports the "-print-multiarch" option
+                default_x11_lib_dirs += [os.path.join("/usr/lib/", triplet)]
+                default_lib_dirs += [os.path.join("/usr/lib/", triplet)]
+
+
+if os.path.join(sys.prefix, 'lib') not in default_lib_dirs:
+    default_lib_dirs.insert(0, os.path.join(sys.prefix, 'lib'))
+    default_include_dirs.append(os.path.join(sys.prefix, 'include'))
+    default_src_dirs.append(os.path.join(sys.prefix, 'src'))
+
+default_lib_dirs = [_m for _m in default_lib_dirs if os.path.isdir(_m)]
+default_runtime_dirs = [_m for _m in default_runtime_dirs if os.path.isdir(_m)]
+default_include_dirs = [_m for _m in default_include_dirs if os.path.isdir(_m)]
+default_src_dirs = [_m for _m in default_src_dirs if os.path.isdir(_m)]
+
+so_ext = get_shared_lib_extension()
+
+
+def get_standard_file(fname):
+    """Returns a list of files named 'fname' from
+    1) System-wide directory (directory-location of this module)
+    2) Users HOME directory (os.environ['HOME'])
+    3) Local directory
+    """
+    # System-wide file
+    filenames = []
+    try:
+        f = __file__
+    except NameError:
+        f = sys.argv[0]
+    sysfile = os.path.join(os.path.split(os.path.abspath(f))[0],
+                           fname)
+    if os.path.isfile(sysfile):
+        filenames.append(sysfile)
+
+    # Home directory
+    # And look for the user config file
+    try:
+        f = os.path.expanduser('~')
+    except KeyError:
+        pass
+    else:
+        user_file = os.path.join(f, fname)
+        if os.path.isfile(user_file):
+            filenames.append(user_file)
+
+    # Local file
+    if os.path.isfile(fname):
+        filenames.append(os.path.abspath(fname))
+
+    return filenames
+
+
+def _parse_env_order(base_order, env):
+    """ Parse an environment variable `env` by splitting with "," and only returning elements from `base_order`
+
+    This method will sequence the environment variable and check for their
+    individual elements in `base_order`.
+
+    The items in the environment variable may be negated via '^item' or '!itema,itemb'.
+    It must start with ^/! to negate all options.
+
+    Raises
+    ------
+    ValueError: for mixed negated and non-negated orders or multiple negated orders
+
+    Parameters
+    ----------
+    base_order : list of str
+       the base list of orders
+    env : str
+       the environment variable to be parsed, if none is found, `base_order` is returned
+
+    Returns
+    -------
+    allow_order : list of str
+        allowed orders in lower-case
+    unknown_order : list of str
+        for values not overlapping with `base_order`
+    """
+    order_str = os.environ.get(env, None)
+
+    # ensure all base-orders are lower-case (for easier comparison)
+    base_order = [order.lower() for order in base_order]
+    if order_str is None:
+        return base_order, []
+
+    neg = order_str.startswith('^') or order_str.startswith('!')
+    # Check format
+    order_str_l = list(order_str)
+    sum_neg = order_str_l.count('^') + order_str_l.count('!')
+    if neg:
+        if sum_neg > 1:
+            raise ValueError(f"Environment variable '{env}' may only contain a single (prefixed) negation: {order_str}")
+        # remove prefix
+        order_str = order_str[1:]
+    elif sum_neg > 0:
+        raise ValueError(f"Environment variable '{env}' may not mix negated an non-negated items: {order_str}")
+
+    # Split and lower case
+    orders = order_str.lower().split(',')
+
+    # to inform callee about non-overlapping elements
+    unknown_order = []
+
+    # if negated, we have to remove from the order
+    if neg:
+        allow_order = base_order.copy()
+
+        for order in orders:
+            if not order:
+                continue
+
+            if order not in base_order:
+                unknown_order.append(order)
+                continue
+
+            if order in allow_order:
+                allow_order.remove(order)
+
+    else:
+        allow_order = []
+
+        for order in orders:
+            if not order:
+                continue
+
+            if order not in base_order:
+                unknown_order.append(order)
+                continue
+
+            if order not in allow_order:
+                allow_order.append(order)
+
+    return allow_order, unknown_order
+
+
+def get_info(name, notfound_action=0):
+    """
+    notfound_action:
+      0 - do nothing
+      1 - display warning message
+      2 - raise error
+    """
+    cl = {'armpl': armpl_info,
+          'blas_armpl': blas_armpl_info,
+          'lapack_armpl': lapack_armpl_info,
+          'fftw3_armpl': fftw3_armpl_info,
+          'atlas': atlas_info,  # use lapack_opt or blas_opt instead
+          'atlas_threads': atlas_threads_info,                # ditto
+          'atlas_blas': atlas_blas_info,
+          'atlas_blas_threads': atlas_blas_threads_info,
+          'lapack_atlas': lapack_atlas_info,  # use lapack_opt instead
+          'lapack_atlas_threads': lapack_atlas_threads_info,  # ditto
+          'atlas_3_10': atlas_3_10_info,  # use lapack_opt or blas_opt instead
+          'atlas_3_10_threads': atlas_3_10_threads_info,                # ditto
+          'atlas_3_10_blas': atlas_3_10_blas_info,
+          'atlas_3_10_blas_threads': atlas_3_10_blas_threads_info,
+          'lapack_atlas_3_10': lapack_atlas_3_10_info,  # use lapack_opt instead
+          'lapack_atlas_3_10_threads': lapack_atlas_3_10_threads_info,  # ditto
+          'flame': flame_info,          # use lapack_opt instead
+          'mkl': mkl_info,
+          'ssl2': ssl2_info,
+          # openblas which may or may not have embedded lapack
+          'openblas': openblas_info,          # use blas_opt instead
+          # openblas with embedded lapack
+          'openblas_lapack': openblas_lapack_info, # use blas_opt instead
+          'openblas_clapack': openblas_clapack_info, # use blas_opt instead
+          'blis': blis_info,                  # use blas_opt instead
+          'lapack_mkl': lapack_mkl_info,      # use lapack_opt instead
+          'blas_mkl': blas_mkl_info,          # use blas_opt instead
+          'lapack_ssl2': lapack_ssl2_info,      
+          'blas_ssl2': blas_ssl2_info,          
+          'accelerate': accelerate_info,      # use blas_opt instead
+          'accelerate_lapack': accelerate_lapack_info,
+          'openblas64_': openblas64__info,
+          'openblas64__lapack': openblas64__lapack_info,
+          'openblas_ilp64': openblas_ilp64_info,
+          'openblas_ilp64_lapack': openblas_ilp64_lapack_info,
+          'x11': x11_info,
+          'fft_opt': fft_opt_info,
+          'fftw': fftw_info,
+          'fftw2': fftw2_info,
+          'fftw3': fftw3_info,
+          'dfftw': dfftw_info,
+          'sfftw': sfftw_info,
+          'fftw_threads': fftw_threads_info,
+          'dfftw_threads': dfftw_threads_info,
+          'sfftw_threads': sfftw_threads_info,
+          'djbfft': djbfft_info,
+          'blas': blas_info,                  # use blas_opt instead
+          'lapack': lapack_info,              # use lapack_opt instead
+          'lapack_src': lapack_src_info,
+          'blas_src': blas_src_info,
+          'numpy': numpy_info,
+          'f2py': f2py_info,
+          'Numeric': Numeric_info,
+          'numeric': Numeric_info,
+          'numarray': numarray_info,
+          'numerix': numerix_info,
+          'lapack_opt': lapack_opt_info,
+          'lapack_ilp64_opt': lapack_ilp64_opt_info,
+          'lapack_ilp64_plain_opt': lapack_ilp64_plain_opt_info,
+          'lapack64__opt': lapack64__opt_info,
+          'blas_opt': blas_opt_info,
+          'blas_ilp64_opt': blas_ilp64_opt_info,
+          'blas_ilp64_plain_opt': blas_ilp64_plain_opt_info,
+          'blas64__opt': blas64__opt_info,
+          'boost_python': boost_python_info,
+          'agg2': agg2_info,
+          'wx': wx_info,
+          'gdk_pixbuf_xlib_2': gdk_pixbuf_xlib_2_info,
+          'gdk-pixbuf-xlib-2.0': gdk_pixbuf_xlib_2_info,
+          'gdk_pixbuf_2': gdk_pixbuf_2_info,
+          'gdk-pixbuf-2.0': gdk_pixbuf_2_info,
+          'gdk': gdk_info,
+          'gdk_2': gdk_2_info,
+          'gdk-2.0': gdk_2_info,
+          'gdk_x11_2': gdk_x11_2_info,
+          'gdk-x11-2.0': gdk_x11_2_info,
+          'gtkp_x11_2': gtkp_x11_2_info,
+          'gtk+-x11-2.0': gtkp_x11_2_info,
+          'gtkp_2': gtkp_2_info,
+          'gtk+-2.0': gtkp_2_info,
+          'xft': xft_info,
+          'freetype2': freetype2_info,
+          'umfpack': umfpack_info,
+          'amd': amd_info,
+          }.get(name.lower(), system_info)
+    return cl().get_info(notfound_action)
+
+
+class NotFoundError(DistutilsError):
+    """Some third-party program or library is not found."""
+
+
+class AliasedOptionError(DistutilsError):
+    """
+    Aliases entries in config files should not be existing.
+    In section '{section}' we found multiple appearances of options {options}."""
+
+
+class AtlasNotFoundError(NotFoundError):
+    """
+    Atlas (http://github.com/math-atlas/math-atlas) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [atlas]) or by setting
+    the ATLAS environment variable."""
+
+
+class FlameNotFoundError(NotFoundError):
+    """
+    FLAME (http://www.cs.utexas.edu/~flame/web/) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [flame])."""
+
+
+class LapackNotFoundError(NotFoundError):
+    """
+    Lapack (http://www.netlib.org/lapack/) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [lapack]) or by setting
+    the LAPACK environment variable."""
+
+
+class LapackSrcNotFoundError(LapackNotFoundError):
+    """
+    Lapack (http://www.netlib.org/lapack/) sources not found.
+    Directories to search for the sources can be specified in the
+    numpy/distutils/site.cfg file (section [lapack_src]) or by setting
+    the LAPACK_SRC environment variable."""
+
+
+class LapackILP64NotFoundError(NotFoundError):
+    """
+    64-bit Lapack libraries not found.
+    Known libraries in numpy/distutils/site.cfg file are:
+    openblas64_, openblas_ilp64
+    """
+
+class BlasOptNotFoundError(NotFoundError):
+    """
+    Optimized (vendor) Blas libraries are not found.
+    Falls back to netlib Blas library which has worse performance.
+    A better performance should be easily gained by switching
+    Blas library."""
+
+class BlasNotFoundError(NotFoundError):
+    """
+    Blas (http://www.netlib.org/blas/) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [blas]) or by setting
+    the BLAS environment variable."""
+
+class BlasILP64NotFoundError(NotFoundError):
+    """
+    64-bit Blas libraries not found.
+    Known libraries in numpy/distutils/site.cfg file are:
+    openblas64_, openblas_ilp64
+    """
+
+class BlasSrcNotFoundError(BlasNotFoundError):
+    """
+    Blas (http://www.netlib.org/blas/) sources not found.
+    Directories to search for the sources can be specified in the
+    numpy/distutils/site.cfg file (section [blas_src]) or by setting
+    the BLAS_SRC environment variable."""
+
+
+class FFTWNotFoundError(NotFoundError):
+    """
+    FFTW (http://www.fftw.org/) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [fftw]) or by setting
+    the FFTW environment variable."""
+
+
+class DJBFFTNotFoundError(NotFoundError):
+    """
+    DJBFFT (https://cr.yp.to/djbfft.html) libraries not found.
+    Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [djbfft]) or by setting
+    the DJBFFT environment variable."""
+
+
+class NumericNotFoundError(NotFoundError):
+    """
+    Numeric (https://www.numpy.org/) module not found.
+    Get it from above location, install it, and retry setup.py."""
+
+
+class X11NotFoundError(NotFoundError):
+    """X11 libraries not found."""
+
+
+class UmfpackNotFoundError(NotFoundError):
+    """
+    UMFPACK sparse solver (https://www.cise.ufl.edu/research/sparse/umfpack/)
+    not found. Directories to search for the libraries can be specified in the
+    numpy/distutils/site.cfg file (section [umfpack]) or by setting
+    the UMFPACK environment variable."""
+
+
+class system_info:
+
+    """ get_info() is the only public method. Don't use others.
+    """
+    dir_env_var = None
+    # XXX: search_static_first is disabled by default, may disappear in
+    # future unless it is proved to be useful.
+    search_static_first = 0
+    # The base-class section name is a random word "ALL" and is not really
+    # intended for general use. It cannot be None nor can it be DEFAULT as
+    # these break the ConfigParser. See gh-15338
+    section = 'ALL'
+    saved_results = {}
+
+    notfounderror = NotFoundError
+
+    def __init__(self,
+                  default_lib_dirs=default_lib_dirs,
+                  default_include_dirs=default_include_dirs,
+                  ):
+        self.__class__.info = {}
+        self.local_prefixes = []
+        defaults = {'library_dirs': os.pathsep.join(default_lib_dirs),
+                    'include_dirs': os.pathsep.join(default_include_dirs),
+                    'runtime_library_dirs': os.pathsep.join(default_runtime_dirs),
+                    'rpath': '',
+                    'src_dirs': os.pathsep.join(default_src_dirs),
+                    'search_static_first': str(self.search_static_first),
+                    'extra_compile_args': '', 'extra_link_args': ''}
+        self.cp = ConfigParser(defaults)
+        self.files = []
+        self.files.extend(get_standard_file('.numpy-site.cfg'))
+        self.files.extend(get_standard_file('site.cfg'))
+        self.parse_config_files()
+
+        if self.section is not None:
+            self.search_static_first = self.cp.getboolean(
+                self.section, 'search_static_first')
+        assert isinstance(self.search_static_first, int)
+
+    def parse_config_files(self):
+        self.cp.read(self.files)
+        if not self.cp.has_section(self.section):
+            if self.section is not None:
+                self.cp.add_section(self.section)
+
+    def calc_libraries_info(self):
+        libs = self.get_libraries()
+        dirs = self.get_lib_dirs()
+        # The extensions use runtime_library_dirs
+        r_dirs = self.get_runtime_lib_dirs()
+        # Intrinsic distutils use rpath, we simply append both entries
+        # as though they were one entry
+        r_dirs.extend(self.get_runtime_lib_dirs(key='rpath'))
+        info = {}
+        for lib in libs:
+            i = self.check_libs(dirs, [lib])
+            if i is not None:
+                dict_append(info, **i)
+            else:
+                log.info('Library %s was not found. Ignoring' % (lib))
+
+            if r_dirs:
+                i = self.check_libs(r_dirs, [lib])
+                if i is not None:
+                    # Swap library keywords found to runtime_library_dirs
+                    # the libraries are insisting on the user having defined
+                    # them using the library_dirs, and not necessarily by
+                    # runtime_library_dirs
+                    del i['libraries']
+                    i['runtime_library_dirs'] = i.pop('library_dirs')
+                    dict_append(info, **i)
+                else:
+                    log.info('Runtime library %s was not found. Ignoring' % (lib))
+
+        return info
+
+    def set_info(self, **info):
+        if info:
+            lib_info = self.calc_libraries_info()
+            dict_append(info, **lib_info)
+            # Update extra information
+            extra_info = self.calc_extra_info()
+            dict_append(info, **extra_info)
+        self.saved_results[self.__class__.__name__] = info
+
+    def get_option_single(self, *options):
+        """ Ensure that only one of `options` are found in the section
+
+        Parameters
+        ----------
+        *options : list of str
+           a list of options to be found in the section (``self.section``)
+
+        Returns
+        -------
+        str :
+            the option that is uniquely found in the section
+
+        Raises
+        ------
+        AliasedOptionError :
+            in case more than one of the options are found
+        """
+        found = [self.cp.has_option(self.section, opt) for opt in options]
+        if sum(found) == 1:
+            return options[found.index(True)]
+        elif sum(found) == 0:
+            # nothing is found anyways
+            return options[0]
+
+        # Else we have more than 1 key found
+        if AliasedOptionError.__doc__ is None:
+            raise AliasedOptionError()
+        raise AliasedOptionError(AliasedOptionError.__doc__.format(
+            section=self.section, options='[{}]'.format(', '.join(options))))
+
+
+    def has_info(self):
+        return self.__class__.__name__ in self.saved_results
+
+    def calc_extra_info(self):
+        """ Updates the information in the current information with
+        respect to these flags:
+          extra_compile_args
+          extra_link_args
+        """
+        info = {}
+        for key in ['extra_compile_args', 'extra_link_args']:
+            # Get values
+            opt = self.cp.get(self.section, key)
+            opt = _shell_utils.NativeParser.split(opt)
+            if opt:
+                tmp = {key: opt}
+                dict_append(info, **tmp)
+        return info
+
+    def get_info(self, notfound_action=0):
+        """ Return a dictionary with items that are compatible
+            with numpy.distutils.setup keyword arguments.
+        """
+        flag = 0
+        if not self.has_info():
+            flag = 1
+            log.info(self.__class__.__name__ + ':')
+            if hasattr(self, 'calc_info'):
+                self.calc_info()
+            if notfound_action:
+                if not self.has_info():
+                    if notfound_action == 1:
+                        warnings.warn(self.notfounderror.__doc__, stacklevel=2)
+                    elif notfound_action == 2:
+                        raise self.notfounderror(self.notfounderror.__doc__)
+                    else:
+                        raise ValueError(repr(notfound_action))
+
+            if not self.has_info():
+                log.info('  NOT AVAILABLE')
+                self.set_info()
+            else:
+                log.info('  FOUND:')
+
+        res = self.saved_results.get(self.__class__.__name__)
+        if log.get_threshold() <= log.INFO and flag:
+            for k, v in res.items():
+                v = str(v)
+                if k in ['sources', 'libraries'] and len(v) > 270:
+                    v = v[:120] + '...\n...\n...' + v[-120:]
+                log.info('    %s = %s', k, v)
+            log.info('')
+
+        return copy.deepcopy(res)
+
+    def get_paths(self, section, key):
+        dirs = self.cp.get(section, key).split(os.pathsep)
+        env_var = self.dir_env_var
+        if env_var:
+            if is_sequence(env_var):
+                e0 = env_var[-1]
+                for e in env_var:
+                    if e in os.environ:
+                        e0 = e
+                        break
+                if not env_var[0] == e0:
+                    log.info('Setting %s=%s' % (env_var[0], e0))
+                env_var = e0
+        if env_var and env_var in os.environ:
+            d = os.environ[env_var]
+            if d == 'None':
+                log.info('Disabled %s: %s',
+                         self.__class__.__name__, '(%s is None)'
+                         % (env_var,))
+                return []
+            if os.path.isfile(d):
+                dirs = [os.path.dirname(d)] + dirs
+                l = getattr(self, '_lib_names', [])
+                if len(l) == 1:
+                    b = os.path.basename(d)
+                    b = os.path.splitext(b)[0]
+                    if b[:3] == 'lib':
+                        log.info('Replacing _lib_names[0]==%r with %r' \
+                              % (self._lib_names[0], b[3:]))
+                        self._lib_names[0] = b[3:]
+            else:
+                ds = d.split(os.pathsep)
+                ds2 = []
+                for d in ds:
+                    if os.path.isdir(d):
+                        ds2.append(d)
+                        for dd in ['include', 'lib']:
+                            d1 = os.path.join(d, dd)
+                            if os.path.isdir(d1):
+                                ds2.append(d1)
+                dirs = ds2 + dirs
+        default_dirs = self.cp.get(self.section, key).split(os.pathsep)
+        dirs.extend(default_dirs)
+        ret = []
+        for d in dirs:
+            if len(d) > 0 and not os.path.isdir(d):
+                warnings.warn('Specified path %s is invalid.' % d, stacklevel=2)
+                continue
+
+            if d not in ret:
+                ret.append(d)
+
+        log.debug('( %s = %s )', key, ':'.join(ret))
+        return ret
+
+    def get_lib_dirs(self, key='library_dirs'):
+        return self.get_paths(self.section, key)
+
+    def get_runtime_lib_dirs(self, key='runtime_library_dirs'):
+        path = self.get_paths(self.section, key)
+        if path == ['']:
+            path = []
+        return path
+
+    def get_include_dirs(self, key='include_dirs'):
+        return self.get_paths(self.section, key)
+
+    def get_src_dirs(self, key='src_dirs'):
+        return self.get_paths(self.section, key)
+
+    def get_libs(self, key, default):
+        try:
+            libs = self.cp.get(self.section, key)
+        except NoOptionError:
+            if not default:
+                return []
+            if is_string(default):
+                return [default]
+            return default
+        return [b for b in [a.strip() for a in libs.split(',')] if b]
+
+    def get_libraries(self, key='libraries'):
+        if hasattr(self, '_lib_names'):
+            return self.get_libs(key, default=self._lib_names)
+        else:
+            return self.get_libs(key, '')
+
+    def library_extensions(self):
+        c = customized_ccompiler()
+        static_exts = []
+        if c.compiler_type != 'msvc':
+            # MSVC doesn't understand binutils
+            static_exts.append('.a')
+        if sys.platform == 'win32':
+            static_exts.append('.lib')  # .lib is used by MSVC and others
+        if self.search_static_first:
+            exts = static_exts + [so_ext]
+        else:
+            exts = [so_ext] + static_exts
+        if sys.platform == 'cygwin':
+            exts.append('.dll.a')
+        if sys.platform == 'darwin':
+            exts.append('.dylib')
+        return exts
+
+    def check_libs(self, lib_dirs, libs, opt_libs=[]):
+        """If static or shared libraries are available then return
+        their info dictionary.
+
+        Checks for all libraries as shared libraries first, then
+        static (or vice versa if self.search_static_first is True).
+        """
+        exts = self.library_extensions()
+        info = None
+        for ext in exts:
+            info = self._check_libs(lib_dirs, libs, opt_libs, [ext])
+            if info is not None:
+                break
+        if not info:
+            log.info('  libraries %s not found in %s', ','.join(libs),
+                     lib_dirs)
+        return info
+
+    def check_libs2(self, lib_dirs, libs, opt_libs=[]):
+        """If static or shared libraries are available then return
+        their info dictionary.
+
+        Checks each library for shared or static.
+        """
+        exts = self.library_extensions()
+        info = self._check_libs(lib_dirs, libs, opt_libs, exts)
+        if not info:
+            log.info('  libraries %s not found in %s', ','.join(libs),
+                     lib_dirs)
+
+        return info
+
+    def _find_lib(self, lib_dir, lib, exts):
+        assert is_string(lib_dir)
+        # under windows first try without 'lib' prefix
+        if sys.platform == 'win32':
+            lib_prefixes = ['', 'lib']
+        else:
+            lib_prefixes = ['lib']
+        # for each library name, see if we can find a file for it.
+        for ext in exts:
+            for prefix in lib_prefixes:
+                p = self.combine_paths(lib_dir, prefix + lib + ext)
+                if p:
+                    break
+            if p:
+                assert len(p) == 1
+                # ??? splitext on p[0] would do this for cygwin
+                # doesn't seem correct
+                if ext == '.dll.a':
+                    lib += '.dll'
+                if ext == '.lib':
+                    lib = prefix + lib
+                return lib
+
+        return False
+
+    def _find_libs(self, lib_dirs, libs, exts):
+        # make sure we preserve the order of libs, as it can be important
+        found_dirs, found_libs = [], []
+        for lib in libs:
+            for lib_dir in lib_dirs:
+                found_lib = self._find_lib(lib_dir, lib, exts)
+                if found_lib:
+                    found_libs.append(found_lib)
+                    if lib_dir not in found_dirs:
+                        found_dirs.append(lib_dir)
+                    break
+        return found_dirs, found_libs
+
+    def _check_libs(self, lib_dirs, libs, opt_libs, exts):
+        """Find mandatory and optional libs in expected paths.
+
+        Missing optional libraries are silently forgotten.
+        """
+        if not is_sequence(lib_dirs):
+            lib_dirs = [lib_dirs]
+        # First, try to find the mandatory libraries
+        found_dirs, found_libs = self._find_libs(lib_dirs, libs, exts)
+        if len(found_libs) > 0 and len(found_libs) == len(libs):
+            # Now, check for optional libraries
+            opt_found_dirs, opt_found_libs = self._find_libs(lib_dirs, opt_libs, exts)
+            found_libs.extend(opt_found_libs)
+            for lib_dir in opt_found_dirs:
+                if lib_dir not in found_dirs:
+                    found_dirs.append(lib_dir)
+            info = {'libraries': found_libs, 'library_dirs': found_dirs}
+            return info
+        else:
+            return None
+
+    def combine_paths(self, *args):
+        """Return a list of existing paths composed by all combinations
+        of items from the arguments.
+        """
+        return combine_paths(*args)
+
+
+class fft_opt_info(system_info):
+
+    def calc_info(self):
+        info = {}
+        fftw_info = get_info('fftw3') or get_info('fftw2') or get_info('dfftw')
+        djbfft_info = get_info('djbfft')
+        if fftw_info:
+            dict_append(info, **fftw_info)
+            if djbfft_info:
+                dict_append(info, **djbfft_info)
+            self.set_info(**info)
+            return
+
+
+class fftw_info(system_info):
+    #variables to override
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    notfounderror = FFTWNotFoundError
+    ver_info = [{'name':'fftw3',
+                    'libs':['fftw3'],
+                    'includes':['fftw3.h'],
+                    'macros':[('SCIPY_FFTW3_H', None)]},
+                  {'name':'fftw2',
+                    'libs':['rfftw', 'fftw'],
+                    'includes':['fftw.h', 'rfftw.h'],
+                    'macros':[('SCIPY_FFTW_H', None)]}]
+
+    def calc_ver_info(self, ver_param):
+        """Returns True on successful version detection, else False"""
+        lib_dirs = self.get_lib_dirs()
+        incl_dirs = self.get_include_dirs()
+
+        opt = self.get_option_single(self.section + '_libs', 'libraries')
+        libs = self.get_libs(opt, ver_param['libs'])
+        info = self.check_libs(lib_dirs, libs)
+        if info is not None:
+            flag = 0
+            for d in incl_dirs:
+                if len(self.combine_paths(d, ver_param['includes'])) \
+                   == len(ver_param['includes']):
+                    dict_append(info, include_dirs=[d])
+                    flag = 1
+                    break
+            if flag:
+                dict_append(info, define_macros=ver_param['macros'])
+            else:
+                info = None
+        if info is not None:
+            self.set_info(**info)
+            return True
+        else:
+            log.info('  %s not found' % (ver_param['name']))
+            return False
+
+    def calc_info(self):
+        for i in self.ver_info:
+            if self.calc_ver_info(i):
+                break
+
+
+class fftw2_info(fftw_info):
+    #variables to override
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    notfounderror = FFTWNotFoundError
+    ver_info = [{'name':'fftw2',
+                    'libs':['rfftw', 'fftw'],
+                    'includes':['fftw.h', 'rfftw.h'],
+                    'macros':[('SCIPY_FFTW_H', None)]}
+                  ]
+
+
+class fftw3_info(fftw_info):
+    #variables to override
+    section = 'fftw3'
+    dir_env_var = 'FFTW3'
+    notfounderror = FFTWNotFoundError
+    ver_info = [{'name':'fftw3',
+                    'libs':['fftw3'],
+                    'includes':['fftw3.h'],
+                    'macros':[('SCIPY_FFTW3_H', None)]},
+                  ]
+
+    
+class fftw3_armpl_info(fftw_info):
+    section = 'fftw3'
+    dir_env_var = 'ARMPL_DIR'
+    notfounderror = FFTWNotFoundError
+    ver_info = [{'name': 'fftw3',
+                    'libs': ['armpl_lp64_mp'],
+                    'includes': ['fftw3.h'],
+                    'macros': [('SCIPY_FFTW3_H', None)]}]
+
+
+class dfftw_info(fftw_info):
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    ver_info = [{'name':'dfftw',
+                    'libs':['drfftw', 'dfftw'],
+                    'includes':['dfftw.h', 'drfftw.h'],
+                    'macros':[('SCIPY_DFFTW_H', None)]}]
+
+
+class sfftw_info(fftw_info):
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    ver_info = [{'name':'sfftw',
+                    'libs':['srfftw', 'sfftw'],
+                    'includes':['sfftw.h', 'srfftw.h'],
+                    'macros':[('SCIPY_SFFTW_H', None)]}]
+
+
+class fftw_threads_info(fftw_info):
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    ver_info = [{'name':'fftw threads',
+                    'libs':['rfftw_threads', 'fftw_threads'],
+                    'includes':['fftw_threads.h', 'rfftw_threads.h'],
+                    'macros':[('SCIPY_FFTW_THREADS_H', None)]}]
+
+
+class dfftw_threads_info(fftw_info):
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    ver_info = [{'name':'dfftw threads',
+                    'libs':['drfftw_threads', 'dfftw_threads'],
+                    'includes':['dfftw_threads.h', 'drfftw_threads.h'],
+                    'macros':[('SCIPY_DFFTW_THREADS_H', None)]}]
+
+
+class sfftw_threads_info(fftw_info):
+    section = 'fftw'
+    dir_env_var = 'FFTW'
+    ver_info = [{'name':'sfftw threads',
+                    'libs':['srfftw_threads', 'sfftw_threads'],
+                    'includes':['sfftw_threads.h', 'srfftw_threads.h'],
+                    'macros':[('SCIPY_SFFTW_THREADS_H', None)]}]
+
+
+class djbfft_info(system_info):
+    section = 'djbfft'
+    dir_env_var = 'DJBFFT'
+    notfounderror = DJBFFTNotFoundError
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend(self.combine_paths(d, ['djbfft']) + [d])
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        incl_dirs = self.get_include_dirs()
+        info = None
+        for d in lib_dirs:
+            p = self.combine_paths(d, ['djbfft.a'])
+            if p:
+                info = {'extra_objects': p}
+                break
+            p = self.combine_paths(d, ['libdjbfft.a', 'libdjbfft' + so_ext])
+            if p:
+                info = {'libraries': ['djbfft'], 'library_dirs': [d]}
+                break
+        if info is None:
+            return
+        for d in incl_dirs:
+            if len(self.combine_paths(d, ['fftc8.h', 'fftfreq.h'])) == 2:
+                dict_append(info, include_dirs=[d],
+                            define_macros=[('SCIPY_DJBFFT_H', None)])
+                self.set_info(**info)
+                return
+        return
+
+
+class mkl_info(system_info):
+    section = 'mkl'
+    dir_env_var = 'MKLROOT'
+    _lib_mkl = ['mkl_rt']
+
+    def get_mkl_rootdir(self):
+        mklroot = os.environ.get('MKLROOT', None)
+        if mklroot is not None:
+            return mklroot
+        paths = os.environ.get('LD_LIBRARY_PATH', '').split(os.pathsep)
+        ld_so_conf = '/etc/ld.so.conf'
+        if os.path.isfile(ld_so_conf):
+            with open(ld_so_conf) as f:
+                for d in f:
+                    d = d.strip()
+                    if d:
+                        paths.append(d)
+        intel_mkl_dirs = []
+        for path in paths:
+            path_atoms = path.split(os.sep)
+            for m in path_atoms:
+                if m.startswith('mkl'):
+                    d = os.sep.join(path_atoms[:path_atoms.index(m) + 2])
+                    intel_mkl_dirs.append(d)
+                    break
+        for d in paths:
+            dirs = glob(os.path.join(d, 'mkl', '*'))
+            dirs += glob(os.path.join(d, 'mkl*'))
+            for sub_dir in dirs:
+                if os.path.isdir(os.path.join(sub_dir, 'lib')):
+                    return sub_dir
+        return None
+
+    def __init__(self):
+        mklroot = self.get_mkl_rootdir()
+        if mklroot is None:
+            system_info.__init__(self)
+        else:
+            from .cpuinfo import cpu
+            if cpu.is_Itanium():
+                plt = '64'
+            elif cpu.is_Intel() and cpu.is_64bit():
+                plt = 'intel64'
+            else:
+                plt = '32'
+            system_info.__init__(
+                self,
+                default_lib_dirs=[os.path.join(mklroot, 'lib', plt)],
+                default_include_dirs=[os.path.join(mklroot, 'include')])
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        incl_dirs = self.get_include_dirs()
+        opt = self.get_option_single('mkl_libs', 'libraries')
+        mkl_libs = self.get_libs(opt, self._lib_mkl)
+        info = self.check_libs2(lib_dirs, mkl_libs)
+        if info is None:
+            return
+        dict_append(info,
+                    define_macros=[('SCIPY_MKL_H', None),
+                                   ('HAVE_CBLAS', None)],
+                    include_dirs=incl_dirs)
+        if sys.platform == 'win32':
+            pass  # win32 has no pthread library
+        else:
+            dict_append(info, libraries=['pthread'])
+        self.set_info(**info)
+
+
+class lapack_mkl_info(mkl_info):
+    pass
+
+
+class blas_mkl_info(mkl_info):
+    pass
+
+
+class ssl2_info(system_info):
+    section = 'ssl2'
+    dir_env_var = 'SSL2_DIR'
+    # Multi-threaded version. Python itself must be built by Fujitsu compiler.
+    _lib_ssl2 = ['fjlapackexsve']
+    # Single-threaded version
+    #_lib_ssl2 = ['fjlapacksve']
+
+    def get_tcsds_rootdir(self):
+        tcsdsroot = os.environ.get('TCSDS_PATH', None)
+        if tcsdsroot is not None:
+            return tcsdsroot
+        return None
+
+    def __init__(self):
+        tcsdsroot = self.get_tcsds_rootdir()
+        if tcsdsroot is None:
+            system_info.__init__(self)
+        else:
+            system_info.__init__(
+                self,
+                default_lib_dirs=[os.path.join(tcsdsroot, 'lib64')],
+                default_include_dirs=[os.path.join(tcsdsroot,
+                    'clang-comp/include')])
+
+    def calc_info(self):
+        tcsdsroot = self.get_tcsds_rootdir()
+
+        lib_dirs = self.get_lib_dirs()
+        if lib_dirs is None:
+            lib_dirs = os.path.join(tcsdsroot, 'lib64')
+
+        incl_dirs = self.get_include_dirs()
+        if incl_dirs is None:
+            incl_dirs = os.path.join(tcsdsroot, 'clang-comp/include')
+
+        ssl2_libs = self.get_libs('ssl2_libs', self._lib_ssl2)
+
+        info = self.check_libs2(lib_dirs, ssl2_libs)
+        if info is None:
+            return
+        dict_append(info,
+                    define_macros=[('HAVE_CBLAS', None),
+                                   ('HAVE_SSL2', 1)],
+                    include_dirs=incl_dirs,)
+        self.set_info(**info)
+
+
+class lapack_ssl2_info(ssl2_info):
+    pass
+
+
+class blas_ssl2_info(ssl2_info):
+    pass
+
+
+
+class armpl_info(system_info):
+    section = 'armpl'
+    dir_env_var = 'ARMPL_DIR'
+    _lib_armpl = ['armpl_lp64_mp']
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        incl_dirs = self.get_include_dirs()
+        armpl_libs = self.get_libs('armpl_libs', self._lib_armpl)
+        info = self.check_libs2(lib_dirs, armpl_libs)
+        if info is None:
+            return
+        dict_append(info,
+                    define_macros=[('SCIPY_MKL_H', None),
+                                   ('HAVE_CBLAS', None)],
+                    include_dirs=incl_dirs)
+        self.set_info(**info)
+
+class lapack_armpl_info(armpl_info):
+    pass
+
+class blas_armpl_info(armpl_info):
+    pass
+
+
+class atlas_info(system_info):
+    section = 'atlas'
+    dir_env_var = 'ATLAS'
+    _lib_names = ['f77blas', 'cblas']
+    if sys.platform[:7] == 'freebsd':
+        _lib_atlas = ['atlas_r']
+        _lib_lapack = ['alapack_r']
+    else:
+        _lib_atlas = ['atlas']
+        _lib_lapack = ['lapack']
+
+    notfounderror = AtlasNotFoundError
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend(self.combine_paths(d, ['atlas*', 'ATLAS*',
+                                         'sse', '3dnow', 'sse2']) + [d])
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        info = {}
+        opt = self.get_option_single('atlas_libs', 'libraries')
+        atlas_libs = self.get_libs(opt, self._lib_names + self._lib_atlas)
+        lapack_libs = self.get_libs('lapack_libs', self._lib_lapack)
+        atlas = None
+        lapack = None
+        atlas_1 = None
+        for d in lib_dirs:
+            atlas = self.check_libs2(d, atlas_libs, [])
+            if atlas is not None:
+                lib_dirs2 = [d] + self.combine_paths(d, ['atlas*', 'ATLAS*'])
+                lapack = self.check_libs2(lib_dirs2, lapack_libs, [])
+                if lapack is not None:
+                    break
+            if atlas:
+                atlas_1 = atlas
+        log.info(self.__class__)
+        if atlas is None:
+            atlas = atlas_1
+        if atlas is None:
+            return
+        include_dirs = self.get_include_dirs()
+        h = (self.combine_paths(lib_dirs + include_dirs, 'cblas.h') or [None])
+        h = h[0]
+        if h:
+            h = os.path.dirname(h)
+            dict_append(info, include_dirs=[h])
+        info['language'] = 'c'
+        if lapack is not None:
+            dict_append(info, **lapack)
+            dict_append(info, **atlas)
+        elif 'lapack_atlas' in atlas['libraries']:
+            dict_append(info, **atlas)
+            dict_append(info,
+                        define_macros=[('ATLAS_WITH_LAPACK_ATLAS', None)])
+            self.set_info(**info)
+            return
+        else:
+            dict_append(info, **atlas)
+            dict_append(info, define_macros=[('ATLAS_WITHOUT_LAPACK', None)])
+            message = textwrap.dedent("""
+                *********************************************************************
+                    Could not find lapack library within the ATLAS installation.
+                *********************************************************************
+                """)
+            warnings.warn(message, stacklevel=2)
+            self.set_info(**info)
+            return
+
+        # Check if lapack library is complete, only warn if it is not.
+        lapack_dir = lapack['library_dirs'][0]
+        lapack_name = lapack['libraries'][0]
+        lapack_lib = None
+        lib_prefixes = ['lib']
+        if sys.platform == 'win32':
+            lib_prefixes.append('')
+        for e in self.library_extensions():
+            for prefix in lib_prefixes:
+                fn = os.path.join(lapack_dir, prefix + lapack_name + e)
+                if os.path.exists(fn):
+                    lapack_lib = fn
+                    break
+            if lapack_lib:
+                break
+        if lapack_lib is not None:
+            sz = os.stat(lapack_lib)[6]
+            if sz <= 4000 * 1024:
+                message = textwrap.dedent("""
+                    *********************************************************************
+                        Lapack library (from ATLAS) is probably incomplete:
+                          size of %s is %sk (expected >4000k)
+
+                        Follow the instructions in the KNOWN PROBLEMS section of the file
+                        numpy/INSTALL.txt.
+                    *********************************************************************
+                    """) % (lapack_lib, sz / 1024)
+                warnings.warn(message, stacklevel=2)
+            else:
+                info['language'] = 'f77'
+
+        atlas_version, atlas_extra_info = get_atlas_version(**atlas)
+        dict_append(info, **atlas_extra_info)
+
+        self.set_info(**info)
+
+
+class atlas_blas_info(atlas_info):
+    _lib_names = ['f77blas', 'cblas']
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        info = {}
+        opt = self.get_option_single('atlas_libs', 'libraries')
+        atlas_libs = self.get_libs(opt, self._lib_names + self._lib_atlas)
+        atlas = self.check_libs2(lib_dirs, atlas_libs, [])
+        if atlas is None:
+            return
+        include_dirs = self.get_include_dirs()
+        h = (self.combine_paths(lib_dirs + include_dirs, 'cblas.h') or [None])
+        h = h[0]
+        if h:
+            h = os.path.dirname(h)
+            dict_append(info, include_dirs=[h])
+        info['language'] = 'c'
+        info['define_macros'] = [('HAVE_CBLAS', None)]
+
+        atlas_version, atlas_extra_info = get_atlas_version(**atlas)
+        dict_append(atlas, **atlas_extra_info)
+
+        dict_append(info, **atlas)
+
+        self.set_info(**info)
+        return
+
+
+class atlas_threads_info(atlas_info):
+    dir_env_var = ['PTATLAS', 'ATLAS']
+    _lib_names = ['ptf77blas', 'ptcblas']
+
+
+class atlas_blas_threads_info(atlas_blas_info):
+    dir_env_var = ['PTATLAS', 'ATLAS']
+    _lib_names = ['ptf77blas', 'ptcblas']
+
+
+class lapack_atlas_info(atlas_info):
+    _lib_names = ['lapack_atlas'] + atlas_info._lib_names
+
+
+class lapack_atlas_threads_info(atlas_threads_info):
+    _lib_names = ['lapack_atlas'] + atlas_threads_info._lib_names
+
+
+class atlas_3_10_info(atlas_info):
+    _lib_names = ['satlas']
+    _lib_atlas = _lib_names
+    _lib_lapack = _lib_names
+
+
+class atlas_3_10_blas_info(atlas_3_10_info):
+    _lib_names = ['satlas']
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        info = {}
+        opt = self.get_option_single('atlas_lib', 'libraries')
+        atlas_libs = self.get_libs(opt, self._lib_names)
+        atlas = self.check_libs2(lib_dirs, atlas_libs, [])
+        if atlas is None:
+            return
+        include_dirs = self.get_include_dirs()
+        h = (self.combine_paths(lib_dirs + include_dirs, 'cblas.h') or [None])
+        h = h[0]
+        if h:
+            h = os.path.dirname(h)
+            dict_append(info, include_dirs=[h])
+        info['language'] = 'c'
+        info['define_macros'] = [('HAVE_CBLAS', None)]
+
+        atlas_version, atlas_extra_info = get_atlas_version(**atlas)
+        dict_append(atlas, **atlas_extra_info)
+
+        dict_append(info, **atlas)
+
+        self.set_info(**info)
+        return
+
+
+class atlas_3_10_threads_info(atlas_3_10_info):
+    dir_env_var = ['PTATLAS', 'ATLAS']
+    _lib_names = ['tatlas']
+    _lib_atlas = _lib_names
+    _lib_lapack = _lib_names
+
+
+class atlas_3_10_blas_threads_info(atlas_3_10_blas_info):
+    dir_env_var = ['PTATLAS', 'ATLAS']
+    _lib_names = ['tatlas']
+
+
+class lapack_atlas_3_10_info(atlas_3_10_info):
+    pass
+
+
+class lapack_atlas_3_10_threads_info(atlas_3_10_threads_info):
+    pass
+
+
+class lapack_info(system_info):
+    section = 'lapack'
+    dir_env_var = 'LAPACK'
+    _lib_names = ['lapack']
+    notfounderror = LapackNotFoundError
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+
+        opt = self.get_option_single('lapack_libs', 'libraries')
+        lapack_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs(lib_dirs, lapack_libs, [])
+        if info is None:
+            return
+        info['language'] = 'f77'
+        self.set_info(**info)
+
+
+class lapack_src_info(system_info):
+    # LAPACK_SRC is deprecated, please do not use this!
+    # Build or install a BLAS library via your package manager or from
+    # source separately.
+    section = 'lapack_src'
+    dir_env_var = 'LAPACK_SRC'
+    notfounderror = LapackSrcNotFoundError
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend([d] + self.combine_paths(d, ['LAPACK*/SRC', 'SRC']))
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        src_dirs = self.get_src_dirs()
+        src_dir = ''
+        for d in src_dirs:
+            if os.path.isfile(os.path.join(d, 'dgesv.f')):
+                src_dir = d
+                break
+        if not src_dir:
+            #XXX: Get sources from netlib. May be ask first.
+            return
+        # The following is extracted from LAPACK-3.0/SRC/Makefile.
+        # Added missing names from lapack-lite-3.1.1/SRC/Makefile
+        # while keeping removed names for Lapack-3.0 compatibility.
+        allaux = '''
+        ilaenv ieeeck lsame lsamen xerbla
+        iparmq
+        '''  # *.f
+        laux = '''
+        bdsdc bdsqr disna labad lacpy ladiv lae2 laebz laed0 laed1
+        laed2 laed3 laed4 laed5 laed6 laed7 laed8 laed9 laeda laev2
+        lagtf lagts lamch lamrg lanst lapy2 lapy3 larnv larrb larre
+        larrf lartg laruv las2 lascl lasd0 lasd1 lasd2 lasd3 lasd4
+        lasd5 lasd6 lasd7 lasd8 lasd9 lasda lasdq lasdt laset lasq1
+        lasq2 lasq3 lasq4 lasq5 lasq6 lasr lasrt lassq lasv2 pttrf
+        stebz stedc steqr sterf
+
+        larra larrc larrd larr larrk larrj larrr laneg laisnan isnan
+        lazq3 lazq4
+        '''  # [s|d]*.f
+        lasrc = '''
+        gbbrd gbcon gbequ gbrfs gbsv gbsvx gbtf2 gbtrf gbtrs gebak
+        gebal gebd2 gebrd gecon geequ gees geesx geev geevx gegs gegv
+        gehd2 gehrd gelq2 gelqf gels gelsd gelss gelsx gelsy geql2
+        geqlf geqp3 geqpf geqr2 geqrf gerfs gerq2 gerqf gesc2 gesdd
+        gesv gesvd gesvx getc2 getf2 getrf getri getrs ggbak ggbal
+        gges ggesx ggev ggevx ggglm gghrd gglse ggqrf ggrqf ggsvd
+        ggsvp gtcon gtrfs gtsv gtsvx gttrf gttrs gtts2 hgeqz hsein
+        hseqr labrd lacon laein lags2 lagtm lahqr lahrd laic1 lals0
+        lalsa lalsd langb lange langt lanhs lansb lansp lansy lantb
+        lantp lantr lapll lapmt laqgb laqge laqp2 laqps laqsb laqsp
+        laqsy lar1v lar2v larf larfb larfg larft larfx largv larrv
+        lartv larz larzb larzt laswp lasyf latbs latdf latps latrd
+        latrs latrz latzm lauu2 lauum pbcon pbequ pbrfs pbstf pbsv
+        pbsvx pbtf2 pbtrf pbtrs pocon poequ porfs posv posvx potf2
+        potrf potri potrs ppcon ppequ pprfs ppsv ppsvx pptrf pptri
+        pptrs ptcon pteqr ptrfs ptsv ptsvx pttrs ptts2 spcon sprfs
+        spsv spsvx sptrf sptri sptrs stegr stein sycon syrfs sysv
+        sysvx sytf2 sytrf sytri sytrs tbcon tbrfs tbtrs tgevc tgex2
+        tgexc tgsen tgsja tgsna tgsy2 tgsyl tpcon tprfs tptri tptrs
+        trcon trevc trexc trrfs trsen trsna trsyl trti2 trtri trtrs
+        tzrqf tzrzf
+
+        lacn2 lahr2 stemr laqr0 laqr1 laqr2 laqr3 laqr4 laqr5
+        '''  # [s|c|d|z]*.f
+        sd_lasrc = '''
+        laexc lag2 lagv2 laln2 lanv2 laqtr lasy2 opgtr opmtr org2l
+        org2r orgbr orghr orgl2 orglq orgql orgqr orgr2 orgrq orgtr
+        orm2l orm2r ormbr ormhr orml2 ormlq ormql ormqr ormr2 ormr3
+        ormrq ormrz ormtr rscl sbev sbevd sbevx sbgst sbgv sbgvd sbgvx
+        sbtrd spev spevd spevx spgst spgv spgvd spgvx sptrd stev stevd
+        stevr stevx syev syevd syevr syevx sygs2 sygst sygv sygvd
+        sygvx sytd2 sytrd
+        '''  # [s|d]*.f
+        cz_lasrc = '''
+        bdsqr hbev hbevd hbevx hbgst hbgv hbgvd hbgvx hbtrd hecon heev
+        heevd heevr heevx hegs2 hegst hegv hegvd hegvx herfs hesv
+        hesvx hetd2 hetf2 hetrd hetrf hetri hetrs hpcon hpev hpevd
+        hpevx hpgst hpgv hpgvd hpgvx hprfs hpsv hpsvx hptrd hptrf
+        hptri hptrs lacgv lacp2 lacpy lacrm lacrt ladiv laed0 laed7
+        laed8 laesy laev2 lahef lanhb lanhe lanhp lanht laqhb laqhe
+        laqhp larcm larnv lartg lascl laset lasr lassq pttrf rot spmv
+        spr stedc steqr symv syr ung2l ung2r ungbr unghr ungl2 unglq
+        ungql ungqr ungr2 ungrq ungtr unm2l unm2r unmbr unmhr unml2
+        unmlq unmql unmqr unmr2 unmr3 unmrq unmrz unmtr upgtr upmtr
+        '''  # [c|z]*.f
+        #######
+        sclaux = laux + ' econd '                  # s*.f
+        dzlaux = laux + ' secnd '                  # d*.f
+        slasrc = lasrc + sd_lasrc                  # s*.f
+        dlasrc = lasrc + sd_lasrc                  # d*.f
+        clasrc = lasrc + cz_lasrc + ' srot srscl '  # c*.f
+        zlasrc = lasrc + cz_lasrc + ' drot drscl '  # z*.f
+        oclasrc = ' icmax1 scsum1 '                # *.f
+        ozlasrc = ' izmax1 dzsum1 '                # *.f
+        sources = ['s%s.f' % f for f in (sclaux + slasrc).split()] \
+                  + ['d%s.f' % f for f in (dzlaux + dlasrc).split()] \
+                  + ['c%s.f' % f for f in (clasrc).split()] \
+                  + ['z%s.f' % f for f in (zlasrc).split()] \
+                  + ['%s.f' % f for f in (allaux + oclasrc + ozlasrc).split()]
+        sources = [os.path.join(src_dir, f) for f in sources]
+        # Lapack 3.1:
+        src_dir2 = os.path.join(src_dir, '..', 'INSTALL')
+        sources += [os.path.join(src_dir2, p + 'lamch.f') for p in 'sdcz']
+        # Lapack 3.2.1:
+        sources += [os.path.join(src_dir, p + 'larfp.f') for p in 'sdcz']
+        sources += [os.path.join(src_dir, 'ila' + p + 'lr.f') for p in 'sdcz']
+        sources += [os.path.join(src_dir, 'ila' + p + 'lc.f') for p in 'sdcz']
+        # Should we check here actual existence of source files?
+        # Yes, the file listing is different between 3.0 and 3.1
+        # versions.
+        sources = [f for f in sources if os.path.isfile(f)]
+        info = {'sources': sources, 'language': 'f77'}
+        self.set_info(**info)
+
+atlas_version_c_text = r'''
+/* This file is generated from numpy/distutils/system_info.py */
+void ATL_buildinfo(void);
+int main(void) {
+  ATL_buildinfo();
+  return 0;
+}
+'''
+
+_cached_atlas_version = {}
+
+
+def get_atlas_version(**config):
+    libraries = config.get('libraries', [])
+    library_dirs = config.get('library_dirs', [])
+    key = (tuple(libraries), tuple(library_dirs))
+    if key in _cached_atlas_version:
+        return _cached_atlas_version[key]
+    c = cmd_config(Distribution())
+    atlas_version = None
+    info = {}
+    try:
+        s, o = c.get_output(atlas_version_c_text,
+                            libraries=libraries, library_dirs=library_dirs,
+                           )
+        if s and re.search(r'undefined reference to `_gfortran', o, re.M):
+            s, o = c.get_output(atlas_version_c_text,
+                                libraries=libraries + ['gfortran'],
+                                library_dirs=library_dirs,
+                               )
+            if not s:
+                warnings.warn(textwrap.dedent("""
+                    *****************************************************
+                    Linkage with ATLAS requires gfortran. Use
+
+                      python setup.py config_fc --fcompiler=gnu95 ...
+
+                    when building extension libraries that use ATLAS.
+                    Make sure that -lgfortran is used for C++ extensions.
+                    *****************************************************
+                    """), stacklevel=2)
+                dict_append(info, language='f90',
+                            define_macros=[('ATLAS_REQUIRES_GFORTRAN', None)])
+    except Exception:  # failed to get version from file -- maybe on Windows
+        # look at directory name
+        for o in library_dirs:
+            m = re.search(r'ATLAS_(?P\d+[.]\d+[.]\d+)_', o)
+            if m:
+                atlas_version = m.group('version')
+            if atlas_version is not None:
+                break
+
+        # final choice --- look at ATLAS_VERSION environment
+        #   variable
+        if atlas_version is None:
+            atlas_version = os.environ.get('ATLAS_VERSION', None)
+        if atlas_version:
+            dict_append(info, define_macros=[(
+                'ATLAS_INFO', _c_string_literal(atlas_version))
+            ])
+        else:
+            dict_append(info, define_macros=[('NO_ATLAS_INFO', -1)])
+        return atlas_version or '?.?.?', info
+
+    if not s:
+        m = re.search(r'ATLAS version (?P\d+[.]\d+[.]\d+)', o)
+        if m:
+            atlas_version = m.group('version')
+    if atlas_version is None:
+        if re.search(r'undefined symbol: ATL_buildinfo', o, re.M):
+            atlas_version = '3.2.1_pre3.3.6'
+        else:
+            log.info('Status: %d', s)
+            log.info('Output: %s', o)
+
+    elif atlas_version == '3.2.1_pre3.3.6':
+        dict_append(info, define_macros=[('NO_ATLAS_INFO', -2)])
+    else:
+        dict_append(info, define_macros=[(
+            'ATLAS_INFO', _c_string_literal(atlas_version))
+        ])
+    result = _cached_atlas_version[key] = atlas_version, info
+    return result
+
+
+class lapack_opt_info(system_info):
+    notfounderror = LapackNotFoundError
+
+    # List of all known LAPACK libraries, in the default order
+    lapack_order = ['armpl', 'mkl', 'ssl2', 'openblas', 'flame',
+                    'accelerate', 'atlas', 'lapack']
+    order_env_var_name = 'NPY_LAPACK_ORDER'
+    
+    def _calc_info_armpl(self):
+        info = get_info('lapack_armpl')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_mkl(self):
+        info = get_info('lapack_mkl')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_ssl2(self):
+        info = get_info('lapack_ssl2')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_openblas(self):
+        info = get_info('openblas_lapack')
+        if info:
+            self.set_info(**info)
+            return True
+        info = get_info('openblas_clapack')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_flame(self):
+        info = get_info('flame')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_atlas(self):
+        info = get_info('atlas_3_10_threads')
+        if not info:
+            info = get_info('atlas_3_10')
+        if not info:
+            info = get_info('atlas_threads')
+        if not info:
+            info = get_info('atlas')
+        if info:
+            # Figure out if ATLAS has lapack...
+            # If not we need the lapack library, but not BLAS!
+            l = info.get('define_macros', [])
+            if ('ATLAS_WITH_LAPACK_ATLAS', None) in l \
+               or ('ATLAS_WITHOUT_LAPACK', None) in l:
+                # Get LAPACK (with possible warnings)
+                # If not found we don't accept anything
+                # since we can't use ATLAS with LAPACK!
+                lapack_info = self._get_info_lapack()
+                if not lapack_info:
+                    return False
+                dict_append(info, **lapack_info)
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_accelerate(self):
+        info = get_info('accelerate')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _get_info_blas(self):
+        # Default to get the optimized BLAS implementation
+        info = get_info('blas_opt')
+        if not info:
+            warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=3)
+            info_src = get_info('blas_src')
+            if not info_src:
+                warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=3)
+                return {}
+            dict_append(info, libraries=[('fblas_src', info_src)])
+        return info
+
+    def _get_info_lapack(self):
+        info = get_info('lapack')
+        if not info:
+            warnings.warn(LapackNotFoundError.__doc__ or '', stacklevel=3)
+            info_src = get_info('lapack_src')
+            if not info_src:
+                warnings.warn(LapackSrcNotFoundError.__doc__ or '', stacklevel=3)
+                return {}
+            dict_append(info, libraries=[('flapack_src', info_src)])
+        return info
+
+    def _calc_info_lapack(self):
+        info = self._get_info_lapack()
+        if info:
+            info_blas = self._get_info_blas()
+            dict_append(info, **info_blas)
+            dict_append(info, define_macros=[('NO_ATLAS_INFO', 1)])
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_from_envvar(self):
+        info = {}
+        info['language'] = 'f77'
+        info['libraries'] = []
+        info['include_dirs'] = []
+        info['define_macros'] = []
+        info['extra_link_args'] = os.environ['NPY_LAPACK_LIBS'].split()
+        self.set_info(**info)
+        return True
+
+    def _calc_info(self, name):
+        return getattr(self, '_calc_info_{}'.format(name))()
+
+    def calc_info(self):
+        lapack_order, unknown_order = _parse_env_order(self.lapack_order, self.order_env_var_name)
+        if len(unknown_order) > 0:
+            raise ValueError("lapack_opt_info user defined "
+                             "LAPACK order has unacceptable "
+                             "values: {}".format(unknown_order))
+
+        if 'NPY_LAPACK_LIBS' in os.environ:
+            # Bypass autodetection, set language to F77 and use env var linker
+            # flags directly
+            self._calc_info_from_envvar()
+            return
+
+        for lapack in lapack_order:
+            if self._calc_info(lapack):
+                return
+
+        if 'lapack' not in lapack_order:
+            # Since the user may request *not* to use any library, we still need
+            # to raise warnings to signal missing packages!
+            warnings.warn(LapackNotFoundError.__doc__ or '', stacklevel=2)
+            warnings.warn(LapackSrcNotFoundError.__doc__ or '', stacklevel=2)
+
+
+class _ilp64_opt_info_mixin:
+    symbol_suffix = None
+    symbol_prefix = None
+
+    def _check_info(self, info):
+        macros = dict(info.get('define_macros', []))
+        prefix = macros.get('BLAS_SYMBOL_PREFIX', '')
+        suffix = macros.get('BLAS_SYMBOL_SUFFIX', '')
+
+        if self.symbol_prefix not in (None, prefix):
+            return False
+
+        if self.symbol_suffix not in (None, suffix):
+            return False
+
+        return bool(info)
+
+
+class lapack_ilp64_opt_info(lapack_opt_info, _ilp64_opt_info_mixin):
+    notfounderror = LapackILP64NotFoundError
+    lapack_order = ['openblas64_', 'openblas_ilp64', 'accelerate']
+    order_env_var_name = 'NPY_LAPACK_ILP64_ORDER'
+
+    def _calc_info(self, name):
+        print('lapack_ilp64_opt_info._calc_info(name=%s)' % (name))
+        info = get_info(name + '_lapack')
+        if self._check_info(info):
+            self.set_info(**info)
+            return True
+        else:
+            print('%s_lapack does not exist' % (name))
+        return False
+
+
+class lapack_ilp64_plain_opt_info(lapack_ilp64_opt_info):
+    # Same as lapack_ilp64_opt_info, but fix symbol names
+    symbol_prefix = ''
+    symbol_suffix = ''
+
+
+class lapack64__opt_info(lapack_ilp64_opt_info):
+    symbol_prefix = ''
+    symbol_suffix = '64_'
+
+
+class blas_opt_info(system_info):
+    notfounderror = BlasNotFoundError
+    # List of all known BLAS libraries, in the default order
+
+    blas_order = ['armpl', 'mkl', 'ssl2', 'blis', 'openblas',
+                  'accelerate', 'atlas', 'blas']
+    order_env_var_name = 'NPY_BLAS_ORDER'
+    
+    def _calc_info_armpl(self):
+        info = get_info('blas_armpl')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_mkl(self):
+        info = get_info('blas_mkl')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_ssl2(self):
+        info = get_info('blas_ssl2')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_blis(self):
+        info = get_info('blis')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_openblas(self):
+        info = get_info('openblas')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_atlas(self):
+        info = get_info('atlas_3_10_blas_threads')
+        if not info:
+            info = get_info('atlas_3_10_blas')
+        if not info:
+            info = get_info('atlas_blas_threads')
+        if not info:
+            info = get_info('atlas_blas')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_accelerate(self):
+        info = get_info('accelerate')
+        if info:
+            self.set_info(**info)
+            return True
+        return False
+
+    def _calc_info_blas(self):
+        # Warn about a non-optimized BLAS library
+        warnings.warn(BlasOptNotFoundError.__doc__ or '', stacklevel=3)
+        info = {}
+        dict_append(info, define_macros=[('NO_ATLAS_INFO', 1)])
+
+        blas = get_info('blas')
+        if blas:
+            dict_append(info, **blas)
+        else:
+            # Not even BLAS was found!
+            warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=3)
+
+            blas_src = get_info('blas_src')
+            if not blas_src:
+                warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=3)
+                return False
+            dict_append(info, libraries=[('fblas_src', blas_src)])
+
+        self.set_info(**info)
+        return True
+
+    def _calc_info_from_envvar(self):
+        info = {}
+        info['language'] = 'f77'
+        info['libraries'] = []
+        info['include_dirs'] = []
+        info['define_macros'] = []
+        info['extra_link_args'] = os.environ['NPY_BLAS_LIBS'].split()
+        if 'NPY_CBLAS_LIBS' in os.environ:
+            info['define_macros'].append(('HAVE_CBLAS', None))
+            info['extra_link_args'].extend(
+                                        os.environ['NPY_CBLAS_LIBS'].split())
+        self.set_info(**info)
+        return True
+
+    def _calc_info(self, name):
+        return getattr(self, '_calc_info_{}'.format(name))()
+
+    def calc_info(self):
+        blas_order, unknown_order = _parse_env_order(self.blas_order, self.order_env_var_name)
+        if len(unknown_order) > 0:
+            raise ValueError("blas_opt_info user defined BLAS order has unacceptable values: {}".format(unknown_order))
+
+        if 'NPY_BLAS_LIBS' in os.environ:
+            # Bypass autodetection, set language to F77 and use env var linker
+            # flags directly
+            self._calc_info_from_envvar()
+            return
+
+        for blas in blas_order:
+            if self._calc_info(blas):
+                return
+
+        if 'blas' not in blas_order:
+            # Since the user may request *not* to use any library, we still need
+            # to raise warnings to signal missing packages!
+            warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=2)
+            warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=2)
+
+
+class blas_ilp64_opt_info(blas_opt_info, _ilp64_opt_info_mixin):
+    notfounderror = BlasILP64NotFoundError
+    blas_order = ['openblas64_', 'openblas_ilp64', 'accelerate']
+    order_env_var_name = 'NPY_BLAS_ILP64_ORDER'
+
+    def _calc_info(self, name):
+        info = get_info(name)
+        if self._check_info(info):
+            self.set_info(**info)
+            return True
+        return False
+
+
+class blas_ilp64_plain_opt_info(blas_ilp64_opt_info):
+    symbol_prefix = ''
+    symbol_suffix = ''
+
+
+class blas64__opt_info(blas_ilp64_opt_info):
+    symbol_prefix = ''
+    symbol_suffix = '64_'
+
+
+class cblas_info(system_info):
+    section = 'cblas'
+    dir_env_var = 'CBLAS'
+    # No default as it's used only in blas_info
+    _lib_names = []
+    notfounderror = BlasNotFoundError
+
+
+class blas_info(system_info):
+    section = 'blas'
+    dir_env_var = 'BLAS'
+    _lib_names = ['blas']
+    notfounderror = BlasNotFoundError
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        opt = self.get_option_single('blas_libs', 'libraries')
+        blas_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs(lib_dirs, blas_libs, [])
+        if info is None:
+            return
+        else:
+            info['include_dirs'] = self.get_include_dirs()
+        if platform.system() == 'Windows':
+            # The check for windows is needed because get_cblas_libs uses the
+            # same compiler that was used to compile Python and msvc is
+            # often not installed when mingw is being used. This rough
+            # treatment is not desirable, but windows is tricky.
+            info['language'] = 'f77'  # XXX: is it generally true?
+            # If cblas is given as an option, use those
+            cblas_info_obj = cblas_info()
+            cblas_opt = cblas_info_obj.get_option_single('cblas_libs', 'libraries')
+            cblas_libs = cblas_info_obj.get_libs(cblas_opt, None)
+            if cblas_libs:
+                info['libraries'] = cblas_libs + blas_libs
+                info['define_macros'] = [('HAVE_CBLAS', None)]
+        else:
+            lib = self.get_cblas_libs(info)
+            if lib is not None:
+                info['language'] = 'c'
+                info['libraries'] = lib
+                info['define_macros'] = [('HAVE_CBLAS', None)]
+        self.set_info(**info)
+
+    def get_cblas_libs(self, info):
+        """ Check whether we can link with CBLAS interface
+
+        This method will search through several combinations of libraries
+        to check whether CBLAS is present:
+
+        1. Libraries in ``info['libraries']``, as is
+        2. As 1. but also explicitly adding ``'cblas'`` as a library
+        3. As 1. but also explicitly adding ``'blas'`` as a library
+        4. Check only library ``'cblas'``
+        5. Check only library ``'blas'``
+
+        Parameters
+        ----------
+        info : dict
+           system information dictionary for compilation and linking
+
+        Returns
+        -------
+        libraries : list of str or None
+            a list of libraries that enables the use of CBLAS interface.
+            Returns None if not found or a compilation error occurs.
+
+            Since 1.17 returns a list.
+        """
+        # primitive cblas check by looking for the header and trying to link
+        # cblas or blas
+        c = customized_ccompiler()
+        tmpdir = tempfile.mkdtemp()
+        s = textwrap.dedent("""\
+            #include 
+            int main(int argc, const char *argv[])
+            {
+                double a[4] = {1,2,3,4};
+                double b[4] = {5,6,7,8};
+                return cblas_ddot(4, a, 1, b, 1) > 10;
+            }""")
+        src = os.path.join(tmpdir, 'source.c')
+        try:
+            with open(src, 'w') as f:
+                f.write(s)
+
+            try:
+                # check we can compile (find headers)
+                obj = c.compile([src], output_dir=tmpdir,
+                                include_dirs=self.get_include_dirs())
+            except (distutils.ccompiler.CompileError, distutils.ccompiler.LinkError):
+                return None
+
+            # check we can link (find library)
+            # some systems have separate cblas and blas libs.
+            for libs in [info['libraries'], ['cblas'] + info['libraries'],
+                         ['blas'] + info['libraries'], ['cblas'], ['blas']]:
+                try:
+                    c.link_executable(obj, os.path.join(tmpdir, "a.out"),
+                                      libraries=libs,
+                                      library_dirs=info['library_dirs'],
+                                      extra_postargs=info.get('extra_link_args', []))
+                    return libs
+                except distutils.ccompiler.LinkError:
+                    pass
+        finally:
+            shutil.rmtree(tmpdir)
+        return None
+
+
+class openblas_info(blas_info):
+    section = 'openblas'
+    dir_env_var = 'OPENBLAS'
+    _lib_names = ['openblas']
+    _require_symbols = []
+    notfounderror = BlasNotFoundError
+
+    @property
+    def symbol_prefix(self):
+        try:
+            return self.cp.get(self.section, 'symbol_prefix')
+        except NoOptionError:
+            return ''
+
+    @property
+    def symbol_suffix(self):
+        try:
+            return self.cp.get(self.section, 'symbol_suffix')
+        except NoOptionError:
+            return ''
+
+    def _calc_info(self):
+        c = customized_ccompiler()
+
+        lib_dirs = self.get_lib_dirs()
+
+        # Prefer to use libraries over openblas_libs
+        opt = self.get_option_single('openblas_libs', 'libraries')
+        openblas_libs = self.get_libs(opt, self._lib_names)
+
+        info = self.check_libs(lib_dirs, openblas_libs, [])
+
+        if c.compiler_type == "msvc" and info is None:
+            from numpy.distutils.fcompiler import new_fcompiler
+            f = new_fcompiler(c_compiler=c)
+            if f and f.compiler_type == 'gnu95':
+                # Try gfortran-compatible library files
+                info = self.check_msvc_gfortran_libs(lib_dirs, openblas_libs)
+                # Skip lapack check, we'd need build_ext to do it
+                skip_symbol_check = True
+        elif info:
+            skip_symbol_check = False
+            info['language'] = 'c'
+
+        if info is None:
+            return None
+
+        # Add extra info for OpenBLAS
+        extra_info = self.calc_extra_info()
+        dict_append(info, **extra_info)
+
+        if not (skip_symbol_check or self.check_symbols(info)):
+            return None
+
+        info['define_macros'] = [('HAVE_CBLAS', None)]
+        if self.symbol_prefix:
+            info['define_macros'] += [('BLAS_SYMBOL_PREFIX', self.symbol_prefix)]
+        if self.symbol_suffix:
+            info['define_macros'] += [('BLAS_SYMBOL_SUFFIX', self.symbol_suffix)]
+
+        return info
+
+    def calc_info(self):
+        info = self._calc_info()
+        if info is not None:
+            self.set_info(**info)
+
+    def check_msvc_gfortran_libs(self, library_dirs, libraries):
+        # First, find the full path to each library directory
+        library_paths = []
+        for library in libraries:
+            for library_dir in library_dirs:
+                # MinGW static ext will be .a
+                fullpath = os.path.join(library_dir, library + '.a')
+                if os.path.isfile(fullpath):
+                    library_paths.append(fullpath)
+                    break
+            else:
+                return None
+
+        # Generate numpy.distutils virtual static library file
+        basename = self.__class__.__name__
+        tmpdir = os.path.join(os.getcwd(), 'build', basename)
+        if not os.path.isdir(tmpdir):
+            os.makedirs(tmpdir)
+
+        info = {'library_dirs': [tmpdir],
+                'libraries': [basename],
+                'language': 'f77'}
+
+        fake_lib_file = os.path.join(tmpdir, basename + '.fobjects')
+        fake_clib_file = os.path.join(tmpdir, basename + '.cobjects')
+        with open(fake_lib_file, 'w') as f:
+            f.write("\n".join(library_paths))
+        with open(fake_clib_file, 'w') as f:
+            pass
+
+        return info
+
+    def check_symbols(self, info):
+        res = False
+        c = customized_ccompiler()
+
+        tmpdir = tempfile.mkdtemp()
+
+        prototypes = "\n".join("void %s%s%s();" % (self.symbol_prefix,
+                                                   symbol_name,
+                                                   self.symbol_suffix)
+                               for symbol_name in self._require_symbols)
+        calls = "\n".join("%s%s%s();" % (self.symbol_prefix,
+                                         symbol_name,
+                                         self.symbol_suffix)
+                          for symbol_name in self._require_symbols)
+        s = textwrap.dedent("""\
+            %(prototypes)s
+            int main(int argc, const char *argv[])
+            {
+                %(calls)s
+                return 0;
+            }""") % dict(prototypes=prototypes, calls=calls)
+        src = os.path.join(tmpdir, 'source.c')
+        out = os.path.join(tmpdir, 'a.out')
+        # Add the additional "extra" arguments
+        try:
+            extra_args = info['extra_link_args']
+        except Exception:
+            extra_args = []
+        try:
+            with open(src, 'w') as f:
+                f.write(s)
+            obj = c.compile([src], output_dir=tmpdir)
+            try:
+                c.link_executable(obj, out, libraries=info['libraries'],
+                                  library_dirs=info['library_dirs'],
+                                  extra_postargs=extra_args)
+                res = True
+            except distutils.ccompiler.LinkError:
+                res = False
+        finally:
+            shutil.rmtree(tmpdir)
+        return res
+
+class openblas_lapack_info(openblas_info):
+    section = 'openblas'
+    dir_env_var = 'OPENBLAS'
+    _lib_names = ['openblas']
+    _require_symbols = ['zungqr_']
+    notfounderror = BlasNotFoundError
+
+class openblas_clapack_info(openblas_lapack_info):
+    _lib_names = ['openblas', 'lapack']
+
+class openblas_ilp64_info(openblas_info):
+    section = 'openblas_ilp64'
+    dir_env_var = 'OPENBLAS_ILP64'
+    _lib_names = ['openblas64']
+    _require_symbols = ['dgemm_', 'cblas_dgemm']
+    notfounderror = BlasILP64NotFoundError
+
+    def _calc_info(self):
+        info = super()._calc_info()
+        if info is not None:
+            info['define_macros'] += [('HAVE_BLAS_ILP64', None)]
+        return info
+
+class openblas_ilp64_lapack_info(openblas_ilp64_info):
+    _require_symbols = ['dgemm_', 'cblas_dgemm', 'zungqr_', 'LAPACKE_zungqr']
+
+    def _calc_info(self):
+        info = super()._calc_info()
+        if info:
+            info['define_macros'] += [('HAVE_LAPACKE', None)]
+        return info
+
+class openblas64__info(openblas_ilp64_info):
+    # ILP64 Openblas, with default symbol suffix
+    section = 'openblas64_'
+    dir_env_var = 'OPENBLAS64_'
+    _lib_names = ['openblas64_']
+    symbol_suffix = '64_'
+    symbol_prefix = ''
+
+class openblas64__lapack_info(openblas_ilp64_lapack_info, openblas64__info):
+    pass
+
+class blis_info(blas_info):
+    section = 'blis'
+    dir_env_var = 'BLIS'
+    _lib_names = ['blis']
+    notfounderror = BlasNotFoundError
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        opt = self.get_option_single('blis_libs', 'libraries')
+        blis_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs2(lib_dirs, blis_libs, [])
+        if info is None:
+            return
+
+        # Add include dirs
+        incl_dirs = self.get_include_dirs()
+        dict_append(info,
+                    language='c',
+                    define_macros=[('HAVE_CBLAS', None)],
+                    include_dirs=incl_dirs)
+        self.set_info(**info)
+
+
+class flame_info(system_info):
+    """ Usage of libflame for LAPACK operations
+
+    This requires libflame to be compiled with lapack wrappers:
+
+    ./configure --enable-lapack2flame ...
+
+    Be aware that libflame 5.1.0 has some missing names in the shared library, so
+    if you have problems, try the static flame library.
+    """
+    section = 'flame'
+    _lib_names = ['flame']
+    notfounderror = FlameNotFoundError
+
+    def check_embedded_lapack(self, info):
+        """ libflame does not necessarily have a wrapper for fortran LAPACK, we need to check """
+        c = customized_ccompiler()
+
+        tmpdir = tempfile.mkdtemp()
+        s = textwrap.dedent("""\
+            void zungqr_();
+            int main(int argc, const char *argv[])
+            {
+                zungqr_();
+                return 0;
+            }""")
+        src = os.path.join(tmpdir, 'source.c')
+        out = os.path.join(tmpdir, 'a.out')
+        # Add the additional "extra" arguments
+        extra_args = info.get('extra_link_args', [])
+        try:
+            with open(src, 'w') as f:
+                f.write(s)
+            obj = c.compile([src], output_dir=tmpdir)
+            try:
+                c.link_executable(obj, out, libraries=info['libraries'],
+                                  library_dirs=info['library_dirs'],
+                                  extra_postargs=extra_args)
+                return True
+            except distutils.ccompiler.LinkError:
+                return False
+        finally:
+            shutil.rmtree(tmpdir)
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+        flame_libs = self.get_libs('libraries', self._lib_names)
+
+        info = self.check_libs2(lib_dirs, flame_libs, [])
+        if info is None:
+            return
+
+        # Add the extra flag args to info
+        extra_info = self.calc_extra_info()
+        dict_append(info, **extra_info)
+
+        if self.check_embedded_lapack(info):
+            # check if the user has supplied all information required
+            self.set_info(**info)
+        else:
+            # Try and get the BLAS lib to see if we can get it to work
+            blas_info = get_info('blas_opt')
+            if not blas_info:
+                # since we already failed once, this ain't going to work either
+                return
+
+            # Now we need to merge the two dictionaries
+            for key in blas_info:
+                if isinstance(blas_info[key], list):
+                    info[key] = info.get(key, []) + blas_info[key]
+                elif isinstance(blas_info[key], tuple):
+                    info[key] = info.get(key, ()) + blas_info[key]
+                else:
+                    info[key] = info.get(key, '') + blas_info[key]
+
+            # Now check again
+            if self.check_embedded_lapack(info):
+                self.set_info(**info)
+
+
+class accelerate_info(system_info):
+    section = 'accelerate'
+    _lib_names = ['accelerate', 'veclib']
+    notfounderror = BlasNotFoundError
+
+    def calc_info(self):
+        # Make possible to enable/disable from config file/env var
+        libraries = os.environ.get('ACCELERATE')
+        if libraries:
+            libraries = [libraries]
+        else:
+            libraries = self.get_libs('libraries', self._lib_names)
+        libraries = [lib.strip().lower() for lib in libraries]
+
+        if (sys.platform == 'darwin' and
+                not os.getenv('_PYTHON_HOST_PLATFORM', None)):
+            # Use the system BLAS from Accelerate or vecLib under OSX
+            args = []
+            link_args = []
+            if get_platform()[-4:] == 'i386' or 'intel' in get_platform() or \
+               'x86_64' in get_platform() or \
+               'i386' in platform.platform():
+                intel = 1
+            else:
+                intel = 0
+            if (os.path.exists('/System/Library/Frameworks'
+                              '/Accelerate.framework/') and
+                    'accelerate' in libraries):
+                if intel:
+                    args.extend(['-msse3'])
+                args.extend([
+                    '-I/System/Library/Frameworks/vecLib.framework/Headers'])
+                link_args.extend(['-Wl,-framework', '-Wl,Accelerate'])
+            elif (os.path.exists('/System/Library/Frameworks'
+                                 '/vecLib.framework/') and
+                      'veclib' in libraries):
+                if intel:
+                    args.extend(['-msse3'])
+                args.extend([
+                    '-I/System/Library/Frameworks/vecLib.framework/Headers'])
+                link_args.extend(['-Wl,-framework', '-Wl,vecLib'])
+
+            if args:
+                macros = [
+                    ('NO_ATLAS_INFO', 3),
+                    ('HAVE_CBLAS', None),
+                    ('ACCELERATE_NEW_LAPACK', None),
+                ]
+                if(os.getenv('NPY_USE_BLAS_ILP64', None)):
+                    print('Setting HAVE_BLAS_ILP64')
+                    macros += [
+                        ('HAVE_BLAS_ILP64', None),
+                        ('ACCELERATE_LAPACK_ILP64', None),
+                    ]
+                self.set_info(extra_compile_args=args,
+                              extra_link_args=link_args,
+                              define_macros=macros)
+
+        return
+
+class accelerate_lapack_info(accelerate_info):
+    def _calc_info(self):
+        return super()._calc_info()
+
+class blas_src_info(system_info):
+    # BLAS_SRC is deprecated, please do not use this!
+    # Build or install a BLAS library via your package manager or from
+    # source separately.
+    section = 'blas_src'
+    dir_env_var = 'BLAS_SRC'
+    notfounderror = BlasSrcNotFoundError
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend([d] + self.combine_paths(d, ['blas']))
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        src_dirs = self.get_src_dirs()
+        src_dir = ''
+        for d in src_dirs:
+            if os.path.isfile(os.path.join(d, 'daxpy.f')):
+                src_dir = d
+                break
+        if not src_dir:
+            #XXX: Get sources from netlib. May be ask first.
+            return
+        blas1 = '''
+        caxpy csscal dnrm2 dzasum saxpy srotg zdotc ccopy cswap drot
+        dznrm2 scasum srotm zdotu cdotc dasum drotg icamax scnrm2
+        srotmg zdrot cdotu daxpy drotm idamax scopy sscal zdscal crotg
+        dcabs1 drotmg isamax sdot sswap zrotg cscal dcopy dscal izamax
+        snrm2 zaxpy zscal csrot ddot dswap sasum srot zcopy zswap
+        scabs1
+        '''
+        blas2 = '''
+        cgbmv chpmv ctrsv dsymv dtrsv sspr2 strmv zhemv ztpmv cgemv
+        chpr dgbmv dsyr lsame ssymv strsv zher ztpsv cgerc chpr2 dgemv
+        dsyr2 sgbmv ssyr xerbla zher2 ztrmv cgeru ctbmv dger dtbmv
+        sgemv ssyr2 zgbmv zhpmv ztrsv chbmv ctbsv dsbmv dtbsv sger
+        stbmv zgemv zhpr chemv ctpmv dspmv dtpmv ssbmv stbsv zgerc
+        zhpr2 cher ctpsv dspr dtpsv sspmv stpmv zgeru ztbmv cher2
+        ctrmv dspr2 dtrmv sspr stpsv zhbmv ztbsv
+        '''
+        blas3 = '''
+        cgemm csymm ctrsm dsyrk sgemm strmm zhemm zsyr2k chemm csyr2k
+        dgemm dtrmm ssymm strsm zher2k zsyrk cher2k csyrk dsymm dtrsm
+        ssyr2k zherk ztrmm cherk ctrmm dsyr2k ssyrk zgemm zsymm ztrsm
+        '''
+        sources = [os.path.join(src_dir, f + '.f') \
+                   for f in (blas1 + blas2 + blas3).split()]
+        #XXX: should we check here actual existence of source files?
+        sources = [f for f in sources if os.path.isfile(f)]
+        info = {'sources': sources, 'language': 'f77'}
+        self.set_info(**info)
+
+
+class x11_info(system_info):
+    section = 'x11'
+    notfounderror = X11NotFoundError
+    _lib_names = ['X11']
+
+    def __init__(self):
+        system_info.__init__(self,
+                             default_lib_dirs=default_x11_lib_dirs,
+                             default_include_dirs=default_x11_include_dirs)
+
+    def calc_info(self):
+        if sys.platform  in ['win32']:
+            return
+        lib_dirs = self.get_lib_dirs()
+        include_dirs = self.get_include_dirs()
+        opt = self.get_option_single('x11_libs', 'libraries')
+        x11_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs(lib_dirs, x11_libs, [])
+        if info is None:
+            return
+        inc_dir = None
+        for d in include_dirs:
+            if self.combine_paths(d, 'X11/X.h'):
+                inc_dir = d
+                break
+        if inc_dir is not None:
+            dict_append(info, include_dirs=[inc_dir])
+        self.set_info(**info)
+
+
+class _numpy_info(system_info):
+    section = 'Numeric'
+    modulename = 'Numeric'
+    notfounderror = NumericNotFoundError
+
+    def __init__(self):
+        include_dirs = []
+        try:
+            module = __import__(self.modulename)
+            prefix = []
+            for name in module.__file__.split(os.sep):
+                if name == 'lib':
+                    break
+                prefix.append(name)
+
+            # Ask numpy for its own include path before attempting
+            # anything else
+            try:
+                include_dirs.append(getattr(module, 'get_include')())
+            except AttributeError:
+                pass
+
+            include_dirs.append(sysconfig.get_path('include'))
+        except ImportError:
+            pass
+        py_incl_dir = sysconfig.get_path('include')
+        include_dirs.append(py_incl_dir)
+        py_pincl_dir = sysconfig.get_path('platinclude')
+        if py_pincl_dir not in include_dirs:
+            include_dirs.append(py_pincl_dir)
+        for d in default_include_dirs:
+            d = os.path.join(d, os.path.basename(py_incl_dir))
+            if d not in include_dirs:
+                include_dirs.append(d)
+        system_info.__init__(self,
+                             default_lib_dirs=[],
+                             default_include_dirs=include_dirs)
+
+    def calc_info(self):
+        try:
+            module = __import__(self.modulename)
+        except ImportError:
+            return
+        info = {}
+        macros = []
+        for v in ['__version__', 'version']:
+            vrs = getattr(module, v, None)
+            if vrs is None:
+                continue
+            macros = [(self.modulename.upper() + '_VERSION',
+                      _c_string_literal(vrs)),
+                      (self.modulename.upper(), None)]
+            break
+        dict_append(info, define_macros=macros)
+        include_dirs = self.get_include_dirs()
+        inc_dir = None
+        for d in include_dirs:
+            if self.combine_paths(d,
+                                  os.path.join(self.modulename,
+                                               'arrayobject.h')):
+                inc_dir = d
+                break
+        if inc_dir is not None:
+            dict_append(info, include_dirs=[inc_dir])
+        if info:
+            self.set_info(**info)
+        return
+
+
+class numarray_info(_numpy_info):
+    section = 'numarray'
+    modulename = 'numarray'
+
+
+class Numeric_info(_numpy_info):
+    section = 'Numeric'
+    modulename = 'Numeric'
+
+
+class numpy_info(_numpy_info):
+    section = 'numpy'
+    modulename = 'numpy'
+
+
+class numerix_info(system_info):
+    section = 'numerix'
+
+    def calc_info(self):
+        which = None, None
+        if os.getenv("NUMERIX"):
+            which = os.getenv("NUMERIX"), "environment var"
+        # If all the above fail, default to numpy.
+        if which[0] is None:
+            which = "numpy", "defaulted"
+            try:
+                import numpy  # noqa: F401
+                which = "numpy", "defaulted"
+            except ImportError as e:
+                msg1 = str(e)
+                try:
+                    import Numeric  # noqa: F401
+                    which = "numeric", "defaulted"
+                except ImportError as e:
+                    msg2 = str(e)
+                    try:
+                        import numarray  # noqa: F401
+                        which = "numarray", "defaulted"
+                    except ImportError as e:
+                        msg3 = str(e)
+                        log.info(msg1)
+                        log.info(msg2)
+                        log.info(msg3)
+        which = which[0].strip().lower(), which[1]
+        if which[0] not in ["numeric", "numarray", "numpy"]:
+            raise ValueError("numerix selector must be either 'Numeric' "
+                             "or 'numarray' or 'numpy' but the value obtained"
+                             " from the %s was '%s'." % (which[1], which[0]))
+        os.environ['NUMERIX'] = which[0]
+        self.set_info(**get_info(which[0]))
+
+
+class f2py_info(system_info):
+    def calc_info(self):
+        try:
+            import numpy.f2py as f2py
+        except ImportError:
+            return
+        f2py_dir = os.path.join(os.path.dirname(f2py.__file__), 'src')
+        self.set_info(sources=[os.path.join(f2py_dir, 'fortranobject.c')],
+                      include_dirs=[f2py_dir])
+        return
+
+
+class boost_python_info(system_info):
+    section = 'boost_python'
+    dir_env_var = 'BOOST'
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend([d] + self.combine_paths(d, ['boost*']))
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        src_dirs = self.get_src_dirs()
+        src_dir = ''
+        for d in src_dirs:
+            if os.path.isfile(os.path.join(d, 'libs', 'python', 'src',
+                                           'module.cpp')):
+                src_dir = d
+                break
+        if not src_dir:
+            return
+        py_incl_dirs = [sysconfig.get_path('include')]
+        py_pincl_dir = sysconfig.get_path('platinclude')
+        if py_pincl_dir not in py_incl_dirs:
+            py_incl_dirs.append(py_pincl_dir)
+        srcs_dir = os.path.join(src_dir, 'libs', 'python', 'src')
+        bpl_srcs = glob(os.path.join(srcs_dir, '*.cpp'))
+        bpl_srcs += glob(os.path.join(srcs_dir, '*', '*.cpp'))
+        info = {'libraries': [('boost_python_src',
+                               {'include_dirs': [src_dir] + py_incl_dirs,
+                                'sources':bpl_srcs}
+                              )],
+                'include_dirs': [src_dir],
+                }
+        if info:
+            self.set_info(**info)
+        return
+
+
+class agg2_info(system_info):
+    section = 'agg2'
+    dir_env_var = 'AGG2'
+
+    def get_paths(self, section, key):
+        pre_dirs = system_info.get_paths(self, section, key)
+        dirs = []
+        for d in pre_dirs:
+            dirs.extend([d] + self.combine_paths(d, ['agg2*']))
+        return [d for d in dirs if os.path.isdir(d)]
+
+    def calc_info(self):
+        src_dirs = self.get_src_dirs()
+        src_dir = ''
+        for d in src_dirs:
+            if os.path.isfile(os.path.join(d, 'src', 'agg_affine_matrix.cpp')):
+                src_dir = d
+                break
+        if not src_dir:
+            return
+        if sys.platform == 'win32':
+            agg2_srcs = glob(os.path.join(src_dir, 'src', 'platform',
+                                          'win32', 'agg_win32_bmp.cpp'))
+        else:
+            agg2_srcs = glob(os.path.join(src_dir, 'src', '*.cpp'))
+            agg2_srcs += [os.path.join(src_dir, 'src', 'platform',
+                                       'X11',
+                                       'agg_platform_support.cpp')]
+
+        info = {'libraries':
+                [('agg2_src',
+                  {'sources': agg2_srcs,
+                   'include_dirs': [os.path.join(src_dir, 'include')],
+                  }
+                 )],
+                'include_dirs': [os.path.join(src_dir, 'include')],
+                }
+        if info:
+            self.set_info(**info)
+        return
+
+
+class _pkg_config_info(system_info):
+    section = None
+    config_env_var = 'PKG_CONFIG'
+    default_config_exe = 'pkg-config'
+    append_config_exe = ''
+    version_macro_name = None
+    release_macro_name = None
+    version_flag = '--modversion'
+    cflags_flag = '--cflags'
+
+    def get_config_exe(self):
+        if self.config_env_var in os.environ:
+            return os.environ[self.config_env_var]
+        return self.default_config_exe
+
+    def get_config_output(self, config_exe, option):
+        cmd = config_exe + ' ' + self.append_config_exe + ' ' + option
+        try:
+            o = subprocess.check_output(cmd)
+        except (OSError, subprocess.CalledProcessError):
+            pass
+        else:
+            o = filepath_from_subprocess_output(o)
+            return o
+
+    def calc_info(self):
+        config_exe = find_executable(self.get_config_exe())
+        if not config_exe:
+            log.warn('File not found: %s. Cannot determine %s info.' \
+                  % (config_exe, self.section))
+            return
+        info = {}
+        macros = []
+        libraries = []
+        library_dirs = []
+        include_dirs = []
+        extra_link_args = []
+        extra_compile_args = []
+        version = self.get_config_output(config_exe, self.version_flag)
+        if version:
+            macros.append((self.__class__.__name__.split('.')[-1].upper(),
+                           _c_string_literal(version)))
+            if self.version_macro_name:
+                macros.append((self.version_macro_name + '_%s'
+                               % (version.replace('.', '_')), None))
+        if self.release_macro_name:
+            release = self.get_config_output(config_exe, '--release')
+            if release:
+                macros.append((self.release_macro_name + '_%s'
+                               % (release.replace('.', '_')), None))
+        opts = self.get_config_output(config_exe, '--libs')
+        if opts:
+            for opt in opts.split():
+                if opt[:2] == '-l':
+                    libraries.append(opt[2:])
+                elif opt[:2] == '-L':
+                    library_dirs.append(opt[2:])
+                else:
+                    extra_link_args.append(opt)
+        opts = self.get_config_output(config_exe, self.cflags_flag)
+        if opts:
+            for opt in opts.split():
+                if opt[:2] == '-I':
+                    include_dirs.append(opt[2:])
+                elif opt[:2] == '-D':
+                    if '=' in opt:
+                        n, v = opt[2:].split('=')
+                        macros.append((n, v))
+                    else:
+                        macros.append((opt[2:], None))
+                else:
+                    extra_compile_args.append(opt)
+        if macros:
+            dict_append(info, define_macros=macros)
+        if libraries:
+            dict_append(info, libraries=libraries)
+        if library_dirs:
+            dict_append(info, library_dirs=library_dirs)
+        if include_dirs:
+            dict_append(info, include_dirs=include_dirs)
+        if extra_link_args:
+            dict_append(info, extra_link_args=extra_link_args)
+        if extra_compile_args:
+            dict_append(info, extra_compile_args=extra_compile_args)
+        if info:
+            self.set_info(**info)
+        return
+
+
+class wx_info(_pkg_config_info):
+    section = 'wx'
+    config_env_var = 'WX_CONFIG'
+    default_config_exe = 'wx-config'
+    append_config_exe = ''
+    version_macro_name = 'WX_VERSION'
+    release_macro_name = 'WX_RELEASE'
+    version_flag = '--version'
+    cflags_flag = '--cxxflags'
+
+
+class gdk_pixbuf_xlib_2_info(_pkg_config_info):
+    section = 'gdk_pixbuf_xlib_2'
+    append_config_exe = 'gdk-pixbuf-xlib-2.0'
+    version_macro_name = 'GDK_PIXBUF_XLIB_VERSION'
+
+
+class gdk_pixbuf_2_info(_pkg_config_info):
+    section = 'gdk_pixbuf_2'
+    append_config_exe = 'gdk-pixbuf-2.0'
+    version_macro_name = 'GDK_PIXBUF_VERSION'
+
+
+class gdk_x11_2_info(_pkg_config_info):
+    section = 'gdk_x11_2'
+    append_config_exe = 'gdk-x11-2.0'
+    version_macro_name = 'GDK_X11_VERSION'
+
+
+class gdk_2_info(_pkg_config_info):
+    section = 'gdk_2'
+    append_config_exe = 'gdk-2.0'
+    version_macro_name = 'GDK_VERSION'
+
+
+class gdk_info(_pkg_config_info):
+    section = 'gdk'
+    append_config_exe = 'gdk'
+    version_macro_name = 'GDK_VERSION'
+
+
+class gtkp_x11_2_info(_pkg_config_info):
+    section = 'gtkp_x11_2'
+    append_config_exe = 'gtk+-x11-2.0'
+    version_macro_name = 'GTK_X11_VERSION'
+
+
+class gtkp_2_info(_pkg_config_info):
+    section = 'gtkp_2'
+    append_config_exe = 'gtk+-2.0'
+    version_macro_name = 'GTK_VERSION'
+
+
+class xft_info(_pkg_config_info):
+    section = 'xft'
+    append_config_exe = 'xft'
+    version_macro_name = 'XFT_VERSION'
+
+
+class freetype2_info(_pkg_config_info):
+    section = 'freetype2'
+    append_config_exe = 'freetype2'
+    version_macro_name = 'FREETYPE2_VERSION'
+
+
+class amd_info(system_info):
+    section = 'amd'
+    dir_env_var = 'AMD'
+    _lib_names = ['amd']
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+
+        opt = self.get_option_single('amd_libs', 'libraries')
+        amd_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs(lib_dirs, amd_libs, [])
+        if info is None:
+            return
+
+        include_dirs = self.get_include_dirs()
+
+        inc_dir = None
+        for d in include_dirs:
+            p = self.combine_paths(d, 'amd.h')
+            if p:
+                inc_dir = os.path.dirname(p[0])
+                break
+        if inc_dir is not None:
+            dict_append(info, include_dirs=[inc_dir],
+                        define_macros=[('SCIPY_AMD_H', None)],
+                        swig_opts=['-I' + inc_dir])
+
+        self.set_info(**info)
+        return
+
+
+class umfpack_info(system_info):
+    section = 'umfpack'
+    dir_env_var = 'UMFPACK'
+    notfounderror = UmfpackNotFoundError
+    _lib_names = ['umfpack']
+
+    def calc_info(self):
+        lib_dirs = self.get_lib_dirs()
+
+        opt = self.get_option_single('umfpack_libs', 'libraries')
+        umfpack_libs = self.get_libs(opt, self._lib_names)
+        info = self.check_libs(lib_dirs, umfpack_libs, [])
+        if info is None:
+            return
+
+        include_dirs = self.get_include_dirs()
+
+        inc_dir = None
+        for d in include_dirs:
+            p = self.combine_paths(d, ['', 'umfpack'], 'umfpack.h')
+            if p:
+                inc_dir = os.path.dirname(p[0])
+                break
+        if inc_dir is not None:
+            dict_append(info, include_dirs=[inc_dir],
+                        define_macros=[('SCIPY_UMFPACK_H', None)],
+                        swig_opts=['-I' + inc_dir])
+
+        dict_append(info, **get_info('amd'))
+
+        self.set_info(**info)
+        return
+
+
+def combine_paths(*args, **kws):
+    """ Return a list of existing paths composed by all combinations of
+        items from arguments.
+    """
+    r = []
+    for a in args:
+        if not a:
+            continue
+        if is_string(a):
+            a = [a]
+        r.append(a)
+    args = r
+    if not args:
+        return []
+    if len(args) == 1:
+        result = reduce(lambda a, b: a + b, map(glob, args[0]), [])
+    elif len(args) == 2:
+        result = []
+        for a0 in args[0]:
+            for a1 in args[1]:
+                result.extend(glob(os.path.join(a0, a1)))
+    else:
+        result = combine_paths(*(combine_paths(args[0], args[1]) + args[2:]))
+    log.debug('(paths: %s)', ','.join(result))
+    return result
+
+language_map = {'c': 0, 'c++': 1, 'f77': 2, 'f90': 3}
+inv_language_map = {0: 'c', 1: 'c++', 2: 'f77', 3: 'f90'}
+
+
+def dict_append(d, **kws):
+    languages = []
+    for k, v in kws.items():
+        if k == 'language':
+            languages.append(v)
+            continue
+        if k in d:
+            if k in ['library_dirs', 'include_dirs',
+                     'extra_compile_args', 'extra_link_args',
+                     'runtime_library_dirs', 'define_macros']:
+                [d[k].append(vv) for vv in v if vv not in d[k]]
+            else:
+                d[k].extend(v)
+        else:
+            d[k] = v
+    if languages:
+        l = inv_language_map[max([language_map.get(l, 0) for l in languages])]
+        d['language'] = l
+    return
+
+
+def parseCmdLine(argv=(None,)):
+    import optparse
+    parser = optparse.OptionParser("usage: %prog [-v] [info objs]")
+    parser.add_option('-v', '--verbose', action='store_true', dest='verbose',
+                      default=False,
+                      help='be verbose and print more messages')
+
+    opts, args = parser.parse_args(args=argv[1:])
+    return opts, args
+
+
+def show_all(argv=None):
+    import inspect
+    if argv is None:
+        argv = sys.argv
+    opts, args = parseCmdLine(argv)
+    if opts.verbose:
+        log.set_threshold(log.DEBUG)
+    else:
+        log.set_threshold(log.INFO)
+    show_only = []
+    for n in args:
+        if n[-5:] != '_info':
+            n = n + '_info'
+        show_only.append(n)
+    show_all = not show_only
+    _gdict_ = globals().copy()
+    for name, c in _gdict_.items():
+        if not inspect.isclass(c):
+            continue
+        if not issubclass(c, system_info) or c is system_info:
+            continue
+        if not show_all:
+            if name not in show_only:
+                continue
+            del show_only[show_only.index(name)]
+        conf = c()
+        conf.verbosity = 2
+        # we don't need the result, but we want
+        # the side effect of printing diagnostics
+        conf.get_info()
+    if show_only:
+        log.info('Info classes not defined: %s', ','.join(show_only))
+
+if __name__ == "__main__":
+    show_all()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_build_ext.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..7124cc407a2f5eef3d67d1eea594fcf9f596f8d2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_build_ext.py
@@ -0,0 +1,74 @@
+'''Tests for numpy.distutils.build_ext.'''
+
+import os
+import subprocess
+import sys
+from textwrap import indent, dedent
+import pytest
+from numpy.testing import IS_WASM
+
+@pytest.mark.skipif(IS_WASM, reason="cannot start subprocess in wasm")
+@pytest.mark.slow
+def test_multi_fortran_libs_link(tmp_path):
+    '''
+    Ensures multiple "fake" static libraries are correctly linked.
+    see gh-18295
+    '''
+
+    # We need to make sure we actually have an f77 compiler.
+    # This is nontrivial, so we'll borrow the utilities
+    # from f2py tests:
+    from numpy.distutils.tests.utilities import has_f77_compiler
+    if not has_f77_compiler():
+        pytest.skip('No F77 compiler found')
+
+    # make some dummy sources
+    with open(tmp_path / '_dummy1.f', 'w') as fid:
+        fid.write(indent(dedent('''\
+            FUNCTION dummy_one()
+            RETURN
+            END FUNCTION'''), prefix=' '*6))
+    with open(tmp_path / '_dummy2.f', 'w') as fid:
+        fid.write(indent(dedent('''\
+            FUNCTION dummy_two()
+            RETURN
+            END FUNCTION'''), prefix=' '*6))
+    with open(tmp_path / '_dummy.c', 'w') as fid:
+        # doesn't need to load - just needs to exist
+        fid.write('int PyInit_dummyext;')
+
+    # make a setup file
+    with open(tmp_path / 'setup.py', 'w') as fid:
+        srctree = os.path.join(os.path.dirname(__file__), '..', '..', '..')
+        fid.write(dedent(f'''\
+            def configuration(parent_package="", top_path=None):
+                from numpy.distutils.misc_util import Configuration
+                config = Configuration("", parent_package, top_path)
+                config.add_library("dummy1", sources=["_dummy1.f"])
+                config.add_library("dummy2", sources=["_dummy2.f"])
+                config.add_extension("dummyext", sources=["_dummy.c"], libraries=["dummy1", "dummy2"])
+                return config
+
+
+            if __name__ == "__main__":
+                import sys
+                sys.path.insert(0, r"{srctree}")
+                from numpy.distutils.core import setup
+                setup(**configuration(top_path="").todict())'''))
+
+    # build the test extension and "install" into a temporary directory
+    build_dir = tmp_path
+    subprocess.check_call([sys.executable, 'setup.py', 'build', 'install',
+                           '--prefix', str(tmp_path / 'installdir'),
+                           '--record', str(tmp_path / 'tmp_install_log.txt'),
+                          ],
+                          cwd=str(build_dir),
+                      )
+    # get the path to the so
+    so = None
+    with open(tmp_path /'tmp_install_log.txt') as fid:
+        for line in fid:
+            if 'dummyext' in line:
+                so = line.strip()
+                break
+    assert so is not None
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..3714aea0e12e05ac8346e51d169664e9c62f4293
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt.py
@@ -0,0 +1,808 @@
+import re, textwrap, os
+from os import sys, path
+from distutils.errors import DistutilsError
+
+is_standalone = __name__ == '__main__' and __package__ is None
+if is_standalone:
+    import unittest, contextlib, tempfile, shutil
+    sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
+    from ccompiler_opt import CCompilerOpt
+
+    # from numpy/testing/_private/utils.py
+    @contextlib.contextmanager
+    def tempdir(*args, **kwargs):
+        tmpdir = tempfile.mkdtemp(*args, **kwargs)
+        try:
+            yield tmpdir
+        finally:
+            shutil.rmtree(tmpdir)
+
+    def assert_(expr, msg=''):
+        if not expr:
+            raise AssertionError(msg)
+else:
+    from numpy.distutils.ccompiler_opt import CCompilerOpt
+    from numpy.testing import assert_, tempdir
+
+# architectures and compilers to test
+arch_compilers = dict(
+    x86 = ("gcc", "clang", "icc", "iccw", "msvc"),
+    x64 = ("gcc", "clang", "icc", "iccw", "msvc"),
+    ppc64 = ("gcc", "clang"),
+    ppc64le = ("gcc", "clang"),
+    armhf = ("gcc", "clang"),
+    aarch64 = ("gcc", "clang", "fcc"),
+    s390x = ("gcc", "clang"),
+    noarch = ("gcc",)
+)
+
+class FakeCCompilerOpt(CCompilerOpt):
+    fake_info = ""
+    def __init__(self, trap_files="", trap_flags="", *args, **kwargs):
+        self.fake_trap_files = trap_files
+        self.fake_trap_flags = trap_flags
+        CCompilerOpt.__init__(self, None, **kwargs)
+
+    def __repr__(self):
+        return textwrap.dedent("""\
+            <<<<
+            march    : {}
+            compiler : {}
+            ----------------
+            {}
+            >>>>
+        """).format(self.cc_march, self.cc_name, self.report())
+
+    def dist_compile(self, sources, flags, **kwargs):
+        assert(isinstance(sources, list))
+        assert(isinstance(flags, list))
+        if self.fake_trap_files:
+            for src in sources:
+                if re.match(self.fake_trap_files, src):
+                    self.dist_error("source is trapped by a fake interface")
+        if self.fake_trap_flags:
+            for f in flags:
+                if re.match(self.fake_trap_flags, f):
+                    self.dist_error("flag is trapped by a fake interface")
+        # fake objects
+        return zip(sources, [' '.join(flags)] * len(sources))
+
+    def dist_info(self):
+        return FakeCCompilerOpt.fake_info
+
+    @staticmethod
+    def dist_log(*args, stderr=False):
+        pass
+
+class _Test_CCompilerOpt:
+    arch = None # x86_64
+    cc   = None # gcc
+
+    def setup_class(self):
+        FakeCCompilerOpt.conf_nocache = True
+        self._opt = None
+
+    def nopt(self, *args, **kwargs):
+        FakeCCompilerOpt.fake_info = (self.arch, self.cc, "")
+        return FakeCCompilerOpt(*args, **kwargs)
+
+    def opt(self):
+        if not self._opt:
+            self._opt = self.nopt()
+        return self._opt
+
+    def march(self):
+        return self.opt().cc_march
+
+    def cc_name(self):
+        return self.opt().cc_name
+
+    def get_targets(self, targets, groups, **kwargs):
+        FakeCCompilerOpt.conf_target_groups = groups
+        opt = self.nopt(
+            cpu_baseline=kwargs.get("baseline", "min"),
+            cpu_dispatch=kwargs.get("dispatch", "max"),
+            trap_files=kwargs.get("trap_files", ""),
+            trap_flags=kwargs.get("trap_flags", "")
+        )
+        with tempdir() as tmpdir:
+            file = os.path.join(tmpdir, "test_targets.c")
+            with open(file, 'w') as f:
+                f.write(targets)
+            gtargets = []
+            gflags = {}
+            fake_objects = opt.try_dispatch([file])
+            for source, flags in fake_objects:
+                gtar = path.basename(source).split('.')[1:-1]
+                glen = len(gtar)
+                if glen == 0:
+                    gtar = "baseline"
+                elif glen == 1:
+                    gtar = gtar[0].upper()
+                else:
+                    # converting multi-target into parentheses str format to be equivalent
+                    # to the configuration statements syntax.
+                    gtar = ('('+' '.join(gtar)+')').upper()
+                gtargets.append(gtar)
+                gflags[gtar] = flags
+
+        has_baseline, targets = opt.sources_status[file]
+        targets = targets + ["baseline"] if has_baseline else targets
+        # convert tuple that represent multi-target into parentheses str format
+        targets = [
+            '('+' '.join(tar)+')' if isinstance(tar, tuple) else tar
+            for tar in targets
+        ]
+        if len(targets) != len(gtargets) or not all(t in gtargets for t in targets):
+            raise AssertionError(
+                "'sources_status' returns different targets than the compiled targets\n"
+                "%s != %s" % (targets, gtargets)
+            )
+        # return targets from 'sources_status' since the order is matters
+        return targets, gflags
+
+    def arg_regex(self, **kwargs):
+        map2origin = dict(
+            x64 = "x86",
+            ppc64le = "ppc64",
+            aarch64 = "armhf",
+            clang = "gcc",
+        )
+        march = self.march(); cc_name = self.cc_name()
+        map_march = map2origin.get(march, march)
+        map_cc = map2origin.get(cc_name, cc_name)
+        for key in (
+            march, cc_name, map_march, map_cc,
+            march + '_' + cc_name,
+            map_march + '_' + cc_name,
+            march + '_' + map_cc,
+            map_march + '_' + map_cc,
+        ) :
+            regex = kwargs.pop(key, None)
+            if regex is not None:
+                break
+        if regex:
+            if isinstance(regex, dict):
+                for k, v in regex.items():
+                    if v[-1:] not in ')}$?\\.+*':
+                        regex[k] = v + '$'
+            else:
+                assert(isinstance(regex, str))
+                if regex[-1:] not in ')}$?\\.+*':
+                    regex += '$'
+        return regex
+
+    def expect(self, dispatch, baseline="", **kwargs):
+        match = self.arg_regex(**kwargs)
+        if match is None:
+            return
+        opt = self.nopt(
+            cpu_baseline=baseline, cpu_dispatch=dispatch,
+            trap_files=kwargs.get("trap_files", ""),
+            trap_flags=kwargs.get("trap_flags", "")
+        )
+        features = ' '.join(opt.cpu_dispatch_names())
+        if not match:
+            if len(features) != 0:
+                raise AssertionError(
+                    'expected empty features, not "%s"' % features
+                )
+            return
+        if not re.match(match, features, re.IGNORECASE):
+            raise AssertionError(
+                'dispatch features "%s" not match "%s"' % (features, match)
+            )
+
+    def expect_baseline(self, baseline, dispatch="", **kwargs):
+        match = self.arg_regex(**kwargs)
+        if match is None:
+            return
+        opt = self.nopt(
+            cpu_baseline=baseline, cpu_dispatch=dispatch,
+            trap_files=kwargs.get("trap_files", ""),
+            trap_flags=kwargs.get("trap_flags", "")
+        )
+        features = ' '.join(opt.cpu_baseline_names())
+        if not match:
+            if len(features) != 0:
+                raise AssertionError(
+                    'expected empty features, not "%s"' % features
+                )
+            return
+        if not re.match(match, features, re.IGNORECASE):
+            raise AssertionError(
+                'baseline features "%s" not match "%s"' % (features, match)
+            )
+
+    def expect_flags(self, baseline, dispatch="", **kwargs):
+        match = self.arg_regex(**kwargs)
+        if match is None:
+            return
+        opt = self.nopt(
+            cpu_baseline=baseline, cpu_dispatch=dispatch,
+            trap_files=kwargs.get("trap_files", ""),
+            trap_flags=kwargs.get("trap_flags", "")
+        )
+        flags = ' '.join(opt.cpu_baseline_flags())
+        if not match:
+            if len(flags) != 0:
+                raise AssertionError(
+                    'expected empty flags not "%s"' % flags
+                )
+            return
+        if not re.match(match, flags):
+            raise AssertionError(
+                'flags "%s" not match "%s"' % (flags, match)
+            )
+
+    def expect_targets(self, targets, groups={}, **kwargs):
+        match = self.arg_regex(**kwargs)
+        if match is None:
+            return
+        targets, _ = self.get_targets(targets=targets, groups=groups, **kwargs)
+        targets = ' '.join(targets)
+        if not match:
+            if len(targets) != 0:
+                raise AssertionError(
+                    'expected empty targets, not "%s"' % targets
+                )
+            return
+        if not re.match(match, targets, re.IGNORECASE):
+            raise AssertionError(
+                'targets "%s" not match "%s"' % (targets, match)
+            )
+
+    def expect_target_flags(self, targets, groups={}, **kwargs):
+        match_dict = self.arg_regex(**kwargs)
+        if match_dict is None:
+            return
+        assert(isinstance(match_dict, dict))
+        _, tar_flags = self.get_targets(targets=targets, groups=groups)
+
+        for match_tar, match_flags in match_dict.items():
+            if match_tar not in tar_flags:
+                raise AssertionError(
+                    'expected to find target "%s"' % match_tar
+                )
+            flags = tar_flags[match_tar]
+            if not match_flags:
+                if len(flags) != 0:
+                    raise AssertionError(
+                        'expected to find empty flags in target "%s"' % match_tar
+                    )
+            if not re.match(match_flags, flags):
+                raise AssertionError(
+                    '"%s" flags "%s" not match "%s"' % (match_tar, flags, match_flags)
+                )
+
+    def test_interface(self):
+        wrong_arch = "ppc64" if self.arch != "ppc64" else "x86"
+        wrong_cc   = "clang" if self.cc   != "clang" else "icc"
+        opt = self.opt()
+        assert_(getattr(opt, "cc_on_" + self.arch))
+        assert_(not getattr(opt, "cc_on_" + wrong_arch))
+        assert_(getattr(opt, "cc_is_" + self.cc))
+        assert_(not getattr(opt, "cc_is_" + wrong_cc))
+
+    def test_args_empty(self):
+        for baseline, dispatch in (
+            ("", "none"),
+            (None, ""),
+            ("none +none", "none - none"),
+            ("none -max", "min - max"),
+            ("+vsx2 -VSX2", "vsx avx2 avx512f -max"),
+            ("max -vsx - avx + avx512f neon -MAX ",
+             "min -min + max -max -vsx + avx2 -avx2 +NONE")
+        ) :
+            opt = self.nopt(cpu_baseline=baseline, cpu_dispatch=dispatch)
+            assert(len(opt.cpu_baseline_names()) == 0)
+            assert(len(opt.cpu_dispatch_names()) == 0)
+
+    def test_args_validation(self):
+        if self.march() == "unknown":
+            return
+        # check sanity of argument's validation
+        for baseline, dispatch in (
+            ("unkown_feature - max +min", "unknown max min"), # unknowing features
+            ("#avx2", "$vsx") # groups and polices aren't acceptable
+        ) :
+            try:
+                self.nopt(cpu_baseline=baseline, cpu_dispatch=dispatch)
+                raise AssertionError("excepted an exception for invalid arguments")
+            except DistutilsError:
+                pass
+
+    def test_skip(self):
+        # only takes what platform supports and skip the others
+        # without casing exceptions
+        self.expect(
+            "sse vsx neon",
+            x86="sse", ppc64="vsx", armhf="neon", unknown=""
+        )
+        self.expect(
+            "sse41 avx avx2 vsx2 vsx3 neon_vfpv4 asimd",
+            x86   = "sse41 avx avx2",
+            ppc64 = "vsx2 vsx3",
+            armhf = "neon_vfpv4 asimd",
+            unknown = ""
+        )
+        # any features in cpu_dispatch must be ignored if it's part of baseline
+        self.expect(
+            "sse neon vsx", baseline="sse neon vsx",
+            x86="", ppc64="", armhf=""
+        )
+        self.expect(
+            "avx2 vsx3 asimdhp", baseline="avx2 vsx3 asimdhp",
+            x86="", ppc64="", armhf=""
+        )
+
+    def test_implies(self):
+        # baseline combining implied features, so we count
+        # on it instead of testing 'feature_implies()'' directly
+        self.expect_baseline(
+            "fma3 avx2 asimd vsx3",
+            # .* between two spaces can validate features in between
+            x86   = "sse .* sse41 .* fma3.*avx2",
+            ppc64 = "vsx vsx2 vsx3",
+            armhf = "neon neon_fp16 neon_vfpv4 asimd"
+        )
+        """
+        special cases
+        """
+        # in icc and msvc, FMA3 and AVX2 can't be separated
+        # both need to implies each other, same for avx512f & cd
+        for f0, f1 in (
+            ("fma3",    "avx2"),
+            ("avx512f", "avx512cd"),
+        ):
+            diff = ".* sse42 .* %s .*%s$" % (f0, f1)
+            self.expect_baseline(f0,
+                x86_gcc=".* sse42 .* %s$" % f0,
+                x86_icc=diff, x86_iccw=diff
+            )
+            self.expect_baseline(f1,
+                x86_gcc=".* avx .* %s$" % f1,
+                x86_icc=diff, x86_iccw=diff
+            )
+        # in msvc, following features can't be separated too
+        for f in (("fma3", "avx2"), ("avx512f", "avx512cd", "avx512_skx")):
+            for ff in f:
+                self.expect_baseline(ff,
+                    x86_msvc=".*%s" % ' '.join(f)
+                )
+
+        # in ppc64le VSX and VSX2 can't be separated
+        self.expect_baseline("vsx", ppc64le="vsx vsx2")
+        # in aarch64 following features can't be separated
+        for f in ("neon", "neon_fp16", "neon_vfpv4", "asimd"):
+            self.expect_baseline(f, aarch64="neon neon_fp16 neon_vfpv4 asimd")
+
+    def test_args_options(self):
+        # max & native
+        for o in ("max", "native"):
+            if o == "native" and self.cc_name() == "msvc":
+                continue
+            self.expect(o,
+                trap_files=".*cpu_(sse|vsx|neon|vx).c",
+                x86="", ppc64="", armhf="", s390x=""
+            )
+            self.expect(o,
+                trap_files=".*cpu_(sse3|vsx2|neon_vfpv4|vxe).c",
+                x86="sse sse2", ppc64="vsx", armhf="neon neon_fp16",
+                aarch64="", ppc64le="", s390x="vx"
+            )
+            self.expect(o,
+                trap_files=".*cpu_(popcnt|vsx3).c",
+                x86="sse .* sse41", ppc64="vsx vsx2",
+                armhf="neon neon_fp16 .* asimd .*",
+                s390x="vx vxe vxe2"
+            )
+            self.expect(o,
+                x86_gcc=".* xop fma4 .* avx512f .* avx512_knl avx512_knm avx512_skx .*",
+                # in icc, xop and fam4 aren't supported
+                x86_icc=".* avx512f .* avx512_knl avx512_knm avx512_skx .*",
+                x86_iccw=".* avx512f .* avx512_knl avx512_knm avx512_skx .*",
+                # in msvc, avx512_knl avx512_knm aren't supported
+                x86_msvc=".* xop fma4 .* avx512f .* avx512_skx .*",
+                armhf=".* asimd asimdhp asimddp .*",
+                ppc64="vsx vsx2 vsx3 vsx4.*",
+                s390x="vx vxe vxe2.*"
+            )
+        # min
+        self.expect("min",
+            x86="sse sse2", x64="sse sse2 sse3",
+            armhf="", aarch64="neon neon_fp16 .* asimd",
+            ppc64="", ppc64le="vsx vsx2", s390x=""
+        )
+        self.expect(
+            "min", trap_files=".*cpu_(sse2|vsx2).c",
+            x86="", ppc64le=""
+        )
+        # an exception must triggered if native flag isn't supported
+        # when option "native" is activated through the args
+        try:
+            self.expect("native",
+                trap_flags=".*(-march=native|-xHost|/QxHost|-mcpu=a64fx).*",
+                x86=".*", ppc64=".*", armhf=".*", s390x=".*", aarch64=".*",
+            )
+            if self.march() != "unknown":
+                raise AssertionError(
+                    "excepted an exception for %s" % self.march()
+                )
+        except DistutilsError:
+            if self.march() == "unknown":
+                raise AssertionError("excepted no exceptions")
+
+    def test_flags(self):
+        self.expect_flags(
+            "sse sse2 vsx vsx2 neon neon_fp16 vx vxe",
+            x86_gcc="-msse -msse2", x86_icc="-msse -msse2",
+            x86_iccw="/arch:SSE2",
+            x86_msvc="/arch:SSE2" if self.march() == "x86" else "",
+            ppc64_gcc= "-mcpu=power8",
+            ppc64_clang="-mcpu=power8",
+            armhf_gcc="-mfpu=neon-fp16 -mfp16-format=ieee",
+            aarch64="",
+            s390x="-mzvector -march=arch12"
+        )
+        # testing normalize -march
+        self.expect_flags(
+            "asimd",
+            aarch64="",
+            armhf_gcc=r"-mfp16-format=ieee -mfpu=neon-fp-armv8 -march=armv8-a\+simd"
+        )
+        self.expect_flags(
+            "asimdhp",
+            aarch64_gcc=r"-march=armv8.2-a\+fp16",
+            armhf_gcc=r"-mfp16-format=ieee -mfpu=neon-fp-armv8 -march=armv8.2-a\+fp16"
+        )
+        self.expect_flags(
+            "asimddp", aarch64_gcc=r"-march=armv8.2-a\+dotprod"
+        )
+        self.expect_flags(
+            # asimdfhm implies asimdhp
+            "asimdfhm", aarch64_gcc=r"-march=armv8.2-a\+fp16\+fp16fml"
+        )
+        self.expect_flags(
+            "asimddp asimdhp asimdfhm",
+            aarch64_gcc=r"-march=armv8.2-a\+dotprod\+fp16\+fp16fml"
+        )
+        self.expect_flags(
+            "vx vxe vxe2",
+            s390x=r"-mzvector -march=arch13"
+        )
+
+    def test_targets_exceptions(self):
+        for targets in (
+            "bla bla", "/*@targets",
+            "/*@targets */",
+            "/*@targets unknown */",
+            "/*@targets $unknown_policy avx2 */",
+            "/*@targets #unknown_group avx2 */",
+            "/*@targets $ */",
+            "/*@targets # vsx */",
+            "/*@targets #$ vsx */",
+            "/*@targets vsx avx2 ) */",
+            "/*@targets vsx avx2 (avx2 */",
+            "/*@targets vsx avx2 () */",
+            "/*@targets vsx avx2 ($autovec) */", # no features
+            "/*@targets vsx avx2 (xxx) */",
+            "/*@targets vsx avx2 (baseline) */",
+        ) :
+            try:
+                self.expect_targets(
+                    targets,
+                    x86="", armhf="", ppc64="", s390x=""
+                )
+                if self.march() != "unknown":
+                    raise AssertionError(
+                        "excepted an exception for %s" % self.march()
+                    )
+            except DistutilsError:
+                if self.march() == "unknown":
+                    raise AssertionError("excepted no exceptions")
+
+    def test_targets_syntax(self):
+        for targets in (
+            "/*@targets $keep_baseline sse vsx neon vx*/",
+            "/*@targets,$keep_baseline,sse,vsx,neon vx*/",
+            "/*@targets*$keep_baseline*sse*vsx*neon*vx*/",
+            """
+            /*
+            ** @targets
+            ** $keep_baseline, sse vsx,neon, vx
+            */
+            """,
+            """
+            /*
+            ************@targets****************
+            ** $keep_baseline, sse vsx, neon, vx
+            ************************************
+            */
+            """,
+            """
+            /*
+            /////////////@targets/////////////////
+            //$keep_baseline//sse//vsx//neon//vx
+            /////////////////////////////////////
+            */
+            """,
+            """
+            /*
+            @targets
+            $keep_baseline
+            SSE VSX NEON VX*/
+            """
+        ) :
+            self.expect_targets(targets,
+                x86="sse", ppc64="vsx", armhf="neon", s390x="vx", unknown=""
+            )
+
+    def test_targets(self):
+        # test skipping baseline features
+        self.expect_targets(
+            """
+            /*@targets
+                sse sse2 sse41 avx avx2 avx512f
+                vsx vsx2 vsx3 vsx4
+                neon neon_fp16 asimdhp asimddp
+                vx vxe vxe2
+            */
+            """,
+            baseline="avx vsx2 asimd vx vxe",
+            x86="avx512f avx2", armhf="asimddp asimdhp", ppc64="vsx4 vsx3",
+            s390x="vxe2"
+        )
+        # test skipping non-dispatch features
+        self.expect_targets(
+            """
+            /*@targets
+                sse41 avx avx2 avx512f
+                vsx2 vsx3 vsx4
+                asimd asimdhp asimddp
+                vx vxe vxe2
+            */
+            """,
+            baseline="", dispatch="sse41 avx2 vsx2 asimd asimddp vxe2",
+            x86="avx2 sse41", armhf="asimddp asimd", ppc64="vsx2", s390x="vxe2"
+        )
+        # test skipping features that not supported
+        self.expect_targets(
+            """
+            /*@targets
+                sse2 sse41 avx2 avx512f
+                vsx2 vsx3 vsx4
+                neon asimdhp asimddp
+                vx vxe vxe2
+            */
+            """,
+            baseline="",
+            trap_files=".*(avx2|avx512f|vsx3|vsx4|asimddp|vxe2).c",
+            x86="sse41 sse2", ppc64="vsx2", armhf="asimdhp neon",
+            s390x="vxe vx"
+        )
+        # test skipping features that implies each other
+        self.expect_targets(
+            """
+            /*@targets
+                sse sse2 avx fma3 avx2 avx512f avx512cd
+                vsx vsx2 vsx3
+                neon neon_vfpv4 neon_fp16 neon_fp16 asimd asimdhp
+                asimddp asimdfhm
+            */
+            """,
+            baseline="",
+            x86_gcc="avx512cd avx512f avx2 fma3 avx sse2",
+            x86_msvc="avx512cd avx2 avx sse2",
+            x86_icc="avx512cd avx2 avx sse2",
+            x86_iccw="avx512cd avx2 avx sse2",
+            ppc64="vsx3 vsx2 vsx",
+            ppc64le="vsx3 vsx2",
+            armhf="asimdfhm asimddp asimdhp asimd neon_vfpv4 neon_fp16 neon",
+            aarch64="asimdfhm asimddp asimdhp asimd"
+        )
+
+    def test_targets_policies(self):
+        # 'keep_baseline', generate objects for baseline features
+        self.expect_targets(
+            """
+            /*@targets
+                $keep_baseline
+                sse2 sse42 avx2 avx512f
+                vsx2 vsx3
+                neon neon_vfpv4 asimd asimddp
+                vx vxe vxe2
+            */
+            """,
+            baseline="sse41 avx2 vsx2 asimd vsx3 vxe",
+            x86="avx512f avx2 sse42 sse2",
+            ppc64="vsx3 vsx2",
+            armhf="asimddp asimd neon_vfpv4 neon",
+            # neon, neon_vfpv4, asimd implies each other
+            aarch64="asimddp asimd",
+            s390x="vxe2 vxe vx"
+        )
+        # 'keep_sort', leave the sort as-is
+        self.expect_targets(
+            """
+            /*@targets
+                $keep_baseline $keep_sort
+                avx512f sse42 avx2 sse2
+                vsx2 vsx3
+                asimd neon neon_vfpv4 asimddp
+                vxe vxe2
+            */
+            """,
+            x86="avx512f sse42 avx2 sse2",
+            ppc64="vsx2 vsx3",
+            armhf="asimd neon neon_vfpv4 asimddp",
+            # neon, neon_vfpv4, asimd implies each other
+            aarch64="asimd asimddp",
+            s390x="vxe vxe2"
+        )
+        # 'autovec', skipping features that can't be
+        # vectorized by the compiler
+        self.expect_targets(
+            """
+            /*@targets
+                $keep_baseline $keep_sort $autovec
+                avx512f avx2 sse42 sse41 sse2
+                vsx3 vsx2
+                asimddp asimd neon_vfpv4 neon
+            */
+            """,
+            x86_gcc="avx512f avx2 sse42 sse41 sse2",
+            x86_icc="avx512f avx2 sse42 sse41 sse2",
+            x86_iccw="avx512f avx2 sse42 sse41 sse2",
+            x86_msvc="avx512f avx2 sse2"
+                     if self.march() == 'x86' else "avx512f avx2",
+            ppc64="vsx3 vsx2",
+            armhf="asimddp asimd neon_vfpv4 neon",
+            # neon, neon_vfpv4, asimd implies each other
+            aarch64="asimddp asimd"
+        )
+        for policy in ("$maxopt", "$autovec"):
+            # 'maxopt' and autovec set the max acceptable optimization flags
+            self.expect_target_flags(
+                "/*@targets baseline %s */" % policy,
+                gcc={"baseline":".*-O3.*"}, icc={"baseline":".*-O3.*"},
+                iccw={"baseline":".*/O3.*"}, msvc={"baseline":".*/O2.*"},
+                unknown={"baseline":".*"}
+            )
+
+        # 'werror', force compilers to treat warnings as errors
+        self.expect_target_flags(
+            "/*@targets baseline $werror */",
+            gcc={"baseline":".*-Werror.*"}, icc={"baseline":".*-Werror.*"},
+            iccw={"baseline":".*/Werror.*"}, msvc={"baseline":".*/WX.*"},
+            unknown={"baseline":".*"}
+        )
+
+    def test_targets_groups(self):
+        self.expect_targets(
+            """
+            /*@targets $keep_baseline baseline #test_group */
+            """,
+            groups=dict(
+                test_group=("""
+                    $keep_baseline
+                    asimddp sse2 vsx2 avx2 vsx3
+                    avx512f asimdhp
+                """)
+            ),
+            x86="avx512f avx2 sse2 baseline",
+            ppc64="vsx3 vsx2 baseline",
+            armhf="asimddp asimdhp baseline"
+        )
+        # test skip duplicating and sorting
+        self.expect_targets(
+            """
+            /*@targets
+             * sse42 avx avx512f
+             * #test_group_1
+             * vsx2
+             * #test_group_2
+             * asimddp asimdfhm
+            */
+            """,
+            groups=dict(
+                test_group_1=("""
+                    VSX2 vsx3 asimd avx2 SSE41
+                """),
+                test_group_2=("""
+                    vsx2 vsx3 asImd aVx2 sse41
+                """)
+            ),
+            x86="avx512f avx2 avx sse42 sse41",
+            ppc64="vsx3 vsx2",
+            # vsx2 part of the default baseline of ppc64le, option ("min")
+            ppc64le="vsx3",
+            armhf="asimdfhm asimddp asimd",
+            # asimd part of the default baseline of aarch64, option ("min")
+            aarch64="asimdfhm asimddp"
+        )
+
+    def test_targets_multi(self):
+        self.expect_targets(
+            """
+            /*@targets
+                (avx512_clx avx512_cnl) (asimdhp asimddp)
+            */
+            """,
+            x86=r"\(avx512_clx avx512_cnl\)",
+            armhf=r"\(asimdhp asimddp\)",
+        )
+        # test skipping implied features and auto-sort
+        self.expect_targets(
+            """
+            /*@targets
+                f16c (sse41 avx sse42) (sse3 avx2 avx512f)
+                vsx2 (vsx vsx3 vsx2)
+                (neon neon_vfpv4 asimd asimdhp asimddp)
+            */
+            """,
+            x86="avx512f f16c avx",
+            ppc64="vsx3 vsx2",
+            ppc64le="vsx3", # vsx2 part of baseline
+            armhf=r"\(asimdhp asimddp\)",
+        )
+        # test skipping implied features and keep sort
+        self.expect_targets(
+            """
+            /*@targets $keep_sort
+                (sse41 avx sse42) (sse3 avx2 avx512f)
+                (vsx vsx3 vsx2)
+                (asimddp neon neon_vfpv4 asimd asimdhp)
+                (vx vxe vxe2)
+            */
+            """,
+            x86="avx avx512f",
+            ppc64="vsx3",
+            armhf=r"\(asimdhp asimddp\)",
+            s390x="vxe2"
+        )
+        # test compiler variety and avoiding duplicating
+        self.expect_targets(
+            """
+            /*@targets $keep_sort
+                fma3 avx2 (fma3 avx2) (avx2 fma3) avx2 fma3
+            */
+            """,
+            x86_gcc=r"fma3 avx2 \(fma3 avx2\)",
+            x86_icc="avx2", x86_iccw="avx2",
+            x86_msvc="avx2"
+        )
+
+def new_test(arch, cc):
+    if is_standalone: return textwrap.dedent("""\
+    class TestCCompilerOpt_{class_name}(_Test_CCompilerOpt, unittest.TestCase):
+        arch = '{arch}'
+        cc   = '{cc}'
+        def __init__(self, methodName="runTest"):
+            unittest.TestCase.__init__(self, methodName)
+            self.setup_class()
+    """).format(
+        class_name=arch + '_' + cc, arch=arch, cc=cc
+    )
+    return textwrap.dedent("""\
+    class TestCCompilerOpt_{class_name}(_Test_CCompilerOpt):
+        arch = '{arch}'
+        cc   = '{cc}'
+    """).format(
+        class_name=arch + '_' + cc, arch=arch, cc=cc
+    )
+"""
+if 1 and is_standalone:
+    FakeCCompilerOpt.fake_info = "x86_icc"
+    cco = FakeCCompilerOpt(None, cpu_baseline="avx2")
+    print(' '.join(cco.cpu_baseline_names()))
+    print(cco.cpu_baseline_flags())
+    unittest.main()
+    sys.exit()
+"""
+for arch, compilers in arch_compilers.items():
+    for cc in compilers:
+        exec(new_test(arch, cc))
+
+if is_standalone:
+    unittest.main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt_conf.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt_conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e8b2b0a8342237b0efd2cc116827a451177fa3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_ccompiler_opt_conf.py
@@ -0,0 +1,176 @@
+import unittest
+from os import sys, path
+
+is_standalone = __name__ == '__main__' and __package__ is None
+if is_standalone:
+    sys.path.append(path.abspath(path.join(path.dirname(__file__), "..")))
+    from ccompiler_opt import CCompilerOpt
+else:
+    from numpy.distutils.ccompiler_opt import CCompilerOpt
+
+arch_compilers = dict(
+    x86 = ("gcc", "clang", "icc", "iccw", "msvc"),
+    x64 = ("gcc", "clang", "icc", "iccw", "msvc"),
+    ppc64 = ("gcc", "clang"),
+    ppc64le = ("gcc", "clang"),
+    armhf = ("gcc", "clang"),
+    aarch64 = ("gcc", "clang"),
+    narch = ("gcc",)
+)
+
+class FakeCCompilerOpt(CCompilerOpt):
+    fake_info = ("arch", "compiler", "extra_args")
+    def __init__(self, *args, **kwargs):
+        CCompilerOpt.__init__(self, None, **kwargs)
+    def dist_compile(self, sources, flags, **kwargs):
+        return sources
+    def dist_info(self):
+        return FakeCCompilerOpt.fake_info
+    @staticmethod
+    def dist_log(*args, stderr=False):
+        pass
+
+class _TestConfFeatures(FakeCCompilerOpt):
+    """A hook to check the sanity of configured features
+-   before it called by the abstract class '_Feature'
+    """
+
+    def conf_features_partial(self):
+        conf_all = self.conf_features
+        for feature_name, feature in conf_all.items():
+            self.test_feature(
+                "attribute conf_features",
+                conf_all, feature_name, feature
+            )
+
+        conf_partial = FakeCCompilerOpt.conf_features_partial(self)
+        for feature_name, feature in conf_partial.items():
+            self.test_feature(
+                "conf_features_partial()",
+                conf_partial, feature_name, feature
+            )
+        return conf_partial
+
+    def test_feature(self, log, search_in, feature_name, feature_dict):
+        error_msg = (
+            "during validate '{}' within feature '{}', "
+            "march '{}' and compiler '{}'\n>> "
+        ).format(log, feature_name, self.cc_march, self.cc_name)
+
+        if not feature_name.isupper():
+            raise AssertionError(error_msg + "feature name must be in uppercase")
+
+        for option, val in feature_dict.items():
+            self.test_option_types(error_msg, option, val)
+            self.test_duplicates(error_msg, option, val)
+
+        self.test_implies(error_msg, search_in, feature_name, feature_dict)
+        self.test_group(error_msg, search_in, feature_name, feature_dict)
+        self.test_extra_checks(error_msg, search_in, feature_name, feature_dict)
+
+    def test_option_types(self, error_msg, option, val):
+        for tp, available in (
+            ((str, list), (
+                "implies", "headers", "flags", "group", "detect", "extra_checks"
+            )),
+            ((str,),  ("disable",)),
+            ((int,),  ("interest",)),
+            ((bool,), ("implies_detect",)),
+            ((bool, type(None)), ("autovec",)),
+        ) :
+            found_it = option in available
+            if not found_it:
+                continue
+            if not isinstance(val, tp):
+                error_tp = [t.__name__ for t in (*tp,)]
+                error_tp = ' or '.join(error_tp)
+                raise AssertionError(error_msg +
+                    "expected '%s' type for option '%s' not '%s'" % (
+                     error_tp, option, type(val).__name__
+                ))
+            break
+
+        if not found_it:
+            raise AssertionError(error_msg + "invalid option name '%s'" % option)
+
+    def test_duplicates(self, error_msg, option, val):
+        if option not in (
+            "implies", "headers", "flags", "group", "detect", "extra_checks"
+        ) : return
+
+        if isinstance(val, str):
+            val = val.split()
+
+        if len(val) != len(set(val)):
+            raise AssertionError(error_msg + "duplicated values in option '%s'" % option)
+
+    def test_implies(self, error_msg, search_in, feature_name, feature_dict):
+        if feature_dict.get("disabled") is not None:
+            return
+        implies = feature_dict.get("implies", "")
+        if not implies:
+            return
+        if isinstance(implies, str):
+            implies = implies.split()
+
+        if feature_name in implies:
+            raise AssertionError(error_msg + "feature implies itself")
+
+        for impl in implies:
+            impl_dict = search_in.get(impl)
+            if impl_dict is not None:
+                if "disable" in impl_dict:
+                    raise AssertionError(error_msg + "implies disabled feature '%s'" % impl)
+                continue
+            raise AssertionError(error_msg + "implies non-exist feature '%s'" % impl)
+
+    def test_group(self, error_msg, search_in, feature_name, feature_dict):
+        if feature_dict.get("disabled") is not None:
+            return
+        group = feature_dict.get("group", "")
+        if not group:
+            return
+        if isinstance(group, str):
+            group = group.split()
+
+        for f in group:
+            impl_dict = search_in.get(f)
+            if not impl_dict or "disable" in impl_dict:
+                continue
+            raise AssertionError(error_msg +
+                "in option 'group', '%s' already exists as a feature name" % f
+            )
+
+    def test_extra_checks(self, error_msg, search_in, feature_name, feature_dict):
+        if feature_dict.get("disabled") is not None:
+            return
+        extra_checks = feature_dict.get("extra_checks", "")
+        if not extra_checks:
+            return
+        if isinstance(extra_checks, str):
+            extra_checks = extra_checks.split()
+
+        for f in extra_checks:
+            impl_dict = search_in.get(f)
+            if not impl_dict or "disable" in impl_dict:
+                continue
+            raise AssertionError(error_msg +
+                "in option 'extra_checks', extra test case '%s' already exists as a feature name" % f
+            )
+
+class TestConfFeatures(unittest.TestCase):
+    def __init__(self, methodName="runTest"):
+        unittest.TestCase.__init__(self, methodName)
+        self._setup()
+
+    def _setup(self):
+        FakeCCompilerOpt.conf_nocache = True
+
+    def test_features(self):
+        for arch, compilers in arch_compilers.items():
+            for cc in compilers:
+                FakeCCompilerOpt.fake_info = (arch, cc, "")
+                _TestConfFeatures()
+
+if is_standalone:
+    unittest.main()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_exec_command.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_exec_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1a20056a5a2a78a76cf36d1bde31a0e82cbb873
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_exec_command.py
@@ -0,0 +1,217 @@
+import os
+import pytest
+import sys
+from tempfile import TemporaryFile
+
+from numpy.distutils import exec_command
+from numpy.distutils.exec_command import get_pythonexe
+from numpy.testing import tempdir, assert_, assert_warns, IS_WASM
+
+
+# In python 3 stdout, stderr are text (unicode compliant) devices, so to
+# emulate them import StringIO from the io module.
+from io import StringIO
+
+class redirect_stdout:
+    """Context manager to redirect stdout for exec_command test."""
+    def __init__(self, stdout=None):
+        self._stdout = stdout or sys.stdout
+
+    def __enter__(self):
+        self.old_stdout = sys.stdout
+        sys.stdout = self._stdout
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._stdout.flush()
+        sys.stdout = self.old_stdout
+        # note: closing sys.stdout won't close it.
+        self._stdout.close()
+
+class redirect_stderr:
+    """Context manager to redirect stderr for exec_command test."""
+    def __init__(self, stderr=None):
+        self._stderr = stderr or sys.stderr
+
+    def __enter__(self):
+        self.old_stderr = sys.stderr
+        sys.stderr = self._stderr
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._stderr.flush()
+        sys.stderr = self.old_stderr
+        # note: closing sys.stderr won't close it.
+        self._stderr.close()
+
+class emulate_nonposix:
+    """Context manager to emulate os.name != 'posix' """
+    def __init__(self, osname='non-posix'):
+        self._new_name = osname
+
+    def __enter__(self):
+        self._old_name = os.name
+        os.name = self._new_name
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        os.name = self._old_name
+
+
+def test_exec_command_stdout():
+    # Regression test for gh-2999 and gh-2915.
+    # There are several packages (nose, scipy.weave.inline, Sage inline
+    # Fortran) that replace stdout, in which case it doesn't have a fileno
+    # method.  This is tested here, with a do-nothing command that fails if the
+    # presence of fileno() is assumed in exec_command.
+
+    # The code has a special case for posix systems, so if we are on posix test
+    # both that the special case works and that the generic code works.
+
+    # Test posix version:
+    with redirect_stdout(StringIO()):
+        with redirect_stderr(TemporaryFile()):
+            with assert_warns(DeprecationWarning):
+                exec_command.exec_command("cd '.'")
+
+    if os.name == 'posix':
+        # Test general (non-posix) version:
+        with emulate_nonposix():
+            with redirect_stdout(StringIO()):
+                with redirect_stderr(TemporaryFile()):
+                    with assert_warns(DeprecationWarning):
+                        exec_command.exec_command("cd '.'")
+
+def test_exec_command_stderr():
+    # Test posix version:
+    with redirect_stdout(TemporaryFile(mode='w+')):
+        with redirect_stderr(StringIO()):
+            with assert_warns(DeprecationWarning):
+                exec_command.exec_command("cd '.'")
+
+    if os.name == 'posix':
+        # Test general (non-posix) version:
+        with emulate_nonposix():
+            with redirect_stdout(TemporaryFile()):
+                with redirect_stderr(StringIO()):
+                    with assert_warns(DeprecationWarning):
+                        exec_command.exec_command("cd '.'")
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+class TestExecCommand:
+    def setup_method(self):
+        self.pyexe = get_pythonexe()
+
+    def check_nt(self, **kws):
+        s, o = exec_command.exec_command('cmd /C echo path=%path%')
+        assert_(s == 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+         '"%s" -c "import sys;sys.stderr.write(sys.platform)"' % self.pyexe)
+        assert_(s == 0)
+        assert_(o == 'win32')
+
+    def check_posix(self, **kws):
+        s, o = exec_command.exec_command("echo Hello", **kws)
+        assert_(s == 0)
+        assert_(o == 'Hello')
+
+        s, o = exec_command.exec_command('echo $AAA', **kws)
+        assert_(s == 0)
+        assert_(o == '')
+
+        s, o = exec_command.exec_command('echo "$AAA"', AAA='Tere', **kws)
+        assert_(s == 0)
+        assert_(o == 'Tere')
+
+        s, o = exec_command.exec_command('echo "$AAA"', **kws)
+        assert_(s == 0)
+        assert_(o == '')
+
+        if 'BBB' not in os.environ:
+            os.environ['BBB'] = 'Hi'
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hi')
+
+            s, o = exec_command.exec_command('echo "$BBB"', BBB='Hey', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hey')
+
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hi')
+
+            del os.environ['BBB']
+
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == '')
+
+
+        s, o = exec_command.exec_command('this_is_not_a_command', **kws)
+        assert_(s != 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command('echo path=$PATH', **kws)
+        assert_(s == 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+             '"%s" -c "import sys,os;sys.stderr.write(os.name)"' %
+             self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == 'posix')
+
+    def check_basic(self, *kws):
+        s, o = exec_command.exec_command(
+                     '"%s" -c "raise \'Ignore me.\'"' % self.pyexe, **kws)
+        assert_(s != 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+             '"%s" -c "import sys;sys.stderr.write(\'0\');'
+             'sys.stderr.write(\'1\');sys.stderr.write(\'2\')"' %
+             self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == '012')
+
+        s, o = exec_command.exec_command(
+                 '"%s" -c "import sys;sys.exit(15)"' % self.pyexe, **kws)
+        assert_(s == 15)
+        assert_(o == '')
+
+        s, o = exec_command.exec_command(
+                     '"%s" -c "print(\'Heipa\'")' % self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == 'Heipa')
+
+    def check_execute_in(self, **kws):
+        with tempdir() as tmpdir:
+            fn = "file"
+            tmpfile = os.path.join(tmpdir, fn)
+            with open(tmpfile, 'w') as f:
+                f.write('Hello')
+
+            s, o = exec_command.exec_command(
+                 '"%s" -c "f = open(\'%s\', \'r\'); f.close()"' %
+                 (self.pyexe, fn), **kws)
+            assert_(s != 0)
+            assert_(o != '')
+            s, o = exec_command.exec_command(
+                     '"%s" -c "f = open(\'%s\', \'r\'); print(f.read()); '
+                     'f.close()"' % (self.pyexe, fn), execute_in=tmpdir, **kws)
+            assert_(s == 0)
+            assert_(o == 'Hello')
+
+    def test_basic(self):
+        with redirect_stdout(StringIO()):
+            with redirect_stderr(StringIO()):
+                with assert_warns(DeprecationWarning):
+                    if os.name == "posix":
+                        self.check_posix(use_tee=0)
+                        self.check_posix(use_tee=1)
+                    elif os.name == "nt":
+                        self.check_nt(use_tee=0)
+                        self.check_nt(use_tee=1)
+                    self.check_execute_in(use_tee=0)
+                    self.check_execute_in(use_tee=1)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd97f1e72afcba2ab379e5ff4dfce15341686534
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler.py
@@ -0,0 +1,43 @@
+from numpy.testing import assert_
+import numpy.distutils.fcompiler
+
+customizable_flags = [
+    ('f77', 'F77FLAGS'),
+    ('f90', 'F90FLAGS'),
+    ('free', 'FREEFLAGS'),
+    ('arch', 'FARCH'),
+    ('debug', 'FDEBUG'),
+    ('flags', 'FFLAGS'),
+    ('linker_so', 'LDFLAGS'),
+]
+
+
+def test_fcompiler_flags(monkeypatch):
+    monkeypatch.setenv('NPY_DISTUTILS_APPEND_FLAGS', '0')
+    fc = numpy.distutils.fcompiler.new_fcompiler(compiler='none')
+    flag_vars = fc.flag_vars.clone(lambda *args, **kwargs: None)
+
+    for opt, envvar in customizable_flags:
+        new_flag = '-dummy-{}-flag'.format(opt)
+        prev_flags = getattr(flag_vars, opt)
+
+        monkeypatch.setenv(envvar, new_flag)
+        new_flags = getattr(flag_vars, opt)
+
+        monkeypatch.delenv(envvar)
+        assert_(new_flags == [new_flag])
+
+    monkeypatch.setenv('NPY_DISTUTILS_APPEND_FLAGS', '1')
+
+    for opt, envvar in customizable_flags:
+        new_flag = '-dummy-{}-flag'.format(opt)
+        prev_flags = getattr(flag_vars, opt)
+        monkeypatch.setenv(envvar, new_flag)
+        new_flags = getattr(flag_vars, opt)
+
+        monkeypatch.delenv(envvar)
+        if prev_flags is None:
+            assert_(new_flags == [new_flag])
+        else:
+            assert_(new_flags == prev_flags + [new_flag])
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_gnu.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_gnu.py
new file mode 100644
index 0000000000000000000000000000000000000000..0817ae58c2140e912eaf3d61e040050016dede54
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_gnu.py
@@ -0,0 +1,55 @@
+from numpy.testing import assert_
+
+import numpy.distutils.fcompiler
+
+g77_version_strings = [
+    ('GNU Fortran 0.5.25 20010319 (prerelease)', '0.5.25'),
+    ('GNU Fortran (GCC 3.2) 3.2 20020814 (release)', '3.2'),
+    ('GNU Fortran (GCC) 3.3.3 20040110 (prerelease) (Debian)', '3.3.3'),
+    ('GNU Fortran (GCC) 3.3.3 (Debian 20040401)', '3.3.3'),
+    ('GNU Fortran (GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-5)) 3.2.2'
+       ' 20030222 (Red Hat Linux 3.2.2-5)', '3.2.2'),
+]
+
+gfortran_version_strings = [
+    ('GNU Fortran 95 (GCC 4.0.3 20051023 (prerelease) (Debian 4.0.2-3))',
+     '4.0.3'),
+    ('GNU Fortran 95 (GCC) 4.1.0', '4.1.0'),
+    ('GNU Fortran 95 (GCC) 4.2.0 20060218 (experimental)', '4.2.0'),
+    ('GNU Fortran (GCC) 4.3.0 20070316 (experimental)', '4.3.0'),
+    ('GNU Fortran (rubenvb-4.8.0) 4.8.0', '4.8.0'),
+    ('4.8.0', '4.8.0'),
+    ('4.0.3-7', '4.0.3'),
+    ("gfortran: warning: couldn't understand kern.osversion '14.1.0\n4.9.1",
+     '4.9.1'),
+    ("gfortran: warning: couldn't understand kern.osversion '14.1.0\n"
+     "gfortran: warning: yet another warning\n4.9.1",
+     '4.9.1'),
+    ('GNU Fortran (crosstool-NG 8a21ab48) 7.2.0', '7.2.0')
+]
+
+class TestG77Versions:
+    def test_g77_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu')
+        for vs, version in g77_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version, (vs, v))
+
+    def test_not_g77(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu')
+        for vs, _ in gfortran_version_strings:
+            v = fc.version_match(vs)
+            assert_(v is None, (vs, v))
+
+class TestGFortranVersions:
+    def test_gfortran_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu95')
+        for vs, version in gfortran_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version, (vs, v))
+
+    def test_not_gfortran(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='gnu95')
+        for vs, _ in g77_version_strings:
+            v = fc.version_match(vs)
+            assert_(v is None, (vs, v))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_intel.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_intel.py
new file mode 100644
index 0000000000000000000000000000000000000000..45c9cdac1910def6b5a50a60b4ab5c8e0092af18
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_intel.py
@@ -0,0 +1,30 @@
+import numpy.distutils.fcompiler
+from numpy.testing import assert_
+
+
+intel_32bit_version_strings = [
+    ("Intel(R) Fortran Intel(R) 32-bit Compiler Professional for applications"
+     "running on Intel(R) 32, Version 11.1", '11.1'),
+]
+
+intel_64bit_version_strings = [
+    ("Intel(R) Fortran IA-64 Compiler Professional for applications"
+     "running on IA-64, Version 11.0", '11.0'),
+    ("Intel(R) Fortran Intel(R) 64 Compiler Professional for applications"
+     "running on Intel(R) 64, Version 11.1", '11.1')
+]
+
+class TestIntelFCompilerVersions:
+    def test_32bit_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intel')
+        for vs, version in intel_32bit_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version)
+
+
+class TestIntelEM64TFCompilerVersions:
+    def test_64bit_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intelem')
+        for vs, version in intel_64bit_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_nagfor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_nagfor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e04f5266dc1e9c5a15f130af5f9c596f8bd7ef9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_fcompiler_nagfor.py
@@ -0,0 +1,22 @@
+from numpy.testing import assert_
+import numpy.distutils.fcompiler
+
+nag_version_strings = [('nagfor', 'NAG Fortran Compiler Release '
+                        '6.2(Chiyoda) Build 6200', '6.2'),
+                       ('nagfor', 'NAG Fortran Compiler Release '
+                        '6.1(Tozai) Build 6136', '6.1'),
+                       ('nagfor', 'NAG Fortran Compiler Release '
+                        '6.0(Hibiya) Build 1021', '6.0'),
+                       ('nagfor', 'NAG Fortran Compiler Release '
+                        '5.3.2(971)', '5.3.2'),
+                       ('nag', 'NAGWare Fortran 95 compiler Release 5.1'
+                        '(347,355-367,375,380-383,389,394,399,401-402,407,'
+                        '431,435,437,446,459-460,463,472,494,496,503,508,'
+                        '511,517,529,555,557,565)', '5.1')]
+
+class TestNagFCompilerVersions:
+    def test_version_match(self):
+        for comp, vs, version in nag_version_strings:
+            fc = numpy.distutils.fcompiler.new_fcompiler(compiler=comp)
+            v = fc.version_match(vs)
+            assert_(v == version)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_from_template.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_from_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..5881754962996460a5900bb211d11411b554a48f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_from_template.py
@@ -0,0 +1,44 @@
+
+from numpy.distutils.from_template import process_str
+from numpy.testing import assert_equal
+
+
+pyf_src = """
+python module foo
+    <_rd=real,double precision>
+    interface
+        subroutine foosub(tol)
+            <_rd>, intent(in,out) :: tol
+        end subroutine foosub
+    end interface
+end python module foo
+"""
+
+expected_pyf = """
+python module foo
+    interface
+        subroutine sfoosub(tol)
+            real, intent(in,out) :: tol
+        end subroutine sfoosub
+        subroutine dfoosub(tol)
+            double precision, intent(in,out) :: tol
+        end subroutine dfoosub
+    end interface
+end python module foo
+"""
+
+
+def normalize_whitespace(s):
+    """
+    Remove leading and trailing whitespace, and convert internal
+    stretches of whitespace to a single space.
+    """
+    return ' '.join(s.split())
+
+
+def test_from_template():
+    """Regression test for gh-10712."""
+    pyf = process_str(pyf_src)
+    normalized_pyf = normalize_whitespace(pyf)
+    normalized_expected_pyf = normalize_whitespace(expected_pyf)
+    assert_equal(normalized_pyf, normalized_expected_pyf)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_log.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..72fddf37370f1b5c81473a24c823a236f9f299bc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_log.py
@@ -0,0 +1,34 @@
+import io
+import re
+from contextlib import redirect_stdout
+
+import pytest
+
+from numpy.distutils import log
+
+
+def setup_module():
+    f = io.StringIO()  # changing verbosity also logs here, capture that
+    with redirect_stdout(f):
+        log.set_verbosity(2, force=True)  # i.e. DEBUG
+
+
+def teardown_module():
+    log.set_verbosity(0, force=True)  # the default
+
+
+r_ansi = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
+
+
+@pytest.mark.parametrize("func_name", ["error", "warn", "info", "debug"])
+def test_log_prefix(func_name):
+    func = getattr(log, func_name)
+    msg = f"{func_name} message"
+    f = io.StringIO()
+    with redirect_stdout(f):
+        func(msg)
+    out = f.getvalue()
+    assert out  # sanity check
+    clean_out = r_ansi.sub("", out)
+    line = next(line for line in clean_out.splitlines())
+    assert line == f"{func_name.upper()}: {msg}"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_mingw32ccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_mingw32ccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebedacb32448f4cab47b4931985a6417f18fd1f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_mingw32ccompiler.py
@@ -0,0 +1,42 @@
+import shutil
+import subprocess
+import sys
+import pytest
+
+from numpy.distutils import mingw32ccompiler
+
+
+@pytest.mark.skipif(sys.platform != 'win32', reason='win32 only test')
+def test_build_import():
+    '''Test the mingw32ccompiler.build_import_library, which builds a
+    `python.a` from the MSVC `python.lib`
+    '''
+
+    # make sure `nm.exe` exists and supports the current python version. This
+    # can get mixed up when the PATH has a 64-bit nm but the python is 32-bit
+    try:
+        out = subprocess.check_output(['nm.exe', '--help'])
+    except FileNotFoundError:
+        pytest.skip("'nm.exe' not on path, is mingw installed?")
+    supported = out[out.find(b'supported targets:'):]
+    if sys.maxsize < 2**32:
+        if b'pe-i386' not in supported:
+            raise ValueError("'nm.exe' found but it does not support 32-bit "
+                             "dlls when using 32-bit python. Supported "
+                             "formats: '%s'" % supported)
+    elif b'pe-x86-64' not in supported:
+        raise ValueError("'nm.exe' found but it does not support 64-bit "
+                         "dlls when using 64-bit python. Supported "
+                         "formats: '%s'" % supported)
+    # Hide the import library to force a build
+    has_import_lib, fullpath = mingw32ccompiler._check_for_import_lib()
+    if has_import_lib: 
+        shutil.move(fullpath, fullpath + '.bak')
+
+    try: 
+        # Whew, now we can actually test the function
+        mingw32ccompiler.build_import_library()
+
+    finally:
+        if has_import_lib:
+            shutil.move(fullpath + '.bak', fullpath)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_misc_util.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_misc_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..40e7606eeb76bd95f81ec48ce2fcc49fd0fe3e71
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_misc_util.py
@@ -0,0 +1,88 @@
+from os.path import join, sep, dirname
+
+import pytest
+
+from numpy.distutils.misc_util import (
+    appendpath, minrelpath, gpaths, get_shared_lib_extension, get_info
+    )
+from numpy.testing import (
+    assert_, assert_equal, IS_EDITABLE
+    )
+
+ajoin = lambda *paths: join(*((sep,)+paths))
+
+class TestAppendpath:
+
+    def test_1(self):
+        assert_equal(appendpath('prefix', 'name'), join('prefix', 'name'))
+        assert_equal(appendpath('/prefix', 'name'), ajoin('prefix', 'name'))
+        assert_equal(appendpath('/prefix', '/name'), ajoin('prefix', 'name'))
+        assert_equal(appendpath('prefix', '/name'), join('prefix', 'name'))
+
+    def test_2(self):
+        assert_equal(appendpath('prefix/sub', 'name'),
+                     join('prefix', 'sub', 'name'))
+        assert_equal(appendpath('prefix/sub', 'sup/name'),
+                     join('prefix', 'sub', 'sup', 'name'))
+        assert_equal(appendpath('/prefix/sub', '/prefix/name'),
+                     ajoin('prefix', 'sub', 'name'))
+
+    def test_3(self):
+        assert_equal(appendpath('/prefix/sub', '/prefix/sup/name'),
+                     ajoin('prefix', 'sub', 'sup', 'name'))
+        assert_equal(appendpath('/prefix/sub/sub2', '/prefix/sup/sup2/name'),
+                     ajoin('prefix', 'sub', 'sub2', 'sup', 'sup2', 'name'))
+        assert_equal(appendpath('/prefix/sub/sub2', '/prefix/sub/sup/name'),
+                     ajoin('prefix', 'sub', 'sub2', 'sup', 'name'))
+
+class TestMinrelpath:
+
+    def test_1(self):
+        n = lambda path: path.replace('/', sep)
+        assert_equal(minrelpath(n('aa/bb')), n('aa/bb'))
+        assert_equal(minrelpath('..'), '..')
+        assert_equal(minrelpath(n('aa/..')), '')
+        assert_equal(minrelpath(n('aa/../bb')), 'bb')
+        assert_equal(minrelpath(n('aa/bb/..')), 'aa')
+        assert_equal(minrelpath(n('aa/bb/../..')), '')
+        assert_equal(minrelpath(n('aa/bb/../cc/../dd')), n('aa/dd'))
+        assert_equal(minrelpath(n('.././..')), n('../..'))
+        assert_equal(minrelpath(n('aa/bb/.././../dd')), n('dd'))
+
+class TestGpaths:
+
+    def test_gpaths(self):
+        local_path = minrelpath(join(dirname(__file__), '..'))
+        ls = gpaths('command/*.py', local_path)
+        assert_(join(local_path, 'command', 'build_src.py') in ls, repr(ls))
+        f = gpaths('system_info.py', local_path)
+        assert_(join(local_path, 'system_info.py') == f[0], repr(f))
+
+class TestSharedExtension:
+
+    def test_get_shared_lib_extension(self):
+        import sys
+        ext = get_shared_lib_extension(is_python_ext=False)
+        if sys.platform.startswith('linux'):
+            assert_equal(ext, '.so')
+        elif sys.platform.startswith('gnukfreebsd'):
+            assert_equal(ext, '.so')
+        elif sys.platform.startswith('darwin'):
+            assert_equal(ext, '.dylib')
+        elif sys.platform.startswith('win'):
+            assert_equal(ext, '.dll')
+        # just check for no crash
+        assert_(get_shared_lib_extension(is_python_ext=True))
+
+
+@pytest.mark.skipif(
+    IS_EDITABLE,
+    reason="`get_info` .ini lookup method incompatible with editable install"
+)
+def test_installed_npymath_ini():
+    # Regression test for gh-7707.  If npymath.ini wasn't installed, then this
+    # will give an error.
+    info = get_info('npymath')
+
+    assert isinstance(info, dict)
+    assert "define_macros" in info
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_npy_pkg_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_npy_pkg_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..b287ebe2e83209fdcf5add161a7af8d988b9d086
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_npy_pkg_config.py
@@ -0,0 +1,84 @@
+import os
+
+from numpy.distutils.npy_pkg_config import read_config, parse_flags
+from numpy.testing import temppath, assert_
+
+simple = """\
+[meta]
+Name = foo
+Description = foo lib
+Version = 0.1
+
+[default]
+cflags = -I/usr/include
+libs = -L/usr/lib
+"""
+simple_d = {'cflags': '-I/usr/include', 'libflags': '-L/usr/lib',
+        'version': '0.1', 'name': 'foo'}
+
+simple_variable = """\
+[meta]
+Name = foo
+Description = foo lib
+Version = 0.1
+
+[variables]
+prefix = /foo/bar
+libdir = ${prefix}/lib
+includedir = ${prefix}/include
+
+[default]
+cflags = -I${includedir}
+libs = -L${libdir}
+"""
+simple_variable_d = {'cflags': '-I/foo/bar/include', 'libflags': '-L/foo/bar/lib',
+        'version': '0.1', 'name': 'foo'}
+
+class TestLibraryInfo:
+    def test_simple(self):
+        with temppath('foo.ini') as path:
+            with open(path,  'w') as f:
+                f.write(simple)
+            pkg = os.path.splitext(path)[0]
+            out = read_config(pkg)
+
+        assert_(out.cflags() == simple_d['cflags'])
+        assert_(out.libs() == simple_d['libflags'])
+        assert_(out.name == simple_d['name'])
+        assert_(out.version == simple_d['version'])
+
+    def test_simple_variable(self):
+        with temppath('foo.ini') as path:
+            with open(path,  'w') as f:
+                f.write(simple_variable)
+            pkg = os.path.splitext(path)[0]
+            out = read_config(pkg)
+
+        assert_(out.cflags() == simple_variable_d['cflags'])
+        assert_(out.libs() == simple_variable_d['libflags'])
+        assert_(out.name == simple_variable_d['name'])
+        assert_(out.version == simple_variable_d['version'])
+        out.vars['prefix'] = '/Users/david'
+        assert_(out.cflags() == '-I/Users/david/include')
+
+class TestParseFlags:
+    def test_simple_cflags(self):
+        d = parse_flags("-I/usr/include")
+        assert_(d['include_dirs'] == ['/usr/include'])
+
+        d = parse_flags("-I/usr/include -DFOO")
+        assert_(d['include_dirs'] == ['/usr/include'])
+        assert_(d['macros'] == ['FOO'])
+
+        d = parse_flags("-I /usr/include -DFOO")
+        assert_(d['include_dirs'] == ['/usr/include'])
+        assert_(d['macros'] == ['FOO'])
+
+    def test_simple_lflags(self):
+        d = parse_flags("-L/usr/lib -lfoo -L/usr/lib -lbar")
+        assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
+        assert_(d['libraries'] == ['foo', 'bar'])
+
+        d = parse_flags("-L /usr/lib -lfoo -L/usr/lib -lbar")
+        assert_(d['library_dirs'] == ['/usr/lib', '/usr/lib'])
+        assert_(d['libraries'] == ['foo', 'bar'])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_shell_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_shell_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..696d38ddd66a41ec5f51f4c93d26d3f0df29b483
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_shell_utils.py
@@ -0,0 +1,79 @@
+import pytest
+import subprocess
+import json
+import sys
+
+from numpy.distutils import _shell_utils
+from numpy.testing import IS_WASM
+
+argv_cases = [
+    [r'exe'],
+    [r'path/exe'],
+    [r'path\exe'],
+    [r'\\server\path\exe'],
+    [r'path to/exe'],
+    [r'path to\exe'],
+
+    [r'exe', '--flag'],
+    [r'path/exe', '--flag'],
+    [r'path\exe', '--flag'],
+    [r'path to/exe', '--flag'],
+    [r'path to\exe', '--flag'],
+
+    # flags containing literal quotes in their name
+    [r'path to/exe', '--flag-"quoted"'],
+    [r'path to\exe', '--flag-"quoted"'],
+    [r'path to/exe', '"--flag-quoted"'],
+    [r'path to\exe', '"--flag-quoted"'],
+]
+
+
+@pytest.fixture(params=[
+    _shell_utils.WindowsParser,
+    _shell_utils.PosixParser
+])
+def Parser(request):
+    return request.param
+
+
+@pytest.fixture
+def runner(Parser):
+    if Parser != _shell_utils.NativeParser:
+        pytest.skip('Unable to run with non-native parser')
+
+    if Parser == _shell_utils.WindowsParser:
+        return lambda cmd: subprocess.check_output(cmd)
+    elif Parser == _shell_utils.PosixParser:
+        # posix has no non-shell string parsing
+        return lambda cmd: subprocess.check_output(cmd, shell=True)
+    else:
+        raise NotImplementedError
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+@pytest.mark.parametrize('argv', argv_cases)
+def test_join_matches_subprocess(Parser, runner, argv):
+    """
+    Test that join produces strings understood by subprocess
+    """
+    # invoke python to return its arguments as json
+    cmd = [
+        sys.executable, '-c',
+        'import json, sys; print(json.dumps(sys.argv[1:]))'
+    ]
+    joined = Parser.join(cmd + argv)
+    json_out = runner(joined).decode()
+    assert json.loads(json_out) == argv
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+@pytest.mark.parametrize('argv', argv_cases)
+def test_roundtrip(Parser, argv):
+    """
+    Test that split is the inverse operation of join
+    """
+    try:
+        joined = Parser.join(argv)
+        assert argv == Parser.split(joined)
+    except NotImplementedError:
+        pytest.skip("Not implemented")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_system_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_system_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bcc09050503e7f1bb3e94eecc902f512a9e42a1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/test_system_info.py
@@ -0,0 +1,334 @@
+import os
+import shutil
+import pytest
+from tempfile import mkstemp, mkdtemp
+from subprocess import Popen, PIPE
+import importlib.metadata
+from distutils.errors import DistutilsError
+
+from numpy.testing import assert_, assert_equal, assert_raises
+from numpy.distutils import ccompiler, customized_ccompiler
+from numpy.distutils.system_info import system_info, ConfigParser, mkl_info
+from numpy.distutils.system_info import AliasedOptionError
+from numpy.distutils.system_info import default_lib_dirs, default_include_dirs
+from numpy.distutils import _shell_utils
+
+
+try:
+    if importlib.metadata.version('setuptools') >= '60':
+        # pkg-resources gives deprecation warnings, and there may be more
+        # issues. We only support setuptools <60
+        pytest.skip("setuptools is too new", allow_module_level=True)
+except importlib.metadata.PackageNotFoundError:
+    # we don't require `setuptools`; if it is not found, continue
+    pass
+
+
+def get_class(name, notfound_action=1):
+    """
+    notfound_action:
+      0 - do nothing
+      1 - display warning message
+      2 - raise error
+    """
+    cl = {'temp1': Temp1Info,
+          'temp2': Temp2Info,
+          'duplicate_options': DuplicateOptionInfo,
+          }.get(name.lower(), _system_info)
+    return cl()
+
+simple_site = """
+[ALL]
+library_dirs = {dir1:s}{pathsep:s}{dir2:s}
+libraries = {lib1:s},{lib2:s}
+extra_compile_args = -I/fake/directory -I"/path with/spaces" -Os
+runtime_library_dirs = {dir1:s}
+
+[temp1]
+library_dirs = {dir1:s}
+libraries = {lib1:s}
+runtime_library_dirs = {dir1:s}
+
+[temp2]
+library_dirs = {dir2:s}
+libraries = {lib2:s}
+extra_link_args = -Wl,-rpath={lib2_escaped:s}
+rpath = {dir2:s}
+
+[duplicate_options]
+mylib_libs = {lib1:s}
+libraries = {lib2:s}
+"""
+site_cfg = simple_site
+
+fakelib_c_text = """
+/* This file is generated from numpy/distutils/testing/test_system_info.py */
+#include
+void foo(void) {
+   printf("Hello foo");
+}
+void bar(void) {
+   printf("Hello bar");
+}
+"""
+
+def have_compiler():
+    """ Return True if there appears to be an executable compiler
+    """
+    compiler = customized_ccompiler()
+    try:
+        cmd = compiler.compiler  # Unix compilers
+    except AttributeError:
+        try:
+            if not compiler.initialized:
+                compiler.initialize()  # MSVC is different
+        except (DistutilsError, ValueError):
+            return False
+        cmd = [compiler.cc]
+    try:
+        p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+        p.stdout.close()
+        p.stderr.close()
+        p.wait()
+    except OSError:
+        return False
+    return True
+
+
+HAVE_COMPILER = have_compiler()
+
+
+class _system_info(system_info):
+
+    def __init__(self,
+                 default_lib_dirs=default_lib_dirs,
+                 default_include_dirs=default_include_dirs,
+                 verbosity=1,
+                 ):
+        self.__class__.info = {}
+        self.local_prefixes = []
+        defaults = {'library_dirs': '',
+                    'include_dirs': '',
+                    'runtime_library_dirs': '',
+                    'rpath': '',
+                    'src_dirs': '',
+                    'search_static_first': "0",
+                    'extra_compile_args': '',
+                    'extra_link_args': ''}
+        self.cp = ConfigParser(defaults)
+        # We have to parse the config files afterwards
+        # to have a consistent temporary filepath
+
+    def _check_libs(self, lib_dirs, libs, opt_libs, exts):
+        """Override _check_libs to return with all dirs """
+        info = {'libraries': libs, 'library_dirs': lib_dirs}
+        return info
+
+
+class Temp1Info(_system_info):
+    """For testing purposes"""
+    section = 'temp1'
+
+
+class Temp2Info(_system_info):
+    """For testing purposes"""
+    section = 'temp2'
+
+class DuplicateOptionInfo(_system_info):
+    """For testing purposes"""
+    section = 'duplicate_options'
+
+
+class TestSystemInfoReading:
+
+    def setup_method(self):
+        """ Create the libraries """
+        # Create 2 sources and 2 libraries
+        self._dir1 = mkdtemp()
+        self._src1 = os.path.join(self._dir1, 'foo.c')
+        self._lib1 = os.path.join(self._dir1, 'libfoo.so')
+        self._dir2 = mkdtemp()
+        self._src2 = os.path.join(self._dir2, 'bar.c')
+        self._lib2 = os.path.join(self._dir2, 'libbar.so')
+        # Update local site.cfg
+        global simple_site, site_cfg
+        site_cfg = simple_site.format(**{
+            'dir1': self._dir1,
+            'lib1': self._lib1,
+            'dir2': self._dir2,
+            'lib2': self._lib2,
+            'pathsep': os.pathsep,
+            'lib2_escaped': _shell_utils.NativeParser.join([self._lib2])
+        })
+        # Write site.cfg
+        fd, self._sitecfg = mkstemp()
+        os.close(fd)
+        with open(self._sitecfg, 'w') as fd:
+            fd.write(site_cfg)
+        # Write the sources
+        with open(self._src1, 'w') as fd:
+            fd.write(fakelib_c_text)
+        with open(self._src2, 'w') as fd:
+            fd.write(fakelib_c_text)
+        # We create all class-instances
+
+        def site_and_parse(c, site_cfg):
+            c.files = [site_cfg]
+            c.parse_config_files()
+            return c
+        self.c_default = site_and_parse(get_class('default'), self._sitecfg)
+        self.c_temp1 = site_and_parse(get_class('temp1'), self._sitecfg)
+        self.c_temp2 = site_and_parse(get_class('temp2'), self._sitecfg)
+        self.c_dup_options = site_and_parse(get_class('duplicate_options'),
+                                            self._sitecfg)
+
+    def teardown_method(self):
+        # Do each removal separately
+        try:
+            shutil.rmtree(self._dir1)
+        except Exception:
+            pass
+        try:
+            shutil.rmtree(self._dir2)
+        except Exception:
+            pass
+        try:
+            os.remove(self._sitecfg)
+        except Exception:
+            pass
+
+    def test_all(self):
+        # Read in all information in the ALL block
+        tsi = self.c_default
+        assert_equal(tsi.get_lib_dirs(), [self._dir1, self._dir2])
+        assert_equal(tsi.get_libraries(), [self._lib1, self._lib2])
+        assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
+        extra = tsi.calc_extra_info()
+        assert_equal(extra['extra_compile_args'], ['-I/fake/directory', '-I/path with/spaces', '-Os'])
+
+    def test_temp1(self):
+        # Read in all information in the temp1 block
+        tsi = self.c_temp1
+        assert_equal(tsi.get_lib_dirs(), [self._dir1])
+        assert_equal(tsi.get_libraries(), [self._lib1])
+        assert_equal(tsi.get_runtime_lib_dirs(), [self._dir1])
+
+    def test_temp2(self):
+        # Read in all information in the temp2 block
+        tsi = self.c_temp2
+        assert_equal(tsi.get_lib_dirs(), [self._dir2])
+        assert_equal(tsi.get_libraries(), [self._lib2])
+        # Now from rpath and not runtime_library_dirs
+        assert_equal(tsi.get_runtime_lib_dirs(key='rpath'), [self._dir2])
+        extra = tsi.calc_extra_info()
+        assert_equal(extra['extra_link_args'], ['-Wl,-rpath=' + self._lib2])
+
+    def test_duplicate_options(self):
+        # Ensure that duplicates are raising an AliasedOptionError
+        tsi = self.c_dup_options
+        assert_raises(AliasedOptionError, tsi.get_option_single, "mylib_libs", "libraries")
+        assert_equal(tsi.get_libs("mylib_libs", [self._lib1]), [self._lib1])
+        assert_equal(tsi.get_libs("libraries", [self._lib2]), [self._lib2])
+
+    @pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
+    def test_compile1(self):
+        # Compile source and link the first source
+        c = customized_ccompiler()
+        previousDir = os.getcwd()
+        try:
+            # Change directory to not screw up directories
+            os.chdir(self._dir1)
+            c.compile([os.path.basename(self._src1)], output_dir=self._dir1)
+            # Ensure that the object exists
+            assert_(os.path.isfile(self._src1.replace('.c', '.o')) or
+                    os.path.isfile(self._src1.replace('.c', '.obj')))
+        finally:
+            os.chdir(previousDir)
+
+    @pytest.mark.skipif(not HAVE_COMPILER, reason="Missing compiler")
+    @pytest.mark.skipif('msvc' in repr(ccompiler.new_compiler()),
+                         reason="Fails with MSVC compiler ")
+    def test_compile2(self):
+        # Compile source and link the second source
+        tsi = self.c_temp2
+        c = customized_ccompiler()
+        extra_link_args = tsi.calc_extra_info()['extra_link_args']
+        previousDir = os.getcwd()
+        try:
+            # Change directory to not screw up directories
+            os.chdir(self._dir2)
+            c.compile([os.path.basename(self._src2)], output_dir=self._dir2,
+                      extra_postargs=extra_link_args)
+            # Ensure that the object exists
+            assert_(os.path.isfile(self._src2.replace('.c', '.o')))
+        finally:
+            os.chdir(previousDir)
+
+    HAS_MKL = "mkl_rt" in mkl_info().calc_libraries_info().get("libraries", [])
+
+    @pytest.mark.xfail(HAS_MKL, reason=("`[DEFAULT]` override doesn't work if "
+                                        "numpy is built with MKL support"))
+    def test_overrides(self):
+        previousDir = os.getcwd()
+        cfg = os.path.join(self._dir1, 'site.cfg')
+        shutil.copy(self._sitecfg, cfg)
+        try:
+            os.chdir(self._dir1)
+            # Check that the '[ALL]' section does not override
+            # missing values from other sections
+            info = mkl_info()
+            lib_dirs = info.cp['ALL']['library_dirs'].split(os.pathsep)
+            assert info.get_lib_dirs() != lib_dirs
+
+            # But if we copy the values to a '[mkl]' section the value
+            # is correct
+            with open(cfg) as fid:
+                mkl = fid.read().replace('[ALL]', '[mkl]', 1)
+            with open(cfg, 'w') as fid:
+                fid.write(mkl)
+            info = mkl_info()
+            assert info.get_lib_dirs() == lib_dirs
+
+            # Also, the values will be taken from a section named '[DEFAULT]'
+            with open(cfg) as fid:
+                dflt = fid.read().replace('[mkl]', '[DEFAULT]', 1)
+            with open(cfg, 'w') as fid:
+                fid.write(dflt)
+            info = mkl_info()
+            assert info.get_lib_dirs() == lib_dirs
+        finally:
+            os.chdir(previousDir)
+
+
+def test_distutils_parse_env_order(monkeypatch):
+    from numpy.distutils.system_info import _parse_env_order
+    env = 'NPY_TESTS_DISTUTILS_PARSE_ENV_ORDER'
+
+    base_order = list('abcdef')
+
+    monkeypatch.setenv(env, 'b,i,e,f')
+    order, unknown = _parse_env_order(base_order, env)
+    assert len(order) == 3
+    assert order == list('bef')
+    assert len(unknown) == 1
+
+    # For when LAPACK/BLAS optimization is disabled
+    monkeypatch.setenv(env, '')
+    order, unknown = _parse_env_order(base_order, env)
+    assert len(order) == 0
+    assert len(unknown) == 0
+
+    for prefix in '^!':
+        monkeypatch.setenv(env, f'{prefix}b,i,e')
+        order, unknown = _parse_env_order(base_order, env)
+        assert len(order) == 4
+        assert order == list('acdf')
+        assert len(unknown) == 1
+
+    with pytest.raises(ValueError):
+        monkeypatch.setenv(env, 'b,^e,i')
+        _parse_env_order(base_order, env)
+
+    with pytest.raises(ValueError):
+        monkeypatch.setenv(env, '!b,^e,i')
+        _parse_env_order(base_order, env)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/utilities.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/utilities.py
new file mode 100644
index 0000000000000000000000000000000000000000..5016a83d2164116dac51f487977e5c9809203cb0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/tests/utilities.py
@@ -0,0 +1,90 @@
+# Kanged out of numpy.f2py.tests.util for test_build_ext
+from numpy.testing import IS_WASM
+import textwrap
+import shutil
+import tempfile
+import os
+import re
+import subprocess
+import sys
+
+#
+# Check if compilers are available at all...
+#
+
+_compiler_status = None
+
+
+def _get_compiler_status():
+    global _compiler_status
+    if _compiler_status is not None:
+        return _compiler_status
+
+    _compiler_status = (False, False, False)
+    if IS_WASM:
+        # Can't run compiler from inside WASM.
+        return _compiler_status
+
+    # XXX: this is really ugly. But I don't know how to invoke Distutils
+    #      in a safer way...
+    code = textwrap.dedent(
+        f"""\
+        import os
+        import sys
+        sys.path = {repr(sys.path)}
+
+        def configuration(parent_name='',top_path=None):
+            global config
+            from numpy.distutils.misc_util import Configuration
+            config = Configuration('', parent_name, top_path)
+            return config
+
+        from numpy.distutils.core import setup
+        setup(configuration=configuration)
+
+        config_cmd = config.get_config_cmd()
+        have_c = config_cmd.try_compile('void foo() {{}}')
+        print('COMPILERS:%%d,%%d,%%d' %% (have_c,
+                                          config.have_f77c(),
+                                          config.have_f90c()))
+        sys.exit(99)
+        """
+    )
+    code = code % dict(syspath=repr(sys.path))
+
+    tmpdir = tempfile.mkdtemp()
+    try:
+        script = os.path.join(tmpdir, "setup.py")
+
+        with open(script, "w") as f:
+            f.write(code)
+
+        cmd = [sys.executable, "setup.py", "config"]
+        p = subprocess.Popen(
+            cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=tmpdir
+        )
+        out, err = p.communicate()
+    finally:
+        shutil.rmtree(tmpdir)
+
+    m = re.search(rb"COMPILERS:(\d+),(\d+),(\d+)", out)
+    if m:
+        _compiler_status = (
+            bool(int(m.group(1))),
+            bool(int(m.group(2))),
+            bool(int(m.group(3))),
+        )
+    # Finished
+    return _compiler_status
+
+
+def has_c_compiler():
+    return _get_compiler_status()[0]
+
+
+def has_f77_compiler():
+    return _get_compiler_status()[1]
+
+
+def has_f90_compiler():
+    return _get_compiler_status()[2]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/unixccompiler.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/unixccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..4884960fdf227497df644b71b129ce561e3b49e0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/distutils/unixccompiler.py
@@ -0,0 +1,141 @@
+"""
+unixccompiler - can handle very long argument lists for ar.
+
+"""
+import os
+import sys
+import subprocess
+import shlex
+
+from distutils.errors import CompileError, DistutilsExecError, LibError
+from distutils.unixccompiler import UnixCCompiler
+from numpy.distutils.ccompiler import replace_method
+from numpy.distutils.misc_util import _commandline_dep_string
+from numpy.distutils import log
+
+# Note that UnixCCompiler._compile appeared in Python 2.3
+def UnixCCompiler__compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+    """Compile a single source files with a Unix-style compiler."""
+    # HP ad-hoc fix, see ticket 1383
+    ccomp = self.compiler_so
+    if ccomp[0] == 'aCC':
+        # remove flags that will trigger ANSI-C mode for aCC
+        if '-Ae' in ccomp:
+            ccomp.remove('-Ae')
+        if '-Aa' in ccomp:
+            ccomp.remove('-Aa')
+        # add flags for (almost) sane C++ handling
+        ccomp += ['-AA']
+        self.compiler_so = ccomp
+    # ensure OPT environment variable is read
+    if 'OPT' in os.environ:
+        # XXX who uses this?
+        from sysconfig import get_config_vars
+        opt = shlex.join(shlex.split(os.environ['OPT']))
+        gcv_opt = shlex.join(shlex.split(get_config_vars('OPT')[0]))
+        ccomp_s = shlex.join(self.compiler_so)
+        if opt not in ccomp_s:
+            ccomp_s = ccomp_s.replace(gcv_opt, opt)
+            self.compiler_so = shlex.split(ccomp_s)
+        llink_s = shlex.join(self.linker_so)
+        if opt not in llink_s:
+            self.linker_so = self.linker_so + shlex.split(opt)
+
+    display = '%s: %s' % (os.path.basename(self.compiler_so[0]), src)
+
+    # gcc style automatic dependencies, outputs a makefile (-MF) that lists
+    # all headers needed by a c file as a side effect of compilation (-MMD)
+    if getattr(self, '_auto_depends', False):
+        deps = ['-MMD', '-MF', obj + '.d']
+    else:
+        deps = []
+
+    try:
+        self.spawn(self.compiler_so + cc_args + [src, '-o', obj] + deps +
+                   extra_postargs, display = display)
+    except DistutilsExecError as e:
+        msg = str(e)
+        raise CompileError(msg) from None
+
+    # add commandline flags to dependency file
+    if deps:
+        # After running the compiler, the file created will be in EBCDIC
+        # but will not be tagged as such. This tags it so the file does not
+        # have multiple different encodings being written to it
+        if sys.platform == 'zos':
+            subprocess.check_output(['chtag', '-tc', 'IBM1047', obj + '.d'])
+        with open(obj + '.d', 'a') as f:
+            f.write(_commandline_dep_string(cc_args, extra_postargs, pp_opts))
+
+replace_method(UnixCCompiler, '_compile', UnixCCompiler__compile)
+
+
+def UnixCCompiler_create_static_lib(self, objects, output_libname,
+                                    output_dir=None, debug=0, target_lang=None):
+    """
+    Build a static library in a separate sub-process.
+
+    Parameters
+    ----------
+    objects : list or tuple of str
+        List of paths to object files used to build the static library.
+    output_libname : str
+        The library name as an absolute or relative (if `output_dir` is used)
+        path.
+    output_dir : str, optional
+        The path to the output directory. Default is None, in which case
+        the ``output_dir`` attribute of the UnixCCompiler instance.
+    debug : bool, optional
+        This parameter is not used.
+    target_lang : str, optional
+        This parameter is not used.
+
+    Returns
+    -------
+    None
+
+    """
+    objects, output_dir = self._fix_object_args(objects, output_dir)
+
+    output_filename = \
+                    self.library_filename(output_libname, output_dir=output_dir)
+
+    if self._need_link(objects, output_filename):
+        try:
+            # previous .a may be screwed up; best to remove it first
+            # and recreate.
+            # Also, ar on OS X doesn't handle updating universal archives
+            os.unlink(output_filename)
+        except OSError:
+            pass
+        self.mkpath(os.path.dirname(output_filename))
+        tmp_objects = objects + self.objects
+        while tmp_objects:
+            objects = tmp_objects[:50]
+            tmp_objects = tmp_objects[50:]
+            display = '%s: adding %d object files to %s' % (
+                           os.path.basename(self.archiver[0]),
+                           len(objects), output_filename)
+            self.spawn(self.archiver + [output_filename] + objects,
+                       display = display)
+
+        # Not many Unices required ranlib anymore -- SunOS 4.x is, I
+        # think the only major Unix that does.  Maybe we need some
+        # platform intelligence here to skip ranlib if it's not
+        # needed -- or maybe Python's configure script took care of
+        # it for us, hence the check for leading colon.
+        if self.ranlib:
+            display = '%s:@ %s' % (os.path.basename(self.ranlib[0]),
+                                   output_filename)
+            try:
+                self.spawn(self.ranlib + [output_filename],
+                           display = display)
+            except DistutilsExecError as e:
+                msg = str(e)
+                raise LibError(msg) from None
+    else:
+        log.debug("skipping %s (up-to-date)", output_filename)
+    return
+
+replace_method(UnixCCompiler, 'create_static_lib',
+               UnixCCompiler_create_static_lib)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/doc/ufuncs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/doc/ufuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7324168e1dc80c3452b170fec2060cddb040d54c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/doc/ufuncs.py
@@ -0,0 +1,138 @@
+"""
+===================
+Universal Functions
+===================
+
+Ufuncs are, generally speaking, mathematical functions or operations that are
+applied element-by-element to the contents of an array. That is, the result
+in each output array element only depends on the value in the corresponding
+input array (or arrays) and on no other array elements. NumPy comes with a
+large suite of ufuncs, and scipy extends that suite substantially. The simplest
+example is the addition operator: ::
+
+ >>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
+ array([1, 3, 2, 6])
+
+The ufunc module lists all the available ufuncs in numpy. Documentation on
+the specific ufuncs may be found in those modules. This documentation is
+intended to address the more general aspects of ufuncs common to most of
+them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
+have equivalent functions defined (e.g. add() for +)
+
+Type coercion
+=============
+
+What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
+two different types? What is the type of the result? Typically, the result is
+the higher of the two types. For example: ::
+
+ float32 + float64 -> float64
+ int8 + int32 -> int32
+ int16 + float32 -> float32
+ float32 + complex64 -> complex64
+
+There are some less obvious cases generally involving mixes of types
+(e.g. uints, ints and floats) where equal bit sizes for each are not
+capable of saving all the information in a different type of equivalent
+bit size. Some examples are int32 vs float32 or uint32 vs int32.
+Generally, the result is the higher type of larger size than both
+(if available). So: ::
+
+ int32 + float32 -> float64
+ uint32 + int32 -> int64
+
+Finally, the type coercion behavior when expressions involve Python
+scalars is different than that seen for arrays. Since Python has a
+limited number of types, combining a Python int with a dtype=np.int8
+array does not coerce to the higher type but instead, the type of the
+array prevails. So the rules for Python scalars combined with arrays is
+that the result will be that of the array equivalent the Python scalar
+if the Python scalar is of a higher 'kind' than the array (e.g., float
+vs. int), otherwise the resultant type will be that of the array.
+For example: ::
+
+  Python int + int8 -> int8
+  Python float + int8 -> float64
+
+ufunc methods
+=============
+
+Binary ufuncs support 4 methods.
+
+**.reduce(arr)** applies the binary operator to elements of the array in
+  sequence. For example: ::
+
+ >>> np.add.reduce(np.arange(10))  # adds all elements of array
+ 45
+
+For multidimensional arrays, the first dimension is reduced by default: ::
+
+ >>> np.add.reduce(np.arange(10).reshape(2,5))
+     array([ 5,  7,  9, 11, 13])
+
+The axis keyword can be used to specify different axes to reduce: ::
+
+ >>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
+ array([10, 35])
+
+**.accumulate(arr)** applies the binary operator and generates an
+equivalently shaped array that includes the accumulated amount for each
+element of the array. A couple examples: ::
+
+ >>> np.add.accumulate(np.arange(10))
+ array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45])
+ >>> np.multiply.accumulate(np.arange(1,9))
+ array([    1,     2,     6,    24,   120,   720,  5040, 40320])
+
+The behavior for multidimensional arrays is the same as for .reduce(),
+as is the use of the axis keyword).
+
+**.reduceat(arr,indices)** allows one to apply reduce to selected parts
+  of an array. It is a difficult method to understand. See the documentation
+  at:
+
+**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
+  arr2. It will work on multidimensional arrays (the shape of the result is
+  the concatenation of the two input shapes.: ::
+
+ >>> np.multiply.outer(np.arange(3),np.arange(4))
+ array([[0, 0, 0, 0],
+        [0, 1, 2, 3],
+        [0, 2, 4, 6]])
+
+Output arguments
+================
+
+All ufuncs accept an optional output array. The array must be of the expected
+output shape. Beware that if the type of the output array is of a different
+(and lower) type than the output result, the results may be silently truncated
+or otherwise corrupted in the downcast to the lower type. This usage is useful
+when one wants to avoid creating large temporary arrays and instead allows one
+to reuse the same array memory repeatedly (at the expense of not being able to
+use more convenient operator notation in expressions). Note that when the
+output argument is used, the ufunc still returns a reference to the result.
+
+ >>> x = np.arange(2)
+ >>> np.add(np.arange(2, dtype=float), np.arange(2, dtype=float), x,
+ ...        casting='unsafe')
+ array([0, 2])
+ >>> x
+ array([0, 2])
+
+and & or as ufuncs
+==================
+
+Invariably people try to use the python 'and' and 'or' as logical operators
+(and quite understandably). But these operators do not behave as normal
+operators since Python treats these quite differently. They cannot be
+overloaded with array equivalents. Thus using 'and' or 'or' with an array
+results in an error. There are two alternatives:
+
+ 1) use the ufunc functions logical_and() and logical_or().
+ 2) use the bitwise operators & and \\|. The drawback of these is that if
+    the arguments to these operators are not boolean arrays, the result is
+    likely incorrect. On the other hand, most usages of logical_and and
+    logical_or are with boolean arrays. As long as one is careful, this is
+    a convenient way to apply these operators.
+
+"""
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/cb_rules.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/cb_rules.py
new file mode 100644
index 0000000000000000000000000000000000000000..faf8dd4013018a3fd1cfc1afdf62104ed98b16b1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/cb_rules.py
@@ -0,0 +1,644 @@
+"""
+Build call-back mechanism for f2py2e.
+
+Copyright 1999 -- 2011 Pearu Peterson all rights reserved.
+Copyright 2011 -- present NumPy Developers.
+Permission to use, modify, and distribute this software is given under the
+terms of the NumPy License.
+
+NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
+"""
+from . import __version__
+from .auxfuncs import (
+    applyrules, debugcapi, dictappend, errmess, getargs, hasnote, isarray,
+    iscomplex, iscomplexarray, iscomplexfunction, isfunction, isintent_c,
+    isintent_hide, isintent_in, isintent_inout, isintent_nothide,
+    isintent_out, isoptional, isrequired, isscalar, isstring,
+    isstringfunction, issubroutine, l_and, l_not, l_or, outmess, replace,
+    stripcomma, throw_error
+)
+from . import cfuncs
+
+f2py_version = __version__.version
+
+
+################## Rules for callback function ##############
+
+cb_routine_rules = {
+    'cbtypedefs': 'typedef #rctype#(*#name#_typedef)(#optargs_td##args_td##strarglens_td##noargs#);',
+    'body': """
+#begintitle#
+typedef struct {
+    PyObject *capi;
+    PyTupleObject *args_capi;
+    int nofargs;
+    jmp_buf jmpbuf;
+} #name#_t;
+
+#if defined(F2PY_THREAD_LOCAL_DECL) && !defined(F2PY_USE_PYTHON_TLS)
+
+static F2PY_THREAD_LOCAL_DECL #name#_t *_active_#name# = NULL;
+
+static #name#_t *swap_active_#name#(#name#_t *ptr) {
+    #name#_t *prev = _active_#name#;
+    _active_#name# = ptr;
+    return prev;
+}
+
+static #name#_t *get_active_#name#(void) {
+    return _active_#name#;
+}
+
+#else
+
+static #name#_t *swap_active_#name#(#name#_t *ptr) {
+    char *key = "__f2py_cb_#name#";
+    return (#name#_t *)F2PySwapThreadLocalCallbackPtr(key, ptr);
+}
+
+static #name#_t *get_active_#name#(void) {
+    char *key = "__f2py_cb_#name#";
+    return (#name#_t *)F2PyGetThreadLocalCallbackPtr(key);
+}
+
+#endif
+
+/*typedef #rctype#(*#name#_typedef)(#optargs_td##args_td##strarglens_td##noargs#);*/
+#static# #rctype# #callbackname# (#optargs##args##strarglens##noargs#) {
+    #name#_t cb_local = { NULL, NULL, 0 };
+    #name#_t *cb = NULL;
+    PyTupleObject *capi_arglist = NULL;
+    PyObject *capi_return = NULL;
+    PyObject *capi_tmp = NULL;
+    PyObject *capi_arglist_list = NULL;
+    int capi_j,capi_i = 0;
+    int capi_longjmp_ok = 1;
+#decl#
+#ifdef F2PY_REPORT_ATEXIT
+f2py_cb_start_clock();
+#endif
+    cb = get_active_#name#();
+    if (cb == NULL) {
+        capi_longjmp_ok = 0;
+        cb = &cb_local;
+    }
+    capi_arglist = cb->args_capi;
+    CFUNCSMESS(\"cb:Call-back function #name# (maxnofargs=#maxnofargs#(-#nofoptargs#))\\n\");
+    CFUNCSMESSPY(\"cb:#name#_capi=\",cb->capi);
+    if (cb->capi==NULL) {
+        capi_longjmp_ok = 0;
+        cb->capi = PyObject_GetAttrString(#modulename#_module,\"#argname#\");
+        CFUNCSMESSPY(\"cb:#name#_capi=\",cb->capi);
+    }
+    if (cb->capi==NULL) {
+        PyErr_SetString(#modulename#_error,\"cb: Callback #argname# not defined (as an argument or module #modulename# attribute).\\n\");
+        goto capi_fail;
+    }
+    if (F2PyCapsule_Check(cb->capi)) {
+    #name#_typedef #name#_cptr;
+    #name#_cptr = F2PyCapsule_AsVoidPtr(cb->capi);
+    #returncptr#(*#name#_cptr)(#optargs_nm##args_nm##strarglens_nm#);
+    #return#
+    }
+    if (capi_arglist==NULL) {
+        capi_longjmp_ok = 0;
+        capi_tmp = PyObject_GetAttrString(#modulename#_module,\"#argname#_extra_args\");
+        if (capi_tmp) {
+            capi_arglist = (PyTupleObject *)PySequence_Tuple(capi_tmp);
+            Py_DECREF(capi_tmp);
+            if (capi_arglist==NULL) {
+                PyErr_SetString(#modulename#_error,\"Failed to convert #modulename#.#argname#_extra_args to tuple.\\n\");
+                goto capi_fail;
+            }
+        } else {
+            PyErr_Clear();
+            capi_arglist = (PyTupleObject *)Py_BuildValue(\"()\");
+        }
+    }
+    if (capi_arglist == NULL) {
+        PyErr_SetString(#modulename#_error,\"Callback #argname# argument list is not set.\\n\");
+        goto capi_fail;
+    }
+#setdims#
+#ifdef PYPY_VERSION
+#define CAPI_ARGLIST_SETITEM(idx, value) PyList_SetItem((PyObject *)capi_arglist_list, idx, value)
+    capi_arglist_list = PySequence_List((PyObject *)capi_arglist);
+    if (capi_arglist_list == NULL) goto capi_fail;
+#else
+#define CAPI_ARGLIST_SETITEM(idx, value) PyTuple_SetItem((PyObject *)capi_arglist, idx, value)
+#endif
+#pyobjfrom#
+#undef CAPI_ARGLIST_SETITEM
+#ifdef PYPY_VERSION
+    CFUNCSMESSPY(\"cb:capi_arglist=\",capi_arglist_list);
+#else
+    CFUNCSMESSPY(\"cb:capi_arglist=\",capi_arglist);
+#endif
+    CFUNCSMESS(\"cb:Call-back calling Python function #argname#.\\n\");
+#ifdef F2PY_REPORT_ATEXIT
+f2py_cb_start_call_clock();
+#endif
+#ifdef PYPY_VERSION
+    capi_return = PyObject_CallObject(cb->capi,(PyObject *)capi_arglist_list);
+    Py_DECREF(capi_arglist_list);
+    capi_arglist_list = NULL;
+#else
+    capi_return = PyObject_CallObject(cb->capi,(PyObject *)capi_arglist);
+#endif
+#ifdef F2PY_REPORT_ATEXIT
+f2py_cb_stop_call_clock();
+#endif
+    CFUNCSMESSPY(\"cb:capi_return=\",capi_return);
+    if (capi_return == NULL) {
+        fprintf(stderr,\"capi_return is NULL\\n\");
+        goto capi_fail;
+    }
+    if (capi_return == Py_None) {
+        Py_DECREF(capi_return);
+        capi_return = Py_BuildValue(\"()\");
+    }
+    else if (!PyTuple_Check(capi_return)) {
+        capi_return = Py_BuildValue(\"(N)\",capi_return);
+    }
+    capi_j = PyTuple_Size(capi_return);
+    capi_i = 0;
+#frompyobj#
+    CFUNCSMESS(\"cb:#name#:successful\\n\");
+    Py_DECREF(capi_return);
+#ifdef F2PY_REPORT_ATEXIT
+f2py_cb_stop_clock();
+#endif
+    goto capi_return_pt;
+capi_fail:
+    fprintf(stderr,\"Call-back #name# failed.\\n\");
+    Py_XDECREF(capi_return);
+    Py_XDECREF(capi_arglist_list);
+    if (capi_longjmp_ok) {
+        longjmp(cb->jmpbuf,-1);
+    }
+capi_return_pt:
+    ;
+#return#
+}
+#endtitle#
+""",
+    'need': ['setjmp.h', 'CFUNCSMESS', 'F2PY_THREAD_LOCAL_DECL'],
+    'maxnofargs': '#maxnofargs#',
+    'nofoptargs': '#nofoptargs#',
+    'docstr': """\
+    def #argname#(#docsignature#): return #docreturn#\\n\\
+#docstrsigns#""",
+    'latexdocstr': """
+{{}\\verb@def #argname#(#latexdocsignature#): return #docreturn#@{}}
+#routnote#
+
+#latexdocstrsigns#""",
+    'docstrshort': 'def #argname#(#docsignature#): return #docreturn#'
+}
+cb_rout_rules = [
+    {  # Init
+        'separatorsfor': {'decl': '\n',
+                          'args': ',', 'optargs': '', 'pyobjfrom': '\n', 'freemem': '\n',
+                          'args_td': ',', 'optargs_td': '',
+                          'args_nm': ',', 'optargs_nm': '',
+                          'frompyobj': '\n', 'setdims': '\n',
+                          'docstrsigns': '\\n"\n"',
+                          'latexdocstrsigns': '\n',
+                          'latexdocstrreq': '\n', 'latexdocstropt': '\n',
+                          'latexdocstrout': '\n', 'latexdocstrcbs': '\n',
+                          },
+        'decl': '/*decl*/', 'pyobjfrom': '/*pyobjfrom*/', 'frompyobj': '/*frompyobj*/',
+        'args': [], 'optargs': '', 'return': '', 'strarglens': '', 'freemem': '/*freemem*/',
+        'args_td': [], 'optargs_td': '', 'strarglens_td': '',
+        'args_nm': [], 'optargs_nm': '', 'strarglens_nm': '',
+        'noargs': '',
+        'setdims': '/*setdims*/',
+        'docstrsigns': '', 'latexdocstrsigns': '',
+        'docstrreq': '    Required arguments:',
+        'docstropt': '    Optional arguments:',
+        'docstrout': '    Return objects:',
+        'docstrcbs': '    Call-back functions:',
+        'docreturn': '', 'docsign': '', 'docsignopt': '',
+        'latexdocstrreq': '\\noindent Required arguments:',
+        'latexdocstropt': '\\noindent Optional arguments:',
+        'latexdocstrout': '\\noindent Return objects:',
+        'latexdocstrcbs': '\\noindent Call-back functions:',
+        'routnote': {hasnote: '--- #note#', l_not(hasnote): ''},
+    }, {  # Function
+        'decl': '    #ctype# return_value = 0;',
+        'frompyobj': [
+            {debugcapi: '    CFUNCSMESS("cb:Getting return_value->");'},
+            '''\
+    if (capi_j>capi_i) {
+        GETSCALARFROMPYTUPLE(capi_return,capi_i++,&return_value,#ctype#,
+          "#ctype#_from_pyobj failed in converting return_value of"
+          " call-back function #name# to C #ctype#\\n");
+    } else {
+        fprintf(stderr,"Warning: call-back function #name# did not provide"
+                       " return value (index=%d, type=#ctype#)\\n",capi_i);
+    }''',
+            {debugcapi:
+             '    fprintf(stderr,"#showvalueformat#.\\n",return_value);'}
+        ],
+        'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'}, 'GETSCALARFROMPYTUPLE'],
+        'return': '    return return_value;',
+        '_check': l_and(isfunction, l_not(isstringfunction), l_not(iscomplexfunction))
+    },
+    {  # String function
+        'pyobjfrom': {debugcapi: '    fprintf(stderr,"debug-capi:cb:#name#:%d:\\n",return_value_len);'},
+        'args': '#ctype# return_value,int return_value_len',
+        'args_nm': 'return_value,&return_value_len',
+        'args_td': '#ctype# ,int',
+        'frompyobj': [
+            {debugcapi: '    CFUNCSMESS("cb:Getting return_value->\\"");'},
+            """\
+    if (capi_j>capi_i) {
+        GETSTRFROMPYTUPLE(capi_return,capi_i++,return_value,return_value_len);
+    } else {
+        fprintf(stderr,"Warning: call-back function #name# did not provide"
+                       " return value (index=%d, type=#ctype#)\\n",capi_i);
+    }""",
+            {debugcapi:
+             '    fprintf(stderr,"#showvalueformat#\\".\\n",return_value);'}
+        ],
+        'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'},
+                 'string.h', 'GETSTRFROMPYTUPLE'],
+        'return': 'return;',
+        '_check': isstringfunction
+    },
+    {  # Complex function
+        'optargs': """
+#ifndef F2PY_CB_RETURNCOMPLEX
+#ctype# *return_value
+#endif
+""",
+        'optargs_nm': """
+#ifndef F2PY_CB_RETURNCOMPLEX
+return_value
+#endif
+""",
+        'optargs_td': """
+#ifndef F2PY_CB_RETURNCOMPLEX
+#ctype# *
+#endif
+""",
+        'decl': """
+#ifdef F2PY_CB_RETURNCOMPLEX
+    #ctype# return_value = {0, 0};
+#endif
+""",
+        'frompyobj': [
+            {debugcapi: '    CFUNCSMESS("cb:Getting return_value->");'},
+            """\
+    if (capi_j>capi_i) {
+#ifdef F2PY_CB_RETURNCOMPLEX
+        GETSCALARFROMPYTUPLE(capi_return,capi_i++,&return_value,#ctype#,
+          \"#ctype#_from_pyobj failed in converting return_value of call-back\"
+          \" function #name# to C #ctype#\\n\");
+#else
+        GETSCALARFROMPYTUPLE(capi_return,capi_i++,return_value,#ctype#,
+          \"#ctype#_from_pyobj failed in converting return_value of call-back\"
+          \" function #name# to C #ctype#\\n\");
+#endif
+    } else {
+        fprintf(stderr,
+                \"Warning: call-back function #name# did not provide\"
+                \" return value (index=%d, type=#ctype#)\\n\",capi_i);
+    }""",
+            {debugcapi: """\
+#ifdef F2PY_CB_RETURNCOMPLEX
+    fprintf(stderr,\"#showvalueformat#.\\n\",(return_value).r,(return_value).i);
+#else
+    fprintf(stderr,\"#showvalueformat#.\\n\",(*return_value).r,(*return_value).i);
+#endif
+"""}
+        ],
+        'return': """
+#ifdef F2PY_CB_RETURNCOMPLEX
+    return return_value;
+#else
+    return;
+#endif
+""",
+        'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'},
+                 'string.h', 'GETSCALARFROMPYTUPLE', '#ctype#'],
+        '_check': iscomplexfunction
+    },
+    {'docstrout': '        #pydocsignout#',
+     'latexdocstrout': ['\\item[]{{}\\verb@#pydocsignout#@{}}',
+                        {hasnote: '--- #note#'}],
+     'docreturn': '#rname#,',
+     '_check': isfunction},
+    {'_check': issubroutine, 'return': 'return;'}
+]
+
+cb_arg_rules = [
+    {  # Doc
+        'docstropt': {l_and(isoptional, isintent_nothide): '        #pydocsign#'},
+        'docstrreq': {l_and(isrequired, isintent_nothide): '        #pydocsign#'},
+        'docstrout': {isintent_out: '        #pydocsignout#'},
+        'latexdocstropt': {l_and(isoptional, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
+                                                                 {hasnote: '--- #note#'}]},
+        'latexdocstrreq': {l_and(isrequired, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
+                                                                 {hasnote: '--- #note#'}]},
+        'latexdocstrout': {isintent_out: ['\\item[]{{}\\verb@#pydocsignout#@{}}',
+                                          {l_and(hasnote, isintent_hide): '--- #note#',
+                                           l_and(hasnote, isintent_nothide): '--- See above.'}]},
+        'docsign': {l_and(isrequired, isintent_nothide): '#varname#,'},
+        'docsignopt': {l_and(isoptional, isintent_nothide): '#varname#,'},
+        'depend': ''
+    },
+    {
+        'args': {
+            l_and(isscalar, isintent_c): '#ctype# #varname_i#',
+            l_and(isscalar, l_not(isintent_c)): '#ctype# *#varname_i#_cb_capi',
+            isarray: '#ctype# *#varname_i#',
+            isstring: '#ctype# #varname_i#'
+        },
+        'args_nm': {
+            l_and(isscalar, isintent_c): '#varname_i#',
+            l_and(isscalar, l_not(isintent_c)): '#varname_i#_cb_capi',
+            isarray: '#varname_i#',
+            isstring: '#varname_i#'
+        },
+        'args_td': {
+            l_and(isscalar, isintent_c): '#ctype#',
+            l_and(isscalar, l_not(isintent_c)): '#ctype# *',
+            isarray: '#ctype# *',
+            isstring: '#ctype#'
+        },
+        'need': {l_or(isscalar, isarray, isstring): '#ctype#'},
+        # untested with multiple args
+        'strarglens': {isstring: ',int #varname_i#_cb_len'},
+        'strarglens_td': {isstring: ',int'},  # untested with multiple args
+        # untested with multiple args
+        'strarglens_nm': {isstring: ',#varname_i#_cb_len'},
+    },
+    {  # Scalars
+        'decl': {l_not(isintent_c): '    #ctype# #varname_i#=(*#varname_i#_cb_capi);'},
+        'error': {l_and(isintent_c, isintent_out,
+                        throw_error('intent(c,out) is forbidden for callback scalar arguments')):
+                  ''},
+        'frompyobj': [{debugcapi: '    CFUNCSMESS("cb:Getting #varname#->");'},
+                      {isintent_out:
+                       '    if (capi_j>capi_i)\n        GETSCALARFROMPYTUPLE(capi_return,capi_i++,#varname_i#_cb_capi,#ctype#,"#ctype#_from_pyobj failed in converting argument #varname# of call-back function #name# to C #ctype#\\n");'},
+                      {l_and(debugcapi, l_and(l_not(iscomplex), isintent_c)):
+                          '    fprintf(stderr,"#showvalueformat#.\\n",#varname_i#);'},
+                      {l_and(debugcapi, l_and(l_not(iscomplex), l_not( isintent_c))):
+                          '    fprintf(stderr,"#showvalueformat#.\\n",*#varname_i#_cb_capi);'},
+                      {l_and(debugcapi, l_and(iscomplex, isintent_c)):
+                          '    fprintf(stderr,"#showvalueformat#.\\n",(#varname_i#).r,(#varname_i#).i);'},
+                      {l_and(debugcapi, l_and(iscomplex, l_not( isintent_c))):
+                          '    fprintf(stderr,"#showvalueformat#.\\n",(*#varname_i#_cb_capi).r,(*#varname_i#_cb_capi).i);'},
+                      ],
+        'need': [{isintent_out: ['#ctype#_from_pyobj', 'GETSCALARFROMPYTUPLE']},
+                 {debugcapi: 'CFUNCSMESS'}],
+        '_check': isscalar
+    }, {
+        'pyobjfrom': [{isintent_in: """\
+    if (cb->nofargs>capi_i)
+        if (CAPI_ARGLIST_SETITEM(capi_i++,pyobj_from_#ctype#1(#varname_i#)))
+            goto capi_fail;"""},
+                      {isintent_inout: """\
+    if (cb->nofargs>capi_i)
+        if (CAPI_ARGLIST_SETITEM(capi_i++,pyarr_from_p_#ctype#1(#varname_i#_cb_capi)))
+            goto capi_fail;"""}],
+        'need': [{isintent_in: 'pyobj_from_#ctype#1'},
+                 {isintent_inout: 'pyarr_from_p_#ctype#1'},
+                 {iscomplex: '#ctype#'}],
+        '_check': l_and(isscalar, isintent_nothide),
+        '_optional': ''
+    }, {  # String
+        'frompyobj': [{debugcapi: '    CFUNCSMESS("cb:Getting #varname#->\\"");'},
+                      """    if (capi_j>capi_i)
+        GETSTRFROMPYTUPLE(capi_return,capi_i++,#varname_i#,#varname_i#_cb_len);""",
+                      {debugcapi:
+                       '    fprintf(stderr,"#showvalueformat#\\":%d:.\\n",#varname_i#,#varname_i#_cb_len);'},
+                      ],
+        'need': ['#ctype#', 'GETSTRFROMPYTUPLE',
+                 {debugcapi: 'CFUNCSMESS'}, 'string.h'],
+        '_check': l_and(isstring, isintent_out)
+    }, {
+        'pyobjfrom': [
+            {debugcapi:
+             ('    fprintf(stderr,"debug-capi:cb:#varname#=#showvalueformat#:'
+              '%d:\\n",#varname_i#,#varname_i#_cb_len);')},
+            {isintent_in: """\
+    if (cb->nofargs>capi_i)
+        if (CAPI_ARGLIST_SETITEM(capi_i++,pyobj_from_#ctype#1size(#varname_i#,#varname_i#_cb_len)))
+            goto capi_fail;"""},
+                      {isintent_inout: """\
+    if (cb->nofargs>capi_i) {
+        int #varname_i#_cb_dims[] = {#varname_i#_cb_len};
+        if (CAPI_ARGLIST_SETITEM(capi_i++,pyarr_from_p_#ctype#1(#varname_i#,#varname_i#_cb_dims)))
+            goto capi_fail;
+    }"""}],
+        'need': [{isintent_in: 'pyobj_from_#ctype#1size'},
+                 {isintent_inout: 'pyarr_from_p_#ctype#1'}],
+        '_check': l_and(isstring, isintent_nothide),
+        '_optional': ''
+    },
+    # Array ...
+    {
+        'decl': '    npy_intp #varname_i#_Dims[#rank#] = {#rank*[-1]#};',
+        'setdims': '    #cbsetdims#;',
+        '_check': isarray,
+        '_depend': ''
+    },
+    {
+        'pyobjfrom': [{debugcapi: '    fprintf(stderr,"debug-capi:cb:#varname#\\n");'},
+                      {isintent_c: """\
+    if (cb->nofargs>capi_i) {
+        /* tmp_arr will be inserted to capi_arglist_list that will be
+           destroyed when leaving callback function wrapper together
+           with tmp_arr. */
+        PyArrayObject *tmp_arr = (PyArrayObject *)PyArray_New(&PyArray_Type,
+          #rank#,#varname_i#_Dims,#atype#,NULL,(char*)#varname_i#,#elsize#,
+          NPY_ARRAY_CARRAY,NULL);
+""",
+                       l_not(isintent_c): """\
+    if (cb->nofargs>capi_i) {
+        /* tmp_arr will be inserted to capi_arglist_list that will be
+           destroyed when leaving callback function wrapper together
+           with tmp_arr. */
+        PyArrayObject *tmp_arr = (PyArrayObject *)PyArray_New(&PyArray_Type,
+          #rank#,#varname_i#_Dims,#atype#,NULL,(char*)#varname_i#,#elsize#,
+          NPY_ARRAY_FARRAY,NULL);
+""",
+                       },
+                      """
+        if (tmp_arr==NULL)
+            goto capi_fail;
+        if (CAPI_ARGLIST_SETITEM(capi_i++,(PyObject *)tmp_arr))
+            goto capi_fail;
+}"""],
+        '_check': l_and(isarray, isintent_nothide, l_or(isintent_in, isintent_inout)),
+        '_optional': '',
+    }, {
+        'frompyobj': [{debugcapi: '    CFUNCSMESS("cb:Getting #varname#->");'},
+                      """    if (capi_j>capi_i) {
+        PyArrayObject *rv_cb_arr = NULL;
+        if ((capi_tmp = PyTuple_GetItem(capi_return,capi_i++))==NULL) goto capi_fail;
+        rv_cb_arr =  array_from_pyobj(#atype#,#varname_i#_Dims,#rank#,F2PY_INTENT_IN""",
+                      {isintent_c: '|F2PY_INTENT_C'},
+                      """,capi_tmp);
+        if (rv_cb_arr == NULL) {
+            fprintf(stderr,\"rv_cb_arr is NULL\\n\");
+            goto capi_fail;
+        }
+        MEMCOPY(#varname_i#,PyArray_DATA(rv_cb_arr),PyArray_NBYTES(rv_cb_arr));
+        if (capi_tmp != (PyObject *)rv_cb_arr) {
+            Py_DECREF(rv_cb_arr);
+        }
+    }""",
+                      {debugcapi: '    fprintf(stderr,"<-.\\n");'},
+                      ],
+        'need': ['MEMCOPY', {iscomplexarray: '#ctype#'}],
+        '_check': l_and(isarray, isintent_out)
+    }, {
+        'docreturn': '#varname#,',
+        '_check': isintent_out
+    }
+]
+
+################## Build call-back module #############
+cb_map = {}
+
+
+def buildcallbacks(m):
+    cb_map[m['name']] = []
+    for bi in m['body']:
+        if bi['block'] == 'interface':
+            for b in bi['body']:
+                if b:
+                    buildcallback(b, m['name'])
+                else:
+                    errmess('warning: empty body for %s\n' % (m['name']))
+
+
+def buildcallback(rout, um):
+    from . import capi_maps
+
+    outmess('    Constructing call-back function "cb_%s_in_%s"\n' %
+            (rout['name'], um))
+    args, depargs = getargs(rout)
+    capi_maps.depargs = depargs
+    var = rout['vars']
+    vrd = capi_maps.cb_routsign2map(rout, um)
+    rd = dictappend({}, vrd)
+    cb_map[um].append([rout['name'], rd['name']])
+    for r in cb_rout_rules:
+        if ('_check' in r and r['_check'](rout)) or ('_check' not in r):
+            ar = applyrules(r, vrd, rout)
+            rd = dictappend(rd, ar)
+    savevrd = {}
+    for i, a in enumerate(args):
+        vrd = capi_maps.cb_sign2map(a, var[a], index=i)
+        savevrd[a] = vrd
+        for r in cb_arg_rules:
+            if '_depend' in r:
+                continue
+            if '_optional' in r and isoptional(var[a]):
+                continue
+            if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
+                ar = applyrules(r, vrd, var[a])
+                rd = dictappend(rd, ar)
+                if '_break' in r:
+                    break
+    for a in args:
+        vrd = savevrd[a]
+        for r in cb_arg_rules:
+            if '_depend' in r:
+                continue
+            if ('_optional' not in r) or ('_optional' in r and isrequired(var[a])):
+                continue
+            if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
+                ar = applyrules(r, vrd, var[a])
+                rd = dictappend(rd, ar)
+                if '_break' in r:
+                    break
+    for a in depargs:
+        vrd = savevrd[a]
+        for r in cb_arg_rules:
+            if '_depend' not in r:
+                continue
+            if '_optional' in r:
+                continue
+            if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
+                ar = applyrules(r, vrd, var[a])
+                rd = dictappend(rd, ar)
+                if '_break' in r:
+                    break
+    if 'args' in rd and 'optargs' in rd:
+        if isinstance(rd['optargs'], list):
+            rd['optargs'] = rd['optargs'] + ["""
+#ifndef F2PY_CB_RETURNCOMPLEX
+,
+#endif
+"""]
+            rd['optargs_nm'] = rd['optargs_nm'] + ["""
+#ifndef F2PY_CB_RETURNCOMPLEX
+,
+#endif
+"""]
+            rd['optargs_td'] = rd['optargs_td'] + ["""
+#ifndef F2PY_CB_RETURNCOMPLEX
+,
+#endif
+"""]
+    if isinstance(rd['docreturn'], list):
+        rd['docreturn'] = stripcomma(
+            replace('#docreturn#', {'docreturn': rd['docreturn']}))
+    optargs = stripcomma(replace('#docsignopt#',
+                                 {'docsignopt': rd['docsignopt']}
+                                 ))
+    if optargs == '':
+        rd['docsignature'] = stripcomma(
+            replace('#docsign#', {'docsign': rd['docsign']}))
+    else:
+        rd['docsignature'] = replace('#docsign#[#docsignopt#]',
+                                     {'docsign': rd['docsign'],
+                                      'docsignopt': optargs,
+                                      })
+    rd['latexdocsignature'] = rd['docsignature'].replace('_', '\\_')
+    rd['latexdocsignature'] = rd['latexdocsignature'].replace(',', ', ')
+    rd['docstrsigns'] = []
+    rd['latexdocstrsigns'] = []
+    for k in ['docstrreq', 'docstropt', 'docstrout', 'docstrcbs']:
+        if k in rd and isinstance(rd[k], list):
+            rd['docstrsigns'] = rd['docstrsigns'] + rd[k]
+        k = 'latex' + k
+        if k in rd and isinstance(rd[k], list):
+            rd['latexdocstrsigns'] = rd['latexdocstrsigns'] + rd[k][0:1] +\
+                ['\\begin{description}'] + rd[k][1:] +\
+                ['\\end{description}']
+    if 'args' not in rd:
+        rd['args'] = ''
+        rd['args_td'] = ''
+        rd['args_nm'] = ''
+    if not (rd.get('args') or rd.get('optargs') or rd.get('strarglens')):
+        rd['noargs'] = 'void'
+
+    ar = applyrules(cb_routine_rules, rd)
+    cfuncs.callbacks[rd['name']] = ar['body']
+    if isinstance(ar['need'], str):
+        ar['need'] = [ar['need']]
+
+    if 'need' in rd:
+        for t in cfuncs.typedefs.keys():
+            if t in rd['need']:
+                ar['need'].append(t)
+
+    cfuncs.typedefs_generated[rd['name'] + '_typedef'] = ar['cbtypedefs']
+    ar['need'].append(rd['name'] + '_typedef')
+    cfuncs.needs[rd['name']] = ar['need']
+
+    capi_maps.lcb2_map[rd['name']] = {'maxnofargs': ar['maxnofargs'],
+                                      'nofoptargs': ar['nofoptargs'],
+                                      'docstr': ar['docstr'],
+                                      'latexdocstr': ar['latexdocstr'],
+                                      'argname': rd['argname']
+                                      }
+    outmess('      %s\n' % (ar['docstrshort']))
+    return
+################## Build call-back function #############
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/setup.cfg b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..14669544cc9ec345373bf5f719e321348fc96a40
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/f2py/setup.cfg
@@ -0,0 +1,3 @@
+[bdist_rpm]
+doc_files = docs/
+            tests/
\ No newline at end of file
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f6e6373e856a428ac56bb4dc7ed36a45aac8f7b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.py
@@ -0,0 +1,215 @@
+"""
+Discrete Fourier Transform (:mod:`numpy.fft`)
+=============================================
+
+.. currentmodule:: numpy.fft
+
+The SciPy module `scipy.fft` is a more comprehensive superset
+of ``numpy.fft``, which includes only a basic set of routines.
+
+Standard FFTs
+-------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fft       Discrete Fourier transform.
+   ifft      Inverse discrete Fourier transform.
+   fft2      Discrete Fourier transform in two dimensions.
+   ifft2     Inverse discrete Fourier transform in two dimensions.
+   fftn      Discrete Fourier transform in N-dimensions.
+   ifftn     Inverse discrete Fourier transform in N dimensions.
+
+Real FFTs
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   rfft      Real discrete Fourier transform.
+   irfft     Inverse real discrete Fourier transform.
+   rfft2     Real discrete Fourier transform in two dimensions.
+   irfft2    Inverse real discrete Fourier transform in two dimensions.
+   rfftn     Real discrete Fourier transform in N dimensions.
+   irfftn    Inverse real discrete Fourier transform in N dimensions.
+
+Hermitian FFTs
+--------------
+
+.. autosummary::
+   :toctree: generated/
+
+   hfft      Hermitian discrete Fourier transform.
+   ihfft     Inverse Hermitian discrete Fourier transform.
+
+Helper routines
+---------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fftfreq   Discrete Fourier Transform sample frequencies.
+   rfftfreq  DFT sample frequencies (for usage with rfft, irfft).
+   fftshift  Shift zero-frequency component to center of spectrum.
+   ifftshift Inverse of fftshift.
+
+
+Background information
+----------------------
+
+Fourier analysis is fundamentally a method for expressing a function as a
+sum of periodic components, and for recovering the function from those
+components.  When both the function and its Fourier transform are
+replaced with discretized counterparts, it is called the discrete Fourier
+transform (DFT).  The DFT has become a mainstay of numerical computing in
+part because of a very fast algorithm for computing it, called the Fast
+Fourier Transform (FFT), which was known to Gauss (1805) and was brought
+to light in its current form by Cooley and Tukey [CT]_.  Press et al. [NR]_
+provide an accessible introduction to Fourier analysis and its
+applications.
+
+Because the discrete Fourier transform separates its input into
+components that contribute at discrete frequencies, it has a great number
+of applications in digital signal processing, e.g., for filtering, and in
+this context the discretized input to the transform is customarily
+referred to as a *signal*, which exists in the *time domain*.  The output
+is called a *spectrum* or *transform* and exists in the *frequency
+domain*.
+
+Implementation details
+----------------------
+
+There are many ways to define the DFT, varying in the sign of the
+exponent, normalization, etc.  In this implementation, the DFT is defined
+as
+
+.. math::
+   A_k =  \\sum_{m=0}^{n-1} a_m \\exp\\left\\{-2\\pi i{mk \\over n}\\right\\}
+   \\qquad k = 0,\\ldots,n-1.
+
+The DFT is in general defined for complex inputs and outputs, and a
+single-frequency component at linear frequency :math:`f` is
+represented by a complex exponential
+:math:`a_m = \\exp\\{2\\pi i\\,f m\\Delta t\\}`, where :math:`\\Delta t`
+is the sampling interval.
+
+The values in the result follow so-called "standard" order: If ``A =
+fft(a, n)``, then ``A[0]`` contains the zero-frequency term (the sum of
+the signal), which is always purely real for real inputs. Then ``A[1:n/2]``
+contains the positive-frequency terms, and ``A[n/2+1:]`` contains the
+negative-frequency terms, in order of decreasingly negative frequency.
+For an even number of input points, ``A[n/2]`` represents both positive and
+negative Nyquist frequency, and is also purely real for real input.  For
+an odd number of input points, ``A[(n-1)/2]`` contains the largest positive
+frequency, while ``A[(n+1)/2]`` contains the largest negative frequency.
+The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies
+of corresponding elements in the output.  The routine
+``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the
+zero-frequency components in the middle, and ``np.fft.ifftshift(A)`` undoes
+that shift.
+
+When the input `a` is a time-domain signal and ``A = fft(a)``, ``np.abs(A)``
+is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum.
+The phase spectrum is obtained by ``np.angle(A)``.
+
+The inverse DFT is defined as
+
+.. math::
+   a_m = \\frac{1}{n}\\sum_{k=0}^{n-1}A_k\\exp\\left\\{2\\pi i{mk\\over n}\\right\\}
+   \\qquad m = 0,\\ldots,n-1.
+
+It differs from the forward transform by the sign of the exponential
+argument and the default normalization by :math:`1/n`.
+
+Type Promotion
+--------------
+
+`numpy.fft` promotes ``float32`` and ``complex64`` arrays to ``float64`` and
+``complex128`` arrays respectively. For an FFT implementation that does not
+promote input arrays, see `scipy.fftpack`.
+
+Normalization
+-------------
+
+The argument ``norm`` indicates which direction of the pair of direct/inverse
+transforms is scaled and with what normalization factor.
+The default normalization (``"backward"``) has the direct (forward) transforms
+unscaled and the inverse (backward) transforms scaled by :math:`1/n`. It is
+possible to obtain unitary transforms by setting the keyword argument ``norm``
+to ``"ortho"`` so that both direct and inverse transforms are scaled by
+:math:`1/\\sqrt{n}`. Finally, setting the keyword argument ``norm`` to
+``"forward"`` has the direct transforms scaled by :math:`1/n` and the inverse
+transforms unscaled (i.e. exactly opposite to the default ``"backward"``).
+`None` is an alias of the default option ``"backward"`` for backward
+compatibility.
+
+Real and Hermitian transforms
+-----------------------------
+
+When the input is purely real, its transform is Hermitian, i.e., the
+component at frequency :math:`f_k` is the complex conjugate of the
+component at frequency :math:`-f_k`, which means that for real
+inputs there is no information in the negative frequency components that
+is not already available from the positive frequency components.
+The family of `rfft` functions is
+designed to operate on real inputs, and exploits this symmetry by
+computing only the positive frequency components, up to and including the
+Nyquist frequency.  Thus, ``n`` input points produce ``n/2+1`` complex
+output points.  The inverses of this family assumes the same symmetry of
+its input, and for an output of ``n`` points uses ``n/2+1`` input points.
+
+Correspondingly, when the spectrum is purely real, the signal is
+Hermitian.  The `hfft` family of functions exploits this symmetry by
+using ``n/2+1`` complex points in the input (time) domain for ``n`` real
+points in the frequency domain.
+
+In higher dimensions, FFTs are used, e.g., for image analysis and
+filtering.  The computational efficiency of the FFT means that it can
+also be a faster way to compute large convolutions, using the property
+that a convolution in the time domain is equivalent to a point-by-point
+multiplication in the frequency domain.
+
+Higher dimensions
+-----------------
+
+In two dimensions, the DFT is defined as
+
+.. math::
+   A_{kl} =  \\sum_{m=0}^{M-1} \\sum_{n=0}^{N-1}
+   a_{mn}\\exp\\left\\{-2\\pi i \\left({mk\\over M}+{nl\\over N}\\right)\\right\\}
+   \\qquad k = 0, \\ldots, M-1;\\quad l = 0, \\ldots, N-1,
+
+which extends in the obvious way to higher dimensions, and the inverses
+in higher dimensions also extend in the same way.
+
+References
+----------
+
+.. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
+        machine calculation of complex Fourier series," *Math. Comput.*
+        19: 297-301.
+
+.. [NR] Press, W., Teukolsky, S., Vetterline, W.T., and Flannery, B.P.,
+        2007, *Numerical Recipes: The Art of Scientific Computing*, ch.
+        12-13.  Cambridge Univ. Press, Cambridge, UK.
+
+Examples
+--------
+
+For examples, see the various functions.
+
+"""
+
+from . import _pocketfft, _helper
+# TODO: `numpy.fft.helper`` was deprecated in NumPy 2.0. It should
+# be deleted once downstream libraries move to `numpy.fft`.
+from . import helper
+from ._pocketfft import *
+from ._helper import *
+
+__all__ = _pocketfft.__all__.copy()
+__all__ += _helper.__all__
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..feac6a7ff8a16026847cc4935811f343d00fc2a7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/__init__.pyi
@@ -0,0 +1,43 @@
+from ._pocketfft import (
+    fft,
+    ifft,
+    rfft,
+    irfft,
+    hfft,
+    ihfft,
+    rfftn,
+    irfftn,
+    rfft2,
+    irfft2,
+    fft2,
+    ifft2,
+    fftn,
+    ifftn,
+)
+from ._helper import (
+    fftshift,
+    ifftshift,
+    fftfreq,
+    rfftfreq,
+)
+
+__all__ = [
+    "fft",
+    "ifft",
+    "rfft",
+    "irfft",
+    "hfft",
+    "ihfft",
+    "rfftn",
+    "irfftn",
+    "rfft2",
+    "irfft2",
+    "fft2",
+    "ifft2",
+    "fftn",
+    "ifftn",
+    "fftshift",
+    "ifftshift",
+    "fftfreq",
+    "rfftfreq",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_helper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6c114bab18d1fc2e12e10ccc2d10f2968b4aaa7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_helper.py
@@ -0,0 +1,235 @@
+"""
+Discrete Fourier Transforms - _helper.py
+
+"""
+from numpy._core import integer, empty, arange, asarray, roll
+from numpy._core.overrides import array_function_dispatch, set_module
+
+# Created by Pearu Peterson, September 2002
+
+__all__ = ['fftshift', 'ifftshift', 'fftfreq', 'rfftfreq']
+
+integer_types = (int, integer)
+
+
+def _fftshift_dispatcher(x, axes=None):
+    return (x,)
+
+
+@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
+def fftshift(x, axes=None):
+    """
+    Shift the zero-frequency component to the center of the spectrum.
+
+    This function swaps half-spaces for all axes listed (defaults to all).
+    Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    axes : int or shape tuple, optional
+        Axes over which to shift.  Default is None, which shifts all axes.
+
+    Returns
+    -------
+    y : ndarray
+        The shifted array.
+
+    See Also
+    --------
+    ifftshift : The inverse of `fftshift`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> freqs = np.fft.fftfreq(10, 0.1)
+    >>> freqs
+    array([ 0.,  1.,  2., ..., -3., -2., -1.])
+    >>> np.fft.fftshift(freqs)
+    array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+
+    Shift the zero-frequency component only along the second axis:
+
+    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
+    >>> freqs
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+    >>> np.fft.fftshift(freqs, axes=(1,))
+    array([[ 2.,  0.,  1.],
+           [-4.,  3.,  4.],
+           [-1., -3., -2.]])
+
+    """
+    x = asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [dim // 2 for dim in x.shape]
+    elif isinstance(axes, integer_types):
+        shift = x.shape[axes] // 2
+    else:
+        shift = [x.shape[ax] // 2 for ax in axes]
+
+    return roll(x, shift, axes)
+
+
+@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
+def ifftshift(x, axes=None):
+    """
+    The inverse of `fftshift`. Although identical for even-length `x`, the
+    functions differ by one sample for odd-length `x`.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    axes : int or shape tuple, optional
+        Axes over which to calculate.  Defaults to None, which shifts all axes.
+
+    Returns
+    -------
+    y : ndarray
+        The shifted array.
+
+    See Also
+    --------
+    fftshift : Shift zero-frequency component to the center of the spectrum.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
+    >>> freqs
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+    >>> np.fft.ifftshift(np.fft.fftshift(freqs))
+    array([[ 0.,  1.,  2.],
+           [ 3.,  4., -4.],
+           [-3., -2., -1.]])
+
+    """
+    x = asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [-(dim // 2) for dim in x.shape]
+    elif isinstance(axes, integer_types):
+        shift = -(x.shape[axes] // 2)
+    else:
+        shift = [-(x.shape[ax] // 2) for ax in axes]
+
+    return roll(x, shift, axes)
+
+
+@set_module('numpy.fft')
+def fftfreq(n, d=1.0, device=None):
+    """
+    Return the Discrete Fourier Transform sample frequencies.
+
+    The returned float array `f` contains the frequency bin centers in cycles
+    per unit of the sample spacing (with zero at the start).  For instance, if
+    the sample spacing is in seconds, then the frequency unit is cycles/second.
+
+    Given a window length `n` and a sample spacing `d`::
+
+      f = [0, 1, ...,   n/2-1,     -n/2, ..., -1] / (d*n)   if n is even
+      f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n)   if n is odd
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar, optional
+        Sample spacing (inverse of the sampling rate). Defaults to 1.
+    device : str, optional
+        The device on which to place the created array. Default: ``None``.
+        For Array-API interoperability only, so must be ``"cpu"`` if passed.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    f : ndarray
+        Array of length `n` containing the sample frequencies.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
+    >>> fourier = np.fft.fft(signal)
+    >>> n = signal.size
+    >>> timestep = 0.1
+    >>> freq = np.fft.fftfreq(n, d=timestep)
+    >>> freq
+    array([ 0.  ,  1.25,  2.5 , ..., -3.75, -2.5 , -1.25])
+
+    """
+    if not isinstance(n, integer_types):
+        raise ValueError("n should be an integer")
+    val = 1.0 / (n * d)
+    results = empty(n, int, device=device)
+    N = (n-1)//2 + 1
+    p1 = arange(0, N, dtype=int, device=device)
+    results[:N] = p1
+    p2 = arange(-(n//2), 0, dtype=int, device=device)
+    results[N:] = p2
+    return results * val
+
+
+@set_module('numpy.fft')
+def rfftfreq(n, d=1.0, device=None):
+    """
+    Return the Discrete Fourier Transform sample frequencies
+    (for usage with rfft, irfft).
+
+    The returned float array `f` contains the frequency bin centers in cycles
+    per unit of the sample spacing (with zero at the start).  For instance, if
+    the sample spacing is in seconds, then the frequency unit is cycles/second.
+
+    Given a window length `n` and a sample spacing `d`::
+
+      f = [0, 1, ...,     n/2-1,     n/2] / (d*n)   if n is even
+      f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n)   if n is odd
+
+    Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`)
+    the Nyquist frequency component is considered to be positive.
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar, optional
+        Sample spacing (inverse of the sampling rate). Defaults to 1.
+    device : str, optional
+        The device on which to place the created array. Default: ``None``.
+        For Array-API interoperability only, so must be ``"cpu"`` if passed.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    f : ndarray
+        Array of length ``n//2 + 1`` containing the sample frequencies.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
+    >>> fourier = np.fft.rfft(signal)
+    >>> n = signal.size
+    >>> sample_rate = 100
+    >>> freq = np.fft.fftfreq(n, d=1./sample_rate)
+    >>> freq
+    array([  0.,  10.,  20., ..., -30., -20., -10.])
+    >>> freq = np.fft.rfftfreq(n, d=1./sample_rate)
+    >>> freq
+    array([  0.,  10.,  20.,  30.,  40.,  50.])
+
+    """
+    if not isinstance(n, integer_types):
+        raise ValueError("n should be an integer")
+    val = 1.0/(n*d)
+    N = n//2 + 1
+    results = arange(0, N, dtype=int, device=device)
+    return results * val
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5b5bfdd8372915268dc5597b13d57d0db8923fa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.py
@@ -0,0 +1,1687 @@
+"""
+Discrete Fourier Transforms
+
+Routines in this module:
+
+fft(a, n=None, axis=-1, norm="backward")
+ifft(a, n=None, axis=-1, norm="backward")
+rfft(a, n=None, axis=-1, norm="backward")
+irfft(a, n=None, axis=-1, norm="backward")
+hfft(a, n=None, axis=-1, norm="backward")
+ihfft(a, n=None, axis=-1, norm="backward")
+fftn(a, s=None, axes=None, norm="backward")
+ifftn(a, s=None, axes=None, norm="backward")
+rfftn(a, s=None, axes=None, norm="backward")
+irfftn(a, s=None, axes=None, norm="backward")
+fft2(a, s=None, axes=(-2,-1), norm="backward")
+ifft2(a, s=None, axes=(-2, -1), norm="backward")
+rfft2(a, s=None, axes=(-2,-1), norm="backward")
+irfft2(a, s=None, axes=(-2, -1), norm="backward")
+
+i = inverse transform
+r = transform of purely real data
+h = Hermite transform
+n = n-dimensional transform
+2 = 2-dimensional transform
+(Note: 2D routines are just nD routines with different default
+behavior.)
+
+"""
+__all__ = ['fft', 'ifft', 'rfft', 'irfft', 'hfft', 'ihfft', 'rfftn',
+           'irfftn', 'rfft2', 'irfft2', 'fft2', 'ifft2', 'fftn', 'ifftn']
+
+import functools
+import warnings
+
+from numpy.lib.array_utils import normalize_axis_index
+from numpy._core import (asarray, empty_like, result_type,
+                         conjugate, take, sqrt, reciprocal)
+from . import _pocketfft_umath as pfu
+from numpy._core import overrides
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy.fft')
+
+
+# `inv_norm` is a float by which the result of the transform needs to be
+# divided. This replaces the original, more intuitive 'fct` parameter to avoid
+# divisions by zero (or alternatively additional checks) in the case of
+# zero-length axes during its computation.
+def _raw_fft(a, n, axis, is_real, is_forward, norm, out=None):
+    if n < 1:
+        raise ValueError(f"Invalid number of FFT data points ({n}) specified.")
+
+    # Calculate the normalization factor, passing in the array dtype to
+    # avoid precision loss in the possible sqrt or reciprocal.
+    if not is_forward:
+        norm = _swap_direction(norm)
+
+    real_dtype = result_type(a.real.dtype, 1.0)
+    if norm is None or norm == "backward":
+        fct = 1
+    elif norm == "ortho":
+        fct = reciprocal(sqrt(n, dtype=real_dtype))
+    elif norm == "forward":
+        fct = reciprocal(n, dtype=real_dtype)
+    else:
+        raise ValueError(f'Invalid norm value {norm}; should be "backward",'
+                         '"ortho" or "forward".')
+
+    n_out = n
+    if is_real:
+        if is_forward:
+            ufunc = pfu.rfft_n_even if n % 2 == 0 else pfu.rfft_n_odd
+            n_out = n // 2 + 1
+        else:
+            ufunc = pfu.irfft
+    else:
+        ufunc = pfu.fft if is_forward else pfu.ifft
+
+    axis = normalize_axis_index(axis, a.ndim)
+
+    if out is None:
+        if is_real and not is_forward:  # irfft, complex in, real output.
+            out_dtype = real_dtype
+        else:  # Others, complex output.
+            out_dtype = result_type(a.dtype, 1j)
+        out = empty_like(a, shape=a.shape[:axis] + (n_out,) + a.shape[axis+1:],
+                         dtype=out_dtype)
+    elif ((shape := getattr(out, "shape", None)) is not None
+          and (len(shape) != a.ndim or shape[axis] != n_out)):
+        raise ValueError("output array has wrong shape.")
+
+    return ufunc(a, fct, axes=[(axis,), (), (axis,)], out=out)
+
+
+_SWAP_DIRECTION_MAP = {"backward": "forward", None: "forward",
+                       "ortho": "ortho", "forward": "backward"}
+
+
+def _swap_direction(norm):
+    try:
+        return _SWAP_DIRECTION_MAP[norm]
+    except KeyError:
+        raise ValueError(f'Invalid norm value {norm}; should be "backward", '
+                         '"ortho" or "forward".') from None
+
+
+def _fft_dispatcher(a, n=None, axis=None, norm=None, out=None):
+    return (a, out)
+
+
+@array_function_dispatch(_fft_dispatcher)
+def fft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Compute the one-dimensional discrete Fourier Transform.
+
+    This function computes the one-dimensional *n*-point discrete Fourier
+    Transform (DFT) with the efficient Fast Fourier Transform (FFT)
+    algorithm [CT].
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex.
+    n : int, optional
+        Length of the transformed axis of the output.
+        If `n` is smaller than the length of the input, the input is cropped.
+        If it is larger, the input is padded with zeros.  If `n` is not given,
+        the length of the input along the axis specified by `axis` is used.
+    axis : int, optional
+        Axis over which to compute the FFT.  If not given, the last axis is
+        used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+
+    Raises
+    ------
+    IndexError
+        If `axis` is not a valid axis of `a`.
+
+    See Also
+    --------
+    numpy.fft : for definition of the DFT and conventions used.
+    ifft : The inverse of `fft`.
+    fft2 : The two-dimensional FFT.
+    fftn : The *n*-dimensional FFT.
+    rfftn : The *n*-dimensional FFT of real input.
+    fftfreq : Frequency bins for given FFT parameters.
+
+    Notes
+    -----
+    FFT (Fast Fourier Transform) refers to a way the discrete Fourier
+    Transform (DFT) can be calculated efficiently, by using symmetries in the
+    calculated terms.  The symmetry is highest when `n` is a power of 2, and
+    the transform is therefore most efficient for these sizes.
+
+    The DFT is defined, with the conventions used in this implementation, in
+    the documentation for the `numpy.fft` module.
+
+    References
+    ----------
+    .. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
+            machine calculation of complex Fourier series," *Math. Comput.*
+            19: 297-301.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
+    array([-2.33486982e-16+1.14423775e-17j,  8.00000000e+00-1.25557246e-15j,
+            2.33486982e-16+2.33486982e-16j,  0.00000000e+00+1.22464680e-16j,
+           -1.14423775e-17+2.33486982e-16j,  0.00000000e+00+5.20784380e-16j,
+            1.14423775e-17+1.14423775e-17j,  0.00000000e+00+1.22464680e-16j])
+
+    In this example, real input has an FFT which is Hermitian, i.e., symmetric
+    in the real part and anti-symmetric in the imaginary part, as described in
+    the `numpy.fft` documentation:
+
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.arange(256)
+    >>> sp = np.fft.fft(np.sin(t))
+    >>> freq = np.fft.fftfreq(t.shape[-1])
+    >>> plt.plot(freq, sp.real, freq, sp.imag)
+    [, ]
+    >>> plt.show()
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = a.shape[axis]
+    output = _raw_fft(a, n, axis, False, True, norm, out)
+    return output
+
+
+@array_function_dispatch(_fft_dispatcher)
+def ifft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Compute the one-dimensional inverse discrete Fourier Transform.
+
+    This function computes the inverse of the one-dimensional *n*-point
+    discrete Fourier transform computed by `fft`.  In other words,
+    ``ifft(fft(a)) == a`` to within numerical accuracy.
+    For a general description of the algorithm and definitions,
+    see `numpy.fft`.
+
+    The input should be ordered in the same way as is returned by `fft`,
+    i.e.,
+
+    * ``a[0]`` should contain the zero frequency term,
+    * ``a[1:n//2]`` should contain the positive-frequency terms,
+    * ``a[n//2 + 1:]`` should contain the negative-frequency terms, in
+      increasing order starting from the most negative frequency.
+
+    For an even number of input points, ``A[n//2]`` represents the sum of
+    the values at the positive and negative Nyquist frequencies, as the two
+    are aliased together. See `numpy.fft` for details.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex.
+    n : int, optional
+        Length of the transformed axis of the output.
+        If `n` is smaller than the length of the input, the input is cropped.
+        If it is larger, the input is padded with zeros.  If `n` is not given,
+        the length of the input along the axis specified by `axis` is used.
+        See notes about padding issues.
+    axis : int, optional
+        Axis over which to compute the inverse DFT.  If not given, the last
+        axis is used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+
+    Raises
+    ------
+    IndexError
+        If `axis` is not a valid axis of `a`.
+
+    See Also
+    --------
+    numpy.fft : An introduction, with definitions and general explanations.
+    fft : The one-dimensional (forward) FFT, of which `ifft` is the inverse
+    ifft2 : The two-dimensional inverse FFT.
+    ifftn : The n-dimensional inverse FFT.
+
+    Notes
+    -----
+    If the input parameter `n` is larger than the size of the input, the input
+    is padded by appending zeros at the end.  Even though this is the common
+    approach, it might lead to surprising results.  If a different padding is
+    desired, it must be performed before calling `ifft`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.fft.ifft([0, 4, 0, 0])
+    array([ 1.+0.j,  0.+1.j, -1.+0.j,  0.-1.j]) # may vary
+
+    Create and plot a band-limited signal with random phases:
+
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.arange(400)
+    >>> n = np.zeros((400,), dtype=complex)
+    >>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,)))
+    >>> s = np.fft.ifft(n)
+    >>> plt.plot(t, s.real, label='real')
+    []
+    >>> plt.plot(t, s.imag, '--', label='imaginary')
+    []
+    >>> plt.legend()
+    
+    >>> plt.show()
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = a.shape[axis]
+    output = _raw_fft(a, n, axis, False, False, norm, out=out)
+    return output
+
+
+@array_function_dispatch(_fft_dispatcher)
+def rfft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Compute the one-dimensional discrete Fourier Transform for real input.
+
+    This function computes the one-dimensional *n*-point discrete Fourier
+    Transform (DFT) of a real-valued array by means of an efficient algorithm
+    called the Fast Fourier Transform (FFT).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+    n : int, optional
+        Number of points along transformation axis in the input to use.
+        If `n` is smaller than the length of the input, the input is cropped.
+        If it is larger, the input is padded with zeros. If `n` is not given,
+        the length of the input along the axis specified by `axis` is used.
+    axis : int, optional
+        Axis over which to compute the FFT. If not given, the last axis is
+        used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+        If `n` is even, the length of the transformed axis is ``(n/2)+1``.
+        If `n` is odd, the length is ``(n+1)/2``.
+
+    Raises
+    ------
+    IndexError
+        If `axis` is not a valid axis of `a`.
+
+    See Also
+    --------
+    numpy.fft : For definition of the DFT and conventions used.
+    irfft : The inverse of `rfft`.
+    fft : The one-dimensional FFT of general (complex) input.
+    fftn : The *n*-dimensional FFT.
+    rfftn : The *n*-dimensional FFT of real input.
+
+    Notes
+    -----
+    When the DFT is computed for purely real input, the output is
+    Hermitian-symmetric, i.e. the negative frequency terms are just the complex
+    conjugates of the corresponding positive-frequency terms, and the
+    negative-frequency terms are therefore redundant.  This function does not
+    compute the negative frequency terms, and the length of the transformed
+    axis of the output is therefore ``n//2 + 1``.
+
+    When ``A = rfft(a)`` and fs is the sampling frequency, ``A[0]`` contains
+    the zero-frequency term 0*fs, which is real due to Hermitian symmetry.
+
+    If `n` is even, ``A[-1]`` contains the term representing both positive
+    and negative Nyquist frequency (+fs/2 and -fs/2), and must also be purely
+    real. If `n` is odd, there is no term at fs/2; ``A[-1]`` contains
+    the largest positive frequency (fs/2*(n-1)/n), and is complex in the
+    general case.
+
+    If the input `a` contains an imaginary part, it is silently discarded.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.fft.fft([0, 1, 0, 0])
+    array([ 1.+0.j,  0.-1.j, -1.+0.j,  0.+1.j]) # may vary
+    >>> np.fft.rfft([0, 1, 0, 0])
+    array([ 1.+0.j,  0.-1.j, -1.+0.j]) # may vary
+
+    Notice how the final element of the `fft` output is the complex conjugate
+    of the second element, for real input. For `rfft`, this symmetry is
+    exploited to compute only the non-negative frequency terms.
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = a.shape[axis]
+    output = _raw_fft(a, n, axis, True, True, norm, out=out)
+    return output
+
+
+@array_function_dispatch(_fft_dispatcher)
+def irfft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Computes the inverse of `rfft`.
+
+    This function computes the inverse of the one-dimensional *n*-point
+    discrete Fourier Transform of real input computed by `rfft`.
+    In other words, ``irfft(rfft(a), len(a)) == a`` to within numerical
+    accuracy. (See Notes below for why ``len(a)`` is necessary here.)
+
+    The input is expected to be in the form returned by `rfft`, i.e. the
+    real zero-frequency term followed by the complex positive frequency terms
+    in order of increasing frequency.  Since the discrete Fourier Transform of
+    real input is Hermitian-symmetric, the negative frequency terms are taken
+    to be the complex conjugates of the corresponding positive frequency terms.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array.
+    n : int, optional
+        Length of the transformed axis of the output.
+        For `n` output points, ``n//2+1`` input points are necessary.  If the
+        input is longer than this, it is cropped.  If it is shorter than this,
+        it is padded with zeros.  If `n` is not given, it is taken to be
+        ``2*(m-1)`` where ``m`` is the length of the input along the axis
+        specified by `axis`.
+    axis : int, optional
+        Axis over which to compute the inverse FFT. If not given, the last
+        axis is used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+        The length of the transformed axis is `n`, or, if `n` is not given,
+        ``2*(m-1)`` where ``m`` is the length of the transformed axis of the
+        input. To get an odd number of output points, `n` must be specified.
+
+    Raises
+    ------
+    IndexError
+        If `axis` is not a valid axis of `a`.
+
+    See Also
+    --------
+    numpy.fft : For definition of the DFT and conventions used.
+    rfft : The one-dimensional FFT of real input, of which `irfft` is inverse.
+    fft : The one-dimensional FFT.
+    irfft2 : The inverse of the two-dimensional FFT of real input.
+    irfftn : The inverse of the *n*-dimensional FFT of real input.
+
+    Notes
+    -----
+    Returns the real valued `n`-point inverse discrete Fourier transform
+    of `a`, where `a` contains the non-negative frequency terms of a
+    Hermitian-symmetric sequence. `n` is the length of the result, not the
+    input.
+
+    If you specify an `n` such that `a` must be zero-padded or truncated, the
+    extra/removed values will be added/removed at high frequencies. One can
+    thus resample a series to `m` points via Fourier interpolation by:
+    ``a_resamp = irfft(rfft(a), m)``.
+
+    The correct interpretation of the hermitian input depends on the length of
+    the original data, as given by `n`. This is because each input shape could
+    correspond to either an odd or even length signal. By default, `irfft`
+    assumes an even output length which puts the last entry at the Nyquist
+    frequency; aliasing with its symmetric counterpart. By Hermitian symmetry,
+    the value is thus treated as purely real. To avoid losing information, the
+    correct length of the real input **must** be given.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.fft.ifft([1, -1j, -1, 1j])
+    array([0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j]) # may vary
+    >>> np.fft.irfft([1, -1j, -1])
+    array([0.,  1.,  0.,  0.])
+
+    Notice how the last term in the input to the ordinary `ifft` is the
+    complex conjugate of the second term, and the output has zero imaginary
+    part everywhere.  When calling `irfft`, the negative frequencies are not
+    specified, and the output array is purely real.
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = (a.shape[axis] - 1) * 2
+    output = _raw_fft(a, n, axis, True, False, norm, out=out)
+    return output
+
+
+@array_function_dispatch(_fft_dispatcher)
+def hfft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Compute the FFT of a signal that has Hermitian symmetry, i.e., a real
+    spectrum.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array.
+    n : int, optional
+        Length of the transformed axis of the output. For `n` output
+        points, ``n//2 + 1`` input points are necessary.  If the input is
+        longer than this, it is cropped.  If it is shorter than this, it is
+        padded with zeros.  If `n` is not given, it is taken to be ``2*(m-1)``
+        where ``m`` is the length of the input along the axis specified by
+        `axis`.
+    axis : int, optional
+        Axis over which to compute the FFT. If not given, the last
+        axis is used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+        The length of the transformed axis is `n`, or, if `n` is not given,
+        ``2*m - 2`` where ``m`` is the length of the transformed axis of
+        the input. To get an odd number of output points, `n` must be
+        specified, for instance as ``2*m - 1`` in the typical case,
+
+    Raises
+    ------
+    IndexError
+        If `axis` is not a valid axis of `a`.
+
+    See also
+    --------
+    rfft : Compute the one-dimensional FFT for real input.
+    ihfft : The inverse of `hfft`.
+
+    Notes
+    -----
+    `hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
+    opposite case: here the signal has Hermitian symmetry in the time
+    domain and is real in the frequency domain. So here it's `hfft` for
+    which you must supply the length of the result if it is to be odd.
+
+    * even: ``ihfft(hfft(a, 2*len(a) - 2)) == a``, within roundoff error,
+    * odd: ``ihfft(hfft(a, 2*len(a) - 1)) == a``, within roundoff error.
+
+    The correct interpretation of the hermitian input depends on the length of
+    the original data, as given by `n`. This is because each input shape could
+    correspond to either an odd or even length signal. By default, `hfft`
+    assumes an even output length which puts the last entry at the Nyquist
+    frequency; aliasing with its symmetric counterpart. By Hermitian symmetry,
+    the value is thus treated as purely real. To avoid losing information, the
+    shape of the full signal **must** be given.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> signal = np.array([1, 2, 3, 4, 3, 2])
+    >>> np.fft.fft(signal)
+    array([15.+0.j,  -4.+0.j,   0.+0.j,  -1.-0.j,   0.+0.j,  -4.+0.j]) # may vary
+    >>> np.fft.hfft(signal[:4]) # Input first half of signal
+    array([15.,  -4.,   0.,  -1.,   0.,  -4.])
+    >>> np.fft.hfft(signal, 6)  # Input entire signal and truncate
+    array([15.,  -4.,   0.,  -1.,   0.,  -4.])
+
+
+    >>> signal = np.array([[1, 1.j], [-1.j, 2]])
+    >>> np.conj(signal.T) - signal   # check Hermitian symmetry
+    array([[ 0.-0.j,  -0.+0.j], # may vary
+           [ 0.+0.j,  0.-0.j]])
+    >>> freq_spectrum = np.fft.hfft(signal)
+    >>> freq_spectrum
+    array([[ 1.,  1.],
+           [ 2., -2.]])
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = (a.shape[axis] - 1) * 2
+    new_norm = _swap_direction(norm)
+    output = irfft(conjugate(a), n, axis, norm=new_norm, out=None)
+    return output
+
+
+@array_function_dispatch(_fft_dispatcher)
+def ihfft(a, n=None, axis=-1, norm=None, out=None):
+    """
+    Compute the inverse FFT of a signal that has Hermitian symmetry.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    n : int, optional
+        Length of the inverse FFT, the number of points along
+        transformation axis in the input to use.  If `n` is smaller than
+        the length of the input, the input is cropped.  If it is larger,
+        the input is padded with zeros. If `n` is not given, the length of
+        the input along the axis specified by `axis` is used.
+    axis : int, optional
+        Axis over which to compute the inverse FFT. If not given, the last
+        axis is used.
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axis
+        indicated by `axis`, or the last one if `axis` is not specified.
+        The length of the transformed axis is ``n//2 + 1``.
+
+    See also
+    --------
+    hfft, irfft
+
+    Notes
+    -----
+    `hfft`/`ihfft` are a pair analogous to `rfft`/`irfft`, but for the
+    opposite case: here the signal has Hermitian symmetry in the time
+    domain and is real in the frequency domain. So here it's `hfft` for
+    which you must supply the length of the result if it is to be odd:
+
+    * even: ``ihfft(hfft(a, 2*len(a) - 2)) == a``, within roundoff error,
+    * odd: ``ihfft(hfft(a, 2*len(a) - 1)) == a``, within roundoff error.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> spectrum = np.array([ 15, -4, 0, -1, 0, -4])
+    >>> np.fft.ifft(spectrum)
+    array([1.+0.j,  2.+0.j,  3.+0.j,  4.+0.j,  3.+0.j,  2.+0.j]) # may vary
+    >>> np.fft.ihfft(spectrum)
+    array([ 1.-0.j,  2.-0.j,  3.-0.j,  4.-0.j]) # may vary
+
+    """
+    a = asarray(a)
+    if n is None:
+        n = a.shape[axis]
+    new_norm = _swap_direction(norm)
+    out = rfft(a, n, axis, norm=new_norm, out=out)
+    return conjugate(out, out=out)
+
+
+def _cook_nd_args(a, s=None, axes=None, invreal=0):
+    if s is None:
+        shapeless = True
+        if axes is None:
+            s = list(a.shape)
+        else:
+            s = take(a.shape, axes)
+    else:
+        shapeless = False
+    s = list(s)
+    if axes is None:
+        if not shapeless:
+            msg = ("`axes` should not be `None` if `s` is not `None` "
+                   "(Deprecated in NumPy 2.0). In a future version of NumPy, "
+                   "this will raise an error and `s[i]` will correspond to "
+                   "the size along the transformed axis specified by "
+                   "`axes[i]`. To retain current behaviour, pass a sequence "
+                   "[0, ..., k-1] to `axes` for an array of dimension k.")
+            warnings.warn(msg, DeprecationWarning, stacklevel=3)
+        axes = list(range(-len(s), 0))
+    if len(s) != len(axes):
+        raise ValueError("Shape and axes have different lengths.")
+    if invreal and shapeless:
+        s[-1] = (a.shape[axes[-1]] - 1) * 2
+    if None in s:
+        msg = ("Passing an array containing `None` values to `s` is "
+               "deprecated in NumPy 2.0 and will raise an error in "
+               "a future version of NumPy. To use the default behaviour "
+               "of the corresponding 1-D transform, pass the value matching "
+               "the default for its `n` parameter. To use the default "
+               "behaviour for every axis, the `s` argument can be omitted.")
+        warnings.warn(msg, DeprecationWarning, stacklevel=3)
+    # use the whole input array along axis `i` if `s[i] == -1`
+    s = [a.shape[_a] if _s == -1 else _s for _s, _a in zip(s, axes)]
+    return s, axes
+
+
+def _raw_fftnd(a, s=None, axes=None, function=fft, norm=None, out=None):
+    a = asarray(a)
+    s, axes = _cook_nd_args(a, s, axes)
+    itl = list(range(len(axes)))
+    itl.reverse()
+    for ii in itl:
+        a = function(a, n=s[ii], axis=axes[ii], norm=norm, out=out)
+    return a
+
+
+def _fftn_dispatcher(a, s=None, axes=None, norm=None, out=None):
+    return (a, out)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def fftn(a, s=None, axes=None, norm=None, out=None):
+    """
+    Compute the N-dimensional discrete Fourier Transform.
+
+    This function computes the *N*-dimensional discrete Fourier Transform over
+    any number of axes in an *M*-dimensional array by means of the Fast Fourier
+    Transform (FFT).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex.
+    s : sequence of ints, optional
+        Shape (length of each transformed axis) of the output
+        (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
+        This corresponds to ``n`` for ``fft(x, n)``.
+        Along any axis, if the given shape is smaller than that of the input,
+        the input is cropped. If it is larger, the input is padded with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes specified
+        by `axes` is used.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the FFT.  If not given, the last ``len(s)``
+        axes are used, or all axes if `s` is also not specified.
+        Repeated indices in `axes` means that the transform over that axis is
+        performed multiple times.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must be explicitly specified too.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for all axes (and hence is
+        incompatible with passing in all but the trivial ``s``).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or by a combination of `s` and `a`,
+        as explained in the parameters section above.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    numpy.fft : Overall view of discrete Fourier transforms, with definitions
+        and conventions used.
+    ifftn : The inverse of `fftn`, the inverse *n*-dimensional FFT.
+    fft : The one-dimensional FFT, with definitions and conventions used.
+    rfftn : The *n*-dimensional FFT of real input.
+    fft2 : The two-dimensional FFT.
+    fftshift : Shifts zero-frequency terms to centre of array
+
+    Notes
+    -----
+    The output, analogously to `fft`, contains the term for zero frequency in
+    the low-order corner of all axes, the positive frequency terms in the
+    first half of all axes, the term for the Nyquist frequency in the middle
+    of all axes and the negative frequency terms in the second half of all
+    axes, in order of decreasingly negative frequency.
+
+    See `numpy.fft` for details, definitions and conventions used.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.mgrid[:3, :3, :3][0]
+    >>> np.fft.fftn(a, axes=(1, 2))
+    array([[[ 0.+0.j,   0.+0.j,   0.+0.j], # may vary
+            [ 0.+0.j,   0.+0.j,   0.+0.j],
+            [ 0.+0.j,   0.+0.j,   0.+0.j]],
+           [[ 9.+0.j,   0.+0.j,   0.+0.j],
+            [ 0.+0.j,   0.+0.j,   0.+0.j],
+            [ 0.+0.j,   0.+0.j,   0.+0.j]],
+           [[18.+0.j,   0.+0.j,   0.+0.j],
+            [ 0.+0.j,   0.+0.j,   0.+0.j],
+            [ 0.+0.j,   0.+0.j,   0.+0.j]]])
+    >>> np.fft.fftn(a, (2, 2), axes=(0, 1))
+    array([[[ 2.+0.j,  2.+0.j,  2.+0.j], # may vary
+            [ 0.+0.j,  0.+0.j,  0.+0.j]],
+           [[-2.+0.j, -2.+0.j, -2.+0.j],
+            [ 0.+0.j,  0.+0.j,  0.+0.j]]])
+
+    >>> import matplotlib.pyplot as plt
+    >>> [X, Y] = np.meshgrid(2 * np.pi * np.arange(200) / 12,
+    ...                      2 * np.pi * np.arange(200) / 34)
+    >>> S = np.sin(X) + np.cos(Y) + np.random.uniform(0, 1, X.shape)
+    >>> FS = np.fft.fftn(S)
+    >>> plt.imshow(np.log(np.abs(np.fft.fftshift(FS))**2))
+    
+    >>> plt.show()
+
+    """
+    return _raw_fftnd(a, s, axes, fft, norm, out=out)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def ifftn(a, s=None, axes=None, norm=None, out=None):
+    """
+    Compute the N-dimensional inverse discrete Fourier Transform.
+
+    This function computes the inverse of the N-dimensional discrete
+    Fourier Transform over any number of axes in an M-dimensional array by
+    means of the Fast Fourier Transform (FFT).  In other words,
+    ``ifftn(fftn(a)) == a`` to within numerical accuracy.
+    For a description of the definitions and conventions used, see `numpy.fft`.
+
+    The input, analogously to `ifft`, should be ordered in the same way as is
+    returned by `fftn`, i.e. it should have the term for zero frequency
+    in all axes in the low-order corner, the positive frequency terms in the
+    first half of all axes, the term for the Nyquist frequency in the middle
+    of all axes and the negative frequency terms in the second half of all
+    axes, in order of decreasingly negative frequency.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex.
+    s : sequence of ints, optional
+        Shape (length of each transformed axis) of the output
+        (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
+        This corresponds to ``n`` for ``ifft(x, n)``.
+        Along any axis, if the given shape is smaller than that of the input,
+        the input is cropped. If it is larger, the input is padded with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes specified
+        by `axes` is used. See notes for issue on `ifft` zero padding.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the IFFT.  If not given, the last ``len(s)``
+        axes are used, or all axes if `s` is also not specified.
+        Repeated indices in `axes` means that the inverse transform over that
+        axis is performed multiple times.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must be explicitly specified too.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for all axes (and hence is
+        incompatible with passing in all but the trivial ``s``).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or by a combination of `s` or `a`,
+        as explained in the parameters section above.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    numpy.fft : Overall view of discrete Fourier transforms, with definitions
+         and conventions used.
+    fftn : The forward *n*-dimensional FFT, of which `ifftn` is the inverse.
+    ifft : The one-dimensional inverse FFT.
+    ifft2 : The two-dimensional inverse FFT.
+    ifftshift : Undoes `fftshift`, shifts zero-frequency terms to beginning
+        of array.
+
+    Notes
+    -----
+    See `numpy.fft` for definitions and conventions used.
+
+    Zero-padding, analogously with `ifft`, is performed by appending zeros to
+    the input along the specified dimension.  Although this is the common
+    approach, it might lead to surprising results.  If another form of zero
+    padding is desired, it must be performed before `ifftn` is called.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.eye(4)
+    >>> np.fft.ifftn(np.fft.fftn(a, axes=(0,)), axes=(1,))
+    array([[1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j], # may vary
+           [0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j],
+           [0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
+           [0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j]])
+
+
+    Create and plot an image with band-limited frequency content:
+
+    >>> import matplotlib.pyplot as plt
+    >>> n = np.zeros((200,200), dtype=complex)
+    >>> n[60:80, 20:40] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20, 20)))
+    >>> im = np.fft.ifftn(n).real
+    >>> plt.imshow(im)
+    
+    >>> plt.show()
+
+    """
+    return _raw_fftnd(a, s, axes, ifft, norm, out=out)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def fft2(a, s=None, axes=(-2, -1), norm=None, out=None):
+    """
+    Compute the 2-dimensional discrete Fourier Transform.
+
+    This function computes the *n*-dimensional discrete Fourier Transform
+    over any axes in an *M*-dimensional array by means of the
+    Fast Fourier Transform (FFT).  By default, the transform is computed over
+    the last two axes of the input array, i.e., a 2-dimensional FFT.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex
+    s : sequence of ints, optional
+        Shape (length of each transformed axis) of the output
+        (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
+        This corresponds to ``n`` for ``fft(x, n)``.
+        Along each axis, if the given shape is smaller than that of the input,
+        the input is cropped. If it is larger, the input is padded with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes specified
+        by `axes` is used.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the FFT.  If not given, the last two
+        axes are used.  A repeated index in `axes` means the transform over
+        that axis is performed multiple times.  A one-element sequence means
+        that a one-dimensional FFT is performed. Default: ``(-2, -1)``.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must not be ``None``.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for all axes (and hence only the
+        last axis can have ``s`` not equal to the shape at that axis).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or the last two axes if `axes` is not given.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length, or `axes` not given and
+        ``len(s) != 2``.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    numpy.fft : Overall view of discrete Fourier transforms, with definitions
+         and conventions used.
+    ifft2 : The inverse two-dimensional FFT.
+    fft : The one-dimensional FFT.
+    fftn : The *n*-dimensional FFT.
+    fftshift : Shifts zero-frequency terms to the center of the array.
+        For two-dimensional input, swaps first and third quadrants, and second
+        and fourth quadrants.
+
+    Notes
+    -----
+    `fft2` is just `fftn` with a different default for `axes`.
+
+    The output, analogously to `fft`, contains the term for zero frequency in
+    the low-order corner of the transformed axes, the positive frequency terms
+    in the first half of these axes, the term for the Nyquist frequency in the
+    middle of the axes and the negative frequency terms in the second half of
+    the axes, in order of decreasingly negative frequency.
+
+    See `fftn` for details and a plotting example, and `numpy.fft` for
+    definitions and conventions used.
+
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.mgrid[:5, :5][0]
+    >>> np.fft.fft2(a)
+    array([[ 50.  +0.j        ,   0.  +0.j        ,   0.  +0.j        , # may vary
+              0.  +0.j        ,   0.  +0.j        ],
+           [-12.5+17.20477401j,   0.  +0.j        ,   0.  +0.j        ,
+              0.  +0.j        ,   0.  +0.j        ],
+           [-12.5 +4.0614962j ,   0.  +0.j        ,   0.  +0.j        ,
+              0.  +0.j        ,   0.  +0.j        ],
+           [-12.5 -4.0614962j ,   0.  +0.j        ,   0.  +0.j        ,
+              0.  +0.j        ,   0.  +0.j        ],
+           [-12.5-17.20477401j,   0.  +0.j        ,   0.  +0.j        ,
+              0.  +0.j        ,   0.  +0.j        ]])
+
+    """
+    return _raw_fftnd(a, s, axes, fft, norm, out=out)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def ifft2(a, s=None, axes=(-2, -1), norm=None, out=None):
+    """
+    Compute the 2-dimensional inverse discrete Fourier Transform.
+
+    This function computes the inverse of the 2-dimensional discrete Fourier
+    Transform over any number of axes in an M-dimensional array by means of
+    the Fast Fourier Transform (FFT).  In other words, ``ifft2(fft2(a)) == a``
+    to within numerical accuracy.  By default, the inverse transform is
+    computed over the last two axes of the input array.
+
+    The input, analogously to `ifft`, should be ordered in the same way as is
+    returned by `fft2`, i.e. it should have the term for zero frequency
+    in the low-order corner of the two axes, the positive frequency terms in
+    the first half of these axes, the term for the Nyquist frequency in the
+    middle of the axes and the negative frequency terms in the second half of
+    both axes, in order of decreasingly negative frequency.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, can be complex.
+    s : sequence of ints, optional
+        Shape (length of each axis) of the output (``s[0]`` refers to axis 0,
+        ``s[1]`` to axis 1, etc.).  This corresponds to `n` for ``ifft(x, n)``.
+        Along each axis, if the given shape is smaller than that of the input,
+        the input is cropped. If it is larger, the input is padded with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes specified
+        by `axes` is used.  See notes for issue on `ifft` zero padding.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the FFT.  If not given, the last two
+        axes are used.  A repeated index in `axes` means the transform over
+        that axis is performed multiple times.  A one-element sequence means
+        that a one-dimensional FFT is performed. Default: ``(-2, -1)``.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must not be ``None``.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for all axes (and hence is
+        incompatible with passing in all but the trivial ``s``).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or the last two axes if `axes` is not given.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length, or `axes` not given and
+        ``len(s) != 2``.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    numpy.fft : Overall view of discrete Fourier transforms, with definitions
+         and conventions used.
+    fft2 : The forward 2-dimensional FFT, of which `ifft2` is the inverse.
+    ifftn : The inverse of the *n*-dimensional FFT.
+    fft : The one-dimensional FFT.
+    ifft : The one-dimensional inverse FFT.
+
+    Notes
+    -----
+    `ifft2` is just `ifftn` with a different default for `axes`.
+
+    See `ifftn` for details and a plotting example, and `numpy.fft` for
+    definition and conventions used.
+
+    Zero-padding, analogously with `ifft`, is performed by appending zeros to
+    the input along the specified dimension.  Although this is the common
+    approach, it might lead to surprising results.  If another form of zero
+    padding is desired, it must be performed before `ifft2` is called.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = 4 * np.eye(4)
+    >>> np.fft.ifft2(a)
+    array([[1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j], # may vary
+           [0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j],
+           [0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
+           [0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j]])
+
+    """
+    return _raw_fftnd(a, s, axes, ifft, norm, out=None)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def rfftn(a, s=None, axes=None, norm=None, out=None):
+    """
+    Compute the N-dimensional discrete Fourier Transform for real input.
+
+    This function computes the N-dimensional discrete Fourier Transform over
+    any number of axes in an M-dimensional real array by means of the Fast
+    Fourier Transform (FFT).  By default, all axes are transformed, with the
+    real transform performed over the last axis, while the remaining
+    transforms are complex.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, taken to be real.
+    s : sequence of ints, optional
+        Shape (length along each transformed axis) to use from the input.
+        (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
+        The final element of `s` corresponds to `n` for ``rfft(x, n)``, while
+        for the remaining axes, it corresponds to `n` for ``fft(x, n)``.
+        Along any axis, if the given shape is smaller than that of the input,
+        the input is cropped. If it is larger, the input is padded with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes specified
+        by `axes` is used.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the FFT.  If not given, the last ``len(s)``
+        axes are used, or all axes if `s` is also not specified.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must be explicitly specified too.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for all axes (and hence is
+        incompatible with passing in all but the trivial ``s``).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : complex ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or by a combination of `s` and `a`,
+        as explained in the parameters section above.
+        The length of the last axis transformed will be ``s[-1]//2+1``,
+        while the remaining transformed axes will have lengths according to
+        `s`, or unchanged from the input.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    irfftn : The inverse of `rfftn`, i.e. the inverse of the n-dimensional FFT
+         of real input.
+    fft : The one-dimensional FFT, with definitions and conventions used.
+    rfft : The one-dimensional FFT of real input.
+    fftn : The n-dimensional FFT.
+    rfft2 : The two-dimensional FFT of real input.
+
+    Notes
+    -----
+    The transform for real input is performed over the last transformation
+    axis, as by `rfft`, then the transform over the remaining axes is
+    performed as by `fftn`.  The order of the output is as for `rfft` for the
+    final transformation axis, and as for `fftn` for the remaining
+    transformation axes.
+
+    See `fft` for details, definitions and conventions used.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ones((2, 2, 2))
+    >>> np.fft.rfftn(a)
+    array([[[8.+0.j,  0.+0.j], # may vary
+            [0.+0.j,  0.+0.j]],
+           [[0.+0.j,  0.+0.j],
+            [0.+0.j,  0.+0.j]]])
+
+    >>> np.fft.rfftn(a, axes=(2, 0))
+    array([[[4.+0.j,  0.+0.j], # may vary
+            [4.+0.j,  0.+0.j]],
+           [[0.+0.j,  0.+0.j],
+            [0.+0.j,  0.+0.j]]])
+
+    """
+    a = asarray(a)
+    s, axes = _cook_nd_args(a, s, axes)
+    a = rfft(a, s[-1], axes[-1], norm, out=out)
+    for ii in range(len(axes)-2, -1, -1):
+        a = fft(a, s[ii], axes[ii], norm, out=out)
+    return a
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def rfft2(a, s=None, axes=(-2, -1), norm=None, out=None):
+    """
+    Compute the 2-dimensional FFT of a real array.
+
+    Parameters
+    ----------
+    a : array
+        Input array, taken to be real.
+    s : sequence of ints, optional
+        Shape of the FFT.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the FFT. Default: ``(-2, -1)``.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must not be ``None``.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : complex ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for the last inverse transform.
+        incompatible with passing in all but the trivial ``s``).
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : ndarray
+        The result of the real 2-D FFT.
+
+    See Also
+    --------
+    rfftn : Compute the N-dimensional discrete Fourier Transform for real
+            input.
+
+    Notes
+    -----
+    This is really just `rfftn` with different default behavior.
+    For more details see `rfftn`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.mgrid[:5, :5][0]
+    >>> np.fft.rfft2(a)
+    array([[ 50.  +0.j        ,   0.  +0.j        ,   0.  +0.j        ],
+           [-12.5+17.20477401j,   0.  +0.j        ,   0.  +0.j        ],
+           [-12.5 +4.0614962j ,   0.  +0.j        ,   0.  +0.j        ],
+           [-12.5 -4.0614962j ,   0.  +0.j        ,   0.  +0.j        ],
+           [-12.5-17.20477401j,   0.  +0.j        ,   0.  +0.j        ]])
+    """
+    return rfftn(a, s, axes, norm, out=out)
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def irfftn(a, s=None, axes=None, norm=None, out=None):
+    """
+    Computes the inverse of `rfftn`.
+
+    This function computes the inverse of the N-dimensional discrete
+    Fourier Transform for real input over any number of axes in an
+    M-dimensional array by means of the Fast Fourier Transform (FFT).  In
+    other words, ``irfftn(rfftn(a), a.shape) == a`` to within numerical
+    accuracy. (The ``a.shape`` is necessary like ``len(a)`` is for `irfft`,
+    and for the same reason.)
+
+    The input should be ordered in the same way as is returned by `rfftn`,
+    i.e. as for `irfft` for the final transformation axis, and as for `ifftn`
+    along all the other axes.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    s : sequence of ints, optional
+        Shape (length of each transformed axis) of the output
+        (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.). `s` is also the
+        number of input points used along this axis, except for the last axis,
+        where ``s[-1]//2+1`` points of the input are used.
+        Along any axis, if the shape indicated by `s` is smaller than that of
+        the input, the input is cropped.  If it is larger, the input is padded
+        with zeros.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        If `s` is not given, the shape of the input along the axes
+        specified by axes is used. Except for the last axis which is taken to
+        be ``2*(m-1)`` where ``m`` is the length of the input along that axis.
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        Axes over which to compute the inverse FFT. If not given, the last
+        `len(s)` axes are used, or all axes if `s` is also not specified.
+        Repeated indices in `axes` means that the inverse transform over that
+        axis is performed multiple times.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must be explicitly specified too.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for the last transformation.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : ndarray
+        The truncated or zero-padded input, transformed along the axes
+        indicated by `axes`, or by a combination of `s` or `a`,
+        as explained in the parameters section above.
+        The length of each transformed axis is as given by the corresponding
+        element of `s`, or the length of the input in every axis except for the
+        last one if `s` is not given.  In the final transformed axis the length
+        of the output when `s` is not given is ``2*(m-1)`` where ``m`` is the
+        length of the final transformed axis of the input.  To get an odd
+        number of output points in the final axis, `s` must be specified.
+
+    Raises
+    ------
+    ValueError
+        If `s` and `axes` have different length.
+    IndexError
+        If an element of `axes` is larger than than the number of axes of `a`.
+
+    See Also
+    --------
+    rfftn : The forward n-dimensional FFT of real input,
+            of which `ifftn` is the inverse.
+    fft : The one-dimensional FFT, with definitions and conventions used.
+    irfft : The inverse of the one-dimensional FFT of real input.
+    irfft2 : The inverse of the two-dimensional FFT of real input.
+
+    Notes
+    -----
+    See `fft` for definitions and conventions used.
+
+    See `rfft` for definitions and conventions used for real input.
+
+    The correct interpretation of the hermitian input depends on the shape of
+    the original data, as given by `s`. This is because each input shape could
+    correspond to either an odd or even length signal. By default, `irfftn`
+    assumes an even output length which puts the last entry at the Nyquist
+    frequency; aliasing with its symmetric counterpart. When performing the
+    final complex to real transform, the last value is thus treated as purely
+    real. To avoid losing information, the correct shape of the real input
+    **must** be given.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.zeros((3, 2, 2))
+    >>> a[0, 0, 0] = 3 * 2 * 2
+    >>> np.fft.irfftn(a)
+    array([[[1.,  1.],
+            [1.,  1.]],
+           [[1.,  1.],
+            [1.,  1.]],
+           [[1.,  1.],
+            [1.,  1.]]])
+
+    """
+    a = asarray(a)
+    s, axes = _cook_nd_args(a, s, axes, invreal=1)
+    for ii in range(len(axes)-1):
+        a = ifft(a, s[ii], axes[ii], norm)
+    a = irfft(a, s[-1], axes[-1], norm, out=out)
+    return a
+
+
+@array_function_dispatch(_fftn_dispatcher)
+def irfft2(a, s=None, axes=(-2, -1), norm=None, out=None):
+    """
+    Computes the inverse of `rfft2`.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array
+    s : sequence of ints, optional
+        Shape of the real output to the inverse FFT.
+
+        .. versionchanged:: 2.0
+
+            If it is ``-1``, the whole input is used (no padding/trimming).
+
+        .. deprecated:: 2.0
+
+            If `s` is not ``None``, `axes` must not be ``None`` either.
+
+        .. deprecated:: 2.0
+
+            `s` must contain only ``int`` s, not ``None`` values. ``None``
+            values currently mean that the default value for ``n`` is used
+            in the corresponding 1-D transform, but this behaviour is
+            deprecated.
+
+    axes : sequence of ints, optional
+        The axes over which to compute the inverse fft.
+        Default: ``(-2, -1)``, the last two axes.
+
+        .. deprecated:: 2.0
+
+            If `s` is specified, the corresponding `axes` to be transformed
+            must not be ``None``.
+
+    norm : {"backward", "ortho", "forward"}, optional
+        Normalization mode (see `numpy.fft`). Default is "backward".
+        Indicates which direction of the forward/backward pair of transforms
+        is scaled and with what normalization factor.
+
+        .. versionadded:: 1.20.0
+
+            The "backward", "forward" values were added.
+
+    out : ndarray, optional
+        If provided, the result will be placed in this array. It should be
+        of the appropriate shape and dtype for the last transformation.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    out : ndarray
+        The result of the inverse real 2-D FFT.
+
+    See Also
+    --------
+    rfft2 : The forward two-dimensional FFT of real input,
+            of which `irfft2` is the inverse.
+    rfft : The one-dimensional FFT for real input.
+    irfft : The inverse of the one-dimensional FFT of real input.
+    irfftn : Compute the inverse of the N-dimensional FFT of real input.
+
+    Notes
+    -----
+    This is really `irfftn` with different defaults.
+    For more details see `irfftn`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.mgrid[:5, :5][0]
+    >>> A = np.fft.rfft2(a)
+    >>> np.fft.irfft2(A, s=a.shape)
+    array([[0., 0., 0., 0., 0.],
+           [1., 1., 1., 1., 1.],
+           [2., 2., 2., 2., 2.],
+           [3., 3., 3., 3., 3.],
+           [4., 4., 4., 4., 4.]])
+    """
+    return irfftn(a, s, axes, norm, out=None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..78f1ff692df0dcb47ac812a5bc4aa5f5afd8bc19
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/_pocketfft.pyi
@@ -0,0 +1,137 @@
+from collections.abc import Sequence
+from typing import Literal as L, TypeAlias
+
+from numpy import complex128, float64
+from numpy._typing import ArrayLike, NDArray, _ArrayLikeNumber_co
+
+__all__ = [
+    "fft",
+    "ifft",
+    "rfft",
+    "irfft",
+    "hfft",
+    "ihfft",
+    "rfftn",
+    "irfftn",
+    "rfft2",
+    "irfft2",
+    "fft2",
+    "ifft2",
+    "fftn",
+    "ifftn",
+]
+
+_NormKind: TypeAlias = L[None, "backward", "ortho", "forward"]
+
+def fft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def ifft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def rfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def irfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[float64] = ...,
+) -> NDArray[float64]: ...
+
+# Input array must be compatible with `np.conjugate`
+def hfft(
+    a: _ArrayLikeNumber_co,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[float64] = ...,
+) -> NDArray[float64]: ...
+
+def ihfft(
+    a: ArrayLike,
+    n: None | int = ...,
+    axis: int = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def fftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def ifftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def rfftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def irfftn(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[float64] = ...,
+) -> NDArray[float64]: ...
+
+def fft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def ifft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def rfft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[complex128] = ...,
+) -> NDArray[complex128]: ...
+
+def irfft2(
+    a: ArrayLike,
+    s: None | Sequence[int] = ...,
+    axes: None | Sequence[int] = ...,
+    norm: _NormKind = ...,
+    out: None | NDArray[float64] = ...,
+) -> NDArray[float64]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/helper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..4375cedf7fcfd43794b205b24f413281f6fa7197
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/helper.py
@@ -0,0 +1,16 @@
+def __getattr__(attr_name):
+    import warnings
+    from numpy.fft import _helper
+    ret = getattr(_helper, attr_name, None)
+    if ret is None:
+        raise AttributeError(
+            f"module 'numpy.fft.helper' has no attribute {attr_name}")
+    warnings.warn(
+        "The numpy.fft.helper has been made private and renamed to "
+        "numpy.fft._helper. All four functions exported by it (i.e. fftshift, "
+        "ifftshift, fftfreq, rfftfreq) are available from numpy.fft. "
+        f"Please use numpy.fft.{attr_name} instead.",
+        DeprecationWarning,
+        stacklevel=3
+    )
+    return ret
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_helper.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..852e6625fff2370380979fd22a8575f692c206aa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_helper.py
@@ -0,0 +1,167 @@
+"""Test functions for fftpack.helper module
+
+Copied from fftpack.helper by Pearu Peterson, October 2005
+
+"""
+import numpy as np
+from numpy.testing import assert_array_almost_equal
+from numpy import fft, pi
+
+
+class TestFFTShift:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4, -4, -3, -2, -1]
+        y = [-4, -3, -2, -1, 0, 1, 2, 3, 4]
+        assert_array_almost_equal(fft.fftshift(x), y)
+        assert_array_almost_equal(fft.ifftshift(y), x)
+        x = [0, 1, 2, 3, 4, -5, -4, -3, -2, -1]
+        y = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
+        assert_array_almost_equal(fft.fftshift(x), y)
+        assert_array_almost_equal(fft.ifftshift(y), x)
+
+    def test_inverse(self):
+        for n in [1, 4, 9, 100, 211]:
+            x = np.random.random((n,))
+            assert_array_almost_equal(fft.ifftshift(fft.fftshift(x)), x)
+
+    def test_axes_keyword(self):
+        freqs = [[0, 1, 2], [3, 4, -4], [-3, -2, -1]]
+        shifted = [[-1, -3, -2], [2, 0, 1], [-4, 3, 4]]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0, 1)), shifted)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=0),
+                                  fft.fftshift(freqs, axes=(0,)))
+        assert_array_almost_equal(fft.ifftshift(shifted, axes=(0, 1)), freqs)
+        assert_array_almost_equal(fft.ifftshift(shifted, axes=0),
+                                  fft.ifftshift(shifted, axes=(0,)))
+
+        assert_array_almost_equal(fft.fftshift(freqs), shifted)
+        assert_array_almost_equal(fft.ifftshift(shifted), freqs)
+
+    def test_uneven_dims(self):
+        """ Test 2D input, which has uneven dimension sizes """
+        freqs = [
+            [0, 1],
+            [2, 3],
+            [4, 5]
+        ]
+
+        # shift in dimension 0
+        shift_dim0 = [
+            [4, 5],
+            [0, 1],
+            [2, 3]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=0), shift_dim0)
+        assert_array_almost_equal(fft.ifftshift(shift_dim0, axes=0), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0,)), shift_dim0)
+        assert_array_almost_equal(fft.ifftshift(shift_dim0, axes=[0]), freqs)
+
+        # shift in dimension 1
+        shift_dim1 = [
+            [1, 0],
+            [3, 2],
+            [5, 4]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=1), shift_dim1)
+        assert_array_almost_equal(fft.ifftshift(shift_dim1, axes=1), freqs)
+
+        # shift in both dimensions
+        shift_dim_both = [
+            [5, 4],
+            [1, 0],
+            [3, 2]
+        ]
+        assert_array_almost_equal(fft.fftshift(freqs, axes=(0, 1)), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=(0, 1)), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs, axes=[0, 1]), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=[0, 1]), freqs)
+
+        # axes=None (default) shift in all dimensions
+        assert_array_almost_equal(fft.fftshift(freqs, axes=None), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both, axes=None), freqs)
+        assert_array_almost_equal(fft.fftshift(freqs), shift_dim_both)
+        assert_array_almost_equal(fft.ifftshift(shift_dim_both), freqs)
+
+    def test_equal_to_original(self):
+        """ Test that the new (>=v1.15) implementation (see #10073) is equal to the original (<=v1.14) """
+        from numpy._core import asarray, concatenate, arange, take
+
+        def original_fftshift(x, axes=None):
+            """ How fftshift was implemented in v1.14"""
+            tmp = asarray(x)
+            ndim = tmp.ndim
+            if axes is None:
+                axes = list(range(ndim))
+            elif isinstance(axes, int):
+                axes = (axes,)
+            y = tmp
+            for k in axes:
+                n = tmp.shape[k]
+                p2 = (n + 1) // 2
+                mylist = concatenate((arange(p2, n), arange(p2)))
+                y = take(y, mylist, k)
+            return y
+
+        def original_ifftshift(x, axes=None):
+            """ How ifftshift was implemented in v1.14 """
+            tmp = asarray(x)
+            ndim = tmp.ndim
+            if axes is None:
+                axes = list(range(ndim))
+            elif isinstance(axes, int):
+                axes = (axes,)
+            y = tmp
+            for k in axes:
+                n = tmp.shape[k]
+                p2 = n - (n + 1) // 2
+                mylist = concatenate((arange(p2, n), arange(p2)))
+                y = take(y, mylist, k)
+            return y
+
+        # create possible 2d array combinations and try all possible keywords
+        # compare output to original functions
+        for i in range(16):
+            for j in range(16):
+                for axes_keyword in [0, 1, None, (0,), (0, 1)]:
+                    inp = np.random.rand(i, j)
+
+                    assert_array_almost_equal(fft.fftshift(inp, axes_keyword),
+                                              original_fftshift(inp, axes_keyword))
+
+                    assert_array_almost_equal(fft.ifftshift(inp, axes_keyword),
+                                              original_ifftshift(inp, axes_keyword))
+
+
+class TestFFTFreq:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4, -4, -3, -2, -1]
+        assert_array_almost_equal(9*fft.fftfreq(9), x)
+        assert_array_almost_equal(9*pi*fft.fftfreq(9, pi), x)
+        x = [0, 1, 2, 3, 4, -5, -4, -3, -2, -1]
+        assert_array_almost_equal(10*fft.fftfreq(10), x)
+        assert_array_almost_equal(10*pi*fft.fftfreq(10, pi), x)
+
+
+class TestRFFTFreq:
+
+    def test_definition(self):
+        x = [0, 1, 2, 3, 4]
+        assert_array_almost_equal(9*fft.rfftfreq(9), x)
+        assert_array_almost_equal(9*pi*fft.rfftfreq(9, pi), x)
+        x = [0, 1, 2, 3, 4, 5]
+        assert_array_almost_equal(10*fft.rfftfreq(10), x)
+        assert_array_almost_equal(10*pi*fft.rfftfreq(10, pi), x)
+
+
+class TestIRFFTN:
+
+    def test_not_last_axis_success(self):
+        ar, ai = np.random.random((2, 16, 8, 32))
+        a = ar + 1j*ai
+
+        axes = (-2,)
+
+        # Should not raise error
+        fft.irfftn(a, axes=axes)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_pocketfft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_pocketfft.py
new file mode 100644
index 0000000000000000000000000000000000000000..dff2c86742d5700468671cde3cd357775418d875
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/fft/tests/test_pocketfft.py
@@ -0,0 +1,589 @@
+import numpy as np
+import pytest
+from numpy.random import random
+from numpy.testing import (
+        assert_array_equal, assert_raises, assert_allclose, IS_WASM
+        )
+import threading
+import queue
+
+
+def fft1(x):
+    L = len(x)
+    phase = -2j * np.pi * (np.arange(L) / L)
+    phase = np.arange(L).reshape(-1, 1) * phase
+    return np.sum(x*np.exp(phase), axis=1)
+
+
+class TestFFTShift:
+
+    def test_fft_n(self):
+        assert_raises(ValueError, np.fft.fft, [1, 2, 3], 0)
+
+
+class TestFFT1D:
+
+    def test_identity(self):
+        maxlen = 512
+        x = random(maxlen) + 1j*random(maxlen)
+        xr = random(maxlen)
+        for i in range(1, maxlen):
+            assert_allclose(np.fft.ifft(np.fft.fft(x[0:i])), x[0:i],
+                            atol=1e-12)
+            assert_allclose(np.fft.irfft(np.fft.rfft(xr[0:i]), i),
+                            xr[0:i], atol=1e-12)
+
+    @pytest.mark.parametrize("dtype", [np.single, np.double, np.longdouble])
+    def test_identity_long_short(self, dtype):
+        # Test with explicitly given number of points, both for n
+        # smaller and for n larger than the input size.
+        maxlen = 16
+        atol = 5 * np.spacing(np.array(1., dtype=dtype))
+        x = random(maxlen).astype(dtype) + 1j*random(maxlen).astype(dtype)
+        xx = np.concatenate([x, np.zeros_like(x)])
+        xr = random(maxlen).astype(dtype)
+        xxr = np.concatenate([xr, np.zeros_like(xr)])
+        for i in range(1, maxlen*2):
+            check_c = np.fft.ifft(np.fft.fft(x, n=i), n=i)
+            assert check_c.real.dtype == dtype
+            assert_allclose(check_c, xx[0:i], atol=atol, rtol=0)
+            check_r = np.fft.irfft(np.fft.rfft(xr, n=i), n=i)
+            assert check_r.dtype == dtype
+            assert_allclose(check_r, xxr[0:i], atol=atol, rtol=0)
+
+    @pytest.mark.parametrize("dtype", [np.single, np.double, np.longdouble])
+    def test_identity_long_short_reversed(self, dtype):
+        # Also test explicitly given number of points in reversed order.
+        maxlen = 16
+        atol = 5 * np.spacing(np.array(1., dtype=dtype))
+        x = random(maxlen).astype(dtype) + 1j*random(maxlen).astype(dtype)
+        xx = np.concatenate([x, np.zeros_like(x)])
+        for i in range(1, maxlen*2):
+            check_via_c = np.fft.fft(np.fft.ifft(x, n=i), n=i)
+            assert check_via_c.dtype == x.dtype
+            assert_allclose(check_via_c, xx[0:i], atol=atol, rtol=0)
+            # For irfft, we can neither recover the imaginary part of
+            # the first element, nor the imaginary part of the last
+            # element if npts is even.  So, set to 0 for the comparison.
+            y = x.copy()
+            n = i // 2 + 1
+            y.imag[0] = 0
+            if i % 2 == 0:
+                y.imag[n-1:] = 0
+            yy = np.concatenate([y, np.zeros_like(y)])
+            check_via_r = np.fft.rfft(np.fft.irfft(x, n=i), n=i)
+            assert check_via_r.dtype == x.dtype
+            assert_allclose(check_via_r, yy[0:n], atol=atol, rtol=0)
+
+    def test_fft(self):
+        x = random(30) + 1j*random(30)
+        assert_allclose(fft1(x), np.fft.fft(x), atol=1e-6)
+        assert_allclose(fft1(x), np.fft.fft(x, norm="backward"), atol=1e-6)
+        assert_allclose(fft1(x) / np.sqrt(30),
+                        np.fft.fft(x, norm="ortho"), atol=1e-6)
+        assert_allclose(fft1(x) / 30.,
+                        np.fft.fft(x, norm="forward"), atol=1e-6)
+
+    @pytest.mark.parametrize("axis", (0, 1))
+    @pytest.mark.parametrize("dtype", (complex, float))
+    @pytest.mark.parametrize("transpose", (True, False))
+    def test_fft_out_argument(self, dtype, transpose, axis):
+        def zeros_like(x):
+            if transpose:
+                return np.zeros_like(x.T).T
+            else:
+                return np.zeros_like(x)
+
+        # tests below only test the out parameter
+        if dtype is complex:
+            y = random((10, 20)) + 1j*random((10, 20))
+            fft, ifft = np.fft.fft, np.fft.ifft
+        else:
+            y = random((10, 20))
+            fft, ifft = np.fft.rfft, np.fft.irfft
+
+        expected = fft(y, axis=axis)
+        out = zeros_like(expected)
+        result = fft(y, out=out, axis=axis)
+        assert result is out
+        assert_array_equal(result, expected)
+
+        expected2 = ifft(expected, axis=axis)
+        out2 = out if dtype is complex else zeros_like(expected2)
+        result2 = ifft(out, out=out2, axis=axis)
+        assert result2 is out2
+        assert_array_equal(result2, expected2)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_fft_inplace_out(self, axis):
+        # Test some weirder in-place combinations
+        y = random((20, 20)) + 1j*random((20, 20))
+        # Fully in-place.
+        y1 = y.copy()
+        expected1 = np.fft.fft(y1, axis=axis)
+        result1 = np.fft.fft(y1, axis=axis, out=y1)
+        assert result1 is y1
+        assert_array_equal(result1, expected1)
+        # In-place of part of the array; rest should be unchanged.
+        y2 = y.copy()
+        out2 = y2[:10] if axis == 0 else y2[:, :10]
+        expected2 = np.fft.fft(y2, n=10, axis=axis)
+        result2 = np.fft.fft(y2, n=10, axis=axis, out=out2)
+        assert result2 is out2
+        assert_array_equal(result2, expected2)
+        if axis == 0:
+            assert_array_equal(y2[10:], y[10:])
+        else:
+            assert_array_equal(y2[:, 10:], y[:, 10:])
+        # In-place of another part of the array.
+        y3 = y.copy()
+        y3_sel = y3[5:] if axis == 0 else y3[:, 5:]
+        out3 = y3[5:15] if axis == 0 else y3[:, 5:15]
+        expected3 = np.fft.fft(y3_sel, n=10, axis=axis)
+        result3 = np.fft.fft(y3_sel, n=10, axis=axis, out=out3)
+        assert result3 is out3
+        assert_array_equal(result3, expected3)
+        if axis == 0:
+            assert_array_equal(y3[:5], y[:5])
+            assert_array_equal(y3[15:], y[15:])
+        else:
+            assert_array_equal(y3[:, :5], y[:, :5])
+            assert_array_equal(y3[:, 15:], y[:, 15:])
+        # In-place with n > nin; rest should be unchanged.
+        y4 = y.copy()
+        y4_sel = y4[:10] if axis == 0 else y4[:, :10]
+        out4 = y4[:15] if axis == 0 else y4[:, :15]
+        expected4 = np.fft.fft(y4_sel, n=15, axis=axis)
+        result4 = np.fft.fft(y4_sel, n=15, axis=axis, out=out4)
+        assert result4 is out4
+        assert_array_equal(result4, expected4)
+        if axis == 0:
+            assert_array_equal(y4[15:], y[15:])
+        else:
+            assert_array_equal(y4[:, 15:], y[:, 15:])
+        # Overwrite in a transpose.
+        y5 = y.copy()
+        out5 = y5.T
+        result5 = np.fft.fft(y5, axis=axis, out=out5)
+        assert result5 is out5
+        assert_array_equal(result5, expected1)
+        # Reverse strides.
+        y6 = y.copy()
+        out6 = y6[::-1] if axis == 0 else y6[:, ::-1]
+        result6 = np.fft.fft(y6, axis=axis, out=out6)
+        assert result6 is out6
+        assert_array_equal(result6, expected1)
+
+    def test_fft_bad_out(self):
+        x = np.arange(30.)
+        with pytest.raises(TypeError, match="must be of ArrayType"):
+            np.fft.fft(x, out="")
+        with pytest.raises(ValueError, match="has wrong shape"):
+            np.fft.fft(x, out=np.zeros_like(x).reshape(5, -1))
+        with pytest.raises(TypeError, match="Cannot cast"):
+            np.fft.fft(x, out=np.zeros_like(x, dtype=float))
+
+    @pytest.mark.parametrize('norm', (None, 'backward', 'ortho', 'forward'))
+    def test_ifft(self, norm):
+        x = random(30) + 1j*random(30)
+        assert_allclose(
+            x, np.fft.ifft(np.fft.fft(x, norm=norm), norm=norm),
+            atol=1e-6)
+        # Ensure we get the correct error message
+        with pytest.raises(ValueError,
+                           match='Invalid number of FFT data points'):
+            np.fft.ifft([], norm=norm)
+
+    def test_fft2(self):
+        x = random((30, 20)) + 1j*random((30, 20))
+        assert_allclose(np.fft.fft(np.fft.fft(x, axis=1), axis=0),
+                        np.fft.fft2(x), atol=1e-6)
+        assert_allclose(np.fft.fft2(x),
+                        np.fft.fft2(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.fft2(x) / np.sqrt(30 * 20),
+                        np.fft.fft2(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.fft2(x) / (30. * 20.),
+                        np.fft.fft2(x, norm="forward"), atol=1e-6)
+
+    def test_ifft2(self):
+        x = random((30, 20)) + 1j*random((30, 20))
+        assert_allclose(np.fft.ifft(np.fft.ifft(x, axis=1), axis=0),
+                        np.fft.ifft2(x), atol=1e-6)
+        assert_allclose(np.fft.ifft2(x),
+                        np.fft.ifft2(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.ifft2(x) * np.sqrt(30 * 20),
+                        np.fft.ifft2(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.ifft2(x) * (30. * 20.),
+                        np.fft.ifft2(x, norm="forward"), atol=1e-6)
+
+    def test_fftn(self):
+        x = random((30, 20, 10)) + 1j*random((30, 20, 10))
+        assert_allclose(
+            np.fft.fft(np.fft.fft(np.fft.fft(x, axis=2), axis=1), axis=0),
+            np.fft.fftn(x), atol=1e-6)
+        assert_allclose(np.fft.fftn(x),
+                        np.fft.fftn(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.fftn(x) / np.sqrt(30 * 20 * 10),
+                        np.fft.fftn(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.fftn(x) / (30. * 20. * 10.),
+                        np.fft.fftn(x, norm="forward"), atol=1e-6)
+
+    def test_ifftn(self):
+        x = random((30, 20, 10)) + 1j*random((30, 20, 10))
+        assert_allclose(
+            np.fft.ifft(np.fft.ifft(np.fft.ifft(x, axis=2), axis=1), axis=0),
+            np.fft.ifftn(x), atol=1e-6)
+        assert_allclose(np.fft.ifftn(x),
+                        np.fft.ifftn(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.ifftn(x) * np.sqrt(30 * 20 * 10),
+                        np.fft.ifftn(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.ifftn(x) * (30. * 20. * 10.),
+                        np.fft.ifftn(x, norm="forward"), atol=1e-6)
+
+    def test_rfft(self):
+        x = random(30)
+        for n in [x.size, 2*x.size]:
+            for norm in [None, 'backward', 'ortho', 'forward']:
+                assert_allclose(
+                    np.fft.fft(x, n=n, norm=norm)[:(n//2 + 1)],
+                    np.fft.rfft(x, n=n, norm=norm), atol=1e-6)
+            assert_allclose(
+                np.fft.rfft(x, n=n),
+                np.fft.rfft(x, n=n, norm="backward"), atol=1e-6)
+            assert_allclose(
+                np.fft.rfft(x, n=n) / np.sqrt(n),
+                np.fft.rfft(x, n=n, norm="ortho"), atol=1e-6)
+            assert_allclose(
+                np.fft.rfft(x, n=n) / n,
+                np.fft.rfft(x, n=n, norm="forward"), atol=1e-6)
+
+    def test_rfft_even(self):
+        x = np.arange(8)
+        n = 4
+        y = np.fft.rfft(x, n)
+        assert_allclose(y, np.fft.fft(x[:n])[:n//2 + 1], rtol=1e-14)
+
+    def test_rfft_odd(self):
+        x = np.array([1, 0, 2, 3, -3])
+        y = np.fft.rfft(x)
+        assert_allclose(y, np.fft.fft(x)[:3], rtol=1e-14)
+
+    def test_irfft(self):
+        x = random(30)
+        assert_allclose(x, np.fft.irfft(np.fft.rfft(x)), atol=1e-6)
+        assert_allclose(x, np.fft.irfft(np.fft.rfft(x, norm="backward"),
+                        norm="backward"), atol=1e-6)
+        assert_allclose(x, np.fft.irfft(np.fft.rfft(x, norm="ortho"),
+                        norm="ortho"), atol=1e-6)
+        assert_allclose(x, np.fft.irfft(np.fft.rfft(x, norm="forward"),
+                        norm="forward"), atol=1e-6)
+
+    def test_rfft2(self):
+        x = random((30, 20))
+        assert_allclose(np.fft.fft2(x)[:, :11], np.fft.rfft2(x), atol=1e-6)
+        assert_allclose(np.fft.rfft2(x),
+                        np.fft.rfft2(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.rfft2(x) / np.sqrt(30 * 20),
+                        np.fft.rfft2(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.rfft2(x) / (30. * 20.),
+                        np.fft.rfft2(x, norm="forward"), atol=1e-6)
+
+    def test_irfft2(self):
+        x = random((30, 20))
+        assert_allclose(x, np.fft.irfft2(np.fft.rfft2(x)), atol=1e-6)
+        assert_allclose(x, np.fft.irfft2(np.fft.rfft2(x, norm="backward"),
+                        norm="backward"), atol=1e-6)
+        assert_allclose(x, np.fft.irfft2(np.fft.rfft2(x, norm="ortho"),
+                        norm="ortho"), atol=1e-6)
+        assert_allclose(x, np.fft.irfft2(np.fft.rfft2(x, norm="forward"),
+                        norm="forward"), atol=1e-6)
+
+    def test_rfftn(self):
+        x = random((30, 20, 10))
+        assert_allclose(np.fft.fftn(x)[:, :, :6], np.fft.rfftn(x), atol=1e-6)
+        assert_allclose(np.fft.rfftn(x),
+                        np.fft.rfftn(x, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.rfftn(x) / np.sqrt(30 * 20 * 10),
+                        np.fft.rfftn(x, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.rfftn(x) / (30. * 20. * 10.),
+                        np.fft.rfftn(x, norm="forward"), atol=1e-6)
+        # Regression test for gh-27159
+        x = np.ones((2, 3))
+        result = np.fft.rfftn(x, axes=(0, 0, 1), s=(10, 20, 40))
+        assert result.shape == (10, 21)
+        expected = np.fft.fft(np.fft.fft(np.fft.rfft(x, axis=1, n=40),
+                            axis=0, n=20), axis=0, n=10)
+        assert expected.shape == (10, 21)
+        assert_allclose(result, expected, atol=1e-6)
+
+    def test_irfftn(self):
+        x = random((30, 20, 10))
+        assert_allclose(x, np.fft.irfftn(np.fft.rfftn(x)), atol=1e-6)
+        assert_allclose(x, np.fft.irfftn(np.fft.rfftn(x, norm="backward"),
+                        norm="backward"), atol=1e-6)
+        assert_allclose(x, np.fft.irfftn(np.fft.rfftn(x, norm="ortho"),
+                        norm="ortho"), atol=1e-6)
+        assert_allclose(x, np.fft.irfftn(np.fft.rfftn(x, norm="forward"),
+                        norm="forward"), atol=1e-6)
+
+    def test_hfft(self):
+        x = random(14) + 1j*random(14)
+        x_herm = np.concatenate((random(1), x, random(1)))
+        x = np.concatenate((x_herm, x[::-1].conj()))
+        assert_allclose(np.fft.fft(x), np.fft.hfft(x_herm), atol=1e-6)
+        assert_allclose(np.fft.hfft(x_herm),
+                        np.fft.hfft(x_herm, norm="backward"), atol=1e-6)
+        assert_allclose(np.fft.hfft(x_herm) / np.sqrt(30),
+                        np.fft.hfft(x_herm, norm="ortho"), atol=1e-6)
+        assert_allclose(np.fft.hfft(x_herm) / 30.,
+                        np.fft.hfft(x_herm, norm="forward"), atol=1e-6)
+
+    def test_ihfft(self):
+        x = random(14) + 1j*random(14)
+        x_herm = np.concatenate((random(1), x, random(1)))
+        x = np.concatenate((x_herm, x[::-1].conj()))
+        assert_allclose(x_herm, np.fft.ihfft(np.fft.hfft(x_herm)), atol=1e-6)
+        assert_allclose(x_herm, np.fft.ihfft(np.fft.hfft(x_herm,
+                        norm="backward"), norm="backward"), atol=1e-6)
+        assert_allclose(x_herm, np.fft.ihfft(np.fft.hfft(x_herm,
+                        norm="ortho"), norm="ortho"), atol=1e-6)
+        assert_allclose(x_herm, np.fft.ihfft(np.fft.hfft(x_herm,
+                        norm="forward"), norm="forward"), atol=1e-6)
+
+    @pytest.mark.parametrize("op", [np.fft.fftn, np.fft.ifftn,
+                                    np.fft.rfftn, np.fft.irfftn])
+    def test_axes(self, op):
+        x = random((30, 20, 10))
+        axes = [(0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)]
+        for a in axes:
+            op_tr = op(np.transpose(x, a))
+            tr_op = np.transpose(op(x, axes=a), a)
+            assert_allclose(op_tr, tr_op, atol=1e-6)
+
+    @pytest.mark.parametrize("op", [np.fft.fftn, np.fft.ifftn,
+                                    np.fft.fft2, np.fft.ifft2])
+    def test_s_negative_1(self, op):
+        x = np.arange(100).reshape(10, 10)
+        # should use the whole input array along the first axis
+        assert op(x, s=(-1, 5), axes=(0, 1)).shape == (10, 5)
+
+    @pytest.mark.parametrize("op", [np.fft.fftn, np.fft.ifftn,
+                                    np.fft.rfftn, np.fft.irfftn])
+    def test_s_axes_none(self, op):
+        x = np.arange(100).reshape(10, 10)
+        with pytest.warns(match='`axes` should not be `None` if `s`'):
+            op(x, s=(-1, 5))
+
+    @pytest.mark.parametrize("op", [np.fft.fft2, np.fft.ifft2])
+    def test_s_axes_none_2D(self, op):
+        x = np.arange(100).reshape(10, 10)
+        with pytest.warns(match='`axes` should not be `None` if `s`'):
+            op(x, s=(-1, 5), axes=None)
+
+    @pytest.mark.parametrize("op", [np.fft.fftn, np.fft.ifftn,
+                                    np.fft.rfftn, np.fft.irfftn,
+                                    np.fft.fft2, np.fft.ifft2])
+    def test_s_contains_none(self, op):
+        x = random((30, 20, 10))
+        with pytest.warns(match='array containing `None` values to `s`'):
+            op(x, s=(10, None, 10), axes=(0, 1, 2))
+
+    def test_all_1d_norm_preserving(self):
+        # verify that round-trip transforms are norm-preserving
+        x = random(30)
+        x_norm = np.linalg.norm(x)
+        n = x.size * 2
+        func_pairs = [(np.fft.fft, np.fft.ifft),
+                      (np.fft.rfft, np.fft.irfft),
+                      # hfft: order so the first function takes x.size samples
+                      #       (necessary for comparison to x_norm above)
+                      (np.fft.ihfft, np.fft.hfft),
+                      ]
+        for forw, back in func_pairs:
+            for n in [x.size, 2*x.size]:
+                for norm in [None, 'backward', 'ortho', 'forward']:
+                    tmp = forw(x, n=n, norm=norm)
+                    tmp = back(tmp, n=n, norm=norm)
+                    assert_allclose(x_norm,
+                                    np.linalg.norm(tmp), atol=1e-6)
+
+    @pytest.mark.parametrize("axes", [(0, 1), (0, 2), None])
+    @pytest.mark.parametrize("dtype", (complex, float))
+    @pytest.mark.parametrize("transpose", (True, False))
+    def test_fftn_out_argument(self, dtype, transpose, axes):
+        def zeros_like(x):
+            if transpose:
+                return np.zeros_like(x.T).T
+            else:
+                return np.zeros_like(x)
+
+        # tests below only test the out parameter
+        if dtype is complex:
+            x = random((10, 5, 6)) + 1j*random((10, 5, 6))
+            fft, ifft = np.fft.fftn, np.fft.ifftn
+        else:
+            x = random((10, 5, 6))
+            fft, ifft = np.fft.rfftn, np.fft.irfftn
+
+        expected = fft(x, axes=axes)
+        out = zeros_like(expected)
+        result = fft(x, out=out, axes=axes)
+        assert result is out
+        assert_array_equal(result, expected)
+
+        expected2 = ifft(expected, axes=axes)
+        out2 = out if dtype is complex else zeros_like(expected2)
+        result2 = ifft(out, out=out2, axes=axes)
+        assert result2 is out2
+        assert_array_equal(result2, expected2)
+
+    @pytest.mark.parametrize("fft", [np.fft.fftn, np.fft.ifftn, np.fft.rfftn])
+    def test_fftn_out_and_s_interaction(self, fft):
+        # With s, shape varies, so generally one cannot pass in out.
+        if fft is np.fft.rfftn:
+            x = random((10, 5, 6))
+        else:
+            x = random((10, 5, 6)) + 1j*random((10, 5, 6))
+        with pytest.raises(ValueError, match="has wrong shape"):
+            fft(x, out=np.zeros_like(x), s=(3, 3, 3), axes=(0, 1, 2))
+        # Except on the first axis done (which is the last of axes).
+        s = (10, 5, 5)
+        expected = fft(x, s=s, axes=(0, 1, 2))
+        out = np.zeros_like(expected)
+        result = fft(x, s=s, axes=(0, 1, 2), out=out)
+        assert result is out
+        assert_array_equal(result, expected)
+
+    @pytest.mark.parametrize("s", [(9, 5, 5), (3, 3, 3)])
+    def test_irfftn_out_and_s_interaction(self, s):
+        # Since for irfftn, the output is real and thus cannot be used for
+        # intermediate steps, it should always work.
+        x = random((9, 5, 6, 2)) + 1j*random((9, 5, 6, 2))
+        expected = np.fft.irfftn(x, s=s, axes=(0, 1, 2))
+        out = np.zeros_like(expected)
+        result = np.fft.irfftn(x, s=s, axes=(0, 1, 2), out=out)
+        assert result is out
+        assert_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+        "dtype",
+        [np.float32, np.float64, np.complex64, np.complex128])
+@pytest.mark.parametrize("order", ["F", 'non-contiguous'])
+@pytest.mark.parametrize(
+        "fft",
+        [np.fft.fft, np.fft.fft2, np.fft.fftn,
+         np.fft.ifft, np.fft.ifft2, np.fft.ifftn])
+def test_fft_with_order(dtype, order, fft):
+    # Check that FFT/IFFT produces identical results for C, Fortran and
+    # non contiguous arrays
+    rng = np.random.RandomState(42)
+    X = rng.rand(8, 7, 13).astype(dtype, copy=False)
+    # See discussion in pull/14178
+    _tol = 8.0 * np.sqrt(np.log2(X.size)) * np.finfo(X.dtype).eps
+    if order == 'F':
+        Y = np.asfortranarray(X)
+    else:
+        # Make a non contiguous array
+        Y = X[::-1]
+        X = np.ascontiguousarray(X[::-1])
+
+    if fft.__name__.endswith('fft'):
+        for axis in range(3):
+            X_res = fft(X, axis=axis)
+            Y_res = fft(Y, axis=axis)
+            assert_allclose(X_res, Y_res, atol=_tol, rtol=_tol)
+    elif fft.__name__.endswith(('fft2', 'fftn')):
+        axes = [(0, 1), (1, 2), (0, 2)]
+        if fft.__name__.endswith('fftn'):
+            axes.extend([(0,), (1,), (2,), None])
+        for ax in axes:
+            X_res = fft(X, axes=ax)
+            Y_res = fft(Y, axes=ax)
+            assert_allclose(X_res, Y_res, atol=_tol, rtol=_tol)
+    else:
+        raise ValueError
+
+
+@pytest.mark.parametrize("order", ["F", "C"])
+@pytest.mark.parametrize("n", [None, 7, 12])
+def test_fft_output_order(order, n):
+    rng = np.random.RandomState(42)
+    x = rng.rand(10)
+    x = np.asarray(x, dtype=np.complex64, order=order)
+    res = np.fft.fft(x, n=n)
+    assert res.flags.c_contiguous == x.flags.c_contiguous
+    assert res.flags.f_contiguous == x.flags.f_contiguous
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start thread")
+class TestFFTThreadSafe:
+    threads = 16
+    input_shape = (800, 200)
+
+    def _test_mtsame(self, func, *args):
+        def worker(args, q):
+            q.put(func(*args))
+
+        q = queue.Queue()
+        expected = func(*args)
+
+        # Spin off a bunch of threads to call the same function simultaneously
+        t = [threading.Thread(target=worker, args=(args, q))
+             for i in range(self.threads)]
+        [x.start() for x in t]
+
+        [x.join() for x in t]
+        # Make sure all threads returned the correct value
+        for i in range(self.threads):
+            assert_array_equal(q.get(timeout=5), expected,
+                'Function returned wrong value in multithreaded context')
+
+    def test_fft(self):
+        a = np.ones(self.input_shape) * 1+0j
+        self._test_mtsame(np.fft.fft, a)
+
+    def test_ifft(self):
+        a = np.ones(self.input_shape) * 1+0j
+        self._test_mtsame(np.fft.ifft, a)
+
+    def test_rfft(self):
+        a = np.ones(self.input_shape)
+        self._test_mtsame(np.fft.rfft, a)
+
+    def test_irfft(self):
+        a = np.ones(self.input_shape) * 1+0j
+        self._test_mtsame(np.fft.irfft, a)
+
+
+def test_irfft_with_n_1_regression():
+    # Regression test for gh-25661
+    x = np.arange(10)
+    np.fft.irfft(x, n=1)
+    np.fft.hfft(x, n=1)
+    np.fft.irfft(np.array([0], complex), n=10)
+
+
+def test_irfft_with_n_large_regression():
+    # Regression test for gh-25679
+    x = np.arange(5) * (1 + 1j)
+    result = np.fft.hfft(x, n=10)
+    expected = np.array([20., 9.91628173, -11.8819096, 7.1048486,
+                         -6.62459848, 4., -3.37540152, -0.16057669,
+                         1.8819096, -20.86055364])
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize("fft", [
+    np.fft.fft, np.fft.ifft, np.fft.rfft, np.fft.irfft
+])
+@pytest.mark.parametrize("data", [
+    np.array([False, True, False]),
+    np.arange(10, dtype=np.uint8),
+    np.arange(5, dtype=np.int16),
+])
+def test_fft_with_integer_or_bool_input(data, fft):
+    # Regression test for gh-25819
+    result = fft(data)
+    float_data = data.astype(np.result_type(data, 1.))
+    expected = fft(float_data)
+    assert_array_equal(result, expected)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..928121ce8f28bf49ab30283d5901aa8bb0414d29
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.py
@@ -0,0 +1,94 @@
+"""
+``numpy.lib`` is mostly a space for implementing functions that don't
+belong in core or in another NumPy submodule with a clear purpose
+(e.g. ``random``, ``fft``, ``linalg``, ``ma``).
+
+``numpy.lib``'s private submodules contain basic functions that are used by
+other public modules and are useful to have in the main name-space.
+
+"""
+
+# Public submodules
+# Note: recfunctions and (maybe) format are public too, but not imported
+from . import array_utils
+from . import introspect
+from . import mixins
+from . import npyio
+from . import scimath
+from . import stride_tricks
+
+# Private submodules
+# load module names. See https://github.com/networkx/networkx/issues/5838
+from . import _type_check_impl
+from . import _index_tricks_impl
+from . import _nanfunctions_impl
+from . import _function_base_impl
+from . import _stride_tricks_impl
+from . import _shape_base_impl
+from . import _twodim_base_impl
+from . import _ufunclike_impl
+from . import _histograms_impl
+from . import _utils_impl
+from . import _arraysetops_impl
+from . import _polynomial_impl
+from . import _npyio_impl
+from . import _arrayterator_impl
+from . import _arraypad_impl
+from . import _version
+
+# numpy.lib namespace members
+from ._arrayterator_impl import Arrayterator
+from ._version import NumpyVersion
+from numpy._core._multiarray_umath import add_docstring, tracemalloc_domain
+from numpy._core.function_base import add_newdoc
+
+__all__ = [
+    "Arrayterator", "add_docstring", "add_newdoc", "array_utils",
+    "introspect", "mixins", "NumpyVersion", "npyio", "scimath",
+    "stride_tricks", "tracemalloc_domain"
+]
+
+add_newdoc.__module__ = "numpy.lib"
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
+
+def __getattr__(attr):
+    # Warn for deprecated/removed aliases
+    import math
+    import warnings
+
+    if attr == "math":
+        warnings.warn(
+            "`np.lib.math` is a deprecated alias for the standard library "
+            "`math` module (Deprecated Numpy 1.25). Replace usages of "
+            "`numpy.lib.math` with `math`", DeprecationWarning, stacklevel=2)
+        return math
+    elif attr == "emath":
+        raise AttributeError(
+            "numpy.lib.emath was an alias for emath module that was removed "
+            "in NumPy 2.0. Replace usages of numpy.lib.emath with "
+            "numpy.emath.",
+            name=None
+        )
+    elif attr in (
+        "histograms", "type_check", "nanfunctions", "function_base",
+        "arraypad", "arraysetops", "ufunclike", "utils", "twodim_base",
+        "shape_base", "polynomial", "index_tricks",
+    ):
+        raise AttributeError(
+            f"numpy.lib.{attr} is now private. If you are using a public "
+            "function, it should be available in the main numpy namespace, "
+            "otherwise check the NumPy 2.0 migration guide.",
+            name=None
+        )
+    elif attr == "arrayterator":
+        raise AttributeError(
+            "numpy.lib.arrayterator submodule is now private. To access "
+            "Arrayterator class use numpy.lib.Arrayterator.",
+            name=None
+        )
+    else:
+        raise AttributeError("module {!r} has no attribute "
+                             "{!r}".format(__name__, attr))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..19d6ea7a4d3f52a70916e9ca25bb2816cdd5e974
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__init__.pyi
@@ -0,0 +1,20 @@
+from numpy._core.multiarray import add_docstring, tracemalloc_domain
+from numpy._core.function_base import add_newdoc
+
+from . import array_utils, format, introspect, mixins, npyio, scimath, stride_tricks  # noqa: F401
+from ._version import NumpyVersion
+from ._arrayterator_impl import Arrayterator
+
+__all__ = [
+    "Arrayterator",
+    "add_docstring",
+    "add_newdoc",
+    "array_utils",
+    "introspect",
+    "mixins",
+    "NumpyVersion",
+    "npyio",
+    "scimath",
+    "stride_tricks",
+    "tracemalloc_domain",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5f778160358a05cc642ef43ebd523cff19e2206
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.py
@@ -0,0 +1,62 @@
+"""
+Miscellaneous utils.
+"""
+from numpy._core import asarray
+from numpy._core.numeric import normalize_axis_tuple, normalize_axis_index
+from numpy._utils import set_module
+
+__all__ = ["byte_bounds", "normalize_axis_tuple", "normalize_axis_index"]
+
+
+@set_module("numpy.lib.array_utils")
+def byte_bounds(a):
+    """
+    Returns pointers to the end-points of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. It must conform to the Python-side of the array
+        interface.
+
+    Returns
+    -------
+    (low, high) : tuple of 2 integers
+        The first integer is the first byte of the array, the second
+        integer is just past the last byte of the array.  If `a` is not
+        contiguous it will not use every byte between the (`low`, `high`)
+        values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> I = np.eye(2, dtype='f'); I.dtype
+    dtype('float32')
+    >>> low, high = np.lib.array_utils.byte_bounds(I)
+    >>> high - low == I.size*I.itemsize
+    True
+    >>> I = np.eye(2); I.dtype
+    dtype('float64')
+    >>> low, high = np.lib.array_utils.byte_bounds(I)
+    >>> high - low == I.size*I.itemsize
+    True
+
+    """
+    ai = a.__array_interface__
+    a_data = ai['data'][0]
+    astrides = ai['strides']
+    ashape = ai['shape']
+    bytes_a = asarray(a).dtype.itemsize
+
+    a_low = a_high = a_data
+    if astrides is None:
+        # contiguous case
+        a_high += a.size * bytes_a
+    else:
+        for shape, stride in zip(ashape, astrides):
+            if stride < 0:
+                a_low += (shape-1)*stride
+            else:
+                a_high += (shape-1)*stride
+        a_high += bytes_a
+    return a_low, a_high
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi
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index 0000000000000000000000000000000000000000..11a2aafb8837dd1ac31f048743202933482da4a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_array_utils_impl.pyi
@@ -0,0 +1,25 @@
+from typing import Any, Iterable
+
+from numpy import generic
+from numpy.typing import NDArray
+
+__all__ = ["byte_bounds", "normalize_axis_tuple", "normalize_axis_index"]
+
+# NOTE: In practice `byte_bounds` can (potentially) take any object
+# implementing the `__array_interface__` protocol. The caveat is
+# that certain keys, marked as optional in the spec, must be present for
+#  `byte_bounds`. This concerns `"strides"` and `"data"`.
+def byte_bounds(a: generic | NDArray[Any]) -> tuple[int, int]: ...
+
+def normalize_axis_tuple(
+    axis: int | Iterable[int],
+    ndim: int = ...,
+    argname: None | str = ...,
+    allow_duplicate: None | bool = ...,
+) -> tuple[int, int]: ...
+
+def normalize_axis_index(
+    axis: int = ...,
+    ndim: int = ...,
+    msg_prefix: None | str = ...,
+) -> int: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e190871722ba8d9fba81bafe66e07a1e5462cdb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.py
@@ -0,0 +1,891 @@
+"""
+The arraypad module contains a group of functions to pad values onto the edges
+of an n-dimensional array.
+
+"""
+import numpy as np
+from numpy._core.overrides import array_function_dispatch
+from numpy.lib._index_tricks_impl import ndindex
+
+
+__all__ = ['pad']
+
+
+###############################################################################
+# Private utility functions.
+
+
+def _round_if_needed(arr, dtype):
+    """
+    Rounds arr inplace if destination dtype is integer.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Input array.
+    dtype : dtype
+        The dtype of the destination array.
+    """
+    if np.issubdtype(dtype, np.integer):
+        arr.round(out=arr)
+
+
+def _slice_at_axis(sl, axis):
+    """
+    Construct tuple of slices to slice an array in the given dimension.
+
+    Parameters
+    ----------
+    sl : slice
+        The slice for the given dimension.
+    axis : int
+        The axis to which `sl` is applied. All other dimensions are left
+        "unsliced".
+
+    Returns
+    -------
+    sl : tuple of slices
+        A tuple with slices matching `shape` in length.
+
+    Examples
+    --------
+    >>> np._slice_at_axis(slice(None, 3, -1), 1)
+    (slice(None, None, None), slice(None, 3, -1), (...,))
+    """
+    return (slice(None),) * axis + (sl,) + (...,)
+
+
+def _view_roi(array, original_area_slice, axis):
+    """
+    Get a view of the current region of interest during iterative padding.
+
+    When padding multiple dimensions iteratively corner values are
+    unnecessarily overwritten multiple times. This function reduces the
+    working area for the first dimensions so that corners are excluded.
+
+    Parameters
+    ----------
+    array : ndarray
+        The array with the region of interest.
+    original_area_slice : tuple of slices
+        Denotes the area with original values of the unpadded array.
+    axis : int
+        The currently padded dimension assuming that `axis` is padded before
+        `axis` + 1.
+
+    Returns
+    -------
+    roi : ndarray
+        The region of interest of the original `array`.
+    """
+    axis += 1
+    sl = (slice(None),) * axis + original_area_slice[axis:]
+    return array[sl]
+
+
+def _pad_simple(array, pad_width, fill_value=None):
+    """
+    Pad array on all sides with either a single value or undefined values.
+
+    Parameters
+    ----------
+    array : ndarray
+        Array to grow.
+    pad_width : sequence of tuple[int, int]
+        Pad width on both sides for each dimension in `arr`.
+    fill_value : scalar, optional
+        If provided the padded area is filled with this value, otherwise
+        the pad area left undefined.
+
+    Returns
+    -------
+    padded : ndarray
+        The padded array with the same dtype as`array`. Its order will default
+        to C-style if `array` is not F-contiguous.
+    original_area_slice : tuple
+        A tuple of slices pointing to the area of the original array.
+    """
+    # Allocate grown array
+    new_shape = tuple(
+        left + size + right
+        for size, (left, right) in zip(array.shape, pad_width)
+    )
+    order = 'F' if array.flags.fnc else 'C'  # Fortran and not also C-order
+    padded = np.empty(new_shape, dtype=array.dtype, order=order)
+
+    if fill_value is not None:
+        padded.fill(fill_value)
+
+    # Copy old array into correct space
+    original_area_slice = tuple(
+        slice(left, left + size)
+        for size, (left, right) in zip(array.shape, pad_width)
+    )
+    padded[original_area_slice] = array
+
+    return padded, original_area_slice
+
+
+def _set_pad_area(padded, axis, width_pair, value_pair):
+    """
+    Set empty-padded area in given dimension.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Array with the pad area which is modified inplace.
+    axis : int
+        Dimension with the pad area to set.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+    value_pair : tuple of scalars or ndarrays
+        Values inserted into the pad area on each side. It must match or be
+        broadcastable to the shape of `arr`.
+    """
+    left_slice = _slice_at_axis(slice(None, width_pair[0]), axis)
+    padded[left_slice] = value_pair[0]
+
+    right_slice = _slice_at_axis(
+        slice(padded.shape[axis] - width_pair[1], None), axis)
+    padded[right_slice] = value_pair[1]
+
+
+def _get_edges(padded, axis, width_pair):
+    """
+    Retrieve edge values from empty-padded array in given dimension.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Empty-padded array.
+    axis : int
+        Dimension in which the edges are considered.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+
+    Returns
+    -------
+    left_edge, right_edge : ndarray
+        Edge values of the valid area in `padded` in the given dimension. Its
+        shape will always match `padded` except for the dimension given by
+        `axis` which will have a length of 1.
+    """
+    left_index = width_pair[0]
+    left_slice = _slice_at_axis(slice(left_index, left_index + 1), axis)
+    left_edge = padded[left_slice]
+
+    right_index = padded.shape[axis] - width_pair[1]
+    right_slice = _slice_at_axis(slice(right_index - 1, right_index), axis)
+    right_edge = padded[right_slice]
+
+    return left_edge, right_edge
+
+
+def _get_linear_ramps(padded, axis, width_pair, end_value_pair):
+    """
+    Construct linear ramps for empty-padded array in given dimension.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Empty-padded array.
+    axis : int
+        Dimension in which the ramps are constructed.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+    end_value_pair : (scalar, scalar)
+        End values for the linear ramps which form the edge of the fully padded
+        array. These values are included in the linear ramps.
+
+    Returns
+    -------
+    left_ramp, right_ramp : ndarray
+        Linear ramps to set on both sides of `padded`.
+    """
+    edge_pair = _get_edges(padded, axis, width_pair)
+
+    left_ramp, right_ramp = (
+        np.linspace(
+            start=end_value,
+            stop=edge.squeeze(axis), # Dimension is replaced by linspace
+            num=width,
+            endpoint=False,
+            dtype=padded.dtype,
+            axis=axis
+        )
+        for end_value, edge, width in zip(
+            end_value_pair, edge_pair, width_pair
+        )
+    )
+
+    # Reverse linear space in appropriate dimension
+    right_ramp = right_ramp[_slice_at_axis(slice(None, None, -1), axis)]
+
+    return left_ramp, right_ramp
+
+
+def _get_stats(padded, axis, width_pair, length_pair, stat_func):
+    """
+    Calculate statistic for the empty-padded array in given dimension.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Empty-padded array.
+    axis : int
+        Dimension in which the statistic is calculated.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+    length_pair : 2-element sequence of None or int
+        Gives the number of values in valid area from each side that is
+        taken into account when calculating the statistic. If None the entire
+        valid area in `padded` is considered.
+    stat_func : function
+        Function to compute statistic. The expected signature is
+        ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``.
+
+    Returns
+    -------
+    left_stat, right_stat : ndarray
+        Calculated statistic for both sides of `padded`.
+    """
+    # Calculate indices of the edges of the area with original values
+    left_index = width_pair[0]
+    right_index = padded.shape[axis] - width_pair[1]
+    # as well as its length
+    max_length = right_index - left_index
+
+    # Limit stat_lengths to max_length
+    left_length, right_length = length_pair
+    if left_length is None or max_length < left_length:
+        left_length = max_length
+    if right_length is None or max_length < right_length:
+        right_length = max_length
+
+    if (left_length == 0 or right_length == 0) \
+            and stat_func in {np.amax, np.amin}:
+        # amax and amin can't operate on an empty array,
+        # raise a more descriptive warning here instead of the default one
+        raise ValueError("stat_length of 0 yields no value for padding")
+
+    # Calculate statistic for the left side
+    left_slice = _slice_at_axis(
+        slice(left_index, left_index + left_length), axis)
+    left_chunk = padded[left_slice]
+    left_stat = stat_func(left_chunk, axis=axis, keepdims=True)
+    _round_if_needed(left_stat, padded.dtype)
+
+    if left_length == right_length == max_length:
+        # return early as right_stat must be identical to left_stat
+        return left_stat, left_stat
+
+    # Calculate statistic for the right side
+    right_slice = _slice_at_axis(
+        slice(right_index - right_length, right_index), axis)
+    right_chunk = padded[right_slice]
+    right_stat = stat_func(right_chunk, axis=axis, keepdims=True)
+    _round_if_needed(right_stat, padded.dtype)
+
+    return left_stat, right_stat
+
+
+def _set_reflect_both(padded, axis, width_pair, method,
+                      original_period, include_edge=False):
+    """
+    Pad `axis` of `arr` with reflection.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Input array of arbitrary shape.
+    axis : int
+        Axis along which to pad `arr`.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+    method : str
+        Controls method of reflection; options are 'even' or 'odd'.
+    original_period : int
+        Original length of data on `axis` of `arr`.
+    include_edge : bool
+        If true, edge value is included in reflection, otherwise the edge
+        value forms the symmetric axis to the reflection.
+
+    Returns
+    -------
+    pad_amt : tuple of ints, length 2
+        New index positions of padding to do along the `axis`. If these are
+        both 0, padding is done in this dimension.
+    """
+    left_pad, right_pad = width_pair
+    old_length = padded.shape[axis] - right_pad - left_pad
+
+    if include_edge:
+        # Avoid wrapping with only a subset of the original area
+        # by ensuring period can only be a multiple of the original
+        # area's length.
+        old_length = old_length // original_period * original_period
+        # Edge is included, we need to offset the pad amount by 1
+        edge_offset = 1
+    else:
+        # Avoid wrapping with only a subset of the original area
+        # by ensuring period can only be a multiple of the original
+        # area's length.
+        old_length = ((old_length - 1) // (original_period - 1)
+            * (original_period - 1) + 1)
+        edge_offset = 0  # Edge is not included, no need to offset pad amount
+        old_length -= 1  # but must be omitted from the chunk
+
+    if left_pad > 0:
+        # Pad with reflected values on left side:
+        # First limit chunk size which can't be larger than pad area
+        chunk_length = min(old_length, left_pad)
+        # Slice right to left, stop on or next to edge, start relative to stop
+        stop = left_pad - edge_offset
+        start = stop + chunk_length
+        left_slice = _slice_at_axis(slice(start, stop, -1), axis)
+        left_chunk = padded[left_slice]
+
+        if method == "odd":
+            # Negate chunk and align with edge
+            edge_slice = _slice_at_axis(slice(left_pad, left_pad + 1), axis)
+            left_chunk = 2 * padded[edge_slice] - left_chunk
+
+        # Insert chunk into padded area
+        start = left_pad - chunk_length
+        stop = left_pad
+        pad_area = _slice_at_axis(slice(start, stop), axis)
+        padded[pad_area] = left_chunk
+        # Adjust pointer to left edge for next iteration
+        left_pad -= chunk_length
+
+    if right_pad > 0:
+        # Pad with reflected values on right side:
+        # First limit chunk size which can't be larger than pad area
+        chunk_length = min(old_length, right_pad)
+        # Slice right to left, start on or next to edge, stop relative to start
+        start = -right_pad + edge_offset - 2
+        stop = start - chunk_length
+        right_slice = _slice_at_axis(slice(start, stop, -1), axis)
+        right_chunk = padded[right_slice]
+
+        if method == "odd":
+            # Negate chunk and align with edge
+            edge_slice = _slice_at_axis(
+                slice(-right_pad - 1, -right_pad), axis)
+            right_chunk = 2 * padded[edge_slice] - right_chunk
+
+        # Insert chunk into padded area
+        start = padded.shape[axis] - right_pad
+        stop = start + chunk_length
+        pad_area = _slice_at_axis(slice(start, stop), axis)
+        padded[pad_area] = right_chunk
+        # Adjust pointer to right edge for next iteration
+        right_pad -= chunk_length
+
+    return left_pad, right_pad
+
+
+def _set_wrap_both(padded, axis, width_pair, original_period):
+    """
+    Pad `axis` of `arr` with wrapped values.
+
+    Parameters
+    ----------
+    padded : ndarray
+        Input array of arbitrary shape.
+    axis : int
+        Axis along which to pad `arr`.
+    width_pair : (int, int)
+        Pair of widths that mark the pad area on both sides in the given
+        dimension.
+    original_period : int
+        Original length of data on `axis` of `arr`.
+
+    Returns
+    -------
+    pad_amt : tuple of ints, length 2
+        New index positions of padding to do along the `axis`. If these are
+        both 0, padding is done in this dimension.
+    """
+    left_pad, right_pad = width_pair
+    period = padded.shape[axis] - right_pad - left_pad
+    # Avoid wrapping with only a subset of the original area by ensuring period
+    # can only be a multiple of the original area's length.
+    period = period // original_period * original_period
+
+    # If the current dimension of `arr` doesn't contain enough valid values
+    # (not part of the undefined pad area) we need to pad multiple times.
+    # Each time the pad area shrinks on both sides which is communicated with
+    # these variables.
+    new_left_pad = 0
+    new_right_pad = 0
+
+    if left_pad > 0:
+        # Pad with wrapped values on left side
+        # First slice chunk from left side of the non-pad area.
+        # Use min(period, left_pad) to ensure that chunk is not larger than
+        # pad area.
+        slice_end = left_pad + period
+        slice_start = slice_end - min(period, left_pad)
+        right_slice = _slice_at_axis(slice(slice_start, slice_end), axis)
+        right_chunk = padded[right_slice]
+
+        if left_pad > period:
+            # Chunk is smaller than pad area
+            pad_area = _slice_at_axis(slice(left_pad - period, left_pad), axis)
+            new_left_pad = left_pad - period
+        else:
+            # Chunk matches pad area
+            pad_area = _slice_at_axis(slice(None, left_pad), axis)
+        padded[pad_area] = right_chunk
+
+    if right_pad > 0:
+        # Pad with wrapped values on right side
+        # First slice chunk from right side of the non-pad area.
+        # Use min(period, right_pad) to ensure that chunk is not larger than
+        # pad area.
+        slice_start = -right_pad - period
+        slice_end = slice_start + min(period, right_pad)
+        left_slice = _slice_at_axis(slice(slice_start, slice_end), axis)
+        left_chunk = padded[left_slice]
+
+        if right_pad > period:
+            # Chunk is smaller than pad area
+            pad_area = _slice_at_axis(
+                slice(-right_pad, -right_pad + period), axis)
+            new_right_pad = right_pad - period
+        else:
+            # Chunk matches pad area
+            pad_area = _slice_at_axis(slice(-right_pad, None), axis)
+        padded[pad_area] = left_chunk
+
+    return new_left_pad, new_right_pad
+
+
+def _as_pairs(x, ndim, as_index=False):
+    """
+    Broadcast `x` to an array with the shape (`ndim`, 2).
+
+    A helper function for `pad` that prepares and validates arguments like
+    `pad_width` for iteration in pairs.
+
+    Parameters
+    ----------
+    x : {None, scalar, array-like}
+        The object to broadcast to the shape (`ndim`, 2).
+    ndim : int
+        Number of pairs the broadcasted `x` will have.
+    as_index : bool, optional
+        If `x` is not None, try to round each element of `x` to an integer
+        (dtype `np.intp`) and ensure every element is positive.
+
+    Returns
+    -------
+    pairs : nested iterables, shape (`ndim`, 2)
+        The broadcasted version of `x`.
+
+    Raises
+    ------
+    ValueError
+        If `as_index` is True and `x` contains negative elements.
+        Or if `x` is not broadcastable to the shape (`ndim`, 2).
+    """
+    if x is None:
+        # Pass through None as a special case, otherwise np.round(x) fails
+        # with an AttributeError
+        return ((None, None),) * ndim
+
+    x = np.array(x)
+    if as_index:
+        x = np.round(x).astype(np.intp, copy=False)
+
+    if x.ndim < 3:
+        # Optimization: Possibly use faster paths for cases where `x` has
+        # only 1 or 2 elements. `np.broadcast_to` could handle these as well
+        # but is currently slower
+
+        if x.size == 1:
+            # x was supplied as a single value
+            x = x.ravel()  # Ensure x[0] works for x.ndim == 0, 1, 2
+            if as_index and x < 0:
+                raise ValueError("index can't contain negative values")
+            return ((x[0], x[0]),) * ndim
+
+        if x.size == 2 and x.shape != (2, 1):
+            # x was supplied with a single value for each side
+            # but except case when each dimension has a single value
+            # which should be broadcasted to a pair,
+            # e.g. [[1], [2]] -> [[1, 1], [2, 2]] not [[1, 2], [1, 2]]
+            x = x.ravel()  # Ensure x[0], x[1] works
+            if as_index and (x[0] < 0 or x[1] < 0):
+                raise ValueError("index can't contain negative values")
+            return ((x[0], x[1]),) * ndim
+
+    if as_index and x.min() < 0:
+        raise ValueError("index can't contain negative values")
+
+    # Converting the array with `tolist` seems to improve performance
+    # when iterating and indexing the result (see usage in `pad`)
+    return np.broadcast_to(x, (ndim, 2)).tolist()
+
+
+def _pad_dispatcher(array, pad_width, mode=None, **kwargs):
+    return (array,)
+
+
+###############################################################################
+# Public functions
+
+
+@array_function_dispatch(_pad_dispatcher, module='numpy')
+def pad(array, pad_width, mode='constant', **kwargs):
+    """
+    Pad an array.
+
+    Parameters
+    ----------
+    array : array_like of rank N
+        The array to pad.
+    pad_width : {sequence, array_like, int}
+        Number of values padded to the edges of each axis.
+        ``((before_1, after_1), ... (before_N, after_N))`` unique pad widths
+        for each axis.
+        ``(before, after)`` or ``((before, after),)`` yields same before
+        and after pad for each axis.
+        ``(pad,)`` or ``int`` is a shortcut for before = after = pad width
+        for all axes.
+    mode : str or function, optional
+        One of the following string values or a user supplied function.
+
+        'constant' (default)
+            Pads with a constant value.
+        'edge'
+            Pads with the edge values of array.
+        'linear_ramp'
+            Pads with the linear ramp between end_value and the
+            array edge value.
+        'maximum'
+            Pads with the maximum value of all or part of the
+            vector along each axis.
+        'mean'
+            Pads with the mean value of all or part of the
+            vector along each axis.
+        'median'
+            Pads with the median value of all or part of the
+            vector along each axis.
+        'minimum'
+            Pads with the minimum value of all or part of the
+            vector along each axis.
+        'reflect'
+            Pads with the reflection of the vector mirrored on
+            the first and last values of the vector along each
+            axis.
+        'symmetric'
+            Pads with the reflection of the vector mirrored
+            along the edge of the array.
+        'wrap'
+            Pads with the wrap of the vector along the axis.
+            The first values are used to pad the end and the
+            end values are used to pad the beginning.
+        'empty'
+            Pads with undefined values.
+
+        
+            Padding function, see Notes.
+    stat_length : sequence or int, optional
+        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
+        values at edge of each axis used to calculate the statistic value.
+
+        ``((before_1, after_1), ... (before_N, after_N))`` unique statistic
+        lengths for each axis.
+
+        ``(before, after)`` or ``((before, after),)`` yields same before
+        and after statistic lengths for each axis.
+
+        ``(stat_length,)`` or ``int`` is a shortcut for
+        ``before = after = statistic`` length for all axes.
+
+        Default is ``None``, to use the entire axis.
+    constant_values : sequence or scalar, optional
+        Used in 'constant'.  The values to set the padded values for each
+        axis.
+
+        ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
+        for each axis.
+
+        ``(before, after)`` or ``((before, after),)`` yields same before
+        and after constants for each axis.
+
+        ``(constant,)`` or ``constant`` is a shortcut for
+        ``before = after = constant`` for all axes.
+
+        Default is 0.
+    end_values : sequence or scalar, optional
+        Used in 'linear_ramp'.  The values used for the ending value of the
+        linear_ramp and that will form the edge of the padded array.
+
+        ``((before_1, after_1), ... (before_N, after_N))`` unique end values
+        for each axis.
+
+        ``(before, after)`` or ``((before, after),)`` yields same before
+        and after end values for each axis.
+
+        ``(constant,)`` or ``constant`` is a shortcut for
+        ``before = after = constant`` for all axes.
+
+        Default is 0.
+    reflect_type : {'even', 'odd'}, optional
+        Used in 'reflect', and 'symmetric'.  The 'even' style is the
+        default with an unaltered reflection around the edge value.  For
+        the 'odd' style, the extended part of the array is created by
+        subtracting the reflected values from two times the edge value.
+
+    Returns
+    -------
+    pad : ndarray
+        Padded array of rank equal to `array` with shape increased
+        according to `pad_width`.
+
+    Notes
+    -----
+    For an array with rank greater than 1, some of the padding of later
+    axes is calculated from padding of previous axes.  This is easiest to
+    think about with a rank 2 array where the corners of the padded array
+    are calculated by using padded values from the first axis.
+
+    The padding function, if used, should modify a rank 1 array in-place. It
+    has the following signature::
+
+        padding_func(vector, iaxis_pad_width, iaxis, kwargs)
+
+    where
+
+    vector : ndarray
+        A rank 1 array already padded with zeros.  Padded values are
+        vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
+    iaxis_pad_width : tuple
+        A 2-tuple of ints, iaxis_pad_width[0] represents the number of
+        values padded at the beginning of vector where
+        iaxis_pad_width[1] represents the number of values padded at
+        the end of vector.
+    iaxis : int
+        The axis currently being calculated.
+    kwargs : dict
+        Any keyword arguments the function requires.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = [1, 2, 3, 4, 5]
+    >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
+    array([4, 4, 1, ..., 6, 6, 6])
+
+    >>> np.pad(a, (2, 3), 'edge')
+    array([1, 1, 1, ..., 5, 5, 5])
+
+    >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
+    array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])
+
+    >>> np.pad(a, (2,), 'maximum')
+    array([5, 5, 1, 2, 3, 4, 5, 5, 5])
+
+    >>> np.pad(a, (2,), 'mean')
+    array([3, 3, 1, 2, 3, 4, 5, 3, 3])
+
+    >>> np.pad(a, (2,), 'median')
+    array([3, 3, 1, 2, 3, 4, 5, 3, 3])
+
+    >>> a = [[1, 2], [3, 4]]
+    >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
+    array([[1, 1, 1, 2, 1, 1, 1],
+           [1, 1, 1, 2, 1, 1, 1],
+           [1, 1, 1, 2, 1, 1, 1],
+           [1, 1, 1, 2, 1, 1, 1],
+           [3, 3, 3, 4, 3, 3, 3],
+           [1, 1, 1, 2, 1, 1, 1],
+           [1, 1, 1, 2, 1, 1, 1]])
+
+    >>> a = [1, 2, 3, 4, 5]
+    >>> np.pad(a, (2, 3), 'reflect')
+    array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
+
+    >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
+    array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])
+
+    >>> np.pad(a, (2, 3), 'symmetric')
+    array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
+
+    >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
+    array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
+
+    >>> np.pad(a, (2, 3), 'wrap')
+    array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
+
+    >>> def pad_with(vector, pad_width, iaxis, kwargs):
+    ...     pad_value = kwargs.get('padder', 10)
+    ...     vector[:pad_width[0]] = pad_value
+    ...     vector[-pad_width[1]:] = pad_value
+    >>> a = np.arange(6)
+    >>> a = a.reshape((2, 3))
+    >>> np.pad(a, 2, pad_with)
+    array([[10, 10, 10, 10, 10, 10, 10],
+           [10, 10, 10, 10, 10, 10, 10],
+           [10, 10,  0,  1,  2, 10, 10],
+           [10, 10,  3,  4,  5, 10, 10],
+           [10, 10, 10, 10, 10, 10, 10],
+           [10, 10, 10, 10, 10, 10, 10]])
+    >>> np.pad(a, 2, pad_with, padder=100)
+    array([[100, 100, 100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100, 100, 100],
+           [100, 100,   0,   1,   2, 100, 100],
+           [100, 100,   3,   4,   5, 100, 100],
+           [100, 100, 100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100, 100, 100]])
+    """
+    array = np.asarray(array)
+    pad_width = np.asarray(pad_width)
+
+    if not pad_width.dtype.kind == 'i':
+        raise TypeError('`pad_width` must be of integral type.')
+
+    # Broadcast to shape (array.ndim, 2)
+    pad_width = _as_pairs(pad_width, array.ndim, as_index=True)
+
+    if callable(mode):
+        # Old behavior: Use user-supplied function with np.apply_along_axis
+        function = mode
+        # Create a new zero padded array
+        padded, _ = _pad_simple(array, pad_width, fill_value=0)
+        # And apply along each axis
+
+        for axis in range(padded.ndim):
+            # Iterate using ndindex as in apply_along_axis, but assuming that
+            # function operates inplace on the padded array.
+
+            # view with the iteration axis at the end
+            view = np.moveaxis(padded, axis, -1)
+
+            # compute indices for the iteration axes, and append a trailing
+            # ellipsis to prevent 0d arrays decaying to scalars (gh-8642)
+            inds = ndindex(view.shape[:-1])
+            inds = (ind + (Ellipsis,) for ind in inds)
+            for ind in inds:
+                function(view[ind], pad_width[axis], axis, kwargs)
+
+        return padded
+
+    # Make sure that no unsupported keywords were passed for the current mode
+    allowed_kwargs = {
+        'empty': [], 'edge': [], 'wrap': [],
+        'constant': ['constant_values'],
+        'linear_ramp': ['end_values'],
+        'maximum': ['stat_length'],
+        'mean': ['stat_length'],
+        'median': ['stat_length'],
+        'minimum': ['stat_length'],
+        'reflect': ['reflect_type'],
+        'symmetric': ['reflect_type'],
+    }
+    try:
+        unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
+    except KeyError:
+        raise ValueError("mode '{}' is not supported".format(mode)) from None
+    if unsupported_kwargs:
+        raise ValueError("unsupported keyword arguments for mode '{}': {}"
+                         .format(mode, unsupported_kwargs))
+
+    stat_functions = {"maximum": np.amax, "minimum": np.amin,
+                      "mean": np.mean, "median": np.median}
+
+    # Create array with final shape and original values
+    # (padded area is undefined)
+    padded, original_area_slice = _pad_simple(array, pad_width)
+    # And prepare iteration over all dimensions
+    # (zipping may be more readable than using enumerate)
+    axes = range(padded.ndim)
+
+    if mode == "constant":
+        values = kwargs.get("constant_values", 0)
+        values = _as_pairs(values, padded.ndim)
+        for axis, width_pair, value_pair in zip(axes, pad_width, values):
+            roi = _view_roi(padded, original_area_slice, axis)
+            _set_pad_area(roi, axis, width_pair, value_pair)
+
+    elif mode == "empty":
+        pass  # Do nothing as _pad_simple already returned the correct result
+
+    elif array.size == 0:
+        # Only modes "constant" and "empty" can extend empty axes, all other
+        # modes depend on `array` not being empty
+        # -> ensure every empty axis is only "padded with 0"
+        for axis, width_pair in zip(axes, pad_width):
+            if array.shape[axis] == 0 and any(width_pair):
+                raise ValueError(
+                    "can't extend empty axis {} using modes other than "
+                    "'constant' or 'empty'".format(axis)
+                )
+        # passed, don't need to do anything more as _pad_simple already
+        # returned the correct result
+
+    elif mode == "edge":
+        for axis, width_pair in zip(axes, pad_width):
+            roi = _view_roi(padded, original_area_slice, axis)
+            edge_pair = _get_edges(roi, axis, width_pair)
+            _set_pad_area(roi, axis, width_pair, edge_pair)
+
+    elif mode == "linear_ramp":
+        end_values = kwargs.get("end_values", 0)
+        end_values = _as_pairs(end_values, padded.ndim)
+        for axis, width_pair, value_pair in zip(axes, pad_width, end_values):
+            roi = _view_roi(padded, original_area_slice, axis)
+            ramp_pair = _get_linear_ramps(roi, axis, width_pair, value_pair)
+            _set_pad_area(roi, axis, width_pair, ramp_pair)
+
+    elif mode in stat_functions:
+        func = stat_functions[mode]
+        length = kwargs.get("stat_length", None)
+        length = _as_pairs(length, padded.ndim, as_index=True)
+        for axis, width_pair, length_pair in zip(axes, pad_width, length):
+            roi = _view_roi(padded, original_area_slice, axis)
+            stat_pair = _get_stats(roi, axis, width_pair, length_pair, func)
+            _set_pad_area(roi, axis, width_pair, stat_pair)
+
+    elif mode in {"reflect", "symmetric"}:
+        method = kwargs.get("reflect_type", "even")
+        include_edge = mode == "symmetric"
+        for axis, (left_index, right_index) in zip(axes, pad_width):
+            if array.shape[axis] == 1 and (left_index > 0 or right_index > 0):
+                # Extending singleton dimension for 'reflect' is legacy
+                # behavior; it really should raise an error.
+                edge_pair = _get_edges(padded, axis, (left_index, right_index))
+                _set_pad_area(
+                    padded, axis, (left_index, right_index), edge_pair)
+                continue
+
+            roi = _view_roi(padded, original_area_slice, axis)
+            while left_index > 0 or right_index > 0:
+                # Iteratively pad until dimension is filled with reflected
+                # values. This is necessary if the pad area is larger than
+                # the length of the original values in the current dimension.
+                left_index, right_index = _set_reflect_both(
+                    roi, axis, (left_index, right_index),
+                    method, array.shape[axis], include_edge
+                )
+
+    elif mode == "wrap":
+        for axis, (left_index, right_index) in zip(axes, pad_width):
+            roi = _view_roi(padded, original_area_slice, axis)
+            original_period = padded.shape[axis] - right_index - left_index
+            while left_index > 0 or right_index > 0:
+                # Iteratively pad until dimension is filled with wrapped
+                # values. This is necessary if the pad area is larger than
+                # the length of the original values in the current dimension.
+                left_index, right_index = _set_wrap_both(
+                    roi, axis, (left_index, right_index), original_period)
+
+    return padded
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3a2c433c338a2eadd2afd83182bb9738ab37fffb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraypad_impl.pyi
@@ -0,0 +1,89 @@
+from typing import (
+    Literal as L,
+    Any,
+    TypeAlias,
+    overload,
+    TypeVar,
+    Protocol,
+    type_check_only,
+)
+
+from numpy import generic
+
+from numpy._typing import (
+    ArrayLike,
+    NDArray,
+    _ArrayLikeInt,
+    _ArrayLike,
+)
+
+__all__ = ["pad"]
+
+_SCT = TypeVar("_SCT", bound=generic)
+
+@type_check_only
+class _ModeFunc(Protocol):
+    def __call__(
+        self,
+        vector: NDArray[Any],
+        iaxis_pad_width: tuple[int, int],
+        iaxis: int,
+        kwargs: dict[str, Any],
+        /,
+    ) -> None: ...
+
+_ModeKind: TypeAlias = L[
+    "constant",
+    "edge",
+    "linear_ramp",
+    "maximum",
+    "mean",
+    "median",
+    "minimum",
+    "reflect",
+    "symmetric",
+    "wrap",
+    "empty",
+]
+
+
+# TODO: In practice each keyword argument is exclusive to one or more
+# specific modes. Consider adding more overloads to express this in the future.
+
+# Expand `**kwargs` into explicit keyword-only arguments
+@overload
+def pad(
+    array: _ArrayLike[_SCT],
+    pad_width: _ArrayLikeInt,
+    mode: _ModeKind = ...,
+    *,
+    stat_length: None | _ArrayLikeInt = ...,
+    constant_values: ArrayLike = ...,
+    end_values: ArrayLike = ...,
+    reflect_type: L["odd", "even"] = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def pad(
+    array: ArrayLike,
+    pad_width: _ArrayLikeInt,
+    mode: _ModeKind = ...,
+    *,
+    stat_length: None | _ArrayLikeInt = ...,
+    constant_values: ArrayLike = ...,
+    end_values: ArrayLike = ...,
+    reflect_type: L["odd", "even"] = ...,
+) -> NDArray[Any]: ...
+@overload
+def pad(
+    array: _ArrayLike[_SCT],
+    pad_width: _ArrayLikeInt,
+    mode: _ModeFunc,
+    **kwargs: Any,
+) -> NDArray[_SCT]: ...
+@overload
+def pad(
+    array: ArrayLike,
+    pad_width: _ArrayLikeInt,
+    mode: _ModeFunc,
+    **kwargs: Any,
+) -> NDArray[Any]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..60b3425682fb2e1de1fb4e7a7c99132b80608986
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.py
@@ -0,0 +1,1215 @@
+"""
+Set operations for arrays based on sorting.
+
+Notes
+-----
+
+For floating point arrays, inaccurate results may appear due to usual round-off
+and floating point comparison issues.
+
+Speed could be gained in some operations by an implementation of
+`numpy.sort`, that can provide directly the permutation vectors, thus avoiding
+calls to `numpy.argsort`.
+
+Original author: Robert Cimrman
+
+"""
+import functools
+import warnings
+from typing import NamedTuple
+
+import numpy as np
+from numpy._core import overrides
+from numpy._core._multiarray_umath import _array_converter
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+__all__ = [
+    "ediff1d", "in1d", "intersect1d", "isin", "setdiff1d", "setxor1d",
+    "union1d", "unique", "unique_all", "unique_counts", "unique_inverse",
+    "unique_values"
+]
+
+
+def _ediff1d_dispatcher(ary, to_end=None, to_begin=None):
+    return (ary, to_end, to_begin)
+
+
+@array_function_dispatch(_ediff1d_dispatcher)
+def ediff1d(ary, to_end=None, to_begin=None):
+    """
+    The differences between consecutive elements of an array.
+
+    Parameters
+    ----------
+    ary : array_like
+        If necessary, will be flattened before the differences are taken.
+    to_end : array_like, optional
+        Number(s) to append at the end of the returned differences.
+    to_begin : array_like, optional
+        Number(s) to prepend at the beginning of the returned differences.
+
+    Returns
+    -------
+    ediff1d : ndarray
+        The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
+
+    See Also
+    --------
+    diff, gradient
+
+    Notes
+    -----
+    When applied to masked arrays, this function drops the mask information
+    if the `to_begin` and/or `to_end` parameters are used.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.ediff1d(x)
+    array([ 1,  2,  3, -7])
+
+    >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
+    array([-99,   1,   2, ...,  -7,  88,  99])
+
+    The returned array is always 1D.
+
+    >>> y = [[1, 2, 4], [1, 6, 24]]
+    >>> np.ediff1d(y)
+    array([ 1,  2, -3,  5, 18])
+
+    """
+    conv = _array_converter(ary)
+    # Convert to (any) array and ravel:
+    ary = conv[0].ravel()
+
+    # enforce that the dtype of `ary` is used for the output
+    dtype_req = ary.dtype
+
+    # fast track default case
+    if to_begin is None and to_end is None:
+        return ary[1:] - ary[:-1]
+
+    if to_begin is None:
+        l_begin = 0
+    else:
+        to_begin = np.asanyarray(to_begin)
+        if not np.can_cast(to_begin, dtype_req, casting="same_kind"):
+            raise TypeError("dtype of `to_begin` must be compatible "
+                            "with input `ary` under the `same_kind` rule.")
+
+        to_begin = to_begin.ravel()
+        l_begin = len(to_begin)
+
+    if to_end is None:
+        l_end = 0
+    else:
+        to_end = np.asanyarray(to_end)
+        if not np.can_cast(to_end, dtype_req, casting="same_kind"):
+            raise TypeError("dtype of `to_end` must be compatible "
+                            "with input `ary` under the `same_kind` rule.")
+
+        to_end = to_end.ravel()
+        l_end = len(to_end)
+
+    # do the calculation in place and copy to_begin and to_end
+    l_diff = max(len(ary) - 1, 0)
+    result = np.empty_like(ary, shape=l_diff + l_begin + l_end)
+
+    if l_begin > 0:
+        result[:l_begin] = to_begin
+    if l_end > 0:
+        result[l_begin + l_diff:] = to_end
+    np.subtract(ary[1:], ary[:-1], result[l_begin:l_begin + l_diff])
+
+    return conv.wrap(result)
+
+
+def _unpack_tuple(x):
+    """ Unpacks one-element tuples for use as return values """
+    if len(x) == 1:
+        return x[0]
+    else:
+        return x
+
+
+def _unique_dispatcher(ar, return_index=None, return_inverse=None,
+                       return_counts=None, axis=None, *, equal_nan=None):
+    return (ar,)
+
+
+@array_function_dispatch(_unique_dispatcher)
+def unique(ar, return_index=False, return_inverse=False,
+           return_counts=False, axis=None, *, equal_nan=True):
+    """
+    Find the unique elements of an array.
+
+    Returns the sorted unique elements of an array. There are three optional
+    outputs in addition to the unique elements:
+
+    * the indices of the input array that give the unique values
+    * the indices of the unique array that reconstruct the input array
+    * the number of times each unique value comes up in the input array
+
+    Parameters
+    ----------
+    ar : array_like
+        Input array. Unless `axis` is specified, this will be flattened if it
+        is not already 1-D.
+    return_index : bool, optional
+        If True, also return the indices of `ar` (along the specified axis,
+        if provided, or in the flattened array) that result in the unique array.
+    return_inverse : bool, optional
+        If True, also return the indices of the unique array (for the specified
+        axis, if provided) that can be used to reconstruct `ar`.
+    return_counts : bool, optional
+        If True, also return the number of times each unique item appears
+        in `ar`.
+    axis : int or None, optional
+        The axis to operate on. If None, `ar` will be flattened. If an integer,
+        the subarrays indexed by the given axis will be flattened and treated
+        as the elements of a 1-D array with the dimension of the given axis,
+        see the notes for more details.  Object arrays or structured arrays
+        that contain objects are not supported if the `axis` kwarg is used. The
+        default is None.
+
+    equal_nan : bool, optional
+        If True, collapses multiple NaN values in the return array into one.
+
+        .. versionadded:: 1.24
+
+    Returns
+    -------
+    unique : ndarray
+        The sorted unique values.
+    unique_indices : ndarray, optional
+        The indices of the first occurrences of the unique values in the
+        original array. Only provided if `return_index` is True.
+    unique_inverse : ndarray, optional
+        The indices to reconstruct the original array from the
+        unique array. Only provided if `return_inverse` is True.
+    unique_counts : ndarray, optional
+        The number of times each of the unique values comes up in the
+        original array. Only provided if `return_counts` is True.
+
+    See Also
+    --------
+    repeat : Repeat elements of an array.
+    sort : Return a sorted copy of an array.
+
+    Notes
+    -----
+    When an axis is specified the subarrays indexed by the axis are sorted.
+    This is done by making the specified axis the first dimension of the array
+    (move the axis to the first dimension to keep the order of the other axes)
+    and then flattening the subarrays in C order. The flattened subarrays are
+    then viewed as a structured type with each element given a label, with the
+    effect that we end up with a 1-D array of structured types that can be
+    treated in the same way as any other 1-D array. The result is that the
+    flattened subarrays are sorted in lexicographic order starting with the
+    first element.
+
+    .. versionchanged:: 1.21
+        Like np.sort, NaN will sort to the end of the values.
+        For complex arrays all NaN values are considered equivalent
+        (no matter whether the NaN is in the real or imaginary part).
+        As the representant for the returned array the smallest one in the
+        lexicographical order is chosen - see np.sort for how the lexicographical
+        order is defined for complex arrays.
+
+    .. versionchanged:: 2.0
+        For multi-dimensional inputs, ``unique_inverse`` is reshaped
+        such that the input can be reconstructed using
+        ``np.take(unique, unique_inverse, axis=axis)``. The result is
+        now not 1-dimensional when ``axis=None``.
+
+        Note that in NumPy 2.0.0 a higher dimensional array was returned also
+        when ``axis`` was not ``None``.  This was reverted, but
+        ``inverse.reshape(-1)`` can be used to ensure compatibility with both
+        versions.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.unique([1, 1, 2, 2, 3, 3])
+    array([1, 2, 3])
+    >>> a = np.array([[1, 1], [2, 3]])
+    >>> np.unique(a)
+    array([1, 2, 3])
+
+    Return the unique rows of a 2D array
+
+    >>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
+    >>> np.unique(a, axis=0)
+    array([[1, 0, 0], [2, 3, 4]])
+
+    Return the indices of the original array that give the unique values:
+
+    >>> a = np.array(['a', 'b', 'b', 'c', 'a'])
+    >>> u, indices = np.unique(a, return_index=True)
+    >>> u
+    array(['a', 'b', 'c'], dtype='>> indices
+    array([0, 1, 3])
+    >>> a[indices]
+    array(['a', 'b', 'c'], dtype='>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> u, indices = np.unique(a, return_inverse=True)
+    >>> u
+    array([1, 2, 3, 4, 6])
+    >>> indices
+    array([0, 1, 4, 3, 1, 2, 1])
+    >>> u[indices]
+    array([1, 2, 6, 4, 2, 3, 2])
+
+    Reconstruct the input values from the unique values and counts:
+
+    >>> a = np.array([1, 2, 6, 4, 2, 3, 2])
+    >>> values, counts = np.unique(a, return_counts=True)
+    >>> values
+    array([1, 2, 3, 4, 6])
+    >>> counts
+    array([1, 3, 1, 1, 1])
+    >>> np.repeat(values, counts)
+    array([1, 2, 2, 2, 3, 4, 6])    # original order not preserved
+
+    """
+    ar = np.asanyarray(ar)
+    if axis is None:
+        ret = _unique1d(ar, return_index, return_inverse, return_counts,
+                        equal_nan=equal_nan, inverse_shape=ar.shape, axis=None)
+        return _unpack_tuple(ret)
+
+    # axis was specified and not None
+    try:
+        ar = np.moveaxis(ar, axis, 0)
+    except np.exceptions.AxisError:
+        # this removes the "axis1" or "axis2" prefix from the error message
+        raise np.exceptions.AxisError(axis, ar.ndim) from None
+    inverse_shape = [1] * ar.ndim
+    inverse_shape[axis] = ar.shape[0]
+
+    # Must reshape to a contiguous 2D array for this to work...
+    orig_shape, orig_dtype = ar.shape, ar.dtype
+    ar = ar.reshape(orig_shape[0], np.prod(orig_shape[1:], dtype=np.intp))
+    ar = np.ascontiguousarray(ar)
+    dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])]
+
+    # At this point, `ar` has shape `(n, m)`, and `dtype` is a structured
+    # data type with `m` fields where each field has the data type of `ar`.
+    # In the following, we create the array `consolidated`, which has
+    # shape `(n,)` with data type `dtype`.
+    try:
+        if ar.shape[1] > 0:
+            consolidated = ar.view(dtype)
+        else:
+            # If ar.shape[1] == 0, then dtype will be `np.dtype([])`, which is
+            # a data type with itemsize 0, and the call `ar.view(dtype)` will
+            # fail.  Instead, we'll use `np.empty` to explicitly create the
+            # array with shape `(len(ar),)`.  Because `dtype` in this case has
+            # itemsize 0, the total size of the result is still 0 bytes.
+            consolidated = np.empty(len(ar), dtype=dtype)
+    except TypeError as e:
+        # There's no good way to do this for object arrays, etc...
+        msg = 'The axis argument to unique is not supported for dtype {dt}'
+        raise TypeError(msg.format(dt=ar.dtype)) from e
+
+    def reshape_uniq(uniq):
+        n = len(uniq)
+        uniq = uniq.view(orig_dtype)
+        uniq = uniq.reshape(n, *orig_shape[1:])
+        uniq = np.moveaxis(uniq, 0, axis)
+        return uniq
+
+    output = _unique1d(consolidated, return_index,
+                       return_inverse, return_counts,
+                       equal_nan=equal_nan, inverse_shape=inverse_shape,
+                       axis=axis)
+    output = (reshape_uniq(output[0]),) + output[1:]
+    return _unpack_tuple(output)
+
+
+def _unique1d(ar, return_index=False, return_inverse=False,
+              return_counts=False, *, equal_nan=True, inverse_shape=None,
+              axis=None):
+    """
+    Find the unique elements of an array, ignoring shape.
+    """
+    ar = np.asanyarray(ar).flatten()
+
+    optional_indices = return_index or return_inverse
+
+    if optional_indices:
+        perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
+        aux = ar[perm]
+    else:
+        ar.sort()
+        aux = ar
+    mask = np.empty(aux.shape, dtype=np.bool)
+    mask[:1] = True
+    if (equal_nan and aux.shape[0] > 0 and aux.dtype.kind in "cfmM" and
+            np.isnan(aux[-1])):
+        if aux.dtype.kind == "c":  # for complex all NaNs are considered equivalent
+            aux_firstnan = np.searchsorted(np.isnan(aux), True, side='left')
+        else:
+            aux_firstnan = np.searchsorted(aux, aux[-1], side='left')
+        if aux_firstnan > 0:
+            mask[1:aux_firstnan] = (
+                aux[1:aux_firstnan] != aux[:aux_firstnan - 1])
+        mask[aux_firstnan] = True
+        mask[aux_firstnan + 1:] = False
+    else:
+        mask[1:] = aux[1:] != aux[:-1]
+
+    ret = (aux[mask],)
+    if return_index:
+        ret += (perm[mask],)
+    if return_inverse:
+        imask = np.cumsum(mask) - 1
+        inv_idx = np.empty(mask.shape, dtype=np.intp)
+        inv_idx[perm] = imask
+        ret += (inv_idx.reshape(inverse_shape) if axis is None else inv_idx,)
+    if return_counts:
+        idx = np.concatenate(np.nonzero(mask) + ([mask.size],))
+        ret += (np.diff(idx),)
+    return ret
+
+
+# Array API set functions
+
+class UniqueAllResult(NamedTuple):
+    values: np.ndarray
+    indices: np.ndarray
+    inverse_indices: np.ndarray
+    counts: np.ndarray
+
+
+class UniqueCountsResult(NamedTuple):
+    values: np.ndarray
+    counts: np.ndarray
+
+
+class UniqueInverseResult(NamedTuple):
+    values: np.ndarray
+    inverse_indices: np.ndarray
+
+
+def _unique_all_dispatcher(x, /):
+    return (x,)
+
+
+@array_function_dispatch(_unique_all_dispatcher)
+def unique_all(x):
+    """
+    Find the unique elements of an array, and counts, inverse, and indices.
+
+    This function is an Array API compatible alternative to::
+
+        np.unique(x, return_index=True, return_inverse=True,
+                  return_counts=True, equal_nan=False)
+
+    but returns a namedtuple for easier access to each output.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    out : namedtuple
+        The result containing:
+
+        * values - The unique elements of an input array.
+        * indices - The first occurring indices for each unique element.
+        * inverse_indices - The indices from the set of unique elements
+          that reconstruct `x`.
+        * counts - The corresponding counts for each unique element.
+
+    See Also
+    --------
+    unique : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = [1, 1, 2]
+    >>> uniq = np.unique_all(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.indices
+    array([0, 2])
+    >>> uniq.inverse_indices
+    array([0, 0, 1])
+    >>> uniq.counts
+    array([2, 1])
+    """
+    result = unique(
+        x,
+        return_index=True,
+        return_inverse=True,
+        return_counts=True,
+        equal_nan=False
+    )
+    return UniqueAllResult(*result)
+
+
+def _unique_counts_dispatcher(x, /):
+    return (x,)
+
+
+@array_function_dispatch(_unique_counts_dispatcher)
+def unique_counts(x):
+    """
+    Find the unique elements and counts of an input array `x`.
+
+    This function is an Array API compatible alternative to::
+
+        np.unique(x, return_counts=True, equal_nan=False)
+
+    but returns a namedtuple for easier access to each output.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    out : namedtuple
+        The result containing:
+
+        * values - The unique elements of an input array.
+        * counts - The corresponding counts for each unique element.
+
+    See Also
+    --------
+    unique : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = [1, 1, 2]
+    >>> uniq = np.unique_counts(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.counts
+    array([2, 1])
+    """
+    result = unique(
+        x,
+        return_index=False,
+        return_inverse=False,
+        return_counts=True,
+        equal_nan=False
+    )
+    return UniqueCountsResult(*result)
+
+
+def _unique_inverse_dispatcher(x, /):
+    return (x,)
+
+
+@array_function_dispatch(_unique_inverse_dispatcher)
+def unique_inverse(x):
+    """
+    Find the unique elements of `x` and indices to reconstruct `x`.
+
+    This function is an Array API compatible alternative to::
+
+        np.unique(x, return_inverse=True, equal_nan=False)
+
+    but returns a namedtuple for easier access to each output.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    out : namedtuple
+        The result containing:
+
+        * values - The unique elements of an input array.
+        * inverse_indices - The indices from the set of unique elements
+          that reconstruct `x`.
+
+    See Also
+    --------
+    unique : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = [1, 1, 2]
+    >>> uniq = np.unique_inverse(x)
+    >>> uniq.values
+    array([1, 2])
+    >>> uniq.inverse_indices
+    array([0, 0, 1])
+    """
+    result = unique(
+        x,
+        return_index=False,
+        return_inverse=True,
+        return_counts=False,
+        equal_nan=False
+    )
+    return UniqueInverseResult(*result)
+
+
+def _unique_values_dispatcher(x, /):
+    return (x,)
+
+
+@array_function_dispatch(_unique_values_dispatcher)
+def unique_values(x):
+    """
+    Returns the unique elements of an input array `x`.
+
+    This function is an Array API compatible alternative to::
+
+        np.unique(x, equal_nan=False)
+
+    Parameters
+    ----------
+    x : array_like
+        Input array. It will be flattened if it is not already 1-D.
+
+    Returns
+    -------
+    out : ndarray
+        The unique elements of an input array.
+
+    See Also
+    --------
+    unique : Find the unique elements of an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.unique_values([1, 1, 2])
+    array([1, 2])
+
+    """
+    return unique(
+        x,
+        return_index=False,
+        return_inverse=False,
+        return_counts=False,
+        equal_nan=False
+    )
+
+
+def _intersect1d_dispatcher(
+        ar1, ar2, assume_unique=None, return_indices=None):
+    return (ar1, ar2)
+
+
+@array_function_dispatch(_intersect1d_dispatcher)
+def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
+    """
+    Find the intersection of two arrays.
+
+    Return the sorted, unique values that are in both of the input arrays.
+
+    Parameters
+    ----------
+    ar1, ar2 : array_like
+        Input arrays. Will be flattened if not already 1D.
+    assume_unique : bool
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation.  If True but ``ar1`` or ``ar2`` are not
+        unique, incorrect results and out-of-bounds indices could result.
+        Default is False.
+    return_indices : bool
+        If True, the indices which correspond to the intersection of the two
+        arrays are returned. The first instance of a value is used if there are
+        multiple. Default is False.
+
+    Returns
+    -------
+    intersect1d : ndarray
+        Sorted 1D array of common and unique elements.
+    comm1 : ndarray
+        The indices of the first occurrences of the common values in `ar1`.
+        Only provided if `return_indices` is True.
+    comm2 : ndarray
+        The indices of the first occurrences of the common values in `ar2`.
+        Only provided if `return_indices` is True.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])
+    array([1, 3])
+
+    To intersect more than two arrays, use functools.reduce:
+
+    >>> from functools import reduce
+    >>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
+    array([3])
+
+    To return the indices of the values common to the input arrays
+    along with the intersected values:
+
+    >>> x = np.array([1, 1, 2, 3, 4])
+    >>> y = np.array([2, 1, 4, 6])
+    >>> xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)
+    >>> x_ind, y_ind
+    (array([0, 2, 4]), array([1, 0, 2]))
+    >>> xy, x[x_ind], y[y_ind]
+    (array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))
+
+    """
+    ar1 = np.asanyarray(ar1)
+    ar2 = np.asanyarray(ar2)
+
+    if not assume_unique:
+        if return_indices:
+            ar1, ind1 = unique(ar1, return_index=True)
+            ar2, ind2 = unique(ar2, return_index=True)
+        else:
+            ar1 = unique(ar1)
+            ar2 = unique(ar2)
+    else:
+        ar1 = ar1.ravel()
+        ar2 = ar2.ravel()
+
+    aux = np.concatenate((ar1, ar2))
+    if return_indices:
+        aux_sort_indices = np.argsort(aux, kind='mergesort')
+        aux = aux[aux_sort_indices]
+    else:
+        aux.sort()
+
+    mask = aux[1:] == aux[:-1]
+    int1d = aux[:-1][mask]
+
+    if return_indices:
+        ar1_indices = aux_sort_indices[:-1][mask]
+        ar2_indices = aux_sort_indices[1:][mask] - ar1.size
+        if not assume_unique:
+            ar1_indices = ind1[ar1_indices]
+            ar2_indices = ind2[ar2_indices]
+
+        return int1d, ar1_indices, ar2_indices
+    else:
+        return int1d
+
+
+def _setxor1d_dispatcher(ar1, ar2, assume_unique=None):
+    return (ar1, ar2)
+
+
+@array_function_dispatch(_setxor1d_dispatcher)
+def setxor1d(ar1, ar2, assume_unique=False):
+    """
+    Find the set exclusive-or of two arrays.
+
+    Return the sorted, unique values that are in only one (not both) of the
+    input arrays.
+
+    Parameters
+    ----------
+    ar1, ar2 : array_like
+        Input arrays.
+    assume_unique : bool
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation. Default is False.
+
+    Returns
+    -------
+    setxor1d : ndarray
+        Sorted 1D array of unique values that are in only one of the input
+        arrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1, 2, 3, 2, 4])
+    >>> b = np.array([2, 3, 5, 7, 5])
+    >>> np.setxor1d(a,b)
+    array([1, 4, 5, 7])
+
+    """
+    if not assume_unique:
+        ar1 = unique(ar1)
+        ar2 = unique(ar2)
+
+    aux = np.concatenate((ar1, ar2), axis=None)
+    if aux.size == 0:
+        return aux
+
+    aux.sort()
+    flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
+    return aux[flag[1:] & flag[:-1]]
+
+
+def _in1d_dispatcher(ar1, ar2, assume_unique=None, invert=None, *,
+                     kind=None):
+    return (ar1, ar2)
+
+
+@array_function_dispatch(_in1d_dispatcher)
+def in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None):
+    """
+    Test whether each element of a 1-D array is also present in a second array.
+
+    .. deprecated:: 2.0
+        Use :func:`isin` instead of `in1d` for new code.
+
+    Returns a boolean array the same length as `ar1` that is True
+    where an element of `ar1` is in `ar2` and False otherwise.
+
+    Parameters
+    ----------
+    ar1 : (M,) array_like
+        Input array.
+    ar2 : array_like
+        The values against which to test each value of `ar1`.
+    assume_unique : bool, optional
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation.  Default is False.
+    invert : bool, optional
+        If True, the values in the returned array are inverted (that is,
+        False where an element of `ar1` is in `ar2` and True otherwise).
+        Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
+        to (but is faster than) ``np.invert(in1d(a, b))``.
+    kind : {None, 'sort', 'table'}, optional
+        The algorithm to use. This will not affect the final result,
+        but will affect the speed and memory use. The default, None,
+        will select automatically based on memory considerations.
+
+        * If 'sort', will use a mergesort-based approach. This will have
+          a memory usage of roughly 6 times the sum of the sizes of
+          `ar1` and `ar2`, not accounting for size of dtypes.
+        * If 'table', will use a lookup table approach similar
+          to a counting sort. This is only available for boolean and
+          integer arrays. This will have a memory usage of the
+          size of `ar1` plus the max-min value of `ar2`. `assume_unique`
+          has no effect when the 'table' option is used.
+        * If None, will automatically choose 'table' if
+          the required memory allocation is less than or equal to
+          6 times the sum of the sizes of `ar1` and `ar2`,
+          otherwise will use 'sort'. This is done to not use
+          a large amount of memory by default, even though
+          'table' may be faster in most cases. If 'table' is chosen,
+          `assume_unique` will have no effect.
+
+    Returns
+    -------
+    in1d : (M,) ndarray, bool
+        The values `ar1[in1d]` are in `ar2`.
+
+    See Also
+    --------
+    isin                  : Version of this function that preserves the
+                            shape of ar1.
+
+    Notes
+    -----
+    `in1d` can be considered as an element-wise function version of the
+    python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
+    equivalent to ``np.array([item in b for item in a])``.
+    However, this idea fails if `ar2` is a set, or similar (non-sequence)
+    container:  As ``ar2`` is converted to an array, in those cases
+    ``asarray(ar2)`` is an object array rather than the expected array of
+    contained values.
+
+    Using ``kind='table'`` tends to be faster than `kind='sort'` if the
+    following relationship is true:
+    ``log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927``,
+    but may use greater memory. The default value for `kind` will
+    be automatically selected based only on memory usage, so one may
+    manually set ``kind='table'`` if memory constraints can be relaxed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> test = np.array([0, 1, 2, 5, 0])
+    >>> states = [0, 2]
+    >>> mask = np.in1d(test, states)
+    >>> mask
+    array([ True, False,  True, False,  True])
+    >>> test[mask]
+    array([0, 2, 0])
+    >>> mask = np.in1d(test, states, invert=True)
+    >>> mask
+    array([False,  True, False,  True, False])
+    >>> test[mask]
+    array([1, 5])
+    """
+
+    # Deprecated in NumPy 2.0, 2023-08-18
+    warnings.warn(
+        "`in1d` is deprecated. Use `np.isin` instead.",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    return _in1d(ar1, ar2, assume_unique, invert, kind=kind)
+
+
+def _in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None):
+    # Ravel both arrays, behavior for the first array could be different
+    ar1 = np.asarray(ar1).ravel()
+    ar2 = np.asarray(ar2).ravel()
+
+    # Ensure that iteration through object arrays yields size-1 arrays
+    if ar2.dtype == object:
+        ar2 = ar2.reshape(-1, 1)
+
+    if kind not in {None, 'sort', 'table'}:
+        raise ValueError(
+            f"Invalid kind: '{kind}'. Please use None, 'sort' or 'table'.")
+
+    # Can use the table method if all arrays are integers or boolean:
+    is_int_arrays = all(ar.dtype.kind in ("u", "i", "b") for ar in (ar1, ar2))
+    use_table_method = is_int_arrays and kind in {None, 'table'}
+
+    if use_table_method:
+        if ar2.size == 0:
+            if invert:
+                return np.ones_like(ar1, dtype=bool)
+            else:
+                return np.zeros_like(ar1, dtype=bool)
+
+        # Convert booleans to uint8 so we can use the fast integer algorithm
+        if ar1.dtype == bool:
+            ar1 = ar1.astype(np.uint8)
+        if ar2.dtype == bool:
+            ar2 = ar2.astype(np.uint8)
+
+        ar2_min = int(np.min(ar2))
+        ar2_max = int(np.max(ar2))
+
+        ar2_range = ar2_max - ar2_min
+
+        # Constraints on whether we can actually use the table method:
+        #  1. Assert memory usage is not too large
+        below_memory_constraint = ar2_range <= 6 * (ar1.size + ar2.size)
+        #  2. Check overflows for (ar2 - ar2_min); dtype=ar2.dtype
+        range_safe_from_overflow = ar2_range <= np.iinfo(ar2.dtype).max
+
+        # Optimal performance is for approximately
+        # log10(size) > (log10(range) - 2.27) / 0.927.
+        # However, here we set the requirement that by default
+        # the intermediate array can only be 6x
+        # the combined memory allocation of the original
+        # arrays. See discussion on
+        # https://github.com/numpy/numpy/pull/12065.
+
+        if (
+            range_safe_from_overflow and
+            (below_memory_constraint or kind == 'table')
+        ):
+
+            if invert:
+                outgoing_array = np.ones_like(ar1, dtype=bool)
+            else:
+                outgoing_array = np.zeros_like(ar1, dtype=bool)
+
+            # Make elements 1 where the integer exists in ar2
+            if invert:
+                isin_helper_ar = np.ones(ar2_range + 1, dtype=bool)
+                isin_helper_ar[ar2 - ar2_min] = 0
+            else:
+                isin_helper_ar = np.zeros(ar2_range + 1, dtype=bool)
+                isin_helper_ar[ar2 - ar2_min] = 1
+
+            # Mask out elements we know won't work
+            basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)
+            in_range_ar1 = ar1[basic_mask]
+            if in_range_ar1.size == 0:
+                # Nothing more to do, since all values are out of range.
+                return outgoing_array
+
+            # Unfortunately, ar2_min can be out of range for `intp` even
+            # if the calculation result must fit in range (and be positive).
+            # In that case, use ar2.dtype which must work for all unmasked
+            # values.
+            try:
+                ar2_min = np.array(ar2_min, dtype=np.intp)
+                dtype = np.intp
+            except OverflowError:
+                dtype = ar2.dtype
+
+            out = np.empty_like(in_range_ar1, dtype=np.intp)
+            outgoing_array[basic_mask] = isin_helper_ar[
+                    np.subtract(in_range_ar1, ar2_min, dtype=dtype,
+                                out=out, casting="unsafe")]
+
+            return outgoing_array
+        elif kind == 'table':  # not range_safe_from_overflow
+            raise RuntimeError(
+                "You have specified kind='table', "
+                "but the range of values in `ar2` or `ar1` exceed the "
+                "maximum integer of the datatype. "
+                "Please set `kind` to None or 'sort'."
+            )
+    elif kind == 'table':
+        raise ValueError(
+            "The 'table' method is only "
+            "supported for boolean or integer arrays. "
+            "Please select 'sort' or None for kind."
+        )
+
+
+    # Check if one of the arrays may contain arbitrary objects
+    contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject
+
+    # This code is run when
+    # a) the first condition is true, making the code significantly faster
+    # b) the second condition is true (i.e. `ar1` or `ar2` may contain
+    #    arbitrary objects), since then sorting is not guaranteed to work
+    if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:
+        if invert:
+            mask = np.ones(len(ar1), dtype=bool)
+            for a in ar2:
+                mask &= (ar1 != a)
+        else:
+            mask = np.zeros(len(ar1), dtype=bool)
+            for a in ar2:
+                mask |= (ar1 == a)
+        return mask
+
+    # Otherwise use sorting
+    if not assume_unique:
+        ar1, rev_idx = np.unique(ar1, return_inverse=True)
+        ar2 = np.unique(ar2)
+
+    ar = np.concatenate((ar1, ar2))
+    # We need this to be a stable sort, so always use 'mergesort'
+    # here. The values from the first array should always come before
+    # the values from the second array.
+    order = ar.argsort(kind='mergesort')
+    sar = ar[order]
+    if invert:
+        bool_ar = (sar[1:] != sar[:-1])
+    else:
+        bool_ar = (sar[1:] == sar[:-1])
+    flag = np.concatenate((bool_ar, [invert]))
+    ret = np.empty(ar.shape, dtype=bool)
+    ret[order] = flag
+
+    if assume_unique:
+        return ret[:len(ar1)]
+    else:
+        return ret[rev_idx]
+
+
+def _isin_dispatcher(element, test_elements, assume_unique=None, invert=None,
+                     *, kind=None):
+    return (element, test_elements)
+
+
+@array_function_dispatch(_isin_dispatcher)
+def isin(element, test_elements, assume_unique=False, invert=False, *,
+         kind=None):
+    """
+    Calculates ``element in test_elements``, broadcasting over `element` only.
+    Returns a boolean array of the same shape as `element` that is True
+    where an element of `element` is in `test_elements` and False otherwise.
+
+    Parameters
+    ----------
+    element : array_like
+        Input array.
+    test_elements : array_like
+        The values against which to test each value of `element`.
+        This argument is flattened if it is an array or array_like.
+        See notes for behavior with non-array-like parameters.
+    assume_unique : bool, optional
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation.  Default is False.
+    invert : bool, optional
+        If True, the values in the returned array are inverted, as if
+        calculating `element not in test_elements`. Default is False.
+        ``np.isin(a, b, invert=True)`` is equivalent to (but faster
+        than) ``np.invert(np.isin(a, b))``.
+    kind : {None, 'sort', 'table'}, optional
+        The algorithm to use. This will not affect the final result,
+        but will affect the speed and memory use. The default, None,
+        will select automatically based on memory considerations.
+
+        * If 'sort', will use a mergesort-based approach. This will have
+          a memory usage of roughly 6 times the sum of the sizes of
+          `element` and `test_elements`, not accounting for size of dtypes.
+        * If 'table', will use a lookup table approach similar
+          to a counting sort. This is only available for boolean and
+          integer arrays. This will have a memory usage of the
+          size of `element` plus the max-min value of `test_elements`.
+          `assume_unique` has no effect when the 'table' option is used.
+        * If None, will automatically choose 'table' if
+          the required memory allocation is less than or equal to
+          6 times the sum of the sizes of `element` and `test_elements`,
+          otherwise will use 'sort'. This is done to not use
+          a large amount of memory by default, even though
+          'table' may be faster in most cases. If 'table' is chosen,
+          `assume_unique` will have no effect.
+
+
+    Returns
+    -------
+    isin : ndarray, bool
+        Has the same shape as `element`. The values `element[isin]`
+        are in `test_elements`.
+
+    Notes
+    -----
+    `isin` is an element-wise function version of the python keyword `in`.
+    ``isin(a, b)`` is roughly equivalent to
+    ``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences.
+
+    `element` and `test_elements` are converted to arrays if they are not
+    already. If `test_elements` is a set (or other non-sequence collection)
+    it will be converted to an object array with one element, rather than an
+    array of the values contained in `test_elements`. This is a consequence
+    of the `array` constructor's way of handling non-sequence collections.
+    Converting the set to a list usually gives the desired behavior.
+
+    Using ``kind='table'`` tends to be faster than `kind='sort'` if the
+    following relationship is true:
+    ``log10(len(test_elements)) >
+    (log10(max(test_elements)-min(test_elements)) - 2.27) / 0.927``,
+    but may use greater memory. The default value for `kind` will
+    be automatically selected based only on memory usage, so one may
+    manually set ``kind='table'`` if memory constraints can be relaxed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> element = 2*np.arange(4).reshape((2, 2))
+    >>> element
+    array([[0, 2],
+           [4, 6]])
+    >>> test_elements = [1, 2, 4, 8]
+    >>> mask = np.isin(element, test_elements)
+    >>> mask
+    array([[False,  True],
+           [ True, False]])
+    >>> element[mask]
+    array([2, 4])
+
+    The indices of the matched values can be obtained with `nonzero`:
+
+    >>> np.nonzero(mask)
+    (array([0, 1]), array([1, 0]))
+
+    The test can also be inverted:
+
+    >>> mask = np.isin(element, test_elements, invert=True)
+    >>> mask
+    array([[ True, False],
+           [False,  True]])
+    >>> element[mask]
+    array([0, 6])
+
+    Because of how `array` handles sets, the following does not
+    work as expected:
+
+    >>> test_set = {1, 2, 4, 8}
+    >>> np.isin(element, test_set)
+    array([[False, False],
+           [False, False]])
+
+    Casting the set to a list gives the expected result:
+
+    >>> np.isin(element, list(test_set))
+    array([[False,  True],
+           [ True, False]])
+    """
+    element = np.asarray(element)
+    return _in1d(element, test_elements, assume_unique=assume_unique,
+                 invert=invert, kind=kind).reshape(element.shape)
+
+
+def _union1d_dispatcher(ar1, ar2):
+    return (ar1, ar2)
+
+
+@array_function_dispatch(_union1d_dispatcher)
+def union1d(ar1, ar2):
+    """
+    Find the union of two arrays.
+
+    Return the unique, sorted array of values that are in either of the two
+    input arrays.
+
+    Parameters
+    ----------
+    ar1, ar2 : array_like
+        Input arrays. They are flattened if they are not already 1D.
+
+    Returns
+    -------
+    union1d : ndarray
+        Unique, sorted union of the input arrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.union1d([-1, 0, 1], [-2, 0, 2])
+    array([-2, -1,  0,  1,  2])
+
+    To find the union of more than two arrays, use functools.reduce:
+
+    >>> from functools import reduce
+    >>> reduce(np.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
+    array([1, 2, 3, 4, 6])
+    """
+    return unique(np.concatenate((ar1, ar2), axis=None))
+
+
+def _setdiff1d_dispatcher(ar1, ar2, assume_unique=None):
+    return (ar1, ar2)
+
+
+@array_function_dispatch(_setdiff1d_dispatcher)
+def setdiff1d(ar1, ar2, assume_unique=False):
+    """
+    Find the set difference of two arrays.
+
+    Return the unique values in `ar1` that are not in `ar2`.
+
+    Parameters
+    ----------
+    ar1 : array_like
+        Input array.
+    ar2 : array_like
+        Input comparison array.
+    assume_unique : bool
+        If True, the input arrays are both assumed to be unique, which
+        can speed up the calculation.  Default is False.
+
+    Returns
+    -------
+    setdiff1d : ndarray
+        1D array of values in `ar1` that are not in `ar2`. The result
+        is sorted when `assume_unique=False`, but otherwise only sorted
+        if the input is sorted.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1, 2, 3, 2, 4, 1])
+    >>> b = np.array([3, 4, 5, 6])
+    >>> np.setdiff1d(a, b)
+    array([1, 2])
+
+    """
+    if assume_unique:
+        ar1 = np.asarray(ar1).ravel()
+    else:
+        ar1 = unique(ar1)
+        ar2 = unique(ar2)
+    return ar1[_in1d(ar1, ar2, assume_unique=True, invert=True)]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..20f2d576bf00b8627a001c81933356746952d258
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arraysetops_impl.pyi
@@ -0,0 +1,379 @@
+from typing import (
+    Any,
+    Generic,
+    Literal as L,
+    NamedTuple,
+    overload,
+    SupportsIndex,
+    TypeVar,
+)
+from typing_extensions import deprecated
+
+import numpy as np
+from numpy import generic, number, int8, intp, timedelta64, object_
+
+from numpy._typing import (
+    ArrayLike,
+    NDArray,
+    _ArrayLike,
+    _ArrayLikeBool_co,
+    _ArrayLikeDT64_co,
+    _ArrayLikeTD64_co,
+    _ArrayLikeObject_co,
+    _ArrayLikeNumber_co,
+)
+
+__all__ = [
+    "ediff1d",
+    "in1d",
+    "intersect1d",
+    "isin",
+    "setdiff1d",
+    "setxor1d",
+    "union1d",
+    "unique",
+    "unique_all",
+    "unique_counts",
+    "unique_inverse",
+    "unique_values",
+]
+
+_SCT = TypeVar("_SCT", bound=generic)
+_NumberType = TypeVar("_NumberType", bound=number[Any])
+
+# Explicitly set all allowed values to prevent accidental castings to
+# abstract dtypes (their common super-type).
+#
+# Only relevant if two or more arguments are parametrized, (e.g. `setdiff1d`)
+# which could result in, for example, `int64` and `float64`producing a
+# `number[_64Bit]` array
+_EitherSCT = TypeVar(
+    "_EitherSCT",
+    np.bool,
+    np.int8, np.int16, np.int32, np.int64, np.intp,
+    np.uint8, np.uint16, np.uint32, np.uint64, np.uintp,
+    np.float16, np.float32, np.float64, np.longdouble,
+    np.complex64, np.complex128, np.clongdouble,
+    np.timedelta64, np.datetime64,
+    np.bytes_, np.str_, np.void, np.object_,
+    np.integer, np.floating, np.complexfloating, np.character,
+)  # fmt: skip
+
+class UniqueAllResult(NamedTuple, Generic[_SCT]):
+    values: NDArray[_SCT]
+    indices: NDArray[intp]
+    inverse_indices: NDArray[intp]
+    counts: NDArray[intp]
+
+class UniqueCountsResult(NamedTuple, Generic[_SCT]):
+    values: NDArray[_SCT]
+    counts: NDArray[intp]
+
+class UniqueInverseResult(NamedTuple, Generic[_SCT]):
+    values: NDArray[_SCT]
+    inverse_indices: NDArray[intp]
+
+@overload
+def ediff1d(
+    ary: _ArrayLikeBool_co,
+    to_end: None | ArrayLike = ...,
+    to_begin: None | ArrayLike = ...,
+) -> NDArray[int8]: ...
+@overload
+def ediff1d(
+    ary: _ArrayLike[_NumberType],
+    to_end: None | ArrayLike = ...,
+    to_begin: None | ArrayLike = ...,
+) -> NDArray[_NumberType]: ...
+@overload
+def ediff1d(
+    ary: _ArrayLikeNumber_co,
+    to_end: None | ArrayLike = ...,
+    to_begin: None | ArrayLike = ...,
+) -> NDArray[Any]: ...
+@overload
+def ediff1d(
+    ary: _ArrayLikeDT64_co | _ArrayLikeTD64_co,
+    to_end: None | ArrayLike = ...,
+    to_begin: None | ArrayLike = ...,
+) -> NDArray[timedelta64]: ...
+@overload
+def ediff1d(
+    ary: _ArrayLikeObject_co,
+    to_end: None | ArrayLike = ...,
+    to_begin: None | ArrayLike = ...,
+) -> NDArray[object_]: ...
+
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[False] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[False] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> NDArray[Any]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[True] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[True] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[False] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[False] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[False] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[False] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[True] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[True] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[False] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[True] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[True] = ...,
+    return_inverse: L[False] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[False] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[False] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: _ArrayLike[_SCT],
+    return_index: L[True] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
+@overload
+def unique(
+    ar: ArrayLike,
+    return_index: L[True] = ...,
+    return_inverse: L[True] = ...,
+    return_counts: L[True] = ...,
+    axis: None | SupportsIndex = ...,
+    *,
+    equal_nan: bool = ...,
+) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
+
+@overload
+def unique_all(
+    x: _ArrayLike[_SCT], /
+) -> UniqueAllResult[_SCT]: ...
+@overload
+def unique_all(
+    x: ArrayLike, /
+) -> UniqueAllResult[Any]: ...
+
+@overload
+def unique_counts(
+    x: _ArrayLike[_SCT], /
+) -> UniqueCountsResult[_SCT]: ...
+@overload
+def unique_counts(
+    x: ArrayLike, /
+) -> UniqueCountsResult[Any]: ...
+
+@overload
+def unique_inverse(x: _ArrayLike[_SCT], /) -> UniqueInverseResult[_SCT]: ...
+@overload
+def unique_inverse(x: ArrayLike, /) -> UniqueInverseResult[Any]: ...
+
+@overload
+def unique_values(x: _ArrayLike[_SCT], /) -> NDArray[_SCT]: ...
+@overload
+def unique_values(x: ArrayLike, /) -> NDArray[Any]: ...
+
+@overload
+def intersect1d(
+    ar1: _ArrayLike[_EitherSCT],
+    ar2: _ArrayLike[_EitherSCT],
+    assume_unique: bool = ...,
+    return_indices: L[False] = ...,
+) -> NDArray[_EitherSCT]: ...
+@overload
+def intersect1d(
+    ar1: ArrayLike,
+    ar2: ArrayLike,
+    assume_unique: bool = ...,
+    return_indices: L[False] = ...,
+) -> NDArray[Any]: ...
+@overload
+def intersect1d(
+    ar1: _ArrayLike[_EitherSCT],
+    ar2: _ArrayLike[_EitherSCT],
+    assume_unique: bool = ...,
+    return_indices: L[True] = ...,
+) -> tuple[NDArray[_EitherSCT], NDArray[intp], NDArray[intp]]: ...
+@overload
+def intersect1d(
+    ar1: ArrayLike,
+    ar2: ArrayLike,
+    assume_unique: bool = ...,
+    return_indices: L[True] = ...,
+) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
+
+@overload
+def setxor1d(
+    ar1: _ArrayLike[_EitherSCT],
+    ar2: _ArrayLike[_EitherSCT],
+    assume_unique: bool = ...,
+) -> NDArray[_EitherSCT]: ...
+@overload
+def setxor1d(
+    ar1: ArrayLike,
+    ar2: ArrayLike,
+    assume_unique: bool = ...,
+) -> NDArray[Any]: ...
+
+def isin(
+    element: ArrayLike,
+    test_elements: ArrayLike,
+    assume_unique: bool = ...,
+    invert: bool = ...,
+    *,
+    kind: None | str = ...,
+) -> NDArray[np.bool]: ...
+
+@deprecated("Use 'isin' instead")
+def in1d(
+    element: ArrayLike,
+    test_elements: ArrayLike,
+    assume_unique: bool = ...,
+    invert: bool = ...,
+    *,
+    kind: None | str = ...,
+) -> NDArray[np.bool]: ...
+
+@overload
+def union1d(
+    ar1: _ArrayLike[_EitherSCT],
+    ar2: _ArrayLike[_EitherSCT],
+) -> NDArray[_EitherSCT]: ...
+@overload
+def union1d(
+    ar1: ArrayLike,
+    ar2: ArrayLike,
+) -> NDArray[Any]: ...
+
+@overload
+def setdiff1d(
+    ar1: _ArrayLike[_EitherSCT],
+    ar2: _ArrayLike[_EitherSCT],
+    assume_unique: bool = ...,
+) -> NDArray[_EitherSCT]: ...
+@overload
+def setdiff1d(
+    ar1: ArrayLike,
+    ar2: ArrayLike,
+    assume_unique: bool = ...,
+) -> NDArray[Any]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..efc529de5cff5c051e5df5b5847a4317d60d75cb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.py
@@ -0,0 +1,224 @@
+"""
+A buffered iterator for big arrays.
+
+This module solves the problem of iterating over a big file-based array
+without having to read it into memory. The `Arrayterator` class wraps
+an array object, and when iterated it will return sub-arrays with at most
+a user-specified number of elements.
+
+"""
+from operator import mul
+from functools import reduce
+
+__all__ = ['Arrayterator']
+
+
+class Arrayterator:
+    """
+    Buffered iterator for big arrays.
+
+    `Arrayterator` creates a buffered iterator for reading big arrays in small
+    contiguous blocks. The class is useful for objects stored in the
+    file system. It allows iteration over the object *without* reading
+    everything in memory; instead, small blocks are read and iterated over.
+
+    `Arrayterator` can be used with any object that supports multidimensional
+    slices. This includes NumPy arrays, but also variables from
+    Scientific.IO.NetCDF or pynetcdf for example.
+
+    Parameters
+    ----------
+    var : array_like
+        The object to iterate over.
+    buf_size : int, optional
+        The buffer size. If `buf_size` is supplied, the maximum amount of
+        data that will be read into memory is `buf_size` elements.
+        Default is None, which will read as many element as possible
+        into memory.
+
+    Attributes
+    ----------
+    var
+    buf_size
+    start
+    stop
+    step
+    shape
+    flat
+
+    See Also
+    --------
+    numpy.ndenumerate : Multidimensional array iterator.
+    numpy.flatiter : Flat array iterator.
+    numpy.memmap : Create a memory-map to an array stored
+                   in a binary file on disk.
+
+    Notes
+    -----
+    The algorithm works by first finding a "running dimension", along which
+    the blocks will be extracted. Given an array of dimensions
+    ``(d1, d2, ..., dn)``, e.g. if `buf_size` is smaller than ``d1``, the
+    first dimension will be used. If, on the other hand,
+    ``d1 < buf_size < d1*d2`` the second dimension will be used, and so on.
+    Blocks are extracted along this dimension, and when the last block is
+    returned the process continues from the next dimension, until all
+    elements have been read.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
+    >>> a_itor = np.lib.Arrayterator(a, 2)
+    >>> a_itor.shape
+    (3, 4, 5, 6)
+
+    Now we can iterate over ``a_itor``, and it will return arrays of size
+    two. Since `buf_size` was smaller than any dimension, the first
+    dimension will be iterated over first:
+
+    >>> for subarr in a_itor:
+    ...     if not subarr.all():
+    ...         print(subarr, subarr.shape) # doctest: +SKIP
+    >>> # [[[[0 1]]]] (1, 1, 1, 2)
+
+    """
+
+    __module__ = "numpy.lib"
+
+    def __init__(self, var, buf_size=None):
+        self.var = var
+        self.buf_size = buf_size
+
+        self.start = [0 for dim in var.shape]
+        self.stop = list(var.shape)
+        self.step = [1 for dim in var.shape]
+
+    def __getattr__(self, attr):
+        return getattr(self.var, attr)
+
+    def __getitem__(self, index):
+        """
+        Return a new arrayterator.
+
+        """
+        # Fix index, handling ellipsis and incomplete slices.
+        if not isinstance(index, tuple):
+            index = (index,)
+        fixed = []
+        length, dims = len(index), self.ndim
+        for slice_ in index:
+            if slice_ is Ellipsis:
+                fixed.extend([slice(None)] * (dims-length+1))
+                length = len(fixed)
+            elif isinstance(slice_, int):
+                fixed.append(slice(slice_, slice_+1, 1))
+            else:
+                fixed.append(slice_)
+        index = tuple(fixed)
+        if len(index) < dims:
+            index += (slice(None),) * (dims-len(index))
+
+        # Return a new arrayterator object.
+        out = self.__class__(self.var, self.buf_size)
+        for i, (start, stop, step, slice_) in enumerate(
+                zip(self.start, self.stop, self.step, index)):
+            out.start[i] = start + (slice_.start or 0)
+            out.step[i] = step * (slice_.step or 1)
+            out.stop[i] = start + (slice_.stop or stop-start)
+            out.stop[i] = min(stop, out.stop[i])
+        return out
+
+    def __array__(self, dtype=None, copy=None):
+        """
+        Return corresponding data.
+
+        """
+        slice_ = tuple(slice(*t) for t in zip(
+                self.start, self.stop, self.step))
+        return self.var[slice_]
+
+    @property
+    def flat(self):
+        """
+        A 1-D flat iterator for Arrayterator objects.
+
+        This iterator returns elements of the array to be iterated over in
+        `~lib.Arrayterator` one by one.
+        It is similar to `flatiter`.
+
+        See Also
+        --------
+        lib.Arrayterator
+        flatiter
+
+        Examples
+        --------
+        >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
+        >>> a_itor = np.lib.Arrayterator(a, 2)
+
+        >>> for subarr in a_itor.flat:
+        ...     if not subarr:
+        ...         print(subarr, type(subarr))
+        ...
+        0 
+
+        """
+        for block in self:
+            yield from block.flat
+
+    @property
+    def shape(self):
+        """
+        The shape of the array to be iterated over.
+
+        For an example, see `Arrayterator`.
+
+        """
+        return tuple(((stop-start-1)//step+1) for start, stop, step in
+                zip(self.start, self.stop, self.step))
+
+    def __iter__(self):
+        # Skip arrays with degenerate dimensions
+        if [dim for dim in self.shape if dim <= 0]:
+            return
+
+        start = self.start[:]
+        stop = self.stop[:]
+        step = self.step[:]
+        ndims = self.var.ndim
+
+        while True:
+            count = self.buf_size or reduce(mul, self.shape)
+
+            # iterate over each dimension, looking for the
+            # running dimension (ie, the dimension along which
+            # the blocks will be built from)
+            rundim = 0
+            for i in range(ndims-1, -1, -1):
+                # if count is zero we ran out of elements to read
+                # along higher dimensions, so we read only a single position
+                if count == 0:
+                    stop[i] = start[i]+1
+                elif count <= self.shape[i]:
+                    # limit along this dimension
+                    stop[i] = start[i] + count*step[i]
+                    rundim = i
+                else:
+                    # read everything along this dimension
+                    stop[i] = self.stop[i]
+                stop[i] = min(self.stop[i], stop[i])
+                count = count//self.shape[i]
+
+            # yield a block
+            slice_ = tuple(slice(*t) for t in zip(start, stop, step))
+            yield self.var[slice_]
+
+            # Update start position, taking care of overflow to
+            # other dimensions
+            start[rundim] = stop[rundim]  # start where we stopped
+            for i in range(ndims-1, 0, -1):
+                if start[i] >= self.stop[i]:
+                    start[i] = self.start[i]
+                    start[i-1] += self.step[i-1]
+            if start[0] >= self.stop[0]:
+                return
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c24fe56ac8a911c38b6d423a274e10fb0838fb98
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_arrayterator_impl.pyi
@@ -0,0 +1,46 @@
+# pyright: reportIncompatibleMethodOverride=false
+
+from collections.abc import Generator
+from types import EllipsisType
+from typing import Any, Final, TypeAlias, overload
+
+from typing_extensions import TypeVar
+
+import numpy as np
+
+__all__ = ["Arrayterator"]
+
+_ShapeT_co = TypeVar("_ShapeT_co", bound=tuple[int, ...], covariant=True)
+_DTypeT = TypeVar("_DTypeT", bound=np.dtype[Any])
+_DTypeT_co = TypeVar("_DTypeT_co", bound=np.dtype[Any], covariant=True)
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+
+_AnyIndex: TypeAlias = EllipsisType | int | slice | tuple[EllipsisType | int | slice, ...]
+
+
+# NOTE: In reality `Arrayterator` does not actually inherit from `ndarray`,
+# but its ``__getattr__` method does wrap around the former and thus has
+# access to all its methods
+
+class Arrayterator(np.ndarray[_ShapeT_co, _DTypeT_co]):
+    var: np.ndarray[_ShapeT_co, _DTypeT_co]  # type: ignore[assignment]
+    buf_size: Final[int | None]
+    start: Final[list[int]]
+    stop: Final[list[int]]
+    step: Final[list[int]]
+
+    @property  # type: ignore[misc]
+    def shape(self) -> _ShapeT_co: ...
+    @property
+    def flat(self: Arrayterator[Any, np.dtype[_ScalarT]]) -> Generator[_ScalarT]: ...  # type: ignore[override]
+
+    #
+    def __init__(self, /, var: np.ndarray[_ShapeT_co, _DTypeT_co], buf_size: int | None = None) -> None: ...
+    def __getitem__(self, index: _AnyIndex, /) -> Arrayterator[tuple[int, ...], _DTypeT_co]: ...  # type: ignore[override]
+    def __iter__(self) -> Generator[np.ndarray[tuple[int, ...], _DTypeT_co]]: ...
+
+    #
+    @overload  # type: ignore[override]
+    def __array__(self, /, dtype: None = None, copy: bool | None = None) -> np.ndarray[_ShapeT_co, _DTypeT_co]: ...
+    @overload
+    def __array__(self, /, dtype: _DTypeT, copy: bool | None = None) -> np.ndarray[_ShapeT_co, _DTypeT]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3d85b8549415c93d3abe33009b22f4f1e6d55fc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.py
@@ -0,0 +1,700 @@
+"""A file interface for handling local and remote data files.
+
+The goal of datasource is to abstract some of the file system operations
+when dealing with data files so the researcher doesn't have to know all the
+low-level details.  Through datasource, a researcher can obtain and use a
+file with one function call, regardless of location of the file.
+
+DataSource is meant to augment standard python libraries, not replace them.
+It should work seamlessly with standard file IO operations and the os
+module.
+
+DataSource files can originate locally or remotely:
+
+- local files : '/home/guido/src/local/data.txt'
+- URLs (http, ftp, ...) : 'http://www.scipy.org/not/real/data.txt'
+
+DataSource files can also be compressed or uncompressed.  Currently only
+gzip, bz2 and xz are supported.
+
+Example::
+
+    >>> # Create a DataSource, use os.curdir (default) for local storage.
+    >>> from numpy import DataSource
+    >>> ds = DataSource()
+    >>>
+    >>> # Open a remote file.
+    >>> # DataSource downloads the file, stores it locally in:
+    >>> #     './www.google.com/index.html'
+    >>> # opens the file and returns a file object.
+    >>> fp = ds.open('http://www.google.com/') # doctest: +SKIP
+    >>>
+    >>> # Use the file as you normally would
+    >>> fp.read() # doctest: +SKIP
+    >>> fp.close() # doctest: +SKIP
+
+"""
+import os
+
+from .._utils import set_module
+
+
+_open = open
+
+
+def _check_mode(mode, encoding, newline):
+    """Check mode and that encoding and newline are compatible.
+
+    Parameters
+    ----------
+    mode : str
+        File open mode.
+    encoding : str
+        File encoding.
+    newline : str
+        Newline for text files.
+
+    """
+    if "t" in mode:
+        if "b" in mode:
+            raise ValueError("Invalid mode: %r" % (mode,))
+    else:
+        if encoding is not None:
+            raise ValueError("Argument 'encoding' not supported in binary mode")
+        if newline is not None:
+            raise ValueError("Argument 'newline' not supported in binary mode")
+
+
+# Using a class instead of a module-level dictionary
+# to reduce the initial 'import numpy' overhead by
+# deferring the import of lzma, bz2 and gzip until needed
+
+# TODO: .zip support, .tar support?
+class _FileOpeners:
+    """
+    Container for different methods to open (un-)compressed files.
+
+    `_FileOpeners` contains a dictionary that holds one method for each
+    supported file format. Attribute lookup is implemented in such a way
+    that an instance of `_FileOpeners` itself can be indexed with the keys
+    of that dictionary. Currently uncompressed files as well as files
+    compressed with ``gzip``, ``bz2`` or ``xz`` compression are supported.
+
+    Notes
+    -----
+    `_file_openers`, an instance of `_FileOpeners`, is made available for
+    use in the `_datasource` module.
+
+    Examples
+    --------
+    >>> import gzip
+    >>> np.lib._datasource._file_openers.keys()
+    [None, '.bz2', '.gz', '.xz', '.lzma']
+    >>> np.lib._datasource._file_openers['.gz'] is gzip.open
+    True
+
+    """
+
+    def __init__(self):
+        self._loaded = False
+        self._file_openers = {None: open}
+
+    def _load(self):
+        if self._loaded:
+            return
+
+        try:
+            import bz2
+            self._file_openers[".bz2"] = bz2.open
+        except ImportError:
+            pass
+
+        try:
+            import gzip
+            self._file_openers[".gz"] = gzip.open
+        except ImportError:
+            pass
+
+        try:
+            import lzma
+            self._file_openers[".xz"] = lzma.open
+            self._file_openers[".lzma"] = lzma.open
+        except (ImportError, AttributeError):
+            # There are incompatible backports of lzma that do not have the
+            # lzma.open attribute, so catch that as well as ImportError.
+            pass
+
+        self._loaded = True
+
+    def keys(self):
+        """
+        Return the keys of currently supported file openers.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        keys : list
+            The keys are None for uncompressed files and the file extension
+            strings (i.e. ``'.gz'``, ``'.xz'``) for supported compression
+            methods.
+
+        """
+        self._load()
+        return list(self._file_openers.keys())
+
+    def __getitem__(self, key):
+        self._load()
+        return self._file_openers[key]
+
+_file_openers = _FileOpeners()
+
+def open(path, mode='r', destpath=os.curdir, encoding=None, newline=None):
+    """
+    Open `path` with `mode` and return the file object.
+
+    If ``path`` is an URL, it will be downloaded, stored in the
+    `DataSource` `destpath` directory and opened from there.
+
+    Parameters
+    ----------
+    path : str or pathlib.Path
+        Local file path or URL to open.
+    mode : str, optional
+        Mode to open `path`. Mode 'r' for reading, 'w' for writing, 'a' to
+        append. Available modes depend on the type of object specified by
+        path.  Default is 'r'.
+    destpath : str, optional
+        Path to the directory where the source file gets downloaded to for
+        use.  If `destpath` is None, a temporary directory will be created.
+        The default path is the current directory.
+    encoding : {None, str}, optional
+        Open text file with given encoding. The default encoding will be
+        what `open` uses.
+    newline : {None, str}, optional
+        Newline to use when reading text file.
+
+    Returns
+    -------
+    out : file object
+        The opened file.
+
+    Notes
+    -----
+    This is a convenience function that instantiates a `DataSource` and
+    returns the file object from ``DataSource.open(path)``.
+
+    """
+
+    ds = DataSource(destpath)
+    return ds.open(path, mode, encoding=encoding, newline=newline)
+
+
+@set_module('numpy.lib.npyio')
+class DataSource:
+    """
+    DataSource(destpath='.')
+
+    A generic data source file (file, http, ftp, ...).
+
+    DataSources can be local files or remote files/URLs.  The files may
+    also be compressed or uncompressed. DataSource hides some of the
+    low-level details of downloading the file, allowing you to simply pass
+    in a valid file path (or URL) and obtain a file object.
+
+    Parameters
+    ----------
+    destpath : str or None, optional
+        Path to the directory where the source file gets downloaded to for
+        use.  If `destpath` is None, a temporary directory will be created.
+        The default path is the current directory.
+
+    Notes
+    -----
+    URLs require a scheme string (``http://``) to be used, without it they
+    will fail::
+
+        >>> repos = np.lib.npyio.DataSource()
+        >>> repos.exists('www.google.com/index.html')
+        False
+        >>> repos.exists('http://www.google.com/index.html')
+        True
+
+    Temporary directories are deleted when the DataSource is deleted.
+
+    Examples
+    --------
+    ::
+
+        >>> ds = np.lib.npyio.DataSource('/home/guido')
+        >>> urlname = 'http://www.google.com/'
+        >>> gfile = ds.open('http://www.google.com/')
+        >>> ds.abspath(urlname)
+        '/home/guido/www.google.com/index.html'
+
+        >>> ds = np.lib.npyio.DataSource(None)  # use with temporary file
+        >>> ds.open('/home/guido/foobar.txt')
+        
+        >>> ds.abspath('/home/guido/foobar.txt')
+        '/tmp/.../home/guido/foobar.txt'
+
+    """
+
+    def __init__(self, destpath=os.curdir):
+        """Create a DataSource with a local path at destpath."""
+        if destpath:
+            self._destpath = os.path.abspath(destpath)
+            self._istmpdest = False
+        else:
+            import tempfile  # deferring import to improve startup time
+            self._destpath = tempfile.mkdtemp()
+            self._istmpdest = True
+
+    def __del__(self):
+        # Remove temp directories
+        if hasattr(self, '_istmpdest') and self._istmpdest:
+            import shutil
+
+            shutil.rmtree(self._destpath)
+
+    def _iszip(self, filename):
+        """Test if the filename is a zip file by looking at the file extension.
+
+        """
+        fname, ext = os.path.splitext(filename)
+        return ext in _file_openers.keys()
+
+    def _iswritemode(self, mode):
+        """Test if the given mode will open a file for writing."""
+
+        # Currently only used to test the bz2 files.
+        _writemodes = ("w", "+")
+        return any(c in _writemodes for c in mode)
+
+    def _splitzipext(self, filename):
+        """Split zip extension from filename and return filename.
+
+        Returns
+        -------
+        base, zip_ext : {tuple}
+
+        """
+
+        if self._iszip(filename):
+            return os.path.splitext(filename)
+        else:
+            return filename, None
+
+    def _possible_names(self, filename):
+        """Return a tuple containing compressed filename variations."""
+        names = [filename]
+        if not self._iszip(filename):
+            for zipext in _file_openers.keys():
+                if zipext:
+                    names.append(filename+zipext)
+        return names
+
+    def _isurl(self, path):
+        """Test if path is a net location.  Tests the scheme and netloc."""
+
+        # We do this here to reduce the 'import numpy' initial import time.
+        from urllib.parse import urlparse
+
+        # BUG : URLs require a scheme string ('http://') to be used.
+        #       www.google.com will fail.
+        #       Should we prepend the scheme for those that don't have it and
+        #       test that also?  Similar to the way we append .gz and test for
+        #       for compressed versions of files.
+
+        scheme, netloc, upath, uparams, uquery, ufrag = urlparse(path)
+        return bool(scheme and netloc)
+
+    def _cache(self, path):
+        """Cache the file specified by path.
+
+        Creates a copy of the file in the datasource cache.
+
+        """
+        # We import these here because importing them is slow and
+        # a significant fraction of numpy's total import time.
+        import shutil
+        from urllib.request import urlopen
+
+        upath = self.abspath(path)
+
+        # ensure directory exists
+        if not os.path.exists(os.path.dirname(upath)):
+            os.makedirs(os.path.dirname(upath))
+
+        # TODO: Doesn't handle compressed files!
+        if self._isurl(path):
+            with urlopen(path) as openedurl:
+                with _open(upath, 'wb') as f:
+                    shutil.copyfileobj(openedurl, f)
+        else:
+            shutil.copyfile(path, upath)
+        return upath
+
+    def _findfile(self, path):
+        """Searches for ``path`` and returns full path if found.
+
+        If path is an URL, _findfile will cache a local copy and return the
+        path to the cached file.  If path is a local file, _findfile will
+        return a path to that local file.
+
+        The search will include possible compressed versions of the file
+        and return the first occurrence found.
+
+        """
+
+        # Build list of possible local file paths
+        if not self._isurl(path):
+            # Valid local paths
+            filelist = self._possible_names(path)
+            # Paths in self._destpath
+            filelist += self._possible_names(self.abspath(path))
+        else:
+            # Cached URLs in self._destpath
+            filelist = self._possible_names(self.abspath(path))
+            # Remote URLs
+            filelist = filelist + self._possible_names(path)
+
+        for name in filelist:
+            if self.exists(name):
+                if self._isurl(name):
+                    name = self._cache(name)
+                return name
+        return None
+
+    def abspath(self, path):
+        """
+        Return absolute path of file in the DataSource directory.
+
+        If `path` is an URL, then `abspath` will return either the location
+        the file exists locally or the location it would exist when opened
+        using the `open` method.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Can be a local file or a remote URL.
+
+        Returns
+        -------
+        out : str
+            Complete path, including the `DataSource` destination directory.
+
+        Notes
+        -----
+        The functionality is based on `os.path.abspath`.
+
+        """
+        # We do this here to reduce the 'import numpy' initial import time.
+        from urllib.parse import urlparse
+
+        # TODO:  This should be more robust.  Handles case where path includes
+        #        the destpath, but not other sub-paths. Failing case:
+        #        path = /home/guido/datafile.txt
+        #        destpath = /home/alex/
+        #        upath = self.abspath(path)
+        #        upath == '/home/alex/home/guido/datafile.txt'
+
+        # handle case where path includes self._destpath
+        splitpath = path.split(self._destpath, 2)
+        if len(splitpath) > 1:
+            path = splitpath[1]
+        scheme, netloc, upath, uparams, uquery, ufrag = urlparse(path)
+        netloc = self._sanitize_relative_path(netloc)
+        upath = self._sanitize_relative_path(upath)
+        return os.path.join(self._destpath, netloc, upath)
+
+    def _sanitize_relative_path(self, path):
+        """Return a sanitised relative path for which
+        os.path.abspath(os.path.join(base, path)).startswith(base)
+        """
+        last = None
+        path = os.path.normpath(path)
+        while path != last:
+            last = path
+            # Note: os.path.join treats '/' as os.sep on Windows
+            path = path.lstrip(os.sep).lstrip('/')
+            path = path.lstrip(os.pardir).removeprefix('..')
+            drive, path = os.path.splitdrive(path)  # for Windows
+        return path
+
+    def exists(self, path):
+        """
+        Test if path exists.
+
+        Test if `path` exists as (and in this order):
+
+        - a local file.
+        - a remote URL that has been downloaded and stored locally in the
+          `DataSource` directory.
+        - a remote URL that has not been downloaded, but is valid and
+          accessible.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Can be a local file or a remote URL.
+
+        Returns
+        -------
+        out : bool
+            True if `path` exists.
+
+        Notes
+        -----
+        When `path` is an URL, `exists` will return True if it's either
+        stored locally in the `DataSource` directory, or is a valid remote
+        URL.  `DataSource` does not discriminate between the two, the file
+        is accessible if it exists in either location.
+
+        """
+
+        # First test for local path
+        if os.path.exists(path):
+            return True
+
+        # We import this here because importing urllib is slow and
+        # a significant fraction of numpy's total import time.
+        from urllib.request import urlopen
+        from urllib.error import URLError
+
+        # Test cached url
+        upath = self.abspath(path)
+        if os.path.exists(upath):
+            return True
+
+        # Test remote url
+        if self._isurl(path):
+            try:
+                netfile = urlopen(path)
+                netfile.close()
+                del(netfile)
+                return True
+            except URLError:
+                return False
+        return False
+
+    def open(self, path, mode='r', encoding=None, newline=None):
+        """
+        Open and return file-like object.
+
+        If `path` is an URL, it will be downloaded, stored in the
+        `DataSource` directory and opened from there.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Local file path or URL to open.
+        mode : {'r', 'w', 'a'}, optional
+            Mode to open `path`.  Mode 'r' for reading, 'w' for writing,
+            'a' to append. Available modes depend on the type of object
+            specified by `path`. Default is 'r'.
+        encoding : {None, str}, optional
+            Open text file with given encoding. The default encoding will be
+            what `open` uses.
+        newline : {None, str}, optional
+            Newline to use when reading text file.
+
+        Returns
+        -------
+        out : file object
+            File object.
+
+        """
+
+        # TODO: There is no support for opening a file for writing which
+        #       doesn't exist yet (creating a file).  Should there be?
+
+        # TODO: Add a ``subdir`` parameter for specifying the subdirectory
+        #       used to store URLs in self._destpath.
+
+        if self._isurl(path) and self._iswritemode(mode):
+            raise ValueError("URLs are not writeable")
+
+        # NOTE: _findfile will fail on a new file opened for writing.
+        found = self._findfile(path)
+        if found:
+            _fname, ext = self._splitzipext(found)
+            if ext == 'bz2':
+                mode.replace("+", "")
+            return _file_openers[ext](found, mode=mode,
+                                      encoding=encoding, newline=newline)
+        else:
+            raise FileNotFoundError(f"{path} not found.")
+
+
+class Repository (DataSource):
+    """
+    Repository(baseurl, destpath='.')
+
+    A data repository where multiple DataSource's share a base
+    URL/directory.
+
+    `Repository` extends `DataSource` by prepending a base URL (or
+    directory) to all the files it handles. Use `Repository` when you will
+    be working with multiple files from one base URL.  Initialize
+    `Repository` with the base URL, then refer to each file by its filename
+    only.
+
+    Parameters
+    ----------
+    baseurl : str
+        Path to the local directory or remote location that contains the
+        data files.
+    destpath : str or None, optional
+        Path to the directory where the source file gets downloaded to for
+        use.  If `destpath` is None, a temporary directory will be created.
+        The default path is the current directory.
+
+    Examples
+    --------
+    To analyze all files in the repository, do something like this
+    (note: this is not self-contained code)::
+
+        >>> repos = np.lib._datasource.Repository('/home/user/data/dir/')
+        >>> for filename in filelist:
+        ...     fp = repos.open(filename)
+        ...     fp.analyze()
+        ...     fp.close()
+
+    Similarly you could use a URL for a repository::
+
+        >>> repos = np.lib._datasource.Repository('http://www.xyz.edu/data')
+
+    """
+
+    def __init__(self, baseurl, destpath=os.curdir):
+        """Create a Repository with a shared url or directory of baseurl."""
+        DataSource.__init__(self, destpath=destpath)
+        self._baseurl = baseurl
+
+    def __del__(self):
+        DataSource.__del__(self)
+
+    def _fullpath(self, path):
+        """Return complete path for path.  Prepends baseurl if necessary."""
+        splitpath = path.split(self._baseurl, 2)
+        if len(splitpath) == 1:
+            result = os.path.join(self._baseurl, path)
+        else:
+            result = path    # path contains baseurl already
+        return result
+
+    def _findfile(self, path):
+        """Extend DataSource method to prepend baseurl to ``path``."""
+        return DataSource._findfile(self, self._fullpath(path))
+
+    def abspath(self, path):
+        """
+        Return absolute path of file in the Repository directory.
+
+        If `path` is an URL, then `abspath` will return either the location
+        the file exists locally or the location it would exist when opened
+        using the `open` method.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Can be a local file or a remote URL. This may, but does not
+            have to, include the `baseurl` with which the `Repository` was
+            initialized.
+
+        Returns
+        -------
+        out : str
+            Complete path, including the `DataSource` destination directory.
+
+        """
+        return DataSource.abspath(self, self._fullpath(path))
+
+    def exists(self, path):
+        """
+        Test if path exists prepending Repository base URL to path.
+
+        Test if `path` exists as (and in this order):
+
+        - a local file.
+        - a remote URL that has been downloaded and stored locally in the
+          `DataSource` directory.
+        - a remote URL that has not been downloaded, but is valid and
+          accessible.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Can be a local file or a remote URL. This may, but does not
+            have to, include the `baseurl` with which the `Repository` was
+            initialized.
+
+        Returns
+        -------
+        out : bool
+            True if `path` exists.
+
+        Notes
+        -----
+        When `path` is an URL, `exists` will return True if it's either
+        stored locally in the `DataSource` directory, or is a valid remote
+        URL.  `DataSource` does not discriminate between the two, the file
+        is accessible if it exists in either location.
+
+        """
+        return DataSource.exists(self, self._fullpath(path))
+
+    def open(self, path, mode='r', encoding=None, newline=None):
+        """
+        Open and return file-like object prepending Repository base URL.
+
+        If `path` is an URL, it will be downloaded, stored in the
+        DataSource directory and opened from there.
+
+        Parameters
+        ----------
+        path : str or pathlib.Path
+            Local file path or URL to open. This may, but does not have to,
+            include the `baseurl` with which the `Repository` was
+            initialized.
+        mode : {'r', 'w', 'a'}, optional
+            Mode to open `path`.  Mode 'r' for reading, 'w' for writing,
+            'a' to append. Available modes depend on the type of object
+            specified by `path`. Default is 'r'.
+        encoding : {None, str}, optional
+            Open text file with given encoding. The default encoding will be
+            what `open` uses.
+        newline : {None, str}, optional
+            Newline to use when reading text file.
+
+        Returns
+        -------
+        out : file object
+            File object.
+
+        """
+        return DataSource.open(self, self._fullpath(path), mode,
+                               encoding=encoding, newline=newline)
+
+    def listdir(self):
+        """
+        List files in the source Repository.
+
+        Returns
+        -------
+        files : list of str or pathlib.Path
+            List of file names (not containing a directory part).
+
+        Notes
+        -----
+        Does not currently work for remote repositories.
+
+        """
+        if self._isurl(self._baseurl):
+            raise NotImplementedError(
+                  "Directory listing of URLs, not supported yet.")
+        else:
+            return os.listdir(self._baseurl)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9f91fdf893a07a3bd3398ae43e2604a60d04d903
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_datasource.pyi
@@ -0,0 +1,31 @@
+from pathlib import Path
+from typing import IO, Any, TypeAlias
+
+from _typeshed import OpenBinaryMode, OpenTextMode
+
+_Mode: TypeAlias = OpenBinaryMode | OpenTextMode
+
+###
+
+# exported in numpy.lib.nppyio
+class DataSource:
+    def __init__(self, /, destpath: Path | str | None = ...) -> None: ...
+    def __del__(self, /) -> None: ...
+    def abspath(self, /, path: str) -> str: ...
+    def exists(self, /, path: str) -> bool: ...
+
+    # Whether the file-object is opened in string or bytes mode (by default)
+    # depends on the file-extension of `path`
+    def open(self, /, path: str, mode: _Mode = "r", encoding: str | None = None, newline: str | None = None) -> IO[Any]: ...
+
+class Repository(DataSource):
+    def __init__(self, /, baseurl: str, destpath: str | None = ...) -> None: ...
+    def listdir(self, /) -> list[str]: ...
+
+def open(
+    path: str,
+    mode: _Mode = "r",
+    destpath: str | None = ...,
+    encoding: str | None = None,
+    newline: str | None = None,
+) -> IO[Any]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fa9c5f99d95352ffc29ade5d33531ccc1f90ead
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.py
@@ -0,0 +1,5827 @@
+import builtins
+import collections.abc
+import functools
+import re
+import sys
+import warnings
+
+import numpy as np
+import numpy._core.numeric as _nx
+from numpy._core import transpose, overrides
+from numpy._core.numeric import (
+    ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
+    ndarray, take, dot, where, intp, integer, isscalar, absolute
+    )
+from numpy._core.umath import (
+    pi, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin,
+    mod, exp, not_equal, subtract, minimum
+    )
+from numpy._core.fromnumeric import (
+    ravel, nonzero, partition, mean, any, sum
+    )
+from numpy._core.numerictypes import typecodes
+from numpy.lib._twodim_base_impl import diag
+from numpy._core.multiarray import (
+    _place, bincount, normalize_axis_index, _monotonicity,
+    interp as compiled_interp, interp_complex as compiled_interp_complex
+    )
+from numpy._core._multiarray_umath import _array_converter
+from numpy._utils import set_module
+
+# needed in this module for compatibility
+from numpy.lib._histograms_impl import histogram, histogramdd  # noqa: F401
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+__all__ = [
+    'select', 'piecewise', 'trim_zeros', 'copy', 'iterable', 'percentile',
+    'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'flip',
+    'rot90', 'extract', 'place', 'vectorize', 'asarray_chkfinite', 'average',
+    'bincount', 'digitize', 'cov', 'corrcoef',
+    'median', 'sinc', 'hamming', 'hanning', 'bartlett',
+    'blackman', 'kaiser', 'trapezoid', 'trapz', 'i0',
+    'meshgrid', 'delete', 'insert', 'append', 'interp',
+    'quantile'
+    ]
+
+# _QuantileMethods is a dictionary listing all the supported methods to
+# compute quantile/percentile.
+#
+# Below virtual_index refers to the index of the element where the percentile
+# would be found in the sorted sample.
+# When the sample contains exactly the percentile wanted, the virtual_index is
+# an integer to the index of this element.
+# When the percentile wanted is in between two elements, the virtual_index
+# is made of a integer part (a.k.a 'i' or 'left') and a fractional part
+# (a.k.a 'g' or 'gamma')
+#
+# Each method in _QuantileMethods has two properties
+# get_virtual_index : Callable
+#   The function used to compute the virtual_index.
+# fix_gamma : Callable
+#   A function used for discrete methods to force the index to a specific value.
+_QuantileMethods = dict(
+    # --- HYNDMAN and FAN METHODS
+    # Discrete methods
+    inverted_cdf=dict(
+        get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles),
+        fix_gamma=None,  # should never be called
+    ),
+    averaged_inverted_cdf=dict(
+        get_virtual_index=lambda n, quantiles: (n * quantiles) - 1,
+        fix_gamma=lambda gamma, _: _get_gamma_mask(
+            shape=gamma.shape,
+            default_value=1.,
+            conditioned_value=0.5,
+            where=gamma == 0),
+    ),
+    closest_observation=dict(
+        get_virtual_index=lambda n, quantiles: _closest_observation(n,
+                                                                    quantiles),
+        fix_gamma=None,  # should never be called
+    ),
+    # Continuous methods
+    interpolated_inverted_cdf=dict(
+        get_virtual_index=lambda n, quantiles:
+        _compute_virtual_index(n, quantiles, 0, 1),
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    hazen=dict(
+        get_virtual_index=lambda n, quantiles:
+        _compute_virtual_index(n, quantiles, 0.5, 0.5),
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    weibull=dict(
+        get_virtual_index=lambda n, quantiles:
+        _compute_virtual_index(n, quantiles, 0, 0),
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    # Default method.
+    # To avoid some rounding issues, `(n-1) * quantiles` is preferred to
+    # `_compute_virtual_index(n, quantiles, 1, 1)`.
+    # They are mathematically equivalent.
+    linear=dict(
+        get_virtual_index=lambda n, quantiles: (n - 1) * quantiles,
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    median_unbiased=dict(
+        get_virtual_index=lambda n, quantiles:
+        _compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0),
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    normal_unbiased=dict(
+        get_virtual_index=lambda n, quantiles:
+        _compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0),
+        fix_gamma=lambda gamma, _: gamma,
+    ),
+    # --- OTHER METHODS
+    lower=dict(
+        get_virtual_index=lambda n, quantiles: np.floor(
+            (n - 1) * quantiles).astype(np.intp),
+        fix_gamma=None,  # should never be called, index dtype is int
+    ),
+    higher=dict(
+        get_virtual_index=lambda n, quantiles: np.ceil(
+            (n - 1) * quantiles).astype(np.intp),
+        fix_gamma=None,  # should never be called, index dtype is int
+    ),
+    midpoint=dict(
+        get_virtual_index=lambda n, quantiles: 0.5 * (
+                np.floor((n - 1) * quantiles)
+                + np.ceil((n - 1) * quantiles)),
+        fix_gamma=lambda gamma, index: _get_gamma_mask(
+            shape=gamma.shape,
+            default_value=0.5,
+            conditioned_value=0.,
+            where=index % 1 == 0),
+    ),
+    nearest=dict(
+        get_virtual_index=lambda n, quantiles: np.around(
+            (n - 1) * quantiles).astype(np.intp),
+        fix_gamma=None,
+        # should never be called, index dtype is int
+    ))
+
+
+def _rot90_dispatcher(m, k=None, axes=None):
+    return (m,)
+
+
+@array_function_dispatch(_rot90_dispatcher)
+def rot90(m, k=1, axes=(0, 1)):
+    """
+    Rotate an array by 90 degrees in the plane specified by axes.
+
+    Rotation direction is from the first towards the second axis.
+    This means for a 2D array with the default `k` and `axes`, the
+    rotation will be counterclockwise.
+
+    Parameters
+    ----------
+    m : array_like
+        Array of two or more dimensions.
+    k : integer
+        Number of times the array is rotated by 90 degrees.
+    axes : (2,) array_like
+        The array is rotated in the plane defined by the axes.
+        Axes must be different.
+
+    Returns
+    -------
+    y : ndarray
+        A rotated view of `m`.
+
+    See Also
+    --------
+    flip : Reverse the order of elements in an array along the given axis.
+    fliplr : Flip an array horizontally.
+    flipud : Flip an array vertically.
+
+    Notes
+    -----
+    ``rot90(m, k=1, axes=(1,0))``  is the reverse of
+    ``rot90(m, k=1, axes=(0,1))``
+
+    ``rot90(m, k=1, axes=(1,0))`` is equivalent to
+    ``rot90(m, k=-1, axes=(0,1))``
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> m = np.array([[1,2],[3,4]], int)
+    >>> m
+    array([[1, 2],
+           [3, 4]])
+    >>> np.rot90(m)
+    array([[2, 4],
+           [1, 3]])
+    >>> np.rot90(m, 2)
+    array([[4, 3],
+           [2, 1]])
+    >>> m = np.arange(8).reshape((2,2,2))
+    >>> np.rot90(m, 1, (1,2))
+    array([[[1, 3],
+            [0, 2]],
+           [[5, 7],
+            [4, 6]]])
+
+    """
+    axes = tuple(axes)
+    if len(axes) != 2:
+        raise ValueError("len(axes) must be 2.")
+
+    m = asanyarray(m)
+
+    if axes[0] == axes[1] or absolute(axes[0] - axes[1]) == m.ndim:
+        raise ValueError("Axes must be different.")
+
+    if (axes[0] >= m.ndim or axes[0] < -m.ndim
+        or axes[1] >= m.ndim or axes[1] < -m.ndim):
+        raise ValueError("Axes={} out of range for array of ndim={}."
+            .format(axes, m.ndim))
+
+    k %= 4
+
+    if k == 0:
+        return m[:]
+    if k == 2:
+        return flip(flip(m, axes[0]), axes[1])
+
+    axes_list = arange(0, m.ndim)
+    (axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]],
+                                                axes_list[axes[0]])
+
+    if k == 1:
+        return transpose(flip(m, axes[1]), axes_list)
+    else:
+        # k == 3
+        return flip(transpose(m, axes_list), axes[1])
+
+
+def _flip_dispatcher(m, axis=None):
+    return (m,)
+
+
+@array_function_dispatch(_flip_dispatcher)
+def flip(m, axis=None):
+    """
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+    axis : None or int or tuple of ints, optional
+         Axis or axes along which to flip over. The default,
+         axis=None, will flip over all of the axes of the input array.
+         If axis is negative it counts from the last to the first axis.
+
+         If axis is a tuple of ints, flipping is performed on all of the axes
+         specified in the tuple.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+
+    See Also
+    --------
+    flipud : Flip an array vertically (axis=0).
+    fliplr : Flip an array horizontally (axis=1).
+
+    Notes
+    -----
+    flip(m, 0) is equivalent to flipud(m).
+
+    flip(m, 1) is equivalent to fliplr(m).
+
+    flip(m, n) corresponds to ``m[...,::-1,...]`` with ``::-1`` at position n.
+
+    flip(m) corresponds to ``m[::-1,::-1,...,::-1]`` with ``::-1`` at all
+    positions.
+
+    flip(m, (0, 1)) corresponds to ``m[::-1,::-1,...]`` with ``::-1`` at
+    position 0 and position 1.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.arange(8).reshape((2,2,2))
+    >>> A
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.flip(A, 0)
+    array([[[4, 5],
+            [6, 7]],
+           [[0, 1],
+            [2, 3]]])
+    >>> np.flip(A, 1)
+    array([[[2, 3],
+            [0, 1]],
+           [[6, 7],
+            [4, 5]]])
+    >>> np.flip(A)
+    array([[[7, 6],
+            [5, 4]],
+           [[3, 2],
+            [1, 0]]])
+    >>> np.flip(A, (0, 2))
+    array([[[5, 4],
+            [7, 6]],
+           [[1, 0],
+            [3, 2]]])
+    >>> rng = np.random.default_rng()
+    >>> A = rng.normal(size=(3,4,5))
+    >>> np.all(np.flip(A,2) == A[:,:,::-1,...])
+    True
+    """
+    if not hasattr(m, 'ndim'):
+        m = asarray(m)
+    if axis is None:
+        indexer = (np.s_[::-1],) * m.ndim
+    else:
+        axis = _nx.normalize_axis_tuple(axis, m.ndim)
+        indexer = [np.s_[:]] * m.ndim
+        for ax in axis:
+            indexer[ax] = np.s_[::-1]
+        indexer = tuple(indexer)
+    return m[indexer]
+
+
+@set_module('numpy')
+def iterable(y):
+    """
+    Check whether or not an object can be iterated over.
+
+    Parameters
+    ----------
+    y : object
+      Input object.
+
+    Returns
+    -------
+    b : bool
+      Return ``True`` if the object has an iterator method or is a
+      sequence and ``False`` otherwise.
+
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.iterable([1, 2, 3])
+    True
+    >>> np.iterable(2)
+    False
+
+    Notes
+    -----
+    In most cases, the results of ``np.iterable(obj)`` are consistent with
+    ``isinstance(obj, collections.abc.Iterable)``. One notable exception is
+    the treatment of 0-dimensional arrays::
+
+        >>> from collections.abc import Iterable
+        >>> a = np.array(1.0)  # 0-dimensional numpy array
+        >>> isinstance(a, Iterable)
+        True
+        >>> np.iterable(a)
+        False
+
+    """
+    try:
+        iter(y)
+    except TypeError:
+        return False
+    return True
+
+
+def _weights_are_valid(weights, a, axis):
+    """Validate weights array.
+
+    We assume, weights is not None.
+    """
+    wgt = np.asanyarray(weights)
+
+    # Sanity checks
+    if a.shape != wgt.shape:
+        if axis is None:
+            raise TypeError(
+                "Axis must be specified when shapes of a and weights "
+                "differ.")
+        if wgt.shape != tuple(a.shape[ax] for ax in axis):
+            raise ValueError(
+                "Shape of weights must be consistent with "
+                "shape of a along specified axis.")
+
+        # setup wgt to broadcast along axis
+        wgt = wgt.transpose(np.argsort(axis))
+        wgt = wgt.reshape(tuple((s if ax in axis else 1)
+                                for ax, s in enumerate(a.shape)))
+    return wgt
+
+
+def _average_dispatcher(a, axis=None, weights=None, returned=None, *,
+                        keepdims=None):
+    return (a, weights)
+
+
+@array_function_dispatch(_average_dispatcher)
+def average(a, axis=None, weights=None, returned=False, *,
+            keepdims=np._NoValue):
+    """
+    Compute the weighted average along the specified axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing data to be averaged. If `a` is not an array, a
+        conversion is attempted.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to average `a`.  The default,
+        `axis=None`, will average over all of the elements of the input array.
+        If axis is negative it counts from the last to the first axis.
+        If axis is a tuple of ints, averaging is performed on all of the axes
+        specified in the tuple instead of a single axis or all the axes as
+        before.
+    weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the average according to its associated weight.
+        The array of weights must be the same shape as `a` if no axis is
+        specified, otherwise the weights must have dimensions and shape
+        consistent with `a` along the specified axis.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        The calculation is::
+
+            avg = sum(a * weights) / sum(weights)
+
+        where the sum is over all included elements.
+        The only constraint on the values of `weights` is that `sum(weights)`
+        must not be 0.
+    returned : bool, optional
+        Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)
+        is returned, otherwise only the average is returned.
+        If `weights=None`, `sum_of_weights` is equivalent to the number of
+        elements over which the average is taken.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+        *Note:* `keepdims` will not work with instances of `numpy.matrix`
+        or other classes whose methods do not support `keepdims`.
+
+        .. versionadded:: 1.23.0
+
+    Returns
+    -------
+    retval, [sum_of_weights] : array_type or double
+        Return the average along the specified axis. When `returned` is `True`,
+        return a tuple with the average as the first element and the sum
+        of the weights as the second element. `sum_of_weights` is of the
+        same type as `retval`. The result dtype follows a general pattern.
+        If `weights` is None, the result dtype will be that of `a` , or ``float64``
+        if `a` is integral. Otherwise, if `weights` is not None and `a` is non-
+        integral, the result type will be the type of lowest precision capable of
+        representing values of both `a` and `weights`. If `a` happens to be
+        integral, the previous rules still applies but the result dtype will
+        at least be ``float64``.
+
+    Raises
+    ------
+    ZeroDivisionError
+        When all weights along axis are zero. See `numpy.ma.average` for a
+        version robust to this type of error.
+    TypeError
+        When `weights` does not have the same shape as `a`, and `axis=None`.
+    ValueError
+        When `weights` does not have dimensions and shape consistent with `a`
+        along specified `axis`.
+
+    See Also
+    --------
+    mean
+
+    ma.average : average for masked arrays -- useful if your data contains
+                 "missing" values
+    numpy.result_type : Returns the type that results from applying the
+                        numpy type promotion rules to the arguments.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> data = np.arange(1, 5)
+    >>> data
+    array([1, 2, 3, 4])
+    >>> np.average(data)
+    2.5
+    >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
+    4.0
+
+    >>> data = np.arange(6).reshape((3, 2))
+    >>> data
+    array([[0, 1],
+           [2, 3],
+           [4, 5]])
+    >>> np.average(data, axis=1, weights=[1./4, 3./4])
+    array([0.75, 2.75, 4.75])
+    >>> np.average(data, weights=[1./4, 3./4])
+    Traceback (most recent call last):
+        ...
+    TypeError: Axis must be specified when shapes of a and weights differ.
+
+    With ``keepdims=True``, the following result has shape (3, 1).
+
+    >>> np.average(data, axis=1, keepdims=True)
+    array([[0.5],
+           [2.5],
+           [4.5]])
+
+    >>> data = np.arange(8).reshape((2, 2, 2))
+    >>> data
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.average(data, axis=(0, 1), weights=[[1./4, 3./4], [1., 1./2]])
+    array([3.4, 4.4])
+    >>> np.average(data, axis=0, weights=[[1./4, 3./4], [1., 1./2]])
+    Traceback (most recent call last):
+        ...
+    ValueError: Shape of weights must be consistent
+    with shape of a along specified axis.
+    """
+    a = np.asanyarray(a)
+
+    if axis is not None:
+        axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
+
+    if keepdims is np._NoValue:
+        # Don't pass on the keepdims argument if one wasn't given.
+        keepdims_kw = {}
+    else:
+        keepdims_kw = {'keepdims': keepdims}
+
+    if weights is None:
+        avg = a.mean(axis, **keepdims_kw)
+        avg_as_array = np.asanyarray(avg)
+        scl = avg_as_array.dtype.type(a.size/avg_as_array.size)
+    else:
+        wgt = _weights_are_valid(weights=weights, a=a, axis=axis)
+
+        if issubclass(a.dtype.type, (np.integer, np.bool)):
+            result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8')
+        else:
+            result_dtype = np.result_type(a.dtype, wgt.dtype)
+
+        scl = wgt.sum(axis=axis, dtype=result_dtype, **keepdims_kw)
+        if np.any(scl == 0.0):
+            raise ZeroDivisionError(
+                "Weights sum to zero, can't be normalized")
+
+        avg = avg_as_array = np.multiply(a, wgt,
+                          dtype=result_dtype).sum(axis, **keepdims_kw) / scl
+
+    if returned:
+        if scl.shape != avg_as_array.shape:
+            scl = np.broadcast_to(scl, avg_as_array.shape).copy()
+        return avg, scl
+    else:
+        return avg
+
+
+@set_module('numpy')
+def asarray_chkfinite(a, dtype=None, order=None):
+    """Convert the input to an array, checking for NaNs or Infs.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data, in any form that can be converted to an array.  This
+        includes lists, lists of tuples, tuples, tuples of tuples, tuples
+        of lists and ndarrays.  Success requires no NaNs or Infs.
+    dtype : data-type, optional
+        By default, the data-type is inferred from the input data.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Memory layout.  'A' and 'K' depend on the order of input array a.
+        'C' row-major (C-style),
+        'F' column-major (Fortran-style) memory representation.
+        'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
+        'K' (keep) preserve input order
+        Defaults to 'C'.
+
+    Returns
+    -------
+    out : ndarray
+        Array interpretation of `a`.  No copy is performed if the input
+        is already an ndarray.  If `a` is a subclass of ndarray, a base
+        class ndarray is returned.
+
+    Raises
+    ------
+    ValueError
+        Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).
+
+    See Also
+    --------
+    asarray : Create and array.
+    asanyarray : Similar function which passes through subclasses.
+    ascontiguousarray : Convert input to a contiguous array.
+    asfortranarray : Convert input to an ndarray with column-major
+                     memory order.
+    fromiter : Create an array from an iterator.
+    fromfunction : Construct an array by executing a function on grid
+                   positions.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Convert a list into an array. If all elements are finite, then
+    ``asarray_chkfinite`` is identical to ``asarray``.
+
+    >>> a = [1, 2]
+    >>> np.asarray_chkfinite(a, dtype=float)
+    array([1., 2.])
+
+    Raises ValueError if array_like contains Nans or Infs.
+
+    >>> a = [1, 2, np.inf]
+    >>> try:
+    ...     np.asarray_chkfinite(a)
+    ... except ValueError:
+    ...     print('ValueError')
+    ...
+    ValueError
+
+    """
+    a = asarray(a, dtype=dtype, order=order)
+    if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
+        raise ValueError(
+            "array must not contain infs or NaNs")
+    return a
+
+
+def _piecewise_dispatcher(x, condlist, funclist, *args, **kw):
+    yield x
+    # support the undocumented behavior of allowing scalars
+    if np.iterable(condlist):
+        yield from condlist
+
+
+@array_function_dispatch(_piecewise_dispatcher)
+def piecewise(x, condlist, funclist, *args, **kw):
+    """
+    Evaluate a piecewise-defined function.
+
+    Given a set of conditions and corresponding functions, evaluate each
+    function on the input data wherever its condition is true.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        The input domain.
+    condlist : list of bool arrays or bool scalars
+        Each boolean array corresponds to a function in `funclist`.  Wherever
+        `condlist[i]` is True, `funclist[i](x)` is used as the output value.
+
+        Each boolean array in `condlist` selects a piece of `x`,
+        and should therefore be of the same shape as `x`.
+
+        The length of `condlist` must correspond to that of `funclist`.
+        If one extra function is given, i.e. if
+        ``len(funclist) == len(condlist) + 1``, then that extra function
+        is the default value, used wherever all conditions are false.
+    funclist : list of callables, f(x,*args,**kw), or scalars
+        Each function is evaluated over `x` wherever its corresponding
+        condition is True.  It should take a 1d array as input and give an 1d
+        array or a scalar value as output.  If, instead of a callable,
+        a scalar is provided then a constant function (``lambda x: scalar``) is
+        assumed.
+    args : tuple, optional
+        Any further arguments given to `piecewise` are passed to the functions
+        upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then
+        each function is called as ``f(x, 1, 'a')``.
+    kw : dict, optional
+        Keyword arguments used in calling `piecewise` are passed to the
+        functions upon execution, i.e., if called
+        ``piecewise(..., ..., alpha=1)``, then each function is called as
+        ``f(x, alpha=1)``.
+
+    Returns
+    -------
+    out : ndarray
+        The output is the same shape and type as x and is found by
+        calling the functions in `funclist` on the appropriate portions of `x`,
+        as defined by the boolean arrays in `condlist`.  Portions not covered
+        by any condition have a default value of 0.
+
+
+    See Also
+    --------
+    choose, select, where
+
+    Notes
+    -----
+    This is similar to choose or select, except that functions are
+    evaluated on elements of `x` that satisfy the corresponding condition from
+    `condlist`.
+
+    The result is::
+
+            |--
+            |funclist[0](x[condlist[0]])
+      out = |funclist[1](x[condlist[1]])
+            |...
+            |funclist[n2](x[condlist[n2]])
+            |--
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Define the signum function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.
+
+    >>> x = np.linspace(-2.5, 2.5, 6)
+    >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])
+    array([-1., -1., -1.,  1.,  1.,  1.])
+
+    Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for
+    ``x >= 0``.
+
+    >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
+    array([2.5,  1.5,  0.5,  0.5,  1.5,  2.5])
+
+    Apply the same function to a scalar value.
+
+    >>> y = -2
+    >>> np.piecewise(y, [y < 0, y >= 0], [lambda x: -x, lambda x: x])
+    array(2)
+
+    """
+    x = asanyarray(x)
+    n2 = len(funclist)
+
+    # undocumented: single condition is promoted to a list of one condition
+    if isscalar(condlist) or (
+            not isinstance(condlist[0], (list, ndarray)) and x.ndim != 0):
+        condlist = [condlist]
+
+    condlist = asarray(condlist, dtype=bool)
+    n = len(condlist)
+
+    if n == n2 - 1:  # compute the "otherwise" condition.
+        condelse = ~np.any(condlist, axis=0, keepdims=True)
+        condlist = np.concatenate([condlist, condelse], axis=0)
+        n += 1
+    elif n != n2:
+        raise ValueError(
+            "with {} condition(s), either {} or {} functions are expected"
+            .format(n, n, n+1)
+        )
+
+    y = zeros_like(x)
+    for cond, func in zip(condlist, funclist):
+        if not isinstance(func, collections.abc.Callable):
+            y[cond] = func
+        else:
+            vals = x[cond]
+            if vals.size > 0:
+                y[cond] = func(vals, *args, **kw)
+
+    return y
+
+
+def _select_dispatcher(condlist, choicelist, default=None):
+    yield from condlist
+    yield from choicelist
+
+
+@array_function_dispatch(_select_dispatcher)
+def select(condlist, choicelist, default=0):
+    """
+    Return an array drawn from elements in choicelist, depending on conditions.
+
+    Parameters
+    ----------
+    condlist : list of bool ndarrays
+        The list of conditions which determine from which array in `choicelist`
+        the output elements are taken. When multiple conditions are satisfied,
+        the first one encountered in `condlist` is used.
+    choicelist : list of ndarrays
+        The list of arrays from which the output elements are taken. It has
+        to be of the same length as `condlist`.
+    default : scalar, optional
+        The element inserted in `output` when all conditions evaluate to False.
+
+    Returns
+    -------
+    output : ndarray
+        The output at position m is the m-th element of the array in
+        `choicelist` where the m-th element of the corresponding array in
+        `condlist` is True.
+
+    See Also
+    --------
+    where : Return elements from one of two arrays depending on condition.
+    take, choose, compress, diag, diagonal
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Beginning with an array of integers from 0 to 5 (inclusive),
+    elements less than ``3`` are negated, elements greater than ``3``
+    are squared, and elements not meeting either of these conditions
+    (exactly ``3``) are replaced with a `default` value of ``42``.
+
+    >>> x = np.arange(6)
+    >>> condlist = [x<3, x>3]
+    >>> choicelist = [x, x**2]
+    >>> np.select(condlist, choicelist, 42)
+    array([ 0,  1,  2, 42, 16, 25])
+
+    When multiple conditions are satisfied, the first one encountered in
+    `condlist` is used.
+
+    >>> condlist = [x<=4, x>3]
+    >>> choicelist = [x, x**2]
+    >>> np.select(condlist, choicelist, 55)
+    array([ 0,  1,  2,  3,  4, 25])
+
+    """
+    # Check the size of condlist and choicelist are the same, or abort.
+    if len(condlist) != len(choicelist):
+        raise ValueError(
+            'list of cases must be same length as list of conditions')
+
+    # Now that the dtype is known, handle the deprecated select([], []) case
+    if len(condlist) == 0:
+        raise ValueError("select with an empty condition list is not possible")
+
+    # TODO: This preserves the Python int, float, complex manually to get the
+    #       right `result_type` with NEP 50.  Most likely we will grow a better
+    #       way to spell this (and this can be replaced).
+    choicelist = [
+        choice if type(choice) in (int, float, complex) else np.asarray(choice)
+        for choice in choicelist]
+    choicelist.append(default if type(default) in (int, float, complex)
+                      else np.asarray(default))
+
+    try:
+        dtype = np.result_type(*choicelist)
+    except TypeError as e:
+        msg = f'Choicelist and default value do not have a common dtype: {e}'
+        raise TypeError(msg) from None
+
+    # Convert conditions to arrays and broadcast conditions and choices
+    # as the shape is needed for the result. Doing it separately optimizes
+    # for example when all choices are scalars.
+    condlist = np.broadcast_arrays(*condlist)
+    choicelist = np.broadcast_arrays(*choicelist)
+
+    # If cond array is not an ndarray in boolean format or scalar bool, abort.
+    for i, cond in enumerate(condlist):
+        if cond.dtype.type is not np.bool:
+            raise TypeError(
+                'invalid entry {} in condlist: should be boolean ndarray'.format(i))
+
+    if choicelist[0].ndim == 0:
+        # This may be common, so avoid the call.
+        result_shape = condlist[0].shape
+    else:
+        result_shape = np.broadcast_arrays(condlist[0], choicelist[0])[0].shape
+
+    result = np.full(result_shape, choicelist[-1], dtype)
+
+    # Use np.copyto to burn each choicelist array onto result, using the
+    # corresponding condlist as a boolean mask. This is done in reverse
+    # order since the first choice should take precedence.
+    choicelist = choicelist[-2::-1]
+    condlist = condlist[::-1]
+    for choice, cond in zip(choicelist, condlist):
+        np.copyto(result, choice, where=cond)
+
+    return result
+
+
+def _copy_dispatcher(a, order=None, subok=None):
+    return (a,)
+
+
+@array_function_dispatch(_copy_dispatcher)
+def copy(a, order='K', subok=False):
+    """
+    Return an array copy of the given object.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout of the copy. 'C' means C-order,
+        'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
+        'C' otherwise. 'K' means match the layout of `a` as closely
+        as possible. (Note that this function and :meth:`ndarray.copy` are very
+        similar, but have different default values for their order=
+        arguments.)
+    subok : bool, optional
+        If True, then sub-classes will be passed-through, otherwise the
+        returned array will be forced to be a base-class array (defaults to False).
+
+    Returns
+    -------
+    arr : ndarray
+        Array interpretation of `a`.
+
+    See Also
+    --------
+    ndarray.copy : Preferred method for creating an array copy
+
+    Notes
+    -----
+    This is equivalent to:
+
+    >>> np.array(a, copy=True)  #doctest: +SKIP
+
+    The copy made of the data is shallow, i.e., for arrays with object dtype,
+    the new array will point to the same objects.
+    See Examples from `ndarray.copy`.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create an array x, with a reference y and a copy z:
+
+    >>> x = np.array([1, 2, 3])
+    >>> y = x
+    >>> z = np.copy(x)
+
+    Note that, when we modify x, y changes, but not z:
+
+    >>> x[0] = 10
+    >>> x[0] == y[0]
+    True
+    >>> x[0] == z[0]
+    False
+
+    Note that, np.copy clears previously set WRITEABLE=False flag.
+
+    >>> a = np.array([1, 2, 3])
+    >>> a.flags["WRITEABLE"] = False
+    >>> b = np.copy(a)
+    >>> b.flags["WRITEABLE"]
+    True
+    >>> b[0] = 3
+    >>> b
+    array([3, 2, 3])
+    """
+    return array(a, order=order, subok=subok, copy=True)
+
+# Basic operations
+
+
+def _gradient_dispatcher(f, *varargs, axis=None, edge_order=None):
+    yield f
+    yield from varargs
+
+
+@array_function_dispatch(_gradient_dispatcher)
+def gradient(f, *varargs, axis=None, edge_order=1):
+    """
+    Return the gradient of an N-dimensional array.
+
+    The gradient is computed using second order accurate central differences
+    in the interior points and either first or second order accurate one-sides
+    (forward or backwards) differences at the boundaries.
+    The returned gradient hence has the same shape as the input array.
+
+    Parameters
+    ----------
+    f : array_like
+        An N-dimensional array containing samples of a scalar function.
+    varargs : list of scalar or array, optional
+        Spacing between f values. Default unitary spacing for all dimensions.
+        Spacing can be specified using:
+
+        1. single scalar to specify a sample distance for all dimensions.
+        2. N scalars to specify a constant sample distance for each dimension.
+           i.e. `dx`, `dy`, `dz`, ...
+        3. N arrays to specify the coordinates of the values along each
+           dimension of F. The length of the array must match the size of
+           the corresponding dimension
+        4. Any combination of N scalars/arrays with the meaning of 2. and 3.
+
+        If `axis` is given, the number of varargs must equal the number of axes.
+        Default: 1. (see Examples below).
+
+    edge_order : {1, 2}, optional
+        Gradient is calculated using N-th order accurate differences
+        at the boundaries. Default: 1.
+    axis : None or int or tuple of ints, optional
+        Gradient is calculated only along the given axis or axes
+        The default (axis = None) is to calculate the gradient for all the axes
+        of the input array. axis may be negative, in which case it counts from
+        the last to the first axis.
+
+    Returns
+    -------
+    gradient : ndarray or tuple of ndarray
+        A tuple of ndarrays (or a single ndarray if there is only one
+        dimension) corresponding to the derivatives of f with respect
+        to each dimension. Each derivative has the same shape as f.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> f = np.array([1, 2, 4, 7, 11, 16])
+    >>> np.gradient(f)
+    array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
+    >>> np.gradient(f, 2)
+    array([0.5 ,  0.75,  1.25,  1.75,  2.25,  2.5 ])
+
+    Spacing can be also specified with an array that represents the coordinates
+    of the values F along the dimensions.
+    For instance a uniform spacing:
+
+    >>> x = np.arange(f.size)
+    >>> np.gradient(f, x)
+    array([1. ,  1.5,  2.5,  3.5,  4.5,  5. ])
+
+    Or a non uniform one:
+
+    >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.])
+    >>> np.gradient(f, x)
+    array([1. ,  3. ,  3.5,  6.7,  6.9,  2.5])
+
+    For two dimensional arrays, the return will be two arrays ordered by
+    axis. In this example the first array stands for the gradient in
+    rows and the second one in columns direction:
+
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]))
+    (array([[ 2.,  2., -1.],
+            [ 2.,  2., -1.]]),
+     array([[1. , 2.5, 4. ],
+            [1. , 1. , 1. ]]))
+
+    In this example the spacing is also specified:
+    uniform for axis=0 and non uniform for axis=1
+
+    >>> dx = 2.
+    >>> y = [1., 1.5, 3.5]
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), dx, y)
+    (array([[ 1. ,  1. , -0.5],
+            [ 1. ,  1. , -0.5]]),
+     array([[2. , 2. , 2. ],
+            [2. , 1.7, 0.5]]))
+
+    It is possible to specify how boundaries are treated using `edge_order`
+
+    >>> x = np.array([0, 1, 2, 3, 4])
+    >>> f = x**2
+    >>> np.gradient(f, edge_order=1)
+    array([1.,  2.,  4.,  6.,  7.])
+    >>> np.gradient(f, edge_order=2)
+    array([0., 2., 4., 6., 8.])
+
+    The `axis` keyword can be used to specify a subset of axes of which the
+    gradient is calculated
+
+    >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), axis=0)
+    array([[ 2.,  2., -1.],
+           [ 2.,  2., -1.]])
+
+    The `varargs` argument defines the spacing between sample points in the
+    input array. It can take two forms:
+
+    1. An array, specifying coordinates, which may be unevenly spaced:
+
+    >>> x = np.array([0., 2., 3., 6., 8.])
+    >>> y = x ** 2
+    >>> np.gradient(y, x, edge_order=2)
+    array([ 0.,  4.,  6., 12., 16.])
+
+    2. A scalar, representing the fixed sample distance:
+
+    >>> dx = 2
+    >>> x = np.array([0., 2., 4., 6., 8.])
+    >>> y = x ** 2
+    >>> np.gradient(y, dx, edge_order=2)
+    array([ 0.,  4.,  8., 12., 16.])
+
+    It's possible to provide different data for spacing along each dimension.
+    The number of arguments must match the number of dimensions in the input
+    data.
+
+    >>> dx = 2
+    >>> dy = 3
+    >>> x = np.arange(0, 6, dx)
+    >>> y = np.arange(0, 9, dy)
+    >>> xs, ys = np.meshgrid(x, y)
+    >>> zs = xs + 2 * ys
+    >>> np.gradient(zs, dy, dx)  # Passing two scalars
+    (array([[2., 2., 2.],
+            [2., 2., 2.],
+            [2., 2., 2.]]),
+     array([[1., 1., 1.],
+            [1., 1., 1.],
+            [1., 1., 1.]]))
+
+    Mixing scalars and arrays is also allowed:
+
+    >>> np.gradient(zs, y, dx)  # Passing one array and one scalar
+    (array([[2., 2., 2.],
+            [2., 2., 2.],
+            [2., 2., 2.]]),
+     array([[1., 1., 1.],
+            [1., 1., 1.],
+            [1., 1., 1.]]))
+
+    Notes
+    -----
+    Assuming that :math:`f\\in C^{3}` (i.e., :math:`f` has at least 3 continuous
+    derivatives) and let :math:`h_{*}` be a non-homogeneous stepsize, we
+    minimize the "consistency error" :math:`\\eta_{i}` between the true gradient
+    and its estimate from a linear combination of the neighboring grid-points:
+
+    .. math::
+
+        \\eta_{i} = f_{i}^{\\left(1\\right)} -
+                    \\left[ \\alpha f\\left(x_{i}\\right) +
+                            \\beta f\\left(x_{i} + h_{d}\\right) +
+                            \\gamma f\\left(x_{i}-h_{s}\\right)
+                    \\right]
+
+    By substituting :math:`f(x_{i} + h_{d})` and :math:`f(x_{i} - h_{s})`
+    with their Taylor series expansion, this translates into solving
+    the following the linear system:
+
+    .. math::
+
+        \\left\\{
+            \\begin{array}{r}
+                \\alpha+\\beta+\\gamma=0 \\\\
+                \\beta h_{d}-\\gamma h_{s}=1 \\\\
+                \\beta h_{d}^{2}+\\gamma h_{s}^{2}=0
+            \\end{array}
+        \\right.
+
+    The resulting approximation of :math:`f_{i}^{(1)}` is the following:
+
+    .. math::
+
+        \\hat f_{i}^{(1)} =
+            \\frac{
+                h_{s}^{2}f\\left(x_{i} + h_{d}\\right)
+                + \\left(h_{d}^{2} - h_{s}^{2}\\right)f\\left(x_{i}\\right)
+                - h_{d}^{2}f\\left(x_{i}-h_{s}\\right)}
+                { h_{s}h_{d}\\left(h_{d} + h_{s}\\right)}
+            + \\mathcal{O}\\left(\\frac{h_{d}h_{s}^{2}
+                                + h_{s}h_{d}^{2}}{h_{d}
+                                + h_{s}}\\right)
+
+    It is worth noting that if :math:`h_{s}=h_{d}`
+    (i.e., data are evenly spaced)
+    we find the standard second order approximation:
+
+    .. math::
+
+        \\hat f_{i}^{(1)}=
+            \\frac{f\\left(x_{i+1}\\right) - f\\left(x_{i-1}\\right)}{2h}
+            + \\mathcal{O}\\left(h^{2}\\right)
+
+    With a similar procedure the forward/backward approximations used for
+    boundaries can be derived.
+
+    References
+    ----------
+    .. [1]  Quarteroni A., Sacco R., Saleri F. (2007) Numerical Mathematics
+            (Texts in Applied Mathematics). New York: Springer.
+    .. [2]  Durran D. R. (1999) Numerical Methods for Wave Equations
+            in Geophysical Fluid Dynamics. New York: Springer.
+    .. [3]  Fornberg B. (1988) Generation of Finite Difference Formulas on
+            Arbitrarily Spaced Grids,
+            Mathematics of Computation 51, no. 184 : 699-706.
+            `PDF `_.
+    """
+    f = np.asanyarray(f)
+    N = f.ndim  # number of dimensions
+
+    if axis is None:
+        axes = tuple(range(N))
+    else:
+        axes = _nx.normalize_axis_tuple(axis, N)
+
+    len_axes = len(axes)
+    n = len(varargs)
+    if n == 0:
+        # no spacing argument - use 1 in all axes
+        dx = [1.0] * len_axes
+    elif n == 1 and np.ndim(varargs[0]) == 0:
+        # single scalar for all axes
+        dx = varargs * len_axes
+    elif n == len_axes:
+        # scalar or 1d array for each axis
+        dx = list(varargs)
+        for i, distances in enumerate(dx):
+            distances = np.asanyarray(distances)
+            if distances.ndim == 0:
+                continue
+            elif distances.ndim != 1:
+                raise ValueError("distances must be either scalars or 1d")
+            if len(distances) != f.shape[axes[i]]:
+                raise ValueError("when 1d, distances must match "
+                                 "the length of the corresponding dimension")
+            if np.issubdtype(distances.dtype, np.integer):
+                # Convert numpy integer types to float64 to avoid modular
+                # arithmetic in np.diff(distances).
+                distances = distances.astype(np.float64)
+            diffx = np.diff(distances)
+            # if distances are constant reduce to the scalar case
+            # since it brings a consistent speedup
+            if (diffx == diffx[0]).all():
+                diffx = diffx[0]
+            dx[i] = diffx
+    else:
+        raise TypeError("invalid number of arguments")
+
+    if edge_order > 2:
+        raise ValueError("'edge_order' greater than 2 not supported")
+
+    # use central differences on interior and one-sided differences on the
+    # endpoints. This preserves second order-accuracy over the full domain.
+
+    outvals = []
+
+    # create slice objects --- initially all are [:, :, ..., :]
+    slice1 = [slice(None)]*N
+    slice2 = [slice(None)]*N
+    slice3 = [slice(None)]*N
+    slice4 = [slice(None)]*N
+
+    otype = f.dtype
+    if otype.type is np.datetime64:
+        # the timedelta dtype with the same unit information
+        otype = np.dtype(otype.name.replace('datetime', 'timedelta'))
+        # view as timedelta to allow addition
+        f = f.view(otype)
+    elif otype.type is np.timedelta64:
+        pass
+    elif np.issubdtype(otype, np.inexact):
+        pass
+    else:
+        # All other types convert to floating point.
+        # First check if f is a numpy integer type; if so, convert f to float64
+        # to avoid modular arithmetic when computing the changes in f.
+        if np.issubdtype(otype, np.integer):
+            f = f.astype(np.float64)
+        otype = np.float64
+
+    for axis, ax_dx in zip(axes, dx):
+        if f.shape[axis] < edge_order + 1:
+            raise ValueError(
+                "Shape of array too small to calculate a numerical gradient, "
+                "at least (edge_order + 1) elements are required.")
+        # result allocation
+        out = np.empty_like(f, dtype=otype)
+
+        # spacing for the current axis
+        uniform_spacing = np.ndim(ax_dx) == 0
+
+        # Numerical differentiation: 2nd order interior
+        slice1[axis] = slice(1, -1)
+        slice2[axis] = slice(None, -2)
+        slice3[axis] = slice(1, -1)
+        slice4[axis] = slice(2, None)
+
+        if uniform_spacing:
+            out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2. * ax_dx)
+        else:
+            dx1 = ax_dx[0:-1]
+            dx2 = ax_dx[1:]
+            a = -(dx2)/(dx1 * (dx1 + dx2))
+            b = (dx2 - dx1) / (dx1 * dx2)
+            c = dx1 / (dx2 * (dx1 + dx2))
+            # fix the shape for broadcasting
+            shape = np.ones(N, dtype=int)
+            shape[axis] = -1
+            a.shape = b.shape = c.shape = shape
+            # 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
+
+        # Numerical differentiation: 1st order edges
+        if edge_order == 1:
+            slice1[axis] = 0
+            slice2[axis] = 1
+            slice3[axis] = 0
+            dx_0 = ax_dx if uniform_spacing else ax_dx[0]
+            # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0])
+            out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
+
+            slice1[axis] = -1
+            slice2[axis] = -1
+            slice3[axis] = -2
+            dx_n = ax_dx if uniform_spacing else ax_dx[-1]
+            # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2])
+            out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
+
+        # Numerical differentiation: 2nd order edges
+        else:
+            slice1[axis] = 0
+            slice2[axis] = 0
+            slice3[axis] = 1
+            slice4[axis] = 2
+            if uniform_spacing:
+                a = -1.5 / ax_dx
+                b = 2. / ax_dx
+                c = -0.5 / ax_dx
+            else:
+                dx1 = ax_dx[0]
+                dx2 = ax_dx[1]
+                a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2))
+                b = (dx1 + dx2) / (dx1 * dx2)
+                c = - dx1 / (dx2 * (dx1 + dx2))
+            # 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
+
+            slice1[axis] = -1
+            slice2[axis] = -3
+            slice3[axis] = -2
+            slice4[axis] = -1
+            if uniform_spacing:
+                a = 0.5 / ax_dx
+                b = -2. / ax_dx
+                c = 1.5 / ax_dx
+            else:
+                dx1 = ax_dx[-2]
+                dx2 = ax_dx[-1]
+                a = (dx2) / (dx1 * (dx1 + dx2))
+                b = - (dx2 + dx1) / (dx1 * dx2)
+                c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2))
+            # 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1]
+            out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
+
+        outvals.append(out)
+
+        # reset the slice object in this dimension to ":"
+        slice1[axis] = slice(None)
+        slice2[axis] = slice(None)
+        slice3[axis] = slice(None)
+        slice4[axis] = slice(None)
+
+    if len_axes == 1:
+        return outvals[0]
+    return tuple(outvals)
+
+
+def _diff_dispatcher(a, n=None, axis=None, prepend=None, append=None):
+    return (a, prepend, append)
+
+
+@array_function_dispatch(_diff_dispatcher)
+def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
+    """
+    Calculate the n-th discrete difference along the given axis.
+
+    The first difference is given by ``out[i] = a[i+1] - a[i]`` along
+    the given axis, higher differences are calculated by using `diff`
+    recursively.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input
+        is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the
+        last axis.
+    prepend, append : array_like, optional
+        Values to prepend or append to `a` along axis prior to
+        performing the difference.  Scalar values are expanded to
+        arrays with length 1 in the direction of axis and the shape
+        of the input array in along all other axes.  Otherwise the
+        dimension and shape must match `a` except along axis.
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences. The shape of the output is the same as `a`
+        except along `axis` where the dimension is smaller by `n`. The
+        type of the output is the same as the type of the difference
+        between any two elements of `a`. This is the same as the type of
+        `a` in most cases. A notable exception is `datetime64`, which
+        results in a `timedelta64` output array.
+
+    See Also
+    --------
+    gradient, ediff1d, cumsum
+
+    Notes
+    -----
+    Type is preserved for boolean arrays, so the result will contain
+    `False` when consecutive elements are the same and `True` when they
+    differ.
+
+    For unsigned integer arrays, the results will also be unsigned. This
+    should not be surprising, as the result is consistent with
+    calculating the difference directly:
+
+    >>> u8_arr = np.array([1, 0], dtype=np.uint8)
+    >>> np.diff(u8_arr)
+    array([255], dtype=uint8)
+    >>> u8_arr[1,...] - u8_arr[0,...]
+    np.uint8(255)
+
+    If this is not desirable, then the array should be cast to a larger
+    integer type first:
+
+    >>> i16_arr = u8_arr.astype(np.int16)
+    >>> np.diff(i16_arr)
+    array([-1], dtype=int16)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+           [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    >>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
+    >>> np.diff(x)
+    array([1, 1], dtype='timedelta64[D]')
+
+    """
+    if n == 0:
+        return a
+    if n < 0:
+        raise ValueError(
+            "order must be non-negative but got " + repr(n))
+
+    a = asanyarray(a)
+    nd = a.ndim
+    if nd == 0:
+        raise ValueError("diff requires input that is at least one dimensional")
+    axis = normalize_axis_index(axis, nd)
+
+    combined = []
+    if prepend is not np._NoValue:
+        prepend = np.asanyarray(prepend)
+        if prepend.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            prepend = np.broadcast_to(prepend, tuple(shape))
+        combined.append(prepend)
+
+    combined.append(a)
+
+    if append is not np._NoValue:
+        append = np.asanyarray(append)
+        if append.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            append = np.broadcast_to(append, tuple(shape))
+        combined.append(append)
+
+    if len(combined) > 1:
+        a = np.concatenate(combined, axis)
+
+    slice1 = [slice(None)] * nd
+    slice2 = [slice(None)] * nd
+    slice1[axis] = slice(1, None)
+    slice2[axis] = slice(None, -1)
+    slice1 = tuple(slice1)
+    slice2 = tuple(slice2)
+
+    op = not_equal if a.dtype == np.bool else subtract
+    for _ in range(n):
+        a = op(a[slice1], a[slice2])
+
+    return a
+
+
+def _interp_dispatcher(x, xp, fp, left=None, right=None, period=None):
+    return (x, xp, fp)
+
+
+@array_function_dispatch(_interp_dispatcher)
+def interp(x, xp, fp, left=None, right=None, period=None):
+    """
+    One-dimensional linear interpolation for monotonically increasing sample points.
+
+    Returns the one-dimensional piecewise linear interpolant to a function
+    with given discrete data points (`xp`, `fp`), evaluated at `x`.
+
+    Parameters
+    ----------
+    x : array_like
+        The x-coordinates at which to evaluate the interpolated values.
+
+    xp : 1-D sequence of floats
+        The x-coordinates of the data points, must be increasing if argument
+        `period` is not specified. Otherwise, `xp` is internally sorted after
+        normalizing the periodic boundaries with ``xp = xp % period``.
+
+    fp : 1-D sequence of float or complex
+        The y-coordinates of the data points, same length as `xp`.
+
+    left : optional float or complex corresponding to fp
+        Value to return for `x < xp[0]`, default is `fp[0]`.
+
+    right : optional float or complex corresponding to fp
+        Value to return for `x > xp[-1]`, default is `fp[-1]`.
+
+    period : None or float, optional
+        A period for the x-coordinates. This parameter allows the proper
+        interpolation of angular x-coordinates. Parameters `left` and `right`
+        are ignored if `period` is specified.
+
+    Returns
+    -------
+    y : float or complex (corresponding to fp) or ndarray
+        The interpolated values, same shape as `x`.
+
+    Raises
+    ------
+    ValueError
+        If `xp` and `fp` have different length
+        If `xp` or `fp` are not 1-D sequences
+        If `period == 0`
+
+    See Also
+    --------
+    scipy.interpolate
+
+    Warnings
+    --------
+    The x-coordinate sequence is expected to be increasing, but this is not
+    explicitly enforced.  However, if the sequence `xp` is non-increasing,
+    interpolation results are meaningless.
+
+    Note that, since NaN is unsortable, `xp` also cannot contain NaNs.
+
+    A simple check for `xp` being strictly increasing is::
+
+        np.all(np.diff(xp) > 0)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> xp = [1, 2, 3]
+    >>> fp = [3, 2, 0]
+    >>> np.interp(2.5, xp, fp)
+    1.0
+    >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
+    array([3.  , 3.  , 2.5 , 0.56, 0.  ])
+    >>> UNDEF = -99.0
+    >>> np.interp(3.14, xp, fp, right=UNDEF)
+    -99.0
+
+    Plot an interpolant to the sine function:
+
+    >>> x = np.linspace(0, 2*np.pi, 10)
+    >>> y = np.sin(x)
+    >>> xvals = np.linspace(0, 2*np.pi, 50)
+    >>> yinterp = np.interp(xvals, x, y)
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(x, y, 'o')
+    []
+    >>> plt.plot(xvals, yinterp, '-x')
+    []
+    >>> plt.show()
+
+    Interpolation with periodic x-coordinates:
+
+    >>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
+    >>> xp = [190, -190, 350, -350]
+    >>> fp = [5, 10, 3, 4]
+    >>> np.interp(x, xp, fp, period=360)
+    array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])
+
+    Complex interpolation:
+
+    >>> x = [1.5, 4.0]
+    >>> xp = [2,3,5]
+    >>> fp = [1.0j, 0, 2+3j]
+    >>> np.interp(x, xp, fp)
+    array([0.+1.j , 1.+1.5j])
+
+    """
+
+    fp = np.asarray(fp)
+
+    if np.iscomplexobj(fp):
+        interp_func = compiled_interp_complex
+        input_dtype = np.complex128
+    else:
+        interp_func = compiled_interp
+        input_dtype = np.float64
+
+    if period is not None:
+        if period == 0:
+            raise ValueError("period must be a non-zero value")
+        period = abs(period)
+        left = None
+        right = None
+
+        x = np.asarray(x, dtype=np.float64)
+        xp = np.asarray(xp, dtype=np.float64)
+        fp = np.asarray(fp, dtype=input_dtype)
+
+        if xp.ndim != 1 or fp.ndim != 1:
+            raise ValueError("Data points must be 1-D sequences")
+        if xp.shape[0] != fp.shape[0]:
+            raise ValueError("fp and xp are not of the same length")
+        # normalizing periodic boundaries
+        x = x % period
+        xp = xp % period
+        asort_xp = np.argsort(xp)
+        xp = xp[asort_xp]
+        fp = fp[asort_xp]
+        xp = np.concatenate((xp[-1:]-period, xp, xp[0:1]+period))
+        fp = np.concatenate((fp[-1:], fp, fp[0:1]))
+
+    return interp_func(x, xp, fp, left, right)
+
+
+def _angle_dispatcher(z, deg=None):
+    return (z,)
+
+
+@array_function_dispatch(_angle_dispatcher)
+def angle(z, deg=False):
+    """
+    Return the angle of the complex argument.
+
+    Parameters
+    ----------
+    z : array_like
+        A complex number or sequence of complex numbers.
+    deg : bool, optional
+        Return angle in degrees if True, radians if False (default).
+
+    Returns
+    -------
+    angle : ndarray or scalar
+        The counterclockwise angle from the positive real axis on the complex
+        plane in the range ``(-pi, pi]``, with dtype as numpy.float64.
+
+    See Also
+    --------
+    arctan2
+    absolute
+
+    Notes
+    -----
+    This function passes the imaginary and real parts of the argument to
+    `arctan2` to compute the result; consequently, it follows the convention
+    of `arctan2` when the magnitude of the argument is zero. See example.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.angle([1.0, 1.0j, 1+1j])               # in radians
+    array([ 0.        ,  1.57079633,  0.78539816]) # may vary
+    >>> np.angle(1+1j, deg=True)                  # in degrees
+    45.0
+    >>> np.angle([0., -0., complex(0., -0.), complex(-0., -0.)])  # convention
+    array([ 0.        ,  3.14159265, -0.        , -3.14159265])
+
+    """
+    z = asanyarray(z)
+    if issubclass(z.dtype.type, _nx.complexfloating):
+        zimag = z.imag
+        zreal = z.real
+    else:
+        zimag = 0
+        zreal = z
+
+    a = arctan2(zimag, zreal)
+    if deg:
+        a *= 180/pi
+    return a
+
+
+def _unwrap_dispatcher(p, discont=None, axis=None, *, period=None):
+    return (p,)
+
+
+@array_function_dispatch(_unwrap_dispatcher)
+def unwrap(p, discont=None, axis=-1, *, period=2*pi):
+    r"""
+    Unwrap by taking the complement of large deltas with respect to the period.
+
+    This unwraps a signal `p` by changing elements which have an absolute
+    difference from their predecessor of more than ``max(discont, period/2)``
+    to their `period`-complementary values.
+
+    For the default case where `period` is :math:`2\pi` and `discont` is
+    :math:`\pi`, this unwraps a radian phase `p` such that adjacent differences
+    are never greater than :math:`\pi` by adding :math:`2k\pi` for some
+    integer :math:`k`.
+
+    Parameters
+    ----------
+    p : array_like
+        Input array.
+    discont : float, optional
+        Maximum discontinuity between values, default is ``period/2``.
+        Values below ``period/2`` are treated as if they were ``period/2``.
+        To have an effect different from the default, `discont` should be
+        larger than ``period/2``.
+    axis : int, optional
+        Axis along which unwrap will operate, default is the last axis.
+    period : float, optional
+        Size of the range over which the input wraps. By default, it is
+        ``2 pi``.
+
+        .. versionadded:: 1.21.0
+
+    Returns
+    -------
+    out : ndarray
+        Output array.
+
+    See Also
+    --------
+    rad2deg, deg2rad
+
+    Notes
+    -----
+    If the discontinuity in `p` is smaller than ``period/2``,
+    but larger than `discont`, no unwrapping is done because taking
+    the complement would only make the discontinuity larger.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> phase = np.linspace(0, np.pi, num=5)
+    >>> phase[3:] += np.pi
+    >>> phase
+    array([ 0.        ,  0.78539816,  1.57079633,  5.49778714,  6.28318531]) # may vary
+    >>> np.unwrap(phase)
+    array([ 0.        ,  0.78539816,  1.57079633, -0.78539816,  0.        ]) # may vary
+    >>> np.unwrap([0, 1, 2, -1, 0], period=4)
+    array([0, 1, 2, 3, 4])
+    >>> np.unwrap([ 1, 2, 3, 4, 5, 6, 1, 2, 3], period=6)
+    array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> np.unwrap([2, 3, 4, 5, 2, 3, 4, 5], period=4)
+    array([2, 3, 4, 5, 6, 7, 8, 9])
+    >>> phase_deg = np.mod(np.linspace(0 ,720, 19), 360) - 180
+    >>> np.unwrap(phase_deg, period=360)
+    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
+            180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
+            540.])
+    """
+    p = asarray(p)
+    nd = p.ndim
+    dd = diff(p, axis=axis)
+    if discont is None:
+        discont = period/2
+    slice1 = [slice(None, None)]*nd     # full slices
+    slice1[axis] = slice(1, None)
+    slice1 = tuple(slice1)
+    dtype = np.result_type(dd, period)
+    if _nx.issubdtype(dtype, _nx.integer):
+        interval_high, rem = divmod(period, 2)
+        boundary_ambiguous = rem == 0
+    else:
+        interval_high = period / 2
+        boundary_ambiguous = True
+    interval_low = -interval_high
+    ddmod = mod(dd - interval_low, period) + interval_low
+    if boundary_ambiguous:
+        # for `mask = (abs(dd) == period/2)`, the above line made
+        # `ddmod[mask] == -period/2`. correct these such that
+        # `ddmod[mask] == sign(dd[mask])*period/2`.
+        _nx.copyto(ddmod, interval_high,
+                   where=(ddmod == interval_low) & (dd > 0))
+    ph_correct = ddmod - dd
+    _nx.copyto(ph_correct, 0, where=abs(dd) < discont)
+    up = array(p, copy=True, dtype=dtype)
+    up[slice1] = p[slice1] + ph_correct.cumsum(axis)
+    return up
+
+
+def _sort_complex(a):
+    return (a,)
+
+
+@array_function_dispatch(_sort_complex)
+def sort_complex(a):
+    """
+    Sort a complex array using the real part first, then the imaginary part.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+
+    Returns
+    -------
+    out : complex ndarray
+        Always returns a sorted complex array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.sort_complex([5, 3, 6, 2, 1])
+    array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
+
+    >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
+    array([1.+2.j,  2.-1.j,  3.-3.j,  3.-2.j,  3.+5.j])
+
+    """
+    b = array(a, copy=True)
+    b.sort()
+    if not issubclass(b.dtype.type, _nx.complexfloating):
+        if b.dtype.char in 'bhBH':
+            return b.astype('F')
+        elif b.dtype.char == 'g':
+            return b.astype('G')
+        else:
+            return b.astype('D')
+    else:
+        return b
+
+
+def _arg_trim_zeros(filt):
+    """Return indices of the first and last non-zero element.
+
+    Parameters
+    ----------
+    filt : array_like
+        Input array.
+
+    Returns
+    -------
+    start, stop : ndarray
+        Two arrays containing the indices of the first and last non-zero
+        element in each dimension.
+
+    See also
+    --------
+    trim_zeros
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> _arg_trim_zeros(np.array([0, 0, 1, 1, 0]))
+    (array([2]), array([3]))
+    """
+    nonzero = (
+        np.argwhere(filt)
+        if filt.dtype != np.object_
+        # Historically, `trim_zeros` treats `None` in an object array
+        # as non-zero while argwhere doesn't, account for that
+        else np.argwhere(filt != 0)
+    )
+    if nonzero.size == 0:
+        start = stop = np.array([], dtype=np.intp)
+    else:
+        start = nonzero.min(axis=0)
+        stop = nonzero.max(axis=0)
+    return start, stop
+
+
+def _trim_zeros(filt, trim=None, axis=None):
+    return (filt,)
+
+
+@array_function_dispatch(_trim_zeros)
+def trim_zeros(filt, trim='fb', axis=None):
+    """Remove values along a dimension which are zero along all other.
+
+    Parameters
+    ----------
+    filt : array_like
+        Input array.
+    trim : {"fb", "f", "b"}, optional
+        A string with 'f' representing trim from front and 'b' to trim from
+        back. By default, zeros are trimmed on both sides.
+        Front and back refer to the edges of a dimension, with "front" refering
+        to the side with the lowest index 0, and "back" refering to the highest
+        index (or index -1).
+    axis : int or sequence, optional
+        If None, `filt` is cropped such, that the smallest bounding box is
+        returned that still contains all values which are not zero.
+        If an axis is specified, `filt` will be sliced in that dimension only
+        on the sides specified by `trim`. The remaining area will be the
+        smallest that still contains all values wich are not zero.
+
+    Returns
+    -------
+    trimmed : ndarray or sequence
+        The result of trimming the input. The number of dimensions and the
+        input data type are preserved.
+
+    Notes
+    -----
+    For all-zero arrays, the first axis is trimmed first.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
+    >>> np.trim_zeros(a)
+    array([1, 2, 3, 0, 2, 1])
+
+    >>> np.trim_zeros(a, trim='b')
+    array([0, 0, 0, ..., 0, 2, 1])
+
+    Multiple dimensions are supported.
+
+    >>> b = np.array([[0, 0, 2, 3, 0, 0],
+    ...               [0, 1, 0, 3, 0, 0],
+    ...               [0, 0, 0, 0, 0, 0]])
+    >>> np.trim_zeros(b)
+    array([[0, 2, 3],
+           [1, 0, 3]])
+
+    >>> np.trim_zeros(b, axis=-1)
+    array([[0, 2, 3],
+           [1, 0, 3],
+           [0, 0, 0]])
+
+    The input data type is preserved, list/tuple in means list/tuple out.
+
+    >>> np.trim_zeros([0, 1, 2, 0])
+    [1, 2]
+
+    """
+    filt_ = np.asarray(filt)
+
+    trim = trim.lower()
+    if trim not in {"fb", "bf", "f", "b"}:
+        raise ValueError(f"unexpected character(s) in `trim`: {trim!r}")
+
+    start, stop = _arg_trim_zeros(filt_)
+    stop += 1  # Adjust for slicing
+
+    if start.size == 0:
+        # filt is all-zero -> assign same values to start and stop so that
+        # resulting slice will be empty
+        start = stop = np.zeros(filt_.ndim, dtype=np.intp)
+    else:
+        if 'f' not in trim:
+            start = (None,) * filt_.ndim
+        if 'b' not in trim:
+            stop = (None,) * filt_.ndim
+
+    if len(start) == 1:
+        # filt is 1D -> don't use multi-dimensional slicing to preserve
+        # non-array input types
+        sl = slice(start[0], stop[0])
+    elif axis is None:
+        # trim all axes
+        sl = tuple(slice(*x) for x in zip(start, stop))
+    else:
+        # only trim single axis
+        axis = normalize_axis_index(axis, filt_.ndim)
+        sl = (slice(None),) * axis + (slice(start[axis], stop[axis]),) + (...,)
+
+    trimmed = filt[sl]
+    return trimmed
+
+
+
+def _extract_dispatcher(condition, arr):
+    return (condition, arr)
+
+
+@array_function_dispatch(_extract_dispatcher)
+def extract(condition, arr):
+    """
+    Return the elements of an array that satisfy some condition.
+
+    This is equivalent to ``np.compress(ravel(condition), ravel(arr))``.  If
+    `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.
+
+    Note that `place` does the exact opposite of `extract`.
+
+    Parameters
+    ----------
+    condition : array_like
+        An array whose nonzero or True entries indicate the elements of `arr`
+        to extract.
+    arr : array_like
+        Input array of the same size as `condition`.
+
+    Returns
+    -------
+    extract : ndarray
+        Rank 1 array of values from `arr` where `condition` is True.
+
+    See Also
+    --------
+    take, put, copyto, compress, place
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.arange(12).reshape((3, 4))
+    >>> arr
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11]])
+    >>> condition = np.mod(arr, 3)==0
+    >>> condition
+    array([[ True, False, False,  True],
+           [False, False,  True, False],
+           [False,  True, False, False]])
+    >>> np.extract(condition, arr)
+    array([0, 3, 6, 9])
+
+
+    If `condition` is boolean:
+
+    >>> arr[condition]
+    array([0, 3, 6, 9])
+
+    """
+    return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
+
+
+def _place_dispatcher(arr, mask, vals):
+    return (arr, mask, vals)
+
+
+@array_function_dispatch(_place_dispatcher)
+def place(arr, mask, vals):
+    """
+    Change elements of an array based on conditional and input values.
+
+    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
+    `place` uses the first N elements of `vals`, where N is the number of
+    True values in `mask`, while `copyto` uses the elements where `mask`
+    is True.
+
+    Note that `extract` does the exact opposite of `place`.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Array to put data into.
+    mask : array_like
+        Boolean mask array. Must have the same size as `a`.
+    vals : 1-D sequence
+        Values to put into `a`. Only the first N elements are used, where
+        N is the number of True values in `mask`. If `vals` is smaller
+        than N, it will be repeated, and if elements of `a` are to be masked,
+        this sequence must be non-empty.
+
+    See Also
+    --------
+    copyto, put, take, extract
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.arange(6).reshape(2, 3)
+    >>> np.place(arr, arr>2, [44, 55])
+    >>> arr
+    array([[ 0,  1,  2],
+           [44, 55, 44]])
+
+    """
+    return _place(arr, mask, vals)
+
+
+def disp(mesg, device=None, linefeed=True):
+    """
+    Display a message on a device.
+
+    .. deprecated:: 2.0
+        Use your own printing function instead.
+
+    Parameters
+    ----------
+    mesg : str
+        Message to display.
+    device : object
+        Device to write message. If None, defaults to ``sys.stdout`` which is
+        very similar to ``print``. `device` needs to have ``write()`` and
+        ``flush()`` methods.
+    linefeed : bool, optional
+        Option whether to print a line feed or not. Defaults to True.
+
+    Raises
+    ------
+    AttributeError
+        If `device` does not have a ``write()`` or ``flush()`` method.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Besides ``sys.stdout``, a file-like object can also be used as it has
+    both required methods:
+
+    >>> from io import StringIO
+    >>> buf = StringIO()
+    >>> np.disp('"Display" in a file', device=buf)
+    >>> buf.getvalue()
+    '"Display" in a file\\n'
+
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`disp` is deprecated, "
+        "use your own printing function instead. "
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    if device is None:
+        device = sys.stdout
+    if linefeed:
+        device.write('%s\n' % mesg)
+    else:
+        device.write('%s' % mesg)
+    device.flush()
+    return
+
+
+# See https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
+_DIMENSION_NAME = r'\w+'
+_CORE_DIMENSION_LIST = '(?:{0:}(?:,{0:})*)?'.format(_DIMENSION_NAME)
+_ARGUMENT = r'\({}\)'.format(_CORE_DIMENSION_LIST)
+_ARGUMENT_LIST = '{0:}(?:,{0:})*'.format(_ARGUMENT)
+_SIGNATURE = '^{0:}->{0:}$'.format(_ARGUMENT_LIST)
+
+
+def _parse_gufunc_signature(signature):
+    """
+    Parse string signatures for a generalized universal function.
+
+    Arguments
+    ---------
+    signature : string
+        Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)``
+        for ``np.matmul``.
+
+    Returns
+    -------
+    Tuple of input and output core dimensions parsed from the signature, each
+    of the form List[Tuple[str, ...]].
+    """
+    signature = re.sub(r'\s+', '', signature)
+
+    if not re.match(_SIGNATURE, signature):
+        raise ValueError(
+            'not a valid gufunc signature: {}'.format(signature))
+    return tuple([tuple(re.findall(_DIMENSION_NAME, arg))
+                  for arg in re.findall(_ARGUMENT, arg_list)]
+                 for arg_list in signature.split('->'))
+
+
+def _update_dim_sizes(dim_sizes, arg, core_dims):
+    """
+    Incrementally check and update core dimension sizes for a single argument.
+
+    Arguments
+    ---------
+    dim_sizes : Dict[str, int]
+        Sizes of existing core dimensions. Will be updated in-place.
+    arg : ndarray
+        Argument to examine.
+    core_dims : Tuple[str, ...]
+        Core dimensions for this argument.
+    """
+    if not core_dims:
+        return
+
+    num_core_dims = len(core_dims)
+    if arg.ndim < num_core_dims:
+        raise ValueError(
+            '%d-dimensional argument does not have enough '
+            'dimensions for all core dimensions %r'
+            % (arg.ndim, core_dims))
+
+    core_shape = arg.shape[-num_core_dims:]
+    for dim, size in zip(core_dims, core_shape):
+        if dim in dim_sizes:
+            if size != dim_sizes[dim]:
+                raise ValueError(
+                    'inconsistent size for core dimension %r: %r vs %r'
+                    % (dim, size, dim_sizes[dim]))
+        else:
+            dim_sizes[dim] = size
+
+
+def _parse_input_dimensions(args, input_core_dims):
+    """
+    Parse broadcast and core dimensions for vectorize with a signature.
+
+    Arguments
+    ---------
+    args : Tuple[ndarray, ...]
+        Tuple of input arguments to examine.
+    input_core_dims : List[Tuple[str, ...]]
+        List of core dimensions corresponding to each input.
+
+    Returns
+    -------
+    broadcast_shape : Tuple[int, ...]
+        Common shape to broadcast all non-core dimensions to.
+    dim_sizes : Dict[str, int]
+        Common sizes for named core dimensions.
+    """
+    broadcast_args = []
+    dim_sizes = {}
+    for arg, core_dims in zip(args, input_core_dims):
+        _update_dim_sizes(dim_sizes, arg, core_dims)
+        ndim = arg.ndim - len(core_dims)
+        dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])
+        broadcast_args.append(dummy_array)
+    broadcast_shape = np.lib._stride_tricks_impl._broadcast_shape(
+        *broadcast_args
+    )
+    return broadcast_shape, dim_sizes
+
+
+def _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims):
+    """Helper for calculating broadcast shapes with core dimensions."""
+    return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims)
+            for core_dims in list_of_core_dims]
+
+
+def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes,
+                   results=None):
+    """Helper for creating output arrays in vectorize."""
+    shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims)
+    if dtypes is None:
+        dtypes = [None] * len(shapes)
+    if results is None:
+        arrays = tuple(np.empty(shape=shape, dtype=dtype)
+                       for shape, dtype in zip(shapes, dtypes))
+    else:
+        arrays = tuple(np.empty_like(result, shape=shape, dtype=dtype)
+                       for result, shape, dtype
+                       in zip(results, shapes, dtypes))
+    return arrays
+
+
+def _get_vectorize_dtype(dtype):
+    if dtype.char in "SU":
+        return dtype.char
+    return dtype
+
+
+@set_module('numpy')
+class vectorize:
+    """
+    vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None,
+    cache=False, signature=None)
+
+    Returns an object that acts like pyfunc, but takes arrays as input.
+
+    Define a vectorized function which takes a nested sequence of objects or
+    numpy arrays as inputs and returns a single numpy array or a tuple of numpy
+    arrays. The vectorized function evaluates `pyfunc` over successive tuples
+    of the input arrays like the python map function, except it uses the
+    broadcasting rules of numpy.
+
+    The data type of the output of `vectorized` is determined by calling
+    the function with the first element of the input.  This can be avoided
+    by specifying the `otypes` argument.
+
+    Parameters
+    ----------
+    pyfunc : callable, optional
+        A python function or method.
+        Can be omitted to produce a decorator with keyword arguments.
+    otypes : str or list of dtypes, optional
+        The output data type. It must be specified as either a string of
+        typecode characters or a list of data type specifiers. There should
+        be one data type specifier for each output.
+    doc : str, optional
+        The docstring for the function. If None, the docstring will be the
+        ``pyfunc.__doc__``.
+    excluded : set, optional
+        Set of strings or integers representing the positional or keyword
+        arguments for which the function will not be vectorized. These will be
+        passed directly to `pyfunc` unmodified.
+
+    cache : bool, optional
+        If `True`, then cache the first function call that determines the number
+        of outputs if `otypes` is not provided.
+
+    signature : string, optional
+        Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for
+        vectorized matrix-vector multiplication. If provided, ``pyfunc`` will
+        be called with (and expected to return) arrays with shapes given by the
+        size of corresponding core dimensions. By default, ``pyfunc`` is
+        assumed to take scalars as input and output.
+
+    Returns
+    -------
+    out : callable
+        A vectorized function if ``pyfunc`` was provided,
+        a decorator otherwise.
+
+    See Also
+    --------
+    frompyfunc : Takes an arbitrary Python function and returns a ufunc
+
+    Notes
+    -----
+    The `vectorize` function is provided primarily for convenience, not for
+    performance. The implementation is essentially a for loop.
+
+    If `otypes` is not specified, then a call to the function with the
+    first argument will be used to determine the number of outputs.  The
+    results of this call will be cached if `cache` is `True` to prevent
+    calling the function twice.  However, to implement the cache, the
+    original function must be wrapped which will slow down subsequent
+    calls, so only do this if your function is expensive.
+
+    The new keyword argument interface and `excluded` argument support
+    further degrades performance.
+
+    References
+    ----------
+    .. [1] :doc:`/reference/c-api/generalized-ufuncs`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> def myfunc(a, b):
+    ...     "Return a-b if a>b, otherwise return a+b"
+    ...     if a > b:
+    ...         return a - b
+    ...     else:
+    ...         return a + b
+
+    >>> vfunc = np.vectorize(myfunc)
+    >>> vfunc([1, 2, 3, 4], 2)
+    array([3, 4, 1, 2])
+
+    The docstring is taken from the input function to `vectorize` unless it
+    is specified:
+
+    >>> vfunc.__doc__
+    'Return a-b if a>b, otherwise return a+b'
+    >>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')
+    >>> vfunc.__doc__
+    'Vectorized `myfunc`'
+
+    The output type is determined by evaluating the first element of the input,
+    unless it is specified:
+
+    >>> out = vfunc([1, 2, 3, 4], 2)
+    >>> type(out[0])
+    
+    >>> vfunc = np.vectorize(myfunc, otypes=[float])
+    >>> out = vfunc([1, 2, 3, 4], 2)
+    >>> type(out[0])
+    
+
+    The `excluded` argument can be used to prevent vectorizing over certain
+    arguments.  This can be useful for array-like arguments of a fixed length
+    such as the coefficients for a polynomial as in `polyval`:
+
+    >>> def mypolyval(p, x):
+    ...     _p = list(p)
+    ...     res = _p.pop(0)
+    ...     while _p:
+    ...         res = res*x + _p.pop(0)
+    ...     return res
+
+    Here, we exclude the zeroth argument from vectorization whether it is
+    passed by position or keyword.
+
+    >>> vpolyval = np.vectorize(mypolyval, excluded={0, 'p'})
+    >>> vpolyval([1, 2, 3], x=[0, 1])
+    array([3, 6])
+    >>> vpolyval(p=[1, 2, 3], x=[0, 1])
+    array([3, 6])
+
+    The `signature` argument allows for vectorizing functions that act on
+    non-scalar arrays of fixed length. For example, you can use it for a
+    vectorized calculation of Pearson correlation coefficient and its p-value:
+
+    >>> import scipy.stats
+    >>> pearsonr = np.vectorize(scipy.stats.pearsonr,
+    ...                 signature='(n),(n)->(),()')
+    >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
+    (array([ 1., -1.]), array([ 0.,  0.]))
+
+    Or for a vectorized convolution:
+
+    >>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
+    >>> convolve(np.eye(4), [1, 2, 1])
+    array([[1., 2., 1., 0., 0., 0.],
+           [0., 1., 2., 1., 0., 0.],
+           [0., 0., 1., 2., 1., 0.],
+           [0., 0., 0., 1., 2., 1.]])
+
+    Decorator syntax is supported.  The decorator can be called as
+    a function to provide keyword arguments:
+
+    >>> @np.vectorize
+    ... def identity(x):
+    ...     return x
+    ...
+    >>> identity([0, 1, 2])
+    array([0, 1, 2])
+    >>> @np.vectorize(otypes=[float])
+    ... def as_float(x):
+    ...     return x
+    ...
+    >>> as_float([0, 1, 2])
+    array([0., 1., 2.])
+    """
+    def __init__(self, pyfunc=np._NoValue, otypes=None, doc=None,
+                 excluded=None, cache=False, signature=None):
+
+        if (pyfunc != np._NoValue) and (not callable(pyfunc)):
+            #Splitting the error message to keep
+            #the length below 79 characters.
+            part1 = "When used as a decorator, "
+            part2 = "only accepts keyword arguments."
+            raise TypeError(part1 + part2)
+
+        self.pyfunc = pyfunc
+        self.cache = cache
+        self.signature = signature
+        if pyfunc != np._NoValue and hasattr(pyfunc, '__name__'):
+            self.__name__ = pyfunc.__name__
+
+        self._ufunc = {}    # Caching to improve default performance
+        self._doc = None
+        self.__doc__ = doc
+        if doc is None and hasattr(pyfunc, '__doc__'):
+            self.__doc__ = pyfunc.__doc__
+        else:
+            self._doc = doc
+
+        if isinstance(otypes, str):
+            for char in otypes:
+                if char not in typecodes['All']:
+                    raise ValueError("Invalid otype specified: %s" % (char,))
+        elif iterable(otypes):
+            otypes = [_get_vectorize_dtype(_nx.dtype(x)) for x in otypes]
+        elif otypes is not None:
+            raise ValueError("Invalid otype specification")
+        self.otypes = otypes
+
+        # Excluded variable support
+        if excluded is None:
+            excluded = set()
+        self.excluded = set(excluded)
+
+        if signature is not None:
+            self._in_and_out_core_dims = _parse_gufunc_signature(signature)
+        else:
+            self._in_and_out_core_dims = None
+
+    def _init_stage_2(self, pyfunc, *args, **kwargs):
+        self.__name__ = pyfunc.__name__
+        self.pyfunc = pyfunc
+        if self._doc is None:
+            self.__doc__ = pyfunc.__doc__
+        else:
+            self.__doc__ = self._doc
+
+    def _call_as_normal(self, *args, **kwargs):
+        """
+        Return arrays with the results of `pyfunc` broadcast (vectorized) over
+        `args` and `kwargs` not in `excluded`.
+        """
+        excluded = self.excluded
+        if not kwargs and not excluded:
+            func = self.pyfunc
+            vargs = args
+        else:
+            # The wrapper accepts only positional arguments: we use `names` and
+            # `inds` to mutate `the_args` and `kwargs` to pass to the original
+            # function.
+            nargs = len(args)
+
+            names = [_n for _n in kwargs if _n not in excluded]
+            inds = [_i for _i in range(nargs) if _i not in excluded]
+            the_args = list(args)
+
+            def func(*vargs):
+                for _n, _i in enumerate(inds):
+                    the_args[_i] = vargs[_n]
+                kwargs.update(zip(names, vargs[len(inds):]))
+                return self.pyfunc(*the_args, **kwargs)
+
+            vargs = [args[_i] for _i in inds]
+            vargs.extend([kwargs[_n] for _n in names])
+
+        return self._vectorize_call(func=func, args=vargs)
+
+    def __call__(self, *args, **kwargs):
+        if self.pyfunc is np._NoValue:
+            self._init_stage_2(*args, **kwargs)
+            return self
+
+        return self._call_as_normal(*args, **kwargs)
+
+    def _get_ufunc_and_otypes(self, func, args):
+        """Return (ufunc, otypes)."""
+        # frompyfunc will fail if args is empty
+        if not args:
+            raise ValueError('args can not be empty')
+
+        if self.otypes is not None:
+            otypes = self.otypes
+
+            # self._ufunc is a dictionary whose keys are the number of
+            # arguments (i.e. len(args)) and whose values are ufuncs created
+            # by frompyfunc. len(args) can be different for different calls if
+            # self.pyfunc has parameters with default values.  We only use the
+            # cache when func is self.pyfunc, which occurs when the call uses
+            # only positional arguments and no arguments are excluded.
+
+            nin = len(args)
+            nout = len(self.otypes)
+            if func is not self.pyfunc or nin not in self._ufunc:
+                ufunc = frompyfunc(func, nin, nout)
+            else:
+                ufunc = None  # We'll get it from self._ufunc
+            if func is self.pyfunc:
+                ufunc = self._ufunc.setdefault(nin, ufunc)
+        else:
+            # Get number of outputs and output types by calling the function on
+            # the first entries of args.  We also cache the result to prevent
+            # the subsequent call when the ufunc is evaluated.
+            # Assumes that ufunc first evaluates the 0th elements in the input
+            # arrays (the input values are not checked to ensure this)
+            args = [asarray(arg) for arg in args]
+            if builtins.any(arg.size == 0 for arg in args):
+                raise ValueError('cannot call `vectorize` on size 0 inputs '
+                                 'unless `otypes` is set')
+
+            inputs = [arg.flat[0] for arg in args]
+            outputs = func(*inputs)
+
+            # Performance note: profiling indicates that -- for simple
+            # functions at least -- this wrapping can almost double the
+            # execution time.
+            # Hence we make it optional.
+            if self.cache:
+                _cache = [outputs]
+
+                def _func(*vargs):
+                    if _cache:
+                        return _cache.pop()
+                    else:
+                        return func(*vargs)
+            else:
+                _func = func
+
+            if isinstance(outputs, tuple):
+                nout = len(outputs)
+            else:
+                nout = 1
+                outputs = (outputs,)
+
+            otypes = ''.join([asarray(outputs[_k]).dtype.char
+                              for _k in range(nout)])
+
+            # Performance note: profiling indicates that creating the ufunc is
+            # not a significant cost compared with wrapping so it seems not
+            # worth trying to cache this.
+            ufunc = frompyfunc(_func, len(args), nout)
+
+        return ufunc, otypes
+
+    def _vectorize_call(self, func, args):
+        """Vectorized call to `func` over positional `args`."""
+        if self.signature is not None:
+            res = self._vectorize_call_with_signature(func, args)
+        elif not args:
+            res = func()
+        else:
+            ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
+
+            # Convert args to object arrays first
+            inputs = [asanyarray(a, dtype=object) for a in args]
+
+            outputs = ufunc(*inputs)
+
+            if ufunc.nout == 1:
+                res = asanyarray(outputs, dtype=otypes[0])
+            else:
+                res = tuple([asanyarray(x, dtype=t)
+                             for x, t in zip(outputs, otypes)])
+        return res
+
+    def _vectorize_call_with_signature(self, func, args):
+        """Vectorized call over positional arguments with a signature."""
+        input_core_dims, output_core_dims = self._in_and_out_core_dims
+
+        if len(args) != len(input_core_dims):
+            raise TypeError('wrong number of positional arguments: '
+                            'expected %r, got %r'
+                            % (len(input_core_dims), len(args)))
+        args = tuple(asanyarray(arg) for arg in args)
+
+        broadcast_shape, dim_sizes = _parse_input_dimensions(
+            args, input_core_dims)
+        input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,
+                                         input_core_dims)
+        args = [np.broadcast_to(arg, shape, subok=True)
+                for arg, shape in zip(args, input_shapes)]
+
+        outputs = None
+        otypes = self.otypes
+        nout = len(output_core_dims)
+
+        for index in np.ndindex(*broadcast_shape):
+            results = func(*(arg[index] for arg in args))
+
+            n_results = len(results) if isinstance(results, tuple) else 1
+
+            if nout != n_results:
+                raise ValueError(
+                    'wrong number of outputs from pyfunc: expected %r, got %r'
+                    % (nout, n_results))
+
+            if nout == 1:
+                results = (results,)
+
+            if outputs is None:
+                for result, core_dims in zip(results, output_core_dims):
+                    _update_dim_sizes(dim_sizes, result, core_dims)
+
+                outputs = _create_arrays(broadcast_shape, dim_sizes,
+                                         output_core_dims, otypes, results)
+
+            for output, result in zip(outputs, results):
+                output[index] = result
+
+        if outputs is None:
+            # did not call the function even once
+            if otypes is None:
+                raise ValueError('cannot call `vectorize` on size 0 inputs '
+                                 'unless `otypes` is set')
+            if builtins.any(dim not in dim_sizes
+                            for dims in output_core_dims
+                            for dim in dims):
+                raise ValueError('cannot call `vectorize` with a signature '
+                                 'including new output dimensions on size 0 '
+                                 'inputs')
+            outputs = _create_arrays(broadcast_shape, dim_sizes,
+                                     output_core_dims, otypes)
+
+        return outputs[0] if nout == 1 else outputs
+
+
+def _cov_dispatcher(m, y=None, rowvar=None, bias=None, ddof=None,
+                    fweights=None, aweights=None, *, dtype=None):
+    return (m, y, fweights, aweights)
+
+
+@array_function_dispatch(_cov_dispatcher)
+def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
+        aweights=None, *, dtype=None):
+    """
+    Estimate a covariance matrix, given data and weights.
+
+    Covariance indicates the level to which two variables vary together.
+    If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
+    then the covariance matrix element :math:`C_{ij}` is the covariance of
+    :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
+    of :math:`x_i`.
+
+    See the notes for an outline of the algorithm.
+
+    Parameters
+    ----------
+    m : array_like
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `m` represents a variable, and each column a single
+        observation of all those variables. Also see `rowvar` below.
+    y : array_like, optional
+        An additional set of variables and observations. `y` has the same form
+        as that of `m`.
+    rowvar : bool, optional
+        If `rowvar` is True (default), then each row represents a
+        variable, with observations in the columns. Otherwise, the relationship
+        is transposed: each column represents a variable, while the rows
+        contain observations.
+    bias : bool, optional
+        Default normalization (False) is by ``(N - 1)``, where ``N`` is the
+        number of observations given (unbiased estimate). If `bias` is True,
+        then normalization is by ``N``. These values can be overridden by using
+        the keyword ``ddof`` in numpy versions >= 1.5.
+    ddof : int, optional
+        If not ``None`` the default value implied by `bias` is overridden.
+        Note that ``ddof=1`` will return the unbiased estimate, even if both
+        `fweights` and `aweights` are specified, and ``ddof=0`` will return
+        the simple average. See the notes for the details. The default value
+        is ``None``.
+    fweights : array_like, int, optional
+        1-D array of integer frequency weights; the number of times each
+        observation vector should be repeated.
+    aweights : array_like, optional
+        1-D array of observation vector weights. These relative weights are
+        typically large for observations considered "important" and smaller for
+        observations considered less "important". If ``ddof=0`` the array of
+        weights can be used to assign probabilities to observation vectors.
+    dtype : data-type, optional
+        Data-type of the result. By default, the return data-type will have
+        at least `numpy.float64` precision.
+
+        .. versionadded:: 1.20
+
+    Returns
+    -------
+    out : ndarray
+        The covariance matrix of the variables.
+
+    See Also
+    --------
+    corrcoef : Normalized covariance matrix
+
+    Notes
+    -----
+    Assume that the observations are in the columns of the observation
+    array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The
+    steps to compute the weighted covariance are as follows::
+
+        >>> m = np.arange(10, dtype=np.float64)
+        >>> f = np.arange(10) * 2
+        >>> a = np.arange(10) ** 2.
+        >>> ddof = 1
+        >>> w = f * a
+        >>> v1 = np.sum(w)
+        >>> v2 = np.sum(w * a)
+        >>> m -= np.sum(m * w, axis=None, keepdims=True) / v1
+        >>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)
+
+    Note that when ``a == 1``, the normalization factor
+    ``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)``
+    as it should.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Consider two variables, :math:`x_0` and :math:`x_1`, which
+    correlate perfectly, but in opposite directions:
+
+    >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
+    >>> x
+    array([[0, 1, 2],
+           [2, 1, 0]])
+
+    Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
+    matrix shows this clearly:
+
+    >>> np.cov(x)
+    array([[ 1., -1.],
+           [-1.,  1.]])
+
+    Note that element :math:`C_{0,1}`, which shows the correlation between
+    :math:`x_0` and :math:`x_1`, is negative.
+
+    Further, note how `x` and `y` are combined:
+
+    >>> x = [-2.1, -1,  4.3]
+    >>> y = [3,  1.1,  0.12]
+    >>> X = np.stack((x, y), axis=0)
+    >>> np.cov(X)
+    array([[11.71      , -4.286     ], # may vary
+           [-4.286     ,  2.144133]])
+    >>> np.cov(x, y)
+    array([[11.71      , -4.286     ], # may vary
+           [-4.286     ,  2.144133]])
+    >>> np.cov(x)
+    array(11.71)
+
+    """
+    # Check inputs
+    if ddof is not None and ddof != int(ddof):
+        raise ValueError(
+            "ddof must be integer")
+
+    # Handles complex arrays too
+    m = np.asarray(m)
+    if m.ndim > 2:
+        raise ValueError("m has more than 2 dimensions")
+
+    if y is not None:
+        y = np.asarray(y)
+        if y.ndim > 2:
+            raise ValueError("y has more than 2 dimensions")
+
+    if dtype is None:
+        if y is None:
+            dtype = np.result_type(m, np.float64)
+        else:
+            dtype = np.result_type(m, y, np.float64)
+
+    X = array(m, ndmin=2, dtype=dtype)
+    if not rowvar and m.ndim != 1:
+        X = X.T
+    if X.shape[0] == 0:
+        return np.array([]).reshape(0, 0)
+    if y is not None:
+        y = array(y, copy=None, ndmin=2, dtype=dtype)
+        if not rowvar and y.shape[0] != 1:
+            y = y.T
+        X = np.concatenate((X, y), axis=0)
+
+    if ddof is None:
+        if bias == 0:
+            ddof = 1
+        else:
+            ddof = 0
+
+    # Get the product of frequencies and weights
+    w = None
+    if fweights is not None:
+        fweights = np.asarray(fweights, dtype=float)
+        if not np.all(fweights == np.around(fweights)):
+            raise TypeError(
+                "fweights must be integer")
+        if fweights.ndim > 1:
+            raise RuntimeError(
+                "cannot handle multidimensional fweights")
+        if fweights.shape[0] != X.shape[1]:
+            raise RuntimeError(
+                "incompatible numbers of samples and fweights")
+        if any(fweights < 0):
+            raise ValueError(
+                "fweights cannot be negative")
+        w = fweights
+    if aweights is not None:
+        aweights = np.asarray(aweights, dtype=float)
+        if aweights.ndim > 1:
+            raise RuntimeError(
+                "cannot handle multidimensional aweights")
+        if aweights.shape[0] != X.shape[1]:
+            raise RuntimeError(
+                "incompatible numbers of samples and aweights")
+        if any(aweights < 0):
+            raise ValueError(
+                "aweights cannot be negative")
+        if w is None:
+            w = aweights
+        else:
+            w *= aweights
+
+    avg, w_sum = average(X, axis=1, weights=w, returned=True)
+    w_sum = w_sum[0]
+
+    # Determine the normalization
+    if w is None:
+        fact = X.shape[1] - ddof
+    elif ddof == 0:
+        fact = w_sum
+    elif aweights is None:
+        fact = w_sum - ddof
+    else:
+        fact = w_sum - ddof*sum(w*aweights)/w_sum
+
+    if fact <= 0:
+        warnings.warn("Degrees of freedom <= 0 for slice",
+                      RuntimeWarning, stacklevel=2)
+        fact = 0.0
+
+    X -= avg[:, None]
+    if w is None:
+        X_T = X.T
+    else:
+        X_T = (X*w).T
+    c = dot(X, X_T.conj())
+    c *= np.true_divide(1, fact)
+    return c.squeeze()
+
+
+def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None, *,
+                         dtype=None):
+    return (x, y)
+
+
+@array_function_dispatch(_corrcoef_dispatcher)
+def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
+             dtype=None):
+    """
+    Return Pearson product-moment correlation coefficients.
+
+    Please refer to the documentation for `cov` for more detail.  The
+    relationship between the correlation coefficient matrix, `R`, and the
+    covariance matrix, `C`, is
+
+    .. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} C_{jj} } }
+
+    The values of `R` are between -1 and 1, inclusive.
+
+    Parameters
+    ----------
+    x : array_like
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `x` represents a variable, and each column a single
+        observation of all those variables. Also see `rowvar` below.
+    y : array_like, optional
+        An additional set of variables and observations. `y` has the same
+        shape as `x`.
+    rowvar : bool, optional
+        If `rowvar` is True (default), then each row represents a
+        variable, with observations in the columns. Otherwise, the relationship
+        is transposed: each column represents a variable, while the rows
+        contain observations.
+    bias : _NoValue, optional
+        Has no effect, do not use.
+
+        .. deprecated:: 1.10.0
+    ddof : _NoValue, optional
+        Has no effect, do not use.
+
+        .. deprecated:: 1.10.0
+    dtype : data-type, optional
+        Data-type of the result. By default, the return data-type will have
+        at least `numpy.float64` precision.
+
+        .. versionadded:: 1.20
+
+    Returns
+    -------
+    R : ndarray
+        The correlation coefficient matrix of the variables.
+
+    See Also
+    --------
+    cov : Covariance matrix
+
+    Notes
+    -----
+    Due to floating point rounding the resulting array may not be Hermitian,
+    the diagonal elements may not be 1, and the elements may not satisfy the
+    inequality abs(a) <= 1. The real and imaginary parts are clipped to the
+    interval [-1,  1] in an attempt to improve on that situation but is not
+    much help in the complex case.
+
+    This function accepts but discards arguments `bias` and `ddof`.  This is
+    for backwards compatibility with previous versions of this function.  These
+    arguments had no effect on the return values of the function and can be
+    safely ignored in this and previous versions of numpy.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    In this example we generate two random arrays, ``xarr`` and ``yarr``, and
+    compute the row-wise and column-wise Pearson correlation coefficients,
+    ``R``. Since ``rowvar`` is  true by  default, we first find the row-wise
+    Pearson correlation coefficients between the variables of ``xarr``.
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng(seed=42)
+    >>> xarr = rng.random((3, 3))
+    >>> xarr
+    array([[0.77395605, 0.43887844, 0.85859792],
+           [0.69736803, 0.09417735, 0.97562235],
+           [0.7611397 , 0.78606431, 0.12811363]])
+    >>> R1 = np.corrcoef(xarr)
+    >>> R1
+    array([[ 1.        ,  0.99256089, -0.68080986],
+           [ 0.99256089,  1.        , -0.76492172],
+           [-0.68080986, -0.76492172,  1.        ]])
+
+    If we add another set of variables and observations ``yarr``, we can
+    compute the row-wise Pearson correlation coefficients between the
+    variables in ``xarr`` and ``yarr``.
+
+    >>> yarr = rng.random((3, 3))
+    >>> yarr
+    array([[0.45038594, 0.37079802, 0.92676499],
+           [0.64386512, 0.82276161, 0.4434142 ],
+           [0.22723872, 0.55458479, 0.06381726]])
+    >>> R2 = np.corrcoef(xarr, yarr)
+    >>> R2
+    array([[ 1.        ,  0.99256089, -0.68080986,  0.75008178, -0.934284  ,
+            -0.99004057],
+           [ 0.99256089,  1.        , -0.76492172,  0.82502011, -0.97074098,
+            -0.99981569],
+           [-0.68080986, -0.76492172,  1.        , -0.99507202,  0.89721355,
+             0.77714685],
+           [ 0.75008178,  0.82502011, -0.99507202,  1.        , -0.93657855,
+            -0.83571711],
+           [-0.934284  , -0.97074098,  0.89721355, -0.93657855,  1.        ,
+             0.97517215],
+           [-0.99004057, -0.99981569,  0.77714685, -0.83571711,  0.97517215,
+             1.        ]])
+
+    Finally if we use the option ``rowvar=False``, the columns are now
+    being treated as the variables and we will find the column-wise Pearson
+    correlation coefficients between variables in ``xarr`` and ``yarr``.
+
+    >>> R3 = np.corrcoef(xarr, yarr, rowvar=False)
+    >>> R3
+    array([[ 1.        ,  0.77598074, -0.47458546, -0.75078643, -0.9665554 ,
+             0.22423734],
+           [ 0.77598074,  1.        , -0.92346708, -0.99923895, -0.58826587,
+            -0.44069024],
+           [-0.47458546, -0.92346708,  1.        ,  0.93773029,  0.23297648,
+             0.75137473],
+           [-0.75078643, -0.99923895,  0.93773029,  1.        ,  0.55627469,
+             0.47536961],
+           [-0.9665554 , -0.58826587,  0.23297648,  0.55627469,  1.        ,
+            -0.46666491],
+           [ 0.22423734, -0.44069024,  0.75137473,  0.47536961, -0.46666491,
+             1.        ]])
+
+    """
+    if bias is not np._NoValue or ddof is not np._NoValue:
+        # 2015-03-15, 1.10
+        warnings.warn('bias and ddof have no effect and are deprecated',
+                      DeprecationWarning, stacklevel=2)
+    c = cov(x, y, rowvar, dtype=dtype)
+    try:
+        d = diag(c)
+    except ValueError:
+        # scalar covariance
+        # nan if incorrect value (nan, inf, 0), 1 otherwise
+        return c / c
+    stddev = sqrt(d.real)
+    c /= stddev[:, None]
+    c /= stddev[None, :]
+
+    # Clip real and imaginary parts to [-1, 1].  This does not guarantee
+    # abs(a[i,j]) <= 1 for complex arrays, but is the best we can do without
+    # excessive work.
+    np.clip(c.real, -1, 1, out=c.real)
+    if np.iscomplexobj(c):
+        np.clip(c.imag, -1, 1, out=c.imag)
+
+    return c
+
+
+@set_module('numpy')
+def blackman(M):
+    """
+    Return the Blackman window.
+
+    The Blackman window is a taper formed by using the first three
+    terms of a summation of cosines. It was designed to have close to the
+    minimal leakage possible.  It is close to optimal, only slightly worse
+    than a Kaiser window.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an empty
+        array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        The window, with the maximum value normalized to one (the value one
+        appears only if the number of samples is odd).
+
+    See Also
+    --------
+    bartlett, hamming, hanning, kaiser
+
+    Notes
+    -----
+    The Blackman window is defined as
+
+    .. math::  w(n) = 0.42 - 0.5 \\cos(2\\pi n/M) + 0.08 \\cos(4\\pi n/M)
+
+    Most references to the Blackman window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function. It is known as a
+    "near optimal" tapering function, almost as good (by some measures)
+    as the kaiser window.
+
+    References
+    ----------
+    Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
+    Dover Publications, New York.
+
+    Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
+    Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> np.blackman(12)
+    array([-1.38777878e-17,   3.26064346e-02,   1.59903635e-01, # may vary
+            4.14397981e-01,   7.36045180e-01,   9.67046769e-01,
+            9.67046769e-01,   7.36045180e-01,   4.14397981e-01,
+            1.59903635e-01,   3.26064346e-02,  -1.38777878e-17])
+
+    Plot the window and the frequency response.
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        from numpy.fft import fft, fftshift
+        window = np.blackman(51)
+        plt.plot(window)
+        plt.title("Blackman window")
+        plt.ylabel("Amplitude")
+        plt.xlabel("Sample")
+        plt.show()  # doctest: +SKIP
+
+        plt.figure()
+        A = fft(window, 2048) / 25.5
+        mag = np.abs(fftshift(A))
+        freq = np.linspace(-0.5, 0.5, len(A))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            response = 20 * np.log10(mag)
+        response = np.clip(response, -100, 100)
+        plt.plot(freq, response)
+        plt.title("Frequency response of Blackman window")
+        plt.ylabel("Magnitude [dB]")
+        plt.xlabel("Normalized frequency [cycles per sample]")
+        plt.axis('tight')
+        plt.show()
+
+    """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
+    if M < 1:
+        return array([], dtype=values.dtype)
+    if M == 1:
+        return ones(1, dtype=values.dtype)
+    n = arange(1-M, M, 2)
+    return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1))
+
+
+@set_module('numpy')
+def bartlett(M):
+    """
+    Return the Bartlett window.
+
+    The Bartlett window is very similar to a triangular window, except
+    that the end points are at zero.  It is often used in signal
+    processing for tapering a signal, without generating too much
+    ripple in the frequency domain.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+
+    Returns
+    -------
+    out : array
+        The triangular window, with the maximum value normalized to one
+        (the value one appears only if the number of samples is odd), with
+        the first and last samples equal to zero.
+
+    See Also
+    --------
+    blackman, hamming, hanning, kaiser
+
+    Notes
+    -----
+    The Bartlett window is defined as
+
+    .. math:: w(n) = \\frac{2}{M-1} \\left(
+              \\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right|
+              \\right)
+
+    Most references to the Bartlett window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  Note that convolution with this window produces linear
+    interpolation.  It is also known as an apodization (which means "removing
+    the foot", i.e. smoothing discontinuities at the beginning and end of the
+    sampled signal) or tapering function. The Fourier transform of the
+    Bartlett window is the product of two sinc functions. Note the excellent
+    discussion in Kanasewich [2]_.
+
+    References
+    ----------
+    .. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
+           Biometrika 37, 1-16, 1950.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
+           The University of Alberta Press, 1975, pp. 109-110.
+    .. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal
+           Processing", Prentice-Hall, 1999, pp. 468-471.
+    .. [4] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [5] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 429.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> np.bartlett(12)
+    array([ 0.        ,  0.18181818,  0.36363636,  0.54545455,  0.72727273, # may vary
+            0.90909091,  0.90909091,  0.72727273,  0.54545455,  0.36363636,
+            0.18181818,  0.        ])
+
+    Plot the window and its frequency response (requires SciPy and matplotlib).
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        from numpy.fft import fft, fftshift
+        window = np.bartlett(51)
+        plt.plot(window)
+        plt.title("Bartlett window")
+        plt.ylabel("Amplitude")
+        plt.xlabel("Sample")
+        plt.show()
+        plt.figure()
+        A = fft(window, 2048) / 25.5
+        mag = np.abs(fftshift(A))
+        freq = np.linspace(-0.5, 0.5, len(A))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            response = 20 * np.log10(mag)
+        response = np.clip(response, -100, 100)
+        plt.plot(freq, response)
+        plt.title("Frequency response of Bartlett window")
+        plt.ylabel("Magnitude [dB]")
+        plt.xlabel("Normalized frequency [cycles per sample]")
+        plt.axis('tight')
+        plt.show()
+
+    """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
+    if M < 1:
+        return array([], dtype=values.dtype)
+    if M == 1:
+        return ones(1, dtype=values.dtype)
+    n = arange(1-M, M, 2)
+    return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1))
+
+
+@set_module('numpy')
+def hanning(M):
+    """
+    Return the Hanning window.
+
+    The Hanning window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+
+    Returns
+    -------
+    out : ndarray, shape(M,)
+        The window, with the maximum value normalized to one (the value
+        one appears only if `M` is odd).
+
+    See Also
+    --------
+    bartlett, blackman, hamming, kaiser
+
+    Notes
+    -----
+    The Hanning window is defined as
+
+    .. math::  w(n) = 0.5 - 0.5\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
+               \\qquad 0 \\leq n \\leq M-1
+
+    The Hanning was named for Julius von Hann, an Austrian meteorologist.
+    It is also known as the Cosine Bell. Some authors prefer that it be
+    called a Hann window, to help avoid confusion with the very similar
+    Hamming window.
+
+    Most references to the Hanning window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
+           The University of Alberta Press, 1975, pp. 106-108.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.hanning(12)
+    array([0.        , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
+           0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
+           0.07937323, 0.        ])
+
+    Plot the window and its frequency response.
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        from numpy.fft import fft, fftshift
+        window = np.hanning(51)
+        plt.plot(window)
+        plt.title("Hann window")
+        plt.ylabel("Amplitude")
+        plt.xlabel("Sample")
+        plt.show()
+
+        plt.figure()
+        A = fft(window, 2048) / 25.5
+        mag = np.abs(fftshift(A))
+        freq = np.linspace(-0.5, 0.5, len(A))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            response = 20 * np.log10(mag)
+        response = np.clip(response, -100, 100)
+        plt.plot(freq, response)
+        plt.title("Frequency response of the Hann window")
+        plt.ylabel("Magnitude [dB]")
+        plt.xlabel("Normalized frequency [cycles per sample]")
+        plt.axis('tight')
+        plt.show()
+
+    """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
+    if M < 1:
+        return array([], dtype=values.dtype)
+    if M == 1:
+        return ones(1, dtype=values.dtype)
+    n = arange(1-M, M, 2)
+    return 0.5 + 0.5*cos(pi*n/(M-1))
+
+
+@set_module('numpy')
+def hamming(M):
+    """
+    Return the Hamming window.
+
+    The Hamming window is a taper formed by using a weighted cosine.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        The window, with the maximum value normalized to one (the value
+        one appears only if the number of samples is odd).
+
+    See Also
+    --------
+    bartlett, blackman, hanning, kaiser
+
+    Notes
+    -----
+    The Hamming window is defined as
+
+    .. math::  w(n) = 0.54 - 0.46\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
+               \\qquad 0 \\leq n \\leq M-1
+
+    The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
+    and is described in Blackman and Tukey. It was recommended for
+    smoothing the truncated autocovariance function in the time domain.
+    Most references to the Hamming window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
+           spectra, Dover Publications, New York.
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
+           University of Alberta Press, 1975, pp. 109-110.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+    .. [4] W.H. Press,  B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
+           "Numerical Recipes", Cambridge University Press, 1986, page 425.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.hamming(12)
+    array([ 0.08      ,  0.15302337,  0.34890909,  0.60546483,  0.84123594, # may vary
+            0.98136677,  0.98136677,  0.84123594,  0.60546483,  0.34890909,
+            0.15302337,  0.08      ])
+
+    Plot the window and the frequency response.
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        from numpy.fft import fft, fftshift
+        window = np.hamming(51)
+        plt.plot(window)
+        plt.title("Hamming window")
+        plt.ylabel("Amplitude")
+        plt.xlabel("Sample")
+        plt.show()
+
+        plt.figure()
+        A = fft(window, 2048) / 25.5
+        mag = np.abs(fftshift(A))
+        freq = np.linspace(-0.5, 0.5, len(A))
+        response = 20 * np.log10(mag)
+        response = np.clip(response, -100, 100)
+        plt.plot(freq, response)
+        plt.title("Frequency response of Hamming window")
+        plt.ylabel("Magnitude [dB]")
+        plt.xlabel("Normalized frequency [cycles per sample]")
+        plt.axis('tight')
+        plt.show()
+
+    """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.
+    values = np.array([0.0, M])
+    M = values[1]
+
+    if M < 1:
+        return array([], dtype=values.dtype)
+    if M == 1:
+        return ones(1, dtype=values.dtype)
+    n = arange(1-M, M, 2)
+    return 0.54 + 0.46*cos(pi*n/(M-1))
+
+
+## Code from cephes for i0
+
+_i0A = [
+    -4.41534164647933937950E-18,
+    3.33079451882223809783E-17,
+    -2.43127984654795469359E-16,
+    1.71539128555513303061E-15,
+    -1.16853328779934516808E-14,
+    7.67618549860493561688E-14,
+    -4.85644678311192946090E-13,
+    2.95505266312963983461E-12,
+    -1.72682629144155570723E-11,
+    9.67580903537323691224E-11,
+    -5.18979560163526290666E-10,
+    2.65982372468238665035E-9,
+    -1.30002500998624804212E-8,
+    6.04699502254191894932E-8,
+    -2.67079385394061173391E-7,
+    1.11738753912010371815E-6,
+    -4.41673835845875056359E-6,
+    1.64484480707288970893E-5,
+    -5.75419501008210370398E-5,
+    1.88502885095841655729E-4,
+    -5.76375574538582365885E-4,
+    1.63947561694133579842E-3,
+    -4.32430999505057594430E-3,
+    1.05464603945949983183E-2,
+    -2.37374148058994688156E-2,
+    4.93052842396707084878E-2,
+    -9.49010970480476444210E-2,
+    1.71620901522208775349E-1,
+    -3.04682672343198398683E-1,
+    6.76795274409476084995E-1
+    ]
+
+_i0B = [
+    -7.23318048787475395456E-18,
+    -4.83050448594418207126E-18,
+    4.46562142029675999901E-17,
+    3.46122286769746109310E-17,
+    -2.82762398051658348494E-16,
+    -3.42548561967721913462E-16,
+    1.77256013305652638360E-15,
+    3.81168066935262242075E-15,
+    -9.55484669882830764870E-15,
+    -4.15056934728722208663E-14,
+    1.54008621752140982691E-14,
+    3.85277838274214270114E-13,
+    7.18012445138366623367E-13,
+    -1.79417853150680611778E-12,
+    -1.32158118404477131188E-11,
+    -3.14991652796324136454E-11,
+    1.18891471078464383424E-11,
+    4.94060238822496958910E-10,
+    3.39623202570838634515E-9,
+    2.26666899049817806459E-8,
+    2.04891858946906374183E-7,
+    2.89137052083475648297E-6,
+    6.88975834691682398426E-5,
+    3.36911647825569408990E-3,
+    8.04490411014108831608E-1
+    ]
+
+
+def _chbevl(x, vals):
+    b0 = vals[0]
+    b1 = 0.0
+
+    for i in range(1, len(vals)):
+        b2 = b1
+        b1 = b0
+        b0 = x*b1 - b2 + vals[i]
+
+    return 0.5*(b0 - b2)
+
+
+def _i0_1(x):
+    return exp(x) * _chbevl(x/2.0-2, _i0A)
+
+
+def _i0_2(x):
+    return exp(x) * _chbevl(32.0/x - 2.0, _i0B) / sqrt(x)
+
+
+def _i0_dispatcher(x):
+    return (x,)
+
+
+@array_function_dispatch(_i0_dispatcher)
+def i0(x):
+    """
+    Modified Bessel function of the first kind, order 0.
+
+    Usually denoted :math:`I_0`.
+
+    Parameters
+    ----------
+    x : array_like of float
+        Argument of the Bessel function.
+
+    Returns
+    -------
+    out : ndarray, shape = x.shape, dtype = float
+        The modified Bessel function evaluated at each of the elements of `x`.
+
+    See Also
+    --------
+    scipy.special.i0, scipy.special.iv, scipy.special.ive
+
+    Notes
+    -----
+    The scipy implementation is recommended over this function: it is a
+    proper ufunc written in C, and more than an order of magnitude faster.
+
+    We use the algorithm published by Clenshaw [1]_ and referenced by
+    Abramowitz and Stegun [2]_, for which the function domain is
+    partitioned into the two intervals [0,8] and (8,inf), and Chebyshev
+    polynomial expansions are employed in each interval. Relative error on
+    the domain [0,30] using IEEE arithmetic is documented [3]_ as having a
+    peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000).
+
+    References
+    ----------
+    .. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in
+           *National Physical Laboratory Mathematical Tables*, vol. 5, London:
+           Her Majesty's Stationery Office, 1962.
+    .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical
+           Functions*, 10th printing, New York: Dover, 1964, pp. 379.
+           https://personal.math.ubc.ca/~cbm/aands/page_379.htm
+    .. [3] https://metacpan.org/pod/distribution/Math-Cephes/lib/Math/Cephes.pod#i0:-Modified-Bessel-function-of-order-zero
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.i0(0.)
+    array(1.0)
+    >>> np.i0([0, 1, 2, 3])
+    array([1.        , 1.26606588, 2.2795853 , 4.88079259])
+
+    """
+    x = np.asanyarray(x)
+    if x.dtype.kind == 'c':
+        raise TypeError("i0 not supported for complex values")
+    if x.dtype.kind != 'f':
+        x = x.astype(float)
+    x = np.abs(x)
+    return piecewise(x, [x <= 8.0], [_i0_1, _i0_2])
+
+## End of cephes code for i0
+
+
+@set_module('numpy')
+def kaiser(M, beta):
+    """
+    Return the Kaiser window.
+
+    The Kaiser window is a taper formed by using a Bessel function.
+
+    Parameters
+    ----------
+    M : int
+        Number of points in the output window. If zero or less, an
+        empty array is returned.
+    beta : float
+        Shape parameter for window.
+
+    Returns
+    -------
+    out : array
+        The window, with the maximum value normalized to one (the value
+        one appears only if the number of samples is odd).
+
+    See Also
+    --------
+    bartlett, blackman, hamming, hanning
+
+    Notes
+    -----
+    The Kaiser window is defined as
+
+    .. math::  w(n) = I_0\\left( \\beta \\sqrt{1-\\frac{4n^2}{(M-1)^2}}
+               \\right)/I_0(\\beta)
+
+    with
+
+    .. math:: \\quad -\\frac{M-1}{2} \\leq n \\leq \\frac{M-1}{2},
+
+    where :math:`I_0` is the modified zeroth-order Bessel function.
+
+    The Kaiser was named for Jim Kaiser, who discovered a simple
+    approximation to the DPSS window based on Bessel functions.  The Kaiser
+    window is a very good approximation to the Digital Prolate Spheroidal
+    Sequence, or Slepian window, which is the transform which maximizes the
+    energy in the main lobe of the window relative to total energy.
+
+    The Kaiser can approximate many other windows by varying the beta
+    parameter.
+
+    ====  =======================
+    beta  Window shape
+    ====  =======================
+    0     Rectangular
+    5     Similar to a Hamming
+    6     Similar to a Hanning
+    8.6   Similar to a Blackman
+    ====  =======================
+
+    A beta value of 14 is probably a good starting point. Note that as beta
+    gets large, the window narrows, and so the number of samples needs to be
+    large enough to sample the increasingly narrow spike, otherwise NaNs will
+    get returned.
+
+    Most references to the Kaiser window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  It is also known as an apodization (which means
+    "removing the foot", i.e. smoothing discontinuities at the beginning
+    and end of the sampled signal) or tapering function.
+
+    References
+    ----------
+    .. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by
+           digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.
+           John Wiley and Sons, New York, (1966).
+    .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
+           University of Alberta Press, 1975, pp. 177-178.
+    .. [3] Wikipedia, "Window function",
+           https://en.wikipedia.org/wiki/Window_function
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> np.kaiser(12, 14)
+     array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary
+            2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
+            9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
+            4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
+
+
+    Plot the window and the frequency response.
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        from numpy.fft import fft, fftshift
+        window = np.kaiser(51, 14)
+        plt.plot(window)
+        plt.title("Kaiser window")
+        plt.ylabel("Amplitude")
+        plt.xlabel("Sample")
+        plt.show()
+
+        plt.figure()
+        A = fft(window, 2048) / 25.5
+        mag = np.abs(fftshift(A))
+        freq = np.linspace(-0.5, 0.5, len(A))
+        response = 20 * np.log10(mag)
+        response = np.clip(response, -100, 100)
+        plt.plot(freq, response)
+        plt.title("Frequency response of Kaiser window")
+        plt.ylabel("Magnitude [dB]")
+        plt.xlabel("Normalized frequency [cycles per sample]")
+        plt.axis('tight')
+        plt.show()
+
+    """
+    # Ensures at least float64 via 0.0.  M should be an integer, but conversion
+    # to double is safe for a range.  (Simplified result_type with 0.0
+    # strongly typed.  result-type is not/less order sensitive, but that mainly
+    # matters for integers anyway.)
+    values = np.array([0.0, M, beta])
+    M = values[1]
+    beta = values[2]
+
+    if M == 1:
+        return np.ones(1, dtype=values.dtype)
+    n = arange(0, M)
+    alpha = (M-1)/2.0
+    return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta)
+
+
+def _sinc_dispatcher(x):
+    return (x,)
+
+
+@array_function_dispatch(_sinc_dispatcher)
+def sinc(x):
+    r"""
+    Return the normalized sinc function.
+
+    The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument
+    :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not
+    only everywhere continuous but also infinitely differentiable.
+
+    .. note::
+
+        Note the normalization factor of ``pi`` used in the definition.
+        This is the most commonly used definition in signal processing.
+        Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function
+        :math:`\sin(x)/x` that is more common in mathematics.
+
+    Parameters
+    ----------
+    x : ndarray
+        Array (possibly multi-dimensional) of values for which to calculate
+        ``sinc(x)``.
+
+    Returns
+    -------
+    out : ndarray
+        ``sinc(x)``, which has the same shape as the input.
+
+    Notes
+    -----
+    The name sinc is short for "sine cardinal" or "sinus cardinalis".
+
+    The sinc function is used in various signal processing applications,
+    including in anti-aliasing, in the construction of a Lanczos resampling
+    filter, and in interpolation.
+
+    For bandlimited interpolation of discrete-time signals, the ideal
+    interpolation kernel is proportional to the sinc function.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
+           Resource. https://mathworld.wolfram.com/SincFunction.html
+    .. [2] Wikipedia, "Sinc function",
+           https://en.wikipedia.org/wiki/Sinc_function
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.linspace(-4, 4, 41)
+    >>> np.sinc(x)
+     array([-3.89804309e-17,  -4.92362781e-02,  -8.40918587e-02, # may vary
+            -8.90384387e-02,  -5.84680802e-02,   3.89804309e-17,
+            6.68206631e-02,   1.16434881e-01,   1.26137788e-01,
+            8.50444803e-02,  -3.89804309e-17,  -1.03943254e-01,
+            -1.89206682e-01,  -2.16236208e-01,  -1.55914881e-01,
+            3.89804309e-17,   2.33872321e-01,   5.04551152e-01,
+            7.56826729e-01,   9.35489284e-01,   1.00000000e+00,
+            9.35489284e-01,   7.56826729e-01,   5.04551152e-01,
+            2.33872321e-01,   3.89804309e-17,  -1.55914881e-01,
+           -2.16236208e-01,  -1.89206682e-01,  -1.03943254e-01,
+           -3.89804309e-17,   8.50444803e-02,   1.26137788e-01,
+            1.16434881e-01,   6.68206631e-02,   3.89804309e-17,
+            -5.84680802e-02,  -8.90384387e-02,  -8.40918587e-02,
+            -4.92362781e-02,  -3.89804309e-17])
+
+    >>> plt.plot(x, np.sinc(x))
+    []
+    >>> plt.title("Sinc Function")
+    Text(0.5, 1.0, 'Sinc Function')
+    >>> plt.ylabel("Amplitude")
+    Text(0, 0.5, 'Amplitude')
+    >>> plt.xlabel("X")
+    Text(0.5, 0, 'X')
+    >>> plt.show()
+
+    """
+    x = np.asanyarray(x)
+    y = pi * where(x == 0, 1.0e-20, x)
+    return sin(y)/y
+
+
+def _ureduce(a, func, keepdims=False, **kwargs):
+    """
+    Internal Function.
+    Call `func` with `a` as first argument swapping the axes to use extended
+    axis on functions that don't support it natively.
+
+    Returns result and a.shape with axis dims set to 1.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    func : callable
+        Reduction function capable of receiving a single axis argument.
+        It is called with `a` as first argument followed by `kwargs`.
+    kwargs : keyword arguments
+        additional keyword arguments to pass to `func`.
+
+    Returns
+    -------
+    result : tuple
+        Result of func(a, **kwargs) and a.shape with axis dims set to 1
+        which can be used to reshape the result to the same shape a ufunc with
+        keepdims=True would produce.
+
+    """
+    a = np.asanyarray(a)
+    axis = kwargs.get('axis', None)
+    out = kwargs.get('out', None)
+
+    if keepdims is np._NoValue:
+        keepdims = False
+
+    nd = a.ndim
+    if axis is not None:
+        axis = _nx.normalize_axis_tuple(axis, nd)
+
+        if keepdims:
+            if out is not None:
+                index_out = tuple(
+                    0 if i in axis else slice(None) for i in range(nd))
+                kwargs['out'] = out[(Ellipsis, ) + index_out]
+
+        if len(axis) == 1:
+            kwargs['axis'] = axis[0]
+        else:
+            keep = set(range(nd)) - set(axis)
+            nkeep = len(keep)
+            # swap axis that should not be reduced to front
+            for i, s in enumerate(sorted(keep)):
+                a = a.swapaxes(i, s)
+            # merge reduced axis
+            a = a.reshape(a.shape[:nkeep] + (-1,))
+            kwargs['axis'] = -1
+    else:
+        if keepdims:
+            if out is not None:
+                index_out = (0, ) * nd
+                kwargs['out'] = out[(Ellipsis, ) + index_out]
+
+    r = func(a, **kwargs)
+
+    if out is not None:
+        return out
+
+    if keepdims:
+        if axis is None:
+            index_r = (np.newaxis, ) * nd
+        else:
+            index_r = tuple(
+                np.newaxis if i in axis else slice(None)
+                for i in range(nd))
+        r = r[(Ellipsis, ) + index_r]
+
+    return r
+
+
+def _median_dispatcher(
+        a, axis=None, out=None, overwrite_input=None, keepdims=None):
+    return (a, out)
+
+
+@array_function_dispatch(_median_dispatcher)
+def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
+    """
+    Compute the median along the specified axis.
+
+    Returns the median of the array elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : {int, sequence of int, None}, optional
+        Axis or axes along which the medians are computed. The default,
+        axis=None, will compute the median along a flattened version of
+        the array. If a sequence of axes, the array is first flattened
+        along the given axes, then the median is computed along the
+        resulting flattened axis.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output,
+        but the type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+       If True, then allow use of memory of input array `a` for
+       calculations. The input array will be modified by the call to
+       `median`. This will save memory when you do not need to preserve
+       the contents of the input array. Treat the input as undefined,
+       but it will probably be fully or partially sorted. Default is
+       False. If `overwrite_input` is ``True`` and `a` is not already an
+       `ndarray`, an error will be raised.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `arr`.
+
+    Returns
+    -------
+    median : ndarray
+        A new array holding the result. If the input contains integers
+        or floats smaller than ``float64``, then the output data-type is
+        ``np.float64``.  Otherwise, the data-type of the output is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    mean, percentile
+
+    Notes
+    -----
+    Given a vector ``V`` of length ``N``, the median of ``V`` is the
+    middle value of a sorted copy of ``V``, ``V_sorted`` - i
+    e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
+    two middle values of ``V_sorted`` when ``N`` is even.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> a
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> np.median(a)
+    np.float64(3.5)
+    >>> np.median(a, axis=0)
+    array([6.5, 4.5, 2.5])
+    >>> np.median(a, axis=1)
+    array([7.,  2.])
+    >>> np.median(a, axis=(0, 1))
+    np.float64(3.5)
+    >>> m = np.median(a, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.median(a, axis=0, out=m)
+    array([6.5,  4.5,  2.5])
+    >>> m
+    array([6.5,  4.5,  2.5])
+    >>> b = a.copy()
+    >>> np.median(b, axis=1, overwrite_input=True)
+    array([7.,  2.])
+    >>> assert not np.all(a==b)
+    >>> b = a.copy()
+    >>> np.median(b, axis=None, overwrite_input=True)
+    np.float64(3.5)
+    >>> assert not np.all(a==b)
+
+    """
+    return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out,
+                    overwrite_input=overwrite_input)
+
+
+def _median(a, axis=None, out=None, overwrite_input=False):
+    # can't be reasonably be implemented in terms of percentile as we have to
+    # call mean to not break astropy
+    a = np.asanyarray(a)
+
+    # Set the partition indexes
+    if axis is None:
+        sz = a.size
+    else:
+        sz = a.shape[axis]
+    if sz % 2 == 0:
+        szh = sz // 2
+        kth = [szh - 1, szh]
+    else:
+        kth = [(sz - 1) // 2]
+
+    # We have to check for NaNs (as of writing 'M' doesn't actually work).
+    supports_nans = np.issubdtype(a.dtype, np.inexact) or a.dtype.kind in 'Mm'
+    if supports_nans:
+        kth.append(-1)
+
+    if overwrite_input:
+        if axis is None:
+            part = a.ravel()
+            part.partition(kth)
+        else:
+            a.partition(kth, axis=axis)
+            part = a
+    else:
+        part = partition(a, kth, axis=axis)
+
+    if part.shape == ():
+        # make 0-D arrays work
+        return part.item()
+    if axis is None:
+        axis = 0
+
+    indexer = [slice(None)] * part.ndim
+    index = part.shape[axis] // 2
+    if part.shape[axis] % 2 == 1:
+        # index with slice to allow mean (below) to work
+        indexer[axis] = slice(index, index+1)
+    else:
+        indexer[axis] = slice(index-1, index+1)
+    indexer = tuple(indexer)
+
+    # Use mean in both odd and even case to coerce data type,
+    # using out array if needed.
+    rout = mean(part[indexer], axis=axis, out=out)
+    if supports_nans and sz > 0:
+        # If nans are possible, warn and replace by nans like mean would.
+        rout = np.lib._utils_impl._median_nancheck(part, rout, axis)
+
+    return rout
+
+
+def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
+                           method=None, keepdims=None, *, weights=None,
+                           interpolation=None):
+    return (a, q, out, weights)
+
+
+@array_function_dispatch(_percentile_dispatcher)
+def percentile(a,
+               q,
+               axis=None,
+               out=None,
+               overwrite_input=False,
+               method="linear",
+               keepdims=False,
+               *,
+               weights=None,
+               interpolation=None):
+    """
+    Compute the q-th percentile of the data along the specified axis.
+
+    Returns the q-th percentile(s) of the array elements.
+
+    Parameters
+    ----------
+    a : array_like of real numbers
+        Input array or object that can be converted to an array.
+    q : array_like of float
+        Percentage or sequence of percentages for the percentiles to compute.
+        Values must be between 0 and 100 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the percentiles are computed. The
+        default is to compute the percentile(s) along a flattened
+        version of the array.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output,
+        but the type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+        If True, then allow the input array `a` to be modified by intermediate
+        calculations, to save memory. In this case, the contents of the input
+        `a` after this function completes is undefined.
+    method : str, optional
+        This parameter specifies the method to use for estimating the
+        percentile.  There are many different methods, some unique to NumPy.
+        See the notes for explanation.  The options sorted by their R type
+        as summarized in the H&F paper [1]_ are:
+
+        1. 'inverted_cdf'
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        The first three methods are discontinuous.  NumPy further defines the
+        following discontinuous variations of the default 'linear' (7.) option:
+
+        * 'lower'
+        * 'higher',
+        * 'midpoint'
+        * 'nearest'
+
+        .. versionchanged:: 1.22.0
+            This argument was previously called "interpolation" and only
+            offered the "linear" default and last four options.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option, the
+        result will broadcast correctly against the original array `a`.
+
+     weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the percentile according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given axis) or of the same shape as `a`.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        Only `method="inverted_cdf"` supports weights.
+        See the notes for more details.
+
+        .. versionadded:: 2.0.0
+
+    interpolation : str, optional
+        Deprecated name for the method keyword argument.
+
+        .. deprecated:: 1.22.0
+
+    Returns
+    -------
+    percentile : scalar or ndarray
+        If `q` is a single percentile and `axis=None`, then the result
+        is a scalar. If multiple percentiles are given, first axis of
+        the result corresponds to the percentiles. The other axes are
+        the axes that remain after the reduction of `a`. If the input
+        contains integers or floats smaller than ``float64``, the output
+        data-type is ``float64``. Otherwise, the output data-type is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    mean
+    median : equivalent to ``percentile(..., 50)``
+    nanpercentile
+    quantile : equivalent to percentile, except q in the range [0, 1].
+
+    Notes
+    -----
+    The behavior of `numpy.percentile` with percentage `q` is
+    that of `numpy.quantile` with argument ``q/100``.
+    For more information, please see `numpy.quantile`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> a
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> np.percentile(a, 50)
+    3.5
+    >>> np.percentile(a, 50, axis=0)
+    array([6.5, 4.5, 2.5])
+    >>> np.percentile(a, 50, axis=1)
+    array([7.,  2.])
+    >>> np.percentile(a, 50, axis=1, keepdims=True)
+    array([[7.],
+           [2.]])
+
+    >>> m = np.percentile(a, 50, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.percentile(a, 50, axis=0, out=out)
+    array([6.5, 4.5, 2.5])
+    >>> m
+    array([6.5, 4.5, 2.5])
+
+    >>> b = a.copy()
+    >>> np.percentile(b, 50, axis=1, overwrite_input=True)
+    array([7.,  2.])
+    >>> assert not np.all(a == b)
+
+    The different methods can be visualized graphically:
+
+    .. plot::
+
+        import matplotlib.pyplot as plt
+
+        a = np.arange(4)
+        p = np.linspace(0, 100, 6001)
+        ax = plt.gca()
+        lines = [
+            ('linear', '-', 'C0'),
+            ('inverted_cdf', ':', 'C1'),
+            # Almost the same as `inverted_cdf`:
+            ('averaged_inverted_cdf', '-.', 'C1'),
+            ('closest_observation', ':', 'C2'),
+            ('interpolated_inverted_cdf', '--', 'C1'),
+            ('hazen', '--', 'C3'),
+            ('weibull', '-.', 'C4'),
+            ('median_unbiased', '--', 'C5'),
+            ('normal_unbiased', '-.', 'C6'),
+            ]
+        for method, style, color in lines:
+            ax.plot(
+                p, np.percentile(a, p, method=method),
+                label=method, linestyle=style, color=color)
+        ax.set(
+            title='Percentiles for different methods and data: ' + str(a),
+            xlabel='Percentile',
+            ylabel='Estimated percentile value',
+            yticks=a)
+        ax.legend(bbox_to_anchor=(1.03, 1))
+        plt.tight_layout()
+        plt.show()
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+       "Sample quantiles in statistical packages,"
+       The American Statistician, 50(4), pp. 361-365, 1996
+
+    """
+    if interpolation is not None:
+        method = _check_interpolation_as_method(
+            method, interpolation, "percentile")
+
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
+    # Use dtype of array if possible (e.g., if q is a python int or float)
+    # by making the divisor have the dtype of the data array.
+    q = np.true_divide(q, a.dtype.type(100) if a.dtype.kind == "f" else 100)
+    q = asanyarray(q)  # undo any decay that the ufunc performed (see gh-13105)
+    if not _quantile_is_valid(q):
+        raise ValueError("Percentiles must be in the range [0, 100]")
+
+    if weights is not None:
+        if method != "inverted_cdf":
+            msg = ("Only method 'inverted_cdf' supports weights. "
+                   f"Got: {method}.")
+            raise ValueError(msg)
+        if axis is not None:
+            axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
+        weights = _weights_are_valid(weights=weights, a=a, axis=axis)
+        if np.any(weights < 0):
+            raise ValueError("Weights must be non-negative.")
+
+    return _quantile_unchecked(
+        a, q, axis, out, overwrite_input, method, keepdims, weights)
+
+
+def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
+                         method=None, keepdims=None, *, weights=None,
+                         interpolation=None):
+    return (a, q, out, weights)
+
+
+@array_function_dispatch(_quantile_dispatcher)
+def quantile(a,
+             q,
+             axis=None,
+             out=None,
+             overwrite_input=False,
+             method="linear",
+             keepdims=False,
+             *,
+             weights=None,
+             interpolation=None):
+    """
+    Compute the q-th quantile of the data along the specified axis.
+
+    Parameters
+    ----------
+    a : array_like of real numbers
+        Input array or object that can be converted to an array.
+    q : array_like of float
+        Probability or sequence of probabilities of the quantiles to compute.
+        Values must be between 0 and 1 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the quantiles are computed. The default is
+        to compute the quantile(s) along a flattened version of the array.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and buffer length as the expected output, but the
+        type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+        If True, then allow the input array `a` to be modified by
+        intermediate calculations, to save memory. In this case, the
+        contents of the input `a` after this function completes is
+        undefined.
+    method : str, optional
+        This parameter specifies the method to use for estimating the
+        quantile.  There are many different methods, some unique to NumPy.
+        The recommended options, numbered as they appear in [1]_, are:
+
+        1. 'inverted_cdf'
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        The first three methods are discontinuous. For backward compatibility
+        with previous versions of NumPy, the following discontinuous variations
+        of the default 'linear' (7.) option are available:
+
+        * 'lower'
+        * 'higher',
+        * 'midpoint'
+        * 'nearest'
+
+        See Notes for details.
+
+        .. versionchanged:: 1.22.0
+            This argument was previously called "interpolation" and only
+            offered the "linear" default and last four options.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option, the
+        result will broadcast correctly against the original array `a`.
+
+    weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the quantile according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given axis) or of the same shape as `a`.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        Only `method="inverted_cdf"` supports weights.
+        See the notes for more details.
+
+        .. versionadded:: 2.0.0
+
+    interpolation : str, optional
+        Deprecated name for the method keyword argument.
+
+        .. deprecated:: 1.22.0
+
+    Returns
+    -------
+    quantile : scalar or ndarray
+        If `q` is a single probability and `axis=None`, then the result
+        is a scalar. If multiple probability levels are given, first axis
+        of the result corresponds to the quantiles. The other axes are
+        the axes that remain after the reduction of `a`. If the input
+        contains integers or floats smaller than ``float64``, the output
+        data-type is ``float64``. Otherwise, the output data-type is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    mean
+    percentile : equivalent to quantile, but with q in the range [0, 100].
+    median : equivalent to ``quantile(..., 0.5)``
+    nanquantile
+
+    Notes
+    -----
+    Given a sample `a` from an underlying distribution, `quantile` provides a
+    nonparametric estimate of the inverse cumulative distribution function.
+
+    By default, this is done by interpolating between adjacent elements in
+    ``y``, a sorted copy of `a`::
+
+        (1-g)*y[j] + g*y[j+1]
+
+    where the index ``j`` and coefficient ``g`` are the integral and
+    fractional components of ``q * (n-1)``, and ``n`` is the number of
+    elements in the sample.
+
+    This is a special case of Equation 1 of H&F [1]_. More generally,
+
+    - ``j = (q*n + m - 1) // 1``, and
+    - ``g = (q*n + m - 1) % 1``,
+
+    where ``m`` may be defined according to several different conventions.
+    The preferred convention may be selected using the ``method`` parameter:
+
+    =============================== =============== ===============
+    ``method``                      number in H&F   ``m``
+    =============================== =============== ===============
+    ``interpolated_inverted_cdf``   4               ``0``
+    ``hazen``                       5               ``1/2``
+    ``weibull``                     6               ``q``
+    ``linear`` (default)            7               ``1 - q``
+    ``median_unbiased``             8               ``q/3 + 1/3``
+    ``normal_unbiased``             9               ``q/4 + 3/8``
+    =============================== =============== ===============
+
+    Note that indices ``j`` and ``j + 1`` are clipped to the range ``0`` to
+    ``n - 1`` when the results of the formula would be outside the allowed
+    range of non-negative indices. The ``- 1`` in the formulas for ``j`` and
+    ``g`` accounts for Python's 0-based indexing.
+
+    The table above includes only the estimators from H&F that are continuous
+    functions of probability `q` (estimators 4-9). NumPy also provides the
+    three discontinuous estimators from H&F (estimators 1-3), where ``j`` is
+    defined as above, ``m`` is defined as follows, and ``g`` is a function
+    of the real-valued ``index = q*n + m - 1`` and ``j``.
+
+    1. ``inverted_cdf``: ``m = 0`` and ``g = int(index - j > 0)``
+    2. ``averaged_inverted_cdf``: ``m = 0`` and
+       ``g = (1 + int(index - j > 0)) / 2``
+    3. ``closest_observation``: ``m = -1/2`` and
+       ``g = 1 - int((index == j) & (j%2 == 1))``
+
+    For backward compatibility with previous versions of NumPy, `quantile`
+    provides four additional discontinuous estimators. Like
+    ``method='linear'``, all have ``m = 1 - q`` so that ``j = q*(n-1) // 1``,
+    but ``g`` is defined as follows.
+
+    - ``lower``: ``g = 0``
+    - ``midpoint``: ``g = 0.5``
+    - ``higher``: ``g = 1``
+    - ``nearest``: ``g = (q*(n-1) % 1) > 0.5``
+
+    **Weighted quantiles:**
+    More formally, the quantile at probability level :math:`q` of a cumulative
+    distribution function :math:`F(y)=P(Y \\leq y)` with probability measure
+    :math:`P` is defined as any number :math:`x` that fulfills the
+    *coverage conditions*
+
+    .. math:: P(Y < x) \\leq q \\quad\\text{and}\\quad P(Y \\leq x) \\geq q
+
+    with random variable :math:`Y\\sim P`.
+    Sample quantiles, the result of `quantile`, provide nonparametric
+    estimation of the underlying population counterparts, represented by the
+    unknown :math:`F`, given a data vector `a` of length ``n``.
+
+    Some of the estimators above arise when one considers :math:`F` as the
+    empirical distribution function of the data, i.e.
+    :math:`F(y) = \\frac{1}{n} \\sum_i 1_{a_i \\leq y}`.
+    Then, different methods correspond to different choices of :math:`x` that
+    fulfill the above coverage conditions. Methods that follow this approach
+    are ``inverted_cdf`` and ``averaged_inverted_cdf``.
+
+    For weighted quantiles, the coverage conditions still hold. The
+    empirical cumulative distribution is simply replaced by its weighted
+    version, i.e.
+    :math:`P(Y \\leq t) = \\frac{1}{\\sum_i w_i} \\sum_i w_i 1_{x_i \\leq t}`.
+    Only ``method="inverted_cdf"`` supports weights.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> a
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> np.quantile(a, 0.5)
+    3.5
+    >>> np.quantile(a, 0.5, axis=0)
+    array([6.5, 4.5, 2.5])
+    >>> np.quantile(a, 0.5, axis=1)
+    array([7.,  2.])
+    >>> np.quantile(a, 0.5, axis=1, keepdims=True)
+    array([[7.],
+           [2.]])
+    >>> m = np.quantile(a, 0.5, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.quantile(a, 0.5, axis=0, out=out)
+    array([6.5, 4.5, 2.5])
+    >>> m
+    array([6.5, 4.5, 2.5])
+    >>> b = a.copy()
+    >>> np.quantile(b, 0.5, axis=1, overwrite_input=True)
+    array([7.,  2.])
+    >>> assert not np.all(a == b)
+
+    See also `numpy.percentile` for a visualization of most methods.
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+       "Sample quantiles in statistical packages,"
+       The American Statistician, 50(4), pp. 361-365, 1996
+
+    """
+    if interpolation is not None:
+        method = _check_interpolation_as_method(
+            method, interpolation, "quantile")
+
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
+    # Use dtype of array if possible (e.g., if q is a python int or float).
+    if isinstance(q, (int, float)) and a.dtype.kind == "f":
+        q = np.asanyarray(q, dtype=a.dtype)
+    else:
+        q = np.asanyarray(q)
+
+    if not _quantile_is_valid(q):
+        raise ValueError("Quantiles must be in the range [0, 1]")
+
+    if weights is not None:
+        if method != "inverted_cdf":
+            msg = ("Only method 'inverted_cdf' supports weights. "
+                   f"Got: {method}.")
+            raise ValueError(msg)
+        if axis is not None:
+            axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
+        weights = _weights_are_valid(weights=weights, a=a, axis=axis)
+        if np.any(weights < 0):
+            raise ValueError("Weights must be non-negative.")
+
+    return _quantile_unchecked(
+        a, q, axis, out, overwrite_input, method, keepdims, weights)
+
+
+def _quantile_unchecked(a,
+                        q,
+                        axis=None,
+                        out=None,
+                        overwrite_input=False,
+                        method="linear",
+                        keepdims=False,
+                        weights=None):
+    """Assumes that q is in [0, 1], and is an ndarray"""
+    return _ureduce(a,
+                    func=_quantile_ureduce_func,
+                    q=q,
+                    weights=weights,
+                    keepdims=keepdims,
+                    axis=axis,
+                    out=out,
+                    overwrite_input=overwrite_input,
+                    method=method)
+
+
+def _quantile_is_valid(q):
+    # avoid expensive reductions, relevant for arrays with < O(1000) elements
+    if q.ndim == 1 and q.size < 10:
+        for i in range(q.size):
+            if not (0.0 <= q[i] <= 1.0):
+                return False
+    else:
+        if not (q.min() >= 0 and q.max() <= 1):
+            return False
+    return True
+
+
+def _check_interpolation_as_method(method, interpolation, fname):
+    # Deprecated NumPy 1.22, 2021-11-08
+    warnings.warn(
+        f"the `interpolation=` argument to {fname} was renamed to "
+        "`method=`, which has additional options.\n"
+        "Users of the modes 'nearest', 'lower', 'higher', or "
+        "'midpoint' are encouraged to review the method they used. "
+        "(Deprecated NumPy 1.22)",
+        DeprecationWarning, stacklevel=4)
+    if method != "linear":
+        # sanity check, we assume this basically never happens
+        raise TypeError(
+            "You shall not pass both `method` and `interpolation`!\n"
+            "(`interpolation` is Deprecated in favor of `method`)")
+    return interpolation
+
+
+def _compute_virtual_index(n, quantiles, alpha: float, beta: float):
+    """
+    Compute the floating point indexes of an array for the linear
+    interpolation of quantiles.
+    n : array_like
+        The sample sizes.
+    quantiles : array_like
+        The quantiles values.
+    alpha : float
+        A constant used to correct the index computed.
+    beta : float
+        A constant used to correct the index computed.
+
+    alpha and beta values depend on the chosen method
+    (see quantile documentation)
+
+    Reference:
+    Hyndman&Fan paper "Sample Quantiles in Statistical Packages",
+    DOI: 10.1080/00031305.1996.10473566
+    """
+    return n * quantiles + (
+            alpha + quantiles * (1 - alpha - beta)
+    ) - 1
+
+
+def _get_gamma(virtual_indexes, previous_indexes, method):
+    """
+    Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation
+    of quantiles.
+
+    virtual_indexes : array_like
+        The indexes where the percentile is supposed to be found in the sorted
+        sample.
+    previous_indexes : array_like
+        The floor values of virtual_indexes.
+    interpolation : dict
+        The interpolation method chosen, which may have a specific rule
+        modifying gamma.
+
+    gamma is usually the fractional part of virtual_indexes but can be modified
+    by the interpolation method.
+    """
+    gamma = np.asanyarray(virtual_indexes - previous_indexes)
+    gamma = method["fix_gamma"](gamma, virtual_indexes)
+    # Ensure both that we have an array, and that we keep the dtype
+    # (which may have been matched to the input array).
+    return np.asanyarray(gamma, dtype=virtual_indexes.dtype)
+
+
+def _lerp(a, b, t, out=None):
+    """
+    Compute the linear interpolation weighted by gamma on each point of
+    two same shape array.
+
+    a : array_like
+        Left bound.
+    b : array_like
+        Right bound.
+    t : array_like
+        The interpolation weight.
+    out : array_like
+        Output array.
+    """
+    diff_b_a = subtract(b, a)
+    # asanyarray is a stop-gap until gh-13105
+    lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out))
+    subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5,
+             casting='unsafe', dtype=type(lerp_interpolation.dtype))
+    if lerp_interpolation.ndim == 0 and out is None:
+        lerp_interpolation = lerp_interpolation[()]  # unpack 0d arrays
+    return lerp_interpolation
+
+
+def _get_gamma_mask(shape, default_value, conditioned_value, where):
+    out = np.full(shape, default_value)
+    np.copyto(out, conditioned_value, where=where, casting="unsafe")
+    return out
+
+
+def _discrete_interpolation_to_boundaries(index, gamma_condition_fun):
+    previous = np.floor(index)
+    next = previous + 1
+    gamma = index - previous
+    res = _get_gamma_mask(shape=index.shape,
+                          default_value=next,
+                          conditioned_value=previous,
+                          where=gamma_condition_fun(gamma, index)
+                          ).astype(np.intp)
+    # Some methods can lead to out-of-bound integers, clip them:
+    res[res < 0] = 0
+    return res
+
+
+def _closest_observation(n, quantiles):
+    # "choose the nearest even order statistic at g=0" (H&F (1996) pp. 362).
+    # Order is 1-based so for zero-based indexing round to nearest odd index.
+    gamma_fun = lambda gamma, index: (gamma == 0) & (np.floor(index) % 2 == 1)
+    return _discrete_interpolation_to_boundaries((n * quantiles) - 1 - 0.5,
+                                                 gamma_fun)
+
+
+def _inverted_cdf(n, quantiles):
+    gamma_fun = lambda gamma, _: (gamma == 0)
+    return _discrete_interpolation_to_boundaries((n * quantiles) - 1,
+                                                 gamma_fun)
+
+
+def _quantile_ureduce_func(
+        a: np.array,
+        q: np.array,
+        weights: np.array,
+        axis: int | None = None,
+        out=None,
+        overwrite_input: bool = False,
+        method="linear",
+) -> np.array:
+    if q.ndim > 2:
+        # The code below works fine for nd, but it might not have useful
+        # semantics. For now, keep the supported dimensions the same as it was
+        # before.
+        raise ValueError("q must be a scalar or 1d")
+    if overwrite_input:
+        if axis is None:
+            axis = 0
+            arr = a.ravel()
+            wgt = None if weights is None else weights.ravel()
+        else:
+            arr = a
+            wgt = weights
+    else:
+        if axis is None:
+            axis = 0
+            arr = a.flatten()
+            wgt = None if weights is None else weights.flatten()
+        else:
+            arr = a.copy()
+            wgt = weights
+    result = _quantile(arr,
+                       quantiles=q,
+                       axis=axis,
+                       method=method,
+                       out=out,
+                       weights=wgt)
+    return result
+
+
+def _get_indexes(arr, virtual_indexes, valid_values_count):
+    """
+    Get the valid indexes of arr neighbouring virtual_indexes.
+    Note
+    This is a companion function to linear interpolation of
+    Quantiles
+
+    Returns
+    -------
+    (previous_indexes, next_indexes): Tuple
+        A Tuple of virtual_indexes neighbouring indexes
+    """
+    previous_indexes = np.asanyarray(np.floor(virtual_indexes))
+    next_indexes = np.asanyarray(previous_indexes + 1)
+    indexes_above_bounds = virtual_indexes >= valid_values_count - 1
+    # When indexes is above max index, take the max value of the array
+    if indexes_above_bounds.any():
+        previous_indexes[indexes_above_bounds] = -1
+        next_indexes[indexes_above_bounds] = -1
+    # When indexes is below min index, take the min value of the array
+    indexes_below_bounds = virtual_indexes < 0
+    if indexes_below_bounds.any():
+        previous_indexes[indexes_below_bounds] = 0
+        next_indexes[indexes_below_bounds] = 0
+    if np.issubdtype(arr.dtype, np.inexact):
+        # After the sort, slices having NaNs will have for last element a NaN
+        virtual_indexes_nans = np.isnan(virtual_indexes)
+        if virtual_indexes_nans.any():
+            previous_indexes[virtual_indexes_nans] = -1
+            next_indexes[virtual_indexes_nans] = -1
+    previous_indexes = previous_indexes.astype(np.intp)
+    next_indexes = next_indexes.astype(np.intp)
+    return previous_indexes, next_indexes
+
+
+def _quantile(
+        arr: np.array,
+        quantiles: np.array,
+        axis: int = -1,
+        method="linear",
+        out=None,
+        weights=None,
+):
+    """
+    Private function that doesn't support extended axis or keepdims.
+    These methods are extended to this function using _ureduce
+    See nanpercentile for parameter usage
+    It computes the quantiles of the array for the given axis.
+    A linear interpolation is performed based on the `interpolation`.
+
+    By default, the method is "linear" where alpha == beta == 1 which
+    performs the 7th method of Hyndman&Fan.
+    With "median_unbiased" we get alpha == beta == 1/3
+    thus the 8th method of Hyndman&Fan.
+    """
+    # --- Setup
+    arr = np.asanyarray(arr)
+    values_count = arr.shape[axis]
+    # The dimensions of `q` are prepended to the output shape, so we need the
+    # axis being sampled from `arr` to be last.
+    if axis != 0:  # But moveaxis is slow, so only call it if necessary.
+        arr = np.moveaxis(arr, axis, destination=0)
+    supports_nans = (
+        np.issubdtype(arr.dtype, np.inexact) or arr.dtype.kind in 'Mm'
+    )
+
+    if weights is None:
+        # --- Computation of indexes
+        # Index where to find the value in the sorted array.
+        # Virtual because it is a floating point value, not an valid index.
+        # The nearest neighbours are used for interpolation
+        try:
+            method_props = _QuantileMethods[method]
+        except KeyError:
+            raise ValueError(
+                f"{method!r} is not a valid method. Use one of: "
+                f"{_QuantileMethods.keys()}") from None
+        virtual_indexes = method_props["get_virtual_index"](values_count,
+                                                            quantiles)
+        virtual_indexes = np.asanyarray(virtual_indexes)
+
+        if method_props["fix_gamma"] is None:
+            supports_integers = True
+        else:
+            int_virtual_indices = np.issubdtype(virtual_indexes.dtype,
+                                                np.integer)
+            supports_integers = method == 'linear' and int_virtual_indices
+
+        if supports_integers:
+            # No interpolation needed, take the points along axis
+            if supports_nans:
+                # may contain nan, which would sort to the end
+                arr.partition(
+                    concatenate((virtual_indexes.ravel(), [-1])), axis=0,
+                )
+                slices_having_nans = np.isnan(arr[-1, ...])
+            else:
+                # cannot contain nan
+                arr.partition(virtual_indexes.ravel(), axis=0)
+                slices_having_nans = np.array(False, dtype=bool)
+            result = take(arr, virtual_indexes, axis=0, out=out)
+        else:
+            previous_indexes, next_indexes = _get_indexes(arr,
+                                                          virtual_indexes,
+                                                          values_count)
+            # --- Sorting
+            arr.partition(
+                np.unique(np.concatenate(([0, -1],
+                                          previous_indexes.ravel(),
+                                          next_indexes.ravel(),
+                                          ))),
+                axis=0)
+            if supports_nans:
+                slices_having_nans = np.isnan(arr[-1, ...])
+            else:
+                slices_having_nans = None
+            # --- Get values from indexes
+            previous = arr[previous_indexes]
+            next = arr[next_indexes]
+            # --- Linear interpolation
+            gamma = _get_gamma(virtual_indexes, previous_indexes, method_props)
+            result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1)
+            gamma = gamma.reshape(result_shape)
+            result = _lerp(previous,
+                        next,
+                        gamma,
+                        out=out)
+    else:
+        # Weighted case
+        # This implements method="inverted_cdf", the only supported weighted
+        # method, which needs to sort anyway.
+        weights = np.asanyarray(weights)
+        if axis != 0:
+            weights = np.moveaxis(weights, axis, destination=0)
+        index_array = np.argsort(arr, axis=0, kind="stable")
+
+        # arr = arr[index_array, ...]  # but this adds trailing dimensions of
+        # 1.
+        arr = np.take_along_axis(arr, index_array, axis=0)
+        if weights.shape == arr.shape:
+            weights = np.take_along_axis(weights, index_array, axis=0)
+        else:
+            # weights is 1d
+            weights = weights.reshape(-1)[index_array, ...]
+
+        if supports_nans:
+            # may contain nan, which would sort to the end
+            slices_having_nans = np.isnan(arr[-1, ...])
+        else:
+            # cannot contain nan
+            slices_having_nans = np.array(False, dtype=bool)
+
+        # We use the weights to calculate the empirical cumulative
+        # distribution function cdf
+        cdf = weights.cumsum(axis=0, dtype=np.float64)
+        cdf /= cdf[-1, ...]  # normalization to 1
+        # Search index i such that
+        #   sum(weights[j], j=0..i-1) < quantile <= sum(weights[j], j=0..i)
+        # is then equivalent to
+        #   cdf[i-1] < quantile <= cdf[i]
+        # Unfortunately, searchsorted only accepts 1-d arrays as first
+        # argument, so we will need to iterate over dimensions.
+
+        # Without the following cast, searchsorted can return surprising
+        # results, e.g.
+        #   np.searchsorted(np.array([0.2, 0.4, 0.6, 0.8, 1.]),
+        #                   np.array(0.4, dtype=np.float32), side="left")
+        # returns 2 instead of 1 because 0.4 is not binary representable.
+        if quantiles.dtype.kind == "f":
+            cdf = cdf.astype(quantiles.dtype)
+        # Weights must be non-negative, so we might have zero weights at the
+        # beginning leading to some leading zeros in cdf. The call to
+        # np.searchsorted for quantiles=0 will then pick the first element,
+        # but should pick the first one larger than zero. We
+        # therefore simply set 0 values in cdf to -1.
+        if np.any(cdf[0, ...] == 0):
+            cdf[cdf == 0] = -1
+
+        def find_cdf_1d(arr, cdf):
+            indices = np.searchsorted(cdf, quantiles, side="left")
+            # We might have reached the maximum with i = len(arr), e.g. for
+            # quantiles = 1, and need to cut it to len(arr) - 1.
+            indices = minimum(indices, values_count - 1)
+            result = take(arr, indices, axis=0)
+            return result
+
+        r_shape = arr.shape[1:]
+        if quantiles.ndim > 0:
+            r_shape = quantiles.shape + r_shape
+        if out is None:
+            result = np.empty_like(arr, shape=r_shape)
+        else:
+            if out.shape != r_shape:
+                msg = (f"Wrong shape of argument 'out', shape={r_shape} is "
+                       f"required; got shape={out.shape}.")
+                raise ValueError(msg)
+            result = out
+
+        # See apply_along_axis, which we do for axis=0. Note that Ni = (,)
+        # always, so we remove it here.
+        Nk = arr.shape[1:]
+        for kk in np.ndindex(Nk):
+            result[(...,) + kk] = find_cdf_1d(
+                arr[np.s_[:, ] + kk], cdf[np.s_[:, ] + kk]
+            )
+
+        # Make result the same as in unweighted inverted_cdf.
+        if result.shape == () and result.dtype == np.dtype("O"):
+            result = result.item()
+
+    if np.any(slices_having_nans):
+        if result.ndim == 0 and out is None:
+            # can't write to a scalar, but indexing will be correct
+            result = arr[-1]
+        else:
+            np.copyto(result, arr[-1, ...], where=slices_having_nans)
+    return result
+
+
+def _trapezoid_dispatcher(y, x=None, dx=None, axis=None):
+    return (y, x)
+
+
+@array_function_dispatch(_trapezoid_dispatcher)
+def trapezoid(y, x=None, dx=1.0, axis=-1):
+    r"""
+    Integrate along the given axis using the composite trapezoidal rule.
+
+    If `x` is provided, the integration happens in sequence along its
+    elements - they are not sorted.
+
+    Integrate `y` (`x`) along each 1d slice on the given axis, compute
+    :math:`\int y(x) dx`.
+    When `x` is specified, this integrates along the parametric curve,
+    computing :math:`\int_t y(t) dt =
+    \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
+
+    .. versionadded:: 2.0.0
+
+    Parameters
+    ----------
+    y : array_like
+        Input array to integrate.
+    x : array_like, optional
+        The sample points corresponding to the `y` values. If `x` is None,
+        the sample points are assumed to be evenly spaced `dx` apart. The
+        default is None.
+    dx : scalar, optional
+        The spacing between sample points when `x` is None. The default is 1.
+    axis : int, optional
+        The axis along which to integrate.
+
+    Returns
+    -------
+    trapezoid : float or ndarray
+        Definite integral of `y` = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
+        then the result is a float. If `n` is greater than 1, then the result
+        is an `n`-1 dimensional array.
+
+    See Also
+    --------
+    sum, cumsum
+
+    Notes
+    -----
+    Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
+    will be taken from `y` array, by default x-axis distances between
+    points will be 1.0, alternatively they can be provided with `x` array
+    or with `dx` scalar.  Return value will be equal to combined area under
+    the red lines.
+
+
+    References
+    ----------
+    .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    .. [2] Illustration image:
+           https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Use the trapezoidal rule on evenly spaced points:
+
+    >>> np.trapezoid([1, 2, 3])
+    4.0
+
+    The spacing between sample points can be selected by either the
+    ``x`` or ``dx`` arguments:
+
+    >>> np.trapezoid([1, 2, 3], x=[4, 6, 8])
+    8.0
+    >>> np.trapezoid([1, 2, 3], dx=2)
+    8.0
+
+    Using a decreasing ``x`` corresponds to integrating in reverse:
+
+    >>> np.trapezoid([1, 2, 3], x=[8, 6, 4])
+    -8.0
+
+    More generally ``x`` is used to integrate along a parametric curve. We can
+    estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
+
+    >>> x = np.linspace(0, 1, num=50)
+    >>> y = x**2
+    >>> np.trapezoid(y, x)
+    0.33340274885464394
+
+    Or estimate the area of a circle, noting we repeat the sample which closes
+    the curve:
+
+    >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+    >>> np.trapezoid(np.cos(theta), x=np.sin(theta))
+    3.141571941375841
+
+    ``np.trapezoid`` can be applied along a specified axis to do multiple
+    computations in one call:
+
+    >>> a = np.arange(6).reshape(2, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> np.trapezoid(a, axis=0)
+    array([1.5, 2.5, 3.5])
+    >>> np.trapezoid(a, axis=1)
+    array([2.,  8.])
+    """
+
+    y = asanyarray(y)
+    if x is None:
+        d = dx
+    else:
+        x = asanyarray(x)
+        if x.ndim == 1:
+            d = diff(x)
+            # reshape to correct shape
+            shape = [1]*y.ndim
+            shape[axis] = d.shape[0]
+            d = d.reshape(shape)
+        else:
+            d = diff(x, axis=axis)
+    nd = y.ndim
+    slice1 = [slice(None)]*nd
+    slice2 = [slice(None)]*nd
+    slice1[axis] = slice(1, None)
+    slice2[axis] = slice(None, -1)
+    try:
+        ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis)
+    except ValueError:
+        # Operations didn't work, cast to ndarray
+        d = np.asarray(d)
+        y = np.asarray(y)
+        ret = add.reduce(d * (y[tuple(slice1)]+y[tuple(slice2)])/2.0, axis)
+    return ret
+
+
+@set_module('numpy')
+def trapz(y, x=None, dx=1.0, axis=-1):
+    """
+    `trapz` is deprecated in NumPy 2.0.
+
+    Please use `trapezoid` instead, or one of the numerical integration
+    functions in `scipy.integrate`.
+    """
+    # Deprecated in NumPy 2.0, 2023-08-18
+    warnings.warn(
+        "`trapz` is deprecated. Use `trapezoid` instead, or one of the "
+        "numerical integration functions in `scipy.integrate`.",
+        DeprecationWarning,
+        stacklevel=2
+    )
+    return trapezoid(y, x=x, dx=dx, axis=axis)
+
+
+def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
+    return xi
+
+
+# Based on scitools meshgrid
+@array_function_dispatch(_meshgrid_dispatcher)
+def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
+    """
+    Return a tuple of coordinate matrices from coordinate vectors.
+
+    Make N-D coordinate arrays for vectorized evaluations of
+    N-D scalar/vector fields over N-D grids, given
+    one-dimensional coordinate arrays x1, x2,..., xn.
+
+    Parameters
+    ----------
+    x1, x2,..., xn : array_like
+        1-D arrays representing the coordinates of a grid.
+    indexing : {'xy', 'ij'}, optional
+        Cartesian ('xy', default) or matrix ('ij') indexing of output.
+        See Notes for more details.
+    sparse : bool, optional
+        If True the shape of the returned coordinate array for dimension *i*
+        is reduced from ``(N1, ..., Ni, ... Nn)`` to
+        ``(1, ..., 1, Ni, 1, ..., 1)``.  These sparse coordinate grids are
+        intended to be use with :ref:`basics.broadcasting`.  When all
+        coordinates are used in an expression, broadcasting still leads to a
+        fully-dimensonal result array.
+
+        Default is False.
+
+    copy : bool, optional
+        If False, a view into the original arrays are returned in order to
+        conserve memory.  Default is True.  Please note that
+        ``sparse=False, copy=False`` will likely return non-contiguous
+        arrays.  Furthermore, more than one element of a broadcast array
+        may refer to a single memory location.  If you need to write to the
+        arrays, make copies first.
+
+    Returns
+    -------
+    X1, X2,..., XN : tuple of ndarrays
+        For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
+        returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
+        or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
+        with the elements of `xi` repeated to fill the matrix along
+        the first dimension for `x1`, the second for `x2` and so on.
+
+    Notes
+    -----
+    This function supports both indexing conventions through the indexing
+    keyword argument.  Giving the string 'ij' returns a meshgrid with
+    matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.
+    In the 2-D case with inputs of length M and N, the outputs are of shape
+    (N, M) for 'xy' indexing and (M, N) for 'ij' indexing.  In the 3-D case
+    with inputs of length M, N and P, outputs are of shape (N, M, P) for
+    'xy' indexing and (M, N, P) for 'ij' indexing.  The difference is
+    illustrated by the following code snippet::
+
+        xv, yv = np.meshgrid(x, y, indexing='ij')
+        for i in range(nx):
+            for j in range(ny):
+                # treat xv[i,j], yv[i,j]
+
+        xv, yv = np.meshgrid(x, y, indexing='xy')
+        for i in range(nx):
+            for j in range(ny):
+                # treat xv[j,i], yv[j,i]
+
+    In the 1-D and 0-D case, the indexing and sparse keywords have no effect.
+
+    See Also
+    --------
+    mgrid : Construct a multi-dimensional "meshgrid" using indexing notation.
+    ogrid : Construct an open multi-dimensional "meshgrid" using indexing
+            notation.
+    :ref:`how-to-index`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> nx, ny = (3, 2)
+    >>> x = np.linspace(0, 1, nx)
+    >>> y = np.linspace(0, 1, ny)
+    >>> xv, yv = np.meshgrid(x, y)
+    >>> xv
+    array([[0. , 0.5, 1. ],
+           [0. , 0.5, 1. ]])
+    >>> yv
+    array([[0.,  0.,  0.],
+           [1.,  1.,  1.]])
+
+    The result of `meshgrid` is a coordinate grid:
+
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(xv, yv, marker='o', color='k', linestyle='none')
+    >>> plt.show()
+
+    You can create sparse output arrays to save memory and computation time.
+
+    >>> xv, yv = np.meshgrid(x, y, sparse=True)
+    >>> xv
+    array([[0. ,  0.5,  1. ]])
+    >>> yv
+    array([[0.],
+           [1.]])
+
+    `meshgrid` is very useful to evaluate functions on a grid. If the
+    function depends on all coordinates, both dense and sparse outputs can be
+    used.
+
+    >>> x = np.linspace(-5, 5, 101)
+    >>> y = np.linspace(-5, 5, 101)
+    >>> # full coordinate arrays
+    >>> xx, yy = np.meshgrid(x, y)
+    >>> zz = np.sqrt(xx**2 + yy**2)
+    >>> xx.shape, yy.shape, zz.shape
+    ((101, 101), (101, 101), (101, 101))
+    >>> # sparse coordinate arrays
+    >>> xs, ys = np.meshgrid(x, y, sparse=True)
+    >>> zs = np.sqrt(xs**2 + ys**2)
+    >>> xs.shape, ys.shape, zs.shape
+    ((1, 101), (101, 1), (101, 101))
+    >>> np.array_equal(zz, zs)
+    True
+
+    >>> h = plt.contourf(x, y, zs)
+    >>> plt.axis('scaled')
+    >>> plt.colorbar()
+    >>> plt.show()
+    """
+    ndim = len(xi)
+
+    if indexing not in ['xy', 'ij']:
+        raise ValueError(
+            "Valid values for `indexing` are 'xy' and 'ij'.")
+
+    s0 = (1,) * ndim
+    output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1:])
+              for i, x in enumerate(xi)]
+
+    if indexing == 'xy' and ndim > 1:
+        # switch first and second axis
+        output[0].shape = (1, -1) + s0[2:]
+        output[1].shape = (-1, 1) + s0[2:]
+
+    if not sparse:
+        # Return the full N-D matrix (not only the 1-D vector)
+        output = np.broadcast_arrays(*output, subok=True)
+
+    if copy:
+        output = tuple(x.copy() for x in output)
+
+    return output
+
+
+def _delete_dispatcher(arr, obj, axis=None):
+    return (arr, obj)
+
+
+@array_function_dispatch(_delete_dispatcher)
+def delete(arr, obj, axis=None):
+    """
+    Return a new array with sub-arrays along an axis deleted. For a one
+    dimensional array, this returns those entries not returned by
+    `arr[obj]`.
+
+    Parameters
+    ----------
+    arr : array_like
+        Input array.
+    obj : slice, int, array-like of ints or bools
+        Indicate indices of sub-arrays to remove along the specified axis.
+
+        .. versionchanged:: 1.19.0
+            Boolean indices are now treated as a mask of elements to remove,
+            rather than being cast to the integers 0 and 1.
+
+    axis : int, optional
+        The axis along which to delete the subarray defined by `obj`.
+        If `axis` is None, `obj` is applied to the flattened array.
+
+    Returns
+    -------
+    out : ndarray
+        A copy of `arr` with the elements specified by `obj` removed. Note
+        that `delete` does not occur in-place. If `axis` is None, `out` is
+        a flattened array.
+
+    See Also
+    --------
+    insert : Insert elements into an array.
+    append : Append elements at the end of an array.
+
+    Notes
+    -----
+    Often it is preferable to use a boolean mask. For example:
+
+    >>> arr = np.arange(12) + 1
+    >>> mask = np.ones(len(arr), dtype=bool)
+    >>> mask[[0,2,4]] = False
+    >>> result = arr[mask,...]
+
+    Is equivalent to ``np.delete(arr, [0,2,4], axis=0)``, but allows further
+    use of `mask`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
+    >>> arr
+    array([[ 1,  2,  3,  4],
+           [ 5,  6,  7,  8],
+           [ 9, 10, 11, 12]])
+    >>> np.delete(arr, 1, 0)
+    array([[ 1,  2,  3,  4],
+           [ 9, 10, 11, 12]])
+
+    >>> np.delete(arr, np.s_[::2], 1)
+    array([[ 2,  4],
+           [ 6,  8],
+           [10, 12]])
+    >>> np.delete(arr, [1,3,5], None)
+    array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])
+
+    """
+    conv = _array_converter(arr)
+    arr, = conv.as_arrays(subok=False)
+
+    ndim = arr.ndim
+    arrorder = 'F' if arr.flags.fnc else 'C'
+    if axis is None:
+        if ndim != 1:
+            arr = arr.ravel()
+        # needed for np.matrix, which is still not 1d after being ravelled
+        ndim = arr.ndim
+        axis = ndim - 1
+    else:
+        axis = normalize_axis_index(axis, ndim)
+
+    slobj = [slice(None)]*ndim
+    N = arr.shape[axis]
+    newshape = list(arr.shape)
+
+    if isinstance(obj, slice):
+        start, stop, step = obj.indices(N)
+        xr = range(start, stop, step)
+        numtodel = len(xr)
+
+        if numtodel <= 0:
+            return conv.wrap(arr.copy(order=arrorder), to_scalar=False)
+
+        # Invert if step is negative:
+        if step < 0:
+            step = -step
+            start = xr[-1]
+            stop = xr[0] + 1
+
+        newshape[axis] -= numtodel
+        new = empty(newshape, arr.dtype, arrorder)
+        # copy initial chunk
+        if start == 0:
+            pass
+        else:
+            slobj[axis] = slice(None, start)
+            new[tuple(slobj)] = arr[tuple(slobj)]
+        # copy end chunk
+        if stop == N:
+            pass
+        else:
+            slobj[axis] = slice(stop-numtodel, None)
+            slobj2 = [slice(None)]*ndim
+            slobj2[axis] = slice(stop, None)
+            new[tuple(slobj)] = arr[tuple(slobj2)]
+        # copy middle pieces
+        if step == 1:
+            pass
+        else:  # use array indexing.
+            keep = ones(stop-start, dtype=bool)
+            keep[:stop-start:step] = False
+            slobj[axis] = slice(start, stop-numtodel)
+            slobj2 = [slice(None)]*ndim
+            slobj2[axis] = slice(start, stop)
+            arr = arr[tuple(slobj2)]
+            slobj2[axis] = keep
+            new[tuple(slobj)] = arr[tuple(slobj2)]
+
+        return conv.wrap(new, to_scalar=False)
+
+    if isinstance(obj, (int, integer)) and not isinstance(obj, bool):
+        single_value = True
+    else:
+        single_value = False
+        _obj = obj
+        obj = np.asarray(obj)
+        # `size == 0` to allow empty lists similar to indexing, but (as there)
+        # is really too generic:
+        if obj.size == 0 and not isinstance(_obj, np.ndarray):
+            obj = obj.astype(intp)
+        elif obj.size == 1 and obj.dtype.kind in "ui":
+            # For a size 1 integer array we can use the single-value path
+            # (most dtypes, except boolean, should just fail later).
+            obj = obj.item()
+            single_value = True
+
+    if single_value:
+        # optimization for a single value
+        if (obj < -N or obj >= N):
+            raise IndexError(
+                "index %i is out of bounds for axis %i with "
+                "size %i" % (obj, axis, N))
+        if (obj < 0):
+            obj += N
+        newshape[axis] -= 1
+        new = empty(newshape, arr.dtype, arrorder)
+        slobj[axis] = slice(None, obj)
+        new[tuple(slobj)] = arr[tuple(slobj)]
+        slobj[axis] = slice(obj, None)
+        slobj2 = [slice(None)]*ndim
+        slobj2[axis] = slice(obj+1, None)
+        new[tuple(slobj)] = arr[tuple(slobj2)]
+    else:
+        if obj.dtype == bool:
+            if obj.shape != (N,):
+                raise ValueError('boolean array argument obj to delete '
+                                 'must be one dimensional and match the axis '
+                                 'length of {}'.format(N))
+
+            # optimization, the other branch is slower
+            keep = ~obj
+        else:
+            keep = ones(N, dtype=bool)
+            keep[obj,] = False
+
+        slobj[axis] = keep
+        new = arr[tuple(slobj)]
+
+    return conv.wrap(new, to_scalar=False)
+
+
+def _insert_dispatcher(arr, obj, values, axis=None):
+    return (arr, obj, values)
+
+
+@array_function_dispatch(_insert_dispatcher)
+def insert(arr, obj, values, axis=None):
+    """
+    Insert values along the given axis before the given indices.
+
+    Parameters
+    ----------
+    arr : array_like
+        Input array.
+    obj : slice, int, array-like of ints or bools
+        Object that defines the index or indices before which `values` is
+        inserted.
+
+        .. versionchanged:: 2.1.2
+            Boolean indices are now treated as a mask of elements to insert,
+            rather than being cast to the integers 0 and 1.
+
+        Support for multiple insertions when `obj` is a single scalar or a
+        sequence with one element (similar to calling insert multiple
+        times).
+    values : array_like
+        Values to insert into `arr`. If the type of `values` is different
+        from that of `arr`, `values` is converted to the type of `arr`.
+        `values` should be shaped so that ``arr[...,obj,...] = values``
+        is legal.
+    axis : int, optional
+        Axis along which to insert `values`.  If `axis` is None then `arr`
+        is flattened first.
+
+    Returns
+    -------
+    out : ndarray
+        A copy of `arr` with `values` inserted.  Note that `insert`
+        does not occur in-place: a new array is returned. If
+        `axis` is None, `out` is a flattened array.
+
+    See Also
+    --------
+    append : Append elements at the end of an array.
+    concatenate : Join a sequence of arrays along an existing axis.
+    delete : Delete elements from an array.
+
+    Notes
+    -----
+    Note that for higher dimensional inserts ``obj=0`` behaves very different
+    from ``obj=[0]`` just like ``arr[:,0,:] = values`` is different from
+    ``arr[:,[0],:] = values``. This is because of the difference between basic
+    and advanced :ref:`indexing `.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(6).reshape(3, 2)
+    >>> a
+    array([[0, 1],
+           [2, 3],
+           [4, 5]])
+    >>> np.insert(a, 1, 6)
+    array([0, 6, 1, 2, 3, 4, 5])
+    >>> np.insert(a, 1, 6, axis=1)
+    array([[0, 6, 1],
+           [2, 6, 3],
+           [4, 6, 5]])
+
+    Difference between sequence and scalars,
+    showing how ``obj=[1]`` behaves different from ``obj=1``:
+
+    >>> np.insert(a, [1], [[7],[8],[9]], axis=1)
+    array([[0, 7, 1],
+           [2, 8, 3],
+           [4, 9, 5]])
+    >>> np.insert(a, 1, [[7],[8],[9]], axis=1)
+    array([[0, 7, 8, 9, 1],
+           [2, 7, 8, 9, 3],
+           [4, 7, 8, 9, 5]])
+    >>> np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1),
+    ...                np.insert(a, [1], [[7],[8],[9]], axis=1))
+    True
+
+    >>> b = a.flatten()
+    >>> b
+    array([0, 1, 2, 3, 4, 5])
+    >>> np.insert(b, [2, 2], [6, 7])
+    array([0, 1, 6, 7, 2, 3, 4, 5])
+
+    >>> np.insert(b, slice(2, 4), [7, 8])
+    array([0, 1, 7, 2, 8, 3, 4, 5])
+
+    >>> np.insert(b, [2, 2], [7.13, False]) # type casting
+    array([0, 1, 7, 0, 2, 3, 4, 5])
+
+    >>> x = np.arange(8).reshape(2, 4)
+    >>> idx = (1, 3)
+    >>> np.insert(x, idx, 999, axis=1)
+    array([[  0, 999,   1,   2, 999,   3],
+           [  4, 999,   5,   6, 999,   7]])
+
+    """
+    conv = _array_converter(arr)
+    arr, = conv.as_arrays(subok=False)
+
+    ndim = arr.ndim
+    arrorder = 'F' if arr.flags.fnc else 'C'
+    if axis is None:
+        if ndim != 1:
+            arr = arr.ravel()
+        # needed for np.matrix, which is still not 1d after being ravelled
+        ndim = arr.ndim
+        axis = ndim - 1
+    else:
+        axis = normalize_axis_index(axis, ndim)
+    slobj = [slice(None)]*ndim
+    N = arr.shape[axis]
+    newshape = list(arr.shape)
+
+    if isinstance(obj, slice):
+        # turn it into a range object
+        indices = arange(*obj.indices(N), dtype=intp)
+    else:
+        # need to copy obj, because indices will be changed in-place
+        indices = np.array(obj)
+        if indices.dtype == bool:
+            if obj.ndim != 1:
+                raise ValueError('boolean array argument obj to insert '
+                                'must be one dimensional')
+            indices = np.flatnonzero(obj)
+        elif indices.ndim > 1:
+            raise ValueError(
+                "index array argument obj to insert must be one dimensional "
+                "or scalar")
+    if indices.size == 1:
+        index = indices.item()
+        if index < -N or index > N:
+            raise IndexError(f"index {obj} is out of bounds for axis {axis} "
+                             f"with size {N}")
+        if (index < 0):
+            index += N
+
+        # There are some object array corner cases here, but we cannot avoid
+        # that:
+        values = array(values, copy=None, ndmin=arr.ndim, dtype=arr.dtype)
+        if indices.ndim == 0:
+            # broadcasting is very different here, since a[:,0,:] = ... behaves
+            # very different from a[:,[0],:] = ...! This changes values so that
+            # it works likes the second case. (here a[:,0:1,:])
+            values = np.moveaxis(values, 0, axis)
+        numnew = values.shape[axis]
+        newshape[axis] += numnew
+        new = empty(newshape, arr.dtype, arrorder)
+        slobj[axis] = slice(None, index)
+        new[tuple(slobj)] = arr[tuple(slobj)]
+        slobj[axis] = slice(index, index+numnew)
+        new[tuple(slobj)] = values
+        slobj[axis] = slice(index+numnew, None)
+        slobj2 = [slice(None)] * ndim
+        slobj2[axis] = slice(index, None)
+        new[tuple(slobj)] = arr[tuple(slobj2)]
+
+        return conv.wrap(new, to_scalar=False)
+
+    elif indices.size == 0 and not isinstance(obj, np.ndarray):
+        # Can safely cast the empty list to intp
+        indices = indices.astype(intp)
+
+    indices[indices < 0] += N
+
+    numnew = len(indices)
+    order = indices.argsort(kind='mergesort')   # stable sort
+    indices[order] += np.arange(numnew)
+
+    newshape[axis] += numnew
+    old_mask = ones(newshape[axis], dtype=bool)
+    old_mask[indices] = False
+
+    new = empty(newshape, arr.dtype, arrorder)
+    slobj2 = [slice(None)]*ndim
+    slobj[axis] = indices
+    slobj2[axis] = old_mask
+    new[tuple(slobj)] = values
+    new[tuple(slobj2)] = arr
+
+    return conv.wrap(new, to_scalar=False)
+
+
+def _append_dispatcher(arr, values, axis=None):
+    return (arr, values)
+
+
+@array_function_dispatch(_append_dispatcher)
+def append(arr, values, axis=None):
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : array_like
+        Values are appended to a copy of this array.
+    values : array_like
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    See Also
+    --------
+    insert : Insert elements into an array.
+    delete : Delete elements from an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
+    array([1, 2, 3, ..., 7, 8, 9])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
+    array([[1, 2, 3],
+           [4, 5, 6],
+           [7, 8, 9]])
+
+    >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
+    Traceback (most recent call last):
+        ...
+    ValueError: all the input arrays must have same number of dimensions, but
+    the array at index 0 has 2 dimension(s) and the array at index 1 has 1
+    dimension(s)
+
+    >>> a = np.array([1, 2], dtype=int)
+    >>> c = np.append(a, [])
+    >>> c
+    array([1., 2.])
+    >>> c.dtype
+    float64
+
+    Default dtype for empty ndarrays is `float64` thus making the output of dtype
+    `float64` when appended with dtype `int64`
+
+    """
+    arr = asanyarray(arr)
+    if axis is None:
+        if arr.ndim != 1:
+            arr = arr.ravel()
+        values = ravel(values)
+        axis = arr.ndim-1
+    return concatenate((arr, values), axis=axis)
+
+
+def _digitize_dispatcher(x, bins, right=None):
+    return (x, bins)
+
+
+@array_function_dispatch(_digitize_dispatcher)
+def digitize(x, bins, right=False):
+    """
+    Return the indices of the bins to which each value in input array belongs.
+
+    =========  =============  ============================
+    `right`    order of bins  returned index `i` satisfies
+    =========  =============  ============================
+    ``False``  increasing     ``bins[i-1] <= x < bins[i]``
+    ``True``   increasing     ``bins[i-1] < x <= bins[i]``
+    ``False``  decreasing     ``bins[i-1] > x >= bins[i]``
+    ``True``   decreasing     ``bins[i-1] >= x > bins[i]``
+    =========  =============  ============================
+
+    If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is
+    returned as appropriate.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array to be binned. Prior to NumPy 1.10.0, this array had to
+        be 1-dimensional, but can now have any shape.
+    bins : array_like
+        Array of bins. It has to be 1-dimensional and monotonic.
+    right : bool, optional
+        Indicating whether the intervals include the right or the left bin
+        edge. Default behavior is (right==False) indicating that the interval
+        does not include the right edge. The left bin end is open in this
+        case, i.e., bins[i-1] <= x < bins[i] is the default behavior for
+        monotonically increasing bins.
+
+    Returns
+    -------
+    indices : ndarray of ints
+        Output array of indices, of same shape as `x`.
+
+    Raises
+    ------
+    ValueError
+        If `bins` is not monotonic.
+    TypeError
+        If the type of the input is complex.
+
+    See Also
+    --------
+    bincount, histogram, unique, searchsorted
+
+    Notes
+    -----
+    If values in `x` are such that they fall outside the bin range,
+    attempting to index `bins` with the indices that `digitize` returns
+    will result in an IndexError.
+
+    .. versionadded:: 1.10.0
+
+    `numpy.digitize` is  implemented in terms of `numpy.searchsorted`.
+    This means that a binary search is used to bin the values, which scales
+    much better for larger number of bins than the previous linear search.
+    It also removes the requirement for the input array to be 1-dimensional.
+
+    For monotonically *increasing* `bins`, the following are equivalent::
+
+        np.digitize(x, bins, right=True)
+        np.searchsorted(bins, x, side='left')
+
+    Note that as the order of the arguments are reversed, the side must be too.
+    The `searchsorted` call is marginally faster, as it does not do any
+    monotonicity checks. Perhaps more importantly, it supports all dtypes.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([0.2, 6.4, 3.0, 1.6])
+    >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
+    >>> inds = np.digitize(x, bins)
+    >>> inds
+    array([1, 4, 3, 2])
+    >>> for n in range(x.size):
+    ...   print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]])
+    ...
+    0.0 <= 0.2 < 1.0
+    4.0 <= 6.4 < 10.0
+    2.5 <= 3.0 < 4.0
+    1.0 <= 1.6 < 2.5
+
+    >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
+    >>> bins = np.array([0, 5, 10, 15, 20])
+    >>> np.digitize(x,bins,right=True)
+    array([1, 2, 3, 4, 4])
+    >>> np.digitize(x,bins,right=False)
+    array([1, 3, 3, 4, 5])
+    """
+    x = _nx.asarray(x)
+    bins = _nx.asarray(bins)
+
+    # here for compatibility, searchsorted below is happy to take this
+    if np.issubdtype(x.dtype, _nx.complexfloating):
+        raise TypeError("x may not be complex")
+
+    mono = _monotonicity(bins)
+    if mono == 0:
+        raise ValueError("bins must be monotonically increasing or decreasing")
+
+    # this is backwards because the arguments below are swapped
+    side = 'left' if right else 'right'
+    if mono == -1:
+        # reverse the bins, and invert the results
+        return len(bins) - _nx.searchsorted(bins[::-1], x, side=side)
+    else:
+        return _nx.searchsorted(bins, x, side=side)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e98dcbb7e74155442daead8e16b6f888c10617fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_function_base_impl.pyi
@@ -0,0 +1,894 @@
+# ruff: noqa: ANN401
+from collections.abc import Callable, Iterable, Sequence
+from typing import (
+    Any,
+    Concatenate,
+    ParamSpec,
+    Protocol,
+    SupportsIndex,
+    SupportsInt,
+    TypeAlias,
+    TypeVar,
+    overload,
+    type_check_only,
+)
+from typing import Literal as L
+
+from _typeshed import Incomplete
+from typing_extensions import TypeIs, deprecated
+
+import numpy as np
+from numpy import (
+    _OrderKACF,
+    bool_,
+    complex128,
+    complexfloating,
+    datetime64,
+    float64,
+    floating,
+    generic,
+    integer,
+    intp,
+    object_,
+    timedelta64,
+    vectorize,
+)
+from numpy._core.multiarray import bincount
+from numpy._globals import _NoValueType
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ArrayLike,
+    _ArrayLikeBool_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeDT64_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _ArrayLikeNumber_co,
+    _ArrayLikeObject_co,
+    _ArrayLikeTD64_co,
+    _ComplexLike_co,
+    _DTypeLike,
+    _FloatLike_co,
+    _NestedSequence,
+    _NumberLike_co,
+    _ScalarLike_co,
+    _ShapeLike,
+)
+
+__all__ = [
+    "select",
+    "piecewise",
+    "trim_zeros",
+    "copy",
+    "iterable",
+    "percentile",
+    "diff",
+    "gradient",
+    "angle",
+    "unwrap",
+    "sort_complex",
+    "flip",
+    "rot90",
+    "extract",
+    "place",
+    "vectorize",
+    "asarray_chkfinite",
+    "average",
+    "bincount",
+    "digitize",
+    "cov",
+    "corrcoef",
+    "median",
+    "sinc",
+    "hamming",
+    "hanning",
+    "bartlett",
+    "blackman",
+    "kaiser",
+    "trapezoid",
+    "trapz",
+    "i0",
+    "meshgrid",
+    "delete",
+    "insert",
+    "append",
+    "interp",
+    "quantile",
+]
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+# The `{}ss` suffix refers to the Python 3.12 syntax: `**P`
+_Pss = ParamSpec("_Pss")
+_SCT = TypeVar("_SCT", bound=generic)
+_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
+
+_2Tuple: TypeAlias = tuple[_T, _T]
+
+@type_check_only
+class _TrimZerosSequence(Protocol[_T_co]):
+    def __len__(self, /) -> int: ...
+    @overload
+    def __getitem__(self, key: int, /) -> object: ...
+    @overload
+    def __getitem__(self, key: slice, /) -> _T_co: ...
+
+###
+
+@overload
+def rot90(
+    m: _ArrayLike[_SCT],
+    k: int = ...,
+    axes: tuple[int, int] = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def rot90(
+    m: ArrayLike,
+    k: int = ...,
+    axes: tuple[int, int] = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def flip(m: _SCT, axis: None = ...) -> _SCT: ...
+@overload
+def flip(m: _ScalarLike_co, axis: None = ...) -> Any: ...
+@overload
+def flip(m: _ArrayLike[_SCT], axis: None | _ShapeLike = ...) -> NDArray[_SCT]: ...
+@overload
+def flip(m: ArrayLike, axis: None | _ShapeLike = ...) -> NDArray[Any]: ...
+
+def iterable(y: object) -> TypeIs[Iterable[Any]]: ...
+
+@overload
+def average(
+    a: _ArrayLikeFloat_co,
+    axis: None = None,
+    weights: _ArrayLikeFloat_co | None = None,
+    returned: L[False] = False,
+    *,
+    keepdims: L[False] | _NoValueType = ...,
+) -> floating: ...
+@overload
+def average(
+    a: _ArrayLikeFloat_co,
+    axis: None = None,
+    weights: _ArrayLikeFloat_co | None = None,
+    *,
+    returned: L[True],
+    keepdims: L[False] | _NoValueType = ...,
+) -> _2Tuple[floating]: ...
+@overload
+def average(
+    a: _ArrayLikeComplex_co,
+    axis: None = None,
+    weights: _ArrayLikeComplex_co | None = None,
+    returned: L[False] = False,
+    *,
+    keepdims: L[False] | _NoValueType = ...,
+) -> complexfloating: ...
+@overload
+def average(
+    a: _ArrayLikeComplex_co,
+    axis: None = None,
+    weights: _ArrayLikeComplex_co | None = None,
+    *,
+    returned: L[True],
+    keepdims: L[False] | _NoValueType = ...,
+) -> _2Tuple[complexfloating]: ...
+@overload
+def average(
+    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    axis: _ShapeLike | None = None,
+    weights: object | None = None,
+    *,
+    returned: L[True],
+    keepdims: bool | bool_ | _NoValueType = ...,
+) -> _2Tuple[Incomplete]: ...
+@overload
+def average(
+    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    axis: _ShapeLike | None = None,
+    weights: object | None = None,
+    returned: bool | bool_ = False,
+    *,
+    keepdims: bool | bool_ | _NoValueType = ...,
+) -> Incomplete: ...
+
+@overload
+def asarray_chkfinite(
+    a: _ArrayLike[_SCT],
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def asarray_chkfinite(
+    a: object,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+) -> NDArray[Any]: ...
+@overload
+def asarray_chkfinite(
+    a: Any,
+    dtype: _DTypeLike[_SCT],
+    order: _OrderKACF = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def asarray_chkfinite(
+    a: Any,
+    dtype: DTypeLike,
+    order: _OrderKACF = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def piecewise(
+    x: _ArrayLike[_SCT],
+    condlist: _ArrayLike[bool_] | Sequence[_ArrayLikeBool_co],
+    funclist: Sequence[
+        Callable[Concatenate[NDArray[_SCT], _Pss], NDArray[_SCT | Any]]
+        | _SCT | object
+    ],
+    /,
+    *args: _Pss.args,
+    **kw: _Pss.kwargs,
+) -> NDArray[_SCT]: ...
+@overload
+def piecewise(
+    x: ArrayLike,
+    condlist: _ArrayLike[bool_] | Sequence[_ArrayLikeBool_co],
+    funclist: Sequence[
+        Callable[Concatenate[NDArray[Any], _Pss], NDArray[Any]]
+        | object
+    ],
+    /,
+    *args: _Pss.args,
+    **kw: _Pss.kwargs,
+) -> NDArray[Any]: ...
+
+def select(
+    condlist: Sequence[ArrayLike],
+    choicelist: Sequence[ArrayLike],
+    default: ArrayLike = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def copy(
+    a: _ArrayType,
+    order: _OrderKACF,
+    subok: L[True],
+) -> _ArrayType: ...
+@overload
+def copy(
+    a: _ArrayType,
+    order: _OrderKACF = ...,
+    *,
+    subok: L[True],
+) -> _ArrayType: ...
+@overload
+def copy(
+    a: _ArrayLike[_SCT],
+    order: _OrderKACF = ...,
+    subok: L[False] = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def copy(
+    a: ArrayLike,
+    order: _OrderKACF = ...,
+    subok: L[False] = ...,
+) -> NDArray[Any]: ...
+
+def gradient(
+    f: ArrayLike,
+    *varargs: ArrayLike,
+    axis: None | _ShapeLike = ...,
+    edge_order: L[1, 2] = ...,
+) -> Any: ...
+
+@overload
+def diff(
+    a: _T,
+    n: L[0],
+    axis: SupportsIndex = ...,
+    prepend: ArrayLike = ...,
+    append: ArrayLike = ...,
+) -> _T: ...
+@overload
+def diff(
+    a: ArrayLike,
+    n: int = ...,
+    axis: SupportsIndex = ...,
+    prepend: ArrayLike = ...,
+    append: ArrayLike = ...,
+) -> NDArray[Any]: ...
+
+@overload  # float scalar
+def interp(
+    x: _FloatLike_co,
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLikeFloat_co,
+    left: _FloatLike_co | None = None,
+    right: _FloatLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> float64: ...
+@overload  # float array
+def interp(
+    x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLikeFloat_co,
+    left: _FloatLike_co | None = None,
+    right: _FloatLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[float64]: ...
+@overload  # float scalar or array
+def interp(
+    x: _ArrayLikeFloat_co,
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLikeFloat_co,
+    left: _FloatLike_co | None = None,
+    right: _FloatLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[float64] | float64: ...
+@overload  # complex scalar
+def interp(
+    x: _FloatLike_co,
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLike[complexfloating],
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> complex128: ...
+@overload  # complex or float scalar
+def interp(
+    x: _FloatLike_co,
+    xp: _ArrayLikeFloat_co,
+    fp: Sequence[complex | complexfloating],
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> complex128 | float64: ...
+@overload  # complex array
+def interp(
+    x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLike[complexfloating],
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[complex128]: ...
+@overload  # complex or float array
+def interp(
+    x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
+    xp: _ArrayLikeFloat_co,
+    fp: Sequence[complex | complexfloating],
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[complex128 | float64]: ...
+@overload  # complex scalar or array
+def interp(
+    x: _ArrayLikeFloat_co,
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLike[complexfloating],
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[complex128] | complex128: ...
+@overload  # complex or float scalar or array
+def interp(
+    x: _ArrayLikeFloat_co,
+    xp: _ArrayLikeFloat_co,
+    fp: _ArrayLikeNumber_co,
+    left: _NumberLike_co | None = None,
+    right: _NumberLike_co | None = None,
+    period: _FloatLike_co | None = None,
+) -> NDArray[complex128 | float64] | complex128 | float64: ...
+
+@overload
+def angle(z: _ComplexLike_co, deg: bool = ...) -> floating[Any]: ...
+@overload
+def angle(z: object_, deg: bool = ...) -> Any: ...
+@overload
+def angle(z: _ArrayLikeComplex_co, deg: bool = ...) -> NDArray[floating[Any]]: ...
+@overload
+def angle(z: _ArrayLikeObject_co, deg: bool = ...) -> NDArray[object_]: ...
+
+@overload
+def unwrap(
+    p: _ArrayLikeFloat_co,
+    discont: None | float = ...,
+    axis: int = ...,
+    *,
+    period: float = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def unwrap(
+    p: _ArrayLikeObject_co,
+    discont: None | float = ...,
+    axis: int = ...,
+    *,
+    period: float = ...,
+) -> NDArray[object_]: ...
+
+def sort_complex(a: ArrayLike) -> NDArray[complexfloating[Any, Any]]: ...
+
+def trim_zeros(
+    filt: _TrimZerosSequence[_T],
+    trim: L["f", "b", "fb", "bf"] = ...,
+) -> _T: ...
+
+@overload
+def extract(condition: ArrayLike, arr: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
+@overload
+def extract(condition: ArrayLike, arr: ArrayLike) -> NDArray[Any]: ...
+
+def place(arr: NDArray[Any], mask: ArrayLike, vals: Any) -> None: ...
+
+@overload
+def cov(
+    m: _ArrayLikeFloat_co,
+    y: None | _ArrayLikeFloat_co = ...,
+    rowvar: bool = ...,
+    bias: bool = ...,
+    ddof: None | SupportsIndex | SupportsInt = ...,
+    fweights: None | ArrayLike = ...,
+    aweights: None | ArrayLike = ...,
+    *,
+    dtype: None = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def cov(
+    m: _ArrayLikeComplex_co,
+    y: None | _ArrayLikeComplex_co = ...,
+    rowvar: bool = ...,
+    bias: bool = ...,
+    ddof: None | SupportsIndex | SupportsInt = ...,
+    fweights: None | ArrayLike = ...,
+    aweights: None | ArrayLike = ...,
+    *,
+    dtype: None = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def cov(
+    m: _ArrayLikeComplex_co,
+    y: None | _ArrayLikeComplex_co = ...,
+    rowvar: bool = ...,
+    bias: bool = ...,
+    ddof: None | SupportsIndex | SupportsInt = ...,
+    fweights: None | ArrayLike = ...,
+    aweights: None | ArrayLike = ...,
+    *,
+    dtype: _DTypeLike[_SCT],
+) -> NDArray[_SCT]: ...
+@overload
+def cov(
+    m: _ArrayLikeComplex_co,
+    y: None | _ArrayLikeComplex_co = ...,
+    rowvar: bool = ...,
+    bias: bool = ...,
+    ddof: None | SupportsIndex | SupportsInt = ...,
+    fweights: None | ArrayLike = ...,
+    aweights: None | ArrayLike = ...,
+    *,
+    dtype: DTypeLike,
+) -> NDArray[Any]: ...
+
+# NOTE `bias` and `ddof` are deprecated and ignored
+@overload
+def corrcoef(
+    m: _ArrayLikeFloat_co,
+    y: _ArrayLikeFloat_co | None = None,
+    rowvar: bool = True,
+    bias: _NoValueType = ...,
+    ddof: _NoValueType = ...,
+    *,
+    dtype: None = None,
+) -> NDArray[floating]: ...
+@overload
+def corrcoef(
+    m: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co | None = None,
+    rowvar: bool = True,
+    bias: _NoValueType = ...,
+    ddof: _NoValueType = ...,
+    *,
+    dtype: None = None,
+) -> NDArray[complexfloating]: ...
+@overload
+def corrcoef(
+    m: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co | None = None,
+    rowvar: bool = True,
+    bias: _NoValueType = ...,
+    ddof: _NoValueType = ...,
+    *,
+    dtype: _DTypeLike[_SCT],
+) -> NDArray[_SCT]: ...
+@overload
+def corrcoef(
+    m: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co | None = None,
+    rowvar: bool = True,
+    bias: _NoValueType = ...,
+    ddof: _NoValueType = ...,
+    *,
+    dtype: DTypeLike | None = None,
+) -> NDArray[Any]: ...
+
+def blackman(M: _FloatLike_co) -> NDArray[floating[Any]]: ...
+
+def bartlett(M: _FloatLike_co) -> NDArray[floating[Any]]: ...
+
+def hanning(M: _FloatLike_co) -> NDArray[floating[Any]]: ...
+
+def hamming(M: _FloatLike_co) -> NDArray[floating[Any]]: ...
+
+def i0(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
+
+def kaiser(
+    M: _FloatLike_co,
+    beta: _FloatLike_co,
+) -> NDArray[floating[Any]]: ...
+
+@overload
+def sinc(x: _FloatLike_co) -> floating[Any]: ...
+@overload
+def sinc(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def sinc(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
+@overload
+def sinc(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def median(
+    a: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    keepdims: L[False] = ...,
+) -> floating[Any]: ...
+@overload
+def median(
+    a: _ArrayLikeComplex_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    keepdims: L[False] = ...,
+) -> complexfloating[Any, Any]: ...
+@overload
+def median(
+    a: _ArrayLikeTD64_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    keepdims: L[False] = ...,
+) -> timedelta64: ...
+@overload
+def median(
+    a: _ArrayLikeObject_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    keepdims: L[False] = ...,
+) -> Any: ...
+@overload
+def median(
+    a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    axis: None | _ShapeLike = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+@overload
+def median(
+    a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    axis: None | _ShapeLike,
+    out: _ArrayType,
+    overwrite_input: bool = ...,
+    keepdims: bool = ...,
+) -> _ArrayType: ...
+@overload
+def median(
+    a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    axis: None | _ShapeLike = ...,
+    *,
+    out: _ArrayType,
+    overwrite_input: bool = ...,
+    keepdims: bool = ...,
+) -> _ArrayType: ...
+
+_MethodKind = L[
+    "inverted_cdf",
+    "averaged_inverted_cdf",
+    "closest_observation",
+    "interpolated_inverted_cdf",
+    "hazen",
+    "weibull",
+    "linear",
+    "median_unbiased",
+    "normal_unbiased",
+    "lower",
+    "higher",
+    "midpoint",
+    "nearest",
+]
+
+@overload
+def percentile(
+    a: _ArrayLikeFloat_co,
+    q: _FloatLike_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> floating[Any]: ...
+@overload
+def percentile(
+    a: _ArrayLikeComplex_co,
+    q: _FloatLike_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> complexfloating[Any, Any]: ...
+@overload
+def percentile(
+    a: _ArrayLikeTD64_co,
+    q: _FloatLike_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> timedelta64: ...
+@overload
+def percentile(
+    a: _ArrayLikeDT64_co,
+    q: _FloatLike_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> datetime64: ...
+@overload
+def percentile(
+    a: _ArrayLikeObject_co,
+    q: _FloatLike_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> Any: ...
+@overload
+def percentile(
+    a: _ArrayLikeFloat_co,
+    q: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def percentile(
+    a: _ArrayLikeComplex_co,
+    q: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def percentile(
+    a: _ArrayLikeTD64_co,
+    q: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[timedelta64]: ...
+@overload
+def percentile(
+    a: _ArrayLikeDT64_co,
+    q: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[datetime64]: ...
+@overload
+def percentile(
+    a: _ArrayLikeObject_co,
+    q: _ArrayLikeFloat_co,
+    axis: None = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: L[False] = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[object_]: ...
+@overload
+def percentile(
+    a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    q: _ArrayLikeFloat_co,
+    axis: None | _ShapeLike = ...,
+    out: None = ...,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: bool = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> Any: ...
+@overload
+def percentile(
+    a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    q: _ArrayLikeFloat_co,
+    axis: None | _ShapeLike,
+    out: _ArrayType,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: bool = ...,
+    *,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> _ArrayType: ...
+@overload
+def percentile(
+    a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    q: _ArrayLikeFloat_co,
+    axis: None | _ShapeLike = ...,
+    *,
+    out: _ArrayType,
+    overwrite_input: bool = ...,
+    method: _MethodKind = ...,
+    keepdims: bool = ...,
+    weights: None | _ArrayLikeFloat_co = ...,
+) -> _ArrayType: ...
+
+# NOTE: Not an alias, but they do have identical signatures
+# (that we can reuse)
+quantile = percentile
+
+
+_SCT_fm = TypeVar(
+    "_SCT_fm",
+    bound=floating[Any] | complexfloating[Any, Any] | timedelta64,
+)
+
+class _SupportsRMulFloat(Protocol[_T_co]):
+    def __rmul__(self, other: float, /) -> _T_co: ...
+
+@overload
+def trapezoid(  # type: ignore[overload-overlap]
+    y: Sequence[_FloatLike_co],
+    x: Sequence[_FloatLike_co] | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> float64: ...
+@overload
+def trapezoid(
+    y: Sequence[_ComplexLike_co],
+    x: Sequence[_ComplexLike_co] | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> complex128: ...
+@overload
+def trapezoid(
+    y: _ArrayLike[bool_ | integer[Any]],
+    x: _ArrayLike[bool_ | integer[Any]] | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> float64 | NDArray[float64]: ...
+@overload
+def trapezoid(  # type: ignore[overload-overlap]
+    y: _ArrayLikeObject_co,
+    x: _ArrayLikeFloat_co | _ArrayLikeObject_co | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> float | NDArray[object_]: ...
+@overload
+def trapezoid(
+    y: _ArrayLike[_SCT_fm],
+    x: _ArrayLike[_SCT_fm] | _ArrayLikeInt_co | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> _SCT_fm | NDArray[_SCT_fm]: ...
+@overload
+def trapezoid(
+    y: Sequence[_SupportsRMulFloat[_T]],
+    x: Sequence[_SupportsRMulFloat[_T] | _T] | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> _T: ...
+@overload
+def trapezoid(
+    y: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    x: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co | None = ...,
+    dx: float = ...,
+    axis: SupportsIndex = ...,
+) -> (
+    floating[Any] | complexfloating[Any, Any] | timedelta64
+    | NDArray[floating[Any] | complexfloating[Any, Any] | timedelta64 | object_]
+): ...
+
+@deprecated("Use 'trapezoid' instead")
+def trapz(y: ArrayLike, x: ArrayLike | None = None, dx: float = 1.0, axis: int = -1) -> generic | NDArray[generic]: ...
+
+def meshgrid(
+    *xi: ArrayLike,
+    copy: bool = ...,
+    sparse: bool = ...,
+    indexing: L["xy", "ij"] = ...,
+) -> tuple[NDArray[Any], ...]: ...
+
+@overload
+def delete(
+    arr: _ArrayLike[_SCT],
+    obj: slice | _ArrayLikeInt_co,
+    axis: None | SupportsIndex = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def delete(
+    arr: ArrayLike,
+    obj: slice | _ArrayLikeInt_co,
+    axis: None | SupportsIndex = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def insert(
+    arr: _ArrayLike[_SCT],
+    obj: slice | _ArrayLikeInt_co,
+    values: ArrayLike,
+    axis: None | SupportsIndex = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def insert(
+    arr: ArrayLike,
+    obj: slice | _ArrayLikeInt_co,
+    values: ArrayLike,
+    axis: None | SupportsIndex = ...,
+) -> NDArray[Any]: ...
+
+def append(
+    arr: ArrayLike,
+    values: ArrayLike,
+    axis: None | SupportsIndex = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def digitize(
+    x: _FloatLike_co,
+    bins: _ArrayLikeFloat_co,
+    right: bool = ...,
+) -> intp: ...
+@overload
+def digitize(
+    x: _ArrayLikeFloat_co,
+    bins: _ArrayLikeFloat_co,
+    right: bool = ...,
+) -> NDArray[intp]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b361bb4f91ac0723f65399cd6b73048649179c2b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.py
@@ -0,0 +1,1090 @@
+"""
+Histogram-related functions
+"""
+import contextlib
+import functools
+import operator
+import warnings
+
+import numpy as np
+from numpy._core import overrides
+
+__all__ = ['histogram', 'histogramdd', 'histogram_bin_edges']
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+# range is a keyword argument to many functions, so save the builtin so they can
+# use it.
+_range = range
+
+
+def _ptp(x):
+    """Peak-to-peak value of x.
+
+    This implementation avoids the problem of signed integer arrays having a
+    peak-to-peak value that cannot be represented with the array's data type.
+    This function returns an unsigned value for signed integer arrays.
+    """
+    return _unsigned_subtract(x.max(), x.min())
+
+
+def _hist_bin_sqrt(x, range):
+    """
+    Square root histogram bin estimator.
+
+    Bin width is inversely proportional to the data size. Used by many
+    programs for its simplicity.
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    return _ptp(x) / np.sqrt(x.size)
+
+
+def _hist_bin_sturges(x, range):
+    """
+    Sturges histogram bin estimator.
+
+    A very simplistic estimator based on the assumption of normality of
+    the data. This estimator has poor performance for non-normal data,
+    which becomes especially obvious for large data sets. The estimate
+    depends only on size of the data.
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    return _ptp(x) / (np.log2(x.size) + 1.0)
+
+
+def _hist_bin_rice(x, range):
+    """
+    Rice histogram bin estimator.
+
+    Another simple estimator with no normality assumption. It has better
+    performance for large data than Sturges, but tends to overestimate
+    the number of bins. The number of bins is proportional to the cube
+    root of data size (asymptotically optimal). The estimate depends
+    only on size of the data.
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    return _ptp(x) / (2.0 * x.size ** (1.0 / 3))
+
+
+def _hist_bin_scott(x, range):
+    """
+    Scott histogram bin estimator.
+
+    The binwidth is proportional to the standard deviation of the data
+    and inversely proportional to the cube root of data size
+    (asymptotically optimal).
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
+
+
+def _hist_bin_stone(x, range):
+    """
+    Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
+
+    The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.
+    The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.
+    https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule
+
+    This paper by Stone appears to be the origination of this rule.
+    https://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+    range : (float, float)
+        The lower and upper range of the bins.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+
+    n = x.size
+    ptp_x = _ptp(x)
+    if n <= 1 or ptp_x == 0:
+        return 0
+
+    def jhat(nbins):
+        hh = ptp_x / nbins
+        p_k = np.histogram(x, bins=nbins, range=range)[0] / n
+        return (2 - (n + 1) * p_k.dot(p_k)) / hh
+
+    nbins_upper_bound = max(100, int(np.sqrt(n)))
+    nbins = min(_range(1, nbins_upper_bound + 1), key=jhat)
+    if nbins == nbins_upper_bound:
+        warnings.warn("The number of bins estimated may be suboptimal.",
+                      RuntimeWarning, stacklevel=3)
+    return ptp_x / nbins
+
+
+def _hist_bin_doane(x, range):
+    """
+    Doane's histogram bin estimator.
+
+    Improved version of Sturges' formula which works better for
+    non-normal data. See
+    stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    if x.size > 2:
+        sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3)))
+        sigma = np.std(x)
+        if sigma > 0.0:
+            # These three operations add up to
+            # g1 = np.mean(((x - np.mean(x)) / sigma)**3)
+            # but use only one temp array instead of three
+            temp = x - np.mean(x)
+            np.true_divide(temp, sigma, temp)
+            np.power(temp, 3, temp)
+            g1 = np.mean(temp)
+            return _ptp(x) / (1.0 + np.log2(x.size) +
+                                    np.log2(1.0 + np.absolute(g1) / sg1))
+    return 0.0
+
+
+def _hist_bin_fd(x, range):
+    """
+    The Freedman-Diaconis histogram bin estimator.
+
+    The Freedman-Diaconis rule uses interquartile range (IQR) to
+    estimate binwidth. It is considered a variation of the Scott rule
+    with more robustness as the IQR is less affected by outliers than
+    the standard deviation. However, the IQR depends on fewer points
+    than the standard deviation, so it is less accurate, especially for
+    long tailed distributions.
+
+    If the IQR is 0, this function returns 0 for the bin width.
+    Binwidth is inversely proportional to the cube root of data size
+    (asymptotically optimal).
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+    """
+    del range  # unused
+    iqr = np.subtract(*np.percentile(x, [75, 25]))
+    return 2.0 * iqr * x.size ** (-1.0 / 3.0)
+
+
+def _hist_bin_auto(x, range):
+    """
+    Histogram bin estimator that uses the minimum width of the
+    Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero.
+    If the bin width from the FD estimator is 0, the Sturges estimator is used.
+
+    The FD estimator is usually the most robust method, but its width
+    estimate tends to be too large for small `x` and bad for data with limited
+    variance. The Sturges estimator is quite good for small (<1000) datasets
+    and is the default in the R language. This method gives good off-the-shelf
+    behaviour.
+
+    If there is limited variance the IQR can be 0, which results in the
+    FD bin width being 0 too. This is not a valid bin width, so
+    ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal.
+    If the IQR is 0, it's unlikely any variance-based estimators will be of
+    use, so we revert to the Sturges estimator, which only uses the size of the
+    dataset in its calculation.
+
+    Parameters
+    ----------
+    x : array_like
+        Input data that is to be histogrammed, trimmed to range. May not
+        be empty.
+
+    Returns
+    -------
+    h : An estimate of the optimal bin width for the given data.
+
+    See Also
+    --------
+    _hist_bin_fd, _hist_bin_sturges
+    """
+    fd_bw = _hist_bin_fd(x, range)
+    sturges_bw = _hist_bin_sturges(x, range)
+    del range  # unused
+    if fd_bw:
+        return min(fd_bw, sturges_bw)
+    else:
+        # limited variance, so we return a len dependent bw estimator
+        return sturges_bw
+
+# Private dict initialized at module load time
+_hist_bin_selectors = {'stone': _hist_bin_stone,
+                       'auto': _hist_bin_auto,
+                       'doane': _hist_bin_doane,
+                       'fd': _hist_bin_fd,
+                       'rice': _hist_bin_rice,
+                       'scott': _hist_bin_scott,
+                       'sqrt': _hist_bin_sqrt,
+                       'sturges': _hist_bin_sturges}
+
+
+def _ravel_and_check_weights(a, weights):
+    """ Check a and weights have matching shapes, and ravel both """
+    a = np.asarray(a)
+
+    # Ensure that the array is a "subtractable" dtype
+    if a.dtype == np.bool:
+        warnings.warn("Converting input from {} to {} for compatibility."
+                      .format(a.dtype, np.uint8),
+                      RuntimeWarning, stacklevel=3)
+        a = a.astype(np.uint8)
+
+    if weights is not None:
+        weights = np.asarray(weights)
+        if weights.shape != a.shape:
+            raise ValueError(
+                'weights should have the same shape as a.')
+        weights = weights.ravel()
+    a = a.ravel()
+    return a, weights
+
+
+def _get_outer_edges(a, range):
+    """
+    Determine the outer bin edges to use, from either the data or the range
+    argument
+    """
+    if range is not None:
+        first_edge, last_edge = range
+        if first_edge > last_edge:
+            raise ValueError(
+                'max must be larger than min in range parameter.')
+        if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
+            raise ValueError(
+                "supplied range of [{}, {}] is not finite".format(first_edge, last_edge))
+    elif a.size == 0:
+        # handle empty arrays. Can't determine range, so use 0-1.
+        first_edge, last_edge = 0, 1
+    else:
+        first_edge, last_edge = a.min(), a.max()
+        if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
+            raise ValueError(
+                "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
+
+    # expand empty range to avoid divide by zero
+    if first_edge == last_edge:
+        first_edge = first_edge - 0.5
+        last_edge = last_edge + 0.5
+
+    return first_edge, last_edge
+
+
+def _unsigned_subtract(a, b):
+    """
+    Subtract two values where a >= b, and produce an unsigned result
+
+    This is needed when finding the difference between the upper and lower
+    bound of an int16 histogram
+    """
+    # coerce to a single type
+    signed_to_unsigned = {
+        np.byte: np.ubyte,
+        np.short: np.ushort,
+        np.intc: np.uintc,
+        np.int_: np.uint,
+        np.longlong: np.ulonglong
+    }
+    dt = np.result_type(a, b)
+    try:
+        unsigned_dt = signed_to_unsigned[dt.type]
+    except KeyError:
+        return np.subtract(a, b, dtype=dt)
+    else:
+        # we know the inputs are integers, and we are deliberately casting
+        # signed to unsigned.  The input may be negative python integers so
+        # ensure we pass in arrays with the initial dtype (related to NEP 50).
+        return np.subtract(np.asarray(a, dtype=dt), np.asarray(b, dtype=dt),
+                           casting='unsafe', dtype=unsigned_dt)
+
+
+def _get_bin_edges(a, bins, range, weights):
+    """
+    Computes the bins used internally by `histogram`.
+
+    Parameters
+    ==========
+    a : ndarray
+        Ravelled data array
+    bins, range
+        Forwarded arguments from `histogram`.
+    weights : ndarray, optional
+        Ravelled weights array, or None
+
+    Returns
+    =======
+    bin_edges : ndarray
+        Array of bin edges
+    uniform_bins : (Number, Number, int):
+        The upper bound, lowerbound, and number of bins, used in the optimized
+        implementation of `histogram` that works on uniform bins.
+    """
+    # parse the overloaded bins argument
+    n_equal_bins = None
+    bin_edges = None
+
+    if isinstance(bins, str):
+        bin_name = bins
+        # if `bins` is a string for an automatic method,
+        # this will replace it with the number of bins calculated
+        if bin_name not in _hist_bin_selectors:
+            raise ValueError(
+                "{!r} is not a valid estimator for `bins`".format(bin_name))
+        if weights is not None:
+            raise TypeError("Automated estimation of the number of "
+                            "bins is not supported for weighted data")
+
+        first_edge, last_edge = _get_outer_edges(a, range)
+
+        # truncate the range if needed
+        if range is not None:
+            keep = (a >= first_edge)
+            keep &= (a <= last_edge)
+            if not np.logical_and.reduce(keep):
+                a = a[keep]
+
+        if a.size == 0:
+            n_equal_bins = 1
+        else:
+            # Do not call selectors on empty arrays
+            width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge))
+            if width:
+                if np.issubdtype(a.dtype, np.integer) and width < 1:
+                    width = 1
+                n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width))
+            else:
+                # Width can be zero for some estimators, e.g. FD when
+                # the IQR of the data is zero.
+                n_equal_bins = 1
+
+    elif np.ndim(bins) == 0:
+        try:
+            n_equal_bins = operator.index(bins)
+        except TypeError as e:
+            raise TypeError(
+                '`bins` must be an integer, a string, or an array') from e
+        if n_equal_bins < 1:
+            raise ValueError('`bins` must be positive, when an integer')
+
+        first_edge, last_edge = _get_outer_edges(a, range)
+
+    elif np.ndim(bins) == 1:
+        bin_edges = np.asarray(bins)
+        if np.any(bin_edges[:-1] > bin_edges[1:]):
+            raise ValueError(
+                '`bins` must increase monotonically, when an array')
+
+    else:
+        raise ValueError('`bins` must be 1d, when an array')
+
+    if n_equal_bins is not None:
+        # gh-10322 means that type resolution rules are dependent on array
+        # shapes. To avoid this causing problems, we pick a type now and stick
+        # with it throughout.
+        bin_type = np.result_type(first_edge, last_edge, a)
+        if np.issubdtype(bin_type, np.integer):
+            bin_type = np.result_type(bin_type, float)
+
+        # bin edges must be computed
+        bin_edges = np.linspace(
+            first_edge, last_edge, n_equal_bins + 1,
+            endpoint=True, dtype=bin_type)
+        if np.any(bin_edges[:-1] >= bin_edges[1:]):
+            raise ValueError(
+                f'Too many bins for data range. Cannot create {n_equal_bins} '
+                f'finite-sized bins.')
+        return bin_edges, (first_edge, last_edge, n_equal_bins)
+    else:
+        return bin_edges, None
+
+
+def _search_sorted_inclusive(a, v):
+    """
+    Like `searchsorted`, but where the last item in `v` is placed on the right.
+
+    In the context of a histogram, this makes the last bin edge inclusive
+    """
+    return np.concatenate((
+        a.searchsorted(v[:-1], 'left'),
+        a.searchsorted(v[-1:], 'right')
+    ))
+
+
+def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None):
+    return (a, bins, weights)
+
+
+@array_function_dispatch(_histogram_bin_edges_dispatcher)
+def histogram_bin_edges(a, bins=10, range=None, weights=None):
+    r"""
+    Function to calculate only the edges of the bins used by the `histogram`
+    function.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data. The histogram is computed over the flattened array.
+    bins : int or sequence of scalars or str, optional
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines the bin edges, including the rightmost
+        edge, allowing for non-uniform bin widths.
+
+        If `bins` is a string from the list below, `histogram_bin_edges` will
+        use the method chosen to calculate the optimal bin width and
+        consequently the number of bins (see the Notes section for more detail
+        on the estimators) from the data that falls within the requested range.
+        While the bin width will be optimal for the actual data
+        in the range, the number of bins will be computed to fill the
+        entire range, including the empty portions. For visualisation,
+        using the 'auto' option is suggested. Weighted data is not
+        supported for automated bin size selection.
+
+        'auto'
+            Minimum bin width between the 'sturges' and 'fd' estimators.
+            Provides good all-around performance.
+
+        'fd' (Freedman Diaconis Estimator)
+            Robust (resilient to outliers) estimator that takes into
+            account data variability and data size.
+
+        'doane'
+            An improved version of Sturges' estimator that works better
+            with non-normal datasets.
+
+        'scott'
+            Less robust estimator that takes into account data variability
+            and data size.
+
+        'stone'
+            Estimator based on leave-one-out cross-validation estimate of
+            the integrated squared error. Can be regarded as a generalization
+            of Scott's rule.
+
+        'rice'
+            Estimator does not take variability into account, only data
+            size. Commonly overestimates number of bins required.
+
+        'sturges'
+            R's default method, only accounts for data size. Only
+            optimal for gaussian data and underestimates number of bins
+            for large non-gaussian datasets.
+
+        'sqrt'
+            Square root (of data size) estimator, used by Excel and
+            other programs for its speed and simplicity.
+
+    range : (float, float), optional
+        The lower and upper range of the bins.  If not provided, range
+        is simply ``(a.min(), a.max())``.  Values outside the range are
+        ignored. The first element of the range must be less than or
+        equal to the second. `range` affects the automatic bin
+        computation as well. While bin width is computed to be optimal
+        based on the actual data within `range`, the bin count will fill
+        the entire range including portions containing no data.
+
+    weights : array_like, optional
+        An array of weights, of the same shape as `a`.  Each value in
+        `a` only contributes its associated weight towards the bin count
+        (instead of 1). This is currently not used by any of the bin estimators,
+        but may be in the future.
+
+    Returns
+    -------
+    bin_edges : array of dtype float
+        The edges to pass into `histogram`
+
+    See Also
+    --------
+    histogram
+
+    Notes
+    -----
+    The methods to estimate the optimal number of bins are well founded
+    in literature, and are inspired by the choices R provides for
+    histogram visualisation. Note that having the number of bins
+    proportional to :math:`n^{1/3}` is asymptotically optimal, which is
+    why it appears in most estimators. These are simply plug-in methods
+    that give good starting points for number of bins. In the equations
+    below, :math:`h` is the binwidth and :math:`n_h` is the number of
+    bins. All estimators that compute bin counts are recast to bin width
+    using the `ptp` of the data. The final bin count is obtained from
+    ``np.round(np.ceil(range / h))``. The final bin width is often less
+    than what is returned by the estimators below.
+
+    'auto' (minimum bin width of the 'sturges' and 'fd' estimators)
+        A compromise to get a good value. For small datasets the Sturges
+        value will usually be chosen, while larger datasets will usually
+        default to FD.  Avoids the overly conservative behaviour of FD
+        and Sturges for small and large datasets respectively.
+        Switchover point is usually :math:`a.size \approx 1000`.
+
+    'fd' (Freedman Diaconis Estimator)
+        .. math:: h = 2 \frac{IQR}{n^{1/3}}
+
+        The binwidth is proportional to the interquartile range (IQR)
+        and inversely proportional to cube root of a.size. Can be too
+        conservative for small datasets, but is quite good for large
+        datasets. The IQR is very robust to outliers.
+
+    'scott'
+        .. math:: h = \sigma \sqrt[3]{\frac{24 \sqrt{\pi}}{n}}
+
+        The binwidth is proportional to the standard deviation of the
+        data and inversely proportional to cube root of ``x.size``. Can
+        be too conservative for small datasets, but is quite good for
+        large datasets. The standard deviation is not very robust to
+        outliers. Values are very similar to the Freedman-Diaconis
+        estimator in the absence of outliers.
+
+    'rice'
+        .. math:: n_h = 2n^{1/3}
+
+        The number of bins is only proportional to cube root of
+        ``a.size``. It tends to overestimate the number of bins and it
+        does not take into account data variability.
+
+    'sturges'
+        .. math:: n_h = \log _{2}(n) + 1
+
+        The number of bins is the base 2 log of ``a.size``.  This
+        estimator assumes normality of data and is too conservative for
+        larger, non-normal datasets. This is the default method in R's
+        ``hist`` method.
+
+    'doane'
+        .. math:: n_h = 1 + \log_{2}(n) +
+                        \log_{2}\left(1 + \frac{|g_1|}{\sigma_{g_1}}\right)
+
+            g_1 = mean\left[\left(\frac{x - \mu}{\sigma}\right)^3\right]
+
+            \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}
+
+        An improved version of Sturges' formula that produces better
+        estimates for non-normal datasets. This estimator attempts to
+        account for the skew of the data.
+
+    'sqrt'
+        .. math:: n_h = \sqrt n
+
+        The simplest and fastest estimator. Only takes into account the
+        data size.
+
+    Additionally, if the data is of integer dtype, then the binwidth will never
+    be less than 1.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])
+    >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1))
+    array([0.  , 0.25, 0.5 , 0.75, 1.  ])
+    >>> np.histogram_bin_edges(arr, bins=2)
+    array([0. , 2.5, 5. ])
+
+    For consistency with histogram, an array of pre-computed bins is
+    passed through unmodified:
+
+    >>> np.histogram_bin_edges(arr, [1, 2])
+    array([1, 2])
+
+    This function allows one set of bins to be computed, and reused across
+    multiple histograms:
+
+    >>> shared_bins = np.histogram_bin_edges(arr, bins='auto')
+    >>> shared_bins
+    array([0., 1., 2., 3., 4., 5.])
+
+    >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])
+    >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins)
+    >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)
+
+    >>> hist_0; hist_1
+    array([1, 1, 0, 1, 0])
+    array([2, 0, 1, 1, 2])
+
+    Which gives more easily comparable results than using separate bins for
+    each histogram:
+
+    >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
+    >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
+    >>> hist_0; hist_1
+    array([1, 1, 1])
+    array([2, 1, 1, 2])
+    >>> bins_0; bins_1
+    array([0., 1., 2., 3.])
+    array([0.  , 1.25, 2.5 , 3.75, 5.  ])
+
+    """
+    a, weights = _ravel_and_check_weights(a, weights)
+    bin_edges, _ = _get_bin_edges(a, bins, range, weights)
+    return bin_edges
+
+
+def _histogram_dispatcher(
+        a, bins=None, range=None, density=None, weights=None):
+    return (a, bins, weights)
+
+
+@array_function_dispatch(_histogram_dispatcher)
+def histogram(a, bins=10, range=None, density=None, weights=None):
+    r"""
+    Compute the histogram of a dataset.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data. The histogram is computed over the flattened array.
+    bins : int or sequence of scalars or str, optional
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+
+    range : (float, float), optional
+        The lower and upper range of the bins.  If not provided, range
+        is simply ``(a.min(), a.max())``.  Values outside the range are
+        ignored. The first element of the range must be less than or
+        equal to the second. `range` affects the automatic bin
+        computation as well. While bin width is computed to be optimal
+        based on the actual data within `range`, the bin count will fill
+        the entire range including portions containing no data.
+    weights : array_like, optional
+        An array of weights, of the same shape as `a`.  Each value in
+        `a` only contributes its associated weight towards the bin count
+        (instead of 1). If `density` is True, the weights are
+        normalized, so that the integral of the density over the range
+        remains 1.
+        Please note that the ``dtype`` of `weights` will also become the
+        ``dtype`` of the returned accumulator (`hist`), so it must be
+        large enough to hold accumulated values as well.
+    density : bool, optional
+        If ``False``, the result will contain the number of samples in
+        each bin. If ``True``, the result is the value of the
+        probability *density* function at the bin, normalized such that
+        the *integral* over the range is 1. Note that the sum of the
+        histogram values will not be equal to 1 unless bins of unity
+        width are chosen; it is not a probability *mass* function.
+
+    Returns
+    -------
+    hist : array
+        The values of the histogram. See `density` and `weights` for a
+        description of the possible semantics.  If `weights` are given,
+        ``hist.dtype`` will be taken from `weights`.
+    bin_edges : array of dtype float
+        Return the bin edges ``(length(hist)+1)``.
+
+
+    See Also
+    --------
+    histogramdd, bincount, searchsorted, digitize, histogram_bin_edges
+
+    Notes
+    -----
+    All but the last (righthand-most) bin is half-open.  In other words,
+    if `bins` is::
+
+      [1, 2, 3, 4]
+
+    then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
+    the second ``[2, 3)``.  The last bin, however, is ``[3, 4]``, which
+    *includes* 4.
+
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
+    (array([0, 2, 1]), array([0, 1, 2, 3]))
+    >>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
+    (array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
+    >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
+    (array([1, 4, 1]), array([0, 1, 2, 3]))
+
+    >>> a = np.arange(5)
+    >>> hist, bin_edges = np.histogram(a, density=True)
+    >>> hist
+    array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
+    >>> hist.sum()
+    2.4999999999999996
+    >>> np.sum(hist * np.diff(bin_edges))
+    1.0
+
+    Automated Bin Selection Methods example, using 2 peak random data
+    with 2000 points.
+
+    .. plot::
+        :include-source:
+
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        rng = np.random.RandomState(10)  # deterministic random data
+        a = np.hstack((rng.normal(size=1000),
+                       rng.normal(loc=5, scale=2, size=1000)))
+        plt.hist(a, bins='auto')  # arguments are passed to np.histogram
+        plt.title("Histogram with 'auto' bins")
+        plt.show()
+
+    """
+    a, weights = _ravel_and_check_weights(a, weights)
+
+    bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
+
+    # Histogram is an integer or a float array depending on the weights.
+    if weights is None:
+        ntype = np.dtype(np.intp)
+    else:
+        ntype = weights.dtype
+
+    # We set a block size, as this allows us to iterate over chunks when
+    # computing histograms, to minimize memory usage.
+    BLOCK = 65536
+
+    # The fast path uses bincount, but that only works for certain types
+    # of weight
+    simple_weights = (
+        weights is None or
+        np.can_cast(weights.dtype, np.double) or
+        np.can_cast(weights.dtype, complex)
+    )
+
+    if uniform_bins is not None and simple_weights:
+        # Fast algorithm for equal bins
+        # We now convert values of a to bin indices, under the assumption of
+        # equal bin widths (which is valid here).
+        first_edge, last_edge, n_equal_bins = uniform_bins
+
+        # Initialize empty histogram
+        n = np.zeros(n_equal_bins, ntype)
+
+        # Pre-compute histogram scaling factor
+        norm_numerator = n_equal_bins
+        norm_denom = _unsigned_subtract(last_edge, first_edge)
+
+        # We iterate over blocks here for two reasons: the first is that for
+        # large arrays, it is actually faster (for example for a 10^8 array it
+        # is 2x as fast) and it results in a memory footprint 3x lower in the
+        # limit of large arrays.
+        for i in _range(0, len(a), BLOCK):
+            tmp_a = a[i:i+BLOCK]
+            if weights is None:
+                tmp_w = None
+            else:
+                tmp_w = weights[i:i + BLOCK]
+
+            # Only include values in the right range
+            keep = (tmp_a >= first_edge)
+            keep &= (tmp_a <= last_edge)
+            if not np.logical_and.reduce(keep):
+                tmp_a = tmp_a[keep]
+                if tmp_w is not None:
+                    tmp_w = tmp_w[keep]
+
+            # This cast ensures no type promotions occur below, which gh-10322
+            # make unpredictable. Getting it wrong leads to precision errors
+            # like gh-8123.
+            tmp_a = tmp_a.astype(bin_edges.dtype, copy=False)
+
+            # Compute the bin indices, and for values that lie exactly on
+            # last_edge we need to subtract one
+            f_indices = ((_unsigned_subtract(tmp_a, first_edge) / norm_denom)
+                         * norm_numerator)
+            indices = f_indices.astype(np.intp)
+            indices[indices == n_equal_bins] -= 1
+
+            # The index computation is not guaranteed to give exactly
+            # consistent results within ~1 ULP of the bin edges.
+            decrement = tmp_a < bin_edges[indices]
+            indices[decrement] -= 1
+            # The last bin includes the right edge. The other bins do not.
+            increment = ((tmp_a >= bin_edges[indices + 1])
+                         & (indices != n_equal_bins - 1))
+            indices[increment] += 1
+
+            # We now compute the histogram using bincount
+            if ntype.kind == 'c':
+                n.real += np.bincount(indices, weights=tmp_w.real,
+                                      minlength=n_equal_bins)
+                n.imag += np.bincount(indices, weights=tmp_w.imag,
+                                      minlength=n_equal_bins)
+            else:
+                n += np.bincount(indices, weights=tmp_w,
+                                 minlength=n_equal_bins).astype(ntype)
+    else:
+        # Compute via cumulative histogram
+        cum_n = np.zeros(bin_edges.shape, ntype)
+        if weights is None:
+            for i in _range(0, len(a), BLOCK):
+                sa = np.sort(a[i:i+BLOCK])
+                cum_n += _search_sorted_inclusive(sa, bin_edges)
+        else:
+            zero = np.zeros(1, dtype=ntype)
+            for i in _range(0, len(a), BLOCK):
+                tmp_a = a[i:i+BLOCK]
+                tmp_w = weights[i:i+BLOCK]
+                sorting_index = np.argsort(tmp_a)
+                sa = tmp_a[sorting_index]
+                sw = tmp_w[sorting_index]
+                cw = np.concatenate((zero, sw.cumsum()))
+                bin_index = _search_sorted_inclusive(sa, bin_edges)
+                cum_n += cw[bin_index]
+
+        n = np.diff(cum_n)
+
+    if density:
+        db = np.array(np.diff(bin_edges), float)
+        return n/db/n.sum(), bin_edges
+
+    return n, bin_edges
+
+
+def _histogramdd_dispatcher(sample, bins=None, range=None, density=None,
+                            weights=None):
+    if hasattr(sample, 'shape'):  # same condition as used in histogramdd
+        yield sample
+    else:
+        yield from sample
+    with contextlib.suppress(TypeError):
+        yield from bins
+    yield weights
+
+
+@array_function_dispatch(_histogramdd_dispatcher)
+def histogramdd(sample, bins=10, range=None, density=None, weights=None):
+    """
+    Compute the multidimensional histogram of some data.
+
+    Parameters
+    ----------
+    sample : (N, D) array, or (N, D) array_like
+        The data to be histogrammed.
+
+        Note the unusual interpretation of sample when an array_like:
+
+        * When an array, each row is a coordinate in a D-dimensional space -
+          such as ``histogramdd(np.array([p1, p2, p3]))``.
+        * When an array_like, each element is the list of values for single
+          coordinate - such as ``histogramdd((X, Y, Z))``.
+
+        The first form should be preferred.
+
+    bins : sequence or int, optional
+        The bin specification:
+
+        * A sequence of arrays describing the monotonically increasing bin
+          edges along each dimension.
+        * The number of bins for each dimension (nx, ny, ... =bins)
+        * The number of bins for all dimensions (nx=ny=...=bins).
+
+    range : sequence, optional
+        A sequence of length D, each an optional (lower, upper) tuple giving
+        the outer bin edges to be used if the edges are not given explicitly in
+        `bins`.
+        An entry of None in the sequence results in the minimum and maximum
+        values being used for the corresponding dimension.
+        The default, None, is equivalent to passing a tuple of D None values.
+    density : bool, optional
+        If False, the default, returns the number of samples in each bin.
+        If True, returns the probability *density* function at the bin,
+        ``bin_count / sample_count / bin_volume``.
+    weights : (N,) array_like, optional
+        An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
+        Weights are normalized to 1 if density is True. If density is False,
+        the values of the returned histogram are equal to the sum of the
+        weights belonging to the samples falling into each bin.
+
+    Returns
+    -------
+    H : ndarray
+        The multidimensional histogram of sample x. See density and weights
+        for the different possible semantics.
+    edges : tuple of ndarrays
+        A tuple of D arrays describing the bin edges for each dimension.
+
+    See Also
+    --------
+    histogram: 1-D histogram
+    histogram2d: 2-D histogram
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> r = rng.normal(size=(100,3))
+    >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
+    >>> H.shape, edges[0].size, edges[1].size, edges[2].size
+    ((5, 8, 4), 6, 9, 5)
+
+    """
+
+    try:
+        # Sample is an ND-array.
+        N, D = sample.shape
+    except (AttributeError, ValueError):
+        # Sample is a sequence of 1D arrays.
+        sample = np.atleast_2d(sample).T
+        N, D = sample.shape
+
+    nbin = np.empty(D, np.intp)
+    edges = D*[None]
+    dedges = D*[None]
+    if weights is not None:
+        weights = np.asarray(weights)
+
+    try:
+        M = len(bins)
+        if M != D:
+            raise ValueError(
+                'The dimension of bins must be equal to the dimension of the '
+                'sample x.')
+    except TypeError:
+        # bins is an integer
+        bins = D*[bins]
+
+    # normalize the range argument
+    if range is None:
+        range = (None,) * D
+    elif len(range) != D:
+        raise ValueError('range argument must have one entry per dimension')
+
+    # Create edge arrays
+    for i in _range(D):
+        if np.ndim(bins[i]) == 0:
+            if bins[i] < 1:
+                raise ValueError(
+                    '`bins[{}]` must be positive, when an integer'.format(i))
+            smin, smax = _get_outer_edges(sample[:,i], range[i])
+            try:
+                n = operator.index(bins[i])
+
+            except TypeError as e:
+                raise TypeError(
+                	"`bins[{}]` must be an integer, when a scalar".format(i)
+                ) from e
+
+            edges[i] = np.linspace(smin, smax, n + 1)
+        elif np.ndim(bins[i]) == 1:
+            edges[i] = np.asarray(bins[i])
+            if np.any(edges[i][:-1] > edges[i][1:]):
+                raise ValueError(
+                    '`bins[{}]` must be monotonically increasing, when an array'
+                    .format(i))
+        else:
+            raise ValueError(
+                '`bins[{}]` must be a scalar or 1d array'.format(i))
+
+        nbin[i] = len(edges[i]) + 1  # includes an outlier on each end
+        dedges[i] = np.diff(edges[i])
+
+    # Compute the bin number each sample falls into.
+    Ncount = tuple(
+        # avoid np.digitize to work around gh-11022
+        np.searchsorted(edges[i], sample[:, i], side='right')
+        for i in _range(D)
+    )
+
+    # Using digitize, values that fall on an edge are put in the right bin.
+    # For the rightmost bin, we want values equal to the right edge to be
+    # counted in the last bin, and not as an outlier.
+    for i in _range(D):
+        # Find which points are on the rightmost edge.
+        on_edge = (sample[:, i] == edges[i][-1])
+        # Shift these points one bin to the left.
+        Ncount[i][on_edge] -= 1
+
+    # Compute the sample indices in the flattened histogram matrix.
+    # This raises an error if the array is too large.
+    xy = np.ravel_multi_index(Ncount, nbin)
+
+    # Compute the number of repetitions in xy and assign it to the
+    # flattened histmat.
+    hist = np.bincount(xy, weights, minlength=nbin.prod())
+
+    # Shape into a proper matrix
+    hist = hist.reshape(nbin)
+
+    # This preserves the (bad) behavior observed in gh-7845, for now.
+    hist = hist.astype(float, casting='safe')
+
+    # Remove outliers (indices 0 and -1 for each dimension).
+    core = D*(slice(1, -1),)
+    hist = hist[core]
+
+    if density:
+        # calculate the probability density function
+        s = hist.sum()
+        for i in _range(D):
+            shape = np.ones(D, int)
+            shape[i] = nbin[i] - 2
+            hist = hist / dedges[i].reshape(shape)
+        hist /= s
+
+    if (hist.shape != nbin - 2).any():
+        raise RuntimeError(
+            "Internal Shape Error")
+    return hist, edges
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e18ab99035b48f9b5d8d85ffe6bb226b5c284ae9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_histograms_impl.pyi
@@ -0,0 +1,48 @@
+from collections.abc import Sequence
+from typing import (
+    Literal as L,
+    Any,
+    SupportsIndex,
+    TypeAlias,
+)
+
+from numpy._typing import (
+    NDArray,
+    ArrayLike,
+)
+
+__all__ = ["histogram", "histogramdd", "histogram_bin_edges"]
+
+_BinKind: TypeAlias = L[
+    "stone",
+    "auto",
+    "doane",
+    "fd",
+    "rice",
+    "scott",
+    "sqrt",
+    "sturges",
+]
+
+def histogram_bin_edges(
+    a: ArrayLike,
+    bins: _BinKind | SupportsIndex | ArrayLike = ...,
+    range: None | tuple[float, float] = ...,
+    weights: None | ArrayLike = ...,
+) -> NDArray[Any]: ...
+
+def histogram(
+    a: ArrayLike,
+    bins: _BinKind | SupportsIndex | ArrayLike = ...,
+    range: None | tuple[float, float] = ...,
+    density: bool = ...,
+    weights: None | ArrayLike = ...,
+) -> tuple[NDArray[Any], NDArray[Any]]: ...
+
+def histogramdd(
+    sample: ArrayLike,
+    bins: SupportsIndex | ArrayLike = ...,
+    range: Sequence[tuple[float, float]] = ...,
+    density: None | bool = ...,
+    weights: None | ArrayLike = ...,
+) -> tuple[NDArray[Any], tuple[NDArray[Any], ...]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..da8fbedc8072b4d73ebc9b2e25229fff761ff073
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.py
@@ -0,0 +1,1069 @@
+import functools
+import sys
+import math
+import warnings
+
+import numpy as np
+from .._utils import set_module
+import numpy._core.numeric as _nx
+from numpy._core.numeric import ScalarType, array
+from numpy._core.numerictypes import issubdtype
+
+import numpy.matrixlib as matrixlib
+from numpy._core.multiarray import ravel_multi_index, unravel_index
+from numpy._core import overrides, linspace
+from numpy.lib.stride_tricks import as_strided
+from numpy.lib._function_base_impl import diff
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+__all__ = [
+    'ravel_multi_index', 'unravel_index', 'mgrid', 'ogrid', 'r_', 'c_',
+    's_', 'index_exp', 'ix_', 'ndenumerate', 'ndindex', 'fill_diagonal',
+    'diag_indices', 'diag_indices_from'
+]
+
+
+def _ix__dispatcher(*args):
+    return args
+
+
+@array_function_dispatch(_ix__dispatcher)
+def ix_(*args):
+    """
+    Construct an open mesh from multiple sequences.
+
+    This function takes N 1-D sequences and returns N outputs with N
+    dimensions each, such that the shape is 1 in all but one dimension
+    and the dimension with the non-unit shape value cycles through all
+    N dimensions.
+
+    Using `ix_` one can quickly construct index arrays that will index
+    the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
+    ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.
+
+    Parameters
+    ----------
+    args : 1-D sequences
+        Each sequence should be of integer or boolean type.
+        Boolean sequences will be interpreted as boolean masks for the
+        corresponding dimension (equivalent to passing in
+        ``np.nonzero(boolean_sequence)``).
+
+    Returns
+    -------
+    out : tuple of ndarrays
+        N arrays with N dimensions each, with N the number of input
+        sequences. Together these arrays form an open mesh.
+
+    See Also
+    --------
+    ogrid, mgrid, meshgrid
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(10).reshape(2, 5)
+    >>> a
+    array([[0, 1, 2, 3, 4],
+           [5, 6, 7, 8, 9]])
+    >>> ixgrid = np.ix_([0, 1], [2, 4])
+    >>> ixgrid
+    (array([[0],
+           [1]]), array([[2, 4]]))
+    >>> ixgrid[0].shape, ixgrid[1].shape
+    ((2, 1), (1, 2))
+    >>> a[ixgrid]
+    array([[2, 4],
+           [7, 9]])
+
+    >>> ixgrid = np.ix_([True, True], [2, 4])
+    >>> a[ixgrid]
+    array([[2, 4],
+           [7, 9]])
+    >>> ixgrid = np.ix_([True, True], [False, False, True, False, True])
+    >>> a[ixgrid]
+    array([[2, 4],
+           [7, 9]])
+
+    """
+    out = []
+    nd = len(args)
+    for k, new in enumerate(args):
+        if not isinstance(new, _nx.ndarray):
+            new = np.asarray(new)
+            if new.size == 0:
+                # Explicitly type empty arrays to avoid float default
+                new = new.astype(_nx.intp)
+        if new.ndim != 1:
+            raise ValueError("Cross index must be 1 dimensional")
+        if issubdtype(new.dtype, _nx.bool):
+            new, = new.nonzero()
+        new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1))
+        out.append(new)
+    return tuple(out)
+
+
+class nd_grid:
+    """
+    Construct a multi-dimensional "meshgrid".
+
+    ``grid = nd_grid()`` creates an instance which will return a mesh-grid
+    when indexed.  The dimension and number of the output arrays are equal
+    to the number of indexing dimensions.  If the step length is not a
+    complex number, then the stop is not inclusive.
+
+    However, if the step length is a **complex number** (e.g. 5j), then the
+    integer part of its magnitude is interpreted as specifying the
+    number of points to create between the start and stop values, where
+    the stop value **is inclusive**.
+
+    If instantiated with an argument of ``sparse=True``, the mesh-grid is
+    open (or not fleshed out) so that only one-dimension of each returned
+    argument is greater than 1.
+
+    Parameters
+    ----------
+    sparse : bool, optional
+        Whether the grid is sparse or not. Default is False.
+
+    Notes
+    -----
+    Two instances of `nd_grid` are made available in the NumPy namespace,
+    `mgrid` and `ogrid`, approximately defined as::
+
+        mgrid = nd_grid(sparse=False)
+        ogrid = nd_grid(sparse=True)
+
+    Users should use these pre-defined instances instead of using `nd_grid`
+    directly.
+    """
+    __slots__ = ('sparse',)
+
+    def __init__(self, sparse=False):
+        self.sparse = sparse
+
+    def __getitem__(self, key):
+        try:
+            size = []
+            # Mimic the behavior of `np.arange` and use a data type
+            # which is at least as large as `np.int_`
+            num_list = [0]
+            for k in range(len(key)):
+                step = key[k].step
+                start = key[k].start
+                stop = key[k].stop
+                if start is None:
+                    start = 0
+                if step is None:
+                    step = 1
+                if isinstance(step, (_nx.complexfloating, complex)):
+                    step = abs(step)
+                    size.append(int(step))
+                else:
+                    size.append(
+                        int(math.ceil((stop - start) / (step*1.0))))
+                num_list += [start, stop, step]
+            typ = _nx.result_type(*num_list)
+            if self.sparse:
+                nn = [_nx.arange(_x, dtype=_t)
+                      for _x, _t in zip(size, (typ,)*len(size))]
+            else:
+                nn = _nx.indices(size, typ)
+            for k, kk in enumerate(key):
+                step = kk.step
+                start = kk.start
+                if start is None:
+                    start = 0
+                if step is None:
+                    step = 1
+                if isinstance(step, (_nx.complexfloating, complex)):
+                    step = int(abs(step))
+                    if step != 1:
+                        step = (kk.stop - start) / float(step - 1)
+                nn[k] = (nn[k]*step+start)
+            if self.sparse:
+                slobj = [_nx.newaxis]*len(size)
+                for k in range(len(size)):
+                    slobj[k] = slice(None, None)
+                    nn[k] = nn[k][tuple(slobj)]
+                    slobj[k] = _nx.newaxis
+                return tuple(nn)  # ogrid -> tuple of arrays
+            return nn  # mgrid -> ndarray
+        except (IndexError, TypeError):
+            step = key.step
+            stop = key.stop
+            start = key.start
+            if start is None:
+                start = 0
+            if isinstance(step, (_nx.complexfloating, complex)):
+                # Prevent the (potential) creation of integer arrays
+                step_float = abs(step)
+                step = length = int(step_float)
+                if step != 1:
+                    step = (key.stop-start)/float(step-1)
+                typ = _nx.result_type(start, stop, step_float)
+                return _nx.arange(0, length, 1, dtype=typ)*step + start
+            else:
+                return _nx.arange(start, stop, step)
+
+
+class MGridClass(nd_grid):
+    """
+    An instance which returns a dense multi-dimensional "meshgrid".
+
+    An instance which returns a dense (or fleshed out) mesh-grid
+    when indexed, so that each returned argument has the same shape.
+    The dimensions and number of the output arrays are equal to the
+    number of indexing dimensions.  If the step length is not a complex
+    number, then the stop is not inclusive.
+
+    However, if the step length is a **complex number** (e.g. 5j), then
+    the integer part of its magnitude is interpreted as specifying the
+    number of points to create between the start and stop values, where
+    the stop value **is inclusive**.
+
+    Returns
+    -------
+    mesh-grid : ndarray
+        A single array, containing a set of `ndarray`\\ s all of the same
+        dimensions. stacked along the first axis.
+
+    See Also
+    --------
+    ogrid : like `mgrid` but returns open (not fleshed out) mesh grids
+    meshgrid: return coordinate matrices from coordinate vectors
+    r_ : array concatenator
+    :ref:`how-to-partition`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.mgrid[0:5, 0:5]
+    array([[[0, 0, 0, 0, 0],
+            [1, 1, 1, 1, 1],
+            [2, 2, 2, 2, 2],
+            [3, 3, 3, 3, 3],
+            [4, 4, 4, 4, 4]],
+           [[0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4],
+            [0, 1, 2, 3, 4]]])
+    >>> np.mgrid[-1:1:5j]
+    array([-1. , -0.5,  0. ,  0.5,  1. ])
+
+    >>> np.mgrid[0:4].shape
+    (4,)
+    >>> np.mgrid[0:4, 0:5].shape
+    (2, 4, 5)
+    >>> np.mgrid[0:4, 0:5, 0:6].shape
+    (3, 4, 5, 6)
+
+    """
+    __slots__ = ()
+
+    def __init__(self):
+        super().__init__(sparse=False)
+
+
+mgrid = MGridClass()
+
+
+class OGridClass(nd_grid):
+    """
+    An instance which returns an open multi-dimensional "meshgrid".
+
+    An instance which returns an open (i.e. not fleshed out) mesh-grid
+    when indexed, so that only one dimension of each returned array is
+    greater than 1.  The dimension and number of the output arrays are
+    equal to the number of indexing dimensions.  If the step length is
+    not a complex number, then the stop is not inclusive.
+
+    However, if the step length is a **complex number** (e.g. 5j), then
+    the integer part of its magnitude is interpreted as specifying the
+    number of points to create between the start and stop values, where
+    the stop value **is inclusive**.
+
+    Returns
+    -------
+    mesh-grid : ndarray or tuple of ndarrays
+        If the input is a single slice, returns an array.
+        If the input is multiple slices, returns a tuple of arrays, with
+        only one dimension not equal to 1.
+
+    See Also
+    --------
+    mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
+    meshgrid: return coordinate matrices from coordinate vectors
+    r_ : array concatenator
+    :ref:`how-to-partition`
+
+    Examples
+    --------
+    >>> from numpy import ogrid
+    >>> ogrid[-1:1:5j]
+    array([-1. , -0.5,  0. ,  0.5,  1. ])
+    >>> ogrid[0:5, 0:5]
+    (array([[0],
+            [1],
+            [2],
+            [3],
+            [4]]),
+     array([[0, 1, 2, 3, 4]]))
+
+    """
+    __slots__ = ()
+
+    def __init__(self):
+        super().__init__(sparse=True)
+
+
+ogrid = OGridClass()
+
+
+class AxisConcatenator:
+    """
+    Translates slice objects to concatenation along an axis.
+
+    For detailed documentation on usage, see `r_`.
+    """
+    __slots__ = ('axis', 'matrix', 'trans1d', 'ndmin')
+
+    # allow ma.mr_ to override this
+    concatenate = staticmethod(_nx.concatenate)
+    makemat = staticmethod(matrixlib.matrix)
+
+    def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1):
+        self.axis = axis
+        self.matrix = matrix
+        self.trans1d = trans1d
+        self.ndmin = ndmin
+
+    def __getitem__(self, key):
+        # handle matrix builder syntax
+        if isinstance(key, str):
+            frame = sys._getframe().f_back
+            mymat = matrixlib.bmat(key, frame.f_globals, frame.f_locals)
+            return mymat
+
+        if not isinstance(key, tuple):
+            key = (key,)
+
+        # copy attributes, since they can be overridden in the first argument
+        trans1d = self.trans1d
+        ndmin = self.ndmin
+        matrix = self.matrix
+        axis = self.axis
+
+        objs = []
+        # dtypes or scalars for weak scalar handling in result_type
+        result_type_objs = []
+
+        for k, item in enumerate(key):
+            scalar = False
+            if isinstance(item, slice):
+                step = item.step
+                start = item.start
+                stop = item.stop
+                if start is None:
+                    start = 0
+                if step is None:
+                    step = 1
+                if isinstance(step, (_nx.complexfloating, complex)):
+                    size = int(abs(step))
+                    newobj = linspace(start, stop, num=size)
+                else:
+                    newobj = _nx.arange(start, stop, step)
+                if ndmin > 1:
+                    newobj = array(newobj, copy=None, ndmin=ndmin)
+                    if trans1d != -1:
+                        newobj = newobj.swapaxes(-1, trans1d)
+            elif isinstance(item, str):
+                if k != 0:
+                    raise ValueError("special directives must be the "
+                                     "first entry.")
+                if item in ('r', 'c'):
+                    matrix = True
+                    col = (item == 'c')
+                    continue
+                if ',' in item:
+                    vec = item.split(',')
+                    try:
+                        axis, ndmin = [int(x) for x in vec[:2]]
+                        if len(vec) == 3:
+                            trans1d = int(vec[2])
+                        continue
+                    except Exception as e:
+                        raise ValueError(
+                            "unknown special directive {!r}".format(item)
+                        ) from e
+                try:
+                    axis = int(item)
+                    continue
+                except (ValueError, TypeError) as e:
+                    raise ValueError("unknown special directive") from e
+            elif type(item) in ScalarType:
+                scalar = True
+                newobj = item
+            else:
+                item_ndim = np.ndim(item)
+                newobj = array(item, copy=None, subok=True, ndmin=ndmin)
+                if trans1d != -1 and item_ndim < ndmin:
+                    k2 = ndmin - item_ndim
+                    k1 = trans1d
+                    if k1 < 0:
+                        k1 += k2 + 1
+                    defaxes = list(range(ndmin))
+                    axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
+                    newobj = newobj.transpose(axes)
+
+            objs.append(newobj)
+            if scalar:
+                result_type_objs.append(item)
+            else:
+                result_type_objs.append(newobj.dtype)
+
+        # Ensure that scalars won't up-cast unless warranted, for 0, drops
+        # through to error in concatenate.
+        if len(result_type_objs) != 0:
+            final_dtype = _nx.result_type(*result_type_objs)
+            # concatenate could do cast, but that can be overridden:
+            objs = [array(obj, copy=None, subok=True,
+                          ndmin=ndmin, dtype=final_dtype) for obj in objs]
+
+        res = self.concatenate(tuple(objs), axis=axis)
+
+        if matrix:
+            oldndim = res.ndim
+            res = self.makemat(res)
+            if oldndim == 1 and col:
+                res = res.T
+        return res
+
+    def __len__(self):
+        return 0
+
+# separate classes are used here instead of just making r_ = concatenator(0),
+# etc. because otherwise we couldn't get the doc string to come out right
+# in help(r_)
+
+
+class RClass(AxisConcatenator):
+    """
+    Translates slice objects to concatenation along the first axis.
+
+    This is a simple way to build up arrays quickly. There are two use cases.
+
+    1. If the index expression contains comma separated arrays, then stack
+       them along their first axis.
+    2. If the index expression contains slice notation or scalars then create
+       a 1-D array with a range indicated by the slice notation.
+
+    If slice notation is used, the syntax ``start:stop:step`` is equivalent
+    to ``np.arange(start, stop, step)`` inside of the brackets. However, if
+    ``step`` is an imaginary number (i.e. 100j) then its integer portion is
+    interpreted as a number-of-points desired and the start and stop are
+    inclusive. In other words ``start:stop:stepj`` is interpreted as
+    ``np.linspace(start, stop, step, endpoint=1)`` inside of the brackets.
+    After expansion of slice notation, all comma separated sequences are
+    concatenated together.
+
+    Optional character strings placed as the first element of the index
+    expression can be used to change the output. The strings 'r' or 'c' result
+    in matrix output. If the result is 1-D and 'r' is specified a 1 x N (row)
+    matrix is produced. If the result is 1-D and 'c' is specified, then a N x 1
+    (column) matrix is produced. If the result is 2-D then both provide the
+    same matrix result.
+
+    A string integer specifies which axis to stack multiple comma separated
+    arrays along. A string of two comma-separated integers allows indication
+    of the minimum number of dimensions to force each entry into as the
+    second integer (the axis to concatenate along is still the first integer).
+
+    A string with three comma-separated integers allows specification of the
+    axis to concatenate along, the minimum number of dimensions to force the
+    entries to, and which axis should contain the start of the arrays which
+    are less than the specified number of dimensions. In other words the third
+    integer allows you to specify where the 1's should be placed in the shape
+    of the arrays that have their shapes upgraded. By default, they are placed
+    in the front of the shape tuple. The third argument allows you to specify
+    where the start of the array should be instead. Thus, a third argument of
+    '0' would place the 1's at the end of the array shape. Negative integers
+    specify where in the new shape tuple the last dimension of upgraded arrays
+    should be placed, so the default is '-1'.
+
+    Parameters
+    ----------
+    Not a function, so takes no parameters
+
+
+    Returns
+    -------
+    A concatenated ndarray or matrix.
+
+    See Also
+    --------
+    concatenate : Join a sequence of arrays along an existing axis.
+    c_ : Translates slice objects to concatenation along the second axis.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])]
+    array([1, 2, 3, ..., 4, 5, 6])
+    >>> np.r_[-1:1:6j, [0]*3, 5, 6]
+    array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ,  0. ,  0. ,  0. ,  5. ,  6. ])
+
+    String integers specify the axis to concatenate along or the minimum
+    number of dimensions to force entries into.
+
+    >>> a = np.array([[0, 1, 2], [3, 4, 5]])
+    >>> np.r_['-1', a, a] # concatenate along last axis
+    array([[0, 1, 2, 0, 1, 2],
+           [3, 4, 5, 3, 4, 5]])
+    >>> np.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, dim>=2
+    array([[1, 2, 3],
+           [4, 5, 6]])
+
+    >>> np.r_['0,2,0', [1,2,3], [4,5,6]]
+    array([[1],
+           [2],
+           [3],
+           [4],
+           [5],
+           [6]])
+    >>> np.r_['1,2,0', [1,2,3], [4,5,6]]
+    array([[1, 4],
+           [2, 5],
+           [3, 6]])
+
+    Using 'r' or 'c' as a first string argument creates a matrix.
+
+    >>> np.r_['r',[1,2,3], [4,5,6]]
+    matrix([[1, 2, 3, 4, 5, 6]])
+
+    """
+    __slots__ = ()
+
+    def __init__(self):
+        AxisConcatenator.__init__(self, 0)
+
+
+r_ = RClass()
+
+
+class CClass(AxisConcatenator):
+    """
+    Translates slice objects to concatenation along the second axis.
+
+    This is short-hand for ``np.r_['-1,2,0', index expression]``, which is
+    useful because of its common occurrence. In particular, arrays will be
+    stacked along their last axis after being upgraded to at least 2-D with
+    1's post-pended to the shape (column vectors made out of 1-D arrays).
+
+    See Also
+    --------
+    column_stack : Stack 1-D arrays as columns into a 2-D array.
+    r_ : For more detailed documentation.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.c_[np.array([1,2,3]), np.array([4,5,6])]
+    array([[1, 4],
+           [2, 5],
+           [3, 6]])
+    >>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
+    array([[1, 2, 3, ..., 4, 5, 6]])
+
+    """
+    __slots__ = ()
+
+    def __init__(self):
+        AxisConcatenator.__init__(self, -1, ndmin=2, trans1d=0)
+
+
+c_ = CClass()
+
+
+@set_module('numpy')
+class ndenumerate:
+    """
+    Multidimensional index iterator.
+
+    Return an iterator yielding pairs of array coordinates and values.
+
+    Parameters
+    ----------
+    arr : ndarray
+      Input array.
+
+    See Also
+    --------
+    ndindex, flatiter
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> for index, x in np.ndenumerate(a):
+    ...     print(index, x)
+    (0, 0) 1
+    (0, 1) 2
+    (1, 0) 3
+    (1, 1) 4
+
+    """
+
+    def __init__(self, arr):
+        self.iter = np.asarray(arr).flat
+
+    def __next__(self):
+        """
+        Standard iterator method, returns the index tuple and array value.
+
+        Returns
+        -------
+        coords : tuple of ints
+            The indices of the current iteration.
+        val : scalar
+            The array element of the current iteration.
+
+        """
+        return self.iter.coords, next(self.iter)
+
+    def __iter__(self):
+        return self
+
+
+@set_module('numpy')
+class ndindex:
+    """
+    An N-dimensional iterator object to index arrays.
+
+    Given the shape of an array, an `ndindex` instance iterates over
+    the N-dimensional index of the array. At each iteration a tuple
+    of indices is returned, the last dimension is iterated over first.
+
+    Parameters
+    ----------
+    shape : ints, or a single tuple of ints
+        The size of each dimension of the array can be passed as
+        individual parameters or as the elements of a tuple.
+
+    See Also
+    --------
+    ndenumerate, flatiter
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Dimensions as individual arguments
+
+    >>> for index in np.ndindex(3, 2, 1):
+    ...     print(index)
+    (0, 0, 0)
+    (0, 1, 0)
+    (1, 0, 0)
+    (1, 1, 0)
+    (2, 0, 0)
+    (2, 1, 0)
+
+    Same dimensions - but in a tuple ``(3, 2, 1)``
+
+    >>> for index in np.ndindex((3, 2, 1)):
+    ...     print(index)
+    (0, 0, 0)
+    (0, 1, 0)
+    (1, 0, 0)
+    (1, 1, 0)
+    (2, 0, 0)
+    (2, 1, 0)
+
+    """
+
+    def __init__(self, *shape):
+        if len(shape) == 1 and isinstance(shape[0], tuple):
+            shape = shape[0]
+        x = as_strided(_nx.zeros(1), shape=shape,
+                       strides=_nx.zeros_like(shape))
+        self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'],
+                              order='C')
+
+    def __iter__(self):
+        return self
+
+    def ndincr(self):
+        """
+        Increment the multi-dimensional index by one.
+
+        This method is for backward compatibility only: do not use.
+
+        .. deprecated:: 1.20.0
+            This method has been advised against since numpy 1.8.0, but only
+            started emitting DeprecationWarning as of this version.
+        """
+        # NumPy 1.20.0, 2020-09-08
+        warnings.warn(
+            "`ndindex.ndincr()` is deprecated, use `next(ndindex)` instead",
+            DeprecationWarning, stacklevel=2)
+        next(self)
+
+    def __next__(self):
+        """
+        Standard iterator method, updates the index and returns the index
+        tuple.
+
+        Returns
+        -------
+        val : tuple of ints
+            Returns a tuple containing the indices of the current
+            iteration.
+
+        """
+        next(self._it)
+        return self._it.multi_index
+
+
+# You can do all this with slice() plus a few special objects,
+# but there's a lot to remember. This version is simpler because
+# it uses the standard array indexing syntax.
+#
+# Written by Konrad Hinsen 
+# last revision: 1999-7-23
+#
+# Cosmetic changes by T. Oliphant 2001
+#
+#
+
+class IndexExpression:
+    """
+    A nicer way to build up index tuples for arrays.
+
+    .. note::
+       Use one of the two predefined instances ``index_exp`` or `s_`
+       rather than directly using `IndexExpression`.
+
+    For any index combination, including slicing and axis insertion,
+    ``a[indices]`` is the same as ``a[np.index_exp[indices]]`` for any
+    array `a`. However, ``np.index_exp[indices]`` can be used anywhere
+    in Python code and returns a tuple of slice objects that can be
+    used in the construction of complex index expressions.
+
+    Parameters
+    ----------
+    maketuple : bool
+        If True, always returns a tuple.
+
+    See Also
+    --------
+    s_ : Predefined instance without tuple conversion:
+       `s_ = IndexExpression(maketuple=False)`.
+       The ``index_exp`` is another predefined instance that
+       always returns a tuple:
+       `index_exp = IndexExpression(maketuple=True)`.
+
+    Notes
+    -----
+    You can do all this with :class:`slice` plus a few special objects,
+    but there's a lot to remember and this version is simpler because
+    it uses the standard array indexing syntax.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.s_[2::2]
+    slice(2, None, 2)
+    >>> np.index_exp[2::2]
+    (slice(2, None, 2),)
+
+    >>> np.array([0, 1, 2, 3, 4])[np.s_[2::2]]
+    array([2, 4])
+
+    """
+    __slots__ = ('maketuple',)
+
+    def __init__(self, maketuple):
+        self.maketuple = maketuple
+
+    def __getitem__(self, item):
+        if self.maketuple and not isinstance(item, tuple):
+            return (item,)
+        else:
+            return item
+
+
+index_exp = IndexExpression(maketuple=True)
+s_ = IndexExpression(maketuple=False)
+
+# End contribution from Konrad.
+
+
+# The following functions complement those in twodim_base, but are
+# applicable to N-dimensions.
+
+
+def _fill_diagonal_dispatcher(a, val, wrap=None):
+    return (a,)
+
+
+@array_function_dispatch(_fill_diagonal_dispatcher)
+def fill_diagonal(a, val, wrap=False):
+    """Fill the main diagonal of the given array of any dimensionality.
+
+    For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
+    values ``a[i, ..., i]`` with indices ``i`` all identical.  This function
+    modifies the input array in-place without returning a value.
+
+    Parameters
+    ----------
+    a : array, at least 2-D.
+      Array whose diagonal is to be filled in-place.
+    val : scalar or array_like
+      Value(s) to write on the diagonal. If `val` is scalar, the value is
+      written along the diagonal. If array-like, the flattened `val` is
+      written along the diagonal, repeating if necessary to fill all
+      diagonal entries.
+
+    wrap : bool
+      For tall matrices in NumPy version up to 1.6.2, the
+      diagonal "wrapped" after N columns. You can have this behavior
+      with this option. This affects only tall matrices.
+
+    See also
+    --------
+    diag_indices, diag_indices_from
+
+    Notes
+    -----
+    This functionality can be obtained via `diag_indices`, but internally
+    this version uses a much faster implementation that never constructs the
+    indices and uses simple slicing.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.zeros((3, 3), int)
+    >>> np.fill_diagonal(a, 5)
+    >>> a
+    array([[5, 0, 0],
+           [0, 5, 0],
+           [0, 0, 5]])
+
+    The same function can operate on a 4-D array:
+
+    >>> a = np.zeros((3, 3, 3, 3), int)
+    >>> np.fill_diagonal(a, 4)
+
+    We only show a few blocks for clarity:
+
+    >>> a[0, 0]
+    array([[4, 0, 0],
+           [0, 0, 0],
+           [0, 0, 0]])
+    >>> a[1, 1]
+    array([[0, 0, 0],
+           [0, 4, 0],
+           [0, 0, 0]])
+    >>> a[2, 2]
+    array([[0, 0, 0],
+           [0, 0, 0],
+           [0, 0, 4]])
+
+    The wrap option affects only tall matrices:
+
+    >>> # tall matrices no wrap
+    >>> a = np.zeros((5, 3), int)
+    >>> np.fill_diagonal(a, 4)
+    >>> a
+    array([[4, 0, 0],
+           [0, 4, 0],
+           [0, 0, 4],
+           [0, 0, 0],
+           [0, 0, 0]])
+
+    >>> # tall matrices wrap
+    >>> a = np.zeros((5, 3), int)
+    >>> np.fill_diagonal(a, 4, wrap=True)
+    >>> a
+    array([[4, 0, 0],
+           [0, 4, 0],
+           [0, 0, 4],
+           [0, 0, 0],
+           [4, 0, 0]])
+
+    >>> # wide matrices
+    >>> a = np.zeros((3, 5), int)
+    >>> np.fill_diagonal(a, 4, wrap=True)
+    >>> a
+    array([[4, 0, 0, 0, 0],
+           [0, 4, 0, 0, 0],
+           [0, 0, 4, 0, 0]])
+
+    The anti-diagonal can be filled by reversing the order of elements
+    using either `numpy.flipud` or `numpy.fliplr`.
+
+    >>> a = np.zeros((3, 3), int);
+    >>> np.fill_diagonal(np.fliplr(a), [1,2,3])  # Horizontal flip
+    >>> a
+    array([[0, 0, 1],
+           [0, 2, 0],
+           [3, 0, 0]])
+    >>> np.fill_diagonal(np.flipud(a), [1,2,3])  # Vertical flip
+    >>> a
+    array([[0, 0, 3],
+           [0, 2, 0],
+           [1, 0, 0]])
+
+    Note that the order in which the diagonal is filled varies depending
+    on the flip function.
+    """
+    if a.ndim < 2:
+        raise ValueError("array must be at least 2-d")
+    end = None
+    if a.ndim == 2:
+        # Explicit, fast formula for the common case.  For 2-d arrays, we
+        # accept rectangular ones.
+        step = a.shape[1] + 1
+        # This is needed to don't have tall matrix have the diagonal wrap.
+        if not wrap:
+            end = a.shape[1] * a.shape[1]
+    else:
+        # For more than d=2, the strided formula is only valid for arrays with
+        # all dimensions equal, so we check first.
+        if not np.all(diff(a.shape) == 0):
+            raise ValueError("All dimensions of input must be of equal length")
+        step = 1 + (np.cumprod(a.shape[:-1])).sum()
+
+    # Write the value out into the diagonal.
+    a.flat[:end:step] = val
+
+
+@set_module('numpy')
+def diag_indices(n, ndim=2):
+    """
+    Return the indices to access the main diagonal of an array.
+
+    This returns a tuple of indices that can be used to access the main
+    diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape
+    (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for
+    ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]``
+    for ``i = [0..n-1]``.
+
+    Parameters
+    ----------
+    n : int
+      The size, along each dimension, of the arrays for which the returned
+      indices can be used.
+
+    ndim : int, optional
+      The number of dimensions.
+
+    See Also
+    --------
+    diag_indices_from
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create a set of indices to access the diagonal of a (4, 4) array:
+
+    >>> di = np.diag_indices(4)
+    >>> di
+    (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+    >>> a[di] = 100
+    >>> a
+    array([[100,   1,   2,   3],
+           [  4, 100,   6,   7],
+           [  8,   9, 100,  11],
+           [ 12,  13,  14, 100]])
+
+    Now, we create indices to manipulate a 3-D array:
+
+    >>> d3 = np.diag_indices(2, 3)
+    >>> d3
+    (array([0, 1]), array([0, 1]), array([0, 1]))
+
+    And use it to set the diagonal of an array of zeros to 1:
+
+    >>> a = np.zeros((2, 2, 2), dtype=int)
+    >>> a[d3] = 1
+    >>> a
+    array([[[1, 0],
+            [0, 0]],
+           [[0, 0],
+            [0, 1]]])
+
+    """
+    idx = np.arange(n)
+    return (idx,) * ndim
+
+
+def _diag_indices_from(arr):
+    return (arr,)
+
+
+@array_function_dispatch(_diag_indices_from)
+def diag_indices_from(arr):
+    """
+    Return the indices to access the main diagonal of an n-dimensional array.
+
+    See `diag_indices` for full details.
+
+    Parameters
+    ----------
+    arr : array, at least 2-D
+
+    See Also
+    --------
+    diag_indices
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create a 4 by 4 array.
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Get the indices of the diagonal elements.
+
+    >>> di = np.diag_indices_from(a)
+    >>> di
+    (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
+
+    >>> a[di]
+    array([ 0,  5, 10, 15])
+
+    This is simply syntactic sugar for diag_indices.
+
+    >>> np.diag_indices(a.shape[0])
+    (array([0, 1, 2, 3]), array([0, 1, 2, 3]))
+
+    """
+
+    if not arr.ndim >= 2:
+        raise ValueError("input array must be at least 2-d")
+    # For more than d=2, the strided formula is only valid for arrays with
+    # all dimensions equal, so we check first.
+    if not np.all(diff(arr.shape) == 0):
+        raise ValueError("All dimensions of input must be of equal length")
+
+    return diag_indices(arr.shape[0], arr.ndim)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4a1426fd4d6cb2eeaf7327b8485efc4ca14670af
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_index_tricks_impl.pyi
@@ -0,0 +1,196 @@
+from collections.abc import Sequence
+from typing import Any, ClassVar, Final, Generic, SupportsIndex, final, overload
+from typing import Literal as L
+
+from _typeshed import Incomplete
+from typing_extensions import Self, TypeVar, deprecated
+
+import numpy as np
+from numpy._core.multiarray import ravel_multi_index, unravel_index
+from numpy._typing import (
+    ArrayLike,
+    NDArray,
+    _FiniteNestedSequence,
+    _NestedSequence,
+    _Shape,
+    _SupportsArray,
+    _SupportsDType,
+)
+
+__all__ = [  # noqa: RUF022
+    "ravel_multi_index",
+    "unravel_index",
+    "mgrid",
+    "ogrid",
+    "r_",
+    "c_",
+    "s_",
+    "index_exp",
+    "ix_",
+    "ndenumerate",
+    "ndindex",
+    "fill_diagonal",
+    "diag_indices",
+    "diag_indices_from",
+]
+
+###
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=tuple[Any, ...])
+_ArrayT = TypeVar("_ArrayT", bound=NDArray[Any])
+_DTypeT = TypeVar("_DTypeT", bound=np.dtype[Any])
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+_ScalarT_co = TypeVar("_ScalarT_co", bound=np.generic, covariant=True)
+_BoolT_co = TypeVar("_BoolT_co", bound=bool, default=bool, covariant=True)
+
+_AxisT_co = TypeVar("_AxisT_co", bound=int, default=L[0], covariant=True)
+_MatrixT_co = TypeVar("_MatrixT_co", bound=bool, default=L[False], covariant=True)
+_NDMinT_co = TypeVar("_NDMinT_co", bound=int, default=L[1], covariant=True)
+_Trans1DT_co = TypeVar("_Trans1DT_co", bound=int, default=L[-1], covariant=True)
+
+###
+
+class ndenumerate(Generic[_ScalarT_co]):
+    @overload
+    def __new__(cls, arr: _FiniteNestedSequence[_SupportsArray[np.dtype[_ScalarT]]]) -> ndenumerate[_ScalarT]: ...
+    @overload
+    def __new__(cls, arr: str | _NestedSequence[str]) -> ndenumerate[np.str_]: ...
+    @overload
+    def __new__(cls, arr: bytes | _NestedSequence[bytes]) -> ndenumerate[np.bytes_]: ...
+    @overload
+    def __new__(cls, arr: bool | _NestedSequence[bool]) -> ndenumerate[np.bool]: ...
+    @overload
+    def __new__(cls, arr: int | _NestedSequence[int]) -> ndenumerate[np.intp]: ...
+    @overload
+    def __new__(cls, arr: float | _NestedSequence[float]) -> ndenumerate[np.float64]: ...
+    @overload
+    def __new__(cls, arr: complex | _NestedSequence[complex]) -> ndenumerate[np.complex128]: ...
+    @overload
+    def __new__(cls, arr: object) -> ndenumerate[Any]: ...
+
+    # The first overload is a (semi-)workaround for a mypy bug (tested with v1.10 and v1.11)
+    @overload
+    def __next__(
+        self: ndenumerate[np.bool | np.number | np.flexible | np.datetime64 | np.timedelta64],
+        /,
+    ) -> tuple[tuple[int, ...], _ScalarT_co]: ...
+    @overload
+    def __next__(self: ndenumerate[np.object_], /) -> tuple[tuple[int, ...], Any]: ...
+    @overload
+    def __next__(self, /) -> tuple[tuple[int, ...], _ScalarT_co]: ...
+
+    #
+    def __iter__(self) -> Self: ...
+
+class ndindex:
+    @overload
+    def __init__(self, shape: tuple[SupportsIndex, ...], /) -> None: ...
+    @overload
+    def __init__(self, /, *shape: SupportsIndex) -> None: ...
+
+    #
+    def __iter__(self) -> Self: ...
+    def __next__(self) -> tuple[int, ...]: ...
+
+    #
+    @deprecated("Deprecated since 1.20.0.")
+    def ndincr(self, /) -> None: ...
+
+class nd_grid(Generic[_BoolT_co]):
+    sparse: _BoolT_co
+    def __init__(self, sparse: _BoolT_co = ...) -> None: ...
+    @overload
+    def __getitem__(self: nd_grid[L[False]], key: slice | Sequence[slice]) -> NDArray[Any]: ...
+    @overload
+    def __getitem__(self: nd_grid[L[True]], key: slice | Sequence[slice]) -> tuple[NDArray[Any], ...]: ...
+
+@final
+class MGridClass(nd_grid[L[False]]):
+    def __init__(self) -> None: ...
+
+@final
+class OGridClass(nd_grid[L[True]]):
+    def __init__(self) -> None: ...
+
+class AxisConcatenator(Generic[_AxisT_co, _MatrixT_co, _NDMinT_co, _Trans1DT_co]):
+    __slots__ = "axis", "matrix", "ndmin", "trans1d"
+
+    makemat: ClassVar[type[np.matrix[tuple[int, int], np.dtype[Any]]]]
+
+    axis: _AxisT_co
+    matrix: _MatrixT_co
+    ndmin: _NDMinT_co
+    trans1d: _Trans1DT_co
+
+    #
+    def __init__(
+        self,
+        /,
+        axis: _AxisT_co = ...,
+        matrix: _MatrixT_co = ...,
+        ndmin: _NDMinT_co = ...,
+        trans1d: _Trans1DT_co = ...,
+    ) -> None: ...
+
+    # TODO(jorenham): annotate this
+    def __getitem__(self, key: Incomplete, /) -> Incomplete: ...
+    def __len__(self, /) -> L[0]: ...
+
+    #
+    @staticmethod
+    @overload
+    def concatenate(*a: ArrayLike, axis: SupportsIndex | None = 0, out: _ArrayT) -> _ArrayT: ...
+    @staticmethod
+    @overload
+    def concatenate(*a: ArrayLike, axis: SupportsIndex | None = 0, out: None = None) -> NDArray[Any]: ...
+
+@final
+class RClass(AxisConcatenator[L[0], L[False], L[1], L[-1]]):
+    def __init__(self, /) -> None: ...
+
+@final
+class CClass(AxisConcatenator[L[-1], L[False], L[2], L[0]]):
+    def __init__(self, /) -> None: ...
+
+class IndexExpression(Generic[_BoolT_co]):
+    maketuple: _BoolT_co
+    def __init__(self, maketuple: _BoolT_co) -> None: ...
+    @overload
+    def __getitem__(self, item: _TupleT) -> _TupleT: ...
+    @overload
+    def __getitem__(self: IndexExpression[L[True]], item: _T) -> tuple[_T]: ...
+    @overload
+    def __getitem__(self: IndexExpression[L[False]], item: _T) -> _T: ...
+
+@overload
+def ix_(*args: _FiniteNestedSequence[_SupportsDType[_DTypeT]]) -> tuple[np.ndarray[_Shape, _DTypeT], ...]: ...
+@overload
+def ix_(*args: str | _NestedSequence[str]) -> tuple[NDArray[np.str_], ...]: ...
+@overload
+def ix_(*args: bytes | _NestedSequence[bytes]) -> tuple[NDArray[np.bytes_], ...]: ...
+@overload
+def ix_(*args: bool | _NestedSequence[bool]) -> tuple[NDArray[np.bool], ...]: ...
+@overload
+def ix_(*args: int | _NestedSequence[int]) -> tuple[NDArray[np.intp], ...]: ...
+@overload
+def ix_(*args: float | _NestedSequence[float]) -> tuple[NDArray[np.float64], ...]: ...
+@overload
+def ix_(*args: complex | _NestedSequence[complex]) -> tuple[NDArray[np.complex128], ...]: ...
+
+#
+def fill_diagonal(a: NDArray[Any], val: object, wrap: bool = ...) -> None: ...
+
+#
+def diag_indices(n: int, ndim: int = ...) -> tuple[NDArray[np.intp], ...]: ...
+def diag_indices_from(arr: ArrayLike) -> tuple[NDArray[np.intp], ...]: ...
+
+#
+mgrid: Final[MGridClass] = ...
+ogrid: Final[OGridClass] = ...
+
+r_: Final[RClass] = ...
+c_: Final[CClass] = ...
+
+index_exp: Final[IndexExpression[L[True]]] = ...
+s_: Final[IndexExpression[L[False]]] = ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_iotools.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_iotools.py
new file mode 100644
index 0000000000000000000000000000000000000000..908ca7762fdd16d9b725e29c7851366ea4d4bec7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_iotools.py
@@ -0,0 +1,899 @@
+"""A collection of functions designed to help I/O with ascii files.
+
+"""
+__docformat__ = "restructuredtext en"
+
+import numpy as np
+import numpy._core.numeric as nx
+from numpy._utils import asbytes, asunicode
+
+
+def _decode_line(line, encoding=None):
+    """Decode bytes from binary input streams.
+
+    Defaults to decoding from 'latin1'. That differs from the behavior of
+    np.compat.asunicode that decodes from 'ascii'.
+
+    Parameters
+    ----------
+    line : str or bytes
+         Line to be decoded.
+    encoding : str
+         Encoding used to decode `line`.
+
+    Returns
+    -------
+    decoded_line : str
+
+    """
+    if type(line) is bytes:
+        if encoding is None:
+            encoding = "latin1"
+        line = line.decode(encoding)
+
+    return line
+
+
+def _is_string_like(obj):
+    """
+    Check whether obj behaves like a string.
+    """
+    try:
+        obj + ''
+    except (TypeError, ValueError):
+        return False
+    return True
+
+
+def _is_bytes_like(obj):
+    """
+    Check whether obj behaves like a bytes object.
+    """
+    try:
+        obj + b''
+    except (TypeError, ValueError):
+        return False
+    return True
+
+
+def has_nested_fields(ndtype):
+    """
+    Returns whether one or several fields of a dtype are nested.
+
+    Parameters
+    ----------
+    ndtype : dtype
+        Data-type of a structured array.
+
+    Raises
+    ------
+    AttributeError
+        If `ndtype` does not have a `names` attribute.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
+    >>> np.lib._iotools.has_nested_fields(dt)
+    False
+
+    """
+    return any(ndtype[name].names is not None for name in ndtype.names or ())
+
+
+def flatten_dtype(ndtype, flatten_base=False):
+    """
+    Unpack a structured data-type by collapsing nested fields and/or fields
+    with a shape.
+
+    Note that the field names are lost.
+
+    Parameters
+    ----------
+    ndtype : dtype
+        The datatype to collapse
+    flatten_base : bool, optional
+       If True, transform a field with a shape into several fields. Default is
+       False.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
+    ...                ('block', int, (2, 3))])
+    >>> np.lib._iotools.flatten_dtype(dt)
+    [dtype('S4'), dtype('float64'), dtype('float64'), dtype('int64')]
+    >>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
+    [dtype('S4'),
+     dtype('float64'),
+     dtype('float64'),
+     dtype('int64'),
+     dtype('int64'),
+     dtype('int64'),
+     dtype('int64'),
+     dtype('int64'),
+     dtype('int64')]
+
+    """
+    names = ndtype.names
+    if names is None:
+        if flatten_base:
+            return [ndtype.base] * int(np.prod(ndtype.shape))
+        return [ndtype.base]
+    else:
+        types = []
+        for field in names:
+            info = ndtype.fields[field]
+            flat_dt = flatten_dtype(info[0], flatten_base)
+            types.extend(flat_dt)
+        return types
+
+
+class LineSplitter:
+    """
+    Object to split a string at a given delimiter or at given places.
+
+    Parameters
+    ----------
+    delimiter : str, int, or sequence of ints, optional
+        If a string, character used to delimit consecutive fields.
+        If an integer or a sequence of integers, width(s) of each field.
+    comments : str, optional
+        Character used to mark the beginning of a comment. Default is '#'.
+    autostrip : bool, optional
+        Whether to strip each individual field. Default is True.
+
+    """
+
+    def autostrip(self, method):
+        """
+        Wrapper to strip each member of the output of `method`.
+
+        Parameters
+        ----------
+        method : function
+            Function that takes a single argument and returns a sequence of
+            strings.
+
+        Returns
+        -------
+        wrapped : function
+            The result of wrapping `method`. `wrapped` takes a single input
+            argument and returns a list of strings that are stripped of
+            white-space.
+
+        """
+        return lambda input: [_.strip() for _ in method(input)]
+
+    def __init__(self, delimiter=None, comments='#', autostrip=True,
+                 encoding=None):
+        delimiter = _decode_line(delimiter)
+        comments = _decode_line(comments)
+
+        self.comments = comments
+
+        # Delimiter is a character
+        if (delimiter is None) or isinstance(delimiter, str):
+            delimiter = delimiter or None
+            _handyman = self._delimited_splitter
+        # Delimiter is a list of field widths
+        elif hasattr(delimiter, '__iter__'):
+            _handyman = self._variablewidth_splitter
+            idx = np.cumsum([0] + list(delimiter))
+            delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
+        # Delimiter is a single integer
+        elif int(delimiter):
+            (_handyman, delimiter) = (
+                    self._fixedwidth_splitter, int(delimiter))
+        else:
+            (_handyman, delimiter) = (self._delimited_splitter, None)
+        self.delimiter = delimiter
+        if autostrip:
+            self._handyman = self.autostrip(_handyman)
+        else:
+            self._handyman = _handyman
+        self.encoding = encoding
+
+    def _delimited_splitter(self, line):
+        """Chop off comments, strip, and split at delimiter. """
+        if self.comments is not None:
+            line = line.split(self.comments)[0]
+        line = line.strip(" \r\n")
+        if not line:
+            return []
+        return line.split(self.delimiter)
+
+    def _fixedwidth_splitter(self, line):
+        if self.comments is not None:
+            line = line.split(self.comments)[0]
+        line = line.strip("\r\n")
+        if not line:
+            return []
+        fixed = self.delimiter
+        slices = [slice(i, i + fixed) for i in range(0, len(line), fixed)]
+        return [line[s] for s in slices]
+
+    def _variablewidth_splitter(self, line):
+        if self.comments is not None:
+            line = line.split(self.comments)[0]
+        if not line:
+            return []
+        slices = self.delimiter
+        return [line[s] for s in slices]
+
+    def __call__(self, line):
+        return self._handyman(_decode_line(line, self.encoding))
+
+
+class NameValidator:
+    """
+    Object to validate a list of strings to use as field names.
+
+    The strings are stripped of any non alphanumeric character, and spaces
+    are replaced by '_'. During instantiation, the user can define a list
+    of names to exclude, as well as a list of invalid characters. Names in
+    the exclusion list are appended a '_' character.
+
+    Once an instance has been created, it can be called with a list of
+    names, and a list of valid names will be created.  The `__call__`
+    method accepts an optional keyword "default" that sets the default name
+    in case of ambiguity. By default this is 'f', so that names will
+    default to `f0`, `f1`, etc.
+
+    Parameters
+    ----------
+    excludelist : sequence, optional
+        A list of names to exclude. This list is appended to the default
+        list ['return', 'file', 'print']. Excluded names are appended an
+        underscore: for example, `file` becomes `file_` if supplied.
+    deletechars : str, optional
+        A string combining invalid characters that must be deleted from the
+        names.
+    case_sensitive : {True, False, 'upper', 'lower'}, optional
+        * If True, field names are case-sensitive.
+        * If False or 'upper', field names are converted to upper case.
+        * If 'lower', field names are converted to lower case.
+
+        The default value is True.
+    replace_space : '_', optional
+        Character(s) used in replacement of white spaces.
+
+    Notes
+    -----
+    Calling an instance of `NameValidator` is the same as calling its
+    method `validate`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> validator = np.lib._iotools.NameValidator()
+    >>> validator(['file', 'field2', 'with space', 'CaSe'])
+    ('file_', 'field2', 'with_space', 'CaSe')
+
+    >>> validator = np.lib._iotools.NameValidator(excludelist=['excl'],
+    ...                                           deletechars='q',
+    ...                                           case_sensitive=False)
+    >>> validator(['excl', 'field2', 'no_q', 'with space', 'CaSe'])
+    ('EXCL', 'FIELD2', 'NO_Q', 'WITH_SPACE', 'CASE')
+
+    """
+
+    defaultexcludelist = ['return', 'file', 'print']
+    defaultdeletechars = set(r"""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
+
+    def __init__(self, excludelist=None, deletechars=None,
+                 case_sensitive=None, replace_space='_'):
+        # Process the exclusion list ..
+        if excludelist is None:
+            excludelist = []
+        excludelist.extend(self.defaultexcludelist)
+        self.excludelist = excludelist
+        # Process the list of characters to delete
+        if deletechars is None:
+            delete = self.defaultdeletechars
+        else:
+            delete = set(deletechars)
+        delete.add('"')
+        self.deletechars = delete
+        # Process the case option .....
+        if (case_sensitive is None) or (case_sensitive is True):
+            self.case_converter = lambda x: x
+        elif (case_sensitive is False) or case_sensitive.startswith('u'):
+            self.case_converter = lambda x: x.upper()
+        elif case_sensitive.startswith('l'):
+            self.case_converter = lambda x: x.lower()
+        else:
+            msg = 'unrecognized case_sensitive value %s.' % case_sensitive
+            raise ValueError(msg)
+
+        self.replace_space = replace_space
+
+    def validate(self, names, defaultfmt="f%i", nbfields=None):
+        """
+        Validate a list of strings as field names for a structured array.
+
+        Parameters
+        ----------
+        names : sequence of str
+            Strings to be validated.
+        defaultfmt : str, optional
+            Default format string, used if validating a given string
+            reduces its length to zero.
+        nbfields : integer, optional
+            Final number of validated names, used to expand or shrink the
+            initial list of names.
+
+        Returns
+        -------
+        validatednames : list of str
+            The list of validated field names.
+
+        Notes
+        -----
+        A `NameValidator` instance can be called directly, which is the
+        same as calling `validate`. For examples, see `NameValidator`.
+
+        """
+        # Initial checks ..............
+        if (names is None):
+            if (nbfields is None):
+                return None
+            names = []
+        if isinstance(names, str):
+            names = [names, ]
+        if nbfields is not None:
+            nbnames = len(names)
+            if (nbnames < nbfields):
+                names = list(names) + [''] * (nbfields - nbnames)
+            elif (nbnames > nbfields):
+                names = names[:nbfields]
+        # Set some shortcuts ...........
+        deletechars = self.deletechars
+        excludelist = self.excludelist
+        case_converter = self.case_converter
+        replace_space = self.replace_space
+        # Initializes some variables ...
+        validatednames = []
+        seen = dict()
+        nbempty = 0
+
+        for item in names:
+            item = case_converter(item).strip()
+            if replace_space:
+                item = item.replace(' ', replace_space)
+            item = ''.join([c for c in item if c not in deletechars])
+            if item == '':
+                item = defaultfmt % nbempty
+                while item in names:
+                    nbempty += 1
+                    item = defaultfmt % nbempty
+                nbempty += 1
+            elif item in excludelist:
+                item += '_'
+            cnt = seen.get(item, 0)
+            if cnt > 0:
+                validatednames.append(item + '_%d' % cnt)
+            else:
+                validatednames.append(item)
+            seen[item] = cnt + 1
+        return tuple(validatednames)
+
+    def __call__(self, names, defaultfmt="f%i", nbfields=None):
+        return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)
+
+
+def str2bool(value):
+    """
+    Tries to transform a string supposed to represent a boolean to a boolean.
+
+    Parameters
+    ----------
+    value : str
+        The string that is transformed to a boolean.
+
+    Returns
+    -------
+    boolval : bool
+        The boolean representation of `value`.
+
+    Raises
+    ------
+    ValueError
+        If the string is not 'True' or 'False' (case independent)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.lib._iotools.str2bool('TRUE')
+    True
+    >>> np.lib._iotools.str2bool('false')
+    False
+
+    """
+    value = value.upper()
+    if value == 'TRUE':
+        return True
+    elif value == 'FALSE':
+        return False
+    else:
+        raise ValueError("Invalid boolean")
+
+
+class ConverterError(Exception):
+    """
+    Exception raised when an error occurs in a converter for string values.
+
+    """
+    pass
+
+
+class ConverterLockError(ConverterError):
+    """
+    Exception raised when an attempt is made to upgrade a locked converter.
+
+    """
+    pass
+
+
+class ConversionWarning(UserWarning):
+    """
+    Warning issued when a string converter has a problem.
+
+    Notes
+    -----
+    In `genfromtxt` a `ConversionWarning` is issued if raising exceptions
+    is explicitly suppressed with the "invalid_raise" keyword.
+
+    """
+    pass
+
+
+class StringConverter:
+    """
+    Factory class for function transforming a string into another object
+    (int, float).
+
+    After initialization, an instance can be called to transform a string
+    into another object. If the string is recognized as representing a
+    missing value, a default value is returned.
+
+    Attributes
+    ----------
+    func : function
+        Function used for the conversion.
+    default : any
+        Default value to return when the input corresponds to a missing
+        value.
+    type : type
+        Type of the output.
+    _status : int
+        Integer representing the order of the conversion.
+    _mapper : sequence of tuples
+        Sequence of tuples (dtype, function, default value) to evaluate in
+        order.
+    _locked : bool
+        Holds `locked` parameter.
+
+    Parameters
+    ----------
+    dtype_or_func : {None, dtype, function}, optional
+        If a `dtype`, specifies the input data type, used to define a basic
+        function and a default value for missing data. For example, when
+        `dtype` is float, the `func` attribute is set to `float` and the
+        default value to `np.nan`.  If a function, this function is used to
+        convert a string to another object. In this case, it is recommended
+        to give an associated default value as input.
+    default : any, optional
+        Value to return by default, that is, when the string to be
+        converted is flagged as missing. If not given, `StringConverter`
+        tries to supply a reasonable default value.
+    missing_values : {None, sequence of str}, optional
+        ``None`` or sequence of strings indicating a missing value. If ``None``
+        then missing values are indicated by empty entries. The default is
+        ``None``.
+    locked : bool, optional
+        Whether the StringConverter should be locked to prevent automatic
+        upgrade or not. Default is False.
+
+    """
+    _mapper = [(nx.bool, str2bool, False),
+               (nx.int_, int, -1),]
+
+    # On 32-bit systems, we need to make sure that we explicitly include
+    # nx.int64 since ns.int_ is nx.int32.
+    if nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize:
+        _mapper.append((nx.int64, int, -1))
+
+    _mapper.extend([(nx.float64, float, nx.nan),
+                    (nx.complex128, complex, nx.nan + 0j),
+                    (nx.longdouble, nx.longdouble, nx.nan),
+                    # If a non-default dtype is passed, fall back to generic
+                    # ones (should only be used for the converter)
+                    (nx.integer, int, -1),
+                    (nx.floating, float, nx.nan),
+                    (nx.complexfloating, complex, nx.nan + 0j),
+                    # Last, try with the string types (must be last, because
+                    # `_mapper[-1]` is used as default in some cases)
+                    (nx.str_, asunicode, '???'),
+                    (nx.bytes_, asbytes, '???'),
+                    ])
+
+    @classmethod
+    def _getdtype(cls, val):
+        """Returns the dtype of the input variable."""
+        return np.array(val).dtype
+
+    @classmethod
+    def _getsubdtype(cls, val):
+        """Returns the type of the dtype of the input variable."""
+        return np.array(val).dtype.type
+
+    @classmethod
+    def _dtypeortype(cls, dtype):
+        """Returns dtype for datetime64 and type of dtype otherwise."""
+
+        # This is a bit annoying. We want to return the "general" type in most
+        # cases (ie. "string" rather than "S10"), but we want to return the
+        # specific type for datetime64 (ie. "datetime64[us]" rather than
+        # "datetime64").
+        if dtype.type == np.datetime64:
+            return dtype
+        return dtype.type
+
+    @classmethod
+    def upgrade_mapper(cls, func, default=None):
+        """
+        Upgrade the mapper of a StringConverter by adding a new function and
+        its corresponding default.
+
+        The input function (or sequence of functions) and its associated
+        default value (if any) is inserted in penultimate position of the
+        mapper.  The corresponding type is estimated from the dtype of the
+        default value.
+
+        Parameters
+        ----------
+        func : var
+            Function, or sequence of functions
+
+        Examples
+        --------
+        >>> import dateutil.parser
+        >>> import datetime
+        >>> dateparser = dateutil.parser.parse
+        >>> defaultdate = datetime.date(2000, 1, 1)
+        >>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
+        """
+        # Func is a single functions
+        if callable(func):
+            cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))
+            return
+        elif hasattr(func, '__iter__'):
+            if isinstance(func[0], (tuple, list)):
+                for _ in func:
+                    cls._mapper.insert(-1, _)
+                return
+            if default is None:
+                default = [None] * len(func)
+            else:
+                default = list(default)
+                default.append([None] * (len(func) - len(default)))
+            for fct, dft in zip(func, default):
+                cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))
+
+    @classmethod
+    def _find_map_entry(cls, dtype):
+        # if a converter for the specific dtype is available use that
+        for i, (deftype, func, default_def) in enumerate(cls._mapper):
+            if dtype.type == deftype:
+                return i, (deftype, func, default_def)
+
+        # otherwise find an inexact match
+        for i, (deftype, func, default_def) in enumerate(cls._mapper):
+            if np.issubdtype(dtype.type, deftype):
+                return i, (deftype, func, default_def)
+
+        raise LookupError
+
+    def __init__(self, dtype_or_func=None, default=None, missing_values=None,
+                 locked=False):
+        # Defines a lock for upgrade
+        self._locked = bool(locked)
+        # No input dtype: minimal initialization
+        if dtype_or_func is None:
+            self.func = str2bool
+            self._status = 0
+            self.default = default or False
+            dtype = np.dtype('bool')
+        else:
+            # Is the input a np.dtype ?
+            try:
+                self.func = None
+                dtype = np.dtype(dtype_or_func)
+            except TypeError:
+                # dtype_or_func must be a function, then
+                if not callable(dtype_or_func):
+                    errmsg = ("The input argument `dtype` is neither a"
+                              " function nor a dtype (got '%s' instead)")
+                    raise TypeError(errmsg % type(dtype_or_func))
+                # Set the function
+                self.func = dtype_or_func
+                # If we don't have a default, try to guess it or set it to
+                # None
+                if default is None:
+                    try:
+                        default = self.func('0')
+                    except ValueError:
+                        default = None
+                dtype = self._getdtype(default)
+
+            # find the best match in our mapper
+            try:
+                self._status, (_, func, default_def) = self._find_map_entry(dtype)
+            except LookupError:
+                # no match
+                self.default = default
+                _, func, _ = self._mapper[-1]
+                self._status = 0
+            else:
+                # use the found default only if we did not already have one
+                if default is None:
+                    self.default = default_def
+                else:
+                    self.default = default
+
+            # If the input was a dtype, set the function to the last we saw
+            if self.func is None:
+                self.func = func
+
+            # If the status is 1 (int), change the function to
+            # something more robust.
+            if self.func == self._mapper[1][1]:
+                if issubclass(dtype.type, np.uint64):
+                    self.func = np.uint64
+                elif issubclass(dtype.type, np.int64):
+                    self.func = np.int64
+                else:
+                    self.func = lambda x: int(float(x))
+        # Store the list of strings corresponding to missing values.
+        if missing_values is None:
+            self.missing_values = {''}
+        else:
+            if isinstance(missing_values, str):
+                missing_values = missing_values.split(",")
+            self.missing_values = set(list(missing_values) + [''])
+
+        self._callingfunction = self._strict_call
+        self.type = self._dtypeortype(dtype)
+        self._checked = False
+        self._initial_default = default
+
+    def _loose_call(self, value):
+        try:
+            return self.func(value)
+        except ValueError:
+            return self.default
+
+    def _strict_call(self, value):
+        try:
+
+            # We check if we can convert the value using the current function
+            new_value = self.func(value)
+
+            # In addition to having to check whether func can convert the
+            # value, we also have to make sure that we don't get overflow
+            # errors for integers.
+            if self.func is int:
+                try:
+                    np.array(value, dtype=self.type)
+                except OverflowError:
+                    raise ValueError
+
+            # We're still here so we can now return the new value
+            return new_value
+
+        except ValueError:
+            if value.strip() in self.missing_values:
+                if not self._status:
+                    self._checked = False
+                return self.default
+            raise ValueError("Cannot convert string '%s'" % value)
+
+    def __call__(self, value):
+        return self._callingfunction(value)
+
+    def _do_upgrade(self):
+        # Raise an exception if we locked the converter...
+        if self._locked:
+            errmsg = "Converter is locked and cannot be upgraded"
+            raise ConverterLockError(errmsg)
+        _statusmax = len(self._mapper)
+        # Complains if we try to upgrade by the maximum
+        _status = self._status
+        if _status == _statusmax:
+            errmsg = "Could not find a valid conversion function"
+            raise ConverterError(errmsg)
+        elif _status < _statusmax - 1:
+            _status += 1
+        self.type, self.func, default = self._mapper[_status]
+        self._status = _status
+        if self._initial_default is not None:
+            self.default = self._initial_default
+        else:
+            self.default = default
+
+    def upgrade(self, value):
+        """
+        Find the best converter for a given string, and return the result.
+
+        The supplied string `value` is converted by testing different
+        converters in order. First the `func` method of the
+        `StringConverter` instance is tried, if this fails other available
+        converters are tried.  The order in which these other converters
+        are tried is determined by the `_status` attribute of the instance.
+
+        Parameters
+        ----------
+        value : str
+            The string to convert.
+
+        Returns
+        -------
+        out : any
+            The result of converting `value` with the appropriate converter.
+
+        """
+        self._checked = True
+        try:
+            return self._strict_call(value)
+        except ValueError:
+            self._do_upgrade()
+            return self.upgrade(value)
+
+    def iterupgrade(self, value):
+        self._checked = True
+        if not hasattr(value, '__iter__'):
+            value = (value,)
+        _strict_call = self._strict_call
+        try:
+            for _m in value:
+                _strict_call(_m)
+        except ValueError:
+            self._do_upgrade()
+            self.iterupgrade(value)
+
+    def update(self, func, default=None, testing_value=None,
+               missing_values='', locked=False):
+        """
+        Set StringConverter attributes directly.
+
+        Parameters
+        ----------
+        func : function
+            Conversion function.
+        default : any, optional
+            Value to return by default, that is, when the string to be
+            converted is flagged as missing. If not given,
+            `StringConverter` tries to supply a reasonable default value.
+        testing_value : str, optional
+            A string representing a standard input value of the converter.
+            This string is used to help defining a reasonable default
+            value.
+        missing_values : {sequence of str, None}, optional
+            Sequence of strings indicating a missing value. If ``None``, then
+            the existing `missing_values` are cleared. The default is ``''``.
+        locked : bool, optional
+            Whether the StringConverter should be locked to prevent
+            automatic upgrade or not. Default is False.
+
+        Notes
+        -----
+        `update` takes the same parameters as the constructor of
+        `StringConverter`, except that `func` does not accept a `dtype`
+        whereas `dtype_or_func` in the constructor does.
+
+        """
+        self.func = func
+        self._locked = locked
+
+        # Don't reset the default to None if we can avoid it
+        if default is not None:
+            self.default = default
+            self.type = self._dtypeortype(self._getdtype(default))
+        else:
+            try:
+                tester = func(testing_value or '1')
+            except (TypeError, ValueError):
+                tester = None
+            self.type = self._dtypeortype(self._getdtype(tester))
+
+        # Add the missing values to the existing set or clear it.
+        if missing_values is None:
+            # Clear all missing values even though the ctor initializes it to
+            # set(['']) when the argument is None.
+            self.missing_values = set()
+        else:
+            if not np.iterable(missing_values):
+                missing_values = [missing_values]
+            if not all(isinstance(v, str) for v in missing_values):
+                raise TypeError("missing_values must be strings or unicode")
+            self.missing_values.update(missing_values)
+
+
+def easy_dtype(ndtype, names=None, defaultfmt="f%i", **validationargs):
+    """
+    Convenience function to create a `np.dtype` object.
+
+    The function processes the input `dtype` and matches it with the given
+    names.
+
+    Parameters
+    ----------
+    ndtype : var
+        Definition of the dtype. Can be any string or dictionary recognized
+        by the `np.dtype` function, or a sequence of types.
+    names : str or sequence, optional
+        Sequence of strings to use as field names for a structured dtype.
+        For convenience, `names` can be a string of a comma-separated list
+        of names.
+    defaultfmt : str, optional
+        Format string used to define missing names, such as ``"f%i"``
+        (default) or ``"fields_%02i"``.
+    validationargs : optional
+        A series of optional arguments used to initialize a
+        `NameValidator`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.lib._iotools.easy_dtype(float)
+    dtype('float64')
+    >>> np.lib._iotools.easy_dtype("i4, f8")
+    dtype([('f0', '>> np.lib._iotools.easy_dtype("i4, f8", defaultfmt="field_%03i")
+    dtype([('field_000', '>> np.lib._iotools.easy_dtype((int, float, float), names="a,b,c")
+    dtype([('a', '>> np.lib._iotools.easy_dtype(float, names="a,b,c")
+    dtype([('a', ' None: ...
+    def __call__(self, /, line: str | bytes) -> list[str]: ...
+    def autostrip(self, /, method: Callable[[_T], Iterable[str]]) -> Callable[[_T], list[str]]: ...
+
+class NameValidator:
+    defaultexcludelist: ClassVar[Sequence[str]]
+    defaultdeletechars: ClassVar[Sequence[str]]
+    excludelist: list[str]
+    deletechars: set[str]
+    case_converter: Callable[[str], str]
+    replace_space: str
+
+    def __init__(
+        self,
+        /,
+        excludelist: Iterable[str] | None = None,
+        deletechars: Iterable[str] | None = None,
+        case_sensitive: Literal["upper", "lower"] | bool | None = None,
+        replace_space: str = "_",
+    ) -> None: ...
+    def __call__(self, /, names: Iterable[str], defaultfmt: str = "f%i", nbfields: int | None = None) -> tuple[str, ...]: ...
+    def validate(self, /, names: Iterable[str], defaultfmt: str = "f%i", nbfields: int | None = None) -> tuple[str, ...]: ...
+
+class StringConverter:
+    func: Callable[[str], Any] | None
+    default: Any
+    missing_values: set[str]
+    type: np.dtype[np.datetime64] | np.generic
+
+    def __init__(
+        self,
+        /,
+        dtype_or_func: npt.DTypeLike | None = None,
+        default: None = None,
+        missing_values: Iterable[str] | None = None,
+        locked: bool = False,
+    ) -> None: ...
+    def update(
+        self,
+        /,
+        func: Callable[[str], Any],
+        default: object | None = None,
+        testing_value: str | None = None,
+        missing_values: str = "",
+        locked: bool = False,
+    ) -> None: ...
+    #
+    def __call__(self, /, value: str) -> Any: ...
+    def upgrade(self, /, value: str) -> Any: ...
+    def iterupgrade(self, /, value: Iterable[str] | str) -> None: ...
+
+    #
+    @classmethod
+    def upgrade_mapper(cls, func: Callable[[str], Any], default: object | None = None) -> None: ...
+
+@overload
+def str2bool(value: Literal["false", "False", "FALSE"]) -> Literal[False]: ...
+@overload
+def str2bool(value: Literal["true", "True", "TRUE"]) -> Literal[True]: ...
+
+#
+def has_nested_fields(ndtype: np.dtype[np.void]) -> bool: ...
+def flatten_dtype(ndtype: np.dtype[np.void], flatten_base: bool = False) -> type[np.dtype[Any]]: ...
+def easy_dtype(
+    ndtype: npt.DTypeLike,
+    names: Iterable[str] | None = None,
+    defaultfmt: str = "f%i",
+    **validationargs: Unpack[_ValidationKwargs],
+) -> np.dtype[np.void]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d0173dbe34018659ab4d9d06bc71ab2189b2fae
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.py
@@ -0,0 +1,2028 @@
+"""
+Functions that ignore NaN.
+
+Functions
+---------
+
+- `nanmin` -- minimum non-NaN value
+- `nanmax` -- maximum non-NaN value
+- `nanargmin` -- index of minimum non-NaN value
+- `nanargmax` -- index of maximum non-NaN value
+- `nansum` -- sum of non-NaN values
+- `nanprod` -- product of non-NaN values
+- `nancumsum` -- cumulative sum of non-NaN values
+- `nancumprod` -- cumulative product of non-NaN values
+- `nanmean` -- mean of non-NaN values
+- `nanvar` -- variance of non-NaN values
+- `nanstd` -- standard deviation of non-NaN values
+- `nanmedian` -- median of non-NaN values
+- `nanquantile` -- qth quantile of non-NaN values
+- `nanpercentile` -- qth percentile of non-NaN values
+
+"""
+import functools
+import warnings
+import numpy as np
+import numpy._core.numeric as _nx
+from numpy.lib import _function_base_impl as fnb
+from numpy.lib._function_base_impl import _weights_are_valid
+from numpy._core import overrides
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+__all__ = [
+    'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean',
+    'nanmedian', 'nanpercentile', 'nanvar', 'nanstd', 'nanprod',
+    'nancumsum', 'nancumprod', 'nanquantile'
+    ]
+
+
+def _nan_mask(a, out=None):
+    """
+    Parameters
+    ----------
+    a : array-like
+        Input array with at least 1 dimension.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``; if provided, it must have the same shape as the
+        expected output and will prevent the allocation of a new array.
+
+    Returns
+    -------
+    y : bool ndarray or True
+        A bool array where ``np.nan`` positions are marked with ``False``
+        and other positions are marked with ``True``. If the type of ``a``
+        is such that it can't possibly contain ``np.nan``, returns ``True``.
+    """
+    # we assume that a is an array for this private function
+
+    if a.dtype.kind not in 'fc':
+        return True
+
+    y = np.isnan(a, out=out)
+    y = np.invert(y, out=y)
+    return y
+
+def _replace_nan(a, val):
+    """
+    If `a` is of inexact type, make a copy of `a`, replace NaNs with
+    the `val` value, and return the copy together with a boolean mask
+    marking the locations where NaNs were present. If `a` is not of
+    inexact type, do nothing and return `a` together with a mask of None.
+
+    Note that scalars will end up as array scalars, which is important
+    for using the result as the value of the out argument in some
+    operations.
+
+    Parameters
+    ----------
+    a : array-like
+        Input array.
+    val : float
+        NaN values are set to val before doing the operation.
+
+    Returns
+    -------
+    y : ndarray
+        If `a` is of inexact type, return a copy of `a` with the NaNs
+        replaced by the fill value, otherwise return `a`.
+    mask: {bool, None}
+        If `a` is of inexact type, return a boolean mask marking locations of
+        NaNs, otherwise return None.
+
+    """
+    a = np.asanyarray(a)
+
+    if a.dtype == np.object_:
+        # object arrays do not support `isnan` (gh-9009), so make a guess
+        mask = np.not_equal(a, a, dtype=bool)
+    elif issubclass(a.dtype.type, np.inexact):
+        mask = np.isnan(a)
+    else:
+        mask = None
+
+    if mask is not None:
+        a = np.array(a, subok=True, copy=True)
+        np.copyto(a, val, where=mask)
+
+    return a, mask
+
+
+def _copyto(a, val, mask):
+    """
+    Replace values in `a` with NaN where `mask` is True.  This differs from
+    copyto in that it will deal with the case where `a` is a numpy scalar.
+
+    Parameters
+    ----------
+    a : ndarray or numpy scalar
+        Array or numpy scalar some of whose values are to be replaced
+        by val.
+    val : numpy scalar
+        Value used a replacement.
+    mask : ndarray, scalar
+        Boolean array. Where True the corresponding element of `a` is
+        replaced by `val`. Broadcasts.
+
+    Returns
+    -------
+    res : ndarray, scalar
+        Array with elements replaced or scalar `val`.
+
+    """
+    if isinstance(a, np.ndarray):
+        np.copyto(a, val, where=mask, casting='unsafe')
+    else:
+        a = a.dtype.type(val)
+    return a
+
+
+def _remove_nan_1d(arr1d, second_arr1d=None, overwrite_input=False):
+    """
+    Equivalent to arr1d[~arr1d.isnan()], but in a different order
+
+    Presumably faster as it incurs fewer copies
+
+    Parameters
+    ----------
+    arr1d : ndarray
+        Array to remove nans from
+    second_arr1d : ndarray or None
+        A second array which will have the same positions removed as arr1d.
+    overwrite_input : bool
+        True if `arr1d` can be modified in place
+
+    Returns
+    -------
+    res : ndarray
+        Array with nan elements removed
+    second_res : ndarray or None
+        Second array with nan element positions of first array removed.
+    overwrite_input : bool
+        True if `res` can be modified in place, given the constraint on the
+        input
+    """
+    if arr1d.dtype == object:
+        # object arrays do not support `isnan` (gh-9009), so make a guess
+        c = np.not_equal(arr1d, arr1d, dtype=bool)
+    else:
+        c = np.isnan(arr1d)
+
+    s = np.nonzero(c)[0]
+    if s.size == arr1d.size:
+        warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                      stacklevel=6)
+        if second_arr1d is None:
+            return arr1d[:0], None, True
+        else:
+            return arr1d[:0], second_arr1d[:0], True
+    elif s.size == 0:
+        return arr1d, second_arr1d, overwrite_input
+    else:
+        if not overwrite_input:
+            arr1d = arr1d.copy()
+        # select non-nans at end of array
+        enonan = arr1d[-s.size:][~c[-s.size:]]
+        # fill nans in beginning of array with non-nans of end
+        arr1d[s[:enonan.size]] = enonan
+
+        if second_arr1d is None:
+            return arr1d[:-s.size], None, True
+        else:
+            if not overwrite_input:
+                second_arr1d = second_arr1d.copy()
+            enonan = second_arr1d[-s.size:][~c[-s.size:]]
+            second_arr1d[s[:enonan.size]] = enonan
+
+            return arr1d[:-s.size], second_arr1d[:-s.size], True
+
+
+def _divide_by_count(a, b, out=None):
+    """
+    Compute a/b ignoring invalid results. If `a` is an array the division
+    is done in place. If `a` is a scalar, then its type is preserved in the
+    output. If out is None, then a is used instead so that the division
+    is in place. Note that this is only called with `a` an inexact type.
+
+    Parameters
+    ----------
+    a : {ndarray, numpy scalar}
+        Numerator. Expected to be of inexact type but not checked.
+    b : {ndarray, numpy scalar}
+        Denominator.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``; if provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary.
+
+    Returns
+    -------
+    ret : {ndarray, numpy scalar}
+        The return value is a/b. If `a` was an ndarray the division is done
+        in place. If `a` is a numpy scalar, the division preserves its type.
+
+    """
+    with np.errstate(invalid='ignore', divide='ignore'):
+        if isinstance(a, np.ndarray):
+            if out is None:
+                return np.divide(a, b, out=a, casting='unsafe')
+            else:
+                return np.divide(a, b, out=out, casting='unsafe')
+        else:
+            if out is None:
+                # Precaution against reduced object arrays
+                try:
+                    return a.dtype.type(a / b)
+                except AttributeError:
+                    return a / b
+            else:
+                # This is questionable, but currently a numpy scalar can
+                # be output to a zero dimensional array.
+                return np.divide(a, b, out=out, casting='unsafe')
+
+
+def _nanmin_dispatcher(a, axis=None, out=None, keepdims=None,
+                       initial=None, where=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanmin_dispatcher)
+def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+           where=np._NoValue):
+    """
+    Return minimum of an array or minimum along an axis, ignoring any NaNs.
+    When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
+    Nan is returned for that slice.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose minimum is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the minimum is computed. The default is to compute
+        the minimum of the flattened array.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``; if provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary. See
+        :ref:`ufuncs-output-type` for more details.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+        If the value is anything but the default, then
+        `keepdims` will be passed through to the `min` method
+        of sub-classes of `ndarray`.  If the sub-classes methods
+        does not implement `keepdims` any exceptions will be raised.
+    initial : scalar, optional
+        The maximum value of an output element. Must be present to allow
+        computation on empty slice. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+    where : array_like of bool, optional
+        Elements to compare for the minimum. See `~numpy.ufunc.reduce`
+        for details.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    nanmin : ndarray
+        An array with the same shape as `a`, with the specified axis
+        removed.  If `a` is a 0-d array, or if axis is None, an ndarray
+        scalar is returned.  The same dtype as `a` is returned.
+
+    See Also
+    --------
+    nanmax :
+        The maximum value of an array along a given axis, ignoring any NaNs.
+    amin :
+        The minimum value of an array along a given axis, propagating any NaNs.
+    fmin :
+        Element-wise minimum of two arrays, ignoring any NaNs.
+    minimum :
+        Element-wise minimum of two arrays, propagating any NaNs.
+    isnan :
+        Shows which elements are Not a Number (NaN).
+    isfinite:
+        Shows which elements are neither NaN nor infinity.
+
+    amax, fmax, maximum
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+    Positive infinity is treated as a very large number and negative
+    infinity is treated as a very small (i.e. negative) number.
+
+    If the input has a integer type the function is equivalent to np.min.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nanmin(a)
+    1.0
+    >>> np.nanmin(a, axis=0)
+    array([1.,  2.])
+    >>> np.nanmin(a, axis=1)
+    array([1.,  3.])
+
+    When positive infinity and negative infinity are present:
+
+    >>> np.nanmin([1, 2, np.nan, np.inf])
+    1.0
+    >>> np.nanmin([1, 2, np.nan, -np.inf])
+    -inf
+
+    """
+    kwargs = {}
+    if keepdims is not np._NoValue:
+        kwargs['keepdims'] = keepdims
+    if initial is not np._NoValue:
+        kwargs['initial'] = initial
+    if where is not np._NoValue:
+        kwargs['where'] = where
+
+    if type(a) is np.ndarray and a.dtype != np.object_:
+        # Fast, but not safe for subclasses of ndarray, or object arrays,
+        # which do not implement isnan (gh-9009), or fmin correctly (gh-8975)
+        res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
+        if np.isnan(res).any():
+            warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                          stacklevel=2)
+    else:
+        # Slow, but safe for subclasses of ndarray
+        a, mask = _replace_nan(a, +np.inf)
+        res = np.amin(a, axis=axis, out=out, **kwargs)
+        if mask is None:
+            return res
+
+        # Check for all-NaN axis
+        kwargs.pop("initial", None)
+        mask = np.all(mask, axis=axis, **kwargs)
+        if np.any(mask):
+            res = _copyto(res, np.nan, mask)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning,
+                          stacklevel=2)
+    return res
+
+
+def _nanmax_dispatcher(a, axis=None, out=None, keepdims=None,
+                       initial=None, where=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanmax_dispatcher)
+def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
+           where=np._NoValue):
+    """
+    Return the maximum of an array or maximum along an axis, ignoring any
+    NaNs.  When all-NaN slices are encountered a ``RuntimeWarning`` is
+    raised and NaN is returned for that slice.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose maximum is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the maximum is computed. The default is to compute
+        the maximum of the flattened array.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``; if provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary. See
+        :ref:`ufuncs-output-type` for more details.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+        If the value is anything but the default, then
+        `keepdims` will be passed through to the `max` method
+        of sub-classes of `ndarray`.  If the sub-classes methods
+        does not implement `keepdims` any exceptions will be raised.
+    initial : scalar, optional
+        The minimum value of an output element. Must be present to allow
+        computation on empty slice. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+    where : array_like of bool, optional
+        Elements to compare for the maximum. See `~numpy.ufunc.reduce`
+        for details.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    nanmax : ndarray
+        An array with the same shape as `a`, with the specified axis removed.
+        If `a` is a 0-d array, or if axis is None, an ndarray scalar is
+        returned.  The same dtype as `a` is returned.
+
+    See Also
+    --------
+    nanmin :
+        The minimum value of an array along a given axis, ignoring any NaNs.
+    amax :
+        The maximum value of an array along a given axis, propagating any NaNs.
+    fmax :
+        Element-wise maximum of two arrays, ignoring any NaNs.
+    maximum :
+        Element-wise maximum of two arrays, propagating any NaNs.
+    isnan :
+        Shows which elements are Not a Number (NaN).
+    isfinite:
+        Shows which elements are neither NaN nor infinity.
+
+    amin, fmin, minimum
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+    Positive infinity is treated as a very large number and negative
+    infinity is treated as a very small (i.e. negative) number.
+
+    If the input has a integer type the function is equivalent to np.max.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nanmax(a)
+    3.0
+    >>> np.nanmax(a, axis=0)
+    array([3.,  2.])
+    >>> np.nanmax(a, axis=1)
+    array([2.,  3.])
+
+    When positive infinity and negative infinity are present:
+
+    >>> np.nanmax([1, 2, np.nan, -np.inf])
+    2.0
+    >>> np.nanmax([1, 2, np.nan, np.inf])
+    inf
+
+    """
+    kwargs = {}
+    if keepdims is not np._NoValue:
+        kwargs['keepdims'] = keepdims
+    if initial is not np._NoValue:
+        kwargs['initial'] = initial
+    if where is not np._NoValue:
+        kwargs['where'] = where
+
+    if type(a) is np.ndarray and a.dtype != np.object_:
+        # Fast, but not safe for subclasses of ndarray, or object arrays,
+        # which do not implement isnan (gh-9009), or fmax correctly (gh-8975)
+        res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
+        if np.isnan(res).any():
+            warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                          stacklevel=2)
+    else:
+        # Slow, but safe for subclasses of ndarray
+        a, mask = _replace_nan(a, -np.inf)
+        res = np.amax(a, axis=axis, out=out, **kwargs)
+        if mask is None:
+            return res
+
+        # Check for all-NaN axis
+        kwargs.pop("initial", None)
+        mask = np.all(mask, axis=axis, **kwargs)
+        if np.any(mask):
+            res = _copyto(res, np.nan, mask)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning,
+                          stacklevel=2)
+    return res
+
+
+def _nanargmin_dispatcher(a, axis=None, out=None, *, keepdims=None):
+    return (a,)
+
+
+@array_function_dispatch(_nanargmin_dispatcher)
+def nanargmin(a, axis=None, out=None, *, keepdims=np._NoValue):
+    """
+    Return the indices of the minimum values in the specified axis ignoring
+    NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
+    cannot be trusted if a slice contains only NaNs and Infs.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default flattened input is used.
+    out : array, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+        .. versionadded:: 1.22.0
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the array.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    index_array : ndarray
+        An array of indices or a single index value.
+
+    See Also
+    --------
+    argmin, nanargmax
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[np.nan, 4], [2, 3]])
+    >>> np.argmin(a)
+    0
+    >>> np.nanargmin(a)
+    2
+    >>> np.nanargmin(a, axis=0)
+    array([1, 1])
+    >>> np.nanargmin(a, axis=1)
+    array([1, 0])
+
+    """
+    a, mask = _replace_nan(a, np.inf)
+    if mask is not None and mask.size:
+        mask = np.all(mask, axis=axis)
+        if np.any(mask):
+            raise ValueError("All-NaN slice encountered")
+    res = np.argmin(a, axis=axis, out=out, keepdims=keepdims)
+    return res
+
+
+def _nanargmax_dispatcher(a, axis=None, out=None, *, keepdims=None):
+    return (a,)
+
+
+@array_function_dispatch(_nanargmax_dispatcher)
+def nanargmax(a, axis=None, out=None, *, keepdims=np._NoValue):
+    """
+    Return the indices of the maximum values in the specified axis ignoring
+    NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
+    results cannot be trusted if a slice contains only NaNs and -Infs.
+
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default flattened input is used.
+    out : array, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+        .. versionadded:: 1.22.0
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the array.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    index_array : ndarray
+        An array of indices or a single index value.
+
+    See Also
+    --------
+    argmax, nanargmin
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[np.nan, 4], [2, 3]])
+    >>> np.argmax(a)
+    0
+    >>> np.nanargmax(a)
+    1
+    >>> np.nanargmax(a, axis=0)
+    array([1, 0])
+    >>> np.nanargmax(a, axis=1)
+    array([1, 1])
+
+    """
+    a, mask = _replace_nan(a, -np.inf)
+    if mask is not None and mask.size:
+        mask = np.all(mask, axis=axis)
+        if np.any(mask):
+            raise ValueError("All-NaN slice encountered")
+    res = np.argmax(a, axis=axis, out=out, keepdims=keepdims)
+    return res
+
+
+def _nansum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
+                       initial=None, where=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nansum_dispatcher)
+def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
+           initial=np._NoValue, where=np._NoValue):
+    """
+    Return the sum of array elements over a given axis treating Not a
+    Numbers (NaNs) as zero.
+
+    In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or
+    empty. In later versions zero is returned.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose sum is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the sum is computed. The default is to compute the
+        sum of the flattened array.
+    dtype : data-type, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed.  By default, the dtype of `a` is used.  An
+        exception is when `a` has an integer type with less precision than
+        the platform (u)intp. In that case, the default will be either
+        (u)int32 or (u)int64 depending on whether the platform is 32 or 64
+        bits. For inexact inputs, dtype must be inexact.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``. If provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary.  See
+        :ref:`ufuncs-output-type` for more details. The casting of NaN to integer
+        can yield unexpected results.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+        If the value is anything but the default, then
+        `keepdims` will be passed through to the `mean` or `sum` methods
+        of sub-classes of `ndarray`.  If the sub-classes methods
+        does not implement `keepdims` any exceptions will be raised.
+    initial : scalar, optional
+        Starting value for the sum. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+    where : array_like of bool, optional
+        Elements to include in the sum. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    nansum : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which it is returned. The result has the same
+        size as `a`, and the same shape as `a` if `axis` is not None
+        or `a` is a 1-d array.
+
+    See Also
+    --------
+    numpy.sum : Sum across array propagating NaNs.
+    isnan : Show which elements are NaN.
+    isfinite : Show which elements are not NaN or +/-inf.
+
+    Notes
+    -----
+    If both positive and negative infinity are present, the sum will be Not
+    A Number (NaN).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.nansum(1)
+    1
+    >>> np.nansum([1])
+    1
+    >>> np.nansum([1, np.nan])
+    1.0
+    >>> a = np.array([[1, 1], [1, np.nan]])
+    >>> np.nansum(a)
+    3.0
+    >>> np.nansum(a, axis=0)
+    array([2.,  1.])
+    >>> np.nansum([1, np.nan, np.inf])
+    inf
+    >>> np.nansum([1, np.nan, -np.inf])
+    -inf
+    >>> from numpy.testing import suppress_warnings
+    >>> with np.errstate(invalid="ignore"):
+    ...     np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
+    np.float64(nan)
+
+    """
+    a, mask = _replace_nan(a, 0)
+    return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
+                  initial=initial, where=where)
+
+
+def _nanprod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
+                        initial=None, where=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanprod_dispatcher)
+def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
+            initial=np._NoValue, where=np._NoValue):
+    """
+    Return the product of array elements over a given axis treating Not a
+    Numbers (NaNs) as ones.
+
+    One is returned for slices that are all-NaN or empty.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose product is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the product is computed. The default is to compute
+        the product of the flattened array.
+    dtype : data-type, optional
+        The type of the returned array and of the accumulator in which the
+        elements are summed.  By default, the dtype of `a` is used.  An
+        exception is when `a` has an integer type with less precision than
+        the platform (u)intp. In that case, the default will be either
+        (u)int32 or (u)int64 depending on whether the platform is 32 or 64
+        bits. For inexact inputs, dtype must be inexact.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``. If provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary. See
+        :ref:`ufuncs-output-type` for more details. The casting of NaN to integer
+        can yield unexpected results.
+    keepdims : bool, optional
+        If True, the axes which are reduced are left in the result as
+        dimensions with size one. With this option, the result will
+        broadcast correctly against the original `arr`.
+    initial : scalar, optional
+        The starting value for this product. See `~numpy.ufunc.reduce`
+        for details.
+
+        .. versionadded:: 1.22.0
+    where : array_like of bool, optional
+        Elements to include in the product. See `~numpy.ufunc.reduce`
+        for details.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    nanprod : ndarray
+        A new array holding the result is returned unless `out` is
+        specified, in which case it is returned.
+
+    See Also
+    --------
+    numpy.prod : Product across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.nanprod(1)
+    1
+    >>> np.nanprod([1])
+    1
+    >>> np.nanprod([1, np.nan])
+    1.0
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nanprod(a)
+    6.0
+    >>> np.nanprod(a, axis=0)
+    array([3., 2.])
+
+    """
+    a, mask = _replace_nan(a, 1)
+    return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
+                   initial=initial, where=where)
+
+
+def _nancumsum_dispatcher(a, axis=None, dtype=None, out=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nancumsum_dispatcher)
+def nancumsum(a, axis=None, dtype=None, out=None):
+    """
+    Return the cumulative sum of array elements over a given axis treating Not a
+    Numbers (NaNs) as zero.  The cumulative sum does not change when NaNs are
+    encountered and leading NaNs are replaced by zeros.
+
+    Zeros are returned for slices that are all-NaN or empty.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default
+        (None) is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the
+        elements are summed.  If `dtype` is not specified, it defaults
+        to the dtype of `a`, unless `a` has an integer dtype with a
+        precision less than that of the default platform integer.  In
+        that case, the default platform integer is used.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary. See :ref:`ufuncs-output-type` for
+        more details.
+
+    Returns
+    -------
+    nancumsum : ndarray.
+        A new array holding the result is returned unless `out` is
+        specified, in which it is returned. The result has the same
+        size as `a`, and the same shape as `a` if `axis` is not None
+        or `a` is a 1-d array.
+
+    See Also
+    --------
+    numpy.cumsum : Cumulative sum across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.nancumsum(1)
+    array([1])
+    >>> np.nancumsum([1])
+    array([1])
+    >>> np.nancumsum([1, np.nan])
+    array([1.,  1.])
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nancumsum(a)
+    array([1.,  3.,  6.,  6.])
+    >>> np.nancumsum(a, axis=0)
+    array([[1.,  2.],
+           [4.,  2.]])
+    >>> np.nancumsum(a, axis=1)
+    array([[1.,  3.],
+           [3.,  3.]])
+
+    """
+    a, mask = _replace_nan(a, 0)
+    return np.cumsum(a, axis=axis, dtype=dtype, out=out)
+
+
+def _nancumprod_dispatcher(a, axis=None, dtype=None, out=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nancumprod_dispatcher)
+def nancumprod(a, axis=None, dtype=None, out=None):
+    """
+    Return the cumulative product of array elements over a given axis treating Not a
+    Numbers (NaNs) as one.  The cumulative product does not change when NaNs are
+    encountered and leading NaNs are replaced by ones.
+
+    Ones are returned for slices that are all-NaN or empty.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative product is computed.  By default
+        the input is flattened.
+    dtype : dtype, optional
+        Type of the returned array, as well as of the accumulator in which
+        the elements are multiplied.  If *dtype* is not specified, it
+        defaults to the dtype of `a`, unless `a` has an integer dtype with
+        a precision less than that of the default platform integer.  In
+        that case, the default platform integer is used instead.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type of the resulting values will be cast if necessary.
+
+    Returns
+    -------
+    nancumprod : ndarray
+        A new array holding the result is returned unless `out` is
+        specified, in which case it is returned.
+
+    See Also
+    --------
+    numpy.cumprod : Cumulative product across array propagating NaNs.
+    isnan : Show which elements are NaN.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.nancumprod(1)
+    array([1])
+    >>> np.nancumprod([1])
+    array([1])
+    >>> np.nancumprod([1, np.nan])
+    array([1.,  1.])
+    >>> a = np.array([[1, 2], [3, np.nan]])
+    >>> np.nancumprod(a)
+    array([1.,  2.,  6.,  6.])
+    >>> np.nancumprod(a, axis=0)
+    array([[1.,  2.],
+           [3.,  2.]])
+    >>> np.nancumprod(a, axis=1)
+    array([[1.,  2.],
+           [3.,  3.]])
+
+    """
+    a, mask = _replace_nan(a, 1)
+    return np.cumprod(a, axis=axis, dtype=dtype, out=out)
+
+
+def _nanmean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
+                        *, where=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanmean_dispatcher)
+def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
+            *, where=np._NoValue):
+    """
+    Compute the arithmetic mean along the specified axis, ignoring NaNs.
+
+    Returns the average of the array elements.  The average is taken over
+    the flattened array by default, otherwise over the specified axis.
+    `float64` intermediate and return values are used for integer inputs.
+
+    For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose mean is desired. If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the means are computed. The default is to compute
+        the mean of the flattened array.
+    dtype : data-type, optional
+        Type to use in computing the mean.  For integer inputs, the default
+        is `float64`; for inexact inputs, it is the same as the input
+        dtype.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  The default
+        is ``None``; if provided, it must have the same shape as the
+        expected output, but the type will be cast if necessary.
+        See :ref:`ufuncs-output-type` for more details.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+        If the value is anything but the default, then
+        `keepdims` will be passed through to the `mean` or `sum` methods
+        of sub-classes of `ndarray`.  If the sub-classes methods
+        does not implement `keepdims` any exceptions will be raised.
+    where : array_like of bool, optional
+        Elements to include in the mean. See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+
+    Returns
+    -------
+    m : ndarray, see dtype parameter above
+        If `out=None`, returns a new array containing the mean values,
+        otherwise a reference to the output array is returned. Nan is
+        returned for slices that contain only NaNs.
+
+    See Also
+    --------
+    average : Weighted average
+    mean : Arithmetic mean taken while not ignoring NaNs
+    var, nanvar
+
+    Notes
+    -----
+    The arithmetic mean is the sum of the non-NaN elements along the axis
+    divided by the number of non-NaN elements.
+
+    Note that for floating-point input, the mean is computed using the same
+    precision the input has.  Depending on the input data, this can cause
+    the results to be inaccurate, especially for `float32`.  Specifying a
+    higher-precision accumulator using the `dtype` keyword can alleviate
+    this issue.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, np.nan], [3, 4]])
+    >>> np.nanmean(a)
+    2.6666666666666665
+    >>> np.nanmean(a, axis=0)
+    array([2.,  4.])
+    >>> np.nanmean(a, axis=1)
+    array([1.,  3.5]) # may vary
+
+    """
+    arr, mask = _replace_nan(a, 0)
+    if mask is None:
+        return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
+                       where=where)
+
+    if dtype is not None:
+        dtype = np.dtype(dtype)
+    if dtype is not None and not issubclass(dtype.type, np.inexact):
+        raise TypeError("If a is inexact, then dtype must be inexact")
+    if out is not None and not issubclass(out.dtype.type, np.inexact):
+        raise TypeError("If a is inexact, then out must be inexact")
+
+    cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims,
+                 where=where)
+    tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
+                 where=where)
+    avg = _divide_by_count(tot, cnt, out=out)
+
+    isbad = (cnt == 0)
+    if isbad.any():
+        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
+        # NaN is the only possible bad value, so no further
+        # action is needed to handle bad results.
+    return avg
+
+
+def _nanmedian1d(arr1d, overwrite_input=False):
+    """
+    Private function for rank 1 arrays. Compute the median ignoring NaNs.
+    See nanmedian for parameter usage
+    """
+    arr1d_parsed, _, overwrite_input = _remove_nan_1d(
+        arr1d, overwrite_input=overwrite_input,
+    )
+
+    if arr1d_parsed.size == 0:
+        # Ensure that a nan-esque scalar of the appropriate type (and unit)
+        # is returned for `timedelta64` and `complexfloating`
+        return arr1d[-1]
+
+    return np.median(arr1d_parsed, overwrite_input=overwrite_input)
+
+
+def _nanmedian(a, axis=None, out=None, overwrite_input=False):
+    """
+    Private function that doesn't support extended axis or keepdims.
+    These methods are extended to this function using _ureduce
+    See nanmedian for parameter usage
+
+    """
+    if axis is None or a.ndim == 1:
+        part = a.ravel()
+        if out is None:
+            return _nanmedian1d(part, overwrite_input)
+        else:
+            out[...] = _nanmedian1d(part, overwrite_input)
+            return out
+    else:
+        # for small medians use sort + indexing which is still faster than
+        # apply_along_axis
+        # benchmarked with shuffled (50, 50, x) containing a few NaN
+        if a.shape[axis] < 600:
+            return _nanmedian_small(a, axis, out, overwrite_input)
+        result = np.apply_along_axis(_nanmedian1d, axis, a, overwrite_input)
+        if out is not None:
+            out[...] = result
+        return result
+
+
+def _nanmedian_small(a, axis=None, out=None, overwrite_input=False):
+    """
+    sort + indexing median, faster for small medians along multiple
+    dimensions due to the high overhead of apply_along_axis
+
+    see nanmedian for parameter usage
+    """
+    a = np.ma.masked_array(a, np.isnan(a))
+    m = np.ma.median(a, axis=axis, overwrite_input=overwrite_input)
+    for i in range(np.count_nonzero(m.mask.ravel())):
+        warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                      stacklevel=5)
+
+    fill_value = np.timedelta64("NaT") if m.dtype.kind == "m" else np.nan
+    if out is not None:
+        out[...] = m.filled(fill_value)
+        return out
+    return m.filled(fill_value)
+
+
+def _nanmedian_dispatcher(
+        a, axis=None, out=None, overwrite_input=None, keepdims=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanmedian_dispatcher)
+def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValue):
+    """
+    Compute the median along the specified axis, while ignoring NaNs.
+
+    Returns the median of the array elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : {int, sequence of int, None}, optional
+        Axis or axes along which the medians are computed. The default
+        is to compute the median along a flattened version of the array.
+        A sequence of axes is supported since version 1.9.0.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output,
+        but the type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+       If True, then allow use of memory of input array `a` for
+       calculations. The input array will be modified by the call to
+       `median`. This will save memory when you do not need to preserve
+       the contents of the input array. Treat the input as undefined,
+       but it will probably be fully or partially sorted. Default is
+       False. If `overwrite_input` is ``True`` and `a` is not already an
+       `ndarray`, an error will be raised.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+        If this is anything but the default value it will be passed
+        through (in the special case of an empty array) to the
+        `mean` function of the underlying array.  If the array is
+        a sub-class and `mean` does not have the kwarg `keepdims` this
+        will raise a RuntimeError.
+
+    Returns
+    -------
+    median : ndarray
+        A new array holding the result. If the input contains integers
+        or floats smaller than ``float64``, then the output data-type is
+        ``np.float64``.  Otherwise, the data-type of the output is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    mean, median, percentile
+
+    Notes
+    -----
+    Given a vector ``V`` of length ``N``, the median of ``V`` is the
+    middle value of a sorted copy of ``V``, ``V_sorted`` - i.e.,
+    ``V_sorted[(N-1)/2]``, when ``N`` is odd and the average of the two
+    middle values of ``V_sorted`` when ``N`` is even.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10.0, 7, 4], [3, 2, 1]])
+    >>> a[0, 1] = np.nan
+    >>> a
+    array([[10., nan,  4.],
+           [ 3.,  2.,  1.]])
+    >>> np.median(a)
+    np.float64(nan)
+    >>> np.nanmedian(a)
+    3.0
+    >>> np.nanmedian(a, axis=0)
+    array([6.5, 2. , 2.5])
+    >>> np.median(a, axis=1)
+    array([nan,  2.])
+    >>> b = a.copy()
+    >>> np.nanmedian(b, axis=1, overwrite_input=True)
+    array([7.,  2.])
+    >>> assert not np.all(a==b)
+    >>> b = a.copy()
+    >>> np.nanmedian(b, axis=None, overwrite_input=True)
+    3.0
+    >>> assert not np.all(a==b)
+
+    """
+    a = np.asanyarray(a)
+    # apply_along_axis in _nanmedian doesn't handle empty arrays well,
+    # so deal them upfront
+    if a.size == 0:
+        return np.nanmean(a, axis, out=out, keepdims=keepdims)
+
+    return fnb._ureduce(a, func=_nanmedian, keepdims=keepdims,
+                        axis=axis, out=out,
+                        overwrite_input=overwrite_input)
+
+
+def _nanpercentile_dispatcher(
+        a, q, axis=None, out=None, overwrite_input=None,
+        method=None, keepdims=None, *, weights=None, interpolation=None):
+    return (a, q, out, weights)
+
+
+@array_function_dispatch(_nanpercentile_dispatcher)
+def nanpercentile(
+        a,
+        q,
+        axis=None,
+        out=None,
+        overwrite_input=False,
+        method="linear",
+        keepdims=np._NoValue,
+        *,
+        weights=None,
+        interpolation=None,
+):
+    """
+    Compute the qth percentile of the data along the specified axis,
+    while ignoring nan values.
+
+    Returns the qth percentile(s) of the array elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array, containing
+        nan values to be ignored.
+    q : array_like of float
+        Percentile or sequence of percentiles to compute, which must be
+        between 0 and 100 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the percentiles are computed. The default
+        is to compute the percentile(s) along a flattened version of the
+        array.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and buffer length as the expected output, but the
+        type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+        If True, then allow the input array `a` to be modified by
+        intermediate calculations, to save memory. In this case, the
+        contents of the input `a` after this function completes is
+        undefined.
+    method : str, optional
+        This parameter specifies the method to use for estimating the
+        percentile.  There are many different methods, some unique to NumPy.
+        See the notes for explanation.  The options sorted by their R type
+        as summarized in the H&F paper [1]_ are:
+
+        1. 'inverted_cdf'
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        The first three methods are discontinuous.  NumPy further defines the
+        following discontinuous variations of the default 'linear' (7.) option:
+
+        * 'lower'
+        * 'higher',
+        * 'midpoint'
+        * 'nearest'
+
+        .. versionchanged:: 1.22.0
+            This argument was previously called "interpolation" and only
+            offered the "linear" default and last four options.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option, the
+        result will broadcast correctly against the original array `a`.
+
+        If this is anything but the default value it will be passed
+        through (in the special case of an empty array) to the
+        `mean` function of the underlying array.  If the array is
+        a sub-class and `mean` does not have the kwarg `keepdims` this
+        will raise a RuntimeError.
+
+    weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the percentile according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given axis) or of the same shape as `a`.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        Only `method="inverted_cdf"` supports weights.
+
+        .. versionadded:: 2.0.0
+
+    interpolation : str, optional
+        Deprecated name for the method keyword argument.
+
+        .. deprecated:: 1.22.0
+
+    Returns
+    -------
+    percentile : scalar or ndarray
+        If `q` is a single percentile and `axis=None`, then the result
+        is a scalar. If multiple percentiles are given, first axis of
+        the result corresponds to the percentiles. The other axes are
+        the axes that remain after the reduction of `a`. If the input
+        contains integers or floats smaller than ``float64``, the output
+        data-type is ``float64``. Otherwise, the output data-type is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    nanmean
+    nanmedian : equivalent to ``nanpercentile(..., 50)``
+    percentile, median, mean
+    nanquantile : equivalent to nanpercentile, except q in range [0, 1].
+
+    Notes
+    -----
+    The behavior of `numpy.nanpercentile` with percentage `q` is that of
+    `numpy.quantile` with argument ``q/100`` (ignoring nan values).
+    For more information, please see `numpy.quantile`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
+    >>> a[0][1] = np.nan
+    >>> a
+    array([[10.,  nan,   4.],
+          [ 3.,   2.,   1.]])
+    >>> np.percentile(a, 50)
+    np.float64(nan)
+    >>> np.nanpercentile(a, 50)
+    3.0
+    >>> np.nanpercentile(a, 50, axis=0)
+    array([6.5, 2. , 2.5])
+    >>> np.nanpercentile(a, 50, axis=1, keepdims=True)
+    array([[7.],
+           [2.]])
+    >>> m = np.nanpercentile(a, 50, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.nanpercentile(a, 50, axis=0, out=out)
+    array([6.5, 2. , 2.5])
+    >>> m
+    array([6.5,  2. ,  2.5])
+
+    >>> b = a.copy()
+    >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)
+    array([7., 2.])
+    >>> assert not np.all(a==b)
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+       "Sample quantiles in statistical packages,"
+       The American Statistician, 50(4), pp. 361-365, 1996
+
+    """
+    if interpolation is not None:
+        method = fnb._check_interpolation_as_method(
+            method, interpolation, "nanpercentile")
+
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
+    q = np.true_divide(q, a.dtype.type(100) if a.dtype.kind == "f" else 100)
+    # undo any decay that the ufunc performed (see gh-13105)
+    q = np.asanyarray(q)
+    if not fnb._quantile_is_valid(q):
+        raise ValueError("Percentiles must be in the range [0, 100]")
+
+    if weights is not None:
+        if method != "inverted_cdf":
+            msg = ("Only method 'inverted_cdf' supports weights. "
+                   f"Got: {method}.")
+            raise ValueError(msg)
+        if axis is not None:
+            axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
+        weights = _weights_are_valid(weights=weights, a=a, axis=axis)
+        if np.any(weights < 0):
+            raise ValueError("Weights must be non-negative.")
+
+    return _nanquantile_unchecked(
+        a, q, axis, out, overwrite_input, method, keepdims, weights)
+
+
+def _nanquantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
+                            method=None, keepdims=None, *, weights=None,
+                            interpolation=None):
+    return (a, q, out, weights)
+
+
+@array_function_dispatch(_nanquantile_dispatcher)
+def nanquantile(
+        a,
+        q,
+        axis=None,
+        out=None,
+        overwrite_input=False,
+        method="linear",
+        keepdims=np._NoValue,
+        *,
+        weights=None,
+        interpolation=None,
+):
+    """
+    Compute the qth quantile of the data along the specified axis,
+    while ignoring nan values.
+    Returns the qth quantile(s) of the array elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array, containing
+        nan values to be ignored
+    q : array_like of float
+        Probability or sequence of probabilities for the quantiles to compute.
+        Values must be between 0 and 1 inclusive.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the quantiles are computed. The
+        default is to compute the quantile(s) along a flattened
+        version of the array.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output,
+        but the type (of the output) will be cast if necessary.
+    overwrite_input : bool, optional
+        If True, then allow the input array `a` to be modified by intermediate
+        calculations, to save memory. In this case, the contents of the input
+        `a` after this function completes is undefined.
+    method : str, optional
+        This parameter specifies the method to use for estimating the
+        quantile.  There are many different methods, some unique to NumPy.
+        See the notes for explanation.  The options sorted by their R type
+        as summarized in the H&F paper [1]_ are:
+
+        1. 'inverted_cdf'
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        The first three methods are discontinuous.  NumPy further defines the
+        following discontinuous variations of the default 'linear' (7.) option:
+
+        * 'lower'
+        * 'higher',
+        * 'midpoint'
+        * 'nearest'
+
+        .. versionchanged:: 1.22.0
+            This argument was previously called "interpolation" and only
+            offered the "linear" default and last four options.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in
+        the result as dimensions with size one. With this option, the
+        result will broadcast correctly against the original array `a`.
+
+        If this is anything but the default value it will be passed
+        through (in the special case of an empty array) to the
+        `mean` function of the underlying array.  If the array is
+        a sub-class and `mean` does not have the kwarg `keepdims` this
+        will raise a RuntimeError.
+
+    weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the quantile according to its associated weight.
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given axis) or of the same shape as `a`.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        Only `method="inverted_cdf"` supports weights.
+
+        .. versionadded:: 2.0.0
+
+    interpolation : str, optional
+        Deprecated name for the method keyword argument.
+
+        .. deprecated:: 1.22.0
+
+    Returns
+    -------
+    quantile : scalar or ndarray
+        If `q` is a single probability and `axis=None`, then the result
+        is a scalar. If multiple probability levels are given, first axis of
+        the result corresponds to the quantiles. The other axes are
+        the axes that remain after the reduction of `a`. If the input
+        contains integers or floats smaller than ``float64``, the output
+        data-type is ``float64``. Otherwise, the output data-type is the
+        same as that of the input. If `out` is specified, that array is
+        returned instead.
+
+    See Also
+    --------
+    quantile
+    nanmean, nanmedian
+    nanmedian : equivalent to ``nanquantile(..., 0.5)``
+    nanpercentile : same as nanquantile, but with q in the range [0, 100].
+
+    Notes
+    -----
+    The behavior of `numpy.nanquantile` is the same as that of
+    `numpy.quantile` (ignoring nan values).
+    For more information, please see `numpy.quantile`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
+    >>> a[0][1] = np.nan
+    >>> a
+    array([[10.,  nan,   4.],
+          [ 3.,   2.,   1.]])
+    >>> np.quantile(a, 0.5)
+    np.float64(nan)
+    >>> np.nanquantile(a, 0.5)
+    3.0
+    >>> np.nanquantile(a, 0.5, axis=0)
+    array([6.5, 2. , 2.5])
+    >>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
+    array([[7.],
+           [2.]])
+    >>> m = np.nanquantile(a, 0.5, axis=0)
+    >>> out = np.zeros_like(m)
+    >>> np.nanquantile(a, 0.5, axis=0, out=out)
+    array([6.5, 2. , 2.5])
+    >>> m
+    array([6.5,  2. ,  2.5])
+    >>> b = a.copy()
+    >>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
+    array([7., 2.])
+    >>> assert not np.all(a==b)
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+       "Sample quantiles in statistical packages,"
+       The American Statistician, 50(4), pp. 361-365, 1996
+
+    """
+
+    if interpolation is not None:
+        method = fnb._check_interpolation_as_method(
+            method, interpolation, "nanquantile")
+
+    a = np.asanyarray(a)
+    if a.dtype.kind == "c":
+        raise TypeError("a must be an array of real numbers")
+
+    # Use dtype of array if possible (e.g., if q is a python int or float).
+    if isinstance(q, (int, float)) and a.dtype.kind == "f":
+        q = np.asanyarray(q, dtype=a.dtype)
+    else:
+        q = np.asanyarray(q)
+
+    if not fnb._quantile_is_valid(q):
+        raise ValueError("Quantiles must be in the range [0, 1]")
+
+    if weights is not None:
+        if method != "inverted_cdf":
+            msg = ("Only method 'inverted_cdf' supports weights. "
+                   f"Got: {method}.")
+            raise ValueError(msg)
+        if axis is not None:
+            axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
+        weights = _weights_are_valid(weights=weights, a=a, axis=axis)
+        if np.any(weights < 0):
+            raise ValueError("Weights must be non-negative.")
+
+    return _nanquantile_unchecked(
+        a, q, axis, out, overwrite_input, method, keepdims, weights)
+
+
+def _nanquantile_unchecked(
+        a,
+        q,
+        axis=None,
+        out=None,
+        overwrite_input=False,
+        method="linear",
+        keepdims=np._NoValue,
+        weights=None,
+):
+    """Assumes that q is in [0, 1], and is an ndarray"""
+    # apply_along_axis in _nanpercentile doesn't handle empty arrays well,
+    # so deal them upfront
+    if a.size == 0:
+        return np.nanmean(a, axis, out=out, keepdims=keepdims)
+    return fnb._ureduce(a,
+                        func=_nanquantile_ureduce_func,
+                        q=q,
+                        weights=weights,
+                        keepdims=keepdims,
+                        axis=axis,
+                        out=out,
+                        overwrite_input=overwrite_input,
+                        method=method)
+
+
+def _nanquantile_ureduce_func(
+        a: np.array,
+        q: np.array,
+        weights: np.array,
+        axis: int | None = None,
+        out=None,
+        overwrite_input: bool = False,
+        method="linear",
+):
+    """
+    Private function that doesn't support extended axis or keepdims.
+    These methods are extended to this function using _ureduce
+    See nanpercentile for parameter usage
+    """
+    if axis is None or a.ndim == 1:
+        part = a.ravel()
+        wgt = None if weights is None else weights.ravel()
+        result = _nanquantile_1d(part, q, overwrite_input, method, weights=wgt)
+    else:
+        # Note that this code could try to fill in `out` right away
+        if weights is None:
+            result = np.apply_along_axis(_nanquantile_1d, axis, a, q,
+                                         overwrite_input, method, weights)
+            # apply_along_axis fills in collapsed axis with results.
+            # Move those axes to the beginning to match percentile's
+            # convention.
+            if q.ndim != 0:
+                from_ax = [axis + i for i in range(q.ndim)]
+                result = np.moveaxis(result, from_ax, list(range(q.ndim)))
+        else:
+            # We need to apply along axis over 2 arrays, a and weights.
+            # move operation axes to end for simplicity:
+            a = np.moveaxis(a, axis, -1)
+            if weights is not None:
+                weights = np.moveaxis(weights, axis, -1)
+            if out is not None:
+                result = out
+            else:
+                # weights are limited to `inverted_cdf` so the result dtype
+                # is known to be identical to that of `a` here:
+                result = np.empty_like(a, shape=q.shape + a.shape[:-1])
+
+            for ii in np.ndindex(a.shape[:-1]):
+                result[(...,) + ii] = _nanquantile_1d(
+                        a[ii], q, weights=weights[ii],
+                        overwrite_input=overwrite_input, method=method,
+                )
+            # This path dealt with `out` already...
+            return result
+
+    if out is not None:
+        out[...] = result
+    return result
+
+
+def _nanquantile_1d(
+    arr1d, q, overwrite_input=False, method="linear", weights=None,
+):
+    """
+    Private function for rank 1 arrays. Compute quantile ignoring NaNs.
+    See nanpercentile for parameter usage
+    """
+    # TODO: What to do when arr1d = [1, np.nan] and weights = [0, 1]?
+    arr1d, weights, overwrite_input = _remove_nan_1d(arr1d,
+        second_arr1d=weights, overwrite_input=overwrite_input)
+    if arr1d.size == 0:
+        # convert to scalar
+        return np.full(q.shape, np.nan, dtype=arr1d.dtype)[()]
+
+    return fnb._quantile_unchecked(
+        arr1d,
+        q,
+        overwrite_input=overwrite_input,
+        method=method,
+        weights=weights,
+    )
+
+
+def _nanvar_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
+                       keepdims=None, *, where=None, mean=None,
+                       correction=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanvar_dispatcher)
+def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
+           *, where=np._NoValue, mean=np._NoValue, correction=np._NoValue):
+    """
+    Compute the variance along the specified axis, while ignoring NaNs.
+
+    Returns the variance of the array elements, a measure of the spread of
+    a distribution.  The variance is computed for the flattened array by
+    default, otherwise over the specified axis.
+
+    For all-NaN slices or slices with zero degrees of freedom, NaN is
+    returned and a `RuntimeWarning` is raised.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose variance is desired.  If `a` is not an
+        array, a conversion is attempted.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the variance is computed.  The default is to compute
+        the variance of the flattened array.
+    dtype : data-type, optional
+        Type to use in computing the variance.  For arrays of integer type
+        the default is `float64`; for arrays of float types it is the same as
+        the array type.
+    out : ndarray, optional
+        Alternate output array in which to place the result.  It must have
+        the same shape as the expected output, but the type is cast if
+        necessary.
+    ddof : {int, float}, optional
+        "Delta Degrees of Freedom": the divisor used in the calculation is
+        ``N - ddof``, where ``N`` represents the number of non-NaN
+        elements. By default `ddof` is zero.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+    where : array_like of bool, optional
+        Elements to include in the variance. See `~numpy.ufunc.reduce` for
+        details.
+
+        .. versionadded:: 1.22.0
+
+    mean : array_like, optional
+        Provide the mean to prevent its recalculation. The mean should have
+        a shape as if it was calculated with ``keepdims=True``.
+        The axis for the calculation of the mean should be the same as used in
+        the call to this var function.
+
+        .. versionadded:: 2.0.0
+
+    correction : {int, float}, optional
+        Array API compatible name for the ``ddof`` parameter. Only one of them
+        can be provided at the same time.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    variance : ndarray, see dtype parameter above
+        If `out` is None, return a new array containing the variance,
+        otherwise return a reference to the output array. If ddof is >= the
+        number of non-NaN elements in a slice or the slice contains only
+        NaNs, then the result for that slice is NaN.
+
+    See Also
+    --------
+    std : Standard deviation
+    mean : Average
+    var : Variance while not ignoring NaNs
+    nanstd, nanmean
+    :ref:`ufuncs-output-type`
+
+    Notes
+    -----
+    The variance is the average of the squared deviations from the mean,
+    i.e.,  ``var = mean(abs(x - x.mean())**2)``.
+
+    The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
+    If, however, `ddof` is specified, the divisor ``N - ddof`` is used
+    instead.  In standard statistical practice, ``ddof=1`` provides an
+    unbiased estimator of the variance of a hypothetical infinite
+    population.  ``ddof=0`` provides a maximum likelihood estimate of the
+    variance for normally distributed variables.
+
+    Note that for complex numbers, the absolute value is taken before
+    squaring, so that the result is always real and nonnegative.
+
+    For floating-point input, the variance is computed using the same
+    precision the input has.  Depending on the input data, this can cause
+    the results to be inaccurate, especially for `float32` (see example
+    below).  Specifying a higher-accuracy accumulator using the ``dtype``
+    keyword can alleviate this issue.
+
+    For this function to work on sub-classes of ndarray, they must define
+    `sum` with the kwarg `keepdims`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, np.nan], [3, 4]])
+    >>> np.nanvar(a)
+    1.5555555555555554
+    >>> np.nanvar(a, axis=0)
+    array([1.,  0.])
+    >>> np.nanvar(a, axis=1)
+    array([0.,  0.25])  # may vary
+
+    """
+    arr, mask = _replace_nan(a, 0)
+    if mask is None:
+        return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof,
+                      keepdims=keepdims, where=where, mean=mean,
+                      correction=correction)
+
+    if dtype is not None:
+        dtype = np.dtype(dtype)
+    if dtype is not None and not issubclass(dtype.type, np.inexact):
+        raise TypeError("If a is inexact, then dtype must be inexact")
+    if out is not None and not issubclass(out.dtype.type, np.inexact):
+        raise TypeError("If a is inexact, then out must be inexact")
+
+    if correction != np._NoValue:
+        if ddof != 0:
+            raise ValueError(
+                "ddof and correction can't be provided simultaneously."
+            )
+        else:
+            ddof = correction
+
+    # Compute mean
+    if type(arr) is np.matrix:
+        _keepdims = np._NoValue
+    else:
+        _keepdims = True
+
+    cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims,
+                     where=where)
+
+    if mean is not np._NoValue:
+        avg = mean
+    else:
+        # we need to special case matrix for reverse compatibility
+        # in order for this to work, these sums need to be called with
+        # keepdims=True, however matrix now raises an error in this case, but
+        # the reason that it drops the keepdims kwarg is to force keepdims=True
+        # so this used to work by serendipity.
+        avg = np.sum(arr, axis=axis, dtype=dtype,
+                     keepdims=_keepdims, where=where)
+        avg = _divide_by_count(avg, cnt)
+
+    # Compute squared deviation from mean.
+    np.subtract(arr, avg, out=arr, casting='unsafe', where=where)
+    arr = _copyto(arr, 0, mask)
+    if issubclass(arr.dtype.type, np.complexfloating):
+        sqr = np.multiply(arr, arr.conj(), out=arr, where=where).real
+    else:
+        sqr = np.multiply(arr, arr, out=arr, where=where)
+
+    # Compute variance.
+    var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims,
+                 where=where)
+
+    # Precaution against reduced object arrays
+    try:
+        var_ndim = var.ndim
+    except AttributeError:
+        var_ndim = np.ndim(var)
+    if var_ndim < cnt.ndim:
+        # Subclasses of ndarray may ignore keepdims, so check here.
+        cnt = cnt.squeeze(axis)
+    dof = cnt - ddof
+    var = _divide_by_count(var, dof)
+
+    isbad = (dof <= 0)
+    if np.any(isbad):
+        warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning,
+                      stacklevel=2)
+        # NaN, inf, or negative numbers are all possible bad
+        # values, so explicitly replace them with NaN.
+        var = _copyto(var, np.nan, isbad)
+    return var
+
+
+def _nanstd_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
+                       keepdims=None, *, where=None, mean=None,
+                       correction=None):
+    return (a, out)
+
+
+@array_function_dispatch(_nanstd_dispatcher)
+def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
+           *, where=np._NoValue, mean=np._NoValue, correction=np._NoValue):
+    """
+    Compute the standard deviation along the specified axis, while
+    ignoring NaNs.
+
+    Returns the standard deviation, a measure of the spread of a
+    distribution, of the non-NaN array elements. The standard deviation is
+    computed for the flattened array by default, otherwise over the
+    specified axis.
+
+    For all-NaN slices or slices with zero degrees of freedom, NaN is
+    returned and a `RuntimeWarning` is raised.
+
+    Parameters
+    ----------
+    a : array_like
+        Calculate the standard deviation of the non-NaN values.
+    axis : {int, tuple of int, None}, optional
+        Axis or axes along which the standard deviation is computed. The default is
+        to compute the standard deviation of the flattened array.
+    dtype : dtype, optional
+        Type to use in computing the standard deviation. For arrays of
+        integer type the default is float64, for arrays of float types it
+        is the same as the array type.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output but the type (of the
+        calculated values) will be cast if necessary.
+    ddof : {int, float}, optional
+        Means Delta Degrees of Freedom.  The divisor used in calculations
+        is ``N - ddof``, where ``N`` represents the number of non-NaN
+        elements.  By default `ddof` is zero.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+
+        If this value is anything but the default it is passed through
+        as-is to the relevant functions of the sub-classes.  If these
+        functions do not have a `keepdims` kwarg, a RuntimeError will
+        be raised.
+    where : array_like of bool, optional
+        Elements to include in the standard deviation.
+        See `~numpy.ufunc.reduce` for details.
+
+        .. versionadded:: 1.22.0
+
+    mean : array_like, optional
+        Provide the mean to prevent its recalculation. The mean should have
+        a shape as if it was calculated with ``keepdims=True``.
+        The axis for the calculation of the mean should be the same as used in
+        the call to this std function.
+
+        .. versionadded:: 2.0.0
+
+    correction : {int, float}, optional
+        Array API compatible name for the ``ddof`` parameter. Only one of them
+        can be provided at the same time.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    standard_deviation : ndarray, see dtype parameter above.
+        If `out` is None, return a new array containing the standard
+        deviation, otherwise return a reference to the output array. If
+        ddof is >= the number of non-NaN elements in a slice or the slice
+        contains only NaNs, then the result for that slice is NaN.
+
+    See Also
+    --------
+    var, mean, std
+    nanvar, nanmean
+    :ref:`ufuncs-output-type`
+
+    Notes
+    -----
+    The standard deviation is the square root of the average of the squared
+    deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
+
+    The average squared deviation is normally calculated as
+    ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is
+    specified, the divisor ``N - ddof`` is used instead. In standard
+    statistical practice, ``ddof=1`` provides an unbiased estimator of the
+    variance of the infinite population. ``ddof=0`` provides a maximum
+    likelihood estimate of the variance for normally distributed variables.
+    The standard deviation computed in this function is the square root of
+    the estimated variance, so even with ``ddof=1``, it will not be an
+    unbiased estimate of the standard deviation per se.
+
+    Note that, for complex numbers, `std` takes the absolute value before
+    squaring, so that the result is always real and nonnegative.
+
+    For floating-point input, the *std* is computed using the same
+    precision the input has. Depending on the input data, this can cause
+    the results to be inaccurate, especially for float32 (see example
+    below).  Specifying a higher-accuracy accumulator using the `dtype`
+    keyword can alleviate this issue.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[1, np.nan], [3, 4]])
+    >>> np.nanstd(a)
+    1.247219128924647
+    >>> np.nanstd(a, axis=0)
+    array([1., 0.])
+    >>> np.nanstd(a, axis=1)
+    array([0.,  0.5]) # may vary
+
+    """
+    var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+                 keepdims=keepdims, where=where, mean=mean,
+                 correction=correction)
+    if isinstance(var, np.ndarray):
+        std = np.sqrt(var, out=var)
+    elif hasattr(var, 'dtype'):
+        std = var.dtype.type(np.sqrt(var))
+    else:
+        std = np.sqrt(var)
+    return std
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..081b53d8ea44b53a5ee1470ab2725e6b8fc7274a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_nanfunctions_impl.pyi
@@ -0,0 +1,53 @@
+from numpy._core.fromnumeric import (
+    amin,
+    amax,
+    argmin,
+    argmax,
+    sum,
+    prod,
+    cumsum,
+    cumprod,
+    mean,
+    var,
+    std
+)
+
+from numpy.lib._function_base_impl import (
+    median,
+    percentile,
+    quantile,
+)
+
+__all__ = [
+    "nansum",
+    "nanmax",
+    "nanmin",
+    "nanargmax",
+    "nanargmin",
+    "nanmean",
+    "nanmedian",
+    "nanpercentile",
+    "nanvar",
+    "nanstd",
+    "nanprod",
+    "nancumsum",
+    "nancumprod",
+    "nanquantile",
+]
+
+# NOTE: In reality these functions are not aliases but distinct functions
+# with identical signatures.
+nanmin = amin
+nanmax = amax
+nanargmin = argmin
+nanargmax = argmax
+nansum = sum
+nanprod = prod
+nancumsum = cumsum
+nancumprod = cumprod
+nanmean = mean
+nanvar = var
+nanstd = std
+nanmedian = median
+nanpercentile = percentile
+nanquantile = quantile
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dc3a4b9b7e2e117b033b103c8ebaf165a1b160c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.py
@@ -0,0 +1,2595 @@
+"""
+IO related functions.
+"""
+import os
+import re
+import functools
+import itertools
+import warnings
+import weakref
+import contextlib
+import operator
+from operator import itemgetter
+from collections.abc import Mapping
+import pickle
+
+import numpy as np
+from . import format
+from ._datasource import DataSource
+from numpy._core import overrides
+from numpy._core.multiarray import packbits, unpackbits
+from numpy._core._multiarray_umath import _load_from_filelike
+from numpy._core.overrides import finalize_array_function_like, set_module
+from ._iotools import (
+    LineSplitter, NameValidator, StringConverter, ConverterError,
+    ConverterLockError, ConversionWarning, _is_string_like,
+    has_nested_fields, flatten_dtype, easy_dtype, _decode_line
+    )
+from numpy._utils import asunicode, asbytes
+
+
+__all__ = [
+    'savetxt', 'loadtxt', 'genfromtxt', 'load', 'save', 'savez',
+    'savez_compressed', 'packbits', 'unpackbits', 'fromregex'
+    ]
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+class BagObj:
+    """
+    BagObj(obj)
+
+    Convert attribute look-ups to getitems on the object passed in.
+
+    Parameters
+    ----------
+    obj : class instance
+        Object on which attribute look-up is performed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib._npyio_impl import BagObj as BO
+    >>> class BagDemo:
+    ...     def __getitem__(self, key): # An instance of BagObj(BagDemo)
+    ...                                 # will call this method when any
+    ...                                 # attribute look-up is required
+    ...         result = "Doesn't matter what you want, "
+    ...         return result + "you're gonna get this"
+    ...
+    >>> demo_obj = BagDemo()
+    >>> bagobj = BO(demo_obj)
+    >>> bagobj.hello_there
+    "Doesn't matter what you want, you're gonna get this"
+    >>> bagobj.I_can_be_anything
+    "Doesn't matter what you want, you're gonna get this"
+
+    """
+
+    def __init__(self, obj):
+        # Use weakref to make NpzFile objects collectable by refcount
+        self._obj = weakref.proxy(obj)
+
+    def __getattribute__(self, key):
+        try:
+            return object.__getattribute__(self, '_obj')[key]
+        except KeyError:
+            raise AttributeError(key) from None
+
+    def __dir__(self):
+        """
+        Enables dir(bagobj) to list the files in an NpzFile.
+
+        This also enables tab-completion in an interpreter or IPython.
+        """
+        return list(object.__getattribute__(self, '_obj').keys())
+
+
+def zipfile_factory(file, *args, **kwargs):
+    """
+    Create a ZipFile.
+
+    Allows for Zip64, and the `file` argument can accept file, str, or
+    pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
+    constructor.
+    """
+    if not hasattr(file, 'read'):
+        file = os.fspath(file)
+    import zipfile
+    kwargs['allowZip64'] = True
+    return zipfile.ZipFile(file, *args, **kwargs)
+
+
+@set_module('numpy.lib.npyio')
+class NpzFile(Mapping):
+    """
+    NpzFile(fid)
+
+    A dictionary-like object with lazy-loading of files in the zipped
+    archive provided on construction.
+
+    `NpzFile` is used to load files in the NumPy ``.npz`` data archive
+    format. It assumes that files in the archive have a ``.npy`` extension,
+    other files are ignored.
+
+    The arrays and file strings are lazily loaded on either
+    getitem access using ``obj['key']`` or attribute lookup using
+    ``obj.f.key``. A list of all files (without ``.npy`` extensions) can
+    be obtained with ``obj.files`` and the ZipFile object itself using
+    ``obj.zip``.
+
+    Attributes
+    ----------
+    files : list of str
+        List of all files in the archive with a ``.npy`` extension.
+    zip : ZipFile instance
+        The ZipFile object initialized with the zipped archive.
+    f : BagObj instance
+        An object on which attribute can be performed as an alternative
+        to getitem access on the `NpzFile` instance itself.
+    allow_pickle : bool, optional
+        Allow loading pickled data. Default: False
+    pickle_kwargs : dict, optional
+        Additional keyword arguments to pass on to pickle.load.
+        These are only useful when loading object arrays saved on
+        Python 2 when using Python 3.
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+        This option is ignored when `allow_pickle` is passed.  In that case
+        the file is by definition trusted and the limit is unnecessary.
+
+    Parameters
+    ----------
+    fid : file, str, or pathlib.Path
+        The zipped archive to open. This is either a file-like object
+        or a string containing the path to the archive.
+    own_fid : bool, optional
+        Whether NpzFile should close the file handle.
+        Requires that `fid` is a file-like object.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from tempfile import TemporaryFile
+    >>> outfile = TemporaryFile()
+    >>> x = np.arange(10)
+    >>> y = np.sin(x)
+    >>> np.savez(outfile, x=x, y=y)
+    >>> _ = outfile.seek(0)
+
+    >>> npz = np.load(outfile)
+    >>> isinstance(npz, np.lib.npyio.NpzFile)
+    True
+    >>> npz
+    NpzFile 'object' with keys: x, y
+    >>> sorted(npz.files)
+    ['x', 'y']
+    >>> npz['x']  # getitem access
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> npz.f.x  # attribute lookup
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+    """
+    # Make __exit__ safe if zipfile_factory raises an exception
+    zip = None
+    fid = None
+    _MAX_REPR_ARRAY_COUNT = 5
+
+    def __init__(self, fid, own_fid=False, allow_pickle=False,
+                 pickle_kwargs=None, *,
+                 max_header_size=format._MAX_HEADER_SIZE):
+        # Import is postponed to here since zipfile depends on gzip, an
+        # optional component of the so-called standard library.
+        _zip = zipfile_factory(fid)
+        self._files = _zip.namelist()
+        self.files = []
+        self.allow_pickle = allow_pickle
+        self.max_header_size = max_header_size
+        self.pickle_kwargs = pickle_kwargs
+        for x in self._files:
+            if x.endswith('.npy'):
+                self.files.append(x[:-4])
+            else:
+                self.files.append(x)
+        self.zip = _zip
+        self.f = BagObj(self)
+        if own_fid:
+            self.fid = fid
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def close(self):
+        """
+        Close the file.
+
+        """
+        if self.zip is not None:
+            self.zip.close()
+            self.zip = None
+        if self.fid is not None:
+            self.fid.close()
+            self.fid = None
+        self.f = None  # break reference cycle
+
+    def __del__(self):
+        self.close()
+
+    # Implement the Mapping ABC
+    def __iter__(self):
+        return iter(self.files)
+
+    def __len__(self):
+        return len(self.files)
+
+    def __getitem__(self, key):
+        # FIXME: This seems like it will copy strings around
+        #   more than is strictly necessary.  The zipfile
+        #   will read the string and then
+        #   the format.read_array will copy the string
+        #   to another place in memory.
+        #   It would be better if the zipfile could read
+        #   (or at least uncompress) the data
+        #   directly into the array memory.
+        member = False
+        if key in self._files:
+            member = True
+        elif key in self.files:
+            member = True
+            key += '.npy'
+        if member:
+            bytes = self.zip.open(key)
+            magic = bytes.read(len(format.MAGIC_PREFIX))
+            bytes.close()
+            if magic == format.MAGIC_PREFIX:
+                bytes = self.zip.open(key)
+                return format.read_array(bytes,
+                                         allow_pickle=self.allow_pickle,
+                                         pickle_kwargs=self.pickle_kwargs,
+                                         max_header_size=self.max_header_size)
+            else:
+                return self.zip.read(key)
+        else:
+            raise KeyError(f"{key} is not a file in the archive")
+
+    def __contains__(self, key):
+        return (key in self._files or key in self.files)
+
+    def __repr__(self):
+        # Get filename or default to `object`
+        if isinstance(self.fid, str):
+            filename = self.fid
+        else:
+            filename = getattr(self.fid, "name", "object")
+
+        # Get the name of arrays
+        array_names = ', '.join(self.files[:self._MAX_REPR_ARRAY_COUNT])
+        if len(self.files) > self._MAX_REPR_ARRAY_COUNT:
+            array_names += "..."
+        return f"NpzFile {filename!r} with keys: {array_names}"
+
+    # Work around problems with the docstrings in the Mapping methods
+    # They contain a `->`, which confuses the type annotation interpretations
+    # of sphinx-docs. See gh-25964
+
+    def get(self, key, default=None, /):
+        """
+        D.get(k,[,d]) returns D[k] if k in D, else d.  d defaults to None.
+        """
+        return Mapping.get(self, key, default)
+
+    def items(self):
+        """
+        D.items() returns a set-like object providing a view on the items
+        """
+        return Mapping.items(self)
+
+    def keys(self):
+        """
+        D.keys() returns a set-like object providing a view on the keys
+        """
+        return Mapping.keys(self)
+
+    def values(self):
+        """
+        D.values() returns a set-like object providing a view on the values
+        """
+        return Mapping.values(self)
+
+
+@set_module('numpy')
+def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
+         encoding='ASCII', *, max_header_size=format._MAX_HEADER_SIZE):
+    """
+    Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
+
+    .. warning:: Loading files that contain object arrays uses the ``pickle``
+                 module, which is not secure against erroneous or maliciously
+                 constructed data. Consider passing ``allow_pickle=False`` to
+                 load data that is known not to contain object arrays for the
+                 safer handling of untrusted sources.
+
+    Parameters
+    ----------
+    file : file-like object, string, or pathlib.Path
+        The file to read. File-like objects must support the
+        ``seek()`` and ``read()`` methods and must always
+        be opened in binary mode.  Pickled files require that the
+        file-like object support the ``readline()`` method as well.
+    mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
+        If not None, then memory-map the file, using the given mode (see
+        `numpy.memmap` for a detailed description of the modes).  A
+        memory-mapped array is kept on disk. However, it can be accessed
+        and sliced like any ndarray.  Memory mapping is especially useful
+        for accessing small fragments of large files without reading the
+        entire file into memory.
+    allow_pickle : bool, optional
+        Allow loading pickled object arrays stored in npy files. Reasons for
+        disallowing pickles include security, as loading pickled data can
+        execute arbitrary code. If pickles are disallowed, loading object
+        arrays will fail. Default: False
+    fix_imports : bool, optional
+        Only useful when loading Python 2 generated pickled files on Python 3,
+        which includes npy/npz files containing object arrays. If `fix_imports`
+        is True, pickle will try to map the old Python 2 names to the new names
+        used in Python 3.
+    encoding : str, optional
+        What encoding to use when reading Python 2 strings. Only useful when
+        loading Python 2 generated pickled files in Python 3, which includes
+        npy/npz files containing object arrays. Values other than 'latin1',
+        'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
+        data. Default: 'ASCII'
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+        This option is ignored when `allow_pickle` is passed.  In that case
+        the file is by definition trusted and the limit is unnecessary.
+
+    Returns
+    -------
+    result : array, tuple, dict, etc.
+        Data stored in the file. For ``.npz`` files, the returned instance
+        of NpzFile class must be closed to avoid leaking file descriptors.
+
+    Raises
+    ------
+    OSError
+        If the input file does not exist or cannot be read.
+    UnpicklingError
+        If ``allow_pickle=True``, but the file cannot be loaded as a pickle.
+    ValueError
+        The file contains an object array, but ``allow_pickle=False`` given.
+    EOFError
+        When calling ``np.load`` multiple times on the same file handle,
+        if all data has already been read
+
+    See Also
+    --------
+    save, savez, savez_compressed, loadtxt
+    memmap : Create a memory-map to an array stored in a file on disk.
+    lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
+
+    Notes
+    -----
+    - If the file contains pickle data, then whatever object is stored
+      in the pickle is returned.
+    - If the file is a ``.npy`` file, then a single array is returned.
+    - If the file is a ``.npz`` file, then a dictionary-like object is
+      returned, containing ``{filename: array}`` key-value pairs, one for
+      each file in the archive.
+    - If the file is a ``.npz`` file, the returned value supports the
+      context manager protocol in a similar fashion to the open function::
+
+        with load('foo.npz') as data:
+            a = data['a']
+
+      The underlying file descriptor is closed when exiting the 'with'
+      block.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Store data to disk, and load it again:
+
+    >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
+    >>> np.load('/tmp/123.npy')
+    array([[1, 2, 3],
+           [4, 5, 6]])
+
+    Store compressed data to disk, and load it again:
+
+    >>> a=np.array([[1, 2, 3], [4, 5, 6]])
+    >>> b=np.array([1, 2])
+    >>> np.savez('/tmp/123.npz', a=a, b=b)
+    >>> data = np.load('/tmp/123.npz')
+    >>> data['a']
+    array([[1, 2, 3],
+           [4, 5, 6]])
+    >>> data['b']
+    array([1, 2])
+    >>> data.close()
+
+    Mem-map the stored array, and then access the second row
+    directly from disk:
+
+    >>> X = np.load('/tmp/123.npy', mmap_mode='r')
+    >>> X[1, :]
+    memmap([4, 5, 6])
+
+    """
+    if encoding not in ('ASCII', 'latin1', 'bytes'):
+        # The 'encoding' value for pickle also affects what encoding
+        # the serialized binary data of NumPy arrays is loaded
+        # in. Pickle does not pass on the encoding information to
+        # NumPy. The unpickling code in numpy._core.multiarray is
+        # written to assume that unicode data appearing where binary
+        # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
+        #
+        # Other encoding values can corrupt binary data, and we
+        # purposefully disallow them. For the same reason, the errors=
+        # argument is not exposed, as values other than 'strict'
+        # result can similarly silently corrupt numerical data.
+        raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
+
+    pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
+
+    with contextlib.ExitStack() as stack:
+        if hasattr(file, 'read'):
+            fid = file
+            own_fid = False
+        else:
+            fid = stack.enter_context(open(os.fspath(file), "rb"))
+            own_fid = True
+
+        # Code to distinguish from NumPy binary files and pickles.
+        _ZIP_PREFIX = b'PK\x03\x04'
+        _ZIP_SUFFIX = b'PK\x05\x06'  # empty zip files start with this
+        N = len(format.MAGIC_PREFIX)
+        magic = fid.read(N)
+        if not magic:
+            raise EOFError("No data left in file")
+        # If the file size is less than N, we need to make sure not
+        # to seek past the beginning of the file
+        fid.seek(-min(N, len(magic)), 1)  # back-up
+        if magic.startswith((_ZIP_PREFIX, _ZIP_SUFFIX)):
+            # zip-file (assume .npz)
+            # Potentially transfer file ownership to NpzFile
+            stack.pop_all()
+            ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
+                          pickle_kwargs=pickle_kwargs,
+                          max_header_size=max_header_size)
+            return ret
+        elif magic == format.MAGIC_PREFIX:
+            # .npy file
+            if mmap_mode:
+                if allow_pickle:
+                    max_header_size = 2**64
+                return format.open_memmap(file, mode=mmap_mode,
+                                          max_header_size=max_header_size)
+            else:
+                return format.read_array(fid, allow_pickle=allow_pickle,
+                                         pickle_kwargs=pickle_kwargs,
+                                         max_header_size=max_header_size)
+        else:
+            # Try a pickle
+            if not allow_pickle:
+                raise ValueError(
+                    "This file contains pickled (object) data. If you trust "
+                    "the file you can load it unsafely using the "
+                    "`allow_pickle=` keyword argument or `pickle.load()`.")
+            try:
+                return pickle.load(fid, **pickle_kwargs)
+            except Exception as e:
+                raise pickle.UnpicklingError(
+                    f"Failed to interpret file {file!r} as a pickle") from e
+
+
+def _save_dispatcher(file, arr, allow_pickle=None, fix_imports=None):
+    return (arr,)
+
+
+@array_function_dispatch(_save_dispatcher)
+def save(file, arr, allow_pickle=True, fix_imports=np._NoValue):
+    """
+    Save an array to a binary file in NumPy ``.npy`` format.
+
+    Parameters
+    ----------
+    file : file, str, or pathlib.Path
+        File or filename to which the data is saved. If file is a file-object,
+        then the filename is unchanged.  If file is a string or Path,
+        a ``.npy`` extension will be appended to the filename if it does not
+        already have one.
+    arr : array_like
+        Array data to be saved.
+    allow_pickle : bool, optional
+        Allow saving object arrays using Python pickles. Reasons for
+        disallowing pickles include security (loading pickled data can execute
+        arbitrary code) and portability (pickled objects may not be loadable
+        on different Python installations, for example if the stored objects
+        require libraries that are not available, and not all pickled data is
+        compatible between different versions of Python).
+        Default: True
+    fix_imports : bool, optional
+        The `fix_imports` flag is deprecated and has no effect.
+
+        .. deprecated:: 2.1
+            This flag is ignored since NumPy 1.17 and was only needed to
+            support loading some files in Python 2 written in Python 3.
+
+    See Also
+    --------
+    savez : Save several arrays into a ``.npz`` archive
+    savetxt, load
+
+    Notes
+    -----
+    For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
+
+    Any data saved to the file is appended to the end of the file.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    >>> from tempfile import TemporaryFile
+    >>> outfile = TemporaryFile()
+
+    >>> x = np.arange(10)
+    >>> np.save(outfile, x)
+
+    >>> _ = outfile.seek(0) # Only needed to simulate closing & reopening file
+    >>> np.load(outfile)
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+
+    >>> with open('test.npy', 'wb') as f:
+    ...     np.save(f, np.array([1, 2]))
+    ...     np.save(f, np.array([1, 3]))
+    >>> with open('test.npy', 'rb') as f:
+    ...     a = np.load(f)
+    ...     b = np.load(f)
+    >>> print(a, b)
+    # [1 2] [1 3]
+    """
+    if fix_imports is not np._NoValue:
+        # Deprecated 2024-05-16, NumPy 2.1
+        warnings.warn(
+            "The 'fix_imports' flag is deprecated and has no effect. "
+            "(Deprecated in NumPy 2.1)",
+            DeprecationWarning, stacklevel=2)
+    if hasattr(file, 'write'):
+        file_ctx = contextlib.nullcontext(file)
+    else:
+        file = os.fspath(file)
+        if not file.endswith('.npy'):
+            file = file + '.npy'
+        file_ctx = open(file, "wb")
+
+    with file_ctx as fid:
+        arr = np.asanyarray(arr)
+        format.write_array(fid, arr, allow_pickle=allow_pickle,
+                           pickle_kwargs=dict(fix_imports=fix_imports))
+
+
+def _savez_dispatcher(file, *args, allow_pickle=True, **kwds):
+    yield from args
+    yield from kwds.values()
+
+
+@array_function_dispatch(_savez_dispatcher)
+def savez(file, *args, allow_pickle=True, **kwds):
+    """Save several arrays into a single file in uncompressed ``.npz`` format.
+
+    Provide arrays as keyword arguments to store them under the
+    corresponding name in the output file: ``savez(fn, x=x, y=y)``.
+
+    If arrays are specified as positional arguments, i.e., ``savez(fn,
+    x, y)``, their names will be `arr_0`, `arr_1`, etc.
+
+    Parameters
+    ----------
+    file : file, str, or pathlib.Path
+        Either the filename (string) or an open file (file-like object)
+        where the data will be saved. If file is a string or a Path, the
+        ``.npz`` extension will be appended to the filename if it is not
+        already there.
+    args : Arguments, optional
+        Arrays to save to the file. Please use keyword arguments (see
+        `kwds` below) to assign names to arrays.  Arrays specified as
+        args will be named "arr_0", "arr_1", and so on.
+    allow_pickle : bool, optional
+        Allow saving object arrays using Python pickles. Reasons for
+        disallowing pickles include security (loading pickled data can execute
+        arbitrary code) and portability (pickled objects may not be loadable
+        on different Python installations, for example if the stored objects
+        require libraries that are not available, and not all pickled data is
+        compatible between different versions of Python).
+        Default: True
+    kwds : Keyword arguments, optional
+        Arrays to save to the file. Each array will be saved to the
+        output file with its corresponding keyword name.
+
+    Returns
+    -------
+    None
+
+    See Also
+    --------
+    save : Save a single array to a binary file in NumPy format.
+    savetxt : Save an array to a file as plain text.
+    savez_compressed : Save several arrays into a compressed ``.npz`` archive
+
+    Notes
+    -----
+    The ``.npz`` file format is a zipped archive of files named after the
+    variables they contain.  The archive is not compressed and each file
+    in the archive contains one variable in ``.npy`` format. For a
+    description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
+
+    When opening the saved ``.npz`` file with `load` a `~lib.npyio.NpzFile`
+    object is returned. This is a dictionary-like object which can be queried
+    for its list of arrays (with the ``.files`` attribute), and for the arrays
+    themselves.
+
+    Keys passed in `kwds` are used as filenames inside the ZIP archive.
+    Therefore, keys should be valid filenames; e.g., avoid keys that begin with
+    ``/`` or contain ``.``.
+
+    When naming variables with keyword arguments, it is not possible to name a
+    variable ``file``, as this would cause the ``file`` argument to be defined
+    twice in the call to ``savez``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from tempfile import TemporaryFile
+    >>> outfile = TemporaryFile()
+    >>> x = np.arange(10)
+    >>> y = np.sin(x)
+
+    Using `savez` with \\*args, the arrays are saved with default names.
+
+    >>> np.savez(outfile, x, y)
+    >>> _ = outfile.seek(0) # Only needed to simulate closing & reopening file
+    >>> npzfile = np.load(outfile)
+    >>> npzfile.files
+    ['arr_0', 'arr_1']
+    >>> npzfile['arr_0']
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+    Using `savez` with \\**kwds, the arrays are saved with the keyword names.
+
+    >>> outfile = TemporaryFile()
+    >>> np.savez(outfile, x=x, y=y)
+    >>> _ = outfile.seek(0)
+    >>> npzfile = np.load(outfile)
+    >>> sorted(npzfile.files)
+    ['x', 'y']
+    >>> npzfile['x']
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+    """
+    _savez(file, args, kwds, False, allow_pickle=allow_pickle)
+
+
+def _savez_compressed_dispatcher(file, *args, allow_pickle=True, **kwds):
+    yield from args
+    yield from kwds.values()
+
+
+@array_function_dispatch(_savez_compressed_dispatcher)
+def savez_compressed(file, *args, allow_pickle=True, **kwds):
+    """
+    Save several arrays into a single file in compressed ``.npz`` format.
+
+    Provide arrays as keyword arguments to store them under the
+    corresponding name in the output file: ``savez_compressed(fn, x=x, y=y)``.
+
+    If arrays are specified as positional arguments, i.e.,
+    ``savez_compressed(fn, x, y)``, their names will be `arr_0`, `arr_1`, etc.
+
+    Parameters
+    ----------
+    file : file, str, or pathlib.Path
+        Either the filename (string) or an open file (file-like object)
+        where the data will be saved. If file is a string or a Path, the
+        ``.npz`` extension will be appended to the filename if it is not
+        already there.
+    args : Arguments, optional
+        Arrays to save to the file. Please use keyword arguments (see
+        `kwds` below) to assign names to arrays.  Arrays specified as
+        args will be named "arr_0", "arr_1", and so on.
+    allow_pickle : bool, optional
+        Allow saving object arrays using Python pickles. Reasons for
+        disallowing pickles include security (loading pickled data can execute
+        arbitrary code) and portability (pickled objects may not be loadable
+        on different Python installations, for example if the stored objects
+        require libraries that are not available, and not all pickled data is
+        compatible between different versions of Python).
+        Default: True
+    kwds : Keyword arguments, optional
+        Arrays to save to the file. Each array will be saved to the
+        output file with its corresponding keyword name.
+
+    Returns
+    -------
+    None
+
+    See Also
+    --------
+    numpy.save : Save a single array to a binary file in NumPy format.
+    numpy.savetxt : Save an array to a file as plain text.
+    numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
+    numpy.load : Load the files created by savez_compressed.
+
+    Notes
+    -----
+    The ``.npz`` file format is a zipped archive of files named after the
+    variables they contain.  The archive is compressed with
+    ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable
+    in ``.npy`` format. For a description of the ``.npy`` format, see
+    :py:mod:`numpy.lib.format`.
+
+
+    When opening the saved ``.npz`` file with `load` a `~lib.npyio.NpzFile`
+    object is returned. This is a dictionary-like object which can be queried
+    for its list of arrays (with the ``.files`` attribute), and for the arrays
+    themselves.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> test_array = np.random.rand(3, 2)
+    >>> test_vector = np.random.rand(4)
+    >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
+    >>> loaded = np.load('/tmp/123.npz')
+    >>> print(np.array_equal(test_array, loaded['a']))
+    True
+    >>> print(np.array_equal(test_vector, loaded['b']))
+    True
+
+    """
+    _savez(file, args, kwds, True, allow_pickle=allow_pickle)
+
+
+def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
+    # Import is postponed to here since zipfile depends on gzip, an optional
+    # component of the so-called standard library.
+    import zipfile
+
+    if not hasattr(file, 'write'):
+        file = os.fspath(file)
+        if not file.endswith('.npz'):
+            file = file + '.npz'
+
+    namedict = kwds
+    for i, val in enumerate(args):
+        key = 'arr_%d' % i
+        if key in namedict.keys():
+            raise ValueError(
+                "Cannot use un-named variables and keyword %s" % key)
+        namedict[key] = val
+
+    if compress:
+        compression = zipfile.ZIP_DEFLATED
+    else:
+        compression = zipfile.ZIP_STORED
+
+    zipf = zipfile_factory(file, mode="w", compression=compression)
+    try:
+        for key, val in namedict.items():
+            fname = key + '.npy'
+            val = np.asanyarray(val)
+            # always force zip64, gh-10776
+            with zipf.open(fname, 'w', force_zip64=True) as fid:
+                format.write_array(fid, val,
+                                   allow_pickle=allow_pickle,
+                                   pickle_kwargs=pickle_kwargs)
+    finally:
+        zipf.close()
+
+
+def _ensure_ndmin_ndarray_check_param(ndmin):
+    """Just checks if the param ndmin is supported on
+        _ensure_ndmin_ndarray. It is intended to be used as
+        verification before running anything expensive.
+        e.g. loadtxt, genfromtxt
+    """
+    # Check correctness of the values of `ndmin`
+    if ndmin not in [0, 1, 2]:
+        raise ValueError(f"Illegal value of ndmin keyword: {ndmin}")
+
+def _ensure_ndmin_ndarray(a, *, ndmin: int):
+    """This is a helper function of loadtxt and genfromtxt to ensure
+        proper minimum dimension as requested
+
+        ndim : int. Supported values 1, 2, 3
+                    ^^ whenever this changes, keep in sync with
+                       _ensure_ndmin_ndarray_check_param
+    """
+    # Verify that the array has at least dimensions `ndmin`.
+    # Tweak the size and shape of the arrays - remove extraneous dimensions
+    if a.ndim > ndmin:
+        a = np.squeeze(a)
+    # and ensure we have the minimum number of dimensions asked for
+    # - has to be in this order for the odd case ndmin=1, a.squeeze().ndim=0
+    if a.ndim < ndmin:
+        if ndmin == 1:
+            a = np.atleast_1d(a)
+        elif ndmin == 2:
+            a = np.atleast_2d(a).T
+
+    return a
+
+
+# amount of lines loadtxt reads in one chunk, can be overridden for testing
+_loadtxt_chunksize = 50000
+
+
+def _check_nonneg_int(value, name="argument"):
+    try:
+        operator.index(value)
+    except TypeError:
+        raise TypeError(f"{name} must be an integer") from None
+    if value < 0:
+        raise ValueError(f"{name} must be nonnegative")
+
+
+def _preprocess_comments(iterable, comments, encoding):
+    """
+    Generator that consumes a line iterated iterable and strips out the
+    multiple (or multi-character) comments from lines.
+    This is a pre-processing step to achieve feature parity with loadtxt
+    (we assume that this feature is a nieche feature).
+    """
+    for line in iterable:
+        if isinstance(line, bytes):
+            # Need to handle conversion here, or the splitting would fail
+            line = line.decode(encoding)
+
+        for c in comments:
+            line = line.split(c, 1)[0]
+
+        yield line
+
+
+# The number of rows we read in one go if confronted with a parametric dtype
+_loadtxt_chunksize = 50000
+
+
+def _read(fname, *, delimiter=',', comment='#', quote='"',
+          imaginary_unit='j', usecols=None, skiplines=0,
+          max_rows=None, converters=None, ndmin=None, unpack=False,
+          dtype=np.float64, encoding=None):
+    r"""
+    Read a NumPy array from a text file.
+    This is a helper function for loadtxt.
+
+    Parameters
+    ----------
+    fname : file, str, or pathlib.Path
+        The filename or the file to be read.
+    delimiter : str, optional
+        Field delimiter of the fields in line of the file.
+        Default is a comma, ','.  If None any sequence of whitespace is
+        considered a delimiter.
+    comment : str or sequence of str or None, optional
+        Character that begins a comment.  All text from the comment
+        character to the end of the line is ignored.
+        Multiple comments or multiple-character comment strings are supported,
+        but may be slower and `quote` must be empty if used.
+        Use None to disable all use of comments.
+    quote : str or None, optional
+        Character that is used to quote string fields. Default is '"'
+        (a double quote). Use None to disable quote support.
+    imaginary_unit : str, optional
+        Character that represent the imaginary unit `sqrt(-1)`.
+        Default is 'j'.
+    usecols : array_like, optional
+        A one-dimensional array of integer column numbers.  These are the
+        columns from the file to be included in the array.  If this value
+        is not given, all the columns are used.
+    skiplines : int, optional
+        Number of lines to skip before interpreting the data in the file.
+    max_rows : int, optional
+        Maximum number of rows of data to read.  Default is to read the
+        entire file.
+    converters : dict or callable, optional
+        A function to parse all columns strings into the desired value, or
+        a dictionary mapping column number to a parser function.
+        E.g. if column 0 is a date string: ``converters = {0: datestr2num}``.
+        Converters can also be used to provide a default value for missing
+        data, e.g. ``converters = lambda s: float(s.strip() or 0)`` will
+        convert empty fields to 0.
+        Default: None
+    ndmin : int, optional
+        Minimum dimension of the array returned.
+        Allowed values are 0, 1 or 2.  Default is 0.
+    unpack : bool, optional
+        If True, the returned array is transposed, so that arguments may be
+        unpacked using ``x, y, z = read(...)``.  When used with a structured
+        data-type, arrays are returned for each field.  Default is False.
+    dtype : numpy data type
+        A NumPy dtype instance, can be a structured dtype to map to the
+        columns of the file.
+    encoding : str, optional
+        Encoding used to decode the inputfile. The special value 'bytes'
+        (the default) enables backwards-compatible behavior for `converters`,
+        ensuring that inputs to the converter functions are encoded
+        bytes objects. The special value 'bytes' has no additional effect if
+        ``converters=None``. If encoding is ``'bytes'`` or ``None``, the
+        default system encoding is used.
+
+    Returns
+    -------
+    ndarray
+        NumPy array.
+    """
+    # Handle special 'bytes' keyword for encoding
+    byte_converters = False
+    if encoding == 'bytes':
+        encoding = None
+        byte_converters = True
+
+    if dtype is None:
+        raise TypeError("a dtype must be provided.")
+    dtype = np.dtype(dtype)
+
+    read_dtype_via_object_chunks = None
+    if dtype.kind in 'SUM' and (
+            dtype == "S0" or dtype == "U0" or dtype == "M8" or dtype == 'm8'):
+        # This is a legacy "flexible" dtype.  We do not truly support
+        # parametric dtypes currently (no dtype discovery step in the core),
+        # but have to support these for backward compatibility.
+        read_dtype_via_object_chunks = dtype
+        dtype = np.dtype(object)
+
+    if usecols is not None:
+        # Allow usecols to be a single int or a sequence of ints, the C-code
+        # handles the rest
+        try:
+            usecols = list(usecols)
+        except TypeError:
+            usecols = [usecols]
+
+    _ensure_ndmin_ndarray_check_param(ndmin)
+
+    if comment is None:
+        comments = None
+    else:
+        # assume comments are a sequence of strings
+        if "" in comment:
+            raise ValueError(
+                "comments cannot be an empty string. Use comments=None to "
+                "disable comments."
+            )
+        comments = tuple(comment)
+        comment = None
+        if len(comments) == 0:
+            comments = None  # No comments at all
+        elif len(comments) == 1:
+            # If there is only one comment, and that comment has one character,
+            # the normal parsing can deal with it just fine.
+            if isinstance(comments[0], str) and len(comments[0]) == 1:
+                comment = comments[0]
+                comments = None
+        else:
+            # Input validation if there are multiple comment characters
+            if delimiter in comments:
+                raise TypeError(
+                    f"Comment characters '{comments}' cannot include the "
+                    f"delimiter '{delimiter}'"
+                )
+
+    # comment is now either a 1 or 0 character string or a tuple:
+    if comments is not None:
+        # Note: An earlier version support two character comments (and could
+        #       have been extended to multiple characters, we assume this is
+        #       rare enough to not optimize for.
+        if quote is not None:
+            raise ValueError(
+                "when multiple comments or a multi-character comment is "
+                "given, quotes are not supported.  In this case quotechar "
+                "must be set to None.")
+
+    if len(imaginary_unit) != 1:
+        raise ValueError('len(imaginary_unit) must be 1.')
+
+    _check_nonneg_int(skiplines)
+    if max_rows is not None:
+        _check_nonneg_int(max_rows)
+    else:
+        # Passing -1 to the C code means "read the entire file".
+        max_rows = -1
+
+    fh_closing_ctx = contextlib.nullcontext()
+    filelike = False
+    try:
+        if isinstance(fname, os.PathLike):
+            fname = os.fspath(fname)
+        if isinstance(fname, str):
+            fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
+            if encoding is None:
+                encoding = getattr(fh, 'encoding', 'latin1')
+
+            fh_closing_ctx = contextlib.closing(fh)
+            data = fh
+            filelike = True
+        else:
+            if encoding is None:
+                encoding = getattr(fname, 'encoding', 'latin1')
+            data = iter(fname)
+    except TypeError as e:
+        raise ValueError(
+            f"fname must be a string, filehandle, list of strings,\n"
+            f"or generator. Got {type(fname)} instead.") from e
+
+    with fh_closing_ctx:
+        if comments is not None:
+            if filelike:
+                data = iter(data)
+                filelike = False
+            data = _preprocess_comments(data, comments, encoding)
+
+        if read_dtype_via_object_chunks is None:
+            arr = _load_from_filelike(
+                data, delimiter=delimiter, comment=comment, quote=quote,
+                imaginary_unit=imaginary_unit,
+                usecols=usecols, skiplines=skiplines, max_rows=max_rows,
+                converters=converters, dtype=dtype,
+                encoding=encoding, filelike=filelike,
+                byte_converters=byte_converters)
+
+        else:
+            # This branch reads the file into chunks of object arrays and then
+            # casts them to the desired actual dtype.  This ensures correct
+            # string-length and datetime-unit discovery (like `arr.astype()`).
+            # Due to chunking, certain error reports are less clear, currently.
+            if filelike:
+                data = iter(data)  # cannot chunk when reading from file
+                filelike = False
+
+            c_byte_converters = False
+            if read_dtype_via_object_chunks == "S":
+                c_byte_converters = True  # Use latin1 rather than ascii
+
+            chunks = []
+            while max_rows != 0:
+                if max_rows < 0:
+                    chunk_size = _loadtxt_chunksize
+                else:
+                    chunk_size = min(_loadtxt_chunksize, max_rows)
+
+                next_arr = _load_from_filelike(
+                    data, delimiter=delimiter, comment=comment, quote=quote,
+                    imaginary_unit=imaginary_unit,
+                    usecols=usecols, skiplines=skiplines, max_rows=chunk_size,
+                    converters=converters, dtype=dtype,
+                    encoding=encoding, filelike=filelike,
+                    byte_converters=byte_converters,
+                    c_byte_converters=c_byte_converters)
+                # Cast here already.  We hope that this is better even for
+                # large files because the storage is more compact.  It could
+                # be adapted (in principle the concatenate could cast).
+                chunks.append(next_arr.astype(read_dtype_via_object_chunks))
+
+                skiplines = 0  # Only have to skip for first chunk
+                if max_rows >= 0:
+                    max_rows -= chunk_size
+                if len(next_arr) < chunk_size:
+                    # There was less data than requested, so we are done.
+                    break
+
+            # Need at least one chunk, but if empty, the last one may have
+            # the wrong shape.
+            if len(chunks) > 1 and len(chunks[-1]) == 0:
+                del chunks[-1]
+            if len(chunks) == 1:
+                arr = chunks[0]
+            else:
+                arr = np.concatenate(chunks, axis=0)
+
+    # NOTE: ndmin works as advertised for structured dtypes, but normally
+    #       these would return a 1D result plus the structured dimension,
+    #       so ndmin=2 adds a third dimension even when no squeezing occurs.
+    #       A `squeeze=False` could be a better solution (pandas uses squeeze).
+    arr = _ensure_ndmin_ndarray(arr, ndmin=ndmin)
+
+    if arr.shape:
+        if arr.shape[0] == 0:
+            warnings.warn(
+                f'loadtxt: input contained no data: "{fname}"',
+                category=UserWarning,
+                stacklevel=3
+            )
+
+    if unpack:
+        # Unpack structured dtypes if requested:
+        dt = arr.dtype
+        if dt.names is not None:
+            # For structured arrays, return an array for each field.
+            return [arr[field] for field in dt.names]
+        else:
+            return arr.T
+    else:
+        return arr
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def loadtxt(fname, dtype=float, comments='#', delimiter=None,
+            converters=None, skiprows=0, usecols=None, unpack=False,
+            ndmin=0, encoding=None, max_rows=None, *, quotechar=None,
+            like=None):
+    r"""
+    Load data from a text file.
+
+    Parameters
+    ----------
+    fname : file, str, pathlib.Path, list of str, generator
+        File, filename, list, or generator to read.  If the filename
+        extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note
+        that generators must return bytes or strings. The strings
+        in a list or produced by a generator are treated as lines.
+    dtype : data-type, optional
+        Data-type of the resulting array; default: float.  If this is a
+        structured data-type, the resulting array will be 1-dimensional, and
+        each row will be interpreted as an element of the array.  In this
+        case, the number of columns used must match the number of fields in
+        the data-type.
+    comments : str or sequence of str or None, optional
+        The characters or list of characters used to indicate the start of a
+        comment. None implies no comments. For backwards compatibility, byte
+        strings will be decoded as 'latin1'. The default is '#'.
+    delimiter : str, optional
+        The character used to separate the values. For backwards compatibility,
+        byte strings will be decoded as 'latin1'. The default is whitespace.
+
+        .. versionchanged:: 1.23.0
+           Only single character delimiters are supported. Newline characters
+           cannot be used as the delimiter.
+
+    converters : dict or callable, optional
+        Converter functions to customize value parsing. If `converters` is
+        callable, the function is applied to all columns, else it must be a
+        dict that maps column number to a parser function.
+        See examples for further details.
+        Default: None.
+
+        .. versionchanged:: 1.23.0
+           The ability to pass a single callable to be applied to all columns
+           was added.
+
+    skiprows : int, optional
+        Skip the first `skiprows` lines, including comments; default: 0.
+    usecols : int or sequence, optional
+        Which columns to read, with 0 being the first. For example,
+        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
+        The default, None, results in all columns being read.
+    unpack : bool, optional
+        If True, the returned array is transposed, so that arguments may be
+        unpacked using ``x, y, z = loadtxt(...)``.  When used with a
+        structured data-type, arrays are returned for each field.
+        Default is False.
+    ndmin : int, optional
+        The returned array will have at least `ndmin` dimensions.
+        Otherwise mono-dimensional axes will be squeezed.
+        Legal values: 0 (default), 1 or 2.
+    encoding : str, optional
+        Encoding used to decode the inputfile. Does not apply to input streams.
+        The special value 'bytes' enables backward compatibility workarounds
+        that ensures you receive byte arrays as results if possible and passes
+        'latin1' encoded strings to converters. Override this value to receive
+        unicode arrays and pass strings as input to converters.  If set to None
+        the system default is used. The default value is 'bytes'.
+
+        .. versionchanged:: 2.0
+            Before NumPy 2, the default was ``'bytes'`` for Python 2
+            compatibility. The default is now ``None``.
+
+    max_rows : int, optional
+        Read `max_rows` rows of content after `skiprows` lines. The default is
+        to read all the rows. Note that empty rows containing no data such as
+        empty lines and comment lines are not counted towards `max_rows`,
+        while such lines are counted in `skiprows`.
+
+        .. versionchanged:: 1.23.0
+            Lines containing no data, including comment lines (e.g., lines
+            starting with '#' or as specified via `comments`) are not counted
+            towards `max_rows`.
+    quotechar : unicode character or None, optional
+        The character used to denote the start and end of a quoted item.
+        Occurrences of the delimiter or comment characters are ignored within
+        a quoted item. The default value is ``quotechar=None``, which means
+        quoting support is disabled.
+
+        If two consecutive instances of `quotechar` are found within a quoted
+        field, the first is treated as an escape character. See examples.
+
+        .. versionadded:: 1.23.0
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Data read from the text file.
+
+    See Also
+    --------
+    load, fromstring, fromregex
+    genfromtxt : Load data with missing values handled as specified.
+    scipy.io.loadmat : reads MATLAB data files
+
+    Notes
+    -----
+    This function aims to be a fast reader for simply formatted files.  The
+    `genfromtxt` function provides more sophisticated handling of, e.g.,
+    lines with missing values.
+
+    Each row in the input text file must have the same number of values to be
+    able to read all values. If all rows do not have same number of values, a
+    subset of up to n columns (where n is the least number of values present
+    in all rows) can be read by specifying the columns via `usecols`.
+
+    The strings produced by the Python float.hex method can be used as
+    input for floats.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from io import StringIO   # StringIO behaves like a file object
+    >>> c = StringIO("0 1\n2 3")
+    >>> np.loadtxt(c)
+    array([[0., 1.],
+           [2., 3.]])
+
+    >>> d = StringIO("M 21 72\nF 35 58")
+    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
+    ...                      'formats': ('S1', 'i4', 'f4')})
+    array([(b'M', 21, 72.), (b'F', 35, 58.)],
+          dtype=[('gender', 'S1'), ('age', '>> c = StringIO("1,0,2\n3,0,4")
+    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
+    >>> x
+    array([1., 3.])
+    >>> y
+    array([2., 4.])
+
+    The `converters` argument is used to specify functions to preprocess the
+    text prior to parsing. `converters` can be a dictionary that maps
+    preprocessing functions to each column:
+
+    >>> s = StringIO("1.618, 2.296\n3.141, 4.669\n")
+    >>> conv = {
+    ...     0: lambda x: np.floor(float(x)),  # conversion fn for column 0
+    ...     1: lambda x: np.ceil(float(x)),  # conversion fn for column 1
+    ... }
+    >>> np.loadtxt(s, delimiter=",", converters=conv)
+    array([[1., 3.],
+           [3., 5.]])
+
+    `converters` can be a callable instead of a dictionary, in which case it
+    is applied to all columns:
+
+    >>> s = StringIO("0xDE 0xAD\n0xC0 0xDE")
+    >>> import functools
+    >>> conv = functools.partial(int, base=16)
+    >>> np.loadtxt(s, converters=conv)
+    array([[222., 173.],
+           [192., 222.]])
+
+    This example shows how `converters` can be used to convert a field
+    with a trailing minus sign into a negative number.
+
+    >>> s = StringIO("10.01 31.25-\n19.22 64.31\n17.57- 63.94")
+    >>> def conv(fld):
+    ...     return -float(fld[:-1]) if fld.endswith("-") else float(fld)
+    ...
+    >>> np.loadtxt(s, converters=conv)
+    array([[ 10.01, -31.25],
+           [ 19.22,  64.31],
+           [-17.57,  63.94]])
+
+    Using a callable as the converter can be particularly useful for handling
+    values with different formatting, e.g. floats with underscores:
+
+    >>> s = StringIO("1 2.7 100_000")
+    >>> np.loadtxt(s, converters=float)
+    array([1.e+00, 2.7e+00, 1.e+05])
+
+    This idea can be extended to automatically handle values specified in
+    many different formats, such as hex values:
+
+    >>> def conv(val):
+    ...     try:
+    ...         return float(val)
+    ...     except ValueError:
+    ...         return float.fromhex(val)
+    >>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2")
+    >>> np.loadtxt(s, delimiter=",", converters=conv)
+    array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00])
+
+    Or a format where the ``-`` sign comes after the number:
+
+    >>> s = StringIO("10.01 31.25-\n19.22 64.31\n17.57- 63.94")
+    >>> conv = lambda x: -float(x[:-1]) if x.endswith("-") else float(x)
+    >>> np.loadtxt(s, converters=conv)
+    array([[ 10.01, -31.25],
+           [ 19.22,  64.31],
+           [-17.57,  63.94]])
+
+    Support for quoted fields is enabled with the `quotechar` parameter.
+    Comment and delimiter characters are ignored when they appear within a
+    quoted item delineated by `quotechar`:
+
+    >>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n')
+    >>> dtype = np.dtype([("label", "U12"), ("value", float)])
+    >>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"')
+    array([('alpha, #42', 10.), ('beta, #64',  2.)],
+          dtype=[('label', '>> s = StringIO('"alpha, #42"       10.0\n"beta, #64" 2.0\n')
+    >>> dtype = np.dtype([("label", "U12"), ("value", float)])
+    >>> np.loadtxt(s, dtype=dtype, delimiter=None, quotechar='"')
+    array([('alpha, #42', 10.), ('beta, #64',  2.)],
+          dtype=[('label', '>> s = StringIO('"Hello, my name is ""Monty""!"')
+    >>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"')
+    array('Hello, my name is "Monty"!', dtype='>> d = StringIO("1 2\n2 4\n3 9 12\n4 16 20")
+    >>> np.loadtxt(d, usecols=(0, 1))
+    array([[ 1.,  2.],
+           [ 2.,  4.],
+           [ 3.,  9.],
+           [ 4., 16.]])
+
+    """
+
+    if like is not None:
+        return _loadtxt_with_like(
+            like, fname, dtype=dtype, comments=comments, delimiter=delimiter,
+            converters=converters, skiprows=skiprows, usecols=usecols,
+            unpack=unpack, ndmin=ndmin, encoding=encoding,
+            max_rows=max_rows
+        )
+
+    if isinstance(delimiter, bytes):
+        delimiter.decode("latin1")
+
+    if dtype is None:
+        dtype = np.float64
+
+    comment = comments
+    # Control character type conversions for Py3 convenience
+    if comment is not None:
+        if isinstance(comment, (str, bytes)):
+            comment = [comment]
+        comment = [
+            x.decode('latin1') if isinstance(x, bytes) else x for x in comment]
+    if isinstance(delimiter, bytes):
+        delimiter = delimiter.decode('latin1')
+
+    arr = _read(fname, dtype=dtype, comment=comment, delimiter=delimiter,
+                converters=converters, skiplines=skiprows, usecols=usecols,
+                unpack=unpack, ndmin=ndmin, encoding=encoding,
+                max_rows=max_rows, quote=quotechar)
+
+    return arr
+
+
+_loadtxt_with_like = array_function_dispatch()(loadtxt)
+
+
+def _savetxt_dispatcher(fname, X, fmt=None, delimiter=None, newline=None,
+                        header=None, footer=None, comments=None,
+                        encoding=None):
+    return (X,)
+
+
+@array_function_dispatch(_savetxt_dispatcher)
+def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
+            footer='', comments='# ', encoding=None):
+    """
+    Save an array to a text file.
+
+    Parameters
+    ----------
+    fname : filename, file handle or pathlib.Path
+        If the filename ends in ``.gz``, the file is automatically saved in
+        compressed gzip format.  `loadtxt` understands gzipped files
+        transparently.
+    X : 1D or 2D array_like
+        Data to be saved to a text file.
+    fmt : str or sequence of strs, optional
+        A single format (%10.5f), a sequence of formats, or a
+        multi-format string, e.g. 'Iteration %d -- %10.5f', in which
+        case `delimiter` is ignored. For complex `X`, the legal options
+        for `fmt` are:
+
+        * a single specifier, ``fmt='%.4e'``, resulting in numbers formatted
+          like ``' (%s+%sj)' % (fmt, fmt)``
+        * a full string specifying every real and imaginary part, e.g.
+          ``' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'`` for 3 columns
+        * a list of specifiers, one per column - in this case, the real
+          and imaginary part must have separate specifiers,
+          e.g. ``['%.3e + %.3ej', '(%.15e%+.15ej)']`` for 2 columns
+    delimiter : str, optional
+        String or character separating columns.
+    newline : str, optional
+        String or character separating lines.
+    header : str, optional
+        String that will be written at the beginning of the file.
+    footer : str, optional
+        String that will be written at the end of the file.
+    comments : str, optional
+        String that will be prepended to the ``header`` and ``footer`` strings,
+        to mark them as comments. Default: '# ',  as expected by e.g.
+        ``numpy.loadtxt``.
+    encoding : {None, str}, optional
+        Encoding used to encode the outputfile. Does not apply to output
+        streams. If the encoding is something other than 'bytes' or 'latin1'
+        you will not be able to load the file in NumPy versions < 1.14. Default
+        is 'latin1'.
+
+    See Also
+    --------
+    save : Save an array to a binary file in NumPy ``.npy`` format
+    savez : Save several arrays into an uncompressed ``.npz`` archive
+    savez_compressed : Save several arrays into a compressed ``.npz`` archive
+
+    Notes
+    -----
+    Further explanation of the `fmt` parameter
+    (``%[flag]width[.precision]specifier``):
+
+    flags:
+        ``-`` : left justify
+
+        ``+`` : Forces to precede result with + or -.
+
+        ``0`` : Left pad the number with zeros instead of space (see width).
+
+    width:
+        Minimum number of characters to be printed. The value is not truncated
+        if it has more characters.
+
+    precision:
+        - For integer specifiers (eg. ``d,i,o,x``), the minimum number of
+          digits.
+        - For ``e, E`` and ``f`` specifiers, the number of digits to print
+          after the decimal point.
+        - For ``g`` and ``G``, the maximum number of significant digits.
+        - For ``s``, the maximum number of characters.
+
+    specifiers:
+        ``c`` : character
+
+        ``d`` or ``i`` : signed decimal integer
+
+        ``e`` or ``E`` : scientific notation with ``e`` or ``E``.
+
+        ``f`` : decimal floating point
+
+        ``g,G`` : use the shorter of ``e,E`` or ``f``
+
+        ``o`` : signed octal
+
+        ``s`` : string of characters
+
+        ``u`` : unsigned decimal integer
+
+        ``x,X`` : unsigned hexadecimal integer
+
+    This explanation of ``fmt`` is not complete, for an exhaustive
+    specification see [1]_.
+
+    References
+    ----------
+    .. [1] `Format Specification Mini-Language
+           `_,
+           Python Documentation.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = y = z = np.arange(0.0,5.0,1.0)
+    >>> np.savetxt('test.out', x, delimiter=',')   # X is an array
+    >>> np.savetxt('test.out', (x,y,z))   # x,y,z equal sized 1D arrays
+    >>> np.savetxt('test.out', x, fmt='%1.4e')   # use exponential notation
+
+    """
+
+    class WriteWrap:
+        """Convert to bytes on bytestream inputs.
+
+        """
+        def __init__(self, fh, encoding):
+            self.fh = fh
+            self.encoding = encoding
+            self.do_write = self.first_write
+
+        def close(self):
+            self.fh.close()
+
+        def write(self, v):
+            self.do_write(v)
+
+        def write_bytes(self, v):
+            if isinstance(v, bytes):
+                self.fh.write(v)
+            else:
+                self.fh.write(v.encode(self.encoding))
+
+        def write_normal(self, v):
+            self.fh.write(asunicode(v))
+
+        def first_write(self, v):
+            try:
+                self.write_normal(v)
+                self.write = self.write_normal
+            except TypeError:
+                # input is probably a bytestream
+                self.write_bytes(v)
+                self.write = self.write_bytes
+
+    own_fh = False
+    if isinstance(fname, os.PathLike):
+        fname = os.fspath(fname)
+    if _is_string_like(fname):
+        # datasource doesn't support creating a new file ...
+        open(fname, 'wt').close()
+        fh = np.lib._datasource.open(fname, 'wt', encoding=encoding)
+        own_fh = True
+    elif hasattr(fname, 'write'):
+        # wrap to handle byte output streams
+        fh = WriteWrap(fname, encoding or 'latin1')
+    else:
+        raise ValueError('fname must be a string or file handle')
+
+    try:
+        X = np.asarray(X)
+
+        # Handle 1-dimensional arrays
+        if X.ndim == 0 or X.ndim > 2:
+            raise ValueError(
+                "Expected 1D or 2D array, got %dD array instead" % X.ndim)
+        elif X.ndim == 1:
+            # Common case -- 1d array of numbers
+            if X.dtype.names is None:
+                X = np.atleast_2d(X).T
+                ncol = 1
+
+            # Complex dtype -- each field indicates a separate column
+            else:
+                ncol = len(X.dtype.names)
+        else:
+            ncol = X.shape[1]
+
+        iscomplex_X = np.iscomplexobj(X)
+        # `fmt` can be a string with multiple insertion points or a
+        # list of formats.  E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
+        if type(fmt) in (list, tuple):
+            if len(fmt) != ncol:
+                raise AttributeError('fmt has wrong shape.  %s' % str(fmt))
+            format = delimiter.join(fmt)
+        elif isinstance(fmt, str):
+            n_fmt_chars = fmt.count('%')
+            error = ValueError('fmt has wrong number of %% formats:  %s' % fmt)
+            if n_fmt_chars == 1:
+                if iscomplex_X:
+                    fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol
+                else:
+                    fmt = [fmt, ] * ncol
+                format = delimiter.join(fmt)
+            elif iscomplex_X and n_fmt_chars != (2 * ncol):
+                raise error
+            elif ((not iscomplex_X) and n_fmt_chars != ncol):
+                raise error
+            else:
+                format = fmt
+        else:
+            raise ValueError('invalid fmt: %r' % (fmt,))
+
+        if len(header) > 0:
+            header = header.replace('\n', '\n' + comments)
+            fh.write(comments + header + newline)
+        if iscomplex_X:
+            for row in X:
+                row2 = []
+                for number in row:
+                    row2.append(number.real)
+                    row2.append(number.imag)
+                s = format % tuple(row2) + newline
+                fh.write(s.replace('+-', '-'))
+        else:
+            for row in X:
+                try:
+                    v = format % tuple(row) + newline
+                except TypeError as e:
+                    raise TypeError("Mismatch between array dtype ('%s') and "
+                                    "format specifier ('%s')"
+                                    % (str(X.dtype), format)) from e
+                fh.write(v)
+
+        if len(footer) > 0:
+            footer = footer.replace('\n', '\n' + comments)
+            fh.write(comments + footer + newline)
+    finally:
+        if own_fh:
+            fh.close()
+
+
+@set_module('numpy')
+def fromregex(file, regexp, dtype, encoding=None):
+    r"""
+    Construct an array from a text file, using regular expression parsing.
+
+    The returned array is always a structured array, and is constructed from
+    all matches of the regular expression in the file. Groups in the regular
+    expression are converted to fields of the structured array.
+
+    Parameters
+    ----------
+    file : file, str, or pathlib.Path
+        Filename or file object to read.
+
+        .. versionchanged:: 1.22.0
+            Now accepts `os.PathLike` implementations.
+
+    regexp : str or regexp
+        Regular expression used to parse the file.
+        Groups in the regular expression correspond to fields in the dtype.
+    dtype : dtype or list of dtypes
+        Dtype for the structured array; must be a structured datatype.
+    encoding : str, optional
+        Encoding used to decode the inputfile. Does not apply to input streams.
+
+    Returns
+    -------
+    output : ndarray
+        The output array, containing the part of the content of `file` that
+        was matched by `regexp`. `output` is always a structured array.
+
+    Raises
+    ------
+    TypeError
+        When `dtype` is not a valid dtype for a structured array.
+
+    See Also
+    --------
+    fromstring, loadtxt
+
+    Notes
+    -----
+    Dtypes for structured arrays can be specified in several forms, but all
+    forms specify at least the data type and field name. For details see
+    `basics.rec`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from io import StringIO
+    >>> text = StringIO("1312 foo\n1534  bar\n444   qux")
+
+    >>> regexp = r"(\d+)\s+(...)"  # match [digits, whitespace, anything]
+    >>> output = np.fromregex(text, regexp,
+    ...                       [('num', np.int64), ('key', 'S3')])
+    >>> output
+    array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')],
+          dtype=[('num', '>> output['num']
+    array([1312, 1534,  444])
+
+    """
+    own_fh = False
+    if not hasattr(file, "read"):
+        file = os.fspath(file)
+        file = np.lib._datasource.open(file, 'rt', encoding=encoding)
+        own_fh = True
+
+    try:
+        if not isinstance(dtype, np.dtype):
+            dtype = np.dtype(dtype)
+        if dtype.names is None:
+            raise TypeError('dtype must be a structured datatype.')
+
+        content = file.read()
+        if isinstance(content, bytes) and isinstance(regexp, str):
+            regexp = asbytes(regexp)
+
+        if not hasattr(regexp, 'match'):
+            regexp = re.compile(regexp)
+        seq = regexp.findall(content)
+        if seq and not isinstance(seq[0], tuple):
+            # Only one group is in the regexp.
+            # Create the new array as a single data-type and then
+            #   re-interpret as a single-field structured array.
+            newdtype = np.dtype(dtype[dtype.names[0]])
+            output = np.array(seq, dtype=newdtype)
+            output.dtype = dtype
+        else:
+            output = np.array(seq, dtype=dtype)
+
+        return output
+    finally:
+        if own_fh:
+            file.close()
+
+
+#####--------------------------------------------------------------------------
+#---- --- ASCII functions ---
+#####--------------------------------------------------------------------------
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
+               skip_header=0, skip_footer=0, converters=None,
+               missing_values=None, filling_values=None, usecols=None,
+               names=None, excludelist=None,
+               deletechars=''.join(sorted(NameValidator.defaultdeletechars)),
+               replace_space='_', autostrip=False, case_sensitive=True,
+               defaultfmt="f%i", unpack=None, usemask=False, loose=True,
+               invalid_raise=True, max_rows=None, encoding=None,
+               *, ndmin=0, like=None):
+    """
+    Load data from a text file, with missing values handled as specified.
+
+    Each line past the first `skip_header` lines is split at the `delimiter`
+    character, and characters following the `comments` character are discarded.
+
+    Parameters
+    ----------
+    fname : file, str, pathlib.Path, list of str, generator
+        File, filename, list, or generator to read.  If the filename
+        extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note
+        that generators must return bytes or strings. The strings
+        in a list or produced by a generator are treated as lines.
+    dtype : dtype, optional
+        Data type of the resulting array.
+        If None, the dtypes will be determined by the contents of each
+        column, individually.
+    comments : str, optional
+        The character used to indicate the start of a comment.
+        All the characters occurring on a line after a comment are discarded.
+    delimiter : str, int, or sequence, optional
+        The string used to separate values.  By default, any consecutive
+        whitespaces act as delimiter.  An integer or sequence of integers
+        can also be provided as width(s) of each field.
+    skiprows : int, optional
+        `skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
+    skip_header : int, optional
+        The number of lines to skip at the beginning of the file.
+    skip_footer : int, optional
+        The number of lines to skip at the end of the file.
+    converters : variable, optional
+        The set of functions that convert the data of a column to a value.
+        The converters can also be used to provide a default value
+        for missing data: ``converters = {3: lambda s: float(s or 0)}``.
+    missing : variable, optional
+        `missing` was removed in numpy 1.10. Please use `missing_values`
+        instead.
+    missing_values : variable, optional
+        The set of strings corresponding to missing data.
+    filling_values : variable, optional
+        The set of values to be used as default when the data are missing.
+    usecols : sequence, optional
+        Which columns to read, with 0 being the first.  For example,
+        ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
+    names : {None, True, str, sequence}, optional
+        If `names` is True, the field names are read from the first line after
+        the first `skip_header` lines. This line can optionally be preceded
+        by a comment delimiter. Any content before the comment delimiter is
+        discarded. If `names` is a sequence or a single-string of
+        comma-separated names, the names will be used to define the field
+        names in a structured dtype. If `names` is None, the names of the
+        dtype fields will be used, if any.
+    excludelist : sequence, optional
+        A list of names to exclude. This list is appended to the default list
+        ['return','file','print']. Excluded names are appended with an
+        underscore: for example, `file` would become `file_`.
+    deletechars : str, optional
+        A string combining invalid characters that must be deleted from the
+        names.
+    defaultfmt : str, optional
+        A format used to define default field names, such as "f%i" or "f_%02i".
+    autostrip : bool, optional
+        Whether to automatically strip white spaces from the variables.
+    replace_space : char, optional
+        Character(s) used in replacement of white spaces in the variable
+        names. By default, use a '_'.
+    case_sensitive : {True, False, 'upper', 'lower'}, optional
+        If True, field names are case sensitive.
+        If False or 'upper', field names are converted to upper case.
+        If 'lower', field names are converted to lower case.
+    unpack : bool, optional
+        If True, the returned array is transposed, so that arguments may be
+        unpacked using ``x, y, z = genfromtxt(...)``.  When used with a
+        structured data-type, arrays are returned for each field.
+        Default is False.
+    usemask : bool, optional
+        If True, return a masked array.
+        If False, return a regular array.
+    loose : bool, optional
+        If True, do not raise errors for invalid values.
+    invalid_raise : bool, optional
+        If True, an exception is raised if an inconsistency is detected in the
+        number of columns.
+        If False, a warning is emitted and the offending lines are skipped.
+    max_rows : int,  optional
+        The maximum number of rows to read. Must not be used with skip_footer
+        at the same time.  If given, the value must be at least 1. Default is
+        to read the entire file.
+    encoding : str, optional
+        Encoding used to decode the inputfile. Does not apply when `fname`
+        is a file object. The special value 'bytes' enables backward
+        compatibility workarounds that ensure that you receive byte arrays
+        when possible and passes latin1 encoded strings to converters.
+        Override this value to receive unicode arrays and pass strings
+        as input to converters.  If set to None the system default is used.
+        The default value is 'bytes'.
+
+        .. versionchanged:: 2.0
+            Before NumPy 2, the default was ``'bytes'`` for Python 2
+            compatibility. The default is now ``None``.
+
+    ndmin : int, optional
+        Same parameter as `loadtxt`
+
+        .. versionadded:: 1.23.0
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Data read from the text file. If `usemask` is True, this is a
+        masked array.
+
+    See Also
+    --------
+    numpy.loadtxt : equivalent function when no data is missing.
+
+    Notes
+    -----
+    * When spaces are used as delimiters, or when no delimiter has been given
+      as input, there should not be any missing data between two fields.
+    * When variables are named (either by a flexible dtype or with a `names`
+      sequence), there must not be any header in the file (else a ValueError
+      exception is raised).
+    * Individual values are not stripped of spaces by default.
+      When using a custom converter, make sure the function does remove spaces.
+    * Custom converters may receive unexpected values due to dtype
+      discovery.
+
+    References
+    ----------
+    .. [1] NumPy User Guide, section `I/O with NumPy
+           `_.
+
+    Examples
+    --------
+    >>> from io import StringIO
+    >>> import numpy as np
+
+    Comma delimited file with mixed dtype
+
+    >>> s = StringIO("1,1.3,abcde")
+    >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
+    ... ('mystring','S5')], delimiter=",")
+    >>> data
+    array((1, 1.3, b'abcde'),
+          dtype=[('myint', '>> _ = s.seek(0) # needed for StringIO example only
+    >>> data = np.genfromtxt(s, dtype=None,
+    ... names = ['myint','myfloat','mystring'], delimiter=",")
+    >>> data
+    array((1, 1.3, 'abcde'),
+          dtype=[('myint', '>> _ = s.seek(0)
+    >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
+    ... names=['myint','myfloat','mystring'], delimiter=",")
+    >>> data
+    array((1, 1.3, b'abcde'),
+          dtype=[('myint', '>> s = StringIO("11.3abcde")
+    >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
+    ...     delimiter=[1,3,5])
+    >>> data
+    array((1, 1.3, 'abcde'),
+          dtype=[('intvar', '>> f = StringIO('''
+    ... text,# of chars
+    ... hello world,11
+    ... numpy,5''')
+    >>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
+    array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
+      dtype=[('f0', 'S12'), ('f1', 'S12')])
+
+    """
+
+    if like is not None:
+        return _genfromtxt_with_like(
+            like, fname, dtype=dtype, comments=comments, delimiter=delimiter,
+            skip_header=skip_header, skip_footer=skip_footer,
+            converters=converters, missing_values=missing_values,
+            filling_values=filling_values, usecols=usecols, names=names,
+            excludelist=excludelist, deletechars=deletechars,
+            replace_space=replace_space, autostrip=autostrip,
+            case_sensitive=case_sensitive, defaultfmt=defaultfmt,
+            unpack=unpack, usemask=usemask, loose=loose,
+            invalid_raise=invalid_raise, max_rows=max_rows, encoding=encoding,
+            ndmin=ndmin,
+        )
+
+    _ensure_ndmin_ndarray_check_param(ndmin)
+
+    if max_rows is not None:
+        if skip_footer:
+            raise ValueError(
+                    "The keywords 'skip_footer' and 'max_rows' can not be "
+                    "specified at the same time.")
+        if max_rows < 1:
+            raise ValueError("'max_rows' must be at least 1.")
+
+    if usemask:
+        from numpy.ma import MaskedArray, make_mask_descr
+    # Check the input dictionary of converters
+    user_converters = converters or {}
+    if not isinstance(user_converters, dict):
+        raise TypeError(
+            "The input argument 'converter' should be a valid dictionary "
+            "(got '%s' instead)" % type(user_converters))
+
+    if encoding == 'bytes':
+        encoding = None
+        byte_converters = True
+    else:
+        byte_converters = False
+
+    # Initialize the filehandle, the LineSplitter and the NameValidator
+    if isinstance(fname, os.PathLike):
+        fname = os.fspath(fname)
+    if isinstance(fname, str):
+        fid = np.lib._datasource.open(fname, 'rt', encoding=encoding)
+        fid_ctx = contextlib.closing(fid)
+    else:
+        fid = fname
+        fid_ctx = contextlib.nullcontext(fid)
+    try:
+        fhd = iter(fid)
+    except TypeError as e:
+        raise TypeError(
+            "fname must be a string, a filehandle, a sequence of strings,\n"
+            f"or an iterator of strings. Got {type(fname)} instead."
+        ) from e
+    with fid_ctx:
+        split_line = LineSplitter(delimiter=delimiter, comments=comments,
+                                  autostrip=autostrip, encoding=encoding)
+        validate_names = NameValidator(excludelist=excludelist,
+                                       deletechars=deletechars,
+                                       case_sensitive=case_sensitive,
+                                       replace_space=replace_space)
+
+        # Skip the first `skip_header` rows
+        try:
+            for i in range(skip_header):
+                next(fhd)
+
+            # Keep on until we find the first valid values
+            first_values = None
+
+            while not first_values:
+                first_line = _decode_line(next(fhd), encoding)
+                if (names is True) and (comments is not None):
+                    if comments in first_line:
+                        first_line = (
+                            ''.join(first_line.split(comments)[1:]))
+                first_values = split_line(first_line)
+        except StopIteration:
+            # return an empty array if the datafile is empty
+            first_line = ''
+            first_values = []
+            warnings.warn(
+                'genfromtxt: Empty input file: "%s"' % fname, stacklevel=2
+            )
+
+        # Should we take the first values as names ?
+        if names is True:
+            fval = first_values[0].strip()
+            if comments is not None:
+                if fval in comments:
+                    del first_values[0]
+
+        # Check the columns to use: make sure `usecols` is a list
+        if usecols is not None:
+            try:
+                usecols = [_.strip() for _ in usecols.split(",")]
+            except AttributeError:
+                try:
+                    usecols = list(usecols)
+                except TypeError:
+                    usecols = [usecols, ]
+        nbcols = len(usecols or first_values)
+
+        # Check the names and overwrite the dtype.names if needed
+        if names is True:
+            names = validate_names([str(_.strip()) for _ in first_values])
+            first_line = ''
+        elif _is_string_like(names):
+            names = validate_names([_.strip() for _ in names.split(',')])
+        elif names:
+            names = validate_names(names)
+        # Get the dtype
+        if dtype is not None:
+            dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
+                               excludelist=excludelist,
+                               deletechars=deletechars,
+                               case_sensitive=case_sensitive,
+                               replace_space=replace_space)
+        # Make sure the names is a list (for 2.5)
+        if names is not None:
+            names = list(names)
+
+        if usecols:
+            for (i, current) in enumerate(usecols):
+                # if usecols is a list of names, convert to a list of indices
+                if _is_string_like(current):
+                    usecols[i] = names.index(current)
+                elif current < 0:
+                    usecols[i] = current + len(first_values)
+            # If the dtype is not None, make sure we update it
+            if (dtype is not None) and (len(dtype) > nbcols):
+                descr = dtype.descr
+                dtype = np.dtype([descr[_] for _ in usecols])
+                names = list(dtype.names)
+            # If `names` is not None, update the names
+            elif (names is not None) and (len(names) > nbcols):
+                names = [names[_] for _ in usecols]
+        elif (names is not None) and (dtype is not None):
+            names = list(dtype.names)
+
+        # Process the missing values ...............................
+        # Rename missing_values for convenience
+        user_missing_values = missing_values or ()
+        if isinstance(user_missing_values, bytes):
+            user_missing_values = user_missing_values.decode('latin1')
+
+        # Define the list of missing_values (one column: one list)
+        missing_values = [[''] for _ in range(nbcols)]
+
+        # We have a dictionary: process it field by field
+        if isinstance(user_missing_values, dict):
+            # Loop on the items
+            for (key, val) in user_missing_values.items():
+                # Is the key a string ?
+                if _is_string_like(key):
+                    try:
+                        # Transform it into an integer
+                        key = names.index(key)
+                    except ValueError:
+                        # We couldn't find it: the name must have been dropped
+                        continue
+                # Redefine the key as needed if it's a column number
+                if usecols:
+                    try:
+                        key = usecols.index(key)
+                    except ValueError:
+                        pass
+                # Transform the value as a list of string
+                if isinstance(val, (list, tuple)):
+                    val = [str(_) for _ in val]
+                else:
+                    val = [str(val), ]
+                # Add the value(s) to the current list of missing
+                if key is None:
+                    # None acts as default
+                    for miss in missing_values:
+                        miss.extend(val)
+                else:
+                    missing_values[key].extend(val)
+        # We have a sequence : each item matches a column
+        elif isinstance(user_missing_values, (list, tuple)):
+            for (value, entry) in zip(user_missing_values, missing_values):
+                value = str(value)
+                if value not in entry:
+                    entry.append(value)
+        # We have a string : apply it to all entries
+        elif isinstance(user_missing_values, str):
+            user_value = user_missing_values.split(",")
+            for entry in missing_values:
+                entry.extend(user_value)
+        # We have something else: apply it to all entries
+        else:
+            for entry in missing_values:
+                entry.extend([str(user_missing_values)])
+
+        # Process the filling_values ...............................
+        # Rename the input for convenience
+        user_filling_values = filling_values
+        if user_filling_values is None:
+            user_filling_values = []
+        # Define the default
+        filling_values = [None] * nbcols
+        # We have a dictionary : update each entry individually
+        if isinstance(user_filling_values, dict):
+            for (key, val) in user_filling_values.items():
+                if _is_string_like(key):
+                    try:
+                        # Transform it into an integer
+                        key = names.index(key)
+                    except ValueError:
+                        # We couldn't find it: the name must have been dropped
+                        continue
+                # Redefine the key if it's a column number
+                # and usecols is defined
+                if usecols:
+                    try:
+                        key = usecols.index(key)
+                    except ValueError:
+                        pass
+                # Add the value to the list
+                filling_values[key] = val
+        # We have a sequence : update on a one-to-one basis
+        elif isinstance(user_filling_values, (list, tuple)):
+            n = len(user_filling_values)
+            if (n <= nbcols):
+                filling_values[:n] = user_filling_values
+            else:
+                filling_values = user_filling_values[:nbcols]
+        # We have something else : use it for all entries
+        else:
+            filling_values = [user_filling_values] * nbcols
+
+        # Initialize the converters ................................
+        if dtype is None:
+            # Note: we can't use a [...]*nbcols, as we would have 3 times
+            # the same converter, instead of 3 different converters.
+            converters = [
+                StringConverter(None, missing_values=miss, default=fill)
+                for (miss, fill) in zip(missing_values, filling_values)
+            ]
+        else:
+            dtype_flat = flatten_dtype(dtype, flatten_base=True)
+            # Initialize the converters
+            if len(dtype_flat) > 1:
+                # Flexible type : get a converter from each dtype
+                zipit = zip(dtype_flat, missing_values, filling_values)
+                converters = [StringConverter(dt,
+                                              locked=True,
+                                              missing_values=miss,
+                                              default=fill)
+                              for (dt, miss, fill) in zipit]
+            else:
+                # Set to a default converter (but w/ different missing values)
+                zipit = zip(missing_values, filling_values)
+                converters = [StringConverter(dtype,
+                                              locked=True,
+                                              missing_values=miss,
+                                              default=fill)
+                              for (miss, fill) in zipit]
+        # Update the converters to use the user-defined ones
+        uc_update = []
+        for (j, conv) in user_converters.items():
+            # If the converter is specified by column names,
+            # use the index instead
+            if _is_string_like(j):
+                try:
+                    j = names.index(j)
+                    i = j
+                except ValueError:
+                    continue
+            elif usecols:
+                try:
+                    i = usecols.index(j)
+                except ValueError:
+                    # Unused converter specified
+                    continue
+            else:
+                i = j
+            # Find the value to test - first_line is not filtered by usecols:
+            if len(first_line):
+                testing_value = first_values[j]
+            else:
+                testing_value = None
+            if conv is bytes:
+                user_conv = asbytes
+            elif byte_converters:
+                # Converters may use decode to workaround numpy's old
+                # behavior, so encode the string again before passing
+                # to the user converter.
+                def tobytes_first(x, conv):
+                    if type(x) is bytes:
+                        return conv(x)
+                    return conv(x.encode("latin1"))
+                user_conv = functools.partial(tobytes_first, conv=conv)
+            else:
+                user_conv = conv
+            converters[i].update(user_conv, locked=True,
+                                 testing_value=testing_value,
+                                 default=filling_values[i],
+                                 missing_values=missing_values[i],)
+            uc_update.append((i, user_conv))
+        # Make sure we have the corrected keys in user_converters...
+        user_converters.update(uc_update)
+
+        # Fixme: possible error as following variable never used.
+        # miss_chars = [_.missing_values for _ in converters]
+
+        # Initialize the output lists ...
+        # ... rows
+        rows = []
+        append_to_rows = rows.append
+        # ... masks
+        if usemask:
+            masks = []
+            append_to_masks = masks.append
+        # ... invalid
+        invalid = []
+        append_to_invalid = invalid.append
+
+        # Parse each line
+        for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
+            values = split_line(line)
+            nbvalues = len(values)
+            # Skip an empty line
+            if nbvalues == 0:
+                continue
+            if usecols:
+                # Select only the columns we need
+                try:
+                    values = [values[_] for _ in usecols]
+                except IndexError:
+                    append_to_invalid((i + skip_header + 1, nbvalues))
+                    continue
+            elif nbvalues != nbcols:
+                append_to_invalid((i + skip_header + 1, nbvalues))
+                continue
+            # Store the values
+            append_to_rows(tuple(values))
+            if usemask:
+                append_to_masks(tuple([v.strip() in m
+                                       for (v, m) in zip(values,
+                                                         missing_values)]))
+            if len(rows) == max_rows:
+                break
+
+    # Upgrade the converters (if needed)
+    if dtype is None:
+        for (i, converter) in enumerate(converters):
+            current_column = [itemgetter(i)(_m) for _m in rows]
+            try:
+                converter.iterupgrade(current_column)
+            except ConverterLockError:
+                errmsg = "Converter #%i is locked and cannot be upgraded: " % i
+                current_column = map(itemgetter(i), rows)
+                for (j, value) in enumerate(current_column):
+                    try:
+                        converter.upgrade(value)
+                    except (ConverterError, ValueError):
+                        errmsg += "(occurred line #%i for value '%s')"
+                        errmsg %= (j + 1 + skip_header, value)
+                        raise ConverterError(errmsg)
+
+    # Check that we don't have invalid values
+    nbinvalid = len(invalid)
+    if nbinvalid > 0:
+        nbrows = len(rows) + nbinvalid - skip_footer
+        # Construct the error message
+        template = "    Line #%%i (got %%i columns instead of %i)" % nbcols
+        if skip_footer > 0:
+            nbinvalid_skipped = len([_ for _ in invalid
+                                     if _[0] > nbrows + skip_header])
+            invalid = invalid[:nbinvalid - nbinvalid_skipped]
+            skip_footer -= nbinvalid_skipped
+#
+#            nbrows -= skip_footer
+#            errmsg = [template % (i, nb)
+#                      for (i, nb) in invalid if i < nbrows]
+#        else:
+        errmsg = [template % (i, nb)
+                  for (i, nb) in invalid]
+        if len(errmsg):
+            errmsg.insert(0, "Some errors were detected !")
+            errmsg = "\n".join(errmsg)
+            # Raise an exception ?
+            if invalid_raise:
+                raise ValueError(errmsg)
+            # Issue a warning ?
+            else:
+                warnings.warn(errmsg, ConversionWarning, stacklevel=2)
+
+    # Strip the last skip_footer data
+    if skip_footer > 0:
+        rows = rows[:-skip_footer]
+        if usemask:
+            masks = masks[:-skip_footer]
+
+    # Convert each value according to the converter:
+    # We want to modify the list in place to avoid creating a new one...
+    if loose:
+        rows = list(
+            zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
+                  for (i, conv) in enumerate(converters)]))
+    else:
+        rows = list(
+            zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
+                  for (i, conv) in enumerate(converters)]))
+
+    # Reset the dtype
+    data = rows
+    if dtype is None:
+        # Get the dtypes from the types of the converters
+        column_types = [conv.type for conv in converters]
+        # Find the columns with strings...
+        strcolidx = [i for (i, v) in enumerate(column_types)
+                     if v == np.str_]
+
+        if byte_converters and strcolidx:
+            # convert strings back to bytes for backward compatibility
+            warnings.warn(
+                "Reading unicode strings without specifying the encoding "
+                "argument is deprecated. Set the encoding, use None for the "
+                "system default.",
+                np.exceptions.VisibleDeprecationWarning, stacklevel=2)
+
+            def encode_unicode_cols(row_tup):
+                row = list(row_tup)
+                for i in strcolidx:
+                    row[i] = row[i].encode('latin1')
+                return tuple(row)
+
+            try:
+                data = [encode_unicode_cols(r) for r in data]
+            except UnicodeEncodeError:
+                pass
+            else:
+                for i in strcolidx:
+                    column_types[i] = np.bytes_
+
+        # Update string types to be the right length
+        sized_column_types = column_types[:]
+        for i, col_type in enumerate(column_types):
+            if np.issubdtype(col_type, np.character):
+                n_chars = max(len(row[i]) for row in data)
+                sized_column_types[i] = (col_type, n_chars)
+
+        if names is None:
+            # If the dtype is uniform (before sizing strings)
+            base = {
+                c_type
+                for c, c_type in zip(converters, column_types)
+                if c._checked}
+            if len(base) == 1:
+                uniform_type, = base
+                (ddtype, mdtype) = (uniform_type, bool)
+            else:
+                ddtype = [(defaultfmt % i, dt)
+                          for (i, dt) in enumerate(sized_column_types)]
+                if usemask:
+                    mdtype = [(defaultfmt % i, bool)
+                              for (i, dt) in enumerate(sized_column_types)]
+        else:
+            ddtype = list(zip(names, sized_column_types))
+            mdtype = list(zip(names, [bool] * len(sized_column_types)))
+        output = np.array(data, dtype=ddtype)
+        if usemask:
+            outputmask = np.array(masks, dtype=mdtype)
+    else:
+        # Overwrite the initial dtype names if needed
+        if names and dtype.names is not None:
+            dtype.names = names
+        # Case 1. We have a structured type
+        if len(dtype_flat) > 1:
+            # Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
+            # First, create the array using a flattened dtype:
+            # [('a', int), ('b1', int), ('b2', float)]
+            # Then, view the array using the specified dtype.
+            if 'O' in (_.char for _ in dtype_flat):
+                if has_nested_fields(dtype):
+                    raise NotImplementedError(
+                        "Nested fields involving objects are not supported...")
+                else:
+                    output = np.array(data, dtype=dtype)
+            else:
+                rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
+                output = rows.view(dtype)
+            # Now, process the rowmasks the same way
+            if usemask:
+                rowmasks = np.array(
+                    masks, dtype=np.dtype([('', bool) for t in dtype_flat]))
+                # Construct the new dtype
+                mdtype = make_mask_descr(dtype)
+                outputmask = rowmasks.view(mdtype)
+        # Case #2. We have a basic dtype
+        else:
+            # We used some user-defined converters
+            if user_converters:
+                ishomogeneous = True
+                descr = []
+                for i, ttype in enumerate([conv.type for conv in converters]):
+                    # Keep the dtype of the current converter
+                    if i in user_converters:
+                        ishomogeneous &= (ttype == dtype.type)
+                        if np.issubdtype(ttype, np.character):
+                            ttype = (ttype, max(len(row[i]) for row in data))
+                        descr.append(('', ttype))
+                    else:
+                        descr.append(('', dtype))
+                # So we changed the dtype ?
+                if not ishomogeneous:
+                    # We have more than one field
+                    if len(descr) > 1:
+                        dtype = np.dtype(descr)
+                    # We have only one field: drop the name if not needed.
+                    else:
+                        dtype = np.dtype(ttype)
+            #
+            output = np.array(data, dtype)
+            if usemask:
+                if dtype.names is not None:
+                    mdtype = [(_, bool) for _ in dtype.names]
+                else:
+                    mdtype = bool
+                outputmask = np.array(masks, dtype=mdtype)
+    # Try to take care of the missing data we missed
+    names = output.dtype.names
+    if usemask and names:
+        for (name, conv) in zip(names, converters):
+            missing_values = [conv(_) for _ in conv.missing_values
+                              if _ != '']
+            for mval in missing_values:
+                outputmask[name] |= (output[name] == mval)
+    # Construct the final array
+    if usemask:
+        output = output.view(MaskedArray)
+        output._mask = outputmask
+
+    output = _ensure_ndmin_ndarray(output, ndmin=ndmin)
+
+    if unpack:
+        if names is None:
+            return output.T
+        elif len(names) == 1:
+            # squeeze single-name dtypes too
+            return output[names[0]]
+        else:
+            # For structured arrays with multiple fields,
+            # return an array for each field.
+            return [output[field] for field in names]
+    return output
+
+
+_genfromtxt_with_like = array_function_dispatch()(genfromtxt)
+
+
+def recfromtxt(fname, **kwargs):
+    """
+    Load ASCII data from a file and return it in a record array.
+
+    If ``usemask=False`` a standard `recarray` is returned,
+    if ``usemask=True`` a MaskedRecords array is returned.
+
+    .. deprecated:: 2.0
+        Use `numpy.genfromtxt` instead.
+
+    Parameters
+    ----------
+    fname, kwargs : For a description of input parameters, see `genfromtxt`.
+
+    See Also
+    --------
+    numpy.genfromtxt : generic function
+
+    Notes
+    -----
+    By default, `dtype` is None, which means that the data-type of the output
+    array will be determined from the data.
+
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`recfromtxt` is deprecated, "
+        "use `numpy.genfromtxt` instead."
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    kwargs.setdefault("dtype", None)
+    usemask = kwargs.get('usemask', False)
+    output = genfromtxt(fname, **kwargs)
+    if usemask:
+        from numpy.ma.mrecords import MaskedRecords
+        output = output.view(MaskedRecords)
+    else:
+        output = output.view(np.recarray)
+    return output
+
+
+def recfromcsv(fname, **kwargs):
+    """
+    Load ASCII data stored in a comma-separated file.
+
+    The returned array is a record array (if ``usemask=False``, see
+    `recarray`) or a masked record array (if ``usemask=True``,
+    see `ma.mrecords.MaskedRecords`).
+
+    .. deprecated:: 2.0
+        Use `numpy.genfromtxt` with comma as `delimiter` instead.
+
+    Parameters
+    ----------
+    fname, kwargs : For a description of input parameters, see `genfromtxt`.
+
+    See Also
+    --------
+    numpy.genfromtxt : generic function to load ASCII data.
+
+    Notes
+    -----
+    By default, `dtype` is None, which means that the data-type of the output
+    array will be determined from the data.
+
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`recfromcsv` is deprecated, "
+        "use `numpy.genfromtxt` with comma as `delimiter` instead. "
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    # Set default kwargs for genfromtxt as relevant to csv import.
+    kwargs.setdefault("case_sensitive", "lower")
+    kwargs.setdefault("names", True)
+    kwargs.setdefault("delimiter", ",")
+    kwargs.setdefault("dtype", None)
+    output = genfromtxt(fname, **kwargs)
+
+    usemask = kwargs.get("usemask", False)
+    if usemask:
+        from numpy.ma.mrecords import MaskedRecords
+        output = output.view(MaskedRecords)
+    else:
+        output = output.view(np.recarray)
+    return output
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..16d009524875c092a7bd209c6b4432301f947080
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_npyio_impl.pyi
@@ -0,0 +1,285 @@
+import types
+import zipfile
+from collections.abc import Callable, Collection, Iterable, Iterator, Mapping, Sequence
+from re import Pattern
+from typing import IO, Any, ClassVar, Generic, Protocol, TypeAlias, overload, type_check_only
+from typing import Literal as L
+
+from _typeshed import StrOrBytesPath, StrPath, SupportsKeysAndGetItem, SupportsRead, SupportsWrite
+from typing_extensions import Self, TypeVar, deprecated, override
+
+import numpy as np
+from numpy._core.multiarray import packbits, unpackbits
+from numpy._typing import ArrayLike, DTypeLike, NDArray, _DTypeLike, _SupportsArrayFunc
+from numpy.ma.mrecords import MaskedRecords
+
+from ._datasource import DataSource as DataSource
+
+__all__ = [
+    "fromregex",
+    "genfromtxt",
+    "load",
+    "loadtxt",
+    "packbits",
+    "save",
+    "savetxt",
+    "savez",
+    "savez_compressed",
+    "unpackbits",
+]
+
+_T_co = TypeVar("_T_co", covariant=True)
+_SCT = TypeVar("_SCT", bound=np.generic)
+_SCT_co = TypeVar("_SCT_co", bound=np.generic, default=Any, covariant=True)
+
+_FName: TypeAlias = StrPath | Iterable[str] | Iterable[bytes]
+_FNameRead: TypeAlias = StrPath | SupportsRead[str] | SupportsRead[bytes]
+_FNameWriteBytes: TypeAlias = StrPath | SupportsWrite[bytes]
+_FNameWrite: TypeAlias = _FNameWriteBytes | SupportsWrite[str]
+
+@type_check_only
+class _SupportsReadSeek(SupportsRead[_T_co], Protocol[_T_co]):
+    def seek(self, offset: int, whence: int, /) -> object: ...
+
+class BagObj(Generic[_T_co]):
+    def __init__(self, /, obj: SupportsKeysAndGetItem[str, _T_co]) -> None: ...
+    def __getattribute__(self, key: str, /) -> _T_co: ...
+    def __dir__(self) -> list[str]: ...
+
+class NpzFile(Mapping[str, NDArray[_SCT_co]]):
+    _MAX_REPR_ARRAY_COUNT: ClassVar[int] = 5
+
+    zip: zipfile.ZipFile
+    fid: IO[str] | None
+    files: list[str]
+    allow_pickle: bool
+    pickle_kwargs: Mapping[str, Any] | None
+    f: BagObj[NpzFile[_SCT_co]]
+
+    #
+    def __init__(
+        self,
+        /,
+        fid: IO[Any],
+        own_fid: bool = False,
+        allow_pickle: bool = False,
+        pickle_kwargs: Mapping[str, object] | None = None,
+        *,
+        max_header_size: int = 10_000,
+    ) -> None: ...
+    def __del__(self) -> None: ...
+    def __enter__(self) -> Self: ...
+    def __exit__(self, cls: type[BaseException] | None, e: BaseException | None, tb: types.TracebackType | None, /) -> None: ...
+    @override
+    def __len__(self) -> int: ...
+    @override
+    def __iter__(self) -> Iterator[str]: ...
+    @override
+    def __getitem__(self, key: str, /) -> NDArray[_SCT_co]: ...
+    def close(self) -> None: ...
+
+# NOTE: Returns a `NpzFile` if file is a zip file;
+# returns an `ndarray`/`memmap` otherwise
+def load(
+    file: StrOrBytesPath | _SupportsReadSeek[bytes],
+    mmap_mode: L["r+", "r", "w+", "c"] | None = None,
+    allow_pickle: bool = False,
+    fix_imports: bool = True,
+    encoding: L["ASCII", "latin1", "bytes"] = "ASCII",
+    *,
+    max_header_size: int = 10_000,
+) -> Any: ...
+
+@overload
+def save(file: _FNameWriteBytes, arr: ArrayLike, allow_pickle: bool = True) -> None: ...
+@overload
+@deprecated("The 'fix_imports' flag is deprecated in NumPy 2.1.")
+def save(file: _FNameWriteBytes, arr: ArrayLike, allow_pickle: bool, fix_imports: bool) -> None: ...
+@overload
+@deprecated("The 'fix_imports' flag is deprecated in NumPy 2.1.")
+def save(file: _FNameWriteBytes, arr: ArrayLike, allow_pickle: bool = True, *, fix_imports: bool) -> None: ...
+
+#
+def savez(file: _FNameWriteBytes, *args: ArrayLike, allow_pickle: bool = True, **kwds: ArrayLike) -> None: ...
+
+#
+def savez_compressed(file: _FNameWriteBytes, *args: ArrayLike, allow_pickle: bool = True, **kwds: ArrayLike) -> None: ...
+
+# File-like objects only have to implement `__iter__` and,
+# optionally, `encoding`
+@overload
+def loadtxt(
+    fname: _FName,
+    dtype: None = None,
+    comments: str | Sequence[str] | None = "#",
+    delimiter: str | None = None,
+    converters: Mapping[int | str, Callable[[str], Any]] | Callable[[str], Any] | None = None,
+    skiprows: int = 0,
+    usecols: int | Sequence[int] | None = None,
+    unpack: bool = False,
+    ndmin: L[0, 1, 2] = 0,
+    encoding: str | None = None,
+    max_rows: int | None = None,
+    *,
+    quotechar: str | None = None,
+    like: _SupportsArrayFunc | None = None,
+) -> NDArray[np.float64]: ...
+@overload
+def loadtxt(
+    fname: _FName,
+    dtype: _DTypeLike[_SCT],
+    comments: str | Sequence[str] | None = "#",
+    delimiter: str | None = None,
+    converters: Mapping[int | str, Callable[[str], Any]] | Callable[[str], Any] | None = None,
+    skiprows: int = 0,
+    usecols: int | Sequence[int] | None = None,
+    unpack: bool = False,
+    ndmin: L[0, 1, 2] = 0,
+    encoding: str | None = None,
+    max_rows: int | None = None,
+    *,
+    quotechar: str | None = None,
+    like: _SupportsArrayFunc | None = None,
+) -> NDArray[_SCT]: ...
+@overload
+def loadtxt(
+    fname: _FName,
+    dtype: DTypeLike,
+    comments: str | Sequence[str] | None = "#",
+    delimiter: str | None = None,
+    converters: Mapping[int | str, Callable[[str], Any]] | Callable[[str], Any] | None = None,
+    skiprows: int = 0,
+    usecols: int | Sequence[int] | None = None,
+    unpack: bool = False,
+    ndmin: L[0, 1, 2] = 0,
+    encoding: str | None = None,
+    max_rows: int | None = None,
+    *,
+    quotechar: str | None = None,
+    like: _SupportsArrayFunc | None = None,
+) -> NDArray[Any]: ...
+
+def savetxt(
+    fname: _FNameWrite,
+    X: ArrayLike,
+    fmt: str | Sequence[str] = "%.18e",
+    delimiter: str = " ",
+    newline: str = "\n",
+    header: str = "",
+    footer: str = "",
+    comments: str = "# ",
+    encoding: str | None = None,
+) -> None: ...
+
+@overload
+def fromregex(
+    file: _FNameRead,
+    regexp: str | bytes | Pattern[Any],
+    dtype: _DTypeLike[_SCT],
+    encoding: str | None = None,
+) -> NDArray[_SCT]: ...
+@overload
+def fromregex(
+    file: _FNameRead,
+    regexp: str | bytes | Pattern[Any],
+    dtype: DTypeLike,
+    encoding: str | None = None,
+) -> NDArray[Any]: ...
+
+@overload
+def genfromtxt(
+    fname: _FName,
+    dtype: None = None,
+    comments: str = ...,
+    delimiter: str | int | Iterable[int] | None = ...,
+    skip_header: int = ...,
+    skip_footer: int = ...,
+    converters: Mapping[int | str, Callable[[str], Any]] | None = ...,
+    missing_values: Any = ...,
+    filling_values: Any = ...,
+    usecols: Sequence[int] | None = ...,
+    names: L[True] | str | Collection[str] | None = ...,
+    excludelist: Sequence[str] | None = ...,
+    deletechars: str = ...,
+    replace_space: str = ...,
+    autostrip: bool = ...,
+    case_sensitive: bool | L["upper", "lower"] = ...,
+    defaultfmt: str = ...,
+    unpack: bool | None = ...,
+    usemask: bool = ...,
+    loose: bool = ...,
+    invalid_raise: bool = ...,
+    max_rows: int | None = ...,
+    encoding: str = ...,
+    *,
+    ndmin: L[0, 1, 2] = ...,
+    like: _SupportsArrayFunc | None = ...,
+) -> NDArray[Any]: ...
+@overload
+def genfromtxt(
+    fname: _FName,
+    dtype: _DTypeLike[_SCT],
+    comments: str = ...,
+    delimiter: str | int | Iterable[int] | None = ...,
+    skip_header: int = ...,
+    skip_footer: int = ...,
+    converters: Mapping[int | str, Callable[[str], Any]] | None = ...,
+    missing_values: Any = ...,
+    filling_values: Any = ...,
+    usecols: Sequence[int] | None = ...,
+    names: L[True] | str | Collection[str] | None = ...,
+    excludelist: Sequence[str] | None = ...,
+    deletechars: str = ...,
+    replace_space: str = ...,
+    autostrip: bool = ...,
+    case_sensitive: bool | L["upper", "lower"] = ...,
+    defaultfmt: str = ...,
+    unpack: bool | None = ...,
+    usemask: bool = ...,
+    loose: bool = ...,
+    invalid_raise: bool = ...,
+    max_rows: int | None = ...,
+    encoding: str = ...,
+    *,
+    ndmin: L[0, 1, 2] = ...,
+    like: _SupportsArrayFunc | None = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def genfromtxt(
+    fname: _FName,
+    dtype: DTypeLike,
+    comments: str = ...,
+    delimiter: str | int | Iterable[int] | None = ...,
+    skip_header: int = ...,
+    skip_footer: int = ...,
+    converters: Mapping[int | str, Callable[[str], Any]] | None = ...,
+    missing_values: Any = ...,
+    filling_values: Any = ...,
+    usecols: Sequence[int] | None = ...,
+    names: L[True] | str | Collection[str] | None = ...,
+    excludelist: Sequence[str] | None = ...,
+    deletechars: str = ...,
+    replace_space: str = ...,
+    autostrip: bool = ...,
+    case_sensitive: bool | L["upper", "lower"] = ...,
+    defaultfmt: str = ...,
+    unpack: bool | None = ...,
+    usemask: bool = ...,
+    loose: bool = ...,
+    invalid_raise: bool = ...,
+    max_rows: int | None = ...,
+    encoding: str = ...,
+    *,
+    ndmin: L[0, 1, 2] = ...,
+    like: _SupportsArrayFunc | None = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def recfromtxt(fname: _FName, *, usemask: L[False] = False, **kwargs: object) -> np.recarray[Any, np.dtype[np.record]]: ...
+@overload
+def recfromtxt(fname: _FName, *, usemask: L[True], **kwargs: object) -> MaskedRecords[Any, np.dtype[np.void]]: ...
+
+@overload
+def recfromcsv(fname: _FName, *, usemask: L[False] = False, **kwargs: object) -> np.recarray[Any, np.dtype[np.record]]: ...
+@overload
+def recfromcsv(fname: _FName, *, usemask: L[True], **kwargs: object) -> MaskedRecords[Any, np.dtype[np.void]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bcf0a3d92a6c8301b25e480f11994ff6feb1efa
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+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.py
@@ -0,0 +1,1458 @@
+"""
+Functions to operate on polynomials.
+
+"""
+__all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
+           'polysub', 'polymul', 'polydiv', 'polyval', 'poly1d',
+           'polyfit']
+
+import functools
+import re
+import warnings
+
+from .._utils import set_module
+import numpy._core.numeric as NX
+
+from numpy._core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
+                        ones)
+from numpy._core import overrides
+from numpy.exceptions import RankWarning
+from numpy.lib._twodim_base_impl import diag, vander
+from numpy.lib._function_base_impl import trim_zeros
+from numpy.lib._type_check_impl import iscomplex, real, imag, mintypecode
+from numpy.linalg import eigvals, lstsq, inv
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+def _poly_dispatcher(seq_of_zeros):
+    return seq_of_zeros
+
+
+@array_function_dispatch(_poly_dispatcher)
+def poly(seq_of_zeros):
+    """
+    Find the coefficients of a polynomial with the given sequence of roots.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Returns the coefficients of the polynomial whose leading coefficient
+    is one for the given sequence of zeros (multiple roots must be included
+    in the sequence as many times as their multiplicity; see Examples).
+    A square matrix (or array, which will be treated as a matrix) can also
+    be given, in which case the coefficients of the characteristic polynomial
+    of the matrix are returned.
+
+    Parameters
+    ----------
+    seq_of_zeros : array_like, shape (N,) or (N, N)
+        A sequence of polynomial roots, or a square array or matrix object.
+
+    Returns
+    -------
+    c : ndarray
+        1D array of polynomial coefficients from highest to lowest degree:
+
+        ``c[0] * x**(N) + c[1] * x**(N-1) + ... + c[N-1] * x + c[N]``
+        where c[0] always equals 1.
+
+    Raises
+    ------
+    ValueError
+        If input is the wrong shape (the input must be a 1-D or square
+        2-D array).
+
+    See Also
+    --------
+    polyval : Compute polynomial values.
+    roots : Return the roots of a polynomial.
+    polyfit : Least squares polynomial fit.
+    poly1d : A one-dimensional polynomial class.
+
+    Notes
+    -----
+    Specifying the roots of a polynomial still leaves one degree of
+    freedom, typically represented by an undetermined leading
+    coefficient. [1]_ In the case of this function, that coefficient -
+    the first one in the returned array - is always taken as one. (If
+    for some reason you have one other point, the only automatic way
+    presently to leverage that information is to use ``polyfit``.)
+
+    The characteristic polynomial, :math:`p_a(t)`, of an `n`-by-`n`
+    matrix **A** is given by
+
+    :math:`p_a(t) = \\mathrm{det}(t\\, \\mathbf{I} - \\mathbf{A})`,
+
+    where **I** is the `n`-by-`n` identity matrix. [2]_
+
+    References
+    ----------
+    .. [1] M. Sullivan and M. Sullivan, III, "Algebra and Trigonometry,
+       Enhanced With Graphing Utilities," Prentice-Hall, pg. 318, 1996.
+
+    .. [2] G. Strang, "Linear Algebra and Its Applications, 2nd Edition,"
+       Academic Press, pg. 182, 1980.
+
+    Examples
+    --------
+    Given a sequence of a polynomial's zeros:
+
+    >>> import numpy as np
+
+    >>> np.poly((0, 0, 0)) # Multiple root example
+    array([1., 0., 0., 0.])
+
+    The line above represents z**3 + 0*z**2 + 0*z + 0.
+
+    >>> np.poly((-1./2, 0, 1./2))
+    array([ 1.  ,  0.  , -0.25,  0.  ])
+
+    The line above represents z**3 - z/4
+
+    >>> np.poly((np.random.random(1)[0], 0, np.random.random(1)[0]))
+    array([ 1.        , -0.77086955,  0.08618131,  0.        ]) # random
+
+    Given a square array object:
+
+    >>> P = np.array([[0, 1./3], [-1./2, 0]])
+    >>> np.poly(P)
+    array([1.        , 0.        , 0.16666667])
+
+    Note how in all cases the leading coefficient is always 1.
+
+    """
+    seq_of_zeros = atleast_1d(seq_of_zeros)
+    sh = seq_of_zeros.shape
+
+    if len(sh) == 2 and sh[0] == sh[1] and sh[0] != 0:
+        seq_of_zeros = eigvals(seq_of_zeros)
+    elif len(sh) == 1:
+        dt = seq_of_zeros.dtype
+        # Let object arrays slip through, e.g. for arbitrary precision
+        if dt != object:
+            seq_of_zeros = seq_of_zeros.astype(mintypecode(dt.char))
+    else:
+        raise ValueError("input must be 1d or non-empty square 2d array.")
+
+    if len(seq_of_zeros) == 0:
+        return 1.0
+    dt = seq_of_zeros.dtype
+    a = ones((1,), dtype=dt)
+    for zero in seq_of_zeros:
+        a = NX.convolve(a, array([1, -zero], dtype=dt), mode='full')
+
+    if issubclass(a.dtype.type, NX.complexfloating):
+        # if complex roots are all complex conjugates, the roots are real.
+        roots = NX.asarray(seq_of_zeros, complex)
+        if NX.all(NX.sort(roots) == NX.sort(roots.conjugate())):
+            a = a.real.copy()
+
+    return a
+
+
+def _roots_dispatcher(p):
+    return p
+
+
+@array_function_dispatch(_roots_dispatcher)
+def roots(p):
+    """
+    Return the roots of a polynomial with coefficients given in p.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    The values in the rank-1 array `p` are coefficients of a polynomial.
+    If the length of `p` is n+1 then the polynomial is described by::
+
+      p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]
+
+    Parameters
+    ----------
+    p : array_like
+        Rank-1 array of polynomial coefficients.
+
+    Returns
+    -------
+    out : ndarray
+        An array containing the roots of the polynomial.
+
+    Raises
+    ------
+    ValueError
+        When `p` cannot be converted to a rank-1 array.
+
+    See also
+    --------
+    poly : Find the coefficients of a polynomial with a given sequence
+           of roots.
+    polyval : Compute polynomial values.
+    polyfit : Least squares polynomial fit.
+    poly1d : A one-dimensional polynomial class.
+
+    Notes
+    -----
+    The algorithm relies on computing the eigenvalues of the
+    companion matrix [1]_.
+
+    References
+    ----------
+    .. [1] R. A. Horn & C. R. Johnson, *Matrix Analysis*.  Cambridge, UK:
+        Cambridge University Press, 1999, pp. 146-7.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> coeff = [3.2, 2, 1]
+    >>> np.roots(coeff)
+    array([-0.3125+0.46351241j, -0.3125-0.46351241j])
+
+    """
+    # If input is scalar, this makes it an array
+    p = atleast_1d(p)
+    if p.ndim != 1:
+        raise ValueError("Input must be a rank-1 array.")
+
+    # find non-zero array entries
+    non_zero = NX.nonzero(NX.ravel(p))[0]
+
+    # Return an empty array if polynomial is all zeros
+    if len(non_zero) == 0:
+        return NX.array([])
+
+    # find the number of trailing zeros -- this is the number of roots at 0.
+    trailing_zeros = len(p) - non_zero[-1] - 1
+
+    # strip leading and trailing zeros
+    p = p[int(non_zero[0]):int(non_zero[-1])+1]
+
+    # casting: if incoming array isn't floating point, make it floating point.
+    if not issubclass(p.dtype.type, (NX.floating, NX.complexfloating)):
+        p = p.astype(float)
+
+    N = len(p)
+    if N > 1:
+        # build companion matrix and find its eigenvalues (the roots)
+        A = diag(NX.ones((N-2,), p.dtype), -1)
+        A[0,:] = -p[1:] / p[0]
+        roots = eigvals(A)
+    else:
+        roots = NX.array([])
+
+    # tack any zeros onto the back of the array
+    roots = hstack((roots, NX.zeros(trailing_zeros, roots.dtype)))
+    return roots
+
+
+def _polyint_dispatcher(p, m=None, k=None):
+    return (p,)
+
+
+@array_function_dispatch(_polyint_dispatcher)
+def polyint(p, m=1, k=None):
+    """
+    Return an antiderivative (indefinite integral) of a polynomial.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    The returned order `m` antiderivative `P` of polynomial `p` satisfies
+    :math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
+    integration constants `k`. The constants determine the low-order
+    polynomial part
+
+    .. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1}
+
+    of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`.
+
+    Parameters
+    ----------
+    p : array_like or poly1d
+        Polynomial to integrate.
+        A sequence is interpreted as polynomial coefficients, see `poly1d`.
+    m : int, optional
+        Order of the antiderivative. (Default: 1)
+    k : list of `m` scalars or scalar, optional
+        Integration constants. They are given in the order of integration:
+        those corresponding to highest-order terms come first.
+
+        If ``None`` (default), all constants are assumed to be zero.
+        If `m = 1`, a single scalar can be given instead of a list.
+
+    See Also
+    --------
+    polyder : derivative of a polynomial
+    poly1d.integ : equivalent method
+
+    Examples
+    --------
+    The defining property of the antiderivative:
+
+    >>> import numpy as np
+
+    >>> p = np.poly1d([1,1,1])
+    >>> P = np.polyint(p)
+    >>> P
+     poly1d([ 0.33333333,  0.5       ,  1.        ,  0.        ]) # may vary
+    >>> np.polyder(P) == p
+    True
+
+    The integration constants default to zero, but can be specified:
+
+    >>> P = np.polyint(p, 3)
+    >>> P(0)
+    0.0
+    >>> np.polyder(P)(0)
+    0.0
+    >>> np.polyder(P, 2)(0)
+    0.0
+    >>> P = np.polyint(p, 3, k=[6,5,3])
+    >>> P
+    poly1d([ 0.01666667,  0.04166667,  0.16666667,  3. ,  5. ,  3. ]) # may vary
+
+    Note that 3 = 6 / 2!, and that the constants are given in the order of
+    integrations. Constant of the highest-order polynomial term comes first:
+
+    >>> np.polyder(P, 2)(0)
+    6.0
+    >>> np.polyder(P, 1)(0)
+    5.0
+    >>> P(0)
+    3.0
+
+    """
+    m = int(m)
+    if m < 0:
+        raise ValueError("Order of integral must be positive (see polyder)")
+    if k is None:
+        k = NX.zeros(m, float)
+    k = atleast_1d(k)
+    if len(k) == 1 and m > 1:
+        k = k[0]*NX.ones(m, float)
+    if len(k) < m:
+        raise ValueError(
+              "k must be a scalar or a rank-1 array of length 1 or >m.")
+
+    truepoly = isinstance(p, poly1d)
+    p = NX.asarray(p)
+    if m == 0:
+        if truepoly:
+            return poly1d(p)
+        return p
+    else:
+        # Note: this must work also with object and integer arrays
+        y = NX.concatenate((p.__truediv__(NX.arange(len(p), 0, -1)), [k[0]]))
+        val = polyint(y, m - 1, k=k[1:])
+        if truepoly:
+            return poly1d(val)
+        return val
+
+
+def _polyder_dispatcher(p, m=None):
+    return (p,)
+
+
+@array_function_dispatch(_polyder_dispatcher)
+def polyder(p, m=1):
+    """
+    Return the derivative of the specified order of a polynomial.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Parameters
+    ----------
+    p : poly1d or sequence
+        Polynomial to differentiate.
+        A sequence is interpreted as polynomial coefficients, see `poly1d`.
+    m : int, optional
+        Order of differentiation (default: 1)
+
+    Returns
+    -------
+    der : poly1d
+        A new polynomial representing the derivative.
+
+    See Also
+    --------
+    polyint : Anti-derivative of a polynomial.
+    poly1d : Class for one-dimensional polynomials.
+
+    Examples
+    --------
+    The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is:
+
+    >>> import numpy as np
+
+    >>> p = np.poly1d([1,1,1,1])
+    >>> p2 = np.polyder(p)
+    >>> p2
+    poly1d([3, 2, 1])
+
+    which evaluates to:
+
+    >>> p2(2.)
+    17.0
+
+    We can verify this, approximating the derivative with
+    ``(f(x + h) - f(x))/h``:
+
+    >>> (p(2. + 0.001) - p(2.)) / 0.001
+    17.007000999997857
+
+    The fourth-order derivative of a 3rd-order polynomial is zero:
+
+    >>> np.polyder(p, 2)
+    poly1d([6, 2])
+    >>> np.polyder(p, 3)
+    poly1d([6])
+    >>> np.polyder(p, 4)
+    poly1d([0])
+
+    """
+    m = int(m)
+    if m < 0:
+        raise ValueError("Order of derivative must be positive (see polyint)")
+
+    truepoly = isinstance(p, poly1d)
+    p = NX.asarray(p)
+    n = len(p) - 1
+    y = p[:-1] * NX.arange(n, 0, -1)
+    if m == 0:
+        val = p
+    else:
+        val = polyder(y, m - 1)
+    if truepoly:
+        val = poly1d(val)
+    return val
+
+
+def _polyfit_dispatcher(x, y, deg, rcond=None, full=None, w=None, cov=None):
+    return (x, y, w)
+
+
+@array_function_dispatch(_polyfit_dispatcher)
+def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
+    """
+    Least squares polynomial fit.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
+    to points `(x, y)`. Returns a vector of coefficients `p` that minimises
+    the squared error in the order `deg`, `deg-1`, ... `0`.
+
+    The `Polynomial.fit ` class
+    method is recommended for new code as it is more stable numerically. See
+    the documentation of the method for more information.
+
+    Parameters
+    ----------
+    x : array_like, shape (M,)
+        x-coordinates of the M sample points ``(x[i], y[i])``.
+    y : array_like, shape (M,) or (M, K)
+        y-coordinates of the sample points. Several data sets of sample
+        points sharing the same x-coordinates can be fitted at once by
+        passing in a 2D-array that contains one dataset per column.
+    deg : int
+        Degree of the fitting polynomial
+    rcond : float, optional
+        Relative condition number of the fit. Singular values smaller than
+        this relative to the largest singular value will be ignored. The
+        default value is len(x)*eps, where eps is the relative precision of
+        the float type, about 2e-16 in most cases.
+    full : bool, optional
+        Switch determining nature of return value. When it is False (the
+        default) just the coefficients are returned, when True diagnostic
+        information from the singular value decomposition is also returned.
+    w : array_like, shape (M,), optional
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
+    cov : bool or str, optional
+        If given and not `False`, return not just the estimate but also its
+        covariance matrix. By default, the covariance are scaled by
+        chi2/dof, where dof = M - (deg + 1), i.e., the weights are presumed
+        to be unreliable except in a relative sense and everything is scaled
+        such that the reduced chi2 is unity. This scaling is omitted if
+        ``cov='unscaled'``, as is relevant for the case that the weights are
+        w = 1/sigma, with sigma known to be a reliable estimate of the
+        uncertainty.
+
+    Returns
+    -------
+    p : ndarray, shape (deg + 1,) or (deg + 1, K)
+        Polynomial coefficients, highest power first.  If `y` was 2-D, the
+        coefficients for `k`-th data set are in ``p[:,k]``.
+
+    residuals, rank, singular_values, rcond
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the effective rank of the scaled Vandermonde
+           coefficient matrix
+        - singular_values -- singular values of the scaled Vandermonde
+           coefficient matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
+
+    V : ndarray, shape (deg + 1, deg + 1) or (deg + 1, deg + 1, K)
+        Present only if ``full == False`` and ``cov == True``.  The covariance
+        matrix of the polynomial coefficient estimates.  The diagonal of
+        this matrix are the variance estimates for each coefficient.  If y
+        is a 2-D array, then the covariance matrix for the `k`-th data set
+        are in ``V[:,:,k]``
+
+
+    Warns
+    -----
+    RankWarning
+        The rank of the coefficient matrix in the least-squares fit is
+        deficient. The warning is only raised if ``full == False``.
+
+        The warnings can be turned off by
+
+        >>> import warnings
+        >>> warnings.simplefilter('ignore', np.exceptions.RankWarning)
+
+    See Also
+    --------
+    polyval : Compute polynomial values.
+    linalg.lstsq : Computes a least-squares fit.
+    scipy.interpolate.UnivariateSpline : Computes spline fits.
+
+    Notes
+    -----
+    The solution minimizes the squared error
+
+    .. math::
+        E = \\sum_{j=0}^k |p(x_j) - y_j|^2
+
+    in the equations::
+
+        x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0]
+        x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1]
+        ...
+        x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k]
+
+    The coefficient matrix of the coefficients `p` is a Vandermonde matrix.
+
+    `polyfit` issues a `~exceptions.RankWarning` when the least-squares fit is
+    badly conditioned. This implies that the best fit is not well-defined due
+    to numerical error. The results may be improved by lowering the polynomial
+    degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter
+    can also be set to a value smaller than its default, but the resulting
+    fit may be spurious: including contributions from the small singular
+    values can add numerical noise to the result.
+
+    Note that fitting polynomial coefficients is inherently badly conditioned
+    when the degree of the polynomial is large or the interval of sample points
+    is badly centered. The quality of the fit should always be checked in these
+    cases. When polynomial fits are not satisfactory, splines may be a good
+    alternative.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Curve fitting",
+           https://en.wikipedia.org/wiki/Curve_fitting
+    .. [2] Wikipedia, "Polynomial interpolation",
+           https://en.wikipedia.org/wiki/Polynomial_interpolation
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import warnings
+    >>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
+    >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
+    >>> z = np.polyfit(x, y, 3)
+    >>> z
+    array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254]) # may vary
+
+    It is convenient to use `poly1d` objects for dealing with polynomials:
+
+    >>> p = np.poly1d(z)
+    >>> p(0.5)
+    0.6143849206349179 # may vary
+    >>> p(3.5)
+    -0.34732142857143039 # may vary
+    >>> p(10)
+    22.579365079365115 # may vary
+
+    High-order polynomials may oscillate wildly:
+
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('ignore', np.exceptions.RankWarning)
+    ...     p30 = np.poly1d(np.polyfit(x, y, 30))
+    ...
+    >>> p30(4)
+    -0.80000000000000204 # may vary
+    >>> p30(5)
+    -0.99999999999999445 # may vary
+    >>> p30(4.5)
+    -0.10547061179440398 # may vary
+
+    Illustration:
+
+    >>> import matplotlib.pyplot as plt
+    >>> xp = np.linspace(-2, 6, 100)
+    >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
+    >>> plt.ylim(-2,2)
+    (-2, 2)
+    >>> plt.show()
+
+    """
+    order = int(deg) + 1
+    x = NX.asarray(x) + 0.0
+    y = NX.asarray(y) + 0.0
+
+    # check arguments.
+    if deg < 0:
+        raise ValueError("expected deg >= 0")
+    if x.ndim != 1:
+        raise TypeError("expected 1D vector for x")
+    if x.size == 0:
+        raise TypeError("expected non-empty vector for x")
+    if y.ndim < 1 or y.ndim > 2:
+        raise TypeError("expected 1D or 2D array for y")
+    if x.shape[0] != y.shape[0]:
+        raise TypeError("expected x and y to have same length")
+
+    # set rcond
+    if rcond is None:
+        rcond = len(x)*finfo(x.dtype).eps
+
+    # set up least squares equation for powers of x
+    lhs = vander(x, order)
+    rhs = y
+
+    # apply weighting
+    if w is not None:
+        w = NX.asarray(w) + 0.0
+        if w.ndim != 1:
+            raise TypeError("expected a 1-d array for weights")
+        if w.shape[0] != y.shape[0]:
+            raise TypeError("expected w and y to have the same length")
+        lhs *= w[:, NX.newaxis]
+        if rhs.ndim == 2:
+            rhs *= w[:, NX.newaxis]
+        else:
+            rhs *= w
+
+    # scale lhs to improve condition number and solve
+    scale = NX.sqrt((lhs*lhs).sum(axis=0))
+    lhs /= scale
+    c, resids, rank, s = lstsq(lhs, rhs, rcond)
+    c = (c.T/scale).T  # broadcast scale coefficients
+
+    # warn on rank reduction, which indicates an ill conditioned matrix
+    if rank != order and not full:
+        msg = "Polyfit may be poorly conditioned"
+        warnings.warn(msg, RankWarning, stacklevel=2)
+
+    if full:
+        return c, resids, rank, s, rcond
+    elif cov:
+        Vbase = inv(dot(lhs.T, lhs))
+        Vbase /= NX.outer(scale, scale)
+        if cov == "unscaled":
+            fac = 1
+        else:
+            if len(x) <= order:
+                raise ValueError("the number of data points must exceed order "
+                                 "to scale the covariance matrix")
+            # note, this used to be: fac = resids / (len(x) - order - 2.0)
+            # it was decided that the "- 2" (originally justified by "Bayesian
+            # uncertainty analysis") is not what the user expects
+            # (see gh-11196 and gh-11197)
+            fac = resids / (len(x) - order)
+        if y.ndim == 1:
+            return c, Vbase * fac
+        else:
+            return c, Vbase[:,:, NX.newaxis] * fac
+    else:
+        return c
+
+
+def _polyval_dispatcher(p, x):
+    return (p, x)
+
+
+@array_function_dispatch(_polyval_dispatcher)
+def polyval(p, x):
+    """
+    Evaluate a polynomial at specific values.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    If `p` is of length N, this function returns the value::
+
+        p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]
+
+    If `x` is a sequence, then ``p(x)`` is returned for each element of ``x``.
+    If `x` is another polynomial then the composite polynomial ``p(x(t))``
+    is returned.
+
+    Parameters
+    ----------
+    p : array_like or poly1d object
+       1D array of polynomial coefficients (including coefficients equal
+       to zero) from highest degree to the constant term, or an
+       instance of poly1d.
+    x : array_like or poly1d object
+       A number, an array of numbers, or an instance of poly1d, at
+       which to evaluate `p`.
+
+    Returns
+    -------
+    values : ndarray or poly1d
+       If `x` is a poly1d instance, the result is the composition of the two
+       polynomials, i.e., `x` is "substituted" in `p` and the simplified
+       result is returned. In addition, the type of `x` - array_like or
+       poly1d - governs the type of the output: `x` array_like => `values`
+       array_like, `x` a poly1d object => `values` is also.
+
+    See Also
+    --------
+    poly1d: A polynomial class.
+
+    Notes
+    -----
+    Horner's scheme [1]_ is used to evaluate the polynomial. Even so,
+    for polynomials of high degree the values may be inaccurate due to
+    rounding errors. Use carefully.
+
+    If `x` is a subtype of `ndarray` the return value will be of the same type.
+
+    References
+    ----------
+    .. [1] I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng.
+       trans. Ed.), *Handbook of Mathematics*, New York, Van Nostrand
+       Reinhold Co., 1985, pg. 720.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.polyval([3,0,1], 5)  # 3 * 5**2 + 0 * 5**1 + 1
+    76
+    >>> np.polyval([3,0,1], np.poly1d(5))
+    poly1d([76])
+    >>> np.polyval(np.poly1d([3,0,1]), 5)
+    76
+    >>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5))
+    poly1d([76])
+
+    """
+    p = NX.asarray(p)
+    if isinstance(x, poly1d):
+        y = 0
+    else:
+        x = NX.asanyarray(x)
+        y = NX.zeros_like(x)
+    for pv in p:
+        y = y * x + pv
+    return y
+
+
+def _binary_op_dispatcher(a1, a2):
+    return (a1, a2)
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def polyadd(a1, a2):
+    """
+    Find the sum of two polynomials.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Returns the polynomial resulting from the sum of two input polynomials.
+    Each input must be either a poly1d object or a 1D sequence of polynomial
+    coefficients, from highest to lowest degree.
+
+    Parameters
+    ----------
+    a1, a2 : array_like or poly1d object
+        Input polynomials.
+
+    Returns
+    -------
+    out : ndarray or poly1d object
+        The sum of the inputs. If either input is a poly1d object, then the
+        output is also a poly1d object. Otherwise, it is a 1D array of
+        polynomial coefficients from highest to lowest degree.
+
+    See Also
+    --------
+    poly1d : A one-dimensional polynomial class.
+    poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.polyadd([1, 2], [9, 5, 4])
+    array([9, 6, 6])
+
+    Using poly1d objects:
+
+    >>> p1 = np.poly1d([1, 2])
+    >>> p2 = np.poly1d([9, 5, 4])
+    >>> print(p1)
+    1 x + 2
+    >>> print(p2)
+       2
+    9 x + 5 x + 4
+    >>> print(np.polyadd(p1, p2))
+       2
+    9 x + 6 x + 6
+
+    """
+    truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
+    a1 = atleast_1d(a1)
+    a2 = atleast_1d(a2)
+    diff = len(a2) - len(a1)
+    if diff == 0:
+        val = a1 + a2
+    elif diff > 0:
+        zr = NX.zeros(diff, a1.dtype)
+        val = NX.concatenate((zr, a1)) + a2
+    else:
+        zr = NX.zeros(abs(diff), a2.dtype)
+        val = a1 + NX.concatenate((zr, a2))
+    if truepoly:
+        val = poly1d(val)
+    return val
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def polysub(a1, a2):
+    """
+    Difference (subtraction) of two polynomials.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
+    `a1` and `a2` can be either array_like sequences of the polynomials'
+    coefficients (including coefficients equal to zero), or `poly1d` objects.
+
+    Parameters
+    ----------
+    a1, a2 : array_like or poly1d
+        Minuend and subtrahend polynomials, respectively.
+
+    Returns
+    -------
+    out : ndarray or poly1d
+        Array or `poly1d` object of the difference polynomial's coefficients.
+
+    See Also
+    --------
+    polyval, polydiv, polymul, polyadd
+
+    Examples
+    --------
+    .. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)
+
+    >>> import numpy as np
+
+    >>> np.polysub([2, 10, -2], [3, 10, -4])
+    array([-1,  0,  2])
+
+    """
+    truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
+    a1 = atleast_1d(a1)
+    a2 = atleast_1d(a2)
+    diff = len(a2) - len(a1)
+    if diff == 0:
+        val = a1 - a2
+    elif diff > 0:
+        zr = NX.zeros(diff, a1.dtype)
+        val = NX.concatenate((zr, a1)) - a2
+    else:
+        zr = NX.zeros(abs(diff), a2.dtype)
+        val = a1 - NX.concatenate((zr, a2))
+    if truepoly:
+        val = poly1d(val)
+    return val
+
+
+@array_function_dispatch(_binary_op_dispatcher)
+def polymul(a1, a2):
+    """
+    Find the product of two polynomials.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    Finds the polynomial resulting from the multiplication of the two input
+    polynomials. Each input must be either a poly1d object or a 1D sequence
+    of polynomial coefficients, from highest to lowest degree.
+
+    Parameters
+    ----------
+    a1, a2 : array_like or poly1d object
+        Input polynomials.
+
+    Returns
+    -------
+    out : ndarray or poly1d object
+        The polynomial resulting from the multiplication of the inputs. If
+        either inputs is a poly1d object, then the output is also a poly1d
+        object. Otherwise, it is a 1D array of polynomial coefficients from
+        highest to lowest degree.
+
+    See Also
+    --------
+    poly1d : A one-dimensional polynomial class.
+    poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval
+    convolve : Array convolution. Same output as polymul, but has parameter
+               for overlap mode.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.polymul([1, 2, 3], [9, 5, 1])
+    array([ 9, 23, 38, 17,  3])
+
+    Using poly1d objects:
+
+    >>> p1 = np.poly1d([1, 2, 3])
+    >>> p2 = np.poly1d([9, 5, 1])
+    >>> print(p1)
+       2
+    1 x + 2 x + 3
+    >>> print(p2)
+       2
+    9 x + 5 x + 1
+    >>> print(np.polymul(p1, p2))
+       4      3      2
+    9 x + 23 x + 38 x + 17 x + 3
+
+    """
+    truepoly = (isinstance(a1, poly1d) or isinstance(a2, poly1d))
+    a1, a2 = poly1d(a1), poly1d(a2)
+    val = NX.convolve(a1, a2)
+    if truepoly:
+        val = poly1d(val)
+    return val
+
+
+def _polydiv_dispatcher(u, v):
+    return (u, v)
+
+
+@array_function_dispatch(_polydiv_dispatcher)
+def polydiv(u, v):
+    """
+    Returns the quotient and remainder of polynomial division.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    The input arrays are the coefficients (including any coefficients
+    equal to zero) of the "numerator" (dividend) and "denominator"
+    (divisor) polynomials, respectively.
+
+    Parameters
+    ----------
+    u : array_like or poly1d
+        Dividend polynomial's coefficients.
+
+    v : array_like or poly1d
+        Divisor polynomial's coefficients.
+
+    Returns
+    -------
+    q : ndarray
+        Coefficients, including those equal to zero, of the quotient.
+    r : ndarray
+        Coefficients, including those equal to zero, of the remainder.
+
+    See Also
+    --------
+    poly, polyadd, polyder, polydiv, polyfit, polyint, polymul, polysub
+    polyval
+
+    Notes
+    -----
+    Both `u` and `v` must be 0-d or 1-d (ndim = 0 or 1), but `u.ndim` need
+    not equal `v.ndim`. In other words, all four possible combinations -
+    ``u.ndim = v.ndim = 0``, ``u.ndim = v.ndim = 1``,
+    ``u.ndim = 1, v.ndim = 0``, and ``u.ndim = 0, v.ndim = 1`` - work.
+
+    Examples
+    --------
+    .. math:: \\frac{3x^2 + 5x + 2}{2x + 1} = 1.5x + 1.75, remainder 0.25
+
+    >>> import numpy as np
+    >>> x = np.array([3.0, 5.0, 2.0])
+    >>> y = np.array([2.0, 1.0])
+    >>> np.polydiv(x, y)
+    (array([1.5 , 1.75]), array([0.25]))
+
+    """
+    truepoly = (isinstance(u, poly1d) or isinstance(v, poly1d))
+    u = atleast_1d(u) + 0.0
+    v = atleast_1d(v) + 0.0
+    # w has the common type
+    w = u[0] + v[0]
+    m = len(u) - 1
+    n = len(v) - 1
+    scale = 1. / v[0]
+    q = NX.zeros((max(m - n + 1, 1),), w.dtype)
+    r = u.astype(w.dtype)
+    for k in range(0, m-n+1):
+        d = scale * r[k]
+        q[k] = d
+        r[k:k+n+1] -= d*v
+    while NX.allclose(r[0], 0, rtol=1e-14) and (r.shape[-1] > 1):
+        r = r[1:]
+    if truepoly:
+        return poly1d(q), poly1d(r)
+    return q, r
+
+_poly_mat = re.compile(r"\*\*([0-9]*)")
+def _raise_power(astr, wrap=70):
+    n = 0
+    line1 = ''
+    line2 = ''
+    output = ' '
+    while True:
+        mat = _poly_mat.search(astr, n)
+        if mat is None:
+            break
+        span = mat.span()
+        power = mat.groups()[0]
+        partstr = astr[n:span[0]]
+        n = span[1]
+        toadd2 = partstr + ' '*(len(power)-1)
+        toadd1 = ' '*(len(partstr)-1) + power
+        if ((len(line2) + len(toadd2) > wrap) or
+                (len(line1) + len(toadd1) > wrap)):
+            output += line1 + "\n" + line2 + "\n "
+            line1 = toadd1
+            line2 = toadd2
+        else:
+            line2 += partstr + ' '*(len(power)-1)
+            line1 += ' '*(len(partstr)-1) + power
+    output += line1 + "\n" + line2
+    return output + astr[n:]
+
+
+@set_module('numpy')
+class poly1d:
+    """
+    A one-dimensional polynomial class.
+
+    .. note::
+       This forms part of the old polynomial API. Since version 1.4, the
+       new polynomial API defined in `numpy.polynomial` is preferred.
+       A summary of the differences can be found in the
+       :doc:`transition guide `.
+
+    A convenience class, used to encapsulate "natural" operations on
+    polynomials so that said operations may take on their customary
+    form in code (see Examples).
+
+    Parameters
+    ----------
+    c_or_r : array_like
+        The polynomial's coefficients, in decreasing powers, or if
+        the value of the second parameter is True, the polynomial's
+        roots (values where the polynomial evaluates to 0).  For example,
+        ``poly1d([1, 2, 3])`` returns an object that represents
+        :math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns
+        one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`.
+    r : bool, optional
+        If True, `c_or_r` specifies the polynomial's roots; the default
+        is False.
+    variable : str, optional
+        Changes the variable used when printing `p` from `x` to `variable`
+        (see Examples).
+
+    Examples
+    --------
+    Construct the polynomial :math:`x^2 + 2x + 3`:
+
+    >>> import numpy as np
+
+    >>> p = np.poly1d([1, 2, 3])
+    >>> print(np.poly1d(p))
+       2
+    1 x + 2 x + 3
+
+    Evaluate the polynomial at :math:`x = 0.5`:
+
+    >>> p(0.5)
+    4.25
+
+    Find the roots:
+
+    >>> p.r
+    array([-1.+1.41421356j, -1.-1.41421356j])
+    >>> p(p.r)
+    array([ -4.44089210e-16+0.j,  -4.44089210e-16+0.j]) # may vary
+
+    These numbers in the previous line represent (0, 0) to machine precision
+
+    Show the coefficients:
+
+    >>> p.c
+    array([1, 2, 3])
+
+    Display the order (the leading zero-coefficients are removed):
+
+    >>> p.order
+    2
+
+    Show the coefficient of the k-th power in the polynomial
+    (which is equivalent to ``p.c[-(i+1)]``):
+
+    >>> p[1]
+    2
+
+    Polynomials can be added, subtracted, multiplied, and divided
+    (returns quotient and remainder):
+
+    >>> p * p
+    poly1d([ 1,  4, 10, 12,  9])
+
+    >>> (p**3 + 4) / p
+    (poly1d([ 1.,  4., 10., 12.,  9.]), poly1d([4.]))
+
+    ``asarray(p)`` gives the coefficient array, so polynomials can be
+    used in all functions that accept arrays:
+
+    >>> p**2 # square of polynomial
+    poly1d([ 1,  4, 10, 12,  9])
+
+    >>> np.square(p) # square of individual coefficients
+    array([1, 4, 9])
+
+    The variable used in the string representation of `p` can be modified,
+    using the `variable` parameter:
+
+    >>> p = np.poly1d([1,2,3], variable='z')
+    >>> print(p)
+       2
+    1 z + 2 z + 3
+
+    Construct a polynomial from its roots:
+
+    >>> np.poly1d([1, 2], True)
+    poly1d([ 1., -3.,  2.])
+
+    This is the same polynomial as obtained by:
+
+    >>> np.poly1d([1, -1]) * np.poly1d([1, -2])
+    poly1d([ 1, -3,  2])
+
+    """
+    __hash__ = None
+
+    @property
+    def coeffs(self):
+        """ The polynomial coefficients """
+        return self._coeffs
+
+    @coeffs.setter
+    def coeffs(self, value):
+        # allowing this makes p.coeffs *= 2 legal
+        if value is not self._coeffs:
+            raise AttributeError("Cannot set attribute")
+
+    @property
+    def variable(self):
+        """ The name of the polynomial variable """
+        return self._variable
+
+    # calculated attributes
+    @property
+    def order(self):
+        """ The order or degree of the polynomial """
+        return len(self._coeffs) - 1
+
+    @property
+    def roots(self):
+        """ The roots of the polynomial, where self(x) == 0 """
+        return roots(self._coeffs)
+
+    # our internal _coeffs property need to be backed by __dict__['coeffs'] for
+    # scipy to work correctly.
+    @property
+    def _coeffs(self):
+        return self.__dict__['coeffs']
+    @_coeffs.setter
+    def _coeffs(self, coeffs):
+        self.__dict__['coeffs'] = coeffs
+
+    # alias attributes
+    r = roots
+    c = coef = coefficients = coeffs
+    o = order
+
+    def __init__(self, c_or_r, r=False, variable=None):
+        if isinstance(c_or_r, poly1d):
+            self._variable = c_or_r._variable
+            self._coeffs = c_or_r._coeffs
+
+            if set(c_or_r.__dict__) - set(self.__dict__):
+                msg = ("In the future extra properties will not be copied "
+                       "across when constructing one poly1d from another")
+                warnings.warn(msg, FutureWarning, stacklevel=2)
+                self.__dict__.update(c_or_r.__dict__)
+
+            if variable is not None:
+                self._variable = variable
+            return
+        if r:
+            c_or_r = poly(c_or_r)
+        c_or_r = atleast_1d(c_or_r)
+        if c_or_r.ndim > 1:
+            raise ValueError("Polynomial must be 1d only.")
+        c_or_r = trim_zeros(c_or_r, trim='f')
+        if len(c_or_r) == 0:
+            c_or_r = NX.array([0], dtype=c_or_r.dtype)
+        self._coeffs = c_or_r
+        if variable is None:
+            variable = 'x'
+        self._variable = variable
+
+    def __array__(self, t=None, copy=None):
+        if t:
+            return NX.asarray(self.coeffs, t, copy=copy)
+        else:
+            return NX.asarray(self.coeffs, copy=copy)
+
+    def __repr__(self):
+        vals = repr(self.coeffs)
+        vals = vals[6:-1]
+        return "poly1d(%s)" % vals
+
+    def __len__(self):
+        return self.order
+
+    def __str__(self):
+        thestr = "0"
+        var = self.variable
+
+        # Remove leading zeros
+        coeffs = self.coeffs[NX.logical_or.accumulate(self.coeffs != 0)]
+        N = len(coeffs)-1
+
+        def fmt_float(q):
+            s = '%.4g' % q
+            if s.endswith('.0000'):
+                s = s[:-5]
+            return s
+
+        for k, coeff in enumerate(coeffs):
+            if not iscomplex(coeff):
+                coefstr = fmt_float(real(coeff))
+            elif real(coeff) == 0:
+                coefstr = '%sj' % fmt_float(imag(coeff))
+            else:
+                coefstr = '(%s + %sj)' % (fmt_float(real(coeff)),
+                                          fmt_float(imag(coeff)))
+
+            power = (N-k)
+            if power == 0:
+                if coefstr != '0':
+                    newstr = '%s' % (coefstr,)
+                else:
+                    if k == 0:
+                        newstr = '0'
+                    else:
+                        newstr = ''
+            elif power == 1:
+                if coefstr == '0':
+                    newstr = ''
+                elif coefstr == 'b':
+                    newstr = var
+                else:
+                    newstr = '%s %s' % (coefstr, var)
+            else:
+                if coefstr == '0':
+                    newstr = ''
+                elif coefstr == 'b':
+                    newstr = '%s**%d' % (var, power,)
+                else:
+                    newstr = '%s %s**%d' % (coefstr, var, power)
+
+            if k > 0:
+                if newstr != '':
+                    if newstr.startswith('-'):
+                        thestr = "%s - %s" % (thestr, newstr[1:])
+                    else:
+                        thestr = "%s + %s" % (thestr, newstr)
+            else:
+                thestr = newstr
+        return _raise_power(thestr)
+
+    def __call__(self, val):
+        return polyval(self.coeffs, val)
+
+    def __neg__(self):
+        return poly1d(-self.coeffs)
+
+    def __pos__(self):
+        return self
+
+    def __mul__(self, other):
+        if isscalar(other):
+            return poly1d(self.coeffs * other)
+        else:
+            other = poly1d(other)
+            return poly1d(polymul(self.coeffs, other.coeffs))
+
+    def __rmul__(self, other):
+        if isscalar(other):
+            return poly1d(other * self.coeffs)
+        else:
+            other = poly1d(other)
+            return poly1d(polymul(self.coeffs, other.coeffs))
+
+    def __add__(self, other):
+        other = poly1d(other)
+        return poly1d(polyadd(self.coeffs, other.coeffs))
+
+    def __radd__(self, other):
+        other = poly1d(other)
+        return poly1d(polyadd(self.coeffs, other.coeffs))
+
+    def __pow__(self, val):
+        if not isscalar(val) or int(val) != val or val < 0:
+            raise ValueError("Power to non-negative integers only.")
+        res = [1]
+        for _ in range(val):
+            res = polymul(self.coeffs, res)
+        return poly1d(res)
+
+    def __sub__(self, other):
+        other = poly1d(other)
+        return poly1d(polysub(self.coeffs, other.coeffs))
+
+    def __rsub__(self, other):
+        other = poly1d(other)
+        return poly1d(polysub(other.coeffs, self.coeffs))
+
+    def __div__(self, other):
+        if isscalar(other):
+            return poly1d(self.coeffs/other)
+        else:
+            other = poly1d(other)
+            return polydiv(self, other)
+
+    __truediv__ = __div__
+
+    def __rdiv__(self, other):
+        if isscalar(other):
+            return poly1d(other/self.coeffs)
+        else:
+            other = poly1d(other)
+            return polydiv(other, self)
+
+    __rtruediv__ = __rdiv__
+
+    def __eq__(self, other):
+        if not isinstance(other, poly1d):
+            return NotImplemented
+        if self.coeffs.shape != other.coeffs.shape:
+            return False
+        return (self.coeffs == other.coeffs).all()
+
+    def __ne__(self, other):
+        if not isinstance(other, poly1d):
+            return NotImplemented
+        return not self.__eq__(other)
+
+
+    def __getitem__(self, val):
+        ind = self.order - val
+        if val > self.order:
+            return self.coeffs.dtype.type(0)
+        if val < 0:
+            return self.coeffs.dtype.type(0)
+        return self.coeffs[ind]
+
+    def __setitem__(self, key, val):
+        ind = self.order - key
+        if key < 0:
+            raise ValueError("Does not support negative powers.")
+        if key > self.order:
+            zr = NX.zeros(key-self.order, self.coeffs.dtype)
+            self._coeffs = NX.concatenate((zr, self.coeffs))
+            ind = 0
+        self._coeffs[ind] = val
+        return
+
+    def __iter__(self):
+        return iter(self.coeffs)
+
+    def integ(self, m=1, k=0):
+        """
+        Return an antiderivative (indefinite integral) of this polynomial.
+
+        Refer to `polyint` for full documentation.
+
+        See Also
+        --------
+        polyint : equivalent function
+
+        """
+        return poly1d(polyint(self.coeffs, m=m, k=k))
+
+    def deriv(self, m=1):
+        """
+        Return a derivative of this polynomial.
+
+        Refer to `polyder` for full documentation.
+
+        See Also
+        --------
+        polyder : equivalent function
+
+        """
+        return poly1d(polyder(self.coeffs, m=m))
+
+# Stuff to do on module import
+
+warnings.simplefilter('always', RankWarning)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..112ec33d2520426fdad8a5c034184c268fe68ea1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_polynomial_impl.pyi
@@ -0,0 +1,315 @@
+from typing import (
+    Literal as L,
+    TypeAlias,
+    overload,
+    Any,
+    SupportsInt,
+    SupportsIndex,
+    TypeVar,
+    NoReturn,
+)
+
+import numpy as np
+from numpy import (
+    poly1d,
+    unsignedinteger,
+    signedinteger,
+    floating,
+    complexfloating,
+    int32,
+    int64,
+    float64,
+    complex128,
+    object_,
+)
+
+from numpy._typing import (
+    NDArray,
+    ArrayLike,
+    _ArrayLikeBool_co,
+    _ArrayLikeUInt_co,
+    _ArrayLikeInt_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeObject_co,
+)
+
+_T = TypeVar("_T")
+
+_2Tup: TypeAlias = tuple[_T, _T]
+_5Tup: TypeAlias = tuple[
+    _T,
+    NDArray[float64],
+    NDArray[int32],
+    NDArray[float64],
+    NDArray[float64],
+]
+
+__all__ = [
+    "poly",
+    "roots",
+    "polyint",
+    "polyder",
+    "polyadd",
+    "polysub",
+    "polymul",
+    "polydiv",
+    "polyval",
+    "poly1d",
+    "polyfit",
+]
+
+def poly(seq_of_zeros: ArrayLike) -> NDArray[floating[Any]]: ...
+
+# Returns either a float or complex array depending on the input values.
+# See `np.linalg.eigvals`.
+def roots(p: ArrayLike) -> NDArray[complexfloating[Any, Any]] | NDArray[floating[Any]]: ...
+
+@overload
+def polyint(
+    p: poly1d,
+    m: SupportsInt | SupportsIndex = ...,
+    k: None | _ArrayLikeComplex_co | _ArrayLikeObject_co = ...,
+) -> poly1d: ...
+@overload
+def polyint(
+    p: _ArrayLikeFloat_co,
+    m: SupportsInt | SupportsIndex = ...,
+    k: None | _ArrayLikeFloat_co = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def polyint(
+    p: _ArrayLikeComplex_co,
+    m: SupportsInt | SupportsIndex = ...,
+    k: None | _ArrayLikeComplex_co = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def polyint(
+    p: _ArrayLikeObject_co,
+    m: SupportsInt | SupportsIndex = ...,
+    k: None | _ArrayLikeObject_co = ...,
+) -> NDArray[object_]: ...
+
+@overload
+def polyder(
+    p: poly1d,
+    m: SupportsInt | SupportsIndex = ...,
+) -> poly1d: ...
+@overload
+def polyder(
+    p: _ArrayLikeFloat_co,
+    m: SupportsInt | SupportsIndex = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def polyder(
+    p: _ArrayLikeComplex_co,
+    m: SupportsInt | SupportsIndex = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def polyder(
+    p: _ArrayLikeObject_co,
+    m: SupportsInt | SupportsIndex = ...,
+) -> NDArray[object_]: ...
+
+@overload
+def polyfit(
+    x: _ArrayLikeFloat_co,
+    y: _ArrayLikeFloat_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[False] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: L[False] = ...,
+) -> NDArray[float64]: ...
+@overload
+def polyfit(
+    x: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[False] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: L[False] = ...,
+) -> NDArray[complex128]: ...
+@overload
+def polyfit(
+    x: _ArrayLikeFloat_co,
+    y: _ArrayLikeFloat_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[False] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: L[True, "unscaled"] = ...,
+) -> _2Tup[NDArray[float64]]: ...
+@overload
+def polyfit(
+    x: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[False] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: L[True, "unscaled"] = ...,
+) -> _2Tup[NDArray[complex128]]: ...
+@overload
+def polyfit(
+    x: _ArrayLikeFloat_co,
+    y: _ArrayLikeFloat_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[True] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: bool | L["unscaled"] = ...,
+) -> _5Tup[NDArray[float64]]: ...
+@overload
+def polyfit(
+    x: _ArrayLikeComplex_co,
+    y: _ArrayLikeComplex_co,
+    deg: SupportsIndex | SupportsInt,
+    rcond: None | float = ...,
+    full: L[True] = ...,
+    w: None | _ArrayLikeFloat_co = ...,
+    cov: bool | L["unscaled"] = ...,
+) -> _5Tup[NDArray[complex128]]: ...
+
+@overload
+def polyval(
+    p: _ArrayLikeBool_co,
+    x: _ArrayLikeBool_co,
+) -> NDArray[int64]: ...
+@overload
+def polyval(
+    p: _ArrayLikeUInt_co,
+    x: _ArrayLikeUInt_co,
+) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def polyval(
+    p: _ArrayLikeInt_co,
+    x: _ArrayLikeInt_co,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def polyval(
+    p: _ArrayLikeFloat_co,
+    x: _ArrayLikeFloat_co,
+) -> NDArray[floating[Any]]: ...
+@overload
+def polyval(
+    p: _ArrayLikeComplex_co,
+    x: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def polyval(
+    p: _ArrayLikeObject_co,
+    x: _ArrayLikeObject_co,
+) -> NDArray[object_]: ...
+
+@overload
+def polyadd(
+    a1: poly1d,
+    a2: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+) -> poly1d: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    a2: poly1d,
+) -> poly1d: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeBool_co,
+    a2: _ArrayLikeBool_co,
+) -> NDArray[np.bool]: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeUInt_co,
+    a2: _ArrayLikeUInt_co,
+) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeInt_co,
+    a2: _ArrayLikeInt_co,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeFloat_co,
+    a2: _ArrayLikeFloat_co,
+) -> NDArray[floating[Any]]: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeComplex_co,
+    a2: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def polyadd(
+    a1: _ArrayLikeObject_co,
+    a2: _ArrayLikeObject_co,
+) -> NDArray[object_]: ...
+
+@overload
+def polysub(
+    a1: poly1d,
+    a2: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+) -> poly1d: ...
+@overload
+def polysub(
+    a1: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    a2: poly1d,
+) -> poly1d: ...
+@overload
+def polysub(
+    a1: _ArrayLikeBool_co,
+    a2: _ArrayLikeBool_co,
+) -> NoReturn: ...
+@overload
+def polysub(
+    a1: _ArrayLikeUInt_co,
+    a2: _ArrayLikeUInt_co,
+) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def polysub(
+    a1: _ArrayLikeInt_co,
+    a2: _ArrayLikeInt_co,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def polysub(
+    a1: _ArrayLikeFloat_co,
+    a2: _ArrayLikeFloat_co,
+) -> NDArray[floating[Any]]: ...
+@overload
+def polysub(
+    a1: _ArrayLikeComplex_co,
+    a2: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def polysub(
+    a1: _ArrayLikeObject_co,
+    a2: _ArrayLikeObject_co,
+) -> NDArray[object_]: ...
+
+# NOTE: Not an alias, but they do have the same signature (that we can reuse)
+polymul = polyadd
+
+@overload
+def polydiv(
+    u: poly1d,
+    v: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+) -> _2Tup[poly1d]: ...
+@overload
+def polydiv(
+    u: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    v: poly1d,
+) -> _2Tup[poly1d]: ...
+@overload
+def polydiv(
+    u: _ArrayLikeFloat_co,
+    v: _ArrayLikeFloat_co,
+) -> _2Tup[NDArray[floating[Any]]]: ...
+@overload
+def polydiv(
+    u: _ArrayLikeComplex_co,
+    v: _ArrayLikeComplex_co,
+) -> _2Tup[NDArray[complexfloating[Any, Any]]]: ...
+@overload
+def polydiv(
+    u: _ArrayLikeObject_co,
+    v: _ArrayLikeObject_co,
+) -> _2Tup[NDArray[Any]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..68e9cd2d5337ea8f7965b27e06f863a4e0cba7a6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.py
@@ -0,0 +1,643 @@
+"""
+Wrapper functions to more user-friendly calling of certain math functions
+whose output data-type is different than the input data-type in certain
+domains of the input.
+
+For example, for functions like `log` with branch cuts, the versions in this
+module provide the mathematically valid answers in the complex plane::
+
+  >>> import math
+  >>> np.emath.log(-math.exp(1)) == (1+1j*math.pi)
+  True
+
+Similarly, `sqrt`, other base logarithms, `power` and trig functions are
+correctly handled.  See their respective docstrings for specific examples.
+
+"""
+import numpy._core.numeric as nx
+import numpy._core.numerictypes as nt
+from numpy._core.numeric import asarray, any
+from numpy._core.overrides import array_function_dispatch, set_module
+from numpy.lib._type_check_impl import isreal
+
+
+__all__ = [
+    'sqrt', 'log', 'log2', 'logn', 'log10', 'power', 'arccos', 'arcsin',
+    'arctanh'
+    ]
+
+
+_ln2 = nx.log(2.0)
+
+
+def _tocomplex(arr):
+    """Convert its input `arr` to a complex array.
+
+    The input is returned as a complex array of the smallest type that will fit
+    the original data: types like single, byte, short, etc. become csingle,
+    while others become cdouble.
+
+    A copy of the input is always made.
+
+    Parameters
+    ----------
+    arr : array
+
+    Returns
+    -------
+    array
+        An array with the same input data as the input but in complex form.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    First, consider an input of type short:
+
+    >>> a = np.array([1,2,3],np.short)
+
+    >>> ac = np.lib.scimath._tocomplex(a); ac
+    array([1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
+
+    >>> ac.dtype
+    dtype('complex64')
+
+    If the input is of type double, the output is correspondingly of the
+    complex double type as well:
+
+    >>> b = np.array([1,2,3],np.double)
+
+    >>> bc = np.lib.scimath._tocomplex(b); bc
+    array([1.+0.j, 2.+0.j, 3.+0.j])
+
+    >>> bc.dtype
+    dtype('complex128')
+
+    Note that even if the input was complex to begin with, a copy is still
+    made, since the astype() method always copies:
+
+    >>> c = np.array([1,2,3],np.csingle)
+
+    >>> cc = np.lib.scimath._tocomplex(c); cc
+    array([1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
+
+    >>> c *= 2; c
+    array([2.+0.j,  4.+0.j,  6.+0.j], dtype=complex64)
+
+    >>> cc
+    array([1.+0.j,  2.+0.j,  3.+0.j], dtype=complex64)
+    """
+    if issubclass(arr.dtype.type, (nt.single, nt.byte, nt.short, nt.ubyte,
+                                   nt.ushort, nt.csingle)):
+        return arr.astype(nt.csingle)
+    else:
+        return arr.astype(nt.cdouble)
+
+
+def _fix_real_lt_zero(x):
+    """Convert `x` to complex if it has real, negative components.
+
+    Otherwise, output is just the array version of the input (via asarray).
+
+    Parameters
+    ----------
+    x : array_like
+
+    Returns
+    -------
+    array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.lib.scimath._fix_real_lt_zero([1,2])
+    array([1, 2])
+
+    >>> np.lib.scimath._fix_real_lt_zero([-1,2])
+    array([-1.+0.j,  2.+0.j])
+
+    """
+    x = asarray(x)
+    if any(isreal(x) & (x < 0)):
+        x = _tocomplex(x)
+    return x
+
+
+def _fix_int_lt_zero(x):
+    """Convert `x` to double if it has real, negative components.
+
+    Otherwise, output is just the array version of the input (via asarray).
+
+    Parameters
+    ----------
+    x : array_like
+
+    Returns
+    -------
+    array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.lib.scimath._fix_int_lt_zero([1,2])
+    array([1, 2])
+
+    >>> np.lib.scimath._fix_int_lt_zero([-1,2])
+    array([-1.,  2.])
+    """
+    x = asarray(x)
+    if any(isreal(x) & (x < 0)):
+        x = x * 1.0
+    return x
+
+
+def _fix_real_abs_gt_1(x):
+    """Convert `x` to complex if it has real components x_i with abs(x_i)>1.
+
+    Otherwise, output is just the array version of the input (via asarray).
+
+    Parameters
+    ----------
+    x : array_like
+
+    Returns
+    -------
+    array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.lib.scimath._fix_real_abs_gt_1([0,1])
+    array([0, 1])
+
+    >>> np.lib.scimath._fix_real_abs_gt_1([0,2])
+    array([0.+0.j, 2.+0.j])
+    """
+    x = asarray(x)
+    if any(isreal(x) & (abs(x) > 1)):
+        x = _tocomplex(x)
+    return x
+
+
+def _unary_dispatcher(x):
+    return (x,)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def sqrt(x):
+    """
+    Compute the square root of x.
+
+    For negative input elements, a complex value is returned
+    (unlike `numpy.sqrt` which returns NaN).
+
+    Parameters
+    ----------
+    x : array_like
+       The input value(s).
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The square root of `x`. If `x` was a scalar, so is `out`,
+       otherwise an array is returned.
+
+    See Also
+    --------
+    numpy.sqrt
+
+    Examples
+    --------
+    For real, non-negative inputs this works just like `numpy.sqrt`:
+
+    >>> import numpy as np
+
+    >>> np.emath.sqrt(1)
+    1.0
+    >>> np.emath.sqrt([1, 4])
+    array([1.,  2.])
+
+    But it automatically handles negative inputs:
+
+    >>> np.emath.sqrt(-1)
+    1j
+    >>> np.emath.sqrt([-1,4])
+    array([0.+1.j, 2.+0.j])
+
+    Different results are expected because:
+    floating point 0.0 and -0.0 are distinct.
+
+    For more control, explicitly use complex() as follows:
+
+    >>> np.emath.sqrt(complex(-4.0, 0.0))
+    2j
+    >>> np.emath.sqrt(complex(-4.0, -0.0))
+    -2j
+    """
+    x = _fix_real_lt_zero(x)
+    return nx.sqrt(x)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def log(x):
+    """
+    Compute the natural logarithm of `x`.
+
+    Return the "principal value" (for a description of this, see `numpy.log`)
+    of :math:`log_e(x)`. For real `x > 0`, this is a real number (``log(0)``
+    returns ``-inf`` and ``log(np.inf)`` returns ``inf``). Otherwise, the
+    complex principle value is returned.
+
+    Parameters
+    ----------
+    x : array_like
+       The value(s) whose log is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The log of the `x` value(s). If `x` was a scalar, so is `out`,
+       otherwise an array is returned.
+
+    See Also
+    --------
+    numpy.log
+
+    Notes
+    -----
+    For a log() that returns ``NAN`` when real `x < 0`, use `numpy.log`
+    (note, however, that otherwise `numpy.log` and this `log` are identical,
+    i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and,
+    notably, the complex principle value if ``x.imag != 0``).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.emath.log(np.exp(1))
+    1.0
+
+    Negative arguments are handled "correctly" (recall that
+    ``exp(log(x)) == x`` does *not* hold for real ``x < 0``):
+
+    >>> np.emath.log(-np.exp(1)) == (1 + np.pi * 1j)
+    True
+
+    """
+    x = _fix_real_lt_zero(x)
+    return nx.log(x)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def log10(x):
+    """
+    Compute the logarithm base 10 of `x`.
+
+    Return the "principal value" (for a description of this, see
+    `numpy.log10`) of :math:`log_{10}(x)`. For real `x > 0`, this
+    is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)``
+    returns ``inf``). Otherwise, the complex principle value is returned.
+
+    Parameters
+    ----------
+    x : array_like or scalar
+       The value(s) whose log base 10 is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`,
+       otherwise an array object is returned.
+
+    See Also
+    --------
+    numpy.log10
+
+    Notes
+    -----
+    For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10`
+    (note, however, that otherwise `numpy.log10` and this `log10` are
+    identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`,
+    and, notably, the complex principle value if ``x.imag != 0``).
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    (We set the printing precision so the example can be auto-tested)
+
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.log10(10**1)
+    1.0
+
+    >>> np.emath.log10([-10**1, -10**2, 10**2])
+    array([1.+1.3644j, 2.+1.3644j, 2.+0.j    ])
+
+    """
+    x = _fix_real_lt_zero(x)
+    return nx.log10(x)
+
+
+def _logn_dispatcher(n, x):
+    return (n, x,)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_logn_dispatcher)
+def logn(n, x):
+    """
+    Take log base n of x.
+
+    If `x` contains negative inputs, the answer is computed and returned in the
+    complex domain.
+
+    Parameters
+    ----------
+    n : array_like
+       The integer base(s) in which the log is taken.
+    x : array_like
+       The value(s) whose log base `n` is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The log base `n` of the `x` value(s). If `x` was a scalar, so is
+       `out`, otherwise an array is returned.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.logn(2, [4, 8])
+    array([2., 3.])
+    >>> np.emath.logn(2, [-4, -8, 8])
+    array([2.+4.5324j, 3.+4.5324j, 3.+0.j    ])
+
+    """
+    x = _fix_real_lt_zero(x)
+    n = _fix_real_lt_zero(n)
+    return nx.log(x)/nx.log(n)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def log2(x):
+    """
+    Compute the logarithm base 2 of `x`.
+
+    Return the "principal value" (for a description of this, see
+    `numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is
+    a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns
+    ``inf``). Otherwise, the complex principle value is returned.
+
+    Parameters
+    ----------
+    x : array_like
+       The value(s) whose log base 2 is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,
+       otherwise an array is returned.
+
+    See Also
+    --------
+    numpy.log2
+
+    Notes
+    -----
+    For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`
+    (note, however, that otherwise `numpy.log2` and this `log2` are
+    identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`,
+    and, notably, the complex principle value if ``x.imag != 0``).
+
+    Examples
+    --------
+
+    We set the printing precision so the example can be auto-tested:
+
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.log2(8)
+    3.0
+    >>> np.emath.log2([-4, -8, 8])
+    array([2.+4.5324j, 3.+4.5324j, 3.+0.j    ])
+
+    """
+    x = _fix_real_lt_zero(x)
+    return nx.log2(x)
+
+
+def _power_dispatcher(x, p):
+    return (x, p)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_power_dispatcher)
+def power(x, p):
+    """
+    Return x to the power p, (x**p).
+
+    If `x` contains negative values, the output is converted to the
+    complex domain.
+
+    Parameters
+    ----------
+    x : array_like
+        The input value(s).
+    p : array_like of ints
+        The power(s) to which `x` is raised. If `x` contains multiple values,
+        `p` has to either be a scalar, or contain the same number of values
+        as `x`. In the latter case, the result is
+        ``x[0]**p[0], x[1]**p[1], ...``.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The result of ``x**p``. If `x` and `p` are scalars, so is `out`,
+        otherwise an array is returned.
+
+    See Also
+    --------
+    numpy.power
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.power(2, 2)
+    4
+
+    >>> np.emath.power([2, 4], 2)
+    array([ 4, 16])
+
+    >>> np.emath.power([2, 4], -2)
+    array([0.25  ,  0.0625])
+
+    >>> np.emath.power([-2, 4], 2)
+    array([ 4.-0.j, 16.+0.j])
+
+    >>> np.emath.power([2, 4], [2, 4])
+    array([ 4, 256])
+
+    """
+    x = _fix_real_lt_zero(x)
+    p = _fix_int_lt_zero(p)
+    return nx.power(x, p)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def arccos(x):
+    """
+    Compute the inverse cosine of x.
+
+    Return the "principal value" (for a description of this, see
+    `numpy.arccos`) of the inverse cosine of `x`. For real `x` such that
+    `abs(x) <= 1`, this is a real number in the closed interval
+    :math:`[0, \\pi]`.  Otherwise, the complex principle value is returned.
+
+    Parameters
+    ----------
+    x : array_like or scalar
+       The value(s) whose arccos is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so
+       is `out`, otherwise an array object is returned.
+
+    See Also
+    --------
+    numpy.arccos
+
+    Notes
+    -----
+    For an arccos() that returns ``NAN`` when real `x` is not in the
+    interval ``[-1,1]``, use `numpy.arccos`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.arccos(1) # a scalar is returned
+    0.0
+
+    >>> np.emath.arccos([1,2])
+    array([0.-0.j   , 0.-1.317j])
+
+    """
+    x = _fix_real_abs_gt_1(x)
+    return nx.arccos(x)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def arcsin(x):
+    """
+    Compute the inverse sine of x.
+
+    Return the "principal value" (for a description of this, see
+    `numpy.arcsin`) of the inverse sine of `x`. For real `x` such that
+    `abs(x) <= 1`, this is a real number in the closed interval
+    :math:`[-\\pi/2, \\pi/2]`.  Otherwise, the complex principle value is
+    returned.
+
+    Parameters
+    ----------
+    x : array_like or scalar
+       The value(s) whose arcsin is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The inverse sine(s) of the `x` value(s). If `x` was a scalar, so
+       is `out`, otherwise an array object is returned.
+
+    See Also
+    --------
+    numpy.arcsin
+
+    Notes
+    -----
+    For an arcsin() that returns ``NAN`` when real `x` is not in the
+    interval ``[-1,1]``, use `numpy.arcsin`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.arcsin(0)
+    0.0
+
+    >>> np.emath.arcsin([0,1])
+    array([0.    , 1.5708])
+
+    """
+    x = _fix_real_abs_gt_1(x)
+    return nx.arcsin(x)
+
+
+@set_module('numpy.lib.scimath')
+@array_function_dispatch(_unary_dispatcher)
+def arctanh(x):
+    """
+    Compute the inverse hyperbolic tangent of `x`.
+
+    Return the "principal value" (for a description of this, see
+    `numpy.arctanh`) of ``arctanh(x)``. For real `x` such that
+    ``abs(x) < 1``, this is a real number.  If `abs(x) > 1`, or if `x` is
+    complex, the result is complex. Finally, `x = 1` returns``inf`` and
+    ``x=-1`` returns ``-inf``.
+
+    Parameters
+    ----------
+    x : array_like
+       The value(s) whose arctanh is (are) required.
+
+    Returns
+    -------
+    out : ndarray or scalar
+       The inverse hyperbolic tangent(s) of the `x` value(s). If `x` was
+       a scalar so is `out`, otherwise an array is returned.
+
+
+    See Also
+    --------
+    numpy.arctanh
+
+    Notes
+    -----
+    For an arctanh() that returns ``NAN`` when real `x` is not in the
+    interval ``(-1,1)``, use `numpy.arctanh` (this latter, however, does
+    return +/-inf for ``x = +/-1``).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.set_printoptions(precision=4)
+
+    >>> np.emath.arctanh(0.5)
+    0.5493061443340549
+
+    >>> from numpy.testing import suppress_warnings
+    >>> with suppress_warnings() as sup:
+    ...     sup.filter(RuntimeWarning)
+    ...     np.emath.arctanh(np.eye(2))
+    array([[inf,  0.],
+           [ 0., inf]])
+    >>> np.emath.arctanh([1j])
+    array([0.+0.7854j])
+
+    """
+    x = _fix_real_abs_gt_1(x)
+    return nx.arctanh(x)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..43b7110b2923453110edce9942a0659508c03eb6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_scimath_impl.pyi
@@ -0,0 +1,94 @@
+from typing import overload, Any
+
+from numpy import complexfloating
+
+from numpy._typing import (
+    NDArray,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ComplexLike_co,
+    _FloatLike_co,
+)
+
+__all__ = ["sqrt", "log", "log2", "logn", "log10", "power", "arccos", "arcsin", "arctanh"]
+
+@overload
+def sqrt(x: _FloatLike_co) -> Any: ...
+@overload
+def sqrt(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def sqrt(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def sqrt(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def log(x: _FloatLike_co) -> Any: ...
+@overload
+def log(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def log(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def log(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def log10(x: _FloatLike_co) -> Any: ...
+@overload
+def log10(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def log10(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def log10(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def log2(x: _FloatLike_co) -> Any: ...
+@overload
+def log2(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def log2(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def log2(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def logn(n: _FloatLike_co, x: _FloatLike_co) -> Any: ...
+@overload
+def logn(n: _ComplexLike_co, x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def logn(n: _ArrayLikeFloat_co, x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def logn(n: _ArrayLikeComplex_co, x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def power(x: _FloatLike_co, p: _FloatLike_co) -> Any: ...
+@overload
+def power(x: _ComplexLike_co, p: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def power(x: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def power(x: _ArrayLikeComplex_co, p: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def arccos(x: _FloatLike_co) -> Any: ...
+@overload
+def arccos(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def arccos(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def arccos(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def arcsin(x: _FloatLike_co) -> Any: ...
+@overload
+def arcsin(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def arcsin(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def arcsin(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def arctanh(x: _FloatLike_co) -> Any: ...
+@overload
+def arctanh(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
+@overload
+def arctanh(x: _ArrayLikeFloat_co) -> NDArray[Any]: ...
+@overload
+def arctanh(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d861bb6f2e00d1c39fb47701b511b2cb2319de1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.py
@@ -0,0 +1,1294 @@
+import functools
+import warnings
+
+import numpy._core.numeric as _nx
+from numpy._core.numeric import asarray, zeros, zeros_like, array, asanyarray
+from numpy._core.fromnumeric import reshape, transpose
+from numpy._core.multiarray import normalize_axis_index
+from numpy._core._multiarray_umath import _array_converter
+from numpy._core import overrides
+from numpy._core import vstack, atleast_3d
+from numpy._core.numeric import normalize_axis_tuple
+from numpy._core.overrides import set_module
+from numpy._core.shape_base import _arrays_for_stack_dispatcher
+from numpy.lib._index_tricks_impl import ndindex
+from numpy.matrixlib.defmatrix import matrix  # this raises all the right alarm bells
+
+
+__all__ = [
+    'column_stack', 'row_stack', 'dstack', 'array_split', 'split',
+    'hsplit', 'vsplit', 'dsplit', 'apply_over_axes', 'expand_dims',
+    'apply_along_axis', 'kron', 'tile', 'take_along_axis',
+    'put_along_axis'
+    ]
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+def _make_along_axis_idx(arr_shape, indices, axis):
+    # compute dimensions to iterate over
+    if not _nx.issubdtype(indices.dtype, _nx.integer):
+        raise IndexError('`indices` must be an integer array')
+    if len(arr_shape) != indices.ndim:
+        raise ValueError(
+            "`indices` and `arr` must have the same number of dimensions")
+    shape_ones = (1,) * indices.ndim
+    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))
+
+    # build a fancy index, consisting of orthogonal aranges, with the
+    # requested index inserted at the right location
+    fancy_index = []
+    for dim, n in zip(dest_dims, arr_shape):
+        if dim is None:
+            fancy_index.append(indices)
+        else:
+            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
+            fancy_index.append(_nx.arange(n).reshape(ind_shape))
+
+    return tuple(fancy_index)
+
+
+def _take_along_axis_dispatcher(arr, indices, axis):
+    return (arr, indices)
+
+
+@array_function_dispatch(_take_along_axis_dispatcher)
+def take_along_axis(arr, indices, axis):
+    """
+    Take values from the input array by matching 1d index and data slices.
+
+    This iterates over matching 1d slices oriented along the specified axis in
+    the index and data arrays, and uses the former to look up values in the
+    latter. These slices can be different lengths.
+
+    Functions returning an index along an axis, like `argsort` and
+    `argpartition`, produce suitable indices for this function.
+
+    Parameters
+    ----------
+    arr : ndarray (Ni..., M, Nk...)
+        Source array
+    indices : ndarray (Ni..., J, Nk...)
+        Indices to take along each 1d slice of `arr`. This must match the
+        dimension of arr, but dimensions Ni and Nj only need to broadcast
+        against `arr`.
+    axis : int
+        The axis to take 1d slices along. If axis is None, the input array is
+        treated as if it had first been flattened to 1d, for consistency with
+        `sort` and `argsort`.
+
+    Returns
+    -------
+    out: ndarray (Ni..., J, Nk...)
+        The indexed result.
+
+    Notes
+    -----
+    This is equivalent to (but faster than) the following use of `ndindex` and
+    `s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices::
+
+        Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
+        J = indices.shape[axis]  # Need not equal M
+        out = np.empty(Ni + (J,) + Nk)
+
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                a_1d       = a      [ii + s_[:,] + kk]
+                indices_1d = indices[ii + s_[:,] + kk]
+                out_1d     = out    [ii + s_[:,] + kk]
+                for j in range(J):
+                    out_1d[j] = a_1d[indices_1d[j]]
+
+    Equivalently, eliminating the inner loop, the last two lines would be::
+
+                out_1d[:] = a_1d[indices_1d]
+
+    See Also
+    --------
+    take : Take along an axis, using the same indices for every 1d slice
+    put_along_axis :
+        Put values into the destination array by matching 1d index and data slices
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    For this sample array
+
+    >>> a = np.array([[10, 30, 20], [60, 40, 50]])
+
+    We can sort either by using sort directly, or argsort and this function
+
+    >>> np.sort(a, axis=1)
+    array([[10, 20, 30],
+           [40, 50, 60]])
+    >>> ai = np.argsort(a, axis=1)
+    >>> ai
+    array([[0, 2, 1],
+           [1, 2, 0]])
+    >>> np.take_along_axis(a, ai, axis=1)
+    array([[10, 20, 30],
+           [40, 50, 60]])
+
+    The same works for max and min, if you maintain the trivial dimension
+    with ``keepdims``:
+
+    >>> np.max(a, axis=1, keepdims=True)
+    array([[30],
+           [60]])
+    >>> ai = np.argmax(a, axis=1, keepdims=True)
+    >>> ai
+    array([[1],
+           [0]])
+    >>> np.take_along_axis(a, ai, axis=1)
+    array([[30],
+           [60]])
+
+    If we want to get the max and min at the same time, we can stack the
+    indices first
+
+    >>> ai_min = np.argmin(a, axis=1, keepdims=True)
+    >>> ai_max = np.argmax(a, axis=1, keepdims=True)
+    >>> ai = np.concatenate([ai_min, ai_max], axis=1)
+    >>> ai
+    array([[0, 1],
+           [1, 0]])
+    >>> np.take_along_axis(a, ai, axis=1)
+    array([[10, 30],
+           [40, 60]])
+    """
+    # normalize inputs
+    if axis is None:
+        if indices.ndim != 1:
+            raise ValueError(
+                'when axis=None, `indices` must have a single dimension.')
+        arr = arr.flat
+        arr_shape = (len(arr),)  # flatiter has no .shape
+        axis = 0
+    else:
+        axis = normalize_axis_index(axis, arr.ndim)
+        arr_shape = arr.shape
+
+    # use the fancy index
+    return arr[_make_along_axis_idx(arr_shape, indices, axis)]
+
+
+def _put_along_axis_dispatcher(arr, indices, values, axis):
+    return (arr, indices, values)
+
+
+@array_function_dispatch(_put_along_axis_dispatcher)
+def put_along_axis(arr, indices, values, axis):
+    """
+    Put values into the destination array by matching 1d index and data slices.
+
+    This iterates over matching 1d slices oriented along the specified axis in
+    the index and data arrays, and uses the former to place values into the
+    latter. These slices can be different lengths.
+
+    Functions returning an index along an axis, like `argsort` and
+    `argpartition`, produce suitable indices for this function.
+
+    Parameters
+    ----------
+    arr : ndarray (Ni..., M, Nk...)
+        Destination array.
+    indices : ndarray (Ni..., J, Nk...)
+        Indices to change along each 1d slice of `arr`. This must match the
+        dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast
+        against `arr`.
+    values : array_like (Ni..., J, Nk...)
+        values to insert at those indices. Its shape and dimension are
+        broadcast to match that of `indices`.
+    axis : int
+        The axis to take 1d slices along. If axis is None, the destination
+        array is treated as if a flattened 1d view had been created of it.
+
+    Notes
+    -----
+    This is equivalent to (but faster than) the following use of `ndindex` and
+    `s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices::
+
+        Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
+        J = indices.shape[axis]  # Need not equal M
+
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                a_1d       = a      [ii + s_[:,] + kk]
+                indices_1d = indices[ii + s_[:,] + kk]
+                values_1d  = values [ii + s_[:,] + kk]
+                for j in range(J):
+                    a_1d[indices_1d[j]] = values_1d[j]
+
+    Equivalently, eliminating the inner loop, the last two lines would be::
+
+                a_1d[indices_1d] = values_1d
+
+    See Also
+    --------
+    take_along_axis :
+        Take values from the input array by matching 1d index and data slices
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    For this sample array
+
+    >>> a = np.array([[10, 30, 20], [60, 40, 50]])
+
+    We can replace the maximum values with:
+
+    >>> ai = np.argmax(a, axis=1, keepdims=True)
+    >>> ai
+    array([[1],
+           [0]])
+    >>> np.put_along_axis(a, ai, 99, axis=1)
+    >>> a
+    array([[10, 99, 20],
+           [99, 40, 50]])
+
+    """
+    # normalize inputs
+    if axis is None:
+        if indices.ndim != 1:
+            raise ValueError(
+                'when axis=None, `indices` must have a single dimension.')
+        arr = arr.flat
+        axis = 0
+        arr_shape = (len(arr),)  # flatiter has no .shape
+    else:
+        axis = normalize_axis_index(axis, arr.ndim)
+        arr_shape = arr.shape
+
+    # use the fancy index
+    arr[_make_along_axis_idx(arr_shape, indices, axis)] = values
+
+
+def _apply_along_axis_dispatcher(func1d, axis, arr, *args, **kwargs):
+    return (arr,)
+
+
+@array_function_dispatch(_apply_along_axis_dispatcher)
+def apply_along_axis(func1d, axis, arr, *args, **kwargs):
+    """
+    Apply a function to 1-D slices along the given axis.
+
+    Execute `func1d(a, *args, **kwargs)` where `func1d` operates on 1-D arrays
+    and `a` is a 1-D slice of `arr` along `axis`.
+
+    This is equivalent to (but faster than) the following use of `ndindex` and
+    `s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indices::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                f = func1d(arr[ii + s_[:,] + kk])
+                Nj = f.shape
+                for jj in ndindex(Nj):
+                    out[ii + jj + kk] = f[jj]
+
+    Equivalently, eliminating the inner loop, this can be expressed as::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
+
+    Parameters
+    ----------
+    func1d : function (M,) -> (Nj...)
+        This function should accept 1-D arrays. It is applied to 1-D
+        slices of `arr` along the specified axis.
+    axis : integer
+        Axis along which `arr` is sliced.
+    arr : ndarray (Ni..., M, Nk...)
+        Input array.
+    args : any
+        Additional arguments to `func1d`.
+    kwargs : any
+        Additional named arguments to `func1d`.
+
+    Returns
+    -------
+    out : ndarray  (Ni..., Nj..., Nk...)
+        The output array. The shape of `out` is identical to the shape of
+        `arr`, except along the `axis` dimension. This axis is removed, and
+        replaced with new dimensions equal to the shape of the return value
+        of `func1d`. So if `func1d` returns a scalar `out` will have one
+        fewer dimensions than `arr`.
+
+    See Also
+    --------
+    apply_over_axes : Apply a function repeatedly over multiple axes.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> def my_func(a):
+    ...     \"\"\"Average first and last element of a 1-D array\"\"\"
+    ...     return (a[0] + a[-1]) * 0.5
+    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
+    >>> np.apply_along_axis(my_func, 0, b)
+    array([4., 5., 6.])
+    >>> np.apply_along_axis(my_func, 1, b)
+    array([2.,  5.,  8.])
+
+    For a function that returns a 1D array, the number of dimensions in
+    `outarr` is the same as `arr`.
+
+    >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
+    >>> np.apply_along_axis(sorted, 1, b)
+    array([[1, 7, 8],
+           [3, 4, 9],
+           [2, 5, 6]])
+
+    For a function that returns a higher dimensional array, those dimensions
+    are inserted in place of the `axis` dimension.
+
+    >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
+    >>> np.apply_along_axis(np.diag, -1, b)
+    array([[[1, 0, 0],
+            [0, 2, 0],
+            [0, 0, 3]],
+           [[4, 0, 0],
+            [0, 5, 0],
+            [0, 0, 6]],
+           [[7, 0, 0],
+            [0, 8, 0],
+            [0, 0, 9]]])
+    """
+    # handle negative axes
+    conv = _array_converter(arr)
+    arr = conv[0]
+
+    nd = arr.ndim
+    axis = normalize_axis_index(axis, nd)
+
+    # arr, with the iteration axis at the end
+    in_dims = list(range(nd))
+    inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis+1:] + [axis])
+
+    # compute indices for the iteration axes, and append a trailing ellipsis to
+    # prevent 0d arrays decaying to scalars, which fixes gh-8642
+    inds = ndindex(inarr_view.shape[:-1])
+    inds = (ind + (Ellipsis,) for ind in inds)
+
+    # invoke the function on the first item
+    try:
+        ind0 = next(inds)
+    except StopIteration:
+        raise ValueError(
+            'Cannot apply_along_axis when any iteration dimensions are 0'
+        ) from None
+    res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
+
+    # build a buffer for storing evaluations of func1d.
+    # remove the requested axis, and add the new ones on the end.
+    # laid out so that each write is contiguous.
+    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
+    if not isinstance(res, matrix):
+        buff = zeros_like(res, shape=inarr_view.shape[:-1] + res.shape)
+    else:
+        # Matrices are nasty with reshaping, so do not preserve them here.
+        buff = zeros(inarr_view.shape[:-1] + res.shape, dtype=res.dtype)
+
+    # permutation of axes such that out = buff.transpose(buff_permute)
+    buff_dims = list(range(buff.ndim))
+    buff_permute = (
+        buff_dims[0 : axis] +
+        buff_dims[buff.ndim-res.ndim : buff.ndim] +
+        buff_dims[axis : buff.ndim-res.ndim]
+    )
+
+    # save the first result, then compute and save all remaining results
+    buff[ind0] = res
+    for ind in inds:
+        buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))
+
+    res = transpose(buff, buff_permute)
+    return conv.wrap(res)
+
+
+def _apply_over_axes_dispatcher(func, a, axes):
+    return (a,)
+
+
+@array_function_dispatch(_apply_over_axes_dispatcher)
+def apply_over_axes(func, a, axes):
+    """
+    Apply a function repeatedly over multiple axes.
+
+    `func` is called as `res = func(a, axis)`, where `axis` is the first
+    element of `axes`.  The result `res` of the function call must have
+    either the same dimensions as `a` or one less dimension.  If `res`
+    has one less dimension than `a`, a dimension is inserted before
+    `axis`.  The call to `func` is then repeated for each axis in `axes`,
+    with `res` as the first argument.
+
+    Parameters
+    ----------
+    func : function
+        This function must take two arguments, `func(a, axis)`.
+    a : array_like
+        Input array.
+    axes : array_like
+        Axes over which `func` is applied; the elements must be integers.
+
+    Returns
+    -------
+    apply_over_axis : ndarray
+        The output array.  The number of dimensions is the same as `a`,
+        but the shape can be different.  This depends on whether `func`
+        changes the shape of its output with respect to its input.
+
+    See Also
+    --------
+    apply_along_axis :
+        Apply a function to 1-D slices of an array along the given axis.
+
+    Notes
+    -----
+    This function is equivalent to tuple axis arguments to reorderable ufuncs
+    with keepdims=True. Tuple axis arguments to ufuncs have been available since
+    version 1.7.0.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(24).reshape(2,3,4)
+    >>> a
+    array([[[ 0,  1,  2,  3],
+            [ 4,  5,  6,  7],
+            [ 8,  9, 10, 11]],
+           [[12, 13, 14, 15],
+            [16, 17, 18, 19],
+            [20, 21, 22, 23]]])
+
+    Sum over axes 0 and 2. The result has same number of dimensions
+    as the original array:
+
+    >>> np.apply_over_axes(np.sum, a, [0,2])
+    array([[[ 60],
+            [ 92],
+            [124]]])
+
+    Tuple axis arguments to ufuncs are equivalent:
+
+    >>> np.sum(a, axis=(0,2), keepdims=True)
+    array([[[ 60],
+            [ 92],
+            [124]]])
+
+    """
+    val = asarray(a)
+    N = a.ndim
+    if array(axes).ndim == 0:
+        axes = (axes,)
+    for axis in axes:
+        if axis < 0:
+            axis = N + axis
+        args = (val, axis)
+        res = func(*args)
+        if res.ndim == val.ndim:
+            val = res
+        else:
+            res = expand_dims(res, axis)
+            if res.ndim == val.ndim:
+                val = res
+            else:
+                raise ValueError("function is not returning "
+                                 "an array of the correct shape")
+    return val
+
+
+def _expand_dims_dispatcher(a, axis):
+    return (a,)
+
+
+@array_function_dispatch(_expand_dims_dispatcher)
+def expand_dims(a, axis):
+    """
+    Expand the shape of an array.
+
+    Insert a new axis that will appear at the `axis` position in the expanded
+    array shape.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int or tuple of ints
+        Position in the expanded axes where the new axis (or axes) is placed.
+
+        .. deprecated:: 1.13.0
+            Passing an axis where ``axis > a.ndim`` will be treated as
+            ``axis == a.ndim``, and passing ``axis < -a.ndim - 1`` will
+            be treated as ``axis == 0``. This behavior is deprecated.
+
+    Returns
+    -------
+    result : ndarray
+        View of `a` with the number of dimensions increased.
+
+    See Also
+    --------
+    squeeze : The inverse operation, removing singleton dimensions
+    reshape : Insert, remove, and combine dimensions, and resize existing ones
+    atleast_1d, atleast_2d, atleast_3d
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([1, 2])
+    >>> x.shape
+    (2,)
+
+    The following is equivalent to ``x[np.newaxis, :]`` or ``x[np.newaxis]``:
+
+    >>> y = np.expand_dims(x, axis=0)
+    >>> y
+    array([[1, 2]])
+    >>> y.shape
+    (1, 2)
+
+    The following is equivalent to ``x[:, np.newaxis]``:
+
+    >>> y = np.expand_dims(x, axis=1)
+    >>> y
+    array([[1],
+           [2]])
+    >>> y.shape
+    (2, 1)
+
+    ``axis`` may also be a tuple:
+
+    >>> y = np.expand_dims(x, axis=(0, 1))
+    >>> y
+    array([[[1, 2]]])
+
+    >>> y = np.expand_dims(x, axis=(2, 0))
+    >>> y
+    array([[[1],
+            [2]]])
+
+    Note that some examples may use ``None`` instead of ``np.newaxis``.  These
+    are the same objects:
+
+    >>> np.newaxis is None
+    True
+
+    """
+    if isinstance(a, matrix):
+        a = asarray(a)
+    else:
+        a = asanyarray(a)
+
+    if type(axis) not in (tuple, list):
+        axis = (axis,)
+
+    out_ndim = len(axis) + a.ndim
+    axis = normalize_axis_tuple(axis, out_ndim)
+
+    shape_it = iter(a.shape)
+    shape = [1 if ax in axis else next(shape_it) for ax in range(out_ndim)]
+
+    return a.reshape(shape)
+
+
+# NOTE: Remove once deprecation period passes
+@set_module("numpy")
+def row_stack(tup, *, dtype=None, casting="same_kind"):
+    # Deprecated in NumPy 2.0, 2023-08-18
+    warnings.warn(
+        "`row_stack` alias is deprecated. "
+        "Use `np.vstack` directly.",
+        DeprecationWarning,
+        stacklevel=2
+    )
+    return vstack(tup, dtype=dtype, casting=casting)
+
+
+row_stack.__doc__ = vstack.__doc__
+
+
+def _column_stack_dispatcher(tup):
+    return _arrays_for_stack_dispatcher(tup)
+
+
+@array_function_dispatch(_column_stack_dispatcher)
+def column_stack(tup):
+    """
+    Stack 1-D arrays as columns into a 2-D array.
+
+    Take a sequence of 1-D arrays and stack them as columns
+    to make a single 2-D array. 2-D arrays are stacked as-is,
+    just like with `hstack`.  1-D arrays are turned into 2-D columns
+    first.
+
+    Parameters
+    ----------
+    tup : sequence of 1-D or 2-D arrays.
+        Arrays to stack. All of them must have the same first dimension.
+
+    Returns
+    -------
+    stacked : 2-D array
+        The array formed by stacking the given arrays.
+
+    See Also
+    --------
+    stack, hstack, vstack, concatenate
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.column_stack((a,b))
+    array([[1, 2],
+           [2, 3],
+           [3, 4]])
+
+    """
+    arrays = []
+    for v in tup:
+        arr = asanyarray(v)
+        if arr.ndim < 2:
+            arr = array(arr, copy=None, subok=True, ndmin=2).T
+        arrays.append(arr)
+    return _nx.concatenate(arrays, 1)
+
+
+def _dstack_dispatcher(tup):
+    return _arrays_for_stack_dispatcher(tup)
+
+
+@array_function_dispatch(_dstack_dispatcher)
+def dstack(tup):
+    """
+    Stack arrays in sequence depth wise (along third axis).
+
+    This is equivalent to concatenation along the third axis after 2-D arrays
+    of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
+    `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
+    `dsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate`, `stack` and
+    `block` provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of arrays
+        The arrays must have the same shape along all but the third axis.
+        1-D or 2-D arrays must have the same shape.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 3-D.
+
+    See Also
+    --------
+    concatenate : Join a sequence of arrays along an existing axis.
+    stack : Join a sequence of arrays along a new axis.
+    block : Assemble an nd-array from nested lists of blocks.
+    vstack : Stack arrays in sequence vertically (row wise).
+    hstack : Stack arrays in sequence horizontally (column wise).
+    column_stack : Stack 1-D arrays as columns into a 2-D array.
+    dsplit : Split array along third axis.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array((1,2,3))
+    >>> b = np.array((2,3,4))
+    >>> np.dstack((a,b))
+    array([[[1, 2],
+            [2, 3],
+            [3, 4]]])
+
+    >>> a = np.array([[1],[2],[3]])
+    >>> b = np.array([[2],[3],[4]])
+    >>> np.dstack((a,b))
+    array([[[1, 2]],
+           [[2, 3]],
+           [[3, 4]]])
+
+    """
+    arrs = atleast_3d(*tup)
+    if not isinstance(arrs, tuple):
+        arrs = (arrs,)
+    return _nx.concatenate(arrs, 2)
+
+
+def _replace_zero_by_x_arrays(sub_arys):
+    for i in range(len(sub_arys)):
+        if _nx.ndim(sub_arys[i]) == 0:
+            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
+        elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
+            sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
+    return sub_arys
+
+
+def _array_split_dispatcher(ary, indices_or_sections, axis=None):
+    return (ary, indices_or_sections)
+
+
+@array_function_dispatch(_array_split_dispatcher)
+def array_split(ary, indices_or_sections, axis=0):
+    """
+    Split an array into multiple sub-arrays.
+
+    Please refer to the ``split`` documentation.  The only difference
+    between these functions is that ``array_split`` allows
+    `indices_or_sections` to be an integer that does *not* equally
+    divide the axis. For an array of length l that should be split
+    into n sections, it returns l % n sub-arrays of size l//n + 1
+    and the rest of size l//n.
+
+    See Also
+    --------
+    split : Split array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(8.0)
+    >>> np.array_split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.])]
+
+    >>> x = np.arange(9)
+    >>> np.array_split(x, 4)
+    [array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]
+
+    """
+    try:
+        Ntotal = ary.shape[axis]
+    except AttributeError:
+        Ntotal = len(ary)
+    try:
+        # handle array case.
+        Nsections = len(indices_or_sections) + 1
+        div_points = [0] + list(indices_or_sections) + [Ntotal]
+    except TypeError:
+        # indices_or_sections is a scalar, not an array.
+        Nsections = int(indices_or_sections)
+        if Nsections <= 0:
+            raise ValueError('number sections must be larger than 0.') from None
+        Neach_section, extras = divmod(Ntotal, Nsections)
+        section_sizes = ([0] +
+                         extras * [Neach_section+1] +
+                         (Nsections-extras) * [Neach_section])
+        div_points = _nx.array(section_sizes, dtype=_nx.intp).cumsum()
+
+    sub_arys = []
+    sary = _nx.swapaxes(ary, axis, 0)
+    for i in range(Nsections):
+        st = div_points[i]
+        end = div_points[i + 1]
+        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))
+
+    return sub_arys
+
+
+def _split_dispatcher(ary, indices_or_sections, axis=None):
+    return (ary, indices_or_sections)
+
+
+@array_function_dispatch(_split_dispatcher)
+def split(ary, indices_or_sections, axis=0):
+    """
+    Split an array into multiple sub-arrays as views into `ary`.
+
+    Parameters
+    ----------
+    ary : ndarray
+        Array to be divided into sub-arrays.
+    indices_or_sections : int or 1-D array
+        If `indices_or_sections` is an integer, N, the array will be divided
+        into N equal arrays along `axis`.  If such a split is not possible,
+        an error is raised.
+
+        If `indices_or_sections` is a 1-D array of sorted integers, the entries
+        indicate where along `axis` the array is split.  For example,
+        ``[2, 3]`` would, for ``axis=0``, result in
+
+        - ary[:2]
+        - ary[2:3]
+        - ary[3:]
+
+        If an index exceeds the dimension of the array along `axis`,
+        an empty sub-array is returned correspondingly.
+    axis : int, optional
+        The axis along which to split, default is 0.
+
+    Returns
+    -------
+    sub-arrays : list of ndarrays
+        A list of sub-arrays as views into `ary`.
+
+    Raises
+    ------
+    ValueError
+        If `indices_or_sections` is given as an integer, but
+        a split does not result in equal division.
+
+    See Also
+    --------
+    array_split : Split an array into multiple sub-arrays of equal or
+                  near-equal size.  Does not raise an exception if
+                  an equal division cannot be made.
+    hsplit : Split array into multiple sub-arrays horizontally (column-wise).
+    vsplit : Split array into multiple sub-arrays vertically (row wise).
+    dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
+    concatenate : Join a sequence of arrays along an existing axis.
+    stack : Join a sequence of arrays along a new axis.
+    hstack : Stack arrays in sequence horizontally (column wise).
+    vstack : Stack arrays in sequence vertically (row wise).
+    dstack : Stack arrays in sequence depth wise (along third dimension).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(9.0)
+    >>> np.split(x, 3)
+    [array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]
+
+    >>> x = np.arange(8.0)
+    >>> np.split(x, [3, 5, 6, 10])
+    [array([0.,  1.,  2.]),
+     array([3.,  4.]),
+     array([5.]),
+     array([6.,  7.]),
+     array([], dtype=float64)]
+
+    """
+    try:
+        len(indices_or_sections)
+    except TypeError:
+        sections = indices_or_sections
+        N = ary.shape[axis]
+        if N % sections:
+            raise ValueError(
+                'array split does not result in an equal division') from None
+    return array_split(ary, indices_or_sections, axis)
+
+
+def _hvdsplit_dispatcher(ary, indices_or_sections):
+    return (ary, indices_or_sections)
+
+
+@array_function_dispatch(_hvdsplit_dispatcher)
+def hsplit(ary, indices_or_sections):
+    """
+    Split an array into multiple sub-arrays horizontally (column-wise).
+
+    Please refer to the `split` documentation.  `hsplit` is equivalent
+    to `split` with ``axis=1``, the array is always split along the second
+    axis except for 1-D arrays, where it is split at ``axis=0``.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,   1.,   2.,   3.],
+           [ 4.,   5.,   6.,   7.],
+           [ 8.,   9.,  10.,  11.],
+           [12.,  13.,  14.,  15.]])
+    >>> np.hsplit(x, 2)
+    [array([[  0.,   1.],
+           [  4.,   5.],
+           [  8.,   9.],
+           [12.,  13.]]),
+     array([[  2.,   3.],
+           [  6.,   7.],
+           [10.,  11.],
+           [14.,  15.]])]
+    >>> np.hsplit(x, np.array([3, 6]))
+    [array([[ 0.,   1.,   2.],
+           [ 4.,   5.,   6.],
+           [ 8.,   9.,  10.],
+           [12.,  13.,  14.]]),
+     array([[ 3.],
+           [ 7.],
+           [11.],
+           [15.]]),
+     array([], shape=(4, 0), dtype=float64)]
+
+    With a higher dimensional array the split is still along the second axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[0.,  1.],
+            [2.,  3.]],
+           [[4.,  5.],
+            [6.,  7.]]])
+    >>> np.hsplit(x, 2)
+    [array([[[0.,  1.]],
+           [[4.,  5.]]]),
+     array([[[2.,  3.]],
+           [[6.,  7.]]])]
+
+    With a 1-D array, the split is along axis 0.
+
+    >>> x = np.array([0, 1, 2, 3, 4, 5])
+    >>> np.hsplit(x, 2)
+    [array([0, 1, 2]), array([3, 4, 5])]
+
+    """
+    if _nx.ndim(ary) == 0:
+        raise ValueError('hsplit only works on arrays of 1 or more dimensions')
+    if ary.ndim > 1:
+        return split(ary, indices_or_sections, 1)
+    else:
+        return split(ary, indices_or_sections, 0)
+
+
+@array_function_dispatch(_hvdsplit_dispatcher)
+def vsplit(ary, indices_or_sections):
+    """
+    Split an array into multiple sub-arrays vertically (row-wise).
+
+    Please refer to the ``split`` documentation.  ``vsplit`` is equivalent
+    to ``split`` with `axis=0` (default), the array is always split along the
+    first axis regardless of the array dimension.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(16.0).reshape(4, 4)
+    >>> x
+    array([[ 0.,   1.,   2.,   3.],
+           [ 4.,   5.,   6.,   7.],
+           [ 8.,   9.,  10.,  11.],
+           [12.,  13.,  14.,  15.]])
+    >>> np.vsplit(x, 2)
+    [array([[0., 1., 2., 3.],
+            [4., 5., 6., 7.]]),
+     array([[ 8.,  9., 10., 11.],
+            [12., 13., 14., 15.]])]
+    >>> np.vsplit(x, np.array([3, 6]))
+    [array([[ 0.,  1.,  2.,  3.],
+            [ 4.,  5.,  6.,  7.],
+            [ 8.,  9., 10., 11.]]),
+     array([[12., 13., 14., 15.]]),
+     array([], shape=(0, 4), dtype=float64)]
+
+    With a higher dimensional array the split is still along the first axis.
+
+    >>> x = np.arange(8.0).reshape(2, 2, 2)
+    >>> x
+    array([[[0.,  1.],
+            [2.,  3.]],
+           [[4.,  5.],
+            [6.,  7.]]])
+    >>> np.vsplit(x, 2)
+    [array([[[0., 1.],
+             [2., 3.]]]),
+     array([[[4., 5.],
+             [6., 7.]]])]
+
+    """
+    if _nx.ndim(ary) < 2:
+        raise ValueError('vsplit only works on arrays of 2 or more dimensions')
+    return split(ary, indices_or_sections, 0)
+
+
+@array_function_dispatch(_hvdsplit_dispatcher)
+def dsplit(ary, indices_or_sections):
+    """
+    Split array into multiple sub-arrays along the 3rd axis (depth).
+
+    Please refer to the `split` documentation.  `dsplit` is equivalent
+    to `split` with ``axis=2``, the array is always split along the third
+    axis provided the array dimension is greater than or equal to 3.
+
+    See Also
+    --------
+    split : Split an array into multiple sub-arrays of equal size.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(16.0).reshape(2, 2, 4)
+    >>> x
+    array([[[ 0.,   1.,   2.,   3.],
+            [ 4.,   5.,   6.,   7.]],
+           [[ 8.,   9.,  10.,  11.],
+            [12.,  13.,  14.,  15.]]])
+    >>> np.dsplit(x, 2)
+    [array([[[ 0.,  1.],
+            [ 4.,  5.]],
+           [[ 8.,  9.],
+            [12., 13.]]]), array([[[ 2.,  3.],
+            [ 6.,  7.]],
+           [[10., 11.],
+            [14., 15.]]])]
+    >>> np.dsplit(x, np.array([3, 6]))
+    [array([[[ 0.,   1.,   2.],
+            [ 4.,   5.,   6.]],
+           [[ 8.,   9.,  10.],
+            [12.,  13.,  14.]]]),
+     array([[[ 3.],
+            [ 7.]],
+           [[11.],
+            [15.]]]),
+    array([], shape=(2, 2, 0), dtype=float64)]
+    """
+    if _nx.ndim(ary) < 3:
+        raise ValueError('dsplit only works on arrays of 3 or more dimensions')
+    return split(ary, indices_or_sections, 2)
+
+
+def get_array_wrap(*args):
+    """Find the wrapper for the array with the highest priority.
+
+    In case of ties, leftmost wins. If no wrapper is found, return None.
+
+    .. deprecated:: 2.0
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`get_array_wrap` is deprecated. "
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    wrappers = sorted((getattr(x, '__array_priority__', 0), -i,
+                 x.__array_wrap__) for i, x in enumerate(args)
+                                   if hasattr(x, '__array_wrap__'))
+    if wrappers:
+        return wrappers[-1][-1]
+    return None
+
+
+def _kron_dispatcher(a, b):
+    return (a, b)
+
+
+@array_function_dispatch(_kron_dispatcher)
+def kron(a, b):
+    """
+    Kronecker product of two arrays.
+
+    Computes the Kronecker product, a composite array made of blocks of the
+    second array scaled by the first.
+
+    Parameters
+    ----------
+    a, b : array_like
+
+    Returns
+    -------
+    out : ndarray
+
+    See Also
+    --------
+    outer : The outer product
+
+    Notes
+    -----
+    The function assumes that the number of dimensions of `a` and `b`
+    are the same, if necessary prepending the smallest with ones.
+    If ``a.shape = (r0,r1,..,rN)`` and ``b.shape = (s0,s1,...,sN)``,
+    the Kronecker product has shape ``(r0*s0, r1*s1, ..., rN*SN)``.
+    The elements are products of elements from `a` and `b`, organized
+    explicitly by::
+
+        kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
+
+    where::
+
+        kt = it * st + jt,  t = 0,...,N
+
+    In the common 2-D case (N=1), the block structure can be visualized::
+
+        [[ a[0,0]*b,   a[0,1]*b,  ... , a[0,-1]*b  ],
+         [  ...                              ...   ],
+         [ a[-1,0]*b,  a[-1,1]*b, ... , a[-1,-1]*b ]]
+
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.kron([1,10,100], [5,6,7])
+    array([  5,   6,   7, ..., 500, 600, 700])
+    >>> np.kron([5,6,7], [1,10,100])
+    array([  5,  50, 500, ...,   7,  70, 700])
+
+    >>> np.kron(np.eye(2), np.ones((2,2)))
+    array([[1.,  1.,  0.,  0.],
+           [1.,  1.,  0.,  0.],
+           [0.,  0.,  1.,  1.],
+           [0.,  0.,  1.,  1.]])
+
+    >>> a = np.arange(100).reshape((2,5,2,5))
+    >>> b = np.arange(24).reshape((2,3,4))
+    >>> c = np.kron(a,b)
+    >>> c.shape
+    (2, 10, 6, 20)
+    >>> I = (1,3,0,2)
+    >>> J = (0,2,1)
+    >>> J1 = (0,) + J             # extend to ndim=4
+    >>> S1 = (1,) + b.shape
+    >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
+    >>> c[K] == a[I]*b[J]
+    True
+
+    """
+    # Working:
+    # 1. Equalise the shapes by prepending smaller array with 1s
+    # 2. Expand shapes of both the arrays by adding new axes at
+    #    odd positions for 1st array and even positions for 2nd
+    # 3. Compute the product of the modified array
+    # 4. The inner most array elements now contain the rows of
+    #    the Kronecker product
+    # 5. Reshape the result to kron's shape, which is same as
+    #    product of shapes of the two arrays.
+    b = asanyarray(b)
+    a = array(a, copy=None, subok=True, ndmin=b.ndim)
+    is_any_mat = isinstance(a, matrix) or isinstance(b, matrix)
+    ndb, nda = b.ndim, a.ndim
+    nd = max(ndb, nda)
+
+    if (nda == 0 or ndb == 0):
+        return _nx.multiply(a, b)
+
+    as_ = a.shape
+    bs = b.shape
+    if not a.flags.contiguous:
+        a = reshape(a, as_)
+    if not b.flags.contiguous:
+        b = reshape(b, bs)
+
+    # Equalise the shapes by prepending smaller one with 1s
+    as_ = (1,)*max(0, ndb-nda) + as_
+    bs = (1,)*max(0, nda-ndb) + bs
+
+    # Insert empty dimensions
+    a_arr = expand_dims(a, axis=tuple(range(ndb-nda)))
+    b_arr = expand_dims(b, axis=tuple(range(nda-ndb)))
+
+    # Compute the product
+    a_arr = expand_dims(a_arr, axis=tuple(range(1, nd*2, 2)))
+    b_arr = expand_dims(b_arr, axis=tuple(range(0, nd*2, 2)))
+    # In case of `mat`, convert result to `array`
+    result = _nx.multiply(a_arr, b_arr, subok=(not is_any_mat))
+
+    # Reshape back
+    result = result.reshape(_nx.multiply(as_, bs))
+
+    return result if not is_any_mat else matrix(result, copy=False)
+
+
+def _tile_dispatcher(A, reps):
+    return (A, reps)
+
+
+@array_function_dispatch(_tile_dispatcher)
+def tile(A, reps):
+    """
+    Construct an array by repeating A the number of times given by reps.
+
+    If `reps` has length ``d``, the result will have dimension of
+    ``max(d, A.ndim)``.
+
+    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
+    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
+    or shape (1, 1, 3) for 3-D replication. If this is not the desired
+    behavior, promote `A` to d-dimensions manually before calling this
+    function.
+
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by prepending 1's to it.
+    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
+    (1, 1, 2, 2).
+
+    Note : Although tile may be used for broadcasting, it is strongly
+    recommended to use numpy's broadcasting operations and functions.
+
+    Parameters
+    ----------
+    A : array_like
+        The input array.
+    reps : array_like
+        The number of repetitions of `A` along each axis.
+
+    Returns
+    -------
+    c : ndarray
+        The tiled output array.
+
+    See Also
+    --------
+    repeat : Repeat elements of an array.
+    broadcast_to : Broadcast an array to a new shape
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([0, 1, 2])
+    >>> np.tile(a, 2)
+    array([0, 1, 2, 0, 1, 2])
+    >>> np.tile(a, (2, 2))
+    array([[0, 1, 2, 0, 1, 2],
+           [0, 1, 2, 0, 1, 2]])
+    >>> np.tile(a, (2, 1, 2))
+    array([[[0, 1, 2, 0, 1, 2]],
+           [[0, 1, 2, 0, 1, 2]]])
+
+    >>> b = np.array([[1, 2], [3, 4]])
+    >>> np.tile(b, 2)
+    array([[1, 2, 1, 2],
+           [3, 4, 3, 4]])
+    >>> np.tile(b, (2, 1))
+    array([[1, 2],
+           [3, 4],
+           [1, 2],
+           [3, 4]])
+
+    >>> c = np.array([1,2,3,4])
+    >>> np.tile(c,(4,1))
+    array([[1, 2, 3, 4],
+           [1, 2, 3, 4],
+           [1, 2, 3, 4],
+           [1, 2, 3, 4]])
+    """
+    try:
+        tup = tuple(reps)
+    except TypeError:
+        tup = (reps,)
+    d = len(tup)
+    if all(x == 1 for x in tup) and isinstance(A, _nx.ndarray):
+        # Fixes the problem that the function does not make a copy if A is a
+        # numpy array and the repetitions are 1 in all dimensions
+        return _nx.array(A, copy=True, subok=True, ndmin=d)
+    else:
+        # Note that no copy of zero-sized arrays is made. However since they
+        # have no data there is no risk of an inadvertent overwrite.
+        c = _nx.array(A, copy=None, subok=True, ndmin=d)
+    if (d < c.ndim):
+        tup = (1,)*(c.ndim-d) + tup
+    shape_out = tuple(s*t for s, t in zip(c.shape, tup))
+    n = c.size
+    if n > 0:
+        for dim_in, nrep in zip(c.shape, tup):
+            if nrep != 1:
+                c = c.reshape(-1, n).repeat(nrep, 0)
+            n //= dim_in
+    return c.reshape(shape_out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..77e5d2de9cb9259efba6d9128705f1a4597d3ef3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_shape_base_impl.pyi
@@ -0,0 +1,225 @@
+from collections.abc import Callable, Sequence
+from typing import (
+    TypeVar,
+    Any,
+    overload,
+    SupportsIndex,
+    Protocol,
+    ParamSpec,
+    Concatenate,
+    type_check_only,
+)
+
+from typing_extensions import deprecated
+
+import numpy as np
+from numpy import _CastingKind, generic, integer, ufunc, unsignedinteger, signedinteger, floating, complexfloating, object_
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ShapeLike,
+    _ArrayLike,
+    _ArrayLikeBool_co,
+    _ArrayLikeUInt_co,
+    _ArrayLikeInt_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeObject_co,
+)
+
+__all__ = [
+    "column_stack",
+    "row_stack",
+    "dstack",
+    "array_split",
+    "split",
+    "hsplit",
+    "vsplit",
+    "dsplit",
+    "apply_over_axes",
+    "expand_dims",
+    "apply_along_axis",
+    "kron",
+    "tile",
+    "take_along_axis",
+    "put_along_axis",
+]
+
+_P = ParamSpec("_P")
+_SCT = TypeVar("_SCT", bound=generic)
+
+# Signature of `__array_wrap__`
+@type_check_only
+class _ArrayWrap(Protocol):
+    def __call__(
+        self,
+        array: NDArray[Any],
+        context: None | tuple[ufunc, tuple[Any, ...], int] = ...,
+        return_scalar: bool = ...,
+        /,
+    ) -> Any: ...
+
+@type_check_only
+class _SupportsArrayWrap(Protocol):
+    @property
+    def __array_wrap__(self) -> _ArrayWrap: ...
+
+###
+
+def take_along_axis(
+    arr: _SCT | NDArray[_SCT],
+    indices: NDArray[integer[Any]],
+    axis: None | int,
+) -> NDArray[_SCT]: ...
+
+def put_along_axis(
+    arr: NDArray[_SCT],
+    indices: NDArray[integer[Any]],
+    values: ArrayLike,
+    axis: None | int,
+) -> None: ...
+
+@overload
+def apply_along_axis(
+    func1d: Callable[Concatenate[NDArray[Any], _P], _ArrayLike[_SCT]],
+    axis: SupportsIndex,
+    arr: ArrayLike,
+    *args: _P.args,
+    **kwargs: _P.kwargs,
+) -> NDArray[_SCT]: ...
+@overload
+def apply_along_axis(
+    func1d: Callable[Concatenate[NDArray[Any], _P], Any],
+    axis: SupportsIndex,
+    arr: ArrayLike,
+    *args: _P.args,
+    **kwargs: _P.kwargs,
+) -> NDArray[Any]: ...
+
+def apply_over_axes(
+    func: Callable[[NDArray[Any], int], NDArray[_SCT]],
+    a: ArrayLike,
+    axes: int | Sequence[int],
+) -> NDArray[_SCT]: ...
+
+@overload
+def expand_dims(
+    a: _ArrayLike[_SCT],
+    axis: _ShapeLike,
+) -> NDArray[_SCT]: ...
+@overload
+def expand_dims(
+    a: ArrayLike,
+    axis: _ShapeLike,
+) -> NDArray[Any]: ...
+
+# Deprecated in NumPy 2.0, 2023-08-18
+@deprecated("`row_stack` alias is deprecated. Use `np.vstack` directly.")
+def row_stack(
+    tup: Sequence[ArrayLike],
+    *,
+    dtype: DTypeLike | None = None,
+    casting: _CastingKind = "same_kind",
+) -> NDArray[Any]: ...
+
+#
+@overload
+def column_stack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ...
+@overload
+def column_stack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ...
+
+@overload
+def dstack(tup: Sequence[_ArrayLike[_SCT]]) -> NDArray[_SCT]: ...
+@overload
+def dstack(tup: Sequence[ArrayLike]) -> NDArray[Any]: ...
+
+@overload
+def array_split(
+    ary: _ArrayLike[_SCT],
+    indices_or_sections: _ShapeLike,
+    axis: SupportsIndex = ...,
+) -> list[NDArray[_SCT]]: ...
+@overload
+def array_split(
+    ary: ArrayLike,
+    indices_or_sections: _ShapeLike,
+    axis: SupportsIndex = ...,
+) -> list[NDArray[Any]]: ...
+
+@overload
+def split(
+    ary: _ArrayLike[_SCT],
+    indices_or_sections: _ShapeLike,
+    axis: SupportsIndex = ...,
+) -> list[NDArray[_SCT]]: ...
+@overload
+def split(
+    ary: ArrayLike,
+    indices_or_sections: _ShapeLike,
+    axis: SupportsIndex = ...,
+) -> list[NDArray[Any]]: ...
+
+@overload
+def hsplit(
+    ary: _ArrayLike[_SCT],
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[_SCT]]: ...
+@overload
+def hsplit(
+    ary: ArrayLike,
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[Any]]: ...
+
+@overload
+def vsplit(
+    ary: _ArrayLike[_SCT],
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[_SCT]]: ...
+@overload
+def vsplit(
+    ary: ArrayLike,
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[Any]]: ...
+
+@overload
+def dsplit(
+    ary: _ArrayLike[_SCT],
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[_SCT]]: ...
+@overload
+def dsplit(
+    ary: ArrayLike,
+    indices_or_sections: _ShapeLike,
+) -> list[NDArray[Any]]: ...
+
+@overload
+def get_array_wrap(*args: _SupportsArrayWrap) -> _ArrayWrap: ...
+@overload
+def get_array_wrap(*args: object) -> None | _ArrayWrap: ...
+
+@overload
+def kron(a: _ArrayLikeBool_co, b: _ArrayLikeBool_co) -> NDArray[np.bool]: ...  # type: ignore[misc]
+@overload
+def kron(a: _ArrayLikeUInt_co, b: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ...  # type: ignore[misc]
+@overload
+def kron(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...  # type: ignore[misc]
+@overload
+def kron(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...  # type: ignore[misc]
+@overload
+def kron(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def kron(a: _ArrayLikeObject_co, b: Any) -> NDArray[object_]: ...
+@overload
+def kron(a: Any, b: _ArrayLikeObject_co) -> NDArray[object_]: ...
+
+@overload
+def tile(
+    A: _ArrayLike[_SCT],
+    reps: int | Sequence[int],
+) -> NDArray[_SCT]: ...
+@overload
+def tile(
+    A: ArrayLike,
+    reps: int | Sequence[int],
+) -> NDArray[Any]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4780783a63812a8e5d6ffe1eb3b0f1ff0b1266c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.py
@@ -0,0 +1,549 @@
+"""
+Utilities that manipulate strides to achieve desirable effects.
+
+An explanation of strides can be found in the :ref:`arrays.ndarray`.
+
+"""
+import numpy as np
+from numpy._core.numeric import normalize_axis_tuple
+from numpy._core.overrides import array_function_dispatch, set_module
+
+__all__ = ['broadcast_to', 'broadcast_arrays', 'broadcast_shapes']
+
+
+class DummyArray:
+    """Dummy object that just exists to hang __array_interface__ dictionaries
+    and possibly keep alive a reference to a base array.
+    """
+
+    def __init__(self, interface, base=None):
+        self.__array_interface__ = interface
+        self.base = base
+
+
+def _maybe_view_as_subclass(original_array, new_array):
+    if type(original_array) is not type(new_array):
+        # if input was an ndarray subclass and subclasses were OK,
+        # then view the result as that subclass.
+        new_array = new_array.view(type=type(original_array))
+        # Since we have done something akin to a view from original_array, we
+        # should let the subclass finalize (if it has it implemented, i.e., is
+        # not None).
+        if new_array.__array_finalize__:
+            new_array.__array_finalize__(original_array)
+    return new_array
+
+
+@set_module("numpy.lib.stride_tricks")
+def as_strided(x, shape=None, strides=None, subok=False, writeable=True):
+    """
+    Create a view into the array with the given shape and strides.
+
+    .. warning:: This function has to be used with extreme care, see notes.
+
+    Parameters
+    ----------
+    x : ndarray
+        Array to create a new.
+    shape : sequence of int, optional
+        The shape of the new array. Defaults to ``x.shape``.
+    strides : sequence of int, optional
+        The strides of the new array. Defaults to ``x.strides``.
+    subok : bool, optional
+        If True, subclasses are preserved.
+    writeable : bool, optional
+        If set to False, the returned array will always be readonly.
+        Otherwise it will be writable if the original array was. It
+        is advisable to set this to False if possible (see Notes).
+
+    Returns
+    -------
+    view : ndarray
+
+    See also
+    --------
+    broadcast_to : broadcast an array to a given shape.
+    reshape : reshape an array.
+    lib.stride_tricks.sliding_window_view :
+        userfriendly and safe function for a creation of sliding window views.
+
+    Notes
+    -----
+    ``as_strided`` creates a view into the array given the exact strides
+    and shape. This means it manipulates the internal data structure of
+    ndarray and, if done incorrectly, the array elements can point to
+    invalid memory and can corrupt results or crash your program.
+    It is advisable to always use the original ``x.strides`` when
+    calculating new strides to avoid reliance on a contiguous memory
+    layout.
+
+    Furthermore, arrays created with this function often contain self
+    overlapping memory, so that two elements are identical.
+    Vectorized write operations on such arrays will typically be
+    unpredictable. They may even give different results for small, large,
+    or transposed arrays.
+
+    Since writing to these arrays has to be tested and done with great
+    care, you may want to use ``writeable=False`` to avoid accidental write
+    operations.
+
+    For these reasons it is advisable to avoid ``as_strided`` when
+    possible.
+    """
+    # first convert input to array, possibly keeping subclass
+    x = np.array(x, copy=None, subok=subok)
+    interface = dict(x.__array_interface__)
+    if shape is not None:
+        interface['shape'] = tuple(shape)
+    if strides is not None:
+        interface['strides'] = tuple(strides)
+
+    array = np.asarray(DummyArray(interface, base=x))
+    # The route via `__interface__` does not preserve structured
+    # dtypes. Since dtype should remain unchanged, we set it explicitly.
+    array.dtype = x.dtype
+
+    view = _maybe_view_as_subclass(x, array)
+
+    if view.flags.writeable and not writeable:
+        view.flags.writeable = False
+
+    return view
+
+
+def _sliding_window_view_dispatcher(x, window_shape, axis=None, *,
+                                    subok=None, writeable=None):
+    return (x,)
+
+
+@array_function_dispatch(
+    _sliding_window_view_dispatcher, module="numpy.lib.stride_tricks"
+)
+def sliding_window_view(x, window_shape, axis=None, *,
+                        subok=False, writeable=False):
+    """
+    Create a sliding window view into the array with the given window shape.
+
+    Also known as rolling or moving window, the window slides across all
+    dimensions of the array and extracts subsets of the array at all window
+    positions.
+
+    .. versionadded:: 1.20.0
+
+    Parameters
+    ----------
+    x : array_like
+        Array to create the sliding window view from.
+    window_shape : int or tuple of int
+        Size of window over each axis that takes part in the sliding window.
+        If `axis` is not present, must have same length as the number of input
+        array dimensions. Single integers `i` are treated as if they were the
+        tuple `(i,)`.
+    axis : int or tuple of int, optional
+        Axis or axes along which the sliding window is applied.
+        By default, the sliding window is applied to all axes and
+        `window_shape[i]` will refer to axis `i` of `x`.
+        If `axis` is given as a `tuple of int`, `window_shape[i]` will refer to
+        the axis `axis[i]` of `x`.
+        Single integers `i` are treated as if they were the tuple `(i,)`.
+    subok : bool, optional
+        If True, sub-classes will be passed-through, otherwise the returned
+        array will be forced to be a base-class array (default).
+    writeable : bool, optional
+        When true, allow writing to the returned view. The default is false,
+        as this should be used with caution: the returned view contains the
+        same memory location multiple times, so writing to one location will
+        cause others to change.
+
+    Returns
+    -------
+    view : ndarray
+        Sliding window view of the array. The sliding window dimensions are
+        inserted at the end, and the original dimensions are trimmed as
+        required by the size of the sliding window.
+        That is, ``view.shape = x_shape_trimmed + window_shape``, where
+        ``x_shape_trimmed`` is ``x.shape`` with every entry reduced by one less
+        than the corresponding window size.
+
+    See Also
+    --------
+    lib.stride_tricks.as_strided: A lower-level and less safe routine for
+        creating arbitrary views from custom shape and strides.
+    broadcast_to: broadcast an array to a given shape.
+
+    Notes
+    -----
+    For many applications using a sliding window view can be convenient, but
+    potentially very slow. Often specialized solutions exist, for example:
+
+    - `scipy.signal.fftconvolve`
+
+    - filtering functions in `scipy.ndimage`
+
+    - moving window functions provided by
+      `bottleneck `_.
+
+    As a rough estimate, a sliding window approach with an input size of `N`
+    and a window size of `W` will scale as `O(N*W)` where frequently a special
+    algorithm can achieve `O(N)`. That means that the sliding window variant
+    for a window size of 100 can be a 100 times slower than a more specialized
+    version.
+
+    Nevertheless, for small window sizes, when no custom algorithm exists, or
+    as a prototyping and developing tool, this function can be a good solution.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib.stride_tricks import sliding_window_view
+    >>> x = np.arange(6)
+    >>> x.shape
+    (6,)
+    >>> v = sliding_window_view(x, 3)
+    >>> v.shape
+    (4, 3)
+    >>> v
+    array([[0, 1, 2],
+           [1, 2, 3],
+           [2, 3, 4],
+           [3, 4, 5]])
+
+    This also works in more dimensions, e.g.
+
+    >>> i, j = np.ogrid[:3, :4]
+    >>> x = 10*i + j
+    >>> x.shape
+    (3, 4)
+    >>> x
+    array([[ 0,  1,  2,  3],
+           [10, 11, 12, 13],
+           [20, 21, 22, 23]])
+    >>> shape = (2,2)
+    >>> v = sliding_window_view(x, shape)
+    >>> v.shape
+    (2, 3, 2, 2)
+    >>> v
+    array([[[[ 0,  1],
+             [10, 11]],
+            [[ 1,  2],
+             [11, 12]],
+            [[ 2,  3],
+             [12, 13]]],
+           [[[10, 11],
+             [20, 21]],
+            [[11, 12],
+             [21, 22]],
+            [[12, 13],
+             [22, 23]]]])
+
+    The axis can be specified explicitly:
+
+    >>> v = sliding_window_view(x, 3, 0)
+    >>> v.shape
+    (1, 4, 3)
+    >>> v
+    array([[[ 0, 10, 20],
+            [ 1, 11, 21],
+            [ 2, 12, 22],
+            [ 3, 13, 23]]])
+
+    The same axis can be used several times. In that case, every use reduces
+    the corresponding original dimension:
+
+    >>> v = sliding_window_view(x, (2, 3), (1, 1))
+    >>> v.shape
+    (3, 1, 2, 3)
+    >>> v
+    array([[[[ 0,  1,  2],
+             [ 1,  2,  3]]],
+           [[[10, 11, 12],
+             [11, 12, 13]]],
+           [[[20, 21, 22],
+             [21, 22, 23]]]])
+
+    Combining with stepped slicing (`::step`), this can be used to take sliding
+    views which skip elements:
+
+    >>> x = np.arange(7)
+    >>> sliding_window_view(x, 5)[:, ::2]
+    array([[0, 2, 4],
+           [1, 3, 5],
+           [2, 4, 6]])
+
+    or views which move by multiple elements
+
+    >>> x = np.arange(7)
+    >>> sliding_window_view(x, 3)[::2, :]
+    array([[0, 1, 2],
+           [2, 3, 4],
+           [4, 5, 6]])
+
+    A common application of `sliding_window_view` is the calculation of running
+    statistics. The simplest example is the
+    `moving average `_:
+
+    >>> x = np.arange(6)
+    >>> x.shape
+    (6,)
+    >>> v = sliding_window_view(x, 3)
+    >>> v.shape
+    (4, 3)
+    >>> v
+    array([[0, 1, 2],
+           [1, 2, 3],
+           [2, 3, 4],
+           [3, 4, 5]])
+    >>> moving_average = v.mean(axis=-1)
+    >>> moving_average
+    array([1., 2., 3., 4.])
+
+    Note that a sliding window approach is often **not** optimal (see Notes).
+    """
+    window_shape = (tuple(window_shape)
+                    if np.iterable(window_shape)
+                    else (window_shape,))
+    # first convert input to array, possibly keeping subclass
+    x = np.array(x, copy=None, subok=subok)
+
+    window_shape_array = np.array(window_shape)
+    if np.any(window_shape_array < 0):
+        raise ValueError('`window_shape` cannot contain negative values')
+
+    if axis is None:
+        axis = tuple(range(x.ndim))
+        if len(window_shape) != len(axis):
+            raise ValueError(f'Since axis is `None`, must provide '
+                             f'window_shape for all dimensions of `x`; '
+                             f'got {len(window_shape)} window_shape elements '
+                             f'and `x.ndim` is {x.ndim}.')
+    else:
+        axis = normalize_axis_tuple(axis, x.ndim, allow_duplicate=True)
+        if len(window_shape) != len(axis):
+            raise ValueError(f'Must provide matching length window_shape and '
+                             f'axis; got {len(window_shape)} window_shape '
+                             f'elements and {len(axis)} axes elements.')
+
+    out_strides = x.strides + tuple(x.strides[ax] for ax in axis)
+
+    # note: same axis can be windowed repeatedly
+    x_shape_trimmed = list(x.shape)
+    for ax, dim in zip(axis, window_shape):
+        if x_shape_trimmed[ax] < dim:
+            raise ValueError(
+                'window shape cannot be larger than input array shape')
+        x_shape_trimmed[ax] -= dim - 1
+    out_shape = tuple(x_shape_trimmed) + window_shape
+    return as_strided(x, strides=out_strides, shape=out_shape,
+                      subok=subok, writeable=writeable)
+
+
+def _broadcast_to(array, shape, subok, readonly):
+    shape = tuple(shape) if np.iterable(shape) else (shape,)
+    array = np.array(array, copy=None, subok=subok)
+    if not shape and array.shape:
+        raise ValueError('cannot broadcast a non-scalar to a scalar array')
+    if any(size < 0 for size in shape):
+        raise ValueError('all elements of broadcast shape must be non-'
+                         'negative')
+    extras = []
+    it = np.nditer(
+        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
+        op_flags=['readonly'], itershape=shape, order='C')
+    with it:
+        # never really has writebackifcopy semantics
+        broadcast = it.itviews[0]
+    result = _maybe_view_as_subclass(array, broadcast)
+    # In a future version this will go away
+    if not readonly and array.flags._writeable_no_warn:
+        result.flags.writeable = True
+        result.flags._warn_on_write = True
+    return result
+
+
+def _broadcast_to_dispatcher(array, shape, subok=None):
+    return (array,)
+
+
+@array_function_dispatch(_broadcast_to_dispatcher, module='numpy')
+def broadcast_to(array, shape, subok=False):
+    """Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array : array_like
+        The array to broadcast.
+    shape : tuple or int
+        The shape of the desired array. A single integer ``i`` is interpreted
+        as ``(i,)``.
+    subok : bool, optional
+        If True, then sub-classes will be passed-through, otherwise
+        the returned array will be forced to be a base-class array (default).
+
+    Returns
+    -------
+    broadcast : array
+        A readonly view on the original array with the given shape. It is
+        typically not contiguous. Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location.
+
+    Raises
+    ------
+    ValueError
+        If the array is not compatible with the new shape according to NumPy's
+        broadcasting rules.
+
+    See Also
+    --------
+    broadcast
+    broadcast_arrays
+    broadcast_shapes
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([1, 2, 3])
+    >>> np.broadcast_to(x, (3, 3))
+    array([[1, 2, 3],
+           [1, 2, 3],
+           [1, 2, 3]])
+    """
+    return _broadcast_to(array, shape, subok=subok, readonly=True)
+
+
+def _broadcast_shape(*args):
+    """Returns the shape of the arrays that would result from broadcasting the
+    supplied arrays against each other.
+    """
+    # use the old-iterator because np.nditer does not handle size 0 arrays
+    # consistently
+    b = np.broadcast(*args[:32])
+    # unfortunately, it cannot handle 32 or more arguments directly
+    for pos in range(32, len(args), 31):
+        # ironically, np.broadcast does not properly handle np.broadcast
+        # objects (it treats them as scalars)
+        # use broadcasting to avoid allocating the full array
+        b = broadcast_to(0, b.shape)
+        b = np.broadcast(b, *args[pos:(pos + 31)])
+    return b.shape
+
+
+_size0_dtype = np.dtype([])
+
+
+@set_module('numpy')
+def broadcast_shapes(*args):
+    """
+    Broadcast the input shapes into a single shape.
+
+    :ref:`Learn more about broadcasting here `.
+
+    .. versionadded:: 1.20.0
+
+    Parameters
+    ----------
+    *args : tuples of ints, or ints
+        The shapes to be broadcast against each other.
+
+    Returns
+    -------
+    tuple
+        Broadcasted shape.
+
+    Raises
+    ------
+    ValueError
+        If the shapes are not compatible and cannot be broadcast according
+        to NumPy's broadcasting rules.
+
+    See Also
+    --------
+    broadcast
+    broadcast_arrays
+    broadcast_to
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.broadcast_shapes((1, 2), (3, 1), (3, 2))
+    (3, 2)
+
+    >>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
+    (5, 6, 7)
+    """
+    arrays = [np.empty(x, dtype=_size0_dtype) for x in args]
+    return _broadcast_shape(*arrays)
+
+
+def _broadcast_arrays_dispatcher(*args, subok=None):
+    return args
+
+
+@array_function_dispatch(_broadcast_arrays_dispatcher, module='numpy')
+def broadcast_arrays(*args, subok=False):
+    """
+    Broadcast any number of arrays against each other.
+
+    Parameters
+    ----------
+    *args : array_likes
+        The arrays to broadcast.
+
+    subok : bool, optional
+        If True, then sub-classes will be passed-through, otherwise
+        the returned arrays will be forced to be a base-class array (default).
+
+    Returns
+    -------
+    broadcasted : tuple of arrays
+        These arrays are views on the original arrays.  They are typically
+        not contiguous.  Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location. If you need
+        to write to the arrays, make copies first. While you can set the
+        ``writable`` flag True, writing to a single output value may end up
+        changing more than one location in the output array.
+
+        .. deprecated:: 1.17
+            The output is currently marked so that if written to, a deprecation
+            warning will be emitted. A future version will set the
+            ``writable`` flag False so writing to it will raise an error.
+
+    See Also
+    --------
+    broadcast
+    broadcast_to
+    broadcast_shapes
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([[1,2,3]])
+    >>> y = np.array([[4],[5]])
+    >>> np.broadcast_arrays(x, y)
+    (array([[1, 2, 3],
+            [1, 2, 3]]),
+     array([[4, 4, 4],
+            [5, 5, 5]]))
+
+    Here is a useful idiom for getting contiguous copies instead of
+    non-contiguous views.
+
+    >>> [np.array(a) for a in np.broadcast_arrays(x, y)]
+    [array([[1, 2, 3],
+            [1, 2, 3]]),
+     array([[4, 4, 4],
+            [5, 5, 5]])]
+
+    """
+    # nditer is not used here to avoid the limit of 32 arrays.
+    # Otherwise, something like the following one-liner would suffice:
+    # return np.nditer(args, flags=['multi_index', 'zerosize_ok'],
+    #                  order='C').itviews
+
+    args = [np.array(_m, copy=None, subok=subok) for _m in args]
+
+    shape = _broadcast_shape(*args)
+
+    result = [array if array.shape == shape
+              else _broadcast_to(array, shape, subok=subok, readonly=False)
+                              for array in args]
+    return tuple(result)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e2284115eeb40276bf4003bb420f3ce83f004fb8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_stride_tricks_impl.pyi
@@ -0,0 +1,80 @@
+from collections.abc import Iterable
+from typing import Any, TypeVar, overload, SupportsIndex
+
+from numpy import generic
+from numpy._typing import (
+    NDArray,
+    ArrayLike,
+    _ShapeLike,
+    _Shape,
+    _ArrayLike
+)
+
+__all__ = ["broadcast_to", "broadcast_arrays", "broadcast_shapes"]
+
+_SCT = TypeVar("_SCT", bound=generic)
+
+class DummyArray:
+    __array_interface__: dict[str, Any]
+    base: None | NDArray[Any]
+    def __init__(
+        self,
+        interface: dict[str, Any],
+        base: None | NDArray[Any] = ...,
+    ) -> None: ...
+
+@overload
+def as_strided(
+    x: _ArrayLike[_SCT],
+    shape: None | Iterable[int] = ...,
+    strides: None | Iterable[int] = ...,
+    subok: bool = ...,
+    writeable: bool = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def as_strided(
+    x: ArrayLike,
+    shape: None | Iterable[int] = ...,
+    strides: None | Iterable[int] = ...,
+    subok: bool = ...,
+    writeable: bool = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def sliding_window_view(
+    x: _ArrayLike[_SCT],
+    window_shape: int | Iterable[int],
+    axis: None | SupportsIndex = ...,
+    *,
+    subok: bool = ...,
+    writeable: bool = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def sliding_window_view(
+    x: ArrayLike,
+    window_shape: int | Iterable[int],
+    axis: None | SupportsIndex = ...,
+    *,
+    subok: bool = ...,
+    writeable: bool = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def broadcast_to(
+    array: _ArrayLike[_SCT],
+    shape: int | Iterable[int],
+    subok: bool = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def broadcast_to(
+    array: ArrayLike,
+    shape: int | Iterable[int],
+    subok: bool = ...,
+) -> NDArray[Any]: ...
+
+def broadcast_shapes(*args: _ShapeLike) -> _Shape: ...
+
+def broadcast_arrays(
+    *args: ArrayLike,
+    subok: bool = ...,
+) -> tuple[NDArray[Any], ...]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8815bede8919c5658243146bd06f4d325126051
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.py
@@ -0,0 +1,1188 @@
+""" Basic functions for manipulating 2d arrays
+
+"""
+import functools
+import operator
+
+from numpy._core._multiarray_umath import _array_converter
+from numpy._core.numeric import (
+    asanyarray, arange, zeros, greater_equal, multiply, ones,
+    asarray, where, int8, int16, int32, int64, intp, empty, promote_types,
+    diagonal, nonzero, indices
+    )
+from numpy._core.overrides import finalize_array_function_like, set_module
+from numpy._core import overrides
+from numpy._core import iinfo
+from numpy.lib._stride_tricks_impl import broadcast_to
+
+
+__all__ = [
+    'diag', 'diagflat', 'eye', 'fliplr', 'flipud', 'tri', 'triu',
+    'tril', 'vander', 'histogram2d', 'mask_indices', 'tril_indices',
+    'tril_indices_from', 'triu_indices', 'triu_indices_from', ]
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+i1 = iinfo(int8)
+i2 = iinfo(int16)
+i4 = iinfo(int32)
+
+
+def _min_int(low, high):
+    """ get small int that fits the range """
+    if high <= i1.max and low >= i1.min:
+        return int8
+    if high <= i2.max and low >= i2.min:
+        return int16
+    if high <= i4.max and low >= i4.min:
+        return int32
+    return int64
+
+
+def _flip_dispatcher(m):
+    return (m,)
+
+
+@array_function_dispatch(_flip_dispatcher)
+def fliplr(m):
+    """
+    Reverse the order of elements along axis 1 (left/right).
+
+    For a 2-D array, this flips the entries in each row in the left/right
+    direction. Columns are preserved, but appear in a different order than
+    before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array, must be at least 2-D.
+
+    Returns
+    -------
+    f : ndarray
+        A view of `m` with the columns reversed.  Since a view
+        is returned, this operation is :math:`\\mathcal O(1)`.
+
+    See Also
+    --------
+    flipud : Flip array in the up/down direction.
+    flip : Flip array in one or more dimensions.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[:,::-1]`` or ``np.flip(m, axis=1)``.
+    Requires the array to be at least 2-D.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.diag([1.,2.,3.])
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.fliplr(A)
+    array([[0.,  0.,  1.],
+           [0.,  2.,  0.],
+           [3.,  0.,  0.]])
+
+    >>> rng = np.random.default_rng()
+    >>> A = rng.normal(size=(2,3,5))
+    >>> np.all(np.fliplr(A) == A[:,::-1,...])
+    True
+
+    """
+    m = asanyarray(m)
+    if m.ndim < 2:
+        raise ValueError("Input must be >= 2-d.")
+    return m[:, ::-1]
+
+
+@array_function_dispatch(_flip_dispatcher)
+def flipud(m):
+    """
+    Reverse the order of elements along axis 0 (up/down).
+
+    For a 2-D array, this flips the entries in each column in the up/down
+    direction. Rows are preserved, but appear in a different order than before.
+
+    Parameters
+    ----------
+    m : array_like
+        Input array.
+
+    Returns
+    -------
+    out : array_like
+        A view of `m` with the rows reversed.  Since a view is
+        returned, this operation is :math:`\\mathcal O(1)`.
+
+    See Also
+    --------
+    fliplr : Flip array in the left/right direction.
+    flip : Flip array in one or more dimensions.
+    rot90 : Rotate array counterclockwise.
+
+    Notes
+    -----
+    Equivalent to ``m[::-1, ...]`` or ``np.flip(m, axis=0)``.
+    Requires the array to be at least 1-D.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.diag([1.0, 2, 3])
+    >>> A
+    array([[1.,  0.,  0.],
+           [0.,  2.,  0.],
+           [0.,  0.,  3.]])
+    >>> np.flipud(A)
+    array([[0.,  0.,  3.],
+           [0.,  2.,  0.],
+           [1.,  0.,  0.]])
+
+    >>> rng = np.random.default_rng()
+    >>> A = rng.normal(size=(2,3,5))
+    >>> np.all(np.flipud(A) == A[::-1,...])
+    True
+
+    >>> np.flipud([1,2])
+    array([2, 1])
+
+    """
+    m = asanyarray(m)
+    if m.ndim < 1:
+        raise ValueError("Input must be >= 1-d.")
+    return m[::-1, ...]
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def eye(N, M=None, k=0, dtype=float, order='C', *, device=None, like=None):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+      Number of rows in the output.
+    M : int, optional
+      Number of columns in the output. If None, defaults to `N`.
+    k : int, optional
+      Index of the diagonal: 0 (the default) refers to the main diagonal,
+      a positive value refers to an upper diagonal, and a negative value
+      to a lower diagonal.
+    dtype : data-type, optional
+      Data-type of the returned array.
+    order : {'C', 'F'}, optional
+        Whether the output should be stored in row-major (C-style) or
+        column-major (Fortran-style) order in memory.
+    device : str, optional
+        The device on which to place the created array. Default: None.
+        For Array-API interoperability only, so must be ``"cpu"`` if passed.
+
+        .. versionadded:: 2.0.0
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    I : ndarray of shape (N,M)
+      An array where all elements are equal to zero, except for the `k`-th
+      diagonal, whose values are equal to one.
+
+    See Also
+    --------
+    identity : (almost) equivalent function
+    diag : diagonal 2-D array from a 1-D array specified by the user.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.eye(2, dtype=int)
+    array([[1, 0],
+           [0, 1]])
+    >>> np.eye(3, k=1)
+    array([[0.,  1.,  0.],
+           [0.,  0.,  1.],
+           [0.,  0.,  0.]])
+
+    """
+    if like is not None:
+        return _eye_with_like(
+            like, N, M=M, k=k, dtype=dtype, order=order, device=device
+        )
+    if M is None:
+        M = N
+    m = zeros((N, M), dtype=dtype, order=order, device=device)
+    if k >= M:
+        return m
+    # Ensure M and k are integers, so we don't get any surprise casting
+    # results in the expressions `M-k` and `M+1` used below.  This avoids
+    # a problem with inputs with type (for example) np.uint64.
+    M = operator.index(M)
+    k = operator.index(k)
+    if k >= 0:
+        i = k
+    else:
+        i = (-k) * M
+    m[:M-k].flat[i::M+1] = 1
+    return m
+
+
+_eye_with_like = array_function_dispatch()(eye)
+
+
+def _diag_dispatcher(v, k=None):
+    return (v,)
+
+
+@array_function_dispatch(_diag_dispatcher)
+def diag(v, k=0):
+    """
+    Extract a diagonal or construct a diagonal array.
+
+    See the more detailed documentation for ``numpy.diagonal`` if you use this
+    function to extract a diagonal and wish to write to the resulting array;
+    whether it returns a copy or a view depends on what version of numpy you
+    are using.
+
+    Parameters
+    ----------
+    v : array_like
+        If `v` is a 2-D array, return a copy of its `k`-th diagonal.
+        If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th
+        diagonal.
+    k : int, optional
+        Diagonal in question. The default is 0. Use `k>0` for diagonals
+        above the main diagonal, and `k<0` for diagonals below the main
+        diagonal.
+
+    Returns
+    -------
+    out : ndarray
+        The extracted diagonal or constructed diagonal array.
+
+    See Also
+    --------
+    diagonal : Return specified diagonals.
+    diagflat : Create a 2-D array with the flattened input as a diagonal.
+    trace : Sum along diagonals.
+    triu : Upper triangle of an array.
+    tril : Lower triangle of an array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(9).reshape((3,3))
+    >>> x
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+
+    >>> np.diag(x)
+    array([0, 4, 8])
+    >>> np.diag(x, k=1)
+    array([1, 5])
+    >>> np.diag(x, k=-1)
+    array([3, 7])
+
+    >>> np.diag(np.diag(x))
+    array([[0, 0, 0],
+           [0, 4, 0],
+           [0, 0, 8]])
+
+    """
+    v = asanyarray(v)
+    s = v.shape
+    if len(s) == 1:
+        n = s[0]+abs(k)
+        res = zeros((n, n), v.dtype)
+        if k >= 0:
+            i = k
+        else:
+            i = (-k) * n
+        res[:n-k].flat[i::n+1] = v
+        return res
+    elif len(s) == 2:
+        return diagonal(v, k)
+    else:
+        raise ValueError("Input must be 1- or 2-d.")
+
+
+@array_function_dispatch(_diag_dispatcher)
+def diagflat(v, k=0):
+    """
+    Create a two-dimensional array with the flattened input as a diagonal.
+
+    Parameters
+    ----------
+    v : array_like
+        Input data, which is flattened and set as the `k`-th
+        diagonal of the output.
+    k : int, optional
+        Diagonal to set; 0, the default, corresponds to the "main" diagonal,
+        a positive (negative) `k` giving the number of the diagonal above
+        (below) the main.
+
+    Returns
+    -------
+    out : ndarray
+        The 2-D output array.
+
+    See Also
+    --------
+    diag : MATLAB work-alike for 1-D and 2-D arrays.
+    diagonal : Return specified diagonals.
+    trace : Sum along diagonals.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.diagflat([[1,2], [3,4]])
+    array([[1, 0, 0, 0],
+           [0, 2, 0, 0],
+           [0, 0, 3, 0],
+           [0, 0, 0, 4]])
+
+    >>> np.diagflat([1,2], 1)
+    array([[0, 1, 0],
+           [0, 0, 2],
+           [0, 0, 0]])
+
+    """
+    conv = _array_converter(v)
+    v, = conv.as_arrays(subok=False)
+    v = v.ravel()
+    s = len(v)
+    n = s + abs(k)
+    res = zeros((n, n), v.dtype)
+    if (k >= 0):
+        i = arange(0, n-k, dtype=intp)
+        fi = i+k+i*n
+    else:
+        i = arange(0, n+k, dtype=intp)
+        fi = i+(i-k)*n
+    res.flat[fi] = v
+
+    return conv.wrap(res)
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def tri(N, M=None, k=0, dtype=float, *, like=None):
+    """
+    An array with ones at and below the given diagonal and zeros elsewhere.
+
+    Parameters
+    ----------
+    N : int
+        Number of rows in the array.
+    M : int, optional
+        Number of columns in the array.
+        By default, `M` is taken equal to `N`.
+    k : int, optional
+        The sub-diagonal at and below which the array is filled.
+        `k` = 0 is the main diagonal, while `k` < 0 is below it,
+        and `k` > 0 is above.  The default is 0.
+    dtype : dtype, optional
+        Data type of the returned array.  The default is float.
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    tri : ndarray of shape (N, M)
+        Array with its lower triangle filled with ones and zero elsewhere;
+        in other words ``T[i,j] == 1`` for ``j <= i + k``, 0 otherwise.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.tri(3, 5, 2, dtype=int)
+    array([[1, 1, 1, 0, 0],
+           [1, 1, 1, 1, 0],
+           [1, 1, 1, 1, 1]])
+
+    >>> np.tri(3, 5, -1)
+    array([[0.,  0.,  0.,  0.,  0.],
+           [1.,  0.,  0.,  0.,  0.],
+           [1.,  1.,  0.,  0.,  0.]])
+
+    """
+    if like is not None:
+        return _tri_with_like(like, N, M=M, k=k, dtype=dtype)
+
+    if M is None:
+        M = N
+
+    m = greater_equal.outer(arange(N, dtype=_min_int(0, N)),
+                            arange(-k, M-k, dtype=_min_int(-k, M - k)))
+
+    # Avoid making a copy if the requested type is already bool
+    m = m.astype(dtype, copy=False)
+
+    return m
+
+
+_tri_with_like = array_function_dispatch()(tri)
+
+
+def _trilu_dispatcher(m, k=None):
+    return (m,)
+
+
+@array_function_dispatch(_trilu_dispatcher)
+def tril(m, k=0):
+    """
+    Lower triangle of an array.
+
+    Return a copy of an array with elements above the `k`-th diagonal zeroed.
+    For arrays with ``ndim`` exceeding 2, `tril` will apply to the final two
+    axes.
+
+    Parameters
+    ----------
+    m : array_like, shape (..., M, N)
+        Input array.
+    k : int, optional
+        Diagonal above which to zero elements.  `k = 0` (the default) is the
+        main diagonal, `k < 0` is below it and `k > 0` is above.
+
+    Returns
+    -------
+    tril : ndarray, shape (..., M, N)
+        Lower triangle of `m`, of same shape and data-type as `m`.
+
+    See Also
+    --------
+    triu : same thing, only for the upper triangle
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
+    array([[ 0,  0,  0],
+           [ 4,  0,  0],
+           [ 7,  8,  0],
+           [10, 11, 12]])
+
+    >>> np.tril(np.arange(3*4*5).reshape(3, 4, 5))
+    array([[[ 0,  0,  0,  0,  0],
+            [ 5,  6,  0,  0,  0],
+            [10, 11, 12,  0,  0],
+            [15, 16, 17, 18,  0]],
+           [[20,  0,  0,  0,  0],
+            [25, 26,  0,  0,  0],
+            [30, 31, 32,  0,  0],
+            [35, 36, 37, 38,  0]],
+           [[40,  0,  0,  0,  0],
+            [45, 46,  0,  0,  0],
+            [50, 51, 52,  0,  0],
+            [55, 56, 57, 58,  0]]])
+
+    """
+    m = asanyarray(m)
+    mask = tri(*m.shape[-2:], k=k, dtype=bool)
+
+    return where(mask, m, zeros(1, m.dtype))
+
+
+@array_function_dispatch(_trilu_dispatcher)
+def triu(m, k=0):
+    """
+    Upper triangle of an array.
+
+    Return a copy of an array with the elements below the `k`-th diagonal
+    zeroed. For arrays with ``ndim`` exceeding 2, `triu` will apply to the
+    final two axes.
+
+    Please refer to the documentation for `tril` for further details.
+
+    See Also
+    --------
+    tril : lower triangle of an array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
+    array([[ 1,  2,  3],
+           [ 4,  5,  6],
+           [ 0,  8,  9],
+           [ 0,  0, 12]])
+
+    >>> np.triu(np.arange(3*4*5).reshape(3, 4, 5))
+    array([[[ 0,  1,  2,  3,  4],
+            [ 0,  6,  7,  8,  9],
+            [ 0,  0, 12, 13, 14],
+            [ 0,  0,  0, 18, 19]],
+           [[20, 21, 22, 23, 24],
+            [ 0, 26, 27, 28, 29],
+            [ 0,  0, 32, 33, 34],
+            [ 0,  0,  0, 38, 39]],
+           [[40, 41, 42, 43, 44],
+            [ 0, 46, 47, 48, 49],
+            [ 0,  0, 52, 53, 54],
+            [ 0,  0,  0, 58, 59]]])
+
+    """
+    m = asanyarray(m)
+    mask = tri(*m.shape[-2:], k=k-1, dtype=bool)
+
+    return where(mask, zeros(1, m.dtype), m)
+
+
+def _vander_dispatcher(x, N=None, increasing=None):
+    return (x,)
+
+
+# Originally borrowed from John Hunter and matplotlib
+@array_function_dispatch(_vander_dispatcher)
+def vander(x, N=None, increasing=False):
+    """
+    Generate a Vandermonde matrix.
+
+    The columns of the output matrix are powers of the input vector. The
+    order of the powers is determined by the `increasing` boolean argument.
+    Specifically, when `increasing` is False, the `i`-th output column is
+    the input vector raised element-wise to the power of ``N - i - 1``. Such
+    a matrix with a geometric progression in each row is named for Alexandre-
+    Theophile Vandermonde.
+
+    Parameters
+    ----------
+    x : array_like
+        1-D input array.
+    N : int, optional
+        Number of columns in the output.  If `N` is not specified, a square
+        array is returned (``N = len(x)``).
+    increasing : bool, optional
+        Order of the powers of the columns.  If True, the powers increase
+        from left to right, if False (the default) they are reversed.
+
+    Returns
+    -------
+    out : ndarray
+        Vandermonde matrix.  If `increasing` is False, the first column is
+        ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is
+        True, the columns are ``x^0, x^1, ..., x^(N-1)``.
+
+    See Also
+    --------
+    polynomial.polynomial.polyvander
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([1, 2, 3, 5])
+    >>> N = 3
+    >>> np.vander(x, N)
+    array([[ 1,  1,  1],
+           [ 4,  2,  1],
+           [ 9,  3,  1],
+           [25,  5,  1]])
+
+    >>> np.column_stack([x**(N-1-i) for i in range(N)])
+    array([[ 1,  1,  1],
+           [ 4,  2,  1],
+           [ 9,  3,  1],
+           [25,  5,  1]])
+
+    >>> x = np.array([1, 2, 3, 5])
+    >>> np.vander(x)
+    array([[  1,   1,   1,   1],
+           [  8,   4,   2,   1],
+           [ 27,   9,   3,   1],
+           [125,  25,   5,   1]])
+    >>> np.vander(x, increasing=True)
+    array([[  1,   1,   1,   1],
+           [  1,   2,   4,   8],
+           [  1,   3,   9,  27],
+           [  1,   5,  25, 125]])
+
+    The determinant of a square Vandermonde matrix is the product
+    of the differences between the values of the input vector:
+
+    >>> np.linalg.det(np.vander(x))
+    48.000000000000043 # may vary
+    >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
+    48
+
+    """
+    x = asarray(x)
+    if x.ndim != 1:
+        raise ValueError("x must be a one-dimensional array or sequence.")
+    if N is None:
+        N = len(x)
+
+    v = empty((len(x), N), dtype=promote_types(x.dtype, int))
+    tmp = v[:, ::-1] if not increasing else v
+
+    if N > 0:
+        tmp[:, 0] = 1
+    if N > 1:
+        tmp[:, 1:] = x[:, None]
+        multiply.accumulate(tmp[:, 1:], out=tmp[:, 1:], axis=1)
+
+    return v
+
+
+def _histogram2d_dispatcher(x, y, bins=None, range=None, density=None,
+                            weights=None):
+    yield x
+    yield y
+
+    # This terrible logic is adapted from the checks in histogram2d
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+    if N == 2:
+        yield from bins  # bins=[x, y]
+    else:
+        yield bins
+
+    yield weights
+
+
+@array_function_dispatch(_histogram2d_dispatcher)
+def histogram2d(x, y, bins=10, range=None, density=None, weights=None):
+    """
+    Compute the bi-dimensional histogram of two data samples.
+
+    Parameters
+    ----------
+    x : array_like, shape (N,)
+        An array containing the x coordinates of the points to be
+        histogrammed.
+    y : array_like, shape (N,)
+        An array containing the y coordinates of the points to be
+        histogrammed.
+    bins : int or array_like or [int, int] or [array, array], optional
+        The bin specification:
+
+        * If int, the number of bins for the two dimensions (nx=ny=bins).
+        * If array_like, the bin edges for the two dimensions
+          (x_edges=y_edges=bins).
+        * If [int, int], the number of bins in each dimension
+          (nx, ny = bins).
+        * If [array, array], the bin edges in each dimension
+          (x_edges, y_edges = bins).
+        * A combination [int, array] or [array, int], where int
+          is the number of bins and array is the bin edges.
+
+    range : array_like, shape(2,2), optional
+        The leftmost and rightmost edges of the bins along each dimension
+        (if not specified explicitly in the `bins` parameters):
+        ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
+        will be considered outliers and not tallied in the histogram.
+    density : bool, optional
+        If False, the default, returns the number of samples in each bin.
+        If True, returns the probability *density* function at the bin,
+        ``bin_count / sample_count / bin_area``.
+    weights : array_like, shape(N,), optional
+        An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.
+        Weights are normalized to 1 if `density` is True. If `density` is
+        False, the values of the returned histogram are equal to the sum of
+        the weights belonging to the samples falling into each bin.
+
+    Returns
+    -------
+    H : ndarray, shape(nx, ny)
+        The bi-dimensional histogram of samples `x` and `y`. Values in `x`
+        are histogrammed along the first dimension and values in `y` are
+        histogrammed along the second dimension.
+    xedges : ndarray, shape(nx+1,)
+        The bin edges along the first dimension.
+    yedges : ndarray, shape(ny+1,)
+        The bin edges along the second dimension.
+
+    See Also
+    --------
+    histogram : 1D histogram
+    histogramdd : Multidimensional histogram
+
+    Notes
+    -----
+    When `density` is True, then the returned histogram is the sample
+    density, defined such that the sum over bins of the product
+    ``bin_value * bin_area`` is 1.
+
+    Please note that the histogram does not follow the Cartesian convention
+    where `x` values are on the abscissa and `y` values on the ordinate
+    axis.  Rather, `x` is histogrammed along the first dimension of the
+    array (vertical), and `y` along the second dimension of the array
+    (horizontal).  This ensures compatibility with `histogramdd`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from matplotlib.image import NonUniformImage
+    >>> import matplotlib.pyplot as plt
+
+    Construct a 2-D histogram with variable bin width. First define the bin
+    edges:
+
+    >>> xedges = [0, 1, 3, 5]
+    >>> yedges = [0, 2, 3, 4, 6]
+
+    Next we create a histogram H with random bin content:
+
+    >>> x = np.random.normal(2, 1, 100)
+    >>> y = np.random.normal(1, 1, 100)
+    >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
+    >>> # Histogram does not follow Cartesian convention (see Notes),
+    >>> # therefore transpose H for visualization purposes.
+    >>> H = H.T
+
+    :func:`imshow ` can only display square bins:
+
+    >>> fig = plt.figure(figsize=(7, 3))
+    >>> ax = fig.add_subplot(131, title='imshow: square bins')
+    >>> plt.imshow(H, interpolation='nearest', origin='lower',
+    ...         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
+    
+
+    :func:`pcolormesh ` can display actual edges:
+
+    >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',
+    ...         aspect='equal')
+    >>> X, Y = np.meshgrid(xedges, yedges)
+    >>> ax.pcolormesh(X, Y, H)
+    
+
+    :class:`NonUniformImage ` can be used to
+    display actual bin edges with interpolation:
+
+    >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',
+    ...         aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])
+    >>> im = NonUniformImage(ax, interpolation='bilinear')
+    >>> xcenters = (xedges[:-1] + xedges[1:]) / 2
+    >>> ycenters = (yedges[:-1] + yedges[1:]) / 2
+    >>> im.set_data(xcenters, ycenters, H)
+    >>> ax.add_image(im)
+    >>> plt.show()
+
+    It is also possible to construct a 2-D histogram without specifying bin
+    edges:
+
+    >>> # Generate non-symmetric test data
+    >>> n = 10000
+    >>> x = np.linspace(1, 100, n)
+    >>> y = 2*np.log(x) + np.random.rand(n) - 0.5
+    >>> # Compute 2d histogram. Note the order of x/y and xedges/yedges
+    >>> H, yedges, xedges = np.histogram2d(y, x, bins=20)
+
+    Now we can plot the histogram using
+    :func:`pcolormesh `, and a
+    :func:`hexbin ` for comparison.
+
+    >>> # Plot histogram using pcolormesh
+    >>> fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True)
+    >>> ax1.pcolormesh(xedges, yedges, H, cmap='rainbow')
+    >>> ax1.plot(x, 2*np.log(x), 'k-')
+    >>> ax1.set_xlim(x.min(), x.max())
+    >>> ax1.set_ylim(y.min(), y.max())
+    >>> ax1.set_xlabel('x')
+    >>> ax1.set_ylabel('y')
+    >>> ax1.set_title('histogram2d')
+    >>> ax1.grid()
+
+    >>> # Create hexbin plot for comparison
+    >>> ax2.hexbin(x, y, gridsize=20, cmap='rainbow')
+    >>> ax2.plot(x, 2*np.log(x), 'k-')
+    >>> ax2.set_title('hexbin')
+    >>> ax2.set_xlim(x.min(), x.max())
+    >>> ax2.set_xlabel('x')
+    >>> ax2.grid()
+
+    >>> plt.show()
+    """
+    from numpy import histogramdd
+
+    if len(x) != len(y):
+        raise ValueError('x and y must have the same length.')
+
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+
+    if N != 1 and N != 2:
+        xedges = yedges = asarray(bins)
+        bins = [xedges, yedges]
+    hist, edges = histogramdd([x, y], bins, range, density, weights)
+    return hist, edges[0], edges[1]
+
+
+@set_module('numpy')
+def mask_indices(n, mask_func, k=0):
+    """
+    Return the indices to access (n, n) arrays, given a masking function.
+
+    Assume `mask_func` is a function that, for a square array a of size
+    ``(n, n)`` with a possible offset argument `k`, when called as
+    ``mask_func(a, k)`` returns a new array with zeros in certain locations
+    (functions like `triu` or `tril` do precisely this). Then this function
+    returns the indices where the non-zero values would be located.
+
+    Parameters
+    ----------
+    n : int
+        The returned indices will be valid to access arrays of shape (n, n).
+    mask_func : callable
+        A function whose call signature is similar to that of `triu`, `tril`.
+        That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.
+        `k` is an optional argument to the function.
+    k : scalar
+        An optional argument which is passed through to `mask_func`. Functions
+        like `triu`, `tril` take a second argument that is interpreted as an
+        offset.
+
+    Returns
+    -------
+    indices : tuple of arrays.
+        The `n` arrays of indices corresponding to the locations where
+        ``mask_func(np.ones((n, n)), k)`` is True.
+
+    See Also
+    --------
+    triu, tril, triu_indices, tril_indices
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    These are the indices that would allow you to access the upper triangular
+    part of any 3x3 array:
+
+    >>> iu = np.mask_indices(3, np.triu)
+
+    For example, if `a` is a 3x3 array:
+
+    >>> a = np.arange(9).reshape(3, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> a[iu]
+    array([0, 1, 2, 4, 5, 8])
+
+    An offset can be passed also to the masking function.  This gets us the
+    indices starting on the first diagonal right of the main one:
+
+    >>> iu1 = np.mask_indices(3, np.triu, 1)
+
+    with which we now extract only three elements:
+
+    >>> a[iu1]
+    array([1, 2, 5])
+
+    """
+    m = ones((n, n), int)
+    a = mask_func(m, k)
+    return nonzero(a != 0)
+
+
+@set_module('numpy')
+def tril_indices(n, k=0, m=None):
+    """
+    Return the indices for the lower-triangle of an (n, m) array.
+
+    Parameters
+    ----------
+    n : int
+        The row dimension of the arrays for which the returned
+        indices will be valid.
+    k : int, optional
+        Diagonal offset (see `tril` for details).
+    m : int, optional
+        The column dimension of the arrays for which the returned
+        arrays will be valid.
+        By default `m` is taken equal to `n`.
+
+
+    Returns
+    -------
+    inds : tuple of arrays
+        The row and column indices, respectively. The row indices are sorted
+        in non-decreasing order, and the correspdonding column indices are
+        strictly increasing for each row.
+
+    See also
+    --------
+    triu_indices : similar function, for upper-triangular.
+    mask_indices : generic function accepting an arbitrary mask function.
+    tril, triu
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Compute two different sets of indices to access 4x4 arrays, one for the
+    lower triangular part starting at the main diagonal, and one starting two
+    diagonals further right:
+
+    >>> il1 = np.tril_indices(4)
+    >>> il1
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    Note that row indices (first array) are non-decreasing, and the corresponding
+    column indices (second array) are strictly increasing for each row.
+    Here is how they can be used with a sample array:
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Both for indexing:
+
+    >>> a[il1]
+    array([ 0,  4,  5, ..., 13, 14, 15])
+
+    And for assigning values:
+
+    >>> a[il1] = -1
+    >>> a
+    array([[-1,  1,  2,  3],
+           [-1, -1,  6,  7],
+           [-1, -1, -1, 11],
+           [-1, -1, -1, -1]])
+
+    These cover almost the whole array (two diagonals right of the main one):
+
+    >>> il2 = np.tril_indices(4, 2)
+    >>> a[il2] = -10
+    >>> a
+    array([[-10, -10, -10,   3],
+           [-10, -10, -10, -10],
+           [-10, -10, -10, -10],
+           [-10, -10, -10, -10]])
+
+    """
+    tri_ = tri(n, m, k=k, dtype=bool)
+
+    return tuple(broadcast_to(inds, tri_.shape)[tri_]
+                 for inds in indices(tri_.shape, sparse=True))
+
+
+def _trilu_indices_form_dispatcher(arr, k=None):
+    return (arr,)
+
+
+@array_function_dispatch(_trilu_indices_form_dispatcher)
+def tril_indices_from(arr, k=0):
+    """
+    Return the indices for the lower-triangle of arr.
+
+    See `tril_indices` for full details.
+
+    Parameters
+    ----------
+    arr : array_like
+        The indices will be valid for square arrays whose dimensions are
+        the same as arr.
+    k : int, optional
+        Diagonal offset (see `tril` for details).
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create a 4 by 4 array
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the lower triangular elements.
+
+    >>> trili = np.tril_indices_from(a)
+    >>> trili
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    >>> a[trili]
+    array([ 0,  4,  5,  8,  9, 10, 12, 13, 14, 15])
+
+    This is syntactic sugar for tril_indices().
+
+    >>> np.tril_indices(a.shape[0])
+    (array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3]), array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3]))
+
+    Use the `k` parameter to return the indices for the lower triangular array
+    up to the k-th diagonal.
+
+    >>> trili1 = np.tril_indices_from(a, k=1)
+    >>> a[trili1]
+    array([ 0,  1,  4,  5,  6,  8,  9, 10, 11, 12, 13, 14, 15])
+
+    See Also
+    --------
+    tril_indices, tril, triu_indices_from
+    """
+    if arr.ndim != 2:
+        raise ValueError("input array must be 2-d")
+    return tril_indices(arr.shape[-2], k=k, m=arr.shape[-1])
+
+
+@set_module('numpy')
+def triu_indices(n, k=0, m=None):
+    """
+    Return the indices for the upper-triangle of an (n, m) array.
+
+    Parameters
+    ----------
+    n : int
+        The size of the arrays for which the returned indices will
+        be valid.
+    k : int, optional
+        Diagonal offset (see `triu` for details).
+    m : int, optional
+        The column dimension of the arrays for which the returned
+        arrays will be valid.
+        By default `m` is taken equal to `n`.
+
+
+    Returns
+    -------
+    inds : tuple, shape(2) of ndarrays, shape(`n`)
+        The row and column indices, respectively. The row indices are sorted
+        in non-decreasing order, and the correspdonding column indices are
+        strictly increasing for each row.
+
+    See also
+    --------
+    tril_indices : similar function, for lower-triangular.
+    mask_indices : generic function accepting an arbitrary mask function.
+    triu, tril
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Compute two different sets of indices to access 4x4 arrays, one for the
+    upper triangular part starting at the main diagonal, and one starting two
+    diagonals further right:
+
+    >>> iu1 = np.triu_indices(4)
+    >>> iu1
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    Note that row indices (first array) are non-decreasing, and the corresponding
+    column indices (second array) are strictly increasing for each row.
+
+    Here is how they can be used with a sample array:
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Both for indexing:
+
+    >>> a[iu1]
+    array([ 0,  1,  2, ..., 10, 11, 15])
+
+    And for assigning values:
+
+    >>> a[iu1] = -1
+    >>> a
+    array([[-1, -1, -1, -1],
+           [ 4, -1, -1, -1],
+           [ 8,  9, -1, -1],
+           [12, 13, 14, -1]])
+
+    These cover only a small part of the whole array (two diagonals right
+    of the main one):
+
+    >>> iu2 = np.triu_indices(4, 2)
+    >>> a[iu2] = -10
+    >>> a
+    array([[ -1,  -1, -10, -10],
+           [  4,  -1,  -1, -10],
+           [  8,   9,  -1,  -1],
+           [ 12,  13,  14,  -1]])
+
+    """
+    tri_ = ~tri(n, m, k=k - 1, dtype=bool)
+
+    return tuple(broadcast_to(inds, tri_.shape)[tri_]
+                 for inds in indices(tri_.shape, sparse=True))
+
+
+@array_function_dispatch(_trilu_indices_form_dispatcher)
+def triu_indices_from(arr, k=0):
+    """
+    Return the indices for the upper-triangle of arr.
+
+    See `triu_indices` for full details.
+
+    Parameters
+    ----------
+    arr : ndarray, shape(N, N)
+        The indices will be valid for square arrays.
+    k : int, optional
+        Diagonal offset (see `triu` for details).
+
+    Returns
+    -------
+    triu_indices_from : tuple, shape(2) of ndarray, shape(N)
+        Indices for the upper-triangle of `arr`.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create a 4 by 4 array
+
+    >>> a = np.arange(16).reshape(4, 4)
+    >>> a
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+
+    Pass the array to get the indices of the upper triangular elements.
+
+    >>> triui = np.triu_indices_from(a)
+    >>> triui
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    >>> a[triui]
+    array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
+
+    This is syntactic sugar for triu_indices().
+
+    >>> np.triu_indices(a.shape[0])
+    (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([0, 1, 2, 3, 1, 2, 3, 2, 3, 3]))
+
+    Use the `k` parameter to return the indices for the upper triangular array
+    from the k-th diagonal.
+
+    >>> triuim1 = np.triu_indices_from(a, k=1)
+    >>> a[triuim1]
+    array([ 1,  2,  3,  6,  7, 11])
+
+
+    See Also
+    --------
+    triu_indices, triu, tril_indices_from
+    """
+    if arr.ndim != 2:
+        raise ValueError("input array must be 2-d")
+    return triu_indices(arr.shape[-2], k=k, m=arr.shape[-1])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5d3ea54511b8c54efaa47ae4b7ecb71cd53b394a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_twodim_base_impl.pyi
@@ -0,0 +1,437 @@
+from collections.abc import Callable, Sequence
+from typing import (
+    Any,
+    TypeAlias,
+    overload,
+    TypeVar,
+    Literal as L,
+)
+
+import numpy as np
+from numpy import (
+    generic,
+    timedelta64,
+    datetime64,
+    int_,
+    intp,
+    float64,
+    complex128,
+    signedinteger,
+    floating,
+    complexfloating,
+    object_,
+    _OrderCF,
+)
+
+from numpy._typing import (
+    DTypeLike,
+    _DTypeLike,
+    ArrayLike,
+    _ArrayLike,
+    NDArray,
+    _SupportsArray,
+    _SupportsArrayFunc,
+    _ArrayLikeInt_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeObject_co,
+)
+
+__all__ = [
+    "diag",
+    "diagflat",
+    "eye",
+    "fliplr",
+    "flipud",
+    "tri",
+    "triu",
+    "tril",
+    "vander",
+    "histogram2d",
+    "mask_indices",
+    "tril_indices",
+    "tril_indices_from",
+    "triu_indices",
+    "triu_indices_from",
+]
+
+###
+
+_T = TypeVar("_T")
+_SCT = TypeVar("_SCT", bound=generic)
+_SCT_complex = TypeVar("_SCT_complex", bound=np.complexfloating)
+_SCT_inexact = TypeVar("_SCT_inexact", bound=np.inexact)
+_SCT_number_co = TypeVar("_SCT_number_co", bound=_Number_co)
+
+# The returned arrays dtype must be compatible with `np.equal`
+_MaskFunc: TypeAlias = Callable[[NDArray[int_], _T], NDArray[_Number_co | timedelta64 | datetime64 | object_]]
+
+_Int_co: TypeAlias = np.integer | np.bool
+_Float_co: TypeAlias = np.floating | _Int_co
+_Number_co: TypeAlias = np.number | np.bool
+
+_ArrayLike1D: TypeAlias = _SupportsArray[np.dtype[_SCT]] | Sequence[_SCT]
+_ArrayLike1DInt_co: TypeAlias = _SupportsArray[np.dtype[_Int_co]] | Sequence[int | _Int_co]
+_ArrayLike1DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[float | _Float_co]
+_ArrayLike2DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[_ArrayLike1DFloat_co]
+_ArrayLike1DNumber_co: TypeAlias = _SupportsArray[np.dtype[_Number_co]] | Sequence[complex | _Number_co]
+
+###
+
+@overload
+def fliplr(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
+@overload
+def fliplr(m: ArrayLike) -> NDArray[Any]: ...
+
+@overload
+def flipud(m: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
+@overload
+def flipud(m: ArrayLike) -> NDArray[Any]: ...
+
+@overload
+def eye(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    dtype: None = ...,
+    order: _OrderCF = ...,
+    *,
+    device: None | L["cpu"] = ...,
+    like: None | _SupportsArrayFunc = ...,
+) -> NDArray[float64]: ...
+@overload
+def eye(
+    N: int,
+    M: None | int,
+    k: int,
+    dtype: _DTypeLike[_SCT],
+    order: _OrderCF = ...,
+    *,
+    device: None | L["cpu"] = ...,
+    like: None | _SupportsArrayFunc = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def eye(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    *,
+    dtype: _DTypeLike[_SCT],
+    order: _OrderCF = ...,
+    device: None | L["cpu"] = ...,
+    like: None | _SupportsArrayFunc = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def eye(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    dtype: DTypeLike = ...,
+    order: _OrderCF = ...,
+    *,
+    device: None | L["cpu"] = ...,
+    like: None | _SupportsArrayFunc = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def diag(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
+@overload
+def diag(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
+
+@overload
+def diagflat(v: _ArrayLike[_SCT], k: int = ...) -> NDArray[_SCT]: ...
+@overload
+def diagflat(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
+
+@overload
+def tri(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    dtype: None = ...,
+    *,
+    like: None | _SupportsArrayFunc = ...
+) -> NDArray[float64]: ...
+@overload
+def tri(
+    N: int,
+    M: None | int,
+    k: int,
+    dtype: _DTypeLike[_SCT],
+    *,
+    like: None | _SupportsArrayFunc = ...
+) -> NDArray[_SCT]: ...
+@overload
+def tri(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    *,
+    dtype: _DTypeLike[_SCT],
+    like: None | _SupportsArrayFunc = ...
+) -> NDArray[_SCT]: ...
+@overload
+def tri(
+    N: int,
+    M: None | int = ...,
+    k: int = ...,
+    dtype: DTypeLike = ...,
+    *,
+    like: None | _SupportsArrayFunc = ...
+) -> NDArray[Any]: ...
+
+@overload
+def tril(m: _ArrayLike[_SCT], k: int = 0) -> NDArray[_SCT]: ...
+@overload
+def tril(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
+
+@overload
+def triu(m: _ArrayLike[_SCT], k: int = 0) -> NDArray[_SCT]: ...
+@overload
+def triu(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
+
+@overload
+def vander(  # type: ignore[misc]
+    x: _ArrayLikeInt_co,
+    N: None | int = ...,
+    increasing: bool = ...,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def vander(  # type: ignore[misc]
+    x: _ArrayLikeFloat_co,
+    N: None | int = ...,
+    increasing: bool = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def vander(
+    x: _ArrayLikeComplex_co,
+    N: None | int = ...,
+    increasing: bool = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def vander(
+    x: _ArrayLikeObject_co,
+    N: None | int = ...,
+    increasing: bool = ...,
+) -> NDArray[object_]: ...
+
+@overload
+def histogram2d(
+    x: _ArrayLike1D[_SCT_complex],
+    y: _ArrayLike1D[_SCT_complex | _Float_co],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_complex],
+    NDArray[_SCT_complex],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1D[_SCT_complex | _Float_co],
+    y: _ArrayLike1D[_SCT_complex],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_complex],
+    NDArray[_SCT_complex],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1D[_SCT_inexact],
+    y: _ArrayLike1D[_SCT_inexact | _Int_co],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_inexact],
+    NDArray[_SCT_inexact],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1D[_SCT_inexact | _Int_co],
+    y: _ArrayLike1D[_SCT_inexact],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_inexact],
+    NDArray[_SCT_inexact],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DInt_co | Sequence[float | int],
+    y: _ArrayLike1DInt_co | Sequence[float | int],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[float64],
+    NDArray[float64],
+]: ...
+@overload
+def histogram2d(
+    x: Sequence[complex | float | int],
+    y: Sequence[complex | float | int],
+    bins: int | Sequence[int] = ...,
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[complex128 | float64],
+    NDArray[complex128 | float64],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DNumber_co,
+    y: _ArrayLike1DNumber_co,
+    bins: _ArrayLike1D[_SCT_number_co] | Sequence[_ArrayLike1D[_SCT_number_co]],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_number_co],
+    NDArray[_SCT_number_co],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1D[_SCT_inexact],
+    y: _ArrayLike1D[_SCT_inexact],
+    bins: Sequence[_ArrayLike1D[_SCT_number_co] | int],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_number_co | _SCT_inexact],
+    NDArray[_SCT_number_co | _SCT_inexact],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DInt_co | Sequence[float | int],
+    y: _ArrayLike1DInt_co | Sequence[float | int],
+    bins: Sequence[_ArrayLike1D[_SCT_number_co] | int],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_number_co | float64],
+    NDArray[_SCT_number_co | float64],
+]: ...
+@overload
+def histogram2d(
+    x: Sequence[complex | float | int],
+    y: Sequence[complex | float | int],
+    bins: Sequence[_ArrayLike1D[_SCT_number_co] | int],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[_SCT_number_co | complex128 | float64],
+    NDArray[_SCT_number_co | complex128 | float64] ,
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DNumber_co,
+    y: _ArrayLike1DNumber_co,
+    bins: Sequence[Sequence[bool]],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[np.bool],
+    NDArray[np.bool],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DNumber_co,
+    y: _ArrayLike1DNumber_co,
+    bins: Sequence[Sequence[int | bool]],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[np.int_ | np.bool],
+    NDArray[np.int_ | np.bool],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DNumber_co,
+    y: _ArrayLike1DNumber_co,
+    bins: Sequence[Sequence[float | int | bool]],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[np.float64 | np.int_ | np.bool],
+    NDArray[np.float64 | np.int_ | np.bool],
+]: ...
+@overload
+def histogram2d(
+    x: _ArrayLike1DNumber_co,
+    y: _ArrayLike1DNumber_co,
+    bins: Sequence[Sequence[complex | float | int | bool]],
+    range: None | _ArrayLike2DFloat_co = ...,
+    density: None | bool = ...,
+    weights: None | _ArrayLike1DFloat_co = ...,
+) -> tuple[
+    NDArray[float64],
+    NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
+    NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
+]: ...
+
+# NOTE: we're assuming/demanding here the `mask_func` returns
+# an ndarray of shape `(n, n)`; otherwise there is the possibility
+# of the output tuple having more or less than 2 elements
+@overload
+def mask_indices(
+    n: int,
+    mask_func: _MaskFunc[int],
+    k: int = ...,
+) -> tuple[NDArray[intp], NDArray[intp]]: ...
+@overload
+def mask_indices(
+    n: int,
+    mask_func: _MaskFunc[_T],
+    k: _T,
+) -> tuple[NDArray[intp], NDArray[intp]]: ...
+
+def tril_indices(
+    n: int,
+    k: int = ...,
+    m: None | int = ...,
+) -> tuple[NDArray[int_], NDArray[int_]]: ...
+
+def tril_indices_from(
+    arr: NDArray[Any],
+    k: int = ...,
+) -> tuple[NDArray[int_], NDArray[int_]]: ...
+
+def triu_indices(
+    n: int,
+    k: int = ...,
+    m: None | int = ...,
+) -> tuple[NDArray[int_], NDArray[int_]]: ...
+
+def triu_indices_from(
+    arr: NDArray[Any],
+    k: int = ...,
+) -> tuple[NDArray[int_], NDArray[int_]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5c9ffbbb8d41049c17f65d56f5afe23a5d5bbcd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.py
@@ -0,0 +1,699 @@
+"""Automatically adapted for numpy Sep 19, 2005 by convertcode.py
+
+"""
+import functools
+
+__all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex',
+           'isreal', 'nan_to_num', 'real', 'real_if_close',
+           'typename', 'mintypecode',
+           'common_type']
+
+from .._utils import set_module
+import numpy._core.numeric as _nx
+from numpy._core.numeric import asarray, asanyarray, isnan, zeros
+from numpy._core import overrides, getlimits
+from ._ufunclike_impl import isneginf, isposinf
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+_typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?'
+
+
+@set_module('numpy')
+def mintypecode(typechars, typeset='GDFgdf', default='d'):
+    """
+    Return the character for the minimum-size type to which given types can
+    be safely cast.
+
+    The returned type character must represent the smallest size dtype such
+    that an array of the returned type can handle the data from an array of
+    all types in `typechars` (or if `typechars` is an array, then its
+    dtype.char).
+
+    Parameters
+    ----------
+    typechars : list of str or array_like
+        If a list of strings, each string should represent a dtype.
+        If array_like, the character representation of the array dtype is used.
+    typeset : str or list of str, optional
+        The set of characters that the returned character is chosen from.
+        The default set is 'GDFgdf'.
+    default : str, optional
+        The default character, this is returned if none of the characters in
+        `typechars` matches a character in `typeset`.
+
+    Returns
+    -------
+    typechar : str
+        The character representing the minimum-size type that was found.
+
+    See Also
+    --------
+    dtype
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.mintypecode(['d', 'f', 'S'])
+    'd'
+    >>> x = np.array([1.1, 2-3.j])
+    >>> np.mintypecode(x)
+    'D'
+
+    >>> np.mintypecode('abceh', default='G')
+    'G'
+
+    """
+    typecodes = ((isinstance(t, str) and t) or asarray(t).dtype.char
+                 for t in typechars)
+    intersection = set(t for t in typecodes if t in typeset)
+    if not intersection:
+        return default
+    if 'F' in intersection and 'd' in intersection:
+        return 'D'
+    return min(intersection, key=_typecodes_by_elsize.index)
+
+
+def _real_dispatcher(val):
+    return (val,)
+
+
+@array_function_dispatch(_real_dispatcher)
+def real(val):
+    """
+    Return the real part of the complex argument.
+
+    Parameters
+    ----------
+    val : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The real component of the complex argument. If `val` is real, the type
+        of `val` is used for the output.  If `val` has complex elements, the
+        returned type is float.
+
+    See Also
+    --------
+    real_if_close, imag, angle
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1+2j, 3+4j, 5+6j])
+    >>> a.real
+    array([1.,  3.,  5.])
+    >>> a.real = 9
+    >>> a
+    array([9.+2.j,  9.+4.j,  9.+6.j])
+    >>> a.real = np.array([9, 8, 7])
+    >>> a
+    array([9.+2.j,  8.+4.j,  7.+6.j])
+    >>> np.real(1 + 1j)
+    1.0
+
+    """
+    try:
+        return val.real
+    except AttributeError:
+        return asanyarray(val).real
+
+
+def _imag_dispatcher(val):
+    return (val,)
+
+
+@array_function_dispatch(_imag_dispatcher)
+def imag(val):
+    """
+    Return the imaginary part of the complex argument.
+
+    Parameters
+    ----------
+    val : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        The imaginary component of the complex argument. If `val` is real,
+        the type of `val` is used for the output.  If `val` has complex
+        elements, the returned type is float.
+
+    See Also
+    --------
+    real, angle, real_if_close
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1+2j, 3+4j, 5+6j])
+    >>> a.imag
+    array([2.,  4.,  6.])
+    >>> a.imag = np.array([8, 10, 12])
+    >>> a
+    array([1. +8.j,  3.+10.j,  5.+12.j])
+    >>> np.imag(1 + 1j)
+    1.0
+
+    """
+    try:
+        return val.imag
+    except AttributeError:
+        return asanyarray(val).imag
+
+
+def _is_type_dispatcher(x):
+    return (x,)
+
+
+@array_function_dispatch(_is_type_dispatcher)
+def iscomplex(x):
+    """
+    Returns a bool array, where True if input element is complex.
+
+    What is tested is whether the input has a non-zero imaginary part, not if
+    the input type is complex.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray of bools
+        Output array.
+
+    See Also
+    --------
+    isreal
+    iscomplexobj : Return True if x is a complex type or an array of complex
+                   numbers.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
+    array([ True, False, False, False, False,  True])
+
+    """
+    ax = asanyarray(x)
+    if issubclass(ax.dtype.type, _nx.complexfloating):
+        return ax.imag != 0
+    res = zeros(ax.shape, bool)
+    return res[()]   # convert to scalar if needed
+
+
+@array_function_dispatch(_is_type_dispatcher)
+def isreal(x):
+    """
+    Returns a bool array, where True if input element is real.
+
+    If element has complex type with zero imaginary part, the return value
+    for that element is True.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+
+    Returns
+    -------
+    out : ndarray, bool
+        Boolean array of same shape as `x`.
+
+    Notes
+    -----
+    `isreal` may behave unexpectedly for string or object arrays (see examples)
+
+    See Also
+    --------
+    iscomplex
+    isrealobj : Return True if x is not a complex type.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=complex)
+    >>> np.isreal(a)
+    array([False,  True,  True,  True,  True, False])
+
+    The function does not work on string arrays.
+
+    >>> a = np.array([2j, "a"], dtype="U")
+    >>> np.isreal(a)  # Warns about non-elementwise comparison
+    False
+
+    Returns True for all elements in input array of ``dtype=object`` even if
+    any of the elements is complex.
+
+    >>> a = np.array([1, "2", 3+4j], dtype=object)
+    >>> np.isreal(a)
+    array([ True,  True,  True])
+
+    isreal should not be used with object arrays
+
+    >>> a = np.array([1+2j, 2+1j], dtype=object)
+    >>> np.isreal(a)
+    array([ True,  True])
+
+    """
+    return imag(x) == 0
+
+
+@array_function_dispatch(_is_type_dispatcher)
+def iscomplexobj(x):
+    """
+    Check for a complex type or an array of complex numbers.
+
+    The type of the input is checked, not the value. Even if the input
+    has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
+
+    Parameters
+    ----------
+    x : any
+        The input can be of any type and shape.
+
+    Returns
+    -------
+    iscomplexobj : bool
+        The return value, True if `x` is of a complex type or has at least
+        one complex element.
+
+    See Also
+    --------
+    isrealobj, iscomplex
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.iscomplexobj(1)
+    False
+    >>> np.iscomplexobj(1+0j)
+    True
+    >>> np.iscomplexobj([3, 1+0j, True])
+    True
+
+    """
+    try:
+        dtype = x.dtype
+        type_ = dtype.type
+    except AttributeError:
+        type_ = asarray(x).dtype.type
+    return issubclass(type_, _nx.complexfloating)
+
+
+@array_function_dispatch(_is_type_dispatcher)
+def isrealobj(x):
+    """
+    Return True if x is a not complex type or an array of complex numbers.
+
+    The type of the input is checked, not the value. So even if the input
+    has an imaginary part equal to zero, `isrealobj` evaluates to False
+    if the data type is complex.
+
+    Parameters
+    ----------
+    x : any
+        The input can be of any type and shape.
+
+    Returns
+    -------
+    y : bool
+        The return value, False if `x` is of a complex type.
+
+    See Also
+    --------
+    iscomplexobj, isreal
+
+    Notes
+    -----
+    The function is only meant for arrays with numerical values but it
+    accepts all other objects. Since it assumes array input, the return
+    value of other objects may be True.
+
+    >>> np.isrealobj('A string')
+    True
+    >>> np.isrealobj(False)
+    True
+    >>> np.isrealobj(None)
+    True
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.isrealobj(1)
+    True
+    >>> np.isrealobj(1+0j)
+    False
+    >>> np.isrealobj([3, 1+0j, True])
+    False
+
+    """
+    return not iscomplexobj(x)
+
+#-----------------------------------------------------------------------------
+
+def _getmaxmin(t):
+    from numpy._core import getlimits
+    f = getlimits.finfo(t)
+    return f.max, f.min
+
+
+def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, neginf=None):
+    return (x,)
+
+
+@array_function_dispatch(_nan_to_num_dispatcher)
+def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
+    """
+    Replace NaN with zero and infinity with large finite numbers (default
+    behaviour) or with the numbers defined by the user using the `nan`,
+    `posinf` and/or `neginf` keywords.
+
+    If `x` is inexact, NaN is replaced by zero or by the user defined value in
+    `nan` keyword, infinity is replaced by the largest finite floating point
+    values representable by ``x.dtype`` or by the user defined value in
+    `posinf` keyword and -infinity is replaced by the most negative finite
+    floating point values representable by ``x.dtype`` or by the user defined
+    value in `neginf` keyword.
+
+    For complex dtypes, the above is applied to each of the real and
+    imaginary components of `x` separately.
+
+    If `x` is not inexact, then no replacements are made.
+
+    Parameters
+    ----------
+    x : scalar or array_like
+        Input data.
+    copy : bool, optional
+        Whether to create a copy of `x` (True) or to replace values
+        in-place (False). The in-place operation only occurs if
+        casting to an array does not require a copy.
+        Default is True.
+    nan : int, float, optional
+        Value to be used to fill NaN values. If no value is passed
+        then NaN values will be replaced with 0.0.
+    posinf : int, float, optional
+        Value to be used to fill positive infinity values. If no value is
+        passed then positive infinity values will be replaced with a very
+        large number.
+    neginf : int, float, optional
+        Value to be used to fill negative infinity values. If no value is
+        passed then negative infinity values will be replaced with a very
+        small (or negative) number.
+
+    Returns
+    -------
+    out : ndarray
+        `x`, with the non-finite values replaced. If `copy` is False, this may
+        be `x` itself.
+
+    See Also
+    --------
+    isinf : Shows which elements are positive or negative infinity.
+    isneginf : Shows which elements are negative infinity.
+    isposinf : Shows which elements are positive infinity.
+    isnan : Shows which elements are Not a Number (NaN).
+    isfinite : Shows which elements are finite (not NaN, not infinity)
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754). This means that Not a Number is not equivalent to infinity.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.nan_to_num(np.inf)
+    1.7976931348623157e+308
+    >>> np.nan_to_num(-np.inf)
+    -1.7976931348623157e+308
+    >>> np.nan_to_num(np.nan)
+    0.0
+    >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
+    >>> np.nan_to_num(x)
+    array([ 1.79769313e+308, -1.79769313e+308,  0.00000000e+000, # may vary
+           -1.28000000e+002,  1.28000000e+002])
+    >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
+    array([ 3.3333333e+07,  3.3333333e+07, -9.9990000e+03,
+           -1.2800000e+02,  1.2800000e+02])
+    >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
+    array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000, # may vary
+         -1.28000000e+002,   1.28000000e+002])
+    >>> np.nan_to_num(y)
+    array([  1.79769313e+308 +0.00000000e+000j, # may vary
+             0.00000000e+000 +0.00000000e+000j,
+             0.00000000e+000 +1.79769313e+308j])
+    >>> np.nan_to_num(y, nan=111111, posinf=222222)
+    array([222222.+111111.j, 111111.     +0.j, 111111.+222222.j])
+    """
+    x = _nx.array(x, subok=True, copy=copy)
+    xtype = x.dtype.type
+
+    isscalar = (x.ndim == 0)
+
+    if not issubclass(xtype, _nx.inexact):
+        return x[()] if isscalar else x
+
+    iscomplex = issubclass(xtype, _nx.complexfloating)
+
+    dest = (x.real, x.imag) if iscomplex else (x,)
+    maxf, minf = _getmaxmin(x.real.dtype)
+    if posinf is not None:
+        maxf = posinf
+    if neginf is not None:
+        minf = neginf
+    for d in dest:
+        idx_nan = isnan(d)
+        idx_posinf = isposinf(d)
+        idx_neginf = isneginf(d)
+        _nx.copyto(d, nan, where=idx_nan)
+        _nx.copyto(d, maxf, where=idx_posinf)
+        _nx.copyto(d, minf, where=idx_neginf)
+    return x[()] if isscalar else x
+
+#-----------------------------------------------------------------------------
+
+def _real_if_close_dispatcher(a, tol=None):
+    return (a,)
+
+
+@array_function_dispatch(_real_if_close_dispatcher)
+def real_if_close(a, tol=100):
+    """
+    If input is complex with all imaginary parts close to zero, return
+    real parts.
+
+    "Close to zero" is defined as `tol` * (machine epsilon of the type for
+    `a`).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    tol : float
+        Tolerance in machine epsilons for the complex part of the elements
+        in the array. If the tolerance is <=1, then the absolute tolerance
+        is used.
+
+    Returns
+    -------
+    out : ndarray
+        If `a` is real, the type of `a` is used for the output.  If `a`
+        has complex elements, the returned type is float.
+
+    See Also
+    --------
+    real, imag, angle
+
+    Notes
+    -----
+    Machine epsilon varies from machine to machine and between data types
+    but Python floats on most platforms have a machine epsilon equal to
+    2.2204460492503131e-16.  You can use 'np.finfo(float).eps' to print
+    out the machine epsilon for floats.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.finfo(float).eps
+    2.2204460492503131e-16 # may vary
+
+    >>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000)
+    array([2.1, 5.2])
+    >>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000)
+    array([2.1+4.e-13j, 5.2 + 3e-15j])
+
+    """
+    a = asanyarray(a)
+    type_ = a.dtype.type
+    if not issubclass(type_, _nx.complexfloating):
+        return a
+    if tol > 1:
+        f = getlimits.finfo(type_)
+        tol = f.eps * tol
+    if _nx.all(_nx.absolute(a.imag) < tol):
+        a = a.real
+    return a
+
+
+#-----------------------------------------------------------------------------
+
+_namefromtype = {'S1': 'character',
+                 '?': 'bool',
+                 'b': 'signed char',
+                 'B': 'unsigned char',
+                 'h': 'short',
+                 'H': 'unsigned short',
+                 'i': 'integer',
+                 'I': 'unsigned integer',
+                 'l': 'long integer',
+                 'L': 'unsigned long integer',
+                 'q': 'long long integer',
+                 'Q': 'unsigned long long integer',
+                 'f': 'single precision',
+                 'd': 'double precision',
+                 'g': 'long precision',
+                 'F': 'complex single precision',
+                 'D': 'complex double precision',
+                 'G': 'complex long double precision',
+                 'S': 'string',
+                 'U': 'unicode',
+                 'V': 'void',
+                 'O': 'object'
+                 }
+
+@set_module('numpy')
+def typename(char):
+    """
+    Return a description for the given data type code.
+
+    Parameters
+    ----------
+    char : str
+        Data type code.
+
+    Returns
+    -------
+    out : str
+        Description of the input data type code.
+
+    See Also
+    --------
+    dtype
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
+    ...              'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
+    >>> for typechar in typechars:
+    ...     print(typechar, ' : ', np.typename(typechar))
+    ...
+    S1  :  character
+    ?  :  bool
+    B  :  unsigned char
+    D  :  complex double precision
+    G  :  complex long double precision
+    F  :  complex single precision
+    I  :  unsigned integer
+    H  :  unsigned short
+    L  :  unsigned long integer
+    O  :  object
+    Q  :  unsigned long long integer
+    S  :  string
+    U  :  unicode
+    V  :  void
+    b  :  signed char
+    d  :  double precision
+    g  :  long precision
+    f  :  single precision
+    i  :  integer
+    h  :  short
+    l  :  long integer
+    q  :  long long integer
+
+    """
+    return _namefromtype[char]
+
+#-----------------------------------------------------------------------------
+
+
+#determine the "minimum common type" for a group of arrays.
+array_type = [[_nx.float16, _nx.float32, _nx.float64, _nx.longdouble],
+              [None, _nx.complex64, _nx.complex128, _nx.clongdouble]]
+array_precision = {_nx.float16: 0,
+                   _nx.float32: 1,
+                   _nx.float64: 2,
+                   _nx.longdouble: 3,
+                   _nx.complex64: 1,
+                   _nx.complex128: 2,
+                   _nx.clongdouble: 3}
+
+
+def _common_type_dispatcher(*arrays):
+    return arrays
+
+
+@array_function_dispatch(_common_type_dispatcher)
+def common_type(*arrays):
+    """
+    Return a scalar type which is common to the input arrays.
+
+    The return type will always be an inexact (i.e. floating point) scalar
+    type, even if all the arrays are integer arrays. If one of the inputs is
+    an integer array, the minimum precision type that is returned is a
+    64-bit floating point dtype.
+
+    All input arrays except int64 and uint64 can be safely cast to the
+    returned dtype without loss of information.
+
+    Parameters
+    ----------
+    array1, array2, ... : ndarrays
+        Input arrays.
+
+    Returns
+    -------
+    out : data type code
+        Data type code.
+
+    See Also
+    --------
+    dtype, mintypecode
+
+    Examples
+    --------
+    >>> np.common_type(np.arange(2, dtype=np.float32))
+    
+    >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
+    
+    >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
+    
+
+    """
+    is_complex = False
+    precision = 0
+    for a in arrays:
+        t = a.dtype.type
+        if iscomplexobj(a):
+            is_complex = True
+        if issubclass(t, _nx.integer):
+            p = 2  # array_precision[_nx.double]
+        else:
+            p = array_precision.get(t)
+            if p is None:
+                raise TypeError("can't get common type for non-numeric array")
+        precision = max(precision, p)
+    if is_complex:
+        return array_type[1][precision]
+    else:
+        return array_type[0][precision]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e195238103fa16d7051b7aa6b2ac5054a9c9a76b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_type_check_impl.pyi
@@ -0,0 +1,201 @@
+from collections.abc import Container, Iterable
+from typing import Literal as L, Any, overload, TypeVar
+
+import numpy as np
+from numpy import (
+    _HasRealAndImag,
+    dtype,
+    generic,
+    floating,
+    complexfloating,
+    integer,
+)
+
+from numpy._typing import (
+    ArrayLike,
+    NBitBase,
+    NDArray,
+    _64Bit,
+    _SupportsDType,
+    _ScalarLike_co,
+    _ArrayLike,
+)
+
+__all__ = [
+    "iscomplexobj",
+    "isrealobj",
+    "imag",
+    "iscomplex",
+    "isreal",
+    "nan_to_num",
+    "real",
+    "real_if_close",
+    "typename",
+    "mintypecode",
+    "common_type",
+]
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+_SCT = TypeVar("_SCT", bound=generic)
+_NBit1 = TypeVar("_NBit1", bound=NBitBase)
+_NBit2 = TypeVar("_NBit2", bound=NBitBase)
+
+
+def mintypecode(
+    typechars: Iterable[str | ArrayLike],
+    typeset: Container[str] = ...,
+    default: str = ...,
+) -> str: ...
+
+@overload
+def real(val: _HasRealAndImag[_T, Any]) -> _T: ...
+@overload
+def real(val: ArrayLike) -> NDArray[Any]: ...
+
+@overload
+def imag(val: _HasRealAndImag[Any, _T]) -> _T: ...
+@overload
+def imag(val: ArrayLike) -> NDArray[Any]: ...
+
+@overload
+def iscomplex(x: _ScalarLike_co) -> np.bool: ...  # type: ignore[misc]
+@overload
+def iscomplex(x: ArrayLike) -> NDArray[np.bool]: ...
+
+@overload
+def isreal(x: _ScalarLike_co) -> np.bool: ...  # type: ignore[misc]
+@overload
+def isreal(x: ArrayLike) -> NDArray[np.bool]: ...
+
+def iscomplexobj(x: _SupportsDType[dtype[Any]] | ArrayLike) -> bool: ...
+
+def isrealobj(x: _SupportsDType[dtype[Any]] | ArrayLike) -> bool: ...
+
+@overload
+def nan_to_num(  # type: ignore[misc]
+    x: _SCT,
+    copy: bool = ...,
+    nan: float = ...,
+    posinf: None | float = ...,
+    neginf: None | float = ...,
+) -> _SCT: ...
+@overload
+def nan_to_num(
+    x: _ScalarLike_co,
+    copy: bool = ...,
+    nan: float = ...,
+    posinf: None | float = ...,
+    neginf: None | float = ...,
+) -> Any: ...
+@overload
+def nan_to_num(
+    x: _ArrayLike[_SCT],
+    copy: bool = ...,
+    nan: float = ...,
+    posinf: None | float = ...,
+    neginf: None | float = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def nan_to_num(
+    x: ArrayLike,
+    copy: bool = ...,
+    nan: float = ...,
+    posinf: None | float = ...,
+    neginf: None | float = ...,
+) -> NDArray[Any]: ...
+
+# If one passes a complex array to `real_if_close`, then one is reasonably
+# expected to verify the output dtype (so we can return an unsafe union here)
+
+@overload
+def real_if_close(  # type: ignore[misc]
+    a: _ArrayLike[complexfloating[_NBit1, _NBit1]],
+    tol: float = ...,
+) -> NDArray[floating[_NBit1]] | NDArray[complexfloating[_NBit1, _NBit1]]: ...
+@overload
+def real_if_close(
+    a: _ArrayLike[_SCT],
+    tol: float = ...,
+) -> NDArray[_SCT]: ...
+@overload
+def real_if_close(
+    a: ArrayLike,
+    tol: float = ...,
+) -> NDArray[Any]: ...
+
+@overload
+def typename(char: L['S1']) -> L['character']: ...
+@overload
+def typename(char: L['?']) -> L['bool']: ...
+@overload
+def typename(char: L['b']) -> L['signed char']: ...
+@overload
+def typename(char: L['B']) -> L['unsigned char']: ...
+@overload
+def typename(char: L['h']) -> L['short']: ...
+@overload
+def typename(char: L['H']) -> L['unsigned short']: ...
+@overload
+def typename(char: L['i']) -> L['integer']: ...
+@overload
+def typename(char: L['I']) -> L['unsigned integer']: ...
+@overload
+def typename(char: L['l']) -> L['long integer']: ...
+@overload
+def typename(char: L['L']) -> L['unsigned long integer']: ...
+@overload
+def typename(char: L['q']) -> L['long long integer']: ...
+@overload
+def typename(char: L['Q']) -> L['unsigned long long integer']: ...
+@overload
+def typename(char: L['f']) -> L['single precision']: ...
+@overload
+def typename(char: L['d']) -> L['double precision']: ...
+@overload
+def typename(char: L['g']) -> L['long precision']: ...
+@overload
+def typename(char: L['F']) -> L['complex single precision']: ...
+@overload
+def typename(char: L['D']) -> L['complex double precision']: ...
+@overload
+def typename(char: L['G']) -> L['complex long double precision']: ...
+@overload
+def typename(char: L['S']) -> L['string']: ...
+@overload
+def typename(char: L['U']) -> L['unicode']: ...
+@overload
+def typename(char: L['V']) -> L['void']: ...
+@overload
+def typename(char: L['O']) -> L['object']: ...
+
+@overload
+def common_type(  # type: ignore[misc]
+    *arrays: _SupportsDType[dtype[
+        integer[Any]
+    ]]
+) -> type[floating[_64Bit]]: ...
+@overload
+def common_type(  # type: ignore[misc]
+    *arrays: _SupportsDType[dtype[
+        floating[_NBit1]
+    ]]
+) -> type[floating[_NBit1]]: ...
+@overload
+def common_type(  # type: ignore[misc]
+    *arrays: _SupportsDType[dtype[
+        integer[Any] | floating[_NBit1]
+    ]]
+) -> type[floating[_NBit1 | _64Bit]]: ...
+@overload
+def common_type(  # type: ignore[misc]
+    *arrays: _SupportsDType[dtype[
+        floating[_NBit1] | complexfloating[_NBit2, _NBit2]
+    ]]
+) -> type[complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]]: ...
+@overload
+def common_type(
+    *arrays: _SupportsDType[dtype[
+        integer[Any] | floating[_NBit1] | complexfloating[_NBit2, _NBit2]
+    ]]
+) -> type[complexfloating[_64Bit | _NBit1 | _NBit2, _64Bit | _NBit1 | _NBit2]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..695aab1b8922385da6ecb8eb4c0bfcba9742dd9c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.py
@@ -0,0 +1,207 @@
+"""
+Module of functions that are like ufuncs in acting on arrays and optionally
+storing results in an output array.
+
+"""
+__all__ = ['fix', 'isneginf', 'isposinf']
+
+import numpy._core.numeric as nx
+from numpy._core.overrides import array_function_dispatch
+
+
+def _dispatcher(x, out=None):
+    return (x, out)
+
+
+@array_function_dispatch(_dispatcher, verify=False, module='numpy')
+def fix(x, out=None):
+    """
+    Round to nearest integer towards zero.
+
+    Round an array of floats element-wise to nearest integer towards zero.
+    The rounded values have the same data-type as the input.
+
+    Parameters
+    ----------
+    x : array_like
+        An array to be rounded
+    out : ndarray, optional
+        A location into which the result is stored. If provided, it must have
+        a shape that the input broadcasts to. If not provided or None, a
+        freshly-allocated array is returned.
+
+    Returns
+    -------
+    out : ndarray of floats
+        An array with the same dimensions and data-type as the input.
+        If second argument is not supplied then a new array is returned
+        with the rounded values.
+
+        If a second argument is supplied the result is stored there.
+        The return value ``out`` is then a reference to that array.
+
+    See Also
+    --------
+    rint, trunc, floor, ceil
+    around : Round to given number of decimals
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.fix(3.14)
+    3.0
+    >>> np.fix(3)
+    3
+    >>> np.fix([2.1, 2.9, -2.1, -2.9])
+    array([ 2.,  2., -2., -2.])
+
+    """
+    # promote back to an array if flattened
+    res = nx.asanyarray(nx.ceil(x, out=out))
+    res = nx.floor(x, out=res, where=nx.greater_equal(x, 0))
+
+    # when no out argument is passed and no subclasses are involved, flatten
+    # scalars
+    if out is None and type(res) is nx.ndarray:
+        res = res[()]
+    return res
+
+
+@array_function_dispatch(_dispatcher, verify=False, module='numpy')
+def isposinf(x, out=None):
+    """
+    Test element-wise for positive infinity, return result as bool array.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    out : array_like, optional
+        A location into which the result is stored. If provided, it must have a
+        shape that the input broadcasts to. If not provided or None, a
+        freshly-allocated boolean array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        A boolean array with the same dimensions as the input.
+        If second argument is not supplied then a boolean array is returned
+        with values True where the corresponding element of the input is
+        positive infinity and values False where the element of the input is
+        not positive infinity.
+
+        If a second argument is supplied the result is stored there. If the
+        type of that array is a numeric type the result is represented as zeros
+        and ones, if the type is boolean then as False and True.
+        The return value `out` is then a reference to that array.
+
+    See Also
+    --------
+    isinf, isneginf, isfinite, isnan
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754).
+
+    Errors result if the second argument is also supplied when x is a scalar
+    input, if first and second arguments have different shapes, or if the
+    first argument has complex values
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.isposinf(np.inf)
+    True
+    >>> np.isposinf(-np.inf)
+    False
+    >>> np.isposinf([-np.inf, 0., np.inf])
+    array([False, False,  True])
+
+    >>> x = np.array([-np.inf, 0., np.inf])
+    >>> y = np.array([2, 2, 2])
+    >>> np.isposinf(x, y)
+    array([0, 0, 1])
+    >>> y
+    array([0, 0, 1])
+
+    """
+    is_inf = nx.isinf(x)
+    try:
+        signbit = ~nx.signbit(x)
+    except TypeError as e:
+        dtype = nx.asanyarray(x).dtype
+        raise TypeError(f'This operation is not supported for {dtype} values '
+                        'because it would be ambiguous.') from e
+    else:
+        return nx.logical_and(is_inf, signbit, out)
+
+
+@array_function_dispatch(_dispatcher, verify=False, module='numpy')
+def isneginf(x, out=None):
+    """
+    Test element-wise for negative infinity, return result as bool array.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    out : array_like, optional
+        A location into which the result is stored. If provided, it must have a
+        shape that the input broadcasts to. If not provided or None, a
+        freshly-allocated boolean array is returned.
+
+    Returns
+    -------
+    out : ndarray
+        A boolean array with the same dimensions as the input.
+        If second argument is not supplied then a numpy boolean array is
+        returned with values True where the corresponding element of the
+        input is negative infinity and values False where the element of
+        the input is not negative infinity.
+
+        If a second argument is supplied the result is stored there. If the
+        type of that array is a numeric type the result is represented as
+        zeros and ones, if the type is boolean then as False and True. The
+        return value `out` is then a reference to that array.
+
+    See Also
+    --------
+    isinf, isposinf, isnan, isfinite
+
+    Notes
+    -----
+    NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
+    (IEEE 754).
+
+    Errors result if the second argument is also supplied when x is a scalar
+    input, if first and second arguments have different shapes, or if the
+    first argument has complex values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.isneginf(-np.inf)
+    True
+    >>> np.isneginf(np.inf)
+    False
+    >>> np.isneginf([-np.inf, 0., np.inf])
+    array([ True, False, False])
+
+    >>> x = np.array([-np.inf, 0., np.inf])
+    >>> y = np.array([2, 2, 2])
+    >>> np.isneginf(x, y)
+    array([1, 0, 0])
+    >>> y
+    array([1, 0, 0])
+
+    """
+    is_inf = nx.isinf(x)
+    try:
+        signbit = nx.signbit(x)
+    except TypeError as e:
+        dtype = nx.asanyarray(x).dtype
+        raise TypeError(f'This operation is not supported for {dtype} values '
+                        'because it would be ambiguous.') from e
+    else:
+        return nx.logical_and(is_inf, signbit, out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8d87ae8bf4c62067e1b19e8b83ac6b951620168d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_ufunclike_impl.pyi
@@ -0,0 +1,67 @@
+from typing import Any, overload, TypeVar
+
+import numpy as np
+from numpy import floating, object_
+from numpy._typing import (
+    NDArray,
+    _FloatLike_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeObject_co,
+)
+
+__all__ = ["fix", "isneginf", "isposinf"]
+
+_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
+
+@overload
+def fix(  # type: ignore[misc]
+    x: _FloatLike_co,
+    out: None = ...,
+) -> floating[Any]: ...
+@overload
+def fix(
+    x: _ArrayLikeFloat_co,
+    out: None = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def fix(
+    x: _ArrayLikeObject_co,
+    out: None = ...,
+) -> NDArray[object_]: ...
+@overload
+def fix(
+    x: _ArrayLikeFloat_co | _ArrayLikeObject_co,
+    out: _ArrayType,
+) -> _ArrayType: ...
+
+@overload
+def isposinf(  # type: ignore[misc]
+    x: _FloatLike_co,
+    out: None = ...,
+) -> np.bool: ...
+@overload
+def isposinf(
+    x: _ArrayLikeFloat_co,
+    out: None = ...,
+) -> NDArray[np.bool]: ...
+@overload
+def isposinf(
+    x: _ArrayLikeFloat_co,
+    out: _ArrayType,
+) -> _ArrayType: ...
+
+@overload
+def isneginf(  # type: ignore[misc]
+    x: _FloatLike_co,
+    out: None = ...,
+) -> np.bool: ...
+@overload
+def isneginf(
+    x: _ArrayLikeFloat_co,
+    out: None = ...,
+) -> NDArray[np.bool]: ...
+@overload
+def isneginf(
+    x: _ArrayLikeFloat_co,
+    out: _ArrayType,
+) -> _ArrayType: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..cae6e0556687a471f28558fb13dd013b966328e4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.py
@@ -0,0 +1,291 @@
+"""
+Container class for backward compatibility with NumArray.
+
+The user_array.container class exists for backward compatibility with NumArray
+and is not meant to be used in new code. If you need to create an array
+container class, we recommend either creating a class that wraps an ndarray
+or subclasses ndarray.
+
+"""
+from numpy._core import (
+    array, asarray, absolute, add, subtract, multiply, divide,
+    remainder, power, left_shift, right_shift, bitwise_and, bitwise_or,
+    bitwise_xor, invert, less, less_equal, not_equal, equal, greater,
+    greater_equal, shape, reshape, arange, sin, sqrt, transpose
+)
+from numpy._core.overrides import set_module
+
+
+@set_module("numpy.lib.user_array")
+class container:
+    """
+    container(data, dtype=None, copy=True)
+
+    Standard container-class for easy multiple-inheritance.
+
+    Methods
+    -------
+    copy
+    tostring
+    byteswap
+    astype
+
+    """
+    def __init__(self, data, dtype=None, copy=True):
+        self.array = array(data, dtype, copy=copy)
+
+    def __repr__(self):
+        if self.ndim > 0:
+            return self.__class__.__name__ + repr(self.array)[len("array"):]
+        else:
+            return self.__class__.__name__ + "(" + repr(self.array) + ")"
+
+    def __array__(self, t=None):
+        if t:
+            return self.array.astype(t)
+        return self.array
+
+    # Array as sequence
+    def __len__(self):
+        return len(self.array)
+
+    def __getitem__(self, index):
+        return self._rc(self.array[index])
+
+    def __setitem__(self, index, value):
+        self.array[index] = asarray(value, self.dtype)
+
+    def __abs__(self):
+        return self._rc(absolute(self.array))
+
+    def __neg__(self):
+        return self._rc(-self.array)
+
+    def __add__(self, other):
+        return self._rc(self.array + asarray(other))
+
+    __radd__ = __add__
+
+    def __iadd__(self, other):
+        add(self.array, other, self.array)
+        return self
+
+    def __sub__(self, other):
+        return self._rc(self.array - asarray(other))
+
+    def __rsub__(self, other):
+        return self._rc(asarray(other) - self.array)
+
+    def __isub__(self, other):
+        subtract(self.array, other, self.array)
+        return self
+
+    def __mul__(self, other):
+        return self._rc(multiply(self.array, asarray(other)))
+
+    __rmul__ = __mul__
+
+    def __imul__(self, other):
+        multiply(self.array, other, self.array)
+        return self
+
+    def __div__(self, other):
+        return self._rc(divide(self.array, asarray(other)))
+
+    def __rdiv__(self, other):
+        return self._rc(divide(asarray(other), self.array))
+
+    def __idiv__(self, other):
+        divide(self.array, other, self.array)
+        return self
+
+    def __mod__(self, other):
+        return self._rc(remainder(self.array, other))
+
+    def __rmod__(self, other):
+        return self._rc(remainder(other, self.array))
+
+    def __imod__(self, other):
+        remainder(self.array, other, self.array)
+        return self
+
+    def __divmod__(self, other):
+        return (self._rc(divide(self.array, other)),
+                self._rc(remainder(self.array, other)))
+
+    def __rdivmod__(self, other):
+        return (self._rc(divide(other, self.array)),
+                self._rc(remainder(other, self.array)))
+
+    def __pow__(self, other):
+        return self._rc(power(self.array, asarray(other)))
+
+    def __rpow__(self, other):
+        return self._rc(power(asarray(other), self.array))
+
+    def __ipow__(self, other):
+        power(self.array, other, self.array)
+        return self
+
+    def __lshift__(self, other):
+        return self._rc(left_shift(self.array, other))
+
+    def __rshift__(self, other):
+        return self._rc(right_shift(self.array, other))
+
+    def __rlshift__(self, other):
+        return self._rc(left_shift(other, self.array))
+
+    def __rrshift__(self, other):
+        return self._rc(right_shift(other, self.array))
+
+    def __ilshift__(self, other):
+        left_shift(self.array, other, self.array)
+        return self
+
+    def __irshift__(self, other):
+        right_shift(self.array, other, self.array)
+        return self
+
+    def __and__(self, other):
+        return self._rc(bitwise_and(self.array, other))
+
+    def __rand__(self, other):
+        return self._rc(bitwise_and(other, self.array))
+
+    def __iand__(self, other):
+        bitwise_and(self.array, other, self.array)
+        return self
+
+    def __xor__(self, other):
+        return self._rc(bitwise_xor(self.array, other))
+
+    def __rxor__(self, other):
+        return self._rc(bitwise_xor(other, self.array))
+
+    def __ixor__(self, other):
+        bitwise_xor(self.array, other, self.array)
+        return self
+
+    def __or__(self, other):
+        return self._rc(bitwise_or(self.array, other))
+
+    def __ror__(self, other):
+        return self._rc(bitwise_or(other, self.array))
+
+    def __ior__(self, other):
+        bitwise_or(self.array, other, self.array)
+        return self
+
+    def __pos__(self):
+        return self._rc(self.array)
+
+    def __invert__(self):
+        return self._rc(invert(self.array))
+
+    def _scalarfunc(self, func):
+        if self.ndim == 0:
+            return func(self[0])
+        else:
+            raise TypeError(
+                "only rank-0 arrays can be converted to Python scalars.")
+
+    def __complex__(self):
+        return self._scalarfunc(complex)
+
+    def __float__(self):
+        return self._scalarfunc(float)
+
+    def __int__(self):
+        return self._scalarfunc(int)
+
+    def __hex__(self):
+        return self._scalarfunc(hex)
+
+    def __oct__(self):
+        return self._scalarfunc(oct)
+
+    def __lt__(self, other):
+        return self._rc(less(self.array, other))
+
+    def __le__(self, other):
+        return self._rc(less_equal(self.array, other))
+
+    def __eq__(self, other):
+        return self._rc(equal(self.array, other))
+
+    def __ne__(self, other):
+        return self._rc(not_equal(self.array, other))
+
+    def __gt__(self, other):
+        return self._rc(greater(self.array, other))
+
+    def __ge__(self, other):
+        return self._rc(greater_equal(self.array, other))
+
+    def copy(self):
+        ""
+        return self._rc(self.array.copy())
+
+    def tostring(self):
+        ""
+        return self.array.tostring()
+
+    def tobytes(self):
+        ""
+        return self.array.tobytes()
+
+    def byteswap(self):
+        ""
+        return self._rc(self.array.byteswap())
+
+    def astype(self, typecode):
+        ""
+        return self._rc(self.array.astype(typecode))
+
+    def _rc(self, a):
+        if len(shape(a)) == 0:
+            return a
+        else:
+            return self.__class__(a)
+
+    def __array_wrap__(self, *args):
+        return self.__class__(args[0])
+
+    def __setattr__(self, attr, value):
+        if attr == 'array':
+            object.__setattr__(self, attr, value)
+            return
+        try:
+            self.array.__setattr__(attr, value)
+        except AttributeError:
+            object.__setattr__(self, attr, value)
+
+    # Only called after other approaches fail.
+    def __getattr__(self, attr):
+        if (attr == 'array'):
+            return object.__getattribute__(self, attr)
+        return self.array.__getattribute__(attr)
+
+
+#############################################################
+# Test of class container
+#############################################################
+if __name__ == '__main__':
+    temp = reshape(arange(10000), (100, 100))
+
+    ua = container(temp)
+    # new object created begin test
+    print(dir(ua))
+    print(shape(ua), ua.shape)  # I have changed Numeric.py
+
+    ua_small = ua[:3, :5]
+    print(ua_small)
+    # this did not change ua[0,0], which is not normal behavior
+    ua_small[0, 0] = 10
+    print(ua_small[0, 0], ua[0, 0])
+    print(sin(ua_small) / 3. * 6. + sqrt(ua_small ** 2))
+    print(less(ua_small, 103), type(less(ua_small, 103)))
+    print(type(ua_small * reshape(arange(15), shape(ua_small))))
+    print(reshape(ua_small, (5, 3)))
+    print(transpose(ua_small))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d5dfb0573c7150a9287e3ff5497546b0ad6a59f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_user_array_impl.pyi
@@ -0,0 +1,220 @@
+from types import EllipsisType
+from typing import Any, Generic, SupportsIndex, TypeAlias, TypeVar, overload
+
+from _typeshed import Incomplete
+from typing_extensions import Self, deprecated, override
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co, _DTypeLike
+
+###
+
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+_ShapeT = TypeVar("_ShapeT", bound=tuple[int, ...])
+_ShapeT_co = TypeVar("_ShapeT_co", bound=tuple[int, ...], default=Any, covariant=True)
+_DTypeT = TypeVar("_DTypeT", bound=np.dtype[Any])
+_DTypeT_co = TypeVar("_DTypeT_co", bound=np.dtype[Any], default=np.dtype[Any], covariant=True)
+
+_BoolArrayT = TypeVar("_BoolArrayT", bound=container[Any, np.dtype[np.bool]])
+_IntegralArrayT = TypeVar("_IntegralArrayT", bound=container[Any, np.dtype[np.bool | np.integer | np.object_]])
+_RealContainerT = TypeVar(
+    "_RealContainerT",
+    bound=container[Any, np.dtype[np.bool | np.integer | np.floating | np.timedelta64 | np.object_]],
+)
+_NumericContainerT = TypeVar("_NumericContainerT", bound=container[Any, np.dtype[np.number | np.timedelta64 | np.object_]])
+
+_ArrayInt_co: TypeAlias = npt.NDArray[np.integer | np.bool]
+
+_ToIndexSlice: TypeAlias = slice | EllipsisType | _ArrayInt_co | None
+_ToIndexSlices: TypeAlias = _ToIndexSlice | tuple[_ToIndexSlice, ...]
+_ToIndex: TypeAlias = SupportsIndex | _ToIndexSlice
+_ToIndices: TypeAlias = _ToIndex | tuple[_ToIndex, ...]
+
+###
+
+class container(Generic[_ShapeT_co, _DTypeT_co]):
+    array: np.ndarray[_ShapeT_co, _DTypeT_co]
+
+    @overload
+    def __init__(
+        self,
+        /,
+        data: container[_ShapeT_co, _DTypeT_co] | np.ndarray[_ShapeT_co, _DTypeT_co],
+        dtype: None = None,
+        copy: bool = True,
+    ) -> None: ...
+    @overload
+    def __init__(
+        self: container[Any, np.dtype[_ScalarT]],
+        /,
+        data: _ArrayLike[_ScalarT],
+        dtype: None = None,
+        copy: bool = True,
+    ) -> None: ...
+    @overload
+    def __init__(
+        self: container[Any, np.dtype[_ScalarT]],
+        /,
+        data: npt.ArrayLike,
+        dtype: _DTypeLike[_ScalarT],
+        copy: bool = True,
+    ) -> None: ...
+    @overload
+    def __init__(self, /, data: npt.ArrayLike, dtype: npt.DTypeLike | None = None, copy: bool = True) -> None: ...
+
+    #
+    def __complex__(self, /) -> complex: ...
+    def __float__(self, /) -> float: ...
+    def __int__(self, /) -> int: ...
+    def __hex__(self, /) -> str: ...
+    def __oct__(self, /) -> str: ...
+
+    #
+    @override
+    def __eq__(self, other: object, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    @override
+    def __ne__(self, other: object, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+
+    #
+    def __lt__(self, other: npt.ArrayLike, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...
+    def __le__(self, other: npt.ArrayLike, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...
+    def __gt__(self, other: npt.ArrayLike, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...
+    def __ge__(self, other: npt.ArrayLike, /) -> container[_ShapeT_co, np.dtype[np.bool]]: ...
+
+    #
+    def __len__(self, /) -> int: ...
+
+    # keep in sync with np.ndarray
+    @overload
+    def __getitem__(self, key: _ArrayInt_co | tuple[_ArrayInt_co, ...], /) -> container[_ShapeT_co, _DTypeT_co]: ...
+    @overload
+    def __getitem__(self, key: _ToIndexSlices, /) -> container[Any, _DTypeT_co]: ...
+    @overload
+    def __getitem__(self, key: _ToIndices, /) -> Any: ...
+    @overload
+    def __getitem__(self: container[Any, np.dtype[np.void]], key: list[str], /) -> container[_ShapeT_co, np.dtype[np.void]]: ...
+    @overload
+    def __getitem__(self: container[Any, np.dtype[np.void]], key: str, /) -> container[_ShapeT_co, np.dtype[Any]]: ...
+
+    # keep in sync with np.ndarray
+    @overload
+    def __setitem__(self, index: _ToIndices, value: object, /) -> None: ...
+    @overload
+    def __setitem__(self: container[Any, np.dtype[np.void]], key: str | list[str], value: object, /) -> None: ...
+
+    # keep in sync with np.ndarray
+    @overload
+    def __abs__(self: container[_ShapeT, np.dtype[np.complex64]], /) -> container[_ShapeT, np.dtype[np.float32]]: ...  # type: ignore[overload-overlap]
+    @overload
+    def __abs__(self: container[_ShapeT, np.dtype[np.complex128]], /) -> container[_ShapeT, np.dtype[np.float64]]: ...
+    @overload
+    def __abs__(self: container[_ShapeT, np.dtype[np.complex192]], /) -> container[_ShapeT, np.dtype[np.float96]]: ...
+    @overload
+    def __abs__(self: container[_ShapeT, np.dtype[np.complex256]], /) -> container[_ShapeT, np.dtype[np.float128]]: ...
+    @overload
+    def __abs__(self: _RealContainerT, /) -> _RealContainerT: ...
+
+    #
+    def __neg__(self: _NumericContainerT, /) -> _NumericContainerT: ...  # noqa: PYI019
+    def __pos__(self: _NumericContainerT, /) -> _NumericContainerT: ...  # noqa: PYI019
+    def __invert__(self: _IntegralArrayT, /) -> _IntegralArrayT: ...  # noqa: PYI019
+
+    # TODO(jorenham): complete these binary ops
+
+    #
+    def __add__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __radd__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __iadd__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __sub__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __rsub__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __isub__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __mul__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __rmul__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __imul__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __div__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __rdiv__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __idiv__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __mod__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __rmod__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __imod__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __divmod__(self, other: npt.ArrayLike, /) -> tuple[Incomplete, Incomplete]: ...
+    def __rdivmod__(self, other: npt.ArrayLike, /) -> tuple[Incomplete, Incomplete]: ...
+
+    #
+    def __pow__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __rpow__(self, other: npt.ArrayLike, /) -> Incomplete: ...
+    def __ipow__(self, other: npt.ArrayLike, /) -> Self: ...
+
+    #
+    def __lshift__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.integer]]: ...
+    def __rlshift__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.integer]]: ...
+    def __ilshift__(self, other: _ArrayLikeInt_co, /) -> Self: ...
+
+    #
+    def __rshift__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.integer]]: ...
+    def __rrshift__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.integer]]: ...
+    def __irshift__(self, other: _ArrayLikeInt_co, /) -> Self: ...
+
+    #
+    @overload
+    def __and__(self: container[Any, np.dtype[np.bool]], other: _ArrayLikeBool_co, /) -> container[Any, np.dtype[np.bool]]: ...
+    @overload
+    def __and__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.bool | np.integer]]: ...
+    __rand__ = __and__
+    @overload
+    def __iand__(self: _BoolArrayT, other: _ArrayLikeBool_co, /) -> _BoolArrayT: ...
+    @overload
+    def __iand__(self, other: _ArrayLikeInt_co, /) -> Self: ...
+
+    #
+    @overload
+    def __xor__(self: container[Any, np.dtype[np.bool]], other: _ArrayLikeBool_co, /) -> container[Any, np.dtype[np.bool]]: ...
+    @overload
+    def __xor__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.bool | np.integer]]: ...
+    __rxor__ = __xor__
+    @overload
+    def __ixor__(self: _BoolArrayT, other: _ArrayLikeBool_co, /) -> _BoolArrayT: ...
+    @overload
+    def __ixor__(self, other: _ArrayLikeInt_co, /) -> Self: ...
+
+    #
+    @overload
+    def __or__(self: container[Any, np.dtype[np.bool]], other: _ArrayLikeBool_co, /) -> container[Any, np.dtype[np.bool]]: ...
+    @overload
+    def __or__(self, other: _ArrayLikeInt_co, /) -> container[Any, np.dtype[np.bool | np.integer]]: ...
+    __ror__ = __or__
+    @overload
+    def __ior__(self: _BoolArrayT, other: _ArrayLikeBool_co, /) -> _BoolArrayT: ...
+    @overload
+    def __ior__(self, other: _ArrayLikeInt_co, /) -> Self: ...
+
+    #
+    @overload
+    def __array__(self, /, t: None = None) -> np.ndarray[_ShapeT_co, _DTypeT_co]: ...
+    @overload
+    def __array__(self, /, t: _DTypeT) -> np.ndarray[_ShapeT_co, _DTypeT]: ...
+
+    #
+    @overload
+    def __array_wrap__(self, arg0: npt.ArrayLike, /) -> container[_ShapeT_co, _DTypeT_co]: ...
+    @overload
+    def __array_wrap__(self, a: np.ndarray[_ShapeT, _DTypeT], c: Any = ..., s: Any = ..., /) -> container[_ShapeT, _DTypeT]: ...
+
+    #
+    def copy(self, /) -> Self: ...
+    @deprecated("tostring() is deprecated. Use tobytes() instead.")
+    def tostring(self, /) -> bytes: ...
+    def tobytes(self, /) -> bytes: ...
+    def byteswap(self, /) -> Self: ...
+    def astype(self, /, typecode: _DTypeLike[_ScalarT]) -> container[_ShapeT_co, np.dtype[_ScalarT]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2f0f31d7bfcc7e96bf7936887788817319ddc5e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.py
@@ -0,0 +1,775 @@
+import os
+import sys
+import textwrap
+import types
+import warnings
+import functools
+import platform
+
+from numpy._core import ndarray
+from numpy._utils import set_module
+import numpy as np
+
+__all__ = [
+    'get_include', 'info', 'show_runtime'
+]
+
+
+@set_module('numpy')
+def show_runtime():
+    """
+    Print information about various resources in the system
+    including available intrinsic support and BLAS/LAPACK library
+    in use
+
+    .. versionadded:: 1.24.0
+
+    See Also
+    --------
+    show_config : Show libraries in the system on which NumPy was built.
+
+    Notes
+    -----
+    1. Information is derived with the help of `threadpoolctl `_
+       library if available.
+    2. SIMD related information is derived from ``__cpu_features__``,
+       ``__cpu_baseline__`` and ``__cpu_dispatch__``
+
+    """
+    from numpy._core._multiarray_umath import (
+        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+    )
+    from pprint import pprint
+    config_found = [{
+        "numpy_version": np.__version__,
+        "python": sys.version,
+        "uname": platform.uname(),
+        }]
+    features_found, features_not_found = [], []
+    for feature in __cpu_dispatch__:
+        if __cpu_features__[feature]:
+            features_found.append(feature)
+        else:
+            features_not_found.append(feature)
+    config_found.append({
+        "simd_extensions": {
+            "baseline": __cpu_baseline__,
+            "found": features_found,
+            "not_found": features_not_found
+        }
+    })
+    try:
+        from threadpoolctl import threadpool_info
+        config_found.extend(threadpool_info())
+    except ImportError:
+        print("WARNING: `threadpoolctl` not found in system!"
+              " Install it by `pip install threadpoolctl`."
+              " Once installed, try `np.show_runtime` again"
+              " for more detailed build information")
+    pprint(config_found)
+
+
+@set_module('numpy')
+def get_include():
+    """
+    Return the directory that contains the NumPy \\*.h header files.
+
+    Extension modules that need to compile against NumPy may need to use this
+    function to locate the appropriate include directory.
+
+    Notes
+    -----
+    When using ``setuptools``, for example in ``setup.py``::
+
+        import numpy as np
+        ...
+        Extension('extension_name', ...
+                  include_dirs=[np.get_include()])
+        ...
+
+    Note that a CLI tool ``numpy-config`` was introduced in NumPy 2.0, using
+    that is likely preferred for build systems other than ``setuptools``::
+
+        $ numpy-config --cflags
+        -I/path/to/site-packages/numpy/_core/include
+
+        # Or rely on pkg-config:
+        $ export PKG_CONFIG_PATH=$(numpy-config --pkgconfigdir)
+        $ pkg-config --cflags
+        -I/path/to/site-packages/numpy/_core/include
+
+    Examples
+    --------
+    >>> np.get_include()
+    '.../site-packages/numpy/core/include'  # may vary
+
+    """
+    import numpy
+    if numpy.show_config is None:
+        # running from numpy source directory
+        d = os.path.join(os.path.dirname(numpy.__file__), '_core', 'include')
+    else:
+        # using installed numpy core headers
+        import numpy._core as _core
+        d = os.path.join(os.path.dirname(_core.__file__), 'include')
+    return d
+
+
+class _Deprecate:
+    """
+    Decorator class to deprecate old functions.
+
+    Refer to `deprecate` for details.
+
+    See Also
+    --------
+    deprecate
+
+    """
+
+    def __init__(self, old_name=None, new_name=None, message=None):
+        self.old_name = old_name
+        self.new_name = new_name
+        self.message = message
+
+    def __call__(self, func, *args, **kwargs):
+        """
+        Decorator call.  Refer to ``decorate``.
+
+        """
+        old_name = self.old_name
+        new_name = self.new_name
+        message = self.message
+
+        if old_name is None:
+            old_name = func.__name__
+        if new_name is None:
+            depdoc = "`%s` is deprecated!" % old_name
+        else:
+            depdoc = "`%s` is deprecated, use `%s` instead!" % \
+                     (old_name, new_name)
+
+        if message is not None:
+            depdoc += "\n" + message
+
+        @functools.wraps(func)
+        def newfunc(*args, **kwds):
+            warnings.warn(depdoc, DeprecationWarning, stacklevel=2)
+            return func(*args, **kwds)
+
+        newfunc.__name__ = old_name
+        doc = func.__doc__
+        if doc is None:
+            doc = depdoc
+        else:
+            lines = doc.expandtabs().split('\n')
+            indent = _get_indent(lines[1:])
+            if lines[0].lstrip():
+                # Indent the original first line to let inspect.cleandoc()
+                # dedent the docstring despite the deprecation notice.
+                doc = indent * ' ' + doc
+            else:
+                # Remove the same leading blank lines as cleandoc() would.
+                skip = len(lines[0]) + 1
+                for line in lines[1:]:
+                    if len(line) > indent:
+                        break
+                    skip += len(line) + 1
+                doc = doc[skip:]
+            depdoc = textwrap.indent(depdoc, ' ' * indent)
+            doc = f'{depdoc}\n\n{doc}'
+        newfunc.__doc__ = doc
+
+        return newfunc
+
+
+def _get_indent(lines):
+    """
+    Determines the leading whitespace that could be removed from all the lines.
+    """
+    indent = sys.maxsize
+    for line in lines:
+        content = len(line.lstrip())
+        if content:
+            indent = min(indent, len(line) - content)
+    if indent == sys.maxsize:
+        indent = 0
+    return indent
+
+
+def deprecate(*args, **kwargs):
+    """
+    Issues a DeprecationWarning, adds warning to `old_name`'s
+    docstring, rebinds ``old_name.__name__`` and returns the new
+    function object.
+
+    This function may also be used as a decorator.
+
+    .. deprecated:: 2.0
+        Use `~warnings.warn` with :exc:`DeprecationWarning` instead.
+
+    Parameters
+    ----------
+    func : function
+        The function to be deprecated.
+    old_name : str, optional
+        The name of the function to be deprecated. Default is None, in
+        which case the name of `func` is used.
+    new_name : str, optional
+        The new name for the function. Default is None, in which case the
+        deprecation message is that `old_name` is deprecated. If given, the
+        deprecation message is that `old_name` is deprecated and `new_name`
+        should be used instead.
+    message : str, optional
+        Additional explanation of the deprecation.  Displayed in the
+        docstring after the warning.
+
+    Returns
+    -------
+    old_func : function
+        The deprecated function.
+
+    Examples
+    --------
+    Note that ``olduint`` returns a value after printing Deprecation
+    Warning:
+
+    >>> olduint = np.lib.utils.deprecate(np.uint)
+    DeprecationWarning: `uint64` is deprecated! # may vary
+    >>> olduint(6)
+    6
+
+    """
+    # Deprecate may be run as a function or as a decorator
+    # If run as a function, we initialise the decorator class
+    # and execute its __call__ method.
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`deprecate` is deprecated, "
+        "use `warn` with `DeprecationWarning` instead. "
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    if args:
+        fn = args[0]
+        args = args[1:]
+
+        return _Deprecate(*args, **kwargs)(fn)
+    else:
+        return _Deprecate(*args, **kwargs)
+
+
+def deprecate_with_doc(msg):
+    """
+    Deprecates a function and includes the deprecation in its docstring.
+
+    .. deprecated:: 2.0
+        Use `~warnings.warn` with :exc:`DeprecationWarning` instead.
+
+    This function is used as a decorator. It returns an object that can be
+    used to issue a DeprecationWarning, by passing the to-be decorated
+    function as argument, this adds warning to the to-be decorated function's
+    docstring and returns the new function object.
+
+    See Also
+    --------
+    deprecate : Decorate a function such that it issues a
+                :exc:`DeprecationWarning`
+
+    Parameters
+    ----------
+    msg : str
+        Additional explanation of the deprecation. Displayed in the
+        docstring after the warning.
+
+    Returns
+    -------
+    obj : object
+
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`deprecate` is deprecated, "
+        "use `warn` with `DeprecationWarning` instead. "
+        "(deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    return _Deprecate(message=msg)
+
+
+#-----------------------------------------------------------------------------
+
+
+# NOTE:  pydoc defines a help function which works similarly to this
+#  except it uses a pager to take over the screen.
+
+# combine name and arguments and split to multiple lines of width
+# characters.  End lines on a comma and begin argument list indented with
+# the rest of the arguments.
+def _split_line(name, arguments, width):
+    firstwidth = len(name)
+    k = firstwidth
+    newstr = name
+    sepstr = ", "
+    arglist = arguments.split(sepstr)
+    for argument in arglist:
+        if k == firstwidth:
+            addstr = ""
+        else:
+            addstr = sepstr
+        k = k + len(argument) + len(addstr)
+        if k > width:
+            k = firstwidth + 1 + len(argument)
+            newstr = newstr + ",\n" + " "*(firstwidth+2) + argument
+        else:
+            newstr = newstr + addstr + argument
+    return newstr
+
+_namedict = None
+_dictlist = None
+
+# Traverse all module directories underneath globals
+# to see if something is defined
+def _makenamedict(module='numpy'):
+    module = __import__(module, globals(), locals(), [])
+    thedict = {module.__name__:module.__dict__}
+    dictlist = [module.__name__]
+    totraverse = [module.__dict__]
+    while True:
+        if len(totraverse) == 0:
+            break
+        thisdict = totraverse.pop(0)
+        for x in thisdict.keys():
+            if isinstance(thisdict[x], types.ModuleType):
+                modname = thisdict[x].__name__
+                if modname not in dictlist:
+                    moddict = thisdict[x].__dict__
+                    dictlist.append(modname)
+                    totraverse.append(moddict)
+                    thedict[modname] = moddict
+    return thedict, dictlist
+
+
+def _info(obj, output=None):
+    """Provide information about ndarray obj.
+
+    Parameters
+    ----------
+    obj : ndarray
+        Must be ndarray, not checked.
+    output
+        Where printed output goes.
+
+    Notes
+    -----
+    Copied over from the numarray module prior to its removal.
+    Adapted somewhat as only numpy is an option now.
+
+    Called by info.
+
+    """
+    extra = ""
+    tic = ""
+    bp = lambda x: x
+    cls = getattr(obj, '__class__', type(obj))
+    nm = getattr(cls, '__name__', cls)
+    strides = obj.strides
+    endian = obj.dtype.byteorder
+
+    if output is None:
+        output = sys.stdout
+
+    print("class: ", nm, file=output)
+    print("shape: ", obj.shape, file=output)
+    print("strides: ", strides, file=output)
+    print("itemsize: ", obj.itemsize, file=output)
+    print("aligned: ", bp(obj.flags.aligned), file=output)
+    print("contiguous: ", bp(obj.flags.contiguous), file=output)
+    print("fortran: ", obj.flags.fortran, file=output)
+    print(
+        "data pointer: %s%s" % (hex(obj.ctypes._as_parameter_.value), extra),
+        file=output
+        )
+    print("byteorder: ", end=' ', file=output)
+    if endian in ['|', '=']:
+        print("%s%s%s" % (tic, sys.byteorder, tic), file=output)
+        byteswap = False
+    elif endian == '>':
+        print("%sbig%s" % (tic, tic), file=output)
+        byteswap = sys.byteorder != "big"
+    else:
+        print("%slittle%s" % (tic, tic), file=output)
+        byteswap = sys.byteorder != "little"
+    print("byteswap: ", bp(byteswap), file=output)
+    print("type: %s" % obj.dtype, file=output)
+
+
+@set_module('numpy')
+def info(object=None, maxwidth=76, output=None, toplevel='numpy'):
+    """
+    Get help information for an array, function, class, or module.
+
+    Parameters
+    ----------
+    object : object or str, optional
+        Input object or name to get information about. If `object` is
+        an `ndarray` instance, information about the array is printed.
+        If `object` is a numpy object, its docstring is given. If it is
+        a string, available modules are searched for matching objects.
+        If None, information about `info` itself is returned.
+    maxwidth : int, optional
+        Printing width.
+    output : file like object, optional
+        File like object that the output is written to, default is
+        ``None``, in which case ``sys.stdout`` will be used.
+        The object has to be opened in 'w' or 'a' mode.
+    toplevel : str, optional
+        Start search at this level.
+
+    Notes
+    -----
+    When used interactively with an object, ``np.info(obj)`` is equivalent
+    to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython
+    prompt.
+
+    Examples
+    --------
+    >>> np.info(np.polyval) # doctest: +SKIP
+       polyval(p, x)
+         Evaluate the polynomial p at x.
+         ...
+
+    When using a string for `object` it is possible to get multiple results.
+
+    >>> np.info('fft') # doctest: +SKIP
+         *** Found in numpy ***
+    Core FFT routines
+    ...
+         *** Found in numpy.fft ***
+     fft(a, n=None, axis=-1)
+    ...
+         *** Repeat reference found in numpy.fft.fftpack ***
+         *** Total of 3 references found. ***
+
+    When the argument is an array, information about the array is printed.
+
+    >>> a = np.array([[1 + 2j, 3, -4], [-5j, 6, 0]], dtype=np.complex64)
+    >>> np.info(a)
+    class:  ndarray
+    shape:  (2, 3)
+    strides:  (24, 8)
+    itemsize:  8
+    aligned:  True
+    contiguous:  True
+    fortran:  False
+    data pointer: 0x562b6e0d2860  # may vary
+    byteorder:  little
+    byteswap:  False
+    type: complex64
+
+    """
+    global _namedict, _dictlist
+    # Local import to speed up numpy's import time.
+    import pydoc
+    import inspect
+
+    if (hasattr(object, '_ppimport_importer') or
+           hasattr(object, '_ppimport_module')):
+        object = object._ppimport_module
+    elif hasattr(object, '_ppimport_attr'):
+        object = object._ppimport_attr
+
+    if output is None:
+        output = sys.stdout
+
+    if object is None:
+        info(info)
+    elif isinstance(object, ndarray):
+        _info(object, output=output)
+    elif isinstance(object, str):
+        if _namedict is None:
+            _namedict, _dictlist = _makenamedict(toplevel)
+        numfound = 0
+        objlist = []
+        for namestr in _dictlist:
+            try:
+                obj = _namedict[namestr][object]
+                if id(obj) in objlist:
+                    print("\n     "
+                          "*** Repeat reference found in %s *** " % namestr,
+                          file=output
+                          )
+                else:
+                    objlist.append(id(obj))
+                    print("     *** Found in %s ***" % namestr, file=output)
+                    info(obj)
+                    print("-"*maxwidth, file=output)
+                numfound += 1
+            except KeyError:
+                pass
+        if numfound == 0:
+            print("Help for %s not found." % object, file=output)
+        else:
+            print("\n     "
+                  "*** Total of %d references found. ***" % numfound,
+                  file=output
+                  )
+
+    elif inspect.isfunction(object) or inspect.ismethod(object):
+        name = object.__name__
+        try:
+            arguments = str(inspect.signature(object))
+        except Exception:
+            arguments = "()"
+
+        if len(name+arguments) > maxwidth:
+            argstr = _split_line(name, arguments, maxwidth)
+        else:
+            argstr = name + arguments
+
+        print(" " + argstr + "\n", file=output)
+        print(inspect.getdoc(object), file=output)
+
+    elif inspect.isclass(object):
+        name = object.__name__
+        try:
+            arguments = str(inspect.signature(object))
+        except Exception:
+            arguments = "()"
+
+        if len(name+arguments) > maxwidth:
+            argstr = _split_line(name, arguments, maxwidth)
+        else:
+            argstr = name + arguments
+
+        print(" " + argstr + "\n", file=output)
+        doc1 = inspect.getdoc(object)
+        if doc1 is None:
+            if hasattr(object, '__init__'):
+                print(inspect.getdoc(object.__init__), file=output)
+        else:
+            print(inspect.getdoc(object), file=output)
+
+        methods = pydoc.allmethods(object)
+
+        public_methods = [meth for meth in methods if meth[0] != '_']
+        if public_methods:
+            print("\n\nMethods:\n", file=output)
+            for meth in public_methods:
+                thisobj = getattr(object, meth, None)
+                if thisobj is not None:
+                    methstr, other = pydoc.splitdoc(
+                            inspect.getdoc(thisobj) or "None"
+                            )
+                print("  %s  --  %s" % (meth, methstr), file=output)
+
+    elif hasattr(object, '__doc__'):
+        print(inspect.getdoc(object), file=output)
+
+
+def safe_eval(source):
+    """
+    Protected string evaluation.
+
+    .. deprecated:: 2.0
+        Use `ast.literal_eval` instead.
+
+    Evaluate a string containing a Python literal expression without
+    allowing the execution of arbitrary non-literal code.
+
+    .. warning::
+
+        This function is identical to :py:meth:`ast.literal_eval` and
+        has the same security implications.  It may not always be safe
+        to evaluate large input strings.
+
+    Parameters
+    ----------
+    source : str
+        The string to evaluate.
+
+    Returns
+    -------
+    obj : object
+       The result of evaluating `source`.
+
+    Raises
+    ------
+    SyntaxError
+        If the code has invalid Python syntax, or if it contains
+        non-literal code.
+
+    Examples
+    --------
+    >>> np.safe_eval('1')
+    1
+    >>> np.safe_eval('[1, 2, 3]')
+    [1, 2, 3]
+    >>> np.safe_eval('{"foo": ("bar", 10.0)}')
+    {'foo': ('bar', 10.0)}
+
+    >>> np.safe_eval('import os')
+    Traceback (most recent call last):
+      ...
+    SyntaxError: invalid syntax
+
+    >>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
+    Traceback (most recent call last):
+      ...
+    ValueError: malformed node or string: <_ast.Call object at 0x...>
+
+    """
+
+    # Deprecated in NumPy 2.0, 2023-07-11
+    warnings.warn(
+        "`safe_eval` is deprecated. Use `ast.literal_eval` instead. "
+        "Be aware of security implications, such as memory exhaustion "
+        "based attacks (deprecated in NumPy 2.0)",
+        DeprecationWarning,
+        stacklevel=2
+    )
+
+    # Local import to speed up numpy's import time.
+    import ast
+    return ast.literal_eval(source)
+
+
+def _median_nancheck(data, result, axis):
+    """
+    Utility function to check median result from data for NaN values at the end
+    and return NaN in that case. Input result can also be a MaskedArray.
+
+    Parameters
+    ----------
+    data : array
+        Sorted input data to median function
+    result : Array or MaskedArray
+        Result of median function.
+    axis : int
+        Axis along which the median was computed.
+
+    Returns
+    -------
+    result : scalar or ndarray
+        Median or NaN in axes which contained NaN in the input.  If the input
+        was an array, NaN will be inserted in-place.  If a scalar, either the
+        input itself or a scalar NaN.
+    """
+    if data.size == 0:
+        return result
+    potential_nans = data.take(-1, axis=axis)
+    n = np.isnan(potential_nans)
+    # masked NaN values are ok, although for masked the copyto may fail for
+    # unmasked ones (this was always broken) when the result is a scalar.
+    if np.ma.isMaskedArray(n):
+        n = n.filled(False)
+
+    if not n.any():
+        return result
+
+    # Without given output, it is possible that the current result is a
+    # numpy scalar, which is not writeable.  If so, just return nan.
+    if isinstance(result, np.generic):
+        return potential_nans
+
+    # Otherwise copy NaNs (if there are any)
+    np.copyto(result, potential_nans, where=n)
+    return result
+
+def _opt_info():
+    """
+    Returns a string containing the CPU features supported
+    by the current build.
+
+    The format of the string can be explained as follows:
+        - Dispatched features supported by the running machine end with `*`.
+        - Dispatched features not supported by the running machine
+          end with `?`.
+        - Remaining features represent the baseline.
+
+    Returns:
+        str: A formatted string indicating the supported CPU features.
+    """
+    from numpy._core._multiarray_umath import (
+        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+    )
+
+    if len(__cpu_baseline__) == 0 and len(__cpu_dispatch__) == 0:
+        return ''
+
+    enabled_features = ' '.join(__cpu_baseline__)
+    for feature in __cpu_dispatch__:
+        if __cpu_features__[feature]:
+            enabled_features += f" {feature}*"
+        else:
+            enabled_features += f" {feature}?"
+
+    return enabled_features
+
+def drop_metadata(dtype, /):
+    """
+    Returns the dtype unchanged if it contained no metadata or a copy of the
+    dtype if it (or any of its structure dtypes) contained metadata.
+
+    This utility is used by `np.save` and `np.savez` to drop metadata before
+    saving.
+
+    .. note::
+
+        Due to its limitation this function may move to a more appropriate
+        home or change in the future and is considered semi-public API only.
+
+    .. warning::
+
+        This function does not preserve more strange things like record dtypes
+        and user dtypes may simply return the wrong thing.  If you need to be
+        sure about the latter, check the result with:
+        ``np.can_cast(new_dtype, dtype, casting="no")``.
+
+    """
+    if dtype.fields is not None:
+        found_metadata = dtype.metadata is not None
+
+        names = []
+        formats = []
+        offsets = []
+        titles = []
+        for name, field in dtype.fields.items():
+            field_dt = drop_metadata(field[0])
+            if field_dt is not field[0]:
+                found_metadata = True
+
+            names.append(name)
+            formats.append(field_dt)
+            offsets.append(field[1])
+            titles.append(None if len(field) < 3 else field[2])
+
+        if not found_metadata:
+            return dtype
+
+        structure = dict(
+            names=names, formats=formats, offsets=offsets, titles=titles,
+            itemsize=dtype.itemsize)
+
+        # NOTE: Could pass (dtype.type, structure) to preserve record dtypes...
+        return np.dtype(structure, align=dtype.isalignedstruct)
+    elif dtype.subdtype is not None:
+        # subarray dtype
+        subdtype, shape = dtype.subdtype
+        new_subdtype = drop_metadata(subdtype)
+        if dtype.metadata is None and new_subdtype is subdtype:
+            return dtype
+
+        return np.dtype((new_subdtype, shape))
+    else:
+        # Normal unstructured dtype
+        if dtype.metadata is None:
+            return dtype
+        # Note that `dt.str` doesn't round-trip e.g. for user-dtypes.
+        return np.dtype(dtype.str)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2a9eb76a5b381ecb476af0e6fdf6d839c4a10f66
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_utils_impl.pyi
@@ -0,0 +1,7 @@
+from _typeshed import SupportsWrite
+
+__all__ = ["get_include", "info", "show_runtime"]
+
+def get_include() -> str: ...
+def show_runtime() -> None: ...
+def info(object: object = ..., maxwidth: int = ..., output: SupportsWrite[str] | None = ..., toplevel: str = ...) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..929f8a1c6685d587663086a78f6379dd6ba684ee
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.py
@@ -0,0 +1,155 @@
+"""Utility to compare (NumPy) version strings.
+
+The NumpyVersion class allows properly comparing numpy version strings.
+The LooseVersion and StrictVersion classes that distutils provides don't
+work; they don't recognize anything like alpha/beta/rc/dev versions.
+
+"""
+import re
+
+
+__all__ = ['NumpyVersion']
+
+
+class NumpyVersion:
+    """Parse and compare numpy version strings.
+
+    NumPy has the following versioning scheme (numbers given are examples; they
+    can be > 9 in principle):
+
+    - Released version: '1.8.0', '1.8.1', etc.
+    - Alpha: '1.8.0a1', '1.8.0a2', etc.
+    - Beta: '1.8.0b1', '1.8.0b2', etc.
+    - Release candidates: '1.8.0rc1', '1.8.0rc2', etc.
+    - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended)
+    - Development versions after a1: '1.8.0a1.dev-f1234afa',
+                                     '1.8.0b2.dev-f1234afa',
+                                     '1.8.1rc1.dev-f1234afa', etc.
+    - Development versions (no git hash available): '1.8.0.dev-Unknown'
+
+    Comparing needs to be done against a valid version string or other
+    `NumpyVersion` instance. Note that all development versions of the same
+    (pre-)release compare equal.
+
+    Parameters
+    ----------
+    vstring : str
+        NumPy version string (``np.__version__``).
+
+    Examples
+    --------
+    >>> from numpy.lib import NumpyVersion
+    >>> if NumpyVersion(np.__version__) < '1.7.0':
+    ...     print('skip')
+    >>> # skip
+
+    >>> NumpyVersion('1.7')  # raises ValueError, add ".0"
+    Traceback (most recent call last):
+        ...
+    ValueError: Not a valid numpy version string
+
+    """
+
+    __module__ = "numpy.lib"
+
+    def __init__(self, vstring):
+        self.vstring = vstring
+        ver_main = re.match(r'\d+\.\d+\.\d+', vstring)
+        if not ver_main:
+            raise ValueError("Not a valid numpy version string")
+
+        self.version = ver_main.group()
+        self.major, self.minor, self.bugfix = [int(x) for x in
+            self.version.split('.')]
+        if len(vstring) == ver_main.end():
+            self.pre_release = 'final'
+        else:
+            alpha = re.match(r'a\d', vstring[ver_main.end():])
+            beta = re.match(r'b\d', vstring[ver_main.end():])
+            rc = re.match(r'rc\d', vstring[ver_main.end():])
+            pre_rel = [m for m in [alpha, beta, rc] if m is not None]
+            if pre_rel:
+                self.pre_release = pre_rel[0].group()
+            else:
+                self.pre_release = ''
+
+        self.is_devversion = bool(re.search(r'.dev', vstring))
+
+    def _compare_version(self, other):
+        """Compare major.minor.bugfix"""
+        if self.major == other.major:
+            if self.minor == other.minor:
+                if self.bugfix == other.bugfix:
+                    vercmp = 0
+                elif self.bugfix > other.bugfix:
+                    vercmp = 1
+                else:
+                    vercmp = -1
+            elif self.minor > other.minor:
+                vercmp = 1
+            else:
+                vercmp = -1
+        elif self.major > other.major:
+            vercmp = 1
+        else:
+            vercmp = -1
+
+        return vercmp
+
+    def _compare_pre_release(self, other):
+        """Compare alpha/beta/rc/final."""
+        if self.pre_release == other.pre_release:
+            vercmp = 0
+        elif self.pre_release == 'final':
+            vercmp = 1
+        elif other.pre_release == 'final':
+            vercmp = -1
+        elif self.pre_release > other.pre_release:
+            vercmp = 1
+        else:
+            vercmp = -1
+
+        return vercmp
+
+    def _compare(self, other):
+        if not isinstance(other, (str, NumpyVersion)):
+            raise ValueError("Invalid object to compare with NumpyVersion.")
+
+        if isinstance(other, str):
+            other = NumpyVersion(other)
+
+        vercmp = self._compare_version(other)
+        if vercmp == 0:
+            # Same x.y.z version, check for alpha/beta/rc
+            vercmp = self._compare_pre_release(other)
+            if vercmp == 0:
+                # Same version and same pre-release, check if dev version
+                if self.is_devversion is other.is_devversion:
+                    vercmp = 0
+                elif self.is_devversion:
+                    vercmp = -1
+                else:
+                    vercmp = 1
+
+        return vercmp
+
+    def __lt__(self, other):
+        return self._compare(other) < 0
+
+    def __le__(self, other):
+        return self._compare(other) <= 0
+
+    def __eq__(self, other):
+        return self._compare(other) == 0
+
+    def __ne__(self, other):
+        return self._compare(other) != 0
+
+    def __gt__(self, other):
+        return self._compare(other) > 0
+
+    def __ge__(self, other):
+        return self._compare(other) >= 0
+
+    def __repr__(self):
+        return "NumpyVersion(%s)" % self.vstring
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c53ef795f9266f5233d8c86e696bd8e1f3699557
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/_version.pyi
@@ -0,0 +1,17 @@
+__all__ = ["NumpyVersion"]
+
+class NumpyVersion:
+    vstring: str
+    version: str
+    major: int
+    minor: int
+    bugfix: int
+    pre_release: str
+    is_devversion: bool
+    def __init__(self, vstring: str) -> None: ...
+    def __lt__(self, other: str | NumpyVersion) -> bool: ...
+    def __le__(self, other: str | NumpyVersion) -> bool: ...
+    def __eq__(self, other: str | NumpyVersion) -> bool: ...  # type: ignore[override]
+    def __ne__(self, other: str | NumpyVersion) -> bool: ...  # type: ignore[override]
+    def __gt__(self, other: str | NumpyVersion) -> bool: ...
+    def __ge__(self, other: str | NumpyVersion) -> bool: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4e7976131d268b307d8ac3bb13034d9e425f6e6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.py
@@ -0,0 +1,7 @@
+from ._array_utils_impl import (
+    __all__,
+    __doc__,
+    byte_bounds,
+    normalize_axis_index,
+    normalize_axis_tuple,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4b9ebe334a1f211a21aac0cfd1605166ca909d1b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/array_utils.pyi
@@ -0,0 +1,6 @@
+from ._array_utils_impl import (
+    __all__ as __all__,
+    byte_bounds as byte_bounds,
+    normalize_axis_index as normalize_axis_index,
+    normalize_axis_tuple as normalize_axis_tuple,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.py
new file mode 100644
index 0000000000000000000000000000000000000000..a22c096b246ce7109ca7b39f7735e4e38ab1e036
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.py
@@ -0,0 +1,1008 @@
+"""
+Binary serialization
+
+NPY format
+==========
+
+A simple format for saving numpy arrays to disk with the full
+information about them.
+
+The ``.npy`` format is the standard binary file format in NumPy for
+persisting a *single* arbitrary NumPy array on disk. The format stores all
+of the shape and dtype information necessary to reconstruct the array
+correctly even on another machine with a different architecture.
+The format is designed to be as simple as possible while achieving
+its limited goals.
+
+The ``.npz`` format is the standard format for persisting *multiple* NumPy
+arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
+files, one for each array.
+
+Capabilities
+------------
+
+- Can represent all NumPy arrays including nested record arrays and
+  object arrays.
+
+- Represents the data in its native binary form.
+
+- Supports Fortran-contiguous arrays directly.
+
+- Stores all of the necessary information to reconstruct the array
+  including shape and dtype on a machine of a different
+  architecture.  Both little-endian and big-endian arrays are
+  supported, and a file with little-endian numbers will yield
+  a little-endian array on any machine reading the file. The
+  types are described in terms of their actual sizes. For example,
+  if a machine with a 64-bit C "long int" writes out an array with
+  "long ints", a reading machine with 32-bit C "long ints" will yield
+  an array with 64-bit integers.
+
+- Is straightforward to reverse engineer. Datasets often live longer than
+  the programs that created them. A competent developer should be
+  able to create a solution in their preferred programming language to
+  read most ``.npy`` files that they have been given without much
+  documentation.
+
+- Allows memory-mapping of the data. See `open_memmap`.
+
+- Can be read from a filelike stream object instead of an actual file.
+
+- Stores object arrays, i.e. arrays containing elements that are arbitrary
+  Python objects. Files with object arrays are not to be mmapable, but
+  can be read and written to disk.
+
+Limitations
+-----------
+
+- Arbitrary subclasses of numpy.ndarray are not completely preserved.
+  Subclasses will be accepted for writing, but only the array data will
+  be written out. A regular numpy.ndarray object will be created
+  upon reading the file.
+
+.. warning::
+
+  Due to limitations in the interpretation of structured dtypes, dtypes
+  with fields with empty names will have the names replaced by 'f0', 'f1',
+  etc. Such arrays will not round-trip through the format entirely
+  accurately. The data is intact; only the field names will differ. We are
+  working on a fix for this. This fix will not require a change in the
+  file format. The arrays with such structures can still be saved and
+  restored, and the correct dtype may be restored by using the
+  ``loadedarray.view(correct_dtype)`` method.
+
+File extensions
+---------------
+
+We recommend using the ``.npy`` and ``.npz`` extensions for files saved
+in this format. This is by no means a requirement; applications may wish
+to use these file formats but use an extension specific to the
+application. In the absence of an obvious alternative, however,
+we suggest using ``.npy`` and ``.npz``.
+
+Version numbering
+-----------------
+
+The version numbering of these formats is independent of NumPy version
+numbering. If the format is upgraded, the code in `numpy.io` will still
+be able to read and write Version 1.0 files.
+
+Format Version 1.0
+------------------
+
+The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
+
+The next 1 byte is an unsigned byte: the major version number of the file
+format, e.g. ``\\x01``.
+
+The next 1 byte is an unsigned byte: the minor version number of the file
+format, e.g. ``\\x00``. Note: the version of the file format is not tied
+to the version of the numpy package.
+
+The next 2 bytes form a little-endian unsigned short int: the length of
+the header data HEADER_LEN.
+
+The next HEADER_LEN bytes form the header data describing the array's
+format. It is an ASCII string which contains a Python literal expression
+of a dictionary. It is terminated by a newline (``\\n``) and padded with
+spaces (``\\x20``) to make the total of
+``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible
+by 64 for alignment purposes.
+
+The dictionary contains three keys:
+
+    "descr" : dtype.descr
+      An object that can be passed as an argument to the `numpy.dtype`
+      constructor to create the array's dtype.
+    "fortran_order" : bool
+      Whether the array data is Fortran-contiguous or not. Since
+      Fortran-contiguous arrays are a common form of non-C-contiguity,
+      we allow them to be written directly to disk for efficiency.
+    "shape" : tuple of int
+      The shape of the array.
+
+For repeatability and readability, the dictionary keys are sorted in
+alphabetic order. This is for convenience only. A writer SHOULD implement
+this if possible. A reader MUST NOT depend on this.
+
+Following the header comes the array data. If the dtype contains Python
+objects (i.e. ``dtype.hasobject is True``), then the data is a Python
+pickle of the array. Otherwise the data is the contiguous (either C-
+or Fortran-, depending on ``fortran_order``) bytes of the array.
+Consumers can figure out the number of bytes by multiplying the number
+of elements given by the shape (noting that ``shape=()`` means there is
+1 element) by ``dtype.itemsize``.
+
+Format Version 2.0
+------------------
+
+The version 1.0 format only allowed the array header to have a total size of
+65535 bytes.  This can be exceeded by structured arrays with a large number of
+columns.  The version 2.0 format extends the header size to 4 GiB.
+`numpy.save` will automatically save in 2.0 format if the data requires it,
+else it will always use the more compatible 1.0 format.
+
+The description of the fourth element of the header therefore has become:
+"The next 4 bytes form a little-endian unsigned int: the length of the header
+data HEADER_LEN."
+
+Format Version 3.0
+------------------
+
+This version replaces the ASCII string (which in practice was latin1) with
+a utf8-encoded string, so supports structured types with any unicode field
+names.
+
+Notes
+-----
+The ``.npy`` format, including motivation for creating it and a comparison of
+alternatives, is described in the
+:doc:`"npy-format" NEP `, however details have
+evolved with time and this document is more current.
+
+"""
+import io
+import os
+import pickle
+import warnings
+
+import numpy
+from numpy.lib._utils_impl import drop_metadata
+
+
+__all__ = []
+
+drop_metadata.__module__ = "numpy.lib.format"
+
+EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'}
+MAGIC_PREFIX = b'\x93NUMPY'
+MAGIC_LEN = len(MAGIC_PREFIX) + 2
+ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096
+BUFFER_SIZE = 2**18  # size of buffer for reading npz files in bytes
+# allow growth within the address space of a 64 bit machine along one axis
+GROWTH_AXIS_MAX_DIGITS = 21  # = len(str(8*2**64-1)) hypothetical int1 dtype
+
+# difference between version 1.0 and 2.0 is a 4 byte (I) header length
+# instead of 2 bytes (H) allowing storage of large structured arrays
+_header_size_info = {
+    (1, 0): (' 255:
+        raise ValueError("major version must be 0 <= major < 256")
+    if minor < 0 or minor > 255:
+        raise ValueError("minor version must be 0 <= minor < 256")
+    return MAGIC_PREFIX + bytes([major, minor])
+
+def read_magic(fp):
+    """ Read the magic string to get the version of the file format.
+
+    Parameters
+    ----------
+    fp : filelike object
+
+    Returns
+    -------
+    major : int
+    minor : int
+    """
+    magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")
+    if magic_str[:-2] != MAGIC_PREFIX:
+        msg = "the magic string is not correct; expected %r, got %r"
+        raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
+    major, minor = magic_str[-2:]
+    return major, minor
+
+
+def dtype_to_descr(dtype):
+    """
+    Get a serializable descriptor from the dtype.
+
+    The .descr attribute of a dtype object cannot be round-tripped through
+    the dtype() constructor. Simple types, like dtype('float32'), have
+    a descr which looks like a record array with one field with '' as
+    a name. The dtype() constructor interprets this as a request to give
+    a default name.  Instead, we construct descriptor that can be passed to
+    dtype().
+
+    Parameters
+    ----------
+    dtype : dtype
+        The dtype of the array that will be written to disk.
+
+    Returns
+    -------
+    descr : object
+        An object that can be passed to `numpy.dtype()` in order to
+        replicate the input dtype.
+
+    """
+    # NOTE: that drop_metadata may not return the right dtype e.g. for user
+    #       dtypes.  In that case our code below would fail the same, though.
+    new_dtype = drop_metadata(dtype)
+    if new_dtype is not dtype:
+        warnings.warn("metadata on a dtype is not saved to an npy/npz. "
+                      "Use another format (such as pickle) to store it.",
+                      UserWarning, stacklevel=2)
+    dtype = new_dtype
+
+    if dtype.names is not None:
+        # This is a record array. The .descr is fine.  XXX: parts of the
+        # record array with an empty name, like padding bytes, still get
+        # fiddled with. This needs to be fixed in the C implementation of
+        # dtype().
+        return dtype.descr
+    elif not type(dtype)._legacy:
+        # this must be a user-defined dtype since numpy does not yet expose any
+        # non-legacy dtypes in the public API
+        #
+        # non-legacy dtypes don't yet have __array_interface__
+        # support. Instead, as a hack, we use pickle to save the array, and lie
+        # that the dtype is object. When the array is loaded, the descriptor is
+        # unpickled with the array and the object dtype in the header is
+        # discarded.
+        #
+        # a future NEP should define a way to serialize user-defined
+        # descriptors and ideally work out the possible security implications
+        warnings.warn("Custom dtypes are saved as python objects using the "
+                      "pickle protocol. Loading this file requires "
+                      "allow_pickle=True to be set.",
+                      UserWarning, stacklevel=2)
+        return "|O"
+    else:
+        return dtype.str
+
+def descr_to_dtype(descr):
+    """
+    Returns a dtype based off the given description.
+
+    This is essentially the reverse of `~lib.format.dtype_to_descr`. It will
+    remove the valueless padding fields created by, i.e. simple fields like
+    dtype('float32'), and then convert the description to its corresponding
+    dtype.
+
+    Parameters
+    ----------
+    descr : object
+        The object retrieved by dtype.descr. Can be passed to
+        `numpy.dtype` in order to replicate the input dtype.
+
+    Returns
+    -------
+    dtype : dtype
+        The dtype constructed by the description.
+
+    """
+    if isinstance(descr, str):
+        # No padding removal needed
+        return numpy.dtype(descr)
+    elif isinstance(descr, tuple):
+        # subtype, will always have a shape descr[1]
+        dt = descr_to_dtype(descr[0])
+        return numpy.dtype((dt, descr[1]))
+
+    titles = []
+    names = []
+    formats = []
+    offsets = []
+    offset = 0
+    for field in descr:
+        if len(field) == 2:
+            name, descr_str = field
+            dt = descr_to_dtype(descr_str)
+        else:
+            name, descr_str, shape = field
+            dt = numpy.dtype((descr_to_dtype(descr_str), shape))
+
+        # Ignore padding bytes, which will be void bytes with '' as name
+        # Once support for blank names is removed, only "if name == ''" needed)
+        is_pad = (name == '' and dt.type is numpy.void and dt.names is None)
+        if not is_pad:
+            title, name = name if isinstance(name, tuple) else (None, name)
+            titles.append(title)
+            names.append(name)
+            formats.append(dt)
+            offsets.append(offset)
+        offset += dt.itemsize
+
+    return numpy.dtype({'names': names, 'formats': formats, 'titles': titles,
+                        'offsets': offsets, 'itemsize': offset})
+
+def header_data_from_array_1_0(array):
+    """ Get the dictionary of header metadata from a numpy.ndarray.
+
+    Parameters
+    ----------
+    array : numpy.ndarray
+
+    Returns
+    -------
+    d : dict
+        This has the appropriate entries for writing its string representation
+        to the header of the file.
+    """
+    d = {'shape': array.shape}
+    if array.flags.c_contiguous:
+        d['fortran_order'] = False
+    elif array.flags.f_contiguous:
+        d['fortran_order'] = True
+    else:
+        # Totally non-contiguous data. We will have to make it C-contiguous
+        # before writing. Note that we need to test for C_CONTIGUOUS first
+        # because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
+        d['fortran_order'] = False
+
+    d['descr'] = dtype_to_descr(array.dtype)
+    return d
+
+
+def _wrap_header(header, version):
+    """
+    Takes a stringified header, and attaches the prefix and padding to it
+    """
+    import struct
+    assert version is not None
+    fmt, encoding = _header_size_info[version]
+    header = header.encode(encoding)
+    hlen = len(header) + 1
+    padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN)
+    try:
+        header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen)
+    except struct.error:
+        msg = "Header length {} too big for version={}".format(hlen, version)
+        raise ValueError(msg) from None
+
+    # Pad the header with spaces and a final newline such that the magic
+    # string, the header-length short and the header are aligned on a
+    # ARRAY_ALIGN byte boundary.  This supports memory mapping of dtypes
+    # aligned up to ARRAY_ALIGN on systems like Linux where mmap()
+    # offset must be page-aligned (i.e. the beginning of the file).
+    return header_prefix + header + b' '*padlen + b'\n'
+
+
+def _wrap_header_guess_version(header):
+    """
+    Like `_wrap_header`, but chooses an appropriate version given the contents
+    """
+    try:
+        return _wrap_header(header, (1, 0))
+    except ValueError:
+        pass
+
+    try:
+        ret = _wrap_header(header, (2, 0))
+    except UnicodeEncodeError:
+        pass
+    else:
+        warnings.warn("Stored array in format 2.0. It can only be"
+                      "read by NumPy >= 1.9", UserWarning, stacklevel=2)
+        return ret
+
+    header = _wrap_header(header, (3, 0))
+    warnings.warn("Stored array in format 3.0. It can only be "
+                  "read by NumPy >= 1.17", UserWarning, stacklevel=2)
+    return header
+
+
+def _write_array_header(fp, d, version=None):
+    """ Write the header for an array and returns the version used
+
+    Parameters
+    ----------
+    fp : filelike object
+    d : dict
+        This has the appropriate entries for writing its string representation
+        to the header of the file.
+    version : tuple or None
+        None means use oldest that works. Providing an explicit version will
+        raise a ValueError if the format does not allow saving this data.
+        Default: None
+    """
+    header = ["{"]
+    for key, value in sorted(d.items()):
+        # Need to use repr here, since we eval these when reading
+        header.append("'%s': %s, " % (key, repr(value)))
+    header.append("}")
+    header = "".join(header)
+
+    # Add some spare space so that the array header can be modified in-place
+    # when changing the array size, e.g. when growing it by appending data at
+    # the end.
+    shape = d['shape']
+    header += " " * ((GROWTH_AXIS_MAX_DIGITS - len(repr(
+        shape[-1 if d['fortran_order'] else 0]
+    ))) if len(shape) > 0 else 0)
+
+    if version is None:
+        header = _wrap_header_guess_version(header)
+    else:
+        header = _wrap_header(header, version)
+    fp.write(header)
+
+def write_array_header_1_0(fp, d):
+    """ Write the header for an array using the 1.0 format.
+
+    Parameters
+    ----------
+    fp : filelike object
+    d : dict
+        This has the appropriate entries for writing its string
+        representation to the header of the file.
+    """
+    _write_array_header(fp, d, (1, 0))
+
+
+def write_array_header_2_0(fp, d):
+    """ Write the header for an array using the 2.0 format.
+        The 2.0 format allows storing very large structured arrays.
+
+    Parameters
+    ----------
+    fp : filelike object
+    d : dict
+        This has the appropriate entries for writing its string
+        representation to the header of the file.
+    """
+    _write_array_header(fp, d, (2, 0))
+
+def read_array_header_1_0(fp, max_header_size=_MAX_HEADER_SIZE):
+    """
+    Read an array header from a filelike object using the 1.0 file format
+    version.
+
+    This will leave the file object located just after the header.
+
+    Parameters
+    ----------
+    fp : filelike object
+        A file object or something with a `.read()` method like a file.
+
+    Returns
+    -------
+    shape : tuple of int
+        The shape of the array.
+    fortran_order : bool
+        The array data will be written out directly if it is either
+        C-contiguous or Fortran-contiguous. Otherwise, it will be made
+        contiguous before writing it out.
+    dtype : dtype
+        The dtype of the file's data.
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+
+    Raises
+    ------
+    ValueError
+        If the data is invalid.
+
+    """
+    return _read_array_header(
+            fp, version=(1, 0), max_header_size=max_header_size)
+
+def read_array_header_2_0(fp, max_header_size=_MAX_HEADER_SIZE):
+    """
+    Read an array header from a filelike object using the 2.0 file format
+    version.
+
+    This will leave the file object located just after the header.
+
+    Parameters
+    ----------
+    fp : filelike object
+        A file object or something with a `.read()` method like a file.
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+
+    Returns
+    -------
+    shape : tuple of int
+        The shape of the array.
+    fortran_order : bool
+        The array data will be written out directly if it is either
+        C-contiguous or Fortran-contiguous. Otherwise, it will be made
+        contiguous before writing it out.
+    dtype : dtype
+        The dtype of the file's data.
+
+    Raises
+    ------
+    ValueError
+        If the data is invalid.
+
+    """
+    return _read_array_header(
+            fp, version=(2, 0), max_header_size=max_header_size)
+
+
+def _filter_header(s):
+    """Clean up 'L' in npz header ints.
+
+    Cleans up the 'L' in strings representing integers. Needed to allow npz
+    headers produced in Python2 to be read in Python3.
+
+    Parameters
+    ----------
+    s : string
+        Npy file header.
+
+    Returns
+    -------
+    header : str
+        Cleaned up header.
+
+    """
+    import tokenize
+    from io import StringIO
+
+    tokens = []
+    last_token_was_number = False
+    for token in tokenize.generate_tokens(StringIO(s).readline):
+        token_type = token[0]
+        token_string = token[1]
+        if (last_token_was_number and
+                token_type == tokenize.NAME and
+                token_string == "L"):
+            continue
+        else:
+            tokens.append(token)
+        last_token_was_number = (token_type == tokenize.NUMBER)
+    return tokenize.untokenize(tokens)
+
+
+def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE):
+    """
+    see read_array_header_1_0
+    """
+    # Read an unsigned, little-endian short int which has the length of the
+    # header.
+    import ast
+    import struct
+    hinfo = _header_size_info.get(version)
+    if hinfo is None:
+        raise ValueError("Invalid version {!r}".format(version))
+    hlength_type, encoding = hinfo
+
+    hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length")
+    header_length = struct.unpack(hlength_type, hlength_str)[0]
+    header = _read_bytes(fp, header_length, "array header")
+    header = header.decode(encoding)
+    if len(header) > max_header_size:
+        raise ValueError(
+            f"Header info length ({len(header)}) is large and may not be safe "
+            "to load securely.\n"
+            "To allow loading, adjust `max_header_size` or fully trust "
+            "the `.npy` file using `allow_pickle=True`.\n"
+            "For safety against large resource use or crashes, sandboxing "
+            "may be necessary.")
+
+    # The header is a pretty-printed string representation of a literal
+    # Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte
+    # boundary. The keys are strings.
+    #   "shape" : tuple of int
+    #   "fortran_order" : bool
+    #   "descr" : dtype.descr
+    # Versions (2, 0) and (1, 0) could have been created by a Python 2
+    # implementation before header filtering was implemented.
+    #
+    # For performance reasons, we try without _filter_header first though
+    try:
+        d = ast.literal_eval(header)
+    except SyntaxError as e:
+        if version <= (2, 0):
+            header = _filter_header(header)
+            try:
+                d = ast.literal_eval(header)
+            except SyntaxError as e2:
+                msg = "Cannot parse header: {!r}"
+                raise ValueError(msg.format(header)) from e2
+            else:
+                warnings.warn(
+                    "Reading `.npy` or `.npz` file required additional "
+                    "header parsing as it was created on Python 2. Save the "
+                    "file again to speed up loading and avoid this warning.",
+                    UserWarning, stacklevel=4)
+        else:
+            msg = "Cannot parse header: {!r}"
+            raise ValueError(msg.format(header)) from e
+    if not isinstance(d, dict):
+        msg = "Header is not a dictionary: {!r}"
+        raise ValueError(msg.format(d))
+
+    if EXPECTED_KEYS != d.keys():
+        keys = sorted(d.keys())
+        msg = "Header does not contain the correct keys: {!r}"
+        raise ValueError(msg.format(keys))
+
+    # Sanity-check the values.
+    if (not isinstance(d['shape'], tuple) or
+            not all(isinstance(x, int) for x in d['shape'])):
+        msg = "shape is not valid: {!r}"
+        raise ValueError(msg.format(d['shape']))
+    if not isinstance(d['fortran_order'], bool):
+        msg = "fortran_order is not a valid bool: {!r}"
+        raise ValueError(msg.format(d['fortran_order']))
+    try:
+        dtype = descr_to_dtype(d['descr'])
+    except TypeError as e:
+        msg = "descr is not a valid dtype descriptor: {!r}"
+        raise ValueError(msg.format(d['descr'])) from e
+
+    return d['shape'], d['fortran_order'], dtype
+
+def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
+    """
+    Write an array to an NPY file, including a header.
+
+    If the array is neither C-contiguous nor Fortran-contiguous AND the
+    file_like object is not a real file object, this function will have to
+    copy data in memory.
+
+    Parameters
+    ----------
+    fp : file_like object
+        An open, writable file object, or similar object with a
+        ``.write()`` method.
+    array : ndarray
+        The array to write to disk.
+    version : (int, int) or None, optional
+        The version number of the format. None means use the oldest
+        supported version that is able to store the data.  Default: None
+    allow_pickle : bool, optional
+        Whether to allow writing pickled data. Default: True
+    pickle_kwargs : dict, optional
+        Additional keyword arguments to pass to pickle.dump, excluding
+        'protocol'. These are only useful when pickling objects in object
+        arrays on Python 3 to Python 2 compatible format.
+
+    Raises
+    ------
+    ValueError
+        If the array cannot be persisted. This includes the case of
+        allow_pickle=False and array being an object array.
+    Various other errors
+        If the array contains Python objects as part of its dtype, the
+        process of pickling them may raise various errors if the objects
+        are not picklable.
+
+    """
+    _check_version(version)
+    _write_array_header(fp, header_data_from_array_1_0(array), version)
+
+    if array.itemsize == 0:
+        buffersize = 0
+    else:
+        # Set buffer size to 16 MiB to hide the Python loop overhead.
+        buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)
+
+    dtype_class = type(array.dtype)
+
+    if array.dtype.hasobject or not dtype_class._legacy:
+        # We contain Python objects so we cannot write out the data
+        # directly.  Instead, we will pickle it out
+        if not allow_pickle:
+            if array.dtype.hasobject:
+                raise ValueError("Object arrays cannot be saved when "
+                                 "allow_pickle=False")
+            if not dtype_class._legacy:
+                raise ValueError("User-defined dtypes cannot be saved "
+                                 "when allow_pickle=False")
+        if pickle_kwargs is None:
+            pickle_kwargs = {}
+        pickle.dump(array, fp, protocol=4, **pickle_kwargs)
+    elif array.flags.f_contiguous and not array.flags.c_contiguous:
+        if isfileobj(fp):
+            array.T.tofile(fp)
+        else:
+            for chunk in numpy.nditer(
+                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
+                    buffersize=buffersize, order='F'):
+                fp.write(chunk.tobytes('C'))
+    else:
+        if isfileobj(fp):
+            array.tofile(fp)
+        else:
+            for chunk in numpy.nditer(
+                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
+                    buffersize=buffersize, order='C'):
+                fp.write(chunk.tobytes('C'))
+
+
+def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,
+               max_header_size=_MAX_HEADER_SIZE):
+    """
+    Read an array from an NPY file.
+
+    Parameters
+    ----------
+    fp : file_like object
+        If this is not a real file object, then this may take extra memory
+        and time.
+    allow_pickle : bool, optional
+        Whether to allow writing pickled data. Default: False
+    pickle_kwargs : dict
+        Additional keyword arguments to pass to pickle.load. These are only
+        useful when loading object arrays saved on Python 2 when using
+        Python 3.
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+        This option is ignored when `allow_pickle` is passed.  In that case
+        the file is by definition trusted and the limit is unnecessary.
+
+    Returns
+    -------
+    array : ndarray
+        The array from the data on disk.
+
+    Raises
+    ------
+    ValueError
+        If the data is invalid, or allow_pickle=False and the file contains
+        an object array.
+
+    """
+    if allow_pickle:
+        # Effectively ignore max_header_size, since `allow_pickle` indicates
+        # that the input is fully trusted.
+        max_header_size = 2**64
+
+    version = read_magic(fp)
+    _check_version(version)
+    shape, fortran_order, dtype = _read_array_header(
+            fp, version, max_header_size=max_header_size)
+    if len(shape) == 0:
+        count = 1
+    else:
+        count = numpy.multiply.reduce(shape, dtype=numpy.int64)
+
+    # Now read the actual data.
+    if dtype.hasobject:
+        # The array contained Python objects. We need to unpickle the data.
+        if not allow_pickle:
+            raise ValueError("Object arrays cannot be loaded when "
+                             "allow_pickle=False")
+        if pickle_kwargs is None:
+            pickle_kwargs = {}
+        try:
+            array = pickle.load(fp, **pickle_kwargs)
+        except UnicodeError as err:
+            # Friendlier error message
+            raise UnicodeError("Unpickling a python object failed: %r\n"
+                               "You may need to pass the encoding= option "
+                               "to numpy.load" % (err,)) from err
+    else:
+        if isfileobj(fp):
+            # We can use the fast fromfile() function.
+            array = numpy.fromfile(fp, dtype=dtype, count=count)
+        else:
+            # This is not a real file. We have to read it the
+            # memory-intensive way.
+            # crc32 module fails on reads greater than 2 ** 32 bytes,
+            # breaking large reads from gzip streams. Chunk reads to
+            # BUFFER_SIZE bytes to avoid issue and reduce memory overhead
+            # of the read. In non-chunked case count < max_read_count, so
+            # only one read is performed.
+
+            # Use np.ndarray instead of np.empty since the latter does
+            # not correctly instantiate zero-width string dtypes; see
+            # https://github.com/numpy/numpy/pull/6430
+            array = numpy.ndarray(count, dtype=dtype)
+
+            if dtype.itemsize > 0:
+                # If dtype.itemsize == 0 then there's nothing more to read
+                max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)
+
+                for i in range(0, count, max_read_count):
+                    read_count = min(max_read_count, count - i)
+                    read_size = int(read_count * dtype.itemsize)
+                    data = _read_bytes(fp, read_size, "array data")
+                    array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
+                                                             count=read_count)
+
+        if fortran_order:
+            array.shape = shape[::-1]
+            array = array.transpose()
+        else:
+            array.shape = shape
+
+    return array
+
+
+def open_memmap(filename, mode='r+', dtype=None, shape=None,
+                fortran_order=False, version=None, *,
+                max_header_size=_MAX_HEADER_SIZE):
+    """
+    Open a .npy file as a memory-mapped array.
+
+    This may be used to read an existing file or create a new one.
+
+    Parameters
+    ----------
+    filename : str or path-like
+        The name of the file on disk.  This may *not* be a file-like
+        object.
+    mode : str, optional
+        The mode in which to open the file; the default is 'r+'.  In
+        addition to the standard file modes, 'c' is also accepted to mean
+        "copy on write."  See `memmap` for the available mode strings.
+    dtype : data-type, optional
+        The data type of the array if we are creating a new file in "write"
+        mode, if not, `dtype` is ignored.  The default value is None, which
+        results in a data-type of `float64`.
+    shape : tuple of int
+        The shape of the array if we are creating a new file in "write"
+        mode, in which case this parameter is required.  Otherwise, this
+        parameter is ignored and is thus optional.
+    fortran_order : bool, optional
+        Whether the array should be Fortran-contiguous (True) or
+        C-contiguous (False, the default) if we are creating a new file in
+        "write" mode.
+    version : tuple of int (major, minor) or None
+        If the mode is a "write" mode, then this is the version of the file
+        format used to create the file.  None means use the oldest
+        supported version that is able to store the data.  Default: None
+    max_header_size : int, optional
+        Maximum allowed size of the header.  Large headers may not be safe
+        to load securely and thus require explicitly passing a larger value.
+        See :py:func:`ast.literal_eval()` for details.
+
+    Returns
+    -------
+    marray : memmap
+        The memory-mapped array.
+
+    Raises
+    ------
+    ValueError
+        If the data or the mode is invalid.
+    OSError
+        If the file is not found or cannot be opened correctly.
+
+    See Also
+    --------
+    numpy.memmap
+
+    """
+    if isfileobj(filename):
+        raise ValueError("Filename must be a string or a path-like object."
+                         "  Memmap cannot use existing file handles.")
+
+    if 'w' in mode:
+        # We are creating the file, not reading it.
+        # Check if we ought to create the file.
+        _check_version(version)
+        # Ensure that the given dtype is an authentic dtype object rather
+        # than just something that can be interpreted as a dtype object.
+        dtype = numpy.dtype(dtype)
+        if dtype.hasobject:
+            msg = "Array can't be memory-mapped: Python objects in dtype."
+            raise ValueError(msg)
+        d = dict(
+            descr=dtype_to_descr(dtype),
+            fortran_order=fortran_order,
+            shape=shape,
+        )
+        # If we got here, then it should be safe to create the file.
+        with open(os.fspath(filename), mode+'b') as fp:
+            _write_array_header(fp, d, version)
+            offset = fp.tell()
+    else:
+        # Read the header of the file first.
+        with open(os.fspath(filename), 'rb') as fp:
+            version = read_magic(fp)
+            _check_version(version)
+
+            shape, fortran_order, dtype = _read_array_header(
+                    fp, version, max_header_size=max_header_size)
+            if dtype.hasobject:
+                msg = "Array can't be memory-mapped: Python objects in dtype."
+                raise ValueError(msg)
+            offset = fp.tell()
+
+    if fortran_order:
+        order = 'F'
+    else:
+        order = 'C'
+
+    # We need to change a write-only mode to a read-write mode since we've
+    # already written data to the file.
+    if mode == 'w+':
+        mode = 'r+'
+
+    marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,
+        mode=mode, offset=offset)
+
+    return marray
+
+
+def _read_bytes(fp, size, error_template="ran out of data"):
+    """
+    Read from file-like object until size bytes are read.
+    Raises ValueError if not EOF is encountered before size bytes are read.
+    Non-blocking objects only supported if they derive from io objects.
+
+    Required as e.g. ZipExtFile in python 2.6 can return less data than
+    requested.
+    """
+    data = bytes()
+    while True:
+        # io files (default in python3) return None or raise on
+        # would-block, python2 file will truncate, probably nothing can be
+        # done about that.  note that regular files can't be non-blocking
+        try:
+            r = fp.read(size - len(data))
+            data += r
+            if len(r) == 0 or len(data) == size:
+                break
+        except BlockingIOError:
+            pass
+    if len(data) != size:
+        msg = "EOF: reading %s, expected %d bytes got %d"
+        raise ValueError(msg % (error_template, size, len(data)))
+    else:
+        return data
+
+
+def isfileobj(f):
+    if not isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter)):
+        return False
+    try:
+        # BufferedReader/Writer may raise OSError when
+        # fetching `fileno()` (e.g. when wrapping BytesIO).
+        f.fileno()
+        return True
+    except OSError:
+        return False
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..57c7e1e206e0a576eb9966898ac439ab3c7c1969
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/format.pyi
@@ -0,0 +1,22 @@
+from typing import Literal, Final
+
+__all__: list[str] = []
+
+EXPECTED_KEYS: Final[set[str]]
+MAGIC_PREFIX: Final[bytes]
+MAGIC_LEN: Literal[8]
+ARRAY_ALIGN: Literal[64]
+BUFFER_SIZE: Literal[262144]  # 2**18
+
+def magic(major, minor): ...
+def read_magic(fp): ...
+def dtype_to_descr(dtype): ...
+def descr_to_dtype(descr): ...
+def header_data_from_array_1_0(array): ...
+def write_array_header_1_0(fp, d): ...
+def write_array_header_2_0(fp, d): ...
+def read_array_header_1_0(fp): ...
+def read_array_header_2_0(fp): ...
+def write_array(fp, array, version=..., allow_pickle=..., pickle_kwargs=...): ...
+def read_array(fp, allow_pickle=..., pickle_kwargs=...): ...
+def open_memmap(filename, mode=..., dtype=..., shape=..., fortran_order=..., version=...): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.py
new file mode 100644
index 0000000000000000000000000000000000000000..4826440dd410948273832e402ccea583bcfb361b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.py
@@ -0,0 +1,95 @@
+"""
+Introspection helper functions.
+"""
+import re
+
+__all__ = ['opt_func_info']
+
+
+def opt_func_info(func_name=None, signature=None):
+    """
+    Returns a dictionary containing the currently supported CPU dispatched
+    features for all optimized functions.
+
+    Parameters
+    ----------
+    func_name : str (optional)
+        Regular expression to filter by function name.
+
+    signature : str (optional)
+        Regular expression to filter by data type.
+
+    Returns
+    -------
+    dict
+        A dictionary where keys are optimized function names and values are
+        nested dictionaries indicating supported targets based on data types.
+
+    Examples
+    --------
+    Retrieve dispatch information for functions named 'add' or 'sub' and
+    data types 'float64' or 'float32':
+
+    >>> import numpy as np
+    >>> dict = np.lib.introspect.opt_func_info(
+    ...     func_name="add|abs", signature="float64|complex64"
+    ... )
+    >>> import json
+    >>> print(json.dumps(dict, indent=2))
+        {
+          "absolute": {
+            "dd": {
+              "current": "SSE41",
+              "available": "SSE41 baseline(SSE SSE2 SSE3)"
+            },
+            "Ff": {
+              "current": "FMA3__AVX2",
+              "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
+            },
+            "Dd": {
+              "current": "FMA3__AVX2",
+              "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
+            }
+          },
+          "add": {
+            "ddd": {
+              "current": "FMA3__AVX2",
+              "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
+            },
+            "FFF": {
+              "current": "FMA3__AVX2",
+              "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
+            }
+          }
+        }
+
+    """
+    from numpy._core._multiarray_umath import (
+        __cpu_targets_info__ as targets, dtype
+    )
+
+    if func_name is not None:
+        func_pattern = re.compile(func_name)
+        matching_funcs = {
+            k: v for k, v in targets.items()
+            if func_pattern.search(k)
+        }
+    else:
+        matching_funcs = targets
+
+    if signature is not None:
+        sig_pattern = re.compile(signature)
+        matching_sigs = {}
+        for k, v in matching_funcs.items():
+            matching_chars = {}
+            for chars, targets in v.items():
+                if any(
+                    sig_pattern.search(c) or sig_pattern.search(dtype(c).name)
+                    for c in chars
+                ):
+                    matching_chars[chars] = targets
+            if matching_chars:
+                matching_sigs[k] = matching_chars
+    else:
+        matching_sigs = matching_funcs
+    return matching_sigs
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7929981cd6362ad7a992b50a74b37c700f00cbe2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/introspect.pyi
@@ -0,0 +1,3 @@
+__all__ = ["opt_func_info"]
+
+def opt_func_info(func_name: str | None = None, signature: str | None = None) -> dict[str, dict[str, dict[str, str]]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e78ac0990b34c9926f7f5a819d9652605d5a0ca
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.py
@@ -0,0 +1,182 @@
+"""
+Mixin classes for custom array types that don't inherit from ndarray.
+"""
+from numpy._core import umath as um
+
+
+__all__ = ['NDArrayOperatorsMixin']
+
+
+def _disables_array_ufunc(obj):
+    """True when __array_ufunc__ is set to None."""
+    try:
+        return obj.__array_ufunc__ is None
+    except AttributeError:
+        return False
+
+
+def _binary_method(ufunc, name):
+    """Implement a forward binary method with a ufunc, e.g., __add__."""
+    def func(self, other):
+        if _disables_array_ufunc(other):
+            return NotImplemented
+        return ufunc(self, other)
+    func.__name__ = '__{}__'.format(name)
+    return func
+
+
+def _reflected_binary_method(ufunc, name):
+    """Implement a reflected binary method with a ufunc, e.g., __radd__."""
+    def func(self, other):
+        if _disables_array_ufunc(other):
+            return NotImplemented
+        return ufunc(other, self)
+    func.__name__ = '__r{}__'.format(name)
+    return func
+
+
+def _inplace_binary_method(ufunc, name):
+    """Implement an in-place binary method with a ufunc, e.g., __iadd__."""
+    def func(self, other):
+        return ufunc(self, other, out=(self,))
+    func.__name__ = '__i{}__'.format(name)
+    return func
+
+
+def _numeric_methods(ufunc, name):
+    """Implement forward, reflected and inplace binary methods with a ufunc."""
+    return (_binary_method(ufunc, name),
+            _reflected_binary_method(ufunc, name),
+            _inplace_binary_method(ufunc, name))
+
+
+def _unary_method(ufunc, name):
+    """Implement a unary special method with a ufunc."""
+    def func(self):
+        return ufunc(self)
+    func.__name__ = '__{}__'.format(name)
+    return func
+
+
+class NDArrayOperatorsMixin:
+    """Mixin defining all operator special methods using __array_ufunc__.
+
+    This class implements the special methods for almost all of Python's
+    builtin operators defined in the `operator` module, including comparisons
+    (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by
+    deferring to the ``__array_ufunc__`` method, which subclasses must
+    implement.
+
+    It is useful for writing classes that do not inherit from `numpy.ndarray`,
+    but that should support arithmetic and numpy universal functions like
+    arrays as described in `A Mechanism for Overriding Ufuncs
+    `_.
+
+    As an trivial example, consider this implementation of an ``ArrayLike``
+    class that simply wraps a NumPy array and ensures that the result of any
+    arithmetic operation is also an ``ArrayLike`` object:
+
+        >>> import numbers
+        >>> class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
+        ...     def __init__(self, value):
+        ...         self.value = np.asarray(value)
+        ...
+        ...     # One might also consider adding the built-in list type to this
+        ...     # list, to support operations like np.add(array_like, list)
+        ...     _HANDLED_TYPES = (np.ndarray, numbers.Number)
+        ...
+        ...     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        ...         out = kwargs.get('out', ())
+        ...         for x in inputs + out:
+        ...             # Only support operations with instances of
+        ...             # _HANDLED_TYPES. Use ArrayLike instead of type(self)
+        ...             # for isinstance to allow subclasses that don't
+        ...             # override __array_ufunc__ to handle ArrayLike objects.
+        ...             if not isinstance(
+        ...                 x, self._HANDLED_TYPES + (ArrayLike,)
+        ...             ):
+        ...                 return NotImplemented
+        ...
+        ...         # Defer to the implementation of the ufunc
+        ...         # on unwrapped values.
+        ...         inputs = tuple(x.value if isinstance(x, ArrayLike) else x
+        ...                     for x in inputs)
+        ...         if out:
+        ...             kwargs['out'] = tuple(
+        ...                 x.value if isinstance(x, ArrayLike) else x
+        ...                 for x in out)
+        ...         result = getattr(ufunc, method)(*inputs, **kwargs)
+        ...
+        ...         if type(result) is tuple:
+        ...             # multiple return values
+        ...             return tuple(type(self)(x) for x in result)
+        ...         elif method == 'at':
+        ...             # no return value
+        ...             return None
+        ...         else:
+        ...             # one return value
+        ...             return type(self)(result)
+        ...
+        ...     def __repr__(self):
+        ...         return '%s(%r)' % (type(self).__name__, self.value)
+
+    In interactions between ``ArrayLike`` objects and numbers or numpy arrays,
+    the result is always another ``ArrayLike``:
+
+        >>> x = ArrayLike([1, 2, 3])
+        >>> x - 1
+        ArrayLike(array([0, 1, 2]))
+        >>> 1 - x
+        ArrayLike(array([ 0, -1, -2]))
+        >>> np.arange(3) - x
+        ArrayLike(array([-1, -1, -1]))
+        >>> x - np.arange(3)
+        ArrayLike(array([1, 1, 1]))
+
+    Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations
+    with arbitrary, unrecognized types. This ensures that interactions with
+    ArrayLike preserve a well-defined casting hierarchy.
+
+    """
+    __slots__ = ()
+    # Like np.ndarray, this mixin class implements "Option 1" from the ufunc
+    # overrides NEP.
+
+    # comparisons don't have reflected and in-place versions
+    __lt__ = _binary_method(um.less, 'lt')
+    __le__ = _binary_method(um.less_equal, 'le')
+    __eq__ = _binary_method(um.equal, 'eq')
+    __ne__ = _binary_method(um.not_equal, 'ne')
+    __gt__ = _binary_method(um.greater, 'gt')
+    __ge__ = _binary_method(um.greater_equal, 'ge')
+
+    # numeric methods
+    __add__, __radd__, __iadd__ = _numeric_methods(um.add, 'add')
+    __sub__, __rsub__, __isub__ = _numeric_methods(um.subtract, 'sub')
+    __mul__, __rmul__, __imul__ = _numeric_methods(um.multiply, 'mul')
+    __matmul__, __rmatmul__, __imatmul__ = _numeric_methods(
+        um.matmul, 'matmul')
+    # Python 3 does not use __div__, __rdiv__, or __idiv__
+    __truediv__, __rtruediv__, __itruediv__ = _numeric_methods(
+        um.true_divide, 'truediv')
+    __floordiv__, __rfloordiv__, __ifloordiv__ = _numeric_methods(
+        um.floor_divide, 'floordiv')
+    __mod__, __rmod__, __imod__ = _numeric_methods(um.remainder, 'mod')
+    __divmod__ = _binary_method(um.divmod, 'divmod')
+    __rdivmod__ = _reflected_binary_method(um.divmod, 'divmod')
+    # __idivmod__ does not exist
+    # TODO: handle the optional third argument for __pow__?
+    __pow__, __rpow__, __ipow__ = _numeric_methods(um.power, 'pow')
+    __lshift__, __rlshift__, __ilshift__ = _numeric_methods(
+        um.left_shift, 'lshift')
+    __rshift__, __rrshift__, __irshift__ = _numeric_methods(
+        um.right_shift, 'rshift')
+    __and__, __rand__, __iand__ = _numeric_methods(um.bitwise_and, 'and')
+    __xor__, __rxor__, __ixor__ = _numeric_methods(um.bitwise_xor, 'xor')
+    __or__, __ror__, __ior__ = _numeric_methods(um.bitwise_or, 'or')
+
+    # unary methods
+    __neg__ = _unary_method(um.negative, 'neg')
+    __pos__ = _unary_method(um.positive, 'pos')
+    __abs__ = _unary_method(um.absolute, 'abs')
+    __invert__ = _unary_method(um.invert, 'invert')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d13d0fe81df48c4e725e6090c699498a9518e4a6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/mixins.pyi
@@ -0,0 +1,74 @@
+from abc import ABCMeta, abstractmethod
+from typing import Literal as L, Any
+
+from numpy import ufunc
+
+__all__ = ["NDArrayOperatorsMixin"]
+
+# NOTE: `NDArrayOperatorsMixin` is not formally an abstract baseclass,
+# even though it's reliant on subclasses implementing `__array_ufunc__`
+
+# NOTE: The accepted input- and output-types of the various dunders are
+# completely dependent on how `__array_ufunc__` is implemented.
+# As such, only little type safety can be provided here.
+
+class NDArrayOperatorsMixin(metaclass=ABCMeta):
+    @abstractmethod
+    def __array_ufunc__(
+        self,
+        ufunc: ufunc,
+        method: L["__call__", "reduce", "reduceat", "accumulate", "outer", "at"],
+        *inputs: Any,
+        **kwargs: Any,
+    ) -> Any: ...
+    def __lt__(self, other: Any) -> Any: ...
+    def __le__(self, other: Any) -> Any: ...
+    def __eq__(self, other: Any) -> Any: ...
+    def __ne__(self, other: Any) -> Any: ...
+    def __gt__(self, other: Any) -> Any: ...
+    def __ge__(self, other: Any) -> Any: ...
+    def __add__(self, other: Any) -> Any: ...
+    def __radd__(self, other: Any) -> Any: ...
+    def __iadd__(self, other: Any) -> Any: ...
+    def __sub__(self, other: Any) -> Any: ...
+    def __rsub__(self, other: Any) -> Any: ...
+    def __isub__(self, other: Any) -> Any: ...
+    def __mul__(self, other: Any) -> Any: ...
+    def __rmul__(self, other: Any) -> Any: ...
+    def __imul__(self, other: Any) -> Any: ...
+    def __matmul__(self, other: Any) -> Any: ...
+    def __rmatmul__(self, other: Any) -> Any: ...
+    def __imatmul__(self, other: Any) -> Any: ...
+    def __truediv__(self, other: Any) -> Any: ...
+    def __rtruediv__(self, other: Any) -> Any: ...
+    def __itruediv__(self, other: Any) -> Any: ...
+    def __floordiv__(self, other: Any) -> Any: ...
+    def __rfloordiv__(self, other: Any) -> Any: ...
+    def __ifloordiv__(self, other: Any) -> Any: ...
+    def __mod__(self, other: Any) -> Any: ...
+    def __rmod__(self, other: Any) -> Any: ...
+    def __imod__(self, other: Any) -> Any: ...
+    def __divmod__(self, other: Any) -> Any: ...
+    def __rdivmod__(self, other: Any) -> Any: ...
+    def __pow__(self, other: Any) -> Any: ...
+    def __rpow__(self, other: Any) -> Any: ...
+    def __ipow__(self, other: Any) -> Any: ...
+    def __lshift__(self, other: Any) -> Any: ...
+    def __rlshift__(self, other: Any) -> Any: ...
+    def __ilshift__(self, other: Any) -> Any: ...
+    def __rshift__(self, other: Any) -> Any: ...
+    def __rrshift__(self, other: Any) -> Any: ...
+    def __irshift__(self, other: Any) -> Any: ...
+    def __and__(self, other: Any) -> Any: ...
+    def __rand__(self, other: Any) -> Any: ...
+    def __iand__(self, other: Any) -> Any: ...
+    def __xor__(self, other: Any) -> Any: ...
+    def __rxor__(self, other: Any) -> Any: ...
+    def __ixor__(self, other: Any) -> Any: ...
+    def __or__(self, other: Any) -> Any: ...
+    def __ror__(self, other: Any) -> Any: ...
+    def __ior__(self, other: Any) -> Any: ...
+    def __neg__(self) -> Any: ...
+    def __pos__(self) -> Any: ...
+    def __abs__(self) -> Any: ...
+    def __invert__(self) -> Any: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.py
new file mode 100644
index 0000000000000000000000000000000000000000..1003ef5be4b1940ddf6943a90d1bee786677c55e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.py
@@ -0,0 +1,3 @@
+from ._npyio_impl import (
+    __doc__, DataSource, NpzFile
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fd3ae8f5a28778764a94fd791e72816b4db51320
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/npyio.pyi
@@ -0,0 +1,5 @@
+from numpy.lib._npyio_impl import (
+    DataSource as DataSource,
+    NpzFile as NpzFile,
+    __doc__ as __doc__,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f4bae4f4721a4c16d9eb5950a97d0d2d3bb2779
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.py
@@ -0,0 +1,1685 @@
+"""
+Collection of utilities to manipulate structured arrays.
+
+Most of these functions were initially implemented by John Hunter for
+matplotlib.  They have been rewritten and extended for convenience.
+
+"""
+import itertools
+
+import numpy as np
+import numpy.ma as ma
+import numpy.ma.mrecords as mrec
+from numpy._core.overrides import array_function_dispatch
+from numpy.lib._iotools import _is_string_like
+
+
+__all__ = [
+    'append_fields', 'apply_along_fields', 'assign_fields_by_name',
+    'drop_fields', 'find_duplicates', 'flatten_descr',
+    'get_fieldstructure', 'get_names', 'get_names_flat',
+    'join_by', 'merge_arrays', 'rec_append_fields',
+    'rec_drop_fields', 'rec_join', 'recursive_fill_fields',
+    'rename_fields', 'repack_fields', 'require_fields',
+    'stack_arrays', 'structured_to_unstructured', 'unstructured_to_structured',
+    ]
+
+
+def _recursive_fill_fields_dispatcher(input, output):
+    return (input, output)
+
+
+@array_function_dispatch(_recursive_fill_fields_dispatcher)
+def recursive_fill_fields(input, output):
+    """
+    Fills fields from output with fields from input,
+    with support for nested structures.
+
+    Parameters
+    ----------
+    input : ndarray
+        Input array.
+    output : ndarray
+        Output array.
+
+    Notes
+    -----
+    * `output` should be at least the same size as `input`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)])
+    >>> b = np.zeros((3,), dtype=a.dtype)
+    >>> rfn.recursive_fill_fields(a, b)
+    array([(1, 10.), (2, 20.), (0,  0.)], dtype=[('A', '>> import numpy as np
+    >>> dt = np.dtype([(('a', 'A'), np.int64), ('b', np.double, 3)])
+    >>> dt.descr
+    [(('a', 'A'), '>> _get_fieldspec(dt)
+    [(('a', 'A'), dtype('int64')), ('b', dtype(('>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> rfn.get_names(np.empty((1,), dtype=[('A', int)]).dtype)
+    ('A',)
+    >>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]).dtype)
+    ('A', 'B')
+    >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
+    >>> rfn.get_names(adtype)
+    ('a', ('b', ('ba', 'bb')))
+    """
+    listnames = []
+    names = adtype.names
+    for name in names:
+        current = adtype[name]
+        if current.names is not None:
+            listnames.append((name, tuple(get_names(current))))
+        else:
+            listnames.append(name)
+    return tuple(listnames)
+
+
+def get_names_flat(adtype):
+    """
+    Returns the field names of the input datatype as a tuple. Input datatype
+    must have fields otherwise error is raised.
+    Nested structure are flattened beforehand.
+
+    Parameters
+    ----------
+    adtype : dtype
+        Input datatype
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A', int)]).dtype) is None
+    False
+    >>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', str)]).dtype)
+    ('A', 'B')
+    >>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
+    >>> rfn.get_names_flat(adtype)
+    ('a', 'b', 'ba', 'bb')
+    """
+    listnames = []
+    names = adtype.names
+    for name in names:
+        listnames.append(name)
+        current = adtype[name]
+        if current.names is not None:
+            listnames.extend(get_names_flat(current))
+    return tuple(listnames)
+
+
+def flatten_descr(ndtype):
+    """
+    Flatten a structured data-type description.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> ndtype = np.dtype([('a', '>> rfn.flatten_descr(ndtype)
+    (('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))
+
+    """
+    names = ndtype.names
+    if names is None:
+        return (('', ndtype),)
+    else:
+        descr = []
+        for field in names:
+            (typ, _) = ndtype.fields[field]
+            if typ.names is not None:
+                descr.extend(flatten_descr(typ))
+            else:
+                descr.append((field, typ))
+        return tuple(descr)
+
+
+def _zip_dtype(seqarrays, flatten=False):
+    newdtype = []
+    if flatten:
+        for a in seqarrays:
+            newdtype.extend(flatten_descr(a.dtype))
+    else:
+        for a in seqarrays:
+            current = a.dtype
+            if current.names is not None and len(current.names) == 1:
+                # special case - dtypes of 1 field are flattened
+                newdtype.extend(_get_fieldspec(current))
+            else:
+                newdtype.append(('', current))
+    return np.dtype(newdtype)
+
+
+def _zip_descr(seqarrays, flatten=False):
+    """
+    Combine the dtype description of a series of arrays.
+
+    Parameters
+    ----------
+    seqarrays : sequence of arrays
+        Sequence of arrays
+    flatten : {boolean}, optional
+        Whether to collapse nested descriptions.
+    """
+    return _zip_dtype(seqarrays, flatten=flatten).descr
+
+
+def get_fieldstructure(adtype, lastname=None, parents=None,):
+    """
+    Returns a dictionary with fields indexing lists of their parent fields.
+
+    This function is used to simplify access to fields nested in other fields.
+
+    Parameters
+    ----------
+    adtype : np.dtype
+        Input datatype
+    lastname : optional
+        Last processed field name (used internally during recursion).
+    parents : dictionary
+        Dictionary of parent fields (used internally during recursion).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> ndtype =  np.dtype([('A', int),
+    ...                     ('B', [('BA', int),
+    ...                            ('BB', [('BBA', int), ('BBB', int)])])])
+    >>> rfn.get_fieldstructure(ndtype)
+    ... # XXX: possible regression, order of BBA and BBB is swapped
+    {'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}
+
+    """
+    if parents is None:
+        parents = {}
+    names = adtype.names
+    for name in names:
+        current = adtype[name]
+        if current.names is not None:
+            if lastname:
+                parents[name] = [lastname, ]
+            else:
+                parents[name] = []
+            parents.update(get_fieldstructure(current, name, parents))
+        else:
+            lastparent = list((parents.get(lastname, []) or []))
+            if lastparent:
+                lastparent.append(lastname)
+            elif lastname:
+                lastparent = [lastname, ]
+            parents[name] = lastparent or []
+    return parents
+
+
+def _izip_fields_flat(iterable):
+    """
+    Returns an iterator of concatenated fields from a sequence of arrays,
+    collapsing any nested structure.
+
+    """
+    for element in iterable:
+        if isinstance(element, np.void):
+            yield from _izip_fields_flat(tuple(element))
+        else:
+            yield element
+
+
+def _izip_fields(iterable):
+    """
+    Returns an iterator of concatenated fields from a sequence of arrays.
+
+    """
+    for element in iterable:
+        if (hasattr(element, '__iter__') and
+                not isinstance(element, str)):
+            yield from _izip_fields(element)
+        elif isinstance(element, np.void) and len(tuple(element)) == 1:
+            # this statement is the same from the previous expression
+            yield from _izip_fields(element)
+        else:
+            yield element
+
+
+def _izip_records(seqarrays, fill_value=None, flatten=True):
+    """
+    Returns an iterator of concatenated items from a sequence of arrays.
+
+    Parameters
+    ----------
+    seqarrays : sequence of arrays
+        Sequence of arrays.
+    fill_value : {None, integer}
+        Value used to pad shorter iterables.
+    flatten : {True, False},
+        Whether to
+    """
+
+    # Should we flatten the items, or just use a nested approach
+    if flatten:
+        zipfunc = _izip_fields_flat
+    else:
+        zipfunc = _izip_fields
+
+    for tup in itertools.zip_longest(*seqarrays, fillvalue=fill_value):
+        yield tuple(zipfunc(tup))
+
+
+def _fix_output(output, usemask=True, asrecarray=False):
+    """
+    Private function: return a recarray, a ndarray, a MaskedArray
+    or a MaskedRecords depending on the input parameters
+    """
+    if not isinstance(output, ma.MaskedArray):
+        usemask = False
+    if usemask:
+        if asrecarray:
+            output = output.view(mrec.MaskedRecords)
+    else:
+        output = ma.filled(output)
+        if asrecarray:
+            output = output.view(np.recarray)
+    return output
+
+
+def _fix_defaults(output, defaults=None):
+    """
+    Update the fill_value and masked data of `output`
+    from the default given in a dictionary defaults.
+    """
+    names = output.dtype.names
+    (data, mask, fill_value) = (output.data, output.mask, output.fill_value)
+    for (k, v) in (defaults or {}).items():
+        if k in names:
+            fill_value[k] = v
+            data[k][mask[k]] = v
+    return output
+
+
+def _merge_arrays_dispatcher(seqarrays, fill_value=None, flatten=None,
+                             usemask=None, asrecarray=None):
+    return seqarrays
+
+
+@array_function_dispatch(_merge_arrays_dispatcher)
+def merge_arrays(seqarrays, fill_value=-1, flatten=False,
+                 usemask=False, asrecarray=False):
+    """
+    Merge arrays field by field.
+
+    Parameters
+    ----------
+    seqarrays : sequence of ndarrays
+        Sequence of arrays
+    fill_value : {float}, optional
+        Filling value used to pad missing data on the shorter arrays.
+    flatten : {False, True}, optional
+        Whether to collapse nested fields.
+    usemask : {False, True}, optional
+        Whether to return a masked array or not.
+    asrecarray : {False, True}, optional
+        Whether to return a recarray (MaskedRecords) or not.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
+    array([( 1, 10.), ( 2, 20.), (-1, 30.)],
+          dtype=[('f0', '>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
+    ...         np.array([10., 20., 30.])), usemask=False)
+     array([(1, 10.0), (2, 20.0), (-1, 30.0)],
+             dtype=[('f0', '>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
+    ...               np.array([10., 20., 30.])),
+    ...              usemask=False, asrecarray=True)
+    rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
+              dtype=[('a', '>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
+    ...   dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])])
+    >>> rfn.drop_fields(a, 'a')
+    array([((2., 3),), ((5., 6),)],
+          dtype=[('b', [('ba', '>> rfn.drop_fields(a, 'ba')
+    array([(1, (3,)), (4, (6,))], dtype=[('a', '>> rfn.drop_fields(a, ['ba', 'bb'])
+    array([(1,), (4,)], dtype=[('a', '>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
+    ...   dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
+    >>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
+    array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))],
+          dtype=[('A', ' 1:
+        data = merge_arrays(data, flatten=True, usemask=usemask,
+                            fill_value=fill_value)
+    else:
+        data = data.pop()
+    #
+    output = ma.masked_all(
+        max(len(base), len(data)),
+        dtype=_get_fieldspec(base.dtype) + _get_fieldspec(data.dtype))
+    output = recursive_fill_fields(base, output)
+    output = recursive_fill_fields(data, output)
+    #
+    return _fix_output(output, usemask=usemask, asrecarray=asrecarray)
+
+
+def _rec_append_fields_dispatcher(base, names, data, dtypes=None):
+    yield base
+    yield from data
+
+
+@array_function_dispatch(_rec_append_fields_dispatcher)
+def rec_append_fields(base, names, data, dtypes=None):
+    """
+    Add new fields to an existing array.
+
+    The names of the fields are given with the `names` arguments,
+    the corresponding values with the `data` arguments.
+    If a single field is appended, `names`, `data` and `dtypes` do not have
+    to be lists but just values.
+
+    Parameters
+    ----------
+    base : array
+        Input array to extend.
+    names : string, sequence
+        String or sequence of strings corresponding to the names
+        of the new fields.
+    data : array or sequence of arrays
+        Array or sequence of arrays storing the fields to add to the base.
+    dtypes : sequence of datatypes, optional
+        Datatype or sequence of datatypes.
+        If None, the datatypes are estimated from the `data`.
+
+    See Also
+    --------
+    append_fields
+
+    Returns
+    -------
+    appended_array : np.recarray
+    """
+    return append_fields(base, names, data=data, dtypes=dtypes,
+                         asrecarray=True, usemask=False)
+
+
+def _repack_fields_dispatcher(a, align=None, recurse=None):
+    return (a,)
+
+
+@array_function_dispatch(_repack_fields_dispatcher)
+def repack_fields(a, align=False, recurse=False):
+    """
+    Re-pack the fields of a structured array or dtype in memory.
+
+    The memory layout of structured datatypes allows fields at arbitrary
+    byte offsets. This means the fields can be separated by padding bytes,
+    their offsets can be non-monotonically increasing, and they can overlap.
+
+    This method removes any overlaps and reorders the fields in memory so they
+    have increasing byte offsets, and adds or removes padding bytes depending
+    on the `align` option, which behaves like the `align` option to
+    `numpy.dtype`.
+
+    If `align=False`, this method produces a "packed" memory layout in which
+    each field starts at the byte the previous field ended, and any padding
+    bytes are removed.
+
+    If `align=True`, this methods produces an "aligned" memory layout in which
+    each field's offset is a multiple of its alignment, and the total itemsize
+    is a multiple of the largest alignment, by adding padding bytes as needed.
+
+    Parameters
+    ----------
+    a : ndarray or dtype
+       array or dtype for which to repack the fields.
+    align : boolean
+       If true, use an "aligned" memory layout, otherwise use a "packed" layout.
+    recurse : boolean
+       If True, also repack nested structures.
+
+    Returns
+    -------
+    repacked : ndarray or dtype
+       Copy of `a` with fields repacked, or `a` itself if no repacking was
+       needed.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    >>> from numpy.lib import recfunctions as rfn
+    >>> def print_offsets(d):
+    ...     print("offsets:", [d.fields[name][1] for name in d.names])
+    ...     print("itemsize:", d.itemsize)
+    ...
+    >>> dt = np.dtype('u1, >> dt
+    dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['u1', '>> print_offsets(dt)
+    offsets: [0, 8, 16]
+    itemsize: 24
+    >>> packed_dt = rfn.repack_fields(dt)
+    >>> packed_dt
+    dtype([('f0', 'u1'), ('f1', '>> print_offsets(packed_dt)
+    offsets: [0, 1, 9]
+    itemsize: 17
+
+    """
+    if not isinstance(a, np.dtype):
+        dt = repack_fields(a.dtype, align=align, recurse=recurse)
+        return a.astype(dt, copy=False)
+
+    if a.names is None:
+        return a
+
+    fieldinfo = []
+    for name in a.names:
+        tup = a.fields[name]
+        if recurse:
+            fmt = repack_fields(tup[0], align=align, recurse=True)
+        else:
+            fmt = tup[0]
+
+        if len(tup) == 3:
+            name = (tup[2], name)
+
+        fieldinfo.append((name, fmt))
+
+    dt = np.dtype(fieldinfo, align=align)
+    return np.dtype((a.type, dt))
+
+def _get_fields_and_offsets(dt, offset=0):
+    """
+    Returns a flat list of (dtype, count, offset) tuples of all the
+    scalar fields in the dtype "dt", including nested fields, in left
+    to right order.
+    """
+
+    # counts up elements in subarrays, including nested subarrays, and returns
+    # base dtype and count
+    def count_elem(dt):
+        count = 1
+        while dt.shape != ():
+            for size in dt.shape:
+                count *= size
+            dt = dt.base
+        return dt, count
+
+    fields = []
+    for name in dt.names:
+        field = dt.fields[name]
+        f_dt, f_offset = field[0], field[1]
+        f_dt, n = count_elem(f_dt)
+
+        if f_dt.names is None:
+            fields.append((np.dtype((f_dt, (n,))), n, f_offset + offset))
+        else:
+            subfields = _get_fields_and_offsets(f_dt, f_offset + offset)
+            size = f_dt.itemsize
+
+            for i in range(n):
+                if i == 0:
+                    # optimization: avoid list comprehension if no subarray
+                    fields.extend(subfields)
+                else:
+                    fields.extend([(d, c, o + i*size) for d, c, o in subfields])
+    return fields
+
+def _common_stride(offsets, counts, itemsize):
+    """
+    Returns the stride between the fields, or None if the stride is not
+    constant. The values in "counts" designate the lengths of
+    subarrays. Subarrays are treated as many contiguous fields, with
+    always positive stride.
+    """
+    if len(offsets) <= 1:
+        return itemsize
+
+    negative = offsets[1] < offsets[0]  # negative stride
+    if negative:
+        # reverse, so offsets will be ascending
+        it = zip(reversed(offsets), reversed(counts))
+    else:
+        it = zip(offsets, counts)
+
+    prev_offset = None
+    stride = None
+    for offset, count in it:
+        if count != 1:  # subarray: always c-contiguous
+            if negative:
+                return None  # subarrays can never have a negative stride
+            if stride is None:
+                stride = itemsize
+            if stride != itemsize:
+                return None
+            end_offset = offset + (count - 1) * itemsize
+        else:
+            end_offset = offset
+
+        if prev_offset is not None:
+            new_stride = offset - prev_offset
+            if stride is None:
+                stride = new_stride
+            if stride != new_stride:
+                return None
+
+        prev_offset = end_offset
+
+    if negative:
+        return -stride
+    return stride
+
+
+def _structured_to_unstructured_dispatcher(arr, dtype=None, copy=None,
+                                           casting=None):
+    return (arr,)
+
+@array_function_dispatch(_structured_to_unstructured_dispatcher)
+def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
+    """
+    Converts an n-D structured array into an (n+1)-D unstructured array.
+
+    The new array will have a new last dimension equal in size to the
+    number of field-elements of the input array. If not supplied, the output
+    datatype is determined from the numpy type promotion rules applied to all
+    the field datatypes.
+
+    Nested fields, as well as each element of any subarray fields, all count
+    as a single field-elements.
+
+    Parameters
+    ----------
+    arr : ndarray
+       Structured array or dtype to convert. Cannot contain object datatype.
+    dtype : dtype, optional
+       The dtype of the output unstructured array.
+    copy : bool, optional
+        If true, always return a copy. If false, a view is returned if
+        possible, such as when the `dtype` and strides of the fields are
+        suitable and the array subtype is one of `numpy.ndarray`,
+        `numpy.recarray` or `numpy.memmap`.
+
+        .. versionchanged:: 1.25.0
+            A view can now be returned if the fields are separated by a
+            uniform stride.
+
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        See casting argument of `numpy.ndarray.astype`. Controls what kind of
+        data casting may occur.
+
+    Returns
+    -------
+    unstructured : ndarray
+       Unstructured array with one more dimension.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    >>> from numpy.lib import recfunctions as rfn
+    >>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
+    >>> a
+    array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]),
+           (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])],
+          dtype=[('a', '>> rfn.structured_to_unstructured(a)
+    array([[0., 0., 0., 0., 0.],
+           [0., 0., 0., 0., 0.],
+           [0., 0., 0., 0., 0.],
+           [0., 0., 0., 0., 0.]])
+
+    >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+    ...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+    >>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1)
+    array([ 3. ,  5.5,  9. , 11. ])
+
+    """
+    if arr.dtype.names is None:
+        raise ValueError('arr must be a structured array')
+
+    fields = _get_fields_and_offsets(arr.dtype)
+    n_fields = len(fields)
+    if n_fields == 0 and dtype is None:
+        raise ValueError("arr has no fields. Unable to guess dtype")
+    elif n_fields == 0:
+        # too many bugs elsewhere for this to work now
+        raise NotImplementedError("arr with no fields is not supported")
+
+    dts, counts, offsets = zip(*fields)
+    names = ['f{}'.format(n) for n in range(n_fields)]
+
+    if dtype is None:
+        out_dtype = np.result_type(*[dt.base for dt in dts])
+    else:
+        out_dtype = np.dtype(dtype)
+
+    # Use a series of views and casts to convert to an unstructured array:
+
+    # first view using flattened fields (doesn't work for object arrays)
+    # Note: dts may include a shape for subarrays
+    flattened_fields = np.dtype({'names': names,
+                                 'formats': dts,
+                                 'offsets': offsets,
+                                 'itemsize': arr.dtype.itemsize})
+    arr = arr.view(flattened_fields)
+
+    # we only allow a few types to be unstructured by manipulating the
+    # strides, because we know it won't work with, for example, np.matrix nor
+    # np.ma.MaskedArray.
+    can_view = type(arr) in (np.ndarray, np.recarray, np.memmap)
+    if (not copy) and can_view and all(dt.base == out_dtype for dt in dts):
+        # all elements have the right dtype already; if they have a common
+        # stride, we can just return a view
+        common_stride = _common_stride(offsets, counts, out_dtype.itemsize)
+        if common_stride is not None:
+            wrap = arr.__array_wrap__
+
+            new_shape = arr.shape + (sum(counts), out_dtype.itemsize)
+            new_strides = arr.strides + (abs(common_stride), 1)
+
+            arr = arr[..., np.newaxis].view(np.uint8)  # view as bytes
+            arr = arr[..., min(offsets):]  # remove the leading unused data
+            arr = np.lib.stride_tricks.as_strided(arr,
+                                                  new_shape,
+                                                  new_strides,
+                                                  subok=True)
+
+            # cast and drop the last dimension again
+            arr = arr.view(out_dtype)[..., 0]
+
+            if common_stride < 0:
+                arr = arr[..., ::-1]  # reverse, if the stride was negative
+            if type(arr) is not type(wrap.__self__):
+                # Some types (e.g. recarray) turn into an ndarray along the
+                # way, so we have to wrap it again in order to match the
+                # behavior with copy=True.
+                arr = wrap(arr)
+            return arr
+
+    # next cast to a packed format with all fields converted to new dtype
+    packed_fields = np.dtype({'names': names,
+                              'formats': [(out_dtype, dt.shape) for dt in dts]})
+    arr = arr.astype(packed_fields, copy=copy, casting=casting)
+
+    # finally is it safe to view the packed fields as the unstructured type
+    return arr.view((out_dtype, (sum(counts),)))
+
+
+def _unstructured_to_structured_dispatcher(arr, dtype=None, names=None,
+                                           align=None, copy=None, casting=None):
+    return (arr,)
+
+@array_function_dispatch(_unstructured_to_structured_dispatcher)
+def unstructured_to_structured(arr, dtype=None, names=None, align=False,
+                               copy=False, casting='unsafe'):
+    """
+    Converts an n-D unstructured array into an (n-1)-D structured array.
+
+    The last dimension of the input array is converted into a structure, with
+    number of field-elements equal to the size of the last dimension of the
+    input array. By default all output fields have the input array's dtype, but
+    an output structured dtype with an equal number of fields-elements can be
+    supplied instead.
+
+    Nested fields, as well as each element of any subarray fields, all count
+    towards the number of field-elements.
+
+    Parameters
+    ----------
+    arr : ndarray
+       Unstructured array or dtype to convert.
+    dtype : dtype, optional
+       The structured dtype of the output array
+    names : list of strings, optional
+       If dtype is not supplied, this specifies the field names for the output
+       dtype, in order. The field dtypes will be the same as the input array.
+    align : boolean, optional
+       Whether to create an aligned memory layout.
+    copy : bool, optional
+        See copy argument to `numpy.ndarray.astype`. If true, always return a
+        copy. If false, and `dtype` requirements are satisfied, a view is
+        returned.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        See casting argument of `numpy.ndarray.astype`. Controls what kind of
+        data casting may occur.
+
+    Returns
+    -------
+    structured : ndarray
+       Structured array with fewer dimensions.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    >>> from numpy.lib import recfunctions as rfn
+    >>> dt = np.dtype([('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
+    >>> a = np.arange(20).reshape((4,5))
+    >>> a
+    array([[ 0,  1,  2,  3,  4],
+           [ 5,  6,  7,  8,  9],
+           [10, 11, 12, 13, 14],
+           [15, 16, 17, 18, 19]])
+    >>> rfn.unstructured_to_structured(a, dt)
+    array([( 0, ( 1.,  2), [ 3.,  4.]), ( 5, ( 6.,  7), [ 8.,  9.]),
+           (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])],
+          dtype=[('a', '>> import numpy as np
+
+    >>> from numpy.lib import recfunctions as rfn
+    >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+    ...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+    >>> rfn.apply_along_fields(np.mean, b)
+    array([ 2.66666667,  5.33333333,  8.66666667, 11.        ])
+    >>> rfn.apply_along_fields(np.mean, b[['x', 'z']])
+    array([ 3. ,  5.5,  9. , 11. ])
+
+    """
+    if arr.dtype.names is None:
+        raise ValueError('arr must be a structured array')
+
+    uarr = structured_to_unstructured(arr)
+    return func(uarr, axis=-1)
+    # works and avoids axis requirement, but very, very slow:
+    #return np.apply_along_axis(func, -1, uarr)
+
+def _assign_fields_by_name_dispatcher(dst, src, zero_unassigned=None):
+    return dst, src
+
+@array_function_dispatch(_assign_fields_by_name_dispatcher)
+def assign_fields_by_name(dst, src, zero_unassigned=True):
+    """
+    Assigns values from one structured array to another by field name.
+
+    Normally in numpy >= 1.14, assignment of one structured array to another
+    copies fields "by position", meaning that the first field from the src is
+    copied to the first field of the dst, and so on, regardless of field name.
+
+    This function instead copies "by field name", such that fields in the dst
+    are assigned from the identically named field in the src. This applies
+    recursively for nested structures. This is how structure assignment worked
+    in numpy >= 1.6 to <= 1.13.
+
+    Parameters
+    ----------
+    dst : ndarray
+    src : ndarray
+        The source and destination arrays during assignment.
+    zero_unassigned : bool, optional
+        If True, fields in the dst for which there was no matching
+        field in the src are filled with the value 0 (zero). This
+        was the behavior of numpy <= 1.13. If False, those fields
+        are not modified.
+    """
+
+    if dst.dtype.names is None:
+        dst[...] = src
+        return
+
+    for name in dst.dtype.names:
+        if name not in src.dtype.names:
+            if zero_unassigned:
+                dst[name] = 0
+        else:
+            assign_fields_by_name(dst[name], src[name],
+                                  zero_unassigned)
+
+def _require_fields_dispatcher(array, required_dtype):
+    return (array,)
+
+@array_function_dispatch(_require_fields_dispatcher)
+def require_fields(array, required_dtype):
+    """
+    Casts a structured array to a new dtype using assignment by field-name.
+
+    This function assigns from the old to the new array by name, so the
+    value of a field in the output array is the value of the field with the
+    same name in the source array. This has the effect of creating a new
+    ndarray containing only the fields "required" by the required_dtype.
+
+    If a field name in the required_dtype does not exist in the
+    input array, that field is created and set to 0 in the output array.
+
+    Parameters
+    ----------
+    a : ndarray
+       array to cast
+    required_dtype : dtype
+       datatype for output array
+
+    Returns
+    -------
+    out : ndarray
+        array with the new dtype, with field values copied from the fields in
+        the input array with the same name
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    >>> from numpy.lib import recfunctions as rfn
+    >>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
+    >>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
+    array([(1., 1), (1., 1), (1., 1), (1., 1)],
+      dtype=[('b', '>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
+    array([(1., 0), (1., 0), (1., 0), (1., 0)],
+      dtype=[('b', '>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> x = np.array([1, 2,])
+    >>> rfn.stack_arrays(x) is x
+    True
+    >>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
+    >>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
+    ...   dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)])
+    >>> test = rfn.stack_arrays((z,zz))
+    >>> test
+    masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0),
+                       (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)],
+                 mask=[(False, False,  True), (False, False,  True),
+                       (False, False, False), (False, False, False),
+                       (False, False, False)],
+           fill_value=(b'N/A', 1e+20, 1e+20),
+                dtype=[('A', 'S3'), ('B', ' '%s'" %
+                                    (cdtype, fdtype))
+    # Only one field: use concatenate
+    if len(newdescr) == 1:
+        output = ma.concatenate(seqarrays)
+    else:
+        #
+        output = ma.masked_all((np.sum(nrecords),), newdescr)
+        offset = np.cumsum(np.r_[0, nrecords])
+        seen = []
+        for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
+            names = a.dtype.names
+            if names is None:
+                output['f%i' % len(seen)][i:j] = a
+            else:
+                for name in n:
+                    output[name][i:j] = a[name]
+                    if name not in seen:
+                        seen.append(name)
+    #
+    return _fix_output(_fix_defaults(output, defaults),
+                       usemask=usemask, asrecarray=asrecarray)
+
+
+def _find_duplicates_dispatcher(
+        a, key=None, ignoremask=None, return_index=None):
+    return (a,)
+
+
+@array_function_dispatch(_find_duplicates_dispatcher)
+def find_duplicates(a, key=None, ignoremask=True, return_index=False):
+    """
+    Find the duplicates in a structured array along a given key
+
+    Parameters
+    ----------
+    a : array-like
+        Input array
+    key : {string, None}, optional
+        Name of the fields along which to check the duplicates.
+        If None, the search is performed by records
+    ignoremask : {True, False}, optional
+        Whether masked data should be discarded or considered as duplicates.
+    return_index : {False, True}, optional
+        Whether to return the indices of the duplicated values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.lib import recfunctions as rfn
+    >>> ndtype = [('a', int)]
+    >>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
+    ...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
+    >>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
+    (masked_array(data=[(1,), (1,), (2,), (2,)],
+                 mask=[(False,), (False,), (False,), (False,)],
+           fill_value=(999999,),
+                dtype=[('a', '= nb1)] - nb1
+    (r1cmn, r2cmn) = (len(idx_1), len(idx_2))
+    if jointype == 'inner':
+        (r1spc, r2spc) = (0, 0)
+    elif jointype == 'outer':
+        idx_out = idx_sort[~flag_in]
+        idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
+        idx_2 = np.concatenate((idx_2, idx_out[(idx_out >= nb1)] - nb1))
+        (r1spc, r2spc) = (len(idx_1) - r1cmn, len(idx_2) - r2cmn)
+    elif jointype == 'leftouter':
+        idx_out = idx_sort[~flag_in]
+        idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
+        (r1spc, r2spc) = (len(idx_1) - r1cmn, 0)
+    # Select the entries from each input
+    (s1, s2) = (r1[idx_1], r2[idx_2])
+    #
+    # Build the new description of the output array .......
+    # Start with the key fields
+    ndtype = _get_fieldspec(r1k.dtype)
+
+    # Add the fields from r1
+    for fname, fdtype in _get_fieldspec(r1.dtype):
+        if fname not in key:
+            ndtype.append((fname, fdtype))
+
+    # Add the fields from r2
+    for fname, fdtype in _get_fieldspec(r2.dtype):
+        # Have we seen the current name already ?
+        # we need to rebuild this list every time
+        names = list(name for name, dtype in ndtype)
+        try:
+            nameidx = names.index(fname)
+        except ValueError:
+            #... we haven't: just add the description to the current list
+            ndtype.append((fname, fdtype))
+        else:
+            # collision
+            _, cdtype = ndtype[nameidx]
+            if fname in key:
+                # The current field is part of the key: take the largest dtype
+                ndtype[nameidx] = (fname, max(fdtype, cdtype))
+            else:
+                # The current field is not part of the key: add the suffixes,
+                # and place the new field adjacent to the old one
+                ndtype[nameidx:nameidx + 1] = [
+                    (fname + r1postfix, cdtype),
+                    (fname + r2postfix, fdtype)
+                ]
+    # Rebuild a dtype from the new fields
+    ndtype = np.dtype(ndtype)
+    # Find the largest nb of common fields :
+    # r1cmn and r2cmn should be equal, but...
+    cmn = max(r1cmn, r2cmn)
+    # Construct an empty array
+    output = ma.masked_all((cmn + r1spc + r2spc,), dtype=ndtype)
+    names = output.dtype.names
+    for f in r1names:
+        selected = s1[f]
+        if f not in names or (f in r2names and not r2postfix and f not in key):
+            f += r1postfix
+        current = output[f]
+        current[:r1cmn] = selected[:r1cmn]
+        if jointype in ('outer', 'leftouter'):
+            current[cmn:cmn + r1spc] = selected[r1cmn:]
+    for f in r2names:
+        selected = s2[f]
+        if f not in names or (f in r1names and not r1postfix and f not in key):
+            f += r2postfix
+        current = output[f]
+        current[:r2cmn] = selected[:r2cmn]
+        if (jointype == 'outer') and r2spc:
+            current[-r2spc:] = selected[r2cmn:]
+    # Sort and finalize the output
+    output.sort(order=key)
+    kwargs = dict(usemask=usemask, asrecarray=asrecarray)
+    return _fix_output(_fix_defaults(output, defaults), **kwargs)
+
+
+def _rec_join_dispatcher(
+        key, r1, r2, jointype=None, r1postfix=None, r2postfix=None,
+        defaults=None):
+    return (r1, r2)
+
+
+@array_function_dispatch(_rec_join_dispatcher)
+def rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2',
+             defaults=None):
+    """
+    Join arrays `r1` and `r2` on keys.
+    Alternative to join_by, that always returns a np.recarray.
+
+    See Also
+    --------
+    join_by : equivalent function
+    """
+    kwargs = dict(jointype=jointype, r1postfix=r1postfix, r2postfix=r2postfix,
+                  defaults=defaults, usemask=False, asrecarray=True)
+    return join_by(key, r1, r2, **kwargs)
+
+
+del array_function_dispatch
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..442530e9cd39803ad0411527dbb24c6b60de54c4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/recfunctions.pyi
@@ -0,0 +1,435 @@
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from typing import Any, Literal, TypeAlias, overload
+
+from _typeshed import Incomplete
+from typing_extensions import TypeVar
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _DTypeLike, _DTypeLikeVoid
+from numpy.ma.mrecords import MaskedRecords
+
+__all__ = [
+    "append_fields",
+    "apply_along_fields",
+    "assign_fields_by_name",
+    "drop_fields",
+    "find_duplicates",
+    "flatten_descr",
+    "get_fieldstructure",
+    "get_names",
+    "get_names_flat",
+    "join_by",
+    "merge_arrays",
+    "rec_append_fields",
+    "rec_drop_fields",
+    "rec_join",
+    "recursive_fill_fields",
+    "rename_fields",
+    "repack_fields",
+    "require_fields",
+    "stack_arrays",
+    "structured_to_unstructured",
+    "unstructured_to_structured",
+]
+
+_T = TypeVar("_T")
+_ShapeT = TypeVar("_ShapeT", bound=tuple[int, ...])
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+_DTypeT = TypeVar("_DTypeT", bound=np.dtype[Any])
+_ArrayT = TypeVar("_ArrayT", bound=npt.NDArray[Any])
+_VoidArrayT = TypeVar("_VoidArrayT", bound=npt.NDArray[np.void])
+_NonVoidDTypeT = TypeVar("_NonVoidDTypeT", bound=_NonVoidDType)
+
+_OneOrMany: TypeAlias = _T | Iterable[_T]
+_BuiltinSequence: TypeAlias = tuple[_T, ...] | list[_T]
+
+_NestedNames: TypeAlias = tuple[str | _NestedNames, ...]
+_NonVoid: TypeAlias = np.bool | np.number | np.character | np.datetime64 | np.timedelta64 | np.object_
+_NonVoidDType: TypeAlias = np.dtype[_NonVoid] | np.dtypes.StringDType
+
+_JoinType: TypeAlias = Literal["inner", "outer", "leftouter"]
+
+###
+
+def recursive_fill_fields(input: npt.NDArray[np.void], output: _VoidArrayT) -> _VoidArrayT: ...
+
+#
+def get_names(adtype: np.dtype[np.void]) -> _NestedNames: ...
+def get_names_flat(adtype: np.dtype[np.void]) -> tuple[str, ...]: ...
+
+#
+@overload
+def flatten_descr(ndtype: _NonVoidDTypeT) -> tuple[tuple[Literal[""], _NonVoidDTypeT]]: ...
+@overload
+def flatten_descr(ndtype: np.dtype[np.void]) -> tuple[tuple[str, np.dtype[Any]]]: ...
+
+#
+def get_fieldstructure(
+    adtype: np.dtype[np.void],
+    lastname: str | None = None,
+    parents: dict[str, list[str]] | None = None,
+) -> dict[str, list[str]]: ...
+
+#
+@overload
+def merge_arrays(
+    seqarrays: Sequence[np.ndarray[_ShapeT, np.dtype[Any]]] | np.ndarray[_ShapeT, np.dtype[Any]],
+    fill_value: float = -1,
+    flatten: bool = False,
+    usemask: bool = False,
+    asrecarray: bool = False,
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def merge_arrays(
+    seqarrays: Sequence[npt.ArrayLike] | np.void,
+    fill_value: float = -1,
+    flatten: bool = False,
+    usemask: bool = False,
+    asrecarray: bool = False,
+) -> np.recarray[Any, np.dtype[np.void]]: ...
+
+#
+@overload
+def drop_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    drop_names: str | Iterable[str],
+    usemask: bool = True,
+    asrecarray: Literal[False] = False,
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def drop_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    drop_names: str | Iterable[str],
+    usemask: bool,
+    asrecarray: Literal[True],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def drop_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    drop_names: str | Iterable[str],
+    usemask: bool = True,
+    *,
+    asrecarray: Literal[True],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+
+#
+@overload
+def rename_fields(
+    base: MaskedRecords[_ShapeT, np.dtype[np.void]],
+    namemapper: Mapping[str, str],
+) -> MaskedRecords[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def rename_fields(
+    base: np.ma.MaskedArray[_ShapeT, np.dtype[np.void]],
+    namemapper: Mapping[str, str],
+) -> np.ma.MaskedArray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def rename_fields(
+    base: np.recarray[_ShapeT, np.dtype[np.void]],
+    namemapper: Mapping[str, str],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def rename_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    namemapper: Mapping[str, str],
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+
+#
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None,
+    fill_value: int,
+    usemask: Literal[False],
+    asrecarray: Literal[False] = False,
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None = None,
+    fill_value: int = -1,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[False] = False,
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None,
+    fill_value: int,
+    usemask: Literal[False],
+    asrecarray: Literal[True],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None = None,
+    fill_value: int = -1,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[True],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None = None,
+    fill_value: int = -1,
+    usemask: Literal[True] = True,
+    asrecarray: Literal[False] = False,
+) -> np.ma.MaskedArray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None,
+    fill_value: int,
+    usemask: Literal[True],
+    asrecarray: Literal[True],
+) -> MaskedRecords[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None = None,
+    fill_value: int = -1,
+    usemask: Literal[True] = True,
+    *,
+    asrecarray: Literal[True],
+) -> MaskedRecords[_ShapeT, np.dtype[np.void]]: ...
+
+#
+def rec_drop_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    drop_names: str | Iterable[str],
+) -> np.recarray[_ShapeT, np.dtype[np.void]]: ...
+
+#
+def rec_append_fields(
+    base: np.ndarray[_ShapeT, np.dtype[np.void]],
+    names: _OneOrMany[str],
+    data: _OneOrMany[npt.NDArray[Any]],
+    dtypes: _BuiltinSequence[np.dtype[Any]] | None = None,
+) -> np.ma.MaskedArray[_ShapeT, np.dtype[np.void]]: ...
+
+# TODO(jorenham): Stop passing `void` directly once structured dtypes are implemented,
+# e.g. using a `TypeVar` with constraints.
+# https://github.com/numpy/numtype/issues/92
+@overload
+def repack_fields(a: _DTypeT, align: bool = False, recurse: bool = False) -> _DTypeT: ...
+@overload
+def repack_fields(a: _ScalarT, align: bool = False, recurse: bool = False) -> _ScalarT: ...
+@overload
+def repack_fields(a: _ArrayT, align: bool = False, recurse: bool = False) -> _ArrayT: ...
+
+# TODO(jorenham): Attempt shape-typing (return type has ndim == arr.ndim + 1)
+@overload
+def structured_to_unstructured(
+    arr: npt.NDArray[np.void],
+    dtype: _DTypeLike[_ScalarT],
+    copy: bool = False,
+    casting: np._CastingKind = "unsafe",
+) -> npt.NDArray[_ScalarT]: ...
+@overload
+def structured_to_unstructured(
+    arr: npt.NDArray[np.void],
+    dtype: npt.DTypeLike | None = None,
+    copy: bool = False,
+    casting: np._CastingKind = "unsafe",
+) -> npt.NDArray[Any]: ...
+
+#
+@overload
+def unstructured_to_structured(
+    arr: npt.NDArray[Any],
+    dtype: npt.DTypeLike,
+    names: None = None,
+    align: bool = False,
+    copy: bool = False,
+    casting: str = "unsafe",
+) -> npt.NDArray[np.void]: ...
+@overload
+def unstructured_to_structured(
+    arr: npt.NDArray[Any],
+    dtype: None,
+    names: _OneOrMany[str],
+    align: bool = False,
+    copy: bool = False,
+    casting: str = "unsafe",
+) -> npt.NDArray[np.void]: ...
+
+#
+def apply_along_fields(
+    func: Callable[[np.ndarray[_ShapeT, Any]], npt.NDArray[Any]],
+    arr: np.ndarray[_ShapeT, np.dtype[np.void]],
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+
+#
+def assign_fields_by_name(dst: npt.NDArray[np.void], src: npt.NDArray[np.void], zero_unassigned: bool = True) -> None: ...
+
+#
+def require_fields(
+    array: np.ndarray[_ShapeT, np.dtype[np.void]],
+    required_dtype: _DTypeLikeVoid,
+) -> np.ndarray[_ShapeT, np.dtype[np.void]]: ...
+
+# TODO(jorenham): Attempt shape-typing
+@overload
+def stack_arrays(
+    arrays: _ArrayT,
+    defaults: Mapping[str, object] | None = None,
+    usemask: bool = True,
+    asrecarray: bool = False,
+    autoconvert: bool = False,
+) -> _ArrayT: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None,
+    usemask: Literal[False],
+    asrecarray: Literal[False] = False,
+    autoconvert: bool = False,
+) -> npt.NDArray[np.void]: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None = None,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[False] = False,
+    autoconvert: bool = False,
+) -> npt.NDArray[np.void]: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None = None,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[True],
+    autoconvert: bool = False,
+) -> np.recarray[tuple[int, ...], np.dtype[np.void]]: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None = None,
+    usemask: Literal[True] = True,
+    asrecarray: Literal[False] = False,
+    autoconvert: bool = False,
+) -> np.ma.MaskedArray[tuple[int, ...], np.dtype[np.void]]: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None,
+    usemask: Literal[True],
+    asrecarray: Literal[True],
+    autoconvert: bool = False,
+) -> MaskedRecords[tuple[int, ...], np.dtype[np.void]]: ...
+@overload
+def stack_arrays(
+    arrays: Sequence[npt.NDArray[Any]],
+    defaults: Mapping[str, Incomplete] | None = None,
+    usemask: Literal[True] = True,
+    *,
+    asrecarray: Literal[True],
+    autoconvert: bool = False,
+) -> MaskedRecords[tuple[int, ...], np.dtype[np.void]]: ...
+
+#
+@overload
+def find_duplicates(
+    a: np.ma.MaskedArray[_ShapeT, np.dtype[np.void]],
+    key: str | None = None,
+    ignoremask: bool = True,
+    return_index: Literal[False] = False,
+) -> np.ma.MaskedArray[_ShapeT, np.dtype[np.void]]: ...
+@overload
+def find_duplicates(
+    a: np.ma.MaskedArray[_ShapeT, np.dtype[np.void]],
+    key: str | None,
+    ignoremask: bool,
+    return_index: Literal[True],
+) -> tuple[np.ma.MaskedArray[_ShapeT, np.dtype[np.void]], np.ndarray[_ShapeT, np.dtype[np.int_]]]: ...
+@overload
+def find_duplicates(
+    a: np.ma.MaskedArray[_ShapeT, np.dtype[np.void]],
+    key: str | None = None,
+    ignoremask: bool = True,
+    *,
+    return_index: Literal[True],
+) -> tuple[np.ma.MaskedArray[_ShapeT, np.dtype[np.void]], np.ndarray[_ShapeT, np.dtype[np.int_]]]: ...
+
+#
+@overload
+def join_by(
+    key: str | Sequence[str],
+    r1: npt.NDArray[np.void],
+    r2: npt.NDArray[np.void],
+    jointype: _JoinType = "inner",
+    r1postfix: str = "1",
+    r2postfix: str = "2",
+    defaults: Mapping[str, object] | None = None,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[False] = False,
+) -> np.ndarray[tuple[int], np.dtype[np.void]]: ...
+@overload
+def join_by(
+    key: str | Sequence[str],
+    r1: npt.NDArray[np.void],
+    r2: npt.NDArray[np.void],
+    jointype: _JoinType = "inner",
+    r1postfix: str = "1",
+    r2postfix: str = "2",
+    defaults: Mapping[str, object] | None = None,
+    *,
+    usemask: Literal[False],
+    asrecarray: Literal[True],
+) -> np.recarray[tuple[int], np.dtype[np.void]]: ...
+@overload
+def join_by(
+    key: str | Sequence[str],
+    r1: npt.NDArray[np.void],
+    r2: npt.NDArray[np.void],
+    jointype: _JoinType = "inner",
+    r1postfix: str = "1",
+    r2postfix: str = "2",
+    defaults: Mapping[str, object] | None = None,
+    usemask: Literal[True] = True,
+    asrecarray: Literal[False] = False,
+) -> np.ma.MaskedArray[tuple[int], np.dtype[np.void]]: ...
+@overload
+def join_by(
+    key: str | Sequence[str],
+    r1: npt.NDArray[np.void],
+    r2: npt.NDArray[np.void],
+    jointype: _JoinType = "inner",
+    r1postfix: str = "1",
+    r2postfix: str = "2",
+    defaults: Mapping[str, object] | None = None,
+    usemask: Literal[True] = True,
+    *,
+    asrecarray: Literal[True],
+) -> MaskedRecords[tuple[int], np.dtype[np.void]]: ...
+
+#
+def rec_join(
+    key: str | Sequence[str],
+    r1: npt.NDArray[np.void],
+    r2: npt.NDArray[np.void],
+    jointype: _JoinType = "inner",
+    r1postfix: str = "1",
+    r2postfix: str = "2",
+    defaults: Mapping[str, object] | None = None,
+) -> np.recarray[tuple[int], np.dtype[np.void]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffd05ef9f364fd64fa9d91d5e6f41a9d90744307
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.py
@@ -0,0 +1,4 @@
+from ._scimath_impl import (
+    __all__, __doc__, sqrt, log, log2, logn, log10, power, arccos, arcsin,
+    arctanh
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cff5b9097faec0c831a99098f3275c591e2be10a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/scimath.pyi
@@ -0,0 +1,12 @@
+from ._scimath_impl import (
+    __all__ as __all__,
+    sqrt as sqrt,
+    log as log,
+    log2 as log2,
+    logn as logn,
+    log10 as log10,
+    power as power,
+    arccos as arccos,
+    arcsin as arcsin,
+    arctanh as arctanh,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba567be0c823408f316178f28ed80a970ee9f516
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.py
@@ -0,0 +1,3 @@
+from ._stride_tricks_impl import (
+    __doc__, as_strided, sliding_window_view
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eb46f28ae5f43e92c7c14df31edb154cf6d1f60b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/stride_tricks.pyi
@@ -0,0 +1,4 @@
+from numpy.lib._stride_tricks_impl import (
+    as_strided as as_strided,
+    sliding_window_view as sliding_window_view,
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__datasource.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__datasource.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8149abc30c40d6b9528fa9f3a98b0340d5914d3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__datasource.py
@@ -0,0 +1,350 @@
+import os
+import pytest
+from tempfile import mkdtemp, mkstemp, NamedTemporaryFile
+from shutil import rmtree
+
+import numpy.lib._datasource as datasource
+from numpy.testing import assert_, assert_equal, assert_raises
+
+import urllib.request as urllib_request
+from urllib.parse import urlparse
+from urllib.error import URLError
+
+
+def urlopen_stub(url, data=None):
+    '''Stub to replace urlopen for testing.'''
+    if url == valid_httpurl():
+        tmpfile = NamedTemporaryFile(prefix='urltmp_')
+        return tmpfile
+    else:
+        raise URLError('Name or service not known')
+
+# setup and teardown
+old_urlopen = None
+
+
+def setup_module():
+    global old_urlopen
+
+    old_urlopen = urllib_request.urlopen
+    urllib_request.urlopen = urlopen_stub
+
+
+def teardown_module():
+    urllib_request.urlopen = old_urlopen
+
+# A valid website for more robust testing
+http_path = 'http://www.google.com/'
+http_file = 'index.html'
+
+http_fakepath = 'http://fake.abc.web/site/'
+http_fakefile = 'fake.txt'
+
+malicious_files = ['/etc/shadow', '../../shadow',
+                   '..\\system.dat', 'c:\\windows\\system.dat']
+
+magic_line = b'three is the magic number'
+
+
+# Utility functions used by many tests
+def valid_textfile(filedir):
+    # Generate and return a valid temporary file.
+    fd, path = mkstemp(suffix='.txt', prefix='dstmp_', dir=filedir, text=True)
+    os.close(fd)
+    return path
+
+
+def invalid_textfile(filedir):
+    # Generate and return an invalid filename.
+    fd, path = mkstemp(suffix='.txt', prefix='dstmp_', dir=filedir)
+    os.close(fd)
+    os.remove(path)
+    return path
+
+
+def valid_httpurl():
+    return http_path+http_file
+
+
+def invalid_httpurl():
+    return http_fakepath+http_fakefile
+
+
+def valid_baseurl():
+    return http_path
+
+
+def invalid_baseurl():
+    return http_fakepath
+
+
+def valid_httpfile():
+    return http_file
+
+
+def invalid_httpfile():
+    return http_fakefile
+
+
+class TestDataSourceOpen:
+    def setup_method(self):
+        self.tmpdir = mkdtemp()
+        self.ds = datasource.DataSource(self.tmpdir)
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+        del self.ds
+
+    def test_ValidHTTP(self):
+        fh = self.ds.open(valid_httpurl())
+        assert_(fh)
+        fh.close()
+
+    def test_InvalidHTTP(self):
+        url = invalid_httpurl()
+        assert_raises(OSError, self.ds.open, url)
+        try:
+            self.ds.open(url)
+        except OSError as e:
+            # Regression test for bug fixed in r4342.
+            assert_(e.errno is None)
+
+    def test_InvalidHTTPCacheURLError(self):
+        assert_raises(URLError, self.ds._cache, invalid_httpurl())
+
+    def test_ValidFile(self):
+        local_file = valid_textfile(self.tmpdir)
+        fh = self.ds.open(local_file)
+        assert_(fh)
+        fh.close()
+
+    def test_InvalidFile(self):
+        invalid_file = invalid_textfile(self.tmpdir)
+        assert_raises(OSError, self.ds.open, invalid_file)
+
+    def test_ValidGzipFile(self):
+        try:
+            import gzip
+        except ImportError:
+            # We don't have the gzip capabilities to test.
+            pytest.skip()
+        # Test datasource's internal file_opener for Gzip files.
+        filepath = os.path.join(self.tmpdir, 'foobar.txt.gz')
+        fp = gzip.open(filepath, 'w')
+        fp.write(magic_line)
+        fp.close()
+        fp = self.ds.open(filepath)
+        result = fp.readline()
+        fp.close()
+        assert_equal(magic_line, result)
+
+    def test_ValidBz2File(self):
+        try:
+            import bz2
+        except ImportError:
+            # We don't have the bz2 capabilities to test.
+            pytest.skip()
+        # Test datasource's internal file_opener for BZip2 files.
+        filepath = os.path.join(self.tmpdir, 'foobar.txt.bz2')
+        fp = bz2.BZ2File(filepath, 'w')
+        fp.write(magic_line)
+        fp.close()
+        fp = self.ds.open(filepath)
+        result = fp.readline()
+        fp.close()
+        assert_equal(magic_line, result)
+
+
+class TestDataSourceExists:
+    def setup_method(self):
+        self.tmpdir = mkdtemp()
+        self.ds = datasource.DataSource(self.tmpdir)
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+        del self.ds
+
+    def test_ValidHTTP(self):
+        assert_(self.ds.exists(valid_httpurl()))
+
+    def test_InvalidHTTP(self):
+        assert_equal(self.ds.exists(invalid_httpurl()), False)
+
+    def test_ValidFile(self):
+        # Test valid file in destpath
+        tmpfile = valid_textfile(self.tmpdir)
+        assert_(self.ds.exists(tmpfile))
+        # Test valid local file not in destpath
+        localdir = mkdtemp()
+        tmpfile = valid_textfile(localdir)
+        assert_(self.ds.exists(tmpfile))
+        rmtree(localdir)
+
+    def test_InvalidFile(self):
+        tmpfile = invalid_textfile(self.tmpdir)
+        assert_equal(self.ds.exists(tmpfile), False)
+
+
+class TestDataSourceAbspath:
+    def setup_method(self):
+        self.tmpdir = os.path.abspath(mkdtemp())
+        self.ds = datasource.DataSource(self.tmpdir)
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+        del self.ds
+
+    def test_ValidHTTP(self):
+        scheme, netloc, upath, pms, qry, frg = urlparse(valid_httpurl())
+        local_path = os.path.join(self.tmpdir, netloc,
+                                  upath.strip(os.sep).strip('/'))
+        assert_equal(local_path, self.ds.abspath(valid_httpurl()))
+
+    def test_ValidFile(self):
+        tmpfile = valid_textfile(self.tmpdir)
+        tmpfilename = os.path.split(tmpfile)[-1]
+        # Test with filename only
+        assert_equal(tmpfile, self.ds.abspath(tmpfilename))
+        # Test filename with complete path
+        assert_equal(tmpfile, self.ds.abspath(tmpfile))
+
+    def test_InvalidHTTP(self):
+        scheme, netloc, upath, pms, qry, frg = urlparse(invalid_httpurl())
+        invalidhttp = os.path.join(self.tmpdir, netloc,
+                                   upath.strip(os.sep).strip('/'))
+        assert_(invalidhttp != self.ds.abspath(valid_httpurl()))
+
+    def test_InvalidFile(self):
+        invalidfile = valid_textfile(self.tmpdir)
+        tmpfile = valid_textfile(self.tmpdir)
+        tmpfilename = os.path.split(tmpfile)[-1]
+        # Test with filename only
+        assert_(invalidfile != self.ds.abspath(tmpfilename))
+        # Test filename with complete path
+        assert_(invalidfile != self.ds.abspath(tmpfile))
+
+    def test_sandboxing(self):
+        tmpfile = valid_textfile(self.tmpdir)
+        tmpfilename = os.path.split(tmpfile)[-1]
+
+        tmp_path = lambda x: os.path.abspath(self.ds.abspath(x))
+
+        assert_(tmp_path(valid_httpurl()).startswith(self.tmpdir))
+        assert_(tmp_path(invalid_httpurl()).startswith(self.tmpdir))
+        assert_(tmp_path(tmpfile).startswith(self.tmpdir))
+        assert_(tmp_path(tmpfilename).startswith(self.tmpdir))
+        for fn in malicious_files:
+            assert_(tmp_path(http_path+fn).startswith(self.tmpdir))
+            assert_(tmp_path(fn).startswith(self.tmpdir))
+
+    def test_windows_os_sep(self):
+        orig_os_sep = os.sep
+        try:
+            os.sep = '\\'
+            self.test_ValidHTTP()
+            self.test_ValidFile()
+            self.test_InvalidHTTP()
+            self.test_InvalidFile()
+            self.test_sandboxing()
+        finally:
+            os.sep = orig_os_sep
+
+
+class TestRepositoryAbspath:
+    def setup_method(self):
+        self.tmpdir = os.path.abspath(mkdtemp())
+        self.repos = datasource.Repository(valid_baseurl(), self.tmpdir)
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+        del self.repos
+
+    def test_ValidHTTP(self):
+        scheme, netloc, upath, pms, qry, frg = urlparse(valid_httpurl())
+        local_path = os.path.join(self.repos._destpath, netloc,
+                                  upath.strip(os.sep).strip('/'))
+        filepath = self.repos.abspath(valid_httpfile())
+        assert_equal(local_path, filepath)
+
+    def test_sandboxing(self):
+        tmp_path = lambda x: os.path.abspath(self.repos.abspath(x))
+        assert_(tmp_path(valid_httpfile()).startswith(self.tmpdir))
+        for fn in malicious_files:
+            assert_(tmp_path(http_path+fn).startswith(self.tmpdir))
+            assert_(tmp_path(fn).startswith(self.tmpdir))
+
+    def test_windows_os_sep(self):
+        orig_os_sep = os.sep
+        try:
+            os.sep = '\\'
+            self.test_ValidHTTP()
+            self.test_sandboxing()
+        finally:
+            os.sep = orig_os_sep
+
+
+class TestRepositoryExists:
+    def setup_method(self):
+        self.tmpdir = mkdtemp()
+        self.repos = datasource.Repository(valid_baseurl(), self.tmpdir)
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+        del self.repos
+
+    def test_ValidFile(self):
+        # Create local temp file
+        tmpfile = valid_textfile(self.tmpdir)
+        assert_(self.repos.exists(tmpfile))
+
+    def test_InvalidFile(self):
+        tmpfile = invalid_textfile(self.tmpdir)
+        assert_equal(self.repos.exists(tmpfile), False)
+
+    def test_RemoveHTTPFile(self):
+        assert_(self.repos.exists(valid_httpurl()))
+
+    def test_CachedHTTPFile(self):
+        localfile = valid_httpurl()
+        # Create a locally cached temp file with an URL based
+        # directory structure.  This is similar to what Repository.open
+        # would do.
+        scheme, netloc, upath, pms, qry, frg = urlparse(localfile)
+        local_path = os.path.join(self.repos._destpath, netloc)
+        os.mkdir(local_path, 0o0700)
+        tmpfile = valid_textfile(local_path)
+        assert_(self.repos.exists(tmpfile))
+
+
+class TestOpenFunc:
+    def setup_method(self):
+        self.tmpdir = mkdtemp()
+
+    def teardown_method(self):
+        rmtree(self.tmpdir)
+
+    def test_DataSourceOpen(self):
+        local_file = valid_textfile(self.tmpdir)
+        # Test case where destpath is passed in
+        fp = datasource.open(local_file, destpath=self.tmpdir)
+        assert_(fp)
+        fp.close()
+        # Test case where default destpath is used
+        fp = datasource.open(local_file)
+        assert_(fp)
+        fp.close()
+
+def test_del_attr_handling():
+    # DataSource __del__ can be called
+    # even if __init__ fails when the
+    # Exception object is caught by the
+    # caller as happens in refguide_check
+    # is_deprecated() function
+
+    ds = datasource.DataSource()
+    # simulate failed __init__ by removing key attribute
+    # produced within __init__ and expected by __del__
+    del ds._istmpdest
+    # should not raise an AttributeError if __del__
+    # gracefully handles failed __init__:
+    ds.__del__()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__iotools.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__iotools.py
new file mode 100644
index 0000000000000000000000000000000000000000..396d4147c6c55bf169cd66e3cb5472e59d5abdb9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__iotools.py
@@ -0,0 +1,353 @@
+import time
+from datetime import date
+
+import numpy as np
+from numpy.testing import (
+    assert_, assert_equal, assert_allclose, assert_raises,
+    )
+from numpy.lib._iotools import (
+    LineSplitter, NameValidator, StringConverter,
+    has_nested_fields, easy_dtype, flatten_dtype
+    )
+
+
+class TestLineSplitter:
+    "Tests the LineSplitter class."
+
+    def test_no_delimiter(self):
+        "Test LineSplitter w/o delimiter"
+        strg = " 1 2 3 4  5 # test"
+        test = LineSplitter()(strg)
+        assert_equal(test, ['1', '2', '3', '4', '5'])
+        test = LineSplitter('')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '5'])
+
+    def test_space_delimiter(self):
+        "Test space delimiter"
+        strg = " 1 2 3 4  5 # test"
+        test = LineSplitter(' ')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '', '5'])
+        test = LineSplitter('  ')(strg)
+        assert_equal(test, ['1 2 3 4', '5'])
+
+    def test_tab_delimiter(self):
+        "Test tab delimiter"
+        strg = " 1\t 2\t 3\t 4\t 5  6"
+        test = LineSplitter('\t')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '5  6'])
+        strg = " 1  2\t 3  4\t 5  6"
+        test = LineSplitter('\t')(strg)
+        assert_equal(test, ['1  2', '3  4', '5  6'])
+
+    def test_other_delimiter(self):
+        "Test LineSplitter on delimiter"
+        strg = "1,2,3,4,,5"
+        test = LineSplitter(',')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '', '5'])
+        #
+        strg = " 1,2,3,4,,5 # test"
+        test = LineSplitter(',')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '', '5'])
+
+        # gh-11028 bytes comment/delimiters should get encoded
+        strg = b" 1,2,3,4,,5 % test"
+        test = LineSplitter(delimiter=b',', comments=b'%')(strg)
+        assert_equal(test, ['1', '2', '3', '4', '', '5'])
+
+    def test_constant_fixed_width(self):
+        "Test LineSplitter w/ fixed-width fields"
+        strg = "  1  2  3  4     5   # test"
+        test = LineSplitter(3)(strg)
+        assert_equal(test, ['1', '2', '3', '4', '', '5', ''])
+        #
+        strg = "  1     3  4  5  6# test"
+        test = LineSplitter(20)(strg)
+        assert_equal(test, ['1     3  4  5  6'])
+        #
+        strg = "  1     3  4  5  6# test"
+        test = LineSplitter(30)(strg)
+        assert_equal(test, ['1     3  4  5  6'])
+
+    def test_variable_fixed_width(self):
+        strg = "  1     3  4  5  6# test"
+        test = LineSplitter((3, 6, 6, 3))(strg)
+        assert_equal(test, ['1', '3', '4  5', '6'])
+        #
+        strg = "  1     3  4  5  6# test"
+        test = LineSplitter((6, 6, 9))(strg)
+        assert_equal(test, ['1', '3  4', '5  6'])
+
+# -----------------------------------------------------------------------------
+
+
+class TestNameValidator:
+
+    def test_case_sensitivity(self):
+        "Test case sensitivity"
+        names = ['A', 'a', 'b', 'c']
+        test = NameValidator().validate(names)
+        assert_equal(test, ['A', 'a', 'b', 'c'])
+        test = NameValidator(case_sensitive=False).validate(names)
+        assert_equal(test, ['A', 'A_1', 'B', 'C'])
+        test = NameValidator(case_sensitive='upper').validate(names)
+        assert_equal(test, ['A', 'A_1', 'B', 'C'])
+        test = NameValidator(case_sensitive='lower').validate(names)
+        assert_equal(test, ['a', 'a_1', 'b', 'c'])
+
+        # check exceptions
+        assert_raises(ValueError, NameValidator, case_sensitive='foobar')
+
+    def test_excludelist(self):
+        "Test excludelist"
+        names = ['dates', 'data', 'Other Data', 'mask']
+        validator = NameValidator(excludelist=['dates', 'data', 'mask'])
+        test = validator.validate(names)
+        assert_equal(test, ['dates_', 'data_', 'Other_Data', 'mask_'])
+
+    def test_missing_names(self):
+        "Test validate missing names"
+        namelist = ('a', 'b', 'c')
+        validator = NameValidator()
+        assert_equal(validator(namelist), ['a', 'b', 'c'])
+        namelist = ('', 'b', 'c')
+        assert_equal(validator(namelist), ['f0', 'b', 'c'])
+        namelist = ('a', 'b', '')
+        assert_equal(validator(namelist), ['a', 'b', 'f0'])
+        namelist = ('', 'f0', '')
+        assert_equal(validator(namelist), ['f1', 'f0', 'f2'])
+
+    def test_validate_nb_names(self):
+        "Test validate nb names"
+        namelist = ('a', 'b', 'c')
+        validator = NameValidator()
+        assert_equal(validator(namelist, nbfields=1), ('a',))
+        assert_equal(validator(namelist, nbfields=5, defaultfmt="g%i"),
+                     ['a', 'b', 'c', 'g0', 'g1'])
+
+    def test_validate_wo_names(self):
+        "Test validate no names"
+        namelist = None
+        validator = NameValidator()
+        assert_(validator(namelist) is None)
+        assert_equal(validator(namelist, nbfields=3), ['f0', 'f1', 'f2'])
+
+# -----------------------------------------------------------------------------
+
+
+def _bytes_to_date(s):
+    return date(*time.strptime(s, "%Y-%m-%d")[:3])
+
+
+class TestStringConverter:
+    "Test StringConverter"
+
+    def test_creation(self):
+        "Test creation of a StringConverter"
+        converter = StringConverter(int, -99999)
+        assert_equal(converter._status, 1)
+        assert_equal(converter.default, -99999)
+
+    def test_upgrade(self):
+        "Tests the upgrade method."
+
+        converter = StringConverter()
+        assert_equal(converter._status, 0)
+
+        # test int
+        assert_equal(converter.upgrade('0'), 0)
+        assert_equal(converter._status, 1)
+
+        # On systems where long defaults to 32-bit, the statuses will be
+        # offset by one, so we check for this here.
+        import numpy._core.numeric as nx
+        status_offset = int(nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize)
+
+        # test int > 2**32
+        assert_equal(converter.upgrade('17179869184'), 17179869184)
+        assert_equal(converter._status, 1 + status_offset)
+
+        # test float
+        assert_allclose(converter.upgrade('0.'), 0.0)
+        assert_equal(converter._status, 2 + status_offset)
+
+        # test complex
+        assert_equal(converter.upgrade('0j'), complex('0j'))
+        assert_equal(converter._status, 3 + status_offset)
+
+        # test str
+        # note that the longdouble type has been skipped, so the
+        # _status increases by 2. Everything should succeed with
+        # unicode conversion (8).
+        for s in ['a', b'a']:
+            res = converter.upgrade(s)
+            assert_(type(res) is str)
+            assert_equal(res, 'a')
+            assert_equal(converter._status, 8 + status_offset)
+
+    def test_missing(self):
+        "Tests the use of missing values."
+        converter = StringConverter(missing_values=('missing',
+                                                    'missed'))
+        converter.upgrade('0')
+        assert_equal(converter('0'), 0)
+        assert_equal(converter(''), converter.default)
+        assert_equal(converter('missing'), converter.default)
+        assert_equal(converter('missed'), converter.default)
+        try:
+            converter('miss')
+        except ValueError:
+            pass
+
+    def test_upgrademapper(self):
+        "Tests updatemapper"
+        dateparser = _bytes_to_date
+        _original_mapper = StringConverter._mapper[:]
+        try:
+            StringConverter.upgrade_mapper(dateparser, date(2000, 1, 1))
+            convert = StringConverter(dateparser, date(2000, 1, 1))
+            test = convert('2001-01-01')
+            assert_equal(test, date(2001, 1, 1))
+            test = convert('2009-01-01')
+            assert_equal(test, date(2009, 1, 1))
+            test = convert('')
+            assert_equal(test, date(2000, 1, 1))
+        finally:
+            StringConverter._mapper = _original_mapper
+
+    def test_string_to_object(self):
+        "Make sure that string-to-object functions are properly recognized"
+        old_mapper = StringConverter._mapper[:]  # copy of list
+        conv = StringConverter(_bytes_to_date)
+        assert_equal(conv._mapper, old_mapper)
+        assert_(hasattr(conv, 'default'))
+
+    def test_keep_default(self):
+        "Make sure we don't lose an explicit default"
+        converter = StringConverter(None, missing_values='',
+                                    default=-999)
+        converter.upgrade('3.14159265')
+        assert_equal(converter.default, -999)
+        assert_equal(converter.type, np.dtype(float))
+        #
+        converter = StringConverter(
+            None, missing_values='', default=0)
+        converter.upgrade('3.14159265')
+        assert_equal(converter.default, 0)
+        assert_equal(converter.type, np.dtype(float))
+
+    def test_keep_default_zero(self):
+        "Check that we don't lose a default of 0"
+        converter = StringConverter(int, default=0,
+                                    missing_values="N/A")
+        assert_equal(converter.default, 0)
+
+    def test_keep_missing_values(self):
+        "Check that we're not losing missing values"
+        converter = StringConverter(int, default=0,
+                                    missing_values="N/A")
+        assert_equal(
+            converter.missing_values, {'', 'N/A'})
+
+    def test_int64_dtype(self):
+        "Check that int64 integer types can be specified"
+        converter = StringConverter(np.int64, default=0)
+        val = "-9223372036854775807"
+        assert_(converter(val) == -9223372036854775807)
+        val = "9223372036854775807"
+        assert_(converter(val) == 9223372036854775807)
+
+    def test_uint64_dtype(self):
+        "Check that uint64 integer types can be specified"
+        converter = StringConverter(np.uint64, default=0)
+        val = "9223372043271415339"
+        assert_(converter(val) == 9223372043271415339)
+
+
+class TestMiscFunctions:
+
+    def test_has_nested_dtype(self):
+        "Test has_nested_dtype"
+        ndtype = np.dtype(float)
+        assert_equal(has_nested_fields(ndtype), False)
+        ndtype = np.dtype([('A', '|S3'), ('B', float)])
+        assert_equal(has_nested_fields(ndtype), False)
+        ndtype = np.dtype([('A', int), ('B', [('BA', float), ('BB', '|S1')])])
+        assert_equal(has_nested_fields(ndtype), True)
+
+    def test_easy_dtype(self):
+        "Test ndtype on dtypes"
+        # Simple case
+        ndtype = float
+        assert_equal(easy_dtype(ndtype), np.dtype(float))
+        # As string w/o names
+        ndtype = "i4, f8"
+        assert_equal(easy_dtype(ndtype),
+                     np.dtype([('f0', "i4"), ('f1', "f8")]))
+        # As string w/o names but different default format
+        assert_equal(easy_dtype(ndtype, defaultfmt="field_%03i"),
+                     np.dtype([('field_000', "i4"), ('field_001', "f8")]))
+        # As string w/ names
+        ndtype = "i4, f8"
+        assert_equal(easy_dtype(ndtype, names="a, b"),
+                     np.dtype([('a', "i4"), ('b', "f8")]))
+        # As string w/ names (too many)
+        ndtype = "i4, f8"
+        assert_equal(easy_dtype(ndtype, names="a, b, c"),
+                     np.dtype([('a', "i4"), ('b', "f8")]))
+        # As string w/ names (not enough)
+        ndtype = "i4, f8"
+        assert_equal(easy_dtype(ndtype, names=", b"),
+                     np.dtype([('f0', "i4"), ('b', "f8")]))
+        # ... (with different default format)
+        assert_equal(easy_dtype(ndtype, names="a", defaultfmt="f%02i"),
+                     np.dtype([('a', "i4"), ('f00', "f8")]))
+        # As list of tuples w/o names
+        ndtype = [('A', int), ('B', float)]
+        assert_equal(easy_dtype(ndtype), np.dtype([('A', int), ('B', float)]))
+        # As list of tuples w/ names
+        assert_equal(easy_dtype(ndtype, names="a,b"),
+                     np.dtype([('a', int), ('b', float)]))
+        # As list of tuples w/ not enough names
+        assert_equal(easy_dtype(ndtype, names="a"),
+                     np.dtype([('a', int), ('f0', float)]))
+        # As list of tuples w/ too many names
+        assert_equal(easy_dtype(ndtype, names="a,b,c"),
+                     np.dtype([('a', int), ('b', float)]))
+        # As list of types w/o names
+        ndtype = (int, float, float)
+        assert_equal(easy_dtype(ndtype),
+                     np.dtype([('f0', int), ('f1', float), ('f2', float)]))
+        # As list of types w names
+        ndtype = (int, float, float)
+        assert_equal(easy_dtype(ndtype, names="a, b, c"),
+                     np.dtype([('a', int), ('b', float), ('c', float)]))
+        # As simple dtype w/ names
+        ndtype = np.dtype(float)
+        assert_equal(easy_dtype(ndtype, names="a, b, c"),
+                     np.dtype([(_, float) for _ in ('a', 'b', 'c')]))
+        # As simple dtype w/o names (but multiple fields)
+        ndtype = np.dtype(float)
+        assert_equal(
+            easy_dtype(ndtype, names=['', '', ''], defaultfmt="f%02i"),
+            np.dtype([(_, float) for _ in ('f00', 'f01', 'f02')]))
+
+    def test_flatten_dtype(self):
+        "Testing flatten_dtype"
+        # Standard dtype
+        dt = np.dtype([("a", "f8"), ("b", "f8")])
+        dt_flat = flatten_dtype(dt)
+        assert_equal(dt_flat, [float, float])
+        # Recursive dtype
+        dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
+        dt_flat = flatten_dtype(dt)
+        assert_equal(dt_flat, [np.dtype('|S1'), np.dtype('|S2'), int])
+        # dtype with shaped fields
+        dt = np.dtype([("a", (float, 2)), ("b", (int, 3))])
+        dt_flat = flatten_dtype(dt)
+        assert_equal(dt_flat, [float, int])
+        dt_flat = flatten_dtype(dt, True)
+        assert_equal(dt_flat, [float] * 2 + [int] * 3)
+        # dtype w/ titles
+        dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")])
+        dt_flat = flatten_dtype(dt)
+        assert_equal(dt_flat, [float, float])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__version.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6d41ad939323792d31faa7ae517e6835ea851d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test__version.py
@@ -0,0 +1,64 @@
+"""Tests for the NumpyVersion class.
+
+"""
+from numpy.testing import assert_, assert_raises
+from numpy.lib import NumpyVersion
+
+
+def test_main_versions():
+    assert_(NumpyVersion('1.8.0') == '1.8.0')
+    for ver in ['1.9.0', '2.0.0', '1.8.1', '10.0.1']:
+        assert_(NumpyVersion('1.8.0') < ver)
+
+    for ver in ['1.7.0', '1.7.1', '0.9.9']:
+        assert_(NumpyVersion('1.8.0') > ver)
+
+
+def test_version_1_point_10():
+    # regression test for gh-2998.
+    assert_(NumpyVersion('1.9.0') < '1.10.0')
+    assert_(NumpyVersion('1.11.0') < '1.11.1')
+    assert_(NumpyVersion('1.11.0') == '1.11.0')
+    assert_(NumpyVersion('1.99.11') < '1.99.12')
+
+
+def test_alpha_beta_rc():
+    assert_(NumpyVersion('1.8.0rc1') == '1.8.0rc1')
+    for ver in ['1.8.0', '1.8.0rc2']:
+        assert_(NumpyVersion('1.8.0rc1') < ver)
+
+    for ver in ['1.8.0a2', '1.8.0b3', '1.7.2rc4']:
+        assert_(NumpyVersion('1.8.0rc1') > ver)
+
+    assert_(NumpyVersion('1.8.0b1') > '1.8.0a2')
+
+
+def test_dev_version():
+    assert_(NumpyVersion('1.9.0.dev-Unknown') < '1.9.0')
+    for ver in ['1.9.0', '1.9.0a1', '1.9.0b2', '1.9.0b2.dev-ffffffff']:
+        assert_(NumpyVersion('1.9.0.dev-f16acvda') < ver)
+
+    assert_(NumpyVersion('1.9.0.dev-f16acvda') == '1.9.0.dev-11111111')
+
+
+def test_dev_a_b_rc_mixed():
+    assert_(NumpyVersion('1.9.0a2.dev-f16acvda') == '1.9.0a2.dev-11111111')
+    assert_(NumpyVersion('1.9.0a2.dev-6acvda54') < '1.9.0a2')
+
+
+def test_dev0_version():
+    assert_(NumpyVersion('1.9.0.dev0+Unknown') < '1.9.0')
+    for ver in ['1.9.0', '1.9.0a1', '1.9.0b2', '1.9.0b2.dev0+ffffffff']:
+        assert_(NumpyVersion('1.9.0.dev0+f16acvda') < ver)
+
+    assert_(NumpyVersion('1.9.0.dev0+f16acvda') == '1.9.0.dev0+11111111')
+
+
+def test_dev0_a_b_rc_mixed():
+    assert_(NumpyVersion('1.9.0a2.dev0+f16acvda') == '1.9.0a2.dev0+11111111')
+    assert_(NumpyVersion('1.9.0a2.dev0+6acvda54') < '1.9.0a2')
+
+
+def test_raises():
+    for ver in ['1.9', '1,9.0', '1.7.x']:
+        assert_raises(ValueError, NumpyVersion, ver)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_array_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_array_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8b2bd4616e0a6d4956bb6c2b2396a33ee8836a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_array_utils.py
@@ -0,0 +1,33 @@
+import numpy as np
+
+from numpy.lib import array_utils
+from numpy.testing import assert_equal
+
+
+class TestByteBounds:
+    def test_byte_bounds(self):
+        # pointer difference matches size * itemsize
+        # due to contiguity
+        a = np.arange(12).reshape(3, 4)
+        low, high = array_utils.byte_bounds(a)
+        assert_equal(high - low, a.size * a.itemsize)
+
+    def test_unusual_order_positive_stride(self):
+        a = np.arange(12).reshape(3, 4)
+        b = a.T
+        low, high = array_utils.byte_bounds(b)
+        assert_equal(high - low, b.size * b.itemsize)
+
+    def test_unusual_order_negative_stride(self):
+        a = np.arange(12).reshape(3, 4)
+        b = a.T[::-1]
+        low, high = array_utils.byte_bounds(b)
+        assert_equal(high - low, b.size * b.itemsize)
+
+    def test_strided(self):
+        a = np.arange(12)
+        b = a[::2]
+        low, high = array_utils.byte_bounds(b)
+        # the largest pointer address is lost (even numbers only in the
+        # stride), and compensate addresses for striding by 2
+        assert_equal(high - low, b.size * 2 * b.itemsize - b.itemsize)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraypad.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraypad.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c1247db8e0c8765a4ddeeee292ec326daeedda2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraypad.py
@@ -0,0 +1,1416 @@
+"""Tests for the array padding functions.
+
+"""
+import pytest
+
+import numpy as np
+from numpy.testing import assert_array_equal, assert_allclose, assert_equal
+from numpy.lib._arraypad_impl import _as_pairs
+
+
+_numeric_dtypes = (
+    np._core.sctypes["uint"]
+    + np._core.sctypes["int"]
+    + np._core.sctypes["float"]
+    + np._core.sctypes["complex"]
+)
+_all_modes = {
+    'constant': {'constant_values': 0},
+    'edge': {},
+    'linear_ramp': {'end_values': 0},
+    'maximum': {'stat_length': None},
+    'mean': {'stat_length': None},
+    'median': {'stat_length': None},
+    'minimum': {'stat_length': None},
+    'reflect': {'reflect_type': 'even'},
+    'symmetric': {'reflect_type': 'even'},
+    'wrap': {},
+    'empty': {}
+}
+
+
+class TestAsPairs:
+    def test_single_value(self):
+        """Test casting for a single value."""
+        expected = np.array([[3, 3]] * 10)
+        for x in (3, [3], [[3]]):
+            result = _as_pairs(x, 10)
+            assert_equal(result, expected)
+        # Test with dtype=object
+        obj = object()
+        assert_equal(
+            _as_pairs(obj, 10),
+            np.array([[obj, obj]] * 10)
+        )
+
+    def test_two_values(self):
+        """Test proper casting for two different values."""
+        # Broadcasting in the first dimension with numbers
+        expected = np.array([[3, 4]] * 10)
+        for x in ([3, 4], [[3, 4]]):
+            result = _as_pairs(x, 10)
+            assert_equal(result, expected)
+        # and with dtype=object
+        obj = object()
+        assert_equal(
+            _as_pairs(["a", obj], 10),
+            np.array([["a", obj]] * 10)
+        )
+
+        # Broadcasting in the second / last dimension with numbers
+        assert_equal(
+            _as_pairs([[3], [4]], 2),
+            np.array([[3, 3], [4, 4]])
+        )
+        # and with dtype=object
+        assert_equal(
+            _as_pairs([["a"], [obj]], 2),
+            np.array([["a", "a"], [obj, obj]])
+        )
+
+    def test_with_none(self):
+        expected = ((None, None), (None, None), (None, None))
+        assert_equal(
+            _as_pairs(None, 3, as_index=False),
+            expected
+        )
+        assert_equal(
+            _as_pairs(None, 3, as_index=True),
+            expected
+        )
+
+    def test_pass_through(self):
+        """Test if `x` already matching desired output are passed through."""
+        expected = np.arange(12).reshape((6, 2))
+        assert_equal(
+            _as_pairs(expected, 6),
+            expected
+        )
+
+    def test_as_index(self):
+        """Test results if `as_index=True`."""
+        assert_equal(
+            _as_pairs([2.6, 3.3], 10, as_index=True),
+            np.array([[3, 3]] * 10, dtype=np.intp)
+        )
+        assert_equal(
+            _as_pairs([2.6, 4.49], 10, as_index=True),
+            np.array([[3, 4]] * 10, dtype=np.intp)
+        )
+        for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]],
+                  [[1, 2]] * 9 + [[1, -2]]):
+            with pytest.raises(ValueError, match="negative values"):
+                _as_pairs(x, 10, as_index=True)
+
+    def test_exceptions(self):
+        """Ensure faulty usage is discovered."""
+        with pytest.raises(ValueError, match="more dimensions than allowed"):
+            _as_pairs([[[3]]], 10)
+        with pytest.raises(ValueError, match="could not be broadcast"):
+            _as_pairs([[1, 2], [3, 4]], 3)
+        with pytest.raises(ValueError, match="could not be broadcast"):
+            _as_pairs(np.ones((2, 3)), 3)
+
+
+class TestConditionalShortcuts:
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_zero_padding_shortcuts(self, mode):
+        test = np.arange(120).reshape(4, 5, 6)
+        pad_amt = [(0, 0) for _ in test.shape]
+        assert_array_equal(test, np.pad(test, pad_amt, mode=mode))
+
+    @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
+    def test_shallow_statistic_range(self, mode):
+        test = np.arange(120).reshape(4, 5, 6)
+        pad_amt = [(1, 1) for _ in test.shape]
+        assert_array_equal(np.pad(test, pad_amt, mode='edge'),
+                           np.pad(test, pad_amt, mode=mode, stat_length=1))
+
+    @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
+    def test_clip_statistic_range(self, mode):
+        test = np.arange(30).reshape(5, 6)
+        pad_amt = [(3, 3) for _ in test.shape]
+        assert_array_equal(np.pad(test, pad_amt, mode=mode),
+                           np.pad(test, pad_amt, mode=mode, stat_length=30))
+
+
+class TestStatistic:
+    def test_check_mean_stat_length(self):
+        a = np.arange(100).astype('f')
+        a = np.pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), ))
+        b = np.array(
+            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
+             0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
+             0.5, 0.5, 0.5, 0.5, 0.5,
+
+             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
+             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
+             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
+             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
+             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
+             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
+             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
+             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
+             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
+             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
+
+             98., 98., 98., 98., 98., 98., 98., 98., 98., 98.,
+             98., 98., 98., 98., 98., 98., 98., 98., 98., 98.
+             ])
+        assert_array_equal(a, b)
+
+    def test_check_maximum_1(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'maximum')
+        b = np.array(
+            [99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
+             99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
+             99, 99, 99, 99, 99,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
+             99, 99, 99, 99, 99, 99, 99, 99, 99, 99]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_maximum_2(self):
+        a = np.arange(100) + 1
+        a = np.pad(a, (25, 20), 'maximum')
+        b = np.array(
+            [100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
+             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
+             100, 100, 100, 100, 100,
+
+             1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
+             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
+             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
+             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
+             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
+             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
+             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
+
+             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
+             100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_maximum_stat_length(self):
+        a = np.arange(100) + 1
+        a = np.pad(a, (25, 20), 'maximum', stat_length=10)
+        b = np.array(
+            [10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+             10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+             10, 10, 10, 10, 10,
+
+              1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
+             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
+             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
+             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
+             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
+             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
+             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
+             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
+
+             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
+             100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_minimum_1(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'minimum')
+        b = np.array(
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+             0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+             0, 0, 0, 0, 0,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+             0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_minimum_2(self):
+        a = np.arange(100) + 2
+        a = np.pad(a, (25, 20), 'minimum')
+        b = np.array(
+            [2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+             2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+             2, 2, 2, 2, 2,
+
+             2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+             12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
+             22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
+             32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
+             42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
+             52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
+             62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+             72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
+             82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
+             92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
+
+             2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+             2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_minimum_stat_length(self):
+        a = np.arange(100) + 1
+        a = np.pad(a, (25, 20), 'minimum', stat_length=10)
+        b = np.array(
+            [ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
+              1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
+              1,  1,  1,  1,  1,
+
+              1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
+             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
+             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
+             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
+             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
+             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
+             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
+             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
+
+             91, 91, 91, 91, 91, 91, 91, 91, 91, 91,
+             91, 91, 91, 91, 91, 91, 91, 91, 91, 91]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_median(self):
+        a = np.arange(100).astype('f')
+        a = np.pad(a, (25, 20), 'median')
+        b = np.array(
+            [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5,
+
+             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
+             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
+             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
+             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
+             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
+             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
+             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
+             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
+             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
+             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
+
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_median_01(self):
+        a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
+        a = np.pad(a, 1, 'median')
+        b = np.array(
+            [[4, 4, 5, 4, 4],
+
+             [3, 3, 1, 4, 3],
+             [5, 4, 5, 9, 5],
+             [8, 9, 8, 2, 8],
+
+             [4, 4, 5, 4, 4]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_median_02(self):
+        a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
+        a = np.pad(a.T, 1, 'median').T
+        b = np.array(
+            [[5, 4, 5, 4, 5],
+
+             [3, 3, 1, 4, 3],
+             [5, 4, 5, 9, 5],
+             [8, 9, 8, 2, 8],
+
+             [5, 4, 5, 4, 5]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_median_stat_length(self):
+        a = np.arange(100).astype('f')
+        a[1] = 2.
+        a[97] = 96.
+        a = np.pad(a, (25, 20), 'median', stat_length=(3, 5))
+        b = np.array(
+            [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
+              2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
+              2.,  2.,  2.,  2.,  2.,
+
+              0.,  2.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.,
+             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
+             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
+             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
+             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
+             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
+             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
+             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
+             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
+             90., 91., 92., 93., 94., 95., 96., 96., 98., 99.,
+
+             96., 96., 96., 96., 96., 96., 96., 96., 96., 96.,
+             96., 96., 96., 96., 96., 96., 96., 96., 96., 96.]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_mean_shape_one(self):
+        a = [[4, 5, 6]]
+        a = np.pad(a, (5, 7), 'mean', stat_length=2)
+        b = np.array(
+            [[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
+             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_mean_2(self):
+        a = np.arange(100).astype('f')
+        a = np.pad(a, (25, 20), 'mean')
+        b = np.array(
+            [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5,
+
+             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
+             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
+             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
+             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
+             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
+             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
+             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
+             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
+             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
+             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
+
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
+             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
+            )
+        assert_array_equal(a, b)
+
+    @pytest.mark.parametrize("mode", [
+        "mean",
+        "median",
+        "minimum",
+        "maximum"
+    ])
+    def test_same_prepend_append(self, mode):
+        """ Test that appended and prepended values are equal """
+        # This test is constructed to trigger floating point rounding errors in
+        # a way that caused gh-11216 for mode=='mean'
+        a = np.array([-1, 2, -1]) + np.array([0, 1e-12, 0], dtype=np.float64)
+        a = np.pad(a, (1, 1), mode)
+        assert_equal(a[0], a[-1])
+
+    @pytest.mark.parametrize("mode", ["mean", "median", "minimum", "maximum"])
+    @pytest.mark.parametrize(
+        "stat_length", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))]
+    )
+    def test_check_negative_stat_length(self, mode, stat_length):
+        arr = np.arange(30).reshape((6, 5))
+        match = "index can't contain negative values"
+        with pytest.raises(ValueError, match=match):
+            np.pad(arr, 2, mode, stat_length=stat_length)
+
+    def test_simple_stat_length(self):
+        a = np.arange(30)
+        a = np.reshape(a, (6, 5))
+        a = np.pad(a, ((2, 3), (3, 2)), mode='mean', stat_length=(3,))
+        b = np.array(
+            [[6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
+             [6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
+
+             [1, 1, 1, 0, 1, 2, 3, 4, 3, 3],
+             [6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
+             [11, 11, 11, 10, 11, 12, 13, 14, 13, 13],
+             [16, 16, 16, 15, 16, 17, 18, 19, 18, 18],
+             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
+             [26, 26, 26, 25, 26, 27, 28, 29, 28, 28],
+
+             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
+             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
+             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23]]
+            )
+        assert_array_equal(a, b)
+
+    @pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning")
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in( scalar)? divide:RuntimeWarning"
+    )
+    @pytest.mark.parametrize("mode", ["mean", "median"])
+    def test_zero_stat_length_valid(self, mode):
+        arr = np.pad([1., 2.], (1, 2), mode, stat_length=0)
+        expected = np.array([np.nan, 1., 2., np.nan, np.nan])
+        assert_equal(arr, expected)
+
+    @pytest.mark.parametrize("mode", ["minimum", "maximum"])
+    def test_zero_stat_length_invalid(self, mode):
+        match = "stat_length of 0 yields no value for padding"
+        with pytest.raises(ValueError, match=match):
+            np.pad([1., 2.], 0, mode, stat_length=0)
+        with pytest.raises(ValueError, match=match):
+            np.pad([1., 2.], 0, mode, stat_length=(1, 0))
+        with pytest.raises(ValueError, match=match):
+            np.pad([1., 2.], 1, mode, stat_length=0)
+        with pytest.raises(ValueError, match=match):
+            np.pad([1., 2.], 1, mode, stat_length=(1, 0))
+
+
+class TestConstant:
+    def test_check_constant(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'constant', constant_values=(10, 20))
+        b = np.array(
+            [10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+             10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+             10, 10, 10, 10, 10,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
+             20, 20, 20, 20, 20, 20, 20, 20, 20, 20]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_constant_zeros(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'constant')
+        b = np.array(
+            [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+              0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+              0,  0,  0,  0,  0,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+              0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+              0,  0,  0,  0,  0,  0,  0,  0,  0,  0]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_constant_float(self):
+        # If input array is int, but constant_values are float, the dtype of
+        # the array to be padded is kept
+        arr = np.arange(30).reshape(5, 6)
+        test = np.pad(arr, (1, 2), mode='constant',
+                   constant_values=1.1)
+        expected = np.array(
+            [[ 1,  1,  1,  1,  1,  1,  1,  1,  1],
+
+             [ 1,  0,  1,  2,  3,  4,  5,  1,  1],
+             [ 1,  6,  7,  8,  9, 10, 11,  1,  1],
+             [ 1, 12, 13, 14, 15, 16, 17,  1,  1],
+             [ 1, 18, 19, 20, 21, 22, 23,  1,  1],
+             [ 1, 24, 25, 26, 27, 28, 29,  1,  1],
+
+             [ 1,  1,  1,  1,  1,  1,  1,  1,  1],
+             [ 1,  1,  1,  1,  1,  1,  1,  1,  1]]
+            )
+        assert_allclose(test, expected)
+
+    def test_check_constant_float2(self):
+        # If input array is float, and constant_values are float, the dtype of
+        # the array to be padded is kept - here retaining the float constants
+        arr = np.arange(30).reshape(5, 6)
+        arr_float = arr.astype(np.float64)
+        test = np.pad(arr_float, ((1, 2), (1, 2)), mode='constant',
+                   constant_values=1.1)
+        expected = np.array(
+            [[  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1],
+
+             [  1.1,   0. ,   1. ,   2. ,   3. ,   4. ,   5. ,   1.1,   1.1],
+             [  1.1,   6. ,   7. ,   8. ,   9. ,  10. ,  11. ,   1.1,   1.1],
+             [  1.1,  12. ,  13. ,  14. ,  15. ,  16. ,  17. ,   1.1,   1.1],
+             [  1.1,  18. ,  19. ,  20. ,  21. ,  22. ,  23. ,   1.1,   1.1],
+             [  1.1,  24. ,  25. ,  26. ,  27. ,  28. ,  29. ,   1.1,   1.1],
+
+             [  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1],
+             [  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1]]
+            )
+        assert_allclose(test, expected)
+
+    def test_check_constant_float3(self):
+        a = np.arange(100, dtype=float)
+        a = np.pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2))
+        b = np.array(
+            [-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
+             -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
+             -1.1, -1.1, -1.1, -1.1, -1.1,
+
+             0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2,
+             -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2]
+            )
+        assert_allclose(a, b)
+
+    def test_check_constant_odd_pad_amount(self):
+        arr = np.arange(30).reshape(5, 6)
+        test = np.pad(arr, ((1,), (2,)), mode='constant',
+                   constant_values=3)
+        expected = np.array(
+            [[ 3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
+
+             [ 3,  3,  0,  1,  2,  3,  4,  5,  3,  3],
+             [ 3,  3,  6,  7,  8,  9, 10, 11,  3,  3],
+             [ 3,  3, 12, 13, 14, 15, 16, 17,  3,  3],
+             [ 3,  3, 18, 19, 20, 21, 22, 23,  3,  3],
+             [ 3,  3, 24, 25, 26, 27, 28, 29,  3,  3],
+
+             [ 3,  3,  3,  3,  3,  3,  3,  3,  3,  3]]
+            )
+        assert_allclose(test, expected)
+
+    def test_check_constant_pad_2d(self):
+        arr = np.arange(4).reshape(2, 2)
+        test = np.pad(arr, ((1, 2), (1, 3)), mode='constant',
+                          constant_values=((1, 2), (3, 4)))
+        expected = np.array(
+            [[3, 1, 1, 4, 4, 4],
+             [3, 0, 1, 4, 4, 4],
+             [3, 2, 3, 4, 4, 4],
+             [3, 2, 2, 4, 4, 4],
+             [3, 2, 2, 4, 4, 4]]
+        )
+        assert_allclose(test, expected)
+
+    def test_check_large_integers(self):
+        uint64_max = 2 ** 64 - 1
+        arr = np.full(5, uint64_max, dtype=np.uint64)
+        test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
+        expected = np.full(7, uint64_max, dtype=np.uint64)
+        assert_array_equal(test, expected)
+
+        int64_max = 2 ** 63 - 1
+        arr = np.full(5, int64_max, dtype=np.int64)
+        test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
+        expected = np.full(7, int64_max, dtype=np.int64)
+        assert_array_equal(test, expected)
+
+    def test_check_object_array(self):
+        arr = np.empty(1, dtype=object)
+        obj_a = object()
+        arr[0] = obj_a
+        obj_b = object()
+        obj_c = object()
+        arr = np.pad(arr, pad_width=1, mode='constant',
+                     constant_values=(obj_b, obj_c))
+
+        expected = np.empty((3,), dtype=object)
+        expected[0] = obj_b
+        expected[1] = obj_a
+        expected[2] = obj_c
+
+        assert_array_equal(arr, expected)
+
+    def test_pad_empty_dimension(self):
+        arr = np.zeros((3, 0, 2))
+        result = np.pad(arr, [(0,), (2,), (1,)], mode="constant")
+        assert result.shape == (3, 4, 4)
+
+
+class TestLinearRamp:
+    def test_check_simple(self):
+        a = np.arange(100).astype('f')
+        a = np.pad(a, (25, 20), 'linear_ramp', end_values=(4, 5))
+        b = np.array(
+            [4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56,
+             2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96,
+             0.80, 0.64, 0.48, 0.32, 0.16,
+
+             0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00,
+             10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0,
+             20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0,
+             30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0,
+             40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0,
+             50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0,
+             60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0,
+             70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0,
+             80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0,
+             90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0,
+
+             94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0,
+             47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.]
+            )
+        assert_allclose(a, b, rtol=1e-5, atol=1e-5)
+
+    def test_check_2d(self):
+        arr = np.arange(20).reshape(4, 5).astype(np.float64)
+        test = np.pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0))
+        expected = np.array(
+            [[0.,   0.,   0.,   0.,   0.,   0.,   0.,    0.,   0.],
+             [0.,   0.,   0.,  0.5,   1.,  1.5,   2.,    1.,   0.],
+             [0.,   0.,   0.,   1.,   2.,   3.,   4.,    2.,   0.],
+             [0.,  2.5,   5.,   6.,   7.,   8.,   9.,   4.5,   0.],
+             [0.,   5.,  10.,  11.,  12.,  13.,  14.,    7.,   0.],
+             [0.,  7.5,  15.,  16.,  17.,  18.,  19.,   9.5,   0.],
+             [0., 3.75,  7.5,   8.,  8.5,   9.,  9.5,  4.75,   0.],
+             [0.,   0.,   0.,   0.,   0.,   0.,   0.,    0.,   0.]])
+        assert_allclose(test, expected)
+
+    @pytest.mark.xfail(exceptions=(AssertionError,))
+    def test_object_array(self):
+        from fractions import Fraction
+        arr = np.array([Fraction(1, 2), Fraction(-1, 2)])
+        actual = np.pad(arr, (2, 3), mode='linear_ramp', end_values=0)
+
+        # deliberately chosen to have a non-power-of-2 denominator such that
+        # rounding to floats causes a failure.
+        expected = np.array([
+            Fraction( 0, 12),
+            Fraction( 3, 12),
+            Fraction( 6, 12),
+            Fraction(-6, 12),
+            Fraction(-4, 12),
+            Fraction(-2, 12),
+            Fraction(-0, 12),
+        ])
+        assert_equal(actual, expected)
+
+    def test_end_values(self):
+        """Ensure that end values are exact."""
+        a = np.pad(np.ones(10).reshape(2, 5), (223, 123), mode="linear_ramp")
+        assert_equal(a[:, 0], 0.)
+        assert_equal(a[:, -1], 0.)
+        assert_equal(a[0, :], 0.)
+        assert_equal(a[-1, :], 0.)
+
+    @pytest.mark.parametrize("dtype", _numeric_dtypes)
+    def test_negative_difference(self, dtype):
+        """
+        Check correct behavior of unsigned dtypes if there is a negative
+        difference between the edge to pad and `end_values`. Check both cases
+        to be independent of implementation. Test behavior for all other dtypes
+        in case dtype casting interferes with complex dtypes. See gh-14191.
+        """
+        x = np.array([3], dtype=dtype)
+        result = np.pad(x, 3, mode="linear_ramp", end_values=0)
+        expected = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype)
+        assert_equal(result, expected)
+
+        x = np.array([0], dtype=dtype)
+        result = np.pad(x, 3, mode="linear_ramp", end_values=3)
+        expected = np.array([3, 2, 1, 0, 1, 2, 3], dtype=dtype)
+        assert_equal(result, expected)
+
+
+class TestReflect:
+    def test_check_simple(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'reflect')
+        b = np.array(
+            [25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
+             15, 14, 13, 12, 11, 10, 9, 8, 7, 6,
+             5, 4, 3, 2, 1,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             98, 97, 96, 95, 94, 93, 92, 91, 90, 89,
+             88, 87, 86, 85, 84, 83, 82, 81, 80, 79]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_odd_method(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'reflect', reflect_type='odd')
+        b = np.array(
+            [-25, -24, -23, -22, -21, -20, -19, -18, -17, -16,
+             -15, -14, -13, -12, -11, -10, -9, -8, -7, -6,
+             -5, -4, -3, -2, -1,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
+             110, 111, 112, 113, 114, 115, 116, 117, 118, 119]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_large_pad(self):
+        a = [[4, 5, 6], [6, 7, 8]]
+        a = np.pad(a, (5, 7), 'reflect')
+        b = np.array(
+            [[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_shape(self):
+        a = [[4, 5, 6]]
+        a = np.pad(a, (5, 7), 'reflect')
+        b = np.array(
+            [[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
+             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_01(self):
+        a = np.pad([1, 2, 3], 2, 'reflect')
+        b = np.array([3, 2, 1, 2, 3, 2, 1])
+        assert_array_equal(a, b)
+
+    def test_check_02(self):
+        a = np.pad([1, 2, 3], 3, 'reflect')
+        b = np.array([2, 3, 2, 1, 2, 3, 2, 1, 2])
+        assert_array_equal(a, b)
+
+    def test_check_03(self):
+        a = np.pad([1, 2, 3], 4, 'reflect')
+        b = np.array([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
+        assert_array_equal(a, b)
+
+    def test_check_04(self):
+        a = np.pad([1, 2, 3], [1, 10], 'reflect')
+        b = np.array([2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1])
+        assert_array_equal(a, b)
+
+    def test_check_05(self):
+        a = np.pad([1, 2, 3, 4], [45, 10], 'reflect')
+        b = np.array(
+            [4, 3, 2, 1, 2, 3, 4, 3, 2, 1,
+             2, 3, 4, 3, 2, 1, 2, 3, 4, 3,
+             2, 1, 2, 3, 4, 3, 2, 1, 2, 3,
+             4, 3, 2, 1, 2, 3, 4, 3, 2, 1,
+             2, 3, 4, 3, 2, 1, 2, 3, 4, 3,
+             2, 1, 2, 3, 4, 3, 2, 1, 2])
+        assert_array_equal(a, b)
+
+    def test_check_06(self):
+        a = np.pad([1, 2, 3, 4], [15, 2], 'symmetric')
+        b = np.array(
+            [2, 3, 4, 4, 3, 2, 1, 1, 2, 3,
+             4, 4, 3, 2, 1, 1, 2, 3, 4, 4,
+             3]
+        )
+        assert_array_equal(a, b)
+
+    def test_check_07(self):
+        a = np.pad([1, 2, 3, 4, 5, 6], [45, 3], 'symmetric')
+        b = np.array(
+            [4, 5, 6, 6, 5, 4, 3, 2, 1, 1,
+             2, 3, 4, 5, 6, 6, 5, 4, 3, 2,
+             1, 1, 2, 3, 4, 5, 6, 6, 5, 4,
+             3, 2, 1, 1, 2, 3, 4, 5, 6, 6,
+             5, 4, 3, 2, 1, 1, 2, 3, 4, 5,
+             6, 6, 5, 4])
+        assert_array_equal(a, b)
+
+
+class TestEmptyArray:
+    """Check how padding behaves on arrays with an empty dimension."""
+
+    @pytest.mark.parametrize(
+        # Keep parametrization ordered, otherwise pytest-xdist might believe
+        # that different tests were collected during parallelization
+        "mode", sorted(_all_modes.keys() - {"constant", "empty"})
+    )
+    def test_pad_empty_dimension(self, mode):
+        match = ("can't extend empty axis 0 using modes other than 'constant' "
+                 "or 'empty'")
+        with pytest.raises(ValueError, match=match):
+            np.pad([], 4, mode=mode)
+        with pytest.raises(ValueError, match=match):
+            np.pad(np.ndarray(0), 4, mode=mode)
+        with pytest.raises(ValueError, match=match):
+            np.pad(np.zeros((0, 3)), ((1,), (0,)), mode=mode)
+
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_pad_non_empty_dimension(self, mode):
+        result = np.pad(np.ones((2, 0, 2)), ((3,), (0,), (1,)), mode=mode)
+        assert result.shape == (8, 0, 4)
+
+
+class TestSymmetric:
+    def test_check_simple(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'symmetric')
+        b = np.array(
+            [24, 23, 22, 21, 20, 19, 18, 17, 16, 15,
+             14, 13, 12, 11, 10, 9, 8, 7, 6, 5,
+             4, 3, 2, 1, 0,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             99, 98, 97, 96, 95, 94, 93, 92, 91, 90,
+             89, 88, 87, 86, 85, 84, 83, 82, 81, 80]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_odd_method(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'symmetric', reflect_type='odd')
+        b = np.array(
+            [-24, -23, -22, -21, -20, -19, -18, -17, -16, -15,
+             -14, -13, -12, -11, -10, -9, -8, -7, -6, -5,
+             -4, -3, -2, -1, 0,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
+             109, 110, 111, 112, 113, 114, 115, 116, 117, 118]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_large_pad(self):
+        a = [[4, 5, 6], [6, 7, 8]]
+        a = np.pad(a, (5, 7), 'symmetric')
+        b = np.array(
+            [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
+            )
+
+        assert_array_equal(a, b)
+
+    def test_check_large_pad_odd(self):
+        a = [[4, 5, 6], [6, 7, 8]]
+        a = np.pad(a, (5, 7), 'symmetric', reflect_type='odd')
+        b = np.array(
+            [[-3, -2, -2, -1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6],
+             [-3, -2, -2, -1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6],
+             [-1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8],
+             [-1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8],
+             [ 1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10],
+
+             [ 1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10],
+             [ 3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12],
+
+             [ 3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12],
+             [ 5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14],
+             [ 5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14],
+             [ 7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
+             [ 7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
+             [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18],
+             [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_shape(self):
+        a = [[4, 5, 6]]
+        a = np.pad(a, (5, 7), 'symmetric')
+        b = np.array(
+            [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
+             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_01(self):
+        a = np.pad([1, 2, 3], 2, 'symmetric')
+        b = np.array([2, 1, 1, 2, 3, 3, 2])
+        assert_array_equal(a, b)
+
+    def test_check_02(self):
+        a = np.pad([1, 2, 3], 3, 'symmetric')
+        b = np.array([3, 2, 1, 1, 2, 3, 3, 2, 1])
+        assert_array_equal(a, b)
+
+    def test_check_03(self):
+        a = np.pad([1, 2, 3], 6, 'symmetric')
+        b = np.array([1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3])
+        assert_array_equal(a, b)
+
+
+class TestWrap:
+    def test_check_simple(self):
+        a = np.arange(100)
+        a = np.pad(a, (25, 20), 'wrap')
+        b = np.array(
+            [75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
+             85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
+             95, 96, 97, 98, 99,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
+             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+
+             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+             10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_large_pad(self):
+        a = np.arange(12)
+        a = np.reshape(a, (3, 4))
+        a = np.pad(a, (10, 12), 'wrap')
+        b = np.array(
+            [[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11],
+             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
+              3, 0, 1, 2, 3, 0, 1, 2, 3],
+             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
+              7, 4, 5, 6, 7, 4, 5, 6, 7],
+             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
+              11, 8, 9, 10, 11, 8, 9, 10, 11]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_01(self):
+        a = np.pad([1, 2, 3], 3, 'wrap')
+        b = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
+        assert_array_equal(a, b)
+
+    def test_check_02(self):
+        a = np.pad([1, 2, 3], 4, 'wrap')
+        b = np.array([3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1])
+        assert_array_equal(a, b)
+
+    def test_pad_with_zero(self):
+        a = np.ones((3, 5))
+        b = np.pad(a, (0, 5), mode="wrap")
+        assert_array_equal(a, b[:-5, :-5])
+
+    def test_repeated_wrapping(self):
+        """
+        Check wrapping on each side individually if the wrapped area is longer
+        than the original array.
+        """
+        a = np.arange(5)
+        b = np.pad(a, (12, 0), mode="wrap")
+        assert_array_equal(np.r_[a, a, a, a][3:], b)
+
+        a = np.arange(5)
+        b = np.pad(a, (0, 12), mode="wrap")
+        assert_array_equal(np.r_[a, a, a, a][:-3], b)
+
+    def test_repeated_wrapping_multiple_origin(self):
+        """
+        Assert that 'wrap' pads only with multiples of the original area if
+        the pad width is larger than the original array.
+        """
+        a = np.arange(4).reshape(2, 2)
+        a = np.pad(a, [(1, 3), (3, 1)], mode='wrap')
+        b = np.array(
+            [[3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0],
+             [3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0],
+             [3, 2, 3, 2, 3, 2],
+             [1, 0, 1, 0, 1, 0]]
+        )
+        assert_array_equal(a, b)
+
+
+class TestEdge:
+    def test_check_simple(self):
+        a = np.arange(12)
+        a = np.reshape(a, (4, 3))
+        a = np.pad(a, ((2, 3), (3, 2)), 'edge')
+        b = np.array(
+            [[0, 0, 0, 0, 1, 2, 2, 2],
+             [0, 0, 0, 0, 1, 2, 2, 2],
+
+             [0, 0, 0, 0, 1, 2, 2, 2],
+             [3, 3, 3, 3, 4, 5, 5, 5],
+             [6, 6, 6, 6, 7, 8, 8, 8],
+             [9, 9, 9, 9, 10, 11, 11, 11],
+
+             [9, 9, 9, 9, 10, 11, 11, 11],
+             [9, 9, 9, 9, 10, 11, 11, 11],
+             [9, 9, 9, 9, 10, 11, 11, 11]]
+            )
+        assert_array_equal(a, b)
+
+    def test_check_width_shape_1_2(self):
+        # Check a pad_width of the form ((1, 2),).
+        # Regression test for issue gh-7808.
+        a = np.array([1, 2, 3])
+        padded = np.pad(a, ((1, 2),), 'edge')
+        expected = np.array([1, 1, 2, 3, 3, 3])
+        assert_array_equal(padded, expected)
+
+        a = np.array([[1, 2, 3], [4, 5, 6]])
+        padded = np.pad(a, ((1, 2),), 'edge')
+        expected = np.pad(a, ((1, 2), (1, 2)), 'edge')
+        assert_array_equal(padded, expected)
+
+        a = np.arange(24).reshape(2, 3, 4)
+        padded = np.pad(a, ((1, 2),), 'edge')
+        expected = np.pad(a, ((1, 2), (1, 2), (1, 2)), 'edge')
+        assert_array_equal(padded, expected)
+
+
+class TestEmpty:
+    def test_simple(self):
+        arr = np.arange(24).reshape(4, 6)
+        result = np.pad(arr, [(2, 3), (3, 1)], mode="empty")
+        assert result.shape == (9, 10)
+        assert_equal(arr, result[2:-3, 3:-1])
+
+    def test_pad_empty_dimension(self):
+        arr = np.zeros((3, 0, 2))
+        result = np.pad(arr, [(0,), (2,), (1,)], mode="empty")
+        assert result.shape == (3, 4, 4)
+
+
+def test_legacy_vector_functionality():
+    def _padwithtens(vector, pad_width, iaxis, kwargs):
+        vector[:pad_width[0]] = 10
+        vector[-pad_width[1]:] = 10
+
+    a = np.arange(6).reshape(2, 3)
+    a = np.pad(a, 2, _padwithtens)
+    b = np.array(
+        [[10, 10, 10, 10, 10, 10, 10],
+         [10, 10, 10, 10, 10, 10, 10],
+
+         [10, 10,  0,  1,  2, 10, 10],
+         [10, 10,  3,  4,  5, 10, 10],
+
+         [10, 10, 10, 10, 10, 10, 10],
+         [10, 10, 10, 10, 10, 10, 10]]
+        )
+    assert_array_equal(a, b)
+
+
+def test_unicode_mode():
+    a = np.pad([1], 2, mode='constant')
+    b = np.array([0, 0, 1, 0, 0])
+    assert_array_equal(a, b)
+
+
+@pytest.mark.parametrize("mode", ["edge", "symmetric", "reflect", "wrap"])
+def test_object_input(mode):
+    # Regression test for issue gh-11395.
+    a = np.full((4, 3), fill_value=None)
+    pad_amt = ((2, 3), (3, 2))
+    b = np.full((9, 8), fill_value=None)
+    assert_array_equal(np.pad(a, pad_amt, mode=mode), b)
+
+
+class TestPadWidth:
+    @pytest.mark.parametrize("pad_width", [
+        (4, 5, 6, 7),
+        ((1,), (2,), (3,)),
+        ((1, 2), (3, 4), (5, 6)),
+        ((3, 4, 5), (0, 1, 2)),
+    ])
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_misshaped_pad_width(self, pad_width, mode):
+        arr = np.arange(30).reshape((6, 5))
+        match = "operands could not be broadcast together"
+        with pytest.raises(ValueError, match=match):
+            np.pad(arr, pad_width, mode)
+
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_misshaped_pad_width_2(self, mode):
+        arr = np.arange(30).reshape((6, 5))
+        match = ("input operand has more dimensions than allowed by the axis "
+                 "remapping")
+        with pytest.raises(ValueError, match=match):
+            np.pad(arr, (((3,), (4,), (5,)), ((0,), (1,), (2,))), mode)
+
+    @pytest.mark.parametrize(
+        "pad_width", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))])
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_negative_pad_width(self, pad_width, mode):
+        arr = np.arange(30).reshape((6, 5))
+        match = "index can't contain negative values"
+        with pytest.raises(ValueError, match=match):
+            np.pad(arr, pad_width, mode)
+
+    @pytest.mark.parametrize("pad_width, dtype", [
+        ("3", None),
+        ("word", None),
+        (None, None),
+        (object(), None),
+        (3.4, None),
+        (((2, 3, 4), (3, 2)), object),
+        (complex(1, -1), None),
+        (((-2.1, 3), (3, 2)), None),
+    ])
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_bad_type(self, pad_width, dtype, mode):
+        arr = np.arange(30).reshape((6, 5))
+        match = "`pad_width` must be of integral type."
+        if dtype is not None:
+            # avoid DeprecationWarning when not specifying dtype
+            with pytest.raises(TypeError, match=match):
+                np.pad(arr, np.array(pad_width, dtype=dtype), mode)
+        else:
+            with pytest.raises(TypeError, match=match):
+                np.pad(arr, pad_width, mode)
+            with pytest.raises(TypeError, match=match):
+                np.pad(arr, np.array(pad_width), mode)
+
+    def test_pad_width_as_ndarray(self):
+        a = np.arange(12)
+        a = np.reshape(a, (4, 3))
+        a = np.pad(a, np.array(((2, 3), (3, 2))), 'edge')
+        b = np.array(
+            [[0,  0,  0,    0,  1,  2,    2,  2],
+             [0,  0,  0,    0,  1,  2,    2,  2],
+
+             [0,  0,  0,    0,  1,  2,    2,  2],
+             [3,  3,  3,    3,  4,  5,    5,  5],
+             [6,  6,  6,    6,  7,  8,    8,  8],
+             [9,  9,  9,    9, 10, 11,   11, 11],
+
+             [9,  9,  9,    9, 10, 11,   11, 11],
+             [9,  9,  9,    9, 10, 11,   11, 11],
+             [9,  9,  9,    9, 10, 11,   11, 11]]
+            )
+        assert_array_equal(a, b)
+
+    @pytest.mark.parametrize("pad_width", [0, (0, 0), ((0, 0), (0, 0))])
+    @pytest.mark.parametrize("mode", _all_modes.keys())
+    def test_zero_pad_width(self, pad_width, mode):
+        arr = np.arange(30).reshape(6, 5)
+        assert_array_equal(arr, np.pad(arr, pad_width, mode=mode))
+
+
+@pytest.mark.parametrize("mode", _all_modes.keys())
+def test_kwargs(mode):
+    """Test behavior of pad's kwargs for the given mode."""
+    allowed = _all_modes[mode]
+    not_allowed = {}
+    for kwargs in _all_modes.values():
+        if kwargs != allowed:
+            not_allowed.update(kwargs)
+    # Test if allowed keyword arguments pass
+    np.pad([1, 2, 3], 1, mode, **allowed)
+    # Test if prohibited keyword arguments of other modes raise an error
+    for key, value in not_allowed.items():
+        match = "unsupported keyword arguments for mode '{}'".format(mode)
+        with pytest.raises(ValueError, match=match):
+            np.pad([1, 2, 3], 1, mode, **{key: value})
+
+
+def test_constant_zero_default():
+    arr = np.array([1, 1])
+    assert_array_equal(np.pad(arr, 2), [0, 0, 1, 1, 0, 0])
+
+
+@pytest.mark.parametrize("mode", [1, "const", object(), None, True, False])
+def test_unsupported_mode(mode):
+    match= "mode '{}' is not supported".format(mode)
+    with pytest.raises(ValueError, match=match):
+        np.pad([1, 2, 3], 4, mode=mode)
+
+
+@pytest.mark.parametrize("mode", _all_modes.keys())
+def test_non_contiguous_array(mode):
+    arr = np.arange(24).reshape(4, 6)[::2, ::2]
+    result = np.pad(arr, (2, 3), mode)
+    assert result.shape == (7, 8)
+    assert_equal(result[2:-3, 2:-3], arr)
+
+
+@pytest.mark.parametrize("mode", _all_modes.keys())
+def test_memory_layout_persistence(mode):
+    """Test if C and F order is preserved for all pad modes."""
+    x = np.ones((5, 10), order='C')
+    assert np.pad(x, 5, mode).flags["C_CONTIGUOUS"]
+    x = np.ones((5, 10), order='F')
+    assert np.pad(x, 5, mode).flags["F_CONTIGUOUS"]
+
+
+@pytest.mark.parametrize("dtype", _numeric_dtypes)
+@pytest.mark.parametrize("mode", _all_modes.keys())
+def test_dtype_persistence(dtype, mode):
+    arr = np.zeros((3, 2, 1), dtype=dtype)
+    result = np.pad(arr, 1, mode=mode)
+    assert result.dtype == dtype
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraysetops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraysetops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9721266036dd9a379efea599cc66108951a4d0c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arraysetops.py
@@ -0,0 +1,1000 @@
+"""Test functions for 1D array set operations.
+
+"""
+import numpy as np
+
+from numpy import (
+    ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, isin
+    )
+from numpy.exceptions import AxisError
+from numpy.testing import (assert_array_equal, assert_equal,
+                           assert_raises, assert_raises_regex)
+import pytest
+
+
+class TestSetOps:
+
+    def test_intersect1d(self):
+        # unique inputs
+        a = np.array([5, 7, 1, 2])
+        b = np.array([2, 4, 3, 1, 5])
+
+        ec = np.array([1, 2, 5])
+        c = intersect1d(a, b, assume_unique=True)
+        assert_array_equal(c, ec)
+
+        # non-unique inputs
+        a = np.array([5, 5, 7, 1, 2])
+        b = np.array([2, 1, 4, 3, 3, 1, 5])
+
+        ed = np.array([1, 2, 5])
+        c = intersect1d(a, b)
+        assert_array_equal(c, ed)
+        assert_array_equal([], intersect1d([], []))
+
+    def test_intersect1d_array_like(self):
+        # See gh-11772
+        class Test:
+            def __array__(self, dtype=None, copy=None):
+                return np.arange(3)
+
+        a = Test()
+        res = intersect1d(a, a)
+        assert_array_equal(res, a)
+        res = intersect1d([1, 2, 3], [1, 2, 3])
+        assert_array_equal(res, [1, 2, 3])
+
+    def test_intersect1d_indices(self):
+        # unique inputs
+        a = np.array([1, 2, 3, 4])
+        b = np.array([2, 1, 4, 6])
+        c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
+        ee = np.array([1, 2, 4])
+        assert_array_equal(c, ee)
+        assert_array_equal(a[i1], ee)
+        assert_array_equal(b[i2], ee)
+
+        # non-unique inputs
+        a = np.array([1, 2, 2, 3, 4, 3, 2])
+        b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
+        c, i1, i2 = intersect1d(a, b, return_indices=True)
+        ef = np.array([1, 2, 3, 4])
+        assert_array_equal(c, ef)
+        assert_array_equal(a[i1], ef)
+        assert_array_equal(b[i2], ef)
+
+        # non1d, unique inputs
+        a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
+        b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
+        c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
+        ui1 = np.unravel_index(i1, a.shape)
+        ui2 = np.unravel_index(i2, b.shape)
+        ea = np.array([2, 6, 7, 8])
+        assert_array_equal(ea, a[ui1])
+        assert_array_equal(ea, b[ui2])
+
+        # non1d, not assumed to be uniqueinputs
+        a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
+        b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
+        c, i1, i2 = intersect1d(a, b, return_indices=True)
+        ui1 = np.unravel_index(i1, a.shape)
+        ui2 = np.unravel_index(i2, b.shape)
+        ea = np.array([2, 7, 8])
+        assert_array_equal(ea, a[ui1])
+        assert_array_equal(ea, b[ui2])
+
+    def test_setxor1d(self):
+        a = np.array([5, 7, 1, 2])
+        b = np.array([2, 4, 3, 1, 5])
+
+        ec = np.array([3, 4, 7])
+        c = setxor1d(a, b)
+        assert_array_equal(c, ec)
+
+        a = np.array([1, 2, 3])
+        b = np.array([6, 5, 4])
+
+        ec = np.array([1, 2, 3, 4, 5, 6])
+        c = setxor1d(a, b)
+        assert_array_equal(c, ec)
+
+        a = np.array([1, 8, 2, 3])
+        b = np.array([6, 5, 4, 8])
+
+        ec = np.array([1, 2, 3, 4, 5, 6])
+        c = setxor1d(a, b)
+        assert_array_equal(c, ec)
+
+        assert_array_equal([], setxor1d([], []))
+
+    def test_setxor1d_unique(self):
+        a = np.array([1, 8, 2, 3])
+        b = np.array([6, 5, 4, 8])
+
+        ec = np.array([1, 2, 3, 4, 5, 6])
+        c = setxor1d(a, b, assume_unique=True)
+        assert_array_equal(c, ec)
+
+        a = np.array([[1], [8], [2], [3]])
+        b = np.array([[6, 5], [4, 8]])
+
+        ec = np.array([1, 2, 3, 4, 5, 6])
+        c = setxor1d(a, b, assume_unique=True)
+        assert_array_equal(c, ec)
+
+    def test_ediff1d(self):
+        zero_elem = np.array([])
+        one_elem = np.array([1])
+        two_elem = np.array([1, 2])
+
+        assert_array_equal([], ediff1d(zero_elem))
+        assert_array_equal([0], ediff1d(zero_elem, to_begin=0))
+        assert_array_equal([0], ediff1d(zero_elem, to_end=0))
+        assert_array_equal([-1, 0], ediff1d(zero_elem, to_begin=-1, to_end=0))
+        assert_array_equal([], ediff1d(one_elem))
+        assert_array_equal([1], ediff1d(two_elem))
+        assert_array_equal([7, 1, 9], ediff1d(two_elem, to_begin=7, to_end=9))
+        assert_array_equal([5, 6, 1, 7, 8],
+                           ediff1d(two_elem, to_begin=[5, 6], to_end=[7, 8]))
+        assert_array_equal([1, 9], ediff1d(two_elem, to_end=9))
+        assert_array_equal([1, 7, 8], ediff1d(two_elem, to_end=[7, 8]))
+        assert_array_equal([7, 1], ediff1d(two_elem, to_begin=7))
+        assert_array_equal([5, 6, 1], ediff1d(two_elem, to_begin=[5, 6]))
+
+    @pytest.mark.parametrize("ary, prepend, append, expected", [
+        # should fail because trying to cast
+        # np.nan standard floating point value
+        # into an integer array:
+        (np.array([1, 2, 3], dtype=np.int64),
+         None,
+         np.nan,
+         'to_end'),
+        # should fail because attempting
+        # to downcast to int type:
+        (np.array([1, 2, 3], dtype=np.int64),
+         np.array([5, 7, 2], dtype=np.float32),
+         None,
+         'to_begin'),
+        # should fail because attempting to cast
+        # two special floating point values
+        # to integers (on both sides of ary),
+        # `to_begin` is in the error message as the impl checks this first:
+        (np.array([1., 3., 9.], dtype=np.int8),
+         np.nan,
+         np.nan,
+         'to_begin'),
+         ])
+    def test_ediff1d_forbidden_type_casts(self, ary, prepend, append, expected):
+        # verify resolution of gh-11490
+
+        # specifically, raise an appropriate
+        # Exception when attempting to append or
+        # prepend with an incompatible type
+        msg = 'dtype of `{}` must be compatible'.format(expected)
+        with assert_raises_regex(TypeError, msg):
+            ediff1d(ary=ary,
+                    to_end=append,
+                    to_begin=prepend)
+
+    @pytest.mark.parametrize(
+        "ary,prepend,append,expected",
+        [
+         (np.array([1, 2, 3], dtype=np.int16),
+          2**16,  # will be cast to int16 under same kind rule.
+          2**16 + 4,
+          np.array([0, 1, 1, 4], dtype=np.int16)),
+         (np.array([1, 2, 3], dtype=np.float32),
+          np.array([5], dtype=np.float64),
+          None,
+          np.array([5, 1, 1], dtype=np.float32)),
+         (np.array([1, 2, 3], dtype=np.int32),
+          0,
+          0,
+          np.array([0, 1, 1, 0], dtype=np.int32)),
+         (np.array([1, 2, 3], dtype=np.int64),
+          3,
+          -9,
+          np.array([3, 1, 1, -9], dtype=np.int64)),
+        ]
+    )
+    def test_ediff1d_scalar_handling(self,
+                                     ary,
+                                     prepend,
+                                     append,
+                                     expected):
+        # maintain backwards-compatibility
+        # of scalar prepend / append behavior
+        # in ediff1d following fix for gh-11490
+        actual = np.ediff1d(ary=ary,
+                            to_end=append,
+                            to_begin=prepend)
+        assert_equal(actual, expected)
+        assert actual.dtype == expected.dtype
+
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin(self, kind):
+        def _isin_slow(a, b):
+            b = np.asarray(b).flatten().tolist()
+            return a in b
+        isin_slow = np.vectorize(_isin_slow, otypes=[bool], excluded={1})
+
+        def assert_isin_equal(a, b):
+            x = isin(a, b, kind=kind)
+            y = isin_slow(a, b)
+            assert_array_equal(x, y)
+
+        # multidimensional arrays in both arguments
+        a = np.arange(24).reshape([2, 3, 4])
+        b = np.array([[10, 20, 30], [0, 1, 3], [11, 22, 33]])
+        assert_isin_equal(a, b)
+
+        # array-likes as both arguments
+        c = [(9, 8), (7, 6)]
+        d = (9, 7)
+        assert_isin_equal(c, d)
+
+        # zero-d array:
+        f = np.array(3)
+        assert_isin_equal(f, b)
+        assert_isin_equal(a, f)
+        assert_isin_equal(f, f)
+
+        # scalar:
+        assert_isin_equal(5, b)
+        assert_isin_equal(a, 6)
+        assert_isin_equal(5, 6)
+
+        # empty array-like:
+        if kind != "table":
+            # An empty list will become float64,
+            # which is invalid for kind="table"
+            x = []
+            assert_isin_equal(x, b)
+            assert_isin_equal(a, x)
+            assert_isin_equal(x, x)
+
+        # empty array with various types:
+        for dtype in [bool, np.int64, np.float64]:
+            if kind == "table" and dtype == np.float64:
+                continue
+
+            if dtype in {np.int64, np.float64}:
+                ar = np.array([10, 20, 30], dtype=dtype)
+            elif dtype in {bool}:
+                ar = np.array([True, False, False])
+
+            empty_array = np.array([], dtype=dtype)
+
+            assert_isin_equal(empty_array, ar)
+            assert_isin_equal(ar, empty_array)
+            assert_isin_equal(empty_array, empty_array)
+
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_additional(self, kind):
+        # we use two different sizes for the b array here to test the
+        # two different paths in isin().
+        for mult in (1, 10):
+            # One check without np.array to make sure lists are handled correct
+            a = [5, 7, 1, 2]
+            b = [2, 4, 3, 1, 5] * mult
+            ec = np.array([True, False, True, True])
+            c = isin(a, b, assume_unique=True, kind=kind)
+            assert_array_equal(c, ec)
+
+            a[0] = 8
+            ec = np.array([False, False, True, True])
+            c = isin(a, b, assume_unique=True, kind=kind)
+            assert_array_equal(c, ec)
+
+            a[0], a[3] = 4, 8
+            ec = np.array([True, False, True, False])
+            c = isin(a, b, assume_unique=True, kind=kind)
+            assert_array_equal(c, ec)
+
+            a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
+            b = [2, 3, 4] * mult
+            ec = [False, True, False, True, True, True, True, True, True,
+                  False, True, False, False, False]
+            c = isin(a, b, kind=kind)
+            assert_array_equal(c, ec)
+
+            b = b + [5, 5, 4] * mult
+            ec = [True, True, True, True, True, True, True, True, True, True,
+                  True, False, True, True]
+            c = isin(a, b, kind=kind)
+            assert_array_equal(c, ec)
+
+            a = np.array([5, 7, 1, 2])
+            b = np.array([2, 4, 3, 1, 5] * mult)
+            ec = np.array([True, False, True, True])
+            c = isin(a, b, kind=kind)
+            assert_array_equal(c, ec)
+
+            a = np.array([5, 7, 1, 1, 2])
+            b = np.array([2, 4, 3, 3, 1, 5] * mult)
+            ec = np.array([True, False, True, True, True])
+            c = isin(a, b, kind=kind)
+            assert_array_equal(c, ec)
+
+            a = np.array([5, 5])
+            b = np.array([2, 2] * mult)
+            ec = np.array([False, False])
+            c = isin(a, b, kind=kind)
+            assert_array_equal(c, ec)
+
+        a = np.array([5])
+        b = np.array([2])
+        ec = np.array([False])
+        c = isin(a, b, kind=kind)
+        assert_array_equal(c, ec)
+
+        if kind in {None, "sort"}:
+            assert_array_equal(isin([], [], kind=kind), [])
+
+    def test_isin_char_array(self):
+        a = np.array(['a', 'b', 'c', 'd', 'e', 'c', 'e', 'b'])
+        b = np.array(['a', 'c'])
+
+        ec = np.array([True, False, True, False, False, True, False, False])
+        c = isin(a, b)
+
+        assert_array_equal(c, ec)
+
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_invert(self, kind):
+        "Test isin's invert parameter"
+        # We use two different sizes for the b array here to test the
+        # two different paths in isin().
+        for mult in (1, 10):
+            a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
+            b = [2, 3, 4] * mult
+            assert_array_equal(np.invert(isin(a, b, kind=kind)),
+                               isin(a, b, invert=True, kind=kind))
+
+        # float:
+        if kind in {None, "sort"}:
+            for mult in (1, 10):
+                a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5],
+                            dtype=np.float32)
+                b = [2, 3, 4] * mult
+                b = np.array(b, dtype=np.float32)
+                assert_array_equal(np.invert(isin(a, b, kind=kind)),
+                                   isin(a, b, invert=True, kind=kind))
+
+    def test_isin_hit_alternate_algorithm(self):
+        """Hit the standard isin code with integers"""
+        # Need extreme range to hit standard code
+        # This hits it without the use of kind='table'
+        a = np.array([5, 4, 5, 3, 4, 4, 1e9], dtype=np.int64)
+        b = np.array([2, 3, 4, 1e9], dtype=np.int64)
+        expected = np.array([0, 1, 0, 1, 1, 1, 1], dtype=bool)
+        assert_array_equal(expected, isin(a, b))
+        assert_array_equal(np.invert(expected), isin(a, b, invert=True))
+
+        a = np.array([5, 7, 1, 2], dtype=np.int64)
+        b = np.array([2, 4, 3, 1, 5, 1e9], dtype=np.int64)
+        ec = np.array([True, False, True, True])
+        c = isin(a, b, assume_unique=True)
+        assert_array_equal(c, ec)
+
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_boolean(self, kind):
+        """Test that isin works for boolean input"""
+        a = np.array([True, False])
+        b = np.array([False, False, False])
+        expected = np.array([False, True])
+        assert_array_equal(expected,
+                           isin(a, b, kind=kind))
+        assert_array_equal(np.invert(expected),
+                           isin(a, b, invert=True, kind=kind))
+
+    @pytest.mark.parametrize("kind", [None, "sort"])
+    def test_isin_timedelta(self, kind):
+        """Test that isin works for timedelta input"""
+        rstate = np.random.RandomState(0)
+        a = rstate.randint(0, 100, size=10)
+        b = rstate.randint(0, 100, size=10)
+        truth = isin(a, b)
+        a_timedelta = a.astype("timedelta64[s]")
+        b_timedelta = b.astype("timedelta64[s]")
+        assert_array_equal(truth, isin(a_timedelta, b_timedelta, kind=kind))
+
+    def test_isin_table_timedelta_fails(self):
+        a = np.array([0, 1, 2], dtype="timedelta64[s]")
+        b = a
+        # Make sure it raises a value error:
+        with pytest.raises(ValueError):
+            isin(a, b, kind="table")
+
+    @pytest.mark.parametrize(
+        "dtype1,dtype2",
+        [
+            (np.int8, np.int16),
+            (np.int16, np.int8),
+            (np.uint8, np.uint16),
+            (np.uint16, np.uint8),
+            (np.uint8, np.int16),
+            (np.int16, np.uint8),
+            (np.uint64, np.int64),
+        ]
+    )
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_mixed_dtype(self, dtype1, dtype2, kind):
+        """Test that isin works as expected for mixed dtype input."""
+        is_dtype2_signed = np.issubdtype(dtype2, np.signedinteger)
+        ar1 = np.array([0, 0, 1, 1], dtype=dtype1)
+
+        if is_dtype2_signed:
+            ar2 = np.array([-128, 0, 127], dtype=dtype2)
+        else:
+            ar2 = np.array([127, 0, 255], dtype=dtype2)
+
+        expected = np.array([True, True, False, False])
+
+        expect_failure = kind == "table" and (
+            dtype1 == np.int16 and dtype2 == np.int8)
+
+        if expect_failure:
+            with pytest.raises(RuntimeError, match="exceed the maximum"):
+                isin(ar1, ar2, kind=kind)
+        else:
+            assert_array_equal(isin(ar1, ar2, kind=kind), expected)
+
+    @pytest.mark.parametrize("data", [
+        np.array([2**63, 2**63+1], dtype=np.uint64),
+        np.array([-2**62, -2**62-1], dtype=np.int64),
+    ])
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_mixed_huge_vals(self, kind, data):
+        """Test values outside intp range (negative ones if 32bit system)"""
+        query = data[1]
+        res = np.isin(data, query, kind=kind)
+        assert_array_equal(res, [False, True])
+        # Also check that nothing weird happens for values can't possibly
+        # in range.
+        data = data.astype(np.int32)  # clearly different values
+        res = np.isin(data, query, kind=kind)
+        assert_array_equal(res, [False, False])
+
+    @pytest.mark.parametrize("kind", [None, "sort", "table"])
+    def test_isin_mixed_boolean(self, kind):
+        """Test that isin works as expected for bool/int input."""
+        for dtype in np.typecodes["AllInteger"]:
+            a = np.array([True, False, False], dtype=bool)
+            b = np.array([0, 0, 0, 0], dtype=dtype)
+            expected = np.array([False, True, True], dtype=bool)
+            assert_array_equal(isin(a, b, kind=kind), expected)
+
+            a, b = b, a
+            expected = np.array([True, True, True, True], dtype=bool)
+            assert_array_equal(isin(a, b, kind=kind), expected)
+
+    def test_isin_first_array_is_object(self):
+        ar1 = [None]
+        ar2 = np.array([1]*10)
+        expected = np.array([False])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+
+    def test_isin_second_array_is_object(self):
+        ar1 = 1
+        ar2 = np.array([None]*10)
+        expected = np.array([False])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+
+    def test_isin_both_arrays_are_object(self):
+        ar1 = [None]
+        ar2 = np.array([None]*10)
+        expected = np.array([True])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+
+    def test_isin_both_arrays_have_structured_dtype(self):
+        # Test arrays of a structured data type containing an integer field
+        # and a field of dtype `object` allowing for arbitrary Python objects
+        dt = np.dtype([('field1', int), ('field2', object)])
+        ar1 = np.array([(1, None)], dtype=dt)
+        ar2 = np.array([(1, None)]*10, dtype=dt)
+        expected = np.array([True])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+
+    def test_isin_with_arrays_containing_tuples(self):
+        ar1 = np.array([(1,), 2], dtype=object)
+        ar2 = np.array([(1,), 2], dtype=object)
+        expected = np.array([True, True])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+        result = np.isin(ar1, ar2, invert=True)
+        assert_array_equal(result, np.invert(expected))
+
+        # An integer is added at the end of the array to make sure
+        # that the array builder will create the array with tuples
+        # and after it's created the integer is removed.
+        # There's a bug in the array constructor that doesn't handle
+        # tuples properly and adding the integer fixes that.
+        ar1 = np.array([(1,), (2, 1), 1], dtype=object)
+        ar1 = ar1[:-1]
+        ar2 = np.array([(1,), (2, 1), 1], dtype=object)
+        ar2 = ar2[:-1]
+        expected = np.array([True, True])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+        result = np.isin(ar1, ar2, invert=True)
+        assert_array_equal(result, np.invert(expected))
+
+        ar1 = np.array([(1,), (2, 3), 1], dtype=object)
+        ar1 = ar1[:-1]
+        ar2 = np.array([(1,), 2], dtype=object)
+        expected = np.array([True, False])
+        result = np.isin(ar1, ar2)
+        assert_array_equal(result, expected)
+        result = np.isin(ar1, ar2, invert=True)
+        assert_array_equal(result, np.invert(expected))
+
+    def test_isin_errors(self):
+        """Test that isin raises expected errors."""
+
+        # Error 1: `kind` is not one of 'sort' 'table' or None.
+        ar1 = np.array([1, 2, 3, 4, 5])
+        ar2 = np.array([2, 4, 6, 8, 10])
+        assert_raises(ValueError, isin, ar1, ar2, kind='quicksort')
+
+        # Error 2: `kind="table"` does not work for non-integral arrays.
+        obj_ar1 = np.array([1, 'a', 3, 'b', 5], dtype=object)
+        obj_ar2 = np.array([1, 'a', 3, 'b', 5], dtype=object)
+        assert_raises(ValueError, isin, obj_ar1, obj_ar2, kind='table')
+
+        for dtype in [np.int32, np.int64]:
+            ar1 = np.array([-1, 2, 3, 4, 5], dtype=dtype)
+            # The range of this array will overflow:
+            overflow_ar2 = np.array([-1, np.iinfo(dtype).max], dtype=dtype)
+
+            # Error 3: `kind="table"` will trigger a runtime error
+            #  if there is an integer overflow expected when computing the
+            #  range of ar2
+            assert_raises(
+                RuntimeError,
+                isin, ar1, overflow_ar2, kind='table'
+            )
+
+            # Non-error: `kind=None` will *not* trigger a runtime error
+            #  if there is an integer overflow, it will switch to
+            #  the `sort` algorithm.
+            result = np.isin(ar1, overflow_ar2, kind=None)
+            assert_array_equal(result, [True] + [False] * 4)
+            result = np.isin(ar1, overflow_ar2, kind='sort')
+            assert_array_equal(result, [True] + [False] * 4)
+
+    def test_union1d(self):
+        a = np.array([5, 4, 7, 1, 2])
+        b = np.array([2, 4, 3, 3, 2, 1, 5])
+
+        ec = np.array([1, 2, 3, 4, 5, 7])
+        c = union1d(a, b)
+        assert_array_equal(c, ec)
+
+        # Tests gh-10340, arguments to union1d should be
+        # flattened if they are not already 1D
+        x = np.array([[0, 1, 2], [3, 4, 5]])
+        y = np.array([0, 1, 2, 3, 4])
+        ez = np.array([0, 1, 2, 3, 4, 5])
+        z = union1d(x, y)
+        assert_array_equal(z, ez)
+
+        assert_array_equal([], union1d([], []))
+
+    def test_setdiff1d(self):
+        a = np.array([6, 5, 4, 7, 1, 2, 7, 4])
+        b = np.array([2, 4, 3, 3, 2, 1, 5])
+
+        ec = np.array([6, 7])
+        c = setdiff1d(a, b)
+        assert_array_equal(c, ec)
+
+        a = np.arange(21)
+        b = np.arange(19)
+        ec = np.array([19, 20])
+        c = setdiff1d(a, b)
+        assert_array_equal(c, ec)
+
+        assert_array_equal([], setdiff1d([], []))
+        a = np.array((), np.uint32)
+        assert_equal(setdiff1d(a, []).dtype, np.uint32)
+
+    def test_setdiff1d_unique(self):
+        a = np.array([3, 2, 1])
+        b = np.array([7, 5, 2])
+        expected = np.array([3, 1])
+        actual = setdiff1d(a, b, assume_unique=True)
+        assert_equal(actual, expected)
+
+    def test_setdiff1d_char_array(self):
+        a = np.array(['a', 'b', 'c'])
+        b = np.array(['a', 'b', 's'])
+        assert_array_equal(setdiff1d(a, b), np.array(['c']))
+
+    def test_manyways(self):
+        a = np.array([5, 7, 1, 2, 8])
+        b = np.array([9, 8, 2, 4, 3, 1, 5])
+
+        c1 = setxor1d(a, b)
+        aux1 = intersect1d(a, b)
+        aux2 = union1d(a, b)
+        c2 = setdiff1d(aux2, aux1)
+        assert_array_equal(c1, c2)
+
+
+class TestUnique:
+
+    def test_unique_1d(self):
+
+        def check_all(a, b, i1, i2, c, dt):
+            base_msg = 'check {0} failed for type {1}'
+
+            msg = base_msg.format('values', dt)
+            v = unique(a)
+            assert_array_equal(v, b, msg)
+
+            msg = base_msg.format('return_index', dt)
+            v, j = unique(a, True, False, False)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j, i1, msg)
+
+            msg = base_msg.format('return_inverse', dt)
+            v, j = unique(a, False, True, False)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j, i2, msg)
+
+            msg = base_msg.format('return_counts', dt)
+            v, j = unique(a, False, False, True)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j, c, msg)
+
+            msg = base_msg.format('return_index and return_inverse', dt)
+            v, j1, j2 = unique(a, True, True, False)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j1, i1, msg)
+            assert_array_equal(j2, i2, msg)
+
+            msg = base_msg.format('return_index and return_counts', dt)
+            v, j1, j2 = unique(a, True, False, True)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j1, i1, msg)
+            assert_array_equal(j2, c, msg)
+
+            msg = base_msg.format('return_inverse and return_counts', dt)
+            v, j1, j2 = unique(a, False, True, True)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j1, i2, msg)
+            assert_array_equal(j2, c, msg)
+
+            msg = base_msg.format(('return_index, return_inverse '
+                                   'and return_counts'), dt)
+            v, j1, j2, j3 = unique(a, True, True, True)
+            assert_array_equal(v, b, msg)
+            assert_array_equal(j1, i1, msg)
+            assert_array_equal(j2, i2, msg)
+            assert_array_equal(j3, c, msg)
+
+        a = [5, 7, 1, 2, 1, 5, 7]*10
+        b = [1, 2, 5, 7]
+        i1 = [2, 3, 0, 1]
+        i2 = [2, 3, 0, 1, 0, 2, 3]*10
+        c = np.multiply([2, 1, 2, 2], 10)
+
+        # test for numeric arrays
+        types = []
+        types.extend(np.typecodes['AllInteger'])
+        types.extend(np.typecodes['AllFloat'])
+        types.append('datetime64[D]')
+        types.append('timedelta64[D]')
+        for dt in types:
+            aa = np.array(a, dt)
+            bb = np.array(b, dt)
+            check_all(aa, bb, i1, i2, c, dt)
+
+        # test for object arrays
+        dt = 'O'
+        aa = np.empty(len(a), dt)
+        aa[:] = a
+        bb = np.empty(len(b), dt)
+        bb[:] = b
+        check_all(aa, bb, i1, i2, c, dt)
+
+        # test for structured arrays
+        dt = [('', 'i'), ('', 'i')]
+        aa = np.array(list(zip(a, a)), dt)
+        bb = np.array(list(zip(b, b)), dt)
+        check_all(aa, bb, i1, i2, c, dt)
+
+        # test for ticket #2799
+        aa = [1. + 0.j, 1 - 1.j, 1]
+        assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j])
+
+        # test for ticket #4785
+        a = [(1, 2), (1, 2), (2, 3)]
+        unq = [1, 2, 3]
+        inv = [[0, 1], [0, 1], [1, 2]]
+        a1 = unique(a)
+        assert_array_equal(a1, unq)
+        a2, a2_inv = unique(a, return_inverse=True)
+        assert_array_equal(a2, unq)
+        assert_array_equal(a2_inv, inv)
+
+        # test for chararrays with return_inverse (gh-5099)
+        a = np.char.chararray(5)
+        a[...] = ''
+        a2, a2_inv = np.unique(a, return_inverse=True)
+        assert_array_equal(a2_inv, np.zeros(5))
+
+        # test for ticket #9137
+        a = []
+        a1_idx = np.unique(a, return_index=True)[1]
+        a2_inv = np.unique(a, return_inverse=True)[1]
+        a3_idx, a3_inv = np.unique(a, return_index=True,
+                                   return_inverse=True)[1:]
+        assert_equal(a1_idx.dtype, np.intp)
+        assert_equal(a2_inv.dtype, np.intp)
+        assert_equal(a3_idx.dtype, np.intp)
+        assert_equal(a3_inv.dtype, np.intp)
+
+        # test for ticket 2111 - float
+        a = [2.0, np.nan, 1.0, np.nan]
+        ua = [1.0, 2.0, np.nan]
+        ua_idx = [2, 0, 1]
+        ua_inv = [1, 2, 0, 2]
+        ua_cnt = [1, 1, 2]
+        assert_equal(np.unique(a), ua)
+        assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
+        assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
+        assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
+
+        # test for ticket 2111 - complex
+        a = [2.0-1j, np.nan, 1.0+1j, complex(0.0, np.nan), complex(1.0, np.nan)]
+        ua = [1.0+1j, 2.0-1j, complex(0.0, np.nan)]
+        ua_idx = [2, 0, 3]
+        ua_inv = [1, 2, 0, 2, 2]
+        ua_cnt = [1, 1, 3]
+        assert_equal(np.unique(a), ua)
+        assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
+        assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
+        assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
+
+        # test for ticket 2111 - datetime64
+        nat = np.datetime64('nat')
+        a = [np.datetime64('2020-12-26'), nat, np.datetime64('2020-12-24'), nat]
+        ua = [np.datetime64('2020-12-24'), np.datetime64('2020-12-26'), nat]
+        ua_idx = [2, 0, 1]
+        ua_inv = [1, 2, 0, 2]
+        ua_cnt = [1, 1, 2]
+        assert_equal(np.unique(a), ua)
+        assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
+        assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
+        assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
+
+        # test for ticket 2111 - timedelta
+        nat = np.timedelta64('nat')
+        a = [np.timedelta64(1, 'D'), nat, np.timedelta64(1, 'h'), nat]
+        ua = [np.timedelta64(1, 'h'), np.timedelta64(1, 'D'), nat]
+        ua_idx = [2, 0, 1]
+        ua_inv = [1, 2, 0, 2]
+        ua_cnt = [1, 1, 2]
+        assert_equal(np.unique(a), ua)
+        assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
+        assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
+        assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
+
+        # test for gh-19300
+        all_nans = [np.nan] * 4
+        ua = [np.nan]
+        ua_idx = [0]
+        ua_inv = [0, 0, 0, 0]
+        ua_cnt = [4]
+        assert_equal(np.unique(all_nans), ua)
+        assert_equal(np.unique(all_nans, return_index=True), (ua, ua_idx))
+        assert_equal(np.unique(all_nans, return_inverse=True), (ua, ua_inv))
+        assert_equal(np.unique(all_nans, return_counts=True), (ua, ua_cnt))
+
+    def test_unique_axis_errors(self):
+        assert_raises(TypeError, self._run_axis_tests, object)
+        assert_raises(TypeError, self._run_axis_tests,
+                      [('a', int), ('b', object)])
+
+        assert_raises(AxisError, unique, np.arange(10), axis=2)
+        assert_raises(AxisError, unique, np.arange(10), axis=-2)
+
+    def test_unique_axis_list(self):
+        msg = "Unique failed on list of lists"
+        inp = [[0, 1, 0], [0, 1, 0]]
+        inp_arr = np.asarray(inp)
+        assert_array_equal(unique(inp, axis=0), unique(inp_arr, axis=0), msg)
+        assert_array_equal(unique(inp, axis=1), unique(inp_arr, axis=1), msg)
+
+    def test_unique_axis(self):
+        types = []
+        types.extend(np.typecodes['AllInteger'])
+        types.extend(np.typecodes['AllFloat'])
+        types.append('datetime64[D]')
+        types.append('timedelta64[D]')
+        types.append([('a', int), ('b', int)])
+        types.append([('a', int), ('b', float)])
+
+        for dtype in types:
+            self._run_axis_tests(dtype)
+
+        msg = 'Non-bitwise-equal booleans test failed'
+        data = np.arange(10, dtype=np.uint8).reshape(-1, 2).view(bool)
+        result = np.array([[False, True], [True, True]], dtype=bool)
+        assert_array_equal(unique(data, axis=0), result, msg)
+
+        msg = 'Negative zero equality test failed'
+        data = np.array([[-0.0, 0.0], [0.0, -0.0], [-0.0, 0.0], [0.0, -0.0]])
+        result = np.array([[-0.0, 0.0]])
+        assert_array_equal(unique(data, axis=0), result, msg)
+
+    @pytest.mark.parametrize("axis", [0, -1])
+    def test_unique_1d_with_axis(self, axis):
+        x = np.array([4, 3, 2, 3, 2, 1, 2, 2])
+        uniq = unique(x, axis=axis)
+        assert_array_equal(uniq, [1, 2, 3, 4])
+
+    @pytest.mark.parametrize("axis", [None, 0, -1])
+    def test_unique_inverse_with_axis(self, axis):
+        x = np.array([[4, 4, 3], [2, 2, 1], [2, 2, 1], [4, 4, 3]])
+        uniq, inv = unique(x, return_inverse=True, axis=axis)
+        assert_equal(inv.ndim, x.ndim if axis is None else 1)
+        assert_array_equal(x, np.take(uniq, inv, axis=axis))
+
+    def test_unique_axis_zeros(self):
+        # issue 15559
+        single_zero = np.empty(shape=(2, 0), dtype=np.int8)
+        uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True,
+                                     return_inverse=True, return_counts=True)
+
+        # there's 1 element of shape (0,) along axis 0
+        assert_equal(uniq.dtype, single_zero.dtype)
+        assert_array_equal(uniq, np.empty(shape=(1, 0)))
+        assert_array_equal(idx, np.array([0]))
+        assert_array_equal(inv, np.array([0, 0]))
+        assert_array_equal(cnt, np.array([2]))
+
+        # there's 0 elements of shape (2,) along axis 1
+        uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True,
+                                     return_inverse=True, return_counts=True)
+
+        assert_equal(uniq.dtype, single_zero.dtype)
+        assert_array_equal(uniq, np.empty(shape=(2, 0)))
+        assert_array_equal(idx, np.array([]))
+        assert_array_equal(inv, np.array([]))
+        assert_array_equal(cnt, np.array([]))
+
+        # test a "complicated" shape
+        shape = (0, 2, 0, 3, 0, 4, 0)
+        multiple_zeros = np.empty(shape=shape)
+        for axis in range(len(shape)):
+            expected_shape = list(shape)
+            if shape[axis] == 0:
+                expected_shape[axis] = 0
+            else:
+                expected_shape[axis] = 1
+
+            assert_array_equal(unique(multiple_zeros, axis=axis),
+                               np.empty(shape=expected_shape))
+
+    def test_unique_masked(self):
+        # issue 8664
+        x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0],
+                     dtype='uint8')
+        y = np.ma.masked_equal(x, 0)
+
+        v = np.unique(y)
+        v2, i, c = np.unique(y, return_index=True, return_counts=True)
+
+        msg = 'Unique returned different results when asked for index'
+        assert_array_equal(v.data, v2.data, msg)
+        assert_array_equal(v.mask, v2.mask, msg)
+
+    def test_unique_sort_order_with_axis(self):
+        # These tests fail if sorting along axis is done by treating subarrays
+        # as unsigned byte strings.  See gh-10495.
+        fmt = "sort order incorrect for integer type '%s'"
+        for dt in 'bhilq':
+            a = np.array([[-1], [0]], dt)
+            b = np.unique(a, axis=0)
+            assert_array_equal(a, b, fmt % dt)
+
+    def _run_axis_tests(self, dtype):
+        data = np.array([[0, 1, 0, 0],
+                         [1, 0, 0, 0],
+                         [0, 1, 0, 0],
+                         [1, 0, 0, 0]]).astype(dtype)
+
+        msg = 'Unique with 1d array and axis=0 failed'
+        result = np.array([0, 1])
+        assert_array_equal(unique(data), result.astype(dtype), msg)
+
+        msg = 'Unique with 2d array and axis=0 failed'
+        result = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
+        assert_array_equal(unique(data, axis=0), result.astype(dtype), msg)
+
+        msg = 'Unique with 2d array and axis=1 failed'
+        result = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
+        assert_array_equal(unique(data, axis=1), result.astype(dtype), msg)
+
+        msg = 'Unique with 3d array and axis=2 failed'
+        data3d = np.array([[[1, 1],
+                            [1, 0]],
+                           [[0, 1],
+                            [0, 0]]]).astype(dtype)
+        result = np.take(data3d, [1, 0], axis=2)
+        assert_array_equal(unique(data3d, axis=2), result, msg)
+
+        uniq, idx, inv, cnt = unique(data, axis=0, return_index=True,
+                                     return_inverse=True, return_counts=True)
+        msg = "Unique's return_index=True failed with axis=0"
+        assert_array_equal(data[idx], uniq, msg)
+        msg = "Unique's return_inverse=True failed with axis=0"
+        assert_array_equal(np.take(uniq, inv, axis=0), data)
+        msg = "Unique's return_counts=True failed with axis=0"
+        assert_array_equal(cnt, np.array([2, 2]), msg)
+
+        uniq, idx, inv, cnt = unique(data, axis=1, return_index=True,
+                                     return_inverse=True, return_counts=True)
+        msg = "Unique's return_index=True failed with axis=1"
+        assert_array_equal(data[:, idx], uniq)
+        msg = "Unique's return_inverse=True failed with axis=1"
+        assert_array_equal(np.take(uniq, inv, axis=1), data)
+        msg = "Unique's return_counts=True failed with axis=1"
+        assert_array_equal(cnt, np.array([2, 1, 1]), msg)
+
+    def test_unique_nanequals(self):
+        # issue 20326
+        a = np.array([1, 1, np.nan, np.nan, np.nan])
+        unq = np.unique(a)
+        not_unq = np.unique(a, equal_nan=False)
+        assert_array_equal(unq, np.array([1, np.nan]))
+        assert_array_equal(not_unq, np.array([1, np.nan, np.nan, np.nan]))
+
+    def test_unique_array_api_functions(self):
+        arr = np.array([np.nan, 1, 4, 1, 3, 4, np.nan, 5, 1])
+
+        for res_unique_array_api, res_unique in [
+            (
+                np.unique_values(arr),
+                np.unique(arr, equal_nan=False)
+            ),
+            (
+                np.unique_counts(arr),
+                np.unique(arr, return_counts=True, equal_nan=False)
+            ),
+            (
+                np.unique_inverse(arr),
+                np.unique(arr, return_inverse=True, equal_nan=False)
+            ),
+            (
+                np.unique_all(arr),
+                np.unique(
+                    arr,
+                    return_index=True,
+                    return_inverse=True,
+                    return_counts=True,
+                    equal_nan=False
+                )
+            )
+        ]:
+            assert len(res_unique_array_api) == len(res_unique)
+            for actual, expected in zip(res_unique_array_api, res_unique):
+                assert_array_equal(actual, expected)
+
+    def test_unique_inverse_shape(self):
+        # Regression test for https://github.com/numpy/numpy/issues/25552
+        arr = np.array([[1, 2, 3], [2, 3, 1]])
+        expected_values, expected_inverse = np.unique(arr, return_inverse=True)
+        expected_inverse = expected_inverse.reshape(arr.shape)
+        for func in np.unique_inverse, np.unique_all:
+            result = func(arr)
+            assert_array_equal(expected_values, result.values)
+            assert_array_equal(expected_inverse, result.inverse_indices)
+            assert_array_equal(arr, result.values[result.inverse_indices])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arrayterator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arrayterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..c00ed13d7f3076d53ec080a46fe7e13ff7dfb5a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_arrayterator.py
@@ -0,0 +1,46 @@
+from operator import mul
+from functools import reduce
+
+import numpy as np
+from numpy.random import randint
+from numpy.lib import Arrayterator
+from numpy.testing import assert_
+
+
+def test():
+    np.random.seed(np.arange(10))
+
+    # Create a random array
+    ndims = randint(5)+1
+    shape = tuple(randint(10)+1 for dim in range(ndims))
+    els = reduce(mul, shape)
+    a = np.arange(els)
+    a.shape = shape
+
+    buf_size = randint(2*els)
+    b = Arrayterator(a, buf_size)
+
+    # Check that each block has at most ``buf_size`` elements
+    for block in b:
+        assert_(len(block.flat) <= (buf_size or els))
+
+    # Check that all elements are iterated correctly
+    assert_(list(b.flat) == list(a.flat))
+
+    # Slice arrayterator
+    start = [randint(dim) for dim in shape]
+    stop = [randint(dim)+1 for dim in shape]
+    step = [randint(dim)+1 for dim in shape]
+    slice_ = tuple(slice(*t) for t in zip(start, stop, step))
+    c = b[slice_]
+    d = a[slice_]
+
+    # Check that each block has at most ``buf_size`` elements
+    for block in c:
+        assert_(len(block.flat) <= (buf_size or els))
+
+    # Check that the arrayterator is sliced correctly
+    assert_(np.all(c.__array__() == d))
+
+    # Check that all elements are iterated correctly
+    assert_(list(c.flat) == list(d.flat))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_format.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_format.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cac8819f5fd3c36f7292da5029b2765d45f56bb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_format.py
@@ -0,0 +1,1026 @@
+# doctest
+r''' Test the .npy file format.
+
+Set up:
+
+    >>> import sys
+    >>> from io import BytesIO
+    >>> from numpy.lib import format
+    >>>
+    >>> scalars = [
+    ...     np.uint8,
+    ...     np.int8,
+    ...     np.uint16,
+    ...     np.int16,
+    ...     np.uint32,
+    ...     np.int32,
+    ...     np.uint64,
+    ...     np.int64,
+    ...     np.float32,
+    ...     np.float64,
+    ...     np.complex64,
+    ...     np.complex128,
+    ...     object,
+    ... ]
+    >>>
+    >>> basic_arrays = []
+    >>>
+    >>> for scalar in scalars:
+    ...     for endian in '<>':
+    ...         dtype = np.dtype(scalar).newbyteorder(endian)
+    ...         basic = np.arange(15).astype(dtype)
+    ...         basic_arrays.extend([
+    ...             np.array([], dtype=dtype),
+    ...             np.array(10, dtype=dtype),
+    ...             basic,
+    ...             basic.reshape((3,5)),
+    ...             basic.reshape((3,5)).T,
+    ...             basic.reshape((3,5))[::-1,::2],
+    ...         ])
+    ...
+    >>>
+    >>> Pdescr = [
+    ...     ('x', 'i4', (2,)),
+    ...     ('y', 'f8', (2, 2)),
+    ...     ('z', 'u1')]
+    >>>
+    >>>
+    >>> PbufferT = [
+    ...     ([3,2], [[6.,4.],[6.,4.]], 8),
+    ...     ([4,3], [[7.,5.],[7.,5.]], 9),
+    ...     ]
+    >>>
+    >>>
+    >>> Ndescr = [
+    ...     ('x', 'i4', (2,)),
+    ...     ('Info', [
+    ...         ('value', 'c16'),
+    ...         ('y2', 'f8'),
+    ...         ('Info2', [
+    ...             ('name', 'S2'),
+    ...             ('value', 'c16', (2,)),
+    ...             ('y3', 'f8', (2,)),
+    ...             ('z3', 'u4', (2,))]),
+    ...         ('name', 'S2'),
+    ...         ('z2', 'b1')]),
+    ...     ('color', 'S2'),
+    ...     ('info', [
+    ...         ('Name', 'U8'),
+    ...         ('Value', 'c16')]),
+    ...     ('y', 'f8', (2, 2)),
+    ...     ('z', 'u1')]
+    >>>
+    >>>
+    >>> NbufferT = [
+    ...     ([3,2], (6j, 6., ('nn', [6j,4j], [6.,4.], [1,2]), 'NN', True), 'cc', ('NN', 6j), [[6.,4.],[6.,4.]], 8),
+    ...     ([4,3], (7j, 7., ('oo', [7j,5j], [7.,5.], [2,1]), 'OO', False), 'dd', ('OO', 7j), [[7.,5.],[7.,5.]], 9),
+    ...     ]
+    >>>
+    >>>
+    >>> record_arrays = [
+    ...     np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('<')),
+    ...     np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('<')),
+    ...     np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('>')),
+    ...     np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('>')),
+    ... ]
+
+Test the magic string writing.
+
+    >>> format.magic(1, 0)
+    '\x93NUMPY\x01\x00'
+    >>> format.magic(0, 0)
+    '\x93NUMPY\x00\x00'
+    >>> format.magic(255, 255)
+    '\x93NUMPY\xff\xff'
+    >>> format.magic(2, 5)
+    '\x93NUMPY\x02\x05'
+
+Test the magic string reading.
+
+    >>> format.read_magic(BytesIO(format.magic(1, 0)))
+    (1, 0)
+    >>> format.read_magic(BytesIO(format.magic(0, 0)))
+    (0, 0)
+    >>> format.read_magic(BytesIO(format.magic(255, 255)))
+    (255, 255)
+    >>> format.read_magic(BytesIO(format.magic(2, 5)))
+    (2, 5)
+
+Test the header writing.
+
+    >>> for arr in basic_arrays + record_arrays:
+    ...     f = BytesIO()
+    ...     format.write_array_header_1_0(f, arr)   # XXX: arr is not a dict, items gets called on it
+    ...     print(repr(f.getvalue()))
+    ...
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '|u1', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '|u1', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '|u1', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '|i1', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '|i1', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '|i1', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'u2', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>u2', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'i2', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>i2', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'u4', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>u4', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'i4', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>i4', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'u8', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>u8', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'i8', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>i8', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'f4', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>f4', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'f8', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>f8', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'c8', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': '>c8', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'c16', 'fortran_order': False, 'shape': (0,)}             \n"
+    "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': ()}               \n"
+    "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (15,)}            \n"
+    "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (3, 5)}           \n"
+    "F\x00{'descr': '>c16', 'fortran_order': True, 'shape': (5, 3)}            \n"
+    "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (3, 3)}           \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': 'O', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (0,)}              \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': ()}                \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (15,)}             \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 5)}            \n"
+    "F\x00{'descr': 'O', 'fortran_order': True, 'shape': (5, 3)}             \n"
+    "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 3)}            \n"
+    "v\x00{'descr': [('x', 'i4', (2,)), ('y', '>f8', (2, 2)), ('z', '|u1')],\n 'fortran_order': False,\n 'shape': (2,)}         \n"
+    "\x16\x02{'descr': [('x', '>i4', (2,)),\n           ('Info',\n            [('value', '>c16'),\n             ('y2', '>f8'),\n             ('Info2',\n              [('name', '|S2'),\n               ('value', '>c16', (2,)),\n               ('y3', '>f8', (2,)),\n               ('z3', '>u4', (2,))]),\n             ('name', '|S2'),\n             ('z2', '|b1')]),\n           ('color', '|S2'),\n           ('info', [('Name', '>U8'), ('Value', '>c16')]),\n           ('y', '>f8', (2, 2)),\n           ('z', '|u1')],\n 'fortran_order': False,\n 'shape': (2,)}      \n"
+'''
+import sys
+import os
+import warnings
+import pytest
+from io import BytesIO
+
+import numpy as np
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises, assert_raises_regex,
+    assert_warns, IS_PYPY, IS_WASM, IS_64BIT
+    )
+from numpy.testing._private.utils import requires_memory
+from numpy.lib import format
+
+
+# Generate some basic arrays to test with.
+scalars = [
+    np.uint8,
+    np.int8,
+    np.uint16,
+    np.int16,
+    np.uint32,
+    np.int32,
+    np.uint64,
+    np.int64,
+    np.float32,
+    np.float64,
+    np.complex64,
+    np.complex128,
+    object,
+]
+basic_arrays = []
+for scalar in scalars:
+    for endian in '<>':
+        dtype = np.dtype(scalar).newbyteorder(endian)
+        basic = np.arange(1500).astype(dtype)
+        basic_arrays.extend([
+            # Empty
+            np.array([], dtype=dtype),
+            # Rank-0
+            np.array(10, dtype=dtype),
+            # 1-D
+            basic,
+            # 2-D C-contiguous
+            basic.reshape((30, 50)),
+            # 2-D F-contiguous
+            basic.reshape((30, 50)).T,
+            # 2-D non-contiguous
+            basic.reshape((30, 50))[::-1, ::2],
+        ])
+
+# More complicated record arrays.
+# This is the structure of the table used for plain objects:
+#
+# +-+-+-+
+# |x|y|z|
+# +-+-+-+
+
+# Structure of a plain array description:
+Pdescr = [
+    ('x', 'i4', (2,)),
+    ('y', 'f8', (2, 2)),
+    ('z', 'u1')]
+
+# A plain list of tuples with values for testing:
+PbufferT = [
+    # x     y                  z
+    ([3, 2], [[6., 4.], [6., 4.]], 8),
+    ([4, 3], [[7., 5.], [7., 5.]], 9),
+    ]
+
+
+# This is the structure of the table used for nested objects (DON'T PANIC!):
+#
+# +-+---------------------------------+-----+----------+-+-+
+# |x|Info                             |color|info      |y|z|
+# | +-----+--+----------------+----+--+     +----+-----+ | |
+# | |value|y2|Info2           |name|z2|     |Name|Value| | |
+# | |     |  +----+-----+--+--+    |  |     |    |     | | |
+# | |     |  |name|value|y3|z3|    |  |     |    |     | | |
+# +-+-----+--+----+-----+--+--+----+--+-----+----+-----+-+-+
+#
+
+# The corresponding nested array description:
+Ndescr = [
+    ('x', 'i4', (2,)),
+    ('Info', [
+        ('value', 'c16'),
+        ('y2', 'f8'),
+        ('Info2', [
+            ('name', 'S2'),
+            ('value', 'c16', (2,)),
+            ('y3', 'f8', (2,)),
+            ('z3', 'u4', (2,))]),
+        ('name', 'S2'),
+        ('z2', 'b1')]),
+    ('color', 'S2'),
+    ('info', [
+        ('Name', 'U8'),
+        ('Value', 'c16')]),
+    ('y', 'f8', (2, 2)),
+    ('z', 'u1')]
+
+NbufferT = [
+    # x     Info                                                color info        y                  z
+    #       value y2 Info2                            name z2         Name Value
+    #                name   value    y3       z3
+    ([3, 2], (6j, 6., ('nn', [6j, 4j], [6., 4.], [1, 2]), 'NN', True),
+     'cc', ('NN', 6j), [[6., 4.], [6., 4.]], 8),
+    ([4, 3], (7j, 7., ('oo', [7j, 5j], [7., 5.], [2, 1]), 'OO', False),
+     'dd', ('OO', 7j), [[7., 5.], [7., 5.]], 9),
+    ]
+
+record_arrays = [
+    np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('<')),
+    np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('<')),
+    np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('>')),
+    np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('>')),
+    np.zeros(1, dtype=[('c', ('= (3, 12), reason="see gh-23988")
+@pytest.mark.xfail(IS_WASM, reason="Emscripten NODEFS has a buggy dup")
+def test_python2_python3_interoperability():
+    fname = 'win64python2.npy'
+    path = os.path.join(os.path.dirname(__file__), 'data', fname)
+    with pytest.warns(UserWarning, match="Reading.*this warning\\."):
+        data = np.load(path)
+    assert_array_equal(data, np.ones(2))
+
+
+def test_pickle_python2_python3():
+    # Test that loading object arrays saved on Python 2 works both on
+    # Python 2 and Python 3 and vice versa
+    data_dir = os.path.join(os.path.dirname(__file__), 'data')
+
+    expected = np.array([None, range, '\u512a\u826f',
+                         b'\xe4\xb8\x8d\xe8\x89\xaf'],
+                        dtype=object)
+
+    for fname in ['py2-np0-objarr.npy', 'py2-objarr.npy', 'py2-objarr.npz',
+                  'py3-objarr.npy', 'py3-objarr.npz']:
+        path = os.path.join(data_dir, fname)
+
+        for encoding in ['bytes', 'latin1']:
+            data_f = np.load(path, allow_pickle=True, encoding=encoding)
+            if fname.endswith('.npz'):
+                data = data_f['x']
+                data_f.close()
+            else:
+                data = data_f
+
+            if encoding == 'latin1' and fname.startswith('py2'):
+                assert_(isinstance(data[3], str))
+                assert_array_equal(data[:-1], expected[:-1])
+                # mojibake occurs
+                assert_array_equal(data[-1].encode(encoding), expected[-1])
+            else:
+                assert_(isinstance(data[3], bytes))
+                assert_array_equal(data, expected)
+
+        if fname.startswith('py2'):
+            if fname.endswith('.npz'):
+                data = np.load(path, allow_pickle=True)
+                assert_raises(UnicodeError, data.__getitem__, 'x')
+                data.close()
+                data = np.load(path, allow_pickle=True, fix_imports=False,
+                               encoding='latin1')
+                assert_raises(ImportError, data.__getitem__, 'x')
+                data.close()
+            else:
+                assert_raises(UnicodeError, np.load, path,
+                              allow_pickle=True)
+                assert_raises(ImportError, np.load, path,
+                              allow_pickle=True, fix_imports=False,
+                              encoding='latin1')
+
+
+def test_pickle_disallow(tmpdir):
+    data_dir = os.path.join(os.path.dirname(__file__), 'data')
+
+    path = os.path.join(data_dir, 'py2-objarr.npy')
+    assert_raises(ValueError, np.load, path,
+                  allow_pickle=False, encoding='latin1')
+
+    path = os.path.join(data_dir, 'py2-objarr.npz')
+    with np.load(path, allow_pickle=False, encoding='latin1') as f:
+        assert_raises(ValueError, f.__getitem__, 'x')
+
+    path = os.path.join(tmpdir, 'pickle-disabled.npy')
+    assert_raises(ValueError, np.save, path, np.array([None], dtype=object),
+                  allow_pickle=False)
+
+@pytest.mark.parametrize('dt', [
+    np.dtype(np.dtype([('a', np.int8),
+                       ('b', np.int16),
+                       ('c', np.int32),
+                      ], align=True),
+             (3,)),
+    np.dtype([('x', np.dtype({'names':['a','b'],
+                              'formats':['i1','i1'],
+                              'offsets':[0,4],
+                              'itemsize':8,
+                             },
+                    (3,)),
+               (4,),
+             )]),
+    np.dtype([('x',
+                   (' 1, a)
+        assert_array_equal(b, [3, 2, 2, 3, 3])
+
+    def test_place(self):
+        # Make sure that non-np.ndarray objects
+        # raise an error instead of doing nothing
+        assert_raises(TypeError, place, [1, 2, 3], [True, False], [0, 1])
+
+        a = np.array([1, 4, 3, 2, 5, 8, 7])
+        place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
+        assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
+
+        place(a, np.zeros(7), [])
+        assert_array_equal(a, np.arange(1, 8))
+
+        place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9])
+        assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9])
+        assert_raises_regex(ValueError, "Cannot insert from an empty array",
+                            lambda: place(a, [0, 0, 0, 0, 0, 1, 0], []))
+
+        # See Issue #6974
+        a = np.array(['12', '34'])
+        place(a, [0, 1], '9')
+        assert_array_equal(a, ['12', '9'])
+
+    def test_both(self):
+        a = rand(10)
+        mask = a > 0.5
+        ac = a.copy()
+        c = extract(mask, a)
+        place(a, mask, 0)
+        place(a, mask, c)
+        assert_array_equal(a, ac)
+
+
+# _foo1 and _foo2 are used in some tests in TestVectorize.
+
+def _foo1(x, y=1.0):
+    return y*math.floor(x)
+
+
+def _foo2(x, y=1.0, z=0.0):
+    return y*math.floor(x) + z
+
+
+class TestVectorize:
+
+    def test_simple(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract)
+        r = f([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_scalar(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract)
+        r = f([0, 3, 6, 9], 5)
+        assert_array_equal(r, [5, 8, 1, 4])
+
+    def test_large(self):
+        x = np.linspace(-3, 2, 10000)
+        f = vectorize(lambda x: x)
+        y = f(x)
+        assert_array_equal(y, x)
+
+    def test_ufunc(self):
+        f = vectorize(math.cos)
+        args = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
+        r1 = f(args)
+        r2 = np.cos(args)
+        assert_array_almost_equal(r1, r2)
+
+    def test_keywords(self):
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo)
+        args = np.array([1, 2, 3])
+        r1 = f(args)
+        r2 = np.array([2, 3, 4])
+        assert_array_equal(r1, r2)
+        r1 = f(args, 2)
+        r2 = np.array([3, 4, 5])
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order1(self):
+        # gh-1620: The second call of f would crash with
+        # `ValueError: invalid number of arguments`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0), 1.0)
+        r2 = f(np.arange(3.0))
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order2(self):
+        # gh-1620: The second call of f would crash with
+        # `ValueError: non-broadcastable output operand with shape ()
+        # doesn't match the broadcast shape (3,)`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0))
+        r2 = f(np.arange(3.0), 1.0)
+        assert_array_equal(r1, r2)
+
+    def test_keywords_with_otypes_order3(self):
+        # gh-1620: The third call of f would crash with
+        # `ValueError: invalid number of arguments`.
+        f = vectorize(_foo1, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(np.arange(3.0))
+        r2 = f(np.arange(3.0), y=1.0)
+        r3 = f(np.arange(3.0))
+        assert_array_equal(r1, r2)
+        assert_array_equal(r1, r3)
+
+    def test_keywords_with_otypes_several_kwd_args1(self):
+        # gh-1620 Make sure different uses of keyword arguments
+        # don't break the vectorized function.
+        f = vectorize(_foo2, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(10.4, z=100)
+        r2 = f(10.4, y=-1)
+        r3 = f(10.4)
+        assert_equal(r1, _foo2(10.4, z=100))
+        assert_equal(r2, _foo2(10.4, y=-1))
+        assert_equal(r3, _foo2(10.4))
+
+    def test_keywords_with_otypes_several_kwd_args2(self):
+        # gh-1620 Make sure different uses of keyword arguments
+        # don't break the vectorized function.
+        f = vectorize(_foo2, otypes=[float])
+        # We're testing the caching of ufuncs by vectorize, so the order
+        # of these function calls is an important part of the test.
+        r1 = f(z=100, x=10.4, y=-1)
+        r2 = f(1, 2, 3)
+        assert_equal(r1, _foo2(z=100, x=10.4, y=-1))
+        assert_equal(r2, _foo2(1, 2, 3))
+
+    def test_keywords_no_func_code(self):
+        # This needs to test a function that has keywords but
+        # no func_code attribute, since otherwise vectorize will
+        # inspect the func_code.
+        import random
+        try:
+            vectorize(random.randrange)  # Should succeed
+        except Exception:
+            raise AssertionError
+
+    def test_keywords2_ticket_2100(self):
+        # Test kwarg support: enhancement ticket 2100
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo)
+        args = np.array([1, 2, 3])
+        r1 = f(a=args)
+        r2 = np.array([2, 3, 4])
+        assert_array_equal(r1, r2)
+        r1 = f(b=1, a=args)
+        assert_array_equal(r1, r2)
+        r1 = f(args, b=2)
+        r2 = np.array([3, 4, 5])
+        assert_array_equal(r1, r2)
+
+    def test_keywords3_ticket_2100(self):
+        # Test excluded with mixed positional and kwargs: ticket 2100
+        def mypolyval(x, p):
+            _p = list(p)
+            res = _p.pop(0)
+            while _p:
+                res = res * x + _p.pop(0)
+            return res
+
+        vpolyval = np.vectorize(mypolyval, excluded=['p', 1])
+        ans = [3, 6]
+        assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
+        assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
+        assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3]))
+
+    def test_keywords4_ticket_2100(self):
+        # Test vectorizing function with no positional args.
+        @vectorize
+        def f(**kw):
+            res = 1.0
+            for _k in kw:
+                res *= kw[_k]
+            return res
+
+        assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8])
+
+    def test_keywords5_ticket_2100(self):
+        # Test vectorizing function with no kwargs args.
+        @vectorize
+        def f(*v):
+            return np.prod(v)
+
+        assert_array_equal(f([1, 2], [3, 4]), [3, 8])
+
+    def test_coverage1_ticket_2100(self):
+        def foo():
+            return 1
+
+        f = vectorize(foo)
+        assert_array_equal(f(), 1)
+
+    def test_assigning_docstring(self):
+        def foo(x):
+            """Original documentation"""
+            return x
+
+        f = vectorize(foo)
+        assert_equal(f.__doc__, foo.__doc__)
+
+        doc = "Provided documentation"
+        f = vectorize(foo, doc=doc)
+        assert_equal(f.__doc__, doc)
+
+    def test_UnboundMethod_ticket_1156(self):
+        # Regression test for issue 1156
+        class Foo:
+            b = 2
+
+            def bar(self, a):
+                return a ** self.b
+
+        assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
+                           np.arange(9) ** 2)
+        assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
+                           np.arange(9) ** 2)
+
+    def test_execution_order_ticket_1487(self):
+        # Regression test for dependence on execution order: issue 1487
+        f1 = vectorize(lambda x: x)
+        res1a = f1(np.arange(3))
+        res1b = f1(np.arange(0.1, 3))
+        f2 = vectorize(lambda x: x)
+        res2b = f2(np.arange(0.1, 3))
+        res2a = f2(np.arange(3))
+        assert_equal(res1a, res2a)
+        assert_equal(res1b, res2b)
+
+    def test_string_ticket_1892(self):
+        # Test vectorization over strings: issue 1892.
+        f = np.vectorize(lambda x: x)
+        s = '0123456789' * 10
+        assert_equal(s, f(s))
+
+    def test_cache(self):
+        # Ensure that vectorized func called exactly once per argument.
+        _calls = [0]
+
+        @vectorize
+        def f(x):
+            _calls[0] += 1
+            return x ** 2
+
+        f.cache = True
+        x = np.arange(5)
+        assert_array_equal(f(x), x * x)
+        assert_equal(_calls[0], len(x))
+
+    def test_otypes(self):
+        f = np.vectorize(lambda x: x)
+        f.otypes = 'i'
+        x = np.arange(5)
+        assert_array_equal(f(x), x)
+
+    def test_parse_gufunc_signature(self):
+        assert_equal(nfb._parse_gufunc_signature('(x)->()'), ([('x',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x,y)->()'),
+                     ([('x', 'y')], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x),(y)->()'),
+                     ([('x',), ('y',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x)->(y)'),
+                     ([('x',)], [('y',)]))
+        assert_equal(nfb._parse_gufunc_signature('(x)->(y),()'),
+                     ([('x',)], [('y',), ()]))
+        assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'),
+                     ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
+
+        # Tests to check if whitespaces are ignored
+        assert_equal(nfb._parse_gufunc_signature('(x )->()'), ([('x',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('( x , y )->(  )'),
+                     ([('x', 'y')], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(x),( y) ->()'),
+                     ([('x',), ('y',)], [()]))
+        assert_equal(nfb._parse_gufunc_signature('(  x)-> (y )  '),
+                     ([('x',)], [('y',)]))
+        assert_equal(nfb._parse_gufunc_signature(' (x)->( y),( )'),
+                     ([('x',)], [('y',), ()]))
+        assert_equal(nfb._parse_gufunc_signature(
+                     '(  ), ( a,  b,c )  ,(  d)   ->   (d  ,  e)'),
+                     ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
+
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('(x)(y)->()')
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('(x),(y)->')
+        with assert_raises(ValueError):
+            nfb._parse_gufunc_signature('((x))->(x)')
+
+    def test_signature_simple(self):
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        f = vectorize(addsubtract, signature='(),()->()')
+        r = f([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_signature_mean_last(self):
+        def mean(a):
+            return a.mean()
+
+        f = vectorize(mean, signature='(n)->()')
+        r = f([[1, 3], [2, 4]])
+        assert_array_equal(r, [2, 3])
+
+    def test_signature_center(self):
+        def center(a):
+            return a - a.mean()
+
+        f = vectorize(center, signature='(n)->(n)')
+        r = f([[1, 3], [2, 4]])
+        assert_array_equal(r, [[-1, 1], [-1, 1]])
+
+    def test_signature_two_outputs(self):
+        f = vectorize(lambda x: (x, x), signature='()->(),()')
+        r = f([1, 2, 3])
+        assert_(isinstance(r, tuple) and len(r) == 2)
+        assert_array_equal(r[0], [1, 2, 3])
+        assert_array_equal(r[1], [1, 2, 3])
+
+    def test_signature_outer(self):
+        f = vectorize(np.outer, signature='(a),(b)->(a,b)')
+        r = f([1, 2], [1, 2, 3])
+        assert_array_equal(r, [[1, 2, 3], [2, 4, 6]])
+
+        r = f([[[1, 2]]], [1, 2, 3])
+        assert_array_equal(r, [[[[1, 2, 3], [2, 4, 6]]]])
+
+        r = f([[1, 0], [2, 0]], [1, 2, 3])
+        assert_array_equal(r, [[[1, 2, 3], [0, 0, 0]],
+                               [[2, 4, 6], [0, 0, 0]]])
+
+        r = f([1, 2], [[1, 2, 3], [0, 0, 0]])
+        assert_array_equal(r, [[[1, 2, 3], [2, 4, 6]],
+                               [[0, 0, 0], [0, 0, 0]]])
+
+    def test_signature_computed_size(self):
+        f = vectorize(lambda x: x[:-1], signature='(n)->(m)')
+        r = f([1, 2, 3])
+        assert_array_equal(r, [1, 2])
+
+        r = f([[1, 2, 3], [2, 3, 4]])
+        assert_array_equal(r, [[1, 2], [2, 3]])
+
+    def test_signature_excluded(self):
+
+        def foo(a, b=1):
+            return a + b
+
+        f = vectorize(foo, signature='()->()', excluded={'b'})
+        assert_array_equal(f([1, 2, 3]), [2, 3, 4])
+        assert_array_equal(f([1, 2, 3], b=0), [1, 2, 3])
+
+    def test_signature_otypes(self):
+        f = vectorize(lambda x: x, signature='(n)->(n)', otypes=['float64'])
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+
+    def test_signature_invalid_inputs(self):
+        f = vectorize(operator.add, signature='(n),(n)->(n)')
+        with assert_raises_regex(TypeError, 'wrong number of positional'):
+            f([1, 2])
+        with assert_raises_regex(
+                ValueError, 'does not have enough dimensions'):
+            f(1, 2)
+        with assert_raises_regex(
+                ValueError, 'inconsistent size for core dimension'):
+            f([1, 2], [1, 2, 3])
+
+        f = vectorize(operator.add, signature='()->()')
+        with assert_raises_regex(TypeError, 'wrong number of positional'):
+            f(1, 2)
+
+    def test_signature_invalid_outputs(self):
+
+        f = vectorize(lambda x: x[:-1], signature='(n)->(n)')
+        with assert_raises_regex(
+                ValueError, 'inconsistent size for core dimension'):
+            f([1, 2, 3])
+
+        f = vectorize(lambda x: x, signature='()->(),()')
+        with assert_raises_regex(ValueError, 'wrong number of outputs'):
+            f(1)
+
+        f = vectorize(lambda x: (x, x), signature='()->()')
+        with assert_raises_regex(ValueError, 'wrong number of outputs'):
+            f([1, 2])
+
+    def test_size_zero_output(self):
+        # see issue 5868
+        f = np.vectorize(lambda x: x)
+        x = np.zeros([0, 5], dtype=int)
+        with assert_raises_regex(ValueError, 'otypes'):
+            f(x)
+
+        f.otypes = 'i'
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='()->()')
+        with assert_raises_regex(ValueError, 'otypes'):
+            f(x)
+
+        f = np.vectorize(lambda x: x, signature='()->()', otypes='i')
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='(n)->(n)', otypes='i')
+        assert_array_equal(f(x), x)
+
+        f = np.vectorize(lambda x: x, signature='(n)->(n)')
+        assert_array_equal(f(x.T), x.T)
+
+        f = np.vectorize(lambda x: [x], signature='()->(n)', otypes='i')
+        with assert_raises_regex(ValueError, 'new output dimensions'):
+            f(x)
+
+    def test_subclasses(self):
+        class subclass(np.ndarray):
+            pass
+
+        m = np.array([[1., 0., 0.],
+                      [0., 0., 1.],
+                      [0., 1., 0.]]).view(subclass)
+        v = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]).view(subclass)
+        # generalized (gufunc)
+        matvec = np.vectorize(np.matmul, signature='(m,m),(m)->(m)')
+        r = matvec(m, v)
+        assert_equal(type(r), subclass)
+        assert_equal(r, [[1., 3., 2.], [4., 6., 5.], [7., 9., 8.]])
+
+        # element-wise (ufunc)
+        mult = np.vectorize(lambda x, y: x*y)
+        r = mult(m, v)
+        assert_equal(type(r), subclass)
+        assert_equal(r, m * v)
+
+    def test_name(self):
+        #See gh-23021
+        @np.vectorize
+        def f2(a, b):
+            return a + b
+
+        assert f2.__name__ == 'f2'
+
+    def test_decorator(self):
+        @vectorize
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_docstring(self):
+        @vectorize
+        def f(x):
+            """Docstring"""
+            return x
+
+        if sys.flags.optimize < 2:
+            assert f.__doc__ == "Docstring"
+
+    def test_partial(self):
+        def foo(x, y):
+            return x + y
+
+        bar = partial(foo, 3)
+        vbar = np.vectorize(bar)
+        assert vbar(1) == 4
+
+    def test_signature_otypes_decorator(self):
+        @vectorize(signature='(n)->(n)', otypes=['float64'])
+        def f(x):
+            return x
+
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+        assert f.__name__ == 'f'
+
+    def test_bad_input(self):
+        with assert_raises(TypeError):
+            A = np.vectorize(pyfunc = 3)
+
+    def test_no_keywords(self):
+        with assert_raises(TypeError):
+            @np.vectorize("string")
+            def foo():
+                return "bar"
+
+    def test_positional_regression_9477(self):
+        # This supplies the first keyword argument as a positional,
+        # to ensure that they are still properly forwarded after the
+        # enhancement for #9477
+        f = vectorize((lambda x: x), ['float64'])
+        r = f([2])
+        assert_equal(r.dtype, np.dtype('float64'))
+
+    def test_datetime_conversion(self):
+        otype = "datetime64[ns]"
+        arr = np.array(['2024-01-01', '2024-01-02', '2024-01-03'],
+                       dtype='datetime64[ns]')
+        assert_array_equal(np.vectorize(lambda x: x, signature="(i)->(j)",
+                                        otypes=[otype])(arr), arr)
+
+
+class TestLeaks:
+    class A:
+        iters = 20
+
+        def bound(self, *args):
+            return 0
+
+        @staticmethod
+        def unbound(*args):
+            return 0
+
+    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+    @pytest.mark.skipif(NOGIL_BUILD,
+                        reason=("Functions are immortalized if a thread is "
+                                "launched, making this test flaky"))
+    @pytest.mark.parametrize('name, incr', [
+            ('bound', A.iters),
+            ('unbound', 0),
+            ])
+    def test_frompyfunc_leaks(self, name, incr):
+        # exposed in gh-11867 as np.vectorized, but the problem stems from
+        # frompyfunc.
+        # class.attribute = np.frompyfunc() creates a
+        # reference cycle if  is a bound class method. It requires a
+        # gc collection cycle to break the cycle (on CPython 3)
+        import gc
+        A_func = getattr(self.A, name)
+        gc.disable()
+        try:
+            refcount = sys.getrefcount(A_func)
+            for i in range(self.A.iters):
+                a = self.A()
+                a.f = np.frompyfunc(getattr(a, name), 1, 1)
+                out = a.f(np.arange(10))
+            a = None
+            # A.func is part of a reference cycle if incr is non-zero
+            assert_equal(sys.getrefcount(A_func), refcount + incr)
+            for i in range(5):
+                gc.collect()
+            assert_equal(sys.getrefcount(A_func), refcount)
+        finally:
+            gc.enable()
+
+
+class TestDigitize:
+
+    def test_forward(self):
+        x = np.arange(-6, 5)
+        bins = np.arange(-5, 5)
+        assert_array_equal(digitize(x, bins), np.arange(11))
+
+    def test_reverse(self):
+        x = np.arange(5, -6, -1)
+        bins = np.arange(5, -5, -1)
+        assert_array_equal(digitize(x, bins), np.arange(11))
+
+    def test_random(self):
+        x = rand(10)
+        bin = np.linspace(x.min(), x.max(), 10)
+        assert_(np.all(digitize(x, bin) != 0))
+
+    def test_right_basic(self):
+        x = [1, 5, 4, 10, 8, 11, 0]
+        bins = [1, 5, 10]
+        default_answer = [1, 2, 1, 3, 2, 3, 0]
+        assert_array_equal(digitize(x, bins), default_answer)
+        right_answer = [0, 1, 1, 2, 2, 3, 0]
+        assert_array_equal(digitize(x, bins, True), right_answer)
+
+    def test_right_open(self):
+        x = np.arange(-6, 5)
+        bins = np.arange(-6, 4)
+        assert_array_equal(digitize(x, bins, True), np.arange(11))
+
+    def test_right_open_reverse(self):
+        x = np.arange(5, -6, -1)
+        bins = np.arange(4, -6, -1)
+        assert_array_equal(digitize(x, bins, True), np.arange(11))
+
+    def test_right_open_random(self):
+        x = rand(10)
+        bins = np.linspace(x.min(), x.max(), 10)
+        assert_(np.all(digitize(x, bins, True) != 10))
+
+    def test_monotonic(self):
+        x = [-1, 0, 1, 2]
+        bins = [0, 0, 1]
+        assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3])
+        assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3])
+        bins = [1, 1, 0]
+        assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0])
+        assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0])
+        bins = [1, 1, 1, 1]
+        assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4])
+        assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4])
+        bins = [0, 0, 1, 0]
+        assert_raises(ValueError, digitize, x, bins)
+        bins = [1, 1, 0, 1]
+        assert_raises(ValueError, digitize, x, bins)
+
+    def test_casting_error(self):
+        x = [1, 2, 3 + 1.j]
+        bins = [1, 2, 3]
+        assert_raises(TypeError, digitize, x, bins)
+        x, bins = bins, x
+        assert_raises(TypeError, digitize, x, bins)
+
+    def test_return_type(self):
+        # Functions returning indices should always return base ndarrays
+        class A(np.ndarray):
+            pass
+        a = np.arange(5).view(A)
+        b = np.arange(1, 3).view(A)
+        assert_(not isinstance(digitize(b, a, False), A))
+        assert_(not isinstance(digitize(b, a, True), A))
+
+    def test_large_integers_increasing(self):
+        # gh-11022
+        x = 2**54  # loses precision in a float
+        assert_equal(np.digitize(x, [x - 1, x + 1]), 1)
+
+    @pytest.mark.xfail(
+        reason="gh-11022: np._core.multiarray._monoticity loses precision")
+    def test_large_integers_decreasing(self):
+        # gh-11022
+        x = 2**54  # loses precision in a float
+        assert_equal(np.digitize(x, [x + 1, x - 1]), 1)
+
+
+class TestUnwrap:
+
+    def test_simple(self):
+        # check that unwrap removes jumps greater that 2*pi
+        assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1])
+        # check that unwrap maintains continuity
+        assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi))
+
+    def test_period(self):
+        # check that unwrap removes jumps greater that 255
+        assert_array_equal(unwrap([1, 1 + 256], period=255), [1, 2])
+        # check that unwrap maintains continuity
+        assert_(np.all(diff(unwrap(rand(10) * 1000, period=255)) < 255))
+        # check simple case
+        simple_seq = np.array([0, 75, 150, 225, 300])
+        wrap_seq = np.mod(simple_seq, 255)
+        assert_array_equal(unwrap(wrap_seq, period=255), simple_seq)
+        # check custom discont value
+        uneven_seq = np.array([0, 75, 150, 225, 300, 430])
+        wrap_uneven = np.mod(uneven_seq, 250)
+        no_discont = unwrap(wrap_uneven, period=250)
+        assert_array_equal(no_discont, [0, 75, 150, 225, 300, 180])
+        sm_discont = unwrap(wrap_uneven, period=250, discont=140)
+        assert_array_equal(sm_discont, [0, 75, 150, 225, 300, 430])
+        assert sm_discont.dtype == wrap_uneven.dtype
+
+
+@pytest.mark.parametrize(
+    "dtype", "O" + np.typecodes["AllInteger"] + np.typecodes["Float"]
+)
+@pytest.mark.parametrize("M", [0, 1, 10])
+class TestFilterwindows:
+
+    def test_hanning(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = hanning(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.500, 4)
+
+    def test_hamming(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = hamming(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.9400, 4)
+
+    def test_bartlett(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = bartlett(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 4.4444, 4)
+
+    def test_blackman(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = blackman(scalar)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 3.7800, 4)
+
+    def test_kaiser(self, dtype: str, M: int) -> None:
+        scalar = np.array(M, dtype=dtype)[()]
+
+        w = kaiser(scalar, 0)
+        if dtype == "O":
+            ref_dtype = np.float64
+        else:
+            ref_dtype = np.result_type(scalar.dtype, np.float64)
+        assert w.dtype == ref_dtype
+
+        # check symmetry
+        assert_equal(w, flipud(w))
+
+        # check known value
+        if scalar < 1:
+            assert_array_equal(w, np.array([]))
+        elif scalar == 1:
+            assert_array_equal(w, np.ones(1))
+        else:
+            assert_almost_equal(np.sum(w, axis=0), 10, 15)
+
+
+class TestTrapezoid:
+
+    def test_simple(self):
+        x = np.arange(-10, 10, .1)
+        r = trapezoid(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1)
+        # check integral of normal equals 1
+        assert_almost_equal(r, 1, 7)
+
+    def test_ndim(self):
+        x = np.linspace(0, 1, 3)
+        y = np.linspace(0, 2, 8)
+        z = np.linspace(0, 3, 13)
+
+        wx = np.ones_like(x) * (x[1] - x[0])
+        wx[0] /= 2
+        wx[-1] /= 2
+        wy = np.ones_like(y) * (y[1] - y[0])
+        wy[0] /= 2
+        wy[-1] /= 2
+        wz = np.ones_like(z) * (z[1] - z[0])
+        wz[0] /= 2
+        wz[-1] /= 2
+
+        q = x[:, None, None] + y[None,:, None] + z[None, None,:]
+
+        qx = (q * wx[:, None, None]).sum(axis=0)
+        qy = (q * wy[None, :, None]).sum(axis=1)
+        qz = (q * wz[None, None, :]).sum(axis=2)
+
+        # n-d `x`
+        r = trapezoid(q, x=x[:, None, None], axis=0)
+        assert_almost_equal(r, qx)
+        r = trapezoid(q, x=y[None, :, None], axis=1)
+        assert_almost_equal(r, qy)
+        r = trapezoid(q, x=z[None, None, :], axis=2)
+        assert_almost_equal(r, qz)
+
+        # 1-d `x`
+        r = trapezoid(q, x=x, axis=0)
+        assert_almost_equal(r, qx)
+        r = trapezoid(q, x=y, axis=1)
+        assert_almost_equal(r, qy)
+        r = trapezoid(q, x=z, axis=2)
+        assert_almost_equal(r, qz)
+
+    def test_masked(self):
+        # Testing that masked arrays behave as if the function is 0 where
+        # masked
+        x = np.arange(5)
+        y = x * x
+        mask = x == 2
+        ym = np.ma.array(y, mask=mask)
+        r = 13.0  # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
+        assert_almost_equal(trapezoid(ym, x), r)
+
+        xm = np.ma.array(x, mask=mask)
+        assert_almost_equal(trapezoid(ym, xm), r)
+
+        xm = np.ma.array(x, mask=mask)
+        assert_almost_equal(trapezoid(y, xm), r)
+
+
+class TestSinc:
+
+    def test_simple(self):
+        assert_(sinc(0) == 1)
+        w = sinc(np.linspace(-1, 1, 100))
+        # check symmetry
+        assert_array_almost_equal(w, flipud(w), 7)
+
+    def test_array_like(self):
+        x = [0, 0.5]
+        y1 = sinc(np.array(x))
+        y2 = sinc(list(x))
+        y3 = sinc(tuple(x))
+        assert_array_equal(y1, y2)
+        assert_array_equal(y1, y3)
+
+
+class TestUnique:
+
+    def test_simple(self):
+        x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0])
+        assert_(np.all(unique(x) == [0, 1, 2, 3, 4]))
+        assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1]))
+        x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
+        assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
+        x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
+        assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
+
+
+class TestCheckFinite:
+
+    def test_simple(self):
+        a = [1, 2, 3]
+        b = [1, 2, np.inf]
+        c = [1, 2, np.nan]
+        np.asarray_chkfinite(a)
+        assert_raises(ValueError, np.asarray_chkfinite, b)
+        assert_raises(ValueError, np.asarray_chkfinite, c)
+
+    def test_dtype_order(self):
+        # Regression test for missing dtype and order arguments
+        a = [1, 2, 3]
+        a = np.asarray_chkfinite(a, order='F', dtype=np.float64)
+        assert_(a.dtype == np.float64)
+
+
+class TestCorrCoef:
+    A = np.array(
+        [[0.15391142, 0.18045767, 0.14197213],
+         [0.70461506, 0.96474128, 0.27906989],
+         [0.9297531, 0.32296769, 0.19267156]])
+    B = np.array(
+        [[0.10377691, 0.5417086, 0.49807457],
+         [0.82872117, 0.77801674, 0.39226705],
+         [0.9314666, 0.66800209, 0.03538394]])
+    res1 = np.array(
+        [[1., 0.9379533, -0.04931983],
+         [0.9379533, 1., 0.30007991],
+         [-0.04931983, 0.30007991, 1.]])
+    res2 = np.array(
+        [[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523],
+         [0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386],
+         [-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601],
+         [0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113],
+         [0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823],
+         [0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]])
+
+    def test_non_array(self):
+        assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]),
+                            [[1., -1.], [-1.,  1.]])
+
+    def test_simple(self):
+        tgt1 = corrcoef(self.A)
+        assert_almost_equal(tgt1, self.res1)
+        assert_(np.all(np.abs(tgt1) <= 1.0))
+
+        tgt2 = corrcoef(self.A, self.B)
+        assert_almost_equal(tgt2, self.res2)
+        assert_(np.all(np.abs(tgt2) <= 1.0))
+
+    def test_ddof(self):
+        # ddof raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1)
+            sup.filter(DeprecationWarning)
+            # ddof has no or negligible effect on the function
+            assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
+            assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
+            assert_almost_equal(corrcoef(self.A, ddof=3), self.res1)
+            assert_almost_equal(corrcoef(self.A, self.B, ddof=3), self.res2)
+
+    def test_bias(self):
+        # bias raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0)
+            assert_warns(DeprecationWarning, corrcoef, self.A, bias=0)
+            sup.filter(DeprecationWarning)
+            # bias has no or negligible effect on the function
+            assert_almost_equal(corrcoef(self.A, bias=1), self.res1)
+
+    def test_complex(self):
+        x = np.array([[1, 2, 3], [1j, 2j, 3j]])
+        res = corrcoef(x)
+        tgt = np.array([[1., -1.j], [1.j, 1.]])
+        assert_allclose(res, tgt)
+        assert_(np.all(np.abs(res) <= 1.0))
+
+    def test_xy(self):
+        x = np.array([[1, 2, 3]])
+        y = np.array([[1j, 2j, 3j]])
+        assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]]))
+
+    def test_empty(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(corrcoef(np.array([])), np.nan)
+            assert_array_equal(corrcoef(np.array([]).reshape(0, 2)),
+                               np.array([]).reshape(0, 0))
+            assert_array_equal(corrcoef(np.array([]).reshape(2, 0)),
+                               np.array([[np.nan, np.nan], [np.nan, np.nan]]))
+
+    def test_extreme(self):
+        x = [[1e-100, 1e100], [1e100, 1e-100]]
+        with np.errstate(all='raise'):
+            c = corrcoef(x)
+        assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]]))
+        assert_(np.all(np.abs(c) <= 1.0))
+
+    @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
+    def test_corrcoef_dtype(self, test_type):
+        cast_A = self.A.astype(test_type)
+        res = corrcoef(cast_A, dtype=test_type)
+        assert test_type == res.dtype
+
+
+class TestCov:
+    x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
+    res1 = np.array([[1., -1.], [-1., 1.]])
+    x2 = np.array([0.0, 1.0, 2.0], ndmin=2)
+    frequencies = np.array([1, 4, 1])
+    x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T
+    res2 = np.array([[0.4, -0.4], [-0.4, 0.4]])
+    unit_frequencies = np.ones(3, dtype=np.int_)
+    weights = np.array([1.0, 4.0, 1.0])
+    res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]])
+    unit_weights = np.ones(3)
+    x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])
+
+    def test_basic(self):
+        assert_allclose(cov(self.x1), self.res1)
+
+    def test_complex(self):
+        x = np.array([[1, 2, 3], [1j, 2j, 3j]])
+        res = np.array([[1., -1.j], [1.j, 1.]])
+        assert_allclose(cov(x), res)
+        assert_allclose(cov(x, aweights=np.ones(3)), res)
+
+    def test_xy(self):
+        x = np.array([[1, 2, 3]])
+        y = np.array([[1j, 2j, 3j]])
+        assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]]))
+
+    def test_empty(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(cov(np.array([])), np.nan)
+            assert_array_equal(cov(np.array([]).reshape(0, 2)),
+                               np.array([]).reshape(0, 0))
+            assert_array_equal(cov(np.array([]).reshape(2, 0)),
+                               np.array([[np.nan, np.nan], [np.nan, np.nan]]))
+
+    def test_wrong_ddof(self):
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter('always', RuntimeWarning)
+            assert_array_equal(cov(self.x1, ddof=5),
+                               np.array([[np.inf, -np.inf],
+                                         [-np.inf, np.inf]]))
+
+    def test_1D_rowvar(self):
+        assert_allclose(cov(self.x3), cov(self.x3, rowvar=False))
+        y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501])
+        assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=False))
+
+    def test_1D_variance(self):
+        assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1))
+
+    def test_fweights(self):
+        assert_allclose(cov(self.x2, fweights=self.frequencies),
+                        cov(self.x2_repeats))
+        assert_allclose(cov(self.x1, fweights=self.frequencies),
+                        self.res2)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies),
+                        self.res1)
+        nonint = self.frequencies + 0.5
+        assert_raises(TypeError, cov, self.x1, fweights=nonint)
+        f = np.ones((2, 3), dtype=np.int_)
+        assert_raises(RuntimeError, cov, self.x1, fweights=f)
+        f = np.ones(2, dtype=np.int_)
+        assert_raises(RuntimeError, cov, self.x1, fweights=f)
+        f = -1 * np.ones(3, dtype=np.int_)
+        assert_raises(ValueError, cov, self.x1, fweights=f)
+
+    def test_aweights(self):
+        assert_allclose(cov(self.x1, aweights=self.weights), self.res3)
+        assert_allclose(cov(self.x1, aweights=3.0 * self.weights),
+                        cov(self.x1, aweights=self.weights))
+        assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1)
+        w = np.ones((2, 3))
+        assert_raises(RuntimeError, cov, self.x1, aweights=w)
+        w = np.ones(2)
+        assert_raises(RuntimeError, cov, self.x1, aweights=w)
+        w = -1.0 * np.ones(3)
+        assert_raises(ValueError, cov, self.x1, aweights=w)
+
+    def test_unit_fweights_and_aweights(self):
+        assert_allclose(cov(self.x2, fweights=self.frequencies,
+                            aweights=self.unit_weights),
+                        cov(self.x2_repeats))
+        assert_allclose(cov(self.x1, fweights=self.frequencies,
+                            aweights=self.unit_weights),
+                        self.res2)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.unit_weights),
+                        self.res1)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.weights),
+                        self.res3)
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=3.0 * self.weights),
+                        cov(self.x1, aweights=self.weights))
+        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
+                            aweights=self.unit_weights),
+                        self.res1)
+
+    @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
+    def test_cov_dtype(self, test_type):
+        cast_x1 = self.x1.astype(test_type)
+        res = cov(cast_x1, dtype=test_type)
+        assert test_type == res.dtype
+
+    def test_gh_27658(self):
+        x = np.ones((3, 1))
+        expected = np.cov(x, ddof=0, rowvar=True)
+        actual = np.cov(x.T, ddof=0, rowvar=False)
+        assert_allclose(actual, expected, strict=True)
+
+
+class Test_I0:
+
+    def test_simple(self):
+        assert_almost_equal(
+            i0(0.5),
+            np.array(1.0634833707413234))
+
+        # need at least one test above 8, as the implementation is piecewise
+        A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549, 10.0])
+        expected = np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049, 2815.71662847])
+        assert_almost_equal(i0(A), expected)
+        assert_almost_equal(i0(-A), expected)
+
+        B = np.array([[0.827002, 0.99959078],
+                      [0.89694769, 0.39298162],
+                      [0.37954418, 0.05206293],
+                      [0.36465447, 0.72446427],
+                      [0.48164949, 0.50324519]])
+        assert_almost_equal(
+            i0(B),
+            np.array([[1.17843223, 1.26583466],
+                      [1.21147086, 1.03898290],
+                      [1.03633899, 1.00067775],
+                      [1.03352052, 1.13557954],
+                      [1.05884290, 1.06432317]]))
+        # Regression test for gh-11205
+        i0_0 = np.i0([0.])
+        assert_equal(i0_0.shape, (1,))
+        assert_array_equal(np.i0([0.]), np.array([1.]))
+
+    def test_non_array(self):
+        a = np.arange(4)
+
+        class array_like:
+            __array_interface__ = a.__array_interface__
+
+            def __array_wrap__(self, arr, context, return_scalar):
+                return self
+
+        # E.g. pandas series survive ufunc calls through array-wrap:
+        assert isinstance(np.abs(array_like()), array_like)
+        exp = np.i0(a)
+        res = np.i0(array_like())
+
+        assert_array_equal(exp, res)
+
+    def test_complex(self):
+        a = np.array([0, 1 + 2j])
+        with pytest.raises(TypeError, match="i0 not supported for complex values"):
+            res = i0(a)
+
+
+class TestKaiser:
+
+    def test_simple(self):
+        assert_(np.isfinite(kaiser(1, 1.0)))
+        assert_almost_equal(kaiser(0, 1.0),
+                            np.array([]))
+        assert_almost_equal(kaiser(2, 1.0),
+                            np.array([0.78984831, 0.78984831]))
+        assert_almost_equal(kaiser(5, 1.0),
+                            np.array([0.78984831, 0.94503323, 1.,
+                                      0.94503323, 0.78984831]))
+        assert_almost_equal(kaiser(5, 1.56789),
+                            np.array([0.58285404, 0.88409679, 1.,
+                                      0.88409679, 0.58285404]))
+
+    def test_int_beta(self):
+        kaiser(3, 4)
+
+
+class TestMeshgrid:
+
+    def test_simple(self):
+        [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
+        assert_array_equal(X, np.array([[1, 2, 3],
+                                        [1, 2, 3],
+                                        [1, 2, 3],
+                                        [1, 2, 3]]))
+        assert_array_equal(Y, np.array([[4, 4, 4],
+                                        [5, 5, 5],
+                                        [6, 6, 6],
+                                        [7, 7, 7]]))
+
+    def test_single_input(self):
+        [X] = meshgrid([1, 2, 3, 4])
+        assert_array_equal(X, np.array([1, 2, 3, 4]))
+
+    def test_no_input(self):
+        args = []
+        assert_array_equal([], meshgrid(*args))
+        assert_array_equal([], meshgrid(*args, copy=False))
+
+    def test_indexing(self):
+        x = [1, 2, 3]
+        y = [4, 5, 6, 7]
+        [X, Y] = meshgrid(x, y, indexing='ij')
+        assert_array_equal(X, np.array([[1, 1, 1, 1],
+                                        [2, 2, 2, 2],
+                                        [3, 3, 3, 3]]))
+        assert_array_equal(Y, np.array([[4, 5, 6, 7],
+                                        [4, 5, 6, 7],
+                                        [4, 5, 6, 7]]))
+
+        # Test expected shapes:
+        z = [8, 9]
+        assert_(meshgrid(x, y)[0].shape == (4, 3))
+        assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4))
+        assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2))
+        assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2))
+
+        assert_raises(ValueError, meshgrid, x, y, indexing='notvalid')
+
+    def test_sparse(self):
+        [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True)
+        assert_array_equal(X, np.array([[1, 2, 3]]))
+        assert_array_equal(Y, np.array([[4], [5], [6], [7]]))
+
+    def test_invalid_arguments(self):
+        # Test that meshgrid complains about invalid arguments
+        # Regression test for issue #4755:
+        # https://github.com/numpy/numpy/issues/4755
+        assert_raises(TypeError, meshgrid,
+                      [1, 2, 3], [4, 5, 6, 7], indices='ij')
+
+    def test_return_type(self):
+        # Test for appropriate dtype in returned arrays.
+        # Regression test for issue #5297
+        # https://github.com/numpy/numpy/issues/5297
+        x = np.arange(0, 10, dtype=np.float32)
+        y = np.arange(10, 20, dtype=np.float64)
+
+        X, Y = np.meshgrid(x,y)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+        # copy
+        X, Y = np.meshgrid(x,y, copy=True)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+        # sparse
+        X, Y = np.meshgrid(x,y, sparse=True)
+
+        assert_(X.dtype == x.dtype)
+        assert_(Y.dtype == y.dtype)
+
+    def test_writeback(self):
+        # Issue 8561
+        X = np.array([1.1, 2.2])
+        Y = np.array([3.3, 4.4])
+        x, y = np.meshgrid(X, Y, sparse=False, copy=True)
+
+        x[0, :] = 0
+        assert_equal(x[0, :], 0)
+        assert_equal(x[1, :], X)
+
+    def test_nd_shape(self):
+        a, b, c, d, e = np.meshgrid(*([0] * i for i in range(1, 6)))
+        expected_shape = (2, 1, 3, 4, 5)
+        assert_equal(a.shape, expected_shape)
+        assert_equal(b.shape, expected_shape)
+        assert_equal(c.shape, expected_shape)
+        assert_equal(d.shape, expected_shape)
+        assert_equal(e.shape, expected_shape)
+
+    def test_nd_values(self):
+        a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5])
+        assert_equal(a, [[[0, 0, 0]], [[0, 0, 0]]])
+        assert_equal(b, [[[1, 1, 1]], [[2, 2, 2]]])
+        assert_equal(c, [[[3, 4, 5]], [[3, 4, 5]]])
+
+    def test_nd_indexing(self):
+        a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5], indexing='ij')
+        assert_equal(a, [[[0, 0, 0], [0, 0, 0]]])
+        assert_equal(b, [[[1, 1, 1], [2, 2, 2]]])
+        assert_equal(c, [[[3, 4, 5], [3, 4, 5]]])
+
+
+class TestPiecewise:
+
+    def test_simple(self):
+        # Condition is single bool list
+        x = piecewise([0, 0], [True, False], [1])
+        assert_array_equal(x, [1, 0])
+
+        # List of conditions: single bool list
+        x = piecewise([0, 0], [[True, False]], [1])
+        assert_array_equal(x, [1, 0])
+
+        # Conditions is single bool array
+        x = piecewise([0, 0], np.array([True, False]), [1])
+        assert_array_equal(x, [1, 0])
+
+        # Condition is single int array
+        x = piecewise([0, 0], np.array([1, 0]), [1])
+        assert_array_equal(x, [1, 0])
+
+        # List of conditions: int array
+        x = piecewise([0, 0], [np.array([1, 0])], [1])
+        assert_array_equal(x, [1, 0])
+
+        x = piecewise([0, 0], [[False, True]], [lambda x:-1])
+        assert_array_equal(x, [0, -1])
+
+        assert_raises_regex(ValueError, '1 or 2 functions are expected',
+            piecewise, [0, 0], [[False, True]], [])
+        assert_raises_regex(ValueError, '1 or 2 functions are expected',
+            piecewise, [0, 0], [[False, True]], [1, 2, 3])
+
+    def test_two_conditions(self):
+        x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
+        assert_array_equal(x, [3, 4])
+
+    def test_scalar_domains_three_conditions(self):
+        x = piecewise(3, [True, False, False], [4, 2, 0])
+        assert_equal(x, 4)
+
+    def test_default(self):
+        # No value specified for x[1], should be 0
+        x = piecewise([1, 2], [True, False], [2])
+        assert_array_equal(x, [2, 0])
+
+        # Should set x[1] to 3
+        x = piecewise([1, 2], [True, False], [2, 3])
+        assert_array_equal(x, [2, 3])
+
+    def test_0d(self):
+        x = np.array(3)
+        y = piecewise(x, x > 3, [4, 0])
+        assert_(y.ndim == 0)
+        assert_(y == 0)
+
+        x = 5
+        y = piecewise(x, [True, False], [1, 0])
+        assert_(y.ndim == 0)
+        assert_(y == 1)
+
+        # With 3 ranges (It was failing, before)
+        y = piecewise(x, [False, False, True], [1, 2, 3])
+        assert_array_equal(y, 3)
+
+    def test_0d_comparison(self):
+        x = 3
+        y = piecewise(x, [x <= 3, x > 3], [4, 0])  # Should succeed.
+        assert_equal(y, 4)
+
+        # With 3 ranges (It was failing, before)
+        x = 4
+        y = piecewise(x, [x <= 3, (x > 3) * (x <= 5), x > 5], [1, 2, 3])
+        assert_array_equal(y, 2)
+
+        assert_raises_regex(ValueError, '2 or 3 functions are expected',
+            piecewise, x, [x <= 3, x > 3], [1])
+        assert_raises_regex(ValueError, '2 or 3 functions are expected',
+            piecewise, x, [x <= 3, x > 3], [1, 1, 1, 1])
+
+    def test_0d_0d_condition(self):
+        x = np.array(3)
+        c = np.array(x > 3)
+        y = piecewise(x, [c], [1, 2])
+        assert_equal(y, 2)
+
+    def test_multidimensional_extrafunc(self):
+        x = np.array([[-2.5, -1.5, -0.5],
+                      [0.5, 1.5, 2.5]])
+        y = piecewise(x, [x < 0, x >= 2], [-1, 1, 3])
+        assert_array_equal(y, np.array([[-1., -1., -1.],
+                                        [3., 3., 1.]]))
+
+    def test_subclasses(self):
+        class subclass(np.ndarray):
+            pass
+        x = np.arange(5.).view(subclass)
+        r = piecewise(x, [x<2., x>=4], [-1., 1., 0.])
+        assert_equal(type(r), subclass)
+        assert_equal(r, [-1., -1., 0., 0., 1.])
+
+
+class TestBincount:
+
+    def test_simple(self):
+        y = np.bincount(np.arange(4))
+        assert_array_equal(y, np.ones(4))
+
+    def test_simple2(self):
+        y = np.bincount(np.array([1, 5, 2, 4, 1]))
+        assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))
+
+    def test_simple_weight(self):
+        x = np.arange(4)
+        w = np.array([0.2, 0.3, 0.5, 0.1])
+        y = np.bincount(x, w)
+        assert_array_equal(y, w)
+
+    def test_simple_weight2(self):
+        x = np.array([1, 2, 4, 5, 2])
+        w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
+        y = np.bincount(x, w)
+        assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
+
+    def test_with_minlength(self):
+        x = np.array([0, 1, 0, 1, 1])
+        y = np.bincount(x, minlength=3)
+        assert_array_equal(y, np.array([2, 3, 0]))
+        x = []
+        y = np.bincount(x, minlength=0)
+        assert_array_equal(y, np.array([]))
+
+    def test_with_minlength_smaller_than_maxvalue(self):
+        x = np.array([0, 1, 1, 2, 2, 3, 3])
+        y = np.bincount(x, minlength=2)
+        assert_array_equal(y, np.array([1, 2, 2, 2]))
+        y = np.bincount(x, minlength=0)
+        assert_array_equal(y, np.array([1, 2, 2, 2]))
+
+    def test_with_minlength_and_weights(self):
+        x = np.array([1, 2, 4, 5, 2])
+        w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
+        y = np.bincount(x, w, 8)
+        assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))
+
+    def test_empty(self):
+        x = np.array([], dtype=int)
+        y = np.bincount(x)
+        assert_array_equal(x, y)
+
+    def test_empty_with_minlength(self):
+        x = np.array([], dtype=int)
+        y = np.bincount(x, minlength=5)
+        assert_array_equal(y, np.zeros(5, dtype=int))
+
+    @pytest.mark.parametrize('minlength', [0, 3])
+    def test_empty_list(self, minlength):
+        assert_array_equal(np.bincount([], minlength=minlength),
+                           np.zeros(minlength, dtype=int))
+
+    def test_with_incorrect_minlength(self):
+        x = np.array([], dtype=int)
+        assert_raises_regex(TypeError,
+                            "'str' object cannot be interpreted",
+                            lambda: np.bincount(x, minlength="foobar"))
+        assert_raises_regex(ValueError,
+                            "must not be negative",
+                            lambda: np.bincount(x, minlength=-1))
+
+        x = np.arange(5)
+        assert_raises_regex(TypeError,
+                            "'str' object cannot be interpreted",
+                            lambda: np.bincount(x, minlength="foobar"))
+        assert_raises_regex(ValueError,
+                            "must not be negative",
+                            lambda: np.bincount(x, minlength=-1))
+
+    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+    def test_dtype_reference_leaks(self):
+        # gh-6805
+        intp_refcount = sys.getrefcount(np.dtype(np.intp))
+        double_refcount = sys.getrefcount(np.dtype(np.double))
+
+        for j in range(10):
+            np.bincount([1, 2, 3])
+        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
+        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
+
+        for j in range(10):
+            np.bincount([1, 2, 3], [4, 5, 6])
+        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
+        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
+
+    @pytest.mark.parametrize("vals", [[[2, 2]], 2])
+    def test_error_not_1d(self, vals):
+        # Test that values has to be 1-D (both as array and nested list)
+        vals_arr = np.asarray(vals)
+        with assert_raises(ValueError):
+            np.bincount(vals_arr)
+        with assert_raises(ValueError):
+            np.bincount(vals)
+
+    @pytest.mark.parametrize("dt", np.typecodes["AllInteger"])
+    def test_gh_28354(self, dt):
+        a = np.array([0, 1, 1, 3, 2, 1, 7], dtype=dt)
+        actual = np.bincount(a)
+        expected = [1, 3, 1, 1, 0, 0, 0, 1]
+        assert_array_equal(actual, expected)
+
+    def test_contiguous_handling(self):
+        # check for absence of hard crash
+        np.bincount(np.arange(10000)[::2])
+
+    def test_gh_28354_array_like(self):
+        class A:
+            def __array__(self):
+                return np.array([0, 1, 1, 3, 2, 1, 7], dtype=np.uint64)
+
+        a = A()
+        actual = np.bincount(a)
+        expected = [1, 3, 1, 1, 0, 0, 0, 1]
+        assert_array_equal(actual, expected)
+
+
+class TestInterp:
+
+    def test_exceptions(self):
+        assert_raises(ValueError, interp, 0, [], [])
+        assert_raises(ValueError, interp, 0, [0], [1, 2])
+        assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0)
+        assert_raises(ValueError, interp, 0, [], [], period=360)
+        assert_raises(ValueError, interp, 0, [0], [1, 2], period=360)
+
+    def test_basic(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.linspace(0, 1, 50)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_right_left_behavior(self):
+        # Needs range of sizes to test different code paths.
+        # size ==1 is special cased, 1 < size < 5 is linear search, and
+        # size >= 5 goes through local search and possibly binary search.
+        for size in range(1, 10):
+            xp = np.arange(size, dtype=np.double)
+            yp = np.ones(size, dtype=np.double)
+            incpts = np.array([-1, 0, size - 1, size], dtype=np.double)
+            decpts = incpts[::-1]
+
+            incres = interp(incpts, xp, yp)
+            decres = interp(decpts, xp, yp)
+            inctgt = np.array([1, 1, 1, 1], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, left=0)
+            decres = interp(decpts, xp, yp, left=0)
+            inctgt = np.array([0, 1, 1, 1], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, right=2)
+            decres = interp(decpts, xp, yp, right=2)
+            inctgt = np.array([1, 1, 1, 2], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+            incres = interp(incpts, xp, yp, left=0, right=2)
+            decres = interp(decpts, xp, yp, left=0, right=2)
+            inctgt = np.array([0, 1, 1, 2], dtype=float)
+            dectgt = inctgt[::-1]
+            assert_equal(incres, inctgt)
+            assert_equal(decres, dectgt)
+
+    def test_scalar_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = 0
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = .3
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float32(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.float64(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+        x0 = np.nan
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+    def test_non_finite_behavior_exact_x(self):
+        x = [1, 2, 2.5, 3, 4]
+        xp = [1, 2, 3, 4]
+        fp = [1, 2, np.inf, 4]
+        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.inf, np.inf, 4])
+        fp = [1, 2, np.nan, 4]
+        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.nan, np.nan, 4])
+
+    @pytest.fixture(params=[
+        lambda x: np.float64(x),
+        lambda x: _make_complex(x, 0),
+        lambda x: _make_complex(0, x),
+        lambda x: _make_complex(x, np.multiply(x, -2))
+    ], ids=[
+        'real',
+        'complex-real',
+        'complex-imag',
+        'complex-both'
+    ])
+    def sc(self, request):
+        """ scale function used by the below tests """
+        return request.param
+
+    def test_non_finite_any_nan(self, sc):
+        """ test that nans are propagated """
+        assert_equal(np.interp(0.5, [np.nan,      1], sc([     0,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0, np.nan], sc([     0,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0,      1], sc([np.nan,     10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [     0,      1], sc([     0, np.nan])), sc(np.nan))
+
+    def test_non_finite_inf(self, sc):
+        """ Test that interp between opposite infs gives nan """
+        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([      0,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0,       1], sc([-np.inf, +np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0,       1], sc([+np.inf, -np.inf])), sc(np.nan))
+
+        # unless the y values are equal
+        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([     10,      10])), sc(10))
+
+    def test_non_finite_half_inf_xf(self, sc):
+        """ Test that interp where both axes have a bound at inf gives nan """
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([-np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([+np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, -np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, +np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([-np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([+np.inf,      10])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, -np.inf])), sc(np.nan))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, +np.inf])), sc(np.nan))
+
+    def test_non_finite_half_inf_x(self, sc):
+        """ Test interp where the x axis has a bound at inf """
+        assert_equal(np.interp(0.5, [-np.inf, -np.inf], sc([0, 10])), sc(10))
+        assert_equal(np.interp(0.5, [-np.inf, 1      ], sc([0, 10])), sc(10))
+        assert_equal(np.interp(0.5, [      0, +np.inf], sc([0, 10])), sc(0))
+        assert_equal(np.interp(0.5, [+np.inf, +np.inf], sc([0, 10])), sc(0))
+
+    def test_non_finite_half_inf_f(self, sc):
+        """ Test interp where the f axis has a bound at inf """
+        assert_equal(np.interp(0.5, [0, 1], sc([      0, -np.inf])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([      0, +np.inf])), sc(+np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf,      10])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf,      10])), sc(+np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, -np.inf])), sc(-np.inf))
+        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, +np.inf])), sc(+np.inf))
+
+    def test_complex_interp(self):
+        # test complex interpolation
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5))*1.0j
+        x0 = 0.3
+        y0 = x0 + (1+x0)*1.0j
+        assert_almost_equal(np.interp(x0, x, y), y0)
+        # test complex left and right
+        x0 = -1
+        left = 2 + 3.0j
+        assert_almost_equal(np.interp(x0, x, y, left=left), left)
+        x0 = 2.0
+        right = 2 + 3.0j
+        assert_almost_equal(np.interp(x0, x, y, right=right), right)
+        # test complex non finite
+        x = [1, 2, 2.5, 3, 4]
+        xp = [1, 2, 3, 4]
+        fp = [1, 2+1j, np.inf, 4]
+        y = [1, 2+1j, np.inf+0.5j, np.inf, 4]
+        assert_almost_equal(np.interp(x, xp, fp), y)
+        # test complex periodic
+        x = [-180, -170, -185, 185, -10, -5, 0, 365]
+        xp = [190, -190, 350, -350]
+        fp = [5+1.0j, 10+2j, 3+3j, 4+4j]
+        y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j,
+             3.5+3.5j, 3.75+3.75j]
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+
+    def test_zero_dimensional_interpolation_point(self):
+        x = np.linspace(0, 1, 5)
+        y = np.linspace(0, 1, 5)
+        x0 = np.array(.3)
+        assert_almost_equal(np.interp(x0, x, y), x0)
+
+        xp = np.array([0, 2, 4])
+        fp = np.array([1, -1, 1])
+
+        actual = np.interp(np.array(1), xp, fp)
+        assert_equal(actual, 0)
+        assert_(isinstance(actual, np.float64))
+
+        actual = np.interp(np.array(4.5), xp, fp, period=4)
+        assert_equal(actual, 0.5)
+        assert_(isinstance(actual, np.float64))
+
+    def test_if_len_x_is_small(self):
+        xp = np.arange(0, 10, 0.0001)
+        fp = np.sin(xp)
+        assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)
+
+    def test_period(self):
+        x = [-180, -170, -185, 185, -10, -5, 0, 365]
+        xp = [190, -190, 350, -350]
+        fp = [5, 10, 3, 4]
+        y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+        x = np.array(x, order='F').reshape(2, -1)
+        y = np.array(y, order='C').reshape(2, -1)
+        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
+
+
+class TestPercentile:
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.percentile(x, 0), 0.)
+        assert_equal(np.percentile(x, 100), 3.5)
+        assert_equal(np.percentile(x, 50), 1.75)
+        x[1] = np.nan
+        assert_equal(np.percentile(x, 0), np.nan)
+        assert_equal(np.percentile(x, 0, method='nearest'), np.nan)
+        assert_equal(np.percentile(x, 0, method='inverted_cdf'), np.nan)
+        assert_equal(
+            np.percentile(x, 0, method='inverted_cdf',
+                          weights=np.ones_like(x)),
+            np.nan,
+        )
+
+    def test_fraction(self):
+        x = [Fraction(i, 2) for i in range(8)]
+
+        p = np.percentile(x, Fraction(0))
+        assert_equal(p, Fraction(0))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, Fraction(100))
+        assert_equal(p, Fraction(7, 2))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, Fraction(50))
+        assert_equal(p, Fraction(7, 4))
+        assert_equal(type(p), Fraction)
+
+        p = np.percentile(x, [Fraction(50)])
+        assert_equal(p, np.array([Fraction(7, 4)]))
+        assert_equal(type(p), np.ndarray)
+
+    def test_api(self):
+        d = np.ones(5)
+        np.percentile(d, 5, None, None, False)
+        np.percentile(d, 5, None, None, False, 'linear')
+        o = np.ones((1,))
+        np.percentile(d, 5, None, o, False, 'linear')
+
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.percentile, arr_c, 0.5)
+
+    def test_2D(self):
+        x = np.array([[1, 1, 1],
+                      [1, 1, 1],
+                      [4, 4, 3],
+                      [1, 1, 1],
+                      [1, 1, 1]])
+        assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1])
+
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
+    def test_linear_nan_1D(self, dtype):
+        # METHOD 1 of H&F
+        arr = np.asarray([15.0, np.nan, 35.0, 40.0, 50.0], dtype=dtype)
+        res = np.percentile(
+            arr,
+            40.0,
+            method="linear")
+        np.testing.assert_equal(res, np.nan)
+        np.testing.assert_equal(res.dtype, arr.dtype)
+
+    H_F_TYPE_CODES = [(int_type, np.float64)
+                      for int_type in np.typecodes["AllInteger"]
+                      ] + [(np.float16, np.float16),
+                           (np.float32, np.float32),
+                           (np.float64, np.float64),
+                           (np.longdouble, np.longdouble),
+                           (np.dtype("O"), np.float64)]
+
+    @pytest.mark.parametrize(["function", "quantile"],
+                             [(np.quantile, 0.4),
+                              (np.percentile, 40.0)])
+    @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES)
+    @pytest.mark.parametrize(["method", "weighted", "expected"],
+                              [("inverted_cdf", False, 20),
+                              ("inverted_cdf", True, 20),
+                              ("averaged_inverted_cdf", False, 27.5),
+                              ("closest_observation", False, 20),
+                              ("interpolated_inverted_cdf", False, 20),
+                              ("hazen", False, 27.5),
+                              ("weibull", False, 26),
+                              ("linear", False, 29),
+                              ("median_unbiased", False, 27),
+                              ("normal_unbiased", False, 27.125),
+                               ])
+    def test_linear_interpolation(self,
+                                  function,
+                                  quantile,
+                                  method,
+                                  weighted,
+                                  expected,
+                                  input_dtype,
+                                  expected_dtype):
+        expected_dtype = np.dtype(expected_dtype)
+
+        arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype)
+        weights = np.ones_like(arr) if weighted else None
+        if input_dtype is np.longdouble:
+            if function is np.quantile:
+                # 0.4 is not exactly representable and it matters
+                # for "averaged_inverted_cdf", so we need to cheat.
+                quantile = input_dtype("0.4")
+            # We want to use nulp, but that does not work for longdouble
+            test_function = np.testing.assert_almost_equal
+        else:
+            test_function = np.testing.assert_array_almost_equal_nulp
+
+        actual = function(arr, quantile, method=method, weights=weights)
+
+        test_function(actual, expected_dtype.type(expected))
+
+        if method in ["inverted_cdf", "closest_observation"]:
+            if input_dtype == "O":
+                np.testing.assert_equal(np.asarray(actual).dtype, np.float64)
+            else:
+                np.testing.assert_equal(np.asarray(actual).dtype,
+                                        np.dtype(input_dtype))
+        else:
+            np.testing.assert_equal(np.asarray(actual).dtype,
+                                    np.dtype(expected_dtype))
+
+    TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["Float"] + "O"
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_lower_higher(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 50,
+                                   method='lower'), 4)
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 50,
+                                   method='higher'), 5)
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_midpoint(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 51,
+                                   method='midpoint'), 4.5)
+        assert_equal(np.percentile(np.arange(9, dtype=dtype) + 1, 50,
+                                   method='midpoint'), 5)
+        assert_equal(np.percentile(np.arange(11, dtype=dtype), 51,
+                                   method='midpoint'), 5.5)
+        assert_equal(np.percentile(np.arange(11, dtype=dtype), 50,
+                                   method='midpoint'), 5)
+
+    @pytest.mark.parametrize("dtype", TYPE_CODES)
+    def test_nearest(self, dtype):
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 51,
+                                   method='nearest'), 5)
+        assert_equal(np.percentile(np.arange(10, dtype=dtype), 49,
+                                   method='nearest'), 4)
+
+    def test_linear_interpolation_extrapolation(self):
+        arr = np.random.rand(5)
+
+        actual = np.percentile(arr, 100)
+        np.testing.assert_equal(actual, arr.max())
+
+        actual = np.percentile(arr, 0)
+        np.testing.assert_equal(actual, arr.min())
+
+    def test_sequence(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75])
+
+    def test_axis(self):
+        x = np.arange(12).reshape(3, 4)
+
+        assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0])
+
+        r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
+        assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0)
+
+        r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]]
+        assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T)
+
+        # ensure qth axis is always first as with np.array(old_percentile(..))
+        x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
+        assert_equal(np.percentile(x, (25, 50)).shape, (2,))
+        assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,))
+        assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5))
+        assert_equal(
+            np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50),
+                                   method="higher").shape, (2,))
+        assert_equal(np.percentile(x, (25, 50, 75),
+                                   method="higher").shape, (3,))
+        assert_equal(np.percentile(x, (25, 50), axis=0,
+                                   method="higher").shape, (2, 4, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=1,
+                                   method="higher").shape, (2, 3, 5, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=2,
+                                   method="higher").shape, (2, 3, 4, 6))
+        assert_equal(np.percentile(x, (25, 50), axis=3,
+                                   method="higher").shape, (2, 3, 4, 5))
+        assert_equal(np.percentile(x, (25, 50, 75), axis=1,
+                                   method="higher").shape, (3, 3, 5, 6))
+
+    def test_scalar_q(self):
+        # test for no empty dimensions for compatibility with old percentile
+        x = np.arange(12).reshape(3, 4)
+        assert_equal(np.percentile(x, 50), 5.5)
+        assert_(np.isscalar(np.percentile(x, 50)))
+        r0 = np.array([4.,  5.,  6.,  7.])
+        assert_equal(np.percentile(x, 50, axis=0), r0)
+        assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape)
+        r1 = np.array([1.5,  5.5,  9.5])
+        assert_almost_equal(np.percentile(x, 50, axis=1), r1)
+        assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape)
+
+        out = np.empty(1)
+        assert_equal(np.percentile(x, 50, out=out), 5.5)
+        assert_equal(out, 5.5)
+        out = np.empty(4)
+        assert_equal(np.percentile(x, 50, axis=0, out=out), r0)
+        assert_equal(out, r0)
+        out = np.empty(3)
+        assert_equal(np.percentile(x, 50, axis=1, out=out), r1)
+        assert_equal(out, r1)
+
+        # test for no empty dimensions for compatibility with old percentile
+        x = np.arange(12).reshape(3, 4)
+        assert_equal(np.percentile(x, 50, method='lower'), 5.)
+        assert_(np.isscalar(np.percentile(x, 50)))
+        r0 = np.array([4.,  5.,  6.,  7.])
+        c0 = np.percentile(x, 50, method='lower', axis=0)
+        assert_equal(c0, r0)
+        assert_equal(c0.shape, r0.shape)
+        r1 = np.array([1.,  5.,  9.])
+        c1 = np.percentile(x, 50, method='lower', axis=1)
+        assert_almost_equal(c1, r1)
+        assert_equal(c1.shape, r1.shape)
+
+        out = np.empty((), dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', out=out)
+        assert_equal(c, 5)
+        assert_equal(out, 5)
+        out = np.empty(4, dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', axis=0, out=out)
+        assert_equal(c, r0)
+        assert_equal(out, r0)
+        out = np.empty(3, dtype=x.dtype)
+        c = np.percentile(x, 50, method='lower', axis=1, out=out)
+        assert_equal(c, r1)
+        assert_equal(out, r1)
+
+    def test_exception(self):
+        assert_raises(ValueError, np.percentile, [1, 2], 56,
+                      method='foobar')
+        assert_raises(ValueError, np.percentile, [1], 101)
+        assert_raises(ValueError, np.percentile, [1], -1)
+        assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101])
+        assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1])
+
+    def test_percentile_list(self):
+        assert_equal(np.percentile([1, 2, 3], 0), 1)
+
+    @pytest.mark.parametrize(
+        "percentile, with_weights",
+        [
+            (np.percentile, False),
+            (partial(np.percentile, method="inverted_cdf"), True),
+        ]
+    )
+    def test_percentile_out(self, percentile, with_weights):
+        out_dtype = int if with_weights else float
+        x = np.array([1, 2, 3])
+        y = np.zeros((3,), dtype=out_dtype)
+        p = (1, 2, 3)
+        weights = np.ones_like(x) if with_weights else None
+        r = percentile(x, p, out=y, weights=weights)
+        assert r is y
+        assert_equal(percentile(x, p, weights=weights), y)
+
+        x = np.array([[1, 2, 3],
+                      [4, 5, 6]])
+        y = np.zeros((3, 3), dtype=out_dtype)
+        weights = np.ones_like(x) if with_weights else None
+        r = percentile(x, p, axis=0, out=y, weights=weights)
+        assert r is y
+        assert_equal(percentile(x, p, weights=weights, axis=0), y)
+
+        y = np.zeros((3, 2), dtype=out_dtype)
+        percentile(x, p, axis=1, out=y, weights=weights)
+        assert_equal(percentile(x, p, weights=weights, axis=1), y)
+
+        x = np.arange(12).reshape(3, 4)
+        # q.dim > 1, float
+        if with_weights:
+            r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
+        else:
+            r0 = np.array([[2., 3., 4., 5.], [4., 5., 6., 7.]])
+        out = np.empty((2, 4), dtype=out_dtype)
+        weights = np.ones_like(x) if with_weights else None
+        assert_equal(
+            percentile(x, (25, 50), axis=0, out=out, weights=weights), r0
+        )
+        assert_equal(out, r0)
+        r1 = np.array([[0.75,  4.75,  8.75], [1.5,  5.5,  9.5]])
+        out = np.empty((2, 3))
+        assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1)
+        assert_equal(out, r1)
+
+        # q.dim > 1, int
+        r0 = np.array([[0,  1,  2, 3], [4, 5, 6, 7]])
+        out = np.empty((2, 4), dtype=x.dtype)
+        c = np.percentile(x, (25, 50), method='lower', axis=0, out=out)
+        assert_equal(c, r0)
+        assert_equal(out, r0)
+        r1 = np.array([[0,  4,  8], [1,  5,  9]])
+        out = np.empty((2, 3), dtype=x.dtype)
+        c = np.percentile(x, (25, 50), method='lower', axis=1, out=out)
+        assert_equal(c, r1)
+        assert_equal(out, r1)
+
+    def test_percentile_empty_dim(self):
+        # empty dims are preserved
+        d = np.arange(11 * 2).reshape(11, 1, 2, 1)
+        assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2))
+        assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2))
+        assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1))
+
+        assert_array_equal(np.percentile(d, 50, axis=2,
+                                         method='midpoint').shape,
+                           (11, 1, 1))
+        assert_array_equal(np.percentile(d, 50, axis=-2,
+                                         method='midpoint').shape,
+                           (11, 1, 1))
+
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape,
+                           (2, 1, 2, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape,
+                           (2, 11, 2, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape,
+                           (2, 11, 1, 1))
+        assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape,
+                           (2, 11, 1, 2))
+
+    def test_percentile_no_overwrite(self):
+        a = np.array([2, 3, 4, 1])
+        np.percentile(a, [50], overwrite_input=False)
+        assert_equal(a, np.array([2, 3, 4, 1]))
+
+        a = np.array([2, 3, 4, 1])
+        np.percentile(a, [50])
+        assert_equal(a, np.array([2, 3, 4, 1]))
+
+    def test_no_p_overwrite(self):
+        p = np.linspace(0., 100., num=5)
+        np.percentile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, np.linspace(0., 100., num=5))
+        p = np.linspace(0., 100., num=5).tolist()
+        np.percentile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, np.linspace(0., 100., num=5).tolist())
+
+    def test_percentile_overwrite(self):
+        a = np.array([2, 3, 4, 1])
+        b = np.percentile(a, [50], overwrite_input=True)
+        assert_equal(b, np.array([2.5]))
+
+        b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True)
+        assert_equal(b, np.array([2.5]))
+
+    def test_extended_axis(self):
+        o = np.random.normal(size=(71, 23))
+        x = np.dstack([o] * 10)
+        assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30))
+        x = np.moveaxis(x, -1, 0)
+        assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30))
+        x = x.swapaxes(0, 1).copy()
+        assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30))
+        x = x.swapaxes(0, 1).copy()
+
+        assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)),
+                     np.percentile(x, [25, 60], axis=None))
+        assert_equal(np.percentile(x, [25, 60], axis=(0,)),
+                     np.percentile(x, [25, 60], axis=0))
+
+        d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
+        np.random.shuffle(d.ravel())
+        assert_equal(np.percentile(d, 25,  axis=(0, 1, 2))[0],
+                     np.percentile(d[:,:,:, 0].flatten(), 25))
+        assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1],
+                     np.percentile(d[:,:, 1,:].flatten(), [10, 90]))
+        assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2],
+                     np.percentile(d[:,:, 2,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2],
+                     np.percentile(d[2,:,:,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1],
+                     np.percentile(d[2, 1,:,:].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1],
+                     np.percentile(d[2,:,:, 1].flatten(), 25))
+        assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2],
+                     np.percentile(d[2,:, 2,:].flatten(), 25))
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(AxisError, np.percentile, d, axis=-5, q=25)
+        assert_raises(AxisError, np.percentile, d, axis=(0, -5), q=25)
+        assert_raises(AxisError, np.percentile, d, axis=4, q=25)
+        assert_raises(AxisError, np.percentile, d, axis=(0, 4), q=25)
+        # each of these refers to the same axis twice
+        assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)
+        assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25)
+        assert_raises(ValueError, np.percentile, d, axis=(3, -1), q=25)
+
+    def test_keepdims(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
+                     (1, 1, 7, 11))
+        assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
+                     (1, 5, 7, 1))
+        assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
+                     (3, 1, 7, 11))
+        assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
+                     (1, 1, 7, 1))
+
+        assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
+                                   keepdims=True).shape, (2, 1, 1, 7, 1))
+        assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
+                                   keepdims=True).shape, (2, 1, 5, 7, 1))
+
+    @pytest.mark.parametrize('q', [7, [1, 7]])
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1,),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, q, axis):
+        d = np.ones((3, 5, 7, 11))
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        shape_out = np.shape(q) + shape_out
+
+        out = np.empty(shape_out)
+        result = np.percentile(d, q, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    def test_out(self):
+        o = np.zeros((4,))
+        d = np.ones((3, 4))
+        assert_equal(np.percentile(d, 0, 0, out=o), o)
+        assert_equal(np.percentile(d, 0, 0, method='nearest', out=o), o)
+        o = np.zeros((3,))
+        assert_equal(np.percentile(d, 1, 1, out=o), o)
+        assert_equal(np.percentile(d, 1, 1, method='nearest', out=o), o)
+
+        o = np.zeros(())
+        assert_equal(np.percentile(d, 2, out=o), o)
+        assert_equal(np.percentile(d, 2, method='nearest', out=o), o)
+
+    @pytest.mark.parametrize("method, weighted", [
+        ("linear", False),
+        ("nearest", False),
+        ("inverted_cdf", False),
+        ("inverted_cdf", True),
+    ])
+    def test_out_nan(self, method, weighted):
+        if weighted:
+            kwargs = {"weights": np.ones((3, 4)), "method": method}
+        else:
+            kwargs = {"method": method}
+        with warnings.catch_warnings(record=True):
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            o = np.zeros((4,))
+            d = np.ones((3, 4))
+            d[2, 1] = np.nan
+            assert_equal(np.percentile(d, 0, 0, out=o, **kwargs), o)
+
+            o = np.zeros((3,))
+            assert_equal(np.percentile(d, 1, 1, out=o, **kwargs), o)
+
+            o = np.zeros(())
+            assert_equal(np.percentile(d, 1, out=o, **kwargs), o)
+
+    def test_nan_behavior(self):
+        a = np.arange(24, dtype=float)
+        a[2] = np.nan
+        assert_equal(np.percentile(a, 0.3), np.nan)
+        assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
+        assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
+                     np.array([np.nan] * 2))
+
+        a = np.arange(24, dtype=float).reshape(2, 3, 4)
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_equal(np.percentile(a, 0.3), np.nan)
+        assert_equal(np.percentile(a, 0.3).ndim, 0)
+
+        # axis0 zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.percentile(a, 0.3, 0), b)
+
+        # axis0 not zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], 0)
+        b[:, 2, 3] = np.nan
+        b[:, 1, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], 0), b)
+
+        # axis1 zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.percentile(a, 0.3, 1), b)
+        # axis1 not zerod
+        b = np.percentile(
+            np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1)
+        b[:, 1, 3] = np.nan
+        b[:, 1, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], 1), b)
+
+        # axis02 zerod
+        b = np.percentile(
+            np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.percentile(a, 0.3, (0, 2)), b)
+        # axis02 not zerod
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], (0, 2))
+        b[:, 1] = np.nan
+        b[:, 2] = np.nan
+        assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
+        # axis02 not zerod with method='nearest'
+        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
+                          [0.3, 0.6], (0, 2), method='nearest')
+        b[:, 1] = np.nan
+        b[:, 2] = np.nan
+        assert_equal(np.percentile(
+            a, [0.3, 0.6], (0, 2), method='nearest'), b)
+
+    def test_nan_q(self):
+        # GH18830
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], np.nan)
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], [np.nan])
+        q = np.linspace(1.0, 99.0, 16)
+        q[0] = np.nan
+        with pytest.raises(ValueError, match="Percentiles must be in"):
+            np.percentile([1, 2, 3, 4.0], q)
+
+    @pytest.mark.parametrize("dtype", ["m8[D]", "M8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_basic(self, dtype, pos):
+        # TODO: Note that times have dubious rounding as of fixing NaTs!
+        # NaT and NaN should behave the same, do basic tests for NaT:
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.percentile(a, 30)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.percentile(a, 30, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
+
+quantile_methods = [
+    'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+    'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear',
+    'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher',
+    'midpoint']
+
+
+methods_supporting_weights = ["inverted_cdf"]
+
+
+class TestQuantile:
+    # most of this is already tested by TestPercentile
+
+    def V(self, x, y, alpha):
+        # Identification function used in several tests.
+        return (x >= y) - alpha
+
+    def test_max_ulp(self):
+        x = [0.0, 0.2, 0.4]
+        a = np.quantile(x, 0.45)
+        # The default linear method would result in 0 + 0.2 * (0.45/2) = 0.18.
+        # 0.18 is not exactly representable and the formula leads to a 1 ULP
+        # different result. Ensure it is this exact within 1 ULP, see gh-20331.
+        np.testing.assert_array_max_ulp(a, 0.18, maxulp=1)
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.quantile(x, 0), 0.)
+        assert_equal(np.quantile(x, 1), 3.5)
+        assert_equal(np.quantile(x, 0.5), 1.75)
+
+    def test_correct_quantile_value(self):
+        a = np.array([True])
+        tf_quant = np.quantile(True, False)
+        assert_equal(tf_quant, a[0])
+        assert_equal(type(tf_quant), a.dtype)
+        a = np.array([False, True, True])
+        quant_res = np.quantile(a, a)
+        assert_array_equal(quant_res, a)
+        assert_equal(quant_res.dtype, a.dtype)
+
+    def test_fraction(self):
+        # fractional input, integral quantile
+        x = [Fraction(i, 2) for i in range(8)]
+        q = np.quantile(x, 0)
+        assert_equal(q, 0)
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, 1)
+        assert_equal(q, Fraction(7, 2))
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, .5)
+        assert_equal(q, 1.75)
+        assert_equal(type(q), np.float64)
+
+        q = np.quantile(x, Fraction(1, 2))
+        assert_equal(q, Fraction(7, 4))
+        assert_equal(type(q), Fraction)
+
+        q = np.quantile(x, [Fraction(1, 2)])
+        assert_equal(q, np.array([Fraction(7, 4)]))
+        assert_equal(type(q), np.ndarray)
+
+        q = np.quantile(x, [[Fraction(1, 2)]])
+        assert_equal(q, np.array([[Fraction(7, 4)]]))
+        assert_equal(type(q), np.ndarray)
+
+        # repeat with integral input but fractional quantile
+        x = np.arange(8)
+        assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2))
+
+    def test_complex(self):
+        #See gh-22652
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.quantile, arr_c, 0.5)
+
+    def test_no_p_overwrite(self):
+        # this is worth retesting, because quantile does not make a copy
+        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
+        p = p0.copy()
+        np.quantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+        p0 = p0.tolist()
+        p = p.tolist()
+        np.quantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
+    def test_quantile_preserve_int_type(self, dtype):
+        res = np.quantile(np.array([1, 2], dtype=dtype), [0.5],
+                          method="nearest")
+        assert res.dtype == dtype
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    def test_q_zero_one(self, method):
+        # gh-24710
+        arr = [10, 11, 12]
+        quantile = np.quantile(arr, q = [0, 1], method=method)
+        assert_equal(quantile,  np.array([10, 12]))
+
+    @pytest.mark.parametrize("method", quantile_methods)
+    def test_quantile_monotonic(self, method):
+        # GH 14685
+        # test that the return value of quantile is monotonic if p0 is ordered
+        # Also tests that the boundary values are not mishandled.
+        p0 = np.linspace(0, 1, 101)
+        quantile = np.quantile(np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9,
+                                         8, 8, 7]) * 0.1, p0, method=method)
+        assert_equal(np.sort(quantile), quantile)
+
+        # Also test one where the number of data points is clearly divisible:
+        quantile = np.quantile([0., 1., 2., 3.], p0, method=method)
+        assert_equal(np.sort(quantile), quantile)
+
+    @hypothesis.given(
+            arr=arrays(dtype=np.float64,
+                       shape=st.integers(min_value=3, max_value=1000),
+                       elements=st.floats(allow_infinity=False, allow_nan=False,
+                                          min_value=-1e300, max_value=1e300)))
+    def test_quantile_monotonic_hypo(self, arr):
+        p0 = np.arange(0, 1, 0.01)
+        quantile = np.quantile(arr, p0)
+        assert_equal(np.sort(quantile), quantile)
+
+    def test_quantile_scalar_nan(self):
+        a = np.array([[10., 7., 4.], [3., 2., 1.]])
+        a[0][1] = np.nan
+        actual = np.quantile(a, 0.5)
+        assert np.isscalar(actual)
+        assert_equal(np.quantile(a, 0.5), np.nan)
+
+    @pytest.mark.parametrize("weights", [False, True])
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_identification_equation(self, weights, method, alpha):
+        # Test that the identification equation holds for the empirical
+        # CDF:
+        #   E[V(x, Y)] = 0  <=>  x is quantile
+        # with Y the random variable for which we have observed values and
+        # V(x, y) the canonical identification function for the quantile (at
+        # level alpha), see
+        # https://doi.org/10.48550/arXiv.0912.0902
+        if weights and method not in methods_supporting_weights:
+            pytest.skip("Weights not supported by method.")
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we cover 3 cases:
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        w = rng.integers(low=0, high=10, size=n) if weights else None
+        x = np.quantile(y, alpha, method=method, weights=w)
+
+        if method in ("higher",):
+            # These methods do not fulfill the identification equation.
+            assert np.abs(np.mean(self.V(x, y, alpha))) > 0.1 / n
+        elif int(n * alpha) == n * alpha and not weights:
+            # We can expect exact results, up to machine precision.
+            assert_allclose(
+                np.average(self.V(x, y, alpha), weights=w), 0, atol=1e-14,
+            )
+        else:
+            # V = (x >= y) - alpha cannot sum to zero exactly but within
+            # "sample precision".
+            assert_allclose(np.average(self.V(x, y, alpha), weights=w), 0,
+                atol=1 / n / np.amin([alpha, 1 - alpha]))
+
+    @pytest.mark.parametrize("weights", [False, True])
+    @pytest.mark.parametrize("method", quantile_methods)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_add_and_multiply_constant(self, weights, method, alpha):
+        # Test that
+        #  1. quantile(c + x) = c + quantile(x)
+        #  2. quantile(c * x) = c * quantile(x)
+        #  3. quantile(-x) = -quantile(x, 1 - alpha)
+        #     On empirical quantiles, this equation does not hold exactly.
+        # Koenker (2005) "Quantile Regression" Chapter 2.2.3 calls these
+        # properties equivariance.
+        if weights and method not in methods_supporting_weights:
+            pytest.skip("Weights not supported by method.")
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we have cases for
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        w = rng.integers(low=0, high=10, size=n) if weights else None
+        q = np.quantile(y, alpha, method=method, weights=w)
+        c = 13.5
+
+        # 1
+        assert_allclose(np.quantile(c + y, alpha, method=method, weights=w),
+                        c + q)
+        # 2
+        assert_allclose(np.quantile(c * y, alpha, method=method, weights=w),
+                        c * q)
+        # 3
+        if weights:
+            # From here on, we would need more methods to support weights.
+            return
+        q = -np.quantile(-y, 1 - alpha, method=method)
+        if method == "inverted_cdf":
+            if (
+                n * alpha == int(n * alpha)
+                or np.round(n * alpha) == int(n * alpha) + 1
+            ):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "closest_observation":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha, method="higher"))
+            elif np.round(n * alpha) == int(n * alpha) + 1:
+                assert_allclose(
+                    q, np.quantile(y, alpha + 1/n, method="higher"))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        elif method == "interpolated_inverted_cdf":
+            assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+        elif method == "nearest":
+            if n * alpha == int(n * alpha):
+                assert_allclose(q, np.quantile(y, alpha + 1/n, method=method))
+            else:
+                assert_allclose(q, np.quantile(y, alpha, method=method))
+        elif method == "lower":
+            assert_allclose(q, np.quantile(y, alpha, method="higher"))
+        elif method == "higher":
+            assert_allclose(q, np.quantile(y, alpha, method="lower"))
+        else:
+            # "averaged_inverted_cdf", "hazen", "weibull", "linear",
+            # "median_unbiased", "normal_unbiased", "midpoint"
+            assert_allclose(q, np.quantile(y, alpha, method=method))
+
+    @pytest.mark.parametrize("method", methods_supporting_weights)
+    @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9])
+    def test_quantile_constant_weights(self, method, alpha):
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we have cases for
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        q = np.quantile(y, alpha, method=method)
+
+        w = np.ones_like(y)
+        qw = np.quantile(y, alpha, method=method, weights=w)
+        assert_allclose(qw, q)
+
+        w = 8.125 * np.ones_like(y)
+        qw = np.quantile(y, alpha, method=method, weights=w)
+        assert_allclose(qw, q)
+
+    @pytest.mark.parametrize("method", methods_supporting_weights)
+    @pytest.mark.parametrize("alpha", [0, 0.2, 0.5, 0.9, 1])
+    def test_quantile_with_integer_weights(self, method, alpha):
+        # Integer weights can be interpreted as repeated observations.
+        rng = np.random.default_rng(4321)
+        # We choose n and alpha such that we have cases for
+        #  - n * alpha is an integer
+        #  - n * alpha is a float that gets rounded down
+        #  - n * alpha is a float that gest rounded up
+        n = 102  # n * alpha = 20.4, 51. , 91.8
+        y = rng.random(n)
+        w = rng.integers(low=0, high=10, size=n, dtype=np.int32)
+
+        qw = np.quantile(y, alpha, method=method, weights=w)
+        q = np.quantile(np.repeat(y, w), alpha, method=method)
+        assert_allclose(qw, q)
+
+    @pytest.mark.parametrize("method", methods_supporting_weights)
+    def test_quantile_with_weights_and_axis(self, method):
+        rng = np.random.default_rng(4321)
+
+        # 1d weight and single alpha
+        y = rng.random((2, 10, 3))
+        w = np.abs(rng.random(10))
+        alpha = 0.5
+        q = np.quantile(y, alpha, weights=w, method=method, axis=1)
+        q_res = np.zeros(shape=(2, 3))
+        for i in range(2):
+            for j in range(3):
+                q_res[i, j] = np.quantile(
+                    y[i, :, j], alpha, method=method, weights=w
+                )
+        assert_allclose(q, q_res)
+
+        # 1d weight and 1d alpha
+        alpha = [0, 0.2, 0.4, 0.6, 0.8, 1]  # shape (6,)
+        q = np.quantile(y, alpha, weights=w, method=method, axis=1)
+        q_res = np.zeros(shape=(6, 2, 3))
+        for i in range(2):
+            for j in range(3):
+                q_res[:, i, j] = np.quantile(
+                    y[i, :, j], alpha, method=method, weights=w
+                )
+        assert_allclose(q, q_res)
+
+        # 1d weight and 2d alpha
+        alpha = [[0, 0.2], [0.4, 0.6], [0.8, 1]]  # shape (3, 2)
+        q = np.quantile(y, alpha, weights=w, method=method, axis=1)
+        q_res = q_res.reshape((3, 2, 2, 3))
+        assert_allclose(q, q_res)
+
+        # shape of weights equals shape of y
+        w = np.abs(rng.random((2, 10, 3)))
+        alpha = 0.5
+        q = np.quantile(y, alpha, weights=w, method=method, axis=1)
+        q_res = np.zeros(shape=(2, 3))
+        for i in range(2):
+            for j in range(3):
+                q_res[i, j] = np.quantile(
+                    y[i, :, j], alpha, method=method, weights=w[i, :, j]
+                )
+        assert_allclose(q, q_res)
+
+    @pytest.mark.parametrize("method", methods_supporting_weights)
+    def test_quantile_weights_min_max(self, method):
+        # Test weighted quantile at 0 and 1 with leading and trailing zero
+        # weights.
+        w = [0, 0, 1, 2, 3, 0]
+        y = np.arange(6)
+        y_min = np.quantile(y, 0, weights=w, method="inverted_cdf")
+        y_max = np.quantile(y, 1, weights=w, method="inverted_cdf")
+        assert y_min == y[2]  # == 2
+        assert y_max == y[4]  # == 4
+
+    def test_quantile_weights_raises_negative_weights(self):
+        y = [1, 2]
+        w = [-0.5, 1]
+        with pytest.raises(ValueError, match="Weights must be non-negative"):
+            np.quantile(y, 0.5, weights=w, method="inverted_cdf")
+
+    @pytest.mark.parametrize(
+            "method",
+            sorted(set(quantile_methods) - set(methods_supporting_weights)),
+    )
+    def test_quantile_weights_raises_unsupported_methods(self, method):
+        y = [1, 2]
+        w = [0.5, 1]
+        msg = "Only method 'inverted_cdf' supports weights"
+        with pytest.raises(ValueError, match=msg):
+            np.quantile(y, 0.5, weights=w, method=method)
+
+    def test_weibull_fraction(self):
+        arr = [Fraction(0, 1), Fraction(1, 10)]
+        quantile = np.quantile(arr, [0, ], method='weibull')
+        assert_equal(quantile, np.array(Fraction(0, 1)))
+        quantile = np.quantile(arr, [Fraction(1, 2)], method='weibull')
+        assert_equal(quantile, np.array(Fraction(1, 20)))
+
+    def test_closest_observation(self):
+        # Round ties to nearest even order statistic (see #26656)
+        m = 'closest_observation'
+        q = 0.5
+        arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        assert_equal(2, np.quantile(arr[0:3], q, method=m))
+        assert_equal(2, np.quantile(arr[0:4], q, method=m))
+        assert_equal(2, np.quantile(arr[0:5], q, method=m))
+        assert_equal(3, np.quantile(arr[0:6], q, method=m))
+        assert_equal(4, np.quantile(arr[0:7], q, method=m))
+        assert_equal(4, np.quantile(arr[0:8], q, method=m))
+        assert_equal(4, np.quantile(arr[0:9], q, method=m))
+        assert_equal(5, np.quantile(arr, q, method=m))
+
+
+class TestLerp:
+    @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
+                                   min_value=0, max_value=1),
+                      t1=st.floats(allow_nan=False, allow_infinity=False,
+                                   min_value=0, max_value=1),
+                      a = st.floats(allow_nan=False, allow_infinity=False,
+                                    min_value=-1e300, max_value=1e300),
+                      b = st.floats(allow_nan=False, allow_infinity=False,
+                                    min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b):
+        l0 = nfb._lerp(a, b, t0)
+        l1 = nfb._lerp(a, b, t1)
+        if t0 == t1 or a == b:
+            assert l0 == l1  # uninteresting
+        elif (t0 < t1) == (a < b):
+            assert l0 <= l1
+        else:
+            assert l0 >= l1
+
+    @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=0, max_value=1),
+                      a=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300),
+                      b=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_bounded(self, t, a, b):
+        if a <= b:
+            assert a <= nfb._lerp(a, b, t) <= b
+        else:
+            assert b <= nfb._lerp(a, b, t) <= a
+
+    @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=0, max_value=1),
+                      a=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300),
+                      b=st.floats(allow_nan=False, allow_infinity=False,
+                                  min_value=-1e300, max_value=1e300))
+    def test_linear_interpolation_formula_symmetric(self, t, a, b):
+        # double subtraction is needed to remove the extra precision of t < 0.5
+        left = nfb._lerp(a, b, 1 - (1 - t))
+        right = nfb._lerp(b, a, 1 - t)
+        assert_allclose(left, right)
+
+    def test_linear_interpolation_formula_0d_inputs(self):
+        a = np.array(2)
+        b = np.array(5)
+        t = np.array(0.2)
+        assert nfb._lerp(a, b, t) == 2.6
+
+
+class TestMedian:
+
+    def test_basic(self):
+        a0 = np.array(1)
+        a1 = np.arange(2)
+        a2 = np.arange(6).reshape(2, 3)
+        assert_equal(np.median(a0), 1)
+        assert_allclose(np.median(a1), 0.5)
+        assert_allclose(np.median(a2), 2.5)
+        assert_allclose(np.median(a2, axis=0), [1.5,  2.5,  3.5])
+        assert_equal(np.median(a2, axis=1), [1, 4])
+        assert_allclose(np.median(a2, axis=None), 2.5)
+
+        a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775])
+        assert_almost_equal((a[1] + a[3]) / 2., np.median(a))
+        a = np.array([0.0463301, 0.0444502, 0.141249])
+        assert_equal(a[0], np.median(a))
+        a = np.array([0.0444502, 0.141249, 0.0463301])
+        assert_equal(a[-1], np.median(a))
+        # check array scalar result
+        assert_equal(np.median(a).ndim, 0)
+        a[1] = np.nan
+        assert_equal(np.median(a).ndim, 0)
+
+    def test_axis_keyword(self):
+        a3 = np.array([[2, 3],
+                       [0, 1],
+                       [6, 7],
+                       [4, 5]])
+        for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]:
+            orig = a.copy()
+            np.median(a, axis=None)
+            for ax in range(a.ndim):
+                np.median(a, axis=ax)
+            assert_array_equal(a, orig)
+
+        assert_allclose(np.median(a3, axis=0), [3,  4])
+        assert_allclose(np.median(a3.T, axis=1), [3,  4])
+        assert_allclose(np.median(a3), 3.5)
+        assert_allclose(np.median(a3, axis=None), 3.5)
+        assert_allclose(np.median(a3.T), 3.5)
+
+    def test_overwrite_keyword(self):
+        a3 = np.array([[2, 3],
+                       [0, 1],
+                       [6, 7],
+                       [4, 5]])
+        a0 = np.array(1)
+        a1 = np.arange(2)
+        a2 = np.arange(6).reshape(2, 3)
+        assert_allclose(np.median(a0.copy(), overwrite_input=True), 1)
+        assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5)
+        assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5)
+        assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0),
+                        [1.5,  2.5,  3.5])
+        assert_allclose(
+            np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4])
+        assert_allclose(
+            np.median(a2.copy(), overwrite_input=True, axis=None), 2.5)
+        assert_allclose(
+            np.median(a3.copy(), overwrite_input=True, axis=0), [3,  4])
+        assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1),
+                        [3,  4])
+
+        a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
+        np.random.shuffle(a4.ravel())
+        assert_allclose(np.median(a4, axis=None),
+                        np.median(a4.copy(), axis=None, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=0),
+                        np.median(a4.copy(), axis=0, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=1),
+                        np.median(a4.copy(), axis=1, overwrite_input=True))
+        assert_allclose(np.median(a4, axis=2),
+                        np.median(a4.copy(), axis=2, overwrite_input=True))
+
+    def test_array_like(self):
+        x = [1, 2, 3]
+        assert_almost_equal(np.median(x), 2)
+        x2 = [x]
+        assert_almost_equal(np.median(x2), 2)
+        assert_allclose(np.median(x2, axis=0), x)
+
+    def test_subclass(self):
+        # gh-3846
+        class MySubClass(np.ndarray):
+
+            def __new__(cls, input_array, info=None):
+                obj = np.asarray(input_array).view(cls)
+                obj.info = info
+                return obj
+
+            def mean(self, axis=None, dtype=None, out=None):
+                return -7
+
+        a = MySubClass([1, 2, 3])
+        assert_equal(np.median(a), -7)
+
+    @pytest.mark.parametrize('arr',
+                             ([1., 2., 3.], [1., np.nan, 3.], np.nan, 0.))
+    def test_subclass2(self, arr):
+        """Check that we return subclasses, even if a NaN scalar."""
+        class MySubclass(np.ndarray):
+            pass
+
+        m = np.median(np.array(arr).view(MySubclass))
+        assert isinstance(m, MySubclass)
+
+    def test_out(self):
+        o = np.zeros((4,))
+        d = np.ones((3, 4))
+        assert_equal(np.median(d, 0, out=o), o)
+        o = np.zeros((3,))
+        assert_equal(np.median(d, 1, out=o), o)
+        o = np.zeros(())
+        assert_equal(np.median(d, out=o), o)
+
+    def test_out_nan(self):
+        with warnings.catch_warnings(record=True):
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            o = np.zeros((4,))
+            d = np.ones((3, 4))
+            d[2, 1] = np.nan
+            assert_equal(np.median(d, 0, out=o), o)
+            o = np.zeros((3,))
+            assert_equal(np.median(d, 1, out=o), o)
+            o = np.zeros(())
+            assert_equal(np.median(d, out=o), o)
+
+    def test_nan_behavior(self):
+        a = np.arange(24, dtype=float)
+        a[2] = np.nan
+        assert_equal(np.median(a), np.nan)
+        assert_equal(np.median(a, axis=0), np.nan)
+
+        a = np.arange(24, dtype=float).reshape(2, 3, 4)
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_equal(np.median(a), np.nan)
+        assert_equal(np.median(a).ndim, 0)
+
+        # axis0
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.median(a, 0), b)
+
+        # axis1
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.median(a, 1), b)
+
+        # axis02
+        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.median(a, (0, 2)), b)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work correctly")
+    def test_empty(self):
+        # mean(empty array) emits two warnings: empty slice and divide by 0
+        a = np.array([], dtype=float)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a), np.nan)
+            assert_(w[0].category is RuntimeWarning)
+            assert_equal(len(w), 2)
+
+        # multiple dimensions
+        a = np.array([], dtype=float, ndmin=3)
+        # no axis
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a), np.nan)
+            assert_(w[0].category is RuntimeWarning)
+
+        # axis 0 and 1
+        b = np.array([], dtype=float, ndmin=2)
+        assert_equal(np.median(a, axis=0), b)
+        assert_equal(np.median(a, axis=1), b)
+
+        # axis 2
+        b = np.array(np.nan, dtype=float, ndmin=2)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.median(a, axis=2), b)
+            assert_(w[0].category is RuntimeWarning)
+
+    def test_object(self):
+        o = np.arange(7.)
+        assert_(type(np.median(o.astype(object))), float)
+        o[2] = np.nan
+        assert_(type(np.median(o.astype(object))), float)
+
+    def test_extended_axis(self):
+        o = np.random.normal(size=(71, 23))
+        x = np.dstack([o] * 10)
+        assert_equal(np.median(x, axis=(0, 1)), np.median(o))
+        x = np.moveaxis(x, -1, 0)
+        assert_equal(np.median(x, axis=(-2, -1)), np.median(o))
+        x = x.swapaxes(0, 1).copy()
+        assert_equal(np.median(x, axis=(0, -1)), np.median(o))
+
+        assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None))
+        assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0))
+        assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1))
+
+        d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
+        np.random.shuffle(d.ravel())
+        assert_equal(np.median(d, axis=(0, 1, 2))[0],
+                     np.median(d[:,:,:, 0].flatten()))
+        assert_equal(np.median(d, axis=(0, 1, 3))[1],
+                     np.median(d[:,:, 1,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 1, -4))[2],
+                     np.median(d[:,:, 2,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 1, 2))[2],
+                     np.median(d[2,:,:,:].flatten()))
+        assert_equal(np.median(d, axis=(3, 2))[2, 1],
+                     np.median(d[2, 1,:,:].flatten()))
+        assert_equal(np.median(d, axis=(1, -2))[2, 1],
+                     np.median(d[2,:,:, 1].flatten()))
+        assert_equal(np.median(d, axis=(1, 3))[2, 2],
+                     np.median(d[2,:, 2,:].flatten()))
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(AxisError, np.median, d, axis=-5)
+        assert_raises(AxisError, np.median, d, axis=(0, -5))
+        assert_raises(AxisError, np.median, d, axis=4)
+        assert_raises(AxisError, np.median, d, axis=(0, 4))
+        assert_raises(ValueError, np.median, d, axis=(1, 1))
+
+    def test_keepdims(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_equal(np.median(d, axis=None, keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape,
+                     (1, 1, 7, 11))
+        assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape,
+                     (1, 5, 7, 1))
+        assert_equal(np.median(d, axis=(1,), keepdims=True).shape,
+                     (3, 1, 7, 11))
+        assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape,
+                     (1, 1, 1, 1))
+        assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape,
+                     (1, 1, 7, 1))
+
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1, ),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, axis):
+        d = np.ones((3, 5, 7, 11))
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        out = np.empty(shape_out)
+        result = np.median(d, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    @pytest.mark.parametrize("dtype", ["m8[s]"])
+    @pytest.mark.parametrize("pos", [0, 23, 10])
+    def test_nat_behavior(self, dtype, pos):
+        # TODO: Median does not support Datetime, due to `mean`.
+        # NaT and NaN should behave the same, do basic tests for NaT.
+        a = np.arange(0, 24, dtype=dtype)
+        a[pos] = "NaT"
+        res = np.median(a)
+        assert res.dtype == dtype
+        assert np.isnat(res)
+        res = np.percentile(a, [30, 60])
+        assert res.dtype == dtype
+        assert np.isnat(res).all()
+
+        a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3)
+        a[pos, 1] = "NaT"
+        res = np.median(a, axis=0)
+        assert_array_equal(np.isnat(res), [False, True, False])
+
+
+class TestSortComplex:
+
+    @pytest.mark.parametrize("type_in, type_out", [
+        ('l', 'D'),
+        ('h', 'F'),
+        ('H', 'F'),
+        ('b', 'F'),
+        ('B', 'F'),
+        ('g', 'G'),
+        ])
+    def test_sort_real(self, type_in, type_out):
+        # sort_complex() type casting for real input types
+        a = np.array([5, 3, 6, 2, 1], dtype=type_in)
+        actual = np.sort_complex(a)
+        expected = np.sort(a).astype(type_out)
+        assert_equal(actual, expected)
+        assert_equal(actual.dtype, expected.dtype)
+
+    def test_sort_complex(self):
+        # sort_complex() handling of complex input
+        a = np.array([2 + 3j, 1 - 2j, 1 - 3j, 2 + 1j], dtype='D')
+        expected = np.array([1 - 3j, 1 - 2j, 2 + 1j, 2 + 3j], dtype='D')
+        actual = np.sort_complex(a)
+        assert_equal(actual, expected)
+        assert_equal(actual.dtype, expected.dtype)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_histograms.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_histograms.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b300624cac76dec3b247c7a7609da440746dde5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_histograms.py
@@ -0,0 +1,841 @@
+import numpy as np
+
+from numpy import histogram, histogramdd, histogram_bin_edges
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_raises, assert_allclose,
+    assert_array_max_ulp, assert_raises_regex, suppress_warnings,
+    )
+import pytest
+
+
+class TestHistogram:
+
+    def setup_method(self):
+        pass
+
+    def teardown_method(self):
+        pass
+
+    def test_simple(self):
+        n = 100
+        v = np.random.rand(n)
+        (a, b) = histogram(v)
+        # check if the sum of the bins equals the number of samples
+        assert_equal(np.sum(a, axis=0), n)
+        # check that the bin counts are evenly spaced when the data is from
+        # a linear function
+        (a, b) = histogram(np.linspace(0, 10, 100))
+        assert_array_equal(a, 10)
+
+    def test_one_bin(self):
+        # Ticket 632
+        hist, edges = histogram([1, 2, 3, 4], [1, 2])
+        assert_array_equal(hist, [2, ])
+        assert_array_equal(edges, [1, 2])
+        assert_raises(ValueError, histogram, [1, 2], bins=0)
+        h, e = histogram([1, 2], bins=1)
+        assert_equal(h, np.array([2]))
+        assert_allclose(e, np.array([1., 2.]))
+
+    def test_density(self):
+        # Check that the integral of the density equals 1.
+        n = 100
+        v = np.random.rand(n)
+        a, b = histogram(v, density=True)
+        area = np.sum(a * np.diff(b))
+        assert_almost_equal(area, 1)
+
+        # Check with non-constant bin widths
+        v = np.arange(10)
+        bins = [0, 1, 3, 6, 10]
+        a, b = histogram(v, bins, density=True)
+        assert_array_equal(a, .1)
+        assert_equal(np.sum(a * np.diff(b)), 1)
+
+        # Test that passing False works too
+        a, b = histogram(v, bins, density=False)
+        assert_array_equal(a, [1, 2, 3, 4])
+
+        # Variable bin widths are especially useful to deal with
+        # infinities.
+        v = np.arange(10)
+        bins = [0, 1, 3, 6, np.inf]
+        a, b = histogram(v, bins, density=True)
+        assert_array_equal(a, [.1, .1, .1, 0.])
+
+        # Taken from a bug report from N. Becker on the numpy-discussion
+        # mailing list Aug. 6, 2010.
+        counts, dmy = np.histogram(
+            [1, 2, 3, 4], [0.5, 1.5, np.inf], density=True)
+        assert_equal(counts, [.25, 0])
+
+    def test_outliers(self):
+        # Check that outliers are not tallied
+        a = np.arange(10) + .5
+
+        # Lower outliers
+        h, b = histogram(a, range=[0, 9])
+        assert_equal(h.sum(), 9)
+
+        # Upper outliers
+        h, b = histogram(a, range=[1, 10])
+        assert_equal(h.sum(), 9)
+
+        # Normalization
+        h, b = histogram(a, range=[1, 9], density=True)
+        assert_almost_equal((h * np.diff(b)).sum(), 1, decimal=15)
+
+        # Weights
+        w = np.arange(10) + .5
+        h, b = histogram(a, range=[1, 9], weights=w, density=True)
+        assert_equal((h * np.diff(b)).sum(), 1)
+
+        h, b = histogram(a, bins=8, range=[1, 9], weights=w)
+        assert_equal(h, w[1:-1])
+
+    def test_arr_weights_mismatch(self):
+        a = np.arange(10) + .5
+        w = np.arange(11) + .5
+        with assert_raises_regex(ValueError, "same shape as"):
+            h, b = histogram(a, range=[1, 9], weights=w, density=True)
+
+
+    def test_type(self):
+        # Check the type of the returned histogram
+        a = np.arange(10) + .5
+        h, b = histogram(a)
+        assert_(np.issubdtype(h.dtype, np.integer))
+
+        h, b = histogram(a, density=True)
+        assert_(np.issubdtype(h.dtype, np.floating))
+
+        h, b = histogram(a, weights=np.ones(10, int))
+        assert_(np.issubdtype(h.dtype, np.integer))
+
+        h, b = histogram(a, weights=np.ones(10, float))
+        assert_(np.issubdtype(h.dtype, np.floating))
+
+    def test_f32_rounding(self):
+        # gh-4799, check that the rounding of the edges works with float32
+        x = np.array([276.318359, -69.593948, 21.329449], dtype=np.float32)
+        y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32)
+        counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100)
+        assert_equal(counts_hist.sum(), 3.)
+
+    def test_bool_conversion(self):
+        # gh-12107
+        # Reference integer histogram
+        a = np.array([1, 1, 0], dtype=np.uint8)
+        int_hist, int_edges = np.histogram(a)
+
+        # Should raise an warning on booleans
+        # Ensure that the histograms are equivalent, need to suppress
+        # the warnings to get the actual outputs
+        with suppress_warnings() as sup:
+            rec = sup.record(RuntimeWarning, 'Converting input from .*')
+            hist, edges = np.histogram([True, True, False])
+            # A warning should be issued
+            assert_equal(len(rec), 1)
+            assert_array_equal(hist, int_hist)
+            assert_array_equal(edges, int_edges)
+
+    def test_weights(self):
+        v = np.random.rand(100)
+        w = np.ones(100) * 5
+        a, b = histogram(v)
+        na, nb = histogram(v, density=True)
+        wa, wb = histogram(v, weights=w)
+        nwa, nwb = histogram(v, weights=w, density=True)
+        assert_array_almost_equal(a * 5, wa)
+        assert_array_almost_equal(na, nwa)
+
+        # Check weights are properly applied.
+        v = np.linspace(0, 10, 10)
+        w = np.concatenate((np.zeros(5), np.ones(5)))
+        wa, wb = histogram(v, bins=np.arange(11), weights=w)
+        assert_array_almost_equal(wa, w)
+
+        # Check with integer weights
+        wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
+        assert_array_equal(wa, [4, 5, 0, 1])
+        wa, wb = histogram(
+            [1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], density=True)
+        assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4)
+
+        # Check weights with non-uniform bin widths
+        a, b = histogram(
+            np.arange(9), [0, 1, 3, 6, 10],
+            weights=[2, 1, 1, 1, 1, 1, 1, 1, 1], density=True)
+        assert_almost_equal(a, [.2, .1, .1, .075])
+
+    def test_exotic_weights(self):
+
+        # Test the use of weights that are not integer or floats, but e.g.
+        # complex numbers or object types.
+
+        # Complex weights
+        values = np.array([1.3, 2.5, 2.3])
+        weights = np.array([1, -1, 2]) + 1j * np.array([2, 1, 2])
+
+        # Check with custom bins
+        wa, wb = histogram(values, bins=[0, 2, 3], weights=weights)
+        assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3]))
+
+        # Check with even bins
+        wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights)
+        assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3]))
+
+        # Decimal weights
+        from decimal import Decimal
+        values = np.array([1.3, 2.5, 2.3])
+        weights = np.array([Decimal(1), Decimal(2), Decimal(3)])
+
+        # Check with custom bins
+        wa, wb = histogram(values, bins=[0, 2, 3], weights=weights)
+        assert_array_almost_equal(wa, [Decimal(1), Decimal(5)])
+
+        # Check with even bins
+        wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights)
+        assert_array_almost_equal(wa, [Decimal(1), Decimal(5)])
+
+    def test_no_side_effects(self):
+        # This is a regression test that ensures that values passed to
+        # ``histogram`` are unchanged.
+        values = np.array([1.3, 2.5, 2.3])
+        np.histogram(values, range=[-10, 10], bins=100)
+        assert_array_almost_equal(values, [1.3, 2.5, 2.3])
+
+    def test_empty(self):
+        a, b = histogram([], bins=([0, 1]))
+        assert_array_equal(a, np.array([0]))
+        assert_array_equal(b, np.array([0, 1]))
+
+    def test_error_binnum_type (self):
+        # Tests if right Error is raised if bins argument is float
+        vals = np.linspace(0.0, 1.0, num=100)
+        histogram(vals, 5)
+        assert_raises(TypeError, histogram, vals, 2.4)
+
+    def test_finite_range(self):
+        # Normal ranges should be fine
+        vals = np.linspace(0.0, 1.0, num=100)
+        histogram(vals, range=[0.25,0.75])
+        assert_raises(ValueError, histogram, vals, range=[np.nan,0.75])
+        assert_raises(ValueError, histogram, vals, range=[0.25,np.inf])
+
+    def test_invalid_range(self):
+        # start of range must be < end of range
+        vals = np.linspace(0.0, 1.0, num=100)
+        with assert_raises_regex(ValueError, "max must be larger than"):
+            np.histogram(vals, range=[0.1, 0.01])
+
+    def test_bin_edge_cases(self):
+        # Ensure that floating-point computations correctly place edge cases.
+        arr = np.array([337, 404, 739, 806, 1007, 1811, 2012])
+        hist, edges = np.histogram(arr, bins=8296, range=(2, 2280))
+        mask = hist > 0
+        left_edges = edges[:-1][mask]
+        right_edges = edges[1:][mask]
+        for x, left, right in zip(arr, left_edges, right_edges):
+            assert_(x >= left)
+            assert_(x < right)
+
+    def test_last_bin_inclusive_range(self):
+        arr = np.array([0.,  0.,  0.,  1.,  2.,  3.,  3.,  4.,  5.])
+        hist, edges = np.histogram(arr, bins=30, range=(-0.5, 5))
+        assert_equal(hist[-1], 1)
+
+    def test_bin_array_dims(self):
+        # gracefully handle bins object > 1 dimension
+        vals = np.linspace(0.0, 1.0, num=100)
+        bins = np.array([[0, 0.5], [0.6, 1.0]])
+        with assert_raises_regex(ValueError, "must be 1d"):
+            np.histogram(vals, bins=bins)
+
+    def test_unsigned_monotonicity_check(self):
+        # Ensures ValueError is raised if bins not increasing monotonically
+        # when bins contain unsigned values (see #9222)
+        arr = np.array([2])
+        bins = np.array([1, 3, 1], dtype='uint64')
+        with assert_raises(ValueError):
+            hist, edges = np.histogram(arr, bins=bins)
+
+    def test_object_array_of_0d(self):
+        # gh-7864
+        assert_raises(ValueError,
+            histogram, [np.array(0.4) for i in range(10)] + [-np.inf])
+        assert_raises(ValueError,
+            histogram, [np.array(0.4) for i in range(10)] + [np.inf])
+
+        # these should not crash
+        np.histogram([np.array(0.5) for i in range(10)] + [.500000000000002])
+        np.histogram([np.array(0.5) for i in range(10)] + [.5])
+
+    def test_some_nan_values(self):
+        # gh-7503
+        one_nan = np.array([0, 1, np.nan])
+        all_nan = np.array([np.nan, np.nan])
+
+        # the internal comparisons with NaN give warnings
+        sup = suppress_warnings()
+        sup.filter(RuntimeWarning)
+        with sup:
+            # can't infer range with nan
+            assert_raises(ValueError, histogram, one_nan, bins='auto')
+            assert_raises(ValueError, histogram, all_nan, bins='auto')
+
+            # explicit range solves the problem
+            h, b = histogram(one_nan, bins='auto', range=(0, 1))
+            assert_equal(h.sum(), 2)  # nan is not counted
+            h, b = histogram(all_nan, bins='auto', range=(0, 1))
+            assert_equal(h.sum(), 0)  # nan is not counted
+
+            # as does an explicit set of bins
+            h, b = histogram(one_nan, bins=[0, 1])
+            assert_equal(h.sum(), 2)  # nan is not counted
+            h, b = histogram(all_nan, bins=[0, 1])
+            assert_equal(h.sum(), 0)  # nan is not counted
+
+    def test_datetime(self):
+        begin = np.datetime64('2000-01-01', 'D')
+        offsets = np.array([0, 0, 1, 1, 2, 3, 5, 10, 20])
+        bins = np.array([0, 2, 7, 20])
+        dates = begin + offsets
+        date_bins = begin + bins
+
+        td = np.dtype('timedelta64[D]')
+
+        # Results should be the same for integer offsets or datetime values.
+        # For now, only explicit bins are supported, since linspace does not
+        # work on datetimes or timedeltas
+        d_count, d_edge = histogram(dates, bins=date_bins)
+        t_count, t_edge = histogram(offsets.astype(td), bins=bins.astype(td))
+        i_count, i_edge = histogram(offsets, bins=bins)
+
+        assert_equal(d_count, i_count)
+        assert_equal(t_count, i_count)
+
+        assert_equal((d_edge - begin).astype(int), i_edge)
+        assert_equal(t_edge.astype(int), i_edge)
+
+        assert_equal(d_edge.dtype, dates.dtype)
+        assert_equal(t_edge.dtype, td)
+
+    def do_signed_overflow_bounds(self, dtype):
+        exponent = 8 * np.dtype(dtype).itemsize - 1
+        arr = np.array([-2**exponent + 4, 2**exponent - 4], dtype=dtype)
+        hist, e = histogram(arr, bins=2)
+        assert_equal(e, [-2**exponent + 4, 0, 2**exponent - 4])
+        assert_equal(hist, [1, 1])
+
+    def test_signed_overflow_bounds(self):
+        self.do_signed_overflow_bounds(np.byte)
+        self.do_signed_overflow_bounds(np.short)
+        self.do_signed_overflow_bounds(np.intc)
+        self.do_signed_overflow_bounds(np.int_)
+        self.do_signed_overflow_bounds(np.longlong)
+
+    def do_precision_lower_bound(self, float_small, float_large):
+        eps = np.finfo(float_large).eps
+
+        arr = np.array([1.0], float_small)
+        range = np.array([1.0 + eps, 2.0], float_large)
+
+        # test is looking for behavior when the bounds change between dtypes
+        if range.astype(float_small)[0] != 1:
+            return
+
+        # previously crashed
+        count, x_loc = np.histogram(arr, bins=1, range=range)
+        assert_equal(count, [0])
+        assert_equal(x_loc.dtype, float_large)
+
+    def do_precision_upper_bound(self, float_small, float_large):
+        eps = np.finfo(float_large).eps
+
+        arr = np.array([1.0], float_small)
+        range = np.array([0.0, 1.0 - eps], float_large)
+
+        # test is looking for behavior when the bounds change between dtypes
+        if range.astype(float_small)[-1] != 1:
+            return
+
+        # previously crashed
+        count, x_loc = np.histogram(arr, bins=1, range=range)
+        assert_equal(count, [0])
+
+        assert_equal(x_loc.dtype, float_large)
+
+    def do_precision(self, float_small, float_large):
+        self.do_precision_lower_bound(float_small, float_large)
+        self.do_precision_upper_bound(float_small, float_large)
+
+    def test_precision(self):
+        # not looping results in a useful stack trace upon failure
+        self.do_precision(np.half, np.single)
+        self.do_precision(np.half, np.double)
+        self.do_precision(np.half, np.longdouble)
+        self.do_precision(np.single, np.double)
+        self.do_precision(np.single, np.longdouble)
+        self.do_precision(np.double, np.longdouble)
+
+    def test_histogram_bin_edges(self):
+        hist, e = histogram([1, 2, 3, 4], [1, 2])
+        edges = histogram_bin_edges([1, 2, 3, 4], [1, 2])
+        assert_array_equal(edges, e)
+
+        arr = np.array([0.,  0.,  0.,  1.,  2.,  3.,  3.,  4.,  5.])
+        hist, e = histogram(arr, bins=30, range=(-0.5, 5))
+        edges = histogram_bin_edges(arr, bins=30, range=(-0.5, 5))
+        assert_array_equal(edges, e)
+
+        hist, e = histogram(arr, bins='auto', range=(0, 1))
+        edges = histogram_bin_edges(arr, bins='auto', range=(0, 1))
+        assert_array_equal(edges, e)
+
+    def test_small_value_range(self):
+        arr = np.array([1, 1 + 2e-16] * 10)
+        with pytest.raises(ValueError, match="Too many bins for data range"):
+            histogram(arr, bins=10)
+
+    # @requires_memory(free_bytes=1e10)
+    # @pytest.mark.slow
+    @pytest.mark.skip(reason="Bad memory reports lead to OOM in ci testing")
+    def test_big_arrays(self):
+        sample = np.zeros([100000000, 3])
+        xbins = 400
+        ybins = 400
+        zbins = np.arange(16000)
+        hist = np.histogramdd(sample=sample, bins=(xbins, ybins, zbins))
+        assert_equal(type(hist), type((1, 2)))
+
+    def test_gh_23110(self):
+        hist, e = np.histogram(np.array([-0.9e-308], dtype='>f8'),
+                               bins=2,
+                               range=(-1e-308, -2e-313))
+        expected_hist = np.array([1, 0])
+        assert_array_equal(hist, expected_hist)
+
+
+class TestHistogramOptimBinNums:
+    """
+    Provide test coverage when using provided estimators for optimal number of
+    bins
+    """
+
+    def test_empty(self):
+        estimator_list = ['fd', 'scott', 'rice', 'sturges',
+                          'doane', 'sqrt', 'auto', 'stone']
+        # check it can deal with empty data
+        for estimator in estimator_list:
+            a, b = histogram([], bins=estimator)
+            assert_array_equal(a, np.array([0]))
+            assert_array_equal(b, np.array([0, 1]))
+
+    def test_simple(self):
+        """
+        Straightforward testing with a mixture of linspace data (for
+        consistency). All test values have been precomputed and the values
+        shouldn't change
+        """
+        # Some basic sanity checking, with some fixed data.
+        # Checking for the correct number of bins
+        basic_test = {50:   {'fd': 4,  'scott': 4,  'rice': 8,  'sturges': 7,
+                             'doane': 8, 'sqrt': 8, 'auto': 7, 'stone': 2},
+                      500:  {'fd': 8,  'scott': 8,  'rice': 16, 'sturges': 10,
+                             'doane': 12, 'sqrt': 23, 'auto': 10, 'stone': 9},
+                      5000: {'fd': 17, 'scott': 17, 'rice': 35, 'sturges': 14,
+                             'doane': 17, 'sqrt': 71, 'auto': 17, 'stone': 20}}
+
+        for testlen, expectedResults in basic_test.items():
+            # Create some sort of non uniform data to test with
+            # (2 peak uniform mixture)
+            x1 = np.linspace(-10, -1, testlen // 5 * 2)
+            x2 = np.linspace(1, 10, testlen // 5 * 3)
+            x = np.concatenate((x1, x2))
+            for estimator, numbins in expectedResults.items():
+                a, b = np.histogram(x, estimator)
+                assert_equal(len(a), numbins, err_msg="For the {0} estimator "
+                             "with datasize of {1}".format(estimator, testlen))
+
+    def test_small(self):
+        """
+        Smaller datasets have the potential to cause issues with the data
+        adaptive methods, especially the FD method. All bin numbers have been
+        precalculated.
+        """
+        small_dat = {1: {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1,
+                         'doane': 1, 'sqrt': 1, 'stone': 1},
+                     2: {'fd': 2, 'scott': 1, 'rice': 3, 'sturges': 2,
+                         'doane': 1, 'sqrt': 2, 'stone': 1},
+                     3: {'fd': 2, 'scott': 2, 'rice': 3, 'sturges': 3,
+                         'doane': 3, 'sqrt': 2, 'stone': 1}}
+
+        for testlen, expectedResults in small_dat.items():
+            testdat = np.arange(testlen).astype(float)
+            for estimator, expbins in expectedResults.items():
+                a, b = np.histogram(testdat, estimator)
+                assert_equal(len(a), expbins, err_msg="For the {0} estimator "
+                             "with datasize of {1}".format(estimator, testlen))
+
+    def test_incorrect_methods(self):
+        """
+        Check a Value Error is thrown when an unknown string is passed in
+        """
+        check_list = ['mad', 'freeman', 'histograms', 'IQR']
+        for estimator in check_list:
+            assert_raises(ValueError, histogram, [1, 2, 3], estimator)
+
+    def test_novariance(self):
+        """
+        Check that methods handle no variance in data
+        Primarily for Scott and FD as the SD and IQR are both 0 in this case
+        """
+        novar_dataset = np.ones(100)
+        novar_resultdict = {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1,
+                            'doane': 1, 'sqrt': 1, 'auto': 1, 'stone': 1}
+
+        for estimator, numbins in novar_resultdict.items():
+            a, b = np.histogram(novar_dataset, estimator)
+            assert_equal(len(a), numbins, err_msg="{0} estimator, "
+                         "No Variance test".format(estimator))
+
+    def test_limited_variance(self):
+        """
+        Check when IQR is 0, but variance exists, we return the sturges value
+        and not the fd value.
+        """
+        lim_var_data = np.ones(1000)
+        lim_var_data[:3] = 0
+        lim_var_data[-4:] = 100
+
+        edges_auto = histogram_bin_edges(lim_var_data, 'auto')
+        assert_equal(edges_auto, np.linspace(0, 100, 12))
+
+        edges_fd = histogram_bin_edges(lim_var_data, 'fd')
+        assert_equal(edges_fd, np.array([0, 100]))
+
+        edges_sturges = histogram_bin_edges(lim_var_data, 'sturges')
+        assert_equal(edges_sturges, np.linspace(0, 100, 12))
+
+    def test_outlier(self):
+        """
+        Check the FD, Scott and Doane with outliers.
+
+        The FD estimates a smaller binwidth since it's less affected by
+        outliers. Since the range is so (artificially) large, this means more
+        bins, most of which will be empty, but the data of interest usually is
+        unaffected. The Scott estimator is more affected and returns fewer bins,
+        despite most of the variance being in one area of the data. The Doane
+        estimator lies somewhere between the other two.
+        """
+        xcenter = np.linspace(-10, 10, 50)
+        outlier_dataset = np.hstack((np.linspace(-110, -100, 5), xcenter))
+
+        outlier_resultdict = {'fd': 21, 'scott': 5, 'doane': 11, 'stone': 6}
+
+        for estimator, numbins in outlier_resultdict.items():
+            a, b = np.histogram(outlier_dataset, estimator)
+            assert_equal(len(a), numbins)
+
+    def test_scott_vs_stone(self):
+        """Verify that Scott's rule and Stone's rule converges for normally distributed data"""
+
+        def nbins_ratio(seed, size):
+            rng = np.random.RandomState(seed)
+            x = rng.normal(loc=0, scale=2, size=size)
+            a, b = len(np.histogram(x, 'stone')[0]), len(np.histogram(x, 'scott')[0])
+            return a / (a + b)
+
+        ll = [[nbins_ratio(seed, size) for size in np.geomspace(start=10, stop=100, num=4).round().astype(int)]
+              for seed in range(10)]
+
+        # the average difference between the two methods decreases as the dataset size increases.
+        avg = abs(np.mean(ll, axis=0) - 0.5)
+        assert_almost_equal(avg, [0.15, 0.09, 0.08, 0.03], decimal=2)
+
+    def test_simple_range(self):
+        """
+        Straightforward testing with a mixture of linspace data (for
+        consistency). Adding in a 3rd mixture that will then be
+        completely ignored. All test values have been precomputed and
+        the shouldn't change.
+        """
+        # some basic sanity checking, with some fixed data.
+        # Checking for the correct number of bins
+        basic_test = {
+                      50:   {'fd': 8,  'scott': 8,  'rice': 15,
+                             'sturges': 14, 'auto': 14, 'stone': 8},
+                      500:  {'fd': 15, 'scott': 16, 'rice': 32,
+                             'sturges': 20, 'auto': 20, 'stone': 80},
+                      5000: {'fd': 33, 'scott': 33, 'rice': 69,
+                             'sturges': 27, 'auto': 33, 'stone': 80}
+                     }
+
+        for testlen, expectedResults in basic_test.items():
+            # create some sort of non uniform data to test with
+            # (3 peak uniform mixture)
+            x1 = np.linspace(-10, -1, testlen // 5 * 2)
+            x2 = np.linspace(1, 10, testlen // 5 * 3)
+            x3 = np.linspace(-100, -50, testlen)
+            x = np.hstack((x1, x2, x3))
+            for estimator, numbins in expectedResults.items():
+                a, b = np.histogram(x, estimator, range = (-20, 20))
+                msg = "For the {0} estimator".format(estimator)
+                msg += " with datasize of {0}".format(testlen)
+                assert_equal(len(a), numbins, err_msg=msg)
+
+    @pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott',
+                                      'stone', 'rice', 'sturges'])
+    def test_signed_integer_data(self, bins):
+        # Regression test for gh-14379.
+        a = np.array([-2, 0, 127], dtype=np.int8)
+        hist, edges = np.histogram(a, bins=bins)
+        hist32, edges32 = np.histogram(a.astype(np.int32), bins=bins)
+        assert_array_equal(hist, hist32)
+        assert_array_equal(edges, edges32)
+
+    @pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott',
+                                      'stone', 'rice', 'sturges'])
+    def test_integer(self, bins):
+        """
+        Test that bin width for integer data is at least 1.
+        """
+        with suppress_warnings() as sup:
+            if bins == 'stone':
+                sup.filter(RuntimeWarning)
+            assert_equal(
+                np.histogram_bin_edges(np.tile(np.arange(9), 1000), bins),
+                np.arange(9))
+
+    def test_integer_non_auto(self):
+        """
+        Test that the bin-width>=1 requirement *only* applies to auto binning.
+        """
+        assert_equal(
+            np.histogram_bin_edges(np.tile(np.arange(9), 1000), 16),
+            np.arange(17) / 2)
+        assert_equal(
+            np.histogram_bin_edges(np.tile(np.arange(9), 1000), [.1, .2]),
+            [.1, .2])
+
+    def test_simple_weighted(self):
+        """
+        Check that weighted data raises a TypeError
+        """
+        estimator_list = ['fd', 'scott', 'rice', 'sturges', 'auto']
+        for estimator in estimator_list:
+            assert_raises(TypeError, histogram, [1, 2, 3],
+                          estimator, weights=[1, 2, 3])
+
+
+class TestHistogramdd:
+
+    def test_simple(self):
+        x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5],
+                      [.5,  .5, 1.5], [.5,  1.5, 2.5], [.5,  2.5, 2.5]])
+        H, edges = histogramdd(x, (2, 3, 3),
+                               range=[[-1, 1], [0, 3], [0, 3]])
+        answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]],
+                           [[0, 1, 0], [0, 0, 1], [0, 0, 1]]])
+        assert_array_equal(H, answer)
+
+        # Check normalization
+        ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
+        H, edges = histogramdd(x, bins=ed, density=True)
+        assert_(np.all(H == answer / 12.))
+
+        # Check that H has the correct shape.
+        H, edges = histogramdd(x, (2, 3, 4),
+                               range=[[-1, 1], [0, 3], [0, 4]],
+                               density=True)
+        answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
+                           [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
+        assert_array_almost_equal(H, answer / 6., 4)
+        # Check that a sequence of arrays is accepted and H has the correct
+        # shape.
+        z = [np.squeeze(y) for y in np.split(x, 3, axis=1)]
+        H, edges = histogramdd(
+            z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
+        answer = np.array([[[0, 0], [0, 0], [0, 0]],
+                           [[0, 1], [0, 0], [1, 0]],
+                           [[0, 1], [0, 0], [0, 0]],
+                           [[0, 0], [0, 0], [0, 0]]])
+        assert_array_equal(H, answer)
+
+        Z = np.zeros((5, 5, 5))
+        Z[list(range(5)), list(range(5)), list(range(5))] = 1.
+        H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
+        assert_array_equal(H, Z)
+
+    def test_shape_3d(self):
+        # All possible permutations for bins of different lengths in 3D.
+        bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
+                (4, 5, 6))
+        r = np.random.rand(10, 3)
+        for b in bins:
+            H, edges = histogramdd(r, b)
+            assert_(H.shape == b)
+
+    def test_shape_4d(self):
+        # All possible permutations for bins of different lengths in 4D.
+        bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
+                (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
+                (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
+                (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
+                (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
+                (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))
+
+        r = np.random.rand(10, 4)
+        for b in bins:
+            H, edges = histogramdd(r, b)
+            assert_(H.shape == b)
+
+    def test_weights(self):
+        v = np.random.rand(100, 2)
+        hist, edges = histogramdd(v)
+        n_hist, edges = histogramdd(v, density=True)
+        w_hist, edges = histogramdd(v, weights=np.ones(100))
+        assert_array_equal(w_hist, hist)
+        w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, density=True)
+        assert_array_equal(w_hist, n_hist)
+        w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
+        assert_array_equal(w_hist, 2 * hist)
+
+    def test_identical_samples(self):
+        x = np.zeros((10, 2), int)
+        hist, edges = histogramdd(x, bins=2)
+        assert_array_equal(edges[0], np.array([-0.5, 0., 0.5]))
+
+    def test_empty(self):
+        a, b = histogramdd([[], []], bins=([0, 1], [0, 1]))
+        assert_array_max_ulp(a, np.array([[0.]]))
+        a, b = np.histogramdd([[], [], []], bins=2)
+        assert_array_max_ulp(a, np.zeros((2, 2, 2)))
+
+    def test_bins_errors(self):
+        # There are two ways to specify bins. Check for the right errors
+        # when mixing those.
+        x = np.arange(8).reshape(2, 4)
+        assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5])
+        assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1])
+        assert_raises(
+            ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
+        assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]]))
+
+    def test_inf_edges(self):
+        # Test using +/-inf bin edges works. See #1788.
+        with np.errstate(invalid='ignore'):
+            x = np.arange(6).reshape(3, 2)
+            expected = np.array([[1, 0], [0, 1], [0, 1]])
+            h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
+            assert_allclose(h, expected)
+            h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
+            assert_allclose(h, expected)
+            h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
+            assert_allclose(h, expected)
+
+    def test_rightmost_binedge(self):
+        # Test event very close to rightmost binedge. See Github issue #4266
+        x = [0.9999999995]
+        bins = [[0., 0.5, 1.0]]
+        hist, _ = histogramdd(x, bins=bins)
+        assert_(hist[0] == 0.0)
+        assert_(hist[1] == 1.)
+        x = [1.0]
+        bins = [[0., 0.5, 1.0]]
+        hist, _ = histogramdd(x, bins=bins)
+        assert_(hist[0] == 0.0)
+        assert_(hist[1] == 1.)
+        x = [1.0000000001]
+        bins = [[0., 0.5, 1.0]]
+        hist, _ = histogramdd(x, bins=bins)
+        assert_(hist[0] == 0.0)
+        assert_(hist[1] == 0.0)
+        x = [1.0001]
+        bins = [[0., 0.5, 1.0]]
+        hist, _ = histogramdd(x, bins=bins)
+        assert_(hist[0] == 0.0)
+        assert_(hist[1] == 0.0)
+
+    def test_finite_range(self):
+        vals = np.random.random((100, 3))
+        histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]])
+        assert_raises(ValueError, histogramdd, vals,
+                      range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]])
+        assert_raises(ValueError, histogramdd, vals,
+                      range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])
+
+    def test_equal_edges(self):
+        """ Test that adjacent entries in an edge array can be equal """
+        x = np.array([0, 1, 2])
+        y = np.array([0, 1, 2])
+        x_edges = np.array([0, 2, 2])
+        y_edges = 1
+        hist, edges = histogramdd((x, y), bins=(x_edges, y_edges))
+
+        hist_expected = np.array([
+            [2.],
+            [1.],  # x == 2 falls in the final bin
+        ])
+        assert_equal(hist, hist_expected)
+
+    def test_edge_dtype(self):
+        """ Test that if an edge array is input, its type is preserved """
+        x = np.array([0, 10, 20])
+        y = x / 10
+        x_edges = np.array([0, 5, 15, 20])
+        y_edges = x_edges / 10
+        hist, edges = histogramdd((x, y), bins=(x_edges, y_edges))
+
+        assert_equal(edges[0].dtype, x_edges.dtype)
+        assert_equal(edges[1].dtype, y_edges.dtype)
+
+    def test_large_integers(self):
+        big = 2**60  # Too large to represent with a full precision float
+
+        x = np.array([0], np.int64)
+        x_edges = np.array([-1, +1], np.int64)
+        y = big + x
+        y_edges = big + x_edges
+
+        hist, edges = histogramdd((x, y), bins=(x_edges, y_edges))
+
+        assert_equal(hist[0, 0], 1)
+
+    def test_density_non_uniform_2d(self):
+        # Defines the following grid:
+        #
+        #    0 2     8
+        #   0+-+-----+
+        #    + |     +
+        #    + |     +
+        #   6+-+-----+
+        #   8+-+-----+
+        x_edges = np.array([0, 2, 8])
+        y_edges = np.array([0, 6, 8])
+        relative_areas = np.array([
+            [3, 9],
+            [1, 3]])
+
+        # ensure the number of points in each region is proportional to its area
+        x = np.array([1] + [1]*3 + [7]*3 + [7]*9)
+        y = np.array([7] + [1]*3 + [7]*3 + [1]*9)
+
+        # sanity check that the above worked as intended
+        hist, edges = histogramdd((y, x), bins=(y_edges, x_edges))
+        assert_equal(hist, relative_areas)
+
+        # resulting histogram should be uniform, since counts and areas are proportional
+        hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), density=True)
+        assert_equal(hist, 1 / (8*8))
+
+    def test_density_non_uniform_1d(self):
+        # compare to histogram to show the results are the same
+        v = np.arange(10)
+        bins = np.array([0, 1, 3, 6, 10])
+        hist, edges = histogram(v, bins, density=True)
+        hist_dd, edges_dd = histogramdd((v,), (bins,), density=True)
+        assert_equal(hist, hist_dd)
+        assert_equal(edges, edges_dd[0])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_index_tricks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_index_tricks.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe1cfce2eaf83272d0a01d3fb1911772693c69d7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_index_tricks.py
@@ -0,0 +1,553 @@
+import pytest
+
+import numpy as np
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_raises, assert_raises_regex,
+    )
+from numpy.lib._index_tricks_impl import (
+    mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from,
+    index_exp, ndindex, c_, r_, s_, ix_
+    )
+
+
+class TestRavelUnravelIndex:
+    def test_basic(self):
+        assert_equal(np.unravel_index(2, (2, 2)), (1, 0))
+
+        # test that new shape argument works properly
+        assert_equal(np.unravel_index(indices=2,
+                                      shape=(2, 2)),
+                                      (1, 0))
+
+        # test that an invalid second keyword argument
+        # is properly handled, including the old name `dims`.
+        with assert_raises(TypeError):
+            np.unravel_index(indices=2, hape=(2, 2))
+
+        with assert_raises(TypeError):
+            np.unravel_index(2, hape=(2, 2))
+
+        with assert_raises(TypeError):
+            np.unravel_index(254, ims=(17, 94))
+
+        with assert_raises(TypeError):
+            np.unravel_index(254, dims=(17, 94))
+
+        assert_equal(np.ravel_multi_index((1, 0), (2, 2)), 2)
+        assert_equal(np.unravel_index(254, (17, 94)), (2, 66))
+        assert_equal(np.ravel_multi_index((2, 66), (17, 94)), 254)
+        assert_raises(ValueError, np.unravel_index, -1, (2, 2))
+        assert_raises(TypeError, np.unravel_index, 0.5, (2, 2))
+        assert_raises(ValueError, np.unravel_index, 4, (2, 2))
+        assert_raises(ValueError, np.ravel_multi_index, (-3, 1), (2, 2))
+        assert_raises(ValueError, np.ravel_multi_index, (2, 1), (2, 2))
+        assert_raises(ValueError, np.ravel_multi_index, (0, -3), (2, 2))
+        assert_raises(ValueError, np.ravel_multi_index, (0, 2), (2, 2))
+        assert_raises(TypeError, np.ravel_multi_index, (0.1, 0.), (2, 2))
+
+        assert_equal(np.unravel_index((2*3 + 1)*6 + 4, (4, 3, 6)), [2, 1, 4])
+        assert_equal(
+            np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (2*3 + 1)*6 + 4)
+
+        arr = np.array([[3, 6, 6], [4, 5, 1]])
+        assert_equal(np.ravel_multi_index(arr, (7, 6)), [22, 41, 37])
+        assert_equal(
+            np.ravel_multi_index(arr, (7, 6), order='F'), [31, 41, 13])
+        assert_equal(
+            np.ravel_multi_index(arr, (4, 6), mode='clip'), [22, 23, 19])
+        assert_equal(np.ravel_multi_index(arr, (4, 4), mode=('clip', 'wrap')),
+                     [12, 13, 13])
+        assert_equal(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), 1621)
+
+        assert_equal(np.unravel_index(np.array([22, 41, 37]), (7, 6)),
+                     [[3, 6, 6], [4, 5, 1]])
+        assert_equal(
+            np.unravel_index(np.array([31, 41, 13]), (7, 6), order='F'),
+            [[3, 6, 6], [4, 5, 1]])
+        assert_equal(np.unravel_index(1621, (6, 7, 8, 9)), [3, 1, 4, 1])
+
+    def test_empty_indices(self):
+        msg1 = 'indices must be integral: the provided empty sequence was'
+        msg2 = 'only int indices permitted'
+        assert_raises_regex(TypeError, msg1, np.unravel_index, [], (10, 3, 5))
+        assert_raises_regex(TypeError, msg1, np.unravel_index, (), (10, 3, 5))
+        assert_raises_regex(TypeError, msg2, np.unravel_index, np.array([]),
+                            (10, 3, 5))
+        assert_equal(np.unravel_index(np.array([],dtype=int), (10, 3, 5)),
+                     [[], [], []])
+        assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], []),
+                            (10, 3))
+        assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], ['abc']),
+                            (10, 3))
+        assert_raises_regex(TypeError, msg2, np.ravel_multi_index,
+                    (np.array([]), np.array([])), (5, 3))
+        assert_equal(np.ravel_multi_index(
+                (np.array([], dtype=int), np.array([], dtype=int)), (5, 3)), [])
+        assert_equal(np.ravel_multi_index(np.array([[], []], dtype=int),
+                     (5, 3)), [])
+
+    def test_big_indices(self):
+        # ravel_multi_index for big indices (issue #7546)
+        if np.intp == np.int64:
+            arr = ([1, 29], [3, 5], [3, 117], [19, 2],
+                   [2379, 1284], [2, 2], [0, 1])
+            assert_equal(
+                np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
+                [5627771580, 117259570957])
+
+        # test unravel_index for big indices (issue #9538)
+        assert_raises(ValueError, np.unravel_index, 1, (2**32-1, 2**31+1))
+
+        # test overflow checking for too big array (issue #7546)
+        dummy_arr = ([0],[0])
+        half_max = np.iinfo(np.intp).max // 2
+        assert_equal(
+            np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
+        assert_raises(ValueError,
+            np.ravel_multi_index, dummy_arr, (half_max+1, 2))
+        assert_equal(
+            np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
+        assert_raises(ValueError,
+            np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F')
+
+    def test_dtypes(self):
+        # Test with different data types
+        for dtype in [np.int16, np.uint16, np.int32,
+                      np.uint32, np.int64, np.uint64]:
+            coords = np.array(
+                [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0]], dtype=dtype)
+            shape = (5, 8)
+            uncoords = 8*coords[0]+coords[1]
+            assert_equal(np.ravel_multi_index(coords, shape), uncoords)
+            assert_equal(coords, np.unravel_index(uncoords, shape))
+            uncoords = coords[0]+5*coords[1]
+            assert_equal(
+                np.ravel_multi_index(coords, shape, order='F'), uncoords)
+            assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
+
+            coords = np.array(
+                [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0], [1, 3, 1, 0, 9, 5]],
+                dtype=dtype)
+            shape = (5, 8, 10)
+            uncoords = 10*(8*coords[0]+coords[1])+coords[2]
+            assert_equal(np.ravel_multi_index(coords, shape), uncoords)
+            assert_equal(coords, np.unravel_index(uncoords, shape))
+            uncoords = coords[0]+5*(coords[1]+8*coords[2])
+            assert_equal(
+                np.ravel_multi_index(coords, shape, order='F'), uncoords)
+            assert_equal(coords, np.unravel_index(uncoords, shape, order='F'))
+
+    def test_clipmodes(self):
+        # Test clipmodes
+        assert_equal(
+            np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode='wrap'),
+            np.ravel_multi_index([1, 1, 6, 2], (4, 3, 7, 12)))
+        assert_equal(np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12),
+                                          mode=(
+                                              'wrap', 'raise', 'clip', 'raise')),
+                     np.ravel_multi_index([1, 1, 0, 2], (4, 3, 7, 12)))
+        assert_raises(
+            ValueError, np.ravel_multi_index, [5, 1, -1, 2], (4, 3, 7, 12))
+
+    def test_writeability(self):
+        # See gh-7269
+        x, y = np.unravel_index([1, 2, 3], (4, 5))
+        assert_(x.flags.writeable)
+        assert_(y.flags.writeable)
+
+    def test_0d(self):
+        # gh-580
+        x = np.unravel_index(0, ())
+        assert_equal(x, ())
+
+        assert_raises_regex(ValueError, "0d array", np.unravel_index, [0], ())
+        assert_raises_regex(
+            ValueError, "out of bounds", np.unravel_index, [1], ())
+
+    @pytest.mark.parametrize("mode", ["clip", "wrap", "raise"])
+    def test_empty_array_ravel(self, mode):
+        res = np.ravel_multi_index(
+                    np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode)
+        assert(res.shape == (0,))
+
+        with assert_raises(ValueError):
+            np.ravel_multi_index(
+                    np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode)
+
+    def test_empty_array_unravel(self):
+        res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0))
+        # res is a tuple of three empty arrays
+        assert(len(res) == 3)
+        assert(all(a.shape == (0,) for a in res))
+
+        with assert_raises(ValueError):
+            np.unravel_index([1], (2, 1, 0))
+
+class TestGrid:
+    def test_basic(self):
+        a = mgrid[-1:1:10j]
+        b = mgrid[-1:1:0.1]
+        assert_(a.shape == (10,))
+        assert_(b.shape == (20,))
+        assert_(a[0] == -1)
+        assert_almost_equal(a[-1], 1)
+        assert_(b[0] == -1)
+        assert_almost_equal(b[1]-b[0], 0.1, 11)
+        assert_almost_equal(b[-1], b[0]+19*0.1, 11)
+        assert_almost_equal(a[1]-a[0], 2.0/9.0, 11)
+
+    def test_linspace_equivalence(self):
+        y, st = np.linspace(2, 10, retstep=True)
+        assert_almost_equal(st, 8/49.0)
+        assert_array_almost_equal(y, mgrid[2:10:50j], 13)
+
+    def test_nd(self):
+        c = mgrid[-1:1:10j, -2:2:10j]
+        d = mgrid[-1:1:0.1, -2:2:0.2]
+        assert_(c.shape == (2, 10, 10))
+        assert_(d.shape == (2, 20, 20))
+        assert_array_equal(c[0][0, :], -np.ones(10, 'd'))
+        assert_array_equal(c[1][:, 0], -2*np.ones(10, 'd'))
+        assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11)
+        assert_array_almost_equal(c[1][:, -1], 2*np.ones(10, 'd'), 11)
+        assert_array_almost_equal(d[0, 1, :] - d[0, 0, :],
+                                  0.1*np.ones(20, 'd'), 11)
+        assert_array_almost_equal(d[1, :, 1] - d[1, :, 0],
+                                  0.2*np.ones(20, 'd'), 11)
+
+    def test_sparse(self):
+        grid_full   = mgrid[-1:1:10j, -2:2:10j]
+        grid_sparse = ogrid[-1:1:10j, -2:2:10j]
+
+        # sparse grids can be made dense by broadcasting
+        grid_broadcast = np.broadcast_arrays(*grid_sparse)
+        for f, b in zip(grid_full, grid_broadcast):
+            assert_equal(f, b)
+
+    @pytest.mark.parametrize("start, stop, step, expected", [
+        (None, 10, 10j, (200, 10)),
+        (-10, 20, None, (1800, 30)),
+        ])
+    def test_mgrid_size_none_handling(self, start, stop, step, expected):
+        # regression test None value handling for
+        # start and step values used by mgrid;
+        # internally, this aims to cover previously
+        # unexplored code paths in nd_grid()
+        grid = mgrid[start:stop:step, start:stop:step]
+        # need a smaller grid to explore one of the
+        # untested code paths
+        grid_small = mgrid[start:stop:step]
+        assert_equal(grid.size, expected[0])
+        assert_equal(grid_small.size, expected[1])
+
+    def test_accepts_npfloating(self):
+        # regression test for #16466
+        grid64 = mgrid[0.1:0.33:0.1, ]
+        grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1), ]
+        assert_array_almost_equal(grid64, grid32)
+        # At some point this was float64, but NEP 50 changed it:
+        assert grid32.dtype == np.float32
+        assert grid64.dtype == np.float64
+
+        # different code path for single slice
+        grid64 = mgrid[0.1:0.33:0.1]
+        grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1)]
+        assert_(grid32.dtype == np.float64)
+        assert_array_almost_equal(grid64, grid32)
+
+    def test_accepts_longdouble(self):
+        # regression tests for #16945
+        grid64 = mgrid[0.1:0.33:0.1, ]
+        grid128 = mgrid[
+            np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1),
+        ]
+        assert_(grid128.dtype == np.longdouble)
+        assert_array_almost_equal(grid64, grid128)
+
+        grid128c_a = mgrid[0:np.longdouble(1):3.4j]
+        grid128c_b = mgrid[0:np.longdouble(1):3.4j, ]
+        assert_(grid128c_a.dtype == grid128c_b.dtype == np.longdouble)
+        assert_array_equal(grid128c_a, grid128c_b[0])
+
+        # different code path for single slice
+        grid64 = mgrid[0.1:0.33:0.1]
+        grid128 = mgrid[
+            np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1)
+        ]
+        assert_(grid128.dtype == np.longdouble)
+        assert_array_almost_equal(grid64, grid128)
+
+    def test_accepts_npcomplexfloating(self):
+        # Related to #16466
+        assert_array_almost_equal(
+            mgrid[0.1:0.3:3j, ], mgrid[0.1:0.3:np.complex64(3j), ]
+        )
+
+        # different code path for single slice
+        assert_array_almost_equal(
+            mgrid[0.1:0.3:3j], mgrid[0.1:0.3:np.complex64(3j)]
+        )
+
+        # Related to #16945
+        grid64_a = mgrid[0.1:0.3:3.3j]
+        grid64_b = mgrid[0.1:0.3:3.3j, ][0]
+        assert_(grid64_a.dtype == grid64_b.dtype == np.float64)
+        assert_array_equal(grid64_a, grid64_b)
+
+        grid128_a = mgrid[0.1:0.3:np.clongdouble(3.3j)]
+        grid128_b = mgrid[0.1:0.3:np.clongdouble(3.3j), ][0]
+        assert_(grid128_a.dtype == grid128_b.dtype == np.longdouble)
+        assert_array_equal(grid64_a, grid64_b)
+
+
+class TestConcatenator:
+    def test_1d(self):
+        assert_array_equal(r_[1, 2, 3, 4, 5, 6], np.array([1, 2, 3, 4, 5, 6]))
+        b = np.ones(5)
+        c = r_[b, 0, 0, b]
+        assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
+
+    def test_mixed_type(self):
+        g = r_[10.1, 1:10]
+        assert_(g.dtype == 'f8')
+
+    def test_more_mixed_type(self):
+        g = r_[-10.1, np.array([1]), np.array([2, 3, 4]), 10.0]
+        assert_(g.dtype == 'f8')
+
+    def test_complex_step(self):
+        # Regression test for #12262
+        g = r_[0:36:100j]
+        assert_(g.shape == (100,))
+
+        # Related to #16466
+        g = r_[0:36:np.complex64(100j)]
+        assert_(g.shape == (100,))
+
+    def test_2d(self):
+        b = np.random.rand(5, 5)
+        c = np.random.rand(5, 5)
+        d = r_['1', b, c]  # append columns
+        assert_(d.shape == (5, 10))
+        assert_array_equal(d[:, :5], b)
+        assert_array_equal(d[:, 5:], c)
+        d = r_[b, c]
+        assert_(d.shape == (10, 5))
+        assert_array_equal(d[:5, :], b)
+        assert_array_equal(d[5:, :], c)
+
+    def test_0d(self):
+        assert_equal(r_[0, np.array(1), 2], [0, 1, 2])
+        assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3])
+        assert_equal(r_[np.array(0), [1, 2, 3]], [0, 1, 2, 3])
+
+
+class TestNdenumerate:
+    def test_basic(self):
+        a = np.array([[1, 2], [3, 4]])
+        assert_equal(list(ndenumerate(a)),
+                     [((0, 0), 1), ((0, 1), 2), ((1, 0), 3), ((1, 1), 4)])
+
+
+class TestIndexExpression:
+    def test_regression_1(self):
+        # ticket #1196
+        a = np.arange(2)
+        assert_equal(a[:-1], a[s_[:-1]])
+        assert_equal(a[:-1], a[index_exp[:-1]])
+
+    def test_simple_1(self):
+        a = np.random.rand(4, 5, 6)
+
+        assert_equal(a[:, :3, [1, 2]], a[index_exp[:, :3, [1, 2]]])
+        assert_equal(a[:, :3, [1, 2]], a[s_[:, :3, [1, 2]]])
+
+
+class TestIx_:
+    def test_regression_1(self):
+        # Test empty untyped inputs create outputs of indexing type, gh-5804
+        a, = np.ix_(range(0))
+        assert_equal(a.dtype, np.intp)
+
+        a, = np.ix_([])
+        assert_equal(a.dtype, np.intp)
+
+        # but if the type is specified, don't change it
+        a, = np.ix_(np.array([], dtype=np.float32))
+        assert_equal(a.dtype, np.float32)
+
+    def test_shape_and_dtype(self):
+        sizes = (4, 5, 3, 2)
+        # Test both lists and arrays
+        for func in (range, np.arange):
+            arrays = np.ix_(*[func(sz) for sz in sizes])
+            for k, (a, sz) in enumerate(zip(arrays, sizes)):
+                assert_equal(a.shape[k], sz)
+                assert_(all(sh == 1 for j, sh in enumerate(a.shape) if j != k))
+                assert_(np.issubdtype(a.dtype, np.integer))
+
+    def test_bool(self):
+        bool_a = [True, False, True, True]
+        int_a, = np.nonzero(bool_a)
+        assert_equal(np.ix_(bool_a)[0], int_a)
+
+    def test_1d_only(self):
+        idx2d = [[1, 2, 3], [4, 5, 6]]
+        assert_raises(ValueError, np.ix_, idx2d)
+
+    def test_repeated_input(self):
+        length_of_vector = 5
+        x = np.arange(length_of_vector)
+        out = ix_(x, x)
+        assert_equal(out[0].shape, (length_of_vector, 1))
+        assert_equal(out[1].shape, (1, length_of_vector))
+        # check that input shape is not modified
+        assert_equal(x.shape, (length_of_vector,))
+
+
+def test_c_():
+    a = c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])]
+    assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]])
+
+
+class TestFillDiagonal:
+    def test_basic(self):
+        a = np.zeros((3, 3), int)
+        fill_diagonal(a, 5)
+        assert_array_equal(
+            a, np.array([[5, 0, 0],
+                         [0, 5, 0],
+                         [0, 0, 5]])
+            )
+
+    def test_tall_matrix(self):
+        a = np.zeros((10, 3), int)
+        fill_diagonal(a, 5)
+        assert_array_equal(
+            a, np.array([[5, 0, 0],
+                         [0, 5, 0],
+                         [0, 0, 5],
+                         [0, 0, 0],
+                         [0, 0, 0],
+                         [0, 0, 0],
+                         [0, 0, 0],
+                         [0, 0, 0],
+                         [0, 0, 0],
+                         [0, 0, 0]])
+            )
+
+    def test_tall_matrix_wrap(self):
+        a = np.zeros((10, 3), int)
+        fill_diagonal(a, 5, True)
+        assert_array_equal(
+            a, np.array([[5, 0, 0],
+                         [0, 5, 0],
+                         [0, 0, 5],
+                         [0, 0, 0],
+                         [5, 0, 0],
+                         [0, 5, 0],
+                         [0, 0, 5],
+                         [0, 0, 0],
+                         [5, 0, 0],
+                         [0, 5, 0]])
+            )
+
+    def test_wide_matrix(self):
+        a = np.zeros((3, 10), int)
+        fill_diagonal(a, 5)
+        assert_array_equal(
+            a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]])
+            )
+
+    def test_operate_4d_array(self):
+        a = np.zeros((3, 3, 3, 3), int)
+        fill_diagonal(a, 4)
+        i = np.array([0, 1, 2])
+        assert_equal(np.where(a != 0), (i, i, i, i))
+
+    def test_low_dim_handling(self):
+        # raise error with low dimensionality
+        a = np.zeros(3, int)
+        with assert_raises_regex(ValueError, "at least 2-d"):
+            fill_diagonal(a, 5)
+
+    def test_hetero_shape_handling(self):
+        # raise error with high dimensionality and
+        # shape mismatch
+        a = np.zeros((3,3,7,3), int)
+        with assert_raises_regex(ValueError, "equal length"):
+            fill_diagonal(a, 2)
+
+
+def test_diag_indices():
+    di = diag_indices(4)
+    a = np.array([[1, 2, 3, 4],
+                  [5, 6, 7, 8],
+                  [9, 10, 11, 12],
+                  [13, 14, 15, 16]])
+    a[di] = 100
+    assert_array_equal(
+        a, np.array([[100, 2, 3, 4],
+                     [5, 100, 7, 8],
+                     [9, 10, 100, 12],
+                     [13, 14, 15, 100]])
+        )
+
+    # Now, we create indices to manipulate a 3-d array:
+    d3 = diag_indices(2, 3)
+
+    # And use it to set the diagonal of a zeros array to 1:
+    a = np.zeros((2, 2, 2), int)
+    a[d3] = 1
+    assert_array_equal(
+        a, np.array([[[1, 0],
+                      [0, 0]],
+                     [[0, 0],
+                      [0, 1]]])
+        )
+
+
+class TestDiagIndicesFrom:
+
+    def test_diag_indices_from(self):
+        x = np.random.random((4, 4))
+        r, c = diag_indices_from(x)
+        assert_array_equal(r, np.arange(4))
+        assert_array_equal(c, np.arange(4))
+
+    def test_error_small_input(self):
+        x = np.ones(7)
+        with assert_raises_regex(ValueError, "at least 2-d"):
+            diag_indices_from(x)
+
+    def test_error_shape_mismatch(self):
+        x = np.zeros((3, 3, 2, 3), int)
+        with assert_raises_regex(ValueError, "equal length"):
+            diag_indices_from(x)
+
+
+def test_ndindex():
+    x = list(ndindex(1, 2, 3))
+    expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))]
+    assert_array_equal(x, expected)
+
+    x = list(ndindex((1, 2, 3)))
+    assert_array_equal(x, expected)
+
+    # Test use of scalars and tuples
+    x = list(ndindex((3,)))
+    assert_array_equal(x, list(ndindex(3)))
+
+    # Make sure size argument is optional
+    x = list(ndindex())
+    assert_equal(x, [()])
+
+    x = list(ndindex(()))
+    assert_equal(x, [()])
+
+    # Make sure 0-sized ndindex works correctly
+    x = list(ndindex(*[0]))
+    assert_equal(x, [])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_io.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..742915e22ef09268d986aee3482a9da9a3602be1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_io.py
@@ -0,0 +1,2821 @@
+import sys
+import gc
+import gzip
+import os
+import threading
+import time
+import warnings
+import re
+import pytest
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+from io import BytesIO, StringIO
+from datetime import datetime
+import locale
+from multiprocessing import Value, get_context
+from ctypes import c_bool
+
+import numpy as np
+import numpy.ma as ma
+from numpy.exceptions import VisibleDeprecationWarning
+from numpy.lib._iotools import ConverterError, ConversionWarning
+from numpy.lib import _npyio_impl
+from numpy.lib._npyio_impl import recfromcsv, recfromtxt
+from numpy.ma.testutils import assert_equal
+from numpy.testing import (
+    assert_warns, assert_, assert_raises_regex, assert_raises,
+    assert_allclose, assert_array_equal, temppath, tempdir, IS_PYPY,
+    HAS_REFCOUNT, suppress_warnings, assert_no_gc_cycles, assert_no_warnings,
+    break_cycles, IS_WASM
+    )
+from numpy.testing._private.utils import requires_memory
+from numpy._utils import asbytes
+
+
+class TextIO(BytesIO):
+    """Helper IO class.
+
+    Writes encode strings to bytes if needed, reads return bytes.
+    This makes it easier to emulate files opened in binary mode
+    without needing to explicitly convert strings to bytes in
+    setting up the test data.
+
+    """
+    def __init__(self, s=""):
+        BytesIO.__init__(self, asbytes(s))
+
+    def write(self, s):
+        BytesIO.write(self, asbytes(s))
+
+    def writelines(self, lines):
+        BytesIO.writelines(self, [asbytes(s) for s in lines])
+
+
+IS_64BIT = sys.maxsize > 2**32
+try:
+    import bz2
+    HAS_BZ2 = True
+except ImportError:
+    HAS_BZ2 = False
+try:
+    import lzma
+    HAS_LZMA = True
+except ImportError:
+    HAS_LZMA = False
+
+
+def strptime(s, fmt=None):
+    """
+    This function is available in the datetime module only from Python >=
+    2.5.
+
+    """
+    if type(s) == bytes:
+        s = s.decode("latin1")
+    return datetime(*time.strptime(s, fmt)[:3])
+
+
+class RoundtripTest:
+    def roundtrip(self, save_func, *args, **kwargs):
+        """
+        save_func : callable
+            Function used to save arrays to file.
+        file_on_disk : bool
+            If true, store the file on disk, instead of in a
+            string buffer.
+        save_kwds : dict
+            Parameters passed to `save_func`.
+        load_kwds : dict
+            Parameters passed to `numpy.load`.
+        args : tuple of arrays
+            Arrays stored to file.
+
+        """
+        save_kwds = kwargs.get('save_kwds', {})
+        load_kwds = kwargs.get('load_kwds', {"allow_pickle": True})
+        file_on_disk = kwargs.get('file_on_disk', False)
+
+        if file_on_disk:
+            target_file = NamedTemporaryFile(delete=False)
+            load_file = target_file.name
+        else:
+            target_file = BytesIO()
+            load_file = target_file
+
+        try:
+            arr = args
+
+            save_func(target_file, *arr, **save_kwds)
+            target_file.flush()
+            target_file.seek(0)
+
+            if sys.platform == 'win32' and not isinstance(target_file, BytesIO):
+                target_file.close()
+
+            arr_reloaded = np.load(load_file, **load_kwds)
+
+            self.arr = arr
+            self.arr_reloaded = arr_reloaded
+        finally:
+            if not isinstance(target_file, BytesIO):
+                target_file.close()
+                # holds an open file descriptor so it can't be deleted on win
+                if 'arr_reloaded' in locals():
+                    if not isinstance(arr_reloaded, np.lib.npyio.NpzFile):
+                        os.remove(target_file.name)
+
+    def check_roundtrips(self, a):
+        self.roundtrip(a)
+        self.roundtrip(a, file_on_disk=True)
+        self.roundtrip(np.asfortranarray(a))
+        self.roundtrip(np.asfortranarray(a), file_on_disk=True)
+        if a.shape[0] > 1:
+            # neither C nor Fortran contiguous for 2D arrays or more
+            self.roundtrip(np.asfortranarray(a)[1:])
+            self.roundtrip(np.asfortranarray(a)[1:], file_on_disk=True)
+
+    def test_array(self):
+        a = np.array([], float)
+        self.check_roundtrips(a)
+
+        a = np.array([[1, 2], [3, 4]], float)
+        self.check_roundtrips(a)
+
+        a = np.array([[1, 2], [3, 4]], int)
+        self.check_roundtrips(a)
+
+        a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.csingle)
+        self.check_roundtrips(a)
+
+        a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.cdouble)
+        self.check_roundtrips(a)
+
+    def test_array_object(self):
+        a = np.array([], object)
+        self.check_roundtrips(a)
+
+        a = np.array([[1, 2], [3, 4]], object)
+        self.check_roundtrips(a)
+
+    def test_1D(self):
+        a = np.array([1, 2, 3, 4], int)
+        self.roundtrip(a)
+
+    @pytest.mark.skipif(sys.platform == 'win32', reason="Fails on Win32")
+    def test_mmap(self):
+        a = np.array([[1, 2.5], [4, 7.3]])
+        self.roundtrip(a, file_on_disk=True, load_kwds={'mmap_mode': 'r'})
+
+        a = np.asfortranarray([[1, 2.5], [4, 7.3]])
+        self.roundtrip(a, file_on_disk=True, load_kwds={'mmap_mode': 'r'})
+
+    def test_record(self):
+        a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+        self.check_roundtrips(a)
+
+    @pytest.mark.slow
+    def test_format_2_0(self):
+        dt = [(("%d" % i) * 100, float) for i in range(500)]
+        a = np.ones(1000, dtype=dt)
+        with warnings.catch_warnings(record=True):
+            warnings.filterwarnings('always', '', UserWarning)
+            self.check_roundtrips(a)
+
+
+class TestSaveLoad(RoundtripTest):
+    def roundtrip(self, *args, **kwargs):
+        RoundtripTest.roundtrip(self, np.save, *args, **kwargs)
+        assert_equal(self.arr[0], self.arr_reloaded)
+        assert_equal(self.arr[0].dtype, self.arr_reloaded.dtype)
+        assert_equal(self.arr[0].flags.fnc, self.arr_reloaded.flags.fnc)
+
+
+class TestSavezLoad(RoundtripTest):
+    def roundtrip(self, *args, **kwargs):
+        RoundtripTest.roundtrip(self, np.savez, *args, **kwargs)
+        try:
+            for n, arr in enumerate(self.arr):
+                reloaded = self.arr_reloaded['arr_%d' % n]
+                assert_equal(arr, reloaded)
+                assert_equal(arr.dtype, reloaded.dtype)
+                assert_equal(arr.flags.fnc, reloaded.flags.fnc)
+        finally:
+            # delete tempfile, must be done here on windows
+            if self.arr_reloaded.fid:
+                self.arr_reloaded.fid.close()
+                os.remove(self.arr_reloaded.fid.name)
+
+    @pytest.mark.skipif(IS_PYPY, reason="Hangs on PyPy")
+    @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform")
+    @pytest.mark.slow
+    def test_big_arrays(self):
+        L = (1 << 31) + 100000
+        a = np.empty(L, dtype=np.uint8)
+        with temppath(prefix="numpy_test_big_arrays_", suffix=".npz") as tmp:
+            np.savez(tmp, a=a)
+            del a
+            npfile = np.load(tmp)
+            a = npfile['a']  # Should succeed
+            npfile.close()
+            del a  # Avoid pyflakes unused variable warning.
+
+    def test_multiple_arrays(self):
+        a = np.array([[1, 2], [3, 4]], float)
+        b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
+        self.roundtrip(a, b)
+
+    def test_named_arrays(self):
+        a = np.array([[1, 2], [3, 4]], float)
+        b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
+        c = BytesIO()
+        np.savez(c, file_a=a, file_b=b)
+        c.seek(0)
+        l = np.load(c)
+        assert_equal(a, l['file_a'])
+        assert_equal(b, l['file_b'])
+
+
+    def test_tuple_getitem_raises(self):
+        # gh-23748
+        a = np.array([1, 2, 3])
+        f = BytesIO()
+        np.savez(f, a=a)
+        f.seek(0)
+        l = np.load(f)
+        with pytest.raises(KeyError, match="(1, 2)"):
+            l[1, 2]
+
+    def test_BagObj(self):
+        a = np.array([[1, 2], [3, 4]], float)
+        b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex)
+        c = BytesIO()
+        np.savez(c, file_a=a, file_b=b)
+        c.seek(0)
+        l = np.load(c)
+        assert_equal(sorted(dir(l.f)), ['file_a','file_b'])
+        assert_equal(a, l.f.file_a)
+        assert_equal(b, l.f.file_b)
+
+    @pytest.mark.skipif(IS_WASM, reason="Cannot start thread")
+    def test_savez_filename_clashes(self):
+        # Test that issue #852 is fixed
+        # and savez functions in multithreaded environment
+
+        def writer(error_list):
+            with temppath(suffix='.npz') as tmp:
+                arr = np.random.randn(500, 500)
+                try:
+                    np.savez(tmp, arr=arr)
+                except OSError as err:
+                    error_list.append(err)
+
+        errors = []
+        threads = [threading.Thread(target=writer, args=(errors,))
+                   for j in range(3)]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+
+        if errors:
+            raise AssertionError(errors)
+
+    def test_not_closing_opened_fid(self):
+        # Test that issue #2178 is fixed:
+        # verify could seek on 'loaded' file
+        with temppath(suffix='.npz') as tmp:
+            with open(tmp, 'wb') as fp:
+                np.savez(fp, data='LOVELY LOAD')
+            with open(tmp, 'rb', 10000) as fp:
+                fp.seek(0)
+                assert_(not fp.closed)
+                np.load(fp)['data']
+                # fp must not get closed by .load
+                assert_(not fp.closed)
+                fp.seek(0)
+                assert_(not fp.closed)
+
+    @pytest.mark.slow_pypy
+    def test_closing_fid(self):
+        # Test that issue #1517 (too many opened files) remains closed
+        # It might be a "weak" test since failed to get triggered on
+        # e.g. Debian sid of 2012 Jul 05 but was reported to
+        # trigger the failure on Ubuntu 10.04:
+        # http://projects.scipy.org/numpy/ticket/1517#comment:2
+        with temppath(suffix='.npz') as tmp:
+            np.savez(tmp, data='LOVELY LOAD')
+            # We need to check if the garbage collector can properly close
+            # numpy npz file returned by np.load when their reference count
+            # goes to zero.  Python 3 running in debug mode raises a
+            # ResourceWarning when file closing is left to the garbage
+            # collector, so we catch the warnings.
+            with suppress_warnings() as sup:
+                sup.filter(ResourceWarning)  # TODO: specify exact message
+                for i in range(1, 1025):
+                    try:
+                        np.load(tmp)["data"]
+                    except Exception as e:
+                        msg = "Failed to load data from a file: %s" % e
+                        raise AssertionError(msg)
+                    finally:
+                        if IS_PYPY:
+                            gc.collect()
+
+    def test_closing_zipfile_after_load(self):
+        # Check that zipfile owns file and can close it.  This needs to
+        # pass a file name to load for the test. On windows failure will
+        # cause a second error will be raised when the attempt to remove
+        # the open file is made.
+        prefix = 'numpy_test_closing_zipfile_after_load_'
+        with temppath(suffix='.npz', prefix=prefix) as tmp:
+            np.savez(tmp, lab='place holder')
+            data = np.load(tmp)
+            fp = data.zip.fp
+            data.close()
+            assert_(fp.closed)
+
+    @pytest.mark.parametrize("count, expected_repr", [
+        (1, "NpzFile {fname!r} with keys: arr_0"),
+        (5, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4"),
+        # _MAX_REPR_ARRAY_COUNT is 5, so files with more than 5 keys are
+        # expected to end in '...'
+        (6, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4..."),
+    ])
+    def test_repr_lists_keys(self, count, expected_repr):
+        a = np.array([[1, 2], [3, 4]], float)
+        with temppath(suffix='.npz') as tmp:
+            np.savez(tmp, *[a]*count)
+            l = np.load(tmp)
+            assert repr(l) == expected_repr.format(fname=tmp)
+            l.close()
+
+
+class TestSaveTxt:
+    def test_array(self):
+        a = np.array([[1, 2], [3, 4]], float)
+        fmt = "%.18e"
+        c = BytesIO()
+        np.savetxt(c, a, fmt=fmt)
+        c.seek(0)
+        assert_equal(c.readlines(),
+                     [asbytes((fmt + ' ' + fmt + '\n') % (1, 2)),
+                      asbytes((fmt + ' ' + fmt + '\n') % (3, 4))])
+
+        a = np.array([[1, 2], [3, 4]], int)
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%d')
+        c.seek(0)
+        assert_equal(c.readlines(), [b'1 2\n', b'3 4\n'])
+
+    def test_1D(self):
+        a = np.array([1, 2, 3, 4], int)
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%d')
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(lines, [b'1\n', b'2\n', b'3\n', b'4\n'])
+
+    def test_0D_3D(self):
+        c = BytesIO()
+        assert_raises(ValueError, np.savetxt, c, np.array(1))
+        assert_raises(ValueError, np.savetxt, c, np.array([[[1], [2]]]))
+
+    def test_structured(self):
+        a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%d')
+        c.seek(0)
+        assert_equal(c.readlines(), [b'1 2\n', b'3 4\n'])
+
+    def test_structured_padded(self):
+        # gh-13297
+        a = np.array([(1, 2, 3),(4, 5, 6)], dtype=[
+            ('foo', 'i4'), ('bar', 'i4'), ('baz', 'i4')
+        ])
+        c = BytesIO()
+        np.savetxt(c, a[['foo', 'baz']], fmt='%d')
+        c.seek(0)
+        assert_equal(c.readlines(), [b'1 3\n', b'4 6\n'])
+
+    def test_multifield_view(self):
+        a = np.ones(1, dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'f4')])
+        v = a[['x', 'z']]
+        with temppath(suffix='.npy') as path:
+            path = Path(path)
+            np.save(path, v)
+            data = np.load(path)
+            assert_array_equal(data, v)
+
+    def test_delimiter(self):
+        a = np.array([[1., 2.], [3., 4.]])
+        c = BytesIO()
+        np.savetxt(c, a, delimiter=',', fmt='%d')
+        c.seek(0)
+        assert_equal(c.readlines(), [b'1,2\n', b'3,4\n'])
+
+    def test_format(self):
+        a = np.array([(1, 2), (3, 4)])
+        c = BytesIO()
+        # Sequence of formats
+        np.savetxt(c, a, fmt=['%02d', '%3.1f'])
+        c.seek(0)
+        assert_equal(c.readlines(), [b'01 2.0\n', b'03 4.0\n'])
+
+        # A single multiformat string
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%02d : %3.1f')
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n'])
+
+        # Specify delimiter, should be overridden
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%02d : %3.1f', delimiter=',')
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n'])
+
+        # Bad fmt, should raise a ValueError
+        c = BytesIO()
+        assert_raises(ValueError, np.savetxt, c, a, fmt=99)
+
+    def test_header_footer(self):
+        # Test the functionality of the header and footer keyword argument.
+
+        c = BytesIO()
+        a = np.array([(1, 2), (3, 4)], dtype=int)
+        test_header_footer = 'Test header / footer'
+        # Test the header keyword argument
+        np.savetxt(c, a, fmt='%1d', header=test_header_footer)
+        c.seek(0)
+        assert_equal(c.read(),
+                     asbytes('# ' + test_header_footer + '\n1 2\n3 4\n'))
+        # Test the footer keyword argument
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%1d', footer=test_header_footer)
+        c.seek(0)
+        assert_equal(c.read(),
+                     asbytes('1 2\n3 4\n# ' + test_header_footer + '\n'))
+        # Test the commentstr keyword argument used on the header
+        c = BytesIO()
+        commentstr = '% '
+        np.savetxt(c, a, fmt='%1d',
+                   header=test_header_footer, comments=commentstr)
+        c.seek(0)
+        assert_equal(c.read(),
+                     asbytes(commentstr + test_header_footer + '\n' + '1 2\n3 4\n'))
+        # Test the commentstr keyword argument used on the footer
+        c = BytesIO()
+        commentstr = '% '
+        np.savetxt(c, a, fmt='%1d',
+                   footer=test_header_footer, comments=commentstr)
+        c.seek(0)
+        assert_equal(c.read(),
+                     asbytes('1 2\n3 4\n' + commentstr + test_header_footer + '\n'))
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    def test_file_roundtrip(self, filename_type):
+        with temppath() as name:
+            a = np.array([(1, 2), (3, 4)])
+            np.savetxt(filename_type(name), a)
+            b = np.loadtxt(filename_type(name))
+            assert_array_equal(a, b)
+
+    def test_complex_arrays(self):
+        ncols = 2
+        nrows = 2
+        a = np.zeros((ncols, nrows), dtype=np.complex128)
+        re = np.pi
+        im = np.e
+        a[:] = re + 1.0j * im
+
+        # One format only
+        c = BytesIO()
+        np.savetxt(c, a, fmt=' %+.3e')
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(
+            lines,
+            [b' ( +3.142e+00+ +2.718e+00j)  ( +3.142e+00+ +2.718e+00j)\n',
+             b' ( +3.142e+00+ +2.718e+00j)  ( +3.142e+00+ +2.718e+00j)\n'])
+
+        # One format for each real and imaginary part
+        c = BytesIO()
+        np.savetxt(c, a, fmt='  %+.3e' * 2 * ncols)
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(
+            lines,
+            [b'  +3.142e+00  +2.718e+00  +3.142e+00  +2.718e+00\n',
+             b'  +3.142e+00  +2.718e+00  +3.142e+00  +2.718e+00\n'])
+
+        # One format for each complex number
+        c = BytesIO()
+        np.savetxt(c, a, fmt=['(%.3e%+.3ej)'] * ncols)
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(
+            lines,
+            [b'(3.142e+00+2.718e+00j) (3.142e+00+2.718e+00j)\n',
+             b'(3.142e+00+2.718e+00j) (3.142e+00+2.718e+00j)\n'])
+
+    def test_complex_negative_exponent(self):
+        # Previous to 1.15, some formats generated x+-yj, gh 7895
+        ncols = 2
+        nrows = 2
+        a = np.zeros((ncols, nrows), dtype=np.complex128)
+        re = np.pi
+        im = np.e
+        a[:] = re - 1.0j * im
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%.3e')
+        c.seek(0)
+        lines = c.readlines()
+        assert_equal(
+            lines,
+            [b' (3.142e+00-2.718e+00j)  (3.142e+00-2.718e+00j)\n',
+             b' (3.142e+00-2.718e+00j)  (3.142e+00-2.718e+00j)\n'])
+
+
+    def test_custom_writer(self):
+
+        class CustomWriter(list):
+            def write(self, text):
+                self.extend(text.split(b'\n'))
+
+        w = CustomWriter()
+        a = np.array([(1, 2), (3, 4)])
+        np.savetxt(w, a)
+        b = np.loadtxt(w)
+        assert_array_equal(a, b)
+
+    def test_unicode(self):
+        utf8 = b'\xcf\x96'.decode('UTF-8')
+        a = np.array([utf8], dtype=np.str_)
+        with tempdir() as tmpdir:
+            # set encoding as on windows it may not be unicode even on py3
+            np.savetxt(os.path.join(tmpdir, 'test.csv'), a, fmt=['%s'],
+                       encoding='UTF-8')
+
+    def test_unicode_roundtrip(self):
+        utf8 = b'\xcf\x96'.decode('UTF-8')
+        a = np.array([utf8], dtype=np.str_)
+        # our gz wrapper support encoding
+        suffixes = ['', '.gz']
+        if HAS_BZ2:
+            suffixes.append('.bz2')
+        if HAS_LZMA:
+            suffixes.extend(['.xz', '.lzma'])
+        with tempdir() as tmpdir:
+            for suffix in suffixes:
+                np.savetxt(os.path.join(tmpdir, 'test.csv' + suffix), a,
+                           fmt=['%s'], encoding='UTF-16-LE')
+                b = np.loadtxt(os.path.join(tmpdir, 'test.csv' + suffix),
+                               encoding='UTF-16-LE', dtype=np.str_)
+                assert_array_equal(a, b)
+
+    def test_unicode_bytestream(self):
+        utf8 = b'\xcf\x96'.decode('UTF-8')
+        a = np.array([utf8], dtype=np.str_)
+        s = BytesIO()
+        np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
+        s.seek(0)
+        assert_equal(s.read().decode('UTF-8'), utf8 + '\n')
+
+    def test_unicode_stringstream(self):
+        utf8 = b'\xcf\x96'.decode('UTF-8')
+        a = np.array([utf8], dtype=np.str_)
+        s = StringIO()
+        np.savetxt(s, a, fmt=['%s'], encoding='UTF-8')
+        s.seek(0)
+        assert_equal(s.read(), utf8 + '\n')
+
+    @pytest.mark.parametrize("iotype", [StringIO, BytesIO])
+    def test_unicode_and_bytes_fmt(self, iotype):
+        # string type of fmt should not matter, see also gh-4053
+        a = np.array([1.])
+        s = iotype()
+        np.savetxt(s, a, fmt="%f")
+        s.seek(0)
+        if iotype is StringIO:
+            assert_equal(s.read(), "%f\n" % 1.)
+        else:
+            assert_equal(s.read(), b"%f\n" % 1.)
+
+    @pytest.mark.skipif(sys.platform=='win32', reason="files>4GB may not work")
+    @pytest.mark.slow
+    @requires_memory(free_bytes=7e9)
+    def test_large_zip(self):
+        def check_large_zip(memoryerror_raised):
+            memoryerror_raised.value = False
+            try:
+                # The test takes at least 6GB of memory, writes a file larger
+                # than 4GB. This tests the ``allowZip64`` kwarg to ``zipfile``
+                test_data = np.asarray([np.random.rand(
+                                        np.random.randint(50,100),4)
+                                        for i in range(800000)], dtype=object)
+                with tempdir() as tmpdir:
+                    np.savez(os.path.join(tmpdir, 'test.npz'),
+                             test_data=test_data)
+            except MemoryError:
+                memoryerror_raised.value = True
+                raise
+        # run in a subprocess to ensure memory is released on PyPy, see gh-15775
+        # Use an object in shared memory to re-raise the MemoryError exception
+        # in our process if needed, see gh-16889
+        memoryerror_raised = Value(c_bool)
+
+        # Since Python 3.8, the default start method for multiprocessing has
+        # been changed from 'fork' to 'spawn' on macOS, causing inconsistency
+        # on memory sharing model, lead to failed test for check_large_zip
+        ctx = get_context('fork')
+        p = ctx.Process(target=check_large_zip, args=(memoryerror_raised,))
+        p.start()
+        p.join()
+        if memoryerror_raised.value:
+            raise MemoryError("Child process raised a MemoryError exception")
+        # -9 indicates a SIGKILL, probably an OOM.
+        if p.exitcode == -9:
+            pytest.xfail("subprocess got a SIGKILL, apparently free memory was not sufficient")
+        assert p.exitcode == 0
+
+class LoadTxtBase:
+    def check_compressed(self, fopen, suffixes):
+        # Test that we can load data from a compressed file
+        wanted = np.arange(6).reshape((2, 3))
+        linesep = ('\n', '\r\n', '\r')
+        for sep in linesep:
+            data = '0 1 2' + sep + '3 4 5'
+            for suffix in suffixes:
+                with temppath(suffix=suffix) as name:
+                    with fopen(name, mode='wt', encoding='UTF-32-LE') as f:
+                        f.write(data)
+                    res = self.loadfunc(name, encoding='UTF-32-LE')
+                    assert_array_equal(res, wanted)
+                    with fopen(name, "rt",  encoding='UTF-32-LE') as f:
+                        res = self.loadfunc(f)
+                    assert_array_equal(res, wanted)
+
+    def test_compressed_gzip(self):
+        self.check_compressed(gzip.open, ('.gz',))
+
+    @pytest.mark.skipif(not HAS_BZ2, reason="Needs bz2")
+    def test_compressed_bz2(self):
+        self.check_compressed(bz2.open, ('.bz2',))
+
+    @pytest.mark.skipif(not HAS_LZMA, reason="Needs lzma")
+    def test_compressed_lzma(self):
+        self.check_compressed(lzma.open, ('.xz', '.lzma'))
+
+    def test_encoding(self):
+        with temppath() as path:
+            with open(path, "wb") as f:
+                f.write('0.\n1.\n2.'.encode("UTF-16"))
+            x = self.loadfunc(path, encoding="UTF-16")
+            assert_array_equal(x, [0., 1., 2.])
+
+    def test_stringload(self):
+        # umlaute
+        nonascii = b'\xc3\xb6\xc3\xbc\xc3\xb6'.decode("UTF-8")
+        with temppath() as path:
+            with open(path, "wb") as f:
+                f.write(nonascii.encode("UTF-16"))
+            x = self.loadfunc(path, encoding="UTF-16", dtype=np.str_)
+            assert_array_equal(x, nonascii)
+
+    def test_binary_decode(self):
+        utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04'
+        v = self.loadfunc(BytesIO(utf16), dtype=np.str_, encoding='UTF-16')
+        assert_array_equal(v, np.array(utf16.decode('UTF-16').split()))
+
+    def test_converters_decode(self):
+        # test converters that decode strings
+        c = TextIO()
+        c.write(b'\xcf\x96')
+        c.seek(0)
+        x = self.loadfunc(c, dtype=np.str_, encoding="bytes",
+                          converters={0: lambda x: x.decode('UTF-8')})
+        a = np.array([b'\xcf\x96'.decode('UTF-8')])
+        assert_array_equal(x, a)
+
+    def test_converters_nodecode(self):
+        # test native string converters enabled by setting an encoding
+        utf8 = b'\xcf\x96'.decode('UTF-8')
+        with temppath() as path:
+            with open(path, 'wt', encoding='UTF-8') as f:
+                f.write(utf8)
+            x = self.loadfunc(path, dtype=np.str_,
+                              converters={0: lambda x: x + 't'},
+                              encoding='UTF-8')
+            a = np.array([utf8 + 't'])
+            assert_array_equal(x, a)
+
+
+class TestLoadTxt(LoadTxtBase):
+    loadfunc = staticmethod(np.loadtxt)
+
+    def setup_method(self):
+        # lower chunksize for testing
+        self.orig_chunk = _npyio_impl._loadtxt_chunksize
+        _npyio_impl._loadtxt_chunksize = 1
+
+    def teardown_method(self):
+        _npyio_impl._loadtxt_chunksize = self.orig_chunk
+
+    def test_record(self):
+        c = TextIO()
+        c.write('1 2\n3 4')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=[('x', np.int32), ('y', np.int32)])
+        a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+        assert_array_equal(x, a)
+
+        d = TextIO()
+        d.write('M 64 75.0\nF 25 60.0')
+        d.seek(0)
+        mydescriptor = {'names': ('gender', 'age', 'weight'),
+                        'formats': ('S1', 'i4', 'f4')}
+        b = np.array([('M', 64.0, 75.0),
+                      ('F', 25.0, 60.0)], dtype=mydescriptor)
+        y = np.loadtxt(d, dtype=mydescriptor)
+        assert_array_equal(y, b)
+
+    def test_array(self):
+        c = TextIO()
+        c.write('1 2\n3 4')
+
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int)
+        a = np.array([[1, 2], [3, 4]], int)
+        assert_array_equal(x, a)
+
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float)
+        a = np.array([[1, 2], [3, 4]], float)
+        assert_array_equal(x, a)
+
+    def test_1D(self):
+        c = TextIO()
+        c.write('1\n2\n3\n4\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int)
+        a = np.array([1, 2, 3, 4], int)
+        assert_array_equal(x, a)
+
+        c = TextIO()
+        c.write('1,2,3,4\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',')
+        a = np.array([1, 2, 3, 4], int)
+        assert_array_equal(x, a)
+
+    def test_missing(self):
+        c = TextIO()
+        c.write('1,2,3,,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       converters={3: lambda s: int(s or - 999)})
+        a = np.array([1, 2, 3, -999, 5], int)
+        assert_array_equal(x, a)
+
+    def test_converters_with_usecols(self):
+        c = TextIO()
+        c.write('1,2,3,,5\n6,7,8,9,10\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       converters={3: lambda s: int(s or - 999)},
+                       usecols=(1, 3,))
+        a = np.array([[2, -999], [7, 9]], int)
+        assert_array_equal(x, a)
+
+    def test_comments_unicode(self):
+        c = TextIO()
+        c.write('# comment\n1,2,3,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       comments='#')
+        a = np.array([1, 2, 3, 5], int)
+        assert_array_equal(x, a)
+
+    def test_comments_byte(self):
+        c = TextIO()
+        c.write('# comment\n1,2,3,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       comments=b'#')
+        a = np.array([1, 2, 3, 5], int)
+        assert_array_equal(x, a)
+
+    def test_comments_multiple(self):
+        c = TextIO()
+        c.write('# comment\n1,2,3\n@ comment2\n4,5,6 // comment3')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       comments=['#', '@', '//'])
+        a = np.array([[1, 2, 3], [4, 5, 6]], int)
+        assert_array_equal(x, a)
+
+    @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                        reason="PyPy bug in error formatting")
+    def test_comments_multi_chars(self):
+        c = TextIO()
+        c.write('/* comment\n1,2,3,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       comments='/*')
+        a = np.array([1, 2, 3, 5], int)
+        assert_array_equal(x, a)
+
+        # Check that '/*' is not transformed to ['/', '*']
+        c = TextIO()
+        c.write('*/ comment\n1,2,3,5\n')
+        c.seek(0)
+        assert_raises(ValueError, np.loadtxt, c, dtype=int, delimiter=',',
+                      comments='/*')
+
+    def test_skiprows(self):
+        c = TextIO()
+        c.write('comment\n1,2,3,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       skiprows=1)
+        a = np.array([1, 2, 3, 5], int)
+        assert_array_equal(x, a)
+
+        c = TextIO()
+        c.write('# comment\n1,2,3,5\n')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       skiprows=1)
+        a = np.array([1, 2, 3, 5], int)
+        assert_array_equal(x, a)
+
+    def test_usecols(self):
+        a = np.array([[1, 2], [3, 4]], float)
+        c = BytesIO()
+        np.savetxt(c, a)
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float, usecols=(1,))
+        assert_array_equal(x, a[:, 1])
+
+        a = np.array([[1, 2, 3], [3, 4, 5]], float)
+        c = BytesIO()
+        np.savetxt(c, a)
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float, usecols=(1, 2))
+        assert_array_equal(x, a[:, 1:])
+
+        # Testing with arrays instead of tuples.
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float, usecols=np.array([1, 2]))
+        assert_array_equal(x, a[:, 1:])
+
+        # Testing with an integer instead of a sequence
+        for int_type in [int, np.int8, np.int16,
+                         np.int32, np.int64, np.uint8, np.uint16,
+                         np.uint32, np.uint64]:
+            to_read = int_type(1)
+            c.seek(0)
+            x = np.loadtxt(c, dtype=float, usecols=to_read)
+            assert_array_equal(x, a[:, 1])
+
+        # Testing with some crazy custom integer type
+        class CrazyInt:
+            def __index__(self):
+                return 1
+
+        crazy_int = CrazyInt()
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float, usecols=crazy_int)
+        assert_array_equal(x, a[:, 1])
+
+        c.seek(0)
+        x = np.loadtxt(c, dtype=float, usecols=(crazy_int,))
+        assert_array_equal(x, a[:, 1])
+
+        # Checking with dtypes defined converters.
+        data = '''JOE 70.1 25.3
+                BOB 60.5 27.9
+                '''
+        c = TextIO(data)
+        names = ['stid', 'temp']
+        dtypes = ['S4', 'f8']
+        arr = np.loadtxt(c, usecols=(0, 2), dtype=list(zip(names, dtypes)))
+        assert_equal(arr['stid'], [b"JOE", b"BOB"])
+        assert_equal(arr['temp'], [25.3, 27.9])
+
+        # Testing non-ints in usecols
+        c.seek(0)
+        bogus_idx = 1.5
+        assert_raises_regex(
+            TypeError,
+            '^usecols must be.*%s' % type(bogus_idx).__name__,
+            np.loadtxt, c, usecols=bogus_idx
+            )
+
+        assert_raises_regex(
+            TypeError,
+            '^usecols must be.*%s' % type(bogus_idx).__name__,
+            np.loadtxt, c, usecols=[0, bogus_idx, 0]
+            )
+
+    def test_bad_usecols(self):
+        with pytest.raises(OverflowError):
+            np.loadtxt(["1\n"], usecols=[2**64], delimiter=",")
+        with pytest.raises((ValueError, OverflowError)):
+            # Overflow error on 32bit platforms
+            np.loadtxt(["1\n"], usecols=[2**62], delimiter=",")
+        with pytest.raises(TypeError,
+                match="If a structured dtype .*. But 1 usecols were given and "
+                      "the number of fields is 3."):
+            np.loadtxt(["1,1\n"], dtype="i,2i", usecols=[0], delimiter=",")
+
+    def test_fancy_dtype(self):
+        c = TextIO()
+        c.write('1,2,3.0\n4,5,6.0\n')
+        c.seek(0)
+        dt = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
+        x = np.loadtxt(c, dtype=dt, delimiter=',')
+        a = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dt)
+        assert_array_equal(x, a)
+
+    def test_shaped_dtype(self):
+        c = TextIO("aaaa  1.0  8.0  1 2 3 4 5 6")
+        dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
+                       ('block', int, (2, 3))])
+        x = np.loadtxt(c, dtype=dt)
+        a = np.array([('aaaa', 1.0, 8.0, [[1, 2, 3], [4, 5, 6]])],
+                     dtype=dt)
+        assert_array_equal(x, a)
+
+    def test_3d_shaped_dtype(self):
+        c = TextIO("aaaa  1.0  8.0  1 2 3 4 5 6 7 8 9 10 11 12")
+        dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
+                       ('block', int, (2, 2, 3))])
+        x = np.loadtxt(c, dtype=dt)
+        a = np.array([('aaaa', 1.0, 8.0,
+                       [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])],
+                     dtype=dt)
+        assert_array_equal(x, a)
+
+    def test_str_dtype(self):
+        # see gh-8033
+        c = ["str1", "str2"]
+
+        for dt in (str, np.bytes_):
+            a = np.array(["str1", "str2"], dtype=dt)
+            x = np.loadtxt(c, dtype=dt)
+            assert_array_equal(x, a)
+
+    def test_empty_file(self):
+        with pytest.warns(UserWarning, match="input contained no data"):
+            c = TextIO()
+            x = np.loadtxt(c)
+            assert_equal(x.shape, (0,))
+            x = np.loadtxt(c, dtype=np.int64)
+            assert_equal(x.shape, (0,))
+            assert_(x.dtype == np.int64)
+
+    def test_unused_converter(self):
+        c = TextIO()
+        c.writelines(['1 21\n', '3 42\n'])
+        c.seek(0)
+        data = np.loadtxt(c, usecols=(1,),
+                          converters={0: lambda s: int(s, 16)})
+        assert_array_equal(data, [21, 42])
+
+        c.seek(0)
+        data = np.loadtxt(c, usecols=(1,),
+                          converters={1: lambda s: int(s, 16)})
+        assert_array_equal(data, [33, 66])
+
+    def test_dtype_with_object(self):
+        # Test using an explicit dtype with an object
+        data = """ 1; 2001-01-01
+                   2; 2002-01-31 """
+        ndtype = [('idx', int), ('code', object)]
+        func = lambda s: strptime(s.strip(), "%Y-%m-%d")
+        converters = {1: func}
+        test = np.loadtxt(TextIO(data), delimiter=";", dtype=ndtype,
+                          converters=converters)
+        control = np.array(
+            [(1, datetime(2001, 1, 1)), (2, datetime(2002, 1, 31))],
+            dtype=ndtype)
+        assert_equal(test, control)
+
+    def test_uint64_type(self):
+        tgt = (9223372043271415339, 9223372043271415853)
+        c = TextIO()
+        c.write("%s %s" % tgt)
+        c.seek(0)
+        res = np.loadtxt(c, dtype=np.uint64)
+        assert_equal(res, tgt)
+
+    def test_int64_type(self):
+        tgt = (-9223372036854775807, 9223372036854775807)
+        c = TextIO()
+        c.write("%s %s" % tgt)
+        c.seek(0)
+        res = np.loadtxt(c, dtype=np.int64)
+        assert_equal(res, tgt)
+
+    def test_from_float_hex(self):
+        # IEEE doubles and floats only, otherwise the float32
+        # conversion may fail.
+        tgt = np.logspace(-10, 10, 5).astype(np.float32)
+        tgt = np.hstack((tgt, -tgt)).astype(float)
+        inp = '\n'.join(map(float.hex, tgt))
+        c = TextIO()
+        c.write(inp)
+        for dt in [float, np.float32]:
+            c.seek(0)
+            res = np.loadtxt(
+                c, dtype=dt, converters=float.fromhex, encoding="latin1")
+            assert_equal(res, tgt, err_msg="%s" % dt)
+
+    @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                        reason="PyPy bug in error formatting")
+    def test_default_float_converter_no_default_hex_conversion(self):
+        """
+        Ensure that fromhex is only used for values with the correct prefix and
+        is not called by default. Regression test related to gh-19598.
+        """
+        c = TextIO("a b c")
+        with pytest.raises(ValueError,
+                match=".*convert string 'a' to float64 at row 0, column 1"):
+            np.loadtxt(c)
+
+    @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                        reason="PyPy bug in error formatting")
+    def test_default_float_converter_exception(self):
+        """
+        Ensure that the exception message raised during failed floating point
+        conversion is correct. Regression test related to gh-19598.
+        """
+        c = TextIO("qrs tuv")  # Invalid values for default float converter
+        with pytest.raises(ValueError,
+                match="could not convert string 'qrs' to float64"):
+            np.loadtxt(c)
+
+    def test_from_complex(self):
+        tgt = (complex(1, 1), complex(1, -1))
+        c = TextIO()
+        c.write("%s %s" % tgt)
+        c.seek(0)
+        res = np.loadtxt(c, dtype=complex)
+        assert_equal(res, tgt)
+
+    def test_complex_misformatted(self):
+        # test for backward compatibility
+        # some complex formats used to generate x+-yj
+        a = np.zeros((2, 2), dtype=np.complex128)
+        re = np.pi
+        im = np.e
+        a[:] = re - 1.0j * im
+        c = BytesIO()
+        np.savetxt(c, a, fmt='%.16e')
+        c.seek(0)
+        txt = c.read()
+        c.seek(0)
+        # misformat the sign on the imaginary part, gh 7895
+        txt_bad = txt.replace(b'e+00-', b'e00+-')
+        assert_(txt_bad != txt)
+        c.write(txt_bad)
+        c.seek(0)
+        res = np.loadtxt(c, dtype=complex)
+        assert_equal(res, a)
+
+    def test_universal_newline(self):
+        with temppath() as name:
+            with open(name, 'w') as f:
+                f.write('1 21\r3 42\r')
+            data = np.loadtxt(name)
+        assert_array_equal(data, [[1, 21], [3, 42]])
+
+    def test_empty_field_after_tab(self):
+        c = TextIO()
+        c.write('1 \t2 \t3\tstart \n4\t5\t6\t  \n7\t8\t9.5\t')
+        c.seek(0)
+        dt = {'names': ('x', 'y', 'z', 'comment'),
+              'formats': (' num rows
+        c = TextIO()
+        c.write('comment\n1,2,3,5\n4,5,7,8\n2,1,4,5')
+        c.seek(0)
+        x = np.loadtxt(c, dtype=int, delimiter=',',
+                       skiprows=1, max_rows=6)
+        a = np.array([[1, 2, 3, 5], [4, 5, 7, 8], [2, 1, 4, 5]], int)
+        assert_array_equal(x, a)
+
+    @pytest.mark.parametrize(["skip", "data"], [
+            (1, ["ignored\n", "1,2\n", "\n", "3,4\n"]),
+            # "Bad" lines that do not end in newlines:
+            (1, ["ignored", "1,2", "", "3,4"]),
+            (1, StringIO("ignored\n1,2\n\n3,4")),
+            # Same as above, but do not skip any lines:
+            (0, ["-1,0\n", "1,2\n", "\n", "3,4\n"]),
+            (0, ["-1,0", "1,2", "", "3,4"]),
+            (0, StringIO("-1,0\n1,2\n\n3,4"))])
+    def test_max_rows_empty_lines(self, skip, data):
+        with pytest.warns(UserWarning,
+                    match=f"Input line 3.*max_rows={3-skip}"):
+            res = np.loadtxt(data, dtype=int, skiprows=skip, delimiter=",",
+                             max_rows=3-skip)
+            assert_array_equal(res, [[-1, 0], [1, 2], [3, 4]][skip:])
+
+        if isinstance(data, StringIO):
+            data.seek(0)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", UserWarning)
+            with pytest.raises(UserWarning):
+                np.loadtxt(data, dtype=int, skiprows=skip, delimiter=",",
+                           max_rows=3-skip)
+
+class Testfromregex:
+    def test_record(self):
+        c = TextIO()
+        c.write('1.312 foo\n1.534 bar\n4.444 qux')
+        c.seek(0)
+
+        dt = [('num', np.float64), ('val', 'S3')]
+        x = np.fromregex(c, r"([0-9.]+)\s+(...)", dt)
+        a = np.array([(1.312, 'foo'), (1.534, 'bar'), (4.444, 'qux')],
+                     dtype=dt)
+        assert_array_equal(x, a)
+
+    def test_record_2(self):
+        c = TextIO()
+        c.write('1312 foo\n1534 bar\n4444 qux')
+        c.seek(0)
+
+        dt = [('num', np.int32), ('val', 'S3')]
+        x = np.fromregex(c, r"(\d+)\s+(...)", dt)
+        a = np.array([(1312, 'foo'), (1534, 'bar'), (4444, 'qux')],
+                     dtype=dt)
+        assert_array_equal(x, a)
+
+    def test_record_3(self):
+        c = TextIO()
+        c.write('1312 foo\n1534 bar\n4444 qux')
+        c.seek(0)
+
+        dt = [('num', np.float64)]
+        x = np.fromregex(c, r"(\d+)\s+...", dt)
+        a = np.array([(1312,), (1534,), (4444,)], dtype=dt)
+        assert_array_equal(x, a)
+
+    @pytest.mark.parametrize("path_type", [str, Path])
+    def test_record_unicode(self, path_type):
+        utf8 = b'\xcf\x96'
+        with temppath() as str_path:
+            path = path_type(str_path)
+            with open(path, 'wb') as f:
+                f.write(b'1.312 foo' + utf8 + b' \n1.534 bar\n4.444 qux')
+
+            dt = [('num', np.float64), ('val', 'U4')]
+            x = np.fromregex(path, r"(?u)([0-9.]+)\s+(\w+)", dt, encoding='UTF-8')
+            a = np.array([(1.312, 'foo' + utf8.decode('UTF-8')), (1.534, 'bar'),
+                           (4.444, 'qux')], dtype=dt)
+            assert_array_equal(x, a)
+
+            regexp = re.compile(r"([0-9.]+)\s+(\w+)", re.UNICODE)
+            x = np.fromregex(path, regexp, dt, encoding='UTF-8')
+            assert_array_equal(x, a)
+
+    def test_compiled_bytes(self):
+        regexp = re.compile(b'(\\d)')
+        c = BytesIO(b'123')
+        dt = [('num', np.float64)]
+        a = np.array([1, 2, 3], dtype=dt)
+        x = np.fromregex(c, regexp, dt)
+        assert_array_equal(x, a)
+
+    def test_bad_dtype_not_structured(self):
+        regexp = re.compile(b'(\\d)')
+        c = BytesIO(b'123')
+        with pytest.raises(TypeError, match='structured datatype'):
+            np.fromregex(c, regexp, dtype=np.float64)
+
+
+#####--------------------------------------------------------------------------
+
+
+class TestFromTxt(LoadTxtBase):
+    loadfunc = staticmethod(np.genfromtxt)
+
+    def test_record(self):
+        # Test w/ explicit dtype
+        data = TextIO('1 2\n3 4')
+        test = np.genfromtxt(data, dtype=[('x', np.int32), ('y', np.int32)])
+        control = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
+        assert_equal(test, control)
+        #
+        data = TextIO('M 64.0 75.0\nF 25.0 60.0')
+        descriptor = {'names': ('gender', 'age', 'weight'),
+                      'formats': ('S1', 'i4', 'f4')}
+        control = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)],
+                           dtype=descriptor)
+        test = np.genfromtxt(data, dtype=descriptor)
+        assert_equal(test, control)
+
+    def test_array(self):
+        # Test outputting a standard ndarray
+        data = TextIO('1 2\n3 4')
+        control = np.array([[1, 2], [3, 4]], dtype=int)
+        test = np.genfromtxt(data, dtype=int)
+        assert_array_equal(test, control)
+        #
+        data.seek(0)
+        control = np.array([[1, 2], [3, 4]], dtype=float)
+        test = np.loadtxt(data, dtype=float)
+        assert_array_equal(test, control)
+
+    def test_1D(self):
+        # Test squeezing to 1D
+        control = np.array([1, 2, 3, 4], int)
+        #
+        data = TextIO('1\n2\n3\n4\n')
+        test = np.genfromtxt(data, dtype=int)
+        assert_array_equal(test, control)
+        #
+        data = TextIO('1,2,3,4\n')
+        test = np.genfromtxt(data, dtype=int, delimiter=',')
+        assert_array_equal(test, control)
+
+    def test_comments(self):
+        # Test the stripping of comments
+        control = np.array([1, 2, 3, 5], int)
+        # Comment on its own line
+        data = TextIO('# comment\n1,2,3,5\n')
+        test = np.genfromtxt(data, dtype=int, delimiter=',', comments='#')
+        assert_equal(test, control)
+        # Comment at the end of a line
+        data = TextIO('1,2,3,5# comment\n')
+        test = np.genfromtxt(data, dtype=int, delimiter=',', comments='#')
+        assert_equal(test, control)
+
+    def test_skiprows(self):
+        # Test row skipping
+        control = np.array([1, 2, 3, 5], int)
+        kwargs = dict(dtype=int, delimiter=',')
+        #
+        data = TextIO('comment\n1,2,3,5\n')
+        test = np.genfromtxt(data, skip_header=1, **kwargs)
+        assert_equal(test, control)
+        #
+        data = TextIO('# comment\n1,2,3,5\n')
+        test = np.loadtxt(data, skiprows=1, **kwargs)
+        assert_equal(test, control)
+
+    def test_skip_footer(self):
+        data = ["# %i" % i for i in range(1, 6)]
+        data.append("A, B, C")
+        data.extend(["%i,%3.1f,%03s" % (i, i, i) for i in range(51)])
+        data[-1] = "99,99"
+        kwargs = dict(delimiter=",", names=True, skip_header=5, skip_footer=10)
+        test = np.genfromtxt(TextIO("\n".join(data)), **kwargs)
+        ctrl = np.array([("%f" % i, "%f" % i, "%f" % i) for i in range(41)],
+                        dtype=[(_, float) for _ in "ABC"])
+        assert_equal(test, ctrl)
+
+    def test_skip_footer_with_invalid(self):
+        with suppress_warnings() as sup:
+            sup.filter(ConversionWarning)
+            basestr = '1 1\n2 2\n3 3\n4 4\n5  \n6  \n7  \n'
+            # Footer too small to get rid of all invalid values
+            assert_raises(ValueError, np.genfromtxt,
+                          TextIO(basestr), skip_footer=1)
+    #        except ValueError:
+    #            pass
+            a = np.genfromtxt(
+                TextIO(basestr), skip_footer=1, invalid_raise=False)
+            assert_equal(a, np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]))
+            #
+            a = np.genfromtxt(TextIO(basestr), skip_footer=3)
+            assert_equal(a, np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]))
+            #
+            basestr = '1 1\n2  \n3 3\n4 4\n5  \n6 6\n7 7\n'
+            a = np.genfromtxt(
+                TextIO(basestr), skip_footer=1, invalid_raise=False)
+            assert_equal(a, np.array([[1., 1.], [3., 3.], [4., 4.], [6., 6.]]))
+            a = np.genfromtxt(
+                TextIO(basestr), skip_footer=3, invalid_raise=False)
+            assert_equal(a, np.array([[1., 1.], [3., 3.], [4., 4.]]))
+
+    def test_header(self):
+        # Test retrieving a header
+        data = TextIO('gender age weight\nM 64.0 75.0\nF 25.0 60.0')
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, dtype=None, names=True,
+                                 encoding='bytes')
+            assert_(w[0].category is VisibleDeprecationWarning)
+        control = {'gender': np.array([b'M', b'F']),
+                   'age': np.array([64.0, 25.0]),
+                   'weight': np.array([75.0, 60.0])}
+        assert_equal(test['gender'], control['gender'])
+        assert_equal(test['age'], control['age'])
+        assert_equal(test['weight'], control['weight'])
+
+    def test_auto_dtype(self):
+        # Test the automatic definition of the output dtype
+        data = TextIO('A 64 75.0 3+4j True\nBCD 25 60.0 5+6j False')
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, dtype=None, encoding='bytes')
+            assert_(w[0].category is VisibleDeprecationWarning)
+        control = [np.array([b'A', b'BCD']),
+                   np.array([64, 25]),
+                   np.array([75.0, 60.0]),
+                   np.array([3 + 4j, 5 + 6j]),
+                   np.array([True, False]), ]
+        assert_equal(test.dtype.names, ['f0', 'f1', 'f2', 'f3', 'f4'])
+        for (i, ctrl) in enumerate(control):
+            assert_equal(test['f%i' % i], ctrl)
+
+    def test_auto_dtype_uniform(self):
+        # Tests whether the output dtype can be uniformized
+        data = TextIO('1 2 3 4\n5 6 7 8\n')
+        test = np.genfromtxt(data, dtype=None)
+        control = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
+        assert_equal(test, control)
+
+    def test_fancy_dtype(self):
+        # Check that a nested dtype isn't MIA
+        data = TextIO('1,2,3.0\n4,5,6.0\n')
+        fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
+        test = np.genfromtxt(data, dtype=fancydtype, delimiter=',')
+        control = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype)
+        assert_equal(test, control)
+
+    def test_names_overwrite(self):
+        # Test overwriting the names of the dtype
+        descriptor = {'names': ('g', 'a', 'w'),
+                      'formats': ('S1', 'i4', 'f4')}
+        data = TextIO(b'M 64.0 75.0\nF 25.0 60.0')
+        names = ('gender', 'age', 'weight')
+        test = np.genfromtxt(data, dtype=descriptor, names=names)
+        descriptor['names'] = names
+        control = np.array([('M', 64.0, 75.0),
+                            ('F', 25.0, 60.0)], dtype=descriptor)
+        assert_equal(test, control)
+
+    def test_bad_fname(self):
+        with pytest.raises(TypeError, match='fname must be a string,'):
+            np.genfromtxt(123)
+
+    def test_commented_header(self):
+        # Check that names can be retrieved even if the line is commented out.
+        data = TextIO("""
+#gender age weight
+M   21  72.100000
+F   35  58.330000
+M   33  21.99
+        """)
+        # The # is part of the first name and should be deleted automatically.
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, names=True, dtype=None,
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        ctrl = np.array([('M', 21, 72.1), ('F', 35, 58.33), ('M', 33, 21.99)],
+                        dtype=[('gender', '|S1'), ('age', int), ('weight', float)])
+        assert_equal(test, ctrl)
+        # Ditto, but we should get rid of the first element
+        data = TextIO(b"""
+# gender age weight
+M   21  72.100000
+F   35  58.330000
+M   33  21.99
+        """)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, names=True, dtype=None,
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        assert_equal(test, ctrl)
+
+    def test_names_and_comments_none(self):
+        # Tests case when names is true but comments is None (gh-10780)
+        data = TextIO('col1 col2\n 1 2\n 3 4')
+        test = np.genfromtxt(data, dtype=(int, int), comments=None, names=True)
+        control = np.array([(1, 2), (3, 4)], dtype=[('col1', int), ('col2', int)])
+        assert_equal(test, control)
+
+    def test_file_is_closed_on_error(self):
+        # gh-13200
+        with tempdir() as tmpdir:
+            fpath = os.path.join(tmpdir, "test.csv")
+            with open(fpath, "wb") as f:
+                f.write('\N{GREEK PI SYMBOL}'.encode())
+
+            # ResourceWarnings are emitted from a destructor, so won't be
+            # detected by regular propagation to errors.
+            with assert_no_warnings():
+                with pytest.raises(UnicodeDecodeError):
+                    np.genfromtxt(fpath, encoding="ascii")
+
+    def test_autonames_and_usecols(self):
+        # Tests names and usecols
+        data = TextIO('A B C D\n aaaa 121 45 9.1')
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, usecols=('A', 'C', 'D'),
+                                names=True, dtype=None, encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        control = np.array(('aaaa', 45, 9.1),
+                           dtype=[('A', '|S4'), ('C', int), ('D', float)])
+        assert_equal(test, control)
+
+    def test_converters_with_usecols(self):
+        # Test the combination user-defined converters and usecol
+        data = TextIO('1,2,3,,5\n6,7,8,9,10\n')
+        test = np.genfromtxt(data, dtype=int, delimiter=',',
+                            converters={3: lambda s: int(s or - 999)},
+                            usecols=(1, 3,))
+        control = np.array([[2, -999], [7, 9]], int)
+        assert_equal(test, control)
+
+    def test_converters_with_usecols_and_names(self):
+        # Tests names and usecols
+        data = TextIO('A B C D\n aaaa 121 45 9.1')
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(data, usecols=('A', 'C', 'D'), names=True,
+                                dtype=None, encoding="bytes",
+                                converters={'C': lambda s: 2 * int(s)})
+            assert_(w[0].category is VisibleDeprecationWarning)
+        control = np.array(('aaaa', 90, 9.1),
+                           dtype=[('A', '|S4'), ('C', int), ('D', float)])
+        assert_equal(test, control)
+
+    def test_converters_cornercases(self):
+        # Test the conversion to datetime.
+        converter = {
+            'date': lambda s: strptime(s, '%Y-%m-%d %H:%M:%SZ')}
+        data = TextIO('2009-02-03 12:00:00Z, 72214.0')
+        test = np.genfromtxt(data, delimiter=',', dtype=None,
+                            names=['date', 'stid'], converters=converter)
+        control = np.array((datetime(2009, 2, 3), 72214.),
+                           dtype=[('date', np.object_), ('stid', float)])
+        assert_equal(test, control)
+
+    def test_converters_cornercases2(self):
+        # Test the conversion to datetime64.
+        converter = {
+            'date': lambda s: np.datetime64(strptime(s, '%Y-%m-%d %H:%M:%SZ'))}
+        data = TextIO('2009-02-03 12:00:00Z, 72214.0')
+        test = np.genfromtxt(data, delimiter=',', dtype=None,
+                            names=['date', 'stid'], converters=converter)
+        control = np.array((datetime(2009, 2, 3), 72214.),
+                           dtype=[('date', 'datetime64[us]'), ('stid', float)])
+        assert_equal(test, control)
+
+    def test_unused_converter(self):
+        # Test whether unused converters are forgotten
+        data = TextIO("1 21\n  3 42\n")
+        test = np.genfromtxt(data, usecols=(1,),
+                            converters={0: lambda s: int(s, 16)})
+        assert_equal(test, [21, 42])
+        #
+        data.seek(0)
+        test = np.genfromtxt(data, usecols=(1,),
+                            converters={1: lambda s: int(s, 16)})
+        assert_equal(test, [33, 66])
+
+    def test_invalid_converter(self):
+        strip_rand = lambda x: float((b'r' in x.lower() and x.split()[-1]) or
+                                     (b'r' not in x.lower() and x.strip() or 0.0))
+        strip_per = lambda x: float((b'%' in x.lower() and x.split()[0]) or
+                                    (b'%' not in x.lower() and x.strip() or 0.0))
+        s = TextIO("D01N01,10/1/2003 ,1 %,R 75,400,600\r\n"
+                   "L24U05,12/5/2003, 2 %,1,300, 150.5\r\n"
+                   "D02N03,10/10/2004,R 1,,7,145.55")
+        kwargs = dict(
+            converters={2: strip_per, 3: strip_rand}, delimiter=",",
+            dtype=None, encoding="bytes")
+        assert_raises(ConverterError, np.genfromtxt, s, **kwargs)
+
+    def test_tricky_converter_bug1666(self):
+        # Test some corner cases
+        s = TextIO('q1,2\nq3,4')
+        cnv = lambda s: float(s[1:])
+        test = np.genfromtxt(s, delimiter=',', converters={0: cnv})
+        control = np.array([[1., 2.], [3., 4.]])
+        assert_equal(test, control)
+
+    def test_dtype_with_converters(self):
+        dstr = "2009; 23; 46"
+        test = np.genfromtxt(TextIO(dstr,),
+                            delimiter=";", dtype=float, converters={0: bytes})
+        control = np.array([('2009', 23., 46)],
+                           dtype=[('f0', '|S4'), ('f1', float), ('f2', float)])
+        assert_equal(test, control)
+        test = np.genfromtxt(TextIO(dstr,),
+                            delimiter=";", dtype=float, converters={0: float})
+        control = np.array([2009., 23., 46],)
+        assert_equal(test, control)
+
+    @pytest.mark.filterwarnings("ignore:.*recfromcsv.*:DeprecationWarning")
+    def test_dtype_with_converters_and_usecols(self):
+        dstr = "1,5,-1,1:1\n2,8,-1,1:n\n3,3,-2,m:n\n"
+        dmap = {'1:1':0, '1:n':1, 'm:1':2, 'm:n':3}
+        dtyp = [('e1','i4'),('e2','i4'),('e3','i2'),('n', 'i1')]
+        conv = {0: int, 1: int, 2: int, 3: lambda r: dmap[r.decode()]}
+        test = recfromcsv(TextIO(dstr,), dtype=dtyp, delimiter=',',
+                          names=None, converters=conv, encoding="bytes")
+        control = np.rec.array([(1,5,-1,0), (2,8,-1,1), (3,3,-2,3)], dtype=dtyp)
+        assert_equal(test, control)
+        dtyp = [('e1', 'i4'), ('e2', 'i4'), ('n', 'i1')]
+        test = recfromcsv(TextIO(dstr,), dtype=dtyp, delimiter=',',
+                          usecols=(0, 1, 3), names=None, converters=conv,
+                          encoding="bytes")
+        control = np.rec.array([(1,5,0), (2,8,1), (3,3,3)], dtype=dtyp)
+        assert_equal(test, control)
+
+    def test_dtype_with_object(self):
+        # Test using an explicit dtype with an object
+        data = """ 1; 2001-01-01
+                   2; 2002-01-31 """
+        ndtype = [('idx', int), ('code', object)]
+        func = lambda s: strptime(s.strip(), "%Y-%m-%d")
+        converters = {1: func}
+        test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype,
+                             converters=converters)
+        control = np.array(
+            [(1, datetime(2001, 1, 1)), (2, datetime(2002, 1, 31))],
+            dtype=ndtype)
+        assert_equal(test, control)
+
+        ndtype = [('nest', [('idx', int), ('code', object)])]
+        with assert_raises_regex(NotImplementedError,
+                                 'Nested fields.* not supported.*'):
+            test = np.genfromtxt(TextIO(data), delimiter=";",
+                                 dtype=ndtype, converters=converters)
+
+        # nested but empty fields also aren't supported
+        ndtype = [('idx', int), ('code', object), ('nest', [])]
+        with assert_raises_regex(NotImplementedError,
+                                 'Nested fields.* not supported.*'):
+            test = np.genfromtxt(TextIO(data), delimiter=";",
+                                 dtype=ndtype, converters=converters)
+
+    def test_dtype_with_object_no_converter(self):
+        # Object without a converter uses bytes:
+        parsed = np.genfromtxt(TextIO("1"), dtype=object)
+        assert parsed[()] == b"1"
+        parsed = np.genfromtxt(TextIO("string"), dtype=object)
+        assert parsed[()] == b"string"
+
+    def test_userconverters_with_explicit_dtype(self):
+        # Test user_converters w/ explicit (standard) dtype
+        data = TextIO('skip,skip,2001-01-01,1.0,skip')
+        test = np.genfromtxt(data, delimiter=",", names=None, dtype=float,
+                             usecols=(2, 3), converters={2: bytes})
+        control = np.array([('2001-01-01', 1.)],
+                           dtype=[('', '|S10'), ('', float)])
+        assert_equal(test, control)
+
+    def test_utf8_userconverters_with_explicit_dtype(self):
+        utf8 = b'\xcf\x96'
+        with temppath() as path:
+            with open(path, 'wb') as f:
+                f.write(b'skip,skip,2001-01-01' + utf8 + b',1.0,skip')
+            test = np.genfromtxt(path, delimiter=",", names=None, dtype=float,
+                                 usecols=(2, 3), converters={2: str},
+                                 encoding='UTF-8')
+        control = np.array([('2001-01-01' + utf8.decode('UTF-8'), 1.)],
+                           dtype=[('', '|U11'), ('', float)])
+        assert_equal(test, control)
+
+    def test_spacedelimiter(self):
+        # Test space delimiter
+        data = TextIO("1  2  3  4   5\n6  7  8  9  10")
+        test = np.genfromtxt(data)
+        control = np.array([[1., 2., 3., 4., 5.],
+                            [6., 7., 8., 9., 10.]])
+        assert_equal(test, control)
+
+    def test_integer_delimiter(self):
+        # Test using an integer for delimiter
+        data = "  1  2  3\n  4  5 67\n890123  4"
+        test = np.genfromtxt(TextIO(data), delimiter=3)
+        control = np.array([[1, 2, 3], [4, 5, 67], [890, 123, 4]])
+        assert_equal(test, control)
+
+    def test_missing(self):
+        data = TextIO('1,2,3,,5\n')
+        test = np.genfromtxt(data, dtype=int, delimiter=',',
+                            converters={3: lambda s: int(s or - 999)})
+        control = np.array([1, 2, 3, -999, 5], int)
+        assert_equal(test, control)
+
+    def test_missing_with_tabs(self):
+        # Test w/ a delimiter tab
+        txt = "1\t2\t3\n\t2\t\n1\t\t3"
+        test = np.genfromtxt(TextIO(txt), delimiter="\t",
+                             usemask=True,)
+        ctrl_d = np.array([(1, 2, 3), (np.nan, 2, np.nan), (1, np.nan, 3)],)
+        ctrl_m = np.array([(0, 0, 0), (1, 0, 1), (0, 1, 0)], dtype=bool)
+        assert_equal(test.data, ctrl_d)
+        assert_equal(test.mask, ctrl_m)
+
+    def test_usecols(self):
+        # Test the selection of columns
+        # Select 1 column
+        control = np.array([[1, 2], [3, 4]], float)
+        data = TextIO()
+        np.savetxt(data, control)
+        data.seek(0)
+        test = np.genfromtxt(data, dtype=float, usecols=(1,))
+        assert_equal(test, control[:, 1])
+        #
+        control = np.array([[1, 2, 3], [3, 4, 5]], float)
+        data = TextIO()
+        np.savetxt(data, control)
+        data.seek(0)
+        test = np.genfromtxt(data, dtype=float, usecols=(1, 2))
+        assert_equal(test, control[:, 1:])
+        # Testing with arrays instead of tuples.
+        data.seek(0)
+        test = np.genfromtxt(data, dtype=float, usecols=np.array([1, 2]))
+        assert_equal(test, control[:, 1:])
+
+    def test_usecols_as_css(self):
+        # Test giving usecols with a comma-separated string
+        data = "1 2 3\n4 5 6"
+        test = np.genfromtxt(TextIO(data),
+                             names="a, b, c", usecols="a, c")
+        ctrl = np.array([(1, 3), (4, 6)], dtype=[(_, float) for _ in "ac"])
+        assert_equal(test, ctrl)
+
+    def test_usecols_with_structured_dtype(self):
+        # Test usecols with an explicit structured dtype
+        data = TextIO("JOE 70.1 25.3\nBOB 60.5 27.9")
+        names = ['stid', 'temp']
+        dtypes = ['S4', 'f8']
+        test = np.genfromtxt(
+            data, usecols=(0, 2), dtype=list(zip(names, dtypes)))
+        assert_equal(test['stid'], [b"JOE", b"BOB"])
+        assert_equal(test['temp'], [25.3, 27.9])
+
+    def test_usecols_with_integer(self):
+        # Test usecols with an integer
+        test = np.genfromtxt(TextIO(b"1 2 3\n4 5 6"), usecols=0)
+        assert_equal(test, np.array([1., 4.]))
+
+    def test_usecols_with_named_columns(self):
+        # Test usecols with named columns
+        ctrl = np.array([(1, 3), (4, 6)], dtype=[('a', float), ('c', float)])
+        data = "1 2 3\n4 5 6"
+        kwargs = dict(names="a, b, c")
+        test = np.genfromtxt(TextIO(data), usecols=(0, -1), **kwargs)
+        assert_equal(test, ctrl)
+        test = np.genfromtxt(TextIO(data),
+                             usecols=('a', 'c'), **kwargs)
+        assert_equal(test, ctrl)
+
+    def test_empty_file(self):
+        # Test that an empty file raises the proper warning.
+        with suppress_warnings() as sup:
+            sup.filter(message="genfromtxt: Empty input file:")
+            data = TextIO()
+            test = np.genfromtxt(data)
+            assert_equal(test, np.array([]))
+
+            # when skip_header > 0
+            test = np.genfromtxt(data, skip_header=1)
+            assert_equal(test, np.array([]))
+
+    def test_fancy_dtype_alt(self):
+        # Check that a nested dtype isn't MIA
+        data = TextIO('1,2,3.0\n4,5,6.0\n')
+        fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
+        test = np.genfromtxt(data, dtype=fancydtype, delimiter=',', usemask=True)
+        control = ma.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype)
+        assert_equal(test, control)
+
+    def test_shaped_dtype(self):
+        c = TextIO("aaaa  1.0  8.0  1 2 3 4 5 6")
+        dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
+                       ('block', int, (2, 3))])
+        x = np.genfromtxt(c, dtype=dt)
+        a = np.array([('aaaa', 1.0, 8.0, [[1, 2, 3], [4, 5, 6]])],
+                     dtype=dt)
+        assert_array_equal(x, a)
+
+    def test_withmissing(self):
+        data = TextIO('A,B\n0,1\n2,N/A')
+        kwargs = dict(delimiter=",", missing_values="N/A", names=True)
+        test = np.genfromtxt(data, dtype=None, usemask=True, **kwargs)
+        control = ma.array([(0, 1), (2, -1)],
+                           mask=[(False, False), (False, True)],
+                           dtype=[('A', int), ('B', int)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        #
+        data.seek(0)
+        test = np.genfromtxt(data, usemask=True, **kwargs)
+        control = ma.array([(0, 1), (2, -1)],
+                           mask=[(False, False), (False, True)],
+                           dtype=[('A', float), ('B', float)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+    def test_user_missing_values(self):
+        data = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
+        basekwargs = dict(dtype=None, delimiter=",", names=True,)
+        mdtype = [('A', int), ('B', float), ('C', complex)]
+        #
+        test = np.genfromtxt(TextIO(data), missing_values="N/A",
+                            **basekwargs)
+        control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
+                            (-9, 2.2, -999j), (3, -99, 3j)],
+                           mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
+                           dtype=mdtype)
+        assert_equal(test, control)
+        #
+        basekwargs['dtype'] = mdtype
+        test = np.genfromtxt(TextIO(data),
+                            missing_values={0: -9, 1: -99, 2: -999j}, usemask=True, **basekwargs)
+        control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
+                            (-9, 2.2, -999j), (3, -99, 3j)],
+                           mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
+                           dtype=mdtype)
+        assert_equal(test, control)
+        #
+        test = np.genfromtxt(TextIO(data),
+                            missing_values={0: -9, 'B': -99, 'C': -999j},
+                            usemask=True,
+                            **basekwargs)
+        control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
+                            (-9, 2.2, -999j), (3, -99, 3j)],
+                           mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
+                           dtype=mdtype)
+        assert_equal(test, control)
+
+    def test_user_filling_values(self):
+        # Test with missing and filling values
+        ctrl = np.array([(0, 3), (4, -999)], dtype=[('a', int), ('b', int)])
+        data = "N/A, 2, 3\n4, ,???"
+        kwargs = dict(delimiter=",",
+                      dtype=int,
+                      names="a,b,c",
+                      missing_values={0: "N/A", 'b': " ", 2: "???"},
+                      filling_values={0: 0, 'b': 0, 2: -999})
+        test = np.genfromtxt(TextIO(data), **kwargs)
+        ctrl = np.array([(0, 2, 3), (4, 0, -999)],
+                        dtype=[(_, int) for _ in "abc"])
+        assert_equal(test, ctrl)
+        #
+        test = np.genfromtxt(TextIO(data), usecols=(0, -1), **kwargs)
+        ctrl = np.array([(0, 3), (4, -999)], dtype=[(_, int) for _ in "ac"])
+        assert_equal(test, ctrl)
+
+        data2 = "1,2,*,4\n5,*,7,8\n"
+        test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
+                             missing_values="*", filling_values=0)
+        ctrl = np.array([[1, 2, 0, 4], [5, 0, 7, 8]])
+        assert_equal(test, ctrl)
+        test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int,
+                             missing_values="*", filling_values=-1)
+        ctrl = np.array([[1, 2, -1, 4], [5, -1, 7, 8]])
+        assert_equal(test, ctrl)
+
+    def test_withmissing_float(self):
+        data = TextIO('A,B\n0,1.5\n2,-999.00')
+        test = np.genfromtxt(data, dtype=None, delimiter=',',
+                            missing_values='-999.0', names=True, usemask=True)
+        control = ma.array([(0, 1.5), (2, -1.)],
+                           mask=[(False, False), (False, True)],
+                           dtype=[('A', int), ('B', float)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+    def test_with_masked_column_uniform(self):
+        # Test masked column
+        data = TextIO('1 2 3\n4 5 6\n')
+        test = np.genfromtxt(data, dtype=None,
+                             missing_values='2,5', usemask=True)
+        control = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[0, 1, 0], [0, 1, 0]])
+        assert_equal(test, control)
+
+    def test_with_masked_column_various(self):
+        # Test masked column
+        data = TextIO('True 2 3\nFalse 5 6\n')
+        test = np.genfromtxt(data, dtype=None,
+                             missing_values='2,5', usemask=True)
+        control = ma.array([(1, 2, 3), (0, 5, 6)],
+                           mask=[(0, 1, 0), (0, 1, 0)],
+                           dtype=[('f0', bool), ('f1', bool), ('f2', int)])
+        assert_equal(test, control)
+
+    def test_invalid_raise(self):
+        # Test invalid raise
+        data = ["1, 1, 1, 1, 1"] * 50
+        for i in range(5):
+            data[10 * i] = "2, 2, 2, 2 2"
+        data.insert(0, "a, b, c, d, e")
+        mdata = TextIO("\n".join(data))
+
+        kwargs = dict(delimiter=",", dtype=None, names=True)
+        def f():
+            return np.genfromtxt(mdata, invalid_raise=False, **kwargs)
+        mtest = assert_warns(ConversionWarning, f)
+        assert_equal(len(mtest), 45)
+        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'abcde']))
+        #
+        mdata.seek(0)
+        assert_raises(ValueError, np.genfromtxt, mdata,
+                      delimiter=",", names=True)
+
+    def test_invalid_raise_with_usecols(self):
+        # Test invalid_raise with usecols
+        data = ["1, 1, 1, 1, 1"] * 50
+        for i in range(5):
+            data[10 * i] = "2, 2, 2, 2 2"
+        data.insert(0, "a, b, c, d, e")
+        mdata = TextIO("\n".join(data))
+
+        kwargs = dict(delimiter=",", dtype=None, names=True,
+                      invalid_raise=False)
+        def f():
+            return np.genfromtxt(mdata, usecols=(0, 4), **kwargs)
+        mtest = assert_warns(ConversionWarning, f)
+        assert_equal(len(mtest), 45)
+        assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'ae']))
+        #
+        mdata.seek(0)
+        mtest = np.genfromtxt(mdata, usecols=(0, 1), **kwargs)
+        assert_equal(len(mtest), 50)
+        control = np.ones(50, dtype=[(_, int) for _ in 'ab'])
+        control[[10 * _ for _ in range(5)]] = (2, 2)
+        assert_equal(mtest, control)
+
+    def test_inconsistent_dtype(self):
+        # Test inconsistent dtype
+        data = ["1, 1, 1, 1, -1.1"] * 50
+        mdata = TextIO("\n".join(data))
+
+        converters = {4: lambda x: "(%s)" % x.decode()}
+        kwargs = dict(delimiter=",", converters=converters,
+                      dtype=[(_, int) for _ in 'abcde'], encoding="bytes")
+        assert_raises(ValueError, np.genfromtxt, mdata, **kwargs)
+
+    def test_default_field_format(self):
+        # Test default format
+        data = "0, 1, 2.3\n4, 5, 6.7"
+        mtest = np.genfromtxt(TextIO(data),
+                             delimiter=",", dtype=None, defaultfmt="f%02i")
+        ctrl = np.array([(0, 1, 2.3), (4, 5, 6.7)],
+                        dtype=[("f00", int), ("f01", int), ("f02", float)])
+        assert_equal(mtest, ctrl)
+
+    def test_single_dtype_wo_names(self):
+        # Test single dtype w/o names
+        data = "0, 1, 2.3\n4, 5, 6.7"
+        mtest = np.genfromtxt(TextIO(data),
+                             delimiter=",", dtype=float, defaultfmt="f%02i")
+        ctrl = np.array([[0., 1., 2.3], [4., 5., 6.7]], dtype=float)
+        assert_equal(mtest, ctrl)
+
+    def test_single_dtype_w_explicit_names(self):
+        # Test single dtype w explicit names
+        data = "0, 1, 2.3\n4, 5, 6.7"
+        mtest = np.genfromtxt(TextIO(data),
+                             delimiter=",", dtype=float, names="a, b, c")
+        ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)],
+                        dtype=[(_, float) for _ in "abc"])
+        assert_equal(mtest, ctrl)
+
+    def test_single_dtype_w_implicit_names(self):
+        # Test single dtype w implicit names
+        data = "a, b, c\n0, 1, 2.3\n4, 5, 6.7"
+        mtest = np.genfromtxt(TextIO(data),
+                             delimiter=",", dtype=float, names=True)
+        ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)],
+                        dtype=[(_, float) for _ in "abc"])
+        assert_equal(mtest, ctrl)
+
+    def test_easy_structured_dtype(self):
+        # Test easy structured dtype
+        data = "0, 1, 2.3\n4, 5, 6.7"
+        mtest = np.genfromtxt(TextIO(data), delimiter=",",
+                             dtype=(int, float, float), defaultfmt="f_%02i")
+        ctrl = np.array([(0, 1., 2.3), (4, 5., 6.7)],
+                        dtype=[("f_00", int), ("f_01", float), ("f_02", float)])
+        assert_equal(mtest, ctrl)
+
+    def test_autostrip(self):
+        # Test autostrip
+        data = "01/01/2003  , 1.3,   abcde"
+        kwargs = dict(delimiter=",", dtype=None, encoding="bytes")
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            mtest = np.genfromtxt(TextIO(data), **kwargs)
+            assert_(w[0].category is VisibleDeprecationWarning)
+        ctrl = np.array([('01/01/2003  ', 1.3, '   abcde')],
+                        dtype=[('f0', '|S12'), ('f1', float), ('f2', '|S8')])
+        assert_equal(mtest, ctrl)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            mtest = np.genfromtxt(TextIO(data), autostrip=True, **kwargs)
+            assert_(w[0].category is VisibleDeprecationWarning)
+        ctrl = np.array([('01/01/2003', 1.3, 'abcde')],
+                        dtype=[('f0', '|S10'), ('f1', float), ('f2', '|S5')])
+        assert_equal(mtest, ctrl)
+
+    def test_replace_space(self):
+        # Test the 'replace_space' option
+        txt = "A.A, B (B), C:C\n1, 2, 3.14"
+        # Test default: replace ' ' by '_' and delete non-alphanum chars
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=None)
+        ctrl_dtype = [("AA", int), ("B_B", int), ("CC", float)]
+        ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+        # Test: no replace, no delete
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=None,
+                             replace_space='', deletechars='')
+        ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", float)]
+        ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+        # Test: no delete (spaces are replaced by _)
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=None,
+                             deletechars='')
+        ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", float)]
+        ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+
+    def test_replace_space_known_dtype(self):
+        # Test the 'replace_space' (and related) options when dtype != None
+        txt = "A.A, B (B), C:C\n1, 2, 3"
+        # Test default: replace ' ' by '_' and delete non-alphanum chars
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=int)
+        ctrl_dtype = [("AA", int), ("B_B", int), ("CC", int)]
+        ctrl = np.array((1, 2, 3), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+        # Test: no replace, no delete
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=int,
+                             replace_space='', deletechars='')
+        ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", int)]
+        ctrl = np.array((1, 2, 3), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+        # Test: no delete (spaces are replaced by _)
+        test = np.genfromtxt(TextIO(txt),
+                             delimiter=",", names=True, dtype=int,
+                             deletechars='')
+        ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", int)]
+        ctrl = np.array((1, 2, 3), dtype=ctrl_dtype)
+        assert_equal(test, ctrl)
+
+    def test_incomplete_names(self):
+        # Test w/ incomplete names
+        data = "A,,C\n0,1,2\n3,4,5"
+        kwargs = dict(delimiter=",", names=True)
+        # w/ dtype=None
+        ctrl = np.array([(0, 1, 2), (3, 4, 5)],
+                        dtype=[(_, int) for _ in ('A', 'f0', 'C')])
+        test = np.genfromtxt(TextIO(data), dtype=None, **kwargs)
+        assert_equal(test, ctrl)
+        # w/ default dtype
+        ctrl = np.array([(0, 1, 2), (3, 4, 5)],
+                        dtype=[(_, float) for _ in ('A', 'f0', 'C')])
+        test = np.genfromtxt(TextIO(data), **kwargs)
+
+    def test_names_auto_completion(self):
+        # Make sure that names are properly completed
+        data = "1 2 3\n 4 5 6"
+        test = np.genfromtxt(TextIO(data),
+                             dtype=(int, float, int), names="a")
+        ctrl = np.array([(1, 2, 3), (4, 5, 6)],
+                        dtype=[('a', int), ('f0', float), ('f1', int)])
+        assert_equal(test, ctrl)
+
+    def test_names_with_usecols_bug1636(self):
+        # Make sure we pick up the right names w/ usecols
+        data = "A,B,C,D,E\n0,1,2,3,4\n0,1,2,3,4\n0,1,2,3,4"
+        ctrl_names = ("A", "C", "E")
+        test = np.genfromtxt(TextIO(data),
+                             dtype=(int, int, int), delimiter=",",
+                             usecols=(0, 2, 4), names=True)
+        assert_equal(test.dtype.names, ctrl_names)
+        #
+        test = np.genfromtxt(TextIO(data),
+                             dtype=(int, int, int), delimiter=",",
+                             usecols=("A", "C", "E"), names=True)
+        assert_equal(test.dtype.names, ctrl_names)
+        #
+        test = np.genfromtxt(TextIO(data),
+                             dtype=int, delimiter=",",
+                             usecols=("A", "C", "E"), names=True)
+        assert_equal(test.dtype.names, ctrl_names)
+
+    def test_fixed_width_names(self):
+        # Test fix-width w/ names
+        data = "    A    B   C\n    0    1 2.3\n   45   67   9."
+        kwargs = dict(delimiter=(5, 5, 4), names=True, dtype=None)
+        ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)],
+                        dtype=[('A', int), ('B', int), ('C', float)])
+        test = np.genfromtxt(TextIO(data), **kwargs)
+        assert_equal(test, ctrl)
+        #
+        kwargs = dict(delimiter=5, names=True, dtype=None)
+        ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)],
+                        dtype=[('A', int), ('B', int), ('C', float)])
+        test = np.genfromtxt(TextIO(data), **kwargs)
+        assert_equal(test, ctrl)
+
+    def test_filling_values(self):
+        # Test missing values
+        data = b"1, 2, 3\n1, , 5\n0, 6, \n"
+        kwargs = dict(delimiter=",", dtype=None, filling_values=-999)
+        ctrl = np.array([[1, 2, 3], [1, -999, 5], [0, 6, -999]], dtype=int)
+        test = np.genfromtxt(TextIO(data), **kwargs)
+        assert_equal(test, ctrl)
+
+    def test_comments_is_none(self):
+        # Github issue 329 (None was previously being converted to 'None').
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(TextIO("test1,testNonetherestofthedata"),
+                                 dtype=None, comments=None, delimiter=',',
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        assert_equal(test[1], b'testNonetherestofthedata')
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(TextIO("test1, testNonetherestofthedata"),
+                                 dtype=None, comments=None, delimiter=',',
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        assert_equal(test[1], b' testNonetherestofthedata')
+
+    def test_latin1(self):
+        latin1 = b'\xf6\xfc\xf6'
+        norm = b"norm1,norm2,norm3\n"
+        enc = b"test1,testNonethe" + latin1 + b",test3\n"
+        s = norm + enc + norm
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(TextIO(s),
+                                 dtype=None, comments=None, delimiter=',',
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        assert_equal(test[1, 0], b"test1")
+        assert_equal(test[1, 1], b"testNonethe" + latin1)
+        assert_equal(test[1, 2], b"test3")
+        test = np.genfromtxt(TextIO(s),
+                             dtype=None, comments=None, delimiter=',',
+                             encoding='latin1')
+        assert_equal(test[1, 0], "test1")
+        assert_equal(test[1, 1], "testNonethe" + latin1.decode('latin1'))
+        assert_equal(test[1, 2], "test3")
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(TextIO(b"0,testNonethe" + latin1),
+                                 dtype=None, comments=None, delimiter=',',
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        assert_equal(test['f0'], 0)
+        assert_equal(test['f1'], b"testNonethe" + latin1)
+
+    def test_binary_decode_autodtype(self):
+        utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04'
+        v = self.loadfunc(BytesIO(utf16), dtype=None, encoding='UTF-16')
+        assert_array_equal(v, np.array(utf16.decode('UTF-16').split()))
+
+    def test_utf8_byte_encoding(self):
+        utf8 = b"\xcf\x96"
+        norm = b"norm1,norm2,norm3\n"
+        enc = b"test1,testNonethe" + utf8 + b",test3\n"
+        s = norm + enc + norm
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', VisibleDeprecationWarning)
+            test = np.genfromtxt(TextIO(s),
+                                 dtype=None, comments=None, delimiter=',',
+                                 encoding="bytes")
+            assert_(w[0].category is VisibleDeprecationWarning)
+        ctl = np.array([
+                 [b'norm1', b'norm2', b'norm3'],
+                 [b'test1', b'testNonethe' + utf8, b'test3'],
+                 [b'norm1', b'norm2', b'norm3']])
+        assert_array_equal(test, ctl)
+
+    def test_utf8_file(self):
+        utf8 = b"\xcf\x96"
+        with temppath() as path:
+            with open(path, "wb") as f:
+                f.write((b"test1,testNonethe" + utf8 + b",test3\n") * 2)
+            test = np.genfromtxt(path, dtype=None, comments=None,
+                                 delimiter=',', encoding="UTF-8")
+            ctl = np.array([
+                     ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"],
+                     ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"]],
+                     dtype=np.str_)
+            assert_array_equal(test, ctl)
+
+            # test a mixed dtype
+            with open(path, "wb") as f:
+                f.write(b"0,testNonethe" + utf8)
+            test = np.genfromtxt(path, dtype=None, comments=None,
+                                 delimiter=',', encoding="UTF-8")
+            assert_equal(test['f0'], 0)
+            assert_equal(test['f1'], "testNonethe" + utf8.decode("UTF-8"))
+
+    def test_utf8_file_nodtype_unicode(self):
+        # bytes encoding with non-latin1 -> unicode upcast
+        utf8 = '\u03d6'
+        latin1 = '\xf6\xfc\xf6'
+
+        # skip test if cannot encode utf8 test string with preferred
+        # encoding. The preferred encoding is assumed to be the default
+        # encoding of open. Will need to change this for PyTest, maybe
+        # using pytest.mark.xfail(raises=***).
+        try:
+            encoding = locale.getpreferredencoding()
+            utf8.encode(encoding)
+        except (UnicodeError, ImportError):
+            pytest.skip('Skipping test_utf8_file_nodtype_unicode, '
+                        'unable to encode utf8 in preferred encoding')
+
+        with temppath() as path:
+            with open(path, "wt") as f:
+                f.write("norm1,norm2,norm3\n")
+                f.write("norm1," + latin1 + ",norm3\n")
+                f.write("test1,testNonethe" + utf8 + ",test3\n")
+            with warnings.catch_warnings(record=True) as w:
+                warnings.filterwarnings('always', '',
+                                        VisibleDeprecationWarning)
+                test = np.genfromtxt(path, dtype=None, comments=None,
+                                     delimiter=',', encoding="bytes")
+                # Check for warning when encoding not specified.
+                assert_(w[0].category is VisibleDeprecationWarning)
+            ctl = np.array([
+                     ["norm1", "norm2", "norm3"],
+                     ["norm1", latin1, "norm3"],
+                     ["test1", "testNonethe" + utf8, "test3"]],
+                     dtype=np.str_)
+            assert_array_equal(test, ctl)
+
+    @pytest.mark.filterwarnings("ignore:.*recfromtxt.*:DeprecationWarning")
+    def test_recfromtxt(self):
+        #
+        data = TextIO('A,B\n0,1\n2,3')
+        kwargs = dict(delimiter=",", missing_values="N/A", names=True)
+        test = recfromtxt(data, **kwargs)
+        control = np.array([(0, 1), (2, 3)],
+                           dtype=[('A', int), ('B', int)])
+        assert_(isinstance(test, np.recarray))
+        assert_equal(test, control)
+        #
+        data = TextIO('A,B\n0,1\n2,N/A')
+        test = recfromtxt(data, dtype=None, usemask=True, **kwargs)
+        control = ma.array([(0, 1), (2, -1)],
+                           mask=[(False, False), (False, True)],
+                           dtype=[('A', int), ('B', int)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        assert_equal(test.A, [0, 2])
+
+    @pytest.mark.filterwarnings("ignore:.*recfromcsv.*:DeprecationWarning")
+    def test_recfromcsv(self):
+        #
+        data = TextIO('A,B\n0,1\n2,3')
+        kwargs = dict(missing_values="N/A", names=True, case_sensitive=True,
+                      encoding="bytes")
+        test = recfromcsv(data, dtype=None, **kwargs)
+        control = np.array([(0, 1), (2, 3)],
+                           dtype=[('A', int), ('B', int)])
+        assert_(isinstance(test, np.recarray))
+        assert_equal(test, control)
+        #
+        data = TextIO('A,B\n0,1\n2,N/A')
+        test = recfromcsv(data, dtype=None, usemask=True, **kwargs)
+        control = ma.array([(0, 1), (2, -1)],
+                           mask=[(False, False), (False, True)],
+                           dtype=[('A', int), ('B', int)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        assert_equal(test.A, [0, 2])
+        #
+        data = TextIO('A,B\n0,1\n2,3')
+        test = recfromcsv(data, missing_values='N/A',)
+        control = np.array([(0, 1), (2, 3)],
+                           dtype=[('a', int), ('b', int)])
+        assert_(isinstance(test, np.recarray))
+        assert_equal(test, control)
+        #
+        data = TextIO('A,B\n0,1\n2,3')
+        dtype = [('a', int), ('b', float)]
+        test = recfromcsv(data, missing_values='N/A', dtype=dtype)
+        control = np.array([(0, 1), (2, 3)],
+                           dtype=dtype)
+        assert_(isinstance(test, np.recarray))
+        assert_equal(test, control)
+
+        #gh-10394
+        data = TextIO('color\n"red"\n"blue"')
+        test = recfromcsv(data, converters={0: lambda x: x.strip('\"')})
+        control = np.array([('red',), ('blue',)], dtype=[('color', (str, 4))])
+        assert_equal(test.dtype, control.dtype)
+        assert_equal(test, control)
+
+    def test_max_rows(self):
+        # Test the `max_rows` keyword argument.
+        data = '1 2\n3 4\n5 6\n7 8\n9 10\n'
+        txt = TextIO(data)
+        a1 = np.genfromtxt(txt, max_rows=3)
+        a2 = np.genfromtxt(txt)
+        assert_equal(a1, [[1, 2], [3, 4], [5, 6]])
+        assert_equal(a2, [[7, 8], [9, 10]])
+
+        # max_rows must be at least 1.
+        assert_raises(ValueError, np.genfromtxt, TextIO(data), max_rows=0)
+
+        # An input with several invalid rows.
+        data = '1 1\n2 2\n0 \n3 3\n4 4\n5  \n6  \n7  \n'
+
+        test = np.genfromtxt(TextIO(data), max_rows=2)
+        control = np.array([[1., 1.], [2., 2.]])
+        assert_equal(test, control)
+
+        # Test keywords conflict
+        assert_raises(ValueError, np.genfromtxt, TextIO(data), skip_footer=1,
+                      max_rows=4)
+
+        # Test with invalid value
+        assert_raises(ValueError, np.genfromtxt, TextIO(data), max_rows=4)
+
+        # Test with invalid not raise
+        with suppress_warnings() as sup:
+            sup.filter(ConversionWarning)
+
+            test = np.genfromtxt(TextIO(data), max_rows=4, invalid_raise=False)
+            control = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]])
+            assert_equal(test, control)
+
+            test = np.genfromtxt(TextIO(data), max_rows=5, invalid_raise=False)
+            control = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]])
+            assert_equal(test, control)
+
+        # Structured array with field names.
+        data = 'a b\n#c d\n1 1\n2 2\n#0 \n3 3\n4 4\n5  5\n'
+
+        # Test with header, names and comments
+        txt = TextIO(data)
+        test = np.genfromtxt(txt, skip_header=1, max_rows=3, names=True)
+        control = np.array([(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)],
+                      dtype=[('c', ' should convert to float
+        # 2**34 = 17179869184 => should convert to int64
+        # 2**10 = 1024 => should convert to int (int32 on 32-bit systems,
+        #                 int64 on 64-bit systems)
+
+        data = TextIO('73786976294838206464 17179869184 1024')
+
+        test = np.genfromtxt(data, dtype=None)
+
+        assert_equal(test.dtype.names, ['f0', 'f1', 'f2'])
+
+        assert_(test.dtype['f0'] == float)
+        assert_(test.dtype['f1'] == np.int64)
+        assert_(test.dtype['f2'] == np.int_)
+
+        assert_allclose(test['f0'], 73786976294838206464.)
+        assert_equal(test['f1'], 17179869184)
+        assert_equal(test['f2'], 1024)
+
+    def test_unpack_float_data(self):
+        txt = TextIO("1,2,3\n4,5,6\n7,8,9\n0.0,1.0,2.0")
+        a, b, c = np.loadtxt(txt, delimiter=",", unpack=True)
+        assert_array_equal(a, np.array([1.0, 4.0, 7.0, 0.0]))
+        assert_array_equal(b, np.array([2.0, 5.0, 8.0, 1.0]))
+        assert_array_equal(c, np.array([3.0, 6.0, 9.0, 2.0]))
+
+    def test_unpack_structured(self):
+        # Regression test for gh-4341
+        # Unpacking should work on structured arrays
+        txt = TextIO("M 21 72\nF 35 58")
+        dt = {'names': ('a', 'b', 'c'), 'formats': ('S1', 'i4', 'f4')}
+        a, b, c = np.genfromtxt(txt, dtype=dt, unpack=True)
+        assert_equal(a.dtype, np.dtype('S1'))
+        assert_equal(b.dtype, np.dtype('i4'))
+        assert_equal(c.dtype, np.dtype('f4'))
+        assert_array_equal(a, np.array([b'M', b'F']))
+        assert_array_equal(b, np.array([21, 35]))
+        assert_array_equal(c, np.array([72.,  58.]))
+
+    def test_unpack_auto_dtype(self):
+        # Regression test for gh-4341
+        # Unpacking should work when dtype=None
+        txt = TextIO("M 21 72.\nF 35 58.")
+        expected = (np.array(["M", "F"]), np.array([21, 35]), np.array([72., 58.]))
+        test = np.genfromtxt(txt, dtype=None, unpack=True, encoding="utf-8")
+        for arr, result in zip(expected, test):
+            assert_array_equal(arr, result)
+            assert_equal(arr.dtype, result.dtype)
+
+    def test_unpack_single_name(self):
+        # Regression test for gh-4341
+        # Unpacking should work when structured dtype has only one field
+        txt = TextIO("21\n35")
+        dt = {'names': ('a',), 'formats': ('i4',)}
+        expected = np.array([21, 35], dtype=np.int32)
+        test = np.genfromtxt(txt, dtype=dt, unpack=True)
+        assert_array_equal(expected, test)
+        assert_equal(expected.dtype, test.dtype)
+
+    def test_squeeze_scalar(self):
+        # Regression test for gh-4341
+        # Unpacking a scalar should give zero-dim output,
+        # even if dtype is structured
+        txt = TextIO("1")
+        dt = {'names': ('a',), 'formats': ('i4',)}
+        expected = np.array((1,), dtype=np.int32)
+        test = np.genfromtxt(txt, dtype=dt, unpack=True)
+        assert_array_equal(expected, test)
+        assert_equal((), test.shape)
+        assert_equal(expected.dtype, test.dtype)
+
+    @pytest.mark.parametrize("ndim", [0, 1, 2])
+    def test_ndmin_keyword(self, ndim: int):
+        # lets have the same behaviour of ndmin as loadtxt
+        # as they should be the same for non-missing values
+        txt = "42"
+
+        a = np.loadtxt(StringIO(txt), ndmin=ndim)
+        b = np.genfromtxt(StringIO(txt), ndmin=ndim)
+
+        assert_array_equal(a, b)
+
+
+class TestPathUsage:
+    # Test that pathlib.Path can be used
+    def test_loadtxt(self):
+        with temppath(suffix='.txt') as path:
+            path = Path(path)
+            a = np.array([[1.1, 2], [3, 4]])
+            np.savetxt(path, a)
+            x = np.loadtxt(path)
+            assert_array_equal(x, a)
+
+    def test_save_load(self):
+        # Test that pathlib.Path instances can be used with save.
+        with temppath(suffix='.npy') as path:
+            path = Path(path)
+            a = np.array([[1, 2], [3, 4]], int)
+            np.save(path, a)
+            data = np.load(path)
+            assert_array_equal(data, a)
+
+    def test_save_load_memmap(self):
+        # Test that pathlib.Path instances can be loaded mem-mapped.
+        with temppath(suffix='.npy') as path:
+            path = Path(path)
+            a = np.array([[1, 2], [3, 4]], int)
+            np.save(path, a)
+            data = np.load(path, mmap_mode='r')
+            assert_array_equal(data, a)
+            # close the mem-mapped file
+            del data
+            if IS_PYPY:
+                break_cycles()
+                break_cycles()
+
+    @pytest.mark.xfail(IS_WASM, reason="memmap doesn't work correctly")
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    def test_save_load_memmap_readwrite(self, filename_type):
+        with temppath(suffix='.npy') as path:
+            path = filename_type(path)
+            a = np.array([[1, 2], [3, 4]], int)
+            np.save(path, a)
+            b = np.load(path, mmap_mode='r+')
+            a[0][0] = 5
+            b[0][0] = 5
+            del b  # closes the file
+            if IS_PYPY:
+                break_cycles()
+                break_cycles()
+            data = np.load(path)
+            assert_array_equal(data, a)
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    def test_savez_load(self, filename_type):
+        with temppath(suffix='.npz') as path:
+            path = filename_type(path)
+            np.savez(path, lab='place holder')
+            with np.load(path) as data:
+                assert_array_equal(data['lab'], 'place holder')
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    def test_savez_compressed_load(self, filename_type):
+        with temppath(suffix='.npz') as path:
+            path = filename_type(path)
+            np.savez_compressed(path, lab='place holder')
+            data = np.load(path)
+            assert_array_equal(data['lab'], 'place holder')
+            data.close()
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    def test_genfromtxt(self, filename_type):
+        with temppath(suffix='.txt') as path:
+            path = filename_type(path)
+            a = np.array([(1, 2), (3, 4)])
+            np.savetxt(path, a)
+            data = np.genfromtxt(path)
+            assert_array_equal(a, data)
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    @pytest.mark.filterwarnings("ignore:.*recfromtxt.*:DeprecationWarning")
+    def test_recfromtxt(self, filename_type):
+        with temppath(suffix='.txt') as path:
+            path = filename_type(path)
+            with open(path, 'w') as f:
+                f.write('A,B\n0,1\n2,3')
+
+            kwargs = dict(delimiter=",", missing_values="N/A", names=True)
+            test = recfromtxt(path, **kwargs)
+            control = np.array([(0, 1), (2, 3)],
+                               dtype=[('A', int), ('B', int)])
+            assert_(isinstance(test, np.recarray))
+            assert_equal(test, control)
+
+    @pytest.mark.parametrize("filename_type", [Path, str])
+    @pytest.mark.filterwarnings("ignore:.*recfromcsv.*:DeprecationWarning")
+    def test_recfromcsv(self, filename_type):
+        with temppath(suffix='.txt') as path:
+            path = filename_type(path)
+            with open(path, 'w') as f:
+                f.write('A,B\n0,1\n2,3')
+
+            kwargs = dict(
+                missing_values="N/A", names=True, case_sensitive=True
+            )
+            test = recfromcsv(path, dtype=None, **kwargs)
+            control = np.array([(0, 1), (2, 3)],
+                               dtype=[('A', int), ('B', int)])
+            assert_(isinstance(test, np.recarray))
+            assert_equal(test, control)
+
+
+def test_gzip_load():
+    a = np.random.random((5, 5))
+
+    s = BytesIO()
+    f = gzip.GzipFile(fileobj=s, mode="w")
+
+    np.save(f, a)
+    f.close()
+    s.seek(0)
+
+    f = gzip.GzipFile(fileobj=s, mode="r")
+    assert_array_equal(np.load(f), a)
+
+
+# These next two classes encode the minimal API needed to save()/load() arrays.
+# The `test_ducktyping` ensures they work correctly
+class JustWriter:
+    def __init__(self, base):
+        self.base = base
+
+    def write(self, s):
+        return self.base.write(s)
+
+    def flush(self):
+        return self.base.flush()
+
+class JustReader:
+    def __init__(self, base):
+        self.base = base
+
+    def read(self, n):
+        return self.base.read(n)
+
+    def seek(self, off, whence=0):
+        return self.base.seek(off, whence)
+
+
+def test_ducktyping():
+    a = np.random.random((5, 5))
+
+    s = BytesIO()
+    f = JustWriter(s)
+
+    np.save(f, a)
+    f.flush()
+    s.seek(0)
+
+    f = JustReader(s)
+    assert_array_equal(np.load(f), a)
+
+
+
+def test_gzip_loadtxt():
+    # Thanks to another windows brokenness, we can't use
+    # NamedTemporaryFile: a file created from this function cannot be
+    # reopened by another open call. So we first put the gzipped string
+    # of the test reference array, write it to a securely opened file,
+    # which is then read from by the loadtxt function
+    s = BytesIO()
+    g = gzip.GzipFile(fileobj=s, mode='w')
+    g.write(b'1 2 3\n')
+    g.close()
+
+    s.seek(0)
+    with temppath(suffix='.gz') as name:
+        with open(name, 'wb') as f:
+            f.write(s.read())
+        res = np.loadtxt(name)
+    s.close()
+
+    assert_array_equal(res, [1, 2, 3])
+
+
+def test_gzip_loadtxt_from_string():
+    s = BytesIO()
+    f = gzip.GzipFile(fileobj=s, mode="w")
+    f.write(b'1 2 3\n')
+    f.close()
+    s.seek(0)
+
+    f = gzip.GzipFile(fileobj=s, mode="r")
+    assert_array_equal(np.loadtxt(f), [1, 2, 3])
+
+
+def test_npzfile_dict():
+    s = BytesIO()
+    x = np.zeros((3, 3))
+    y = np.zeros((3, 3))
+
+    np.savez(s, x=x, y=y)
+    s.seek(0)
+
+    z = np.load(s)
+
+    assert_('x' in z)
+    assert_('y' in z)
+    assert_('x' in z.keys())
+    assert_('y' in z.keys())
+
+    for f, a in z.items():
+        assert_(f in ['x', 'y'])
+        assert_equal(a.shape, (3, 3))
+
+    for a in z.values():
+        assert_equal(a.shape, (3, 3))
+
+    assert_(len(z.items()) == 2)
+
+    for f in z:
+        assert_(f in ['x', 'y'])
+
+    assert_('x' in z.keys())
+    assert (z.get('x') == z['x']).all()
+
+
+@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+def test_load_refcount():
+    # Check that objects returned by np.load are directly freed based on
+    # their refcount, rather than needing the gc to collect them.
+
+    f = BytesIO()
+    np.savez(f, [1, 2, 3])
+    f.seek(0)
+
+    with assert_no_gc_cycles():
+        np.load(f)
+
+    f.seek(0)
+    dt = [("a", 'u1', 2), ("b", 'u1', 2)]
+    with assert_no_gc_cycles():
+        x = np.loadtxt(TextIO("0 1 2 3"), dtype=dt)
+        assert_equal(x, np.array([((0, 1), (2, 3))], dtype=dt))
+
+
+def test_load_multiple_arrays_until_eof():
+    f = BytesIO()
+    np.save(f, 1)
+    np.save(f, 2)
+    f.seek(0)
+    out1 = np.load(f)
+    assert out1 == 1
+    out2 = np.load(f)
+    assert out2 == 2
+    with pytest.raises(EOFError):
+        np.load(f)
+
+
+def test_savez_nopickle():
+    obj_array = np.array([1, 'hello'], dtype=object)
+    with temppath(suffix='.npz') as tmp:
+        np.savez(tmp, obj_array)
+
+    with temppath(suffix='.npz') as tmp:
+        with pytest.raises(ValueError, match="Object arrays cannot be saved when.*"):
+            np.savez(tmp, obj_array, allow_pickle=False)
+
+    with temppath(suffix='.npz') as tmp:
+        np.savez_compressed(tmp, obj_array)
+
+    with temppath(suffix='.npz') as tmp:
+        with pytest.raises(ValueError, match="Object arrays cannot be saved when.*"):
+            np.savez_compressed(tmp, obj_array, allow_pickle=False)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_loadtxt.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_loadtxt.py
new file mode 100644
index 0000000000000000000000000000000000000000..60717be3bd9af7c8159beee4900bec2ce411c7cb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_loadtxt.py
@@ -0,0 +1,1100 @@
+"""
+Tests specific to `np.loadtxt` added during the move of loadtxt to be backed
+by C code.
+These tests complement those found in `test_io.py`.
+"""
+
+import sys
+import os
+import pytest
+from tempfile import NamedTemporaryFile, mkstemp
+from io import StringIO
+
+import numpy as np
+from numpy.ma.testutils import assert_equal
+from numpy.testing import assert_array_equal, HAS_REFCOUNT, IS_PYPY
+
+
+def test_scientific_notation():
+    """Test that both 'e' and 'E' are parsed correctly."""
+    data = StringIO(
+
+            "1.0e-1,2.0E1,3.0\n"
+            "4.0e-2,5.0E-1,6.0\n"
+            "7.0e-3,8.0E1,9.0\n"
+            "0.0e-4,1.0E-1,2.0"
+
+    )
+    expected = np.array(
+        [[0.1, 20., 3.0], [0.04, 0.5, 6], [0.007, 80., 9], [0, 0.1, 2]]
+    )
+    assert_array_equal(np.loadtxt(data, delimiter=","), expected)
+
+
+@pytest.mark.parametrize("comment", ["..", "//", "@-", "this is a comment:"])
+def test_comment_multiple_chars(comment):
+    content = "# IGNORE\n1.5, 2.5# ABC\n3.0,4.0# XXX\n5.5,6.0\n"
+    txt = StringIO(content.replace("#", comment))
+    a = np.loadtxt(txt, delimiter=",", comments=comment)
+    assert_equal(a, [[1.5, 2.5], [3.0, 4.0], [5.5, 6.0]])
+
+
+@pytest.fixture
+def mixed_types_structured():
+    """
+    Fixture providing heterogeneous input data with a structured dtype, along
+    with the associated structured array.
+    """
+    data = StringIO(
+
+            "1000;2.4;alpha;-34\n"
+            "2000;3.1;beta;29\n"
+            "3500;9.9;gamma;120\n"
+            "4090;8.1;delta;0\n"
+            "5001;4.4;epsilon;-99\n"
+            "6543;7.8;omega;-1\n"
+
+    )
+    dtype = np.dtype(
+        [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)]
+    )
+    expected = np.array(
+        [
+            (1000, 2.4, "alpha", -34),
+            (2000, 3.1, "beta", 29),
+            (3500, 9.9, "gamma", 120),
+            (4090, 8.1, "delta", 0),
+            (5001, 4.4, "epsilon", -99),
+            (6543, 7.8, "omega", -1)
+        ],
+        dtype=dtype
+    )
+    return data, dtype, expected
+
+
+@pytest.mark.parametrize('skiprows', [0, 1, 2, 3])
+def test_structured_dtype_and_skiprows_no_empty_lines(
+        skiprows, mixed_types_structured):
+    data, dtype, expected = mixed_types_structured
+    a = np.loadtxt(data, dtype=dtype, delimiter=";", skiprows=skiprows)
+    assert_array_equal(a, expected[skiprows:])
+
+
+def test_unpack_structured(mixed_types_structured):
+    data, dtype, expected = mixed_types_structured
+
+    a, b, c, d = np.loadtxt(data, dtype=dtype, delimiter=";", unpack=True)
+    assert_array_equal(a, expected["f0"])
+    assert_array_equal(b, expected["f1"])
+    assert_array_equal(c, expected["f2"])
+    assert_array_equal(d, expected["f3"])
+
+
+def test_structured_dtype_with_shape():
+    dtype = np.dtype([("a", "u1", 2), ("b", "u1", 2)])
+    data = StringIO("0,1,2,3\n6,7,8,9\n")
+    expected = np.array([((0, 1), (2, 3)), ((6, 7), (8, 9))], dtype=dtype)
+    assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dtype), expected)
+
+
+def test_structured_dtype_with_multi_shape():
+    dtype = np.dtype([("a", "u1", (2, 2))])
+    data = StringIO("0 1 2 3\n")
+    expected = np.array([(((0, 1), (2, 3)),)], dtype=dtype)
+    assert_array_equal(np.loadtxt(data, dtype=dtype), expected)
+
+
+def test_nested_structured_subarray():
+    # Test from gh-16678
+    point = np.dtype([('x', float), ('y', float)])
+    dt = np.dtype([('code', int), ('points', point, (2,))])
+    data = StringIO("100,1,2,3,4\n200,5,6,7,8\n")
+    expected = np.array(
+        [
+            (100, [(1., 2.), (3., 4.)]),
+            (200, [(5., 6.), (7., 8.)]),
+        ],
+        dtype=dt
+    )
+    assert_array_equal(np.loadtxt(data, dtype=dt, delimiter=","), expected)
+
+
+def test_structured_dtype_offsets():
+    # An aligned structured dtype will have additional padding
+    dt = np.dtype("i1, i4, i1, i4, i1, i4", align=True)
+    data = StringIO("1,2,3,4,5,6\n7,8,9,10,11,12\n")
+    expected = np.array([(1, 2, 3, 4, 5, 6), (7, 8, 9, 10, 11, 12)], dtype=dt)
+    assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dt), expected)
+
+
+@pytest.mark.parametrize("param", ("skiprows", "max_rows"))
+def test_exception_negative_row_limits(param):
+    """skiprows and max_rows should raise for negative parameters."""
+    with pytest.raises(ValueError, match="argument must be nonnegative"):
+        np.loadtxt("foo.bar", **{param: -3})
+
+
+@pytest.mark.parametrize("param", ("skiprows", "max_rows"))
+def test_exception_noninteger_row_limits(param):
+    with pytest.raises(TypeError, match="argument must be an integer"):
+        np.loadtxt("foo.bar", **{param: 1.0})
+
+
+@pytest.mark.parametrize(
+    "data, shape",
+    [
+        ("1 2 3 4 5\n", (1, 5)),  # Single row
+        ("1\n2\n3\n4\n5\n", (5, 1)),  # Single column
+    ]
+)
+def test_ndmin_single_row_or_col(data, shape):
+    arr = np.array([1, 2, 3, 4, 5])
+    arr2d = arr.reshape(shape)
+
+    assert_array_equal(np.loadtxt(StringIO(data), dtype=int), arr)
+    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=0), arr)
+    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=1), arr)
+    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=2), arr2d)
+
+
+@pytest.mark.parametrize("badval", [-1, 3, None, "plate of shrimp"])
+def test_bad_ndmin(badval):
+    with pytest.raises(ValueError, match="Illegal value of ndmin keyword"):
+        np.loadtxt("foo.bar", ndmin=badval)
+
+
+@pytest.mark.parametrize(
+    "ws",
+    (
+            " ",  # space
+            "\t",  # tab
+            "\u2003",  # em
+            "\u00A0",  # non-break
+            "\u3000",  # ideographic space
+    )
+)
+def test_blank_lines_spaces_delimit(ws):
+    txt = StringIO(
+        f"1 2{ws}30\n\n{ws}\n"
+        f"4 5 60{ws}\n  {ws}  \n"
+        f"7 8 {ws} 90\n  # comment\n"
+        f"3 2 1"
+    )
+    # NOTE: It is unclear that the `  # comment` should succeed. Except
+    #       for delimiter=None, which should use any whitespace (and maybe
+    #       should just be implemented closer to Python
+    expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]])
+    assert_equal(
+        np.loadtxt(txt, dtype=int, delimiter=None, comments="#"), expected
+    )
+
+
+def test_blank_lines_normal_delimiter():
+    txt = StringIO('1,2,30\n\n4,5,60\n\n7,8,90\n# comment\n3,2,1')
+    expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]])
+    assert_equal(
+        np.loadtxt(txt, dtype=int, delimiter=',', comments="#"), expected
+    )
+
+
+@pytest.mark.parametrize("dtype", (float, object))
+def test_maxrows_no_blank_lines(dtype):
+    txt = StringIO("1.5,2.5\n3.0,4.0\n5.5,6.0")
+    res = np.loadtxt(txt, dtype=dtype, delimiter=",", max_rows=2)
+    assert_equal(res.dtype, dtype)
+    assert_equal(res, np.array([["1.5", "2.5"], ["3.0", "4.0"]], dtype=dtype))
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype", (np.dtype("f8"), np.dtype("i2")))
+def test_exception_message_bad_values(dtype):
+    txt = StringIO("1,2\n3,XXX\n5,6")
+    msg = f"could not convert string 'XXX' to {dtype} at row 1, column 2"
+    with pytest.raises(ValueError, match=msg):
+        np.loadtxt(txt, dtype=dtype, delimiter=",")
+
+
+def test_converters_negative_indices():
+    txt = StringIO('1.5,2.5\n3.0,XXX\n5.5,6.0')
+    conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
+    expected = np.array([[1.5, 2.5], [3.0, np.nan], [5.5, 6.0]])
+    res = np.loadtxt(txt, dtype=np.float64, delimiter=",", converters=conv)
+    assert_equal(res, expected)
+
+
+def test_converters_negative_indices_with_usecols():
+    txt = StringIO('1.5,2.5,3.5\n3.0,4.0,XXX\n5.5,6.0,7.5\n')
+    conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
+    expected = np.array([[1.5, 3.5], [3.0, np.nan], [5.5, 7.5]])
+    res = np.loadtxt(
+        txt,
+        dtype=np.float64,
+        delimiter=",",
+        converters=conv,
+        usecols=[0, -1],
+    )
+    assert_equal(res, expected)
+
+    # Second test with variable number of rows:
+    res = np.loadtxt(StringIO('''0,1,2\n0,1,2,3,4'''), delimiter=",",
+                     usecols=[0, -1], converters={-1: (lambda x: -1)})
+    assert_array_equal(res, [[0, -1], [0, -1]])
+
+
+def test_ragged_error():
+    rows = ["1,2,3", "1,2,3", "4,3,2,1"]
+    with pytest.raises(ValueError,
+            match="the number of columns changed from 3 to 4 at row 3"):
+        np.loadtxt(rows, delimiter=",")
+
+
+def test_ragged_usecols():
+    # usecols, and negative ones, work even with varying number of columns.
+    txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
+    expected = np.array([[0, 0], [0, 0], [0, 0]])
+    res = np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2])
+    assert_equal(res, expected)
+
+    txt = StringIO("0,0,XXX\n0\n0,XXX,XXX,0,XXX\n")
+    with pytest.raises(ValueError,
+                match="invalid column index -2 at row 2 with 1 columns"):
+        # There is no -2 column in the second row:
+        np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2])
+
+
+def test_empty_usecols():
+    txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
+    res = np.loadtxt(txt, dtype=np.dtype([]), delimiter=",", usecols=[])
+    assert res.shape == (3,)
+    assert res.dtype == np.dtype([])
+
+
+@pytest.mark.parametrize("c1", ["a", "の", "🫕"])
+@pytest.mark.parametrize("c2", ["a", "の", "🫕"])
+def test_large_unicode_characters(c1, c2):
+    # c1 and c2 span ascii, 16bit and 32bit range.
+    txt = StringIO(f"a,{c1},c,1.0\ne,{c2},2.0,g")
+    res = np.loadtxt(txt, dtype=np.dtype('U12'), delimiter=",")
+    expected = np.array(
+        [f"a,{c1},c,1.0".split(","), f"e,{c2},2.0,g".split(",")],
+        dtype=np.dtype('U12')
+    )
+    assert_equal(res, expected)
+
+
+def test_unicode_with_converter():
+    txt = StringIO("cat,dog\nαβγ,δεζ\nabc,def\n")
+    conv = {0: lambda s: s.upper()}
+    res = np.loadtxt(
+        txt,
+        dtype=np.dtype("U12"),
+        converters=conv,
+        delimiter=",",
+        encoding=None
+    )
+    expected = np.array([['CAT', 'dog'], ['ΑΒΓ', 'δεζ'], ['ABC', 'def']])
+    assert_equal(res, expected)
+
+
+def test_converter_with_structured_dtype():
+    txt = StringIO('1.5,2.5,Abc\n3.0,4.0,dEf\n5.5,6.0,ghI\n')
+    dt = np.dtype([('m', np.int32), ('r', np.float32), ('code', 'U8')])
+    conv = {0: lambda s: int(10*float(s)), -1: lambda s: s.upper()}
+    res = np.loadtxt(txt, dtype=dt, delimiter=",", converters=conv)
+    expected = np.array(
+        [(15, 2.5, 'ABC'), (30, 4.0, 'DEF'), (55, 6.0, 'GHI')], dtype=dt
+    )
+    assert_equal(res, expected)
+
+
+def test_converter_with_unicode_dtype():
+    """
+    With the 'bytes' encoding, tokens are encoded prior to being
+    passed to the converter. This means that the output of the converter may
+    be bytes instead of unicode as expected by `read_rows`.
+
+    This test checks that outputs from the above scenario are properly decoded
+    prior to parsing by `read_rows`.
+    """
+    txt = StringIO('abc,def\nrst,xyz')
+    conv = bytes.upper
+    res = np.loadtxt(
+            txt, dtype=np.dtype("U3"), converters=conv, delimiter=",",
+            encoding="bytes")
+    expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']])
+    assert_equal(res, expected)
+
+
+def test_read_huge_row():
+    row = "1.5, 2.5," * 50000
+    row = row[:-1] + "\n"
+    txt = StringIO(row * 2)
+    res = np.loadtxt(txt, delimiter=",", dtype=float)
+    assert_equal(res, np.tile([1.5, 2.5], (2, 50000)))
+
+
+@pytest.mark.parametrize("dtype", "edfgFDG")
+def test_huge_float(dtype):
+    # Covers a non-optimized path that is rarely taken:
+    field = "0" * 1000 + ".123456789"
+    dtype = np.dtype(dtype)
+    value = np.loadtxt([field], dtype=dtype)[()]
+    assert value == dtype.type("0.123456789")
+
+
+@pytest.mark.parametrize(
+    ("given_dtype", "expected_dtype"),
+    [
+        ("S", np.dtype("S5")),
+        ("U", np.dtype("U5")),
+    ],
+)
+def test_string_no_length_given(given_dtype, expected_dtype):
+    """
+    The given dtype is just 'S' or 'U' with no length. In these cases, the
+    length of the resulting dtype is determined by the longest string found
+    in the file.
+    """
+    txt = StringIO("AAA,5-1\nBBBBB,0-3\nC,4-9\n")
+    res = np.loadtxt(txt, dtype=given_dtype, delimiter=",")
+    expected = np.array(
+        [['AAA', '5-1'], ['BBBBB', '0-3'], ['C', '4-9']], dtype=expected_dtype
+    )
+    assert_equal(res, expected)
+    assert_equal(res.dtype, expected_dtype)
+
+
+def test_float_conversion():
+    """
+    Some tests that the conversion to float64 works as accurately as the
+    Python built-in `float` function. In a naive version of the float parser,
+    these strings resulted in values that were off by an ULP or two.
+    """
+    strings = [
+        '0.9999999999999999',
+        '9876543210.123456',
+        '5.43215432154321e+300',
+        '0.901',
+        '0.333',
+    ]
+    txt = StringIO('\n'.join(strings))
+    res = np.loadtxt(txt)
+    expected = np.array([float(s) for s in strings])
+    assert_equal(res, expected)
+
+
+def test_bool():
+    # Simple test for bool via integer
+    txt = StringIO("1, 0\n10, -1")
+    res = np.loadtxt(txt, dtype=bool, delimiter=",")
+    assert res.dtype == bool
+    assert_array_equal(res, [[True, False], [True, True]])
+    # Make sure we use only 1 and 0 on the byte level:
+    assert_array_equal(res.view(np.uint8), [[1, 0], [1, 1]])
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
+@pytest.mark.filterwarnings("error:.*integer via a float.*:DeprecationWarning")
+def test_integer_signs(dtype):
+    dtype = np.dtype(dtype)
+    assert np.loadtxt(["+2"], dtype=dtype) == 2
+    if dtype.kind == "u":
+        with pytest.raises(ValueError):
+            np.loadtxt(["-1\n"], dtype=dtype)
+    else:
+        assert np.loadtxt(["-2\n"], dtype=dtype) == -2
+
+    for sign in ["++", "+-", "--", "-+"]:
+        with pytest.raises(ValueError):
+            np.loadtxt([f"{sign}2\n"], dtype=dtype)
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
+@pytest.mark.filterwarnings("error:.*integer via a float.*:DeprecationWarning")
+def test_implicit_cast_float_to_int_fails(dtype):
+    txt = StringIO("1.0, 2.1, 3.7\n4, 5, 6")
+    with pytest.raises(ValueError):
+        np.loadtxt(txt, dtype=dtype, delimiter=",")
+
+@pytest.mark.parametrize("dtype", (np.complex64, np.complex128))
+@pytest.mark.parametrize("with_parens", (False, True))
+def test_complex_parsing(dtype, with_parens):
+    s = "(1.0-2.5j),3.75,(7+-5.0j)\n(4),(-19e2j),(0)"
+    if not with_parens:
+        s = s.replace("(", "").replace(")", "")
+
+    res = np.loadtxt(StringIO(s), dtype=dtype, delimiter=",")
+    expected = np.array(
+        [[1.0-2.5j, 3.75, 7-5j], [4.0, -1900j, 0]], dtype=dtype
+    )
+    assert_equal(res, expected)
+
+
+def test_read_from_generator():
+    def gen():
+        for i in range(4):
+            yield f"{i},{2*i},{i**2}"
+
+    res = np.loadtxt(gen(), dtype=int, delimiter=",")
+    expected = np.array([[0, 0, 0], [1, 2, 1], [2, 4, 4], [3, 6, 9]])
+    assert_equal(res, expected)
+
+
+def test_read_from_generator_multitype():
+    def gen():
+        for i in range(3):
+            yield f"{i} {i / 4}"
+
+    res = np.loadtxt(gen(), dtype="i, d", delimiter=" ")
+    expected = np.array([(0, 0.0), (1, 0.25), (2, 0.5)], dtype="i, d")
+    assert_equal(res, expected)
+
+
+def test_read_from_bad_generator():
+    def gen():
+        yield from ["1,2", b"3, 5", 12738]
+
+    with pytest.raises(
+            TypeError, match=r"non-string returned while reading data"):
+        np.loadtxt(gen(), dtype="i, i", delimiter=",")
+
+
+@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+def test_object_cleanup_on_read_error():
+    sentinel = object()
+    already_read = 0
+
+    def conv(x):
+        nonlocal already_read
+        if already_read > 4999:
+            raise ValueError("failed half-way through!")
+        already_read += 1
+        return sentinel
+
+    txt = StringIO("x\n" * 10000)
+
+    with pytest.raises(ValueError, match="at row 5000, column 1"):
+        np.loadtxt(txt, dtype=object, converters={0: conv})
+
+    assert sys.getrefcount(sentinel) == 2
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+def test_character_not_bytes_compatible():
+    """Test exception when a character cannot be encoded as 'S'."""
+    data = StringIO("–")  # == \u2013
+    with pytest.raises(ValueError):
+        np.loadtxt(data, dtype="S5")
+
+
+@pytest.mark.parametrize("conv", (0, [float], ""))
+def test_invalid_converter(conv):
+    msg = (
+        "converters must be a dictionary mapping columns to converter "
+        "functions or a single callable."
+    )
+    with pytest.raises(TypeError, match=msg):
+        np.loadtxt(StringIO("1 2\n3 4"), converters=conv)
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+def test_converters_dict_raises_non_integer_key():
+    with pytest.raises(TypeError, match="keys of the converters dict"):
+        np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int})
+    with pytest.raises(TypeError, match="keys of the converters dict"):
+        np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int}, usecols=0)
+
+
+@pytest.mark.parametrize("bad_col_ind", (3, -3))
+def test_converters_dict_raises_non_col_key(bad_col_ind):
+    data = StringIO("1 2\n3 4")
+    with pytest.raises(ValueError, match="converter specified for column"):
+        np.loadtxt(data, converters={bad_col_ind: int})
+
+
+def test_converters_dict_raises_val_not_callable():
+    with pytest.raises(TypeError,
+                match="values of the converters dictionary must be callable"):
+        np.loadtxt(StringIO("1 2\n3 4"), converters={0: 1})
+
+
+@pytest.mark.parametrize("q", ('"', "'", "`"))
+def test_quoted_field(q):
+    txt = StringIO(
+        f"{q}alpha, x{q}, 2.5\n{q}beta, y{q}, 4.5\n{q}gamma, z{q}, 5.0\n"
+    )
+    dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)])
+    expected = np.array(
+        [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype
+    )
+
+    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar=q)
+    assert_array_equal(res, expected)
+
+
+@pytest.mark.parametrize("q", ('"', "'", "`"))
+def test_quoted_field_with_whitepace_delimiter(q):
+    txt = StringIO(
+        f"{q}alpha, x{q}     2.5\n{q}beta, y{q} 4.5\n{q}gamma, z{q}   5.0\n"
+    )
+    dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)])
+    expected = np.array(
+        [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype
+    )
+
+    res = np.loadtxt(txt, dtype=dtype, delimiter=None, quotechar=q)
+    assert_array_equal(res, expected)
+
+
+def test_quote_support_default():
+    """Support for quoted fields is disabled by default."""
+    txt = StringIO('"lat,long", 45, 30\n')
+    dtype = np.dtype([('f0', 'U24'), ('f1', np.float64), ('f2', np.float64)])
+
+    with pytest.raises(ValueError,
+            match="the dtype passed requires 3 columns but 4 were"):
+        np.loadtxt(txt, dtype=dtype, delimiter=",")
+
+    # Enable quoting support with non-None value for quotechar param
+    txt.seek(0)
+    expected = np.array([("lat,long", 45., 30.)], dtype=dtype)
+
+    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"')
+    assert_array_equal(res, expected)
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+def test_quotechar_multichar_error():
+    txt = StringIO("1,2\n3,4")
+    msg = r".*must be a single unicode character or None"
+    with pytest.raises(TypeError, match=msg):
+        np.loadtxt(txt, delimiter=",", quotechar="''")
+
+
+def test_comment_multichar_error_with_quote():
+    txt = StringIO("1,2\n3,4")
+    msg = (
+        "when multiple comments or a multi-character comment is given, "
+        "quotes are not supported."
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.loadtxt(txt, delimiter=",", comments="123", quotechar='"')
+    with pytest.raises(ValueError, match=msg):
+        np.loadtxt(txt, delimiter=",", comments=["#", "%"], quotechar='"')
+
+    # A single character string in a tuple is unpacked though:
+    res = np.loadtxt(txt, delimiter=",", comments=("#",), quotechar="'")
+    assert_equal(res, [[1, 2], [3, 4]])
+
+
+def test_structured_dtype_with_quotes():
+    data = StringIO(
+
+            "1000;2.4;'alpha';-34\n"
+            "2000;3.1;'beta';29\n"
+            "3500;9.9;'gamma';120\n"
+            "4090;8.1;'delta';0\n"
+            "5001;4.4;'epsilon';-99\n"
+            "6543;7.8;'omega';-1\n"
+
+    )
+    dtype = np.dtype(
+        [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)]
+    )
+    expected = np.array(
+        [
+            (1000, 2.4, "alpha", -34),
+            (2000, 3.1, "beta", 29),
+            (3500, 9.9, "gamma", 120),
+            (4090, 8.1, "delta", 0),
+            (5001, 4.4, "epsilon", -99),
+            (6543, 7.8, "omega", -1)
+        ],
+        dtype=dtype
+    )
+    res = np.loadtxt(data, dtype=dtype, delimiter=";", quotechar="'")
+    assert_array_equal(res, expected)
+
+
+def test_quoted_field_is_not_empty():
+    txt = StringIO('1\n\n"4"\n""')
+    expected = np.array(["1", "4", ""], dtype="U1")
+    res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"')
+    assert_equal(res, expected)
+
+def test_quoted_field_is_not_empty_nonstrict():
+    # Same as test_quoted_field_is_not_empty but check that we are not strict
+    # about missing closing quote (this is the `csv.reader` default also)
+    txt = StringIO('1\n\n"4"\n"')
+    expected = np.array(["1", "4", ""], dtype="U1")
+    res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"')
+    assert_equal(res, expected)
+
+def test_consecutive_quotechar_escaped():
+    txt = StringIO('"Hello, my name is ""Monty""!"')
+    expected = np.array('Hello, my name is "Monty"!', dtype="U40")
+    res = np.loadtxt(txt, dtype="U40", delimiter=",", quotechar='"')
+    assert_equal(res, expected)
+
+
+@pytest.mark.parametrize("data", ("", "\n\n\n", "# 1 2 3\n# 4 5 6\n"))
+@pytest.mark.parametrize("ndmin", (0, 1, 2))
+@pytest.mark.parametrize("usecols", [None, (1, 2, 3)])
+def test_warn_on_no_data(data, ndmin, usecols):
+    """Check that a UserWarning is emitted when no data is read from input."""
+    if usecols is not None:
+        expected_shape = (0, 3)
+    elif ndmin == 2:
+        expected_shape = (0, 1)  # guess a single column?!
+    else:
+        expected_shape = (0,)
+
+    txt = StringIO(data)
+    with pytest.warns(UserWarning, match="input contained no data"):
+        res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols)
+    assert res.shape == expected_shape
+
+    with NamedTemporaryFile(mode="w") as fh:
+        fh.write(data)
+        fh.seek(0)
+        with pytest.warns(UserWarning, match="input contained no data"):
+            res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols)
+        assert res.shape == expected_shape
+
+@pytest.mark.parametrize("skiprows", (2, 3))
+def test_warn_on_skipped_data(skiprows):
+    data = "1 2 3\n4 5 6"
+    txt = StringIO(data)
+    with pytest.warns(UserWarning, match="input contained no data"):
+        np.loadtxt(txt, skiprows=skiprows)
+
+
+@pytest.mark.parametrize(["dtype", "value"], [
+        ("i2", 0x0001), ("u2", 0x0001),
+        ("i4", 0x00010203), ("u4", 0x00010203),
+        ("i8", 0x0001020304050607), ("u8", 0x0001020304050607),
+        # The following values are constructed to lead to unique bytes:
+        ("float16", 3.07e-05),
+        ("float32", 9.2557e-41), ("complex64", 9.2557e-41+2.8622554e-29j),
+        ("float64", -1.758571353180402e-24),
+        # Here and below, the repr side-steps a small loss of precision in
+        # complex `str` in PyPy (which is probably fine, as repr works):
+        ("complex128", repr(5.406409232372729e-29-1.758571353180402e-24j)),
+        # Use integer values that fit into double.  Everything else leads to
+        # problems due to longdoubles going via double and decimal strings
+        # causing rounding errors.
+        ("longdouble", 0x01020304050607),
+        ("clongdouble", repr(0x01020304050607 + (0x00121314151617 * 1j))),
+        ("U2", "\U00010203\U000a0b0c")])
+@pytest.mark.parametrize("swap", [True, False])
+def test_byteswapping_and_unaligned(dtype, value, swap):
+    # Try to create "interesting" values within the valid unicode range:
+    dtype = np.dtype(dtype)
+    data = [f"x,{value}\n"]  # repr as PyPy `str` truncates some
+    if swap:
+        dtype = dtype.newbyteorder()
+    full_dt = np.dtype([("a", "S1"), ("b", dtype)], align=False)
+    # The above ensures that the interesting "b" field is unaligned:
+    assert full_dt.fields["b"][1] == 1
+    res = np.loadtxt(data, dtype=full_dt, delimiter=",",
+                     max_rows=1)  # max-rows prevents over-allocation
+    assert res["b"] == dtype.type(value)
+
+
+@pytest.mark.parametrize("dtype",
+        np.typecodes["AllInteger"] + "efdFD" + "?")
+def test_unicode_whitespace_stripping(dtype):
+    # Test that all numeric types (and bool) strip whitespace correctly
+    # \u202F is a narrow no-break space, `\n` is just a whitespace if quoted.
+    # Currently, skip float128 as it did not always support this and has no
+    # "custom" parsing:
+    txt = StringIO(' 3 ,"\u202F2\n"')
+    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"')
+    assert_array_equal(res, np.array([3, 2]).astype(dtype))
+
+
+@pytest.mark.parametrize("dtype", "FD")
+def test_unicode_whitespace_stripping_complex(dtype):
+    # Complex has a few extra cases since it has two components and
+    # parentheses
+    line = " 1 , 2+3j , ( 4+5j ), ( 6+-7j )  , 8j , ( 9j ) \n"
+    data = [line, line.replace(" ", "\u202F")]
+    res = np.loadtxt(data, dtype=dtype, delimiter=',')
+    assert_array_equal(res, np.array([[1, 2+3j, 4+5j, 6-7j, 8j, 9j]] * 2))
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype", "FD")
+@pytest.mark.parametrize("field",
+        ["1 +2j", "1+ 2j", "1+2 j", "1+-+3", "(1j", "(1", "(1+2j", "1+2j)"])
+def test_bad_complex(dtype, field):
+    with pytest.raises(ValueError):
+        np.loadtxt([field + "\n"], dtype=dtype, delimiter=",")
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype",
+            np.typecodes["AllInteger"] + "efgdFDG" + "?")
+def test_nul_character_error(dtype):
+    # Test that a \0 character is correctly recognized as an error even if
+    # what comes before is valid (not everything gets parsed internally).
+    if dtype.lower() == "g":
+        pytest.xfail("longdouble/clongdouble assignment may misbehave.")
+    with pytest.raises(ValueError):
+        np.loadtxt(["1\000"], dtype=dtype, delimiter=",", quotechar='"')
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+@pytest.mark.parametrize("dtype",
+        np.typecodes["AllInteger"] + "efgdFDG" + "?")
+def test_no_thousands_support(dtype):
+    # Mainly to document behaviour, Python supports thousands like 1_1.
+    # (e and G may end up using different conversion and support it, this is
+    # a bug but happens...)
+    if dtype == "e":
+        pytest.skip("half assignment currently uses Python float converter")
+    if dtype in "eG":
+        pytest.xfail("clongdouble assignment is buggy (uses `complex`?).")
+
+    assert int("1_1") == float("1_1") == complex("1_1") == 11
+    with pytest.raises(ValueError):
+        np.loadtxt(["1_1\n"], dtype=dtype)
+
+
+@pytest.mark.parametrize("data", [
+    ["1,2\n", "2\n,3\n"],
+    ["1,2\n", "2\r,3\n"]])
+def test_bad_newline_in_iterator(data):
+    # In NumPy <=1.22 this was accepted, because newlines were completely
+    # ignored when the input was an iterable.  This could be changed, but right
+    # now, we raise an error.
+    msg = "Found an unquoted embedded newline within a single line"
+    with pytest.raises(ValueError, match=msg):
+        np.loadtxt(data, delimiter=",")
+
+
+@pytest.mark.parametrize("data", [
+    ["1,2\n", "2,3\r\n"],  # a universal newline
+    ["1,2\n", "'2\n',3\n"],  # a quoted newline
+    ["1,2\n", "'2\r',3\n"],
+    ["1,2\n", "'2\r\n',3\n"],
+])
+def test_good_newline_in_iterator(data):
+    # The quoted newlines will be untransformed here, but are just whitespace.
+    res = np.loadtxt(data, delimiter=",", quotechar="'")
+    assert_array_equal(res, [[1., 2.], [2., 3.]])
+
+
+@pytest.mark.parametrize("newline", ["\n", "\r", "\r\n"])
+def test_universal_newlines_quoted(newline):
+    # Check that universal newline support within the tokenizer is not applied
+    # to quoted fields.  (note that lines must end in newline or quoted
+    # fields will not include a newline at all)
+    data = ['1,"2\n"\n', '3,"4\n', '1"\n']
+    data = [row.replace("\n", newline) for row in data]
+    res = np.loadtxt(data, dtype=object, delimiter=",", quotechar='"')
+    assert_array_equal(res, [['1', f'2{newline}'], ['3', f'4{newline}1']])
+
+
+def test_null_character():
+    # Basic tests to check that the NUL character is not special:
+    res = np.loadtxt(["1\0002\0003\n", "4\0005\0006"], delimiter="\000")
+    assert_array_equal(res, [[1, 2, 3], [4, 5, 6]])
+
+    # Also not as part of a field (avoid unicode/arrays as unicode strips \0)
+    res = np.loadtxt(["1\000,2\000,3\n", "4\000,5\000,6"],
+                     delimiter=",", dtype=object)
+    assert res.tolist() == [["1\000", "2\000", "3"], ["4\000", "5\000", "6"]]
+
+
+def test_iterator_fails_getting_next_line():
+    class BadSequence:
+        def __len__(self):
+            return 100
+
+        def __getitem__(self, item):
+            if item == 50:
+                raise RuntimeError("Bad things happened!")
+            return f"{item}, {item+1}"
+
+    with pytest.raises(RuntimeError, match="Bad things happened!"):
+        np.loadtxt(BadSequence(), dtype=int, delimiter=",")
+
+
+class TestCReaderUnitTests:
+    # These are internal tests for path that should not be possible to hit
+    # unless things go very very wrong somewhere.
+    def test_not_an_filelike(self):
+        with pytest.raises(AttributeError, match=".*read"):
+            np._core._multiarray_umath._load_from_filelike(
+                object(), dtype=np.dtype("i"), filelike=True)
+
+    def test_filelike_read_fails(self):
+        # Can only be reached if loadtxt opens the file, so it is hard to do
+        # via the public interface (although maybe not impossible considering
+        # the current "DataClass" backing).
+        class BadFileLike:
+            counter = 0
+
+            def read(self, size):
+                self.counter += 1
+                if self.counter > 20:
+                    raise RuntimeError("Bad bad bad!")
+                return "1,2,3\n"
+
+        with pytest.raises(RuntimeError, match="Bad bad bad!"):
+            np._core._multiarray_umath._load_from_filelike(
+                BadFileLike(), dtype=np.dtype("i"), filelike=True)
+
+    def test_filelike_bad_read(self):
+        # Can only be reached if loadtxt opens the file, so it is hard to do
+        # via the public interface (although maybe not impossible considering
+        # the current "DataClass" backing).
+
+        class BadFileLike:
+            counter = 0
+
+            def read(self, size):
+                return 1234  # not a string!
+
+        with pytest.raises(TypeError,
+                    match="non-string returned while reading data"):
+            np._core._multiarray_umath._load_from_filelike(
+                BadFileLike(), dtype=np.dtype("i"), filelike=True)
+
+    def test_not_an_iter(self):
+        with pytest.raises(TypeError,
+                    match="error reading from object, expected an iterable"):
+            np._core._multiarray_umath._load_from_filelike(
+                object(), dtype=np.dtype("i"), filelike=False)
+
+    def test_bad_type(self):
+        with pytest.raises(TypeError, match="internal error: dtype must"):
+            np._core._multiarray_umath._load_from_filelike(
+                object(), dtype="i", filelike=False)
+
+    def test_bad_encoding(self):
+        with pytest.raises(TypeError, match="encoding must be a unicode"):
+            np._core._multiarray_umath._load_from_filelike(
+                object(), dtype=np.dtype("i"), filelike=False, encoding=123)
+
+    @pytest.mark.parametrize("newline", ["\r", "\n", "\r\n"])
+    def test_manual_universal_newlines(self, newline):
+        # This is currently not available to users, because we should always
+        # open files with universal newlines enabled `newlines=None`.
+        # (And reading from an iterator uses slightly different code paths.)
+        # We have no real support for `newline="\r"` or `newline="\n" as the
+        # user cannot specify those options.
+        data = StringIO('0\n1\n"2\n"\n3\n4 #\n'.replace("\n", newline),
+                        newline="")
+
+        res = np._core._multiarray_umath._load_from_filelike(
+            data, dtype=np.dtype("U10"), filelike=True,
+            quote='"', comment="#", skiplines=1)
+        assert_array_equal(res[:, 0], ["1", f"2{newline}", "3", "4 "])
+
+
+def test_delimiter_comment_collision_raises():
+    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
+        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=",")
+
+
+def test_delimiter_quotechar_collision_raises():
+    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
+        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", quotechar=",")
+
+
+def test_comment_quotechar_collision_raises():
+    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
+        np.loadtxt(StringIO("1 2 3"), comments="#", quotechar="#")
+
+
+def test_delimiter_and_multiple_comments_collision_raises():
+    with pytest.raises(
+        TypeError, match="Comment characters.*cannot include the delimiter"
+    ):
+        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=["#", ","])
+
+
+@pytest.mark.parametrize(
+    "ws",
+    (
+        " ",  # space
+        "\t",  # tab
+        "\u2003",  # em
+        "\u00A0",  # non-break
+        "\u3000",  # ideographic space
+    )
+)
+def test_collision_with_default_delimiter_raises(ws):
+    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
+        np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), comments=ws)
+    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
+        np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), quotechar=ws)
+
+
+@pytest.mark.parametrize("nl", ("\n", "\r"))
+def test_control_character_newline_raises(nl):
+    txt = StringIO(f"1{nl}2{nl}3{nl}{nl}4{nl}5{nl}6{nl}{nl}")
+    msg = "control character.*cannot be a newline"
+    with pytest.raises(TypeError, match=msg):
+        np.loadtxt(txt, delimiter=nl)
+    with pytest.raises(TypeError, match=msg):
+        np.loadtxt(txt, comments=nl)
+    with pytest.raises(TypeError, match=msg):
+        np.loadtxt(txt, quotechar=nl)
+
+
+@pytest.mark.parametrize(
+    ("generic_data", "long_datum", "unitless_dtype", "expected_dtype"),
+    [
+        ("2012-03", "2013-01-15", "M8", "M8[D]"),  # Datetimes
+        ("spam-a-lot", "tis_but_a_scratch", "U", "U17"),  # str
+    ],
+)
+@pytest.mark.parametrize("nrows", (10, 50000, 60000))  # lt, eq, gt chunksize
+def test_parametric_unit_discovery(
+    generic_data, long_datum, unitless_dtype, expected_dtype, nrows
+):
+    """Check that the correct unit (e.g. month, day, second) is discovered from
+    the data when a user specifies a unitless datetime."""
+    # Unit should be "D" (days) due to last entry
+    data = [generic_data] * nrows + [long_datum]
+    expected = np.array(data, dtype=expected_dtype)
+    assert len(data) == nrows+1
+    assert len(data) == len(expected)
+
+    # file-like path
+    txt = StringIO("\n".join(data))
+    a = np.loadtxt(txt, dtype=unitless_dtype)
+    assert len(a) == len(expected)
+    assert a.dtype == expected.dtype
+    assert_equal(a, expected)
+
+    # file-obj path
+    fd, fname = mkstemp()
+    os.close(fd)
+    with open(fname, "w") as fh:
+        fh.write("\n".join(data)+"\n")
+    # loading the full file...
+    a = np.loadtxt(fname, dtype=unitless_dtype)
+    assert len(a) == len(expected)
+    assert a.dtype == expected.dtype
+    assert_equal(a, expected)
+    # loading half of the file...
+    a = np.loadtxt(fname, dtype=unitless_dtype, max_rows=int(nrows/2))
+    os.remove(fname)
+    assert len(a) == int(nrows/2)
+    assert_equal(a, expected[:int(nrows/2)])
+
+
+def test_str_dtype_unit_discovery_with_converter():
+    data = ["spam-a-lot"] * 60000 + ["XXXtis_but_a_scratch"]
+    expected = np.array(
+        ["spam-a-lot"] * 60000 + ["tis_but_a_scratch"], dtype="U17"
+    )
+    conv = lambda s: s.removeprefix("XXX")
+
+    # file-like path
+    txt = StringIO("\n".join(data))
+    a = np.loadtxt(txt, dtype="U", converters=conv)
+    assert a.dtype == expected.dtype
+    assert_equal(a, expected)
+
+    # file-obj path
+    fd, fname = mkstemp()
+    os.close(fd)
+    with open(fname, "w") as fh:
+        fh.write("\n".join(data))
+    a = np.loadtxt(fname, dtype="U", converters=conv)
+    os.remove(fname)
+    assert a.dtype == expected.dtype
+    assert_equal(a, expected)
+
+
+@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
+                    reason="PyPy bug in error formatting")
+def test_control_character_empty():
+    with pytest.raises(TypeError, match="Text reading control character must"):
+        np.loadtxt(StringIO("1 2 3"), delimiter="")
+    with pytest.raises(TypeError, match="Text reading control character must"):
+        np.loadtxt(StringIO("1 2 3"), quotechar="")
+    with pytest.raises(ValueError, match="comments cannot be an empty string"):
+        np.loadtxt(StringIO("1 2 3"), comments="")
+    with pytest.raises(ValueError, match="comments cannot be an empty string"):
+        np.loadtxt(StringIO("1 2 3"), comments=["#", ""])
+
+
+def test_control_characters_as_bytes():
+    """Byte control characters (comments, delimiter) are supported."""
+    a = np.loadtxt(StringIO("#header\n1,2,3"), comments=b"#", delimiter=b",")
+    assert_equal(a, [1, 2, 3])
+
+
+@pytest.mark.filterwarnings('ignore::UserWarning')
+def test_field_growing_cases():
+    # Test empty field appending/growing (each field still takes 1 character)
+    # to see if the final field appending does not create issues.
+    res = np.loadtxt([""], delimiter=",", dtype=bytes)
+    assert len(res) == 0
+
+    for i in range(1, 1024):
+        res = np.loadtxt(["," * i], delimiter=",", dtype=bytes, max_rows=10)
+        assert len(res) == i+1
+
+@pytest.mark.parametrize("nmax", (10000, 50000, 55000, 60000))
+def test_maxrows_exceeding_chunksize(nmax):
+    # tries to read all of the file,
+    # or less, equal, greater than _loadtxt_chunksize
+    file_length = 60000
+
+    # file-like path
+    data = ["a 0.5 1"]*file_length
+    txt = StringIO("\n".join(data))
+    res = np.loadtxt(txt, dtype=str, delimiter=" ", max_rows=nmax)
+    assert len(res) == nmax
+
+    # file-obj path
+    fd, fname = mkstemp()
+    os.close(fd)
+    with open(fname, "w") as fh:
+        fh.write("\n".join(data))
+    res = np.loadtxt(fname, dtype=str, delimiter=" ", max_rows=nmax)
+    os.remove(fname)
+    assert len(res) == nmax
+
+@pytest.mark.parametrize("nskip", (0, 10000, 12345, 50000, 67891, 100000))
+def test_skiprow_exceeding_maxrows_exceeding_chunksize(tmpdir, nskip):
+    # tries to read a file in chunks by skipping a variable amount of lines,
+    # less, equal, greater than max_rows
+    file_length = 110000
+    data = "\n".join(f"{i} a 0.5 1" for i in range(1, file_length + 1))
+    expected_length = min(60000, file_length - nskip)
+    expected = np.arange(nskip + 1, nskip + 1 + expected_length).astype(str)
+
+    # file-like path
+    txt = StringIO(data)
+    res = np.loadtxt(txt, dtype='str', delimiter=" ", skiprows=nskip, max_rows=60000)
+    assert len(res) == expected_length
+    # are the right lines read in res?
+    assert_array_equal(expected, res[:, 0])
+
+    # file-obj path
+    tmp_file = tmpdir / "test_data.txt"
+    tmp_file.write(data)
+    fname = str(tmp_file)
+    res = np.loadtxt(fname, dtype='str', delimiter=" ", skiprows=nskip, max_rows=60000)
+    assert len(res) == expected_length
+    # are the right lines read in res?
+    assert_array_equal(expected, res[:, 0])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_mixins.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_mixins.py
new file mode 100644
index 0000000000000000000000000000000000000000..632058763b7d9e826122af6834bb72d4bd970434
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_mixins.py
@@ -0,0 +1,216 @@
+import numbers
+import operator
+
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_raises
+
+
+# NOTE: This class should be kept as an exact copy of the example from the
+# docstring for NDArrayOperatorsMixin.
+
+class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
+    def __init__(self, value):
+        self.value = np.asarray(value)
+
+    # One might also consider adding the built-in list type to this
+    # list, to support operations like np.add(array_like, list)
+    _HANDLED_TYPES = (np.ndarray, numbers.Number)
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        out = kwargs.get('out', ())
+        for x in inputs + out:
+            # Only support operations with instances of _HANDLED_TYPES.
+            # Use ArrayLike instead of type(self) for isinstance to
+            # allow subclasses that don't override __array_ufunc__ to
+            # handle ArrayLike objects.
+            if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
+                return NotImplemented
+
+        # Defer to the implementation of the ufunc on unwrapped values.
+        inputs = tuple(x.value if isinstance(x, ArrayLike) else x
+                       for x in inputs)
+        if out:
+            kwargs['out'] = tuple(
+                x.value if isinstance(x, ArrayLike) else x
+                for x in out)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+
+        if type(result) is tuple:
+            # multiple return values
+            return tuple(type(self)(x) for x in result)
+        elif method == 'at':
+            # no return value
+            return None
+        else:
+            # one return value
+            return type(self)(result)
+
+    def __repr__(self):
+        return '%s(%r)' % (type(self).__name__, self.value)
+
+
+def wrap_array_like(result):
+    if type(result) is tuple:
+        return tuple(ArrayLike(r) for r in result)
+    else:
+        return ArrayLike(result)
+
+
+def _assert_equal_type_and_value(result, expected, err_msg=None):
+    assert_equal(type(result), type(expected), err_msg=err_msg)
+    if isinstance(result, tuple):
+        assert_equal(len(result), len(expected), err_msg=err_msg)
+        for result_item, expected_item in zip(result, expected):
+            _assert_equal_type_and_value(result_item, expected_item, err_msg)
+    else:
+        assert_equal(result.value, expected.value, err_msg=err_msg)
+        assert_equal(getattr(result.value, 'dtype', None),
+                     getattr(expected.value, 'dtype', None), err_msg=err_msg)
+
+
+_ALL_BINARY_OPERATORS = [
+    operator.lt,
+    operator.le,
+    operator.eq,
+    operator.ne,
+    operator.gt,
+    operator.ge,
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.floordiv,
+    operator.mod,
+    divmod,
+    pow,
+    operator.lshift,
+    operator.rshift,
+    operator.and_,
+    operator.xor,
+    operator.or_,
+]
+
+
+class TestNDArrayOperatorsMixin:
+
+    def test_array_like_add(self):
+
+        def check(result):
+            _assert_equal_type_and_value(result, ArrayLike(0))
+
+        check(ArrayLike(0) + 0)
+        check(0 + ArrayLike(0))
+
+        check(ArrayLike(0) + np.array(0))
+        check(np.array(0) + ArrayLike(0))
+
+        check(ArrayLike(np.array(0)) + 0)
+        check(0 + ArrayLike(np.array(0)))
+
+        check(ArrayLike(np.array(0)) + np.array(0))
+        check(np.array(0) + ArrayLike(np.array(0)))
+
+    def test_inplace(self):
+        array_like = ArrayLike(np.array([0]))
+        array_like += 1
+        _assert_equal_type_and_value(array_like, ArrayLike(np.array([1])))
+
+        array = np.array([0])
+        array += ArrayLike(1)
+        _assert_equal_type_and_value(array, ArrayLike(np.array([1])))
+
+    def test_opt_out(self):
+
+        class OptOut:
+            """Object that opts out of __array_ufunc__."""
+            __array_ufunc__ = None
+
+            def __add__(self, other):
+                return self
+
+            def __radd__(self, other):
+                return self
+
+        array_like = ArrayLike(1)
+        opt_out = OptOut()
+
+        # supported operations
+        assert_(array_like + opt_out is opt_out)
+        assert_(opt_out + array_like is opt_out)
+
+        # not supported
+        with assert_raises(TypeError):
+            # don't use the Python default, array_like = array_like + opt_out
+            array_like += opt_out
+        with assert_raises(TypeError):
+            array_like - opt_out
+        with assert_raises(TypeError):
+            opt_out - array_like
+
+    def test_subclass(self):
+
+        class SubArrayLike(ArrayLike):
+            """Should take precedence over ArrayLike."""
+
+        x = ArrayLike(0)
+        y = SubArrayLike(1)
+        _assert_equal_type_and_value(x + y, y)
+        _assert_equal_type_and_value(y + x, y)
+
+    def test_object(self):
+        x = ArrayLike(0)
+        obj = object()
+        with assert_raises(TypeError):
+            x + obj
+        with assert_raises(TypeError):
+            obj + x
+        with assert_raises(TypeError):
+            x += obj
+
+    def test_unary_methods(self):
+        array = np.array([-1, 0, 1, 2])
+        array_like = ArrayLike(array)
+        for op in [operator.neg,
+                   operator.pos,
+                   abs,
+                   operator.invert]:
+            _assert_equal_type_and_value(op(array_like), ArrayLike(op(array)))
+
+    def test_forward_binary_methods(self):
+        array = np.array([-1, 0, 1, 2])
+        array_like = ArrayLike(array)
+        for op in _ALL_BINARY_OPERATORS:
+            expected = wrap_array_like(op(array, 1))
+            actual = op(array_like, 1)
+            err_msg = 'failed for operator {}'.format(op)
+            _assert_equal_type_and_value(expected, actual, err_msg=err_msg)
+
+    def test_reflected_binary_methods(self):
+        for op in _ALL_BINARY_OPERATORS:
+            expected = wrap_array_like(op(2, 1))
+            actual = op(2, ArrayLike(1))
+            err_msg = 'failed for operator {}'.format(op)
+            _assert_equal_type_and_value(expected, actual, err_msg=err_msg)
+
+    def test_matmul(self):
+        array = np.array([1, 2], dtype=np.float64)
+        array_like = ArrayLike(array)
+        expected = ArrayLike(np.float64(5))
+        _assert_equal_type_and_value(expected, np.matmul(array_like, array))
+        _assert_equal_type_and_value(
+            expected, operator.matmul(array_like, array))
+        _assert_equal_type_and_value(
+            expected, operator.matmul(array, array_like))
+
+    def test_ufunc_at(self):
+        array = ArrayLike(np.array([1, 2, 3, 4]))
+        assert_(np.negative.at(array, np.array([0, 1])) is None)
+        _assert_equal_type_and_value(array, ArrayLike([-1, -2, 3, 4]))
+
+    def test_ufunc_two_outputs(self):
+        mantissa, exponent = np.frexp(2 ** -3)
+        expected = (ArrayLike(mantissa), ArrayLike(exponent))
+        _assert_equal_type_and_value(
+            np.frexp(ArrayLike(2 ** -3)), expected)
+        _assert_equal_type_and_value(
+            np.frexp(ArrayLike(np.array(2 ** -3))), expected)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_nanfunctions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_nanfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8fa7df86b24602ae5ae9cf3ad465a17b4ce20ea
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_nanfunctions.py
@@ -0,0 +1,1418 @@
+import warnings
+import pytest
+import inspect
+from functools import partial
+
+import numpy as np
+from numpy._core.numeric import normalize_axis_tuple
+from numpy.exceptions import AxisError, ComplexWarning
+from numpy.lib._nanfunctions_impl import _nan_mask, _replace_nan
+from numpy.testing import (
+    assert_, assert_equal, assert_almost_equal, assert_raises,
+    assert_raises_regex, assert_array_equal, suppress_warnings
+    )
+
+
+# Test data
+_ndat = np.array([[0.6244, np.nan, 0.2692, 0.0116, np.nan, 0.1170],
+                  [0.5351, -0.9403, np.nan, 0.2100, 0.4759, 0.2833],
+                  [np.nan, np.nan, np.nan, 0.1042, np.nan, -0.5954],
+                  [0.1610, np.nan, np.nan, 0.1859, 0.3146, np.nan]])
+
+
+# Rows of _ndat with nans removed
+_rdat = [np.array([0.6244, 0.2692, 0.0116, 0.1170]),
+         np.array([0.5351, -0.9403, 0.2100, 0.4759, 0.2833]),
+         np.array([0.1042, -0.5954]),
+         np.array([0.1610, 0.1859, 0.3146])]
+
+# Rows of _ndat with nans converted to ones
+_ndat_ones = np.array([[0.6244, 1.0, 0.2692, 0.0116, 1.0, 0.1170],
+                       [0.5351, -0.9403, 1.0, 0.2100, 0.4759, 0.2833],
+                       [1.0, 1.0, 1.0, 0.1042, 1.0, -0.5954],
+                       [0.1610, 1.0, 1.0, 0.1859, 0.3146, 1.0]])
+
+# Rows of _ndat with nans converted to zeros
+_ndat_zeros = np.array([[0.6244, 0.0, 0.2692, 0.0116, 0.0, 0.1170],
+                        [0.5351, -0.9403, 0.0, 0.2100, 0.4759, 0.2833],
+                        [0.0, 0.0, 0.0, 0.1042, 0.0, -0.5954],
+                        [0.1610, 0.0, 0.0, 0.1859, 0.3146, 0.0]])
+
+
+class TestSignatureMatch:
+    NANFUNCS = {
+        np.nanmin: np.amin,
+        np.nanmax: np.amax,
+        np.nanargmin: np.argmin,
+        np.nanargmax: np.argmax,
+        np.nansum: np.sum,
+        np.nanprod: np.prod,
+        np.nancumsum: np.cumsum,
+        np.nancumprod: np.cumprod,
+        np.nanmean: np.mean,
+        np.nanmedian: np.median,
+        np.nanpercentile: np.percentile,
+        np.nanquantile: np.quantile,
+        np.nanvar: np.var,
+        np.nanstd: np.std,
+    }
+    IDS = [k.__name__ for k in NANFUNCS]
+
+    @staticmethod
+    def get_signature(func, default="..."):
+        """Construct a signature and replace all default parameter-values."""
+        prm_list = []
+        signature = inspect.signature(func)
+        for prm in signature.parameters.values():
+            if prm.default is inspect.Parameter.empty:
+                prm_list.append(prm)
+            else:
+                prm_list.append(prm.replace(default=default))
+        return inspect.Signature(prm_list)
+
+    @pytest.mark.parametrize("nan_func,func", NANFUNCS.items(), ids=IDS)
+    def test_signature_match(self, nan_func, func):
+        # Ignore the default parameter-values as they can sometimes differ
+        # between the two functions (*e.g.* one has `False` while the other
+        # has `np._NoValue`)
+        signature = self.get_signature(func)
+        nan_signature = self.get_signature(nan_func)
+        np.testing.assert_equal(signature, nan_signature)
+
+    def test_exhaustiveness(self):
+        """Validate that all nan functions are actually tested."""
+        np.testing.assert_equal(
+            set(self.IDS), set(np.lib._nanfunctions_impl.__all__)
+        )
+
+
+class TestNanFunctions_MinMax:
+
+    nanfuncs = [np.nanmin, np.nanmax]
+    stdfuncs = [np.min, np.max]
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for axis in [None, 0, 1]:
+                tgt = rf(mat, axis=axis, keepdims=True)
+                res = nf(mat, axis=axis, keepdims=True)
+                assert_(res.ndim == tgt.ndim)
+
+    def test_out(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            resout = np.zeros(3)
+            tgt = rf(mat, axis=1)
+            res = nf(mat, axis=1, out=resout)
+            assert_almost_equal(res, resout)
+            assert_almost_equal(res, tgt)
+
+    def test_dtype_from_input(self):
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                mat = np.eye(3, dtype=c)
+                tgt = rf(mat, axis=1).dtype.type
+                res = nf(mat, axis=1).dtype.type
+                assert_(res is tgt)
+                # scalar case
+                tgt = rf(mat, axis=None).dtype.type
+                res = nf(mat, axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_result_values(self):
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            tgt = [rf(d) for d in _rdat]
+            res = nf(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        match = "All-NaN slice encountered"
+        for func in self.nanfuncs:
+            with pytest.warns(RuntimeWarning, match=match):
+                out = func(array, axis=axis)
+            assert np.isnan(out).all()
+            assert out.dtype == array.dtype
+
+    def test_masked(self):
+        mat = np.ma.fix_invalid(_ndat)
+        msk = mat._mask.copy()
+        for f in [np.nanmin]:
+            res = f(mat, axis=1)
+            tgt = f(_ndat, axis=1)
+            assert_equal(res, tgt)
+            assert_equal(mat._mask, msk)
+            assert_(not np.isinf(mat).any())
+
+    def test_scalar(self):
+        for f in self.nanfuncs:
+            assert_(f(0.) == 0.)
+
+    def test_subclass(self):
+        class MyNDArray(np.ndarray):
+            pass
+
+        # Check that it works and that type and
+        # shape are preserved
+        mine = np.eye(3).view(MyNDArray)
+        for f in self.nanfuncs:
+            res = f(mine, axis=0)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == (3,))
+            res = f(mine, axis=1)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == (3,))
+            res = f(mine)
+            assert_(res.shape == ())
+
+        # check that rows of nan are dealt with for subclasses (#4628)
+        mine[1] = np.nan
+        for f in self.nanfuncs:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                res = f(mine, axis=0)
+                assert_(isinstance(res, MyNDArray))
+                assert_(not np.any(np.isnan(res)))
+                assert_(len(w) == 0)
+
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                res = f(mine, axis=1)
+                assert_(isinstance(res, MyNDArray))
+                assert_(np.isnan(res[1]) and not np.isnan(res[0])
+                        and not np.isnan(res[2]))
+                assert_(len(w) == 1, 'no warning raised')
+                assert_(issubclass(w[0].category, RuntimeWarning))
+
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                res = f(mine)
+                assert_(res.shape == ())
+                assert_(res != np.nan)
+                assert_(len(w) == 0)
+
+    def test_object_array(self):
+        arr = np.array([[1.0, 2.0], [np.nan, 4.0], [np.nan, np.nan]], dtype=object)
+        assert_equal(np.nanmin(arr), 1.0)
+        assert_equal(np.nanmin(arr, axis=0), [1.0, 2.0])
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            # assert_equal does not work on object arrays of nan
+            assert_equal(list(np.nanmin(arr, axis=1)), [1.0, 4.0, np.nan])
+            assert_(len(w) == 1, 'no warning raised')
+            assert_(issubclass(w[0].category, RuntimeWarning))
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_initial(self, dtype):
+        class MyNDArray(np.ndarray):
+            pass
+
+        ar = np.arange(9).astype(dtype)
+        ar[:5] = np.nan
+
+        for f in self.nanfuncs:
+            initial = 100 if f is np.nanmax else 0
+
+            ret1 = f(ar, initial=initial)
+            assert ret1.dtype == dtype
+            assert ret1 == initial
+
+            ret2 = f(ar.view(MyNDArray), initial=initial)
+            assert ret2.dtype == dtype
+            assert ret2 == initial
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_where(self, dtype):
+        class MyNDArray(np.ndarray):
+            pass
+
+        ar = np.arange(9).reshape(3, 3).astype(dtype)
+        ar[0, :] = np.nan
+        where = np.ones_like(ar, dtype=np.bool)
+        where[:, 0] = False
+
+        for f in self.nanfuncs:
+            reference = 4 if f is np.nanmin else 8
+
+            ret1 = f(ar, where=where, initial=5)
+            assert ret1.dtype == dtype
+            assert ret1 == reference
+
+            ret2 = f(ar.view(MyNDArray), where=where, initial=5)
+            assert ret2.dtype == dtype
+            assert ret2 == reference
+
+
+class TestNanFunctions_ArgminArgmax:
+
+    nanfuncs = [np.nanargmin, np.nanargmax]
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_result_values(self):
+        for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
+            for row in _ndat:
+                with suppress_warnings() as sup:
+                    sup.filter(RuntimeWarning, "invalid value encountered in")
+                    ind = f(row)
+                    val = row[ind]
+                    # comparing with NaN is tricky as the result
+                    # is always false except for NaN != NaN
+                    assert_(not np.isnan(val))
+                    assert_(not fcmp(val, row).any())
+                    assert_(not np.equal(val, row[:ind]).any())
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        for func in self.nanfuncs:
+            with pytest.raises(ValueError, match="All-NaN slice encountered"):
+                func(array, axis=axis)
+
+    def test_empty(self):
+        mat = np.zeros((0, 3))
+        for f in self.nanfuncs:
+            for axis in [0, None]:
+                assert_raises_regex(
+                        ValueError,
+                        "attempt to get argm.. of an empty sequence",
+                        f, mat, axis=axis)
+            for axis in [1]:
+                res = f(mat, axis=axis)
+                assert_equal(res, np.zeros(0))
+
+    def test_scalar(self):
+        for f in self.nanfuncs:
+            assert_(f(0.) == 0.)
+
+    def test_subclass(self):
+        class MyNDArray(np.ndarray):
+            pass
+
+        # Check that it works and that type and
+        # shape are preserved
+        mine = np.eye(3).view(MyNDArray)
+        for f in self.nanfuncs:
+            res = f(mine, axis=0)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == (3,))
+            res = f(mine, axis=1)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == (3,))
+            res = f(mine)
+            assert_(res.shape == ())
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_keepdims(self, dtype):
+        ar = np.arange(9).astype(dtype)
+        ar[:5] = np.nan
+
+        for f in self.nanfuncs:
+            reference = 5 if f is np.nanargmin else 8
+            ret = f(ar, keepdims=True)
+            assert ret.ndim == ar.ndim
+            assert ret == reference
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_out(self, dtype):
+        ar = np.arange(9).astype(dtype)
+        ar[:5] = np.nan
+
+        for f in self.nanfuncs:
+            out = np.zeros((), dtype=np.intp)
+            reference = 5 if f is np.nanargmin else 8
+            ret = f(ar, out=out)
+            assert ret is out
+            assert ret == reference
+
+
+
+_TEST_ARRAYS = {
+    "0d": np.array(5),
+    "1d": np.array([127, 39, 93, 87, 46])
+}
+for _v in _TEST_ARRAYS.values():
+    _v.setflags(write=False)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O",
+)
+@pytest.mark.parametrize("mat", _TEST_ARRAYS.values(), ids=_TEST_ARRAYS.keys())
+class TestNanFunctions_NumberTypes:
+    nanfuncs = {
+        np.nanmin: np.min,
+        np.nanmax: np.max,
+        np.nanargmin: np.argmin,
+        np.nanargmax: np.argmax,
+        np.nansum: np.sum,
+        np.nanprod: np.prod,
+        np.nancumsum: np.cumsum,
+        np.nancumprod: np.cumprod,
+        np.nanmean: np.mean,
+        np.nanmedian: np.median,
+        np.nanvar: np.var,
+        np.nanstd: np.std,
+    }
+    nanfunc_ids = [i.__name__ for i in nanfuncs]
+
+    @pytest.mark.parametrize("nanfunc,func", nanfuncs.items(), ids=nanfunc_ids)
+    @np.errstate(over="ignore")
+    def test_nanfunc(self, mat, dtype, nanfunc, func):
+        mat = mat.astype(dtype)
+        tgt = func(mat)
+        out = nanfunc(mat)
+
+        assert_almost_equal(out, tgt)
+        if dtype == "O":
+            assert type(out) is type(tgt)
+        else:
+            assert out.dtype == tgt.dtype
+
+    @pytest.mark.parametrize(
+        "nanfunc,func",
+        [(np.nanquantile, np.quantile), (np.nanpercentile, np.percentile)],
+        ids=["nanquantile", "nanpercentile"],
+    )
+    def test_nanfunc_q(self, mat, dtype, nanfunc, func):
+        mat = mat.astype(dtype)
+        if mat.dtype.kind == "c":
+            assert_raises(TypeError, func, mat, q=1)
+            assert_raises(TypeError, nanfunc, mat, q=1)
+
+        else:
+            tgt = func(mat, q=1)
+            out = nanfunc(mat, q=1)
+
+            assert_almost_equal(out, tgt)
+
+            if dtype == "O":
+                assert type(out) is type(tgt)
+            else:
+                assert out.dtype == tgt.dtype
+
+    @pytest.mark.parametrize(
+        "nanfunc,func",
+        [(np.nanvar, np.var), (np.nanstd, np.std)],
+        ids=["nanvar", "nanstd"],
+    )
+    def test_nanfunc_ddof(self, mat, dtype, nanfunc, func):
+        mat = mat.astype(dtype)
+        tgt = func(mat, ddof=0.5)
+        out = nanfunc(mat, ddof=0.5)
+
+        assert_almost_equal(out, tgt)
+        if dtype == "O":
+            assert type(out) is type(tgt)
+        else:
+            assert out.dtype == tgt.dtype
+
+    @pytest.mark.parametrize(
+        "nanfunc", [np.nanvar, np.nanstd]
+    )
+    def test_nanfunc_correction(self, mat, dtype, nanfunc):
+        mat = mat.astype(dtype)
+        assert_almost_equal(
+            nanfunc(mat, correction=0.5), nanfunc(mat, ddof=0.5)
+        )
+
+        err_msg = "ddof and correction can't be provided simultaneously."
+        with assert_raises_regex(ValueError, err_msg):
+            nanfunc(mat, ddof=0.5, correction=0.5)
+
+        with assert_raises_regex(ValueError, err_msg):
+            nanfunc(mat, ddof=1, correction=0)
+
+
+class SharedNanFunctionsTestsMixin:
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for axis in [None, 0, 1]:
+                tgt = rf(mat, axis=axis, keepdims=True)
+                res = nf(mat, axis=axis, keepdims=True)
+                assert_(res.ndim == tgt.ndim)
+
+    def test_out(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            resout = np.zeros(3)
+            tgt = rf(mat, axis=1)
+            res = nf(mat, axis=1, out=resout)
+            assert_almost_equal(res, resout)
+            assert_almost_equal(res, tgt)
+
+    def test_dtype_from_dtype(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                with suppress_warnings() as sup:
+                    if nf in {np.nanstd, np.nanvar} and c in 'FDG':
+                        # Giving the warning is a small bug, see gh-8000
+                        sup.filter(ComplexWarning)
+                    tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
+                    res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
+                    assert_(res is tgt)
+                    # scalar case
+                    tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
+                    res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
+                    assert_(res is tgt)
+
+    def test_dtype_from_char(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                with suppress_warnings() as sup:
+                    if nf in {np.nanstd, np.nanvar} and c in 'FDG':
+                        # Giving the warning is a small bug, see gh-8000
+                        sup.filter(ComplexWarning)
+                    tgt = rf(mat, dtype=c, axis=1).dtype.type
+                    res = nf(mat, dtype=c, axis=1).dtype.type
+                    assert_(res is tgt)
+                    # scalar case
+                    tgt = rf(mat, dtype=c, axis=None).dtype.type
+                    res = nf(mat, dtype=c, axis=None).dtype.type
+                    assert_(res is tgt)
+
+    def test_dtype_from_input(self):
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                mat = np.eye(3, dtype=c)
+                tgt = rf(mat, axis=1).dtype.type
+                res = nf(mat, axis=1).dtype.type
+                assert_(res is tgt, "res %s, tgt %s" % (res, tgt))
+                # scalar case
+                tgt = rf(mat, axis=None).dtype.type
+                res = nf(mat, axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_result_values(self):
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            tgt = [rf(d) for d in _rdat]
+            res = nf(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    def test_scalar(self):
+        for f in self.nanfuncs:
+            assert_(f(0.) == 0.)
+
+    def test_subclass(self):
+        class MyNDArray(np.ndarray):
+            pass
+
+        # Check that it works and that type and
+        # shape are preserved
+        array = np.eye(3)
+        mine = array.view(MyNDArray)
+        for f in self.nanfuncs:
+            expected_shape = f(array, axis=0).shape
+            res = f(mine, axis=0)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == expected_shape)
+            expected_shape = f(array, axis=1).shape
+            res = f(mine, axis=1)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == expected_shape)
+            expected_shape = f(array).shape
+            res = f(mine)
+            assert_(isinstance(res, MyNDArray))
+            assert_(res.shape == expected_shape)
+
+
+class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin):
+
+    nanfuncs = [np.nansum, np.nanprod]
+    stdfuncs = [np.sum, np.prod]
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        for func, identity in zip(self.nanfuncs, [0, 1]):
+            out = func(array, axis=axis)
+            assert np.all(out == identity)
+            assert out.dtype == array.dtype
+
+    def test_empty(self):
+        for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
+            mat = np.zeros((0, 3))
+            tgt = [tgt_value]*3
+            res = f(mat, axis=0)
+            assert_equal(res, tgt)
+            tgt = []
+            res = f(mat, axis=1)
+            assert_equal(res, tgt)
+            tgt = tgt_value
+            res = f(mat, axis=None)
+            assert_equal(res, tgt)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_initial(self, dtype):
+        ar = np.arange(9).astype(dtype)
+        ar[:5] = np.nan
+
+        for f in self.nanfuncs:
+            reference = 28 if f is np.nansum else 3360
+            ret = f(ar, initial=2)
+            assert ret.dtype == dtype
+            assert ret == reference
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_where(self, dtype):
+        ar = np.arange(9).reshape(3, 3).astype(dtype)
+        ar[0, :] = np.nan
+        where = np.ones_like(ar, dtype=np.bool)
+        where[:, 0] = False
+
+        for f in self.nanfuncs:
+            reference = 26 if f is np.nansum else 2240
+            ret = f(ar, where=where, initial=2)
+            assert ret.dtype == dtype
+            assert ret == reference
+
+
+class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin):
+
+    nanfuncs = [np.nancumsum, np.nancumprod]
+    stdfuncs = [np.cumsum, np.cumprod]
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan)
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        for func, identity in zip(self.nanfuncs, [0, 1]):
+            out = func(array)
+            assert np.all(out == identity)
+            assert out.dtype == array.dtype
+
+    def test_empty(self):
+        for f, tgt_value in zip(self.nanfuncs, [0, 1]):
+            mat = np.zeros((0, 3))
+            tgt = tgt_value*np.ones((0, 3))
+            res = f(mat, axis=0)
+            assert_equal(res, tgt)
+            tgt = mat
+            res = f(mat, axis=1)
+            assert_equal(res, tgt)
+            tgt = np.zeros(0)
+            res = f(mat, axis=None)
+            assert_equal(res, tgt)
+
+    def test_keepdims(self):
+        for f, g in zip(self.nanfuncs, self.stdfuncs):
+            mat = np.eye(3)
+            for axis in [None, 0, 1]:
+                tgt = f(mat, axis=axis, out=None)
+                res = g(mat, axis=axis, out=None)
+                assert_(res.ndim == tgt.ndim)
+
+        for f in self.nanfuncs:
+            d = np.ones((3, 5, 7, 11))
+            # Randomly set some elements to NaN:
+            rs = np.random.RandomState(0)
+            d[rs.rand(*d.shape) < 0.5] = np.nan
+            res = f(d, axis=None)
+            assert_equal(res.shape, (1155,))
+            for axis in np.arange(4):
+                res = f(d, axis=axis)
+                assert_equal(res.shape, (3, 5, 7, 11))
+
+    def test_result_values(self):
+        for axis in (-2, -1, 0, 1, None):
+            tgt = np.cumprod(_ndat_ones, axis=axis)
+            res = np.nancumprod(_ndat, axis=axis)
+            assert_almost_equal(res, tgt)
+            tgt = np.cumsum(_ndat_zeros,axis=axis)
+            res = np.nancumsum(_ndat, axis=axis)
+            assert_almost_equal(res, tgt)
+
+    def test_out(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            resout = np.eye(3)
+            for axis in (-2, -1, 0, 1):
+                tgt = rf(mat, axis=axis)
+                res = nf(mat, axis=axis, out=resout)
+                assert_almost_equal(res, resout)
+                assert_almost_equal(res, tgt)
+
+
+class TestNanFunctions_MeanVarStd(SharedNanFunctionsTestsMixin):
+
+    nanfuncs = [np.nanmean, np.nanvar, np.nanstd]
+    stdfuncs = [np.mean, np.var, np.std]
+
+    def test_dtype_error(self):
+        for f in self.nanfuncs:
+            for dtype in [np.bool, np.int_, np.object_]:
+                assert_raises(TypeError, f, _ndat, axis=1, dtype=dtype)
+
+    def test_out_dtype_error(self):
+        for f in self.nanfuncs:
+            for dtype in [np.bool, np.int_, np.object_]:
+                out = np.empty(_ndat.shape[0], dtype=dtype)
+                assert_raises(TypeError, f, _ndat, axis=1, out=out)
+
+    def test_ddof(self):
+        nanfuncs = [np.nanvar, np.nanstd]
+        stdfuncs = [np.var, np.std]
+        for nf, rf in zip(nanfuncs, stdfuncs):
+            for ddof in [0, 1]:
+                tgt = [rf(d, ddof=ddof) for d in _rdat]
+                res = nf(_ndat, axis=1, ddof=ddof)
+                assert_almost_equal(res, tgt)
+
+    def test_ddof_too_big(self):
+        nanfuncs = [np.nanvar, np.nanstd]
+        stdfuncs = [np.var, np.std]
+        dsize = [len(d) for d in _rdat]
+        for nf, rf in zip(nanfuncs, stdfuncs):
+            for ddof in range(5):
+                with suppress_warnings() as sup:
+                    sup.record(RuntimeWarning)
+                    sup.filter(ComplexWarning)
+                    tgt = [ddof >= d for d in dsize]
+                    res = nf(_ndat, axis=1, ddof=ddof)
+                    assert_equal(np.isnan(res), tgt)
+                    if any(tgt):
+                        assert_(len(sup.log) == 1)
+                    else:
+                        assert_(len(sup.log) == 0)
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        match = "(Degrees of freedom <= 0 for slice.)|(Mean of empty slice)"
+        for func in self.nanfuncs:
+            with pytest.warns(RuntimeWarning, match=match):
+                out = func(array, axis=axis)
+            assert np.isnan(out).all()
+
+            # `nanvar` and `nanstd` convert complex inputs to their
+            # corresponding floating dtype
+            if func is np.nanmean:
+                assert out.dtype == array.dtype
+            else:
+                assert out.dtype == np.abs(array).dtype
+
+    def test_empty(self):
+        mat = np.zeros((0, 3))
+        for f in self.nanfuncs:
+            for axis in [0, None]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    assert_(np.isnan(f(mat, axis=axis)).all())
+                    assert_(len(w) == 1)
+                    assert_(issubclass(w[0].category, RuntimeWarning))
+            for axis in [1]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    assert_equal(f(mat, axis=axis), np.zeros([]))
+                    assert_(len(w) == 0)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
+    def test_where(self, dtype):
+        ar = np.arange(9).reshape(3, 3).astype(dtype)
+        ar[0, :] = np.nan
+        where = np.ones_like(ar, dtype=np.bool)
+        where[:, 0] = False
+
+        for f, f_std in zip(self.nanfuncs, self.stdfuncs):
+            reference = f_std(ar[where][2:])
+            dtype_reference = dtype if f is np.nanmean else ar.real.dtype
+
+            ret = f(ar, where=where)
+            assert ret.dtype == dtype_reference
+            np.testing.assert_allclose(ret, reference)
+
+    def test_nanstd_with_mean_keyword(self):
+        # Setting the seed to make the test reproducible
+        rng = np.random.RandomState(1234)
+        A = rng.randn(10, 20, 5) + 0.5
+        A[:, 5, :] = np.nan
+
+        mean_out = np.zeros((10, 1, 5))
+        std_out = np.zeros((10, 1, 5))
+
+        mean = np.nanmean(A,
+                       out=mean_out,
+                       axis=1,
+                       keepdims=True)
+
+        # The returned  object should be the object specified during calling
+        assert mean_out is mean
+
+        std = np.nanstd(A,
+                     out=std_out,
+                     axis=1,
+                     keepdims=True,
+                     mean=mean)
+
+        # The returned  object should be the object specified during calling
+        assert std_out is std
+
+        # Shape of returned mean and std should be same
+        assert std.shape == mean.shape
+        assert std.shape == (10, 1, 5)
+
+        # Output should be the same as from the individual algorithms
+        std_old = np.nanstd(A, axis=1, keepdims=True)
+
+        assert std_old.shape == mean.shape
+        assert_almost_equal(std, std_old)
+
+_TIME_UNITS = (
+    "Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as"
+)
+
+# All `inexact` + `timdelta64` type codes
+_TYPE_CODES = list(np.typecodes["AllFloat"])
+_TYPE_CODES += [f"m8[{unit}]" for unit in _TIME_UNITS]
+
+
+class TestNanFunctions_Median:
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        np.nanmedian(ndat)
+        assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for axis in [None, 0, 1]:
+            tgt = np.median(mat, axis=axis, out=None, overwrite_input=False)
+            res = np.nanmedian(mat, axis=axis, out=None, overwrite_input=False)
+            assert_(res.ndim == tgt.ndim)
+
+        d = np.ones((3, 5, 7, 11))
+        # Randomly set some elements to NaN:
+        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
+        w = w.astype(np.intp)
+        d[tuple(w)] = np.nan
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            res = np.nanmedian(d, axis=None, keepdims=True)
+            assert_equal(res.shape, (1, 1, 1, 1))
+            res = np.nanmedian(d, axis=(0, 1), keepdims=True)
+            assert_equal(res.shape, (1, 1, 7, 11))
+            res = np.nanmedian(d, axis=(0, 3), keepdims=True)
+            assert_equal(res.shape, (1, 5, 7, 1))
+            res = np.nanmedian(d, axis=(1,), keepdims=True)
+            assert_equal(res.shape, (3, 1, 7, 11))
+            res = np.nanmedian(d, axis=(0, 1, 2, 3), keepdims=True)
+            assert_equal(res.shape, (1, 1, 1, 1))
+            res = np.nanmedian(d, axis=(0, 1, 3), keepdims=True)
+            assert_equal(res.shape, (1, 1, 7, 1))
+
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1, ),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
+    def test_keepdims_out(self, axis):
+        d = np.ones((3, 5, 7, 11))
+        # Randomly set some elements to NaN:
+        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
+        w = w.astype(np.intp)
+        d[tuple(w)] = np.nan
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        out = np.empty(shape_out)
+        result = np.nanmedian(d, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    def test_out(self):
+        mat = np.random.rand(3, 3)
+        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
+        resout = np.zeros(3)
+        tgt = np.median(mat, axis=1)
+        res = np.nanmedian(nan_mat, axis=1, out=resout)
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+        # 0-d output:
+        resout = np.zeros(())
+        tgt = np.median(mat, axis=None)
+        res = np.nanmedian(nan_mat, axis=None, out=resout)
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+        res = np.nanmedian(nan_mat, axis=(0, 1), out=resout)
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+
+    def test_small_large(self):
+        # test the small and large code paths, current cutoff 400 elements
+        for s in [5, 20, 51, 200, 1000]:
+            d = np.random.randn(4, s)
+            # Randomly set some elements to NaN:
+            w = np.random.randint(0, d.size, size=d.size // 5)
+            d.ravel()[w] = np.nan
+            d[:,0] = 1.  # ensure at least one good value
+            # use normal median without nans to compare
+            tgt = []
+            for x in d:
+                nonan = np.compress(~np.isnan(x), x)
+                tgt.append(np.median(nonan, overwrite_input=True))
+
+            assert_array_equal(np.nanmedian(d, axis=-1), tgt)
+
+    def test_result_values(self):
+            tgt = [np.median(d) for d in _rdat]
+            res = np.nanmedian(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", _TYPE_CODES)
+    def test_allnans(self, dtype, axis):
+        mat = np.full((3, 3), np.nan).astype(dtype)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+
+            output = np.nanmedian(mat, axis=axis)
+            assert output.dtype == mat.dtype
+            assert np.isnan(output).all()
+
+            if axis is None:
+                assert_(len(sup.log) == 1)
+            else:
+                assert_(len(sup.log) == 3)
+
+            # Check scalar
+            scalar = np.array(np.nan).astype(dtype)[()]
+            output_scalar = np.nanmedian(scalar)
+            assert output_scalar.dtype == scalar.dtype
+            assert np.isnan(output_scalar)
+
+            if axis is None:
+                assert_(len(sup.log) == 2)
+            else:
+                assert_(len(sup.log) == 4)
+
+    def test_empty(self):
+        mat = np.zeros((0, 3))
+        for axis in [0, None]:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                assert_(np.isnan(np.nanmedian(mat, axis=axis)).all())
+                assert_(len(w) == 1)
+                assert_(issubclass(w[0].category, RuntimeWarning))
+        for axis in [1]:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                assert_equal(np.nanmedian(mat, axis=axis), np.zeros([]))
+                assert_(len(w) == 0)
+
+    def test_scalar(self):
+        assert_(np.nanmedian(0.) == 0.)
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(AxisError, np.nanmedian, d, axis=-5)
+        assert_raises(AxisError, np.nanmedian, d, axis=(0, -5))
+        assert_raises(AxisError, np.nanmedian, d, axis=4)
+        assert_raises(AxisError, np.nanmedian, d, axis=(0, 4))
+        assert_raises(ValueError, np.nanmedian, d, axis=(1, 1))
+
+    def test_float_special(self):
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            for inf in [np.inf, -np.inf]:
+                a = np.array([[inf,  np.nan], [np.nan, np.nan]])
+                assert_equal(np.nanmedian(a, axis=0), [inf,  np.nan])
+                assert_equal(np.nanmedian(a, axis=1), [inf,  np.nan])
+                assert_equal(np.nanmedian(a), inf)
+
+                # minimum fill value check
+                a = np.array([[np.nan, np.nan, inf],
+                             [np.nan, np.nan, inf]])
+                assert_equal(np.nanmedian(a), inf)
+                assert_equal(np.nanmedian(a, axis=0), [np.nan, np.nan, inf])
+                assert_equal(np.nanmedian(a, axis=1), inf)
+
+                # no mask path
+                a = np.array([[inf, inf], [inf, inf]])
+                assert_equal(np.nanmedian(a, axis=1), inf)
+
+                a = np.array([[inf, 7, -inf, -9],
+                              [-10, np.nan, np.nan, 5],
+                              [4, np.nan, np.nan, inf]],
+                              dtype=np.float32)
+                if inf > 0:
+                    assert_equal(np.nanmedian(a, axis=0), [4., 7., -inf, 5.])
+                    assert_equal(np.nanmedian(a), 4.5)
+                else:
+                    assert_equal(np.nanmedian(a, axis=0), [-10., 7., -inf, -9.])
+                    assert_equal(np.nanmedian(a), -2.5)
+                assert_equal(np.nanmedian(a, axis=-1), [-1., -2.5, inf])
+
+                for i in range(0, 10):
+                    for j in range(1, 10):
+                        a = np.array([([np.nan] * i) + ([inf] * j)] * 2)
+                        assert_equal(np.nanmedian(a), inf)
+                        assert_equal(np.nanmedian(a, axis=1), inf)
+                        assert_equal(np.nanmedian(a, axis=0),
+                                     ([np.nan] * i) + [inf] * j)
+
+                        a = np.array([([np.nan] * i) + ([-inf] * j)] * 2)
+                        assert_equal(np.nanmedian(a), -inf)
+                        assert_equal(np.nanmedian(a, axis=1), -inf)
+                        assert_equal(np.nanmedian(a, axis=0),
+                                     ([np.nan] * i) + [-inf] * j)
+
+
+class TestNanFunctions_Percentile:
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        np.nanpercentile(ndat, 30)
+        assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for axis in [None, 0, 1]:
+            tgt = np.percentile(mat, 70, axis=axis, out=None,
+                                overwrite_input=False)
+            res = np.nanpercentile(mat, 70, axis=axis, out=None,
+                                   overwrite_input=False)
+            assert_(res.ndim == tgt.ndim)
+
+        d = np.ones((3, 5, 7, 11))
+        # Randomly set some elements to NaN:
+        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
+        w = w.astype(np.intp)
+        d[tuple(w)] = np.nan
+        with suppress_warnings() as sup:
+            sup.filter(RuntimeWarning)
+            res = np.nanpercentile(d, 90, axis=None, keepdims=True)
+            assert_equal(res.shape, (1, 1, 1, 1))
+            res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
+            assert_equal(res.shape, (1, 1, 7, 11))
+            res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
+            assert_equal(res.shape, (1, 5, 7, 1))
+            res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
+            assert_equal(res.shape, (3, 1, 7, 11))
+            res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
+            assert_equal(res.shape, (1, 1, 1, 1))
+            res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
+            assert_equal(res.shape, (1, 1, 7, 1))
+
+    @pytest.mark.parametrize('q', [7, [1, 7]])
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1,),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
+    def test_keepdims_out(self, q, axis):
+        d = np.ones((3, 5, 7, 11))
+        # Randomly set some elements to NaN:
+        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
+        w = w.astype(np.intp)
+        d[tuple(w)] = np.nan
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        shape_out = np.shape(q) + shape_out
+
+        out = np.empty(shape_out)
+        result = np.nanpercentile(d, q, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    @pytest.mark.parametrize("weighted", [False, True])
+    def test_out(self, weighted):
+        mat = np.random.rand(3, 3)
+        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
+        resout = np.zeros(3)
+        if weighted:
+            w_args = {"weights": np.ones_like(mat), "method": "inverted_cdf"}
+            nan_w_args = {
+                "weights": np.ones_like(nan_mat), "method": "inverted_cdf"
+            }
+        else:
+            w_args = dict()
+            nan_w_args = dict()
+        tgt = np.percentile(mat, 42, axis=1, **w_args)
+        res = np.nanpercentile(nan_mat, 42, axis=1, out=resout, **nan_w_args)
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+        # 0-d output:
+        resout = np.zeros(())
+        tgt = np.percentile(mat, 42, axis=None, **w_args)
+        res = np.nanpercentile(
+            nan_mat, 42, axis=None, out=resout, **nan_w_args
+        )
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+        res = np.nanpercentile(
+            nan_mat, 42, axis=(0, 1), out=resout, **nan_w_args
+        )
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.nanpercentile, arr_c, 0.5)
+
+    @pytest.mark.parametrize("weighted", [False, True])
+    @pytest.mark.parametrize("use_out", [False, True])
+    def test_result_values(self, weighted, use_out):
+        if weighted:
+            percentile = partial(np.percentile, method="inverted_cdf")
+            nanpercentile = partial(np.nanpercentile, method="inverted_cdf")
+
+            def gen_weights(d):
+                return np.ones_like(d)
+
+        else:
+            percentile = np.percentile
+            nanpercentile = np.nanpercentile
+
+            def gen_weights(d):
+                return None
+
+        tgt = [percentile(d, 28, weights=gen_weights(d)) for d in _rdat]
+        out = np.empty_like(tgt) if use_out else None
+        res = nanpercentile(_ndat, 28, axis=1,
+                            weights=gen_weights(_ndat), out=out)
+        assert_almost_equal(res, tgt)
+        # Transpose the array to fit the output convention of numpy.percentile
+        tgt = np.transpose([percentile(d, (28, 98), weights=gen_weights(d))
+                            for d in _rdat])
+        out = np.empty_like(tgt) if use_out else None
+        res = nanpercentile(_ndat, (28, 98), axis=1,
+                            weights=gen_weights(_ndat), out=out)
+        assert_almost_equal(res, tgt)
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
+            out = np.nanpercentile(array, 60, axis=axis)
+        assert np.isnan(out).all()
+        assert out.dtype == array.dtype
+
+    def test_empty(self):
+        mat = np.zeros((0, 3))
+        for axis in [0, None]:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
+                assert_(len(w) == 1)
+                assert_(issubclass(w[0].category, RuntimeWarning))
+        for axis in [1]:
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter('always')
+                assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
+                assert_(len(w) == 0)
+
+    def test_scalar(self):
+        assert_equal(np.nanpercentile(0., 100), 0.)
+        a = np.arange(6)
+        r = np.nanpercentile(a, 50, axis=0)
+        assert_equal(r, 2.5)
+        assert_(np.isscalar(r))
+
+    def test_extended_axis_invalid(self):
+        d = np.ones((3, 5, 7, 11))
+        assert_raises(AxisError, np.nanpercentile, d, q=5, axis=-5)
+        assert_raises(AxisError, np.nanpercentile, d, q=5, axis=(0, -5))
+        assert_raises(AxisError, np.nanpercentile, d, q=5, axis=4)
+        assert_raises(AxisError, np.nanpercentile, d, q=5, axis=(0, 4))
+        assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1))
+
+    def test_multiple_percentiles(self):
+        perc = [50, 100]
+        mat = np.ones((4, 3))
+        nan_mat = np.nan * mat
+        # For checking consistency in higher dimensional case
+        large_mat = np.ones((3, 4, 5))
+        large_mat[:, 0:2:4, :] = 0
+        large_mat[:, :, 3:] *= 2
+        for axis in [None, 0, 1]:
+            for keepdim in [False, True]:
+                with suppress_warnings() as sup:
+                    sup.filter(RuntimeWarning, "All-NaN slice encountered")
+                    val = np.percentile(mat, perc, axis=axis, keepdims=keepdim)
+                    nan_val = np.nanpercentile(nan_mat, perc, axis=axis,
+                                               keepdims=keepdim)
+                    assert_equal(nan_val.shape, val.shape)
+
+                    val = np.percentile(large_mat, perc, axis=axis,
+                                        keepdims=keepdim)
+                    nan_val = np.nanpercentile(large_mat, perc, axis=axis,
+                                               keepdims=keepdim)
+                    assert_equal(nan_val, val)
+
+        megamat = np.ones((3, 4, 5, 6))
+        assert_equal(
+            np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6)
+        )
+
+    @pytest.mark.parametrize("nan_weight", [0, 1, 2, 3, 1e200])
+    def test_nan_value_with_weight(self, nan_weight):
+        x = [1, np.nan, 2, 3]
+        result = np.float64(2.0)
+        q_unweighted = np.nanpercentile(x, 50, method="inverted_cdf")
+        assert_equal(q_unweighted, result)
+
+        # The weight value at the nan position should not matter.
+        w = [1.0, nan_weight, 1.0, 1.0]
+        q_weighted = np.nanpercentile(x, 50, weights=w, method="inverted_cdf")
+        assert_equal(q_weighted, result)
+
+    @pytest.mark.parametrize("axis", [0, 1, 2])
+    def test_nan_value_with_weight_ndim(self, axis):
+        # Create a multi-dimensional array to test
+        np.random.seed(1)
+        x_no_nan = np.random.random(size=(100, 99, 2))
+        # Set some places to NaN (not particularly smart) so there is always
+        # some non-Nan.
+        x = x_no_nan.copy()
+        x[np.arange(99), np.arange(99), 0] = np.nan
+
+        p = np.array([[20., 50., 30], [70, 33, 80]])
+
+        # We just use ones as weights, but replace it with 0 or 1e200 at the
+        # NaN positions below.
+        weights = np.ones_like(x)
+
+        # For comparison use weighted normal percentile with nan weights at
+        # 0 (and no NaNs); not sure this is strictly identical but should be
+        # sufficiently so (if a percentile lies exactly on a 0 value).
+        weights[np.isnan(x)] = 0
+        p_expected = np.percentile(
+            x_no_nan, p, axis=axis, weights=weights, method="inverted_cdf")
+
+        p_unweighted = np.nanpercentile(
+            x, p, axis=axis, method="inverted_cdf")
+        # The normal and unweighted versions should be identical:
+        assert_equal(p_unweighted, p_expected)
+
+        weights[np.isnan(x)] = 1e200  # huge value, shouldn't matter
+        p_weighted = np.nanpercentile(
+            x, p, axis=axis, weights=weights, method="inverted_cdf")
+        assert_equal(p_weighted, p_expected)
+        # Also check with out passed:
+        out = np.empty_like(p_weighted)
+        res = np.nanpercentile(
+            x, p, axis=axis, weights=weights, out=out, method="inverted_cdf")
+
+        assert res is out
+        assert_equal(out, p_expected)
+
+
+class TestNanFunctions_Quantile:
+    # most of this is already tested by TestPercentile
+
+    @pytest.mark.parametrize("weighted", [False, True])
+    def test_regression(self, weighted):
+        ar = np.arange(24).reshape(2, 3, 4).astype(float)
+        ar[0][1] = np.nan
+        if weighted:
+            w_args = {"weights": np.ones_like(ar), "method": "inverted_cdf"}
+        else:
+            w_args = dict()
+
+        assert_equal(np.nanquantile(ar, q=0.5, **w_args),
+                     np.nanpercentile(ar, q=50, **w_args))
+        assert_equal(np.nanquantile(ar, q=0.5, axis=0, **w_args),
+                     np.nanpercentile(ar, q=50, axis=0, **w_args))
+        assert_equal(np.nanquantile(ar, q=0.5, axis=1, **w_args),
+                     np.nanpercentile(ar, q=50, axis=1, **w_args))
+        assert_equal(np.nanquantile(ar, q=[0.5], axis=1, **w_args),
+                     np.nanpercentile(ar, q=[50], axis=1, **w_args))
+        assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1, **w_args),
+                     np.nanpercentile(ar, q=[25, 50, 75], axis=1, **w_args))
+
+    def test_basic(self):
+        x = np.arange(8) * 0.5
+        assert_equal(np.nanquantile(x, 0), 0.)
+        assert_equal(np.nanquantile(x, 1), 3.5)
+        assert_equal(np.nanquantile(x, 0.5), 1.75)
+
+    def test_complex(self):
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+        arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F')
+        assert_raises(TypeError, np.nanquantile, arr_c, 0.5)
+
+    def test_no_p_overwrite(self):
+        # this is worth retesting, because quantile does not make a copy
+        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
+        p = p0.copy()
+        np.nanquantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+        p0 = p0.tolist()
+        p = p.tolist()
+        np.nanquantile(np.arange(100.), p, method="midpoint")
+        assert_array_equal(p, p0)
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize("dtype", np.typecodes["Float"])
+    @pytest.mark.parametrize("array", [
+        np.array(np.nan),
+        np.full((3, 3), np.nan),
+    ], ids=["0d", "2d"])
+    def test_allnans(self, axis, dtype, array):
+        if axis is not None and array.ndim == 0:
+            pytest.skip("`axis != None` not supported for 0d arrays")
+
+        array = array.astype(dtype)
+        with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
+            out = np.nanquantile(array, 1, axis=axis)
+        assert np.isnan(out).all()
+        assert out.dtype == array.dtype
+
+@pytest.mark.parametrize("arr, expected", [
+    # array of floats with some nans
+    (np.array([np.nan, 5.0, np.nan, np.inf]),
+     np.array([False, True, False, True])),
+    # int64 array that can't possibly have nans
+    (np.array([1, 5, 7, 9], dtype=np.int64),
+     True),
+    # bool array that can't possibly have nans
+    (np.array([False, True, False, True]),
+     True),
+    # 2-D complex array with nans
+    (np.array([[np.nan, 5.0],
+               [np.nan, np.inf]], dtype=np.complex64),
+     np.array([[False, True],
+               [False, True]])),
+    ])
+def test__nan_mask(arr, expected):
+    for out in [None, np.empty(arr.shape, dtype=np.bool)]:
+        actual = _nan_mask(arr, out=out)
+        assert_equal(actual, expected)
+        # the above won't distinguish between True proper
+        # and an array of True values; we want True proper
+        # for types that can't possibly contain NaN
+        if type(expected) is not np.ndarray:
+            assert actual is True
+
+
+def test__replace_nan():
+    """ Test that _replace_nan returns the original array if there are no
+    NaNs, not a copy.
+    """
+    for dtype in [np.bool, np.int32, np.int64]:
+        arr = np.array([0, 1], dtype=dtype)
+        result, mask = _replace_nan(arr, 0)
+        assert mask is None
+        # do not make a copy if there are no nans
+        assert result is arr
+
+    for dtype in [np.float32, np.float64]:
+        arr = np.array([0, 1], dtype=dtype)
+        result, mask = _replace_nan(arr, 2)
+        assert (mask == False).all()
+        # mask is not None, so we make a copy
+        assert result is not arr
+        assert_equal(result, arr)
+
+        arr_nan = np.array([0, 1, np.nan], dtype=dtype)
+        result_nan, mask_nan = _replace_nan(arr_nan, 2)
+        assert_equal(mask_nan, np.array([False, False, True]))
+        assert result_nan is not arr_nan
+        assert_equal(result_nan, np.array([0, 1, 2]))
+        assert np.isnan(arr_nan[-1])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_packbits.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_packbits.py
new file mode 100644
index 0000000000000000000000000000000000000000..a446156327cd4f1fc0c088fbc61c3ca713f379e4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_packbits.py
@@ -0,0 +1,376 @@
+import numpy as np
+from numpy.testing import assert_array_equal, assert_equal, assert_raises
+import pytest
+from itertools import chain
+
+def test_packbits():
+    # Copied from the docstring.
+    a = [[[1, 0, 1], [0, 1, 0]],
+         [[1, 1, 0], [0, 0, 1]]]
+    for dt in '?bBhHiIlLqQ':
+        arr = np.array(a, dtype=dt)
+        b = np.packbits(arr, axis=-1)
+        assert_equal(b.dtype, np.uint8)
+        assert_array_equal(b, np.array([[[160], [64]], [[192], [32]]]))
+
+    assert_raises(TypeError, np.packbits, np.array(a, dtype=float))
+
+
+def test_packbits_empty():
+    shapes = [
+        (0,), (10, 20, 0), (10, 0, 20), (0, 10, 20), (20, 0, 0), (0, 20, 0),
+        (0, 0, 20), (0, 0, 0),
+    ]
+    for dt in '?bBhHiIlLqQ':
+        for shape in shapes:
+            a = np.empty(shape, dtype=dt)
+            b = np.packbits(a)
+            assert_equal(b.dtype, np.uint8)
+            assert_equal(b.shape, (0,))
+
+
+def test_packbits_empty_with_axis():
+    # Original shapes and lists of packed shapes for different axes.
+    shapes = [
+        ((0,), [(0,)]),
+        ((10, 20, 0), [(2, 20, 0), (10, 3, 0), (10, 20, 0)]),
+        ((10, 0, 20), [(2, 0, 20), (10, 0, 20), (10, 0, 3)]),
+        ((0, 10, 20), [(0, 10, 20), (0, 2, 20), (0, 10, 3)]),
+        ((20, 0, 0), [(3, 0, 0), (20, 0, 0), (20, 0, 0)]),
+        ((0, 20, 0), [(0, 20, 0), (0, 3, 0), (0, 20, 0)]),
+        ((0, 0, 20), [(0, 0, 20), (0, 0, 20), (0, 0, 3)]),
+        ((0, 0, 0), [(0, 0, 0), (0, 0, 0), (0, 0, 0)]),
+    ]
+    for dt in '?bBhHiIlLqQ':
+        for in_shape, out_shapes in shapes:
+            for ax, out_shape in enumerate(out_shapes):
+                a = np.empty(in_shape, dtype=dt)
+                b = np.packbits(a, axis=ax)
+                assert_equal(b.dtype, np.uint8)
+                assert_equal(b.shape, out_shape)
+
+@pytest.mark.parametrize('bitorder', ('little', 'big'))
+def test_packbits_large(bitorder):
+    # test data large enough for 16 byte vectorization
+    a = np.array([1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,
+                  0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
+                  1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,
+                  1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,
+                  1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1,
+                  1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,
+                  1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
+                  0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,
+                  1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,
+                  1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1,
+                  1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
+                  0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,
+                  1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0,
+                  1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,
+                  1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0])
+    a = a.repeat(3)
+    for dtype in '?bBhHiIlLqQ':
+        arr = np.array(a, dtype=dtype)
+        b = np.packbits(arr, axis=None, bitorder=bitorder)
+        assert_equal(b.dtype, np.uint8)
+        r = [252, 127, 192, 3, 254, 7, 252, 0, 7, 31, 240, 0, 28, 1, 255, 252,
+             113, 248, 3, 255, 192, 28, 15, 192, 28, 126, 0, 224, 127, 255,
+             227, 142, 7, 31, 142, 63, 28, 126, 56, 227, 240, 0, 227, 128, 63,
+             224, 14, 56, 252, 112, 56, 255, 241, 248, 3, 240, 56, 224, 112,
+             63, 255, 255, 199, 224, 14, 0, 31, 143, 192, 3, 255, 199, 0, 1,
+             255, 224, 1, 255, 252, 126, 63, 0, 1, 192, 252, 14, 63, 0, 15,
+             199, 252, 113, 255, 3, 128, 56, 252, 14, 7, 0, 113, 255, 255, 142, 56, 227,
+             129, 248, 227, 129, 199, 31, 128]
+        if bitorder == 'big':
+            assert_array_equal(b, r)
+        # equal for size being multiple of 8
+        assert_array_equal(np.unpackbits(b, bitorder=bitorder)[:-4], a)
+
+        # check last byte of different remainders (16 byte vectorization)
+        b = [np.packbits(arr[:-i], axis=None)[-1] for i in range(1, 16)]
+        assert_array_equal(b, [128, 128, 128, 31, 30, 28, 24, 16, 0, 0, 0, 199,
+                               198, 196, 192])
+
+
+        arr = arr.reshape(36, 25)
+        b = np.packbits(arr, axis=0)
+        assert_equal(b.dtype, np.uint8)
+        assert_array_equal(b, [[190, 186, 178, 178, 150, 215, 87, 83, 83, 195,
+                                199, 206, 204, 204, 140, 140, 136, 136, 8, 40, 105,
+                                107, 75, 74, 88],
+                               [72, 216, 248, 241, 227, 195, 202, 90, 90, 83,
+                                83, 119, 127, 109, 73, 64, 208, 244, 189, 45,
+                                41, 104, 122, 90, 18],
+                               [113, 120, 248, 216, 152, 24, 60, 52, 182, 150,
+                                150, 150, 146, 210, 210, 246, 255, 255, 223,
+                                151, 21, 17, 17, 131, 163],
+                               [214, 210, 210, 64, 68, 5, 5, 1, 72, 88, 92,
+                                92, 78, 110, 39, 181, 149, 220, 222, 218, 218,
+                                202, 234, 170, 168],
+                               [0, 128, 128, 192, 80, 112, 48, 160, 160, 224,
+                                240, 208, 144, 128, 160, 224, 240, 208, 144,
+                                144, 176, 240, 224, 192, 128]])
+
+        b = np.packbits(arr, axis=1)
+        assert_equal(b.dtype, np.uint8)
+        assert_array_equal(b, [[252, 127, 192,   0],
+                               [  7, 252,  15, 128],
+                               [240,   0,  28,   0],
+                               [255, 128,   0, 128],
+                               [192,  31, 255, 128],
+                               [142,  63,   0,   0],
+                               [255, 240,   7,   0],
+                               [  7, 224,  14,   0],
+                               [126,   0, 224,   0],
+                               [255, 255, 199,   0],
+                               [ 56,  28, 126,   0],
+                               [113, 248, 227, 128],
+                               [227, 142,  63,   0],
+                               [  0,  28, 112,   0],
+                               [ 15, 248,   3, 128],
+                               [ 28, 126,  56,   0],
+                               [ 56, 255, 241, 128],
+                               [240,   7, 224,   0],
+                               [227, 129, 192, 128],
+                               [255, 255, 254,   0],
+                               [126,   0, 224,   0],
+                               [  3, 241, 248,   0],
+                               [  0, 255, 241, 128],
+                               [128,   0, 255, 128],
+                               [224,   1, 255, 128],
+                               [248, 252, 126,   0],
+                               [  0,   7,   3, 128],
+                               [224, 113, 248,   0],
+                               [  0, 252, 127, 128],
+                               [142,  63, 224,   0],
+                               [224,  14,  63,   0],
+                               [  7,   3, 128,   0],
+                               [113, 255, 255, 128],
+                               [ 28, 113, 199,   0],
+                               [  7, 227, 142,   0],
+                               [ 14,  56, 252,   0]])
+
+        arr = arr.T.copy()
+        b = np.packbits(arr, axis=0)
+        assert_equal(b.dtype, np.uint8)
+        assert_array_equal(b, [[252, 7, 240, 255, 192, 142, 255, 7, 126, 255,
+                                56, 113, 227, 0, 15, 28, 56, 240, 227, 255,
+                                126, 3, 0, 128, 224, 248, 0, 224, 0, 142, 224,
+                                7, 113, 28, 7, 14],
+                                [127, 252, 0, 128, 31, 63, 240, 224, 0, 255,
+                                 28, 248, 142, 28, 248, 126, 255, 7, 129, 255,
+                                 0, 241, 255, 0, 1, 252, 7, 113, 252, 63, 14,
+                                 3, 255, 113, 227, 56],
+                                [192, 15, 28, 0, 255, 0, 7, 14, 224, 199, 126,
+                                 227, 63, 112, 3, 56, 241, 224, 192, 254, 224,
+                                 248, 241, 255, 255, 126, 3, 248, 127, 224, 63,
+                                 128, 255, 199, 142, 252],
+                                [0, 128, 0, 128, 128, 0, 0, 0, 0, 0, 0, 128, 0,
+                                 0, 128, 0, 128, 0, 128, 0, 0, 0, 128, 128,
+                                 128, 0, 128, 0, 128, 0, 0, 0, 128, 0, 0, 0]])
+
+        b = np.packbits(arr, axis=1)
+        assert_equal(b.dtype, np.uint8)
+        assert_array_equal(b, [[190,  72, 113, 214,   0],
+                               [186, 216, 120, 210, 128],
+                               [178, 248, 248, 210, 128],
+                               [178, 241, 216,  64, 192],
+                               [150, 227, 152,  68,  80],
+                               [215, 195,  24,   5, 112],
+                               [ 87, 202,  60,   5,  48],
+                               [ 83,  90,  52,   1, 160],
+                               [ 83,  90, 182,  72, 160],
+                               [195,  83, 150,  88, 224],
+                               [199,  83, 150,  92, 240],
+                               [206, 119, 150,  92, 208],
+                               [204, 127, 146,  78, 144],
+                               [204, 109, 210, 110, 128],
+                               [140,  73, 210,  39, 160],
+                               [140,  64, 246, 181, 224],
+                               [136, 208, 255, 149, 240],
+                               [136, 244, 255, 220, 208],
+                               [  8, 189, 223, 222, 144],
+                               [ 40,  45, 151, 218, 144],
+                               [105,  41,  21, 218, 176],
+                               [107, 104,  17, 202, 240],
+                               [ 75, 122,  17, 234, 224],
+                               [ 74,  90, 131, 170, 192],
+                               [ 88,  18, 163, 168, 128]])
+
+
+    # result is the same if input is multiplied with a nonzero value
+    for dtype in 'bBhHiIlLqQ':
+        arr = np.array(a, dtype=dtype)
+        rnd = np.random.randint(low=np.iinfo(dtype).min,
+                                high=np.iinfo(dtype).max, size=arr.size,
+                                dtype=dtype)
+        rnd[rnd == 0] = 1
+        arr *= rnd.astype(dtype)
+        b = np.packbits(arr, axis=-1)
+        assert_array_equal(np.unpackbits(b)[:-4], a)
+
+    assert_raises(TypeError, np.packbits, np.array(a, dtype=float))
+
+
+def test_packbits_very_large():
+    # test some with a larger arrays gh-8637
+    # code is covered earlier but larger array makes crash on bug more likely
+    for s in range(950, 1050):
+        for dt in '?bBhHiIlLqQ':
+            x = np.ones((200, s), dtype=bool)
+            np.packbits(x, axis=1)
+
+
+def test_unpackbits():
+    # Copied from the docstring.
+    a = np.array([[2], [7], [23]], dtype=np.uint8)
+    b = np.unpackbits(a, axis=1)
+    assert_equal(b.dtype, np.uint8)
+    assert_array_equal(b, np.array([[0, 0, 0, 0, 0, 0, 1, 0],
+                                    [0, 0, 0, 0, 0, 1, 1, 1],
+                                    [0, 0, 0, 1, 0, 1, 1, 1]]))
+
+def test_pack_unpack_order():
+    a = np.array([[2], [7], [23]], dtype=np.uint8)
+    b = np.unpackbits(a, axis=1)
+    assert_equal(b.dtype, np.uint8)
+    b_little = np.unpackbits(a, axis=1, bitorder='little')
+    b_big = np.unpackbits(a, axis=1, bitorder='big')
+    assert_array_equal(b, b_big)
+    assert_array_equal(a, np.packbits(b_little, axis=1, bitorder='little'))
+    assert_array_equal(b[:,::-1], b_little)
+    assert_array_equal(a, np.packbits(b_big, axis=1, bitorder='big'))
+    assert_raises(ValueError, np.unpackbits, a, bitorder='r')
+    assert_raises(TypeError, np.unpackbits, a, bitorder=10)
+
+
+
+def test_unpackbits_empty():
+    a = np.empty((0,), dtype=np.uint8)
+    b = np.unpackbits(a)
+    assert_equal(b.dtype, np.uint8)
+    assert_array_equal(b, np.empty((0,)))
+
+
+def test_unpackbits_empty_with_axis():
+    # Lists of packed shapes for different axes and unpacked shapes.
+    shapes = [
+        ([(0,)], (0,)),
+        ([(2, 24, 0), (16, 3, 0), (16, 24, 0)], (16, 24, 0)),
+        ([(2, 0, 24), (16, 0, 24), (16, 0, 3)], (16, 0, 24)),
+        ([(0, 16, 24), (0, 2, 24), (0, 16, 3)], (0, 16, 24)),
+        ([(3, 0, 0), (24, 0, 0), (24, 0, 0)], (24, 0, 0)),
+        ([(0, 24, 0), (0, 3, 0), (0, 24, 0)], (0, 24, 0)),
+        ([(0, 0, 24), (0, 0, 24), (0, 0, 3)], (0, 0, 24)),
+        ([(0, 0, 0), (0, 0, 0), (0, 0, 0)], (0, 0, 0)),
+    ]
+    for in_shapes, out_shape in shapes:
+        for ax, in_shape in enumerate(in_shapes):
+            a = np.empty(in_shape, dtype=np.uint8)
+            b = np.unpackbits(a, axis=ax)
+            assert_equal(b.dtype, np.uint8)
+            assert_equal(b.shape, out_shape)
+
+
+def test_unpackbits_large():
+    # test all possible numbers via comparison to already tested packbits
+    d = np.arange(277, dtype=np.uint8)
+    assert_array_equal(np.packbits(np.unpackbits(d)), d)
+    assert_array_equal(np.packbits(np.unpackbits(d[::2])), d[::2])
+    d = np.tile(d, (3, 1))
+    assert_array_equal(np.packbits(np.unpackbits(d, axis=1), axis=1), d)
+    d = d.T.copy()
+    assert_array_equal(np.packbits(np.unpackbits(d, axis=0), axis=0), d)
+
+
+class TestCount:
+    x = np.array([
+        [1, 0, 1, 0, 0, 1, 0],
+        [0, 1, 1, 1, 0, 0, 0],
+        [0, 0, 1, 0, 0, 1, 1],
+        [1, 1, 0, 0, 0, 1, 1],
+        [1, 0, 1, 0, 1, 0, 1],
+        [0, 0, 1, 1, 1, 0, 0],
+        [0, 1, 0, 1, 0, 1, 0],
+    ], dtype=np.uint8)
+    padded1 = np.zeros(57, dtype=np.uint8)
+    padded1[:49] = x.ravel()
+    padded1b = np.zeros(57, dtype=np.uint8)
+    padded1b[:49] = x[::-1].copy().ravel()
+    padded2 = np.zeros((9, 9), dtype=np.uint8)
+    padded2[:7, :7] = x
+
+    @pytest.mark.parametrize('bitorder', ('little', 'big'))
+    @pytest.mark.parametrize('count', chain(range(58), range(-1, -57, -1)))
+    def test_roundtrip(self, bitorder, count):
+        if count < 0:
+            # one extra zero of padding
+            cutoff = count - 1
+        else:
+            cutoff = count
+        # test complete invertibility of packbits and unpackbits with count
+        packed = np.packbits(self.x, bitorder=bitorder)
+        unpacked = np.unpackbits(packed, count=count, bitorder=bitorder)
+        assert_equal(unpacked.dtype, np.uint8)
+        assert_array_equal(unpacked, self.padded1[:cutoff])
+
+    @pytest.mark.parametrize('kwargs', [
+                    {}, {'count': None},
+                    ])
+    def test_count(self, kwargs):
+        packed = np.packbits(self.x)
+        unpacked = np.unpackbits(packed, **kwargs)
+        assert_equal(unpacked.dtype, np.uint8)
+        assert_array_equal(unpacked, self.padded1[:-1])
+
+    @pytest.mark.parametrize('bitorder', ('little', 'big'))
+    # delta==-1 when count<0 because one extra zero of padding
+    @pytest.mark.parametrize('count', chain(range(8), range(-1, -9, -1)))
+    def test_roundtrip_axis(self, bitorder, count):
+        if count < 0:
+            # one extra zero of padding
+            cutoff = count - 1
+        else:
+            cutoff = count
+        packed0 = np.packbits(self.x, axis=0, bitorder=bitorder)
+        unpacked0 = np.unpackbits(packed0, axis=0, count=count,
+                                  bitorder=bitorder)
+        assert_equal(unpacked0.dtype, np.uint8)
+        assert_array_equal(unpacked0, self.padded2[:cutoff, :self.x.shape[1]])
+
+        packed1 = np.packbits(self.x, axis=1, bitorder=bitorder)
+        unpacked1 = np.unpackbits(packed1, axis=1, count=count,
+                                  bitorder=bitorder)
+        assert_equal(unpacked1.dtype, np.uint8)
+        assert_array_equal(unpacked1, self.padded2[:self.x.shape[0], :cutoff])
+
+    @pytest.mark.parametrize('kwargs', [
+                    {}, {'count': None},
+                    {'bitorder' : 'little'},
+                    {'bitorder': 'little', 'count': None},
+                    {'bitorder' : 'big'},
+                    {'bitorder': 'big', 'count': None},
+                    ])
+    def test_axis_count(self, kwargs):
+        packed0 = np.packbits(self.x, axis=0)
+        unpacked0 = np.unpackbits(packed0, axis=0, **kwargs)
+        assert_equal(unpacked0.dtype, np.uint8)
+        if kwargs.get('bitorder', 'big') == 'big':
+            assert_array_equal(unpacked0, self.padded2[:-1, :self.x.shape[1]])
+        else:
+            assert_array_equal(unpacked0[::-1, :], self.padded2[:-1, :self.x.shape[1]])
+
+        packed1 = np.packbits(self.x, axis=1)
+        unpacked1 = np.unpackbits(packed1, axis=1, **kwargs)
+        assert_equal(unpacked1.dtype, np.uint8)
+        if kwargs.get('bitorder', 'big') == 'big':
+            assert_array_equal(unpacked1, self.padded2[:self.x.shape[0], :-1])
+        else:
+            assert_array_equal(unpacked1[:, ::-1], self.padded2[:self.x.shape[0], :-1])
+
+    def test_bad_count(self):
+        packed0 = np.packbits(self.x, axis=0)
+        assert_raises(ValueError, np.unpackbits, packed0, axis=0, count=-9)
+        packed1 = np.packbits(self.x, axis=1)
+        assert_raises(ValueError, np.unpackbits, packed1, axis=1, count=-9)
+        packed = np.packbits(self.x)
+        assert_raises(ValueError, np.unpackbits, packed, count=-57)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_polynomial.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_polynomial.py
new file mode 100644
index 0000000000000000000000000000000000000000..460de9985fa0c6d803f42661018672708a7e14dc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_polynomial.py
@@ -0,0 +1,303 @@
+import numpy as np
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_raises, assert_allclose
+    )
+
+import pytest
+
+# `poly1d` has some support for `np.bool` and `np.timedelta64`,
+# but it is limited and they are therefore excluded here
+TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O"
+
+
+class TestPolynomial:
+    def test_poly1d_str_and_repr(self):
+        p = np.poly1d([1., 2, 3])
+        assert_equal(repr(p), 'poly1d([1., 2., 3.])')
+        assert_equal(str(p),
+                     '   2\n'
+                     '1 x + 2 x + 3')
+
+        q = np.poly1d([3., 2, 1])
+        assert_equal(repr(q), 'poly1d([3., 2., 1.])')
+        assert_equal(str(q),
+                     '   2\n'
+                     '3 x + 2 x + 1')
+
+        r = np.poly1d([1.89999 + 2j, -3j, -5.12345678, 2 + 1j])
+        assert_equal(str(r),
+                     '            3      2\n'
+                     '(1.9 + 2j) x - 3j x - 5.123 x + (2 + 1j)')
+
+        assert_equal(str(np.poly1d([-3, -2, -1])),
+                     '    2\n'
+                     '-3 x - 2 x - 1')
+
+    def test_poly1d_resolution(self):
+        p = np.poly1d([1., 2, 3])
+        q = np.poly1d([3., 2, 1])
+        assert_equal(p(0), 3.0)
+        assert_equal(p(5), 38.0)
+        assert_equal(q(0), 1.0)
+        assert_equal(q(5), 86.0)
+
+    def test_poly1d_math(self):
+        # here we use some simple coeffs to make calculations easier
+        p = np.poly1d([1., 2, 4])
+        q = np.poly1d([4., 2, 1])
+        assert_equal(p/q, (np.poly1d([0.25]), np.poly1d([1.5, 3.75])))
+        assert_equal(p.integ(), np.poly1d([1/3, 1., 4., 0.]))
+        assert_equal(p.integ(1), np.poly1d([1/3, 1., 4., 0.]))
+
+        p = np.poly1d([1., 2, 3])
+        q = np.poly1d([3., 2, 1])
+        assert_equal(p * q, np.poly1d([3., 8., 14., 8., 3.]))
+        assert_equal(p + q, np.poly1d([4., 4., 4.]))
+        assert_equal(p - q, np.poly1d([-2., 0., 2.]))
+        assert_equal(p ** 4, np.poly1d([1., 8., 36., 104., 214., 312., 324., 216., 81.]))
+        assert_equal(p(q), np.poly1d([9., 12., 16., 8., 6.]))
+        assert_equal(q(p), np.poly1d([3., 12., 32., 40., 34.]))
+        assert_equal(p.deriv(), np.poly1d([2., 2.]))
+        assert_equal(p.deriv(2), np.poly1d([2.]))
+        assert_equal(np.polydiv(np.poly1d([1, 0, -1]), np.poly1d([1, 1])),
+                     (np.poly1d([1., -1.]), np.poly1d([0.])))
+
+    @pytest.mark.parametrize("type_code", TYPE_CODES)
+    def test_poly1d_misc(self, type_code: str) -> None:
+        dtype = np.dtype(type_code)
+        ar = np.array([1, 2, 3], dtype=dtype)
+        p = np.poly1d(ar)
+
+        # `__eq__`
+        assert_equal(np.asarray(p), ar)
+        assert_equal(np.asarray(p).dtype, dtype)
+        assert_equal(len(p), 2)
+
+        # `__getitem__`
+        comparison_dct = {-1: 0, 0: 3, 1: 2, 2: 1, 3: 0}
+        for index, ref in comparison_dct.items():
+            scalar = p[index]
+            assert_equal(scalar, ref)
+            if dtype == np.object_:
+                assert isinstance(scalar, int)
+            else:
+                assert_equal(scalar.dtype, dtype)
+
+    def test_poly1d_variable_arg(self):
+        q = np.poly1d([1., 2, 3], variable='y')
+        assert_equal(str(q),
+                     '   2\n'
+                     '1 y + 2 y + 3')
+        q = np.poly1d([1., 2, 3], variable='lambda')
+        assert_equal(str(q),
+                     '        2\n'
+                     '1 lambda + 2 lambda + 3')
+
+    def test_poly(self):
+        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
+                                  [1, -3, -2, 6])
+
+        # From matlab docs
+        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
+        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])
+
+        # Should produce real output for perfect conjugates
+        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
+        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
+                                      1-2j, 1.+3.5j, 1-3.5j])))
+        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
+        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
+        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
+        assert_(np.isrealobj(np.poly([1j, -1j])))
+        assert_(np.isrealobj(np.poly([1, -1])))
+
+        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))
+
+        np.random.seed(42)
+        a = np.random.randn(100) + 1j*np.random.randn(100)
+        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a))))))
+
+    def test_roots(self):
+        assert_array_equal(np.roots([1, 0, 0]), [0, 0])
+
+    def test_str_leading_zeros(self):
+        p = np.poly1d([4, 3, 2, 1])
+        p[3] = 0
+        assert_equal(str(p),
+                     "   2\n"
+                     "3 x + 2 x + 1")
+
+        p = np.poly1d([1, 2])
+        p[0] = 0
+        p[1] = 0
+        assert_equal(str(p), " \n0")
+
+    def test_polyfit(self):
+        c = np.array([3., 2., 1.])
+        x = np.linspace(0, 2, 7)
+        y = np.polyval(c, x)
+        err = [1, -1, 1, -1, 1, -1, 1]
+        weights = np.arange(8, 1, -1)**2/7.0
+
+        # Check exception when too few points for variance estimate. Note that
+        # the estimate requires the number of data points to exceed
+        # degree + 1
+        assert_raises(ValueError, np.polyfit,
+                      [1], [1], deg=0, cov=True)
+
+        # check 1D case
+        m, cov = np.polyfit(x, y+err, 2, cov=True)
+        est = [3.8571, 0.2857, 1.619]
+        assert_almost_equal(est, m, decimal=4)
+        val0 = [[ 1.4694, -2.9388,  0.8163],
+                [-2.9388,  6.3673, -2.1224],
+                [ 0.8163, -2.1224,  1.161 ]]
+        assert_almost_equal(val0, cov, decimal=4)
+
+        m2, cov2 = np.polyfit(x, y+err, 2, w=weights, cov=True)
+        assert_almost_equal([4.8927, -1.0177, 1.7768], m2, decimal=4)
+        val = [[ 4.3964, -5.0052,  0.4878],
+               [-5.0052,  6.8067, -0.9089],
+               [ 0.4878, -0.9089,  0.3337]]
+        assert_almost_equal(val, cov2, decimal=4)
+
+        m3, cov3 = np.polyfit(x, y+err, 2, w=weights, cov="unscaled")
+        assert_almost_equal([4.8927, -1.0177, 1.7768], m3, decimal=4)
+        val = [[ 0.1473, -0.1677,  0.0163],
+               [-0.1677,  0.228 , -0.0304],
+               [ 0.0163, -0.0304,  0.0112]]
+        assert_almost_equal(val, cov3, decimal=4)
+
+        # check 2D (n,1) case
+        y = y[:, np.newaxis]
+        c = c[:, np.newaxis]
+        assert_almost_equal(c, np.polyfit(x, y, 2))
+        # check 2D (n,2) case
+        yy = np.concatenate((y, y), axis=1)
+        cc = np.concatenate((c, c), axis=1)
+        assert_almost_equal(cc, np.polyfit(x, yy, 2))
+
+        m, cov = np.polyfit(x, yy + np.array(err)[:, np.newaxis], 2, cov=True)
+        assert_almost_equal(est, m[:, 0], decimal=4)
+        assert_almost_equal(est, m[:, 1], decimal=4)
+        assert_almost_equal(val0, cov[:, :, 0], decimal=4)
+        assert_almost_equal(val0, cov[:, :, 1], decimal=4)
+
+        # check order 1 (deg=0) case, were the analytic results are simple
+        np.random.seed(123)
+        y = np.random.normal(size=(4, 10000))
+        mean, cov = np.polyfit(np.zeros(y.shape[0]), y, deg=0, cov=True)
+        # Should get sigma_mean = sigma/sqrt(N) = 1./sqrt(4) = 0.5.
+        assert_allclose(mean.std(), 0.5, atol=0.01)
+        assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01)
+        # Without scaling, since reduced chi2 is 1, the result should be the same.
+        mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=np.ones(y.shape[0]),
+                               deg=0, cov="unscaled")
+        assert_allclose(mean.std(), 0.5, atol=0.01)
+        assert_almost_equal(np.sqrt(cov.mean()), 0.5)
+        # If we estimate our errors wrong, no change with scaling:
+        w = np.full(y.shape[0], 1./0.5)
+        mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov=True)
+        assert_allclose(mean.std(), 0.5, atol=0.01)
+        assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01)
+        # But if we do not scale, our estimate for the error in the mean will
+        # differ.
+        mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov="unscaled")
+        assert_allclose(mean.std(), 0.5, atol=0.01)
+        assert_almost_equal(np.sqrt(cov.mean()), 0.25)
+
+    def test_objects(self):
+        from decimal import Decimal
+        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
+        p2 = p * Decimal('1.333333333333333')
+        assert_(p2[1] == Decimal("3.9999999999999990"))
+        p2 = p.deriv()
+        assert_(p2[1] == Decimal('8.0'))
+        p2 = p.integ()
+        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
+        assert_(p2[2] == Decimal('1.5'))
+        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
+        p = np.poly([Decimal(1), Decimal(2)])
+        assert_equal(np.poly([Decimal(1), Decimal(2)]),
+                     [1, Decimal(-3), Decimal(2)])
+
+    def test_complex(self):
+        p = np.poly1d([3j, 2j, 1j])
+        p2 = p.integ()
+        assert_((p2.coeffs == [1j, 1j, 1j, 0]).all())
+        p2 = p.deriv()
+        assert_((p2.coeffs == [6j, 2j]).all())
+
+    def test_integ_coeffs(self):
+        p = np.poly1d([3, 2, 1])
+        p2 = p.integ(3, k=[9, 7, 6])
+        assert_(
+            (p2.coeffs == [1/4./5., 1/3./4., 1/2./3., 9/1./2., 7, 6]).all())
+
+    def test_zero_dims(self):
+        try:
+            np.poly(np.zeros((0, 0)))
+        except ValueError:
+            pass
+
+    def test_poly_int_overflow(self):
+        """
+        Regression test for gh-5096.
+        """
+        v = np.arange(1, 21)
+        assert_almost_equal(np.poly(v), np.poly(np.diag(v)))
+
+    def test_zero_poly_dtype(self):
+        """
+        Regression test for gh-16354.
+        """
+        z = np.array([0, 0, 0])
+        p = np.poly1d(z.astype(np.int64))
+        assert_equal(p.coeffs.dtype, np.int64)
+
+        p = np.poly1d(z.astype(np.float32))
+        assert_equal(p.coeffs.dtype, np.float32)
+
+        p = np.poly1d(z.astype(np.complex64))
+        assert_equal(p.coeffs.dtype, np.complex64)
+
+    def test_poly_eq(self):
+        p = np.poly1d([1, 2, 3])
+        p2 = np.poly1d([1, 2, 4])
+        assert_equal(p == None, False)  # noqa: E711
+        assert_equal(p != None, True)  # noqa: E711
+        assert_equal(p == p, True)
+        assert_equal(p == p2, False)
+        assert_equal(p != p2, True)
+
+    def test_polydiv(self):
+        b = np.poly1d([2, 6, 6, 1])
+        a = np.poly1d([-1j, (1+2j), -(2+1j), 1])
+        q, r = np.polydiv(b, a)
+        assert_equal(q.coeffs.dtype, np.complex128)
+        assert_equal(r.coeffs.dtype, np.complex128)
+        assert_equal(q*a + r, b)
+
+        c = [1, 2, 3]
+        d = np.poly1d([1, 2, 3])
+        s, t = np.polydiv(c, d)
+        assert isinstance(s, np.poly1d)
+        assert isinstance(t, np.poly1d)
+        u, v = np.polydiv(d, c)
+        assert isinstance(u, np.poly1d)
+        assert isinstance(v, np.poly1d)
+
+    def test_poly_coeffs_mutable(self):
+        """ Coefficients should be modifiable """
+        p = np.poly1d([1, 2, 3])
+
+        p.coeffs += 1
+        assert_equal(p.coeffs, [2, 3, 4])
+
+        p.coeffs[2] += 10
+        assert_equal(p.coeffs, [2, 3, 14])
+
+        # this never used to be allowed - let's not add features to deprecated
+        # APIs
+        assert_raises(AttributeError, setattr, p, 'coeffs', np.array(1))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_recfunctions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_recfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..37ab6d390ac8d5b9692f419e61dc791743ef2469
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_recfunctions.py
@@ -0,0 +1,1042 @@
+
+import numpy as np
+import numpy.ma as ma
+from numpy.ma.mrecords import MaskedRecords
+from numpy.ma.testutils import assert_equal
+from numpy.testing import assert_, assert_raises
+from numpy.lib.recfunctions import (
+    drop_fields, rename_fields, get_fieldstructure, recursive_fill_fields,
+    find_duplicates, merge_arrays, append_fields, stack_arrays, join_by,
+    repack_fields, unstructured_to_structured, structured_to_unstructured,
+    apply_along_fields, require_fields, assign_fields_by_name)
+get_fieldspec = np.lib.recfunctions._get_fieldspec
+get_names = np.lib.recfunctions.get_names
+get_names_flat = np.lib.recfunctions.get_names_flat
+zip_descr = np.lib.recfunctions._zip_descr
+zip_dtype = np.lib.recfunctions._zip_dtype
+
+
+class TestRecFunctions:
+    # Misc tests
+
+    def setup_method(self):
+        x = np.array([1, 2, ])
+        y = np.array([10, 20, 30])
+        z = np.array([('A', 1.), ('B', 2.)],
+                     dtype=[('A', '|S3'), ('B', float)])
+        w = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
+                     dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+        self.data = (w, x, y, z)
+
+    def test_zip_descr(self):
+        # Test zip_descr
+        (w, x, y, z) = self.data
+
+        # Std array
+        test = zip_descr((x, x), flatten=True)
+        assert_equal(test,
+                     np.dtype([('', int), ('', int)]))
+        test = zip_descr((x, x), flatten=False)
+        assert_equal(test,
+                     np.dtype([('', int), ('', int)]))
+
+        # Std & flexible-dtype
+        test = zip_descr((x, z), flatten=True)
+        assert_equal(test,
+                     np.dtype([('', int), ('A', '|S3'), ('B', float)]))
+        test = zip_descr((x, z), flatten=False)
+        assert_equal(test,
+                     np.dtype([('', int),
+                               ('', [('A', '|S3'), ('B', float)])]))
+
+        # Standard & nested dtype
+        test = zip_descr((x, w), flatten=True)
+        assert_equal(test,
+                     np.dtype([('', int),
+                               ('a', int),
+                               ('ba', float), ('bb', int)]))
+        test = zip_descr((x, w), flatten=False)
+        assert_equal(test,
+                     np.dtype([('', int),
+                               ('', [('a', int),
+                                     ('b', [('ba', float), ('bb', int)])])]))
+
+    def test_drop_fields(self):
+        # Test drop_fields
+        a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
+                     dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+
+        # A basic field
+        test = drop_fields(a, 'a')
+        control = np.array([((2, 3.0),), ((5, 6.0),)],
+                           dtype=[('b', [('ba', float), ('bb', int)])])
+        assert_equal(test, control)
+
+        # Another basic field (but nesting two fields)
+        test = drop_fields(a, 'b')
+        control = np.array([(1,), (4,)], dtype=[('a', int)])
+        assert_equal(test, control)
+
+        # A nested sub-field
+        test = drop_fields(a, ['ba', ])
+        control = np.array([(1, (3.0,)), (4, (6.0,))],
+                           dtype=[('a', int), ('b', [('bb', int)])])
+        assert_equal(test, control)
+
+        # All the nested sub-field from a field: zap that field
+        test = drop_fields(a, ['ba', 'bb'])
+        control = np.array([(1,), (4,)], dtype=[('a', int)])
+        assert_equal(test, control)
+
+        # dropping all fields results in an array with no fields
+        test = drop_fields(a, ['a', 'b'])
+        control = np.array([(), ()], dtype=[])
+        assert_equal(test, control)
+
+    def test_rename_fields(self):
+        # Test rename fields
+        a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
+                     dtype=[('a', int),
+                            ('b', [('ba', float), ('bb', (float, 2))])])
+        test = rename_fields(a, {'a': 'A', 'bb': 'BB'})
+        newdtype = [('A', int), ('b', [('ba', float), ('BB', (float, 2))])]
+        control = a.view(newdtype)
+        assert_equal(test.dtype, newdtype)
+        assert_equal(test, control)
+
+    def test_get_names(self):
+        # Test get_names
+        ndtype = np.dtype([('A', '|S3'), ('B', float)])
+        test = get_names(ndtype)
+        assert_equal(test, ('A', 'B'))
+
+        ndtype = np.dtype([('a', int), ('b', [('ba', float), ('bb', int)])])
+        test = get_names(ndtype)
+        assert_equal(test, ('a', ('b', ('ba', 'bb'))))
+
+        ndtype = np.dtype([('a', int), ('b', [])])
+        test = get_names(ndtype)
+        assert_equal(test, ('a', ('b', ())))
+
+        ndtype = np.dtype([])
+        test = get_names(ndtype)
+        assert_equal(test, ())
+
+    def test_get_names_flat(self):
+        # Test get_names_flat
+        ndtype = np.dtype([('A', '|S3'), ('B', float)])
+        test = get_names_flat(ndtype)
+        assert_equal(test, ('A', 'B'))
+
+        ndtype = np.dtype([('a', int), ('b', [('ba', float), ('bb', int)])])
+        test = get_names_flat(ndtype)
+        assert_equal(test, ('a', 'b', 'ba', 'bb'))
+
+        ndtype = np.dtype([('a', int), ('b', [])])
+        test = get_names_flat(ndtype)
+        assert_equal(test, ('a', 'b'))
+
+        ndtype = np.dtype([])
+        test = get_names_flat(ndtype)
+        assert_equal(test, ())
+
+    def test_get_fieldstructure(self):
+        # Test get_fieldstructure
+
+        # No nested fields
+        ndtype = np.dtype([('A', '|S3'), ('B', float)])
+        test = get_fieldstructure(ndtype)
+        assert_equal(test, {'A': [], 'B': []})
+
+        # One 1-nested field
+        ndtype = np.dtype([('A', int), ('B', [('BA', float), ('BB', '|S1')])])
+        test = get_fieldstructure(ndtype)
+        assert_equal(test, {'A': [], 'B': [], 'BA': ['B', ], 'BB': ['B']})
+
+        # One 2-nested fields
+        ndtype = np.dtype([('A', int),
+                           ('B', [('BA', int),
+                                  ('BB', [('BBA', int), ('BBB', int)])])])
+        test = get_fieldstructure(ndtype)
+        control = {'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'],
+                   'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}
+        assert_equal(test, control)
+
+        # 0 fields
+        ndtype = np.dtype([])
+        test = get_fieldstructure(ndtype)
+        assert_equal(test, {})
+
+    def test_find_duplicates(self):
+        # Test find_duplicates
+        a = ma.array([(2, (2., 'B')), (1, (2., 'B')), (2, (2., 'B')),
+                      (1, (1., 'B')), (2, (2., 'B')), (2, (2., 'C'))],
+                     mask=[(0, (0, 0)), (0, (0, 0)), (0, (0, 0)),
+                           (0, (0, 0)), (1, (0, 0)), (0, (1, 0))],
+                     dtype=[('A', int), ('B', [('BA', float), ('BB', '|S1')])])
+
+        test = find_duplicates(a, ignoremask=False, return_index=True)
+        control = [0, 2]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+        test = find_duplicates(a, key='A', return_index=True)
+        control = [0, 1, 2, 3, 5]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+        test = find_duplicates(a, key='B', return_index=True)
+        control = [0, 1, 2, 4]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+        test = find_duplicates(a, key='BA', return_index=True)
+        control = [0, 1, 2, 4]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+        test = find_duplicates(a, key='BB', return_index=True)
+        control = [0, 1, 2, 3, 4]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+    def test_find_duplicates_ignoremask(self):
+        # Test the ignoremask option of find_duplicates
+        ndtype = [('a', int)]
+        a = ma.array([1, 1, 1, 2, 2, 3, 3],
+                     mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
+        test = find_duplicates(a, ignoremask=True, return_index=True)
+        control = [0, 1, 3, 4]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+        test = find_duplicates(a, ignoremask=False, return_index=True)
+        control = [0, 1, 2, 3, 4, 6]
+        assert_equal(sorted(test[-1]), control)
+        assert_equal(test[0], a[test[-1]])
+
+    def test_repack_fields(self):
+        dt = np.dtype('u1,f4,i8', align=True)
+        a = np.zeros(2, dtype=dt)
+
+        assert_equal(repack_fields(dt), np.dtype('u1,f4,i8'))
+        assert_equal(repack_fields(a).itemsize, 13)
+        assert_equal(repack_fields(repack_fields(dt), align=True), dt)
+
+        # make sure type is preserved
+        dt = np.dtype((np.record, dt))
+        assert_(repack_fields(dt).type is np.record)
+
+    def test_structured_to_unstructured(self, tmp_path):
+        a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
+        out = structured_to_unstructured(a)
+        assert_equal(out, np.zeros((4,5), dtype='f8'))
+
+        b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+                     dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+        out = np.mean(structured_to_unstructured(b[['x', 'z']]), axis=-1)
+        assert_equal(out, np.array([ 3. ,  5.5,  9. , 11. ]))
+        out = np.mean(structured_to_unstructured(b[['x']]), axis=-1)
+        assert_equal(out, np.array([ 1. ,  4. ,  7. , 10. ]))
+
+        c = np.arange(20).reshape((4,5))
+        out = unstructured_to_structured(c, a.dtype)
+        want = np.array([( 0, ( 1.,  2), [ 3.,  4.]),
+                         ( 5, ( 6.,  7), [ 8.,  9.]),
+                         (10, (11., 12), [13., 14.]),
+                         (15, (16., 17), [18., 19.])],
+                     dtype=[('a', 'i4'),
+                            ('b', [('f0', 'f4'), ('f1', 'u2')]),
+                            ('c', 'f4', (2,))])
+        assert_equal(out, want)
+
+        d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+                     dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+        assert_equal(apply_along_fields(np.mean, d),
+                     np.array([ 8.0/3,  16.0/3,  26.0/3, 11. ]))
+        assert_equal(apply_along_fields(np.mean, d[['x', 'z']]),
+                     np.array([ 3. ,  5.5,  9. , 11. ]))
+
+        # check that for uniform field dtypes we get a view, not a copy:
+        d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+                     dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'i4')])
+        dd = structured_to_unstructured(d)
+        ddd = unstructured_to_structured(dd, d.dtype)
+        assert_(np.shares_memory(dd, d))
+        assert_(np.shares_memory(ddd, d))
+
+        # check that reversing the order of attributes works
+        dd_attrib_rev = structured_to_unstructured(d[['z', 'x']])
+        assert_equal(dd_attrib_rev, [[5, 1], [7, 4], [11, 7], [12, 10]])
+        assert_(np.shares_memory(dd_attrib_rev, d))
+
+        # including uniform fields with subarrays unpacked
+        d = np.array([(1, [2,  3], [[ 4,  5], [ 6,  7]]),
+                      (8, [9, 10], [[11, 12], [13, 14]])],
+                     dtype=[('x0', 'i4'), ('x1', ('i4', 2)),
+                            ('x2', ('i4', (2, 2)))])
+        dd = structured_to_unstructured(d)
+        ddd = unstructured_to_structured(dd, d.dtype)
+        assert_(np.shares_memory(dd, d))
+        assert_(np.shares_memory(ddd, d))
+
+        # check that reversing with sub-arrays works as expected
+        d_rev = d[::-1]
+        dd_rev = structured_to_unstructured(d_rev)
+        assert_equal(dd_rev, [[8, 9, 10, 11, 12, 13, 14],
+                              [1, 2, 3, 4, 5, 6, 7]])
+
+        # check that sub-arrays keep the order of their values
+        d_attrib_rev = d[['x2', 'x1', 'x0']]
+        dd_attrib_rev = structured_to_unstructured(d_attrib_rev)
+        assert_equal(dd_attrib_rev, [[4, 5, 6, 7, 2, 3, 1],
+                                     [11, 12, 13, 14, 9, 10, 8]])
+
+        # with ignored field at the end
+        d = np.array([(1, [2,  3], [[4, 5], [6, 7]], 32),
+                      (8, [9, 10], [[11, 12], [13, 14]], 64)],
+                     dtype=[('x0', 'i4'), ('x1', ('i4', 2)),
+                            ('x2', ('i4', (2, 2))), ('ignored', 'u1')])
+        dd = structured_to_unstructured(d[['x0', 'x1', 'x2']])
+        assert_(np.shares_memory(dd, d))
+        assert_equal(dd, [[1, 2, 3, 4, 5, 6, 7],
+                          [8, 9, 10, 11, 12, 13, 14]])
+
+        # test that nested fields with identical names don't break anything
+        point = np.dtype([('x', int), ('y', int)])
+        triangle = np.dtype([('a', point), ('b', point), ('c', point)])
+        arr = np.zeros(10, triangle)
+        res = structured_to_unstructured(arr, dtype=int)
+        assert_equal(res, np.zeros((10, 6), dtype=int))
+
+
+        # test nested combinations of subarrays and structured arrays, gh-13333
+        def subarray(dt, shape):
+            return np.dtype((dt, shape))
+
+        def structured(*dts):
+            return np.dtype([('x{}'.format(i), dt) for i, dt in enumerate(dts)])
+
+        def inspect(dt, dtype=None):
+            arr = np.zeros((), dt)
+            ret = structured_to_unstructured(arr, dtype=dtype)
+            backarr = unstructured_to_structured(ret, dt)
+            return ret.shape, ret.dtype, backarr.dtype
+
+        dt = structured(subarray(structured(np.int32, np.int32), 3))
+        assert_equal(inspect(dt), ((6,), np.int32, dt))
+
+        dt = structured(subarray(subarray(np.int32, 2), 2))
+        assert_equal(inspect(dt), ((4,), np.int32, dt))
+
+        dt = structured(np.int32)
+        assert_equal(inspect(dt), ((1,), np.int32, dt))
+
+        dt = structured(np.int32, subarray(subarray(np.int32, 2), 2))
+        assert_equal(inspect(dt), ((5,), np.int32, dt))
+
+        dt = structured()
+        assert_raises(ValueError, structured_to_unstructured, np.zeros(3, dt))
+
+        # these currently don't work, but we may make it work in the future
+        assert_raises(NotImplementedError, structured_to_unstructured,
+                                           np.zeros(3, dt), dtype=np.int32)
+        assert_raises(NotImplementedError, unstructured_to_structured,
+                                           np.zeros((3,0), dtype=np.int32))
+
+        # test supported ndarray subclasses
+        d_plain = np.array([(1, 2), (3, 4)], dtype=[('a', 'i4'), ('b', 'i4')])
+        dd_expected = structured_to_unstructured(d_plain, copy=True)
+
+        # recarray
+        d = d_plain.view(np.recarray)
+
+        dd = structured_to_unstructured(d, copy=False)
+        ddd = structured_to_unstructured(d, copy=True)
+        assert_(np.shares_memory(d, dd))
+        assert_(type(dd) is np.recarray)
+        assert_(type(ddd) is np.recarray)
+        assert_equal(dd, dd_expected)
+        assert_equal(ddd, dd_expected)
+
+        # memmap
+        d = np.memmap(tmp_path / 'memmap',
+                      mode='w+',
+                      dtype=d_plain.dtype,
+                      shape=d_plain.shape)
+        d[:] = d_plain
+        dd = structured_to_unstructured(d, copy=False)
+        ddd = structured_to_unstructured(d, copy=True)
+        assert_(np.shares_memory(d, dd))
+        assert_(type(dd) is np.memmap)
+        assert_(type(ddd) is np.memmap)
+        assert_equal(dd, dd_expected)
+        assert_equal(ddd, dd_expected)
+
+    def test_unstructured_to_structured(self):
+        # test if dtype is the args of np.dtype
+        a = np.zeros((20, 2))
+        test_dtype_args = [('x', float), ('y', float)]
+        test_dtype = np.dtype(test_dtype_args)
+        field1 = unstructured_to_structured(a, dtype=test_dtype_args)  # now
+        field2 = unstructured_to_structured(a, dtype=test_dtype)  # before
+        assert_equal(field1, field2)
+
+    def test_field_assignment_by_name(self):
+        a = np.ones(2, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
+        newdt = [('b', 'f4'), ('c', 'u1')]
+
+        assert_equal(require_fields(a, newdt), np.ones(2, newdt))
+
+        b = np.array([(1,2), (3,4)], dtype=newdt)
+        assign_fields_by_name(a, b, zero_unassigned=False)
+        assert_equal(a, np.array([(1,1,2),(1,3,4)], dtype=a.dtype))
+        assign_fields_by_name(a, b)
+        assert_equal(a, np.array([(0,1,2),(0,3,4)], dtype=a.dtype))
+
+        # test nested fields
+        a = np.ones(2, dtype=[('a', [('b', 'f8'), ('c', 'u1')])])
+        newdt = [('a', [('c', 'u1')])]
+        assert_equal(require_fields(a, newdt), np.ones(2, newdt))
+        b = np.array([((2,),), ((3,),)], dtype=newdt)
+        assign_fields_by_name(a, b, zero_unassigned=False)
+        assert_equal(a, np.array([((1,2),), ((1,3),)], dtype=a.dtype))
+        assign_fields_by_name(a, b)
+        assert_equal(a, np.array([((0,2),), ((0,3),)], dtype=a.dtype))
+
+        # test unstructured code path for 0d arrays
+        a, b = np.array(3), np.array(0)
+        assign_fields_by_name(b, a)
+        assert_equal(b[()], 3)
+
+
+class TestRecursiveFillFields:
+    # Test recursive_fill_fields.
+    def test_simple_flexible(self):
+        # Test recursive_fill_fields on flexible-array
+        a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)])
+        b = np.zeros((3,), dtype=a.dtype)
+        test = recursive_fill_fields(a, b)
+        control = np.array([(1, 10.), (2, 20.), (0, 0.)],
+                           dtype=[('A', int), ('B', float)])
+        assert_equal(test, control)
+
+    def test_masked_flexible(self):
+        # Test recursive_fill_fields on masked flexible-array
+        a = ma.array([(1, 10.), (2, 20.)], mask=[(0, 1), (1, 0)],
+                     dtype=[('A', int), ('B', float)])
+        b = ma.zeros((3,), dtype=a.dtype)
+        test = recursive_fill_fields(a, b)
+        control = ma.array([(1, 10.), (2, 20.), (0, 0.)],
+                           mask=[(0, 1), (1, 0), (0, 0)],
+                           dtype=[('A', int), ('B', float)])
+        assert_equal(test, control)
+
+
+class TestMergeArrays:
+    # Test merge_arrays
+
+    def setup_method(self):
+        x = np.array([1, 2, ])
+        y = np.array([10, 20, 30])
+        z = np.array(
+            [('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)])
+        w = np.array(
+            [(1, (2, 3.0, ())), (4, (5, 6.0, ()))],
+            dtype=[('a', int), ('b', [('ba', float), ('bb', int), ('bc', [])])])
+        self.data = (w, x, y, z)
+
+    def test_solo(self):
+        # Test merge_arrays on a single array.
+        (_, x, _, z) = self.data
+
+        test = merge_arrays(x)
+        control = np.array([(1,), (2,)], dtype=[('f0', int)])
+        assert_equal(test, control)
+        test = merge_arrays((x,))
+        assert_equal(test, control)
+
+        test = merge_arrays(z, flatten=False)
+        assert_equal(test, z)
+        test = merge_arrays(z, flatten=True)
+        assert_equal(test, z)
+
+    def test_solo_w_flatten(self):
+        # Test merge_arrays on a single array w & w/o flattening
+        w = self.data[0]
+        test = merge_arrays(w, flatten=False)
+        assert_equal(test, w)
+
+        test = merge_arrays(w, flatten=True)
+        control = np.array([(1, 2, 3.0), (4, 5, 6.0)],
+                           dtype=[('a', int), ('ba', float), ('bb', int)])
+        assert_equal(test, control)
+
+    def test_standard(self):
+        # Test standard & standard
+        # Test merge arrays
+        (_, x, y, _) = self.data
+        test = merge_arrays((x, y), usemask=False)
+        control = np.array([(1, 10), (2, 20), (-1, 30)],
+                           dtype=[('f0', int), ('f1', int)])
+        assert_equal(test, control)
+
+        test = merge_arrays((x, y), usemask=True)
+        control = ma.array([(1, 10), (2, 20), (-1, 30)],
+                           mask=[(0, 0), (0, 0), (1, 0)],
+                           dtype=[('f0', int), ('f1', int)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+    def test_flatten(self):
+        # Test standard & flexible
+        (_, x, _, z) = self.data
+        test = merge_arrays((x, z), flatten=True)
+        control = np.array([(1, 'A', 1.), (2, 'B', 2.)],
+                           dtype=[('f0', int), ('A', '|S3'), ('B', float)])
+        assert_equal(test, control)
+
+        test = merge_arrays((x, z), flatten=False)
+        control = np.array([(1, ('A', 1.)), (2, ('B', 2.))],
+                           dtype=[('f0', int),
+                                  ('f1', [('A', '|S3'), ('B', float)])])
+        assert_equal(test, control)
+
+    def test_flatten_wflexible(self):
+        # Test flatten standard & nested
+        (w, x, _, _) = self.data
+        test = merge_arrays((x, w), flatten=True)
+        control = np.array([(1, 1, 2, 3.0), (2, 4, 5, 6.0)],
+                           dtype=[('f0', int),
+                                  ('a', int), ('ba', float), ('bb', int)])
+        assert_equal(test, control)
+
+        test = merge_arrays((x, w), flatten=False)
+        controldtype = [('f0', int),
+                                ('f1', [('a', int),
+                                        ('b', [('ba', float), ('bb', int), ('bc', [])])])]
+        control = np.array([(1., (1, (2, 3.0, ()))), (2, (4, (5, 6.0, ())))],
+                           dtype=controldtype)
+        assert_equal(test, control)
+
+    def test_wmasked_arrays(self):
+        # Test merge_arrays masked arrays
+        (_, x, _, _) = self.data
+        mx = ma.array([1, 2, 3], mask=[1, 0, 0])
+        test = merge_arrays((x, mx), usemask=True)
+        control = ma.array([(1, 1), (2, 2), (-1, 3)],
+                           mask=[(0, 1), (0, 0), (1, 0)],
+                           dtype=[('f0', int), ('f1', int)])
+        assert_equal(test, control)
+        test = merge_arrays((x, mx), usemask=True, asrecarray=True)
+        assert_equal(test, control)
+        assert_(isinstance(test, MaskedRecords))
+
+    def test_w_singlefield(self):
+        # Test single field
+        test = merge_arrays((np.array([1, 2]).view([('a', int)]),
+                             np.array([10., 20., 30.])),)
+        control = ma.array([(1, 10.), (2, 20.), (-1, 30.)],
+                           mask=[(0, 0), (0, 0), (1, 0)],
+                           dtype=[('a', int), ('f1', float)])
+        assert_equal(test, control)
+
+    def test_w_shorter_flex(self):
+        # Test merge_arrays w/ a shorter flexndarray.
+        z = self.data[-1]
+
+        # Fixme, this test looks incomplete and broken
+        #test = merge_arrays((z, np.array([10, 20, 30]).view([('C', int)])))
+        #control = np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)],
+        #                   dtype=[('A', '|S3'), ('B', float), ('C', int)])
+        #assert_equal(test, control)
+
+        # Hack to avoid pyflakes warnings about unused variables
+        merge_arrays((z, np.array([10, 20, 30]).view([('C', int)])))
+        np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)],
+                 dtype=[('A', '|S3'), ('B', float), ('C', int)])
+
+    def test_singlerecord(self):
+        (_, x, y, z) = self.data
+        test = merge_arrays((x[0], y[0], z[0]), usemask=False)
+        control = np.array([(1, 10, ('A', 1))],
+                           dtype=[('f0', int),
+                                  ('f1', int),
+                                  ('f2', [('A', '|S3'), ('B', float)])])
+        assert_equal(test, control)
+
+
+class TestAppendFields:
+    # Test append_fields
+
+    def setup_method(self):
+        x = np.array([1, 2, ])
+        y = np.array([10, 20, 30])
+        z = np.array(
+            [('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)])
+        w = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
+                     dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+        self.data = (w, x, y, z)
+
+    def test_append_single(self):
+        # Test simple case
+        (_, x, _, _) = self.data
+        test = append_fields(x, 'A', data=[10, 20, 30])
+        control = ma.array([(1, 10), (2, 20), (-1, 30)],
+                           mask=[(0, 0), (0, 0), (1, 0)],
+                           dtype=[('f0', int), ('A', int)],)
+        assert_equal(test, control)
+
+    def test_append_double(self):
+        # Test simple case
+        (_, x, _, _) = self.data
+        test = append_fields(x, ('A', 'B'), data=[[10, 20, 30], [100, 200]])
+        control = ma.array([(1, 10, 100), (2, 20, 200), (-1, 30, -1)],
+                           mask=[(0, 0, 0), (0, 0, 0), (1, 0, 1)],
+                           dtype=[('f0', int), ('A', int), ('B', int)],)
+        assert_equal(test, control)
+
+    def test_append_on_flex(self):
+        # Test append_fields on flexible type arrays
+        z = self.data[-1]
+        test = append_fields(z, 'C', data=[10, 20, 30])
+        control = ma.array([('A', 1., 10), ('B', 2., 20), (-1, -1., 30)],
+                           mask=[(0, 0, 0), (0, 0, 0), (1, 1, 0)],
+                           dtype=[('A', '|S3'), ('B', float), ('C', int)],)
+        assert_equal(test, control)
+
+    def test_append_on_nested(self):
+        # Test append_fields on nested fields
+        w = self.data[0]
+        test = append_fields(w, 'C', data=[10, 20, 30])
+        control = ma.array([(1, (2, 3.0), 10),
+                            (4, (5, 6.0), 20),
+                            (-1, (-1, -1.), 30)],
+                           mask=[(
+                               0, (0, 0), 0), (0, (0, 0), 0), (1, (1, 1), 0)],
+                           dtype=[('a', int),
+                                  ('b', [('ba', float), ('bb', int)]),
+                                  ('C', int)],)
+        assert_equal(test, control)
+
+
+class TestStackArrays:
+    # Test stack_arrays
+    def setup_method(self):
+        x = np.array([1, 2, ])
+        y = np.array([10, 20, 30])
+        z = np.array(
+            [('A', 1.), ('B', 2.)], dtype=[('A', '|S3'), ('B', float)])
+        w = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
+                     dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
+        self.data = (w, x, y, z)
+
+    def test_solo(self):
+        # Test stack_arrays on single arrays
+        (_, x, _, _) = self.data
+        test = stack_arrays((x,))
+        assert_equal(test, x)
+        assert_(test is x)
+
+        test = stack_arrays(x)
+        assert_equal(test, x)
+        assert_(test is x)
+
+    def test_unnamed_fields(self):
+        # Tests combinations of arrays w/o named fields
+        (_, x, y, _) = self.data
+
+        test = stack_arrays((x, x), usemask=False)
+        control = np.array([1, 2, 1, 2])
+        assert_equal(test, control)
+
+        test = stack_arrays((x, y), usemask=False)
+        control = np.array([1, 2, 10, 20, 30])
+        assert_equal(test, control)
+
+        test = stack_arrays((y, x), usemask=False)
+        control = np.array([10, 20, 30, 1, 2])
+        assert_equal(test, control)
+
+    def test_unnamed_and_named_fields(self):
+        # Test combination of arrays w/ & w/o named fields
+        (_, x, _, z) = self.data
+
+        test = stack_arrays((x, z))
+        control = ma.array([(1, -1, -1), (2, -1, -1),
+                            (-1, 'A', 1), (-1, 'B', 2)],
+                           mask=[(0, 1, 1), (0, 1, 1),
+                                 (1, 0, 0), (1, 0, 0)],
+                           dtype=[('f0', int), ('A', '|S3'), ('B', float)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+        test = stack_arrays((z, x))
+        control = ma.array([('A', 1, -1), ('B', 2, -1),
+                            (-1, -1, 1), (-1, -1, 2), ],
+                           mask=[(0, 0, 1), (0, 0, 1),
+                                 (1, 1, 0), (1, 1, 0)],
+                           dtype=[('A', '|S3'), ('B', float), ('f2', int)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+        test = stack_arrays((z, z, x))
+        control = ma.array([('A', 1, -1), ('B', 2, -1),
+                            ('A', 1, -1), ('B', 2, -1),
+                            (-1, -1, 1), (-1, -1, 2), ],
+                           mask=[(0, 0, 1), (0, 0, 1),
+                                 (0, 0, 1), (0, 0, 1),
+                                 (1, 1, 0), (1, 1, 0)],
+                           dtype=[('A', '|S3'), ('B', float), ('f2', int)])
+        assert_equal(test, control)
+
+    def test_matching_named_fields(self):
+        # Test combination of arrays w/ matching field names
+        (_, x, _, z) = self.data
+        zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
+                      dtype=[('A', '|S3'), ('B', float), ('C', float)])
+        test = stack_arrays((z, zz))
+        control = ma.array([('A', 1, -1), ('B', 2, -1),
+                            (
+                                'a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
+                           dtype=[('A', '|S3'), ('B', float), ('C', float)],
+                           mask=[(0, 0, 1), (0, 0, 1),
+                                 (0, 0, 0), (0, 0, 0), (0, 0, 0)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+        test = stack_arrays((z, zz, x))
+        ndtype = [('A', '|S3'), ('B', float), ('C', float), ('f3', int)]
+        control = ma.array([('A', 1, -1, -1), ('B', 2, -1, -1),
+                            ('a', 10., 100., -1), ('b', 20., 200., -1),
+                            ('c', 30., 300., -1),
+                            (-1, -1, -1, 1), (-1, -1, -1, 2)],
+                           dtype=ndtype,
+                           mask=[(0, 0, 1, 1), (0, 0, 1, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (1, 1, 1, 0), (1, 1, 1, 0)])
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+    def test_defaults(self):
+        # Test defaults: no exception raised if keys of defaults are not fields.
+        (_, _, _, z) = self.data
+        zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
+                      dtype=[('A', '|S3'), ('B', float), ('C', float)])
+        defaults = {'A': '???', 'B': -999., 'C': -9999., 'D': -99999.}
+        test = stack_arrays((z, zz), defaults=defaults)
+        control = ma.array([('A', 1, -9999.), ('B', 2, -9999.),
+                            (
+                                'a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
+                           dtype=[('A', '|S3'), ('B', float), ('C', float)],
+                           mask=[(0, 0, 1), (0, 0, 1),
+                                 (0, 0, 0), (0, 0, 0), (0, 0, 0)])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+    def test_autoconversion(self):
+        # Tests autoconversion
+        adtype = [('A', int), ('B', bool), ('C', float)]
+        a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype)
+        bdtype = [('A', int), ('B', float), ('C', float)]
+        b = ma.array([(4, 5, 6)], dtype=bdtype)
+        control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)],
+                           dtype=bdtype)
+        test = stack_arrays((a, b), autoconvert=True)
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        with assert_raises(TypeError):
+            stack_arrays((a, b), autoconvert=False)
+
+    def test_checktitles(self):
+        # Test using titles in the field names
+        adtype = [(('a', 'A'), int), (('b', 'B'), bool), (('c', 'C'), float)]
+        a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype)
+        bdtype = [(('a', 'A'), int), (('b', 'B'), bool), (('c', 'C'), float)]
+        b = ma.array([(4, 5, 6)], dtype=bdtype)
+        test = stack_arrays((a, b))
+        control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)],
+                           dtype=bdtype)
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+
+    def test_subdtype(self):
+        z = np.array([
+            ('A', 1), ('B', 2)
+        ], dtype=[('A', '|S3'), ('B', float, (1,))])
+        zz = np.array([
+            ('a', [10.], 100.), ('b', [20.], 200.), ('c', [30.], 300.)
+        ], dtype=[('A', '|S3'), ('B', float, (1,)), ('C', float)])
+
+        res = stack_arrays((z, zz))
+        expected = ma.array(
+            data=[
+                (b'A', [1.0], 0),
+                (b'B', [2.0], 0),
+                (b'a', [10.0], 100.0),
+                (b'b', [20.0], 200.0),
+                (b'c', [30.0], 300.0)],
+            mask=[
+                (False, [False],  True),
+                (False, [False],  True),
+                (False, [False], False),
+                (False, [False], False),
+                (False, [False], False)
+            ],
+            dtype=zz.dtype
+        )
+        assert_equal(res.dtype, expected.dtype)
+        assert_equal(res, expected)
+        assert_equal(res.mask, expected.mask)
+
+
+class TestJoinBy:
+    def setup_method(self):
+        self.a = np.array(list(zip(np.arange(10), np.arange(50, 60),
+                                   np.arange(100, 110))),
+                          dtype=[('a', int), ('b', int), ('c', int)])
+        self.b = np.array(list(zip(np.arange(5, 15), np.arange(65, 75),
+                                   np.arange(100, 110))),
+                          dtype=[('a', int), ('b', int), ('d', int)])
+
+    def test_inner_join(self):
+        # Basic test of join_by
+        a, b = self.a, self.b
+
+        test = join_by('a', a, b, jointype='inner')
+        control = np.array([(5, 55, 65, 105, 100), (6, 56, 66, 106, 101),
+                            (7, 57, 67, 107, 102), (8, 58, 68, 108, 103),
+                            (9, 59, 69, 109, 104)],
+                           dtype=[('a', int), ('b1', int), ('b2', int),
+                                  ('c', int), ('d', int)])
+        assert_equal(test, control)
+
+    def test_join(self):
+        a, b = self.a, self.b
+
+        # Fixme, this test is broken
+        #test = join_by(('a', 'b'), a, b)
+        #control = np.array([(5, 55, 105, 100), (6, 56, 106, 101),
+        #                    (7, 57, 107, 102), (8, 58, 108, 103),
+        #                    (9, 59, 109, 104)],
+        #                   dtype=[('a', int), ('b', int),
+        #                          ('c', int), ('d', int)])
+        #assert_equal(test, control)
+
+        # Hack to avoid pyflakes unused variable warnings
+        join_by(('a', 'b'), a, b)
+        np.array([(5, 55, 105, 100), (6, 56, 106, 101),
+                  (7, 57, 107, 102), (8, 58, 108, 103),
+                  (9, 59, 109, 104)],
+                  dtype=[('a', int), ('b', int),
+                         ('c', int), ('d', int)])
+
+    def test_join_subdtype(self):
+        # tests the bug in https://stackoverflow.com/q/44769632/102441
+        foo = np.array([(1,)],
+                       dtype=[('key', int)])
+        bar = np.array([(1, np.array([1,2,3]))],
+                       dtype=[('key', int), ('value', 'uint16', 3)])
+        res = join_by('key', foo, bar)
+        assert_equal(res, bar.view(ma.MaskedArray))
+
+    def test_outer_join(self):
+        a, b = self.a, self.b
+
+        test = join_by(('a', 'b'), a, b, 'outer')
+        control = ma.array([(0, 50, 100, -1), (1, 51, 101, -1),
+                            (2, 52, 102, -1), (3, 53, 103, -1),
+                            (4, 54, 104, -1), (5, 55, 105, -1),
+                            (5, 65, -1, 100), (6, 56, 106, -1),
+                            (6, 66, -1, 101), (7, 57, 107, -1),
+                            (7, 67, -1, 102), (8, 58, 108, -1),
+                            (8, 68, -1, 103), (9, 59, 109, -1),
+                            (9, 69, -1, 104), (10, 70, -1, 105),
+                            (11, 71, -1, 106), (12, 72, -1, 107),
+                            (13, 73, -1, 108), (14, 74, -1, 109)],
+                           mask=[(0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 1, 0), (0, 0, 0, 1),
+                                 (0, 0, 1, 0), (0, 0, 0, 1),
+                                 (0, 0, 1, 0), (0, 0, 0, 1),
+                                 (0, 0, 1, 0), (0, 0, 0, 1),
+                                 (0, 0, 1, 0), (0, 0, 1, 0),
+                                 (0, 0, 1, 0), (0, 0, 1, 0),
+                                 (0, 0, 1, 0), (0, 0, 1, 0)],
+                           dtype=[('a', int), ('b', int),
+                                  ('c', int), ('d', int)])
+        assert_equal(test, control)
+
+    def test_leftouter_join(self):
+        a, b = self.a, self.b
+
+        test = join_by(('a', 'b'), a, b, 'leftouter')
+        control = ma.array([(0, 50, 100, -1), (1, 51, 101, -1),
+                            (2, 52, 102, -1), (3, 53, 103, -1),
+                            (4, 54, 104, -1), (5, 55, 105, -1),
+                            (6, 56, 106, -1), (7, 57, 107, -1),
+                            (8, 58, 108, -1), (9, 59, 109, -1)],
+                           mask=[(0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1),
+                                 (0, 0, 0, 1), (0, 0, 0, 1)],
+                           dtype=[('a', int), ('b', int), ('c', int), ('d', int)])
+        assert_equal(test, control)
+
+    def test_different_field_order(self):
+        # gh-8940
+        a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')])
+        b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')])
+        # this should not give a FutureWarning:
+        j = join_by(['c', 'b'], a, b, jointype='inner', usemask=False)
+        assert_equal(j.dtype.names, ['b', 'c', 'a1', 'a2'])
+
+    def test_duplicate_keys(self):
+        a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')])
+        b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')])
+        assert_raises(ValueError, join_by, ['a', 'b', 'b'], a, b)
+
+    def test_same_name_different_dtypes_key(self):
+        a_dtype = np.dtype([('key', 'S5'), ('value', ' 2**32
+
+
+def _add_keepdims(func):
+    """ hack in keepdims behavior into a function taking an axis """
+    @functools.wraps(func)
+    def wrapped(a, axis, **kwargs):
+        res = func(a, axis=axis, **kwargs)
+        if axis is None:
+            axis = 0  # res is now a scalar, so we can insert this anywhere
+        return np.expand_dims(res, axis=axis)
+    return wrapped
+
+
+class TestTakeAlongAxis:
+    def test_argequivalent(self):
+        """ Test it translates from arg to  """
+        from numpy.random import rand
+        a = rand(3, 4, 5)
+
+        funcs = [
+            (np.sort, np.argsort, dict()),
+            (_add_keepdims(np.min), _add_keepdims(np.argmin), dict()),
+            (_add_keepdims(np.max), _add_keepdims(np.argmax), dict()),
+            #(np.partition, np.argpartition, dict(kth=2)),
+        ]
+
+        for func, argfunc, kwargs in funcs:
+            for axis in list(range(a.ndim)) + [None]:
+                a_func = func(a, axis=axis, **kwargs)
+                ai_func = argfunc(a, axis=axis, **kwargs)
+                assert_equal(a_func, take_along_axis(a, ai_func, axis=axis))
+
+    def test_invalid(self):
+        """ Test it errors when indices has too few dimensions """
+        a = np.ones((10, 10))
+        ai = np.ones((10, 2), dtype=np.intp)
+
+        # sanity check
+        take_along_axis(a, ai, axis=1)
+
+        # not enough indices
+        assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
+        # bool arrays not allowed
+        assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
+        # float arrays not allowed
+        assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
+        # invalid axis
+        assert_raises(AxisError, take_along_axis, a, ai, axis=10)
+        # invalid indices
+        assert_raises(ValueError, take_along_axis, a, ai, axis=None)
+
+    def test_empty(self):
+        """ Test everything is ok with empty results, even with inserted dims """
+        a  = np.ones((3, 4, 5))
+        ai = np.ones((3, 0, 5), dtype=np.intp)
+
+        actual = take_along_axis(a, ai, axis=1)
+        assert_equal(actual.shape, ai.shape)
+
+    def test_broadcast(self):
+        """ Test that non-indexing dimensions are broadcast in both directions """
+        a  = np.ones((3, 4, 1))
+        ai = np.ones((1, 2, 5), dtype=np.intp)
+        actual = take_along_axis(a, ai, axis=1)
+        assert_equal(actual.shape, (3, 2, 5))
+
+
+class TestPutAlongAxis:
+    def test_replace_max(self):
+        a_base = np.array([[10, 30, 20], [60, 40, 50]])
+
+        for axis in list(range(a_base.ndim)) + [None]:
+            # we mutate this in the loop
+            a = a_base.copy()
+
+            # replace the max with a small value
+            i_max = _add_keepdims(np.argmax)(a, axis=axis)
+            put_along_axis(a, i_max, -99, axis=axis)
+
+            # find the new minimum, which should max
+            i_min = _add_keepdims(np.argmin)(a, axis=axis)
+
+            assert_equal(i_min, i_max)
+
+    def test_broadcast(self):
+        """ Test that non-indexing dimensions are broadcast in both directions """
+        a  = np.ones((3, 4, 1))
+        ai = np.arange(10, dtype=np.intp).reshape((1, 2, 5)) % 4
+        put_along_axis(a, ai, 20, axis=1)
+        assert_equal(take_along_axis(a, ai, axis=1), 20)
+
+    def test_invalid(self):
+        """ Test invalid inputs """
+        a_base = np.array([[10, 30, 20], [60, 40, 50]])
+        indices = np.array([[0], [1]])
+        values = np.array([[2], [1]])
+
+        # sanity check
+        a = a_base.copy()
+        put_along_axis(a, indices, values, axis=0)
+        assert np.all(a == [[2, 2, 2], [1, 1, 1]])
+
+        # invalid indices
+        a = a_base.copy()
+        with assert_raises(ValueError) as exc:
+            put_along_axis(a, indices, values, axis=None)
+        assert "single dimension" in str(exc.exception)
+
+
+
+class TestApplyAlongAxis:
+    def test_simple(self):
+        a = np.ones((20, 10), 'd')
+        assert_array_equal(
+            apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
+
+    def test_simple101(self):
+        a = np.ones((10, 101), 'd')
+        assert_array_equal(
+            apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
+
+    def test_3d(self):
+        a = np.arange(27).reshape((3, 3, 3))
+        assert_array_equal(apply_along_axis(np.sum, 0, a),
+                           [[27, 30, 33], [36, 39, 42], [45, 48, 51]])
+
+    def test_preserve_subclass(self):
+        def double(row):
+            return row * 2
+
+        class MyNDArray(np.ndarray):
+            pass
+
+        m = np.array([[0, 1], [2, 3]]).view(MyNDArray)
+        expected = np.array([[0, 2], [4, 6]]).view(MyNDArray)
+
+        result = apply_along_axis(double, 0, m)
+        assert_(isinstance(result, MyNDArray))
+        assert_array_equal(result, expected)
+
+        result = apply_along_axis(double, 1, m)
+        assert_(isinstance(result, MyNDArray))
+        assert_array_equal(result, expected)
+
+    def test_subclass(self):
+        class MinimalSubclass(np.ndarray):
+            data = 1
+
+        def minimal_function(array):
+            return array.data
+
+        a = np.zeros((6, 3)).view(MinimalSubclass)
+
+        assert_array_equal(
+            apply_along_axis(minimal_function, 0, a), np.array([1, 1, 1])
+        )
+
+    def test_scalar_array(self, cls=np.ndarray):
+        a = np.ones((6, 3)).view(cls)
+        res = apply_along_axis(np.sum, 0, a)
+        assert_(isinstance(res, cls))
+        assert_array_equal(res, np.array([6, 6, 6]).view(cls))
+
+    def test_0d_array(self, cls=np.ndarray):
+        def sum_to_0d(x):
+            """ Sum x, returning a 0d array of the same class """
+            assert_equal(x.ndim, 1)
+            return np.squeeze(np.sum(x, keepdims=True))
+        a = np.ones((6, 3)).view(cls)
+        res = apply_along_axis(sum_to_0d, 0, a)
+        assert_(isinstance(res, cls))
+        assert_array_equal(res, np.array([6, 6, 6]).view(cls))
+
+        res = apply_along_axis(sum_to_0d, 1, a)
+        assert_(isinstance(res, cls))
+        assert_array_equal(res, np.array([3, 3, 3, 3, 3, 3]).view(cls))
+
+    def test_axis_insertion(self, cls=np.ndarray):
+        def f1to2(x):
+            """produces an asymmetric non-square matrix from x"""
+            assert_equal(x.ndim, 1)
+            return (x[::-1] * x[1:,None]).view(cls)
+
+        a2d = np.arange(6*3).reshape((6, 3))
+
+        # 2d insertion along first axis
+        actual = apply_along_axis(f1to2, 0, a2d)
+        expected = np.stack([
+            f1to2(a2d[:,i]) for i in range(a2d.shape[1])
+        ], axis=-1).view(cls)
+        assert_equal(type(actual), type(expected))
+        assert_equal(actual, expected)
+
+        # 2d insertion along last axis
+        actual = apply_along_axis(f1to2, 1, a2d)
+        expected = np.stack([
+            f1to2(a2d[i,:]) for i in range(a2d.shape[0])
+        ], axis=0).view(cls)
+        assert_equal(type(actual), type(expected))
+        assert_equal(actual, expected)
+
+        # 3d insertion along middle axis
+        a3d = np.arange(6*5*3).reshape((6, 5, 3))
+
+        actual = apply_along_axis(f1to2, 1, a3d)
+        expected = np.stack([
+            np.stack([
+                f1to2(a3d[i,:,j]) for i in range(a3d.shape[0])
+            ], axis=0)
+            for j in range(a3d.shape[2])
+        ], axis=-1).view(cls)
+        assert_equal(type(actual), type(expected))
+        assert_equal(actual, expected)
+
+    def test_subclass_preservation(self):
+        class MinimalSubclass(np.ndarray):
+            pass
+        self.test_scalar_array(MinimalSubclass)
+        self.test_0d_array(MinimalSubclass)
+        self.test_axis_insertion(MinimalSubclass)
+
+    def test_axis_insertion_ma(self):
+        def f1to2(x):
+            """produces an asymmetric non-square matrix from x"""
+            assert_equal(x.ndim, 1)
+            res = x[::-1] * x[1:,None]
+            return np.ma.masked_where(res%5==0, res)
+        a = np.arange(6*3).reshape((6, 3))
+        res = apply_along_axis(f1to2, 0, a)
+        assert_(isinstance(res, np.ma.masked_array))
+        assert_equal(res.ndim, 3)
+        assert_array_equal(res[:,:,0].mask, f1to2(a[:,0]).mask)
+        assert_array_equal(res[:,:,1].mask, f1to2(a[:,1]).mask)
+        assert_array_equal(res[:,:,2].mask, f1to2(a[:,2]).mask)
+
+    def test_tuple_func1d(self):
+        def sample_1d(x):
+            return x[1], x[0]
+        res = np.apply_along_axis(sample_1d, 1, np.array([[1, 2], [3, 4]]))
+        assert_array_equal(res, np.array([[2, 1], [4, 3]]))
+
+    def test_empty(self):
+        # can't apply_along_axis when there's no chance to call the function
+        def never_call(x):
+            assert_(False) # should never be reached
+
+        a = np.empty((0, 0))
+        assert_raises(ValueError, np.apply_along_axis, never_call, 0, a)
+        assert_raises(ValueError, np.apply_along_axis, never_call, 1, a)
+
+        # but it's sometimes ok with some non-zero dimensions
+        def empty_to_1(x):
+            assert_(len(x) == 0)
+            return 1
+
+        a = np.empty((10, 0))
+        actual = np.apply_along_axis(empty_to_1, 1, a)
+        assert_equal(actual, np.ones(10))
+        assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a)
+
+    def test_with_iterable_object(self):
+        # from issue 5248
+        d = np.array([
+            [{1, 11}, {2, 22}, {3, 33}],
+            [{4, 44}, {5, 55}, {6, 66}]
+        ])
+        actual = np.apply_along_axis(lambda a: set.union(*a), 0, d)
+        expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}])
+
+        assert_equal(actual, expected)
+
+        # issue 8642 - assert_equal doesn't detect this!
+        for i in np.ndindex(actual.shape):
+            assert_equal(type(actual[i]), type(expected[i]))
+
+
+class TestApplyOverAxes:
+    def test_simple(self):
+        a = np.arange(24).reshape(2, 3, 4)
+        aoa_a = apply_over_axes(np.sum, a, [0, 2])
+        assert_array_equal(aoa_a, np.array([[[60], [92], [124]]]))
+
+
+class TestExpandDims:
+    def test_functionality(self):
+        s = (2, 3, 4, 5)
+        a = np.empty(s)
+        for axis in range(-5, 4):
+            b = expand_dims(a, axis)
+            assert_(b.shape[axis] == 1)
+            assert_(np.squeeze(b).shape == s)
+
+    def test_axis_tuple(self):
+        a = np.empty((3, 3, 3))
+        assert np.expand_dims(a, axis=(0, 1, 2)).shape == (1, 1, 1, 3, 3, 3)
+        assert np.expand_dims(a, axis=(0, -1, -2)).shape == (1, 3, 3, 3, 1, 1)
+        assert np.expand_dims(a, axis=(0, 3, 5)).shape == (1, 3, 3, 1, 3, 1)
+        assert np.expand_dims(a, axis=(0, -3, -5)).shape == (1, 1, 3, 1, 3, 3)
+
+    def test_axis_out_of_range(self):
+        s = (2, 3, 4, 5)
+        a = np.empty(s)
+        assert_raises(AxisError, expand_dims, a, -6)
+        assert_raises(AxisError, expand_dims, a, 5)
+
+        a = np.empty((3, 3, 3))
+        assert_raises(AxisError, expand_dims, a, (0, -6))
+        assert_raises(AxisError, expand_dims, a, (0, 5))
+
+    def test_repeated_axis(self):
+        a = np.empty((3, 3, 3))
+        assert_raises(ValueError, expand_dims, a, axis=(1, 1))
+
+    def test_subclasses(self):
+        a = np.arange(10).reshape((2, 5))
+        a = np.ma.array(a, mask=a%3 == 0)
+
+        expanded = np.expand_dims(a, axis=1)
+        assert_(isinstance(expanded, np.ma.MaskedArray))
+        assert_equal(expanded.shape, (2, 1, 5))
+        assert_equal(expanded.mask.shape, (2, 1, 5))
+
+
+class TestArraySplit:
+    def test_integer_0_split(self):
+        a = np.arange(10)
+        assert_raises(ValueError, array_split, a, 0)
+
+    def test_integer_split(self):
+        a = np.arange(10)
+        res = array_split(a, 1)
+        desired = [np.arange(10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 2)
+        desired = [np.arange(5), np.arange(5, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 3)
+        desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 4)
+        desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8),
+                   np.arange(8, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 5)
+        desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
+                   np.arange(6, 8), np.arange(8, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 6)
+        desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
+                   np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 7)
+        desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
+                   np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
+                   np.arange(9, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 8)
+        desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5),
+                   np.arange(5, 6), np.arange(6, 7), np.arange(7, 8),
+                   np.arange(8, 9), np.arange(9, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 9)
+        desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4),
+                   np.arange(4, 5), np.arange(5, 6), np.arange(6, 7),
+                   np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 10)
+        desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
+                   np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
+                   np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
+                   np.arange(9, 10)]
+        compare_results(res, desired)
+
+        res = array_split(a, 11)
+        desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
+                   np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
+                   np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
+                   np.arange(9, 10), np.array([])]
+        compare_results(res, desired)
+
+    def test_integer_split_2D_rows(self):
+        a = np.array([np.arange(10), np.arange(10)])
+        res = array_split(a, 3, axis=0)
+        tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
+                   np.zeros((0, 10))]
+        compare_results(res, tgt)
+        assert_(a.dtype.type is res[-1].dtype.type)
+
+        # Same thing for manual splits:
+        res = array_split(a, [0, 1], axis=0)
+        tgt = [np.zeros((0, 10)), np.array([np.arange(10)]),
+               np.array([np.arange(10)])]
+        compare_results(res, tgt)
+        assert_(a.dtype.type is res[-1].dtype.type)
+
+    def test_integer_split_2D_cols(self):
+        a = np.array([np.arange(10), np.arange(10)])
+        res = array_split(a, 3, axis=-1)
+        desired = [np.array([np.arange(4), np.arange(4)]),
+                   np.array([np.arange(4, 7), np.arange(4, 7)]),
+                   np.array([np.arange(7, 10), np.arange(7, 10)])]
+        compare_results(res, desired)
+
+    def test_integer_split_2D_default(self):
+        """ This will fail if we change default axis
+        """
+        a = np.array([np.arange(10), np.arange(10)])
+        res = array_split(a, 3)
+        tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
+                   np.zeros((0, 10))]
+        compare_results(res, tgt)
+        assert_(a.dtype.type is res[-1].dtype.type)
+        # perhaps should check higher dimensions
+
+    @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform")
+    def test_integer_split_2D_rows_greater_max_int32(self):
+        a = np.broadcast_to([0], (1 << 32, 2))
+        res = array_split(a, 4)
+        chunk = np.broadcast_to([0], (1 << 30, 2))
+        tgt = [chunk] * 4
+        for i in range(len(tgt)):
+            assert_equal(res[i].shape, tgt[i].shape)
+
+    def test_index_split_simple(self):
+        a = np.arange(10)
+        indices = [1, 5, 7]
+        res = array_split(a, indices, axis=-1)
+        desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7),
+                   np.arange(7, 10)]
+        compare_results(res, desired)
+
+    def test_index_split_low_bound(self):
+        a = np.arange(10)
+        indices = [0, 5, 7]
+        res = array_split(a, indices, axis=-1)
+        desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
+                   np.arange(7, 10)]
+        compare_results(res, desired)
+
+    def test_index_split_high_bound(self):
+        a = np.arange(10)
+        indices = [0, 5, 7, 10, 12]
+        res = array_split(a, indices, axis=-1)
+        desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
+                   np.arange(7, 10), np.array([]), np.array([])]
+        compare_results(res, desired)
+
+
+class TestSplit:
+    # The split function is essentially the same as array_split,
+    # except that it test if splitting will result in an
+    # equal split.  Only test for this case.
+
+    def test_equal_split(self):
+        a = np.arange(10)
+        res = split(a, 2)
+        desired = [np.arange(5), np.arange(5, 10)]
+        compare_results(res, desired)
+
+    def test_unequal_split(self):
+        a = np.arange(10)
+        assert_raises(ValueError, split, a, 3)
+
+
+class TestColumnStack:
+    def test_non_iterable(self):
+        assert_raises(TypeError, column_stack, 1)
+
+    def test_1D_arrays(self):
+        # example from docstring
+        a = np.array((1, 2, 3))
+        b = np.array((2, 3, 4))
+        expected = np.array([[1, 2],
+                             [2, 3],
+                             [3, 4]])
+        actual = np.column_stack((a, b))
+        assert_equal(actual, expected)
+
+    def test_2D_arrays(self):
+        # same as hstack 2D docstring example
+        a = np.array([[1], [2], [3]])
+        b = np.array([[2], [3], [4]])
+        expected = np.array([[1, 2],
+                             [2, 3],
+                             [3, 4]])
+        actual = np.column_stack((a, b))
+        assert_equal(actual, expected)
+
+    def test_generator(self):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
+            column_stack(np.arange(3) for _ in range(2))
+
+
+class TestDstack:
+    def test_non_iterable(self):
+        assert_raises(TypeError, dstack, 1)
+
+    def test_0D_array(self):
+        a = np.array(1)
+        b = np.array(2)
+        res = dstack([a, b])
+        desired = np.array([[[1, 2]]])
+        assert_array_equal(res, desired)
+
+    def test_1D_array(self):
+        a = np.array([1])
+        b = np.array([2])
+        res = dstack([a, b])
+        desired = np.array([[[1, 2]]])
+        assert_array_equal(res, desired)
+
+    def test_2D_array(self):
+        a = np.array([[1], [2]])
+        b = np.array([[1], [2]])
+        res = dstack([a, b])
+        desired = np.array([[[1, 1]], [[2, 2, ]]])
+        assert_array_equal(res, desired)
+
+    def test_2D_array2(self):
+        a = np.array([1, 2])
+        b = np.array([1, 2])
+        res = dstack([a, b])
+        desired = np.array([[[1, 1], [2, 2]]])
+        assert_array_equal(res, desired)
+
+    def test_generator(self):
+        with pytest.raises(TypeError, match="arrays to stack must be"):
+            dstack(np.arange(3) for _ in range(2))
+
+
+# array_split has more comprehensive test of splitting.
+# only do simple test on hsplit, vsplit, and dsplit
+class TestHsplit:
+    """Only testing for integer splits.
+
+    """
+    def test_non_iterable(self):
+        assert_raises(ValueError, hsplit, 1, 1)
+
+    def test_0D_array(self):
+        a = np.array(1)
+        try:
+            hsplit(a, 2)
+            assert_(0)
+        except ValueError:
+            pass
+
+    def test_1D_array(self):
+        a = np.array([1, 2, 3, 4])
+        res = hsplit(a, 2)
+        desired = [np.array([1, 2]), np.array([3, 4])]
+        compare_results(res, desired)
+
+    def test_2D_array(self):
+        a = np.array([[1, 2, 3, 4],
+                  [1, 2, 3, 4]])
+        res = hsplit(a, 2)
+        desired = [np.array([[1, 2], [1, 2]]), np.array([[3, 4], [3, 4]])]
+        compare_results(res, desired)
+
+
+class TestVsplit:
+    """Only testing for integer splits.
+
+    """
+    def test_non_iterable(self):
+        assert_raises(ValueError, vsplit, 1, 1)
+
+    def test_0D_array(self):
+        a = np.array(1)
+        assert_raises(ValueError, vsplit, a, 2)
+
+    def test_1D_array(self):
+        a = np.array([1, 2, 3, 4])
+        try:
+            vsplit(a, 2)
+            assert_(0)
+        except ValueError:
+            pass
+
+    def test_2D_array(self):
+        a = np.array([[1, 2, 3, 4],
+                  [1, 2, 3, 4]])
+        res = vsplit(a, 2)
+        desired = [np.array([[1, 2, 3, 4]]), np.array([[1, 2, 3, 4]])]
+        compare_results(res, desired)
+
+
+class TestDsplit:
+    # Only testing for integer splits.
+    def test_non_iterable(self):
+        assert_raises(ValueError, dsplit, 1, 1)
+
+    def test_0D_array(self):
+        a = np.array(1)
+        assert_raises(ValueError, dsplit, a, 2)
+
+    def test_1D_array(self):
+        a = np.array([1, 2, 3, 4])
+        assert_raises(ValueError, dsplit, a, 2)
+
+    def test_2D_array(self):
+        a = np.array([[1, 2, 3, 4],
+                  [1, 2, 3, 4]])
+        try:
+            dsplit(a, 2)
+            assert_(0)
+        except ValueError:
+            pass
+
+    def test_3D_array(self):
+        a = np.array([[[1, 2, 3, 4],
+                   [1, 2, 3, 4]],
+                  [[1, 2, 3, 4],
+                   [1, 2, 3, 4]]])
+        res = dsplit(a, 2)
+        desired = [np.array([[[1, 2], [1, 2]], [[1, 2], [1, 2]]]),
+                   np.array([[[3, 4], [3, 4]], [[3, 4], [3, 4]]])]
+        compare_results(res, desired)
+
+
+class TestSqueeze:
+    def test_basic(self):
+        from numpy.random import rand
+
+        a = rand(20, 10, 10, 1, 1)
+        b = rand(20, 1, 10, 1, 20)
+        c = rand(1, 1, 20, 10)
+        assert_array_equal(np.squeeze(a), np.reshape(a, (20, 10, 10)))
+        assert_array_equal(np.squeeze(b), np.reshape(b, (20, 10, 20)))
+        assert_array_equal(np.squeeze(c), np.reshape(c, (20, 10)))
+
+        # Squeezing to 0-dim should still give an ndarray
+        a = [[[1.5]]]
+        res = np.squeeze(a)
+        assert_equal(res, 1.5)
+        assert_equal(res.ndim, 0)
+        assert_equal(type(res), np.ndarray)
+
+
+class TestKron:
+    def test_basic(self):
+        # Using 0-dimensional ndarray
+        a = np.array(1)
+        b = np.array([[1, 2], [3, 4]])
+        k = np.array([[1, 2], [3, 4]])
+        assert_array_equal(np.kron(a, b), k)
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array(1)
+        assert_array_equal(np.kron(a, b), k)
+
+        # Using 1-dimensional ndarray
+        a = np.array([3])
+        b = np.array([[1, 2], [3, 4]])
+        k = np.array([[3, 6], [9, 12]])
+        assert_array_equal(np.kron(a, b), k)
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array([3])
+        assert_array_equal(np.kron(a, b), k)
+
+        # Using 3-dimensional ndarray
+        a = np.array([[[1]], [[2]]])
+        b = np.array([[1, 2], [3, 4]])
+        k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]])
+        assert_array_equal(np.kron(a, b), k)
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array([[[1]], [[2]]])
+        k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]])
+        assert_array_equal(np.kron(a, b), k)
+
+    def test_return_type(self):
+        class myarray(np.ndarray):
+            __array_priority__ = 1.0
+
+        a = np.ones([2, 2])
+        ma = myarray(a.shape, a.dtype, a.data)
+        assert_equal(type(kron(a, a)), np.ndarray)
+        assert_equal(type(kron(ma, ma)), myarray)
+        assert_equal(type(kron(a, ma)), myarray)
+        assert_equal(type(kron(ma, a)), myarray)
+
+    @pytest.mark.parametrize(
+        "array_class", [np.asarray, np.asmatrix]
+    )
+    def test_kron_smoke(self, array_class):
+        a = array_class(np.ones([3, 3]))
+        b = array_class(np.ones([3, 3]))
+        k = array_class(np.ones([9, 9]))
+
+        assert_array_equal(np.kron(a, b), k)
+
+    def test_kron_ma(self):
+        x = np.ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
+        k = np.ma.array(np.diag([1, 4, 4, 16]),
+                mask=~np.array(np.identity(4), dtype=bool))
+
+        assert_array_equal(k, np.kron(x, x))
+
+    @pytest.mark.parametrize(
+        "shape_a,shape_b", [
+            ((1, 1), (1, 1)),
+            ((1, 2, 3), (4, 5, 6)),
+            ((2, 2), (2, 2, 2)),
+            ((1, 0), (1, 1)),
+            ((2, 0, 2), (2, 2)),
+            ((2, 0, 0, 2), (2, 0, 2)),
+        ])
+    def test_kron_shape(self, shape_a, shape_b):
+        a = np.ones(shape_a)
+        b = np.ones(shape_b)
+        normalised_shape_a = (1,) * max(0, len(shape_b)-len(shape_a)) + shape_a
+        normalised_shape_b = (1,) * max(0, len(shape_a)-len(shape_b)) + shape_b
+        expected_shape = np.multiply(normalised_shape_a, normalised_shape_b)
+
+        k = np.kron(a, b)
+        assert np.array_equal(
+                k.shape, expected_shape), "Unexpected shape from kron"
+
+
+class TestTile:
+    def test_basic(self):
+        a = np.array([0, 1, 2])
+        b = [[1, 2], [3, 4]]
+        assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2])
+        assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]])
+        assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]])
+        assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]])
+        assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]])
+        assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4],
+                                       [1, 2, 1, 2], [3, 4, 3, 4]])
+
+    def test_tile_one_repetition_on_array_gh4679(self):
+        a = np.arange(5)
+        b = tile(a, 1)
+        b += 2
+        assert_equal(a, np.arange(5))
+
+    def test_empty(self):
+        a = np.array([[[]]])
+        b = np.array([[], []])
+        c = tile(b, 2).shape
+        d = tile(a, (3, 2, 5)).shape
+        assert_equal(c, (2, 0))
+        assert_equal(d, (3, 2, 0))
+
+    def test_kroncompare(self):
+        from numpy.random import randint
+
+        reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
+        shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
+        for s in shape:
+            b = randint(0, 10, size=s)
+            for r in reps:
+                a = np.ones(r, b.dtype)
+                large = tile(b, r)
+                klarge = kron(a, b)
+                assert_equal(large, klarge)
+
+
+class TestMayShareMemory:
+    def test_basic(self):
+        d = np.ones((50, 60))
+        d2 = np.ones((30, 60, 6))
+        assert_(np.may_share_memory(d, d))
+        assert_(np.may_share_memory(d, d[::-1]))
+        assert_(np.may_share_memory(d, d[::2]))
+        assert_(np.may_share_memory(d, d[1:, ::-1]))
+
+        assert_(not np.may_share_memory(d[::-1], d2))
+        assert_(not np.may_share_memory(d[::2], d2))
+        assert_(not np.may_share_memory(d[1:, ::-1], d2))
+        assert_(np.may_share_memory(d2[1:, ::-1], d2))
+
+
+# Utility
+def compare_results(res, desired):
+    """Compare lists of arrays."""
+    if len(res) != len(desired):
+        raise ValueError("Iterables have different lengths")
+    # See also PEP 618 for Python 3.10
+    for x, y in zip(res, desired):
+        assert_array_equal(x, y)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_stride_tricks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_stride_tricks.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cbebbdd552eb92b3072b4e7286f29a09328358a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_stride_tricks.py
@@ -0,0 +1,647 @@
+import numpy as np
+from numpy._core._rational_tests import rational
+from numpy.testing import (
+    assert_equal, assert_array_equal, assert_raises, assert_,
+    assert_raises_regex, assert_warns,
+    )
+from numpy.lib._stride_tricks_impl import (
+    as_strided, broadcast_arrays, _broadcast_shape, broadcast_to,
+    broadcast_shapes, sliding_window_view,
+    )
+import pytest
+
+
+def assert_shapes_correct(input_shapes, expected_shape):
+    # Broadcast a list of arrays with the given input shapes and check the
+    # common output shape.
+
+    inarrays = [np.zeros(s) for s in input_shapes]
+    outarrays = broadcast_arrays(*inarrays)
+    outshapes = [a.shape for a in outarrays]
+    expected = [expected_shape] * len(inarrays)
+    assert_equal(outshapes, expected)
+
+
+def assert_incompatible_shapes_raise(input_shapes):
+    # Broadcast a list of arrays with the given (incompatible) input shapes
+    # and check that they raise a ValueError.
+
+    inarrays = [np.zeros(s) for s in input_shapes]
+    assert_raises(ValueError, broadcast_arrays, *inarrays)
+
+
+def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
+    # Broadcast two shapes against each other and check that the data layout
+    # is the same as if a ufunc did the broadcasting.
+
+    x0 = np.zeros(shape0, dtype=int)
+    # Note that multiply.reduce's identity element is 1.0, so when shape1==(),
+    # this gives the desired n==1.
+    n = int(np.multiply.reduce(shape1))
+    x1 = np.arange(n).reshape(shape1)
+    if transposed:
+        x0 = x0.T
+        x1 = x1.T
+    if flipped:
+        x0 = x0[::-1]
+        x1 = x1[::-1]
+    # Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
+    # result should be exactly the same as the broadcasted view of x1.
+    y = x0 + x1
+    b0, b1 = broadcast_arrays(x0, x1)
+    assert_array_equal(y, b1)
+
+
+def test_same():
+    x = np.arange(10)
+    y = np.arange(10)
+    bx, by = broadcast_arrays(x, y)
+    assert_array_equal(x, bx)
+    assert_array_equal(y, by)
+
+def test_broadcast_kwargs():
+    # ensure that a TypeError is appropriately raised when
+    # np.broadcast_arrays() is called with any keyword
+    # argument other than 'subok'
+    x = np.arange(10)
+    y = np.arange(10)
+
+    with assert_raises_regex(TypeError, 'got an unexpected keyword'):
+        broadcast_arrays(x, y, dtype='float64')
+
+
+def test_one_off():
+    x = np.array([[1, 2, 3]])
+    y = np.array([[1], [2], [3]])
+    bx, by = broadcast_arrays(x, y)
+    bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
+    by0 = bx0.T
+    assert_array_equal(bx0, bx)
+    assert_array_equal(by0, by)
+
+
+def test_same_input_shapes():
+    # Check that the final shape is just the input shape.
+
+    data = [
+        (),
+        (1,),
+        (3,),
+        (0, 1),
+        (0, 3),
+        (1, 0),
+        (3, 0),
+        (1, 3),
+        (3, 1),
+        (3, 3),
+    ]
+    for shape in data:
+        input_shapes = [shape]
+        # Single input.
+        assert_shapes_correct(input_shapes, shape)
+        # Double input.
+        input_shapes2 = [shape, shape]
+        assert_shapes_correct(input_shapes2, shape)
+        # Triple input.
+        input_shapes3 = [shape, shape, shape]
+        assert_shapes_correct(input_shapes3, shape)
+
+
+def test_two_compatible_by_ones_input_shapes():
+    # Check that two different input shapes of the same length, but some have
+    # ones, broadcast to the correct shape.
+
+    data = [
+        [[(1,), (3,)], (3,)],
+        [[(1, 3), (3, 3)], (3, 3)],
+        [[(3, 1), (3, 3)], (3, 3)],
+        [[(1, 3), (3, 1)], (3, 3)],
+        [[(1, 1), (3, 3)], (3, 3)],
+        [[(1, 1), (1, 3)], (1, 3)],
+        [[(1, 1), (3, 1)], (3, 1)],
+        [[(1, 0), (0, 0)], (0, 0)],
+        [[(0, 1), (0, 0)], (0, 0)],
+        [[(1, 0), (0, 1)], (0, 0)],
+        [[(1, 1), (0, 0)], (0, 0)],
+        [[(1, 1), (1, 0)], (1, 0)],
+        [[(1, 1), (0, 1)], (0, 1)],
+    ]
+    for input_shapes, expected_shape in data:
+        assert_shapes_correct(input_shapes, expected_shape)
+        # Reverse the input shapes since broadcasting should be symmetric.
+        assert_shapes_correct(input_shapes[::-1], expected_shape)
+
+
+def test_two_compatible_by_prepending_ones_input_shapes():
+    # Check that two different input shapes (of different lengths) broadcast
+    # to the correct shape.
+
+    data = [
+        [[(), (3,)], (3,)],
+        [[(3,), (3, 3)], (3, 3)],
+        [[(3,), (3, 1)], (3, 3)],
+        [[(1,), (3, 3)], (3, 3)],
+        [[(), (3, 3)], (3, 3)],
+        [[(1, 1), (3,)], (1, 3)],
+        [[(1,), (3, 1)], (3, 1)],
+        [[(1,), (1, 3)], (1, 3)],
+        [[(), (1, 3)], (1, 3)],
+        [[(), (3, 1)], (3, 1)],
+        [[(), (0,)], (0,)],
+        [[(0,), (0, 0)], (0, 0)],
+        [[(0,), (0, 1)], (0, 0)],
+        [[(1,), (0, 0)], (0, 0)],
+        [[(), (0, 0)], (0, 0)],
+        [[(1, 1), (0,)], (1, 0)],
+        [[(1,), (0, 1)], (0, 1)],
+        [[(1,), (1, 0)], (1, 0)],
+        [[(), (1, 0)], (1, 0)],
+        [[(), (0, 1)], (0, 1)],
+    ]
+    for input_shapes, expected_shape in data:
+        assert_shapes_correct(input_shapes, expected_shape)
+        # Reverse the input shapes since broadcasting should be symmetric.
+        assert_shapes_correct(input_shapes[::-1], expected_shape)
+
+
+def test_incompatible_shapes_raise_valueerror():
+    # Check that a ValueError is raised for incompatible shapes.
+
+    data = [
+        [(3,), (4,)],
+        [(2, 3), (2,)],
+        [(3,), (3,), (4,)],
+        [(1, 3, 4), (2, 3, 3)],
+    ]
+    for input_shapes in data:
+        assert_incompatible_shapes_raise(input_shapes)
+        # Reverse the input shapes since broadcasting should be symmetric.
+        assert_incompatible_shapes_raise(input_shapes[::-1])
+
+
+def test_same_as_ufunc():
+    # Check that the data layout is the same as if a ufunc did the operation.
+
+    data = [
+        [[(1,), (3,)], (3,)],
+        [[(1, 3), (3, 3)], (3, 3)],
+        [[(3, 1), (3, 3)], (3, 3)],
+        [[(1, 3), (3, 1)], (3, 3)],
+        [[(1, 1), (3, 3)], (3, 3)],
+        [[(1, 1), (1, 3)], (1, 3)],
+        [[(1, 1), (3, 1)], (3, 1)],
+        [[(1, 0), (0, 0)], (0, 0)],
+        [[(0, 1), (0, 0)], (0, 0)],
+        [[(1, 0), (0, 1)], (0, 0)],
+        [[(1, 1), (0, 0)], (0, 0)],
+        [[(1, 1), (1, 0)], (1, 0)],
+        [[(1, 1), (0, 1)], (0, 1)],
+        [[(), (3,)], (3,)],
+        [[(3,), (3, 3)], (3, 3)],
+        [[(3,), (3, 1)], (3, 3)],
+        [[(1,), (3, 3)], (3, 3)],
+        [[(), (3, 3)], (3, 3)],
+        [[(1, 1), (3,)], (1, 3)],
+        [[(1,), (3, 1)], (3, 1)],
+        [[(1,), (1, 3)], (1, 3)],
+        [[(), (1, 3)], (1, 3)],
+        [[(), (3, 1)], (3, 1)],
+        [[(), (0,)], (0,)],
+        [[(0,), (0, 0)], (0, 0)],
+        [[(0,), (0, 1)], (0, 0)],
+        [[(1,), (0, 0)], (0, 0)],
+        [[(), (0, 0)], (0, 0)],
+        [[(1, 1), (0,)], (1, 0)],
+        [[(1,), (0, 1)], (0, 1)],
+        [[(1,), (1, 0)], (1, 0)],
+        [[(), (1, 0)], (1, 0)],
+        [[(), (0, 1)], (0, 1)],
+    ]
+    for input_shapes, expected_shape in data:
+        assert_same_as_ufunc(input_shapes[0], input_shapes[1],
+                             "Shapes: %s %s" % (input_shapes[0], input_shapes[1]))
+        # Reverse the input shapes since broadcasting should be symmetric.
+        assert_same_as_ufunc(input_shapes[1], input_shapes[0])
+        # Try them transposed, too.
+        assert_same_as_ufunc(input_shapes[0], input_shapes[1], True)
+        # ... and flipped for non-rank-0 inputs in order to test negative
+        # strides.
+        if () not in input_shapes:
+            assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True)
+            assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True)
+
+
+def test_broadcast_to_succeeds():
+    data = [
+        [np.array(0), (0,), np.array(0)],
+        [np.array(0), (1,), np.zeros(1)],
+        [np.array(0), (3,), np.zeros(3)],
+        [np.ones(1), (1,), np.ones(1)],
+        [np.ones(1), (2,), np.ones(2)],
+        [np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
+        [np.arange(3), (3,), np.arange(3)],
+        [np.arange(3), (1, 3), np.arange(3).reshape(1, -1)],
+        [np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])],
+        # test if shape is not a tuple
+        [np.ones(0), 0, np.ones(0)],
+        [np.ones(1), 1, np.ones(1)],
+        [np.ones(1), 2, np.ones(2)],
+        # these cases with size 0 are strange, but they reproduce the behavior
+        # of broadcasting with ufuncs (see test_same_as_ufunc above)
+        [np.ones(1), (0,), np.ones(0)],
+        [np.ones((1, 2)), (0, 2), np.ones((0, 2))],
+        [np.ones((2, 1)), (2, 0), np.ones((2, 0))],
+    ]
+    for input_array, shape, expected in data:
+        actual = broadcast_to(input_array, shape)
+        assert_array_equal(expected, actual)
+
+
+def test_broadcast_to_raises():
+    data = [
+        [(0,), ()],
+        [(1,), ()],
+        [(3,), ()],
+        [(3,), (1,)],
+        [(3,), (2,)],
+        [(3,), (4,)],
+        [(1, 2), (2, 1)],
+        [(1, 1), (1,)],
+        [(1,), -1],
+        [(1,), (-1,)],
+        [(1, 2), (-1, 2)],
+    ]
+    for orig_shape, target_shape in data:
+        arr = np.zeros(orig_shape)
+        assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))
+
+
+def test_broadcast_shape():
+    # tests internal _broadcast_shape
+    # _broadcast_shape is already exercised indirectly by broadcast_arrays
+    # _broadcast_shape is also exercised by the public broadcast_shapes function
+    assert_equal(_broadcast_shape(), ())
+    assert_equal(_broadcast_shape([1, 2]), (2,))
+    assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1))
+    assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4))
+    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
+    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))
+
+    # regression tests for gh-5862
+    assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
+    bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
+    assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))
+
+
+def test_broadcast_shapes_succeeds():
+    # tests public broadcast_shapes
+    data = [
+        [[], ()],
+        [[()], ()],
+        [[(7,)], (7,)],
+        [[(1, 2), (2,)], (1, 2)],
+        [[(1, 1)], (1, 1)],
+        [[(1, 1), (3, 4)], (3, 4)],
+        [[(6, 7), (5, 6, 1), (7,), (5, 1, 7)], (5, 6, 7)],
+        [[(5, 6, 1)], (5, 6, 1)],
+        [[(1, 3), (3, 1)], (3, 3)],
+        [[(1, 0), (0, 0)], (0, 0)],
+        [[(0, 1), (0, 0)], (0, 0)],
+        [[(1, 0), (0, 1)], (0, 0)],
+        [[(1, 1), (0, 0)], (0, 0)],
+        [[(1, 1), (1, 0)], (1, 0)],
+        [[(1, 1), (0, 1)], (0, 1)],
+        [[(), (0,)], (0,)],
+        [[(0,), (0, 0)], (0, 0)],
+        [[(0,), (0, 1)], (0, 0)],
+        [[(1,), (0, 0)], (0, 0)],
+        [[(), (0, 0)], (0, 0)],
+        [[(1, 1), (0,)], (1, 0)],
+        [[(1,), (0, 1)], (0, 1)],
+        [[(1,), (1, 0)], (1, 0)],
+        [[(), (1, 0)], (1, 0)],
+        [[(), (0, 1)], (0, 1)],
+        [[(1,), (3,)], (3,)],
+        [[2, (3, 2)], (3, 2)],
+    ]
+    for input_shapes, target_shape in data:
+        assert_equal(broadcast_shapes(*input_shapes), target_shape)
+
+    assert_equal(broadcast_shapes(*([(1, 2)] * 32)), (1, 2))
+    assert_equal(broadcast_shapes(*([(1, 2)] * 100)), (1, 2))
+
+    # regression tests for gh-5862
+    assert_equal(broadcast_shapes(*([(2,)] * 32)), (2,))
+
+
+def test_broadcast_shapes_raises():
+    # tests public broadcast_shapes
+    data = [
+        [(3,), (4,)],
+        [(2, 3), (2,)],
+        [(3,), (3,), (4,)],
+        [(1, 3, 4), (2, 3, 3)],
+        [(1, 2), (3, 1), (3, 2), (10, 5)],
+        [2, (2, 3)],
+    ]
+    for input_shapes in data:
+        assert_raises(ValueError, lambda: broadcast_shapes(*input_shapes))
+
+    bad_args = [(2,)] * 32 + [(3,)] * 32
+    assert_raises(ValueError, lambda: broadcast_shapes(*bad_args))
+
+
+def test_as_strided():
+    a = np.array([None])
+    a_view = as_strided(a)
+    expected = np.array([None])
+    assert_array_equal(a_view, np.array([None]))
+
+    a = np.array([1, 2, 3, 4])
+    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
+    expected = np.array([1, 3])
+    assert_array_equal(a_view, expected)
+
+    a = np.array([1, 2, 3, 4])
+    a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
+    expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
+    assert_array_equal(a_view, expected)
+
+    # Regression test for gh-5081
+    dt = np.dtype([('num', 'i4'), ('obj', 'O')])
+    a = np.empty((4,), dtype=dt)
+    a['num'] = np.arange(1, 5)
+    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
+    expected_num = [[1, 2, 3, 4]] * 3
+    expected_obj = [[None]*4]*3
+    assert_equal(a_view.dtype, dt)
+    assert_array_equal(expected_num, a_view['num'])
+    assert_array_equal(expected_obj, a_view['obj'])
+
+    # Make sure that void types without fields are kept unchanged
+    a = np.empty((4,), dtype='V4')
+    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
+    assert_equal(a.dtype, a_view.dtype)
+
+    # Make sure that the only type that could fail is properly handled
+    dt = np.dtype({'names': [''], 'formats': ['V4']})
+    a = np.empty((4,), dtype=dt)
+    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
+    assert_equal(a.dtype, a_view.dtype)
+
+    # Custom dtypes should not be lost (gh-9161)
+    r = [rational(i) for i in range(4)]
+    a = np.array(r, dtype=rational)
+    a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
+    assert_equal(a.dtype, a_view.dtype)
+    assert_array_equal([r] * 3, a_view)
+
+
+class TestSlidingWindowView:
+    def test_1d(self):
+        arr = np.arange(5)
+        arr_view = sliding_window_view(arr, 2)
+        expected = np.array([[0, 1],
+                             [1, 2],
+                             [2, 3],
+                             [3, 4]])
+        assert_array_equal(arr_view, expected)
+
+    def test_2d(self):
+        i, j = np.ogrid[:3, :4]
+        arr = 10*i + j
+        shape = (2, 2)
+        arr_view = sliding_window_view(arr, shape)
+        expected = np.array([[[[0, 1], [10, 11]],
+                              [[1, 2], [11, 12]],
+                              [[2, 3], [12, 13]]],
+                             [[[10, 11], [20, 21]],
+                              [[11, 12], [21, 22]],
+                              [[12, 13], [22, 23]]]])
+        assert_array_equal(arr_view, expected)
+
+    def test_2d_with_axis(self):
+        i, j = np.ogrid[:3, :4]
+        arr = 10*i + j
+        arr_view = sliding_window_view(arr, 3, 0)
+        expected = np.array([[[0, 10, 20],
+                              [1, 11, 21],
+                              [2, 12, 22],
+                              [3, 13, 23]]])
+        assert_array_equal(arr_view, expected)
+
+    def test_2d_repeated_axis(self):
+        i, j = np.ogrid[:3, :4]
+        arr = 10*i + j
+        arr_view = sliding_window_view(arr, (2, 3), (1, 1))
+        expected = np.array([[[[0, 1, 2],
+                               [1, 2, 3]]],
+                             [[[10, 11, 12],
+                               [11, 12, 13]]],
+                             [[[20, 21, 22],
+                               [21, 22, 23]]]])
+        assert_array_equal(arr_view, expected)
+
+    def test_2d_without_axis(self):
+        i, j = np.ogrid[:4, :4]
+        arr = 10*i + j
+        shape = (2, 3)
+        arr_view = sliding_window_view(arr, shape)
+        expected = np.array([[[[0, 1, 2], [10, 11, 12]],
+                              [[1, 2, 3], [11, 12, 13]]],
+                             [[[10, 11, 12], [20, 21, 22]],
+                              [[11, 12, 13], [21, 22, 23]]],
+                             [[[20, 21, 22], [30, 31, 32]],
+                              [[21, 22, 23], [31, 32, 33]]]])
+        assert_array_equal(arr_view, expected)
+
+    def test_errors(self):
+        i, j = np.ogrid[:4, :4]
+        arr = 10*i + j
+        with pytest.raises(ValueError, match='cannot contain negative values'):
+            sliding_window_view(arr, (-1, 3))
+        with pytest.raises(
+                ValueError,
+                match='must provide window_shape for all dimensions of `x`'):
+            sliding_window_view(arr, (1,))
+        with pytest.raises(
+                ValueError,
+                match='Must provide matching length window_shape and axis'):
+            sliding_window_view(arr, (1, 3, 4), axis=(0, 1))
+        with pytest.raises(
+                ValueError,
+                match='window shape cannot be larger than input array'):
+            sliding_window_view(arr, (5, 5))
+
+    def test_writeable(self):
+        arr = np.arange(5)
+        view = sliding_window_view(arr, 2, writeable=False)
+        assert_(not view.flags.writeable)
+        with pytest.raises(
+                ValueError,
+                match='assignment destination is read-only'):
+            view[0, 0] = 3
+        view = sliding_window_view(arr, 2, writeable=True)
+        assert_(view.flags.writeable)
+        view[0, 1] = 3
+        assert_array_equal(arr, np.array([0, 3, 2, 3, 4]))
+
+    def test_subok(self):
+        class MyArray(np.ndarray):
+            pass
+
+        arr = np.arange(5).view(MyArray)
+        assert_(not isinstance(sliding_window_view(arr, 2,
+                                                   subok=False),
+                               MyArray))
+        assert_(isinstance(sliding_window_view(arr, 2, subok=True), MyArray))
+        # Default behavior
+        assert_(not isinstance(sliding_window_view(arr, 2), MyArray))
+
+
+def as_strided_writeable():
+    arr = np.ones(10)
+    view = as_strided(arr, writeable=False)
+    assert_(not view.flags.writeable)
+
+    # Check that writeable also is fine:
+    view = as_strided(arr, writeable=True)
+    assert_(view.flags.writeable)
+    view[...] = 3
+    assert_array_equal(arr, np.full_like(arr, 3))
+
+    # Test that things do not break down for readonly:
+    arr.flags.writeable = False
+    view = as_strided(arr, writeable=False)
+    view = as_strided(arr, writeable=True)
+    assert_(not view.flags.writeable)
+
+
+class VerySimpleSubClass(np.ndarray):
+    def __new__(cls, *args, **kwargs):
+        return np.array(*args, subok=True, **kwargs).view(cls)
+
+
+class SimpleSubClass(VerySimpleSubClass):
+    def __new__(cls, *args, **kwargs):
+        self = np.array(*args, subok=True, **kwargs).view(cls)
+        self.info = 'simple'
+        return self
+
+    def __array_finalize__(self, obj):
+        self.info = getattr(obj, 'info', '') + ' finalized'
+
+
+def test_subclasses():
+    # test that subclass is preserved only if subok=True
+    a = VerySimpleSubClass([1, 2, 3, 4])
+    assert_(type(a) is VerySimpleSubClass)
+    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
+    assert_(type(a_view) is np.ndarray)
+    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
+    assert_(type(a_view) is VerySimpleSubClass)
+    # test that if a subclass has __array_finalize__, it is used
+    a = SimpleSubClass([1, 2, 3, 4])
+    a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
+    assert_(type(a_view) is SimpleSubClass)
+    assert_(a_view.info == 'simple finalized')
+
+    # similar tests for broadcast_arrays
+    b = np.arange(len(a)).reshape(-1, 1)
+    a_view, b_view = broadcast_arrays(a, b)
+    assert_(type(a_view) is np.ndarray)
+    assert_(type(b_view) is np.ndarray)
+    assert_(a_view.shape == b_view.shape)
+    a_view, b_view = broadcast_arrays(a, b, subok=True)
+    assert_(type(a_view) is SimpleSubClass)
+    assert_(a_view.info == 'simple finalized')
+    assert_(type(b_view) is np.ndarray)
+    assert_(a_view.shape == b_view.shape)
+
+    # and for broadcast_to
+    shape = (2, 4)
+    a_view = broadcast_to(a, shape)
+    assert_(type(a_view) is np.ndarray)
+    assert_(a_view.shape == shape)
+    a_view = broadcast_to(a, shape, subok=True)
+    assert_(type(a_view) is SimpleSubClass)
+    assert_(a_view.info == 'simple finalized')
+    assert_(a_view.shape == shape)
+
+
+def test_writeable():
+    # broadcast_to should return a readonly array
+    original = np.array([1, 2, 3])
+    result = broadcast_to(original, (2, 3))
+    assert_equal(result.flags.writeable, False)
+    assert_raises(ValueError, result.__setitem__, slice(None), 0)
+
+    # but the result of broadcast_arrays needs to be writeable, to
+    # preserve backwards compatibility
+    test_cases = [((False,), broadcast_arrays(original,)),
+                  ((True, False), broadcast_arrays(0, original))]
+    for is_broadcast, results in test_cases:
+        for array_is_broadcast, result in zip(is_broadcast, results):
+            # This will change to False in a future version
+            if array_is_broadcast:
+                with assert_warns(FutureWarning):
+                    assert_equal(result.flags.writeable, True)
+                with assert_warns(DeprecationWarning):
+                    result[:] = 0
+                # Warning not emitted, writing to the array resets it
+                assert_equal(result.flags.writeable, True)
+            else:
+                # No warning:
+                assert_equal(result.flags.writeable, True)
+
+    for results in [broadcast_arrays(original),
+                    broadcast_arrays(0, original)]:
+        for result in results:
+            # resets the warn_on_write DeprecationWarning
+            result.flags.writeable = True
+            # check: no warning emitted
+            assert_equal(result.flags.writeable, True)
+            result[:] = 0
+
+    # keep readonly input readonly
+    original.flags.writeable = False
+    _, result = broadcast_arrays(0, original)
+    assert_equal(result.flags.writeable, False)
+
+    # regression test for GH6491
+    shape = (2,)
+    strides = [0]
+    tricky_array = as_strided(np.array(0), shape, strides)
+    other = np.zeros((1,))
+    first, second = broadcast_arrays(tricky_array, other)
+    assert_(first.shape == second.shape)
+
+
+def test_writeable_memoryview():
+    # The result of broadcast_arrays exports as a non-writeable memoryview
+    # because otherwise there is no good way to opt in to the new behaviour
+    # (i.e. you would need to set writeable to False explicitly).
+    # See gh-13929.
+    original = np.array([1, 2, 3])
+
+    test_cases = [((False, ), broadcast_arrays(original,)),
+                  ((True, False), broadcast_arrays(0, original))]
+    for is_broadcast, results in test_cases:
+        for array_is_broadcast, result in zip(is_broadcast, results):
+            # This will change to False in a future version
+            if array_is_broadcast:
+                # memoryview(result, writable=True) will give warning but cannot
+                # be tested using the python API.
+                assert memoryview(result).readonly
+            else:
+                assert not memoryview(result).readonly
+
+
+def test_reference_types():
+    input_array = np.array('a', dtype=object)
+    expected = np.array(['a'] * 3, dtype=object)
+    actual = broadcast_to(input_array, (3,))
+    assert_array_equal(expected, actual)
+
+    actual, _ = broadcast_arrays(input_array, np.ones(3))
+    assert_array_equal(expected, actual)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_twodim_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_twodim_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb008c6002c86c94b180533230f849c909d10f39
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_twodim_base.py
@@ -0,0 +1,541 @@
+"""Test functions for matrix module
+
+"""
+from numpy.testing import (
+    assert_equal, assert_array_equal, assert_array_max_ulp,
+    assert_array_almost_equal, assert_raises, assert_
+)
+from numpy import (
+    arange, add, fliplr, flipud, zeros, ones, eye, array, diag, histogram2d,
+    tri, mask_indices, triu_indices, triu_indices_from, tril_indices,
+    tril_indices_from, vander,
+)
+import numpy as np
+
+import pytest
+
+
+def get_mat(n):
+    data = arange(n)
+    data = add.outer(data, data)
+    return data
+
+
+class TestEye:
+    def test_basic(self):
+        assert_equal(eye(4),
+                     array([[1, 0, 0, 0],
+                            [0, 1, 0, 0],
+                            [0, 0, 1, 0],
+                            [0, 0, 0, 1]]))
+
+        assert_equal(eye(4, dtype='f'),
+                     array([[1, 0, 0, 0],
+                            [0, 1, 0, 0],
+                            [0, 0, 1, 0],
+                            [0, 0, 0, 1]], 'f'))
+
+        assert_equal(eye(3) == 1,
+                     eye(3, dtype=bool))
+
+    def test_uint64(self):
+        # Regression test for gh-9982
+        assert_equal(eye(np.uint64(2), dtype=int), array([[1, 0], [0, 1]]))
+        assert_equal(eye(np.uint64(2), M=np.uint64(4), k=np.uint64(1)),
+                     array([[0, 1, 0, 0], [0, 0, 1, 0]]))
+
+    def test_diag(self):
+        assert_equal(eye(4, k=1),
+                     array([[0, 1, 0, 0],
+                            [0, 0, 1, 0],
+                            [0, 0, 0, 1],
+                            [0, 0, 0, 0]]))
+
+        assert_equal(eye(4, k=-1),
+                     array([[0, 0, 0, 0],
+                            [1, 0, 0, 0],
+                            [0, 1, 0, 0],
+                            [0, 0, 1, 0]]))
+
+    def test_2d(self):
+        assert_equal(eye(4, 3),
+                     array([[1, 0, 0],
+                            [0, 1, 0],
+                            [0, 0, 1],
+                            [0, 0, 0]]))
+
+        assert_equal(eye(3, 4),
+                     array([[1, 0, 0, 0],
+                            [0, 1, 0, 0],
+                            [0, 0, 1, 0]]))
+
+    def test_diag2d(self):
+        assert_equal(eye(3, 4, k=2),
+                     array([[0, 0, 1, 0],
+                            [0, 0, 0, 1],
+                            [0, 0, 0, 0]]))
+
+        assert_equal(eye(4, 3, k=-2),
+                     array([[0, 0, 0],
+                            [0, 0, 0],
+                            [1, 0, 0],
+                            [0, 1, 0]]))
+
+    def test_eye_bounds(self):
+        assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]])
+        assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]])
+        assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]])
+        assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]])
+        assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]])
+        assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]])
+        assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]])
+        assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]])
+        assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]])
+
+    def test_strings(self):
+        assert_equal(eye(2, 2, dtype='S3'),
+                     [[b'1', b''], [b'', b'1']])
+
+    def test_bool(self):
+        assert_equal(eye(2, 2, dtype=bool), [[True, False], [False, True]])
+
+    def test_order(self):
+        mat_c = eye(4, 3, k=-1)
+        mat_f = eye(4, 3, k=-1, order='F')
+        assert_equal(mat_c, mat_f)
+        assert mat_c.flags.c_contiguous
+        assert not mat_c.flags.f_contiguous
+        assert not mat_f.flags.c_contiguous
+        assert mat_f.flags.f_contiguous
+
+
+class TestDiag:
+    def test_vector(self):
+        vals = (100 * arange(5)).astype('l')
+        b = zeros((5, 5))
+        for k in range(5):
+            b[k, k] = vals[k]
+        assert_equal(diag(vals), b)
+        b = zeros((7, 7))
+        c = b.copy()
+        for k in range(5):
+            b[k, k + 2] = vals[k]
+            c[k + 2, k] = vals[k]
+        assert_equal(diag(vals, k=2), b)
+        assert_equal(diag(vals, k=-2), c)
+
+    def test_matrix(self, vals=None):
+        if vals is None:
+            vals = (100 * get_mat(5) + 1).astype('l')
+        b = zeros((5,))
+        for k in range(5):
+            b[k] = vals[k, k]
+        assert_equal(diag(vals), b)
+        b = b * 0
+        for k in range(3):
+            b[k] = vals[k, k + 2]
+        assert_equal(diag(vals, 2), b[:3])
+        for k in range(3):
+            b[k] = vals[k + 2, k]
+        assert_equal(diag(vals, -2), b[:3])
+
+    def test_fortran_order(self):
+        vals = array((100 * get_mat(5) + 1), order='F', dtype='l')
+        self.test_matrix(vals)
+
+    def test_diag_bounds(self):
+        A = [[1, 2], [3, 4], [5, 6]]
+        assert_equal(diag(A, k=2), [])
+        assert_equal(diag(A, k=1), [2])
+        assert_equal(diag(A, k=0), [1, 4])
+        assert_equal(diag(A, k=-1), [3, 6])
+        assert_equal(diag(A, k=-2), [5])
+        assert_equal(diag(A, k=-3), [])
+
+    def test_failure(self):
+        assert_raises(ValueError, diag, [[[1]]])
+
+
+class TestFliplr:
+    def test_basic(self):
+        assert_raises(ValueError, fliplr, ones(4))
+        a = get_mat(4)
+        b = a[:, ::-1]
+        assert_equal(fliplr(a), b)
+        a = [[0, 1, 2],
+             [3, 4, 5]]
+        b = [[2, 1, 0],
+             [5, 4, 3]]
+        assert_equal(fliplr(a), b)
+
+
+class TestFlipud:
+    def test_basic(self):
+        a = get_mat(4)
+        b = a[::-1, :]
+        assert_equal(flipud(a), b)
+        a = [[0, 1, 2],
+             [3, 4, 5]]
+        b = [[3, 4, 5],
+             [0, 1, 2]]
+        assert_equal(flipud(a), b)
+
+
+class TestHistogram2d:
+    def test_simple(self):
+        x = array(
+            [0.41702200, 0.72032449, 1.1437481e-4, 0.302332573, 0.146755891])
+        y = array(
+            [0.09233859, 0.18626021, 0.34556073, 0.39676747, 0.53881673])
+        xedges = np.linspace(0, 1, 10)
+        yedges = np.linspace(0, 1, 10)
+        H = histogram2d(x, y, (xedges, yedges))[0]
+        answer = array(
+            [[0, 0, 0, 1, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 1, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [1, 0, 1, 0, 0, 0, 0, 0, 0],
+             [0, 1, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+        assert_array_equal(H.T, answer)
+        H = histogram2d(x, y, xedges)[0]
+        assert_array_equal(H.T, answer)
+        H, xedges, yedges = histogram2d(list(range(10)), list(range(10)))
+        assert_array_equal(H, eye(10, 10))
+        assert_array_equal(xedges, np.linspace(0, 9, 11))
+        assert_array_equal(yedges, np.linspace(0, 9, 11))
+
+    def test_asym(self):
+        x = array([1, 1, 2, 3, 4, 4, 4, 5])
+        y = array([1, 3, 2, 0, 1, 2, 3, 4])
+        H, xed, yed = histogram2d(
+            x, y, (6, 5), range=[[0, 6], [0, 5]], density=True)
+        answer = array(
+            [[0., 0, 0, 0, 0],
+             [0, 1, 0, 1, 0],
+             [0, 0, 1, 0, 0],
+             [1, 0, 0, 0, 0],
+             [0, 1, 1, 1, 0],
+             [0, 0, 0, 0, 1]])
+        assert_array_almost_equal(H, answer/8., 3)
+        assert_array_equal(xed, np.linspace(0, 6, 7))
+        assert_array_equal(yed, np.linspace(0, 5, 6))
+
+    def test_density(self):
+        x = array([1, 2, 3, 1, 2, 3, 1, 2, 3])
+        y = array([1, 1, 1, 2, 2, 2, 3, 3, 3])
+        H, xed, yed = histogram2d(
+            x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], density=True)
+        answer = array([[1, 1, .5],
+                        [1, 1, .5],
+                        [.5, .5, .25]])/9.
+        assert_array_almost_equal(H, answer, 3)
+
+    def test_all_outliers(self):
+        r = np.random.rand(100) + 1. + 1e6  # histogramdd rounds by decimal=6
+        H, xed, yed = histogram2d(r, r, (4, 5), range=([0, 1], [0, 1]))
+        assert_array_equal(H, 0)
+
+    def test_empty(self):
+        a, edge1, edge2 = histogram2d([], [], bins=([0, 1], [0, 1]))
+        assert_array_max_ulp(a, array([[0.]]))
+
+        a, edge1, edge2 = histogram2d([], [], bins=4)
+        assert_array_max_ulp(a, np.zeros((4, 4)))
+
+    def test_binparameter_combination(self):
+        x = array(
+            [0, 0.09207008, 0.64575234, 0.12875982, 0.47390599,
+             0.59944483, 1])
+        y = array(
+            [0, 0.14344267, 0.48988575, 0.30558665, 0.44700682,
+             0.15886423, 1])
+        edges = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
+        H, xe, ye = histogram2d(x, y, (edges, 4))
+        answer = array(
+            [[2., 0., 0., 0.],
+             [0., 1., 0., 0.],
+             [0., 0., 0., 0.],
+             [0., 0., 0., 0.],
+             [0., 1., 0., 0.],
+             [1., 0., 0., 0.],
+             [0., 1., 0., 0.],
+             [0., 0., 0., 0.],
+             [0., 0., 0., 0.],
+             [0., 0., 0., 1.]])
+        assert_array_equal(H, answer)
+        assert_array_equal(ye, array([0., 0.25, 0.5, 0.75, 1]))
+        H, xe, ye = histogram2d(x, y, (4, edges))
+        answer = array(
+            [[1., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
+             [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
+             [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],
+             [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
+        assert_array_equal(H, answer)
+        assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1]))
+
+    def test_dispatch(self):
+        class ShouldDispatch:
+            def __array_function__(self, function, types, args, kwargs):
+                return types, args, kwargs
+
+        xy = [1, 2]
+        s_d = ShouldDispatch()
+        r = histogram2d(s_d, xy)
+        # Cannot use assert_equal since that dispatches...
+        assert_(r == ((ShouldDispatch,), (s_d, xy), {}))
+        r = histogram2d(xy, s_d)
+        assert_(r == ((ShouldDispatch,), (xy, s_d), {}))
+        r = histogram2d(xy, xy, bins=s_d)
+        assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=s_d)))
+        r = histogram2d(xy, xy, bins=[s_d, 5])
+        assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=[s_d, 5])))
+        assert_raises(Exception, histogram2d, xy, xy, bins=[s_d])
+        r = histogram2d(xy, xy, weights=s_d)
+        assert_(r, ((ShouldDispatch,), (xy, xy), dict(weights=s_d)))
+
+    @pytest.mark.parametrize(("x_len", "y_len"), [(10, 11), (20, 19)])
+    def test_bad_length(self, x_len, y_len):
+        x, y = np.ones(x_len), np.ones(y_len)
+        with pytest.raises(ValueError,
+                           match='x and y must have the same length.'):
+            histogram2d(x, y)
+
+
+class TestTri:
+    def test_dtype(self):
+        out = array([[1, 0, 0],
+                     [1, 1, 0],
+                     [1, 1, 1]])
+        assert_array_equal(tri(3), out)
+        assert_array_equal(tri(3, dtype=bool), out.astype(bool))
+
+
+def test_tril_triu_ndim2():
+    for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
+        a = np.ones((2, 2), dtype=dtype)
+        b = np.tril(a)
+        c = np.triu(a)
+        assert_array_equal(b, [[1, 0], [1, 1]])
+        assert_array_equal(c, b.T)
+        # should return the same dtype as the original array
+        assert_equal(b.dtype, a.dtype)
+        assert_equal(c.dtype, a.dtype)
+
+
+def test_tril_triu_ndim3():
+    for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
+        a = np.array([
+            [[1, 1], [1, 1]],
+            [[1, 1], [1, 0]],
+            [[1, 1], [0, 0]],
+            ], dtype=dtype)
+        a_tril_desired = np.array([
+            [[1, 0], [1, 1]],
+            [[1, 0], [1, 0]],
+            [[1, 0], [0, 0]],
+            ], dtype=dtype)
+        a_triu_desired = np.array([
+            [[1, 1], [0, 1]],
+            [[1, 1], [0, 0]],
+            [[1, 1], [0, 0]],
+            ], dtype=dtype)
+        a_triu_observed = np.triu(a)
+        a_tril_observed = np.tril(a)
+        assert_array_equal(a_triu_observed, a_triu_desired)
+        assert_array_equal(a_tril_observed, a_tril_desired)
+        assert_equal(a_triu_observed.dtype, a.dtype)
+        assert_equal(a_tril_observed.dtype, a.dtype)
+
+
+def test_tril_triu_with_inf():
+    # Issue 4859
+    arr = np.array([[1, 1, np.inf],
+                    [1, 1, 1],
+                    [np.inf, 1, 1]])
+    out_tril = np.array([[1, 0, 0],
+                         [1, 1, 0],
+                         [np.inf, 1, 1]])
+    out_triu = out_tril.T
+    assert_array_equal(np.triu(arr), out_triu)
+    assert_array_equal(np.tril(arr), out_tril)
+
+
+def test_tril_triu_dtype():
+    # Issue 4916
+    # tril and triu should return the same dtype as input
+    for c in np.typecodes['All']:
+        if c == 'V':
+            continue
+        arr = np.zeros((3, 3), dtype=c)
+        assert_equal(np.triu(arr).dtype, arr.dtype)
+        assert_equal(np.tril(arr).dtype, arr.dtype)
+
+    # check special cases
+    arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
+                    ['2004-01-01T12:00', '2003-01-03T13:45']],
+                   dtype='datetime64')
+    assert_equal(np.triu(arr).dtype, arr.dtype)
+    assert_equal(np.tril(arr).dtype, arr.dtype)
+
+    arr = np.zeros((3, 3), dtype='f4,f4')
+    assert_equal(np.triu(arr).dtype, arr.dtype)
+    assert_equal(np.tril(arr).dtype, arr.dtype)
+
+
+def test_mask_indices():
+    # simple test without offset
+    iu = mask_indices(3, np.triu)
+    a = np.arange(9).reshape(3, 3)
+    assert_array_equal(a[iu], array([0, 1, 2, 4, 5, 8]))
+    # Now with an offset
+    iu1 = mask_indices(3, np.triu, 1)
+    assert_array_equal(a[iu1], array([1, 2, 5]))
+
+
+def test_tril_indices():
+    # indices without and with offset
+    il1 = tril_indices(4)
+    il2 = tril_indices(4, k=2)
+    il3 = tril_indices(4, m=5)
+    il4 = tril_indices(4, k=2, m=5)
+
+    a = np.array([[1, 2, 3, 4],
+                  [5, 6, 7, 8],
+                  [9, 10, 11, 12],
+                  [13, 14, 15, 16]])
+    b = np.arange(1, 21).reshape(4, 5)
+
+    # indexing:
+    assert_array_equal(a[il1],
+                       array([1, 5, 6, 9, 10, 11, 13, 14, 15, 16]))
+    assert_array_equal(b[il3],
+                       array([1, 6, 7, 11, 12, 13, 16, 17, 18, 19]))
+
+    # And for assigning values:
+    a[il1] = -1
+    assert_array_equal(a,
+                       array([[-1, 2, 3, 4],
+                              [-1, -1, 7, 8],
+                              [-1, -1, -1, 12],
+                              [-1, -1, -1, -1]]))
+    b[il3] = -1
+    assert_array_equal(b,
+                       array([[-1, 2, 3, 4, 5],
+                              [-1, -1, 8, 9, 10],
+                              [-1, -1, -1, 14, 15],
+                              [-1, -1, -1, -1, 20]]))
+    # These cover almost the whole array (two diagonals right of the main one):
+    a[il2] = -10
+    assert_array_equal(a,
+                       array([[-10, -10, -10, 4],
+                              [-10, -10, -10, -10],
+                              [-10, -10, -10, -10],
+                              [-10, -10, -10, -10]]))
+    b[il4] = -10
+    assert_array_equal(b,
+                       array([[-10, -10, -10, 4, 5],
+                              [-10, -10, -10, -10, 10],
+                              [-10, -10, -10, -10, -10],
+                              [-10, -10, -10, -10, -10]]))
+
+
+class TestTriuIndices:
+    def test_triu_indices(self):
+        iu1 = triu_indices(4)
+        iu2 = triu_indices(4, k=2)
+        iu3 = triu_indices(4, m=5)
+        iu4 = triu_indices(4, k=2, m=5)
+
+        a = np.array([[1, 2, 3, 4],
+                      [5, 6, 7, 8],
+                      [9, 10, 11, 12],
+                      [13, 14, 15, 16]])
+        b = np.arange(1, 21).reshape(4, 5)
+
+        # Both for indexing:
+        assert_array_equal(a[iu1],
+                           array([1, 2, 3, 4, 6, 7, 8, 11, 12, 16]))
+        assert_array_equal(b[iu3],
+                           array([1, 2, 3, 4, 5, 7, 8, 9,
+                                  10, 13, 14, 15, 19, 20]))
+
+        # And for assigning values:
+        a[iu1] = -1
+        assert_array_equal(a,
+                           array([[-1, -1, -1, -1],
+                                  [5, -1, -1, -1],
+                                  [9, 10, -1, -1],
+                                  [13, 14, 15, -1]]))
+        b[iu3] = -1
+        assert_array_equal(b,
+                           array([[-1, -1, -1, -1, -1],
+                                  [6, -1, -1, -1, -1],
+                                  [11, 12, -1, -1, -1],
+                                  [16, 17, 18, -1, -1]]))
+
+        # These cover almost the whole array (two diagonals right of the
+        # main one):
+        a[iu2] = -10
+        assert_array_equal(a,
+                           array([[-1, -1, -10, -10],
+                                  [5, -1, -1, -10],
+                                  [9, 10, -1, -1],
+                                  [13, 14, 15, -1]]))
+        b[iu4] = -10
+        assert_array_equal(b,
+                           array([[-1, -1, -10, -10, -10],
+                                  [6, -1, -1, -10, -10],
+                                  [11, 12, -1, -1, -10],
+                                  [16, 17, 18, -1, -1]]))
+
+
+class TestTrilIndicesFrom:
+    def test_exceptions(self):
+        assert_raises(ValueError, tril_indices_from, np.ones((2,)))
+        assert_raises(ValueError, tril_indices_from, np.ones((2, 2, 2)))
+        # assert_raises(ValueError, tril_indices_from, np.ones((2, 3)))
+
+
+class TestTriuIndicesFrom:
+    def test_exceptions(self):
+        assert_raises(ValueError, triu_indices_from, np.ones((2,)))
+        assert_raises(ValueError, triu_indices_from, np.ones((2, 2, 2)))
+        # assert_raises(ValueError, triu_indices_from, np.ones((2, 3)))
+
+
+class TestVander:
+    def test_basic(self):
+        c = np.array([0, 1, -2, 3])
+        v = vander(c)
+        powers = np.array([[0, 0, 0, 0, 1],
+                           [1, 1, 1, 1, 1],
+                           [16, -8, 4, -2, 1],
+                           [81, 27, 9, 3, 1]])
+        # Check default value of N:
+        assert_array_equal(v, powers[:, 1:])
+        # Check a range of N values, including 0 and 5 (greater than default)
+        m = powers.shape[1]
+        for n in range(6):
+            v = vander(c, N=n)
+            assert_array_equal(v, powers[:, m-n:m])
+
+    def test_dtypes(self):
+        c = array([11, -12, 13], dtype=np.int8)
+        v = vander(c)
+        expected = np.array([[121, 11, 1],
+                             [144, -12, 1],
+                             [169, 13, 1]])
+        assert_array_equal(v, expected)
+
+        c = array([1.0+1j, 1.0-1j])
+        v = vander(c, N=3)
+        expected = np.array([[2j, 1+1j, 1],
+                             [-2j, 1-1j, 1]])
+        # The data is floating point, but the values are small integers,
+        # so assert_array_equal *should* be safe here (rather than, say,
+        # assert_array_almost_equal).
+        assert_array_equal(v, expected)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_type_check.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_type_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..01c888bef6f1bc524226142b12fda4bc149f5ce0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_type_check.py
@@ -0,0 +1,465 @@
+import numpy as np
+from numpy import (
+    common_type, mintypecode, isreal, iscomplex, isposinf, isneginf,
+    nan_to_num, isrealobj, iscomplexobj, real_if_close
+    )
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_raises
+    )
+
+
+def assert_all(x):
+    assert_(np.all(x), x)
+
+
+class TestCommonType:
+    def test_basic(self):
+        ai32 = np.array([[1, 2], [3, 4]], dtype=np.int32)
+        af16 = np.array([[1, 2], [3, 4]], dtype=np.float16)
+        af32 = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        af64 = np.array([[1, 2], [3, 4]], dtype=np.float64)
+        acs = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.complex64)
+        acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.complex128)
+        assert_(common_type(ai32) == np.float64)
+        assert_(common_type(af16) == np.float16)
+        assert_(common_type(af32) == np.float32)
+        assert_(common_type(af64) == np.float64)
+        assert_(common_type(acs) == np.complex64)
+        assert_(common_type(acd) == np.complex128)
+
+
+class TestMintypecode:
+
+    def test_default_1(self):
+        for itype in '1bcsuwil':
+            assert_equal(mintypecode(itype), 'd')
+        assert_equal(mintypecode('f'), 'f')
+        assert_equal(mintypecode('d'), 'd')
+        assert_equal(mintypecode('F'), 'F')
+        assert_equal(mintypecode('D'), 'D')
+
+    def test_default_2(self):
+        for itype in '1bcsuwil':
+            assert_equal(mintypecode(itype+'f'), 'f')
+            assert_equal(mintypecode(itype+'d'), 'd')
+            assert_equal(mintypecode(itype+'F'), 'F')
+            assert_equal(mintypecode(itype+'D'), 'D')
+        assert_equal(mintypecode('ff'), 'f')
+        assert_equal(mintypecode('fd'), 'd')
+        assert_equal(mintypecode('fF'), 'F')
+        assert_equal(mintypecode('fD'), 'D')
+        assert_equal(mintypecode('df'), 'd')
+        assert_equal(mintypecode('dd'), 'd')
+        #assert_equal(mintypecode('dF',savespace=1),'F')
+        assert_equal(mintypecode('dF'), 'D')
+        assert_equal(mintypecode('dD'), 'D')
+        assert_equal(mintypecode('Ff'), 'F')
+        #assert_equal(mintypecode('Fd',savespace=1),'F')
+        assert_equal(mintypecode('Fd'), 'D')
+        assert_equal(mintypecode('FF'), 'F')
+        assert_equal(mintypecode('FD'), 'D')
+        assert_equal(mintypecode('Df'), 'D')
+        assert_equal(mintypecode('Dd'), 'D')
+        assert_equal(mintypecode('DF'), 'D')
+        assert_equal(mintypecode('DD'), 'D')
+
+    def test_default_3(self):
+        assert_equal(mintypecode('fdF'), 'D')
+        #assert_equal(mintypecode('fdF',savespace=1),'F')
+        assert_equal(mintypecode('fdD'), 'D')
+        assert_equal(mintypecode('fFD'), 'D')
+        assert_equal(mintypecode('dFD'), 'D')
+
+        assert_equal(mintypecode('ifd'), 'd')
+        assert_equal(mintypecode('ifF'), 'F')
+        assert_equal(mintypecode('ifD'), 'D')
+        assert_equal(mintypecode('idF'), 'D')
+        #assert_equal(mintypecode('idF',savespace=1),'F')
+        assert_equal(mintypecode('idD'), 'D')
+
+
+class TestIsscalar:
+
+    def test_basic(self):
+        assert_(np.isscalar(3))
+        assert_(not np.isscalar([3]))
+        assert_(not np.isscalar((3,)))
+        assert_(np.isscalar(3j))
+        assert_(np.isscalar(4.0))
+
+
+class TestReal:
+
+    def test_real(self):
+        y = np.random.rand(10,)
+        assert_array_equal(y, np.real(y))
+
+        y = np.array(1)
+        out = np.real(y)
+        assert_array_equal(y, out)
+        assert_(isinstance(out, np.ndarray))
+
+        y = 1
+        out = np.real(y)
+        assert_equal(y, out)
+        assert_(not isinstance(out, np.ndarray))
+
+    def test_cmplx(self):
+        y = np.random.rand(10,)+1j*np.random.rand(10,)
+        assert_array_equal(y.real, np.real(y))
+
+        y = np.array(1 + 1j)
+        out = np.real(y)
+        assert_array_equal(y.real, out)
+        assert_(isinstance(out, np.ndarray))
+
+        y = 1 + 1j
+        out = np.real(y)
+        assert_equal(1.0, out)
+        assert_(not isinstance(out, np.ndarray))
+
+
+class TestImag:
+
+    def test_real(self):
+        y = np.random.rand(10,)
+        assert_array_equal(0, np.imag(y))
+
+        y = np.array(1)
+        out = np.imag(y)
+        assert_array_equal(0, out)
+        assert_(isinstance(out, np.ndarray))
+
+        y = 1
+        out = np.imag(y)
+        assert_equal(0, out)
+        assert_(not isinstance(out, np.ndarray))
+
+    def test_cmplx(self):
+        y = np.random.rand(10,)+1j*np.random.rand(10,)
+        assert_array_equal(y.imag, np.imag(y))
+
+        y = np.array(1 + 1j)
+        out = np.imag(y)
+        assert_array_equal(y.imag, out)
+        assert_(isinstance(out, np.ndarray))
+
+        y = 1 + 1j
+        out = np.imag(y)
+        assert_equal(1.0, out)
+        assert_(not isinstance(out, np.ndarray))
+
+
+class TestIscomplex:
+
+    def test_fail(self):
+        z = np.array([-1, 0, 1])
+        res = iscomplex(z)
+        assert_(not np.any(res, axis=0))
+
+    def test_pass(self):
+        z = np.array([-1j, 1, 0])
+        res = iscomplex(z)
+        assert_array_equal(res, [1, 0, 0])
+
+
+class TestIsreal:
+
+    def test_pass(self):
+        z = np.array([-1, 0, 1j])
+        res = isreal(z)
+        assert_array_equal(res, [1, 1, 0])
+
+    def test_fail(self):
+        z = np.array([-1j, 1, 0])
+        res = isreal(z)
+        assert_array_equal(res, [0, 1, 1])
+
+
+class TestIscomplexobj:
+
+    def test_basic(self):
+        z = np.array([-1, 0, 1])
+        assert_(not iscomplexobj(z))
+        z = np.array([-1j, 0, -1])
+        assert_(iscomplexobj(z))
+
+    def test_scalar(self):
+        assert_(not iscomplexobj(1.0))
+        assert_(iscomplexobj(1+0j))
+
+    def test_list(self):
+        assert_(iscomplexobj([3, 1+0j, True]))
+        assert_(not iscomplexobj([3, 1, True]))
+
+    def test_duck(self):
+        class DummyComplexArray:
+            @property
+            def dtype(self):
+                return np.dtype(complex)
+        dummy = DummyComplexArray()
+        assert_(iscomplexobj(dummy))
+
+    def test_pandas_duck(self):
+        # This tests a custom np.dtype duck-typed class, such as used by pandas
+        # (pandas.core.dtypes)
+        class PdComplex(np.complex128):
+            pass
+        class PdDtype:
+            name = 'category'
+            names = None
+            type = PdComplex
+            kind = 'c'
+            str = ' 1e10) and assert_all(np.isfinite(vals[2]))
+        assert_equal(type(vals), np.ndarray)
+
+        # perform the same tests but with nan, posinf and neginf keywords
+        with np.errstate(divide='ignore', invalid='ignore'):
+            vals = nan_to_num(np.array((-1., 0, 1))/0.,
+                              nan=10, posinf=20, neginf=30)
+        assert_equal(vals, [30, 10, 20])
+        assert_all(np.isfinite(vals[[0, 2]]))
+        assert_equal(type(vals), np.ndarray)
+
+        # perform the same test but in-place
+        with np.errstate(divide='ignore', invalid='ignore'):
+            vals = np.array((-1., 0, 1))/0.
+        result = nan_to_num(vals, copy=False)
+
+        assert_(result is vals)
+        assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
+        assert_(vals[1] == 0)
+        assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))
+        assert_equal(type(vals), np.ndarray)
+
+        # perform the same test but in-place
+        with np.errstate(divide='ignore', invalid='ignore'):
+            vals = np.array((-1., 0, 1))/0.
+        result = nan_to_num(vals, copy=False, nan=10, posinf=20, neginf=30)
+
+        assert_(result is vals)
+        assert_equal(vals, [30, 10, 20])
+        assert_all(np.isfinite(vals[[0, 2]]))
+        assert_equal(type(vals), np.ndarray)
+
+    def test_array(self):
+        vals = nan_to_num([1])
+        assert_array_equal(vals, np.array([1], int))
+        assert_equal(type(vals), np.ndarray)
+        vals = nan_to_num([1], nan=10, posinf=20, neginf=30)
+        assert_array_equal(vals, np.array([1], int))
+        assert_equal(type(vals), np.ndarray)
+
+    def test_integer(self):
+        vals = nan_to_num(1)
+        assert_all(vals == 1)
+        assert_equal(type(vals), np.int_)
+        vals = nan_to_num(1, nan=10, posinf=20, neginf=30)
+        assert_all(vals == 1)
+        assert_equal(type(vals), np.int_)
+
+    def test_float(self):
+        vals = nan_to_num(1.0)
+        assert_all(vals == 1.0)
+        assert_equal(type(vals), np.float64)
+        vals = nan_to_num(1.1, nan=10, posinf=20, neginf=30)
+        assert_all(vals == 1.1)
+        assert_equal(type(vals), np.float64)
+
+    def test_complex_good(self):
+        vals = nan_to_num(1+1j)
+        assert_all(vals == 1+1j)
+        assert_equal(type(vals), np.complex128)
+        vals = nan_to_num(1+1j, nan=10, posinf=20, neginf=30)
+        assert_all(vals == 1+1j)
+        assert_equal(type(vals), np.complex128)
+
+    def test_complex_bad(self):
+        with np.errstate(divide='ignore', invalid='ignore'):
+            v = 1 + 1j
+            v += np.array(0+1.j)/0.
+        vals = nan_to_num(v)
+        # !! This is actually (unexpectedly) zero
+        assert_all(np.isfinite(vals))
+        assert_equal(type(vals), np.complex128)
+
+    def test_complex_bad2(self):
+        with np.errstate(divide='ignore', invalid='ignore'):
+            v = 1 + 1j
+            v += np.array(-1+1.j)/0.
+        vals = nan_to_num(v)
+        assert_all(np.isfinite(vals))
+        assert_equal(type(vals), np.complex128)
+        # Fixme
+        #assert_all(vals.imag > 1e10)  and assert_all(np.isfinite(vals))
+        # !! This is actually (unexpectedly) positive
+        # !! inf.  Comment out for now, and see if it
+        # !! changes
+        #assert_all(vals.real < -1e10) and assert_all(np.isfinite(vals))
+
+    def test_do_not_rewrite_previous_keyword(self):
+        # This is done to test that when, for instance, nan=np.inf then these
+        # values are not rewritten by posinf keyword to the posinf value.
+        with np.errstate(divide='ignore', invalid='ignore'):
+            vals = nan_to_num(np.array((-1., 0, 1))/0., nan=np.inf, posinf=999)
+        assert_all(np.isfinite(vals[[0, 2]]))
+        assert_all(vals[0] < -1e10)
+        assert_equal(vals[[1, 2]], [np.inf, 999])
+        assert_equal(type(vals), np.ndarray)
+
+
+class TestRealIfClose:
+
+    def test_basic(self):
+        a = np.random.rand(10)
+        b = real_if_close(a+1e-15j)
+        assert_all(isrealobj(b))
+        assert_array_equal(a, b)
+        b = real_if_close(a+1e-7j)
+        assert_all(iscomplexobj(b))
+        b = real_if_close(a+1e-7j, tol=1e-6)
+        assert_all(isrealobj(b))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_ufunclike.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_ufunclike.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b5d11010e0f0e4dcd94c3b0067caee7a849ea58
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_ufunclike.py
@@ -0,0 +1,100 @@
+import numpy as np
+
+from numpy import fix, isposinf, isneginf
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_raises
+)
+
+
+class TestUfunclike:
+
+    def test_isposinf(self):
+        a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0])
+        out = np.zeros(a.shape, bool)
+        tgt = np.array([True, False, False, False, False, False])
+
+        res = isposinf(a)
+        assert_equal(res, tgt)
+        res = isposinf(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+
+        a = a.astype(np.complex128)
+        with assert_raises(TypeError):
+            isposinf(a)
+
+    def test_isneginf(self):
+        a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0])
+        out = np.zeros(a.shape, bool)
+        tgt = np.array([False, True, False, False, False, False])
+
+        res = isneginf(a)
+        assert_equal(res, tgt)
+        res = isneginf(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+
+        a = a.astype(np.complex128)
+        with assert_raises(TypeError):
+            isneginf(a)
+
+    def test_fix(self):
+        a = np.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]])
+        out = np.zeros(a.shape, float)
+        tgt = np.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]])
+
+        res = fix(a)
+        assert_equal(res, tgt)
+        res = fix(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+        assert_equal(fix(3.14), 3)
+
+    def test_fix_with_subclass(self):
+        class MyArray(np.ndarray):
+            def __new__(cls, data, metadata=None):
+                res = np.array(data, copy=True).view(cls)
+                res.metadata = metadata
+                return res
+
+            def __array_wrap__(self, obj, context=None, return_scalar=False):
+                if not isinstance(obj, MyArray):
+                    obj = obj.view(MyArray)
+                if obj.metadata is None:
+                    obj.metadata = self.metadata
+                return obj
+
+            def __array_finalize__(self, obj):
+                self.metadata = getattr(obj, 'metadata', None)
+                return self
+
+        a = np.array([1.1, -1.1])
+        m = MyArray(a, metadata='foo')
+        f = fix(m)
+        assert_array_equal(f, np.array([1, -1]))
+        assert_(isinstance(f, MyArray))
+        assert_equal(f.metadata, 'foo')
+
+        # check 0d arrays don't decay to scalars
+        m0d = m[0,...]
+        m0d.metadata = 'bar'
+        f0d = fix(m0d)
+        assert_(isinstance(f0d, MyArray))
+        assert_equal(f0d.metadata, 'bar')
+
+    def test_scalar(self):
+        x = np.inf
+        actual = np.isposinf(x)
+        expected = np.True_
+        assert_equal(actual, expected)
+        assert_equal(type(actual), type(expected))
+
+        x = -3.4
+        actual = np.fix(x)
+        expected = np.float64(-3.0)
+        assert_equal(actual, expected)
+        assert_equal(type(actual), type(expected))
+
+        out = np.array(0.0)
+        actual = np.fix(x, out=out)
+        assert_(actual is out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..644912d941e3c233741ba415f6469b1cebf994b2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/lib/tests/test_utils.py
@@ -0,0 +1,80 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_raises_regex
+import numpy.lib._utils_impl as _utils_impl
+
+from io import StringIO
+
+
+def test_assert_raises_regex_context_manager():
+    with assert_raises_regex(ValueError, 'no deprecation warning'):
+        raise ValueError('no deprecation warning')
+
+
+def test_info_method_heading():
+    # info(class) should only print "Methods:" heading if methods exist
+
+    class NoPublicMethods:
+        pass
+
+    class WithPublicMethods:
+        def first_method():
+            pass
+
+    def _has_method_heading(cls):
+        out = StringIO()
+        np.info(cls, output=out)
+        return 'Methods:' in out.getvalue()
+
+    assert _has_method_heading(WithPublicMethods)
+    assert not _has_method_heading(NoPublicMethods)
+
+
+def test_drop_metadata():
+    def _compare_dtypes(dt1, dt2):
+        return np.can_cast(dt1, dt2, casting='no')
+
+    # structured dtype
+    dt = np.dtype([('l1', [('l2', np.dtype('S8', metadata={'msg': 'toto'}))])],
+                  metadata={'msg': 'titi'})
+    dt_m = _utils_impl.drop_metadata(dt)
+    assert _compare_dtypes(dt, dt_m) is True
+    assert dt_m.metadata is None
+    assert dt_m['l1'].metadata is None
+    assert dt_m['l1']['l2'].metadata is None
+
+    # alignment
+    dt = np.dtype([('x', '>> from numpy import linalg as LA
+    >>> LA.inv(np.zeros((2,2)))
+    Traceback (most recent call last):
+      File "", line 1, in 
+      File "...linalg.py", line 350,
+        in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype)))
+      File "...linalg.py", line 249,
+        in solve
+        raise LinAlgError('Singular matrix')
+    numpy.linalg.LinAlgError: Singular matrix
+
+    """
+
+
+def _raise_linalgerror_singular(err, flag):
+    raise LinAlgError("Singular matrix")
+
+def _raise_linalgerror_nonposdef(err, flag):
+    raise LinAlgError("Matrix is not positive definite")
+
+def _raise_linalgerror_eigenvalues_nonconvergence(err, flag):
+    raise LinAlgError("Eigenvalues did not converge")
+
+def _raise_linalgerror_svd_nonconvergence(err, flag):
+    raise LinAlgError("SVD did not converge")
+
+def _raise_linalgerror_lstsq(err, flag):
+    raise LinAlgError("SVD did not converge in Linear Least Squares")
+
+def _raise_linalgerror_qr(err, flag):
+    raise LinAlgError("Incorrect argument found while performing "
+                      "QR factorization")
+
+
+def _makearray(a):
+    new = asarray(a)
+    wrap = getattr(a, "__array_wrap__", new.__array_wrap__)
+    return new, wrap
+
+def isComplexType(t):
+    return issubclass(t, complexfloating)
+
+
+_real_types_map = {single: single,
+                   double: double,
+                   csingle: single,
+                   cdouble: double}
+
+_complex_types_map = {single: csingle,
+                      double: cdouble,
+                      csingle: csingle,
+                      cdouble: cdouble}
+
+def _realType(t, default=double):
+    return _real_types_map.get(t, default)
+
+def _complexType(t, default=cdouble):
+    return _complex_types_map.get(t, default)
+
+def _commonType(*arrays):
+    # in lite version, use higher precision (always double or cdouble)
+    result_type = single
+    is_complex = False
+    for a in arrays:
+        type_ = a.dtype.type
+        if issubclass(type_, inexact):
+            if isComplexType(type_):
+                is_complex = True
+            rt = _realType(type_, default=None)
+            if rt is double:
+                result_type = double
+            elif rt is None:
+                # unsupported inexact scalar
+                raise TypeError("array type %s is unsupported in linalg" %
+                        (a.dtype.name,))
+        else:
+            result_type = double
+    if is_complex:
+        result_type = _complex_types_map[result_type]
+        return cdouble, result_type
+    else:
+        return double, result_type
+
+
+def _to_native_byte_order(*arrays):
+    ret = []
+    for arr in arrays:
+        if arr.dtype.byteorder not in ('=', '|'):
+            ret.append(asarray(arr, dtype=arr.dtype.newbyteorder('=')))
+        else:
+            ret.append(arr)
+    if len(ret) == 1:
+        return ret[0]
+    else:
+        return ret
+
+
+def _assert_2d(*arrays):
+    for a in arrays:
+        if a.ndim != 2:
+            raise LinAlgError('%d-dimensional array given. Array must be '
+                    'two-dimensional' % a.ndim)
+
+def _assert_stacked_2d(*arrays):
+    for a in arrays:
+        if a.ndim < 2:
+            raise LinAlgError('%d-dimensional array given. Array must be '
+                    'at least two-dimensional' % a.ndim)
+
+def _assert_stacked_square(*arrays):
+    for a in arrays:
+        m, n = a.shape[-2:]
+        if m != n:
+            raise LinAlgError('Last 2 dimensions of the array must be square')
+
+def _assert_finite(*arrays):
+    for a in arrays:
+        if not isfinite(a).all():
+            raise LinAlgError("Array must not contain infs or NaNs")
+
+def _is_empty_2d(arr):
+    # check size first for efficiency
+    return arr.size == 0 and prod(arr.shape[-2:]) == 0
+
+
+def transpose(a):
+    """
+    Transpose each matrix in a stack of matrices.
+
+    Unlike np.transpose, this only swaps the last two axes, rather than all of
+    them
+
+    Parameters
+    ----------
+    a : (...,M,N) array_like
+
+    Returns
+    -------
+    aT : (...,N,M) ndarray
+    """
+    return swapaxes(a, -1, -2)
+
+# Linear equations
+
+def _tensorsolve_dispatcher(a, b, axes=None):
+    return (a, b)
+
+
+@array_function_dispatch(_tensorsolve_dispatcher)
+def tensorsolve(a, b, axes=None):
+    """
+    Solve the tensor equation ``a x = b`` for x.
+
+    It is assumed that all indices of `x` are summed over in the product,
+    together with the rightmost indices of `a`, as is done in, for example,
+    ``tensordot(a, x, axes=x.ndim)``.
+
+    Parameters
+    ----------
+    a : array_like
+        Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
+        the shape of that sub-tensor of `a` consisting of the appropriate
+        number of its rightmost indices, and must be such that
+        ``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
+        'square').
+    b : array_like
+        Right-hand tensor, which can be of any shape.
+    axes : tuple of ints, optional
+        Axes in `a` to reorder to the right, before inversion.
+        If None (default), no reordering is done.
+
+    Returns
+    -------
+    x : ndarray, shape Q
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    numpy.tensordot, tensorinv, numpy.einsum
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.eye(2*3*4)
+    >>> a.shape = (2*3, 4, 2, 3, 4)
+    >>> rng = np.random.default_rng()
+    >>> b = rng.normal(size=(2*3, 4))
+    >>> x = np.linalg.tensorsolve(a, b)
+    >>> x.shape
+    (2, 3, 4)
+    >>> np.allclose(np.tensordot(a, x, axes=3), b)
+    True
+
+    """
+    a, wrap = _makearray(a)
+    b = asarray(b)
+    an = a.ndim
+
+    if axes is not None:
+        allaxes = list(range(0, an))
+        for k in axes:
+            allaxes.remove(k)
+            allaxes.insert(an, k)
+        a = a.transpose(allaxes)
+
+    oldshape = a.shape[-(an-b.ndim):]
+    prod = 1
+    for k in oldshape:
+        prod *= k
+
+    if a.size != prod ** 2:
+        raise LinAlgError(
+            "Input arrays must satisfy the requirement \
+            prod(a.shape[b.ndim:]) == prod(a.shape[:b.ndim])"
+        )
+
+    a = a.reshape(prod, prod)
+    b = b.ravel()
+    res = wrap(solve(a, b))
+    res.shape = oldshape
+    return res
+
+
+def _solve_dispatcher(a, b):
+    return (a, b)
+
+
+@array_function_dispatch(_solve_dispatcher)
+def solve(a, b):
+    """
+    Solve a linear matrix equation, or system of linear scalar equations.
+
+    Computes the "exact" solution, `x`, of the well-determined, i.e., full
+    rank, linear matrix equation `ax = b`.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Coefficient matrix.
+    b : {(M,), (..., M, K)}, array_like
+        Ordinate or "dependent variable" values.
+
+    Returns
+    -------
+    x : {(..., M,), (..., M, K)} ndarray
+        Solution to the system a x = b.  Returned shape is (..., M) if b is
+        shape (M,) and (..., M, K) if b is (..., M, K), where the "..." part is
+        broadcasted between a and b.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular or not square.
+
+    See Also
+    --------
+    scipy.linalg.solve : Similar function in SciPy.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The solutions are computed using LAPACK routine ``_gesv``.
+
+    `a` must be square and of full-rank, i.e., all rows (or, equivalently,
+    columns) must be linearly independent; if either is not true, use
+    `lstsq` for the least-squares best "solution" of the
+    system/equation.
+
+    .. versionchanged:: 2.0
+
+       The b array is only treated as a shape (M,) column vector if it is
+       exactly 1-dimensional. In all other instances it is treated as a stack
+       of (M, K) matrices. Previously b would be treated as a stack of (M,)
+       vectors if b.ndim was equal to a.ndim - 1.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pg. 22.
+
+    Examples
+    --------
+    Solve the system of equations:
+    ``x0 + 2 * x1 = 1`` and
+    ``3 * x0 + 5 * x1 = 2``:
+
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, 5]])
+    >>> b = np.array([1, 2])
+    >>> x = np.linalg.solve(a, b)
+    >>> x
+    array([-1.,  1.])
+
+    Check that the solution is correct:
+
+    >>> np.allclose(np.dot(a, x), b)
+    True
+
+    """
+    a, _ = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    b, wrap = _makearray(b)
+    t, result_t = _commonType(a, b)
+
+    # We use the b = (..., M,) logic, only if the number of extra dimensions
+    # match exactly
+    if b.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    with errstate(call=_raise_linalgerror_singular, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        r = gufunc(a, b, signature=signature)
+
+    return wrap(r.astype(result_t, copy=False))
+
+
+def _tensorinv_dispatcher(a, ind=None):
+    return (a,)
+
+
+@array_function_dispatch(_tensorinv_dispatcher)
+def tensorinv(a, ind=2):
+    """
+    Compute the 'inverse' of an N-dimensional array.
+
+    The result is an inverse for `a` relative to the tensordot operation
+    ``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
+    ``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
+    tensordot operation.
+
+    Parameters
+    ----------
+    a : array_like
+        Tensor to 'invert'. Its shape must be 'square', i. e.,
+        ``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
+    ind : int, optional
+        Number of first indices that are involved in the inverse sum.
+        Must be a positive integer, default is 2.
+
+    Returns
+    -------
+    b : ndarray
+        `a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is singular or not 'square' (in the above sense).
+
+    See Also
+    --------
+    numpy.tensordot, tensorsolve
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.eye(4*6)
+    >>> a.shape = (4, 6, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=2)
+    >>> ainv.shape
+    (8, 3, 4, 6)
+    >>> rng = np.random.default_rng()
+    >>> b = rng.normal(size=(4, 6))
+    >>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
+    True
+
+    >>> a = np.eye(4*6)
+    >>> a.shape = (24, 8, 3)
+    >>> ainv = np.linalg.tensorinv(a, ind=1)
+    >>> ainv.shape
+    (8, 3, 24)
+    >>> rng = np.random.default_rng()
+    >>> b = rng.normal(size=24)
+    >>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+    True
+
+    """
+    a = asarray(a)
+    oldshape = a.shape
+    prod = 1
+    if ind > 0:
+        invshape = oldshape[ind:] + oldshape[:ind]
+        for k in oldshape[ind:]:
+            prod *= k
+    else:
+        raise ValueError("Invalid ind argument.")
+    a = a.reshape(prod, -1)
+    ia = inv(a)
+    return ia.reshape(*invshape)
+
+
+# Matrix inversion
+
+def _unary_dispatcher(a):
+    return (a,)
+
+
+@array_function_dispatch(_unary_dispatcher)
+def inv(a):
+    """
+    Compute the inverse of a matrix.
+
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``a @ ainv = ainv @ a = eye(a.shape[0])``.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Matrix to be inverted.
+
+    Returns
+    -------
+    ainv : (..., M, M) ndarray or matrix
+        Inverse of the matrix `a`.
+
+    Raises
+    ------
+    LinAlgError
+        If `a` is not square or inversion fails.
+
+    See Also
+    --------
+    scipy.linalg.inv : Similar function in SciPy.
+    numpy.linalg.cond : Compute the condition number of a matrix.
+    numpy.linalg.svd : Compute the singular value decomposition of a matrix.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    If `a` is detected to be singular, a `LinAlgError` is raised. If `a` is
+    ill-conditioned, a `LinAlgError` may or may not be raised, and results may
+    be inaccurate due to floating-point errors.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Condition number",
+           https://en.wikipedia.org/wiki/Condition_number
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.linalg import inv
+    >>> a = np.array([[1., 2.], [3., 4.]])
+    >>> ainv = inv(a)
+    >>> np.allclose(a @ ainv, np.eye(2))
+    True
+    >>> np.allclose(ainv @ a, np.eye(2))
+    True
+
+    If a is a matrix object, then the return value is a matrix as well:
+
+    >>> ainv = inv(np.matrix(a))
+    >>> ainv
+    matrix([[-2. ,  1. ],
+            [ 1.5, -0.5]])
+
+    Inverses of several matrices can be computed at once:
+
+    >>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
+    >>> inv(a)
+    array([[[-2.  ,  1.  ],
+            [ 1.5 , -0.5 ]],
+           [[-1.25,  0.75],
+            [ 0.75, -0.25]]])
+
+    If a matrix is close to singular, the computed inverse may not satisfy
+    ``a @ ainv = ainv @ a = eye(a.shape[0])`` even if a `LinAlgError`
+    is not raised:
+
+    >>> a = np.array([[2,4,6],[2,0,2],[6,8,14]])
+    >>> inv(a)  # No errors raised
+    array([[-1.12589991e+15, -5.62949953e+14,  5.62949953e+14],
+       [-1.12589991e+15, -5.62949953e+14,  5.62949953e+14],
+       [ 1.12589991e+15,  5.62949953e+14, -5.62949953e+14]])
+    >>> a @ inv(a)
+    array([[ 0.   , -0.5  ,  0.   ],  # may vary
+           [-0.5  ,  0.625,  0.25 ],
+           [ 0.   ,  0.   ,  1.   ]])
+
+    To detect ill-conditioned matrices, you can use `numpy.linalg.cond` to
+    compute its *condition number* [1]_. The larger the condition number, the
+    more ill-conditioned the matrix is. As a rule of thumb, if the condition
+    number ``cond(a) = 10**k``, then you may lose up to ``k`` digits of
+    accuracy on top of what would be lost to the numerical method due to loss
+    of precision from arithmetic methods.
+
+    >>> from numpy.linalg import cond
+    >>> cond(a)
+    np.float64(8.659885634118668e+17)  # may vary
+
+    It is also possible to detect ill-conditioning by inspecting the matrix's
+    singular values directly. The ratio between the largest and the smallest
+    singular value is the condition number:
+
+    >>> from numpy.linalg import svd
+    >>> sigma = svd(a, compute_uv=False)  # Do not compute singular vectors
+    >>> sigma.max()/sigma.min()
+    8.659885634118668e+17  # may vary
+
+    """
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+
+    signature = 'D->D' if isComplexType(t) else 'd->d'
+    with errstate(call=_raise_linalgerror_singular, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        ainv = _umath_linalg.inv(a, signature=signature)
+    return wrap(ainv.astype(result_t, copy=False))
+
+
+def _matrix_power_dispatcher(a, n):
+    return (a,)
+
+
+@array_function_dispatch(_matrix_power_dispatcher)
+def matrix_power(a, n):
+    """
+    Raise a square matrix to the (integer) power `n`.
+
+    For positive integers `n`, the power is computed by repeated matrix
+    squarings and matrix multiplications. If ``n == 0``, the identity matrix
+    of the same shape as M is returned. If ``n < 0``, the inverse
+    is computed and then raised to the ``abs(n)``.
+
+    .. note:: Stacks of object matrices are not currently supported.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Matrix to be "powered".
+    n : int
+        The exponent can be any integer or long integer, positive,
+        negative, or zero.
+
+    Returns
+    -------
+    a**n : (..., M, M) ndarray or matrix object
+        The return value is the same shape and type as `M`;
+        if the exponent is positive or zero then the type of the
+        elements is the same as those of `M`. If the exponent is
+        negative the elements are floating-point.
+
+    Raises
+    ------
+    LinAlgError
+        For matrices that are not square or that (for negative powers) cannot
+        be inverted numerically.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.linalg import matrix_power
+    >>> i = np.array([[0, 1], [-1, 0]]) # matrix equiv. of the imaginary unit
+    >>> matrix_power(i, 3) # should = -i
+    array([[ 0, -1],
+           [ 1,  0]])
+    >>> matrix_power(i, 0)
+    array([[1, 0],
+           [0, 1]])
+    >>> matrix_power(i, -3) # should = 1/(-i) = i, but w/ f.p. elements
+    array([[ 0.,  1.],
+           [-1.,  0.]])
+
+    Somewhat more sophisticated example
+
+    >>> q = np.zeros((4, 4))
+    >>> q[0:2, 0:2] = -i
+    >>> q[2:4, 2:4] = i
+    >>> q # one of the three quaternion units not equal to 1
+    array([[ 0., -1.,  0.,  0.],
+           [ 1.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  1.],
+           [ 0.,  0., -1.,  0.]])
+    >>> matrix_power(q, 2) # = -np.eye(4)
+    array([[-1.,  0.,  0.,  0.],
+           [ 0., -1.,  0.,  0.],
+           [ 0.,  0., -1.,  0.],
+           [ 0.,  0.,  0., -1.]])
+
+    """
+    a = asanyarray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+
+    try:
+        n = operator.index(n)
+    except TypeError as e:
+        raise TypeError("exponent must be an integer") from e
+
+    # Fall back on dot for object arrays. Object arrays are not supported by
+    # the current implementation of matmul using einsum
+    if a.dtype != object:
+        fmatmul = matmul
+    elif a.ndim == 2:
+        fmatmul = dot
+    else:
+        raise NotImplementedError(
+            "matrix_power not supported for stacks of object arrays")
+
+    if n == 0:
+        a = empty_like(a)
+        a[...] = eye(a.shape[-2], dtype=a.dtype)
+        return a
+
+    elif n < 0:
+        a = inv(a)
+        n = abs(n)
+
+    # short-cuts.
+    if n == 1:
+        return a
+
+    elif n == 2:
+        return fmatmul(a, a)
+
+    elif n == 3:
+        return fmatmul(fmatmul(a, a), a)
+
+    # Use binary decomposition to reduce the number of matrix multiplications.
+    # Here, we iterate over the bits of n, from LSB to MSB, raise `a` to
+    # increasing powers of 2, and multiply into the result as needed.
+    z = result = None
+    while n > 0:
+        z = a if z is None else fmatmul(z, z)
+        n, bit = divmod(n, 2)
+        if bit:
+            result = z if result is None else fmatmul(result, z)
+
+    return result
+
+
+# Cholesky decomposition
+
+def _cholesky_dispatcher(a, /, *, upper=None):
+    return (a,)
+
+
+@array_function_dispatch(_cholesky_dispatcher)
+def cholesky(a, /, *, upper=False):
+    """
+    Cholesky decomposition.
+
+    Return the lower or upper Cholesky decomposition, ``L * L.H`` or
+    ``U.H * U``, of the square matrix ``a``, where ``L`` is lower-triangular,
+    ``U`` is upper-triangular, and ``.H`` is the conjugate transpose operator
+    (which is the ordinary transpose if ``a`` is real-valued). ``a`` must be
+    Hermitian (symmetric if real-valued) and positive-definite. No checking is
+    performed to verify whether ``a`` is Hermitian or not. In addition, only
+    the lower or upper-triangular and diagonal elements of ``a`` are used.
+    Only ``L`` or ``U`` is actually returned.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Hermitian (symmetric if all elements are real), positive-definite
+        input matrix.
+    upper : bool
+        If ``True``, the result must be the upper-triangular Cholesky factor.
+        If ``False``, the result must be the lower-triangular Cholesky factor.
+        Default: ``False``.
+
+    Returns
+    -------
+    L : (..., M, M) array_like
+        Lower or upper-triangular Cholesky factor of `a`. Returns a matrix
+        object if `a` is a matrix object.
+
+    Raises
+    ------
+    LinAlgError
+       If the decomposition fails, for example, if `a` is not
+       positive-definite.
+
+    See Also
+    --------
+    scipy.linalg.cholesky : Similar function in SciPy.
+    scipy.linalg.cholesky_banded : Cholesky decompose a banded Hermitian
+                                   positive-definite matrix.
+    scipy.linalg.cho_factor : Cholesky decomposition of a matrix, to use in
+                              `scipy.linalg.cho_solve`.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The Cholesky decomposition is often used as a fast way of solving
+
+    .. math:: A \\mathbf{x} = \\mathbf{b}
+
+    (when `A` is both Hermitian/symmetric and positive-definite).
+
+    First, we solve for :math:`\\mathbf{y}` in
+
+    .. math:: L \\mathbf{y} = \\mathbf{b},
+
+    and then for :math:`\\mathbf{x}` in
+
+    .. math:: L^{H} \\mathbf{x} = \\mathbf{y}.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.array([[1,-2j],[2j,5]])
+    >>> A
+    array([[ 1.+0.j, -0.-2.j],
+           [ 0.+2.j,  5.+0.j]])
+    >>> L = np.linalg.cholesky(A)
+    >>> L
+    array([[1.+0.j, 0.+0.j],
+           [0.+2.j, 1.+0.j]])
+    >>> np.dot(L, L.T.conj()) # verify that L * L.H = A
+    array([[1.+0.j, 0.-2.j],
+           [0.+2.j, 5.+0.j]])
+    >>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like?
+    >>> np.linalg.cholesky(A) # an ndarray object is returned
+    array([[1.+0.j, 0.+0.j],
+           [0.+2.j, 1.+0.j]])
+    >>> # But a matrix object is returned if A is a matrix object
+    >>> np.linalg.cholesky(np.matrix(A))
+    matrix([[ 1.+0.j,  0.+0.j],
+            [ 0.+2.j,  1.+0.j]])
+    >>> # The upper-triangular Cholesky factor can also be obtained.
+    >>> np.linalg.cholesky(A, upper=True)
+    array([[1.-0.j, 0.-2.j],
+           [0.-0.j, 1.-0.j]])
+
+    """
+    gufunc = _umath_linalg.cholesky_up if upper else _umath_linalg.cholesky_lo
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+    signature = 'D->D' if isComplexType(t) else 'd->d'
+    with errstate(call=_raise_linalgerror_nonposdef, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        r = gufunc(a, signature=signature)
+    return wrap(r.astype(result_t, copy=False))
+
+
+# outer product
+
+
+def _outer_dispatcher(x1, x2):
+    return (x1, x2)
+
+
+@array_function_dispatch(_outer_dispatcher)
+def outer(x1, x2, /):
+    """
+    Compute the outer product of two vectors.
+
+    This function is Array API compatible. Compared to ``np.outer``
+    it accepts 1-dimensional inputs only.
+
+    Parameters
+    ----------
+    x1 : (M,) array_like
+        One-dimensional input array of size ``N``.
+        Must have a numeric data type.
+    x2 : (N,) array_like
+        One-dimensional input array of size ``M``.
+        Must have a numeric data type.
+
+    Returns
+    -------
+    out : (M, N) ndarray
+        ``out[i, j] = a[i] * b[j]``
+
+    See also
+    --------
+    outer
+
+    Examples
+    --------
+    Make a (*very* coarse) grid for computing a Mandelbrot set:
+
+    >>> rl = np.linalg.outer(np.ones((5,)), np.linspace(-2, 2, 5))
+    >>> rl
+    array([[-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.],
+           [-2., -1.,  0.,  1.,  2.]])
+    >>> im = np.linalg.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
+    >>> im
+    array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],
+           [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],
+           [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
+           [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],
+           [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])
+    >>> grid = rl + im
+    >>> grid
+    array([[-2.+2.j, -1.+2.j,  0.+2.j,  1.+2.j,  2.+2.j],
+           [-2.+1.j, -1.+1.j,  0.+1.j,  1.+1.j,  2.+1.j],
+           [-2.+0.j, -1.+0.j,  0.+0.j,  1.+0.j,  2.+0.j],
+           [-2.-1.j, -1.-1.j,  0.-1.j,  1.-1.j,  2.-1.j],
+           [-2.-2.j, -1.-2.j,  0.-2.j,  1.-2.j,  2.-2.j]])
+
+    An example using a "vector" of letters:
+
+    >>> x = np.array(['a', 'b', 'c'], dtype=object)
+    >>> np.linalg.outer(x, [1, 2, 3])
+    array([['a', 'aa', 'aaa'],
+           ['b', 'bb', 'bbb'],
+           ['c', 'cc', 'ccc']], dtype=object)
+
+    """
+    x1 = asanyarray(x1)
+    x2 = asanyarray(x2)
+    if x1.ndim != 1 or x2.ndim != 1:
+        raise ValueError(
+            "Input arrays must be one-dimensional, but they are "
+            f"{x1.ndim=} and {x2.ndim=}."
+        )
+    return _core_outer(x1, x2, out=None)
+
+
+# QR decomposition
+
+
+def _qr_dispatcher(a, mode=None):
+    return (a,)
+
+
+@array_function_dispatch(_qr_dispatcher)
+def qr(a, mode='reduced'):
+    """
+    Compute the qr factorization of a matrix.
+
+    Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is
+    upper-triangular.
+
+    Parameters
+    ----------
+    a : array_like, shape (..., M, N)
+        An array-like object with the dimensionality of at least 2.
+    mode : {'reduced', 'complete', 'r', 'raw'}, optional, default: 'reduced'
+        If K = min(M, N), then
+
+        * 'reduced'  : returns Q, R with dimensions (..., M, K), (..., K, N)
+        * 'complete' : returns Q, R with dimensions (..., M, M), (..., M, N)
+        * 'r'        : returns R only with dimensions (..., K, N)
+        * 'raw'      : returns h, tau with dimensions (..., N, M), (..., K,)
+
+        The options 'reduced', 'complete, and 'raw' are new in numpy 1.8,
+        see the notes for more information. The default is 'reduced', and to
+        maintain backward compatibility with earlier versions of numpy both
+        it and the old default 'full' can be omitted. Note that array h
+        returned in 'raw' mode is transposed for calling Fortran. The
+        'economic' mode is deprecated.  The modes 'full' and 'economic' may
+        be passed using only the first letter for backwards compatibility,
+        but all others must be spelled out. See the Notes for more
+        explanation.
+
+
+    Returns
+    -------
+    When mode is 'reduced' or 'complete', the result will be a namedtuple with
+    the attributes `Q` and `R`.
+
+    Q : ndarray of float or complex, optional
+        A matrix with orthonormal columns. When mode = 'complete' the
+        result is an orthogonal/unitary matrix depending on whether or not
+        a is real/complex. The determinant may be either +/- 1 in that
+        case. In case the number of dimensions in the input array is
+        greater than 2 then a stack of the matrices with above properties
+        is returned.
+    R : ndarray of float or complex, optional
+        The upper-triangular matrix or a stack of upper-triangular
+        matrices if the number of dimensions in the input array is greater
+        than 2.
+    (h, tau) : ndarrays of np.double or np.cdouble, optional
+        The array h contains the Householder reflectors that generate q
+        along with r. The tau array contains scaling factors for the
+        reflectors. In the deprecated  'economic' mode only h is returned.
+
+    Raises
+    ------
+    LinAlgError
+        If factoring fails.
+
+    See Also
+    --------
+    scipy.linalg.qr : Similar function in SciPy.
+    scipy.linalg.rq : Compute RQ decomposition of a matrix.
+
+    Notes
+    -----
+    This is an interface to the LAPACK routines ``dgeqrf``, ``zgeqrf``,
+    ``dorgqr``, and ``zungqr``.
+
+    For more information on the qr factorization, see for example:
+    https://en.wikipedia.org/wiki/QR_factorization
+
+    Subclasses of `ndarray` are preserved except for the 'raw' mode. So if
+    `a` is of type `matrix`, all the return values will be matrices too.
+
+    New 'reduced', 'complete', and 'raw' options for mode were added in
+    NumPy 1.8.0 and the old option 'full' was made an alias of 'reduced'.  In
+    addition the options 'full' and 'economic' were deprecated.  Because
+    'full' was the previous default and 'reduced' is the new default,
+    backward compatibility can be maintained by letting `mode` default.
+    The 'raw' option was added so that LAPACK routines that can multiply
+    arrays by q using the Householder reflectors can be used. Note that in
+    this case the returned arrays are of type np.double or np.cdouble and
+    the h array is transposed to be FORTRAN compatible.  No routines using
+    the 'raw' return are currently exposed by numpy, but some are available
+    in lapack_lite and just await the necessary work.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> a = rng.normal(size=(9, 6))
+    >>> Q, R = np.linalg.qr(a)
+    >>> np.allclose(a, np.dot(Q, R))  # a does equal QR
+    True
+    >>> R2 = np.linalg.qr(a, mode='r')
+    >>> np.allclose(R, R2)  # mode='r' returns the same R as mode='full'
+    True
+    >>> a = np.random.normal(size=(3, 2, 2)) # Stack of 2 x 2 matrices as input
+    >>> Q, R = np.linalg.qr(a)
+    >>> Q.shape
+    (3, 2, 2)
+    >>> R.shape
+    (3, 2, 2)
+    >>> np.allclose(a, np.matmul(Q, R))
+    True
+
+    Example illustrating a common use of `qr`: solving of least squares
+    problems
+
+    What are the least-squares-best `m` and `y0` in ``y = y0 + mx`` for
+    the following data: {(0,1), (1,0), (1,2), (2,1)}. (Graph the points
+    and you'll see that it should be y0 = 0, m = 1.)  The answer is provided
+    by solving the over-determined matrix equation ``Ax = b``, where::
+
+      A = array([[0, 1], [1, 1], [1, 1], [2, 1]])
+      x = array([[y0], [m]])
+      b = array([[1], [0], [2], [1]])
+
+    If A = QR such that Q is orthonormal (which is always possible via
+    Gram-Schmidt), then ``x = inv(R) * (Q.T) * b``.  (In numpy practice,
+    however, we simply use `lstsq`.)
+
+    >>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]])
+    >>> A
+    array([[0, 1],
+           [1, 1],
+           [1, 1],
+           [2, 1]])
+    >>> b = np.array([1, 2, 2, 3])
+    >>> Q, R = np.linalg.qr(A)
+    >>> p = np.dot(Q.T, b)
+    >>> np.dot(np.linalg.inv(R), p)
+    array([  1.,   1.])
+
+    """
+    if mode not in ('reduced', 'complete', 'r', 'raw'):
+        if mode in ('f', 'full'):
+            # 2013-04-01, 1.8
+            msg = (
+                "The 'full' option is deprecated in favor of 'reduced'.\n"
+                "For backward compatibility let mode default."
+            )
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            mode = 'reduced'
+        elif mode in ('e', 'economic'):
+            # 2013-04-01, 1.8
+            msg = "The 'economic' option is deprecated."
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            mode = 'economic'
+        else:
+            raise ValueError(f"Unrecognized mode '{mode}'")
+
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    m, n = a.shape[-2:]
+    t, result_t = _commonType(a)
+    a = a.astype(t, copy=True)
+    a = _to_native_byte_order(a)
+    mn = min(m, n)
+
+    signature = 'D->D' if isComplexType(t) else 'd->d'
+    with errstate(call=_raise_linalgerror_qr, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        tau = _umath_linalg.qr_r_raw(a, signature=signature)
+
+    # handle modes that don't return q
+    if mode == 'r':
+        r = triu(a[..., :mn, :])
+        r = r.astype(result_t, copy=False)
+        return wrap(r)
+
+    if mode == 'raw':
+        q = transpose(a)
+        q = q.astype(result_t, copy=False)
+        tau = tau.astype(result_t, copy=False)
+        return wrap(q), tau
+
+    if mode == 'economic':
+        a = a.astype(result_t, copy=False)
+        return wrap(a)
+
+    # mc is the number of columns in the resulting q
+    # matrix. If the mode is complete then it is
+    # same as number of rows, and if the mode is reduced,
+    # then it is the minimum of number of rows and columns.
+    if mode == 'complete' and m > n:
+        mc = m
+        gufunc = _umath_linalg.qr_complete
+    else:
+        mc = mn
+        gufunc = _umath_linalg.qr_reduced
+
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    with errstate(call=_raise_linalgerror_qr, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        q = gufunc(a, tau, signature=signature)
+    r = triu(a[..., :mc, :])
+
+    q = q.astype(result_t, copy=False)
+    r = r.astype(result_t, copy=False)
+
+    return QRResult(wrap(q), wrap(r))
+
+# Eigenvalues
+
+
+@array_function_dispatch(_unary_dispatcher)
+def eigvals(a):
+    """
+    Compute the eigenvalues of a general matrix.
+
+    Main difference between `eigvals` and `eig`: the eigenvectors aren't
+    returned.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        A complex- or real-valued matrix whose eigenvalues will be computed.
+
+    Returns
+    -------
+    w : (..., M,) ndarray
+        The eigenvalues, each repeated according to its multiplicity.
+        They are not necessarily ordered, nor are they necessarily
+        real for real matrices.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eig : eigenvalues and right eigenvectors of general arrays
+    eigvalsh : eigenvalues of real symmetric or complex Hermitian
+               (conjugate symmetric) arrays.
+    eigh : eigenvalues and eigenvectors of real symmetric or complex
+           Hermitian (conjugate symmetric) arrays.
+    scipy.linalg.eigvals : Similar function in SciPy.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    This is implemented using the ``_geev`` LAPACK routines which compute
+    the eigenvalues and eigenvectors of general square arrays.
+
+    Examples
+    --------
+    Illustration, using the fact that the eigenvalues of a diagonal matrix
+    are its diagonal elements, that multiplying a matrix on the left
+    by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose
+    of `Q`), preserves the eigenvalues of the "middle" matrix. In other words,
+    if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as
+    ``A``:
+
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+    >>> x = np.random.random()
+    >>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]])
+    >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :])
+    (1.0, 1.0, 0.0)
+
+    Now multiply a diagonal matrix by ``Q`` on one side and
+    by ``Q.T`` on the other:
+
+    >>> D = np.diag((-1,1))
+    >>> LA.eigvals(D)
+    array([-1.,  1.])
+    >>> A = np.dot(Q, D)
+    >>> A = np.dot(A, Q.T)
+    >>> LA.eigvals(A)
+    array([ 1., -1.]) # random
+
+    """
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    _assert_finite(a)
+    t, result_t = _commonType(a)
+
+    signature = 'D->D' if isComplexType(t) else 'd->D'
+    with errstate(call=_raise_linalgerror_eigenvalues_nonconvergence,
+                  invalid='call', over='ignore', divide='ignore',
+                  under='ignore'):
+        w = _umath_linalg.eigvals(a, signature=signature)
+
+    if not isComplexType(t):
+        if all(w.imag == 0):
+            w = w.real
+            result_t = _realType(result_t)
+        else:
+            result_t = _complexType(result_t)
+
+    return w.astype(result_t, copy=False)
+
+
+def _eigvalsh_dispatcher(a, UPLO=None):
+    return (a,)
+
+
+@array_function_dispatch(_eigvalsh_dispatcher)
+def eigvalsh(a, UPLO='L'):
+    """
+    Compute the eigenvalues of a complex Hermitian or real symmetric matrix.
+
+    Main difference from eigh: the eigenvectors are not computed.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        A complex- or real-valued matrix whose eigenvalues are to be
+        computed.
+    UPLO : {'L', 'U'}, optional
+        Specifies whether the calculation is done with the lower triangular
+        part of `a` ('L', default) or the upper triangular part ('U').
+        Irrespective of this value only the real parts of the diagonal will
+        be considered in the computation to preserve the notion of a Hermitian
+        matrix. It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+
+    Returns
+    -------
+    w : (..., M,) ndarray
+        The eigenvalues in ascending order, each repeated according to
+        its multiplicity.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigh : eigenvalues and eigenvectors of real symmetric or complex Hermitian
+           (conjugate symmetric) arrays.
+    eigvals : eigenvalues of general real or complex arrays.
+    eig : eigenvalues and right eigenvectors of general real or complex
+          arrays.
+    scipy.linalg.eigvalsh : Similar function in SciPy.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The eigenvalues are computed using LAPACK routines ``_syevd``, ``_heevd``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, -2j], [2j, 5]])
+    >>> LA.eigvalsh(a)
+    array([ 0.17157288,  5.82842712]) # may vary
+
+    >>> # demonstrate the treatment of the imaginary part of the diagonal
+    >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
+    >>> a
+    array([[5.+2.j, 9.-2.j],
+           [0.+2.j, 2.-1.j]])
+    >>> # with UPLO='L' this is numerically equivalent to using LA.eigvals()
+    >>> # with:
+    >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
+    >>> b
+    array([[5.+0.j, 0.-2.j],
+           [0.+2.j, 2.+0.j]])
+    >>> wa = LA.eigvalsh(a)
+    >>> wb = LA.eigvals(b)
+    >>> wa; wb
+    array([1., 6.])
+    array([6.+0.j, 1.+0.j])
+
+    """
+    UPLO = UPLO.upper()
+    if UPLO not in ('L', 'U'):
+        raise ValueError("UPLO argument must be 'L' or 'U'")
+
+    if UPLO == 'L':
+        gufunc = _umath_linalg.eigvalsh_lo
+    else:
+        gufunc = _umath_linalg.eigvalsh_up
+
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+    signature = 'D->d' if isComplexType(t) else 'd->d'
+    with errstate(call=_raise_linalgerror_eigenvalues_nonconvergence,
+                  invalid='call', over='ignore', divide='ignore',
+                  under='ignore'):
+        w = gufunc(a, signature=signature)
+    return w.astype(_realType(result_t), copy=False)
+
+def _convertarray(a):
+    t, result_t = _commonType(a)
+    a = a.astype(t).T.copy()
+    return a, t, result_t
+
+
+# Eigenvectors
+
+
+@array_function_dispatch(_unary_dispatcher)
+def eig(a):
+    """
+    Compute the eigenvalues and right eigenvectors of a square array.
+
+    Parameters
+    ----------
+    a : (..., M, M) array
+        Matrices for which the eigenvalues and right eigenvectors will
+        be computed
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    eigenvalues : (..., M) array
+        The eigenvalues, each repeated according to its multiplicity.
+        The eigenvalues are not necessarily ordered. The resulting
+        array will be of complex type, unless the imaginary part is
+        zero in which case it will be cast to a real type. When `a`
+        is real the resulting eigenvalues will be real (0 imaginary
+        part) or occur in conjugate pairs
+
+    eigenvectors : (..., M, M) array
+        The normalized (unit "length") eigenvectors, such that the
+        column ``eigenvectors[:,i]`` is the eigenvector corresponding to the
+        eigenvalue ``eigenvalues[i]``.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigvals : eigenvalues of a non-symmetric array.
+    eigh : eigenvalues and eigenvectors of a real symmetric or complex
+           Hermitian (conjugate symmetric) array.
+    eigvalsh : eigenvalues of a real symmetric or complex Hermitian
+               (conjugate symmetric) array.
+    scipy.linalg.eig : Similar function in SciPy that also solves the
+                       generalized eigenvalue problem.
+    scipy.linalg.schur : Best choice for unitary and other non-Hermitian
+                         normal matrices.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    This is implemented using the ``_geev`` LAPACK routines which compute
+    the eigenvalues and eigenvectors of general square arrays.
+
+    The number `w` is an eigenvalue of `a` if there exists a vector `v` such
+    that ``a @ v = w * v``. Thus, the arrays `a`, `eigenvalues`, and
+    `eigenvectors` satisfy the equations ``a @ eigenvectors[:,i] =
+    eigenvalues[i] * eigenvectors[:,i]`` for :math:`i \\in \\{0,...,M-1\\}`.
+
+    The array `eigenvectors` may not be of maximum rank, that is, some of the
+    columns may be linearly dependent, although round-off error may obscure
+    that fact. If the eigenvalues are all different, then theoretically the
+    eigenvectors are linearly independent and `a` can be diagonalized by a
+    similarity transformation using `eigenvectors`, i.e, ``inv(eigenvectors) @
+    a @ eigenvectors`` is diagonal.
+
+    For non-Hermitian normal matrices the SciPy function `scipy.linalg.schur`
+    is preferred because the matrix `eigenvectors` is guaranteed to be
+    unitary, which is not the case when using `eig`. The Schur factorization
+    produces an upper triangular matrix rather than a diagonal matrix, but for
+    normal matrices only the diagonal of the upper triangular matrix is
+    needed, the rest is roundoff error.
+
+    Finally, it is emphasized that `eigenvectors` consists of the *right* (as
+    in right-hand side) eigenvectors of `a`. A vector `y` satisfying ``y.T @ a
+    = z * y.T`` for some number `z` is called a *left* eigenvector of `a`,
+    and, in general, the left and right eigenvectors of a matrix are not
+    necessarily the (perhaps conjugate) transposes of each other.
+
+    References
+    ----------
+    G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, FL,
+    Academic Press, Inc., 1980, Various pp.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+
+    (Almost) trivial example with real eigenvalues and eigenvectors.
+
+    >>> eigenvalues, eigenvectors = LA.eig(np.diag((1, 2, 3)))
+    >>> eigenvalues
+    array([1., 2., 3.])
+    >>> eigenvectors
+    array([[1., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 1.]])
+
+    Real matrix possessing complex eigenvalues and eigenvectors;
+    note that the eigenvalues are complex conjugates of each other.
+
+    >>> eigenvalues, eigenvectors = LA.eig(np.array([[1, -1], [1, 1]]))
+    >>> eigenvalues
+    array([1.+1.j, 1.-1.j])
+    >>> eigenvectors
+    array([[0.70710678+0.j        , 0.70710678-0.j        ],
+           [0.        -0.70710678j, 0.        +0.70710678j]])
+
+    Complex-valued matrix with real eigenvalues (but complex-valued
+    eigenvectors); note that ``a.conj().T == a``, i.e., `a` is Hermitian.
+
+    >>> a = np.array([[1, 1j], [-1j, 1]])
+    >>> eigenvalues, eigenvectors = LA.eig(a)
+    >>> eigenvalues
+    array([2.+0.j, 0.+0.j])
+    >>> eigenvectors
+    array([[ 0.        +0.70710678j,  0.70710678+0.j        ], # may vary
+           [ 0.70710678+0.j        , -0.        +0.70710678j]])
+
+    Be careful about round-off error!
+
+    >>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]])
+    >>> # Theor. eigenvalues are 1 +/- 1e-9
+    >>> eigenvalues, eigenvectors = LA.eig(a)
+    >>> eigenvalues
+    array([1., 1.])
+    >>> eigenvectors
+    array([[1., 0.],
+           [0., 1.]])
+
+    """
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    _assert_finite(a)
+    t, result_t = _commonType(a)
+
+    signature = 'D->DD' if isComplexType(t) else 'd->DD'
+    with errstate(call=_raise_linalgerror_eigenvalues_nonconvergence,
+                  invalid='call', over='ignore', divide='ignore',
+                  under='ignore'):
+        w, vt = _umath_linalg.eig(a, signature=signature)
+
+    if not isComplexType(t) and all(w.imag == 0.0):
+        w = w.real
+        vt = vt.real
+        result_t = _realType(result_t)
+    else:
+        result_t = _complexType(result_t)
+
+    vt = vt.astype(result_t, copy=False)
+    return EigResult(w.astype(result_t, copy=False), wrap(vt))
+
+
+@array_function_dispatch(_eigvalsh_dispatcher)
+def eigh(a, UPLO='L'):
+    """
+    Return the eigenvalues and eigenvectors of a complex Hermitian
+    (conjugate symmetric) or a real symmetric matrix.
+
+    Returns two objects, a 1-D array containing the eigenvalues of `a`, and
+    a 2-D square array or matrix (depending on the input type) of the
+    corresponding eigenvectors (in columns).
+
+    Parameters
+    ----------
+    a : (..., M, M) array
+        Hermitian or real symmetric matrices whose eigenvalues and
+        eigenvectors are to be computed.
+    UPLO : {'L', 'U'}, optional
+        Specifies whether the calculation is done with the lower triangular
+        part of `a` ('L', default) or the upper triangular part ('U').
+        Irrespective of this value only the real parts of the diagonal will
+        be considered in the computation to preserve the notion of a Hermitian
+        matrix. It therefore follows that the imaginary part of the diagonal
+        will always be treated as zero.
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    eigenvalues : (..., M) ndarray
+        The eigenvalues in ascending order, each repeated according to
+        its multiplicity.
+    eigenvectors : {(..., M, M) ndarray, (..., M, M) matrix}
+        The column ``eigenvectors[:, i]`` is the normalized eigenvector
+        corresponding to the eigenvalue ``eigenvalues[i]``.  Will return a
+        matrix object if `a` is a matrix object.
+
+    Raises
+    ------
+    LinAlgError
+        If the eigenvalue computation does not converge.
+
+    See Also
+    --------
+    eigvalsh : eigenvalues of real symmetric or complex Hermitian
+               (conjugate symmetric) arrays.
+    eig : eigenvalues and right eigenvectors for non-symmetric arrays.
+    eigvals : eigenvalues of non-symmetric arrays.
+    scipy.linalg.eigh : Similar function in SciPy (but also solves the
+                        generalized eigenvalue problem).
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The eigenvalues/eigenvectors are computed using LAPACK routines ``_syevd``,
+    ``_heevd``.
+
+    The eigenvalues of real symmetric or complex Hermitian matrices are always
+    real. [1]_ The array `eigenvalues` of (column) eigenvectors is unitary and
+    `a`, `eigenvalues`, and `eigenvectors` satisfy the equations ``dot(a,
+    eigenvectors[:, i]) = eigenvalues[i] * eigenvectors[:, i]``.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pg. 222.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, -2j], [2j, 5]])
+    >>> a
+    array([[ 1.+0.j, -0.-2.j],
+           [ 0.+2.j,  5.+0.j]])
+    >>> eigenvalues, eigenvectors = LA.eigh(a)
+    >>> eigenvalues
+    array([0.17157288, 5.82842712])
+    >>> eigenvectors
+    array([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
+           [ 0.        +0.38268343j,  0.        -0.92387953j]])
+
+    >>> (np.dot(a, eigenvectors[:, 0]) -
+    ... eigenvalues[0] * eigenvectors[:, 0])  # verify 1st eigenval/vec pair
+    array([5.55111512e-17+0.0000000e+00j, 0.00000000e+00+1.2490009e-16j])
+    >>> (np.dot(a, eigenvectors[:, 1]) -
+    ... eigenvalues[1] * eigenvectors[:, 1])  # verify 2nd eigenval/vec pair
+    array([0.+0.j, 0.+0.j])
+
+    >>> A = np.matrix(a) # what happens if input is a matrix object
+    >>> A
+    matrix([[ 1.+0.j, -0.-2.j],
+            [ 0.+2.j,  5.+0.j]])
+    >>> eigenvalues, eigenvectors = LA.eigh(A)
+    >>> eigenvalues
+    array([0.17157288, 5.82842712])
+    >>> eigenvectors
+    matrix([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
+            [ 0.        +0.38268343j,  0.        -0.92387953j]])
+
+    >>> # demonstrate the treatment of the imaginary part of the diagonal
+    >>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
+    >>> a
+    array([[5.+2.j, 9.-2.j],
+           [0.+2.j, 2.-1.j]])
+    >>> # with UPLO='L' this is numerically equivalent to using LA.eig() with:
+    >>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
+    >>> b
+    array([[5.+0.j, 0.-2.j],
+           [0.+2.j, 2.+0.j]])
+    >>> wa, va = LA.eigh(a)
+    >>> wb, vb = LA.eig(b)
+    >>> wa
+    array([1., 6.])
+    >>> wb
+    array([6.+0.j, 1.+0.j])
+    >>> va
+    array([[-0.4472136 +0.j        , -0.89442719+0.j        ], # may vary
+           [ 0.        +0.89442719j,  0.        -0.4472136j ]])
+    >>> vb
+    array([[ 0.89442719+0.j       , -0.        +0.4472136j],
+           [-0.        +0.4472136j,  0.89442719+0.j       ]])
+
+    """
+    UPLO = UPLO.upper()
+    if UPLO not in ('L', 'U'):
+        raise ValueError("UPLO argument must be 'L' or 'U'")
+
+    a, wrap = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+
+    if UPLO == 'L':
+        gufunc = _umath_linalg.eigh_lo
+    else:
+        gufunc = _umath_linalg.eigh_up
+
+    signature = 'D->dD' if isComplexType(t) else 'd->dd'
+    with errstate(call=_raise_linalgerror_eigenvalues_nonconvergence,
+                  invalid='call', over='ignore', divide='ignore',
+                  under='ignore'):
+        w, vt = gufunc(a, signature=signature)
+    w = w.astype(_realType(result_t), copy=False)
+    vt = vt.astype(result_t, copy=False)
+    return EighResult(w, wrap(vt))
+
+
+# Singular value decomposition
+
+def _svd_dispatcher(a, full_matrices=None, compute_uv=None, hermitian=None):
+    return (a,)
+
+
+@array_function_dispatch(_svd_dispatcher)
+def svd(a, full_matrices=True, compute_uv=True, hermitian=False):
+    """
+    Singular Value Decomposition.
+
+    When `a` is a 2D array, and ``full_matrices=False``, then it is
+    factorized as ``u @ np.diag(s) @ vh = (u * s) @ vh``, where
+    `u` and the Hermitian transpose of `vh` are 2D arrays with
+    orthonormal columns and `s` is a 1D array of `a`'s singular
+    values. When `a` is higher-dimensional, SVD is applied in
+    stacked mode as explained below.
+
+    Parameters
+    ----------
+    a : (..., M, N) array_like
+        A real or complex array with ``a.ndim >= 2``.
+    full_matrices : bool, optional
+        If True (default), `u` and `vh` have the shapes ``(..., M, M)`` and
+        ``(..., N, N)``, respectively.  Otherwise, the shapes are
+        ``(..., M, K)`` and ``(..., K, N)``, respectively, where
+        ``K = min(M, N)``.
+    compute_uv : bool, optional
+        Whether or not to compute `u` and `vh` in addition to `s`.  True
+        by default.
+    hermitian : bool, optional
+        If True, `a` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+
+    Returns
+    -------
+    When `compute_uv` is True, the result is a namedtuple with the following
+    attribute names:
+
+    U : { (..., M, M), (..., M, K) } array
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
+    S : (..., K) array
+        Vector(s) with the singular values, within each vector sorted in
+        descending order. The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`.
+    Vh : { (..., N, N), (..., K, N) } array
+        Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
+        size as those of the input `a`. The size of the last two dimensions
+        depends on the value of `full_matrices`. Only returned when
+        `compute_uv` is True.
+
+    Raises
+    ------
+    LinAlgError
+        If SVD computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.svd : Similar function in SciPy.
+    scipy.linalg.svdvals : Compute singular values of a matrix.
+
+    Notes
+    -----
+    The decomposition is performed using LAPACK routine ``_gesdd``.
+
+    SVD is usually described for the factorization of a 2D matrix :math:`A`.
+    The higher-dimensional case will be discussed below. In the 2D case, SVD is
+    written as :math:`A = U S V^H`, where :math:`A = a`, :math:`U= u`,
+    :math:`S= \\mathtt{np.diag}(s)` and :math:`V^H = vh`. The 1D array `s`
+    contains the singular values of `a` and `u` and `vh` are unitary. The rows
+    of `vh` are the eigenvectors of :math:`A^H A` and the columns of `u` are
+    the eigenvectors of :math:`A A^H`. In both cases the corresponding
+    (possibly non-zero) eigenvalues are given by ``s**2``.
+
+    If `a` has more than two dimensions, then broadcasting rules apply, as
+    explained in :ref:`routines.linalg-broadcasting`. This means that SVD is
+    working in "stacked" mode: it iterates over all indices of the first
+    ``a.ndim - 2`` dimensions and for each combination SVD is applied to the
+    last two indices. The matrix `a` can be reconstructed from the
+    decomposition with either ``(u * s[..., None, :]) @ vh`` or
+    ``u @ (s[..., None] * vh)``. (The ``@`` operator can be replaced by the
+    function ``np.matmul`` for python versions below 3.5.)
+
+    If `a` is a ``matrix`` object (as opposed to an ``ndarray``), then so are
+    all the return values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> a = rng.normal(size=(9, 6)) + 1j*rng.normal(size=(9, 6))
+    >>> b = rng.normal(size=(2, 7, 8, 3)) + 1j*rng.normal(size=(2, 7, 8, 3))
+
+
+    Reconstruction based on full SVD, 2D case:
+
+    >>> U, S, Vh = np.linalg.svd(a, full_matrices=True)
+    >>> U.shape, S.shape, Vh.shape
+    ((9, 9), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(U[:, :6] * S, Vh))
+    True
+    >>> smat = np.zeros((9, 6), dtype=complex)
+    >>> smat[:6, :6] = np.diag(S)
+    >>> np.allclose(a, np.dot(U, np.dot(smat, Vh)))
+    True
+
+    Reconstruction based on reduced SVD, 2D case:
+
+    >>> U, S, Vh = np.linalg.svd(a, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
+    ((9, 6), (6,), (6, 6))
+    >>> np.allclose(a, np.dot(U * S, Vh))
+    True
+    >>> smat = np.diag(S)
+    >>> np.allclose(a, np.dot(U, np.dot(smat, Vh)))
+    True
+
+    Reconstruction based on full SVD, 4D case:
+
+    >>> U, S, Vh = np.linalg.svd(b, full_matrices=True)
+    >>> U.shape, S.shape, Vh.shape
+    ((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(U[..., :3] * S[..., None, :], Vh))
+    True
+    >>> np.allclose(b, np.matmul(U[..., :3], S[..., None] * Vh))
+    True
+
+    Reconstruction based on reduced SVD, 4D case:
+
+    >>> U, S, Vh = np.linalg.svd(b, full_matrices=False)
+    >>> U.shape, S.shape, Vh.shape
+    ((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3))
+    >>> np.allclose(b, np.matmul(U * S[..., None, :], Vh))
+    True
+    >>> np.allclose(b, np.matmul(U, S[..., None] * Vh))
+    True
+
+    """
+    import numpy as _nx
+    a, wrap = _makearray(a)
+
+    if hermitian:
+        # note: lapack svd returns eigenvalues with s ** 2 sorted descending,
+        # but eig returns s sorted ascending, so we re-order the eigenvalues
+        # and related arrays to have the correct order
+        if compute_uv:
+            s, u = eigh(a)
+            sgn = sign(s)
+            s = abs(s)
+            sidx = argsort(s)[..., ::-1]
+            sgn = _nx.take_along_axis(sgn, sidx, axis=-1)
+            s = _nx.take_along_axis(s, sidx, axis=-1)
+            u = _nx.take_along_axis(u, sidx[..., None, :], axis=-1)
+            # singular values are unsigned, move the sign into v
+            vt = transpose(u * sgn[..., None, :]).conjugate()
+            return SVDResult(wrap(u), s, wrap(vt))
+        else:
+            s = eigvalsh(a)
+            s = abs(s)
+            return sort(s)[..., ::-1]
+
+    _assert_stacked_2d(a)
+    t, result_t = _commonType(a)
+
+    m, n = a.shape[-2:]
+    if compute_uv:
+        if full_matrices:
+            gufunc = _umath_linalg.svd_f
+        else:
+            gufunc = _umath_linalg.svd_s
+
+        signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
+        with errstate(call=_raise_linalgerror_svd_nonconvergence,
+                      invalid='call', over='ignore', divide='ignore',
+                      under='ignore'):
+            u, s, vh = gufunc(a, signature=signature)
+        u = u.astype(result_t, copy=False)
+        s = s.astype(_realType(result_t), copy=False)
+        vh = vh.astype(result_t, copy=False)
+        return SVDResult(wrap(u), s, wrap(vh))
+    else:
+        signature = 'D->d' if isComplexType(t) else 'd->d'
+        with errstate(call=_raise_linalgerror_svd_nonconvergence,
+                      invalid='call', over='ignore', divide='ignore',
+                      under='ignore'):
+            s = _umath_linalg.svd(a, signature=signature)
+        s = s.astype(_realType(result_t), copy=False)
+        return s
+
+
+def _svdvals_dispatcher(x):
+    return (x,)
+
+
+@array_function_dispatch(_svdvals_dispatcher)
+def svdvals(x, /):
+    """
+    Returns the singular values of a matrix (or a stack of matrices) ``x``.
+    When x is a stack of matrices, the function will compute the singular
+    values for each matrix in the stack.
+
+    This function is Array API compatible.
+
+    Calling ``np.svdvals(x)`` to get singular values is the same as
+    ``np.svd(x, compute_uv=False, hermitian=False)``.
+
+    Parameters
+    ----------
+    x : (..., M, N) array_like
+        Input array having shape (..., M, N) and whose last two
+        dimensions form matrices on which to perform singular value
+        decomposition. Should have a floating-point data type.
+
+    Returns
+    -------
+    out : ndarray
+        An array with shape (..., K) that contains the vector(s)
+        of singular values of length K, where K = min(M, N).
+
+    See Also
+    --------
+    scipy.linalg.svdvals : Compute singular values of a matrix.
+
+    Examples
+    --------
+
+    >>> np.linalg.svdvals([[1, 2, 3, 4, 5],
+    ...                    [1, 4, 9, 16, 25],
+    ...                    [1, 8, 27, 64, 125]])
+    array([146.68862757,   5.57510612,   0.60393245])
+
+    Determine the rank of a matrix using singular values:
+
+    >>> s = np.linalg.svdvals([[1, 2, 3],
+    ...                        [2, 4, 6],
+    ...                        [-1, 1, -1]]); s
+    array([8.38434191e+00, 1.64402274e+00, 2.31534378e-16])
+    >>> np.count_nonzero(s > 1e-10)  # Matrix of rank 2
+    2
+
+    """
+    return svd(x, compute_uv=False, hermitian=False)
+
+
+def _cond_dispatcher(x, p=None):
+    return (x,)
+
+
+@array_function_dispatch(_cond_dispatcher)
+def cond(x, p=None):
+    """
+    Compute the condition number of a matrix.
+
+    This function is capable of returning the condition number using
+    one of seven different norms, depending on the value of `p` (see
+    Parameters below).
+
+    Parameters
+    ----------
+    x : (..., M, N) array_like
+        The matrix whose condition number is sought.
+    p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional
+        Order of the norm used in the condition number computation:
+
+        =====  ============================
+        p      norm for matrices
+        =====  ============================
+        None   2-norm, computed directly using the ``SVD``
+        'fro'  Frobenius norm
+        inf    max(sum(abs(x), axis=1))
+        -inf   min(sum(abs(x), axis=1))
+        1      max(sum(abs(x), axis=0))
+        -1     min(sum(abs(x), axis=0))
+        2      2-norm (largest sing. value)
+        -2     smallest singular value
+        =====  ============================
+
+        inf means the `numpy.inf` object, and the Frobenius norm is
+        the root-of-sum-of-squares norm.
+
+    Returns
+    -------
+    c : {float, inf}
+        The condition number of the matrix. May be infinite.
+
+    See Also
+    --------
+    numpy.linalg.norm
+
+    Notes
+    -----
+    The condition number of `x` is defined as the norm of `x` times the
+    norm of the inverse of `x` [1]_; the norm can be the usual L2-norm
+    (root-of-sum-of-squares) or one of a number of other matrix norms.
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, Orlando, FL,
+           Academic Press, Inc., 1980, pg. 285.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+    >>> a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]])
+    >>> a
+    array([[ 1,  0, -1],
+           [ 0,  1,  0],
+           [ 1,  0,  1]])
+    >>> LA.cond(a)
+    1.4142135623730951
+    >>> LA.cond(a, 'fro')
+    3.1622776601683795
+    >>> LA.cond(a, np.inf)
+    2.0
+    >>> LA.cond(a, -np.inf)
+    1.0
+    >>> LA.cond(a, 1)
+    2.0
+    >>> LA.cond(a, -1)
+    1.0
+    >>> LA.cond(a, 2)
+    1.4142135623730951
+    >>> LA.cond(a, -2)
+    0.70710678118654746 # may vary
+    >>> (min(LA.svd(a, compute_uv=False)) *
+    ... min(LA.svd(LA.inv(a), compute_uv=False)))
+    0.70710678118654746 # may vary
+
+    """
+    x = asarray(x)  # in case we have a matrix
+    if _is_empty_2d(x):
+        raise LinAlgError("cond is not defined on empty arrays")
+    if p is None or p == 2 or p == -2:
+        s = svd(x, compute_uv=False)
+        with errstate(all='ignore'):
+            if p == -2:
+                r = s[..., -1] / s[..., 0]
+            else:
+                r = s[..., 0] / s[..., -1]
+    else:
+        # Call inv(x) ignoring errors. The result array will
+        # contain nans in the entries where inversion failed.
+        _assert_stacked_2d(x)
+        _assert_stacked_square(x)
+        t, result_t = _commonType(x)
+        signature = 'D->D' if isComplexType(t) else 'd->d'
+        with errstate(all='ignore'):
+            invx = _umath_linalg.inv(x, signature=signature)
+            r = norm(x, p, axis=(-2, -1)) * norm(invx, p, axis=(-2, -1))
+        r = r.astype(result_t, copy=False)
+
+    # Convert nans to infs unless the original array had nan entries
+    r = asarray(r)
+    nan_mask = isnan(r)
+    if nan_mask.any():
+        nan_mask &= ~isnan(x).any(axis=(-2, -1))
+        if r.ndim > 0:
+            r[nan_mask] = inf
+        elif nan_mask:
+            r[()] = inf
+
+    # Convention is to return scalars instead of 0d arrays
+    if r.ndim == 0:
+        r = r[()]
+
+    return r
+
+
+def _matrix_rank_dispatcher(A, tol=None, hermitian=None, *, rtol=None):
+    return (A,)
+
+
+@array_function_dispatch(_matrix_rank_dispatcher)
+def matrix_rank(A, tol=None, hermitian=False, *, rtol=None):
+    """
+    Return matrix rank of array using SVD method
+
+    Rank of the array is the number of singular values of the array that are
+    greater than `tol`.
+
+    Parameters
+    ----------
+    A : {(M,), (..., M, N)} array_like
+        Input vector or stack of matrices.
+    tol : (...) array_like, float, optional
+        Threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M, N) * eps``.
+    hermitian : bool, optional
+        If True, `A` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+    rtol : (...) array_like, float, optional
+        Parameter for the relative tolerance component. Only ``tol`` or
+        ``rtol`` can be set at a time. Defaults to ``max(M, N) * eps``.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    rank : (...) array_like
+        Rank of A.
+
+    Notes
+    -----
+    The default threshold to detect rank deficiency is a test on the magnitude
+    of the singular values of `A`.  By default, we identify singular values
+    less than ``S.max() * max(M, N) * eps`` as indicating rank deficiency
+    (with the symbols defined above). This is the algorithm MATLAB uses [1].
+    It also appears in *Numerical recipes* in the discussion of SVD solutions
+    for linear least squares [2].
+
+    This default threshold is designed to detect rank deficiency accounting
+    for the numerical errors of the SVD computation. Imagine that there
+    is a column in `A` that is an exact (in floating point) linear combination
+    of other columns in `A`. Computing the SVD on `A` will not produce
+    a singular value exactly equal to 0 in general: any difference of
+    the smallest SVD value from 0 will be caused by numerical imprecision
+    in the calculation of the SVD. Our threshold for small SVD values takes
+    this numerical imprecision into account, and the default threshold will
+    detect such numerical rank deficiency. The threshold may declare a matrix
+    `A` rank deficient even if the linear combination of some columns of `A`
+    is not exactly equal to another column of `A` but only numerically very
+    close to another column of `A`.
+
+    We chose our default threshold because it is in wide use. Other thresholds
+    are possible.  For example, elsewhere in the 2007 edition of *Numerical
+    recipes* there is an alternative threshold of ``S.max() *
+    np.finfo(A.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+    this threshold as being based on "expected roundoff error" (p 71).
+
+    The thresholds above deal with floating point roundoff error in the
+    calculation of the SVD.  However, you may have more information about
+    the sources of error in `A` that would make you consider other tolerance
+    values to detect *effective* rank deficiency. The most useful measure
+    of the tolerance depends on the operations you intend to use on your
+    matrix. For example, if your data come from uncertain measurements with
+    uncertainties greater than floating point epsilon, choosing a tolerance
+    near that uncertainty may be preferable. The tolerance may be absolute
+    if the uncertainties are absolute rather than relative.
+
+    References
+    ----------
+    .. [1] MATLAB reference documentation, "Rank"
+           https://www.mathworks.com/help/techdoc/ref/rank.html
+    .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+           "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+           page 795.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from numpy.linalg import matrix_rank
+    >>> matrix_rank(np.eye(4)) # Full rank matrix
+    4
+    >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+    >>> matrix_rank(I)
+    3
+    >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+    1
+    >>> matrix_rank(np.zeros((4,)))
+    0
+    """
+    if rtol is not None and tol is not None:
+        raise ValueError("`tol` and `rtol` can't be both set.")
+
+    A = asarray(A)
+    if A.ndim < 2:
+        return int(not all(A == 0))
+    S = svd(A, compute_uv=False, hermitian=hermitian)
+
+    if tol is None:
+        if rtol is None:
+            rtol = max(A.shape[-2:]) * finfo(S.dtype).eps
+        else:
+            rtol = asarray(rtol)[..., newaxis]
+        tol = S.max(axis=-1, keepdims=True) * rtol
+    else:
+        tol = asarray(tol)[..., newaxis]
+
+    return count_nonzero(S > tol, axis=-1)
+
+
+# Generalized inverse
+
+def _pinv_dispatcher(a, rcond=None, hermitian=None, *, rtol=None):
+    return (a,)
+
+
+@array_function_dispatch(_pinv_dispatcher)
+def pinv(a, rcond=None, hermitian=False, *, rtol=_NoValue):
+    """
+    Compute the (Moore-Penrose) pseudo-inverse of a matrix.
+
+    Calculate the generalized inverse of a matrix using its
+    singular-value decomposition (SVD) and including all
+    *large* singular values.
+
+    Parameters
+    ----------
+    a : (..., M, N) array_like
+        Matrix or stack of matrices to be pseudo-inverted.
+    rcond : (...) array_like of float, optional
+        Cutoff for small singular values.
+        Singular values less than or equal to
+        ``rcond * largest_singular_value`` are set to zero.
+        Broadcasts against the stack of matrices. Default: ``1e-15``.
+    hermitian : bool, optional
+        If True, `a` is assumed to be Hermitian (symmetric if real-valued),
+        enabling a more efficient method for finding singular values.
+        Defaults to False.
+    rtol : (...) array_like of float, optional
+        Same as `rcond`, but it's an Array API compatible parameter name.
+        Only `rcond` or `rtol` can be set at a time. If none of them are
+        provided then NumPy's ``1e-15`` default is used. If ``rtol=None``
+        is passed then the API standard default is used.
+
+        .. versionadded:: 2.0.0
+
+    Returns
+    -------
+    B : (..., N, M) ndarray
+        The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
+        is `B`.
+
+    Raises
+    ------
+    LinAlgError
+        If the SVD computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.pinv : Similar function in SciPy.
+    scipy.linalg.pinvh : Compute the (Moore-Penrose) pseudo-inverse of a
+                         Hermitian matrix.
+
+    Notes
+    -----
+    The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
+    defined as: "the matrix that 'solves' [the least-squares problem]
+    :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
+    :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.
+
+    It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular
+    value decomposition of A, then
+    :math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
+    orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting
+    of A's so-called singular values, (followed, typically, by
+    zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix
+    consisting of the reciprocals of A's singular values
+    (again, followed by zeros). [1]_
+
+    References
+    ----------
+    .. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
+           FL, Academic Press, Inc., 1980, pp. 139-142.
+
+    Examples
+    --------
+    The following example checks that ``a * a+ * a == a`` and
+    ``a+ * a * a+ == a+``:
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> a = rng.normal(size=(9, 6))
+    >>> B = np.linalg.pinv(a)
+    >>> np.allclose(a, np.dot(a, np.dot(B, a)))
+    True
+    >>> np.allclose(B, np.dot(B, np.dot(a, B)))
+    True
+
+    """
+    a, wrap = _makearray(a)
+    if rcond is None:
+        if rtol is _NoValue:
+            rcond = 1e-15
+        elif rtol is None:
+            rcond = max(a.shape[-2:]) * finfo(a.dtype).eps
+        else:
+            rcond = rtol
+    elif rtol is not _NoValue:
+        raise ValueError("`rtol` and `rcond` can't be both set.")
+    else:
+        # NOTE: Deprecate `rcond` in a few versions.
+        pass
+
+    rcond = asarray(rcond)
+    if _is_empty_2d(a):
+        m, n = a.shape[-2:]
+        res = empty(a.shape[:-2] + (n, m), dtype=a.dtype)
+        return wrap(res)
+    a = a.conjugate()
+    u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
+
+    # discard small singular values
+    cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)
+    large = s > cutoff
+    s = divide(1, s, where=large, out=s)
+    s[~large] = 0
+
+    res = matmul(transpose(vt), multiply(s[..., newaxis], transpose(u)))
+    return wrap(res)
+
+
+# Determinant
+
+
+@array_function_dispatch(_unary_dispatcher)
+def slogdet(a):
+    """
+    Compute the sign and (natural) logarithm of the determinant of an array.
+
+    If an array has a very small or very large determinant, then a call to
+    `det` may overflow or underflow. This routine is more robust against such
+    issues, because it computes the logarithm of the determinant rather than
+    the determinant itself.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Input array, has to be a square 2-D array.
+
+    Returns
+    -------
+    A namedtuple with the following attributes:
+
+    sign : (...) array_like
+        A number representing the sign of the determinant. For a real matrix,
+        this is 1, 0, or -1. For a complex matrix, this is a complex number
+        with absolute value 1 (i.e., it is on the unit circle), or else 0.
+    logabsdet : (...) array_like
+        The natural log of the absolute value of the determinant.
+
+    If the determinant is zero, then `sign` will be 0 and `logabsdet`
+    will be -inf. In all cases, the determinant is equal to
+    ``sign * np.exp(logabsdet)``.
+
+    See Also
+    --------
+    det
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine ``z/dgetrf``.
+
+    Examples
+    --------
+    The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
+
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> (sign, logabsdet) = np.linalg.slogdet(a)
+    >>> (sign, logabsdet)
+    (-1, 0.69314718055994529) # may vary
+    >>> sign * np.exp(logabsdet)
+    -2.0
+
+    Computing log-determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> sign, logabsdet = np.linalg.slogdet(a)
+    >>> (sign, logabsdet)
+    (array([-1., -1., -1.]), array([ 0.69314718,  1.09861229,  2.07944154]))
+    >>> sign * np.exp(logabsdet)
+    array([-2., -3., -8.])
+
+    This routine succeeds where ordinary `det` does not:
+
+    >>> np.linalg.det(np.eye(500) * 0.1)
+    0.0
+    >>> np.linalg.slogdet(np.eye(500) * 0.1)
+    (1, -1151.2925464970228)
+
+    """
+    a = asarray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+    real_t = _realType(result_t)
+    signature = 'D->Dd' if isComplexType(t) else 'd->dd'
+    sign, logdet = _umath_linalg.slogdet(a, signature=signature)
+    sign = sign.astype(result_t, copy=False)
+    logdet = logdet.astype(real_t, copy=False)
+    return SlogdetResult(sign, logdet)
+
+
+@array_function_dispatch(_unary_dispatcher)
+def det(a):
+    """
+    Compute the determinant of an array.
+
+    Parameters
+    ----------
+    a : (..., M, M) array_like
+        Input array to compute determinants for.
+
+    Returns
+    -------
+    det : (...) array_like
+        Determinant of `a`.
+
+    See Also
+    --------
+    slogdet : Another way to represent the determinant, more suitable
+      for large matrices where underflow/overflow may occur.
+    scipy.linalg.det : Similar function in SciPy.
+
+    Notes
+    -----
+    Broadcasting rules apply, see the `numpy.linalg` documentation for
+    details.
+
+    The determinant is computed via LU factorization using the LAPACK
+    routine ``z/dgetrf``.
+
+    Examples
+    --------
+    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
+
+    >>> import numpy as np
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.det(a)
+    -2.0 # may vary
+
+    Computing determinants for a stack of matrices:
+
+    >>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
+    >>> a.shape
+    (3, 2, 2)
+    >>> np.linalg.det(a)
+    array([-2., -3., -8.])
+
+    """
+    a = asarray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    t, result_t = _commonType(a)
+    signature = 'D->D' if isComplexType(t) else 'd->d'
+    r = _umath_linalg.det(a, signature=signature)
+    r = r.astype(result_t, copy=False)
+    return r
+
+
+# Linear Least Squares
+
+def _lstsq_dispatcher(a, b, rcond=None):
+    return (a, b)
+
+
+@array_function_dispatch(_lstsq_dispatcher)
+def lstsq(a, b, rcond=None):
+    r"""
+    Return the least-squares solution to a linear matrix equation.
+
+    Computes the vector `x` that approximately solves the equation
+    ``a @ x = b``. The equation may be under-, well-, or over-determined
+    (i.e., the number of linearly independent rows of `a` can be less than,
+    equal to, or greater than its number of linearly independent columns).
+    If `a` is square and of full rank, then `x` (but for round-off error)
+    is the "exact" solution of the equation. Else, `x` minimizes the
+    Euclidean 2-norm :math:`||b - ax||`. If there are multiple minimizing
+    solutions, the one with the smallest 2-norm :math:`||x||` is returned.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        "Coefficient" matrix.
+    b : {(M,), (M, K)} array_like
+        Ordinate or "dependent variable" values. If `b` is two-dimensional,
+        the least-squares solution is calculated for each of the `K` columns
+        of `b`.
+    rcond : float, optional
+        Cut-off ratio for small singular values of `a`.
+        For the purposes of rank determination, singular values are treated
+        as zero if they are smaller than `rcond` times the largest singular
+        value of `a`.
+        The default uses the machine precision times ``max(M, N)``.  Passing
+        ``-1`` will use machine precision.
+
+        .. versionchanged:: 2.0
+            Previously, the default was ``-1``, but a warning was given that
+            this would change.
+
+    Returns
+    -------
+    x : {(N,), (N, K)} ndarray
+        Least-squares solution. If `b` is two-dimensional,
+        the solutions are in the `K` columns of `x`.
+    residuals : {(1,), (K,), (0,)} ndarray
+        Sums of squared residuals: Squared Euclidean 2-norm for each column in
+        ``b - a @ x``.
+        If the rank of `a` is < N or M <= N, this is an empty array.
+        If `b` is 1-dimensional, this is a (1,) shape array.
+        Otherwise the shape is (K,).
+    rank : int
+        Rank of matrix `a`.
+    s : (min(M, N),) ndarray
+        Singular values of `a`.
+
+    Raises
+    ------
+    LinAlgError
+        If computation does not converge.
+
+    See Also
+    --------
+    scipy.linalg.lstsq : Similar function in SciPy.
+
+    Notes
+    -----
+    If `b` is a matrix, then all array results are returned as matrices.
+
+    Examples
+    --------
+    Fit a line, ``y = mx + c``, through some noisy data-points:
+
+    >>> import numpy as np
+    >>> x = np.array([0, 1, 2, 3])
+    >>> y = np.array([-1, 0.2, 0.9, 2.1])
+
+    By examining the coefficients, we see that the line should have a
+    gradient of roughly 1 and cut the y-axis at, more or less, -1.
+
+    We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
+    and ``p = [[m], [c]]``.  Now use `lstsq` to solve for `p`:
+
+    >>> A = np.vstack([x, np.ones(len(x))]).T
+    >>> A
+    array([[ 0.,  1.],
+           [ 1.,  1.],
+           [ 2.,  1.],
+           [ 3.,  1.]])
+
+    >>> m, c = np.linalg.lstsq(A, y)[0]
+    >>> m, c
+    (1.0 -0.95) # may vary
+
+    Plot the data along with the fitted line:
+
+    >>> import matplotlib.pyplot as plt
+    >>> _ = plt.plot(x, y, 'o', label='Original data', markersize=10)
+    >>> _ = plt.plot(x, m*x + c, 'r', label='Fitted line')
+    >>> _ = plt.legend()
+    >>> plt.show()
+
+    """
+    a, _ = _makearray(a)
+    b, wrap = _makearray(b)
+    is_1d = b.ndim == 1
+    if is_1d:
+        b = b[:, newaxis]
+    _assert_2d(a, b)
+    m, n = a.shape[-2:]
+    m2, n_rhs = b.shape[-2:]
+    if m != m2:
+        raise LinAlgError('Incompatible dimensions')
+
+    t, result_t = _commonType(a, b)
+    result_real_t = _realType(result_t)
+
+    if rcond is None:
+        rcond = finfo(t).eps * max(n, m)
+
+    signature = 'DDd->Ddid' if isComplexType(t) else 'ddd->ddid'
+    if n_rhs == 0:
+        # lapack can't handle n_rhs = 0 - so allocate
+        # the array one larger in that axis
+        b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
+
+    with errstate(call=_raise_linalgerror_lstsq, invalid='call',
+                  over='ignore', divide='ignore', under='ignore'):
+        x, resids, rank, s = _umath_linalg.lstsq(a, b, rcond,
+                                                 signature=signature)
+    if m == 0:
+        x[...] = 0
+    if n_rhs == 0:
+        # remove the item we added
+        x = x[..., :n_rhs]
+        resids = resids[..., :n_rhs]
+
+    # remove the axis we added
+    if is_1d:
+        x = x.squeeze(axis=-1)
+        # we probably should squeeze resids too, but we can't
+        # without breaking compatibility.
+
+    # as documented
+    if rank != n or m <= n:
+        resids = array([], result_real_t)
+
+    # coerce output arrays
+    s = s.astype(result_real_t, copy=False)
+    resids = resids.astype(result_real_t, copy=False)
+    # Copying lets the memory in r_parts be freed
+    x = x.astype(result_t, copy=True)
+    return wrap(x), wrap(resids), rank, s
+
+
+def _multi_svd_norm(x, row_axis, col_axis, op):
+    """Compute a function of the singular values of the 2-D matrices in `x`.
+
+    This is a private utility function used by `numpy.linalg.norm()`.
+
+    Parameters
+    ----------
+    x : ndarray
+    row_axis, col_axis : int
+        The axes of `x` that hold the 2-D matrices.
+    op : callable
+        This should be either numpy.amin or `numpy.amax` or `numpy.sum`.
+
+    Returns
+    -------
+    result : float or ndarray
+        If `x` is 2-D, the return values is a float.
+        Otherwise, it is an array with ``x.ndim - 2`` dimensions.
+        The return values are either the minimum or maximum or sum of the
+        singular values of the matrices, depending on whether `op`
+        is `numpy.amin` or `numpy.amax` or `numpy.sum`.
+
+    """
+    y = moveaxis(x, (row_axis, col_axis), (-2, -1))
+    result = op(svd(y, compute_uv=False), axis=-1)
+    return result
+
+
+def _norm_dispatcher(x, ord=None, axis=None, keepdims=None):
+    return (x,)
+
+
+@array_function_dispatch(_norm_dispatcher)
+def norm(x, ord=None, axis=None, keepdims=False):
+    """
+    Matrix or vector norm.
+
+    This function is able to return one of eight different matrix norms,
+    or one of an infinite number of vector norms (described below), depending
+    on the value of the ``ord`` parameter.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.  If `axis` is None, `x` must be 1-D or 2-D, unless `ord`
+        is None. If both `axis` and `ord` are None, the 2-norm of
+        ``x.ravel`` will be returned.
+    ord : {int, float, inf, -inf, 'fro', 'nuc'}, optional
+        Order of the norm (see table under ``Notes`` for what values are
+        supported for matrices and vectors respectively). inf means numpy's
+        `inf` object. The default is None.
+    axis : {None, int, 2-tuple of ints}, optional.
+        If `axis` is an integer, it specifies the axis of `x` along which to
+        compute the vector norms.  If `axis` is a 2-tuple, it specifies the
+        axes that hold 2-D matrices, and the matrix norms of these matrices
+        are computed.  If `axis` is None then either a vector norm (when `x`
+        is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default
+        is None.
+
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in the
+        result as dimensions with size one.  With this option the result will
+        broadcast correctly against the original `x`.
+
+    Returns
+    -------
+    n : float or ndarray
+        Norm of the matrix or vector(s).
+
+    See Also
+    --------
+    scipy.linalg.norm : Similar function in SciPy.
+
+    Notes
+    -----
+    For values of ``ord < 1``, the result is, strictly speaking, not a
+    mathematical 'norm', but it may still be useful for various numerical
+    purposes.
+
+    The following norms can be calculated:
+
+    =====  ============================  ==========================
+    ord    norm for matrices             norm for vectors
+    =====  ============================  ==========================
+    None   Frobenius norm                2-norm
+    'fro'  Frobenius norm                --
+    'nuc'  nuclear norm                  --
+    inf    max(sum(abs(x), axis=1))      max(abs(x))
+    -inf   min(sum(abs(x), axis=1))      min(abs(x))
+    0      --                            sum(x != 0)
+    1      max(sum(abs(x), axis=0))      as below
+    -1     min(sum(abs(x), axis=0))      as below
+    2      2-norm (largest sing. value)  as below
+    -2     smallest singular value       as below
+    other  --                            sum(abs(x)**ord)**(1./ord)
+    =====  ============================  ==========================
+
+    The Frobenius norm is given by [1]_:
+
+    :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
+
+    The nuclear norm is the sum of the singular values.
+
+    Both the Frobenius and nuclear norm orders are only defined for
+    matrices and raise a ValueError when ``x.ndim != 2``.
+
+    References
+    ----------
+    .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
+           Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4, -3, -2, ...,  2,  3,  4])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4, -3, -2],
+           [-1,  0,  1],
+           [ 2,  3,  4]])
+
+    >>> LA.norm(a)
+    7.745966692414834
+    >>> LA.norm(b)
+    7.745966692414834
+    >>> LA.norm(b, 'fro')
+    7.745966692414834
+    >>> LA.norm(a, np.inf)
+    4.0
+    >>> LA.norm(b, np.inf)
+    9.0
+    >>> LA.norm(a, -np.inf)
+    0.0
+    >>> LA.norm(b, -np.inf)
+    2.0
+
+    >>> LA.norm(a, 1)
+    20.0
+    >>> LA.norm(b, 1)
+    7.0
+    >>> LA.norm(a, -1)
+    -4.6566128774142013e-010
+    >>> LA.norm(b, -1)
+    6.0
+    >>> LA.norm(a, 2)
+    7.745966692414834
+    >>> LA.norm(b, 2)
+    7.3484692283495345
+
+    >>> LA.norm(a, -2)
+    0.0
+    >>> LA.norm(b, -2)
+    1.8570331885190563e-016 # may vary
+    >>> LA.norm(a, 3)
+    5.8480354764257312 # may vary
+    >>> LA.norm(a, -3)
+    0.0
+
+    Using the `axis` argument to compute vector norms:
+
+    >>> c = np.array([[ 1, 2, 3],
+    ...               [-1, 1, 4]])
+    >>> LA.norm(c, axis=0)
+    array([ 1.41421356,  2.23606798,  5.        ])
+    >>> LA.norm(c, axis=1)
+    array([ 3.74165739,  4.24264069])
+    >>> LA.norm(c, ord=1, axis=1)
+    array([ 6.,  6.])
+
+    Using the `axis` argument to compute matrix norms:
+
+    >>> m = np.arange(8).reshape(2,2,2)
+    >>> LA.norm(m, axis=(1,2))
+    array([  3.74165739,  11.22497216])
+    >>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
+    (3.7416573867739413, 11.224972160321824)
+
+    """
+    x = asarray(x)
+
+    if not issubclass(x.dtype.type, (inexact, object_)):
+        x = x.astype(float)
+
+    # Immediately handle some default, simple, fast, and common cases.
+    if axis is None:
+        ndim = x.ndim
+        if (
+            (ord is None) or
+            (ord in ('f', 'fro') and ndim == 2) or
+            (ord == 2 and ndim == 1)
+        ):
+            x = x.ravel(order='K')
+            if isComplexType(x.dtype.type):
+                x_real = x.real
+                x_imag = x.imag
+                sqnorm = x_real.dot(x_real) + x_imag.dot(x_imag)
+            else:
+                sqnorm = x.dot(x)
+            ret = sqrt(sqnorm)
+            if keepdims:
+                ret = ret.reshape(ndim*[1])
+            return ret
+
+    # Normalize the `axis` argument to a tuple.
+    nd = x.ndim
+    if axis is None:
+        axis = tuple(range(nd))
+    elif not isinstance(axis, tuple):
+        try:
+            axis = int(axis)
+        except Exception as e:
+            raise TypeError(
+                "'axis' must be None, an integer or a tuple of integers"
+            ) from e
+        axis = (axis,)
+
+    if len(axis) == 1:
+        if ord == inf:
+            return abs(x).max(axis=axis, keepdims=keepdims)
+        elif ord == -inf:
+            return abs(x).min(axis=axis, keepdims=keepdims)
+        elif ord == 0:
+            # Zero norm
+            return (
+                (x != 0)
+                .astype(x.real.dtype)
+                .sum(axis=axis, keepdims=keepdims)
+            )
+        elif ord == 1:
+            # special case for speedup
+            return add.reduce(abs(x), axis=axis, keepdims=keepdims)
+        elif ord is None or ord == 2:
+            # special case for speedup
+            s = (x.conj() * x).real
+            return sqrt(add.reduce(s, axis=axis, keepdims=keepdims))
+        # None of the str-type keywords for ord ('fro', 'nuc')
+        # are valid for vectors
+        elif isinstance(ord, str):
+            raise ValueError(f"Invalid norm order '{ord}' for vectors")
+        else:
+            absx = abs(x)
+            absx **= ord
+            ret = add.reduce(absx, axis=axis, keepdims=keepdims)
+            ret **= reciprocal(ord, dtype=ret.dtype)
+            return ret
+    elif len(axis) == 2:
+        row_axis, col_axis = axis
+        row_axis = normalize_axis_index(row_axis, nd)
+        col_axis = normalize_axis_index(col_axis, nd)
+        if row_axis == col_axis:
+            raise ValueError('Duplicate axes given.')
+        if ord == 2:
+            ret = _multi_svd_norm(x, row_axis, col_axis, amax)
+        elif ord == -2:
+            ret = _multi_svd_norm(x, row_axis, col_axis, amin)
+        elif ord == 1:
+            if col_axis > row_axis:
+                col_axis -= 1
+            ret = add.reduce(abs(x), axis=row_axis).max(axis=col_axis)
+        elif ord == inf:
+            if row_axis > col_axis:
+                row_axis -= 1
+            ret = add.reduce(abs(x), axis=col_axis).max(axis=row_axis)
+        elif ord == -1:
+            if col_axis > row_axis:
+                col_axis -= 1
+            ret = add.reduce(abs(x), axis=row_axis).min(axis=col_axis)
+        elif ord == -inf:
+            if row_axis > col_axis:
+                row_axis -= 1
+            ret = add.reduce(abs(x), axis=col_axis).min(axis=row_axis)
+        elif ord in [None, 'fro', 'f']:
+            ret = sqrt(add.reduce((x.conj() * x).real, axis=axis))
+        elif ord == 'nuc':
+            ret = _multi_svd_norm(x, row_axis, col_axis, sum)
+        else:
+            raise ValueError("Invalid norm order for matrices.")
+        if keepdims:
+            ret_shape = list(x.shape)
+            ret_shape[axis[0]] = 1
+            ret_shape[axis[1]] = 1
+            ret = ret.reshape(ret_shape)
+        return ret
+    else:
+        raise ValueError("Improper number of dimensions to norm.")
+
+
+# multi_dot
+
+def _multidot_dispatcher(arrays, *, out=None):
+    yield from arrays
+    yield out
+
+
+@array_function_dispatch(_multidot_dispatcher)
+def multi_dot(arrays, *, out=None):
+    """
+    Compute the dot product of two or more arrays in a single function call,
+    while automatically selecting the fastest evaluation order.
+
+    `multi_dot` chains `numpy.dot` and uses optimal parenthesization
+    of the matrices [1]_ [2]_. Depending on the shapes of the matrices,
+    this can speed up the multiplication a lot.
+
+    If the first argument is 1-D it is treated as a row vector.
+    If the last argument is 1-D it is treated as a column vector.
+    The other arguments must be 2-D.
+
+    Think of `multi_dot` as::
+
+        def multi_dot(arrays): return functools.reduce(np.dot, arrays)
+
+
+    Parameters
+    ----------
+    arrays : sequence of array_like
+        If the first argument is 1-D it is treated as row vector.
+        If the last argument is 1-D it is treated as column vector.
+        The other arguments must be 2-D.
+    out : ndarray, optional
+        Output argument. This must have the exact kind that would be returned
+        if it was not used. In particular, it must have the right type, must be
+        C-contiguous, and its dtype must be the dtype that would be returned
+        for `dot(a, b)`. This is a performance feature. Therefore, if these
+        conditions are not met, an exception is raised, instead of attempting
+        to be flexible.
+
+    Returns
+    -------
+    output : ndarray
+        Returns the dot product of the supplied arrays.
+
+    See Also
+    --------
+    numpy.dot : dot multiplication with two arguments.
+
+    References
+    ----------
+
+    .. [1] Cormen, "Introduction to Algorithms", Chapter 15.2, p. 370-378
+    .. [2] https://en.wikipedia.org/wiki/Matrix_chain_multiplication
+
+    Examples
+    --------
+    `multi_dot` allows you to write::
+
+    >>> import numpy as np
+    >>> from numpy.linalg import multi_dot
+    >>> # Prepare some data
+    >>> A = np.random.random((10000, 100))
+    >>> B = np.random.random((100, 1000))
+    >>> C = np.random.random((1000, 5))
+    >>> D = np.random.random((5, 333))
+    >>> # the actual dot multiplication
+    >>> _ = multi_dot([A, B, C, D])
+
+    instead of::
+
+    >>> _ = np.dot(np.dot(np.dot(A, B), C), D)
+    >>> # or
+    >>> _ = A.dot(B).dot(C).dot(D)
+
+    Notes
+    -----
+    The cost for a matrix multiplication can be calculated with the
+    following function::
+
+        def cost(A, B):
+            return A.shape[0] * A.shape[1] * B.shape[1]
+
+    Assume we have three matrices
+    :math:`A_{10x100}, B_{100x5}, C_{5x50}`.
+
+    The costs for the two different parenthesizations are as follows::
+
+        cost((AB)C) = 10*100*5 + 10*5*50   = 5000 + 2500   = 7500
+        cost(A(BC)) = 10*100*50 + 100*5*50 = 50000 + 25000 = 75000
+
+    """
+    n = len(arrays)
+    # optimization only makes sense for len(arrays) > 2
+    if n < 2:
+        raise ValueError("Expecting at least two arrays.")
+    elif n == 2:
+        return dot(arrays[0], arrays[1], out=out)
+
+    arrays = [asanyarray(a) for a in arrays]
+
+    # save original ndim to reshape the result array into the proper form later
+    ndim_first, ndim_last = arrays[0].ndim, arrays[-1].ndim
+    # Explicitly convert vectors to 2D arrays to keep the logic of the internal
+    # _multi_dot_* functions as simple as possible.
+    if arrays[0].ndim == 1:
+        arrays[0] = atleast_2d(arrays[0])
+    if arrays[-1].ndim == 1:
+        arrays[-1] = atleast_2d(arrays[-1]).T
+    _assert_2d(*arrays)
+
+    # _multi_dot_three is much faster than _multi_dot_matrix_chain_order
+    if n == 3:
+        result = _multi_dot_three(arrays[0], arrays[1], arrays[2], out=out)
+    else:
+        order = _multi_dot_matrix_chain_order(arrays)
+        result = _multi_dot(arrays, order, 0, n - 1, out=out)
+
+    # return proper shape
+    if ndim_first == 1 and ndim_last == 1:
+        return result[0, 0]  # scalar
+    elif ndim_first == 1 or ndim_last == 1:
+        return result.ravel()  # 1-D
+    else:
+        return result
+
+
+def _multi_dot_three(A, B, C, out=None):
+    """
+    Find the best order for three arrays and do the multiplication.
+
+    For three arguments `_multi_dot_three` is approximately 15 times faster
+    than `_multi_dot_matrix_chain_order`
+
+    """
+    a0, a1b0 = A.shape
+    b1c0, c1 = C.shape
+    # cost1 = cost((AB)C) = a0*a1b0*b1c0 + a0*b1c0*c1
+    cost1 = a0 * b1c0 * (a1b0 + c1)
+    # cost2 = cost(A(BC)) = a1b0*b1c0*c1 + a0*a1b0*c1
+    cost2 = a1b0 * c1 * (a0 + b1c0)
+
+    if cost1 < cost2:
+        return dot(dot(A, B), C, out=out)
+    else:
+        return dot(A, dot(B, C), out=out)
+
+
+def _multi_dot_matrix_chain_order(arrays, return_costs=False):
+    """
+    Return a np.array that encodes the optimal order of multiplications.
+
+    The optimal order array is then used by `_multi_dot()` to do the
+    multiplication.
+
+    Also return the cost matrix if `return_costs` is `True`
+
+    The implementation CLOSELY follows Cormen, "Introduction to Algorithms",
+    Chapter 15.2, p. 370-378.  Note that Cormen uses 1-based indices.
+
+        cost[i, j] = min([
+            cost[prefix] + cost[suffix] + cost_mult(prefix, suffix)
+            for k in range(i, j)])
+
+    """
+    n = len(arrays)
+    # p stores the dimensions of the matrices
+    # Example for p: A_{10x100}, B_{100x5}, C_{5x50} --> p = [10, 100, 5, 50]
+    p = [a.shape[0] for a in arrays] + [arrays[-1].shape[1]]
+    # m is a matrix of costs of the subproblems
+    # m[i,j]: min number of scalar multiplications needed to compute A_{i..j}
+    m = zeros((n, n), dtype=double)
+    # s is the actual ordering
+    # s[i, j] is the value of k at which we split the product A_i..A_j
+    s = empty((n, n), dtype=intp)
+
+    for l in range(1, n):
+        for i in range(n - l):
+            j = i + l
+            m[i, j] = inf
+            for k in range(i, j):
+                q = m[i, k] + m[k+1, j] + p[i]*p[k+1]*p[j+1]
+                if q < m[i, j]:
+                    m[i, j] = q
+                    s[i, j] = k  # Note that Cormen uses 1-based index
+
+    return (s, m) if return_costs else s
+
+
+def _multi_dot(arrays, order, i, j, out=None):
+    """Actually do the multiplication with the given order."""
+    if i == j:
+        # the initial call with non-None out should never get here
+        assert out is None
+
+        return arrays[i]
+    else:
+        return dot(_multi_dot(arrays, order, i, order[i, j]),
+                   _multi_dot(arrays, order, order[i, j] + 1, j),
+                   out=out)
+
+
+# diagonal
+
+def _diagonal_dispatcher(x, /, *, offset=None):
+    return (x,)
+
+
+@array_function_dispatch(_diagonal_dispatcher)
+def diagonal(x, /, *, offset=0):
+    """
+    Returns specified diagonals of a matrix (or a stack of matrices) ``x``.
+
+    This function is Array API compatible, contrary to
+    :py:func:`numpy.diagonal`, the matrix is assumed
+    to be defined by the last two dimensions.
+
+    Parameters
+    ----------
+    x : (...,M,N) array_like
+        Input array having shape (..., M, N) and whose innermost two
+        dimensions form MxN matrices.
+    offset : int, optional
+        Offset specifying the off-diagonal relative to the main diagonal,
+        where::
+
+            * offset = 0: the main diagonal.
+            * offset > 0: off-diagonal above the main diagonal.
+            * offset < 0: off-diagonal below the main diagonal.
+
+    Returns
+    -------
+    out : (...,min(N,M)) ndarray
+        An array containing the diagonals and whose shape is determined by
+        removing the last two dimensions and appending a dimension equal to
+        the size of the resulting diagonals. The returned array must have
+        the same data type as ``x``.
+
+    See Also
+    --------
+    numpy.diagonal
+
+    Examples
+    --------
+    >>> a = np.arange(4).reshape(2, 2); a
+    array([[0, 1],
+           [2, 3]])
+    >>> np.linalg.diagonal(a)
+    array([0, 3])
+
+    A 3-D example:
+
+    >>> a = np.arange(8).reshape(2, 2, 2); a
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.linalg.diagonal(a)
+    array([[0, 3],
+           [4, 7]])
+
+    Diagonals adjacent to the main diagonal can be obtained by using the
+    `offset` argument:
+
+    >>> a = np.arange(9).reshape(3, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> np.linalg.diagonal(a, offset=1)  # First superdiagonal
+    array([1, 5])
+    >>> np.linalg.diagonal(a, offset=2)  # Second superdiagonal
+    array([2])
+    >>> np.linalg.diagonal(a, offset=-1)  # First subdiagonal
+    array([3, 7])
+    >>> np.linalg.diagonal(a, offset=-2)  # Second subdiagonal
+    array([6])
+
+    The anti-diagonal can be obtained by reversing the order of elements
+    using either `numpy.flipud` or `numpy.fliplr`.
+
+    >>> a = np.arange(9).reshape(3, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> np.linalg.diagonal(np.fliplr(a))  # Horizontal flip
+    array([2, 4, 6])
+    >>> np.linalg.diagonal(np.flipud(a))  # Vertical flip
+    array([6, 4, 2])
+
+    Note that the order in which the diagonal is retrieved varies depending
+    on the flip function.
+
+    """
+    return _core_diagonal(x, offset, axis1=-2, axis2=-1)
+
+
+# trace
+
+def _trace_dispatcher(x, /, *, offset=None, dtype=None):
+    return (x,)
+
+
+@array_function_dispatch(_trace_dispatcher)
+def trace(x, /, *, offset=0, dtype=None):
+    """
+    Returns the sum along the specified diagonals of a matrix
+    (or a stack of matrices) ``x``.
+
+    This function is Array API compatible, contrary to
+    :py:func:`numpy.trace`.
+
+    Parameters
+    ----------
+    x : (...,M,N) array_like
+        Input array having shape (..., M, N) and whose innermost two
+        dimensions form MxN matrices.
+    offset : int, optional
+        Offset specifying the off-diagonal relative to the main diagonal,
+        where::
+
+            * offset = 0: the main diagonal.
+            * offset > 0: off-diagonal above the main diagonal.
+            * offset < 0: off-diagonal below the main diagonal.
+
+    dtype : dtype, optional
+        Data type of the returned array.
+
+    Returns
+    -------
+    out : ndarray
+        An array containing the traces and whose shape is determined by
+        removing the last two dimensions and storing the traces in the last
+        array dimension. For example, if x has rank k and shape:
+        (I, J, K, ..., L, M, N), then an output array has rank k-2 and shape:
+        (I, J, K, ..., L) where::
+
+            out[i, j, k, ..., l] = trace(a[i, j, k, ..., l, :, :])
+
+        The returned array must have a data type as described by the dtype
+        parameter above.
+
+    See Also
+    --------
+    numpy.trace
+
+    Examples
+    --------
+    >>> np.linalg.trace(np.eye(3))
+    3.0
+    >>> a = np.arange(8).reshape((2, 2, 2))
+    >>> np.linalg.trace(a)
+    array([3, 11])
+
+    Trace is computed with the last two axes as the 2-d sub-arrays.
+    This behavior differs from :py:func:`numpy.trace` which uses the first two
+    axes by default.
+
+    >>> a = np.arange(24).reshape((3, 2, 2, 2))
+    >>> np.linalg.trace(a).shape
+    (3, 2)
+
+    Traces adjacent to the main diagonal can be obtained by using the
+    `offset` argument:
+
+    >>> a = np.arange(9).reshape((3, 3)); a
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+    >>> np.linalg.trace(a, offset=1)  # First superdiagonal
+    6
+    >>> np.linalg.trace(a, offset=2)  # Second superdiagonal
+    2
+    >>> np.linalg.trace(a, offset=-1)  # First subdiagonal
+    10
+    >>> np.linalg.trace(a, offset=-2)  # Second subdiagonal
+    6
+
+    """
+    return _core_trace(x, offset, axis1=-2, axis2=-1, dtype=dtype)
+
+
+# cross
+
+def _cross_dispatcher(x1, x2, /, *, axis=None):
+    return (x1, x2,)
+
+
+@array_function_dispatch(_cross_dispatcher)
+def cross(x1, x2, /, *, axis=-1):
+    """
+    Returns the cross product of 3-element vectors.
+
+    If ``x1`` and/or ``x2`` are multi-dimensional arrays, then
+    the cross-product of each pair of corresponding 3-element vectors
+    is independently computed.
+
+    This function is Array API compatible, contrary to
+    :func:`numpy.cross`.
+
+    Parameters
+    ----------
+    x1 : array_like
+        The first input array.
+    x2 : array_like
+        The second input array. Must be compatible with ``x1`` for all
+        non-compute axes. The size of the axis over which to compute
+        the cross-product must be the same size as the respective axis
+        in ``x1``.
+    axis : int, optional
+        The axis (dimension) of ``x1`` and ``x2`` containing the vectors for
+        which to compute the cross-product. Default: ``-1``.
+
+    Returns
+    -------
+    out : ndarray
+        An array containing the cross products.
+
+    See Also
+    --------
+    numpy.cross
+
+    Examples
+    --------
+    Vector cross-product.
+
+    >>> x = np.array([1, 2, 3])
+    >>> y = np.array([4, 5, 6])
+    >>> np.linalg.cross(x, y)
+    array([-3,  6, -3])
+
+    Multiple vector cross-products. Note that the direction of the cross
+    product vector is defined by the *right-hand rule*.
+
+    >>> x = np.array([[1,2,3], [4,5,6]])
+    >>> y = np.array([[4,5,6], [1,2,3]])
+    >>> np.linalg.cross(x, y)
+    array([[-3,  6, -3],
+           [ 3, -6,  3]])
+
+    >>> x = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> y = np.array([[4, 5], [6, 1], [2, 3]])
+    >>> np.linalg.cross(x, y, axis=0)
+    array([[-24,  6],
+           [ 18, 24],
+           [-6,  -18]])
+
+    """
+    x1 = asanyarray(x1)
+    x2 = asanyarray(x2)
+
+    if x1.shape[axis] != 3 or x2.shape[axis] != 3:
+        raise ValueError(
+            "Both input arrays must be (arrays of) 3-dimensional vectors, "
+            f"but they are {x1.shape[axis]} and {x2.shape[axis]} "
+            "dimensional instead."
+        )
+
+    return _core_cross(x1, x2, axis=axis)
+
+
+# matmul
+
+def _matmul_dispatcher(x1, x2, /):
+    return (x1, x2)
+
+
+@array_function_dispatch(_matmul_dispatcher)
+def matmul(x1, x2, /):
+    """
+    Computes the matrix product.
+
+    This function is Array API compatible, contrary to
+    :func:`numpy.matmul`.
+
+    Parameters
+    ----------
+    x1 : array_like
+        The first input array.
+    x2 : array_like
+        The second input array.
+
+    Returns
+    -------
+    out : ndarray
+        The matrix product of the inputs.
+        This is a scalar only when both ``x1``, ``x2`` are 1-d vectors.
+
+    Raises
+    ------
+    ValueError
+        If the last dimension of ``x1`` is not the same size as
+        the second-to-last dimension of ``x2``.
+
+        If a scalar value is passed in.
+
+    See Also
+    --------
+    numpy.matmul
+
+    Examples
+    --------
+    For 2-D arrays it is the matrix product:
+
+    >>> a = np.array([[1, 0],
+    ...               [0, 1]])
+    >>> b = np.array([[4, 1],
+    ...               [2, 2]])
+    >>> np.linalg.matmul(a, b)
+    array([[4, 1],
+           [2, 2]])
+
+    For 2-D mixed with 1-D, the result is the usual.
+
+    >>> a = np.array([[1, 0],
+    ...               [0, 1]])
+    >>> b = np.array([1, 2])
+    >>> np.linalg.matmul(a, b)
+    array([1, 2])
+    >>> np.linalg.matmul(b, a)
+    array([1, 2])
+
+
+    Broadcasting is conventional for stacks of arrays
+
+    >>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4))
+    >>> b = np.arange(2 * 2 * 4).reshape((2, 4, 2))
+    >>> np.linalg.matmul(a,b).shape
+    (2, 2, 2)
+    >>> np.linalg.matmul(a, b)[0, 1, 1]
+    98
+    >>> sum(a[0, 1, :] * b[0 , :, 1])
+    98
+
+    Vector, vector returns the scalar inner product, but neither argument
+    is complex-conjugated:
+
+    >>> np.linalg.matmul([2j, 3j], [2j, 3j])
+    (-13+0j)
+
+    Scalar multiplication raises an error.
+
+    >>> np.linalg.matmul([1,2], 3)
+    Traceback (most recent call last):
+    ...
+    ValueError: matmul: Input operand 1 does not have enough dimensions ...
+
+    """
+    return _core_matmul(x1, x2)
+
+
+# tensordot
+
+def _tensordot_dispatcher(x1, x2, /, *, axes=None):
+    return (x1, x2)
+
+
+@array_function_dispatch(_tensordot_dispatcher)
+def tensordot(x1, x2, /, *, axes=2):
+    return _core_tensordot(x1, x2, axes=axes)
+
+
+tensordot.__doc__ = _core_tensordot.__doc__
+
+
+# matrix_transpose
+
+def _matrix_transpose_dispatcher(x):
+    return (x,)
+
+@array_function_dispatch(_matrix_transpose_dispatcher)
+def matrix_transpose(x, /):
+    return _core_matrix_transpose(x)
+
+
+matrix_transpose.__doc__ = _core_matrix_transpose.__doc__
+
+
+# matrix_norm
+
+def _matrix_norm_dispatcher(x, /, *, keepdims=None, ord=None):
+    return (x,)
+
+@array_function_dispatch(_matrix_norm_dispatcher)
+def matrix_norm(x, /, *, keepdims=False, ord="fro"):
+    """
+    Computes the matrix norm of a matrix (or a stack of matrices) ``x``.
+
+    This function is Array API compatible.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array having shape (..., M, N) and whose two innermost
+        dimensions form ``MxN`` matrices.
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in
+        the result as dimensions with size one. Default: False.
+    ord : {1, -1, 2, -2, inf, -inf, 'fro', 'nuc'}, optional
+        The order of the norm. For details see the table under ``Notes``
+        in `numpy.linalg.norm`.
+
+    See Also
+    --------
+    numpy.linalg.norm : Generic norm function
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4, -3, -2, ...,  2,  3,  4])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4, -3, -2],
+           [-1,  0,  1],
+           [ 2,  3,  4]])
+
+    >>> LA.matrix_norm(b)
+    7.745966692414834
+    >>> LA.matrix_norm(b, ord='fro')
+    7.745966692414834
+    >>> LA.matrix_norm(b, ord=np.inf)
+    9.0
+    >>> LA.matrix_norm(b, ord=-np.inf)
+    2.0
+
+    >>> LA.matrix_norm(b, ord=1)
+    7.0
+    >>> LA.matrix_norm(b, ord=-1)
+    6.0
+    >>> LA.matrix_norm(b, ord=2)
+    7.3484692283495345
+    >>> LA.matrix_norm(b, ord=-2)
+    1.8570331885190563e-016 # may vary
+
+    """
+    x = asanyarray(x)
+    return norm(x, axis=(-2, -1), keepdims=keepdims, ord=ord)
+
+
+# vector_norm
+
+def _vector_norm_dispatcher(x, /, *, axis=None, keepdims=None, ord=None):
+    return (x,)
+
+@array_function_dispatch(_vector_norm_dispatcher)
+def vector_norm(x, /, *, axis=None, keepdims=False, ord=2):
+    """
+    Computes the vector norm of a vector (or batch of vectors) ``x``.
+
+    This function is Array API compatible.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    axis : {None, int, 2-tuple of ints}, optional
+        If an integer, ``axis`` specifies the axis (dimension) along which
+        to compute vector norms. If an n-tuple, ``axis`` specifies the axes
+        (dimensions) along which to compute batched vector norms. If ``None``,
+        the vector norm must be computed over all array values (i.e.,
+        equivalent to computing the vector norm of a flattened array).
+        Default: ``None``.
+    keepdims : bool, optional
+        If this is set to True, the axes which are normed over are left in
+        the result as dimensions with size one. Default: False.
+    ord : {int, float, inf, -inf}, optional
+        The order of the norm. For details see the table under ``Notes``
+        in `numpy.linalg.norm`.
+
+    See Also
+    --------
+    numpy.linalg.norm : Generic norm function
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) + 1
+    >>> a
+    array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[1, 2, 3],
+           [4, 5, 6],
+           [7, 8, 9]])
+
+    >>> LA.vector_norm(b)
+    16.881943016134134
+    >>> LA.vector_norm(b, ord=np.inf)
+    9.0
+    >>> LA.vector_norm(b, ord=-np.inf)
+    1.0
+
+    >>> LA.vector_norm(b, ord=0)
+    9.0
+    >>> LA.vector_norm(b, ord=1)
+    45.0
+    >>> LA.vector_norm(b, ord=-1)
+    0.3534857623790153
+    >>> LA.vector_norm(b, ord=2)
+    16.881943016134134
+    >>> LA.vector_norm(b, ord=-2)
+    0.8058837395885292
+
+    """
+    x = asanyarray(x)
+    shape = list(x.shape)
+    if axis is None:
+        # Note: np.linalg.norm() doesn't handle 0-D arrays
+        x = x.ravel()
+        _axis = 0
+    elif isinstance(axis, tuple):
+        # Note: The axis argument supports any number of axes, whereas
+        # np.linalg.norm() only supports a single axis for vector norm.
+        normalized_axis = normalize_axis_tuple(axis, x.ndim)
+        rest = tuple(i for i in range(x.ndim) if i not in normalized_axis)
+        newshape = axis + rest
+        x = _core_transpose(x, newshape).reshape(
+            (
+                prod([x.shape[i] for i in axis], dtype=int),
+                *[x.shape[i] for i in rest]
+            )
+        )
+        _axis = 0
+    else:
+        _axis = axis
+
+    res = norm(x, axis=_axis, ord=ord)
+
+    if keepdims:
+        # We can't reuse np.linalg.norm(keepdims) because of the reshape hacks
+        # above to avoid matrix norm logic.
+        _axis = normalize_axis_tuple(
+            range(len(shape)) if axis is None else axis, len(shape)
+        )
+        for i in _axis:
+            shape[i] = 1
+        res = res.reshape(tuple(shape))
+
+    return res
+
+
+# vecdot
+
+def _vecdot_dispatcher(x1, x2, /, *, axis=None):
+    return (x1, x2)
+
+@array_function_dispatch(_vecdot_dispatcher)
+def vecdot(x1, x2, /, *, axis=-1):
+    """
+    Computes the vector dot product.
+
+    This function is restricted to arguments compatible with the Array API,
+    contrary to :func:`numpy.vecdot`.
+
+    Let :math:`\\mathbf{a}` be a vector in ``x1`` and :math:`\\mathbf{b}` be
+    a corresponding vector in ``x2``. The dot product is defined as:
+
+    .. math::
+       \\mathbf{a} \\cdot \\mathbf{b} = \\sum_{i=0}^{n-1} \\overline{a_i}b_i
+
+    over the dimension specified by ``axis`` and where :math:`\\overline{a_i}`
+    denotes the complex conjugate if :math:`a_i` is complex and the identity
+    otherwise.
+
+    Parameters
+    ----------
+    x1 : array_like
+        First input array.
+    x2 : array_like
+        Second input array.
+    axis : int, optional
+        Axis over which to compute the dot product. Default: ``-1``.
+
+    Returns
+    -------
+    output : ndarray
+        The vector dot product of the input.
+
+    See Also
+    --------
+    numpy.vecdot
+
+    Examples
+    --------
+    Get the projected size along a given normal for an array of vectors.
+
+    >>> v = np.array([[0., 5., 0.], [0., 0., 10.], [0., 6., 8.]])
+    >>> n = np.array([0., 0.6, 0.8])
+    >>> np.linalg.vecdot(v, n)
+    array([ 3.,  8., 10.])
+
+    """
+    return _core_vecdot(x1, x2, axis=axis)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_linalg.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9f646ec94037bb3bcbf91fbb186596ec5692929c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_linalg.pyi
@@ -0,0 +1,482 @@
+from collections.abc import Iterable
+from typing import (
+    Literal as L,
+    overload,
+    TypeAlias,
+    TypeVar,
+    Any,
+    SupportsIndex,
+    SupportsInt,
+    NamedTuple,
+)
+
+import numpy as np
+from numpy import (
+    # re-exports
+    vecdot,
+
+    # other
+    floating,
+    complexfloating,
+    signedinteger,
+    unsignedinteger,
+    timedelta64,
+    object_,
+    int32,
+    float64,
+    complex128,
+)
+from numpy.linalg import LinAlgError
+from numpy._core.fromnumeric import matrix_transpose
+from numpy._core.numeric import tensordot
+from numpy._typing import (
+    NDArray,
+    ArrayLike,
+    DTypeLike,
+    _ArrayLikeUnknown,
+    _ArrayLikeBool_co,
+    _ArrayLikeInt_co,
+    _ArrayLikeUInt_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _ArrayLikeTD64_co,
+    _ArrayLikeObject_co,
+)
+
+__all__ = [
+    "matrix_power",
+    "solve",
+    "tensorsolve",
+    "tensorinv",
+    "inv",
+    "cholesky",
+    "eigvals",
+    "eigvalsh",
+    "pinv",
+    "slogdet",
+    "det",
+    "svd",
+    "svdvals",
+    "eig",
+    "eigh",
+    "lstsq",
+    "norm",
+    "qr",
+    "cond",
+    "matrix_rank",
+    "LinAlgError",
+    "multi_dot",
+    "trace",
+    "diagonal",
+    "cross",
+    "outer",
+    "tensordot",
+    "matmul",
+    "matrix_transpose",
+    "matrix_norm",
+    "vector_norm",
+    "vecdot",
+]
+
+_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
+
+_ModeKind: TypeAlias = L["reduced", "complete", "r", "raw"]
+
+###
+
+fortran_int = np.intc
+
+class EigResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class EighResult(NamedTuple):
+    eigenvalues: NDArray[Any]
+    eigenvectors: NDArray[Any]
+
+class QRResult(NamedTuple):
+    Q: NDArray[Any]
+    R: NDArray[Any]
+
+class SlogdetResult(NamedTuple):
+    # TODO: `sign` and `logabsdet` are scalars for input 2D arrays and
+    # a `(x.ndim - 2)`` dimensionl arrays otherwise
+    sign: Any
+    logabsdet: Any
+
+class SVDResult(NamedTuple):
+    U: NDArray[Any]
+    S: NDArray[Any]
+    Vh: NDArray[Any]
+
+@overload
+def tensorsolve(
+    a: _ArrayLikeInt_co,
+    b: _ArrayLikeInt_co,
+    axes: None | Iterable[int] =...,
+) -> NDArray[float64]: ...
+@overload
+def tensorsolve(
+    a: _ArrayLikeFloat_co,
+    b: _ArrayLikeFloat_co,
+    axes: None | Iterable[int] =...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def tensorsolve(
+    a: _ArrayLikeComplex_co,
+    b: _ArrayLikeComplex_co,
+    axes: None | Iterable[int] =...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def solve(
+    a: _ArrayLikeInt_co,
+    b: _ArrayLikeInt_co,
+) -> NDArray[float64]: ...
+@overload
+def solve(
+    a: _ArrayLikeFloat_co,
+    b: _ArrayLikeFloat_co,
+) -> NDArray[floating[Any]]: ...
+@overload
+def solve(
+    a: _ArrayLikeComplex_co,
+    b: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def tensorinv(
+    a: _ArrayLikeInt_co,
+    ind: int = ...,
+) -> NDArray[float64]: ...
+@overload
+def tensorinv(
+    a: _ArrayLikeFloat_co,
+    ind: int = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def tensorinv(
+    a: _ArrayLikeComplex_co,
+    ind: int = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def inv(a: _ArrayLikeInt_co) -> NDArray[float64]: ...
+@overload
+def inv(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
+@overload
+def inv(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+# TODO: The supported input and output dtypes are dependent on the value of `n`.
+# For example: `n < 0` always casts integer types to float64
+def matrix_power(
+    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
+    n: SupportsIndex,
+) -> NDArray[Any]: ...
+
+@overload
+def cholesky(a: _ArrayLikeInt_co, /, *, upper: bool = False) -> NDArray[float64]: ...
+@overload
+def cholesky(a: _ArrayLikeFloat_co, /, *, upper: bool = False) -> NDArray[floating[Any]]: ...
+@overload
+def cholesky(a: _ArrayLikeComplex_co, /, *, upper: bool = False) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def outer(x1: _ArrayLikeUnknown, x2: _ArrayLikeUnknown) -> NDArray[Any]: ...
+@overload
+def outer(x1: _ArrayLikeBool_co, x2: _ArrayLikeBool_co) -> NDArray[np.bool]: ...
+@overload
+def outer(x1: _ArrayLikeUInt_co, x2: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def outer(x1: _ArrayLikeInt_co, x2: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
+@overload
+def outer(x1: _ArrayLikeFloat_co, x2: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
+@overload
+def outer(
+    x1: _ArrayLikeComplex_co,
+    x2: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
+@overload
+def outer(
+    x1: _ArrayLikeTD64_co,
+    x2: _ArrayLikeTD64_co,
+    out: None = ...,
+) -> NDArray[timedelta64]: ...
+@overload
+def outer(x1: _ArrayLikeObject_co, x2: _ArrayLikeObject_co) -> NDArray[object_]: ...
+@overload
+def outer(
+    x1: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+    x2: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
+) -> _ArrayType: ...
+
+@overload
+def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> QRResult: ...
+@overload
+def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> QRResult: ...
+@overload
+def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> QRResult: ...
+
+@overload
+def eigvals(a: _ArrayLikeInt_co) -> NDArray[float64] | NDArray[complex128]: ...
+@overload
+def eigvals(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]] | NDArray[complexfloating[Any, Any]]: ...
+@overload
+def eigvals(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def eigvalsh(a: _ArrayLikeInt_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[float64]: ...
+@overload
+def eigvalsh(a: _ArrayLikeComplex_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[floating[Any]]: ...
+
+@overload
+def eig(a: _ArrayLikeInt_co) -> EigResult: ...
+@overload
+def eig(a: _ArrayLikeFloat_co) -> EigResult: ...
+@overload
+def eig(a: _ArrayLikeComplex_co) -> EigResult: ...
+
+@overload
+def eigh(
+    a: _ArrayLikeInt_co,
+    UPLO: L["L", "U", "l", "u"] = ...,
+) -> EighResult: ...
+@overload
+def eigh(
+    a: _ArrayLikeFloat_co,
+    UPLO: L["L", "U", "l", "u"] = ...,
+) -> EighResult: ...
+@overload
+def eigh(
+    a: _ArrayLikeComplex_co,
+    UPLO: L["L", "U", "l", "u"] = ...,
+) -> EighResult: ...
+
+@overload
+def svd(
+    a: _ArrayLikeInt_co,
+    full_matrices: bool = ...,
+    compute_uv: L[True] = ...,
+    hermitian: bool = ...,
+) -> SVDResult: ...
+@overload
+def svd(
+    a: _ArrayLikeFloat_co,
+    full_matrices: bool = ...,
+    compute_uv: L[True] = ...,
+    hermitian: bool = ...,
+) -> SVDResult: ...
+@overload
+def svd(
+    a: _ArrayLikeComplex_co,
+    full_matrices: bool = ...,
+    compute_uv: L[True] = ...,
+    hermitian: bool = ...,
+) -> SVDResult: ...
+@overload
+def svd(
+    a: _ArrayLikeInt_co,
+    full_matrices: bool = ...,
+    compute_uv: L[False] = ...,
+    hermitian: bool = ...,
+) -> NDArray[float64]: ...
+@overload
+def svd(
+    a: _ArrayLikeComplex_co,
+    full_matrices: bool = ...,
+    compute_uv: L[False] = ...,
+    hermitian: bool = ...,
+) -> NDArray[floating[Any]]: ...
+
+def svdvals(
+    x: _ArrayLikeInt_co | _ArrayLikeFloat_co | _ArrayLikeComplex_co
+) -> NDArray[floating[Any]]: ...
+
+# TODO: Returns a scalar for 2D arrays and
+# a `(x.ndim - 2)`` dimensionl array otherwise
+def cond(x: _ArrayLikeComplex_co, p: None | float | L["fro", "nuc"] = ...) -> Any: ...
+
+# TODO: Returns `int` for <2D arrays and `intp` otherwise
+def matrix_rank(
+    A: _ArrayLikeComplex_co,
+    tol: None | _ArrayLikeFloat_co = ...,
+    hermitian: bool = ...,
+    *,
+    rtol: None | _ArrayLikeFloat_co = ...,
+) -> Any: ...
+
+@overload
+def pinv(
+    a: _ArrayLikeInt_co,
+    rcond: _ArrayLikeFloat_co = ...,
+    hermitian: bool = ...,
+) -> NDArray[float64]: ...
+@overload
+def pinv(
+    a: _ArrayLikeFloat_co,
+    rcond: _ArrayLikeFloat_co = ...,
+    hermitian: bool = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def pinv(
+    a: _ArrayLikeComplex_co,
+    rcond: _ArrayLikeFloat_co = ...,
+    hermitian: bool = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+
+# TODO: Returns a 2-tuple of scalars for 2D arrays and
+# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
+def slogdet(a: _ArrayLikeComplex_co) -> SlogdetResult: ...
+
+# TODO: Returns a 2-tuple of scalars for 2D arrays and
+# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
+def det(a: _ArrayLikeComplex_co) -> Any: ...
+
+@overload
+def lstsq(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, rcond: None | float = ...) -> tuple[
+    NDArray[float64],
+    NDArray[float64],
+    int32,
+    NDArray[float64],
+]: ...
+@overload
+def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: None | float = ...) -> tuple[
+    NDArray[floating[Any]],
+    NDArray[floating[Any]],
+    int32,
+    NDArray[floating[Any]],
+]: ...
+@overload
+def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: None | float = ...) -> tuple[
+    NDArray[complexfloating[Any, Any]],
+    NDArray[floating[Any]],
+    int32,
+    NDArray[floating[Any]],
+]: ...
+
+@overload
+def norm(
+    x: ArrayLike,
+    ord: None | float | L["fro", "nuc"] = ...,
+    axis: None = ...,
+    keepdims: bool = ...,
+) -> floating[Any]: ...
+@overload
+def norm(
+    x: ArrayLike,
+    ord: None | float | L["fro", "nuc"] = ...,
+    axis: SupportsInt | SupportsIndex | tuple[int, ...] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+@overload
+def matrix_norm(
+    x: ArrayLike,
+    /,
+    *,
+    ord: None | float | L["fro", "nuc"] = ...,
+    keepdims: bool = ...,
+) -> floating[Any]: ...
+@overload
+def matrix_norm(
+    x: ArrayLike,
+    /,
+    *,
+    ord: None | float | L["fro", "nuc"] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+@overload
+def vector_norm(
+    x: ArrayLike,
+    /,
+    *,
+    axis: None = ...,
+    ord: None | float = ...,
+    keepdims: bool = ...,
+) -> floating[Any]: ...
+@overload
+def vector_norm(
+    x: ArrayLike,
+    /,
+    *,
+    axis: SupportsInt | SupportsIndex | tuple[int, ...] = ...,
+    ord: None | float = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+# TODO: Returns a scalar or array
+def multi_dot(
+    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
+    *,
+    out: None | NDArray[Any] = ...,
+) -> Any: ...
+
+def diagonal(
+    x: ArrayLike,  # >= 2D array
+    /,
+    *,
+    offset: SupportsIndex = ...,
+) -> NDArray[Any]: ...
+
+def trace(
+    x: ArrayLike,  # >= 2D array
+    /,
+    *,
+    offset: SupportsIndex = ...,
+    dtype: DTypeLike = ...,
+) -> Any: ...
+
+@overload
+def cross(
+    x1: _ArrayLikeUInt_co,
+    x2: _ArrayLikeUInt_co,
+    /,
+    *,
+    axis: int = ...,
+) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def cross(
+    x1: _ArrayLikeInt_co,
+    x2: _ArrayLikeInt_co,
+    /,
+    *,
+    axis: int = ...,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def cross(
+    x1: _ArrayLikeFloat_co,
+    x2: _ArrayLikeFloat_co,
+    /,
+    *,
+    axis: int = ...,
+) -> NDArray[floating[Any]]: ...
+@overload
+def cross(
+    x1: _ArrayLikeComplex_co,
+    x2: _ArrayLikeComplex_co,
+    /,
+    *,
+    axis: int = ...,
+) -> NDArray[complexfloating[Any, Any]]: ...
+
+@overload
+def matmul(
+    x1: _ArrayLikeInt_co,
+    x2: _ArrayLikeInt_co,
+) -> NDArray[signedinteger[Any]]: ...
+@overload
+def matmul(
+    x1: _ArrayLikeUInt_co,
+    x2: _ArrayLikeUInt_co,
+) -> NDArray[unsignedinteger[Any]]: ...
+@overload
+def matmul(
+    x1: _ArrayLikeFloat_co,
+    x2: _ArrayLikeFloat_co,
+) -> NDArray[floating[Any]]: ...
+@overload
+def matmul(
+    x1: _ArrayLikeComplex_co,
+    x2: _ArrayLikeComplex_co,
+) -> NDArray[complexfloating[Any, Any]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_umath_linalg.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_umath_linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cd07acdb1f9ed16811bf9898a0aa02c58d95f41e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/_umath_linalg.pyi
@@ -0,0 +1,61 @@
+from typing import Final
+from typing import Literal as L
+
+import numpy as np
+from numpy._typing._ufunc import _GUFunc_Nin2_Nout1
+
+__version__: Final[str] = ...
+_ilp64: Final[bool] = ...
+
+###
+# 1 -> 1
+
+# (m,m) -> ()
+det: Final[np.ufunc] = ...
+# (m,m) -> (m)
+cholesky_lo: Final[np.ufunc] = ...
+cholesky_up: Final[np.ufunc] = ...
+eigvals: Final[np.ufunc] = ...
+eigvalsh_lo: Final[np.ufunc] = ...
+eigvalsh_up: Final[np.ufunc] = ...
+# (m,m) -> (m,m)
+inv: Final[np.ufunc] = ...
+# (m,n) -> (p)
+qr_r_raw: Final[np.ufunc] = ...
+svd: Final[np.ufunc] = ...
+
+###
+# 1 -> 2
+
+# (m,m) -> (), ()
+slogdet: Final[np.ufunc] = ...
+# (m,m) -> (m), (m,m)
+eig: Final[np.ufunc] = ...
+eigh_lo: Final[np.ufunc] = ...
+eigh_up: Final[np.ufunc] = ...
+
+###
+# 2 -> 1
+
+# (m,n), (n) -> (m,m)
+qr_complete: Final[_GUFunc_Nin2_Nout1[L["qr_complete"], L[2], None, L["(m,n),(n)->(m,m)"]]] = ...
+# (m,n), (k) -> (m,k)
+qr_reduced: Final[_GUFunc_Nin2_Nout1[L["qr_reduced"], L[2], None, L["(m,n),(k)->(m,k)"]]] = ...
+# (m,m), (m,n) -> (m,n)
+solve: Final[_GUFunc_Nin2_Nout1[L["solve"], L[4], None, L["(m,m),(m,n)->(m,n)"]]] = ...
+# (m,m), (m) -> (m)
+solve1: Final[_GUFunc_Nin2_Nout1[L["solve1"], L[4], None, L["(m,m),(m)->(m)"]]] = ...
+
+###
+# 1 -> 3
+
+# (m,n) -> (m,m), (p), (n,n)
+svd_f: Final[np.ufunc] = ...
+# (m,n) -> (m,p), (p), (p,n)
+svd_s: Final[np.ufunc] = ...
+
+###
+# 3 -> 4
+
+# (m,n), (m,k), () -> (n,k), (k), (), (p)
+lstsq: Final[np.ufunc] = ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..1efefb1b740506870b53375940f27918a9c5711a
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.cpython-310-x86_64-linux-gnu.so differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0f6bfa3a022be08e963995b0570f82c19e56a3ad
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/lapack_lite.pyi
@@ -0,0 +1,141 @@
+from typing import Any, Final, TypedDict, type_check_only
+
+import numpy as np
+from numpy._typing import NDArray
+
+from ._linalg import fortran_int
+
+###
+
+@type_check_only
+class _GELSD(TypedDict):
+    m: int
+    n: int
+    nrhs: int
+    lda: int
+    ldb: int
+    rank: int
+    lwork: int
+    info: int
+
+@type_check_only
+class _DGELSD(_GELSD):
+    dgelsd_: int
+    rcond: float
+
+@type_check_only
+class _ZGELSD(_GELSD):
+    zgelsd_: int
+
+@type_check_only
+class _GEQRF(TypedDict):
+    m: int
+    n: int
+    lda: int
+    lwork: int
+    info: int
+
+@type_check_only
+class _DGEQRF(_GEQRF):
+    dgeqrf_: int
+
+@type_check_only
+class _ZGEQRF(_GEQRF):
+    zgeqrf_: int
+
+@type_check_only
+class _DORGQR(TypedDict):
+    dorgqr_: int
+    info: int
+
+@type_check_only
+class _ZUNGQR(TypedDict):
+    zungqr_: int
+    info: int
+
+###
+
+_ilp64: Final[bool] = ...
+
+def dgelsd(
+    m: int,
+    n: int,
+    nrhs: int,
+    a: NDArray[np.float64],
+    lda: int,
+    b: NDArray[np.float64],
+    ldb: int,
+    s: NDArray[np.float64],
+    rcond: float,
+    rank: int,
+    work: NDArray[np.float64],
+    lwork: int,
+    iwork: NDArray[fortran_int],
+    info: int,
+) -> _DGELSD: ...
+def zgelsd(
+    m: int,
+    n: int,
+    nrhs: int,
+    a: NDArray[np.complex128],
+    lda: int,
+    b: NDArray[np.complex128],
+    ldb: int,
+    s: NDArray[np.float64],
+    rcond: float,
+    rank: int,
+    work: NDArray[np.complex128],
+    lwork: int,
+    rwork: NDArray[np.float64],
+    iwork: NDArray[fortran_int],
+    info: int,
+) -> _ZGELSD: ...
+
+#
+def dgeqrf(
+    m: int,
+    n: int,
+    a: NDArray[np.float64],  # in/out, shape: (lda, n)
+    lda: int,
+    tau: NDArray[np.float64],  # out, shape: (min(m, n),)
+    work: NDArray[np.float64],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _DGEQRF: ...
+def zgeqrf(
+    m: int,
+    n: int,
+    a: NDArray[np.complex128],  # in/out, shape: (lda, n)
+    lda: int,
+    tau: NDArray[np.complex128],  # out, shape: (min(m, n),)
+    work: NDArray[np.complex128],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _ZGEQRF: ...
+
+#
+def dorgqr(
+    m: int,  # >=0
+    n: int,  # m >= n >= 0
+    k: int,  # n >= k >= 0
+    a: NDArray[np.float64],  # in/out, shape: (lda, n)
+    lda: int,  # >= max(1, m)
+    tau: NDArray[np.float64],  # in, shape: (k,)
+    work: NDArray[np.float64],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _DORGQR: ...
+def zungqr(
+    m: int,
+    n: int,
+    k: int,
+    a: NDArray[np.complex128],
+    lda: int,
+    tau: NDArray[np.complex128],
+    work: NDArray[np.complex128],
+    lwork: int,
+    info: int,
+) -> _ZUNGQR: ...
+
+#
+def xerbla(srname: object, info: int) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..d75b07342b587a1ed0e77f43fb4f6be4e1a87a41
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.py
@@ -0,0 +1,16 @@
+def __getattr__(attr_name):
+    import warnings
+    from numpy.linalg import _linalg
+    ret = getattr(_linalg, attr_name, None)
+    if ret is None:
+        raise AttributeError(
+            f"module 'numpy.linalg.linalg' has no attribute {attr_name}")
+    warnings.warn(
+        "The numpy.linalg.linalg has been made private and renamed to "
+        "numpy.linalg._linalg. All public functions exported by it are "
+        f"available from numpy.linalg. Please use numpy.linalg.{attr_name} "
+        "instead.",
+        DeprecationWarning,
+        stacklevel=3
+    )
+    return ret
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..dbe9becfb8d51b449542039c7881e62e72bfa535
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/linalg.pyi
@@ -0,0 +1,69 @@
+from ._linalg import (
+    LinAlgError,
+    cholesky,
+    cond,
+    cross,
+    det,
+    diagonal,
+    eig,
+    eigh,
+    eigvals,
+    eigvalsh,
+    inv,
+    lstsq,
+    matmul,
+    matrix_norm,
+    matrix_power,
+    matrix_rank,
+    matrix_transpose,
+    multi_dot,
+    norm,
+    outer,
+    pinv,
+    qr,
+    slogdet,
+    solve,
+    svd,
+    svdvals,
+    tensordot,
+    tensorinv,
+    tensorsolve,
+    trace,
+    vecdot,
+    vector_norm,
+)
+
+__all__ = [
+    "LinAlgError",
+    "cholesky",
+    "cond",
+    "cross",
+    "det",
+    "diagonal",
+    "eig",
+    "eigh",
+    "eigvals",
+    "eigvalsh",
+    "inv",
+    "lstsq",
+    "matmul",
+    "matrix_norm",
+    "matrix_power",
+    "matrix_rank",
+    "matrix_transpose",
+    "multi_dot",
+    "norm",
+    "outer",
+    "pinv",
+    "qr",
+    "slogdet",
+    "solve",
+    "svd",
+    "svdvals",
+    "tensordot",
+    "tensorinv",
+    "tensorsolve",
+    "trace",
+    "vecdot",
+    "vector_norm",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_deprecations.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_deprecations.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd4c10832e7e7240175571605a07541f0c188f89
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_deprecations.py
@@ -0,0 +1,20 @@
+"""Test deprecation and future warnings.
+
+"""
+import numpy as np
+from numpy.testing import assert_warns
+
+
+def test_qr_mode_full_future_warning():
+    """Check mode='full' FutureWarning.
+
+    In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
+    deprecated. The release date will probably be sometime in the summer
+    of 2013.
+
+    """
+    a = np.eye(2)
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..0745654a07309eff196cf22c6b72832427e9c667
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_linalg.py
@@ -0,0 +1,2386 @@
+""" Test functions for linalg module
+
+"""
+import os
+import sys
+import itertools
+import threading
+import traceback
+import textwrap
+import subprocess
+import pytest
+
+import numpy as np
+from numpy import array, single, double, csingle, cdouble, dot, identity, matmul
+from numpy._core import swapaxes
+from numpy.exceptions import AxisError
+from numpy import multiply, atleast_2d, inf, asarray
+from numpy import linalg
+from numpy.linalg import matrix_power, norm, matrix_rank, multi_dot, LinAlgError
+from numpy.linalg._linalg import _multi_dot_matrix_chain_order
+from numpy.testing import (
+    assert_, assert_equal, assert_raises, assert_array_equal,
+    assert_almost_equal, assert_allclose, suppress_warnings,
+    assert_raises_regex, HAS_LAPACK64, IS_WASM
+    )
+try:
+    import numpy.linalg.lapack_lite
+except ImportError:
+    # May be broken when numpy was built without BLAS/LAPACK present
+    # If so, ensure we don't break the whole test suite - the `lapack_lite`
+    # submodule should be removed, it's only used in two tests in this file.
+    pass
+
+
+def consistent_subclass(out, in_):
+    # For ndarray subclass input, our output should have the same subclass
+    # (non-ndarray input gets converted to ndarray).
+    return type(out) is (type(in_) if isinstance(in_, np.ndarray)
+                         else np.ndarray)
+
+
+old_assert_almost_equal = assert_almost_equal
+
+
+def assert_almost_equal(a, b, single_decimal=6, double_decimal=12, **kw):
+    if asarray(a).dtype.type in (single, csingle):
+        decimal = single_decimal
+    else:
+        decimal = double_decimal
+    old_assert_almost_equal(a, b, decimal=decimal, **kw)
+
+
+def get_real_dtype(dtype):
+    return {single: single, double: double,
+            csingle: single, cdouble: double}[dtype]
+
+
+def get_complex_dtype(dtype):
+    return {single: csingle, double: cdouble,
+            csingle: csingle, cdouble: cdouble}[dtype]
+
+
+def get_rtol(dtype):
+    # Choose a safe rtol
+    if dtype in (single, csingle):
+        return 1e-5
+    else:
+        return 1e-11
+
+
+# used to categorize tests
+all_tags = {
+  'square', 'nonsquare', 'hermitian',  # mutually exclusive
+  'generalized', 'size-0', 'strided' # optional additions
+}
+
+
+class LinalgCase:
+    def __init__(self, name, a, b, tags=set()):
+        """
+        A bundle of arguments to be passed to a test case, with an identifying
+        name, the operands a and b, and a set of tags to filter the tests
+        """
+        assert_(isinstance(name, str))
+        self.name = name
+        self.a = a
+        self.b = b
+        self.tags = frozenset(tags)  # prevent shared tags
+
+    def check(self, do):
+        """
+        Run the function `do` on this test case, expanding arguments
+        """
+        do(self.a, self.b, tags=self.tags)
+
+    def __repr__(self):
+        return f''
+
+
+def apply_tag(tag, cases):
+    """
+    Add the given tag (a string) to each of the cases (a list of LinalgCase
+    objects)
+    """
+    assert tag in all_tags, "Invalid tag"
+    for case in cases:
+        case.tags = case.tags | {tag}
+    return cases
+
+
+#
+# Base test cases
+#
+
+np.random.seed(1234)
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+    LinalgCase("single",
+               array([[1., 2.], [3., 4.]], dtype=single),
+               array([2., 1.], dtype=single)),
+    LinalgCase("double",
+               array([[1., 2.], [3., 4.]], dtype=double),
+               array([2., 1.], dtype=double)),
+    LinalgCase("double_2",
+               array([[1., 2.], [3., 4.]], dtype=double),
+               array([[2., 1., 4.], [3., 4., 6.]], dtype=double)),
+    LinalgCase("csingle",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j], dtype=csingle)),
+    LinalgCase("cdouble",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j], dtype=cdouble)),
+    LinalgCase("cdouble_2",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j, 1 + 3j], [1 - 2j, 1 - 3j, 1 - 6j]], dtype=cdouble)),
+    LinalgCase("0x0",
+               np.empty((0, 0), dtype=double),
+               np.empty((0,), dtype=double),
+               tags={'size-0'}),
+    LinalgCase("8x8",
+               np.random.rand(8, 8),
+               np.random.rand(8)),
+    LinalgCase("1x1",
+               np.random.rand(1, 1),
+               np.random.rand(1)),
+    LinalgCase("nonarray",
+               [[1, 2], [3, 4]],
+               [2, 1]),
+])
+
+# non-square test-cases
+CASES += apply_tag('nonsquare', [
+    LinalgCase("single_nsq_1",
+               array([[1., 2., 3.], [3., 4., 6.]], dtype=single),
+               array([2., 1.], dtype=single)),
+    LinalgCase("single_nsq_2",
+               array([[1., 2.], [3., 4.], [5., 6.]], dtype=single),
+               array([2., 1., 3.], dtype=single)),
+    LinalgCase("double_nsq_1",
+               array([[1., 2., 3.], [3., 4., 6.]], dtype=double),
+               array([2., 1.], dtype=double)),
+    LinalgCase("double_nsq_2",
+               array([[1., 2.], [3., 4.], [5., 6.]], dtype=double),
+               array([2., 1., 3.], dtype=double)),
+    LinalgCase("csingle_nsq_1",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j], dtype=csingle)),
+    LinalgCase("csingle_nsq_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j, 3. - 3j], dtype=csingle)),
+    LinalgCase("cdouble_nsq_1",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j, 3. - 3j], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_1_2",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_2_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
+    LinalgCase("8x11",
+               np.random.rand(8, 11),
+               np.random.rand(8)),
+    LinalgCase("1x5",
+               np.random.rand(1, 5),
+               np.random.rand(1)),
+    LinalgCase("5x1",
+               np.random.rand(5, 1),
+               np.random.rand(5)),
+    LinalgCase("0x4",
+               np.random.rand(0, 4),
+               np.random.rand(0),
+               tags={'size-0'}),
+    LinalgCase("4x0",
+               np.random.rand(4, 0),
+               np.random.rand(4),
+               tags={'size-0'}),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+    LinalgCase("hsingle",
+               array([[1., 2.], [2., 1.]], dtype=single),
+               None),
+    LinalgCase("hdouble",
+               array([[1., 2.], [2., 1.]], dtype=double),
+               None),
+    LinalgCase("hcsingle",
+               array([[1., 2 + 3j], [2 - 3j, 1]], dtype=csingle),
+               None),
+    LinalgCase("hcdouble",
+               array([[1., 2 + 3j], [2 - 3j, 1]], dtype=cdouble),
+               None),
+    LinalgCase("hempty",
+               np.empty((0, 0), dtype=double),
+               None,
+               tags={'size-0'}),
+    LinalgCase("hnonarray",
+               [[1, 2], [2, 1]],
+               None),
+    LinalgCase("matrix_b_only",
+               array([[1., 2.], [2., 1.]]),
+               None),
+    LinalgCase("hmatrix_1x1",
+               np.random.rand(1, 1),
+               None),
+])
+
+
+#
+# Gufunc test cases
+#
+def _make_generalized_cases():
+    new_cases = []
+
+    for case in CASES:
+        if not isinstance(case.a, np.ndarray):
+            continue
+
+        a = np.array([case.a, 2 * case.a, 3 * case.a])
+        if case.b is None:
+            b = None
+        elif case.b.ndim == 1:
+            b = case.b
+        else:
+            b = np.array([case.b, 7 * case.b, 6 * case.b])
+        new_case = LinalgCase(case.name + "_tile3", a, b,
+                              tags=case.tags | {'generalized'})
+        new_cases.append(new_case)
+
+        a = np.array([case.a] * 2 * 3).reshape((3, 2) + case.a.shape)
+        if case.b is None:
+            b = None
+        elif case.b.ndim == 1:
+            b = np.array([case.b] * 2 * 3 * a.shape[-1])\
+                  .reshape((3, 2) + case.a.shape[-2:])
+        else:
+            b = np.array([case.b] * 2 * 3).reshape((3, 2) + case.b.shape)
+        new_case = LinalgCase(case.name + "_tile213", a, b,
+                              tags=case.tags | {'generalized'})
+        new_cases.append(new_case)
+
+    return new_cases
+
+
+CASES += _make_generalized_cases()
+
+
+#
+# Generate stride combination variations of the above
+#
+def _stride_comb_iter(x):
+    """
+    Generate cartesian product of strides for all axes
+    """
+
+    if not isinstance(x, np.ndarray):
+        yield x, "nop"
+        return
+
+    stride_set = [(1,)] * x.ndim
+    stride_set[-1] = (1, 3, -4)
+    if x.ndim > 1:
+        stride_set[-2] = (1, 3, -4)
+    if x.ndim > 2:
+        stride_set[-3] = (1, -4)
+
+    for repeats in itertools.product(*tuple(stride_set)):
+        new_shape = [abs(a * b) for a, b in zip(x.shape, repeats)]
+        slices = tuple([slice(None, None, repeat) for repeat in repeats])
+
+        # new array with different strides, but same data
+        xi = np.empty(new_shape, dtype=x.dtype)
+        xi.view(np.uint32).fill(0xdeadbeef)
+        xi = xi[slices]
+        xi[...] = x
+        xi = xi.view(x.__class__)
+        assert_(np.all(xi == x))
+        yield xi, "stride_" + "_".join(["%+d" % j for j in repeats])
+
+        # generate also zero strides if possible
+        if x.ndim >= 1 and x.shape[-1] == 1:
+            s = list(x.strides)
+            s[-1] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0"
+        if x.ndim >= 2 and x.shape[-2] == 1:
+            s = list(x.strides)
+            s[-2] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0_x"
+        if x.ndim >= 2 and x.shape[:-2] == (1, 1):
+            s = list(x.strides)
+            s[-1] = 0
+            s[-2] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0_0"
+
+
+def _make_strided_cases():
+    new_cases = []
+    for case in CASES:
+        for a, a_label in _stride_comb_iter(case.a):
+            for b, b_label in _stride_comb_iter(case.b):
+                new_case = LinalgCase(case.name + "_" + a_label + "_" + b_label, a, b,
+                                      tags=case.tags | {'strided'})
+                new_cases.append(new_case)
+    return new_cases
+
+
+CASES += _make_strided_cases()
+
+
+#
+# Test different routines against the above cases
+#
+class LinalgTestCase:
+    TEST_CASES = CASES
+
+    def check_cases(self, require=set(), exclude=set()):
+        """
+        Run func on each of the cases with all of the tags in require, and none
+        of the tags in exclude
+        """
+        for case in self.TEST_CASES:
+            # filter by require and exclude
+            if case.tags & require != require:
+                continue
+            if case.tags & exclude:
+                continue
+
+            try:
+                case.check(self.do)
+            except Exception as e:
+                msg = f'In test case: {case!r}\n\n'
+                msg += traceback.format_exc()
+                raise AssertionError(msg) from e
+
+
+class LinalgSquareTestCase(LinalgTestCase):
+
+    def test_sq_cases(self):
+        self.check_cases(require={'square'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_sq_cases(self):
+        self.check_cases(require={'square', 'size-0'},
+                         exclude={'generalized'})
+
+
+class LinalgNonsquareTestCase(LinalgTestCase):
+
+    def test_nonsq_cases(self):
+        self.check_cases(require={'nonsquare'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_nonsq_cases(self):
+        self.check_cases(require={'nonsquare', 'size-0'},
+                         exclude={'generalized'})
+
+
+class HermitianTestCase(LinalgTestCase):
+
+    def test_herm_cases(self):
+        self.check_cases(require={'hermitian'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_herm_cases(self):
+        self.check_cases(require={'hermitian', 'size-0'},
+                         exclude={'generalized'})
+
+
+class LinalgGeneralizedSquareTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_sq_cases(self):
+        self.check_cases(require={'generalized', 'square'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_sq_cases(self):
+        self.check_cases(require={'generalized', 'square', 'size-0'})
+
+
+class LinalgGeneralizedNonsquareTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_nonsq_cases(self):
+        self.check_cases(require={'generalized', 'nonsquare'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_nonsq_cases(self):
+        self.check_cases(require={'generalized', 'nonsquare', 'size-0'})
+
+
+class HermitianGeneralizedTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_herm_cases(self):
+        self.check_cases(require={'generalized', 'hermitian'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_herm_cases(self):
+        self.check_cases(require={'generalized', 'hermitian', 'size-0'},
+                         exclude={'none'})
+
+
+def identity_like_generalized(a):
+    a = asarray(a)
+    if a.ndim >= 3:
+        r = np.empty(a.shape, dtype=a.dtype)
+        r[...] = identity(a.shape[-2])
+        return r
+    else:
+        return identity(a.shape[0])
+
+
+class SolveCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+    # kept apart from TestSolve for use for testing with matrices.
+    def do(self, a, b, tags):
+        x = linalg.solve(a, b)
+        if np.array(b).ndim == 1:
+            # When a is (..., M, M) and b is (M,), it is the same as when b is
+            # (M, 1), except the result has shape (..., M)
+            adotx = matmul(a, x[..., None])[..., 0]
+            assert_almost_equal(np.broadcast_to(b, adotx.shape), adotx)
+        else:
+            adotx = matmul(a, x)
+            assert_almost_equal(b, adotx)
+        assert_(consistent_subclass(x, b))
+
+
+class TestSolve(SolveCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.solve(x, x).dtype, dtype)
+
+    def test_1_d(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.arange(8).reshape(2, 2, 2)
+        b = np.arange(2).view(ArraySubclass)
+        result = linalg.solve(a, b)
+        assert result.shape == (2, 2)
+
+        # If b is anything other than 1-D it should be treated as a stack of
+        # matrices
+        b = np.arange(4).reshape(2, 2).view(ArraySubclass)
+        result = linalg.solve(a, b)
+        assert result.shape == (2, 2, 2)
+
+        b = np.arange(2).reshape(1, 2).view(ArraySubclass)
+        assert_raises(ValueError, linalg.solve, a, b)
+
+    def test_0_size(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        # Test system of 0x0 matrices
+        a = np.arange(8).reshape(2, 2, 2)
+        b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass)
+
+        expected = linalg.solve(a, b)[:, 0:0, :]
+        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, :])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        # Test errors for non-square and only b's dimension being 0
+        assert_raises(linalg.LinAlgError, linalg.solve, a[:, 0:0, 0:1], b)
+        assert_raises(ValueError, linalg.solve, a, b[:, 0:0, :])
+
+        # Test broadcasting error
+        b = np.arange(6).reshape(1, 3, 2)  # broadcasting error
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+
+        # Test zero "single equations" with 0x0 matrices.
+        b = np.arange(2).view(ArraySubclass)
+        expected = linalg.solve(a, b)[:, 0:0]
+        result = linalg.solve(a[:, 0:0, 0:0], b[0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        b = np.arange(3).reshape(1, 3)
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+        assert_raises(ValueError, linalg.solve, a[:, 0:0, 0:0], b)
+
+    def test_0_size_k(self):
+        # test zero multiple equation (K=0) case.
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.arange(4).reshape(1, 2, 2)
+        b = np.arange(6).reshape(3, 2, 1).view(ArraySubclass)
+
+        expected = linalg.solve(a, b)[:, :, 0:0]
+        result = linalg.solve(a, b[:, :, 0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        # test both zero.
+        expected = linalg.solve(a, b)[:, 0:0, 0:0]
+        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, 0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+
+class InvCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        a_inv = linalg.inv(a)
+        assert_almost_equal(matmul(a, a_inv),
+                            identity_like_generalized(a))
+        assert_(consistent_subclass(a_inv, a))
+
+
+class TestInv(InvCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.inv(x).dtype, dtype)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res.shape)
+        assert_(isinstance(res, ArraySubclass))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal(a.shape, res.shape)
+        assert_(isinstance(res, ArraySubclass))
+
+
+class EigvalsCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        ev = linalg.eigvals(a)
+        evalues, evectors = linalg.eig(a)
+        assert_almost_equal(ev, evalues)
+
+
+class TestEigvals(EigvalsCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.eigvals(x).dtype, dtype)
+        x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
+        assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.eigvals(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.eigvals(a)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+
+class EigCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        res = linalg.eig(a)
+        eigenvalues, eigenvectors = res.eigenvalues, res.eigenvectors
+        assert_allclose(matmul(a, eigenvectors),
+                        np.asarray(eigenvectors) * np.asarray(eigenvalues)[..., None, :],
+                        rtol=get_rtol(eigenvalues.dtype))
+        assert_(consistent_subclass(eigenvectors, a))
+
+
+class TestEig(EigCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w, v = np.linalg.eig(x)
+        assert_equal(w.dtype, dtype)
+        assert_equal(v.dtype, dtype)
+
+        x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
+        w, v = np.linalg.eig(x)
+        assert_equal(w.dtype, get_complex_dtype(dtype))
+        assert_equal(v.dtype, get_complex_dtype(dtype))
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res, res_v = linalg.eig(a)
+        assert_(res_v.dtype.type is np.float64)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res, res_v = linalg.eig(a)
+        assert_(res_v.dtype.type is np.complex64)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+
+class SVDBaseTests:
+    hermitian = False
+
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        res = linalg.svd(x)
+        U, S, Vh = res.U, res.S, res.Vh
+        assert_equal(U.dtype, dtype)
+        assert_equal(S.dtype, get_real_dtype(dtype))
+        assert_equal(Vh.dtype, dtype)
+        s = linalg.svd(x, compute_uv=False, hermitian=self.hermitian)
+        assert_equal(s.dtype, get_real_dtype(dtype))
+
+
+class SVDCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        u, s, vt = linalg.svd(a, False)
+        assert_allclose(a, matmul(np.asarray(u) * np.asarray(s)[..., None, :],
+                                           np.asarray(vt)),
+                        rtol=get_rtol(u.dtype))
+        assert_(consistent_subclass(u, a))
+        assert_(consistent_subclass(vt, a))
+
+
+class TestSVD(SVDCases, SVDBaseTests):
+    def test_empty_identity(self):
+        """ Empty input should put an identity matrix in u or vh """
+        x = np.empty((4, 0))
+        u, s, vh = linalg.svd(x, compute_uv=True, hermitian=self.hermitian)
+        assert_equal(u.shape, (4, 4))
+        assert_equal(vh.shape, (0, 0))
+        assert_equal(u, np.eye(4))
+
+        x = np.empty((0, 4))
+        u, s, vh = linalg.svd(x, compute_uv=True, hermitian=self.hermitian)
+        assert_equal(u.shape, (0, 0))
+        assert_equal(vh.shape, (4, 4))
+        assert_equal(vh, np.eye(4))
+
+    def test_svdvals(self):
+        x = np.array([[1, 0.5], [0.5, 1]])
+        s_from_svd = linalg.svd(x, compute_uv=False, hermitian=self.hermitian)
+        s_from_svdvals = linalg.svdvals(x)
+        assert_almost_equal(s_from_svd, s_from_svdvals)
+
+
+class SVDHermitianCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        u, s, vt = linalg.svd(a, False, hermitian=True)
+        assert_allclose(a, matmul(np.asarray(u) * np.asarray(s)[..., None, :],
+                                           np.asarray(vt)),
+                        rtol=get_rtol(u.dtype))
+        def hermitian(mat):
+            axes = list(range(mat.ndim))
+            axes[-1], axes[-2] = axes[-2], axes[-1]
+            return np.conj(np.transpose(mat, axes=axes))
+
+        assert_almost_equal(np.matmul(u, hermitian(u)), np.broadcast_to(np.eye(u.shape[-1]), u.shape))
+        assert_almost_equal(np.matmul(vt, hermitian(vt)), np.broadcast_to(np.eye(vt.shape[-1]), vt.shape))
+        assert_equal(np.sort(s)[..., ::-1], s)
+        assert_(consistent_subclass(u, a))
+        assert_(consistent_subclass(vt, a))
+
+
+class TestSVDHermitian(SVDHermitianCases, SVDBaseTests):
+    hermitian = True
+
+
+class CondCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+    # cond(x, p) for p in (None, 2, -2)
+
+    def do(self, a, b, tags):
+        c = asarray(a)  # a might be a matrix
+        if 'size-0' in tags:
+            assert_raises(LinAlgError, linalg.cond, c)
+            return
+
+        # +-2 norms
+        s = linalg.svd(c, compute_uv=False)
+        assert_almost_equal(
+            linalg.cond(a), s[..., 0] / s[..., -1],
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, 2), s[..., 0] / s[..., -1],
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -2), s[..., -1] / s[..., 0],
+            single_decimal=5, double_decimal=11)
+
+        # Other norms
+        cinv = np.linalg.inv(c)
+        assert_almost_equal(
+            linalg.cond(a, 1),
+            abs(c).sum(-2).max(-1) * abs(cinv).sum(-2).max(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -1),
+            abs(c).sum(-2).min(-1) * abs(cinv).sum(-2).min(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, np.inf),
+            abs(c).sum(-1).max(-1) * abs(cinv).sum(-1).max(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -np.inf),
+            abs(c).sum(-1).min(-1) * abs(cinv).sum(-1).min(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, 'fro'),
+            np.sqrt((abs(c)**2).sum(-1).sum(-1)
+                    * (abs(cinv)**2).sum(-1).sum(-1)),
+            single_decimal=5, double_decimal=11)
+
+
+class TestCond(CondCases):
+    def test_basic_nonsvd(self):
+        # Smoketest the non-svd norms
+        A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]])
+        assert_almost_equal(linalg.cond(A, inf), 4)
+        assert_almost_equal(linalg.cond(A, -inf), 2/3)
+        assert_almost_equal(linalg.cond(A, 1), 4)
+        assert_almost_equal(linalg.cond(A, -1), 0.5)
+        assert_almost_equal(linalg.cond(A, 'fro'), np.sqrt(265 / 12))
+
+    def test_singular(self):
+        # Singular matrices have infinite condition number for
+        # positive norms, and negative norms shouldn't raise
+        # exceptions
+        As = [np.zeros((2, 2)), np.ones((2, 2))]
+        p_pos = [None, 1, 2, 'fro']
+        p_neg = [-1, -2]
+        for A, p in itertools.product(As, p_pos):
+            # Inversion may not hit exact infinity, so just check the
+            # number is large
+            assert_(linalg.cond(A, p) > 1e15)
+        for A, p in itertools.product(As, p_neg):
+            linalg.cond(A, p)
+
+    @pytest.mark.xfail(True, run=False,
+                       reason="Platform/LAPACK-dependent failure, "
+                              "see gh-18914")
+    def test_nan(self):
+        # nans should be passed through, not converted to infs
+        ps = [None, 1, -1, 2, -2, 'fro']
+        p_pos = [None, 1, 2, 'fro']
+
+        A = np.ones((2, 2))
+        A[0,1] = np.nan
+        for p in ps:
+            c = linalg.cond(A, p)
+            assert_(isinstance(c, np.float64))
+            assert_(np.isnan(c))
+
+        A = np.ones((3, 2, 2))
+        A[1,0,1] = np.nan
+        for p in ps:
+            c = linalg.cond(A, p)
+            assert_(np.isnan(c[1]))
+            if p in p_pos:
+                assert_(c[0] > 1e15)
+                assert_(c[2] > 1e15)
+            else:
+                assert_(not np.isnan(c[0]))
+                assert_(not np.isnan(c[2]))
+
+    def test_stacked_singular(self):
+        # Check behavior when only some of the stacked matrices are
+        # singular
+        np.random.seed(1234)
+        A = np.random.rand(2, 2, 2, 2)
+        A[0,0] = 0
+        A[1,1] = 0
+
+        for p in (None, 1, 2, 'fro', -1, -2):
+            c = linalg.cond(A, p)
+            assert_equal(c[0,0], np.inf)
+            assert_equal(c[1,1], np.inf)
+            assert_(np.isfinite(c[0,1]))
+            assert_(np.isfinite(c[1,0]))
+
+
+class PinvCases(LinalgSquareTestCase,
+                LinalgNonsquareTestCase,
+                LinalgGeneralizedSquareTestCase,
+                LinalgGeneralizedNonsquareTestCase):
+
+    def do(self, a, b, tags):
+        a_ginv = linalg.pinv(a)
+        # `a @ a_ginv == I` does not hold if a is singular
+        dot = matmul
+        assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11)
+        assert_(consistent_subclass(a_ginv, a))
+
+
+class TestPinv(PinvCases):
+    pass
+
+
+class PinvHermitianCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        a_ginv = linalg.pinv(a, hermitian=True)
+        # `a @ a_ginv == I` does not hold if a is singular
+        dot = matmul
+        assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11)
+        assert_(consistent_subclass(a_ginv, a))
+
+
+class TestPinvHermitian(PinvHermitianCases):
+    pass
+
+
+def test_pinv_rtol_arg():
+    a = np.array([[1, 2, 3], [4, 1, 1], [2, 3, 1]])
+
+    assert_almost_equal(
+        np.linalg.pinv(a, rcond=0.5),
+        np.linalg.pinv(a, rtol=0.5),
+    )
+
+    with pytest.raises(
+        ValueError, match=r"`rtol` and `rcond` can't be both set."
+    ):
+        np.linalg.pinv(a, rcond=0.5, rtol=0.5)
+
+
+class DetCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        d = linalg.det(a)
+        res = linalg.slogdet(a)
+        s, ld = res.sign, res.logabsdet
+        if asarray(a).dtype.type in (single, double):
+            ad = asarray(a).astype(double)
+        else:
+            ad = asarray(a).astype(cdouble)
+        ev = linalg.eigvals(ad)
+        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
+        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
+
+        s = np.atleast_1d(s)
+        ld = np.atleast_1d(ld)
+        m = (s != 0)
+        assert_almost_equal(np.abs(s[m]), 1)
+        assert_equal(ld[~m], -inf)
+
+
+class TestDet(DetCases):
+    def test_zero(self):
+        assert_equal(linalg.det([[0.0]]), 0.0)
+        assert_equal(type(linalg.det([[0.0]])), double)
+        assert_equal(linalg.det([[0.0j]]), 0.0)
+        assert_equal(type(linalg.det([[0.0j]])), cdouble)
+
+        assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
+        assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
+        assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
+        assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
+        assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
+        assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)
+
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(np.linalg.det(x).dtype, dtype)
+        ph, s = np.linalg.slogdet(x)
+        assert_equal(s.dtype, get_real_dtype(dtype))
+        assert_equal(ph.dtype, dtype)
+
+    def test_0_size(self):
+        a = np.zeros((0, 0), dtype=np.complex64)
+        res = linalg.det(a)
+        assert_equal(res, 1.)
+        assert_(res.dtype.type is np.complex64)
+        res = linalg.slogdet(a)
+        assert_equal(res, (1, 0))
+        assert_(res[0].dtype.type is np.complex64)
+        assert_(res[1].dtype.type is np.float32)
+
+        a = np.zeros((0, 0), dtype=np.float64)
+        res = linalg.det(a)
+        assert_equal(res, 1.)
+        assert_(res.dtype.type is np.float64)
+        res = linalg.slogdet(a)
+        assert_equal(res, (1, 0))
+        assert_(res[0].dtype.type is np.float64)
+        assert_(res[1].dtype.type is np.float64)
+
+
+class LstsqCases(LinalgSquareTestCase, LinalgNonsquareTestCase):
+
+    def do(self, a, b, tags):
+        arr = np.asarray(a)
+        m, n = arr.shape
+        u, s, vt = linalg.svd(a, False)
+        x, residuals, rank, sv = linalg.lstsq(a, b, rcond=-1)
+        if m == 0:
+            assert_((x == 0).all())
+        if m <= n:
+            assert_almost_equal(b, dot(a, x))
+            assert_equal(rank, m)
+        else:
+            assert_equal(rank, n)
+        assert_almost_equal(sv, sv.__array_wrap__(s))
+        if rank == n and m > n:
+            expect_resids = (
+                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
+            expect_resids = np.asarray(expect_resids)
+            if np.asarray(b).ndim == 1:
+                expect_resids.shape = (1,)
+                assert_equal(residuals.shape, expect_resids.shape)
+        else:
+            expect_resids = np.array([]).view(type(x))
+        assert_almost_equal(residuals, expect_resids)
+        assert_(np.issubdtype(residuals.dtype, np.floating))
+        assert_(consistent_subclass(x, b))
+        assert_(consistent_subclass(residuals, b))
+
+
+class TestLstsq(LstsqCases):
+    def test_rcond(self):
+        a = np.array([[0., 1.,  0.,  1.,  2.,  0.],
+                      [0., 2.,  0.,  0.,  1.,  0.],
+                      [1., 0.,  1.,  0.,  0.,  4.],
+                      [0., 0.,  0.,  2.,  3.,  0.]]).T
+
+        b = np.array([1, 0, 0, 0, 0, 0])
+
+        x, residuals, rank, s = linalg.lstsq(a, b, rcond=-1)
+        assert_(rank == 4)
+        x, residuals, rank, s = linalg.lstsq(a, b)
+        assert_(rank == 3)
+        x, residuals, rank, s = linalg.lstsq(a, b, rcond=None)
+        assert_(rank == 3)
+
+    @pytest.mark.parametrize(["m", "n", "n_rhs"], [
+        (4, 2, 2),
+        (0, 4, 1),
+        (0, 4, 2),
+        (4, 0, 1),
+        (4, 0, 2),
+        (4, 2, 0),
+        (0, 0, 0)
+    ])
+    def test_empty_a_b(self, m, n, n_rhs):
+        a = np.arange(m * n).reshape(m, n)
+        b = np.ones((m, n_rhs))
+        x, residuals, rank, s = linalg.lstsq(a, b, rcond=None)
+        if m == 0:
+            assert_((x == 0).all())
+        assert_equal(x.shape, (n, n_rhs))
+        assert_equal(residuals.shape, ((n_rhs,) if m > n else (0,)))
+        if m > n and n_rhs > 0:
+            # residuals are exactly the squared norms of b's columns
+            r = b - np.dot(a, x)
+            assert_almost_equal(residuals, (r * r).sum(axis=-2))
+        assert_equal(rank, min(m, n))
+        assert_equal(s.shape, (min(m, n),))
+
+    def test_incompatible_dims(self):
+        # use modified version of docstring example
+        x = np.array([0, 1, 2, 3])
+        y = np.array([-1, 0.2, 0.9, 2.1, 3.3])
+        A = np.vstack([x, np.ones(len(x))]).T
+        with assert_raises_regex(LinAlgError, "Incompatible dimensions"):
+            linalg.lstsq(A, y, rcond=None)
+
+
+@pytest.mark.parametrize('dt', [np.dtype(c) for c in '?bBhHiIqQefdgFDGO'])
+class TestMatrixPower:
+
+    rshft_0 = np.eye(4)
+    rshft_1 = rshft_0[[3, 0, 1, 2]]
+    rshft_2 = rshft_0[[2, 3, 0, 1]]
+    rshft_3 = rshft_0[[1, 2, 3, 0]]
+    rshft_all = [rshft_0, rshft_1, rshft_2, rshft_3]
+    noninv = array([[1, 0], [0, 0]])
+    stacked = np.block([[[rshft_0]]]*2)
+    #FIXME the 'e' dtype might work in future
+    dtnoinv = [object, np.dtype('e'), np.dtype('g'), np.dtype('G')]
+
+    def test_large_power(self, dt):
+        rshft = self.rshft_1.astype(dt)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 0), self.rshft_0)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 1), self.rshft_1)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 2), self.rshft_2)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 3), self.rshft_3)
+
+    def test_power_is_zero(self, dt):
+        def tz(M):
+            mz = matrix_power(M, 0)
+            assert_equal(mz, identity_like_generalized(M))
+            assert_equal(mz.dtype, M.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_one(self, dt):
+        def tz(mat):
+            mz = matrix_power(mat, 1)
+            assert_equal(mz, mat)
+            assert_equal(mz.dtype, mat.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_two(self, dt):
+        def tz(mat):
+            mz = matrix_power(mat, 2)
+            mmul = matmul if mat.dtype != object else dot
+            assert_equal(mz, mmul(mat, mat))
+            assert_equal(mz.dtype, mat.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_minus_one(self, dt):
+        def tz(mat):
+            invmat = matrix_power(mat, -1)
+            mmul = matmul if mat.dtype != object else dot
+            assert_almost_equal(
+                mmul(invmat, mat), identity_like_generalized(mat))
+
+        for mat in self.rshft_all:
+            if dt not in self.dtnoinv:
+                tz(mat.astype(dt))
+
+    def test_exceptions_bad_power(self, dt):
+        mat = self.rshft_0.astype(dt)
+        assert_raises(TypeError, matrix_power, mat, 1.5)
+        assert_raises(TypeError, matrix_power, mat, [1])
+
+    def test_exceptions_non_square(self, dt):
+        assert_raises(LinAlgError, matrix_power, np.array([1], dt), 1)
+        assert_raises(LinAlgError, matrix_power, np.array([[1], [2]], dt), 1)
+        assert_raises(LinAlgError, matrix_power, np.ones((4, 3, 2), dt), 1)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    def test_exceptions_not_invertible(self, dt):
+        if dt in self.dtnoinv:
+            return
+        mat = self.noninv.astype(dt)
+        assert_raises(LinAlgError, matrix_power, mat, -1)
+
+
+class TestEigvalshCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        # note that eigenvalue arrays returned by eig must be sorted since
+        # their order isn't guaranteed.
+        ev = linalg.eigvalsh(a, 'L')
+        evalues, evectors = linalg.eig(a)
+        evalues.sort(axis=-1)
+        assert_allclose(ev, evalues, rtol=get_rtol(ev.dtype))
+
+        ev2 = linalg.eigvalsh(a, 'U')
+        assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype))
+
+
+class TestEigvalsh:
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w = np.linalg.eigvalsh(x)
+        assert_equal(w.dtype, get_real_dtype(dtype))
+
+    def test_invalid(self):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
+        assert_raises(ValueError, np.linalg.eigvalsh, x, UPLO="lrong")
+        assert_raises(ValueError, np.linalg.eigvalsh, x, "lower")
+        assert_raises(ValueError, np.linalg.eigvalsh, x, "upper")
+
+    def test_UPLO(self):
+        Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
+        Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
+        tgt = np.array([-1, 1], dtype=np.double)
+        rtol = get_rtol(np.double)
+
+        # Check default is 'L'
+        w = np.linalg.eigvalsh(Klo)
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'L'
+        w = np.linalg.eigvalsh(Klo, UPLO='L')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'l'
+        w = np.linalg.eigvalsh(Klo, UPLO='l')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'U'
+        w = np.linalg.eigvalsh(Kup, UPLO='U')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'u'
+        w = np.linalg.eigvalsh(Kup, UPLO='u')
+        assert_allclose(w, tgt, rtol=rtol)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.eigvalsh(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.eigvalsh(a)
+        assert_(res.dtype.type is np.float32)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+
+class TestEighCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        # note that eigenvalue arrays returned by eig must be sorted since
+        # their order isn't guaranteed.
+        res = linalg.eigh(a)
+        ev, evc = res.eigenvalues, res.eigenvectors
+        evalues, evectors = linalg.eig(a)
+        evalues.sort(axis=-1)
+        assert_almost_equal(ev, evalues)
+
+        assert_allclose(matmul(a, evc),
+                        np.asarray(ev)[..., None, :] * np.asarray(evc),
+                        rtol=get_rtol(ev.dtype))
+
+        ev2, evc2 = linalg.eigh(a, 'U')
+        assert_almost_equal(ev2, evalues)
+
+        assert_allclose(matmul(a, evc2),
+                        np.asarray(ev2)[..., None, :] * np.asarray(evc2),
+                        rtol=get_rtol(ev.dtype), err_msg=repr(a))
+
+
+class TestEigh:
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w, v = np.linalg.eigh(x)
+        assert_equal(w.dtype, get_real_dtype(dtype))
+        assert_equal(v.dtype, dtype)
+
+    def test_invalid(self):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
+        assert_raises(ValueError, np.linalg.eigh, x, UPLO="lrong")
+        assert_raises(ValueError, np.linalg.eigh, x, "lower")
+        assert_raises(ValueError, np.linalg.eigh, x, "upper")
+
+    def test_UPLO(self):
+        Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
+        Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
+        tgt = np.array([-1, 1], dtype=np.double)
+        rtol = get_rtol(np.double)
+
+        # Check default is 'L'
+        w, v = np.linalg.eigh(Klo)
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'L'
+        w, v = np.linalg.eigh(Klo, UPLO='L')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'l'
+        w, v = np.linalg.eigh(Klo, UPLO='l')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'U'
+        w, v = np.linalg.eigh(Kup, UPLO='U')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'u'
+        w, v = np.linalg.eigh(Kup, UPLO='u')
+        assert_allclose(w, tgt, rtol=rtol)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res, res_v = linalg.eigh(a)
+        assert_(res_v.dtype.type is np.float64)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res, res_v = linalg.eigh(a)
+        assert_(res_v.dtype.type is np.complex64)
+        assert_(res.dtype.type is np.float32)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+
+class _TestNormBase:
+    dt = None
+    dec = None
+
+    @staticmethod
+    def check_dtype(x, res):
+        if issubclass(x.dtype.type, np.inexact):
+            assert_equal(res.dtype, x.real.dtype)
+        else:
+            # For integer input, don't have to test float precision of output.
+            assert_(issubclass(res.dtype.type, np.floating))
+
+
+class _TestNormGeneral(_TestNormBase):
+
+    def test_empty(self):
+        assert_equal(norm([]), 0.0)
+        assert_equal(norm(array([], dtype=self.dt)), 0.0)
+        assert_equal(norm(atleast_2d(array([], dtype=self.dt))), 0.0)
+
+    def test_vector_return_type(self):
+        a = np.array([1, 0, 1])
+
+        exact_types = np.typecodes['AllInteger']
+        inexact_types = np.typecodes['AllFloat']
+
+        all_types = exact_types + inexact_types
+
+        for each_type in all_types:
+            at = a.astype(each_type)
+
+            an = norm(at, -np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 0.0)
+
+            with suppress_warnings() as sup:
+                sup.filter(RuntimeWarning, "divide by zero encountered")
+                an = norm(at, -1)
+                self.check_dtype(at, an)
+                assert_almost_equal(an, 0.0)
+
+            an = norm(at, 0)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2)
+
+            an = norm(at, 1)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/2.0))
+
+            an = norm(at, 4)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/4.0))
+
+            an = norm(at, np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 1.0)
+
+    def test_vector(self):
+        a = [1, 2, 3, 4]
+        b = [-1, -2, -3, -4]
+        c = [-1, 2, -3, 4]
+
+        def _test(v):
+            np.testing.assert_almost_equal(norm(v), 30 ** 0.5,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, inf), 4.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -inf), 1.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 1), 10.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -1), 12.0 / 25,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 2), 30 ** 0.5,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -2), ((205. / 144) ** -0.5),
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 0), 4,
+                                           decimal=self.dec)
+
+        for v in (a, b, c,):
+            _test(v)
+
+        for v in (array(a, dtype=self.dt), array(b, dtype=self.dt),
+                  array(c, dtype=self.dt)):
+            _test(v)
+
+    def test_axis(self):
+        # Vector norms.
+        # Compare the use of `axis` with computing the norm of each row
+        # or column separately.
+        A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
+        for order in [None, -1, 0, 1, 2, 3, np.inf, -np.inf]:
+            expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])]
+            assert_almost_equal(norm(A, ord=order, axis=0), expected0)
+            expected1 = [norm(A[k, :], ord=order) for k in range(A.shape[0])]
+            assert_almost_equal(norm(A, ord=order, axis=1), expected1)
+
+        # Matrix norms.
+        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+        nd = B.ndim
+        for order in [None, -2, 2, -1, 1, np.inf, -np.inf, 'fro']:
+            for axis in itertools.combinations(range(-nd, nd), 2):
+                row_axis, col_axis = axis
+                if row_axis < 0:
+                    row_axis += nd
+                if col_axis < 0:
+                    col_axis += nd
+                if row_axis == col_axis:
+                    assert_raises(ValueError, norm, B, ord=order, axis=axis)
+                else:
+                    n = norm(B, ord=order, axis=axis)
+
+                    # The logic using k_index only works for nd = 3.
+                    # This has to be changed if nd is increased.
+                    k_index = nd - (row_axis + col_axis)
+                    if row_axis < col_axis:
+                        expected = [norm(B[:].take(k, axis=k_index), ord=order)
+                                    for k in range(B.shape[k_index])]
+                    else:
+                        expected = [norm(B[:].take(k, axis=k_index).T, ord=order)
+                                    for k in range(B.shape[k_index])]
+                    assert_almost_equal(n, expected)
+
+    def test_keepdims(self):
+        A = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+
+        allclose_err = 'order {0}, axis = {1}'
+        shape_err = 'Shape mismatch found {0}, expected {1}, order={2}, axis={3}'
+
+        # check the order=None, axis=None case
+        expected = norm(A, ord=None, axis=None)
+        found = norm(A, ord=None, axis=None, keepdims=True)
+        assert_allclose(np.squeeze(found), expected,
+                        err_msg=allclose_err.format(None, None))
+        expected_shape = (1, 1, 1)
+        assert_(found.shape == expected_shape,
+                shape_err.format(found.shape, expected_shape, None, None))
+
+        # Vector norms.
+        for order in [None, -1, 0, 1, 2, 3, np.inf, -np.inf]:
+            for k in range(A.ndim):
+                expected = norm(A, ord=order, axis=k)
+                found = norm(A, ord=order, axis=k, keepdims=True)
+                assert_allclose(np.squeeze(found), expected,
+                                err_msg=allclose_err.format(order, k))
+                expected_shape = list(A.shape)
+                expected_shape[k] = 1
+                expected_shape = tuple(expected_shape)
+                assert_(found.shape == expected_shape,
+                        shape_err.format(found.shape, expected_shape, order, k))
+
+        # Matrix norms.
+        for order in [None, -2, 2, -1, 1, np.inf, -np.inf, 'fro', 'nuc']:
+            for k in itertools.permutations(range(A.ndim), 2):
+                expected = norm(A, ord=order, axis=k)
+                found = norm(A, ord=order, axis=k, keepdims=True)
+                assert_allclose(np.squeeze(found), expected,
+                                err_msg=allclose_err.format(order, k))
+                expected_shape = list(A.shape)
+                expected_shape[k[0]] = 1
+                expected_shape[k[1]] = 1
+                expected_shape = tuple(expected_shape)
+                assert_(found.shape == expected_shape,
+                        shape_err.format(found.shape, expected_shape, order, k))
+
+
+class _TestNorm2D(_TestNormBase):
+    # Define the part for 2d arrays separately, so we can subclass this
+    # and run the tests using np.matrix in matrixlib.tests.test_matrix_linalg.
+    array = np.array
+
+    def test_matrix_empty(self):
+        assert_equal(norm(self.array([[]], dtype=self.dt)), 0.0)
+
+    def test_matrix_return_type(self):
+        a = self.array([[1, 0, 1], [0, 1, 1]])
+
+        exact_types = np.typecodes['AllInteger']
+
+        # float32, complex64, float64, complex128 types are the only types
+        # allowed by `linalg`, which performs the matrix operations used
+        # within `norm`.
+        inexact_types = 'fdFD'
+
+        all_types = exact_types + inexact_types
+
+        for each_type in all_types:
+            at = a.astype(each_type)
+
+            an = norm(at, -np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            with suppress_warnings() as sup:
+                sup.filter(RuntimeWarning, "divide by zero encountered")
+                an = norm(at, -1)
+                self.check_dtype(at, an)
+                assert_almost_equal(an, 1.0)
+
+            an = norm(at, 1)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 3.0**(1.0/2.0))
+
+            an = norm(at, -2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 1.0)
+
+            an = norm(at, np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 'fro')
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 'nuc')
+            self.check_dtype(at, an)
+            # Lower bar needed to support low precision floats.
+            # They end up being off by 1 in the 7th place.
+            np.testing.assert_almost_equal(an, 2.7320508075688772, decimal=6)
+
+    def test_matrix_2x2(self):
+        A = self.array([[1, 3], [5, 7]], dtype=self.dt)
+        assert_almost_equal(norm(A), 84 ** 0.5)
+        assert_almost_equal(norm(A, 'fro'), 84 ** 0.5)
+        assert_almost_equal(norm(A, 'nuc'), 10.0)
+        assert_almost_equal(norm(A, inf), 12.0)
+        assert_almost_equal(norm(A, -inf), 4.0)
+        assert_almost_equal(norm(A, 1), 10.0)
+        assert_almost_equal(norm(A, -1), 6.0)
+        assert_almost_equal(norm(A, 2), 9.1231056256176615)
+        assert_almost_equal(norm(A, -2), 0.87689437438234041)
+
+        assert_raises(ValueError, norm, A, 'nofro')
+        assert_raises(ValueError, norm, A, -3)
+        assert_raises(ValueError, norm, A, 0)
+
+    def test_matrix_3x3(self):
+        # This test has been added because the 2x2 example
+        # happened to have equal nuclear norm and induced 1-norm.
+        # The 1/10 scaling factor accommodates the absolute tolerance
+        # used in assert_almost_equal.
+        A = (1 / 10) * \
+            self.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt)
+        assert_almost_equal(norm(A), (1 / 10) * 89 ** 0.5)
+        assert_almost_equal(norm(A, 'fro'), (1 / 10) * 89 ** 0.5)
+        assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836)
+        assert_almost_equal(norm(A, inf), 1.1)
+        assert_almost_equal(norm(A, -inf), 0.6)
+        assert_almost_equal(norm(A, 1), 1.0)
+        assert_almost_equal(norm(A, -1), 0.4)
+        assert_almost_equal(norm(A, 2), 0.88722940323461277)
+        assert_almost_equal(norm(A, -2), 0.19456584790481812)
+
+    def test_bad_args(self):
+        # Check that bad arguments raise the appropriate exceptions.
+
+        A = self.array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
+        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+
+        # Using `axis=` or passing in a 1-D array implies vector
+        # norms are being computed, so also using `ord='fro'`
+        # or `ord='nuc'` or any other string raises a ValueError.
+        assert_raises(ValueError, norm, A, 'fro', 0)
+        assert_raises(ValueError, norm, A, 'nuc', 0)
+        assert_raises(ValueError, norm, [3, 4], 'fro', None)
+        assert_raises(ValueError, norm, [3, 4], 'nuc', None)
+        assert_raises(ValueError, norm, [3, 4], 'test', None)
+
+        # Similarly, norm should raise an exception when ord is any finite
+        # number other than 1, 2, -1 or -2 when computing matrix norms.
+        for order in [0, 3]:
+            assert_raises(ValueError, norm, A, order, None)
+            assert_raises(ValueError, norm, A, order, (0, 1))
+            assert_raises(ValueError, norm, B, order, (1, 2))
+
+        # Invalid axis
+        assert_raises(AxisError, norm, B, None, 3)
+        assert_raises(AxisError, norm, B, None, (2, 3))
+        assert_raises(ValueError, norm, B, None, (0, 1, 2))
+
+
+class _TestNorm(_TestNorm2D, _TestNormGeneral):
+    pass
+
+
+class TestNorm_NonSystematic:
+
+    def test_longdouble_norm(self):
+        # Non-regression test: p-norm of longdouble would previously raise
+        # UnboundLocalError.
+        x = np.arange(10, dtype=np.longdouble)
+        old_assert_almost_equal(norm(x, ord=3), 12.65, decimal=2)
+
+    def test_intmin(self):
+        # Non-regression test: p-norm of signed integer would previously do
+        # float cast and abs in the wrong order.
+        x = np.array([-2 ** 31], dtype=np.int32)
+        old_assert_almost_equal(norm(x, ord=3), 2 ** 31, decimal=5)
+
+    def test_complex_high_ord(self):
+        # gh-4156
+        d = np.empty((2,), dtype=np.clongdouble)
+        d[0] = 6 + 7j
+        d[1] = -6 + 7j
+        res = 11.615898132184
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=10)
+        d = d.astype(np.complex128)
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=9)
+        d = d.astype(np.complex64)
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=5)
+
+
+# Separate definitions so we can use them for matrix tests.
+class _TestNormDoubleBase(_TestNormBase):
+    dt = np.double
+    dec = 12
+
+
+class _TestNormSingleBase(_TestNormBase):
+    dt = np.float32
+    dec = 6
+
+
+class _TestNormInt64Base(_TestNormBase):
+    dt = np.int64
+    dec = 12
+
+
+class TestNormDouble(_TestNorm, _TestNormDoubleBase):
+    pass
+
+
+class TestNormSingle(_TestNorm, _TestNormSingleBase):
+    pass
+
+
+class TestNormInt64(_TestNorm, _TestNormInt64Base):
+    pass
+
+
+class TestMatrixRank:
+
+    def test_matrix_rank(self):
+        # Full rank matrix
+        assert_equal(4, matrix_rank(np.eye(4)))
+        # rank deficient matrix
+        I = np.eye(4)
+        I[-1, -1] = 0.
+        assert_equal(matrix_rank(I), 3)
+        # All zeros - zero rank
+        assert_equal(matrix_rank(np.zeros((4, 4))), 0)
+        # 1 dimension - rank 1 unless all 0
+        assert_equal(matrix_rank([1, 0, 0, 0]), 1)
+        assert_equal(matrix_rank(np.zeros((4,))), 0)
+        # accepts array-like
+        assert_equal(matrix_rank([1]), 1)
+        # greater than 2 dimensions treated as stacked matrices
+        ms = np.array([I, np.eye(4), np.zeros((4,4))])
+        assert_equal(matrix_rank(ms), np.array([3, 4, 0]))
+        # works on scalar
+        assert_equal(matrix_rank(1), 1)
+
+        with assert_raises_regex(
+            ValueError, "`tol` and `rtol` can\'t be both set."
+        ):
+            matrix_rank(I, tol=0.01, rtol=0.01)
+
+    def test_symmetric_rank(self):
+        assert_equal(4, matrix_rank(np.eye(4), hermitian=True))
+        assert_equal(1, matrix_rank(np.ones((4, 4)), hermitian=True))
+        assert_equal(0, matrix_rank(np.zeros((4, 4)), hermitian=True))
+        # rank deficient matrix
+        I = np.eye(4)
+        I[-1, -1] = 0.
+        assert_equal(3, matrix_rank(I, hermitian=True))
+        # manually supplied tolerance
+        I[-1, -1] = 1e-8
+        assert_equal(4, matrix_rank(I, hermitian=True, tol=0.99e-8))
+        assert_equal(3, matrix_rank(I, hermitian=True, tol=1.01e-8))
+
+
+def test_reduced_rank():
+    # Test matrices with reduced rank
+    rng = np.random.RandomState(20120714)
+    for i in range(100):
+        # Make a rank deficient matrix
+        X = rng.normal(size=(40, 10))
+        X[:, 0] = X[:, 1] + X[:, 2]
+        # Assert that matrix_rank detected deficiency
+        assert_equal(matrix_rank(X), 9)
+        X[:, 3] = X[:, 4] + X[:, 5]
+        assert_equal(matrix_rank(X), 8)
+
+
+class TestQR:
+    # Define the array class here, so run this on matrices elsewhere.
+    array = np.array
+
+    def check_qr(self, a):
+        # This test expects the argument `a` to be an ndarray or
+        # a subclass of an ndarray of inexact type.
+        a_type = type(a)
+        a_dtype = a.dtype
+        m, n = a.shape
+        k = min(m, n)
+
+        # mode == 'complete'
+        res = linalg.qr(a, mode='complete')
+        Q, R = res.Q, res.R
+        assert_(Q.dtype == a_dtype)
+        assert_(R.dtype == a_dtype)
+        assert_(isinstance(Q, a_type))
+        assert_(isinstance(R, a_type))
+        assert_(Q.shape == (m, m))
+        assert_(R.shape == (m, n))
+        assert_almost_equal(dot(Q, R), a)
+        assert_almost_equal(dot(Q.T.conj(), Q), np.eye(m))
+        assert_almost_equal(np.triu(R), R)
+
+        # mode == 'reduced'
+        q1, r1 = linalg.qr(a, mode='reduced')
+        assert_(q1.dtype == a_dtype)
+        assert_(r1.dtype == a_dtype)
+        assert_(isinstance(q1, a_type))
+        assert_(isinstance(r1, a_type))
+        assert_(q1.shape == (m, k))
+        assert_(r1.shape == (k, n))
+        assert_almost_equal(dot(q1, r1), a)
+        assert_almost_equal(dot(q1.T.conj(), q1), np.eye(k))
+        assert_almost_equal(np.triu(r1), r1)
+
+        # mode == 'r'
+        r2 = linalg.qr(a, mode='r')
+        assert_(r2.dtype == a_dtype)
+        assert_(isinstance(r2, a_type))
+        assert_almost_equal(r2, r1)
+
+
+    @pytest.mark.parametrize(["m", "n"], [
+        (3, 0),
+        (0, 3),
+        (0, 0)
+    ])
+    def test_qr_empty(self, m, n):
+        k = min(m, n)
+        a = np.empty((m, n))
+
+        self.check_qr(a)
+
+        h, tau = np.linalg.qr(a, mode='raw')
+        assert_equal(h.dtype, np.double)
+        assert_equal(tau.dtype, np.double)
+        assert_equal(h.shape, (n, m))
+        assert_equal(tau.shape, (k,))
+
+    def test_mode_raw(self):
+        # The factorization is not unique and varies between libraries,
+        # so it is not possible to check against known values. Functional
+        # testing is a possibility, but awaits the exposure of more
+        # of the functions in lapack_lite. Consequently, this test is
+        # very limited in scope. Note that the results are in FORTRAN
+        # order, hence the h arrays are transposed.
+        a = self.array([[1, 2], [3, 4], [5, 6]], dtype=np.double)
+
+        # Test double
+        h, tau = linalg.qr(a, mode='raw')
+        assert_(h.dtype == np.double)
+        assert_(tau.dtype == np.double)
+        assert_(h.shape == (2, 3))
+        assert_(tau.shape == (2,))
+
+        h, tau = linalg.qr(a.T, mode='raw')
+        assert_(h.dtype == np.double)
+        assert_(tau.dtype == np.double)
+        assert_(h.shape == (3, 2))
+        assert_(tau.shape == (2,))
+
+    def test_mode_all_but_economic(self):
+        a = self.array([[1, 2], [3, 4]])
+        b = self.array([[1, 2], [3, 4], [5, 6]])
+        for dt in "fd":
+            m1 = a.astype(dt)
+            m2 = b.astype(dt)
+            self.check_qr(m1)
+            self.check_qr(m2)
+            self.check_qr(m2.T)
+
+        for dt in "fd":
+            m1 = 1 + 1j * a.astype(dt)
+            m2 = 1 + 1j * b.astype(dt)
+            self.check_qr(m1)
+            self.check_qr(m2)
+            self.check_qr(m2.T)
+
+    def check_qr_stacked(self, a):
+        # This test expects the argument `a` to be an ndarray or
+        # a subclass of an ndarray of inexact type.
+        a_type = type(a)
+        a_dtype = a.dtype
+        m, n = a.shape[-2:]
+        k = min(m, n)
+
+        # mode == 'complete'
+        q, r = linalg.qr(a, mode='complete')
+        assert_(q.dtype == a_dtype)
+        assert_(r.dtype == a_dtype)
+        assert_(isinstance(q, a_type))
+        assert_(isinstance(r, a_type))
+        assert_(q.shape[-2:] == (m, m))
+        assert_(r.shape[-2:] == (m, n))
+        assert_almost_equal(matmul(q, r), a)
+        I_mat = np.identity(q.shape[-1])
+        stack_I_mat = np.broadcast_to(I_mat,
+                        q.shape[:-2] + (q.shape[-1],)*2)
+        assert_almost_equal(matmul(swapaxes(q, -1, -2).conj(), q), stack_I_mat)
+        assert_almost_equal(np.triu(r[..., :, :]), r)
+
+        # mode == 'reduced'
+        q1, r1 = linalg.qr(a, mode='reduced')
+        assert_(q1.dtype == a_dtype)
+        assert_(r1.dtype == a_dtype)
+        assert_(isinstance(q1, a_type))
+        assert_(isinstance(r1, a_type))
+        assert_(q1.shape[-2:] == (m, k))
+        assert_(r1.shape[-2:] == (k, n))
+        assert_almost_equal(matmul(q1, r1), a)
+        I_mat = np.identity(q1.shape[-1])
+        stack_I_mat = np.broadcast_to(I_mat,
+                        q1.shape[:-2] + (q1.shape[-1],)*2)
+        assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1),
+                            stack_I_mat)
+        assert_almost_equal(np.triu(r1[..., :, :]), r1)
+
+        # mode == 'r'
+        r2 = linalg.qr(a, mode='r')
+        assert_(r2.dtype == a_dtype)
+        assert_(isinstance(r2, a_type))
+        assert_almost_equal(r2, r1)
+
+    @pytest.mark.parametrize("size", [
+        (3, 4), (4, 3), (4, 4),
+        (3, 0), (0, 3)])
+    @pytest.mark.parametrize("outer_size", [
+        (2, 2), (2,), (2, 3, 4)])
+    @pytest.mark.parametrize("dt", [
+        np.single, np.double,
+        np.csingle, np.cdouble])
+    def test_stacked_inputs(self, outer_size, size, dt):
+
+        rng = np.random.default_rng(123)
+        A = rng.normal(size=outer_size + size).astype(dt)
+        B = rng.normal(size=outer_size + size).astype(dt)
+        self.check_qr_stacked(A)
+        self.check_qr_stacked(A + 1.j*B)
+
+
+class TestCholesky:
+
+    @pytest.mark.parametrize(
+        'shape', [(1, 1), (2, 2), (3, 3), (50, 50), (3, 10, 10)]
+    )
+    @pytest.mark.parametrize(
+        'dtype', (np.float32, np.float64, np.complex64, np.complex128)
+    )
+    @pytest.mark.parametrize(
+        'upper', [False, True])
+    def test_basic_property(self, shape, dtype, upper):
+        np.random.seed(1)
+        a = np.random.randn(*shape)
+        if np.issubdtype(dtype, np.complexfloating):
+            a = a + 1j*np.random.randn(*shape)
+
+        t = list(range(len(shape)))
+        t[-2:] = -1, -2
+
+        a = np.matmul(a.transpose(t).conj(), a)
+        a = np.asarray(a, dtype=dtype)
+
+        c = np.linalg.cholesky(a, upper=upper)
+
+        # Check A = L L^H or A = U^H U
+        if upper:
+            b = np.matmul(c.transpose(t).conj(), c)
+        else:
+            b = np.matmul(c, c.transpose(t).conj())
+
+        atol = 500 * a.shape[0] * np.finfo(dtype).eps
+        assert_allclose(b, a, atol=atol, err_msg=f'{shape} {dtype}\n{a}\n{c}')
+
+        # Check diag(L or U) is real and positive
+        d = np.diagonal(c, axis1=-2, axis2=-1)
+        assert_(np.all(np.isreal(d)))
+        assert_(np.all(d >= 0))
+
+    def test_0_size(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.cholesky(a)
+        assert_equal(a.shape, res.shape)
+        assert_(res.dtype.type is np.float64)
+        # for documentation purpose:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((1, 0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.cholesky(a)
+        assert_equal(a.shape, res.shape)
+        assert_(res.dtype.type is np.complex64)
+        assert_(isinstance(res, np.ndarray))
+
+    def test_upper_lower_arg(self):
+        # Explicit test of upper argument that also checks the default.
+        a = np.array([[1+0j, 0-2j], [0+2j, 5+0j]])
+
+        assert_equal(linalg.cholesky(a), linalg.cholesky(a, upper=False))
+
+        assert_equal(
+            linalg.cholesky(a, upper=True),
+            linalg.cholesky(a).T.conj()
+        )
+
+
+class TestOuter:
+    arr1 = np.arange(3)
+    arr2 = np.arange(3)
+    expected = np.array(
+        [[0, 0, 0],
+         [0, 1, 2],
+         [0, 2, 4]]
+    )
+
+    assert_array_equal(np.linalg.outer(arr1, arr2), expected)
+
+    with assert_raises_regex(
+        ValueError, "Input arrays must be one-dimensional"
+    ):
+        np.linalg.outer(arr1[:, np.newaxis], arr2)
+
+
+def test_byteorder_check():
+    # Byte order check should pass for native order
+    if sys.byteorder == 'little':
+        native = '<'
+    else:
+        native = '>'
+
+    for dtt in (np.float32, np.float64):
+        arr = np.eye(4, dtype=dtt)
+        n_arr = arr.view(arr.dtype.newbyteorder(native))
+        sw_arr = arr.view(arr.dtype.newbyteorder("S")).byteswap()
+        assert_equal(arr.dtype.byteorder, '=')
+        for routine in (linalg.inv, linalg.det, linalg.pinv):
+            # Normal call
+            res = routine(arr)
+            # Native but not '='
+            assert_array_equal(res, routine(n_arr))
+            # Swapped
+            assert_array_equal(res, routine(sw_arr))
+
+
+@pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+def test_generalized_raise_multiloop():
+    # It should raise an error even if the error doesn't occur in the
+    # last iteration of the ufunc inner loop
+
+    invertible = np.array([[1, 2], [3, 4]])
+    non_invertible = np.array([[1, 1], [1, 1]])
+
+    x = np.zeros([4, 4, 2, 2])[1::2]
+    x[...] = invertible
+    x[0, 0] = non_invertible
+
+    assert_raises(np.linalg.LinAlgError, np.linalg.inv, x)
+
+@pytest.mark.skipif(
+    threading.active_count() > 1,
+    reason="skipping test that uses fork because there are multiple threads")
+def test_xerbla_override():
+    # Check that our xerbla has been successfully linked in. If it is not,
+    # the default xerbla routine is called, which prints a message to stdout
+    # and may, or may not, abort the process depending on the LAPACK package.
+
+    XERBLA_OK = 255
+
+    try:
+        pid = os.fork()
+    except (OSError, AttributeError):
+        # fork failed, or not running on POSIX
+        pytest.skip("Not POSIX or fork failed.")
+
+    if pid == 0:
+        # child; close i/o file handles
+        os.close(1)
+        os.close(0)
+        # Avoid producing core files.
+        import resource
+        resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
+        # These calls may abort.
+        try:
+            np.linalg.lapack_lite.xerbla()
+        except ValueError:
+            pass
+        except Exception:
+            os._exit(os.EX_CONFIG)
+
+        try:
+            a = np.array([[1.]])
+            np.linalg.lapack_lite.dorgqr(
+                1, 1, 1, a,
+                0,  # <- invalid value
+                a, a, 0, 0)
+        except ValueError as e:
+            if "DORGQR parameter number 5" in str(e):
+                # success, reuse error code to mark success as
+                # FORTRAN STOP returns as success.
+                os._exit(XERBLA_OK)
+
+        # Did not abort, but our xerbla was not linked in.
+        os._exit(os.EX_CONFIG)
+    else:
+        # parent
+        pid, status = os.wait()
+        if os.WEXITSTATUS(status) != XERBLA_OK:
+            pytest.skip('Numpy xerbla not linked in.')
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+@pytest.mark.slow
+def test_sdot_bug_8577():
+    # Regression test that loading certain other libraries does not
+    # result to wrong results in float32 linear algebra.
+    #
+    # There's a bug gh-8577 on OSX that can trigger this, and perhaps
+    # there are also other situations in which it occurs.
+    #
+    # Do the check in a separate process.
+
+    bad_libs = ['PyQt5.QtWidgets', 'IPython']
+
+    template = textwrap.dedent("""
+    import sys
+    {before}
+    try:
+        import {bad_lib}
+    except ImportError:
+        sys.exit(0)
+    {after}
+    x = np.ones(2, dtype=np.float32)
+    sys.exit(0 if np.allclose(x.dot(x), 2.0) else 1)
+    """)
+
+    for bad_lib in bad_libs:
+        code = template.format(before="import numpy as np", after="",
+                               bad_lib=bad_lib)
+        subprocess.check_call([sys.executable, "-c", code])
+
+        # Swapped import order
+        code = template.format(after="import numpy as np", before="",
+                               bad_lib=bad_lib)
+        subprocess.check_call([sys.executable, "-c", code])
+
+
+class TestMultiDot:
+
+    def test_basic_function_with_three_arguments(self):
+        # multi_dot with three arguments uses a fast hand coded algorithm to
+        # determine the optimal order. Therefore test it separately.
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+
+        assert_almost_equal(multi_dot([A, B, C]), A.dot(B).dot(C))
+        assert_almost_equal(multi_dot([A, B, C]), np.dot(A, np.dot(B, C)))
+
+    def test_basic_function_with_two_arguments(self):
+        # separate code path with two arguments
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+
+        assert_almost_equal(multi_dot([A, B]), A.dot(B))
+        assert_almost_equal(multi_dot([A, B]), np.dot(A, B))
+
+    def test_basic_function_with_dynamic_programming_optimization(self):
+        # multi_dot with four or more arguments uses the dynamic programming
+        # optimization and therefore deserve a separate
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 1))
+        assert_almost_equal(multi_dot([A, B, C, D]), A.dot(B).dot(C).dot(D))
+
+    def test_vector_as_first_argument(self):
+        # The first argument can be 1-D
+        A1d = np.random.random(2)  # 1-D
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 2))
+
+        # the result should be 1-D
+        assert_equal(multi_dot([A1d, B, C, D]).shape, (2,))
+
+    def test_vector_as_last_argument(self):
+        # The last argument can be 1-D
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D1d = np.random.random(2)  # 1-D
+
+        # the result should be 1-D
+        assert_equal(multi_dot([A, B, C, D1d]).shape, (6,))
+
+    def test_vector_as_first_and_last_argument(self):
+        # The first and last arguments can be 1-D
+        A1d = np.random.random(2)  # 1-D
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D1d = np.random.random(2)  # 1-D
+
+        # the result should be a scalar
+        assert_equal(multi_dot([A1d, B, C, D1d]).shape, ())
+
+    def test_three_arguments_and_out(self):
+        # multi_dot with three arguments uses a fast hand coded algorithm to
+        # determine the optimal order. Therefore test it separately.
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+
+        out = np.zeros((6, 2))
+        ret = multi_dot([A, B, C], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B).dot(C))
+        assert_almost_equal(out, np.dot(A, np.dot(B, C)))
+
+    def test_two_arguments_and_out(self):
+        # separate code path with two arguments
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        out = np.zeros((6, 6))
+        ret = multi_dot([A, B], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B))
+        assert_almost_equal(out, np.dot(A, B))
+
+    def test_dynamic_programming_optimization_and_out(self):
+        # multi_dot with four or more arguments uses the dynamic programming
+        # optimization and therefore deserve a separate test
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 1))
+        out = np.zeros((6, 1))
+        ret = multi_dot([A, B, C, D], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B).dot(C).dot(D))
+
+    def test_dynamic_programming_logic(self):
+        # Test for the dynamic programming part
+        # This test is directly taken from Cormen page 376.
+        arrays = [np.random.random((30, 35)),
+                  np.random.random((35, 15)),
+                  np.random.random((15, 5)),
+                  np.random.random((5, 10)),
+                  np.random.random((10, 20)),
+                  np.random.random((20, 25))]
+        m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
+                               [0.,     0., 2625., 4375.,  7125., 10500.],
+                               [0.,     0.,    0.,  750.,  2500.,  5375.],
+                               [0.,     0.,    0.,    0.,  1000.,  3500.],
+                               [0.,     0.,    0.,    0.,     0.,  5000.],
+                               [0.,     0.,    0.,    0.,     0.,     0.]])
+        s_expected = np.array([[0,  1,  1,  3,  3,  3],
+                               [0,  0,  2,  3,  3,  3],
+                               [0,  0,  0,  3,  3,  3],
+                               [0,  0,  0,  0,  4,  5],
+                               [0,  0,  0,  0,  0,  5],
+                               [0,  0,  0,  0,  0,  0]], dtype=int)
+        s_expected -= 1  # Cormen uses 1-based index, python does not.
+
+        s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
+
+        # Only the upper triangular part (without the diagonal) is interesting.
+        assert_almost_equal(np.triu(s[:-1, 1:]),
+                            np.triu(s_expected[:-1, 1:]))
+        assert_almost_equal(np.triu(m), np.triu(m_expected))
+
+    def test_too_few_input_arrays(self):
+        assert_raises(ValueError, multi_dot, [])
+        assert_raises(ValueError, multi_dot, [np.random.random((3, 3))])
+
+
+class TestTensorinv:
+
+    @pytest.mark.parametrize("arr, ind", [
+        (np.ones((4, 6, 8, 2)), 2),
+        (np.ones((3, 3, 2)), 1),
+        ])
+    def test_non_square_handling(self, arr, ind):
+        with assert_raises(LinAlgError):
+            linalg.tensorinv(arr, ind=ind)
+
+    @pytest.mark.parametrize("shape, ind", [
+        # examples from docstring
+        ((4, 6, 8, 3), 2),
+        ((24, 8, 3), 1),
+        ])
+    def test_tensorinv_shape(self, shape, ind):
+        a = np.eye(24)
+        a.shape = shape
+        ainv = linalg.tensorinv(a=a, ind=ind)
+        expected = a.shape[ind:] + a.shape[:ind]
+        actual = ainv.shape
+        assert_equal(actual, expected)
+
+    @pytest.mark.parametrize("ind", [
+        0, -2,
+        ])
+    def test_tensorinv_ind_limit(self, ind):
+        a = np.eye(24)
+        a.shape = (4, 6, 8, 3)
+        with assert_raises(ValueError):
+            linalg.tensorinv(a=a, ind=ind)
+
+    def test_tensorinv_result(self):
+        # mimic a docstring example
+        a = np.eye(24)
+        a.shape = (24, 8, 3)
+        ainv = linalg.tensorinv(a, ind=1)
+        b = np.ones(24)
+        assert_allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+
+
+class TestTensorsolve:
+
+    @pytest.mark.parametrize("a, axes", [
+        (np.ones((4, 6, 8, 2)), None),
+        (np.ones((3, 3, 2)), (0, 2)),
+        ])
+    def test_non_square_handling(self, a, axes):
+        with assert_raises(LinAlgError):
+            b = np.ones(a.shape[:2])
+            linalg.tensorsolve(a, b, axes=axes)
+
+    @pytest.mark.parametrize("shape",
+        [(2, 3, 6), (3, 4, 4, 3), (0, 3, 3, 0)],
+    )
+    def test_tensorsolve_result(self, shape):
+        a = np.random.randn(*shape)
+        b = np.ones(a.shape[:2])
+        x = np.linalg.tensorsolve(a, b)
+        assert_allclose(np.tensordot(a, x, axes=len(x.shape)), b)
+
+
+def test_unsupported_commontype():
+    # linalg gracefully handles unsupported type
+    arr = np.array([[1, -2], [2, 5]], dtype='float16')
+    with assert_raises_regex(TypeError, "unsupported in linalg"):
+        linalg.cholesky(arr)
+
+
+#@pytest.mark.slow
+#@pytest.mark.xfail(not HAS_LAPACK64, run=False,
+#                   reason="Numpy not compiled with 64-bit BLAS/LAPACK")
+#@requires_memory(free_bytes=16e9)
+@pytest.mark.skip(reason="Bad memory reports lead to OOM in ci testing")
+def test_blas64_dot():
+    n = 2**32
+    a = np.zeros([1, n], dtype=np.float32)
+    b = np.ones([1, 1], dtype=np.float32)
+    a[0,-1] = 1
+    c = np.dot(b, a)
+    assert_equal(c[0,-1], 1)
+
+
+@pytest.mark.xfail(not HAS_LAPACK64,
+                   reason="Numpy not compiled with 64-bit BLAS/LAPACK")
+def test_blas64_geqrf_lwork_smoketest():
+    # Smoke test LAPACK geqrf lwork call with 64-bit integers
+    dtype = np.float64
+    lapack_routine = np.linalg.lapack_lite.dgeqrf
+
+    m = 2**32 + 1
+    n = 2**32 + 1
+    lda = m
+
+    # Dummy arrays, not referenced by the lapack routine, so don't
+    # need to be of the right size
+    a = np.zeros([1, 1], dtype=dtype)
+    work = np.zeros([1], dtype=dtype)
+    tau = np.zeros([1], dtype=dtype)
+
+    # Size query
+    results = lapack_routine(m, n, a, lda, tau, work, -1, 0)
+    assert_equal(results['info'], 0)
+    assert_equal(results['m'], m)
+    assert_equal(results['n'], m)
+
+    # Should result to an integer of a reasonable size
+    lwork = int(work.item())
+    assert_(2**32 < lwork < 2**42)
+
+
+def test_diagonal():
+    # Here we only test if selected axes are compatible
+    # with Array API (last two). Core implementation
+    # of `diagonal` is tested in `test_multiarray.py`.
+    x = np.arange(60).reshape((3, 4, 5))
+    actual = np.linalg.diagonal(x)
+    expected = np.array(
+        [
+            [0,  6, 12, 18],
+            [20, 26, 32, 38],
+            [40, 46, 52, 58],
+        ]
+    )
+    assert_equal(actual, expected)
+
+
+def test_trace():
+    # Here we only test if selected axes are compatible
+    # with Array API (last two). Core implementation
+    # of `trace` is tested in `test_multiarray.py`.
+    x = np.arange(60).reshape((3, 4, 5))
+    actual = np.linalg.trace(x)
+    expected = np.array([36, 116, 196])
+
+    assert_equal(actual, expected)
+
+
+def test_cross():
+    x = np.arange(9).reshape((3, 3))
+    actual = np.linalg.cross(x, x + 1)
+    expected = np.array([
+        [-1, 2, -1],
+        [-1, 2, -1],
+        [-1, 2, -1],
+    ])
+
+    assert_equal(actual, expected)
+
+    # We test that lists are converted to arrays.
+    u = [1, 2, 3]
+    v = [4, 5, 6]
+    actual = np.linalg.cross(u, v)
+    expected = array([-3,  6, -3])
+
+    assert_equal(actual, expected)
+
+    with assert_raises_regex(
+        ValueError,
+        r"input arrays must be \(arrays of\) 3-dimensional vectors"
+    ):
+        x_2dim = x[:, 1:]
+        np.linalg.cross(x_2dim, x_2dim)
+
+
+def test_tensordot():
+    # np.linalg.tensordot is just an alias for np.tensordot
+    x = np.arange(6).reshape((2, 3))
+
+    assert np.linalg.tensordot(x, x) == 55
+    assert np.linalg.tensordot(x, x, axes=[(0, 1), (0, 1)]) == 55
+
+
+def test_matmul():
+    # np.linalg.matmul and np.matmul only differs in the number
+    # of arguments in the signature
+    x = np.arange(6).reshape((2, 3))
+    actual = np.linalg.matmul(x, x.T)
+    expected = np.array([[5, 14], [14, 50]])
+
+    assert_equal(actual, expected)
+
+
+def test_matrix_transpose():
+    x = np.arange(6).reshape((2, 3))
+    actual = np.linalg.matrix_transpose(x)
+    expected = x.T
+
+    assert_equal(actual, expected)
+
+    with assert_raises_regex(
+        ValueError, "array must be at least 2-dimensional"
+    ):
+        np.linalg.matrix_transpose(x[:, 0])
+
+
+def test_matrix_norm():
+    x = np.arange(9).reshape((3, 3))
+    actual = np.linalg.matrix_norm(x)
+
+    assert_almost_equal(actual, np.float64(14.2828), double_decimal=3)
+
+    actual = np.linalg.matrix_norm(x, keepdims=True)
+
+    assert_almost_equal(actual, np.array([[14.2828]]), double_decimal=3)
+
+
+def test_vector_norm():
+    x = np.arange(9).reshape((3, 3))
+    actual = np.linalg.vector_norm(x)
+
+    assert_almost_equal(actual, np.float64(14.2828), double_decimal=3)
+
+    actual = np.linalg.vector_norm(x, axis=0)
+
+    assert_almost_equal(
+        actual, np.array([6.7082, 8.124, 9.6436]), double_decimal=3
+    )
+
+    actual = np.linalg.vector_norm(x, keepdims=True)
+    expected = np.full((1, 1), 14.2828, dtype='float64')
+    assert_equal(actual.shape, expected.shape)
+    assert_almost_equal(actual, expected, double_decimal=3)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_regression.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dd058e0fd1e7112af3d23af5e61e7335f80eb18
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/linalg/tests/test_regression.py
@@ -0,0 +1,177 @@
+""" Test functions for linalg module
+"""
+
+import pytest
+
+import numpy as np
+from numpy import linalg, arange, float64, array, dot, transpose
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_array_equal,
+    assert_array_almost_equal, assert_array_less
+)
+
+
+class TestRegression:
+
+    def test_eig_build(self):
+        # Ticket #652
+        rva = array([1.03221168e+02 + 0.j,
+                     -1.91843603e+01 + 0.j,
+                     -6.04004526e-01 + 15.84422474j,
+                     -6.04004526e-01 - 15.84422474j,
+                     -1.13692929e+01 + 0.j,
+                     -6.57612485e-01 + 10.41755503j,
+                     -6.57612485e-01 - 10.41755503j,
+                     1.82126812e+01 + 0.j,
+                     1.06011014e+01 + 0.j,
+                     7.80732773e+00 + 0.j,
+                     -7.65390898e-01 + 0.j,
+                     1.51971555e-15 + 0.j,
+                     -1.51308713e-15 + 0.j])
+        a = arange(13 * 13, dtype=float64)
+        a.shape = (13, 13)
+        a = a % 17
+        va, ve = linalg.eig(a)
+        va.sort()
+        rva.sort()
+        assert_array_almost_equal(va, rva)
+
+    def test_eigh_build(self):
+        # Ticket 662.
+        rvals = [68.60568999, 89.57756725, 106.67185574]
+
+        cov = array([[77.70273908,   3.51489954,  15.64602427],
+                     [3.51489954,  88.97013878,  -1.07431931],
+                     [15.64602427,  -1.07431931,  98.18223512]])
+
+        vals, vecs = linalg.eigh(cov)
+        assert_array_almost_equal(vals, rvals)
+
+    def test_svd_build(self):
+        # Ticket 627.
+        a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
+        m, n = a.shape
+        u, s, vh = linalg.svd(a)
+
+        b = dot(transpose(u[:, n:]), a)
+
+        assert_array_almost_equal(b, np.zeros((2, 2)))
+
+    def test_norm_vector_badarg(self):
+        # Regression for #786: Frobenius norm for vectors raises
+        # ValueError.
+        assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
+
+    def test_lapack_endian(self):
+        # For bug #1482
+        a = array([[5.7998084,  -2.1825367],
+                   [-2.1825367,   9.85910595]], dtype='>f8')
+        b = array(a, dtype=' 0.5)
+                assert_equal(c, 1)
+                assert_equal(np.linalg.matrix_rank(a), 1)
+                assert_array_less(1, np.linalg.norm(a, ord=2))
+
+                w_svdvals = linalg.svdvals(a)
+                assert_array_almost_equal(w, w_svdvals)
+
+    def test_norm_object_array(self):
+        # gh-7575
+        testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
+
+        norm = linalg.norm(testvector)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=1)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype != np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=2)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(ValueError, linalg.norm, testvector, ord='fro')
+        assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
+        assert_raises(ValueError, linalg.norm, testvector, ord=0)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-1)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-2)
+
+        testmatrix = np.array([[np.array([0, 1]), 0, 0],
+                               [0,                0, 0]], dtype=object)
+
+        norm = linalg.norm(testmatrix)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testmatrix, ord='fro')
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
+
+    def test_lstsq_complex_larger_rhs(self):
+        # gh-9891
+        size = 20
+        n_rhs = 70
+        G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
+        u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
+        b = G.dot(u)
+        # This should work without segmentation fault.
+        u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
+        # check results just in case
+        assert_array_almost_equal(u_lstsq, u)
+
+    @pytest.mark.parametrize("upper", [True, False])
+    def test_cholesky_empty_array(self, upper):
+        # gh-25840 - upper=True hung before.
+        res = np.linalg.cholesky(np.zeros((0, 0)), upper=upper)
+        assert res.size == 0
+
+    @pytest.mark.parametrize("rtol", [0.0, [0.0] * 4, np.zeros((4,))])
+    def test_matrix_rank_rtol_argument(self, rtol):
+        # gh-25877
+        x = np.zeros((4, 3, 2))
+        res = np.linalg.matrix_rank(x, rtol=rtol)
+        assert res.shape == (4,)
+
+    def test_openblas_threading(self):
+        # gh-27036
+        # Test whether matrix multiplication involving a large matrix always
+        # gives the same (correct) answer
+        x = np.arange(500000, dtype=np.float64)
+        src = np.vstack((x, -10*x)).T
+        matrix = np.array([[0, 1], [1, 0]])
+        expected = np.vstack((-10*x, x)).T  # src @ matrix
+        for i in range(200):
+            result = src @ matrix
+            mismatches = (~np.isclose(result, expected)).sum()
+            if mismatches != 0:
+                assert False, ("unexpected result from matmul, "
+                    "probably due to OpenBLAS threading issues")
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/API_CHANGES.txt b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/API_CHANGES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a3d792a1fad983fc0b8403870c2e2d801dabf314
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/API_CHANGES.txt
@@ -0,0 +1,135 @@
+.. -*- rest -*-
+
+==================================================
+API changes in the new masked array implementation
+==================================================
+
+Masked arrays are subclasses of ndarray
+---------------------------------------
+
+Contrary to the original implementation, masked arrays are now regular
+ndarrays::
+
+  >>> x = masked_array([1,2,3],mask=[0,0,1])
+  >>> print isinstance(x, numpy.ndarray)
+  True
+
+
+``_data`` returns a view of the masked array
+--------------------------------------------
+
+Masked arrays are composed of a ``_data`` part and a ``_mask``. Accessing the
+``_data`` part will return a regular ndarray or any of its subclass, depending
+on the initial data::
+
+  >>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]])
+  >>> print x._data
+  [[1 2]
+   [3 4]]
+  >>> print type(x._data)
+  
+
+
+In practice, ``_data`` is implemented as a property, not as an attribute.
+Therefore, you cannot access it directly, and some simple tests such as the
+following one will fail::
+
+  >>>x._data is x._data
+  False
+
+
+``filled(x)`` can return a subclass of ndarray
+----------------------------------------------
+The function ``filled(a)`` returns an array of the same type as ``a._data``::
+
+  >>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]])
+  >>> y = filled(x)
+  >>> print type(y)
+  
+  >>> print y
+  matrix([[     1,      2],
+          [     3, 999999]])
+
+
+``put``, ``putmask`` behave like their ndarray counterparts
+-----------------------------------------------------------
+
+Previously, ``putmask`` was used like this::
+
+  mask = [False,True,True]
+  x = array([1,4,7],mask=mask)
+  putmask(x,mask,[3])
+
+which translated to::
+
+  x[~mask] = [3]
+
+(Note that a ``True``-value in a mask suppresses a value.)
+
+In other words, the mask had the same length as ``x``, whereas
+``values`` had ``sum(~mask)`` elements.
+
+Now, the behaviour is similar to that of ``ndarray.putmask``, where
+the mask and the values are both the same length as ``x``, i.e.
+
+::
+
+  putmask(x,mask,[3,0,0])
+
+
+``fill_value`` is a property
+----------------------------
+
+``fill_value`` is no longer a method, but a property::
+
+  >>> print x.fill_value
+  999999
+
+``cumsum`` and ``cumprod`` ignore missing values
+------------------------------------------------
+
+Missing values are assumed to be the identity element, i.e. 0 for
+``cumsum`` and 1 for ``cumprod``::
+
+  >>> x = N.ma.array([1,2,3,4],mask=[False,True,False,False])
+  >>> print x
+  [1 -- 3 4]
+  >>> print x.cumsum()
+  [1 -- 4 8]
+  >> print x.cumprod()
+  [1 -- 3 12]
+
+``bool(x)`` raises a ValueError
+-------------------------------
+
+Masked arrays now behave like regular ``ndarrays``, in that they cannot be
+converted to booleans:
+
+::
+
+  >>> x = N.ma.array([1,2,3])
+  >>> bool(x)
+  Traceback (most recent call last):
+    File "", line 1, in 
+  ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
+
+
+==================================
+New features (non exhaustive list)
+==================================
+
+``mr_``
+-------
+
+``mr_`` mimics the behavior of ``r_`` for masked arrays::
+
+  >>> np.ma.mr_[3,4,5]
+  masked_array(data = [3 4 5],
+        mask = False,
+        fill_value=999999)
+
+
+``anom``
+--------
+
+The ``anom`` method returns the deviations from the average (anomalies).
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/LICENSE b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b41aae0c89a0f2486843d395f972db759c73c4b8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/LICENSE
@@ -0,0 +1,24 @@
+* Copyright (c) 2006, University of Georgia and Pierre G.F. Gerard-Marchant
+* All rights reserved.
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+*     * Redistributions of source code must retain the above copyright
+*       notice, this list of conditions and the following disclaimer.
+*     * Redistributions in binary form must reproduce the above copyright
+*       notice, this list of conditions and the following disclaimer in the
+*       documentation and/or other materials provided with the distribution.
+*     * Neither the name of the University of Georgia nor the
+*       names of its contributors may be used to endorse or promote products
+*       derived from this software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY
+* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+* DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
+* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/README.rst b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/README.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cd1010329de627e2fe89e51d593e28e9892f69c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/README.rst
@@ -0,0 +1,236 @@
+==================================
+A guide to masked arrays in NumPy
+==================================
+
+.. Contents::
+
+See http://www.scipy.org/scipy/numpy/wiki/MaskedArray (dead link)
+for updates of this document.
+
+
+History
+-------
+
+As a regular user of MaskedArray, I (Pierre G.F. Gerard-Marchant) became
+increasingly frustrated with the subclassing of masked arrays (even if
+I can only blame my inexperience). I needed to develop a class of arrays
+that could store some additional information along with numerical values,
+while keeping the possibility for missing data (picture storing a series
+of dates along with measurements, what would later become the `TimeSeries
+Scikit `__
+(dead link).
+
+I started to implement such a class, but then quickly realized that
+any additional information disappeared when processing these subarrays
+(for example, adding a constant value to a subarray would erase its
+dates). I ended up writing the equivalent of *numpy.core.ma* for my
+particular class, ufuncs included. Everything went fine until I needed to
+subclass my new class, when more problems showed up: some attributes of
+the new subclass were lost during processing. I identified the culprit as
+MaskedArray, which returns masked ndarrays when I expected masked
+arrays of my class. I was preparing myself to rewrite *numpy.core.ma*
+when I forced myself to learn how to subclass ndarrays. As I became more
+familiar with the *__new__* and *__array_finalize__* methods,
+I started to wonder why masked arrays were objects, and not ndarrays,
+and whether it wouldn't be more convenient for subclassing if they did
+behave like regular ndarrays.
+
+The new *maskedarray* is what I eventually come up with. The
+main differences with the initial *numpy.core.ma* package are
+that MaskedArray is now a subclass of *ndarray* and that the
+*_data* section can now be any subclass of *ndarray*. Apart from a
+couple of issues listed below, the behavior of the new MaskedArray
+class reproduces the old one. Initially the *maskedarray*
+implementation was marginally slower than *numpy.ma* in some areas,
+but work is underway to speed it up; the expectation is that it can be
+made substantially faster than the present *numpy.ma*.
+
+
+Note that if the subclass has some special methods and
+attributes, they are not propagated to the masked version:
+this would require a modification of the *__getattribute__*
+method (first trying *ndarray.__getattribute__*, then trying
+*self._data.__getattribute__* if an exception is raised in the first
+place), which really slows things down.
+
+Main differences
+----------------
+
+ * The *_data* part of the masked array can be any subclass of ndarray (but not recarray, cf below).
+ * *fill_value* is now a property, not a function.
+ * in the majority of cases, the mask is forced to *nomask* when no value is actually masked. A notable exception is when a masked array (with no masked values) has just been unpickled.
+ * I got rid of the *share_mask* flag, I never understood its purpose.
+ * *put*, *putmask* and *take* now mimic the ndarray methods, to avoid unpleasant surprises. Moreover, *put* and *putmask* both update the mask when needed.  * if *a* is a masked array, *bool(a)* raises a *ValueError*, as it does with ndarrays.
+ * in the same way, the comparison of two masked arrays is a masked array, not a boolean
+ * *filled(a)* returns an array of the same subclass as *a._data*, and no test is performed on whether it is contiguous or not.
+ * the mask is always printed, even if it's *nomask*, which makes things easy (for me at least) to remember that a masked array is used.
+ * *cumsum* works as if the *_data* array was filled with 0. The mask is preserved, but not updated.
+ * *cumprod* works as if the *_data* array was filled with 1. The mask is preserved, but not updated.
+
+New features
+------------
+
+This list is non-exhaustive...
+
+ * the *mr_* function mimics *r_* for masked arrays.
+ * the *anom* method returns the anomalies (deviations from the average)
+
+Using the new package with numpy.core.ma
+----------------------------------------
+
+I tried to make sure that the new package can understand old masked
+arrays. Unfortunately, there's no upward compatibility.
+
+For example:
+
+>>> import numpy.core.ma as old_ma
+>>> import maskedarray as new_ma
+>>> x = old_ma.array([1,2,3,4,5], mask=[0,0,1,0,0])
+>>> x
+array(data =
+ [     1      2 999999      4      5],
+      mask =
+ [False False True False False],
+      fill_value=999999)
+>>> y = new_ma.array([1,2,3,4,5], mask=[0,0,1,0,0])
+>>> y
+array(data = [1 2 -- 4 5],
+      mask = [False False True False False],
+      fill_value=999999)
+>>> x==y
+array(data =
+ [True True True True True],
+      mask =
+ [False False True False False],
+      fill_value=?)
+>>> old_ma.getmask(x) == new_ma.getmask(x)
+array([True, True, True, True, True])
+>>> old_ma.getmask(y) == new_ma.getmask(y)
+array([True, True, False, True, True])
+>>> old_ma.getmask(y)
+False
+
+
+Using maskedarray with matplotlib
+---------------------------------
+
+Starting with matplotlib 0.91.2, the masked array importing will work with
+the maskedarray branch) as well as with earlier versions.
+
+By default matplotlib still uses numpy.ma, but there is an rcParams setting
+that you can use to select maskedarray instead.  In the matplotlibrc file
+you will find::
+
+  #maskedarray : False       # True to use external maskedarray module
+                             # instead of numpy.ma; this is a temporary #
+                             setting for testing maskedarray.
+
+
+Uncomment and set to True to select maskedarray everywhere.
+Alternatively, you can test a script with maskedarray by using a
+command-line option, e.g.::
+
+  python simple_plot.py --maskedarray
+
+
+Masked records
+--------------
+
+Like *numpy.ma.core*, the *ndarray*-based implementation
+of MaskedArray is limited when working with records: you can
+mask any record of the array, but not a field in a record. If you
+need this feature, you may want to give the *mrecords* package
+a try (available in the *maskedarray* directory in the scipy
+sandbox). This module defines a new class, *MaskedRecord*. An
+instance of this class accepts a *recarray* as data, and uses two
+masks: the *fieldmask* has as many entries as records in the array,
+each entry with the same fields as a record, but of boolean types:
+they indicate whether the field is masked or not; a record entry
+is flagged as masked in the *mask* array if all the fields are
+masked. A few examples in the file should give you an idea of what
+can be done. Note that *mrecords* is still experimental...
+
+Optimizing maskedarray
+----------------------
+
+Should masked arrays be filled before processing or not?
+--------------------------------------------------------
+
+In the current implementation, most operations on masked arrays involve
+the following steps:
+
+ * the input arrays are filled
+ * the operation is performed on the filled arrays
+ * the mask is set for the results, from the combination of the input masks and the mask corresponding to the domain of the operation.
+
+For example, consider the division of two masked arrays::
+
+  import numpy
+  import maskedarray as ma
+  x = ma.array([1,2,3,4],mask=[1,0,0,0], dtype=numpy.float64)
+  y = ma.array([-1,0,1,2], mask=[0,0,0,1], dtype=numpy.float64)
+
+The division of x by y is then computed as::
+
+  d1 = x.filled(0) # d1 = array([0., 2., 3., 4.])
+  d2 = y.filled(1) # array([-1.,  0.,  1.,  1.])
+  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m =
+  array([True,False,False,True])
+  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
+  result = (d1/d2).view(MaskedArray) # masked_array([-0. inf, 3., 4.])
+  result._mask = logical_or(m, dm)
+
+Note that a division by zero takes place. To avoid it, we can consider
+to fill the input arrays, taking the domain mask into account, so that::
+
+  d1 = x._data.copy() # d1 = array([1., 2., 3., 4.])
+  d2 = y._data.copy() # array([-1.,  0.,  1.,  2.])
+  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
+  numpy.putmask(d2, dm, 1) # d2 = array([-1.,  1.,  1.,  2.])
+  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m =
+  array([True,False,False,True])
+  result = (d1/d2).view(MaskedArray) # masked_array([-1. 0., 3., 2.])
+  result._mask = logical_or(m, dm)
+
+Note that the *.copy()* is required to avoid updating the inputs with
+*putmask*.  The *.filled()* method also involves a *.copy()*.
+
+A third possibility consists in avoid filling the arrays::
+
+  d1 = x._data # d1 = array([1., 2., 3., 4.])
+  d2 = y._data # array([-1.,  0.,  1.,  2.])
+  dm = ma.divide.domain(d1,d2) # array([False,  True, False, False])
+  m = ma.mask_or(ma.getmask(x), ma.getmask(y)) # m =
+  array([True,False,False,True])
+  result = (d1/d2).view(MaskedArray) # masked_array([-1. inf, 3., 2.])
+  result._mask = logical_or(m, dm)
+
+Note that here again the division by zero takes place.
+
+A quick benchmark gives the following results:
+
+ * *numpy.ma.divide*  : 2.69 ms per loop
+ * classical division     : 2.21 ms per loop
+ * division w/ prefilling : 2.34 ms per loop
+ * division w/o filling   : 1.55 ms per loop
+
+So, is it worth filling the arrays beforehand ? Yes, if we are interested
+in avoiding floating-point exceptions that may fill the result with infs
+and nans. No, if we are only interested into speed...
+
+
+Thanks
+------
+
+I'd like to thank Paul Dubois, Travis Oliphant and Sasha for the
+original masked array package: without you, I would never have started
+that (it might be argued that I shouldn't have anyway, but that's
+another story...).  I also wish to extend these thanks to Reggie Dugard
+and Eric Firing for their suggestions and numerous improvements.
+
+
+Revision notes
+--------------
+
+  * 08/25/2007 : Creation of this page
+  * 01/23/2007 : The package has been moved to the SciPy sandbox, and is regularly updated: please check out your SVN version!
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03e9fcd075cc06da022f48845e03ed9e28eca765
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.py
@@ -0,0 +1,54 @@
+"""
+=============
+Masked Arrays
+=============
+
+Arrays sometimes contain invalid or missing data.  When doing operations
+on such arrays, we wish to suppress invalid values, which is the purpose masked
+arrays fulfill (an example of typical use is given below).
+
+For example, examine the following array:
+
+>>> x = np.array([2, 1, 3, np.nan, 5, 2, 3, np.nan])
+
+When we try to calculate the mean of the data, the result is undetermined:
+
+>>> np.mean(x)
+nan
+
+The mean is calculated using roughly ``np.sum(x)/len(x)``, but since
+any number added to ``NaN`` [1]_ produces ``NaN``, this doesn't work.  Enter
+masked arrays:
+
+>>> m = np.ma.masked_array(x, np.isnan(x))
+>>> m
+masked_array(data=[2.0, 1.0, 3.0, --, 5.0, 2.0, 3.0, --],
+             mask=[False, False, False, True, False, False, False, True],
+      fill_value=1e+20)
+
+Here, we construct a masked array that suppress all ``NaN`` values.  We
+may now proceed to calculate the mean of the other values:
+
+>>> np.mean(m)
+2.6666666666666665
+
+.. [1] Not-a-Number, a floating point value that is the result of an
+       invalid operation.
+
+.. moduleauthor:: Pierre Gerard-Marchant
+.. moduleauthor:: Jarrod Millman
+
+"""
+from . import core
+from .core import *
+
+from . import extras
+from .extras import *
+
+__all__ = ['core', 'extras']
+__all__ += core.__all__
+__all__ += extras.__all__
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7e38d1793460bf1e8965809def8b826564eed915
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/__init__.pyi
@@ -0,0 +1,458 @@
+from . import core, extras
+from .core import (
+    MAError,
+    MaskError,
+    MaskType,
+    MaskedArray,
+    abs,
+    absolute,
+    add,
+    all,
+    allclose,
+    allequal,
+    alltrue,
+    amax,
+    amin,
+    angle,
+    anom,
+    anomalies,
+    any,
+    append,
+    arange,
+    arccos,
+    arccosh,
+    arcsin,
+    arcsinh,
+    arctan,
+    arctan2,
+    arctanh,
+    argmax,
+    argmin,
+    argsort,
+    around,
+    array,
+    asanyarray,
+    asarray,
+    bool_,
+    bitwise_and,
+    bitwise_or,
+    bitwise_xor,
+    ceil,
+    choose,
+    clip,
+    common_fill_value,
+    compress,
+    compressed,
+    concatenate,
+    conjugate,
+    convolve,
+    copy,
+    correlate,
+    cos,
+    cosh,
+    count,
+    cumprod,
+    cumsum,
+    default_fill_value,
+    diag,
+    diagonal,
+    diff,
+    divide,
+    empty,
+    empty_like,
+    equal,
+    exp,
+    expand_dims,
+    fabs,
+    filled,
+    fix_invalid,
+    flatten_mask,
+    flatten_structured_array,
+    floor,
+    floor_divide,
+    fmod,
+    frombuffer,
+    fromflex,
+    fromfunction,
+    getdata,
+    getmask,
+    getmaskarray,
+    greater,
+    greater_equal,
+    harden_mask,
+    hypot,
+    identity,
+    ids,
+    indices,
+    inner,
+    innerproduct,
+    isMA,
+    isMaskedArray,
+    is_mask,
+    is_masked,
+    isarray,
+    left_shift,
+    less,
+    less_equal,
+    log,
+    log10,
+    log2,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+    make_mask,
+    make_mask_descr,
+    make_mask_none,
+    mask_or,
+    masked,
+    masked_array,
+    masked_equal,
+    masked_greater,
+    masked_greater_equal,
+    masked_inside,
+    masked_invalid,
+    masked_less,
+    masked_less_equal,
+    masked_not_equal,
+    masked_object,
+    masked_outside,
+    masked_print_option,
+    masked_singleton,
+    masked_values,
+    masked_where,
+    max,
+    maximum,
+    maximum_fill_value,
+    mean,
+    min,
+    minimum,
+    minimum_fill_value,
+    mod,
+    multiply,
+    mvoid,
+    ndim,
+    negative,
+    nomask,
+    nonzero,
+    not_equal,
+    ones,
+    ones_like,
+    outer,
+    outerproduct,
+    power,
+    prod,
+    product,
+    ptp,
+    put,
+    putmask,
+    ravel,
+    remainder,
+    repeat,
+    reshape,
+    resize,
+    right_shift,
+    round,
+    round_,
+    set_fill_value,
+    shape,
+    sin,
+    sinh,
+    size,
+    soften_mask,
+    sometrue,
+    sort,
+    sqrt,
+    squeeze,
+    std,
+    subtract,
+    sum,
+    swapaxes,
+    take,
+    tan,
+    tanh,
+    trace,
+    transpose,
+    true_divide,
+    var,
+    where,
+    zeros,
+    zeros_like,
+)
+from .extras import (
+    apply_along_axis,
+    apply_over_axes,
+    atleast_1d,
+    atleast_2d,
+    atleast_3d,
+    average,
+    clump_masked,
+    clump_unmasked,
+    column_stack,
+    compress_cols,
+    compress_nd,
+    compress_rowcols,
+    compress_rows,
+    count_masked,
+    corrcoef,
+    cov,
+    diagflat,
+    dot,
+    dstack,
+    ediff1d,
+    flatnotmasked_contiguous,
+    flatnotmasked_edges,
+    hsplit,
+    hstack,
+    isin,
+    in1d,
+    intersect1d,
+    mask_cols,
+    mask_rowcols,
+    mask_rows,
+    masked_all,
+    masked_all_like,
+    median,
+    mr_,
+    ndenumerate,
+    notmasked_contiguous,
+    notmasked_edges,
+    polyfit,
+    row_stack,
+    setdiff1d,
+    setxor1d,
+    stack,
+    unique,
+    union1d,
+    vander,
+    vstack,
+)
+
+__all__ = [
+    "core",
+    "extras",
+    "MAError",
+    "MaskError",
+    "MaskType",
+    "MaskedArray",
+    "abs",
+    "absolute",
+    "add",
+    "all",
+    "allclose",
+    "allequal",
+    "alltrue",
+    "amax",
+    "amin",
+    "angle",
+    "anom",
+    "anomalies",
+    "any",
+    "append",
+    "arange",
+    "arccos",
+    "arccosh",
+    "arcsin",
+    "arcsinh",
+    "arctan",
+    "arctan2",
+    "arctanh",
+    "argmax",
+    "argmin",
+    "argsort",
+    "around",
+    "array",
+    "asanyarray",
+    "asarray",
+    "bitwise_and",
+    "bitwise_or",
+    "bitwise_xor",
+    "bool_",
+    "ceil",
+    "choose",
+    "clip",
+    "common_fill_value",
+    "compress",
+    "compressed",
+    "concatenate",
+    "conjugate",
+    "convolve",
+    "copy",
+    "correlate",
+    "cos",
+    "cosh",
+    "count",
+    "cumprod",
+    "cumsum",
+    "default_fill_value",
+    "diag",
+    "diagonal",
+    "diff",
+    "divide",
+    "empty",
+    "empty_like",
+    "equal",
+    "exp",
+    "expand_dims",
+    "fabs",
+    "filled",
+    "fix_invalid",
+    "flatten_mask",
+    "flatten_structured_array",
+    "floor",
+    "floor_divide",
+    "fmod",
+    "frombuffer",
+    "fromflex",
+    "fromfunction",
+    "getdata",
+    "getmask",
+    "getmaskarray",
+    "greater",
+    "greater_equal",
+    "harden_mask",
+    "hypot",
+    "identity",
+    "ids",
+    "indices",
+    "inner",
+    "innerproduct",
+    "isMA",
+    "isMaskedArray",
+    "is_mask",
+    "is_masked",
+    "isarray",
+    "left_shift",
+    "less",
+    "less_equal",
+    "log",
+    "log10",
+    "log2",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "make_mask",
+    "make_mask_descr",
+    "make_mask_none",
+    "mask_or",
+    "masked",
+    "masked_array",
+    "masked_equal",
+    "masked_greater",
+    "masked_greater_equal",
+    "masked_inside",
+    "masked_invalid",
+    "masked_less",
+    "masked_less_equal",
+    "masked_not_equal",
+    "masked_object",
+    "masked_outside",
+    "masked_print_option",
+    "masked_singleton",
+    "masked_values",
+    "masked_where",
+    "max",
+    "maximum",
+    "maximum_fill_value",
+    "mean",
+    "min",
+    "minimum",
+    "minimum_fill_value",
+    "mod",
+    "multiply",
+    "mvoid",
+    "ndim",
+    "negative",
+    "nomask",
+    "nonzero",
+    "not_equal",
+    "ones",
+    "ones_like",
+    "outer",
+    "outerproduct",
+    "power",
+    "prod",
+    "product",
+    "ptp",
+    "put",
+    "putmask",
+    "ravel",
+    "remainder",
+    "repeat",
+    "reshape",
+    "resize",
+    "right_shift",
+    "round",
+    "round_",
+    "set_fill_value",
+    "shape",
+    "sin",
+    "sinh",
+    "size",
+    "soften_mask",
+    "sometrue",
+    "sort",
+    "sqrt",
+    "squeeze",
+    "std",
+    "subtract",
+    "sum",
+    "swapaxes",
+    "take",
+    "tan",
+    "tanh",
+    "trace",
+    "transpose",
+    "true_divide",
+    "var",
+    "where",
+    "zeros",
+    "zeros_like",
+    "apply_along_axis",
+    "apply_over_axes",
+    "atleast_1d",
+    "atleast_2d",
+    "atleast_3d",
+    "average",
+    "clump_masked",
+    "clump_unmasked",
+    "column_stack",
+    "compress_cols",
+    "compress_nd",
+    "compress_rowcols",
+    "compress_rows",
+    "count_masked",
+    "corrcoef",
+    "cov",
+    "diagflat",
+    "dot",
+    "dstack",
+    "ediff1d",
+    "flatnotmasked_contiguous",
+    "flatnotmasked_edges",
+    "hsplit",
+    "hstack",
+    "isin",
+    "in1d",
+    "intersect1d",
+    "mask_cols",
+    "mask_rowcols",
+    "mask_rows",
+    "masked_all",
+    "masked_all_like",
+    "median",
+    "mr_",
+    "ndenumerate",
+    "notmasked_contiguous",
+    "notmasked_edges",
+    "polyfit",
+    "row_stack",
+    "setdiff1d",
+    "setxor1d",
+    "stack",
+    "unique",
+    "union1d",
+    "vander",
+    "vstack",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.py
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+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.py
@@ -0,0 +1,8959 @@
+"""
+numpy.ma : a package to handle missing or invalid values.
+
+This package was initially written for numarray by Paul F. Dubois
+at Lawrence Livermore National Laboratory.
+In 2006, the package was completely rewritten by Pierre Gerard-Marchant
+(University of Georgia) to make the MaskedArray class a subclass of ndarray,
+and to improve support of structured arrays.
+
+
+Copyright 1999, 2000, 2001 Regents of the University of California.
+Released for unlimited redistribution.
+
+* Adapted for numpy_core 2005 by Travis Oliphant and (mainly) Paul Dubois.
+* Subclassing of the base `ndarray` 2006 by Pierre Gerard-Marchant
+  (pgmdevlist_AT_gmail_DOT_com)
+* Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com)
+
+.. moduleauthor:: Pierre Gerard-Marchant
+
+"""
+# pylint: disable-msg=E1002
+import builtins
+import functools
+import inspect
+import operator
+import warnings
+import textwrap
+import re
+from typing import Dict
+
+import numpy as np
+import numpy._core.umath as umath
+import numpy._core.numerictypes as ntypes
+from numpy._core import multiarray as mu
+from numpy import ndarray, amax, amin, iscomplexobj, bool_, _NoValue, angle
+from numpy import array as narray, expand_dims, iinfo, finfo
+from numpy._core.numeric import normalize_axis_tuple
+from numpy._utils._inspect import getargspec, formatargspec
+from numpy._utils import set_module
+
+
+__all__ = [
+    'MAError', 'MaskError', 'MaskType', 'MaskedArray', 'abs', 'absolute',
+    'add', 'all', 'allclose', 'allequal', 'alltrue', 'amax', 'amin',
+    'angle', 'anom', 'anomalies', 'any', 'append', 'arange', 'arccos',
+    'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh',
+    'argmax', 'argmin', 'argsort', 'around', 'array', 'asanyarray',
+    'asarray', 'bitwise_and', 'bitwise_or', 'bitwise_xor', 'bool_', 'ceil',
+    'choose', 'clip', 'common_fill_value', 'compress', 'compressed',
+    'concatenate', 'conjugate', 'convolve', 'copy', 'correlate', 'cos', 'cosh',
+    'count', 'cumprod', 'cumsum', 'default_fill_value', 'diag', 'diagonal',
+    'diff', 'divide', 'empty', 'empty_like', 'equal', 'exp',
+    'expand_dims', 'fabs', 'filled', 'fix_invalid', 'flatten_mask',
+    'flatten_structured_array', 'floor', 'floor_divide', 'fmod',
+    'frombuffer', 'fromflex', 'fromfunction', 'getdata', 'getmask',
+    'getmaskarray', 'greater', 'greater_equal', 'harden_mask', 'hypot',
+    'identity', 'ids', 'indices', 'inner', 'innerproduct', 'isMA',
+    'isMaskedArray', 'is_mask', 'is_masked', 'isarray', 'left_shift',
+    'less', 'less_equal', 'log', 'log10', 'log2',
+    'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'make_mask',
+    'make_mask_descr', 'make_mask_none', 'mask_or', 'masked',
+    'masked_array', 'masked_equal', 'masked_greater',
+    'masked_greater_equal', 'masked_inside', 'masked_invalid',
+    'masked_less', 'masked_less_equal', 'masked_not_equal',
+    'masked_object', 'masked_outside', 'masked_print_option',
+    'masked_singleton', 'masked_values', 'masked_where', 'max', 'maximum',
+    'maximum_fill_value', 'mean', 'min', 'minimum', 'minimum_fill_value',
+    'mod', 'multiply', 'mvoid', 'ndim', 'negative', 'nomask', 'nonzero',
+    'not_equal', 'ones', 'ones_like', 'outer', 'outerproduct', 'power', 'prod',
+    'product', 'ptp', 'put', 'putmask', 'ravel', 'remainder',
+    'repeat', 'reshape', 'resize', 'right_shift', 'round', 'round_',
+    'set_fill_value', 'shape', 'sin', 'sinh', 'size', 'soften_mask',
+    'sometrue', 'sort', 'sqrt', 'squeeze', 'std', 'subtract', 'sum',
+    'swapaxes', 'take', 'tan', 'tanh', 'trace', 'transpose', 'true_divide',
+    'var', 'where', 'zeros', 'zeros_like',
+    ]
+
+MaskType = np.bool
+nomask = MaskType(0)
+
+class MaskedArrayFutureWarning(FutureWarning):
+    pass
+
+def _deprecate_argsort_axis(arr):
+    """
+    Adjust the axis passed to argsort, warning if necessary
+
+    Parameters
+    ----------
+    arr
+        The array which argsort was called on
+
+    np.ma.argsort has a long-term bug where the default of the axis argument
+    is wrong (gh-8701), which now must be kept for backwards compatibility.
+    Thankfully, this only makes a difference when arrays are 2- or more-
+    dimensional, so we only need a warning then.
+    """
+    if arr.ndim <= 1:
+        # no warning needed - but switch to -1 anyway, to avoid surprising
+        # subclasses, which are more likely to implement scalar axes.
+        return -1
+    else:
+        # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default
+        warnings.warn(
+            "In the future the default for argsort will be axis=-1, not the "
+            "current None, to match its documentation and np.argsort. "
+            "Explicitly pass -1 or None to silence this warning.",
+            MaskedArrayFutureWarning, stacklevel=3)
+        return None
+
+
+def doc_note(initialdoc, note):
+    """
+    Adds a Notes section to an existing docstring.
+
+    """
+    if initialdoc is None:
+        return
+    if note is None:
+        return initialdoc
+
+    notesplit = re.split(r'\n\s*?Notes\n\s*?-----', inspect.cleandoc(initialdoc))
+    notedoc = "\n\nNotes\n-----\n%s\n" % inspect.cleandoc(note)
+
+    return ''.join(notesplit[:1] + [notedoc] + notesplit[1:])
+
+
+def get_object_signature(obj):
+    """
+    Get the signature from obj
+
+    """
+    try:
+        sig = formatargspec(*getargspec(obj))
+    except TypeError:
+        sig = ''
+    return sig
+
+
+###############################################################################
+#                              Exceptions                                     #
+###############################################################################
+
+
+class MAError(Exception):
+    """
+    Class for masked array related errors.
+
+    """
+    pass
+
+
+class MaskError(MAError):
+    """
+    Class for mask related errors.
+
+    """
+    pass
+
+
+###############################################################################
+#                           Filling options                                   #
+###############################################################################
+
+
+# b: boolean - c: complex - f: floats - i: integer - O: object - S: string
+default_filler = {'b': True,
+                  'c': 1.e20 + 0.0j,
+                  'f': 1.e20,
+                  'i': 999999,
+                  'O': '?',
+                  'S': b'N/A',
+                  'u': 999999,
+                  'V': b'???',
+                  'U': 'N/A'
+                  }
+
+# Add datetime64 and timedelta64 types
+for v in ["Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps",
+          "fs", "as"]:
+    default_filler["M8[" + v + "]"] = np.datetime64("NaT", v)
+    default_filler["m8[" + v + "]"] = np.timedelta64("NaT", v)
+
+float_types_list = [np.half, np.single, np.double, np.longdouble,
+                    np.csingle, np.cdouble, np.clongdouble]
+
+_minvals: Dict[type, int] = {}
+_maxvals: Dict[type, int] = {}
+
+for sctype in ntypes.sctypeDict.values():
+    scalar_dtype = np.dtype(sctype)
+
+    if scalar_dtype.kind in "Mm":
+        info = np.iinfo(np.int64)
+        min_val, max_val = info.min + 1, info.max
+    elif np.issubdtype(scalar_dtype, np.integer):
+        info = np.iinfo(sctype)
+        min_val, max_val = info.min, info.max
+    elif np.issubdtype(scalar_dtype, np.floating):
+        info = np.finfo(sctype)
+        min_val, max_val = info.min, info.max
+    elif scalar_dtype.kind == "b":
+        min_val, max_val = 0, 1
+    else:
+        min_val, max_val = None, None
+
+    _minvals[sctype] = min_val
+    _maxvals[sctype] = max_val
+
+max_filler = _minvals
+max_filler.update([(k, -np.inf) for k in float_types_list[:4]])
+max_filler.update([(k, complex(-np.inf, -np.inf)) for k in float_types_list[-3:]])
+
+min_filler = _maxvals
+min_filler.update([(k,  +np.inf) for k in float_types_list[:4]])
+min_filler.update([(k, complex(+np.inf, +np.inf)) for k in float_types_list[-3:]])
+
+del float_types_list
+
+def _recursive_fill_value(dtype, f):
+    """
+    Recursively produce a fill value for `dtype`, calling f on scalar dtypes
+    """
+    if dtype.names is not None:
+        # We wrap into `array` here, which ensures we use NumPy cast rules
+        # for integer casts, this allows the use of 99999 as a fill value
+        # for int8.
+        # TODO: This is probably a mess, but should best preserve behavior?
+        vals = tuple(
+                np.array(_recursive_fill_value(dtype[name], f))
+                for name in dtype.names)
+        return np.array(vals, dtype=dtype)[()]  # decay to void scalar from 0d
+    elif dtype.subdtype:
+        subtype, shape = dtype.subdtype
+        subval = _recursive_fill_value(subtype, f)
+        return np.full(shape, subval)
+    else:
+        return f(dtype)
+
+
+def _get_dtype_of(obj):
+    """ Convert the argument for *_fill_value into a dtype """
+    if isinstance(obj, np.dtype):
+        return obj
+    elif hasattr(obj, 'dtype'):
+        return obj.dtype
+    else:
+        return np.asanyarray(obj).dtype
+
+
+def default_fill_value(obj):
+    """
+    Return the default fill value for the argument object.
+
+    The default filling value depends on the datatype of the input
+    array or the type of the input scalar:
+
+       ========  ========
+       datatype  default
+       ========  ========
+       bool      True
+       int       999999
+       float     1.e20
+       complex   1.e20+0j
+       object    '?'
+       string    'N/A'
+       ========  ========
+
+    For structured types, a structured scalar is returned, with each field the
+    default fill value for its type.
+
+    For subarray types, the fill value is an array of the same size containing
+    the default scalar fill value.
+
+    Parameters
+    ----------
+    obj : ndarray, dtype or scalar
+        The array data-type or scalar for which the default fill value
+        is returned.
+
+    Returns
+    -------
+    fill_value : scalar
+        The default fill value.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.ma.default_fill_value(1)
+    999999
+    >>> np.ma.default_fill_value(np.array([1.1, 2., np.pi]))
+    1e+20
+    >>> np.ma.default_fill_value(np.dtype(complex))
+    (1e+20+0j)
+
+    """
+    def _scalar_fill_value(dtype):
+        if dtype.kind in 'Mm':
+            return default_filler.get(dtype.str[1:], '?')
+        else:
+            return default_filler.get(dtype.kind, '?')
+
+    dtype = _get_dtype_of(obj)
+    return _recursive_fill_value(dtype, _scalar_fill_value)
+
+
+def _extremum_fill_value(obj, extremum, extremum_name):
+
+    def _scalar_fill_value(dtype):
+        try:
+            return extremum[dtype.type]
+        except KeyError as e:
+            raise TypeError(
+                f"Unsuitable type {dtype} for calculating {extremum_name}."
+            ) from None
+
+    dtype = _get_dtype_of(obj)
+    return _recursive_fill_value(dtype, _scalar_fill_value)
+
+
+def minimum_fill_value(obj):
+    """
+    Return the maximum value that can be represented by the dtype of an object.
+
+    This function is useful for calculating a fill value suitable for
+    taking the minimum of an array with a given dtype.
+
+    Parameters
+    ----------
+    obj : ndarray, dtype or scalar
+        An object that can be queried for it's numeric type.
+
+    Returns
+    -------
+    val : scalar
+        The maximum representable value.
+
+    Raises
+    ------
+    TypeError
+        If `obj` isn't a suitable numeric type.
+
+    See Also
+    --------
+    maximum_fill_value : The inverse function.
+    set_fill_value : Set the filling value of a masked array.
+    MaskedArray.fill_value : Return current fill value.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.int8()
+    >>> ma.minimum_fill_value(a)
+    127
+    >>> a = np.int32()
+    >>> ma.minimum_fill_value(a)
+    2147483647
+
+    An array of numeric data can also be passed.
+
+    >>> a = np.array([1, 2, 3], dtype=np.int8)
+    >>> ma.minimum_fill_value(a)
+    127
+    >>> a = np.array([1, 2, 3], dtype=np.float32)
+    >>> ma.minimum_fill_value(a)
+    inf
+
+    """
+    return _extremum_fill_value(obj, min_filler, "minimum")
+
+
+def maximum_fill_value(obj):
+    """
+    Return the minimum value that can be represented by the dtype of an object.
+
+    This function is useful for calculating a fill value suitable for
+    taking the maximum of an array with a given dtype.
+
+    Parameters
+    ----------
+    obj : ndarray, dtype or scalar
+        An object that can be queried for it's numeric type.
+
+    Returns
+    -------
+    val : scalar
+        The minimum representable value.
+
+    Raises
+    ------
+    TypeError
+        If `obj` isn't a suitable numeric type.
+
+    See Also
+    --------
+    minimum_fill_value : The inverse function.
+    set_fill_value : Set the filling value of a masked array.
+    MaskedArray.fill_value : Return current fill value.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.int8()
+    >>> ma.maximum_fill_value(a)
+    -128
+    >>> a = np.int32()
+    >>> ma.maximum_fill_value(a)
+    -2147483648
+
+    An array of numeric data can also be passed.
+
+    >>> a = np.array([1, 2, 3], dtype=np.int8)
+    >>> ma.maximum_fill_value(a)
+    -128
+    >>> a = np.array([1, 2, 3], dtype=np.float32)
+    >>> ma.maximum_fill_value(a)
+    -inf
+
+    """
+    return _extremum_fill_value(obj, max_filler, "maximum")
+
+
+def _recursive_set_fill_value(fillvalue, dt):
+    """
+    Create a fill value for a structured dtype.
+
+    Parameters
+    ----------
+    fillvalue : scalar or array_like
+        Scalar or array representing the fill value. If it is of shorter
+        length than the number of fields in dt, it will be resized.
+    dt : dtype
+        The structured dtype for which to create the fill value.
+
+    Returns
+    -------
+    val : tuple
+        A tuple of values corresponding to the structured fill value.
+
+    """
+    fillvalue = np.resize(fillvalue, len(dt.names))
+    output_value = []
+    for (fval, name) in zip(fillvalue, dt.names):
+        cdtype = dt[name]
+        if cdtype.subdtype:
+            cdtype = cdtype.subdtype[0]
+
+        if cdtype.names is not None:
+            output_value.append(tuple(_recursive_set_fill_value(fval, cdtype)))
+        else:
+            output_value.append(np.array(fval, dtype=cdtype).item())
+    return tuple(output_value)
+
+
+def _check_fill_value(fill_value, ndtype):
+    """
+    Private function validating the given `fill_value` for the given dtype.
+
+    If fill_value is None, it is set to the default corresponding to the dtype.
+
+    If fill_value is not None, its value is forced to the given dtype.
+
+    The result is always a 0d array.
+
+    """
+    ndtype = np.dtype(ndtype)
+    if fill_value is None:
+        fill_value = default_fill_value(ndtype)
+        # TODO: It seems better to always store a valid fill_value, the oddity
+        #       about is that `_fill_value = None` would behave even more
+        #       different then.
+        #       (e.g. this allows arr_uint8.astype(int64) to have the default
+        #       fill value again...)
+        # The one thing that changed in 2.0/2.1 around cast safety is that the
+        # default `int(99...)` is not a same-kind cast anymore, so if we
+        # have a uint, use the default uint.
+        if ndtype.kind == "u":
+            fill_value = np.uint(fill_value)
+    elif ndtype.names is not None:
+        if isinstance(fill_value, (ndarray, np.void)):
+            try:
+                fill_value = np.asarray(fill_value, dtype=ndtype)
+            except ValueError as e:
+                err_msg = "Unable to transform %s to dtype %s"
+                raise ValueError(err_msg % (fill_value, ndtype)) from e
+        else:
+            fill_value = np.asarray(fill_value, dtype=object)
+            fill_value = np.array(_recursive_set_fill_value(fill_value, ndtype),
+                                  dtype=ndtype)
+    else:
+        if isinstance(fill_value, str) and (ndtype.char not in 'OSVU'):
+            # Note this check doesn't work if fill_value is not a scalar
+            err_msg = "Cannot set fill value of string with array of dtype %s"
+            raise TypeError(err_msg % ndtype)
+        else:
+            # In case we want to convert 1e20 to int.
+            # Also in case of converting string arrays.
+            try:
+                fill_value = np.asarray(fill_value, dtype=ndtype)
+            except (OverflowError, ValueError) as e:
+                # Raise TypeError instead of OverflowError or ValueError.
+                # OverflowError is seldom used, and the real problem here is
+                # that the passed fill_value is not compatible with the ndtype.
+                err_msg = "Cannot convert fill_value %s to dtype %s"
+                raise TypeError(err_msg % (fill_value, ndtype)) from e
+    return np.array(fill_value)
+
+
+def set_fill_value(a, fill_value):
+    """
+    Set the filling value of a, if a is a masked array.
+
+    This function changes the fill value of the masked array `a` in place.
+    If `a` is not a masked array, the function returns silently, without
+    doing anything.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    fill_value : dtype
+        Filling value. A consistency test is performed to make sure
+        the value is compatible with the dtype of `a`.
+
+    Returns
+    -------
+    None
+        Nothing returned by this function.
+
+    See Also
+    --------
+    maximum_fill_value : Return the default fill value for a dtype.
+    MaskedArray.fill_value : Return current fill value.
+    MaskedArray.set_fill_value : Equivalent method.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(5)
+    >>> a
+    array([0, 1, 2, 3, 4])
+    >>> a = ma.masked_where(a < 3, a)
+    >>> a
+    masked_array(data=[--, --, --, 3, 4],
+                 mask=[ True,  True,  True, False, False],
+           fill_value=999999)
+    >>> ma.set_fill_value(a, -999)
+    >>> a
+    masked_array(data=[--, --, --, 3, 4],
+                 mask=[ True,  True,  True, False, False],
+           fill_value=-999)
+
+    Nothing happens if `a` is not a masked array.
+
+    >>> a = list(range(5))
+    >>> a
+    [0, 1, 2, 3, 4]
+    >>> ma.set_fill_value(a, 100)
+    >>> a
+    [0, 1, 2, 3, 4]
+    >>> a = np.arange(5)
+    >>> a
+    array([0, 1, 2, 3, 4])
+    >>> ma.set_fill_value(a, 100)
+    >>> a
+    array([0, 1, 2, 3, 4])
+
+    """
+    if isinstance(a, MaskedArray):
+        a.set_fill_value(fill_value)
+    return
+
+
+def get_fill_value(a):
+    """
+    Return the filling value of a, if any.  Otherwise, returns the
+    default filling value for that type.
+
+    """
+    if isinstance(a, MaskedArray):
+        result = a.fill_value
+    else:
+        result = default_fill_value(a)
+    return result
+
+
+def common_fill_value(a, b):
+    """
+    Return the common filling value of two masked arrays, if any.
+
+    If ``a.fill_value == b.fill_value``, return the fill value,
+    otherwise return None.
+
+    Parameters
+    ----------
+    a, b : MaskedArray
+        The masked arrays for which to compare fill values.
+
+    Returns
+    -------
+    fill_value : scalar or None
+        The common fill value, or None.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([0, 1.], fill_value=3)
+    >>> y = np.ma.array([0, 1.], fill_value=3)
+    >>> np.ma.common_fill_value(x, y)
+    3.0
+
+    """
+    t1 = get_fill_value(a)
+    t2 = get_fill_value(b)
+    if t1 == t2:
+        return t1
+    return None
+
+
+def filled(a, fill_value=None):
+    """
+    Return input as an `~numpy.ndarray`, with masked values replaced by
+    `fill_value`.
+
+    If `a` is not a `MaskedArray`, `a` itself is returned.
+    If `a` is a `MaskedArray` with no masked values, then ``a.data`` is
+    returned.
+    If `a` is a `MaskedArray` and `fill_value` is None, `fill_value` is set to
+    ``a.fill_value``.
+
+    Parameters
+    ----------
+    a : MaskedArray or array_like
+        An input object.
+    fill_value : array_like, optional.
+        Can be scalar or non-scalar. If non-scalar, the
+        resulting filled array should be broadcastable
+        over input array. Default is None.
+
+    Returns
+    -------
+    a : ndarray
+        The filled array.
+
+    See Also
+    --------
+    compressed
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
+    ...                                                [1, 0, 0],
+    ...                                                [0, 0, 0]])
+    >>> x.filled()
+    array([[999999,      1,      2],
+           [999999,      4,      5],
+           [     6,      7,      8]])
+    >>> x.filled(fill_value=333)
+    array([[333,   1,   2],
+           [333,   4,   5],
+           [  6,   7,   8]])
+    >>> x.filled(fill_value=np.arange(3))
+    array([[0, 1, 2],
+           [0, 4, 5],
+           [6, 7, 8]])
+
+    """
+    if hasattr(a, 'filled'):
+        return a.filled(fill_value)
+
+    elif isinstance(a, ndarray):
+        # Should we check for contiguity ? and a.flags['CONTIGUOUS']:
+        return a
+    elif isinstance(a, dict):
+        return np.array(a, 'O')
+    else:
+        return np.array(a)
+
+
+def get_masked_subclass(*arrays):
+    """
+    Return the youngest subclass of MaskedArray from a list of (masked) arrays.
+
+    In case of siblings, the first listed takes over.
+
+    """
+    if len(arrays) == 1:
+        arr = arrays[0]
+        if isinstance(arr, MaskedArray):
+            rcls = type(arr)
+        else:
+            rcls = MaskedArray
+    else:
+        arrcls = [type(a) for a in arrays]
+        rcls = arrcls[0]
+        if not issubclass(rcls, MaskedArray):
+            rcls = MaskedArray
+        for cls in arrcls[1:]:
+            if issubclass(cls, rcls):
+                rcls = cls
+    # Don't return MaskedConstant as result: revert to MaskedArray
+    if rcls.__name__ == 'MaskedConstant':
+        return MaskedArray
+    return rcls
+
+
+def getdata(a, subok=True):
+    """
+    Return the data of a masked array as an ndarray.
+
+    Return the data of `a` (if any) as an ndarray if `a` is a ``MaskedArray``,
+    else return `a` as a ndarray or subclass (depending on `subok`) if not.
+
+    Parameters
+    ----------
+    a : array_like
+        Input ``MaskedArray``, alternatively a ndarray or a subclass thereof.
+    subok : bool
+        Whether to force the output to be a `pure` ndarray (False) or to
+        return a subclass of ndarray if appropriate (True, default).
+
+    See Also
+    --------
+    getmask : Return the mask of a masked array, or nomask.
+    getmaskarray : Return the mask of a masked array, or full array of False.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.masked_equal([[1,2],[3,4]], 2)
+    >>> a
+    masked_array(
+      data=[[1, --],
+            [3, 4]],
+      mask=[[False,  True],
+            [False, False]],
+      fill_value=2)
+    >>> ma.getdata(a)
+    array([[1, 2],
+           [3, 4]])
+
+    Equivalently use the ``MaskedArray`` `data` attribute.
+
+    >>> a.data
+    array([[1, 2],
+           [3, 4]])
+
+    """
+    try:
+        data = a._data
+    except AttributeError:
+        data = np.array(a, copy=None, subok=subok)
+    if not subok:
+        return data.view(ndarray)
+    return data
+
+
+get_data = getdata
+
+
+def fix_invalid(a, mask=nomask, copy=True, fill_value=None):
+    """
+    Return input with invalid data masked and replaced by a fill value.
+
+    Invalid data means values of `nan`, `inf`, etc.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, a (subclass of) ndarray.
+    mask : sequence, optional
+        Mask. Must be convertible to an array of booleans with the same
+        shape as `data`. True indicates a masked (i.e. invalid) data.
+    copy : bool, optional
+        Whether to use a copy of `a` (True) or to fix `a` in place (False).
+        Default is True.
+    fill_value : scalar, optional
+        Value used for fixing invalid data. Default is None, in which case
+        the ``a.fill_value`` is used.
+
+    Returns
+    -------
+    b : MaskedArray
+        The input array with invalid entries fixed.
+
+    Notes
+    -----
+    A copy is performed by default.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3)
+    >>> x
+    masked_array(data=[--, -1.0, nan, inf],
+                 mask=[ True, False, False, False],
+           fill_value=1e+20)
+    >>> np.ma.fix_invalid(x)
+    masked_array(data=[--, -1.0, --, --],
+                 mask=[ True, False,  True,  True],
+           fill_value=1e+20)
+
+    >>> fixed = np.ma.fix_invalid(x)
+    >>> fixed.data
+    array([ 1.e+00, -1.e+00,  1.e+20,  1.e+20])
+    >>> x.data
+    array([ 1., -1., nan, inf])
+
+    """
+    a = masked_array(a, copy=copy, mask=mask, subok=True)
+    invalid = np.logical_not(np.isfinite(a._data))
+    if not invalid.any():
+        return a
+    a._mask |= invalid
+    if fill_value is None:
+        fill_value = a.fill_value
+    a._data[invalid] = fill_value
+    return a
+
+def is_string_or_list_of_strings(val):
+    return (isinstance(val, str) or
+            (isinstance(val, list) and val and
+             builtins.all(isinstance(s, str) for s in val)))
+
+###############################################################################
+#                                  Ufuncs                                     #
+###############################################################################
+
+
+ufunc_domain = {}
+ufunc_fills = {}
+
+
+class _DomainCheckInterval:
+    """
+    Define a valid interval, so that :
+
+    ``domain_check_interval(a,b)(x) == True`` where
+    ``x < a`` or ``x > b``.
+
+    """
+
+    def __init__(self, a, b):
+        "domain_check_interval(a,b)(x) = true where x < a or y > b"
+        if a > b:
+            (a, b) = (b, a)
+        self.a = a
+        self.b = b
+
+    def __call__(self, x):
+        "Execute the call behavior."
+        # nans at masked positions cause RuntimeWarnings, even though
+        # they are masked. To avoid this we suppress warnings.
+        with np.errstate(invalid='ignore'):
+            return umath.logical_or(umath.greater(x, self.b),
+                                    umath.less(x, self.a))
+
+
+class _DomainTan:
+    """
+    Define a valid interval for the `tan` function, so that:
+
+    ``domain_tan(eps) = True`` where ``abs(cos(x)) < eps``
+
+    """
+
+    def __init__(self, eps):
+        "domain_tan(eps) = true where abs(cos(x)) < eps)"
+        self.eps = eps
+
+    def __call__(self, x):
+        "Executes the call behavior."
+        with np.errstate(invalid='ignore'):
+            return umath.less(umath.absolute(umath.cos(x)), self.eps)
+
+
+class _DomainSafeDivide:
+    """
+    Define a domain for safe division.
+
+    """
+
+    def __init__(self, tolerance=None):
+        self.tolerance = tolerance
+
+    def __call__(self, a, b):
+        # Delay the selection of the tolerance to here in order to reduce numpy
+        # import times. The calculation of these parameters is a substantial
+        # component of numpy's import time.
+        if self.tolerance is None:
+            self.tolerance = np.finfo(float).tiny
+        # don't call ma ufuncs from __array_wrap__ which would fail for scalars
+        a, b = np.asarray(a), np.asarray(b)
+        with np.errstate(all='ignore'):
+            return umath.absolute(a) * self.tolerance >= umath.absolute(b)
+
+
+class _DomainGreater:
+    """
+    DomainGreater(v)(x) is True where x <= v.
+
+    """
+
+    def __init__(self, critical_value):
+        "DomainGreater(v)(x) = true where x <= v"
+        self.critical_value = critical_value
+
+    def __call__(self, x):
+        "Executes the call behavior."
+        with np.errstate(invalid='ignore'):
+            return umath.less_equal(x, self.critical_value)
+
+
+class _DomainGreaterEqual:
+    """
+    DomainGreaterEqual(v)(x) is True where x < v.
+
+    """
+
+    def __init__(self, critical_value):
+        "DomainGreaterEqual(v)(x) = true where x < v"
+        self.critical_value = critical_value
+
+    def __call__(self, x):
+        "Executes the call behavior."
+        with np.errstate(invalid='ignore'):
+            return umath.less(x, self.critical_value)
+
+
+class _MaskedUFunc:
+    def __init__(self, ufunc):
+        self.f = ufunc
+        self.__doc__ = ufunc.__doc__
+        self.__name__ = ufunc.__name__
+        self.__qualname__ = ufunc.__qualname__
+
+    def __str__(self):
+        return f"Masked version of {self.f}"
+
+
+class _MaskedUnaryOperation(_MaskedUFunc):
+    """
+    Defines masked version of unary operations, where invalid values are
+    pre-masked.
+
+    Parameters
+    ----------
+    mufunc : callable
+        The function for which to define a masked version. Made available
+        as ``_MaskedUnaryOperation.f``.
+    fill : scalar, optional
+        Filling value, default is 0.
+    domain : class instance
+        Domain for the function. Should be one of the ``_Domain*``
+        classes. Default is None.
+
+    """
+
+    def __init__(self, mufunc, fill=0, domain=None):
+        super().__init__(mufunc)
+        self.fill = fill
+        self.domain = domain
+        ufunc_domain[mufunc] = domain
+        ufunc_fills[mufunc] = fill
+
+    def __call__(self, a, *args, **kwargs):
+        """
+        Execute the call behavior.
+
+        """
+        d = getdata(a)
+        # Deal with domain
+        if self.domain is not None:
+            # Case 1.1. : Domained function
+            # nans at masked positions cause RuntimeWarnings, even though
+            # they are masked. To avoid this we suppress warnings.
+            with np.errstate(divide='ignore', invalid='ignore'):
+                result = self.f(d, *args, **kwargs)
+            # Make a mask
+            m = ~umath.isfinite(result)
+            m |= self.domain(d)
+            m |= getmask(a)
+        else:
+            # Case 1.2. : Function without a domain
+            # Get the result and the mask
+            with np.errstate(divide='ignore', invalid='ignore'):
+                result = self.f(d, *args, **kwargs)
+            m = getmask(a)
+
+        if not result.ndim:
+            # Case 2.1. : The result is scalarscalar
+            if m:
+                return masked
+            return result
+
+        if m is not nomask:
+            # Case 2.2. The result is an array
+            # We need to fill the invalid data back w/ the input Now,
+            # that's plain silly: in C, we would just skip the element and
+            # keep the original, but we do have to do it that way in Python
+
+            # In case result has a lower dtype than the inputs (as in
+            # equal)
+            try:
+                np.copyto(result, d, where=m)
+            except TypeError:
+                pass
+        # Transform to
+        masked_result = result.view(get_masked_subclass(a))
+        masked_result._mask = m
+        masked_result._update_from(a)
+        return masked_result
+
+
+class _MaskedBinaryOperation(_MaskedUFunc):
+    """
+    Define masked version of binary operations, where invalid
+    values are pre-masked.
+
+    Parameters
+    ----------
+    mbfunc : function
+        The function for which to define a masked version. Made available
+        as ``_MaskedBinaryOperation.f``.
+    domain : class instance
+        Default domain for the function. Should be one of the ``_Domain*``
+        classes. Default is None.
+    fillx : scalar, optional
+        Filling value for the first argument, default is 0.
+    filly : scalar, optional
+        Filling value for the second argument, default is 0.
+
+    """
+
+    def __init__(self, mbfunc, fillx=0, filly=0):
+        """
+        abfunc(fillx, filly) must be defined.
+
+        abfunc(x, filly) = x for all x to enable reduce.
+
+        """
+        super().__init__(mbfunc)
+        self.fillx = fillx
+        self.filly = filly
+        ufunc_domain[mbfunc] = None
+        ufunc_fills[mbfunc] = (fillx, filly)
+
+    def __call__(self, a, b, *args, **kwargs):
+        """
+        Execute the call behavior.
+
+        """
+        # Get the data, as ndarray
+        (da, db) = (getdata(a), getdata(b))
+        # Get the result
+        with np.errstate():
+            np.seterr(divide='ignore', invalid='ignore')
+            result = self.f(da, db, *args, **kwargs)
+        # Get the mask for the result
+        (ma, mb) = (getmask(a), getmask(b))
+        if ma is nomask:
+            if mb is nomask:
+                m = nomask
+            else:
+                m = umath.logical_or(getmaskarray(a), mb)
+        elif mb is nomask:
+            m = umath.logical_or(ma, getmaskarray(b))
+        else:
+            m = umath.logical_or(ma, mb)
+
+        # Case 1. : scalar
+        if not result.ndim:
+            if m:
+                return masked
+            return result
+
+        # Case 2. : array
+        # Revert result to da where masked
+        if m is not nomask and m.any():
+            # any errors, just abort; impossible to guarantee masked values
+            try:
+                np.copyto(result, da, casting='unsafe', where=m)
+            except Exception:
+                pass
+
+        # Transforms to a (subclass of) MaskedArray
+        masked_result = result.view(get_masked_subclass(a, b))
+        masked_result._mask = m
+        if isinstance(a, MaskedArray):
+            masked_result._update_from(a)
+        elif isinstance(b, MaskedArray):
+            masked_result._update_from(b)
+        return masked_result
+
+    def reduce(self, target, axis=0, dtype=None):
+        """
+        Reduce `target` along the given `axis`.
+
+        """
+        tclass = get_masked_subclass(target)
+        m = getmask(target)
+        t = filled(target, self.filly)
+        if t.shape == ():
+            t = t.reshape(1)
+            if m is not nomask:
+                m = make_mask(m, copy=True)
+                m.shape = (1,)
+
+        if m is nomask:
+            tr = self.f.reduce(t, axis)
+            mr = nomask
+        else:
+            tr = self.f.reduce(t, axis, dtype=dtype)
+            mr = umath.logical_and.reduce(m, axis)
+
+        if not tr.shape:
+            if mr:
+                return masked
+            else:
+                return tr
+        masked_tr = tr.view(tclass)
+        masked_tr._mask = mr
+        return masked_tr
+
+    def outer(self, a, b):
+        """
+        Return the function applied to the outer product of a and b.
+
+        """
+        (da, db) = (getdata(a), getdata(b))
+        d = self.f.outer(da, db)
+        ma = getmask(a)
+        mb = getmask(b)
+        if ma is nomask and mb is nomask:
+            m = nomask
+        else:
+            ma = getmaskarray(a)
+            mb = getmaskarray(b)
+            m = umath.logical_or.outer(ma, mb)
+        if (not m.ndim) and m:
+            return masked
+        if m is not nomask:
+            np.copyto(d, da, where=m)
+        if not d.shape:
+            return d
+        masked_d = d.view(get_masked_subclass(a, b))
+        masked_d._mask = m
+        return masked_d
+
+    def accumulate(self, target, axis=0):
+        """Accumulate `target` along `axis` after filling with y fill
+        value.
+
+        """
+        tclass = get_masked_subclass(target)
+        t = filled(target, self.filly)
+        result = self.f.accumulate(t, axis)
+        masked_result = result.view(tclass)
+        return masked_result
+
+
+
+class _DomainedBinaryOperation(_MaskedUFunc):
+    """
+    Define binary operations that have a domain, like divide.
+
+    They have no reduce, outer or accumulate.
+
+    Parameters
+    ----------
+    mbfunc : function
+        The function for which to define a masked version. Made available
+        as ``_DomainedBinaryOperation.f``.
+    domain : class instance
+        Default domain for the function. Should be one of the ``_Domain*``
+        classes.
+    fillx : scalar, optional
+        Filling value for the first argument, default is 0.
+    filly : scalar, optional
+        Filling value for the second argument, default is 0.
+
+    """
+
+    def __init__(self, dbfunc, domain, fillx=0, filly=0):
+        """abfunc(fillx, filly) must be defined.
+           abfunc(x, filly) = x for all x to enable reduce.
+        """
+        super().__init__(dbfunc)
+        self.domain = domain
+        self.fillx = fillx
+        self.filly = filly
+        ufunc_domain[dbfunc] = domain
+        ufunc_fills[dbfunc] = (fillx, filly)
+
+    def __call__(self, a, b, *args, **kwargs):
+        "Execute the call behavior."
+        # Get the data
+        (da, db) = (getdata(a), getdata(b))
+        # Get the result
+        with np.errstate(divide='ignore', invalid='ignore'):
+            result = self.f(da, db, *args, **kwargs)
+        # Get the mask as a combination of the source masks and invalid
+        m = ~umath.isfinite(result)
+        m |= getmask(a)
+        m |= getmask(b)
+        # Apply the domain
+        domain = ufunc_domain.get(self.f, None)
+        if domain is not None:
+            m |= domain(da, db)
+        # Take care of the scalar case first
+        if not m.ndim:
+            if m:
+                return masked
+            else:
+                return result
+        # When the mask is True, put back da if possible
+        # any errors, just abort; impossible to guarantee masked values
+        try:
+            np.copyto(result, 0, casting='unsafe', where=m)
+            # avoid using "*" since this may be overlaid
+            masked_da = umath.multiply(m, da)
+            # only add back if it can be cast safely
+            if np.can_cast(masked_da.dtype, result.dtype, casting='safe'):
+                result += masked_da
+        except Exception:
+            pass
+
+        # Transforms to a (subclass of) MaskedArray
+        masked_result = result.view(get_masked_subclass(a, b))
+        masked_result._mask = m
+        if isinstance(a, MaskedArray):
+            masked_result._update_from(a)
+        elif isinstance(b, MaskedArray):
+            masked_result._update_from(b)
+        return masked_result
+
+
+# Unary ufuncs
+exp = _MaskedUnaryOperation(umath.exp)
+conjugate = _MaskedUnaryOperation(umath.conjugate)
+sin = _MaskedUnaryOperation(umath.sin)
+cos = _MaskedUnaryOperation(umath.cos)
+arctan = _MaskedUnaryOperation(umath.arctan)
+arcsinh = _MaskedUnaryOperation(umath.arcsinh)
+sinh = _MaskedUnaryOperation(umath.sinh)
+cosh = _MaskedUnaryOperation(umath.cosh)
+tanh = _MaskedUnaryOperation(umath.tanh)
+abs = absolute = _MaskedUnaryOperation(umath.absolute)
+angle = _MaskedUnaryOperation(angle)
+fabs = _MaskedUnaryOperation(umath.fabs)
+negative = _MaskedUnaryOperation(umath.negative)
+floor = _MaskedUnaryOperation(umath.floor)
+ceil = _MaskedUnaryOperation(umath.ceil)
+around = _MaskedUnaryOperation(np.around)
+logical_not = _MaskedUnaryOperation(umath.logical_not)
+
+# Domained unary ufuncs
+sqrt = _MaskedUnaryOperation(umath.sqrt, 0.0,
+                             _DomainGreaterEqual(0.0))
+log = _MaskedUnaryOperation(umath.log, 1.0,
+                            _DomainGreater(0.0))
+log2 = _MaskedUnaryOperation(umath.log2, 1.0,
+                             _DomainGreater(0.0))
+log10 = _MaskedUnaryOperation(umath.log10, 1.0,
+                              _DomainGreater(0.0))
+tan = _MaskedUnaryOperation(umath.tan, 0.0,
+                            _DomainTan(1e-35))
+arcsin = _MaskedUnaryOperation(umath.arcsin, 0.0,
+                               _DomainCheckInterval(-1.0, 1.0))
+arccos = _MaskedUnaryOperation(umath.arccos, 0.0,
+                               _DomainCheckInterval(-1.0, 1.0))
+arccosh = _MaskedUnaryOperation(umath.arccosh, 1.0,
+                                _DomainGreaterEqual(1.0))
+arctanh = _MaskedUnaryOperation(umath.arctanh, 0.0,
+                                _DomainCheckInterval(-1.0 + 1e-15, 1.0 - 1e-15))
+
+# Binary ufuncs
+add = _MaskedBinaryOperation(umath.add)
+subtract = _MaskedBinaryOperation(umath.subtract)
+multiply = _MaskedBinaryOperation(umath.multiply, 1, 1)
+arctan2 = _MaskedBinaryOperation(umath.arctan2, 0.0, 1.0)
+equal = _MaskedBinaryOperation(umath.equal)
+equal.reduce = None
+not_equal = _MaskedBinaryOperation(umath.not_equal)
+not_equal.reduce = None
+less_equal = _MaskedBinaryOperation(umath.less_equal)
+less_equal.reduce = None
+greater_equal = _MaskedBinaryOperation(umath.greater_equal)
+greater_equal.reduce = None
+less = _MaskedBinaryOperation(umath.less)
+less.reduce = None
+greater = _MaskedBinaryOperation(umath.greater)
+greater.reduce = None
+logical_and = _MaskedBinaryOperation(umath.logical_and)
+alltrue = _MaskedBinaryOperation(umath.logical_and, 1, 1).reduce
+logical_or = _MaskedBinaryOperation(umath.logical_or)
+sometrue = logical_or.reduce
+logical_xor = _MaskedBinaryOperation(umath.logical_xor)
+bitwise_and = _MaskedBinaryOperation(umath.bitwise_and)
+bitwise_or = _MaskedBinaryOperation(umath.bitwise_or)
+bitwise_xor = _MaskedBinaryOperation(umath.bitwise_xor)
+hypot = _MaskedBinaryOperation(umath.hypot)
+
+# Domained binary ufuncs
+divide = _DomainedBinaryOperation(umath.divide, _DomainSafeDivide(), 0, 1)
+true_divide = _DomainedBinaryOperation(umath.true_divide,
+                                       _DomainSafeDivide(), 0, 1)
+floor_divide = _DomainedBinaryOperation(umath.floor_divide,
+                                        _DomainSafeDivide(), 0, 1)
+remainder = _DomainedBinaryOperation(umath.remainder,
+                                     _DomainSafeDivide(), 0, 1)
+fmod = _DomainedBinaryOperation(umath.fmod, _DomainSafeDivide(), 0, 1)
+mod = _DomainedBinaryOperation(umath.mod, _DomainSafeDivide(), 0, 1)
+
+
+###############################################################################
+#                        Mask creation functions                              #
+###############################################################################
+
+
+def _replace_dtype_fields_recursive(dtype, primitive_dtype):
+    "Private function allowing recursion in _replace_dtype_fields."
+    _recurse = _replace_dtype_fields_recursive
+
+    # Do we have some name fields ?
+    if dtype.names is not None:
+        descr = []
+        for name in dtype.names:
+            field = dtype.fields[name]
+            if len(field) == 3:
+                # Prepend the title to the name
+                name = (field[-1], name)
+            descr.append((name, _recurse(field[0], primitive_dtype)))
+        new_dtype = np.dtype(descr)
+
+    # Is this some kind of composite a la (float,2)
+    elif dtype.subdtype:
+        descr = list(dtype.subdtype)
+        descr[0] = _recurse(dtype.subdtype[0], primitive_dtype)
+        new_dtype = np.dtype(tuple(descr))
+
+    # this is a primitive type, so do a direct replacement
+    else:
+        new_dtype = primitive_dtype
+
+    # preserve identity of dtypes
+    if new_dtype == dtype:
+        new_dtype = dtype
+
+    return new_dtype
+
+
+def _replace_dtype_fields(dtype, primitive_dtype):
+    """
+    Construct a dtype description list from a given dtype.
+
+    Returns a new dtype object, with all fields and subtypes in the given type
+    recursively replaced with `primitive_dtype`.
+
+    Arguments are coerced to dtypes first.
+    """
+    dtype = np.dtype(dtype)
+    primitive_dtype = np.dtype(primitive_dtype)
+    return _replace_dtype_fields_recursive(dtype, primitive_dtype)
+
+
+def make_mask_descr(ndtype):
+    """
+    Construct a dtype description list from a given dtype.
+
+    Returns a new dtype object, with the type of all fields in `ndtype` to a
+    boolean type. Field names are not altered.
+
+    Parameters
+    ----------
+    ndtype : dtype
+        The dtype to convert.
+
+    Returns
+    -------
+    result : dtype
+        A dtype that looks like `ndtype`, the type of all fields is boolean.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> dtype = np.dtype({'names':['foo', 'bar'],
+    ...                   'formats':[np.float32, np.int64]})
+    >>> dtype
+    dtype([('foo', '>> ma.make_mask_descr(dtype)
+    dtype([('foo', '|b1'), ('bar', '|b1')])
+    >>> ma.make_mask_descr(np.float32)
+    dtype('bool')
+
+    """
+    return _replace_dtype_fields(ndtype, MaskType)
+
+
+def getmask(a):
+    """
+    Return the mask of a masked array, or nomask.
+
+    Return the mask of `a` as an ndarray if `a` is a `MaskedArray` and the
+    mask is not `nomask`, else return `nomask`. To guarantee a full array
+    of booleans of the same shape as a, use `getmaskarray`.
+
+    Parameters
+    ----------
+    a : array_like
+        Input `MaskedArray` for which the mask is required.
+
+    See Also
+    --------
+    getdata : Return the data of a masked array as an ndarray.
+    getmaskarray : Return the mask of a masked array, or full array of False.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.masked_equal([[1,2],[3,4]], 2)
+    >>> a
+    masked_array(
+      data=[[1, --],
+            [3, 4]],
+      mask=[[False,  True],
+            [False, False]],
+      fill_value=2)
+    >>> ma.getmask(a)
+    array([[False,  True],
+           [False, False]])
+
+    Equivalently use the `MaskedArray` `mask` attribute.
+
+    >>> a.mask
+    array([[False,  True],
+           [False, False]])
+
+    Result when mask == `nomask`
+
+    >>> b = ma.masked_array([[1,2],[3,4]])
+    >>> b
+    masked_array(
+      data=[[1, 2],
+            [3, 4]],
+      mask=False,
+      fill_value=999999)
+    >>> ma.nomask
+    False
+    >>> ma.getmask(b) == ma.nomask
+    True
+    >>> b.mask == ma.nomask
+    True
+
+    """
+    return getattr(a, '_mask', nomask)
+
+
+get_mask = getmask
+
+
+def getmaskarray(arr):
+    """
+    Return the mask of a masked array, or full boolean array of False.
+
+    Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and
+    the mask is not `nomask`, else return a full boolean array of False of
+    the same shape as `arr`.
+
+    Parameters
+    ----------
+    arr : array_like
+        Input `MaskedArray` for which the mask is required.
+
+    See Also
+    --------
+    getmask : Return the mask of a masked array, or nomask.
+    getdata : Return the data of a masked array as an ndarray.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.masked_equal([[1,2],[3,4]], 2)
+    >>> a
+    masked_array(
+      data=[[1, --],
+            [3, 4]],
+      mask=[[False,  True],
+            [False, False]],
+      fill_value=2)
+    >>> ma.getmaskarray(a)
+    array([[False,  True],
+           [False, False]])
+
+    Result when mask == ``nomask``
+
+    >>> b = ma.masked_array([[1,2],[3,4]])
+    >>> b
+    masked_array(
+      data=[[1, 2],
+            [3, 4]],
+      mask=False,
+      fill_value=999999)
+    >>> ma.getmaskarray(b)
+    array([[False, False],
+           [False, False]])
+
+    """
+    mask = getmask(arr)
+    if mask is nomask:
+        mask = make_mask_none(np.shape(arr), getattr(arr, 'dtype', None))
+    return mask
+
+
+def is_mask(m):
+    """
+    Return True if m is a valid, standard mask.
+
+    This function does not check the contents of the input, only that the
+    type is MaskType. In particular, this function returns False if the
+    mask has a flexible dtype.
+
+    Parameters
+    ----------
+    m : array_like
+        Array to test.
+
+    Returns
+    -------
+    result : bool
+        True if `m.dtype.type` is MaskType, False otherwise.
+
+    See Also
+    --------
+    ma.isMaskedArray : Test whether input is an instance of MaskedArray.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)
+    >>> m
+    masked_array(data=[--, 1, --, 2, 3],
+                 mask=[ True, False,  True, False, False],
+           fill_value=0)
+    >>> ma.is_mask(m)
+    False
+    >>> ma.is_mask(m.mask)
+    True
+
+    Input must be an ndarray (or have similar attributes)
+    for it to be considered a valid mask.
+
+    >>> m = [False, True, False]
+    >>> ma.is_mask(m)
+    False
+    >>> m = np.array([False, True, False])
+    >>> m
+    array([False,  True, False])
+    >>> ma.is_mask(m)
+    True
+
+    Arrays with complex dtypes don't return True.
+
+    >>> dtype = np.dtype({'names':['monty', 'pithon'],
+    ...                   'formats':[bool, bool]})
+    >>> dtype
+    dtype([('monty', '|b1'), ('pithon', '|b1')])
+    >>> m = np.array([(True, False), (False, True), (True, False)],
+    ...              dtype=dtype)
+    >>> m
+    array([( True, False), (False,  True), ( True, False)],
+          dtype=[('monty', '?'), ('pithon', '?')])
+    >>> ma.is_mask(m)
+    False
+
+    """
+    try:
+        return m.dtype.type is MaskType
+    except AttributeError:
+        return False
+
+
+def _shrink_mask(m):
+    """
+    Shrink a mask to nomask if possible
+    """
+    if m.dtype.names is None and not m.any():
+        return nomask
+    else:
+        return m
+
+
+def make_mask(m, copy=False, shrink=True, dtype=MaskType):
+    """
+    Create a boolean mask from an array.
+
+    Return `m` as a boolean mask, creating a copy if necessary or requested.
+    The function can accept any sequence that is convertible to integers,
+    or ``nomask``.  Does not require that contents must be 0s and 1s, values
+    of 0 are interpreted as False, everything else as True.
+
+    Parameters
+    ----------
+    m : array_like
+        Potential mask.
+    copy : bool, optional
+        Whether to return a copy of `m` (True) or `m` itself (False).
+    shrink : bool, optional
+        Whether to shrink `m` to ``nomask`` if all its values are False.
+    dtype : dtype, optional
+        Data-type of the output mask. By default, the output mask has a
+        dtype of MaskType (bool). If the dtype is flexible, each field has
+        a boolean dtype. This is ignored when `m` is ``nomask``, in which
+        case ``nomask`` is always returned.
+
+    Returns
+    -------
+    result : ndarray
+        A boolean mask derived from `m`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> m = [True, False, True, True]
+    >>> ma.make_mask(m)
+    array([ True, False,  True,  True])
+    >>> m = [1, 0, 1, 1]
+    >>> ma.make_mask(m)
+    array([ True, False,  True,  True])
+    >>> m = [1, 0, 2, -3]
+    >>> ma.make_mask(m)
+    array([ True, False,  True,  True])
+
+    Effect of the `shrink` parameter.
+
+    >>> m = np.zeros(4)
+    >>> m
+    array([0., 0., 0., 0.])
+    >>> ma.make_mask(m)
+    False
+    >>> ma.make_mask(m, shrink=False)
+    array([False, False, False, False])
+
+    Using a flexible `dtype`.
+
+    >>> m = [1, 0, 1, 1]
+    >>> n = [0, 1, 0, 0]
+    >>> arr = []
+    >>> for man, mouse in zip(m, n):
+    ...     arr.append((man, mouse))
+    >>> arr
+    [(1, 0), (0, 1), (1, 0), (1, 0)]
+    >>> dtype = np.dtype({'names':['man', 'mouse'],
+    ...                   'formats':[np.int64, np.int64]})
+    >>> arr = np.array(arr, dtype=dtype)
+    >>> arr
+    array([(1, 0), (0, 1), (1, 0), (1, 0)],
+          dtype=[('man', '>> ma.make_mask(arr, dtype=dtype)
+    array([(True, False), (False, True), (True, False), (True, False)],
+          dtype=[('man', '|b1'), ('mouse', '|b1')])
+
+    """
+    if m is nomask:
+        return nomask
+
+    # Make sure the input dtype is valid.
+    dtype = make_mask_descr(dtype)
+
+    # legacy boolean special case: "existence of fields implies true"
+    if isinstance(m, ndarray) and m.dtype.fields and dtype == np.bool:
+        return np.ones(m.shape, dtype=dtype)
+
+    # Fill the mask in case there are missing data; turn it into an ndarray.
+    copy = None if not copy else True
+    result = np.array(filled(m, True), copy=copy, dtype=dtype, subok=True)
+    # Bas les masques !
+    if shrink:
+        result = _shrink_mask(result)
+    return result
+
+
+def make_mask_none(newshape, dtype=None):
+    """
+    Return a boolean mask of the given shape, filled with False.
+
+    This function returns a boolean ndarray with all entries False, that can
+    be used in common mask manipulations. If a complex dtype is specified, the
+    type of each field is converted to a boolean type.
+
+    Parameters
+    ----------
+    newshape : tuple
+        A tuple indicating the shape of the mask.
+    dtype : {None, dtype}, optional
+        If None, use a MaskType instance. Otherwise, use a new datatype with
+        the same fields as `dtype`, converted to boolean types.
+
+    Returns
+    -------
+    result : ndarray
+        An ndarray of appropriate shape and dtype, filled with False.
+
+    See Also
+    --------
+    make_mask : Create a boolean mask from an array.
+    make_mask_descr : Construct a dtype description list from a given dtype.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> ma.make_mask_none((3,))
+    array([False, False, False])
+
+    Defining a more complex dtype.
+
+    >>> dtype = np.dtype({'names':['foo', 'bar'],
+    ...                   'formats':[np.float32, np.int64]})
+    >>> dtype
+    dtype([('foo', '>> ma.make_mask_none((3,), dtype=dtype)
+    array([(False, False), (False, False), (False, False)],
+          dtype=[('foo', '|b1'), ('bar', '|b1')])
+
+    """
+    if dtype is None:
+        result = np.zeros(newshape, dtype=MaskType)
+    else:
+        result = np.zeros(newshape, dtype=make_mask_descr(dtype))
+    return result
+
+
+def _recursive_mask_or(m1, m2, newmask):
+    names = m1.dtype.names
+    for name in names:
+        current1 = m1[name]
+        if current1.dtype.names is not None:
+            _recursive_mask_or(current1, m2[name], newmask[name])
+        else:
+            umath.logical_or(current1, m2[name], newmask[name])
+
+
+def mask_or(m1, m2, copy=False, shrink=True):
+    """
+    Combine two masks with the ``logical_or`` operator.
+
+    The result may be a view on `m1` or `m2` if the other is `nomask`
+    (i.e. False).
+
+    Parameters
+    ----------
+    m1, m2 : array_like
+        Input masks.
+    copy : bool, optional
+        If copy is False and one of the inputs is `nomask`, return a view
+        of the other input mask. Defaults to False.
+    shrink : bool, optional
+        Whether to shrink the output to `nomask` if all its values are
+        False. Defaults to True.
+
+    Returns
+    -------
+    mask : output mask
+        The result masks values that are masked in either `m1` or `m2`.
+
+    Raises
+    ------
+    ValueError
+        If `m1` and `m2` have different flexible dtypes.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> m1 = np.ma.make_mask([0, 1, 1, 0])
+    >>> m2 = np.ma.make_mask([1, 0, 0, 0])
+    >>> np.ma.mask_or(m1, m2)
+    array([ True,  True,  True, False])
+
+    """
+
+    if (m1 is nomask) or (m1 is False):
+        dtype = getattr(m2, 'dtype', MaskType)
+        return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype)
+    if (m2 is nomask) or (m2 is False):
+        dtype = getattr(m1, 'dtype', MaskType)
+        return make_mask(m1, copy=copy, shrink=shrink, dtype=dtype)
+    if m1 is m2 and is_mask(m1):
+        return _shrink_mask(m1) if shrink else m1
+    (dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None))
+    if dtype1 != dtype2:
+        raise ValueError("Incompatible dtypes '%s'<>'%s'" % (dtype1, dtype2))
+    if dtype1.names is not None:
+        # Allocate an output mask array with the properly broadcast shape.
+        newmask = np.empty(np.broadcast(m1, m2).shape, dtype1)
+        _recursive_mask_or(m1, m2, newmask)
+        return newmask
+    return make_mask(umath.logical_or(m1, m2), copy=copy, shrink=shrink)
+
+
+def flatten_mask(mask):
+    """
+    Returns a completely flattened version of the mask, where nested fields
+    are collapsed.
+
+    Parameters
+    ----------
+    mask : array_like
+        Input array, which will be interpreted as booleans.
+
+    Returns
+    -------
+    flattened_mask : ndarray of bools
+        The flattened input.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> mask = np.array([0, 0, 1])
+    >>> np.ma.flatten_mask(mask)
+    array([False, False,  True])
+
+    >>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])
+    >>> np.ma.flatten_mask(mask)
+    array([False, False, False,  True])
+
+    >>> mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
+    >>> mask = np.array([(0, (0, 0)), (0, (0, 1))], dtype=mdtype)
+    >>> np.ma.flatten_mask(mask)
+    array([False, False, False, False, False,  True])
+
+    """
+
+    def _flatmask(mask):
+        "Flatten the mask and returns a (maybe nested) sequence of booleans."
+        mnames = mask.dtype.names
+        if mnames is not None:
+            return [flatten_mask(mask[name]) for name in mnames]
+        else:
+            return mask
+
+    def _flatsequence(sequence):
+        "Generates a flattened version of the sequence."
+        try:
+            for element in sequence:
+                if hasattr(element, '__iter__'):
+                    yield from _flatsequence(element)
+                else:
+                    yield element
+        except TypeError:
+            yield sequence
+
+    mask = np.asarray(mask)
+    flattened = _flatsequence(_flatmask(mask))
+    return np.array(list(flattened), dtype=bool)
+
+
+def _check_mask_axis(mask, axis, keepdims=np._NoValue):
+    "Check whether there are masked values along the given axis"
+    kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+    if mask is not nomask:
+        return mask.all(axis=axis, **kwargs)
+    return nomask
+
+
+###############################################################################
+#                             Masking functions                               #
+###############################################################################
+
+def masked_where(condition, a, copy=True):
+    """
+    Mask an array where a condition is met.
+
+    Return `a` as an array masked where `condition` is True.
+    Any masked values of `a` or `condition` are also masked in the output.
+
+    Parameters
+    ----------
+    condition : array_like
+        Masking condition.  When `condition` tests floating point values for
+        equality, consider using ``masked_values`` instead.
+    a : array_like
+        Array to mask.
+    copy : bool
+        If True (default) make a copy of `a` in the result.  If False modify
+        `a` in place and return a view.
+
+    Returns
+    -------
+    result : MaskedArray
+        The result of masking `a` where `condition` is True.
+
+    See Also
+    --------
+    masked_values : Mask using floating point equality.
+    masked_equal : Mask where equal to a given value.
+    masked_not_equal : Mask where *not* equal to a given value.
+    masked_less_equal : Mask where less than or equal to a given value.
+    masked_greater_equal : Mask where greater than or equal to a given value.
+    masked_less : Mask where less than a given value.
+    masked_greater : Mask where greater than a given value.
+    masked_inside : Mask inside a given interval.
+    masked_outside : Mask outside a given interval.
+    masked_invalid : Mask invalid values (NaNs or infs).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_where(a <= 2, a)
+    masked_array(data=[--, --, --, 3],
+                 mask=[ True,  True,  True, False],
+           fill_value=999999)
+
+    Mask array `b` conditional on `a`.
+
+    >>> b = ['a', 'b', 'c', 'd']
+    >>> ma.masked_where(a == 2, b)
+    masked_array(data=['a', 'b', --, 'd'],
+                 mask=[False, False,  True, False],
+           fill_value='N/A',
+                dtype='>> c = ma.masked_where(a <= 2, a)
+    >>> c
+    masked_array(data=[--, --, --, 3],
+                 mask=[ True,  True,  True, False],
+           fill_value=999999)
+    >>> c[0] = 99
+    >>> c
+    masked_array(data=[99, --, --, 3],
+                 mask=[False,  True,  True, False],
+           fill_value=999999)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> c = ma.masked_where(a <= 2, a, copy=False)
+    >>> c[0] = 99
+    >>> c
+    masked_array(data=[99, --, --, 3],
+                 mask=[False,  True,  True, False],
+           fill_value=999999)
+    >>> a
+    array([99,  1,  2,  3])
+
+    When `condition` or `a` contain masked values.
+
+    >>> a = np.arange(4)
+    >>> a = ma.masked_where(a == 2, a)
+    >>> a
+    masked_array(data=[0, 1, --, 3],
+                 mask=[False, False,  True, False],
+           fill_value=999999)
+    >>> b = np.arange(4)
+    >>> b = ma.masked_where(b == 0, b)
+    >>> b
+    masked_array(data=[--, 1, 2, 3],
+                 mask=[ True, False, False, False],
+           fill_value=999999)
+    >>> ma.masked_where(a == 3, b)
+    masked_array(data=[--, 1, --, --],
+                 mask=[ True, False,  True,  True],
+           fill_value=999999)
+
+    """
+    # Make sure that condition is a valid standard-type mask.
+    cond = make_mask(condition, shrink=False)
+    a = np.array(a, copy=copy, subok=True)
+
+    (cshape, ashape) = (cond.shape, a.shape)
+    if cshape and cshape != ashape:
+        raise IndexError("Inconsistent shape between the condition and the input"
+                         " (got %s and %s)" % (cshape, ashape))
+    if hasattr(a, '_mask'):
+        cond = mask_or(cond, a._mask)
+        cls = type(a)
+    else:
+        cls = MaskedArray
+    result = a.view(cls)
+    # Assign to *.mask so that structured masks are handled correctly.
+    result.mask = _shrink_mask(cond)
+    # There is no view of a boolean so when 'a' is a MaskedArray with nomask
+    # the update to the result's mask has no effect.
+    if not copy and hasattr(a, '_mask') and getmask(a) is nomask:
+        a._mask = result._mask.view()
+    return result
+
+
+def masked_greater(x, value, copy=True):
+    """
+    Mask an array where greater than a given value.
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = (x > value).
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_greater(a, 2)
+    masked_array(data=[0, 1, 2, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+
+    """
+    return masked_where(greater(x, value), x, copy=copy)
+
+
+def masked_greater_equal(x, value, copy=True):
+    """
+    Mask an array where greater than or equal to a given value.
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = (x >= value).
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_greater_equal(a, 2)
+    masked_array(data=[0, 1, --, --],
+                 mask=[False, False,  True,  True],
+           fill_value=999999)
+
+    """
+    return masked_where(greater_equal(x, value), x, copy=copy)
+
+
+def masked_less(x, value, copy=True):
+    """
+    Mask an array where less than a given value.
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = (x < value).
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_less(a, 2)
+    masked_array(data=[--, --, 2, 3],
+                 mask=[ True,  True, False, False],
+           fill_value=999999)
+
+    """
+    return masked_where(less(x, value), x, copy=copy)
+
+
+def masked_less_equal(x, value, copy=True):
+    """
+    Mask an array where less than or equal to a given value.
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = (x <= value).
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_less_equal(a, 2)
+    masked_array(data=[--, --, --, 3],
+                 mask=[ True,  True,  True, False],
+           fill_value=999999)
+
+    """
+    return masked_where(less_equal(x, value), x, copy=copy)
+
+
+def masked_not_equal(x, value, copy=True):
+    """
+    Mask an array where *not* equal to a given value.
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = (x != value).
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_not_equal(a, 2)
+    masked_array(data=[--, --, 2, --],
+                 mask=[ True,  True, False,  True],
+           fill_value=999999)
+
+    """
+    return masked_where(not_equal(x, value), x, copy=copy)
+
+
+def masked_equal(x, value, copy=True):
+    """
+    Mask an array where equal to a given value.
+
+    Return a MaskedArray, masked where the data in array `x` are
+    equal to `value`. The fill_value of the returned MaskedArray
+    is set to `value`.
+
+    For floating point arrays, consider using ``masked_values(x, value)``.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+    masked_values : Mask using floating point equality.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(4)
+    >>> a
+    array([0, 1, 2, 3])
+    >>> ma.masked_equal(a, 2)
+    masked_array(data=[0, 1, --, 3],
+                 mask=[False, False,  True, False],
+           fill_value=2)
+
+    """
+    output = masked_where(equal(x, value), x, copy=copy)
+    output.fill_value = value
+    return output
+
+
+def masked_inside(x, v1, v2, copy=True):
+    """
+    Mask an array inside a given interval.
+
+    Shortcut to ``masked_where``, where `condition` is True for `x` inside
+    the interval [v1,v2] (v1 <= x <= v2).  The boundaries `v1` and `v2`
+    can be given in either order.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Notes
+    -----
+    The array `x` is prefilled with its filling value.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]
+    >>> ma.masked_inside(x, -0.3, 0.3)
+    masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
+                 mask=[False, False,  True,  True, False, False],
+           fill_value=1e+20)
+
+    The order of `v1` and `v2` doesn't matter.
+
+    >>> ma.masked_inside(x, 0.3, -0.3)
+    masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1],
+                 mask=[False, False,  True,  True, False, False],
+           fill_value=1e+20)
+
+    """
+    if v2 < v1:
+        (v1, v2) = (v2, v1)
+    xf = filled(x)
+    condition = (xf >= v1) & (xf <= v2)
+    return masked_where(condition, x, copy=copy)
+
+
+def masked_outside(x, v1, v2, copy=True):
+    """
+    Mask an array outside a given interval.
+
+    Shortcut to ``masked_where``, where `condition` is True for `x` outside
+    the interval [v1,v2] (x < v1)|(x > v2).
+    The boundaries `v1` and `v2` can be given in either order.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Notes
+    -----
+    The array `x` is prefilled with its filling value.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]
+    >>> ma.masked_outside(x, -0.3, 0.3)
+    masked_array(data=[--, --, 0.01, 0.2, --, --],
+                 mask=[ True,  True, False, False,  True,  True],
+           fill_value=1e+20)
+
+    The order of `v1` and `v2` doesn't matter.
+
+    >>> ma.masked_outside(x, 0.3, -0.3)
+    masked_array(data=[--, --, 0.01, 0.2, --, --],
+                 mask=[ True,  True, False, False,  True,  True],
+           fill_value=1e+20)
+
+    """
+    if v2 < v1:
+        (v1, v2) = (v2, v1)
+    xf = filled(x)
+    condition = (xf < v1) | (xf > v2)
+    return masked_where(condition, x, copy=copy)
+
+
+def masked_object(x, value, copy=True, shrink=True):
+    """
+    Mask the array `x` where the data are exactly equal to value.
+
+    This function is similar to `masked_values`, but only suitable
+    for object arrays: for floating point, use `masked_values` instead.
+
+    Parameters
+    ----------
+    x : array_like
+        Array to mask
+    value : object
+        Comparison value
+    copy : {True, False}, optional
+        Whether to return a copy of `x`.
+    shrink : {True, False}, optional
+        Whether to collapse a mask full of False to nomask
+
+    Returns
+    -------
+    result : MaskedArray
+        The result of masking `x` where equal to `value`.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+    masked_equal : Mask where equal to a given value (integers).
+    masked_values : Mask using floating point equality.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> food = np.array(['green_eggs', 'ham'], dtype=object)
+    >>> # don't eat spoiled food
+    >>> eat = ma.masked_object(food, 'green_eggs')
+    >>> eat
+    masked_array(data=[--, 'ham'],
+                 mask=[ True, False],
+           fill_value='green_eggs',
+                dtype=object)
+    >>> # plain ol` ham is boring
+    >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)
+    >>> eat = ma.masked_object(fresh_food, 'green_eggs')
+    >>> eat
+    masked_array(data=['cheese', 'ham', 'pineapple'],
+                 mask=False,
+           fill_value='green_eggs',
+                dtype=object)
+
+    Note that `mask` is set to ``nomask`` if possible.
+
+    >>> eat
+    masked_array(data=['cheese', 'ham', 'pineapple'],
+                 mask=False,
+           fill_value='green_eggs',
+                dtype=object)
+
+    """
+    if isMaskedArray(x):
+        condition = umath.equal(x._data, value)
+        mask = x._mask
+    else:
+        condition = umath.equal(np.asarray(x), value)
+        mask = nomask
+    mask = mask_or(mask, make_mask(condition, shrink=shrink))
+    return masked_array(x, mask=mask, copy=copy, fill_value=value)
+
+
+def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):
+    """
+    Mask using floating point equality.
+
+    Return a MaskedArray, masked where the data in array `x` are approximately
+    equal to `value`, determined using `isclose`. The default tolerances for
+    `masked_values` are the same as those for `isclose`.
+
+    For integer types, exact equality is used, in the same way as
+    `masked_equal`.
+
+    The fill_value is set to `value` and the mask is set to ``nomask`` if
+    possible.
+
+    Parameters
+    ----------
+    x : array_like
+        Array to mask.
+    value : float
+        Masking value.
+    rtol, atol : float, optional
+        Tolerance parameters passed on to `isclose`
+    copy : bool, optional
+        Whether to return a copy of `x`.
+    shrink : bool, optional
+        Whether to collapse a mask full of False to ``nomask``.
+
+    Returns
+    -------
+    result : MaskedArray
+        The result of masking `x` where approximately equal to `value`.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+    masked_equal : Mask where equal to a given value (integers).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = np.array([1, 1.1, 2, 1.1, 3])
+    >>> ma.masked_values(x, 1.1)
+    masked_array(data=[1.0, --, 2.0, --, 3.0],
+                 mask=[False,  True, False,  True, False],
+           fill_value=1.1)
+
+    Note that `mask` is set to ``nomask`` if possible.
+
+    >>> ma.masked_values(x, 2.1)
+    masked_array(data=[1. , 1.1, 2. , 1.1, 3. ],
+                 mask=False,
+           fill_value=2.1)
+
+    Unlike `masked_equal`, `masked_values` can perform approximate equalities.
+
+    >>> ma.masked_values(x, 2.1, atol=1e-1)
+    masked_array(data=[1.0, 1.1, --, 1.1, 3.0],
+                 mask=[False, False,  True, False, False],
+           fill_value=2.1)
+
+    """
+    xnew = filled(x, value)
+    if np.issubdtype(xnew.dtype, np.floating):
+        mask = np.isclose(xnew, value, atol=atol, rtol=rtol)
+    else:
+        mask = umath.equal(xnew, value)
+    ret = masked_array(xnew, mask=mask, copy=copy, fill_value=value)
+    if shrink:
+        ret.shrink_mask()
+    return ret
+
+
+def masked_invalid(a, copy=True):
+    """
+    Mask an array where invalid values occur (NaNs or infs).
+
+    This function is a shortcut to ``masked_where``, with
+    `condition` = ~(np.isfinite(a)). Any pre-existing mask is conserved.
+    Only applies to arrays with a dtype where NaNs or infs make sense
+    (i.e. floating point types), but accepts any array_like object.
+
+    See Also
+    --------
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.arange(5, dtype=float)
+    >>> a[2] = np.nan
+    >>> a[3] = np.inf
+    >>> a
+    array([ 0.,  1., nan, inf,  4.])
+    >>> ma.masked_invalid(a)
+    masked_array(data=[0.0, 1.0, --, --, 4.0],
+                 mask=[False, False,  True,  True, False],
+           fill_value=1e+20)
+
+    """
+    a = np.array(a, copy=None, subok=True)
+    res = masked_where(~(np.isfinite(a)), a, copy=copy)
+    # masked_invalid previously never returned nomask as a mask and doing so
+    # threw off matplotlib (gh-22842).  So use shrink=False:
+    if res._mask is nomask:
+        res._mask = make_mask_none(res.shape, res.dtype)
+    return res
+
+###############################################################################
+#                            Printing options                                 #
+###############################################################################
+
+
+class _MaskedPrintOption:
+    """
+    Handle the string used to represent missing data in a masked array.
+
+    """
+
+    def __init__(self, display):
+        """
+        Create the masked_print_option object.
+
+        """
+        self._display = display
+        self._enabled = True
+
+    def display(self):
+        """
+        Display the string to print for masked values.
+
+        """
+        return self._display
+
+    def set_display(self, s):
+        """
+        Set the string to print for masked values.
+
+        """
+        self._display = s
+
+    def enabled(self):
+        """
+        Is the use of the display value enabled?
+
+        """
+        return self._enabled
+
+    def enable(self, shrink=1):
+        """
+        Set the enabling shrink to `shrink`.
+
+        """
+        self._enabled = shrink
+
+    def __str__(self):
+        return str(self._display)
+
+    __repr__ = __str__
+
+# if you single index into a masked location you get this object.
+masked_print_option = _MaskedPrintOption('--')
+
+
+def _recursive_printoption(result, mask, printopt):
+    """
+    Puts printoptions in result where mask is True.
+
+    Private function allowing for recursion
+
+    """
+    names = result.dtype.names
+    if names is not None:
+        for name in names:
+            curdata = result[name]
+            curmask = mask[name]
+            _recursive_printoption(curdata, curmask, printopt)
+    else:
+        np.copyto(result, printopt, where=mask)
+    return
+
+# For better or worse, these end in a newline
+_legacy_print_templates = dict(
+    long_std=textwrap.dedent("""\
+        masked_%(name)s(data =
+         %(data)s,
+        %(nlen)s        mask =
+         %(mask)s,
+        %(nlen)s  fill_value = %(fill)s)
+        """),
+    long_flx=textwrap.dedent("""\
+        masked_%(name)s(data =
+         %(data)s,
+        %(nlen)s        mask =
+         %(mask)s,
+        %(nlen)s  fill_value = %(fill)s,
+        %(nlen)s       dtype = %(dtype)s)
+        """),
+    short_std=textwrap.dedent("""\
+        masked_%(name)s(data = %(data)s,
+        %(nlen)s        mask = %(mask)s,
+        %(nlen)s  fill_value = %(fill)s)
+        """),
+    short_flx=textwrap.dedent("""\
+        masked_%(name)s(data = %(data)s,
+        %(nlen)s        mask = %(mask)s,
+        %(nlen)s  fill_value = %(fill)s,
+        %(nlen)s       dtype = %(dtype)s)
+        """)
+)
+
+###############################################################################
+#                          MaskedArray class                                  #
+###############################################################################
+
+
+def _recursive_filled(a, mask, fill_value):
+    """
+    Recursively fill `a` with `fill_value`.
+
+    """
+    names = a.dtype.names
+    for name in names:
+        current = a[name]
+        if current.dtype.names is not None:
+            _recursive_filled(current, mask[name], fill_value[name])
+        else:
+            np.copyto(current, fill_value[name], where=mask[name])
+
+
+def flatten_structured_array(a):
+    """
+    Flatten a structured array.
+
+    The data type of the output is chosen such that it can represent all of the
+    (nested) fields.
+
+    Parameters
+    ----------
+    a : structured array
+
+    Returns
+    -------
+    output : masked array or ndarray
+        A flattened masked array if the input is a masked array, otherwise a
+        standard ndarray.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> ndtype = [('a', int), ('b', float)]
+    >>> a = np.array([(1, 1), (2, 2)], dtype=ndtype)
+    >>> np.ma.flatten_structured_array(a)
+    array([[1., 1.],
+           [2., 2.]])
+
+    """
+
+    def flatten_sequence(iterable):
+        """
+        Flattens a compound of nested iterables.
+
+        """
+        for elm in iter(iterable):
+            if hasattr(elm, '__iter__'):
+                yield from flatten_sequence(elm)
+            else:
+                yield elm
+
+    a = np.asanyarray(a)
+    inishape = a.shape
+    a = a.ravel()
+    if isinstance(a, MaskedArray):
+        out = np.array([tuple(flatten_sequence(d.item())) for d in a._data])
+        out = out.view(MaskedArray)
+        out._mask = np.array([tuple(flatten_sequence(d.item()))
+                              for d in getmaskarray(a)])
+    else:
+        out = np.array([tuple(flatten_sequence(d.item())) for d in a])
+    if len(inishape) > 1:
+        newshape = list(out.shape)
+        newshape[0] = inishape
+        out.shape = tuple(flatten_sequence(newshape))
+    return out
+
+
+def _arraymethod(funcname, onmask=True):
+    """
+    Return a class method wrapper around a basic array method.
+
+    Creates a class method which returns a masked array, where the new
+    ``_data`` array is the output of the corresponding basic method called
+    on the original ``_data``.
+
+    If `onmask` is True, the new mask is the output of the method called
+    on the initial mask. Otherwise, the new mask is just a reference
+    to the initial mask.
+
+    Parameters
+    ----------
+    funcname : str
+        Name of the function to apply on data.
+    onmask : bool
+        Whether the mask must be processed also (True) or left
+        alone (False). Default is True. Make available as `_onmask`
+        attribute.
+
+    Returns
+    -------
+    method : instancemethod
+        Class method wrapper of the specified basic array method.
+
+    """
+    def wrapped_method(self, *args, **params):
+        result = getattr(self._data, funcname)(*args, **params)
+        result = result.view(type(self))
+        result._update_from(self)
+        mask = self._mask
+        if not onmask:
+            result.__setmask__(mask)
+        elif mask is not nomask:
+            # __setmask__ makes a copy, which we don't want
+            result._mask = getattr(mask, funcname)(*args, **params)
+        return result
+    methdoc = getattr(ndarray, funcname, None) or getattr(np, funcname, None)
+    if methdoc is not None:
+        wrapped_method.__doc__ = methdoc.__doc__
+    wrapped_method.__name__ = funcname
+    return wrapped_method
+
+
+class MaskedIterator:
+    """
+    Flat iterator object to iterate over masked arrays.
+
+    A `MaskedIterator` iterator is returned by ``x.flat`` for any masked array
+    `x`. It allows iterating over the array as if it were a 1-D array,
+    either in a for-loop or by calling its `next` method.
+
+    Iteration is done in C-contiguous style, with the last index varying the
+    fastest. The iterator can also be indexed using basic slicing or
+    advanced indexing.
+
+    See Also
+    --------
+    MaskedArray.flat : Return a flat iterator over an array.
+    MaskedArray.flatten : Returns a flattened copy of an array.
+
+    Notes
+    -----
+    `MaskedIterator` is not exported by the `ma` module. Instead of
+    instantiating a `MaskedIterator` directly, use `MaskedArray.flat`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array(arange(6).reshape(2, 3))
+    >>> fl = x.flat
+    >>> type(fl)
+    
+    >>> for item in fl:
+    ...     print(item)
+    ...
+    0
+    1
+    2
+    3
+    4
+    5
+
+    Extracting more than a single element b indexing the `MaskedIterator`
+    returns a masked array:
+
+    >>> fl[2:4]
+    masked_array(data = [2 3],
+                 mask = False,
+           fill_value = 999999)
+
+    """
+
+    def __init__(self, ma):
+        self.ma = ma
+        self.dataiter = ma._data.flat
+
+        if ma._mask is nomask:
+            self.maskiter = None
+        else:
+            self.maskiter = ma._mask.flat
+
+    def __iter__(self):
+        return self
+
+    def __getitem__(self, indx):
+        result = self.dataiter.__getitem__(indx).view(type(self.ma))
+        if self.maskiter is not None:
+            _mask = self.maskiter.__getitem__(indx)
+            if isinstance(_mask, ndarray):
+                # set shape to match that of data; this is needed for matrices
+                _mask.shape = result.shape
+                result._mask = _mask
+            elif isinstance(_mask, np.void):
+                return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)
+            elif _mask:  # Just a scalar, masked
+                return masked
+        return result
+
+    # This won't work if ravel makes a copy
+    def __setitem__(self, index, value):
+        self.dataiter[index] = getdata(value)
+        if self.maskiter is not None:
+            self.maskiter[index] = getmaskarray(value)
+
+    def __next__(self):
+        """
+        Return the next value, or raise StopIteration.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([3, 2], mask=[0, 1])
+        >>> fl = x.flat
+        >>> next(fl)
+        3
+        >>> next(fl)
+        masked
+        >>> next(fl)
+        Traceback (most recent call last):
+          ...
+        StopIteration
+
+        """
+        d = next(self.dataiter)
+        if self.maskiter is not None:
+            m = next(self.maskiter)
+            if isinstance(m, np.void):
+                return mvoid(d, mask=m, hardmask=self.ma._hardmask)
+            elif m:  # Just a scalar, masked
+                return masked
+        return d
+
+
+@set_module("numpy.ma")
+class MaskedArray(ndarray):
+    """
+    An array class with possibly masked values.
+
+    Masked values of True exclude the corresponding element from any
+    computation.
+
+    Construction::
+
+      x = MaskedArray(data, mask=nomask, dtype=None, copy=False, subok=True,
+                      ndmin=0, fill_value=None, keep_mask=True, hard_mask=None,
+                      shrink=True, order=None)
+
+    Parameters
+    ----------
+    data : array_like
+        Input data.
+    mask : sequence, optional
+        Mask. Must be convertible to an array of booleans with the same
+        shape as `data`. True indicates a masked (i.e. invalid) data.
+    dtype : dtype, optional
+        Data type of the output.
+        If `dtype` is None, the type of the data argument (``data.dtype``)
+        is used. If `dtype` is not None and different from ``data.dtype``,
+        a copy is performed.
+    copy : bool, optional
+        Whether to copy the input data (True), or to use a reference instead.
+        Default is False.
+    subok : bool, optional
+        Whether to return a subclass of `MaskedArray` if possible (True) or a
+        plain `MaskedArray`. Default is True.
+    ndmin : int, optional
+        Minimum number of dimensions. Default is 0.
+    fill_value : scalar, optional
+        Value used to fill in the masked values when necessary.
+        If None, a default based on the data-type is used.
+    keep_mask : bool, optional
+        Whether to combine `mask` with the mask of the input data, if any
+        (True), or to use only `mask` for the output (False). Default is True.
+    hard_mask : bool, optional
+        Whether to use a hard mask or not. With a hard mask, masked values
+        cannot be unmasked. Default is False.
+    shrink : bool, optional
+        Whether to force compression of an empty mask. Default is True.
+    order : {'C', 'F', 'A'}, optional
+        Specify the order of the array.  If order is 'C', then the array
+        will be in C-contiguous order (last-index varies the fastest).
+        If order is 'F', then the returned array will be in
+        Fortran-contiguous order (first-index varies the fastest).
+        If order is 'A' (default), then the returned array may be
+        in any order (either C-, Fortran-contiguous, or even discontiguous),
+        unless a copy is required, in which case it will be C-contiguous.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    The ``mask`` can be initialized with an array of boolean values
+    with the same shape as ``data``.
+
+    >>> data = np.arange(6).reshape((2, 3))
+    >>> np.ma.MaskedArray(data, mask=[[False, True, False],
+    ...                               [False, False, True]])
+    masked_array(
+      data=[[0, --, 2],
+            [3, 4, --]],
+      mask=[[False,  True, False],
+            [False, False,  True]],
+      fill_value=999999)
+
+    Alternatively, the ``mask`` can be initialized to homogeneous boolean
+    array with the same shape as ``data`` by passing in a scalar
+    boolean value:
+
+    >>> np.ma.MaskedArray(data, mask=False)
+    masked_array(
+      data=[[0, 1, 2],
+            [3, 4, 5]],
+      mask=[[False, False, False],
+            [False, False, False]],
+      fill_value=999999)
+
+    >>> np.ma.MaskedArray(data, mask=True)
+    masked_array(
+      data=[[--, --, --],
+            [--, --, --]],
+      mask=[[ True,  True,  True],
+            [ True,  True,  True]],
+      fill_value=999999,
+      dtype=int64)
+
+    .. note::
+        The recommended practice for initializing ``mask`` with a scalar
+        boolean value is to use ``True``/``False`` rather than
+        ``np.True_``/``np.False_``. The reason is :attr:`nomask`
+        is represented internally as ``np.False_``.
+
+        >>> np.False_ is np.ma.nomask
+        True
+
+    """
+
+    __array_priority__ = 15
+    _defaultmask = nomask
+    _defaulthardmask = False
+    _baseclass = ndarray
+
+    # Maximum number of elements per axis used when printing an array. The
+    # 1d case is handled separately because we need more values in this case.
+    _print_width = 100
+    _print_width_1d = 1500
+
+    def __new__(cls, data=None, mask=nomask, dtype=None, copy=False,
+                subok=True, ndmin=0, fill_value=None, keep_mask=True,
+                hard_mask=None, shrink=True, order=None):
+        """
+        Create a new masked array from scratch.
+
+        Notes
+        -----
+        A masked array can also be created by taking a .view(MaskedArray).
+
+        """
+        # Process data.
+        copy = None if not copy else True
+        _data = np.array(data, dtype=dtype, copy=copy,
+                         order=order, subok=True, ndmin=ndmin)
+        _baseclass = getattr(data, '_baseclass', type(_data))
+        # Check that we're not erasing the mask.
+        if isinstance(data, MaskedArray) and (data.shape != _data.shape):
+            copy = True
+
+        # Here, we copy the _view_, so that we can attach new properties to it
+        # we must never do .view(MaskedConstant), as that would create a new
+        # instance of np.ma.masked, which make identity comparison fail
+        if isinstance(data, cls) and subok and not isinstance(data, MaskedConstant):
+            _data = ndarray.view(_data, type(data))
+        else:
+            _data = ndarray.view(_data, cls)
+
+        # Handle the case where data is not a subclass of ndarray, but
+        # still has the _mask attribute like MaskedArrays
+        if hasattr(data, '_mask') and not isinstance(data, ndarray):
+            _data._mask = data._mask
+            # FIXME: should we set `_data._sharedmask = True`?
+        # Process mask.
+        # Type of the mask
+        mdtype = make_mask_descr(_data.dtype)
+        if mask is nomask:
+            # Case 1. : no mask in input.
+            # Erase the current mask ?
+            if not keep_mask:
+                # With a reduced version
+                if shrink:
+                    _data._mask = nomask
+                # With full version
+                else:
+                    _data._mask = np.zeros(_data.shape, dtype=mdtype)
+            # Check whether we missed something
+            elif isinstance(data, (tuple, list)):
+                try:
+                    # If data is a sequence of masked array
+                    mask = np.array(
+                        [getmaskarray(np.asanyarray(m, dtype=_data.dtype))
+                         for m in data], dtype=mdtype)
+                except (ValueError, TypeError):
+                    # If data is nested
+                    mask = nomask
+                # Force shrinking of the mask if needed (and possible)
+                if (mdtype == MaskType) and mask.any():
+                    _data._mask = mask
+                    _data._sharedmask = False
+            else:
+                _data._sharedmask = not copy
+                if copy:
+                    _data._mask = _data._mask.copy()
+                    # Reset the shape of the original mask
+                    if getmask(data) is not nomask:
+                        # gh-21022 encounters an issue here
+                        # because data._mask.shape is not writeable, but
+                        # the op was also pointless in that case, because
+                        # the shapes were the same, so we can at least
+                        # avoid that path
+                        if data._mask.shape != data.shape:
+                            data._mask.shape = data.shape
+        else:
+            # Case 2. : With a mask in input.
+            # If mask is boolean, create an array of True or False
+
+            # if users pass `mask=None` be forgiving here and cast it False
+            # for speed; although the default is `mask=nomask` and can differ.
+            if mask is None:
+                mask = False
+
+            if mask is True and mdtype == MaskType:
+                mask = np.ones(_data.shape, dtype=mdtype)
+            elif mask is False and mdtype == MaskType:
+                mask = np.zeros(_data.shape, dtype=mdtype)
+            else:
+                # Read the mask with the current mdtype
+                try:
+                    mask = np.array(mask, copy=copy, dtype=mdtype)
+                # Or assume it's a sequence of bool/int
+                except TypeError:
+                    mask = np.array([tuple([m] * len(mdtype)) for m in mask],
+                                    dtype=mdtype)
+            # Make sure the mask and the data have the same shape
+            if mask.shape != _data.shape:
+                (nd, nm) = (_data.size, mask.size)
+                if nm == 1:
+                    mask = np.resize(mask, _data.shape)
+                elif nm == nd:
+                    mask = np.reshape(mask, _data.shape)
+                else:
+                    msg = "Mask and data not compatible: data size is %i, " + \
+                          "mask size is %i."
+                    raise MaskError(msg % (nd, nm))
+                copy = True
+            # Set the mask to the new value
+            if _data._mask is nomask:
+                _data._mask = mask
+                _data._sharedmask = not copy
+            else:
+                if not keep_mask:
+                    _data._mask = mask
+                    _data._sharedmask = not copy
+                else:
+                    if _data.dtype.names is not None:
+                        def _recursive_or(a, b):
+                            "do a|=b on each field of a, recursively"
+                            for name in a.dtype.names:
+                                (af, bf) = (a[name], b[name])
+                                if af.dtype.names is not None:
+                                    _recursive_or(af, bf)
+                                else:
+                                    af |= bf
+
+                        _recursive_or(_data._mask, mask)
+                    else:
+                        _data._mask = np.logical_or(mask, _data._mask)
+                    _data._sharedmask = False
+
+        # Update fill_value.
+        if fill_value is None:
+            fill_value = getattr(data, '_fill_value', None)
+        # But don't run the check unless we have something to check.
+        if fill_value is not None:
+            _data._fill_value = _check_fill_value(fill_value, _data.dtype)
+        # Process extra options ..
+        if hard_mask is None:
+            _data._hardmask = getattr(data, '_hardmask', False)
+        else:
+            _data._hardmask = hard_mask
+        _data._baseclass = _baseclass
+        return _data
+
+
+    def _update_from(self, obj):
+        """
+        Copies some attributes of obj to self.
+
+        """
+        if isinstance(obj, ndarray):
+            _baseclass = type(obj)
+        else:
+            _baseclass = ndarray
+        # We need to copy the _basedict to avoid backward propagation
+        _optinfo = {}
+        _optinfo.update(getattr(obj, '_optinfo', {}))
+        _optinfo.update(getattr(obj, '_basedict', {}))
+        if not isinstance(obj, MaskedArray):
+            _optinfo.update(getattr(obj, '__dict__', {}))
+        _dict = dict(_fill_value=getattr(obj, '_fill_value', None),
+                     _hardmask=getattr(obj, '_hardmask', False),
+                     _sharedmask=getattr(obj, '_sharedmask', False),
+                     _isfield=getattr(obj, '_isfield', False),
+                     _baseclass=getattr(obj, '_baseclass', _baseclass),
+                     _optinfo=_optinfo,
+                     _basedict=_optinfo)
+        self.__dict__.update(_dict)
+        self.__dict__.update(_optinfo)
+        return
+
+    def __array_finalize__(self, obj):
+        """
+        Finalizes the masked array.
+
+        """
+        # Get main attributes.
+        self._update_from(obj)
+
+        # We have to decide how to initialize self.mask, based on
+        # obj.mask. This is very difficult.  There might be some
+        # correspondence between the elements in the array we are being
+        # created from (= obj) and us. Or there might not. This method can
+        # be called in all kinds of places for all kinds of reasons -- could
+        # be empty_like, could be slicing, could be a ufunc, could be a view.
+        # The numpy subclassing interface simply doesn't give us any way
+        # to know, which means that at best this method will be based on
+        # guesswork and heuristics. To make things worse, there isn't even any
+        # clear consensus about what the desired behavior is. For instance,
+        # most users think that np.empty_like(marr) -- which goes via this
+        # method -- should return a masked array with an empty mask (see
+        # gh-3404 and linked discussions), but others disagree, and they have
+        # existing code which depends on empty_like returning an array that
+        # matches the input mask.
+        #
+        # Historically our algorithm was: if the template object mask had the
+        # same *number of elements* as us, then we used *it's mask object
+        # itself* as our mask, so that writes to us would also write to the
+        # original array. This is horribly broken in multiple ways.
+        #
+        # Now what we do instead is, if the template object mask has the same
+        # number of elements as us, and we do not have the same base pointer
+        # as the template object (b/c views like arr[...] should keep the same
+        # mask), then we make a copy of the template object mask and use
+        # that. This is also horribly broken but somewhat less so. Maybe.
+        if isinstance(obj, ndarray):
+            # XX: This looks like a bug -- shouldn't it check self.dtype
+            # instead?
+            if obj.dtype.names is not None:
+                _mask = getmaskarray(obj)
+            else:
+                _mask = getmask(obj)
+
+            # If self and obj point to exactly the same data, then probably
+            # self is a simple view of obj (e.g., self = obj[...]), so they
+            # should share the same mask. (This isn't 100% reliable, e.g. self
+            # could be the first row of obj, or have strange strides, but as a
+            # heuristic it's not bad.) In all other cases, we make a copy of
+            # the mask, so that future modifications to 'self' do not end up
+            # side-effecting 'obj' as well.
+            if (_mask is not nomask and obj.__array_interface__["data"][0]
+                    != self.__array_interface__["data"][0]):
+                # We should make a copy. But we could get here via astype,
+                # in which case the mask might need a new dtype as well
+                # (e.g., changing to or from a structured dtype), and the
+                # order could have changed. So, change the mask type if
+                # needed and use astype instead of copy.
+                if self.dtype == obj.dtype:
+                    _mask_dtype = _mask.dtype
+                else:
+                    _mask_dtype = make_mask_descr(self.dtype)
+
+                if self.flags.c_contiguous:
+                    order = "C"
+                elif self.flags.f_contiguous:
+                    order = "F"
+                else:
+                    order = "K"
+
+                _mask = _mask.astype(_mask_dtype, order)
+            else:
+                # Take a view so shape changes, etc., do not propagate back.
+                _mask = _mask.view()
+        else:
+            _mask = nomask
+
+        self._mask = _mask
+        # Finalize the mask
+        if self._mask is not nomask:
+            try:
+                self._mask.shape = self.shape
+            except ValueError:
+                self._mask = nomask
+            except (TypeError, AttributeError):
+                # When _mask.shape is not writable (because it's a void)
+                pass
+
+        # Finalize the fill_value
+        if self._fill_value is not None:
+            self._fill_value = _check_fill_value(self._fill_value, self.dtype)
+        elif self.dtype.names is not None:
+            # Finalize the default fill_value for structured arrays
+            self._fill_value = _check_fill_value(None, self.dtype)
+
+    def __array_wrap__(self, obj, context=None, return_scalar=False):
+        """
+        Special hook for ufuncs.
+
+        Wraps the numpy array and sets the mask according to context.
+
+        """
+        if obj is self:  # for in-place operations
+            result = obj
+        else:
+            result = obj.view(type(self))
+            result._update_from(self)
+
+        if context is not None:
+            result._mask = result._mask.copy()
+            func, args, out_i = context
+            # args sometimes contains outputs (gh-10459), which we don't want
+            input_args = args[:func.nin]
+            m = functools.reduce(mask_or, [getmaskarray(arg) for arg in input_args])
+            # Get the domain mask
+            domain = ufunc_domain.get(func)
+            if domain is not None:
+                # Take the domain, and make sure it's a ndarray
+                with np.errstate(divide='ignore', invalid='ignore'):
+                    # The result may be masked for two (unary) domains.
+                    # That can't really be right as some domains drop
+                    # the mask and some don't behaving differently here.
+                    d = domain(*input_args).astype(bool, copy=False)
+                    d = filled(d, True)
+
+                if d.any():
+                    # Fill the result where the domain is wrong
+                    try:
+                        # Binary domain: take the last value
+                        fill_value = ufunc_fills[func][-1]
+                    except TypeError:
+                        # Unary domain: just use this one
+                        fill_value = ufunc_fills[func]
+                    except KeyError:
+                        # Domain not recognized, use fill_value instead
+                        fill_value = self.fill_value
+
+                    np.copyto(result, fill_value, where=d)
+
+                    # Update the mask
+                    if m is nomask:
+                        m = d
+                    else:
+                        # Don't modify inplace, we risk back-propagation
+                        m = (m | d)
+
+            # Make sure the mask has the proper size
+            if result is not self and result.shape == () and m:
+                return masked
+            else:
+                result._mask = m
+                result._sharedmask = False
+
+        return result
+
+    def view(self, dtype=None, type=None, fill_value=None):
+        """
+        Return a view of the MaskedArray data.
+
+        Parameters
+        ----------
+        dtype : data-type or ndarray sub-class, optional
+            Data-type descriptor of the returned view, e.g., float32 or int16.
+            The default, None, results in the view having the same data-type
+            as `a`. As with ``ndarray.view``, dtype can also be specified as
+            an ndarray sub-class, which then specifies the type of the
+            returned object (this is equivalent to setting the ``type``
+            parameter).
+        type : Python type, optional
+            Type of the returned view, either ndarray or a subclass.  The
+            default None results in type preservation.
+        fill_value : scalar, optional
+            The value to use for invalid entries (None by default).
+            If None, then this argument is inferred from the passed `dtype`, or
+            in its absence the original array, as discussed in the notes below.
+
+        See Also
+        --------
+        numpy.ndarray.view : Equivalent method on ndarray object.
+
+        Notes
+        -----
+
+        ``a.view()`` is used two different ways:
+
+        ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
+        of the array's memory with a different data-type.  This can cause a
+        reinterpretation of the bytes of memory.
+
+        ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
+        returns an instance of `ndarray_subclass` that looks at the same array
+        (same shape, dtype, etc.)  This does not cause a reinterpretation of the
+        memory.
+
+        If `fill_value` is not specified, but `dtype` is specified (and is not
+        an ndarray sub-class), the `fill_value` of the MaskedArray will be
+        reset. If neither `fill_value` nor `dtype` are specified (or if
+        `dtype` is an ndarray sub-class), then the fill value is preserved.
+        Finally, if `fill_value` is specified, but `dtype` is not, the fill
+        value is set to the specified value.
+
+        For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
+        bytes per entry than the previous dtype (for example, converting a
+        regular array to a structured array), then the behavior of the view
+        cannot be predicted just from the superficial appearance of ``a`` (shown
+        by ``print(a)``). It also depends on exactly how ``a`` is stored in
+        memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
+        defined as a slice or transpose, etc., the view may give different
+        results.
+        """
+
+        if dtype is None:
+            if type is None:
+                output = ndarray.view(self)
+            else:
+                output = ndarray.view(self, type)
+        elif type is None:
+            try:
+                if issubclass(dtype, ndarray):
+                    output = ndarray.view(self, dtype)
+                    dtype = None
+                else:
+                    output = ndarray.view(self, dtype)
+            except TypeError:
+                output = ndarray.view(self, dtype)
+        else:
+            output = ndarray.view(self, dtype, type)
+
+        # also make the mask be a view (so attr changes to the view's
+        # mask do no affect original object's mask)
+        # (especially important to avoid affecting np.masked singleton)
+        if getmask(output) is not nomask:
+            output._mask = output._mask.view()
+
+        # Make sure to reset the _fill_value if needed
+        if getattr(output, '_fill_value', None) is not None:
+            if fill_value is None:
+                if dtype is None:
+                    pass  # leave _fill_value as is
+                else:
+                    output._fill_value = None
+            else:
+                output.fill_value = fill_value
+        return output
+
+    def __getitem__(self, indx):
+        """
+        x.__getitem__(y) <==> x[y]
+
+        Return the item described by i, as a masked array.
+
+        """
+        # We could directly use ndarray.__getitem__ on self.
+        # But then we would have to modify __array_finalize__ to prevent the
+        # mask of being reshaped if it hasn't been set up properly yet
+        # So it's easier to stick to the current version
+        dout = self.data[indx]
+        _mask = self._mask
+
+        def _is_scalar(m):
+            return not isinstance(m, np.ndarray)
+
+        def _scalar_heuristic(arr, elem):
+            """
+            Return whether `elem` is a scalar result of indexing `arr`, or None
+            if undecidable without promoting nomask to a full mask
+            """
+            # obviously a scalar
+            if not isinstance(elem, np.ndarray):
+                return True
+
+            # object array scalar indexing can return anything
+            elif arr.dtype.type is np.object_:
+                if arr.dtype is not elem.dtype:
+                    # elem is an array, but dtypes do not match, so must be
+                    # an element
+                    return True
+
+            # well-behaved subclass that only returns 0d arrays when
+            # expected - this is not a scalar
+            elif type(arr).__getitem__ == ndarray.__getitem__:
+                return False
+
+            return None
+
+        if _mask is not nomask:
+            # _mask cannot be a subclass, so it tells us whether we should
+            # expect a scalar. It also cannot be of dtype object.
+            mout = _mask[indx]
+            scalar_expected = _is_scalar(mout)
+
+        else:
+            # attempt to apply the heuristic to avoid constructing a full mask
+            mout = nomask
+            scalar_expected = _scalar_heuristic(self.data, dout)
+            if scalar_expected is None:
+                # heuristics have failed
+                # construct a full array, so we can be certain. This is costly.
+                # we could also fall back on ndarray.__getitem__(self.data, indx)
+                scalar_expected = _is_scalar(getmaskarray(self)[indx])
+
+        # Did we extract a single item?
+        if scalar_expected:
+            # A record
+            if isinstance(dout, np.void):
+                # We should always re-cast to mvoid, otherwise users can
+                # change masks on rows that already have masked values, but not
+                # on rows that have no masked values, which is inconsistent.
+                return mvoid(dout, mask=mout, hardmask=self._hardmask)
+
+            # special case introduced in gh-5962
+            elif (self.dtype.type is np.object_ and
+                  isinstance(dout, np.ndarray) and
+                  dout is not masked):
+                # If masked, turn into a MaskedArray, with everything masked.
+                if mout:
+                    return MaskedArray(dout, mask=True)
+                else:
+                    return dout
+
+            # Just a scalar
+            else:
+                if mout:
+                    return masked
+                else:
+                    return dout
+        else:
+            # Force dout to MA
+            dout = dout.view(type(self))
+            # Inherit attributes from self
+            dout._update_from(self)
+            # Check the fill_value
+            if is_string_or_list_of_strings(indx):
+                if self._fill_value is not None:
+                    dout._fill_value = self._fill_value[indx]
+
+                    # Something like gh-15895 has happened if this check fails.
+                    # _fill_value should always be an ndarray.
+                    if not isinstance(dout._fill_value, np.ndarray):
+                        raise RuntimeError('Internal NumPy error.')
+                    # If we're indexing a multidimensional field in a
+                    # structured array (such as dtype("(2,)i2,(2,)i1")),
+                    # dimensionality goes up (M[field].ndim == M.ndim +
+                    # M.dtype[field].ndim).  That's fine for
+                    # M[field] but problematic for M[field].fill_value
+                    # which should have shape () to avoid breaking several
+                    # methods. There is no great way out, so set to
+                    # first element. See issue #6723.
+                    if dout._fill_value.ndim > 0:
+                        if not (dout._fill_value ==
+                                dout._fill_value.flat[0]).all():
+                            warnings.warn(
+                                "Upon accessing multidimensional field "
+                                f"{indx!s}, need to keep dimensionality "
+                                "of fill_value at 0. Discarding "
+                                "heterogeneous fill_value and setting "
+                                f"all to {dout._fill_value[0]!s}.",
+                                stacklevel=2)
+                        # Need to use `.flat[0:1].squeeze(...)` instead of just
+                        # `.flat[0]` to ensure the result is a 0d array and not
+                        # a scalar.
+                        dout._fill_value = dout._fill_value.flat[0:1].squeeze(axis=0)
+                dout._isfield = True
+            # Update the mask if needed
+            if mout is not nomask:
+                # set shape to match that of data; this is needed for matrices
+                dout._mask = reshape(mout, dout.shape)
+                dout._sharedmask = True
+                # Note: Don't try to check for m.any(), that'll take too long
+        return dout
+
+    # setitem may put NaNs into integer arrays or occasionally overflow a
+    # float.  But this may happen in masked values, so avoid otherwise
+    # correct warnings (as is typical also in masked calculations).
+    @np.errstate(over='ignore', invalid='ignore')
+    def __setitem__(self, indx, value):
+        """
+        x.__setitem__(i, y) <==> x[i]=y
+
+        Set item described by index. If value is masked, masks those
+        locations.
+
+        """
+        if self is masked:
+            raise MaskError('Cannot alter the masked element.')
+        _data = self._data
+        _mask = self._mask
+        if isinstance(indx, str):
+            _data[indx] = value
+            if _mask is nomask:
+                self._mask = _mask = make_mask_none(self.shape, self.dtype)
+            _mask[indx] = getmask(value)
+            return
+
+        _dtype = _data.dtype
+
+        if value is masked:
+            # The mask wasn't set: create a full version.
+            if _mask is nomask:
+                _mask = self._mask = make_mask_none(self.shape, _dtype)
+            # Now, set the mask to its value.
+            if _dtype.names is not None:
+                _mask[indx] = tuple([True] * len(_dtype.names))
+            else:
+                _mask[indx] = True
+            return
+
+        # Get the _data part of the new value
+        dval = getattr(value, '_data', value)
+        # Get the _mask part of the new value
+        mval = getmask(value)
+        if _dtype.names is not None and mval is nomask:
+            mval = tuple([False] * len(_dtype.names))
+        if _mask is nomask:
+            # Set the data, then the mask
+            _data[indx] = dval
+            if mval is not nomask:
+                _mask = self._mask = make_mask_none(self.shape, _dtype)
+                _mask[indx] = mval
+        elif not self._hardmask:
+            # Set the data, then the mask
+            if (isinstance(indx, masked_array) and
+                    not isinstance(value, masked_array)):
+                _data[indx.data] = dval
+            else:
+                _data[indx] = dval
+                _mask[indx] = mval
+        elif hasattr(indx, 'dtype') and (indx.dtype == MaskType):
+            indx = indx * umath.logical_not(_mask)
+            _data[indx] = dval
+        else:
+            if _dtype.names is not None:
+                err_msg = "Flexible 'hard' masks are not yet supported."
+                raise NotImplementedError(err_msg)
+            mindx = mask_or(_mask[indx], mval, copy=True)
+            dindx = self._data[indx]
+            if dindx.size > 1:
+                np.copyto(dindx, dval, where=~mindx)
+            elif mindx is nomask:
+                dindx = dval
+            _data[indx] = dindx
+            _mask[indx] = mindx
+        return
+
+    # Define so that we can overwrite the setter.
+    @property
+    def dtype(self):
+        return super().dtype
+
+    @dtype.setter
+    def dtype(self, dtype):
+        super(MaskedArray, type(self)).dtype.__set__(self, dtype)
+        if self._mask is not nomask:
+            self._mask = self._mask.view(make_mask_descr(dtype), ndarray)
+            # Try to reset the shape of the mask (if we don't have a void).
+            # This raises a ValueError if the dtype change won't work.
+            try:
+                self._mask.shape = self.shape
+            except (AttributeError, TypeError):
+                pass
+
+    @property
+    def shape(self):
+        return super().shape
+
+    @shape.setter
+    def shape(self, shape):
+        super(MaskedArray, type(self)).shape.__set__(self, shape)
+        # Cannot use self._mask, since it may not (yet) exist when a
+        # masked matrix sets the shape.
+        if getmask(self) is not nomask:
+            self._mask.shape = self.shape
+
+    def __setmask__(self, mask, copy=False):
+        """
+        Set the mask.
+
+        """
+        idtype = self.dtype
+        current_mask = self._mask
+        if mask is masked:
+            mask = True
+
+        if current_mask is nomask:
+            # Make sure the mask is set
+            # Just don't do anything if there's nothing to do.
+            if mask is nomask:
+                return
+            current_mask = self._mask = make_mask_none(self.shape, idtype)
+
+        if idtype.names is None:
+            # No named fields.
+            # Hardmask: don't unmask the data
+            if self._hardmask:
+                current_mask |= mask
+            # Softmask: set everything to False
+            # If it's obviously a compatible scalar, use a quick update
+            # method.
+            elif isinstance(mask, (int, float, np.bool, np.number)):
+                current_mask[...] = mask
+            # Otherwise fall back to the slower, general purpose way.
+            else:
+                current_mask.flat = mask
+        else:
+            # Named fields w/
+            mdtype = current_mask.dtype
+            mask = np.asarray(mask)
+            # Mask is a singleton
+            if not mask.ndim:
+                # It's a boolean : make a record
+                if mask.dtype.kind == 'b':
+                    mask = np.array(tuple([mask.item()] * len(mdtype)),
+                                    dtype=mdtype)
+                # It's a record: make sure the dtype is correct
+                else:
+                    mask = mask.astype(mdtype)
+            # Mask is a sequence
+            else:
+                # Make sure the new mask is a ndarray with the proper dtype
+                try:
+                    copy = None if not copy else True
+                    mask = np.array(mask, copy=copy, dtype=mdtype)
+                # Or assume it's a sequence of bool/int
+                except TypeError:
+                    mask = np.array([tuple([m] * len(mdtype)) for m in mask],
+                                    dtype=mdtype)
+            # Hardmask: don't unmask the data
+            if self._hardmask:
+                for n in idtype.names:
+                    current_mask[n] |= mask[n]
+            # Softmask: set everything to False
+            # If it's obviously a compatible scalar, use a quick update
+            # method.
+            elif isinstance(mask, (int, float, np.bool, np.number)):
+                current_mask[...] = mask
+            # Otherwise fall back to the slower, general purpose way.
+            else:
+                current_mask.flat = mask
+        # Reshape if needed
+        if current_mask.shape:
+            current_mask.shape = self.shape
+        return
+
+    _set_mask = __setmask__
+
+    @property
+    def mask(self):
+        """ Current mask. """
+
+        # We could try to force a reshape, but that wouldn't work in some
+        # cases.
+        # Return a view so that the dtype and shape cannot be changed in place
+        # This still preserves nomask by identity
+        return self._mask.view()
+
+    @mask.setter
+    def mask(self, value):
+        self.__setmask__(value)
+
+    @property
+    def recordmask(self):
+        """
+        Get or set the mask of the array if it has no named fields. For
+        structured arrays, returns a ndarray of booleans where entries are
+        ``True`` if **all** the fields are masked, ``False`` otherwise:
+
+        >>> x = np.ma.array([(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)],
+        ...         mask=[(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)],
+        ...        dtype=[('a', int), ('b', int)])
+        >>> x.recordmask
+        array([False, False,  True, False, False])
+        """
+
+        _mask = self._mask.view(ndarray)
+        if _mask.dtype.names is None:
+            return _mask
+        return np.all(flatten_structured_array(_mask), axis=-1)
+
+    @recordmask.setter
+    def recordmask(self, mask):
+        raise NotImplementedError("Coming soon: setting the mask per records!")
+
+    def harden_mask(self):
+        """
+        Force the mask to hard, preventing unmasking by assignment.
+
+        Whether the mask of a masked array is hard or soft is determined by
+        its `~ma.MaskedArray.hardmask` property. `harden_mask` sets
+        `~ma.MaskedArray.hardmask` to ``True`` (and returns the modified
+        self).
+
+        See Also
+        --------
+        ma.MaskedArray.hardmask
+        ma.MaskedArray.soften_mask
+
+        """
+        self._hardmask = True
+        return self
+
+    def soften_mask(self):
+        """
+        Force the mask to soft (default), allowing unmasking by assignment.
+
+        Whether the mask of a masked array is hard or soft is determined by
+        its `~ma.MaskedArray.hardmask` property. `soften_mask` sets
+        `~ma.MaskedArray.hardmask` to ``False`` (and returns the modified
+        self).
+
+        See Also
+        --------
+        ma.MaskedArray.hardmask
+        ma.MaskedArray.harden_mask
+
+        """
+        self._hardmask = False
+        return self
+
+    @property
+    def hardmask(self):
+        """
+        Specifies whether values can be unmasked through assignments.
+
+        By default, assigning definite values to masked array entries will
+        unmask them.  When `hardmask` is ``True``, the mask will not change
+        through assignments.
+
+        See Also
+        --------
+        ma.MaskedArray.harden_mask
+        ma.MaskedArray.soften_mask
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.arange(10)
+        >>> m = np.ma.masked_array(x, x>5)
+        >>> assert not m.hardmask
+
+        Since `m` has a soft mask, assigning an element value unmasks that
+        element:
+
+        >>> m[8] = 42
+        >>> m
+        masked_array(data=[0, 1, 2, 3, 4, 5, --, --, 42, --],
+                     mask=[False, False, False, False, False, False,
+                           True, True, False, True],
+               fill_value=999999)
+
+        After hardening, the mask is not affected by assignments:
+
+        >>> hardened = np.ma.harden_mask(m)
+        >>> assert m.hardmask and hardened is m
+        >>> m[:] = 23
+        >>> m
+        masked_array(data=[23, 23, 23, 23, 23, 23, --, --, 23, --],
+                     mask=[False, False, False, False, False, False,
+                           True, True, False, True],
+               fill_value=999999)
+
+        """
+        return self._hardmask
+
+    def unshare_mask(self):
+        """
+        Copy the mask and set the `sharedmask` flag to ``False``.
+
+        Whether the mask is shared between masked arrays can be seen from
+        the `sharedmask` property. `unshare_mask` ensures the mask is not
+        shared. A copy of the mask is only made if it was shared.
+
+        See Also
+        --------
+        sharedmask
+
+        """
+        if self._sharedmask:
+            self._mask = self._mask.copy()
+            self._sharedmask = False
+        return self
+
+    @property
+    def sharedmask(self):
+        """ Share status of the mask (read-only). """
+        return self._sharedmask
+
+    def shrink_mask(self):
+        """
+        Reduce a mask to nomask when possible.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4)
+        >>> x.mask
+        array([[False, False],
+               [False, False]])
+        >>> x.shrink_mask()
+        masked_array(
+          data=[[1, 2],
+                [3, 4]],
+          mask=False,
+          fill_value=999999)
+        >>> x.mask
+        False
+
+        """
+        self._mask = _shrink_mask(self._mask)
+        return self
+
+    @property
+    def baseclass(self):
+        """ Class of the underlying data (read-only). """
+        return self._baseclass
+
+    def _get_data(self):
+        """
+        Returns the underlying data, as a view of the masked array.
+
+        If the underlying data is a subclass of :class:`numpy.ndarray`, it is
+        returned as such.
+
+        >>> x = np.ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])
+        >>> x.data
+        matrix([[1, 2],
+                [3, 4]])
+
+        The type of the data can be accessed through the :attr:`baseclass`
+        attribute.
+        """
+        return ndarray.view(self, self._baseclass)
+
+    _data = property(fget=_get_data)
+    data = property(fget=_get_data)
+
+    @property
+    def flat(self):
+        """ Return a flat iterator, or set a flattened version of self to value. """
+        return MaskedIterator(self)
+
+    @flat.setter
+    def flat(self, value):
+        y = self.ravel()
+        y[:] = value
+
+    @property
+    def fill_value(self):
+        """
+        The filling value of the masked array is a scalar. When setting, None
+        will set to a default based on the data type.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> for dt in [np.int32, np.int64, np.float64, np.complex128]:
+        ...     np.ma.array([0, 1], dtype=dt).get_fill_value()
+        ...
+        np.int64(999999)
+        np.int64(999999)
+        np.float64(1e+20)
+        np.complex128(1e+20+0j)
+
+        >>> x = np.ma.array([0, 1.], fill_value=-np.inf)
+        >>> x.fill_value
+        np.float64(-inf)
+        >>> x.fill_value = np.pi
+        >>> x.fill_value
+        np.float64(3.1415926535897931)
+
+        Reset to default:
+
+        >>> x.fill_value = None
+        >>> x.fill_value
+        np.float64(1e+20)
+
+        """
+        if self._fill_value is None:
+            self._fill_value = _check_fill_value(None, self.dtype)
+
+        # Temporary workaround to account for the fact that str and bytes
+        # scalars cannot be indexed with (), whereas all other numpy
+        # scalars can. See issues #7259 and #7267.
+        # The if-block can be removed after #7267 has been fixed.
+        if isinstance(self._fill_value, ndarray):
+            return self._fill_value[()]
+        return self._fill_value
+
+    @fill_value.setter
+    def fill_value(self, value=None):
+        target = _check_fill_value(value, self.dtype)
+        if not target.ndim == 0:
+            # 2019-11-12, 1.18.0
+            warnings.warn(
+                "Non-scalar arrays for the fill value are deprecated. Use "
+                "arrays with scalar values instead. The filled function "
+                "still supports any array as `fill_value`.",
+                DeprecationWarning, stacklevel=2)
+
+        _fill_value = self._fill_value
+        if _fill_value is None:
+            # Create the attribute if it was undefined
+            self._fill_value = target
+        else:
+            # Don't overwrite the attribute, just fill it (for propagation)
+            _fill_value[()] = target
+
+    # kept for compatibility
+    get_fill_value = fill_value.fget
+    set_fill_value = fill_value.fset
+
+    def filled(self, fill_value=None):
+        """
+        Return a copy of self, with masked values filled with a given value.
+        **However**, if there are no masked values to fill, self will be
+        returned instead as an ndarray.
+
+        Parameters
+        ----------
+        fill_value : array_like, optional
+            The value to use for invalid entries. Can be scalar or non-scalar.
+            If non-scalar, the resulting ndarray must be broadcastable over
+            input array. Default is None, in which case, the `fill_value`
+            attribute of the array is used instead.
+
+        Returns
+        -------
+        filled_array : ndarray
+            A copy of ``self`` with invalid entries replaced by *fill_value*
+            (be it the function argument or the attribute of ``self``), or
+            ``self`` itself as an ndarray if there are no invalid entries to
+            be replaced.
+
+        Notes
+        -----
+        The result is **not** a MaskedArray!
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)
+        >>> x.filled()
+        array([   1,    2, -999,    4, -999])
+        >>> x.filled(fill_value=1000)
+        array([   1,    2, 1000,    4, 1000])
+        >>> type(x.filled())
+        
+
+        Subclassing is preserved. This means that if, e.g., the data part of
+        the masked array is a recarray, `filled` returns a recarray:
+
+        >>> x = np.array([(-1, 2), (-3, 4)], dtype='i8,i8').view(np.recarray)
+        >>> m = np.ma.array(x, mask=[(True, False), (False, True)])
+        >>> m.filled()
+        rec.array([(999999,      2), (    -3, 999999)],
+                  dtype=[('f0', '>> import numpy as np
+        >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)
+        >>> x.compressed()
+        array([0, 1])
+        >>> type(x.compressed())
+        
+
+        N-D arrays are compressed to 1-D.
+
+        >>> arr = [[1, 2], [3, 4]]
+        >>> mask = [[1, 0], [0, 1]]
+        >>> x = np.ma.array(arr, mask=mask)
+        >>> x.compressed()
+        array([2, 3])
+
+        """
+        data = ndarray.ravel(self._data)
+        if self._mask is not nomask:
+            data = data.compress(np.logical_not(ndarray.ravel(self._mask)))
+        return data
+
+    def compress(self, condition, axis=None, out=None):
+        """
+        Return `a` where condition is ``True``.
+
+        If condition is a `~ma.MaskedArray`, missing values are considered
+        as ``False``.
+
+        Parameters
+        ----------
+        condition : var
+            Boolean 1-d array selecting which entries to return. If len(condition)
+            is less than the size of a along the axis, then output is truncated
+            to length of condition array.
+        axis : {None, int}, optional
+            Axis along which the operation must be performed.
+        out : {None, ndarray}, optional
+            Alternative output array in which to place the result. It must have
+            the same shape as the expected output but the type will be cast if
+            necessary.
+
+        Returns
+        -------
+        result : MaskedArray
+            A :class:`~ma.MaskedArray` object.
+
+        Notes
+        -----
+        Please note the difference with :meth:`compressed` !
+        The output of :meth:`compress` has a mask, the output of
+        :meth:`compressed` does not.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
+        >>> x
+        masked_array(
+          data=[[1, --, 3],
+                [--, 5, --],
+                [7, --, 9]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+        >>> x.compress([1, 0, 1])
+        masked_array(data=[1, 3],
+                     mask=[False, False],
+               fill_value=999999)
+
+        >>> x.compress([1, 0, 1], axis=1)
+        masked_array(
+          data=[[1, 3],
+                [--, --],
+                [7, 9]],
+          mask=[[False, False],
+                [ True,  True],
+                [False, False]],
+          fill_value=999999)
+
+        """
+        # Get the basic components
+        (_data, _mask) = (self._data, self._mask)
+
+        # Force the condition to a regular ndarray and forget the missing
+        # values.
+        condition = np.asarray(condition)
+
+        _new = _data.compress(condition, axis=axis, out=out).view(type(self))
+        _new._update_from(self)
+        if _mask is not nomask:
+            _new._mask = _mask.compress(condition, axis=axis)
+        return _new
+
+    def _insert_masked_print(self):
+        """
+        Replace masked values with masked_print_option, casting all innermost
+        dtypes to object.
+        """
+        if masked_print_option.enabled():
+            mask = self._mask
+            if mask is nomask:
+                res = self._data
+            else:
+                # convert to object array to make filled work
+                data = self._data
+                # For big arrays, to avoid a costly conversion to the
+                # object dtype, extract the corners before the conversion.
+                print_width = (self._print_width if self.ndim > 1
+                               else self._print_width_1d)
+                for axis in range(self.ndim):
+                    if data.shape[axis] > print_width:
+                        ind = print_width // 2
+                        arr = np.split(data, (ind, -ind), axis=axis)
+                        data = np.concatenate((arr[0], arr[2]), axis=axis)
+                        arr = np.split(mask, (ind, -ind), axis=axis)
+                        mask = np.concatenate((arr[0], arr[2]), axis=axis)
+
+                rdtype = _replace_dtype_fields(self.dtype, "O")
+                res = data.astype(rdtype)
+                _recursive_printoption(res, mask, masked_print_option)
+        else:
+            res = self.filled(self.fill_value)
+        return res
+
+    def __str__(self):
+        return str(self._insert_masked_print())
+
+    def __repr__(self):
+        """
+        Literal string representation.
+
+        """
+        if self._baseclass is np.ndarray:
+            name = 'array'
+        else:
+            name = self._baseclass.__name__
+
+
+        # 2016-11-19: Demoted to legacy format
+        if np._core.arrayprint._get_legacy_print_mode() <= 113:
+            is_long = self.ndim > 1
+            parameters = dict(
+                name=name,
+                nlen=" " * len(name),
+                data=str(self),
+                mask=str(self._mask),
+                fill=str(self.fill_value),
+                dtype=str(self.dtype)
+            )
+            is_structured = bool(self.dtype.names)
+            key = '{}_{}'.format(
+                'long' if is_long else 'short',
+                'flx' if is_structured else 'std'
+            )
+            return _legacy_print_templates[key] % parameters
+
+        prefix = f"masked_{name}("
+
+        dtype_needed = (
+            not np._core.arrayprint.dtype_is_implied(self.dtype) or
+            np.all(self.mask) or
+            self.size == 0
+        )
+
+        # determine which keyword args need to be shown
+        keys = ['data', 'mask', 'fill_value']
+        if dtype_needed:
+            keys.append('dtype')
+
+        # array has only one row (non-column)
+        is_one_row = builtins.all(dim == 1 for dim in self.shape[:-1])
+
+        # choose what to indent each keyword with
+        min_indent = 2
+        if is_one_row:
+            # first key on the same line as the type, remaining keys
+            # aligned by equals
+            indents = {}
+            indents[keys[0]] = prefix
+            for k in keys[1:]:
+                n = builtins.max(min_indent, len(prefix + keys[0]) - len(k))
+                indents[k] = ' ' * n
+            prefix = ''  # absorbed into the first indent
+        else:
+            # each key on its own line, indented by two spaces
+            indents = {k: ' ' * min_indent for k in keys}
+            prefix = prefix + '\n'  # first key on the next line
+
+        # format the field values
+        reprs = {}
+        reprs['data'] = np.array2string(
+            self._insert_masked_print(),
+            separator=", ",
+            prefix=indents['data'] + 'data=',
+            suffix=',')
+        reprs['mask'] = np.array2string(
+            self._mask,
+            separator=", ",
+            prefix=indents['mask'] + 'mask=',
+            suffix=',')
+
+        if self._fill_value is None:
+            self.fill_value  # initialize fill_value
+
+        if (self._fill_value.dtype.kind in ("S", "U")
+                and self.dtype.kind == self._fill_value.dtype.kind):
+            # Allow strings: "N/A" has length 3 so would mismatch.
+            fill_repr = repr(self.fill_value.item())
+        elif self._fill_value.dtype == self.dtype and not self.dtype == object:
+            # Guess that it is OK to use the string as item repr.  To really
+            # fix this, it needs new logic (shared with structured scalars)
+            fill_repr = str(self.fill_value)
+        else:
+            fill_repr = repr(self.fill_value)
+
+        reprs['fill_value'] = fill_repr
+        if dtype_needed:
+            reprs['dtype'] = np._core.arrayprint.dtype_short_repr(self.dtype)
+
+        # join keys with values and indentations
+        result = ',\n'.join(
+            '{}{}={}'.format(indents[k], k, reprs[k])
+            for k in keys
+        )
+        return prefix + result + ')'
+
+    def _delegate_binop(self, other):
+        # This emulates the logic in
+        #     private/binop_override.h:forward_binop_should_defer
+        if isinstance(other, type(self)):
+            return False
+        array_ufunc = getattr(other, "__array_ufunc__", False)
+        if array_ufunc is False:
+            other_priority = getattr(other, "__array_priority__", -1000000)
+            return self.__array_priority__ < other_priority
+        else:
+            # If array_ufunc is not None, it will be called inside the ufunc;
+            # None explicitly tells us to not call the ufunc, i.e., defer.
+            return array_ufunc is None
+
+    def _comparison(self, other, compare):
+        """Compare self with other using operator.eq or operator.ne.
+
+        When either of the elements is masked, the result is masked as well,
+        but the underlying boolean data are still set, with self and other
+        considered equal if both are masked, and unequal otherwise.
+
+        For structured arrays, all fields are combined, with masked values
+        ignored. The result is masked if all fields were masked, with self
+        and other considered equal only if both were fully masked.
+        """
+        omask = getmask(other)
+        smask = self.mask
+        mask = mask_or(smask, omask, copy=True)
+
+        odata = getdata(other)
+        if mask.dtype.names is not None:
+            # only == and != are reasonably defined for structured dtypes,
+            # so give up early for all other comparisons:
+            if compare not in (operator.eq, operator.ne):
+                return NotImplemented
+            # For possibly masked structured arrays we need to be careful,
+            # since the standard structured array comparison will use all
+            # fields, masked or not. To avoid masked fields influencing the
+            # outcome, we set all masked fields in self to other, so they'll
+            # count as equal.  To prepare, we ensure we have the right shape.
+            broadcast_shape = np.broadcast(self, odata).shape
+            sbroadcast = np.broadcast_to(self, broadcast_shape, subok=True)
+            sbroadcast._mask = mask
+            sdata = sbroadcast.filled(odata)
+            # Now take care of the mask; the merged mask should have an item
+            # masked if all fields were masked (in one and/or other).
+            mask = (mask == np.ones((), mask.dtype))
+            # Ensure we can compare masks below if other was not masked.
+            if omask is np.False_:
+                omask = np.zeros((), smask.dtype)
+
+        else:
+            # For regular arrays, just use the data as they come.
+            sdata = self.data
+
+        check = compare(sdata, odata)
+
+        if isinstance(check, (np.bool, bool)):
+            return masked if mask else check
+
+        if mask is not nomask:
+            if compare in (operator.eq, operator.ne):
+                # Adjust elements that were masked, which should be treated
+                # as equal if masked in both, unequal if masked in one.
+                # Note that this works automatically for structured arrays too.
+                # Ignore this for operations other than `==` and `!=`
+                check = np.where(mask, compare(smask, omask), check)
+
+            if mask.shape != check.shape:
+                # Guarantee consistency of the shape, making a copy since the
+                # the mask may need to get written to later.
+                mask = np.broadcast_to(mask, check.shape).copy()
+
+        check = check.view(type(self))
+        check._update_from(self)
+        check._mask = mask
+
+        # Cast fill value to np.bool if needed. If it cannot be cast, the
+        # default boolean fill value is used.
+        if check._fill_value is not None:
+            try:
+                fill = _check_fill_value(check._fill_value, np.bool)
+            except (TypeError, ValueError):
+                fill = _check_fill_value(None, np.bool)
+            check._fill_value = fill
+
+        return check
+
+    def __eq__(self, other):
+        """Check whether other equals self elementwise.
+
+        When either of the elements is masked, the result is masked as well,
+        but the underlying boolean data are still set, with self and other
+        considered equal if both are masked, and unequal otherwise.
+
+        For structured arrays, all fields are combined, with masked values
+        ignored. The result is masked if all fields were masked, with self
+        and other considered equal only if both were fully masked.
+        """
+        return self._comparison(other, operator.eq)
+
+    def __ne__(self, other):
+        """Check whether other does not equal self elementwise.
+
+        When either of the elements is masked, the result is masked as well,
+        but the underlying boolean data are still set, with self and other
+        considered equal if both are masked, and unequal otherwise.
+
+        For structured arrays, all fields are combined, with masked values
+        ignored. The result is masked if all fields were masked, with self
+        and other considered equal only if both were fully masked.
+        """
+        return self._comparison(other, operator.ne)
+
+    # All other comparisons:
+    def __le__(self, other):
+        return self._comparison(other, operator.le)
+
+    def __lt__(self, other):
+        return self._comparison(other, operator.lt)
+
+    def __ge__(self, other):
+        return self._comparison(other, operator.ge)
+
+    def __gt__(self, other):
+        return self._comparison(other, operator.gt)
+
+    def __add__(self, other):
+        """
+        Add self to other, and return a new masked array.
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return add(self, other)
+
+    def __radd__(self, other):
+        """
+        Add other to self, and return a new masked array.
+
+        """
+        # In analogy with __rsub__ and __rdiv__, use original order:
+        # we get here from `other + self`.
+        return add(other, self)
+
+    def __sub__(self, other):
+        """
+        Subtract other from self, and return a new masked array.
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return subtract(self, other)
+
+    def __rsub__(self, other):
+        """
+        Subtract self from other, and return a new masked array.
+
+        """
+        return subtract(other, self)
+
+    def __mul__(self, other):
+        "Multiply self by other, and return a new masked array."
+        if self._delegate_binop(other):
+            return NotImplemented
+        return multiply(self, other)
+
+    def __rmul__(self, other):
+        """
+        Multiply other by self, and return a new masked array.
+
+        """
+        # In analogy with __rsub__ and __rdiv__, use original order:
+        # we get here from `other * self`.
+        return multiply(other, self)
+
+    def __div__(self, other):
+        """
+        Divide other into self, and return a new masked array.
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return divide(self, other)
+
+    def __truediv__(self, other):
+        """
+        Divide other into self, and return a new masked array.
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return true_divide(self, other)
+
+    def __rtruediv__(self, other):
+        """
+        Divide self into other, and return a new masked array.
+
+        """
+        return true_divide(other, self)
+
+    def __floordiv__(self, other):
+        """
+        Divide other into self, and return a new masked array.
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return floor_divide(self, other)
+
+    def __rfloordiv__(self, other):
+        """
+        Divide self into other, and return a new masked array.
+
+        """
+        return floor_divide(other, self)
+
+    def __pow__(self, other):
+        """
+        Raise self to the power other, masking the potential NaNs/Infs
+
+        """
+        if self._delegate_binop(other):
+            return NotImplemented
+        return power(self, other)
+
+    def __rpow__(self, other):
+        """
+        Raise other to the power self, masking the potential NaNs/Infs
+
+        """
+        return power(other, self)
+
+    def __iadd__(self, other):
+        """
+        Add other to self in-place.
+
+        """
+        m = getmask(other)
+        if self._mask is nomask:
+            if m is not nomask and m.any():
+                self._mask = make_mask_none(self.shape, self.dtype)
+                self._mask += m
+        else:
+            if m is not nomask:
+                self._mask += m
+        other_data = getdata(other)
+        other_data = np.where(self._mask, other_data.dtype.type(0), other_data)
+        self._data.__iadd__(other_data)
+        return self
+
+    def __isub__(self, other):
+        """
+        Subtract other from self in-place.
+
+        """
+        m = getmask(other)
+        if self._mask is nomask:
+            if m is not nomask and m.any():
+                self._mask = make_mask_none(self.shape, self.dtype)
+                self._mask += m
+        elif m is not nomask:
+            self._mask += m
+        other_data = getdata(other)
+        other_data = np.where(self._mask, other_data.dtype.type(0), other_data)
+        self._data.__isub__(other_data)
+        return self
+
+    def __imul__(self, other):
+        """
+        Multiply self by other in-place.
+
+        """
+        m = getmask(other)
+        if self._mask is nomask:
+            if m is not nomask and m.any():
+                self._mask = make_mask_none(self.shape, self.dtype)
+                self._mask += m
+        elif m is not nomask:
+            self._mask += m
+        other_data = getdata(other)
+        other_data = np.where(self._mask, other_data.dtype.type(1), other_data)
+        self._data.__imul__(other_data)
+        return self
+
+    def __idiv__(self, other):
+        """
+        Divide self by other in-place.
+
+        """
+        other_data = getdata(other)
+        dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
+        other_mask = getmask(other)
+        new_mask = mask_or(other_mask, dom_mask)
+        # The following 4 lines control the domain filling
+        if dom_mask.any():
+            (_, fval) = ufunc_fills[np.divide]
+            other_data = np.where(
+                    dom_mask, other_data.dtype.type(fval), other_data)
+        self._mask |= new_mask
+        other_data = np.where(self._mask, other_data.dtype.type(1), other_data)
+        self._data.__idiv__(other_data)
+        return self
+
+    def __ifloordiv__(self, other):
+        """
+        Floor divide self by other in-place.
+
+        """
+        other_data = getdata(other)
+        dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
+        other_mask = getmask(other)
+        new_mask = mask_or(other_mask, dom_mask)
+        # The following 3 lines control the domain filling
+        if dom_mask.any():
+            (_, fval) = ufunc_fills[np.floor_divide]
+            other_data = np.where(
+                    dom_mask, other_data.dtype.type(fval), other_data)
+        self._mask |= new_mask
+        other_data = np.where(self._mask, other_data.dtype.type(1), other_data)
+        self._data.__ifloordiv__(other_data)
+        return self
+
+    def __itruediv__(self, other):
+        """
+        True divide self by other in-place.
+
+        """
+        other_data = getdata(other)
+        dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
+        other_mask = getmask(other)
+        new_mask = mask_or(other_mask, dom_mask)
+        # The following 3 lines control the domain filling
+        if dom_mask.any():
+            (_, fval) = ufunc_fills[np.true_divide]
+            other_data = np.where(
+                    dom_mask, other_data.dtype.type(fval), other_data)
+        self._mask |= new_mask
+        other_data = np.where(self._mask, other_data.dtype.type(1), other_data)
+        self._data.__itruediv__(other_data)
+        return self
+
+    def __ipow__(self, other):
+        """
+        Raise self to the power other, in place.
+
+        """
+        other_data = getdata(other)
+        other_data = np.where(self._mask, other_data.dtype.type(1), other_data)
+        other_mask = getmask(other)
+        with np.errstate(divide='ignore', invalid='ignore'):
+            self._data.__ipow__(other_data)
+        invalid = np.logical_not(np.isfinite(self._data))
+        if invalid.any():
+            if self._mask is not nomask:
+                self._mask |= invalid
+            else:
+                self._mask = invalid
+            np.copyto(self._data, self.fill_value, where=invalid)
+        new_mask = mask_or(other_mask, invalid)
+        self._mask = mask_or(self._mask, new_mask)
+        return self
+
+    def __float__(self):
+        """
+        Convert to float.
+
+        """
+        if self.size > 1:
+            raise TypeError("Only length-1 arrays can be converted "
+                            "to Python scalars")
+        elif self._mask:
+            warnings.warn("Warning: converting a masked element to nan.", stacklevel=2)
+            return np.nan
+        return float(self.item())
+
+    def __int__(self):
+        """
+        Convert to int.
+
+        """
+        if self.size > 1:
+            raise TypeError("Only length-1 arrays can be converted "
+                            "to Python scalars")
+        elif self._mask:
+            raise MaskError('Cannot convert masked element to a Python int.')
+        return int(self.item())
+
+    @property
+    def imag(self):
+        """
+        The imaginary part of the masked array.
+
+        This property is a view on the imaginary part of this `MaskedArray`.
+
+        See Also
+        --------
+        real
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])
+        >>> x.imag
+        masked_array(data=[1.0, --, 1.6],
+                     mask=[False,  True, False],
+               fill_value=1e+20)
+
+        """
+        result = self._data.imag.view(type(self))
+        result.__setmask__(self._mask)
+        return result
+
+    # kept for compatibility
+    get_imag = imag.fget
+
+    @property
+    def real(self):
+        """
+        The real part of the masked array.
+
+        This property is a view on the real part of this `MaskedArray`.
+
+        See Also
+        --------
+        imag
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])
+        >>> x.real
+        masked_array(data=[1.0, --, 3.45],
+                     mask=[False,  True, False],
+               fill_value=1e+20)
+
+        """
+        result = self._data.real.view(type(self))
+        result.__setmask__(self._mask)
+        return result
+
+    # kept for compatibility
+    get_real = real.fget
+
+    def count(self, axis=None, keepdims=np._NoValue):
+        """
+        Count the non-masked elements of the array along the given axis.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Axis or axes along which the count is performed.
+            The default, None, performs the count over all
+            the dimensions of the input array. `axis` may be negative, in
+            which case it counts from the last to the first axis.
+            If this is a tuple of ints, the count is performed on multiple
+            axes, instead of a single axis or all the axes as before.
+        keepdims : bool, optional
+            If this is set to True, the axes which are reduced are left
+            in the result as dimensions with size one. With this option,
+            the result will broadcast correctly against the array.
+
+        Returns
+        -------
+        result : ndarray or scalar
+            An array with the same shape as the input array, with the specified
+            axis removed. If the array is a 0-d array, or if `axis` is None, a
+            scalar is returned.
+
+        See Also
+        --------
+        ma.count_masked : Count masked elements in array or along a given axis.
+
+        Examples
+        --------
+        >>> import numpy.ma as ma
+        >>> a = ma.arange(6).reshape((2, 3))
+        >>> a[1, :] = ma.masked
+        >>> a
+        masked_array(
+          data=[[0, 1, 2],
+                [--, --, --]],
+          mask=[[False, False, False],
+                [ True,  True,  True]],
+          fill_value=999999)
+        >>> a.count()
+        3
+
+        When the `axis` keyword is specified an array of appropriate size is
+        returned.
+
+        >>> a.count(axis=0)
+        array([1, 1, 1])
+        >>> a.count(axis=1)
+        array([3, 0])
+
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        m = self._mask
+        # special case for matrices (we assume no other subclasses modify
+        # their dimensions)
+        if isinstance(self.data, np.matrix):
+            if m is nomask:
+                m = np.zeros(self.shape, dtype=np.bool)
+            m = m.view(type(self.data))
+
+        if m is nomask:
+            # compare to _count_reduce_items in _methods.py
+
+            if self.shape == ():
+                if axis not in (None, 0):
+                    raise np.exceptions.AxisError(axis=axis, ndim=self.ndim)
+                return 1
+            elif axis is None:
+                if kwargs.get('keepdims', False):
+                    return np.array(self.size, dtype=np.intp, ndmin=self.ndim)
+                return self.size
+
+            axes = normalize_axis_tuple(axis, self.ndim)
+            items = 1
+            for ax in axes:
+                items *= self.shape[ax]
+
+            if kwargs.get('keepdims', False):
+                out_dims = list(self.shape)
+                for a in axes:
+                    out_dims[a] = 1
+            else:
+                out_dims = [d for n, d in enumerate(self.shape)
+                            if n not in axes]
+            # make sure to return a 0-d array if axis is supplied
+            return np.full(out_dims, items, dtype=np.intp)
+
+        # take care of the masked singleton
+        if self is masked:
+            return 0
+
+        return (~m).sum(axis=axis, dtype=np.intp, **kwargs)
+
+    def ravel(self, order='C'):
+        """
+        Returns a 1D version of self, as a view.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            The elements of `a` are read using this index order. 'C' means to
+            index the elements in C-like order, with the last axis index
+            changing fastest, back to the first axis index changing slowest.
+            'F' means to index the elements in Fortran-like index order, with
+            the first index changing fastest, and the last index changing
+            slowest. Note that the 'C' and 'F' options take no account of the
+            memory layout of the underlying array, and only refer to the order
+            of axis indexing.  'A' means to read the elements in Fortran-like
+            index order if `m` is Fortran *contiguous* in memory, C-like order
+            otherwise.  'K' means to read the elements in the order they occur
+            in memory, except for reversing the data when strides are negative.
+            By default, 'C' index order is used.
+            (Masked arrays currently use 'A' on the data when 'K' is passed.)
+
+        Returns
+        -------
+        MaskedArray
+            Output view is of shape ``(self.size,)`` (or
+            ``(np.ma.product(self.shape),)``).
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
+        >>> x
+        masked_array(
+          data=[[1, --, 3],
+                [--, 5, --],
+                [7, --, 9]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+        >>> x.ravel()
+        masked_array(data=[1, --, 3, --, 5, --, 7, --, 9],
+                     mask=[False,  True, False,  True, False,  True, False,  True,
+                           False],
+               fill_value=999999)
+
+        """
+        # The order of _data and _mask could be different (it shouldn't be
+        # normally).  Passing order `K` or `A` would be incorrect.
+        # So we ignore the mask memory order.
+        # TODO: We don't actually support K, so use A instead.  We could
+        #       try to guess this correct by sorting strides or deprecate.
+        if order in "kKaA":
+            order = "F" if self._data.flags.fnc else "C"
+        r = ndarray.ravel(self._data, order=order).view(type(self))
+        r._update_from(self)
+        if self._mask is not nomask:
+            r._mask = ndarray.ravel(self._mask, order=order).reshape(r.shape)
+        else:
+            r._mask = nomask
+        return r
+
+
+    def reshape(self, *s, **kwargs):
+        """
+        Give a new shape to the array without changing its data.
+
+        Returns a masked array containing the same data, but with a new shape.
+        The result is a view on the original array; if this is not possible, a
+        ValueError is raised.
+
+        Parameters
+        ----------
+        shape : int or tuple of ints
+            The new shape should be compatible with the original shape. If an
+            integer is supplied, then the result will be a 1-D array of that
+            length.
+        order : {'C', 'F'}, optional
+            Determines whether the array data should be viewed as in C
+            (row-major) or FORTRAN (column-major) order.
+
+        Returns
+        -------
+        reshaped_array : array
+            A new view on the array.
+
+        See Also
+        --------
+        reshape : Equivalent function in the masked array module.
+        numpy.ndarray.reshape : Equivalent method on ndarray object.
+        numpy.reshape : Equivalent function in the NumPy module.
+
+        Notes
+        -----
+        The reshaping operation cannot guarantee that a copy will not be made,
+        to modify the shape in place, use ``a.shape = s``
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
+        >>> x
+        masked_array(
+          data=[[--, 2],
+                [3, --]],
+          mask=[[ True, False],
+                [False,  True]],
+          fill_value=999999)
+        >>> x = x.reshape((4,1))
+        >>> x
+        masked_array(
+          data=[[--],
+                [2],
+                [3],
+                [--]],
+          mask=[[ True],
+                [False],
+                [False],
+                [ True]],
+          fill_value=999999)
+
+        """
+        kwargs.update(order=kwargs.get('order', 'C'))
+        result = self._data.reshape(*s, **kwargs).view(type(self))
+        result._update_from(self)
+        mask = self._mask
+        if mask is not nomask:
+            result._mask = mask.reshape(*s, **kwargs)
+        return result
+
+    def resize(self, newshape, refcheck=True, order=False):
+        """
+        .. warning::
+
+            This method does nothing, except raise a ValueError exception. A
+            masked array does not own its data and therefore cannot safely be
+            resized in place. Use the `numpy.ma.resize` function instead.
+
+        This method is difficult to implement safely and may be deprecated in
+        future releases of NumPy.
+
+        """
+        # Note : the 'order' keyword looks broken, let's just drop it
+        errmsg = "A masked array does not own its data "\
+                 "and therefore cannot be resized.\n" \
+                 "Use the numpy.ma.resize function instead."
+        raise ValueError(errmsg)
+
+    def put(self, indices, values, mode='raise'):
+        """
+        Set storage-indexed locations to corresponding values.
+
+        Sets self._data.flat[n] = values[n] for each n in indices.
+        If `values` is shorter than `indices` then it will repeat.
+        If `values` has some masked values, the initial mask is updated
+        in consequence, else the corresponding values are unmasked.
+
+        Parameters
+        ----------
+        indices : 1-D array_like
+            Target indices, interpreted as integers.
+        values : array_like
+            Values to place in self._data copy at target indices.
+        mode : {'raise', 'wrap', 'clip'}, optional
+            Specifies how out-of-bounds indices will behave.
+            'raise' : raise an error.
+            'wrap' : wrap around.
+            'clip' : clip to the range.
+
+        Notes
+        -----
+        `values` can be a scalar or length 1 array.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
+        >>> x
+        masked_array(
+          data=[[1, --, 3],
+                [--, 5, --],
+                [7, --, 9]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+        >>> x.put([0,4,8],[10,20,30])
+        >>> x
+        masked_array(
+          data=[[10, --, 3],
+                [--, 20, --],
+                [7, --, 30]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+
+        >>> x.put(4,999)
+        >>> x
+        masked_array(
+          data=[[10, --, 3],
+                [--, 999, --],
+                [7, --, 30]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+
+        """
+        # Hard mask: Get rid of the values/indices that fall on masked data
+        if self._hardmask and self._mask is not nomask:
+            mask = self._mask[indices]
+            indices = narray(indices, copy=None)
+            values = narray(values, copy=None, subok=True)
+            values.resize(indices.shape)
+            indices = indices[~mask]
+            values = values[~mask]
+
+        self._data.put(indices, values, mode=mode)
+
+        # short circuit if neither self nor values are masked
+        if self._mask is nomask and getmask(values) is nomask:
+            return
+
+        m = getmaskarray(self)
+
+        if getmask(values) is nomask:
+            m.put(indices, False, mode=mode)
+        else:
+            m.put(indices, values._mask, mode=mode)
+        m = make_mask(m, copy=False, shrink=True)
+        self._mask = m
+        return
+
+    def ids(self):
+        """
+        Return the addresses of the data and mask areas.
+
+        Parameters
+        ----------
+        None
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1])
+        >>> x.ids()
+        (166670640, 166659832) # may vary
+
+        If the array has no mask, the address of `nomask` is returned. This address
+        is typically not close to the data in memory:
+
+        >>> x = np.ma.array([1, 2, 3])
+        >>> x.ids()
+        (166691080, 3083169284) # may vary
+
+        """
+        if self._mask is nomask:
+            return (self.ctypes.data, id(nomask))
+        return (self.ctypes.data, self._mask.ctypes.data)
+
+    def iscontiguous(self):
+        """
+        Return a boolean indicating whether the data is contiguous.
+
+        Parameters
+        ----------
+        None
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([1, 2, 3])
+        >>> x.iscontiguous()
+        True
+
+        `iscontiguous` returns one of the flags of the masked array:
+
+        >>> x.flags
+          C_CONTIGUOUS : True
+          F_CONTIGUOUS : True
+          OWNDATA : False
+          WRITEABLE : True
+          ALIGNED : True
+          WRITEBACKIFCOPY : False
+
+        """
+        return self.flags['CONTIGUOUS']
+
+    def all(self, axis=None, out=None, keepdims=np._NoValue):
+        """
+        Returns True if all elements evaluate to True.
+
+        The output array is masked where all the values along the given axis
+        are masked: if the output would have been a scalar and that all the
+        values are masked, then the output is `masked`.
+
+        Refer to `numpy.all` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.all : corresponding function for ndarrays
+        numpy.all : equivalent function
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> np.ma.array([1,2,3]).all()
+        True
+        >>> a = np.ma.array([1,2,3], mask=True)
+        >>> (a.all() is np.ma.masked)
+        True
+
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        mask = _check_mask_axis(self._mask, axis, **kwargs)
+        if out is None:
+            d = self.filled(True).all(axis=axis, **kwargs).view(type(self))
+            if d.ndim:
+                d.__setmask__(mask)
+            elif mask:
+                return masked
+            return d
+        self.filled(True).all(axis=axis, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            if out.ndim or mask:
+                out.__setmask__(mask)
+        return out
+
+    def any(self, axis=None, out=None, keepdims=np._NoValue):
+        """
+        Returns True if any of the elements of `a` evaluate to True.
+
+        Masked values are considered as False during computation.
+
+        Refer to `numpy.any` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.any : corresponding function for ndarrays
+        numpy.any : equivalent function
+
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        mask = _check_mask_axis(self._mask, axis, **kwargs)
+        if out is None:
+            d = self.filled(False).any(axis=axis, **kwargs).view(type(self))
+            if d.ndim:
+                d.__setmask__(mask)
+            elif mask:
+                d = masked
+            return d
+        self.filled(False).any(axis=axis, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            if out.ndim or mask:
+                out.__setmask__(mask)
+        return out
+
+    def nonzero(self):
+        """
+        Return the indices of unmasked elements that are not zero.
+
+        Returns a tuple of arrays, one for each dimension, containing the
+        indices of the non-zero elements in that dimension. The corresponding
+        non-zero values can be obtained with::
+
+            a[a.nonzero()]
+
+        To group the indices by element, rather than dimension, use
+        instead::
+
+            np.transpose(a.nonzero())
+
+        The result of this is always a 2d array, with a row for each non-zero
+        element.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        tuple_of_arrays : tuple
+            Indices of elements that are non-zero.
+
+        See Also
+        --------
+        numpy.nonzero :
+            Function operating on ndarrays.
+        flatnonzero :
+            Return indices that are non-zero in the flattened version of the input
+            array.
+        numpy.ndarray.nonzero :
+            Equivalent ndarray method.
+        count_nonzero :
+            Counts the number of non-zero elements in the input array.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import numpy.ma as ma
+        >>> x = ma.array(np.eye(3))
+        >>> x
+        masked_array(
+          data=[[1., 0., 0.],
+                [0., 1., 0.],
+                [0., 0., 1.]],
+          mask=False,
+          fill_value=1e+20)
+        >>> x.nonzero()
+        (array([0, 1, 2]), array([0, 1, 2]))
+
+        Masked elements are ignored.
+
+        >>> x[1, 1] = ma.masked
+        >>> x
+        masked_array(
+          data=[[1.0, 0.0, 0.0],
+                [0.0, --, 0.0],
+                [0.0, 0.0, 1.0]],
+          mask=[[False, False, False],
+                [False,  True, False],
+                [False, False, False]],
+          fill_value=1e+20)
+        >>> x.nonzero()
+        (array([0, 2]), array([0, 2]))
+
+        Indices can also be grouped by element.
+
+        >>> np.transpose(x.nonzero())
+        array([[0, 0],
+               [2, 2]])
+
+        A common use for ``nonzero`` is to find the indices of an array, where
+        a condition is True.  Given an array `a`, the condition `a` > 3 is a
+        boolean array and since False is interpreted as 0, ma.nonzero(a > 3)
+        yields the indices of the `a` where the condition is true.
+
+        >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
+        >>> a > 3
+        masked_array(
+          data=[[False, False, False],
+                [ True,  True,  True],
+                [ True,  True,  True]],
+          mask=False,
+          fill_value=True)
+        >>> ma.nonzero(a > 3)
+        (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
+
+        The ``nonzero`` method of the condition array can also be called.
+
+        >>> (a > 3).nonzero()
+        (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
+
+        """
+        return np.asarray(self.filled(0)).nonzero()
+
+    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
+        """
+        (this docstring should be overwritten)
+        """
+        #!!!: implement out + test!
+        m = self._mask
+        if m is nomask:
+            result = super().trace(offset=offset, axis1=axis1, axis2=axis2,
+                                   out=out)
+            return result.astype(dtype)
+        else:
+            D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)
+            return D.astype(dtype).filled(0).sum(axis=-1, out=out)
+    trace.__doc__ = ndarray.trace.__doc__
+
+    def dot(self, b, out=None, strict=False):
+        """
+        a.dot(b, out=None)
+
+        Masked dot product of two arrays. Note that `out` and `strict` are
+        located in different positions than in `ma.dot`. In order to
+        maintain compatibility with the functional version, it is
+        recommended that the optional arguments be treated as keyword only.
+        At some point that may be mandatory.
+
+        Parameters
+        ----------
+        b : masked_array_like
+            Inputs array.
+        out : masked_array, optional
+            Output argument. This must have the exact kind that would be
+            returned if it was not used. In particular, it must have the
+            right type, must be C-contiguous, and its dtype must be the
+            dtype that would be returned for `ma.dot(a,b)`. This is a
+            performance feature. Therefore, if these conditions are not
+            met, an exception is raised, instead of attempting to be
+            flexible.
+        strict : bool, optional
+            Whether masked data are propagated (True) or set to 0 (False)
+            for the computation. Default is False.  Propagating the mask
+            means that if a masked value appears in a row or column, the
+            whole row or column is considered masked.
+
+        See Also
+        --------
+        numpy.ma.dot : equivalent function
+
+        """
+        return dot(self, b, out=out, strict=strict)
+
+    def sum(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
+        """
+        Return the sum of the array elements over the given axis.
+
+        Masked elements are set to 0 internally.
+
+        Refer to `numpy.sum` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.sum : corresponding function for ndarrays
+        numpy.sum : equivalent function
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
+        >>> x
+        masked_array(
+          data=[[1, --, 3],
+                [--, 5, --],
+                [7, --, 9]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+        >>> x.sum()
+        25
+        >>> x.sum(axis=1)
+        masked_array(data=[4, 5, 16],
+                     mask=[False, False, False],
+               fill_value=999999)
+        >>> x.sum(axis=0)
+        masked_array(data=[8, 5, 12],
+                     mask=[False, False, False],
+               fill_value=999999)
+        >>> print(type(x.sum(axis=0, dtype=np.int64)[0]))
+        
+
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        _mask = self._mask
+        newmask = _check_mask_axis(_mask, axis, **kwargs)
+        # No explicit output
+        if out is None:
+            result = self.filled(0).sum(axis, dtype=dtype, **kwargs)
+            rndim = getattr(result, 'ndim', 0)
+            if rndim:
+                result = result.view(type(self))
+                result.__setmask__(newmask)
+            elif newmask:
+                result = masked
+            return result
+        # Explicit output
+        result = self.filled(0).sum(axis, dtype=dtype, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            outmask = getmask(out)
+            if outmask is nomask:
+                outmask = out._mask = make_mask_none(out.shape)
+            outmask.flat = newmask
+        return out
+
+    def cumsum(self, axis=None, dtype=None, out=None):
+        """
+        Return the cumulative sum of the array elements over the given axis.
+
+        Masked values are set to 0 internally during the computation.
+        However, their position is saved, and the result will be masked at
+        the same locations.
+
+        Refer to `numpy.cumsum` for full documentation.
+
+        Notes
+        -----
+        The mask is lost if `out` is not a valid :class:`ma.MaskedArray` !
+
+        Arithmetic is modular when using integer types, and no error is
+        raised on overflow.
+
+        See Also
+        --------
+        numpy.ndarray.cumsum : corresponding function for ndarrays
+        numpy.cumsum : equivalent function
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0])
+        >>> marr.cumsum()
+        masked_array(data=[0, 1, 3, --, --, --, 9, 16, 24, 33],
+                     mask=[False, False, False,  True,  True,  True, False, False,
+                           False, False],
+               fill_value=999999)
+
+        """
+        result = self.filled(0).cumsum(axis=axis, dtype=dtype, out=out)
+        if out is not None:
+            if isinstance(out, MaskedArray):
+                out.__setmask__(self.mask)
+            return out
+        result = result.view(type(self))
+        result.__setmask__(self._mask)
+        return result
+
+    def prod(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
+        """
+        Return the product of the array elements over the given axis.
+
+        Masked elements are set to 1 internally for computation.
+
+        Refer to `numpy.prod` for full documentation.
+
+        Notes
+        -----
+        Arithmetic is modular when using integer types, and no error is raised
+        on overflow.
+
+        See Also
+        --------
+        numpy.ndarray.prod : corresponding function for ndarrays
+        numpy.prod : equivalent function
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        _mask = self._mask
+        newmask = _check_mask_axis(_mask, axis, **kwargs)
+        # No explicit output
+        if out is None:
+            result = self.filled(1).prod(axis, dtype=dtype, **kwargs)
+            rndim = getattr(result, 'ndim', 0)
+            if rndim:
+                result = result.view(type(self))
+                result.__setmask__(newmask)
+            elif newmask:
+                result = masked
+            return result
+        # Explicit output
+        result = self.filled(1).prod(axis, dtype=dtype, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            outmask = getmask(out)
+            if outmask is nomask:
+                outmask = out._mask = make_mask_none(out.shape)
+            outmask.flat = newmask
+        return out
+    product = prod
+
+    def cumprod(self, axis=None, dtype=None, out=None):
+        """
+        Return the cumulative product of the array elements over the given axis.
+
+        Masked values are set to 1 internally during the computation.
+        However, their position is saved, and the result will be masked at
+        the same locations.
+
+        Refer to `numpy.cumprod` for full documentation.
+
+        Notes
+        -----
+        The mask is lost if `out` is not a valid MaskedArray !
+
+        Arithmetic is modular when using integer types, and no error is
+        raised on overflow.
+
+        See Also
+        --------
+        numpy.ndarray.cumprod : corresponding function for ndarrays
+        numpy.cumprod : equivalent function
+        """
+        result = self.filled(1).cumprod(axis=axis, dtype=dtype, out=out)
+        if out is not None:
+            if isinstance(out, MaskedArray):
+                out.__setmask__(self._mask)
+            return out
+        result = result.view(type(self))
+        result.__setmask__(self._mask)
+        return result
+
+    def mean(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
+        """
+        Returns the average of the array elements along given axis.
+
+        Masked entries are ignored, and result elements which are not
+        finite will be masked.
+
+        Refer to `numpy.mean` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.mean : corresponding function for ndarrays
+        numpy.mean : Equivalent function
+        numpy.ma.average : Weighted average.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.ma.array([1,2,3], mask=[False, False, True])
+        >>> a
+        masked_array(data=[1, 2, --],
+                     mask=[False, False,  True],
+               fill_value=999999)
+        >>> a.mean()
+        1.5
+
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+        if self._mask is nomask:
+            result = super().mean(axis=axis, dtype=dtype, **kwargs)[()]
+        else:
+            is_float16_result = False
+            if dtype is None:
+                if issubclass(self.dtype.type, (ntypes.integer, ntypes.bool)):
+                    dtype = mu.dtype('f8')
+                elif issubclass(self.dtype.type, ntypes.float16):
+                    dtype = mu.dtype('f4')
+                    is_float16_result = True
+            dsum = self.sum(axis=axis, dtype=dtype, **kwargs)
+            cnt = self.count(axis=axis, **kwargs)
+            if cnt.shape == () and (cnt == 0):
+                result = masked
+            elif is_float16_result:
+                result = self.dtype.type(dsum * 1. / cnt)
+            else:
+                result = dsum * 1. / cnt
+        if out is not None:
+            out.flat = result
+            if isinstance(out, MaskedArray):
+                outmask = getmask(out)
+                if outmask is nomask:
+                    outmask = out._mask = make_mask_none(out.shape)
+                outmask.flat = getmask(result)
+            return out
+        return result
+
+    def anom(self, axis=None, dtype=None):
+        """
+        Compute the anomalies (deviations from the arithmetic mean)
+        along the given axis.
+
+        Returns an array of anomalies, with the same shape as the input and
+        where the arithmetic mean is computed along the given axis.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis over which the anomalies are taken.
+            The default is to use the mean of the flattened array as reference.
+        dtype : dtype, optional
+            Type to use in computing the variance. For arrays of integer type
+             the default is float32; for arrays of float types it is the same as
+             the array type.
+
+        See Also
+        --------
+        mean : Compute the mean of the array.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.ma.array([1,2,3])
+        >>> a.anom()
+        masked_array(data=[-1.,  0.,  1.],
+                     mask=False,
+               fill_value=1e+20)
+
+        """
+        m = self.mean(axis, dtype)
+        if not axis:
+            return self - m
+        else:
+            return self - expand_dims(m, axis)
+
+    def var(self, axis=None, dtype=None, out=None, ddof=0,
+            keepdims=np._NoValue, mean=np._NoValue):
+        """
+        Returns the variance of the array elements along given axis.
+
+        Masked entries are ignored, and result elements which are not
+        finite will be masked.
+
+        Refer to `numpy.var` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.var : corresponding function for ndarrays
+        numpy.var : Equivalent function
+        """
+        kwargs = {}
+
+        if keepdims is not np._NoValue:
+            kwargs['keepdims'] = keepdims
+
+        # Easy case: nomask, business as usual
+        if self._mask is nomask:
+
+            if mean is not np._NoValue:
+                kwargs['mean'] = mean
+
+            ret = super().var(axis=axis, dtype=dtype, out=out, ddof=ddof,
+                              **kwargs)[()]
+            if out is not None:
+                if isinstance(out, MaskedArray):
+                    out.__setmask__(nomask)
+                return out
+            return ret
+
+        # Some data are masked, yay!
+        cnt = self.count(axis=axis, **kwargs) - ddof
+
+        if mean is not np._NoValue:
+            danom = self - mean
+        else:
+            danom = self - self.mean(axis, dtype, keepdims=True)
+
+        if iscomplexobj(self):
+            danom = umath.absolute(danom) ** 2
+        else:
+            danom *= danom
+        dvar = divide(danom.sum(axis, **kwargs), cnt).view(type(self))
+        # Apply the mask if it's not a scalar
+        if dvar.ndim:
+            dvar._mask = mask_or(self._mask.all(axis, **kwargs), (cnt <= 0))
+            dvar._update_from(self)
+        elif getmask(dvar):
+            # Make sure that masked is returned when the scalar is masked.
+            dvar = masked
+            if out is not None:
+                if isinstance(out, MaskedArray):
+                    out.flat = 0
+                    out.__setmask__(True)
+                elif out.dtype.kind in 'biu':
+                    errmsg = "Masked data information would be lost in one or "\
+                             "more location."
+                    raise MaskError(errmsg)
+                else:
+                    out.flat = np.nan
+                return out
+        # In case with have an explicit output
+        if out is not None:
+            # Set the data
+            out.flat = dvar
+            # Set the mask if needed
+            if isinstance(out, MaskedArray):
+                out.__setmask__(dvar.mask)
+            return out
+        return dvar
+    var.__doc__ = np.var.__doc__
+
+    def std(self, axis=None, dtype=None, out=None, ddof=0,
+            keepdims=np._NoValue, mean=np._NoValue):
+        """
+        Returns the standard deviation of the array elements along given axis.
+
+        Masked entries are ignored.
+
+        Refer to `numpy.std` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.std : corresponding function for ndarrays
+        numpy.std : Equivalent function
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        dvar = self.var(axis, dtype, out, ddof, **kwargs)
+        if dvar is not masked:
+            if out is not None:
+                np.power(out, 0.5, out=out, casting='unsafe')
+                return out
+            dvar = sqrt(dvar)
+        return dvar
+
+    def round(self, decimals=0, out=None):
+        """
+        Return each element rounded to the given number of decimals.
+
+        Refer to `numpy.around` for full documentation.
+
+        See Also
+        --------
+        numpy.ndarray.round : corresponding function for ndarrays
+        numpy.around : equivalent function
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import numpy.ma as ma
+        >>> x = ma.array([1.35, 2.5, 1.5, 1.75, 2.25, 2.75],
+        ...              mask=[0, 0, 0, 1, 0, 0])
+        >>> ma.round(x)
+        masked_array(data=[1.0, 2.0, 2.0, --, 2.0, 3.0],
+                     mask=[False, False, False,  True, False, False],
+                fill_value=1e+20)
+
+        """
+        result = self._data.round(decimals=decimals, out=out).view(type(self))
+        if result.ndim > 0:
+            result._mask = self._mask
+            result._update_from(self)
+        elif self._mask:
+            # Return masked when the scalar is masked
+            result = masked
+        # No explicit output: we're done
+        if out is None:
+            return result
+        if isinstance(out, MaskedArray):
+            out.__setmask__(self._mask)
+        return out
+
+    def argsort(self, axis=np._NoValue, kind=None, order=None, endwith=True,
+                fill_value=None, *, stable=False):
+        """
+        Return an ndarray of indices that sort the array along the
+        specified axis.  Masked values are filled beforehand to
+        `fill_value`.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis along which to sort. If None, the default, the flattened array
+            is used.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            The sorting algorithm used.
+        order : list, optional
+            When `a` is an array with fields defined, this argument specifies
+            which fields to compare first, second, etc.  Not all fields need be
+            specified.
+        endwith : {True, False}, optional
+            Whether missing values (if any) should be treated as the largest values
+            (True) or the smallest values (False)
+            When the array contains unmasked values at the same extremes of the
+            datatype, the ordering of these values and the masked values is
+            undefined.
+        fill_value : scalar or None, optional
+            Value used internally for the masked values.
+            If ``fill_value`` is not None, it supersedes ``endwith``.
+        stable : bool, optional
+            Only for compatibility with ``np.argsort``. Ignored.
+
+        Returns
+        -------
+        index_array : ndarray, int
+            Array of indices that sort `a` along the specified axis.
+            In other words, ``a[index_array]`` yields a sorted `a`.
+
+        See Also
+        --------
+        ma.MaskedArray.sort : Describes sorting algorithms used.
+        lexsort : Indirect stable sort with multiple keys.
+        numpy.ndarray.sort : Inplace sort.
+
+        Notes
+        -----
+        See `sort` for notes on the different sorting algorithms.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.ma.array([3,2,1], mask=[False, False, True])
+        >>> a
+        masked_array(data=[3, 2, --],
+                     mask=[False, False,  True],
+               fill_value=999999)
+        >>> a.argsort()
+        array([1, 0, 2])
+
+        """
+        if stable:
+            raise ValueError(
+                "`stable` parameter is not supported for masked arrays."
+            )
+
+        # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default
+        if axis is np._NoValue:
+            axis = _deprecate_argsort_axis(self)
+
+        if fill_value is None:
+            if endwith:
+                # nan > inf
+                if np.issubdtype(self.dtype, np.floating):
+                    fill_value = np.nan
+                else:
+                    fill_value = minimum_fill_value(self)
+            else:
+                fill_value = maximum_fill_value(self)
+
+        filled = self.filled(fill_value)
+        return filled.argsort(axis=axis, kind=kind, order=order)
+
+    def argmin(self, axis=None, fill_value=None, out=None, *,
+                keepdims=np._NoValue):
+        """
+        Return array of indices to the minimum values along the given axis.
+
+        Parameters
+        ----------
+        axis : {None, integer}
+            If None, the index is into the flattened array, otherwise along
+            the specified axis
+        fill_value : scalar or None, optional
+            Value used to fill in the masked values.  If None, the output of
+            minimum_fill_value(self._data) is used instead.
+        out : {None, array}, optional
+            Array into which the result can be placed. Its type is preserved
+            and it must be of the right shape to hold the output.
+
+        Returns
+        -------
+        ndarray or scalar
+            If multi-dimension input, returns a new ndarray of indices to the
+            minimum values along the given axis.  Otherwise, returns a scalar
+            of index to the minimum values along the given axis.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array(np.arange(4), mask=[1,1,0,0])
+        >>> x.shape = (2,2)
+        >>> x
+        masked_array(
+          data=[[--, --],
+                [2, 3]],
+          mask=[[ True,  True],
+                [False, False]],
+          fill_value=999999)
+        >>> x.argmin(axis=0, fill_value=-1)
+        array([0, 0])
+        >>> x.argmin(axis=0, fill_value=9)
+        array([1, 1])
+
+        """
+        if fill_value is None:
+            fill_value = minimum_fill_value(self)
+        d = self.filled(fill_value).view(ndarray)
+        keepdims = False if keepdims is np._NoValue else bool(keepdims)
+        return d.argmin(axis, out=out, keepdims=keepdims)
+
+    def argmax(self, axis=None, fill_value=None, out=None, *,
+                keepdims=np._NoValue):
+        """
+        Returns array of indices of the maximum values along the given axis.
+        Masked values are treated as if they had the value fill_value.
+
+        Parameters
+        ----------
+        axis : {None, integer}
+            If None, the index is into the flattened array, otherwise along
+            the specified axis
+        fill_value : scalar or None, optional
+            Value used to fill in the masked values.  If None, the output of
+            maximum_fill_value(self._data) is used instead.
+        out : {None, array}, optional
+            Array into which the result can be placed. Its type is preserved
+            and it must be of the right shape to hold the output.
+
+        Returns
+        -------
+        index_array : {integer_array}
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.arange(6).reshape(2,3)
+        >>> a.argmax()
+        5
+        >>> a.argmax(0)
+        array([1, 1, 1])
+        >>> a.argmax(1)
+        array([2, 2])
+
+        """
+        if fill_value is None:
+            fill_value = maximum_fill_value(self._data)
+        d = self.filled(fill_value).view(ndarray)
+        keepdims = False if keepdims is np._NoValue else bool(keepdims)
+        return d.argmax(axis, out=out, keepdims=keepdims)
+
+    def sort(self, axis=-1, kind=None, order=None, endwith=True,
+             fill_value=None, *, stable=False):
+        """
+        Sort the array, in-place
+
+        Parameters
+        ----------
+        a : array_like
+            Array to be sorted.
+        axis : int, optional
+            Axis along which to sort. If None, the array is flattened before
+            sorting. The default is -1, which sorts along the last axis.
+        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
+            The sorting algorithm used.
+        order : list, optional
+            When `a` is a structured array, this argument specifies which fields
+            to compare first, second, and so on.  This list does not need to
+            include all of the fields.
+        endwith : {True, False}, optional
+            Whether missing values (if any) should be treated as the largest values
+            (True) or the smallest values (False)
+            When the array contains unmasked values sorting at the same extremes of the
+            datatype, the ordering of these values and the masked values is
+            undefined.
+        fill_value : scalar or None, optional
+            Value used internally for the masked values.
+            If ``fill_value`` is not None, it supersedes ``endwith``.
+        stable : bool, optional
+            Only for compatibility with ``np.sort``. Ignored.
+
+        Returns
+        -------
+        sorted_array : ndarray
+            Array of the same type and shape as `a`.
+
+        See Also
+        --------
+        numpy.ndarray.sort : Method to sort an array in-place.
+        argsort : Indirect sort.
+        lexsort : Indirect stable sort on multiple keys.
+        searchsorted : Find elements in a sorted array.
+
+        Notes
+        -----
+        See ``sort`` for notes on the different sorting algorithms.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+        >>> # Default
+        >>> a.sort()
+        >>> a
+        masked_array(data=[1, 3, 5, --, --],
+                     mask=[False, False, False,  True,  True],
+               fill_value=999999)
+
+        >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+        >>> # Put missing values in the front
+        >>> a.sort(endwith=False)
+        >>> a
+        masked_array(data=[--, --, 1, 3, 5],
+                     mask=[ True,  True, False, False, False],
+               fill_value=999999)
+
+        >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
+        >>> # fill_value takes over endwith
+        >>> a.sort(endwith=False, fill_value=3)
+        >>> a
+        masked_array(data=[1, --, --, 3, 5],
+                     mask=[False,  True,  True, False, False],
+               fill_value=999999)
+
+        """
+        if stable:
+            raise ValueError(
+                "`stable` parameter is not supported for masked arrays."
+            )
+
+        if self._mask is nomask:
+            ndarray.sort(self, axis=axis, kind=kind, order=order)
+            return
+
+        if self is masked:
+            return
+
+        sidx = self.argsort(axis=axis, kind=kind, order=order,
+                            fill_value=fill_value, endwith=endwith)
+
+        self[...] = np.take_along_axis(self, sidx, axis=axis)
+
+    def min(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
+        """
+        Return the minimum along a given axis.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Axis along which to operate.  By default, ``axis`` is None and the
+            flattened input is used.
+            If this is a tuple of ints, the minimum is selected over multiple
+            axes, instead of a single axis or all the axes as before.
+        out : array_like, optional
+            Alternative output array in which to place the result.  Must be of
+            the same shape and buffer length as the expected output.
+        fill_value : scalar or None, optional
+            Value used to fill in the masked values.
+            If None, use the output of `minimum_fill_value`.
+        keepdims : bool, optional
+            If this is set to True, the axes which are reduced are left
+            in the result as dimensions with size one. With this option,
+            the result will broadcast correctly against the array.
+
+        Returns
+        -------
+        amin : array_like
+            New array holding the result.
+            If ``out`` was specified, ``out`` is returned.
+
+        See Also
+        --------
+        ma.minimum_fill_value
+            Returns the minimum filling value for a given datatype.
+
+        Examples
+        --------
+        >>> import numpy.ma as ma
+        >>> x = [[1., -2., 3.], [0.2, -0.7, 0.1]]
+        >>> mask = [[1, 1, 0], [0, 0, 1]]
+        >>> masked_x = ma.masked_array(x, mask)
+        >>> masked_x
+        masked_array(
+          data=[[--, --, 3.0],
+                [0.2, -0.7, --]],
+          mask=[[ True,  True, False],
+                [False, False,  True]],
+          fill_value=1e+20)
+        >>> ma.min(masked_x)
+        -0.7
+        >>> ma.min(masked_x, axis=-1)
+        masked_array(data=[3.0, -0.7],
+                     mask=[False, False],
+                fill_value=1e+20)
+        >>> ma.min(masked_x, axis=0, keepdims=True)
+        masked_array(data=[[0.2, -0.7, 3.0]],
+                     mask=[[False, False, False]],
+                fill_value=1e+20)
+        >>> mask = [[1, 1, 1,], [1, 1, 1]]
+        >>> masked_x = ma.masked_array(x, mask)
+        >>> ma.min(masked_x, axis=0)
+        masked_array(data=[--, --, --],
+                     mask=[ True,  True,  True],
+                fill_value=1e+20,
+                    dtype=float64)
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        _mask = self._mask
+        newmask = _check_mask_axis(_mask, axis, **kwargs)
+        if fill_value is None:
+            fill_value = minimum_fill_value(self)
+        # No explicit output
+        if out is None:
+            result = self.filled(fill_value).min(
+                axis=axis, out=out, **kwargs).view(type(self))
+            if result.ndim:
+                # Set the mask
+                result.__setmask__(newmask)
+                # Get rid of Infs
+                if newmask.ndim:
+                    np.copyto(result, result.fill_value, where=newmask)
+            elif newmask:
+                result = masked
+            return result
+        # Explicit output
+        self.filled(fill_value).min(axis=axis, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            outmask = getmask(out)
+            if outmask is nomask:
+                outmask = out._mask = make_mask_none(out.shape)
+            outmask.flat = newmask
+        else:
+            if out.dtype.kind in 'biu':
+                errmsg = "Masked data information would be lost in one or more"\
+                         " location."
+                raise MaskError(errmsg)
+            np.copyto(out, np.nan, where=newmask)
+        return out
+
+    def max(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
+        """
+        Return the maximum along a given axis.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Axis along which to operate.  By default, ``axis`` is None and the
+            flattened input is used.
+            If this is a tuple of ints, the maximum is selected over multiple
+            axes, instead of a single axis or all the axes as before.
+        out : array_like, optional
+            Alternative output array in which to place the result.  Must
+            be of the same shape and buffer length as the expected output.
+        fill_value : scalar or None, optional
+            Value used to fill in the masked values.
+            If None, use the output of maximum_fill_value().
+        keepdims : bool, optional
+            If this is set to True, the axes which are reduced are left
+            in the result as dimensions with size one. With this option,
+            the result will broadcast correctly against the array.
+
+        Returns
+        -------
+        amax : array_like
+            New array holding the result.
+            If ``out`` was specified, ``out`` is returned.
+
+        See Also
+        --------
+        ma.maximum_fill_value
+            Returns the maximum filling value for a given datatype.
+
+        Examples
+        --------
+        >>> import numpy.ma as ma
+        >>> x = [[-1., 2.5], [4., -2.], [3., 0.]]
+        >>> mask = [[0, 0], [1, 0], [1, 0]]
+        >>> masked_x = ma.masked_array(x, mask)
+        >>> masked_x
+        masked_array(
+          data=[[-1.0, 2.5],
+                [--, -2.0],
+                [--, 0.0]],
+          mask=[[False, False],
+                [ True, False],
+                [ True, False]],
+          fill_value=1e+20)
+        >>> ma.max(masked_x)
+        2.5
+        >>> ma.max(masked_x, axis=0)
+        masked_array(data=[-1.0, 2.5],
+                     mask=[False, False],
+               fill_value=1e+20)
+        >>> ma.max(masked_x, axis=1, keepdims=True)
+        masked_array(
+          data=[[2.5],
+                [-2.0],
+                [0.0]],
+          mask=[[False],
+                [False],
+                [False]],
+          fill_value=1e+20)
+        >>> mask = [[1, 1], [1, 1], [1, 1]]
+        >>> masked_x = ma.masked_array(x, mask)
+        >>> ma.max(masked_x, axis=1)
+        masked_array(data=[--, --, --],
+                     mask=[ True,  True,  True],
+               fill_value=1e+20,
+                    dtype=float64)
+        """
+        kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+        _mask = self._mask
+        newmask = _check_mask_axis(_mask, axis, **kwargs)
+        if fill_value is None:
+            fill_value = maximum_fill_value(self)
+        # No explicit output
+        if out is None:
+            result = self.filled(fill_value).max(
+                axis=axis, out=out, **kwargs).view(type(self))
+            if result.ndim:
+                # Set the mask
+                result.__setmask__(newmask)
+                # Get rid of Infs
+                if newmask.ndim:
+                    np.copyto(result, result.fill_value, where=newmask)
+            elif newmask:
+                result = masked
+            return result
+        # Explicit output
+        self.filled(fill_value).max(axis=axis, out=out, **kwargs)
+        if isinstance(out, MaskedArray):
+            outmask = getmask(out)
+            if outmask is nomask:
+                outmask = out._mask = make_mask_none(out.shape)
+            outmask.flat = newmask
+        else:
+
+            if out.dtype.kind in 'biu':
+                errmsg = "Masked data information would be lost in one or more"\
+                         " location."
+                raise MaskError(errmsg)
+            np.copyto(out, np.nan, where=newmask)
+        return out
+
+    def ptp(self, axis=None, out=None, fill_value=None, keepdims=False):
+        """
+        Return (maximum - minimum) along the given dimension
+        (i.e. peak-to-peak value).
+
+        .. warning::
+            `ptp` preserves the data type of the array. This means the
+            return value for an input of signed integers with n bits
+            (e.g. `np.int8`, `np.int16`, etc) is also a signed integer
+            with n bits.  In that case, peak-to-peak values greater than
+            ``2**(n-1)-1`` will be returned as negative values. An example
+            with a work-around is shown below.
+
+        Parameters
+        ----------
+        axis : {None, int}, optional
+            Axis along which to find the peaks.  If None (default) the
+            flattened array is used.
+        out : {None, array_like}, optional
+            Alternative output array in which to place the result. It must
+            have the same shape and buffer length as the expected output
+            but the type will be cast if necessary.
+        fill_value : scalar or None, optional
+            Value used to fill in the masked values.
+        keepdims : bool, optional
+            If this is set to True, the axes which are reduced are left
+            in the result as dimensions with size one. With this option,
+            the result will broadcast correctly against the array.
+
+        Returns
+        -------
+        ptp : ndarray.
+            A new array holding the result, unless ``out`` was
+            specified, in which case a reference to ``out`` is returned.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.MaskedArray([[4, 9, 2, 10],
+        ...                        [6, 9, 7, 12]])
+
+        >>> x.ptp(axis=1)
+        masked_array(data=[8, 6],
+                     mask=False,
+               fill_value=999999)
+
+        >>> x.ptp(axis=0)
+        masked_array(data=[2, 0, 5, 2],
+                     mask=False,
+               fill_value=999999)
+
+        >>> x.ptp()
+        10
+
+        This example shows that a negative value can be returned when
+        the input is an array of signed integers.
+
+        >>> y = np.ma.MaskedArray([[1, 127],
+        ...                        [0, 127],
+        ...                        [-1, 127],
+        ...                        [-2, 127]], dtype=np.int8)
+        >>> y.ptp(axis=1)
+        masked_array(data=[ 126,  127, -128, -127],
+                     mask=False,
+               fill_value=np.int64(999999),
+                    dtype=int8)
+
+        A work-around is to use the `view()` method to view the result as
+        unsigned integers with the same bit width:
+
+        >>> y.ptp(axis=1).view(np.uint8)
+        masked_array(data=[126, 127, 128, 129],
+                     mask=False,
+               fill_value=np.uint64(999999),
+                    dtype=uint8)
+        """
+        if out is None:
+            result = self.max(axis=axis, fill_value=fill_value,
+                              keepdims=keepdims)
+            result -= self.min(axis=axis, fill_value=fill_value,
+                               keepdims=keepdims)
+            return result
+        out.flat = self.max(axis=axis, out=out, fill_value=fill_value,
+                            keepdims=keepdims)
+        min_value = self.min(axis=axis, fill_value=fill_value,
+                             keepdims=keepdims)
+        np.subtract(out, min_value, out=out, casting='unsafe')
+        return out
+
+    def partition(self, *args, **kwargs):
+        warnings.warn("Warning: 'partition' will ignore the 'mask' "
+                      f"of the {self.__class__.__name__}.",
+                      stacklevel=2)
+        return super().partition(*args, **kwargs)
+
+    def argpartition(self, *args, **kwargs):
+        warnings.warn("Warning: 'argpartition' will ignore the 'mask' "
+                      f"of the {self.__class__.__name__}.",
+                      stacklevel=2)
+        return super().argpartition(*args, **kwargs)
+
+    def take(self, indices, axis=None, out=None, mode='raise'):
+        """
+        Take elements from a masked array along an axis.
+
+        This function does the same thing as "fancy" indexing (indexing arrays
+        using arrays) for masked arrays. It can be easier to use if you need
+        elements along a given axis.
+
+        Parameters
+        ----------
+        a : masked_array
+            The source masked array.
+        indices : array_like
+            The indices of the values to extract. Also allow scalars for indices.
+        axis : int, optional
+            The axis over which to select values. By default, the flattened
+            input array is used.
+        out : MaskedArray, optional
+            If provided, the result will be placed in this array. It should
+            be of the appropriate shape and dtype. Note that `out` is always
+            buffered if `mode='raise'`; use other modes for better performance.
+        mode : {'raise', 'wrap', 'clip'}, optional
+            Specifies how out-of-bounds indices will behave.
+
+            * 'raise' -- raise an error (default)
+            * 'wrap' -- wrap around
+            * 'clip' -- clip to the range
+
+            'clip' mode means that all indices that are too large are replaced
+            by the index that addresses the last element along that axis. Note
+            that this disables indexing with negative numbers.
+
+        Returns
+        -------
+        out : MaskedArray
+            The returned array has the same type as `a`.
+
+        See Also
+        --------
+        numpy.take : Equivalent function for ndarrays.
+        compress : Take elements using a boolean mask.
+        take_along_axis : Take elements by matching the array and the index arrays.
+
+        Notes
+        -----
+        This function behaves similarly to `numpy.take`, but it handles masked
+        values. The mask is retained in the output array, and masked values
+        in the input array remain masked in the output.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> a = np.ma.array([4, 3, 5, 7, 6, 8], mask=[0, 0, 1, 0, 1, 0])
+        >>> indices = [0, 1, 4]
+        >>> np.ma.take(a, indices)
+        masked_array(data=[4, 3, --],
+                    mask=[False, False,  True],
+            fill_value=999999)
+
+        When `indices` is not one-dimensional, the output also has these dimensions:
+
+        >>> np.ma.take(a, [[0, 1], [2, 3]])
+        masked_array(data=[[4, 3],
+                        [--, 7]],
+                    mask=[[False, False],
+                        [ True, False]],
+            fill_value=999999)
+        """ 
+        (_data, _mask) = (self._data, self._mask)
+        cls = type(self)
+        # Make sure the indices are not masked
+        maskindices = getmask(indices)
+        if maskindices is not nomask:
+            indices = indices.filled(0)
+        # Get the data, promoting scalars to 0d arrays with [...] so that
+        # .view works correctly
+        if out is None:
+            out = _data.take(indices, axis=axis, mode=mode)[...].view(cls)
+        else:
+            np.take(_data, indices, axis=axis, mode=mode, out=out)
+        # Get the mask
+        if isinstance(out, MaskedArray):
+            if _mask is nomask:
+                outmask = maskindices
+            else:
+                outmask = _mask.take(indices, axis=axis, mode=mode)
+                outmask |= maskindices
+            out.__setmask__(outmask)
+        # demote 0d arrays back to scalars, for consistency with ndarray.take
+        return out[()]
+
+    # Array methods
+    copy = _arraymethod('copy')
+    diagonal = _arraymethod('diagonal')
+    flatten = _arraymethod('flatten')
+    repeat = _arraymethod('repeat')
+    squeeze = _arraymethod('squeeze')
+    swapaxes = _arraymethod('swapaxes')
+    T = property(fget=lambda self: self.transpose())
+    transpose = _arraymethod('transpose')
+
+    @property
+    def mT(self):
+        """
+        Return the matrix-transpose of the masked array.
+
+        The matrix transpose is the transpose of the last two dimensions, even
+        if the array is of higher dimension.
+
+        .. versionadded:: 2.0
+
+        Returns
+        -------
+        result: MaskedArray
+            The masked array with the last two dimensions transposed
+
+        Raises
+        ------
+        ValueError
+            If the array is of dimension less than 2.
+
+        See Also
+        --------
+        ndarray.mT:
+            Equivalent method for arrays
+        """
+
+        if self.ndim < 2:
+            raise ValueError("matrix transpose with ndim < 2 is undefined")
+
+        if self._mask is nomask:
+            return masked_array(data=self._data.mT)
+        else:
+            return masked_array(data=self.data.mT, mask=self.mask.mT)
+
+
+    def tolist(self, fill_value=None):
+        """
+        Return the data portion of the masked array as a hierarchical Python list.
+
+        Data items are converted to the nearest compatible Python type.
+        Masked values are converted to `fill_value`. If `fill_value` is None,
+        the corresponding entries in the output list will be ``None``.
+
+        Parameters
+        ----------
+        fill_value : scalar, optional
+            The value to use for invalid entries. Default is None.
+
+        Returns
+        -------
+        result : list
+            The Python list representation of the masked array.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3], [4,5,6], [7,8,9]], mask=[0] + [1,0]*4)
+        >>> x.tolist()
+        [[1, None, 3], [None, 5, None], [7, None, 9]]
+        >>> x.tolist(-999)
+        [[1, -999, 3], [-999, 5, -999], [7, -999, 9]]
+
+        """
+        _mask = self._mask
+        # No mask ? Just return .data.tolist ?
+        if _mask is nomask:
+            return self._data.tolist()
+        # Explicit fill_value: fill the array and get the list
+        if fill_value is not None:
+            return self.filled(fill_value).tolist()
+        # Structured array.
+        names = self.dtype.names
+        if names:
+            result = self._data.astype([(_, object) for _ in names])
+            for n in names:
+                result[n][_mask[n]] = None
+            return result.tolist()
+        # Standard arrays.
+        if _mask is nomask:
+            return [None]
+        # Set temps to save time when dealing w/ marrays.
+        inishape = self.shape
+        result = np.array(self._data.ravel(), dtype=object)
+        result[_mask.ravel()] = None
+        result.shape = inishape
+        return result.tolist()
+
+    def tostring(self, fill_value=None, order='C'):
+        r"""
+        A compatibility alias for `tobytes`, with exactly the same behavior.
+
+        Despite its name, it returns `bytes` not `str`\ s.
+
+        .. deprecated:: 1.19.0
+        """
+        # 2020-03-30, Numpy 1.19.0
+        warnings.warn(
+            "tostring() is deprecated. Use tobytes() instead.",
+            DeprecationWarning, stacklevel=2)
+
+        return self.tobytes(fill_value, order=order)
+
+    def tobytes(self, fill_value=None, order='C'):
+        """
+        Return the array data as a string containing the raw bytes in the array.
+
+        The array is filled with a fill value before the string conversion.
+
+        Parameters
+        ----------
+        fill_value : scalar, optional
+            Value used to fill in the masked values. Default is None, in which
+            case `MaskedArray.fill_value` is used.
+        order : {'C','F','A'}, optional
+            Order of the data item in the copy. Default is 'C'.
+
+            - 'C'   -- C order (row major).
+            - 'F'   -- Fortran order (column major).
+            - 'A'   -- Any, current order of array.
+            - None  -- Same as 'A'.
+
+        See Also
+        --------
+        numpy.ndarray.tobytes
+        tolist, tofile
+
+        Notes
+        -----
+        As for `ndarray.tobytes`, information about the shape, dtype, etc.,
+        but also about `fill_value`, will be lost.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])
+        >>> x.tobytes()
+        b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x00\\x00\\x00\\x00'
+
+        """
+        return self.filled(fill_value).tobytes(order=order)
+
+    def tofile(self, fid, sep="", format="%s"):
+        """
+        Save a masked array to a file in binary format.
+
+        .. warning::
+          This function is not implemented yet.
+
+        Raises
+        ------
+        NotImplementedError
+            When `tofile` is called.
+
+        """
+        raise NotImplementedError("MaskedArray.tofile() not implemented yet.")
+
+    def toflex(self):
+        """
+        Transforms a masked array into a flexible-type array.
+
+        The flexible type array that is returned will have two fields:
+
+        * the ``_data`` field stores the ``_data`` part of the array.
+        * the ``_mask`` field stores the ``_mask`` part of the array.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        record : ndarray
+            A new flexible-type `ndarray` with two fields: the first element
+            containing a value, the second element containing the corresponding
+            mask boolean. The returned record shape matches self.shape.
+
+        Notes
+        -----
+        A side-effect of transforming a masked array into a flexible `ndarray` is
+        that meta information (``fill_value``, ...) will be lost.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
+        >>> x
+        masked_array(
+          data=[[1, --, 3],
+                [--, 5, --],
+                [7, --, 9]],
+          mask=[[False,  True, False],
+                [ True, False,  True],
+                [False,  True, False]],
+          fill_value=999999)
+        >>> x.toflex()
+        array([[(1, False), (2,  True), (3, False)],
+               [(4,  True), (5, False), (6,  True)],
+               [(7, False), (8,  True), (9, False)]],
+              dtype=[('_data', 'i2", (2,))])
+            # x = A[0]; y = x["A"]; then y.mask["A"].size==2
+            # and we can not say masked/unmasked.
+            # The result is no longer mvoid!
+            # See also issue #6724.
+            return masked_array(
+                data=self._data[indx], mask=m[indx],
+                fill_value=self._fill_value[indx],
+                hard_mask=self._hardmask)
+        if m is not nomask and m[indx]:
+            return masked
+        return self._data[indx]
+
+    def __setitem__(self, indx, value):
+        self._data[indx] = value
+        if self._hardmask:
+            self._mask[indx] |= getattr(value, "_mask", False)
+        else:
+            self._mask[indx] = getattr(value, "_mask", False)
+
+    def __str__(self):
+        m = self._mask
+        if m is nomask:
+            return str(self._data)
+
+        rdtype = _replace_dtype_fields(self._data.dtype, "O")
+        data_arr = super()._data
+        res = data_arr.astype(rdtype)
+        _recursive_printoption(res, self._mask, masked_print_option)
+        return str(res)
+
+    __repr__ = __str__
+
+    def __iter__(self):
+        "Defines an iterator for mvoid"
+        (_data, _mask) = (self._data, self._mask)
+        if _mask is nomask:
+            yield from _data
+        else:
+            for (d, m) in zip(_data, _mask):
+                if m:
+                    yield masked
+                else:
+                    yield d
+
+    def __len__(self):
+        return self._data.__len__()
+
+    def filled(self, fill_value=None):
+        """
+        Return a copy with masked fields filled with a given value.
+
+        Parameters
+        ----------
+        fill_value : array_like, optional
+            The value to use for invalid entries. Can be scalar or
+            non-scalar. If latter is the case, the filled array should
+            be broadcastable over input array. Default is None, in
+            which case the `fill_value` attribute is used instead.
+
+        Returns
+        -------
+        filled_void
+            A `np.void` object
+
+        See Also
+        --------
+        MaskedArray.filled
+
+        """
+        return asarray(self).filled(fill_value)[()]
+
+    def tolist(self):
+        """
+    Transforms the mvoid object into a tuple.
+
+    Masked fields are replaced by None.
+
+    Returns
+    -------
+    returned_tuple
+        Tuple of fields
+        """
+        _mask = self._mask
+        if _mask is nomask:
+            return self._data.tolist()
+        result = []
+        for (d, m) in zip(self._data, self._mask):
+            if m:
+                result.append(None)
+            else:
+                # .item() makes sure we return a standard Python object
+                result.append(d.item())
+        return tuple(result)
+
+
+##############################################################################
+#                                Shortcuts                                   #
+##############################################################################
+
+
+def isMaskedArray(x):
+    """
+    Test whether input is an instance of MaskedArray.
+
+    This function returns True if `x` is an instance of MaskedArray
+    and returns False otherwise.  Any object is accepted as input.
+
+    Parameters
+    ----------
+    x : object
+        Object to test.
+
+    Returns
+    -------
+    result : bool
+        True if `x` is a MaskedArray.
+
+    See Also
+    --------
+    isMA : Alias to isMaskedArray.
+    isarray : Alias to isMaskedArray.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = np.eye(3, 3)
+    >>> a
+    array([[ 1.,  0.,  0.],
+           [ 0.,  1.,  0.],
+           [ 0.,  0.,  1.]])
+    >>> m = ma.masked_values(a, 0)
+    >>> m
+    masked_array(
+      data=[[1.0, --, --],
+            [--, 1.0, --],
+            [--, --, 1.0]],
+      mask=[[False,  True,  True],
+            [ True, False,  True],
+            [ True,  True, False]],
+      fill_value=0.0)
+    >>> ma.isMaskedArray(a)
+    False
+    >>> ma.isMaskedArray(m)
+    True
+    >>> ma.isMaskedArray([0, 1, 2])
+    False
+
+    """
+    return isinstance(x, MaskedArray)
+
+
+isarray = isMaskedArray
+isMA = isMaskedArray  # backward compatibility
+
+
+class MaskedConstant(MaskedArray):
+    # the lone np.ma.masked instance
+    __singleton = None
+
+    @classmethod
+    def __has_singleton(cls):
+        # second case ensures `cls.__singleton` is not just a view on the
+        # superclass singleton
+        return cls.__singleton is not None and type(cls.__singleton) is cls
+
+    def __new__(cls):
+        if not cls.__has_singleton():
+            # We define the masked singleton as a float for higher precedence.
+            # Note that it can be tricky sometimes w/ type comparison
+            data = np.array(0.)
+            mask = np.array(True)
+
+            # prevent any modifications
+            data.flags.writeable = False
+            mask.flags.writeable = False
+
+            # don't fall back on MaskedArray.__new__(MaskedConstant), since
+            # that might confuse it - this way, the construction is entirely
+            # within our control
+            cls.__singleton = MaskedArray(data, mask=mask).view(cls)
+
+        return cls.__singleton
+
+    def __array_finalize__(self, obj):
+        if not self.__has_singleton():
+            # this handles the `.view` in __new__, which we want to copy across
+            # properties normally
+            return super().__array_finalize__(obj)
+        elif self is self.__singleton:
+            # not clear how this can happen, play it safe
+            pass
+        else:
+            # everywhere else, we want to downcast to MaskedArray, to prevent a
+            # duplicate maskedconstant.
+            self.__class__ = MaskedArray
+            MaskedArray.__array_finalize__(self, obj)
+
+    def __array_wrap__(self, obj, context=None, return_scalar=False):
+        return self.view(MaskedArray).__array_wrap__(obj, context)
+
+    def __str__(self):
+        return str(masked_print_option._display)
+
+    def __repr__(self):
+        if self is MaskedConstant.__singleton:
+            return 'masked'
+        else:
+            # it's a subclass, or something is wrong, make it obvious
+            return object.__repr__(self)
+
+    def __format__(self, format_spec):
+        # Replace ndarray.__format__ with the default, which supports no format characters.
+        # Supporting format characters is unwise here, because we do not know what type
+        # the user was expecting - better to not guess.
+        try:
+            return object.__format__(self, format_spec)
+        except TypeError:
+            # 2020-03-23, NumPy 1.19.0
+            warnings.warn(
+                "Format strings passed to MaskedConstant are ignored, but in future may "
+                "error or produce different behavior",
+                FutureWarning, stacklevel=2
+            )
+            return object.__format__(self, "")
+
+    def __reduce__(self):
+        """Override of MaskedArray's __reduce__.
+        """
+        return (self.__class__, ())
+
+    # inplace operations have no effect. We have to override them to avoid
+    # trying to modify the readonly data and mask arrays
+    def __iop__(self, other):
+        return self
+    __iadd__ = \
+    __isub__ = \
+    __imul__ = \
+    __ifloordiv__ = \
+    __itruediv__ = \
+    __ipow__ = \
+        __iop__
+    del __iop__  # don't leave this around
+
+    def copy(self, *args, **kwargs):
+        """ Copy is a no-op on the maskedconstant, as it is a scalar """
+        # maskedconstant is a scalar, so copy doesn't need to copy. There's
+        # precedent for this with `np.bool` scalars.
+        return self
+
+    def __copy__(self):
+        return self
+
+    def __deepcopy__(self, memo):
+        return self
+
+    def __setattr__(self, attr, value):
+        if not self.__has_singleton():
+            # allow the singleton to be initialized
+            return super().__setattr__(attr, value)
+        elif self is self.__singleton:
+            raise AttributeError(
+                f"attributes of {self!r} are not writeable")
+        else:
+            # duplicate instance - we can end up here from __array_finalize__,
+            # where we set the __class__ attribute
+            return super().__setattr__(attr, value)
+
+
+masked = masked_singleton = MaskedConstant()
+masked_array = MaskedArray
+
+
+def array(data, dtype=None, copy=False, order=None,
+          mask=nomask, fill_value=None, keep_mask=True,
+          hard_mask=False, shrink=True, subok=True, ndmin=0):
+    """
+    Shortcut to MaskedArray.
+
+    The options are in a different order for convenience and backwards
+    compatibility.
+
+    """
+    return MaskedArray(data, mask=mask, dtype=dtype, copy=copy,
+                       subok=subok, keep_mask=keep_mask,
+                       hard_mask=hard_mask, fill_value=fill_value,
+                       ndmin=ndmin, shrink=shrink, order=order)
+array.__doc__ = masked_array.__doc__
+
+
+def is_masked(x):
+    """
+    Determine whether input has masked values.
+
+    Accepts any object as input, but always returns False unless the
+    input is a MaskedArray containing masked values.
+
+    Parameters
+    ----------
+    x : array_like
+        Array to check for masked values.
+
+    Returns
+    -------
+    result : bool
+        True if `x` is a MaskedArray with masked values, False otherwise.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)
+    >>> x
+    masked_array(data=[--, 1, --, 2, 3],
+                 mask=[ True, False,  True, False, False],
+           fill_value=0)
+    >>> ma.is_masked(x)
+    True
+    >>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)
+    >>> x
+    masked_array(data=[0, 1, 0, 2, 3],
+                 mask=False,
+           fill_value=42)
+    >>> ma.is_masked(x)
+    False
+
+    Always returns False if `x` isn't a MaskedArray.
+
+    >>> x = [False, True, False]
+    >>> ma.is_masked(x)
+    False
+    >>> x = 'a string'
+    >>> ma.is_masked(x)
+    False
+
+    """
+    m = getmask(x)
+    if m is nomask:
+        return False
+    elif m.any():
+        return True
+    return False
+
+
+##############################################################################
+#                             Extrema functions                              #
+##############################################################################
+
+
+class _extrema_operation(_MaskedUFunc):
+    """
+    Generic class for maximum/minimum functions.
+
+    .. note::
+      This is the base class for `_maximum_operation` and
+      `_minimum_operation`.
+
+    """
+    def __init__(self, ufunc, compare, fill_value):
+        super().__init__(ufunc)
+        self.compare = compare
+        self.fill_value_func = fill_value
+
+    def __call__(self, a, b):
+        "Executes the call behavior."
+
+        return where(self.compare(a, b), a, b)
+
+    def reduce(self, target, axis=np._NoValue):
+        "Reduce target along the given axis."
+        target = narray(target, copy=None, subok=True)
+        m = getmask(target)
+
+        if axis is np._NoValue and target.ndim > 1:
+            # 2017-05-06, Numpy 1.13.0: warn on axis default
+            warnings.warn(
+                f"In the future the default for ma.{self.__name__}.reduce will be axis=0, "
+                f"not the current None, to match np.{self.__name__}.reduce. "
+                "Explicitly pass 0 or None to silence this warning.",
+                MaskedArrayFutureWarning, stacklevel=2)
+            axis = None
+
+        if axis is not np._NoValue:
+            kwargs = dict(axis=axis)
+        else:
+            kwargs = dict()
+
+        if m is nomask:
+            t = self.f.reduce(target, **kwargs)
+        else:
+            target = target.filled(
+                self.fill_value_func(target)).view(type(target))
+            t = self.f.reduce(target, **kwargs)
+            m = umath.logical_and.reduce(m, **kwargs)
+            if hasattr(t, '_mask'):
+                t._mask = m
+            elif m:
+                t = masked
+        return t
+
+    def outer(self, a, b):
+        "Return the function applied to the outer product of a and b."
+        ma = getmask(a)
+        mb = getmask(b)
+        if ma is nomask and mb is nomask:
+            m = nomask
+        else:
+            ma = getmaskarray(a)
+            mb = getmaskarray(b)
+            m = logical_or.outer(ma, mb)
+        result = self.f.outer(filled(a), filled(b))
+        if not isinstance(result, MaskedArray):
+            result = result.view(MaskedArray)
+        result._mask = m
+        return result
+
+def min(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
+    kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+    try:
+        return obj.min(axis=axis, fill_value=fill_value, out=out, **kwargs)
+    except (AttributeError, TypeError):
+        # If obj doesn't have a min method, or if the method doesn't accept a
+        # fill_value argument
+        return asanyarray(obj).min(axis=axis, fill_value=fill_value,
+                                   out=out, **kwargs)
+min.__doc__ = MaskedArray.min.__doc__
+
+def max(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
+    kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+
+    try:
+        return obj.max(axis=axis, fill_value=fill_value, out=out, **kwargs)
+    except (AttributeError, TypeError):
+        # If obj doesn't have a max method, or if the method doesn't accept a
+        # fill_value argument
+        return asanyarray(obj).max(axis=axis, fill_value=fill_value,
+                                   out=out, **kwargs)
+max.__doc__ = MaskedArray.max.__doc__
+
+
+def ptp(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
+    kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
+    try:
+        return obj.ptp(axis, out=out, fill_value=fill_value, **kwargs)
+    except (AttributeError, TypeError):
+        # If obj doesn't have a ptp method or if the method doesn't accept
+        # a fill_value argument
+        return asanyarray(obj).ptp(axis=axis, fill_value=fill_value,
+                                   out=out, **kwargs)
+ptp.__doc__ = MaskedArray.ptp.__doc__
+
+
+##############################################################################
+#           Definition of functions from the corresponding methods           #
+##############################################################################
+
+
+class _frommethod:
+    """
+    Define functions from existing MaskedArray methods.
+
+    Parameters
+    ----------
+    methodname : str
+        Name of the method to transform.
+
+    """
+
+    def __init__(self, methodname, reversed=False):
+        self.__name__ = methodname
+        self.__qualname__ = methodname
+        self.__doc__ = self.getdoc()
+        self.reversed = reversed
+
+    def getdoc(self):
+        "Return the doc of the function (from the doc of the method)."
+        meth = getattr(MaskedArray, self.__name__, None) or\
+            getattr(np, self.__name__, None)
+        signature = self.__name__ + get_object_signature(meth)
+        if meth is not None:
+            doc = """    %s\n%s""" % (
+                signature, getattr(meth, '__doc__', None))
+            return doc
+
+    def __call__(self, a, *args, **params):
+        if self.reversed:
+            args = list(args)
+            a, args[0] = args[0], a
+
+        marr = asanyarray(a)
+        method_name = self.__name__
+        method = getattr(type(marr), method_name, None)
+        if method is None:
+            # use the corresponding np function
+            method = getattr(np, method_name)
+
+        return method(marr, *args, **params)
+
+
+all = _frommethod('all')
+anomalies = anom = _frommethod('anom')
+any = _frommethod('any')
+compress = _frommethod('compress', reversed=True)
+cumprod = _frommethod('cumprod')
+cumsum = _frommethod('cumsum')
+copy = _frommethod('copy')
+diagonal = _frommethod('diagonal')
+harden_mask = _frommethod('harden_mask')
+ids = _frommethod('ids')
+maximum = _extrema_operation(umath.maximum, greater, maximum_fill_value)
+mean = _frommethod('mean')
+minimum = _extrema_operation(umath.minimum, less, minimum_fill_value)
+nonzero = _frommethod('nonzero')
+prod = _frommethod('prod')
+product = _frommethod('prod')
+ravel = _frommethod('ravel')
+repeat = _frommethod('repeat')
+shrink_mask = _frommethod('shrink_mask')
+soften_mask = _frommethod('soften_mask')
+std = _frommethod('std')
+sum = _frommethod('sum')
+swapaxes = _frommethod('swapaxes')
+#take = _frommethod('take')
+trace = _frommethod('trace')
+var = _frommethod('var')
+
+count = _frommethod('count')
+
+def take(a, indices, axis=None, out=None, mode='raise'):
+    """
+
+    """
+    a = masked_array(a)
+    return a.take(indices, axis=axis, out=out, mode=mode)
+
+
+def power(a, b, third=None):
+    """
+    Returns element-wise base array raised to power from second array.
+
+    This is the masked array version of `numpy.power`. For details see
+    `numpy.power`.
+
+    See Also
+    --------
+    numpy.power
+
+    Notes
+    -----
+    The *out* argument to `numpy.power` is not supported, `third` has to be
+    None.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [11.2, -3.973, 0.801, -1.41]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11.2, -3.973, 0.801, --],
+             mask=[False, False, False,  True],
+       fill_value=1e+20)
+    >>> ma.power(masked_x, 2)
+    masked_array(data=[125.43999999999998, 15.784728999999999,
+                   0.6416010000000001, --],
+             mask=[False, False, False,  True],
+       fill_value=1e+20)
+    >>> y = [-0.5, 2, 0, 17]
+    >>> masked_y = ma.masked_array(y, mask)
+    >>> masked_y
+    masked_array(data=[-0.5, 2.0, 0.0, --],
+             mask=[False, False, False,  True],
+       fill_value=1e+20)
+    >>> ma.power(masked_x, masked_y)
+    masked_array(data=[0.2988071523335984, 15.784728999999999, 1.0, --],
+             mask=[False, False, False,  True],
+       fill_value=1e+20)
+
+    """
+    if third is not None:
+        raise MaskError("3-argument power not supported.")
+    # Get the masks
+    ma = getmask(a)
+    mb = getmask(b)
+    m = mask_or(ma, mb)
+    # Get the rawdata
+    fa = getdata(a)
+    fb = getdata(b)
+    # Get the type of the result (so that we preserve subclasses)
+    if isinstance(a, MaskedArray):
+        basetype = type(a)
+    else:
+        basetype = MaskedArray
+    # Get the result and view it as a (subclass of) MaskedArray
+    with np.errstate(divide='ignore', invalid='ignore'):
+        result = np.where(m, fa, umath.power(fa, fb)).view(basetype)
+    result._update_from(a)
+    # Find where we're in trouble w/ NaNs and Infs
+    invalid = np.logical_not(np.isfinite(result.view(ndarray)))
+    # Add the initial mask
+    if m is not nomask:
+        if not result.ndim:
+            return masked
+        result._mask = np.logical_or(m, invalid)
+    # Fix the invalid parts
+    if invalid.any():
+        if not result.ndim:
+            return masked
+        elif result._mask is nomask:
+            result._mask = invalid
+        result._data[invalid] = result.fill_value
+    return result
+
+argmin = _frommethod('argmin')
+argmax = _frommethod('argmax')
+
+def argsort(a, axis=np._NoValue, kind=None, order=None, endwith=True,
+            fill_value=None, *, stable=None):
+    "Function version of the eponymous method."
+    a = np.asanyarray(a)
+
+    # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default
+    if axis is np._NoValue:
+        axis = _deprecate_argsort_axis(a)
+
+    if isinstance(a, MaskedArray):
+        return a.argsort(axis=axis, kind=kind, order=order, endwith=endwith,
+                         fill_value=fill_value, stable=None)
+    else:
+        return a.argsort(axis=axis, kind=kind, order=order, stable=None)
+argsort.__doc__ = MaskedArray.argsort.__doc__
+
+def sort(a, axis=-1, kind=None, order=None, endwith=True, fill_value=None, *,
+         stable=None):
+    """
+    Return a sorted copy of the masked array.
+
+    Equivalent to creating a copy of the array
+    and applying the  MaskedArray ``sort()`` method.
+
+    Refer to ``MaskedArray.sort`` for the full documentation
+
+    See Also
+    --------
+    MaskedArray.sort : equivalent method
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [11.2, -3.973, 0.801, -1.41]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11.2, -3.973, 0.801, --],
+                 mask=[False, False, False,  True],
+           fill_value=1e+20)
+    >>> ma.sort(masked_x)
+    masked_array(data=[-3.973, 0.801, 11.2, --],
+                 mask=[False, False, False,  True],
+           fill_value=1e+20)
+    """
+    a = np.array(a, copy=True, subok=True)
+    if axis is None:
+        a = a.flatten()
+        axis = 0
+
+    if isinstance(a, MaskedArray):
+        a.sort(axis=axis, kind=kind, order=order, endwith=endwith,
+               fill_value=fill_value, stable=stable)
+    else:
+        a.sort(axis=axis, kind=kind, order=order, stable=stable)
+    return a
+
+
+def compressed(x):
+    """
+    Return all the non-masked data as a 1-D array.
+
+    This function is equivalent to calling the "compressed" method of a
+    `ma.MaskedArray`, see `ma.MaskedArray.compressed` for details.
+
+    See Also
+    --------
+    ma.MaskedArray.compressed : Equivalent method.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create an array with negative values masked:
+
+    >>> import numpy as np
+    >>> x = np.array([[1, -1, 0], [2, -1, 3], [7, 4, -1]])
+    >>> masked_x = np.ma.masked_array(x, mask=x < 0)
+    >>> masked_x
+    masked_array(
+      data=[[1, --, 0],
+            [2, --, 3],
+            [7, 4, --]],
+      mask=[[False,  True, False],
+            [False,  True, False],
+            [False, False,  True]],
+      fill_value=999999)
+
+    Compress the masked array into a 1-D array of non-masked values:
+
+    >>> np.ma.compressed(masked_x)
+    array([1, 0, 2, 3, 7, 4])
+
+    """
+    return asanyarray(x).compressed()
+
+
+def concatenate(arrays, axis=0):
+    """
+    Concatenate a sequence of arrays along the given axis.
+
+    Parameters
+    ----------
+    arrays : sequence of array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int, optional
+        The axis along which the arrays will be joined. Default is 0.
+
+    Returns
+    -------
+    result : MaskedArray
+        The concatenated array with any masked entries preserved.
+
+    See Also
+    --------
+    numpy.concatenate : Equivalent function in the top-level NumPy module.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.arange(3)
+    >>> a[1] = ma.masked
+    >>> b = ma.arange(2, 5)
+    >>> a
+    masked_array(data=[0, --, 2],
+                 mask=[False,  True, False],
+           fill_value=999999)
+    >>> b
+    masked_array(data=[2, 3, 4],
+                 mask=False,
+           fill_value=999999)
+    >>> ma.concatenate([a, b])
+    masked_array(data=[0, --, 2, 2, 3, 4],
+                 mask=[False,  True, False, False, False, False],
+           fill_value=999999)
+
+    """
+    d = np.concatenate([getdata(a) for a in arrays], axis)
+    rcls = get_masked_subclass(*arrays)
+    data = d.view(rcls)
+    # Check whether one of the arrays has a non-empty mask.
+    for x in arrays:
+        if getmask(x) is not nomask:
+            break
+    else:
+        return data
+    # OK, so we have to concatenate the masks
+    dm = np.concatenate([getmaskarray(a) for a in arrays], axis)
+    dm = dm.reshape(d.shape)
+
+    # If we decide to keep a '_shrinkmask' option, we want to check that
+    # all of them are True, and then check for dm.any()
+    data._mask = _shrink_mask(dm)
+    return data
+
+
+def diag(v, k=0):
+    """
+    Extract a diagonal or construct a diagonal array.
+
+    This function is the equivalent of `numpy.diag` that takes masked
+    values into account, see `numpy.diag` for details.
+
+    See Also
+    --------
+    numpy.diag : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+
+    Create an array with negative values masked:
+
+    >>> import numpy as np
+    >>> x = np.array([[11.2, -3.973, 18], [0.801, -1.41, 12], [7, 33, -12]])
+    >>> masked_x = np.ma.masked_array(x, mask=x < 0)
+    >>> masked_x
+    masked_array(
+      data=[[11.2, --, 18.0],
+            [0.801, --, 12.0],
+            [7.0, 33.0, --]],
+      mask=[[False,  True, False],
+            [False,  True, False],
+            [False, False,  True]],
+      fill_value=1e+20)
+
+    Isolate the main diagonal from the masked array:
+
+    >>> np.ma.diag(masked_x)
+    masked_array(data=[11.2, --, --],
+                 mask=[False,  True,  True],
+           fill_value=1e+20)
+
+    Isolate the first diagonal below the main diagonal:
+
+    >>> np.ma.diag(masked_x, -1)
+    masked_array(data=[0.801, 33.0],
+                 mask=[False, False],
+           fill_value=1e+20)
+
+    """
+    output = np.diag(v, k).view(MaskedArray)
+    if getmask(v) is not nomask:
+        output._mask = np.diag(v._mask, k)
+    return output
+
+
+def left_shift(a, n):
+    """
+    Shift the bits of an integer to the left.
+
+    This is the masked array version of `numpy.left_shift`, for details
+    see that function.
+
+    See Also
+    --------
+    numpy.left_shift
+
+    Examples
+    --------
+    Shift with a masked array:
+
+    >>> arr = np.ma.array([10, 20, 30], mask=[False, True, False])
+    >>> np.ma.left_shift(arr, 1)
+    masked_array(data=[20, --, 60],
+                 mask=[False,  True, False],
+           fill_value=999999)
+
+    Large shift:
+
+    >>> np.ma.left_shift(10, 10)
+    masked_array(data=10240,
+                 mask=False,
+           fill_value=999999)
+
+    Shift with a scalar and an array:
+
+    >>> scalar = 10
+    >>> arr = np.ma.array([1, 2, 3], mask=[False, True, False])
+    >>> np.ma.left_shift(scalar, arr)
+    masked_array(data=[20, --, 80],
+                 mask=[False,  True, False],
+           fill_value=999999)
+
+
+    """
+    m = getmask(a)
+    if m is nomask:
+        d = umath.left_shift(filled(a), n)
+        return masked_array(d)
+    else:
+        d = umath.left_shift(filled(a, 0), n)
+        return masked_array(d, mask=m)
+
+
+def right_shift(a, n):
+    """
+    Shift the bits of an integer to the right.
+
+    This is the masked array version of `numpy.right_shift`, for details
+    see that function.
+
+    See Also
+    --------
+    numpy.right_shift
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [11, 3, 8, 1]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11, 3, 8, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+    >>> ma.right_shift(masked_x,1)
+    masked_array(data=[5, 1, 4, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+
+    """
+    m = getmask(a)
+    if m is nomask:
+        d = umath.right_shift(filled(a), n)
+        return masked_array(d)
+    else:
+        d = umath.right_shift(filled(a, 0), n)
+        return masked_array(d, mask=m)
+
+
+def put(a, indices, values, mode='raise'):
+    """
+    Set storage-indexed locations to corresponding values.
+
+    This function is equivalent to `MaskedArray.put`, see that method
+    for details.
+
+    See Also
+    --------
+    MaskedArray.put
+
+    Examples
+    --------
+    Putting values in a masked array:
+
+    >>> a = np.ma.array([1, 2, 3, 4], mask=[False, True, False, False])
+    >>> np.ma.put(a, [1, 3], [10, 30])
+    >>> a
+    masked_array(data=[ 1, 10,  3, 30],
+                 mask=False,
+           fill_value=999999)
+
+    Using put with a 2D array:
+
+    >>> b = np.ma.array([[1, 2], [3, 4]], mask=[[False, True], [False, False]])
+    >>> np.ma.put(b, [[0, 1], [1, 0]], [[10, 20], [30, 40]])
+    >>> b
+    masked_array(
+      data=[[40, 30],
+            [ 3,  4]],
+      mask=False,
+      fill_value=999999)
+
+    """
+    # We can't use 'frommethod', the order of arguments is different
+    try:
+        return a.put(indices, values, mode=mode)
+    except AttributeError:
+        return np.asarray(a).put(indices, values, mode=mode)
+
+
+def putmask(a, mask, values):  # , mode='raise'):
+    """
+    Changes elements of an array based on conditional and input values.
+
+    This is the masked array version of `numpy.putmask`, for details see
+    `numpy.putmask`.
+
+    See Also
+    --------
+    numpy.putmask
+
+    Notes
+    -----
+    Using a masked array as `values` will **not** transform a `ndarray` into
+    a `MaskedArray`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = [[1, 2], [3, 4]]
+    >>> mask = [[1, 0], [0, 0]]
+    >>> x = np.ma.array(arr, mask=mask)
+    >>> np.ma.putmask(x, x < 4, 10*x)
+    >>> x
+    masked_array(
+      data=[[--, 20],
+            [30, 4]],
+      mask=[[ True, False],
+            [False, False]],
+      fill_value=999999)
+    >>> x.data
+    array([[10, 20],
+           [30,  4]])
+
+    """
+    # We can't use 'frommethod', the order of arguments is different
+    if not isinstance(a, MaskedArray):
+        a = a.view(MaskedArray)
+    (valdata, valmask) = (getdata(values), getmask(values))
+    if getmask(a) is nomask:
+        if valmask is not nomask:
+            a._sharedmask = True
+            a._mask = make_mask_none(a.shape, a.dtype)
+            np.copyto(a._mask, valmask, where=mask)
+    elif a._hardmask:
+        if valmask is not nomask:
+            m = a._mask.copy()
+            np.copyto(m, valmask, where=mask)
+            a.mask |= m
+    else:
+        if valmask is nomask:
+            valmask = getmaskarray(values)
+        np.copyto(a._mask, valmask, where=mask)
+    np.copyto(a._data, valdata, where=mask)
+    return
+
+
+def transpose(a, axes=None):
+    """
+    Permute the dimensions of an array.
+
+    This function is exactly equivalent to `numpy.transpose`.
+
+    See Also
+    --------
+    numpy.transpose : Equivalent function in top-level NumPy module.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = ma.arange(4).reshape((2,2))
+    >>> x[1, 1] = ma.masked
+    >>> x
+    masked_array(
+      data=[[0, 1],
+            [2, --]],
+      mask=[[False, False],
+            [False,  True]],
+      fill_value=999999)
+
+    >>> ma.transpose(x)
+    masked_array(
+      data=[[0, 2],
+            [1, --]],
+      mask=[[False, False],
+            [False,  True]],
+      fill_value=999999)
+    """
+    # We can't use 'frommethod', as 'transpose' doesn't take keywords
+    try:
+        return a.transpose(axes)
+    except AttributeError:
+        return np.asarray(a).transpose(axes).view(MaskedArray)
+
+
+def reshape(a, new_shape, order='C'):
+    """
+    Returns an array containing the same data with a new shape.
+
+    Refer to `MaskedArray.reshape` for full documentation.
+
+    See Also
+    --------
+    MaskedArray.reshape : equivalent function
+
+    Examples
+    --------
+    Reshaping a 1-D array:
+
+    >>> a = np.ma.array([1, 2, 3, 4])
+    >>> np.ma.reshape(a, (2, 2))
+    masked_array(
+      data=[[1, 2],
+            [3, 4]],
+      mask=False,
+      fill_value=999999)
+
+    Reshaping a 2-D array:
+
+    >>> b = np.ma.array([[1, 2], [3, 4]])
+    >>> np.ma.reshape(b, (1, 4))
+    masked_array(data=[[1, 2, 3, 4]],
+                 mask=False,
+           fill_value=999999)
+
+    Reshaping a 1-D array with a mask:
+
+    >>> c = np.ma.array([1, 2, 3, 4], mask=[False, True, False, False])
+    >>> np.ma.reshape(c, (2, 2))
+    masked_array(
+      data=[[1, --],
+            [3, 4]],
+      mask=[[False,  True],
+            [False, False]],
+      fill_value=999999)
+
+    """
+    # We can't use 'frommethod', it whine about some parameters. Dmmit.
+    try:
+        return a.reshape(new_shape, order=order)
+    except AttributeError:
+        _tmp = np.asarray(a).reshape(new_shape, order=order)
+        return _tmp.view(MaskedArray)
+
+
+def resize(x, new_shape):
+    """
+    Return a new masked array with the specified size and shape.
+
+    This is the masked equivalent of the `numpy.resize` function. The new
+    array is filled with repeated copies of `x` (in the order that the
+    data are stored in memory). If `x` is masked, the new array will be
+    masked, and the new mask will be a repetition of the old one.
+
+    See Also
+    --------
+    numpy.resize : Equivalent function in the top level NumPy module.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.array([[1, 2] ,[3, 4]])
+    >>> a[0, 1] = ma.masked
+    >>> a
+    masked_array(
+      data=[[1, --],
+            [3, 4]],
+      mask=[[False,  True],
+            [False, False]],
+      fill_value=999999)
+    >>> np.resize(a, (3, 3))
+    masked_array(
+      data=[[1, 2, 3],
+            [4, 1, 2],
+            [3, 4, 1]],
+      mask=False,
+      fill_value=999999)
+    >>> ma.resize(a, (3, 3))
+    masked_array(
+      data=[[1, --, 3],
+            [4, 1, --],
+            [3, 4, 1]],
+      mask=[[False,  True, False],
+            [False, False,  True],
+            [False, False, False]],
+      fill_value=999999)
+
+    A MaskedArray is always returned, regardless of the input type.
+
+    >>> a = np.array([[1, 2] ,[3, 4]])
+    >>> ma.resize(a, (3, 3))
+    masked_array(
+      data=[[1, 2, 3],
+            [4, 1, 2],
+            [3, 4, 1]],
+      mask=False,
+      fill_value=999999)
+
+    """
+    # We can't use _frommethods here, as N.resize is notoriously whiny.
+    m = getmask(x)
+    if m is not nomask:
+        m = np.resize(m, new_shape)
+    result = np.resize(x, new_shape).view(get_masked_subclass(x))
+    if result.ndim:
+        result._mask = m
+    return result
+
+
+def ndim(obj):
+    """
+    maskedarray version of the numpy function.
+
+    """
+    return np.ndim(getdata(obj))
+
+ndim.__doc__ = np.ndim.__doc__
+
+
+def shape(obj):
+    "maskedarray version of the numpy function."
+    return np.shape(getdata(obj))
+shape.__doc__ = np.shape.__doc__
+
+
+def size(obj, axis=None):
+    "maskedarray version of the numpy function."
+    return np.size(getdata(obj), axis)
+size.__doc__ = np.size.__doc__
+
+
+def diff(a, /, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
+    """
+    Calculate the n-th discrete difference along the given axis.
+    The first difference is given by ``out[i] = a[i+1] - a[i]`` along
+    the given axis, higher differences are calculated by using `diff`
+    recursively.
+    Preserves the input mask.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input
+        is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the
+        last axis.
+    prepend, append : array_like, optional
+        Values to prepend or append to `a` along axis prior to
+        performing the difference.  Scalar values are expanded to
+        arrays with length 1 in the direction of axis and the shape
+        of the input array in along all other axes.  Otherwise the
+        dimension and shape must match `a` except along axis.
+
+    Returns
+    -------
+    diff : MaskedArray
+        The n-th differences. The shape of the output is the same as `a`
+        except along `axis` where the dimension is smaller by `n`. The
+        type of the output is the same as the type of the difference
+        between any two elements of `a`. This is the same as the type of
+        `a` in most cases. A notable exception is `datetime64`, which
+        results in a `timedelta64` output array.
+
+    See Also
+    --------
+    numpy.diff : Equivalent function in the top-level NumPy module.
+
+    Notes
+    -----
+    Type is preserved for boolean arrays, so the result will contain
+    `False` when consecutive elements are the same and `True` when they
+    differ.
+
+    For unsigned integer arrays, the results will also be unsigned. This
+    should not be surprising, as the result is consistent with
+    calculating the difference directly:
+
+    >>> u8_arr = np.array([1, 0], dtype=np.uint8)
+    >>> np.ma.diff(u8_arr)
+    masked_array(data=[255],
+                 mask=False,
+           fill_value=np.uint64(999999),
+                dtype=uint8)
+    >>> u8_arr[1,...] - u8_arr[0,...]
+    np.uint8(255)
+
+    If this is not desirable, then the array should be cast to a larger
+    integer type first:
+
+    >>> i16_arr = u8_arr.astype(np.int16)
+    >>> np.ma.diff(i16_arr)
+    masked_array(data=[-1],
+                 mask=False,
+           fill_value=np.int64(999999),
+                dtype=int16)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([1, 2, 3, 4, 7, 0, 2, 3])
+    >>> x = np.ma.masked_where(a < 2, a)
+    >>> np.ma.diff(x)
+    masked_array(data=[--, 1, 1, 3, --, --, 1],
+            mask=[ True, False, False, False,  True,  True, False],
+        fill_value=999999)
+
+    >>> np.ma.diff(x, n=2)
+    masked_array(data=[--, 0, 2, --, --, --],
+                mask=[ True, False, False,  True,  True,  True],
+        fill_value=999999)
+
+    >>> a = np.array([[1, 3, 1, 5, 10], [0, 1, 5, 6, 8]])
+    >>> x = np.ma.masked_equal(a, value=1)
+    >>> np.ma.diff(x)
+    masked_array(
+        data=[[--, --, --, 5],
+                [--, --, 1, 2]],
+        mask=[[ True,  True,  True, False],
+                [ True,  True, False, False]],
+        fill_value=1)
+
+    >>> np.ma.diff(x, axis=0)
+    masked_array(data=[[--, --, --, 1, -2]],
+            mask=[[ True,  True,  True, False, False]],
+        fill_value=1)
+
+    """
+    if n == 0:
+        return a
+    if n < 0:
+        raise ValueError("order must be non-negative but got " + repr(n))
+
+    a = np.ma.asanyarray(a)
+    if a.ndim == 0:
+        raise ValueError(
+            "diff requires input that is at least one dimensional"
+            )
+
+    combined = []
+    if prepend is not np._NoValue:
+        prepend = np.ma.asanyarray(prepend)
+        if prepend.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            prepend = np.broadcast_to(prepend, tuple(shape))
+        combined.append(prepend)
+
+    combined.append(a)
+
+    if append is not np._NoValue:
+        append = np.ma.asanyarray(append)
+        if append.ndim == 0:
+            shape = list(a.shape)
+            shape[axis] = 1
+            append = np.broadcast_to(append, tuple(shape))
+        combined.append(append)
+
+    if len(combined) > 1:
+        a = np.ma.concatenate(combined, axis)
+
+    # GH 22465 np.diff without prepend/append preserves the mask
+    return np.diff(a, n, axis)
+
+
+##############################################################################
+#                            Extra functions                                 #
+##############################################################################
+
+
+def where(condition, x=_NoValue, y=_NoValue):
+    """
+    Return a masked array with elements from `x` or `y`, depending on condition.
+
+    .. note::
+        When only `condition` is provided, this function is identical to
+        `nonzero`. The rest of this documentation covers only the case where
+        all three arguments are provided.
+
+    Parameters
+    ----------
+    condition : array_like, bool
+        Where True, yield `x`, otherwise yield `y`.
+    x, y : array_like, optional
+        Values from which to choose. `x`, `y` and `condition` need to be
+        broadcastable to some shape.
+
+    Returns
+    -------
+    out : MaskedArray
+        An masked array with `masked` elements where the condition is masked,
+        elements from `x` where `condition` is True, and elements from `y`
+        elsewhere.
+
+    See Also
+    --------
+    numpy.where : Equivalent function in the top-level NumPy module.
+    nonzero : The function that is called when x and y are omitted
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],
+    ...                                                    [1, 0, 1],
+    ...                                                    [0, 1, 0]])
+    >>> x
+    masked_array(
+      data=[[0.0, --, 2.0],
+            [--, 4.0, --],
+            [6.0, --, 8.0]],
+      mask=[[False,  True, False],
+            [ True, False,  True],
+            [False,  True, False]],
+      fill_value=1e+20)
+    >>> np.ma.where(x > 5, x, -3.1416)
+    masked_array(
+      data=[[-3.1416, --, -3.1416],
+            [--, -3.1416, --],
+            [6.0, --, 8.0]],
+      mask=[[False,  True, False],
+            [ True, False,  True],
+            [False,  True, False]],
+      fill_value=1e+20)
+
+    """
+
+    # handle the single-argument case
+    missing = (x is _NoValue, y is _NoValue).count(True)
+    if missing == 1:
+        raise ValueError("Must provide both 'x' and 'y' or neither.")
+    if missing == 2:
+        return nonzero(condition)
+
+    # we only care if the condition is true - false or masked pick y
+    cf = filled(condition, False)
+    xd = getdata(x)
+    yd = getdata(y)
+
+    # we need the full arrays here for correct final dimensions
+    cm = getmaskarray(condition)
+    xm = getmaskarray(x)
+    ym = getmaskarray(y)
+
+    # deal with the fact that masked.dtype == float64, but we don't actually
+    # want to treat it as that.
+    if x is masked and y is not masked:
+        xd = np.zeros((), dtype=yd.dtype)
+        xm = np.ones((),  dtype=ym.dtype)
+    elif y is masked and x is not masked:
+        yd = np.zeros((), dtype=xd.dtype)
+        ym = np.ones((),  dtype=xm.dtype)
+
+    data = np.where(cf, xd, yd)
+    mask = np.where(cf, xm, ym)
+    mask = np.where(cm, np.ones((), dtype=mask.dtype), mask)
+
+    # collapse the mask, for backwards compatibility
+    mask = _shrink_mask(mask)
+
+    return masked_array(data, mask=mask)
+
+
+def choose(indices, choices, out=None, mode='raise'):
+    """
+    Use an index array to construct a new array from a list of choices.
+
+    Given an array of integers and a list of n choice arrays, this method
+    will create a new array that merges each of the choice arrays.  Where a
+    value in `index` is i, the new array will have the value that choices[i]
+    contains in the same place.
+
+    Parameters
+    ----------
+    indices : ndarray of ints
+        This array must contain integers in ``[0, n-1]``, where n is the
+        number of choices.
+    choices : sequence of arrays
+        Choice arrays. The index array and all of the choices should be
+        broadcastable to the same shape.
+    out : array, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and `dtype`.
+    mode : {'raise', 'wrap', 'clip'}, optional
+        Specifies how out-of-bounds indices will behave.
+
+        * 'raise' : raise an error
+        * 'wrap' : wrap around
+        * 'clip' : clip to the range
+
+    Returns
+    -------
+    merged_array : array
+
+    See Also
+    --------
+    choose : equivalent function
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]])
+    >>> a = np.array([2, 1, 0])
+    >>> np.ma.choose(a, choice)
+    masked_array(data=[3, 2, 1],
+                 mask=False,
+           fill_value=999999)
+
+    """
+    def fmask(x):
+        "Returns the filled array, or True if masked."
+        if x is masked:
+            return True
+        return filled(x)
+
+    def nmask(x):
+        "Returns the mask, True if ``masked``, False if ``nomask``."
+        if x is masked:
+            return True
+        return getmask(x)
+    # Get the indices.
+    c = filled(indices, 0)
+    # Get the masks.
+    masks = [nmask(x) for x in choices]
+    data = [fmask(x) for x in choices]
+    # Construct the mask
+    outputmask = np.choose(c, masks, mode=mode)
+    outputmask = make_mask(mask_or(outputmask, getmask(indices)),
+                           copy=False, shrink=True)
+    # Get the choices.
+    d = np.choose(c, data, mode=mode, out=out).view(MaskedArray)
+    if out is not None:
+        if isinstance(out, MaskedArray):
+            out.__setmask__(outputmask)
+        return out
+    d.__setmask__(outputmask)
+    return d
+
+
+def round_(a, decimals=0, out=None):
+    """
+    Return a copy of a, rounded to 'decimals' places.
+
+    When 'decimals' is negative, it specifies the number of positions
+    to the left of the decimal point.  The real and imaginary parts of
+    complex numbers are rounded separately. Nothing is done if the
+    array is not of float type and 'decimals' is greater than or equal
+    to 0.
+
+    Parameters
+    ----------
+    decimals : int
+        Number of decimals to round to. May be negative.
+    out : array_like
+        Existing array to use for output.
+        If not given, returns a default copy of a.
+
+    Notes
+    -----
+    If out is given and does not have a mask attribute, the mask of a
+    is lost!
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> x = [11.2, -3.973, 0.801, -1.41]
+    >>> mask = [0, 0, 0, 1]
+    >>> masked_x = ma.masked_array(x, mask)
+    >>> masked_x
+    masked_array(data=[11.2, -3.973, 0.801, --],
+                 mask=[False, False, False, True],
+        fill_value=1e+20)
+    >>> ma.round_(masked_x)
+    masked_array(data=[11.0, -4.0, 1.0, --],
+                 mask=[False, False, False, True],
+        fill_value=1e+20)
+    >>> ma.round(masked_x, decimals=1)
+    masked_array(data=[11.2, -4.0, 0.8, --],
+                 mask=[False, False, False, True],
+        fill_value=1e+20)
+    >>> ma.round_(masked_x, decimals=-1)
+    masked_array(data=[10.0, -0.0, 0.0, --],
+                 mask=[False, False, False, True],
+        fill_value=1e+20)
+    """
+    if out is None:
+        return np.round(a, decimals, out)
+    else:
+        np.round(getdata(a), decimals, out)
+        if hasattr(out, '_mask'):
+            out._mask = getmask(a)
+        return out
+round = round_
+
+
+def _mask_propagate(a, axis):
+    """
+    Mask whole 1-d vectors of an array that contain masked values.
+    """
+    a = array(a, subok=False)
+    m = getmask(a)
+    if m is nomask or not m.any() or axis is None:
+        return a
+    a._mask = a._mask.copy()
+    axes = normalize_axis_tuple(axis, a.ndim)
+    for ax in axes:
+        a._mask |= m.any(axis=ax, keepdims=True)
+    return a
+
+
+# Include masked dot here to avoid import problems in getting it from
+# extras.py. Note that it is not included in __all__, but rather exported
+# from extras in order to avoid backward compatibility problems.
+def dot(a, b, strict=False, out=None):
+    """
+    Return the dot product of two arrays.
+
+    This function is the equivalent of `numpy.dot` that takes masked values
+    into account. Note that `strict` and `out` are in different position
+    than in the method version. In order to maintain compatibility with the
+    corresponding method, it is recommended that the optional arguments be
+    treated as keyword only.  At some point that may be mandatory.
+
+    Parameters
+    ----------
+    a, b : masked_array_like
+        Inputs arrays.
+    strict : bool, optional
+        Whether masked data are propagated (True) or set to 0 (False) for
+        the computation. Default is False.  Propagating the mask means that
+        if a masked value appears in a row or column, the whole row or
+        column is considered masked.
+    out : masked_array, optional
+        Output argument. This must have the exact kind that would be returned
+        if it was not used. In particular, it must have the right type, must be
+        C-contiguous, and its dtype must be the dtype that would be returned
+        for `dot(a,b)`. This is a performance feature. Therefore, if these
+        conditions are not met, an exception is raised, instead of attempting
+        to be flexible.
+
+    See Also
+    --------
+    numpy.dot : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])
+    >>> b = np.ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]])
+    >>> np.ma.dot(a, b)
+    masked_array(
+      data=[[21, 26],
+            [45, 64]],
+      mask=[[False, False],
+            [False, False]],
+      fill_value=999999)
+    >>> np.ma.dot(a, b, strict=True)
+    masked_array(
+      data=[[--, --],
+            [--, 64]],
+      mask=[[ True,  True],
+            [ True, False]],
+      fill_value=999999)
+
+    """
+    if strict is True:
+        if np.ndim(a) == 0 or np.ndim(b) == 0:
+            pass
+        elif b.ndim == 1:
+            a = _mask_propagate(a, a.ndim - 1)
+            b = _mask_propagate(b, b.ndim - 1)
+        else:
+            a = _mask_propagate(a, a.ndim - 1)
+            b = _mask_propagate(b, b.ndim - 2)
+    am = ~getmaskarray(a)
+    bm = ~getmaskarray(b)
+
+    if out is None:
+        d = np.dot(filled(a, 0), filled(b, 0))
+        m = ~np.dot(am, bm)
+        if np.ndim(d) == 0:
+            d = np.asarray(d)
+        r = d.view(get_masked_subclass(a, b))
+        r.__setmask__(m)
+        return r
+    else:
+        d = np.dot(filled(a, 0), filled(b, 0), out._data)
+        if out.mask.shape != d.shape:
+            out._mask = np.empty(d.shape, MaskType)
+        np.dot(am, bm, out._mask)
+        np.logical_not(out._mask, out._mask)
+        return out
+
+
+def inner(a, b):
+    """
+    Returns the inner product of a and b for arrays of floating point types.
+
+    Like the generic NumPy equivalent the product sum is over the last dimension
+    of a and b. The first argument is not conjugated.
+
+    """
+    fa = filled(a, 0)
+    fb = filled(b, 0)
+    if fa.ndim == 0:
+        fa.shape = (1,)
+    if fb.ndim == 0:
+        fb.shape = (1,)
+    return np.inner(fa, fb).view(MaskedArray)
+inner.__doc__ = doc_note(np.inner.__doc__,
+                         "Masked values are replaced by 0.")
+innerproduct = inner
+
+
+def outer(a, b):
+    "maskedarray version of the numpy function."
+    fa = filled(a, 0).ravel()
+    fb = filled(b, 0).ravel()
+    d = np.outer(fa, fb)
+    ma = getmask(a)
+    mb = getmask(b)
+    if ma is nomask and mb is nomask:
+        return masked_array(d)
+    ma = getmaskarray(a)
+    mb = getmaskarray(b)
+    m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=False)
+    return masked_array(d, mask=m)
+outer.__doc__ = doc_note(np.outer.__doc__,
+                         "Masked values are replaced by 0.")
+outerproduct = outer
+
+
+def _convolve_or_correlate(f, a, v, mode, propagate_mask):
+    """
+    Helper function for ma.correlate and ma.convolve
+    """
+    if propagate_mask:
+        # results which are contributed to by either item in any pair being invalid
+        mask = (
+            f(getmaskarray(a), np.ones(np.shape(v), dtype=bool), mode=mode)
+          | f(np.ones(np.shape(a), dtype=bool), getmaskarray(v), mode=mode)
+        )
+        data = f(getdata(a), getdata(v), mode=mode)
+    else:
+        # results which are not contributed to by any pair of valid elements
+        mask = ~f(~getmaskarray(a), ~getmaskarray(v), mode=mode)
+        data = f(filled(a, 0), filled(v, 0), mode=mode)
+
+    return masked_array(data, mask=mask)
+
+
+def correlate(a, v, mode='valid', propagate_mask=True):
+    """
+    Cross-correlation of two 1-dimensional sequences.
+
+    Parameters
+    ----------
+    a, v : array_like
+        Input sequences.
+    mode : {'valid', 'same', 'full'}, optional
+        Refer to the `np.convolve` docstring.  Note that the default
+        is 'valid', unlike `convolve`, which uses 'full'.
+    propagate_mask : bool
+        If True, then a result element is masked if any masked element contributes towards it.
+        If False, then a result element is only masked if no non-masked element
+        contribute towards it
+
+    Returns
+    -------
+    out : MaskedArray
+        Discrete cross-correlation of `a` and `v`.
+
+    See Also
+    --------
+    numpy.correlate : Equivalent function in the top-level NumPy module.
+
+    Examples
+    --------
+    Basic correlation:
+
+    >>> a = np.ma.array([1, 2, 3])
+    >>> v = np.ma.array([0, 1, 0])
+    >>> np.ma.correlate(a, v, mode='valid')
+    masked_array(data=[2],
+                 mask=[False],
+           fill_value=999999)
+
+    Correlation with masked elements:
+
+    >>> a = np.ma.array([1, 2, 3], mask=[False, True, False])
+    >>> v = np.ma.array([0, 1, 0])
+    >>> np.ma.correlate(a, v, mode='valid', propagate_mask=True)
+    masked_array(data=[--],
+                 mask=[ True],
+           fill_value=999999,
+                dtype=int64)
+
+    Correlation with different modes and mixed array types:
+
+    >>> a = np.ma.array([1, 2, 3])
+    >>> v = np.ma.array([0, 1, 0])
+    >>> np.ma.correlate(a, v, mode='full')
+    masked_array(data=[0, 1, 2, 3, 0],
+                 mask=[False, False, False, False, False],
+           fill_value=999999)
+
+    """
+    return _convolve_or_correlate(np.correlate, a, v, mode, propagate_mask)
+
+
+def convolve(a, v, mode='full', propagate_mask=True):
+    """
+    Returns the discrete, linear convolution of two one-dimensional sequences.
+
+    Parameters
+    ----------
+    a, v : array_like
+        Input sequences.
+    mode : {'valid', 'same', 'full'}, optional
+        Refer to the `np.convolve` docstring.
+    propagate_mask : bool
+        If True, then if any masked element is included in the sum for a result
+        element, then the result is masked.
+        If False, then the result element is only masked if no non-masked cells
+        contribute towards it
+
+    Returns
+    -------
+    out : MaskedArray
+        Discrete, linear convolution of `a` and `v`.
+
+    See Also
+    --------
+    numpy.convolve : Equivalent function in the top-level NumPy module.
+    """
+    return _convolve_or_correlate(np.convolve, a, v, mode, propagate_mask)
+
+
+def allequal(a, b, fill_value=True):
+    """
+    Return True if all entries of a and b are equal, using
+    fill_value as a truth value where either or both are masked.
+
+    Parameters
+    ----------
+    a, b : array_like
+        Input arrays to compare.
+    fill_value : bool, optional
+        Whether masked values in a or b are considered equal (True) or not
+        (False).
+
+    Returns
+    -------
+    y : bool
+        Returns True if the two arrays are equal within the given
+        tolerance, False otherwise. If either array contains NaN,
+        then False is returned.
+
+    See Also
+    --------
+    all, any
+    numpy.ma.allclose
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
+    >>> a
+    masked_array(data=[10000000000.0, 1e-07, --],
+                 mask=[False, False,  True],
+           fill_value=1e+20)
+
+    >>> b = np.array([1e10, 1e-7, -42.0])
+    >>> b
+    array([  1.00000000e+10,   1.00000000e-07,  -4.20000000e+01])
+    >>> np.ma.allequal(a, b, fill_value=False)
+    False
+    >>> np.ma.allequal(a, b)
+    True
+
+    """
+    m = mask_or(getmask(a), getmask(b))
+    if m is nomask:
+        x = getdata(a)
+        y = getdata(b)
+        d = umath.equal(x, y)
+        return d.all()
+    elif fill_value:
+        x = getdata(a)
+        y = getdata(b)
+        d = umath.equal(x, y)
+        dm = array(d, mask=m, copy=False)
+        return dm.filled(True).all(None)
+    else:
+        return False
+
+
+def allclose(a, b, masked_equal=True, rtol=1e-5, atol=1e-8):
+    """
+    Returns True if two arrays are element-wise equal within a tolerance.
+
+    This function is equivalent to `allclose` except that masked values
+    are treated as equal (default) or unequal, depending on the `masked_equal`
+    argument.
+
+    Parameters
+    ----------
+    a, b : array_like
+        Input arrays to compare.
+    masked_equal : bool, optional
+        Whether masked values in `a` and `b` are considered equal (True) or not
+        (False). They are considered equal by default.
+    rtol : float, optional
+        Relative tolerance. The relative difference is equal to ``rtol * b``.
+        Default is 1e-5.
+    atol : float, optional
+        Absolute tolerance. The absolute difference is equal to `atol`.
+        Default is 1e-8.
+
+    Returns
+    -------
+    y : bool
+        Returns True if the two arrays are equal within the given
+        tolerance, False otherwise. If either array contains NaN, then
+        False is returned.
+
+    See Also
+    --------
+    all, any
+    numpy.allclose : the non-masked `allclose`.
+
+    Notes
+    -----
+    If the following equation is element-wise True, then `allclose` returns
+    True::
+
+      absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
+
+    Return True if all elements of `a` and `b` are equal subject to
+    given tolerances.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
+    >>> a
+    masked_array(data=[10000000000.0, 1e-07, --],
+                 mask=[False, False,  True],
+           fill_value=1e+20)
+    >>> b = np.ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])
+    >>> np.ma.allclose(a, b)
+    False
+
+    >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
+    >>> b = np.ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])
+    >>> np.ma.allclose(a, b)
+    True
+    >>> np.ma.allclose(a, b, masked_equal=False)
+    False
+
+    Masked values are not compared directly.
+
+    >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
+    >>> b = np.ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1])
+    >>> np.ma.allclose(a, b)
+    True
+    >>> np.ma.allclose(a, b, masked_equal=False)
+    False
+
+    """
+    x = masked_array(a, copy=False)
+    y = masked_array(b, copy=False)
+
+    # make sure y is an inexact type to avoid abs(MIN_INT); will cause
+    # casting of x later.
+    # NOTE: We explicitly allow timedelta, which used to work. This could
+    #       possibly be deprecated. See also gh-18286.
+    #       timedelta works if `atol` is an integer or also a timedelta.
+    #       Although, the default tolerances are unlikely to be useful
+    if y.dtype.kind != "m":
+        dtype = np.result_type(y, 1.)
+        if y.dtype != dtype:
+            y = masked_array(y, dtype=dtype, copy=False)
+
+    m = mask_or(getmask(x), getmask(y))
+    xinf = np.isinf(masked_array(x, copy=False, mask=m)).filled(False)
+    # If we have some infs, they should fall at the same place.
+    if not np.all(xinf == filled(np.isinf(y), False)):
+        return False
+    # No infs at all
+    if not np.any(xinf):
+        d = filled(less_equal(absolute(x - y), atol + rtol * absolute(y)),
+                   masked_equal)
+        return np.all(d)
+
+    if not np.all(filled(x[xinf] == y[xinf], masked_equal)):
+        return False
+    x = x[~xinf]
+    y = y[~xinf]
+
+    d = filled(less_equal(absolute(x - y), atol + rtol * absolute(y)),
+               masked_equal)
+
+    return np.all(d)
+
+
+def asarray(a, dtype=None, order=None):
+    """
+    Convert the input to a masked array of the given data-type.
+
+    No copy is performed if the input is already an `ndarray`. If `a` is
+    a subclass of `MaskedArray`, a base class `MaskedArray` is returned.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data, in any form that can be converted to a masked array. This
+        includes lists, lists of tuples, tuples, tuples of tuples, tuples
+        of lists, ndarrays and masked arrays.
+    dtype : dtype, optional
+        By default, the data-type is inferred from the input data.
+    order : {'C', 'F'}, optional
+        Whether to use row-major ('C') or column-major ('FORTRAN') memory
+        representation.  Default is 'C'.
+
+    Returns
+    -------
+    out : MaskedArray
+        Masked array interpretation of `a`.
+
+    See Also
+    --------
+    asanyarray : Similar to `asarray`, but conserves subclasses.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(10.).reshape(2, 5)
+    >>> x
+    array([[0., 1., 2., 3., 4.],
+           [5., 6., 7., 8., 9.]])
+    >>> np.ma.asarray(x)
+    masked_array(
+      data=[[0., 1., 2., 3., 4.],
+            [5., 6., 7., 8., 9.]],
+      mask=False,
+      fill_value=1e+20)
+    >>> type(np.ma.asarray(x))
+    
+
+    """
+    order = order or 'C'
+    return masked_array(a, dtype=dtype, copy=False, keep_mask=True,
+                        subok=False, order=order)
+
+
+def asanyarray(a, dtype=None):
+    """
+    Convert the input to a masked array, conserving subclasses.
+
+    If `a` is a subclass of `MaskedArray`, its class is conserved.
+    No copy is performed if the input is already an `ndarray`.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data, in any form that can be converted to an array.
+    dtype : dtype, optional
+        By default, the data-type is inferred from the input data.
+    order : {'C', 'F'}, optional
+        Whether to use row-major ('C') or column-major ('FORTRAN') memory
+        representation.  Default is 'C'.
+
+    Returns
+    -------
+    out : MaskedArray
+        MaskedArray interpretation of `a`.
+
+    See Also
+    --------
+    asarray : Similar to `asanyarray`, but does not conserve subclass.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(10.).reshape(2, 5)
+    >>> x
+    array([[0., 1., 2., 3., 4.],
+           [5., 6., 7., 8., 9.]])
+    >>> np.ma.asanyarray(x)
+    masked_array(
+      data=[[0., 1., 2., 3., 4.],
+            [5., 6., 7., 8., 9.]],
+      mask=False,
+      fill_value=1e+20)
+    >>> type(np.ma.asanyarray(x))
+    
+
+    """
+    # workaround for #8666, to preserve identity. Ideally the bottom line
+    # would handle this for us.
+    if isinstance(a, MaskedArray) and (dtype is None or dtype == a.dtype):
+        return a
+    return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)
+
+
+##############################################################################
+#                               Pickling                                     #
+##############################################################################
+
+
+def fromfile(file, dtype=float, count=-1, sep=''):
+    raise NotImplementedError(
+        "fromfile() not yet implemented for a MaskedArray.")
+
+
+def fromflex(fxarray):
+    """
+    Build a masked array from a suitable flexible-type array.
+
+    The input array has to have a data-type with ``_data`` and ``_mask``
+    fields. This type of array is output by `MaskedArray.toflex`.
+
+    Parameters
+    ----------
+    fxarray : ndarray
+        The structured input array, containing ``_data`` and ``_mask``
+        fields. If present, other fields are discarded.
+
+    Returns
+    -------
+    result : MaskedArray
+        The constructed masked array.
+
+    See Also
+    --------
+    MaskedArray.toflex : Build a flexible-type array from a masked array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)
+    >>> rec = x.toflex()
+    >>> rec
+    array([[(0, False), (1,  True), (2, False)],
+           [(3,  True), (4, False), (5,  True)],
+           [(6, False), (7,  True), (8, False)]],
+          dtype=[('_data', '>> x2 = np.ma.fromflex(rec)
+    >>> x2
+    masked_array(
+      data=[[0, --, 2],
+            [--, 4, --],
+            [6, --, 8]],
+      mask=[[False,  True, False],
+            [ True, False,  True],
+            [False,  True, False]],
+      fill_value=999999)
+
+    Extra fields can be present in the structured array but are discarded:
+
+    >>> dt = [('_data', '>> rec2 = np.zeros((2, 2), dtype=dt)
+    >>> rec2
+    array([[(0, False, 0.), (0, False, 0.)],
+           [(0, False, 0.), (0, False, 0.)]],
+          dtype=[('_data', '>> y = np.ma.fromflex(rec2)
+    >>> y
+    masked_array(
+      data=[[0, 0],
+            [0, 0]],
+      mask=[[False, False],
+            [False, False]],
+      fill_value=np.int64(999999),
+      dtype=int32)
+
+    """
+    return masked_array(fxarray['_data'], mask=fxarray['_mask'])
+
+
+class _convert2ma:
+
+    """
+    Convert functions from numpy to numpy.ma.
+
+    Parameters
+    ----------
+        _methodname : string
+            Name of the method to transform.
+
+    """
+    __doc__ = None
+
+    def __init__(self, funcname, np_ret, np_ma_ret, params=None):
+        self._func = getattr(np, funcname)
+        self.__doc__ = self.getdoc(np_ret, np_ma_ret)
+        self._extras = params or {}
+
+    def getdoc(self, np_ret, np_ma_ret):
+        "Return the doc of the function (from the doc of the method)."
+        doc = getattr(self._func, '__doc__', None)
+        sig = get_object_signature(self._func)
+        if doc:
+            doc = self._replace_return_type(doc, np_ret, np_ma_ret)
+            # Add the signature of the function at the beginning of the doc
+            if sig:
+                sig = "%s%s\n" % (self._func.__name__, sig)
+            doc = sig + doc
+        return doc
+
+    def _replace_return_type(self, doc, np_ret, np_ma_ret):
+        """
+        Replace documentation of ``np`` function's return type.
+
+        Replaces it with the proper type for the ``np.ma`` function.
+
+        Parameters
+        ----------
+        doc : str
+            The documentation of the ``np`` method.
+        np_ret : str
+            The return type string of the ``np`` method that we want to
+            replace. (e.g. "out : ndarray")
+        np_ma_ret : str
+            The return type string of the ``np.ma`` method.
+            (e.g. "out : MaskedArray")
+        """
+        if np_ret not in doc:
+            raise RuntimeError(
+                f"Failed to replace `{np_ret}` with `{np_ma_ret}`. "
+                f"The documentation string for return type, {np_ret}, is not "
+                f"found in the docstring for `np.{self._func.__name__}`. "
+                f"Fix the docstring for `np.{self._func.__name__}` or "
+                "update the expected string for return type."
+            )
+
+        return doc.replace(np_ret, np_ma_ret)
+
+    def __call__(self, *args, **params):
+        # Find the common parameters to the call and the definition
+        _extras = self._extras
+        common_params = set(params).intersection(_extras)
+        # Drop the common parameters from the call
+        for p in common_params:
+            _extras[p] = params.pop(p)
+        # Get the result
+        result = self._func.__call__(*args, **params).view(MaskedArray)
+        if "fill_value" in common_params:
+            result.fill_value = _extras.get("fill_value", None)
+        if "hardmask" in common_params:
+            result._hardmask = bool(_extras.get("hard_mask", False))
+        return result
+
+
+arange = _convert2ma(
+    'arange',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='arange : ndarray',
+    np_ma_ret='arange : MaskedArray',
+)
+clip = _convert2ma(
+    'clip',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='clipped_array : ndarray',
+    np_ma_ret='clipped_array : MaskedArray',
+)
+empty = _convert2ma(
+    'empty',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+empty_like = _convert2ma(
+    'empty_like',
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+frombuffer = _convert2ma(
+    'frombuffer',
+    np_ret='out : ndarray',
+    np_ma_ret='out: MaskedArray',
+)
+fromfunction = _convert2ma(
+   'fromfunction',
+   np_ret='fromfunction : any',
+   np_ma_ret='fromfunction: MaskedArray',
+)
+identity = _convert2ma(
+    'identity',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+indices = _convert2ma(
+    'indices',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='grid : one ndarray or tuple of ndarrays',
+    np_ma_ret='grid : one MaskedArray or tuple of MaskedArrays',
+)
+ones = _convert2ma(
+    'ones',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+ones_like = _convert2ma(
+    'ones_like',
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+squeeze = _convert2ma(
+    'squeeze',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='squeezed : ndarray',
+    np_ma_ret='squeezed : MaskedArray',
+)
+zeros = _convert2ma(
+    'zeros',
+    params=dict(fill_value=None, hardmask=False),
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+zeros_like = _convert2ma(
+    'zeros_like',
+    np_ret='out : ndarray',
+    np_ma_ret='out : MaskedArray',
+)
+
+
+def append(a, b, axis=None):
+    """Append values to the end of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Values are appended to a copy of this array.
+    b : array_like
+        These values are appended to a copy of `a`.  It must be of the
+        correct shape (the same shape as `a`, excluding `axis`).  If `axis`
+        is not specified, `b` can be any shape and will be flattened
+        before use.
+    axis : int, optional
+        The axis along which `v` are appended.  If `axis` is not given,
+        both `a` and `b` are flattened before use.
+
+    Returns
+    -------
+    append : MaskedArray
+        A copy of `a` with `b` appended to `axis`.  Note that `append`
+        does not occur in-place: a new array is allocated and filled.  If
+        `axis` is None, the result is a flattened array.
+
+    See Also
+    --------
+    numpy.append : Equivalent function in the top-level NumPy module.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import numpy.ma as ma
+    >>> a = ma.masked_values([1, 2, 3], 2)
+    >>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)
+    >>> ma.append(a, b)
+    masked_array(data=[1, --, 3, 4, 5, 6, --, 8, 9],
+                 mask=[False,  True, False, False, False, False,  True, False,
+                       False],
+           fill_value=999999)
+    """
+    return concatenate([a, b], axis)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..83c0636ce4a70f921871160af0c9a0397bdb90aa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/core.pyi
@@ -0,0 +1,656 @@
+# pyright: reportIncompatibleMethodOverride=false
+# ruff: noqa: ANN001, ANN002, ANN003, ANN201, ANN202 ANN204
+
+from typing import Any, SupportsIndex, TypeVar
+
+from _typeshed import Incomplete
+from typing_extensions import deprecated
+
+from numpy import _OrderKACF, amax, amin, bool_, dtype, expand_dims, float64, ndarray
+from numpy._typing import ArrayLike, _DTypeLikeBool
+
+__all__ = [
+    "MAError",
+    "MaskError",
+    "MaskType",
+    "MaskedArray",
+    "abs",
+    "absolute",
+    "add",
+    "all",
+    "allclose",
+    "allequal",
+    "alltrue",
+    "amax",
+    "amin",
+    "angle",
+    "anom",
+    "anomalies",
+    "any",
+    "append",
+    "arange",
+    "arccos",
+    "arccosh",
+    "arcsin",
+    "arcsinh",
+    "arctan",
+    "arctan2",
+    "arctanh",
+    "argmax",
+    "argmin",
+    "argsort",
+    "around",
+    "array",
+    "asanyarray",
+    "asarray",
+    "bitwise_and",
+    "bitwise_or",
+    "bitwise_xor",
+    "bool_",
+    "ceil",
+    "choose",
+    "clip",
+    "common_fill_value",
+    "compress",
+    "compressed",
+    "concatenate",
+    "conjugate",
+    "convolve",
+    "copy",
+    "correlate",
+    "cos",
+    "cosh",
+    "count",
+    "cumprod",
+    "cumsum",
+    "default_fill_value",
+    "diag",
+    "diagonal",
+    "diff",
+    "divide",
+    "empty",
+    "empty_like",
+    "equal",
+    "exp",
+    "expand_dims",
+    "fabs",
+    "filled",
+    "fix_invalid",
+    "flatten_mask",
+    "flatten_structured_array",
+    "floor",
+    "floor_divide",
+    "fmod",
+    "frombuffer",
+    "fromflex",
+    "fromfunction",
+    "getdata",
+    "getmask",
+    "getmaskarray",
+    "greater",
+    "greater_equal",
+    "harden_mask",
+    "hypot",
+    "identity",
+    "ids",
+    "indices",
+    "inner",
+    "innerproduct",
+    "isMA",
+    "isMaskedArray",
+    "is_mask",
+    "is_masked",
+    "isarray",
+    "left_shift",
+    "less",
+    "less_equal",
+    "log",
+    "log2",
+    "log10",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "make_mask",
+    "make_mask_descr",
+    "make_mask_none",
+    "mask_or",
+    "masked",
+    "masked_array",
+    "masked_equal",
+    "masked_greater",
+    "masked_greater_equal",
+    "masked_inside",
+    "masked_invalid",
+    "masked_less",
+    "masked_less_equal",
+    "masked_not_equal",
+    "masked_object",
+    "masked_outside",
+    "masked_print_option",
+    "masked_singleton",
+    "masked_values",
+    "masked_where",
+    "max",
+    "maximum",
+    "maximum_fill_value",
+    "mean",
+    "min",
+    "minimum",
+    "minimum_fill_value",
+    "mod",
+    "multiply",
+    "mvoid",
+    "ndim",
+    "negative",
+    "nomask",
+    "nonzero",
+    "not_equal",
+    "ones",
+    "ones_like",
+    "outer",
+    "outerproduct",
+    "power",
+    "prod",
+    "product",
+    "ptp",
+    "put",
+    "putmask",
+    "ravel",
+    "remainder",
+    "repeat",
+    "reshape",
+    "resize",
+    "right_shift",
+    "round",
+    "round_",
+    "set_fill_value",
+    "shape",
+    "sin",
+    "sinh",
+    "size",
+    "soften_mask",
+    "sometrue",
+    "sort",
+    "sqrt",
+    "squeeze",
+    "std",
+    "subtract",
+    "sum",
+    "swapaxes",
+    "take",
+    "tan",
+    "tanh",
+    "trace",
+    "transpose",
+    "true_divide",
+    "var",
+    "where",
+    "zeros",
+    "zeros_like",
+]
+
+_ShapeType_co = TypeVar("_ShapeType_co", bound=tuple[int, ...], covariant=True)
+_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True)
+
+
+MaskType = bool
+nomask: bool
+
+class MaskedArrayFutureWarning(FutureWarning): ...
+class MAError(Exception): ...
+class MaskError(MAError): ...
+
+def default_fill_value(obj): ...
+def minimum_fill_value(obj): ...
+def maximum_fill_value(obj): ...
+def set_fill_value(a, fill_value): ...
+def common_fill_value(a, b): ...
+def filled(a, fill_value=...): ...
+def getdata(a, subok=...): ...
+get_data = getdata
+
+def fix_invalid(a, mask=..., copy=..., fill_value=...): ...
+
+class _MaskedUFunc:
+    f: Any
+    __doc__: Any
+    __name__: Any
+    def __init__(self, ufunc): ...
+
+class _MaskedUnaryOperation(_MaskedUFunc):
+    fill: Any
+    domain: Any
+    def __init__(self, mufunc, fill=..., domain=...): ...
+    def __call__(self, a, *args, **kwargs): ...
+
+class _MaskedBinaryOperation(_MaskedUFunc):
+    fillx: Any
+    filly: Any
+    def __init__(self, mbfunc, fillx=..., filly=...): ...
+    def __call__(self, a, b, *args, **kwargs): ...
+    def reduce(self, target, axis=..., dtype=...): ...
+    def outer(self, a, b): ...
+    def accumulate(self, target, axis=...): ...
+
+class _DomainedBinaryOperation(_MaskedUFunc):
+    domain: Any
+    fillx: Any
+    filly: Any
+    def __init__(self, dbfunc, domain, fillx=..., filly=...): ...
+    def __call__(self, a, b, *args, **kwargs): ...
+
+exp: _MaskedUnaryOperation
+conjugate: _MaskedUnaryOperation
+sin: _MaskedUnaryOperation
+cos: _MaskedUnaryOperation
+arctan: _MaskedUnaryOperation
+arcsinh: _MaskedUnaryOperation
+sinh: _MaskedUnaryOperation
+cosh: _MaskedUnaryOperation
+tanh: _MaskedUnaryOperation
+abs: _MaskedUnaryOperation
+absolute: _MaskedUnaryOperation
+angle: _MaskedUnaryOperation
+fabs: _MaskedUnaryOperation
+negative: _MaskedUnaryOperation
+floor: _MaskedUnaryOperation
+ceil: _MaskedUnaryOperation
+around: _MaskedUnaryOperation
+logical_not: _MaskedUnaryOperation
+sqrt: _MaskedUnaryOperation
+log: _MaskedUnaryOperation
+log2: _MaskedUnaryOperation
+log10: _MaskedUnaryOperation
+tan: _MaskedUnaryOperation
+arcsin: _MaskedUnaryOperation
+arccos: _MaskedUnaryOperation
+arccosh: _MaskedUnaryOperation
+arctanh: _MaskedUnaryOperation
+
+add: _MaskedBinaryOperation
+subtract: _MaskedBinaryOperation
+multiply: _MaskedBinaryOperation
+arctan2: _MaskedBinaryOperation
+equal: _MaskedBinaryOperation
+not_equal: _MaskedBinaryOperation
+less_equal: _MaskedBinaryOperation
+greater_equal: _MaskedBinaryOperation
+less: _MaskedBinaryOperation
+greater: _MaskedBinaryOperation
+logical_and: _MaskedBinaryOperation
+def alltrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
+logical_or: _MaskedBinaryOperation
+def sometrue(target: ArrayLike, axis: SupportsIndex | None = 0, dtype: _DTypeLikeBool | None = None) -> Incomplete: ...
+logical_xor: _MaskedBinaryOperation
+bitwise_and: _MaskedBinaryOperation
+bitwise_or: _MaskedBinaryOperation
+bitwise_xor: _MaskedBinaryOperation
+hypot: _MaskedBinaryOperation
+
+divide: _DomainedBinaryOperation
+true_divide: _DomainedBinaryOperation
+floor_divide: _DomainedBinaryOperation
+remainder: _DomainedBinaryOperation
+fmod: _DomainedBinaryOperation
+mod: _DomainedBinaryOperation
+
+def make_mask_descr(ndtype): ...
+def getmask(a): ...
+get_mask = getmask
+
+def getmaskarray(arr): ...
+def is_mask(m): ...
+def make_mask(m, copy=..., shrink=..., dtype=...): ...
+def make_mask_none(newshape, dtype=...): ...
+def mask_or(m1, m2, copy=..., shrink=...): ...
+def flatten_mask(mask): ...
+def masked_where(condition, a, copy=...): ...
+def masked_greater(x, value, copy=...): ...
+def masked_greater_equal(x, value, copy=...): ...
+def masked_less(x, value, copy=...): ...
+def masked_less_equal(x, value, copy=...): ...
+def masked_not_equal(x, value, copy=...): ...
+def masked_equal(x, value, copy=...): ...
+def masked_inside(x, v1, v2, copy=...): ...
+def masked_outside(x, v1, v2, copy=...): ...
+def masked_object(x, value, copy=..., shrink=...): ...
+def masked_values(x, value, rtol=..., atol=..., copy=..., shrink=...): ...
+def masked_invalid(a, copy=...): ...
+
+class _MaskedPrintOption:
+    def __init__(self, display): ...
+    def display(self): ...
+    def set_display(self, s): ...
+    def enabled(self): ...
+    def enable(self, shrink=...): ...
+
+masked_print_option: _MaskedPrintOption
+
+def flatten_structured_array(a): ...
+
+class MaskedIterator:
+    ma: Any
+    dataiter: Any
+    maskiter: Any
+    def __init__(self, ma): ...
+    def __iter__(self): ...
+    def __getitem__(self, indx): ...
+    def __setitem__(self, index, value): ...
+    def __next__(self): ...
+
+class MaskedArray(ndarray[_ShapeType_co, _DType_co]):
+    __array_priority__: Any
+    def __new__(cls, data=..., mask=..., dtype=..., copy=..., subok=..., ndmin=..., fill_value=..., keep_mask=..., hard_mask=..., shrink=..., order=...): ...
+    def __array_finalize__(self, obj): ...
+    def __array_wrap__(self, obj, context=..., return_scalar=...): ...
+    def view(self, dtype=..., type=..., fill_value=...): ...
+    def __getitem__(self, indx): ...
+    def __setitem__(self, indx, value): ...
+    @property
+    def dtype(self): ...
+    @dtype.setter
+    def dtype(self, dtype): ...
+    @property
+    def shape(self): ...
+    @shape.setter
+    def shape(self, shape): ...
+    def __setmask__(self, mask, copy=...): ...
+    @property
+    def mask(self): ...
+    @mask.setter
+    def mask(self, value): ...
+    @property
+    def recordmask(self): ...
+    @recordmask.setter
+    def recordmask(self, mask): ...
+    def harden_mask(self): ...
+    def soften_mask(self): ...
+    @property
+    def hardmask(self): ...
+    def unshare_mask(self): ...
+    @property
+    def sharedmask(self): ...
+    def shrink_mask(self): ...
+    @property
+    def baseclass(self): ...
+    data: Any
+    @property
+    def flat(self): ...
+    @flat.setter
+    def flat(self, value): ...
+    @property
+    def fill_value(self): ...
+    @fill_value.setter
+    def fill_value(self, value=...): ...
+    get_fill_value: Any
+    set_fill_value: Any
+    def filled(self, fill_value=...): ...
+    def compressed(self): ...
+    def compress(self, condition, axis=..., out=...): ...
+    def __eq__(self, other): ...
+    def __ne__(self, other): ...
+    def __ge__(self, other): ...
+    def __gt__(self, other): ...
+    def __le__(self, other): ...
+    def __lt__(self, other): ...
+    def __add__(self, other): ...
+    def __radd__(self, other): ...
+    def __sub__(self, other): ...
+    def __rsub__(self, other): ...
+    def __mul__(self, other): ...
+    def __rmul__(self, other): ...
+    def __div__(self, other): ...
+    def __truediv__(self, other): ...
+    def __rtruediv__(self, other): ...
+    def __floordiv__(self, other): ...
+    def __rfloordiv__(self, other): ...
+    def __pow__(self, other): ...
+    def __rpow__(self, other): ...
+    def __iadd__(self, other): ...
+    def __isub__(self, other): ...
+    def __imul__(self, other): ...
+    def __idiv__(self, other): ...
+    def __ifloordiv__(self, other): ...
+    def __itruediv__(self, other): ...
+    def __ipow__(self, other): ...
+    def __float__(self): ...
+    def __int__(self): ...
+    @property  # type: ignore[misc]
+    def imag(self): ...
+    get_imag: Any
+    @property  # type: ignore[misc]
+    def real(self): ...
+    get_real: Any
+    def count(self, axis=..., keepdims=...): ...
+    def ravel(self, order=...): ...
+    def reshape(self, *s, **kwargs): ...
+    def resize(self, newshape, refcheck=..., order=...): ...
+    def put(self, indices, values, mode=...): ...
+    def ids(self): ...
+    def iscontiguous(self): ...
+    def all(self, axis=..., out=..., keepdims=...): ...
+    def any(self, axis=..., out=..., keepdims=...): ...
+    def nonzero(self): ...
+    def trace(self, offset=..., axis1=..., axis2=..., dtype=..., out=...): ...
+    def dot(self, b, out=..., strict=...): ...
+    def sum(self, axis=..., dtype=..., out=..., keepdims=...): ...
+    def cumsum(self, axis=..., dtype=..., out=...): ...
+    def prod(self, axis=..., dtype=..., out=..., keepdims=...): ...
+    product: Any
+    def cumprod(self, axis=..., dtype=..., out=...): ...
+    def mean(self, axis=..., dtype=..., out=..., keepdims=...): ...
+    def anom(self, axis=..., dtype=...): ...
+    def var(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ...
+    def std(self, axis=..., dtype=..., out=..., ddof=..., keepdims=...): ...
+    def round(self, decimals=..., out=...): ...
+    def argsort(self, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
+    def argmin(self, axis=..., fill_value=..., out=..., *, keepdims=...): ...
+    def argmax(self, axis=..., fill_value=..., out=..., *, keepdims=...): ...
+    def sort(self, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
+    def min(self, axis=..., out=..., fill_value=..., keepdims=...): ...
+    # NOTE: deprecated
+    # def tostring(self, fill_value=..., order=...): ...
+    def max(self, axis=..., out=..., fill_value=..., keepdims=...): ...
+    def ptp(self, axis=..., out=..., fill_value=..., keepdims=...): ...
+    def partition(self, *args, **kwargs): ...
+    def argpartition(self, *args, **kwargs): ...
+    def take(self, indices, axis=..., out=..., mode=...): ...
+
+    copy: Any
+    diagonal: Any
+    flatten: Any
+    repeat: Any
+    squeeze: Any
+    swapaxes: Any
+    T: Any
+    transpose: Any
+
+    @property  # type: ignore[misc]
+    def mT(self): ...
+
+    #
+    def toflex(self) -> Incomplete: ...
+    def torecords(self) -> Incomplete: ...
+    def tolist(self, fill_value: Incomplete | None = None) -> Incomplete: ...
+    @deprecated("tostring() is deprecated. Use tobytes() instead.")
+    def tostring(self, /, fill_value: Incomplete | None = None, order: _OrderKACF = "C") -> bytes: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def tobytes(self, /, fill_value: Incomplete | None = None, order: _OrderKACF = "C") -> bytes: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def tofile(self, /, fid: Incomplete, sep: str = "", format: str = "%s") -> Incomplete: ...
+
+    #
+    def __reduce__(self): ...
+    def __deepcopy__(self, memo=...): ...
+
+class mvoid(MaskedArray[_ShapeType_co, _DType_co]):
+    def __new__(
+        self,  # pyright: ignore[reportSelfClsParameterName]
+        data,
+        mask=...,
+        dtype=...,
+        fill_value=...,
+        hardmask=...,
+        copy=...,
+        subok=...,
+    ): ...
+    def __getitem__(self, indx): ...
+    def __setitem__(self, indx, value): ...
+    def __iter__(self): ...
+    def __len__(self): ...
+    def filled(self, fill_value=...): ...
+    def tolist(self): ...
+
+def isMaskedArray(x): ...
+isarray = isMaskedArray
+isMA = isMaskedArray
+
+# 0D float64 array
+class MaskedConstant(MaskedArray[Any, dtype[float64]]):
+    def __new__(cls): ...
+    __class__: Any
+    def __array_finalize__(self, obj): ...
+    def __array_wrap__(self, obj, context=..., return_scalar=...): ...
+    def __format__(self, format_spec): ...
+    def __reduce__(self): ...
+    def __iop__(self, other): ...
+    __iadd__: Any
+    __isub__: Any
+    __imul__: Any
+    __ifloordiv__: Any
+    __itruediv__: Any
+    __ipow__: Any
+    def copy(self, *args, **kwargs): ...
+    def __copy__(self): ...
+    def __deepcopy__(self, memo): ...
+    def __setattr__(self, attr, value): ...
+
+masked: MaskedConstant
+masked_singleton: MaskedConstant
+masked_array = MaskedArray
+
+def array(
+    data,
+    dtype=...,
+    copy=...,
+    order=...,
+    mask=...,
+    fill_value=...,
+    keep_mask=...,
+    hard_mask=...,
+    shrink=...,
+    subok=...,
+    ndmin=...,
+): ...
+def is_masked(x): ...
+
+class _extrema_operation(_MaskedUFunc):
+    compare: Any
+    fill_value_func: Any
+    def __init__(self, ufunc, compare, fill_value): ...
+    # NOTE: in practice `b` has a default value, but users should
+    # explicitly provide a value here as the default is deprecated
+    def __call__(self, a, b): ...
+    def reduce(self, target, axis=...): ...
+    def outer(self, a, b): ...
+
+def min(obj, axis=..., out=..., fill_value=..., keepdims=...): ...
+def max(obj, axis=..., out=..., fill_value=..., keepdims=...): ...
+def ptp(obj, axis=..., out=..., fill_value=..., keepdims=...): ...
+
+class _frommethod:
+    __name__: Any
+    __doc__: Any
+    reversed: Any
+    def __init__(self, methodname, reversed=...): ...
+    def getdoc(self): ...
+    def __call__(self, a, *args, **params): ...
+
+all: _frommethod
+anomalies: _frommethod
+anom: _frommethod
+any: _frommethod
+compress: _frommethod
+cumprod: _frommethod
+cumsum: _frommethod
+copy: _frommethod
+diagonal: _frommethod
+harden_mask: _frommethod
+ids: _frommethod
+mean: _frommethod
+nonzero: _frommethod
+prod: _frommethod
+product: _frommethod
+ravel: _frommethod
+repeat: _frommethod
+soften_mask: _frommethod
+std: _frommethod
+sum: _frommethod
+swapaxes: _frommethod
+trace: _frommethod
+var: _frommethod
+count: _frommethod
+argmin: _frommethod
+argmax: _frommethod
+
+minimum: _extrema_operation
+maximum: _extrema_operation
+
+def take(a, indices, axis=..., out=..., mode=...): ...
+def power(a, b, third=...): ...
+def argsort(a, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
+def sort(a, axis=..., kind=..., order=..., endwith=..., fill_value=..., *, stable=...): ...
+def compressed(x): ...
+def concatenate(arrays, axis=...): ...
+def diag(v, k=...): ...
+def left_shift(a, n): ...
+def right_shift(a, n): ...
+def put(a, indices, values, mode=...): ...
+def putmask(a, mask, values): ...
+def transpose(a, axes=...): ...
+def reshape(a, new_shape, order=...): ...
+def resize(x, new_shape): ...
+def ndim(obj): ...
+def shape(obj): ...
+def size(obj, axis=...): ...
+def diff(a, /, n=..., axis=..., prepend=..., append=...): ...
+def where(condition, x=..., y=...): ...
+def choose(indices, choices, out=..., mode=...): ...
+def round_(a, decimals=..., out=...): ...
+round = round_
+
+def inner(a, b): ...
+innerproduct = inner
+
+def outer(a, b): ...
+outerproduct = outer
+
+def correlate(a, v, mode=..., propagate_mask=...): ...
+def convolve(a, v, mode=..., propagate_mask=...): ...
+def allequal(a, b, fill_value=...): ...
+def allclose(a, b, masked_equal=..., rtol=..., atol=...): ...
+def asarray(a, dtype=..., order=...): ...
+def asanyarray(a, dtype=...): ...
+def fromflex(fxarray): ...
+
+class _convert2ma:
+    def __init__(self, /, funcname: str, np_ret: str, np_ma_ret: str, params: dict[str, Any] | None = None) -> None: ...
+    def __call__(self, /, *args: object, **params: object) -> Any: ...  # noqa: ANN401
+    def getdoc(self, /, np_ret: str, np_ma_ret: str) -> str | None: ...
+
+arange: _convert2ma
+clip: _convert2ma
+empty: _convert2ma
+empty_like: _convert2ma
+frombuffer: _convert2ma
+fromfunction: _convert2ma
+identity: _convert2ma
+indices: _convert2ma
+ones: _convert2ma
+ones_like: _convert2ma
+squeeze: _convert2ma
+zeros: _convert2ma
+zeros_like: _convert2ma
+
+def append(a, b, axis=...): ...
+def dot(a, b, strict=..., out=...): ...
+def mask_rowcols(a, axis=...): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdc35c424ce3843feb7f599745f413ebdd1dbe81
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.py
@@ -0,0 +1,2321 @@
+"""
+Masked arrays add-ons.
+
+A collection of utilities for `numpy.ma`.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+
+"""
+__all__ = [
+    'apply_along_axis', 'apply_over_axes', 'atleast_1d', 'atleast_2d',
+    'atleast_3d', 'average', 'clump_masked', 'clump_unmasked', 'column_stack',
+    'compress_cols', 'compress_nd', 'compress_rowcols', 'compress_rows',
+    'count_masked', 'corrcoef', 'cov', 'diagflat', 'dot', 'dstack', 'ediff1d',
+    'flatnotmasked_contiguous', 'flatnotmasked_edges', 'hsplit', 'hstack',
+    'isin', 'in1d', 'intersect1d', 'mask_cols', 'mask_rowcols', 'mask_rows',
+    'masked_all', 'masked_all_like', 'median', 'mr_', 'ndenumerate',
+    'notmasked_contiguous', 'notmasked_edges', 'polyfit', 'row_stack',
+    'setdiff1d', 'setxor1d', 'stack', 'unique', 'union1d', 'vander', 'vstack',
+    ]
+
+import itertools
+import warnings
+
+from . import core as ma
+from .core import (
+    MaskedArray, MAError, add, array, asarray, concatenate, filled, count,
+    getmask, getmaskarray, make_mask_descr, masked, masked_array, mask_or,
+    nomask, ones, sort, zeros, getdata, get_masked_subclass, dot
+    )
+
+import numpy as np
+from numpy import ndarray, array as nxarray
+from numpy.lib.array_utils import normalize_axis_index, normalize_axis_tuple
+from numpy.lib._function_base_impl import _ureduce
+from numpy.lib._index_tricks_impl import AxisConcatenator
+
+
+def issequence(seq):
+    """
+    Is seq a sequence (ndarray, list or tuple)?
+
+    """
+    return isinstance(seq, (ndarray, tuple, list))
+
+
+def count_masked(arr, axis=None):
+    """
+    Count the number of masked elements along the given axis.
+
+    Parameters
+    ----------
+    arr : array_like
+        An array with (possibly) masked elements.
+    axis : int, optional
+        Axis along which to count. If None (default), a flattened
+        version of the array is used.
+
+    Returns
+    -------
+    count : int, ndarray
+        The total number of masked elements (axis=None) or the number
+        of masked elements along each slice of the given axis.
+
+    See Also
+    --------
+    MaskedArray.count : Count non-masked elements.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(9).reshape((3,3))
+    >>> a = np.ma.array(a)
+    >>> a[1, 0] = np.ma.masked
+    >>> a[1, 2] = np.ma.masked
+    >>> a[2, 1] = np.ma.masked
+    >>> a
+    masked_array(
+      data=[[0, 1, 2],
+            [--, 4, --],
+            [6, --, 8]],
+      mask=[[False, False, False],
+            [ True, False,  True],
+            [False,  True, False]],
+      fill_value=999999)
+    >>> np.ma.count_masked(a)
+    3
+
+    When the `axis` keyword is used an array is returned.
+
+    >>> np.ma.count_masked(a, axis=0)
+    array([1, 1, 1])
+    >>> np.ma.count_masked(a, axis=1)
+    array([0, 2, 1])
+
+    """
+    m = getmaskarray(arr)
+    return m.sum(axis)
+
+
+def masked_all(shape, dtype=float):
+    """
+    Empty masked array with all elements masked.
+
+    Return an empty masked array of the given shape and dtype, where all the
+    data are masked.
+
+    Parameters
+    ----------
+    shape : int or tuple of ints
+        Shape of the required MaskedArray, e.g., ``(2, 3)`` or ``2``.
+    dtype : dtype, optional
+        Data type of the output.
+
+    Returns
+    -------
+    a : MaskedArray
+        A masked array with all data masked.
+
+    See Also
+    --------
+    masked_all_like : Empty masked array modelled on an existing array.
+
+    Notes
+    -----
+    Unlike other masked array creation functions (e.g. `numpy.ma.zeros`,
+    `numpy.ma.ones`, `numpy.ma.full`), `masked_all` does not initialize the
+    values of the array, and may therefore be marginally faster. However,
+    the values stored in the newly allocated array are arbitrary. For
+    reproducible behavior, be sure to set each element of the array before
+    reading.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.ma.masked_all((3, 3))
+    masked_array(
+      data=[[--, --, --],
+            [--, --, --],
+            [--, --, --]],
+      mask=[[ True,  True,  True],
+            [ True,  True,  True],
+            [ True,  True,  True]],
+      fill_value=1e+20,
+      dtype=float64)
+
+    The `dtype` parameter defines the underlying data type.
+
+    >>> a = np.ma.masked_all((3, 3))
+    >>> a.dtype
+    dtype('float64')
+    >>> a = np.ma.masked_all((3, 3), dtype=np.int32)
+    >>> a.dtype
+    dtype('int32')
+
+    """
+    a = masked_array(np.empty(shape, dtype),
+                     mask=np.ones(shape, make_mask_descr(dtype)))
+    return a
+
+
+def masked_all_like(arr):
+    """
+    Empty masked array with the properties of an existing array.
+
+    Return an empty masked array of the same shape and dtype as
+    the array `arr`, where all the data are masked.
+
+    Parameters
+    ----------
+    arr : ndarray
+        An array describing the shape and dtype of the required MaskedArray.
+
+    Returns
+    -------
+    a : MaskedArray
+        A masked array with all data masked.
+
+    Raises
+    ------
+    AttributeError
+        If `arr` doesn't have a shape attribute (i.e. not an ndarray)
+
+    See Also
+    --------
+    masked_all : Empty masked array with all elements masked.
+
+    Notes
+    -----
+    Unlike other masked array creation functions (e.g. `numpy.ma.zeros_like`,
+    `numpy.ma.ones_like`, `numpy.ma.full_like`), `masked_all_like` does not
+    initialize the values of the array, and may therefore be marginally
+    faster. However, the values stored in the newly allocated array are
+    arbitrary. For reproducible behavior, be sure to set each element of the
+    array before reading.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.zeros((2, 3), dtype=np.float32)
+    >>> arr
+    array([[0., 0., 0.],
+           [0., 0., 0.]], dtype=float32)
+    >>> np.ma.masked_all_like(arr)
+    masked_array(
+      data=[[--, --, --],
+            [--, --, --]],
+      mask=[[ True,  True,  True],
+            [ True,  True,  True]],
+      fill_value=np.float64(1e+20),
+      dtype=float32)
+
+    The dtype of the masked array matches the dtype of `arr`.
+
+    >>> arr.dtype
+    dtype('float32')
+    >>> np.ma.masked_all_like(arr).dtype
+    dtype('float32')
+
+    """
+    a = np.empty_like(arr).view(MaskedArray)
+    a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))
+    return a
+
+
+#####--------------------------------------------------------------------------
+#---- --- Standard functions ---
+#####--------------------------------------------------------------------------
+class _fromnxfunction:
+    """
+    Defines a wrapper to adapt NumPy functions to masked arrays.
+
+
+    An instance of `_fromnxfunction` can be called with the same parameters
+    as the wrapped NumPy function. The docstring of `newfunc` is adapted from
+    the wrapped function as well, see `getdoc`.
+
+    This class should not be used directly. Instead, one of its extensions that
+    provides support for a specific type of input should be used.
+
+    Parameters
+    ----------
+    funcname : str
+        The name of the function to be adapted. The function should be
+        in the NumPy namespace (i.e. ``np.funcname``).
+
+    """
+
+    def __init__(self, funcname):
+        self.__name__ = funcname
+        self.__qualname__ = funcname
+        self.__doc__ = self.getdoc()
+
+    def getdoc(self):
+        """
+        Retrieve the docstring and signature from the function.
+
+        The ``__doc__`` attribute of the function is used as the docstring for
+        the new masked array version of the function. A note on application
+        of the function to the mask is appended.
+
+        Parameters
+        ----------
+        None
+
+        """
+        npfunc = getattr(np, self.__name__, None)
+        doc = getattr(npfunc, '__doc__', None)
+        if doc:
+            sig = ma.get_object_signature(npfunc)
+            doc = ma.doc_note(doc, "The function is applied to both the _data "
+                                   "and the _mask, if any.")
+            if sig:
+                sig = self.__name__ + sig + "\n\n"
+            return sig + doc
+        return
+
+    def __call__(self, *args, **params):
+        pass
+
+
+class _fromnxfunction_single(_fromnxfunction):
+    """
+    A version of `_fromnxfunction` that is called with a single array
+    argument followed by auxiliary args that are passed verbatim for
+    both the data and mask calls.
+    """
+    def __call__(self, x, *args, **params):
+        func = getattr(np, self.__name__)
+        if isinstance(x, ndarray):
+            _d = func(x.__array__(), *args, **params)
+            _m = func(getmaskarray(x), *args, **params)
+            return masked_array(_d, mask=_m)
+        else:
+            _d = func(np.asarray(x), *args, **params)
+            _m = func(getmaskarray(x), *args, **params)
+            return masked_array(_d, mask=_m)
+
+
+class _fromnxfunction_seq(_fromnxfunction):
+    """
+    A version of `_fromnxfunction` that is called with a single sequence
+    of arrays followed by auxiliary args that are passed verbatim for
+    both the data and mask calls.
+    """
+    def __call__(self, x, *args, **params):
+        func = getattr(np, self.__name__)
+        _d = func(tuple([np.asarray(a) for a in x]), *args, **params)
+        _m = func(tuple([getmaskarray(a) for a in x]), *args, **params)
+        return masked_array(_d, mask=_m)
+
+
+class _fromnxfunction_args(_fromnxfunction):
+    """
+    A version of `_fromnxfunction` that is called with multiple array
+    arguments. The first non-array-like input marks the beginning of the
+    arguments that are passed verbatim for both the data and mask calls.
+    Array arguments are processed independently and the results are
+    returned in a list. If only one array is found, the return value is
+    just the processed array instead of a list.
+    """
+    def __call__(self, *args, **params):
+        func = getattr(np, self.__name__)
+        arrays = []
+        args = list(args)
+        while len(args) > 0 and issequence(args[0]):
+            arrays.append(args.pop(0))
+        res = []
+        for x in arrays:
+            _d = func(np.asarray(x), *args, **params)
+            _m = func(getmaskarray(x), *args, **params)
+            res.append(masked_array(_d, mask=_m))
+        if len(arrays) == 1:
+            return res[0]
+        return res
+
+
+class _fromnxfunction_allargs(_fromnxfunction):
+    """
+    A version of `_fromnxfunction` that is called with multiple array
+    arguments. Similar to `_fromnxfunction_args` except that all args
+    are converted to arrays even if they are not so already. This makes
+    it possible to process scalars as 1-D arrays. Only keyword arguments
+    are passed through verbatim for the data and mask calls. Arrays
+    arguments are processed independently and the results are returned
+    in a list. If only one arg is present, the return value is just the
+    processed array instead of a list.
+    """
+    def __call__(self, *args, **params):
+        func = getattr(np, self.__name__)
+        res = []
+        for x in args:
+            _d = func(np.asarray(x), **params)
+            _m = func(getmaskarray(x), **params)
+            res.append(masked_array(_d, mask=_m))
+        if len(args) == 1:
+            return res[0]
+        return res
+
+
+atleast_1d = _fromnxfunction_allargs('atleast_1d')
+atleast_2d = _fromnxfunction_allargs('atleast_2d')
+atleast_3d = _fromnxfunction_allargs('atleast_3d')
+
+vstack = row_stack = _fromnxfunction_seq('vstack')
+hstack = _fromnxfunction_seq('hstack')
+column_stack = _fromnxfunction_seq('column_stack')
+dstack = _fromnxfunction_seq('dstack')
+stack = _fromnxfunction_seq('stack')
+
+hsplit = _fromnxfunction_single('hsplit')
+
+diagflat = _fromnxfunction_single('diagflat')
+
+
+#####--------------------------------------------------------------------------
+#----
+#####--------------------------------------------------------------------------
+def flatten_inplace(seq):
+    """Flatten a sequence in place."""
+    k = 0
+    while (k != len(seq)):
+        while hasattr(seq[k], '__iter__'):
+            seq[k:(k + 1)] = seq[k]
+        k += 1
+    return seq
+
+
+def apply_along_axis(func1d, axis, arr, *args, **kwargs):
+    """
+    (This docstring should be overwritten)
+    """
+    arr = array(arr, copy=False, subok=True)
+    nd = arr.ndim
+    axis = normalize_axis_index(axis, nd)
+    ind = [0] * (nd - 1)
+    i = np.zeros(nd, 'O')
+    indlist = list(range(nd))
+    indlist.remove(axis)
+    i[axis] = slice(None, None)
+    outshape = np.asarray(arr.shape).take(indlist)
+    i.put(indlist, ind)
+    res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
+    #  if res is a number, then we have a smaller output array
+    asscalar = np.isscalar(res)
+    if not asscalar:
+        try:
+            len(res)
+        except TypeError:
+            asscalar = True
+    # Note: we shouldn't set the dtype of the output from the first result
+    # so we force the type to object, and build a list of dtypes.  We'll
+    # just take the largest, to avoid some downcasting
+    dtypes = []
+    if asscalar:
+        dtypes.append(np.asarray(res).dtype)
+        outarr = zeros(outshape, object)
+        outarr[tuple(ind)] = res
+        Ntot = np.prod(outshape)
+        k = 1
+        while k < Ntot:
+            # increment the index
+            ind[-1] += 1
+            n = -1
+            while (ind[n] >= outshape[n]) and (n > (1 - nd)):
+                ind[n - 1] += 1
+                ind[n] = 0
+                n -= 1
+            i.put(indlist, ind)
+            res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
+            outarr[tuple(ind)] = res
+            dtypes.append(asarray(res).dtype)
+            k += 1
+    else:
+        res = array(res, copy=False, subok=True)
+        j = i.copy()
+        j[axis] = ([slice(None, None)] * res.ndim)
+        j.put(indlist, ind)
+        Ntot = np.prod(outshape)
+        holdshape = outshape
+        outshape = list(arr.shape)
+        outshape[axis] = res.shape
+        dtypes.append(asarray(res).dtype)
+        outshape = flatten_inplace(outshape)
+        outarr = zeros(outshape, object)
+        outarr[tuple(flatten_inplace(j.tolist()))] = res
+        k = 1
+        while k < Ntot:
+            # increment the index
+            ind[-1] += 1
+            n = -1
+            while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
+                ind[n - 1] += 1
+                ind[n] = 0
+                n -= 1
+            i.put(indlist, ind)
+            j.put(indlist, ind)
+            res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
+            outarr[tuple(flatten_inplace(j.tolist()))] = res
+            dtypes.append(asarray(res).dtype)
+            k += 1
+    max_dtypes = np.dtype(np.asarray(dtypes).max())
+    if not hasattr(arr, '_mask'):
+        result = np.asarray(outarr, dtype=max_dtypes)
+    else:
+        result = asarray(outarr, dtype=max_dtypes)
+        result.fill_value = ma.default_fill_value(result)
+    return result
+apply_along_axis.__doc__ = np.apply_along_axis.__doc__
+
+
+def apply_over_axes(func, a, axes):
+    """
+    (This docstring will be overwritten)
+    """
+    val = asarray(a)
+    N = a.ndim
+    if array(axes).ndim == 0:
+        axes = (axes,)
+    for axis in axes:
+        if axis < 0:
+            axis = N + axis
+        args = (val, axis)
+        res = func(*args)
+        if res.ndim == val.ndim:
+            val = res
+        else:
+            res = ma.expand_dims(res, axis)
+            if res.ndim == val.ndim:
+                val = res
+            else:
+                raise ValueError("function is not returning "
+                        "an array of the correct shape")
+    return val
+
+
+if apply_over_axes.__doc__ is not None:
+    apply_over_axes.__doc__ = np.apply_over_axes.__doc__[
+        :np.apply_over_axes.__doc__.find('Notes')].rstrip() + \
+    """
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.arange(24).reshape(2,3,4)
+    >>> a[:,0,1] = np.ma.masked
+    >>> a[:,1,:] = np.ma.masked
+    >>> a
+    masked_array(
+      data=[[[0, --, 2, 3],
+             [--, --, --, --],
+             [8, 9, 10, 11]],
+            [[12, --, 14, 15],
+             [--, --, --, --],
+             [20, 21, 22, 23]]],
+      mask=[[[False,  True, False, False],
+             [ True,  True,  True,  True],
+             [False, False, False, False]],
+            [[False,  True, False, False],
+             [ True,  True,  True,  True],
+             [False, False, False, False]]],
+      fill_value=999999)
+    >>> np.ma.apply_over_axes(np.ma.sum, a, [0,2])
+    masked_array(
+      data=[[[46],
+             [--],
+             [124]]],
+      mask=[[[False],
+             [ True],
+             [False]]],
+      fill_value=999999)
+
+    Tuple axis arguments to ufuncs are equivalent:
+
+    >>> np.ma.sum(a, axis=(0,2)).reshape((1,-1,1))
+    masked_array(
+      data=[[[46],
+             [--],
+             [124]]],
+      mask=[[[False],
+             [ True],
+             [False]]],
+      fill_value=999999)
+    """
+
+
+def average(a, axis=None, weights=None, returned=False, *,
+            keepdims=np._NoValue):
+    """
+    Return the weighted average of array over the given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        Data to be averaged.
+        Masked entries are not taken into account in the computation.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to average `a`.  The default,
+        `axis=None`, will average over all of the elements of the input array.
+        If axis is a tuple of ints, averaging is performed on all of the axes
+        specified in the tuple instead of a single axis or all the axes as
+        before.
+    weights : array_like, optional
+        An array of weights associated with the values in `a`. Each value in
+        `a` contributes to the average according to its associated weight.
+        The array of weights must be the same shape as `a` if no axis is
+        specified, otherwise the weights must have dimensions and shape
+        consistent with `a` along the specified axis.
+        If `weights=None`, then all data in `a` are assumed to have a
+        weight equal to one.
+        The calculation is::
+
+            avg = sum(a * weights) / sum(weights)
+
+        where the sum is over all included elements.
+        The only constraint on the values of `weights` is that `sum(weights)`
+        must not be 0.
+    returned : bool, optional
+        Flag indicating whether a tuple ``(result, sum of weights)``
+        should be returned as output (True), or just the result (False).
+        Default is False.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `a`.
+        *Note:* `keepdims` will not work with instances of `numpy.matrix`
+        or other classes whose methods do not support `keepdims`.
+
+        .. versionadded:: 1.23.0
+
+    Returns
+    -------
+    average, [sum_of_weights] : (tuple of) scalar or MaskedArray
+        The average along the specified axis. When returned is `True`,
+        return a tuple with the average as the first element and the sum
+        of the weights as the second element. The return type is `np.float64`
+        if `a` is of integer type and floats smaller than `float64`, or the
+        input data-type, otherwise. If returned, `sum_of_weights` is always
+        `float64`.
+
+    Raises
+    ------
+    ZeroDivisionError
+        When all weights along axis are zero. See `numpy.ma.average` for a
+        version robust to this type of error.
+    TypeError
+        When `weights` does not have the same shape as `a`, and `axis=None`.
+    ValueError
+        When `weights` does not have dimensions and shape consistent with `a`
+        along specified `axis`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True])
+    >>> np.ma.average(a, weights=[3, 1, 0, 0])
+    1.25
+
+    >>> x = np.ma.arange(6.).reshape(3, 2)
+    >>> x
+    masked_array(
+      data=[[0., 1.],
+            [2., 3.],
+            [4., 5.]],
+      mask=False,
+      fill_value=1e+20)
+    >>> data = np.arange(8).reshape((2, 2, 2))
+    >>> data
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.ma.average(data, axis=(0, 1), weights=[[1./4, 3./4], [1., 1./2]])
+    masked_array(data=[3.4, 4.4],
+             mask=[False, False],
+       fill_value=1e+20)
+    >>> np.ma.average(data, axis=0, weights=[[1./4, 3./4], [1., 1./2]])
+    Traceback (most recent call last):
+        ...
+    ValueError: Shape of weights must be consistent
+    with shape of a along specified axis.
+
+    >>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],
+    ...                                 returned=True)
+    >>> avg
+    masked_array(data=[2.6666666666666665, 3.6666666666666665],
+                 mask=[False, False],
+           fill_value=1e+20)
+
+    With ``keepdims=True``, the following result has shape (3, 1).
+
+    >>> np.ma.average(x, axis=1, keepdims=True)
+    masked_array(
+      data=[[0.5],
+            [2.5],
+            [4.5]],
+      mask=False,
+      fill_value=1e+20)
+    """
+    a = asarray(a)
+    m = getmask(a)
+
+    if axis is not None:
+        axis = normalize_axis_tuple(axis, a.ndim, argname="axis")
+
+    if keepdims is np._NoValue:
+        # Don't pass on the keepdims argument if one wasn't given.
+        keepdims_kw = {}
+    else:
+        keepdims_kw = {'keepdims': keepdims}
+
+    if weights is None:
+        avg = a.mean(axis, **keepdims_kw)
+        scl = avg.dtype.type(a.count(axis))
+    else:
+        wgt = asarray(weights)
+
+        if issubclass(a.dtype.type, (np.integer, np.bool)):
+            result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8')
+        else:
+            result_dtype = np.result_type(a.dtype, wgt.dtype)
+
+        # Sanity checks
+        if a.shape != wgt.shape:
+            if axis is None:
+                raise TypeError(
+                    "Axis must be specified when shapes of a and weights "
+                    "differ.")
+            if wgt.shape != tuple(a.shape[ax] for ax in axis):
+                raise ValueError(
+                    "Shape of weights must be consistent with "
+                    "shape of a along specified axis.")
+
+            # setup wgt to broadcast along axis
+            wgt = wgt.transpose(np.argsort(axis))
+            wgt = wgt.reshape(tuple((s if ax in axis else 1)
+                                    for ax, s in enumerate(a.shape)))
+
+        if m is not nomask:
+            wgt = wgt*(~a.mask)
+            wgt.mask |= a.mask
+
+        scl = wgt.sum(axis=axis, dtype=result_dtype, **keepdims_kw)
+        avg = np.multiply(a, wgt,
+                          dtype=result_dtype).sum(axis, **keepdims_kw) / scl
+
+    if returned:
+        if scl.shape != avg.shape:
+            scl = np.broadcast_to(scl, avg.shape).copy()
+        return avg, scl
+    else:
+        return avg
+
+
+def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
+    """
+    Compute the median along the specified axis.
+
+    Returns the median of the array elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : int, optional
+        Axis along which the medians are computed. The default (None) is
+        to compute the median along a flattened version of the array.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must
+        have the same shape and buffer length as the expected output
+        but the type will be cast if necessary.
+    overwrite_input : bool, optional
+        If True, then allow use of memory of input array (a) for
+        calculations. The input array will be modified by the call to
+        median. This will save memory when you do not need to preserve
+        the contents of the input array. Treat the input as undefined,
+        but it will probably be fully or partially sorted. Default is
+        False. Note that, if `overwrite_input` is True, and the input
+        is not already an `ndarray`, an error will be raised.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+
+    Returns
+    -------
+    median : ndarray
+        A new array holding the result is returned unless out is
+        specified, in which case a reference to out is returned.
+        Return data-type is `float64` for integers and floats smaller than
+        `float64`, or the input data-type, otherwise.
+
+    See Also
+    --------
+    mean
+
+    Notes
+    -----
+    Given a vector ``V`` with ``N`` non masked values, the median of ``V``
+    is the middle value of a sorted copy of ``V`` (``Vs``) - i.e.
+    ``Vs[(N-1)/2]``, when ``N`` is odd, or ``{Vs[N/2 - 1] + Vs[N/2]}/2``
+    when ``N`` is even.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array(np.arange(8), mask=[0]*4 + [1]*4)
+    >>> np.ma.median(x)
+    1.5
+
+    >>> x = np.ma.array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
+    >>> np.ma.median(x)
+    2.5
+    >>> np.ma.median(x, axis=-1, overwrite_input=True)
+    masked_array(data=[2.0, 5.0],
+                 mask=[False, False],
+           fill_value=1e+20)
+
+    """
+    if not hasattr(a, 'mask'):
+        m = np.median(getdata(a, subok=True), axis=axis,
+                      out=out, overwrite_input=overwrite_input,
+                      keepdims=keepdims)
+        if isinstance(m, np.ndarray) and 1 <= m.ndim:
+            return masked_array(m, copy=False)
+        else:
+            return m
+
+    return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out,
+                    overwrite_input=overwrite_input)
+
+
+def _median(a, axis=None, out=None, overwrite_input=False):
+    # when an unmasked NaN is present return it, so we need to sort the NaN
+    # values behind the mask
+    if np.issubdtype(a.dtype, np.inexact):
+        fill_value = np.inf
+    else:
+        fill_value = None
+    if overwrite_input:
+        if axis is None:
+            asorted = a.ravel()
+            asorted.sort(fill_value=fill_value)
+        else:
+            a.sort(axis=axis, fill_value=fill_value)
+            asorted = a
+    else:
+        asorted = sort(a, axis=axis, fill_value=fill_value)
+
+    if axis is None:
+        axis = 0
+    else:
+        axis = normalize_axis_index(axis, asorted.ndim)
+
+    if asorted.shape[axis] == 0:
+        # for empty axis integer indices fail so use slicing to get same result
+        # as median (which is mean of empty slice = nan)
+        indexer = [slice(None)] * asorted.ndim
+        indexer[axis] = slice(0, 0)
+        indexer = tuple(indexer)
+        return np.ma.mean(asorted[indexer], axis=axis, out=out)
+
+    if asorted.ndim == 1:
+        idx, odd = divmod(count(asorted), 2)
+        mid = asorted[idx + odd - 1:idx + 1]
+        if np.issubdtype(asorted.dtype, np.inexact) and asorted.size > 0:
+            # avoid inf / x = masked
+            s = mid.sum(out=out)
+            if not odd:
+                s = np.true_divide(s, 2., casting='safe', out=out)
+            s = np.lib._utils_impl._median_nancheck(asorted, s, axis)
+        else:
+            s = mid.mean(out=out)
+
+        # if result is masked either the input contained enough
+        # minimum_fill_value so that it would be the median or all values
+        # masked
+        if np.ma.is_masked(s) and not np.all(asorted.mask):
+            return np.ma.minimum_fill_value(asorted)
+        return s
+
+    counts = count(asorted, axis=axis, keepdims=True)
+    h = counts // 2
+
+    # duplicate high if odd number of elements so mean does nothing
+    odd = counts % 2 == 1
+    l = np.where(odd, h, h-1)
+
+    lh = np.concatenate([l,h], axis=axis)
+
+    # get low and high median
+    low_high = np.take_along_axis(asorted, lh, axis=axis)
+
+    def replace_masked(s):
+        # Replace masked entries with minimum_full_value unless it all values
+        # are masked. This is required as the sort order of values equal or
+        # larger than the fill value is undefined and a valid value placed
+        # elsewhere, e.g. [4, --, inf].
+        if np.ma.is_masked(s):
+            rep = (~np.all(asorted.mask, axis=axis, keepdims=True)) & s.mask
+            s.data[rep] = np.ma.minimum_fill_value(asorted)
+            s.mask[rep] = False
+
+    replace_masked(low_high)
+
+    if np.issubdtype(asorted.dtype, np.inexact):
+        # avoid inf / x = masked
+        s = np.ma.sum(low_high, axis=axis, out=out)
+        np.true_divide(s.data, 2., casting='unsafe', out=s.data)
+
+        s = np.lib._utils_impl._median_nancheck(asorted, s, axis)
+    else:
+        s = np.ma.mean(low_high, axis=axis, out=out)
+
+    return s
+
+
+def compress_nd(x, axis=None):
+    """Suppress slices from multiple dimensions which contain masked values.
+
+    Parameters
+    ----------
+    x : array_like, MaskedArray
+        The array to operate on. If not a MaskedArray instance (or if no array
+        elements are masked), `x` is interpreted as a MaskedArray with `mask`
+        set to `nomask`.
+    axis : tuple of ints or int, optional
+        Which dimensions to suppress slices from can be configured with this
+        parameter.
+        - If axis is a tuple of ints, those are the axes to suppress slices from.
+        - If axis is an int, then that is the only axis to suppress slices from.
+        - If axis is None, all axis are selected.
+
+    Returns
+    -------
+    compress_array : ndarray
+        The compressed array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = [[1, 2], [3, 4]]
+    >>> mask = [[0, 1], [0, 0]]
+    >>> x = np.ma.array(arr, mask=mask)
+    >>> np.ma.compress_nd(x, axis=0)
+    array([[3, 4]])
+    >>> np.ma.compress_nd(x, axis=1)
+    array([[1],
+           [3]])
+    >>> np.ma.compress_nd(x)
+    array([[3]])
+
+    """
+    x = asarray(x)
+    m = getmask(x)
+    # Set axis to tuple of ints
+    if axis is None:
+        axis = tuple(range(x.ndim))
+    else:
+        axis = normalize_axis_tuple(axis, x.ndim)
+
+    # Nothing is masked: return x
+    if m is nomask or not m.any():
+        return x._data
+    # All is masked: return empty
+    if m.all():
+        return nxarray([])
+    # Filter elements through boolean indexing
+    data = x._data
+    for ax in axis:
+        axes = tuple(list(range(ax)) + list(range(ax + 1, x.ndim)))
+        data = data[(slice(None),)*ax + (~m.any(axis=axes),)]
+    return data
+
+
+def compress_rowcols(x, axis=None):
+    """
+    Suppress the rows and/or columns of a 2-D array that contain
+    masked values.
+
+    The suppression behavior is selected with the `axis` parameter.
+
+    - If axis is None, both rows and columns are suppressed.
+    - If axis is 0, only rows are suppressed.
+    - If axis is 1 or -1, only columns are suppressed.
+
+    Parameters
+    ----------
+    x : array_like, MaskedArray
+        The array to operate on.  If not a MaskedArray instance (or if no array
+        elements are masked), `x` is interpreted as a MaskedArray with
+        `mask` set to `nomask`. Must be a 2D array.
+    axis : int, optional
+        Axis along which to perform the operation. Default is None.
+
+    Returns
+    -------
+    compressed_array : ndarray
+        The compressed array.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
+    ...                                                   [1, 0, 0],
+    ...                                                   [0, 0, 0]])
+    >>> x
+    masked_array(
+      data=[[--, 1, 2],
+            [--, 4, 5],
+            [6, 7, 8]],
+      mask=[[ True, False, False],
+            [ True, False, False],
+            [False, False, False]],
+      fill_value=999999)
+
+    >>> np.ma.compress_rowcols(x)
+    array([[7, 8]])
+    >>> np.ma.compress_rowcols(x, 0)
+    array([[6, 7, 8]])
+    >>> np.ma.compress_rowcols(x, 1)
+    array([[1, 2],
+           [4, 5],
+           [7, 8]])
+
+    """
+    if asarray(x).ndim != 2:
+        raise NotImplementedError("compress_rowcols works for 2D arrays only.")
+    return compress_nd(x, axis=axis)
+
+
+def compress_rows(a):
+    """
+    Suppress whole rows of a 2-D array that contain masked values.
+
+    This is equivalent to ``np.ma.compress_rowcols(a, 0)``, see
+    `compress_rowcols` for details.
+
+    Parameters
+    ----------
+    x : array_like, MaskedArray
+        The array to operate on. If not a MaskedArray instance (or if no array
+        elements are masked), `x` is interpreted as a MaskedArray with
+        `mask` set to `nomask`. Must be a 2D array.
+
+    Returns
+    -------
+    compressed_array : ndarray
+        The compressed array.
+
+    See Also
+    --------
+    compress_rowcols
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
+    ...                                                   [1, 0, 0],
+    ...                                                   [0, 0, 0]])
+    >>> np.ma.compress_rows(a)
+    array([[6, 7, 8]])
+
+    """
+    a = asarray(a)
+    if a.ndim != 2:
+        raise NotImplementedError("compress_rows works for 2D arrays only.")
+    return compress_rowcols(a, 0)
+
+
+def compress_cols(a):
+    """
+    Suppress whole columns of a 2-D array that contain masked values.
+
+    This is equivalent to ``np.ma.compress_rowcols(a, 1)``, see
+    `compress_rowcols` for details.
+
+    Parameters
+    ----------
+    x : array_like, MaskedArray
+        The array to operate on.  If not a MaskedArray instance (or if no array
+        elements are masked), `x` is interpreted as a MaskedArray with
+        `mask` set to `nomask`. Must be a 2D array.
+
+    Returns
+    -------
+    compressed_array : ndarray
+        The compressed array.
+
+    See Also
+    --------
+    compress_rowcols
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],
+    ...                                                   [1, 0, 0],
+    ...                                                   [0, 0, 0]])
+    >>> np.ma.compress_cols(a)
+    array([[1, 2],
+           [4, 5],
+           [7, 8]])
+
+    """
+    a = asarray(a)
+    if a.ndim != 2:
+        raise NotImplementedError("compress_cols works for 2D arrays only.")
+    return compress_rowcols(a, 1)
+
+
+def mask_rowcols(a, axis=None):
+    """
+    Mask rows and/or columns of a 2D array that contain masked values.
+
+    Mask whole rows and/or columns of a 2D array that contain
+    masked values.  The masking behavior is selected using the
+    `axis` parameter.
+
+      - If `axis` is None, rows *and* columns are masked.
+      - If `axis` is 0, only rows are masked.
+      - If `axis` is 1 or -1, only columns are masked.
+
+    Parameters
+    ----------
+    a : array_like, MaskedArray
+        The array to mask.  If not a MaskedArray instance (or if no array
+        elements are masked), the result is a MaskedArray with `mask` set
+        to `nomask` (False). Must be a 2D array.
+    axis : int, optional
+        Axis along which to perform the operation. If None, applies to a
+        flattened version of the array.
+
+    Returns
+    -------
+    a : MaskedArray
+        A modified version of the input array, masked depending on the value
+        of the `axis` parameter.
+
+    Raises
+    ------
+    NotImplementedError
+        If input array `a` is not 2D.
+
+    See Also
+    --------
+    mask_rows : Mask rows of a 2D array that contain masked values.
+    mask_cols : Mask cols of a 2D array that contain masked values.
+    masked_where : Mask where a condition is met.
+
+    Notes
+    -----
+    The input array's mask is modified by this function.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.zeros((3, 3), dtype=int)
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0, 0, 0],
+           [0, 1, 0],
+           [0, 0, 0]])
+    >>> a = np.ma.masked_equal(a, 1)
+    >>> a
+    masked_array(
+      data=[[0, 0, 0],
+            [0, --, 0],
+            [0, 0, 0]],
+      mask=[[False, False, False],
+            [False,  True, False],
+            [False, False, False]],
+      fill_value=1)
+    >>> np.ma.mask_rowcols(a)
+    masked_array(
+      data=[[0, --, 0],
+            [--, --, --],
+            [0, --, 0]],
+      mask=[[False,  True, False],
+            [ True,  True,  True],
+            [False,  True, False]],
+      fill_value=1)
+
+    """
+    a = array(a, subok=False)
+    if a.ndim != 2:
+        raise NotImplementedError("mask_rowcols works for 2D arrays only.")
+    m = getmask(a)
+    # Nothing is masked: return a
+    if m is nomask or not m.any():
+        return a
+    maskedval = m.nonzero()
+    a._mask = a._mask.copy()
+    if not axis:
+        a[np.unique(maskedval[0])] = masked
+    if axis in [None, 1, -1]:
+        a[:, np.unique(maskedval[1])] = masked
+    return a
+
+
+def mask_rows(a, axis=np._NoValue):
+    """
+    Mask rows of a 2D array that contain masked values.
+
+    This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0.
+
+    See Also
+    --------
+    mask_rowcols : Mask rows and/or columns of a 2D array.
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.zeros((3, 3), dtype=int)
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0, 0, 0],
+           [0, 1, 0],
+           [0, 0, 0]])
+    >>> a = np.ma.masked_equal(a, 1)
+    >>> a
+    masked_array(
+      data=[[0, 0, 0],
+            [0, --, 0],
+            [0, 0, 0]],
+      mask=[[False, False, False],
+            [False,  True, False],
+            [False, False, False]],
+      fill_value=1)
+
+    >>> np.ma.mask_rows(a)
+    masked_array(
+      data=[[0, 0, 0],
+            [--, --, --],
+            [0, 0, 0]],
+      mask=[[False, False, False],
+            [ True,  True,  True],
+            [False, False, False]],
+      fill_value=1)
+
+    """
+    if axis is not np._NoValue:
+        # remove the axis argument when this deprecation expires
+        # NumPy 1.18.0, 2019-11-28
+        warnings.warn(
+            "The axis argument has always been ignored, in future passing it "
+            "will raise TypeError", DeprecationWarning, stacklevel=2)
+    return mask_rowcols(a, 0)
+
+
+def mask_cols(a, axis=np._NoValue):
+    """
+    Mask columns of a 2D array that contain masked values.
+
+    This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.
+
+    See Also
+    --------
+    mask_rowcols : Mask rows and/or columns of a 2D array.
+    masked_where : Mask where a condition is met.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.zeros((3, 3), dtype=int)
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0, 0, 0],
+           [0, 1, 0],
+           [0, 0, 0]])
+    >>> a = np.ma.masked_equal(a, 1)
+    >>> a
+    masked_array(
+      data=[[0, 0, 0],
+            [0, --, 0],
+            [0, 0, 0]],
+      mask=[[False, False, False],
+            [False,  True, False],
+            [False, False, False]],
+      fill_value=1)
+    >>> np.ma.mask_cols(a)
+    masked_array(
+      data=[[0, --, 0],
+            [0, --, 0],
+            [0, --, 0]],
+      mask=[[False,  True, False],
+            [False,  True, False],
+            [False,  True, False]],
+      fill_value=1)
+
+    """
+    if axis is not np._NoValue:
+        # remove the axis argument when this deprecation expires
+        # NumPy 1.18.0, 2019-11-28
+        warnings.warn(
+            "The axis argument has always been ignored, in future passing it "
+            "will raise TypeError", DeprecationWarning, stacklevel=2)
+    return mask_rowcols(a, 1)
+
+
+#####--------------------------------------------------------------------------
+#---- --- arraysetops ---
+#####--------------------------------------------------------------------------
+
+def ediff1d(arr, to_end=None, to_begin=None):
+    """
+    Compute the differences between consecutive elements of an array.
+
+    This function is the equivalent of `numpy.ediff1d` that takes masked
+    values into account, see `numpy.ediff1d` for details.
+
+    See Also
+    --------
+    numpy.ediff1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> arr = np.ma.array([1, 2, 4, 7, 0])
+    >>> np.ma.ediff1d(arr)
+    masked_array(data=[ 1,  2,  3, -7],
+                 mask=False,
+           fill_value=999999)
+
+    """
+    arr = ma.asanyarray(arr).flat
+    ed = arr[1:] - arr[:-1]
+    arrays = [ed]
+    #
+    if to_begin is not None:
+        arrays.insert(0, to_begin)
+    if to_end is not None:
+        arrays.append(to_end)
+    #
+    if len(arrays) != 1:
+        # We'll save ourselves a copy of a potentially large array in the common
+        # case where neither to_begin or to_end was given.
+        ed = hstack(arrays)
+    #
+    return ed
+
+
+def unique(ar1, return_index=False, return_inverse=False):
+    """
+    Finds the unique elements of an array.
+
+    Masked values are considered the same element (masked). The output array
+    is always a masked array. See `numpy.unique` for more details.
+
+    See Also
+    --------
+    numpy.unique : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = [1, 2, 1000, 2, 3]
+    >>> mask = [0, 0, 1, 0, 0]
+    >>> masked_a = np.ma.masked_array(a, mask)
+    >>> masked_a
+    masked_array(data=[1, 2, --, 2, 3],
+                mask=[False, False,  True, False, False],
+        fill_value=999999)
+    >>> np.ma.unique(masked_a)
+    masked_array(data=[1, 2, 3, --],
+                mask=[False, False, False,  True],
+        fill_value=999999)
+    >>> np.ma.unique(masked_a, return_index=True)
+    (masked_array(data=[1, 2, 3, --],
+                mask=[False, False, False,  True],
+        fill_value=999999), array([0, 1, 4, 2]))
+    >>> np.ma.unique(masked_a, return_inverse=True)
+    (masked_array(data=[1, 2, 3, --],
+                mask=[False, False, False,  True],
+        fill_value=999999), array([0, 1, 3, 1, 2]))
+    >>> np.ma.unique(masked_a, return_index=True, return_inverse=True)
+    (masked_array(data=[1, 2, 3, --],
+                mask=[False, False, False,  True],
+        fill_value=999999), array([0, 1, 4, 2]), array([0, 1, 3, 1, 2]))
+    """
+    output = np.unique(ar1,
+                       return_index=return_index,
+                       return_inverse=return_inverse)
+    if isinstance(output, tuple):
+        output = list(output)
+        output[0] = output[0].view(MaskedArray)
+        output = tuple(output)
+    else:
+        output = output.view(MaskedArray)
+    return output
+
+
+def intersect1d(ar1, ar2, assume_unique=False):
+    """
+    Returns the unique elements common to both arrays.
+
+    Masked values are considered equal one to the other.
+    The output is always a masked array.
+
+    See `numpy.intersect1d` for more details.
+
+    See Also
+    --------
+    numpy.intersect1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([1, 3, 3, 3], mask=[0, 0, 0, 1])
+    >>> y = np.ma.array([3, 1, 1, 1], mask=[0, 0, 0, 1])
+    >>> np.ma.intersect1d(x, y)
+    masked_array(data=[1, 3, --],
+                 mask=[False, False,  True],
+           fill_value=999999)
+
+    """
+    if assume_unique:
+        aux = ma.concatenate((ar1, ar2))
+    else:
+        # Might be faster than unique( intersect1d( ar1, ar2 ) )?
+        aux = ma.concatenate((unique(ar1), unique(ar2)))
+    aux.sort()
+    return aux[:-1][aux[1:] == aux[:-1]]
+
+
+def setxor1d(ar1, ar2, assume_unique=False):
+    """
+    Set exclusive-or of 1-D arrays with unique elements.
+
+    The output is always a masked array. See `numpy.setxor1d` for more details.
+
+    See Also
+    --------
+    numpy.setxor1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> ar1 = np.ma.array([1, 2, 3, 2, 4])
+    >>> ar2 = np.ma.array([2, 3, 5, 7, 5])
+    >>> np.ma.setxor1d(ar1, ar2)
+    masked_array(data=[1, 4, 5, 7],
+                 mask=False,
+           fill_value=999999)
+
+    """
+    if not assume_unique:
+        ar1 = unique(ar1)
+        ar2 = unique(ar2)
+
+    aux = ma.concatenate((ar1, ar2), axis=None)
+    if aux.size == 0:
+        return aux
+    aux.sort()
+    auxf = aux.filled()
+#    flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
+    flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True]))
+#    flag2 = ediff1d( flag ) == 0
+    flag2 = (flag[1:] == flag[:-1])
+    return aux[flag2]
+
+
+def in1d(ar1, ar2, assume_unique=False, invert=False):
+    """
+    Test whether each element of an array is also present in a second
+    array.
+
+    The output is always a masked array. See `numpy.in1d` for more details.
+
+    We recommend using :func:`isin` instead of `in1d` for new code.
+
+    See Also
+    --------
+    isin       : Version of this function that preserves the shape of ar1.
+    numpy.in1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> ar1 = np.ma.array([0, 1, 2, 5, 0])
+    >>> ar2 = [0, 2]
+    >>> np.ma.in1d(ar1, ar2)
+    masked_array(data=[ True, False,  True, False,  True],
+                 mask=False,
+           fill_value=True)
+
+    """
+    if not assume_unique:
+        ar1, rev_idx = unique(ar1, return_inverse=True)
+        ar2 = unique(ar2)
+
+    ar = ma.concatenate((ar1, ar2))
+    # We need this to be a stable sort, so always use 'mergesort'
+    # here. The values from the first array should always come before
+    # the values from the second array.
+    order = ar.argsort(kind='mergesort')
+    sar = ar[order]
+    if invert:
+        bool_ar = (sar[1:] != sar[:-1])
+    else:
+        bool_ar = (sar[1:] == sar[:-1])
+    flag = ma.concatenate((bool_ar, [invert]))
+    indx = order.argsort(kind='mergesort')[:len(ar1)]
+
+    if assume_unique:
+        return flag[indx]
+    else:
+        return flag[indx][rev_idx]
+
+
+def isin(element, test_elements, assume_unique=False, invert=False):
+    """
+    Calculates `element in test_elements`, broadcasting over
+    `element` only.
+
+    The output is always a masked array of the same shape as `element`.
+    See `numpy.isin` for more details.
+
+    See Also
+    --------
+    in1d       : Flattened version of this function.
+    numpy.isin : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> element = np.ma.array([1, 2, 3, 4, 5, 6])
+    >>> test_elements = [0, 2]
+    >>> np.ma.isin(element, test_elements)
+    masked_array(data=[False,  True, False, False, False, False],
+                 mask=False,
+           fill_value=True)
+
+    """
+    element = ma.asarray(element)
+    return in1d(element, test_elements, assume_unique=assume_unique,
+                invert=invert).reshape(element.shape)
+
+
+def union1d(ar1, ar2):
+    """
+    Union of two arrays.
+
+    The output is always a masked array. See `numpy.union1d` for more details.
+
+    See Also
+    --------
+    numpy.union1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> ar1 = np.ma.array([1, 2, 3, 4])
+    >>> ar2 = np.ma.array([3, 4, 5, 6])
+    >>> np.ma.union1d(ar1, ar2)
+    masked_array(data=[1, 2, 3, 4, 5, 6],
+             mask=False,
+       fill_value=999999)
+
+    """
+    return unique(ma.concatenate((ar1, ar2), axis=None))
+
+
+def setdiff1d(ar1, ar2, assume_unique=False):
+    """
+    Set difference of 1D arrays with unique elements.
+
+    The output is always a masked array. See `numpy.setdiff1d` for more
+    details.
+
+    See Also
+    --------
+    numpy.setdiff1d : Equivalent function for ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])
+    >>> np.ma.setdiff1d(x, [1, 2])
+    masked_array(data=[3, --],
+                 mask=[False,  True],
+           fill_value=999999)
+
+    """
+    if assume_unique:
+        ar1 = ma.asarray(ar1).ravel()
+    else:
+        ar1 = unique(ar1)
+        ar2 = unique(ar2)
+    return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]
+
+
+###############################################################################
+#                                Covariance                                   #
+###############################################################################
+
+
+def _covhelper(x, y=None, rowvar=True, allow_masked=True):
+    """
+    Private function for the computation of covariance and correlation
+    coefficients.
+
+    """
+    x = ma.array(x, ndmin=2, copy=True, dtype=float)
+    xmask = ma.getmaskarray(x)
+    # Quick exit if we can't process masked data
+    if not allow_masked and xmask.any():
+        raise ValueError("Cannot process masked data.")
+    #
+    if x.shape[0] == 1:
+        rowvar = True
+    # Make sure that rowvar is either 0 or 1
+    rowvar = int(bool(rowvar))
+    axis = 1 - rowvar
+    if rowvar:
+        tup = (slice(None), None)
+    else:
+        tup = (None, slice(None))
+    #
+    if y is None:
+        # Check if we can guarantee that the integers in the (N - ddof)
+        # normalisation can be accurately represented with single-precision
+        # before computing the dot product.
+        if x.shape[0] > 2 ** 24 or x.shape[1] > 2 ** 24:
+            xnm_dtype = np.float64
+        else:
+            xnm_dtype = np.float32
+        xnotmask = np.logical_not(xmask).astype(xnm_dtype)
+    else:
+        y = array(y, copy=False, ndmin=2, dtype=float)
+        ymask = ma.getmaskarray(y)
+        if not allow_masked and ymask.any():
+            raise ValueError("Cannot process masked data.")
+        if xmask.any() or ymask.any():
+            if y.shape == x.shape:
+                # Define some common mask
+                common_mask = np.logical_or(xmask, ymask)
+                if common_mask is not nomask:
+                    xmask = x._mask = y._mask = ymask = common_mask
+                    x._sharedmask = False
+                    y._sharedmask = False
+        x = ma.concatenate((x, y), axis)
+        # Check if we can guarantee that the integers in the (N - ddof)
+        # normalisation can be accurately represented with single-precision
+        # before computing the dot product.
+        if x.shape[0] > 2 ** 24 or x.shape[1] > 2 ** 24:
+            xnm_dtype = np.float64
+        else:
+            xnm_dtype = np.float32
+        xnotmask = np.logical_not(np.concatenate((xmask, ymask), axis)).astype(
+            xnm_dtype
+        )
+    x -= x.mean(axis=rowvar)[tup]
+    return (x, xnotmask, rowvar)
+
+
+def cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None):
+    """
+    Estimate the covariance matrix.
+
+    Except for the handling of missing data this function does the same as
+    `numpy.cov`. For more details and examples, see `numpy.cov`.
+
+    By default, masked values are recognized as such. If `x` and `y` have the
+    same shape, a common mask is allocated: if ``x[i,j]`` is masked, then
+    ``y[i,j]`` will also be masked.
+    Setting `allow_masked` to False will raise an exception if values are
+    missing in either of the input arrays.
+
+    Parameters
+    ----------
+    x : array_like
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `x` represents a variable, and each column a single
+        observation of all those variables. Also see `rowvar` below.
+    y : array_like, optional
+        An additional set of variables and observations. `y` has the same
+        shape as `x`.
+    rowvar : bool, optional
+        If `rowvar` is True (default), then each row represents a
+        variable, with observations in the columns. Otherwise, the relationship
+        is transposed: each column represents a variable, while the rows
+        contain observations.
+    bias : bool, optional
+        Default normalization (False) is by ``(N-1)``, where ``N`` is the
+        number of observations given (unbiased estimate). If `bias` is True,
+        then normalization is by ``N``. This keyword can be overridden by
+        the keyword ``ddof`` in numpy versions >= 1.5.
+    allow_masked : bool, optional
+        If True, masked values are propagated pair-wise: if a value is masked
+        in `x`, the corresponding value is masked in `y`.
+        If False, raises a `ValueError` exception when some values are missing.
+    ddof : {None, int}, optional
+        If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
+        the number of observations; this overrides the value implied by
+        ``bias``. The default value is ``None``.
+
+    Raises
+    ------
+    ValueError
+        Raised if some values are missing and `allow_masked` is False.
+
+    See Also
+    --------
+    numpy.cov
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
+    >>> y = np.ma.array([[1, 0], [0, 1]], mask=[0, 0, 1, 1])
+    >>> np.ma.cov(x, y)
+    masked_array(
+    data=[[--, --, --, --],
+          [--, --, --, --],
+          [--, --, --, --],
+          [--, --, --, --]],
+    mask=[[ True,  True,  True,  True],
+          [ True,  True,  True,  True],
+          [ True,  True,  True,  True],
+          [ True,  True,  True,  True]],
+    fill_value=1e+20,
+    dtype=float64)
+
+    """
+    # Check inputs
+    if ddof is not None and ddof != int(ddof):
+        raise ValueError("ddof must be an integer")
+    # Set up ddof
+    if ddof is None:
+        if bias:
+            ddof = 0
+        else:
+            ddof = 1
+
+    (x, xnotmask, rowvar) = _covhelper(x, y, rowvar, allow_masked)
+    if not rowvar:
+        fact = np.dot(xnotmask.T, xnotmask) - ddof
+        mask = np.less_equal(fact, 0, dtype=bool)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            data = np.dot(filled(x.T, 0), filled(x.conj(), 0)) / fact
+        result = ma.array(data, mask=mask).squeeze()
+    else:
+        fact = np.dot(xnotmask, xnotmask.T) - ddof
+        mask = np.less_equal(fact, 0, dtype=bool)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            data = np.dot(filled(x, 0), filled(x.T.conj(), 0)) / fact
+        result = ma.array(data, mask=mask).squeeze()
+    return result
+
+
+def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True,
+             ddof=np._NoValue):
+    """
+    Return Pearson product-moment correlation coefficients.
+
+    Except for the handling of missing data this function does the same as
+    `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`.
+
+    Parameters
+    ----------
+    x : array_like
+        A 1-D or 2-D array containing multiple variables and observations.
+        Each row of `x` represents a variable, and each column a single
+        observation of all those variables. Also see `rowvar` below.
+    y : array_like, optional
+        An additional set of variables and observations. `y` has the same
+        shape as `x`.
+    rowvar : bool, optional
+        If `rowvar` is True (default), then each row represents a
+        variable, with observations in the columns. Otherwise, the relationship
+        is transposed: each column represents a variable, while the rows
+        contain observations.
+    bias : _NoValue, optional
+        Has no effect, do not use.
+
+        .. deprecated:: 1.10.0
+    allow_masked : bool, optional
+        If True, masked values are propagated pair-wise: if a value is masked
+        in `x`, the corresponding value is masked in `y`.
+        If False, raises an exception.  Because `bias` is deprecated, this
+        argument needs to be treated as keyword only to avoid a warning.
+    ddof : _NoValue, optional
+        Has no effect, do not use.
+
+        .. deprecated:: 1.10.0
+
+    See Also
+    --------
+    numpy.corrcoef : Equivalent function in top-level NumPy module.
+    cov : Estimate the covariance matrix.
+
+    Notes
+    -----
+    This function accepts but discards arguments `bias` and `ddof`.  This is
+    for backwards compatibility with previous versions of this function.  These
+    arguments had no effect on the return values of the function and can be
+    safely ignored in this and previous versions of numpy.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
+    >>> np.ma.corrcoef(x)
+    masked_array(
+      data=[[--, --],
+            [--, --]],
+      mask=[[ True,  True],
+            [ True,  True]],
+      fill_value=1e+20,
+      dtype=float64)
+
+    """
+    msg = 'bias and ddof have no effect and are deprecated'
+    if bias is not np._NoValue or ddof is not np._NoValue:
+        # 2015-03-15, 1.10
+        warnings.warn(msg, DeprecationWarning, stacklevel=2)
+    # Estimate the covariance matrix.
+    corr = cov(x, y, rowvar, allow_masked=allow_masked)
+    # The non-masked version returns a masked value for a scalar.
+    try:
+        std = ma.sqrt(ma.diagonal(corr))
+    except ValueError:
+        return ma.MaskedConstant()
+    corr /= ma.multiply.outer(std, std)
+    return corr
+
+#####--------------------------------------------------------------------------
+#---- --- Concatenation helpers ---
+#####--------------------------------------------------------------------------
+
+class MAxisConcatenator(AxisConcatenator):
+    """
+    Translate slice objects to concatenation along an axis.
+
+    For documentation on usage, see `mr_class`.
+
+    See Also
+    --------
+    mr_class
+
+    """
+    __slots__ = ()
+
+    concatenate = staticmethod(concatenate)
+
+    @classmethod
+    def makemat(cls, arr):
+        # There used to be a view as np.matrix here, but we may eventually
+        # deprecate that class. In preparation, we use the unmasked version
+        # to construct the matrix (with copy=False for backwards compatibility
+        # with the .view)
+        data = super().makemat(arr.data, copy=False)
+        return array(data, mask=arr.mask)
+
+    def __getitem__(self, key):
+        # matrix builder syntax, like 'a, b; c, d'
+        if isinstance(key, str):
+            raise MAError("Unavailable for masked array.")
+
+        return super().__getitem__(key)
+
+
+class mr_class(MAxisConcatenator):
+    """
+    Translate slice objects to concatenation along the first axis.
+
+    This is the masked array version of `r_`.
+
+    See Also
+    --------
+    r_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])]
+    masked_array(data=[1, 2, 3, ..., 4, 5, 6],
+                 mask=False,
+           fill_value=999999)
+
+    """
+    __slots__ = ()
+
+    def __init__(self):
+        MAxisConcatenator.__init__(self, 0)
+
+mr_ = mr_class()
+
+
+#####--------------------------------------------------------------------------
+#---- Find unmasked data ---
+#####--------------------------------------------------------------------------
+
+def ndenumerate(a, compressed=True):
+    """
+    Multidimensional index iterator.
+
+    Return an iterator yielding pairs of array coordinates and values,
+    skipping elements that are masked. With `compressed=False`,
+    `ma.masked` is yielded as the value of masked elements. This
+    behavior differs from that of `numpy.ndenumerate`, which yields the
+    value of the underlying data array.
+
+    Notes
+    -----
+    .. versionadded:: 1.23.0
+
+    Parameters
+    ----------
+    a : array_like
+        An array with (possibly) masked elements.
+    compressed : bool, optional
+        If True (default), masked elements are skipped.
+
+    See Also
+    --------
+    numpy.ndenumerate : Equivalent function ignoring any mask.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.arange(9).reshape((3, 3))
+    >>> a[1, 0] = np.ma.masked
+    >>> a[1, 2] = np.ma.masked
+    >>> a[2, 1] = np.ma.masked
+    >>> a
+    masked_array(
+      data=[[0, 1, 2],
+            [--, 4, --],
+            [6, --, 8]],
+      mask=[[False, False, False],
+            [ True, False,  True],
+            [False,  True, False]],
+      fill_value=999999)
+    >>> for index, x in np.ma.ndenumerate(a):
+    ...     print(index, x)
+    (0, 0) 0
+    (0, 1) 1
+    (0, 2) 2
+    (1, 1) 4
+    (2, 0) 6
+    (2, 2) 8
+
+    >>> for index, x in np.ma.ndenumerate(a, compressed=False):
+    ...     print(index, x)
+    (0, 0) 0
+    (0, 1) 1
+    (0, 2) 2
+    (1, 0) --
+    (1, 1) 4
+    (1, 2) --
+    (2, 0) 6
+    (2, 1) --
+    (2, 2) 8
+    """
+    for it, mask in zip(np.ndenumerate(a), getmaskarray(a).flat):
+        if not mask:
+            yield it
+        elif not compressed:
+            yield it[0], masked
+
+
+def flatnotmasked_edges(a):
+    """
+    Find the indices of the first and last unmasked values.
+
+    Expects a 1-D `MaskedArray`, returns None if all values are masked.
+
+    Parameters
+    ----------
+    a : array_like
+        Input 1-D `MaskedArray`
+
+    Returns
+    -------
+    edges : ndarray or None
+        The indices of first and last non-masked value in the array.
+        Returns None if all values are masked.
+
+    See Also
+    --------
+    flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges
+    clump_masked, clump_unmasked
+
+    Notes
+    -----
+    Only accepts 1-D arrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.arange(10)
+    >>> np.ma.flatnotmasked_edges(a)
+    array([0, 9])
+
+    >>> mask = (a < 3) | (a > 8) | (a == 5)
+    >>> a[mask] = np.ma.masked
+    >>> np.array(a[~a.mask])
+    array([3, 4, 6, 7, 8])
+
+    >>> np.ma.flatnotmasked_edges(a)
+    array([3, 8])
+
+    >>> a[:] = np.ma.masked
+    >>> print(np.ma.flatnotmasked_edges(a))
+    None
+
+    """
+    m = getmask(a)
+    if m is nomask or not np.any(m):
+        return np.array([0, a.size - 1])
+    unmasked = np.flatnonzero(~m)
+    if len(unmasked) > 0:
+        return unmasked[[0, -1]]
+    else:
+        return None
+
+
+def notmasked_edges(a, axis=None):
+    """
+    Find the indices of the first and last unmasked values along an axis.
+
+    If all values are masked, return None.  Otherwise, return a list
+    of two tuples, corresponding to the indices of the first and last
+    unmasked values respectively.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array.
+    axis : int, optional
+        Axis along which to perform the operation.
+        If None (default), applies to a flattened version of the array.
+
+    Returns
+    -------
+    edges : ndarray or list
+        An array of start and end indexes if there are any masked data in
+        the array. If there are no masked data in the array, `edges` is a
+        list of the first and last index.
+
+    See Also
+    --------
+    flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous
+    clump_masked, clump_unmasked
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(9).reshape((3, 3))
+    >>> m = np.zeros_like(a)
+    >>> m[1:, 1:] = 1
+
+    >>> am = np.ma.array(a, mask=m)
+    >>> np.array(am[~am.mask])
+    array([0, 1, 2, 3, 6])
+
+    >>> np.ma.notmasked_edges(am)
+    array([0, 6])
+
+    """
+    a = asarray(a)
+    if axis is None or a.ndim == 1:
+        return flatnotmasked_edges(a)
+    m = getmaskarray(a)
+    idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim))
+    return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
+            tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ]
+
+
+def flatnotmasked_contiguous(a):
+    """
+    Find contiguous unmasked data in a masked array.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array.
+
+    Returns
+    -------
+    slice_list : list
+        A sorted sequence of `slice` objects (start index, end index).
+
+    See Also
+    --------
+    flatnotmasked_edges, notmasked_contiguous, notmasked_edges
+    clump_masked, clump_unmasked
+
+    Notes
+    -----
+    Only accepts 2-D arrays at most.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.arange(10)
+    >>> np.ma.flatnotmasked_contiguous(a)
+    [slice(0, 10, None)]
+
+    >>> mask = (a < 3) | (a > 8) | (a == 5)
+    >>> a[mask] = np.ma.masked
+    >>> np.array(a[~a.mask])
+    array([3, 4, 6, 7, 8])
+
+    >>> np.ma.flatnotmasked_contiguous(a)
+    [slice(3, 5, None), slice(6, 9, None)]
+    >>> a[:] = np.ma.masked
+    >>> np.ma.flatnotmasked_contiguous(a)
+    []
+
+    """
+    m = getmask(a)
+    if m is nomask:
+        return [slice(0, a.size)]
+    i = 0
+    result = []
+    for (k, g) in itertools.groupby(m.ravel()):
+        n = len(list(g))
+        if not k:
+            result.append(slice(i, i + n))
+        i += n
+    return result
+
+
+def notmasked_contiguous(a, axis=None):
+    """
+    Find contiguous unmasked data in a masked array along the given axis.
+
+    Parameters
+    ----------
+    a : array_like
+        The input array.
+    axis : int, optional
+        Axis along which to perform the operation.
+        If None (default), applies to a flattened version of the array, and this
+        is the same as `flatnotmasked_contiguous`.
+
+    Returns
+    -------
+    endpoints : list
+        A list of slices (start and end indexes) of unmasked indexes
+        in the array.
+
+        If the input is 2d and axis is specified, the result is a list of lists.
+
+    See Also
+    --------
+    flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
+    clump_masked, clump_unmasked
+
+    Notes
+    -----
+    Only accepts 2-D arrays at most.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.arange(12).reshape((3, 4))
+    >>> mask = np.zeros_like(a)
+    >>> mask[1:, :-1] = 1; mask[0, 1] = 1; mask[-1, 0] = 0
+    >>> ma = np.ma.array(a, mask=mask)
+    >>> ma
+    masked_array(
+      data=[[0, --, 2, 3],
+            [--, --, --, 7],
+            [8, --, --, 11]],
+      mask=[[False,  True, False, False],
+            [ True,  True,  True, False],
+            [False,  True,  True, False]],
+      fill_value=999999)
+    >>> np.array(ma[~ma.mask])
+    array([ 0,  2,  3,  7, 8, 11])
+
+    >>> np.ma.notmasked_contiguous(ma)
+    [slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]
+
+    >>> np.ma.notmasked_contiguous(ma, axis=0)
+    [[slice(0, 1, None), slice(2, 3, None)], [], [slice(0, 1, None)], [slice(0, 3, None)]]
+
+    >>> np.ma.notmasked_contiguous(ma, axis=1)
+    [[slice(0, 1, None), slice(2, 4, None)], [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, None)]]
+
+    """
+    a = asarray(a)
+    nd = a.ndim
+    if nd > 2:
+        raise NotImplementedError("Currently limited to at most 2D array.")
+    if axis is None or nd == 1:
+        return flatnotmasked_contiguous(a)
+    #
+    result = []
+    #
+    other = (axis + 1) % 2
+    idx = [0, 0]
+    idx[axis] = slice(None, None)
+    #
+    for i in range(a.shape[other]):
+        idx[other] = i
+        result.append(flatnotmasked_contiguous(a[tuple(idx)]))
+    return result
+
+
+def _ezclump(mask):
+    """
+    Finds the clumps (groups of data with the same values) for a 1D bool array.
+
+    Returns a series of slices.
+    """
+    if mask.ndim > 1:
+        mask = mask.ravel()
+    idx = (mask[1:] ^ mask[:-1]).nonzero()
+    idx = idx[0] + 1
+
+    if mask[0]:
+        if len(idx) == 0:
+            return [slice(0, mask.size)]
+
+        r = [slice(0, idx[0])]
+        r.extend((slice(left, right)
+                  for left, right in zip(idx[1:-1:2], idx[2::2])))
+    else:
+        if len(idx) == 0:
+            return []
+
+        r = [slice(left, right) for left, right in zip(idx[:-1:2], idx[1::2])]
+
+    if mask[-1]:
+        r.append(slice(idx[-1], mask.size))
+    return r
+
+
+def clump_unmasked(a):
+    """
+    Return list of slices corresponding to the unmasked clumps of a 1-D array.
+    (A "clump" is defined as a contiguous region of the array).
+
+    Parameters
+    ----------
+    a : ndarray
+        A one-dimensional masked array.
+
+    Returns
+    -------
+    slices : list of slice
+        The list of slices, one for each continuous region of unmasked
+        elements in `a`.
+
+    See Also
+    --------
+    flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
+    notmasked_contiguous, clump_masked
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.masked_array(np.arange(10))
+    >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked
+    >>> np.ma.clump_unmasked(a)
+    [slice(3, 6, None), slice(7, 8, None)]
+
+    """
+    mask = getattr(a, '_mask', nomask)
+    if mask is nomask:
+        return [slice(0, a.size)]
+    return _ezclump(~mask)
+
+
+def clump_masked(a):
+    """
+    Returns a list of slices corresponding to the masked clumps of a 1-D array.
+    (A "clump" is defined as a contiguous region of the array).
+
+    Parameters
+    ----------
+    a : ndarray
+        A one-dimensional masked array.
+
+    Returns
+    -------
+    slices : list of slice
+        The list of slices, one for each continuous region of masked elements
+        in `a`.
+
+    See Also
+    --------
+    flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
+    notmasked_contiguous, clump_unmasked
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.ma.masked_array(np.arange(10))
+    >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked
+    >>> np.ma.clump_masked(a)
+    [slice(0, 3, None), slice(6, 7, None), slice(8, 10, None)]
+
+    """
+    mask = ma.getmask(a)
+    if mask is nomask:
+        return []
+    return _ezclump(mask)
+
+
+###############################################################################
+#                              Polynomial fit                                 #
+###############################################################################
+
+
+def vander(x, n=None):
+    """
+    Masked values in the input array result in rows of zeros.
+
+    """
+    _vander = np.vander(x, n)
+    m = getmask(x)
+    if m is not nomask:
+        _vander[m] = 0
+    return _vander
+
+vander.__doc__ = ma.doc_note(np.vander.__doc__, vander.__doc__)
+
+
+def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
+    """
+    Any masked values in x is propagated in y, and vice-versa.
+
+    """
+    x = asarray(x)
+    y = asarray(y)
+
+    m = getmask(x)
+    if y.ndim == 1:
+        m = mask_or(m, getmask(y))
+    elif y.ndim == 2:
+        my = getmask(mask_rows(y))
+        if my is not nomask:
+            m = mask_or(m, my[:, 0])
+    else:
+        raise TypeError("Expected a 1D or 2D array for y!")
+
+    if w is not None:
+        w = asarray(w)
+        if w.ndim != 1:
+            raise TypeError("expected a 1-d array for weights")
+        if w.shape[0] != y.shape[0]:
+            raise TypeError("expected w and y to have the same length")
+        m = mask_or(m, getmask(w))
+
+    if m is not nomask:
+        not_m = ~m
+        if w is not None:
+            w = w[not_m]
+        return np.polyfit(x[not_m], y[not_m], deg, rcond, full, w, cov)
+    else:
+        return np.polyfit(x, y, deg, rcond, full, w, cov)
+
+polyfit.__doc__ = ma.doc_note(np.polyfit.__doc__, polyfit.__doc__)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ba76f351752604a546140d1ecb13debbf62b8f8b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/extras.pyi
@@ -0,0 +1,134 @@
+from _typeshed import Incomplete
+
+import numpy as np
+from numpy.lib._function_base_impl import average
+from numpy.lib._index_tricks_impl import AxisConcatenator
+
+from .core import MaskedArray, dot
+
+__all__ = [
+    "apply_along_axis",
+    "apply_over_axes",
+    "atleast_1d",
+    "atleast_2d",
+    "atleast_3d",
+    "average",
+    "clump_masked",
+    "clump_unmasked",
+    "column_stack",
+    "compress_cols",
+    "compress_nd",
+    "compress_rowcols",
+    "compress_rows",
+    "corrcoef",
+    "count_masked",
+    "cov",
+    "diagflat",
+    "dot",
+    "dstack",
+    "ediff1d",
+    "flatnotmasked_contiguous",
+    "flatnotmasked_edges",
+    "hsplit",
+    "hstack",
+    "in1d",
+    "intersect1d",
+    "isin",
+    "mask_cols",
+    "mask_rowcols",
+    "mask_rows",
+    "masked_all",
+    "masked_all_like",
+    "median",
+    "mr_",
+    "ndenumerate",
+    "notmasked_contiguous",
+    "notmasked_edges",
+    "polyfit",
+    "row_stack",
+    "setdiff1d",
+    "setxor1d",
+    "stack",
+    "union1d",
+    "unique",
+    "vander",
+    "vstack",
+]
+
+def count_masked(arr, axis=...): ...
+def masked_all(shape, dtype = ...): ...
+def masked_all_like(arr): ...
+
+class _fromnxfunction:
+    __name__: Incomplete
+    __doc__: Incomplete
+    def __init__(self, funcname) -> None: ...
+    def getdoc(self): ...
+    def __call__(self, *args, **params): ...
+
+class _fromnxfunction_single(_fromnxfunction):
+    def __call__(self, x, *args, **params): ...
+
+class _fromnxfunction_seq(_fromnxfunction):
+    def __call__(self, x, *args, **params): ...
+
+class _fromnxfunction_allargs(_fromnxfunction):
+    def __call__(self, *args, **params): ...
+
+atleast_1d: _fromnxfunction_allargs
+atleast_2d: _fromnxfunction_allargs
+atleast_3d: _fromnxfunction_allargs
+
+vstack: _fromnxfunction_seq
+row_stack: _fromnxfunction_seq
+hstack: _fromnxfunction_seq
+column_stack: _fromnxfunction_seq
+dstack: _fromnxfunction_seq
+stack: _fromnxfunction_seq
+
+hsplit: _fromnxfunction_single
+diagflat: _fromnxfunction_single
+
+def apply_along_axis(func1d, axis, arr, *args, **kwargs): ...
+def apply_over_axes(func, a, axes): ...
+def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ...
+def compress_nd(x, axis=...): ...
+def compress_rowcols(x, axis=...): ...
+def compress_rows(a): ...
+def compress_cols(a): ...
+def mask_rows(a, axis = ...): ...
+def mask_cols(a, axis = ...): ...
+def ediff1d(arr, to_end=..., to_begin=...): ...
+def unique(ar1, return_index=..., return_inverse=...): ...
+def intersect1d(ar1, ar2, assume_unique=...): ...
+def setxor1d(ar1, ar2, assume_unique=...): ...
+def in1d(ar1, ar2, assume_unique=..., invert=...): ...
+def isin(element, test_elements, assume_unique=..., invert=...): ...
+def union1d(ar1, ar2): ...
+def setdiff1d(ar1, ar2, assume_unique=...): ...
+def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
+def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ...
+
+class MAxisConcatenator(AxisConcatenator):
+    @staticmethod
+    def concatenate(arrays: Incomplete, axis: int = 0) -> Incomplete: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    @classmethod
+    def makemat(cls, arr: Incomplete) -> Incomplete: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleVariableOverride]
+
+class mr_class(MAxisConcatenator):
+    def __init__(self) -> None: ...
+
+mr_: mr_class
+
+def ndenumerate(a, compressed=...): ...
+def flatnotmasked_edges(a): ...
+def notmasked_edges(a, axis=...): ...
+def flatnotmasked_contiguous(a): ...
+def notmasked_contiguous(a, axis=...): ...
+def clump_unmasked(a): ...
+def clump_masked(a): ...
+def vander(x, n=...): ...
+def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ...
+
+#
+def mask_rowcols(a: Incomplete, axis: Incomplete | None = None) -> MaskedArray[Incomplete, np.dtype[Incomplete]]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..10e9e834cb88db9b031af83d33b51b3b65c52994
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.py
@@ -0,0 +1,774 @@
+""":mod:`numpy.ma..mrecords`
+
+Defines the equivalent of :class:`numpy.recarrays` for masked arrays,
+where fields can be accessed as attributes.
+Note that :class:`numpy.ma.MaskedArray` already supports structured datatypes
+and the masking of individual fields.
+
+.. moduleauthor:: Pierre Gerard-Marchant
+
+"""
+#  We should make sure that no field is called '_mask','mask','_fieldmask',
+#  or whatever restricted keywords.  An idea would be to no bother in the
+#  first place, and then rename the invalid fields with a trailing
+#  underscore. Maybe we could just overload the parser function ?
+
+import warnings
+
+import numpy as np
+import numpy.ma as ma
+
+
+_byteorderconv = np._core.records._byteorderconv
+
+
+_check_fill_value = ma.core._check_fill_value
+
+
+__all__ = [
+    'MaskedRecords', 'mrecarray', 'fromarrays', 'fromrecords',
+    'fromtextfile', 'addfield',
+]
+
+reserved_fields = ['_data', '_mask', '_fieldmask', 'dtype']
+
+
+def _checknames(descr, names=None):
+    """
+    Checks that field names ``descr`` are not reserved keywords.
+
+    If this is the case, a default 'f%i' is substituted.  If the argument
+    `names` is not None, updates the field names to valid names.
+
+    """
+    ndescr = len(descr)
+    default_names = ['f%i' % i for i in range(ndescr)]
+    if names is None:
+        new_names = default_names
+    else:
+        if isinstance(names, (tuple, list)):
+            new_names = names
+        elif isinstance(names, str):
+            new_names = names.split(',')
+        else:
+            raise NameError(f'illegal input names {names!r}')
+        nnames = len(new_names)
+        if nnames < ndescr:
+            new_names += default_names[nnames:]
+    ndescr = []
+    for (n, d, t) in zip(new_names, default_names, descr.descr):
+        if n in reserved_fields:
+            if t[0] in reserved_fields:
+                ndescr.append((d, t[1]))
+            else:
+                ndescr.append(t)
+        else:
+            ndescr.append((n, t[1]))
+    return np.dtype(ndescr)
+
+
+def _get_fieldmask(self):
+    mdescr = [(n, '|b1') for n in self.dtype.names]
+    fdmask = np.empty(self.shape, dtype=mdescr)
+    fdmask.flat = tuple([False] * len(mdescr))
+    return fdmask
+
+
+class MaskedRecords(ma.MaskedArray):
+    """
+
+    Attributes
+    ----------
+    _data : recarray
+        Underlying data, as a record array.
+    _mask : boolean array
+        Mask of the records. A record is masked when all its fields are
+        masked.
+    _fieldmask : boolean recarray
+        Record array of booleans, setting the mask of each individual field
+        of each record.
+    _fill_value : record
+        Filling values for each field.
+
+    """
+
+    def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,
+                formats=None, names=None, titles=None,
+                byteorder=None, aligned=False,
+                mask=ma.nomask, hard_mask=False, fill_value=None, keep_mask=True,
+                copy=False,
+                **options):
+
+        self = np.recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,
+                                   strides=strides, formats=formats, names=names,
+                                   titles=titles, byteorder=byteorder,
+                                   aligned=aligned,)
+
+        mdtype = ma.make_mask_descr(self.dtype)
+        if mask is ma.nomask or not np.size(mask):
+            if not keep_mask:
+                self._mask = tuple([False] * len(mdtype))
+        else:
+            mask = np.array(mask, copy=copy)
+            if mask.shape != self.shape:
+                (nd, nm) = (self.size, mask.size)
+                if nm == 1:
+                    mask = np.resize(mask, self.shape)
+                elif nm == nd:
+                    mask = np.reshape(mask, self.shape)
+                else:
+                    msg = "Mask and data not compatible: data size is %i, " + \
+                          "mask size is %i."
+                    raise ma.MAError(msg % (nd, nm))
+            if not keep_mask:
+                self.__setmask__(mask)
+                self._sharedmask = True
+            else:
+                if mask.dtype == mdtype:
+                    _mask = mask
+                else:
+                    _mask = np.array([tuple([m] * len(mdtype)) for m in mask],
+                                     dtype=mdtype)
+                self._mask = _mask
+        return self
+
+    def __array_finalize__(self, obj):
+        # Make sure we have a _fieldmask by default
+        _mask = getattr(obj, '_mask', None)
+        if _mask is None:
+            objmask = getattr(obj, '_mask', ma.nomask)
+            _dtype = np.ndarray.__getattribute__(self, 'dtype')
+            if objmask is ma.nomask:
+                _mask = ma.make_mask_none(self.shape, dtype=_dtype)
+            else:
+                mdescr = ma.make_mask_descr(_dtype)
+                _mask = np.array([tuple([m] * len(mdescr)) for m in objmask],
+                               dtype=mdescr).view(np.recarray)
+        # Update some of the attributes
+        _dict = self.__dict__
+        _dict.update(_mask=_mask)
+        self._update_from(obj)
+        if _dict['_baseclass'] == np.ndarray:
+            _dict['_baseclass'] = np.recarray
+        return
+
+    @property
+    def _data(self):
+        """
+        Returns the data as a recarray.
+
+        """
+        return np.ndarray.view(self, np.recarray)
+
+    @property
+    def _fieldmask(self):
+        """
+        Alias to mask.
+
+        """
+        return self._mask
+
+    def __len__(self):
+        """
+        Returns the length
+
+        """
+        # We have more than one record
+        if self.ndim:
+            return len(self._data)
+        # We have only one record: return the nb of fields
+        return len(self.dtype)
+
+    def __getattribute__(self, attr):
+        try:
+            return object.__getattribute__(self, attr)
+        except AttributeError:
+            # attr must be a fieldname
+            pass
+        fielddict = np.ndarray.__getattribute__(self, 'dtype').fields
+        try:
+            res = fielddict[attr][:2]
+        except (TypeError, KeyError) as e:
+            raise AttributeError(
+                f'record array has no attribute {attr}') from e
+        # So far, so good
+        _localdict = np.ndarray.__getattribute__(self, '__dict__')
+        _data = np.ndarray.view(self, _localdict['_baseclass'])
+        obj = _data.getfield(*res)
+        if obj.dtype.names is not None:
+            raise NotImplementedError("MaskedRecords is currently limited to"
+                                      "simple records.")
+        # Get some special attributes
+        # Reset the object's mask
+        hasmasked = False
+        _mask = _localdict.get('_mask', None)
+        if _mask is not None:
+            try:
+                _mask = _mask[attr]
+            except IndexError:
+                # Couldn't find a mask: use the default (nomask)
+                pass
+            tp_len = len(_mask.dtype)
+            hasmasked = _mask.view((bool, ((tp_len,) if tp_len else ()))).any()
+        if (obj.shape or hasmasked):
+            obj = obj.view(ma.MaskedArray)
+            obj._baseclass = np.ndarray
+            obj._isfield = True
+            obj._mask = _mask
+            # Reset the field values
+            _fill_value = _localdict.get('_fill_value', None)
+            if _fill_value is not None:
+                try:
+                    obj._fill_value = _fill_value[attr]
+                except ValueError:
+                    obj._fill_value = None
+        else:
+            obj = obj.item()
+        return obj
+
+    def __setattr__(self, attr, val):
+        """
+        Sets the attribute attr to the value val.
+
+        """
+        # Should we call __setmask__ first ?
+        if attr in ['mask', 'fieldmask']:
+            self.__setmask__(val)
+            return
+        # Create a shortcut (so that we don't have to call getattr all the time)
+        _localdict = object.__getattribute__(self, '__dict__')
+        # Check whether we're creating a new field
+        newattr = attr not in _localdict
+        try:
+            # Is attr a generic attribute ?
+            ret = object.__setattr__(self, attr, val)
+        except Exception:
+            # Not a generic attribute: exit if it's not a valid field
+            fielddict = np.ndarray.__getattribute__(self, 'dtype').fields or {}
+            optinfo = np.ndarray.__getattribute__(self, '_optinfo') or {}
+            if not (attr in fielddict or attr in optinfo):
+                raise
+        else:
+            # Get the list of names
+            fielddict = np.ndarray.__getattribute__(self, 'dtype').fields or {}
+            # Check the attribute
+            if attr not in fielddict:
+                return ret
+            if newattr:
+                # We just added this one or this setattr worked on an
+                # internal attribute.
+                try:
+                    object.__delattr__(self, attr)
+                except Exception:
+                    return ret
+        # Let's try to set the field
+        try:
+            res = fielddict[attr][:2]
+        except (TypeError, KeyError) as e:
+            raise AttributeError(
+                f'record array has no attribute {attr}') from e
+
+        if val is ma.masked:
+            _fill_value = _localdict['_fill_value']
+            if _fill_value is not None:
+                dval = _localdict['_fill_value'][attr]
+            else:
+                dval = val
+            mval = True
+        else:
+            dval = ma.filled(val)
+            mval = ma.getmaskarray(val)
+        obj = np.ndarray.__getattribute__(self, '_data').setfield(dval, *res)
+        _localdict['_mask'].__setitem__(attr, mval)
+        return obj
+
+    def __getitem__(self, indx):
+        """
+        Returns all the fields sharing the same fieldname base.
+
+        The fieldname base is either `_data` or `_mask`.
+
+        """
+        _localdict = self.__dict__
+        _mask = np.ndarray.__getattribute__(self, '_mask')
+        _data = np.ndarray.view(self, _localdict['_baseclass'])
+        # We want a field
+        if isinstance(indx, str):
+            # Make sure _sharedmask is True to propagate back to _fieldmask
+            # Don't use _set_mask, there are some copies being made that
+            # break propagation Don't force the mask to nomask, that wreaks
+            # easy masking
+            obj = _data[indx].view(ma.MaskedArray)
+            obj._mask = _mask[indx]
+            obj._sharedmask = True
+            fval = _localdict['_fill_value']
+            if fval is not None:
+                obj._fill_value = fval[indx]
+            # Force to masked if the mask is True
+            if not obj.ndim and obj._mask:
+                return ma.masked
+            return obj
+        # We want some elements.
+        # First, the data.
+        obj = np.asarray(_data[indx]).view(mrecarray)
+        obj._mask = np.asarray(_mask[indx]).view(np.recarray)
+        return obj
+
+    def __setitem__(self, indx, value):
+        """
+        Sets the given record to value.
+
+        """
+        ma.MaskedArray.__setitem__(self, indx, value)
+        if isinstance(indx, str):
+            self._mask[indx] = ma.getmaskarray(value)
+
+    def __str__(self):
+        """
+        Calculates the string representation.
+
+        """
+        if self.size > 1:
+            mstr = [f"({','.join([str(i) for i in s])})"
+                    for s in zip(*[getattr(self, f) for f in self.dtype.names])]
+            return f"[{', '.join(mstr)}]"
+        else:
+            mstr = [f"{','.join([str(i) for i in s])}"
+                    for s in zip([getattr(self, f) for f in self.dtype.names])]
+            return f"({', '.join(mstr)})"
+
+    def __repr__(self):
+        """
+        Calculates the repr representation.
+
+        """
+        _names = self.dtype.names
+        fmt = "%%%is : %%s" % (max([len(n) for n in _names]) + 4,)
+        reprstr = [fmt % (f, getattr(self, f)) for f in self.dtype.names]
+        reprstr.insert(0, 'masked_records(')
+        reprstr.extend([fmt % ('    fill_value', self.fill_value),
+                        '              )'])
+        return str("\n".join(reprstr))
+
+    def view(self, dtype=None, type=None):
+        """
+        Returns a view of the mrecarray.
+
+        """
+        # OK, basic copy-paste from MaskedArray.view.
+        if dtype is None:
+            if type is None:
+                output = np.ndarray.view(self)
+            else:
+                output = np.ndarray.view(self, type)
+        # Here again.
+        elif type is None:
+            try:
+                if issubclass(dtype, np.ndarray):
+                    output = np.ndarray.view(self, dtype)
+                else:
+                    output = np.ndarray.view(self, dtype)
+            # OK, there's the change
+            except TypeError:
+                dtype = np.dtype(dtype)
+                # we need to revert to MaskedArray, but keeping the possibility
+                # of subclasses (eg, TimeSeriesRecords), so we'll force a type
+                # set to the first parent
+                if dtype.fields is None:
+                    basetype = self.__class__.__bases__[0]
+                    output = self.__array__().view(dtype, basetype)
+                    output._update_from(self)
+                else:
+                    output = np.ndarray.view(self, dtype)
+                output._fill_value = None
+        else:
+            output = np.ndarray.view(self, dtype, type)
+        # Update the mask, just like in MaskedArray.view
+        if (getattr(output, '_mask', ma.nomask) is not ma.nomask):
+            mdtype = ma.make_mask_descr(output.dtype)
+            output._mask = self._mask.view(mdtype, np.ndarray)
+            output._mask.shape = output.shape
+        return output
+
+    def harden_mask(self):
+        """
+        Forces the mask to hard.
+
+        """
+        self._hardmask = True
+
+    def soften_mask(self):
+        """
+        Forces the mask to soft
+
+        """
+        self._hardmask = False
+
+    def copy(self):
+        """
+        Returns a copy of the masked record.
+
+        """
+        copied = self._data.copy().view(type(self))
+        copied._mask = self._mask.copy()
+        return copied
+
+    def tolist(self, fill_value=None):
+        """
+        Return the data portion of the array as a list.
+
+        Data items are converted to the nearest compatible Python type.
+        Masked values are converted to fill_value. If fill_value is None,
+        the corresponding entries in the output list will be ``None``.
+
+        """
+        if fill_value is not None:
+            return self.filled(fill_value).tolist()
+        result = np.array(self.filled().tolist(), dtype=object)
+        mask = np.array(self._mask.tolist())
+        result[mask] = None
+        return result.tolist()
+
+    def __getstate__(self):
+        """Return the internal state of the masked array.
+
+        This is for pickling.
+
+        """
+        state = (1,
+                 self.shape,
+                 self.dtype,
+                 self.flags.fnc,
+                 self._data.tobytes(),
+                 self._mask.tobytes(),
+                 self._fill_value,
+                 )
+        return state
+
+    def __setstate__(self, state):
+        """
+        Restore the internal state of the masked array.
+
+        This is for pickling.  ``state`` is typically the output of the
+        ``__getstate__`` output, and is a 5-tuple:
+
+        - class name
+        - a tuple giving the shape of the data
+        - a typecode for the data
+        - a binary string for the data
+        - a binary string for the mask.
+
+        """
+        (ver, shp, typ, isf, raw, msk, flv) = state
+        np.ndarray.__setstate__(self, (shp, typ, isf, raw))
+        mdtype = np.dtype([(k, np.bool) for (k, _) in self.dtype.descr])
+        self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))
+        self.fill_value = flv
+
+    def __reduce__(self):
+        """
+        Return a 3-tuple for pickling a MaskedArray.
+
+        """
+        return (_mrreconstruct,
+                (self.__class__, self._baseclass, (0,), 'b',),
+                self.__getstate__())
+
+
+def _mrreconstruct(subtype, baseclass, baseshape, basetype,):
+    """
+    Build a new MaskedArray from the information stored in a pickle.
+
+    """
+    _data = np.ndarray.__new__(baseclass, baseshape, basetype).view(subtype)
+    _mask = np.ndarray.__new__(np.ndarray, baseshape, 'b1')
+    return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)
+
+mrecarray = MaskedRecords
+
+
+###############################################################################
+#                             Constructors                                    #
+###############################################################################
+
+
+def fromarrays(arraylist, dtype=None, shape=None, formats=None,
+               names=None, titles=None, aligned=False, byteorder=None,
+               fill_value=None):
+    """
+    Creates a mrecarray from a (flat) list of masked arrays.
+
+    Parameters
+    ----------
+    arraylist : sequence
+        A list of (masked) arrays. Each element of the sequence is first converted
+        to a masked array if needed. If a 2D array is passed as argument, it is
+        processed line by line
+    dtype : {None, dtype}, optional
+        Data type descriptor.
+    shape : {None, integer}, optional
+        Number of records. If None, shape is defined from the shape of the
+        first array in the list.
+    formats : {None, sequence}, optional
+        Sequence of formats for each individual field. If None, the formats will
+        be autodetected by inspecting the fields and selecting the highest dtype
+        possible.
+    names : {None, sequence}, optional
+        Sequence of the names of each field.
+    fill_value : {None, sequence}, optional
+        Sequence of data to be used as filling values.
+
+    Notes
+    -----
+    Lists of tuples should be preferred over lists of lists for faster processing.
+
+    """
+    datalist = [ma.getdata(x) for x in arraylist]
+    masklist = [np.atleast_1d(ma.getmaskarray(x)) for x in arraylist]
+    _array = np.rec.fromarrays(datalist,
+                               dtype=dtype, shape=shape, formats=formats,
+                               names=names, titles=titles, aligned=aligned,
+                               byteorder=byteorder).view(mrecarray)
+    _array._mask.flat = list(zip(*masklist))
+    if fill_value is not None:
+        _array.fill_value = fill_value
+    return _array
+
+
+def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
+                titles=None, aligned=False, byteorder=None,
+                fill_value=None, mask=ma.nomask):
+    """
+    Creates a MaskedRecords from a list of records.
+
+    Parameters
+    ----------
+    reclist : sequence
+        A list of records. Each element of the sequence is first converted
+        to a masked array if needed. If a 2D array is passed as argument, it is
+        processed line by line
+    dtype : {None, dtype}, optional
+        Data type descriptor.
+    shape : {None,int}, optional
+        Number of records. If None, ``shape`` is defined from the shape of the
+        first array in the list.
+    formats : {None, sequence}, optional
+        Sequence of formats for each individual field. If None, the formats will
+        be autodetected by inspecting the fields and selecting the highest dtype
+        possible.
+    names : {None, sequence}, optional
+        Sequence of the names of each field.
+    fill_value : {None, sequence}, optional
+        Sequence of data to be used as filling values.
+    mask : {nomask, sequence}, optional.
+        External mask to apply on the data.
+
+    Notes
+    -----
+    Lists of tuples should be preferred over lists of lists for faster processing.
+
+    """
+    # Grab the initial _fieldmask, if needed:
+    _mask = getattr(reclist, '_mask', None)
+    # Get the list of records.
+    if isinstance(reclist, np.ndarray):
+        # Make sure we don't have some hidden mask
+        if isinstance(reclist, ma.MaskedArray):
+            reclist = reclist.filled().view(np.ndarray)
+        # Grab the initial dtype, just in case
+        if dtype is None:
+            dtype = reclist.dtype
+        reclist = reclist.tolist()
+    mrec = np.rec.fromrecords(reclist, dtype=dtype, shape=shape, formats=formats,
+                          names=names, titles=titles,
+                          aligned=aligned, byteorder=byteorder).view(mrecarray)
+    # Set the fill_value if needed
+    if fill_value is not None:
+        mrec.fill_value = fill_value
+    # Now, let's deal w/ the mask
+    if mask is not ma.nomask:
+        mask = np.asarray(mask)
+        maskrecordlength = len(mask.dtype)
+        if maskrecordlength:
+            mrec._mask.flat = mask
+        elif mask.ndim == 2:
+            mrec._mask.flat = [tuple(m) for m in mask]
+        else:
+            mrec.__setmask__(mask)
+    if _mask is not None:
+        mrec._mask[:] = _mask
+    return mrec
+
+
+def _guessvartypes(arr):
+    """
+    Tries to guess the dtypes of the str_ ndarray `arr`.
+
+    Guesses by testing element-wise conversion. Returns a list of dtypes.
+    The array is first converted to ndarray. If the array is 2D, the test
+    is performed on the first line. An exception is raised if the file is
+    3D or more.
+
+    """
+    vartypes = []
+    arr = np.asarray(arr)
+    if arr.ndim == 2:
+        arr = arr[0]
+    elif arr.ndim > 2:
+        raise ValueError("The array should be 2D at most!")
+    # Start the conversion loop.
+    for f in arr:
+        try:
+            int(f)
+        except (ValueError, TypeError):
+            try:
+                float(f)
+            except (ValueError, TypeError):
+                try:
+                    complex(f)
+                except (ValueError, TypeError):
+                    vartypes.append(arr.dtype)
+                else:
+                    vartypes.append(np.dtype(complex))
+            else:
+                vartypes.append(np.dtype(float))
+        else:
+            vartypes.append(np.dtype(int))
+    return vartypes
+
+
+def openfile(fname):
+    """
+    Opens the file handle of file `fname`.
+
+    """
+    # A file handle
+    if hasattr(fname, 'readline'):
+        return fname
+    # Try to open the file and guess its type
+    try:
+        f = open(fname)
+    except FileNotFoundError as e:
+        raise FileNotFoundError(f"No such file: '{fname}'") from e
+    if f.readline()[:2] != "\\x":
+        f.seek(0, 0)
+        return f
+    f.close()
+    raise NotImplementedError("Wow, binary file")
+
+
+def fromtextfile(fname, delimiter=None, commentchar='#', missingchar='',
+                 varnames=None, vartypes=None,
+                 *, delimitor=np._NoValue):  # backwards compatibility
+    """
+    Creates a mrecarray from data stored in the file `filename`.
+
+    Parameters
+    ----------
+    fname : {file name/handle}
+        Handle of an opened file.
+    delimiter : {None, string}, optional
+        Alphanumeric character used to separate columns in the file.
+        If None, any (group of) white spacestring(s) will be used.
+    commentchar : {'#', string}, optional
+        Alphanumeric character used to mark the start of a comment.
+    missingchar : {'', string}, optional
+        String indicating missing data, and used to create the masks.
+    varnames : {None, sequence}, optional
+        Sequence of the variable names. If None, a list will be created from
+        the first non empty line of the file.
+    vartypes : {None, sequence}, optional
+        Sequence of the variables dtypes. If None, it will be estimated from
+        the first non-commented line.
+
+
+    Ultra simple: the varnames are in the header, one line"""
+    if delimitor is not np._NoValue:
+        if delimiter is not None:
+            raise TypeError("fromtextfile() got multiple values for argument "
+                            "'delimiter'")
+        # NumPy 1.22.0, 2021-09-23
+        warnings.warn("The 'delimitor' keyword argument of "
+                      "numpy.ma.mrecords.fromtextfile() is deprecated "
+                      "since NumPy 1.22.0, use 'delimiter' instead.",
+                      DeprecationWarning, stacklevel=2)
+        delimiter = delimitor
+
+    # Try to open the file.
+    ftext = openfile(fname)
+
+    # Get the first non-empty line as the varnames
+    while True:
+        line = ftext.readline()
+        firstline = line[:line.find(commentchar)].strip()
+        _varnames = firstline.split(delimiter)
+        if len(_varnames) > 1:
+            break
+    if varnames is None:
+        varnames = _varnames
+
+    # Get the data.
+    _variables = ma.masked_array([line.strip().split(delimiter) for line in ftext
+                                 if line[0] != commentchar and len(line) > 1])
+    (_, nfields) = _variables.shape
+    ftext.close()
+
+    # Try to guess the dtype.
+    if vartypes is None:
+        vartypes = _guessvartypes(_variables[0])
+    else:
+        vartypes = [np.dtype(v) for v in vartypes]
+        if len(vartypes) != nfields:
+            msg = "Attempting to %i dtypes for %i fields!"
+            msg += " Reverting to default."
+            warnings.warn(msg % (len(vartypes), nfields), stacklevel=2)
+            vartypes = _guessvartypes(_variables[0])
+
+    # Construct the descriptor.
+    mdescr = list(zip(varnames, vartypes))
+    mfillv = [ma.default_fill_value(f) for f in vartypes]
+
+    # Get the data and the mask.
+    # We just need a list of masked_arrays. It's easier to create it like that:
+    _mask = (_variables.T == missingchar)
+    _datalist = [ma.masked_array(a, mask=m, dtype=t, fill_value=f)
+                 for (a, m, t, f) in zip(_variables.T, _mask, vartypes, mfillv)]
+
+    return fromarrays(_datalist, dtype=mdescr)
+
+
+def addfield(mrecord, newfield, newfieldname=None):
+    """Adds a new field to the masked record array
+
+    Uses `newfield` as data and `newfieldname` as name. If `newfieldname`
+    is None, the new field name is set to 'fi', where `i` is the number of
+    existing fields.
+
+    """
+    _data = mrecord._data
+    _mask = mrecord._mask
+    if newfieldname is None or newfieldname in reserved_fields:
+        newfieldname = 'f%i' % len(_data.dtype)
+    newfield = ma.array(newfield)
+    # Get the new data.
+    # Create a new empty recarray
+    newdtype = np.dtype(_data.dtype.descr + [(newfieldname, newfield.dtype)])
+    newdata = np.recarray(_data.shape, newdtype)
+    # Add the existing field
+    [newdata.setfield(_data.getfield(*f), *f)
+     for f in _data.dtype.fields.values()]
+    # Add the new field
+    newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])
+    newdata = newdata.view(MaskedRecords)
+    # Get the new mask
+    # Create a new empty recarray
+    newmdtype = np.dtype([(n, np.bool) for n in newdtype.names])
+    newmask = np.recarray(_data.shape, newmdtype)
+    # Add the old masks
+    [newmask.setfield(_mask.getfield(*f), *f)
+     for f in _mask.dtype.fields.values()]
+    # Add the mask of the new field
+    newmask.setfield(ma.getmaskarray(newfield),
+                     *newmask.dtype.fields[newfieldname])
+    newdata._mask = newmask
+    return newdata
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7e2fdb1e92c616cc863429e3fcaba8f7fdc45b91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/mrecords.pyi
@@ -0,0 +1,95 @@
+from typing import Any, TypeVar
+
+from numpy import dtype
+from . import MaskedArray
+
+__all__ = [
+    "MaskedRecords",
+    "mrecarray",
+    "fromarrays",
+    "fromrecords",
+    "fromtextfile",
+    "addfield",
+]
+
+_ShapeType_co = TypeVar("_ShapeType_co", covariant=True, bound=tuple[int, ...])
+_DType_co = TypeVar("_DType_co", bound=dtype[Any], covariant=True)
+
+class MaskedRecords(MaskedArray[_ShapeType_co, _DType_co]):
+    def __new__(
+        cls,
+        shape,
+        dtype=...,
+        buf=...,
+        offset=...,
+        strides=...,
+        formats=...,
+        names=...,
+        titles=...,
+        byteorder=...,
+        aligned=...,
+        mask=...,
+        hard_mask=...,
+        fill_value=...,
+        keep_mask=...,
+        copy=...,
+        **options,
+    ): ...
+    _mask: Any
+    _fill_value: Any
+    @property
+    def _data(self): ...
+    @property
+    def _fieldmask(self): ...
+    def __array_finalize__(self, obj): ...
+    def __len__(self): ...
+    def __getattribute__(self, attr): ...
+    def __setattr__(self, attr, val): ...
+    def __getitem__(self, indx): ...
+    def __setitem__(self, indx, value): ...
+    def view(self, dtype=..., type=...): ...
+    def harden_mask(self): ...
+    def soften_mask(self): ...
+    def copy(self): ...
+    def tolist(self, fill_value=...): ...
+    def __reduce__(self): ...
+
+mrecarray = MaskedRecords
+
+def fromarrays(
+    arraylist,
+    dtype=...,
+    shape=...,
+    formats=...,
+    names=...,
+    titles=...,
+    aligned=...,
+    byteorder=...,
+    fill_value=...,
+): ...
+
+def fromrecords(
+    reclist,
+    dtype=...,
+    shape=...,
+    formats=...,
+    names=...,
+    titles=...,
+    aligned=...,
+    byteorder=...,
+    fill_value=...,
+    mask=...,
+): ...
+
+def fromtextfile(
+    fname,
+    delimiter=...,
+    commentchar=...,
+    missingchar=...,
+    varnames=...,
+    vartypes=...,
+    # NOTE: deprecated: NumPy 1.22.0, 2021-09-23
+    # delimitor=...,
+): ...
+
+def addfield(mrecord, newfield, newfieldname=...): ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_arrayobject.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_arrayobject.py
new file mode 100644
index 0000000000000000000000000000000000000000..2000cea226fce9dea890553601594d7c1255d77c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_arrayobject.py
@@ -0,0 +1,40 @@
+import pytest
+
+import numpy as np
+from numpy.ma import masked_array
+from numpy.testing import assert_array_equal
+
+
+def test_matrix_transpose_raises_error_for_1d():
+    msg = "matrix transpose with ndim < 2 is undefined"
+    ma_arr = masked_array(data=[1, 2, 3, 4, 5, 6],
+                          mask=[1, 0, 1, 1, 1, 0])
+    with pytest.raises(ValueError, match=msg):
+        ma_arr.mT
+
+
+def test_matrix_transpose_equals_transpose_2d():
+    ma_arr = masked_array(data=[[1, 2, 3], [4, 5, 6]],
+                          mask=[[1, 0, 1], [1, 1, 0]])
+    assert_array_equal(ma_arr.T, ma_arr.mT)
+
+
+ARRAY_SHAPES_TO_TEST = (
+    (5, 2),
+    (5, 2, 3),
+    (5, 2, 3, 4),
+)
+
+
+@pytest.mark.parametrize("shape", ARRAY_SHAPES_TO_TEST)
+def test_matrix_transpose_equals_swapaxes(shape):
+    num_of_axes = len(shape)
+    vec = np.arange(shape[-1])
+    arr = np.broadcast_to(vec, shape)
+
+    rng = np.random.default_rng(42)
+    mask = rng.choice([0, 1], size=shape)
+    ma_arr = masked_array(data=arr, mask=mask)
+
+    tgt = np.swapaxes(arr, num_of_axes - 2, num_of_axes - 1)
+    assert_array_equal(tgt, ma_arr.mT)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_core.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..53651004db9a6021223a1a68b9be5a469cd572e0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_core.py
@@ -0,0 +1,5791 @@
+# pylint: disable-msg=W0400,W0511,W0611,W0612,W0614,R0201,E1102
+"""Tests suite for MaskedArray & subclassing.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+"""
+__author__ = "Pierre GF Gerard-Marchant"
+
+import sys
+import warnings
+import copy
+import operator
+import itertools
+import textwrap
+import pickle
+from functools import reduce
+
+import pytest
+
+import numpy as np
+import numpy.ma.core
+import numpy._core.fromnumeric as fromnumeric
+import numpy._core.umath as umath
+from numpy.exceptions import AxisError
+from numpy.testing import (
+    assert_raises, assert_warns, suppress_warnings, IS_WASM, temppath
+    )
+from numpy.testing._private.utils import requires_memory
+from numpy import ndarray
+from numpy._utils import asbytes
+from numpy.ma.testutils import (
+    assert_, assert_array_equal, assert_equal, assert_almost_equal,
+    assert_equal_records, fail_if_equal, assert_not_equal,
+    assert_mask_equal
+    )
+from numpy.ma.core import (
+    MAError, MaskError, MaskType, MaskedArray, abs, absolute, add, all,
+    allclose, allequal, alltrue, angle, anom, arange, arccos, arccosh, arctan2,
+    arcsin, arctan, argsort, array, asarray, choose, concatenate,
+    conjugate, cos, cosh, count, default_fill_value, diag, divide, doc_note,
+    empty, empty_like, equal, exp, flatten_mask, filled, fix_invalid,
+    flatten_structured_array, fromflex, getmask, getmaskarray, greater,
+    greater_equal, identity, inner, isMaskedArray, less, less_equal, log,
+    log10, make_mask, make_mask_descr, mask_or, masked, masked_array,
+    masked_equal, masked_greater, masked_greater_equal, masked_inside,
+    masked_less, masked_less_equal, masked_not_equal, masked_outside,
+    masked_print_option, masked_values, masked_where, max, maximum,
+    maximum_fill_value, min, minimum, minimum_fill_value, mod, multiply,
+    mvoid, nomask, not_equal, ones, ones_like, outer, power, product, put,
+    putmask, ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort,
+    sqrt, subtract, sum, take, tan, tanh, transpose, where, zeros, zeros_like,
+    )
+
+pi = np.pi
+
+
+suppress_copy_mask_on_assignment = suppress_warnings()
+suppress_copy_mask_on_assignment.filter(
+    numpy.ma.core.MaskedArrayFutureWarning,
+    "setting an item on a masked array which has a shared mask will not copy")
+
+
+# For parametrized numeric testing
+num_dts = [np.dtype(dt_) for dt_ in '?bhilqBHILQefdgFD']
+num_ids = [dt_.char for dt_ in num_dts]
+
+
+class TestMaskedArray:
+    # Base test class for MaskedArrays.
+
+    def setup_method(self):
+        # Base data definition.
+        x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+        a10 = 10.
+        m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
+        xm = masked_array(x, mask=m1)
+        ym = masked_array(y, mask=m2)
+        z = np.array([-.5, 0., .5, .8])
+        zm = masked_array(z, mask=[0, 1, 0, 0])
+        xf = np.where(m1, 1e+20, x)
+        xm.set_fill_value(1e+20)
+        self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
+
+    def test_basicattributes(self):
+        # Tests some basic array attributes.
+        a = array([1, 3, 2])
+        b = array([1, 3, 2], mask=[1, 0, 1])
+        assert_equal(a.ndim, 1)
+        assert_equal(b.ndim, 1)
+        assert_equal(a.size, 3)
+        assert_equal(b.size, 3)
+        assert_equal(a.shape, (3,))
+        assert_equal(b.shape, (3,))
+
+    def test_basic0d(self):
+        # Checks masking a scalar
+        x = masked_array(0)
+        assert_equal(str(x), '0')
+        x = masked_array(0, mask=True)
+        assert_equal(str(x), str(masked_print_option))
+        x = masked_array(0, mask=False)
+        assert_equal(str(x), '0')
+        x = array(0, mask=1)
+        assert_(x.filled().dtype is x._data.dtype)
+
+    def test_basic1d(self):
+        # Test of basic array creation and properties in 1 dimension.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        assert_(not isMaskedArray(x))
+        assert_(isMaskedArray(xm))
+        assert_((xm - ym).filled(0).any())
+        fail_if_equal(xm.mask.astype(int), ym.mask.astype(int))
+        s = x.shape
+        assert_equal(np.shape(xm), s)
+        assert_equal(xm.shape, s)
+        assert_equal(xm.dtype, x.dtype)
+        assert_equal(zm.dtype, z.dtype)
+        assert_equal(xm.size, reduce(lambda x, y:x * y, s))
+        assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
+        assert_array_equal(xm, xf)
+        assert_array_equal(filled(xm, 1.e20), xf)
+        assert_array_equal(x, xm)
+
+    def test_basic2d(self):
+        # Test of basic array creation and properties in 2 dimensions.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        for s in [(4, 3), (6, 2)]:
+            x.shape = s
+            y.shape = s
+            xm.shape = s
+            ym.shape = s
+            xf.shape = s
+
+            assert_(not isMaskedArray(x))
+            assert_(isMaskedArray(xm))
+            assert_equal(shape(xm), s)
+            assert_equal(xm.shape, s)
+            assert_equal(xm.size, reduce(lambda x, y:x * y, s))
+            assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
+            assert_equal(xm, xf)
+            assert_equal(filled(xm, 1.e20), xf)
+            assert_equal(x, xm)
+
+    def test_concatenate_basic(self):
+        # Tests concatenations.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        # basic concatenation
+        assert_equal(np.concatenate((x, y)), concatenate((xm, ym)))
+        assert_equal(np.concatenate((x, y)), concatenate((x, y)))
+        assert_equal(np.concatenate((x, y)), concatenate((xm, y)))
+        assert_equal(np.concatenate((x, y, x)), concatenate((x, ym, x)))
+
+    def test_concatenate_alongaxis(self):
+        # Tests concatenations.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        # Concatenation along an axis
+        s = (3, 4)
+        x.shape = y.shape = xm.shape = ym.shape = s
+        assert_equal(xm.mask, np.reshape(m1, s))
+        assert_equal(ym.mask, np.reshape(m2, s))
+        xmym = concatenate((xm, ym), 1)
+        assert_equal(np.concatenate((x, y), 1), xmym)
+        assert_equal(np.concatenate((xm.mask, ym.mask), 1), xmym._mask)
+
+        x = zeros(2)
+        y = array(ones(2), mask=[False, True])
+        z = concatenate((x, y))
+        assert_array_equal(z, [0, 0, 1, 1])
+        assert_array_equal(z.mask, [False, False, False, True])
+        z = concatenate((y, x))
+        assert_array_equal(z, [1, 1, 0, 0])
+        assert_array_equal(z.mask, [False, True, False, False])
+
+    def test_concatenate_flexible(self):
+        # Tests the concatenation on flexible arrays.
+        data = masked_array(list(zip(np.random.rand(10),
+                                     np.arange(10))),
+                            dtype=[('a', float), ('b', int)])
+
+        test = concatenate([data[:5], data[5:]])
+        assert_equal_records(test, data)
+
+    def test_creation_ndmin(self):
+        # Check the use of ndmin
+        x = array([1, 2, 3], mask=[1, 0, 0], ndmin=2)
+        assert_equal(x.shape, (1, 3))
+        assert_equal(x._data, [[1, 2, 3]])
+        assert_equal(x._mask, [[1, 0, 0]])
+
+    def test_creation_ndmin_from_maskedarray(self):
+        # Make sure we're not losing the original mask w/ ndmin
+        x = array([1, 2, 3])
+        x[-1] = masked
+        xx = array(x, ndmin=2, dtype=float)
+        assert_equal(x.shape, x._mask.shape)
+        assert_equal(xx.shape, xx._mask.shape)
+
+    def test_creation_maskcreation(self):
+        # Tests how masks are initialized at the creation of Maskedarrays.
+        data = arange(24, dtype=float)
+        data[[3, 6, 15]] = masked
+        dma_1 = MaskedArray(data)
+        assert_equal(dma_1.mask, data.mask)
+        dma_2 = MaskedArray(dma_1)
+        assert_equal(dma_2.mask, dma_1.mask)
+        dma_3 = MaskedArray(dma_1, mask=[1, 0, 0, 0] * 6)
+        fail_if_equal(dma_3.mask, dma_1.mask)
+
+        x = array([1, 2, 3], mask=True)
+        assert_equal(x._mask, [True, True, True])
+        x = array([1, 2, 3], mask=False)
+        assert_equal(x._mask, [False, False, False])
+        y = array([1, 2, 3], mask=x._mask, copy=False)
+        assert_(np.may_share_memory(x.mask, y.mask))
+        y = array([1, 2, 3], mask=x._mask, copy=True)
+        assert_(not np.may_share_memory(x.mask, y.mask))
+        x = array([1, 2, 3], mask=None)
+        assert_equal(x._mask, [False, False, False])
+
+    def test_masked_singleton_array_creation_warns(self):
+        # The first works, but should not (ideally), there may be no way
+        # to solve this, however, as long as `np.ma.masked` is an ndarray.
+        np.array(np.ma.masked)
+        with pytest.warns(UserWarning):
+            # Tries to create a float array, using `float(np.ma.masked)`.
+            # We may want to define this is invalid behaviour in the future!
+            # (requiring np.ma.masked to be a known NumPy scalar probably
+            # with a DType.)
+            np.array([3., np.ma.masked])
+
+    def test_creation_with_list_of_maskedarrays(self):
+        # Tests creating a masked array from a list of masked arrays.
+        x = array(np.arange(5), mask=[1, 0, 0, 0, 0])
+        data = array((x, x[::-1]))
+        assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
+        assert_equal(data._mask, [[1, 0, 0, 0, 0], [0, 0, 0, 0, 1]])
+
+        x.mask = nomask
+        data = array((x, x[::-1]))
+        assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
+        assert_(data.mask is nomask)
+
+    def test_creation_with_list_of_maskedarrays_no_bool_cast(self):
+        # Tests the regression in gh-18551
+        masked_str = np.ma.masked_array(['a', 'b'], mask=[True, False])
+        normal_int = np.arange(2)
+        res = np.ma.asarray([masked_str, normal_int], dtype="U21")
+        assert_array_equal(res.mask, [[True, False], [False, False]])
+
+        # The above only failed due a long chain of oddity, try also with
+        # an object array that cannot be converted to bool always:
+        class NotBool:
+            def __bool__(self):
+                raise ValueError("not a bool!")
+        masked_obj = np.ma.masked_array([NotBool(), 'b'], mask=[True, False])
+        # Check that the NotBool actually fails like we would expect:
+        with pytest.raises(ValueError, match="not a bool!"):
+            np.asarray([masked_obj], dtype=bool)
+
+        res = np.ma.asarray([masked_obj, normal_int])
+        assert_array_equal(res.mask, [[True, False], [False, False]])
+
+    def test_creation_from_ndarray_with_padding(self):
+        x = np.array([('A', 0)], dtype={'names':['f0','f1'],
+                                        'formats':['S4','i8'],
+                                        'offsets':[0,8]})
+        array(x)  # used to fail due to 'V' padding field in x.dtype.descr
+
+    def test_unknown_keyword_parameter(self):
+        with pytest.raises(TypeError, match="unexpected keyword argument"):
+            MaskedArray([1, 2, 3], maks=[0, 1, 0])  # `mask` is misspelled.
+
+    def test_asarray(self):
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        xm.fill_value = -9999
+        xm._hardmask = True
+        xmm = asarray(xm)
+        assert_equal(xmm._data, xm._data)
+        assert_equal(xmm._mask, xm._mask)
+        assert_equal(xmm.fill_value, xm.fill_value)
+        assert_equal(xmm._hardmask, xm._hardmask)
+
+    def test_asarray_default_order(self):
+        # See Issue #6646
+        m = np.eye(3).T
+        assert_(not m.flags.c_contiguous)
+
+        new_m = asarray(m)
+        assert_(new_m.flags.c_contiguous)
+
+    def test_asarray_enforce_order(self):
+        # See Issue #6646
+        m = np.eye(3).T
+        assert_(not m.flags.c_contiguous)
+
+        new_m = asarray(m, order='C')
+        assert_(new_m.flags.c_contiguous)
+
+    def test_fix_invalid(self):
+        # Checks fix_invalid.
+        with np.errstate(invalid='ignore'):
+            data = masked_array([np.nan, 0., 1.], mask=[0, 0, 1])
+            data_fixed = fix_invalid(data)
+            assert_equal(data_fixed._data, [data.fill_value, 0., 1.])
+            assert_equal(data_fixed._mask, [1., 0., 1.])
+
+    def test_maskedelement(self):
+        # Test of masked element
+        x = arange(6)
+        x[1] = masked
+        assert_(str(masked) == '--')
+        assert_(x[1] is masked)
+        assert_equal(filled(x[1], 0), 0)
+
+    def test_set_element_as_object(self):
+        # Tests setting elements with object
+        a = empty(1, dtype=object)
+        x = (1, 2, 3, 4, 5)
+        a[0] = x
+        assert_equal(a[0], x)
+        assert_(a[0] is x)
+
+        import datetime
+        dt = datetime.datetime.now()
+        a[0] = dt
+        assert_(a[0] is dt)
+
+    def test_indexing(self):
+        # Tests conversions and indexing
+        x1 = np.array([1, 2, 4, 3])
+        x2 = array(x1, mask=[1, 0, 0, 0])
+        x3 = array(x1, mask=[0, 1, 0, 1])
+        x4 = array(x1)
+        # test conversion to strings
+        str(x2)  # raises?
+        repr(x2)  # raises?
+        assert_equal(np.sort(x1), sort(x2, endwith=False))
+        # tests of indexing
+        assert_(type(x2[1]) is type(x1[1]))
+        assert_(x1[1] == x2[1])
+        assert_(x2[0] is masked)
+        assert_equal(x1[2], x2[2])
+        assert_equal(x1[2:5], x2[2:5])
+        assert_equal(x1[:], x2[:])
+        assert_equal(x1[1:], x3[1:])
+        x1[2] = 9
+        x2[2] = 9
+        assert_equal(x1, x2)
+        x1[1:3] = 99
+        x2[1:3] = 99
+        assert_equal(x1, x2)
+        x2[1] = masked
+        assert_equal(x1, x2)
+        x2[1:3] = masked
+        assert_equal(x1, x2)
+        x2[:] = x1
+        x2[1] = masked
+        assert_(allequal(getmask(x2), array([0, 1, 0, 0])))
+        x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        assert_(allequal(getmask(x3), array([0, 1, 1, 0])))
+        x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        assert_(allequal(getmask(x4), array([0, 1, 1, 0])))
+        assert_(allequal(x4, array([1, 2, 3, 4])))
+        x1 = np.arange(5) * 1.0
+        x2 = masked_values(x1, 3.0)
+        assert_equal(x1, x2)
+        assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
+        assert_equal(3.0, x2.fill_value)
+        x1 = array([1, 'hello', 2, 3], object)
+        x2 = np.array([1, 'hello', 2, 3], object)
+        s1 = x1[1]
+        s2 = x2[1]
+        assert_equal(type(s2), str)
+        assert_equal(type(s1), str)
+        assert_equal(s1, s2)
+        assert_(x1[1:1].shape == (0,))
+
+    def test_setitem_no_warning(self):
+        # Setitem shouldn't warn, because the assignment might be masked
+        # and warning for a masked assignment is weird (see gh-23000)
+        # (When the value is masked, otherwise a warning would be acceptable
+        # but is not given currently.)
+        x = np.ma.arange(60).reshape((6, 10))
+        index = (slice(1, 5, 2), [7, 5])
+        value = np.ma.masked_all((2, 2))
+        value._data[...] = np.inf  # not a valid integer...
+        x[index] = value
+        # The masked scalar is special cased, but test anyway (it's NaN):
+        x[...] = np.ma.masked
+        # Finally, a large value that cannot be cast to the float32 `x`
+        x = np.ma.arange(3., dtype=np.float32)
+        value = np.ma.array([2e234, 1, 1], mask=[True, False, False])
+        x[...] = value
+        x[[0, 1, 2]] = value
+
+    @suppress_copy_mask_on_assignment
+    def test_copy(self):
+        # Tests of some subtle points of copying and sizing.
+        n = [0, 0, 1, 0, 0]
+        m = make_mask(n)
+        m2 = make_mask(m)
+        assert_(m is m2)
+        m3 = make_mask(m, copy=True)
+        assert_(m is not m3)
+
+        x1 = np.arange(5)
+        y1 = array(x1, mask=m)
+        assert_equal(y1._data.__array_interface__, x1.__array_interface__)
+        assert_(allequal(x1, y1.data))
+        assert_equal(y1._mask.__array_interface__, m.__array_interface__)
+
+        y1a = array(y1)
+        # Default for masked array is not to copy; see gh-10318.
+        assert_(y1a._data.__array_interface__ ==
+                        y1._data.__array_interface__)
+        assert_(y1a._mask.__array_interface__ ==
+                        y1._mask.__array_interface__)
+
+        y2 = array(x1, mask=m3)
+        assert_(y2._data.__array_interface__ == x1.__array_interface__)
+        assert_(y2._mask.__array_interface__ == m3.__array_interface__)
+        assert_(y2[2] is masked)
+        y2[2] = 9
+        assert_(y2[2] is not masked)
+        assert_(y2._mask.__array_interface__ == m3.__array_interface__)
+        assert_(allequal(y2.mask, 0))
+
+        y2a = array(x1, mask=m, copy=1)
+        assert_(y2a._data.__array_interface__ != x1.__array_interface__)
+        #assert_( y2a._mask is not m)
+        assert_(y2a._mask.__array_interface__ != m.__array_interface__)
+        assert_(y2a[2] is masked)
+        y2a[2] = 9
+        assert_(y2a[2] is not masked)
+        #assert_( y2a._mask is not m)
+        assert_(y2a._mask.__array_interface__ != m.__array_interface__)
+        assert_(allequal(y2a.mask, 0))
+
+        y3 = array(x1 * 1.0, mask=m)
+        assert_(filled(y3).dtype is (x1 * 1.0).dtype)
+
+        x4 = arange(4)
+        x4[2] = masked
+        y4 = resize(x4, (8,))
+        assert_equal(concatenate([x4, x4]), y4)
+        assert_equal(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])
+        y5 = repeat(x4, (2, 2, 2, 2), axis=0)
+        assert_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3])
+        y6 = repeat(x4, 2, axis=0)
+        assert_equal(y5, y6)
+        y7 = x4.repeat((2, 2, 2, 2), axis=0)
+        assert_equal(y5, y7)
+        y8 = x4.repeat(2, 0)
+        assert_equal(y5, y8)
+
+        y9 = x4.copy()
+        assert_equal(y9._data, x4._data)
+        assert_equal(y9._mask, x4._mask)
+
+        x = masked_array([1, 2, 3], mask=[0, 1, 0])
+        # Copy is False by default
+        y = masked_array(x)
+        assert_equal(y._data.ctypes.data, x._data.ctypes.data)
+        assert_equal(y._mask.ctypes.data, x._mask.ctypes.data)
+        y = masked_array(x, copy=True)
+        assert_not_equal(y._data.ctypes.data, x._data.ctypes.data)
+        assert_not_equal(y._mask.ctypes.data, x._mask.ctypes.data)
+
+    def test_copy_0d(self):
+        # gh-9430
+        x = np.ma.array(43, mask=True)
+        xc = x.copy()
+        assert_equal(xc.mask, True)
+
+    def test_copy_on_python_builtins(self):
+        # Tests copy works on python builtins (issue#8019)
+        assert_(isMaskedArray(np.ma.copy([1,2,3])))
+        assert_(isMaskedArray(np.ma.copy((1,2,3))))
+
+    def test_copy_immutable(self):
+        # Tests that the copy method is immutable, GitHub issue #5247
+        a = np.ma.array([1, 2, 3])
+        b = np.ma.array([4, 5, 6])
+        a_copy_method = a.copy
+        b.copy
+        assert_equal(a_copy_method(), [1, 2, 3])
+
+    def test_deepcopy(self):
+        from copy import deepcopy
+        a = array([0, 1, 2], mask=[False, True, False])
+        copied = deepcopy(a)
+        assert_equal(copied.mask, a.mask)
+        assert_not_equal(id(a._mask), id(copied._mask))
+
+        copied[1] = 1
+        assert_equal(copied.mask, [0, 0, 0])
+        assert_equal(a.mask, [0, 1, 0])
+
+        copied = deepcopy(a)
+        assert_equal(copied.mask, a.mask)
+        copied.mask[1] = False
+        assert_equal(copied.mask, [0, 0, 0])
+        assert_equal(a.mask, [0, 1, 0])
+
+    def test_format(self):
+        a = array([0, 1, 2], mask=[False, True, False])
+        assert_equal(format(a), "[0 -- 2]")
+        assert_equal(format(masked), "--")
+        assert_equal(format(masked, ""), "--")
+
+        # Postponed from PR #15410, perhaps address in the future.
+        # assert_equal(format(masked, " >5"), "   --")
+        # assert_equal(format(masked, " <5"), "--   ")
+
+        # Expect a FutureWarning for using format_spec with MaskedElement
+        with assert_warns(FutureWarning):
+            with_format_string = format(masked, " >5")
+        assert_equal(with_format_string, "--")
+
+    def test_str_repr(self):
+        a = array([0, 1, 2], mask=[False, True, False])
+        assert_equal(str(a), '[0 -- 2]')
+        assert_equal(
+            repr(a),
+            textwrap.dedent('''\
+            masked_array(data=[0, --, 2],
+                         mask=[False,  True, False],
+                   fill_value=999999)''')
+        )
+
+        # arrays with a continuation
+        a = np.ma.arange(2000)
+        a[1:50] = np.ma.masked
+        assert_equal(
+            repr(a),
+            textwrap.dedent('''\
+            masked_array(data=[0, --, --, ..., 1997, 1998, 1999],
+                         mask=[False,  True,  True, ..., False, False, False],
+                   fill_value=999999)''')
+        )
+
+        # line-wrapped 1d arrays are correctly aligned
+        a = np.ma.arange(20)
+        assert_equal(
+            repr(a),
+            textwrap.dedent('''\
+            masked_array(data=[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13,
+                               14, 15, 16, 17, 18, 19],
+                         mask=False,
+                   fill_value=999999)''')
+        )
+
+        # 2d arrays cause wrapping
+        a = array([[1, 2, 3], [4, 5, 6]], dtype=np.int8)
+        a[1,1] = np.ma.masked
+        assert_equal(
+            repr(a),
+            textwrap.dedent(f'''\
+            masked_array(
+              data=[[1, 2, 3],
+                    [4, --, 6]],
+              mask=[[False, False, False],
+                    [False,  True, False]],
+              fill_value={np.array(999999)[()]!r},
+              dtype=int8)''')
+        )
+
+        # but not it they're a row vector
+        assert_equal(
+            repr(a[:1]),
+            textwrap.dedent(f'''\
+            masked_array(data=[[1, 2, 3]],
+                         mask=[[False, False, False]],
+                   fill_value={np.array(999999)[()]!r},
+                        dtype=int8)''')
+        )
+
+        # dtype=int is implied, so not shown
+        assert_equal(
+            repr(a.astype(int)),
+            textwrap.dedent('''\
+            masked_array(
+              data=[[1, 2, 3],
+                    [4, --, 6]],
+              mask=[[False, False, False],
+                    [False,  True, False]],
+              fill_value=999999)''')
+        )
+
+    def test_str_repr_legacy(self):
+        oldopts = np.get_printoptions()
+        np.set_printoptions(legacy='1.13')
+        try:
+            a = array([0, 1, 2], mask=[False, True, False])
+            assert_equal(str(a), '[0 -- 2]')
+            assert_equal(repr(a), 'masked_array(data = [0 -- 2],\n'
+                                  '             mask = [False  True False],\n'
+                                  '       fill_value = 999999)\n')
+
+            a = np.ma.arange(2000)
+            a[1:50] = np.ma.masked
+            assert_equal(
+                repr(a),
+                'masked_array(data = [0 -- -- ..., 1997 1998 1999],\n'
+                '             mask = [False  True  True ..., False False False],\n'
+                '       fill_value = 999999)\n'
+            )
+        finally:
+            np.set_printoptions(**oldopts)
+
+    def test_0d_unicode(self):
+        u = 'caf\xe9'
+        utype = type(u)
+
+        arr_nomask = np.ma.array(u)
+        arr_masked = np.ma.array(u, mask=True)
+
+        assert_equal(utype(arr_nomask), u)
+        assert_equal(utype(arr_masked), '--')
+
+    def test_pickling(self):
+        # Tests pickling
+        for dtype in (int, float, str, object):
+            a = arange(10).astype(dtype)
+            a.fill_value = 999
+
+            masks = ([0, 0, 0, 1, 0, 1, 0, 1, 0, 1],  # partially masked
+                     True,                            # Fully masked
+                     False)                           # Fully unmasked
+
+            for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+                for mask in masks:
+                    a.mask = mask
+                    a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+                    assert_equal(a_pickled._mask, a._mask)
+                    assert_equal(a_pickled._data, a._data)
+                    if dtype in (object, int):
+                        assert_equal(a_pickled.fill_value, 999)
+                    else:
+                        assert_equal(a_pickled.fill_value, dtype(999))
+                    assert_array_equal(a_pickled.mask, mask)
+
+    def test_pickling_subbaseclass(self):
+        # Test pickling w/ a subclass of ndarray
+        x = np.array([(1.0, 2), (3.0, 4)],
+                     dtype=[('x', float), ('y', int)]).view(np.recarray)
+        a = masked_array(x, mask=[(True, False), (False, True)])
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+            assert_equal(a_pickled._mask, a._mask)
+            assert_equal(a_pickled, a)
+            assert_(isinstance(a_pickled._data, np.recarray))
+
+    def test_pickling_maskedconstant(self):
+        # Test pickling MaskedConstant
+        mc = np.ma.masked
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            mc_pickled = pickle.loads(pickle.dumps(mc, protocol=proto))
+            assert_equal(mc_pickled._baseclass, mc._baseclass)
+            assert_equal(mc_pickled._mask, mc._mask)
+            assert_equal(mc_pickled._data, mc._data)
+
+    def test_pickling_wstructured(self):
+        # Tests pickling w/ structured array
+        a = array([(1, 1.), (2, 2.)], mask=[(0, 0), (0, 1)],
+                  dtype=[('a', int), ('b', float)])
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+            assert_equal(a_pickled._mask, a._mask)
+            assert_equal(a_pickled, a)
+
+    def test_pickling_keepalignment(self):
+        # Tests pickling w/ F_CONTIGUOUS arrays
+        a = arange(10)
+        a.shape = (-1, 2)
+        b = a.T
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            test = pickle.loads(pickle.dumps(b, protocol=proto))
+            assert_equal(test, b)
+
+    def test_single_element_subscript(self):
+        # Tests single element subscripts of Maskedarrays.
+        a = array([1, 3, 2])
+        b = array([1, 3, 2], mask=[1, 0, 1])
+        assert_equal(a[0].shape, ())
+        assert_equal(b[0].shape, ())
+        assert_equal(b[1].shape, ())
+
+    def test_topython(self):
+        # Tests some communication issues with Python.
+        assert_equal(1, int(array(1)))
+        assert_equal(1.0, float(array(1)))
+        assert_equal(1, int(array([[[1]]])))
+        assert_equal(1.0, float(array([[1]])))
+        assert_raises(TypeError, float, array([1, 1]))
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning, 'Warning: converting a masked element')
+            assert_(np.isnan(float(array([1], mask=[1]))))
+
+            a = array([1, 2, 3], mask=[1, 0, 0])
+            assert_raises(TypeError, lambda: float(a))
+            assert_equal(float(a[-1]), 3.)
+            assert_(np.isnan(float(a[0])))
+        assert_raises(TypeError, int, a)
+        assert_equal(int(a[-1]), 3)
+        assert_raises(MAError, lambda:int(a[0]))
+
+    def test_oddfeatures_1(self):
+        # Test of other odd features
+        x = arange(20)
+        x = x.reshape(4, 5)
+        x.flat[5] = 12
+        assert_(x[1, 0] == 12)
+        z = x + 10j * x
+        assert_equal(z.real, x)
+        assert_equal(z.imag, 10 * x)
+        assert_equal((z * conjugate(z)).real, 101 * x * x)
+        z.imag[...] = 0.0
+
+        x = arange(10)
+        x[3] = masked
+        assert_(str(x[3]) == str(masked))
+        c = x >= 8
+        assert_(count(where(c, masked, masked)) == 0)
+        assert_(shape(where(c, masked, masked)) == c.shape)
+
+        z = masked_where(c, x)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is not masked)
+        assert_(z[7] is not masked)
+        assert_(z[8] is masked)
+        assert_(z[9] is masked)
+        assert_equal(x, z)
+
+    def test_oddfeatures_2(self):
+        # Tests some more features.
+        x = array([1., 2., 3., 4., 5.])
+        c = array([1, 1, 1, 0, 0])
+        x[2] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        c[0] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+
+    @suppress_copy_mask_on_assignment
+    def test_oddfeatures_3(self):
+        # Tests some generic features
+        atest = array([10], mask=True)
+        btest = array([20])
+        idx = atest.mask
+        atest[idx] = btest[idx]
+        assert_equal(atest, [20])
+
+    def test_filled_with_object_dtype(self):
+        a = np.ma.masked_all(1, dtype='O')
+        assert_equal(a.filled('x')[0], 'x')
+
+    def test_filled_with_flexible_dtype(self):
+        # Test filled w/ flexible dtype
+        flexi = array([(1, 1, 1)],
+                      dtype=[('i', int), ('s', '|S8'), ('f', float)])
+        flexi[0] = masked
+        assert_equal(flexi.filled(),
+                     np.array([(default_fill_value(0),
+                                default_fill_value('0'),
+                                default_fill_value(0.),)], dtype=flexi.dtype))
+        flexi[0] = masked
+        assert_equal(flexi.filled(1),
+                     np.array([(1, '1', 1.)], dtype=flexi.dtype))
+
+    def test_filled_with_mvoid(self):
+        # Test filled w/ mvoid
+        ndtype = [('a', int), ('b', float)]
+        a = mvoid((1, 2.), mask=[(0, 1)], dtype=ndtype)
+        # Filled using default
+        test = a.filled()
+        assert_equal(tuple(test), (1, default_fill_value(1.)))
+        # Explicit fill_value
+        test = a.filled((-1, -1))
+        assert_equal(tuple(test), (1, -1))
+        # Using predefined filling values
+        a.fill_value = (-999, -999)
+        assert_equal(tuple(a.filled()), (1, -999))
+
+    def test_filled_with_nested_dtype(self):
+        # Test filled w/ nested dtype
+        ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
+        a = array([(1, (1, 1)), (2, (2, 2))],
+                  mask=[(0, (1, 0)), (0, (0, 1))], dtype=ndtype)
+        test = a.filled(0)
+        control = np.array([(1, (0, 1)), (2, (2, 0))], dtype=ndtype)
+        assert_equal(test, control)
+
+        test = a['B'].filled(0)
+        control = np.array([(0, 1), (2, 0)], dtype=a['B'].dtype)
+        assert_equal(test, control)
+
+        # test if mask gets set correctly (see #6760)
+        Z = numpy.ma.zeros(2, numpy.dtype([("A", "(2,2)i1,(2,2)i1", (2,2))]))
+        assert_equal(Z.data.dtype, numpy.dtype([('A', [('f0', 'i1', (2, 2)),
+                                          ('f1', 'i1', (2, 2))], (2, 2))]))
+        assert_equal(Z.mask.dtype, numpy.dtype([('A', [('f0', '?', (2, 2)),
+                                          ('f1', '?', (2, 2))], (2, 2))]))
+
+    def test_filled_with_f_order(self):
+        # Test filled w/ F-contiguous array
+        a = array(np.array([(0, 1, 2), (4, 5, 6)], order='F'),
+                  mask=np.array([(0, 0, 1), (1, 0, 0)], order='F'),
+                  order='F')  # this is currently ignored
+        assert_(a.flags['F_CONTIGUOUS'])
+        assert_(a.filled(0).flags['F_CONTIGUOUS'])
+
+    def test_optinfo_propagation(self):
+        # Checks that _optinfo dictionary isn't back-propagated
+        x = array([1, 2, 3, ], dtype=float)
+        x._optinfo['info'] = '???'
+        y = x.copy()
+        assert_equal(y._optinfo['info'], '???')
+        y._optinfo['info'] = '!!!'
+        assert_equal(x._optinfo['info'], '???')
+
+    def test_optinfo_forward_propagation(self):
+        a = array([1,2,2,4])
+        a._optinfo["key"] = "value"
+        assert_equal(a._optinfo["key"], (a == 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a != 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a > 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a >= 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a <= 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a + 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a - 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a * 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], (a / 2)._optinfo["key"])
+        assert_equal(a._optinfo["key"], a[:2]._optinfo["key"])
+        assert_equal(a._optinfo["key"], a[[0,0,2]]._optinfo["key"])
+        assert_equal(a._optinfo["key"], np.exp(a)._optinfo["key"])
+        assert_equal(a._optinfo["key"], np.abs(a)._optinfo["key"])
+        assert_equal(a._optinfo["key"], array(a, copy=True)._optinfo["key"])
+        assert_equal(a._optinfo["key"], np.zeros_like(a)._optinfo["key"])
+
+    def test_fancy_printoptions(self):
+        # Test printing a masked array w/ fancy dtype.
+        fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
+        test = array([(1, (2, 3.0)), (4, (5, 6.0))],
+                     mask=[(1, (0, 1)), (0, (1, 0))],
+                     dtype=fancydtype)
+        control = "[(--, (2, --)) (4, (--, 6.0))]"
+        assert_equal(str(test), control)
+
+        # Test 0-d array with multi-dimensional dtype
+        t_2d0 = masked_array(data = (0, [[0.0, 0.0, 0.0],
+                                        [0.0, 0.0, 0.0]],
+                                    0.0),
+                             mask = (False, [[True, False, True],
+                                             [False, False, True]],
+                                     False),
+                             dtype = "int, (2,3)float, float")
+        control = "(0, [[--, 0.0, --], [0.0, 0.0, --]], 0.0)"
+        assert_equal(str(t_2d0), control)
+
+    def test_flatten_structured_array(self):
+        # Test flatten_structured_array on arrays
+        # On ndarray
+        ndtype = [('a', int), ('b', float)]
+        a = np.array([(1, 1), (2, 2)], dtype=ndtype)
+        test = flatten_structured_array(a)
+        control = np.array([[1., 1.], [2., 2.]], dtype=float)
+        assert_equal(test, control)
+        assert_equal(test.dtype, control.dtype)
+        # On masked_array
+        a = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
+        test = flatten_structured_array(a)
+        control = array([[1., 1.], [2., 2.]],
+                        mask=[[0, 1], [1, 0]], dtype=float)
+        assert_equal(test, control)
+        assert_equal(test.dtype, control.dtype)
+        assert_equal(test.mask, control.mask)
+        # On masked array with nested structure
+        ndtype = [('a', int), ('b', [('ba', int), ('bb', float)])]
+        a = array([(1, (1, 1.1)), (2, (2, 2.2))],
+                  mask=[(0, (1, 0)), (1, (0, 1))], dtype=ndtype)
+        test = flatten_structured_array(a)
+        control = array([[1., 1., 1.1], [2., 2., 2.2]],
+                        mask=[[0, 1, 0], [1, 0, 1]], dtype=float)
+        assert_equal(test, control)
+        assert_equal(test.dtype, control.dtype)
+        assert_equal(test.mask, control.mask)
+        # Keeping the initial shape
+        ndtype = [('a', int), ('b', float)]
+        a = np.array([[(1, 1), ], [(2, 2), ]], dtype=ndtype)
+        test = flatten_structured_array(a)
+        control = np.array([[[1., 1.], ], [[2., 2.], ]], dtype=float)
+        assert_equal(test, control)
+        assert_equal(test.dtype, control.dtype)
+
+    def test_void0d(self):
+        # Test creating a mvoid object
+        ndtype = [('a', int), ('b', int)]
+        a = np.array([(1, 2,)], dtype=ndtype)[0]
+        f = mvoid(a)
+        assert_(isinstance(f, mvoid))
+
+        a = masked_array([(1, 2)], mask=[(1, 0)], dtype=ndtype)[0]
+        assert_(isinstance(a, mvoid))
+
+        a = masked_array([(1, 2), (1, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
+        f = mvoid(a._data[0], a._mask[0])
+        assert_(isinstance(f, mvoid))
+
+    def test_mvoid_getitem(self):
+        # Test mvoid.__getitem__
+        ndtype = [('a', int), ('b', int)]
+        a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
+                         dtype=ndtype)
+        # w/o mask
+        f = a[0]
+        assert_(isinstance(f, mvoid))
+        assert_equal((f[0], f['a']), (1, 1))
+        assert_equal(f['b'], 2)
+        # w/ mask
+        f = a[1]
+        assert_(isinstance(f, mvoid))
+        assert_(f[0] is masked)
+        assert_(f['a'] is masked)
+        assert_equal(f[1], 4)
+
+        # exotic dtype
+        A = masked_array(data=[([0,1],)],
+                         mask=[([True, False],)],
+                         dtype=[("A", ">i2", (2,))])
+        assert_equal(A[0]["A"], A["A"][0])
+        assert_equal(A[0]["A"], masked_array(data=[0, 1],
+                         mask=[True, False], dtype=">i2"))
+
+    def test_mvoid_iter(self):
+        # Test iteration on __getitem__
+        ndtype = [('a', int), ('b', int)]
+        a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
+                         dtype=ndtype)
+        # w/o mask
+        assert_equal(list(a[0]), [1, 2])
+        # w/ mask
+        assert_equal(list(a[1]), [masked, 4])
+
+    def test_mvoid_print(self):
+        # Test printing a mvoid
+        mx = array([(1, 1), (2, 2)], dtype=[('a', int), ('b', int)])
+        assert_equal(str(mx[0]), "(1, 1)")
+        mx['b'][0] = masked
+        ini_display = masked_print_option._display
+        masked_print_option.set_display("-X-")
+        try:
+            assert_equal(str(mx[0]), "(1, -X-)")
+            assert_equal(repr(mx[0]), "(1, -X-)")
+        finally:
+            masked_print_option.set_display(ini_display)
+
+        # also check if there are object datatypes (see gh-7493)
+        mx = array([(1,), (2,)], dtype=[('a', 'O')])
+        assert_equal(str(mx[0]), "(1,)")
+
+    def test_mvoid_multidim_print(self):
+
+        # regression test for gh-6019
+        t_ma = masked_array(data = [([1, 2, 3],)],
+                            mask = [([False, True, False],)],
+                            fill_value = ([999999, 999999, 999999],),
+                            dtype = [('a', ' 1:
+            assert_equal(np.concatenate((x, y), 1), concatenate((xm, ym), 1))
+            assert_equal(np.add.reduce(x, 1), add.reduce(x, 1))
+            assert_equal(np.sum(x, 1), sum(x, 1))
+            assert_equal(np.prod(x, 1), product(x, 1))
+
+    def test_binops_d2D(self):
+        # Test binary operations on 2D data
+        a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
+        b = array([[2., 3.], [4., 5.], [6., 7.]])
+
+        test = a * b
+        control = array([[2., 3.], [2., 2.], [3., 3.]],
+                        mask=[[0, 0], [1, 1], [1, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        test = b * a
+        control = array([[2., 3.], [4., 5.], [6., 7.]],
+                        mask=[[0, 0], [1, 1], [1, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        a = array([[1.], [2.], [3.]])
+        b = array([[2., 3.], [4., 5.], [6., 7.]],
+                  mask=[[0, 0], [0, 0], [0, 1]])
+        test = a * b
+        control = array([[2, 3], [8, 10], [18, 3]],
+                        mask=[[0, 0], [0, 0], [0, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        test = b * a
+        control = array([[2, 3], [8, 10], [18, 7]],
+                        mask=[[0, 0], [0, 0], [0, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+    def test_domained_binops_d2D(self):
+        # Test domained binary operations on 2D data
+        a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
+        b = array([[2., 3.], [4., 5.], [6., 7.]])
+
+        test = a / b
+        control = array([[1. / 2., 1. / 3.], [2., 2.], [3., 3.]],
+                        mask=[[0, 0], [1, 1], [1, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        test = b / a
+        control = array([[2. / 1., 3. / 1.], [4., 5.], [6., 7.]],
+                        mask=[[0, 0], [1, 1], [1, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        a = array([[1.], [2.], [3.]])
+        b = array([[2., 3.], [4., 5.], [6., 7.]],
+                  mask=[[0, 0], [0, 0], [0, 1]])
+        test = a / b
+        control = array([[1. / 2, 1. / 3], [2. / 4, 2. / 5], [3. / 6, 3]],
+                        mask=[[0, 0], [0, 0], [0, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+        test = b / a
+        control = array([[2 / 1., 3 / 1.], [4 / 2., 5 / 2.], [6 / 3., 7]],
+                        mask=[[0, 0], [0, 0], [0, 1]])
+        assert_equal(test, control)
+        assert_equal(test.data, control.data)
+        assert_equal(test.mask, control.mask)
+
+    def test_noshrinking(self):
+        # Check that we don't shrink a mask when not wanted
+        # Binary operations
+        a = masked_array([1., 2., 3.], mask=[False, False, False],
+                         shrink=False)
+        b = a + 1
+        assert_equal(b.mask, [0, 0, 0])
+        # In place binary operation
+        a += 1
+        assert_equal(a.mask, [0, 0, 0])
+        # Domained binary operation
+        b = a / 1.
+        assert_equal(b.mask, [0, 0, 0])
+        # In place binary operation
+        a /= 1.
+        assert_equal(a.mask, [0, 0, 0])
+
+    def test_ufunc_nomask(self):
+        # check the case ufuncs should set the mask to false
+        m = np.ma.array([1])
+        # check we don't get array([False], dtype=bool)
+        assert_equal(np.true_divide(m, 5).mask.shape, ())
+
+    def test_noshink_on_creation(self):
+        # Check that the mask is not shrunk on array creation when not wanted
+        a = np.ma.masked_values([1., 2.5, 3.1], 1.5, shrink=False)
+        assert_equal(a.mask, [0, 0, 0])
+
+    def test_mod(self):
+        # Tests mod
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
+        assert_equal(mod(x, y), mod(xm, ym))
+        test = mod(ym, xm)
+        assert_equal(test, np.mod(ym, xm))
+        assert_equal(test.mask, mask_or(xm.mask, ym.mask))
+        test = mod(xm, ym)
+        assert_equal(test, np.mod(xm, ym))
+        assert_equal(test.mask, mask_or(mask_or(xm.mask, ym.mask), (ym == 0)))
+
+    def test_TakeTransposeInnerOuter(self):
+        # Test of take, transpose, inner, outer products
+        x = arange(24)
+        y = np.arange(24)
+        x[5:6] = masked
+        x = x.reshape(2, 3, 4)
+        y = y.reshape(2, 3, 4)
+        assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
+        assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
+        assert_equal(np.inner(filled(x, 0), filled(y, 0)),
+                     inner(x, y))
+        assert_equal(np.outer(filled(x, 0), filled(y, 0)),
+                     outer(x, y))
+        y = array(['abc', 1, 'def', 2, 3], object)
+        y[2] = masked
+        t = take(y, [0, 3, 4])
+        assert_(t[0] == 'abc')
+        assert_(t[1] == 2)
+        assert_(t[2] == 3)
+
+    def test_imag_real(self):
+        # Check complex
+        xx = array([1 + 10j, 20 + 2j], mask=[1, 0])
+        assert_equal(xx.imag, [10, 2])
+        assert_equal(xx.imag.filled(), [1e+20, 2])
+        assert_equal(xx.imag.dtype, xx._data.imag.dtype)
+        assert_equal(xx.real, [1, 20])
+        assert_equal(xx.real.filled(), [1e+20, 20])
+        assert_equal(xx.real.dtype, xx._data.real.dtype)
+
+    def test_methods_with_output(self):
+        xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
+        xm[:, 0] = xm[0] = xm[-1, -1] = masked
+
+        funclist = ('sum', 'prod', 'var', 'std', 'max', 'min', 'ptp', 'mean',)
+
+        for funcname in funclist:
+            npfunc = getattr(np, funcname)
+            xmmeth = getattr(xm, funcname)
+            # A ndarray as explicit input
+            output = np.empty(4, dtype=float)
+            output.fill(-9999)
+            result = npfunc(xm, axis=0, out=output)
+            # ... the result should be the given output
+            assert_(result is output)
+            assert_equal(result, xmmeth(axis=0, out=output))
+
+            output = empty(4, dtype=int)
+            result = xmmeth(axis=0, out=output)
+            assert_(result is output)
+            assert_(output[0] is masked)
+
+    def test_eq_on_structured(self):
+        # Test the equality of structured arrays
+        ndtype = [('A', int), ('B', int)]
+        a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
+
+        test = (a == a)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        test = (a == a[0])
+        assert_equal(test.data, [True, False])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
+        test = (a == b)
+        assert_equal(test.data, [False, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (a[0] == b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
+        test = (a == b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        # complicated dtype, 2-dimensional array.
+        ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
+        a = array([[(1, (1, 1)), (2, (2, 2))],
+                   [(3, (3, 3)), (4, (4, 4))]],
+                  mask=[[(0, (1, 0)), (0, (0, 1))],
+                        [(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
+        test = (a[0, 0] == a)
+        assert_equal(test.data, [[True, False], [False, False]])
+        assert_equal(test.mask, [[False, False], [False, True]])
+        assert_(test.fill_value == True)
+
+    def test_ne_on_structured(self):
+        # Test the equality of structured arrays
+        ndtype = [('A', int), ('B', int)]
+        a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
+
+        test = (a != a)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        test = (a != a[0])
+        assert_equal(test.data, [False, True])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
+        test = (a != b)
+        assert_equal(test.data, [True, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (a[0] != b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
+        test = (a != b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [False, False])
+        assert_(test.fill_value == True)
+
+        # complicated dtype, 2-dimensional array.
+        ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
+        a = array([[(1, (1, 1)), (2, (2, 2))],
+                   [(3, (3, 3)), (4, (4, 4))]],
+                  mask=[[(0, (1, 0)), (0, (0, 1))],
+                        [(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
+        test = (a[0, 0] != a)
+        assert_equal(test.data, [[False, True], [True, True]])
+        assert_equal(test.mask, [[False, False], [False, True]])
+        assert_(test.fill_value == True)
+
+    def test_eq_ne_structured_with_non_masked(self):
+        a = array([(1, 1), (2, 2), (3, 4)],
+                  mask=[(0, 1), (0, 0), (1, 1)], dtype='i4,i4')
+        eq = a == a.data
+        ne = a.data != a
+        # Test the obvious.
+        assert_(np.all(eq))
+        assert_(not np.any(ne))
+        # Expect the mask set only for items with all fields masked.
+        expected_mask = a.mask == np.ones((), a.mask.dtype)
+        assert_array_equal(eq.mask, expected_mask)
+        assert_array_equal(ne.mask, expected_mask)
+        # The masked element will indicated not equal, because the
+        # masks did not match.
+        assert_equal(eq.data, [True, True, False])
+        assert_array_equal(eq.data, ~ne.data)
+
+    def test_eq_ne_structured_extra(self):
+        # ensure simple examples are symmetric and make sense.
+        # from https://github.com/numpy/numpy/pull/8590#discussion_r101126465
+        dt = np.dtype('i4,i4')
+        for m1 in (mvoid((1, 2), mask=(0, 0), dtype=dt),
+                   mvoid((1, 2), mask=(0, 1), dtype=dt),
+                   mvoid((1, 2), mask=(1, 0), dtype=dt),
+                   mvoid((1, 2), mask=(1, 1), dtype=dt)):
+            ma1 = m1.view(MaskedArray)
+            r1 = ma1.view('2i4')
+            for m2 in (np.array((1, 1), dtype=dt),
+                       mvoid((1, 1), dtype=dt),
+                       mvoid((1, 0), mask=(0, 1), dtype=dt),
+                       mvoid((3, 2), mask=(0, 1), dtype=dt)):
+                ma2 = m2.view(MaskedArray)
+                r2 = ma2.view('2i4')
+                eq_expected = (r1 == r2).all()
+                assert_equal(m1 == m2, eq_expected)
+                assert_equal(m2 == m1, eq_expected)
+                assert_equal(ma1 == m2, eq_expected)
+                assert_equal(m1 == ma2, eq_expected)
+                assert_equal(ma1 == ma2, eq_expected)
+                # Also check it is the same if we do it element by element.
+                el_by_el = [m1[name] == m2[name] for name in dt.names]
+                assert_equal(array(el_by_el, dtype=bool).all(), eq_expected)
+                ne_expected = (r1 != r2).any()
+                assert_equal(m1 != m2, ne_expected)
+                assert_equal(m2 != m1, ne_expected)
+                assert_equal(ma1 != m2, ne_expected)
+                assert_equal(m1 != ma2, ne_expected)
+                assert_equal(ma1 != ma2, ne_expected)
+                el_by_el = [m1[name] != m2[name] for name in dt.names]
+                assert_equal(array(el_by_el, dtype=bool).any(), ne_expected)
+
+    @pytest.mark.parametrize('dt', ['S', 'U'])
+    @pytest.mark.parametrize('fill', [None, 'A'])
+    def test_eq_for_strings(self, dt, fill):
+        # Test the equality of structured arrays
+        a = array(['a', 'b'], dtype=dt, mask=[0, 1], fill_value=fill)
+
+        test = (a == a)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        test = (a == a[0])
+        assert_equal(test.data, [True, False])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        b = array(['a', 'b'], dtype=dt, mask=[1, 0], fill_value=fill)
+        test = (a == b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, True])
+        assert_(test.fill_value == True)
+
+        test = (a[0] == b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (b == a[0])
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+    @pytest.mark.parametrize('dt', ['S', 'U'])
+    @pytest.mark.parametrize('fill', [None, 'A'])
+    def test_ne_for_strings(self, dt, fill):
+        # Test the equality of structured arrays
+        a = array(['a', 'b'], dtype=dt, mask=[0, 1], fill_value=fill)
+
+        test = (a != a)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        test = (a != a[0])
+        assert_equal(test.data, [False, True])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        b = array(['a', 'b'], dtype=dt, mask=[1, 0], fill_value=fill)
+        test = (a != b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, True])
+        assert_(test.fill_value == True)
+
+        test = (a[0] != b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (b != a[0])
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+    @pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('fill', [None, 1])
+    def test_eq_for_numeric(self, dt1, dt2, fill):
+        # Test the equality of structured arrays
+        a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
+
+        test = (a == a)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        test = (a == a[0])
+        assert_equal(test.data, [True, False])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
+        test = (a == b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, True])
+        assert_(test.fill_value == True)
+
+        test = (a[0] == b)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (b == a[0])
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+    @pytest.mark.parametrize("op", [operator.eq, operator.lt])
+    def test_eq_broadcast_with_unmasked(self, op):
+        a = array([0, 1], mask=[0, 1])
+        b = np.arange(10).reshape(5, 2)
+        result = op(a, b)
+        assert_(result.mask.shape == b.shape)
+        assert_equal(result.mask, np.zeros(b.shape, bool) | a.mask)
+
+    @pytest.mark.parametrize("op", [operator.eq, operator.gt])
+    def test_comp_no_mask_not_broadcast(self, op):
+        # Regression test for failing doctest in MaskedArray.nonzero
+        # after gh-24556.
+        a = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        result = op(a, 3)
+        assert_(not result.mask.shape)
+        assert_(result.mask is nomask)
+
+    @pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('fill', [None, 1])
+    def test_ne_for_numeric(self, dt1, dt2, fill):
+        # Test the equality of structured arrays
+        a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
+
+        test = (a != a)
+        assert_equal(test.data, [False, False])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        test = (a != a[0])
+        assert_equal(test.data, [False, True])
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
+        test = (a != b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, True])
+        assert_(test.fill_value == True)
+
+        test = (a[0] != b)
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = (b != a[0])
+        assert_equal(test.data, [True, True])
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+    @pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
+    @pytest.mark.parametrize('fill', [None, 1])
+    @pytest.mark.parametrize('op',
+            [operator.le, operator.lt, operator.ge, operator.gt])
+    def test_comparisons_for_numeric(self, op, dt1, dt2, fill):
+        # Test the equality of structured arrays
+        a = array([0, 1], dtype=dt1, mask=[0, 1], fill_value=fill)
+
+        test = op(a, a)
+        assert_equal(test.data, op(a._data, a._data))
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        test = op(a, a[0])
+        assert_equal(test.data, op(a._data, a._data[0]))
+        assert_equal(test.mask, [False, True])
+        assert_(test.fill_value == True)
+
+        b = array([0, 1], dtype=dt2, mask=[1, 0], fill_value=fill)
+        test = op(a, b)
+        assert_equal(test.data, op(a._data, b._data))
+        assert_equal(test.mask, [True, True])
+        assert_(test.fill_value == True)
+
+        test = op(a[0], b)
+        assert_equal(test.data, op(a._data[0], b._data))
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+        test = op(b, a[0])
+        assert_equal(test.data, op(b._data, a._data[0]))
+        assert_equal(test.mask, [True, False])
+        assert_(test.fill_value == True)
+
+    @pytest.mark.parametrize('op',
+            [operator.le, operator.lt, operator.ge, operator.gt])
+    @pytest.mark.parametrize('fill', [None, "N/A"])
+    def test_comparisons_strings(self, op, fill):
+        # See gh-21770, mask propagation is broken for strings (and some other
+        # cases) so we explicitly test strings here.
+        # In principle only == and != may need special handling...
+        ma1 = masked_array(["a", "b", "cde"], mask=[0, 1, 0], fill_value=fill)
+        ma2 = masked_array(["cde", "b", "a"], mask=[0, 1, 0], fill_value=fill)
+        assert_equal(op(ma1, ma2)._data, op(ma1._data, ma2._data))
+
+    def test_eq_with_None(self):
+        # Really, comparisons with None should not be done, but check them
+        # anyway. Note that pep8 will flag these tests.
+        # Deprecation is in place for arrays, and when it happens this
+        # test will fail (and have to be changed accordingly).
+
+        # With partial mask
+        with suppress_warnings() as sup:
+            sup.filter(FutureWarning, "Comparison to `None`")
+            a = array([None, 1], mask=[0, 1])
+            assert_equal(a == None, array([True, False], mask=[0, 1]))  # noqa: E711
+            assert_equal(a.data == None, [True, False])  # noqa: E711
+            assert_equal(a != None, array([False, True], mask=[0, 1]))  # noqa: E711
+            # With nomask
+            a = array([None, 1], mask=False)
+            assert_equal(a == None, [True, False])  # noqa: E711
+            assert_equal(a != None, [False, True])  # noqa: E711
+            # With complete mask
+            a = array([None, 2], mask=True)
+            assert_equal(a == None, array([False, True], mask=True))  # noqa: E711
+            assert_equal(a != None, array([True, False], mask=True))  # noqa: E711
+            # Fully masked, even comparison to None should return "masked"
+            a = masked
+            assert_equal(a == None, masked)  # noqa: E711
+
+    def test_eq_with_scalar(self):
+        a = array(1)
+        assert_equal(a == 1, True)
+        assert_equal(a == 0, False)
+        assert_equal(a != 1, False)
+        assert_equal(a != 0, True)
+        b = array(1, mask=True)
+        assert_equal(b == 0, masked)
+        assert_equal(b == 1, masked)
+        assert_equal(b != 0, masked)
+        assert_equal(b != 1, masked)
+
+    def test_eq_different_dimensions(self):
+        m1 = array([1, 1], mask=[0, 1])
+        # test comparison with both masked and regular arrays.
+        for m2 in (array([[0, 1], [1, 2]]),
+                   np.array([[0, 1], [1, 2]])):
+            test = (m1 == m2)
+            assert_equal(test.data, [[False, False],
+                                     [True, False]])
+            assert_equal(test.mask, [[False, True],
+                                     [False, True]])
+
+    def test_numpyarithmetic(self):
+        # Check that the mask is not back-propagated when using numpy functions
+        a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1])
+        control = masked_array([np.nan, np.nan, 0, np.log(2), -1],
+                               mask=[1, 1, 0, 0, 1])
+
+        test = log(a)
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        assert_equal(a.mask, [0, 0, 0, 0, 1])
+
+        test = np.log(a)
+        assert_equal(test, control)
+        assert_equal(test.mask, control.mask)
+        assert_equal(a.mask, [0, 0, 0, 0, 1])
+
+
+class TestMaskedArrayAttributes:
+
+    def test_keepmask(self):
+        # Tests the keep mask flag
+        x = masked_array([1, 2, 3], mask=[1, 0, 0])
+        mx = masked_array(x)
+        assert_equal(mx.mask, x.mask)
+        mx = masked_array(x, mask=[0, 1, 0], keep_mask=False)
+        assert_equal(mx.mask, [0, 1, 0])
+        mx = masked_array(x, mask=[0, 1, 0], keep_mask=True)
+        assert_equal(mx.mask, [1, 1, 0])
+        # We default to true
+        mx = masked_array(x, mask=[0, 1, 0])
+        assert_equal(mx.mask, [1, 1, 0])
+
+    def test_hardmask(self):
+        # Test hard_mask
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        xh = array(d, mask=m, hard_mask=True)
+        # We need to copy, to avoid updating d in xh !
+        xs = array(d, mask=m, hard_mask=False, copy=True)
+        xh[[1, 4]] = [10, 40]
+        xs[[1, 4]] = [10, 40]
+        assert_equal(xh._data, [0, 10, 2, 3, 4])
+        assert_equal(xs._data, [0, 10, 2, 3, 40])
+        assert_equal(xs.mask, [0, 0, 0, 1, 0])
+        assert_(xh._hardmask)
+        assert_(not xs._hardmask)
+        xh[1:4] = [10, 20, 30]
+        xs[1:4] = [10, 20, 30]
+        assert_equal(xh._data, [0, 10, 20, 3, 4])
+        assert_equal(xs._data, [0, 10, 20, 30, 40])
+        assert_equal(xs.mask, nomask)
+        xh[0] = masked
+        xs[0] = masked
+        assert_equal(xh.mask, [1, 0, 0, 1, 1])
+        assert_equal(xs.mask, [1, 0, 0, 0, 0])
+        xh[:] = 1
+        xs[:] = 1
+        assert_equal(xh._data, [0, 1, 1, 3, 4])
+        assert_equal(xs._data, [1, 1, 1, 1, 1])
+        assert_equal(xh.mask, [1, 0, 0, 1, 1])
+        assert_equal(xs.mask, nomask)
+        # Switch to soft mask
+        xh.soften_mask()
+        xh[:] = arange(5)
+        assert_equal(xh._data, [0, 1, 2, 3, 4])
+        assert_equal(xh.mask, nomask)
+        # Switch back to hard mask
+        xh.harden_mask()
+        xh[xh < 3] = masked
+        assert_equal(xh._data, [0, 1, 2, 3, 4])
+        assert_equal(xh._mask, [1, 1, 1, 0, 0])
+        xh[filled(xh > 1, False)] = 5
+        assert_equal(xh._data, [0, 1, 2, 5, 5])
+        assert_equal(xh._mask, [1, 1, 1, 0, 0])
+
+        xh = array([[1, 2], [3, 4]], mask=[[1, 0], [0, 0]], hard_mask=True)
+        xh[0] = 0
+        assert_equal(xh._data, [[1, 0], [3, 4]])
+        assert_equal(xh._mask, [[1, 0], [0, 0]])
+        xh[-1, -1] = 5
+        assert_equal(xh._data, [[1, 0], [3, 5]])
+        assert_equal(xh._mask, [[1, 0], [0, 0]])
+        xh[filled(xh < 5, False)] = 2
+        assert_equal(xh._data, [[1, 2], [2, 5]])
+        assert_equal(xh._mask, [[1, 0], [0, 0]])
+
+    def test_hardmask_again(self):
+        # Another test of hardmask
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        xh = array(d, mask=m, hard_mask=True)
+        xh[4:5] = 999
+        xh[0:1] = 999
+        assert_equal(xh._data, [999, 1, 2, 3, 4])
+
+    def test_hardmask_oncemore_yay(self):
+        # OK, yet another test of hardmask
+        # Make sure that harden_mask/soften_mask//unshare_mask returns self
+        a = array([1, 2, 3], mask=[1, 0, 0])
+        b = a.harden_mask()
+        assert_equal(a, b)
+        b[0] = 0
+        assert_equal(a, b)
+        assert_equal(b, array([1, 2, 3], mask=[1, 0, 0]))
+        a = b.soften_mask()
+        a[0] = 0
+        assert_equal(a, b)
+        assert_equal(b, array([0, 2, 3], mask=[0, 0, 0]))
+
+    def test_smallmask(self):
+        # Checks the behaviour of _smallmask
+        a = arange(10)
+        a[1] = masked
+        a[1] = 1
+        assert_equal(a._mask, nomask)
+        a = arange(10)
+        a._smallmask = False
+        a[1] = masked
+        a[1] = 1
+        assert_equal(a._mask, zeros(10))
+
+    def test_shrink_mask(self):
+        # Tests .shrink_mask()
+        a = array([1, 2, 3], mask=[0, 0, 0])
+        b = a.shrink_mask()
+        assert_equal(a, b)
+        assert_equal(a.mask, nomask)
+
+        # Mask cannot be shrunk on structured types, so is a no-op
+        a = np.ma.array([(1, 2.0)], [('a', int), ('b', float)])
+        b = a.copy()
+        a.shrink_mask()
+        assert_equal(a.mask, b.mask)
+
+    def test_flat(self):
+        # Test that flat can return all types of items [#4585, #4615]
+        # test 2-D record array
+        # ... on structured array w/ masked records
+        x = array([[(1, 1.1, 'one'), (2, 2.2, 'two'), (3, 3.3, 'thr')],
+                   [(4, 4.4, 'fou'), (5, 5.5, 'fiv'), (6, 6.6, 'six')]],
+                  dtype=[('a', int), ('b', float), ('c', '|S8')])
+        x['a'][0, 1] = masked
+        x['b'][1, 0] = masked
+        x['c'][0, 2] = masked
+        x[-1, -1] = masked
+        xflat = x.flat
+        assert_equal(xflat[0], x[0, 0])
+        assert_equal(xflat[1], x[0, 1])
+        assert_equal(xflat[2], x[0, 2])
+        assert_equal(xflat[:3], x[0])
+        assert_equal(xflat[3], x[1, 0])
+        assert_equal(xflat[4], x[1, 1])
+        assert_equal(xflat[5], x[1, 2])
+        assert_equal(xflat[3:], x[1])
+        assert_equal(xflat[-1], x[-1, -1])
+        i = 0
+        j = 0
+        for xf in xflat:
+            assert_equal(xf, x[j, i])
+            i += 1
+            if i >= x.shape[-1]:
+                i = 0
+                j += 1
+
+    def test_assign_dtype(self):
+        # check that the mask's dtype is updated when dtype is changed
+        a = np.zeros(4, dtype='f4,i4')
+
+        m = np.ma.array(a)
+        m.dtype = np.dtype('f4')
+        repr(m)  # raises?
+        assert_equal(m.dtype, np.dtype('f4'))
+
+        # check that dtype changes that change shape of mask too much
+        # are not allowed
+        def assign():
+            m = np.ma.array(a)
+            m.dtype = np.dtype('f8')
+        assert_raises(ValueError, assign)
+
+        b = a.view(dtype='f4', type=np.ma.MaskedArray)  # raises?
+        assert_equal(b.dtype, np.dtype('f4'))
+
+        # check that nomask is preserved
+        a = np.zeros(4, dtype='f4')
+        m = np.ma.array(a)
+        m.dtype = np.dtype('f4,i4')
+        assert_equal(m.dtype, np.dtype('f4,i4'))
+        assert_equal(m._mask, np.ma.nomask)
+
+
+class TestFillingValues:
+
+    def test_check_on_scalar(self):
+        # Test _check_fill_value set to valid and invalid values
+        _check_fill_value = np.ma.core._check_fill_value
+
+        fval = _check_fill_value(0, int)
+        assert_equal(fval, 0)
+        fval = _check_fill_value(None, int)
+        assert_equal(fval, default_fill_value(0))
+
+        fval = _check_fill_value(0, "|S3")
+        assert_equal(fval, b"0")
+        fval = _check_fill_value(None, "|S3")
+        assert_equal(fval, default_fill_value(b"camelot!"))
+        assert_raises(TypeError, _check_fill_value, 1e+20, int)
+        assert_raises(TypeError, _check_fill_value, 'stuff', int)
+
+    def test_check_on_fields(self):
+        # Tests _check_fill_value with records
+        _check_fill_value = np.ma.core._check_fill_value
+        ndtype = [('a', int), ('b', float), ('c', "|S3")]
+        # A check on a list should return a single record
+        fval = _check_fill_value([-999, -12345678.9, "???"], ndtype)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), [-999, -12345678.9, b"???"])
+        # A check on None should output the defaults
+        fval = _check_fill_value(None, ndtype)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), [default_fill_value(0),
+                                   default_fill_value(0.),
+                                   asbytes(default_fill_value("0"))])
+        #.....Using a structured type as fill_value should work
+        fill_val = np.array((-999, -12345678.9, "???"), dtype=ndtype)
+        fval = _check_fill_value(fill_val, ndtype)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), [-999, -12345678.9, b"???"])
+
+        #.....Using a flexible type w/ a different type shouldn't matter
+        # BEHAVIOR in 1.5 and earlier, and 1.13 and later: match structured
+        # types by position
+        fill_val = np.array((-999, -12345678.9, "???"),
+                            dtype=[("A", int), ("B", float), ("C", "|S3")])
+        fval = _check_fill_value(fill_val, ndtype)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), [-999, -12345678.9, b"???"])
+
+        #.....Using an object-array shouldn't matter either
+        fill_val = np.ndarray(shape=(1,), dtype=object)
+        fill_val[0] = (-999, -12345678.9, b"???")
+        fval = _check_fill_value(fill_val, object)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), [-999, -12345678.9, b"???"])
+        # NOTE: This test was never run properly as "fill_value" rather than
+        # "fill_val" was assigned.  Written properly, it fails.
+        #fill_val = np.array((-999, -12345678.9, "???"))
+        #fval = _check_fill_value(fill_val, ndtype)
+        #assert_(isinstance(fval, ndarray))
+        #assert_equal(fval.item(), [-999, -12345678.9, b"???"])
+        #.....One-field-only flexible type should work as well
+        ndtype = [("a", int)]
+        fval = _check_fill_value(-999999999, ndtype)
+        assert_(isinstance(fval, ndarray))
+        assert_equal(fval.item(), (-999999999,))
+
+    def test_fillvalue_conversion(self):
+        # Tests the behavior of fill_value during conversion
+        # We had a tailored comment to make sure special attributes are
+        # properly dealt with
+        a = array([b'3', b'4', b'5'])
+        a._optinfo.update({'comment':"updated!"})
+
+        b = array(a, dtype=int)
+        assert_equal(b._data, [3, 4, 5])
+        assert_equal(b.fill_value, default_fill_value(0))
+
+        b = array(a, dtype=float)
+        assert_equal(b._data, [3, 4, 5])
+        assert_equal(b.fill_value, default_fill_value(0.))
+
+        b = a.astype(int)
+        assert_equal(b._data, [3, 4, 5])
+        assert_equal(b.fill_value, default_fill_value(0))
+        assert_equal(b._optinfo['comment'], "updated!")
+
+        b = a.astype([('a', '|S3')])
+        assert_equal(b['a']._data, a._data)
+        assert_equal(b['a'].fill_value, a.fill_value)
+
+    def test_default_fill_value(self):
+        # check all calling conventions
+        f1 = default_fill_value(1.)
+        f2 = default_fill_value(np.array(1.))
+        f3 = default_fill_value(np.array(1.).dtype)
+        assert_equal(f1, f2)
+        assert_equal(f1, f3)
+
+    def test_default_fill_value_structured(self):
+        fields = array([(1, 1, 1)],
+                      dtype=[('i', int), ('s', '|S8'), ('f', float)])
+
+        f1 = default_fill_value(fields)
+        f2 = default_fill_value(fields.dtype)
+        expected = np.array((default_fill_value(0),
+                             default_fill_value('0'),
+                             default_fill_value(0.)), dtype=fields.dtype)
+        assert_equal(f1, expected)
+        assert_equal(f2, expected)
+
+    def test_default_fill_value_void(self):
+        dt = np.dtype([('v', 'V7')])
+        f = default_fill_value(dt)
+        assert_equal(f['v'], np.array(default_fill_value(dt['v']), dt['v']))
+
+    def test_fillvalue(self):
+        # Yet more fun with the fill_value
+        data = masked_array([1, 2, 3], fill_value=-999)
+        series = data[[0, 2, 1]]
+        assert_equal(series._fill_value, data._fill_value)
+
+        mtype = [('f', float), ('s', '|S3')]
+        x = array([(1, 'a'), (2, 'b'), (pi, 'pi')], dtype=mtype)
+        x.fill_value = 999
+        assert_equal(x.fill_value.item(), [999., b'999'])
+        assert_equal(x['f'].fill_value, 999)
+        assert_equal(x['s'].fill_value, b'999')
+
+        x.fill_value = (9, '???')
+        assert_equal(x.fill_value.item(), (9, b'???'))
+        assert_equal(x['f'].fill_value, 9)
+        assert_equal(x['s'].fill_value, b'???')
+
+        x = array([1, 2, 3.1])
+        x.fill_value = 999
+        assert_equal(np.asarray(x.fill_value).dtype, float)
+        assert_equal(x.fill_value, 999.)
+        assert_equal(x._fill_value, np.array(999.))
+
+    def test_subarray_fillvalue(self):
+        # gh-10483   test multi-field index fill value
+        fields = array([(1, 1, 1)],
+                      dtype=[('i', int), ('s', '|S8'), ('f', float)])
+        with suppress_warnings() as sup:
+            sup.filter(FutureWarning, "Numpy has detected")
+            subfields = fields[['i', 'f']]
+            assert_equal(tuple(subfields.fill_value), (999999, 1.e+20))
+            # test comparison does not raise:
+            subfields[1:] == subfields[:-1]
+
+    def test_fillvalue_exotic_dtype(self):
+        # Tests yet more exotic flexible dtypes
+        _check_fill_value = np.ma.core._check_fill_value
+        ndtype = [('i', int), ('s', '|S8'), ('f', float)]
+        control = np.array((default_fill_value(0),
+                            default_fill_value('0'),
+                            default_fill_value(0.),),
+                           dtype=ndtype)
+        assert_equal(_check_fill_value(None, ndtype), control)
+        # The shape shouldn't matter
+        ndtype = [('f0', float, (2, 2))]
+        control = np.array((default_fill_value(0.),),
+                           dtype=[('f0', float)]).astype(ndtype)
+        assert_equal(_check_fill_value(None, ndtype), control)
+        control = np.array((0,), dtype=[('f0', float)]).astype(ndtype)
+        assert_equal(_check_fill_value(0, ndtype), control)
+
+        ndtype = np.dtype("int, (2,3)float, float")
+        control = np.array((default_fill_value(0),
+                            default_fill_value(0.),
+                            default_fill_value(0.),),
+                           dtype="int, float, float").astype(ndtype)
+        test = _check_fill_value(None, ndtype)
+        assert_equal(test, control)
+        control = np.array((0, 0, 0), dtype="int, float, float").astype(ndtype)
+        assert_equal(_check_fill_value(0, ndtype), control)
+        # but when indexing, fill value should become scalar not tuple
+        # See issue #6723
+        M = masked_array(control)
+        assert_equal(M["f1"].fill_value.ndim, 0)
+
+    def test_fillvalue_datetime_timedelta(self):
+        # Test default fillvalue for datetime64 and timedelta64 types.
+        # See issue #4476, this would return '?' which would cause errors
+        # elsewhere
+
+        for timecode in ("as", "fs", "ps", "ns", "us", "ms", "s", "m",
+                         "h", "D", "W", "M", "Y"):
+            control = numpy.datetime64("NaT", timecode)
+            test = default_fill_value(numpy.dtype(" 0
+
+            # test different unary domains
+            sqrt(m)
+            log(m)
+            tan(m)
+            arcsin(m)
+            arccos(m)
+            arccosh(m)
+
+            # test binary domains
+            divide(m, 2)
+
+            # also check that allclose uses ma ufuncs, to avoid warning
+            allclose(m, 0.5)
+
+    def test_masked_array_underflow(self):
+        x = np.arange(0, 3, 0.1)
+        X = np.ma.array(x)
+        with np.errstate(under="raise"):
+            X2 = X/2.0
+            np.testing.assert_array_equal(X2, x/2)
+
+class TestMaskedArrayInPlaceArithmetic:
+    # Test MaskedArray Arithmetic
+
+    def setup_method(self):
+        x = arange(10)
+        y = arange(10)
+        xm = arange(10)
+        xm[2] = masked
+        self.intdata = (x, y, xm)
+        self.floatdata = (x.astype(float), y.astype(float), xm.astype(float))
+        self.othertypes = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
+        self.othertypes = [np.dtype(_).type for _ in self.othertypes]
+        self.uint8data = (
+            x.astype(np.uint8),
+            y.astype(np.uint8),
+            xm.astype(np.uint8)
+        )
+
+    def test_inplace_addition_scalar(self):
+        # Test of inplace additions
+        (x, y, xm) = self.intdata
+        xm[2] = masked
+        x += 1
+        assert_equal(x, y + 1)
+        xm += 1
+        assert_equal(xm, y + 1)
+
+        (x, _, xm) = self.floatdata
+        id1 = x.data.ctypes.data
+        x += 1.
+        assert_(id1 == x.data.ctypes.data)
+        assert_equal(x, y + 1.)
+
+    def test_inplace_addition_array(self):
+        # Test of inplace additions
+        (x, y, xm) = self.intdata
+        m = xm.mask
+        a = arange(10, dtype=np.int16)
+        a[-1] = masked
+        x += a
+        xm += a
+        assert_equal(x, y + a)
+        assert_equal(xm, y + a)
+        assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_subtraction_scalar(self):
+        # Test of inplace subtractions
+        (x, y, xm) = self.intdata
+        x -= 1
+        assert_equal(x, y - 1)
+        xm -= 1
+        assert_equal(xm, y - 1)
+
+    def test_inplace_subtraction_array(self):
+        # Test of inplace subtractions
+        (x, y, xm) = self.floatdata
+        m = xm.mask
+        a = arange(10, dtype=float)
+        a[-1] = masked
+        x -= a
+        xm -= a
+        assert_equal(x, y - a)
+        assert_equal(xm, y - a)
+        assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_multiplication_scalar(self):
+        # Test of inplace multiplication
+        (x, y, xm) = self.floatdata
+        x *= 2.0
+        assert_equal(x, y * 2)
+        xm *= 2.0
+        assert_equal(xm, y * 2)
+
+    def test_inplace_multiplication_array(self):
+        # Test of inplace multiplication
+        (x, y, xm) = self.floatdata
+        m = xm.mask
+        a = arange(10, dtype=float)
+        a[-1] = masked
+        x *= a
+        xm *= a
+        assert_equal(x, y * a)
+        assert_equal(xm, y * a)
+        assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_division_scalar_int(self):
+        # Test of inplace division
+        (x, y, xm) = self.intdata
+        x = arange(10) * 2
+        xm = arange(10) * 2
+        xm[2] = masked
+        x //= 2
+        assert_equal(x, y)
+        xm //= 2
+        assert_equal(xm, y)
+
+    def test_inplace_division_scalar_float(self):
+        # Test of inplace division
+        (x, y, xm) = self.floatdata
+        x /= 2.0
+        assert_equal(x, y / 2.0)
+        xm /= arange(10)
+        assert_equal(xm, ones((10,)))
+
+    def test_inplace_division_array_float(self):
+        # Test of inplace division
+        (x, y, xm) = self.floatdata
+        m = xm.mask
+        a = arange(10, dtype=float)
+        a[-1] = masked
+        x /= a
+        xm /= a
+        assert_equal(x, y / a)
+        assert_equal(xm, y / a)
+        assert_equal(xm.mask, mask_or(mask_or(m, a.mask), (a == 0)))
+
+    def test_inplace_division_misc(self):
+
+        x = [1., 1., 1., -2., pi / 2., 4., 5., -10., 10., 1., 2., 3.]
+        y = [5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.]
+        m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
+        xm = masked_array(x, mask=m1)
+        ym = masked_array(y, mask=m2)
+
+        z = xm / ym
+        assert_equal(z._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
+        assert_equal(z._data,
+                     [1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
+
+        xm = xm.copy()
+        xm /= ym
+        assert_equal(xm._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
+        assert_equal(z._data,
+                     [1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
+
+    def test_datafriendly_add(self):
+        # Test keeping data w/ (inplace) addition
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        # Test add w/ scalar
+        xx = x + 1
+        assert_equal(xx.data, [2, 3, 3])
+        assert_equal(xx.mask, [0, 0, 1])
+        # Test iadd w/ scalar
+        x += 1
+        assert_equal(x.data, [2, 3, 3])
+        assert_equal(x.mask, [0, 0, 1])
+        # Test add w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x + array([1, 2, 3], mask=[1, 0, 0])
+        assert_equal(xx.data, [1, 4, 3])
+        assert_equal(xx.mask, [1, 0, 1])
+        # Test iadd w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        x += array([1, 2, 3], mask=[1, 0, 0])
+        assert_equal(x.data, [1, 4, 3])
+        assert_equal(x.mask, [1, 0, 1])
+
+    def test_datafriendly_sub(self):
+        # Test keeping data w/ (inplace) subtraction
+        # Test sub w/ scalar
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x - 1
+        assert_equal(xx.data, [0, 1, 3])
+        assert_equal(xx.mask, [0, 0, 1])
+        # Test isub w/ scalar
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        x -= 1
+        assert_equal(x.data, [0, 1, 3])
+        assert_equal(x.mask, [0, 0, 1])
+        # Test sub w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x - array([1, 2, 3], mask=[1, 0, 0])
+        assert_equal(xx.data, [1, 0, 3])
+        assert_equal(xx.mask, [1, 0, 1])
+        # Test isub w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        x -= array([1, 2, 3], mask=[1, 0, 0])
+        assert_equal(x.data, [1, 0, 3])
+        assert_equal(x.mask, [1, 0, 1])
+
+    def test_datafriendly_mul(self):
+        # Test keeping data w/ (inplace) multiplication
+        # Test mul w/ scalar
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x * 2
+        assert_equal(xx.data, [2, 4, 3])
+        assert_equal(xx.mask, [0, 0, 1])
+        # Test imul w/ scalar
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        x *= 2
+        assert_equal(x.data, [2, 4, 3])
+        assert_equal(x.mask, [0, 0, 1])
+        # Test mul w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x * array([10, 20, 30], mask=[1, 0, 0])
+        assert_equal(xx.data, [1, 40, 3])
+        assert_equal(xx.mask, [1, 0, 1])
+        # Test imul w/ array
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        x *= array([10, 20, 30], mask=[1, 0, 0])
+        assert_equal(x.data, [1, 40, 3])
+        assert_equal(x.mask, [1, 0, 1])
+
+    def test_datafriendly_div(self):
+        # Test keeping data w/ (inplace) division
+        # Test div on scalar
+        x = array([1, 2, 3], mask=[0, 0, 1])
+        xx = x / 2.
+        assert_equal(xx.data, [1 / 2., 2 / 2., 3])
+        assert_equal(xx.mask, [0, 0, 1])
+        # Test idiv on scalar
+        x = array([1., 2., 3.], mask=[0, 0, 1])
+        x /= 2.
+        assert_equal(x.data, [1 / 2., 2 / 2., 3])
+        assert_equal(x.mask, [0, 0, 1])
+        # Test div on array
+        x = array([1., 2., 3.], mask=[0, 0, 1])
+        xx = x / array([10., 20., 30.], mask=[1, 0, 0])
+        assert_equal(xx.data, [1., 2. / 20., 3.])
+        assert_equal(xx.mask, [1, 0, 1])
+        # Test idiv on array
+        x = array([1., 2., 3.], mask=[0, 0, 1])
+        x /= array([10., 20., 30.], mask=[1, 0, 0])
+        assert_equal(x.data, [1., 2 / 20., 3.])
+        assert_equal(x.mask, [1, 0, 1])
+
+    def test_datafriendly_pow(self):
+        # Test keeping data w/ (inplace) power
+        # Test pow on scalar
+        x = array([1., 2., 3.], mask=[0, 0, 1])
+        xx = x ** 2.5
+        assert_equal(xx.data, [1., 2. ** 2.5, 3.])
+        assert_equal(xx.mask, [0, 0, 1])
+        # Test ipow on scalar
+        x **= 2.5
+        assert_equal(x.data, [1., 2. ** 2.5, 3])
+        assert_equal(x.mask, [0, 0, 1])
+
+    def test_datafriendly_add_arrays(self):
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 0])
+        a += b
+        assert_equal(a, [[2, 2], [4, 4]])
+        if a.mask is not nomask:
+            assert_equal(a.mask, [[0, 0], [0, 0]])
+
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 1])
+        a += b
+        assert_equal(a, [[2, 2], [4, 4]])
+        assert_equal(a.mask, [[0, 1], [0, 1]])
+
+    def test_datafriendly_sub_arrays(self):
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 0])
+        a -= b
+        assert_equal(a, [[0, 0], [2, 2]])
+        if a.mask is not nomask:
+            assert_equal(a.mask, [[0, 0], [0, 0]])
+
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 1])
+        a -= b
+        assert_equal(a, [[0, 0], [2, 2]])
+        assert_equal(a.mask, [[0, 1], [0, 1]])
+
+    def test_datafriendly_mul_arrays(self):
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 0])
+        a *= b
+        assert_equal(a, [[1, 1], [3, 3]])
+        if a.mask is not nomask:
+            assert_equal(a.mask, [[0, 0], [0, 0]])
+
+        a = array([[1, 1], [3, 3]])
+        b = array([1, 1], mask=[0, 1])
+        a *= b
+        assert_equal(a, [[1, 1], [3, 3]])
+        assert_equal(a.mask, [[0, 1], [0, 1]])
+
+    def test_inplace_addition_scalar_type(self):
+        # Test of inplace additions
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                xm[2] = masked
+                x += t(1)
+                assert_equal(x, y + t(1))
+                xm += t(1)
+                assert_equal(xm, y + t(1))
+
+    def test_inplace_addition_array_type(self):
+        # Test of inplace additions
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                m = xm.mask
+                a = arange(10, dtype=t)
+                a[-1] = masked
+                x += a
+                xm += a
+                assert_equal(x, y + a)
+                assert_equal(xm, y + a)
+                assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_subtraction_scalar_type(self):
+        # Test of inplace subtractions
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                x -= t(1)
+                assert_equal(x, y - t(1))
+                xm -= t(1)
+                assert_equal(xm, y - t(1))
+
+    def test_inplace_subtraction_array_type(self):
+        # Test of inplace subtractions
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                m = xm.mask
+                a = arange(10, dtype=t)
+                a[-1] = masked
+                x -= a
+                xm -= a
+                assert_equal(x, y - a)
+                assert_equal(xm, y - a)
+                assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_multiplication_scalar_type(self):
+        # Test of inplace multiplication
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                x *= t(2)
+                assert_equal(x, y * t(2))
+                xm *= t(2)
+                assert_equal(xm, y * t(2))
+
+    def test_inplace_multiplication_array_type(self):
+        # Test of inplace multiplication
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                m = xm.mask
+                a = arange(10, dtype=t)
+                a[-1] = masked
+                x *= a
+                xm *= a
+                assert_equal(x, y * a)
+                assert_equal(xm, y * a)
+                assert_equal(xm.mask, mask_or(m, a.mask))
+
+    def test_inplace_floor_division_scalar_type(self):
+        # Test of inplace division
+        # Check for TypeError in case of unsupported types
+        unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]}
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                x = arange(10, dtype=t) * t(2)
+                xm = arange(10, dtype=t) * t(2)
+                xm[2] = masked
+                try:
+                    x //= t(2)
+                    xm //= t(2)
+                    assert_equal(x, y)
+                    assert_equal(xm, y)
+                except TypeError:
+                    msg = f"Supported type {t} throwing TypeError"
+                    assert t in unsupported, msg
+
+    def test_inplace_floor_division_array_type(self):
+        # Test of inplace division
+        # Check for TypeError in case of unsupported types
+        unsupported = {np.dtype(t).type for t in np.typecodes["Complex"]}
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                m = xm.mask
+                a = arange(10, dtype=t)
+                a[-1] = masked
+                try:
+                    x //= a
+                    xm //= a
+                    assert_equal(x, y // a)
+                    assert_equal(xm, y // a)
+                    assert_equal(
+                        xm.mask,
+                        mask_or(mask_or(m, a.mask), (a == t(0)))
+                    )
+                except TypeError:
+                    msg = f"Supported type {t} throwing TypeError"
+                    assert t in unsupported, msg
+
+    def test_inplace_division_scalar_type(self):
+        # Test of inplace division
+        for t in self.othertypes:
+            with suppress_warnings() as sup:
+                sup.record(UserWarning)
+
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                x = arange(10, dtype=t) * t(2)
+                xm = arange(10, dtype=t) * t(2)
+                xm[2] = masked
+
+                # May get a DeprecationWarning or a TypeError.
+                #
+                # This is a consequence of the fact that this is true divide
+                # and will require casting to float for calculation and
+                # casting back to the original type. This will only be raised
+                # with integers. Whether it is an error or warning is only
+                # dependent on how stringent the casting rules are.
+                #
+                # Will handle the same way.
+                try:
+                    x /= t(2)
+                    assert_equal(x, y)
+                except (DeprecationWarning, TypeError) as e:
+                    warnings.warn(str(e), stacklevel=1)
+                try:
+                    xm /= t(2)
+                    assert_equal(xm, y)
+                except (DeprecationWarning, TypeError) as e:
+                    warnings.warn(str(e), stacklevel=1)
+
+                if issubclass(t, np.integer):
+                    assert_equal(len(sup.log), 2, f'Failed on type={t}.')
+                else:
+                    assert_equal(len(sup.log), 0, f'Failed on type={t}.')
+
+    def test_inplace_division_array_type(self):
+        # Test of inplace division
+        for t in self.othertypes:
+            with suppress_warnings() as sup:
+                sup.record(UserWarning)
+                (x, y, xm) = (_.astype(t) for _ in self.uint8data)
+                m = xm.mask
+                a = arange(10, dtype=t)
+                a[-1] = masked
+
+                # May get a DeprecationWarning or a TypeError.
+                #
+                # This is a consequence of the fact that this is true divide
+                # and will require casting to float for calculation and
+                # casting back to the original type. This will only be raised
+                # with integers. Whether it is an error or warning is only
+                # dependent on how stringent the casting rules are.
+                #
+                # Will handle the same way.
+                try:
+                    x /= a
+                    assert_equal(x, y / a)
+                except (DeprecationWarning, TypeError) as e:
+                    warnings.warn(str(e), stacklevel=1)
+                try:
+                    xm /= a
+                    assert_equal(xm, y / a)
+                    assert_equal(
+                        xm.mask,
+                        mask_or(mask_or(m, a.mask), (a == t(0)))
+                    )
+                except (DeprecationWarning, TypeError) as e:
+                    warnings.warn(str(e), stacklevel=1)
+
+                if issubclass(t, np.integer):
+                    assert_equal(len(sup.log), 2, f'Failed on type={t}.')
+                else:
+                    assert_equal(len(sup.log), 0, f'Failed on type={t}.')
+
+    def test_inplace_pow_type(self):
+        # Test keeping data w/ (inplace) power
+        for t in self.othertypes:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("error")
+                # Test pow on scalar
+                x = array([1, 2, 3], mask=[0, 0, 1], dtype=t)
+                xx = x ** t(2)
+                xx_r = array([1, 2 ** 2, 3], mask=[0, 0, 1], dtype=t)
+                assert_equal(xx.data, xx_r.data)
+                assert_equal(xx.mask, xx_r.mask)
+                # Test ipow on scalar
+                x **= t(2)
+                assert_equal(x.data, xx_r.data)
+                assert_equal(x.mask, xx_r.mask)
+
+
+class TestMaskedArrayMethods:
+    # Test class for miscellaneous MaskedArrays methods.
+    def setup_method(self):
+        # Base data definition.
+        x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
+                      7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+        X = x.reshape(6, 6)
+        XX = x.reshape(3, 2, 2, 3)
+
+        m = np.array([0, 1, 0, 1, 0, 0,
+                     1, 0, 1, 1, 0, 1,
+                     0, 0, 0, 1, 0, 1,
+                     0, 0, 0, 1, 1, 1,
+                     1, 0, 0, 1, 0, 0,
+                     0, 0, 1, 0, 1, 0])
+        mx = array(data=x, mask=m)
+        mX = array(data=X, mask=m.reshape(X.shape))
+        mXX = array(data=XX, mask=m.reshape(XX.shape))
+
+        m2 = np.array([1, 1, 0, 1, 0, 0,
+                      1, 1, 1, 1, 0, 1,
+                      0, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 1, 0,
+                      0, 0, 1, 0, 1, 1])
+        m2x = array(data=x, mask=m2)
+        m2X = array(data=X, mask=m2.reshape(X.shape))
+        m2XX = array(data=XX, mask=m2.reshape(XX.shape))
+        self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
+
+    def test_generic_methods(self):
+        # Tests some MaskedArray methods.
+        a = array([1, 3, 2])
+        assert_equal(a.any(), a._data.any())
+        assert_equal(a.all(), a._data.all())
+        assert_equal(a.argmax(), a._data.argmax())
+        assert_equal(a.argmin(), a._data.argmin())
+        assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
+        assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
+        assert_equal(a.conj(), a._data.conj())
+        assert_equal(a.conjugate(), a._data.conjugate())
+
+        m = array([[1, 2], [3, 4]])
+        assert_equal(m.diagonal(), m._data.diagonal())
+        assert_equal(a.sum(), a._data.sum())
+        assert_equal(a.take([1, 2]), a._data.take([1, 2]))
+        assert_equal(m.transpose(), m._data.transpose())
+
+    def test_allclose(self):
+        # Tests allclose on arrays
+        a = np.random.rand(10)
+        b = a + np.random.rand(10) * 1e-8
+        assert_(allclose(a, b))
+        # Test allclose w/ infs
+        a[0] = np.inf
+        assert_(not allclose(a, b))
+        b[0] = np.inf
+        assert_(allclose(a, b))
+        # Test allclose w/ masked
+        a = masked_array(a)
+        a[-1] = masked
+        assert_(allclose(a, b, masked_equal=True))
+        assert_(not allclose(a, b, masked_equal=False))
+        # Test comparison w/ scalar
+        a *= 1e-8
+        a[0] = 0
+        assert_(allclose(a, 0, masked_equal=True))
+
+        # Test that the function works for MIN_INT integer typed arrays
+        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
+        assert_(allclose(a, a))
+
+    def test_allclose_timedelta(self):
+        # Allclose currently works for timedelta64 as long as `atol` is
+        # an integer or also a timedelta64
+        a = np.array([[1, 2, 3, 4]], dtype="m8[ns]")
+        assert allclose(a, a, atol=0)
+        assert allclose(a, a, atol=np.timedelta64(1, "ns"))
+
+    def test_allany(self):
+        # Checks the any/all methods/functions.
+        x = np.array([[0.13, 0.26, 0.90],
+                      [0.28, 0.33, 0.63],
+                      [0.31, 0.87, 0.70]])
+        m = np.array([[True, False, False],
+                      [False, False, False],
+                      [True, True, False]], dtype=np.bool)
+        mx = masked_array(x, mask=m)
+        mxbig = (mx > 0.5)
+        mxsmall = (mx < 0.5)
+
+        assert_(not mxbig.all())
+        assert_(mxbig.any())
+        assert_equal(mxbig.all(0), [False, False, True])
+        assert_equal(mxbig.all(1), [False, False, True])
+        assert_equal(mxbig.any(0), [False, False, True])
+        assert_equal(mxbig.any(1), [True, True, True])
+
+        assert_(not mxsmall.all())
+        assert_(mxsmall.any())
+        assert_equal(mxsmall.all(0), [True, True, False])
+        assert_equal(mxsmall.all(1), [False, False, False])
+        assert_equal(mxsmall.any(0), [True, True, False])
+        assert_equal(mxsmall.any(1), [True, True, False])
+
+    def test_allany_oddities(self):
+        # Some fun with all and any
+        store = empty((), dtype=bool)
+        full = array([1, 2, 3], mask=True)
+
+        assert_(full.all() is masked)
+        full.all(out=store)
+        assert_(store)
+        assert_(store._mask, True)
+        assert_(store is not masked)
+
+        store = empty((), dtype=bool)
+        assert_(full.any() is masked)
+        full.any(out=store)
+        assert_(not store)
+        assert_(store._mask, True)
+        assert_(store is not masked)
+
+    def test_argmax_argmin(self):
+        # Tests argmin & argmax on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+
+        assert_equal(mx.argmin(), 35)
+        assert_equal(mX.argmin(), 35)
+        assert_equal(m2x.argmin(), 4)
+        assert_equal(m2X.argmin(), 4)
+        assert_equal(mx.argmax(), 28)
+        assert_equal(mX.argmax(), 28)
+        assert_equal(m2x.argmax(), 31)
+        assert_equal(m2X.argmax(), 31)
+
+        assert_equal(mX.argmin(0), [2, 2, 2, 5, 0, 5])
+        assert_equal(m2X.argmin(0), [2, 2, 4, 5, 0, 4])
+        assert_equal(mX.argmax(0), [0, 5, 0, 5, 4, 0])
+        assert_equal(m2X.argmax(0), [5, 5, 0, 5, 1, 0])
+
+        assert_equal(mX.argmin(1), [4, 1, 0, 0, 5, 5, ])
+        assert_equal(m2X.argmin(1), [4, 4, 0, 0, 5, 3])
+        assert_equal(mX.argmax(1), [2, 4, 1, 1, 4, 1])
+        assert_equal(m2X.argmax(1), [2, 4, 1, 1, 1, 1])
+
+    def test_clip(self):
+        # Tests clip on MaskedArrays.
+        x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
+                      7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+        m = np.array([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0])
+        mx = array(x, mask=m)
+        clipped = mx.clip(2, 8)
+        assert_equal(clipped.mask, mx.mask)
+        assert_equal(clipped._data, x.clip(2, 8))
+        assert_equal(clipped._data, mx._data.clip(2, 8))
+
+    def test_clip_out(self):
+        # gh-14140
+        a = np.arange(10)
+        m = np.ma.MaskedArray(a, mask=[0, 1] * 5)
+        m.clip(0, 5, out=m)
+        assert_equal(m.mask, [0, 1] * 5)
+
+    def test_compress(self):
+        # test compress
+        a = masked_array([1., 2., 3., 4., 5.], fill_value=9999)
+        condition = (a > 1.5) & (a < 3.5)
+        assert_equal(a.compress(condition), [2., 3.])
+
+        a[[2, 3]] = masked
+        b = a.compress(condition)
+        assert_equal(b._data, [2., 3.])
+        assert_equal(b._mask, [0, 1])
+        assert_equal(b.fill_value, 9999)
+        assert_equal(b, a[condition])
+
+        condition = (a < 4.)
+        b = a.compress(condition)
+        assert_equal(b._data, [1., 2., 3.])
+        assert_equal(b._mask, [0, 0, 1])
+        assert_equal(b.fill_value, 9999)
+        assert_equal(b, a[condition])
+
+        a = masked_array([[10, 20, 30], [40, 50, 60]],
+                         mask=[[0, 0, 1], [1, 0, 0]])
+        b = a.compress(a.ravel() >= 22)
+        assert_equal(b._data, [30, 40, 50, 60])
+        assert_equal(b._mask, [1, 1, 0, 0])
+
+        x = np.array([3, 1, 2])
+        b = a.compress(x >= 2, axis=1)
+        assert_equal(b._data, [[10, 30], [40, 60]])
+        assert_equal(b._mask, [[0, 1], [1, 0]])
+
+    def test_compressed(self):
+        # Tests compressed
+        a = array([1, 2, 3, 4], mask=[0, 0, 0, 0])
+        b = a.compressed()
+        assert_equal(b, a)
+        a[0] = masked
+        b = a.compressed()
+        assert_equal(b, [2, 3, 4])
+
+    def test_empty(self):
+        # Tests empty/like
+        datatype = [('a', int), ('b', float), ('c', '|S8')]
+        a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
+                         dtype=datatype)
+        assert_equal(len(a.fill_value.item()), len(datatype))
+
+        b = empty_like(a)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        b = empty(len(a), dtype=datatype)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        # check empty_like mask handling
+        a = masked_array([1, 2, 3], mask=[False, True, False])
+        b = empty_like(a)
+        assert_(not np.may_share_memory(a.mask, b.mask))
+        b = a.view(masked_array)
+        assert_(np.may_share_memory(a.mask, b.mask))
+
+    def test_zeros(self):
+        # Tests zeros/like
+        datatype = [('a', int), ('b', float), ('c', '|S8')]
+        a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
+                         dtype=datatype)
+        assert_equal(len(a.fill_value.item()), len(datatype))
+
+        b = zeros(len(a), dtype=datatype)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        b = zeros_like(a)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        # check zeros_like mask handling
+        a = masked_array([1, 2, 3], mask=[False, True, False])
+        b = zeros_like(a)
+        assert_(not np.may_share_memory(a.mask, b.mask))
+        b = a.view()
+        assert_(np.may_share_memory(a.mask, b.mask))
+
+    def test_ones(self):
+        # Tests ones/like
+        datatype = [('a', int), ('b', float), ('c', '|S8')]
+        a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
+                         dtype=datatype)
+        assert_equal(len(a.fill_value.item()), len(datatype))
+
+        b = ones(len(a), dtype=datatype)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        b = ones_like(a)
+        assert_equal(b.shape, a.shape)
+        assert_equal(b.fill_value, a.fill_value)
+
+        # check ones_like mask handling
+        a = masked_array([1, 2, 3], mask=[False, True, False])
+        b = ones_like(a)
+        assert_(not np.may_share_memory(a.mask, b.mask))
+        b = a.view()
+        assert_(np.may_share_memory(a.mask, b.mask))
+
+    @suppress_copy_mask_on_assignment
+    def test_put(self):
+        # Tests put.
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        x = array(d, mask=m)
+        assert_(x[3] is masked)
+        assert_(x[4] is masked)
+        x[[1, 4]] = [10, 40]
+        assert_(x[3] is masked)
+        assert_(x[4] is not masked)
+        assert_equal(x, [0, 10, 2, -1, 40])
+
+        x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
+        i = [0, 2, 4, 6]
+        x.put(i, [6, 4, 2, 0])
+        assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
+        assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
+        x.put(i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
+        assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
+        assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
+
+        x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
+        put(x, i, [6, 4, 2, 0])
+        assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
+        assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
+        put(x, i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
+        assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
+        assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
+
+    def test_put_nomask(self):
+        # GitHub issue 6425
+        x = zeros(10)
+        z = array([3., -1.], mask=[False, True])
+
+        x.put([1, 2], z)
+        assert_(x[0] is not masked)
+        assert_equal(x[0], 0)
+        assert_(x[1] is not masked)
+        assert_equal(x[1], 3)
+        assert_(x[2] is masked)
+        assert_(x[3] is not masked)
+        assert_equal(x[3], 0)
+
+    def test_put_hardmask(self):
+        # Tests put on hardmask
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        xh = array(d + 1, mask=m, hard_mask=True, copy=True)
+        xh.put([4, 2, 0, 1, 3], [1, 2, 3, 4, 5])
+        assert_equal(xh._data, [3, 4, 2, 4, 5])
+
+    def test_putmask(self):
+        x = arange(6) + 1
+        mx = array(x, mask=[0, 0, 0, 1, 1, 1])
+        mask = [0, 0, 1, 0, 0, 1]
+        # w/o mask, w/o masked values
+        xx = x.copy()
+        putmask(xx, mask, 99)
+        assert_equal(xx, [1, 2, 99, 4, 5, 99])
+        # w/ mask, w/o masked values
+        mxx = mx.copy()
+        putmask(mxx, mask, 99)
+        assert_equal(mxx._data, [1, 2, 99, 4, 5, 99])
+        assert_equal(mxx._mask, [0, 0, 0, 1, 1, 0])
+        # w/o mask, w/ masked values
+        values = array([10, 20, 30, 40, 50, 60], mask=[1, 1, 1, 0, 0, 0])
+        xx = x.copy()
+        putmask(xx, mask, values)
+        assert_equal(xx._data, [1, 2, 30, 4, 5, 60])
+        assert_equal(xx._mask, [0, 0, 1, 0, 0, 0])
+        # w/ mask, w/ masked values
+        mxx = mx.copy()
+        putmask(mxx, mask, values)
+        assert_equal(mxx._data, [1, 2, 30, 4, 5, 60])
+        assert_equal(mxx._mask, [0, 0, 1, 1, 1, 0])
+        # w/ mask, w/ masked values + hardmask
+        mxx = mx.copy()
+        mxx.harden_mask()
+        putmask(mxx, mask, values)
+        assert_equal(mxx, [1, 2, 30, 4, 5, 60])
+
+    def test_ravel(self):
+        # Tests ravel
+        a = array([[1, 2, 3, 4, 5]], mask=[[0, 1, 0, 0, 0]])
+        aravel = a.ravel()
+        assert_equal(aravel._mask.shape, aravel.shape)
+        a = array([0, 0], mask=[1, 1])
+        aravel = a.ravel()
+        assert_equal(aravel._mask.shape, a.shape)
+        # Checks that small_mask is preserved
+        a = array([1, 2, 3, 4], mask=[0, 0, 0, 0], shrink=False)
+        assert_equal(a.ravel()._mask, [0, 0, 0, 0])
+        # Test that the fill_value is preserved
+        a.fill_value = -99
+        a.shape = (2, 2)
+        ar = a.ravel()
+        assert_equal(ar._mask, [0, 0, 0, 0])
+        assert_equal(ar._data, [1, 2, 3, 4])
+        assert_equal(ar.fill_value, -99)
+        # Test index ordering
+        assert_equal(a.ravel(order='C'), [1, 2, 3, 4])
+        assert_equal(a.ravel(order='F'), [1, 3, 2, 4])
+
+    @pytest.mark.parametrize("order", "AKCF")
+    @pytest.mark.parametrize("data_order", "CF")
+    def test_ravel_order(self, order, data_order):
+        # Ravelling must ravel mask and data in the same order always to avoid
+        # misaligning the two in the ravel result.
+        arr = np.ones((5, 10), order=data_order)
+        arr[0, :] = 0
+        mask = np.ones((10, 5), dtype=bool, order=data_order).T
+        mask[0, :] = False
+        x = array(arr, mask=mask)
+        assert x._data.flags.fnc != x._mask.flags.fnc
+        assert (x.filled(0) == 0).all()
+        raveled = x.ravel(order)
+        assert (raveled.filled(0) == 0).all()
+
+        # NOTE: Can be wrong if arr order is neither C nor F and `order="K"`
+        assert_array_equal(arr.ravel(order), x.ravel(order)._data)
+
+    def test_reshape(self):
+        # Tests reshape
+        x = arange(4)
+        x[0] = masked
+        y = x.reshape(2, 2)
+        assert_equal(y.shape, (2, 2,))
+        assert_equal(y._mask.shape, (2, 2,))
+        assert_equal(x.shape, (4,))
+        assert_equal(x._mask.shape, (4,))
+
+    def test_sort(self):
+        # Test sort
+        x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
+
+        sortedx = sort(x)
+        assert_equal(sortedx._data, [1, 2, 3, 4])
+        assert_equal(sortedx._mask, [0, 0, 0, 1])
+
+        sortedx = sort(x, endwith=False)
+        assert_equal(sortedx._data, [4, 1, 2, 3])
+        assert_equal(sortedx._mask, [1, 0, 0, 0])
+
+        x.sort()
+        assert_equal(x._data, [1, 2, 3, 4])
+        assert_equal(x._mask, [0, 0, 0, 1])
+
+        x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
+        x.sort(endwith=False)
+        assert_equal(x._data, [4, 1, 2, 3])
+        assert_equal(x._mask, [1, 0, 0, 0])
+
+        x = [1, 4, 2, 3]
+        sortedx = sort(x)
+        assert_(not isinstance(sorted, MaskedArray))
+
+        x = array([0, 1, -1, -2, 2], mask=nomask, dtype=np.int8)
+        sortedx = sort(x, endwith=False)
+        assert_equal(sortedx._data, [-2, -1, 0, 1, 2])
+        x = array([0, 1, -1, -2, 2], mask=[0, 1, 0, 0, 1], dtype=np.int8)
+        sortedx = sort(x, endwith=False)
+        assert_equal(sortedx._data, [1, 2, -2, -1, 0])
+        assert_equal(sortedx._mask, [1, 1, 0, 0, 0])
+
+        x = array([0, -1], dtype=np.int8)
+        sortedx = sort(x, kind="stable")
+        assert_equal(sortedx, array([-1, 0], dtype=np.int8))
+
+    def test_stable_sort(self):
+        x = array([1, 2, 3, 1, 2, 3], dtype=np.uint8)
+        expected = array([0, 3, 1, 4, 2, 5])
+        computed = argsort(x, kind='stable')
+        assert_equal(computed, expected)
+
+    def test_argsort_matches_sort(self):
+        x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
+
+        for kwargs in [dict(),
+                       dict(endwith=True),
+                       dict(endwith=False),
+                       dict(fill_value=2),
+                       dict(fill_value=2, endwith=True),
+                       dict(fill_value=2, endwith=False)]:
+            sortedx = sort(x, **kwargs)
+            argsortedx = x[argsort(x, **kwargs)]
+            assert_equal(sortedx._data, argsortedx._data)
+            assert_equal(sortedx._mask, argsortedx._mask)
+
+    def test_sort_2d(self):
+        # Check sort of 2D array.
+        # 2D array w/o mask
+        a = masked_array([[8, 4, 1], [2, 0, 9]])
+        a.sort(0)
+        assert_equal(a, [[2, 0, 1], [8, 4, 9]])
+        a = masked_array([[8, 4, 1], [2, 0, 9]])
+        a.sort(1)
+        assert_equal(a, [[1, 4, 8], [0, 2, 9]])
+        # 2D array w/mask
+        a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
+        a.sort(0)
+        assert_equal(a, [[2, 0, 1], [8, 4, 9]])
+        assert_equal(a._mask, [[0, 0, 0], [1, 0, 1]])
+        a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
+        a.sort(1)
+        assert_equal(a, [[1, 4, 8], [0, 2, 9]])
+        assert_equal(a._mask, [[0, 0, 1], [0, 0, 1]])
+        # 3D
+        a = masked_array([[[7, 8, 9], [4, 5, 6], [1, 2, 3]],
+                          [[1, 2, 3], [7, 8, 9], [4, 5, 6]],
+                          [[7, 8, 9], [1, 2, 3], [4, 5, 6]],
+                          [[4, 5, 6], [1, 2, 3], [7, 8, 9]]])
+        a[a % 4 == 0] = masked
+        am = a.copy()
+        an = a.filled(99)
+        am.sort(0)
+        an.sort(0)
+        assert_equal(am, an)
+        am = a.copy()
+        an = a.filled(99)
+        am.sort(1)
+        an.sort(1)
+        assert_equal(am, an)
+        am = a.copy()
+        an = a.filled(99)
+        am.sort(2)
+        an.sort(2)
+        assert_equal(am, an)
+
+    def test_sort_flexible(self):
+        # Test sort on structured dtype.
+        a = array(
+            data=[(3, 3), (3, 2), (2, 2), (2, 1), (1, 0), (1, 1), (1, 2)],
+            mask=[(0, 0), (0, 1), (0, 0), (0, 0), (1, 0), (0, 0), (0, 0)],
+            dtype=[('A', int), ('B', int)])
+        mask_last = array(
+            data=[(1, 1), (1, 2), (2, 1), (2, 2), (3, 3), (3, 2), (1, 0)],
+            mask=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (1, 0)],
+            dtype=[('A', int), ('B', int)])
+        mask_first = array(
+            data=[(1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (3, 2), (3, 3)],
+            mask=[(1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0)],
+            dtype=[('A', int), ('B', int)])
+
+        test = sort(a)
+        assert_equal(test, mask_last)
+        assert_equal(test.mask, mask_last.mask)
+
+        test = sort(a, endwith=False)
+        assert_equal(test, mask_first)
+        assert_equal(test.mask, mask_first.mask)
+
+        # Test sort on dtype with subarray (gh-8069)
+        # Just check that the sort does not error, structured array subarrays
+        # are treated as byte strings and that leads to differing behavior
+        # depending on endianness and `endwith`.
+        dt = np.dtype([('v', int, 2)])
+        a = a.view(dt)
+        test = sort(a)
+        test = sort(a, endwith=False)
+
+    def test_argsort(self):
+        # Test argsort
+        a = array([1, 5, 2, 4, 3], mask=[1, 0, 0, 1, 0])
+        assert_equal(np.argsort(a), argsort(a))
+
+    def test_squeeze(self):
+        # Check squeeze
+        data = masked_array([[1, 2, 3]])
+        assert_equal(data.squeeze(), [1, 2, 3])
+        data = masked_array([[1, 2, 3]], mask=[[1, 1, 1]])
+        assert_equal(data.squeeze(), [1, 2, 3])
+        assert_equal(data.squeeze()._mask, [1, 1, 1])
+
+        # normal ndarrays return a view
+        arr = np.array([[1]])
+        arr_sq = arr.squeeze()
+        assert_equal(arr_sq, 1)
+        arr_sq[...] = 2
+        assert_equal(arr[0,0], 2)
+
+        # so maskedarrays should too
+        m_arr = masked_array([[1]], mask=True)
+        m_arr_sq = m_arr.squeeze()
+        assert_(m_arr_sq is not np.ma.masked)
+        assert_equal(m_arr_sq.mask, True)
+        m_arr_sq[...] = 2
+        assert_equal(m_arr[0,0], 2)
+
+    def test_swapaxes(self):
+        # Tests swapaxes on MaskedArrays.
+        x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
+                      7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+        m = np.array([0, 1, 0, 1, 0, 0,
+                      1, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 0, 1,
+                      0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 0, 0,
+                      0, 0, 1, 0, 1, 0])
+        mX = array(x, mask=m).reshape(6, 6)
+        mXX = mX.reshape(3, 2, 2, 3)
+
+        mXswapped = mX.swapaxes(0, 1)
+        assert_equal(mXswapped[-1], mX[:, -1])
+
+        mXXswapped = mXX.swapaxes(0, 2)
+        assert_equal(mXXswapped.shape, (2, 2, 3, 3))
+
+    def test_take(self):
+        # Tests take
+        x = masked_array([10, 20, 30, 40], [0, 1, 0, 1])
+        assert_equal(x.take([0, 0, 3]), masked_array([10, 10, 40], [0, 0, 1]))
+        assert_equal(x.take([0, 0, 3]), x[[0, 0, 3]])
+        assert_equal(x.take([[0, 1], [0, 1]]),
+                     masked_array([[10, 20], [10, 20]], [[0, 1], [0, 1]]))
+
+        # assert_equal crashes when passed np.ma.mask
+        assert_(x[1] is np.ma.masked)
+        assert_(x.take(1) is np.ma.masked)
+
+        x = array([[10, 20, 30], [40, 50, 60]], mask=[[0, 0, 1], [1, 0, 0, ]])
+        assert_equal(x.take([0, 2], axis=1),
+                     array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
+        assert_equal(take(x, [0, 2], axis=1),
+                     array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
+
+    def test_take_masked_indices(self):
+        # Test take w/ masked indices
+        a = np.array((40, 18, 37, 9, 22))
+        indices = np.arange(3)[None,:] + np.arange(5)[:, None]
+        mindices = array(indices, mask=(indices >= len(a)))
+        # No mask
+        test = take(a, mindices, mode='clip')
+        ctrl = array([[40, 18, 37],
+                      [18, 37, 9],
+                      [37, 9, 22],
+                      [9, 22, 22],
+                      [22, 22, 22]])
+        assert_equal(test, ctrl)
+        # Masked indices
+        test = take(a, mindices)
+        ctrl = array([[40, 18, 37],
+                      [18, 37, 9],
+                      [37, 9, 22],
+                      [9, 22, 40],
+                      [22, 40, 40]])
+        ctrl[3, 2] = ctrl[4, 1] = ctrl[4, 2] = masked
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, ctrl.mask)
+        # Masked input + masked indices
+        a = array((40, 18, 37, 9, 22), mask=(0, 1, 0, 0, 0))
+        test = take(a, mindices)
+        ctrl[0, 1] = ctrl[1, 0] = masked
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, ctrl.mask)
+
+    def test_tolist(self):
+        # Tests to list
+        # ... on 1D
+        x = array(np.arange(12))
+        x[[1, -2]] = masked
+        xlist = x.tolist()
+        assert_(xlist[1] is None)
+        assert_(xlist[-2] is None)
+        # ... on 2D
+        x.shape = (3, 4)
+        xlist = x.tolist()
+        ctrl = [[0, None, 2, 3], [4, 5, 6, 7], [8, 9, None, 11]]
+        assert_equal(xlist[0], [0, None, 2, 3])
+        assert_equal(xlist[1], [4, 5, 6, 7])
+        assert_equal(xlist[2], [8, 9, None, 11])
+        assert_equal(xlist, ctrl)
+        # ... on structured array w/ masked records
+        x = array(list(zip([1, 2, 3],
+                           [1.1, 2.2, 3.3],
+                           ['one', 'two', 'thr'])),
+                  dtype=[('a', int), ('b', float), ('c', '|S8')])
+        x[-1] = masked
+        assert_equal(x.tolist(),
+                     [(1, 1.1, b'one'),
+                      (2, 2.2, b'two'),
+                      (None, None, None)])
+        # ... on structured array w/ masked fields
+        a = array([(1, 2,), (3, 4)], mask=[(0, 1), (0, 0)],
+                  dtype=[('a', int), ('b', int)])
+        test = a.tolist()
+        assert_equal(test, [[1, None], [3, 4]])
+        # ... on mvoid
+        a = a[0]
+        test = a.tolist()
+        assert_equal(test, [1, None])
+
+    def test_tolist_specialcase(self):
+        # Test mvoid.tolist: make sure we return a standard Python object
+        a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
+        # w/o mask: each entry is a np.void whose elements are standard Python
+        for entry in a:
+            for item in entry.tolist():
+                assert_(not isinstance(item, np.generic))
+        # w/ mask: each entry is a ma.void whose elements should be
+        # standard Python
+        a.mask[0] = (0, 1)
+        for entry in a:
+            for item in entry.tolist():
+                assert_(not isinstance(item, np.generic))
+
+    def test_toflex(self):
+        # Test the conversion to records
+        data = arange(10)
+        record = data.toflex()
+        assert_equal(record['_data'], data._data)
+        assert_equal(record['_mask'], data._mask)
+
+        data[[0, 1, 2, -1]] = masked
+        record = data.toflex()
+        assert_equal(record['_data'], data._data)
+        assert_equal(record['_mask'], data._mask)
+
+        ndtype = [('i', int), ('s', '|S3'), ('f', float)]
+        data = array(list(zip(np.arange(10),
+                              'ABCDEFGHIJKLM',
+                              np.random.rand(10))),
+                     dtype=ndtype)
+        data[[0, 1, 2, -1]] = masked
+        record = data.toflex()
+        assert_equal(record['_data'], data._data)
+        assert_equal(record['_mask'], data._mask)
+
+        ndtype = np.dtype("int, (2,3)float, float")
+        data = array(list(zip(np.arange(10),
+                              np.random.rand(10),
+                              np.random.rand(10))),
+                     dtype=ndtype)
+        data[[0, 1, 2, -1]] = masked
+        record = data.toflex()
+        assert_equal_records(record['_data'], data._data)
+        assert_equal_records(record['_mask'], data._mask)
+
+    def test_fromflex(self):
+        # Test the reconstruction of a masked_array from a record
+        a = array([1, 2, 3])
+        test = fromflex(a.toflex())
+        assert_equal(test, a)
+        assert_equal(test.mask, a.mask)
+
+        a = array([1, 2, 3], mask=[0, 0, 1])
+        test = fromflex(a.toflex())
+        assert_equal(test, a)
+        assert_equal(test.mask, a.mask)
+
+        a = array([(1, 1.), (2, 2.), (3, 3.)], mask=[(1, 0), (0, 0), (0, 1)],
+                  dtype=[('A', int), ('B', float)])
+        test = fromflex(a.toflex())
+        assert_equal(test, a)
+        assert_equal(test.data, a.data)
+
+    def test_arraymethod(self):
+        # Test a _arraymethod w/ n argument
+        marray = masked_array([[1, 2, 3, 4, 5]], mask=[0, 0, 1, 0, 0])
+        control = masked_array([[1], [2], [3], [4], [5]],
+                               mask=[0, 0, 1, 0, 0])
+        assert_equal(marray.T, control)
+        assert_equal(marray.transpose(), control)
+
+        assert_equal(MaskedArray.cumsum(marray.T, 0), control.cumsum(0))
+
+    def test_arraymethod_0d(self):
+        # gh-9430
+        x = np.ma.array(42, mask=True)
+        assert_equal(x.T.mask, x.mask)
+        assert_equal(x.T.data, x.data)
+
+    def test_transpose_view(self):
+        x = np.ma.array([[1, 2, 3], [4, 5, 6]])
+        x[0,1] = np.ma.masked
+        xt = x.T
+
+        xt[1,0] = 10
+        xt[0,1] = np.ma.masked
+
+        assert_equal(x.data, xt.T.data)
+        assert_equal(x.mask, xt.T.mask)
+
+    def test_diagonal_view(self):
+        x = np.ma.zeros((3,3))
+        x[0,0] = 10
+        x[1,1] = np.ma.masked
+        x[2,2] = 20
+        xd = x.diagonal()
+        x[1,1] = 15
+        assert_equal(xd.mask, x.diagonal().mask)
+        assert_equal(xd.data, x.diagonal().data)
+
+
+class TestMaskedArrayMathMethods:
+
+    def setup_method(self):
+        # Base data definition.
+        x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
+                      7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+        X = x.reshape(6, 6)
+        XX = x.reshape(3, 2, 2, 3)
+
+        m = np.array([0, 1, 0, 1, 0, 0,
+                     1, 0, 1, 1, 0, 1,
+                     0, 0, 0, 1, 0, 1,
+                     0, 0, 0, 1, 1, 1,
+                     1, 0, 0, 1, 0, 0,
+                     0, 0, 1, 0, 1, 0])
+        mx = array(data=x, mask=m)
+        mX = array(data=X, mask=m.reshape(X.shape))
+        mXX = array(data=XX, mask=m.reshape(XX.shape))
+
+        m2 = np.array([1, 1, 0, 1, 0, 0,
+                      1, 1, 1, 1, 0, 1,
+                      0, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 1, 0,
+                      0, 0, 1, 0, 1, 1])
+        m2x = array(data=x, mask=m2)
+        m2X = array(data=X, mask=m2.reshape(X.shape))
+        m2XX = array(data=XX, mask=m2.reshape(XX.shape))
+        self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
+
+    def test_cumsumprod(self):
+        # Tests cumsum & cumprod on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        mXcp = mX.cumsum(0)
+        assert_equal(mXcp._data, mX.filled(0).cumsum(0))
+        mXcp = mX.cumsum(1)
+        assert_equal(mXcp._data, mX.filled(0).cumsum(1))
+
+        mXcp = mX.cumprod(0)
+        assert_equal(mXcp._data, mX.filled(1).cumprod(0))
+        mXcp = mX.cumprod(1)
+        assert_equal(mXcp._data, mX.filled(1).cumprod(1))
+
+    def test_cumsumprod_with_output(self):
+        # Tests cumsum/cumprod w/ output
+        xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
+        xm[:, 0] = xm[0] = xm[-1, -1] = masked
+
+        for funcname in ('cumsum', 'cumprod'):
+            npfunc = getattr(np, funcname)
+            xmmeth = getattr(xm, funcname)
+
+            # A ndarray as explicit input
+            output = np.empty((3, 4), dtype=float)
+            output.fill(-9999)
+            result = npfunc(xm, axis=0, out=output)
+            # ... the result should be the given output
+            assert_(result is output)
+            assert_equal(result, xmmeth(axis=0, out=output))
+
+            output = empty((3, 4), dtype=int)
+            result = xmmeth(axis=0, out=output)
+            assert_(result is output)
+
+    def test_ptp(self):
+        # Tests ptp on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        (n, m) = X.shape
+        assert_equal(mx.ptp(), np.ptp(mx.compressed()))
+        rows = np.zeros(n, float)
+        cols = np.zeros(m, float)
+        for k in range(m):
+            cols[k] = np.ptp(mX[:, k].compressed())
+        for k in range(n):
+            rows[k] = np.ptp(mX[k].compressed())
+        assert_equal(mX.ptp(0), cols)
+        assert_equal(mX.ptp(1), rows)
+
+    def test_add_object(self):
+        x = masked_array(['a', 'b'], mask=[1, 0], dtype=object)
+        y = x + 'x'
+        assert_equal(y[1], 'bx')
+        assert_(y.mask[0])
+
+    def test_sum_object(self):
+        # Test sum on object dtype
+        a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
+        assert_equal(a.sum(), 5)
+        a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
+        assert_equal(a.sum(axis=0), [5, 7, 9])
+
+    def test_prod_object(self):
+        # Test prod on object dtype
+        a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
+        assert_equal(a.prod(), 2 * 3)
+        a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
+        assert_equal(a.prod(axis=0), [4, 10, 18])
+
+    def test_meananom_object(self):
+        # Test mean/anom on object dtype
+        a = masked_array([1, 2, 3], dtype=object)
+        assert_equal(a.mean(), 2)
+        assert_equal(a.anom(), [-1, 0, 1])
+
+    def test_anom_shape(self):
+        a = masked_array([1, 2, 3])
+        assert_equal(a.anom().shape, a.shape)
+        a.mask = True
+        assert_equal(a.anom().shape, a.shape)
+        assert_(np.ma.is_masked(a.anom()))
+
+    def test_anom(self):
+        a = masked_array(np.arange(1, 7).reshape(2, 3))
+        assert_almost_equal(a.anom(),
+                            [[-2.5, -1.5, -0.5], [0.5, 1.5, 2.5]])
+        assert_almost_equal(a.anom(axis=0),
+                            [[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]])
+        assert_almost_equal(a.anom(axis=1),
+                            [[-1., 0., 1.], [-1., 0., 1.]])
+        a.mask = [[0, 0, 1], [0, 1, 0]]
+        mval = -99
+        assert_almost_equal(a.anom().filled(mval),
+                            [[-2.25, -1.25, mval], [0.75, mval, 2.75]])
+        assert_almost_equal(a.anom(axis=0).filled(mval),
+                            [[-1.5, 0.0, mval], [1.5, mval, 0.0]])
+        assert_almost_equal(a.anom(axis=1).filled(mval),
+                            [[-0.5, 0.5, mval], [-1.0, mval, 1.0]])
+
+    def test_trace(self):
+        # Tests trace on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        mXdiag = mX.diagonal()
+        assert_equal(mX.trace(), mX.diagonal().compressed().sum())
+        assert_almost_equal(mX.trace(),
+                            X.trace() - sum(mXdiag.mask * X.diagonal(),
+                                            axis=0))
+        assert_equal(np.trace(mX), mX.trace())
+
+        # gh-5560
+        arr = np.arange(2*4*4).reshape(2,4,4)
+        m_arr = np.ma.masked_array(arr, False)
+        assert_equal(arr.trace(axis1=1, axis2=2), m_arr.trace(axis1=1, axis2=2))
+
+    def test_dot(self):
+        # Tests dot on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        fx = mx.filled(0)
+        r = mx.dot(mx)
+        assert_almost_equal(r.filled(0), fx.dot(fx))
+        assert_(r.mask is nomask)
+
+        fX = mX.filled(0)
+        r = mX.dot(mX)
+        assert_almost_equal(r.filled(0), fX.dot(fX))
+        assert_(r.mask[1,3])
+        r1 = empty_like(r)
+        mX.dot(mX, out=r1)
+        assert_almost_equal(r, r1)
+
+        mYY = mXX.swapaxes(-1, -2)
+        fXX, fYY = mXX.filled(0), mYY.filled(0)
+        r = mXX.dot(mYY)
+        assert_almost_equal(r.filled(0), fXX.dot(fYY))
+        r1 = empty_like(r)
+        mXX.dot(mYY, out=r1)
+        assert_almost_equal(r, r1)
+
+    def test_dot_shape_mismatch(self):
+        # regression test
+        x = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
+        y = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
+        z = masked_array([[0,1],[3,3]])
+        x.dot(y, out=z)
+        assert_almost_equal(z.filled(0), [[1, 0], [15, 16]])
+        assert_almost_equal(z.mask, [[0, 1], [0, 0]])
+
+    def test_varmean_nomask(self):
+        # gh-5769
+        foo = array([1,2,3,4], dtype='f8')
+        bar = array([1,2,3,4], dtype='f8')
+        assert_equal(type(foo.mean()), np.float64)
+        assert_equal(type(foo.var()), np.float64)
+        assert((foo.mean() == bar.mean()) is np.bool(True))
+
+        # check array type is preserved and out works
+        foo = array(np.arange(16).reshape((4,4)), dtype='f8')
+        bar = empty(4, dtype='f4')
+        assert_equal(type(foo.mean(axis=1)), MaskedArray)
+        assert_equal(type(foo.var(axis=1)), MaskedArray)
+        assert_(foo.mean(axis=1, out=bar) is bar)
+        assert_(foo.var(axis=1, out=bar) is bar)
+
+    def test_varstd(self):
+        # Tests var & std on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        assert_almost_equal(mX.var(axis=None), mX.compressed().var())
+        assert_almost_equal(mX.std(axis=None), mX.compressed().std())
+        assert_almost_equal(mX.std(axis=None, ddof=1),
+                            mX.compressed().std(ddof=1))
+        assert_almost_equal(mX.var(axis=None, ddof=1),
+                            mX.compressed().var(ddof=1))
+        assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
+        assert_equal(mX.var().shape, X.var().shape)
+        (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
+        assert_almost_equal(mX.var(axis=None, ddof=2),
+                            mX.compressed().var(ddof=2))
+        assert_almost_equal(mX.std(axis=None, ddof=2),
+                            mX.compressed().std(ddof=2))
+        for k in range(6):
+            assert_almost_equal(mXvar1[k], mX[k].compressed().var())
+            assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
+            assert_almost_equal(np.sqrt(mXvar0[k]),
+                                mX[:, k].compressed().std())
+
+    @suppress_copy_mask_on_assignment
+    def test_varstd_specialcases(self):
+        # Test a special case for var
+        nout = np.array(-1, dtype=float)
+        mout = array(-1, dtype=float)
+
+        x = array(arange(10), mask=True)
+        for methodname in ('var', 'std'):
+            method = getattr(x, methodname)
+            assert_(method() is masked)
+            assert_(method(0) is masked)
+            assert_(method(-1) is masked)
+            # Using a masked array as explicit output
+            method(out=mout)
+            assert_(mout is not masked)
+            assert_equal(mout.mask, True)
+            # Using a ndarray as explicit output
+            method(out=nout)
+            assert_(np.isnan(nout))
+
+        x = array(arange(10), mask=True)
+        x[-1] = 9
+        for methodname in ('var', 'std'):
+            method = getattr(x, methodname)
+            assert_(method(ddof=1) is masked)
+            assert_(method(0, ddof=1) is masked)
+            assert_(method(-1, ddof=1) is masked)
+            # Using a masked array as explicit output
+            method(out=mout, ddof=1)
+            assert_(mout is not masked)
+            assert_equal(mout.mask, True)
+            # Using a ndarray as explicit output
+            method(out=nout, ddof=1)
+            assert_(np.isnan(nout))
+
+    def test_varstd_ddof(self):
+        a = array([[1, 1, 0], [1, 1, 0]], mask=[[0, 0, 1], [0, 0, 1]])
+        test = a.std(axis=0, ddof=0)
+        assert_equal(test.filled(0), [0, 0, 0])
+        assert_equal(test.mask, [0, 0, 1])
+        test = a.std(axis=0, ddof=1)
+        assert_equal(test.filled(0), [0, 0, 0])
+        assert_equal(test.mask, [0, 0, 1])
+        test = a.std(axis=0, ddof=2)
+        assert_equal(test.filled(0), [0, 0, 0])
+        assert_equal(test.mask, [1, 1, 1])
+
+    def test_diag(self):
+        # Test diag
+        x = arange(9).reshape((3, 3))
+        x[1, 1] = masked
+        out = np.diag(x)
+        assert_equal(out, [0, 4, 8])
+        out = diag(x)
+        assert_equal(out, [0, 4, 8])
+        assert_equal(out.mask, [0, 1, 0])
+        out = diag(out)
+        control = array([[0, 0, 0], [0, 4, 0], [0, 0, 8]],
+                        mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(out, control)
+
+    def test_axis_methods_nomask(self):
+        # Test the combination nomask & methods w/ axis
+        a = array([[1, 2, 3], [4, 5, 6]])
+
+        assert_equal(a.sum(0), [5, 7, 9])
+        assert_equal(a.sum(-1), [6, 15])
+        assert_equal(a.sum(1), [6, 15])
+
+        assert_equal(a.prod(0), [4, 10, 18])
+        assert_equal(a.prod(-1), [6, 120])
+        assert_equal(a.prod(1), [6, 120])
+
+        assert_equal(a.min(0), [1, 2, 3])
+        assert_equal(a.min(-1), [1, 4])
+        assert_equal(a.min(1), [1, 4])
+
+        assert_equal(a.max(0), [4, 5, 6])
+        assert_equal(a.max(-1), [3, 6])
+        assert_equal(a.max(1), [3, 6])
+
+    @requires_memory(free_bytes=2 * 10000 * 1000 * 2)
+    def test_mean_overflow(self):
+        # Test overflow in masked arrays
+        # gh-20272
+        a = masked_array(np.full((10000, 10000), 65535, dtype=np.uint16),
+                         mask=np.zeros((10000, 10000)))
+        assert_equal(a.mean(), 65535.0)
+
+    def test_diff_with_prepend(self):
+        # GH 22465
+        x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
+
+        a = np.ma.masked_equal(x[3:], value=2)
+        a_prep = np.ma.masked_equal(x[:3], value=2)
+        diff1 = np.ma.diff(a, prepend=a_prep, axis=0)
+
+        b = np.ma.masked_equal(x, value=2)
+        diff2 = np.ma.diff(b, axis=0)
+
+        assert_(np.ma.allequal(diff1, diff2))
+
+    def test_diff_with_append(self):
+        # GH 22465
+        x = np.array([1, 2, 2, 3, 4, 2, 1, 1])
+
+        a = np.ma.masked_equal(x[:3], value=2)
+        a_app = np.ma.masked_equal(x[3:], value=2)
+        diff1 = np.ma.diff(a, append=a_app, axis=0)
+
+        b = np.ma.masked_equal(x, value=2)
+        diff2 = np.ma.diff(b, axis=0)
+
+        assert_(np.ma.allequal(diff1, diff2))
+
+    def test_diff_with_dim_0(self):
+        with pytest.raises(
+            ValueError,
+            match="diff requires input that is at least one dimensional"
+            ):
+            np.ma.diff(np.array(1))
+
+    def test_diff_with_n_0(self):
+        a = np.ma.masked_equal([1, 2, 2, 3, 4, 2, 1, 1], value=2)
+        diff = np.ma.diff(a, n=0, axis=0)
+
+        assert_(np.ma.allequal(a, diff))
+
+
+class TestMaskedArrayMathMethodsComplex:
+    # Test class for miscellaneous MaskedArrays methods.
+    def setup_method(self):
+        # Base data definition.
+        x = np.array([8.375j, 7.545j, 8.828j, 8.5j, 1.757j, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479j,
+                      7.189j, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993j])
+        X = x.reshape(6, 6)
+        XX = x.reshape(3, 2, 2, 3)
+
+        m = np.array([0, 1, 0, 1, 0, 0,
+                     1, 0, 1, 1, 0, 1,
+                     0, 0, 0, 1, 0, 1,
+                     0, 0, 0, 1, 1, 1,
+                     1, 0, 0, 1, 0, 0,
+                     0, 0, 1, 0, 1, 0])
+        mx = array(data=x, mask=m)
+        mX = array(data=X, mask=m.reshape(X.shape))
+        mXX = array(data=XX, mask=m.reshape(XX.shape))
+
+        m2 = np.array([1, 1, 0, 1, 0, 0,
+                      1, 1, 1, 1, 0, 1,
+                      0, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 1, 0,
+                      0, 0, 1, 0, 1, 1])
+        m2x = array(data=x, mask=m2)
+        m2X = array(data=X, mask=m2.reshape(X.shape))
+        m2XX = array(data=XX, mask=m2.reshape(XX.shape))
+        self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
+
+    def test_varstd(self):
+        # Tests var & std on MaskedArrays.
+        (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
+        assert_almost_equal(mX.var(axis=None), mX.compressed().var())
+        assert_almost_equal(mX.std(axis=None), mX.compressed().std())
+        assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
+        assert_equal(mX.var().shape, X.var().shape)
+        (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
+        assert_almost_equal(mX.var(axis=None, ddof=2),
+                            mX.compressed().var(ddof=2))
+        assert_almost_equal(mX.std(axis=None, ddof=2),
+                            mX.compressed().std(ddof=2))
+        for k in range(6):
+            assert_almost_equal(mXvar1[k], mX[k].compressed().var())
+            assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
+            assert_almost_equal(np.sqrt(mXvar0[k]),
+                                mX[:, k].compressed().std())
+
+
+class TestMaskedArrayFunctions:
+    # Test class for miscellaneous functions.
+
+    def setup_method(self):
+        x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+        m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
+        xm = masked_array(x, mask=m1)
+        ym = masked_array(y, mask=m2)
+        xm.set_fill_value(1e+20)
+        self.info = (xm, ym)
+
+    def test_masked_where_bool(self):
+        x = [1, 2]
+        y = masked_where(False, x)
+        assert_equal(y, [1, 2])
+        assert_equal(y[1], 2)
+
+    def test_masked_equal_wlist(self):
+        x = [1, 2, 3]
+        mx = masked_equal(x, 3)
+        assert_equal(mx, x)
+        assert_equal(mx._mask, [0, 0, 1])
+        mx = masked_not_equal(x, 3)
+        assert_equal(mx, x)
+        assert_equal(mx._mask, [1, 1, 0])
+
+    def test_masked_equal_fill_value(self):
+        x = [1, 2, 3]
+        mx = masked_equal(x, 3)
+        assert_equal(mx._mask, [0, 0, 1])
+        assert_equal(mx.fill_value, 3)
+
+    def test_masked_where_condition(self):
+        # Tests masking functions.
+        x = array([1., 2., 3., 4., 5.])
+        x[2] = masked
+        assert_equal(masked_where(greater(x, 2), x), masked_greater(x, 2))
+        assert_equal(masked_where(greater_equal(x, 2), x),
+                     masked_greater_equal(x, 2))
+        assert_equal(masked_where(less(x, 2), x), masked_less(x, 2))
+        assert_equal(masked_where(less_equal(x, 2), x),
+                     masked_less_equal(x, 2))
+        assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
+        assert_equal(masked_where(equal(x, 2), x), masked_equal(x, 2))
+        assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
+        assert_equal(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
+                     [99, 99, 3, 4, 5])
+
+    def test_masked_where_oddities(self):
+        # Tests some generic features.
+        atest = ones((10, 10, 10), dtype=float)
+        btest = zeros(atest.shape, MaskType)
+        ctest = masked_where(btest, atest)
+        assert_equal(atest, ctest)
+
+    def test_masked_where_shape_constraint(self):
+        a = arange(10)
+        with assert_raises(IndexError):
+            masked_equal(1, a)
+        test = masked_equal(a, 1)
+        assert_equal(test.mask, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    def test_masked_where_structured(self):
+        # test that masked_where on a structured array sets a structured
+        # mask (see issue #2972)
+        a = np.zeros(10, dtype=[("A", " 6, x)
+
+    def test_masked_otherfunctions(self):
+        assert_equal(masked_inside(list(range(5)), 1, 3),
+                     [0, 199, 199, 199, 4])
+        assert_equal(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199])
+        assert_equal(masked_inside(array(list(range(5)),
+                                         mask=[1, 0, 0, 0, 0]), 1, 3).mask,
+                     [1, 1, 1, 1, 0])
+        assert_equal(masked_outside(array(list(range(5)),
+                                          mask=[0, 1, 0, 0, 0]), 1, 3).mask,
+                     [1, 1, 0, 0, 1])
+        assert_equal(masked_equal(array(list(range(5)),
+                                        mask=[1, 0, 0, 0, 0]), 2).mask,
+                     [1, 0, 1, 0, 0])
+        assert_equal(masked_not_equal(array([2, 2, 1, 2, 1],
+                                            mask=[1, 0, 0, 0, 0]), 2).mask,
+                     [1, 0, 1, 0, 1])
+
+    def test_round(self):
+        a = array([1.23456, 2.34567, 3.45678, 4.56789, 5.67890],
+                  mask=[0, 1, 0, 0, 0])
+        assert_equal(a.round(), [1., 2., 3., 5., 6.])
+        assert_equal(a.round(1), [1.2, 2.3, 3.5, 4.6, 5.7])
+        assert_equal(a.round(3), [1.235, 2.346, 3.457, 4.568, 5.679])
+        b = empty_like(a)
+        a.round(out=b)
+        assert_equal(b, [1., 2., 3., 5., 6.])
+
+        x = array([1., 2., 3., 4., 5.])
+        c = array([1, 1, 1, 0, 0])
+        x[2] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        c[0] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+
+    def test_round_with_output(self):
+        # Testing round with an explicit output
+
+        xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
+        xm[:, 0] = xm[0] = xm[-1, -1] = masked
+
+        # A ndarray as explicit input
+        output = np.empty((3, 4), dtype=float)
+        output.fill(-9999)
+        result = np.round(xm, decimals=2, out=output)
+        # ... the result should be the given output
+        assert_(result is output)
+        assert_equal(result, xm.round(decimals=2, out=output))
+
+        output = empty((3, 4), dtype=float)
+        result = xm.round(decimals=2, out=output)
+        assert_(result is output)
+
+    def test_round_with_scalar(self):
+        # Testing round with scalar/zero dimension input
+        # GH issue 2244
+        a = array(1.1, mask=[False])
+        assert_equal(a.round(), 1)
+
+        a = array(1.1, mask=[True])
+        assert_(a.round() is masked)
+
+        a = array(1.1, mask=[False])
+        output = np.empty(1, dtype=float)
+        output.fill(-9999)
+        a.round(out=output)
+        assert_equal(output, 1)
+
+        a = array(1.1, mask=[False])
+        output = array(-9999., mask=[True])
+        a.round(out=output)
+        assert_equal(output[()], 1)
+
+        a = array(1.1, mask=[True])
+        output = array(-9999., mask=[False])
+        a.round(out=output)
+        assert_(output[()] is masked)
+
+    def test_identity(self):
+        a = identity(5)
+        assert_(isinstance(a, MaskedArray))
+        assert_equal(a, np.identity(5))
+
+    def test_power(self):
+        x = -1.1
+        assert_almost_equal(power(x, 2.), 1.21)
+        assert_(power(x, masked) is masked)
+        x = array([-1.1, -1.1, 1.1, 1.1, 0.])
+        b = array([0.5, 2., 0.5, 2., -1.], mask=[0, 0, 0, 0, 1])
+        y = power(x, b)
+        assert_almost_equal(y, [0, 1.21, 1.04880884817, 1.21, 0.])
+        assert_equal(y._mask, [1, 0, 0, 0, 1])
+        b.mask = nomask
+        y = power(x, b)
+        assert_equal(y._mask, [1, 0, 0, 0, 1])
+        z = x ** b
+        assert_equal(z._mask, y._mask)
+        assert_almost_equal(z, y)
+        assert_almost_equal(z._data, y._data)
+        x **= b
+        assert_equal(x._mask, y._mask)
+        assert_almost_equal(x, y)
+        assert_almost_equal(x._data, y._data)
+
+    def test_power_with_broadcasting(self):
+        # Test power w/ broadcasting
+        a2 = np.array([[1., 2., 3.], [4., 5., 6.]])
+        a2m = array(a2, mask=[[1, 0, 0], [0, 0, 1]])
+        b1 = np.array([2, 4, 3])
+        b2 = np.array([b1, b1])
+        b2m = array(b2, mask=[[0, 1, 0], [0, 1, 0]])
+
+        ctrl = array([[1 ** 2, 2 ** 4, 3 ** 3], [4 ** 2, 5 ** 4, 6 ** 3]],
+                     mask=[[1, 1, 0], [0, 1, 1]])
+        # No broadcasting, base & exp w/ mask
+        test = a2m ** b2m
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, ctrl.mask)
+        # No broadcasting, base w/ mask, exp w/o mask
+        test = a2m ** b2
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, a2m.mask)
+        # No broadcasting, base w/o mask, exp w/ mask
+        test = a2 ** b2m
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, b2m.mask)
+
+        ctrl = array([[2 ** 2, 4 ** 4, 3 ** 3], [2 ** 2, 4 ** 4, 3 ** 3]],
+                     mask=[[0, 1, 0], [0, 1, 0]])
+        test = b1 ** b2m
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, ctrl.mask)
+        test = b2m ** b1
+        assert_equal(test, ctrl)
+        assert_equal(test.mask, ctrl.mask)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    def test_where(self):
+        # Test the where function
+        x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+        m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
+        xm = masked_array(x, mask=m1)
+        ym = masked_array(y, mask=m2)
+        xm.set_fill_value(1e+20)
+
+        d = where(xm > 2, xm, -9)
+        assert_equal(d, [-9., -9., -9., -9., -9., 4.,
+                         -9., -9., 10., -9., -9., 3.])
+        assert_equal(d._mask, xm._mask)
+        d = where(xm > 2, -9, ym)
+        assert_equal(d, [5., 0., 3., 2., -1., -9.,
+                         -9., -10., -9., 1., 0., -9.])
+        assert_equal(d._mask, [1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0])
+        d = where(xm > 2, xm, masked)
+        assert_equal(d, [-9., -9., -9., -9., -9., 4.,
+                         -9., -9., 10., -9., -9., 3.])
+        tmp = xm._mask.copy()
+        tmp[(xm <= 2).filled(True)] = True
+        assert_equal(d._mask, tmp)
+
+        with np.errstate(invalid="warn"):
+            # The fill value is 1e20, it cannot be converted to `int`:
+            with pytest.warns(RuntimeWarning, match="invalid value"):
+                ixm = xm.astype(int)
+        d = where(ixm > 2, ixm, masked)
+        assert_equal(d, [-9, -9, -9, -9, -9, 4, -9, -9, 10, -9, -9, 3])
+        assert_equal(d.dtype, ixm.dtype)
+
+    def test_where_object(self):
+        a = np.array(None)
+        b = masked_array(None)
+        r = b.copy()
+        assert_equal(np.ma.where(True, a, a), r)
+        assert_equal(np.ma.where(True, b, b), r)
+
+    def test_where_with_masked_choice(self):
+        x = arange(10)
+        x[3] = masked
+        c = x >= 8
+        # Set False to masked
+        z = where(c, x, masked)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is masked)
+        assert_(z[7] is masked)
+        assert_(z[8] is not masked)
+        assert_(z[9] is not masked)
+        assert_equal(x, z)
+        # Set True to masked
+        z = where(c, masked, x)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is not masked)
+        assert_(z[7] is not masked)
+        assert_(z[8] is masked)
+        assert_(z[9] is masked)
+
+    def test_where_with_masked_condition(self):
+        x = array([1., 2., 3., 4., 5.])
+        c = array([1, 1, 1, 0, 0])
+        x[2] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        c[0] = masked
+        z = where(c, x, -x)
+        assert_equal(z, [1., 2., 0., -4., -5])
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+
+        x = arange(1, 6)
+        x[-1] = masked
+        y = arange(1, 6) * 10
+        y[2] = masked
+        c = array([1, 1, 1, 0, 0], mask=[1, 0, 0, 0, 0])
+        cm = c.filled(1)
+        z = where(c, x, y)
+        zm = where(cm, x, y)
+        assert_equal(z, zm)
+        assert_(getmask(zm) is nomask)
+        assert_equal(zm, [1, 2, 3, 40, 50])
+        z = where(c, masked, 1)
+        assert_equal(z, [99, 99, 99, 1, 1])
+        z = where(c, 1, masked)
+        assert_equal(z, [99, 1, 1, 99, 99])
+
+    def test_where_type(self):
+        # Test the type conservation with where
+        x = np.arange(4, dtype=np.int32)
+        y = np.arange(4, dtype=np.float32) * 2.2
+        test = where(x > 1.5, y, x).dtype
+        control = np.result_type(np.int32, np.float32)
+        assert_equal(test, control)
+
+    def test_where_broadcast(self):
+        # Issue 8599
+        x = np.arange(9).reshape(3, 3)
+        y = np.zeros(3)
+        core = np.where([1, 0, 1], x, y)
+        ma = where([1, 0, 1], x, y)
+
+        assert_equal(core, ma)
+        assert_equal(core.dtype, ma.dtype)
+
+    def test_where_structured(self):
+        # Issue 8600
+        dt = np.dtype([('a', int), ('b', int)])
+        x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
+        y = np.array((10, 20), dtype=dt)
+        core = np.where([0, 1, 1], x, y)
+        ma = np.where([0, 1, 1], x, y)
+
+        assert_equal(core, ma)
+        assert_equal(core.dtype, ma.dtype)
+
+    def test_where_structured_masked(self):
+        dt = np.dtype([('a', int), ('b', int)])
+        x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
+
+        ma = where([0, 1, 1], x, masked)
+        expected = masked_where([1, 0, 0], x)
+
+        assert_equal(ma.dtype, expected.dtype)
+        assert_equal(ma, expected)
+        assert_equal(ma.mask, expected.mask)
+
+    def test_masked_invalid_error(self):
+        a = np.arange(5, dtype=object)
+        a[3] = np.inf
+        a[2] = np.nan
+        with pytest.raises(TypeError,
+                           match="not supported for the input types"):
+            np.ma.masked_invalid(a)
+
+    def test_masked_invalid_pandas(self):
+        # getdata() used to be bad for pandas series due to its _data
+        # attribute.  This test is a regression test mainly and may be
+        # removed if getdata() is adjusted.
+        class Series:
+            _data = "nonsense"
+
+            def __array__(self, dtype=None, copy=None):
+                return np.array([5, np.nan, np.inf])
+
+        arr = np.ma.masked_invalid(Series())
+        assert_array_equal(arr._data, np.array(Series()))
+        assert_array_equal(arr._mask, [False, True, True])
+
+    @pytest.mark.parametrize("copy", [True, False])
+    def test_masked_invalid_full_mask(self, copy):
+        # Matplotlib relied on masked_invalid always returning a full mask
+        # (Also astropy projects, but were ok with it gh-22720 and gh-22842)
+        a = np.ma.array([1, 2, 3, 4])
+        assert a._mask is nomask
+        res = np.ma.masked_invalid(a, copy=copy)
+        assert res.mask is not nomask
+        # mask of a should not be mutated
+        assert a.mask is nomask
+        assert np.may_share_memory(a._data, res._data) != copy
+
+    def test_choose(self):
+        # Test choose
+        choices = [[0, 1, 2, 3], [10, 11, 12, 13],
+                   [20, 21, 22, 23], [30, 31, 32, 33]]
+        chosen = choose([2, 3, 1, 0], choices)
+        assert_equal(chosen, array([20, 31, 12, 3]))
+        chosen = choose([2, 4, 1, 0], choices, mode='clip')
+        assert_equal(chosen, array([20, 31, 12, 3]))
+        chosen = choose([2, 4, 1, 0], choices, mode='wrap')
+        assert_equal(chosen, array([20, 1, 12, 3]))
+        # Check with some masked indices
+        indices_ = array([2, 4, 1, 0], mask=[1, 0, 0, 1])
+        chosen = choose(indices_, choices, mode='wrap')
+        assert_equal(chosen, array([99, 1, 12, 99]))
+        assert_equal(chosen.mask, [1, 0, 0, 1])
+        # Check with some masked choices
+        choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
+                                       [1, 0, 0, 0], [0, 0, 0, 0]])
+        indices_ = [2, 3, 1, 0]
+        chosen = choose(indices_, choices, mode='wrap')
+        assert_equal(chosen, array([20, 31, 12, 3]))
+        assert_equal(chosen.mask, [1, 0, 0, 1])
+
+    def test_choose_with_out(self):
+        # Test choose with an explicit out keyword
+        choices = [[0, 1, 2, 3], [10, 11, 12, 13],
+                   [20, 21, 22, 23], [30, 31, 32, 33]]
+        store = empty(4, dtype=int)
+        chosen = choose([2, 3, 1, 0], choices, out=store)
+        assert_equal(store, array([20, 31, 12, 3]))
+        assert_(store is chosen)
+        # Check with some masked indices + out
+        store = empty(4, dtype=int)
+        indices_ = array([2, 3, 1, 0], mask=[1, 0, 0, 1])
+        chosen = choose(indices_, choices, mode='wrap', out=store)
+        assert_equal(store, array([99, 31, 12, 99]))
+        assert_equal(store.mask, [1, 0, 0, 1])
+        # Check with some masked choices + out ina ndarray !
+        choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
+                                       [1, 0, 0, 0], [0, 0, 0, 0]])
+        indices_ = [2, 3, 1, 0]
+        store = empty(4, dtype=int).view(ndarray)
+        chosen = choose(indices_, choices, mode='wrap', out=store)
+        assert_equal(store, array([999999, 31, 12, 999999]))
+
+    def test_reshape(self):
+        a = arange(10)
+        a[0] = masked
+        # Try the default
+        b = a.reshape((5, 2))
+        assert_equal(b.shape, (5, 2))
+        assert_(b.flags['C'])
+        # Try w/ arguments as list instead of tuple
+        b = a.reshape(5, 2)
+        assert_equal(b.shape, (5, 2))
+        assert_(b.flags['C'])
+        # Try w/ order
+        b = a.reshape((5, 2), order='F')
+        assert_equal(b.shape, (5, 2))
+        assert_(b.flags['F'])
+        # Try w/ order
+        b = a.reshape(5, 2, order='F')
+        assert_equal(b.shape, (5, 2))
+        assert_(b.flags['F'])
+
+        c = np.reshape(a, (2, 5))
+        assert_(isinstance(c, MaskedArray))
+        assert_equal(c.shape, (2, 5))
+        assert_(c[0, 0] is masked)
+        assert_(c.flags['C'])
+
+    def test_make_mask_descr(self):
+        # Flexible
+        ntype = [('a', float), ('b', float)]
+        test = make_mask_descr(ntype)
+        assert_equal(test, [('a', bool), ('b', bool)])
+        assert_(test is make_mask_descr(test))
+
+        # Standard w/ shape
+        ntype = (float, 2)
+        test = make_mask_descr(ntype)
+        assert_equal(test, (bool, 2))
+        assert_(test is make_mask_descr(test))
+
+        # Standard standard
+        ntype = float
+        test = make_mask_descr(ntype)
+        assert_equal(test, np.dtype(bool))
+        assert_(test is make_mask_descr(test))
+
+        # Nested
+        ntype = [('a', float), ('b', [('ba', float), ('bb', float)])]
+        test = make_mask_descr(ntype)
+        control = np.dtype([('a', 'b1'), ('b', [('ba', 'b1'), ('bb', 'b1')])])
+        assert_equal(test, control)
+        assert_(test is make_mask_descr(test))
+
+        # Named+ shape
+        ntype = [('a', (float, 2))]
+        test = make_mask_descr(ntype)
+        assert_equal(test, np.dtype([('a', (bool, 2))]))
+        assert_(test is make_mask_descr(test))
+
+        # 2 names
+        ntype = [(('A', 'a'), float)]
+        test = make_mask_descr(ntype)
+        assert_equal(test, np.dtype([(('A', 'a'), bool)]))
+        assert_(test is make_mask_descr(test))
+
+        # nested boolean types should preserve identity
+        base_type = np.dtype([('a', int, 3)])
+        base_mtype = make_mask_descr(base_type)
+        sub_type = np.dtype([('a', int), ('b', base_mtype)])
+        test = make_mask_descr(sub_type)
+        assert_equal(test, np.dtype([('a', bool), ('b', [('a', bool, 3)])]))
+        assert_(test.fields['b'][0] is base_mtype)
+
+    def test_make_mask(self):
+        # Test make_mask
+        # w/ a list as an input
+        mask = [0, 1]
+        test = make_mask(mask)
+        assert_equal(test.dtype, MaskType)
+        assert_equal(test, [0, 1])
+        # w/ a ndarray as an input
+        mask = np.array([0, 1], dtype=bool)
+        test = make_mask(mask)
+        assert_equal(test.dtype, MaskType)
+        assert_equal(test, [0, 1])
+        # w/ a flexible-type ndarray as an input - use default
+        mdtype = [('a', bool), ('b', bool)]
+        mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
+        test = make_mask(mask)
+        assert_equal(test.dtype, MaskType)
+        assert_equal(test, [1, 1])
+        # w/ a flexible-type ndarray as an input - use input dtype
+        mdtype = [('a', bool), ('b', bool)]
+        mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
+        test = make_mask(mask, dtype=mask.dtype)
+        assert_equal(test.dtype, mdtype)
+        assert_equal(test, mask)
+        # w/ a flexible-type ndarray as an input - use input dtype
+        mdtype = [('a', float), ('b', float)]
+        bdtype = [('a', bool), ('b', bool)]
+        mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
+        test = make_mask(mask, dtype=mask.dtype)
+        assert_equal(test.dtype, bdtype)
+        assert_equal(test, np.array([(0, 0), (0, 1)], dtype=bdtype))
+        # Ensure this also works for void
+        mask = np.array((False, True), dtype='?,?')[()]
+        assert_(isinstance(mask, np.void))
+        test = make_mask(mask, dtype=mask.dtype)
+        assert_equal(test, mask)
+        assert_(test is not mask)
+        mask = np.array((0, 1), dtype='i4,i4')[()]
+        test2 = make_mask(mask, dtype=mask.dtype)
+        assert_equal(test2, test)
+        # test that nomask is returned when m is nomask.
+        bools = [True, False]
+        dtypes = [MaskType, float]
+        msgformat = 'copy=%s, shrink=%s, dtype=%s'
+        for cpy, shr, dt in itertools.product(bools, bools, dtypes):
+            res = make_mask(nomask, copy=cpy, shrink=shr, dtype=dt)
+            assert_(res is nomask, msgformat % (cpy, shr, dt))
+
+    def test_mask_or(self):
+        # Initialize
+        mtype = [('a', bool), ('b', bool)]
+        mask = np.array([(0, 0), (0, 1), (1, 0), (0, 0)], dtype=mtype)
+        # Test using nomask as input
+        test = mask_or(mask, nomask)
+        assert_equal(test, mask)
+        test = mask_or(nomask, mask)
+        assert_equal(test, mask)
+        # Using False as input
+        test = mask_or(mask, False)
+        assert_equal(test, mask)
+        # Using another array w / the same dtype
+        other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=mtype)
+        test = mask_or(mask, other)
+        control = np.array([(0, 1), (0, 1), (1, 1), (0, 1)], dtype=mtype)
+        assert_equal(test, control)
+        # Using another array w / a different dtype
+        othertype = [('A', bool), ('B', bool)]
+        other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=othertype)
+        try:
+            test = mask_or(mask, other)
+        except ValueError:
+            pass
+        # Using nested arrays
+        dtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
+        amask = np.array([(0, (1, 0)), (0, (1, 0))], dtype=dtype)
+        bmask = np.array([(1, (0, 1)), (0, (0, 0))], dtype=dtype)
+        cntrl = np.array([(1, (1, 1)), (0, (1, 0))], dtype=dtype)
+        assert_equal(mask_or(amask, bmask), cntrl)
+
+        a = np.array([False, False])
+        assert mask_or(a, a) is nomask  # gh-27360
+
+    def test_allequal(self):
+        x = array([1, 2, 3], mask=[0, 0, 0])
+        y = array([1, 2, 3], mask=[1, 0, 0])
+        z = array([[1, 2, 3], [4, 5, 6]], mask=[[0, 0, 0], [1, 1, 1]])
+
+        assert allequal(x, y)
+        assert not allequal(x, y, fill_value=False)
+        assert allequal(x, z)
+
+        # test allequal for the same input, with mask=nomask, this test is for
+        # the scenario raised in https://github.com/numpy/numpy/issues/27201
+        assert allequal(x, x)
+        assert allequal(x, x, fill_value=False)
+
+        assert allequal(y, y)
+        assert not allequal(y, y, fill_value=False)
+
+    def test_flatten_mask(self):
+        # Tests flatten mask
+        # Standard dtype
+        mask = np.array([0, 0, 1], dtype=bool)
+        assert_equal(flatten_mask(mask), mask)
+        # Flexible dtype
+        mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])
+        test = flatten_mask(mask)
+        control = np.array([0, 0, 0, 1], dtype=bool)
+        assert_equal(test, control)
+
+        mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
+        data = [(0, (0, 0)), (0, (0, 1))]
+        mask = np.array(data, dtype=mdtype)
+        test = flatten_mask(mask)
+        control = np.array([0, 0, 0, 0, 0, 1], dtype=bool)
+        assert_equal(test, control)
+
+    def test_on_ndarray(self):
+        # Test functions on ndarrays
+        a = np.array([1, 2, 3, 4])
+        m = array(a, mask=False)
+        test = anom(a)
+        assert_equal(test, m.anom())
+        test = reshape(a, (2, 2))
+        assert_equal(test, m.reshape(2, 2))
+
+    def test_compress(self):
+        # Test compress function on ndarray and masked array
+        # Address Github #2495.
+        arr = np.arange(8)
+        arr.shape = 4, 2
+        cond = np.array([True, False, True, True])
+        control = arr[[0, 2, 3]]
+        test = np.ma.compress(cond, arr, axis=0)
+        assert_equal(test, control)
+        marr = np.ma.array(arr)
+        test = np.ma.compress(cond, marr, axis=0)
+        assert_equal(test, control)
+
+    def test_compressed(self):
+        # Test ma.compressed function.
+        # Address gh-4026
+        a = np.ma.array([1, 2])
+        test = np.ma.compressed(a)
+        assert_(type(test) is np.ndarray)
+
+        # Test case when input data is ndarray subclass
+        class A(np.ndarray):
+            pass
+
+        a = np.ma.array(A(shape=0))
+        test = np.ma.compressed(a)
+        assert_(type(test) is A)
+
+        # Test that compress flattens
+        test = np.ma.compressed([[1],[2]])
+        assert_equal(test.ndim, 1)
+        test = np.ma.compressed([[[[[1]]]]])
+        assert_equal(test.ndim, 1)
+
+        # Test case when input is MaskedArray subclass
+        class M(MaskedArray):
+            pass
+
+        test = np.ma.compressed(M([[[]], [[]]]))
+        assert_equal(test.ndim, 1)
+
+        # with .compressed() overridden
+        class M(MaskedArray):
+            def compressed(self):
+                return 42
+
+        test = np.ma.compressed(M([[[]], [[]]]))
+        assert_equal(test, 42)
+
+    def test_convolve(self):
+        a = masked_equal(np.arange(5), 2)
+        b = np.array([1, 1])
+
+        result = masked_equal([0, 1, -1, -1, 7, 4], -1)
+        test = np.ma.convolve(a, b, mode='full')
+        assert_equal(test, result)
+
+        test = np.ma.convolve(a, b, mode='same')
+        assert_equal(test, result[:-1])
+
+        test = np.ma.convolve(a, b, mode='valid')
+        assert_equal(test, result[1:-1])
+
+        result = masked_equal([0, 1, 1, 3, 7, 4], -1)
+        test = np.ma.convolve(a, b, mode='full', propagate_mask=False)
+        assert_equal(test, result)
+
+        test = np.ma.convolve(a, b, mode='same', propagate_mask=False)
+        assert_equal(test, result[:-1])
+
+        test = np.ma.convolve(a, b, mode='valid', propagate_mask=False)
+        assert_equal(test, result[1:-1])
+
+        test = np.ma.convolve([1, 1], [1, 1, 1])
+        assert_equal(test, masked_equal([1, 2, 2, 1], -1))
+
+        a = [1, 1]
+        b = masked_equal([1, -1, -1, 1], -1)
+        test = np.ma.convolve(a, b, propagate_mask=False)
+        assert_equal(test, masked_equal([1, 1, -1, 1, 1], -1))
+        test = np.ma.convolve(a, b, propagate_mask=True)
+        assert_equal(test, masked_equal([-1, -1, -1, -1, -1], -1))
+
+
+class TestMaskedFields:
+
+    def setup_method(self):
+        ilist = [1, 2, 3, 4, 5]
+        flist = [1.1, 2.2, 3.3, 4.4, 5.5]
+        slist = ['one', 'two', 'three', 'four', 'five']
+        ddtype = [('a', int), ('b', float), ('c', '|S8')]
+        mdtype = [('a', bool), ('b', bool), ('c', bool)]
+        mask = [0, 1, 0, 0, 1]
+        base = array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
+        self.data = dict(base=base, mask=mask, ddtype=ddtype, mdtype=mdtype)
+
+    def test_set_records_masks(self):
+        base = self.data['base']
+        mdtype = self.data['mdtype']
+        # Set w/ nomask or masked
+        base.mask = nomask
+        assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
+        base.mask = masked
+        assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
+        # Set w/ simple boolean
+        base.mask = False
+        assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
+        base.mask = True
+        assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
+        # Set w/ list
+        base.mask = [0, 0, 0, 1, 1]
+        assert_equal_records(base._mask,
+                             np.array([(x, x, x) for x in [0, 0, 0, 1, 1]],
+                                      dtype=mdtype))
+
+    def test_set_record_element(self):
+        # Check setting an element of a record)
+        base = self.data['base']
+        (base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
+        base[0] = (pi, pi, 'pi')
+
+        assert_equal(base_a.dtype, int)
+        assert_equal(base_a._data, [3, 2, 3, 4, 5])
+
+        assert_equal(base_b.dtype, float)
+        assert_equal(base_b._data, [pi, 2.2, 3.3, 4.4, 5.5])
+
+        assert_equal(base_c.dtype, '|S8')
+        assert_equal(base_c._data,
+                     [b'pi', b'two', b'three', b'four', b'five'])
+
+    def test_set_record_slice(self):
+        base = self.data['base']
+        (base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
+        base[:3] = (pi, pi, 'pi')
+
+        assert_equal(base_a.dtype, int)
+        assert_equal(base_a._data, [3, 3, 3, 4, 5])
+
+        assert_equal(base_b.dtype, float)
+        assert_equal(base_b._data, [pi, pi, pi, 4.4, 5.5])
+
+        assert_equal(base_c.dtype, '|S8')
+        assert_equal(base_c._data,
+                     [b'pi', b'pi', b'pi', b'four', b'five'])
+
+    def test_mask_element(self):
+        "Check record access"
+        base = self.data['base']
+        base[0] = masked
+
+        for n in ('a', 'b', 'c'):
+            assert_equal(base[n].mask, [1, 1, 0, 0, 1])
+            assert_equal(base[n]._data, base._data[n])
+
+    def test_getmaskarray(self):
+        # Test getmaskarray on flexible dtype
+        ndtype = [('a', int), ('b', float)]
+        test = empty(3, dtype=ndtype)
+        assert_equal(getmaskarray(test),
+                     np.array([(0, 0), (0, 0), (0, 0)],
+                              dtype=[('a', '|b1'), ('b', '|b1')]))
+        test[:] = masked
+        assert_equal(getmaskarray(test),
+                     np.array([(1, 1), (1, 1), (1, 1)],
+                              dtype=[('a', '|b1'), ('b', '|b1')]))
+
+    def test_view(self):
+        # Test view w/ flexible dtype
+        iterator = list(zip(np.arange(10), np.random.rand(10)))
+        data = np.array(iterator)
+        a = array(iterator, dtype=[('a', float), ('b', float)])
+        a.mask[0] = (1, 0)
+        controlmask = np.array([1] + 19 * [0], dtype=bool)
+        # Transform globally to simple dtype
+        test = a.view(float)
+        assert_equal(test, data.ravel())
+        assert_equal(test.mask, controlmask)
+        # Transform globally to dty
+        test = a.view((float, 2))
+        assert_equal(test, data)
+        assert_equal(test.mask, controlmask.reshape(-1, 2))
+
+    def test_getitem(self):
+        ndtype = [('a', float), ('b', float)]
+        a = array(list(zip(np.random.rand(10), np.arange(10))), dtype=ndtype)
+        a.mask = np.array(list(zip([0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
+                                   [1, 0, 0, 0, 0, 0, 0, 0, 1, 0])),
+                          dtype=[('a', bool), ('b', bool)])
+
+        def _test_index(i):
+            assert_equal(type(a[i]), mvoid)
+            assert_equal_records(a[i]._data, a._data[i])
+            assert_equal_records(a[i]._mask, a._mask[i])
+
+            assert_equal(type(a[i, ...]), MaskedArray)
+            assert_equal_records(a[i,...]._data, a._data[i,...])
+            assert_equal_records(a[i,...]._mask, a._mask[i,...])
+
+        _test_index(1)   # No mask
+        _test_index(0)   # One element masked
+        _test_index(-2)  # All element masked
+
+    def test_setitem(self):
+        # Issue 4866: check that one can set individual items in [record][col]
+        # and [col][record] order
+        ndtype = np.dtype([('a', float), ('b', int)])
+        ma = np.ma.MaskedArray([(1.0, 1), (2.0, 2)], dtype=ndtype)
+        ma['a'][1] = 3.0
+        assert_equal(ma['a'], np.array([1.0, 3.0]))
+        ma[1]['a'] = 4.0
+        assert_equal(ma['a'], np.array([1.0, 4.0]))
+        # Issue 2403
+        mdtype = np.dtype([('a', bool), ('b', bool)])
+        # soft mask
+        control = np.array([(False, True), (True, True)], dtype=mdtype)
+        a = np.ma.masked_all((2,), dtype=ndtype)
+        a['a'][0] = 2
+        assert_equal(a.mask, control)
+        a = np.ma.masked_all((2,), dtype=ndtype)
+        a[0]['a'] = 2
+        assert_equal(a.mask, control)
+        # hard mask
+        control = np.array([(True, True), (True, True)], dtype=mdtype)
+        a = np.ma.masked_all((2,), dtype=ndtype)
+        a.harden_mask()
+        a['a'][0] = 2
+        assert_equal(a.mask, control)
+        a = np.ma.masked_all((2,), dtype=ndtype)
+        a.harden_mask()
+        a[0]['a'] = 2
+        assert_equal(a.mask, control)
+
+    def test_setitem_scalar(self):
+        # 8510
+        mask_0d = np.ma.masked_array(1, mask=True)
+        arr = np.ma.arange(3)
+        arr[0] = mask_0d
+        assert_array_equal(arr.mask, [True, False, False])
+
+    def test_element_len(self):
+        # check that len() works for mvoid (Github issue #576)
+        for rec in self.data['base']:
+            assert_equal(len(rec), len(self.data['ddtype']))
+
+
+class TestMaskedObjectArray:
+
+    def test_getitem(self):
+        arr = np.ma.array([None, None])
+        for dt in [float, object]:
+            a0 = np.eye(2).astype(dt)
+            a1 = np.eye(3).astype(dt)
+            arr[0] = a0
+            arr[1] = a1
+
+            assert_(arr[0] is a0)
+            assert_(arr[1] is a1)
+            assert_(isinstance(arr[0,...], MaskedArray))
+            assert_(isinstance(arr[1,...], MaskedArray))
+            assert_(arr[0,...][()] is a0)
+            assert_(arr[1,...][()] is a1)
+
+            arr[0] = np.ma.masked
+
+            assert_(arr[1] is a1)
+            assert_(isinstance(arr[0,...], MaskedArray))
+            assert_(isinstance(arr[1,...], MaskedArray))
+            assert_equal(arr[0,...].mask, True)
+            assert_(arr[1,...][()] is a1)
+
+            # gh-5962 - object arrays of arrays do something special
+            assert_equal(arr[0].data, a0)
+            assert_equal(arr[0].mask, True)
+            assert_equal(arr[0,...][()].data, a0)
+            assert_equal(arr[0,...][()].mask, True)
+
+    def test_nested_ma(self):
+
+        arr = np.ma.array([None, None])
+        # set the first object to be an unmasked masked constant. A little fiddly
+        arr[0,...] = np.array([np.ma.masked], object)[0,...]
+
+        # check the above line did what we were aiming for
+        assert_(arr.data[0] is np.ma.masked)
+
+        # test that getitem returned the value by identity
+        assert_(arr[0] is np.ma.masked)
+
+        # now mask the masked value!
+        arr[0] = np.ma.masked
+        assert_(arr[0] is np.ma.masked)
+
+
+class TestMaskedView:
+
+    def setup_method(self):
+        iterator = list(zip(np.arange(10), np.random.rand(10)))
+        data = np.array(iterator)
+        a = array(iterator, dtype=[('a', float), ('b', float)])
+        a.mask[0] = (1, 0)
+        controlmask = np.array([1] + 19 * [0], dtype=bool)
+        self.data = (data, a, controlmask)
+
+    def test_view_to_nothing(self):
+        (data, a, controlmask) = self.data
+        test = a.view()
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test._data, a._data)
+        assert_equal(test._mask, a._mask)
+
+    def test_view_to_type(self):
+        (data, a, controlmask) = self.data
+        test = a.view(np.ndarray)
+        assert_(not isinstance(test, MaskedArray))
+        assert_equal(test, a._data)
+        assert_equal_records(test, data.view(a.dtype).squeeze())
+
+    def test_view_to_simple_dtype(self):
+        (data, a, controlmask) = self.data
+        # View globally
+        test = a.view(float)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, data.ravel())
+        assert_equal(test.mask, controlmask)
+
+    def test_view_to_flexible_dtype(self):
+        (data, a, controlmask) = self.data
+
+        test = a.view([('A', float), ('B', float)])
+        assert_equal(test.mask.dtype.names, ('A', 'B'))
+        assert_equal(test['A'], a['a'])
+        assert_equal(test['B'], a['b'])
+
+        test = a[0].view([('A', float), ('B', float)])
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.mask.dtype.names, ('A', 'B'))
+        assert_equal(test['A'], a['a'][0])
+        assert_equal(test['B'], a['b'][0])
+
+        test = a[-1].view([('A', float), ('B', float)])
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.dtype.names, ('A', 'B'))
+        assert_equal(test['A'], a['a'][-1])
+        assert_equal(test['B'], a['b'][-1])
+
+    def test_view_to_subdtype(self):
+        (data, a, controlmask) = self.data
+        # View globally
+        test = a.view((float, 2))
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, data)
+        assert_equal(test.mask, controlmask.reshape(-1, 2))
+        # View on 1 masked element
+        test = a[0].view((float, 2))
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, data[0])
+        assert_equal(test.mask, (1, 0))
+        # View on 1 unmasked element
+        test = a[-1].view((float, 2))
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, data[-1])
+
+    def test_view_to_dtype_and_type(self):
+        (data, a, controlmask) = self.data
+
+        test = a.view((float, 2), np.recarray)
+        assert_equal(test, data)
+        assert_(isinstance(test, np.recarray))
+        assert_(not isinstance(test, MaskedArray))
+
+
+class TestOptionalArgs:
+    def test_ndarrayfuncs(self):
+        # test axis arg behaves the same as ndarray (including multiple axes)
+
+        d = np.arange(24.0).reshape((2,3,4))
+        m = np.zeros(24, dtype=bool).reshape((2,3,4))
+        # mask out last element of last dimension
+        m[:,:,-1] = True
+        a = np.ma.array(d, mask=m)
+
+        def testaxis(f, a, d):
+            numpy_f = numpy.__getattribute__(f)
+            ma_f = np.ma.__getattribute__(f)
+
+            # test axis arg
+            assert_equal(ma_f(a, axis=1)[...,:-1], numpy_f(d[...,:-1], axis=1))
+            assert_equal(ma_f(a, axis=(0,1))[...,:-1],
+                         numpy_f(d[...,:-1], axis=(0,1)))
+
+        def testkeepdims(f, a, d):
+            numpy_f = numpy.__getattribute__(f)
+            ma_f = np.ma.__getattribute__(f)
+
+            # test keepdims arg
+            assert_equal(ma_f(a, keepdims=True).shape,
+                         numpy_f(d, keepdims=True).shape)
+            assert_equal(ma_f(a, keepdims=False).shape,
+                         numpy_f(d, keepdims=False).shape)
+
+            # test both at once
+            assert_equal(ma_f(a, axis=1, keepdims=True)[...,:-1],
+                         numpy_f(d[...,:-1], axis=1, keepdims=True))
+            assert_equal(ma_f(a, axis=(0,1), keepdims=True)[...,:-1],
+                         numpy_f(d[...,:-1], axis=(0,1), keepdims=True))
+
+        for f in ['sum', 'prod', 'mean', 'var', 'std']:
+            testaxis(f, a, d)
+            testkeepdims(f, a, d)
+
+        for f in ['min', 'max']:
+            testaxis(f, a, d)
+
+        d = (np.arange(24).reshape((2,3,4))%2 == 0)
+        a = np.ma.array(d, mask=m)
+        for f in ['all', 'any']:
+            testaxis(f, a, d)
+            testkeepdims(f, a, d)
+
+    def test_count(self):
+        # test np.ma.count specially
+
+        d = np.arange(24.0).reshape((2,3,4))
+        m = np.zeros(24, dtype=bool).reshape((2,3,4))
+        m[:,0,:] = True
+        a = np.ma.array(d, mask=m)
+
+        assert_equal(count(a), 16)
+        assert_equal(count(a, axis=1), 2*ones((2,4)))
+        assert_equal(count(a, axis=(0,1)), 4*ones((4,)))
+        assert_equal(count(a, keepdims=True), 16*ones((1,1,1)))
+        assert_equal(count(a, axis=1, keepdims=True), 2*ones((2,1,4)))
+        assert_equal(count(a, axis=(0,1), keepdims=True), 4*ones((1,1,4)))
+        assert_equal(count(a, axis=-2), 2*ones((2,4)))
+        assert_raises(ValueError, count, a, axis=(1,1))
+        assert_raises(AxisError, count, a, axis=3)
+
+        # check the 'nomask' path
+        a = np.ma.array(d, mask=nomask)
+
+        assert_equal(count(a), 24)
+        assert_equal(count(a, axis=1), 3*ones((2,4)))
+        assert_equal(count(a, axis=(0,1)), 6*ones((4,)))
+        assert_equal(count(a, keepdims=True), 24*ones((1,1,1)))
+        assert_equal(np.ndim(count(a, keepdims=True)), 3)
+        assert_equal(count(a, axis=1, keepdims=True), 3*ones((2,1,4)))
+        assert_equal(count(a, axis=(0,1), keepdims=True), 6*ones((1,1,4)))
+        assert_equal(count(a, axis=-2), 3*ones((2,4)))
+        assert_raises(ValueError, count, a, axis=(1,1))
+        assert_raises(AxisError, count, a, axis=3)
+
+        # check the 'masked' singleton
+        assert_equal(count(np.ma.masked), 0)
+
+        # check 0-d arrays do not allow axis > 0
+        assert_raises(AxisError, count, np.ma.array(1), axis=1)
+
+
+class TestMaskedConstant:
+    def _do_add_test(self, add):
+        # sanity check
+        assert_(add(np.ma.masked, 1) is np.ma.masked)
+
+        # now try with a vector
+        vector = np.array([1, 2, 3])
+        result = add(np.ma.masked, vector)
+
+        # lots of things could go wrong here
+        assert_(result is not np.ma.masked)
+        assert_(not isinstance(result, np.ma.core.MaskedConstant))
+        assert_equal(result.shape, vector.shape)
+        assert_equal(np.ma.getmask(result), np.ones(vector.shape, dtype=bool))
+
+    def test_ufunc(self):
+        self._do_add_test(np.add)
+
+    def test_operator(self):
+        self._do_add_test(lambda a, b: a + b)
+
+    def test_ctor(self):
+        m = np.ma.array(np.ma.masked)
+
+        # most importantly, we do not want to create a new MaskedConstant
+        # instance
+        assert_(not isinstance(m, np.ma.core.MaskedConstant))
+        assert_(m is not np.ma.masked)
+
+    def test_repr(self):
+        # copies should not exist, but if they do, it should be obvious that
+        # something is wrong
+        assert_equal(repr(np.ma.masked), 'masked')
+
+        # create a new instance in a weird way
+        masked2 = np.ma.MaskedArray.__new__(np.ma.core.MaskedConstant)
+        assert_not_equal(repr(masked2), 'masked')
+
+    def test_pickle(self):
+        from io import BytesIO
+
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            with BytesIO() as f:
+                pickle.dump(np.ma.masked, f, protocol=proto)
+                f.seek(0)
+                res = pickle.load(f)
+            assert_(res is np.ma.masked)
+
+    def test_copy(self):
+        # gh-9328
+        # copy is a no-op, like it is with np.True_
+        assert_equal(
+            np.ma.masked.copy() is np.ma.masked,
+            np.True_.copy() is np.True_)
+
+    def test__copy(self):
+        import copy
+        assert_(
+            copy.copy(np.ma.masked) is np.ma.masked)
+
+    def test_deepcopy(self):
+        import copy
+        assert_(
+            copy.deepcopy(np.ma.masked) is np.ma.masked)
+
+    def test_immutable(self):
+        orig = np.ma.masked
+        assert_raises(np.ma.core.MaskError, operator.setitem, orig, (), 1)
+        assert_raises(ValueError,operator.setitem, orig.data, (), 1)
+        assert_raises(ValueError, operator.setitem, orig.mask, (), False)
+
+        view = np.ma.masked.view(np.ma.MaskedArray)
+        assert_raises(ValueError, operator.setitem, view, (), 1)
+        assert_raises(ValueError, operator.setitem, view.data, (), 1)
+        assert_raises(ValueError, operator.setitem, view.mask, (), False)
+
+    def test_coercion_int(self):
+        a_i = np.zeros((), int)
+        assert_raises(MaskError, operator.setitem, a_i, (), np.ma.masked)
+        assert_raises(MaskError, int, np.ma.masked)
+
+    def test_coercion_float(self):
+        a_f = np.zeros((), float)
+        assert_warns(UserWarning, operator.setitem, a_f, (), np.ma.masked)
+        assert_(np.isnan(a_f[()]))
+
+    @pytest.mark.xfail(reason="See gh-9750")
+    def test_coercion_unicode(self):
+        a_u = np.zeros((), 'U10')
+        a_u[()] = np.ma.masked
+        assert_equal(a_u[()], '--')
+
+    @pytest.mark.xfail(reason="See gh-9750")
+    def test_coercion_bytes(self):
+        a_b = np.zeros((), 'S10')
+        a_b[()] = np.ma.masked
+        assert_equal(a_b[()], b'--')
+
+    def test_subclass(self):
+        # https://github.com/astropy/astropy/issues/6645
+        class Sub(type(np.ma.masked)):
+            pass
+
+        a = Sub()
+        assert_(a is Sub())
+        assert_(a is not np.ma.masked)
+        assert_not_equal(repr(a), 'masked')
+
+    def test_attributes_readonly(self):
+        assert_raises(AttributeError, setattr, np.ma.masked, 'shape', (1,))
+        assert_raises(AttributeError, setattr, np.ma.masked, 'dtype', np.int64)
+
+
+class TestMaskedWhereAliases:
+
+    # TODO: Test masked_object, masked_equal, ...
+
+    def test_masked_values(self):
+        res = masked_values(np.array([-32768.0]), np.int16(-32768))
+        assert_equal(res.mask, [True])
+
+        res = masked_values(np.inf, np.inf)
+        assert_equal(res.mask, True)
+
+        res = np.ma.masked_values(np.inf, -np.inf)
+        assert_equal(res.mask, False)
+
+        res = np.ma.masked_values([1, 2, 3, 4], 5, shrink=True)
+        assert_(res.mask is np.ma.nomask)
+
+        res = np.ma.masked_values([1, 2, 3, 4], 5, shrink=False)
+        assert_equal(res.mask, [False] * 4)
+
+
+def test_masked_array():
+    a = np.ma.array([0, 1, 2, 3], mask=[0, 0, 1, 0])
+    assert_equal(np.argwhere(a), [[1], [3]])
+
+def test_masked_array_no_copy():
+    # check nomask array is updated in place
+    a = np.ma.array([1, 2, 3, 4])
+    _ = np.ma.masked_where(a == 3, a, copy=False)
+    assert_array_equal(a.mask, [False, False, True, False])
+    # check masked array is updated in place
+    a = np.ma.array([1, 2, 3, 4], mask=[1, 0, 0, 0])
+    _ = np.ma.masked_where(a == 3, a, copy=False)
+    assert_array_equal(a.mask, [True, False, True, False])
+    # check masked array with masked_invalid is updated in place
+    a = np.ma.array([np.inf, 1, 2, 3, 4])
+    _ = np.ma.masked_invalid(a, copy=False)
+    assert_array_equal(a.mask, [True, False, False, False, False])
+
+def test_append_masked_array():
+    a = np.ma.masked_equal([1,2,3], value=2)
+    b = np.ma.masked_equal([4,3,2], value=2)
+
+    result = np.ma.append(a, b)
+    expected_data = [1, 2, 3, 4, 3, 2]
+    expected_mask = [False, True, False, False, False, True]
+    assert_array_equal(result.data, expected_data)
+    assert_array_equal(result.mask, expected_mask)
+
+    a = np.ma.masked_all((2,2))
+    b = np.ma.ones((3,1))
+
+    result = np.ma.append(a, b)
+    expected_data = [1] * 3
+    expected_mask = [True] * 4 + [False] * 3
+    assert_array_equal(result.data[-3], expected_data)
+    assert_array_equal(result.mask, expected_mask)
+
+    result = np.ma.append(a, b, axis=None)
+    assert_array_equal(result.data[-3], expected_data)
+    assert_array_equal(result.mask, expected_mask)
+
+
+def test_append_masked_array_along_axis():
+    a = np.ma.masked_equal([1,2,3], value=2)
+    b = np.ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)
+
+    # When `axis` is specified, `values` must have the correct shape.
+    assert_raises(ValueError, np.ma.append, a, b, axis=0)
+
+    result = np.ma.append(a[np.newaxis,:], b, axis=0)
+    expected = np.ma.arange(1, 10)
+    expected[[1, 6]] = np.ma.masked
+    expected = expected.reshape((3,3))
+    assert_array_equal(result.data, expected.data)
+    assert_array_equal(result.mask, expected.mask)
+
+def test_default_fill_value_complex():
+    # regression test for Python 3, where 'unicode' was not defined
+    assert_(default_fill_value(1 + 1j) == 1.e20 + 0.0j)
+
+
+def test_ufunc_with_output():
+    # check that giving an output argument always returns that output.
+    # Regression test for gh-8416.
+    x = array([1., 2., 3.], mask=[0, 0, 1])
+    y = np.add(x, 1., out=x)
+    assert_(y is x)
+
+
+def test_ufunc_with_out_varied():
+    """ Test that masked arrays are immune to gh-10459 """
+    # the mask of the output should not affect the result, however it is passed
+    a        = array([ 1,  2,  3], mask=[1, 0, 0])
+    b        = array([10, 20, 30], mask=[1, 0, 0])
+    out      = array([ 0,  0,  0], mask=[0, 0, 1])
+    expected = array([11, 22, 33], mask=[1, 0, 0])
+
+    out_pos = out.copy()
+    res_pos = np.add(a, b, out_pos)
+
+    out_kw = out.copy()
+    res_kw = np.add(a, b, out=out_kw)
+
+    out_tup = out.copy()
+    res_tup = np.add(a, b, out=(out_tup,))
+
+    assert_equal(res_kw.mask,  expected.mask)
+    assert_equal(res_kw.data,  expected.data)
+    assert_equal(res_tup.mask, expected.mask)
+    assert_equal(res_tup.data, expected.data)
+    assert_equal(res_pos.mask, expected.mask)
+    assert_equal(res_pos.data, expected.data)
+
+
+def test_astype_mask_ordering():
+    descr = np.dtype([('v', int, 3), ('x', [('y', float)])])
+    x = array([
+        [([1, 2, 3], (1.0,)),  ([1, 2, 3], (2.0,))],
+        [([1, 2, 3], (3.0,)),  ([1, 2, 3], (4.0,))]], dtype=descr)
+    x[0]['v'][0] = np.ma.masked
+
+    x_a = x.astype(descr)
+    assert x_a.dtype.names == np.dtype(descr).names
+    assert x_a.mask.dtype.names == np.dtype(descr).names
+    assert_equal(x, x_a)
+
+    assert_(x is x.astype(x.dtype, copy=False))
+    assert_equal(type(x.astype(x.dtype, subok=False)), np.ndarray)
+
+    x_f = x.astype(x.dtype, order='F')
+    assert_(x_f.flags.f_contiguous)
+    assert_(x_f.mask.flags.f_contiguous)
+
+    # Also test the same indirectly, via np.array
+    x_a2 = np.array(x, dtype=descr, subok=True)
+    assert x_a2.dtype.names == np.dtype(descr).names
+    assert x_a2.mask.dtype.names == np.dtype(descr).names
+    assert_equal(x, x_a2)
+
+    assert_(x is np.array(x, dtype=descr, copy=None, subok=True))
+
+    x_f2 = np.array(x, dtype=x.dtype, order='F', subok=True)
+    assert_(x_f2.flags.f_contiguous)
+    assert_(x_f2.mask.flags.f_contiguous)
+
+
+@pytest.mark.parametrize('dt1', num_dts, ids=num_ids)
+@pytest.mark.parametrize('dt2', num_dts, ids=num_ids)
+@pytest.mark.filterwarnings('ignore::numpy.exceptions.ComplexWarning')
+def test_astype_basic(dt1, dt2):
+    # See gh-12070
+    src = np.ma.array(ones(3, dt1), fill_value=1)
+    dst = src.astype(dt2)
+
+    assert_(src.fill_value == 1)
+    assert_(src.dtype == dt1)
+    assert_(src.fill_value.dtype == dt1)
+
+    assert_(dst.fill_value == 1)
+    assert_(dst.dtype == dt2)
+    assert_(dst.fill_value.dtype == dt2)
+
+    assert_equal(src, dst)
+
+
+def test_fieldless_void():
+    dt = np.dtype([])  # a void dtype with no fields
+    x = np.empty(4, dt)
+
+    # these arrays contain no values, so there's little to test - but this
+    # shouldn't crash
+    mx = np.ma.array(x)
+    assert_equal(mx.dtype, x.dtype)
+    assert_equal(mx.shape, x.shape)
+
+    mx = np.ma.array(x, mask=x)
+    assert_equal(mx.dtype, x.dtype)
+    assert_equal(mx.shape, x.shape)
+
+
+def test_mask_shape_assignment_does_not_break_masked():
+    a = np.ma.masked
+    b = np.ma.array(1, mask=a.mask)
+    b.shape = (1,)
+    assert_equal(a.mask.shape, ())
+
+@pytest.mark.skipif(sys.flags.optimize > 1,
+                    reason="no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1")
+def test_doc_note():
+    def method(self):
+        """This docstring
+
+        Has multiple lines
+
+        And notes
+
+        Notes
+        -----
+        original note
+        """
+        pass
+
+    expected_doc = """This docstring
+
+Has multiple lines
+
+And notes
+
+Notes
+-----
+note
+
+original note"""
+
+    assert_equal(np.ma.core.doc_note(method.__doc__, "note"), expected_doc)
+
+
+def test_gh_22556():
+    source = np.ma.array([0, [0, 1, 2]], dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[1].append('this should not appear in source')
+    assert len(source[1]) == 3
+
+
+def test_gh_21022():
+    # testing for absence of reported error
+    source = np.ma.masked_array(data=[-1, -1], mask=True, dtype=np.float64)
+    axis = np.array(0)
+    result = np.prod(source, axis=axis, keepdims=False)
+    result = np.ma.masked_array(result,
+                                mask=np.ones(result.shape, dtype=np.bool))
+    array = np.ma.masked_array(data=-1, mask=True, dtype=np.float64)
+    copy.deepcopy(array)
+    copy.deepcopy(result)
+
+
+def test_deepcopy_2d_obj():
+    source = np.ma.array([[0, "dog"],
+                          [1, 1],
+                          [[1, 2], "cat"]],
+                        mask=[[0, 1],
+                              [0, 0],
+                              [0, 0]],
+                        dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[2, 0].extend(['this should not appear in source', 3])
+    assert len(source[2, 0]) == 2
+    assert len(deepcopy[2, 0]) == 4
+    assert_equal(deepcopy._mask, source._mask)
+    deepcopy._mask[0, 0] = 1
+    assert source._mask[0, 0] == 0
+
+
+def test_deepcopy_0d_obj():
+    source = np.ma.array(0, mask=[0], dtype=object)
+    deepcopy = copy.deepcopy(source)
+    deepcopy[...] = 17
+    assert_equal(source, 0)
+    assert_equal(deepcopy, 17)
+
+
+def test_uint_fill_value_and_filled():
+    # See also gh-27269
+    a = np.ma.MaskedArray([1, 1], [True, False], dtype="uint16")
+    # the fill value should likely not be 99999, but for now guarantee it:
+    assert a.fill_value == 999999
+    # However, it's type is uint:
+    assert a.fill_value.dtype.kind == "u"
+    # And this ensures things like filled work:
+    np.testing.assert_array_equal(
+        a.filled(), np.array([999999, 1]).astype("uint16"), strict=True)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_deprecations.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_deprecations.py
new file mode 100644
index 0000000000000000000000000000000000000000..40c8418f5c1809130672dca46e8c43469692da09
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_deprecations.py
@@ -0,0 +1,84 @@
+"""Test deprecation and future warnings.
+
+"""
+import pytest
+import numpy as np
+from numpy.testing import assert_warns
+from numpy.ma.testutils import assert_equal
+from numpy.ma.core import MaskedArrayFutureWarning
+import io
+import textwrap
+
+class TestArgsort:
+    """ gh-8701 """
+    def _test_base(self, argsort, cls):
+        arr_0d = np.array(1).view(cls)
+        argsort(arr_0d)
+
+        arr_1d = np.array([1, 2, 3]).view(cls)
+        argsort(arr_1d)
+
+        # argsort has a bad default for >1d arrays
+        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
+        result = assert_warns(
+            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
+        assert_equal(result, argsort(arr_2d, axis=None))
+
+        # should be no warnings for explicitly specifying it
+        argsort(arr_2d, axis=None)
+        argsort(arr_2d, axis=-1)
+
+    def test_function_ndarray(self):
+        return self._test_base(np.ma.argsort, np.ndarray)
+
+    def test_function_maskedarray(self):
+        return self._test_base(np.ma.argsort, np.ma.MaskedArray)
+
+    def test_method(self):
+        return self._test_base(np.ma.MaskedArray.argsort, np.ma.MaskedArray)
+
+
+class TestMinimumMaximum:
+
+    def test_axis_default(self):
+        # NumPy 1.13, 2017-05-06
+
+        data1d = np.ma.arange(6)
+        data2d = data1d.reshape(2, 3)
+
+        ma_min = np.ma.minimum.reduce
+        ma_max = np.ma.maximum.reduce
+
+        # check that the default axis is still None, but warns on 2d arrays
+        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
+        assert_equal(result, ma_max(data2d, axis=None))
+
+        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
+        assert_equal(result, ma_min(data2d, axis=None))
+
+        # no warnings on 1d, as both new and old defaults are equivalent
+        result = ma_min(data1d)
+        assert_equal(result, ma_min(data1d, axis=None))
+        assert_equal(result, ma_min(data1d, axis=0))
+
+        result = ma_max(data1d)
+        assert_equal(result, ma_max(data1d, axis=None))
+        assert_equal(result, ma_max(data1d, axis=0))
+
+
+class TestFromtextfile:
+    def test_fromtextfile_delimitor(self):
+        # NumPy 1.22.0, 2021-09-23
+
+        textfile = io.StringIO(textwrap.dedent(
+            """
+            A,B,C,D
+            'string 1';1;1.0;'mixed column'
+            'string 2';2;2.0;
+            'string 3';3;3.0;123
+            'string 4';4;4.0;3.14
+            """
+        ))
+
+        with pytest.warns(DeprecationWarning):
+            result = np.ma.mrecords.fromtextfile(textfile, delimitor=';')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_extras.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..daf376b766d5bb98b3c7602bf2afc37b07e2f5a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_extras.py
@@ -0,0 +1,1960 @@
+# pylint: disable-msg=W0611, W0612, W0511
+"""Tests suite for MaskedArray.
+Adapted from the original test_ma by Pierre Gerard-Marchant
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+
+"""
+import warnings
+import itertools
+import pytest
+
+import numpy as np
+from numpy._core.numeric import normalize_axis_tuple
+from numpy.testing import (
+    assert_warns, suppress_warnings
+    )
+from numpy.ma.testutils import (
+    assert_, assert_array_equal, assert_equal, assert_almost_equal
+    )
+from numpy.ma.core import (
+    array, arange, masked, MaskedArray, masked_array, getmaskarray, shape,
+    nomask, ones, zeros, count
+    )
+from numpy.ma.extras import (
+    atleast_1d, atleast_2d, atleast_3d, mr_, dot, polyfit, cov, corrcoef,
+    median, average, unique, setxor1d, setdiff1d, union1d, intersect1d, in1d,
+    ediff1d, apply_over_axes, apply_along_axis, compress_nd, compress_rowcols,
+    mask_rowcols, clump_masked, clump_unmasked, flatnotmasked_contiguous,
+    notmasked_contiguous, notmasked_edges, masked_all, masked_all_like, isin,
+    diagflat, ndenumerate, stack, vstack, _covhelper
+    )
+
+
+class TestGeneric:
+    #
+    def test_masked_all(self):
+        # Tests masked_all
+        # Standard dtype
+        test = masked_all((2,), dtype=float)
+        control = array([1, 1], mask=[1, 1], dtype=float)
+        assert_equal(test, control)
+        # Flexible dtype
+        dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
+        test = masked_all((2,), dtype=dt)
+        control = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((2, 2), dtype=dt)
+        control = array([[(0, 0), (0, 0)], [(0, 0), (0, 0)]],
+                        mask=[[(1, 1), (1, 1)], [(1, 1), (1, 1)]],
+                        dtype=dt)
+        assert_equal(test, control)
+        # Nested dtype
+        dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
+        test = masked_all((2,), dtype=dt)
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((2,), dtype=dt)
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((1, 1), dtype=dt)
+        control = array([[(1, (1, 1))]], mask=[[(1, (1, 1))]], dtype=dt)
+        assert_equal(test, control)
+
+    def test_masked_all_with_object_nested(self):
+        # Test masked_all works with nested array with dtype of an 'object'
+        # refers to issue #15895
+        my_dtype = np.dtype([('b', ([('c', object)], (1,)))])
+        masked_arr = np.ma.masked_all((1,), my_dtype)
+
+        assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
+        assert_equal(type(masked_arr['b']['c']), np.ma.core.MaskedArray)
+        assert_equal(len(masked_arr['b']['c']), 1)
+        assert_equal(masked_arr['b']['c'].shape, (1, 1))
+        assert_equal(masked_arr['b']['c']._fill_value.shape, ())
+
+    def test_masked_all_with_object(self):
+        # same as above except that the array is not nested
+        my_dtype = np.dtype([('b', (object, (1,)))])
+        masked_arr = np.ma.masked_all((1,), my_dtype)
+
+        assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
+        assert_equal(len(masked_arr['b']), 1)
+        assert_equal(masked_arr['b'].shape, (1, 1))
+        assert_equal(masked_arr['b']._fill_value.shape, ())
+
+    def test_masked_all_like(self):
+        # Tests masked_all
+        # Standard dtype
+        base = array([1, 2], dtype=float)
+        test = masked_all_like(base)
+        control = array([1, 1], mask=[1, 1], dtype=float)
+        assert_equal(test, control)
+        # Flexible dtype
+        dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
+        base = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
+        test = masked_all_like(base)
+        control = array([(10, 10), (10, 10)], mask=[(1, 1), (1, 1)], dtype=dt)
+        assert_equal(test, control)
+        # Nested dtype
+        dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        test = masked_all_like(control)
+        assert_equal(test, control)
+
+    def check_clump(self, f):
+        for i in range(1, 7):
+            for j in range(2**i):
+                k = np.arange(i, dtype=int)
+                ja = np.full(i, j, dtype=int)
+                a = masked_array(2**k)
+                a.mask = (ja & (2**k)) != 0
+                s = 0
+                for sl in f(a):
+                    s += a.data[sl].sum()
+                if f == clump_unmasked:
+                    assert_equal(a.compressed().sum(), s)
+                else:
+                    a.mask = ~a.mask
+                    assert_equal(a.compressed().sum(), s)
+
+    def test_clump_masked(self):
+        # Test clump_masked
+        a = masked_array(np.arange(10))
+        a[[0, 1, 2, 6, 8, 9]] = masked
+        #
+        test = clump_masked(a)
+        control = [slice(0, 3), slice(6, 7), slice(8, 10)]
+        assert_equal(test, control)
+
+        self.check_clump(clump_masked)
+
+    def test_clump_unmasked(self):
+        # Test clump_unmasked
+        a = masked_array(np.arange(10))
+        a[[0, 1, 2, 6, 8, 9]] = masked
+        test = clump_unmasked(a)
+        control = [slice(3, 6), slice(7, 8), ]
+        assert_equal(test, control)
+
+        self.check_clump(clump_unmasked)
+
+    def test_flatnotmasked_contiguous(self):
+        # Test flatnotmasked_contiguous
+        a = arange(10)
+        # No mask
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [slice(0, a.size)])
+        # mask of all false
+        a.mask = np.zeros(10, dtype=bool)
+        assert_equal(test, [slice(0, a.size)])
+        # Some mask
+        a[(a < 3) | (a > 8) | (a == 5)] = masked
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [slice(3, 5), slice(6, 9)])
+        #
+        a[:] = masked
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [])
+
+
+class TestAverage:
+    # Several tests of average. Why so many ? Good point...
+    def test_testAverage1(self):
+        # Test of average.
+        ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
+        assert_equal(2.0, average(ott, axis=0))
+        assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
+        result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
+        assert_equal(2.0, result)
+        assert_(wts == 4.0)
+        ott[:] = masked
+        assert_equal(average(ott, axis=0).mask, [True])
+        ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
+        ott = ott.reshape(2, 2)
+        ott[:, 1] = masked
+        assert_equal(average(ott, axis=0), [2.0, 0.0])
+        assert_equal(average(ott, axis=1).mask[0], [True])
+        assert_equal([2., 0.], average(ott, axis=0))
+        result, wts = average(ott, axis=0, returned=True)
+        assert_equal(wts, [1., 0.])
+
+    def test_testAverage2(self):
+        # More tests of average.
+        w1 = [0, 1, 1, 1, 1, 0]
+        w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
+        x = arange(6, dtype=np.float64)
+        assert_equal(average(x, axis=0), 2.5)
+        assert_equal(average(x, axis=0, weights=w1), 2.5)
+        y = array([arange(6, dtype=np.float64), 2.0 * arange(6)])
+        assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)
+        assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.)
+        assert_equal(average(y, axis=1),
+                     [average(x, axis=0), average(x, axis=0) * 2.0])
+        assert_equal(average(y, None, weights=w2), 20. / 6.)
+        assert_equal(average(y, axis=0, weights=w2),
+                     [0., 1., 2., 3., 4., 10.])
+        assert_equal(average(y, axis=1),
+                     [average(x, axis=0), average(x, axis=0) * 2.0])
+        m1 = zeros(6)
+        m2 = [0, 0, 1, 1, 0, 0]
+        m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
+        m4 = ones(6)
+        m5 = [0, 1, 1, 1, 1, 1]
+        assert_equal(average(masked_array(x, m1), axis=0), 2.5)
+        assert_equal(average(masked_array(x, m2), axis=0), 2.5)
+        assert_equal(average(masked_array(x, m4), axis=0).mask, [True])
+        assert_equal(average(masked_array(x, m5), axis=0), 0.0)
+        assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
+        z = masked_array(y, m3)
+        assert_equal(average(z, None), 20. / 6.)
+        assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
+        assert_equal(average(z, axis=1), [2.5, 5.0])
+        assert_equal(average(z, axis=0, weights=w2),
+                     [0., 1., 99., 99., 4.0, 10.0])
+
+    def test_testAverage3(self):
+        # Yet more tests of average!
+        a = arange(6)
+        b = arange(6) * 3
+        r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
+        assert_equal(shape(r1), shape(w1))
+        assert_equal(r1.shape, w1.shape)
+        r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        a2d = array([[1, 2], [0, 4]], float)
+        a2dm = masked_array(a2d, [[False, False], [True, False]])
+        a2da = average(a2d, axis=0)
+        assert_equal(a2da, [0.5, 3.0])
+        a2dma = average(a2dm, axis=0)
+        assert_equal(a2dma, [1.0, 3.0])
+        a2dma = average(a2dm, axis=None)
+        assert_equal(a2dma, 7. / 3.)
+        a2dma = average(a2dm, axis=1)
+        assert_equal(a2dma, [1.5, 4.0])
+
+    def test_testAverage4(self):
+        # Test that `keepdims` works with average
+        x = np.array([2, 3, 4]).reshape(3, 1)
+        b = np.ma.array(x, mask=[[False], [False], [True]])
+        w = np.array([4, 5, 6]).reshape(3, 1)
+        actual = average(b, weights=w, axis=1, keepdims=True)
+        desired = masked_array([[2.], [3.], [4.]], [[False], [False], [True]])
+        assert_equal(actual, desired)
+
+    def test_weight_and_input_dims_different(self):
+        # this test mirrors a test for np.average()
+        # in lib/test/test_function_base.py
+        y = np.arange(12).reshape(2, 2, 3)
+        w = np.array([0., 0., 1., .5, .5, 0., 0., .5, .5, 1., 0., 0.])\
+            .reshape(2, 2, 3)
+
+        m = np.full((2, 2, 3), False)
+        yma = np.ma.array(y, mask=m)
+        subw0 = w[:, :, 0]
+
+        actual = average(yma, axis=(0, 1), weights=subw0)
+        desired = masked_array([7., 8., 9.], mask=[False, False, False])
+        assert_almost_equal(actual, desired)
+
+        m = np.full((2, 2, 3), False)
+        m[:, :, 0] = True
+        m[0, 0, 1] = True
+        yma = np.ma.array(y, mask=m)
+        actual = average(yma, axis=(0, 1), weights=subw0)
+        desired = masked_array(
+            [np.nan, 8., 9.],
+            mask=[True, False, False])
+        assert_almost_equal(actual, desired)
+
+        m = np.full((2, 2, 3), False)
+        yma = np.ma.array(y, mask=m)
+
+        subw1 = w[1, :, :]
+        actual = average(yma, axis=(1, 2), weights=subw1)
+        desired = masked_array([2.25, 8.25], mask=[False, False])
+        assert_almost_equal(actual, desired)
+
+        # here the weights have the wrong shape for the specified axes
+        with pytest.raises(
+                ValueError,
+                match="Shape of weights must be consistent with "
+                      "shape of a along specified axis"):
+            average(yma, axis=(0, 1, 2), weights=subw0)
+
+        with pytest.raises(
+                ValueError,
+                match="Shape of weights must be consistent with "
+                      "shape of a along specified axis"):
+            average(yma, axis=(0, 1), weights=subw1)
+
+        # swapping the axes should be same as transposing weights
+        actual = average(yma, axis=(1, 0), weights=subw0)
+        desired = average(yma, axis=(0, 1), weights=subw0.T)
+        assert_almost_equal(actual, desired)
+
+    def test_onintegers_with_mask(self):
+        # Test average on integers with mask
+        a = average(array([1, 2]))
+        assert_equal(a, 1.5)
+        a = average(array([1, 2, 3, 4], mask=[False, False, True, True]))
+        assert_equal(a, 1.5)
+
+    def test_complex(self):
+        # Test with complex data.
+        # (Regression test for https://github.com/numpy/numpy/issues/2684)
+        mask = np.array([[0, 0, 0, 1, 0],
+                         [0, 1, 0, 0, 0]], dtype=bool)
+        a = masked_array([[0, 1+2j, 3+4j, 5+6j, 7+8j],
+                          [9j, 0+1j, 2+3j, 4+5j, 7+7j]],
+                         mask=mask)
+
+        av = average(a)
+        expected = np.average(a.compressed())
+        assert_almost_equal(av.real, expected.real)
+        assert_almost_equal(av.imag, expected.imag)
+
+        av0 = average(a, axis=0)
+        expected0 = average(a.real, axis=0) + average(a.imag, axis=0)*1j
+        assert_almost_equal(av0.real, expected0.real)
+        assert_almost_equal(av0.imag, expected0.imag)
+
+        av1 = average(a, axis=1)
+        expected1 = average(a.real, axis=1) + average(a.imag, axis=1)*1j
+        assert_almost_equal(av1.real, expected1.real)
+        assert_almost_equal(av1.imag, expected1.imag)
+
+        # Test with the 'weights' argument.
+        wts = np.array([[0.5, 1.0, 2.0, 1.0, 0.5],
+                        [1.0, 1.0, 1.0, 1.0, 1.0]])
+        wav = average(a, weights=wts)
+        expected = np.average(a.compressed(), weights=wts[~mask])
+        assert_almost_equal(wav.real, expected.real)
+        assert_almost_equal(wav.imag, expected.imag)
+
+        wav0 = average(a, weights=wts, axis=0)
+        expected0 = (average(a.real, weights=wts, axis=0) +
+                     average(a.imag, weights=wts, axis=0)*1j)
+        assert_almost_equal(wav0.real, expected0.real)
+        assert_almost_equal(wav0.imag, expected0.imag)
+
+        wav1 = average(a, weights=wts, axis=1)
+        expected1 = (average(a.real, weights=wts, axis=1) +
+                     average(a.imag, weights=wts, axis=1)*1j)
+        assert_almost_equal(wav1.real, expected1.real)
+        assert_almost_equal(wav1.imag, expected1.imag)
+
+    @pytest.mark.parametrize(
+        'x, axis, expected_avg, weights, expected_wavg, expected_wsum',
+        [([1, 2, 3], None, [2.0], [3, 4, 1], [1.75], [8.0]),
+         ([[1, 2, 5], [1, 6, 11]], 0, [[1.0, 4.0, 8.0]],
+          [1, 3], [[1.0, 5.0, 9.5]], [[4, 4, 4]])],
+    )
+    def test_basic_keepdims(self, x, axis, expected_avg,
+                            weights, expected_wavg, expected_wsum):
+        avg = np.ma.average(x, axis=axis, keepdims=True)
+        assert avg.shape == np.shape(expected_avg)
+        assert_array_equal(avg, expected_avg)
+
+        wavg = np.ma.average(x, axis=axis, weights=weights, keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+
+        wavg, wsum = np.ma.average(x, axis=axis, weights=weights,
+                                   returned=True, keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+        assert wsum.shape == np.shape(expected_wsum)
+        assert_array_equal(wsum, expected_wsum)
+
+    def test_masked_weights(self):
+        # Test with masked weights.
+        # (Regression test for https://github.com/numpy/numpy/issues/10438)
+        a = np.ma.array(np.arange(9).reshape(3, 3),
+                        mask=[[1, 0, 0], [1, 0, 0], [0, 0, 0]])
+        weights_unmasked = masked_array([5, 28, 31], mask=False)
+        weights_masked = masked_array([5, 28, 31], mask=[1, 0, 0])
+
+        avg_unmasked = average(a, axis=0,
+                               weights=weights_unmasked, returned=False)
+        expected_unmasked = np.array([6.0, 5.21875, 6.21875])
+        assert_almost_equal(avg_unmasked, expected_unmasked)
+
+        avg_masked = average(a, axis=0, weights=weights_masked, returned=False)
+        expected_masked = np.array([6.0, 5.576271186440678, 6.576271186440678])
+        assert_almost_equal(avg_masked, expected_masked)
+
+        # weights should be masked if needed
+        # depending on the array mask. This is to avoid summing
+        # masked nan or other values that are not cancelled by a zero
+        a = np.ma.array([1.0,   2.0,   3.0,  4.0],
+                   mask=[False, False, True, True])
+        avg_unmasked = average(a, weights=[1, 1, 1, np.nan])
+
+        assert_almost_equal(avg_unmasked, 1.5)
+
+        a = np.ma.array([
+            [1.0, 2.0, 3.0, 4.0],
+            [5.0, 6.0, 7.0, 8.0],
+            [9.0, 1.0, 2.0, 3.0],
+        ], mask=[
+            [False, True, True, False],
+            [True, False, True, True],
+            [True, False, True, False],
+        ])
+
+        avg_masked = np.ma.average(a, weights=[1, np.nan, 1], axis=0)
+        avg_expected = np.ma.array([1.0, np.nan, np.nan, 3.5],
+                              mask=[False, True, True, False])
+
+        assert_almost_equal(avg_masked, avg_expected)
+        assert_equal(avg_masked.mask, avg_expected.mask)
+
+
+class TestConcatenator:
+    # Tests for mr_, the equivalent of r_ for masked arrays.
+
+    def test_1d(self):
+        # Tests mr_ on 1D arrays.
+        assert_array_equal(mr_[1, 2, 3, 4, 5, 6], array([1, 2, 3, 4, 5, 6]))
+        b = ones(5)
+        m = [1, 0, 0, 0, 0]
+        d = masked_array(b, mask=m)
+        c = mr_[d, 0, 0, d]
+        assert_(isinstance(c, MaskedArray))
+        assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
+        assert_array_equal(c.mask, mr_[m, 0, 0, m])
+
+    def test_2d(self):
+        # Tests mr_ on 2D arrays.
+        a_1 = np.random.rand(5, 5)
+        a_2 = np.random.rand(5, 5)
+        m_1 = np.round(np.random.rand(5, 5), 0)
+        m_2 = np.round(np.random.rand(5, 5), 0)
+        b_1 = masked_array(a_1, mask=m_1)
+        b_2 = masked_array(a_2, mask=m_2)
+        # append columns
+        d = mr_['1', b_1, b_2]
+        assert_(d.shape == (5, 10))
+        assert_array_equal(d[:, :5], b_1)
+        assert_array_equal(d[:, 5:], b_2)
+        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
+        d = mr_[b_1, b_2]
+        assert_(d.shape == (10, 5))
+        assert_array_equal(d[:5,:], b_1)
+        assert_array_equal(d[5:,:], b_2)
+        assert_array_equal(d.mask, np.r_[m_1, m_2])
+
+    def test_masked_constant(self):
+        actual = mr_[np.ma.masked, 1]
+        assert_equal(actual.mask, [True, False])
+        assert_equal(actual.data[1], 1)
+
+        actual = mr_[[1, 2], np.ma.masked]
+        assert_equal(actual.mask, [False, False, True])
+        assert_equal(actual.data[:2], [1, 2])
+
+
+class TestNotMasked:
+    # Tests notmasked_edges and notmasked_contiguous.
+
+    def test_edges(self):
+        # Tests unmasked_edges
+        data = masked_array(np.arange(25).reshape(5, 5),
+                            mask=[[0, 0, 1, 0, 0],
+                                  [0, 0, 0, 1, 1],
+                                  [1, 1, 0, 0, 0],
+                                  [0, 0, 0, 0, 0],
+                                  [1, 1, 1, 0, 0]],)
+        test = notmasked_edges(data, None)
+        assert_equal(test, [0, 24])
+        test = notmasked_edges(data, 0)
+        assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(3, 3, 3, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data, 1)
+        assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 2, 0, 3)])
+        assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 2, 4, 4, 4)])
+        #
+        test = notmasked_edges(data.data, None)
+        assert_equal(test, [0, 24])
+        test = notmasked_edges(data.data, 0)
+        assert_equal(test[0], [(0, 0, 0, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(4, 4, 4, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data.data, -1)
+        assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 0, 0, 0)])
+        assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 4, 4, 4, 4)])
+        #
+        data[-2] = masked
+        test = notmasked_edges(data, 0)
+        assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(1, 1, 2, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data, -1)
+        assert_equal(test[0], [(0, 1, 2, 4), (0, 0, 2, 3)])
+        assert_equal(test[1], [(0, 1, 2, 4), (4, 2, 4, 4)])
+
+    def test_contiguous(self):
+        # Tests notmasked_contiguous
+        a = masked_array(np.arange(24).reshape(3, 8),
+                         mask=[[0, 0, 0, 0, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 1, 1, 1],
+                               [0, 0, 0, 0, 0, 0, 1, 0]])
+        tmp = notmasked_contiguous(a, None)
+        assert_equal(tmp, [
+            slice(0, 4, None),
+            slice(16, 22, None),
+            slice(23, 24, None)
+        ])
+
+        tmp = notmasked_contiguous(a, 0)
+        assert_equal(tmp, [
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(2, 3, None)],
+            [slice(2, 3, None)],
+            [],
+            [slice(2, 3, None)]
+        ])
+        #
+        tmp = notmasked_contiguous(a, 1)
+        assert_equal(tmp, [
+            [slice(0, 4, None)],
+            [],
+            [slice(0, 6, None), slice(7, 8, None)]
+        ])
+
+
+class TestCompressFunctions:
+
+    def test_compress_nd(self):
+        # Tests compress_nd
+        x = np.array(list(range(3*4*5))).reshape(3, 4, 5)
+        m = np.zeros((3,4,5)).astype(bool)
+        m[1,1,1] = True
+        x = array(x, mask=m)
+
+        # axis=None
+        a = compress_nd(x)
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[40, 42, 43, 44],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        # axis=0
+        a = compress_nd(x, 0)
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [ 5,  6,  7,  8,  9],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[40, 41, 42, 43, 44],
+                          [45, 46, 47, 48, 49],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+
+        # axis=1
+        a = compress_nd(x, 1)
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[20, 21, 22, 23, 24],
+                          [30, 31, 32, 33, 34],
+                          [35, 36, 37, 38, 39]],
+                         [[40, 41, 42, 43, 44],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (1,))
+        a3 = compress_nd(x, -2)
+        a4 = compress_nd(x, (-2,))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=2
+        a = compress_nd(x, 2)
+        assert_equal(a, [[[ 0, 2,  3,  4],
+                          [ 5, 7,  8,  9],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[20, 22, 23, 24],
+                          [25, 27, 28, 29],
+                          [30, 32, 33, 34],
+                          [35, 37, 38, 39]],
+                         [[40, 42, 43, 44],
+                          [45, 47, 48, 49],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (2,))
+        a3 = compress_nd(x, -1)
+        a4 = compress_nd(x, (-1,))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=(0, 1)
+        a = compress_nd(x, (0, 1))
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[40, 41, 42, 43, 44],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+        a2 = compress_nd(x, (0, -2))
+        assert_equal(a, a2)
+
+        # axis=(1, 2)
+        a = compress_nd(x, (1, 2))
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[20, 22, 23, 24],
+                          [30, 32, 33, 34],
+                          [35, 37, 38, 39]],
+                         [[40, 42, 43, 44],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (-2, 2))
+        a3 = compress_nd(x, (1, -1))
+        a4 = compress_nd(x, (-2, -1))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=(0, 2)
+        a = compress_nd(x, (0, 2))
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [ 5,  7,  8,  9],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[40, 42, 43, 44],
+                          [45, 47, 48, 49],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (0, -1))
+        assert_equal(a, a2)
+
+    def test_compress_rowcols(self):
+        # Tests compress_rowcols
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[4, 5], [7, 8]])
+        assert_equal(compress_rowcols(x, 0), [[3, 4, 5], [6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1), [[1, 2], [4, 5], [7, 8]])
+        x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[0, 2], [6, 8]])
+        assert_equal(compress_rowcols(x, 0), [[0, 1, 2], [6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1), [[0, 2], [3, 5], [6, 8]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[8]])
+        assert_equal(compress_rowcols(x, 0), [[6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1,), [[2], [5], [8]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        assert_equal(compress_rowcols(x).size, 0)
+        assert_equal(compress_rowcols(x, 0).size, 0)
+        assert_equal(compress_rowcols(x, 1).size, 0)
+
+    def test_mask_rowcols(self):
+        # Tests mask_rowcols.
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[1, 1, 1], [0, 0, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1).mask,
+                     [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
+        x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[0, 0, 0], [1, 1, 1], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1).mask,
+                     [[0, 1, 0], [0, 1, 0], [0, 1, 0]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[1, 1, 1], [1, 1, 1], [1, 1, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[1, 1, 1], [1, 1, 1], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1,).mask,
+                     [[1, 1, 0], [1, 1, 0], [1, 1, 0]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        assert_(mask_rowcols(x).all() is masked)
+        assert_(mask_rowcols(x, 0).all() is masked)
+        assert_(mask_rowcols(x, 1).all() is masked)
+        assert_(mask_rowcols(x).mask.all())
+        assert_(mask_rowcols(x, 0).mask.all())
+        assert_(mask_rowcols(x, 1).mask.all())
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize(["func", "rowcols_axis"],
+                             [(np.ma.mask_rows, 0), (np.ma.mask_cols, 1)])
+    def test_mask_row_cols_axis_deprecation(self, axis, func, rowcols_axis):
+        # Test deprecation of the axis argument to `mask_rows` and `mask_cols`
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+
+        with assert_warns(DeprecationWarning):
+            res = func(x, axis=axis)
+            assert_equal(res, mask_rowcols(x, rowcols_axis))
+
+    def test_dot(self):
+        # Tests dot product
+        n = np.arange(1, 7)
+        #
+        m = [1, 0, 0, 0, 0, 0]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [1, 0]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        m = [0, 0, 0, 0, 0, 1]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[0, 1], [1, 1]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        assert_equal(c, dot(a, b))
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        m = [0, 0, 0, 0, 0, 0]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b)
+        assert_equal(c.mask, nomask)
+        c = dot(b, a)
+        assert_equal(c.mask, nomask)
+        #
+        a = masked_array(n, mask=[1, 0, 0, 0, 0, 0]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [0, 0]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[0, 0], [1, 1]])
+        c = dot(a, b)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 1, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 0], [1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[0, 0], [0, 0]], [[0, 0], [0, 1]]])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 1], [1, 1]], [[0, 0], [0, 1]]],
+                      [[[0, 0], [0, 1]], [[0, 0], [0, 1]]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 1]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 0], [0, 0]], [[1, 0], [0, 0]]],
+                      [[[1, 0], [0, 0]], [[1, 1], [1, 1]]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 0]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[1, 0], [0, 0]]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = 5.
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(2), mask=[0, 1])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[1, 0], [0, 0]])
+
+    def test_dot_returns_maskedarray(self):
+        # See gh-6611
+        a = np.eye(3)
+        b = array(a)
+        assert_(type(dot(a, a)) is MaskedArray)
+        assert_(type(dot(a, b)) is MaskedArray)
+        assert_(type(dot(b, a)) is MaskedArray)
+        assert_(type(dot(b, b)) is MaskedArray)
+
+    def test_dot_out(self):
+        a = array(np.eye(3))
+        out = array(np.zeros((3, 3)))
+        res = dot(a, a, out=out)
+        assert_(res is out)
+        assert_equal(a, res)
+
+
+class TestApplyAlongAxis:
+    # Tests 2D functions
+    def test_3d(self):
+        a = arange(12.).reshape(2, 2, 3)
+
+        def myfunc(b):
+            return b[1]
+
+        xa = apply_along_axis(myfunc, 2, a)
+        assert_equal(xa, [[1, 4], [7, 10]])
+
+    # Tests kwargs functions
+    def test_3d_kwargs(self):
+        a = arange(12).reshape(2, 2, 3)
+
+        def myfunc(b, offset=0):
+            return b[1+offset]
+
+        xa = apply_along_axis(myfunc, 2, a, offset=1)
+        assert_equal(xa, [[2, 5], [8, 11]])
+
+
+class TestApplyOverAxes:
+    # Tests apply_over_axes
+    def test_basic(self):
+        a = arange(24).reshape(2, 3, 4)
+        test = apply_over_axes(np.sum, a, [0, 2])
+        ctrl = np.array([[[60], [92], [124]]])
+        assert_equal(test, ctrl)
+        a[(a % 2).astype(bool)] = masked
+        test = apply_over_axes(np.sum, a, [0, 2])
+        ctrl = np.array([[[28], [44], [60]]])
+        assert_equal(test, ctrl)
+
+
+class TestMedian:
+    def test_pytype(self):
+        r = np.ma.median([[np.inf, np.inf], [np.inf, np.inf]], axis=-1)
+        assert_equal(r, np.inf)
+
+    def test_inf(self):
+        # test that even which computes handles inf / x = masked
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]]), axis=-1)
+        assert_equal(r, np.inf)
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]]), axis=None)
+        assert_equal(r, np.inf)
+        # all masked
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]], mask=True),
+                         axis=-1)
+        assert_equal(r.mask, True)
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]], mask=True),
+                         axis=None)
+        assert_equal(r.mask, True)
+
+    def test_non_masked(self):
+        x = np.arange(9)
+        assert_equal(np.ma.median(x), 4.)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = range(8)
+        assert_equal(np.ma.median(x), 3.5)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = 5
+        assert_equal(np.ma.median(x), 5.)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = np.arange(9 * 8).reshape(9, 8)
+        assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
+        assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
+        assert_(np.ma.median(x, axis=1) is not MaskedArray)
+        # float
+        x = np.arange(9 * 8.).reshape(9, 8)
+        assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
+        assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
+        assert_(np.ma.median(x, axis=1) is not MaskedArray)
+
+    def test_docstring_examples(self):
+        "test the examples given in the docstring of ma.median"
+        x = array(np.arange(8), mask=[0]*4 + [1]*4)
+        assert_equal(np.ma.median(x), 1.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        ma_x = np.ma.median(x, axis=-1, overwrite_input=True)
+        assert_equal(ma_x, [2., 5.])
+        assert_equal(ma_x.shape, (2,), "shape mismatch")
+        assert_(type(ma_x) is MaskedArray)
+
+    def test_axis_argument_errors(self):
+        msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
+        for ndmin in range(5):
+            for mask in [False, True]:
+                x = array(1, ndmin=ndmin, mask=mask)
+
+                # Valid axis values should not raise exception
+                args = itertools.product(range(-ndmin, ndmin), [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except Exception:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+                # Invalid axis values should raise exception
+                args = itertools.product([-(ndmin + 1), ndmin], [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except np.exceptions.AxisError:
+                        pass
+                    else:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+    def test_masked_0d(self):
+        # Check values
+        x = array(1, mask=False)
+        assert_equal(np.ma.median(x), 1)
+        x = array(1, mask=True)
+        assert_equal(np.ma.median(x), np.ma.masked)
+
+    def test_masked_1d(self):
+        x = array(np.arange(5), mask=True)
+        assert_equal(np.ma.median(x), np.ma.masked)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is np.ma.core.MaskedConstant)
+        x = array(np.arange(5), mask=False)
+        assert_equal(np.ma.median(x), 2.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(5), mask=[0,1,0,0,0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(5), mask=[0,1,1,1,1])
+        assert_equal(np.ma.median(x), 0.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = array(np.arange(5), mask=[0,1,1,0,0])
+        assert_equal(np.ma.median(x), 3.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # float
+        x = array(np.arange(5.), mask=[0,1,1,0,0])
+        assert_equal(np.ma.median(x), 3.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = array(np.arange(6), mask=[0,1,1,1,1,0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # float
+        x = array(np.arange(6.), mask=[0,1,1,1,1,0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+
+    def test_1d_shape_consistency(self):
+        assert_equal(np.ma.median(array([1,2,3],mask=[0,0,0])).shape,
+                     np.ma.median(array([1,2,3],mask=[0,1,0])).shape )
+
+    def test_2d(self):
+        # Tests median w/ 2D
+        (n, p) = (101, 30)
+        x = masked_array(np.linspace(-1., 1., n),)
+        x[:10] = x[-10:] = masked
+        z = masked_array(np.empty((n, p), dtype=float))
+        z[:, 0] = x[:]
+        idx = np.arange(len(x))
+        for i in range(1, p):
+            np.random.shuffle(idx)
+            z[:, i] = x[idx]
+        assert_equal(median(z[:, 0]), 0)
+        assert_equal(median(z), 0)
+        assert_equal(median(z, axis=0), np.zeros(p))
+        assert_equal(median(z.T, axis=1), np.zeros(p))
+
+    def test_2d_waxis(self):
+        # Tests median w/ 2D arrays and different axis.
+        x = masked_array(np.arange(30).reshape(10, 3))
+        x[:3] = x[-3:] = masked
+        assert_equal(median(x), 14.5)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        assert_equal(median(x, axis=0), [13.5, 14.5, 15.5])
+        assert_(type(np.ma.median(x, axis=0)) is MaskedArray)
+        assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
+        assert_(type(np.ma.median(x, axis=1)) is MaskedArray)
+        assert_equal(median(x, axis=1).mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])
+
+    def test_3d(self):
+        # Tests median w/ 3D
+        x = np.ma.arange(24).reshape(3, 4, 2)
+        x[x % 3 == 0] = masked
+        assert_equal(median(x, 0), [[12, 9], [6, 15], [12, 9], [18, 15]])
+        x.shape = (4, 3, 2)
+        assert_equal(median(x, 0), [[99, 10], [11, 99], [13, 14]])
+        x = np.ma.arange(24).reshape(4, 3, 2)
+        x[x % 5 == 0] = masked
+        assert_equal(median(x, 0), [[12, 10], [8, 9], [16, 17]])
+
+    def test_neg_axis(self):
+        x = masked_array(np.arange(30).reshape(10, 3))
+        x[:3] = x[-3:] = masked
+        assert_equal(median(x, axis=-1), median(x, axis=1))
+
+    def test_out_1d(self):
+        # integer float even odd
+        for v in (30, 30., 31, 31.):
+            x = masked_array(np.arange(v))
+            x[:3] = x[-3:] = masked
+            out = masked_array(np.ones(()))
+            r = median(x, out=out)
+            if v == 30:
+                assert_equal(out, 14.5)
+            else:
+                assert_equal(out, 15.)
+            assert_(r is out)
+            assert_(type(r) is MaskedArray)
+
+    def test_out(self):
+        # integer float even odd
+        for v in (40, 40., 30, 30.):
+            x = masked_array(np.arange(v).reshape(10, -1))
+            x[:3] = x[-3:] = masked
+            out = masked_array(np.ones(10))
+            r = median(x, axis=1, out=out)
+            if v == 30:
+                e = masked_array([0.]*3 + [10, 13, 16, 19] + [0.]*3,
+                                 mask=[True] * 3 + [False] * 4 + [True] * 3)
+            else:
+                e = masked_array([0.]*3 + [13.5, 17.5, 21.5, 25.5] + [0.]*3,
+                                 mask=[True]*3 + [False]*4 + [True]*3)
+            assert_equal(r, e)
+            assert_(r is out)
+            assert_(type(r) is MaskedArray)
+
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1, ),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, axis):
+        mask = np.zeros((3, 5, 7, 11), dtype=bool)
+        # Randomly set some elements to True:
+        w = np.random.random((4, 200)) * np.array(mask.shape)[:, None]
+        w = w.astype(np.intp)
+        mask[tuple(w)] = np.nan
+        d = masked_array(np.ones(mask.shape), mask=mask)
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        out = masked_array(np.empty(shape_out))
+        result = median(d, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    def test_single_non_masked_value_on_axis(self):
+        data = [[1., 0.],
+                [0., 3.],
+                [0., 0.]]
+        masked_arr = np.ma.masked_equal(data, 0)
+        expected = [1., 3.]
+        assert_array_equal(np.ma.median(masked_arr, axis=0),
+                           expected)
+
+    def test_nan(self):
+        for mask in (False, np.zeros(6, dtype=bool)):
+            dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
+            dm.mask = mask
+
+            # scalar result
+            r = np.ma.median(dm, axis=None)
+            assert_(np.isscalar(r))
+            assert_array_equal(r, np.nan)
+            r = np.ma.median(dm.ravel(), axis=0)
+            assert_(np.isscalar(r))
+            assert_array_equal(r, np.nan)
+
+            r = np.ma.median(dm, axis=0)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [1, np.nan, 3])
+            r = np.ma.median(dm, axis=1)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [np.nan, 2])
+            r = np.ma.median(dm, axis=-1)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [np.nan, 2])
+
+        dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
+        dm[:, 2] = np.ma.masked
+        assert_array_equal(np.ma.median(dm, axis=None), np.nan)
+        assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3])
+        assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5])
+
+    def test_out_nan(self):
+        o = np.ma.masked_array(np.zeros((4,)))
+        d = np.ma.masked_array(np.ones((3, 4)))
+        d[2, 1] = np.nan
+        d[2, 2] = np.ma.masked
+        assert_equal(np.ma.median(d, 0, out=o), o)
+        o = np.ma.masked_array(np.zeros((3,)))
+        assert_equal(np.ma.median(d, 1, out=o), o)
+        o = np.ma.masked_array(np.zeros(()))
+        assert_equal(np.ma.median(d, out=o), o)
+
+    def test_nan_behavior(self):
+        a = np.ma.masked_array(np.arange(24, dtype=float))
+        a[::3] = np.ma.masked
+        a[2] = np.nan
+        assert_array_equal(np.ma.median(a), np.nan)
+        assert_array_equal(np.ma.median(a, axis=0), np.nan)
+
+        a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4))
+        a.mask = np.arange(a.size) % 2 == 1
+        aorig = a.copy()
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_array_equal(np.ma.median(a), np.nan)
+        assert_(np.isscalar(np.ma.median(a)))
+
+        # axis0
+        b = np.ma.median(aorig, axis=0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.ma.median(a, 0), b)
+
+        # axis1
+        b = np.ma.median(aorig, axis=1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.ma.median(a, 1), b)
+
+        # axis02
+        b = np.ma.median(aorig, axis=(0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.ma.median(a, (0, 2)), b)
+
+    def test_ambigous_fill(self):
+        # 255 is max value, used as filler for sort
+        a = np.array([[3, 3, 255], [3, 3, 255]], dtype=np.uint8)
+        a = np.ma.masked_array(a, mask=a == 3)
+        assert_array_equal(np.ma.median(a, axis=1), 255)
+        assert_array_equal(np.ma.median(a, axis=1).mask, False)
+        assert_array_equal(np.ma.median(a, axis=0), a[0])
+        assert_array_equal(np.ma.median(a), 255)
+
+    def test_special(self):
+        for inf in [np.inf, -np.inf]:
+            a = np.array([[inf,  np.nan], [np.nan, np.nan]])
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            assert_equal(np.ma.median(a, axis=0), [inf,  np.nan])
+            assert_equal(np.ma.median(a, axis=1), [inf,  np.nan])
+            assert_equal(np.ma.median(a), inf)
+
+            a = np.array([[np.nan, np.nan, inf], [np.nan, np.nan, inf]])
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            assert_array_equal(np.ma.median(a, axis=1), inf)
+            assert_array_equal(np.ma.median(a, axis=1).mask, False)
+            assert_array_equal(np.ma.median(a, axis=0), a[0])
+            assert_array_equal(np.ma.median(a), inf)
+
+            # no mask
+            a = np.array([[inf, inf], [inf, inf]])
+            assert_equal(np.ma.median(a), inf)
+            assert_equal(np.ma.median(a, axis=0), inf)
+            assert_equal(np.ma.median(a, axis=1), inf)
+
+            a = np.array([[inf, 7, -inf, -9],
+                          [-10, np.nan, np.nan, 5],
+                          [4, np.nan, np.nan, inf]],
+                          dtype=np.float32)
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            if inf > 0:
+                assert_equal(np.ma.median(a, axis=0), [4., 7., -inf, 5.])
+                assert_equal(np.ma.median(a), 4.5)
+            else:
+                assert_equal(np.ma.median(a, axis=0), [-10., 7., -inf, -9.])
+                assert_equal(np.ma.median(a), -2.5)
+            assert_equal(np.ma.median(a, axis=1), [-1., -2.5, inf])
+
+            for i in range(0, 10):
+                for j in range(1, 10):
+                    a = np.array([([np.nan] * i) + ([inf] * j)] * 2)
+                    a = np.ma.masked_array(a, mask=np.isnan(a))
+                    assert_equal(np.ma.median(a), inf)
+                    assert_equal(np.ma.median(a, axis=1), inf)
+                    assert_equal(np.ma.median(a, axis=0),
+                                 ([np.nan] * i) + [inf] * j)
+
+    def test_empty(self):
+        # empty arrays
+        a = np.ma.masked_array(np.array([], dtype=float))
+        with suppress_warnings() as w:
+            w.record(RuntimeWarning)
+            assert_array_equal(np.ma.median(a), np.nan)
+            assert_(w.log[0].category is RuntimeWarning)
+
+        # multiple dimensions
+        a = np.ma.masked_array(np.array([], dtype=float, ndmin=3))
+        # no axis
+        with suppress_warnings() as w:
+            w.record(RuntimeWarning)
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_array_equal(np.ma.median(a), np.nan)
+            assert_(w.log[0].category is RuntimeWarning)
+
+        # axis 0 and 1
+        b = np.ma.masked_array(np.array([], dtype=float, ndmin=2))
+        assert_equal(np.ma.median(a, axis=0), b)
+        assert_equal(np.ma.median(a, axis=1), b)
+
+        # axis 2
+        b = np.ma.masked_array(np.array(np.nan, dtype=float, ndmin=2))
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.ma.median(a, axis=2), b)
+            assert_(w[0].category is RuntimeWarning)
+
+    def test_object(self):
+        o = np.ma.masked_array(np.arange(7.))
+        assert_(type(np.ma.median(o.astype(object))), float)
+        o[2] = np.nan
+        assert_(type(np.ma.median(o.astype(object))), float)
+
+
+class TestCov:
+
+    def setup_method(self):
+        self.data = array(np.random.rand(12))
+
+    def test_covhelper(self):
+        x = self.data
+        # Test not mask output type is a float.
+        assert_(_covhelper(x, rowvar=True)[1].dtype, np.float32)
+        assert_(_covhelper(x, y=x, rowvar=False)[1].dtype, np.float32)
+        # Test not mask output is equal after casting to float.
+        mask = x > 0.5
+        assert_array_equal(
+            _covhelper(
+                np.ma.masked_array(x, mask), rowvar=True
+            )[1].astype(bool),
+            ~mask.reshape(1, -1),
+        )
+        assert_array_equal(
+            _covhelper(
+                np.ma.masked_array(x, mask), y=x, rowvar=False
+            )[1].astype(bool),
+            np.vstack((~mask, ~mask)),
+        )
+
+    def test_1d_without_missing(self):
+        # Test cov on 1D variable w/o missing values
+        x = self.data
+        assert_almost_equal(np.cov(x), cov(x))
+        assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(x, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+
+    def test_2d_without_missing(self):
+        # Test cov on 1 2D variable w/o missing values
+        x = self.data.reshape(3, 4)
+        assert_almost_equal(np.cov(x), cov(x))
+        assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(x, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+
+    def test_1d_with_missing(self):
+        # Test cov 1 1D variable w/missing values
+        x = self.data
+        x[-1] = masked
+        x -= x.mean()
+        nx = x.compressed()
+        assert_almost_equal(np.cov(nx), cov(x))
+        assert_almost_equal(np.cov(nx, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(nx, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+        #
+        try:
+            cov(x, allow_masked=False)
+        except ValueError:
+            pass
+        #
+        # 2 1D variables w/ missing values
+        nx = x[1:-1]
+        assert_almost_equal(np.cov(nx, nx[::-1]), cov(x, x[::-1]))
+        assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False),
+                            cov(x, x[::-1], rowvar=False))
+        assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False, bias=True),
+                            cov(x, x[::-1], rowvar=False, bias=True))
+
+    def test_2d_with_missing(self):
+        # Test cov on 2D variable w/ missing value
+        x = self.data
+        x[-1] = masked
+        x = x.reshape(3, 4)
+        valid = np.logical_not(getmaskarray(x)).astype(int)
+        frac = np.dot(valid, valid.T)
+        xf = (x - x.mean(1)[:, None]).filled(0)
+        assert_almost_equal(cov(x),
+                            np.cov(xf) * (x.shape[1] - 1) / (frac - 1.))
+        assert_almost_equal(cov(x, bias=True),
+                            np.cov(xf, bias=True) * x.shape[1] / frac)
+        frac = np.dot(valid.T, valid)
+        xf = (x - x.mean(0)).filled(0)
+        assert_almost_equal(cov(x, rowvar=False),
+                            (np.cov(xf, rowvar=False) *
+                             (x.shape[0] - 1) / (frac - 1.)))
+        assert_almost_equal(cov(x, rowvar=False, bias=True),
+                            (np.cov(xf, rowvar=False, bias=True) *
+                             x.shape[0] / frac))
+
+
+class TestCorrcoef:
+
+    def setup_method(self):
+        self.data = array(np.random.rand(12))
+        self.data2 = array(np.random.rand(12))
+
+    def test_ddof(self):
+        # ddof raises DeprecationWarning
+        x, y = self.data, self.data2
+        expected = np.corrcoef(x)
+        expected2 = np.corrcoef(x, y)
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, x, ddof=-1)
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof has no or negligible effect on the function
+            assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
+            assert_almost_equal(corrcoef(x, ddof=-1), expected)
+            assert_almost_equal(corrcoef(x, y, ddof=-1), expected2)
+            assert_almost_equal(corrcoef(x, ddof=3), expected)
+            assert_almost_equal(corrcoef(x, y, ddof=3), expected2)
+
+    def test_bias(self):
+        x, y = self.data, self.data2
+        expected = np.corrcoef(x)
+        # bias raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
+            assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
+            assert_warns(DeprecationWarning, corrcoef, x, bias=False)
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # bias has no or negligible effect on the function
+            assert_almost_equal(corrcoef(x, bias=1), expected)
+
+    def test_1d_without_missing(self):
+        # Test cov on 1D variable w/o missing values
+        x = self.data
+        assert_almost_equal(np.corrcoef(x), corrcoef(x))
+        assert_almost_equal(np.corrcoef(x, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+
+    def test_2d_without_missing(self):
+        # Test corrcoef on 1 2D variable w/o missing values
+        x = self.data.reshape(3, 4)
+        assert_almost_equal(np.corrcoef(x), corrcoef(x))
+        assert_almost_equal(np.corrcoef(x, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+
+    def test_1d_with_missing(self):
+        # Test corrcoef 1 1D variable w/missing values
+        x = self.data
+        x[-1] = masked
+        x -= x.mean()
+        nx = x.compressed()
+        assert_almost_equal(np.corrcoef(nx), corrcoef(x))
+        assert_almost_equal(np.corrcoef(nx, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+        try:
+            corrcoef(x, allow_masked=False)
+        except ValueError:
+            pass
+        # 2 1D variables w/ missing values
+        nx = x[1:-1]
+        assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
+        assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
+                            corrcoef(x, x[::-1], rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof and bias have no or negligible effect on the function
+            assert_almost_equal(np.corrcoef(nx, nx[::-1]),
+                                corrcoef(x, x[::-1], bias=1))
+            assert_almost_equal(np.corrcoef(nx, nx[::-1]),
+                                corrcoef(x, x[::-1], ddof=2))
+
+    def test_2d_with_missing(self):
+        # Test corrcoef on 2D variable w/ missing value
+        x = self.data
+        x[-1] = masked
+        x = x.reshape(3, 4)
+
+        test = corrcoef(x)
+        control = np.corrcoef(x)
+        assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof and bias have no or negligible effect on the function
+            assert_almost_equal(corrcoef(x, ddof=-2)[:-1, :-1],
+                                control[:-1, :-1])
+            assert_almost_equal(corrcoef(x, ddof=3)[:-1, :-1],
+                                control[:-1, :-1])
+            assert_almost_equal(corrcoef(x, bias=1)[:-1, :-1],
+                                control[:-1, :-1])
+
+
+class TestPolynomial:
+    #
+    def test_polyfit(self):
+        # Tests polyfit
+        # On ndarrays
+        x = np.random.rand(10)
+        y = np.random.rand(20).reshape(-1, 2)
+        assert_almost_equal(polyfit(x, y, 3), np.polyfit(x, y, 3))
+        # ON 1D maskedarrays
+        x = x.view(MaskedArray)
+        x[0] = masked
+        y = y.view(MaskedArray)
+        y[0, 0] = y[-1, -1] = masked
+        #
+        (C, R, K, S, D) = polyfit(x, y[:, 0], 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:], y[1:, 0].compressed(), 3,
+                                     full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        (C, R, K, S, D) = polyfit(x, y[:, -1], 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, -1], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1,:], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        w = np.random.rand(10) + 1
+        wo = w.copy()
+        xs = x[1:-1]
+        ys = y[1:-1]
+        ws = w[1:-1]
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True, w=w)
+        (c, r, k, s, d) = np.polyfit(xs, ys, 3, full=True, w=ws)
+        assert_equal(w, wo)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+
+    def test_polyfit_with_masked_NaNs(self):
+        x = np.random.rand(10)
+        y = np.random.rand(20).reshape(-1, 2)
+
+        x[0] = np.nan
+        y[-1,-1] = np.nan
+        x = x.view(MaskedArray)
+        y = y.view(MaskedArray)
+        x[0] = masked
+        y[-1,-1] = masked
+
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1,:], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+
+
+class TestArraySetOps:
+
+    def test_unique_onlist(self):
+        # Test unique on list
+        data = [1, 1, 1, 2, 2, 3]
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_(isinstance(test[0], MaskedArray))
+        assert_equal(test[0], masked_array([1, 2, 3], mask=[0, 0, 0]))
+        assert_equal(test[1], [0, 3, 5])
+        assert_equal(test[2], [0, 0, 0, 1, 1, 2])
+
+    def test_unique_onmaskedarray(self):
+        # Test unique on masked data w/use_mask=True
+        data = masked_array([1, 1, 1, 2, 2, 3], mask=[0, 0, 1, 0, 1, 0])
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
+        assert_equal(test[1], [0, 3, 5, 2])
+        assert_equal(test[2], [0, 0, 3, 1, 3, 2])
+        #
+        data.fill_value = 3
+        data = masked_array(data=[1, 1, 1, 2, 2, 3],
+                            mask=[0, 0, 1, 0, 1, 0], fill_value=3)
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
+        assert_equal(test[1], [0, 3, 5, 2])
+        assert_equal(test[2], [0, 0, 3, 1, 3, 2])
+
+    def test_unique_allmasked(self):
+        # Test all masked
+        data = masked_array([1, 1, 1], mask=True)
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, ], mask=[True]))
+        assert_equal(test[1], [0])
+        assert_equal(test[2], [0, 0, 0])
+        #
+        # Test masked
+        data = masked
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array(masked))
+        assert_equal(test[1], [0])
+        assert_equal(test[2], [0])
+
+    def test_ediff1d(self):
+        # Tests mediff1d
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        control = array([1, 1, 1, 4], mask=[1, 0, 0, 1])
+        test = ediff1d(x)
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_tobegin(self):
+        # Test ediff1d w/ to_begin
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_begin=masked)
+        control = array([0, 1, 1, 1, 4], mask=[1, 1, 0, 0, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_begin=[1, 2, 3])
+        control = array([1, 2, 3, 1, 1, 1, 4], mask=[0, 0, 0, 1, 0, 0, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_toend(self):
+        # Test ediff1d w/ to_end
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_end=masked)
+        control = array([1, 1, 1, 4, 0], mask=[1, 0, 0, 1, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=[1, 2, 3])
+        control = array([1, 1, 1, 4, 1, 2, 3], mask=[1, 0, 0, 1, 0, 0, 0])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_tobegin_toend(self):
+        # Test ediff1d w/ to_begin and to_end
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_end=masked, to_begin=masked)
+        control = array([0, 1, 1, 1, 4, 0], mask=[1, 1, 0, 0, 1, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=[1, 2, 3], to_begin=masked)
+        control = array([0, 1, 1, 1, 4, 1, 2, 3],
+                        mask=[1, 1, 0, 0, 1, 0, 0, 0])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_ndarray(self):
+        # Test ediff1d w/ a ndarray
+        x = np.arange(5)
+        test = ediff1d(x)
+        control = array([1, 1, 1, 1], mask=[0, 0, 0, 0])
+        assert_equal(test, control)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=masked, to_begin=masked)
+        control = array([0, 1, 1, 1, 1, 0], mask=[1, 0, 0, 0, 0, 1])
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_intersect1d(self):
+        # Test intersect1d
+        x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
+        y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
+        test = intersect1d(x, y)
+        control = array([1, 3, -1], mask=[0, 0, 1])
+        assert_equal(test, control)
+
+    def test_setxor1d(self):
+        # Test setxor1d
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = setxor1d(a, b)
+        assert_equal(test, array([3, 4, 7]))
+        #
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = [1, 2, 3, 4, 5]
+        test = setxor1d(a, b)
+        assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
+        #
+        a = array([1, 2, 3])
+        b = array([6, 5, 4])
+        test = setxor1d(a, b)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
+        b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
+        test = setxor1d(a, b)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        assert_array_equal([], setxor1d([], []))
+
+    def test_setxor1d_unique(self):
+        # Test setxor1d with assume_unique=True
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = [1, 2, 3, 4, 5]
+        test = setxor1d(a, b, assume_unique=True)
+        assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
+        #
+        a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
+        b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
+        test = setxor1d(a, b, assume_unique=True)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        a = array([[1], [8], [2], [3]])
+        b = array([[6, 5], [4, 8]])
+        test = setxor1d(a, b, assume_unique=True)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+
+    def test_isin(self):
+        # the tests for in1d cover most of isin's behavior
+        # if in1d is removed, would need to change those tests to test
+        # isin instead.
+        a = np.arange(24).reshape([2, 3, 4])
+        mask = np.zeros([2, 3, 4])
+        mask[1, 2, 0] = 1
+        a = array(a, mask=mask)
+        b = array(data=[0, 10, 20, 30,  1,  3, 11, 22, 33],
+                  mask=[0,  1,  0,  1,  0,  1,  0,  1,  0])
+        ec = zeros((2, 3, 4), dtype=bool)
+        ec[0, 0, 0] = True
+        ec[0, 0, 1] = True
+        ec[0, 2, 3] = True
+        c = isin(a, b)
+        assert_(isinstance(c, MaskedArray))
+        assert_array_equal(c, ec)
+        #compare results of np.isin to ma.isin
+        d = np.isin(a, b[~b.mask]) & ~a.mask
+        assert_array_equal(c, d)
+
+    def test_in1d(self):
+        # Test in1d
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = in1d(a, b)
+        assert_equal(test, [True, True, True, False, True])
+        #
+        a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 5, -1], mask=[0, 0, 1])
+        test = in1d(a, b)
+        assert_equal(test, [True, True, False, True, True])
+        #
+        assert_array_equal([], in1d([], []))
+
+    def test_in1d_invert(self):
+        # Test in1d's invert parameter
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
+
+        a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 5, -1], mask=[0, 0, 1])
+        assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
+
+        assert_array_equal([], in1d([], [], invert=True))
+
+    def test_union1d(self):
+        # Test union1d
+        a = array([1, 2, 5, 7, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = union1d(a, b)
+        control = array([1, 2, 3, 4, 5, 7, -1], mask=[0, 0, 0, 0, 0, 0, 1])
+        assert_equal(test, control)
+
+        # Tests gh-10340, arguments to union1d should be
+        # flattened if they are not already 1D
+        x = array([[0, 1, 2], [3, 4, 5]], mask=[[0, 0, 0], [0, 0, 1]])
+        y = array([0, 1, 2, 3, 4], mask=[0, 0, 0, 0, 1])
+        ez = array([0, 1, 2, 3, 4, 5], mask=[0, 0, 0, 0, 0, 1])
+        z = union1d(x, y)
+        assert_equal(z, ez)
+        #
+        assert_array_equal([], union1d([], []))
+
+    def test_setdiff1d(self):
+        # Test setdiff1d
+        a = array([6, 5, 4, 7, 7, 1, 2, 1], mask=[0, 0, 0, 0, 0, 0, 0, 1])
+        b = array([2, 4, 3, 3, 2, 1, 5])
+        test = setdiff1d(a, b)
+        assert_equal(test, array([6, 7, -1], mask=[0, 0, 1]))
+        #
+        a = arange(10)
+        b = arange(8)
+        assert_equal(setdiff1d(a, b), array([8, 9]))
+        a = array([], np.uint32, mask=[])
+        assert_equal(setdiff1d(a, []).dtype, np.uint32)
+
+    def test_setdiff1d_char_array(self):
+        # Test setdiff1d_charray
+        a = np.array(['a', 'b', 'c'])
+        b = np.array(['a', 'b', 's'])
+        assert_array_equal(setdiff1d(a, b), np.array(['c']))
+
+
+class TestShapeBase:
+
+    def test_atleast_2d(self):
+        # Test atleast_2d
+        a = masked_array([0, 1, 2], mask=[0, 1, 0])
+        b = atleast_2d(a)
+        assert_equal(b.shape, (1, 3))
+        assert_equal(b.mask.shape, b.data.shape)
+        assert_equal(a.shape, (3,))
+        assert_equal(a.mask.shape, a.data.shape)
+        assert_equal(b.mask.shape, b.data.shape)
+
+    def test_shape_scalar(self):
+        # the atleast and diagflat function should work with scalars
+        # GitHub issue #3367
+        # Additionally, the atleast functions should accept multiple scalars
+        # correctly
+        b = atleast_1d(1.0)
+        assert_equal(b.shape, (1,))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_1d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1,))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = atleast_2d(1.0)
+        assert_equal(b.shape, (1, 1))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_2d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1, 1))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = atleast_3d(1.0)
+        assert_equal(b.shape, (1, 1, 1))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_3d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1, 1, 1))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = diagflat(1.0)
+        assert_equal(b.shape, (1, 1))
+        assert_equal(b.mask.shape, b.data.shape)
+
+
+class TestNDEnumerate:
+
+    def test_ndenumerate_nomasked(self):
+        ordinary = np.arange(6.).reshape((1, 3, 2))
+        empty_mask = np.zeros_like(ordinary, dtype=bool)
+        with_mask = masked_array(ordinary, mask=empty_mask)
+        assert_equal(list(np.ndenumerate(ordinary)),
+                     list(ndenumerate(ordinary)))
+        assert_equal(list(ndenumerate(ordinary)),
+                     list(ndenumerate(with_mask)))
+        assert_equal(list(ndenumerate(with_mask)),
+                     list(ndenumerate(with_mask, compressed=False)))
+
+    def test_ndenumerate_allmasked(self):
+        a = masked_all(())
+        b = masked_all((100,))
+        c = masked_all((2, 3, 4))
+        assert_equal(list(ndenumerate(a)), [])
+        assert_equal(list(ndenumerate(b)), [])
+        assert_equal(list(ndenumerate(b, compressed=False)),
+                     list(zip(np.ndindex((100,)), 100 * [masked])))
+        assert_equal(list(ndenumerate(c)), [])
+        assert_equal(list(ndenumerate(c, compressed=False)),
+                     list(zip(np.ndindex((2, 3, 4)), 2 * 3 * 4 * [masked])))
+
+    def test_ndenumerate_mixedmasked(self):
+        a = masked_array(np.arange(12).reshape((3, 4)),
+                         mask=[[1, 1, 1, 1],
+                               [1, 1, 0, 1],
+                               [0, 0, 0, 0]])
+        items = [((1, 2), 6),
+                 ((2, 0), 8), ((2, 1), 9), ((2, 2), 10), ((2, 3), 11)]
+        assert_equal(list(ndenumerate(a)), items)
+        assert_equal(len(list(ndenumerate(a, compressed=False))), a.size)
+        for coordinate, value in ndenumerate(a, compressed=False):
+            assert_equal(a[coordinate], value)
+
+
+class TestStack:
+
+    def test_stack_1d(self):
+        a = masked_array([0, 1, 2], mask=[0, 1, 0])
+        b = masked_array([9, 8, 7], mask=[1, 0, 0])
+
+        c = stack([a, b], axis=0)
+        assert_equal(c.shape, (2, 3))
+        assert_array_equal(a.mask, c[0].mask)
+        assert_array_equal(b.mask, c[1].mask)
+
+        d = vstack([a, b])
+        assert_array_equal(c.data, d.data)
+        assert_array_equal(c.mask, d.mask)
+
+        c = stack([a, b], axis=1)
+        assert_equal(c.shape, (3, 2))
+        assert_array_equal(a.mask, c[:, 0].mask)
+        assert_array_equal(b.mask, c[:, 1].mask)
+
+    def test_stack_masks(self):
+        a = masked_array([0, 1, 2], mask=True)
+        b = masked_array([9, 8, 7], mask=False)
+
+        c = stack([a, b], axis=0)
+        assert_equal(c.shape, (2, 3))
+        assert_array_equal(a.mask, c[0].mask)
+        assert_array_equal(b.mask, c[1].mask)
+
+        d = vstack([a, b])
+        assert_array_equal(c.data, d.data)
+        assert_array_equal(c.mask, d.mask)
+
+        c = stack([a, b], axis=1)
+        assert_equal(c.shape, (3, 2))
+        assert_array_equal(a.mask, c[:, 0].mask)
+        assert_array_equal(b.mask, c[:, 1].mask)
+
+    def test_stack_nd(self):
+        # 2D
+        shp = (3, 2)
+        d1 = np.random.randint(0, 10, shp)
+        d2 = np.random.randint(0, 10, shp)
+        m1 = np.random.randint(0, 2, shp).astype(bool)
+        m2 = np.random.randint(0, 2, shp).astype(bool)
+        a1 = masked_array(d1, mask=m1)
+        a2 = masked_array(d2, mask=m2)
+
+        c = stack([a1, a2], axis=0)
+        c_shp = (2,) + shp
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[0].mask)
+        assert_array_equal(a2.mask, c[1].mask)
+
+        c = stack([a1, a2], axis=-1)
+        c_shp = shp + (2,)
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[..., 0].mask)
+        assert_array_equal(a2.mask, c[..., 1].mask)
+
+        # 4D
+        shp = (3, 2, 4, 5,)
+        d1 = np.random.randint(0, 10, shp)
+        d2 = np.random.randint(0, 10, shp)
+        m1 = np.random.randint(0, 2, shp).astype(bool)
+        m2 = np.random.randint(0, 2, shp).astype(bool)
+        a1 = masked_array(d1, mask=m1)
+        a2 = masked_array(d2, mask=m2)
+
+        c = stack([a1, a2], axis=0)
+        c_shp = (2,) + shp
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[0].mask)
+        assert_array_equal(a2.mask, c[1].mask)
+
+        c = stack([a1, a2], axis=-1)
+        c_shp = shp + (2,)
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[..., 0].mask)
+        assert_array_equal(a2.mask, c[..., 1].mask)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_mrecords.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_mrecords.py
new file mode 100644
index 0000000000000000000000000000000000000000..a364268a344bf9baccff6de535533936783312f7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_mrecords.py
@@ -0,0 +1,493 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for mrecords.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+
+"""
+import pickle
+
+import numpy as np
+import numpy.ma as ma
+from numpy.ma import masked, nomask
+from numpy.testing import temppath
+from numpy._core.records import (
+    recarray, fromrecords as recfromrecords, fromarrays as recfromarrays
+    )
+from numpy.ma.mrecords import (
+    MaskedRecords, mrecarray, fromarrays, fromtextfile, fromrecords,
+    addfield
+    )
+from numpy.ma.testutils import (
+    assert_, assert_equal,
+    assert_equal_records,
+    )
+
+
+class TestMRecords:
+
+    ilist = [1, 2, 3, 4, 5]
+    flist = [1.1, 2.2, 3.3, 4.4, 5.5]
+    slist = [b'one', b'two', b'three', b'four', b'five']
+    ddtype = [('a', int), ('b', float), ('c', '|S8')]
+    mask = [0, 1, 0, 0, 1]
+    base = ma.array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
+
+    def test_byview(self):
+        # Test creation by view
+        base = self.base
+        mbase = base.view(mrecarray)
+        assert_equal(mbase.recordmask, base.recordmask)
+        assert_equal_records(mbase._mask, base._mask)
+        assert_(isinstance(mbase._data, recarray))
+        assert_equal_records(mbase._data, base._data.view(recarray))
+        for field in ('a', 'b', 'c'):
+            assert_equal(base[field], mbase[field])
+        assert_equal_records(mbase.view(mrecarray), mbase)
+
+    def test_get(self):
+        # Tests fields retrieval
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # As fields..........
+        for field in ('a', 'b', 'c'):
+            assert_equal(getattr(mbase, field), mbase[field])
+            assert_equal(base[field], mbase[field])
+        # as elements .......
+        mbase_first = mbase[0]
+        assert_(isinstance(mbase_first, mrecarray))
+        assert_equal(mbase_first.dtype, mbase.dtype)
+        assert_equal(mbase_first.tolist(), (1, 1.1, b'one'))
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_first.recordmask, nomask)
+        assert_equal(mbase_first._mask.item(), (False, False, False))
+        assert_equal(mbase_first['a'], mbase['a'][0])
+        mbase_last = mbase[-1]
+        assert_(isinstance(mbase_last, mrecarray))
+        assert_equal(mbase_last.dtype, mbase.dtype)
+        assert_equal(mbase_last.tolist(), (None, None, None))
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_last.recordmask, True)
+        assert_equal(mbase_last._mask.item(), (True, True, True))
+        assert_equal(mbase_last['a'], mbase['a'][-1])
+        assert_(mbase_last['a'] is masked)
+        # as slice ..........
+        mbase_sl = mbase[:2]
+        assert_(isinstance(mbase_sl, mrecarray))
+        assert_equal(mbase_sl.dtype, mbase.dtype)
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_sl.recordmask, [0, 1])
+        assert_equal_records(mbase_sl.mask,
+                             np.array([(False, False, False),
+                                       (True, True, True)],
+                                      dtype=mbase._mask.dtype))
+        assert_equal_records(mbase_sl, base[:2].view(mrecarray))
+        for field in ('a', 'b', 'c'):
+            assert_equal(getattr(mbase_sl, field), base[:2][field])
+
+    def test_set_fields(self):
+        # Tests setting fields.
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase = mbase.copy()
+        mbase.fill_value = (999999, 1e20, 'N/A')
+        # Change the data, the mask should be conserved
+        mbase.a._data[:] = 5
+        assert_equal(mbase['a']._data, [5, 5, 5, 5, 5])
+        assert_equal(mbase['a']._mask, [0, 1, 0, 0, 1])
+        # Change the elements, and the mask will follow
+        mbase.a = 1
+        assert_equal(mbase['a']._data, [1]*5)
+        assert_equal(ma.getmaskarray(mbase['a']), [0]*5)
+        # Use to be _mask, now it's recordmask
+        assert_equal(mbase.recordmask, [False]*5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 0),
+                               (0, 1, 1),
+                               (0, 0, 0),
+                               (0, 0, 0),
+                               (0, 1, 1)],
+                              dtype=bool))
+        # Set a field to mask ........................
+        mbase.c = masked
+        # Use to be mask, and now it's still mask !
+        assert_equal(mbase.c.mask, [1]*5)
+        assert_equal(mbase.c.recordmask, [1]*5)
+        assert_equal(ma.getmaskarray(mbase['c']), [1]*5)
+        assert_equal(ma.getdata(mbase['c']), [b'N/A']*5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 1),
+                               (0, 1, 1),
+                               (0, 0, 1),
+                               (0, 0, 1),
+                               (0, 1, 1)],
+                              dtype=bool))
+        # Set fields by slices .......................
+        mbase = base.view(mrecarray).copy()
+        mbase.a[3:] = 5
+        assert_equal(mbase.a, [1, 2, 3, 5, 5])
+        assert_equal(mbase.a._mask, [0, 1, 0, 0, 0])
+        mbase.b[3:] = masked
+        assert_equal(mbase.b, base['b'])
+        assert_equal(mbase.b._mask, [0, 1, 0, 1, 1])
+        # Set fields globally..........................
+        ndtype = [('alpha', '|S1'), ('num', int)]
+        data = ma.array([('a', 1), ('b', 2), ('c', 3)], dtype=ndtype)
+        rdata = data.view(MaskedRecords)
+        val = ma.array([10, 20, 30], mask=[1, 0, 0])
+
+        rdata['num'] = val
+        assert_equal(rdata.num, val)
+        assert_equal(rdata.num.mask, [1, 0, 0])
+
+    def test_set_fields_mask(self):
+        # Tests setting the mask of a field.
+        base = self.base.copy()
+        # This one has already a mask....
+        mbase = base.view(mrecarray)
+        mbase['a'][-2] = masked
+        assert_equal(mbase.a, [1, 2, 3, 4, 5])
+        assert_equal(mbase.a._mask, [0, 1, 0, 1, 1])
+        # This one has not yet
+        mbase = fromarrays([np.arange(5), np.random.rand(5)],
+                           dtype=[('a', int), ('b', float)])
+        mbase['a'][-2] = masked
+        assert_equal(mbase.a, [0, 1, 2, 3, 4])
+        assert_equal(mbase.a._mask, [0, 0, 0, 1, 0])
+
+    def test_set_mask(self):
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # Set the mask to True .......................
+        mbase.mask = masked
+        assert_equal(ma.getmaskarray(mbase['b']), [1]*5)
+        assert_equal(mbase['a']._mask, mbase['b']._mask)
+        assert_equal(mbase['a']._mask, mbase['c']._mask)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(1, 1, 1)]*5, dtype=bool))
+        # Delete the mask ............................
+        mbase.mask = nomask
+        assert_equal(ma.getmaskarray(mbase['c']), [0]*5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 0)]*5, dtype=bool))
+
+    def test_set_mask_fromarray(self):
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # Sets the mask w/ an array
+        mbase.mask = [1, 0, 0, 0, 1]
+        assert_equal(mbase.a.mask, [1, 0, 0, 0, 1])
+        assert_equal(mbase.b.mask, [1, 0, 0, 0, 1])
+        assert_equal(mbase.c.mask, [1, 0, 0, 0, 1])
+        # Yay, once more !
+        mbase.mask = [0, 0, 0, 0, 1]
+        assert_equal(mbase.a.mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.b.mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.c.mask, [0, 0, 0, 0, 1])
+
+    def test_set_mask_fromfields(self):
+        mbase = self.base.copy().view(mrecarray)
+
+        nmask = np.array(
+            [(0, 1, 0), (0, 1, 0), (1, 0, 1), (1, 0, 1), (0, 0, 0)],
+            dtype=[('a', bool), ('b', bool), ('c', bool)])
+        mbase.mask = nmask
+        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
+        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
+        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
+        # Reinitialize and redo
+        mbase.mask = False
+        mbase.fieldmask = nmask
+        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
+        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
+        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
+
+    def test_set_elements(self):
+        base = self.base.copy()
+        # Set an element to mask .....................
+        mbase = base.view(mrecarray).copy()
+        mbase[-2] = masked
+        assert_equal(
+            mbase._mask.tolist(),
+            np.array([(0, 0, 0), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1)],
+                     dtype=bool))
+        # Used to be mask, now it's recordmask!
+        assert_equal(mbase.recordmask, [0, 1, 0, 1, 1])
+        # Set slices .................................
+        mbase = base.view(mrecarray).copy()
+        mbase[:2] = (5, 5, 5)
+        assert_equal(mbase.a._data, [5, 5, 3, 4, 5])
+        assert_equal(mbase.a._mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.b._data, [5., 5., 3.3, 4.4, 5.5])
+        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.c._data,
+                     [b'5', b'5', b'three', b'four', b'five'])
+        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
+
+        mbase = base.view(mrecarray).copy()
+        mbase[:2] = masked
+        assert_equal(mbase.a._data, [1, 2, 3, 4, 5])
+        assert_equal(mbase.a._mask, [1, 1, 0, 0, 1])
+        assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 4.4, 5.5])
+        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
+        assert_equal(mbase.c._data,
+                     [b'one', b'two', b'three', b'four', b'five'])
+        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
+
+    def test_setslices_hardmask(self):
+        # Tests setting slices w/ hardmask.
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase.harden_mask()
+        try:
+            mbase[-2:] = (5, 5, 5)
+            assert_equal(mbase.a._data, [1, 2, 3, 5, 5])
+            assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 5, 5.5])
+            assert_equal(mbase.c._data,
+                         [b'one', b'two', b'three', b'5', b'five'])
+            assert_equal(mbase.a._mask, [0, 1, 0, 0, 1])
+            assert_equal(mbase.b._mask, mbase.a._mask)
+            assert_equal(mbase.b._mask, mbase.c._mask)
+        except NotImplementedError:
+            # OK, not implemented yet...
+            pass
+        except AssertionError:
+            raise
+        else:
+            raise Exception("Flexible hard masks should be supported !")
+        # Not using a tuple should crash
+        try:
+            mbase[-2:] = 3
+        except (NotImplementedError, TypeError):
+            pass
+        else:
+            raise TypeError("Should have expected a readable buffer object!")
+
+    def test_hardmask(self):
+        # Test hardmask
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase.harden_mask()
+        assert_(mbase._hardmask)
+        mbase.mask = nomask
+        assert_equal_records(mbase._mask, base._mask)
+        mbase.soften_mask()
+        assert_(not mbase._hardmask)
+        mbase.mask = nomask
+        # So, the mask of a field is no longer set to nomask...
+        assert_equal_records(mbase._mask,
+                             ma.make_mask_none(base.shape, base.dtype))
+        assert_(ma.make_mask(mbase['b']._mask) is nomask)
+        assert_equal(mbase['a']._mask, mbase['b']._mask)
+
+    def test_pickling(self):
+        # Test pickling
+        base = self.base.copy()
+        mrec = base.view(mrecarray)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            _ = pickle.dumps(mrec, protocol=proto)
+            mrec_ = pickle.loads(_)
+            assert_equal(mrec_.dtype, mrec.dtype)
+            assert_equal_records(mrec_._data, mrec._data)
+            assert_equal(mrec_._mask, mrec._mask)
+            assert_equal_records(mrec_._mask, mrec._mask)
+
+    def test_filled(self):
+        # Test filling the array
+        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+        _c = ma.array(['one', 'two', 'three'], mask=[0, 0, 1], dtype='|S8')
+        ddtype = [('a', int), ('b', float), ('c', '|S8')]
+        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
+                          fill_value=(99999, 99999., 'N/A'))
+        mrecfilled = mrec.filled()
+        assert_equal(mrecfilled['a'], np.array((1, 2, 99999), dtype=int))
+        assert_equal(mrecfilled['b'], np.array((1.1, 2.2, 99999.),
+                                               dtype=float))
+        assert_equal(mrecfilled['c'], np.array(('one', 'two', 'N/A'),
+                                               dtype='|S8'))
+
+    def test_tolist(self):
+        # Test tolist.
+        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+        _c = ma.array(['one', 'two', 'three'], mask=[1, 0, 0], dtype='|S8')
+        ddtype = [('a', int), ('b', float), ('c', '|S8')]
+        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
+                          fill_value=(99999, 99999., 'N/A'))
+
+        assert_equal(mrec.tolist(),
+                     [(1, 1.1, None), (2, 2.2, b'two'),
+                      (None, None, b'three')])
+
+    def test_withnames(self):
+        # Test the creation w/ format and names
+        x = mrecarray(1, formats=float, names='base')
+        x[0]['base'] = 10
+        assert_equal(x['base'][0], 10)
+
+    def test_exotic_formats(self):
+        # Test that 'exotic' formats are processed properly
+        easy = mrecarray(1, dtype=[('i', int), ('s', '|S8'), ('f', float)])
+        easy[0] = masked
+        assert_equal(easy.filled(1).item(), (1, b'1', 1.))
+
+        solo = mrecarray(1, dtype=[('f0', ' 1:
+            assert_(eq(np.concatenate((x, y), 1),
+                               concatenate((xm, ym), 1)))
+            assert_(eq(np.add.reduce(x, 1), add.reduce(x, 1)))
+            assert_(eq(np.sum(x, 1), sum(x, 1)))
+            assert_(eq(np.prod(x, 1), product(x, 1)))
+
+    def test_testCI(self):
+        # Test of conversions and indexing
+        x1 = np.array([1, 2, 4, 3])
+        x2 = array(x1, mask=[1, 0, 0, 0])
+        x3 = array(x1, mask=[0, 1, 0, 1])
+        x4 = array(x1)
+        # test conversion to strings
+        str(x2)  # raises?
+        repr(x2)  # raises?
+        assert_(eq(np.sort(x1), sort(x2, fill_value=0)))
+        # tests of indexing
+        assert_(type(x2[1]) is type(x1[1]))
+        assert_(x1[1] == x2[1])
+        assert_(x2[0] is masked)
+        assert_(eq(x1[2], x2[2]))
+        assert_(eq(x1[2:5], x2[2:5]))
+        assert_(eq(x1[:], x2[:]))
+        assert_(eq(x1[1:], x3[1:]))
+        x1[2] = 9
+        x2[2] = 9
+        assert_(eq(x1, x2))
+        x1[1:3] = 99
+        x2[1:3] = 99
+        assert_(eq(x1, x2))
+        x2[1] = masked
+        assert_(eq(x1, x2))
+        x2[1:3] = masked
+        assert_(eq(x1, x2))
+        x2[:] = x1
+        x2[1] = masked
+        assert_(allequal(getmask(x2), array([0, 1, 0, 0])))
+        x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        assert_(allequal(getmask(x3), array([0, 1, 1, 0])))
+        x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        assert_(allequal(getmask(x4), array([0, 1, 1, 0])))
+        assert_(allequal(x4, array([1, 2, 3, 4])))
+        x1 = np.arange(5) * 1.0
+        x2 = masked_values(x1, 3.0)
+        assert_(eq(x1, x2))
+        assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
+        assert_(eq(3.0, x2.fill_value))
+        x1 = array([1, 'hello', 2, 3], object)
+        x2 = np.array([1, 'hello', 2, 3], object)
+        s1 = x1[1]
+        s2 = x2[1]
+        assert_equal(type(s2), str)
+        assert_equal(type(s1), str)
+        assert_equal(s1, s2)
+        assert_(x1[1:1].shape == (0,))
+
+    def test_testCopySize(self):
+        # Tests of some subtle points of copying and sizing.
+        n = [0, 0, 1, 0, 0]
+        m = make_mask(n)
+        m2 = make_mask(m)
+        assert_(m is m2)
+        m3 = make_mask(m, copy=True)
+        assert_(m is not m3)
+
+        x1 = np.arange(5)
+        y1 = array(x1, mask=m)
+        assert_(y1._data is not x1)
+        assert_(allequal(x1, y1._data))
+        assert_(y1._mask is m)
+
+        y1a = array(y1, copy=0)
+        # For copy=False, one might expect that the array would just
+        # passed on, i.e., that it would be "is" instead of "==".
+        # See gh-4043 for discussion.
+        assert_(y1a._mask.__array_interface__ ==
+                y1._mask.__array_interface__)
+
+        y2 = array(x1, mask=m3, copy=0)
+        assert_(y2._mask is m3)
+        assert_(y2[2] is masked)
+        y2[2] = 9
+        assert_(y2[2] is not masked)
+        assert_(y2._mask is m3)
+        assert_(allequal(y2.mask, 0))
+
+        y2a = array(x1, mask=m, copy=1)
+        assert_(y2a._mask is not m)
+        assert_(y2a[2] is masked)
+        y2a[2] = 9
+        assert_(y2a[2] is not masked)
+        assert_(y2a._mask is not m)
+        assert_(allequal(y2a.mask, 0))
+
+        y3 = array(x1 * 1.0, mask=m)
+        assert_(filled(y3).dtype is (x1 * 1.0).dtype)
+
+        x4 = arange(4)
+        x4[2] = masked
+        y4 = resize(x4, (8,))
+        assert_(eq(concatenate([x4, x4]), y4))
+        assert_(eq(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0]))
+        y5 = repeat(x4, (2, 2, 2, 2), axis=0)
+        assert_(eq(y5, [0, 0, 1, 1, 2, 2, 3, 3]))
+        y6 = repeat(x4, 2, axis=0)
+        assert_(eq(y5, y6))
+
+    def test_testPut(self):
+        # Test of put
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        m2 = m.copy()
+        x = array(d, mask=m)
+        assert_(x[3] is masked)
+        assert_(x[4] is masked)
+        x[[1, 4]] = [10, 40]
+        assert_(x._mask is m)
+        assert_(x[3] is masked)
+        assert_(x[4] is not masked)
+        assert_(eq(x, [0, 10, 2, -1, 40]))
+
+        x = array(d, mask=m2, copy=True)
+        x.put([0, 1, 2], [-1, 100, 200])
+        assert_(x._mask is not m2)
+        assert_(x[3] is masked)
+        assert_(x[4] is masked)
+        assert_(eq(x, [-1, 100, 200, 0, 0]))
+
+    def test_testPut2(self):
+        # Test of put
+        d = arange(5)
+        x = array(d, mask=[0, 0, 0, 0, 0])
+        z = array([10, 40], mask=[1, 0])
+        assert_(x[2] is not masked)
+        assert_(x[3] is not masked)
+        x[2:4] = z
+        assert_(x[2] is masked)
+        assert_(x[3] is not masked)
+        assert_(eq(x, [0, 1, 10, 40, 4]))
+
+        d = arange(5)
+        x = array(d, mask=[0, 0, 0, 0, 0])
+        y = x[2:4]
+        z = array([10, 40], mask=[1, 0])
+        assert_(x[2] is not masked)
+        assert_(x[3] is not masked)
+        y[:] = z
+        assert_(y[0] is masked)
+        assert_(y[1] is not masked)
+        assert_(eq(y, [10, 40]))
+        assert_(x[2] is masked)
+        assert_(x[3] is not masked)
+        assert_(eq(x, [0, 1, 10, 40, 4]))
+
+    def test_testMaPut(self):
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]
+        i = np.nonzero(m)[0]
+        put(ym, i, zm)
+        assert_(all(take(ym, i, axis=0) == zm))
+
+    def test_testOddFeatures(self):
+        # Test of other odd features
+        x = arange(20)
+        x = x.reshape(4, 5)
+        x.flat[5] = 12
+        assert_(x[1, 0] == 12)
+        z = x + 10j * x
+        assert_(eq(z.real, x))
+        assert_(eq(z.imag, 10 * x))
+        assert_(eq((z * conjugate(z)).real, 101 * x * x))
+        z.imag[...] = 0.0
+
+        x = arange(10)
+        x[3] = masked
+        assert_(str(x[3]) == str(masked))
+        c = x >= 8
+        assert_(count(where(c, masked, masked)) == 0)
+        assert_(shape(where(c, masked, masked)) == c.shape)
+        z = where(c, x, masked)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is masked)
+        assert_(z[7] is masked)
+        assert_(z[8] is not masked)
+        assert_(z[9] is not masked)
+        assert_(eq(x, z))
+        z = where(c, masked, x)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is not masked)
+        assert_(z[7] is not masked)
+        assert_(z[8] is masked)
+        assert_(z[9] is masked)
+        z = masked_where(c, x)
+        assert_(z.dtype is x.dtype)
+        assert_(z[3] is masked)
+        assert_(z[4] is not masked)
+        assert_(z[7] is not masked)
+        assert_(z[8] is masked)
+        assert_(z[9] is masked)
+        assert_(eq(x, z))
+        x = array([1., 2., 3., 4., 5.])
+        c = array([1, 1, 1, 0, 0])
+        x[2] = masked
+        z = where(c, x, -x)
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        c[0] = masked
+        z = where(c, x, -x)
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+        assert_(eq(masked_where(greater(x, 2), x), masked_greater(x, 2)))
+        assert_(eq(masked_where(greater_equal(x, 2), x),
+                   masked_greater_equal(x, 2)))
+        assert_(eq(masked_where(less(x, 2), x), masked_less(x, 2)))
+        assert_(eq(masked_where(less_equal(x, 2), x), masked_less_equal(x, 2)))
+        assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
+        assert_(eq(masked_where(equal(x, 2), x), masked_equal(x, 2)))
+        assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
+        assert_(eq(masked_inside(list(range(5)), 1, 3), [0, 199, 199, 199, 4]))
+        assert_(eq(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199]))
+        assert_(eq(masked_inside(array(list(range(5)),
+                                       mask=[1, 0, 0, 0, 0]), 1, 3).mask,
+                   [1, 1, 1, 1, 0]))
+        assert_(eq(masked_outside(array(list(range(5)),
+                                        mask=[0, 1, 0, 0, 0]), 1, 3).mask,
+                   [1, 1, 0, 0, 1]))
+        assert_(eq(masked_equal(array(list(range(5)),
+                                      mask=[1, 0, 0, 0, 0]), 2).mask,
+                   [1, 0, 1, 0, 0]))
+        assert_(eq(masked_not_equal(array([2, 2, 1, 2, 1],
+                                          mask=[1, 0, 0, 0, 0]), 2).mask,
+                   [1, 0, 1, 0, 1]))
+        assert_(eq(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
+                   [99, 99, 3, 4, 5]))
+        atest = ones((10, 10, 10), dtype=np.float32)
+        btest = zeros(atest.shape, MaskType)
+        ctest = masked_where(btest, atest)
+        assert_(eq(atest, ctest))
+        z = choose(c, (-x, x))
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+        x = arange(6)
+        x[5] = masked
+        y = arange(6) * 10
+        y[2] = masked
+        c = array([1, 1, 1, 0, 0, 0], mask=[1, 0, 0, 0, 0, 0])
+        cm = c.filled(1)
+        z = where(c, x, y)
+        zm = where(cm, x, y)
+        assert_(eq(z, zm))
+        assert_(getmask(zm) is nomask)
+        assert_(eq(zm, [0, 1, 2, 30, 40, 50]))
+        z = where(c, masked, 1)
+        assert_(eq(z, [99, 99, 99, 1, 1, 1]))
+        z = where(c, 1, masked)
+        assert_(eq(z, [99, 1, 1, 99, 99, 99]))
+
+    def test_testMinMax2(self):
+        # Test of minimum, maximum.
+        assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
+        assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
+        x = arange(5)
+        y = arange(5) - 2
+        x[3] = masked
+        y[0] = masked
+        assert_(eq(minimum(x, y), where(less(x, y), x, y)))
+        assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
+        assert_(minimum.reduce(x) == 0)
+        assert_(maximum.reduce(x) == 4)
+
+    def test_testTakeTransposeInnerOuter(self):
+        # Test of take, transpose, inner, outer products
+        x = arange(24)
+        y = np.arange(24)
+        x[5:6] = masked
+        x = x.reshape(2, 3, 4)
+        y = y.reshape(2, 3, 4)
+        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
+        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
+        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
+                   inner(x, y)))
+        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
+                   outer(x, y)))
+        y = array(['abc', 1, 'def', 2, 3], object)
+        y[2] = masked
+        t = take(y, [0, 3, 4])
+        assert_(t[0] == 'abc')
+        assert_(t[1] == 2)
+        assert_(t[2] == 3)
+
+    def test_testInplace(self):
+        # Test of inplace operations and rich comparisons
+        y = arange(10)
+
+        x = arange(10)
+        xm = arange(10)
+        xm[2] = masked
+        x += 1
+        assert_(eq(x, y + 1))
+        xm += 1
+        assert_(eq(x, y + 1))
+
+        x = arange(10)
+        xm = arange(10)
+        xm[2] = masked
+        x -= 1
+        assert_(eq(x, y - 1))
+        xm -= 1
+        assert_(eq(xm, y - 1))
+
+        x = arange(10) * 1.0
+        xm = arange(10) * 1.0
+        xm[2] = masked
+        x *= 2.0
+        assert_(eq(x, y * 2))
+        xm *= 2.0
+        assert_(eq(xm, y * 2))
+
+        x = arange(10) * 2
+        xm = arange(10)
+        xm[2] = masked
+        x //= 2
+        assert_(eq(x, y))
+        xm //= 2
+        assert_(eq(x, y))
+
+        x = arange(10) * 1.0
+        xm = arange(10) * 1.0
+        xm[2] = masked
+        x /= 2.0
+        assert_(eq(x, y / 2.0))
+        xm /= arange(10)
+        assert_(eq(xm, ones((10,))))
+
+        x = arange(10).astype(np.float32)
+        xm = arange(10)
+        xm[2] = masked
+        x += 1.
+        assert_(eq(x, y + 1.))
+
+    def test_testPickle(self):
+        # Test of pickling
+        x = arange(12)
+        x[4:10:2] = masked
+        x = x.reshape(4, 3)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            s = pickle.dumps(x, protocol=proto)
+            y = pickle.loads(s)
+            assert_(eq(x, y))
+
+    def test_testMasked(self):
+        # Test of masked element
+        xx = arange(6)
+        xx[1] = masked
+        assert_(str(masked) == '--')
+        assert_(xx[1] is masked)
+        assert_equal(filled(xx[1], 0), 0)
+
+    def test_testAverage1(self):
+        # Test of average.
+        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        assert_(eq(2.0, average(ott, axis=0)))
+        assert_(eq(2.0, average(ott, weights=[1., 1., 2., 1.])))
+        result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
+        assert_(eq(2.0, result))
+        assert_(wts == 4.0)
+        ott[:] = masked
+        assert_(average(ott, axis=0) is masked)
+        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        ott = ott.reshape(2, 2)
+        ott[:, 1] = masked
+        assert_(eq(average(ott, axis=0), [2.0, 0.0]))
+        assert_(average(ott, axis=1)[0] is masked)
+        assert_(eq([2., 0.], average(ott, axis=0)))
+        result, wts = average(ott, axis=0, returned=True)
+        assert_(eq(wts, [1., 0.]))
+
+    def test_testAverage2(self):
+        # More tests of average.
+        w1 = [0, 1, 1, 1, 1, 0]
+        w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
+        x = arange(6)
+        assert_(allclose(average(x, axis=0), 2.5))
+        assert_(allclose(average(x, axis=0, weights=w1), 2.5))
+        y = array([arange(6), 2.0 * arange(6)])
+        assert_(allclose(average(y, None),
+                                 np.add.reduce(np.arange(6)) * 3. / 12.))
+        assert_(allclose(average(y, axis=0), np.arange(6) * 3. / 2.))
+        assert_(allclose(average(y, axis=1),
+                                 [average(x, axis=0), average(x, axis=0)*2.0]))
+        assert_(allclose(average(y, None, weights=w2), 20. / 6.))
+        assert_(allclose(average(y, axis=0, weights=w2),
+                                 [0., 1., 2., 3., 4., 10.]))
+        assert_(allclose(average(y, axis=1),
+                                 [average(x, axis=0), average(x, axis=0)*2.0]))
+        m1 = zeros(6)
+        m2 = [0, 0, 1, 1, 0, 0]
+        m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
+        m4 = ones(6)
+        m5 = [0, 1, 1, 1, 1, 1]
+        assert_(allclose(average(masked_array(x, m1), axis=0), 2.5))
+        assert_(allclose(average(masked_array(x, m2), axis=0), 2.5))
+        assert_(average(masked_array(x, m4), axis=0) is masked)
+        assert_equal(average(masked_array(x, m5), axis=0), 0.0)
+        assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
+        z = masked_array(y, m3)
+        assert_(allclose(average(z, None), 20. / 6.))
+        assert_(allclose(average(z, axis=0),
+                                 [0., 1., 99., 99., 4.0, 7.5]))
+        assert_(allclose(average(z, axis=1), [2.5, 5.0]))
+        assert_(allclose(average(z, axis=0, weights=w2),
+                                 [0., 1., 99., 99., 4.0, 10.0]))
+
+        a = arange(6)
+        b = arange(6) * 3
+        r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
+        assert_equal(shape(r1), shape(w1))
+        assert_equal(r1.shape, w1.shape)
+        r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
+        assert_(shape(w2) == shape(r2))
+        a2d = array([[1, 2], [0, 4]], float)
+        a2dm = masked_array(a2d, [[0, 0], [1, 0]])
+        a2da = average(a2d, axis=0)
+        assert_(eq(a2da, [0.5, 3.0]))
+        a2dma = average(a2dm, axis=0)
+        assert_(eq(a2dma, [1.0, 3.0]))
+        a2dma = average(a2dm, axis=None)
+        assert_(eq(a2dma, 7. / 3.))
+        a2dma = average(a2dm, axis=1)
+        assert_(eq(a2dma, [1.5, 4.0]))
+
+    def test_testToPython(self):
+        assert_equal(1, int(array(1)))
+        assert_equal(1.0, float(array(1)))
+        assert_equal(1, int(array([[[1]]])))
+        assert_equal(1.0, float(array([[1]])))
+        assert_raises(TypeError, float, array([1, 1]))
+        assert_raises(ValueError, bool, array([0, 1]))
+        assert_raises(ValueError, bool, array([0, 0], mask=[0, 1]))
+
+    def test_testScalarArithmetic(self):
+        xm = array(0, mask=1)
+        #TODO FIXME: Find out what the following raises a warning in r8247
+        with np.errstate(divide='ignore'):
+            assert_((1 / array(0)).mask)
+        assert_((1 + xm).mask)
+        assert_((-xm).mask)
+        assert_((-xm).mask)
+        assert_(maximum(xm, xm).mask)
+        assert_(minimum(xm, xm).mask)
+        assert_(xm.filled().dtype is xm._data.dtype)
+        x = array(0, mask=0)
+        assert_(x.filled() == x._data)
+        assert_equal(str(xm), str(masked_print_option))
+
+    def test_testArrayMethods(self):
+        a = array([1, 3, 2])
+        assert_(eq(a.any(), a._data.any()))
+        assert_(eq(a.all(), a._data.all()))
+        assert_(eq(a.argmax(), a._data.argmax()))
+        assert_(eq(a.argmin(), a._data.argmin()))
+        assert_(eq(a.choose(0, 1, 2, 3, 4),
+                           a._data.choose(0, 1, 2, 3, 4)))
+        assert_(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
+        assert_(eq(a.conj(), a._data.conj()))
+        assert_(eq(a.conjugate(), a._data.conjugate()))
+        m = array([[1, 2], [3, 4]])
+        assert_(eq(m.diagonal(), m._data.diagonal()))
+        assert_(eq(a.sum(), a._data.sum()))
+        assert_(eq(a.take([1, 2]), a._data.take([1, 2])))
+        assert_(eq(m.transpose(), m._data.transpose()))
+
+    def test_testArrayAttributes(self):
+        a = array([1, 3, 2])
+        assert_equal(a.ndim, 1)
+
+    def test_testAPI(self):
+        assert_(not [m for m in dir(np.ndarray)
+                     if m not in dir(MaskedArray) and
+                     not m.startswith('_')])
+
+    def test_testSingleElementSubscript(self):
+        a = array([1, 3, 2])
+        b = array([1, 3, 2], mask=[1, 0, 1])
+        assert_equal(a[0].shape, ())
+        assert_equal(b[0].shape, ())
+        assert_equal(b[1].shape, ())
+
+    def test_assignment_by_condition(self):
+        # Test for gh-18951
+        a = array([1, 2, 3, 4], mask=[1, 0, 1, 0])
+        c = a >= 3
+        a[c] = 5
+        assert_(a[2] is masked)
+
+    def test_assignment_by_condition_2(self):
+        # gh-19721
+        a = masked_array([0, 1], mask=[False, False])
+        b = masked_array([0, 1], mask=[True, True])
+        mask = a < 1
+        b[mask] = a[mask]
+        expected_mask = [False, True]
+        assert_equal(b.mask, expected_mask)
+
+
+class TestUfuncs:
+    def setup_method(self):
+        self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
+                  array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
+
+    def test_testUfuncRegression(self):
+        f_invalid_ignore = [
+            'sqrt', 'arctanh', 'arcsin', 'arccos',
+            'arccosh', 'arctanh', 'log', 'log10', 'divide',
+            'true_divide', 'floor_divide', 'remainder', 'fmod']
+        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
+                  'sin', 'cos', 'tan',
+                  'arcsin', 'arccos', 'arctan',
+                  'sinh', 'cosh', 'tanh',
+                  'arcsinh',
+                  'arccosh',
+                  'arctanh',
+                  'absolute', 'fabs', 'negative',
+                  'floor', 'ceil',
+                  'logical_not',
+                  'add', 'subtract', 'multiply',
+                  'divide', 'true_divide', 'floor_divide',
+                  'remainder', 'fmod', 'hypot', 'arctan2',
+                  'equal', 'not_equal', 'less_equal', 'greater_equal',
+                  'less', 'greater',
+                  'logical_and', 'logical_or', 'logical_xor']:
+            try:
+                uf = getattr(umath, f)
+            except AttributeError:
+                uf = getattr(fromnumeric, f)
+            mf = getattr(np.ma, f)
+            args = self.d[:uf.nin]
+            with np.errstate():
+                if f in f_invalid_ignore:
+                    np.seterr(invalid='ignore')
+                if f in ['arctanh', 'log', 'log10']:
+                    np.seterr(divide='ignore')
+                ur = uf(*args)
+                mr = mf(*args)
+            assert_(eq(ur.filled(0), mr.filled(0), f))
+            assert_(eqmask(ur.mask, mr.mask))
+
+    def test_reduce(self):
+        a = self.d[0]
+        assert_(not alltrue(a, axis=0))
+        assert_(sometrue(a, axis=0))
+        assert_equal(sum(a[:3], axis=0), 0)
+        assert_equal(product(a, axis=0), 0)
+
+    def test_minmax(self):
+        a = arange(1, 13).reshape(3, 4)
+        amask = masked_where(a < 5, a)
+        assert_equal(amask.max(), a.max())
+        assert_equal(amask.min(), 5)
+        assert_((amask.max(0) == a.max(0)).all())
+        assert_((amask.min(0) == [5, 6, 7, 8]).all())
+        assert_(amask.max(1)[0].mask)
+        assert_(amask.min(1)[0].mask)
+
+    def test_nonzero(self):
+        for t in "?bhilqpBHILQPfdgFDGO":
+            x = array([1, 0, 2, 0], mask=[0, 0, 1, 1])
+            assert_(eq(nonzero(x), [0]))
+
+
+class TestArrayMethods:
+
+    def setup_method(self):
+        x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
+                      8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
+                      3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
+                      6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
+                      7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
+                      7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
+        X = x.reshape(6, 6)
+        XX = x.reshape(3, 2, 2, 3)
+
+        m = np.array([0, 1, 0, 1, 0, 0,
+                      1, 0, 1, 1, 0, 1,
+                      0, 0, 0, 1, 0, 1,
+                      0, 0, 0, 1, 1, 1,
+                      1, 0, 0, 1, 0, 0,
+                      0, 0, 1, 0, 1, 0])
+        mx = array(data=x, mask=m)
+        mX = array(data=X, mask=m.reshape(X.shape))
+        mXX = array(data=XX, mask=m.reshape(XX.shape))
+
+        self.d = (x, X, XX, m, mx, mX, mXX)
+
+    def test_trace(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXdiag = mX.diagonal()
+        assert_equal(mX.trace(), mX.diagonal().compressed().sum())
+        assert_(eq(mX.trace(),
+                           X.trace() - sum(mXdiag.mask * X.diagonal(),
+                                           axis=0)))
+
+    def test_clip(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        clipped = mx.clip(2, 8)
+        assert_(eq(clipped.mask, mx.mask))
+        assert_(eq(clipped._data, x.clip(2, 8)))
+        assert_(eq(clipped._data, mx._data.clip(2, 8)))
+
+    def test_ptp(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        (n, m) = X.shape
+        # print(type(mx), mx.compressed())
+        # raise Exception()
+        assert_equal(mx.ptp(), np.ptp(mx.compressed()))
+        rows = np.zeros(n, np.float64)
+        cols = np.zeros(m, np.float64)
+        for k in range(m):
+            cols[k] = np.ptp(mX[:, k].compressed())
+        for k in range(n):
+            rows[k] = np.ptp(mX[k].compressed())
+        assert_(eq(mX.ptp(0), cols))
+        assert_(eq(mX.ptp(1), rows))
+
+    def test_swapaxes(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXswapped = mX.swapaxes(0, 1)
+        assert_(eq(mXswapped[-1], mX[:, -1]))
+        mXXswapped = mXX.swapaxes(0, 2)
+        assert_equal(mXXswapped.shape, (2, 2, 3, 3))
+
+    def test_cumprod(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXcp = mX.cumprod(0)
+        assert_(eq(mXcp._data, mX.filled(1).cumprod(0)))
+        mXcp = mX.cumprod(1)
+        assert_(eq(mXcp._data, mX.filled(1).cumprod(1)))
+
+    def test_cumsum(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXcp = mX.cumsum(0)
+        assert_(eq(mXcp._data, mX.filled(0).cumsum(0)))
+        mXcp = mX.cumsum(1)
+        assert_(eq(mXcp._data, mX.filled(0).cumsum(1)))
+
+    def test_varstd(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        assert_(eq(mX.var(axis=None), mX.compressed().var()))
+        assert_(eq(mX.std(axis=None), mX.compressed().std()))
+        assert_(eq(mXX.var(axis=3).shape, XX.var(axis=3).shape))
+        assert_(eq(mX.var().shape, X.var().shape))
+        (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
+        for k in range(6):
+            assert_(eq(mXvar1[k], mX[k].compressed().var()))
+            assert_(eq(mXvar0[k], mX[:, k].compressed().var()))
+            assert_(eq(np.sqrt(mXvar0[k]),
+                               mX[:, k].compressed().std()))
+
+
+def eqmask(m1, m2):
+    if m1 is nomask:
+        return m2 is nomask
+    if m2 is nomask:
+        return m1 is nomask
+    return (m1 == m2).all()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_regression.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4f32cc7a98b4567076c9ccb340f6063321ba607
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_regression.py
@@ -0,0 +1,97 @@
+import numpy as np
+from numpy.testing import (
+    assert_, assert_array_equal, assert_allclose, suppress_warnings
+    )
+
+
+class TestRegression:
+    def test_masked_array_create(self):
+        # Ticket #17
+        x = np.ma.masked_array([0, 1, 2, 3, 0, 4, 5, 6],
+                               mask=[0, 0, 0, 1, 1, 1, 0, 0])
+        assert_array_equal(np.ma.nonzero(x), [[1, 2, 6, 7]])
+
+    def test_masked_array(self):
+        # Ticket #61
+        np.ma.array(1, mask=[1])
+
+    def test_mem_masked_where(self):
+        # Ticket #62
+        from numpy.ma import masked_where, MaskType
+        a = np.zeros((1, 1))
+        b = np.zeros(a.shape, MaskType)
+        c = masked_where(b, a)
+        a-c
+
+    def test_masked_array_multiply(self):
+        # Ticket #254
+        a = np.ma.zeros((4, 1))
+        a[2, 0] = np.ma.masked
+        b = np.zeros((4, 2))
+        a*b
+        b*a
+
+    def test_masked_array_repeat(self):
+        # Ticket #271
+        np.ma.array([1], mask=False).repeat(10)
+
+    def test_masked_array_repr_unicode(self):
+        # Ticket #1256
+        repr(np.ma.array("Unicode"))
+
+    def test_atleast_2d(self):
+        # Ticket #1559
+        a = np.ma.masked_array([0.0, 1.2, 3.5], mask=[False, True, False])
+        b = np.atleast_2d(a)
+        assert_(a.mask.ndim == 1)
+        assert_(b.mask.ndim == 2)
+
+    def test_set_fill_value_unicode_py3(self):
+        # Ticket #2733
+        a = np.ma.masked_array(['a', 'b', 'c'], mask=[1, 0, 0])
+        a.fill_value = 'X'
+        assert_(a.fill_value == 'X')
+
+    def test_var_sets_maskedarray_scalar(self):
+        # Issue gh-2757
+        a = np.ma.array(np.arange(5), mask=True)
+        mout = np.ma.array(-1, dtype=float)
+        a.var(out=mout)
+        assert_(mout._data == 0)
+
+    def test_ddof_corrcoef(self):
+        # See gh-3336
+        x = np.ma.masked_equal([1, 2, 3, 4, 5], 4)
+        y = np.array([2, 2.5, 3.1, 3, 5])
+        # this test can be removed after deprecation.
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            r0 = np.ma.corrcoef(x, y, ddof=0)
+            r1 = np.ma.corrcoef(x, y, ddof=1)
+            # ddof should not have an effect (it gets cancelled out)
+            assert_allclose(r0.data, r1.data)
+
+    def test_mask_not_backmangled(self):
+        # See gh-10314.  Test case taken from gh-3140.
+        a = np.ma.MaskedArray([1., 2.], mask=[False, False])
+        assert_(a.mask.shape == (2,))
+        b = np.tile(a, (2, 1))
+        # Check that the above no longer changes a.shape to (1, 2)
+        assert_(a.mask.shape == (2,))
+        assert_(b.shape == (2, 2))
+        assert_(b.mask.shape == (2, 2))
+
+    def test_empty_list_on_structured(self):
+        # See gh-12464. Indexing with empty list should give empty result.
+        ma = np.ma.MaskedArray([(1, 1.), (2, 2.), (3, 3.)], dtype='i4,f4')
+        assert_array_equal(ma[[]], ma[:0])
+
+    def test_masked_array_tobytes_fortran(self):
+        ma = np.ma.arange(4).reshape((2,2))
+        assert_array_equal(ma.tobytes(order='F'), ma.T.tobytes())
+
+    def test_structured_array(self):
+        # see gh-22041
+        np.ma.array((1, (b"", b"")),
+                    dtype=[("x", np.int_),
+                          ("y", [("i", np.void), ("j", np.void)])])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_subclassing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_subclassing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c454af09bb196692015438415a6e85fb82ef6a1b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/tests/test_subclassing.py
@@ -0,0 +1,460 @@
+# pylint: disable-msg=W0611, W0612, W0511,R0201
+"""Tests suite for MaskedArray & subclassing.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_subclassing.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+
+"""
+import numpy as np
+from numpy.lib.mixins import NDArrayOperatorsMixin
+from numpy.testing import assert_, assert_raises
+from numpy.ma.testutils import assert_equal
+from numpy.ma.core import (
+    array, arange, masked, MaskedArray, masked_array, log, add, hypot,
+    divide, asarray, asanyarray, nomask
+    )
+# from numpy.ma.core import (
+
+def assert_startswith(a, b):
+    # produces a better error message than assert_(a.startswith(b))
+    assert_equal(a[:len(b)], b)
+
+class SubArray(np.ndarray):
+    # Defines a generic np.ndarray subclass, that stores some metadata
+    # in the  dictionary `info`.
+    def __new__(cls,arr,info={}):
+        x = np.asanyarray(arr).view(cls)
+        x.info = info.copy()
+        return x
+
+    def __array_finalize__(self, obj):
+        super().__array_finalize__(obj)
+        self.info = getattr(obj, 'info', {}).copy()
+        return
+
+    def __add__(self, other):
+        result = super().__add__(other)
+        result.info['added'] = result.info.get('added', 0) + 1
+        return result
+
+    def __iadd__(self, other):
+        result = super().__iadd__(other)
+        result.info['iadded'] = result.info.get('iadded', 0) + 1
+        return result
+
+
+subarray = SubArray
+
+
+class SubMaskedArray(MaskedArray):
+    """Pure subclass of MaskedArray, keeping some info on subclass."""
+    def __new__(cls, info=None, **kwargs):
+        obj = super().__new__(cls, **kwargs)
+        obj._optinfo['info'] = info
+        return obj
+
+
+class MSubArray(SubArray, MaskedArray):
+
+    def __new__(cls, data, info={}, mask=nomask):
+        subarr = SubArray(data, info)
+        _data = MaskedArray.__new__(cls, data=subarr, mask=mask)
+        _data.info = subarr.info
+        return _data
+
+    @property
+    def _series(self):
+        _view = self.view(MaskedArray)
+        _view._sharedmask = False
+        return _view
+
+msubarray = MSubArray
+
+
+# Also a subclass that overrides __str__, __repr__ and __setitem__, disallowing
+# setting to non-class values (and thus np.ma.core.masked_print_option)
+# and overrides __array_wrap__, updating the info dict, to check that this
+# doesn't get destroyed by MaskedArray._update_from.  But this one also needs
+# its own iterator...
+class CSAIterator:
+    """
+    Flat iterator object that uses its own setter/getter
+    (works around ndarray.flat not propagating subclass setters/getters
+    see https://github.com/numpy/numpy/issues/4564)
+    roughly following MaskedIterator
+    """
+    def __init__(self, a):
+        self._original = a
+        self._dataiter = a.view(np.ndarray).flat
+
+    def __iter__(self):
+        return self
+
+    def __getitem__(self, indx):
+        out = self._dataiter.__getitem__(indx)
+        if not isinstance(out, np.ndarray):
+            out = out.__array__()
+        out = out.view(type(self._original))
+        return out
+
+    def __setitem__(self, index, value):
+        self._dataiter[index] = self._original._validate_input(value)
+
+    def __next__(self):
+        return next(self._dataiter).__array__().view(type(self._original))
+
+
+class ComplicatedSubArray(SubArray):
+
+    def __str__(self):
+        return f'myprefix {self.view(SubArray)} mypostfix'
+
+    def __repr__(self):
+        # Return a repr that does not start with 'name('
+        return f'<{self.__class__.__name__} {self}>'
+
+    def _validate_input(self, value):
+        if not isinstance(value, ComplicatedSubArray):
+            raise ValueError("Can only set to MySubArray values")
+        return value
+
+    def __setitem__(self, item, value):
+        # validation ensures direct assignment with ndarray or
+        # masked_print_option will fail
+        super().__setitem__(item, self._validate_input(value))
+
+    def __getitem__(self, item):
+        # ensure getter returns our own class also for scalars
+        value = super().__getitem__(item)
+        if not isinstance(value, np.ndarray):  # scalar
+            value = value.__array__().view(ComplicatedSubArray)
+        return value
+
+    @property
+    def flat(self):
+        return CSAIterator(self)
+
+    @flat.setter
+    def flat(self, value):
+        y = self.ravel()
+        y[:] = value
+
+    def __array_wrap__(self, obj, context=None, return_scalar=False):
+        obj = super().__array_wrap__(obj, context, return_scalar)
+        if context is not None and context[0] is np.multiply:
+            obj.info['multiplied'] = obj.info.get('multiplied', 0) + 1
+
+        return obj
+
+
+class WrappedArray(NDArrayOperatorsMixin):
+    """
+    Wrapping a MaskedArray rather than subclassing to test that
+    ufunc deferrals are commutative.
+    See: https://github.com/numpy/numpy/issues/15200)
+    """
+    __slots__ = ('_array', 'attrs')
+    __array_priority__ = 20
+
+    def __init__(self, array, **attrs):
+        self._array = array
+        self.attrs = attrs
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(\n{self._array}\n{self.attrs}\n)"
+
+    def __array__(self, dtype=None, copy=None):
+        return np.asarray(self._array)
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        if method == '__call__':
+            inputs = [arg._array if isinstance(arg, self.__class__) else arg
+                      for arg in inputs]
+            return self.__class__(ufunc(*inputs, **kwargs), **self.attrs)
+        else:
+            return NotImplemented
+
+
+class TestSubclassing:
+    # Test suite for masked subclasses of ndarray.
+
+    def setup_method(self):
+        x = np.arange(5, dtype='float')
+        mx = msubarray(x, mask=[0, 1, 0, 0, 0])
+        self.data = (x, mx)
+
+    def test_data_subclassing(self):
+        # Tests whether the subclass is kept.
+        x = np.arange(5)
+        m = [0, 0, 1, 0, 0]
+        xsub = SubArray(x)
+        xmsub = masked_array(xsub, mask=m)
+        assert_(isinstance(xmsub, MaskedArray))
+        assert_equal(xmsub._data, xsub)
+        assert_(isinstance(xmsub._data, SubArray))
+
+    def test_maskedarray_subclassing(self):
+        # Tests subclassing MaskedArray
+        (x, mx) = self.data
+        assert_(isinstance(mx._data, subarray))
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (x, mx) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(log(mx), msubarray))
+            assert_equal(log(x), np.log(x))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (x, mx) = self.data
+        # Result should be a msubarray
+        assert_(isinstance(add(mx, mx), msubarray))
+        assert_(isinstance(add(mx, x), msubarray))
+        # Result should work
+        assert_equal(add(mx, x), mx+x)
+        assert_(isinstance(add(mx, mx)._data, subarray))
+        assert_(isinstance(add.outer(mx, mx), msubarray))
+        assert_(isinstance(hypot(mx, mx), msubarray))
+        assert_(isinstance(hypot(mx, x), msubarray))
+
+    def test_masked_binary_operations2(self):
+        # Tests domained_masked_binary_operation
+        (x, mx) = self.data
+        xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+        assert_(isinstance(divide(mx, mx), msubarray))
+        assert_(isinstance(divide(mx, x), msubarray))
+        assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+    def test_attributepropagation(self):
+        x = array(arange(5), mask=[0]+[1]*4)
+        my = masked_array(subarray(x))
+        ym = msubarray(x)
+        #
+        z = (my+1)
+        assert_(isinstance(z, MaskedArray))
+        assert_(not isinstance(z, MSubArray))
+        assert_(isinstance(z._data, SubArray))
+        assert_equal(z._data.info, {})
+        #
+        z = (ym+1)
+        assert_(isinstance(z, MaskedArray))
+        assert_(isinstance(z, MSubArray))
+        assert_(isinstance(z._data, SubArray))
+        assert_(z._data.info['added'] > 0)
+        # Test that inplace methods from data get used (gh-4617)
+        ym += 1
+        assert_(isinstance(ym, MaskedArray))
+        assert_(isinstance(ym, MSubArray))
+        assert_(isinstance(ym._data, SubArray))
+        assert_(ym._data.info['iadded'] > 0)
+        #
+        ym._set_mask([1, 0, 0, 0, 1])
+        assert_equal(ym._mask, [1, 0, 0, 0, 1])
+        ym._series._set_mask([0, 0, 0, 0, 1])
+        assert_equal(ym._mask, [0, 0, 0, 0, 1])
+        #
+        xsub = subarray(x, info={'name':'x'})
+        mxsub = masked_array(xsub)
+        assert_(hasattr(mxsub, 'info'))
+        assert_equal(mxsub.info, xsub.info)
+
+    def test_subclasspreservation(self):
+        # Checks that masked_array(...,subok=True) preserves the class.
+        x = np.arange(5)
+        m = [0, 0, 1, 0, 0]
+        xinfo = list(zip(x, m))
+        xsub = MSubArray(x, mask=m, info={'xsub':xinfo})
+        #
+        mxsub = masked_array(xsub, subok=False)
+        assert_(not isinstance(mxsub, MSubArray))
+        assert_(isinstance(mxsub, MaskedArray))
+        assert_equal(mxsub._mask, m)
+        #
+        mxsub = asarray(xsub)
+        assert_(not isinstance(mxsub, MSubArray))
+        assert_(isinstance(mxsub, MaskedArray))
+        assert_equal(mxsub._mask, m)
+        #
+        mxsub = masked_array(xsub, subok=True)
+        assert_(isinstance(mxsub, MSubArray))
+        assert_equal(mxsub.info, xsub.info)
+        assert_equal(mxsub._mask, xsub._mask)
+        #
+        mxsub = asanyarray(xsub)
+        assert_(isinstance(mxsub, MSubArray))
+        assert_equal(mxsub.info, xsub.info)
+        assert_equal(mxsub._mask, m)
+
+    def test_subclass_items(self):
+        """test that getter and setter go via baseclass"""
+        x = np.arange(5)
+        xcsub = ComplicatedSubArray(x)
+        mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
+        # getter should  return a ComplicatedSubArray, even for single item
+        # first check we wrote ComplicatedSubArray correctly
+        assert_(isinstance(xcsub[1], ComplicatedSubArray))
+        assert_(isinstance(xcsub[1,...], ComplicatedSubArray))
+        assert_(isinstance(xcsub[1:4], ComplicatedSubArray))
+
+        # now that it propagates inside the MaskedArray
+        assert_(isinstance(mxcsub[1], ComplicatedSubArray))
+        assert_(isinstance(mxcsub[1,...].data, ComplicatedSubArray))
+        assert_(mxcsub[0] is masked)
+        assert_(isinstance(mxcsub[0,...].data, ComplicatedSubArray))
+        assert_(isinstance(mxcsub[1:4].data, ComplicatedSubArray))
+
+        # also for flattened version (which goes via MaskedIterator)
+        assert_(isinstance(mxcsub.flat[1].data, ComplicatedSubArray))
+        assert_(mxcsub.flat[0] is masked)
+        assert_(isinstance(mxcsub.flat[1:4].base, ComplicatedSubArray))
+
+        # setter should only work with ComplicatedSubArray input
+        # first check we wrote ComplicatedSubArray correctly
+        assert_raises(ValueError, xcsub.__setitem__, 1, x[4])
+        # now that it propagates inside the MaskedArray
+        assert_raises(ValueError, mxcsub.__setitem__, 1, x[4])
+        assert_raises(ValueError, mxcsub.__setitem__, slice(1, 4), x[1:4])
+        mxcsub[1] = xcsub[4]
+        mxcsub[1:4] = xcsub[1:4]
+        # also for flattened version (which goes via MaskedIterator)
+        assert_raises(ValueError, mxcsub.flat.__setitem__, 1, x[4])
+        assert_raises(ValueError, mxcsub.flat.__setitem__, slice(1, 4), x[1:4])
+        mxcsub.flat[1] = xcsub[4]
+        mxcsub.flat[1:4] = xcsub[1:4]
+
+    def test_subclass_nomask_items(self):
+        x = np.arange(5)
+        xcsub = ComplicatedSubArray(x)
+        mxcsub_nomask = masked_array(xcsub)
+
+        assert_(isinstance(mxcsub_nomask[1,...].data, ComplicatedSubArray))
+        assert_(isinstance(mxcsub_nomask[0,...].data, ComplicatedSubArray))
+
+        assert_(isinstance(mxcsub_nomask[1], ComplicatedSubArray))
+        assert_(isinstance(mxcsub_nomask[0], ComplicatedSubArray))
+
+    def test_subclass_repr(self):
+        """test that repr uses the name of the subclass
+        and 'array' for np.ndarray"""
+        x = np.arange(5)
+        mx = masked_array(x, mask=[True, False, True, False, False])
+        assert_startswith(repr(mx), 'masked_array')
+        xsub = SubArray(x)
+        mxsub = masked_array(xsub, mask=[True, False, True, False, False])
+        assert_startswith(repr(mxsub),
+            f'masked_{SubArray.__name__}(data=[--, 1, --, 3, 4]')
+
+    def test_subclass_str(self):
+        """test str with subclass that has overridden str, setitem"""
+        # first without override
+        x = np.arange(5)
+        xsub = SubArray(x)
+        mxsub = masked_array(xsub, mask=[True, False, True, False, False])
+        assert_equal(str(mxsub), '[-- 1 -- 3 4]')
+
+        xcsub = ComplicatedSubArray(x)
+        assert_raises(ValueError, xcsub.__setitem__, 0,
+                      np.ma.core.masked_print_option)
+        mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
+        assert_equal(str(mxcsub), 'myprefix [-- 1 -- 3 4] mypostfix')
+
+    def test_pure_subclass_info_preservation(self):
+        # Test that ufuncs and methods conserve extra information consistently;
+        # see gh-7122.
+        arr1 = SubMaskedArray('test', data=[1,2,3,4,5,6])
+        arr2 = SubMaskedArray(data=[0,1,2,3,4,5])
+        diff1 = np.subtract(arr1, arr2)
+        assert_('info' in diff1._optinfo)
+        assert_(diff1._optinfo['info'] == 'test')
+        diff2 = arr1 - arr2
+        assert_('info' in diff2._optinfo)
+        assert_(diff2._optinfo['info'] == 'test')
+
+
+class ArrayNoInheritance:
+    """Quantity-like class that does not inherit from ndarray"""
+    def __init__(self, data, units):
+        self.magnitude = data
+        self.units = units
+
+    def __getattr__(self, attr):
+        return getattr(self.magnitude, attr)
+
+
+def test_array_no_inheritance():
+    data_masked = np.ma.array([1, 2, 3], mask=[True, False, True])
+    data_masked_units = ArrayNoInheritance(data_masked, 'meters')
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test sharing the mask
+    data_masked.mask = [True, False, False]
+    assert_equal(data_masked.mask, new_array.mask)
+    assert_(new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, copy=True)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test that the mask is not shared when copy=True
+    data_masked.mask = [True, False, True]
+    assert_equal([True, False, False], new_array.mask)
+    assert_(not new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, keep_mask=False)
+    assert_equal(data_masked.data, new_array.data)
+    # The change did not affect the original mask
+    assert_equal(data_masked.mask, [True, False, True])
+    # Test that the mask is False and not shared when keep_mask=False
+    assert_(not new_array.mask)
+    assert_(not new_array.sharedmask)
+
+
+class TestClassWrapping:
+    # Test suite for classes that wrap MaskedArrays
+
+    def setup_method(self):
+        m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
+        wm = WrappedArray(m)
+        self.data = (m, wm)
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (m, wm) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(np.log(wm), WrappedArray))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (m, wm) = self.data
+        # Result should be a WrappedArray
+        assert_(isinstance(np.add(wm, wm), WrappedArray))
+        assert_(isinstance(np.add(m, wm), WrappedArray))
+        assert_(isinstance(np.add(wm, m), WrappedArray))
+        # add and '+' should call the same ufunc
+        assert_equal(np.add(m, wm), m + wm)
+        assert_(isinstance(np.hypot(m, wm), WrappedArray))
+        assert_(isinstance(np.hypot(wm, m), WrappedArray))
+        # Test domained binary operations
+        assert_(isinstance(np.divide(wm, m), WrappedArray))
+        assert_(isinstance(np.divide(m, wm), WrappedArray))
+        assert_equal(np.divide(wm, m) * m, np.divide(m, m) * wm)
+        # Test broadcasting
+        m2 = np.stack([m, m])
+        assert_(isinstance(np.divide(wm, m2), WrappedArray))
+        assert_(isinstance(np.divide(m2, wm), WrappedArray))
+        assert_equal(np.divide(m2, wm), np.divide(wm, m2))
+
+    def test_mixins_have_slots(self):
+        mixin = NDArrayOperatorsMixin()
+        # Should raise an error
+        assert_raises(AttributeError, mixin.__setattr__, "not_a_real_attr", 1)
+
+        m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
+        wm = WrappedArray(m)
+        assert_raises(AttributeError, wm.__setattr__, "not_an_attr", 2)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/testutils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/testutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c51256047c2735bdde9c5806726ba747eb3ca9b1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/testutils.py
@@ -0,0 +1,292 @@
+"""Miscellaneous functions for testing masked arrays and subclasses
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: testutils.py 3529 2007-11-13 08:01:14Z jarrod.millman $
+
+"""
+import operator
+
+import numpy as np
+from numpy import ndarray
+import numpy._core.umath as umath
+import numpy.testing
+from numpy.testing import (
+    assert_, assert_allclose, assert_array_almost_equal_nulp,
+    assert_raises, build_err_msg
+    )
+from .core import mask_or, getmask, masked_array, nomask, masked, filled
+
+__all__masked = [
+    'almost', 'approx', 'assert_almost_equal', 'assert_array_almost_equal',
+    'assert_array_approx_equal', 'assert_array_compare',
+    'assert_array_equal', 'assert_array_less', 'assert_close',
+    'assert_equal', 'assert_equal_records', 'assert_mask_equal',
+    'assert_not_equal', 'fail_if_array_equal',
+    ]
+
+# Include some normal test functions to avoid breaking other projects who
+# have mistakenly included them from this file. SciPy is one. That is
+# unfortunate, as some of these functions are not intended to work with
+# masked arrays. But there was no way to tell before.
+from unittest import TestCase
+__some__from_testing = [
+    'TestCase', 'assert_', 'assert_allclose', 'assert_array_almost_equal_nulp',
+    'assert_raises'
+    ]
+
+__all__ = __all__masked + __some__from_testing
+
+
+def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
+    """
+    Returns true if all components of a and b are equal to given tolerances.
+
+    If fill_value is True, masked values considered equal. Otherwise,
+    masked values are considered unequal.  The relative error rtol should
+    be positive and << 1.0 The absolute error atol comes into play for
+    those elements of b that are very small or zero; it says how small a
+    must be also.
+
+    """
+    m = mask_or(getmask(a), getmask(b))
+    d1 = filled(a)
+    d2 = filled(b)
+    if d1.dtype.char == "O" or d2.dtype.char == "O":
+        return np.equal(d1, d2).ravel()
+    x = filled(
+        masked_array(d1, copy=False, mask=m), fill_value
+    ).astype(np.float64)
+    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(np.float64)
+    d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
+    return d.ravel()
+
+
+def almost(a, b, decimal=6, fill_value=True):
+    """
+    Returns True if a and b are equal up to decimal places.
+
+    If fill_value is True, masked values considered equal. Otherwise,
+    masked values are considered unequal.
+
+    """
+    m = mask_or(getmask(a), getmask(b))
+    d1 = filled(a)
+    d2 = filled(b)
+    if d1.dtype.char == "O" or d2.dtype.char == "O":
+        return np.equal(d1, d2).ravel()
+    x = filled(
+        masked_array(d1, copy=False, mask=m), fill_value
+    ).astype(np.float64)
+    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(np.float64)
+    d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
+    return d.ravel()
+
+
+def _assert_equal_on_sequences(actual, desired, err_msg=''):
+    """
+    Asserts the equality of two non-array sequences.
+
+    """
+    assert_equal(len(actual), len(desired), err_msg)
+    for k in range(len(desired)):
+        assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}')
+    return
+
+
+def assert_equal_records(a, b):
+    """
+    Asserts that two records are equal.
+
+    Pretty crude for now.
+
+    """
+    assert_equal(a.dtype, b.dtype)
+    for f in a.dtype.names:
+        (af, bf) = (operator.getitem(a, f), operator.getitem(b, f))
+        if not (af is masked) and not (bf is masked):
+            assert_equal(operator.getitem(a, f), operator.getitem(b, f))
+    return
+
+
+def assert_equal(actual, desired, err_msg=''):
+    """
+    Asserts that two items are equal.
+
+    """
+    # Case #1: dictionary .....
+    if isinstance(desired, dict):
+        if not isinstance(actual, dict):
+            raise AssertionError(repr(type(actual)))
+        assert_equal(len(actual), len(desired), err_msg)
+        for k, i in desired.items():
+            if k not in actual:
+                raise AssertionError(f"{k} not in {actual}")
+            assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}')
+        return
+    # Case #2: lists .....
+    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
+        return _assert_equal_on_sequences(actual, desired, err_msg='')
+    if not (isinstance(actual, ndarray) or isinstance(desired, ndarray)):
+        msg = build_err_msg([actual, desired], err_msg,)
+        if not desired == actual:
+            raise AssertionError(msg)
+        return
+    # Case #4. arrays or equivalent
+    if ((actual is masked) and not (desired is masked)) or \
+            ((desired is masked) and not (actual is masked)):
+        msg = build_err_msg([actual, desired],
+                            err_msg, header='', names=('x', 'y'))
+        raise ValueError(msg)
+    actual = np.asanyarray(actual)
+    desired = np.asanyarray(desired)
+    (actual_dtype, desired_dtype) = (actual.dtype, desired.dtype)
+    if actual_dtype.char == "S" and desired_dtype.char == "S":
+        return _assert_equal_on_sequences(actual.tolist(),
+                                          desired.tolist(),
+                                          err_msg='')
+    return assert_array_equal(actual, desired, err_msg)
+
+
+def fail_if_equal(actual, desired, err_msg='',):
+    """
+    Raises an assertion error if two items are equal.
+
+    """
+    if isinstance(desired, dict):
+        if not isinstance(actual, dict):
+            raise AssertionError(repr(type(actual)))
+        fail_if_equal(len(actual), len(desired), err_msg)
+        for k, i in desired.items():
+            if k not in actual:
+                raise AssertionError(repr(k))
+            fail_if_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}')
+        return
+    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
+        fail_if_equal(len(actual), len(desired), err_msg)
+        for k in range(len(desired)):
+            fail_if_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}')
+        return
+    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
+        return fail_if_array_equal(actual, desired, err_msg)
+    msg = build_err_msg([actual, desired], err_msg)
+    if not desired != actual:
+        raise AssertionError(msg)
+
+
+assert_not_equal = fail_if_equal
+
+
+def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):
+    """
+    Asserts that two items are almost equal.
+
+    The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal).
+
+    """
+    if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
+        return assert_array_almost_equal(actual, desired, decimal=decimal,
+                                         err_msg=err_msg, verbose=verbose)
+    msg = build_err_msg([actual, desired],
+                        err_msg=err_msg, verbose=verbose)
+    if not round(abs(desired - actual), decimal) == 0:
+        raise AssertionError(msg)
+
+
+assert_close = assert_almost_equal
+
+
+def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
+                         fill_value=True):
+    """
+    Asserts that comparison between two masked arrays is satisfied.
+
+    The comparison is elementwise.
+
+    """
+    # Allocate a common mask and refill
+    m = mask_or(getmask(x), getmask(y))
+    x = masked_array(x, copy=False, mask=m, keep_mask=False, subok=False)
+    y = masked_array(y, copy=False, mask=m, keep_mask=False, subok=False)
+    if ((x is masked) and not (y is masked)) or \
+            ((y is masked) and not (x is masked)):
+        msg = build_err_msg([x, y], err_msg=err_msg, verbose=verbose,
+                            header=header, names=('x', 'y'))
+        raise ValueError(msg)
+    # OK, now run the basic tests on filled versions
+    return np.testing.assert_array_compare(comparison,
+                                           x.filled(fill_value),
+                                           y.filled(fill_value),
+                                           err_msg=err_msg,
+                                           verbose=verbose, header=header)
+
+
+def assert_array_equal(x, y, err_msg='', verbose=True):
+    """
+    Checks the elementwise equality of two masked arrays.
+
+    """
+    assert_array_compare(operator.__eq__, x, y,
+                         err_msg=err_msg, verbose=verbose,
+                         header='Arrays are not equal')
+
+
+def fail_if_array_equal(x, y, err_msg='', verbose=True):
+    """
+    Raises an assertion error if two masked arrays are not equal elementwise.
+
+    """
+    def compare(x, y):
+        return (not np.all(approx(x, y)))
+    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
+                         header='Arrays are not equal')
+
+
+def assert_array_approx_equal(x, y, decimal=6, err_msg='', verbose=True):
+    """
+    Checks the equality of two masked arrays, up to given number odecimals.
+
+    The equality is checked elementwise.
+
+    """
+    def compare(x, y):
+        "Returns the result of the loose comparison between x and y)."
+        return approx(x, y, rtol=10. ** -decimal)
+    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
+                         header='Arrays are not almost equal')
+
+
+def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True):
+    """
+    Checks the equality of two masked arrays, up to given number odecimals.
+
+    The equality is checked elementwise.
+
+    """
+    def compare(x, y):
+        "Returns the result of the loose comparison between x and y)."
+        return almost(x, y, decimal)
+    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
+                         header='Arrays are not almost equal')
+
+
+def assert_array_less(x, y, err_msg='', verbose=True):
+    """
+    Checks that x is smaller than y elementwise.
+
+    """
+    assert_array_compare(operator.__lt__, x, y,
+                         err_msg=err_msg, verbose=verbose,
+                         header='Arrays are not less-ordered')
+
+
+def assert_mask_equal(m1, m2, err_msg=''):
+    """
+    Asserts the equality of two masks.
+
+    """
+    if m1 is nomask:
+        assert_(m2 is nomask)
+    if m2 is nomask:
+        assert_(m1 is nomask)
+    assert_array_equal(m1, m2, err_msg=err_msg)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/timer_comparison.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/timer_comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c157308fcbd9ef7dbe8376720f0d651c9f4722d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/ma/timer_comparison.py
@@ -0,0 +1,442 @@
+import functools
+import timeit
+
+import numpy as np
+import numpy._core.fromnumeric as fromnumeric
+
+from numpy.testing import build_err_msg
+
+
+pi = np.pi
+
+class ModuleTester:
+    def __init__(self, module):
+        self.module = module
+        self.allequal = module.allequal
+        self.arange = module.arange
+        self.array = module.array
+        self.concatenate = module.concatenate
+        self.count = module.count
+        self.equal = module.equal
+        self.filled = module.filled
+        self.getmask = module.getmask
+        self.getmaskarray = module.getmaskarray
+        self.id = id
+        self.inner = module.inner
+        self.make_mask = module.make_mask
+        self.masked = module.masked
+        self.masked_array = module.masked_array
+        self.masked_values = module.masked_values
+        self.mask_or = module.mask_or
+        self.nomask = module.nomask
+        self.ones = module.ones
+        self.outer = module.outer
+        self.repeat = module.repeat
+        self.resize = module.resize
+        self.sort = module.sort
+        self.take = module.take
+        self.transpose = module.transpose
+        self.zeros = module.zeros
+        self.MaskType = module.MaskType
+        try:
+            self.umath = module.umath
+        except AttributeError:
+            self.umath = module.core.umath
+        self.testnames = []
+
+    def assert_array_compare(self, comparison, x, y, err_msg='', header='',
+                         fill_value=True):
+        """
+        Assert that a comparison of two masked arrays is satisfied elementwise.
+
+        """
+        xf = self.filled(x)
+        yf = self.filled(y)
+        m = self.mask_or(self.getmask(x), self.getmask(y))
+
+        x = self.filled(self.masked_array(xf, mask=m), fill_value)
+        y = self.filled(self.masked_array(yf, mask=m), fill_value)
+        if (x.dtype.char != "O"):
+            x = x.astype(np.float64)
+            if isinstance(x, np.ndarray) and x.size > 1:
+                x[np.isnan(x)] = 0
+            elif np.isnan(x):
+                x = 0
+        if (y.dtype.char != "O"):
+            y = y.astype(np.float64)
+            if isinstance(y, np.ndarray) and y.size > 1:
+                y[np.isnan(y)] = 0
+            elif np.isnan(y):
+                y = 0
+        try:
+            cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
+            if not cond:
+                msg = build_err_msg([x, y],
+                                    err_msg
+                                    + f'\n(shapes {x.shape}, {y.shape} mismatch)',
+                                    header=header,
+                                    names=('x', 'y'))
+                assert cond, msg
+            val = comparison(x, y)
+            if m is not self.nomask and fill_value:
+                val = self.masked_array(val, mask=m)
+            if isinstance(val, bool):
+                cond = val
+                reduced = [0]
+            else:
+                reduced = val.ravel()
+                cond = reduced.all()
+                reduced = reduced.tolist()
+            if not cond:
+                match = 100-100.0*reduced.count(1)/len(reduced)
+                msg = build_err_msg([x, y],
+                                    err_msg
+                                    + '\n(mismatch %s%%)' % (match,),
+                                    header=header,
+                                    names=('x', 'y'))
+                assert cond, msg
+        except ValueError as e:
+            msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
+            raise ValueError(msg) from e
+
+    def assert_array_equal(self, x, y, err_msg=''):
+        """
+        Checks the elementwise equality of two masked arrays.
+
+        """
+        self.assert_array_compare(self.equal, x, y, err_msg=err_msg,
+                                  header='Arrays are not equal')
+
+    @np.errstate(all='ignore')
+    def test_0(self):
+        """
+        Tests creation
+
+        """
+        x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        xm = self.masked_array(x, mask=m)
+        xm[0]
+
+    @np.errstate(all='ignore')
+    def test_1(self):
+        """
+        Tests creation
+
+        """
+        x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
+        y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
+        m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
+        m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
+        xm = self.masked_array(x, mask=m1)
+        ym = self.masked_array(y, mask=m2)
+        xf = np.where(m1, 1.e+20, x)
+        xm.set_fill_value(1.e+20)
+
+        assert (xm-ym).filled(0).any()
+        s = x.shape
+        assert xm.size == functools.reduce(lambda x, y: x*y, s)
+        assert self.count(xm) == len(m1) - functools.reduce(lambda x, y: x+y, m1)
+
+        for s in [(4, 3), (6, 2)]:
+            x.shape = s
+            y.shape = s
+            xm.shape = s
+            ym.shape = s
+            xf.shape = s
+            assert self.count(xm) == len(m1) - functools.reduce(lambda x, y: x+y, m1)
+
+    @np.errstate(all='ignore')
+    def test_2(self):
+        """
+        Tests conversions and indexing.
+
+        """
+        x1 = np.array([1, 2, 4, 3])
+        x2 = self.array(x1, mask=[1, 0, 0, 0])
+        x3 = self.array(x1, mask=[0, 1, 0, 1])
+        x4 = self.array(x1)
+        # test conversion to strings, no errors
+        str(x2)
+        repr(x2)
+        # tests of indexing
+        assert type(x2[1]) is type(x1[1])
+        assert x1[1] == x2[1]
+        x1[2] = 9
+        x2[2] = 9
+        self.assert_array_equal(x1, x2)
+        x1[1:3] = 99
+        x2[1:3] = 99
+        x2[1] = self.masked
+        x2[1:3] = self.masked
+        x2[:] = x1
+        x2[1] = self.masked
+        x3[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        x4[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
+        x1 = np.arange(5)*1.0
+        x2 = self.masked_values(x1, 3.0)
+        x1 = self.array([1, 'hello', 2, 3], object)
+        x2 = np.array([1, 'hello', 2, 3], object)
+        # check that no error occurs.
+        x1[1]
+        x2[1]
+        assert x1[1:1].shape == (0,)
+        # Tests copy-size
+        n = [0, 0, 1, 0, 0]
+        m = self.make_mask(n)
+        m2 = self.make_mask(m)
+        assert(m is m2)
+        m3 = self.make_mask(m, copy=1)
+        assert(m is not m3)
+
+    @np.errstate(all='ignore')
+    def test_3(self):
+        """
+        Tests resize/repeat
+
+        """
+        x4 = self.arange(4)
+        x4[2] = self.masked
+        y4 = self.resize(x4, (8,))
+        assert self.allequal(self.concatenate([x4, x4]), y4)
+        assert self.allequal(self.getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])
+        y5 = self.repeat(x4, (2, 2, 2, 2), axis=0)
+        self.assert_array_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3])
+        y6 = self.repeat(x4, 2, axis=0)
+        assert self.allequal(y5, y6)
+        y7 = x4.repeat((2, 2, 2, 2), axis=0)
+        assert self.allequal(y5, y7)
+        y8 = x4.repeat(2, 0)
+        assert self.allequal(y5, y8)
+
+    @np.errstate(all='ignore')
+    def test_4(self):
+        """
+        Test of take, transpose, inner, outer products.
+
+        """
+        x = self.arange(24)
+        y = np.arange(24)
+        x[5:6] = self.masked
+        x = x.reshape(2, 3, 4)
+        y = y.reshape(2, 3, 4)
+        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
+        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
+        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
+                            self.inner(x, y))
+        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
+                            self.outer(x, y))
+        y = self.array(['abc', 1, 'def', 2, 3], object)
+        y[2] = self.masked
+        t = self.take(y, [0, 3, 4])
+        assert t[0] == 'abc'
+        assert t[1] == 2
+        assert t[2] == 3
+
+    @np.errstate(all='ignore')
+    def test_5(self):
+        """
+        Tests inplace w/ scalar
+
+        """
+        x = self.arange(10)
+        y = self.arange(10)
+        xm = self.arange(10)
+        xm[2] = self.masked
+        x += 1
+        assert self.allequal(x, y+1)
+        xm += 1
+        assert self.allequal(xm, y+1)
+
+        x = self.arange(10)
+        xm = self.arange(10)
+        xm[2] = self.masked
+        x -= 1
+        assert self.allequal(x, y-1)
+        xm -= 1
+        assert self.allequal(xm, y-1)
+
+        x = self.arange(10)*1.0
+        xm = self.arange(10)*1.0
+        xm[2] = self.masked
+        x *= 2.0
+        assert self.allequal(x, y*2)
+        xm *= 2.0
+        assert self.allequal(xm, y*2)
+
+        x = self.arange(10)*2
+        xm = self.arange(10)*2
+        xm[2] = self.masked
+        x /= 2
+        assert self.allequal(x, y)
+        xm /= 2
+        assert self.allequal(xm, y)
+
+        x = self.arange(10)*1.0
+        xm = self.arange(10)*1.0
+        xm[2] = self.masked
+        x /= 2.0
+        assert self.allequal(x, y/2.0)
+        xm /= self.arange(10)
+        self.assert_array_equal(xm, self.ones((10,)))
+
+        x = self.arange(10).astype(np.float64)
+        xm = self.arange(10)
+        xm[2] = self.masked
+        x += 1.
+        assert self.allequal(x, y + 1.)
+
+    @np.errstate(all='ignore')
+    def test_6(self):
+        """
+        Tests inplace w/ array
+
+        """
+        x = self.arange(10, dtype=np.float64)
+        y = self.arange(10)
+        xm = self.arange(10, dtype=np.float64)
+        xm[2] = self.masked
+        m = xm.mask
+        a = self.arange(10, dtype=np.float64)
+        a[-1] = self.masked
+        x += a
+        xm += a
+        assert self.allequal(x, y+a)
+        assert self.allequal(xm, y+a)
+        assert self.allequal(xm.mask, self.mask_or(m, a.mask))
+
+        x = self.arange(10, dtype=np.float64)
+        xm = self.arange(10, dtype=np.float64)
+        xm[2] = self.masked
+        m = xm.mask
+        a = self.arange(10, dtype=np.float64)
+        a[-1] = self.masked
+        x -= a
+        xm -= a
+        assert self.allequal(x, y-a)
+        assert self.allequal(xm, y-a)
+        assert self.allequal(xm.mask, self.mask_or(m, a.mask))
+
+        x = self.arange(10, dtype=np.float64)
+        xm = self.arange(10, dtype=np.float64)
+        xm[2] = self.masked
+        m = xm.mask
+        a = self.arange(10, dtype=np.float64)
+        a[-1] = self.masked
+        x *= a
+        xm *= a
+        assert self.allequal(x, y*a)
+        assert self.allequal(xm, y*a)
+        assert self.allequal(xm.mask, self.mask_or(m, a.mask))
+
+        x = self.arange(10, dtype=np.float64)
+        xm = self.arange(10, dtype=np.float64)
+        xm[2] = self.masked
+        m = xm.mask
+        a = self.arange(10, dtype=np.float64)
+        a[-1] = self.masked
+        x /= a
+        xm /= a
+
+    @np.errstate(all='ignore')
+    def test_7(self):
+        "Tests ufunc"
+        d = (self.array([1.0, 0, -1, pi/2]*2, mask=[0, 1]+[0]*6),
+             self.array([1.0, 0, -1, pi/2]*2, mask=[1, 0]+[0]*6),)
+        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
+#                  'sin', 'cos', 'tan',
+#                  'arcsin', 'arccos', 'arctan',
+#                  'sinh', 'cosh', 'tanh',
+#                  'arcsinh',
+#                  'arccosh',
+#                  'arctanh',
+#                  'absolute', 'fabs', 'negative',
+#                  # 'nonzero', 'around',
+#                  'floor', 'ceil',
+#                  # 'sometrue', 'alltrue',
+#                  'logical_not',
+#                  'add', 'subtract', 'multiply',
+#                  'divide', 'true_divide', 'floor_divide',
+#                  'remainder', 'fmod', 'hypot', 'arctan2',
+#                  'equal', 'not_equal', 'less_equal', 'greater_equal',
+#                  'less', 'greater',
+#                  'logical_and', 'logical_or', 'logical_xor',
+                  ]:
+            try:
+                uf = getattr(self.umath, f)
+            except AttributeError:
+                uf = getattr(fromnumeric, f)
+            mf = getattr(self.module, f)
+            args = d[:uf.nin]
+            ur = uf(*args)
+            mr = mf(*args)
+            self.assert_array_equal(ur.filled(0), mr.filled(0), f)
+            self.assert_array_equal(ur._mask, mr._mask)
+
+    @np.errstate(all='ignore')
+    def test_99(self):
+        # test average
+        ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        self.assert_array_equal(2.0, self.average(ott, axis=0))
+        self.assert_array_equal(2.0, self.average(ott, weights=[1., 1., 2., 1.]))
+        result, wts = self.average(ott, weights=[1., 1., 2., 1.], returned=1)
+        self.assert_array_equal(2.0, result)
+        assert(wts == 4.0)
+        ott[:] = self.masked
+        assert(self.average(ott, axis=0) is self.masked)
+        ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        ott = ott.reshape(2, 2)
+        ott[:, 1] = self.masked
+        self.assert_array_equal(self.average(ott, axis=0), [2.0, 0.0])
+        assert(self.average(ott, axis=1)[0] is self.masked)
+        self.assert_array_equal([2., 0.], self.average(ott, axis=0))
+        result, wts = self.average(ott, axis=0, returned=1)
+        self.assert_array_equal(wts, [1., 0.])
+        w1 = [0, 1, 1, 1, 1, 0]
+        w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
+        x = self.arange(6)
+        self.assert_array_equal(self.average(x, axis=0), 2.5)
+        self.assert_array_equal(self.average(x, axis=0, weights=w1), 2.5)
+        y = self.array([self.arange(6), 2.0*self.arange(6)])
+        self.assert_array_equal(self.average(y, None), np.add.reduce(np.arange(6))*3./12.)
+        self.assert_array_equal(self.average(y, axis=0), np.arange(6) * 3./2.)
+        self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0])
+        self.assert_array_equal(self.average(y, None, weights=w2), 20./6.)
+        self.assert_array_equal(self.average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.])
+        self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0])
+        m1 = self.zeros(6)
+        m2 = [0, 0, 1, 1, 0, 0]
+        m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
+        m4 = self.ones(6)
+        m5 = [0, 1, 1, 1, 1, 1]
+        self.assert_array_equal(self.average(self.masked_array(x, m1), axis=0), 2.5)
+        self.assert_array_equal(self.average(self.masked_array(x, m2), axis=0), 2.5)
+        self.assert_array_equal(self.average(self.masked_array(x, m5), axis=0), 0.0)
+        self.assert_array_equal(self.count(self.average(self.masked_array(x, m4), axis=0)), 0)
+        z = self.masked_array(y, m3)
+        self.assert_array_equal(self.average(z, None), 20./6.)
+        self.assert_array_equal(self.average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
+        self.assert_array_equal(self.average(z, axis=1), [2.5, 5.0])
+        self.assert_array_equal(self.average(z, axis=0, weights=w2), [0., 1., 99., 99., 4.0, 10.0])
+
+    @np.errstate(all='ignore')
+    def test_A(self):
+        x = self.arange(24)
+        x[5:6] = self.masked
+        x = x.reshape(2, 3, 4)
+
+
+if __name__ == '__main__':
+    setup_base = ("from __main__ import ModuleTester \n"
+                  "import numpy\n"
+                  "tester = ModuleTester(module)\n")
+    setup_cur = "import numpy.ma.core as module\n" + setup_base
+    (nrepeat, nloop) = (10, 10)
+
+    for i in range(1, 8):
+        func = 'tester.test_%i()' % i
+        cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10)
+        cur = np.sort(cur)
+        print("#%i" % i + 50*'.')
+        print(eval("ModuleTester.test_%i.__doc__" % i))
+        print(f'core_current : {cur[0]:.3f} - {cur[1]:.3f}')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a7597d30387c98c0e7e66a0bfc82f5e64823d95
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.py
@@ -0,0 +1,11 @@
+"""Sub-package containing the matrix class and related functions.
+
+"""
+from . import defmatrix
+from .defmatrix import *
+
+__all__ = defmatrix.__all__
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e8ec8b2488664922c0594dc4ad8313d1612058fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__init__.pyi
@@ -0,0 +1,4 @@
+from numpy import matrix
+from .defmatrix import bmat, asmatrix
+
+__all__ = ["matrix", "bmat", "asmatrix"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..6512a0246db6aa6adc9a0619ca22e75f833b61e2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.py
@@ -0,0 +1,1118 @@
+__all__ = ['matrix', 'bmat', 'asmatrix']
+
+import sys
+import warnings
+import ast
+
+from .._utils import set_module
+import numpy._core.numeric as N
+from numpy._core.numeric import concatenate, isscalar
+# While not in __all__, matrix_power used to be defined here, so we import
+# it for backward compatibility.
+from numpy.linalg import matrix_power
+
+
+def _convert_from_string(data):
+    for char in '[]':
+        data = data.replace(char, '')
+
+    rows = data.split(';')
+    newdata = []
+    for count, row in enumerate(rows):
+        trow = row.split(',')
+        newrow = []
+        for col in trow:
+            temp = col.split()
+            newrow.extend(map(ast.literal_eval, temp))
+        if count == 0:
+            Ncols = len(newrow)
+        elif len(newrow) != Ncols:
+            raise ValueError("Rows not the same size.")
+        newdata.append(newrow)
+    return newdata
+
+
+@set_module('numpy')
+def asmatrix(data, dtype=None):
+    """
+    Interpret the input as a matrix.
+
+    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
+    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.
+
+    Parameters
+    ----------
+    data : array_like
+        Input data.
+    dtype : data-type
+       Data-type of the output matrix.
+
+    Returns
+    -------
+    mat : matrix
+        `data` interpreted as a matrix.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.array([[1, 2], [3, 4]])
+
+    >>> m = np.asmatrix(x)
+
+    >>> x[0,0] = 5
+
+    >>> m
+    matrix([[5, 2],
+            [3, 4]])
+
+    """
+    return matrix(data, dtype=dtype, copy=False)
+
+
+@set_module('numpy')
+class matrix(N.ndarray):
+    """
+    matrix(data, dtype=None, copy=True)
+
+    Returns a matrix from an array-like object, or from a string of data.
+
+    A matrix is a specialized 2-D array that retains its 2-D nature
+    through operations.  It has certain special operators, such as ``*``
+    (matrix multiplication) and ``**`` (matrix power).
+
+    .. note:: It is no longer recommended to use this class, even for linear
+              algebra. Instead use regular arrays. The class may be removed
+              in the future.
+
+    Parameters
+    ----------
+    data : array_like or string
+       If `data` is a string, it is interpreted as a matrix with commas
+       or spaces separating columns, and semicolons separating rows.
+    dtype : data-type
+       Data-type of the output matrix.
+    copy : bool
+       If `data` is already an `ndarray`, then this flag determines
+       whether the data is copied (the default), or whether a view is
+       constructed.
+
+    See Also
+    --------
+    array
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.matrix('1 2; 3 4')
+    >>> a
+    matrix([[1, 2],
+            [3, 4]])
+
+    >>> np.matrix([[1, 2], [3, 4]])
+    matrix([[1, 2],
+            [3, 4]])
+
+    """
+    __array_priority__ = 10.0
+    def __new__(subtype, data, dtype=None, copy=True):
+        warnings.warn('the matrix subclass is not the recommended way to '
+                      'represent matrices or deal with linear algebra (see '
+                      'https://docs.scipy.org/doc/numpy/user/'
+                      'numpy-for-matlab-users.html). '
+                      'Please adjust your code to use regular ndarray.',
+                      PendingDeprecationWarning, stacklevel=2)
+        if isinstance(data, matrix):
+            dtype2 = data.dtype
+            if (dtype is None):
+                dtype = dtype2
+            if (dtype2 == dtype) and (not copy):
+                return data
+            return data.astype(dtype)
+
+        if isinstance(data, N.ndarray):
+            if dtype is None:
+                intype = data.dtype
+            else:
+                intype = N.dtype(dtype)
+            new = data.view(subtype)
+            if intype != data.dtype:
+                return new.astype(intype)
+            if copy:
+                return new.copy()
+            else:
+                return new
+
+        if isinstance(data, str):
+            data = _convert_from_string(data)
+
+        # now convert data to an array
+        copy = None if not copy else True
+        arr = N.array(data, dtype=dtype, copy=copy)
+        ndim = arr.ndim
+        shape = arr.shape
+        if (ndim > 2):
+            raise ValueError("matrix must be 2-dimensional")
+        elif ndim == 0:
+            shape = (1, 1)
+        elif ndim == 1:
+            shape = (1, shape[0])
+
+        order = 'C'
+        if (ndim == 2) and arr.flags.fortran:
+            order = 'F'
+
+        if not (order or arr.flags.contiguous):
+            arr = arr.copy()
+
+        ret = N.ndarray.__new__(subtype, shape, arr.dtype,
+                                buffer=arr,
+                                order=order)
+        return ret
+
+    def __array_finalize__(self, obj):
+        self._getitem = False
+        if (isinstance(obj, matrix) and obj._getitem):
+            return
+        ndim = self.ndim
+        if (ndim == 2):
+            return
+        if (ndim > 2):
+            newshape = tuple([x for x in self.shape if x > 1])
+            ndim = len(newshape)
+            if ndim == 2:
+                self.shape = newshape
+                return
+            elif (ndim > 2):
+                raise ValueError("shape too large to be a matrix.")
+        else:
+            newshape = self.shape
+        if ndim == 0:
+            self.shape = (1, 1)
+        elif ndim == 1:
+            self.shape = (1, newshape[0])
+        return
+
+    def __getitem__(self, index):
+        self._getitem = True
+
+        try:
+            out = N.ndarray.__getitem__(self, index)
+        finally:
+            self._getitem = False
+
+        if not isinstance(out, N.ndarray):
+            return out
+
+        if out.ndim == 0:
+            return out[()]
+        if out.ndim == 1:
+            sh = out.shape[0]
+            # Determine when we should have a column array
+            try:
+                n = len(index)
+            except Exception:
+                n = 0
+            if n > 1 and isscalar(index[1]):
+                out.shape = (sh, 1)
+            else:
+                out.shape = (1, sh)
+        return out
+
+    def __mul__(self, other):
+        if isinstance(other, (N.ndarray, list, tuple)) :
+            # This promotes 1-D vectors to row vectors
+            return N.dot(self, asmatrix(other))
+        if isscalar(other) or not hasattr(other, '__rmul__') :
+            return N.dot(self, other)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        return N.dot(other, self)
+
+    def __imul__(self, other):
+        self[:] = self * other
+        return self
+
+    def __pow__(self, other):
+        return matrix_power(self, other)
+
+    def __ipow__(self, other):
+        self[:] = self ** other
+        return self
+
+    def __rpow__(self, other):
+        return NotImplemented
+
+    def _align(self, axis):
+        """A convenience function for operations that need to preserve axis
+        orientation.
+        """
+        if axis is None:
+            return self[0, 0]
+        elif axis==0:
+            return self
+        elif axis==1:
+            return self.transpose()
+        else:
+            raise ValueError("unsupported axis")
+
+    def _collapse(self, axis):
+        """A convenience function for operations that want to collapse
+        to a scalar like _align, but are using keepdims=True
+        """
+        if axis is None:
+            return self[0, 0]
+        else:
+            return self
+
+    # Necessary because base-class tolist expects dimension
+    #  reduction by x[0]
+    def tolist(self):
+        """
+        Return the matrix as a (possibly nested) list.
+
+        See `ndarray.tolist` for full documentation.
+
+        See Also
+        --------
+        ndarray.tolist
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.tolist()
+        [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]
+
+        """
+        return self.__array__().tolist()
+
+    # To preserve orientation of result...
+    def sum(self, axis=None, dtype=None, out=None):
+        """
+        Returns the sum of the matrix elements, along the given axis.
+
+        Refer to `numpy.sum` for full documentation.
+
+        See Also
+        --------
+        numpy.sum
+
+        Notes
+        -----
+        This is the same as `ndarray.sum`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix([[1, 2], [4, 3]])
+        >>> x.sum()
+        10
+        >>> x.sum(axis=1)
+        matrix([[3],
+                [7]])
+        >>> x.sum(axis=1, dtype='float')
+        matrix([[3.],
+                [7.]])
+        >>> out = np.zeros((2, 1), dtype='float')
+        >>> x.sum(axis=1, dtype='float', out=np.asmatrix(out))
+        matrix([[3.],
+                [7.]])
+
+        """
+        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+
+    # To update docstring from array to matrix...
+    def squeeze(self, axis=None):
+        """
+        Return a possibly reshaped matrix.
+
+        Refer to `numpy.squeeze` for more documentation.
+
+        Parameters
+        ----------
+        axis : None or int or tuple of ints, optional
+            Selects a subset of the axes of length one in the shape.
+            If an axis is selected with shape entry greater than one,
+            an error is raised.
+
+        Returns
+        -------
+        squeezed : matrix
+            The matrix, but as a (1, N) matrix if it had shape (N, 1).
+
+        See Also
+        --------
+        numpy.squeeze : related function
+
+        Notes
+        -----
+        If `m` has a single column then that column is returned
+        as the single row of a matrix.  Otherwise `m` is returned.
+        The returned matrix is always either `m` itself or a view into `m`.
+        Supplying an axis keyword argument will not affect the returned matrix
+        but it may cause an error to be raised.
+
+        Examples
+        --------
+        >>> c = np.matrix([[1], [2]])
+        >>> c
+        matrix([[1],
+                [2]])
+        >>> c.squeeze()
+        matrix([[1, 2]])
+        >>> r = c.T
+        >>> r
+        matrix([[1, 2]])
+        >>> r.squeeze()
+        matrix([[1, 2]])
+        >>> m = np.matrix([[1, 2], [3, 4]])
+        >>> m.squeeze()
+        matrix([[1, 2],
+                [3, 4]])
+
+        """
+        return N.ndarray.squeeze(self, axis=axis)
+
+
+    # To update docstring from array to matrix...
+    def flatten(self, order='C'):
+        """
+        Return a flattened copy of the matrix.
+
+        All `N` elements of the matrix are placed into a single row.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            'C' means to flatten in row-major (C-style) order. 'F' means to
+            flatten in column-major (Fortran-style) order. 'A' means to
+            flatten in column-major order if `m` is Fortran *contiguous* in
+            memory, row-major order otherwise. 'K' means to flatten `m` in
+            the order the elements occur in memory. The default is 'C'.
+
+        Returns
+        -------
+        y : matrix
+            A copy of the matrix, flattened to a `(1, N)` matrix where `N`
+            is the number of elements in the original matrix.
+
+        See Also
+        --------
+        ravel : Return a flattened array.
+        flat : A 1-D flat iterator over the matrix.
+
+        Examples
+        --------
+        >>> m = np.matrix([[1,2], [3,4]])
+        >>> m.flatten()
+        matrix([[1, 2, 3, 4]])
+        >>> m.flatten('F')
+        matrix([[1, 3, 2, 4]])
+
+        """
+        return N.ndarray.flatten(self, order=order)
+
+    def mean(self, axis=None, dtype=None, out=None):
+        """
+        Returns the average of the matrix elements along the given axis.
+
+        Refer to `numpy.mean` for full documentation.
+
+        See Also
+        --------
+        numpy.mean
+
+        Notes
+        -----
+        Same as `ndarray.mean` except that, where that returns an `ndarray`,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.mean()
+        5.5
+        >>> x.mean(0)
+        matrix([[4., 5., 6., 7.]])
+        >>> x.mean(1)
+        matrix([[ 1.5],
+                [ 5.5],
+                [ 9.5]])
+
+        """
+        return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+    def std(self, axis=None, dtype=None, out=None, ddof=0):
+        """
+        Return the standard deviation of the array elements along the given axis.
+
+        Refer to `numpy.std` for full documentation.
+
+        See Also
+        --------
+        numpy.std
+
+        Notes
+        -----
+        This is the same as `ndarray.std`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.std()
+        3.4520525295346629 # may vary
+        >>> x.std(0)
+        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary
+        >>> x.std(1)
+        matrix([[ 1.11803399],
+                [ 1.11803399],
+                [ 1.11803399]])
+
+        """
+        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
+
+    def var(self, axis=None, dtype=None, out=None, ddof=0):
+        """
+        Returns the variance of the matrix elements, along the given axis.
+
+        Refer to `numpy.var` for full documentation.
+
+        See Also
+        --------
+        numpy.var
+
+        Notes
+        -----
+        This is the same as `ndarray.var`, except that where an `ndarray` would
+        be returned, a `matrix` object is returned instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
+        >>> x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.var()
+        11.916666666666666
+        >>> x.var(0)
+        matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]]) # may vary
+        >>> x.var(1)
+        matrix([[1.25],
+                [1.25],
+                [1.25]])
+
+        """
+        return N.ndarray.var(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
+
+    def prod(self, axis=None, dtype=None, out=None):
+        """
+        Return the product of the array elements over the given axis.
+
+        Refer to `prod` for full documentation.
+
+        See Also
+        --------
+        prod, ndarray.prod
+
+        Notes
+        -----
+        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
+        returns a `matrix` object instead.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.prod()
+        0
+        >>> x.prod(0)
+        matrix([[  0,  45, 120, 231]])
+        >>> x.prod(1)
+        matrix([[   0],
+                [ 840],
+                [7920]])
+
+        """
+        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
+
+    def any(self, axis=None, out=None):
+        """
+        Test whether any array element along a given axis evaluates to True.
+
+        Refer to `numpy.any` for full documentation.
+
+        Parameters
+        ----------
+        axis : int, optional
+            Axis along which logical OR is performed
+        out : ndarray, optional
+            Output to existing array instead of creating new one, must have
+            same shape as expected output
+
+        Returns
+        -------
+            any : bool, ndarray
+                Returns a single bool if `axis` is ``None``; otherwise,
+                returns `ndarray`
+
+        """
+        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis)
+
+    def all(self, axis=None, out=None):
+        """
+        Test whether all matrix elements along a given axis evaluate to True.
+
+        Parameters
+        ----------
+        See `numpy.all` for complete descriptions
+
+        See Also
+        --------
+        numpy.all
+
+        Notes
+        -----
+        This is the same as `ndarray.all`, but it returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> y = x[0]; y
+        matrix([[0, 1, 2, 3]])
+        >>> (x == y)
+        matrix([[ True,  True,  True,  True],
+                [False, False, False, False],
+                [False, False, False, False]])
+        >>> (x == y).all()
+        False
+        >>> (x == y).all(0)
+        matrix([[False, False, False, False]])
+        >>> (x == y).all(1)
+        matrix([[ True],
+                [False],
+                [False]])
+
+        """
+        return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
+
+    def max(self, axis=None, out=None):
+        """
+        Return the maximum value along an axis.
+
+        Parameters
+        ----------
+        See `amax` for complete descriptions
+
+        See Also
+        --------
+        amax, ndarray.max
+
+        Notes
+        -----
+        This is the same as `ndarray.max`, but returns a `matrix` object
+        where `ndarray.max` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.max()
+        11
+        >>> x.max(0)
+        matrix([[ 8,  9, 10, 11]])
+        >>> x.max(1)
+        matrix([[ 3],
+                [ 7],
+                [11]])
+
+        """
+        return N.ndarray.max(self, axis, out, keepdims=True)._collapse(axis)
+
+    def argmax(self, axis=None, out=None):
+        """
+        Indexes of the maximum values along an axis.
+
+        Return the indexes of the first occurrences of the maximum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmax` for complete descriptions
+
+        See Also
+        --------
+        numpy.argmax
+
+        Notes
+        -----
+        This is the same as `ndarray.argmax`, but returns a `matrix` object
+        where `ndarray.argmax` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.argmax()
+        11
+        >>> x.argmax(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmax(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ndarray.argmax(self, axis, out)._align(axis)
+
+    def min(self, axis=None, out=None):
+        """
+        Return the minimum value along an axis.
+
+        Parameters
+        ----------
+        See `amin` for complete descriptions.
+
+        See Also
+        --------
+        amin, ndarray.min
+
+        Notes
+        -----
+        This is the same as `ndarray.min`, but returns a `matrix` object
+        where `ndarray.min` would return an ndarray.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.min()
+        -11
+        >>> x.min(0)
+        matrix([[ -8,  -9, -10, -11]])
+        >>> x.min(1)
+        matrix([[ -3],
+                [ -7],
+                [-11]])
+
+        """
+        return N.ndarray.min(self, axis, out, keepdims=True)._collapse(axis)
+
+    def argmin(self, axis=None, out=None):
+        """
+        Indexes of the minimum values along an axis.
+
+        Return the indexes of the first occurrences of the minimum values
+        along the specified axis.  If axis is None, the index is for the
+        flattened matrix.
+
+        Parameters
+        ----------
+        See `numpy.argmin` for complete descriptions.
+
+        See Also
+        --------
+        numpy.argmin
+
+        Notes
+        -----
+        This is the same as `ndarray.argmin`, but returns a `matrix` object
+        where `ndarray.argmin` would return an `ndarray`.
+
+        Examples
+        --------
+        >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[  0,  -1,  -2,  -3],
+                [ -4,  -5,  -6,  -7],
+                [ -8,  -9, -10, -11]])
+        >>> x.argmin()
+        11
+        >>> x.argmin(0)
+        matrix([[2, 2, 2, 2]])
+        >>> x.argmin(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ndarray.argmin(self, axis, out)._align(axis)
+
+    def ptp(self, axis=None, out=None):
+        """
+        Peak-to-peak (maximum - minimum) value along the given axis.
+
+        Refer to `numpy.ptp` for full documentation.
+
+        See Also
+        --------
+        numpy.ptp
+
+        Notes
+        -----
+        Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
+        this returns a `matrix` object.
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.ptp()
+        11
+        >>> x.ptp(0)
+        matrix([[8, 8, 8, 8]])
+        >>> x.ptp(1)
+        matrix([[3],
+                [3],
+                [3]])
+
+        """
+        return N.ptp(self, axis, out)._align(axis)
+
+    @property
+    def I(self):
+        """
+        Returns the (multiplicative) inverse of invertible `self`.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            If `self` is non-singular, `ret` is such that ``ret * self`` ==
+            ``self * ret`` == ``np.matrix(np.eye(self[0,:].size))`` all return
+            ``True``.
+
+        Raises
+        ------
+        numpy.linalg.LinAlgError: Singular matrix
+            If `self` is singular.
+
+        See Also
+        --------
+        linalg.inv
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]'); m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getI()
+        matrix([[-2. ,  1. ],
+                [ 1.5, -0.5]])
+        >>> m.getI() * m
+        matrix([[ 1.,  0.], # may vary
+                [ 0.,  1.]])
+
+        """
+        M, N = self.shape
+        if M == N:
+            from numpy.linalg import inv as func
+        else:
+            from numpy.linalg import pinv as func
+        return asmatrix(func(self))
+
+    @property
+    def A(self):
+        """
+        Return `self` as an `ndarray` object.
+
+        Equivalent to ``np.asarray(self)``.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : ndarray
+            `self` as an `ndarray`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.getA()
+        array([[ 0,  1,  2,  3],
+               [ 4,  5,  6,  7],
+               [ 8,  9, 10, 11]])
+
+        """
+        return self.__array__()
+
+    @property
+    def A1(self):
+        """
+        Return `self` as a flattened `ndarray`.
+
+        Equivalent to ``np.asarray(x).ravel()``
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : ndarray
+            `self`, 1-D, as an `ndarray`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
+        matrix([[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]])
+        >>> x.getA1()
+        array([ 0,  1,  2, ...,  9, 10, 11])
+
+
+        """
+        return self.__array__().ravel()
+
+
+    def ravel(self, order='C'):
+        """
+        Return a flattened matrix.
+
+        Refer to `numpy.ravel` for more documentation.
+
+        Parameters
+        ----------
+        order : {'C', 'F', 'A', 'K'}, optional
+            The elements of `m` are read using this index order. 'C' means to
+            index the elements in C-like order, with the last axis index
+            changing fastest, back to the first axis index changing slowest.
+            'F' means to index the elements in Fortran-like index order, with
+            the first index changing fastest, and the last index changing
+            slowest. Note that the 'C' and 'F' options take no account of the
+            memory layout of the underlying array, and only refer to the order
+            of axis indexing.  'A' means to read the elements in Fortran-like
+            index order if `m` is Fortran *contiguous* in memory, C-like order
+            otherwise.  'K' means to read the elements in the order they occur
+            in memory, except for reversing the data when strides are negative.
+            By default, 'C' index order is used.
+
+        Returns
+        -------
+        ret : matrix
+            Return the matrix flattened to shape `(1, N)` where `N`
+            is the number of elements in the original matrix.
+            A copy is made only if necessary.
+
+        See Also
+        --------
+        matrix.flatten : returns a similar output matrix but always a copy
+        matrix.flat : a flat iterator on the array.
+        numpy.ravel : related function which returns an ndarray
+
+        """
+        return N.ndarray.ravel(self, order=order)
+
+    @property
+    def T(self):
+        """
+        Returns the transpose of the matrix.
+
+        Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            The (non-conjugated) transpose of the matrix.
+
+        See Also
+        --------
+        transpose, getH
+
+        Examples
+        --------
+        >>> m = np.matrix('[1, 2; 3, 4]')
+        >>> m
+        matrix([[1, 2],
+                [3, 4]])
+        >>> m.getT()
+        matrix([[1, 3],
+                [2, 4]])
+
+        """
+        return self.transpose()
+
+    @property
+    def H(self):
+        """
+        Returns the (complex) conjugate transpose of `self`.
+
+        Equivalent to ``np.transpose(self)`` if `self` is real-valued.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        ret : matrix object
+            complex conjugate transpose of `self`
+
+        Examples
+        --------
+        >>> x = np.matrix(np.arange(12).reshape((3,4)))
+        >>> z = x - 1j*x; z
+        matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
+                [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
+                [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
+        >>> z.getH()
+        matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
+                [ 1. +1.j,  5. +5.j,  9. +9.j],
+                [ 2. +2.j,  6. +6.j, 10.+10.j],
+                [ 3. +3.j,  7. +7.j, 11.+11.j]])
+
+        """
+        if issubclass(self.dtype.type, N.complexfloating):
+            return self.transpose().conjugate()
+        else:
+            return self.transpose()
+
+    # kept for compatibility
+    getT = T.fget
+    getA = A.fget
+    getA1 = A1.fget
+    getH = H.fget
+    getI = I.fget
+
+def _from_string(str, gdict, ldict):
+    rows = str.split(';')
+    rowtup = []
+    for row in rows:
+        trow = row.split(',')
+        newrow = []
+        for x in trow:
+            newrow.extend(x.split())
+        trow = newrow
+        coltup = []
+        for col in trow:
+            col = col.strip()
+            try:
+                thismat = ldict[col]
+            except KeyError:
+                try:
+                    thismat = gdict[col]
+                except KeyError as e:
+                    raise NameError(f"name {col!r} is not defined") from None
+
+            coltup.append(thismat)
+        rowtup.append(concatenate(coltup, axis=-1))
+    return concatenate(rowtup, axis=0)
+
+
+@set_module('numpy')
+def bmat(obj, ldict=None, gdict=None):
+    """
+    Build a matrix object from a string, nested sequence, or array.
+
+    Parameters
+    ----------
+    obj : str or array_like
+        Input data. If a string, variables in the current scope may be
+        referenced by name.
+    ldict : dict, optional
+        A dictionary that replaces local operands in current frame.
+        Ignored if `obj` is not a string or `gdict` is None.
+    gdict : dict, optional
+        A dictionary that replaces global operands in current frame.
+        Ignored if `obj` is not a string.
+
+    Returns
+    -------
+    out : matrix
+        Returns a matrix object, which is a specialized 2-D array.
+
+    See Also
+    --------
+    block :
+        A generalization of this function for N-d arrays, that returns normal
+        ndarrays.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> A = np.asmatrix('1 1; 1 1')
+    >>> B = np.asmatrix('2 2; 2 2')
+    >>> C = np.asmatrix('3 4; 5 6')
+    >>> D = np.asmatrix('7 8; 9 0')
+
+    All the following expressions construct the same block matrix:
+
+    >>> np.bmat([[A, B], [C, D]])
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+    >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+    >>> np.bmat('A,B; C,D')
+    matrix([[1, 1, 2, 2],
+            [1, 1, 2, 2],
+            [3, 4, 7, 8],
+            [5, 6, 9, 0]])
+
+    """
+    if isinstance(obj, str):
+        if gdict is None:
+            # get previous frame
+            frame = sys._getframe().f_back
+            glob_dict = frame.f_globals
+            loc_dict = frame.f_locals
+        else:
+            glob_dict = gdict
+            loc_dict = ldict
+
+        return matrix(_from_string(obj, glob_dict, loc_dict))
+
+    if isinstance(obj, (tuple, list)):
+        # [[A,B],[C,D]]
+        arr_rows = []
+        for row in obj:
+            if isinstance(row, N.ndarray):  # not 2-d
+                return matrix(concatenate(obj, axis=-1))
+            else:
+                arr_rows.append(concatenate(row, axis=-1))
+        return matrix(concatenate(arr_rows, axis=0))
+    if isinstance(obj, N.ndarray):
+        return matrix(obj)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a6095cc1155ab6a97be8142c5704dd553aedd602
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/defmatrix.pyi
@@ -0,0 +1,17 @@
+from collections.abc import Mapping, Sequence
+from typing import Any
+
+from numpy import matrix
+from numpy._typing import ArrayLike, DTypeLike, NDArray
+
+__all__ = ["asmatrix", "bmat", "matrix"]
+
+def bmat(
+    obj: str | Sequence[ArrayLike] | NDArray[Any],
+    ldict: None | Mapping[str, Any] = ...,
+    gdict: None | Mapping[str, Any] = ...,
+) -> matrix[tuple[int, int], Any]: ...
+
+def asmatrix(
+    data: ArrayLike, dtype: DTypeLike = ...
+) -> matrix[tuple[int, int], Any]: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..81d955e86fa863043b82fa126f09528b02a3cff3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_defmatrix.py
@@ -0,0 +1,453 @@
+import collections.abc
+
+import numpy as np
+from numpy import matrix, asmatrix, bmat
+from numpy.testing import (
+    assert_, assert_equal, assert_almost_equal, assert_array_equal,
+    assert_array_almost_equal, assert_raises
+    )
+from numpy.linalg import matrix_power
+
+class TestCtor:
+    def test_basic(self):
+        A = np.array([[1, 2], [3, 4]])
+        mA = matrix(A)
+        assert_(np.all(mA.A == A))
+
+        B = bmat("A,A;A,A")
+        C = bmat([[A, A], [A, A]])
+        D = np.array([[1, 2, 1, 2],
+                      [3, 4, 3, 4],
+                      [1, 2, 1, 2],
+                      [3, 4, 3, 4]])
+        assert_(np.all(B.A == D))
+        assert_(np.all(C.A == D))
+
+        E = np.array([[5, 6], [7, 8]])
+        AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
+        assert_(np.all(bmat([A, E]) == AEresult))
+
+        vec = np.arange(5)
+        mvec = matrix(vec)
+        assert_(mvec.shape == (1, 5))
+
+    def test_exceptions(self):
+        # Check for ValueError when called with invalid string data.
+        assert_raises(ValueError, matrix, "invalid")
+
+    def test_bmat_nondefault_str(self):
+        A = np.array([[1, 2], [3, 4]])
+        B = np.array([[5, 6], [7, 8]])
+        Aresult = np.array([[1, 2, 1, 2],
+                            [3, 4, 3, 4],
+                            [1, 2, 1, 2],
+                            [3, 4, 3, 4]])
+        mixresult = np.array([[1, 2, 5, 6],
+                              [3, 4, 7, 8],
+                              [5, 6, 1, 2],
+                              [7, 8, 3, 4]])
+        assert_(np.all(bmat("A,A;A,A") == Aresult))
+        assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
+        assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
+        assert_(
+            np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
+        b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
+        assert_(np.all(b2 == mixresult))
+
+
+class TestProperties:
+    def test_sum(self):
+        """Test whether matrix.sum(axis=1) preserves orientation.
+        Fails in NumPy <= 0.9.6.2127.
+        """
+        M = matrix([[1, 2, 0, 0],
+                   [3, 4, 0, 0],
+                   [1, 2, 1, 2],
+                   [3, 4, 3, 4]])
+        sum0 = matrix([8, 12, 4, 6])
+        sum1 = matrix([3, 7, 6, 14]).T
+        sumall = 30
+        assert_array_equal(sum0, M.sum(axis=0))
+        assert_array_equal(sum1, M.sum(axis=1))
+        assert_equal(sumall, M.sum())
+
+        assert_array_equal(sum0, np.sum(M, axis=0))
+        assert_array_equal(sum1, np.sum(M, axis=1))
+        assert_equal(sumall, np.sum(M))
+
+    def test_prod(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.prod(), 720)
+        assert_equal(x.prod(0), matrix([[4, 10, 18]]))
+        assert_equal(x.prod(1), matrix([[6], [120]]))
+
+        assert_equal(np.prod(x), 720)
+        assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
+        assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
+
+        y = matrix([0, 1, 3])
+        assert_(y.prod() == 0)
+
+    def test_max(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.max(), 6)
+        assert_equal(x.max(0), matrix([[4, 5, 6]]))
+        assert_equal(x.max(1), matrix([[3], [6]]))
+
+        assert_equal(np.max(x), 6)
+        assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
+        assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
+
+    def test_min(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.min(), 1)
+        assert_equal(x.min(0), matrix([[1, 2, 3]]))
+        assert_equal(x.min(1), matrix([[1], [4]]))
+
+        assert_equal(np.min(x), 1)
+        assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
+        assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
+
+    def test_ptp(self):
+        x = np.arange(4).reshape((2, 2))
+        mx = x.view(np.matrix)
+        assert_(mx.ptp() == 3)
+        assert_(np.all(mx.ptp(0) == np.array([2, 2])))
+        assert_(np.all(mx.ptp(1) == np.array([1, 1])))
+
+    def test_var(self):
+        x = np.arange(9).reshape((3, 3))
+        mx = x.view(np.matrix)
+        assert_equal(x.var(ddof=0), mx.var(ddof=0))
+        assert_equal(x.var(ddof=1), mx.var(ddof=1))
+
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.],
+                      [3., 4.]])
+        mA = matrix(A)
+        assert_(np.allclose(linalg.inv(A), mA.I))
+        assert_(np.all(np.array(np.transpose(A) == mA.T)))
+        assert_(np.all(np.array(np.transpose(A) == mA.H)))
+        assert_(np.all(A == mA.A))
+
+        B = A + 2j*A
+        mB = matrix(B)
+        assert_(np.allclose(linalg.inv(B), mB.I))
+        assert_(np.all(np.array(np.transpose(B) == mB.T)))
+        assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
+
+    def test_pinv(self):
+        x = matrix(np.arange(6).reshape(2, 3))
+        xpinv = matrix([[-0.77777778,  0.27777778],
+                        [-0.11111111,  0.11111111],
+                        [ 0.55555556, -0.05555556]])
+        assert_almost_equal(x.I, xpinv)
+
+    def test_comparisons(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+        mB = matrix(A) + 0.1
+        assert_(np.all(mB == A+0.1))
+        assert_(np.all(mB == matrix(A+0.1)))
+        assert_(not np.any(mB == matrix(A-0.1)))
+        assert_(np.all(mA < mB))
+        assert_(np.all(mA <= mB))
+        assert_(np.all(mA <= mA))
+        assert_(not np.any(mA < mA))
+
+        assert_(not np.any(mB < mA))
+        assert_(np.all(mB >= mA))
+        assert_(np.all(mB >= mB))
+        assert_(not np.any(mB > mB))
+
+        assert_(np.all(mA == mA))
+        assert_(not np.any(mA == mB))
+        assert_(np.all(mB != mA))
+
+        assert_(not np.all(abs(mA) > 0))
+        assert_(np.all(abs(mB > 0)))
+
+    def test_asmatrix(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = asmatrix(A)
+        A[0, 0] = -10
+        assert_(A[0, 0] == mA[0, 0])
+
+    def test_noaxis(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(A.sum() == matrix(2))
+        assert_(A.mean() == matrix(0.5))
+
+    def test_repr(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(repr(A) == "matrix([[1, 0],\n        [0, 1]])")
+
+    def test_make_bool_matrix_from_str(self):
+        A = matrix('True; True; False')
+        B = matrix([[True], [True], [False]])
+        assert_array_equal(A, B)
+
+class TestCasting:
+    def test_basic(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+
+        mB = mA.copy()
+        O = np.ones((10, 10), np.float64) * 0.1
+        mB = mB + O
+        assert_(mB.dtype.type == np.float64)
+        assert_(np.all(mA != mB))
+        assert_(np.all(mB == mA+0.1))
+
+        mC = mA.copy()
+        O = np.ones((10, 10), np.complex128)
+        mC = mC * O
+        assert_(mC.dtype.type == np.complex128)
+        assert_(np.all(mA != mB))
+
+
+class TestAlgebra:
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.], [3., 4.]])
+        mA = matrix(A)
+
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** i).A, B))
+            B = np.dot(B, A)
+
+        Ainv = linalg.inv(A)
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** -i).A, B))
+            B = np.dot(B, Ainv)
+
+        assert_(np.allclose((mA * mA).A, np.dot(A, A)))
+        assert_(np.allclose((mA + mA).A, (A + A)))
+        assert_(np.allclose((3*mA).A, (3*A)))
+
+        mA2 = matrix(A)
+        mA2 *= 3
+        assert_(np.allclose(mA2.A, 3*A))
+
+    def test_pow(self):
+        """Test raising a matrix to an integer power works as expected."""
+        m = matrix("1. 2.; 3. 4.")
+        m2 = m.copy()
+        m2 **= 2
+        mi = m.copy()
+        mi **= -1
+        m4 = m2.copy()
+        m4 **= 2
+        assert_array_almost_equal(m2, m**2)
+        assert_array_almost_equal(m4, np.dot(m2, m2))
+        assert_array_almost_equal(np.dot(mi, m), np.eye(2))
+
+    def test_scalar_type_pow(self):
+        m = matrix([[1, 2], [3, 4]])
+        for scalar_t in [np.int8, np.uint8]:
+            two = scalar_t(2)
+            assert_array_almost_equal(m ** 2, m ** two)
+
+    def test_notimplemented(self):
+        '''Check that 'not implemented' operations produce a failure.'''
+        A = matrix([[1., 2.],
+                    [3., 4.]])
+
+        # __rpow__
+        with assert_raises(TypeError):
+            1.0**A
+
+        # __mul__ with something not a list, ndarray, tuple, or scalar
+        with assert_raises(TypeError):
+            A*object()
+
+
+class TestMatrixReturn:
+    def test_instance_methods(self):
+        a = matrix([1.0], dtype='f8')
+        methodargs = {
+            'astype': ('intc',),
+            'clip': (0.0, 1.0),
+            'compress': ([1],),
+            'repeat': (1,),
+            'reshape': (1,),
+            'swapaxes': (0, 0),
+            'dot': np.array([1.0]),
+            }
+        excluded_methods = [
+            'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
+            'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
+            'searchsorted', 'setflags', 'setfield', 'sort',
+            'partition', 'argpartition', 'newbyteorder', 'to_device',
+            'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any',
+            'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
+            'prod', 'std', 'ctypes', 'itemset', 'bitwise_count',
+            ]
+        for attrib in dir(a):
+            if attrib.startswith('_') or attrib in excluded_methods:
+                continue
+            f = getattr(a, attrib)
+            if isinstance(f, collections.abc.Callable):
+                # reset contents of a
+                a.astype('f8')
+                a.fill(1.0)
+                if attrib in methodargs:
+                    args = methodargs[attrib]
+                else:
+                    args = ()
+                b = f(*args)
+                assert_(type(b) is matrix, "%s" % attrib)
+        assert_(type(a.real) is matrix)
+        assert_(type(a.imag) is matrix)
+        c, d = matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+
+class TestIndexing:
+    def test_basic(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        y = np.zeros((3, 1), float)
+        y[:, 0] = [0.8, 0.2, 0.3]
+        x[:, 1] = y > 0.5
+        assert_equal(x, [[0, 1], [0, 0], [0, 0]])
+
+
+class TestNewScalarIndexing:
+    a = matrix([[1, 2], [3, 4]])
+
+    def test_dimesions(self):
+        a = self.a
+        x = a[0]
+        assert_equal(x.ndim, 2)
+
+    def test_array_from_matrix_list(self):
+        a = self.a
+        x = np.array([a, a])
+        assert_equal(x.shape, [2, 2, 2])
+
+    def test_array_to_list(self):
+        a = self.a
+        assert_equal(a.tolist(), [[1, 2], [3, 4]])
+
+    def test_fancy_indexing(self):
+        a = self.a
+        x = a[1, [0, 1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3,  4,  3]]))
+        x = a[[1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3,  4], [1, 2]]))
+        x = a[[[1], [0]], [[1, 0], [0, 1]]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[4,  3], [1,  2]]))
+
+    def test_matrix_element(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x[0][0], matrix([[1, 2, 3]]))
+        assert_equal(x[0][0].shape, (1, 3))
+        assert_equal(x[0].shape, (1, 3))
+        assert_equal(x[:, 0].shape, (2, 1))
+
+        x = matrix(0)
+        assert_equal(x[0, 0], 0)
+        assert_equal(x[0], 0)
+        assert_equal(x[:, 0].shape, x.shape)
+
+    def test_scalar_indexing(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        assert_equal(x[0, 0], x[0][0])
+
+    def test_row_column_indexing(self):
+        x = asmatrix(np.eye(2))
+        assert_array_equal(x[0,:], [[1, 0]])
+        assert_array_equal(x[1,:], [[0, 1]])
+        assert_array_equal(x[:, 0], [[1], [0]])
+        assert_array_equal(x[:, 1], [[0], [1]])
+
+    def test_boolean_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, np.array([True, False])], x[:, 0])
+        assert_array_equal(x[np.array([True, False, False]),:], x[0,:])
+
+    def test_list_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, [1, 0]], x[:, ::-1])
+        assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
+
+
+class TestPower:
+    def test_returntype(self):
+        a = np.array([[0, 1], [0, 0]])
+        assert_(type(matrix_power(a, 2)) is np.ndarray)
+        a = asmatrix(a)
+        assert_(type(matrix_power(a, 2)) is matrix)
+
+    def test_list(self):
+        assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
+
+
+class TestShape:
+
+    a = np.array([[1], [2]])
+    m = matrix([[1], [2]])
+
+    def test_shape(self):
+        assert_equal(self.a.shape, (2, 1))
+        assert_equal(self.m.shape, (2, 1))
+
+    def test_numpy_ravel(self):
+        assert_equal(np.ravel(self.a).shape, (2,))
+        assert_equal(np.ravel(self.m).shape, (2,))
+
+    def test_member_ravel(self):
+        assert_equal(self.a.ravel().shape, (2,))
+        assert_equal(self.m.ravel().shape, (1, 2))
+
+    def test_member_flatten(self):
+        assert_equal(self.a.flatten().shape, (2,))
+        assert_equal(self.m.flatten().shape, (1, 2))
+
+    def test_numpy_ravel_order(self):
+        x = np.array([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+
+    def test_matrix_ravel_order(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
+        assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
+
+    def test_array_memory_sharing(self):
+        assert_(np.may_share_memory(self.a, self.a.ravel()))
+        assert_(not np.may_share_memory(self.a, self.a.flatten()))
+
+    def test_matrix_memory_sharing(self):
+        assert_(np.may_share_memory(self.m, self.m.ravel()))
+        assert_(not np.may_share_memory(self.m, self.m.flatten()))
+
+    def test_expand_dims_matrix(self):
+        # matrices are always 2d - so expand_dims only makes sense when the
+        # type is changed away from matrix.
+        a = np.arange(10).reshape((2, 5)).view(np.matrix)
+        expanded = np.expand_dims(a, axis=1)
+        assert_equal(expanded.ndim, 3)
+        assert_(not isinstance(expanded, np.matrix))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c6bf210e46e4f6a8fd53f4762acf27f1c74e6a1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_interaction.py
@@ -0,0 +1,354 @@
+"""Tests of interaction of matrix with other parts of numpy.
+
+Note that tests with MaskedArray and linalg are done in separate files.
+"""
+import pytest
+
+import textwrap
+import warnings
+
+import numpy as np
+from numpy.testing import (assert_, assert_equal, assert_raises,
+                           assert_raises_regex, assert_array_equal,
+                           assert_almost_equal, assert_array_almost_equal)
+
+
+def test_fancy_indexing():
+    # The matrix class messes with the shape. While this is always
+    # weird (getitem is not used, it does not have setitem nor knows
+    # about fancy indexing), this tests gh-3110
+    # 2018-04-29: moved here from core.tests.test_index.
+    m = np.matrix([[1, 2], [3, 4]])
+
+    assert_(isinstance(m[[0, 1, 0], :], np.matrix))
+
+    # gh-3110. Note the transpose currently because matrices do *not*
+    # support dimension fixing for fancy indexing correctly.
+    x = np.asmatrix(np.arange(50).reshape(5, 10))
+    assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
+
+
+def test_polynomial_mapdomain():
+    # test that polynomial preserved matrix subtype.
+    # 2018-04-29: moved here from polynomial.tests.polyutils.
+    dom1 = [0, 4]
+    dom2 = [1, 3]
+    x = np.matrix([dom1, dom1])
+    res = np.polynomial.polyutils.mapdomain(x, dom1, dom2)
+    assert_(isinstance(res, np.matrix))
+
+
+def test_sort_matrix_none():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.sort(a, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_partition_matrix_none():
+    # gh-4301
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.partition(a, 1, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_dot_scalar_and_matrix_of_objects():
+    # Ticket #2469
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.dot(arr, 3), desired)
+    assert_equal(np.dot(3, arr), desired)
+
+
+def test_inner_scalar_and_matrix():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
+        sca = np.array(3, dtype=dt)[()]
+        arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
+        desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
+        assert_equal(np.inner(arr, sca), desired)
+        assert_equal(np.inner(sca, arr), desired)
+
+
+def test_inner_scalar_and_matrix_of_objects():
+    # Ticket #4482
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.inner(arr, 3), desired)
+    assert_equal(np.inner(3, arr), desired)
+
+
+def test_iter_allocate_output_subtype():
+    # Make sure that the subtype with priority wins
+    # 2018-04-29: moved here from core.tests.test_nditer, given the
+    # matrix specific shape test.
+
+    # matrix vs ndarray
+    a = np.matrix([[1, 2], [3, 4]])
+    b = np.arange(4).reshape(2, 2).T
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    assert_(type(i.operands[2]) is np.matrix)
+    assert_(type(i.operands[2]) is not np.ndarray)
+    assert_equal(i.operands[2].shape, (2, 2))
+
+    # matrix always wants things to be 2D
+    b = np.arange(4).reshape(1, 2, 2)
+    assert_raises(RuntimeError, np.nditer, [a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    # but if subtypes are disabled, the result can still work
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'],
+                   ['writeonly', 'allocate', 'no_subtype']])
+    assert_(type(i.operands[2]) is np.ndarray)
+    assert_(type(i.operands[2]) is not np.matrix)
+    assert_equal(i.operands[2].shape, (1, 2, 2))
+
+
+def like_function():
+    # 2018-04-29: moved here from core.tests.test_numeric
+    a = np.matrix([[1, 2], [3, 4]])
+    for like_function in np.zeros_like, np.ones_like, np.empty_like:
+        b = like_function(a)
+        assert_(type(b) is np.matrix)
+
+        c = like_function(a, subok=False)
+        assert_(type(c) is not np.matrix)
+
+
+def test_array_astype():
+    # 2018-04-29: copied here from core.tests.test_api
+    # subok=True passes through a matrix
+    a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
+    b = a.astype('f4', subok=True, copy=False)
+    assert_(a is b)
+
+    # subok=True is default, and creates a subtype on a cast
+    b = a.astype('i4', copy=False)
+    assert_equal(a, b)
+    assert_equal(type(b), np.matrix)
+
+    # subok=False never returns a matrix
+    b = a.astype('f4', subok=False, copy=False)
+    assert_equal(a, b)
+    assert_(not (a is b))
+    assert_(type(b) is not np.matrix)
+
+
+def test_stack():
+    # 2018-04-29: copied here from core.tests.test_shape_base
+    # check np.matrix cannot be stacked
+    m = np.matrix([[1, 2], [3, 4]])
+    assert_raises_regex(ValueError, 'shape too large to be a matrix',
+                        np.stack, [m, m])
+
+
+def test_object_scalar_multiply():
+    # Tickets #2469 and #4482
+    # 2018-04-29: moved here from core.tests.test_ufunc
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.multiply(arr, 3), desired)
+    assert_equal(np.multiply(3, arr), desired)
+
+
+def test_nanfunctions_matrices():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in [np.nanmin, np.nanmax]:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+    # check that rows of nan are dealt with for subclasses (#4628)
+    mat[1] = np.nan
+    for f in [np.nanmin, np.nanmax]:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=0)
+            assert_(isinstance(res, np.matrix))
+            assert_(not np.any(np.isnan(res)))
+            assert_(len(w) == 0)
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=1)
+            assert_(isinstance(res, np.matrix))
+            assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
+                    and not np.isnan(res[2, 0]))
+            assert_(len(w) == 1, 'no warning raised')
+            assert_(issubclass(w[0].category, RuntimeWarning))
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat)
+            assert_(np.isscalar(res))
+            assert_(res != np.nan)
+            assert_(len(w) == 0)
+
+
+def test_nanfunctions_matrices_general():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
+              np.nanmean, np.nanvar, np.nanstd):
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+
+    for f in np.nancumsum, np.nancumprod:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3*3))
+
+
+def test_average_matrix():
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    y = np.matrix(np.random.rand(5, 5))
+    assert_array_equal(y.mean(0), np.average(y, 0))
+
+    a = np.matrix([[1, 2], [3, 4]])
+    w = np.matrix([[1, 2], [3, 4]])
+
+    r = np.average(a, axis=0, weights=w)
+    assert_equal(type(r), np.matrix)
+    assert_equal(r, [[2.5, 10.0/3]])
+
+
+def test_dot_matrix():
+    # Test to make sure matrices give the same answer as ndarrays
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    x = np.linspace(0, 5)
+    y = np.linspace(-5, 0)
+    mx = np.matrix(x)
+    my = np.matrix(y)
+    r = np.dot(x, y)
+    mr = np.dot(mx, my.T)
+    assert_almost_equal(mr, r)
+
+
+def test_ediff1d_matrix():
+    # 2018-04-29: moved here from core.tests.test_arraysetops.
+    assert(isinstance(np.ediff1d(np.matrix(1)), np.matrix))
+    assert(isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix))
+
+
+def test_apply_along_axis_matrix():
+    # this test is particularly malicious because matrix
+    # refuses to become 1d
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    def double(row):
+        return row * 2
+
+    m = np.matrix([[0, 1], [2, 3]])
+    expected = np.matrix([[0, 2], [4, 6]])
+
+    result = np.apply_along_axis(double, 0, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+    result = np.apply_along_axis(double, 1, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+
+def test_kron_matrix():
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    a = np.ones([2, 2])
+    m = np.asmatrix(a)
+    assert_equal(type(np.kron(a, a)), np.ndarray)
+    assert_equal(type(np.kron(m, m)), np.matrix)
+    assert_equal(type(np.kron(a, m)), np.matrix)
+    assert_equal(type(np.kron(m, a)), np.matrix)
+
+
+class TestConcatenatorMatrix:
+    # 2018-04-29: moved here from core.tests.test_index_tricks.
+    def test_matrix(self):
+        a = [1, 2]
+        b = [3, 4]
+
+        ab_r = np.r_['r', a, b]
+        ab_c = np.r_['c', a, b]
+
+        assert_equal(type(ab_r), np.matrix)
+        assert_equal(type(ab_c), np.matrix)
+
+        assert_equal(np.array(ab_r), [[1, 2, 3, 4]])
+        assert_equal(np.array(ab_c), [[1], [2], [3], [4]])
+
+        assert_raises(ValueError, lambda: np.r_['rc', a, b])
+
+    def test_matrix_scalar(self):
+        r = np.r_['r', [1, 2], 3]
+        assert_equal(type(r), np.matrix)
+        assert_equal(np.array(r), [[1, 2, 3]])
+
+    def test_matrix_builder(self):
+        a = np.array([1])
+        b = np.array([2])
+        c = np.array([3])
+        d = np.array([4])
+        actual = np.r_['a, b; c, d']
+        expected = np.bmat([[a, b], [c, d]])
+
+        assert_equal(actual, expected)
+        assert_equal(type(actual), type(expected))
+
+
+def test_array_equal_error_message_matrix():
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    with pytest.raises(AssertionError) as exc_info:
+        assert_equal(np.array([1, 2]), np.matrix([1, 2]))
+    msg = str(exc_info.value)
+    msg_reference = textwrap.dedent("""\
+
+    Arrays are not equal
+
+    (shapes (2,), (1, 2) mismatch)
+     ACTUAL: array([1, 2])
+     DESIRED: matrix([[1, 2]])""")
+    assert_equal(msg, msg_reference)
+
+
+def test_array_almost_equal_matrix():
+    # Matrix slicing keeps things 2-D, while array does not necessarily.
+    # See gh-8452.
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    m1 = np.matrix([[1., 2.]])
+    m2 = np.matrix([[1., np.nan]])
+    m3 = np.matrix([[1., -np.inf]])
+    m4 = np.matrix([[np.nan, np.inf]])
+    m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
+    for assert_func in assert_array_almost_equal, assert_almost_equal:
+        for m in m1, m2, m3, m4, m5:
+            assert_func(m, m)
+            a = np.array(m)
+            assert_func(a, m)
+            assert_func(m, a)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..5303e6ce723f69b6fa8007857e6f0943e5010f5a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_masked_matrix.py
@@ -0,0 +1,232 @@
+import pickle
+
+import numpy as np
+from numpy.testing import assert_warns
+from numpy.ma.testutils import (assert_, assert_equal, assert_raises,
+                                assert_array_equal)
+from numpy.ma.core import (masked_array, masked_values, masked, allequal,
+                           MaskType, getmask, MaskedArray, nomask,
+                           log, add, hypot, divide)
+from numpy.ma.extras import mr_
+
+
+class MMatrix(MaskedArray, np.matrix,):
+
+    def __new__(cls, data, mask=nomask):
+        mat = np.matrix(data)
+        _data = MaskedArray.__new__(cls, data=mat, mask=mask)
+        return _data
+
+    def __array_finalize__(self, obj):
+        np.matrix.__array_finalize__(self, obj)
+        MaskedArray.__array_finalize__(self, obj)
+        return
+
+    @property
+    def _series(self):
+        _view = self.view(MaskedArray)
+        _view._sharedmask = False
+        return _view
+
+
+class TestMaskedMatrix:
+    def test_matrix_indexing(self):
+        # Tests conversions and indexing
+        x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
+        x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
+        x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
+        x4 = masked_array(x1)
+        # test conversion to strings
+        str(x2)  # raises?
+        repr(x2)  # raises?
+        # tests of indexing
+        assert_(type(x2[1, 0]) is type(x1[1, 0]))
+        assert_(x1[1, 0] == x2[1, 0])
+        assert_(x2[1, 1] is masked)
+        assert_equal(x1[0, 2], x2[0, 2])
+        assert_equal(x1[0, 1:], x2[0, 1:])
+        assert_equal(x1[:, 2], x2[:, 2])
+        assert_equal(x1[:], x2[:])
+        assert_equal(x1[1:], x3[1:])
+        x1[0, 2] = 9
+        x2[0, 2] = 9
+        assert_equal(x1, x2)
+        x1[0, 1:] = 99
+        x2[0, 1:] = 99
+        assert_equal(x1, x2)
+        x2[0, 1] = masked
+        assert_equal(x1, x2)
+        x2[0, 1:] = masked
+        assert_equal(x1, x2)
+        x2[0, :] = x1[0, :]
+        x2[0, 1] = masked
+        assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
+        x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+        assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0])))
+        assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
+        x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+        assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
+        assert_(allequal(x4[1], masked_array([1, 2, 3])))
+        x1 = np.matrix(np.arange(5) * 1.0)
+        x2 = masked_values(x1, 3.0)
+        assert_equal(x1, x2)
+        assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType),
+                         x2.mask))
+        assert_equal(3.0, x2.fill_value)
+
+    def test_pickling_subbaseclass(self):
+        # Test pickling w/ a subclass of ndarray
+        a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+            assert_equal(a_pickled._mask, a._mask)
+            assert_equal(a_pickled, a)
+            assert_(isinstance(a_pickled._data, np.matrix))
+
+    def test_count_mean_with_matrix(self):
+        m = masked_array(np.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2)))
+
+        assert_equal(m.count(axis=0).shape, (1, 2))
+        assert_equal(m.count(axis=1).shape, (2, 1))
+
+        # Make sure broadcasting inside mean and var work
+        assert_equal(m.mean(axis=0), [[2., 3.]])
+        assert_equal(m.mean(axis=1), [[1.5], [3.5]])
+
+    def test_flat(self):
+        # Test that flat can return items even for matrices [#4585, #4615]
+        # test simple access
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        assert_equal(test.flat[1], 2)
+        assert_equal(test.flat[2], masked)
+        assert_(np.all(test.flat[0:2] == test[0, 0:2]))
+        # Test flat on masked_matrices
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
+        control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
+        assert_equal(test, control)
+        # Test setting
+        test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
+        testflat = test.flat
+        testflat[:] = testflat[[2, 1, 0]]
+        assert_equal(test, control)
+        testflat[0] = 9
+        # test that matrices keep the correct shape (#4615)
+        a = masked_array(np.matrix(np.eye(2)), mask=0)
+        b = a.flat
+        b01 = b[:2]
+        assert_equal(b01.data, np.array([[1., 0.]]))
+        assert_equal(b01.mask, np.array([[False, False]]))
+
+    def test_allany_onmatrices(self):
+        x = np.array([[0.13, 0.26, 0.90],
+                      [0.28, 0.33, 0.63],
+                      [0.31, 0.87, 0.70]])
+        X = np.matrix(x)
+        m = np.array([[True, False, False],
+                      [False, False, False],
+                      [True, True, False]], dtype=np.bool)
+        mX = masked_array(X, mask=m)
+        mXbig = (mX > 0.5)
+        mXsmall = (mX < 0.5)
+
+        assert_(not mXbig.all())
+        assert_(mXbig.any())
+        assert_equal(mXbig.all(0), np.matrix([False, False, True]))
+        assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
+        assert_equal(mXbig.any(0), np.matrix([False, False, True]))
+        assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
+
+        assert_(not mXsmall.all())
+        assert_(mXsmall.any())
+        assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
+        assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
+        assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
+        assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
+
+    def test_compressed(self):
+        a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
+        b = a.compressed()
+        assert_equal(b, a)
+        assert_(isinstance(b, np.matrix))
+        a[0, 0] = masked
+        b = a.compressed()
+        assert_equal(b, [[2, 3, 4]])
+
+    def test_ravel(self):
+        a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
+        aravel = a.ravel()
+        assert_equal(aravel.shape, (1, 5))
+        assert_equal(aravel._mask.shape, a.shape)
+
+    def test_view(self):
+        # Test view w/ flexible dtype
+        iterator = list(zip(np.arange(10), np.random.rand(10)))
+        data = np.array(iterator)
+        a = masked_array(iterator, dtype=[('a', float), ('b', float)])
+        a.mask[0] = (1, 0)
+        test = a.view((float, 2), np.matrix)
+        assert_equal(test, data)
+        assert_(isinstance(test, np.matrix))
+        assert_(not isinstance(test, MaskedArray))
+
+
+class TestSubclassing:
+    # Test suite for masked subclasses of ndarray.
+
+    def setup_method(self):
+        x = np.arange(5, dtype='float')
+        mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
+        self.data = (x, mx)
+
+    def test_maskedarray_subclassing(self):
+        # Tests subclassing MaskedArray
+        (x, mx) = self.data
+        assert_(isinstance(mx._data, np.matrix))
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (x, mx) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(log(mx), MMatrix))
+            assert_equal(log(x), np.log(x))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (x, mx) = self.data
+        # Result should be a MMatrix
+        assert_(isinstance(add(mx, mx), MMatrix))
+        assert_(isinstance(add(mx, x), MMatrix))
+        # Result should work
+        assert_equal(add(mx, x), mx+x)
+        assert_(isinstance(add(mx, mx)._data, np.matrix))
+        with assert_warns(DeprecationWarning):
+            assert_(isinstance(add.outer(mx, mx), MMatrix))
+        assert_(isinstance(hypot(mx, mx), MMatrix))
+        assert_(isinstance(hypot(mx, x), MMatrix))
+
+    def test_masked_binary_operations2(self):
+        # Tests domained_masked_binary_operation
+        (x, mx) = self.data
+        xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+        assert_(isinstance(divide(mx, mx), MMatrix))
+        assert_(isinstance(divide(mx, x), MMatrix))
+        assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+class TestConcatenator:
+    # Tests for mr_, the equivalent of r_ for masked arrays.
+
+    def test_matrix_builder(self):
+        assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
+
+    def test_matrix(self):
+        # Test consistency with unmasked version.  If we ever deprecate
+        # matrix, this test should either still pass, or both actual and
+        # expected should fail to be build.
+        actual = mr_['r', 1, 2, 3]
+        expected = np.ma.array(np.r_['r', 1, 2, 3])
+        assert_array_equal(actual, expected)
+
+        # outer type is masked array, inner type is matrix
+        assert_equal(type(actual), type(expected))
+        assert_equal(type(actual.data), type(expected.data))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..106c2e38217a633829329a94df077c097fbcbf7a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py
@@ -0,0 +1,93 @@
+""" Test functions for linalg module using the matrix class."""
+import numpy as np
+
+from numpy.linalg.tests.test_linalg import (
+    LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase,
+    _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base,
+    SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases,
+    PinvCases, DetCases, LstsqCases)
+
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+    LinalgCase("0x0_matrix",
+               np.empty((0, 0), dtype=np.double).view(np.matrix),
+               np.empty((0, 1), dtype=np.double).view(np.matrix),
+               tags={'size-0'}),
+    LinalgCase("matrix_b_only",
+               np.array([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+    LinalgCase("matrix_a_and_b",
+               np.matrix([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+    LinalgCase("hmatrix_a_and_b",
+               np.matrix([[1., 2.], [2., 1.]]),
+               None),
+])
+# No need to make generalized or strided cases for matrices.
+
+
+class MatrixTestCase(LinalgTestCase):
+    TEST_CASES = CASES
+
+
+class TestSolveMatrix(SolveCases, MatrixTestCase):
+    pass
+
+
+class TestInvMatrix(InvCases, MatrixTestCase):
+    pass
+
+
+class TestEigvalsMatrix(EigvalsCases, MatrixTestCase):
+    pass
+
+
+class TestEigMatrix(EigCases, MatrixTestCase):
+    pass
+
+
+class TestSVDMatrix(SVDCases, MatrixTestCase):
+    pass
+
+
+class TestCondMatrix(CondCases, MatrixTestCase):
+    pass
+
+
+class TestPinvMatrix(PinvCases, MatrixTestCase):
+    pass
+
+
+class TestDetMatrix(DetCases, MatrixTestCase):
+    pass
+
+
+class TestLstsqMatrix(LstsqCases, MatrixTestCase):
+    pass
+
+
+class _TestNorm2DMatrix(_TestNorm2D):
+    array = np.matrix
+
+
+class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase):
+    pass
+
+
+class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase):
+    pass
+
+
+class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base):
+    pass
+
+
+class TestQRMatrix(_TestQR):
+    array = np.matrix
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..638d0d1534deba060140ffda3b61950a0b4f815d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/matrixlib/tests/test_multiarray.py
@@ -0,0 +1,16 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_array_equal
+
+class TestView:
+    def test_type(self):
+        x = np.array([1, 2, 3])
+        assert_(isinstance(x.view(np.matrix), np.matrix))
+
+    def test_keywords(self):
+        x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
+        # We must be specific about the endianness here:
+        y = x.view(dtype=',
+)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1f9057296ba9475574a191cf231dc04ace3f910c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pxd
@@ -0,0 +1,14 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+from numpy.random.bit_generator cimport BitGenerator, SeedSequence
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e8f99fe3045b9c2b691a8ece67d0f06d9d73b08
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.py
@@ -0,0 +1,215 @@
+"""
+========================
+Random Number Generation
+========================
+
+Use ``default_rng()`` to create a `Generator` and call its methods.
+
+=============== =========================================================
+Generator
+--------------- ---------------------------------------------------------
+Generator       Class implementing all of the random number distributions
+default_rng     Default constructor for ``Generator``
+=============== =========================================================
+
+============================================= ===
+BitGenerator Streams that work with Generator
+--------------------------------------------- ---
+MT19937
+PCG64
+PCG64DXSM
+Philox
+SFC64
+============================================= ===
+
+============================================= ===
+Getting entropy to initialize a BitGenerator
+--------------------------------------------- ---
+SeedSequence
+============================================= ===
+
+
+Legacy
+------
+
+For backwards compatibility with previous versions of numpy before 1.17, the
+various aliases to the global `RandomState` methods are left alone and do not
+use the new `Generator` API.
+
+==================== =========================================================
+Utility functions
+-------------------- ---------------------------------------------------------
+random               Uniformly distributed floats over ``[0, 1)``
+bytes                Uniformly distributed random bytes.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+choice               Random sample from 1-D array.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility
+functions - removed
+in the new API
+-------------------- ---------------------------------------------------------
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+random_integers      Uniformly distributed integers in a given range.
+                     (deprecated, use ``integers(..., closed=True)`` instead)
+random_sample        Alias for `random_sample`
+randint              Uniformly distributed integers in a given range
+seed                 Seed the legacy random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Univariate
+distributions
+-------------------- ---------------------------------------------------------
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== ==========================================================
+Multivariate
+distributions
+-------------------- ----------------------------------------------------------
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== ==========================================================
+
+==================== =========================================================
+Standard
+distributions
+-------------------- ---------------------------------------------------------
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+-------------------- ---------------------------------------------------------
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+
+"""
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random',
+    'random_integers',
+    'random_sample',
+    'ranf',
+    'rayleigh',
+    'sample',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf',
+]
+
+# add these for module-freeze analysis (like PyInstaller)
+from . import _pickle
+from . import _common
+from . import _bounded_integers
+
+from ._generator import Generator, default_rng
+from .bit_generator import SeedSequence, BitGenerator
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .mtrand import *
+
+__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
+            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
+            'BitGenerator']
+
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this
+    function's entire purpose is to return a newly allocated RandomState whose
+    state pickle can set.  Consequently the RandomState returned by this function
+    is a freshly allocated copy with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8cfa9c0e1369812c5afe6e353d29e39793358715
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__init__.pyi
@@ -0,0 +1,126 @@
+from ._generator import Generator
+from ._generator import default_rng
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .bit_generator import BitGenerator
+from .bit_generator import SeedSequence
+from .mtrand import (
+    RandomState,
+    beta,
+    binomial,
+    bytes,
+    chisquare,
+    choice,
+    dirichlet,
+    exponential,
+    f,
+    gamma,
+    geometric,
+    get_bit_generator,  # noqa: F401
+    get_state,
+    gumbel,
+    hypergeometric,
+    laplace,
+    logistic,
+    lognormal,
+    logseries,
+    multinomial,
+    multivariate_normal,
+    negative_binomial,
+    noncentral_chisquare,
+    noncentral_f,
+    normal,
+    pareto,
+    permutation,
+    poisson,
+    power,
+    rand,
+    randint,
+    randn,
+    random,
+    random_integers,
+    random_sample,
+    ranf,
+    rayleigh,
+    sample,
+    seed,
+    set_bit_generator,  # noqa: F401
+    set_state,
+    shuffle,
+    standard_cauchy,
+    standard_exponential,
+    standard_gamma,
+    standard_normal,
+    standard_t,
+    triangular,
+    uniform,
+    vonmises,
+    wald,
+    weibull,
+    zipf,
+)
+
+__all__ = [
+    "beta",
+    "binomial",
+    "bytes",
+    "chisquare",
+    "choice",
+    "dirichlet",
+    "exponential",
+    "f",
+    "gamma",
+    "geometric",
+    "get_state",
+    "gumbel",
+    "hypergeometric",
+    "laplace",
+    "logistic",
+    "lognormal",
+    "logseries",
+    "multinomial",
+    "multivariate_normal",
+    "negative_binomial",
+    "noncentral_chisquare",
+    "noncentral_f",
+    "normal",
+    "pareto",
+    "permutation",
+    "poisson",
+    "power",
+    "rand",
+    "randint",
+    "randn",
+    "random",
+    "random_integers",
+    "random_sample",
+    "ranf",
+    "rayleigh",
+    "sample",
+    "seed",
+    "set_state",
+    "shuffle",
+    "standard_cauchy",
+    "standard_exponential",
+    "standard_gamma",
+    "standard_normal",
+    "standard_t",
+    "triangular",
+    "uniform",
+    "vonmises",
+    "wald",
+    "weibull",
+    "zipf",
+    "Generator",
+    "RandomState",
+    "SeedSequence",
+    "MT19937",
+    "Philox",
+    "PCG64",
+    "PCG64DXSM",
+    "SFC64",
+    "default_rng",
+    "BitGenerator",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__pycache__/_pickle.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/__pycache__/_pickle.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..607014cbf5b42737669f699471082ab5642910d1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd
@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) noexcept nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_common.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_common.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0de4456d778f409f63d237d53eb083bf2c9949ae
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_common.pxd
@@ -0,0 +1,107 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
+
+import numpy as np
+cimport numpy as np
+
+from numpy.random cimport bitgen_t
+
+cdef double POISSON_LAM_MAX
+cdef double LEGACY_POISSON_LAM_MAX
+cdef uint64_t MAXSIZE
+
+cdef enum ConstraintType:
+    CONS_NONE
+    CONS_NON_NEGATIVE
+    CONS_POSITIVE
+    CONS_POSITIVE_NOT_NAN
+    CONS_BOUNDED_0_1
+    CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
+    CONS_GT_1
+    CONS_GTE_1
+    CONS_POISSON
+    LEGACY_CONS_POISSON
+    LEGACY_CONS_NON_NEGATIVE_INBOUNDS_LONG
+
+ctypedef ConstraintType constraint_type
+
+cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
+cdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)
+cdef object prepare_cffi(bitgen_t *bitgen)
+cdef object prepare_ctypes(bitgen_t *bitgen)
+cdef int check_constraint(double val, object name, constraint_type cons) except -1
+cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1
+
+cdef extern from "include/aligned_malloc.h":
+    cdef void *PyArray_realloc_aligned(void *p, size_t n)
+    cdef void *PyArray_malloc_aligned(size_t n)
+    cdef void *PyArray_calloc_aligned(size_t n, size_t s)
+    cdef void PyArray_free_aligned(void *p)
+
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out)  noexcept nogil
+ctypedef double (*random_double_0)(void *state)  noexcept nogil
+ctypedef double (*random_double_1)(void *state, double a)  noexcept nogil
+ctypedef double (*random_double_2)(void *state, double a, double b)  noexcept nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c)  noexcept nogil
+
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out)  noexcept nogil
+ctypedef float (*random_float_0)(bitgen_t *state)  noexcept nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a)  noexcept nogil
+
+ctypedef int64_t (*random_uint_0)(void *state)  noexcept nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a)  noexcept nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b)  noexcept nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b)  noexcept nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a)  noexcept nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c)  noexcept nogil
+
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state)  noexcept nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a)  noexcept nogil
+
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b)  noexcept nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b)  noexcept nogil
+
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept
+
+cdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:
+    return (rnd >> 11) * (1.0 / 9007199254740992.0)
+
+cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object wrap_int(object val, object bits)
+
+cdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)
+
+cdef validate_output_shape(iter_shape, np.ndarray output)
+
+cdef object cont(void *func, void *state, object size, object lock, int narg,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint,
+                 object out)
+
+cdef object disc(void *func, void *state, object size, object lock,
+                 int narg_double, int narg_int64,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint)
+
+cdef object cont_f(void *func, bitgen_t *state, object size, object lock,
+                   object a, object a_name, constraint_type a_constraint,
+                   object out)
+
+cdef object cont_broadcast_3(void *func, void *state, object size, object lock,
+                             np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                             np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                             np.ndarray c_arr, object c_name, constraint_type c_constraint)
+
+cdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,
+                                   np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                                   np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                                   np.ndarray c_arr, object c_name, constraint_type c_constraint)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/extending.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..8440d400ea9178bb17efc68fde1f8cca1f66c189
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/extending.py
@@ -0,0 +1,40 @@
+"""
+Use cffi to access any of the underlying C functions from distributions.h
+"""
+import os
+import numpy as np
+import cffi
+from .parse import parse_distributions_h
+ffi = cffi.FFI()
+
+inc_dir = os.path.join(np.get_include(), 'numpy')
+
+# Basic numpy types
+ffi.cdef('''
+    typedef intptr_t npy_intp;
+    typedef unsigned char npy_bool;
+
+''')
+
+parse_distributions_h(ffi, inc_dir)
+
+lib = ffi.dlopen(np.random._generator.__file__)
+
+# Compare the distributions.h random_standard_normal_fill to
+# Generator.standard_random
+bit_gen = np.random.PCG64()
+rng = np.random.Generator(bit_gen)
+state = bit_gen.state
+
+interface = rng.bit_generator.cffi
+n = 100
+vals_cffi = ffi.new('double[%d]' % n)
+lib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)
+
+# reset the state
+bit_gen.state = state
+
+vals = rng.standard_normal(n)
+
+for i in range(n):
+    assert vals[i] == vals_cffi[i]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/parse.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/parse.py
new file mode 100644
index 0000000000000000000000000000000000000000..993cedee05eb0219e3748c41efb575b87a0c56a7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cffi/parse.py
@@ -0,0 +1,54 @@
+import os
+
+
+def parse_distributions_h(ffi, inc_dir):
+    """
+    Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
+
+    Read the function declarations without the "#define ..." macros that will
+    be filled in when loading the library.
+    """
+
+    with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:
+        s = []
+        for line in fid:
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
+    with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:
+        s = []
+        in_skip = 0
+        ignoring = False
+        for line in fid:
+            # check for and remove extern "C" guards
+            if ignoring:
+                if line.strip().startswith('#endif'):
+                    ignoring = False
+                continue
+            if line.strip().startswith('#ifdef __cplusplus'):
+                ignoring = True
+
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+
+            # skip any inlined function definition
+            # which starts with 'static inline xxx(...) {'
+            # and ends with a closing '}'
+            if line.strip().startswith('static inline'):
+                in_skip += line.count('{')
+                continue
+            elif in_skip > 0:
+                in_skip += line.count('{')
+                in_skip -= line.count('}')
+                continue
+
+            # replace defines with their value or remove them
+            line = line.replace('DECLDIR', '')
+            line = line.replace('RAND_INT_TYPE', 'int64_t')
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..6a0f45e1be9e6f32ac9ac39952cd01597b93a2e9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending.pyx
@@ -0,0 +1,77 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+
+import numpy as np
+cimport numpy as np
+cimport cython
+
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+
+np.import_array()
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniform_mean(Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+    cdef np.ndarray randoms
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng =  PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n)
+    # Best practice is to acquire the lock whenever generating random values.
+    # This prevents other threads from modifying the state. Acquiring the lock
+    # is only necessary if the GIL is also released, as in this example.
+    with x.lock, nogil:
+        for i in range(n):
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+    return randoms.mean()
+
+
+# This function is declared nogil so it can be used without the GIL below
+cdef uint32_t bounded_uint(uint32_t lb, uint32_t ub, bitgen_t *rng) nogil:
+    cdef uint32_t mask, delta, val
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = rng.next_uint32(rng.state) & mask
+    while val > delta:
+        val = rng.next_uint32(rng.state) & mask
+
+    return lb + val
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def bounded_uints(uint32_t lb, uint32_t ub, Py_ssize_t n):
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef uint32_t[::1] out
+    cdef const char *capsule_name = "BitGenerator"
+
+    x = PCG64()
+    out = np.empty(n, dtype=np.uint32)
+    capsule = x.capsule
+
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = PyCapsule_GetPointer(capsule, capsule_name)
+
+    with x.lock, nogil:
+        for i in range(n):
+            out[i] = bounded_uint(lb, ub, rng)
+    return np.asarray(out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..59ecc4b36366f76d21289286d5c8780b3852e660
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx
@@ -0,0 +1,116 @@
+#cython: language_level=3
+"""
+This file shows how the to use a BitGenerator to create a distribution.
+"""
+import numpy as np
+cimport numpy as np
+cimport cython
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+from libc.stdint cimport uint16_t, uint64_t
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+from numpy.random.c_distributions cimport (
+      random_standard_uniform_fill, random_standard_uniform_fill_f)
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniforms(Py_ssize_t n):
+    """
+    Create an array of `n` uniformly distributed doubles.
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+
+    x = PCG64()
+    capsule = x.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng =  PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='float64')
+    with x.lock, nogil:
+        for i in range(n):
+            # Call the function
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+
+    return randoms
+
+# cython example 2
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uint10_uniforms(Py_ssize_t n):
+    """Uniform 10 bit integers stored as 16-bit unsigned integers"""
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef uint16_t[::1] random_values
+    cdef int bits_remaining
+    cdef int width = 10
+    cdef uint64_t buff, mask = 0x3FF
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng =  PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='uint16')
+    # Best practice is to release GIL and acquire the lock
+    bits_remaining = 0
+    with x.lock, nogil:
+        for i in range(n):
+            if bits_remaining < width:
+                buff = rng.next_uint64(rng.state)
+            random_values[i] = buff & mask
+            buff >>= width
+
+    randoms = np.asarray(random_values)
+    return randoms
+
+# cython example 3
+def uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64):
+    """
+    Create an array of `n` uniformly distributed doubles via a "fill" function.
+
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+
+    Parameters
+    ----------
+    bit_generator: BitGenerator instance
+    n: int
+        Output vector length
+    dtype: {str, dtype}, optional
+        Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The
+        default dtype value is 'd'
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef np.ndarray randoms
+
+    capsule = bit_generator.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng =  PyCapsule_GetPointer(capsule, capsule_name)
+
+    _dtype = np.dtype(dtype)
+    randoms = np.empty(n, dtype=_dtype)
+    if _dtype == np.float32:
+        with bit_generator.lock:
+            random_standard_uniform_fill_f(rng, n, np.PyArray_DATA(randoms))
+    elif _dtype == np.float64:
+        with bit_generator.lock:
+            random_standard_uniform_fill(rng, n, np.PyArray_DATA(randoms))
+    else:
+        raise TypeError('Unsupported dtype %r for random' % _dtype)
+    return randoms
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/meson.build b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..7aa367d13787c4f7ad5c2910bb044670b07eb012
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/cython/meson.build
@@ -0,0 +1,53 @@
+project('random-build-examples', 'c', 'cpp', 'cython')
+
+py_mod = import('python')
+py3 = py_mod.find_installation(pure: false)
+
+cc = meson.get_compiler('c')
+cy = meson.get_compiler('cython')
+
+# Keep synced with pyproject.toml
+if not cy.version().version_compare('>=3.0.6')
+  error('tests requires Cython >= 3.0.6')
+endif
+
+base_cython_args = []
+if cy.version().version_compare('>=3.1.0')
+  base_cython_args += ['-Xfreethreading_compatible=True']
+endif
+
+_numpy_abs = run_command(py3, ['-c',
+               'import os; os.chdir(".."); import numpy; print(os.path.abspath(numpy.get_include() + "../../.."))'],
+                         check: true).stdout().strip()
+
+npymath_path = _numpy_abs / '_core' / 'lib'
+npy_include_path = _numpy_abs / '_core' / 'include'
+npyrandom_path = _numpy_abs / 'random' / 'lib'
+npymath_lib = cc.find_library('npymath', dirs: npymath_path)
+npyrandom_lib = cc.find_library('npyrandom', dirs: npyrandom_path)
+
+py3.extension_module(
+    'extending_distributions',
+    'extending_distributions.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+    cython_args: base_cython_args,
+)
+py3.extension_module(
+    'extending',
+    'extending.pyx',
+    install: false,
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+    cython_args: base_cython_args,
+)
+py3.extension_module(
+    'extending_cpp',
+    'extending_distributions.pyx',
+    install: false,
+    override_options : ['cython_language=cpp'],
+    cython_args: base_cython_args + ['--module-name', 'extending_cpp'],
+    include_directories: [npy_include_path],
+    dependencies: [npyrandom_lib, npymath_lib],
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..f387db69502a4bfe8731d540a7a741b062fea861
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending.py
@@ -0,0 +1,84 @@
+import numpy as np
+import numba as nb
+
+from numpy.random import PCG64
+from timeit import timeit
+
+bit_gen = PCG64()
+next_d = bit_gen.cffi.next_double
+state_addr = bit_gen.cffi.state_address
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * next_d(state) - 1.0
+        x2 = 2.0 * next_d(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * next_d(state) - 1.0
+            x2 = 2.0 * next_d(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+# Compile using Numba
+normalsj = nb.jit(normals, nopython=True)
+# Must use state address not state with numba
+n = 10000
+
+def numbacall():
+    return normalsj(n, state_addr)
+
+rg = np.random.Generator(PCG64())
+
+def numpycall():
+    return rg.normal(size=n)
+
+# Check that the functions work
+r1 = numbacall()
+r2 = numpycall()
+assert r1.shape == (n,)
+assert r1.shape == r2.shape
+
+t1 = timeit(numbacall, number=1000)
+print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
+t2 = timeit(numpycall, number=1000)
+print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
+
+# example 2
+
+next_u32 = bit_gen.ctypes.next_uint32
+ctypes_state = bit_gen.ctypes.state
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = next_u32(state) & mask
+    while val > delta:
+        val = next_u32(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, ctypes_state.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, ctypes_state.value)
+
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ef0753d71d1a1033c0225f332bf1b75d832a598
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_examples/numba/extending_distributions.py
@@ -0,0 +1,67 @@
+r"""
+Building the required library in this example requires a source distribution
+of NumPy or clone of the NumPy git repository since distributions.c is not
+included in binary distributions.
+
+On *nix, execute in numpy/random/src/distributions
+
+export ${PYTHON_VERSION}=3.8 # Python version
+export PYTHON_INCLUDE=#path to Python's include folder, usually \
+    ${PYTHON_HOME}/include/python${PYTHON_VERSION}m
+export NUMPY_INCLUDE=#path to numpy's include folder, usually \
+    ${PYTHON_HOME}/lib/python${PYTHON_VERSION}/site-packages/numpy/_core/include
+gcc -shared -o libdistributions.so -fPIC distributions.c \
+    -I${NUMPY_INCLUDE} -I${PYTHON_INCLUDE}
+mv libdistributions.so ../../_examples/numba/
+
+On Windows
+
+rem PYTHON_HOME and PYTHON_VERSION are setup dependent, this is an example
+set PYTHON_HOME=c:\Anaconda
+set PYTHON_VERSION=38
+cl.exe /LD .\distributions.c -DDLL_EXPORT \
+    -I%PYTHON_HOME%\lib\site-packages\numpy\_core\include \
+    -I%PYTHON_HOME%\include %PYTHON_HOME%\libs\python%PYTHON_VERSION%.lib
+move distributions.dll ../../_examples/numba/
+"""
+import os
+
+import numba as nb
+import numpy as np
+from cffi import FFI
+
+from numpy.random import PCG64
+
+ffi = FFI()
+if os.path.exists('./distributions.dll'):
+    lib = ffi.dlopen('./distributions.dll')
+elif os.path.exists('./libdistributions.so'):
+    lib = ffi.dlopen('./libdistributions.so')
+else:
+    raise RuntimeError('Required DLL/so file was not found.')
+
+ffi.cdef("""
+double random_standard_normal(void *bitgen_state);
+""")
+x = PCG64()
+xffi = x.cffi
+bit_generator = xffi.bit_generator
+
+random_standard_normal = lib.random_standard_normal
+
+
+def normals(n, bit_generator):
+    out = np.empty(n)
+    for i in range(n):
+        out[i] = random_standard_normal(bit_generator)
+    return out
+
+
+normalsj = nb.jit(normals, nopython=True)
+
+# Numba requires a memory address for void *
+# Can also get address from x.ctypes.bit_generator.value
+bit_generator_address = int(ffi.cast('uintptr_t', bit_generator))
+
+norm = normalsj(1000, bit_generator_address)
+print(norm[:12])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_generator.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7ed4a959625f92b19ec6e56bab54403706b6604f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_generator.pyi
@@ -0,0 +1,856 @@
+from collections.abc import Callable
+from typing import Any, Literal, TypeAlias, TypeVar, overload
+
+import numpy as np
+from numpy import dtype, float32, float64, int64
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _BoolCodes,
+    _DoubleCodes,
+    _DTypeLike,
+    _DTypeLikeBool,
+    _Float32Codes,
+    _Float64Codes,
+    _FloatLike_co,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntPCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntPCodes,
+)
+from numpy.random import BitGenerator, RandomState, SeedSequence
+
+_IntegerT = TypeVar("_IntegerT", bound=np.integer)
+
+_DTypeLikeFloat32: TypeAlias = (
+    dtype[float32]
+    | _SupportsDType[dtype[float32]]
+    | type[float32]
+    | _Float32Codes
+    | _SingleCodes
+)
+
+_DTypeLikeFloat64: TypeAlias = (
+    dtype[float64]
+    | _SupportsDType[dtype[float64]]
+    | type[float]
+    | type[float64]
+    | _Float64Codes
+    | _DoubleCodes
+)
+
+class Generator:
+    def __init__(self, bit_generator: BitGenerator) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> None: ...
+    def __setstate__(self, state: dict[str, Any] | None) -> None: ...
+    def __reduce__(self) -> tuple[
+        Callable[[BitGenerator], Generator],
+        tuple[BitGenerator],
+        None]: ...
+    @property
+    def bit_generator(self) -> BitGenerator: ...
+    def spawn(self, n_children: int) -> list[Generator]: ...
+    def bytes(self, length: int) -> bytes: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def permutation(self, x: int, axis: int = ...) -> NDArray[int64]: ...
+    @overload
+    def permutation(self, x: ArrayLike, axis: int = ...) -> NDArray[Any]: ...
+    @overload
+    def standard_exponential(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def random(
+        self,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def beta(
+        self,
+        a: _FloatLike_co,
+        b: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self,
+        a: _ArrayLikeFloat_co,
+        b: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> NDArray[float64]: ...
+
+    #
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
+        endpoint: bool = False,
+    ) -> np.int64: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: type[bool],
+        endpoint: bool = False,
+    ) -> bool: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: type[int],
+        endpoint: bool = False,
+    ) -> int: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _DTypeLike[np.bool] | _BoolCodes,
+        endpoint: bool = False,
+    ) -> np.bool: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _DTypeLike[_IntegerT],
+        endpoint: bool = False,
+    ) -> _IntegerT: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
+        endpoint: bool = False,
+    ) -> NDArray[np.int64]: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _DTypeLikeBool,
+        endpoint: bool = False,
+    ) -> NDArray[np.bool]: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _DTypeLike[_IntegerT],
+        endpoint: bool = False,
+    ) -> NDArray[_IntegerT]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int8Codes,
+        endpoint: bool = False,
+    ) -> np.int8: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int8Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int8]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt8Codes,
+        endpoint: bool = False,
+    ) -> np.uint8: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt8Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint8]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int16Codes,
+        endpoint: bool = False,
+    ) -> np.int16: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int16Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int16]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt16Codes,
+        endpoint: bool = False,
+    ) -> np.uint16: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt16Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint16]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _Int32Codes,
+        endpoint: bool = False,
+    ) -> np.int32: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _Int32Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.int32]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt32Codes,
+        endpoint: bool = False,
+    ) -> np.uint32: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt32Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint32]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UInt64Codes,
+        endpoint: bool = False,
+    ) -> np.uint64: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UInt64Codes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uint64]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _IntPCodes,
+        endpoint: bool = False,
+    ) -> np.intp: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _IntPCodes,
+        endpoint: bool = False,
+    ) -> NDArray[np.intp]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        *,
+        dtype: _UIntPCodes,
+        endpoint: bool = False,
+    ) -> np.uintp: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        *,
+        dtype: _UIntPCodes,
+        endpoint: bool = False,
+    ) -> NDArray[np.uintp]: ...
+    @overload
+    def integers(
+        self,
+        low: int,
+        high: int | None = None,
+        size: None = None,
+        dtype: DTypeLike = ...,
+        endpoint: bool = False,
+    ) -> Any: ...
+    @overload
+    def integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: _ArrayLikeInt_co | None = None,
+        size: _ShapeLike | None = None,
+        dtype: DTypeLike = ...,
+        endpoint: bool = False,
+    ) -> NDArray[Any]: ...
+
+    # TODO: Use a TypeVar _T here to get away from Any output?
+    #       Should be int->NDArray[int64], ArrayLike[_T] -> _T | NDArray[Any]
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> NDArray[Any]: ...
+    @overload
+    def uniform(
+        self,
+        low: _FloatLike_co = ...,
+        high: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def normal(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: _FloatLike_co,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        *,
+        out: NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | NDArray[float32] = ...,
+    ) -> NDArray[float32]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | NDArray[float64] = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gamma(
+        self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def f(
+        self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _FloatLike_co,
+        dfden: _FloatLike_co,
+        nonc: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_chisquare(
+        self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self,
+        df: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def vonmises(
+        self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self,
+        mu: _ArrayLikeFloat_co,
+        kappa: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def power(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...
+    @overload
+    def laplace(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gumbel(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def logistic(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def lognormal(
+        self,
+        mean: _FloatLike_co = ...,
+        sigma: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def wald(
+        self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self,
+        mean: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def triangular(
+        self,
+        left: _FloatLike_co,
+        mode: _FloatLike_co,
+        right: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def negative_binomial(
+        self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...
+    ) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self,
+        n: _ArrayLikeFloat_co,
+        p: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    @overload
+    def hypergeometric(
+        self, ngood: int, nbad: int, nsample: int, size: None = ...
+    ) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+        *,
+        method: Literal["svd", "eigh", "cholesky"] = ...,
+    ) -> NDArray[float64]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co,
+            pvals: _ArrayLikeFloat_co,
+            size: None | _ShapeLike = ...
+    ) -> NDArray[int64]: ...
+    def multivariate_hypergeometric(
+        self,
+        colors: _ArrayLikeInt_co,
+        nsample: int,
+        size: None | _ShapeLike = ...,
+        method: Literal["marginals", "count"] = ...,
+    ) -> NDArray[int64]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    def permuted(
+        self, x: ArrayLike, *, axis: None | int = ..., out: None | NDArray[Any] = ...
+    ) -> NDArray[Any]: ...
+    def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
+
+def default_rng(
+    seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator | RandomState = ...
+) -> Generator: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_mt19937.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_mt19937.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..430dd8041f50221c92b297a3ee5e9fe767a8d176
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_mt19937.pyi
@@ -0,0 +1,25 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint32
+from numpy.typing import NDArray
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _MT19937Internal(TypedDict):
+    key: NDArray[uint32]
+    pos: int
+
+@type_check_only
+class _MT19937State(TypedDict):
+    bit_generator: str
+    state: _MT19937Internal
+
+class MT19937(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...
+    def jumped(self, jumps: int = ...) -> MT19937: ...
+    @property
+    def state(self) -> _MT19937State: ...
+    @state.setter
+    def state(self, value: _MT19937State) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pcg64.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pcg64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..15bb0525c9a532af49715242b0b2da6a5e7dbdbc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pcg64.pyi
@@ -0,0 +1,44 @@
+from typing import TypedDict, type_check_only
+
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _PCG64Internal(TypedDict):
+    state: int
+    inc: int
+
+@type_check_only
+class _PCG64State(TypedDict):
+    bit_generator: str
+    state: _PCG64Internal
+    has_uint32: int
+    uinteger: int
+
+class PCG64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64: ...
+
+class PCG64DXSM(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64DXSM: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64DXSM: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_philox.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_philox.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7206ae9702c002a7f893bbee3d485eef4c6ca240
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_philox.pyi
@@ -0,0 +1,39 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint64
+from numpy.typing import NDArray
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+@type_check_only
+class _PhiloxInternal(TypedDict):
+    counter: NDArray[uint64]
+    key: NDArray[uint64]
+
+@type_check_only
+class _PhiloxState(TypedDict):
+    bit_generator: str
+    state: _PhiloxInternal
+    buffer: NDArray[uint64]
+    buffer_pos: int
+    has_uint32: int
+    uinteger: int
+
+class Philox(BitGenerator):
+    def __init__(
+        self,
+        seed: None | _ArrayLikeInt_co | SeedSequence = ...,
+        counter: None | _ArrayLikeInt_co = ...,
+        key: None | _ArrayLikeInt_co = ...,
+    ) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _PhiloxState: ...
+    @state.setter
+    def state(
+        self,
+        value: _PhiloxState,
+    ) -> None: ...
+    def jumped(self, jumps: int = ...) -> Philox: ...
+    def advance(self, delta: int) -> Philox: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..842bd441a50237765a543a13c878ce1ece828892
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.py
@@ -0,0 +1,89 @@
+from .bit_generator import BitGenerator
+from .mtrand import RandomState
+from ._philox import Philox
+from ._pcg64 import PCG64, PCG64DXSM
+from ._sfc64 import SFC64
+
+from ._generator import Generator
+from ._mt19937 import MT19937
+
+BitGenerators = {'MT19937': MT19937,
+                 'PCG64': PCG64,
+                 'PCG64DXSM': PCG64DXSM,
+                 'Philox': Philox,
+                 'SFC64': SFC64,
+                 }
+
+
+def __bit_generator_ctor(bit_generator: str | type[BitGenerator] = 'MT19937'):
+    """
+    Pickling helper function that returns a bit generator object
+
+    Parameters
+    ----------
+    bit_generator : type[BitGenerator] or str
+        BitGenerator class or string containing the name of the BitGenerator
+
+    Returns
+    -------
+    BitGenerator
+        BitGenerator instance
+    """
+    if isinstance(bit_generator, type):
+        bit_gen_class = bit_generator
+    elif bit_generator in BitGenerators:
+        bit_gen_class = BitGenerators[bit_generator]
+    else:
+        raise ValueError(
+            str(bit_generator) + ' is not a known BitGenerator module.'
+        )
+
+    return bit_gen_class()
+
+
+def __generator_ctor(bit_generator_name="MT19937",
+                     bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a Generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str or BitGenerator
+        String containing the core BitGenerator's name or a
+        BitGenerator instance
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rg : Generator
+        Generator using the named core BitGenerator
+    """
+    if isinstance(bit_generator_name, BitGenerator):
+        return Generator(bit_generator_name)
+    # Legacy path that uses a bit generator name and ctor
+    return Generator(bit_generator_ctor(bit_generator_name))
+
+
+def __randomstate_ctor(bit_generator_name="MT19937",
+                       bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a legacy RandomState-like object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rs : RandomState
+        Legacy RandomState using the named core BitGenerator
+    """
+    if isinstance(bit_generator_name, BitGenerator):
+        return RandomState(bit_generator_name)
+    return RandomState(bit_generator_ctor(bit_generator_name))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d4c6e8155ae9a800d3b0e3b320e6e552ce85f177
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_pickle.pyi
@@ -0,0 +1,43 @@
+from collections.abc import Callable
+from typing import Final, Literal, TypeVar, TypedDict, overload, type_check_only
+
+from numpy.random._generator import Generator
+from numpy.random._mt19937 import MT19937
+from numpy.random._pcg64 import PCG64, PCG64DXSM
+from numpy.random._philox import Philox
+from numpy.random._sfc64 import SFC64
+from numpy.random.bit_generator import BitGenerator
+from numpy.random.mtrand import RandomState
+
+_T = TypeVar("_T", bound=BitGenerator)
+
+@type_check_only
+class _BitGenerators(TypedDict):
+    MT19937: type[MT19937]
+    PCG64: type[PCG64]
+    PCG64DXSM: type[PCG64DXSM]
+    Philox: type[Philox]
+    SFC64: type[SFC64]
+
+BitGenerators: Final[_BitGenerators] = ...
+
+@overload
+def __bit_generator_ctor(bit_generator: Literal["MT19937"] = "MT19937") -> MT19937: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["PCG64"]) -> PCG64: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["PCG64DXSM"]) -> PCG64DXSM: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["Philox"]) -> Philox: ...
+@overload
+def __bit_generator_ctor(bit_generator: Literal["SFC64"]) -> SFC64: ...
+@overload
+def __bit_generator_ctor(bit_generator: type[_T]) -> _T: ...
+def __generator_ctor(
+    bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",
+    bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,
+) -> Generator: ...
+def __randomstate_ctor(
+    bit_generator_name: str | type[BitGenerator] | BitGenerator = "MT19937",
+    bit_generator_ctor: Callable[[str | type[BitGenerator]], BitGenerator] = ...,
+) -> RandomState: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..56bd3a7a29e025a6aaed9bd36b6df48b2483e583
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..baaae7c668fb61f950489f4486c1880ae7cd44e1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/_sfc64.pyi
@@ -0,0 +1,28 @@
+from typing import TypedDict, type_check_only
+
+from numpy import uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import NDArray, _ArrayLikeInt_co
+
+@type_check_only
+class _SFC64Internal(TypedDict):
+    state: NDArray[uint64]
+
+@type_check_only
+class _SFC64State(TypedDict):
+    bit_generator: str
+    state: _SFC64Internal
+    has_uint32: int
+    uinteger: int
+
+class SFC64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _SFC64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _SFC64State,
+    ) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..dfa7d0a71c085dfa3dfb2819f47493cb8501d198
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pxd
@@ -0,0 +1,35 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+cdef class BitGenerator():
+    cdef readonly object _seed_seq
+    cdef readonly object lock
+    cdef bitgen_t _bitgen
+    cdef readonly object _ctypes
+    cdef readonly object _cffi
+    cdef readonly object capsule
+
+
+cdef class SeedSequence():
+    cdef readonly object entropy
+    cdef readonly tuple spawn_key
+    cdef readonly Py_ssize_t pool_size
+    cdef readonly object pool
+    cdef readonly uint32_t n_children_spawned
+
+    cdef mix_entropy(self, np.ndarray[np.npy_uint32, ndim=1] mixer,
+                     np.ndarray[np.npy_uint32, ndim=1] entropy_array)
+    cdef get_assembled_entropy(self)
+
+cdef class SeedlessSequence():
+    pass
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..78fb769683d32f0ae2c1c663ff76d79429b2e6e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pyi
@@ -0,0 +1,107 @@
+import abc
+from collections.abc import Callable, Mapping, Sequence
+from threading import Lock
+from typing import Any, ClassVar, Literal, NamedTuple, TypeAlias, TypedDict, overload, type_check_only
+
+from _typeshed import Incomplete
+from typing_extensions import CapsuleType, Self
+
+import numpy as np
+from numpy._typing import NDArray, _ArrayLikeInt_co, _DTypeLike, _ShapeLike, _UInt32Codes, _UInt64Codes
+
+__all__ = ["BitGenerator", "SeedSequence"]
+
+###
+
+_DTypeLikeUint_: TypeAlias = _DTypeLike[np.uint32 | np.uint64] | _UInt32Codes | _UInt64Codes
+
+@type_check_only
+class _SeedSeqState(TypedDict):
+    entropy: int | Sequence[int] | None
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+
+@type_check_only
+class _Interface(NamedTuple):
+    state_address: Incomplete
+    state: Incomplete
+    next_uint64: Incomplete
+    next_uint32: Incomplete
+    next_double: Incomplete
+    bit_generator: Incomplete
+
+@type_check_only
+class _CythonMixin:
+    def __setstate_cython__(self, pyx_state: object, /) -> None: ...
+    def __reduce_cython__(self) -> Any: ...  # noqa: ANN401
+
+@type_check_only
+class _GenerateStateMixin(_CythonMixin):
+    def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
+
+###
+
+class ISeedSequence(abc.ABC):
+    @abc.abstractmethod
+    def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
+
+class ISpawnableSeedSequence(ISeedSequence, abc.ABC):
+    @abc.abstractmethod
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+
+class SeedlessSeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+
+class SeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
+    __pyx_vtable__: ClassVar[CapsuleType] = ...
+
+    entropy: int | Sequence[int] | None
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+    pool: NDArray[np.uint32]
+
+    def __init__(
+        self,
+        /,
+        entropy: _ArrayLikeInt_co | None = None,
+        *,
+        spawn_key: Sequence[int] = (),
+        pool_size: int = 4,
+        n_children_spawned: int = ...,
+    ) -> None: ...
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+    @property
+    def state(self) -> _SeedSeqState: ...
+
+class BitGenerator(_CythonMixin, abc.ABC):
+    lock: Lock
+    @property
+    def state(self) -> Mapping[str, Any]: ...
+    @state.setter
+    def state(self, value: Mapping[str, Any], /) -> None: ...
+    @property
+    def seed_seq(self) -> ISeedSequence: ...
+    @property
+    def ctypes(self) -> _Interface: ...
+    @property
+    def cffi(self) -> _Interface: ...
+    @property
+    def capsule(self) -> CapsuleType: ...
+
+    #
+    def __init__(self, /, seed: _ArrayLikeInt_co | SeedSequence | None = None) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], Self], tuple[str], tuple[Mapping[str, Any], ISeedSequence]]: ...
+    def spawn(self, /, n_children: int) -> list[Self]: ...
+    def _benchmark(self, /, cnt: int, method: str = "uint64") -> None: ...
+
+    #
+    @overload
+    def random_raw(self, /, size: None = None, output: Literal[True] = True) -> int: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike, output: Literal[True] = True) -> NDArray[np.uint64]: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike | None, output: Literal[False]) -> None: ...
+    @overload
+    def random_raw(self, /, size: _ShapeLike | None = None, *, output: Literal[False]) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/c_distributions.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/c_distributions.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..da790ca499df2aadb503d6a98182575fb0de67ed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/c_distributions.pxd
@@ -0,0 +1,119 @@
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
+from numpy cimport npy_intp
+
+from libc.stdint cimport (uint64_t, int32_t, int64_t)
+from numpy.random cimport bitgen_t
+
+cdef extern from "numpy/random/distributions.h":
+
+    struct s_binomial_t:
+        int has_binomial
+        double psave
+        int64_t nsave
+        double r
+        double q
+        double fm
+        int64_t m
+        double p1
+        double xm
+        double xl
+        double xr
+        double c
+        double laml
+        double lamr
+        double p2
+        double p3
+        double p4
+
+    ctypedef s_binomial_t binomial_t
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    double random_standard_uniform(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_exponential(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
+    void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
+    void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
+    double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
+    float random_standard_normal_f(bitgen_t* bitgen_state) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    int64_t random_positive_int64(bitgen_t *bitgen_state) nogil
+    int32_t random_positive_int32(bitgen_t *bitgen_state) nogil
+    int64_t random_positive_int(bitgen_t *bitgen_state) nogil
+    uint64_t random_uint(bitgen_t *bitgen_state) nogil
+
+    double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil
+
+    double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil
+    float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil
+
+    double random_exponential(bitgen_t *bitgen_state, double scale) nogil
+    double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil
+    double random_beta(bitgen_t *bitgen_state, double a, double b) nogil
+    double random_chisquare(bitgen_t *bitgen_state, double df) nogil
+    double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil
+    double random_standard_cauchy(bitgen_t *bitgen_state) nogil
+    double random_pareto(bitgen_t *bitgen_state, double a) nogil
+    double random_weibull(bitgen_t *bitgen_state, double a) nogil
+    double random_power(bitgen_t *bitgen_state, double a) nogil
+    double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil
+    double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil
+    double random_standard_t(bitgen_t *bitgen_state, double df) nogil
+    double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
+                                       double nonc) nogil
+    double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
+                               double dfden, double nonc) nogil
+    double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil
+    double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil
+    double random_triangular(bitgen_t *bitgen_state, double left, double mode,
+                             double right) nogil
+
+    int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil
+    int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil
+    int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil
+    int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil
+    int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,
+                                    int64_t sample) nogil
+
+    uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil
+
+    # Generate random uint64 numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+
+    void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,
+                            double *pix, npy_intp d, binomial_t *binomial) nogil
+
+    int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+                          int64_t total,
+                          size_t num_colors, int64_t *colors,
+                          int64_t nsample,
+                          size_t num_variates, int64_t *variates) nogil
+    void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+                               int64_t total,
+                               size_t num_colors, int64_t *colors,
+                               int64_t nsample,
+                               size_t num_variates, int64_t *variates) nogil
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/lib/libnpyrandom.a b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/lib/libnpyrandom.a
new file mode 100644
index 0000000000000000000000000000000000000000..946b059f1c5740b06d124641d66276cfc32ebccc
Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/lib/libnpyrandom.a differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/mtrand.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/mtrand.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..16a722c0038e4180cc68d4e528ef806a629fc3f5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/mtrand.pyi
@@ -0,0 +1,658 @@
+import builtins
+from collections.abc import Callable
+from typing import Any, overload, Literal
+
+import numpy as np
+from numpy import (
+    dtype,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    long,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    uint,
+    ulong,
+)
+from numpy.random.bit_generator import BitGenerator
+from numpy._typing import (
+    ArrayLike,
+    NDArray,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DTypeLikeBool,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _LongCodes,
+    _ShapeLike,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+    _ULongCodes,
+)
+
+
+class RandomState:
+    _bit_generator: BitGenerator
+    def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[BitGenerator], RandomState], tuple[BitGenerator], dict[str, Any]]: ...
+    def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
+    @overload
+    def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
+    @overload
+    def get_state(
+        self, legacy: Literal[True] = ...
+    ) -> dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]: ...
+    def set_state(
+        self, state: dict[str, Any] | tuple[str, NDArray[uint32], int, int, float]
+    ) -> None: ...
+    @overload
+    def random_sample(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random_sample(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def random(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_exponential(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_exponential(self, size: _ShapeLike) -> NDArray[float64]: ...
+    @overload
+    def tomaxint(self, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    # Generates long values, but stores it in a 64bit int:
+    def tomaxint(self, size: _ShapeLike) -> NDArray[int64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[bool] = ...,
+    ) -> bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[np.bool] = ...,
+    ) -> np.bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: type[int] = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> uint8: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> uint16: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> uint32: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+    ) -> uint: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ...,
+    ) -> ulong: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> uint64: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> int8: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> int16: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> int32: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int_] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+    ) -> int_: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[long] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ...,
+    ) -> long: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> int64: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> NDArray[np.bool]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> NDArray[int8]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> NDArray[int16]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> NDArray[int32]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> NDArray[int64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> NDArray[uint8]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> NDArray[uint16]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> NDArray[uint32]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> NDArray[uint64]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[long] | type[int] | type[long] | _LongCodes | _SupportsDType[dtype[long]] = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[ulong] | type[ulong] | _ULongCodes | _SupportsDType[dtype[ulong]] = ...,
+    ) -> NDArray[ulong]: ...
+    def bytes(self, length: int) -> builtins.bytes: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> NDArray[Any]: ...
+    @overload
+    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rand(self) -> float: ...
+    @overload
+    def rand(self, *args: int) -> NDArray[float64]: ...
+    @overload
+    def randn(self) -> float: ...
+    @overload
+    def randn(self, *args: int) -> NDArray[float64]: ...
+    @overload
+    def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def standard_normal(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: float,
+        size: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...
+    @overload
+    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    @overload
+    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[float64]: ...
+    @overload
+    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> NDArray[long]: ...
+    @overload
+    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+    ) -> NDArray[float64]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[long]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> NDArray[float64]: ...
+    def shuffle(self, x: ArrayLike) -> None: ...
+    @overload
+    def permutation(self, x: int) -> NDArray[long]: ...
+    @overload
+    def permutation(self, x: ArrayLike) -> NDArray[Any]: ...
+
+_rand: RandomState
+
+beta = _rand.beta
+binomial = _rand.binomial
+bytes = _rand.bytes
+chisquare = _rand.chisquare
+choice = _rand.choice
+dirichlet = _rand.dirichlet
+exponential = _rand.exponential
+f = _rand.f
+gamma = _rand.gamma
+get_state = _rand.get_state
+geometric = _rand.geometric
+gumbel = _rand.gumbel
+hypergeometric = _rand.hypergeometric
+laplace = _rand.laplace
+logistic = _rand.logistic
+lognormal = _rand.lognormal
+logseries = _rand.logseries
+multinomial = _rand.multinomial
+multivariate_normal = _rand.multivariate_normal
+negative_binomial = _rand.negative_binomial
+noncentral_chisquare = _rand.noncentral_chisquare
+noncentral_f = _rand.noncentral_f
+normal = _rand.normal
+pareto = _rand.pareto
+permutation = _rand.permutation
+poisson = _rand.poisson
+power = _rand.power
+rand = _rand.rand
+randint = _rand.randint
+randn = _rand.randn
+random = _rand.random
+random_integers = _rand.random_integers
+random_sample = _rand.random_sample
+rayleigh = _rand.rayleigh
+seed = _rand.seed
+set_state = _rand.set_state
+shuffle = _rand.shuffle
+standard_cauchy = _rand.standard_cauchy
+standard_exponential = _rand.standard_exponential
+standard_gamma = _rand.standard_gamma
+standard_normal = _rand.standard_normal
+standard_t = _rand.standard_t
+triangular = _rand.triangular
+uniform = _rand.uniform
+vonmises = _rand.vonmises
+wald = _rand.wald
+weibull = _rand.weibull
+zipf = _rand.zipf
+# Two legacy that are trivial wrappers around random_sample
+sample = _rand.random_sample
+ranf = _rand.random_sample
+
+def set_bit_generator(bitgen: BitGenerator) -> None:
+    ...
+
+def get_bit_generator() -> BitGenerator:
+    ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-1.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b97bfa66f72f1be31cdb32317905d805fe90f4d5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0xc816921f
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cdb8e4794ccdb30fa4fea3045b30a188f6ff5357
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0c8271fab6dff757e0b94443be8bc0e8b5f1e047
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv
new file mode 100644
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..39cef057f4490a70d7041bcfde4d3fec844732bb
--- /dev/null
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv
new file mode 100644
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--- /dev/null
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e448cbf73cc0774eca74df0d3d8866d8bd35bab3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-1.csv
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..69d24c38c28959361283624e02f8891fa69b6c5f
--- /dev/null
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-1.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4fffe69591fea3f399f35034057096089d4017fb
--- /dev/null
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..70aebd5d539256e0abd453bb45828b691c6abdf0
--- /dev/null
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_direct.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_direct.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ef94b63ac590c9dfb4224df785f977e59b05b51
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_direct.py
@@ -0,0 +1,580 @@
+import os
+from os.path import join
+import sys
+
+import numpy as np
+from numpy.testing import (assert_equal, assert_allclose, assert_array_equal,
+                           assert_raises)
+import pytest
+
+from numpy.random import (
+    Generator, MT19937, PCG64, PCG64DXSM, Philox, RandomState, SeedSequence,
+    SFC64, default_rng
+)
+from numpy.random._common import interface
+
+try:
+    import cffi  # noqa: F401
+
+    MISSING_CFFI = False
+except ImportError:
+    MISSING_CFFI = True
+
+try:
+    import ctypes  # noqa: F401
+
+    MISSING_CTYPES = False
+except ImportError:
+    MISSING_CTYPES = False
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    MISSING_CFFI = True
+
+
+pwd = os.path.dirname(os.path.abspath(__file__))
+
+
+def assert_state_equal(actual, target):
+    for key in actual:
+        if isinstance(actual[key], dict):
+            assert_state_equal(actual[key], target[key])
+        elif isinstance(actual[key], np.ndarray):
+            assert_array_equal(actual[key], target[key])
+        else:
+            assert actual[key] == target[key]
+
+
+def uint32_to_float32(u):
+    return ((u >> np.uint32(8)) * (1.0 / 2**24)).astype(np.float32)
+
+
+def uniform32_from_uint64(x):
+    x = np.uint64(x)
+    upper = np.array(x >> np.uint64(32), dtype=np.uint32)
+    lower = np.uint64(0xffffffff)
+    lower = np.array(x & lower, dtype=np.uint32)
+    joined = np.column_stack([lower, upper]).ravel()
+    return uint32_to_float32(joined)
+
+
+def uniform32_from_uint53(x):
+    x = np.uint64(x) >> np.uint64(16)
+    x = np.uint32(x & np.uint64(0xffffffff))
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint32(x):
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint(x, bits):
+    if bits == 64:
+        return uniform32_from_uint64(x)
+    elif bits == 53:
+        return uniform32_from_uint53(x)
+    elif bits == 32:
+        return uniform32_from_uint32(x)
+    else:
+        raise NotImplementedError
+
+
+def uniform_from_uint(x, bits):
+    if bits in (64, 63, 53):
+        return uniform_from_uint64(x)
+    elif bits == 32:
+        return uniform_from_uint32(x)
+
+
+def uniform_from_uint64(x):
+    return (x >> np.uint64(11)) * (1.0 / 9007199254740992.0)
+
+
+def uniform_from_uint32(x):
+    out = np.empty(len(x) // 2)
+    for i in range(0, len(x), 2):
+        a = x[i] >> 5
+        b = x[i + 1] >> 6
+        out[i // 2] = (a * 67108864.0 + b) / 9007199254740992.0
+    return out
+
+
+def uniform_from_dsfmt(x):
+    return x.view(np.double) - 1.0
+
+
+def gauss_from_uint(x, n, bits):
+    if bits in (64, 63):
+        doubles = uniform_from_uint64(x)
+    elif bits == 32:
+        doubles = uniform_from_uint32(x)
+    else:  # bits == 'dsfmt'
+        doubles = uniform_from_dsfmt(x)
+    gauss = []
+    loc = 0
+    x1 = x2 = 0.0
+    while len(gauss) < n:
+        r2 = 2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * doubles[loc] - 1.0
+            x2 = 2.0 * doubles[loc + 1] - 1.0
+            r2 = x1 * x1 + x2 * x2
+            loc += 2
+
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        gauss.append(f * x2)
+        gauss.append(f * x1)
+
+    return gauss[:n]
+
+
+def test_seedsequence():
+    from numpy.random.bit_generator import (ISeedSequence,
+                                            ISpawnableSeedSequence,
+                                            SeedlessSeedSequence)
+
+    s1 = SeedSequence(range(10), spawn_key=(1, 2), pool_size=6)
+    s1.spawn(10)
+    s2 = SeedSequence(**s1.state)
+    assert_equal(s1.state, s2.state)
+    assert_equal(s1.n_children_spawned, s2.n_children_spawned)
+
+    # The interfaces cannot be instantiated themselves.
+    assert_raises(TypeError, ISeedSequence)
+    assert_raises(TypeError, ISpawnableSeedSequence)
+    dummy = SeedlessSeedSequence()
+    assert_raises(NotImplementedError, dummy.generate_state, 10)
+    assert len(dummy.spawn(10)) == 10
+
+
+def test_generator_spawning():
+    """ Test spawning new generators and bit_generators directly.
+    """
+    rng = np.random.default_rng()
+    seq = rng.bit_generator.seed_seq
+    new_ss = seq.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5)]
+    assert [c.spawn_key for c in new_ss] == expected_keys
+
+    new_bgs = rng.bit_generator.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]
+    assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys
+
+    new_rngs = rng.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]
+    found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]
+    assert found_keys == expected_keys
+
+    # Sanity check that streams are actually different:
+    assert new_rngs[0].uniform() != new_rngs[1].uniform()
+
+
+def test_non_spawnable():
+    from numpy.random.bit_generator import ISeedSequence
+
+    class FakeSeedSequence:
+        def generate_state(self, n_words, dtype=np.uint32):
+            return np.zeros(n_words, dtype=dtype)
+
+    ISeedSequence.register(FakeSeedSequence)
+
+    rng = np.random.default_rng(FakeSeedSequence())
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.spawn(5)
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.bit_generator.spawn(5)
+
+
+class Base:
+    dtype = np.uint64
+    data2 = data1 = {}
+
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = []
+
+    @classmethod
+    def _read_csv(cls, filename):
+        with open(filename) as csv:
+            seed = csv.readline()
+            seed = seed.split(',')
+            seed = [int(s.strip(), 0) for s in seed[1:]]
+            data = []
+            for line in csv:
+                data.append(int(line.split(',')[-1].strip(), 0))
+            return {'seed': seed, 'data': np.array(data, dtype=cls.dtype)}
+
+    def test_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data1['data'])
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw()
+        assert_equal(uints, self.data1['data'][0])
+
+        bit_generator = self.bit_generator(*self.data2['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data2['data'])
+
+    def test_random_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(output=False)
+        assert uints is None
+        uints = bit_generator.random_raw(1000, output=False)
+        assert uints is None
+
+    def test_gauss_inv(self):
+        n = 25
+        rs = RandomState(self.bit_generator(*self.data1['seed']))
+        gauss = rs.standard_normal(n)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data1['data'], n, self.bits))
+
+        rs = RandomState(self.bit_generator(*self.data2['seed']))
+        gauss = rs.standard_normal(25)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data2['data'], n, self.bits))
+
+    def test_uniform_double(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+    def test_uniform_float(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform32_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform32_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+    def test_repr(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in repr(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') in repr(rs)
+
+    def test_str(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in str(rs)
+        assert str(self.bit_generator.__name__) in str(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') not in str(rs)
+
+    def test_pickle(self):
+        import pickle
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        bitgen_pkl = pickle.dumps(bit_generator)
+        reloaded = pickle.loads(bitgen_pkl)
+        reloaded_state = reloaded.state
+        assert_array_equal(Generator(bit_generator).standard_normal(1000),
+                           Generator(reloaded).standard_normal(1000))
+        assert bit_generator is not reloaded
+        assert_state_equal(reloaded_state, state)
+
+        ss = SeedSequence(100)
+        aa = pickle.loads(pickle.dumps(ss))
+        assert_equal(ss.state, aa.state)
+
+    def test_pickle_preserves_seed_sequence(self):
+        # GH 26234
+        # Add explicit test that bit generators preserve seed sequences
+        import pickle
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        ss = bit_generator.seed_seq
+        bg_plk = pickle.loads(pickle.dumps(bit_generator))
+        ss_plk = bg_plk.seed_seq
+        assert_equal(ss.state, ss_plk.state)
+        assert_equal(ss.pool, ss_plk.pool)
+
+        bit_generator.seed_seq.spawn(10)
+        bg_plk = pickle.loads(pickle.dumps(bit_generator))
+        ss_plk = bg_plk.seed_seq
+        assert_equal(ss.state, ss_plk.state)
+        assert_equal(ss.n_children_spawned, ss_plk.n_children_spawned)
+
+    def test_invalid_state_type(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        with pytest.raises(TypeError):
+            bit_generator.state = {'1'}
+
+    def test_invalid_state_value(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        state['bit_generator'] = 'otherBitGenerator'
+        with pytest.raises(ValueError):
+            bit_generator.state = state
+
+    def test_invalid_init_type(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_types:
+            with pytest.raises(TypeError):
+                bit_generator(*st)
+
+    def test_invalid_init_values(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_values:
+            with pytest.raises((ValueError, OverflowError)):
+                bit_generator(*st)
+
+    def test_benchmark(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        bit_generator._benchmark(1)
+        bit_generator._benchmark(1, 'double')
+        with pytest.raises(ValueError):
+            bit_generator._benchmark(1, 'int32')
+
+    @pytest.mark.skipif(MISSING_CFFI, reason='cffi not available')
+    def test_cffi(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        cffi_interface = bit_generator.cffi
+        assert isinstance(cffi_interface, interface)
+        other_cffi_interface = bit_generator.cffi
+        assert other_cffi_interface is cffi_interface
+
+    @pytest.mark.skipif(MISSING_CTYPES, reason='ctypes not available')
+    def test_ctypes(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        ctypes_interface = bit_generator.ctypes
+        assert isinstance(ctypes_interface, interface)
+        other_ctypes_interface = bit_generator.ctypes
+        assert other_ctypes_interface is ctypes_interface
+
+    def test_getstate(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        alt_state = bit_generator.__getstate__()
+        assert isinstance(alt_state, tuple)
+        assert_state_equal(state, alt_state[0])
+        assert isinstance(alt_state[1], SeedSequence)
+
+class TestPhilox(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/philox-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/philox-testset-2.csv'))
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(1, None, 1), (-1,), (None, None, 2 ** 257 + 1)]
+
+    def test_set_key(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        keyed = self.bit_generator(counter=state['state']['counter'],
+                                   key=state['state']['key'])
+        assert_state_equal(bit_generator.state, keyed.state)
+
+
+class TestPCG64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state["state"]
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 135275564607035429730177404003164635391
+        assert state["state"] == advanced_state
+
+
+
+class TestPCG64DXSM(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"]["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 277778083536782149546677086420637664879
+        assert state["state"] == advanced_state
+
+
+class TestMT19937(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.bits = 32
+        cls.dtype = np.uint32
+        cls.data1 = cls._read_csv(join(pwd, './data/mt19937-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/mt19937-testset-2.csv'))
+        cls.seed_error_type = ValueError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(-1,)]
+
+    def test_seed_float_array(self):
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([-np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi, -np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([0, np.pi]))
+        assert_raises(TypeError, self.bit_generator, [np.pi])
+        assert_raises(TypeError, self.bit_generator, [0, np.pi])
+
+    def test_state_tuple(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        bit_generator = rs.bit_generator
+        state = bit_generator.state
+        desired = rs.integers(2 ** 16)
+        tup = (state['bit_generator'], state['state']['key'],
+               state['state']['pos'])
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+        tup = tup + (0, 0.0)
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+
+
+class TestSFC64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_legacy_pickle(self):
+        # Pickling format was changed in 2.0.x
+        import gzip
+        import pickle
+
+        expected_state = np.array(
+            [
+                9957867060933711493,
+                532597980065565856,
+                14769588338631205282,
+                13
+            ],
+            dtype=np.uint64
+        )
+
+        base_path = os.path.split(os.path.abspath(__file__))[0]
+        pkl_file = os.path.join(base_path, "data", "sfc64_np126.pkl.gz")
+        with gzip.open(pkl_file) as gz:
+            sfc = pickle.load(gz)
+
+        assert isinstance(sfc, SFC64)
+        assert_equal(sfc.state["state"]["state"], expected_state)
+
+
+class TestDefaultRNG:
+    def test_seed(self):
+        for args in [(), (None,), (1234,), ([1234, 5678],)]:
+            rg = default_rng(*args)
+            assert isinstance(rg.bit_generator, PCG64)
+
+    def test_passthrough(self):
+        bg = Philox()
+        rg = default_rng(bg)
+        assert rg.bit_generator is bg
+        rg2 = default_rng(rg)
+        assert rg2 is rg
+        assert rg2.bit_generator is bg
+
+    def test_coercion_RandomState_Generator(self):
+        # use default_rng to coerce RandomState to Generator
+        rs = RandomState(1234)
+        rg = default_rng(rs)
+        assert isinstance(rg.bit_generator, MT19937)
+        assert rg.bit_generator is rs._bit_generator
+
+        # RandomState with a non MT19937 bit generator
+        _original = np.random.get_bit_generator()
+        bg = PCG64(12342298)
+        np.random.set_bit_generator(bg)
+        rs = np.random.mtrand._rand
+        rg = default_rng(rs)
+        assert rg.bit_generator is bg
+
+        # vital to get global state back to original, otherwise
+        # other tests start to fail.
+        np.random.set_bit_generator(_original)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_extending.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6ffea0b2dbf060180a747f2e549f5fc98d1cfed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_extending.py
@@ -0,0 +1,126 @@
+from importlib.util import spec_from_file_location, module_from_spec
+import os
+import pytest
+import shutil
+import subprocess
+import sys
+import sysconfig
+import warnings
+
+import numpy as np
+from numpy.testing import IS_WASM, IS_EDITABLE
+
+
+try:
+    import cffi
+except ImportError:
+    cffi = None
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    cffi = None
+
+try:
+    with warnings.catch_warnings(record=True) as w:
+        # numba issue gh-4733
+        warnings.filterwarnings('always', '', DeprecationWarning)
+        import numba
+except (ImportError, SystemError):
+    # Certain numpy/numba versions trigger a SystemError due to a numba bug
+    numba = None
+
+try:
+    import cython
+    from Cython.Compiler.Version import version as cython_version
+except ImportError:
+    cython = None
+else:
+    from numpy._utils import _pep440
+    # Note: keep in sync with the one in pyproject.toml
+    required_version = '3.0.6'
+    if _pep440.parse(cython_version) < _pep440.Version(required_version):
+        # too old or wrong cython, skip the test
+        cython = None
+
+
+@pytest.mark.skipif(
+    IS_EDITABLE,
+    reason='Editable install cannot find .pxd headers'
+)
+@pytest.mark.skipif(
+        sys.platform == "win32" and sys.maxsize < 2**32,
+        reason="Failing in 32-bit Windows wheel build job, skip for now"
+)
+@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")
+@pytest.mark.skipif(cython is None, reason="requires cython")
+@pytest.mark.slow
+def test_cython(tmp_path):
+    import glob
+    # build the examples in a temporary directory
+    srcdir = os.path.join(os.path.dirname(__file__), '..')
+    shutil.copytree(srcdir, tmp_path / 'random')
+    build_dir = tmp_path / 'random' / '_examples' / 'cython'
+    target_dir = build_dir / "build"
+    os.makedirs(target_dir, exist_ok=True)
+    # Ensure we use the correct Python interpreter even when `meson` is
+    # installed in a different Python environment (see gh-24956)
+    native_file = str(build_dir / 'interpreter-native-file.ini')
+    with open(native_file, 'w') as f:
+        f.write("[binaries]\n")
+        f.write(f"python = '{sys.executable}'\n")
+        f.write(f"python3 = '{sys.executable}'")
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release",
+                               "--vsenv", "--native-file", native_file,
+                               str(build_dir)],
+                              cwd=target_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup",
+                               "--native-file", native_file, str(build_dir)],
+                              cwd=target_dir
+                              )
+    subprocess.check_call(["meson", "compile", "-vv"], cwd=target_dir)
+
+    # gh-16162: make sure numpy's __init__.pxd was used for cython
+    # not really part of this test, but it is a convenient place to check
+
+    g = glob.glob(str(target_dir / "*" / "extending.pyx.c"))
+    with open(g[0]) as fid:
+        txt_to_find = 'NumPy API declarations from "numpy/__init__'
+        for line in fid:
+            if txt_to_find in line:
+                break
+        else:
+            assert False, ("Could not find '{}' in C file, "
+                           "wrong pxd used".format(txt_to_find))
+    # import without adding the directory to sys.path
+    suffix = sysconfig.get_config_var('EXT_SUFFIX')
+
+    def load(modname):
+        so = (target_dir / modname).with_suffix(suffix)
+        spec = spec_from_file_location(modname, so)
+        mod = module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        return mod
+
+    # test that the module can be imported
+    load("extending")
+    load("extending_cpp")
+    # actually test the cython c-extension
+    extending_distributions = load("extending_distributions")
+    from numpy.random import PCG64
+    values = extending_distributions.uniforms_ex(PCG64(0), 10, 'd')
+    assert values.shape == (10,)
+    assert values.dtype == np.float64
+
+@pytest.mark.skipif(numba is None or cffi is None,
+                    reason="requires numba and cffi")
+def test_numba():
+    from numpy.random._examples.numba import extending  # noqa: F401
+
+@pytest.mark.skipif(cffi is None, reason="requires cffi")
+def test_cffi():
+    from numpy.random._examples.cffi import extending  # noqa: F401
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9dc81e96a37fbd13549272d70161847554923b0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937.py
@@ -0,0 +1,2797 @@
+import os.path
+import sys
+import hashlib
+
+import pytest
+
+import numpy as np
+from numpy.exceptions import AxisError
+from numpy.linalg import LinAlgError
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_allclose,
+    assert_warns, assert_no_warnings, assert_array_equal,
+    assert_array_almost_equal, suppress_warnings, IS_WASM)
+
+from numpy.random import Generator, MT19937, SeedSequence, RandomState
+
+random = Generator(MT19937())
+
+JUMP_TEST_DATA = [
+    {
+        "seed": 0,
+        "steps": 10,
+        "initial": {"key_sha256": "bb1636883c2707b51c5b7fc26c6927af4430f2e0785a8c7bc886337f919f9edf", "pos": 9},
+        "jumped": {"key_sha256": "ff682ac12bb140f2d72fba8d3506cf4e46817a0db27aae1683867629031d8d55", "pos": 598},
+    },
+    {
+        "seed":384908324,
+        "steps":312,
+        "initial": {"key_sha256": "16b791a1e04886ccbbb4d448d6ff791267dc458ae599475d08d5cced29d11614", "pos": 311},
+        "jumped": {"key_sha256": "a0110a2cf23b56be0feaed8f787a7fc84bef0cb5623003d75b26bdfa1c18002c", "pos": 276},
+    },
+    {
+        "seed": [839438204, 980239840, 859048019, 821],
+        "steps": 511,
+        "initial": {"key_sha256": "d306cf01314d51bd37892d874308200951a35265ede54d200f1e065004c3e9ea", "pos": 510},
+        "jumped": {"key_sha256": "0e00ab449f01a5195a83b4aee0dfbc2ce8d46466a640b92e33977d2e42f777f8", "pos": 475},
+    },
+]
+
+
+@pytest.fixture(scope='module', params=[True, False])
+def endpoint(request):
+    return request.param
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = Generator(MT19937(0))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937(4294967295))
+        assert_equal(s.integers(1000), 324)
+
+    def test_array(self):
+        s = Generator(MT19937(range(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937(np.arange(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937([0]))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937([4294967295]))
+        assert_equal(s.integers(1000), 324)
+
+    def test_seedsequence(self):
+        s = MT19937(SeedSequence(0))
+        assert_equal(s.random_raw(1), 2058676884)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, MT19937, -0.5)
+        assert_raises(ValueError, MT19937, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned integer
+        assert_raises(TypeError, MT19937, [-0.5])
+        assert_raises(ValueError, MT19937, [-1])
+        assert_raises(ValueError, MT19937, [1, -2, 4294967296])
+
+    def test_noninstantized_bitgen(self):
+        assert_raises(ValueError, Generator, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.integers(-5, -1) < -1)
+        x = random.integers(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+        assert_raises(ValueError, random.multinomial, [-1] * 10, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random = Generator(MT19937(1432985819))
+        non_contig = random.multinomial(100, pvals=pvals)
+        random = Generator(MT19937(1432985819))
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        random = Generator(MT19937(1432985819))
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+
+class TestMultivariateHypergeometric:
+
+    def setup_method(self):
+        self.seed = 8675309
+
+    def test_argument_validation(self):
+        # Error cases...
+
+        # `colors` must be a 1-d sequence
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      10, 4)
+
+        # Negative nsample
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], -1)
+
+        # Negative color
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [-1, 2, 3], 2)
+
+        # nsample exceeds sum(colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], 10)
+
+        # nsample exceeds sum(colors) (edge case of empty colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [], 1)
+
+        # Validation errors associated with very large values in colors.
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [999999999, 101], 5, 1, 'marginals')
+
+        int64_info = np.iinfo(np.int64)
+        max_int64 = int64_info.max
+        max_int64_index = max_int64 // int64_info.dtype.itemsize
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [max_int64_index - 100, 101], 5, 1, 'count')
+
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    def test_edge_cases(self, method):
+        # Set the seed, but in fact, all the results in this test are
+        # deterministic, so we don't really need this.
+        random = Generator(MT19937(self.seed))
+
+        x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([], 0, method=method)
+        assert_array_equal(x, [])
+
+        x = random.multivariate_hypergeometric([], 0, size=1, method=method)
+        assert_array_equal(x, np.empty((1, 0), dtype=np.int64))
+
+        x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method)
+        assert_array_equal(x, [3, 0, 0])
+
+        colors = [1, 1, 0, 1, 1]
+        x = random.multivariate_hypergeometric(colors, sum(colors),
+                                               method=method)
+        assert_array_equal(x, colors)
+
+        x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3,
+                                               method=method)
+        assert_array_equal(x, [[3, 4, 5]]*3)
+
+    # Cases for nsample:
+    #     nsample < 10
+    #     10 <= nsample < colors.sum()/2
+    #     colors.sum()/2 < nsample < colors.sum() - 10
+    #     colors.sum() - 10 < nsample < colors.sum()
+    @pytest.mark.parametrize('nsample', [8, 25, 45, 55])
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    @pytest.mark.parametrize('size', [5, (2, 3), 150000])
+    def test_typical_cases(self, nsample, method, size):
+        random = Generator(MT19937(self.seed))
+
+        colors = np.array([10, 5, 20, 25])
+        sample = random.multivariate_hypergeometric(colors, nsample, size,
+                                                    method=method)
+        if isinstance(size, int):
+            expected_shape = (size,) + colors.shape
+        else:
+            expected_shape = size + colors.shape
+        assert_equal(sample.shape, expected_shape)
+        assert_((sample >= 0).all())
+        assert_((sample <= colors).all())
+        assert_array_equal(sample.sum(axis=-1),
+                           np.full(size, fill_value=nsample, dtype=int))
+        if isinstance(size, int) and size >= 100000:
+            # This sample is large enough to compare its mean to
+            # the expected values.
+            assert_allclose(sample.mean(axis=0),
+                            nsample * colors / colors.sum(),
+                            rtol=1e-3, atol=0.005)
+
+    def test_repeatability1(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5,
+                                                    method='count')
+        expected = np.array([[2, 1, 2],
+                             [2, 1, 2],
+                             [1, 1, 3],
+                             [2, 0, 3],
+                             [2, 1, 2]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability2(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 50,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[ 9, 17, 24],
+                             [ 7, 13, 30],
+                             [ 9, 15, 26],
+                             [ 9, 17, 24],
+                             [12, 14, 24]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability3(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 12,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[2, 3, 7],
+                             [5, 3, 4],
+                             [2, 5, 5],
+                             [5, 3, 4],
+                             [1, 5, 6]])
+        assert_array_equal(sample, expected)
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.rg = Generator(MT19937(self.seed))
+        self.bit_generator = self.rg.bit_generator
+        self.state = self.bit_generator.state
+        self.legacy_state = (self.state['bit_generator'],
+                             self.state['state']['key'],
+                             self.state['state']['pos'])
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = self.state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.rg.standard_normal()
+        state = self.bit_generator.state
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.rg.negative_binomial(0.5, 0.5)
+
+
+class TestIntegers:
+    rfunc = random.integers
+
+    # valid integer/boolean types
+    itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self, endpoint):
+        assert_raises(TypeError, self.rfunc, 1, endpoint=endpoint, dtype=float)
+
+    def test_bounds_checking(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, endpoint=endpoint,
+                          dtype=dt)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1], ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd], [ubnd + 1],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd], [lbnd],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, [0],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
+                          endpoint=endpoint, dtype=dt)
+
+    def test_bounds_checking_array(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + (not endpoint)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1] * 2, [ubnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd] * 2,
+                          [ubnd + 1] * 2, endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, [lbnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [1] * 2, 0,
+                          endpoint=endpoint, dtype=dt)
+
+    def test_rng_zero_and_extremes(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            is_open = not endpoint
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc(tgt, [tgt + is_open], size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], [tgt + is_open],
+                                    size=1000, endpoint=endpoint, dtype=dt),
+                         tgt)
+
+    def test_rng_zero_and_extremes_array(self, endpoint):
+        size = 1000
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+    def test_full_range(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_full_range_array(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc([lbnd] * 2, [ubnd], endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self, endpoint):
+        # Don't use fixed seed
+        random = Generator(MT19937())
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd - endpoint, size=2 ** 16,
+                                  endpoint=endpoint, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2 - endpoint, size=2 ** 16, endpoint=endpoint,
+                          dtype=bool)
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_scalar_array_equiv(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            size = 1000
+            random = Generator(MT19937(1234))
+            scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint,
+                                dtype=dt)
+
+            random = Generator(MT19937(1234))
+            scalar_array = random.integers([lbnd], [ubnd], size=size,
+                                      endpoint=endpoint, dtype=dt)
+
+            random = Generator(MT19937(1234))
+            array = random.integers([lbnd] * size, [ubnd] *
+                               size, size=size, endpoint=endpoint, dtype=dt)
+            assert_array_equal(scalar, scalar_array)
+            assert_array_equal(scalar, array)
+
+    def test_repeatability(self, endpoint):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool':   '053594a9b82d656f967c54869bc6970aa0358cf94ad469c81478459c6a90eee3',
+               'int16':  '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'int32':  'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'int64':  '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'int8':   '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1',
+               'uint16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'uint32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'uint64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'uint8':  '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1'}
+
+        for dt in self.itype[1:]:
+            random = Generator(MT19937(1234))
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt)
+            else:
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt).byteswap()
+
+            res = hashlib.sha256(val).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random = Generator(MT19937(1234))
+        val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint,
+                         dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_repeatability_broadcasting(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt in (bool, np.bool) else np.iinfo(dt).min
+            ubnd = 2 if dt in (bool, np.bool) else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # view as little endian for hash
+            random = Generator(MT19937(1234))
+            val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint,
+                             dtype=dt)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint,
+                                dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000,
+                                endpoint=endpoint, dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+    @pytest.mark.parametrize(
+        'bound, expected',
+        [(2**32 - 1, np.array([517043486, 1364798665, 1733884389, 1353720612,
+                               3769704066, 1170797179, 4108474671])),
+         (2**32, np.array([517043487, 1364798666, 1733884390, 1353720613,
+                           3769704067, 1170797180, 4108474672])),
+         (2**32 + 1, np.array([517043487, 1733884390, 3769704068, 4108474673,
+                               1831631863, 1215661561, 3869512430]))]
+    )
+    def test_repeatability_32bit_boundary(self, bound, expected):
+        for size in [None, len(expected)]:
+            random = Generator(MT19937(1234))
+            x = random.integers(bound, size=size)
+            assert_equal(x, expected if size is not None else expected[0])
+
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[1622936284, 3620788691, 1659384060],
+                             [1417365545,  760222891, 1909653332],
+                             [3788118662,  660249498, 4092002593]],
+                            [[3625610153, 2979601262, 3844162757],
+                             [ 685800658,  120261497, 2694012896],
+                             [1207779440, 1586594375, 3854335050]],
+                            [[3004074748, 2310761796, 3012642217],
+                             [2067714190, 2786677879, 1363865881],
+                             [ 791663441, 1867303284, 2169727960]],
+                            [[1939603804, 1250951100,  298950036],
+                             [1040128489, 3791912209, 3317053765],
+                             [3155528714,   61360675, 2305155588]],
+                            [[ 817688762, 1335621943, 3288952434],
+                             [1770890872, 1102951817, 1957607470],
+                             [3099996017,  798043451,   48334215]]])
+        for size in [None, (5, 3, 3)]:
+            random = Generator(MT19937(12345))
+            x = random.integers([[-1], [0], [1]],
+                                [2**32 - 1, 2**32, 2**32 + 1],
+                                size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_broadcast_exceptions(self, endpoint):
+        configs = {np.uint64: ((0, 2**65), (-1, 2**62), (10, 9), (0, 0)),
+                   np.int64: ((0, 2**64), (-(2**64), 2**62), (10, 9), (0, 0),
+                              (-2**63-1, -2**63-1))}
+        for dtype in configs:
+            for config in configs[dtype]:
+                low, high = config
+                high = high - endpoint
+                low_a = np.array([[low]*10])
+                high_a = np.array([high] * 10)
+                assert_raises(ValueError, random.integers, low, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_a,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high_a,
+                              endpoint=endpoint, dtype=dtype)
+
+                low_o = np.array([[low]*10], dtype=object)
+                high_o = np.array([high] * 10, dtype=object)
+                assert_raises(ValueError, random.integers, low_o, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_o,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_o, high_o,
+                              endpoint=endpoint, dtype=dtype)
+
+    def test_int64_uint64_corner_case(self, endpoint):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1 - endpoint)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.integers(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool if dt is bool else dt
+
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+        for dt in (bool, int):
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert not hasattr(sample, 'dtype')
+            assert_equal(type(sample), dt)
+
+    def test_respect_dtype_array(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool if dt is bool else dt
+
+            sample = self.rfunc([lbnd], [ubnd], endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+            sample = self.rfunc([lbnd] * 2, [ubnd] * 2, endpoint=endpoint,
+                                dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+    def test_zero_size(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            sample = self.rfunc(0, 0, (3, 0, 4), endpoint=endpoint, dtype=dt)
+            assert sample.shape == (3, 0, 4)
+            assert sample.dtype == dt
+            assert self.rfunc(0, -10, 0, endpoint=endpoint,
+                              dtype=dt).shape == (0,)
+            assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape,
+                         (3, 0, 4))
+            assert_equal(random.integers(0, -10, size=0).shape, (0,))
+            assert_equal(random.integers(10, 10, size=0).shape, (0,))
+
+    def test_error_byteorder(self):
+        other_byteord_dt = 'i4'
+        with pytest.raises(ValueError):
+            random.integers(0, 200, size=10, dtype=other_byteord_dt)
+
+    # chi2max is the maximum acceptable chi-squared value.
+    @pytest.mark.slow
+    @pytest.mark.parametrize('sample_size,high,dtype,chi2max',
+        [(5000000, 5, np.int8, 125.0),          # p-value ~4.6e-25
+         (5000000, 7, np.uint8, 150.0),         # p-value ~7.7e-30
+         (10000000, 2500, np.int16, 3300.0),    # p-value ~3.0e-25
+         (50000000, 5000, np.uint16, 6500.0),   # p-value ~3.5e-25
+        ])
+    def test_integers_small_dtype_chisquared(self, sample_size, high,
+                                             dtype, chi2max):
+        # Regression test for gh-14774.
+        samples = random.integers(high, size=sample_size, dtype=dtype)
+
+        values, counts = np.unique(samples, return_counts=True)
+        expected = sample_size / high
+        chi2 = ((counts - expected)**2 / expected).sum()
+        assert chi2 < chi2max
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_integers(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2))
+        desired = np.array([[-80, -56], [41, 37], [-83, -16]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_masked(self):
+        # Test masked rejection sampling algorithm to generate array of
+        # uint32 in an interval.
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32)
+        desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32)
+        assert_array_equal(actual, desired)
+
+    def test_integers_closed(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2), endpoint=True)
+        desired = np.array([[-80, -56], [ 41, 38], [-83, -15]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_max_int(self):
+        # Tests whether integers with closed=True can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        actual = random.integers(np.iinfo('l').max, np.iinfo('l').max,
+                                 endpoint=True)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.096999199829214, 0.707517457682192],
+                            [0.084364834598269, 0.767731206553125],
+                            [0.665069021359413, 0.715487190596693]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.random()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_random_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.0969992 , 0.70751746],
+                            [0.08436483, 0.76773121],
+                            [0.66506902, 0.71548719]])
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    def test_random_float_scalar(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random(dtype=np.float32)
+        desired = 0.0969992
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    @pytest.mark.parametrize('dtype, uint_view_type',
+                             [(np.float32, np.uint32),
+                              (np.float64, np.uint64)])
+    def test_random_distribution_of_lsb(self, dtype, uint_view_type):
+        random = Generator(MT19937(self.seed))
+        sample = random.random(100000, dtype=dtype)
+        num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1)
+        # The probability of a 1 in the least significant bit is 0.25.
+        # With a sample size of 100000, the probability that num_ones_in_lsb
+        # is outside the following range is less than 5e-11.
+        assert 24100 < num_ones_in_lsb < 25900
+
+    def test_random_unsupported_type(self):
+        assert_raises(TypeError, random.random, dtype='int32')
+
+    def test_choice_uniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4)
+        desired = np.array([0, 0, 2, 2], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([0, 1, 0, 1], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([2, 0, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+        actual = random.choice(4, 4, replace=False, shuffle=False)
+        desired = np.arange(4, dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([0, 2, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['a', 'a', 'c', 'c'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_default_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3)
+        desired = np.array([[0, 1], [0, 1], [4, 5]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1)
+        desired = np.array([[0], [2], [4], [6]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.integers(0, -10, size=0).shape, (0,))
+        assert_equal(random.integers(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random = Generator(MT19937(self.seed))
+        non_contig = random.choice(5, 3, p=p[::2])
+        random = Generator(MT19937(self.seed))
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_choice_return_type(self):
+        # gh 9867
+        p = np.ones(4) / 4.
+        actual = random.choice(4, 2)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, replace=False)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p, replace=False)
+        assert actual.dtype == np.int64
+
+    def test_choice_large_sample(self):
+        choice_hash = '4266599d12bfcfb815213303432341c06b4349f5455890446578877bb322e222'
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(10000, 5000, replace=False)
+        if sys.byteorder != 'little':
+            actual = actual.byteswap()
+        res = hashlib.sha256(actual.view(np.int8)).hexdigest()
+        assert_(choice_hash == res)
+
+    def test_choice_array_size_empty_tuple(self):
+        random = Generator(MT19937(self.seed))
+        assert_array_equal(random.choice([1, 2, 3], size=()), np.array(1),
+                           strict=True)
+        assert_array_equal(random.choice([[1, 2, 3]], size=()), [1, 2, 3])
+        assert_array_equal(random.choice([[1]], size=()), [1], strict=True)
+        assert_array_equal(random.choice([[1]], size=(), axis=1), [1],
+                           strict=True)
+
+    def test_bytes(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.bytes(10)
+        desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random = Generator(MT19937(self.seed))
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=1)
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis_empty(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([]).reshape((0, 6))
+        for axis in (0, 1):
+            actual = np.array([]).reshape((0, 6))
+            random.shuffle(actual, axis=axis)
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_axis_nonsquare(self):
+        y1 = np.arange(20).reshape(2, 10)
+        y2 = y1.copy()
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y1, axis=1)
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y2.T)
+        assert_array_equal(y1, y2)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    def test_shuffle_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(AxisError, random.shuffle, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(AxisError, random.shuffle, arr, 3)
+        assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None))
+        arr = [[1, 2, 3], [4, 5, 6]]
+        assert_raises(NotImplementedError, random.shuffle, arr, 1)
+
+        arr = np.array(3)
+        assert_raises(TypeError, random.shuffle, arr)
+        arr = np.ones((3, 2))
+        assert_raises(AxisError, random.shuffle, arr, 2)
+
+    def test_shuffle_not_writeable(self):
+        random = Generator(MT19937(self.seed))
+        a = np.zeros(5)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.shuffle(a)
+
+    def test_permutation(self):
+        random = Generator(MT19937(self.seed))
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7]
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        bad_x_str = "abcd"
+        assert_raises(AxisError, random.permutation, bad_x_str)
+
+        bad_x_float = 1.2
+        assert_raises(AxisError, random.permutation, bad_x_float)
+
+        random = Generator(MT19937(self.seed))
+        integer_val = 10
+        desired = [3, 0, 8, 7, 9, 4, 2, 5, 1, 6]
+
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_custom_axis(self):
+        a = np.arange(16).reshape((4, 4))
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=1)
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(AxisError, random.permutation, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(AxisError, random.permutation, arr, 3)
+        assert_raises(TypeError, random.permutation, arr, slice(1, 2, None))
+
+    @pytest.mark.parametrize("dtype", [int, object])
+    @pytest.mark.parametrize("axis, expected",
+                             [(None, np.array([[3, 7, 0, 9, 10, 11],
+                                               [8, 4, 2, 5,  1,  6]])),
+                              (0, np.array([[6, 1, 2, 9, 10, 11],
+                                            [0, 7, 8, 3,  4,  5]])),
+                              (1, np.array([[ 5, 3,  4, 0, 2, 1],
+                                            [11, 9, 10, 6, 8, 7]]))])
+    def test_permuted(self, dtype, axis, expected):
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        random.permuted(x, axis=axis, out=x)
+        assert_array_equal(x, expected)
+
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        y = random.permuted(x, axis=axis)
+        assert y.dtype == dtype
+        assert_array_equal(y, expected)
+
+    def test_permuted_with_strides(self):
+        random = Generator(MT19937(self.seed))
+        x0 = np.arange(22).reshape(2, 11)
+        x1 = x0.copy()
+        x = x0[:, ::3]
+        y = random.permuted(x, axis=1, out=x)
+        expected = np.array([[0, 9, 3, 6],
+                             [14, 20, 11, 17]])
+        assert_array_equal(y, expected)
+        x1[:, ::3] = expected
+        # Verify that the original x0 was modified in-place as expected.
+        assert_array_equal(x1, x0)
+
+    def test_permuted_empty(self):
+        y = random.permuted([])
+        assert_array_equal(y, [])
+
+    @pytest.mark.parametrize('outshape', [(2, 3), 5])
+    def test_permuted_out_with_wrong_shape(self, outshape):
+        a = np.array([1, 2, 3])
+        out = np.zeros(outshape, dtype=a.dtype)
+        with pytest.raises(ValueError, match='same shape'):
+            random.permuted(a, out=out)
+
+    def test_permuted_out_with_wrong_type(self):
+        out = np.zeros((3, 5), dtype=np.int32)
+        x = np.ones((3, 5))
+        with pytest.raises(TypeError, match='Cannot cast'):
+            random.permuted(x, axis=1, out=out)
+
+    def test_permuted_not_writeable(self):
+        x = np.zeros((2, 5))
+        x.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.permuted(x, axis=1, out=x)
+
+    def test_beta(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+            [[1.083029353267698e-10, 2.449965303168024e-11],
+             [2.397085162969853e-02, 3.590779671820755e-08],
+             [2.830254190078299e-04, 1.744709918330393e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[42, 41],
+                            [42, 48],
+                            [44, 50]])
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456)
+        desired = 42
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[32.9850547060149, 39.0219480493301],
+                            [56.2006134779419, 57.3474165711485],
+                            [55.4243733880198, 55.4209797925213]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.5439892869558927,  0.45601071304410745],
+                             [0.5588917345860708,  0.4411082654139292 ]],
+                            [[0.5632074165063435,  0.43679258349365657],
+                             [0.54862581112627,    0.45137418887373015]],
+                            [[0.49961831357047226, 0.5003816864295278 ],
+                             [0.52374806183482,    0.47625193816517997]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random = Generator(MT19937(self.seed))
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random = Generator(MT19937(self.seed))
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_dirichlet_small_alpha(self):
+        eps = 1.0e-9  # 1.0e-10 -> runtime x 10; 1e-11 -> runtime x 200, etc.
+        alpha = eps * np.array([1., 1.0e-3])
+        random = Generator(MT19937(self.seed))
+        actual = random.dirichlet(alpha, size=(3, 2))
+        expected = np.array([
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]]
+        ])
+        assert_array_almost_equal(actual, expected, decimal=15)
+
+    @pytest.mark.slow
+    def test_dirichlet_moderately_small_alpha(self):
+        # Use alpha.max() < 0.1 to trigger stick breaking code path
+        alpha = np.array([0.02, 0.04])
+        exact_mean = alpha / alpha.sum()
+        random = Generator(MT19937(self.seed))
+        sample = random.dirichlet(alpha, size=20000000)
+        sample_mean = sample.mean(axis=0)
+        assert_allclose(sample_mean, exact_mean, rtol=1e-3)
+
+    # This set of parameters includes inputs with alpha.max() >= 0.1 and
+    # alpha.max() < 0.1 to exercise both generation methods within the
+    # dirichlet code.
+    @pytest.mark.parametrize(
+        'alpha',
+        [[5, 9, 0, 8],
+         [0.5, 0, 0, 0],
+         [1, 5, 0, 0, 1.5, 0, 0, 0],
+         [0.01, 0.03, 0, 0.005],
+         [1e-5, 0, 0, 0],
+         [0.002, 0.015, 0, 0, 0.04, 0, 0, 0],
+         [0.0],
+         [0, 0, 0]],
+    )
+    def test_dirichlet_multiple_zeros_in_alpha(self, alpha):
+        alpha = np.array(alpha)
+        y = random.dirichlet(alpha)
+        assert_equal(y[alpha == 0], 0.0)
+
+    def test_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[0.098845481066258, 1.560752510746964],
+                            [0.075730916041636, 1.769098974710777],
+                            [1.488602544592235, 2.49684815275751 ]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[0.461720027077085, 1.100441958872451],
+                            [1.100337455217484, 0.91421736740018 ],
+                            [0.500811891303113, 0.826802454552058]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[ 5.03850858902096,  7.9228656732049 ],
+                            [18.73983605132985, 19.57961681699238],
+                            [18.17897755150825, 18.17653912505234]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[1, 11],
+                            [1, 12],
+                            [11, 17]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[ 4.688397515056245, -0.289514845417841],
+                            [ 4.981176042584683, -0.633224272589149],
+                            [-0.055915275687488, -0.333962478257953]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[ 9, 9],
+                            [ 9, 9],
+                            [10, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.156353949272393,  1.195863024830054],
+                            [-3.435458081645966,  1.656882398925444],
+                            [ 0.924824032467446,  1.251116432209336]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-4.338584631510999,  1.890171436749954],
+                            [-4.64547787337966 ,  2.514545562919217],
+                            [ 1.495389489198666,  1.967827627577474]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[ 0.0268252166335, 13.9534486483053],
+                            [ 0.1204014788936,  2.2422077497792],
+                            [ 4.2484199496128, 12.0093343977523]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[14, 17],
+                            [3, 18],
+                            [5, 1]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        random = Generator(MT19937(self.seed))
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        random = Generator(MT19937(self.seed))
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[1, 5, 1, 6, 4, 3],
+                             [4, 2, 6, 2, 4, 2]],
+                            [[5, 3, 2, 6, 3, 1],
+                             [4, 4, 0, 2, 3, 7]],
+                            [[6, 3, 1, 5, 3, 2],
+                             [5, 5, 3, 1, 2, 4]]])
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal(self, method):
+        random = Generator(MT19937(self.seed))
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size, method=method)
+        desired = np.array([[[-1.747478062846581,  11.25613495182354  ],
+                             [-0.9967333370066214, 10.342002097029821 ]],
+                            [[ 0.7850019631242964, 11.181113712443013 ],
+                             [ 0.8901349653255224,  8.873825399642492 ]],
+                            [[ 0.7130260107430003,  9.551628690083056 ],
+                             [ 0.7127098726541128, 11.991709234143173 ]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov, method=method)
+        desired = np.array([0.233278563284287, 9.424140804347195])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        # Check that non symmetric covariance input raises exception when
+        # check_valid='raises' if using default svd method.
+        mean = [0, 0]
+        cov = [[1, 2], [1, 2]]
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov,
+                     method='eigh')
+        assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                      method='cholesky')
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise', method='eigh')
+
+        # check degenerate samples from singular covariance matrix
+        cov = [[1, 1], [1, 1]]
+        if method in ('svd', 'eigh'):
+            samples = random.multivariate_normal(mean, cov, size=(3, 2),
+                                                 method=method)
+            assert_array_almost_equal(samples[..., 0], samples[..., 1],
+                                      decimal=6)
+        else:
+            assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                          method='cholesky')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov, method=method)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    @pytest.mark.parametrize('mean, cov', [([0], [[1+1j]]), ([0j], [[1]])])
+    def test_multivariate_normal_disallow_complex(self, mean, cov):
+        random = Generator(MT19937(self.seed))
+        with pytest.raises(TypeError, match="must not be complex"):
+            random.multivariate_normal(mean, cov)
+
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal_basic_stats(self, method):
+        random = Generator(MT19937(self.seed))
+        n_s = 1000
+        mean = np.array([1, 2])
+        cov = np.array([[2, 1], [1, 2]])
+        s = random.multivariate_normal(mean, cov, size=(n_s,), method=method)
+        s_center = s - mean
+        cov_emp = (s_center.T @ s_center) / (n_s - 1)
+        # these are pretty loose and are only designed to detect major errors
+        assert np.all(np.abs(s_center.mean(-2)) < 0.1)
+        assert np.all(np.abs(cov_emp - cov) < 0.2)
+
+    def test_negative_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[543, 727],
+                            [775, 760],
+                            [600, 674]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_negative_binomial_p0_exception(self):
+        # Verify that p=0 raises an exception.
+        with assert_raises(ValueError):
+            x = random.negative_binomial(1, 0)
+
+    def test_negative_binomial_invalid_p_n_combination(self):
+        # Verify that values of p and n that would result in an overflow
+        # or infinite loop raise an exception.
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 2**62, 0.1)
+            assert_raises(ValueError, random.negative_binomial, [2**62], [0.1])
+
+    def test_noncentral_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[ 1.70561552362133, 15.97378184942111],
+                            [13.71483425173724, 20.17859633310629],
+                            [11.3615477156643 ,  3.67891108738029]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04],
+                            [1.14554372041263e+00, 1.38187755933435e-03],
+                            [1.90659181905387e+00, 1.21772577941822e+00]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[0.82947954590419, 1.80139670767078],
+                            [6.58720057417794, 7.00491463609814],
+                            [6.31101879073157, 6.30982307753005]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[0.060310671139  , 0.23866058175939],
+                            [0.86860246709073, 0.2668510459738 ],
+                            [0.23375780078364, 1.88922102885943]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.618412914693162,  2.635726692647081],
+                            [-2.116923463013243,  0.807460983059643],
+                            [ 1.446547137248593,  2.485684213886024]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04],
+                            [7.2640150889064703e-01, 3.4650454783825594e+05],
+                            [4.5852344481994740e+04, 6.5851383009539105e+07]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [0, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('int64').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02],
+                            [2.482442984543471e-10, 1.527108843266079e-01],
+                            [8.188283434244285e-02, 3.950547209346948e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[4.19494429102666, 16.66920198906598],
+                            [3.67184544902662, 17.74695521962917],
+                            [16.27935397855501, 21.08355560691792]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[-1.489437778266206, -3.275389641569784],
+                            [ 0.560102864910406, -0.680780916282552],
+                            [-1.314912905226277,  0.295852965660225]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_exponential(size=(3, 2), method='inv')
+        desired = np.array([[0.102031839440643, 1.229350298474972],
+                            [0.088137284693098, 1.459859985522667],
+                            [1.093830802293668, 1.256977002164613]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_expoential_type_error(self):
+        assert_raises(TypeError, random.standard_exponential, dtype=np.int32)
+
+    def test_standard_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62970724056362, 1.22379851271008],
+                            [3.899412530884  , 4.12479964250139],
+                            [3.74994102464584, 3.74929307690815]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gammma_scalar_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(3, dtype=np.float32)
+        desired = 2.9242148399353027
+        assert_array_almost_equal(actual, desired, decimal=6)
+
+    def test_standard_gamma_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62971, 1.2238 ],
+                            [3.89941, 4.1248 ],
+                            [3.74994, 3.74929]])
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gammma_float_out(self):
+        actual = np.zeros((3, 2), dtype=np.float32)
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, dtype=np.float32)
+        desired = np.array([[10.14987,  7.87012],
+                             [ 9.46284, 12.56832],
+                             [13.82495,  7.81533]], dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gamma_unknown_type(self):
+        assert_raises(TypeError, random.standard_gamma, 1.,
+                      dtype='int32')
+
+    def test_out_size_mismatch(self):
+        out = np.zeros(10)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=20,
+                      out=out)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=(10, 1),
+                      out=out)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[-1.870934851846581,  1.25613495182354 ],
+                            [-1.120190126006621,  0.342002097029821],
+                            [ 0.661545174124296,  1.181113712443012]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_normal_unsupported_type(self):
+        assert_raises(TypeError, random.standard_normal, dtype=np.int32)
+
+    def test_standard_t(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[-1.484666193042647,  0.30597891831161 ],
+                            [ 1.056684299648085, -0.407312602088507],
+                            [ 0.130704414281157, -2.038053410490321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[ 7.86664070590917, 13.6313848513185 ],
+                            [ 7.68152445215983, 14.36169131136546],
+                            [13.16105603911429, 13.72341621856971]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[2.13306255040998 , 7.816987531021207],
+                            [2.015436610109887, 8.377577533009589],
+                            [7.421792588856135, 7.891185744455209]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_uniform_zero_range(self):
+        func = random.uniform
+        result = func(1.5, 1.5)
+        assert_allclose(result, 1.5)
+        result = func([0.0, np.pi], [0.0, np.pi])
+        assert_allclose(result, [0.0, np.pi])
+        result = func([[2145.12], [2145.12]], [2145.12, 2145.12])
+        assert_allclose(result, 2145.12 + np.zeros((2, 2)))
+
+    def test_uniform_neg_range(self):
+        func = random.uniform
+        assert_raises(ValueError, func, 2, 1)
+        assert_raises(ValueError, func,  [1, 2], [1, 1])
+        assert_raises(ValueError, func,  [[0, 1],[2, 3]], 2)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[ 1.107972248690106,  2.841536476232361],
+                            [ 1.832602376042457,  1.945511926976032],
+                            [-0.260147475776542,  2.058047492231698]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_nan(self):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    @pytest.mark.parametrize("kappa", [1e4, 1e15])
+    def test_vonmises_large_kappa(self, kappa):
+        random = Generator(MT19937(self.seed))
+        rs = RandomState(random.bit_generator)
+        state = random.bit_generator.state
+
+        random_state_vals = rs.vonmises(0, kappa, size=10)
+        random.bit_generator.state = state
+        gen_vals = random.vonmises(0, kappa, size=10)
+        if kappa < 1e6:
+            assert_allclose(random_state_vals, gen_vals)
+        else:
+            assert np.all(random_state_vals != gen_vals)
+
+    @pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2])
+    @pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15])
+    def test_vonmises_large_kappa_range(self, mu, kappa):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu, kappa, 50)
+        assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_wald(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[0.26871721804551, 3.2233942732115 ],
+                            [2.20328374987066, 2.40958405189353],
+                            [2.07093587449261, 0.73073890064369]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.138613914769468, 1.306463419753191],
+                            [0.111623365934763, 1.446570494646721],
+                            [1.257145775276011, 1.914247725027957]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random = Generator(MT19937(self.seed))
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[  1,   1],
+                            [ 10, 867],
+                            [354,   2]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        random = Generator(MT19937(self.seed))
+        desired = np.array([-0.38736406738527,  0.79594375042255,  0.0197076236097])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.normal, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        normal = random.normal
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455])
+
+        random = Generator(MT19937(self.seed))
+        beta = random.beta
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        std_gamma = random.standard_gamma
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258])
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763])
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629])
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([0.04714867120827, 0.1239390327694])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399])
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202])
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081])
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276])
+
+        random = Generator(MT19937(self.seed))
+        lognormal = random.lognormal
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean, sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array(
+            [1.1597068009872629,
+             0.6539188836253857,
+             1.1981526554349398]
+        )
+
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean, scale * 3)
+        assert_raises(ValueError, random.wald, mean, bad_scale * 3)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326])
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        binom = random.binomial
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 2, 1], dtype=np.int64)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+
+        lam = [1]
+        bad_lam_one = [-1]
+        desired = np.array([0, 0, 3])
+
+        random = Generator(MT19937(self.seed))
+        max_lam = random._poisson_lam_max
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        desired = np.array([1, 8, 1])
+
+        random = Generator(MT19937(self.seed))
+        zipf = random.zipf
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([1, 1, 3])
+
+        random = Generator(MT19937(self.seed))
+        geometric = random.geometric
+        actual = geometric(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geometric, bad_p_one * 3)
+        assert_raises(ValueError, geometric, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [-1]
+        bad_nsample_two = [4]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        hypergeom = random.hypergeometric
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, -1)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+        # ValueError for arguments that are too big.
+        assert_raises(ValueError, hypergeom, 2**30, 10, 20)
+        assert_raises(ValueError, hypergeom, 999, 2**31, 50)
+        assert_raises(ValueError, hypergeom, 999, [2**29, 2**30], 1000)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        desired = np.array([1, 1, 1])
+
+        random = Generator(MT19937(self.seed))
+        logseries = random.logseries
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [2, 3, 6, 4, 2, 3]],
+                            [[1, 0, 1, 0, 2, 1],
+                             [7, 2, 2, 1, 4, 4]],
+                            [[0, 2, 0, 1, 2, 0],
+                             [3, 2, 3, 3, 4, 5]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2)
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [0, 0, 2, 1, 1, 1]],
+                            [[4, 2, 3, 3, 5, 3],
+                             [7, 2, 2, 1, 4, 4]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.parametrize("n", [10,
+                                   np.array([10, 10]),
+                                   np.array([[[10]], [[10]]])
+                                   ]
+                             )
+    def test_multinomial_pval_broadcast(self, n):
+        random = Generator(MT19937(self.seed))
+        pvals = np.array([1 / 4] * 4)
+        actual = random.multinomial(n, pvals)
+        n_shape = tuple() if isinstance(n, int) else n.shape
+        expected_shape = n_shape + (4,)
+        assert actual.shape == expected_shape
+        pvals = np.vstack([pvals, pvals])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,)
+        assert actual.shape == expected_shape
+
+        pvals = np.vstack([[pvals], [pvals]])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1])
+        assert actual.shape == expected_shape + (4,)
+        actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape)
+        assert actual.shape == (3, 2) + expected_shape + (4,)
+
+        with pytest.raises(ValueError):
+            # Ensure that size is not broadcast
+            actual = random.multinomial(n, pvals, size=(1,) * 6)
+
+    def test_invalid_pvals_broadcast(self):
+        random = Generator(MT19937(self.seed))
+        pvals = [[1 / 6] * 6, [1 / 4] * 6]
+        assert_raises(ValueError, random.multinomial, 1, pvals)
+        assert_raises(ValueError, random.multinomial, 6, 0.5)
+
+    def test_empty_outputs(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6)
+        assert actual.shape == (10, 0, 6, 6)
+        actual = random.multinomial(12, np.empty((10, 0, 10)))
+        assert actual.shape == (10, 0, 10)
+        actual = random.multinomial(np.empty((3, 0, 7), "i8"),
+                                    np.empty((3, 0, 7, 4)))
+        assert actual.shape == (3, 0, 7, 4)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(Generator(MT19937(s)), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(Generator(MT19937(s)), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_integers(self, endpoint):
+        itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = random.integers
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+@pytest.mark.parametrize("config", JUMP_TEST_DATA)
+def test_jumped(config):
+    # Each config contains the initial seed, a number of raw steps
+    # the sha256 hashes of the initial and the final states' keys and
+    # the position of the initial and the final state.
+    # These were produced using the original C implementation.
+    seed = config["seed"]
+    steps = config["steps"]
+
+    mt19937 = MT19937(seed)
+    # Burn step
+    mt19937.random_raw(steps)
+    key = mt19937.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert mt19937.state["state"]["pos"] == config["initial"]["pos"]
+    assert sha256.hexdigest() == config["initial"]["key_sha256"]
+
+    jumped = mt19937.jumped()
+    key = jumped.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert jumped.state["state"]["pos"] == config["jumped"]["pos"]
+    assert sha256.hexdigest() == config["jumped"]["key_sha256"]
+
+
+def test_broadcast_size_error():
+    mu = np.ones(3)
+    sigma = np.ones((4, 3))
+    size = (10, 4, 2)
+    assert random.normal(mu, sigma, size=(5, 4, 3)).shape == (5, 4, 3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=size)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(1, 3))
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(4, 1, 1))
+    # 1 arg
+    shape = np.ones((4, 3))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=(3,))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=3)
+    # Check out
+    out = np.empty(size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, out=out)
+
+    # 2 arg
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.multinomial([2, 2], [.3, .7], size=(2, 1))
+
+    # 3 arg
+    a = random.chisquare(5, size=3)
+    b = random.chisquare(5, size=(4, 3))
+    c = random.chisquare(5, size=(5, 4, 3))
+    assert random.noncentral_f(a, b, c).shape == (5, 4, 3)
+    with pytest.raises(ValueError, match=r"Output size \(6, 5, 1, 1\) is"):
+        random.noncentral_f(a, b, c, size=(6, 5, 1, 1))
+
+
+def test_broadcast_size_scalar():
+    mu = np.ones(3)
+    sigma = np.ones(3)
+    random.normal(mu, sigma, size=3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=2)
+
+
+def test_ragged_shuffle():
+    # GH 18142
+    seq = [[], [], 1]
+    gen = Generator(MT19937(0))
+    assert_no_warnings(gen.shuffle, seq)
+    assert seq == [1, [], []]
+
+
+@pytest.mark.parametrize("high", [-2, [-2]])
+@pytest.mark.parametrize("endpoint", [True, False])
+def test_single_arg_integer_exception(high, endpoint):
+    # GH 14333
+    gen = Generator(MT19937(0))
+    msg = 'high < 0' if endpoint else 'high <= 0'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(high, endpoint=endpoint)
+    msg = 'low > high' if endpoint else 'low >= high'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(-1, high, endpoint=endpoint)
+    with pytest.raises(ValueError, match=msg):
+        gen.integers([-1], high, endpoint=endpoint)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+def test_c_contig_req_out(dtype):
+    # GH 18704
+    out = np.empty((2, 3), order="F", dtype=dtype)
+    shape = [1, 2, 3]
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, dtype=dtype)
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, size=out.shape, dtype=dtype)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+@pytest.mark.parametrize("order", ["F", "C"])
+@pytest.mark.parametrize("dist", [random.standard_normal, random.random])
+def test_contig_req_out(dist, order, dtype):
+    # GH 18704
+    out = np.empty((2, 3), dtype=dtype, order=order)
+    variates = dist(out=out, dtype=dtype)
+    assert variates is out
+    variates = dist(out=out, dtype=dtype, size=out.shape)
+    assert variates is out
+
+
+def test_generator_ctor_old_style_pickle():
+    rg = np.random.Generator(np.random.PCG64DXSM(0))
+    rg.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, (bit_gen, ), _ = rg.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert bit_gen.__class__.__name__ == "PCG64DXSM"
+    print(ctor)
+    b = ctor(*("PCG64DXSM",))
+    print(b)
+    b.bit_generator.state = bit_gen.state
+    state_b = b.bit_generator.state
+    assert bit_gen.state == state_b
+
+
+def test_pickle_preserves_seed_sequence():
+    # GH 26234
+    # Add explicit test that bit generators preserve seed sequences
+    import pickle
+
+    rg = np.random.Generator(np.random.PCG64DXSM(20240411))
+    ss = rg.bit_generator.seed_seq
+    rg_plk = pickle.loads(pickle.dumps(rg))
+    ss_plk = rg_plk.bit_generator.seed_seq
+    assert_equal(ss.state, ss_plk.state)
+    assert_equal(ss.pool, ss_plk.pool)
+
+    rg.bit_generator.seed_seq.spawn(10)
+    rg_plk = pickle.loads(pickle.dumps(rg))
+    ss_plk = rg_plk.bit_generator.seed_seq
+    assert_equal(ss.state, ss_plk.state)
+
+
+@pytest.mark.parametrize("version", [121, 126])
+def test_legacy_pickle(version):
+    # Pickling format was changes in 1.22.x and in 2.0.x
+    import pickle
+    import gzip
+
+    base_path = os.path.split(os.path.abspath(__file__))[0]
+    pkl_file = os.path.join(
+        base_path, "data", f"generator_pcg64_np{version}.pkl.gz"
+    )
+    with gzip.open(pkl_file) as gz:
+        rg = pickle.load(gz)
+    state = rg.bit_generator.state['state']
+
+    assert isinstance(rg, Generator)
+    assert isinstance(rg.bit_generator, np.random.PCG64)
+    assert state['state'] == 35399562948360463058890781895381311971
+    assert state['inc'] == 87136372517582989555478159403783844777
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c34e6bb3ba74f2f9084a7400fc608776d8e278c0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
@@ -0,0 +1,206 @@
+from numpy.testing import (assert_, assert_array_equal)
+import numpy as np
+import pytest
+from numpy.random import Generator, MT19937
+
+
+class TestRegression:
+
+    def setup_method(self):
+        self.mt19937 = Generator(MT19937(121263137472525314065))
+
+    def test_vonmises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = self.mt19937.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = (2**20 - 2, 2**20 - 2, 2**20 - 2)  # Check for 32-bit systems
+        assert_(self.mt19937.hypergeometric(*args) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        rvsn = self.mt19937.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            mt19937 = Generator(MT19937(12345))
+            shuffled = np.array(t, dtype=object)
+            mt19937.shuffle(shuffled)
+            expected = np.array([t[2], t[0], t[3], t[1]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom BitGenerator does not call into global state
+        res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4])
+        for i in range(3):
+            mt19937 = Generator(MT19937(i))
+            m = Generator(MT19937(4321))
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        self.mt19937.multivariate_normal([0], [[0]], size=1)
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        x = self.mt19937.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
+
+    def test_beta_very_small_parameters(self):
+        # gh-24203: beta would hang with very small parameters.
+        self.mt19937.beta(1e-49, 1e-40)
+
+    def test_beta_ridiculously_small_parameters(self):
+        # gh-24266: beta would generate nan when the parameters
+        # were subnormal or a small multiple of the smallest normal.
+        tiny = np.finfo(1.0).tiny
+        x = self.mt19937.beta(tiny/32, tiny/40, size=50)
+        assert not np.any(np.isnan(x))
+
+    def test_beta_expected_zero_frequency(self):
+        # gh-24475: For small a and b (e.g. a=0.0025, b=0.0025), beta
+        # would generate too many zeros.
+        a = 0.0025
+        b = 0.0025
+        n = 1000000
+        x = self.mt19937.beta(a, b, size=n)
+        nzeros = np.count_nonzero(x == 0)
+        # beta CDF at x = np.finfo(np.double).smallest_subnormal/2
+        # is p = 0.0776169083131899, e.g,
+        #
+        #    import numpy as np
+        #    from mpmath import mp
+        #    mp.dps = 160
+        #    x = mp.mpf(np.finfo(np.float64).smallest_subnormal)/2
+        #    # CDF of the beta distribution at x:
+        #    p = mp.betainc(a, b, x1=0, x2=x, regularized=True)
+        #    n = 1000000
+        #    exprected_freq = float(n*p)
+        #
+        expected_freq = 77616.90831318991
+        assert 0.95*expected_freq < nzeros < 1.05*expected_freq
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = self.mt19937.choice(a, p=probs)
+            assert_(c in a)
+            with pytest.raises(ValueError):
+                self.mt19937.choice(a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+
+        class N(np.ndarray):
+            pass
+
+        mt19937 = Generator(MT19937(1))
+        orig = np.arange(3).view(N)
+        perm = mt19937.permutation(orig)
+        assert_array_equal(perm, np.array([2, 0, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        mt19937 = Generator(MT19937(1))
+        m = M()
+        perm = mt19937.permutation(m)
+        assert_array_equal(perm, np.array([4, 1, 3, 0, 2]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_gamma_0(self):
+        assert self.mt19937.standard_gamma(0.0) == 0.0
+        assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)
+
+        actual = self.mt19937.standard_gamma([0.0], dtype='float')
+        expected = np.array([0.], dtype=np.float32)
+        assert_array_equal(actual, expected)
+
+    def test_geometric_tiny_prob(self):
+        # Regression test for gh-17007.
+        # When p = 1e-30, the probability that a sample will exceed 2**63-1
+        # is 0.9999999999907766, so we expect the result to be all 2**63-1.
+        assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),
+                           np.iinfo(np.int64).max)
+
+    def test_zipf_large_parameter(self):
+        # Regression test for part of gh-9829: a call such as rng.zipf(10000)
+        # would hang.
+        n = 8
+        sample = self.mt19937.zipf(10000, size=n)
+        assert_array_equal(sample, np.ones(n, dtype=np.int64))
+
+    def test_zipf_a_near_1(self):
+        # Regression test for gh-9829: a call such as rng.zipf(1.0000000000001)
+        # would hang.
+        n = 100000
+        sample = self.mt19937.zipf(1.0000000000001, size=n)
+        # Not much of a test, but let's do something more than verify that
+        # it doesn't hang.  Certainly for a monotonically decreasing
+        # discrete distribution truncated to signed 64 bit integers, more
+        # than half should be less than 2**62.
+        assert np.count_nonzero(sample < 2**62) > n/2
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_random.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..c98584aeda9df3ea64209038521784c4b2c24ba9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_random.py
@@ -0,0 +1,1751 @@
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+from numpy import random
+import sys
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = np.random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = np.random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = np.random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = np.random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, -0.5)
+        assert_raises(ValueError, np.random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, [-0.5])
+        assert_raises(ValueError, np.random.RandomState, [-1])
+        assert_raises(ValueError, np.random.RandomState, [4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, np.random.RandomState,
+                      np.array([], dtype=np.int64))
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],
+                                                          [4, 5, 6]])
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, np.random.multinomial, 1, p,
+                      float(1))
+
+    def test_multidimensional_pvals(self):
+        assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])
+        assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.prng = random.RandomState(self.seed)
+        self.state = self.prng.get_state()
+
+    def test_basic(self):
+        old = self.prng.tomaxint(16)
+        self.prng.set_state(self.state)
+        new = self.prng.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(self.state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.prng.standard_normal()
+        state = self.prng.get_state()
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.prng.standard_normal(size=16)
+        self.prng.set_state(old_state)
+        x2 = self.prng.standard_normal(size=16)
+        self.prng.set_state(self.state)
+        x3 = self.prng.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.prng.negative_binomial(0.5, 0.5)
+
+    def test_set_invalid_state(self):
+        # gh-25402
+        with pytest.raises(IndexError):
+            self.prng.set_state(())
+
+
+class TestRandint:
+
+    rfunc = np.random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        np.random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        import hashlib
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            np.random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        np.random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = np.random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            # The legacy rng uses "long" as the default integer:
+            lbnd = 0 if dt is bool else np.iinfo("long").min
+            ubnd = 2 if dt is bool else np.iinfo("long").max + 1
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        np.random.seed(self.seed)
+        actual = np.random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        np.random.seed(self.seed)
+        actual = np.random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randint(self):
+        np.random.seed(self.seed)
+        actual = np.random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        np.random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(np.iinfo('l').max,
+                                               np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random(self):
+        np.random.seed(self.seed)
+        actual = np.random.random((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_choice_uniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False,
+                                  p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = np.random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(np.random.choice(2, replace=True)))
+        assert_(np.isscalar(np.random.choice(2, replace=False)))
+        assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
+        assert_(np.random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
+        assert_(np.random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(np.random.choice(6, s, replace=True).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False).shape, s)
+        assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(np.random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(np.random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(np.random.choice(0, size=0).shape, (0,))
+        assert_equal(np.random.choice([], size=(0,)).shape, (0,))
+        assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, np.random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, np.random.choice, a, p=p)
+
+    def test_bytes(self):
+        np.random.seed(self.seed)
+        actual = np.random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object), ("b", np.int32)])]:
+            np.random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            np.random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            np.random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            np.random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    @pytest.mark.parametrize("random",
+            [np.random, np.random.RandomState(), np.random.default_rng()])
+    def test_shuffle_untyped_warning(self, random):
+        # Create a dict works like a sequence but isn't one
+        values = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
+        with pytest.warns(UserWarning,
+                match="you are shuffling a 'dict' object") as rec:
+            random.shuffle(values)
+        assert "test_random" in rec[0].filename
+
+    @pytest.mark.parametrize("random",
+        [np.random, np.random.RandomState(), np.random.default_rng()])
+    @pytest.mark.parametrize("use_array_like", [True, False])
+    def test_shuffle_no_object_unpacking(self, random, use_array_like):
+        class MyArr(np.ndarray):
+            pass
+
+        items = [
+            None, np.array([3]), np.float64(3), np.array(10), np.float64(7)
+        ]
+        arr = np.array(items, dtype=object)
+        item_ids = {id(i) for i in items}
+        if use_array_like:
+            arr = arr.view(MyArr)
+
+        # The array was created fine, and did not modify any objects:
+        assert all(id(i) in item_ids for i in arr)
+
+        if use_array_like and not isinstance(random, np.random.Generator):
+            # The old API gives incorrect results, but warns about it.
+            with pytest.warns(UserWarning,
+                    match="Shuffling a one dimensional array.*"):
+                random.shuffle(arr)
+        else:
+            random.shuffle(arr)
+            assert all(id(i) in item_ids for i in arr)
+
+    def test_shuffle_memoryview(self):
+        # gh-18273
+        # allow graceful handling of memoryviews
+        # (treat the same as arrays)
+        np.random.seed(self.seed)
+        a = np.arange(5).data
+        np.random.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 4, 3, 2])
+        rng = np.random.RandomState(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 2, 3, 4])
+        rng = np.random.default_rng(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [4, 1, 0, 3, 2])
+
+    def test_shuffle_not_writeable(self):
+        a = np.zeros(3)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            np.random.shuffle(a)
+
+    def test_beta(self):
+        np.random.seed(self.seed)
+        actual = np.random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.binomial(100, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        np.random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, np.random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(np.random.exponential(scale=0), 0)
+        assert_raises(ValueError, np.random.exponential, scale=-0.)
+
+    def test_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(np.random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_gumbel(self):
+        np.random.seed(self.seed)
+        actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(np.random.gumbel(scale=0), 0)
+        assert_raises(ValueError, np.random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.hypergeometric(10, 5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = np.random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = np.random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(np.random.laplace(scale=0), 0)
+        assert_raises(ValueError, np.random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        np.random.seed(self.seed)
+        actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        np.random.seed(self.seed)
+        actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(np.random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, np.random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        np.random.seed(self.seed)
+        actual = np.random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_multinomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        np.random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = np.random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = np.random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(np.random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, np.random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            np.random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+    def test_negative_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_noncentral_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                        size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(np.random.normal(scale=0), 0)
+        assert_raises(ValueError, np.random.normal, scale=-0.)
+
+    def test_pareto(self):
+        np.random.seed(self.seed)
+        actual = np.random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        np.random.seed(self.seed)
+        actual = np.random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, np.random.poisson, lamneg)
+        assert_raises(ValueError, np.random.poisson, [lamneg]*10)
+        assert_raises(ValueError, np.random.poisson, lambig)
+        assert_raises(ValueError, np.random.poisson, [lambig]*10)
+
+    def test_power(self):
+        np.random.seed(self.seed)
+        actual = np.random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        np.random.seed(self.seed)
+        actual = np.random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(np.random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, np.random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(np.random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, np.random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        np.random.seed(self.seed)
+        actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,
+                                      size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        np.random.seed(self.seed)
+        actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = np.random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func,  0,      np.inf)
+        assert_raises(OverflowError, func,  fmin,   fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        np.random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, np.random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+            __index__ = __int__
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        np.random.seed(self.seed)
+        actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        np.random.seed(self.seed)
+        r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        np.testing.assert_(np.isfinite(r).all())
+
+    def test_wald(self):
+        np.random.seed(self.seed)
+        actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        np.random.seed(self.seed)
+        actual = np.random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        np.random.seed(self.seed)
+        assert_equal(np.random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, np.random.weibull, a=-0.)
+
+    def test_zipf(self):
+        np.random.seed(self.seed)
+        actual = np.random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def setSeed(self):
+        np.random.seed(self.seed)
+
+    # TODO: Include test for randint once it can broadcast
+    # Can steal the test written in PR #6938
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = np.random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.setSeed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = np.random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.setSeed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = np.random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.setSeed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.setSeed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = np.random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = np.random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = np.random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.setSeed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.setSeed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = np.random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.setSeed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.setSeed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = np.random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.setSeed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.setSeed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = np.random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.setSeed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = np.random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.setSeed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = np.random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.setSeed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = np.random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.setSeed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.setSeed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = np.random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.setSeed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = np.random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = np.random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = np.random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.setSeed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = np.random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.setSeed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = np.random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.setSeed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = np.random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.setSeed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        self.setSeed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = np.random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.setSeed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = np.random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.setSeed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+
+        self.setSeed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = np.random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.setSeed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = np.random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = np.random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.setSeed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = np.random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = np.random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.setSeed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = np.random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.setSeed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = np.random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.setSeed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = np.random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = np.random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(np.random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(np.random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1/6.]*6, size=10000)
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (np.random.exponential, np.random.standard_gamma,
+                 np.random.chisquare, np.random.standard_t,
+                 np.random.pareto, np.random.weibull,
+                 np.random.power, np.random.rayleigh,
+                 np.random.poisson, np.random.zipf,
+                 np.random.geometric, np.random.logseries)
+
+        probfuncs = (np.random.geometric, np.random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (np.random.uniform, np.random.normal,
+                 np.random.beta, np.random.gamma,
+                 np.random.f, np.random.noncentral_chisquare,
+                 np.random.vonmises, np.random.laplace,
+                 np.random.gumbel, np.random.logistic,
+                 np.random.lognormal, np.random.wald,
+                 np.random.binomial, np.random.negative_binomial)
+
+        probfuncs = (np.random.binomial, np.random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_randint(self):
+        itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = np.random.randint
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [np.random.noncentral_f, np.random.triangular,
+                 np.random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate.py
new file mode 100644
index 0000000000000000000000000000000000000000..5121a684f693a14febd473272509d491a4438631
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate.py
@@ -0,0 +1,2124 @@
+import hashlib
+import pickle
+import sys
+import warnings
+
+import numpy as np
+import pytest
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+
+from numpy.random import MT19937, PCG64
+from numpy import random
+
+INT_FUNCS = {'binomial': (100.0, 0.6),
+             'geometric': (.5,),
+             'hypergeometric': (20, 20, 10),
+             'logseries': (.5,),
+             'multinomial': (20, np.ones(6) / 6.0),
+             'negative_binomial': (100, .5),
+             'poisson': (10.0,),
+             'zipf': (2,),
+             }
+
+if np.iinfo(np.long).max < 2**32:
+    # Windows and some 32-bit platforms, e.g., ARM
+    INT_FUNC_HASHES = {'binomial': '2fbead005fc63942decb5326d36a1f32fe2c9d32c904ee61e46866b88447c263',
+                       'logseries': '23ead5dcde35d4cfd4ef2c105e4c3d43304b45dc1b1444b7823b9ee4fa144ebb',
+                       'geometric': '0d764db64f5c3bad48c8c33551c13b4d07a1e7b470f77629bef6c985cac76fcf',
+                       'hypergeometric': '7b59bf2f1691626c5815cdcd9a49e1dd68697251d4521575219e4d2a1b8b2c67',
+                       'multinomial': 'd754fa5b92943a38ec07630de92362dd2e02c43577fc147417dc5b9db94ccdd3',
+                       'negative_binomial': '8eb216f7cb2a63cf55605422845caaff002fddc64a7dc8b2d45acd477a49e824',
+                       'poisson': '70c891d76104013ebd6f6bcf30d403a9074b886ff62e4e6b8eb605bf1a4673b7',
+                       'zipf': '01f074f97517cd5d21747148ac6ca4074dde7fcb7acbaec0a936606fecacd93f',
+                       }
+else:
+    INT_FUNC_HASHES = {'binomial': '8626dd9d052cb608e93d8868de0a7b347258b199493871a1dc56e2a26cacb112',
+                       'geometric': '8edd53d272e49c4fc8fbbe6c7d08d563d62e482921f3131d0a0e068af30f0db9',
+                       'hypergeometric': '83496cc4281c77b786c9b7ad88b74d42e01603a55c60577ebab81c3ba8d45657',
+                       'logseries': '65878a38747c176bc00e930ebafebb69d4e1e16cd3a704e264ea8f5e24f548db',
+                       'multinomial': '7a984ae6dca26fd25374479e118b22f55db0aedccd5a0f2584ceada33db98605',
+                       'negative_binomial': 'd636d968e6a24ae92ab52fe11c46ac45b0897e98714426764e820a7d77602a61',
+                       'poisson': '956552176f77e7c9cb20d0118fc9cf690be488d790ed4b4c4747b965e61b0bb4',
+                       'zipf': 'f84ba7feffda41e606e20b28dfc0f1ea9964a74574513d4a4cbc98433a8bfa45',
+                       }
+
+
+@pytest.fixture(scope='module', params=INT_FUNCS)
+def int_func(request):
+    return (request.param, INT_FUNCS[request.param],
+            INT_FUNC_HASHES[request.param])
+
+
+@pytest.fixture
+def restore_singleton_bitgen():
+    """Ensures that the singleton bitgen is restored after a test"""
+    orig_bitgen = np.random.get_bit_generator()
+    yield
+    np.random.set_bit_generator(orig_bitgen)
+
+
+def assert_mt19937_state_equal(a, b):
+    assert_equal(a['bit_generator'], b['bit_generator'])
+    assert_array_equal(a['state']['key'], b['state']['key'])
+    assert_array_equal(a['state']['pos'], b['state']['pos'])
+    assert_equal(a['has_gauss'], b['has_gauss'])
+    assert_equal(a['gauss'], b['gauss'])
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, -0.5)
+        assert_raises(ValueError, random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, [-0.5])
+        assert_raises(ValueError, random.RandomState, [-1])
+        assert_raises(ValueError, random.RandomState, [4294967296])
+        assert_raises(ValueError, random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, random.RandomState, np.array([],
+                                                               dtype=np.int64))
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3],
+                                                       [4, 5, 6]])
+
+    def test_cannot_seed(self):
+        rs = random.RandomState(PCG64(0))
+        with assert_raises(TypeError):
+            rs.seed(1234)
+
+    def test_invalid_initialization(self):
+        assert_raises(ValueError, random.RandomState, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random.seed(1432985819)
+        non_contig = random.multinomial(100, pvals=pvals)
+        random.seed(1432985819)
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+    def test_multinomial_n_float(self):
+        # Non-index integer types should gracefully truncate floats
+        random.multinomial(100.5, [0.2, 0.8])
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.random_state = random.RandomState(self.seed)
+        self.state = self.random_state.get_state()
+
+    def test_basic(self):
+        old = self.random_state.tomaxint(16)
+        self.random_state.set_state(self.state)
+        new = self.random_state.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(self.state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.random_state.standard_normal()
+        state = self.random_state.get_state()
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(old_state)
+        x2 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(self.state)
+        x3 = self.random_state.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.random_state.negative_binomial(0.5, 0.5)
+
+    def test_get_state_warning(self):
+        rs = random.RandomState(PCG64())
+        with suppress_warnings() as sup:
+            w = sup.record(RuntimeWarning)
+            state = rs.get_state()
+            assert_(len(w) == 1)
+            assert isinstance(state, dict)
+            assert state['bit_generator'] == 'PCG64'
+
+    def test_invalid_legacy_state_setting(self):
+        state = self.random_state.get_state()
+        new_state = ('Unknown', ) + state[1:]
+        assert_raises(ValueError, self.random_state.set_state, new_state)
+        assert_raises(TypeError, self.random_state.set_state,
+                      np.array(new_state, dtype=object))
+        state = self.random_state.get_state(legacy=False)
+        del state['bit_generator']
+        assert_raises(ValueError, self.random_state.set_state, state)
+
+    def test_pickle(self):
+        self.random_state.seed(0)
+        self.random_state.random_sample(100)
+        self.random_state.standard_normal()
+        pickled = self.random_state.get_state(legacy=False)
+        assert_equal(pickled['has_gauss'], 1)
+        rs_unpick = pickle.loads(pickle.dumps(self.random_state))
+        unpickled = rs_unpick.get_state(legacy=False)
+        assert_mt19937_state_equal(pickled, unpickled)
+
+    def test_state_setting(self):
+        attr_state = self.random_state.__getstate__()
+        self.random_state.standard_normal()
+        self.random_state.__setstate__(attr_state)
+        state = self.random_state.get_state(legacy=False)
+        assert_mt19937_state_equal(attr_state, state)
+
+    def test_repr(self):
+        assert repr(self.random_state).startswith('RandomState(MT19937)')
+
+
+class TestRandint:
+
+    rfunc = random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[3992670689, 2438360420, 2557845020],
+                             [4107320065, 4142558326, 3216529513],
+                             [1605979228, 2807061240,  665605495]],
+                            [[3211410639, 4128781000,  457175120],
+                             [1712592594, 1282922662, 3081439808],
+                             [3997822960, 2008322436, 1563495165]],
+                            [[1398375547, 4269260146,  115316740],
+                             [3414372578, 3437564012, 2112038651],
+                             [3572980305, 2260248732, 3908238631]],
+                            [[2561372503,  223155946, 3127879445],
+                             [ 441282060, 3514786552, 2148440361],
+                             [1629275283, 3479737011, 3003195987]],
+                            [[ 412181688,  940383289, 3047321305],
+                             [2978368172,  764731833, 2282559898],
+                             [ 105711276,  720447391, 3596512484]]])
+        for size in [None, (5, 3, 3)]:
+            random.seed(12345)
+            x = self.rfunc([[-1], [0], [1]], [2**32 - 1, 2**32, 2**32 + 1],
+                           size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            # The legacy random generation forces the use of "long" on this
+            # branch even when the input is `int` and the default dtype
+            # for int changed (dtype=int is also the functions default)
+            op_dtype = "long" if dt is int else "bool"
+            lbnd = 0 if dt is bool else np.iinfo(op_dtype).min
+            ubnd = 2 if dt is bool else np.iinfo(op_dtype).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        random.seed(self.seed)
+        actual = random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rand_singleton(self):
+        random.seed(self.seed)
+        actual = random.rand()
+        desired = 0.61879477158567997
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        random.seed(self.seed)
+        actual = random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.randn()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_randint(self):
+        random.seed(self.seed)
+        actual = random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(198, size=(3, 2))
+            assert_(len(w) == 1)
+        assert_array_equal(actual, desired + 100)
+
+    def test_tomaxint(self):
+        random.seed(self.seed)
+        rs = random.RandomState(self.seed)
+        actual = rs.tomaxint(size=(3, 2))
+        if np.iinfo(np.long).max == 2147483647:
+            desired = np.array([[1328851649,  731237375],
+                                [1270502067,  320041495],
+                                [1908433478,  499156889]], dtype=np.int64)
+        else:
+            desired = np.array([[5707374374421908479, 5456764827585442327],
+                                [8196659375100692377, 8224063923314595285],
+                                [4220315081820346526, 7177518203184491332]],
+                               dtype=np.int64)
+
+        assert_equal(actual, desired)
+
+        rs.seed(self.seed)
+        actual = rs.tomaxint()
+        assert_equal(actual, desired[0, 0])
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(np.iinfo('l').max,
+                                            np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            typer = np.dtype('l').type
+            actual = random.random_integers(typer(np.iinfo('l').max),
+                                            typer(np.iinfo('l').max))
+            assert_(len(w) == 1)
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random_sample(self):
+        random.seed(self.seed)
+        actual = random.random_sample((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.random_sample()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_choice_uniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random.seed(self.seed)
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random.seed(self.seed)
+        non_contig = random.choice(5, 3, p=p[::2])
+        random.seed(self.seed)
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_bytes(self):
+        random.seed(self.seed)
+        actual = random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+        def test_shuffle_invalid_objects(self):
+            x = np.array(3)
+            assert_raises(TypeError, random.shuffle, x)
+
+    def test_permutation(self):
+        random.seed(self.seed)
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3]
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        random.seed(self.seed)
+        bad_x_str = "abcd"
+        assert_raises(IndexError, random.permutation, bad_x_str)
+
+        random.seed(self.seed)
+        bad_x_float = 1.2
+        assert_raises(IndexError, random.permutation, bad_x_float)
+
+        integer_val = 10
+        desired = [9, 0, 8, 5, 1, 3, 4, 7, 6, 2]
+
+        random.seed(self.seed)
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_beta(self):
+        random.seed(self.seed)
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456)
+        desired = 37
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random.seed(self.seed)
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random.seed(self.seed)
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random.seed(self.seed)
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_exponential(self):
+        random.seed(self.seed)
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random.seed(self.seed)
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random.seed(self.seed)
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random.seed(self.seed)
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random.seed(self.seed)
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random.seed(self.seed)
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random.seed(self.seed)
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random.seed(self.seed)
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random.seed(self.seed)
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random.seed(self.seed)
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random.seed(self.seed)
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    def test_negative_binomial(self):
+        random.seed(self.seed)
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_noncentral_chisquare(self):
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random.seed(self.seed)
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random.seed(self.seed)
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random.seed(self.seed)
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random.seed(self.seed)
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random.seed(self.seed)
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random.seed(self.seed)
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random.seed(self.seed)
+        actual = random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        random.seed(self.seed)
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random.seed(self.seed)
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn_singleton(self):
+        random.seed(self.seed)
+        actual = random.randn()
+        desired = np.array(1.34016345771863121)
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        random.seed(self.seed)
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random.seed(self.seed)
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random.seed(self.seed)
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random.seed(self.seed)
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_large(self):
+        # guard against changes in RandomState when Generator is fixed
+        random.seed(self.seed)
+        actual = random.vonmises(mu=0., kappa=1e7, size=3)
+        desired = np.array([4.634253748521111e-04,
+                            3.558873596114509e-04,
+                            -2.337119622577433e-04])
+        assert_array_almost_equal(actual, desired, decimal=8)
+
+    def test_vonmises_nan(self):
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    def test_wald(self):
+        random.seed(self.seed)
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random.seed(self.seed)
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random.seed(self.seed)
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random.seed(self.seed)
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def set_seed(self):
+        random.seed(self.seed)
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.set_seed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.set_seed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.set_seed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.set_seed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.set_seed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.set_seed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.set_seed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.set_seed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.set_seed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.set_seed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.set_seed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.set_seed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.set_seed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.set_seed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.set_seed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.set_seed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.set_seed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.set_seed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.set_seed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.set_seed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+        assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma)
+
+        self.set_seed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.set_seed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.set_seed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        self.set_seed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.set_seed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.set_seed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.set_seed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.set_seed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.set_seed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, 0)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+# Ensure returned array dtype is correct for platform
+def test_integer_dtype(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    actual = f(*args, size=2)
+    assert_(actual.dtype == np.dtype('l'))
+
+
+def test_integer_repeat(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    val = f(*args, size=1000000)
+    if sys.byteorder != 'little':
+        val = val.byteswap()
+    res = hashlib.sha256(val.view(np.int8)).hexdigest()
+    assert_(res == sha256)
+
+
+def test_broadcast_size_error():
+    # GH-16833
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+
+
+def test_randomstate_ctor_old_style_pickle():
+    rs = np.random.RandomState(MT19937(0))
+    rs.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, args, state_a = rs.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert args[0].__class__.__name__ == "MT19937"
+    b = ctor(*("MT19937",))
+    b.set_state(state_a)
+    state_b = b.get_state(legacy=False)
+
+    assert_equal(state_a['bit_generator'], state_b['bit_generator'])
+    assert_array_equal(state_a['state']['key'], state_b['state']['key'])
+    assert_array_equal(state_a['state']['pos'], state_b['state']['pos'])
+    assert_equal(state_a['has_gauss'], state_b['has_gauss'])
+    assert_equal(state_a['gauss'], state_b['gauss'])
+
+
+def test_hot_swap(restore_singleton_bitgen):
+    # GH 21808
+    def_bg = np.random.default_rng(0)
+    bg = def_bg.bit_generator
+    np.random.set_bit_generator(bg)
+    assert isinstance(np.random.mtrand._rand._bit_generator, type(bg))
+
+    second_bg = np.random.get_bit_generator()
+    assert bg is second_bg
+
+
+def test_seed_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    state = np.random.get_state(legacy=False)
+    np.random.seed(1)
+    new_state = np.random.get_state(legacy=False)
+    print(state)
+    print(new_state)
+    assert state["bit_generator"] == "PCG64"
+    assert state["state"]["state"] != new_state["state"]["state"]
+    assert state["state"]["inc"] != new_state["state"]["inc"]
+
+
+def test_state_error_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    state = np.random.get_state()
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    with pytest.raises(ValueError, match="state must be for a PCG64"):
+        np.random.set_state(state)
+
+
+def test_swap_worked(restore_singleton_bitgen):
+    # GH 21808
+    np.random.seed(98765)
+    vals = np.random.randint(0, 2 ** 30, 10)
+    bg = PCG64(0)
+    state = bg.state
+    np.random.set_bit_generator(bg)
+    state_direct = np.random.get_state(legacy=False)
+    for field in state:
+        assert state[field] == state_direct[field]
+    np.random.seed(98765)
+    pcg_vals = np.random.randint(0, 2 ** 30, 10)
+    assert not np.all(vals == pcg_vals)
+    new_state = bg.state
+    assert new_state["state"]["state"] != state["state"]["state"]
+    assert new_state["state"]["inc"] == new_state["state"]["inc"]
+
+
+def test_swapped_singleton_against_direct(restore_singleton_bitgen):
+    np.random.set_bit_generator(PCG64(98765))
+    singleton_vals = np.random.randint(0, 2 ** 30, 10)
+    rg = np.random.RandomState(PCG64(98765))
+    non_singleton_vals = rg.randint(0, 2 ** 30, 10)
+    assert_equal(non_singleton_vals, singleton_vals)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fd8776c7f969c71c7d0046142598219bd3374b3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_randomstate_regression.py
@@ -0,0 +1,216 @@
+import sys
+
+import pytest
+
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+import numpy as np
+
+from numpy import random
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        random.seed(0)
+        rvsn = random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        random.multivariate_normal([0], [[0]], size=1)
+        random.multivariate_normal([0], [[0]], size=np.int_(1))
+        random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        random.seed(1234567890)
+        x = random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        random.seed(1)
+        m = M()
+        perm = random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_warns_byteorder(self):
+        # GH 13159
+        other_byteord_dt = 'i4'
+        with pytest.deprecated_call(match='non-native byteorder is not'):
+            random.randint(0, 200, size=10, dtype=other_byteord_dt)
+
+    def test_named_argument_initialization(self):
+        # GH 13669
+        rs1 = np.random.RandomState(123456789)
+        rs2 = np.random.RandomState(seed=123456789)
+        assert rs1.randint(0, 100) == rs2.randint(0, 100)
+
+    def test_choice_retun_dtype(self):
+        # GH 9867, now long since the NumPy default changed.
+        c = np.random.choice(10, p=[.1]*10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, p=[.1]*10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = np.random.choice(10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_randint_117(self):
+        # GH 14189
+        random.seed(0)
+        expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
+                             2588848963, 3684848379, 2340255427, 3638918503,
+                             1819583497, 2678185683], dtype='int64')
+        actual = random.randint(2**32, size=10)
+        assert_array_equal(actual, expected)
+
+    def test_p_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(12345)
+        assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
+                           [0, 0, 0, 1, 1])
+
+    def test_n_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(8675309)
+        expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                             [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
+        assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
+                           expected)
+
+
+def test_multinomial_empty():
+    # gh-20483
+    # Ensure that empty p-vals are correctly handled
+    assert random.multinomial(10, []).shape == (0,)
+    assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)
+
+
+def test_multinomial_1d_pval():
+    # gh-20483
+    with pytest.raises(TypeError, match="pvals must be a 1-d"):
+        random.multinomial(10, 0.3)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_regression.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7b02dc4f7d7f161631e193caf43e3a3e109909c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_regression.py
@@ -0,0 +1,149 @@
+import sys
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+from numpy import random
+import numpy as np
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.mtrand.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(np.random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        np.random.seed(0)
+        rvsn = np.random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            np.random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = np.random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            np.random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        np.random.multivariate_normal([0], [[0]], size=1)
+        np.random.multivariate_normal([0], [[0]], size=np.int_(1))
+        np.random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        np.random.seed(1234567890)
+        x = np.random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        np.random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = np.random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        np.random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        np.random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        np.random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = np.random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        np.random.seed(1)
+        m = M()
+        perm = np.random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..f08cf80faafa2fc1a369eaf7dd4d6fcccd5e9158
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_seed_sequence.py
@@ -0,0 +1,80 @@
+import numpy as np
+from numpy.testing import assert_array_equal, assert_array_compare
+
+from numpy.random import SeedSequence
+
+
+def test_reference_data():
+    """ Check that SeedSequence generates data the same as the C++ reference.
+
+    https://gist.github.com/imneme/540829265469e673d045
+    """
+    inputs = [
+        [3735928559, 195939070, 229505742, 305419896],
+        [3668361503, 4165561550, 1661411377, 3634257570],
+        [164546577, 4166754639, 1765190214, 1303880213],
+        [446610472, 3941463886, 522937693, 1882353782],
+        [1864922766, 1719732118, 3882010307, 1776744564],
+        [4141682960, 3310988675, 553637289, 902896340],
+        [1134851934, 2352871630, 3699409824, 2648159817],
+        [1240956131, 3107113773, 1283198141, 1924506131],
+        [2669565031, 579818610, 3042504477, 2774880435],
+        [2766103236, 2883057919, 4029656435, 862374500],
+    ]
+    outputs = [
+        [3914649087, 576849849, 3593928901, 2229911004],
+        [2240804226, 3691353228, 1365957195, 2654016646],
+        [3562296087, 3191708229, 1147942216, 3726991905],
+        [1403443605, 3591372999, 1291086759, 441919183],
+        [1086200464, 2191331643, 560336446, 3658716651],
+        [3249937430, 2346751812, 847844327, 2996632307],
+        [2584285912, 4034195531, 3523502488, 169742686],
+        [959045797, 3875435559, 1886309314, 359682705],
+        [3978441347, 432478529, 3223635119, 138903045],
+        [296367413, 4262059219, 13109864, 3283683422],
+    ]
+    outputs64 = [
+        [2477551240072187391, 9577394838764454085],
+        [15854241394484835714, 11398914698975566411],
+        [13708282465491374871, 16007308345579681096],
+        [15424829579845884309, 1898028439751125927],
+        [9411697742461147792, 15714068361935982142],
+        [10079222287618677782, 12870437757549876199],
+        [17326737873898640088, 729039288628699544],
+        [16644868984619524261, 1544825456798124994],
+        [1857481142255628931, 596584038813451439],
+        [18305404959516669237, 14103312907920476776],
+    ]
+    for seed, expected, expected64 in zip(inputs, outputs, outputs64):
+        expected = np.array(expected, dtype=np.uint32)
+        ss = SeedSequence(seed)
+        state = ss.generate_state(len(expected))
+        assert_array_equal(state, expected)
+        state64 = ss.generate_state(len(expected64), dtype=np.uint64)
+        assert_array_equal(state64, expected64)
+
+
+def test_zero_padding():
+    """ Ensure that the implicit zero-padding does not cause problems.
+    """
+    # Ensure that large integers are inserted in little-endian fashion to avoid
+    # trailing 0s.
+    ss0 = SeedSequence(42)
+    ss1 = SeedSequence(42 << 32)
+    assert_array_compare(
+        np.not_equal,
+        ss0.generate_state(4),
+        ss1.generate_state(4))
+
+    # Ensure backwards compatibility with the original 0.17 release for small
+    # integers and no spawn key.
+    expected42 = np.array([3444837047, 2669555309, 2046530742, 3581440988],
+                          dtype=np.uint32)
+    assert_array_equal(SeedSequence(42).generate_state(4), expected42)
+
+    # Regression test for gh-16539 to ensure that the implicit 0s don't
+    # conflict with spawn keys.
+    assert_array_compare(
+        np.not_equal,
+        SeedSequence(42, spawn_key=(0,)).generate_state(4),
+        expected42)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_smoke.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_smoke.py
new file mode 100644
index 0000000000000000000000000000000000000000..b402e87384d6fcee08b6351bbbf8ef7587b890e8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/random/tests/test_smoke.py
@@ -0,0 +1,818 @@
+import pickle
+from functools import partial
+
+import numpy as np
+import pytest
+from numpy.testing import assert_equal, assert_, assert_array_equal
+from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64)
+
+@pytest.fixture(scope='module',
+                params=(np.bool, np.int8, np.int16, np.int32, np.int64,
+                        np.uint8, np.uint16, np.uint32, np.uint64))
+def dtype(request):
+    return request.param
+
+
+def params_0(f):
+    val = f()
+    assert_(np.isscalar(val))
+    val = f(10)
+    assert_(val.shape == (10,))
+    val = f((10, 10))
+    assert_(val.shape == (10, 10))
+    val = f((10, 10, 10))
+    assert_(val.shape == (10, 10, 10))
+    val = f(size=(5, 5))
+    assert_(val.shape == (5, 5))
+
+
+def params_1(f, bounded=False):
+    a = 5.0
+    b = np.arange(2.0, 12.0)
+    c = np.arange(2.0, 102.0).reshape((10, 10))
+    d = np.arange(2.0, 1002.0).reshape((10, 10, 10))
+    e = np.array([2.0, 3.0])
+    g = np.arange(2.0, 12.0).reshape((1, 10, 1))
+    if bounded:
+        a = 0.5
+        b = b / (1.5 * b.max())
+        c = c / (1.5 * c.max())
+        d = d / (1.5 * d.max())
+        e = e / (1.5 * e.max())
+        g = g / (1.5 * g.max())
+
+    # Scalar
+    f(a)
+    # Scalar - size
+    f(a, size=(10, 10))
+    # 1d
+    f(b)
+    # 2d
+    f(c)
+    # 3d
+    f(d)
+    # 1d size
+    f(b, size=10)
+    # 2d - size - broadcast
+    f(e, size=(10, 2))
+    # 3d - size
+    f(g, size=(10, 10, 10))
+
+
+def comp_state(state1, state2):
+    identical = True
+    if isinstance(state1, dict):
+        for key in state1:
+            identical &= comp_state(state1[key], state2[key])
+    elif type(state1) != type(state2):
+        identical &= type(state1) == type(state2)
+    else:
+        if (isinstance(state1, (list, tuple, np.ndarray)) and isinstance(
+                state2, (list, tuple, np.ndarray))):
+            for s1, s2 in zip(state1, state2):
+                identical &= comp_state(s1, s2)
+        else:
+            identical &= state1 == state2
+    return identical
+
+
+def warmup(rg, n=None):
+    if n is None:
+        n = 11 + np.random.randint(0, 20)
+    rg.standard_normal(n)
+    rg.standard_normal(n)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.integers(0, 2 ** 24, n, dtype=np.uint64)
+    rg.integers(0, 2 ** 48, n, dtype=np.uint64)
+    rg.standard_gamma(11.0, n)
+    rg.standard_gamma(11.0, n, dtype=np.float32)
+    rg.random(n, dtype=np.float64)
+    rg.random(n, dtype=np.float32)
+
+
+class RNG:
+    @classmethod
+    def setup_class(cls):
+        # Overridden in test classes. Place holder to silence IDE noise
+        cls.bit_generator = PCG64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    @classmethod
+    def _extra_setup(cls):
+        cls.vec_1d = np.arange(2.0, 102.0)
+        cls.vec_2d = np.arange(2.0, 102.0)[None, :]
+        cls.mat = np.arange(2.0, 102.0, 0.01).reshape((100, 100))
+        cls.seed_error = TypeError
+
+    def _reset_state(self):
+        self.rg.bit_generator.state = self.initial_state
+
+    def test_init(self):
+        rg = Generator(self.bit_generator())
+        state = rg.bit_generator.state
+        rg.standard_normal(1)
+        rg.standard_normal(1)
+        rg.bit_generator.state = state
+        new_state = rg.bit_generator.state
+        assert_(comp_state(state, new_state))
+
+    def test_advance(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'advance'):
+            self.rg.bit_generator.advance(self.advance)
+            assert_(not comp_state(state, self.rg.bit_generator.state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            pytest.skip(f'Advance is not supported by {bitgen_name}')
+
+    def test_jump(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'jumped'):
+            bit_gen2 = self.rg.bit_generator.jumped()
+            jumped_state = bit_gen2.state
+            assert_(not comp_state(state, jumped_state))
+            self.rg.random(2 * 3 * 5 * 7 * 11 * 13 * 17)
+            self.rg.bit_generator.state = state
+            bit_gen3 = self.rg.bit_generator.jumped()
+            rejumped_state = bit_gen3.state
+            assert_(comp_state(jumped_state, rejumped_state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            if bitgen_name not in ('SFC64',):
+                raise AttributeError(f'no "jumped" in {bitgen_name}')
+            pytest.skip(f'Jump is not supported by {bitgen_name}')
+
+    def test_uniform(self):
+        r = self.rg.uniform(-1.0, 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_uniform_array(self):
+        r = self.rg.uniform(np.array([-1.0] * 10), 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(np.array([-1.0] * 10),
+                            np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(-1.0, np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_random(self):
+        assert_(len(self.rg.random(10)) == 10)
+        params_0(self.rg.random)
+
+    def test_standard_normal_zig(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+
+    def test_standard_normal(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+        params_0(self.rg.standard_normal)
+
+    def test_standard_gamma(self):
+        assert_(len(self.rg.standard_gamma(10, 10)) == 10)
+        assert_(len(self.rg.standard_gamma(np.array([10] * 10), 10)) == 10)
+        params_1(self.rg.standard_gamma)
+
+    def test_standard_exponential(self):
+        assert_(len(self.rg.standard_exponential(10)) == 10)
+        params_0(self.rg.standard_exponential)
+
+    def test_standard_exponential_float(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32'))
+
+    def test_standard_exponential_float_log(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32',
+                                               method='inv')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32',
+                         method='inv'))
+
+    def test_standard_cauchy(self):
+        assert_(len(self.rg.standard_cauchy(10)) == 10)
+        params_0(self.rg.standard_cauchy)
+
+    def test_standard_t(self):
+        assert_(len(self.rg.standard_t(10, 10)) == 10)
+        params_1(self.rg.standard_t)
+
+    def test_binomial(self):
+        assert_(self.rg.binomial(10, .5) >= 0)
+        assert_(self.rg.binomial(1000, .5) >= 0)
+
+    def test_reset_state(self):
+        state = self.rg.bit_generator.state
+        int_1 = self.rg.integers(2**31)
+        self.rg.bit_generator.state = state
+        int_2 = self.rg.integers(2**31)
+        assert_(int_1 == int_2)
+
+    def test_entropy_init(self):
+        rg = Generator(self.bit_generator())
+        rg2 = Generator(self.bit_generator())
+        assert_(not comp_state(rg.bit_generator.state,
+                               rg2.bit_generator.state))
+
+    def test_seed(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg2 = Generator(self.bit_generator(*self.seed))
+        rg.random()
+        rg2.random()
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_reset_state_gauss(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.standard_normal()
+        state = rg.bit_generator.state
+        n1 = rg.standard_normal(size=10)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.standard_normal(size=10)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_uint32(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.integers(0, 2 ** 24, 120, dtype=np.uint32)
+        state = rg.bit_generator.state
+        n1 = rg.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_float(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.random(dtype='float32')
+        state = rg.bit_generator.state
+        n1 = rg.random(size=10, dtype='float32')
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.random(size=10, dtype='float32')
+        assert_((n1 == n2).all())
+
+    def test_shuffle(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_permutation(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_beta(self):
+        vals = self.rg.beta(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), 2.0)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(2.0, np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([[2.0]] * 10))
+        assert_(vals.shape == (10, 10))
+
+    def test_bytes(self):
+        vals = self.rg.bytes(10)
+        assert_(len(vals) == 10)
+
+    def test_chisquare(self):
+        vals = self.rg.chisquare(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.chisquare)
+
+    def test_exponential(self):
+        vals = self.rg.exponential(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential)
+
+    def test_f(self):
+        vals = self.rg.f(3, 1000, 10)
+        assert_(len(vals) == 10)
+
+    def test_gamma(self):
+        vals = self.rg.gamma(3, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_geometric(self):
+        vals = self.rg.geometric(0.5, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential, bounded=True)
+
+    def test_gumbel(self):
+        vals = self.rg.gumbel(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_laplace(self):
+        vals = self.rg.laplace(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logitic(self):
+        vals = self.rg.logistic(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logseries(self):
+        vals = self.rg.logseries(0.5, 10)
+        assert_(len(vals) == 10)
+
+    def test_negative_binomial(self):
+        vals = self.rg.negative_binomial(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_chisquare(self):
+        vals = self.rg.noncentral_chisquare(10, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_f(self):
+        vals = self.rg.noncentral_f(3, 1000, 2, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(np.array([3] * 10), 1000, 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, np.array([1000] * 10), 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, 1000, np.array([2] * 10))
+        assert_(len(vals) == 10)
+
+    def test_normal(self):
+        vals = self.rg.normal(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_pareto(self):
+        vals = self.rg.pareto(3.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_poisson(self):
+        vals = self.rg.poisson(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.poisson(np.array([10] * 10))
+        assert_(len(vals) == 10)
+        params_1(self.rg.poisson)
+
+    def test_power(self):
+        vals = self.rg.power(0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_integers(self):
+        vals = self.rg.integers(10, 20, 10)
+        assert_(len(vals) == 10)
+
+    def test_rayleigh(self):
+        vals = self.rg.rayleigh(0.2, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.rayleigh, bounded=True)
+
+    def test_vonmises(self):
+        vals = self.rg.vonmises(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_wald(self):
+        vals = self.rg.wald(1.0, 1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_weibull(self):
+        vals = self.rg.weibull(1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_zipf(self):
+        vals = self.rg.zipf(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.zipf(self.vec_1d)
+        assert_(len(vals) == 100)
+        vals = self.rg.zipf(self.vec_2d)
+        assert_(vals.shape == (1, 100))
+        vals = self.rg.zipf(self.mat)
+        assert_(vals.shape == (100, 100))
+
+    def test_hypergeometric(self):
+        vals = self.rg.hypergeometric(25, 25, 20)
+        assert_(np.isscalar(vals))
+        vals = self.rg.hypergeometric(np.array([25] * 10), 25, 20)
+        assert_(vals.shape == (10,))
+
+    def test_triangular(self):
+        vals = self.rg.triangular(-5, 0, 5)
+        assert_(np.isscalar(vals))
+        vals = self.rg.triangular(-5, np.array([0] * 10), 5)
+        assert_(vals.shape == (10,))
+
+    def test_multivariate_normal(self):
+        mean = [0, 0]
+        cov = [[1, 0], [0, 100]]  # diagonal covariance
+        x = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_zig = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_inv = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        assert_((x_zig != x_inv).any())
+
+    def test_multinomial(self):
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3])
+        assert_(vals.shape == (2,))
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3], size=10)
+        assert_(vals.shape == (10, 2))
+
+    def test_dirichlet(self):
+        s = self.rg.dirichlet((10, 5, 3), 20)
+        assert_(s.shape == (20, 3))
+
+    def test_pickle(self):
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_(type(self.rg) == type(unpick))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_(type(self.rg) == type(unpick))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+    def test_seed_array(self):
+        if self.seed_vector_bits is None:
+            bitgen_name = self.bit_generator.__name__
+            pytest.skip(f'Vector seeding is not supported by {bitgen_name}')
+
+        if self.seed_vector_bits == 32:
+            dtype = np.uint32
+        else:
+            dtype = np.uint64
+        seed = np.array([1], dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(1)
+        state2 = bg.state
+        assert_(comp_state(state1, state2))
+
+        seed = np.arange(4, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = np.arange(1500, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = 2 ** np.mod(np.arange(1500, dtype=dtype),
+                           self.seed_vector_bits - 1) + 1
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg  = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+    def test_uniform_float(self):
+        rg = Generator(self.bit_generator(12345))
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.random(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.random(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_gamma_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_gamma(4.0, 11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_gamma(4.0, 11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_zig_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_output_fill(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size)
+        assert_equal(direct, existing)
+
+        sized = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=sized, size=sized.shape)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_uniform(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.random(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.random(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_exponential(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma_broadcast(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        mu = np.arange(97.0) + 1.0
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_fill_error(self):
+        rg = self.rg
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_normal(out=existing[::3])
+        existing = np.empty(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float64)
+
+        existing = np.zeros(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float64)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3], dtype=np.float32)
+        existing = np.zeros(size, dtype=np.float64)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3])
+
+    def test_integers_broadcast(self, dtype):
+        if dtype == np.bool:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        self._reset_state()
+        a = self.rg.integers(lower, [upper] * 10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([lower] * 10, upper, dtype=dtype)
+        assert_equal(a, b)
+        self._reset_state()
+        c = self.rg.integers(lower, upper, size=10, dtype=dtype)
+        assert_equal(a, c)
+        self._reset_state()
+        d = self.rg.integers(np.array(
+            [lower] * 10), np.array([upper], dtype=object), size=10,
+            dtype=dtype)
+        assert_equal(a, d)
+        self._reset_state()
+        e = self.rg.integers(
+            np.array([lower] * 10), np.array([upper] * 10), size=10,
+            dtype=dtype)
+        assert_equal(a, e)
+
+        self._reset_state()
+        a = self.rg.integers(0, upper, size=10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([upper] * 10, dtype=dtype)
+        assert_equal(a, b)
+
+    def test_integers_numpy(self, dtype):
+        high = np.array([1])
+        low = np.array([0])
+
+        out = self.rg.integers(low, high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low[0], high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low, high[0], dtype=dtype)
+        assert out.shape == (1,)
+
+    def test_integers_broadcast_errors(self, dtype):
+        if dtype == np.bool:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        with pytest.raises(ValueError):
+            self.rg.integers(lower, [upper + 1] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers(lower - 1, [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([lower - 1], [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([0], [0], dtype=dtype)
+
+
+class TestMT19937(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.advance = None
+        cls.seed = [2 ** 21 + 2 ** 16 + 2 ** 5 + 1]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 32
+        cls._extra_setup()
+        cls.seed_error = ValueError
+
+    def test_numpy_state(self):
+        nprg = np.random.RandomState()
+        nprg.standard_normal(99)
+        state = nprg.get_state()
+        self.rg.bit_generator.state = state
+        state2 = self.rg.bit_generator.state
+        assert_((state[1] == state2['state']['key']).all())
+        assert_(state[2] == state2['state']['pos'])
+
+
+class TestPhilox(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestSFC64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 192
+        cls._extra_setup()
+
+
+class TestPCG64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestPCG64DXSM(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestDefaultRNG(RNG):
+    @classmethod
+    def setup_class(cls):
+        # This will duplicate some tests that directly instantiate a fresh
+        # Generator(), but that's okay.
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = np.random.default_rng(*cls.seed)
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    def test_default_is_pcg64(self):
+        # In order to change the default BitGenerator, we'll go through
+        # a deprecation cycle to move to a different function.
+        assert_(isinstance(self.rg.bit_generator, PCG64))
+
+    def test_seed(self):
+        np.random.default_rng()
+        np.random.default_rng(None)
+        np.random.default_rng(12345)
+        np.random.default_rng(0)
+        np.random.default_rng(43660444402423911716352051725018508569)
+        np.random.default_rng([43660444402423911716352051725018508569,
+                               279705150948142787361475340226491943209])
+        with pytest.raises(ValueError):
+            np.random.default_rng(-1)
+        with pytest.raises(ValueError):
+            np.random.default_rng([12345, -1])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f370ba71f296be0129c3e7aebc9af769dd83e94e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.py
@@ -0,0 +1,2 @@
+from numpy._core.strings import __all__, __doc__
+from numpy._core.strings import *
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fb03e9c8b5e62912182623c5787ea554ed2a0bb9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/strings/__init__.pyi
@@ -0,0 +1,95 @@
+from numpy._core.strings import (
+    equal,
+    not_equal,
+    greater_equal,
+    less_equal,
+    greater,
+    less,
+    add,
+    multiply,
+    mod,
+    isalpha,
+    isalnum,
+    isdigit,
+    isspace,
+    isnumeric,
+    isdecimal,
+    islower,
+    isupper,
+    istitle,
+    str_len,
+    find,
+    rfind,
+    index,
+    rindex,
+    count,
+    startswith,
+    endswith,
+    decode,
+    encode,
+    expandtabs,
+    center,
+    ljust,
+    rjust,
+    lstrip,
+    rstrip,
+    strip,
+    zfill,
+    upper,
+    lower,
+    swapcase,
+    capitalize,
+    title,
+    replace,
+    partition,
+    rpartition,
+    translate,
+)
+
+__all__ = [
+    "equal",
+    "not_equal",
+    "less",
+    "less_equal",
+    "greater",
+    "greater_equal",
+    "add",
+    "multiply",
+    "isalpha",
+    "isdigit",
+    "isspace",
+    "isalnum",
+    "islower",
+    "isupper",
+    "istitle",
+    "isdecimal",
+    "isnumeric",
+    "str_len",
+    "find",
+    "rfind",
+    "index",
+    "rindex",
+    "count",
+    "startswith",
+    "endswith",
+    "lstrip",
+    "rstrip",
+    "strip",
+    "replace",
+    "expandtabs",
+    "center",
+    "ljust",
+    "rjust",
+    "zfill",
+    "partition",
+    "rpartition",
+    "upper",
+    "lower",
+    "swapcase",
+    "capitalize",
+    "title",
+    "mod",
+    "decode",
+    "encode",
+    "translate",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a34221e4dde5f8a1eeab7446193344915467769
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.py
@@ -0,0 +1,22 @@
+"""Common test support for all numpy test scripts.
+
+This single module should provide all the common functionality for numpy tests
+in a single location, so that test scripts can just import it and work right
+away.
+
+"""
+from unittest import TestCase
+
+from . import _private
+from ._private.utils import *
+from ._private.utils import (_assert_valid_refcount, _gen_alignment_data)
+from ._private import extbuild
+from . import overrides
+
+__all__ = (
+    _private.utils.__all__ + ['TestCase', 'overrides']
+)
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ba3c9a2b7a44bb8f4639fb8e4ab2e528b0a4e572
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/__init__.pyi
@@ -0,0 +1,102 @@
+from unittest import TestCase
+
+from . import overrides
+from ._private.utils import (
+    HAS_LAPACK64,
+    HAS_REFCOUNT,
+    IS_EDITABLE,
+    IS_INSTALLED,
+    IS_MUSL,
+    IS_PYPY,
+    IS_PYSTON,
+    IS_WASM,
+    NOGIL_BUILD,
+    NUMPY_ROOT,
+    IgnoreException,
+    KnownFailureException,
+    SkipTest,
+    assert_,
+    assert_allclose,
+    assert_almost_equal,
+    assert_approx_equal,
+    assert_array_almost_equal,
+    assert_array_almost_equal_nulp,
+    assert_array_compare,
+    assert_array_equal,
+    assert_array_less,
+    assert_array_max_ulp,
+    assert_equal,
+    assert_no_gc_cycles,
+    assert_no_warnings,
+    assert_raises,
+    assert_raises_regex,
+    assert_string_equal,
+    assert_warns,
+    break_cycles,
+    build_err_msg,
+    check_support_sve,
+    clear_and_catch_warnings,
+    decorate_methods,
+    jiffies,
+    measure,
+    memusage,
+    print_assert_equal,
+    run_threaded,
+    rundocs,
+    runstring,
+    suppress_warnings,
+    tempdir,
+    temppath,
+    verbose,
+)
+
+__all__ = [
+    "HAS_LAPACK64",
+    "HAS_REFCOUNT",
+    "IS_EDITABLE",
+    "IS_INSTALLED",
+    "IS_MUSL",
+    "IS_PYPY",
+    "IS_PYSTON",
+    "IS_WASM",
+    "NOGIL_BUILD",
+    "NUMPY_ROOT",
+    "IgnoreException",
+    "KnownFailureException",
+    "SkipTest",
+    "TestCase",
+    "assert_",
+    "assert_allclose",
+    "assert_almost_equal",
+    "assert_approx_equal",
+    "assert_array_almost_equal",
+    "assert_array_almost_equal_nulp",
+    "assert_array_compare",
+    "assert_array_equal",
+    "assert_array_less",
+    "assert_array_max_ulp",
+    "assert_equal",
+    "assert_no_gc_cycles",
+    "assert_no_warnings",
+    "assert_raises",
+    "assert_raises_regex",
+    "assert_string_equal",
+    "assert_warns",
+    "break_cycles",
+    "build_err_msg",
+    "check_support_sve",
+    "clear_and_catch_warnings",
+    "decorate_methods",
+    "jiffies",
+    "measure",
+    "memusage",
+    "overrides",
+    "print_assert_equal",
+    "run_threaded",
+    "rundocs",
+    "runstring",
+    "suppress_warnings",
+    "tempdir",
+    "temppath",
+    "verbose",
+]
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fd0d839f24962d17b974d59b08ded8c03277fac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.py
@@ -0,0 +1,252 @@
+"""
+Build a c-extension module on-the-fly in tests.
+See build_and_import_extensions for usage hints
+
+"""
+
+import os
+import pathlib
+import subprocess
+import sys
+import sysconfig
+import textwrap
+
+__all__ = ['build_and_import_extension', 'compile_extension_module']
+
+
+def build_and_import_extension(
+        modname, functions, *, prologue="", build_dir=None,
+        include_dirs=[], more_init=""):
+    """
+    Build and imports a c-extension module `modname` from a list of function
+    fragments `functions`.
+
+
+    Parameters
+    ----------
+    functions : list of fragments
+        Each fragment is a sequence of func_name, calling convention, snippet.
+    prologue : string
+        Code to precede the rest, usually extra ``#include`` or ``#define``
+        macros.
+    build_dir : pathlib.Path
+        Where to build the module, usually a temporary directory
+    include_dirs : list
+        Extra directories to find include files when compiling
+    more_init : string
+        Code to appear in the module PyMODINIT_FUNC
+
+    Returns
+    -------
+    out: module
+        The module will have been loaded and is ready for use
+
+    Examples
+    --------
+    >>> functions = [("test_bytes", "METH_O", \"\"\"
+        if ( !PyBytesCheck(args)) {
+            Py_RETURN_FALSE;
+        }
+        Py_RETURN_TRUE;
+    \"\"\")]
+    >>> mod = build_and_import_extension("testme", functions)
+    >>> assert not mod.test_bytes('abc')
+    >>> assert mod.test_bytes(b'abc')
+    """
+    body = prologue + _make_methods(functions, modname)
+    init = """
+    PyObject *mod = PyModule_Create(&moduledef);
+    #ifdef Py_GIL_DISABLED
+    PyUnstable_Module_SetGIL(mod, Py_MOD_GIL_NOT_USED);
+    #endif
+           """
+    if not build_dir:
+        build_dir = pathlib.Path('.')
+    if more_init:
+        init += """#define INITERROR return NULL
+                """
+        init += more_init
+    init += "\nreturn mod;"
+    source_string = _make_source(modname, init, body)
+    try:
+        mod_so = compile_extension_module(
+            modname, build_dir, include_dirs, source_string)
+    except Exception as e:
+        # shorten the exception chain
+        raise RuntimeError(f"could not compile in {build_dir}:") from e
+    import importlib.util
+    spec = importlib.util.spec_from_file_location(modname, mod_so)
+    foo = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(foo)
+    return foo
+
+
+def compile_extension_module(
+        name, builddir, include_dirs,
+        source_string, libraries=[], library_dirs=[]):
+    """
+    Build an extension module and return the filename of the resulting
+    native code file.
+
+    Parameters
+    ----------
+    name : string
+        name of the module, possibly including dots if it is a module inside a
+        package.
+    builddir : pathlib.Path
+        Where to build the module, usually a temporary directory
+    include_dirs : list
+        Extra directories to find include files when compiling
+    libraries : list
+        Libraries to link into the extension module
+    library_dirs: list
+        Where to find the libraries, ``-L`` passed to the linker
+    """
+    modname = name.split('.')[-1]
+    dirname = builddir / name
+    dirname.mkdir(exist_ok=True)
+    cfile = _convert_str_to_file(source_string, dirname)
+    include_dirs = include_dirs + [sysconfig.get_config_var('INCLUDEPY')]
+
+    return _c_compile(
+        cfile, outputfilename=dirname / modname,
+        include_dirs=include_dirs, libraries=[], library_dirs=[],
+        )
+
+
+def _convert_str_to_file(source, dirname):
+    """Helper function to create a file ``source.c`` in `dirname` that contains
+    the string in `source`. Returns the file name
+    """
+    filename = dirname / 'source.c'
+    with filename.open('w') as f:
+        f.write(str(source))
+    return filename
+
+
+def _make_methods(functions, modname):
+    """ Turns the name, signature, code in functions into complete functions
+    and lists them in a methods_table. Then turns the methods_table into a
+    ``PyMethodDef`` structure and returns the resulting code fragment ready
+    for compilation
+    """
+    methods_table = []
+    codes = []
+    for funcname, flags, code in functions:
+        cfuncname = "%s_%s" % (modname, funcname)
+        if 'METH_KEYWORDS' in flags:
+            signature = '(PyObject *self, PyObject *args, PyObject *kwargs)'
+        else:
+            signature = '(PyObject *self, PyObject *args)'
+        methods_table.append(
+            "{\"%s\", (PyCFunction)%s, %s}," % (funcname, cfuncname, flags))
+        func_code = """
+        static PyObject* {cfuncname}{signature}
+        {{
+        {code}
+        }}
+        """.format(cfuncname=cfuncname, signature=signature, code=code)
+        codes.append(func_code)
+
+    body = "\n".join(codes) + """
+    static PyMethodDef methods[] = {
+    %(methods)s
+    { NULL }
+    };
+    static struct PyModuleDef moduledef = {
+        PyModuleDef_HEAD_INIT,
+        "%(modname)s",  /* m_name */
+        NULL,           /* m_doc */
+        -1,             /* m_size */
+        methods,        /* m_methods */
+    };
+    """ % dict(methods='\n'.join(methods_table), modname=modname)
+    return body
+
+
+def _make_source(name, init, body):
+    """ Combines the code fragments into source code ready to be compiled
+    """
+    code = """
+    #include 
+
+    %(body)s
+
+    PyMODINIT_FUNC
+    PyInit_%(name)s(void) {
+    %(init)s
+    }
+    """ % dict(
+        name=name, init=init, body=body,
+    )
+    return code
+
+
+def _c_compile(cfile, outputfilename, include_dirs=[], libraries=[],
+               library_dirs=[]):
+    if sys.platform == 'win32':
+        compile_extra = ["/we4013"]
+        link_extra = ["/LIBPATH:" + os.path.join(sys.base_prefix, 'libs')]
+    elif sys.platform.startswith('linux'):
+        compile_extra = [
+            "-O0", "-g", "-Werror=implicit-function-declaration", "-fPIC"]
+        link_extra = []
+    else:
+        compile_extra = link_extra = []
+        pass
+    if sys.platform == 'win32':
+        link_extra = link_extra + ['/DEBUG']  # generate .pdb file
+    if sys.platform == 'darwin':
+        # support Fink & Darwinports
+        for s in ('/sw/', '/opt/local/'):
+            if (s + 'include' not in include_dirs
+                    and os.path.exists(s + 'include')):
+                include_dirs.append(s + 'include')
+            if s + 'lib' not in library_dirs and os.path.exists(s + 'lib'):
+                library_dirs.append(s + 'lib')
+
+    outputfilename = outputfilename.with_suffix(get_so_suffix())
+    build(
+        cfile, outputfilename,
+        compile_extra, link_extra,
+        include_dirs, libraries, library_dirs)
+    return outputfilename
+
+
+def build(cfile, outputfilename, compile_extra, link_extra,
+          include_dirs, libraries, library_dirs):
+    "use meson to build"
+
+    build_dir = cfile.parent / "build"
+    os.makedirs(build_dir, exist_ok=True)
+    so_name = outputfilename.parts[-1]
+    with open(cfile.parent / "meson.build", "wt") as fid:
+        includes = ['-I' + d for d in include_dirs]
+        link_dirs = ['-L' + d for d in library_dirs]
+        fid.write(textwrap.dedent(f"""\
+            project('foo', 'c')
+            shared_module('{so_name}', '{cfile.parts[-1]}',
+                c_args: {includes} + {compile_extra},
+                link_args: {link_dirs} + {link_extra},
+                link_with: {libraries},
+                name_prefix: '',
+                name_suffix: 'dummy',
+            )
+        """))
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release",
+                               "--vsenv", ".."],
+                              cwd=build_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup", "--vsenv", ".."],
+                              cwd=build_dir
+                              )
+    subprocess.check_call(["meson", "compile"], cwd=build_dir)
+    os.rename(str(build_dir / so_name) + ".dummy", cfile.parent / so_name)
+
+def get_so_suffix():
+    ret = sysconfig.get_config_var('EXT_SUFFIX')
+    assert ret
+    return ret
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..609a45e79d1614bb920b312ecd4449ef3b05a3f2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/extbuild.pyi
@@ -0,0 +1,25 @@
+import pathlib
+import types
+from collections.abc import Sequence
+
+__all__ = ["build_and_import_extension", "compile_extension_module"]
+
+def build_and_import_extension(
+    modname: str,
+    functions: Sequence[tuple[str, str, str]],
+    *,
+    prologue: str = "",
+    build_dir: pathlib.Path | None = None,
+    include_dirs: Sequence[str] = [],
+    more_init: str = "",
+) -> types.ModuleType: ...
+
+#
+def compile_extension_module(
+    name: str,
+    builddir: pathlib.Path,
+    include_dirs: Sequence[str],
+    source_string: str,
+    libraries: Sequence[str] = [],
+    library_dirs: Sequence[str] = [],
+) -> pathlib.Path: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e43e21f37bb68695c6383232ea2082c39898a4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.py
@@ -0,0 +1,2760 @@
+"""
+Utility function to facilitate testing.
+
+"""
+import os
+import sys
+import pathlib
+import platform
+import re
+import gc
+import operator
+import warnings
+from functools import partial, wraps
+import shutil
+import contextlib
+from tempfile import mkdtemp, mkstemp
+from unittest.case import SkipTest
+from warnings import WarningMessage
+import pprint
+import sysconfig
+import concurrent.futures
+import threading
+import importlib.metadata
+
+import numpy as np
+from numpy._core import (
+     intp, float32, empty, arange, array_repr, ndarray, isnat, array)
+from numpy import isfinite, isnan, isinf
+import numpy.linalg._umath_linalg
+from numpy._utils import _rename_parameter
+from numpy._core.tests._natype import pd_NA
+
+from io import StringIO
+
+
+__all__ = [
+        'assert_equal', 'assert_almost_equal', 'assert_approx_equal',
+        'assert_array_equal', 'assert_array_less', 'assert_string_equal',
+        'assert_array_almost_equal', 'assert_raises', 'build_err_msg',
+        'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',
+        'rundocs', 'runstring', 'verbose', 'measure',
+        'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',
+        'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',
+        'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',
+        'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',
+        'HAS_REFCOUNT', "IS_WASM", 'suppress_warnings', 'assert_array_compare',
+        'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON',
+        'IS_MUSL', 'check_support_sve', 'NOGIL_BUILD',
+        'IS_EDITABLE', 'IS_INSTALLED', 'NUMPY_ROOT', 'run_threaded', 'IS_64BIT',
+        ]
+
+
+class KnownFailureException(Exception):
+    '''Raise this exception to mark a test as a known failing test.'''
+    pass
+
+
+KnownFailureTest = KnownFailureException  # backwards compat
+verbose = 0
+
+NUMPY_ROOT = pathlib.Path(np.__file__).parent
+
+try:
+    np_dist = importlib.metadata.distribution('numpy')
+except importlib.metadata.PackageNotFoundError:
+    IS_INSTALLED = IS_EDITABLE = False
+else:
+    IS_INSTALLED = True
+    try:
+        if sys.version_info >= (3, 13):
+            IS_EDITABLE = np_dist.origin.dir_info.editable
+        else:
+            # Backport importlib.metadata.Distribution.origin
+            import json, types  # noqa: E401
+            origin = json.loads(
+                np_dist.read_text('direct_url.json') or '{}',
+                object_hook=lambda data: types.SimpleNamespace(**data),
+            )
+            IS_EDITABLE = origin.dir_info.editable
+    except AttributeError:
+        IS_EDITABLE = False
+
+    # spin installs numpy directly via meson, instead of using meson-python, and
+    # runs the module by setting PYTHONPATH. This is problematic because the
+    # resulting installation lacks the Python metadata (.dist-info), and numpy
+    # might already be installed on the environment, causing us to find its
+    # metadata, even though we are not actually loading that package.
+    # Work around this issue by checking if the numpy root matches.
+    if not IS_EDITABLE and np_dist.locate_file('numpy') != NUMPY_ROOT:
+        IS_INSTALLED = False
+
+IS_WASM = platform.machine() in ["wasm32", "wasm64"]
+IS_PYPY = sys.implementation.name == 'pypy'
+IS_PYSTON = hasattr(sys, "pyston_version_info")
+HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON
+HAS_LAPACK64 = numpy.linalg._umath_linalg._ilp64
+
+IS_MUSL = False
+# alternate way is
+# from packaging.tags import sys_tags
+#     _tags = list(sys_tags())
+#     if 'musllinux' in _tags[0].platform:
+_v = sysconfig.get_config_var('HOST_GNU_TYPE') or ''
+if 'musl' in _v:
+    IS_MUSL = True
+
+NOGIL_BUILD = bool(sysconfig.get_config_var("Py_GIL_DISABLED"))
+IS_64BIT = np.dtype(np.intp).itemsize == 8
+
+def assert_(val, msg=''):
+    """
+    Assert that works in release mode.
+    Accepts callable msg to allow deferring evaluation until failure.
+
+    The Python built-in ``assert`` does not work when executing code in
+    optimized mode (the ``-O`` flag) - no byte-code is generated for it.
+
+    For documentation on usage, refer to the Python documentation.
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if not val:
+        try:
+            smsg = msg()
+        except TypeError:
+            smsg = msg
+        raise AssertionError(smsg)
+
+
+if os.name == 'nt':
+    # Code "stolen" from enthought/debug/memusage.py
+    def GetPerformanceAttributes(object, counter, instance=None,
+                                 inum=-1, format=None, machine=None):
+        # NOTE: Many counters require 2 samples to give accurate results,
+        # including "% Processor Time" (as by definition, at any instant, a
+        # thread's CPU usage is either 0 or 100).  To read counters like this,
+        # you should copy this function, but keep the counter open, and call
+        # CollectQueryData() each time you need to know.
+        # See http://msdn.microsoft.com/library/en-us/dnperfmo/html/perfmonpt2.asp
+        #(dead link)
+        # My older explanation for this was that the "AddCounter" process
+        # forced the CPU to 100%, but the above makes more sense :)
+        import win32pdh
+        if format is None:
+            format = win32pdh.PDH_FMT_LONG
+        path = win32pdh.MakeCounterPath((machine, object, instance, None,
+                                         inum, counter))
+        hq = win32pdh.OpenQuery()
+        try:
+            hc = win32pdh.AddCounter(hq, path)
+            try:
+                win32pdh.CollectQueryData(hq)
+                type, val = win32pdh.GetFormattedCounterValue(hc, format)
+                return val
+            finally:
+                win32pdh.RemoveCounter(hc)
+        finally:
+            win32pdh.CloseQuery(hq)
+
+    def memusage(processName="python", instance=0):
+        # from win32pdhutil, part of the win32all package
+        import win32pdh
+        return GetPerformanceAttributes("Process", "Virtual Bytes",
+                                        processName, instance,
+                                        win32pdh.PDH_FMT_LONG, None)
+elif sys.platform[:5] == 'linux':
+
+    def memusage(_proc_pid_stat=f'/proc/{os.getpid()}/stat'):
+        """
+        Return virtual memory size in bytes of the running python.
+
+        """
+        try:
+            with open(_proc_pid_stat) as f:
+                l = f.readline().split(' ')
+            return int(l[22])
+        except Exception:
+            return
+else:
+    def memusage():
+        """
+        Return memory usage of running python. [Not implemented]
+
+        """
+        raise NotImplementedError
+
+
+if sys.platform[:5] == 'linux':
+    def jiffies(_proc_pid_stat=f'/proc/{os.getpid()}/stat', _load_time=[]):
+        """
+        Return number of jiffies elapsed.
+
+        Return number of jiffies (1/100ths of a second) that this
+        process has been scheduled in user mode. See man 5 proc.
+
+        """
+        import time
+        if not _load_time:
+            _load_time.append(time.time())
+        try:
+            with open(_proc_pid_stat) as f:
+                l = f.readline().split(' ')
+            return int(l[13])
+        except Exception:
+            return int(100 * (time.time() - _load_time[0]))
+else:
+    # os.getpid is not in all platforms available.
+    # Using time is safe but inaccurate, especially when process
+    # was suspended or sleeping.
+    def jiffies(_load_time=[]):
+        """
+        Return number of jiffies elapsed.
+
+        Return number of jiffies (1/100ths of a second) that this
+        process has been scheduled in user mode. See man 5 proc.
+
+        """
+        import time
+        if not _load_time:
+            _load_time.append(time.time())
+        return int(100 * (time.time() - _load_time[0]))
+
+
+def build_err_msg(arrays, err_msg, header='Items are not equal:',
+                  verbose=True, names=('ACTUAL', 'DESIRED'), precision=8):
+    msg = ['\n' + header]
+    err_msg = str(err_msg)
+    if err_msg:
+        if err_msg.find('\n') == -1 and len(err_msg) < 79 - len(header):
+            msg = [msg[0] + ' ' + err_msg]
+        else:
+            msg.append(err_msg)
+    if verbose:
+        for i, a in enumerate(arrays):
+
+            if isinstance(a, ndarray):
+                # precision argument is only needed if the objects are ndarrays
+                r_func = partial(array_repr, precision=precision)
+            else:
+                r_func = repr
+
+            try:
+                r = r_func(a)
+            except Exception as exc:
+                r = f'[repr failed for <{type(a).__name__}>: {exc}]'
+            if r.count('\n') > 3:
+                r = '\n'.join(r.splitlines()[:3])
+                r += '...'
+            msg.append(f' {names[i]}: {r}')
+    return '\n'.join(msg)
+
+
+def assert_equal(actual, desired, err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two objects are not equal.
+
+    Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
+    check that all elements of these objects are equal. An exception is raised
+    at the first conflicting values.
+
+    This function handles NaN comparisons as if NaN was a "normal" number.
+    That is, AssertionError is not raised if both objects have NaNs in the same
+    positions.  This is in contrast to the IEEE standard on NaNs, which says
+    that NaN compared to anything must return False.
+
+    Parameters
+    ----------
+    actual : array_like
+        The object to check.
+    desired : array_like
+        The expected object.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True and either of the `actual` and `desired` arguments is an array,
+        raise an ``AssertionError`` when either the shape or the data type of
+        the arguments does not match. If neither argument is an array, this
+        parameter has no effect.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal.
+
+    See Also
+    --------
+    assert_allclose
+    assert_array_almost_equal_nulp,
+    assert_array_max_ulp,
+
+    Notes
+    -----
+    By default, when one of `actual` and `desired` is a scalar and the other is
+    an array, the function checks that each element of the array is equal to
+    the scalar. This behaviour can be disabled by setting ``strict==True``.
+
+    Examples
+    --------
+    >>> np.testing.assert_equal([4, 5], [4, 6])
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Items are not equal:
+    item=1
+     ACTUAL: 5
+     DESIRED: 6
+
+    The following comparison does not raise an exception.  There are NaNs
+    in the inputs, but they are in the same positions.
+
+    >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])
+
+    As mentioned in the Notes section, `assert_equal` has special
+    handling for scalars when one of the arguments is an array.
+    Here, the test checks that each value in `x` is 3:
+
+    >>> x = np.full((2, 5), fill_value=3)
+    >>> np.testing.assert_equal(x, 3)
+
+    Use `strict` to raise an AssertionError when comparing a scalar with an
+    array of a different shape:
+
+    >>> np.testing.assert_equal(x, 3, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    
+    (shapes (2, 5), () mismatch)
+     ACTUAL: array([[3, 3, 3, 3, 3],
+           [3, 3, 3, 3, 3]])
+     DESIRED: array(3)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> x = np.array([2, 2, 2])
+    >>> y = np.array([2., 2., 2.], dtype=np.float32)
+    >>> np.testing.assert_equal(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    
+    (dtypes int64, float32 mismatch)
+     ACTUAL: array([2, 2, 2])
+     DESIRED: array([2., 2., 2.], dtype=float32)
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if isinstance(desired, dict):
+        if not isinstance(actual, dict):
+            raise AssertionError(repr(type(actual)))
+        assert_equal(len(actual), len(desired), err_msg, verbose)
+        for k, i in desired.items():
+            if k not in actual:
+                raise AssertionError(repr(k))
+            assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}',
+                         verbose)
+        return
+    if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
+        assert_equal(len(actual), len(desired), err_msg, verbose)
+        for k in range(len(desired)):
+            assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}',
+                         verbose)
+        return
+    from numpy._core import ndarray, isscalar, signbit
+    from numpy import iscomplexobj, real, imag
+    if isinstance(actual, ndarray) or isinstance(desired, ndarray):
+        return assert_array_equal(actual, desired, err_msg, verbose,
+                                  strict=strict)
+    msg = build_err_msg([actual, desired], err_msg, verbose=verbose)
+
+    # Handle complex numbers: separate into real/imag to handle
+    # nan/inf/negative zero correctly
+    # XXX: catch ValueError for subclasses of ndarray where iscomplex fail
+    try:
+        usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
+    except (ValueError, TypeError):
+        usecomplex = False
+
+    if usecomplex:
+        if iscomplexobj(actual):
+            actualr = real(actual)
+            actuali = imag(actual)
+        else:
+            actualr = actual
+            actuali = 0
+        if iscomplexobj(desired):
+            desiredr = real(desired)
+            desiredi = imag(desired)
+        else:
+            desiredr = desired
+            desiredi = 0
+        try:
+            assert_equal(actualr, desiredr)
+            assert_equal(actuali, desiredi)
+        except AssertionError:
+            raise AssertionError(msg)
+
+    # isscalar test to check cases such as [np.nan] != np.nan
+    if isscalar(desired) != isscalar(actual):
+        raise AssertionError(msg)
+
+    try:
+        isdesnat = isnat(desired)
+        isactnat = isnat(actual)
+        dtypes_match = (np.asarray(desired).dtype.type ==
+                        np.asarray(actual).dtype.type)
+        if isdesnat and isactnat:
+            # If both are NaT (and have the same dtype -- datetime or
+            # timedelta) they are considered equal.
+            if dtypes_match:
+                return
+            else:
+                raise AssertionError(msg)
+
+    except (TypeError, ValueError, NotImplementedError):
+        pass
+
+    # Inf/nan/negative zero handling
+    try:
+        isdesnan = isnan(desired)
+        isactnan = isnan(actual)
+        if isdesnan and isactnan:
+            return  # both nan, so equal
+
+        # handle signed zero specially for floats
+        array_actual = np.asarray(actual)
+        array_desired = np.asarray(desired)
+        if (array_actual.dtype.char in 'Mm' or
+                array_desired.dtype.char in 'Mm'):
+            # version 1.18
+            # until this version, isnan failed for datetime64 and timedelta64.
+            # Now it succeeds but comparison to scalar with a different type
+            # emits a DeprecationWarning.
+            # Avoid that by skipping the next check
+            raise NotImplementedError('cannot compare to a scalar '
+                                      'with a different type')
+
+        if desired == 0 and actual == 0:
+            if not signbit(desired) == signbit(actual):
+                raise AssertionError(msg)
+
+    except (TypeError, ValueError, NotImplementedError):
+        pass
+
+    try:
+        # Explicitly use __eq__ for comparison, gh-2552
+        if not (desired == actual):
+            raise AssertionError(msg)
+
+    except (DeprecationWarning, FutureWarning) as e:
+        # this handles the case when the two types are not even comparable
+        if 'elementwise == comparison' in e.args[0]:
+            raise AssertionError(msg)
+        else:
+            raise
+
+
+def print_assert_equal(test_string, actual, desired):
+    """
+    Test if two objects are equal, and print an error message if test fails.
+
+    The test is performed with ``actual == desired``.
+
+    Parameters
+    ----------
+    test_string : str
+        The message supplied to AssertionError.
+    actual : object
+        The object to test for equality against `desired`.
+    desired : object
+        The expected result.
+
+    Examples
+    --------
+    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
+    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
+    Traceback (most recent call last):
+    ...
+    AssertionError: Test XYZ of func xyz failed
+    ACTUAL:
+    [0, 1]
+    DESIRED:
+    [0, 2]
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import pprint
+
+    if not (actual == desired):
+        msg = StringIO()
+        msg.write(test_string)
+        msg.write(' failed\nACTUAL: \n')
+        pprint.pprint(actual, msg)
+        msg.write('DESIRED: \n')
+        pprint.pprint(desired, msg)
+        raise AssertionError(msg.getvalue())
+
+
+def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True):
+    """
+    Raises an AssertionError if two items are not equal up to desired
+    precision.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    The test verifies that the elements of `actual` and `desired` satisfy::
+
+        abs(desired-actual) < float64(1.5 * 10**(-decimal))
+
+    That is a looser test than originally documented, but agrees with what the
+    actual implementation in `assert_array_almost_equal` did up to rounding
+    vagaries. An exception is raised at conflicting values. For ndarrays this
+    delegates to assert_array_almost_equal
+
+    Parameters
+    ----------
+    actual : array_like
+        The object to check.
+    desired : array_like
+        The expected object.
+    decimal : int, optional
+        Desired precision, default is 7.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+      If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    >>> from numpy.testing import assert_almost_equal
+    >>> assert_almost_equal(2.3333333333333, 2.33333334)
+    >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 10 decimals
+     ACTUAL: 2.3333333333333
+     DESIRED: 2.33333334
+
+    >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
+    ...                     np.array([1.0,2.33333334]), decimal=9)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 9 decimals
+    
+    Mismatched elements: 1 / 2 (50%)
+    Max absolute difference among violations: 6.66669964e-09
+    Max relative difference among violations: 2.85715698e-09
+     ACTUAL: array([1.         , 2.333333333])
+     DESIRED: array([1.        , 2.33333334])
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import ndarray
+    from numpy import iscomplexobj, real, imag
+
+    # Handle complex numbers: separate into real/imag to handle
+    # nan/inf/negative zero correctly
+    # XXX: catch ValueError for subclasses of ndarray where iscomplex fail
+    try:
+        usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
+    except ValueError:
+        usecomplex = False
+
+    def _build_err_msg():
+        header = ('Arrays are not almost equal to %d decimals' % decimal)
+        return build_err_msg([actual, desired], err_msg, verbose=verbose,
+                             header=header)
+
+    if usecomplex:
+        if iscomplexobj(actual):
+            actualr = real(actual)
+            actuali = imag(actual)
+        else:
+            actualr = actual
+            actuali = 0
+        if iscomplexobj(desired):
+            desiredr = real(desired)
+            desiredi = imag(desired)
+        else:
+            desiredr = desired
+            desiredi = 0
+        try:
+            assert_almost_equal(actualr, desiredr, decimal=decimal)
+            assert_almost_equal(actuali, desiredi, decimal=decimal)
+        except AssertionError:
+            raise AssertionError(_build_err_msg())
+
+    if isinstance(actual, (ndarray, tuple, list)) \
+            or isinstance(desired, (ndarray, tuple, list)):
+        return assert_array_almost_equal(actual, desired, decimal, err_msg)
+    try:
+        # If one of desired/actual is not finite, handle it specially here:
+        # check that both are nan if any is a nan, and test for equality
+        # otherwise
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
+                    raise AssertionError(_build_err_msg())
+            else:
+                if not desired == actual:
+                    raise AssertionError(_build_err_msg())
+            return
+    except (NotImplementedError, TypeError):
+        pass
+    if abs(desired - actual) >= np.float64(1.5 * 10.0**(-decimal)):
+        raise AssertionError(_build_err_msg())
+
+
+def assert_approx_equal(actual, desired, significant=7, err_msg='',
+                        verbose=True):
+    """
+    Raises an AssertionError if two items are not equal up to significant
+    digits.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    Given two numbers, check that they are approximately equal.
+    Approximately equal is defined as the number of significant digits
+    that agree.
+
+    Parameters
+    ----------
+    actual : scalar
+        The object to check.
+    desired : scalar
+        The expected object.
+    significant : int, optional
+        Desired precision, default is 7.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+      If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
+    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
+    ...                                significant=8)
+    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
+    ...                                significant=8)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Items are not equal to 8 significant digits:
+     ACTUAL: 1.234567e-21
+     DESIRED: 1.2345672e-21
+
+    the evaluated condition that raises the exception is
+
+    >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
+    True
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+
+    (actual, desired) = map(float, (actual, desired))
+    if desired == actual:
+        return
+    # Normalized the numbers to be in range (-10.0,10.0)
+    # scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))
+    with np.errstate(invalid='ignore'):
+        scale = 0.5 * (np.abs(desired) + np.abs(actual))
+        scale = np.power(10, np.floor(np.log10(scale)))
+    try:
+        sc_desired = desired / scale
+    except ZeroDivisionError:
+        sc_desired = 0.0
+    try:
+        sc_actual = actual / scale
+    except ZeroDivisionError:
+        sc_actual = 0.0
+    msg = build_err_msg(
+        [actual, desired], err_msg,
+        header='Items are not equal to %d significant digits:' % significant,
+        verbose=verbose)
+    try:
+        # If one of desired/actual is not finite, handle it specially here:
+        # check that both are nan if any is a nan, and test for equality
+        # otherwise
+        if not (isfinite(desired) and isfinite(actual)):
+            if isnan(desired) or isnan(actual):
+                if not (isnan(desired) and isnan(actual)):
+                    raise AssertionError(msg)
+            else:
+                if not desired == actual:
+                    raise AssertionError(msg)
+            return
+    except (TypeError, NotImplementedError):
+        pass
+    if np.abs(sc_desired - sc_actual) >= np.power(10., -(significant - 1)):
+        raise AssertionError(msg)
+
+
+def assert_array_compare(comparison, x, y, err_msg='', verbose=True, header='',
+                         precision=6, equal_nan=True, equal_inf=True,
+                         *, strict=False, names=('ACTUAL', 'DESIRED')):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import (array2string, isnan, inf, errstate,
+                            all, max, object_)
+
+    x = np.asanyarray(x)
+    y = np.asanyarray(y)
+
+    # original array for output formatting
+    ox, oy = x, y
+
+    def isnumber(x):
+        return x.dtype.char in '?bhilqpBHILQPefdgFDG'
+
+    def istime(x):
+        return x.dtype.char in "Mm"
+
+    def isvstring(x):
+        return x.dtype.char == "T"
+
+    def func_assert_same_pos(x, y, func=isnan, hasval='nan'):
+        """Handling nan/inf.
+
+        Combine results of running func on x and y, checking that they are True
+        at the same locations.
+
+        """
+        __tracebackhide__ = True  # Hide traceback for py.test
+
+        x_id = func(x)
+        y_id = func(y)
+        # We include work-arounds here to handle three types of slightly
+        # pathological ndarray subclasses:
+        # (1) all() on `masked` array scalars can return masked arrays, so we
+        #     use != True
+        # (2) __eq__ on some ndarray subclasses returns Python booleans
+        #     instead of element-wise comparisons, so we cast to np.bool() and
+        #     use isinstance(..., bool) checks
+        # (3) subclasses with bare-bones __array_function__ implementations may
+        #     not implement np.all(), so favor using the .all() method
+        # We are not committed to supporting such subclasses, but it's nice to
+        # support them if possible.
+        if np.bool(x_id == y_id).all() != True:
+            msg = build_err_msg(
+                [x, y],
+                err_msg + '\n%s location mismatch:'
+                % (hasval), verbose=verbose, header=header,
+                names=names,
+                precision=precision)
+            raise AssertionError(msg)
+        # If there is a scalar, then here we know the array has the same
+        # flag as it everywhere, so we should return the scalar flag.
+        if isinstance(x_id, bool) or x_id.ndim == 0:
+            return np.bool(x_id)
+        elif isinstance(y_id, bool) or y_id.ndim == 0:
+            return np.bool(y_id)
+        else:
+            return y_id
+
+    try:
+        if strict:
+            cond = x.shape == y.shape and x.dtype == y.dtype
+        else:
+            cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
+        if not cond:
+            if x.shape != y.shape:
+                reason = f'\n(shapes {x.shape}, {y.shape} mismatch)'
+            else:
+                reason = f'\n(dtypes {x.dtype}, {y.dtype} mismatch)'
+            msg = build_err_msg([x, y],
+                                err_msg
+                                + reason,
+                                verbose=verbose, header=header,
+                                names=names,
+                                precision=precision)
+            raise AssertionError(msg)
+
+        flagged = np.bool(False)
+        if isnumber(x) and isnumber(y):
+            if equal_nan:
+                flagged = func_assert_same_pos(x, y, func=isnan, hasval='nan')
+
+            if equal_inf:
+                flagged |= func_assert_same_pos(x, y,
+                                                func=lambda xy: xy == +inf,
+                                                hasval='+inf')
+                flagged |= func_assert_same_pos(x, y,
+                                                func=lambda xy: xy == -inf,
+                                                hasval='-inf')
+
+        elif istime(x) and istime(y):
+            # If one is datetime64 and the other timedelta64 there is no point
+            if equal_nan and x.dtype.type == y.dtype.type:
+                flagged = func_assert_same_pos(x, y, func=isnat, hasval="NaT")
+
+        elif isvstring(x) and isvstring(y):
+            dt = x.dtype
+            if equal_nan and dt == y.dtype and hasattr(dt, 'na_object'):
+                is_nan = (isinstance(dt.na_object, float) and
+                          np.isnan(dt.na_object))
+                bool_errors = 0
+                try:
+                    bool(dt.na_object)
+                except TypeError:
+                    bool_errors = 1
+                if is_nan or bool_errors:
+                    # nan-like NA object
+                    flagged = func_assert_same_pos(
+                        x, y, func=isnan, hasval=x.dtype.na_object)
+
+        if flagged.ndim > 0:
+            x, y = x[~flagged], y[~flagged]
+            # Only do the comparison if actual values are left
+            if x.size == 0:
+                return
+        elif flagged:
+            # no sense doing comparison if everything is flagged.
+            return
+
+        val = comparison(x, y)
+        invalids = np.logical_not(val)
+
+        if isinstance(val, bool):
+            cond = val
+            reduced = array([val])
+        else:
+            reduced = val.ravel()
+            cond = reduced.all()
+
+        # The below comparison is a hack to ensure that fully masked
+        # results, for which val.ravel().all() returns np.ma.masked,
+        # do not trigger a failure (np.ma.masked != True evaluates as
+        # np.ma.masked, which is falsy).
+        if cond != True:
+            n_mismatch = reduced.size - reduced.sum(dtype=intp)
+            n_elements = flagged.size if flagged.ndim != 0 else reduced.size
+            percent_mismatch = 100 * n_mismatch / n_elements
+            remarks = [
+                'Mismatched elements: {} / {} ({:.3g}%)'.format(
+                    n_mismatch, n_elements, percent_mismatch)]
+
+            with errstate(all='ignore'):
+                # ignore errors for non-numeric types
+                with contextlib.suppress(TypeError):
+                    error = abs(x - y)
+                    if np.issubdtype(x.dtype, np.unsignedinteger):
+                        error2 = abs(y - x)
+                        np.minimum(error, error2, out=error)
+
+                    reduced_error = error[invalids]
+                    max_abs_error = max(reduced_error)
+                    if getattr(error, 'dtype', object_) == object_:
+                        remarks.append(
+                            'Max absolute difference among violations: '
+                            + str(max_abs_error))
+                    else:
+                        remarks.append(
+                            'Max absolute difference among violations: '
+                            + array2string(max_abs_error))
+
+                    # note: this definition of relative error matches that one
+                    # used by assert_allclose (found in np.isclose)
+                    # Filter values where the divisor would be zero
+                    nonzero = np.bool(y != 0)
+                    nonzero_and_invalid = np.logical_and(invalids, nonzero)
+
+                    if all(~nonzero_and_invalid):
+                        max_rel_error = array(inf)
+                    else:
+                        nonzero_invalid_error = error[nonzero_and_invalid]
+                        broadcasted_y = np.broadcast_to(y, error.shape)
+                        nonzero_invalid_y = broadcasted_y[nonzero_and_invalid]
+                        max_rel_error = max(nonzero_invalid_error
+                                            / abs(nonzero_invalid_y))
+
+                    if getattr(error, 'dtype', object_) == object_:
+                        remarks.append(
+                            'Max relative difference among violations: '
+                            + str(max_rel_error))
+                    else:
+                        remarks.append(
+                            'Max relative difference among violations: '
+                            + array2string(max_rel_error))
+            err_msg = str(err_msg)
+            err_msg += '\n' + '\n'.join(remarks)
+            msg = build_err_msg([ox, oy], err_msg,
+                                verbose=verbose, header=header,
+                                names=names,
+                                precision=precision)
+            raise AssertionError(msg)
+    except ValueError:
+        import traceback
+        efmt = traceback.format_exc()
+        header = f'error during assertion:\n\n{efmt}\n\n{header}'
+
+        msg = build_err_msg([x, y], err_msg, verbose=verbose, header=header,
+                            names=names, precision=precision)
+        raise ValueError(msg)
+
+
+@_rename_parameter(['x', 'y'], ['actual', 'desired'], dep_version='2.0.0')
+def assert_array_equal(actual, desired, err_msg='', verbose=True, *,
+                       strict=False):
+    """
+    Raises an AssertionError if two array_like objects are not equal.
+
+    Given two array_like objects, check that the shape is equal and all
+    elements of these objects are equal (but see the Notes for the special
+    handling of a scalar). An exception is raised at shape mismatch or
+    conflicting values. In contrast to the standard usage in numpy, NaNs
+    are compared like numbers, no assertion is raised if both objects have
+    NaNs in the same positions.
+
+    The usual caution for verifying equality with floating point numbers is
+    advised.
+
+    .. note:: When either `actual` or `desired` is already an instance of
+        `numpy.ndarray` and `desired` is not a ``dict``, the behavior of
+        ``assert_equal(actual, desired)`` is identical to the behavior of this
+        function. Otherwise, this function performs `np.asanyarray` on the
+        inputs before comparison, whereas `assert_equal` defines special
+        comparison rules for common Python types. For example, only
+        `assert_equal` can be used to compare nested Python lists. In new code,
+        consider using only `assert_equal`, explicitly converting either
+        `actual` or `desired` to arrays if the behavior of `assert_array_equal`
+        is desired.
+
+    Parameters
+    ----------
+    actual : array_like
+        The actual object to check.
+    desired : array_like
+        The desired, expected object.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an AssertionError when either the shape or the data
+        type of the array_like objects does not match. The special
+        handling for scalars mentioned in the Notes section is disabled.
+
+        .. versionadded:: 1.24.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired objects are not equal.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Notes
+    -----
+    When one of `actual` and `desired` is a scalar and the other is array_like,
+    the function checks that each element of the array_like object is equal to
+    the scalar. This behaviour can be disabled with the `strict` parameter.
+
+    Examples
+    --------
+    The first assert does not raise an exception:
+
+    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
+    ...                               [np.exp(0),2.33333, np.nan])
+
+    Assert fails with numerical imprecision with floats:
+
+    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
+    ...                               [1, np.sqrt(np.pi)**2, np.nan])
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 4.4408921e-16
+    Max relative difference among violations: 1.41357986e-16
+     ACTUAL: array([1.      , 3.141593,      nan])
+     DESIRED: array([1.      , 3.141593,      nan])
+
+    Use `assert_allclose` or one of the nulp (number of floating point values)
+    functions for these cases instead:
+
+    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
+    ...                            [1, np.sqrt(np.pi)**2, np.nan],
+    ...                            rtol=1e-10, atol=0)
+
+    As mentioned in the Notes section, `assert_array_equal` has special
+    handling for scalars. Here the test checks that each value in `x` is 3:
+
+    >>> x = np.full((2, 5), fill_value=3)
+    >>> np.testing.assert_array_equal(x, 3)
+
+    Use `strict` to raise an AssertionError when comparing a scalar with an
+    array:
+
+    >>> np.testing.assert_array_equal(x, 3, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    
+    (shapes (2, 5), () mismatch)
+     ACTUAL: array([[3, 3, 3, 3, 3],
+           [3, 3, 3, 3, 3]])
+     DESIRED: array(3)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> x = np.array([2, 2, 2])
+    >>> y = np.array([2., 2., 2.], dtype=np.float32)
+    >>> np.testing.assert_array_equal(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not equal
+    
+    (dtypes int64, float32 mismatch)
+     ACTUAL: array([2, 2, 2])
+     DESIRED: array([2., 2., 2.], dtype=float32)
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    assert_array_compare(operator.__eq__, actual, desired, err_msg=err_msg,
+                         verbose=verbose, header='Arrays are not equal',
+                         strict=strict)
+
+
+@_rename_parameter(['x', 'y'], ['actual', 'desired'], dep_version='2.0.0')
+def assert_array_almost_equal(actual, desired, decimal=6, err_msg='',
+                              verbose=True):
+    """
+    Raises an AssertionError if two objects are not equal up to desired
+    precision.
+
+    .. note:: It is recommended to use one of `assert_allclose`,
+              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
+              instead of this function for more consistent floating point
+              comparisons.
+
+    The test verifies identical shapes and that the elements of ``actual`` and
+    ``desired`` satisfy::
+
+        abs(desired-actual) < 1.5 * 10**(-decimal)
+
+    That is a looser test than originally documented, but agrees with what the
+    actual implementation did up to rounding vagaries. An exception is raised
+    at shape mismatch or conflicting values. In contrast to the standard usage
+    in numpy, NaNs are compared like numbers, no assertion is raised if both
+    objects have NaNs in the same positions.
+
+    Parameters
+    ----------
+    actual : array_like
+        The actual object to check.
+    desired : array_like
+        The desired, expected object.
+    decimal : int, optional
+        Desired precision, default is 6.
+    err_msg : str, optional
+      The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_allclose: Compare two array_like objects for equality with desired
+                     relative and/or absolute precision.
+    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+
+    Examples
+    --------
+    the first assert does not raise an exception
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
+    ...                                      [1.0,2.333,np.nan])
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
+    ...                                      [1.0,2.33339,np.nan], decimal=5)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 5 decimals
+    
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 6.e-05
+    Max relative difference among violations: 2.57136612e-05
+     ACTUAL: array([1.     , 2.33333,     nan])
+     DESIRED: array([1.     , 2.33339,     nan])
+
+    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
+    ...                                      [1.0,2.33333, 5], decimal=5)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not almost equal to 5 decimals
+    
+    nan location mismatch:
+     ACTUAL: array([1.     , 2.33333,     nan])
+     DESIRED: array([1.     , 2.33333, 5.     ])
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    from numpy._core import number, result_type
+    from numpy._core.numerictypes import issubdtype
+    from numpy._core.fromnumeric import any as npany
+
+    def compare(x, y):
+        try:
+            if npany(isinf(x)) or npany(isinf(y)):
+                xinfid = isinf(x)
+                yinfid = isinf(y)
+                if not (xinfid == yinfid).all():
+                    return False
+                # if one item, x and y is +- inf
+                if x.size == y.size == 1:
+                    return x == y
+                x = x[~xinfid]
+                y = y[~yinfid]
+        except (TypeError, NotImplementedError):
+            pass
+
+        # make sure y is an inexact type to avoid abs(MIN_INT); will cause
+        # casting of x later.
+        dtype = result_type(y, 1.)
+        y = np.asanyarray(y, dtype)
+        z = abs(x - y)
+
+        if not issubdtype(z.dtype, number):
+            z = z.astype(np.float64)  # handle object arrays
+
+        return z < 1.5 * 10.0**(-decimal)
+
+    assert_array_compare(compare, actual, desired, err_msg=err_msg,
+                         verbose=verbose,
+             header=('Arrays are not almost equal to %d decimals' % decimal),
+             precision=decimal)
+
+
+def assert_array_less(x, y, err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two array_like objects are not ordered by less
+    than.
+
+    Given two array_like objects `x` and `y`, check that the shape is equal and
+    all elements of `x` are strictly less than the corresponding elements of
+    `y` (but see the Notes for the special handling of a scalar). An exception
+    is raised at shape mismatch or values that are not correctly ordered. In
+    contrast to the  standard usage in NumPy, no assertion is raised if both
+    objects have NaNs in the same positions.
+
+    Parameters
+    ----------
+    x : array_like
+      The smaller object to check.
+    y : array_like
+      The larger object to compare.
+    err_msg : string
+      The error message to be printed in case of failure.
+    verbose : bool
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an AssertionError when either the shape or the data
+        type of the array_like objects does not match. The special
+        handling for scalars mentioned in the Notes section is disabled.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+      If x is not strictly smaller than y, element-wise.
+
+    See Also
+    --------
+    assert_array_equal: tests objects for equality
+    assert_array_almost_equal: test objects for equality up to precision
+
+    Notes
+    -----
+    When one of `x` and `y` is a scalar and the other is array_like, the
+    function performs the comparison as though the scalar were broadcasted
+    to the shape of the array. This behaviour can be disabled with the `strict`
+    parameter.
+
+    Examples
+    --------
+    The following assertion passes because each finite element of `x` is
+    strictly less than the corresponding element of `y`, and the NaNs are in
+    corresponding locations.
+
+    >>> x = [1.0, 1.0, np.nan]
+    >>> y = [1.1, 2.0, np.nan]
+    >>> np.testing.assert_array_less(x, y)
+
+    The following assertion fails because the zeroth element of `x` is no
+    longer strictly less than the zeroth element of `y`.
+
+    >>> y[0] = 1
+    >>> np.testing.assert_array_less(x, y)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    
+    Mismatched elements: 1 / 3 (33.3%)
+    Max absolute difference among violations: 0.
+    Max relative difference among violations: 0.
+     x: array([ 1.,  1., nan])
+     y: array([ 1.,  2., nan])
+
+    Here, `y` is a scalar, so each element of `x` is compared to `y`, and
+    the assertion passes.
+
+    >>> x = [1.0, 4.0]
+    >>> y = 5.0
+    >>> np.testing.assert_array_less(x, y)
+
+    However, with ``strict=True``, the assertion will fail because the shapes
+    do not match.
+
+    >>> np.testing.assert_array_less(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    
+    (shapes (2,), () mismatch)
+     x: array([1., 4.])
+     y: array(5.)
+
+    With ``strict=True``, the assertion also fails if the dtypes of the two
+    arrays do not match.
+
+    >>> y = [5, 5]
+    >>> np.testing.assert_array_less(x, y, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Arrays are not strictly ordered `x < y`
+    
+    (dtypes float64, int64 mismatch)
+     x: array([1., 4.])
+     y: array([5, 5])
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    assert_array_compare(operator.__lt__, x, y, err_msg=err_msg,
+                         verbose=verbose,
+                         header='Arrays are not strictly ordered `x < y`',
+                         equal_inf=False,
+                         strict=strict,
+                         names=('x', 'y'))
+
+
+def runstring(astr, dict):
+    exec(astr, dict)
+
+
+def assert_string_equal(actual, desired):
+    """
+    Test if two strings are equal.
+
+    If the given strings are equal, `assert_string_equal` does nothing.
+    If they are not equal, an AssertionError is raised, and the diff
+    between the strings is shown.
+
+    Parameters
+    ----------
+    actual : str
+        The string to test for equality against the expected string.
+    desired : str
+        The expected string.
+
+    Examples
+    --------
+    >>> np.testing.assert_string_equal('abc', 'abc')
+    >>> np.testing.assert_string_equal('abc', 'abcd')
+    Traceback (most recent call last):
+      File "", line 1, in 
+    ...
+    AssertionError: Differences in strings:
+    - abc+ abcd?    +
+
+    """
+    # delay import of difflib to reduce startup time
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import difflib
+
+    if not isinstance(actual, str):
+        raise AssertionError(repr(type(actual)))
+    if not isinstance(desired, str):
+        raise AssertionError(repr(type(desired)))
+    if desired == actual:
+        return
+
+    diff = list(difflib.Differ().compare(actual.splitlines(True),
+                desired.splitlines(True)))
+    diff_list = []
+    while diff:
+        d1 = diff.pop(0)
+        if d1.startswith('  '):
+            continue
+        if d1.startswith('- '):
+            l = [d1]
+            d2 = diff.pop(0)
+            if d2.startswith('? '):
+                l.append(d2)
+                d2 = diff.pop(0)
+            if not d2.startswith('+ '):
+                raise AssertionError(repr(d2))
+            l.append(d2)
+            if diff:
+                d3 = diff.pop(0)
+                if d3.startswith('? '):
+                    l.append(d3)
+                else:
+                    diff.insert(0, d3)
+            if d2[2:] == d1[2:]:
+                continue
+            diff_list.extend(l)
+            continue
+        raise AssertionError(repr(d1))
+    if not diff_list:
+        return
+    msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}"
+    if actual != desired:
+        raise AssertionError(msg)
+
+
+def rundocs(filename=None, raise_on_error=True):
+    """
+    Run doctests found in the given file.
+
+    By default `rundocs` raises an AssertionError on failure.
+
+    Parameters
+    ----------
+    filename : str
+        The path to the file for which the doctests are run.
+    raise_on_error : bool
+        Whether to raise an AssertionError when a doctest fails. Default is
+        True.
+
+    Notes
+    -----
+    The doctests can be run by the user/developer by adding the ``doctests``
+    argument to the ``test()`` call. For example, to run all tests (including
+    doctests) for ``numpy.lib``:
+
+    >>> np.lib.test(doctests=True)  # doctest: +SKIP
+    """
+    from numpy.distutils.misc_util import exec_mod_from_location
+    import doctest
+    if filename is None:
+        f = sys._getframe(1)
+        filename = f.f_globals['__file__']
+    name = os.path.splitext(os.path.basename(filename))[0]
+    m = exec_mod_from_location(name, filename)
+
+    tests = doctest.DocTestFinder().find(m)
+    runner = doctest.DocTestRunner(verbose=False)
+
+    msg = []
+    if raise_on_error:
+        out = lambda s: msg.append(s)
+    else:
+        out = None
+
+    for test in tests:
+        runner.run(test, out=out)
+
+    if runner.failures > 0 and raise_on_error:
+        raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))
+
+
+def check_support_sve(__cache=[]):
+    """
+    gh-22982
+    """
+
+    if __cache:
+        return __cache[0]
+
+    import subprocess
+    cmd = 'lscpu'
+    try:
+        output = subprocess.run(cmd, capture_output=True, text=True)
+        result = 'sve' in output.stdout
+    except (OSError, subprocess.SubprocessError):
+        result = False
+    __cache.append(result)
+    return __cache[0]
+
+
+#
+# assert_raises and assert_raises_regex are taken from unittest.
+#
+import unittest
+
+
+class _Dummy(unittest.TestCase):
+    def nop(self):
+        pass
+
+
+_d = _Dummy('nop')
+
+
+def assert_raises(*args, **kwargs):
+    """
+    assert_raises(exception_class, callable, *args, **kwargs)
+    assert_raises(exception_class)
+
+    Fail unless an exception of class exception_class is thrown
+    by callable when invoked with arguments args and keyword
+    arguments kwargs. If a different type of exception is
+    thrown, it will not be caught, and the test case will be
+    deemed to have suffered an error, exactly as for an
+    unexpected exception.
+
+    Alternatively, `assert_raises` can be used as a context manager:
+
+    >>> from numpy.testing import assert_raises
+    >>> with assert_raises(ZeroDivisionError):
+    ...     1 / 0
+
+    is equivalent to
+
+    >>> def div(x, y):
+    ...     return x / y
+    >>> assert_raises(ZeroDivisionError, div, 1, 0)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    return _d.assertRaises(*args, **kwargs)
+
+
+def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
+    """
+    assert_raises_regex(exception_class, expected_regexp, callable, *args,
+                        **kwargs)
+    assert_raises_regex(exception_class, expected_regexp)
+
+    Fail unless an exception of class exception_class and with message that
+    matches expected_regexp is thrown by callable when invoked with arguments
+    args and keyword arguments kwargs.
+
+    Alternatively, can be used as a context manager like `assert_raises`.
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs)
+
+
+def decorate_methods(cls, decorator, testmatch=None):
+    """
+    Apply a decorator to all methods in a class matching a regular expression.
+
+    The given decorator is applied to all public methods of `cls` that are
+    matched by the regular expression `testmatch`
+    (``testmatch.search(methodname)``). Methods that are private, i.e. start
+    with an underscore, are ignored.
+
+    Parameters
+    ----------
+    cls : class
+        Class whose methods to decorate.
+    decorator : function
+        Decorator to apply to methods
+    testmatch : compiled regexp or str, optional
+        The regular expression. Default value is None, in which case the
+        nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
+        is used.
+        If `testmatch` is a string, it is compiled to a regular expression
+        first.
+
+    """
+    if testmatch is None:
+        testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)
+    else:
+        testmatch = re.compile(testmatch)
+    cls_attr = cls.__dict__
+
+    # delayed import to reduce startup time
+    from inspect import isfunction
+
+    methods = [_m for _m in cls_attr.values() if isfunction(_m)]
+    for function in methods:
+        try:
+            if hasattr(function, 'compat_func_name'):
+                funcname = function.compat_func_name
+            else:
+                funcname = function.__name__
+        except AttributeError:
+            # not a function
+            continue
+        if testmatch.search(funcname) and not funcname.startswith('_'):
+            setattr(cls, funcname, decorator(function))
+    return
+
+
+def measure(code_str, times=1, label=None):
+    """
+    Return elapsed time for executing code in the namespace of the caller.
+
+    The supplied code string is compiled with the Python builtin ``compile``.
+    The precision of the timing is 10 milli-seconds. If the code will execute
+    fast on this timescale, it can be executed many times to get reasonable
+    timing accuracy.
+
+    Parameters
+    ----------
+    code_str : str
+        The code to be timed.
+    times : int, optional
+        The number of times the code is executed. Default is 1. The code is
+        only compiled once.
+    label : str, optional
+        A label to identify `code_str` with. This is passed into ``compile``
+        as the second argument (for run-time error messages).
+
+    Returns
+    -------
+    elapsed : float
+        Total elapsed time in seconds for executing `code_str` `times` times.
+
+    Examples
+    --------
+    >>> times = 10
+    >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)', times=times)
+    >>> print("Time for a single execution : ", etime / times, "s")  # doctest: +SKIP
+    Time for a single execution :  0.005 s
+
+    """
+    frame = sys._getframe(1)
+    locs, globs = frame.f_locals, frame.f_globals
+
+    code = compile(code_str, f'Test name: {label} ', 'exec')
+    i = 0
+    elapsed = jiffies()
+    while i < times:
+        i += 1
+        exec(code, globs, locs)
+    elapsed = jiffies() - elapsed
+    return 0.01 * elapsed
+
+
+def _assert_valid_refcount(op):
+    """
+    Check that ufuncs don't mishandle refcount of object `1`.
+    Used in a few regression tests.
+    """
+    if not HAS_REFCOUNT:
+        return True
+
+    import gc
+    import numpy as np
+
+    b = np.arange(100 * 100).reshape(100, 100)
+    c = b
+    i = 1
+
+    gc.disable()
+    try:
+        rc = sys.getrefcount(i)
+        for j in range(15):
+            d = op(b, c)
+        assert_(sys.getrefcount(i) >= rc)
+    finally:
+        gc.enable()
+    del d  # for pyflakes
+
+
+def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
+                    err_msg='', verbose=True, *, strict=False):
+    """
+    Raises an AssertionError if two objects are not equal up to desired
+    tolerance.
+
+    Given two array_like objects, check that their shapes and all elements
+    are equal (but see the Notes for the special handling of a scalar). An
+    exception is raised if the shapes mismatch or any values conflict. In
+    contrast to the standard usage in numpy, NaNs are compared like numbers,
+    no assertion is raised if both objects have NaNs in the same positions.
+
+    The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note
+    that ``allclose`` has different default values). It compares the difference
+    between `actual` and `desired` to ``atol + rtol * abs(desired)``.
+
+    Parameters
+    ----------
+    actual : array_like
+        Array obtained.
+    desired : array_like
+        Array desired.
+    rtol : float, optional
+        Relative tolerance.
+    atol : float, optional
+        Absolute tolerance.
+    equal_nan : bool, optional.
+        If True, NaNs will compare equal.
+    err_msg : str, optional
+        The error message to be printed in case of failure.
+    verbose : bool, optional
+        If True, the conflicting values are appended to the error message.
+    strict : bool, optional
+        If True, raise an ``AssertionError`` when either the shape or the data
+        type of the arguments does not match. The special handling of scalars
+        mentioned in the Notes section is disabled.
+
+        .. versionadded:: 2.0.0
+
+    Raises
+    ------
+    AssertionError
+        If actual and desired are not equal up to specified precision.
+
+    See Also
+    --------
+    assert_array_almost_equal_nulp, assert_array_max_ulp
+
+    Notes
+    -----
+    When one of `actual` and `desired` is a scalar and the other is
+    array_like, the function performs the comparison as if the scalar were
+    broadcasted to the shape of the array.
+    This behaviour can be disabled with the `strict` parameter.
+
+    Examples
+    --------
+    >>> x = [1e-5, 1e-3, 1e-1]
+    >>> y = np.arccos(np.cos(x))
+    >>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)
+
+    As mentioned in the Notes section, `assert_allclose` has special
+    handling for scalars. Here, the test checks that the value of `numpy.sin`
+    is nearly zero at integer multiples of π.
+
+    >>> x = np.arange(3) * np.pi
+    >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15)
+
+    Use `strict` to raise an ``AssertionError`` when comparing an array
+    with one or more dimensions against a scalar.
+
+    >>> np.testing.assert_allclose(np.sin(x), 0, atol=1e-15, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Not equal to tolerance rtol=1e-07, atol=1e-15
+    
+    (shapes (3,), () mismatch)
+     ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
+     DESIRED: array(0)
+
+    The `strict` parameter also ensures that the array data types match:
+
+    >>> y = np.zeros(3, dtype=np.float32)
+    >>> np.testing.assert_allclose(np.sin(x), y, atol=1e-15, strict=True)
+    Traceback (most recent call last):
+        ...
+    AssertionError:
+    Not equal to tolerance rtol=1e-07, atol=1e-15
+    
+    (dtypes float64, float32 mismatch)
+     ACTUAL: array([ 0.000000e+00,  1.224647e-16, -2.449294e-16])
+     DESIRED: array([0., 0., 0.], dtype=float32)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+
+    def compare(x, y):
+        return np._core.numeric.isclose(x, y, rtol=rtol, atol=atol,
+                                       equal_nan=equal_nan)
+
+    actual, desired = np.asanyarray(actual), np.asanyarray(desired)
+    header = f'Not equal to tolerance rtol={rtol:g}, atol={atol:g}'
+    assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
+                         verbose=verbose, header=header, equal_nan=equal_nan,
+                         strict=strict)
+
+
+def assert_array_almost_equal_nulp(x, y, nulp=1):
+    """
+    Compare two arrays relatively to their spacing.
+
+    This is a relatively robust method to compare two arrays whose amplitude
+    is variable.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Input arrays.
+    nulp : int, optional
+        The maximum number of unit in the last place for tolerance (see Notes).
+        Default is 1.
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    AssertionError
+        If the spacing between `x` and `y` for one or more elements is larger
+        than `nulp`.
+
+    See Also
+    --------
+    assert_array_max_ulp : Check that all items of arrays differ in at most
+        N Units in the Last Place.
+    spacing : Return the distance between x and the nearest adjacent number.
+
+    Notes
+    -----
+    An assertion is raised if the following condition is not met::
+
+        abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))
+
+    Examples
+    --------
+    >>> x = np.array([1., 1e-10, 1e-20])
+    >>> eps = np.finfo(x.dtype).eps
+    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)
+
+    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
+    Traceback (most recent call last):
+      ...
+    AssertionError: Arrays are not equal to 1 ULP (max is 2)
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+    ax = np.abs(x)
+    ay = np.abs(y)
+    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
+    if not np.all(np.abs(x - y) <= ref):
+        if np.iscomplexobj(x) or np.iscomplexobj(y):
+            msg = f"Arrays are not equal to {nulp} ULP"
+        else:
+            max_nulp = np.max(nulp_diff(x, y))
+            msg = f"Arrays are not equal to {nulp} ULP (max is {max_nulp:g})"
+        raise AssertionError(msg)
+
+
+def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
+    """
+    Check that all items of arrays differ in at most N Units in the Last Place.
+
+    Parameters
+    ----------
+    a, b : array_like
+        Input arrays to be compared.
+    maxulp : int, optional
+        The maximum number of units in the last place that elements of `a` and
+        `b` can differ. Default is 1.
+    dtype : dtype, optional
+        Data-type to convert `a` and `b` to if given. Default is None.
+
+    Returns
+    -------
+    ret : ndarray
+        Array containing number of representable floating point numbers between
+        items in `a` and `b`.
+
+    Raises
+    ------
+    AssertionError
+        If one or more elements differ by more than `maxulp`.
+
+    Notes
+    -----
+    For computing the ULP difference, this API does not differentiate between
+    various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
+    is zero).
+
+    See Also
+    --------
+    assert_array_almost_equal_nulp : Compare two arrays relatively to their
+        spacing.
+
+    Examples
+    --------
+    >>> a = np.linspace(0., 1., 100)
+    >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))
+
+    """
+    __tracebackhide__ = True  # Hide traceback for py.test
+    import numpy as np
+    ret = nulp_diff(a, b, dtype)
+    if not np.all(ret <= maxulp):
+        raise AssertionError("Arrays are not almost equal up to %g "
+                             "ULP (max difference is %g ULP)" %
+                             (maxulp, np.max(ret)))
+    return ret
+
+
+def nulp_diff(x, y, dtype=None):
+    """For each item in x and y, return the number of representable floating
+    points between them.
+
+    Parameters
+    ----------
+    x : array_like
+        first input array
+    y : array_like
+        second input array
+    dtype : dtype, optional
+        Data-type to convert `x` and `y` to if given. Default is None.
+
+    Returns
+    -------
+    nulp : array_like
+        number of representable floating point numbers between each item in x
+        and y.
+
+    Notes
+    -----
+    For computing the ULP difference, this API does not differentiate between
+    various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
+    is zero).
+
+    Examples
+    --------
+    # By definition, epsilon is the smallest number such as 1 + eps != 1, so
+    # there should be exactly one ULP between 1 and 1 + eps
+    >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)
+    1.0
+    """
+    import numpy as np
+    if dtype:
+        x = np.asarray(x, dtype=dtype)
+        y = np.asarray(y, dtype=dtype)
+    else:
+        x = np.asarray(x)
+        y = np.asarray(y)
+
+    t = np.common_type(x, y)
+    if np.iscomplexobj(x) or np.iscomplexobj(y):
+        raise NotImplementedError("_nulp not implemented for complex array")
+
+    x = np.array([x], dtype=t)
+    y = np.array([y], dtype=t)
+
+    x[np.isnan(x)] = np.nan
+    y[np.isnan(y)] = np.nan
+
+    if not x.shape == y.shape:
+        raise ValueError("Arrays do not have the same shape: %s - %s" %
+                         (x.shape, y.shape))
+
+    def _diff(rx, ry, vdt):
+        diff = np.asarray(rx - ry, dtype=vdt)
+        return np.abs(diff)
+
+    rx = integer_repr(x)
+    ry = integer_repr(y)
+    return _diff(rx, ry, t)
+
+
+def _integer_repr(x, vdt, comp):
+    # Reinterpret binary representation of the float as sign-magnitude:
+    # take into account two-complement representation
+    # See also
+    # https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
+    rx = x.view(vdt)
+    if not (rx.size == 1):
+        rx[rx < 0] = comp - rx[rx < 0]
+    else:
+        if rx < 0:
+            rx = comp - rx
+
+    return rx
+
+
+def integer_repr(x):
+    """Return the signed-magnitude interpretation of the binary representation
+    of x."""
+    import numpy as np
+    if x.dtype == np.float16:
+        return _integer_repr(x, np.int16, np.int16(-2**15))
+    elif x.dtype == np.float32:
+        return _integer_repr(x, np.int32, np.int32(-2**31))
+    elif x.dtype == np.float64:
+        return _integer_repr(x, np.int64, np.int64(-2**63))
+    else:
+        raise ValueError(f'Unsupported dtype {x.dtype}')
+
+
+@contextlib.contextmanager
+def _assert_warns_context(warning_class, name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    with suppress_warnings() as sup:
+        l = sup.record(warning_class)
+        yield
+        if not len(l) > 0:
+            name_str = f' when calling {name}' if name is not None else ''
+            raise AssertionError("No warning raised" + name_str)
+
+
+def assert_warns(warning_class, *args, **kwargs):
+    """
+    Fail unless the given callable throws the specified warning.
+
+    A warning of class warning_class should be thrown by the callable when
+    invoked with arguments args and keyword arguments kwargs.
+    If a different type of warning is thrown, it will not be caught.
+
+    If called with all arguments other than the warning class omitted, may be
+    used as a context manager::
+
+        with assert_warns(SomeWarning):
+            do_something()
+
+    The ability to be used as a context manager is new in NumPy v1.11.0.
+
+    Parameters
+    ----------
+    warning_class : class
+        The class defining the warning that `func` is expected to throw.
+    func : callable, optional
+        Callable to test
+    *args : Arguments
+        Arguments for `func`.
+    **kwargs : Kwargs
+        Keyword arguments for `func`.
+
+    Returns
+    -------
+    The value returned by `func`.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def deprecated_func(num):
+    ...     warnings.warn("Please upgrade", DeprecationWarning)
+    ...     return num*num
+    >>> with np.testing.assert_warns(DeprecationWarning):
+    ...     assert deprecated_func(4) == 16
+    >>> # or passing a func
+    >>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)
+    >>> assert ret == 16
+    """
+    if not args and not kwargs:
+        return _assert_warns_context(warning_class)
+    elif len(args) < 1:
+        if "match" in kwargs:
+            raise RuntimeError(
+                "assert_warns does not use 'match' kwarg, "
+                "use pytest.warns instead"
+                )
+        raise RuntimeError("assert_warns(...) needs at least one arg")
+
+    func = args[0]
+    args = args[1:]
+    with _assert_warns_context(warning_class, name=func.__name__):
+        return func(*args, **kwargs)
+
+
+@contextlib.contextmanager
+def _assert_no_warnings_context(name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    with warnings.catch_warnings(record=True) as l:
+        warnings.simplefilter('always')
+        yield
+        if len(l) > 0:
+            name_str = f' when calling {name}' if name is not None else ''
+            raise AssertionError(f'Got warnings{name_str}: {l}')
+
+
+def assert_no_warnings(*args, **kwargs):
+    """
+    Fail if the given callable produces any warnings.
+
+    If called with all arguments omitted, may be used as a context manager::
+
+        with assert_no_warnings():
+            do_something()
+
+    The ability to be used as a context manager is new in NumPy v1.11.0.
+
+    Parameters
+    ----------
+    func : callable
+        The callable to test.
+    \\*args : Arguments
+        Arguments passed to `func`.
+    \\*\\*kwargs : Kwargs
+        Keyword arguments passed to `func`.
+
+    Returns
+    -------
+    The value returned by `func`.
+
+    """
+    if not args:
+        return _assert_no_warnings_context()
+
+    func = args[0]
+    args = args[1:]
+    with _assert_no_warnings_context(name=func.__name__):
+        return func(*args, **kwargs)
+
+
+def _gen_alignment_data(dtype=float32, type='binary', max_size=24):
+    """
+    generator producing data with different alignment and offsets
+    to test simd vectorization
+
+    Parameters
+    ----------
+    dtype : dtype
+        data type to produce
+    type : string
+        'unary': create data for unary operations, creates one input
+                 and output array
+        'binary': create data for unary operations, creates two input
+                 and output array
+    max_size : integer
+        maximum size of data to produce
+
+    Returns
+    -------
+    if type is 'unary' yields one output, one input array and a message
+    containing information on the data
+    if type is 'binary' yields one output array, two input array and a message
+    containing information on the data
+
+    """
+    ufmt = 'unary offset=(%d, %d), size=%d, dtype=%r, %s'
+    bfmt = 'binary offset=(%d, %d, %d), size=%d, dtype=%r, %s'
+    for o in range(3):
+        for s in range(o + 2, max(o + 3, max_size)):
+            if type == 'unary':
+                inp = lambda: arange(s, dtype=dtype)[o:]
+                out = empty((s,), dtype=dtype)[o:]
+                yield out, inp(), ufmt % (o, o, s, dtype, 'out of place')
+                d = inp()
+                yield d, d, ufmt % (o, o, s, dtype, 'in place')
+                yield out[1:], inp()[:-1], ufmt % \
+                    (o + 1, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp()[1:], ufmt % \
+                    (o, o + 1, s - 1, dtype, 'out of place')
+                yield inp()[:-1], inp()[1:], ufmt % \
+                    (o, o + 1, s - 1, dtype, 'aliased')
+                yield inp()[1:], inp()[:-1], ufmt % \
+                    (o + 1, o, s - 1, dtype, 'aliased')
+            if type == 'binary':
+                inp1 = lambda: arange(s, dtype=dtype)[o:]
+                inp2 = lambda: arange(s, dtype=dtype)[o:]
+                out = empty((s,), dtype=dtype)[o:]
+                yield out, inp1(), inp2(),  bfmt % \
+                    (o, o, o, s, dtype, 'out of place')
+                d = inp1()
+                yield d, d, inp2(), bfmt % \
+                    (o, o, o, s, dtype, 'in place1')
+                d = inp2()
+                yield d, inp1(), d, bfmt % \
+                    (o, o, o, s, dtype, 'in place2')
+                yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % \
+                    (o + 1, o, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % \
+                    (o, o + 1, o, s - 1, dtype, 'out of place')
+                yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % \
+                    (o, o, o + 1, s - 1, dtype, 'out of place')
+                yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % \
+                    (o + 1, o, o, s - 1, dtype, 'aliased')
+                yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % \
+                    (o, o + 1, o, s - 1, dtype, 'aliased')
+                yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % \
+                    (o, o, o + 1, s - 1, dtype, 'aliased')
+
+
+class IgnoreException(Exception):
+    "Ignoring this exception due to disabled feature"
+    pass
+
+
+@contextlib.contextmanager
+def tempdir(*args, **kwargs):
+    """Context manager to provide a temporary test folder.
+
+    All arguments are passed as this to the underlying tempfile.mkdtemp
+    function.
+
+    """
+    tmpdir = mkdtemp(*args, **kwargs)
+    try:
+        yield tmpdir
+    finally:
+        shutil.rmtree(tmpdir)
+
+
+@contextlib.contextmanager
+def temppath(*args, **kwargs):
+    """Context manager for temporary files.
+
+    Context manager that returns the path to a closed temporary file. Its
+    parameters are the same as for tempfile.mkstemp and are passed directly
+    to that function. The underlying file is removed when the context is
+    exited, so it should be closed at that time.
+
+    Windows does not allow a temporary file to be opened if it is already
+    open, so the underlying file must be closed after opening before it
+    can be opened again.
+
+    """
+    fd, path = mkstemp(*args, **kwargs)
+    os.close(fd)
+    try:
+        yield path
+    finally:
+        os.remove(path)
+
+
+class clear_and_catch_warnings(warnings.catch_warnings):
+    """ Context manager that resets warning registry for catching warnings
+
+    Warnings can be slippery, because, whenever a warning is triggered, Python
+    adds a ``__warningregistry__`` member to the *calling* module.  This makes
+    it impossible to retrigger the warning in this module, whatever you put in
+    the warnings filters.  This context manager accepts a sequence of `modules`
+    as a keyword argument to its constructor and:
+
+    * stores and removes any ``__warningregistry__`` entries in given `modules`
+      on entry;
+    * resets ``__warningregistry__`` to its previous state on exit.
+
+    This makes it possible to trigger any warning afresh inside the context
+    manager without disturbing the state of warnings outside.
+
+    For compatibility with Python 3.0, please consider all arguments to be
+    keyword-only.
+
+    Parameters
+    ----------
+    record : bool, optional
+        Specifies whether warnings should be captured by a custom
+        implementation of ``warnings.showwarning()`` and be appended to a list
+        returned by the context manager. Otherwise None is returned by the
+        context manager. The objects appended to the list are arguments whose
+        attributes mirror the arguments to ``showwarning()``.
+    modules : sequence, optional
+        Sequence of modules for which to reset warnings registry on entry and
+        restore on exit. To work correctly, all 'ignore' filters should
+        filter by one of these modules.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> with np.testing.clear_and_catch_warnings(
+    ...         modules=[np._core.fromnumeric]):
+    ...     warnings.simplefilter('always')
+    ...     warnings.filterwarnings('ignore', module='np._core.fromnumeric')
+    ...     # do something that raises a warning but ignore those in
+    ...     # np._core.fromnumeric
+    """
+    class_modules = ()
+
+    def __init__(self, record=False, modules=()):
+        self.modules = set(modules).union(self.class_modules)
+        self._warnreg_copies = {}
+        super().__init__(record=record)
+
+    def __enter__(self):
+        for mod in self.modules:
+            if hasattr(mod, '__warningregistry__'):
+                mod_reg = mod.__warningregistry__
+                self._warnreg_copies[mod] = mod_reg.copy()
+                mod_reg.clear()
+        return super().__enter__()
+
+    def __exit__(self, *exc_info):
+        super().__exit__(*exc_info)
+        for mod in self.modules:
+            if hasattr(mod, '__warningregistry__'):
+                mod.__warningregistry__.clear()
+            if mod in self._warnreg_copies:
+                mod.__warningregistry__.update(self._warnreg_copies[mod])
+
+
+class suppress_warnings:
+    """
+    Context manager and decorator doing much the same as
+    ``warnings.catch_warnings``.
+
+    However, it also provides a filter mechanism to work around
+    https://bugs.python.org/issue4180.
+
+    This bug causes Python before 3.4 to not reliably show warnings again
+    after they have been ignored once (even within catch_warnings). It
+    means that no "ignore" filter can be used easily, since following
+    tests might need to see the warning. Additionally it allows easier
+    specificity for testing warnings and can be nested.
+
+    Parameters
+    ----------
+    forwarding_rule : str, optional
+        One of "always", "once", "module", or "location". Analogous to
+        the usual warnings module filter mode, it is useful to reduce
+        noise mostly on the outmost level. Unsuppressed and unrecorded
+        warnings will be forwarded based on this rule. Defaults to "always".
+        "location" is equivalent to the warnings "default", match by exact
+        location the warning warning originated from.
+
+    Notes
+    -----
+    Filters added inside the context manager will be discarded again
+    when leaving it. Upon entering all filters defined outside a
+    context will be applied automatically.
+
+    When a recording filter is added, matching warnings are stored in the
+    ``log`` attribute as well as in the list returned by ``record``.
+
+    If filters are added and the ``module`` keyword is given, the
+    warning registry of this module will additionally be cleared when
+    applying it, entering the context, or exiting it. This could cause
+    warnings to appear a second time after leaving the context if they
+    were configured to be printed once (default) and were already
+    printed before the context was entered.
+
+    Nesting this context manager will work as expected when the
+    forwarding rule is "always" (default). Unfiltered and unrecorded
+    warnings will be passed out and be matched by the outer level.
+    On the outmost level they will be printed (or caught by another
+    warnings context). The forwarding rule argument can modify this
+    behaviour.
+
+    Like ``catch_warnings`` this context manager is not threadsafe.
+
+    Examples
+    --------
+
+    With a context manager::
+
+        with np.testing.suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "Some text")
+            sup.filter(module=np.ma.core)
+            log = sup.record(FutureWarning, "Does this occur?")
+            command_giving_warnings()
+            # The FutureWarning was given once, the filtered warnings were
+            # ignored. All other warnings abide outside settings (may be
+            # printed/error)
+            assert_(len(log) == 1)
+            assert_(len(sup.log) == 1)  # also stored in log attribute
+
+    Or as a decorator::
+
+        sup = np.testing.suppress_warnings()
+        sup.filter(module=np.ma.core)  # module must match exactly
+        @sup
+        def some_function():
+            # do something which causes a warning in np.ma.core
+            pass
+    """
+    def __init__(self, forwarding_rule="always"):
+        self._entered = False
+
+        # Suppressions are either instance or defined inside one with block:
+        self._suppressions = []
+
+        if forwarding_rule not in {"always", "module", "once", "location"}:
+            raise ValueError("unsupported forwarding rule.")
+        self._forwarding_rule = forwarding_rule
+
+    def _clear_registries(self):
+        if hasattr(warnings, "_filters_mutated"):
+            # clearing the registry should not be necessary on new pythons,
+            # instead the filters should be mutated.
+            warnings._filters_mutated()
+            return
+        # Simply clear the registry, this should normally be harmless,
+        # note that on new pythons it would be invalidated anyway.
+        for module in self._tmp_modules:
+            if hasattr(module, "__warningregistry__"):
+                module.__warningregistry__.clear()
+
+    def _filter(self, category=Warning, message="", module=None, record=False):
+        if record:
+            record = []  # The log where to store warnings
+        else:
+            record = None
+        if self._entered:
+            if module is None:
+                warnings.filterwarnings(
+                    "always", category=category, message=message)
+            else:
+                module_regex = module.__name__.replace('.', r'\.') + '$'
+                warnings.filterwarnings(
+                    "always", category=category, message=message,
+                    module=module_regex)
+                self._tmp_modules.add(module)
+                self._clear_registries()
+
+            self._tmp_suppressions.append(
+                (category, message, re.compile(message, re.I), module, record))
+        else:
+            self._suppressions.append(
+                (category, message, re.compile(message, re.I), module, record))
+
+        return record
+
+    def filter(self, category=Warning, message="", module=None):
+        """
+        Add a new suppressing filter or apply it if the state is entered.
+
+        Parameters
+        ----------
+        category : class, optional
+            Warning class to filter
+        message : string, optional
+            Regular expression matching the warning message.
+        module : module, optional
+            Module to filter for. Note that the module (and its file)
+            must match exactly and cannot be a submodule. This may make
+            it unreliable for external modules.
+
+        Notes
+        -----
+        When added within a context, filters are only added inside
+        the context and will be forgotten when the context is exited.
+        """
+        self._filter(category=category, message=message, module=module,
+                     record=False)
+
+    def record(self, category=Warning, message="", module=None):
+        """
+        Append a new recording filter or apply it if the state is entered.
+
+        All warnings matching will be appended to the ``log`` attribute.
+
+        Parameters
+        ----------
+        category : class, optional
+            Warning class to filter
+        message : string, optional
+            Regular expression matching the warning message.
+        module : module, optional
+            Module to filter for. Note that the module (and its file)
+            must match exactly and cannot be a submodule. This may make
+            it unreliable for external modules.
+
+        Returns
+        -------
+        log : list
+            A list which will be filled with all matched warnings.
+
+        Notes
+        -----
+        When added within a context, filters are only added inside
+        the context and will be forgotten when the context is exited.
+        """
+        return self._filter(category=category, message=message, module=module,
+                            record=True)
+
+    def __enter__(self):
+        if self._entered:
+            raise RuntimeError("cannot enter suppress_warnings twice.")
+
+        self._orig_show = warnings.showwarning
+        self._filters = warnings.filters
+        warnings.filters = self._filters[:]
+
+        self._entered = True
+        self._tmp_suppressions = []
+        self._tmp_modules = set()
+        self._forwarded = set()
+
+        self.log = []  # reset global log (no need to keep same list)
+
+        for cat, mess, _, mod, log in self._suppressions:
+            if log is not None:
+                del log[:]  # clear the log
+            if mod is None:
+                warnings.filterwarnings(
+                    "always", category=cat, message=mess)
+            else:
+                module_regex = mod.__name__.replace('.', r'\.') + '$'
+                warnings.filterwarnings(
+                    "always", category=cat, message=mess,
+                    module=module_regex)
+                self._tmp_modules.add(mod)
+        warnings.showwarning = self._showwarning
+        self._clear_registries()
+
+        return self
+
+    def __exit__(self, *exc_info):
+        warnings.showwarning = self._orig_show
+        warnings.filters = self._filters
+        self._clear_registries()
+        self._entered = False
+        del self._orig_show
+        del self._filters
+
+    def _showwarning(self, message, category, filename, lineno,
+                     *args, use_warnmsg=None, **kwargs):
+        for cat, _, pattern, mod, rec in (
+                self._suppressions + self._tmp_suppressions)[::-1]:
+            if (issubclass(category, cat) and
+                    pattern.match(message.args[0]) is not None):
+                if mod is None:
+                    # Message and category match, either recorded or ignored
+                    if rec is not None:
+                        msg = WarningMessage(message, category, filename,
+                                             lineno, **kwargs)
+                        self.log.append(msg)
+                        rec.append(msg)
+                    return
+                # Use startswith, because warnings strips the c or o from
+                # .pyc/.pyo files.
+                elif mod.__file__.startswith(filename):
+                    # The message and module (filename) match
+                    if rec is not None:
+                        msg = WarningMessage(message, category, filename,
+                                             lineno, **kwargs)
+                        self.log.append(msg)
+                        rec.append(msg)
+                    return
+
+        # There is no filter in place, so pass to the outside handler
+        # unless we should only pass it once
+        if self._forwarding_rule == "always":
+            if use_warnmsg is None:
+                self._orig_show(message, category, filename, lineno,
+                                *args, **kwargs)
+            else:
+                self._orig_showmsg(use_warnmsg)
+            return
+
+        if self._forwarding_rule == "once":
+            signature = (message.args, category)
+        elif self._forwarding_rule == "module":
+            signature = (message.args, category, filename)
+        elif self._forwarding_rule == "location":
+            signature = (message.args, category, filename, lineno)
+
+        if signature in self._forwarded:
+            return
+        self._forwarded.add(signature)
+        if use_warnmsg is None:
+            self._orig_show(message, category, filename, lineno, *args,
+                            **kwargs)
+        else:
+            self._orig_showmsg(use_warnmsg)
+
+    def __call__(self, func):
+        """
+        Function decorator to apply certain suppressions to a whole
+        function.
+        """
+        @wraps(func)
+        def new_func(*args, **kwargs):
+            with self:
+                return func(*args, **kwargs)
+
+        return new_func
+
+
+@contextlib.contextmanager
+def _assert_no_gc_cycles_context(name=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+
+    # not meaningful to test if there is no refcounting
+    if not HAS_REFCOUNT:
+        yield
+        return
+
+    assert_(gc.isenabled())
+    gc.disable()
+    gc_debug = gc.get_debug()
+    try:
+        for i in range(100):
+            if gc.collect() == 0:
+                break
+        else:
+            raise RuntimeError(
+                "Unable to fully collect garbage - perhaps a __del__ method "
+                "is creating more reference cycles?")
+
+        gc.set_debug(gc.DEBUG_SAVEALL)
+        yield
+        # gc.collect returns the number of unreachable objects in cycles that
+        # were found -- we are checking that no cycles were created in the context
+        n_objects_in_cycles = gc.collect()
+        objects_in_cycles = gc.garbage[:]
+    finally:
+        del gc.garbage[:]
+        gc.set_debug(gc_debug)
+        gc.enable()
+
+    if n_objects_in_cycles:
+        name_str = f' when calling {name}' if name is not None else ''
+        raise AssertionError(
+            "Reference cycles were found{}: {} objects were collected, "
+            "of which {} are shown below:{}"
+            .format(
+                name_str,
+                n_objects_in_cycles,
+                len(objects_in_cycles),
+                ''.join(
+                    "\n  {} object with id={}:\n    {}".format(
+                        type(o).__name__,
+                        id(o),
+                        pprint.pformat(o).replace('\n', '\n    ')
+                    ) for o in objects_in_cycles
+                )
+            )
+        )
+
+
+def assert_no_gc_cycles(*args, **kwargs):
+    """
+    Fail if the given callable produces any reference cycles.
+
+    If called with all arguments omitted, may be used as a context manager::
+
+        with assert_no_gc_cycles():
+            do_something()
+
+    Parameters
+    ----------
+    func : callable
+        The callable to test.
+    \\*args : Arguments
+        Arguments passed to `func`.
+    \\*\\*kwargs : Kwargs
+        Keyword arguments passed to `func`.
+
+    Returns
+    -------
+    Nothing. The result is deliberately discarded to ensure that all cycles
+    are found.
+
+    """
+    if not args:
+        return _assert_no_gc_cycles_context()
+
+    func = args[0]
+    args = args[1:]
+    with _assert_no_gc_cycles_context(name=func.__name__):
+        func(*args, **kwargs)
+
+
+def break_cycles():
+    """
+    Break reference cycles by calling gc.collect
+    Objects can call other objects' methods (for instance, another object's
+     __del__) inside their own __del__. On PyPy, the interpreter only runs
+    between calls to gc.collect, so multiple calls are needed to completely
+    release all cycles.
+    """
+
+    gc.collect()
+    if IS_PYPY:
+        # a few more, just to make sure all the finalizers are called
+        gc.collect()
+        gc.collect()
+        gc.collect()
+        gc.collect()
+
+
+def requires_memory(free_bytes):
+    """Decorator to skip a test if not enough memory is available"""
+    import pytest
+
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*a, **kw):
+            msg = check_free_memory(free_bytes)
+            if msg is not None:
+                pytest.skip(msg)
+
+            try:
+                return func(*a, **kw)
+            except MemoryError:
+                # Probably ran out of memory regardless: don't regard as failure
+                pytest.xfail("MemoryError raised")
+
+        return wrapper
+
+    return decorator
+
+
+def check_free_memory(free_bytes):
+    """
+    Check whether `free_bytes` amount of memory is currently free.
+    Returns: None if enough memory available, otherwise error message
+    """
+    env_var = 'NPY_AVAILABLE_MEM'
+    env_value = os.environ.get(env_var)
+    if env_value is not None:
+        try:
+            mem_free = _parse_size(env_value)
+        except ValueError as exc:
+            raise ValueError(f'Invalid environment variable {env_var}: {exc}')
+
+        msg = (f'{free_bytes / 1e9} GB memory required, but environment variable '
+               f'NPY_AVAILABLE_MEM={env_value} set')
+    else:
+        mem_free = _get_mem_available()
+
+        if mem_free is None:
+            msg = ("Could not determine available memory; set NPY_AVAILABLE_MEM "
+                   "environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run "
+                   "the test.")
+            mem_free = -1
+        else:
+            free_bytes_gb = free_bytes / 1e9
+            mem_free_gb = mem_free / 1e9
+            msg = f'{free_bytes_gb} GB memory required, but {mem_free_gb} GB available'
+
+    return msg if mem_free < free_bytes else None
+
+
+def _parse_size(size_str):
+    """Convert memory size strings ('12 GB' etc.) to float"""
+    suffixes = {'': 1, 'b': 1,
+                'k': 1000, 'm': 1000**2, 'g': 1000**3, 't': 1000**4,
+                'kb': 1000, 'mb': 1000**2, 'gb': 1000**3, 'tb': 1000**4,
+                'kib': 1024, 'mib': 1024**2, 'gib': 1024**3, 'tib': 1024**4}
+
+    size_re = re.compile(r'^\s*(\d+|\d+\.\d+)\s*({0})\s*$'.format(
+        '|'.join(suffixes.keys())), re.I)
+
+    m = size_re.match(size_str.lower())
+    if not m or m.group(2) not in suffixes:
+        raise ValueError(f'value {size_str!r} not a valid size')
+    return int(float(m.group(1)) * suffixes[m.group(2)])
+
+
+def _get_mem_available():
+    """Return available memory in bytes, or None if unknown."""
+    try:
+        import psutil
+        return psutil.virtual_memory().available
+    except (ImportError, AttributeError):
+        pass
+
+    if sys.platform.startswith('linux'):
+        info = {}
+        with open('/proc/meminfo') as f:
+            for line in f:
+                p = line.split()
+                info[p[0].strip(':').lower()] = int(p[1]) * 1024
+
+        if 'memavailable' in info:
+            # Linux >= 3.14
+            return info['memavailable']
+        else:
+            return info['memfree'] + info['cached']
+
+    return None
+
+
+def _no_tracing(func):
+    """
+    Decorator to temporarily turn off tracing for the duration of a test.
+    Needed in tests that check refcounting, otherwise the tracing itself
+    influences the refcounts
+    """
+    if not hasattr(sys, 'gettrace'):
+        return func
+    else:
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            original_trace = sys.gettrace()
+            try:
+                sys.settrace(None)
+                return func(*args, **kwargs)
+            finally:
+                sys.settrace(original_trace)
+        return wrapper
+
+
+def _get_glibc_version():
+    try:
+        ver = os.confstr('CS_GNU_LIBC_VERSION').rsplit(' ')[1]
+    except Exception:
+        ver = '0.0'
+
+    return ver
+
+
+_glibcver = _get_glibc_version()
+_glibc_older_than = lambda x: (_glibcver != '0.0' and _glibcver < x)
+
+
+def run_threaded(func, max_workers=8, pass_count=False,
+                 pass_barrier=False, outer_iterations=1,
+                 prepare_args=None):
+    """Runs a function many times in parallel"""
+    for _ in range(outer_iterations):
+        with (concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
+              as tpe):
+            if prepare_args is None:
+                args = []
+            else:
+                args = prepare_args()
+            if pass_barrier:
+                barrier = threading.Barrier(max_workers)
+                args.append(barrier)
+            if pass_count:
+                all_args = [(func, i, *args) for i in range(max_workers)]
+            else:
+                all_args = [(func, *args) for i in range(max_workers)]
+            try:
+                futures = []
+                for arg in all_args:
+                    futures.append(tpe.submit(*arg))
+            finally:
+                if len(futures) < max_workers and pass_barrier:
+                    barrier.abort()
+            for f in futures:
+                f.result()
+
+
+def get_stringdtype_dtype(na_object, coerce=True):
+    # explicit is check for pd_NA because != with pd_NA returns pd_NA
+    if na_object is pd_NA or na_object != "unset":
+        return np.dtypes.StringDType(na_object=na_object, coerce=coerce)
+    else:
+        return np.dtypes.StringDType(coerce=coerce)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..75ea45d3a72118fa6d17298fe85ccf7078caaed3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/_private/utils.pyi
@@ -0,0 +1,496 @@
+import ast
+import sys
+import types
+import unittest
+import warnings
+from collections.abc import Callable, Iterable, Sequence
+from contextlib import _GeneratorContextManager
+from pathlib import Path
+from re import Pattern
+from typing import (
+    Any,
+    AnyStr,
+    ClassVar,
+    Final,
+    Generic,
+    NoReturn,
+    SupportsIndex,
+    TypeAlias,
+    overload,
+    type_check_only,
+)
+from typing import Literal as L
+from unittest.case import SkipTest
+
+from _typeshed import ConvertibleToFloat, GenericPath, StrOrBytesPath, StrPath
+from typing_extensions import ParamSpec, Self, TypeVar, TypeVarTuple, Unpack
+
+import numpy as np
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NDArray,
+    _ArrayLikeDT64_co,
+    _ArrayLikeNumber_co,
+    _ArrayLikeObject_co,
+    _ArrayLikeTD64_co,
+)
+
+__all__ = [  # noqa: RUF022
+    "IS_EDITABLE",
+    "IS_MUSL",
+    "IS_PYPY",
+    "IS_PYSTON",
+    "IS_WASM",
+    "HAS_LAPACK64",
+    "HAS_REFCOUNT",
+    "NOGIL_BUILD",
+    "assert_",
+    "assert_array_almost_equal_nulp",
+    "assert_raises_regex",
+    "assert_array_max_ulp",
+    "assert_warns",
+    "assert_no_warnings",
+    "assert_allclose",
+    "assert_equal",
+    "assert_almost_equal",
+    "assert_approx_equal",
+    "assert_array_equal",
+    "assert_array_less",
+    "assert_string_equal",
+    "assert_array_almost_equal",
+    "assert_raises",
+    "build_err_msg",
+    "decorate_methods",
+    "jiffies",
+    "memusage",
+    "print_assert_equal",
+    "rundocs",
+    "runstring",
+    "verbose",
+    "measure",
+    "IgnoreException",
+    "clear_and_catch_warnings",
+    "SkipTest",
+    "KnownFailureException",
+    "temppath",
+    "tempdir",
+    "suppress_warnings",
+    "assert_array_compare",
+    "assert_no_gc_cycles",
+    "break_cycles",
+    "check_support_sve",
+    "run_threaded",
+]
+
+###
+
+_T = TypeVar("_T")
+_Ts = TypeVarTuple("_Ts")
+_Tss = ParamSpec("_Tss")
+_ET = TypeVar("_ET", bound=BaseException, default=BaseException)
+_FT = TypeVar("_FT", bound=Callable[..., Any])
+_W_co = TypeVar("_W_co", bound=_WarnLog | None, default=_WarnLog | None, covariant=True)
+_T_or_bool = TypeVar("_T_or_bool", default=bool)
+
+_StrLike: TypeAlias = str | bytes
+_RegexLike: TypeAlias = _StrLike | Pattern[Any]
+_NumericArrayLike: TypeAlias = _ArrayLikeNumber_co | _ArrayLikeObject_co
+
+_ExceptionSpec: TypeAlias = type[_ET] | tuple[type[_ET], ...]
+_WarningSpec: TypeAlias = type[Warning]
+_WarnLog: TypeAlias = list[warnings.WarningMessage]
+_ToModules: TypeAlias = Iterable[types.ModuleType]
+
+# Must return a bool or an ndarray/generic type that is supported by `np.logical_and.reduce`
+_ComparisonFunc: TypeAlias = Callable[
+    [NDArray[Any], NDArray[Any]],
+    bool | np.bool | np.number | NDArray[np.bool | np.number | np.object_],
+]
+
+# Type-check only `clear_and_catch_warnings` subclasses for both values of the
+# `record` parameter. Copied from the stdlib `warnings` stubs.
+@type_check_only
+class _clear_and_catch_warnings_with_records(clear_and_catch_warnings):
+    def __enter__(self) -> list[warnings.WarningMessage]: ...
+
+@type_check_only
+class _clear_and_catch_warnings_without_records(clear_and_catch_warnings):
+    def __enter__(self) -> None: ...
+
+###
+
+verbose: int = 0
+NUMPY_ROOT: Final[Path] = ...
+IS_INSTALLED: Final[bool] = ...
+IS_EDITABLE: Final[bool] = ...
+IS_MUSL: Final[bool] = ...
+IS_PYPY: Final[bool] = ...
+IS_PYSTON: Final[bool] = ...
+IS_WASM: Final[bool] = ...
+HAS_REFCOUNT: Final[bool] = ...
+HAS_LAPACK64: Final[bool] = ...
+NOGIL_BUILD: Final[bool] = ...
+
+class KnownFailureException(Exception): ...
+class IgnoreException(Exception): ...
+
+# NOTE: `warnings.catch_warnings` is incorrectly defined as invariant in typeshed
+class clear_and_catch_warnings(warnings.catch_warnings[_W_co], Generic[_W_co]):  # type: ignore[type-var]  # pyright: ignore[reportInvalidTypeArguments]
+    class_modules: ClassVar[tuple[types.ModuleType, ...]] = ()
+    modules: Final[set[types.ModuleType]]
+    @overload  # record: True
+    def __init__(self: clear_and_catch_warnings[_WarnLog], /, record: L[True], modules: _ToModules = ()) -> None: ...
+    @overload  # record: False (default)
+    def __init__(self: clear_and_catch_warnings[None], /, record: L[False] = False, modules: _ToModules = ()) -> None: ...
+    @overload  # record; bool
+    def __init__(self, /, record: bool, modules: _ToModules = ()) -> None: ...
+
+class suppress_warnings:
+    log: Final[_WarnLog]
+    def __init__(self, /, forwarding_rule: L["always", "module", "once", "location"] = "always") -> None: ...
+    def __enter__(self) -> Self: ...
+    def __exit__(self, cls: type[BaseException] | None, exc: BaseException | None, tb: types.TracebackType | None, /) -> None: ...
+    def __call__(self, /, func: _FT) -> _FT: ...
+
+    #
+    def filter(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> None: ...
+    def record(self, /, category: type[Warning] = ..., message: str = "", module: types.ModuleType | None = None) -> _WarnLog: ...
+
+# Contrary to runtime we can't do `os.name` checks while type checking,
+# only `sys.platform` checks
+if sys.platform == "win32" or sys.platform == "cygwin":
+    def memusage(processName: str = ..., instance: int = ...) -> int: ...
+elif sys.platform == "linux":
+    def memusage(_proc_pid_stat: StrOrBytesPath = ...) -> int | None: ...
+else:
+    def memusage() -> NoReturn: ...
+
+if sys.platform == "linux":
+    def jiffies(_proc_pid_stat: StrOrBytesPath = ..., _load_time: list[float] = []) -> int: ...
+else:
+    def jiffies(_load_time: list[float] = []) -> int: ...
+
+#
+def build_err_msg(
+    arrays: Iterable[object],
+    err_msg: object,
+    header: str = ...,
+    verbose: bool = ...,
+    names: Sequence[str] = ...,
+    precision: SupportsIndex | None = ...,
+) -> str: ...
+
+#
+def print_assert_equal(test_string: str, actual: object, desired: object) -> None: ...
+
+#
+def assert_(val: object, msg: str | Callable[[], str] = "") -> None: ...
+
+#
+def assert_equal(
+    actual: object,
+    desired: object,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+def assert_almost_equal(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    decimal: int = 7,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+#
+def assert_approx_equal(
+    actual: ConvertibleToFloat,
+    desired: ConvertibleToFloat,
+    significant: int = 7,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+#
+def assert_array_compare(
+    comparison: _ComparisonFunc,
+    x: ArrayLike,
+    y: ArrayLike,
+    err_msg: object = "",
+    verbose: bool = True,
+    header: str = "",
+    precision: SupportsIndex = 6,
+    equal_nan: bool = True,
+    equal_inf: bool = True,
+    *,
+    strict: bool = False,
+    names: tuple[str, str] = ("ACTUAL", "DESIRED"),
+) -> None: ...
+
+#
+def assert_array_equal(
+    actual: object,
+    desired: object,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_array_almost_equal(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    decimal: float = 6,
+    err_msg: object = "",
+    verbose: bool = True,
+) -> None: ...
+
+@overload
+def assert_array_less(
+    x: _ArrayLikeDT64_co,
+    y: _ArrayLikeDT64_co,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_array_less(
+    x: _ArrayLikeTD64_co,
+    y: _ArrayLikeTD64_co,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_array_less(
+    x: _NumericArrayLike,
+    y: _NumericArrayLike,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_string_equal(actual: str, desired: str) -> None: ...
+
+#
+@overload
+def assert_raises(
+    exception_class: _ExceptionSpec[_ET],
+    /,
+    *,
+    msg: str | None = None,
+) -> unittest.case._AssertRaisesContext[_ET]: ...
+@overload
+def assert_raises(
+    exception_class: _ExceptionSpec,
+    callable: Callable[_Tss, Any],
+    /,
+    *args: _Tss.args,
+    **kwargs: _Tss.kwargs,
+) -> None: ...
+
+#
+@overload
+def assert_raises_regex(
+    exception_class: _ExceptionSpec[_ET],
+    expected_regexp: _RegexLike,
+    *,
+    msg: str | None = None,
+) -> unittest.case._AssertRaisesContext[_ET]: ...
+@overload
+def assert_raises_regex(
+    exception_class: _ExceptionSpec,
+    expected_regexp: _RegexLike,
+    callable: Callable[_Tss, Any],
+    *args: _Tss.args,
+    **kwargs: _Tss.kwargs,
+) -> None: ...
+
+#
+@overload
+def assert_allclose(
+    actual: _ArrayLikeTD64_co,
+    desired: _ArrayLikeTD64_co,
+    rtol: float = 1e-7,
+    atol: float = 0,
+    equal_nan: bool = True,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+@overload
+def assert_allclose(
+    actual: _NumericArrayLike,
+    desired: _NumericArrayLike,
+    rtol: float = 1e-7,
+    atol: float = 0,
+    equal_nan: bool = True,
+    err_msg: object = "",
+    verbose: bool = True,
+    *,
+    strict: bool = False,
+) -> None: ...
+
+#
+def assert_array_almost_equal_nulp(
+    x: _ArrayLikeNumber_co,
+    y: _ArrayLikeNumber_co,
+    nulp: float = 1,
+) -> None: ...
+
+#
+def assert_array_max_ulp(
+    a: _ArrayLikeNumber_co,
+    b: _ArrayLikeNumber_co,
+    maxulp: float = 1,
+    dtype: DTypeLike | None = None,
+) -> NDArray[Any]: ...
+
+#
+@overload
+def assert_warns(warning_class: _WarningSpec) -> _GeneratorContextManager[None]: ...
+@overload
+def assert_warns(warning_class: _WarningSpec, func: Callable[_Tss, _T], *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...
+
+#
+@overload
+def assert_no_warnings() -> _GeneratorContextManager[None]: ...
+@overload
+def assert_no_warnings(func: Callable[_Tss, _T], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> _T: ...
+
+#
+@overload
+def assert_no_gc_cycles() -> _GeneratorContextManager[None]: ...
+@overload
+def assert_no_gc_cycles(func: Callable[_Tss, Any], /, *args: _Tss.args, **kwargs: _Tss.kwargs) -> None: ...
+
+###
+
+#
+@overload
+def tempdir(
+    suffix: None = None,
+    prefix: None = None,
+    dir: None = None,
+) -> _GeneratorContextManager[str]: ...
+@overload
+def tempdir(
+    suffix: AnyStr | None = None,
+    prefix: AnyStr | None = None,
+    *,
+    dir: GenericPath[AnyStr],
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def tempdir(
+    suffix: AnyStr | None = None,
+    *,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def tempdir(
+    suffix: AnyStr,
+    prefix: AnyStr | None = None,
+    dir: GenericPath[AnyStr] | None = None,
+) -> _GeneratorContextManager[AnyStr]: ...
+
+#
+@overload
+def temppath(
+    suffix: None = None,
+    prefix: None = None,
+    dir: None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[str]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None,
+    prefix: AnyStr | None,
+    dir: GenericPath[AnyStr],
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None = None,
+    prefix: AnyStr | None = None,
+    *,
+    dir: GenericPath[AnyStr],
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr | None = None,
+    *,
+    prefix: AnyStr,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+@overload
+def temppath(
+    suffix: AnyStr,
+    prefix: AnyStr | None = None,
+    dir: GenericPath[AnyStr] | None = None,
+    text: bool = False,
+) -> _GeneratorContextManager[AnyStr]: ...
+
+#
+def check_support_sve(__cache: list[_T_or_bool] = []) -> _T_or_bool: ...  # noqa: PYI063
+
+#
+def decorate_methods(
+    cls: type,
+    decorator: Callable[[Callable[..., Any]], Any],
+    testmatch: _RegexLike | None = None,
+) -> None: ...
+
+#
+@overload
+def run_threaded(
+    func: Callable[[], None],
+    max_workers: int = 8,
+    pass_count: bool = False,
+    pass_barrier: bool = False,
+    outer_iterations: int = 1,
+    prepare_args: None = None,
+) -> None: ...
+@overload
+def run_threaded(
+    func: Callable[[Unpack[_Ts]], None],
+    max_workers: int,
+    pass_count: bool,
+    pass_barrier: bool,
+    outer_iterations: int,
+    prepare_args: tuple[Unpack[_Ts]],
+) -> None: ...
+@overload
+def run_threaded(
+    func: Callable[[Unpack[_Ts]], None],
+    max_workers: int = 8,
+    pass_count: bool = False,
+    pass_barrier: bool = False,
+    outer_iterations: int = 1,
+    *,
+    prepare_args: tuple[Unpack[_Ts]],
+) -> None: ...
+
+#
+def runstring(astr: _StrLike | types.CodeType, dict: dict[str, Any] | None) -> Any: ...  # noqa: ANN401
+def rundocs(filename: StrPath | None = None, raise_on_error: bool = True) -> None: ...
+def measure(code_str: _StrLike | ast.AST, times: int = 1, label: str | None = None) -> float: ...
+def break_cycles() -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e61534c323648f3def69c24e61d7d6e6c79d970
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.py
@@ -0,0 +1,83 @@
+"""Tools for testing implementations of __array_function__ and ufunc overrides
+
+
+"""
+
+from numpy._core.overrides import ARRAY_FUNCTIONS as _array_functions
+from numpy import ufunc as _ufunc
+import numpy._core.umath as _umath
+
+def get_overridable_numpy_ufuncs():
+    """List all numpy ufuncs overridable via `__array_ufunc__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all overridable ufuncs in the public numpy API.
+    """
+    ufuncs = {obj for obj in _umath.__dict__.values()
+              if isinstance(obj, _ufunc)}
+    return ufuncs
+
+
+def allows_array_ufunc_override(func):
+    """Determine if a function can be overridden via `__array_ufunc__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_ufunc__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is overridable via `__array_ufunc__` and
+        `False` otherwise.
+
+    Notes
+    -----
+    This function is equivalent to ``isinstance(func, np.ufunc)`` and
+    will work correctly for ufuncs defined outside of Numpy.
+
+    """
+    return isinstance(func, _ufunc)
+
+
+def get_overridable_numpy_array_functions():
+    """List all numpy functions overridable via `__array_function__`
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    set
+        A set containing all functions in the public numpy API that are
+        overridable via `__array_function__`.
+
+    """
+    # 'import numpy' doesn't import recfunctions, so make sure it's imported
+    # so ufuncs defined there show up in the ufunc listing
+    from numpy.lib import recfunctions  # noqa: F401
+    return _array_functions.copy()
+
+def allows_array_function_override(func):
+    """Determine if a Numpy function can be overridden via `__array_function__`
+
+    Parameters
+    ----------
+    func : callable
+        Function that may be overridable via `__array_function__`
+
+    Returns
+    -------
+    bool
+        `True` if `func` is a function in the Numpy API that is
+        overridable via `__array_function__` and `False` otherwise.
+    """
+    return func in _array_functions
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3fefc3f350dacbd223c1fcc94db1c634d1b6c6b1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/overrides.pyi
@@ -0,0 +1,11 @@
+from collections.abc import Callable, Hashable
+from typing import Any
+
+from typing_extensions import TypeIs
+
+import numpy as np
+
+def get_overridable_numpy_ufuncs() -> set[np.ufunc]: ...
+def get_overridable_numpy_array_functions() -> set[Callable[..., Any]]: ...
+def allows_array_ufunc_override(func: object) -> TypeIs[np.ufunc]: ...
+def allows_array_function_override(func: Hashable) -> bool: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.py
new file mode 100644
index 0000000000000000000000000000000000000000..649c1cd6bc21720ace7d4a6597061242a9d2ccde
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.py
@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""Prints type-coercion tables for the built-in NumPy types
+
+"""
+import numpy as np
+from numpy._core.numerictypes import obj2sctype
+from collections import namedtuple
+
+# Generic object that can be added, but doesn't do anything else
+class GenericObject:
+    def __init__(self, v):
+        self.v = v
+
+    def __add__(self, other):
+        return self
+
+    def __radd__(self, other):
+        return self
+
+    dtype = np.dtype('O')
+
+def print_cancast_table(ntypes):
+    print('X', end=' ')
+    for char in ntypes:
+        print(char, end=' ')
+    print()
+    for row in ntypes:
+        print(row, end=' ')
+        for col in ntypes:
+            if np.can_cast(row, col, "equiv"):
+                cast = "#"
+            elif np.can_cast(row, col, "safe"):
+                cast = "="
+            elif np.can_cast(row, col, "same_kind"):
+                cast = "~"
+            elif np.can_cast(row, col, "unsafe"):
+                cast = "."
+            else:
+                cast = " "
+            print(cast, end=' ')
+        print()
+
+def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
+    print('+', end=' ')
+    for char in ntypes:
+        print(char, end=' ')
+    print()
+    for row in ntypes:
+        if row == 'O':
+            rowtype = GenericObject
+        else:
+            rowtype = obj2sctype(row)
+
+        print(row, end=' ')
+        for col in ntypes:
+            if col == 'O':
+                coltype = GenericObject
+            else:
+                coltype = obj2sctype(col)
+            try:
+                if firstarray:
+                    rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
+                else:
+                    rowvalue = rowtype(inputfirstvalue)
+                colvalue = coltype(inputsecondvalue)
+                if use_promote_types:
+                    char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
+                else:
+                    value = np.add(rowvalue, colvalue)
+                    if isinstance(value, np.ndarray):
+                        char = value.dtype.char
+                    else:
+                        char = np.dtype(type(value)).char
+            except ValueError:
+                char = '!'
+            except OverflowError:
+                char = '@'
+            except TypeError:
+                char = '#'
+            print(char, end=' ')
+        print()
+
+
+def print_new_cast_table(*, can_cast=True, legacy=False, flags=False):
+    """Prints new casts, the values given are default "can-cast" values, not
+    actual ones.
+    """
+    from numpy._core._multiarray_tests import get_all_cast_information
+
+    cast_table = {
+        -1: " ",
+        0: "#",  # No cast (classify as equivalent here)
+        1: "#",  # equivalent casting
+        2: "=",  # safe casting
+        3: "~",  # same-kind casting
+        4: ".",  # unsafe casting
+    }
+    flags_table = {
+        0 : "▗", 7: "█",
+        1: "▚", 2: "▐", 4: "▄",
+                3: "▜", 5: "▙",
+                        6: "▟",
+    }
+
+    cast_info = namedtuple("cast_info", ["can_cast", "legacy", "flags"])
+    no_cast_info = cast_info(" ", " ", " ")
+
+    casts = get_all_cast_information()
+    table = {}
+    dtypes = set()
+    for cast in casts:
+        dtypes.add(cast["from"])
+        dtypes.add(cast["to"])
+
+        if cast["from"] not in table:
+            table[cast["from"]] = {}
+        to_dict = table[cast["from"]]
+
+        can_cast = cast_table[cast["casting"]]
+        legacy = "L" if cast["legacy"] else "."
+        flags = 0
+        if cast["requires_pyapi"]:
+            flags |= 1
+        if cast["supports_unaligned"]:
+            flags |= 2
+        if cast["no_floatingpoint_errors"]:
+            flags |= 4
+
+        flags = flags_table[flags]
+        to_dict[cast["to"]] = cast_info(can_cast=can_cast, legacy=legacy, flags=flags)
+
+    # The np.dtype(x.type) is a bit strange, because dtype classes do
+    # not expose much yet.
+    types = np.typecodes["All"]
+    def sorter(x):
+        # This is a bit weird hack, to get a table as close as possible to
+        # the one printing all typecodes (but expecting user-dtypes).
+        dtype = np.dtype(x.type)
+        try:
+            indx = types.index(dtype.char)
+        except ValueError:
+            indx = np.inf
+        return (indx, dtype.char)
+
+    dtypes = sorted(dtypes, key=sorter)
+
+    def print_table(field="can_cast"):
+        print('X', end=' ')
+        for dt in dtypes:
+            print(np.dtype(dt.type).char, end=' ')
+        print()
+        for from_dt in dtypes:
+            print(np.dtype(from_dt.type).char, end=' ')
+            row = table.get(from_dt, {})
+            for to_dt in dtypes:
+                print(getattr(row.get(to_dt, no_cast_info), field), end=' ')
+            print()
+
+    if can_cast:
+        # Print the actual table:
+        print()
+        print("Casting: # is equivalent, = is safe, ~ is same-kind, and . is unsafe")
+        print()
+        print_table("can_cast")
+
+    if legacy:
+        print()
+        print("L denotes a legacy cast . a non-legacy one.")
+        print()
+        print_table("legacy")
+
+    if flags:
+        print()
+        print(f"{flags_table[0]}: no flags, {flags_table[1]}: PyAPI, "
+              f"{flags_table[2]}: supports unaligned, {flags_table[4]}: no-float-errors")
+        print()
+        print_table("flags")
+
+
+if __name__ == '__main__':
+    print("can cast")
+    print_cancast_table(np.typecodes['All'])
+    print()
+    print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")
+    print()
+    print("scalar + scalar")
+    print_coercion_table(np.typecodes['All'], 0, 0, False)
+    print()
+    print("scalar + neg scalar")
+    print_coercion_table(np.typecodes['All'], 0, -1, False)
+    print()
+    print("array + scalar")
+    print_coercion_table(np.typecodes['All'], 0, 0, True)
+    print()
+    print("array + neg scalar")
+    print_coercion_table(np.typecodes['All'], 0, -1, True)
+    print()
+    print("promote_types")
+    print_coercion_table(np.typecodes['All'], 0, 0, False, True)
+    print("New casting type promotion:")
+    print_new_cast_table(can_cast=True, legacy=True, flags=True)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e6430304675e430753a8caa72ffcb2570736a618
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/print_coercion_tables.pyi
@@ -0,0 +1,27 @@
+from collections.abc import Iterable
+from typing import ClassVar, Generic
+
+from typing_extensions import Self, TypeVar
+
+import numpy as np
+
+_VT_co = TypeVar("_VT_co", default=object, covariant=True)
+
+# undocumented
+class GenericObject(Generic[_VT_co]):
+    dtype: ClassVar[np.dtype[np.object_]] = ...
+    v: _VT_co
+
+    def __init__(self, /, v: _VT_co) -> None: ...
+    def __add__(self, other: object, /) -> Self: ...
+    def __radd__(self, other: object, /) -> Self: ...
+
+def print_cancast_table(ntypes: Iterable[str]) -> None: ...
+def print_coercion_table(
+    ntypes: Iterable[str],
+    inputfirstvalue: int,
+    inputsecondvalue: int,
+    firstarray: bool,
+    use_promote_types: bool = False,
+) -> None: ...
+def print_new_cast_table(*, can_cast: bool = True, legacy: bool = False, flags: bool = False) -> None: ...
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/tests/test_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/tests/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..df9fce8fd79afbcec85d94bb37ee034a9d1f4668
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/testing/tests/test_utils.py
@@ -0,0 +1,1929 @@
+import warnings
+import sys
+import os
+import itertools
+import pytest
+import weakref
+import re
+
+import numpy as np
+import numpy._core._multiarray_umath as ncu
+from numpy.testing import (
+    assert_equal, assert_array_equal, assert_almost_equal,
+    assert_array_almost_equal, assert_array_less, build_err_msg,
+    assert_raises, assert_warns, assert_no_warnings, assert_allclose,
+    assert_approx_equal, assert_array_almost_equal_nulp, assert_array_max_ulp,
+    clear_and_catch_warnings, suppress_warnings, assert_string_equal, assert_,
+    tempdir, temppath, assert_no_gc_cycles, HAS_REFCOUNT
+)
+
+
+class _GenericTest:
+
+    def _test_equal(self, a, b):
+        self._assert_func(a, b)
+
+    def _test_not_equal(self, a, b):
+        with assert_raises(AssertionError):
+            self._assert_func(a, b)
+
+    def test_array_rank1_eq(self):
+        """Test two equal array of rank 1 are found equal."""
+        a = np.array([1, 2])
+        b = np.array([1, 2])
+
+        self._test_equal(a, b)
+
+    def test_array_rank1_noteq(self):
+        """Test two different array of rank 1 are found not equal."""
+        a = np.array([1, 2])
+        b = np.array([2, 2])
+
+        self._test_not_equal(a, b)
+
+    def test_array_rank2_eq(self):
+        """Test two equal array of rank 2 are found equal."""
+        a = np.array([[1, 2], [3, 4]])
+        b = np.array([[1, 2], [3, 4]])
+
+        self._test_equal(a, b)
+
+    def test_array_diffshape(self):
+        """Test two arrays with different shapes are found not equal."""
+        a = np.array([1, 2])
+        b = np.array([[1, 2], [1, 2]])
+
+        self._test_not_equal(a, b)
+
+    def test_objarray(self):
+        """Test object arrays."""
+        a = np.array([1, 1], dtype=object)
+        self._test_equal(a, 1)
+
+    def test_array_likes(self):
+        self._test_equal([1, 2, 3], (1, 2, 3))
+
+
+class TestArrayEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_array_equal
+
+    def test_generic_rank1(self):
+        """Test rank 1 array for all dtypes."""
+        def foo(t):
+            a = np.empty(2, t)
+            a.fill(1)
+            b = a.copy()
+            c = a.copy()
+            c.fill(0)
+            self._test_equal(a, b)
+            self._test_not_equal(c, b)
+
+        # Test numeric types and object
+        for t in '?bhilqpBHILQPfdgFDG':
+            foo(t)
+
+        # Test strings
+        for t in ['S1', 'U1']:
+            foo(t)
+
+    def test_0_ndim_array(self):
+        x = np.array(473963742225900817127911193656584771)
+        y = np.array(18535119325151578301457182298393896)
+
+        with pytest.raises(AssertionError) as exc_info:
+            self._assert_func(x, y)
+        msg = str(exc_info.value)
+        assert_('Mismatched elements: 1 / 1 (100%)\n'
+                in msg)
+
+        y = x
+        self._assert_func(x, y)
+
+        x = np.array(4395065348745.5643764887869876)
+        y = np.array(0)
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '4.39506535e+12\n'
+                        'Max relative difference among violations: inf\n')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        x = y
+        self._assert_func(x, y)
+
+    def test_generic_rank3(self):
+        """Test rank 3 array for all dtypes."""
+        def foo(t):
+            a = np.empty((4, 2, 3), t)
+            a.fill(1)
+            b = a.copy()
+            c = a.copy()
+            c.fill(0)
+            self._test_equal(a, b)
+            self._test_not_equal(c, b)
+
+        # Test numeric types and object
+        for t in '?bhilqpBHILQPfdgFDG':
+            foo(t)
+
+        # Test strings
+        for t in ['S1', 'U1']:
+            foo(t)
+
+    def test_nan_array(self):
+        """Test arrays with nan values in them."""
+        a = np.array([1, 2, np.nan])
+        b = np.array([1, 2, np.nan])
+
+        self._test_equal(a, b)
+
+        c = np.array([1, 2, 3])
+        self._test_not_equal(c, b)
+
+    def test_string_arrays(self):
+        """Test two arrays with different shapes are found not equal."""
+        a = np.array(['floupi', 'floupa'])
+        b = np.array(['floupi', 'floupa'])
+
+        self._test_equal(a, b)
+
+        c = np.array(['floupipi', 'floupa'])
+
+        self._test_not_equal(c, b)
+
+    def test_recarrays(self):
+        """Test record arrays."""
+        a = np.empty(2, [('floupi', float), ('floupa', float)])
+        a['floupi'] = [1, 2]
+        a['floupa'] = [1, 2]
+        b = a.copy()
+
+        self._test_equal(a, b)
+
+        c = np.empty(2, [('floupipi', float),
+                         ('floupi', float), ('floupa', float)])
+        c['floupipi'] = a['floupi'].copy()
+        c['floupa'] = a['floupa'].copy()
+
+        with pytest.raises(TypeError):
+            self._test_not_equal(c, b)
+
+    def test_masked_nan_inf(self):
+        # Regression test for gh-11121
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[False, True, False])
+        b = np.array([3., np.nan, 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])
+        b = np.array([np.inf, 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+
+    def test_subclass_that_overrides_eq(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return bool(np.equal(self, other).all())
+
+            def __ne__(self, other):
+                return not self == other
+
+        a = np.array([1., 2.]).view(MyArray)
+        b = np.array([2., 3.]).view(MyArray)
+        assert_(type(a == a), bool)
+        assert_(a == a)
+        assert_(a != b)
+        self._test_equal(a, a)
+        self._test_not_equal(a, b)
+        self._test_not_equal(b, a)
+
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._test_equal(a, b)
+
+        c = np.array([0., 2.9]).view(MyArray)
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 2.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._test_equal(b, c)
+
+    def test_subclass_that_does_not_implement_npall(self):
+        class MyArray(np.ndarray):
+            def __array_function__(self, *args, **kwargs):
+                return NotImplemented
+
+        a = np.array([1., 2.]).view(MyArray)
+        b = np.array([2., 3.]).view(MyArray)
+        with assert_raises(TypeError):
+            np.all(a)
+        self._test_equal(a, a)
+        self._test_not_equal(a, b)
+        self._test_not_equal(b, a)
+
+    def test_suppress_overflow_warnings(self):
+        # Based on issue #18992
+        with pytest.raises(AssertionError):
+            with np.errstate(all="raise"):
+                np.testing.assert_array_equal(
+                    np.array([1, 2, 3], np.float32),
+                    np.array([1, 1e-40, 3], np.float32))
+
+    def test_array_vs_scalar_is_equal(self):
+        """Test comparing an array with a scalar when all values are equal."""
+        a = np.array([1., 1., 1.])
+        b = 1.
+
+        self._test_equal(a, b)
+
+    def test_array_vs_array_not_equal(self):
+        """Test comparing an array with a scalar when not all values equal."""
+        a = np.array([34986, 545676, 439655, 563766])
+        b = np.array([34986, 545676, 439655, 0])
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 563766\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+        a = np.array([34986, 545676, 439655.2, 563766])
+        expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '563766.\n'
+                        'Max relative difference among violations: '
+                        '4.54902139e-07')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_array_vs_scalar_strict(self):
+        """Test comparing an array with a scalar with strict option."""
+        a = np.array([1., 1., 1.])
+        b = 1.
+
+        with pytest.raises(AssertionError):
+            self._assert_func(a, b, strict=True)
+
+    def test_array_vs_array_strict(self):
+        """Test comparing two arrays with strict option."""
+        a = np.array([1., 1., 1.])
+        b = np.array([1., 1., 1.])
+
+        self._assert_func(a, b, strict=True)
+
+    def test_array_vs_float_array_strict(self):
+        """Test comparing two arrays with strict option."""
+        a = np.array([1, 1, 1])
+        b = np.array([1., 1., 1.])
+
+        with pytest.raises(AssertionError):
+            self._assert_func(a, b, strict=True)
+
+
+class TestBuildErrorMessage:
+
+    def test_build_err_msg_defaults(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg)
+        b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
+             '1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '
+             '2.00003, 3.00004])')
+        assert_equal(a, b)
+
+    def test_build_err_msg_no_verbose(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, verbose=False)
+        b = '\nItems are not equal: There is a mismatch'
+        assert_equal(a, b)
+
+    def test_build_err_msg_custom_names(self):
+        x = np.array([1.00001, 2.00002, 3.00003])
+        y = np.array([1.00002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))
+        b = ('\nItems are not equal: There is a mismatch\n FOO: array(['
+             '1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '
+             '3.00004])')
+        assert_equal(a, b)
+
+    def test_build_err_msg_custom_precision(self):
+        x = np.array([1.000000001, 2.00002, 3.00003])
+        y = np.array([1.000000002, 2.00003, 3.00004])
+        err_msg = 'There is a mismatch'
+
+        a = build_err_msg([x, y], err_msg, precision=10)
+        b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
+             '1.000000001, 2.00002    , 3.00003    ])\n DESIRED: array(['
+             '1.000000002, 2.00003    , 3.00004    ])')
+        assert_equal(a, b)
+
+
+class TestEqual(TestArrayEqual):
+
+    def setup_method(self):
+        self._assert_func = assert_equal
+
+    def test_nan_items(self):
+        self._assert_func(np.nan, np.nan)
+        self._assert_func([np.nan], [np.nan])
+        self._test_not_equal(np.nan, [np.nan])
+        self._test_not_equal(np.nan, 1)
+
+    def test_inf_items(self):
+        self._assert_func(np.inf, np.inf)
+        self._assert_func([np.inf], [np.inf])
+        self._test_not_equal(np.inf, [np.inf])
+
+    def test_datetime(self):
+        self._test_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-01", "s")
+        )
+        self._test_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-01", "m")
+        )
+
+        # gh-10081
+        self._test_not_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-02", "s")
+        )
+        self._test_not_equal(
+            np.datetime64("2017-01-01", "s"),
+            np.datetime64("2017-01-02", "m")
+        )
+
+    def test_nat_items(self):
+        # not a datetime
+        nadt_no_unit = np.datetime64("NaT")
+        nadt_s = np.datetime64("NaT", "s")
+        nadt_d = np.datetime64("NaT", "ns")
+        # not a timedelta
+        natd_no_unit = np.timedelta64("NaT")
+        natd_s = np.timedelta64("NaT", "s")
+        natd_d = np.timedelta64("NaT", "ns")
+
+        dts = [nadt_no_unit, nadt_s, nadt_d]
+        tds = [natd_no_unit, natd_s, natd_d]
+        for a, b in itertools.product(dts, dts):
+            self._assert_func(a, b)
+            self._assert_func([a], [b])
+            self._test_not_equal([a], b)
+
+        for a, b in itertools.product(tds, tds):
+            self._assert_func(a, b)
+            self._assert_func([a], [b])
+            self._test_not_equal([a], b)
+
+        for a, b in itertools.product(tds, dts):
+            self._test_not_equal(a, b)
+            self._test_not_equal(a, [b])
+            self._test_not_equal([a], [b])
+            self._test_not_equal([a], np.datetime64("2017-01-01", "s"))
+            self._test_not_equal([b], np.datetime64("2017-01-01", "s"))
+            self._test_not_equal([a], np.timedelta64(123, "s"))
+            self._test_not_equal([b], np.timedelta64(123, "s"))
+
+    def test_non_numeric(self):
+        self._assert_func('ab', 'ab')
+        self._test_not_equal('ab', 'abb')
+
+    def test_complex_item(self):
+        self._assert_func(complex(1, 2), complex(1, 2))
+        self._assert_func(complex(1, np.nan), complex(1, np.nan))
+        self._test_not_equal(complex(1, np.nan), complex(1, 2))
+        self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
+        self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
+
+    def test_negative_zero(self):
+        self._test_not_equal(ncu.PZERO, ncu.NZERO)
+
+    def test_complex(self):
+        x = np.array([complex(1, 2), complex(1, np.nan)])
+        y = np.array([complex(1, 2), complex(1, 2)])
+        self._assert_func(x, x)
+        self._test_not_equal(x, y)
+
+    def test_object(self):
+        # gh-12942
+        import datetime
+        a = np.array([datetime.datetime(2000, 1, 1),
+                      datetime.datetime(2000, 1, 2)])
+        self._test_not_equal(a, a[::-1])
+
+
+class TestArrayAlmostEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_array_almost_equal
+
+    def test_closeness(self):
+        # Note that in the course of time we ended up with
+        #     `abs(x - y) < 1.5 * 10**(-decimal)`
+        # instead of the previously documented
+        #     `abs(x - y) < 0.5 * 10**(-decimal)`
+        # so this check serves to preserve the wrongness.
+
+        # test scalars
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.5\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(1.5, 0.0, decimal=0)
+
+        # test arrays
+        self._assert_func([1.499999], [0.0], decimal=0)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.5\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func([1.5], [0.0], decimal=0)
+
+        a = [1.4999999, 0.00003]
+        b = [1.49999991, 0]
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 3.e-05\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=7)
+
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 3.e-05\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=7)
+
+    def test_simple(self):
+        x = np.array([1234.2222])
+        y = np.array([1234.2223])
+
+        self._assert_func(x, y, decimal=3)
+        self._assert_func(x, y, decimal=4)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '1.e-04\n'
+                        'Max relative difference among violations: '
+                        '8.10226812e-08')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y, decimal=5)
+
+    def test_array_vs_scalar(self):
+        a = [5498.42354, 849.54345, 0.00]
+        b = 5498.42354
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=9)
+
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: 5.4722099')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=9)
+
+        a = [5498.42354, 0.00]
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(b, a, decimal=7)
+
+        b = 0
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: '
+                        '5498.42354\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b, decimal=7)
+
+    def test_nan(self):
+        anan = np.array([np.nan])
+        aone = np.array([1])
+        ainf = np.array([np.inf])
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(ainf, anan))
+
+    def test_inf(self):
+        a = np.array([[1., 2.], [3., 4.]])
+        b = a.copy()
+        a[0, 0] = np.inf
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(a, b))
+        b[0, 0] = -np.inf
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(a, b))
+
+    def test_subclass(self):
+        a = np.array([[1., 2.], [3., 4.]])
+        b = np.ma.masked_array([[1., 2.], [0., 4.]],
+                               [[False, False], [True, False]])
+        self._assert_func(a, b)
+        self._assert_func(b, a)
+        self._assert_func(b, b)
+
+        # Test fully masked as well (see gh-11123).
+        a = np.ma.MaskedArray(3.5, mask=True)
+        b = np.array([3., 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.masked
+        b = np.array([3., 4., 6.5])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
+        b = np.array([1., 2., 3.])
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, True, True])
+        b = np.array(1.)
+        self._test_equal(a, b)
+        self._test_equal(b, a)
+
+    def test_subclass_2(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                return all(self)
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+        z = np.array([True, True]).view(MyArray)
+        all(z)
+        b = np.array([1., 202]).view(MyArray)
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 200.\n'
+                        'Max relative difference among violations: 0.99009')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_subclass_that_cannot_be_bool(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                raise NotImplementedError
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+
+class TestAlmostEqual(_GenericTest):
+
+    def setup_method(self):
+        self._assert_func = assert_almost_equal
+
+    def test_closeness(self):
+        # Note that in the course of time we ended up with
+        #     `abs(x - y) < 1.5 * 10**(-decimal)`
+        # instead of the previously documented
+        #     `abs(x - y) < 0.5 * 10**(-decimal)`
+        # so this check serves to preserve the wrongness.
+
+        # test scalars
+        self._assert_func(1.499999, 0.0, decimal=0)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(1.5, 0.0, decimal=0))
+
+        # test arrays
+        self._assert_func([1.499999], [0.0], decimal=0)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func([1.5], [0.0], decimal=0))
+
+    def test_nan_item(self):
+        self._assert_func(np.nan, np.nan)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.nan, 1))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.nan, np.inf))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.inf, np.nan))
+
+    def test_inf_item(self):
+        self._assert_func(np.inf, np.inf)
+        self._assert_func(-np.inf, -np.inf)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(np.inf, 1))
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(-np.inf, np.inf))
+
+    def test_simple_item(self):
+        self._test_not_equal(1, 2)
+
+    def test_complex_item(self):
+        self._assert_func(complex(1, 2), complex(1, 2))
+        self._assert_func(complex(1, np.nan), complex(1, np.nan))
+        self._assert_func(complex(np.inf, np.nan), complex(np.inf, np.nan))
+        self._test_not_equal(complex(1, np.nan), complex(1, 2))
+        self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
+        self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
+
+    def test_complex(self):
+        x = np.array([complex(1, 2), complex(1, np.nan)])
+        z = np.array([complex(1, 2), complex(np.nan, 1)])
+        y = np.array([complex(1, 2), complex(1, 2)])
+        self._assert_func(x, x)
+        self._test_not_equal(x, y)
+        self._test_not_equal(x, z)
+
+    def test_error_message(self):
+        """Check the message is formatted correctly for the decimal value.
+           Also check the message when input includes inf or nan (gh12200)"""
+        x = np.array([1.00000000001, 2.00000000002, 3.00003])
+        y = np.array([1.00000000002, 2.00000000003, 3.00004])
+
+        # Test with a different amount of decimal digits
+        expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'
+                        'Max absolute difference among violations: 1.e-05\n'
+                        'Max relative difference among violations: '
+                        '3.33328889e-06\n'
+                        ' ACTUAL: array([1.00000000001, '
+                        '2.00000000002, '
+                        '3.00003      ])\n'
+                        ' DESIRED: array([1.00000000002, 2.00000000003, '
+                        '3.00004      ])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y, decimal=12)
+
+        # With the default value of decimal digits, only the 3rd element
+        # differs. Note that we only check for the formatting of the arrays
+        # themselves.
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 1.e-05\n'
+                        'Max relative difference among violations: '
+                        '3.33328889e-06\n'
+                        ' ACTUAL: array([1.     , 2.     , 3.00003])\n'
+                        ' DESIRED: array([1.     , 2.     , 3.00004])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        # Check the error message when input includes inf
+        x = np.array([np.inf, 0])
+        y = np.array([np.inf, 1])
+        expected_msg = ('Mismatched elements: 1 / 2 (50%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 1.\n'
+                        ' ACTUAL: array([inf,  0.])\n'
+                        ' DESIRED: array([inf,  1.])')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        # Check the error message when dividing by zero
+        x = np.array([1, 2])
+        y = np.array([0, 0])
+        expected_msg = ('Mismatched elements: 2 / 2 (100%)\n'
+                        'Max absolute difference among violations: 2\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+    def test_error_message_2(self):
+        """Check the message is formatted correctly """
+        """when either x or y is a scalar."""
+        x = 2
+        y = np.ones(20)
+        expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        y = 2
+        x = np.ones(20)
+        expected_msg = ('Mismatched elements: 20 / 20 (100%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+    def test_subclass_that_cannot_be_bool(self):
+        # While we cannot guarantee testing functions will always work for
+        # subclasses, the tests should ideally rely only on subclasses having
+        # comparison operators, not on them being able to store booleans
+        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
+        class MyArray(np.ndarray):
+            def __eq__(self, other):
+                return super().__eq__(other).view(np.ndarray)
+
+            def __lt__(self, other):
+                return super().__lt__(other).view(np.ndarray)
+
+            def all(self, *args, **kwargs):
+                raise NotImplementedError
+
+        a = np.array([1., 2.]).view(MyArray)
+        self._assert_func(a, a)
+
+
+class TestApproxEqual:
+
+    def setup_method(self):
+        self._assert_func = assert_approx_equal
+
+    def test_simple_0d_arrays(self):
+        x = np.array(1234.22)
+        y = np.array(1234.23)
+
+        self._assert_func(x, y, significant=5)
+        self._assert_func(x, y, significant=6)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(x, y, significant=7))
+
+    def test_simple_items(self):
+        x = 1234.22
+        y = 1234.23
+
+        self._assert_func(x, y, significant=4)
+        self._assert_func(x, y, significant=5)
+        self._assert_func(x, y, significant=6)
+        assert_raises(AssertionError,
+                      lambda: self._assert_func(x, y, significant=7))
+
+    def test_nan_array(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+    def test_nan_items(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+
+class TestArrayAssertLess:
+
+    def setup_method(self):
+        self._assert_func = assert_array_less
+
+    def test_simple_arrays(self):
+        x = np.array([1.1, 2.2])
+        y = np.array([1.2, 2.3])
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y = np.array([1.0, 2.3])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        a = np.array([1, 3, 6, 20])
+        b = np.array([2, 4, 6, 8])
+
+        expected_msg = ('Mismatched elements: 2 / 4 (50%)\n'
+                        'Max absolute difference among violations: 12\n'
+                        'Max relative difference among violations: 1.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(a, b)
+
+    def test_rank2(self):
+        x = np.array([[1.1, 2.2], [3.3, 4.4]])
+        y = np.array([[1.2, 2.3], [3.4, 4.5]])
+
+        self._assert_func(x, y)
+        expected_msg = ('Mismatched elements: 4 / 4 (100%)\n'
+                        'Max absolute difference among violations: 0.1\n'
+                        'Max relative difference among violations: 0.09090909')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = np.array([[1.0, 2.3], [3.4, 4.5]])
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+    def test_rank3(self):
+        x = np.ones(shape=(2, 2, 2))
+        y = np.ones(shape=(2, 2, 2))+1
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y[0, 0, 0] = 0
+        expected_msg = ('Mismatched elements: 1 / 8 (12.5%)\n'
+                        'Max absolute difference among violations: 1.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+    def test_simple_items(self):
+        x = 1.1
+        y = 2.2
+
+        self._assert_func(x, y)
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.1\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = np.array([2.2, 3.3])
+
+        self._assert_func(x, y)
+        assert_raises(AssertionError, lambda: self._assert_func(y, x))
+
+        y = np.array([1.0, 3.3])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+
+    def test_simple_items_and_array(self):
+        x = np.array([[621.345454, 390.5436, 43.54657, 626.4535],
+                      [54.54, 627.3399, 13., 405.5435],
+                      [543.545, 8.34, 91.543, 333.3]])
+        y = 627.34
+        self._assert_func(x, y)
+
+        y = 8.339999
+        self._assert_func(y, x)
+
+        x = np.array([[3.4536, 2390.5436, 435.54657, 324525.4535],
+                      [5449.54, 999090.54, 130303.54, 405.5435],
+                      [543.545, 8.34, 91.543, 999090.53999]])
+        y = 999090.54
+
+        expected_msg = ('Mismatched elements: 1 / 12 (8.33%)\n'
+                        'Max absolute difference among violations: 0.\n'
+                        'Max relative difference among violations: 0.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 12 / 12 (100%)\n'
+                        'Max absolute difference among violations: '
+                        '999087.0864\n'
+                        'Max relative difference among violations: '
+                        '289288.5934676')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+    def test_zeroes(self):
+        x = np.array([546456., 0, 15.455])
+        y = np.array(87654.)
+
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 458802.\n'
+                        'Max relative difference among violations: 5.23423917')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 2 / 3 (66.7%)\n'
+                        'Max absolute difference among violations: 87654.\n'
+                        'Max relative difference among violations: '
+                        '5670.5626011')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+        y = 0
+
+        expected_msg = ('Mismatched elements: 3 / 3 (100%)\n'
+                        'Max absolute difference among violations: 546456.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(x, y)
+
+        expected_msg = ('Mismatched elements: 1 / 3 (33.3%)\n'
+                        'Max absolute difference among violations: 0.\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            self._assert_func(y, x)
+
+    def test_nan_noncompare(self):
+        anan = np.array(np.nan)
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+        self._assert_func(anan, anan)
+        assert_raises(AssertionError, lambda: self._assert_func(aone, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, anan))
+
+    def test_nan_noncompare_array(self):
+        x = np.array([1.1, 2.2, 3.3])
+        anan = np.array(np.nan)
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, x))
+
+        x = np.array([1.1, 2.2, np.nan])
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, anan))
+        assert_raises(AssertionError, lambda: self._assert_func(anan, x))
+
+        y = np.array([1.0, 2.0, np.nan])
+
+        self._assert_func(y, x)
+        assert_raises(AssertionError, lambda: self._assert_func(x, y))
+
+    def test_inf_compare(self):
+        aone = np.array(1)
+        ainf = np.array(np.inf)
+
+        self._assert_func(aone, ainf)
+        self._assert_func(-ainf, aone)
+        self._assert_func(-ainf, ainf)
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, aone))
+        assert_raises(AssertionError, lambda: self._assert_func(aone, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-ainf, -ainf))
+
+    def test_inf_compare_array(self):
+        x = np.array([1.1, 2.2, np.inf])
+        ainf = np.array(np.inf)
+
+        assert_raises(AssertionError, lambda: self._assert_func(x, ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(ainf, x))
+        assert_raises(AssertionError, lambda: self._assert_func(x, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-x, -ainf))
+        assert_raises(AssertionError, lambda: self._assert_func(-ainf, -x))
+        self._assert_func(-ainf, x)
+
+    def test_strict(self):
+        """Test the behavior of the `strict` option."""
+        x = np.zeros(3)
+        y = np.ones(())
+        self._assert_func(x, y)
+        with pytest.raises(AssertionError):
+            self._assert_func(x, y, strict=True)
+        y = np.broadcast_to(y, x.shape)
+        self._assert_func(x, y)
+        with pytest.raises(AssertionError):
+            self._assert_func(x, y.astype(np.float32), strict=True)
+
+
+class TestWarns:
+
+    def test_warn(self):
+        def f():
+            warnings.warn("yo")
+            return 3
+
+        before_filters = sys.modules['warnings'].filters[:]
+        assert_equal(assert_warns(UserWarning, f), 3)
+        after_filters = sys.modules['warnings'].filters
+
+        assert_raises(AssertionError, assert_no_warnings, f)
+        assert_equal(assert_no_warnings(lambda x: x, 1), 1)
+
+        # Check that the warnings state is unchanged
+        assert_equal(before_filters, after_filters,
+                     "assert_warns does not preserver warnings state")
+
+    def test_context_manager(self):
+
+        before_filters = sys.modules['warnings'].filters[:]
+        with assert_warns(UserWarning):
+            warnings.warn("yo")
+        after_filters = sys.modules['warnings'].filters
+
+        def no_warnings():
+            with assert_no_warnings():
+                warnings.warn("yo")
+
+        assert_raises(AssertionError, no_warnings)
+        assert_equal(before_filters, after_filters,
+                     "assert_warns does not preserver warnings state")
+
+    def test_args(self):
+        def f(a=0, b=1):
+            warnings.warn("yo")
+            return a + b
+
+        assert assert_warns(UserWarning, f, b=20) == 20
+
+        with pytest.raises(RuntimeError) as exc:
+            # assert_warns cannot do regexp matching, use pytest.warns
+            with assert_warns(UserWarning, match="A"):
+                warnings.warn("B", UserWarning)
+        assert "assert_warns" in str(exc)
+        assert "pytest.warns" in str(exc)
+
+        with pytest.raises(RuntimeError) as exc:
+            # assert_warns cannot do regexp matching, use pytest.warns
+            with assert_warns(UserWarning, wrong="A"):
+                warnings.warn("B", UserWarning)
+        assert "assert_warns" in str(exc)
+        assert "pytest.warns" not in str(exc)
+
+    def test_warn_wrong_warning(self):
+        def f():
+            warnings.warn("yo", DeprecationWarning)
+
+        failed = False
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+            try:
+                # Should raise a DeprecationWarning
+                assert_warns(UserWarning, f)
+                failed = True
+            except DeprecationWarning:
+                pass
+
+        if failed:
+            raise AssertionError("wrong warning caught by assert_warn")
+
+
+class TestAssertAllclose:
+
+    def test_simple(self):
+        x = 1e-3
+        y = 1e-9
+
+        assert_allclose(x, y, atol=1)
+        assert_raises(AssertionError, assert_allclose, x, y)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: 999999.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(x, y)
+
+        z = 0
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.e-09\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(y, z)
+
+        expected_msg = ('Mismatched elements: 1 / 1 (100%)\n'
+                        'Max absolute difference among violations: 1.e-09\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(z, y)
+
+        a = np.array([x, y, x, y])
+        b = np.array([x, y, x, x])
+
+        assert_allclose(a, b, atol=1)
+        assert_raises(AssertionError, assert_allclose, a, b)
+
+        b[-1] = y * (1 + 1e-8)
+        assert_allclose(a, b)
+        assert_raises(AssertionError, assert_allclose, a, b, rtol=1e-9)
+
+        assert_allclose(6, 10, rtol=0.5)
+        assert_raises(AssertionError, assert_allclose, 10, 6, rtol=0.5)
+
+        b = np.array([x, y, x, x])
+        c = np.array([x, y, x, z])
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: inf')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(b, c)
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 0.001\n'
+                        'Max relative difference among violations: 1.')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(c, b)
+
+    def test_min_int(self):
+        a = np.array([np.iinfo(np.int_).min], dtype=np.int_)
+        # Should not raise:
+        assert_allclose(a, a)
+
+    def test_report_fail_percentage(self):
+        a = np.array([1, 1, 1, 1])
+        b = np.array([1, 1, 1, 2])
+
+        expected_msg = ('Mismatched elements: 1 / 4 (25%)\n'
+                        'Max absolute difference among violations: 1\n'
+                        'Max relative difference among violations: 0.5')
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(a, b)
+
+    def test_equal_nan(self):
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        # Should not raise:
+        assert_allclose(a, b, equal_nan=True)
+
+    def test_not_equal_nan(self):
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        assert_raises(AssertionError, assert_allclose, a, b, equal_nan=False)
+
+    def test_equal_nan_default(self):
+        # Make sure equal_nan default behavior remains unchanged. (All
+        # of these functions use assert_array_compare under the hood.)
+        # None of these should raise.
+        a = np.array([np.nan])
+        b = np.array([np.nan])
+        assert_array_equal(a, b)
+        assert_array_almost_equal(a, b)
+        assert_array_less(a, b)
+        assert_allclose(a, b)
+
+    def test_report_max_relative_error(self):
+        a = np.array([0, 1])
+        b = np.array([0, 2])
+
+        expected_msg = 'Max relative difference among violations: 0.5'
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(a, b)
+
+    def test_timedelta(self):
+        # see gh-18286
+        a = np.array([[1, 2, 3, "NaT"]], dtype="m8[ns]")
+        assert_allclose(a, a)
+
+    def test_error_message_unsigned(self):
+        """Check the message is formatted correctly when overflow can occur
+           (gh21768)"""
+        # Ensure to test for potential overflow in the case of:
+        #        x - y
+        # and
+        #        y - x
+        x = np.asarray([0, 1, 8], dtype='uint8')
+        y = np.asarray([4, 4, 4], dtype='uint8')
+        expected_msg = 'Max absolute difference among violations: 4'
+        with pytest.raises(AssertionError, match=re.escape(expected_msg)):
+            assert_allclose(x, y, atol=3)
+
+    def test_strict(self):
+        """Test the behavior of the `strict` option."""
+        x = np.ones(3)
+        y = np.ones(())
+        assert_allclose(x, y)
+        with pytest.raises(AssertionError):
+            assert_allclose(x, y, strict=True)
+        assert_allclose(x, x)
+        with pytest.raises(AssertionError):
+            assert_allclose(x, x.astype(np.float32), strict=True)
+
+
+class TestArrayAlmostEqualNulp:
+
+    def test_float64_pass(self):
+        # The number of units of least precision
+        # In this case, use a few places above the lowest level (ie nulp=1)
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        # Addition
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        # Subtraction
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float64_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float64_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint64(0xffffffff)
+        nan1_i64 = np.array(np.nan, dtype=np.float64).view(np.uint64)
+        nan2_i64 = nan1_i64 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f64 = nan1_i64.view(np.float64)
+        nan2_f64 = nan2_i64.view(np.float64)
+        assert_array_max_ulp(nan1_f64, nan2_f64, 0)
+
+    def test_float32_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float32_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float32_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint32(0xffff)
+        nan1_i32 = np.array(np.nan, dtype=np.float32).view(np.uint32)
+        nan2_i32 = nan1_i32 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f32 = nan1_i32.view(np.float32)
+        nan2_f32 = nan2_i32.view(np.float32)
+        assert_array_max_ulp(nan1_f32, nan2_f32, 0)
+
+    def test_float16_pass(self):
+        nulp = 5
+        x = np.linspace(-4, 4, 10, dtype=np.float16)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(x, y, nulp)
+
+    def test_float16_fail(self):
+        nulp = 5
+        x = np.linspace(-4, 4, 10, dtype=np.float16)
+        x = 10**x
+        x = np.r_[-x, x]
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      x, y, nulp)
+
+    def test_float16_ignore_nan(self):
+        # Ignore ULP differences between various NAN's
+        # Note that MIPS may reverse quiet and signaling nans
+        # so we use the builtin version as a base.
+        offset = np.uint16(0xff)
+        nan1_i16 = np.array(np.nan, dtype=np.float16).view(np.uint16)
+        nan2_i16 = nan1_i16 ^ offset  # nan payload on MIPS is all ones.
+        nan1_f16 = nan1_i16.view(np.float16)
+        nan2_f16 = nan2_i16.view(np.float16)
+        assert_array_max_ulp(nan1_f16, nan2_f16, 0)
+
+    def test_complex128_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        # The test condition needs to be at least a factor of sqrt(2) smaller
+        # because the real and imaginary parts both change
+        y = x + x*eps*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+    def test_complex128_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float64)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        # The test condition needs to be at least a factor of sqrt(2) smaller
+        # because the real and imaginary parts both change
+        y = x + x*eps*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+    def test_complex64_pass(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x + x*eps*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp/2.
+        assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
+        assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp/4.
+        assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
+
+    def test_complex64_fail(self):
+        nulp = 5
+        x = np.linspace(-20, 20, 50, dtype=np.float32)
+        x = 10**x
+        x = np.r_[-x, x]
+        xi = x + x*1j
+
+        eps = np.finfo(x.dtype).eps
+        y = x + x*eps*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x + x*eps*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+        epsneg = np.finfo(x.dtype).epsneg
+        y = x - x*epsneg*nulp*2.
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, x + y*1j, nulp)
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + x*1j, nulp)
+        y = x - x*epsneg*nulp
+        assert_raises(AssertionError, assert_array_almost_equal_nulp,
+                      xi, y + y*1j, nulp)
+
+
+class TestULP:
+
+    def test_equal(self):
+        x = np.random.randn(10)
+        assert_array_max_ulp(x, x, maxulp=0)
+
+    def test_single(self):
+        # Generate 1 + small deviation, check that adding eps gives a few UNL
+        x = np.ones(10).astype(np.float32)
+        x += 0.01 * np.random.randn(10).astype(np.float32)
+        eps = np.finfo(np.float32).eps
+        assert_array_max_ulp(x, x+eps, maxulp=20)
+
+    def test_double(self):
+        # Generate 1 + small deviation, check that adding eps gives a few UNL
+        x = np.ones(10).astype(np.float64)
+        x += 0.01 * np.random.randn(10).astype(np.float64)
+        eps = np.finfo(np.float64).eps
+        assert_array_max_ulp(x, x+eps, maxulp=200)
+
+    def test_inf(self):
+        for dt in [np.float32, np.float64]:
+            inf = np.array([np.inf]).astype(dt)
+            big = np.array([np.finfo(dt).max])
+            assert_array_max_ulp(inf, big, maxulp=200)
+
+    def test_nan(self):
+        # Test that nan is 'far' from small, tiny, inf, max and min
+        for dt in [np.float32, np.float64]:
+            if dt == np.float32:
+                maxulp = 1e6
+            else:
+                maxulp = 1e12
+            inf = np.array([np.inf]).astype(dt)
+            nan = np.array([np.nan]).astype(dt)
+            big = np.array([np.finfo(dt).max])
+            tiny = np.array([np.finfo(dt).tiny])
+            zero = np.array([0.0]).astype(dt)
+            nzero = np.array([-0.0]).astype(dt)
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, inf,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, big,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, tiny,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, zero,
+                                                       maxulp=maxulp))
+            assert_raises(AssertionError,
+                          lambda: assert_array_max_ulp(nan, nzero,
+                                                       maxulp=maxulp))
+
+
+class TestStringEqual:
+    def test_simple(self):
+        assert_string_equal("hello", "hello")
+        assert_string_equal("hello\nmultiline", "hello\nmultiline")
+
+        with pytest.raises(AssertionError) as exc_info:
+            assert_string_equal("foo\nbar", "hello\nbar")
+        msg = str(exc_info.value)
+        assert_equal(msg, "Differences in strings:\n- foo\n+ hello")
+
+        assert_raises(AssertionError,
+                      lambda: assert_string_equal("foo", "hello"))
+
+    def test_regex(self):
+        assert_string_equal("a+*b", "a+*b")
+
+        assert_raises(AssertionError,
+                      lambda: assert_string_equal("aaa", "a+b"))
+
+
+def assert_warn_len_equal(mod, n_in_context):
+    try:
+        mod_warns = mod.__warningregistry__
+    except AttributeError:
+        # the lack of a __warningregistry__
+        # attribute means that no warning has
+        # occurred; this can be triggered in
+        # a parallel test scenario, while in
+        # a serial test scenario an initial
+        # warning (and therefore the attribute)
+        # are always created first
+        mod_warns = {}
+
+    num_warns = len(mod_warns)
+
+    if 'version' in mod_warns:
+        # Python 3 adds a 'version' entry to the registry,
+        # do not count it.
+        num_warns -= 1
+
+    assert_equal(num_warns, n_in_context)
+
+
+def test_warn_len_equal_call_scenarios():
+    # assert_warn_len_equal is called under
+    # varying circumstances depending on serial
+    # vs. parallel test scenarios; this test
+    # simply aims to probe both code paths and
+    # check that no assertion is uncaught
+
+    # parallel scenario -- no warning issued yet
+    class mod:
+        pass
+
+    mod_inst = mod()
+
+    assert_warn_len_equal(mod=mod_inst,
+                          n_in_context=0)
+
+    # serial test scenario -- the __warningregistry__
+    # attribute should be present
+    class mod:
+        def __init__(self):
+            self.__warningregistry__ = {'warning1': 1,
+                                        'warning2': 2}
+
+    mod_inst = mod()
+    assert_warn_len_equal(mod=mod_inst,
+                          n_in_context=2)
+
+
+def _get_fresh_mod():
+    # Get this module, with warning registry empty
+    my_mod = sys.modules[__name__]
+    try:
+        my_mod.__warningregistry__.clear()
+    except AttributeError:
+        # will not have a __warningregistry__ unless warning has been
+        # raised in the module at some point
+        pass
+    return my_mod
+
+
+def test_clear_and_catch_warnings():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+    with clear_and_catch_warnings(modules=[my_mod]):
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_equal(my_mod.__warningregistry__, {})
+    # Without specified modules, don't clear warnings during context.
+    # catch_warnings doesn't make an entry for 'ignore'.
+    with clear_and_catch_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Manually adding two warnings to the registry:
+    my_mod.__warningregistry__ = {'warning1': 1,
+                                  'warning2': 2}
+
+    # Confirm that specifying module keeps old warning, does not add new
+    with clear_and_catch_warnings(modules=[my_mod]):
+        warnings.simplefilter('ignore')
+        warnings.warn('Another warning')
+    assert_warn_len_equal(my_mod, 2)
+
+    # Another warning, no module spec it clears up registry
+    with clear_and_catch_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Another warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_module():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+
+    def warn_other_module():
+        # Apply along axis is implemented in python; stacklevel=2 means
+        # we end up inside its module, not ours.
+        def warn(arr):
+            warnings.warn("Some warning 2", stacklevel=2)
+            return arr
+        np.apply_along_axis(warn, 0, [0])
+
+    # Test module based warning suppression:
+    assert_warn_len_equal(my_mod, 0)
+    with suppress_warnings() as sup:
+        sup.record(UserWarning)
+        # suppress warning from other module (may have .pyc ending),
+        # if apply_along_axis is moved, had to be changed.
+        sup.filter(module=np.lib._shape_base_impl)
+        warnings.warn("Some warning")
+        warn_other_module()
+    # Check that the suppression did test the file correctly (this module
+    # got filtered)
+    assert_equal(len(sup.log), 1)
+    assert_equal(sup.log[0].message.args[0], "Some warning")
+    assert_warn_len_equal(my_mod, 0)
+    sup = suppress_warnings()
+    # Will have to be changed if apply_along_axis is moved:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    # And test repeat works:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Without specified modules
+    with suppress_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_type():
+    # Initial state of module, no warnings
+    my_mod = _get_fresh_mod()
+    assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
+
+    # Test module based warning suppression:
+    with suppress_warnings() as sup:
+        sup.filter(UserWarning)
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    sup = suppress_warnings()
+    sup.filter(UserWarning)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+    # And test repeat works:
+    sup.filter(module=my_mod)
+    with sup:
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+    # Without specified modules
+    with suppress_warnings():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_warn_len_equal(my_mod, 0)
+
+
+def test_suppress_warnings_decorate_no_record():
+    sup = suppress_warnings()
+    sup.filter(UserWarning)
+
+    @sup
+    def warn(category):
+        warnings.warn('Some warning', category)
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        warn(UserWarning)  # should be suppressed
+        warn(RuntimeWarning)
+        assert_equal(len(w), 1)
+
+
+def test_suppress_warnings_record():
+    sup = suppress_warnings()
+    log1 = sup.record()
+
+    with sup:
+        log2 = sup.record(message='Some other warning 2')
+        sup.filter(message='Some warning')
+        warnings.warn('Some warning')
+        warnings.warn('Some other warning')
+        warnings.warn('Some other warning 2')
+
+        assert_equal(len(sup.log), 2)
+        assert_equal(len(log1), 1)
+        assert_equal(len(log2), 1)
+        assert_equal(log2[0].message.args[0], 'Some other warning 2')
+
+    # Do it again, with the same context to see if some warnings survived:
+    with sup:
+        log2 = sup.record(message='Some other warning 2')
+        sup.filter(message='Some warning')
+        warnings.warn('Some warning')
+        warnings.warn('Some other warning')
+        warnings.warn('Some other warning 2')
+
+        assert_equal(len(sup.log), 2)
+        assert_equal(len(log1), 1)
+        assert_equal(len(log2), 1)
+        assert_equal(log2[0].message.args[0], 'Some other warning 2')
+
+    # Test nested:
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings() as sup2:
+            sup2.record(message='Some warning')
+            warnings.warn('Some warning')
+            warnings.warn('Some other warning')
+            assert_equal(len(sup2.log), 1)
+        assert_equal(len(sup.log), 1)
+
+
+def test_suppress_warnings_forwarding():
+    def warn_other_module():
+        # Apply along axis is implemented in python; stacklevel=2 means
+        # we end up inside its module, not ours.
+        def warn(arr):
+            warnings.warn("Some warning", stacklevel=2)
+            return arr
+        np.apply_along_axis(warn, 0, [0])
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("always"):
+            for i in range(2):
+                warnings.warn("Some warning")
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("location"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some warning")
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("module"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some warning")
+                warn_other_module()
+
+        assert_equal(len(sup.log), 2)
+
+    with suppress_warnings() as sup:
+        sup.record()
+        with suppress_warnings("once"):
+            for i in range(2):
+                warnings.warn("Some warning")
+                warnings.warn("Some other warning")
+                warn_other_module()
+
+        assert_equal(len(sup.log), 2)
+
+
+def test_tempdir():
+    with tempdir() as tdir:
+        fpath = os.path.join(tdir, 'tmp')
+        with open(fpath, 'w'):
+            pass
+    assert_(not os.path.isdir(tdir))
+
+    raised = False
+    try:
+        with tempdir() as tdir:
+            raise ValueError
+    except ValueError:
+        raised = True
+    assert_(raised)
+    assert_(not os.path.isdir(tdir))
+
+
+def test_temppath():
+    with temppath() as fpath:
+        with open(fpath, 'w'):
+            pass
+    assert_(not os.path.isfile(fpath))
+
+    raised = False
+    try:
+        with temppath() as fpath:
+            raise ValueError
+    except ValueError:
+        raised = True
+    assert_(raised)
+    assert_(not os.path.isfile(fpath))
+
+
+class my_cacw(clear_and_catch_warnings):
+
+    class_modules = (sys.modules[__name__],)
+
+
+def test_clear_and_catch_warnings_inherit():
+    # Test can subclass and add default modules
+    my_mod = _get_fresh_mod()
+    with my_cacw():
+        warnings.simplefilter('ignore')
+        warnings.warn('Some warning')
+    assert_equal(my_mod.__warningregistry__, {})
+
+
+@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+class TestAssertNoGcCycles:
+    """ Test assert_no_gc_cycles """
+
+    def test_passes(self):
+        def no_cycle():
+            b = []
+            b.append([])
+            return b
+
+        with assert_no_gc_cycles():
+            no_cycle()
+
+        assert_no_gc_cycles(no_cycle)
+
+    def test_asserts(self):
+        def make_cycle():
+            a = []
+            a.append(a)
+            a.append(a)
+            return a
+
+        with assert_raises(AssertionError):
+            with assert_no_gc_cycles():
+                make_cycle()
+
+        with assert_raises(AssertionError):
+            assert_no_gc_cycles(make_cycle)
+
+    @pytest.mark.slow
+    def test_fails(self):
+        """
+        Test that in cases where the garbage cannot be collected, we raise an
+        error, instead of hanging forever trying to clear it.
+        """
+
+        class ReferenceCycleInDel:
+            """
+            An object that not only contains a reference cycle, but creates new
+            cycles whenever it's garbage-collected and its __del__ runs
+            """
+            make_cycle = True
+
+            def __init__(self):
+                self.cycle = self
+
+            def __del__(self):
+                # break the current cycle so that `self` can be freed
+                self.cycle = None
+
+                if ReferenceCycleInDel.make_cycle:
+                    # but create a new one so that the garbage collector has more
+                    # work to do.
+                    ReferenceCycleInDel()
+
+        try:
+            w = weakref.ref(ReferenceCycleInDel())
+            try:
+                with assert_raises(RuntimeError):
+                    # this will be unable to get a baseline empty garbage
+                    assert_no_gc_cycles(lambda: None)
+            except AssertionError:
+                # the above test is only necessary if the GC actually tried to free
+                # our object anyway, which python 2.7 does not.
+                if w() is not None:
+                    pytest.skip("GC does not call __del__ on cyclic objects")
+                    raise
+
+        finally:
+            # make sure that we stop creating reference cycles
+            ReferenceCycleInDel.make_cycle = False
+
+
+@pytest.mark.parametrize('assert_func', [assert_array_equal,
+                                         assert_array_almost_equal])
+def test_xy_rename(assert_func):
+    # Test that keywords `x` and `y` have been renamed to `actual` and
+    # `desired`, respectively. These tests and use of `_rename_parameter`
+    # decorator can be removed before the release of NumPy 2.2.0.
+    assert_func(1, 1)
+    assert_func(actual=1, desired=1)
+
+    assert_message = "Arrays are not..."
+    with pytest.raises(AssertionError, match=assert_message):
+        assert_func(1, 2)
+    with pytest.raises(AssertionError, match=assert_message):
+        assert_func(actual=1, desired=2)
+
+    dep_message = 'Use of keyword argument...'
+    with pytest.warns(DeprecationWarning, match=dep_message):
+        assert_func(x=1, desired=1)
+    with pytest.warns(DeprecationWarning, match=dep_message):
+        assert_func(1, y=1)
+
+    type_message = '...got multiple values for argument'
+    with (pytest.warns(DeprecationWarning, match=dep_message),
+          pytest.raises(TypeError, match=type_message)):
+        assert_func(1, x=1)
+        assert_func(1, 2, y=2)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b247921818e27d603dc653098b51f53d8e3187e1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__init__.py
@@ -0,0 +1,175 @@
+"""
+============================
+Typing (:mod:`numpy.typing`)
+============================
+
+.. versionadded:: 1.20
+
+Large parts of the NumPy API have :pep:`484`-style type annotations. In
+addition a number of type aliases are available to users, most prominently
+the two below:
+
+- `ArrayLike`: objects that can be converted to arrays
+- `DTypeLike`: objects that can be converted to dtypes
+
+.. _typing-extensions: https://pypi.org/project/typing-extensions/
+
+Mypy plugin
+-----------
+
+.. versionadded:: 1.21
+
+.. automodule:: numpy.typing.mypy_plugin
+
+.. currentmodule:: numpy.typing
+
+Differences from the runtime NumPy API
+--------------------------------------
+
+NumPy is very flexible. Trying to describe the full range of
+possibilities statically would result in types that are not very
+helpful. For that reason, the typed NumPy API is often stricter than
+the runtime NumPy API. This section describes some notable
+differences.
+
+ArrayLike
+~~~~~~~~~
+
+The `ArrayLike` type tries to avoid creating object arrays. For
+example,
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))
+    array( at ...>, dtype=object)
+
+is valid NumPy code which will create a 0-dimensional object
+array. Type checkers will complain about the above example when using
+the NumPy types however. If you really intended to do the above, then
+you can either use a ``# type: ignore`` comment:
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))  # type: ignore
+
+or explicitly type the array like object as `~typing.Any`:
+
+.. code-block:: python
+
+    >>> from typing import Any
+    >>> array_like: Any = (x**2 for x in range(10))
+    >>> np.array(array_like)
+    array( at ...>, dtype=object)
+
+ndarray
+~~~~~~~
+
+It's possible to mutate the dtype of an array at runtime. For example,
+the following code is valid:
+
+.. code-block:: python
+
+    >>> x = np.array([1, 2])
+    >>> x.dtype = np.bool
+
+This sort of mutation is not allowed by the types. Users who want to
+write statically typed code should instead use the `numpy.ndarray.view`
+method to create a view of the array with a different dtype.
+
+DTypeLike
+~~~~~~~~~
+
+The `DTypeLike` type tries to avoid creation of dtype objects using
+dictionary of fields like below:
+
+.. code-block:: python
+
+    >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)})
+
+Although this is valid NumPy code, the type checker will complain about it,
+since its usage is discouraged.
+Please see : :ref:`Data type objects `
+
+Number precision
+~~~~~~~~~~~~~~~~
+
+The precision of `numpy.number` subclasses is treated as a invariant generic
+parameter (see :class:`~NBitBase`), simplifying the annotating of processes
+involving precision-based casting.
+
+.. code-block:: python
+
+    >>> from typing import TypeVar
+    >>> import numpy as np
+    >>> import numpy.typing as npt
+
+    >>> T = TypeVar("T", bound=npt.NBitBase)
+    >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]":
+    ...     ...
+
+Consequently, the likes of `~numpy.float16`, `~numpy.float32` and
+`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to
+runtime, they're not necessarily considered as sub-classes.
+
+Timedelta64
+~~~~~~~~~~~
+
+The `~numpy.timedelta64` class is not considered a subclass of
+`~numpy.signedinteger`, the former only inheriting from `~numpy.generic`
+while static type checking.
+
+0D arrays
+~~~~~~~~~
+
+During runtime numpy aggressively casts any passed 0D arrays into their
+corresponding `~numpy.generic` instance. Until the introduction of shape
+typing (see :pep:`646`) it is unfortunately not possible to make the
+necessary distinction between 0D and >0D arrays. While thus not strictly
+correct, all operations are that can potentially perform a 0D-array -> scalar
+cast are currently annotated as exclusively returning an `~numpy.ndarray`.
+
+If it is known in advance that an operation *will* perform a
+0D-array -> scalar cast, then one can consider manually remedying the
+situation with either `typing.cast` or a ``# type: ignore`` comment.
+
+Record array dtypes
+~~~~~~~~~~~~~~~~~~~
+
+The dtype of `numpy.recarray`, and the :ref:`routines.array-creation.rec`
+functions in general, can be specified in one of two ways:
+
+* Directly via the ``dtype`` argument.
+* With up to five helper arguments that operate via `numpy.rec.format_parser`:
+  ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``.
+
+These two approaches are currently typed as being mutually exclusive,
+*i.e.* if ``dtype`` is specified than one may not specify ``formats``.
+While this mutual exclusivity is not (strictly) enforced during runtime,
+combining both dtype specifiers can lead to unexpected or even downright
+buggy behavior.
+
+API
+---
+
+"""
+# NOTE: The API section will be appended with additional entries
+# further down in this file
+
+from numpy._typing import (
+    ArrayLike,
+    DTypeLike,
+    NBitBase,
+    NDArray,
+)
+
+__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"]
+
+if __doc__ is not None:
+    from numpy._typing._add_docstring import _docstrings
+    __doc__ += _docstrings
+    __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n'
+    del _docstrings
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__pycache__/__init__.cpython-310.pyc
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Binary files /dev/null and b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/__pycache__/__init__.cpython-310.pyc differ
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/mypy_plugin.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/mypy_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce9b0d9582ad164d3c6d2747dc941e841907eba6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/mypy_plugin.py
@@ -0,0 +1,199 @@
+"""A mypy_ plugin for managing a number of platform-specific annotations.
+Its functionality can be split into three distinct parts:
+
+* Assigning the (platform-dependent) precisions of certain `~numpy.number`
+  subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and
+  `~numpy.longlong`. See the documentation on
+  :ref:`scalar types ` for a comprehensive overview
+  of the affected classes. Without the plugin the precision of all relevant
+  classes will be inferred as `~typing.Any`.
+* Removing all extended-precision `~numpy.number` subclasses that are
+  unavailable for the platform in question. Most notably this includes the
+  likes of `~numpy.float128` and `~numpy.complex256`. Without the plugin *all*
+  extended-precision types will, as far as mypy is concerned, be available
+  to all platforms.
+* Assigning the (platform-dependent) precision of `~numpy.ctypeslib.c_intp`.
+  Without the plugin the type will default to `ctypes.c_int64`.
+
+  .. versionadded:: 1.22
+
+Examples
+--------
+To enable the plugin, one must add it to their mypy `configuration file`_:
+
+.. code-block:: ini
+
+    [mypy]
+    plugins = numpy.typing.mypy_plugin
+
+.. _mypy: https://mypy-lang.org/
+.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html
+
+"""
+
+from __future__ import annotations
+
+from typing import Final, TYPE_CHECKING, Callable
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+try:
+    import mypy.types
+    from mypy.types import Type
+    from mypy.plugin import Plugin, AnalyzeTypeContext
+    from mypy.nodes import MypyFile, ImportFrom, Statement
+    from mypy.build import PRI_MED
+
+    _HookFunc = Callable[[AnalyzeTypeContext], Type]
+    MYPY_EX: None | ModuleNotFoundError = None
+except ModuleNotFoundError as ex:
+    MYPY_EX = ex
+
+__all__: list[str] = []
+
+
+def _get_precision_dict() -> dict[str, str]:
+    names = [
+        ("_NBitByte", np.byte),
+        ("_NBitShort", np.short),
+        ("_NBitIntC", np.intc),
+        ("_NBitIntP", np.intp),
+        ("_NBitInt", np.int_),
+        ("_NBitLong", np.long),
+        ("_NBitLongLong", np.longlong),
+
+        ("_NBitHalf", np.half),
+        ("_NBitSingle", np.single),
+        ("_NBitDouble", np.double),
+        ("_NBitLongDouble", np.longdouble),
+    ]
+    ret = {}
+    module = "numpy._typing"
+    for name, typ in names:
+        n: int = 8 * typ().dtype.itemsize
+        ret[f'{module}._nbit.{name}'] = f"{module}._nbit_base._{n}Bit"
+    return ret
+
+
+def _get_extended_precision_list() -> list[str]:
+    extended_names = [
+        "uint128",
+        "uint256",
+        "int128",
+        "int256",
+        "float80",
+        "float96",
+        "float128",
+        "float256",
+        "complex160",
+        "complex192",
+        "complex256",
+        "complex512",
+    ]
+    return [i for i in extended_names if hasattr(np, i)]
+
+def _get_c_intp_name() -> str:
+    # Adapted from `np.core._internal._getintp_ctype`
+    char = np.dtype('n').char
+    if char == 'i':
+        return "c_int"
+    elif char == 'l':
+        return "c_long"
+    elif char == 'q':
+        return "c_longlong"
+    else:
+        return "c_long"
+
+
+#: A dictionary mapping type-aliases in `numpy._typing._nbit` to
+#: concrete `numpy.typing.NBitBase` subclasses.
+_PRECISION_DICT: Final = _get_precision_dict()
+
+#: A list with the names of all extended precision `np.number` subclasses.
+_EXTENDED_PRECISION_LIST: Final = _get_extended_precision_list()
+
+#: The name of the ctypes equivalent of `np.intp`
+_C_INTP: Final = _get_c_intp_name()
+
+
+def _hook(ctx: AnalyzeTypeContext) -> Type:
+    """Replace a type-alias with a concrete ``NBitBase`` subclass."""
+    typ, _, api = ctx
+    name = typ.name.split(".")[-1]
+    name_new = _PRECISION_DICT[f"numpy._typing._nbit.{name}"]
+    return api.named_type(name_new)
+
+
+if TYPE_CHECKING or MYPY_EX is None:
+    def _index(iterable: Iterable[Statement], id: str) -> int:
+        """Identify the first ``ImportFrom`` instance the specified `id`."""
+        for i, value in enumerate(iterable):
+            if getattr(value, "id", None) == id:
+                return i
+        raise ValueError("Failed to identify a `ImportFrom` instance "
+                         f"with the following id: {id!r}")
+
+    def _override_imports(
+        file: MypyFile,
+        module: str,
+        imports: list[tuple[str, None | str]],
+    ) -> None:
+        """Override the first `module`-based import with new `imports`."""
+        # Construct a new `from module import y` statement
+        import_obj = ImportFrom(module, 0, names=imports)
+        import_obj.is_top_level = True
+
+        # Replace the first `module`-based import statement with `import_obj`
+        for lst in [file.defs, file.imports]:  # type: list[Statement]
+            i = _index(lst, module)
+            lst[i] = import_obj
+
+    class _NumpyPlugin(Plugin):
+        """A mypy plugin for handling versus numpy-specific typing tasks."""
+
+        def get_type_analyze_hook(self, fullname: str) -> None | _HookFunc:
+            """Set the precision of platform-specific `numpy.number`
+            subclasses.
+
+            For example: `numpy.int_`, `numpy.longlong` and `numpy.longdouble`.
+            """
+            if fullname in _PRECISION_DICT:
+                return _hook
+            return None
+
+        def get_additional_deps(
+            self, file: MypyFile
+        ) -> list[tuple[int, str, int]]:
+            """Handle all import-based overrides.
+
+            * Import platform-specific extended-precision `numpy.number`
+              subclasses (*e.g.* `numpy.float96`, `numpy.float128` and
+              `numpy.complex256`).
+            * Import the appropriate `ctypes` equivalent to `numpy.intp`.
+
+            """
+            ret = [(PRI_MED, file.fullname, -1)]
+
+            if file.fullname == "numpy":
+                _override_imports(
+                    file, "numpy._typing._extended_precision",
+                    imports=[(v, v) for v in _EXTENDED_PRECISION_LIST],
+                )
+            elif file.fullname == "numpy.ctypeslib":
+                _override_imports(
+                    file, "ctypes",
+                    imports=[(_C_INTP, "_c_intp")],
+                )
+            return ret
+
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        return _NumpyPlugin
+
+else:
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        raise MYPY_EX
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..29f3ab4e28d36b02b537880d4d5656d6cb4e5481
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi
@@ -0,0 +1,128 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+b_ = np.bool()
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint32]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.longdouble]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+ANY: Any
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_m: list[np.timedelta64]
+AR_LIKE_M: list[np.datetime64]
+
+# Array subtraction
+
+# NOTE: mypys `NoReturn` errors are, unfortunately, not that great
+_1 = AR_b - AR_LIKE_b  # E: Need type annotation
+_2 = AR_LIKE_b - AR_b  # E: Need type annotation
+AR_i - bytes()  # E: No overload variant
+
+AR_f - AR_LIKE_m  # E: Unsupported operand types
+AR_f - AR_LIKE_M  # E: Unsupported operand types
+AR_c - AR_LIKE_m  # E: Unsupported operand types
+AR_c - AR_LIKE_M  # E: Unsupported operand types
+
+AR_m - AR_LIKE_f  # E: Unsupported operand types
+AR_M - AR_LIKE_f  # E: Unsupported operand types
+AR_m - AR_LIKE_c  # E: Unsupported operand types
+AR_M - AR_LIKE_c  # E: Unsupported operand types
+
+AR_m - AR_LIKE_M  # E: Unsupported operand types
+AR_LIKE_m - AR_M  # E: Unsupported operand types
+
+# array floor division
+
+AR_M // AR_LIKE_b  # E: Unsupported operand types
+AR_M // AR_LIKE_u  # E: Unsupported operand types
+AR_M // AR_LIKE_i  # E: Unsupported operand types
+AR_M // AR_LIKE_f  # E: Unsupported operand types
+AR_M // AR_LIKE_c  # E: Unsupported operand types
+AR_M // AR_LIKE_m  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+AR_b // AR_LIKE_M  # E: Unsupported operand types
+AR_u // AR_LIKE_M  # E: Unsupported operand types
+AR_i // AR_LIKE_M  # E: Unsupported operand types
+AR_f // AR_LIKE_M  # E: Unsupported operand types
+AR_c // AR_LIKE_M  # E: Unsupported operand types
+AR_m // AR_LIKE_M  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+_3 = AR_m // AR_LIKE_b  # E: Need type annotation
+AR_m // AR_LIKE_c  # E: Unsupported operand types
+
+AR_b // AR_LIKE_m  # E: Unsupported operand types
+AR_u // AR_LIKE_m  # E: Unsupported operand types
+AR_i // AR_LIKE_m  # E: Unsupported operand types
+AR_f // AR_LIKE_m  # E: Unsupported operand types
+AR_c // AR_LIKE_m  # E: Unsupported operand types
+
+# regression tests for https://github.com/numpy/numpy/issues/28957
+AR_c // 2  # type: ignore[operator]
+AR_c // AR_i  # type: ignore[operator]
+AR_c // AR_c  # type: ignore[operator]
+
+# Array multiplication
+
+AR_b *= AR_LIKE_u  # E: incompatible type
+AR_b *= AR_LIKE_i  # E: incompatible type
+AR_b *= AR_LIKE_f  # E: incompatible type
+AR_b *= AR_LIKE_c  # E: incompatible type
+AR_b *= AR_LIKE_m  # E: incompatible type
+
+AR_u *= AR_LIKE_i  # E: incompatible type
+AR_u *= AR_LIKE_f  # E: incompatible type
+AR_u *= AR_LIKE_c  # E: incompatible type
+AR_u *= AR_LIKE_m  # E: incompatible type
+
+AR_i *= AR_LIKE_f  # E: incompatible type
+AR_i *= AR_LIKE_c  # E: incompatible type
+AR_i *= AR_LIKE_m  # E: incompatible type
+
+AR_f *= AR_LIKE_c  # E: incompatible type
+AR_f *= AR_LIKE_m  # E: incompatible type
+
+# Array power
+
+AR_b **= AR_LIKE_b  # E: Invalid self argument
+AR_b **= AR_LIKE_u  # E: Invalid self argument
+AR_b **= AR_LIKE_i  # E: Invalid self argument
+AR_b **= AR_LIKE_f  # E: Invalid self argument
+AR_b **= AR_LIKE_c  # E: Invalid self argument
+
+AR_u **= AR_LIKE_i  # E: incompatible type
+AR_u **= AR_LIKE_f  # E: incompatible type
+AR_u **= AR_LIKE_c  # E: incompatible type
+
+AR_i **= AR_LIKE_f  # E: incompatible type
+AR_i **= AR_LIKE_c  # E: incompatible type
+
+AR_f **= AR_LIKE_c  # E: incompatible type
+
+# Scalars
+
+b_ - b_  # E: No overload variant
+
+dt + dt  # E: Unsupported operand types
+td - dt  # E: Unsupported operand types
+td % 1  # E: Unsupported operand types
+td / dt  # E: No overload
+td % dt  # E: Unsupported operand types
+
+-b_  # E: Unsupported operand type
++b_  # E: Unsupported operand type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..27eefe3c918d87c871d018f5d29246d681ed4033
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi
@@ -0,0 +1,34 @@
+import numpy as np
+import numpy.typing as npt
+
+a: npt.NDArray[np.float64]
+generator = (i for i in range(10))
+
+np.require(a, requirements=1)  # E: No overload variant
+np.require(a, requirements="TEST")  # E: incompatible type
+
+np.zeros("test")  # E: incompatible type
+np.zeros()  # E: require at least one argument
+
+np.ones("test")  # E: incompatible type
+np.ones()  # E: require at least one argument
+
+np.array(0, float, True)  # E: No overload variant
+
+np.linspace(None, 'bob')  # E: No overload variant
+np.linspace(0, 2, num=10.0)  # E: No overload variant
+np.linspace(0, 2, endpoint='True')  # E: No overload variant
+np.linspace(0, 2, retstep=b'False')  # E: No overload variant
+np.linspace(0, 2, dtype=0)  # E: No overload variant
+np.linspace(0, 2, axis=None)  # E: No overload variant
+
+np.logspace(None, 'bob')  # E: No overload variant
+np.logspace(0, 2, base=None)  # E: No overload variant
+
+np.geomspace(None, 'bob')  # E: No overload variant
+
+np.stack(generator)  # E: No overload variant
+np.hstack({1, 2})  # E: No overload variant
+np.vstack(1)  # E: No overload variant
+
+np.array([1], like=1)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a21101a993c768210f0befbba319ca21ca50723b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_like.pyi
@@ -0,0 +1,18 @@
+import numpy as np
+from numpy._typing import ArrayLike
+
+
+class A:
+    pass
+
+
+x1: ArrayLike = (i for i in range(10))  # E: Incompatible types in assignment
+x2: ArrayLike = A()  # E: Incompatible types in assignment
+x3: ArrayLike = {1: "foo", 2: "bar"}  # E: Incompatible types in assignment
+
+scalar = np.int64(1)
+scalar.__array__(dtype=np.float64)  # E: No overload variant
+array = np.array([1])
+array.__array__(dtype=np.float64)  # E: No overload variant
+
+array.setfield(np.eye(1), np.int32, (0, 1))  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2be51a87181dcc14068d7036fe44d1d3cc9d9d6f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/array_pad.pyi
@@ -0,0 +1,6 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.pad(AR_i8, 2, mode="bob")  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..486c11e79868f8d88ac093d789633ec5fb0953d4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi
@@ -0,0 +1,16 @@
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR: npt.NDArray[np.float64]
+func1: Callable[[Any], str]
+func2: Callable[[np.integer], str]
+
+np.array2string(AR, style=None)  # E: No overload variant
+np.array2string(AR, legacy="1.14")  # E: No overload variant
+np.array2string(AR, sign="*")  # E: No overload variant
+np.array2string(AR, floatmode="default")  # E: No overload variant
+np.array2string(AR, formatter={"A": func1})  # E: No overload variant
+np.array2string(AR, formatter={"float": func2})  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..00280b3a6a2c523ff0f92ed5f2110a103a2a8740
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+np.lib.Arrayterator(np.int64())  # E: incompatible type
+ar_iter.shape = (10, 5)  # E: is read-only
+ar_iter[None]  # E: Invalid index type
+ar_iter[None, 1]  # E: Invalid index type
+ar_iter[np.intp()]  # E: Invalid index type
+ar_iter[np.intp(), ...]  # E: Invalid index type
+ar_iter[AR_i8]  # E: Invalid index type
+ar_iter[AR_i8, :]  # E: Invalid index type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..13b47c485b417fdf2008f09706b98ed3311bb8f7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi
@@ -0,0 +1,21 @@
+import numpy as np
+
+i8 = np.int64()
+i4 = np.int32()
+u8 = np.uint64()
+b_ = np.bool()
+i = int()
+
+f8 = np.float64()
+
+b_ >> f8  # E: No overload variant
+i8 << f8  # E: No overload variant
+i | f8  # E: Unsupported operand types
+i8 ^ f8  # E: No overload variant
+u8 & f8  # E: No overload variant
+~f8  # E: Unsupported operand type
+# TODO: Certain mixes like i4 << u8 go to float and thus should fail
+
+# mypys' error message for `NoReturn` is unfortunately pretty bad
+# TODO: Re-enable this once we add support for numerical precision for `number`s
+# a = u8 | 0  # E: Need type annotation
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..542a273baef54c802d0033019926b7a50861c21f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/char.pyi
@@ -0,0 +1,69 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.char.equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.not_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.less_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater(AR_U, AR_S)  # E: incompatible type
+
+np.char.less(AR_U, AR_S)  # E: incompatible type
+
+np.char.encode(AR_S)  # E: incompatible type
+np.char.decode(AR_U)  # E: incompatible type
+
+np.char.join(AR_U, b"_")  # E: incompatible type
+np.char.join(AR_S, "_")  # E: incompatible type
+
+np.char.ljust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.ljust(AR_S, 5, fillchar="a")  # E: incompatible type
+np.char.rjust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.rjust(AR_S, 5, fillchar="a")  # E: incompatible type
+
+np.char.lstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.lstrip(AR_S, chars="a")  # E: incompatible type
+np.char.strip(AR_U, chars=b"a")  # E: incompatible type
+np.char.strip(AR_S, chars="a")  # E: incompatible type
+np.char.rstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.rstrip(AR_S, chars="a")  # E: incompatible type
+
+np.char.partition(AR_U, b"a")  # E: incompatible type
+np.char.partition(AR_S, "a")  # E: incompatible type
+np.char.rpartition(AR_U, b"a")  # E: incompatible type
+np.char.rpartition(AR_S, "a")  # E: incompatible type
+
+np.char.replace(AR_U, b"_", b"-")  # E: incompatible type
+np.char.replace(AR_S, "_", "-")  # E: incompatible type
+
+np.char.split(AR_U, b"_")  # E: incompatible type
+np.char.split(AR_S, "_")  # E: incompatible type
+np.char.rsplit(AR_U, b"_")  # E: incompatible type
+np.char.rsplit(AR_S, "_")  # E: incompatible type
+
+np.char.count(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.count(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.endswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.endswith(AR_S, "a", end=9)  # E: incompatible type
+np.char.startswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.startswith(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.find(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.find(AR_S, "a", end=9)  # E: incompatible type
+np.char.rfind(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rfind(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.index(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.index(AR_S, "a", end=9)  # E: incompatible type
+np.char.rindex(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rindex(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.isdecimal(AR_S)  # E: incompatible type
+np.char.isnumeric(AR_S)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e484b644e4b8ec75cdc8ea404a457cf9d1a78b58
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/chararray.pyi
@@ -0,0 +1,61 @@
+import numpy as np
+
+AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+AR_S: np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]]
+
+AR_S.encode()  # E: Invalid self argument
+AR_U.decode()  # E: Invalid self argument
+
+AR_U.join(b"_")  # E: incompatible type
+AR_S.join("_")  # E: incompatible type
+
+AR_U.ljust(5, fillchar=b"a")  # E: incompatible type
+AR_S.ljust(5, fillchar="a")  # E: incompatible type
+AR_U.rjust(5, fillchar=b"a")  # E: incompatible type
+AR_S.rjust(5, fillchar="a")  # E: incompatible type
+
+AR_U.lstrip(chars=b"a")  # E: incompatible type
+AR_S.lstrip(chars="a")  # E: incompatible type
+AR_U.strip(chars=b"a")  # E: incompatible type
+AR_S.strip(chars="a")  # E: incompatible type
+AR_U.rstrip(chars=b"a")  # E: incompatible type
+AR_S.rstrip(chars="a")  # E: incompatible type
+
+AR_U.partition(b"a")  # E: incompatible type
+AR_S.partition("a")  # E: incompatible type
+AR_U.rpartition(b"a")  # E: incompatible type
+AR_S.rpartition("a")  # E: incompatible type
+
+AR_U.replace(b"_", b"-")  # E: incompatible type
+AR_S.replace("_", "-")  # E: incompatible type
+
+AR_U.split(b"_")  # E: incompatible type
+AR_S.split("_")  # E: incompatible type
+AR_S.split(1)  # E: incompatible type
+AR_U.rsplit(b"_")  # E: incompatible type
+AR_S.rsplit("_")  # E: incompatible type
+
+AR_U.count(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.count("a", end=9)  # E: incompatible type
+
+AR_U.endswith(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.endswith("a", end=9)  # E: incompatible type
+AR_U.startswith(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.startswith("a", end=9)  # E: incompatible type
+
+AR_U.find(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.find("a", end=9)  # E: incompatible type
+AR_U.rfind(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.rfind("a", end=9)  # E: incompatible type
+
+AR_U.index(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.index("a", end=9)  # E: incompatible type
+AR_U.rindex(b"a", start=[1, 2, 3])  # E: incompatible type
+AR_S.rindex("a", end=9)  # E: incompatible type
+
+AR_U == AR_S  # E: Unsupported operand types
+AR_U != AR_S  # E: Unsupported operand types
+AR_U >= AR_S  # E: Unsupported operand types
+AR_U <= AR_S  # E: Unsupported operand types
+AR_U > AR_S  # E: Unsupported operand types
+AR_U < AR_S  # E: Unsupported operand types
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1ae8149082b6b726989a1e2dd01933388f49eece
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/comparisons.pyi
@@ -0,0 +1,27 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+AR_f > AR_m  # E: Unsupported operand types
+AR_c > AR_m  # E: Unsupported operand types
+
+AR_m > AR_f  # E: Unsupported operand types
+AR_m > AR_c  # E: Unsupported operand types
+
+AR_i > AR_M  # E: Unsupported operand types
+AR_f > AR_M  # E: Unsupported operand types
+AR_m > AR_M  # E: Unsupported operand types
+
+AR_M > AR_i  # E: Unsupported operand types
+AR_M > AR_f  # E: Unsupported operand types
+AR_M > AR_m  # E: Unsupported operand types
+
+AR_i > str()  # E: No overload variant
+AR_i > bytes()  # E: No overload variant
+str() > AR_M  # E: Unsupported operand types
+bytes() > AR_M  # E: Unsupported operand types
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b5d6d27eae46086e8241b276895516f8bad226c5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/constants.pyi
@@ -0,0 +1,3 @@
+import numpy as np
+
+np.little_endian = np.little_endian  # E: Cannot assign to final
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..44f4fa27307addb3ee54ebfe840ccbfa1e4cc72d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/datasource.pyi
@@ -0,0 +1,15 @@
+from pathlib import Path
+import numpy as np
+
+path: Path
+d1: np.lib.npyio.DataSource
+
+d1.abspath(path)  # E: incompatible type
+d1.abspath(b"...")  # E: incompatible type
+
+d1.exists(path)  # E: incompatible type
+d1.exists(b"...")  # E: incompatible type
+
+d1.open(path, "r")  # E: incompatible type
+d1.open(b"...", encoding="utf8")  # E: incompatible type
+d1.open(None, newline="/n")  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0f3810f3c014aafac0e149cfc6da0ec38c61f165
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/dtype.pyi
@@ -0,0 +1,20 @@
+import numpy as np
+
+
+class Test1:
+    not_dtype = np.dtype(float)
+
+
+class Test2:
+    dtype = float
+
+
+np.dtype(Test1())  # E: No overload variant of "dtype" matches
+np.dtype(Test2())  # E: incompatible type
+
+np.dtype(  # E: No overload variant of "dtype" matches
+    {
+        "field1": (float, 1),
+        "field2": (int, 3),
+    }
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e51f72e47b25b798c9655b749fc01911c824c2dd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_m: npt.NDArray[np.timedelta64]
+AR_U: npt.NDArray[np.str_]
+
+np.einsum("i,i->i", AR_i, AR_m)  # E: incompatible type
+np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32)  # E: incompatible type
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U)  # E: Value of type variable "_ArrayType" of "einsum" cannot be
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U, casting="unsafe")  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b0c3b023f16b3b36a28421938fe4eda4d411a68b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/flatiter.pyi
@@ -0,0 +1,25 @@
+from typing import Any
+
+import numpy as np
+import numpy._typing as npt
+
+
+class Index:
+    def __index__(self) -> int:
+        ...
+
+
+a: np.flatiter[npt.NDArray[np.float64]]
+supports_array: npt._SupportsArray[np.dtype[np.float64]]
+
+a.base = Any  # E: Property "base" defined in "flatiter" is read-only
+a.coords = Any  # E: Property "coords" defined in "flatiter" is read-only
+a.index = Any  # E: Property "index" defined in "flatiter" is read-only
+a.copy(order='C')  # E: Unexpected keyword argument
+
+# NOTE: Contrary to `ndarray.__getitem__` its counterpart in `flatiter`
+# does not accept objects with the `__array__` or `__index__` protocols;
+# boolean indexing is just plain broken (gh-17175)
+a[np.bool()]  # E: No overload variant of "__getitem__"
+a[Index()]  # E: No overload variant of "__getitem__"
+a[supports_array]  # E: No overload variant of "__getitem__"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fb666986a7e03fc6eb7aceeba235299ec9bfa14f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi
@@ -0,0 +1,165 @@
+"""Tests for :mod:`numpy._core.fromnumeric`."""
+
+import numpy as np
+import numpy.typing as npt
+
+A = np.array(True, ndmin=2, dtype=bool)
+A.setflags(write=False)
+AR_U: npt.NDArray[np.str_]
+AR_M: npt.NDArray[np.datetime64]
+
+a = np.bool(True)
+
+np.take(a, None)  # E: No overload variant
+np.take(a, axis=1.0)  # E: No overload variant
+np.take(A, out=1)  # E: No overload variant
+np.take(A, mode="bob")  # E: No overload variant
+
+np.reshape(a, None)  # E: No overload variant
+np.reshape(A, 1, order="bob")  # E: No overload variant
+
+np.choose(a, None)  # E: No overload variant
+np.choose(a, out=1.0)  # E: No overload variant
+np.choose(A, mode="bob")  # E: No overload variant
+
+np.repeat(a, None)  # E: No overload variant
+np.repeat(A, 1, axis=1.0)  # E: No overload variant
+
+np.swapaxes(A, None, 1)  # E: No overload variant
+np.swapaxes(A, 1, [0])  # E: No overload variant
+
+np.transpose(A, axes=1.0)  # E: No overload variant
+
+np.partition(a, None)  # E: No overload variant
+np.partition(  # E: No overload variant
+    a, 0, axis="bob"
+)
+np.partition(  # E: No overload variant
+    A, 0, kind="bob"
+)
+np.partition(
+    A, 0, order=range(5)  # E: Argument "order" to "partition" has incompatible type
+)
+
+np.argpartition(
+    a, None  # E: incompatible type
+)
+np.argpartition(
+    a, 0, axis="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, kind="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, order=range(5)  # E: Argument "order" to "argpartition" has incompatible type
+)
+
+np.sort(A, axis="bob")  # E: No overload variant
+np.sort(A, kind="bob")  # E: No overload variant
+np.sort(A, order=range(5))  # E: Argument "order" to "sort" has incompatible type
+
+np.argsort(A, axis="bob")  # E: Argument "axis" to "argsort" has incompatible type
+np.argsort(A, kind="bob")  # E: Argument "kind" to "argsort" has incompatible type
+np.argsort(A, order=range(5))  # E: Argument "order" to "argsort" has incompatible type
+
+np.argmax(A, axis="bob")  # E: No overload variant of "argmax" matches argument type
+np.argmax(A, kind="bob")  # E: No overload variant of "argmax" matches argument type
+
+np.argmin(A, axis="bob")  # E: No overload variant of "argmin" matches argument type
+np.argmin(A, kind="bob")  # E: No overload variant of "argmin" matches argument type
+
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, side="bob"
+)
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, sorter=1.0
+)
+
+np.resize(A, 1.0)  # E: No overload variant
+
+np.squeeze(A, 1.0)  # E: No overload variant of "squeeze" matches argument type
+
+np.diagonal(A, offset=None)  # E: No overload variant
+np.diagonal(A, axis1="bob")  # E: No overload variant
+np.diagonal(A, axis2=[])  # E: No overload variant
+
+np.trace(A, offset=None)  # E: No overload variant
+np.trace(A, axis1="bob")  # E: No overload variant
+np.trace(A, axis2=[])  # E: No overload variant
+
+np.ravel(a, order="bob")  # E: No overload variant
+
+np.nonzero(0)  # E: No overload variant
+
+np.compress(  # E: No overload variant
+    [True], A, axis=1.0
+)
+
+np.clip(a, 1, 2, out=1)  # E: No overload variant of "clip" matches argument type
+
+np.sum(a, axis=1.0)  # E: No overload variant
+np.sum(a, keepdims=1.0)  # E: No overload variant
+np.sum(a, initial=[1])  # E: No overload variant
+
+np.all(a, axis=1.0)  # E: No overload variant
+np.all(a, keepdims=1.0)  # E: No overload variant
+np.all(a, out=1.0)  # E: No overload variant
+
+np.any(a, axis=1.0)  # E: No overload variant
+np.any(a, keepdims=1.0)  # E: No overload variant
+np.any(a, out=1.0)  # E: No overload variant
+
+np.cumsum(a, axis=1.0)  # E: No overload variant
+np.cumsum(a, dtype=1.0)  # E: No overload variant
+np.cumsum(a, out=1.0)  # E: No overload variant
+
+np.ptp(a, axis=1.0)  # E: No overload variant
+np.ptp(a, keepdims=1.0)  # E: No overload variant
+np.ptp(a, out=1.0)  # E: No overload variant
+
+np.amax(a, axis=1.0)  # E: No overload variant
+np.amax(a, keepdims=1.0)  # E: No overload variant
+np.amax(a, out=1.0)  # E: No overload variant
+np.amax(a, initial=[1.0])  # E: No overload variant
+np.amax(a, where=[1.0])  # E: incompatible type
+
+np.amin(a, axis=1.0)  # E: No overload variant
+np.amin(a, keepdims=1.0)  # E: No overload variant
+np.amin(a, out=1.0)  # E: No overload variant
+np.amin(a, initial=[1.0])  # E: No overload variant
+np.amin(a, where=[1.0])  # E: incompatible type
+
+np.prod(a, axis=1.0)  # E: No overload variant
+np.prod(a, out=False)  # E: No overload variant
+np.prod(a, keepdims=1.0)  # E: No overload variant
+np.prod(a, initial=int)  # E: No overload variant
+np.prod(a, where=1.0)  # E: No overload variant
+np.prod(AR_U)  # E: incompatible type
+
+np.cumprod(a, axis=1.0)  # E: No overload variant
+np.cumprod(a, out=False)  # E: No overload variant
+np.cumprod(AR_U)  # E: incompatible type
+
+np.size(a, axis=1.0)  # E: Argument "axis" to "size" has incompatible type
+
+np.around(a, decimals=1.0)  # E: No overload variant
+np.around(a, out=type)  # E: No overload variant
+np.around(AR_U)  # E: incompatible type
+
+np.mean(a, axis=1.0)  # E: No overload variant
+np.mean(a, out=False)  # E: No overload variant
+np.mean(a, keepdims=1.0)  # E: No overload variant
+np.mean(AR_U)  # E: incompatible type
+np.mean(AR_M)  # E: incompatible type
+
+np.std(a, axis=1.0)  # E: No overload variant
+np.std(a, out=False)  # E: No overload variant
+np.std(a, ddof='test')  # E: No overload variant
+np.std(a, keepdims=1.0)  # E: No overload variant
+np.std(AR_U)  # E: incompatible type
+
+np.var(a, axis=1.0)  # E: No overload variant
+np.var(a, out=False)  # E: No overload variant
+np.var(a, ddof='test')  # E: No overload variant
+np.var(a, keepdims=1.0)  # E: No overload variant
+np.var(AR_U)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22499d39175ac4252d6ebc7a8c9c63421d64faee
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/histograms.pyi
@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+np.histogram_bin_edges(AR_i8, range=(0, 1, 2))  # E: incompatible type
+
+np.histogram(AR_i8, range=(0, 1, 2))  # E: incompatible type
+
+np.histogramdd(AR_i8, range=(0, 1))  # E: incompatible type
+np.histogramdd(AR_i8, range=[(0, 1, 2)])  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f6f4a61e8e11079e40d3755b0c01200ffdf762
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi
@@ -0,0 +1,14 @@
+import numpy as np
+
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+
+np.ndindex([1, 2, 3])  # E: No overload variant
+np.unravel_index(AR_LIKE_f, (1, 2, 3))  # E: incompatible type
+np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob")  # E: No overload variant
+np.mgrid[1]  # E: Invalid index type
+np.mgrid[...]  # E: Invalid index type
+np.ogrid[1]  # E: Invalid index type
+np.ogrid[...]  # E: Invalid index type
+np.fill_diagonal(AR_LIKE_f, 2)  # E: incompatible type
+np.diag_indices(1.0)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..de4e56b07ba1ed189300ae9cbdf585144bb0d880
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi
@@ -0,0 +1,62 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_b_list: list[npt.NDArray[np.bool]]
+
+def fn_none_i(a: None, /) -> npt.NDArray[Any]: ...
+def fn_ar_i(a: npt.NDArray[np.float64], posarg: int, /) -> npt.NDArray[Any]: ...
+
+np.average(AR_m)  # E: incompatible type
+np.select(1, [AR_f8])  # E: incompatible type
+np.angle(AR_m)  # E: incompatible type
+np.unwrap(AR_m)  # E: incompatible type
+np.unwrap(AR_c16)  # E: incompatible type
+np.trim_zeros(1)  # E: incompatible type
+np.place(1, [True], 1.5)  # E: incompatible type
+np.vectorize(1)  # E: incompatible type
+np.place(AR_f8, slice(None), 5)  # E: incompatible type
+
+np.piecewise(AR_f8, True, [fn_ar_i], 42)  # E: No overload variants
+# TODO: enable these once mypy actually supports ParamSpec (released in 2021)
+# NOTE: pyright correctly reports errors for these (`reportCallIssue`)
+# np.piecewise(AR_f8, AR_b_list, [fn_none_i])  # E: No overload variants
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i])  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 3.14)  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, None)  # E: No overload variant
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, _=None)  # E: No overload variant
+
+np.interp(AR_f8, AR_c16, AR_f8)  # E: incompatible type
+np.interp(AR_c16, AR_f8, AR_f8)  # E: incompatible type
+np.interp(AR_f8, AR_f8, AR_f8, period=AR_c16)  # E: No overload variant
+np.interp(AR_f8, AR_f8, AR_O)  # E: incompatible type
+
+np.cov(AR_m)  # E: incompatible type
+np.cov(AR_O)  # E: incompatible type
+np.corrcoef(AR_m)  # E: incompatible type
+np.corrcoef(AR_O)  # E: incompatible type
+np.corrcoef(AR_f8, bias=True)  # E: No overload variant
+np.corrcoef(AR_f8, ddof=2)  # E: No overload variant
+np.blackman(1j)  # E: incompatible type
+np.bartlett(1j)  # E: incompatible type
+np.hanning(1j)  # E: incompatible type
+np.hamming(1j)  # E: incompatible type
+np.hamming(AR_c16)  # E: incompatible type
+np.kaiser(1j, 1)  # E: incompatible type
+np.sinc(AR_O)  # E: incompatible type
+np.median(AR_M)  # E: incompatible type
+
+np.percentile(AR_f8, 50j)  # E: No overload variant
+np.percentile(AR_f8, 50, interpolation="bob")  # E: No overload variant
+np.quantile(AR_f8, 0.5j)  # E: No overload variant
+np.quantile(AR_f8, 0.5, interpolation="bob")  # E: No overload variant
+np.meshgrid(AR_f8, AR_f8, indexing="bob")  # E: incompatible type
+np.delete(AR_f8, AR_f8)  # E: incompatible type
+np.insert(AR_f8, AR_f8, 1.5)  # E: incompatible type
+np.digitize(AR_f8, 1j)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e51b6b58e30751bef821a44127e96be71483005b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi
@@ -0,0 +1,29 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+AR_U: npt.NDArray[np.str_]
+
+poly_obj: np.poly1d
+
+np.polymul(AR_f8, AR_U)  # E: incompatible type
+np.polydiv(AR_f8, AR_U)  # E: incompatible type
+
+5**poly_obj  # E: No overload variant
+
+np.polyint(AR_U)  # E: incompatible type
+np.polyint(AR_f8, m=1j)  # E: No overload variant
+
+np.polyder(AR_U)  # E: incompatible type
+np.polyder(AR_f8, m=1j)  # E: No overload variant
+
+np.polyfit(AR_O, AR_f8, 1)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, rcond=1j)  # E: No overload variant
+np.polyfit(AR_f8, AR_f8, 1, w=AR_c16)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, cov="bob")  # E: No overload variant
+
+np.polyval(AR_f8, AR_U)  # E: incompatible type
+np.polyadd(AR_f8, AR_U)  # E: incompatible type
+np.polysub(AR_f8, AR_U)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8b8482eeff6d93510dcabeece5bf7d6f85f627aa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi
@@ -0,0 +1,3 @@
+import numpy.lib.array_utils as array_utils
+
+array_utils.byte_bounds(1)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2758cfe4043883eaaa3651efe726bd31b853e603
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/lib_version.pyi
@@ -0,0 +1,6 @@
+from numpy.lib import NumpyVersion
+
+version: NumpyVersion
+
+NumpyVersion(b"1.8.0")  # E: incompatible type
+version >= b"1.8.0"  # E: Unsupported operand types
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..da9390328bd7ca1ebcab5a1ce0736f7f4df57d96
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/linalg.pyi
@@ -0,0 +1,48 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_O: npt.NDArray[np.object_]
+AR_M: npt.NDArray[np.datetime64]
+
+np.linalg.tensorsolve(AR_O, AR_O)  # E: incompatible type
+
+np.linalg.solve(AR_O, AR_O)  # E: incompatible type
+
+np.linalg.tensorinv(AR_O)  # E: incompatible type
+
+np.linalg.inv(AR_O)  # E: incompatible type
+
+np.linalg.matrix_power(AR_M, 5)  # E: incompatible type
+
+np.linalg.cholesky(AR_O)  # E: incompatible type
+
+np.linalg.qr(AR_O)  # E: incompatible type
+np.linalg.qr(AR_f8, mode="bob")  # E: No overload variant
+
+np.linalg.eigvals(AR_O)  # E: incompatible type
+
+np.linalg.eigvalsh(AR_O)  # E: incompatible type
+np.linalg.eigvalsh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.eig(AR_O)  # E: incompatible type
+
+np.linalg.eigh(AR_O)  # E: incompatible type
+np.linalg.eigh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.svd(AR_O)  # E: incompatible type
+
+np.linalg.cond(AR_O)  # E: incompatible type
+np.linalg.cond(AR_f8, p="bob")  # E: incompatible type
+
+np.linalg.matrix_rank(AR_O)  # E: incompatible type
+
+np.linalg.pinv(AR_O)  # E: incompatible type
+
+np.linalg.slogdet(AR_O)  # E: incompatible type
+
+np.linalg.det(AR_O)  # E: incompatible type
+
+np.linalg.norm(AR_f8, ord="bob")  # E: No overload variant
+
+np.linalg.multi_dot([AR_M])  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..434870b60e41948afc6fb3f593742deb2cc11e3e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/memmap.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+with open("file.txt", "r") as f:
+    np.memmap(f)  # E: No overload variant
+np.memmap("test.txt", shape=[10, 5])  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..541be15b24ae3a20c7ce24d474e9af6adc804c69
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/modules.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+
+np.testing.bob  # E: Module has no attribute
+np.bob  # E: Module has no attribute
+
+# Stdlib modules in the namespace by accident
+np.warnings  # E: Module has no attribute
+np.sys  # E: Module has no attribute
+np.os  # E: Module "numpy" does not explicitly export
+np.math  # E: Module has no attribute
+
+# Public sub-modules that are not imported to their parent module by default;
+# e.g. one must first execute `import numpy.lib.recfunctions`
+np.lib.recfunctions  # E: Module has no attribute
+
+np.__deprecated_attrs__  # E: Module has no attribute
+np.__expired_functions__  # E: Module has no attribute
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..0ee6c11c6dfff268efd1af3083210d430699f04c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/multiarray.pyi
@@ -0,0 +1,53 @@
+import numpy as np
+import numpy.typing as npt
+
+i8: np.int64
+
+AR_b: npt.NDArray[np.bool]
+AR_u1: npt.NDArray[np.uint8]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+
+M: np.datetime64
+
+AR_LIKE_f: list[float]
+
+def func(a: int) -> None: ...
+
+np.where(AR_b, 1)  # E: No overload variant
+
+np.can_cast(AR_f8, 1)  # E: incompatible type
+
+np.vdot(AR_M, AR_M)  # E: incompatible type
+
+np.copyto(AR_LIKE_f, AR_f8)  # E: incompatible type
+
+np.putmask(AR_LIKE_f, [True, True, False], 1.5)  # E: incompatible type
+
+np.packbits(AR_f8)  # E: incompatible type
+np.packbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.unpackbits(AR_i8)  # E: incompatible type
+np.unpackbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.shares_memory(1, 1, max_work=i8)  # E: incompatible type
+np.may_share_memory(1, 1, max_work=i8)  # E: incompatible type
+
+np.arange(M)  # E: No overload variant
+np.arange(stop=10)  # E: No overload variant
+
+np.datetime_data(int)  # E: incompatible type
+
+np.busday_offset("2012", 10)  # E: No overload variant
+
+np.datetime_as_string("2012")  # E: No overload variant
+
+np.char.compare_chararrays("a", b"a", "==", False)  # E: No overload variant
+
+np.nested_iters([AR_i8, AR_i8])  # E: Missing positional argument
+np.nested_iters([AR_i8, AR_i8], 0)  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [0])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["test"])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["test"]])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], buffersize=1.0)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..5ecae02e6178c3ced44031358a6f24047552651e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray.pyi
@@ -0,0 +1,11 @@
+import numpy as np
+
+# Ban setting dtype since mutating the type of the array in place
+# makes having ndarray be generic over dtype impossible. Generally
+# users should use `ndarray.view` in this situation anyway. See
+#
+# https://github.com/numpy/numpy-stubs/issues/7
+#
+# for more context.
+float_array = np.array([1.0])
+float_array.dtype = np.bool  # E: Property "dtype" defined in "ndarray" is read-only
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..489aefca7ffcc3d2673f23b80ea5da16742d189e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi
@@ -0,0 +1,36 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+import numpy as np
+import numpy.typing as npt
+
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_b: npt.NDArray[np.bool]
+
+ctypes_obj = AR_f8.ctypes
+
+f8.argpartition(0)  # E: has no attribute
+f8.diagonal()  # E: has no attribute
+f8.dot(1)  # E: has no attribute
+f8.nonzero()  # E: has no attribute
+f8.partition(0)  # E: has no attribute
+f8.put(0, 2)  # E: has no attribute
+f8.setfield(2, np.float64)  # E: has no attribute
+f8.sort()  # E: has no attribute
+f8.trace()  # E: has no attribute
+
+AR_M.__complex__()  # E: Invalid self argument
+AR_b.__index__()  # E: Invalid self argument
+
+AR_f8[1.5]  # E: No overload variant
+AR_f8["field_a"]  # E: No overload variant
+AR_f8[["field_a", "field_b"]]  # E: Invalid index type
+
+AR_f8.__array_finalize__(object())  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1e8e37ee5fe09373a6be5e8a2b2ddb9f84725eb0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nditer.pyi
@@ -0,0 +1,8 @@
+import numpy as np
+
+class Test(np.nditer): ...  # E: Cannot inherit from final class
+
+np.nditer([0, 1], flags=["test"])  # E: incompatible type
+np.nditer([0, 1], op_flags=[["test"]])  # E: incompatible type
+np.nditer([0, 1], itershape=(1.0,))  # E: incompatible type
+np.nditer([0, 1], buffersize=1.0)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6301e51769fee30db50bfaf1e2777bf894166de8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi
@@ -0,0 +1,17 @@
+from collections.abc import Sequence
+from numpy._typing import _NestedSequence
+
+a: Sequence[float]
+b: list[complex]
+c: tuple[str, ...]
+d: int
+e: str
+
+def func(a: _NestedSequence[int]) -> None:
+    ...
+
+reveal_type(func(a))  # E: incompatible type
+reveal_type(func(b))  # E: incompatible type
+reveal_type(func(c))  # E: incompatible type
+reveal_type(func(d))  # E: incompatible type
+reveal_type(func(e))  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6ba6a6be17978fbb68a300f2d67c56a22b80a4a8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/npyio.pyi
@@ -0,0 +1,25 @@
+import pathlib
+from typing import IO
+
+import numpy.typing as npt
+import numpy as np
+
+str_path: str
+bytes_path: bytes
+pathlib_path: pathlib.Path
+str_file: IO[str]
+AR_i8: npt.NDArray[np.int64]
+
+np.load(str_file)  # E: incompatible type
+
+np.save(bytes_path, AR_i8)  # E: No overload variant
+# https://github.com/python/mypy/issues/16111
+# np.save(str_path, AR_i8, fix_imports=True)  # W: deprecated
+
+np.savez(bytes_path, AR_i8)  # E: incompatible type
+
+np.savez_compressed(bytes_path, AR_i8)  # E: incompatible type
+
+np.loadtxt(bytes_path)  # E: incompatible type
+
+np.fromregex(bytes_path, ".", np.int64)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..29a3cf30dd959d7e05ce688413741b9ccc0060a3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi
@@ -0,0 +1,5 @@
+import numpy as np
+
+np.isdtype(1, np.int64)  # E: incompatible type
+
+np.issubdtype(1, np.int64)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aa1eae4424e2ba07b6f662ccc5e2f523fcb6dcf8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/random.pyi
@@ -0,0 +1,62 @@
+import numpy as np
+import numpy.typing as npt
+
+SEED_FLOAT: float = 457.3
+SEED_ARR_FLOAT: npt.NDArray[np.float64] = np.array([1.0, 2, 3, 4])
+SEED_ARRLIKE_FLOAT: list[float] = [1.0, 2.0, 3.0, 4.0]
+SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
+SEED_STR: str = "String seeding not allowed"
+
+# default rng
+np.random.default_rng(SEED_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_STR)  # E: incompatible type
+
+# Seed Sequence
+np.random.SeedSequence(SEED_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_SEED_SEQ)  # E: incompatible type
+np.random.SeedSequence(SEED_STR)  # E: incompatible type
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence()
+seed_seq.spawn(11.5)  # E: incompatible type
+seed_seq.generate_state(3.14)  # E: incompatible type
+seed_seq.generate_state(3, np.uint8)  # E: incompatible type
+seed_seq.generate_state(3, "uint8")  # E: incompatible type
+seed_seq.generate_state(3, "u1")  # E: incompatible type
+seed_seq.generate_state(3, np.uint16)  # E: incompatible type
+seed_seq.generate_state(3, "uint16")  # E: incompatible type
+seed_seq.generate_state(3, "u2")  # E: incompatible type
+seed_seq.generate_state(3, np.int32)  # E: incompatible type
+seed_seq.generate_state(3, "int32")  # E: incompatible type
+seed_seq.generate_state(3, "i4")  # E: incompatible type
+
+# Bit Generators
+np.random.MT19937(SEED_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_STR)  # E: incompatible type
+
+np.random.PCG64(SEED_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_STR)  # E: incompatible type
+
+np.random.Philox(SEED_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_STR)  # E: incompatible type
+
+np.random.SFC64(SEED_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_STR)  # E: incompatible type
+
+# Generator
+np.random.Generator(None)  # E: incompatible type
+np.random.Generator(12333283902830213)  # E: incompatible type
+np.random.Generator("OxFEEDF00D")  # E: incompatible type
+np.random.Generator([123, 234])  # E: incompatible type
+np.random.Generator(np.array([123, 234], dtype="u4"))  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a57f1ba27d74504ff59232a4a5929ccaf55dd445
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/rec.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.rec.fromarrays(1)  # E: No overload variant
+np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromrecords(AR_i8)  # E: incompatible type
+np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromstring("string", dtype=[("f8", "f8")])  # E: No overload variant
+np.rec.fromstring(b"bytes")  # E: No overload variant
+np.rec.fromstring(b"(1.5,)", dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+with open("test", "r") as f:
+    np.rec.fromfile(f, dtype=[("f8", "f8")])  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e847d8d6c45a0158818aa5f90da756ec36080b5d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/scalars.pyi
@@ -0,0 +1,89 @@
+import sys
+import numpy as np
+
+f2: np.float16
+f8: np.float64
+c8: np.complex64
+
+# Construction
+
+np.float32(3j)  # E: incompatible type
+
+# Technically the following examples are valid NumPy code. But they
+# are not considered a best practice, and people who wish to use the
+# stubs should instead do
+#
+# np.array([1.0, 0.0, 0.0], dtype=np.float32)
+# np.array([], dtype=np.complex64)
+#
+# See e.g. the discussion on the mailing list
+#
+# https://mail.python.org/pipermail/numpy-discussion/2020-April/080566.html
+#
+# and the issue
+#
+# https://github.com/numpy/numpy-stubs/issues/41
+#
+# for more context.
+np.float32([1.0, 0.0, 0.0])  # E: incompatible type
+np.complex64([])  # E: incompatible type
+
+# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
+
+np.datetime64(0)  # E: No overload variant
+
+class A:
+    def __float__(self):
+        return 1.0
+
+
+np.int8(A())  # E: incompatible type
+np.int16(A())  # E: incompatible type
+np.int32(A())  # E: incompatible type
+np.int64(A())  # E: incompatible type
+np.uint8(A())  # E: incompatible type
+np.uint16(A())  # E: incompatible type
+np.uint32(A())  # E: incompatible type
+np.uint64(A())  # E: incompatible type
+
+np.void("test")  # E: No overload variant
+np.void("test", dtype=None)  # E: No overload variant
+
+np.generic(1)  # E: Cannot instantiate abstract class
+np.number(1)  # E: Cannot instantiate abstract class
+np.integer(1)  # E: Cannot instantiate abstract class
+np.inexact(1)  # E: Cannot instantiate abstract class
+np.character("test")  # E: Cannot instantiate abstract class
+np.flexible(b"test")  # E: Cannot instantiate abstract class
+
+np.float64(value=0.0)  # E: Unexpected keyword argument
+np.int64(value=0)  # E: Unexpected keyword argument
+np.uint64(value=0)  # E: Unexpected keyword argument
+np.complex128(value=0.0j)  # E: No overload variant
+np.str_(value='bob')  # E: No overload variant
+np.bytes_(value=b'test')  # E: No overload variant
+np.void(value=b'test')  # E: No overload variant
+np.bool(value=True)  # E: Unexpected keyword argument
+np.datetime64(value="2019")  # E: No overload variant
+np.timedelta64(value=0)  # E: Unexpected keyword argument
+
+np.bytes_(b"hello", encoding='utf-8')  # E: No overload variant
+np.str_("hello", encoding='utf-8')  # E: No overload variant
+
+f8.item(1)  # E: incompatible type
+f8.item((0, 1))  # E: incompatible type
+f8.squeeze(axis=1)  # E: incompatible type
+f8.squeeze(axis=(0, 1))  # E: incompatible type
+f8.transpose(1)  # E: incompatible type
+
+def func(a: np.float32) -> None: ...
+
+func(f2)  # E: incompatible type
+func(f8)  # E: incompatible type
+
+c8.__getnewargs__()  # E: Invalid self argument
+f2.__getnewargs__()  # E: Invalid self argument
+f2.hex()  # E: Invalid self argument
+np.float16.fromhex("0x0.0p+0")  # E: Invalid self argument
+f2.__trunc__()  # E: Invalid self argument
+f2.__getformat__("float")  # E: Invalid self argument
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3dd6d14f4222de8872f85bde7d69cc3a65aef4a7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape.pyi
@@ -0,0 +1,6 @@
+from typing import Any
+import numpy as np
+
+# test bounds of _ShapeType_co
+
+np.ndarray[tuple[str, str], Any]  # E: Value of type variable
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e709741b7935ec7269affd836f5256a0842ddd0a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/shape_base.pyi
@@ -0,0 +1,8 @@
+import numpy as np
+
+class DTypeLike:
+    dtype: np.dtype[np.int_]
+
+dtype_like: DTypeLike
+
+np.expand_dims(dtype_like, (5, 10))  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f2bfba7432a89b41e095377e1d7e0e5f87d07109
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi
@@ -0,0 +1,9 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.lib.stride_tricks.as_strided(AR_f8, shape=8)  # E: No overload variant
+np.lib.stride_tricks.as_strided(AR_f8, strides=8)  # E: No overload variant
+
+np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,))  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..25c3c2ecc0d76c6bbd2ec897ef9f8b09b4078715
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/strings.pyi
@@ -0,0 +1,59 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.strings.equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.not_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.greater_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.less_equal(AR_U, AR_S)  # E: incompatible type
+
+np.strings.greater(AR_U, AR_S)  # E: incompatible type
+
+np.strings.less(AR_U, AR_S)  # E: incompatible type
+
+np.strings.encode(AR_S)  # E: incompatible type
+np.strings.decode(AR_U)  # E: incompatible type
+
+np.strings.join(AR_U, b"_")  # E: incompatible type
+np.strings.join(AR_S, "_")  # E: incompatible type
+
+np.strings.lstrip(AR_U, b"a")  # E: incompatible type
+np.strings.lstrip(AR_S, "a")  # E: incompatible type
+np.strings.strip(AR_U, b"a")  # E: incompatible type
+np.strings.strip(AR_S, "a")  # E: incompatible type
+np.strings.rstrip(AR_U, b"a")  # E: incompatible type
+np.strings.rstrip(AR_S, "a")  # E: incompatible type
+
+np.strings.partition(AR_U, b"a")  # E: incompatible type
+np.strings.partition(AR_S, "a")  # E: incompatible type
+np.strings.rpartition(AR_U, b"a")  # E: incompatible type
+np.strings.rpartition(AR_S, "a")  # E: incompatible type
+
+np.strings.count(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.count(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.endswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.endswith(AR_S, "a", 0, 9)  # E: incompatible type
+np.strings.startswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.startswith(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.find(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.strings.find(AR_S, "a", 0, 9)  # E: incompatible type
+np.strings.rfind(AR_U, b"a", [1, 2, 3], [1, 2 , 3])  # E: incompatible type
+np.strings.rfind(AR_S, "a", 0, 9)  # E: incompatible type
+
+np.strings.index(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.strings.index(AR_S, "a", end=9)  # E: incompatible type
+np.strings.rindex(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.strings.rindex(AR_S, "a", end=9)  # E: incompatible type
+
+np.strings.isdecimal(AR_S)  # E: incompatible type
+np.strings.isnumeric(AR_S)  # E: incompatible type
+
+np.strings.replace(AR_U, b"_", b"-", 10)  # E: incompatible type
+np.strings.replace(AR_S, "_", "-", 1)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f7eaa7d20836946144d7e1f7aa2d40a6d0f00948
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/testing.pyi
@@ -0,0 +1,28 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+
+def func(x: object) -> bool: ...
+
+np.testing.assert_(True, msg=1)  # E: incompatible type
+np.testing.build_err_msg(1, "test")  # E: incompatible type
+np.testing.assert_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_approx_equal([1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.testing.assert_array_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_less(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_string_equal(b"a", b"a")  # E: incompatible type
+
+np.testing.assert_raises(expected_exception=TypeError, callable=func)  # E: No overload variant
+np.testing.assert_raises_regex(expected_exception=TypeError, expected_regex="T", callable=func)  # E: No overload variant
+
+np.testing.assert_allclose(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_almost_equal_nulp(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_max_ulp(AR_U, AR_U)  # E: incompatible type
+
+np.testing.assert_warns(RuntimeWarning, func)  # E: No overload variant
+np.testing.assert_no_warnings(func=func)  # E: No overload variant
+np.testing.assert_no_warnings(func)  # E: Too many arguments
+np.testing.assert_no_warnings(func, y=None)  # E: No overload variant
+
+np.testing.assert_no_gc_cycles(func=func)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..76186285669b34e42f0e387e94435f593954781f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi
@@ -0,0 +1,37 @@
+from typing import Any, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+
+def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]:
+    pass
+
+
+def func2(ar: npt.NDArray[Any], a: float) -> float:
+    pass
+
+
+AR_b: npt.NDArray[np.bool]
+AR_m: npt.NDArray[np.timedelta64]
+
+AR_LIKE_b: list[bool]
+
+np.eye(10, M=20.0)  # E: No overload variant
+np.eye(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.diag(AR_b, k=0.5)  # E: No overload variant
+np.diagflat(AR_b, k=0.5)  # E: No overload variant
+
+np.tri(10, M=20.0)  # E: No overload variant
+np.tri(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.tril(AR_b, k=0.5)  # E: No overload variant
+np.triu(AR_b, k=0.5)  # E: No overload variant
+
+np.vander(AR_m)  # E: incompatible type
+
+np.histogram2d(AR_m)  # E: No overload variant
+
+np.mask_indices(10, func1)  # E: incompatible type
+np.mask_indices(10, func2, 10.5)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..95f52bfbd260914c429cbf0ca57f1ff4b03cbb1d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/type_check.pyi
@@ -0,0 +1,13 @@
+import numpy as np
+import numpy.typing as npt
+
+DTYPE_i8: np.dtype[np.int64]
+
+np.mintypecode(DTYPE_i8)  # E: incompatible type
+np.iscomplexobj(DTYPE_i8)  # E: incompatible type
+np.isrealobj(DTYPE_i8)  # E: incompatible type
+
+np.typename(DTYPE_i8)  # E: No overload variant
+np.typename("invalid")  # E: No overload variant
+
+np.common_type(np.timedelta64())  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b080804b0fcf21a09cedb2abeba9f2baa9592dde
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi
@@ -0,0 +1,21 @@
+"""Typing tests for `numpy._core._ufunc_config`."""
+
+import numpy as np
+
+def func1(a: str, b: int, c: float) -> None: ...
+def func2(a: str, *, b: int) -> None: ...
+
+class Write1:
+    def write1(self, a: str) -> None: ...
+
+class Write2:
+    def write(self, a: str, b: str) -> None: ...
+
+class Write3:
+    def write(self, *, a: str) -> None: ...
+
+np.seterrcall(func1)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(func2)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write1())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write2())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write3())  # E: Argument 1 to "seterrcall" has incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..be5e6a1530c2e5415042840ee83ed4a9c8182daf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi
@@ -0,0 +1,21 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+np.fix(AR_c)  # E: incompatible type
+np.fix(AR_m)  # E: incompatible type
+np.fix(AR_M)  # E: incompatible type
+
+np.isposinf(AR_c)  # E: incompatible type
+np.isposinf(AR_m)  # E: incompatible type
+np.isposinf(AR_M)  # E: incompatible type
+np.isposinf(AR_O)  # E: incompatible type
+
+np.isneginf(AR_c)  # E: incompatible type
+np.isneginf(AR_m)  # E: incompatible type
+np.isneginf(AR_M)  # E: incompatible type
+np.isneginf(AR_O)  # E: incompatible type
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bbab0dfe3fc2fe363dc50a7a1f3c0b11f3d22f97
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi
@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.sin.nin + "foo"  # E: Unsupported operand types
+np.sin(1, foo="bar")  # E: No overload variant
+
+np.abs(None)  # E: No overload variant
+
+np.add(1, 1, 1)  # E: No overload variant
+np.add(1, 1, axis=0)  # E: No overload variant
+
+np.matmul(AR_f8, AR_f8, where=True)  # E: No overload variant
+
+np.frexp(AR_f8, out=None)  # E: No overload variant
+np.frexp(AR_f8, out=AR_f8)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fae96d6bf01641d0cf7dc8f7883f09c9492cb383
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi
@@ -0,0 +1,5 @@
+import numpy.exceptions as ex
+
+ex.AxisError(1.0)  # E: No overload variant
+ex.AxisError(1, ndim=2.0)  # E: No overload variant
+ex.AxisError(2, msg_prefix=404)  # E: No overload variant
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..78d8d93c6560616c3495dcdf801befce51997c00
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi
@@ -0,0 +1,25 @@
+import sys
+
+import numpy as np
+from numpy._typing import _80Bit, _96Bit, _128Bit, _256Bit
+
+if sys.version_info >= (3, 11):
+    from typing import assert_type
+else:
+    from typing_extensions import assert_type
+
+assert_type(np.uint128(), np.unsignedinteger[_128Bit])
+assert_type(np.uint256(), np.unsignedinteger[_256Bit])
+
+assert_type(np.int128(), np.signedinteger[_128Bit])
+assert_type(np.int256(), np.signedinteger[_256Bit])
+
+assert_type(np.float80(), np.floating[_80Bit])
+assert_type(np.float96(), np.floating[_96Bit])
+assert_type(np.float128(), np.floating[_128Bit])
+assert_type(np.float256(), np.floating[_256Bit])
+
+assert_type(np.complex160(), np.complexfloating[_80Bit, _80Bit])
+assert_type(np.complex192(), np.complexfloating[_96Bit, _96Bit])
+assert_type(np.complex256(), np.complexfloating[_128Bit, _128Bit])
+assert_type(np.complex512(), np.complexfloating[_256Bit, _256Bit])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/mypy.ini b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/mypy.ini
new file mode 100644
index 0000000000000000000000000000000000000000..3bd7887c12091fc77cf4b872a61ec364d77d3eb5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/mypy.ini
@@ -0,0 +1,10 @@
+[mypy]
+plugins = numpy.typing.mypy_plugin
+show_absolute_path = True
+implicit_reexport = False
+pretty = True
+disallow_any_unimported = True
+disallow_any_generics = True
+; https://github.com/python/mypy/issues/15313
+disable_bytearray_promotion = true
+disable_memoryview_promotion = true
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..93fda1d291c043b109ac2d8d151b83e45bd0f43a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arithmetic.py
@@ -0,0 +1,596 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+import numpy.typing as npt
+import pytest
+
+c16 = np.complex128(1)
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+c8 = np.complex64(1)
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+dt = np.datetime64(1, "D")
+td = np.timedelta64(1, "D")
+
+b_ = np.bool(1)
+
+b = bool(1)
+c = complex(1)
+f = float(1)
+i = int(1)
+
+
+class Object:
+    def __array__(self, dtype: np.typing.DTypeLike = None,
+                  copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+    def __sub__(self, value: Any) -> Object:
+        return self
+
+    def __rsub__(self, value: Any) -> Object:
+        return self
+
+    def __floordiv__(self, value: Any) -> Object:
+        return self
+
+    def __rfloordiv__(self, value: Any) -> Object:
+        return self
+
+    def __mul__(self, value: Any) -> Object:
+        return self
+
+    def __rmul__(self, value: Any) -> Object:
+        return self
+
+    def __pow__(self, value: Any) -> Object:
+        return self
+
+    def __rpow__(self, value: Any) -> Object:
+        return self
+
+
+AR_b: npt.NDArray[np.bool] = np.array([True])
+AR_u: npt.NDArray[np.uint32] = np.array([1], dtype=np.uint32)
+AR_i: npt.NDArray[np.int64] = np.array([1])
+AR_f: npt.NDArray[np.float64] = np.array([1.0])
+AR_c: npt.NDArray[np.complex128] = np.array([1j])
+AR_m: npt.NDArray[np.timedelta64] = np.array([np.timedelta64(1, "D")])
+AR_M: npt.NDArray[np.datetime64] = np.array([np.datetime64(1, "D")])
+AR_O: npt.NDArray[np.object_] = np.array([Object()])
+
+AR_LIKE_b = [True]
+AR_LIKE_u = [np.uint32(1)]
+AR_LIKE_i = [1]
+AR_LIKE_f = [1.0]
+AR_LIKE_c = [1j]
+AR_LIKE_m = [np.timedelta64(1, "D")]
+AR_LIKE_M = [np.datetime64(1, "D")]
+AR_LIKE_O = [Object()]
+
+# Array subtractions
+
+AR_b - AR_LIKE_u
+AR_b - AR_LIKE_i
+AR_b - AR_LIKE_f
+AR_b - AR_LIKE_c
+AR_b - AR_LIKE_m
+AR_b - AR_LIKE_O
+
+AR_LIKE_u - AR_b
+AR_LIKE_i - AR_b
+AR_LIKE_f - AR_b
+AR_LIKE_c - AR_b
+AR_LIKE_m - AR_b
+AR_LIKE_M - AR_b
+AR_LIKE_O - AR_b
+
+AR_u - AR_LIKE_b
+AR_u - AR_LIKE_u
+AR_u - AR_LIKE_i
+AR_u - AR_LIKE_f
+AR_u - AR_LIKE_c
+AR_u - AR_LIKE_m
+AR_u - AR_LIKE_O
+
+AR_LIKE_b - AR_u
+AR_LIKE_u - AR_u
+AR_LIKE_i - AR_u
+AR_LIKE_f - AR_u
+AR_LIKE_c - AR_u
+AR_LIKE_m - AR_u
+AR_LIKE_M - AR_u
+AR_LIKE_O - AR_u
+
+AR_i - AR_LIKE_b
+AR_i - AR_LIKE_u
+AR_i - AR_LIKE_i
+AR_i - AR_LIKE_f
+AR_i - AR_LIKE_c
+AR_i - AR_LIKE_m
+AR_i - AR_LIKE_O
+
+AR_LIKE_b - AR_i
+AR_LIKE_u - AR_i
+AR_LIKE_i - AR_i
+AR_LIKE_f - AR_i
+AR_LIKE_c - AR_i
+AR_LIKE_m - AR_i
+AR_LIKE_M - AR_i
+AR_LIKE_O - AR_i
+
+AR_f - AR_LIKE_b
+AR_f - AR_LIKE_u
+AR_f - AR_LIKE_i
+AR_f - AR_LIKE_f
+AR_f - AR_LIKE_c
+AR_f - AR_LIKE_O
+
+AR_LIKE_b - AR_f
+AR_LIKE_u - AR_f
+AR_LIKE_i - AR_f
+AR_LIKE_f - AR_f
+AR_LIKE_c - AR_f
+AR_LIKE_O - AR_f
+
+AR_c - AR_LIKE_b
+AR_c - AR_LIKE_u
+AR_c - AR_LIKE_i
+AR_c - AR_LIKE_f
+AR_c - AR_LIKE_c
+AR_c - AR_LIKE_O
+
+AR_LIKE_b - AR_c
+AR_LIKE_u - AR_c
+AR_LIKE_i - AR_c
+AR_LIKE_f - AR_c
+AR_LIKE_c - AR_c
+AR_LIKE_O - AR_c
+
+AR_m - AR_LIKE_b
+AR_m - AR_LIKE_u
+AR_m - AR_LIKE_i
+AR_m - AR_LIKE_m
+
+AR_LIKE_b - AR_m
+AR_LIKE_u - AR_m
+AR_LIKE_i - AR_m
+AR_LIKE_m - AR_m
+AR_LIKE_M - AR_m
+
+AR_M - AR_LIKE_b
+AR_M - AR_LIKE_u
+AR_M - AR_LIKE_i
+AR_M - AR_LIKE_m
+AR_M - AR_LIKE_M
+
+AR_LIKE_M - AR_M
+
+AR_O - AR_LIKE_b
+AR_O - AR_LIKE_u
+AR_O - AR_LIKE_i
+AR_O - AR_LIKE_f
+AR_O - AR_LIKE_c
+AR_O - AR_LIKE_O
+
+AR_LIKE_b - AR_O
+AR_LIKE_u - AR_O
+AR_LIKE_i - AR_O
+AR_LIKE_f - AR_O
+AR_LIKE_c - AR_O
+AR_LIKE_O - AR_O
+
+AR_u += AR_b
+AR_u += AR_u
+AR_u += 1  # Allowed during runtime as long as the object is 0D and >=0
+
+# Array floor division
+
+AR_b // AR_LIKE_b
+AR_b // AR_LIKE_u
+AR_b // AR_LIKE_i
+AR_b // AR_LIKE_f
+AR_b // AR_LIKE_O
+
+AR_LIKE_b // AR_b
+AR_LIKE_u // AR_b
+AR_LIKE_i // AR_b
+AR_LIKE_f // AR_b
+AR_LIKE_O // AR_b
+
+AR_u // AR_LIKE_b
+AR_u // AR_LIKE_u
+AR_u // AR_LIKE_i
+AR_u // AR_LIKE_f
+AR_u // AR_LIKE_O
+
+AR_LIKE_b // AR_u
+AR_LIKE_u // AR_u
+AR_LIKE_i // AR_u
+AR_LIKE_f // AR_u
+AR_LIKE_m // AR_u
+AR_LIKE_O // AR_u
+
+AR_i // AR_LIKE_b
+AR_i // AR_LIKE_u
+AR_i // AR_LIKE_i
+AR_i // AR_LIKE_f
+AR_i // AR_LIKE_O
+
+AR_LIKE_b // AR_i
+AR_LIKE_u // AR_i
+AR_LIKE_i // AR_i
+AR_LIKE_f // AR_i
+AR_LIKE_m // AR_i
+AR_LIKE_O // AR_i
+
+AR_f // AR_LIKE_b
+AR_f // AR_LIKE_u
+AR_f // AR_LIKE_i
+AR_f // AR_LIKE_f
+AR_f // AR_LIKE_O
+
+AR_LIKE_b // AR_f
+AR_LIKE_u // AR_f
+AR_LIKE_i // AR_f
+AR_LIKE_f // AR_f
+AR_LIKE_m // AR_f
+AR_LIKE_O // AR_f
+
+AR_m // AR_LIKE_u
+AR_m // AR_LIKE_i
+AR_m // AR_LIKE_f
+AR_m // AR_LIKE_m
+
+AR_LIKE_m // AR_m
+
+AR_O // AR_LIKE_b
+AR_O // AR_LIKE_u
+AR_O // AR_LIKE_i
+AR_O // AR_LIKE_f
+AR_O // AR_LIKE_O
+
+AR_LIKE_b // AR_O
+AR_LIKE_u // AR_O
+AR_LIKE_i // AR_O
+AR_LIKE_f // AR_O
+AR_LIKE_O // AR_O
+
+# Inplace multiplication
+
+AR_b *= AR_LIKE_b
+
+AR_u *= AR_LIKE_b
+AR_u *= AR_LIKE_u
+
+AR_i *= AR_LIKE_b
+AR_i *= AR_LIKE_u
+AR_i *= AR_LIKE_i
+
+AR_f *= AR_LIKE_b
+AR_f *= AR_LIKE_u
+AR_f *= AR_LIKE_i
+AR_f *= AR_LIKE_f
+
+AR_c *= AR_LIKE_b
+AR_c *= AR_LIKE_u
+AR_c *= AR_LIKE_i
+AR_c *= AR_LIKE_f
+AR_c *= AR_LIKE_c
+
+AR_m *= AR_LIKE_b
+AR_m *= AR_LIKE_u
+AR_m *= AR_LIKE_i
+AR_m *= AR_LIKE_f
+
+AR_O *= AR_LIKE_b
+AR_O *= AR_LIKE_u
+AR_O *= AR_LIKE_i
+AR_O *= AR_LIKE_f
+AR_O *= AR_LIKE_c
+AR_O *= AR_LIKE_O
+
+# Inplace power
+
+AR_u **= AR_LIKE_b
+AR_u **= AR_LIKE_u
+
+AR_i **= AR_LIKE_b
+AR_i **= AR_LIKE_u
+AR_i **= AR_LIKE_i
+
+AR_f **= AR_LIKE_b
+AR_f **= AR_LIKE_u
+AR_f **= AR_LIKE_i
+AR_f **= AR_LIKE_f
+
+AR_c **= AR_LIKE_b
+AR_c **= AR_LIKE_u
+AR_c **= AR_LIKE_i
+AR_c **= AR_LIKE_f
+AR_c **= AR_LIKE_c
+
+AR_O **= AR_LIKE_b
+AR_O **= AR_LIKE_u
+AR_O **= AR_LIKE_i
+AR_O **= AR_LIKE_f
+AR_O **= AR_LIKE_c
+AR_O **= AR_LIKE_O
+
+# unary ops
+
+-c16
+-c8
+-f8
+-f4
+-i8
+-i4
+with pytest.warns(RuntimeWarning):
+    -u8
+    -u4
+-td
+-AR_f
+
++c16
++c8
++f8
++f4
++i8
++i4
++u8
++u4
++td
++AR_f
+
+abs(c16)
+abs(c8)
+abs(f8)
+abs(f4)
+abs(i8)
+abs(i4)
+abs(u8)
+abs(u4)
+abs(td)
+abs(b_)
+abs(AR_f)
+
+# Time structures
+
+dt + td
+dt + i
+dt + i4
+dt + i8
+dt - dt
+dt - i
+dt - i4
+dt - i8
+
+td + td
+td + i
+td + i4
+td + i8
+td - td
+td - i
+td - i4
+td - i8
+td / f
+td / f4
+td / f8
+td / td
+td // td
+td % td
+
+
+# boolean
+
+b_ / b
+b_ / b_
+b_ / i
+b_ / i8
+b_ / i4
+b_ / u8
+b_ / u4
+b_ / f
+b_ / f8
+b_ / f4
+b_ / c
+b_ / c16
+b_ / c8
+
+b / b_
+b_ / b_
+i / b_
+i8 / b_
+i4 / b_
+u8 / b_
+u4 / b_
+f / b_
+f8 / b_
+f4 / b_
+c / b_
+c16 / b_
+c8 / b_
+
+# Complex
+
+c16 + c16
+c16 + f8
+c16 + i8
+c16 + c8
+c16 + f4
+c16 + i4
+c16 + b_
+c16 + b
+c16 + c
+c16 + f
+c16 + i
+c16 + AR_f
+
+c16 + c16
+f8 + c16
+i8 + c16
+c8 + c16
+f4 + c16
+i4 + c16
+b_ + c16
+b + c16
+c + c16
+f + c16
+i + c16
+AR_f + c16
+
+c8 + c16
+c8 + f8
+c8 + i8
+c8 + c8
+c8 + f4
+c8 + i4
+c8 + b_
+c8 + b
+c8 + c
+c8 + f
+c8 + i
+c8 + AR_f
+
+c16 + c8
+f8 + c8
+i8 + c8
+c8 + c8
+f4 + c8
+i4 + c8
+b_ + c8
+b + c8
+c + c8
+f + c8
+i + c8
+AR_f + c8
+
+# Float
+
+f8 + f8
+f8 + i8
+f8 + f4
+f8 + i4
+f8 + b_
+f8 + b
+f8 + c
+f8 + f
+f8 + i
+f8 + AR_f
+
+f8 + f8
+i8 + f8
+f4 + f8
+i4 + f8
+b_ + f8
+b + f8
+c + f8
+f + f8
+i + f8
+AR_f + f8
+
+f4 + f8
+f4 + i8
+f4 + f4
+f4 + i4
+f4 + b_
+f4 + b
+f4 + c
+f4 + f
+f4 + i
+f4 + AR_f
+
+f8 + f4
+i8 + f4
+f4 + f4
+i4 + f4
+b_ + f4
+b + f4
+c + f4
+f + f4
+i + f4
+AR_f + f4
+
+# Int
+
+i8 + i8
+i8 + u8
+i8 + i4
+i8 + u4
+i8 + b_
+i8 + b
+i8 + c
+i8 + f
+i8 + i
+i8 + AR_f
+
+u8 + u8
+u8 + i4
+u8 + u4
+u8 + b_
+u8 + b
+u8 + c
+u8 + f
+u8 + i
+u8 + AR_f
+
+i8 + i8
+u8 + i8
+i4 + i8
+u4 + i8
+b_ + i8
+b + i8
+c + i8
+f + i8
+i + i8
+AR_f + i8
+
+u8 + u8
+i4 + u8
+u4 + u8
+b_ + u8
+b + u8
+c + u8
+f + u8
+i + u8
+AR_f + u8
+
+i4 + i8
+i4 + i4
+i4 + i
+i4 + b_
+i4 + b
+i4 + AR_f
+
+u4 + i8
+u4 + i4
+u4 + u8
+u4 + u4
+u4 + i
+u4 + b_
+u4 + b
+u4 + AR_f
+
+i8 + i4
+i4 + i4
+i + i4
+b_ + i4
+b + i4
+AR_f + i4
+
+i8 + u4
+i4 + u4
+u8 + u4
+u4 + u4
+b_ + u4
+b + u4
+i + u4
+AR_f + u4
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..17b6fab93ad877b5eff31e65afadabcda8bd1c9e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_constructors.py
@@ -0,0 +1,137 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+class Index:
+    def __index__(self) -> int:
+        return 0
+
+
+class SubClass(npt.NDArray[np.float64]):
+    pass
+
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass:
+    return B
+
+
+i8 = np.int64(1)
+
+A = np.array([1])
+B = A.view(SubClass).copy()
+B_stack = np.array([[1], [1]]).view(SubClass)
+C = [1]
+
+np.ndarray(Index())
+np.ndarray([Index()])
+
+np.array(1, dtype=float)
+np.array(1, copy=None)
+np.array(1, order='F')
+np.array(1, order=None)
+np.array(1, subok=True)
+np.array(1, ndmin=3)
+np.array(1, str, copy=True, order='C', subok=False, ndmin=2)
+
+np.asarray(A)
+np.asarray(B)
+np.asarray(C)
+
+np.asanyarray(A)
+np.asanyarray(B)
+np.asanyarray(B, dtype=int)
+np.asanyarray(C)
+
+np.ascontiguousarray(A)
+np.ascontiguousarray(B)
+np.ascontiguousarray(C)
+
+np.asfortranarray(A)
+np.asfortranarray(B)
+np.asfortranarray(C)
+
+np.require(A)
+np.require(B)
+np.require(B, dtype=int)
+np.require(B, requirements=None)
+np.require(B, requirements="E")
+np.require(B, requirements=["ENSUREARRAY"])
+np.require(B, requirements={"F", "E"})
+np.require(B, requirements=["C", "OWNDATA"])
+np.require(B, requirements="W")
+np.require(B, requirements="A")
+np.require(C)
+
+np.linspace(0, 2)
+np.linspace(0.5, [0, 1, 2])
+np.linspace([0, 1, 2], 3)
+np.linspace(0j, 2)
+np.linspace(0, 2, num=10)
+np.linspace(0, 2, endpoint=True)
+np.linspace(0, 2, retstep=True)
+np.linspace(0j, 2j, retstep=True)
+np.linspace(0, 2, dtype=bool)
+np.linspace([0, 1], [2, 3], axis=Index())
+
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=[1j, 2j], num=2)
+
+np.geomspace(1, 2)
+
+np.zeros_like(A)
+np.zeros_like(C)
+np.zeros_like(B)
+np.zeros_like(B, dtype=np.int64)
+
+np.ones_like(A)
+np.ones_like(C)
+np.ones_like(B)
+np.ones_like(B, dtype=np.int64)
+
+np.empty_like(A)
+np.empty_like(C)
+np.empty_like(B)
+np.empty_like(B, dtype=np.int64)
+
+np.full_like(A, i8)
+np.full_like(C, i8)
+np.full_like(B, i8)
+np.full_like(B, i8, dtype=np.int64)
+
+np.ones(1)
+np.ones([1, 1, 1])
+
+np.full(1, i8)
+np.full([1, 1, 1], i8)
+
+np.indices([1, 2, 3])
+np.indices([1, 2, 3], sparse=True)
+
+np.fromfunction(func, (3, 5))
+
+np.identity(10)
+
+np.atleast_1d(C)
+np.atleast_1d(A)
+np.atleast_1d(C, C)
+np.atleast_1d(C, A)
+np.atleast_1d(A, A)
+
+np.atleast_2d(C)
+
+np.atleast_3d(C)
+
+np.vstack([C, C])
+np.vstack([C, A])
+np.vstack([A, A])
+
+np.hstack([C, C])
+
+np.stack([C, C])
+np.stack([C, C], axis=0)
+np.stack([C, C], out=B_stack)
+
+np.block([[C, C], [C, C]])
+np.block(A)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py
new file mode 100644
index 0000000000000000000000000000000000000000..730eb46d1c925474508e863a133e256007bd734b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/array_like.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from numpy._typing import NDArray, ArrayLike, _SupportsArray
+
+x1: ArrayLike = True
+x2: ArrayLike = 5
+x3: ArrayLike = 1.0
+x4: ArrayLike = 1 + 1j
+x5: ArrayLike = np.int8(1)
+x6: ArrayLike = np.float64(1)
+x7: ArrayLike = np.complex128(1)
+x8: ArrayLike = np.array([1, 2, 3])
+x9: ArrayLike = [1, 2, 3]
+x10: ArrayLike = (1, 2, 3)
+x11: ArrayLike = "foo"
+x12: ArrayLike = memoryview(b'foo')
+
+
+class A:
+    def __array__(
+        self, dtype: None | np.dtype[Any] = None
+    ) -> NDArray[np.float64]:
+        return np.array([1.0, 2.0, 3.0])
+
+
+x13: ArrayLike = A()
+
+scalar: _SupportsArray[np.dtype[np.int64]] = np.int64(1)
+scalar.__array__()
+array: _SupportsArray[np.dtype[np.int_]] = np.array(1)
+array.__array__()
+
+a: _SupportsArray[np.dtype[np.float64]] = A()
+a.__array__()
+a.__array__()
+
+# Escape hatch for when you mean to make something like an object
+# array.
+object_array_scalar: object = (i for i in range(10))
+np.array(object_array_scalar)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c704c755570d1508424af92a0eb5aa1353666a0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayprint.py
@@ -0,0 +1,37 @@
+import numpy as np
+
+AR = np.arange(10)
+AR.setflags(write=False)
+
+with np.printoptions():
+    np.set_printoptions(
+        precision=1,
+        threshold=2,
+        edgeitems=3,
+        linewidth=4,
+        suppress=False,
+        nanstr="Bob",
+        infstr="Bill",
+        formatter={},
+        sign="+",
+        floatmode="unique",
+    )
+    np.get_printoptions()
+    str(AR)
+
+    np.array2string(
+        AR,
+        max_line_width=5,
+        precision=2,
+        suppress_small=True,
+        separator=";",
+        prefix="test",
+        threshold=5,
+        floatmode="fixed",
+        suffix="?",
+        legacy="1.13",
+    )
+    np.format_float_scientific(1, precision=5)
+    np.format_float_positional(1, trim="k")
+    np.array_repr(AR)
+    np.array_str(AR)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..572be5e2fe29ba978b78c8b65b116b5b54a4d01a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/arrayterator.py
@@ -0,0 +1,27 @@
+
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+AR_i8: np.ndarray[Any, np.dtype[np.int_]] = np.arange(10)
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+ar_iter.var
+ar_iter.buf_size
+ar_iter.start
+ar_iter.stop
+ar_iter.step
+ar_iter.shape
+ar_iter.flat
+
+ar_iter.__array__()
+
+for i in ar_iter:
+    pass
+
+ar_iter[0]
+ar_iter[...]
+ar_iter[:]
+ar_iter[0, 0, 0]
+ar_iter[..., 0, :]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..22a245d2180979e6e0fa0cb5780713aa3ac86439
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py
@@ -0,0 +1,131 @@
+import numpy as np
+
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+b_ = np.bool(1)
+
+b = bool(1)
+i = int(1)
+
+AR = np.array([0, 1, 2], dtype=np.int32)
+AR.setflags(write=False)
+
+
+i8 << i8
+i8 >> i8
+i8 | i8
+i8 ^ i8
+i8 & i8
+
+i << AR
+i >> AR
+i | AR
+i ^ AR
+i & AR
+
+i8 << AR
+i8 >> AR
+i8 | AR
+i8 ^ AR
+i8 & AR
+
+i4 << i4
+i4 >> i4
+i4 | i4
+i4 ^ i4
+i4 & i4
+
+i8 << i4
+i8 >> i4
+i8 | i4
+i8 ^ i4
+i8 & i4
+
+i8 << i
+i8 >> i
+i8 | i
+i8 ^ i
+i8 & i
+
+i8 << b_
+i8 >> b_
+i8 | b_
+i8 ^ b_
+i8 & b_
+
+i8 << b
+i8 >> b
+i8 | b
+i8 ^ b
+i8 & b
+
+u8 << u8
+u8 >> u8
+u8 | u8
+u8 ^ u8
+u8 & u8
+
+u4 << u4
+u4 >> u4
+u4 | u4
+u4 ^ u4
+u4 & u4
+
+u4 << i4
+u4 >> i4
+u4 | i4
+u4 ^ i4
+u4 & i4
+
+u4 << i
+u4 >> i
+u4 | i
+u4 ^ i
+u4 & i
+
+u8 << b_
+u8 >> b_
+u8 | b_
+u8 ^ b_
+u8 & b_
+
+u8 << b
+u8 >> b
+u8 | b
+u8 ^ b
+u8 & b
+
+b_ << b_
+b_ >> b_
+b_ | b_
+b_ ^ b_
+b_ & b_
+
+b_ << AR
+b_ >> AR
+b_ | AR
+b_ ^ AR
+b_ & AR
+
+b_ << b
+b_ >> b
+b_ | b
+b_ ^ b
+b_ & b
+
+b_ << i
+b_ >> i
+b_ | i
+b_ ^ i
+b_ & i
+
+~i8
+~i4
+~u8
+~u4
+~b_
+~AR
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py
new file mode 100644
index 0000000000000000000000000000000000000000..a461d8b660da6f38ca6ddd416d4857d7b865aa9e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/comparisons.py
@@ -0,0 +1,315 @@
+from __future__ import annotations
+
+from typing import cast, Any
+import numpy as np
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+SEQ = (0, 1, 2, 3, 4)
+
+AR_b: np.ndarray[Any, np.dtype[np.bool]] = np.array([True])
+AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
+AR_i: np.ndarray[Any, np.dtype[np.int_]] = np.array([1])
+AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
+AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1.0j])
+AR_S: np.ndarray[Any, np.dtype[np.bytes_]] = np.array([b"a"], "S")
+AR_T = cast(np.ndarray[Any, np.dtypes.StringDType], np.array(["a"], "T"))
+AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.array(["a"], "U")
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64("1")])
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64("1")])
+AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([1], dtype=object)
+
+# Arrays
+
+AR_b > AR_b
+AR_b > AR_u
+AR_b > AR_i
+AR_b > AR_f
+AR_b > AR_c
+
+AR_u > AR_b
+AR_u > AR_u
+AR_u > AR_i
+AR_u > AR_f
+AR_u > AR_c
+
+AR_i > AR_b
+AR_i > AR_u
+AR_i > AR_i
+AR_i > AR_f
+AR_i > AR_c
+
+AR_f > AR_b
+AR_f > AR_u
+AR_f > AR_i
+AR_f > AR_f
+AR_f > AR_c
+
+AR_c > AR_b
+AR_c > AR_u
+AR_c > AR_i
+AR_c > AR_f
+AR_c > AR_c
+
+AR_S > AR_S
+AR_S > b""
+
+AR_T > AR_T
+AR_T > AR_U
+AR_T > ""
+
+AR_U > AR_U
+AR_U > AR_T
+AR_U > ""
+
+AR_m > AR_b
+AR_m > AR_u
+AR_m > AR_i
+AR_b > AR_m
+AR_u > AR_m
+AR_i > AR_m
+
+AR_M > AR_M
+
+AR_O > AR_O
+1 > AR_O
+AR_O > 1
+
+# Time structures
+
+dt > dt
+
+td > td
+td > i
+td > i4
+td > i8
+td > AR_i
+td > SEQ
+
+# boolean
+
+b_ > b
+b_ > b_
+b_ > i
+b_ > i8
+b_ > i4
+b_ > u8
+b_ > u4
+b_ > f
+b_ > f8
+b_ > f4
+b_ > c
+b_ > c16
+b_ > c8
+b_ > AR_i
+b_ > SEQ
+
+# Complex
+
+c16 > c16
+c16 > f8
+c16 > i8
+c16 > c8
+c16 > f4
+c16 > i4
+c16 > b_
+c16 > b
+c16 > c
+c16 > f
+c16 > i
+c16 > AR_i
+c16 > SEQ
+
+c16 > c16
+f8 > c16
+i8 > c16
+c8 > c16
+f4 > c16
+i4 > c16
+b_ > c16
+b > c16
+c > c16
+f > c16
+i > c16
+AR_i > c16
+SEQ > c16
+
+c8 > c16
+c8 > f8
+c8 > i8
+c8 > c8
+c8 > f4
+c8 > i4
+c8 > b_
+c8 > b
+c8 > c
+c8 > f
+c8 > i
+c8 > AR_i
+c8 > SEQ
+
+c16 > c8
+f8 > c8
+i8 > c8
+c8 > c8
+f4 > c8
+i4 > c8
+b_ > c8
+b > c8
+c > c8
+f > c8
+i > c8
+AR_i > c8
+SEQ > c8
+
+# Float
+
+f8 > f8
+f8 > i8
+f8 > f4
+f8 > i4
+f8 > b_
+f8 > b
+f8 > c
+f8 > f
+f8 > i
+f8 > AR_i
+f8 > SEQ
+
+f8 > f8
+i8 > f8
+f4 > f8
+i4 > f8
+b_ > f8
+b > f8
+c > f8
+f > f8
+i > f8
+AR_i > f8
+SEQ > f8
+
+f4 > f8
+f4 > i8
+f4 > f4
+f4 > i4
+f4 > b_
+f4 > b
+f4 > c
+f4 > f
+f4 > i
+f4 > AR_i
+f4 > SEQ
+
+f8 > f4
+i8 > f4
+f4 > f4
+i4 > f4
+b_ > f4
+b > f4
+c > f4
+f > f4
+i > f4
+AR_i > f4
+SEQ > f4
+
+# Int
+
+i8 > i8
+i8 > u8
+i8 > i4
+i8 > u4
+i8 > b_
+i8 > b
+i8 > c
+i8 > f
+i8 > i
+i8 > AR_i
+i8 > SEQ
+
+u8 > u8
+u8 > i4
+u8 > u4
+u8 > b_
+u8 > b
+u8 > c
+u8 > f
+u8 > i
+u8 > AR_i
+u8 > SEQ
+
+i8 > i8
+u8 > i8
+i4 > i8
+u4 > i8
+b_ > i8
+b > i8
+c > i8
+f > i8
+i > i8
+AR_i > i8
+SEQ > i8
+
+u8 > u8
+i4 > u8
+u4 > u8
+b_ > u8
+b > u8
+c > u8
+f > u8
+i > u8
+AR_i > u8
+SEQ > u8
+
+i4 > i8
+i4 > i4
+i4 > i
+i4 > b_
+i4 > b
+i4 > AR_i
+i4 > SEQ
+
+u4 > i8
+u4 > i4
+u4 > u8
+u4 > u4
+u4 > i
+u4 > b_
+u4 > b
+u4 > AR_i
+u4 > SEQ
+
+i8 > i4
+i4 > i4
+i > i4
+b_ > i4
+b > i4
+AR_i > i4
+SEQ > i4
+
+i8 > u4
+i4 > u4
+u8 > u4
+u4 > u4
+b_ > u4
+b > u4
+i > u4
+AR_i > u4
+SEQ > u4
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f11518276c842f2601e3f1420020f00e3b775fb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/dtype.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+dtype_obj = np.dtype(np.str_)
+void_dtype_obj = np.dtype([("f0", np.float64), ("f1", np.float32)])
+
+np.dtype(dtype=np.int64)
+np.dtype(int)
+np.dtype("int")
+np.dtype(None)
+
+np.dtype((int, 2))
+np.dtype((int, (1,)))
+
+np.dtype({"names": ["a", "b"], "formats": [int, float]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object()]})
+
+np.dtype([("name", np.str_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
+
+np.dtype(
+    {
+        "names": ["a", "b"],
+        "formats": [int, float],
+        "itemsize": 9,
+        "aligned": False,
+        "titles": ["x", "y"],
+        "offsets": [0, 1],
+    }
+)
+
+np.dtype((np.float64, float))
+
+
+class Test:
+    dtype = np.dtype(float)
+
+
+np.dtype(Test())
+
+# Methods and attributes
+dtype_obj.base
+dtype_obj.subdtype
+dtype_obj.newbyteorder()
+dtype_obj.type
+dtype_obj.name
+dtype_obj.names
+
+dtype_obj * 0
+dtype_obj * 2
+
+0 * dtype_obj
+2 * dtype_obj
+
+void_dtype_obj["f0"]
+void_dtype_obj[0]
+void_dtype_obj[["f0", "f1"]]
+void_dtype_obj[["f0"]]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..429764e67eccc7855d363da20d432fdb45e66971
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/einsumfunc.py
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+AR_LIKE_b = [True, True, True]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_c = [1j, 2j, 3j]
+AR_LIKE_U = ["1", "2", "3"]
+
+OUT_f: np.ndarray[Any, np.dtype[np.float64]] = np.empty(3, dtype=np.float64)
+OUT_c: np.ndarray[Any, np.dtype[np.complex128]] = np.empty(3, dtype=np.complex128)
+
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)
+
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, out=OUT_c)
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=int, casting="unsafe", out=OUT_f)
+
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum_path("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum_path("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum_path("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum_path("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum_path("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py
new file mode 100644
index 0000000000000000000000000000000000000000..63c839af4b23f0ba3bea8c56f2bbb7c03e7bc44a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/flatiter.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+a = np.empty((2, 2)).flat
+
+a.base
+a.copy()
+a.coords
+a.index
+iter(a)
+next(a)
+a[0]
+a[[0, 1, 2]]
+a[...]
+a[:]
+a.__array__()
+a.__array__(np.dtype(np.float64))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cc2bcfd8b50f235081e737c3420e1db34191637
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/fromnumeric.py
@@ -0,0 +1,272 @@
+"""Tests for :mod:`numpy._core.fromnumeric`."""
+
+import numpy as np
+
+A = np.array(True, ndmin=2, dtype=bool)
+B = np.array(1.0, ndmin=2, dtype=np.float32)
+A.setflags(write=False)
+B.setflags(write=False)
+
+a = np.bool(True)
+b = np.float32(1.0)
+c = 1.0
+d = np.array(1.0, dtype=np.float32)  # writeable
+
+np.take(a, 0)
+np.take(b, 0)
+np.take(c, 0)
+np.take(A, 0)
+np.take(B, 0)
+np.take(A, [0])
+np.take(B, [0])
+
+np.reshape(a, 1)
+np.reshape(b, 1)
+np.reshape(c, 1)
+np.reshape(A, 1)
+np.reshape(B, 1)
+
+np.choose(a, [True, True])
+np.choose(A, [1.0, 1.0])
+
+np.repeat(a, 1)
+np.repeat(b, 1)
+np.repeat(c, 1)
+np.repeat(A, 1)
+np.repeat(B, 1)
+
+np.swapaxes(A, 0, 0)
+np.swapaxes(B, 0, 0)
+
+np.transpose(a)
+np.transpose(b)
+np.transpose(c)
+np.transpose(A)
+np.transpose(B)
+
+np.partition(a, 0, axis=None)
+np.partition(b, 0, axis=None)
+np.partition(c, 0, axis=None)
+np.partition(A, 0)
+np.partition(B, 0)
+
+np.argpartition(a, 0)
+np.argpartition(b, 0)
+np.argpartition(c, 0)
+np.argpartition(A, 0)
+np.argpartition(B, 0)
+
+np.sort(A, 0)
+np.sort(B, 0)
+
+np.argsort(A, 0)
+np.argsort(B, 0)
+
+np.argmax(A)
+np.argmax(B)
+np.argmax(A, axis=0)
+np.argmax(B, axis=0)
+
+np.argmin(A)
+np.argmin(B)
+np.argmin(A, axis=0)
+np.argmin(B, axis=0)
+
+np.searchsorted(A[0], 0)
+np.searchsorted(B[0], 0)
+np.searchsorted(A[0], [0])
+np.searchsorted(B[0], [0])
+
+np.resize(a, (5, 5))
+np.resize(b, (5, 5))
+np.resize(c, (5, 5))
+np.resize(A, (5, 5))
+np.resize(B, (5, 5))
+
+np.squeeze(a)
+np.squeeze(b)
+np.squeeze(c)
+np.squeeze(A)
+np.squeeze(B)
+
+np.diagonal(A)
+np.diagonal(B)
+
+np.trace(A)
+np.trace(B)
+
+np.ravel(a)
+np.ravel(b)
+np.ravel(c)
+np.ravel(A)
+np.ravel(B)
+
+np.nonzero(A)
+np.nonzero(B)
+
+np.shape(a)
+np.shape(b)
+np.shape(c)
+np.shape(A)
+np.shape(B)
+
+np.compress([True], a)
+np.compress([True], b)
+np.compress([True], c)
+np.compress([True], A)
+np.compress([True], B)
+
+np.clip(a, 0, 1.0)
+np.clip(b, -1, 1)
+np.clip(a, 0, None)
+np.clip(b, None, 1)
+np.clip(c, 0, 1)
+np.clip(A, 0, 1)
+np.clip(B, 0, 1)
+np.clip(B, [0, 1], [1, 2])
+
+np.sum(a)
+np.sum(b)
+np.sum(c)
+np.sum(A)
+np.sum(B)
+np.sum(A, axis=0)
+np.sum(B, axis=0)
+
+np.all(a)
+np.all(b)
+np.all(c)
+np.all(A)
+np.all(B)
+np.all(A, axis=0)
+np.all(B, axis=0)
+np.all(A, keepdims=True)
+np.all(B, keepdims=True)
+
+np.any(a)
+np.any(b)
+np.any(c)
+np.any(A)
+np.any(B)
+np.any(A, axis=0)
+np.any(B, axis=0)
+np.any(A, keepdims=True)
+np.any(B, keepdims=True)
+
+np.cumsum(a)
+np.cumsum(b)
+np.cumsum(c)
+np.cumsum(A)
+np.cumsum(B)
+
+np.cumulative_sum(a)
+np.cumulative_sum(b)
+np.cumulative_sum(c)
+np.cumulative_sum(A, axis=0)
+np.cumulative_sum(B, axis=0)
+
+np.ptp(b)
+np.ptp(c)
+np.ptp(B)
+np.ptp(B, axis=0)
+np.ptp(B, keepdims=True)
+
+np.amax(a)
+np.amax(b)
+np.amax(c)
+np.amax(A)
+np.amax(B)
+np.amax(A, axis=0)
+np.amax(B, axis=0)
+np.amax(A, keepdims=True)
+np.amax(B, keepdims=True)
+
+np.amin(a)
+np.amin(b)
+np.amin(c)
+np.amin(A)
+np.amin(B)
+np.amin(A, axis=0)
+np.amin(B, axis=0)
+np.amin(A, keepdims=True)
+np.amin(B, keepdims=True)
+
+np.prod(a)
+np.prod(b)
+np.prod(c)
+np.prod(A)
+np.prod(B)
+np.prod(a, dtype=None)
+np.prod(A, dtype=None)
+np.prod(A, axis=0)
+np.prod(B, axis=0)
+np.prod(A, keepdims=True)
+np.prod(B, keepdims=True)
+np.prod(b, out=d)
+np.prod(B, out=d)
+
+np.cumprod(a)
+np.cumprod(b)
+np.cumprod(c)
+np.cumprod(A)
+np.cumprod(B)
+
+np.cumulative_prod(a)
+np.cumulative_prod(b)
+np.cumulative_prod(c)
+np.cumulative_prod(A, axis=0)
+np.cumulative_prod(B, axis=0)
+
+np.ndim(a)
+np.ndim(b)
+np.ndim(c)
+np.ndim(A)
+np.ndim(B)
+
+np.size(a)
+np.size(b)
+np.size(c)
+np.size(A)
+np.size(B)
+
+np.around(a)
+np.around(b)
+np.around(c)
+np.around(A)
+np.around(B)
+
+np.mean(a)
+np.mean(b)
+np.mean(c)
+np.mean(A)
+np.mean(B)
+np.mean(A, axis=0)
+np.mean(B, axis=0)
+np.mean(A, keepdims=True)
+np.mean(B, keepdims=True)
+np.mean(b, out=d)
+np.mean(B, out=d)
+
+np.std(a)
+np.std(b)
+np.std(c)
+np.std(A)
+np.std(B)
+np.std(A, axis=0)
+np.std(B, axis=0)
+np.std(A, keepdims=True)
+np.std(B, keepdims=True)
+np.std(b, out=d)
+np.std(B, out=d)
+
+np.var(a)
+np.var(b)
+np.var(c)
+np.var(A)
+np.var(B)
+np.var(A, axis=0)
+np.var(B, axis=0)
+np.var(A, keepdims=True)
+np.var(B, keepdims=True)
+np.var(b, out=d)
+np.var(B, out=d)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfc4ff2f314aa2417cd5205a86386d4bd6211273
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/index_tricks.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+AR_LIKE_b = [[True, True], [True, True]]
+AR_LIKE_i = [[1, 2], [3, 4]]
+AR_LIKE_f = [[1.0, 2.0], [3.0, 4.0]]
+AR_LIKE_U = [["1", "2"], ["3", "4"]]
+
+AR_i8: np.ndarray[Any, np.dtype[np.int64]] = np.array(AR_LIKE_i, dtype=np.int64)
+
+np.ndenumerate(AR_i8)
+np.ndenumerate(AR_LIKE_f)
+np.ndenumerate(AR_LIKE_U)
+
+next(np.ndenumerate(AR_i8))
+next(np.ndenumerate(AR_LIKE_f))
+next(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndenumerate(AR_i8))
+iter(np.ndenumerate(AR_LIKE_f))
+iter(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndindex(1, 2, 3))
+next(np.ndindex(1, 2, 3))
+
+np.unravel_index([22, 41, 37], (7, 6))
+np.unravel_index([31, 41, 13], (7, 6), order='F')
+np.unravel_index(1621, (6, 7, 8, 9))
+
+np.ravel_multi_index(AR_LIKE_i, (7, 6))
+np.ravel_multi_index(AR_LIKE_i, (7, 6), order='F')
+np.ravel_multi_index(AR_LIKE_i, (4, 6), mode='clip')
+np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=('clip', 'wrap'))
+np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))
+
+np.mgrid[1:1:2]
+np.mgrid[1:1:2, None:10]
+
+np.ogrid[1:1:2]
+np.ogrid[1:1:2, None:10]
+
+np.index_exp[0:1]
+np.index_exp[0:1, None:3]
+np.index_exp[0, 0:1, ..., [0, 1, 3]]
+
+np.s_[0:1]
+np.s_[0:1, None:3]
+np.s_[0, 0:1, ..., [0, 1, 3]]
+
+np.ix_(AR_LIKE_b[0])
+np.ix_(AR_LIKE_i[0], AR_LIKE_f[0])
+np.ix_(AR_i8[0])
+
+np.fill_diagonal(AR_i8, 5)
+
+np.diag_indices(4)
+np.diag_indices(2, 3)
+
+np.diag_indices_from(AR_i8)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b7e85d7ff1e49fecbbe88a921c931a5b8ae745
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_user_array.py
@@ -0,0 +1,22 @@
+"""Based on the `if __name__ == "__main__"` test code in `lib/_user_array_impl.py`."""
+
+from __future__ import annotations
+
+import numpy as np
+from numpy.lib.user_array import container
+
+N = 10_000
+W = H = int(N**0.5)
+
+a: np.ndarray[tuple[int, int], np.dtype[np.int32]]
+ua: container[tuple[int, int], np.dtype[np.int32]]
+
+a = np.arange(N, dtype=np.int32).reshape(W, H)
+ua = container(a)
+
+ua_small: container[tuple[int, int], np.dtype[np.int32]] = ua[:3, :5]
+ua_small[0, 0] = 10
+
+ua_bool: container[tuple[int, int], np.dtype[np.bool]] = ua_small > 1
+
+# shape: tuple[int, int] = np.shape(ua)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b3381e13d26adf44130fc62aa8bfaacfe6a658
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_utils.py
@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from io import StringIO
+
+import numpy as np
+import numpy.lib.array_utils as array_utils
+
+FILE = StringIO()
+AR = np.arange(10, dtype=np.float64)
+
+
+def func(a: int) -> bool:
+    return True
+
+
+array_utils.byte_bounds(AR)
+array_utils.byte_bounds(np.float64())
+
+np.info(1, output=FILE)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3825eca524795f4d9742873a773cd7749636e2a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/lib_version.py
@@ -0,0 +1,18 @@
+from numpy.lib import NumpyVersion
+
+version = NumpyVersion("1.8.0")
+
+version.vstring
+version.version
+version.major
+version.minor
+version.bugfix
+version.pre_release
+version.is_devversion
+
+version == version
+version != version
+version < "1.8.0"
+version <= version
+version > version
+version >= "1.8.0"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py
new file mode 100644
index 0000000000000000000000000000000000000000..2238618eb67c904335d731332d5afa5eb988d7f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/literal.py
@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+from functools import partial
+
+import pytest
+import numpy as np
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+AR = np.array(0)
+AR.setflags(write=False)
+
+KACF = frozenset({None, "K", "A", "C", "F"})
+ACF = frozenset({None, "A", "C", "F"})
+CF = frozenset({None, "C", "F"})
+
+order_list: list[tuple[frozenset[str | None], Callable[..., Any]]] = [
+    (KACF, partial(np.ndarray, 1)),
+    (KACF, AR.tobytes),
+    (KACF, partial(AR.astype, int)),
+    (KACF, AR.copy),
+    (ACF, partial(AR.reshape, 1)),
+    (KACF, AR.flatten),
+    (KACF, AR.ravel),
+    (KACF, partial(np.array, 1)),
+    # NOTE: __call__ is needed due to mypy 1.11 bugs (#17620, #17631)
+    (CF, partial(np.zeros.__call__, 1)),
+    (CF, partial(np.ones.__call__, 1)),
+    (CF, partial(np.empty.__call__, 1)),
+    (CF, partial(np.full, 1, 1)),
+    (KACF, partial(np.zeros_like, AR)),
+    (KACF, partial(np.ones_like, AR)),
+    (KACF, partial(np.empty_like, AR)),
+    (KACF, partial(np.full_like, AR, 1)),
+    (KACF, partial(np.add.__call__, 1, 1)),  # i.e. np.ufunc.__call__
+    (ACF, partial(np.reshape, AR, 1)),
+    (KACF, partial(np.ravel, AR)),
+    (KACF, partial(np.asarray, 1)),
+    (KACF, partial(np.asanyarray, 1)),
+]
+
+for order_set, func in order_list:
+    for order in order_set:
+        func(order=order)
+
+    invalid_orders = KACF - order_set
+    for order in invalid_orders:
+        with pytest.raises(ValueError):
+            func(order=order)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b3b138119bbd01a8241a68f4f172ce1d26b5833
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ma.py
@@ -0,0 +1,8 @@
+from typing import Any
+
+import numpy as np
+import numpy.ma
+
+
+m : np.ma.MaskedArray[Any, np.dtype[np.float64]] = np.ma.masked_array([1.5, 2, 3], mask=[True, False, True])
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b7e6cd85c73cfce452c3bd8bf91f174f134966c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/mod.py
@@ -0,0 +1,149 @@
+import numpy as np
+
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+td = np.timedelta64(1, "D")
+b_ = np.bool(1)
+
+b = bool(1)
+f = float(1)
+i = int(1)
+
+AR = np.array([1], dtype=np.bool)
+AR.setflags(write=False)
+
+AR2 = np.array([1], dtype=np.timedelta64)
+AR2.setflags(write=False)
+
+# Time structures
+
+td % td
+td % AR2
+AR2 % td
+
+divmod(td, td)
+divmod(td, AR2)
+divmod(AR2, td)
+
+# Bool
+
+b_ % b
+b_ % i
+b_ % f
+b_ % b_
+b_ % i8
+b_ % u8
+b_ % f8
+b_ % AR
+
+divmod(b_, b)
+divmod(b_, i)
+divmod(b_, f)
+divmod(b_, b_)
+divmod(b_, i8)
+divmod(b_, u8)
+divmod(b_, f8)
+divmod(b_, AR)
+
+b % b_
+i % b_
+f % b_
+b_ % b_
+i8 % b_
+u8 % b_
+f8 % b_
+AR % b_
+
+divmod(b, b_)
+divmod(i, b_)
+divmod(f, b_)
+divmod(b_, b_)
+divmod(i8, b_)
+divmod(u8, b_)
+divmod(f8, b_)
+divmod(AR, b_)
+
+# int
+
+i8 % b
+i8 % i
+i8 % f
+i8 % i8
+i8 % f8
+i4 % i8
+i4 % f8
+i4 % i4
+i4 % f4
+i8 % AR
+
+divmod(i8, b)
+divmod(i8, i)
+divmod(i8, f)
+divmod(i8, i8)
+divmod(i8, f8)
+divmod(i8, i4)
+divmod(i8, f4)
+divmod(i4, i4)
+divmod(i4, f4)
+divmod(i8, AR)
+
+b % i8
+i % i8
+f % i8
+i8 % i8
+f8 % i8
+i8 % i4
+f8 % i4
+i4 % i4
+f4 % i4
+AR % i8
+
+divmod(b, i8)
+divmod(i, i8)
+divmod(f, i8)
+divmod(i8, i8)
+divmod(f8, i8)
+divmod(i4, i8)
+divmod(f4, i8)
+divmod(i4, i4)
+divmod(f4, i4)
+divmod(AR, i8)
+
+# float
+
+f8 % b
+f8 % i
+f8 % f
+i8 % f4
+f4 % f4
+f8 % AR
+
+divmod(f8, b)
+divmod(f8, i)
+divmod(f8, f)
+divmod(f8, f8)
+divmod(f8, f4)
+divmod(f4, f4)
+divmod(f8, AR)
+
+b % f8
+i % f8
+f % f8
+f8 % f8
+f8 % f8
+f4 % f4
+AR % f8
+
+divmod(b, f8)
+divmod(i, f8)
+divmod(f, f8)
+divmod(f8, f8)
+divmod(f4, f8)
+divmod(f4, f4)
+divmod(AR, f8)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c2fd4b7e7c3a2d0ed1be39928edf5b2d591e16c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/modules.py
@@ -0,0 +1,45 @@
+import numpy as np
+from numpy import f2py
+
+np.char
+np.ctypeslib
+np.emath
+np.fft
+np.lib
+np.linalg
+np.ma
+np.matrixlib
+np.polynomial
+np.random
+np.rec
+np.strings
+np.testing
+np.version
+
+np.lib.format
+np.lib.mixins
+np.lib.scimath
+np.lib.stride_tricks
+np.lib.array_utils
+np.ma.extras
+np.polynomial.chebyshev
+np.polynomial.hermite
+np.polynomial.hermite_e
+np.polynomial.laguerre
+np.polynomial.legendre
+np.polynomial.polynomial
+
+np.__path__
+np.__version__
+
+np.__all__
+np.char.__all__
+np.ctypeslib.__all__
+np.emath.__all__
+np.lib.__all__
+np.ma.__all__
+np.random.__all__
+np.rec.__all__
+np.strings.__all__
+np.testing.__all__
+f2py.__all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..26cedfd77566e7b7865345e0775af88153e74ffc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/multiarray.py
@@ -0,0 +1,76 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64] = np.array([1.0])
+AR_i4 = np.array([1], dtype=np.int32)
+AR_u1 = np.array([1], dtype=np.uint8)
+
+AR_LIKE_f = [1.5]
+AR_LIKE_i = [1]
+
+b_f8 = np.broadcast(AR_f8)
+b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8)
+
+next(b_f8)
+b_f8.reset()
+b_f8.index
+b_f8.iters
+b_f8.nd
+b_f8.ndim
+b_f8.numiter
+b_f8.shape
+b_f8.size
+
+next(b_i4_f8_f8)
+b_i4_f8_f8.reset()
+b_i4_f8_f8.ndim
+b_i4_f8_f8.index
+b_i4_f8_f8.iters
+b_i4_f8_f8.nd
+b_i4_f8_f8.numiter
+b_i4_f8_f8.shape
+b_i4_f8_f8.size
+
+np.inner(AR_f8, AR_i4)
+
+np.where([True, True, False])
+np.where([True, True, False], 1, 0)
+
+np.lexsort([0, 1, 2])
+
+np.can_cast(np.dtype("i8"), int)
+np.can_cast(AR_f8, "f8")
+np.can_cast(AR_f8, np.complex128, casting="unsafe")
+
+np.min_scalar_type([1])
+np.min_scalar_type(AR_f8)
+
+np.result_type(int, AR_i4)
+np.result_type(AR_f8, AR_u1)
+np.result_type(AR_f8, np.complex128)
+
+np.dot(AR_LIKE_f, AR_i4)
+np.dot(AR_u1, 1)
+np.dot(1.5j, 1)
+np.dot(AR_u1, 1, out=AR_f8)
+
+np.vdot(AR_LIKE_f, AR_i4)
+np.vdot(AR_u1, 1)
+np.vdot(1.5j, 1)
+
+np.bincount(AR_i4)
+
+np.copyto(AR_f8, [1.6])
+
+np.putmask(AR_f8, [True], 1.5)
+
+np.packbits(AR_i4)
+np.packbits(AR_u1)
+
+np.unpackbits(AR_u1)
+
+np.shares_memory(1, 2)
+np.shares_memory(AR_f8, AR_f8, max_work=1)
+
+np.may_share_memory(1, 2)
+np.may_share_memory(AR_f8, AR_f8, max_work=1)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..76da1dadd327861fdb576f1f492fbc44a4881168
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py
@@ -0,0 +1,87 @@
+import os
+import tempfile
+
+import numpy as np
+
+nd = np.array([[1, 2], [3, 4]])
+scalar_array = np.array(1)
+
+# item
+scalar_array.item()
+nd.item(1)
+nd.item(0, 1)
+nd.item((0, 1))
+
+# tobytes
+nd.tobytes()
+nd.tobytes("C")
+nd.tobytes(None)
+
+# tofile
+if os.name != "nt":
+    with tempfile.NamedTemporaryFile(suffix=".txt") as tmp:
+        nd.tofile(tmp.name)
+        nd.tofile(tmp.name, "")
+        nd.tofile(tmp.name, sep="")
+
+        nd.tofile(tmp.name, "", "%s")
+        nd.tofile(tmp.name, format="%s")
+
+        nd.tofile(tmp)
+
+# dump is pretty simple
+# dumps is pretty simple
+
+# astype
+nd.astype("float")
+nd.astype(float)
+
+nd.astype(float, "K")
+nd.astype(float, order="K")
+
+nd.astype(float, "K", "unsafe")
+nd.astype(float, casting="unsafe")
+
+nd.astype(float, "K", "unsafe", True)
+nd.astype(float, subok=True)
+
+nd.astype(float, "K", "unsafe", True, True)
+nd.astype(float, copy=True)
+
+# byteswap
+nd.byteswap()
+nd.byteswap(True)
+
+# copy
+nd.copy()
+nd.copy("C")
+
+# view
+nd.view()
+nd.view(np.int64)
+nd.view(dtype=np.int64)
+nd.view(np.int64, np.matrix)
+nd.view(type=np.matrix)
+
+# getfield
+complex_array = np.array([[1 + 1j, 0], [0, 1 - 1j]], dtype=np.complex128)
+
+complex_array.getfield("float")
+complex_array.getfield(float)
+
+complex_array.getfield("float", 8)
+complex_array.getfield(float, offset=8)
+
+# setflags
+nd.setflags()
+
+nd.setflags(True)
+nd.setflags(write=True)
+
+nd.setflags(True, True)
+nd.setflags(write=True, align=True)
+
+nd.setflags(True, True, False)
+nd.setflags(write=True, align=True, uic=False)
+
+# fill is pretty simple
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..758626e18dd69077380ea177cc698e7a535e7937
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py
@@ -0,0 +1,196 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import cast, Any
+
+import numpy as np
+import numpy.typing as npt
+
+class SubClass(npt.NDArray[np.float64]): ...
+
+i4 = np.int32(1)
+A: np.ndarray[Any, np.dtype[np.int32]] = np.array([[1]], dtype=np.int32)
+B0 = np.empty((), dtype=np.int32).view(SubClass)
+B1 = np.empty((1,), dtype=np.int32).view(SubClass)
+B2 = np.empty((1, 1), dtype=np.int32).view(SubClass)
+C: np.ndarray[Any, np.dtype[np.int32]] = np.array([0, 1, 2], dtype=np.int32)
+D = np.ones(3).view(SubClass)
+
+ctypes_obj = A.ctypes
+
+i4.all()
+A.all()
+A.all(axis=0)
+A.all(keepdims=True)
+A.all(out=B0)
+
+i4.any()
+A.any()
+A.any(axis=0)
+A.any(keepdims=True)
+A.any(out=B0)
+
+i4.argmax()
+A.argmax()
+A.argmax(axis=0)
+A.argmax(out=B0)
+
+i4.argmin()
+A.argmin()
+A.argmin(axis=0)
+A.argmin(out=B0)
+
+i4.argsort()
+A.argsort()
+
+i4.choose([()])
+_choices = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=np.int32)
+C.choose(_choices)
+C.choose(_choices, out=D)
+
+i4.clip(1)
+A.clip(1)
+A.clip(None, 1)
+A.clip(1, out=B2)
+A.clip(None, 1, out=B2)
+
+i4.compress([1])
+A.compress([1])
+A.compress([1], out=B1)
+
+i4.conj()
+A.conj()
+B0.conj()
+
+i4.conjugate()
+A.conjugate()
+B0.conjugate()
+
+i4.cumprod()
+A.cumprod()
+A.cumprod(out=B1)
+
+i4.cumsum()
+A.cumsum()
+A.cumsum(out=B1)
+
+i4.max()
+A.max()
+A.max(axis=0)
+A.max(keepdims=True)
+A.max(out=B0)
+
+i4.mean()
+A.mean()
+A.mean(axis=0)
+A.mean(keepdims=True)
+A.mean(out=B0)
+
+i4.min()
+A.min()
+A.min(axis=0)
+A.min(keepdims=True)
+A.min(out=B0)
+
+i4.prod()
+A.prod()
+A.prod(axis=0)
+A.prod(keepdims=True)
+A.prod(out=B0)
+
+i4.round()
+A.round()
+A.round(out=B2)
+
+i4.repeat(1)
+A.repeat(1)
+B0.repeat(1)
+
+i4.std()
+A.std()
+A.std(axis=0)
+A.std(keepdims=True)
+A.std(out=B0.astype(np.float64))
+
+i4.sum()
+A.sum()
+A.sum(axis=0)
+A.sum(keepdims=True)
+A.sum(out=B0)
+
+i4.take(0)
+A.take(0)
+A.take([0])
+A.take(0, out=B0)
+A.take([0], out=B1)
+
+i4.var()
+A.var()
+A.var(axis=0)
+A.var(keepdims=True)
+A.var(out=B0)
+
+A.argpartition([0])
+
+A.diagonal()
+
+A.dot(1)
+A.dot(1, out=B2)
+
+A.nonzero()
+
+C.searchsorted(1)
+
+A.trace()
+A.trace(out=B0)
+
+void = cast(np.void, np.array(1, dtype=[("f", np.float64)]).take(0))
+void.setfield(10, np.float64)
+
+A.item(0)
+C.item(0)
+
+A.ravel()
+C.ravel()
+
+A.flatten()
+C.flatten()
+
+A.reshape(1)
+C.reshape(3)
+
+int(np.array(1.0, dtype=np.float64))
+int(np.array("1", dtype=np.str_))
+
+float(np.array(1.0, dtype=np.float64))
+float(np.array("1", dtype=np.str_))
+
+complex(np.array(1.0, dtype=np.float64))
+
+operator.index(np.array(1, dtype=np.int64))
+
+# this fails on numpy 2.2.1
+# https://github.com/scipy/scipy/blob/a755ee77ec47a64849abe42c349936475a6c2f24/scipy/io/arff/tests/test_arffread.py#L41-L44
+A_float = np.array([[1, 5], [2, 4], [np.nan, np.nan]])
+A_void: npt.NDArray[np.void] = np.empty(3, [("yop", float), ("yap", float)])
+A_void["yop"] = A_float[:, 0]
+A_void["yap"] = A_float[:, 1]
+
+# deprecated
+
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_data()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_shape()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_strides()  # pyright: ignore[reportDeprecated]
+with np.testing.assert_warns(DeprecationWarning):
+    ctypes_obj.get_as_parameter()  # pyright: ignore[reportDeprecated]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ca3dff392e12a4122740ade2ceb71b3d6e7bb08
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py
@@ -0,0 +1,47 @@
+import numpy as np
+
+nd1 = np.array([[1, 2], [3, 4]])
+
+# reshape
+nd1.reshape(4)
+nd1.reshape(2, 2)
+nd1.reshape((2, 2))
+
+nd1.reshape((2, 2), order="C")
+nd1.reshape(4, order="C")
+
+# resize
+nd1.resize()
+nd1.resize(4)
+nd1.resize(2, 2)
+nd1.resize((2, 2))
+
+nd1.resize((2, 2), refcheck=True)
+nd1.resize(4, refcheck=True)
+
+nd2 = np.array([[1, 2], [3, 4]])
+
+# transpose
+nd2.transpose()
+nd2.transpose(1, 0)
+nd2.transpose((1, 0))
+
+# swapaxes
+nd2.swapaxes(0, 1)
+
+# flatten
+nd2.flatten()
+nd2.flatten("C")
+
+# ravel
+nd2.ravel()
+nd2.ravel("C")
+
+# squeeze
+nd2.squeeze()
+
+nd3 = np.array([[1, 2]])
+nd3.squeeze(0)
+
+nd4 = np.array([[[1, 2]]])
+nd4.squeeze((0, 1))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py
new file mode 100644
index 0000000000000000000000000000000000000000..25a5b44d7aecf6486be76ae0dca0e3e8ef60699b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/nditer.py
@@ -0,0 +1,4 @@
+import numpy as np
+
+arr = np.array([1])
+np.nditer([arr, None])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e12fb5d70e60eb1d9a29f7d79e8e1ca4fd47efa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numeric.py
@@ -0,0 +1,95 @@
+"""
+Tests for :mod:`numpy._core.numeric`.
+
+Does not include tests which fall under ``array_constructors``.
+
+"""
+
+from __future__ import annotations
+from typing import cast
+
+import numpy as np
+import numpy.typing as npt
+
+class SubClass(npt.NDArray[np.float64]):
+    ...
+
+i8 = np.int64(1)
+
+A = cast(
+    np.ndarray[tuple[int, int, int], np.dtype[np.intp]],
+    np.arange(27).reshape(3, 3, 3),
+)
+B: list[list[list[int]]] = A.tolist()
+C = np.empty((27, 27)).view(SubClass)
+
+np.count_nonzero(i8)
+np.count_nonzero(A)
+np.count_nonzero(B)
+np.count_nonzero(A, keepdims=True)
+np.count_nonzero(A, axis=0)
+
+np.isfortran(i8)
+np.isfortran(A)
+
+np.argwhere(i8)
+np.argwhere(A)
+
+np.flatnonzero(i8)
+np.flatnonzero(A)
+
+np.correlate(B[0][0], A.ravel(), mode="valid")
+np.correlate(A.ravel(), A.ravel(), mode="same")
+
+np.convolve(B[0][0], A.ravel(), mode="valid")
+np.convolve(A.ravel(), A.ravel(), mode="same")
+
+np.outer(i8, A)
+np.outer(B, A)
+np.outer(A, A)
+np.outer(A, A, out=C)
+
+np.tensordot(B, A)
+np.tensordot(A, A)
+np.tensordot(A, A, axes=0)
+np.tensordot(A, A, axes=(0, 1))
+
+np.isscalar(i8)
+np.isscalar(A)
+np.isscalar(B)
+
+np.roll(A, 1)
+np.roll(A, (1, 2))
+np.roll(B, 1)
+
+np.rollaxis(A, 0, 1)
+
+np.moveaxis(A, 0, 1)
+np.moveaxis(A, (0, 1), (1, 2))
+
+np.cross(B, A)
+np.cross(A, A)
+
+np.indices([0, 1, 2])
+np.indices([0, 1, 2], sparse=False)
+np.indices([0, 1, 2], sparse=True)
+
+np.binary_repr(1)
+
+np.base_repr(1)
+
+np.allclose(i8, A)
+np.allclose(B, A)
+np.allclose(A, A)
+
+np.isclose(i8, A)
+np.isclose(B, A)
+np.isclose(A, A)
+
+np.array_equal(i8, A)
+np.array_equal(B, A)
+np.array_equal(A, A)
+
+np.array_equiv(i8, A)
+np.array_equiv(B, A)
+np.array_equiv(A, A)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e1a9986d88ab320bdfab54a050ed27aec4d8b5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/numerictypes.py
@@ -0,0 +1,17 @@
+import numpy as np
+
+np.isdtype(np.float64, (np.int64, np.float64))
+np.isdtype(np.int64, "signed integer")
+
+np.issubdtype("S1", np.bytes_)
+np.issubdtype(np.float64, np.float32)
+
+np.ScalarType
+np.ScalarType[0]
+np.ScalarType[3]
+np.ScalarType[8]
+np.ScalarType[10]
+
+np.typecodes["Character"]
+np.typecodes["Complex"]
+np.typecodes["All"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/random.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..bce204a7378e8a8c8761d02a08686206f59ca6ce
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/random.py
@@ -0,0 +1,1497 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+SEED_NONE = None
+SEED_INT = 4579435749574957634658964293569
+SEED_ARR: np.ndarray[Any, np.dtype[np.int64]] = np.array([1, 2, 3, 4], dtype=np.int64)
+SEED_ARRLIKE: list[int] = [1, 2, 3, 4]
+SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
+SEED_MT19937: np.random.MT19937 = np.random.MT19937(0)
+SEED_PCG64: np.random.PCG64 = np.random.PCG64(0)
+SEED_PHILOX: np.random.Philox = np.random.Philox(0)
+SEED_SFC64: np.random.SFC64 = np.random.SFC64(0)
+
+# default rng
+np.random.default_rng()
+np.random.default_rng(SEED_NONE)
+np.random.default_rng(SEED_INT)
+np.random.default_rng(SEED_ARR)
+np.random.default_rng(SEED_ARRLIKE)
+np.random.default_rng(SEED_SEED_SEQ)
+np.random.default_rng(SEED_MT19937)
+np.random.default_rng(SEED_PCG64)
+np.random.default_rng(SEED_PHILOX)
+np.random.default_rng(SEED_SFC64)
+
+# Seed Sequence
+np.random.SeedSequence(SEED_NONE)
+np.random.SeedSequence(SEED_INT)
+np.random.SeedSequence(SEED_ARR)
+np.random.SeedSequence(SEED_ARRLIKE)
+
+# Bit Generators
+np.random.MT19937(SEED_NONE)
+np.random.MT19937(SEED_INT)
+np.random.MT19937(SEED_ARR)
+np.random.MT19937(SEED_ARRLIKE)
+np.random.MT19937(SEED_SEED_SEQ)
+
+np.random.PCG64(SEED_NONE)
+np.random.PCG64(SEED_INT)
+np.random.PCG64(SEED_ARR)
+np.random.PCG64(SEED_ARRLIKE)
+np.random.PCG64(SEED_SEED_SEQ)
+
+np.random.Philox(SEED_NONE)
+np.random.Philox(SEED_INT)
+np.random.Philox(SEED_ARR)
+np.random.Philox(SEED_ARRLIKE)
+np.random.Philox(SEED_SEED_SEQ)
+
+np.random.SFC64(SEED_NONE)
+np.random.SFC64(SEED_INT)
+np.random.SFC64(SEED_ARR)
+np.random.SFC64(SEED_ARRLIKE)
+np.random.SFC64(SEED_SEED_SEQ)
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence(SEED_NONE)
+seed_seq.spawn(10)
+seed_seq.generate_state(3)
+seed_seq.generate_state(3, "u4")
+seed_seq.generate_state(3, "uint32")
+seed_seq.generate_state(3, "u8")
+seed_seq.generate_state(3, "uint64")
+seed_seq.generate_state(3, np.uint32)
+seed_seq.generate_state(3, np.uint64)
+
+
+def_gen: np.random.Generator = np.random.default_rng()
+
+D_arr_0p1: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.1])
+D_arr_0p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.5])
+D_arr_0p9: np.ndarray[Any, np.dtype[np.float64]] = np.array([0.9])
+D_arr_1p5: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.5])
+I_arr_10: np.ndarray[Any, np.dtype[np.int_]] = np.array([10], dtype=np.int_)
+I_arr_20: np.ndarray[Any, np.dtype[np.int_]] = np.array([20], dtype=np.int_)
+D_arr_like_0p1: list[float] = [0.1]
+D_arr_like_0p5: list[float] = [0.5]
+D_arr_like_0p9: list[float] = [0.9]
+D_arr_like_1p5: list[float] = [1.5]
+I_arr_like_10: list[int] = [10]
+I_arr_like_20: list[int] = [20]
+D_2D_like: list[list[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]]
+D_2D: np.ndarray[Any, np.dtype[np.float64]] = np.array(D_2D_like)
+
+S_out: np.ndarray[Any, np.dtype[np.float32]] = np.empty(1, dtype=np.float32)
+D_out: np.ndarray[Any, np.dtype[np.float64]] = np.empty(1)
+
+def_gen.standard_normal()
+def_gen.standard_normal(dtype=np.float32)
+def_gen.standard_normal(dtype="float32")
+def_gen.standard_normal(dtype="double")
+def_gen.standard_normal(dtype=np.float64)
+def_gen.standard_normal(size=None)
+def_gen.standard_normal(size=1)
+def_gen.standard_normal(size=1, dtype=np.float32)
+def_gen.standard_normal(size=1, dtype="f4")
+def_gen.standard_normal(size=1, dtype="float32", out=S_out)
+def_gen.standard_normal(dtype=np.float32, out=S_out)
+def_gen.standard_normal(size=1, dtype=np.float64)
+def_gen.standard_normal(size=1, dtype="float64")
+def_gen.standard_normal(size=1, dtype="f8")
+def_gen.standard_normal(out=D_out)
+def_gen.standard_normal(size=1, dtype="float64")
+def_gen.standard_normal(size=1, dtype="float64", out=D_out)
+
+def_gen.random()
+def_gen.random(dtype=np.float32)
+def_gen.random(dtype="float32")
+def_gen.random(dtype="double")
+def_gen.random(dtype=np.float64)
+def_gen.random(size=None)
+def_gen.random(size=1)
+def_gen.random(size=1, dtype=np.float32)
+def_gen.random(size=1, dtype="f4")
+def_gen.random(size=1, dtype="float32", out=S_out)
+def_gen.random(dtype=np.float32, out=S_out)
+def_gen.random(size=1, dtype=np.float64)
+def_gen.random(size=1, dtype="float64")
+def_gen.random(size=1, dtype="f8")
+def_gen.random(out=D_out)
+def_gen.random(size=1, dtype="float64")
+def_gen.random(size=1, dtype="float64", out=D_out)
+
+def_gen.standard_cauchy()
+def_gen.standard_cauchy(size=None)
+def_gen.standard_cauchy(size=1)
+
+def_gen.standard_exponential()
+def_gen.standard_exponential(method="inv")
+def_gen.standard_exponential(dtype=np.float32)
+def_gen.standard_exponential(dtype="float32")
+def_gen.standard_exponential(dtype="double")
+def_gen.standard_exponential(dtype=np.float64)
+def_gen.standard_exponential(size=None)
+def_gen.standard_exponential(size=None, method="inv")
+def_gen.standard_exponential(size=1, method="inv")
+def_gen.standard_exponential(size=1, dtype=np.float32)
+def_gen.standard_exponential(size=1, dtype="f4", method="inv")
+def_gen.standard_exponential(size=1, dtype="float32", out=S_out)
+def_gen.standard_exponential(dtype=np.float32, out=S_out)
+def_gen.standard_exponential(size=1, dtype=np.float64, method="inv")
+def_gen.standard_exponential(size=1, dtype="float64")
+def_gen.standard_exponential(size=1, dtype="f8")
+def_gen.standard_exponential(out=D_out)
+def_gen.standard_exponential(size=1, dtype="float64")
+def_gen.standard_exponential(size=1, dtype="float64", out=D_out)
+
+def_gen.zipf(1.5)
+def_gen.zipf(1.5, size=None)
+def_gen.zipf(1.5, size=1)
+def_gen.zipf(D_arr_1p5)
+def_gen.zipf(D_arr_1p5, size=1)
+def_gen.zipf(D_arr_like_1p5)
+def_gen.zipf(D_arr_like_1p5, size=1)
+
+def_gen.weibull(0.5)
+def_gen.weibull(0.5, size=None)
+def_gen.weibull(0.5, size=1)
+def_gen.weibull(D_arr_0p5)
+def_gen.weibull(D_arr_0p5, size=1)
+def_gen.weibull(D_arr_like_0p5)
+def_gen.weibull(D_arr_like_0p5, size=1)
+
+def_gen.standard_t(0.5)
+def_gen.standard_t(0.5, size=None)
+def_gen.standard_t(0.5, size=1)
+def_gen.standard_t(D_arr_0p5)
+def_gen.standard_t(D_arr_0p5, size=1)
+def_gen.standard_t(D_arr_like_0p5)
+def_gen.standard_t(D_arr_like_0p5, size=1)
+
+def_gen.poisson(0.5)
+def_gen.poisson(0.5, size=None)
+def_gen.poisson(0.5, size=1)
+def_gen.poisson(D_arr_0p5)
+def_gen.poisson(D_arr_0p5, size=1)
+def_gen.poisson(D_arr_like_0p5)
+def_gen.poisson(D_arr_like_0p5, size=1)
+
+def_gen.power(0.5)
+def_gen.power(0.5, size=None)
+def_gen.power(0.5, size=1)
+def_gen.power(D_arr_0p5)
+def_gen.power(D_arr_0p5, size=1)
+def_gen.power(D_arr_like_0p5)
+def_gen.power(D_arr_like_0p5, size=1)
+
+def_gen.pareto(0.5)
+def_gen.pareto(0.5, size=None)
+def_gen.pareto(0.5, size=1)
+def_gen.pareto(D_arr_0p5)
+def_gen.pareto(D_arr_0p5, size=1)
+def_gen.pareto(D_arr_like_0p5)
+def_gen.pareto(D_arr_like_0p5, size=1)
+
+def_gen.chisquare(0.5)
+def_gen.chisquare(0.5, size=None)
+def_gen.chisquare(0.5, size=1)
+def_gen.chisquare(D_arr_0p5)
+def_gen.chisquare(D_arr_0p5, size=1)
+def_gen.chisquare(D_arr_like_0p5)
+def_gen.chisquare(D_arr_like_0p5, size=1)
+
+def_gen.exponential(0.5)
+def_gen.exponential(0.5, size=None)
+def_gen.exponential(0.5, size=1)
+def_gen.exponential(D_arr_0p5)
+def_gen.exponential(D_arr_0p5, size=1)
+def_gen.exponential(D_arr_like_0p5)
+def_gen.exponential(D_arr_like_0p5, size=1)
+
+def_gen.geometric(0.5)
+def_gen.geometric(0.5, size=None)
+def_gen.geometric(0.5, size=1)
+def_gen.geometric(D_arr_0p5)
+def_gen.geometric(D_arr_0p5, size=1)
+def_gen.geometric(D_arr_like_0p5)
+def_gen.geometric(D_arr_like_0p5, size=1)
+
+def_gen.logseries(0.5)
+def_gen.logseries(0.5, size=None)
+def_gen.logseries(0.5, size=1)
+def_gen.logseries(D_arr_0p5)
+def_gen.logseries(D_arr_0p5, size=1)
+def_gen.logseries(D_arr_like_0p5)
+def_gen.logseries(D_arr_like_0p5, size=1)
+
+def_gen.rayleigh(0.5)
+def_gen.rayleigh(0.5, size=None)
+def_gen.rayleigh(0.5, size=1)
+def_gen.rayleigh(D_arr_0p5)
+def_gen.rayleigh(D_arr_0p5, size=1)
+def_gen.rayleigh(D_arr_like_0p5)
+def_gen.rayleigh(D_arr_like_0p5, size=1)
+
+def_gen.standard_gamma(0.5)
+def_gen.standard_gamma(0.5, size=None)
+def_gen.standard_gamma(0.5, dtype="float32")
+def_gen.standard_gamma(0.5, size=None, dtype="float32")
+def_gen.standard_gamma(0.5, size=1)
+def_gen.standard_gamma(D_arr_0p5)
+def_gen.standard_gamma(D_arr_0p5, dtype="f4")
+def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out)
+def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out)
+def_gen.standard_gamma(D_arr_0p5, size=1)
+def_gen.standard_gamma(D_arr_like_0p5)
+def_gen.standard_gamma(D_arr_like_0p5, size=1)
+def_gen.standard_gamma(0.5, out=D_out)
+def_gen.standard_gamma(D_arr_like_0p5, out=D_out)
+def_gen.standard_gamma(D_arr_like_0p5, size=1)
+def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64)
+
+def_gen.vonmises(0.5, 0.5)
+def_gen.vonmises(0.5, 0.5, size=None)
+def_gen.vonmises(0.5, 0.5, size=1)
+def_gen.vonmises(D_arr_0p5, 0.5)
+def_gen.vonmises(0.5, D_arr_0p5)
+def_gen.vonmises(D_arr_0p5, 0.5, size=1)
+def_gen.vonmises(0.5, D_arr_0p5, size=1)
+def_gen.vonmises(D_arr_like_0p5, 0.5)
+def_gen.vonmises(0.5, D_arr_like_0p5)
+def_gen.vonmises(D_arr_0p5, D_arr_0p5)
+def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.wald(0.5, 0.5)
+def_gen.wald(0.5, 0.5, size=None)
+def_gen.wald(0.5, 0.5, size=1)
+def_gen.wald(D_arr_0p5, 0.5)
+def_gen.wald(0.5, D_arr_0p5)
+def_gen.wald(D_arr_0p5, 0.5, size=1)
+def_gen.wald(0.5, D_arr_0p5, size=1)
+def_gen.wald(D_arr_like_0p5, 0.5)
+def_gen.wald(0.5, D_arr_like_0p5)
+def_gen.wald(D_arr_0p5, D_arr_0p5)
+def_gen.wald(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.wald(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.uniform(0.5, 0.5)
+def_gen.uniform(0.5, 0.5, size=None)
+def_gen.uniform(0.5, 0.5, size=1)
+def_gen.uniform(D_arr_0p5, 0.5)
+def_gen.uniform(0.5, D_arr_0p5)
+def_gen.uniform(D_arr_0p5, 0.5, size=1)
+def_gen.uniform(0.5, D_arr_0p5, size=1)
+def_gen.uniform(D_arr_like_0p5, 0.5)
+def_gen.uniform(0.5, D_arr_like_0p5)
+def_gen.uniform(D_arr_0p5, D_arr_0p5)
+def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.beta(0.5, 0.5)
+def_gen.beta(0.5, 0.5, size=None)
+def_gen.beta(0.5, 0.5, size=1)
+def_gen.beta(D_arr_0p5, 0.5)
+def_gen.beta(0.5, D_arr_0p5)
+def_gen.beta(D_arr_0p5, 0.5, size=1)
+def_gen.beta(0.5, D_arr_0p5, size=1)
+def_gen.beta(D_arr_like_0p5, 0.5)
+def_gen.beta(0.5, D_arr_like_0p5)
+def_gen.beta(D_arr_0p5, D_arr_0p5)
+def_gen.beta(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.beta(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.f(0.5, 0.5)
+def_gen.f(0.5, 0.5, size=None)
+def_gen.f(0.5, 0.5, size=1)
+def_gen.f(D_arr_0p5, 0.5)
+def_gen.f(0.5, D_arr_0p5)
+def_gen.f(D_arr_0p5, 0.5, size=1)
+def_gen.f(0.5, D_arr_0p5, size=1)
+def_gen.f(D_arr_like_0p5, 0.5)
+def_gen.f(0.5, D_arr_like_0p5)
+def_gen.f(D_arr_0p5, D_arr_0p5)
+def_gen.f(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.f(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.gamma(0.5, 0.5)
+def_gen.gamma(0.5, 0.5, size=None)
+def_gen.gamma(0.5, 0.5, size=1)
+def_gen.gamma(D_arr_0p5, 0.5)
+def_gen.gamma(0.5, D_arr_0p5)
+def_gen.gamma(D_arr_0p5, 0.5, size=1)
+def_gen.gamma(0.5, D_arr_0p5, size=1)
+def_gen.gamma(D_arr_like_0p5, 0.5)
+def_gen.gamma(0.5, D_arr_like_0p5)
+def_gen.gamma(D_arr_0p5, D_arr_0p5)
+def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.gumbel(0.5, 0.5)
+def_gen.gumbel(0.5, 0.5, size=None)
+def_gen.gumbel(0.5, 0.5, size=1)
+def_gen.gumbel(D_arr_0p5, 0.5)
+def_gen.gumbel(0.5, D_arr_0p5)
+def_gen.gumbel(D_arr_0p5, 0.5, size=1)
+def_gen.gumbel(0.5, D_arr_0p5, size=1)
+def_gen.gumbel(D_arr_like_0p5, 0.5)
+def_gen.gumbel(0.5, D_arr_like_0p5)
+def_gen.gumbel(D_arr_0p5, D_arr_0p5)
+def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.laplace(0.5, 0.5)
+def_gen.laplace(0.5, 0.5, size=None)
+def_gen.laplace(0.5, 0.5, size=1)
+def_gen.laplace(D_arr_0p5, 0.5)
+def_gen.laplace(0.5, D_arr_0p5)
+def_gen.laplace(D_arr_0p5, 0.5, size=1)
+def_gen.laplace(0.5, D_arr_0p5, size=1)
+def_gen.laplace(D_arr_like_0p5, 0.5)
+def_gen.laplace(0.5, D_arr_like_0p5)
+def_gen.laplace(D_arr_0p5, D_arr_0p5)
+def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.logistic(0.5, 0.5)
+def_gen.logistic(0.5, 0.5, size=None)
+def_gen.logistic(0.5, 0.5, size=1)
+def_gen.logistic(D_arr_0p5, 0.5)
+def_gen.logistic(0.5, D_arr_0p5)
+def_gen.logistic(D_arr_0p5, 0.5, size=1)
+def_gen.logistic(0.5, D_arr_0p5, size=1)
+def_gen.logistic(D_arr_like_0p5, 0.5)
+def_gen.logistic(0.5, D_arr_like_0p5)
+def_gen.logistic(D_arr_0p5, D_arr_0p5)
+def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.lognormal(0.5, 0.5)
+def_gen.lognormal(0.5, 0.5, size=None)
+def_gen.lognormal(0.5, 0.5, size=1)
+def_gen.lognormal(D_arr_0p5, 0.5)
+def_gen.lognormal(0.5, D_arr_0p5)
+def_gen.lognormal(D_arr_0p5, 0.5, size=1)
+def_gen.lognormal(0.5, D_arr_0p5, size=1)
+def_gen.lognormal(D_arr_like_0p5, 0.5)
+def_gen.lognormal(0.5, D_arr_like_0p5)
+def_gen.lognormal(D_arr_0p5, D_arr_0p5)
+def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.noncentral_chisquare(0.5, 0.5)
+def_gen.noncentral_chisquare(0.5, 0.5, size=None)
+def_gen.noncentral_chisquare(0.5, 0.5, size=1)
+def_gen.noncentral_chisquare(D_arr_0p5, 0.5)
+def_gen.noncentral_chisquare(0.5, D_arr_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1)
+def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1)
+def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5)
+def_gen.noncentral_chisquare(0.5, D_arr_like_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5)
+def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.normal(0.5, 0.5)
+def_gen.normal(0.5, 0.5, size=None)
+def_gen.normal(0.5, 0.5, size=1)
+def_gen.normal(D_arr_0p5, 0.5)
+def_gen.normal(0.5, D_arr_0p5)
+def_gen.normal(D_arr_0p5, 0.5, size=1)
+def_gen.normal(0.5, D_arr_0p5, size=1)
+def_gen.normal(D_arr_like_0p5, 0.5)
+def_gen.normal(0.5, D_arr_like_0p5)
+def_gen.normal(D_arr_0p5, D_arr_0p5)
+def_gen.normal(D_arr_like_0p5, D_arr_like_0p5)
+def_gen.normal(D_arr_0p5, D_arr_0p5, size=1)
+def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+def_gen.triangular(0.1, 0.5, 0.9)
+def_gen.triangular(0.1, 0.5, 0.9, size=None)
+def_gen.triangular(0.1, 0.5, 0.9, size=1)
+def_gen.triangular(D_arr_0p1, 0.5, 0.9)
+def_gen.triangular(0.1, D_arr_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1)
+def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)
+def_gen.triangular(0.5, D_arr_like_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9)
+def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+def_gen.noncentral_f(0.1, 0.5, 0.9)
+def_gen.noncentral_f(0.1, 0.5, 0.9, size=None)
+def_gen.noncentral_f(0.1, 0.5, 0.9, size=1)
+def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9)
+def_gen.noncentral_f(0.1, D_arr_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)
+def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)
+def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)
+def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+def_gen.binomial(10, 0.5)
+def_gen.binomial(10, 0.5, size=None)
+def_gen.binomial(10, 0.5, size=1)
+def_gen.binomial(I_arr_10, 0.5)
+def_gen.binomial(10, D_arr_0p5)
+def_gen.binomial(I_arr_10, 0.5, size=1)
+def_gen.binomial(10, D_arr_0p5, size=1)
+def_gen.binomial(I_arr_like_10, 0.5)
+def_gen.binomial(10, D_arr_like_0p5)
+def_gen.binomial(I_arr_10, D_arr_0p5)
+def_gen.binomial(I_arr_like_10, D_arr_like_0p5)
+def_gen.binomial(I_arr_10, D_arr_0p5, size=1)
+def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+def_gen.negative_binomial(10, 0.5)
+def_gen.negative_binomial(10, 0.5, size=None)
+def_gen.negative_binomial(10, 0.5, size=1)
+def_gen.negative_binomial(I_arr_10, 0.5)
+def_gen.negative_binomial(10, D_arr_0p5)
+def_gen.negative_binomial(I_arr_10, 0.5, size=1)
+def_gen.negative_binomial(10, D_arr_0p5, size=1)
+def_gen.negative_binomial(I_arr_like_10, 0.5)
+def_gen.negative_binomial(10, D_arr_like_0p5)
+def_gen.negative_binomial(I_arr_10, D_arr_0p5)
+def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5)
+def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1)
+def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+def_gen.hypergeometric(20, 20, 10)
+def_gen.hypergeometric(20, 20, 10, size=None)
+def_gen.hypergeometric(20, 20, 10, size=1)
+def_gen.hypergeometric(I_arr_20, 20, 10)
+def_gen.hypergeometric(20, I_arr_20, 10)
+def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)
+def_gen.hypergeometric(20, I_arr_20, 10, size=1)
+def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10)
+def_gen.hypergeometric(20, I_arr_like_20, 10)
+def_gen.hypergeometric(I_arr_20, I_arr_20, 10)
+def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10)
+def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)
+def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)
+
+I_int64_100: np.ndarray[Any, np.dtype[np.int64]] = np.array([100], dtype=np.int64)
+
+def_gen.integers(0, 100)
+def_gen.integers(100)
+def_gen.integers([100])
+def_gen.integers(0, [100])
+
+I_bool_low: np.ndarray[Any, np.dtype[np.bool]] = np.array([0], dtype=np.bool)
+I_bool_low_like: list[int] = [0]
+I_bool_high_open: np.ndarray[Any, np.dtype[np.bool]] = np.array([1], dtype=np.bool)
+I_bool_high_closed: np.ndarray[Any, np.dtype[np.bool]] = np.array([1], dtype=np.bool)
+
+def_gen.integers(2, dtype=bool)
+def_gen.integers(0, 2, dtype=bool)
+def_gen.integers(1, dtype=bool, endpoint=True)
+def_gen.integers(0, 1, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_high_open, dtype=bool)
+def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool)
+def_gen.integers(0, I_bool_high_open, dtype=bool)
+def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True)
+def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True)
+def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True)
+
+def_gen.integers(2, dtype=np.bool)
+def_gen.integers(0, 2, dtype=np.bool)
+def_gen.integers(1, dtype=np.bool, endpoint=True)
+def_gen.integers(0, 1, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_low_like, 1, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_high_open, dtype=np.bool)
+def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool)
+def_gen.integers(0, I_bool_high_open, dtype=np.bool)
+def_gen.integers(I_bool_high_closed, dtype=np.bool, endpoint=True)
+def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool, endpoint=True)
+def_gen.integers(0, I_bool_high_closed, dtype=np.bool, endpoint=True)
+
+I_u1_low: np.ndarray[Any, np.dtype[np.uint8]] = np.array([0], dtype=np.uint8)
+I_u1_low_like: list[int] = [0]
+I_u1_high_open: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8)
+I_u1_high_closed: np.ndarray[Any, np.dtype[np.uint8]] = np.array([255], dtype=np.uint8)
+
+def_gen.integers(256, dtype="u1")
+def_gen.integers(0, 256, dtype="u1")
+def_gen.integers(255, dtype="u1", endpoint=True)
+def_gen.integers(0, 255, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_high_open, dtype="u1")
+def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1")
+def_gen.integers(0, I_u1_high_open, dtype="u1")
+def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True)
+
+def_gen.integers(256, dtype="uint8")
+def_gen.integers(0, 256, dtype="uint8")
+def_gen.integers(255, dtype="uint8", endpoint=True)
+def_gen.integers(0, 255, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_high_open, dtype="uint8")
+def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8")
+def_gen.integers(0, I_u1_high_open, dtype="uint8")
+def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True)
+
+def_gen.integers(256, dtype=np.uint8)
+def_gen.integers(0, 256, dtype=np.uint8)
+def_gen.integers(255, dtype=np.uint8, endpoint=True)
+def_gen.integers(0, 255, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_high_open, dtype=np.uint8)
+def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8)
+def_gen.integers(0, I_u1_high_open, dtype=np.uint8)
+def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True)
+def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True)
+def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True)
+
+I_u2_low: np.ndarray[Any, np.dtype[np.uint16]] = np.array([0], dtype=np.uint16)
+I_u2_low_like: list[int] = [0]
+I_u2_high_open: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16)
+I_u2_high_closed: np.ndarray[Any, np.dtype[np.uint16]] = np.array([65535], dtype=np.uint16)
+
+def_gen.integers(65536, dtype="u2")
+def_gen.integers(0, 65536, dtype="u2")
+def_gen.integers(65535, dtype="u2", endpoint=True)
+def_gen.integers(0, 65535, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_high_open, dtype="u2")
+def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2")
+def_gen.integers(0, I_u2_high_open, dtype="u2")
+def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True)
+
+def_gen.integers(65536, dtype="uint16")
+def_gen.integers(0, 65536, dtype="uint16")
+def_gen.integers(65535, dtype="uint16", endpoint=True)
+def_gen.integers(0, 65535, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_high_open, dtype="uint16")
+def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16")
+def_gen.integers(0, I_u2_high_open, dtype="uint16")
+def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True)
+
+def_gen.integers(65536, dtype=np.uint16)
+def_gen.integers(0, 65536, dtype=np.uint16)
+def_gen.integers(65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_high_open, dtype=np.uint16)
+def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16)
+def_gen.integers(0, I_u2_high_open, dtype=np.uint16)
+def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True)
+def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True)
+def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True)
+
+I_u4_low: np.ndarray[Any, np.dtype[np.uint32]] = np.array([0], dtype=np.uint32)
+I_u4_low_like: list[int] = [0]
+I_u4_high_open: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32)
+I_u4_high_closed: np.ndarray[Any, np.dtype[np.uint32]] = np.array([4294967295], dtype=np.uint32)
+
+def_gen.integers(4294967296, dtype="u4")
+def_gen.integers(0, 4294967296, dtype="u4")
+def_gen.integers(4294967295, dtype="u4", endpoint=True)
+def_gen.integers(0, 4294967295, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_high_open, dtype="u4")
+def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4")
+def_gen.integers(0, I_u4_high_open, dtype="u4")
+def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True)
+
+def_gen.integers(4294967296, dtype="uint32")
+def_gen.integers(0, 4294967296, dtype="uint32")
+def_gen.integers(4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_high_open, dtype="uint32")
+def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32")
+def_gen.integers(0, I_u4_high_open, dtype="uint32")
+def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True)
+
+def_gen.integers(4294967296, dtype=np.uint32)
+def_gen.integers(0, 4294967296, dtype=np.uint32)
+def_gen.integers(4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_high_open, dtype=np.uint32)
+def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32)
+def_gen.integers(0, I_u4_high_open, dtype=np.uint32)
+def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True)
+def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True)
+def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True)
+
+I_u8_low: np.ndarray[Any, np.dtype[np.uint64]] = np.array([0], dtype=np.uint64)
+I_u8_low_like: list[int] = [0]
+I_u8_high_open: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64)
+I_u8_high_closed: np.ndarray[Any, np.dtype[np.uint64]] = np.array([18446744073709551615], dtype=np.uint64)
+
+def_gen.integers(18446744073709551616, dtype="u8")
+def_gen.integers(0, 18446744073709551616, dtype="u8")
+def_gen.integers(18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_high_open, dtype="u8")
+def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8")
+def_gen.integers(0, I_u8_high_open, dtype="u8")
+def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True)
+
+def_gen.integers(18446744073709551616, dtype="uint64")
+def_gen.integers(0, 18446744073709551616, dtype="uint64")
+def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_high_open, dtype="uint64")
+def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64")
+def_gen.integers(0, I_u8_high_open, dtype="uint64")
+def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True)
+
+def_gen.integers(18446744073709551616, dtype=np.uint64)
+def_gen.integers(0, 18446744073709551616, dtype=np.uint64)
+def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_high_open, dtype=np.uint64)
+def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64)
+def_gen.integers(0, I_u8_high_open, dtype=np.uint64)
+def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True)
+def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True)
+def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True)
+
+I_i1_low: np.ndarray[Any, np.dtype[np.int8]] = np.array([-128], dtype=np.int8)
+I_i1_low_like: list[int] = [-128]
+I_i1_high_open: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8)
+I_i1_high_closed: np.ndarray[Any, np.dtype[np.int8]] = np.array([127], dtype=np.int8)
+
+def_gen.integers(128, dtype="i1")
+def_gen.integers(-128, 128, dtype="i1")
+def_gen.integers(127, dtype="i1", endpoint=True)
+def_gen.integers(-128, 127, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_high_open, dtype="i1")
+def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1")
+def_gen.integers(-128, I_i1_high_open, dtype="i1")
+def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True)
+
+def_gen.integers(128, dtype="int8")
+def_gen.integers(-128, 128, dtype="int8")
+def_gen.integers(127, dtype="int8", endpoint=True)
+def_gen.integers(-128, 127, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_high_open, dtype="int8")
+def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8")
+def_gen.integers(-128, I_i1_high_open, dtype="int8")
+def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True)
+
+def_gen.integers(128, dtype=np.int8)
+def_gen.integers(-128, 128, dtype=np.int8)
+def_gen.integers(127, dtype=np.int8, endpoint=True)
+def_gen.integers(-128, 127, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_high_open, dtype=np.int8)
+def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8)
+def_gen.integers(-128, I_i1_high_open, dtype=np.int8)
+def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True)
+def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True)
+def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True)
+
+I_i2_low: np.ndarray[Any, np.dtype[np.int16]] = np.array([-32768], dtype=np.int16)
+I_i2_low_like: list[int] = [-32768]
+I_i2_high_open: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16)
+I_i2_high_closed: np.ndarray[Any, np.dtype[np.int16]] = np.array([32767], dtype=np.int16)
+
+def_gen.integers(32768, dtype="i2")
+def_gen.integers(-32768, 32768, dtype="i2")
+def_gen.integers(32767, dtype="i2", endpoint=True)
+def_gen.integers(-32768, 32767, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_high_open, dtype="i2")
+def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2")
+def_gen.integers(-32768, I_i2_high_open, dtype="i2")
+def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True)
+
+def_gen.integers(32768, dtype="int16")
+def_gen.integers(-32768, 32768, dtype="int16")
+def_gen.integers(32767, dtype="int16", endpoint=True)
+def_gen.integers(-32768, 32767, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_high_open, dtype="int16")
+def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16")
+def_gen.integers(-32768, I_i2_high_open, dtype="int16")
+def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True)
+
+def_gen.integers(32768, dtype=np.int16)
+def_gen.integers(-32768, 32768, dtype=np.int16)
+def_gen.integers(32767, dtype=np.int16, endpoint=True)
+def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_high_open, dtype=np.int16)
+def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16)
+def_gen.integers(-32768, I_i2_high_open, dtype=np.int16)
+def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True)
+def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True)
+def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True)
+
+I_i4_low: np.ndarray[Any, np.dtype[np.int32]] = np.array([-2147483648], dtype=np.int32)
+I_i4_low_like: list[int] = [-2147483648]
+I_i4_high_open: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32)
+I_i4_high_closed: np.ndarray[Any, np.dtype[np.int32]] = np.array([2147483647], dtype=np.int32)
+
+def_gen.integers(2147483648, dtype="i4")
+def_gen.integers(-2147483648, 2147483648, dtype="i4")
+def_gen.integers(2147483647, dtype="i4", endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_high_open, dtype="i4")
+def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4")
+def_gen.integers(-2147483648, I_i4_high_open, dtype="i4")
+def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True)
+
+def_gen.integers(2147483648, dtype="int32")
+def_gen.integers(-2147483648, 2147483648, dtype="int32")
+def_gen.integers(2147483647, dtype="int32", endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_high_open, dtype="int32")
+def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32")
+def_gen.integers(-2147483648, I_i4_high_open, dtype="int32")
+def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True)
+
+def_gen.integers(2147483648, dtype=np.int32)
+def_gen.integers(-2147483648, 2147483648, dtype=np.int32)
+def_gen.integers(2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_high_open, dtype=np.int32)
+def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32)
+def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32)
+def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True)
+def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True)
+def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True)
+
+I_i8_low: np.ndarray[Any, np.dtype[np.int64]] = np.array([-9223372036854775808], dtype=np.int64)
+I_i8_low_like: list[int] = [-9223372036854775808]
+I_i8_high_open: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64)
+I_i8_high_closed: np.ndarray[Any, np.dtype[np.int64]] = np.array([9223372036854775807], dtype=np.int64)
+
+def_gen.integers(9223372036854775808, dtype="i8")
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8")
+def_gen.integers(9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_high_open, dtype="i8")
+def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8")
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8")
+def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True)
+
+def_gen.integers(9223372036854775808, dtype="int64")
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64")
+def_gen.integers(9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_high_open, dtype="int64")
+def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64")
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64")
+def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True)
+
+def_gen.integers(9223372036854775808, dtype=np.int64)
+def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64)
+def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_high_open, dtype=np.int64)
+def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64)
+def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64)
+def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True)
+def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True)
+def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True)
+
+
+def_gen.bit_generator
+
+def_gen.bytes(2)
+
+def_gen.choice(5)
+def_gen.choice(5, 3)
+def_gen.choice(5, 3, replace=True)
+def_gen.choice(5, 3, p=[1 / 5] * 5)
+def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False)
+
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"])
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)
+def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))
+
+def_gen.dirichlet([0.5, 0.5])
+def_gen.dirichlet(np.array([0.5, 0.5]))
+def_gen.dirichlet(np.array([0.5, 0.5]), size=3)
+
+def_gen.multinomial(20, [1 / 6.0] * 6)
+def_gen.multinomial(20, np.array([0.5, 0.5]))
+def_gen.multinomial(20, [1 / 6.0] * 6, size=2)
+def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2))
+def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2))
+
+def_gen.multivariate_hypergeometric([3, 5, 7], 2)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4)
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7))
+def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count")
+def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals")
+
+def_gen.multivariate_normal([0.0], [[1.0]])
+def_gen.multivariate_normal([0.0], np.array([[1.0]]))
+def_gen.multivariate_normal(np.array([0.0]), [[1.0]])
+def_gen.multivariate_normal([0.0], np.array([[1.0]]))
+
+def_gen.permutation(10)
+def_gen.permutation([1, 2, 3, 4])
+def_gen.permutation(np.array([1, 2, 3, 4]))
+def_gen.permutation(D_2D, axis=1)
+def_gen.permuted(D_2D)
+def_gen.permuted(D_2D_like)
+def_gen.permuted(D_2D, axis=1)
+def_gen.permuted(D_2D, out=D_2D)
+def_gen.permuted(D_2D_like, out=D_2D)
+def_gen.permuted(D_2D_like, out=D_2D)
+def_gen.permuted(D_2D, axis=1, out=D_2D)
+
+def_gen.shuffle(np.arange(10))
+def_gen.shuffle([1, 2, 3, 4, 5])
+def_gen.shuffle(D_2D, axis=1)
+
+def_gen.__str__()
+def_gen.__repr__()
+def_gen.__setstate__(dict(def_gen.bit_generator.state))
+
+# RandomState
+random_st: np.random.RandomState = np.random.RandomState()
+
+random_st.standard_normal()
+random_st.standard_normal(size=None)
+random_st.standard_normal(size=1)
+
+random_st.random()
+random_st.random(size=None)
+random_st.random(size=1)
+
+random_st.standard_cauchy()
+random_st.standard_cauchy(size=None)
+random_st.standard_cauchy(size=1)
+
+random_st.standard_exponential()
+random_st.standard_exponential(size=None)
+random_st.standard_exponential(size=1)
+
+random_st.zipf(1.5)
+random_st.zipf(1.5, size=None)
+random_st.zipf(1.5, size=1)
+random_st.zipf(D_arr_1p5)
+random_st.zipf(D_arr_1p5, size=1)
+random_st.zipf(D_arr_like_1p5)
+random_st.zipf(D_arr_like_1p5, size=1)
+
+random_st.weibull(0.5)
+random_st.weibull(0.5, size=None)
+random_st.weibull(0.5, size=1)
+random_st.weibull(D_arr_0p5)
+random_st.weibull(D_arr_0p5, size=1)
+random_st.weibull(D_arr_like_0p5)
+random_st.weibull(D_arr_like_0p5, size=1)
+
+random_st.standard_t(0.5)
+random_st.standard_t(0.5, size=None)
+random_st.standard_t(0.5, size=1)
+random_st.standard_t(D_arr_0p5)
+random_st.standard_t(D_arr_0p5, size=1)
+random_st.standard_t(D_arr_like_0p5)
+random_st.standard_t(D_arr_like_0p5, size=1)
+
+random_st.poisson(0.5)
+random_st.poisson(0.5, size=None)
+random_st.poisson(0.5, size=1)
+random_st.poisson(D_arr_0p5)
+random_st.poisson(D_arr_0p5, size=1)
+random_st.poisson(D_arr_like_0p5)
+random_st.poisson(D_arr_like_0p5, size=1)
+
+random_st.power(0.5)
+random_st.power(0.5, size=None)
+random_st.power(0.5, size=1)
+random_st.power(D_arr_0p5)
+random_st.power(D_arr_0p5, size=1)
+random_st.power(D_arr_like_0p5)
+random_st.power(D_arr_like_0p5, size=1)
+
+random_st.pareto(0.5)
+random_st.pareto(0.5, size=None)
+random_st.pareto(0.5, size=1)
+random_st.pareto(D_arr_0p5)
+random_st.pareto(D_arr_0p5, size=1)
+random_st.pareto(D_arr_like_0p5)
+random_st.pareto(D_arr_like_0p5, size=1)
+
+random_st.chisquare(0.5)
+random_st.chisquare(0.5, size=None)
+random_st.chisquare(0.5, size=1)
+random_st.chisquare(D_arr_0p5)
+random_st.chisquare(D_arr_0p5, size=1)
+random_st.chisquare(D_arr_like_0p5)
+random_st.chisquare(D_arr_like_0p5, size=1)
+
+random_st.exponential(0.5)
+random_st.exponential(0.5, size=None)
+random_st.exponential(0.5, size=1)
+random_st.exponential(D_arr_0p5)
+random_st.exponential(D_arr_0p5, size=1)
+random_st.exponential(D_arr_like_0p5)
+random_st.exponential(D_arr_like_0p5, size=1)
+
+random_st.geometric(0.5)
+random_st.geometric(0.5, size=None)
+random_st.geometric(0.5, size=1)
+random_st.geometric(D_arr_0p5)
+random_st.geometric(D_arr_0p5, size=1)
+random_st.geometric(D_arr_like_0p5)
+random_st.geometric(D_arr_like_0p5, size=1)
+
+random_st.logseries(0.5)
+random_st.logseries(0.5, size=None)
+random_st.logseries(0.5, size=1)
+random_st.logseries(D_arr_0p5)
+random_st.logseries(D_arr_0p5, size=1)
+random_st.logseries(D_arr_like_0p5)
+random_st.logseries(D_arr_like_0p5, size=1)
+
+random_st.rayleigh(0.5)
+random_st.rayleigh(0.5, size=None)
+random_st.rayleigh(0.5, size=1)
+random_st.rayleigh(D_arr_0p5)
+random_st.rayleigh(D_arr_0p5, size=1)
+random_st.rayleigh(D_arr_like_0p5)
+random_st.rayleigh(D_arr_like_0p5, size=1)
+
+random_st.standard_gamma(0.5)
+random_st.standard_gamma(0.5, size=None)
+random_st.standard_gamma(0.5, size=1)
+random_st.standard_gamma(D_arr_0p5)
+random_st.standard_gamma(D_arr_0p5, size=1)
+random_st.standard_gamma(D_arr_like_0p5)
+random_st.standard_gamma(D_arr_like_0p5, size=1)
+random_st.standard_gamma(D_arr_like_0p5, size=1)
+
+random_st.vonmises(0.5, 0.5)
+random_st.vonmises(0.5, 0.5, size=None)
+random_st.vonmises(0.5, 0.5, size=1)
+random_st.vonmises(D_arr_0p5, 0.5)
+random_st.vonmises(0.5, D_arr_0p5)
+random_st.vonmises(D_arr_0p5, 0.5, size=1)
+random_st.vonmises(0.5, D_arr_0p5, size=1)
+random_st.vonmises(D_arr_like_0p5, 0.5)
+random_st.vonmises(0.5, D_arr_like_0p5)
+random_st.vonmises(D_arr_0p5, D_arr_0p5)
+random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5)
+random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1)
+random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.wald(0.5, 0.5)
+random_st.wald(0.5, 0.5, size=None)
+random_st.wald(0.5, 0.5, size=1)
+random_st.wald(D_arr_0p5, 0.5)
+random_st.wald(0.5, D_arr_0p5)
+random_st.wald(D_arr_0p5, 0.5, size=1)
+random_st.wald(0.5, D_arr_0p5, size=1)
+random_st.wald(D_arr_like_0p5, 0.5)
+random_st.wald(0.5, D_arr_like_0p5)
+random_st.wald(D_arr_0p5, D_arr_0p5)
+random_st.wald(D_arr_like_0p5, D_arr_like_0p5)
+random_st.wald(D_arr_0p5, D_arr_0p5, size=1)
+random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.uniform(0.5, 0.5)
+random_st.uniform(0.5, 0.5, size=None)
+random_st.uniform(0.5, 0.5, size=1)
+random_st.uniform(D_arr_0p5, 0.5)
+random_st.uniform(0.5, D_arr_0p5)
+random_st.uniform(D_arr_0p5, 0.5, size=1)
+random_st.uniform(0.5, D_arr_0p5, size=1)
+random_st.uniform(D_arr_like_0p5, 0.5)
+random_st.uniform(0.5, D_arr_like_0p5)
+random_st.uniform(D_arr_0p5, D_arr_0p5)
+random_st.uniform(D_arr_like_0p5, D_arr_like_0p5)
+random_st.uniform(D_arr_0p5, D_arr_0p5, size=1)
+random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.beta(0.5, 0.5)
+random_st.beta(0.5, 0.5, size=None)
+random_st.beta(0.5, 0.5, size=1)
+random_st.beta(D_arr_0p5, 0.5)
+random_st.beta(0.5, D_arr_0p5)
+random_st.beta(D_arr_0p5, 0.5, size=1)
+random_st.beta(0.5, D_arr_0p5, size=1)
+random_st.beta(D_arr_like_0p5, 0.5)
+random_st.beta(0.5, D_arr_like_0p5)
+random_st.beta(D_arr_0p5, D_arr_0p5)
+random_st.beta(D_arr_like_0p5, D_arr_like_0p5)
+random_st.beta(D_arr_0p5, D_arr_0p5, size=1)
+random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.f(0.5, 0.5)
+random_st.f(0.5, 0.5, size=None)
+random_st.f(0.5, 0.5, size=1)
+random_st.f(D_arr_0p5, 0.5)
+random_st.f(0.5, D_arr_0p5)
+random_st.f(D_arr_0p5, 0.5, size=1)
+random_st.f(0.5, D_arr_0p5, size=1)
+random_st.f(D_arr_like_0p5, 0.5)
+random_st.f(0.5, D_arr_like_0p5)
+random_st.f(D_arr_0p5, D_arr_0p5)
+random_st.f(D_arr_like_0p5, D_arr_like_0p5)
+random_st.f(D_arr_0p5, D_arr_0p5, size=1)
+random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.gamma(0.5, 0.5)
+random_st.gamma(0.5, 0.5, size=None)
+random_st.gamma(0.5, 0.5, size=1)
+random_st.gamma(D_arr_0p5, 0.5)
+random_st.gamma(0.5, D_arr_0p5)
+random_st.gamma(D_arr_0p5, 0.5, size=1)
+random_st.gamma(0.5, D_arr_0p5, size=1)
+random_st.gamma(D_arr_like_0p5, 0.5)
+random_st.gamma(0.5, D_arr_like_0p5)
+random_st.gamma(D_arr_0p5, D_arr_0p5)
+random_st.gamma(D_arr_like_0p5, D_arr_like_0p5)
+random_st.gamma(D_arr_0p5, D_arr_0p5, size=1)
+random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.gumbel(0.5, 0.5)
+random_st.gumbel(0.5, 0.5, size=None)
+random_st.gumbel(0.5, 0.5, size=1)
+random_st.gumbel(D_arr_0p5, 0.5)
+random_st.gumbel(0.5, D_arr_0p5)
+random_st.gumbel(D_arr_0p5, 0.5, size=1)
+random_st.gumbel(0.5, D_arr_0p5, size=1)
+random_st.gumbel(D_arr_like_0p5, 0.5)
+random_st.gumbel(0.5, D_arr_like_0p5)
+random_st.gumbel(D_arr_0p5, D_arr_0p5)
+random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5)
+random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1)
+random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.laplace(0.5, 0.5)
+random_st.laplace(0.5, 0.5, size=None)
+random_st.laplace(0.5, 0.5, size=1)
+random_st.laplace(D_arr_0p5, 0.5)
+random_st.laplace(0.5, D_arr_0p5)
+random_st.laplace(D_arr_0p5, 0.5, size=1)
+random_st.laplace(0.5, D_arr_0p5, size=1)
+random_st.laplace(D_arr_like_0p5, 0.5)
+random_st.laplace(0.5, D_arr_like_0p5)
+random_st.laplace(D_arr_0p5, D_arr_0p5)
+random_st.laplace(D_arr_like_0p5, D_arr_like_0p5)
+random_st.laplace(D_arr_0p5, D_arr_0p5, size=1)
+random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.logistic(0.5, 0.5)
+random_st.logistic(0.5, 0.5, size=None)
+random_st.logistic(0.5, 0.5, size=1)
+random_st.logistic(D_arr_0p5, 0.5)
+random_st.logistic(0.5, D_arr_0p5)
+random_st.logistic(D_arr_0p5, 0.5, size=1)
+random_st.logistic(0.5, D_arr_0p5, size=1)
+random_st.logistic(D_arr_like_0p5, 0.5)
+random_st.logistic(0.5, D_arr_like_0p5)
+random_st.logistic(D_arr_0p5, D_arr_0p5)
+random_st.logistic(D_arr_like_0p5, D_arr_like_0p5)
+random_st.logistic(D_arr_0p5, D_arr_0p5, size=1)
+random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.lognormal(0.5, 0.5)
+random_st.lognormal(0.5, 0.5, size=None)
+random_st.lognormal(0.5, 0.5, size=1)
+random_st.lognormal(D_arr_0p5, 0.5)
+random_st.lognormal(0.5, D_arr_0p5)
+random_st.lognormal(D_arr_0p5, 0.5, size=1)
+random_st.lognormal(0.5, D_arr_0p5, size=1)
+random_st.lognormal(D_arr_like_0p5, 0.5)
+random_st.lognormal(0.5, D_arr_like_0p5)
+random_st.lognormal(D_arr_0p5, D_arr_0p5)
+random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5)
+random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1)
+random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.noncentral_chisquare(0.5, 0.5)
+random_st.noncentral_chisquare(0.5, 0.5, size=None)
+random_st.noncentral_chisquare(0.5, 0.5, size=1)
+random_st.noncentral_chisquare(D_arr_0p5, 0.5)
+random_st.noncentral_chisquare(0.5, D_arr_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1)
+random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1)
+random_st.noncentral_chisquare(D_arr_like_0p5, 0.5)
+random_st.noncentral_chisquare(0.5, D_arr_like_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5)
+random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5)
+random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1)
+random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.normal(0.5, 0.5)
+random_st.normal(0.5, 0.5, size=None)
+random_st.normal(0.5, 0.5, size=1)
+random_st.normal(D_arr_0p5, 0.5)
+random_st.normal(0.5, D_arr_0p5)
+random_st.normal(D_arr_0p5, 0.5, size=1)
+random_st.normal(0.5, D_arr_0p5, size=1)
+random_st.normal(D_arr_like_0p5, 0.5)
+random_st.normal(0.5, D_arr_like_0p5)
+random_st.normal(D_arr_0p5, D_arr_0p5)
+random_st.normal(D_arr_like_0p5, D_arr_like_0p5)
+random_st.normal(D_arr_0p5, D_arr_0p5, size=1)
+random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1)
+
+random_st.triangular(0.1, 0.5, 0.9)
+random_st.triangular(0.1, 0.5, 0.9, size=None)
+random_st.triangular(0.1, 0.5, 0.9, size=1)
+random_st.triangular(D_arr_0p1, 0.5, 0.9)
+random_st.triangular(0.1, D_arr_0p5, 0.9)
+random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+random_st.triangular(0.1, D_arr_0p5, 0.9, size=1)
+random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9)
+random_st.triangular(0.5, D_arr_like_0p5, 0.9)
+random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9)
+random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+random_st.noncentral_f(0.1, 0.5, 0.9)
+random_st.noncentral_f(0.1, 0.5, 0.9, size=None)
+random_st.noncentral_f(0.1, 0.5, 0.9, size=1)
+random_st.noncentral_f(D_arr_0p1, 0.5, 0.9)
+random_st.noncentral_f(0.1, D_arr_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1)
+random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1)
+random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9)
+random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9)
+random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9)
+random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1)
+random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1)
+
+random_st.binomial(10, 0.5)
+random_st.binomial(10, 0.5, size=None)
+random_st.binomial(10, 0.5, size=1)
+random_st.binomial(I_arr_10, 0.5)
+random_st.binomial(10, D_arr_0p5)
+random_st.binomial(I_arr_10, 0.5, size=1)
+random_st.binomial(10, D_arr_0p5, size=1)
+random_st.binomial(I_arr_like_10, 0.5)
+random_st.binomial(10, D_arr_like_0p5)
+random_st.binomial(I_arr_10, D_arr_0p5)
+random_st.binomial(I_arr_like_10, D_arr_like_0p5)
+random_st.binomial(I_arr_10, D_arr_0p5, size=1)
+random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+random_st.negative_binomial(10, 0.5)
+random_st.negative_binomial(10, 0.5, size=None)
+random_st.negative_binomial(10, 0.5, size=1)
+random_st.negative_binomial(I_arr_10, 0.5)
+random_st.negative_binomial(10, D_arr_0p5)
+random_st.negative_binomial(I_arr_10, 0.5, size=1)
+random_st.negative_binomial(10, D_arr_0p5, size=1)
+random_st.negative_binomial(I_arr_like_10, 0.5)
+random_st.negative_binomial(10, D_arr_like_0p5)
+random_st.negative_binomial(I_arr_10, D_arr_0p5)
+random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5)
+random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1)
+random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1)
+
+random_st.hypergeometric(20, 20, 10)
+random_st.hypergeometric(20, 20, 10, size=None)
+random_st.hypergeometric(20, 20, 10, size=1)
+random_st.hypergeometric(I_arr_20, 20, 10)
+random_st.hypergeometric(20, I_arr_20, 10)
+random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1)
+random_st.hypergeometric(20, I_arr_20, 10, size=1)
+random_st.hypergeometric(I_arr_like_20, 20, I_arr_10)
+random_st.hypergeometric(20, I_arr_like_20, 10)
+random_st.hypergeometric(I_arr_20, I_arr_20, 10)
+random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10)
+random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1)
+random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1)
+
+random_st.randint(0, 100)
+random_st.randint(100)
+random_st.randint([100])
+random_st.randint(0, [100])
+
+random_st.randint(2, dtype=bool)
+random_st.randint(0, 2, dtype=bool)
+random_st.randint(I_bool_high_open, dtype=bool)
+random_st.randint(I_bool_low, I_bool_high_open, dtype=bool)
+random_st.randint(0, I_bool_high_open, dtype=bool)
+
+random_st.randint(2, dtype=np.bool)
+random_st.randint(0, 2, dtype=np.bool)
+random_st.randint(I_bool_high_open, dtype=np.bool)
+random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool)
+random_st.randint(0, I_bool_high_open, dtype=np.bool)
+
+random_st.randint(256, dtype="u1")
+random_st.randint(0, 256, dtype="u1")
+random_st.randint(I_u1_high_open, dtype="u1")
+random_st.randint(I_u1_low, I_u1_high_open, dtype="u1")
+random_st.randint(0, I_u1_high_open, dtype="u1")
+
+random_st.randint(256, dtype="uint8")
+random_st.randint(0, 256, dtype="uint8")
+random_st.randint(I_u1_high_open, dtype="uint8")
+random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8")
+random_st.randint(0, I_u1_high_open, dtype="uint8")
+
+random_st.randint(256, dtype=np.uint8)
+random_st.randint(0, 256, dtype=np.uint8)
+random_st.randint(I_u1_high_open, dtype=np.uint8)
+random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8)
+random_st.randint(0, I_u1_high_open, dtype=np.uint8)
+
+random_st.randint(65536, dtype="u2")
+random_st.randint(0, 65536, dtype="u2")
+random_st.randint(I_u2_high_open, dtype="u2")
+random_st.randint(I_u2_low, I_u2_high_open, dtype="u2")
+random_st.randint(0, I_u2_high_open, dtype="u2")
+
+random_st.randint(65536, dtype="uint16")
+random_st.randint(0, 65536, dtype="uint16")
+random_st.randint(I_u2_high_open, dtype="uint16")
+random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16")
+random_st.randint(0, I_u2_high_open, dtype="uint16")
+
+random_st.randint(65536, dtype=np.uint16)
+random_st.randint(0, 65536, dtype=np.uint16)
+random_st.randint(I_u2_high_open, dtype=np.uint16)
+random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16)
+random_st.randint(0, I_u2_high_open, dtype=np.uint16)
+
+random_st.randint(4294967296, dtype="u4")
+random_st.randint(0, 4294967296, dtype="u4")
+random_st.randint(I_u4_high_open, dtype="u4")
+random_st.randint(I_u4_low, I_u4_high_open, dtype="u4")
+random_st.randint(0, I_u4_high_open, dtype="u4")
+
+random_st.randint(4294967296, dtype="uint32")
+random_st.randint(0, 4294967296, dtype="uint32")
+random_st.randint(I_u4_high_open, dtype="uint32")
+random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32")
+random_st.randint(0, I_u4_high_open, dtype="uint32")
+
+random_st.randint(4294967296, dtype=np.uint32)
+random_st.randint(0, 4294967296, dtype=np.uint32)
+random_st.randint(I_u4_high_open, dtype=np.uint32)
+random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32)
+random_st.randint(0, I_u4_high_open, dtype=np.uint32)
+
+
+random_st.randint(18446744073709551616, dtype="u8")
+random_st.randint(0, 18446744073709551616, dtype="u8")
+random_st.randint(I_u8_high_open, dtype="u8")
+random_st.randint(I_u8_low, I_u8_high_open, dtype="u8")
+random_st.randint(0, I_u8_high_open, dtype="u8")
+
+random_st.randint(18446744073709551616, dtype="uint64")
+random_st.randint(0, 18446744073709551616, dtype="uint64")
+random_st.randint(I_u8_high_open, dtype="uint64")
+random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64")
+random_st.randint(0, I_u8_high_open, dtype="uint64")
+
+random_st.randint(18446744073709551616, dtype=np.uint64)
+random_st.randint(0, 18446744073709551616, dtype=np.uint64)
+random_st.randint(I_u8_high_open, dtype=np.uint64)
+random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64)
+random_st.randint(0, I_u8_high_open, dtype=np.uint64)
+
+random_st.randint(128, dtype="i1")
+random_st.randint(-128, 128, dtype="i1")
+random_st.randint(I_i1_high_open, dtype="i1")
+random_st.randint(I_i1_low, I_i1_high_open, dtype="i1")
+random_st.randint(-128, I_i1_high_open, dtype="i1")
+
+random_st.randint(128, dtype="int8")
+random_st.randint(-128, 128, dtype="int8")
+random_st.randint(I_i1_high_open, dtype="int8")
+random_st.randint(I_i1_low, I_i1_high_open, dtype="int8")
+random_st.randint(-128, I_i1_high_open, dtype="int8")
+
+random_st.randint(128, dtype=np.int8)
+random_st.randint(-128, 128, dtype=np.int8)
+random_st.randint(I_i1_high_open, dtype=np.int8)
+random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8)
+random_st.randint(-128, I_i1_high_open, dtype=np.int8)
+
+random_st.randint(32768, dtype="i2")
+random_st.randint(-32768, 32768, dtype="i2")
+random_st.randint(I_i2_high_open, dtype="i2")
+random_st.randint(I_i2_low, I_i2_high_open, dtype="i2")
+random_st.randint(-32768, I_i2_high_open, dtype="i2")
+random_st.randint(32768, dtype="int16")
+random_st.randint(-32768, 32768, dtype="int16")
+random_st.randint(I_i2_high_open, dtype="int16")
+random_st.randint(I_i2_low, I_i2_high_open, dtype="int16")
+random_st.randint(-32768, I_i2_high_open, dtype="int16")
+random_st.randint(32768, dtype=np.int16)
+random_st.randint(-32768, 32768, dtype=np.int16)
+random_st.randint(I_i2_high_open, dtype=np.int16)
+random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16)
+random_st.randint(-32768, I_i2_high_open, dtype=np.int16)
+
+random_st.randint(2147483648, dtype="i4")
+random_st.randint(-2147483648, 2147483648, dtype="i4")
+random_st.randint(I_i4_high_open, dtype="i4")
+random_st.randint(I_i4_low, I_i4_high_open, dtype="i4")
+random_st.randint(-2147483648, I_i4_high_open, dtype="i4")
+
+random_st.randint(2147483648, dtype="int32")
+random_st.randint(-2147483648, 2147483648, dtype="int32")
+random_st.randint(I_i4_high_open, dtype="int32")
+random_st.randint(I_i4_low, I_i4_high_open, dtype="int32")
+random_st.randint(-2147483648, I_i4_high_open, dtype="int32")
+
+random_st.randint(2147483648, dtype=np.int32)
+random_st.randint(-2147483648, 2147483648, dtype=np.int32)
+random_st.randint(I_i4_high_open, dtype=np.int32)
+random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32)
+random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32)
+
+random_st.randint(9223372036854775808, dtype="i8")
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8")
+random_st.randint(I_i8_high_open, dtype="i8")
+random_st.randint(I_i8_low, I_i8_high_open, dtype="i8")
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8")
+
+random_st.randint(9223372036854775808, dtype="int64")
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64")
+random_st.randint(I_i8_high_open, dtype="int64")
+random_st.randint(I_i8_low, I_i8_high_open, dtype="int64")
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64")
+
+random_st.randint(9223372036854775808, dtype=np.int64)
+random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64)
+random_st.randint(I_i8_high_open, dtype=np.int64)
+random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64)
+random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64)
+
+bg: np.random.BitGenerator = random_st._bit_generator
+
+random_st.bytes(2)
+
+random_st.choice(5)
+random_st.choice(5, 3)
+random_st.choice(5, 3, replace=True)
+random_st.choice(5, 3, p=[1 / 5] * 5)
+random_st.choice(5, 3, p=[1 / 5] * 5, replace=False)
+
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"])
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True)
+random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4]))
+
+random_st.dirichlet([0.5, 0.5])
+random_st.dirichlet(np.array([0.5, 0.5]))
+random_st.dirichlet(np.array([0.5, 0.5]), size=3)
+
+random_st.multinomial(20, [1 / 6.0] * 6)
+random_st.multinomial(20, np.array([0.5, 0.5]))
+random_st.multinomial(20, [1 / 6.0] * 6, size=2)
+
+random_st.multivariate_normal([0.0], [[1.0]])
+random_st.multivariate_normal([0.0], np.array([[1.0]]))
+random_st.multivariate_normal(np.array([0.0]), [[1.0]])
+random_st.multivariate_normal([0.0], np.array([[1.0]]))
+
+random_st.permutation(10)
+random_st.permutation([1, 2, 3, 4])
+random_st.permutation(np.array([1, 2, 3, 4]))
+random_st.permutation(D_2D)
+
+random_st.shuffle(np.arange(10))
+random_st.shuffle([1, 2, 3, 4, 5])
+random_st.shuffle(D_2D)
+
+np.random.RandomState(SEED_PCG64)
+np.random.RandomState(0)
+np.random.RandomState([0, 1, 2])
+random_st.__str__()
+random_st.__repr__()
+random_st_state = random_st.__getstate__()
+random_st.__setstate__(random_st_state)
+random_st.seed()
+random_st.seed(1)
+random_st.seed([0, 1])
+random_st_get_state = random_st.get_state()
+random_st_get_state_legacy = random_st.get_state(legacy=True)
+random_st.set_state(random_st_get_state)
+
+random_st.rand()
+random_st.rand(1)
+random_st.rand(1, 2)
+random_st.randn()
+random_st.randn(1)
+random_st.randn(1, 2)
+random_st.random_sample()
+random_st.random_sample(1)
+random_st.random_sample(size=(1, 2))
+
+random_st.tomaxint()
+random_st.tomaxint(1)
+random_st.tomaxint((1,))
+
+np.random.mtrand.set_bit_generator(SEED_PCG64)
+np.random.mtrand.get_bit_generator()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..03322e064be4407c8748d415cc26f7e5861e1b7a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/recfunctions.py
@@ -0,0 +1,162 @@
+"""These tests are based on the doctests from `numpy/lib/recfunctions.py`."""
+
+from typing import Any
+from typing_extensions import assert_type
+
+import numpy as np
+import numpy.typing as npt
+from numpy.lib import recfunctions as rfn
+
+
+def test_recursive_fill_fields() -> None:
+    a: npt.NDArray[np.void] = np.array(
+        [(1, 10.0), (2, 20.0)],
+        dtype=[("A", np.int64), ("B", np.float64)],
+    )
+    b = np.zeros((int(3),), dtype=a.dtype)
+    out = rfn.recursive_fill_fields(a, b)
+    assert_type(out, np.ndarray[tuple[int], np.dtype[np.void]])
+
+
+def test_get_names() -> None:
+    names: tuple[str | Any, ...]
+    names = rfn.get_names(np.empty((1,), dtype=[("A", int)]).dtype)
+    names = rfn.get_names(np.empty((1,), dtype=[("A", int), ("B", float)]).dtype)
+
+    adtype = np.dtype([("a", int), ("b", [("b_a", int), ("b_b", int)])])
+    names = rfn.get_names(adtype)
+
+
+def test_get_names_flat() -> None:
+    names: tuple[str, ...]
+    names = rfn.get_names_flat(np.empty((1,), dtype=[("A", int)]).dtype)
+    names = rfn.get_names_flat(np.empty((1,), dtype=[("A", int), ("B", float)]).dtype)
+
+    adtype = np.dtype([("a", int), ("b", [("b_a", int), ("b_b", int)])])
+    names = rfn.get_names_flat(adtype)
+
+
+def test_flatten_descr() -> None:
+    ndtype = np.dtype([("a", " None:
+    ndtype = np.dtype([
+        ("A", int),
+        ("B", [("B_A", int), ("B_B", [("B_B_A", int), ("B_B_B", int)])]),
+    ])
+    assert_type(rfn.get_fieldstructure(ndtype), dict[str, list[str]])
+
+
+def test_merge_arrays() -> None:
+    assert_type(
+        rfn.merge_arrays((
+            np.ones((int(2),), np.int_),
+            np.ones((int(3),), np.float64),
+        )),
+        np.recarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_drop_fields() -> None:
+    ndtype = [("a", np.int64), ("b", [("b_a", np.double), ("b_b", np.int64)])]
+    a = np.ones((int(3),), dtype=ndtype)
+
+    assert_type(
+        rfn.drop_fields(a, "a"),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+    assert_type(
+        rfn.drop_fields(a, "a", asrecarray=True),
+        np.rec.recarray[tuple[int], np.dtype[np.void]],
+    )
+    assert_type(
+        rfn.rec_drop_fields(a, "a"),
+        np.rec.recarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_rename_fields() -> None:
+    ndtype = [("a", np.int64), ("b", [("b_a", np.double), ("b_b", np.int64)])]
+    a = np.ones((int(3),), dtype=ndtype)
+
+    assert_type(
+        rfn.rename_fields(a, {"a": "A", "b_b": "B_B"}),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_repack_fields() -> None:
+    dt: np.dtype[np.void] = np.dtype("u1,  None:
+    a = np.zeros(4, dtype=[("a", "i4"), ("b", "f4,u2"), ("c", "f4", 2)])
+    assert_type(rfn.structured_to_unstructured(a), npt.NDArray[Any])
+
+
+def unstructured_to_structured() -> None:
+    dt: np.dtype[np.void] = np.dtype([("a", "i4"), ("b", "f4,u2"), ("c", "f4", 2)])
+    a = np.arange(20, dtype=np.int32).reshape((4, 5))
+    assert_type(rfn.unstructured_to_structured(a, dt), npt.NDArray[np.void])
+
+
+def test_apply_along_fields() -> None:
+    b = np.ones(4, dtype=[("x", "i4"), ("y", "f4"), ("z", "f8")])
+    assert_type(
+        rfn.apply_along_fields(np.mean, b),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_assign_fields_by_name() -> None:
+    b = np.ones(4, dtype=[("x", "i4"), ("y", "f4"), ("z", "f8")])
+    assert_type(
+        rfn.apply_along_fields(np.mean, b),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_require_fields() -> None:
+    a = np.ones(4, dtype=[("a", "i4"), ("b", "f8"), ("c", "u1")])
+    assert_type(
+        rfn.require_fields(a, [("b", "f4"), ("c", "u1")]),
+        np.ndarray[tuple[int], np.dtype[np.void]],
+    )
+
+
+def test_stack_arrays() -> None:
+    x = np.zeros((int(2),), np.int32)
+    assert_type(
+        rfn.stack_arrays(x),
+        np.ndarray[tuple[int], np.dtype[np.int32]],
+    )
+
+    z = np.ones((int(2),), [("A", "|S3"), ("B", float)])
+    zz = np.ones((int(2),), [("A", "|S3"), ("B", np.float64), ("C", np.float64)])
+    assert_type(
+        rfn.stack_arrays((z, zz)),
+        np.ma.MaskedArray[tuple[int, ...], np.dtype[np.void]],
+    )
+
+
+def test_find_duplicates() -> None:
+    ndtype = np.dtype([("a", int)])
+
+    a = np.ma.ones(7, mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
+    assert_type(rfn.find_duplicates(a), np.ma.MaskedArray[Any, np.dtype[np.void]])
+    assert_type(
+        rfn.find_duplicates(a, ignoremask=True, return_index=True),
+        tuple[
+            np.ma.MaskedArray[Any, np.dtype[np.void]],
+            np.ndarray[Any, np.dtype[np.int_]],
+        ],
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py
new file mode 100644
index 0000000000000000000000000000000000000000..89f24cb92991ba6586b21e1d707fb0d023731b89
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/scalars.py
@@ -0,0 +1,248 @@
+import datetime as dt
+
+import pytest
+import numpy as np
+
+b =  np.bool()
+b_ = np.bool_()
+u8 = np.uint64()
+i8 = np.int64()
+f8 = np.float64()
+c16 = np.complex128()
+U = np.str_()
+S = np.bytes_()
+
+
+# Construction
+class D:
+    def __index__(self) -> int:
+        return 0
+
+
+class C:
+    def __complex__(self) -> complex:
+        return 3j
+
+
+class B:
+    def __int__(self) -> int:
+        return 4
+
+
+class A:
+    def __float__(self) -> float:
+        return 4.0
+
+
+np.complex64(3j)
+np.complex64(A())
+np.complex64(C())
+np.complex128(3j)
+np.complex128(C())
+np.complex128(None)
+np.complex64("1.2")
+np.complex128(b"2j")
+
+np.int8(4)
+np.int16(3.4)
+np.int32(4)
+np.int64(-1)
+np.uint8(B())
+np.uint32()
+np.int32("1")
+np.int64(b"2")
+
+np.float16(A())
+np.float32(16)
+np.float64(3.0)
+np.float64(None)
+np.float32("1")
+np.float16(b"2.5")
+
+np.uint64(D())
+np.float32(D())
+np.complex64(D())
+
+np.bytes_(b"hello")
+np.bytes_("hello", 'utf-8')
+np.bytes_("hello", encoding='utf-8')
+np.str_("hello")
+np.str_(b"hello", 'utf-8')
+np.str_(b"hello", encoding='utf-8')
+
+# Array-ish semantics
+np.int8().real
+np.int16().imag
+np.int32().data
+np.int64().flags
+
+np.uint8().itemsize * 2
+np.uint16().ndim + 1
+np.uint32().strides
+np.uint64().shape
+
+# Time structures
+np.datetime64()
+np.datetime64(0, "D")
+np.datetime64(0, b"D")
+np.datetime64(0, ('ms', 3))
+np.datetime64("2019")
+np.datetime64(b"2019")
+np.datetime64("2019", "D")
+np.datetime64("2019", "us")
+np.datetime64("2019", "as")
+np.datetime64(np.datetime64())
+np.datetime64(np.datetime64())
+np.datetime64(dt.datetime(2000, 5, 3))
+np.datetime64(dt.datetime(2000, 5, 3), "D")
+np.datetime64(dt.datetime(2000, 5, 3), "us")
+np.datetime64(dt.datetime(2000, 5, 3), "as")
+np.datetime64(dt.date(2000, 5, 3))
+np.datetime64(dt.date(2000, 5, 3), "D")
+np.datetime64(dt.date(2000, 5, 3), "us")
+np.datetime64(dt.date(2000, 5, 3), "as")
+np.datetime64(None)
+np.datetime64(None, "D")
+
+np.timedelta64()
+np.timedelta64(0)
+np.timedelta64(0, "D")
+np.timedelta64(0, ('ms', 3))
+np.timedelta64(0, b"D")
+np.timedelta64("3")
+np.timedelta64(b"5")
+np.timedelta64(np.timedelta64(2))
+np.timedelta64(dt.timedelta(2))
+np.timedelta64(None)
+np.timedelta64(None, "D")
+
+np.void(1)
+np.void(np.int64(1))
+np.void(True)
+np.void(np.bool(True))
+np.void(b"test")
+np.void(np.bytes_("test"))
+np.void(object(), [("a", "O"), ("b", "O")])
+np.void(object(), dtype=[("a", "O"), ("b", "O")])
+
+# Protocols
+i8 = np.int64()
+u8 = np.uint64()
+f8 = np.float64()
+c16 = np.complex128()
+b = np.bool()
+td = np.timedelta64()
+U = np.str_("1")
+S = np.bytes_("1")
+AR = np.array(1, dtype=np.float64)
+
+int(i8)
+int(u8)
+int(f8)
+int(b)
+int(td)
+int(U)
+int(S)
+int(AR)
+with pytest.warns(np.exceptions.ComplexWarning):
+    int(c16)
+
+float(i8)
+float(u8)
+float(f8)
+float(b_)
+float(td)
+float(U)
+float(S)
+float(AR)
+with pytest.warns(np.exceptions.ComplexWarning):
+    float(c16)
+
+complex(i8)
+complex(u8)
+complex(f8)
+complex(c16)
+complex(b_)
+complex(td)
+complex(U)
+complex(AR)
+
+
+# Misc
+c16.dtype
+c16.real
+c16.imag
+c16.real.real
+c16.real.imag
+c16.ndim
+c16.size
+c16.itemsize
+c16.shape
+c16.strides
+c16.squeeze()
+c16.byteswap()
+c16.transpose()
+
+# Aliases
+np.byte()
+np.short()
+np.intc()
+np.intp()
+np.int_()
+np.longlong()
+
+np.ubyte()
+np.ushort()
+np.uintc()
+np.uintp()
+np.uint()
+np.ulonglong()
+
+np.half()
+np.single()
+np.double()
+np.longdouble()
+
+np.csingle()
+np.cdouble()
+np.clongdouble()
+
+b.item()
+i8.item()
+u8.item()
+f8.item()
+c16.item()
+U.item()
+S.item()
+
+b.tolist()
+i8.tolist()
+u8.tolist()
+f8.tolist()
+c16.tolist()
+U.tolist()
+S.tolist()
+
+b.ravel()
+i8.ravel()
+u8.ravel()
+f8.ravel()
+c16.ravel()
+U.ravel()
+S.ravel()
+
+b.flatten()
+i8.flatten()
+u8.flatten()
+f8.flatten()
+c16.flatten()
+U.flatten()
+S.flatten()
+
+b.reshape(1)
+i8.reshape(1)
+u8.reshape(1)
+f8.reshape(1)
+c16.reshape(1)
+U.reshape(1)
+S.reshape(1)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab1ae3d9bc79e19d6000bee350b18fe34d9abdc0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/shape.py
@@ -0,0 +1,21 @@
+from typing import Any, NamedTuple, cast
+
+import numpy as np
+
+
+# Subtype of tuple[int, int]
+class XYGrid(NamedTuple):
+    x_axis: int
+    y_axis: int
+
+# TODO: remove this cast after: https://github.com/numpy/numpy/pull/27171
+arr: np.ndarray[XYGrid, Any] = cast(
+    np.ndarray[XYGrid, Any],
+    np.empty(XYGrid(2, 2)),
+)
+
+# Test variance of _ShapeType_co
+def accepts_2d(a: np.ndarray[tuple[int, int], Any]) -> None:
+    return None
+
+accepts_2d(arr)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f44e6e76f8353d55a29f6cc0608d39e7db433dc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple.py
@@ -0,0 +1,168 @@
+"""Simple expression that should pass with mypy."""
+import operator
+
+import numpy as np
+import numpy.typing as npt
+from collections.abc import Iterable
+
+# Basic checks
+array = np.array([1, 2])
+
+
+def ndarray_func(x: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
+    return x
+
+
+ndarray_func(np.array([1, 2], dtype=np.float64))
+array == 1
+array.dtype == float
+
+# Dtype construction
+np.dtype(float)
+np.dtype(np.float64)
+np.dtype(None)
+np.dtype("float64")
+np.dtype(np.dtype(float))
+np.dtype(("U", 10))
+np.dtype((np.int32, (2, 2)))
+# Define the arguments on the previous line to prevent bidirectional
+# type inference in mypy from broadening the types.
+two_tuples_dtype = [("R", "u1"), ("G", "u1"), ("B", "u1")]
+np.dtype(two_tuples_dtype)
+
+three_tuples_dtype = [("R", "u1", 2)]
+np.dtype(three_tuples_dtype)
+
+mixed_tuples_dtype = [("R", "u1"), ("G", np.str_, 1)]
+np.dtype(mixed_tuples_dtype)
+
+shape_tuple_dtype = [("R", "u1", (2, 2))]
+np.dtype(shape_tuple_dtype)
+
+shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.str_, 1)]
+np.dtype(shape_like_dtype)
+
+object_dtype = [("field1", object)]
+np.dtype(object_dtype)
+
+np.dtype((np.int32, (np.int8, 4)))
+
+# Dtype comparison
+np.dtype(float) == float
+np.dtype(float) != np.float64
+np.dtype(float) < None
+np.dtype(float) <= "float64"
+np.dtype(float) > np.dtype(float)
+np.dtype(float) >= np.dtype(("U", 10))
+
+# Iteration and indexing
+def iterable_func(x: Iterable[object]) -> Iterable[object]:
+    return x
+
+
+iterable_func(array)
+list(array)
+iter(array)
+zip(array, array)
+array[1]
+array[:]
+array[...]
+array[:] = 0
+
+array_2d = np.ones((3, 3))
+array_2d[:2, :2]
+array_2d[:2, :2] = 0
+array_2d[..., 0]
+array_2d[..., 0] = 2
+array_2d[-1, -1] = None
+
+array_obj = np.zeros(1, dtype=np.object_)
+array_obj[0] = slice(None)
+
+# Other special methods
+len(array)
+str(array)
+array_scalar = np.array(1)
+int(array_scalar)
+float(array_scalar)
+complex(array_scalar)
+bytes(array_scalar)
+operator.index(array_scalar)
+bool(array_scalar)
+
+# comparisons
+array < 1
+array <= 1
+array == 1
+array != 1
+array > 1
+array >= 1
+1 < array
+1 <= array
+1 == array
+1 != array
+1 > array
+1 >= array
+
+# binary arithmetic
+array + 1
+1 + array
+array += 1
+
+array - 1
+1 - array
+array -= 1
+
+array * 1
+1 * array
+array *= 1
+
+nonzero_array = np.array([1, 2])
+array / 1
+1 / nonzero_array
+float_array = np.array([1.0, 2.0])
+float_array /= 1
+
+array // 1
+1 // nonzero_array
+array //= 1
+
+array % 1
+1 % nonzero_array
+array %= 1
+
+divmod(array, 1)
+divmod(1, nonzero_array)
+
+array ** 1
+1 ** array
+array **= 1
+
+array << 1
+1 << array
+array <<= 1
+
+array >> 1
+1 >> array
+array >>= 1
+
+array & 1
+1 & array
+array &= 1
+
+array ^ 1
+1 ^ array
+array ^= 1
+
+array | 1
+1 | array
+array |= 1
+
+# unary arithmetic
+-array
++array
+abs(array)
+~array
+
+# Other methods
+np.array([1, 2]).transpose()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c05a1ce612ac5e40d0914732c4c72ad1d3f2d552
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/simple_py3.py
@@ -0,0 +1,6 @@
+import numpy as np
+
+array = np.array([1, 2])
+
+# The @ operator is not in python 2
+array @ array
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..778e1b57f7e3831464f0bc9ba37e4baddcdf49d8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunc_config.py
@@ -0,0 +1,64 @@
+"""Typing tests for `numpy._core._ufunc_config`."""
+
+import numpy as np
+
+
+def func1(a: str, b: int) -> None:
+    return None
+
+
+def func2(a: str, b: int, c: float = 1.0) -> None:
+    return None
+
+
+def func3(a: str, b: int) -> int:
+    return 0
+
+
+class Write1:
+    def write(self, a: str) -> None:
+        return None
+
+
+class Write2:
+    def write(self, a: str, b: int = 1) -> None:
+        return None
+
+
+class Write3:
+    def write(self, a: str) -> int:
+        return 0
+
+
+_err_default = np.geterr()
+_bufsize_default = np.getbufsize()
+_errcall_default = np.geterrcall()
+
+try:
+    np.seterr(all=None)
+    np.seterr(divide="ignore")
+    np.seterr(over="warn")
+    np.seterr(under="call")
+    np.seterr(invalid="raise")
+    np.geterr()
+
+    np.setbufsize(4096)
+    np.getbufsize()
+
+    np.seterrcall(func1)
+    np.seterrcall(func2)
+    np.seterrcall(func3)
+    np.seterrcall(Write1())
+    np.seterrcall(Write2())
+    np.seterrcall(Write3())
+    np.geterrcall()
+
+    with np.errstate(call=func1, all="call"):
+        pass
+    with np.errstate(call=Write1(), divide="log", over="log"):
+        pass
+
+finally:
+    np.seterr(**_err_default)
+    np.setbufsize(_bufsize_default)
+    np.seterrcall(_errcall_default)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py
new file mode 100644
index 0000000000000000000000000000000000000000..f993939ddba12851c5c93d3eed0f6d0923d0b98d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufunclike.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+
+class Object:
+    def __ceil__(self) -> Object:
+        return self
+
+    def __floor__(self) -> Object:
+        return self
+
+    def __ge__(self, value: object) -> bool:
+        return True
+
+    def __array__(self, dtype: np.typing.DTypeLike | None = None,
+                  copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+
+AR_LIKE_b = [True, True, False]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_O = [Object(), Object(), Object()]
+AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.zeros(3, dtype="U5")
+
+np.fix(AR_LIKE_b)
+np.fix(AR_LIKE_u)
+np.fix(AR_LIKE_i)
+np.fix(AR_LIKE_f)
+np.fix(AR_LIKE_O)
+np.fix(AR_LIKE_f, out=AR_U)
+
+np.isposinf(AR_LIKE_b)
+np.isposinf(AR_LIKE_u)
+np.isposinf(AR_LIKE_i)
+np.isposinf(AR_LIKE_f)
+np.isposinf(AR_LIKE_f, out=AR_U)
+
+np.isneginf(AR_LIKE_b)
+np.isneginf(AR_LIKE_u)
+np.isneginf(AR_LIKE_i)
+np.isneginf(AR_LIKE_f)
+np.isneginf(AR_LIKE_f, out=AR_U)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbc61bb0b17b594865bc909787798238c5b32bf5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/ufuncs.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+np.sin(1)
+np.sin([1, 2, 3])
+np.sin(1, out=np.empty(1))
+np.matmul(np.ones((2, 2, 2)), np.ones((2, 2, 2)), axes=[(0, 1), (0, 1), (0, 1)])
+np.sin(1, signature="D->D")
+# NOTE: `np.generic` subclasses are not guaranteed to support addition;
+# re-enable this we can infer the exact return type of `np.sin(...)`.
+#
+# np.sin(1) + np.sin(1)
+np.sin.types[0]
+np.sin.__name__
+np.sin.__doc__
+
+np.abs(np.array([1]))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c351afb084c57b2d1264a3ff020ed1e63703a623
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py
@@ -0,0 +1,6 @@
+import numpy.exceptions as ex
+
+ex.AxisError("test")
+ex.AxisError(1, ndim=2)
+ex.AxisError(1, ndim=2, msg_prefix="error")
+ex.AxisError(1, ndim=2, msg_prefix=None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c0b94bae08a10aca0a72cba8ee54ef87e19c7ab7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi
@@ -0,0 +1,677 @@
+import datetime as dt
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _32Bit,_64Bit, _128Bit
+
+from typing_extensions import assert_type
+
+b: bool
+c: complex
+f: float
+i: int
+
+c16: np.complex128
+c8: np.complex64
+
+# Can't directly import `np.float128` as it is not available on all platforms
+f16: np.floating[_128Bit]
+f8: np.float64
+f4: np.float32
+
+i8: np.int64
+i4: np.int32
+
+u8: np.uint64
+u4: np.uint32
+
+b_: np.bool
+
+M8: np.datetime64
+M8_none: np.datetime64[None]
+M8_date: np.datetime64[dt.date]
+M8_time: np.datetime64[dt.datetime]
+M8_int: np.datetime64[int]
+date: dt.date
+time: dt.datetime
+
+m8: np.timedelta64
+m8_none: np.timedelta64[None]
+m8_int: np.timedelta64[int]
+m8_delta: np.timedelta64[dt.timedelta]
+delta: dt.timedelta
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint32]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_floating: npt.NDArray[np.floating]
+AR_number: npt.NDArray[np.number]
+AR_Any: npt.NDArray[Any]
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_m: list[np.timedelta64]
+AR_LIKE_M: list[np.datetime64]
+AR_LIKE_O: list[np.object_]
+
+
+# Array subtraction
+
+assert_type(AR_number - AR_number, npt.NDArray[np.number[Any]])
+
+assert_type(AR_b - AR_LIKE_u, npt.NDArray[np.uint32])
+assert_type(AR_b - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_b - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_b - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_b - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_b - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_u - AR_b, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_i - AR_b, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_b, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_b, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_b, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_b, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_b, Any)
+
+assert_type(AR_u - AR_LIKE_b, npt.NDArray[np.uint32])
+assert_type(AR_u - AR_LIKE_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_u - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_u - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_u - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_u - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_u - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_u, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_u - AR_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_LIKE_i - AR_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_u, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_u, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_u, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_u, Any)
+
+assert_type(AR_i - AR_LIKE_b, npt.NDArray[np.int64])
+assert_type(AR_i - AR_LIKE_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i - AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i - AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_i - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_i - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_i - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_i, npt.NDArray[np.int64])
+assert_type(AR_LIKE_u - AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_i - AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f - AR_i, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_c - AR_i, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_m - AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_i, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_i, Any)
+
+assert_type(AR_f - AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f - AR_LIKE_c, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_f - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f - AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_c - AR_f, npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(AR_LIKE_O - AR_f, Any)
+
+assert_type(AR_c - AR_LIKE_b, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_u, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_i, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_f, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_c, npt.NDArray[np.complex128])
+assert_type(AR_c - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_u - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_i - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_f - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_c - AR_c, npt.NDArray[np.complex128])
+assert_type(AR_LIKE_O - AR_c, Any)
+
+assert_type(AR_m - AR_LIKE_b, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_m, npt.NDArray[np.timedelta64])
+assert_type(AR_m - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_u - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_i - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_m - AR_m, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_M - AR_m, npt.NDArray[np.datetime64])
+assert_type(AR_LIKE_O - AR_m, Any)
+
+assert_type(AR_M - AR_LIKE_b, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_u, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_i, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_m, npt.NDArray[np.datetime64])
+assert_type(AR_M - AR_LIKE_M, npt.NDArray[np.timedelta64])
+assert_type(AR_M - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_M - AR_M, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O - AR_M, Any)
+
+assert_type(AR_O - AR_LIKE_b, Any)
+assert_type(AR_O - AR_LIKE_u, Any)
+assert_type(AR_O - AR_LIKE_i, Any)
+assert_type(AR_O - AR_LIKE_f, Any)
+assert_type(AR_O - AR_LIKE_c, Any)
+assert_type(AR_O - AR_LIKE_m, Any)
+assert_type(AR_O - AR_LIKE_M, Any)
+assert_type(AR_O - AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b - AR_O, Any)
+assert_type(AR_LIKE_u - AR_O, Any)
+assert_type(AR_LIKE_i - AR_O, Any)
+assert_type(AR_LIKE_f - AR_O, Any)
+assert_type(AR_LIKE_c - AR_O, Any)
+assert_type(AR_LIKE_m - AR_O, Any)
+assert_type(AR_LIKE_M - AR_O, Any)
+assert_type(AR_LIKE_O - AR_O, Any)
+
+# Array "true" division
+
+assert_type(AR_f / b, npt.NDArray[np.float64])
+assert_type(AR_f / i, npt.NDArray[np.float64])
+assert_type(AR_f / f, npt.NDArray[np.float64])
+
+assert_type(b / AR_f, npt.NDArray[np.float64])
+assert_type(i / AR_f, npt.NDArray[np.float64])
+assert_type(f / AR_f, npt.NDArray[np.float64])
+
+assert_type(AR_b / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_b / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_b, npt.NDArray[np.float64])
+assert_type(AR_LIKE_O / AR_b, Any)
+
+assert_type(AR_u / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_u / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_u, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_u, Any)
+
+assert_type(AR_i / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_i / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_i, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_i, Any)
+
+assert_type(AR_f / AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f / AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m / AR_f, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O / AR_f, Any)
+
+assert_type(AR_m / AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_f, npt.NDArray[np.timedelta64])
+assert_type(AR_m / AR_LIKE_m, npt.NDArray[np.float64])
+assert_type(AR_m / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_m / AR_m, npt.NDArray[np.float64])
+assert_type(AR_LIKE_O / AR_m, Any)
+
+assert_type(AR_O / AR_LIKE_b, Any)
+assert_type(AR_O / AR_LIKE_u, Any)
+assert_type(AR_O / AR_LIKE_i, Any)
+assert_type(AR_O / AR_LIKE_f, Any)
+assert_type(AR_O / AR_LIKE_m, Any)
+assert_type(AR_O / AR_LIKE_M, Any)
+assert_type(AR_O / AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b / AR_O, Any)
+assert_type(AR_LIKE_u / AR_O, Any)
+assert_type(AR_LIKE_i / AR_O, Any)
+assert_type(AR_LIKE_f / AR_O, Any)
+assert_type(AR_LIKE_m / AR_O, Any)
+assert_type(AR_LIKE_M / AR_O, Any)
+assert_type(AR_LIKE_O / AR_O, Any)
+
+# Array floor division
+
+assert_type(AR_b // AR_LIKE_b, npt.NDArray[np.int8])
+assert_type(AR_b // AR_LIKE_u, npt.NDArray[np.uint32])
+assert_type(AR_b // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_b // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_b // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_b, npt.NDArray[np.int8])
+assert_type(AR_LIKE_u // AR_b, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_i // AR_b, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_b, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_O // AR_b, Any)
+
+assert_type(AR_u // AR_LIKE_b, npt.NDArray[np.uint32])
+assert_type(AR_u // AR_LIKE_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_u // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_u // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_u // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_u, npt.NDArray[np.uint32])
+assert_type(AR_LIKE_u // AR_u, npt.NDArray[np.unsignedinteger[Any]])
+assert_type(AR_LIKE_i // AR_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_u, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_m // AR_u, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_u, Any)
+
+assert_type(AR_i // AR_LIKE_b, npt.NDArray[np.int64])
+assert_type(AR_i // AR_LIKE_u, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i // AR_LIKE_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_i // AR_LIKE_f, npt.NDArray[np.floating[Any]])
+assert_type(AR_i // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_i, npt.NDArray[np.int64])
+assert_type(AR_LIKE_u // AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_i // AR_i, npt.NDArray[np.signedinteger[Any]])
+assert_type(AR_LIKE_f // AR_i, npt.NDArray[np.floating[Any]])
+assert_type(AR_LIKE_m // AR_i, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_i, Any)
+
+assert_type(AR_f // AR_LIKE_b, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_u, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_i, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_f, npt.NDArray[np.float64])
+assert_type(AR_f // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_u // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_i // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_f // AR_f, npt.NDArray[np.float64])
+assert_type(AR_LIKE_m // AR_f, npt.NDArray[np.timedelta64])
+assert_type(AR_LIKE_O // AR_f, Any)
+
+assert_type(AR_m // AR_LIKE_u, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_i, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_f, npt.NDArray[np.timedelta64])
+assert_type(AR_m // AR_LIKE_m, npt.NDArray[np.int64])
+assert_type(AR_m // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_m // AR_m, npt.NDArray[np.int64])
+assert_type(AR_LIKE_O // AR_m, Any)
+
+assert_type(AR_O // AR_LIKE_b, Any)
+assert_type(AR_O // AR_LIKE_u, Any)
+assert_type(AR_O // AR_LIKE_i, Any)
+assert_type(AR_O // AR_LIKE_f, Any)
+assert_type(AR_O // AR_LIKE_m, Any)
+assert_type(AR_O // AR_LIKE_M, Any)
+assert_type(AR_O // AR_LIKE_O, Any)
+
+assert_type(AR_LIKE_b // AR_O, Any)
+assert_type(AR_LIKE_u // AR_O, Any)
+assert_type(AR_LIKE_i // AR_O, Any)
+assert_type(AR_LIKE_f // AR_O, Any)
+assert_type(AR_LIKE_m // AR_O, Any)
+assert_type(AR_LIKE_M // AR_O, Any)
+assert_type(AR_LIKE_O // AR_O, Any)
+
+# unary ops
+
+assert_type(-f16, np.floating[_128Bit])
+assert_type(-c16, np.complex128)
+assert_type(-c8, np.complex64)
+assert_type(-f8, np.float64)
+assert_type(-f4, np.float32)
+assert_type(-i8, np.int64)
+assert_type(-i4, np.int32)
+assert_type(-u8, np.uint64)
+assert_type(-u4, np.uint32)
+assert_type(-m8, np.timedelta64)
+assert_type(-m8_none, np.timedelta64[None])
+assert_type(-m8_int, np.timedelta64[int])
+assert_type(-m8_delta, np.timedelta64[dt.timedelta])
+assert_type(-AR_f, npt.NDArray[np.float64])
+
+assert_type(+f16, np.floating[_128Bit])
+assert_type(+c16, np.complex128)
+assert_type(+c8, np.complex64)
+assert_type(+f8, np.float64)
+assert_type(+f4, np.float32)
+assert_type(+i8, np.int64)
+assert_type(+i4, np.int32)
+assert_type(+u8, np.uint64)
+assert_type(+u4, np.uint32)
+assert_type(+m8_none, np.timedelta64[None])
+assert_type(+m8_int, np.timedelta64[int])
+assert_type(+m8_delta, np.timedelta64[dt.timedelta])
+assert_type(+AR_f, npt.NDArray[np.float64])
+
+assert_type(abs(f16), np.floating[_128Bit])
+assert_type(abs(c16), np.float64)
+assert_type(abs(c8), np.float32)
+assert_type(abs(f8), np.float64)
+assert_type(abs(f4), np.float32)
+assert_type(abs(i8), np.int64)
+assert_type(abs(i4), np.int32)
+assert_type(abs(u8), np.uint64)
+assert_type(abs(u4), np.uint32)
+assert_type(abs(m8), np.timedelta64)
+assert_type(abs(m8_none), np.timedelta64[None])
+assert_type(abs(m8_int), np.timedelta64[int])
+assert_type(abs(m8_delta), np.timedelta64[dt.timedelta])
+assert_type(abs(b_), np.bool)
+assert_type(abs(AR_O), npt.NDArray[np.object_])
+
+# Time structures
+
+assert_type(M8 + m8, np.datetime64)
+assert_type(M8 + i, np.datetime64)
+assert_type(M8 + i8, np.datetime64)
+assert_type(M8 - M8, np.timedelta64)
+assert_type(M8 - i, np.datetime64)
+assert_type(M8 - i8, np.datetime64)
+
+assert_type(M8_none + m8, np.datetime64[None])
+assert_type(M8_none + i, np.datetime64[None])
+assert_type(M8_none + i8, np.datetime64[None])
+assert_type(M8_none - M8, np.timedelta64[None])
+assert_type(M8_none - m8, np.datetime64[None])
+assert_type(M8_none - i, np.datetime64[None])
+assert_type(M8_none - i8, np.datetime64[None])
+
+assert_type(m8 + m8, np.timedelta64)
+assert_type(m8 + i, np.timedelta64)
+assert_type(m8 + i8, np.timedelta64)
+assert_type(m8 - m8, np.timedelta64)
+assert_type(m8 - i, np.timedelta64)
+assert_type(m8 - i8, np.timedelta64)
+assert_type(m8 * f, np.timedelta64)
+assert_type(m8 * f4, np.timedelta64)
+assert_type(m8 * np.True_, np.timedelta64)
+assert_type(m8 / f, np.timedelta64)
+assert_type(m8 / f4, np.timedelta64)
+assert_type(m8 / m8, np.float64)
+assert_type(m8 // m8, np.int64)
+assert_type(m8 % m8, np.timedelta64)
+assert_type(divmod(m8, m8), tuple[np.int64, np.timedelta64])
+
+assert_type(m8_none + m8, np.timedelta64[None])
+assert_type(m8_none + i, np.timedelta64[None])
+assert_type(m8_none + i8, np.timedelta64[None])
+assert_type(m8_none - i, np.timedelta64[None])
+assert_type(m8_none - i8, np.timedelta64[None])
+
+assert_type(m8_int + i, np.timedelta64[int])
+assert_type(m8_int + m8_delta, np.timedelta64[int])
+assert_type(m8_int + m8, np.timedelta64[int | None])
+assert_type(m8_int - i, np.timedelta64[int])
+assert_type(m8_int - m8_delta, np.timedelta64[int])
+assert_type(m8_int - m8, np.timedelta64[int | None])
+
+assert_type(m8_delta + date, dt.date)
+assert_type(m8_delta + time, dt.datetime)
+assert_type(m8_delta + delta, dt.timedelta)
+assert_type(m8_delta - delta, dt.timedelta)
+assert_type(m8_delta / delta, float)
+assert_type(m8_delta // delta, int)
+assert_type(m8_delta % delta, dt.timedelta)
+assert_type(divmod(m8_delta, delta), tuple[int, dt.timedelta])
+
+# boolean
+
+assert_type(b_ / b, np.float64)
+assert_type(b_ / b_, np.float64)
+assert_type(b_ / i, np.float64)
+assert_type(b_ / i8, np.float64)
+assert_type(b_ / i4, np.float64)
+assert_type(b_ / u8, np.float64)
+assert_type(b_ / u4, np.float64)
+assert_type(b_ / f, np.float64)
+assert_type(b_ / f16, np.floating[_128Bit])
+assert_type(b_ / f8, np.float64)
+assert_type(b_ / f4, np.float32)
+assert_type(b_ / c, np.complex128)
+assert_type(b_ / c16, np.complex128)
+assert_type(b_ / c8, np.complex64)
+
+assert_type(b / b_, np.float64)
+assert_type(b_ / b_, np.float64)
+assert_type(i / b_, np.float64)
+assert_type(i8 / b_, np.float64)
+assert_type(i4 / b_, np.float64)
+assert_type(u8 / b_, np.float64)
+assert_type(u4 / b_, np.float64)
+assert_type(f / b_, np.float64)
+assert_type(f16 / b_, np.floating[_128Bit])
+assert_type(f8 / b_, np.float64)
+assert_type(f4 / b_, np.float32)
+assert_type(c / b_, np.complex128)
+assert_type(c16 / b_, np.complex128)
+assert_type(c8 / b_, np.complex64)
+
+# Complex
+
+assert_type(c16 + f16, np.complex128 | np.complexfloating[_128Bit, _128Bit])
+assert_type(c16 + c16, np.complex128)
+assert_type(c16 + f8, np.complex128)
+assert_type(c16 + i8, np.complex128)
+assert_type(c16 + c8, np.complex128)
+assert_type(c16 + f4, np.complex128)
+assert_type(c16 + i4, np.complex128)
+assert_type(c16 + b_, np.complex128)
+assert_type(c16 + b, np.complex128)
+assert_type(c16 + c, np.complex128)
+assert_type(c16 + f, np.complex128)
+assert_type(c16 + AR_f, npt.NDArray[np.complex128])
+
+assert_type(f16 + c16, np.complex128 | np.complexfloating[_128Bit, _128Bit])
+assert_type(c16 + c16, np.complex128)
+assert_type(f8 + c16, np.complex128)
+assert_type(i8 + c16, np.complex128)
+assert_type(c8 + c16, np.complex128 | np.complex64)
+assert_type(f4 + c16, np.complex128 | np.complex64)
+assert_type(i4 + c16, np.complex128)
+assert_type(b_ + c16, np.complex128)
+assert_type(b + c16, np.complex128)
+assert_type(c + c16, np.complex128)
+assert_type(f + c16, np.complex128)
+assert_type(AR_f + c16, npt.NDArray[np.complex128])
+
+assert_type(c8 + f16, np.complexfloating[_32Bit, _32Bit] | np.complexfloating[_128Bit, _128Bit])
+assert_type(c8 + c16, np.complex64 | np.complex128)
+assert_type(c8 + f8, np.complex64 | np.complex128)
+assert_type(c8 + i8, np.complexfloating[_32Bit, _32Bit] | np.complexfloating[_64Bit, _64Bit])
+assert_type(c8 + c8, np.complex64)
+assert_type(c8 + f4, np.complex64)
+assert_type(c8 + i4, np.complex64)
+assert_type(c8 + b_, np.complex64)
+assert_type(c8 + b, np.complex64)
+assert_type(c8 + c, np.complex64 | np.complex128)
+assert_type(c8 + f, np.complex64 | np.complex128)
+assert_type(c8 + AR_f, npt.NDArray[np.complexfloating])
+
+assert_type(f16 + c8, np.complexfloating[_128Bit, _128Bit] | np.complex64)
+assert_type(c16 + c8, np.complex128)
+assert_type(f8 + c8, np.complexfloating[_64Bit, _64Bit])
+assert_type(i8 + c8, np.complexfloating[_64Bit, _64Bit] | np.complex64)
+assert_type(c8 + c8, np.complex64)
+assert_type(f4 + c8, np.complex64)
+assert_type(i4 + c8, np.complex64)
+assert_type(b_ + c8, np.complex64)
+assert_type(b + c8, np.complex64)
+assert_type(c + c8, np.complex64 | np.complex128)
+assert_type(f + c8, np.complex64 | np.complex128)
+assert_type(AR_f + c8, npt.NDArray[np.complexfloating])
+
+# Float
+
+assert_type(f8 + f16, np.float64| np.floating[_128Bit])
+assert_type(f8 + f8, np.float64)
+assert_type(f8 + i8, np.float64)
+assert_type(f8 + f4, np.float64)
+assert_type(f8 + i4, np.float64)
+assert_type(f8 + b_, np.float64)
+assert_type(f8 + b, np.float64)
+assert_type(f8 + c, np.float64 | np.complex128)
+assert_type(f8 + f, np.float64)
+assert_type(f8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(f16 + f8, np.floating[_128Bit] | np.float64)
+assert_type(f8 + f8, np.float64)
+assert_type(i8 + f8, np.float64)
+assert_type(f4 + f8, np.float32 | np.float64)
+assert_type(i4 + f8,np.float64)
+assert_type(b_ + f8, np.float64)
+assert_type(b + f8, np.float64)
+assert_type(c + f8, np.complex128 | np.float64)
+assert_type(f + f8, np.float64)
+assert_type(AR_f + f8, npt.NDArray[np.float64])
+
+assert_type(f4 + f16, np.float32 | np.floating[_128Bit])
+assert_type(f4 + f8, np.float32 | np.float64)
+assert_type(f4 + i8, np.float32 | np.floating[_64Bit])
+assert_type(f4 + f4, np.float32)
+assert_type(f4 + i4, np.float32)
+assert_type(f4 + b_, np.float32)
+assert_type(f4 + b, np.float32)
+assert_type(f4 + c, np.complex64 | np.complex128)
+assert_type(f4 + f, np.float32 | np.float64)
+assert_type(f4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(f16 + f4, np.floating[_128Bit] | np.float32)
+assert_type(f8 + f4, np.float64)
+assert_type(i8 + f4, np.floating[_32Bit] | np.floating[_64Bit])
+assert_type(f4 + f4, np.float32)
+assert_type(i4 + f4, np.float32)
+assert_type(b_ + f4, np.float32)
+assert_type(b + f4, np.float32)
+assert_type(c + f4, np.complex64 | np.complex128)
+assert_type(f + f4, np.float64 | np.float32)
+assert_type(AR_f + f4, npt.NDArray[np.float64])
+
+# Int
+
+assert_type(i8 + i8, np.int64)
+assert_type(i8 + u8, Any)
+assert_type(i8 + i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 + u4, Any)
+assert_type(i8 + b_, np.int64)
+assert_type(i8 + b, np.int64)
+assert_type(i8 + c, np.complex128)
+assert_type(i8 + f, np.float64)
+assert_type(i8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(u8 + u8, np.uint64)
+assert_type(u8 + i4, Any)
+assert_type(u8 + u4, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u8 + b_, np.uint64)
+assert_type(u8 + b, np.uint64)
+assert_type(u8 + c, np.complex128)
+assert_type(u8 + f, np.float64)
+assert_type(u8 + AR_f, npt.NDArray[np.float64])
+
+assert_type(i8 + i8, np.int64)
+assert_type(u8 + i8, Any)
+assert_type(i4 + i8, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(u4 + i8, Any)
+assert_type(b_ + i8, np.int64)
+assert_type(b + i8, np.int64)
+assert_type(c + i8, np.complex128)
+assert_type(f + i8, np.float64)
+assert_type(AR_f + i8, npt.NDArray[np.float64])
+
+assert_type(u8 + u8, np.uint64)
+assert_type(i4 + u8, Any)
+assert_type(u4 + u8, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(b_ + u8, np.uint64)
+assert_type(b + u8, np.uint64)
+assert_type(c + u8, np.complex128)
+assert_type(f + u8, np.float64)
+assert_type(AR_f + u8, npt.NDArray[np.float64])
+
+assert_type(i4 + i8, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i4 + i4, np.int32)
+assert_type(i4 + b_, np.int32)
+assert_type(i4 + b, np.int32)
+assert_type(i4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(u4 + i8, Any)
+assert_type(u4 + i4, Any)
+assert_type(u4 + u8, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u4 + u4, np.uint32)
+assert_type(u4 + b_, np.uint32)
+assert_type(u4 + b, np.uint32)
+assert_type(u4 + AR_f, npt.NDArray[np.float64])
+
+assert_type(i8 + i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i4 + i4, np.int32)
+assert_type(b_ + i4, np.int32)
+assert_type(b + i4, np.int32)
+assert_type(AR_f + i4, npt.NDArray[np.float64])
+
+assert_type(i8 + u4, Any)
+assert_type(i4 + u4, Any)
+assert_type(u8 + u4, np.unsignedinteger[_32Bit] | np.unsignedinteger[_64Bit])
+assert_type(u4 + u4, np.uint32)
+assert_type(b_ + u4, np.uint32)
+assert_type(b + u4, np.uint32)
+assert_type(AR_f + u4, npt.NDArray[np.float64])
+
+# Any
+
+assert_type(AR_Any + 2, npt.NDArray[Any])
+
+# regression tests for https://github.com/numpy/numpy/issues/28805
+
+assert_type(AR_floating + f, npt.NDArray[np.floating])
+assert_type(AR_floating - f, npt.NDArray[np.floating])
+assert_type(AR_floating * f, npt.NDArray[np.floating])
+assert_type(AR_floating ** f, npt.NDArray[np.floating])
+assert_type(AR_floating / f, npt.NDArray[np.floating])
+assert_type(AR_floating // f, npt.NDArray[np.floating])
+assert_type(AR_floating % f, npt.NDArray[np.floating])
+assert_type(divmod(AR_floating, f), tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]])
+
+assert_type(f + AR_floating, npt.NDArray[np.floating])
+assert_type(f - AR_floating, npt.NDArray[np.floating])
+assert_type(f * AR_floating, npt.NDArray[np.floating])
+assert_type(f ** AR_floating, npt.NDArray[np.floating])
+assert_type(f / AR_floating, npt.NDArray[np.floating])
+assert_type(f // AR_floating, npt.NDArray[np.floating])
+assert_type(f % AR_floating, npt.NDArray[np.floating])
+assert_type(divmod(f, AR_floating), tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e4110b7344e2324b59c2c421cffeeb596dd4ca9a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_api_info.pyi
@@ -0,0 +1,72 @@
+from typing import Literal
+
+import numpy as np
+
+from typing_extensions import Never, assert_type
+
+info = np.__array_namespace_info__()
+
+assert_type(info.__module__, Literal["numpy"])
+
+assert_type(info.default_device(), Literal["cpu"])
+assert_type(info.devices()[0], Literal["cpu"])
+assert_type(info.devices()[-1], Literal["cpu"])
+
+assert_type(info.capabilities()["boolean indexing"], Literal[True])
+assert_type(info.capabilities()["data-dependent shapes"], Literal[True])
+
+assert_type(info.default_dtypes()["real floating"], np.dtype[np.float64])
+assert_type(info.default_dtypes()["complex floating"], np.dtype[np.complex128])
+assert_type(info.default_dtypes()["integral"], np.dtype[np.int_])
+assert_type(info.default_dtypes()["indexing"], np.dtype[np.intp])
+
+assert_type(info.dtypes()["bool"], np.dtype[np.bool])
+assert_type(info.dtypes()["int8"], np.dtype[np.int8])
+assert_type(info.dtypes()["uint8"], np.dtype[np.uint8])
+assert_type(info.dtypes()["float32"], np.dtype[np.float32])
+assert_type(info.dtypes()["complex64"], np.dtype[np.complex64])
+
+assert_type(info.dtypes(kind="bool")["bool"], np.dtype[np.bool])
+assert_type(info.dtypes(kind="signed integer")["int64"], np.dtype[np.int64])
+assert_type(info.dtypes(kind="unsigned integer")["uint64"], np.dtype[np.uint64])
+assert_type(info.dtypes(kind="integral")["int32"], np.dtype[np.int32])
+assert_type(info.dtypes(kind="integral")["uint32"], np.dtype[np.uint32])
+assert_type(info.dtypes(kind="real floating")["float64"], np.dtype[np.float64])
+assert_type(info.dtypes(kind="complex floating")["complex128"], np.dtype[np.complex128])
+assert_type(info.dtypes(kind="numeric")["int16"], np.dtype[np.int16])
+assert_type(info.dtypes(kind="numeric")["uint16"], np.dtype[np.uint16])
+assert_type(info.dtypes(kind="numeric")["float64"], np.dtype[np.float64])
+assert_type(info.dtypes(kind="numeric")["complex128"], np.dtype[np.complex128])
+
+assert_type(info.dtypes(kind=()), dict[Never, Never])
+
+assert_type(info.dtypes(kind=("bool",))["bool"], np.dtype[np.bool])
+assert_type(info.dtypes(kind=("signed integer",))["int64"], np.dtype[np.int64])
+assert_type(info.dtypes(kind=("integral",))["uint32"], np.dtype[np.uint32])
+assert_type(info.dtypes(kind=("complex floating",))["complex128"], np.dtype[np.complex128])
+assert_type(info.dtypes(kind=("numeric",))["float64"], np.dtype[np.float64])
+
+assert_type(
+    info.dtypes(kind=("signed integer", "unsigned integer"))["int8"],
+    np.dtype[np.int8],
+)
+assert_type(
+    info.dtypes(kind=("signed integer", "unsigned integer"))["uint8"],
+    np.dtype[np.uint8],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["int16"],
+    np.dtype[np.int16],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["uint16"],
+    np.dtype[np.uint16],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["float32"],
+    np.dtype[np.float32],
+)
+assert_type(
+    info.dtypes(kind=("integral", "real floating", "complex floating"))["complex64"],
+    np.dtype[np.complex64],
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..35861cc0e94205cb13739187647975f1e0cf3a1e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi
@@ -0,0 +1,244 @@
+import sys
+from typing import Any, Literal as L, TypeVar
+from pathlib import Path
+from collections import deque
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic, covariant=True)
+
+class SubClass(npt.NDArray[_SCT]): ...
+
+i8: np.int64
+
+A: npt.NDArray[np.float64]
+B: SubClass[np.float64]
+C: list[int]
+D: SubClass[np.float64 | np.int64]
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ...
+
+assert_type(np.empty_like(A), npt.NDArray[np.float64])
+assert_type(np.empty_like(B), SubClass[np.float64])
+assert_type(np.empty_like([1, 1.0]), npt.NDArray[Any])
+assert_type(np.empty_like(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.empty_like(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.array(A), npt.NDArray[np.float64])
+assert_type(np.array(B), npt.NDArray[np.float64])
+assert_type(np.array([1, 1.0]), npt.NDArray[Any])
+assert_type(np.array(deque([1, 2, 3])), npt.NDArray[Any])
+assert_type(np.array(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.array(A, dtype='c16'), npt.NDArray[Any])
+assert_type(np.array(A, like=A), npt.NDArray[np.float64])
+assert_type(np.array(A, subok=True), npt.NDArray[np.float64])
+assert_type(np.array(B, subok=True), SubClass[np.float64])
+assert_type(np.array(B, subok=True, ndmin=0), SubClass[np.float64])
+assert_type(np.array(B, subok=True, ndmin=1), SubClass[np.float64])
+assert_type(np.array(D), npt.NDArray[np.float64 | np.int64])
+
+assert_type(np.zeros([1, 5, 6]), npt.NDArray[np.float64])
+assert_type(np.zeros([1, 5, 6], dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.zeros([1, 5, 6], dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.empty([1, 5, 6]), npt.NDArray[np.float64])
+assert_type(np.empty([1, 5, 6], dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.empty([1, 5, 6], dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.concatenate(A), npt.NDArray[np.float64])
+assert_type(np.concatenate([A, A]), npt.NDArray[Any])
+assert_type(np.concatenate([[1], A]), npt.NDArray[Any])
+assert_type(np.concatenate([[1], [1]]), npt.NDArray[Any])
+assert_type(np.concatenate((A, A)), npt.NDArray[np.float64])
+assert_type(np.concatenate(([1], [1])), npt.NDArray[Any])
+assert_type(np.concatenate([1, 1.0]), npt.NDArray[Any])
+assert_type(np.concatenate(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.concatenate(A, dtype='c16'), npt.NDArray[Any])
+assert_type(np.concatenate([1, 1.0], out=A), npt.NDArray[np.float64])
+
+assert_type(np.asarray(A), npt.NDArray[np.float64])
+assert_type(np.asarray(B), npt.NDArray[np.float64])
+assert_type(np.asarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.asanyarray(A), npt.NDArray[np.float64])
+assert_type(np.asanyarray(B), SubClass[np.float64])
+assert_type(np.asanyarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asanyarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asanyarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.ascontiguousarray(A), npt.NDArray[np.float64])
+assert_type(np.ascontiguousarray(B), npt.NDArray[np.float64])
+assert_type(np.ascontiguousarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.ascontiguousarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.ascontiguousarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.asfortranarray(A), npt.NDArray[np.float64])
+assert_type(np.asfortranarray(B), npt.NDArray[np.float64])
+assert_type(np.asfortranarray([1, 1.0]), npt.NDArray[Any])
+assert_type(np.asfortranarray(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.asfortranarray(A, dtype='c16'), npt.NDArray[Any])
+
+assert_type(np.fromstring("1 1 1", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromstring(b"1 1 1", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromstring("1 1 1", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromstring("1 1 1", dtype="c16", sep=" "), npt.NDArray[Any])
+assert_type(np.fromstring(b"1 1 1", dtype="c16", sep=" "), npt.NDArray[Any])
+
+assert_type(np.fromfile("test.txt", sep=" "), npt.NDArray[np.float64])
+assert_type(np.fromfile("test.txt", dtype=np.int64, sep=" "), npt.NDArray[np.int64])
+assert_type(np.fromfile("test.txt", dtype="c16", sep=" "), npt.NDArray[Any])
+with open("test.txt") as f:
+    assert_type(np.fromfile(f, sep=" "), npt.NDArray[np.float64])
+    assert_type(np.fromfile(b"test.txt", sep=" "), npt.NDArray[np.float64])
+    assert_type(np.fromfile(Path("test.txt"), sep=" "), npt.NDArray[np.float64])
+
+assert_type(np.fromiter("12345", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromiter("12345", float), npt.NDArray[Any])
+
+assert_type(np.frombuffer(A), npt.NDArray[np.float64])
+assert_type(np.frombuffer(A, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.frombuffer(A, dtype="c16"), npt.NDArray[Any])
+
+assert_type(np.arange(False, True), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(10), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(0, 10, step=2), np.ndarray[tuple[int], np.dtype[np.signedinteger[Any]]])
+assert_type(np.arange(10.0), np.ndarray[tuple[int], np.dtype[np.floating[Any]]])
+assert_type(np.arange(start=0, stop=10.0), np.ndarray[tuple[int], np.dtype[np.floating[Any]]])
+assert_type(np.arange(np.timedelta64(0)), np.ndarray[tuple[int], np.dtype[np.timedelta64]])
+assert_type(np.arange(0, np.timedelta64(10)), np.ndarray[tuple[int], np.dtype[np.timedelta64]])
+assert_type(np.arange(np.datetime64("0"), np.datetime64("10")), np.ndarray[tuple[int], np.dtype[np.datetime64]])
+assert_type(np.arange(10, dtype=np.float64), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.arange(0, 10, step=2, dtype=np.int16), np.ndarray[tuple[int], np.dtype[np.int16]])
+assert_type(np.arange(10, dtype=int), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.arange(0, 10, dtype="f8"), np.ndarray[tuple[int], np.dtype[Any]])
+
+assert_type(np.require(A), npt.NDArray[np.float64])
+assert_type(np.require(B), SubClass[np.float64])
+assert_type(np.require(B, requirements=None), SubClass[np.float64])
+assert_type(np.require(B, dtype=int), npt.NDArray[Any])
+assert_type(np.require(B, requirements="E"), npt.NDArray[Any])
+assert_type(np.require(B, requirements=["ENSUREARRAY"]), npt.NDArray[Any])
+assert_type(np.require(B, requirements={"F", "E"}), npt.NDArray[Any])
+assert_type(np.require(B, requirements=["C", "OWNDATA"]), SubClass[np.float64])
+assert_type(np.require(B, requirements="W"), SubClass[np.float64])
+assert_type(np.require(B, requirements="A"), SubClass[np.float64])
+assert_type(np.require(C), npt.NDArray[Any])
+
+assert_type(np.linspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.linspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.linspace(0, 10, dtype=int), npt.NDArray[Any])
+assert_type(np.linspace(0, 10, retstep=True), tuple[npt.NDArray[np.floating[Any]], np.floating[Any]])
+assert_type(np.linspace(0j, 10, retstep=True), tuple[npt.NDArray[np.complexfloating[Any, Any]], np.complexfloating[Any, Any]])
+assert_type(np.linspace(0, 10, retstep=True, dtype=np.int64), tuple[npt.NDArray[np.int64], np.int64])
+assert_type(np.linspace(0j, 10, retstep=True, dtype=int), tuple[npt.NDArray[Any], Any])
+
+assert_type(np.logspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.logspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.logspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.logspace(0, 10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.geomspace(0, 10), npt.NDArray[np.floating[Any]])
+assert_type(np.geomspace(0, 10j), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.geomspace(0, 10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.geomspace(0, 10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.zeros_like(A), npt.NDArray[np.float64])
+assert_type(np.zeros_like(C), npt.NDArray[Any])
+assert_type(np.zeros_like(A, dtype=float), npt.NDArray[Any])
+assert_type(np.zeros_like(B), SubClass[np.float64])
+assert_type(np.zeros_like(B, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(np.ones_like(A), npt.NDArray[np.float64])
+assert_type(np.ones_like(C), npt.NDArray[Any])
+assert_type(np.ones_like(A, dtype=float), npt.NDArray[Any])
+assert_type(np.ones_like(B), SubClass[np.float64])
+assert_type(np.ones_like(B, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(np.full_like(A, i8), npt.NDArray[np.float64])
+assert_type(np.full_like(C, i8), npt.NDArray[Any])
+assert_type(np.full_like(A, i8, dtype=int), npt.NDArray[Any])
+assert_type(np.full_like(B, i8), SubClass[np.float64])
+assert_type(np.full_like(B, i8, dtype=np.int64), npt.NDArray[np.int64])
+
+_size: int
+_shape_0d: tuple[()]
+_shape_1d: tuple[int]
+_shape_2d: tuple[int, int]
+_shape_nd: tuple[int, ...]
+_shape_like: list[int]
+
+assert_type(np.ones(_shape_0d), np.ndarray[tuple[()], np.dtype[np.float64]])
+assert_type(np.ones(_size), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.ones(_shape_2d), np.ndarray[tuple[int, int], np.dtype[np.float64]])
+assert_type(np.ones(_shape_nd), np.ndarray[tuple[int, ...], np.dtype[np.float64]])
+assert_type(np.ones(_shape_1d, dtype=np.int64), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(np.ones(_shape_like), npt.NDArray[np.float64])
+assert_type(np.ones(_shape_like, dtype=np.dtypes.Int64DType()), np.ndarray[Any, np.dtypes.Int64DType])
+assert_type(np.ones(_shape_like, dtype=int), npt.NDArray[Any])
+
+assert_type(np.full(_size, i8), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(np.full(_shape_2d, i8), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(np.full(_shape_like, i8), npt.NDArray[np.int64])
+assert_type(np.full(_shape_like, 42), npt.NDArray[Any])
+assert_type(np.full(_size, i8, dtype=np.float64), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(np.full(_size, i8, dtype=float), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.full(_shape_like, 42, dtype=float), npt.NDArray[Any])
+assert_type(np.full(_shape_0d, i8, dtype=object), np.ndarray[tuple[()], np.dtype[Any]])
+
+assert_type(np.indices([1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.indices([1, 2, 3], sparse=True), tuple[npt.NDArray[np.int_], ...])
+
+assert_type(np.fromfunction(func, (3, 5)), SubClass[np.float64])
+
+assert_type(np.identity(10), npt.NDArray[np.float64])
+assert_type(np.identity(10, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.identity(10, dtype=int), npt.NDArray[Any])
+
+assert_type(np.atleast_1d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_1d(C), npt.NDArray[Any])
+assert_type(np.atleast_1d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_1d(A, C), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.atleast_1d(C, C), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.atleast_1d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.atleast_1d(C, C, C), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.atleast_2d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_2d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_2d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+
+assert_type(np.atleast_3d(A), npt.NDArray[np.float64])
+assert_type(np.atleast_3d(A, A), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+assert_type(np.atleast_3d(A, A, A), tuple[npt.NDArray[np.float64], ...])
+
+assert_type(np.vstack([A, A]), npt.NDArray[np.float64])
+assert_type(np.vstack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.vstack([A, C]), npt.NDArray[Any])
+assert_type(np.vstack([C, C]), npt.NDArray[Any])
+
+assert_type(np.hstack([A, A]), npt.NDArray[np.float64])
+assert_type(np.hstack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+
+assert_type(np.stack([A, A]), npt.NDArray[np.float64])
+assert_type(np.stack([A, A], dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.stack([A, C]), npt.NDArray[Any])
+assert_type(np.stack([C, C]), npt.NDArray[Any])
+assert_type(np.stack([A, A], axis=0), npt.NDArray[np.float64])
+assert_type(np.stack([A, A], out=B), SubClass[np.float64])
+
+assert_type(np.block([[A, A], [A, A]]), npt.NDArray[Any])
+assert_type(np.block(C), npt.NDArray[Any])
+
+if sys.version_info >= (3, 12):
+    from collections.abc import Buffer
+
+    def create_array(obj: npt.ArrayLike) -> npt.NDArray[Any]: ...
+
+    buffer: Buffer
+    assert_type(create_array(buffer), npt.NDArray[Any])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d053dab1c76f9383f0d50041e20301bd2cde52fc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi
@@ -0,0 +1,24 @@
+from collections.abc import Mapping
+from typing import Any, SupportsIndex
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+def mode_func(
+    ar: npt.NDArray[np.number[Any]],
+    width: tuple[int, int],
+    iaxis: SupportsIndex,
+    kwargs: Mapping[str, Any],
+) -> None: ...
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE: list[int]
+
+assert_type(np.pad(AR_i8, (2, 3), "constant"), npt.NDArray[np.int64])
+assert_type(np.pad(AR_LIKE, (2, 3), "constant"), npt.NDArray[Any])
+
+assert_type(np.pad(AR_f8, (2, 3), mode_func), npt.NDArray[np.float64])
+assert_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2), npt.NDArray[np.float64])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f19f1536d416edec0d49b6a2ad043b68d92c7ad9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi
@@ -0,0 +1,27 @@
+import contextlib
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy._core.arrayprint import _FormatOptions
+
+from typing_extensions import assert_type
+
+AR: npt.NDArray[np.int64]
+func_float: Callable[[np.floating[Any]], str]
+func_int: Callable[[np.integer[Any]], str]
+
+assert_type(np.get_printoptions(), _FormatOptions)
+assert_type(
+    np.array2string(AR, formatter={'float_kind': func_float, 'int_kind': func_int}),
+    str,
+)
+assert_type(np.format_float_scientific(1.0), str)
+assert_type(np.format_float_positional(1), str)
+assert_type(np.array_repr(AR), str)
+assert_type(np.array_str(AR), str)
+
+assert_type(np.printoptions(), contextlib._GeneratorContextManager[_FormatOptions])
+with np.printoptions() as dct:
+    assert_type(dct, _FormatOptions)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eabc7677cde9655914e0366cfadc13c9758830dc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi
@@ -0,0 +1,72 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy.lib._arraysetops_impl import UniqueAllResult, UniqueCountsResult, UniqueInverseResult
+
+from typing_extensions import assert_type
+
+AR_b: npt.NDArray[np.bool]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+AR_LIKE_f8: list[float]
+
+assert_type(np.ediff1d(AR_b), npt.NDArray[np.int8])
+assert_type(np.ediff1d(AR_i8, to_end=[1, 2, 3]), npt.NDArray[np.int64])
+assert_type(np.ediff1d(AR_M), npt.NDArray[np.timedelta64])
+assert_type(np.ediff1d(AR_O), npt.NDArray[np.object_])
+assert_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5]), npt.NDArray[Any])
+
+assert_type(np.intersect1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.intersect1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.intersect1d(AR_f8, AR_i8), npt.NDArray[Any])
+assert_type(
+    np.intersect1d(AR_f8, AR_f8, return_indices=True),
+    tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]],
+)
+
+assert_type(np.setxor1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.setxor1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.setxor1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.isin(AR_i8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isin(AR_M, AR_M, assume_unique=True), npt.NDArray[np.bool])
+assert_type(np.isin(AR_f8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isin(AR_f8, AR_LIKE_f8, invert=True), npt.NDArray[np.bool])
+
+assert_type(np.union1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.union1d(AR_M, AR_M), npt.NDArray[np.datetime64])
+assert_type(np.union1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.setdiff1d(AR_i8, AR_i8), npt.NDArray[np.int64])
+assert_type(np.setdiff1d(AR_M, AR_M, assume_unique=True), npt.NDArray[np.datetime64])
+assert_type(np.setdiff1d(AR_f8, AR_i8), npt.NDArray[Any])
+
+assert_type(np.unique(AR_f8), npt.NDArray[np.float64])
+assert_type(np.unique(AR_LIKE_f8, axis=0), npt.NDArray[Any])
+assert_type(np.unique(AR_f8, return_index=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_inverse=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_inverse=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_inverse=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_inverse=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True), tuple[npt.NDArray[np.float64], npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True), tuple[npt.NDArray[Any], npt.NDArray[np.intp], npt.NDArray[np.intp], npt.NDArray[np.intp]])
+
+assert_type(np.unique_all(AR_f8), UniqueAllResult[np.float64])
+assert_type(np.unique_all(AR_LIKE_f8), UniqueAllResult[Any])
+assert_type(np.unique_counts(AR_f8), UniqueCountsResult[np.float64])
+assert_type(np.unique_counts(AR_LIKE_f8), UniqueCountsResult[Any])
+assert_type(np.unique_inverse(AR_f8), UniqueInverseResult[np.float64])
+assert_type(np.unique_inverse(AR_LIKE_f8), UniqueInverseResult[Any])
+assert_type(np.unique_values(AR_f8), npt.NDArray[np.float64])
+assert_type(np.unique_values(AR_LIKE_f8), npt.NDArray[Any])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..332e5da9bc961ef1a49883449ed4641d55aaf216
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi
@@ -0,0 +1,29 @@
+from typing import Any
+from collections.abc import Generator
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+assert_type(ar_iter.var, npt.NDArray[np.int64])
+assert_type(ar_iter.buf_size, None | int)
+assert_type(ar_iter.start, list[int])
+assert_type(ar_iter.stop, list[int])
+assert_type(ar_iter.step, list[int])
+assert_type(ar_iter.shape, tuple[int, ...])
+assert_type(ar_iter.flat, Generator[np.int64, None, None])
+
+assert_type(ar_iter.__array__(), npt.NDArray[np.int64])
+
+for i in ar_iter:
+    assert_type(i, npt.NDArray[np.int64])
+
+assert_type(ar_iter[0], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[...], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[:], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[0, 0, 0], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
+assert_type(ar_iter[..., 0, :], np.lib.Arrayterator[tuple[int, ...], np.dtype[np.int64]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..384932a2c823528dad842aaac26a00b0c666133d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/bitwise_ops.pyi
@@ -0,0 +1,170 @@
+from typing import Any, Literal as L, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit, _32Bit
+
+from typing_extensions import assert_type
+
+FalseType: TypeAlias = L[False]
+TrueType: TypeAlias = L[True]
+
+i4: np.int32
+i8: np.int64
+
+u4: np.uint32
+u8: np.uint64
+
+b_: np.bool[bool]
+b0_: np.bool[FalseType]
+b1_: np.bool[TrueType]
+
+b: bool
+b0: FalseType
+b1: TrueType
+
+i: int
+
+AR: npt.NDArray[np.int32]
+
+
+assert_type(i8 << i8, np.int64)
+assert_type(i8 >> i8, np.int64)
+assert_type(i8 | i8, np.int64)
+assert_type(i8 ^ i8, np.int64)
+assert_type(i8 & i8, np.int64)
+
+assert_type(i8 << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(i8 & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(i4 << i4, np.int32)
+assert_type(i4 >> i4, np.int32)
+assert_type(i4 | i4, np.int32)
+assert_type(i4 ^ i4, np.int32)
+assert_type(i4 & i4, np.int32)
+
+assert_type(i8 << i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 >> i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 | i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 ^ i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+assert_type(i8 & i4, np.signedinteger[_32Bit] | np.signedinteger[_64Bit])
+
+assert_type(i8 << b_, np.int64)
+assert_type(i8 >> b_, np.int64)
+assert_type(i8 | b_, np.int64)
+assert_type(i8 ^ b_, np.int64)
+assert_type(i8 & b_, np.int64)
+
+assert_type(i8 << b, np.int64)
+assert_type(i8 >> b, np.int64)
+assert_type(i8 | b, np.int64)
+assert_type(i8 ^ b, np.int64)
+assert_type(i8 & b, np.int64)
+
+assert_type(u8 << u8, np.uint64)
+assert_type(u8 >> u8, np.uint64)
+assert_type(u8 | u8, np.uint64)
+assert_type(u8 ^ u8, np.uint64)
+assert_type(u8 & u8, np.uint64)
+
+assert_type(u8 << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(u8 & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(u4 << u4, np.uint32)
+assert_type(u4 >> u4, np.uint32)
+assert_type(u4 | u4, np.uint32)
+assert_type(u4 ^ u4, np.uint32)
+assert_type(u4 & u4, np.uint32)
+
+assert_type(u4 << i4, np.signedinteger[Any])
+assert_type(u4 >> i4, np.signedinteger[Any])
+assert_type(u4 | i4, np.signedinteger[Any])
+assert_type(u4 ^ i4, np.signedinteger[Any])
+assert_type(u4 & i4, np.signedinteger[Any])
+
+assert_type(u4 << i, np.signedinteger[Any])
+assert_type(u4 >> i, np.signedinteger[Any])
+assert_type(u4 | i, np.signedinteger[Any])
+assert_type(u4 ^ i, np.signedinteger[Any])
+assert_type(u4 & i, np.signedinteger[Any])
+
+assert_type(u8 << b_, np.uint64)
+assert_type(u8 >> b_, np.uint64)
+assert_type(u8 | b_, np.uint64)
+assert_type(u8 ^ b_, np.uint64)
+assert_type(u8 & b_, np.uint64)
+
+assert_type(u8 << b, np.uint64)
+assert_type(u8 >> b, np.uint64)
+assert_type(u8 | b, np.uint64)
+assert_type(u8 ^ b, np.uint64)
+assert_type(u8 & b, np.uint64)
+
+assert_type(b_ << b_, np.int8)
+assert_type(b_ >> b_, np.int8)
+assert_type(b_ | b_, np.bool)
+assert_type(b_ ^ b_, np.bool)
+assert_type(b_ & b_, np.bool)
+
+assert_type(b_ << AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ >> AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ | AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ ^ AR, npt.NDArray[np.signedinteger[Any]])
+assert_type(b_ & AR, npt.NDArray[np.signedinteger[Any]])
+
+assert_type(b_ << b, np.int8)
+assert_type(b_ >> b, np.int8)
+assert_type(b_ | b, np.bool)
+assert_type(b_ ^ b, np.bool)
+assert_type(b_ & b, np.bool)
+
+assert_type(b_ << i, np.int_)
+assert_type(b_ >> i, np.int_)
+assert_type(b_ | i, np.bool | np.int_)
+assert_type(b_ ^ i, np.bool | np.int_)
+assert_type(b_ & i, np.bool | np.int_)
+
+assert_type(~i8, np.int64)
+assert_type(~i4, np.int32)
+assert_type(~u8, np.uint64)
+assert_type(~u4, np.uint32)
+assert_type(~b_, np.bool)
+assert_type(~b0_, np.bool[TrueType])
+assert_type(~b1_, np.bool[FalseType])
+assert_type(~AR, npt.NDArray[np.int32])
+
+assert_type(b_ | b0_, np.bool)
+assert_type(b0_ | b_, np.bool)
+assert_type(b_ | b1_, np.bool[TrueType])
+assert_type(b1_ | b_, np.bool[TrueType])
+
+assert_type(b_ ^ b0_, np.bool)
+assert_type(b0_ ^ b_, np.bool)
+assert_type(b_ ^ b1_, np.bool)
+assert_type(b1_ ^ b_, np.bool)
+
+assert_type(b_ & b0_, np.bool[FalseType])
+assert_type(b0_ & b_, np.bool[FalseType])
+assert_type(b_ & b1_, np.bool)
+assert_type(b1_ & b_, np.bool)
+
+assert_type(b0_ | b0_, np.bool[FalseType])
+assert_type(b0_ | b1_, np.bool[TrueType])
+assert_type(b1_ | b0_, np.bool[TrueType])
+assert_type(b1_ | b1_, np.bool[TrueType])
+
+assert_type(b0_ ^ b0_, np.bool[FalseType])
+assert_type(b0_ ^ b1_, np.bool[TrueType])
+assert_type(b1_ ^ b0_, np.bool[TrueType])
+assert_type(b1_ ^ b1_, np.bool[FalseType])
+
+assert_type(b0_ & b0_, np.bool[FalseType])
+assert_type(b0_ & b1_, np.bool[FalseType])
+assert_type(b1_ & b0_, np.bool[FalseType])
+assert_type(b1_ & b1_, np.bool[TrueType])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..19ca211bec1a906cdf75fbd619239b9fda68d435
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/char.pyi
@@ -0,0 +1,219 @@
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as np_t
+
+from typing_extensions import assert_type
+from typing import TypeAlias
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+AR_T: np.ndarray[np_t._Shape, np.dtypes.StringDType]
+
+AR_T_alias: TypeAlias = np.ndarray[np_t._Shape, np.dtypes.StringDType]
+AR_TU_alias: TypeAlias = AR_T_alias | npt.NDArray[np.str_]
+
+assert_type(np.char.equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.not_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.not_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.not_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.greater_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.greater_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.greater_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.less_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.less_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.less_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.greater(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.greater(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.greater(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.less(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.char.less(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.char.less(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.multiply(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.multiply(AR_S, [5, 4, 3]), npt.NDArray[np.bytes_])
+assert_type(np.char.multiply(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.mod(AR_U, "test"), npt.NDArray[np.str_])
+assert_type(np.char.mod(AR_S, "test"), npt.NDArray[np.bytes_])
+assert_type(np.char.mod(AR_T, "test"), AR_T_alias)
+
+assert_type(np.char.capitalize(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.capitalize(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.capitalize(AR_T), AR_T_alias)
+
+assert_type(np.char.center(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.center(AR_S, [2, 3, 4], b"a"), npt.NDArray[np.bytes_])
+assert_type(np.char.center(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.encode(AR_U), npt.NDArray[np.bytes_])
+assert_type(np.char.encode(AR_T), npt.NDArray[np.bytes_])
+assert_type(np.char.decode(AR_S), npt.NDArray[np.str_])
+
+assert_type(np.char.expandtabs(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.expandtabs(AR_S, tabsize=4), npt.NDArray[np.bytes_])
+assert_type(np.char.expandtabs(AR_T), AR_T_alias)
+
+assert_type(np.char.join(AR_U, "_"), npt.NDArray[np.str_])
+assert_type(np.char.join(AR_S, [b"_", b""]), npt.NDArray[np.bytes_])
+assert_type(np.char.join(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.ljust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.ljust(AR_T, 5), AR_T_alias)
+assert_type(np.char.ljust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_TU_alias)
+
+assert_type(np.char.rjust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.rjust(AR_T, 5), AR_T_alias)
+assert_type(np.char.rjust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_TU_alias)
+
+assert_type(np.char.lstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.lstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.lstrip(AR_T), AR_T_alias)
+assert_type(np.char.lstrip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.rstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.rstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.rstrip(AR_T), AR_T_alias)
+assert_type(np.char.rstrip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.strip(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.strip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.char.strip(AR_T), AR_T_alias)
+assert_type(np.char.strip(AR_T, "_"), AR_TU_alias)
+
+assert_type(np.char.count(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_T, AR_T, start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.count(AR_T, ["a", "b", "c"], end=9), npt.NDArray[np.int_])
+
+assert_type(np.char.partition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.char.partition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.partition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.char.rpartition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.char.rpartition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.char.rpartition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.char.replace(AR_U, "_", "-"), npt.NDArray[np.str_])
+assert_type(np.char.replace(AR_S, [b"_", b""], [b"a", b"b"]), npt.NDArray[np.bytes_])
+assert_type(np.char.replace(AR_T, "_", "_"), AR_TU_alias)
+
+assert_type(np.char.split(AR_U, "_"), npt.NDArray[np.object_])
+assert_type(np.char.split(AR_S, maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(np.char.split(AR_T, "_"), npt.NDArray[np.object_])
+
+assert_type(np.char.rsplit(AR_U, "_"), npt.NDArray[np.object_])
+assert_type(np.char.rsplit(AR_S, maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(np.char.rsplit(AR_T, "_"), npt.NDArray[np.object_])
+
+assert_type(np.char.splitlines(AR_U), npt.NDArray[np.object_])
+assert_type(np.char.splitlines(AR_S, keepends=[True, True, False]), npt.NDArray[np.object_])
+assert_type(np.char.splitlines(AR_T), npt.NDArray[np.object_])
+
+assert_type(np.char.lower(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.lower(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.lower(AR_T), AR_T_alias)
+
+assert_type(np.char.upper(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.upper(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.upper(AR_T), AR_T_alias)
+
+assert_type(np.char.swapcase(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.swapcase(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.swapcase(AR_T), AR_T_alias)
+
+assert_type(np.char.title(AR_U), npt.NDArray[np.str_])
+assert_type(np.char.title(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.char.title(AR_T), AR_T_alias)
+
+assert_type(np.char.zfill(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.char.zfill(AR_S, [2, 3, 4]), npt.NDArray[np.bytes_])
+assert_type(np.char.zfill(AR_T, 5), AR_T_alias)
+
+assert_type(np.char.endswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.char.endswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.char.endswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.char.startswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.char.startswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.char.startswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.char.find(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.find(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.find(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.rfind(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.rfind(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.rfind(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.index(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.index(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.index(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.rindex(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.char.rindex(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.char.rindex(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.char.isalpha(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isalpha(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isalpha(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isalnum(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isalnum(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isalnum(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isdecimal(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isdecimal(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isdigit(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isdigit(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isdigit(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.islower(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.islower(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.islower(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isnumeric(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isnumeric(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isspace(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isspace(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isspace(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.istitle(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.istitle(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.istitle(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.isupper(AR_U), npt.NDArray[np.bool])
+assert_type(np.char.isupper(AR_S), npt.NDArray[np.bool])
+assert_type(np.char.isupper(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.char.str_len(AR_U), npt.NDArray[np.int_])
+assert_type(np.char.str_len(AR_S), npt.NDArray[np.int_])
+assert_type(np.char.str_len(AR_T), npt.NDArray[np.int_])
+
+assert_type(np.char.translate(AR_U, ""), npt.NDArray[np.str_])
+assert_type(np.char.translate(AR_S, ""), npt.NDArray[np.bytes_])
+assert_type(np.char.translate(AR_T, ""), AR_T_alias)
+
+assert_type(np.char.array(AR_U), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.array(AR_S, order="K"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array("bob", copy=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.array(b"bob", itemsize=5), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array(1, unicode=False), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.array(1, unicode=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(np.char.asarray(AR_U), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.asarray(AR_S, order="K"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray("bob"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.char.asarray(b"bob", itemsize=5), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray(1, unicode=False), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(np.char.asarray(1, unicode=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..116880f44356564e4e42cebd2a4854f42ca8fe70
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/chararray.pyi
@@ -0,0 +1,136 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+AR_S: np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]]
+
+assert_type(AR_U == AR_U, npt.NDArray[np.bool])
+assert_type(AR_S == AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U != AR_U, npt.NDArray[np.bool])
+assert_type(AR_S != AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U >= AR_U, npt.NDArray[np.bool])
+assert_type(AR_S >= AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U <= AR_U, npt.NDArray[np.bool])
+assert_type(AR_S <= AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U > AR_U, npt.NDArray[np.bool])
+assert_type(AR_S > AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U < AR_U, npt.NDArray[np.bool])
+assert_type(AR_S < AR_S, npt.NDArray[np.bool])
+
+assert_type(AR_U * 5, np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S * [5], np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U % "test", np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S % b"test", np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.capitalize(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.capitalize(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.center(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.center([2, 3, 4], b"a"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.encode(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_S.decode(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(AR_U.expandtabs(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.expandtabs(tabsize=4), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.join("_"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.join([b"_", b""]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.ljust(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.ljust([4, 3, 1], fillchar=[b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rjust(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rjust([4, 3, 1], fillchar=[b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.lstrip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.lstrip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rstrip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rstrip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.strip(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.strip(chars=b"_"), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.partition("\n"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.partition([b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+assert_type(AR_U.rpartition("\n"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.rpartition([b"a", b"b", b"c"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.replace("_", "-"), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.replace([b"_", b""], [b"a", b"b"]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.split("_"), npt.NDArray[np.object_])
+assert_type(AR_S.split(maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+assert_type(AR_U.rsplit("_"), npt.NDArray[np.object_])
+assert_type(AR_S.rsplit(maxsplit=[1, 2, 3]), npt.NDArray[np.object_])
+
+assert_type(AR_U.splitlines(), npt.NDArray[np.object_])
+assert_type(AR_S.splitlines(keepends=[True, True, False]), npt.NDArray[np.object_])
+
+assert_type(AR_U.swapcase(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.swapcase(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.title(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.title(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.upper(), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.upper(), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.zfill(5), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(AR_S.zfill([2, 3, 4]), np.char.chararray[tuple[int, ...], np.dtype[np.bytes_]])
+
+assert_type(AR_U.count("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.count([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.endswith("a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(AR_S.endswith([b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(AR_U.startswith("a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(AR_S.startswith([b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+
+assert_type(AR_U.find("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.find([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(AR_U.rfind("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.rfind([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.index("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.index([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(AR_U.rindex("a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(AR_S.rindex([b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+
+assert_type(AR_U.isalpha(), npt.NDArray[np.bool])
+assert_type(AR_S.isalpha(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isalnum(), npt.NDArray[np.bool])
+assert_type(AR_S.isalnum(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isdecimal(), npt.NDArray[np.bool])
+assert_type(AR_S.isdecimal(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isdigit(), npt.NDArray[np.bool])
+assert_type(AR_S.isdigit(), npt.NDArray[np.bool])
+
+assert_type(AR_U.islower(), npt.NDArray[np.bool])
+assert_type(AR_S.islower(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isnumeric(), npt.NDArray[np.bool])
+assert_type(AR_S.isnumeric(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isspace(), npt.NDArray[np.bool])
+assert_type(AR_S.isspace(), npt.NDArray[np.bool])
+
+assert_type(AR_U.istitle(), npt.NDArray[np.bool])
+assert_type(AR_S.istitle(), npt.NDArray[np.bool])
+
+assert_type(AR_U.isupper(), npt.NDArray[np.bool])
+assert_type(AR_S.isupper(), npt.NDArray[np.bool])
+
+assert_type(AR_U.__array_finalize__(object()), None)
+assert_type(AR_S.__array_finalize__(object()), None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b71ef1d1b79f952cc03e9924f30c52bd821cb72b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/comparisons.pyi
@@ -0,0 +1,266 @@
+import fractions
+import decimal
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+AR = np.array([0], dtype=np.int64)
+AR.setflags(write=False)
+
+SEQ = (0, 1, 2, 3, 4)
+
+# object-like comparisons
+
+assert_type(i8 > fractions.Fraction(1, 5), np.bool)
+assert_type(i8 > [fractions.Fraction(1, 5)], npt.NDArray[np.bool])
+assert_type(i8 > decimal.Decimal("1.5"), np.bool)
+assert_type(i8 > [decimal.Decimal("1.5")], npt.NDArray[np.bool])
+
+# Time structures
+
+assert_type(dt > dt, np.bool)
+
+assert_type(td > td, np.bool)
+assert_type(td > i, np.bool)
+assert_type(td > i4, np.bool)
+assert_type(td > i8, np.bool)
+
+assert_type(td > AR, npt.NDArray[np.bool])
+assert_type(td > SEQ, npt.NDArray[np.bool])
+assert_type(AR > SEQ, npt.NDArray[np.bool])
+assert_type(AR > td, npt.NDArray[np.bool])
+assert_type(SEQ > td, npt.NDArray[np.bool])
+assert_type(SEQ > AR, npt.NDArray[np.bool])
+
+# boolean
+
+assert_type(b_ > b, np.bool)
+assert_type(b_ > b_, np.bool)
+assert_type(b_ > i, np.bool)
+assert_type(b_ > i8, np.bool)
+assert_type(b_ > i4, np.bool)
+assert_type(b_ > u8, np.bool)
+assert_type(b_ > u4, np.bool)
+assert_type(b_ > f, np.bool)
+assert_type(b_ > f8, np.bool)
+assert_type(b_ > f4, np.bool)
+assert_type(b_ > c, np.bool)
+assert_type(b_ > c16, np.bool)
+assert_type(b_ > c8, np.bool)
+assert_type(b_ > AR, npt.NDArray[np.bool])
+assert_type(b_ > SEQ, npt.NDArray[np.bool])
+
+# Complex
+
+assert_type(c16 > c16, np.bool)
+assert_type(c16 > f8, np.bool)
+assert_type(c16 > i8, np.bool)
+assert_type(c16 > c8, np.bool)
+assert_type(c16 > f4, np.bool)
+assert_type(c16 > i4, np.bool)
+assert_type(c16 > b_, np.bool)
+assert_type(c16 > b, np.bool)
+assert_type(c16 > c, np.bool)
+assert_type(c16 > f, np.bool)
+assert_type(c16 > i, np.bool)
+assert_type(c16 > AR, npt.NDArray[np.bool])
+assert_type(c16 > SEQ, npt.NDArray[np.bool])
+
+assert_type(c16 > c16, np.bool)
+assert_type(f8 > c16, np.bool)
+assert_type(i8 > c16, np.bool)
+assert_type(c8 > c16, np.bool)
+assert_type(f4 > c16, np.bool)
+assert_type(i4 > c16, np.bool)
+assert_type(b_ > c16, np.bool)
+assert_type(b > c16, np.bool)
+assert_type(c > c16, np.bool)
+assert_type(f > c16, np.bool)
+assert_type(i > c16, np.bool)
+assert_type(AR > c16, npt.NDArray[np.bool])
+assert_type(SEQ > c16, npt.NDArray[np.bool])
+
+assert_type(c8 > c16, np.bool)
+assert_type(c8 > f8, np.bool)
+assert_type(c8 > i8, np.bool)
+assert_type(c8 > c8, np.bool)
+assert_type(c8 > f4, np.bool)
+assert_type(c8 > i4, np.bool)
+assert_type(c8 > b_, np.bool)
+assert_type(c8 > b, np.bool)
+assert_type(c8 > c, np.bool)
+assert_type(c8 > f, np.bool)
+assert_type(c8 > i, np.bool)
+assert_type(c8 > AR, npt.NDArray[np.bool])
+assert_type(c8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(c16 > c8, np.bool)
+assert_type(f8 > c8, np.bool)
+assert_type(i8 > c8, np.bool)
+assert_type(c8 > c8, np.bool)
+assert_type(f4 > c8, np.bool)
+assert_type(i4 > c8, np.bool)
+assert_type(b_ > c8, np.bool)
+assert_type(b > c8, np.bool)
+assert_type(c > c8, np.bool)
+assert_type(f > c8, np.bool)
+assert_type(i > c8, np.bool)
+assert_type(AR > c8, npt.NDArray[np.bool])
+assert_type(SEQ > c8, npt.NDArray[np.bool])
+
+# Float
+
+assert_type(f8 > f8, np.bool)
+assert_type(f8 > i8, np.bool)
+assert_type(f8 > f4, np.bool)
+assert_type(f8 > i4, np.bool)
+assert_type(f8 > b_, np.bool)
+assert_type(f8 > b, np.bool)
+assert_type(f8 > c, np.bool)
+assert_type(f8 > f, np.bool)
+assert_type(f8 > i, np.bool)
+assert_type(f8 > AR, npt.NDArray[np.bool])
+assert_type(f8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(f8 > f8, np.bool)
+assert_type(i8 > f8, np.bool)
+assert_type(f4 > f8, np.bool)
+assert_type(i4 > f8, np.bool)
+assert_type(b_ > f8, np.bool)
+assert_type(b > f8, np.bool)
+assert_type(c > f8, np.bool)
+assert_type(f > f8, np.bool)
+assert_type(i > f8, np.bool)
+assert_type(AR > f8, npt.NDArray[np.bool])
+assert_type(SEQ > f8, npt.NDArray[np.bool])
+
+assert_type(f4 > f8, np.bool)
+assert_type(f4 > i8, np.bool)
+assert_type(f4 > f4, np.bool)
+assert_type(f4 > i4, np.bool)
+assert_type(f4 > b_, np.bool)
+assert_type(f4 > b, np.bool)
+assert_type(f4 > c, np.bool)
+assert_type(f4 > f, np.bool)
+assert_type(f4 > i, np.bool)
+assert_type(f4 > AR, npt.NDArray[np.bool])
+assert_type(f4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(f8 > f4, np.bool)
+assert_type(i8 > f4, np.bool)
+assert_type(f4 > f4, np.bool)
+assert_type(i4 > f4, np.bool)
+assert_type(b_ > f4, np.bool)
+assert_type(b > f4, np.bool)
+assert_type(c > f4, np.bool)
+assert_type(f > f4, np.bool)
+assert_type(i > f4, np.bool)
+assert_type(AR > f4, npt.NDArray[np.bool])
+assert_type(SEQ > f4, npt.NDArray[np.bool])
+
+# Int
+
+assert_type(i8 > i8, np.bool)
+assert_type(i8 > u8, np.bool)
+assert_type(i8 > i4, np.bool)
+assert_type(i8 > u4, np.bool)
+assert_type(i8 > b_, np.bool)
+assert_type(i8 > b, np.bool)
+assert_type(i8 > c, np.bool)
+assert_type(i8 > f, np.bool)
+assert_type(i8 > i, np.bool)
+assert_type(i8 > AR, npt.NDArray[np.bool])
+assert_type(i8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(u8 > u8, np.bool)
+assert_type(u8 > i4, np.bool)
+assert_type(u8 > u4, np.bool)
+assert_type(u8 > b_, np.bool)
+assert_type(u8 > b, np.bool)
+assert_type(u8 > c, np.bool)
+assert_type(u8 > f, np.bool)
+assert_type(u8 > i, np.bool)
+assert_type(u8 > AR, npt.NDArray[np.bool])
+assert_type(u8 > SEQ, npt.NDArray[np.bool])
+
+assert_type(i8 > i8, np.bool)
+assert_type(u8 > i8, np.bool)
+assert_type(i4 > i8, np.bool)
+assert_type(u4 > i8, np.bool)
+assert_type(b_ > i8, np.bool)
+assert_type(b > i8, np.bool)
+assert_type(c > i8, np.bool)
+assert_type(f > i8, np.bool)
+assert_type(i > i8, np.bool)
+assert_type(AR > i8, npt.NDArray[np.bool])
+assert_type(SEQ > i8, npt.NDArray[np.bool])
+
+assert_type(u8 > u8, np.bool)
+assert_type(i4 > u8, np.bool)
+assert_type(u4 > u8, np.bool)
+assert_type(b_ > u8, np.bool)
+assert_type(b > u8, np.bool)
+assert_type(c > u8, np.bool)
+assert_type(f > u8, np.bool)
+assert_type(i > u8, np.bool)
+assert_type(AR > u8, npt.NDArray[np.bool])
+assert_type(SEQ > u8, npt.NDArray[np.bool])
+
+assert_type(i4 > i8, np.bool)
+assert_type(i4 > i4, np.bool)
+assert_type(i4 > i, np.bool)
+assert_type(i4 > b_, np.bool)
+assert_type(i4 > b, np.bool)
+assert_type(i4 > AR, npt.NDArray[np.bool])
+assert_type(i4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(u4 > i8, np.bool)
+assert_type(u4 > i4, np.bool)
+assert_type(u4 > u8, np.bool)
+assert_type(u4 > u4, np.bool)
+assert_type(u4 > i, np.bool)
+assert_type(u4 > b_, np.bool)
+assert_type(u4 > b, np.bool)
+assert_type(u4 > AR, npt.NDArray[np.bool])
+assert_type(u4 > SEQ, npt.NDArray[np.bool])
+
+assert_type(i8 > i4, np.bool)
+assert_type(i4 > i4, np.bool)
+assert_type(i > i4, np.bool)
+assert_type(b_ > i4, np.bool)
+assert_type(b > i4, np.bool)
+assert_type(AR > i4, npt.NDArray[np.bool])
+assert_type(SEQ > i4, npt.NDArray[np.bool])
+
+assert_type(i8 > u4, np.bool)
+assert_type(i4 > u4, np.bool)
+assert_type(u8 > u4, np.bool)
+assert_type(u4 > u4, np.bool)
+assert_type(b_ > u4, np.bool)
+assert_type(b > u4, np.bool)
+assert_type(i > u4, np.bool)
+assert_type(AR > u4, npt.NDArray[np.bool])
+assert_type(SEQ > u4, npt.NDArray[np.bool])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..146a40cf467ffd22355a261eef5ad1687a62ad2e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/constants.pyi
@@ -0,0 +1,15 @@
+from typing import Literal
+from typing_extensions import assert_type
+
+import numpy as np
+
+assert_type(np.e, float)
+assert_type(np.euler_gamma, float)
+assert_type(np.inf, float)
+assert_type(np.nan, float)
+assert_type(np.pi, float)
+
+assert_type(np.little_endian, bool)
+
+assert_type(np.True_, np.bool[Literal[True]])
+assert_type(np.False_, np.bool[Literal[False]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..80928a93444cf0a1af8b92fcdaccf8b45be0d98b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ctypeslib.pyi
@@ -0,0 +1,93 @@
+import sys
+import ctypes as ct
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy import ctypeslib
+
+from typing_extensions import assert_type
+
+AR_bool: npt.NDArray[np.bool]
+AR_ubyte: npt.NDArray[np.ubyte]
+AR_ushort: npt.NDArray[np.ushort]
+AR_uintc: npt.NDArray[np.uintc]
+AR_ulong: npt.NDArray[np.ulong]
+AR_ulonglong: npt.NDArray[np.ulonglong]
+AR_byte: npt.NDArray[np.byte]
+AR_short: npt.NDArray[np.short]
+AR_intc: npt.NDArray[np.intc]
+AR_long: npt.NDArray[np.long]
+AR_longlong: npt.NDArray[np.longlong]
+AR_single: npt.NDArray[np.single]
+AR_double: npt.NDArray[np.double]
+AR_longdouble: npt.NDArray[np.longdouble]
+AR_void: npt.NDArray[np.void]
+
+pointer: ct._Pointer[Any]
+
+assert_type(np.ctypeslib.c_intp(), ctypeslib.c_intp)
+
+assert_type(np.ctypeslib.ndpointer(), type[ctypeslib._ndptr[None]])
+assert_type(np.ctypeslib.ndpointer(dtype=np.float64), type[ctypeslib._ndptr[np.dtype[np.float64]]])
+assert_type(np.ctypeslib.ndpointer(dtype=float), type[ctypeslib._ndptr[np.dtype[Any]]])
+assert_type(np.ctypeslib.ndpointer(shape=(10, 3)), type[ctypeslib._ndptr[None]])
+assert_type(np.ctypeslib.ndpointer(np.int64, shape=(10, 3)), type[ctypeslib._concrete_ndptr[np.dtype[np.int64]]])
+assert_type(np.ctypeslib.ndpointer(int, shape=(1,)), type[np.ctypeslib._concrete_ndptr[np.dtype[Any]]])
+
+assert_type(np.ctypeslib.as_ctypes_type(np.bool), type[ct.c_bool])
+assert_type(np.ctypeslib.as_ctypes_type(np.ubyte), type[ct.c_ubyte])
+assert_type(np.ctypeslib.as_ctypes_type(np.ushort), type[ct.c_ushort])
+assert_type(np.ctypeslib.as_ctypes_type(np.uintc), type[ct.c_uint])
+assert_type(np.ctypeslib.as_ctypes_type(np.byte), type[ct.c_byte])
+assert_type(np.ctypeslib.as_ctypes_type(np.short), type[ct.c_short])
+assert_type(np.ctypeslib.as_ctypes_type(np.intc), type[ct.c_int])
+assert_type(np.ctypeslib.as_ctypes_type(np.single), type[ct.c_float])
+assert_type(np.ctypeslib.as_ctypes_type(np.double), type[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes_type(ct.c_double), type[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes_type("q"), type[ct.c_longlong])
+assert_type(np.ctypeslib.as_ctypes_type([("i8", np.int64), ("f8", np.float64)]), type[Any])
+assert_type(np.ctypeslib.as_ctypes_type("i8"), type[Any])
+assert_type(np.ctypeslib.as_ctypes_type("f8"), type[Any])
+
+assert_type(np.ctypeslib.as_ctypes(AR_bool.take(0)), ct.c_bool)
+assert_type(np.ctypeslib.as_ctypes(AR_ubyte.take(0)), ct.c_ubyte)
+assert_type(np.ctypeslib.as_ctypes(AR_ushort.take(0)), ct.c_ushort)
+assert_type(np.ctypeslib.as_ctypes(AR_uintc.take(0)), ct.c_uint)
+
+assert_type(np.ctypeslib.as_ctypes(AR_byte.take(0)), ct.c_byte)
+assert_type(np.ctypeslib.as_ctypes(AR_short.take(0)), ct.c_short)
+assert_type(np.ctypeslib.as_ctypes(AR_intc.take(0)), ct.c_int)
+assert_type(np.ctypeslib.as_ctypes(AR_single.take(0)), ct.c_float)
+assert_type(np.ctypeslib.as_ctypes(AR_double.take(0)), ct.c_double)
+assert_type(np.ctypeslib.as_ctypes(AR_void.take(0)), Any)
+assert_type(np.ctypeslib.as_ctypes(AR_bool), ct.Array[ct.c_bool])
+assert_type(np.ctypeslib.as_ctypes(AR_ubyte), ct.Array[ct.c_ubyte])
+assert_type(np.ctypeslib.as_ctypes(AR_ushort), ct.Array[ct.c_ushort])
+assert_type(np.ctypeslib.as_ctypes(AR_uintc), ct.Array[ct.c_uint])
+assert_type(np.ctypeslib.as_ctypes(AR_byte), ct.Array[ct.c_byte])
+assert_type(np.ctypeslib.as_ctypes(AR_short), ct.Array[ct.c_short])
+assert_type(np.ctypeslib.as_ctypes(AR_intc), ct.Array[ct.c_int])
+assert_type(np.ctypeslib.as_ctypes(AR_single), ct.Array[ct.c_float])
+assert_type(np.ctypeslib.as_ctypes(AR_double), ct.Array[ct.c_double])
+assert_type(np.ctypeslib.as_ctypes(AR_void), ct.Array[Any])
+
+assert_type(np.ctypeslib.as_array(AR_ubyte), npt.NDArray[np.ubyte])
+assert_type(np.ctypeslib.as_array(1), npt.NDArray[Any])
+assert_type(np.ctypeslib.as_array(pointer), npt.NDArray[Any])
+
+if sys.platform == "win32":
+    # Mainly on windows int is the same size as long but gets picked first:
+    assert_type(np.ctypeslib.as_ctypes_type(np.long), type[ct.c_int])
+    assert_type(np.ctypeslib.as_ctypes_type(np.ulong), type[ct.c_uint])
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong), ct.Array[ct.c_uint])
+    assert_type(np.ctypeslib.as_ctypes(AR_long), ct.Array[ct.c_int])
+    assert_type(np.ctypeslib.as_ctypes(AR_long.take(0)), ct.c_int)
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong.take(0)), ct.c_uint)
+else:
+    assert_type(np.ctypeslib.as_ctypes_type(np.long), type[ct.c_long])
+    assert_type(np.ctypeslib.as_ctypes_type(np.ulong), type[ct.c_ulong])
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong), ct.Array[ct.c_ulong])
+    assert_type(np.ctypeslib.as_ctypes(AR_long), ct.Array[ct.c_long])
+    assert_type(np.ctypeslib.as_ctypes(AR_long.take(0)), ct.c_long)
+    assert_type(np.ctypeslib.as_ctypes(AR_ulong.take(0)), ct.c_ulong)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..88f2b076be8481ffb58618afa4b25d51bf18bf60
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/datasource.pyi
@@ -0,0 +1,25 @@
+from pathlib import Path
+from typing import IO, Any
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+path1: Path
+path2: str
+
+d1 = np.lib.npyio.DataSource(path1)
+d2 = np.lib.npyio.DataSource(path2)
+d3 = np.lib.npyio.DataSource(None)
+
+assert_type(d1.abspath("..."), str)
+assert_type(d2.abspath("..."), str)
+assert_type(d3.abspath("..."), str)
+
+assert_type(d1.exists("..."), bool)
+assert_type(d2.exists("..."), bool)
+assert_type(d3.exists("..."), bool)
+
+assert_type(d1.open("...", "r"), IO[Any])
+assert_type(d2.open("...", encoding="utf8"), IO[Any])
+assert_type(d3.open("...", newline="/n"), IO[Any])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..da37778b177bd08c7919f3bcc0a4ee83a07f94a2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/dtype.pyi
@@ -0,0 +1,140 @@
+import ctypes as ct
+import datetime as dt
+from decimal import Decimal
+from fractions import Fraction
+from typing import Any, Literal, TypeAlias
+
+import numpy as np
+from numpy.dtypes import StringDType
+
+from typing_extensions import LiteralString, assert_type
+
+# a combination of likely `object` dtype-like candidates (no `_co`)
+_PyObjectLike: TypeAlias = Decimal | Fraction | dt.datetime | dt.timedelta
+
+dtype_U: np.dtype[np.str_]
+dtype_V: np.dtype[np.void]
+dtype_i8: np.dtype[np.int64]
+
+py_int_co: type[int | bool]
+py_float_co: type[float | int | bool]
+py_complex_co: type[complex | float | int | bool]
+py_object: type[_PyObjectLike]
+py_character: type[str | bytes]
+py_flexible: type[str | bytes | memoryview]
+
+ct_floating: type[ct.c_float | ct.c_double | ct.c_longdouble]
+ct_number: type[ct.c_uint8 | ct.c_float]
+ct_generic: type[ct.c_bool | ct.c_char]
+
+cs_integer: Literal["u1", "V", "S"]
+cs_generic: Literal["H", "U", "h", "|M8[Y]", "?"]
+
+dt_inexact: np.dtype[np.inexact[Any]]
+dt_string: StringDType
+
+
+assert_type(np.dtype(np.float64), np.dtype[np.float64])
+assert_type(np.dtype(np.float64, metadata={"test": "test"}), np.dtype[np.float64])
+assert_type(np.dtype(np.int64), np.dtype[np.int64])
+
+# String aliases
+assert_type(np.dtype("float64"), np.dtype[np.float64])
+assert_type(np.dtype("float32"), np.dtype[np.float32])
+assert_type(np.dtype("int64"), np.dtype[np.int64])
+assert_type(np.dtype("int32"), np.dtype[np.int32])
+assert_type(np.dtype("bool"), np.dtype[np.bool])
+assert_type(np.dtype("bytes"), np.dtype[np.bytes_])
+assert_type(np.dtype("str"), np.dtype[np.str_])
+
+# Python types
+assert_type(np.dtype(bool), np.dtype[np.bool])
+assert_type(np.dtype(py_int_co), np.dtype[np.int_ | np.bool])
+assert_type(np.dtype(int), np.dtype[np.int_ | np.bool])
+assert_type(np.dtype(py_float_co), np.dtype[np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(float), np.dtype[np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(py_complex_co), np.dtype[np.complex128 | np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(complex), np.dtype[np.complex128 | np.float64 | np.int_ | np.bool])
+assert_type(np.dtype(py_object), np.dtype[np.object_])
+assert_type(np.dtype(str), np.dtype[np.str_])
+assert_type(np.dtype(bytes), np.dtype[np.bytes_])
+assert_type(np.dtype(py_character), np.dtype[np.character])
+assert_type(np.dtype(memoryview), np.dtype[np.void])
+assert_type(np.dtype(py_flexible), np.dtype[np.flexible])
+
+assert_type(np.dtype(list), np.dtype[np.object_])
+assert_type(np.dtype(dt.datetime), np.dtype[np.object_])
+assert_type(np.dtype(dt.timedelta), np.dtype[np.object_])
+assert_type(np.dtype(Decimal), np.dtype[np.object_])
+assert_type(np.dtype(Fraction), np.dtype[np.object_])
+
+# char-codes
+assert_type(np.dtype("?"), np.dtype[np.bool])
+assert_type(np.dtype("|b1"), np.dtype[np.bool])
+assert_type(np.dtype("u1"), np.dtype[np.uint8])
+assert_type(np.dtype("l"), np.dtype[np.long])
+assert_type(np.dtype("longlong"), np.dtype[np.longlong])
+assert_type(np.dtype(">g"), np.dtype[np.longdouble])
+assert_type(np.dtype(cs_integer), np.dtype[np.integer[Any]])
+assert_type(np.dtype(cs_number), np.dtype[np.number[Any]])
+assert_type(np.dtype(cs_flex), np.dtype[np.flexible])
+assert_type(np.dtype(cs_generic), np.dtype[np.generic])
+
+# ctypes
+assert_type(np.dtype(ct.c_double), np.dtype[np.double])
+assert_type(np.dtype(ct.c_longlong), np.dtype[np.longlong])
+assert_type(np.dtype(ct.c_uint32), np.dtype[np.uint32])
+assert_type(np.dtype(ct.c_bool), np.dtype[np.bool])
+assert_type(np.dtype(ct.c_char), np.dtype[np.bytes_])
+assert_type(np.dtype(ct.py_object), np.dtype[np.object_])
+
+# Special case for None
+assert_type(np.dtype(None), np.dtype[np.float64])
+
+# Dypes of dtypes
+assert_type(np.dtype(np.dtype(np.float64)), np.dtype[np.float64])
+assert_type(np.dtype(dt_inexact), np.dtype[np.inexact[Any]])
+
+# Parameterized dtypes
+assert_type(np.dtype("S8"), np.dtype[Any])
+
+# Void
+assert_type(np.dtype(("U", 10)), np.dtype[np.void])
+
+# StringDType
+assert_type(np.dtype(dt_string), StringDType)
+assert_type(np.dtype("T"), StringDType)
+assert_type(np.dtype("=T"), StringDType)
+assert_type(np.dtype("|T"), StringDType)
+
+
+# Methods and attributes
+assert_type(dtype_U.base, np.dtype[Any])
+assert_type(dtype_U.subdtype, None | tuple[np.dtype[Any], tuple[int, ...]])
+assert_type(dtype_U.newbyteorder(), np.dtype[np.str_])
+assert_type(dtype_U.type, type[np.str_])
+assert_type(dtype_U.name, LiteralString)
+assert_type(dtype_U.names, None | tuple[str, ...])
+
+assert_type(dtype_U * 0, np.dtype[np.str_])
+assert_type(dtype_U * 1, np.dtype[np.str_])
+assert_type(dtype_U * 2, np.dtype[np.str_])
+
+assert_type(dtype_i8 * 0, np.dtype[np.void])
+assert_type(dtype_i8 * 1, np.dtype[np.int64])
+assert_type(dtype_i8 * 2, np.dtype[np.void])
+
+assert_type(0 * dtype_U, np.dtype[np.str_])
+assert_type(1 * dtype_U, np.dtype[np.str_])
+assert_type(2 * dtype_U, np.dtype[np.str_])
+
+assert_type(0 * dtype_i8, np.dtype[Any])
+assert_type(1 * dtype_i8, np.dtype[Any])
+assert_type(2 * dtype_i8, np.dtype[Any])
+
+assert_type(dtype_V["f0"], np.dtype[Any])
+assert_type(dtype_V[0], np.dtype[Any])
+assert_type(dtype_V[["f0", "f1"]], np.dtype[np.void])
+assert_type(dtype_V[["f0"]], np.dtype[np.void])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6dc44e23bda0523c528f502ed224aa2d64a1970a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/einsumfunc.pyi
@@ -0,0 +1,41 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_U: list[str]
+AR_o: npt.NDArray[np.object_]
+
+OUT_f: npt.NDArray[np.float64]
+
+assert_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b), Any)
+assert_type(np.einsum("i,i->i", AR_o, AR_o), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i), Any)
+assert_type(np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c), Any)
+
+assert_type(np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c, out=OUT_f), npt.NDArray[np.float64])
+assert_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe", out=OUT_f), npt.NDArray[np.float64])
+assert_type(np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16"), Any)
+assert_type(np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe"), Any)
+
+assert_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_u, AR_LIKE_u), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_i, AR_LIKE_i), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_f, AR_LIKE_f), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_c, AR_LIKE_c), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_i), tuple[list[Any], str])
+assert_type(np.einsum_path("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c), tuple[list[Any], str])
+
+assert_type(np.einsum([[1, 1], [1, 1]], AR_LIKE_i, AR_LIKE_i), Any)
+assert_type(np.einsum_path([[1, 1], [1, 1]], AR_LIKE_i, AR_LIKE_i), tuple[list[Any], str])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cc6579cf3b33a9cc831720ae9d6382ab2b710c3b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/emath.pyi
@@ -0,0 +1,56 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+f8: np.float64
+c16: np.complex128
+
+assert_type(np.emath.sqrt(f8), Any)
+assert_type(np.emath.sqrt(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.sqrt(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.sqrt(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log(f8), Any)
+assert_type(np.emath.log(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log10(f8), Any)
+assert_type(np.emath.log10(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log10(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log10(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.log2(f8), Any)
+assert_type(np.emath.log2(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.log2(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.log2(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.logn(f8, 2), Any)
+assert_type(np.emath.logn(AR_f8, 4), npt.NDArray[Any])
+assert_type(np.emath.logn(f8, 1j), np.complexfloating[Any, Any])
+assert_type(np.emath.logn(AR_c16, 1.5), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.power(f8, 2), Any)
+assert_type(np.emath.power(AR_f8, 4), npt.NDArray[Any])
+assert_type(np.emath.power(f8, 2j), np.complexfloating[Any, Any])
+assert_type(np.emath.power(AR_c16, 1.5), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arccos(f8), Any)
+assert_type(np.emath.arccos(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arccos(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arccos(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arcsin(f8), Any)
+assert_type(np.emath.arcsin(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arcsin(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arcsin(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.emath.arctanh(f8), Any)
+assert_type(np.emath.arctanh(AR_f8), npt.NDArray[Any])
+assert_type(np.emath.arctanh(c16), np.complexfloating[Any, Any])
+assert_type(np.emath.arctanh(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f3a29c75615ce86d9d977445b82b41ab33f470e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fft.pyi
@@ -0,0 +1,39 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_LIKE_f8: list[float]
+
+assert_type(np.fft.fftshift(AR_f8), npt.NDArray[np.float64])
+assert_type(np.fft.fftshift(AR_LIKE_f8, axes=0), npt.NDArray[Any])
+
+assert_type(np.fft.ifftshift(AR_f8), npt.NDArray[np.float64])
+assert_type(np.fft.ifftshift(AR_LIKE_f8, axes=0), npt.NDArray[Any])
+
+assert_type(np.fft.fftfreq(5, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.fft.fftfreq(np.int64(), AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.fft.fftfreq(5, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.fft.fftfreq(np.int64(), AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.fft.fft(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifft(AR_f8, axis=1), npt.NDArray[np.complex128])
+assert_type(np.fft.rfft(AR_f8, n=None), npt.NDArray[np.complex128])
+assert_type(np.fft.irfft(AR_f8, norm="ortho"), npt.NDArray[np.float64])
+assert_type(np.fft.hfft(AR_f8, n=2), npt.NDArray[np.float64])
+assert_type(np.fft.ihfft(AR_f8), npt.NDArray[np.complex128])
+
+assert_type(np.fft.fftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.rfftn(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.irfftn(AR_f8), npt.NDArray[np.float64])
+
+assert_type(np.fft.rfft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.ifft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.fft2(AR_f8), npt.NDArray[np.complex128])
+assert_type(np.fft.irfft2(AR_f8), npt.NDArray[np.float64])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6891ce7382fe93b32e5a7cb87def3fde36dfb2a5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/flatiter.pyi
@@ -0,0 +1,49 @@
+from typing import Literal, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+a: np.flatiter[npt.NDArray[np.str_]]
+a_1d: np.flatiter[np.ndarray[tuple[int], np.dtype[np.bytes_]]]
+
+Size: TypeAlias = Literal[42]
+a_1d_fixed: np.flatiter[np.ndarray[tuple[Size], np.dtype[np.object_]]]
+
+assert_type(a.base, npt.NDArray[np.str_])
+assert_type(a.copy(), npt.NDArray[np.str_])
+assert_type(a.coords, tuple[int, ...])
+assert_type(a.index, int)
+assert_type(iter(a), np.flatiter[npt.NDArray[np.str_]])
+assert_type(next(a), np.str_)
+assert_type(a[0], np.str_)
+assert_type(a[[0, 1, 2]], npt.NDArray[np.str_])
+assert_type(a[...], npt.NDArray[np.str_])
+assert_type(a[:], npt.NDArray[np.str_])
+assert_type(a[(...,)], npt.NDArray[np.str_])
+assert_type(a[(0,)], np.str_)
+
+assert_type(a.__array__(), npt.NDArray[np.str_])
+assert_type(a.__array__(np.dtype(np.float64)), npt.NDArray[np.float64])
+assert_type(
+    a_1d.__array__(),
+    np.ndarray[tuple[int], np.dtype[np.bytes_]],
+)
+assert_type(
+    a_1d.__array__(np.dtype(np.float64)),
+    np.ndarray[tuple[int], np.dtype[np.float64]],
+)
+assert_type(
+    a_1d_fixed.__array__(),
+    np.ndarray[tuple[Size], np.dtype[np.object_]],
+)
+assert_type(
+    a_1d_fixed.__array__(np.dtype(np.float64)),
+    np.ndarray[tuple[Size], np.dtype[np.float64]],
+)
+
+a[0] = "a"
+a[:5] = "a"
+a[...] = "a"
+a[(...,)] = "a"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7e778dc58410e3cdf2a552497bde5c92df9a9b34
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/fromnumeric.pyi
@@ -0,0 +1,343 @@
+"""Tests for :mod:`_core.fromnumeric`."""
+
+from typing import Any, Literal as L
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+class NDArraySubclass(npt.NDArray[np.complex128]):
+    ...
+
+AR_b: npt.NDArray[np.bool]
+AR_f4: npt.NDArray[np.float32]
+AR_c16: npt.NDArray[np.complex128]
+AR_u8: npt.NDArray[np.uint64]
+AR_i8: npt.NDArray[np.int64]
+AR_O: npt.NDArray[np.object_]
+AR_subclass: NDArraySubclass
+AR_m: npt.NDArray[np.timedelta64]
+AR_0d: np.ndarray[tuple[()], np.dtype[Any]]
+AR_1d: np.ndarray[tuple[int], np.dtype[Any]]
+AR_nd: np.ndarray[tuple[int, ...], np.dtype[Any]]
+
+b: np.bool
+f4: np.float32
+i8: np.int64
+f: float
+
+assert_type(np.take(b, 0), np.bool)
+assert_type(np.take(f4, 0), np.float32)
+assert_type(np.take(f, 0), Any)
+assert_type(np.take(AR_b, 0), np.bool)
+assert_type(np.take(AR_f4, 0), np.float32)
+assert_type(np.take(AR_b, [0]), npt.NDArray[np.bool])
+assert_type(np.take(AR_f4, [0]), npt.NDArray[np.float32])
+assert_type(np.take([1], [0]), npt.NDArray[Any])
+assert_type(np.take(AR_f4, [0], out=AR_subclass), NDArraySubclass)
+
+assert_type(np.reshape(b, 1), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.reshape(f4, 1), np.ndarray[tuple[int], np.dtype[np.float32]])
+assert_type(np.reshape(f, 1), np.ndarray[tuple[int], np.dtype[Any]])
+assert_type(np.reshape(AR_b, 1), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.reshape(AR_f4, 1), np.ndarray[tuple[int], np.dtype[np.float32]])
+
+assert_type(np.choose(1, [True, True]), Any)
+assert_type(np.choose([1], [True, True]), npt.NDArray[Any])
+assert_type(np.choose([1], AR_b), npt.NDArray[np.bool])
+assert_type(np.choose([1], AR_b, out=AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.repeat(b, 1), npt.NDArray[np.bool])
+assert_type(np.repeat(f4, 1), npt.NDArray[np.float32])
+assert_type(np.repeat(f, 1), npt.NDArray[Any])
+assert_type(np.repeat(AR_b, 1), npt.NDArray[np.bool])
+assert_type(np.repeat(AR_f4, 1), npt.NDArray[np.float32])
+
+# TODO: array_bdd tests for np.put()
+
+assert_type(np.swapaxes([[0, 1]], 0, 0), npt.NDArray[Any])
+assert_type(np.swapaxes(AR_b, 0, 0), npt.NDArray[np.bool])
+assert_type(np.swapaxes(AR_f4, 0, 0), npt.NDArray[np.float32])
+
+assert_type(np.transpose(b), npt.NDArray[np.bool])
+assert_type(np.transpose(f4), npt.NDArray[np.float32])
+assert_type(np.transpose(f), npt.NDArray[Any])
+assert_type(np.transpose(AR_b), npt.NDArray[np.bool])
+assert_type(np.transpose(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.partition(b, 0, axis=None), npt.NDArray[np.bool])
+assert_type(np.partition(f4, 0, axis=None), npt.NDArray[np.float32])
+assert_type(np.partition(f, 0, axis=None), npt.NDArray[Any])
+assert_type(np.partition(AR_b, 0), npt.NDArray[np.bool])
+assert_type(np.partition(AR_f4, 0), npt.NDArray[np.float32])
+
+assert_type(np.argpartition(b, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(f4, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(f, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(AR_b, 0), npt.NDArray[np.intp])
+assert_type(np.argpartition(AR_f4, 0), npt.NDArray[np.intp])
+
+assert_type(np.sort([2, 1], 0), npt.NDArray[Any])
+assert_type(np.sort(AR_b, 0), npt.NDArray[np.bool])
+assert_type(np.sort(AR_f4, 0), npt.NDArray[np.float32])
+
+assert_type(np.argsort(AR_b, 0), npt.NDArray[np.intp])
+assert_type(np.argsort(AR_f4, 0), npt.NDArray[np.intp])
+
+assert_type(np.argmax(AR_b), np.intp)
+assert_type(np.argmax(AR_f4), np.intp)
+assert_type(np.argmax(AR_b, axis=0), Any)
+assert_type(np.argmax(AR_f4, axis=0), Any)
+assert_type(np.argmax(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.argmin(AR_b), np.intp)
+assert_type(np.argmin(AR_f4), np.intp)
+assert_type(np.argmin(AR_b, axis=0), Any)
+assert_type(np.argmin(AR_f4, axis=0), Any)
+assert_type(np.argmin(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.searchsorted(AR_b[0], 0), np.intp)
+assert_type(np.searchsorted(AR_f4[0], 0), np.intp)
+assert_type(np.searchsorted(AR_b[0], [0]), npt.NDArray[np.intp])
+assert_type(np.searchsorted(AR_f4[0], [0]), npt.NDArray[np.intp])
+
+assert_type(np.resize(b, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.bool]])
+assert_type(np.resize(f4, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.float32]])
+assert_type(np.resize(f, (5, 5)), np.ndarray[tuple[int, int], np.dtype[Any]])
+assert_type(np.resize(AR_b, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.bool]])
+assert_type(np.resize(AR_f4, (5, 5)), np.ndarray[tuple[int, int], np.dtype[np.float32]])
+
+assert_type(np.squeeze(b), np.bool)
+assert_type(np.squeeze(f4), np.float32)
+assert_type(np.squeeze(f), npt.NDArray[Any])
+assert_type(np.squeeze(AR_b), npt.NDArray[np.bool])
+assert_type(np.squeeze(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.diagonal(AR_b), npt.NDArray[np.bool])
+assert_type(np.diagonal(AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.trace(AR_b), Any)
+assert_type(np.trace(AR_f4), Any)
+assert_type(np.trace(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ravel(b), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.ravel(f4), np.ndarray[tuple[int], np.dtype[np.float32]])
+assert_type(np.ravel(f), np.ndarray[tuple[int], np.dtype[np.float64 | np.int_ | np.bool]])
+assert_type(np.ravel(AR_b), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(np.ravel(AR_f4), np.ndarray[tuple[int], np.dtype[np.float32]])
+
+assert_type(np.nonzero(AR_b), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_f4), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_1d), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.nonzero(AR_nd), tuple[npt.NDArray[np.intp], ...])
+
+assert_type(np.shape(b), tuple[()])
+assert_type(np.shape(f), tuple[()])
+assert_type(np.shape([1]), tuple[int])
+assert_type(np.shape([[2]]), tuple[int, int])
+assert_type(np.shape([[[3]]]), tuple[int, ...])
+assert_type(np.shape(AR_b), tuple[int, ...])
+assert_type(np.shape(AR_nd), tuple[int, ...])
+# these fail on mypy, but it works as expected with pyright/pylance
+# assert_type(np.shape(AR_0d), tuple[()])
+# assert_type(np.shape(AR_1d), tuple[int])
+# assert_type(np.shape(AR_2d), tuple[int, int])
+
+assert_type(np.compress([True], b), npt.NDArray[np.bool])
+assert_type(np.compress([True], f4), npt.NDArray[np.float32])
+assert_type(np.compress([True], f), npt.NDArray[Any])
+assert_type(np.compress([True], AR_b), npt.NDArray[np.bool])
+assert_type(np.compress([True], AR_f4), npt.NDArray[np.float32])
+
+assert_type(np.clip(b, 0, 1.0), np.bool)
+assert_type(np.clip(f4, -1, 1), np.float32)
+assert_type(np.clip(f, 0, 1), Any)
+assert_type(np.clip(AR_b, 0, 1), npt.NDArray[np.bool])
+assert_type(np.clip(AR_f4, 0, 1), npt.NDArray[np.float32])
+assert_type(np.clip([0], 0, 1), npt.NDArray[Any])
+assert_type(np.clip(AR_b, 0, 1, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.sum(b), np.bool)
+assert_type(np.sum(f4), np.float32)
+assert_type(np.sum(f), Any)
+assert_type(np.sum(AR_b), np.bool)
+assert_type(np.sum(AR_f4), np.float32)
+assert_type(np.sum(AR_b, axis=0), Any)
+assert_type(np.sum(AR_f4, axis=0), Any)
+assert_type(np.sum(AR_f4, out=AR_subclass), NDArraySubclass)
+assert_type(np.sum(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.sum(AR_f4, None, np.float64), np.float64)
+assert_type(np.sum(AR_f4, dtype=np.float64, keepdims=False), np.float64)
+assert_type(np.sum(AR_f4, None, np.float64, keepdims=False), np.float64)
+assert_type(np.sum(AR_f4, dtype=np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+assert_type(np.sum(AR_f4, None, np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+
+assert_type(np.all(b), np.bool)
+assert_type(np.all(f4), np.bool)
+assert_type(np.all(f), np.bool)
+assert_type(np.all(AR_b), np.bool)
+assert_type(np.all(AR_f4), np.bool)
+assert_type(np.all(AR_b, axis=0), Any)
+assert_type(np.all(AR_f4, axis=0), Any)
+assert_type(np.all(AR_b, keepdims=True), Any)
+assert_type(np.all(AR_f4, keepdims=True), Any)
+assert_type(np.all(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.any(b), np.bool)
+assert_type(np.any(f4), np.bool)
+assert_type(np.any(f), np.bool)
+assert_type(np.any(AR_b), np.bool)
+assert_type(np.any(AR_f4), np.bool)
+assert_type(np.any(AR_b, axis=0), Any)
+assert_type(np.any(AR_f4, axis=0), Any)
+assert_type(np.any(AR_b, keepdims=True), Any)
+assert_type(np.any(AR_f4, keepdims=True), Any)
+assert_type(np.any(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumsum(b), npt.NDArray[np.bool])
+assert_type(np.cumsum(f4), npt.NDArray[np.float32])
+assert_type(np.cumsum(f), npt.NDArray[Any])
+assert_type(np.cumsum(AR_b), npt.NDArray[np.bool])
+assert_type(np.cumsum(AR_f4), npt.NDArray[np.float32])
+assert_type(np.cumsum(f, dtype=float), npt.NDArray[Any])
+assert_type(np.cumsum(f, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumsum(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumulative_sum(b), npt.NDArray[np.bool])
+assert_type(np.cumulative_sum(f4), npt.NDArray[np.float32])
+assert_type(np.cumulative_sum(f), npt.NDArray[Any])
+assert_type(np.cumulative_sum(AR_b), npt.NDArray[np.bool])
+assert_type(np.cumulative_sum(AR_f4), npt.NDArray[np.float32])
+assert_type(np.cumulative_sum(f, dtype=float), npt.NDArray[Any])
+assert_type(np.cumulative_sum(f, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumulative_sum(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ptp(b), np.bool)
+assert_type(np.ptp(f4), np.float32)
+assert_type(np.ptp(f), Any)
+assert_type(np.ptp(AR_b), np.bool)
+assert_type(np.ptp(AR_f4), np.float32)
+assert_type(np.ptp(AR_b, axis=0), Any)
+assert_type(np.ptp(AR_f4, axis=0), Any)
+assert_type(np.ptp(AR_b, keepdims=True), Any)
+assert_type(np.ptp(AR_f4, keepdims=True), Any)
+assert_type(np.ptp(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.amax(b), np.bool)
+assert_type(np.amax(f4), np.float32)
+assert_type(np.amax(f), Any)
+assert_type(np.amax(AR_b), np.bool)
+assert_type(np.amax(AR_f4), np.float32)
+assert_type(np.amax(AR_b, axis=0), Any)
+assert_type(np.amax(AR_f4, axis=0), Any)
+assert_type(np.amax(AR_b, keepdims=True), Any)
+assert_type(np.amax(AR_f4, keepdims=True), Any)
+assert_type(np.amax(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.amin(b), np.bool)
+assert_type(np.amin(f4), np.float32)
+assert_type(np.amin(f), Any)
+assert_type(np.amin(AR_b), np.bool)
+assert_type(np.amin(AR_f4), np.float32)
+assert_type(np.amin(AR_b, axis=0), Any)
+assert_type(np.amin(AR_f4, axis=0), Any)
+assert_type(np.amin(AR_b, keepdims=True), Any)
+assert_type(np.amin(AR_f4, keepdims=True), Any)
+assert_type(np.amin(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.prod(AR_b), np.int_)
+assert_type(np.prod(AR_u8), np.uint64)
+assert_type(np.prod(AR_i8), np.int64)
+assert_type(np.prod(AR_f4), np.floating[Any])
+assert_type(np.prod(AR_c16), np.complexfloating[Any, Any])
+assert_type(np.prod(AR_O), Any)
+assert_type(np.prod(AR_f4, axis=0), Any)
+assert_type(np.prod(AR_f4, keepdims=True), Any)
+assert_type(np.prod(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.prod(AR_f4, dtype=float), Any)
+assert_type(np.prod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumprod(AR_b), npt.NDArray[np.int_])
+assert_type(np.cumprod(AR_u8), npt.NDArray[np.uint64])
+assert_type(np.cumprod(AR_i8), npt.NDArray[np.int64])
+assert_type(np.cumprod(AR_f4), npt.NDArray[np.floating[Any]])
+assert_type(np.cumprod(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cumprod(AR_O), npt.NDArray[np.object_])
+assert_type(np.cumprod(AR_f4, axis=0), npt.NDArray[np.floating[Any]])
+assert_type(np.cumprod(AR_f4, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumprod(AR_f4, dtype=float), npt.NDArray[Any])
+assert_type(np.cumprod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.cumulative_prod(AR_b), npt.NDArray[np.int_])
+assert_type(np.cumulative_prod(AR_u8), npt.NDArray[np.uint64])
+assert_type(np.cumulative_prod(AR_i8), npt.NDArray[np.int64])
+assert_type(np.cumulative_prod(AR_f4), npt.NDArray[np.floating[Any]])
+assert_type(np.cumulative_prod(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cumulative_prod(AR_O), npt.NDArray[np.object_])
+assert_type(np.cumulative_prod(AR_f4, axis=0), npt.NDArray[np.floating[Any]])
+assert_type(np.cumulative_prod(AR_f4, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.cumulative_prod(AR_f4, dtype=float), npt.NDArray[Any])
+assert_type(np.cumulative_prod(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.ndim(b), int)
+assert_type(np.ndim(f4), int)
+assert_type(np.ndim(f), int)
+assert_type(np.ndim(AR_b), int)
+assert_type(np.ndim(AR_f4), int)
+
+assert_type(np.size(b), int)
+assert_type(np.size(f4), int)
+assert_type(np.size(f), int)
+assert_type(np.size(AR_b), int)
+assert_type(np.size(AR_f4), int)
+
+assert_type(np.around(b), np.float16)
+assert_type(np.around(f), Any)
+assert_type(np.around(i8), np.int64)
+assert_type(np.around(f4), np.float32)
+assert_type(np.around(AR_b), npt.NDArray[np.float16])
+assert_type(np.around(AR_i8), npt.NDArray[np.int64])
+assert_type(np.around(AR_f4), npt.NDArray[np.float32])
+assert_type(np.around([1.5]), npt.NDArray[Any])
+assert_type(np.around(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.mean(AR_b), np.floating[Any])
+assert_type(np.mean(AR_i8), np.floating[Any])
+assert_type(np.mean(AR_f4), np.floating[Any])
+assert_type(np.mean(AR_m), np.timedelta64)
+assert_type(np.mean(AR_c16), np.complexfloating[Any, Any])
+assert_type(np.mean(AR_O), Any)
+assert_type(np.mean(AR_f4, axis=0), Any)
+assert_type(np.mean(AR_f4, keepdims=True), Any)
+assert_type(np.mean(AR_f4, dtype=float), Any)
+assert_type(np.mean(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.mean(AR_f4, out=AR_subclass), NDArraySubclass)
+assert_type(np.mean(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.mean(AR_f4, None, np.float64), np.float64)
+assert_type(np.mean(AR_f4, dtype=np.float64, keepdims=False), np.float64)
+assert_type(np.mean(AR_f4, None, np.float64, keepdims=False), np.float64)
+assert_type(np.mean(AR_f4, dtype=np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+assert_type(np.mean(AR_f4, None, np.float64, keepdims=True), np.float64 | npt.NDArray[np.float64])
+
+assert_type(np.std(AR_b), np.floating[Any])
+assert_type(np.std(AR_i8), np.floating[Any])
+assert_type(np.std(AR_f4), np.floating[Any])
+assert_type(np.std(AR_c16), np.floating[Any])
+assert_type(np.std(AR_O), Any)
+assert_type(np.std(AR_f4, axis=0), Any)
+assert_type(np.std(AR_f4, keepdims=True), Any)
+assert_type(np.std(AR_f4, dtype=float), Any)
+assert_type(np.std(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.std(AR_f4, out=AR_subclass), NDArraySubclass)
+
+assert_type(np.var(AR_b), np.floating[Any])
+assert_type(np.var(AR_i8), np.floating[Any])
+assert_type(np.var(AR_f4), np.floating[Any])
+assert_type(np.var(AR_c16), np.floating[Any])
+assert_type(np.var(AR_O), Any)
+assert_type(np.var(AR_f4, axis=0), Any)
+assert_type(np.var(AR_f4, keepdims=True), Any)
+assert_type(np.var(AR_f4, dtype=float), Any)
+assert_type(np.var(AR_f4, dtype=np.float64), np.float64)
+assert_type(np.var(AR_f4, out=AR_subclass), NDArraySubclass)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f058382f2042e38267fccf0e772907420c4dc51c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/getlimits.pyi
@@ -0,0 +1,53 @@
+from typing import Any
+
+import numpy as np
+from numpy._typing import _64Bit
+
+from typing_extensions import assert_type, LiteralString
+
+f: float
+f8: np.float64
+c8: np.complex64
+
+i: int
+i8: np.int64
+u4: np.uint32
+
+finfo_f8: np.finfo[np.float64]
+iinfo_i8: np.iinfo[np.int64]
+
+assert_type(np.finfo(f), np.finfo[np.float64])
+assert_type(np.finfo(f8), np.finfo[np.floating[_64Bit]])
+assert_type(np.finfo(c8), np.finfo[np.float32])
+assert_type(np.finfo('f2'), np.finfo[np.floating[Any]])
+
+assert_type(finfo_f8.dtype, np.dtype[np.float64])
+assert_type(finfo_f8.bits, int)
+assert_type(finfo_f8.eps, np.float64)
+assert_type(finfo_f8.epsneg, np.float64)
+assert_type(finfo_f8.iexp, int)
+assert_type(finfo_f8.machep, int)
+assert_type(finfo_f8.max, np.float64)
+assert_type(finfo_f8.maxexp, int)
+assert_type(finfo_f8.min, np.float64)
+assert_type(finfo_f8.minexp, int)
+assert_type(finfo_f8.negep, int)
+assert_type(finfo_f8.nexp, int)
+assert_type(finfo_f8.nmant, int)
+assert_type(finfo_f8.precision, int)
+assert_type(finfo_f8.resolution, np.float64)
+assert_type(finfo_f8.tiny, np.float64)
+assert_type(finfo_f8.smallest_normal, np.float64)
+assert_type(finfo_f8.smallest_subnormal, np.float64)
+
+assert_type(np.iinfo(i), np.iinfo[np.int_])
+assert_type(np.iinfo(i8), np.iinfo[np.int64])
+assert_type(np.iinfo(u4), np.iinfo[np.uint32])
+assert_type(np.iinfo('i2'), np.iinfo[Any])
+
+assert_type(iinfo_i8.dtype, np.dtype[np.int64])
+assert_type(iinfo_i8.kind, LiteralString)
+assert_type(iinfo_i8.bits, int)
+assert_type(iinfo_i8.key, LiteralString)
+assert_type(iinfo_i8.min, int)
+assert_type(iinfo_i8.max, int)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..91a7d0394d20c575af6a7763ef35a9469d369a2b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/histograms.pyi
@@ -0,0 +1,27 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+assert_type(np.histogram_bin_edges(AR_i8, bins="auto"), npt.NDArray[Any])
+assert_type(np.histogram_bin_edges(AR_i8, bins="rice", range=(0, 3)), npt.NDArray[Any])
+assert_type(np.histogram_bin_edges(AR_i8, bins="scott", weights=AR_f8), npt.NDArray[Any])
+
+assert_type(np.histogram(AR_i8, bins="auto"), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_i8, bins="rice", range=(0, 3)), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_i8, bins="scott", weights=AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+assert_type(np.histogram(AR_f8, bins=1, density=True), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.histogramdd(AR_i8, bins=[1]),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_i8, range=[(0, 3)]),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_i8, weights=AR_f8),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
+assert_type(np.histogramdd(AR_f8, density=True),
+            tuple[npt.NDArray[Any], tuple[npt.NDArray[Any], ...]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..06071feddd79f9bd3c0d61ec10c489d62e06e0fa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/index_tricks.pyi
@@ -0,0 +1,72 @@
+from types import EllipsisType
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_U: list[str]
+AR_LIKE_O: list[object]
+
+AR_i8: npt.NDArray[np.int64]
+AR_O: npt.NDArray[np.object_]
+
+assert_type(np.ndenumerate(AR_i8), np.ndenumerate[np.int64])
+assert_type(np.ndenumerate(AR_LIKE_f), np.ndenumerate[np.float64])
+assert_type(np.ndenumerate(AR_LIKE_U), np.ndenumerate[np.str_])
+assert_type(np.ndenumerate(AR_LIKE_O), np.ndenumerate[Any])
+
+assert_type(next(np.ndenumerate(AR_i8)), tuple[tuple[int, ...], np.int64])
+assert_type(next(np.ndenumerate(AR_LIKE_f)), tuple[tuple[int, ...], np.float64])
+assert_type(next(np.ndenumerate(AR_LIKE_U)), tuple[tuple[int, ...], np.str_])
+assert_type(next(np.ndenumerate(AR_LIKE_O)), tuple[tuple[int, ...], Any])
+
+assert_type(iter(np.ndenumerate(AR_i8)), np.ndenumerate[np.int64])
+assert_type(iter(np.ndenumerate(AR_LIKE_f)), np.ndenumerate[np.float64])
+assert_type(iter(np.ndenumerate(AR_LIKE_U)), np.ndenumerate[np.str_])
+assert_type(iter(np.ndenumerate(AR_LIKE_O)), np.ndenumerate[Any])
+
+assert_type(np.ndindex(1, 2, 3), np.ndindex)
+assert_type(np.ndindex((1, 2, 3)), np.ndindex)
+assert_type(iter(np.ndindex(1, 2, 3)), np.ndindex)
+assert_type(next(np.ndindex(1, 2, 3)), tuple[int, ...])
+
+assert_type(np.unravel_index([22, 41, 37], (7, 6)), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.unravel_index([31, 41, 13], (7, 6), order="F"), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.unravel_index(1621, (6, 7, 8, 9)), tuple[np.intp, ...])
+
+assert_type(np.ravel_multi_index([[1]], (7, 6)), npt.NDArray[np.intp])
+assert_type(np.ravel_multi_index(AR_LIKE_i, (7, 6)), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (7, 6), order="F"), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (4, 6), mode="clip"), np.intp)
+assert_type(np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=("clip", "wrap")), np.intp)
+assert_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), np.intp)
+
+assert_type(np.mgrid[1:1:2], npt.NDArray[Any])
+assert_type(np.mgrid[1:1:2, None:10], npt.NDArray[Any])
+
+assert_type(np.ogrid[1:1:2], tuple[npt.NDArray[Any], ...])
+assert_type(np.ogrid[1:1:2, None:10], tuple[npt.NDArray[Any], ...])
+
+assert_type(np.index_exp[0:1], tuple[slice[int, int, None]])
+assert_type(np.index_exp[0:1, None:3], tuple[slice[int, int, None], slice[None, int, None]])
+assert_type(np.index_exp[0, 0:1, ..., [0, 1, 3]], tuple[Literal[0], slice[int, int, None], EllipsisType, list[int]])
+
+assert_type(np.s_[0:1], slice[int, int, None])
+assert_type(np.s_[0:1, None:3], tuple[slice[int, int, None], slice[None, int, None]])
+assert_type(np.s_[0, 0:1, ..., [0, 1, 3]], tuple[Literal[0], slice[int, int, None], EllipsisType, list[int]])
+
+assert_type(np.ix_(AR_LIKE_b), tuple[npt.NDArray[np.bool], ...])
+assert_type(np.ix_(AR_LIKE_i, AR_LIKE_f), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.ix_(AR_i8), tuple[npt.NDArray[np.int64], ...])
+
+assert_type(np.fill_diagonal(AR_i8, 5), None)
+
+assert_type(np.diag_indices(4), tuple[npt.NDArray[np.int_], ...])
+assert_type(np.diag_indices(2, 3), tuple[npt.NDArray[np.int_], ...])
+
+assert_type(np.diag_indices_from(AR_i8), tuple[npt.NDArray[np.int_], ...])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9cd06a36f3e08e21b0b8e538bc45d1cfd37de083
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_function_base.pyi
@@ -0,0 +1,210 @@
+from fractions import Fraction
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+vectorized_func: np.vectorize
+
+f8: np.float64
+AR_LIKE_f8: list[float]
+AR_LIKE_c16: list[complex]
+AR_LIKE_O: list[Fraction]
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_b: npt.NDArray[np.bool]
+AR_U: npt.NDArray[np.str_]
+CHAR_AR_U: np.char.chararray[tuple[int, ...], np.dtype[np.str_]]
+
+AR_b_list: list[npt.NDArray[np.bool]]
+
+def func(
+    a: npt.NDArray[Any],
+    posarg: bool = ...,
+    /,
+    arg: int = ...,
+    *,
+    kwarg: str = ...,
+) -> npt.NDArray[Any]: ...
+
+assert_type(vectorized_func.pyfunc, Callable[..., Any])
+assert_type(vectorized_func.cache, bool)
+assert_type(vectorized_func.signature, None | str)
+assert_type(vectorized_func.otypes, None | str)
+assert_type(vectorized_func.excluded, set[int | str])
+assert_type(vectorized_func.__doc__, None | str)
+assert_type(vectorized_func([1]), Any)
+assert_type(np.vectorize(int), np.vectorize)
+assert_type(
+    np.vectorize(int, otypes="i", doc="doc", excluded=(), cache=True, signature=None),
+    np.vectorize,
+)
+
+assert_type(np.rot90(AR_f8, k=2), npt.NDArray[np.float64])
+assert_type(np.rot90(AR_LIKE_f8, axes=(0, 1)), npt.NDArray[Any])
+
+assert_type(np.flip(f8), np.float64)
+assert_type(np.flip(1.0), Any)
+assert_type(np.flip(AR_f8, axis=(0, 1)), npt.NDArray[np.float64])
+assert_type(np.flip(AR_LIKE_f8, axis=0), npt.NDArray[Any])
+
+assert_type(np.iterable(1), bool)
+assert_type(np.iterable([1]), bool)
+
+assert_type(np.average(AR_f8), np.floating[Any])
+assert_type(np.average(AR_f8, weights=AR_c16), np.complexfloating[Any, Any])
+assert_type(np.average(AR_O), Any)
+assert_type(np.average(AR_f8, returned=True), tuple[np.floating[Any], np.floating[Any]])
+assert_type(np.average(AR_f8, weights=AR_c16, returned=True), tuple[np.complexfloating[Any, Any], np.complexfloating[Any, Any]])
+assert_type(np.average(AR_O, returned=True), tuple[Any, Any])
+assert_type(np.average(AR_f8, axis=0), Any)
+assert_type(np.average(AR_f8, axis=0, returned=True), tuple[Any, Any])
+
+assert_type(np.asarray_chkfinite(AR_f8), npt.NDArray[np.float64])
+assert_type(np.asarray_chkfinite(AR_LIKE_f8), npt.NDArray[Any])
+assert_type(np.asarray_chkfinite(AR_f8, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.asarray_chkfinite(AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.piecewise(AR_f8, AR_b, [func]), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func]), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func], True, -1, kwarg=''), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_f8, AR_b_list, [func], True, arg=-1, kwarg=''), npt.NDArray[np.float64])
+assert_type(np.piecewise(AR_LIKE_f8, AR_b_list, [func]), npt.NDArray[Any])
+
+assert_type(np.select([AR_f8], [AR_f8]), npt.NDArray[Any])
+
+assert_type(np.copy(AR_LIKE_f8), npt.NDArray[Any])
+assert_type(np.copy(AR_U), npt.NDArray[np.str_])
+assert_type(np.copy(CHAR_AR_U), npt.NDArray[np.str_])
+assert_type(np.copy(CHAR_AR_U, "K", subok=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+assert_type(np.copy(CHAR_AR_U, subok=True), np.char.chararray[tuple[int, ...], np.dtype[np.str_]])
+
+assert_type(np.gradient(AR_f8, axis=None), Any)
+assert_type(np.gradient(AR_LIKE_f8, edge_order=2), Any)
+
+assert_type(np.diff("bob", n=0), str)
+assert_type(np.diff(AR_f8, axis=0), npt.NDArray[Any])
+assert_type(np.diff(AR_LIKE_f8, prepend=1.5), npt.NDArray[Any])
+
+assert_type(np.interp(1, [1], AR_f8), np.float64)
+assert_type(np.interp(1, [1], [1]), np.float64)
+assert_type(np.interp(1, [1], AR_c16), np.complex128)
+assert_type(np.interp(1, [1], [1j]), np.complex128)  # pyright correctly infers `complex128 | float64`
+assert_type(np.interp([1], [1], AR_f8), npt.NDArray[np.float64])
+assert_type(np.interp([1], [1], [1]), npt.NDArray[np.float64])
+assert_type(np.interp([1], [1], AR_c16), npt.NDArray[np.complex128])
+assert_type(np.interp([1], [1], [1j]), npt.NDArray[np.complex128])  # pyright correctly infers `NDArray[complex128 | float64]`
+
+assert_type(np.angle(f8), np.floating[Any])
+assert_type(np.angle(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.angle(AR_c16, deg=True), npt.NDArray[np.floating[Any]])
+assert_type(np.angle(AR_O), npt.NDArray[np.object_])
+
+assert_type(np.unwrap(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.unwrap(AR_O), npt.NDArray[np.object_])
+
+assert_type(np.sort_complex(AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.trim_zeros(AR_f8), npt.NDArray[np.float64])
+assert_type(np.trim_zeros(AR_LIKE_f8), list[float])
+
+assert_type(np.extract(AR_i8, AR_f8), npt.NDArray[np.float64])
+assert_type(np.extract(AR_i8, AR_LIKE_f8), npt.NDArray[Any])
+
+assert_type(np.place(AR_f8, mask=AR_i8, vals=5.0), None)
+
+assert_type(np.cov(AR_f8, bias=True), npt.NDArray[np.floating[Any]])
+assert_type(np.cov(AR_f8, AR_c16, ddof=1), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cov(AR_f8, aweights=AR_f8, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.cov(AR_f8, fweights=AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.corrcoef(AR_f8, rowvar=True), npt.NDArray[np.floating[Any]])
+assert_type(np.corrcoef(AR_f8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.corrcoef(AR_f8, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(np.corrcoef(AR_f8, dtype=float), npt.NDArray[Any])
+
+assert_type(np.blackman(5), npt.NDArray[np.floating[Any]])
+assert_type(np.bartlett(6), npt.NDArray[np.floating[Any]])
+assert_type(np.hanning(4.5), npt.NDArray[np.floating[Any]])
+assert_type(np.hamming(0), npt.NDArray[np.floating[Any]])
+assert_type(np.i0(AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.kaiser(4, 5.9), npt.NDArray[np.floating[Any]])
+
+assert_type(np.sinc(1.0), np.floating[Any])
+assert_type(np.sinc(1j), np.complexfloating[Any, Any])
+assert_type(np.sinc(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.sinc(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.median(AR_f8, keepdims=False), np.floating[Any])
+assert_type(np.median(AR_c16, overwrite_input=True), np.complexfloating[Any, Any])
+assert_type(np.median(AR_m), np.timedelta64)
+assert_type(np.median(AR_O), Any)
+assert_type(np.median(AR_f8, keepdims=True), Any)
+assert_type(np.median(AR_c16, axis=0), Any)
+assert_type(np.median(AR_LIKE_f8, out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.percentile(AR_f8, 50), np.floating[Any])
+assert_type(np.percentile(AR_c16, 50), np.complexfloating[Any, Any])
+assert_type(np.percentile(AR_m, 50), np.timedelta64)
+assert_type(np.percentile(AR_M, 50, overwrite_input=True), np.datetime64)
+assert_type(np.percentile(AR_O, 50), Any)
+assert_type(np.percentile(AR_f8, [50]), npt.NDArray[np.floating[Any]])
+assert_type(np.percentile(AR_c16, [50]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.percentile(AR_m, [50]), npt.NDArray[np.timedelta64])
+assert_type(np.percentile(AR_M, [50], method="nearest"), npt.NDArray[np.datetime64])
+assert_type(np.percentile(AR_O, [50]), npt.NDArray[np.object_])
+assert_type(np.percentile(AR_f8, [50], keepdims=True), Any)
+assert_type(np.percentile(AR_f8, [50], axis=[1]), Any)
+assert_type(np.percentile(AR_f8, [50], out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.quantile(AR_f8, 0.5), np.floating[Any])
+assert_type(np.quantile(AR_c16, 0.5), np.complexfloating[Any, Any])
+assert_type(np.quantile(AR_m, 0.5), np.timedelta64)
+assert_type(np.quantile(AR_M, 0.5, overwrite_input=True), np.datetime64)
+assert_type(np.quantile(AR_O, 0.5), Any)
+assert_type(np.quantile(AR_f8, [0.5]), npt.NDArray[np.floating[Any]])
+assert_type(np.quantile(AR_c16, [0.5]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.quantile(AR_m, [0.5]), npt.NDArray[np.timedelta64])
+assert_type(np.quantile(AR_M, [0.5], method="nearest"), npt.NDArray[np.datetime64])
+assert_type(np.quantile(AR_O, [0.5]), npt.NDArray[np.object_])
+assert_type(np.quantile(AR_f8, [0.5], keepdims=True), Any)
+assert_type(np.quantile(AR_f8, [0.5], axis=[1]), Any)
+assert_type(np.quantile(AR_f8, [0.5], out=AR_c16), npt.NDArray[np.complex128])
+
+assert_type(np.trapezoid(AR_LIKE_f8), np.float64)
+assert_type(np.trapezoid(AR_LIKE_f8, AR_LIKE_f8), np.float64)
+assert_type(np.trapezoid(AR_LIKE_c16), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_c16, AR_LIKE_f8), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_f8, AR_LIKE_c16), np.complex128)
+assert_type(np.trapezoid(AR_LIKE_O), float)
+assert_type(np.trapezoid(AR_LIKE_O, AR_LIKE_f8), float)
+assert_type(np.trapezoid(AR_f8), np.float64 | npt.NDArray[np.float64])
+assert_type(np.trapezoid(AR_f8, AR_f8), np.float64 | npt.NDArray[np.float64])
+assert_type(np.trapezoid(AR_c16), np.complex128 | npt.NDArray[np.complex128])
+assert_type(np.trapezoid(AR_c16, AR_c16), np.complex128 | npt.NDArray[np.complex128])
+assert_type(np.trapezoid(AR_m), np.timedelta64 | npt.NDArray[np.timedelta64])
+assert_type(np.trapezoid(AR_O), float | npt.NDArray[np.object_])
+assert_type(np.trapezoid(AR_O, AR_LIKE_f8), float | npt.NDArray[np.object_])
+
+assert_type(np.meshgrid(AR_f8, AR_i8, copy=False), tuple[npt.NDArray[Any], ...])
+assert_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij"), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.delete(AR_f8, np.s_[:5]), npt.NDArray[np.float64])
+assert_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0), npt.NDArray[Any])
+
+assert_type(np.insert(AR_f8, np.s_[:5], 5), npt.NDArray[np.float64])
+assert_type(np.insert(AR_LIKE_f8, [0, 4, 9], [0.5, 9.2, 7], axis=0), npt.NDArray[Any])
+
+assert_type(np.append(AR_f8, 5), npt.NDArray[Any])
+assert_type(np.append(AR_LIKE_f8, 1j, axis=0), npt.NDArray[Any])
+
+assert_type(np.digitize(4.5, [1]), np.intp)
+assert_type(np.digitize(AR_f8, [1, 2, 3]), npt.NDArray[np.intp])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d41b1d56b75a7d3f2e119bc0801b052cbc8ec858
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_polynomial.pyi
@@ -0,0 +1,146 @@
+from typing import Any, NoReturn
+from collections.abc import Iterator
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_b: npt.NDArray[np.bool]
+AR_u4: npt.NDArray[np.uint32]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+poly_obj: np.poly1d
+
+assert_type(poly_obj.variable, str)
+assert_type(poly_obj.order, int)
+assert_type(poly_obj.o, int)
+assert_type(poly_obj.roots, npt.NDArray[Any])
+assert_type(poly_obj.r, npt.NDArray[Any])
+assert_type(poly_obj.coeffs, npt.NDArray[Any])
+assert_type(poly_obj.c, npt.NDArray[Any])
+assert_type(poly_obj.coef, npt.NDArray[Any])
+assert_type(poly_obj.coefficients, npt.NDArray[Any])
+assert_type(poly_obj.__hash__, None)
+
+assert_type(poly_obj(1), Any)
+assert_type(poly_obj([1]), npt.NDArray[Any])
+assert_type(poly_obj(poly_obj), np.poly1d)
+
+assert_type(len(poly_obj), int)
+assert_type(-poly_obj, np.poly1d)
+assert_type(+poly_obj, np.poly1d)
+
+assert_type(poly_obj * 5, np.poly1d)
+assert_type(5 * poly_obj, np.poly1d)
+assert_type(poly_obj + 5, np.poly1d)
+assert_type(5 + poly_obj, np.poly1d)
+assert_type(poly_obj - 5, np.poly1d)
+assert_type(5 - poly_obj, np.poly1d)
+assert_type(poly_obj**1, np.poly1d)
+assert_type(poly_obj**1.0, np.poly1d)
+assert_type(poly_obj / 5, np.poly1d)
+assert_type(5 / poly_obj, np.poly1d)
+
+assert_type(poly_obj[0], Any)
+poly_obj[0] = 5
+assert_type(iter(poly_obj), Iterator[Any])
+assert_type(poly_obj.deriv(), np.poly1d)
+assert_type(poly_obj.integ(), np.poly1d)
+
+assert_type(np.poly(poly_obj), npt.NDArray[np.floating[Any]])
+assert_type(np.poly(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.poly(AR_c16), npt.NDArray[np.floating[Any]])
+
+assert_type(np.polyint(poly_obj), np.poly1d)
+assert_type(np.polyint(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyint(AR_f8, k=AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyint(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(np.polyder(poly_obj), np.poly1d)
+assert_type(np.polyder(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyder(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyder(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(np.polyfit(AR_f8, AR_f8, 2), npt.NDArray[np.float64])
+assert_type(
+    np.polyfit(AR_f8, AR_i8, 1, full=True),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.int32],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.polyfit(AR_u4, AR_f8, 1.0, cov="unscaled"),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(np.polyfit(AR_c16, AR_f8, 2), npt.NDArray[np.complex128])
+assert_type(
+    np.polyfit(AR_f8, AR_c16, 1, full=True),
+    tuple[
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.int32],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.polyfit(AR_u4, AR_c16, 1.0, cov=True),
+    tuple[
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+
+assert_type(np.polyval(AR_b, AR_b), npt.NDArray[np.int64])
+assert_type(np.polyval(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polyval(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polyval(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyval(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyval(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polyadd(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polyadd(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polyadd(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.polyadd(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polyadd(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polyadd(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polyadd(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polyadd(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polysub(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polysub(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polysub(AR_b, AR_b), NoReturn)
+assert_type(np.polysub(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polysub(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polysub(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polysub(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polysub(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polymul(poly_obj, AR_i8), np.poly1d)
+assert_type(np.polymul(AR_f8, poly_obj), np.poly1d)
+assert_type(np.polymul(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.polymul(AR_u4, AR_b), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.polymul(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.polymul(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(np.polymul(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.polymul(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.polydiv(poly_obj, AR_i8), tuple[np.poly1d, np.poly1d])
+assert_type(np.polydiv(AR_f8, poly_obj), tuple[np.poly1d, np.poly1d])
+assert_type(np.polydiv(AR_b, AR_b), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_u4, AR_b), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_i8, AR_i8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_f8, AR_i8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]]])
+assert_type(np.polydiv(AR_i8, AR_c16), tuple[npt.NDArray[np.complexfloating[Any, Any]], npt.NDArray[np.complexfloating[Any, Any]]])
+assert_type(np.polydiv(AR_O, AR_O), tuple[npt.NDArray[Any], npt.NDArray[Any]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..44ae59234c42405cdb717b74bb213f057449cfad
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_utils.pyi
@@ -0,0 +1,18 @@
+from io import StringIO
+
+import numpy as np
+import numpy.typing as npt
+import numpy.lib.array_utils as array_utils
+
+from typing_extensions import assert_type
+
+AR: npt.NDArray[np.float64]
+AR_DICT: dict[str, npt.NDArray[np.float64]]
+FILE: StringIO
+
+def func(a: int) -> bool: ...
+
+assert_type(array_utils.byte_bounds(AR), tuple[int, int])
+assert_type(array_utils.byte_bounds(np.float64()), tuple[int, int])
+
+assert_type(np.info(1, output=FILE), None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..52c1218e9dfde3b0241ebb739aec6e27f91e0eb0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/lib_version.pyi
@@ -0,0 +1,20 @@
+from numpy.lib import NumpyVersion
+
+from typing_extensions import assert_type
+
+version = NumpyVersion("1.8.0")
+
+assert_type(version.vstring, str)
+assert_type(version.version, str)
+assert_type(version.major, int)
+assert_type(version.minor, int)
+assert_type(version.bugfix, int)
+assert_type(version.pre_release, str)
+assert_type(version.is_devversion, bool)
+
+assert_type(version == version, bool)
+assert_type(version != version, bool)
+assert_type(version < "1.8.0", bool)
+assert_type(version <= version, bool)
+assert_type(version > version, bool)
+assert_type(version >= "1.8.0", bool)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f9aaa71ef4bc44a4fdc22f9224912b16e086a439
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/linalg.pyi
@@ -0,0 +1,130 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from numpy.linalg._linalg import (
+    QRResult, EigResult, EighResult, SVDResult, SlogdetResult
+)
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+AR_m: npt.NDArray[np.timedelta64]
+AR_S: npt.NDArray[np.str_]
+AR_b: npt.NDArray[np.bool]
+
+assert_type(np.linalg.tensorsolve(AR_i8, AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.tensorsolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.tensorsolve(AR_c16, AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.solve(AR_i8, AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.solve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.solve(AR_c16, AR_f8), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.tensorinv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.tensorinv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.tensorinv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.inv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.inv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.inv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.matrix_power(AR_i8, -1), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_f8, 0), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_c16, 1), npt.NDArray[Any])
+assert_type(np.linalg.matrix_power(AR_O, 2), npt.NDArray[Any])
+
+assert_type(np.linalg.cholesky(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.cholesky(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.cholesky(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.outer(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.outer(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.outer(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linalg.outer(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.linalg.outer(AR_O, AR_O), npt.NDArray[np.object_])
+assert_type(np.linalg.outer(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+
+assert_type(np.linalg.qr(AR_i8), QRResult)
+assert_type(np.linalg.qr(AR_f8), QRResult)
+assert_type(np.linalg.qr(AR_c16), QRResult)
+
+assert_type(np.linalg.eigvals(AR_i8), npt.NDArray[np.float64] | npt.NDArray[np.complex128])
+assert_type(np.linalg.eigvals(AR_f8), npt.NDArray[np.floating[Any]] | npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.linalg.eigvals(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.eigvalsh(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.eigvalsh(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.eigvalsh(AR_c16), npt.NDArray[np.floating[Any]])
+
+assert_type(np.linalg.eig(AR_i8), EigResult)
+assert_type(np.linalg.eig(AR_f8), EigResult)
+assert_type(np.linalg.eig(AR_c16), EigResult)
+
+assert_type(np.linalg.eigh(AR_i8), EighResult)
+assert_type(np.linalg.eigh(AR_f8), EighResult)
+assert_type(np.linalg.eigh(AR_c16), EighResult)
+
+assert_type(np.linalg.svd(AR_i8), SVDResult)
+assert_type(np.linalg.svd(AR_f8), SVDResult)
+assert_type(np.linalg.svd(AR_c16), SVDResult)
+assert_type(np.linalg.svd(AR_i8, compute_uv=False), npt.NDArray[np.float64])
+assert_type(np.linalg.svd(AR_f8, compute_uv=False), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.svd(AR_c16, compute_uv=False), npt.NDArray[np.floating[Any]])
+
+assert_type(np.linalg.cond(AR_i8), Any)
+assert_type(np.linalg.cond(AR_f8), Any)
+assert_type(np.linalg.cond(AR_c16), Any)
+
+assert_type(np.linalg.matrix_rank(AR_i8), Any)
+assert_type(np.linalg.matrix_rank(AR_f8), Any)
+assert_type(np.linalg.matrix_rank(AR_c16), Any)
+
+assert_type(np.linalg.pinv(AR_i8), npt.NDArray[np.float64])
+assert_type(np.linalg.pinv(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.pinv(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.slogdet(AR_i8), SlogdetResult)
+assert_type(np.linalg.slogdet(AR_f8), SlogdetResult)
+assert_type(np.linalg.slogdet(AR_c16), SlogdetResult)
+
+assert_type(np.linalg.det(AR_i8), Any)
+assert_type(np.linalg.det(AR_f8), Any)
+assert_type(np.linalg.det(AR_c16), Any)
+
+assert_type(np.linalg.lstsq(AR_i8, AR_i8), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], np.int32, npt.NDArray[np.float64]])
+assert_type(np.linalg.lstsq(AR_i8, AR_f8), tuple[npt.NDArray[np.floating[Any]], npt.NDArray[np.floating[Any]], np.int32, npt.NDArray[np.floating[Any]]])
+assert_type(np.linalg.lstsq(AR_f8, AR_c16), tuple[npt.NDArray[np.complexfloating[Any, Any]], npt.NDArray[np.floating[Any]], np.int32, npt.NDArray[np.floating[Any]]])
+
+assert_type(np.linalg.norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.norm(AR_S), np.floating[Any])
+assert_type(np.linalg.norm(AR_f8, axis=0), Any)
+
+assert_type(np.linalg.matrix_norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.matrix_norm(AR_S), np.floating[Any])
+
+assert_type(np.linalg.vector_norm(AR_i8), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_f8), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_c16), np.floating[Any])
+assert_type(np.linalg.vector_norm(AR_S), np.floating[Any])
+
+assert_type(np.linalg.multi_dot([AR_i8, AR_i8]), Any)
+assert_type(np.linalg.multi_dot([AR_i8, AR_f8]), Any)
+assert_type(np.linalg.multi_dot([AR_f8, AR_c16]), Any)
+assert_type(np.linalg.multi_dot([AR_O, AR_O]), Any)
+assert_type(np.linalg.multi_dot([AR_m, AR_m]), Any)
+
+assert_type(np.linalg.cross(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.cross(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.cross(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+
+assert_type(np.linalg.matmul(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.linalg.matmul(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.linalg.matmul(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..28a2531b4db26b0c074236906bdedb1022217d4a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/matrix.pyi
@@ -0,0 +1,74 @@
+from typing import Any, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_Shape2D: TypeAlias = tuple[int, int]
+
+mat: np.matrix[_Shape2D, np.dtype[np.int64]]
+ar_f8: npt.NDArray[np.float64]
+
+assert_type(mat * 5, np.matrix[_Shape2D, Any])
+assert_type(5 * mat, np.matrix[_Shape2D, Any])
+mat *= 5
+
+assert_type(mat**5, np.matrix[_Shape2D, Any])
+mat **= 5
+
+assert_type(mat.sum(), Any)
+assert_type(mat.mean(), Any)
+assert_type(mat.std(), Any)
+assert_type(mat.var(), Any)
+assert_type(mat.prod(), Any)
+assert_type(mat.any(), np.bool)
+assert_type(mat.all(), np.bool)
+assert_type(mat.max(), np.int64)
+assert_type(mat.min(), np.int64)
+assert_type(mat.argmax(), np.intp)
+assert_type(mat.argmin(), np.intp)
+assert_type(mat.ptp(), np.int64)
+
+assert_type(mat.sum(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.mean(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.std(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.var(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.prod(axis=0), np.matrix[_Shape2D, Any])
+assert_type(mat.any(axis=0), np.matrix[_Shape2D, np.dtype[np.bool]])
+assert_type(mat.all(axis=0), np.matrix[_Shape2D, np.dtype[np.bool]])
+assert_type(mat.max(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.min(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.argmax(axis=0), np.matrix[_Shape2D, np.dtype[np.intp]])
+assert_type(mat.argmin(axis=0), np.matrix[_Shape2D, np.dtype[np.intp]])
+assert_type(mat.ptp(axis=0), np.matrix[_Shape2D, np.dtype[np.int64]])
+
+assert_type(mat.sum(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.mean(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.std(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.var(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.prod(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.any(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.all(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.max(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.min(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.argmax(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.argmin(out=ar_f8), npt.NDArray[np.float64])
+assert_type(mat.ptp(out=ar_f8), npt.NDArray[np.float64])
+
+assert_type(mat.T, np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.I, np.matrix[_Shape2D, Any])
+assert_type(mat.A, np.ndarray[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.A1, npt.NDArray[np.int64])
+assert_type(mat.H, np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getT(), np.matrix[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getI(), np.matrix[_Shape2D, Any])
+assert_type(mat.getA(), np.ndarray[_Shape2D, np.dtype[np.int64]])
+assert_type(mat.getA1(), npt.NDArray[np.int64])
+assert_type(mat.getH(), np.matrix[_Shape2D, np.dtype[np.int64]])
+
+assert_type(np.bmat(ar_f8), np.matrix[_Shape2D, Any])
+assert_type(np.bmat([[0, 1, 2]]), np.matrix[_Shape2D, Any])
+assert_type(np.bmat("mat"), np.matrix[_Shape2D, Any])
+
+assert_type(np.asmatrix(ar_f8, dtype=np.int64), np.matrix[_Shape2D, Any])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b1f985382c6b63b2dd9f6619ab5d60c90efc783b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/memmap.pyi
@@ -0,0 +1,21 @@
+from typing import Any
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+memmap_obj: np.memmap[Any, np.dtype[np.str_]]
+
+assert_type(np.memmap.__array_priority__, float)
+assert_type(memmap_obj.__array_priority__, float)
+assert_type(memmap_obj.filename, str | None)
+assert_type(memmap_obj.offset, int)
+assert_type(memmap_obj.mode, str)
+assert_type(memmap_obj.flush(), None)
+
+assert_type(np.memmap("file.txt", offset=5), np.memmap[Any, np.dtype[np.uint8]])
+assert_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3)), np.memmap[Any, np.dtype[np.float64]])
+with open("file.txt", "rb") as f:
+    assert_type(np.memmap(f, dtype=float, order="K"), np.memmap[Any, np.dtype[Any]])
+
+assert_type(memmap_obj.__array_finalize__(object()), None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..db79504fdd1f472bb004f5db1ef3864f1a0e5ab1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/mod.pyi
@@ -0,0 +1,181 @@
+import datetime as dt
+from typing import Literal as L
+
+from typing_extensions import assert_type
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit
+
+f8: np.float64
+i8: np.int64
+u8: np.uint64
+
+f4: np.float32
+i4: np.int32
+u4: np.uint32
+
+m: np.timedelta64
+m_nat: np.timedelta64[None]
+m_int0: np.timedelta64[L[0]]
+m_int: np.timedelta64[int]
+m_td: np.timedelta64[dt.timedelta]
+
+b_: np.bool
+
+b: bool
+i: int
+f: float
+
+AR_b: npt.NDArray[np.bool]
+AR_m: npt.NDArray[np.timedelta64]
+
+# Time structures
+
+assert_type(m % m, np.timedelta64)
+assert_type(m % m_nat, np.timedelta64[None])
+assert_type(m % m_int0, np.timedelta64[None])
+assert_type(m % m_int, np.timedelta64[int | None])
+assert_type(m_nat % m, np.timedelta64[None])
+assert_type(m_int % m_nat, np.timedelta64[None])
+assert_type(m_int % m_int0, np.timedelta64[None])
+assert_type(m_int % m_int, np.timedelta64[int | None])
+assert_type(m_int % m_td, np.timedelta64[int | None])
+assert_type(m_td % m_nat, np.timedelta64[None])
+assert_type(m_td % m_int0, np.timedelta64[None])
+assert_type(m_td % m_int, np.timedelta64[int | None])
+assert_type(m_td % m_td, np.timedelta64[dt.timedelta | None])
+
+assert_type(AR_m % m, npt.NDArray[np.timedelta64])
+assert_type(m % AR_m, npt.NDArray[np.timedelta64])
+
+assert_type(divmod(m, m), tuple[np.int64, np.timedelta64])
+assert_type(divmod(m, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m, m_int0), tuple[np.int64, np.timedelta64[None]])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(m.__divmod__(m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_nat, m), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_int0), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_int, m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_int, m_td), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_td, m_nat), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_td, m_int0), tuple[np.int64, np.timedelta64[None]])
+assert_type(divmod(m_td, m_int), tuple[np.int64, np.timedelta64[int | None]])
+assert_type(divmod(m_td, m_td), tuple[np.int64, np.timedelta64[dt.timedelta | None]])
+
+assert_type(divmod(AR_m, m), tuple[npt.NDArray[np.int64], npt.NDArray[np.timedelta64]])
+assert_type(divmod(m, AR_m), tuple[npt.NDArray[np.int64], npt.NDArray[np.timedelta64]])
+
+# Bool
+
+assert_type(b_ % b, np.int8)
+assert_type(b_ % i, np.int_)
+assert_type(b_ % f, np.float64)
+assert_type(b_ % b_, np.int8)
+assert_type(b_ % i8, np.int64)
+assert_type(b_ % u8, np.uint64)
+assert_type(b_ % f8, np.float64)
+assert_type(b_ % AR_b, npt.NDArray[np.int8])
+
+assert_type(divmod(b_, b), tuple[np.int8, np.int8])
+assert_type(divmod(b_, b_), tuple[np.int8, np.int8])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(b_.__divmod__(i), tuple[np.int_, np.int_])
+assert_type(b_.__divmod__(f), tuple[np.float64, np.float64])
+assert_type(b_.__divmod__(i8), tuple[np.int64, np.int64])
+assert_type(b_.__divmod__(u8), tuple[np.uint64, np.uint64])
+assert_type(divmod(b_, f8), tuple[np.float64, np.float64])
+assert_type(divmod(b_, AR_b), tuple[npt.NDArray[np.int8], npt.NDArray[np.int8]])
+
+assert_type(b % b_, np.int8)
+assert_type(i % b_, np.int_)
+assert_type(f % b_, np.float64)
+assert_type(b_ % b_, np.int8)
+assert_type(i8 % b_, np.int64)
+assert_type(u8 % b_, np.uint64)
+assert_type(f8 % b_, np.float64)
+assert_type(AR_b % b_, npt.NDArray[np.int8])
+
+assert_type(divmod(b, b_), tuple[np.int8, np.int8])
+assert_type(divmod(i, b_), tuple[np.int_, np.int_])
+assert_type(divmod(f, b_), tuple[np.float64, np.float64])
+assert_type(divmod(b_, b_), tuple[np.int8, np.int8])
+assert_type(divmod(i8, b_), tuple[np.int64, np.int64])
+assert_type(divmod(u8, b_), tuple[np.uint64, np.uint64])
+assert_type(divmod(f8, b_), tuple[np.float64, np.float64])
+assert_type(divmod(AR_b, b_), tuple[npt.NDArray[np.int8], npt.NDArray[np.int8]])
+
+# int
+
+assert_type(i8 % b, np.int64)
+assert_type(i8 % i8, np.int64)
+assert_type(i8 % f, np.float64 | np.floating[_64Bit])
+assert_type(i8 % f8, np.float64 | np.floating[_64Bit])
+assert_type(i4 % i8, np.int64 | np.int32)
+assert_type(i4 % f8, np.float64 | np.float32)
+assert_type(i4 % i4, np.int32)
+assert_type(i4 % f4, np.float32)
+assert_type(i8 % AR_b, npt.NDArray[np.int64])
+
+assert_type(divmod(i8, b), tuple[np.int64, np.int64])
+assert_type(divmod(i8, i4), tuple[np.int64, np.int64] | tuple[np.int32, np.int32])
+assert_type(divmod(i8, i8), tuple[np.int64, np.int64])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(i8.__divmod__(f), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(i8.__divmod__(f8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(divmod(i8, f4), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float32, np.float32])
+assert_type(divmod(i4, i4), tuple[np.int32, np.int32])
+assert_type(divmod(i4, f4), tuple[np.float32, np.float32])
+assert_type(divmod(i8, AR_b), tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]])
+
+assert_type(b % i8, np.int64)
+assert_type(f % i8, np.float64 | np.floating[_64Bit])
+assert_type(i8 % i8, np.int64)
+assert_type(f8 % i8, np.float64)
+assert_type(i8 % i4, np.int64 | np.int32)
+assert_type(f8 % i4, np.float64)
+assert_type(i4 % i4, np.int32)
+assert_type(f4 % i4, np.float32)
+assert_type(AR_b % i8, npt.NDArray[np.int64])
+
+assert_type(divmod(b, i8), tuple[np.int64, np.int64])
+assert_type(divmod(f, i8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float64, np.float64])
+assert_type(divmod(i8, i8), tuple[np.int64, np.int64])
+assert_type(divmod(f8, i8), tuple[np.float64, np.float64])
+assert_type(divmod(i4, i8), tuple[np.int64, np.int64] | tuple[np.int32, np.int32])
+assert_type(divmod(i4, i4), tuple[np.int32, np.int32])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(f4.__divmod__(i8), tuple[np.floating[_64Bit], np.floating[_64Bit]] | tuple[np.float32, np.float32])
+assert_type(f4.__divmod__(i4), tuple[np.float32, np.float32])
+assert_type(AR_b.__divmod__(i8), tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]])
+
+# float
+
+assert_type(f8 % b, np.float64)
+assert_type(f8 % f, np.float64)
+assert_type(i8 % f4, np.floating[_64Bit] | np.float32)
+assert_type(f4 % f4, np.float32)
+assert_type(f8 % AR_b, npt.NDArray[np.float64])
+
+assert_type(divmod(f8, b), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f4), tuple[np.float64, np.float64])
+assert_type(divmod(f4, f4), tuple[np.float32, np.float32])
+assert_type(divmod(f8, AR_b), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
+
+assert_type(b % f8, np.float64)
+assert_type(f % f8, np.float64)  # pyright: ignore[reportAssertTypeFailure]  # pyright incorrectly infers `builtins.float`
+assert_type(f8 % f8, np.float64)
+assert_type(f8 % f8, np.float64)
+assert_type(f4 % f4, np.float32)
+assert_type(AR_b % f8, npt.NDArray[np.float64])
+
+assert_type(divmod(b, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f8, f8), tuple[np.float64, np.float64])
+assert_type(divmod(f4, f4), tuple[np.float32, np.float32])
+# workarounds for https://github.com/microsoft/pyright/issues/9663
+assert_type(f8.__rdivmod__(f), tuple[np.float64, np.float64])
+assert_type(f8.__rdivmod__(f4), tuple[np.float64, np.float64])
+assert_type(AR_b.__divmod__(f8), tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1e4e895bf5f864b902c68c27f0725f7fa3da553e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/modules.pyi
@@ -0,0 +1,52 @@
+import types
+
+import numpy as np
+from numpy import f2py
+
+from typing_extensions import assert_type
+
+assert_type(np, types.ModuleType)
+
+assert_type(np.char, types.ModuleType)
+assert_type(np.ctypeslib, types.ModuleType)
+assert_type(np.emath, types.ModuleType)
+assert_type(np.fft, types.ModuleType)
+assert_type(np.lib, types.ModuleType)
+assert_type(np.linalg, types.ModuleType)
+assert_type(np.ma, types.ModuleType)
+assert_type(np.matrixlib, types.ModuleType)
+assert_type(np.polynomial, types.ModuleType)
+assert_type(np.random, types.ModuleType)
+assert_type(np.rec, types.ModuleType)
+assert_type(np.testing, types.ModuleType)
+assert_type(np.version, types.ModuleType)
+assert_type(np.exceptions, types.ModuleType)
+assert_type(np.dtypes, types.ModuleType)
+
+assert_type(np.lib.format, types.ModuleType)
+assert_type(np.lib.mixins, types.ModuleType)
+assert_type(np.lib.scimath, types.ModuleType)
+assert_type(np.lib.stride_tricks, types.ModuleType)
+assert_type(np.ma.extras, types.ModuleType)
+assert_type(np.polynomial.chebyshev, types.ModuleType)
+assert_type(np.polynomial.hermite, types.ModuleType)
+assert_type(np.polynomial.hermite_e, types.ModuleType)
+assert_type(np.polynomial.laguerre, types.ModuleType)
+assert_type(np.polynomial.legendre, types.ModuleType)
+assert_type(np.polynomial.polynomial, types.ModuleType)
+
+assert_type(np.__path__, list[str])
+assert_type(np.__version__, str)
+assert_type(np.test, np._pytesttester.PytestTester)
+assert_type(np.test.module_name, str)
+
+assert_type(np.__all__, list[str])
+assert_type(np.char.__all__, list[str])
+assert_type(np.ctypeslib.__all__, list[str])
+assert_type(np.emath.__all__, list[str])
+assert_type(np.lib.__all__, list[str])
+assert_type(np.ma.__all__, list[str])
+assert_type(np.random.__all__, list[str])
+assert_type(np.rec.__all__, list[str])
+assert_type(np.testing.__all__, list[str])
+assert_type(f2py.__all__, list[str])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..cae14ee57e22f492629a23d8bed0e2038741e0b3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/multiarray.pyi
@@ -0,0 +1,196 @@
+import datetime as dt
+from typing import Any, Literal, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import Unpack, assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic, covariant=True)
+
+class SubClass(npt.NDArray[_SCT]): ...
+
+subclass: SubClass[np.float64]
+
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+AR_u1: npt.NDArray[np.uint8]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+AR_LIKE_f: list[float]
+AR_LIKE_i: list[int]
+
+m: np.timedelta64
+M: np.datetime64
+
+b_f8 = np.broadcast(AR_f8)
+b_i8_f8_f8 = np.broadcast(AR_i8, AR_f8, AR_f8)
+
+nditer_obj: np.nditer
+
+date_scalar: dt.date
+date_seq: list[dt.date]
+timedelta_seq: list[dt.timedelta]
+
+n1: Literal[1]
+n2: Literal[2]
+n3: Literal[3]
+
+f8: np.float64
+
+def func11(a: int) -> bool: ...
+def func21(a: int, b: int) -> int: ...
+def func12(a: int) -> tuple[complex, bool]: ...
+
+assert_type(next(b_f8), tuple[Any, ...])
+assert_type(b_f8.reset(), None)
+assert_type(b_f8.index, int)
+assert_type(b_f8.iters, tuple[np.flatiter[Any], ...])
+assert_type(b_f8.nd, int)
+assert_type(b_f8.ndim, int)
+assert_type(b_f8.numiter, int)
+assert_type(b_f8.shape, tuple[int, ...])
+assert_type(b_f8.size, int)
+
+assert_type(next(b_i8_f8_f8), tuple[Any, ...])
+assert_type(b_i8_f8_f8.reset(), None)
+assert_type(b_i8_f8_f8.index, int)
+assert_type(b_i8_f8_f8.iters, tuple[np.flatiter[Any], ...])
+assert_type(b_i8_f8_f8.nd, int)
+assert_type(b_i8_f8_f8.ndim, int)
+assert_type(b_i8_f8_f8.numiter, int)
+assert_type(b_i8_f8_f8.shape, tuple[int, ...])
+assert_type(b_i8_f8_f8.size, int)
+
+assert_type(np.inner(AR_f8, AR_i8), Any)
+
+assert_type(np.where([True, True, False]), tuple[npt.NDArray[np.intp], ...])
+assert_type(np.where([True, True, False], 1, 0), npt.NDArray[Any])
+
+assert_type(np.lexsort([0, 1, 2]), Any)
+
+assert_type(np.can_cast(np.dtype("i8"), int), bool)
+assert_type(np.can_cast(AR_f8, "f8"), bool)
+assert_type(np.can_cast(AR_f8, np.complex128, casting="unsafe"), bool)
+
+assert_type(np.min_scalar_type([1]), np.dtype[Any])
+assert_type(np.min_scalar_type(AR_f8), np.dtype[Any])
+
+assert_type(np.result_type(int, [1]), np.dtype[Any])
+assert_type(np.result_type(AR_f8, AR_u1), np.dtype[Any])
+assert_type(np.result_type(AR_f8, np.complex128), np.dtype[Any])
+
+assert_type(np.dot(AR_LIKE_f, AR_i8), Any)
+assert_type(np.dot(AR_u1, 1), Any)
+assert_type(np.dot(1.5j, 1), Any)
+assert_type(np.dot(AR_u1, 1, out=AR_f8), npt.NDArray[np.float64])
+
+assert_type(np.vdot(AR_LIKE_f, AR_i8), np.floating[Any])
+assert_type(np.vdot(AR_u1, 1), np.signedinteger[Any])
+assert_type(np.vdot(1.5j, 1), np.complexfloating[Any, Any])
+
+assert_type(np.bincount(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.copyto(AR_f8, [1., 1.5, 1.6]), None)
+
+assert_type(np.putmask(AR_f8, [True, True, False], 1.5), None)
+
+assert_type(np.packbits(AR_i8), npt.NDArray[np.uint8])
+assert_type(np.packbits(AR_u1), npt.NDArray[np.uint8])
+
+assert_type(np.unpackbits(AR_u1), npt.NDArray[np.uint8])
+
+assert_type(np.shares_memory(1, 2), bool)
+assert_type(np.shares_memory(AR_f8, AR_f8, max_work=1), bool)
+
+assert_type(np.may_share_memory(1, 2), bool)
+assert_type(np.may_share_memory(AR_f8, AR_f8, max_work=1), bool)
+
+assert_type(np.promote_types(np.int32, np.int64), np.dtype[Any])
+assert_type(np.promote_types("f4", float), np.dtype[Any])
+
+assert_type(np.frompyfunc(func11, n1, n1).nin, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).nout, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).nargs, Literal[2])
+assert_type(np.frompyfunc(func11, n1, n1).ntypes, Literal[1])
+assert_type(np.frompyfunc(func11, n1, n1).identity, None)
+assert_type(np.frompyfunc(func11, n1, n1).signature, None)
+assert_type(np.frompyfunc(func11, n1, n1)(f8), bool)
+assert_type(np.frompyfunc(func11, n1, n1)(AR_f8), bool | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func11, n1, n1).at(AR_f8, AR_i8), None)
+
+assert_type(np.frompyfunc(func21, n2, n1).nin, Literal[2])
+assert_type(np.frompyfunc(func21, n2, n1).nout, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1).nargs, Literal[3])
+assert_type(np.frompyfunc(func21, n2, n1).ntypes, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1).identity, None)
+assert_type(np.frompyfunc(func21, n2, n1).signature, None)
+assert_type(np.frompyfunc(func21, n2, n1)(f8, f8), int)
+assert_type(np.frompyfunc(func21, n2, n1)(AR_f8, f8), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1)(f8, AR_f8), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).reduce(AR_f8, axis=0), int | npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).accumulate(AR_f8), npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).reduceat(AR_f8, AR_i8), npt.NDArray[np.object_])
+assert_type(np.frompyfunc(func21, n2, n1).outer(f8, f8), int)
+assert_type(np.frompyfunc(func21, n2, n1).outer(AR_f8, f8), int | npt.NDArray[np.object_])
+
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nin, Literal[2])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nout, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).nargs, Literal[3])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).ntypes, Literal[1])
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).identity, int)
+assert_type(np.frompyfunc(func21, n2, n1, identity=0).signature, None)
+
+assert_type(np.frompyfunc(func12, n1, n2).nin, Literal[1])
+assert_type(np.frompyfunc(func12, n1, n2).nout, Literal[2])
+assert_type(np.frompyfunc(func12, n1, n2).nargs, int)
+assert_type(np.frompyfunc(func12, n1, n2).ntypes, Literal[1])
+assert_type(np.frompyfunc(func12, n1, n2).identity, None)
+assert_type(np.frompyfunc(func12, n1, n2).signature, None)
+assert_type(
+    np.frompyfunc(func12, n2, n2)(f8, f8),
+    tuple[complex, complex, Unpack[tuple[complex, ...]]],
+)
+assert_type(
+    np.frompyfunc(func12, n2, n2)(AR_f8, f8),
+    tuple[
+        complex | npt.NDArray[np.object_],
+        complex | npt.NDArray[np.object_],
+        Unpack[tuple[complex | npt.NDArray[np.object_], ...]],
+    ],
+)
+
+assert_type(np.datetime_data("m8[D]"), tuple[str, int])
+assert_type(np.datetime_data(np.datetime64), tuple[str, int])
+assert_type(np.datetime_data(np.dtype(np.timedelta64)), tuple[str, int])
+
+assert_type(np.busday_count("2011-01", "2011-02"), np.int_)
+assert_type(np.busday_count(["2011-01"], "2011-02"), npt.NDArray[np.int_])
+assert_type(np.busday_count(["2011-01"], date_scalar), npt.NDArray[np.int_])
+
+assert_type(np.busday_offset(M, m), np.datetime64)
+assert_type(np.busday_offset(date_scalar, m), np.datetime64)
+assert_type(np.busday_offset(M, 5), np.datetime64)
+assert_type(np.busday_offset(AR_M, m), npt.NDArray[np.datetime64])
+assert_type(np.busday_offset(M, timedelta_seq), npt.NDArray[np.datetime64])
+assert_type(np.busday_offset("2011-01", "2011-02", roll="forward"), np.datetime64)
+assert_type(np.busday_offset(["2011-01"], "2011-02", roll="forward"), npt.NDArray[np.datetime64])
+
+assert_type(np.is_busday("2012"), np.bool)
+assert_type(np.is_busday(date_scalar), np.bool)
+assert_type(np.is_busday(["2012"]), npt.NDArray[np.bool])
+
+assert_type(np.datetime_as_string(M), np.str_)
+assert_type(np.datetime_as_string(AR_M), npt.NDArray[np.str_])
+
+assert_type(np.busdaycalendar(holidays=date_seq), np.busdaycalendar)
+assert_type(np.busdaycalendar(holidays=[M]), np.busdaycalendar)
+
+assert_type(np.char.compare_chararrays("a", "b", "!=", rstrip=False), npt.NDArray[np.bool])
+assert_type(np.char.compare_chararrays(b"a", b"a", "==", True), npt.NDArray[np.bool])
+
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"]), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]]), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_), tuple[np.nditer, ...])
+assert_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no"), tuple[np.nditer, ...])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..add031ac884affb8ea7f5e3c8d48b1dc366ac206
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nbit_base_example.pyi
@@ -0,0 +1,23 @@
+from typing import TypeVar
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _64Bit, _32Bit
+
+from typing_extensions import assert_type
+
+T1 = TypeVar("T1", bound=npt.NBitBase)
+T2 = TypeVar("T2", bound=npt.NBitBase)
+
+def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
+    return a + b
+
+i8: np.int64
+i4: np.int32
+f8: np.float64
+f4: np.float32
+
+assert_type(add(f8, i8), np.floating[_64Bit])
+assert_type(add(f4, i8), np.floating[_32Bit | _64Bit])
+assert_type(add(f8, i4), np.floating[_32Bit | _64Bit])
+assert_type(add(f4, i4), np.floating[_32Bit])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..22f0d005a7d2eec43b30cd7be51847233acda211
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_assignability.pyi
@@ -0,0 +1,79 @@
+from typing import Protocol, TypeAlias, TypeVar
+from typing_extensions import assert_type
+import numpy as np
+
+from numpy._typing import _64Bit
+
+
+_T = TypeVar("_T")
+_T_co = TypeVar("_T_co", covariant=True)
+
+class CanAbs(Protocol[_T_co]):
+    def __abs__(self, /) -> _T_co: ...
+
+class CanInvert(Protocol[_T_co]):
+    def __invert__(self, /) -> _T_co: ...
+
+class CanNeg(Protocol[_T_co]):
+    def __neg__(self, /) -> _T_co: ...
+
+class CanPos(Protocol[_T_co]):
+    def __pos__(self, /) -> _T_co: ...
+
+def do_abs(x: CanAbs[_T]) -> _T: ...
+def do_invert(x: CanInvert[_T]) -> _T: ...
+def do_neg(x: CanNeg[_T]) -> _T: ...
+def do_pos(x: CanPos[_T]) -> _T: ...
+
+_Bool_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.bool]]
+_UInt8_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.uint8]]
+_Int16_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.int16]]
+_LongLong_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.longlong]]
+_Float32_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float32]]
+_Float64_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float64]]
+_LongDouble_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.longdouble]]
+_Complex64_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex64]]
+_Complex128_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex128]]
+_CLongDouble_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[np.clongdouble]]
+
+b1_1d: _Bool_1d
+u1_1d: _UInt8_1d
+i2_1d: _Int16_1d
+q_1d: _LongLong_1d
+f4_1d: _Float32_1d
+f8_1d: _Float64_1d
+g_1d: _LongDouble_1d
+c8_1d: _Complex64_1d
+c16_1d: _Complex128_1d
+G_1d: _CLongDouble_1d
+
+assert_type(do_abs(b1_1d), _Bool_1d)
+assert_type(do_abs(u1_1d), _UInt8_1d)
+assert_type(do_abs(i2_1d), _Int16_1d)
+assert_type(do_abs(q_1d), _LongLong_1d)
+assert_type(do_abs(f4_1d), _Float32_1d)
+assert_type(do_abs(f8_1d), _Float64_1d)
+assert_type(do_abs(g_1d), _LongDouble_1d)
+
+assert_type(do_abs(c8_1d), _Float32_1d)
+# NOTE: Unfortunately it's not possible to have this return a `float64` sctype, see
+# https://github.com/python/mypy/issues/14070
+assert_type(do_abs(c16_1d), np.ndarray[tuple[int], np.dtype[np.floating[_64Bit]]])
+assert_type(do_abs(G_1d), _LongDouble_1d)
+
+assert_type(do_invert(b1_1d), _Bool_1d)
+assert_type(do_invert(u1_1d), _UInt8_1d)
+assert_type(do_invert(i2_1d), _Int16_1d)
+assert_type(do_invert(q_1d), _LongLong_1d)
+
+assert_type(do_neg(u1_1d), _UInt8_1d)
+assert_type(do_neg(i2_1d), _Int16_1d)
+assert_type(do_neg(q_1d), _LongLong_1d)
+assert_type(do_neg(f4_1d), _Float32_1d)
+assert_type(do_neg(c16_1d), _Complex128_1d)
+
+assert_type(do_pos(u1_1d), _UInt8_1d)
+assert_type(do_pos(i2_1d), _Int16_1d)
+assert_type(do_pos(q_1d), _LongLong_1d)
+assert_type(do_pos(f4_1d), _Float32_1d)
+assert_type(do_pos(c16_1d), _Complex128_1d)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..49181d2c98a61730e86748dd8823fe7549a781ff
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_conversion.pyi
@@ -0,0 +1,88 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+b1_0d: np.ndarray[tuple[()], np.dtype[np.bool]]
+u2_1d: np.ndarray[tuple[int], np.dtype[np.uint16]]
+i4_2d: np.ndarray[tuple[int, int], np.dtype[np.int32]]
+f8_3d: np.ndarray[tuple[int, int, int], np.dtype[np.float64]]
+cG_4d: np.ndarray[tuple[int, int, int, int], np.dtype[np.clongdouble]]
+i0_nd: npt.NDArray[np.int_]
+uncertain_dtype: np.int32 | np.float64 | np.str_
+
+# item
+assert_type(i0_nd.item(), int)
+assert_type(i0_nd.item(1), int)
+assert_type(i0_nd.item(0, 1), int)
+assert_type(i0_nd.item((0, 1)), int)
+
+assert_type(b1_0d.item(()), bool)
+assert_type(u2_1d.item((0,)), int)
+assert_type(i4_2d.item(-1, 2), int)
+assert_type(f8_3d.item(2, 1, -1), float)
+assert_type(cG_4d.item(-0xEd_fed_Deb_a_dead_bee), complex)  # c'mon Ed, we talked about this...
+
+# tolist
+assert_type(b1_0d.tolist(), bool)
+assert_type(u2_1d.tolist(), list[int])
+assert_type(i4_2d.tolist(), list[list[int]])
+assert_type(f8_3d.tolist(), list[list[list[float]]])
+assert_type(cG_4d.tolist(), Any)
+assert_type(i0_nd.tolist(), Any)
+
+# regression tests for numpy/numpy#27944
+any_dtype: np.ndarray[Any, Any]
+any_sctype: np.ndarray[Any, Any]
+assert_type(any_dtype.tolist(), Any)
+assert_type(any_sctype.tolist(), Any)
+
+
+# itemset does not return a value
+# tostring is pretty simple
+# tobytes is pretty simple
+# tofile does not return a value
+# dump does not return a value
+# dumps is pretty simple
+
+# astype
+assert_type(i0_nd.astype("float"), npt.NDArray[Any])
+assert_type(i0_nd.astype(float), npt.NDArray[Any])
+assert_type(i0_nd.astype(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K"), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe"), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe", True), npt.NDArray[np.float64])
+assert_type(i0_nd.astype(np.float64, "K", "unsafe", True, True), npt.NDArray[np.float64])
+
+assert_type(np.astype(i0_nd, np.float64), npt.NDArray[np.float64])
+
+assert_type(i4_2d.astype(np.uint16), np.ndarray[tuple[int, int], np.dtype[np.uint16]])
+assert_type(np.astype(i4_2d, np.uint16), np.ndarray[tuple[int, int], np.dtype[np.uint16]])
+assert_type(f8_3d.astype(np.int16), np.ndarray[tuple[int, int, int], np.dtype[np.int16]])
+assert_type(np.astype(f8_3d, np.int16), np.ndarray[tuple[int, int, int], np.dtype[np.int16]])
+assert_type(i4_2d.astype(uncertain_dtype), np.ndarray[tuple[int, int], np.dtype[np.generic[Any]]])
+assert_type(np.astype(i4_2d, uncertain_dtype), np.ndarray[tuple[int, int], np.dtype[Any]])
+
+# byteswap
+assert_type(i0_nd.byteswap(), npt.NDArray[np.int_])
+assert_type(i0_nd.byteswap(True), npt.NDArray[np.int_])
+
+# copy
+assert_type(i0_nd.copy(), npt.NDArray[np.int_])
+assert_type(i0_nd.copy("C"), npt.NDArray[np.int_])
+
+assert_type(i0_nd.view(), npt.NDArray[np.int_])
+assert_type(i0_nd.view(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.view(float), npt.NDArray[Any])
+assert_type(i0_nd.view(np.float64, np.matrix), np.matrix[tuple[int, int], Any])
+
+# getfield
+assert_type(i0_nd.getfield("float"), npt.NDArray[Any])
+assert_type(i0_nd.getfield(float), npt.NDArray[Any])
+assert_type(i0_nd.getfield(np.float64), npt.NDArray[np.float64])
+assert_type(i0_nd.getfield(np.float64, 8), npt.NDArray[np.float64])
+
+# setflags does not return a value
+# fill does not return a value
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7c619c1e156e313f1eb3d8f4af6c535065ec0b55
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_misc.pyi
@@ -0,0 +1,234 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+import operator
+import ctypes as ct
+from types import ModuleType
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import CapsuleType, assert_type
+
+class SubClass(npt.NDArray[np.object_]): ...
+
+f8: np.float64
+i8: np.int64
+B: SubClass
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+AR_u1: npt.NDArray[np.uint8]
+AR_c8: npt.NDArray[np.complex64]
+AR_m: npt.NDArray[np.timedelta64]
+AR_U: npt.NDArray[np.str_]
+AR_V: npt.NDArray[np.void]
+
+ctypes_obj = AR_f8.ctypes
+
+assert_type(AR_f8.__dlpack__(), CapsuleType)
+assert_type(AR_f8.__dlpack_device__(), tuple[Literal[1], Literal[0]])
+
+assert_type(ctypes_obj.data, int)
+assert_type(ctypes_obj.shape, ct.Array[np.ctypeslib.c_intp])
+assert_type(ctypes_obj.strides, ct.Array[np.ctypeslib.c_intp])
+assert_type(ctypes_obj._as_parameter_, ct.c_void_p)
+
+assert_type(ctypes_obj.data_as(ct.c_void_p), ct.c_void_p)
+assert_type(ctypes_obj.shape_as(ct.c_longlong), ct.Array[ct.c_longlong])
+assert_type(ctypes_obj.strides_as(ct.c_ubyte), ct.Array[ct.c_ubyte])
+
+assert_type(f8.all(), np.bool)
+assert_type(AR_f8.all(), np.bool)
+assert_type(AR_f8.all(axis=0), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.all(keepdims=True), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.all(out=B), SubClass)
+
+assert_type(f8.any(), np.bool)
+assert_type(AR_f8.any(), np.bool)
+assert_type(AR_f8.any(axis=0), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.any(keepdims=True), np.bool | npt.NDArray[np.bool])
+assert_type(AR_f8.any(out=B), SubClass)
+
+assert_type(f8.argmax(), np.intp)
+assert_type(AR_f8.argmax(), np.intp)
+assert_type(AR_f8.argmax(axis=0), Any)
+assert_type(AR_f8.argmax(out=B), SubClass)
+
+assert_type(f8.argmin(), np.intp)
+assert_type(AR_f8.argmin(), np.intp)
+assert_type(AR_f8.argmin(axis=0), Any)
+assert_type(AR_f8.argmin(out=B), SubClass)
+
+assert_type(f8.argsort(), npt.NDArray[Any])
+assert_type(AR_f8.argsort(), npt.NDArray[Any])
+
+assert_type(f8.astype(np.int64).choose([()]), npt.NDArray[Any])
+assert_type(AR_f8.choose([0]), npt.NDArray[Any])
+assert_type(AR_f8.choose([0], out=B), SubClass)
+
+assert_type(f8.clip(1), npt.NDArray[Any])
+assert_type(AR_f8.clip(1), npt.NDArray[Any])
+assert_type(AR_f8.clip(None, 1), npt.NDArray[Any])
+assert_type(AR_f8.clip(1, out=B), SubClass)
+assert_type(AR_f8.clip(None, 1, out=B), SubClass)
+
+assert_type(f8.compress([0]), npt.NDArray[Any])
+assert_type(AR_f8.compress([0]), npt.NDArray[Any])
+assert_type(AR_f8.compress([0], out=B), SubClass)
+
+assert_type(f8.conj(), np.float64)
+assert_type(AR_f8.conj(), npt.NDArray[np.float64])
+assert_type(B.conj(), SubClass)
+
+assert_type(f8.conjugate(), np.float64)
+assert_type(AR_f8.conjugate(), npt.NDArray[np.float64])
+assert_type(B.conjugate(), SubClass)
+
+assert_type(f8.cumprod(), npt.NDArray[Any])
+assert_type(AR_f8.cumprod(), npt.NDArray[Any])
+assert_type(AR_f8.cumprod(out=B), SubClass)
+
+assert_type(f8.cumsum(), npt.NDArray[Any])
+assert_type(AR_f8.cumsum(), npt.NDArray[Any])
+assert_type(AR_f8.cumsum(out=B), SubClass)
+
+assert_type(f8.max(), Any)
+assert_type(AR_f8.max(), Any)
+assert_type(AR_f8.max(axis=0), Any)
+assert_type(AR_f8.max(keepdims=True), Any)
+assert_type(AR_f8.max(out=B), SubClass)
+
+assert_type(f8.mean(), Any)
+assert_type(AR_f8.mean(), Any)
+assert_type(AR_f8.mean(axis=0), Any)
+assert_type(AR_f8.mean(keepdims=True), Any)
+assert_type(AR_f8.mean(out=B), SubClass)
+
+assert_type(f8.min(), Any)
+assert_type(AR_f8.min(), Any)
+assert_type(AR_f8.min(axis=0), Any)
+assert_type(AR_f8.min(keepdims=True), Any)
+assert_type(AR_f8.min(out=B), SubClass)
+
+assert_type(f8.prod(), Any)
+assert_type(AR_f8.prod(), Any)
+assert_type(AR_f8.prod(axis=0), Any)
+assert_type(AR_f8.prod(keepdims=True), Any)
+assert_type(AR_f8.prod(out=B), SubClass)
+
+assert_type(f8.round(), np.float64)
+assert_type(AR_f8.round(), npt.NDArray[np.float64])
+assert_type(AR_f8.round(out=B), SubClass)
+
+assert_type(f8.repeat(1), npt.NDArray[np.float64])
+assert_type(AR_f8.repeat(1), npt.NDArray[np.float64])
+assert_type(B.repeat(1), npt.NDArray[np.object_])
+
+assert_type(f8.std(), Any)
+assert_type(AR_f8.std(), Any)
+assert_type(AR_f8.std(axis=0), Any)
+assert_type(AR_f8.std(keepdims=True), Any)
+assert_type(AR_f8.std(out=B), SubClass)
+
+assert_type(f8.sum(), Any)
+assert_type(AR_f8.sum(), Any)
+assert_type(AR_f8.sum(axis=0), Any)
+assert_type(AR_f8.sum(keepdims=True), Any)
+assert_type(AR_f8.sum(out=B), SubClass)
+
+assert_type(f8.take(0), np.float64)
+assert_type(AR_f8.take(0), np.float64)
+assert_type(AR_f8.take([0]), npt.NDArray[np.float64])
+assert_type(AR_f8.take(0, out=B), SubClass)
+assert_type(AR_f8.take([0], out=B), SubClass)
+
+assert_type(f8.var(), Any)
+assert_type(AR_f8.var(), Any)
+assert_type(AR_f8.var(axis=0), Any)
+assert_type(AR_f8.var(keepdims=True), Any)
+assert_type(AR_f8.var(out=B), SubClass)
+
+assert_type(AR_f8.argpartition([0]), npt.NDArray[np.intp])
+
+assert_type(AR_f8.diagonal(), npt.NDArray[np.float64])
+
+assert_type(AR_f8.dot(1), npt.NDArray[Any])
+assert_type(AR_f8.dot([1]), Any)
+assert_type(AR_f8.dot(1, out=B), SubClass)
+
+assert_type(AR_f8.nonzero(), tuple[npt.NDArray[np.intp], ...])
+
+assert_type(AR_f8.searchsorted(1), np.intp)
+assert_type(AR_f8.searchsorted([1]), npt.NDArray[np.intp])
+
+assert_type(AR_f8.trace(), Any)
+assert_type(AR_f8.trace(out=B), SubClass)
+
+assert_type(AR_f8.item(), float)
+assert_type(AR_U.item(), str)
+
+assert_type(AR_f8.ravel(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_U.ravel(), np.ndarray[tuple[int], np.dtype[np.str_]])
+
+assert_type(AR_f8.flatten(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_U.flatten(), np.ndarray[tuple[int], np.dtype[np.str_]])
+
+assert_type(AR_i8.reshape(None), npt.NDArray[np.int64])
+assert_type(AR_f8.reshape(-1), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(AR_c8.reshape(2, 3, 4, 5), np.ndarray[tuple[int, int, int, int], np.dtype[np.complex64]])
+assert_type(AR_m.reshape(()), np.ndarray[tuple[()], np.dtype[np.timedelta64]])
+assert_type(AR_U.reshape([]), np.ndarray[tuple[()], np.dtype[np.str_]])
+assert_type(AR_V.reshape((480, 720, 4)), np.ndarray[tuple[int, int, int], np.dtype[np.void]])
+
+assert_type(int(AR_f8), int)
+assert_type(int(AR_U), int)
+
+assert_type(float(AR_f8), float)
+assert_type(float(AR_U), float)
+
+assert_type(complex(AR_f8), complex)
+
+assert_type(operator.index(AR_i8), int)
+
+assert_type(AR_f8.__array_wrap__(B), npt.NDArray[np.object_])
+
+assert_type(AR_V[0], Any)
+assert_type(AR_V[0, 0], Any)
+assert_type(AR_V[AR_i8], npt.NDArray[np.void])
+assert_type(AR_V[AR_i8, AR_i8], npt.NDArray[np.void])
+assert_type(AR_V[AR_i8, None], npt.NDArray[np.void])
+assert_type(AR_V[0, ...], npt.NDArray[np.void])
+assert_type(AR_V[[0]], npt.NDArray[np.void])
+assert_type(AR_V[[0], [0]], npt.NDArray[np.void])
+assert_type(AR_V[:], npt.NDArray[np.void])
+assert_type(AR_V["a"], npt.NDArray[Any])
+assert_type(AR_V[["a", "b"]], npt.NDArray[np.void])
+
+assert_type(AR_f8.dump("test_file"), None)
+assert_type(AR_f8.dump(b"test_file"), None)
+with open("test_file", "wb") as f:
+    assert_type(AR_f8.dump(f), None)
+
+assert_type(AR_f8.__array_finalize__(None), None)
+assert_type(AR_f8.__array_finalize__(B), None)
+assert_type(AR_f8.__array_finalize__(AR_f8), None)
+
+assert_type(f8.device, Literal["cpu"])
+assert_type(AR_f8.device, Literal["cpu"])
+
+assert_type(f8.to_device("cpu"), np.float64)
+assert_type(i8.to_device("cpu"), np.int64)
+assert_type(AR_f8.to_device("cpu"), npt.NDArray[np.float64])
+assert_type(AR_i8.to_device("cpu"), npt.NDArray[np.int64])
+assert_type(AR_u1.to_device("cpu"), npt.NDArray[np.uint8])
+assert_type(AR_c8.to_device("cpu"), npt.NDArray[np.complex64])
+assert_type(AR_m.to_device("cpu"), npt.NDArray[np.timedelta64])
+
+assert_type(f8.__array_namespace__(), ModuleType)
+assert_type(AR_f8.__array_namespace__(), ModuleType)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..25637134088c7c9990c4fc257ed4e779d9ac34aa
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi
@@ -0,0 +1,39 @@
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+nd: npt.NDArray[np.int64]
+
+# reshape
+assert_type(nd.reshape(None), npt.NDArray[np.int64])
+assert_type(nd.reshape(4), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.reshape((4,)), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.reshape(2, 2), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(nd.reshape((2, 2)), np.ndarray[tuple[int, int], np.dtype[np.int64]])
+
+assert_type(nd.reshape((2, 2), order="C"),  np.ndarray[tuple[int, int], np.dtype[np.int64]])
+assert_type(nd.reshape(4, order="C"),  np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# resize does not return a value
+
+# transpose
+assert_type(nd.transpose(), npt.NDArray[np.int64])
+assert_type(nd.transpose(1, 0), npt.NDArray[np.int64])
+assert_type(nd.transpose((1, 0)), npt.NDArray[np.int64])
+
+# swapaxes
+assert_type(nd.swapaxes(0, 1), npt.NDArray[np.int64])
+
+# flatten
+assert_type(nd.flatten(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.flatten("C"), np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# ravel
+assert_type(nd.ravel(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(nd.ravel("C"), np.ndarray[tuple[int], np.dtype[np.int64]])
+
+# squeeze
+assert_type(nd.squeeze(), npt.NDArray[np.int64])
+assert_type(nd.squeeze(0), npt.NDArray[np.int64])
+assert_type(nd.squeeze((0, 2)), npt.NDArray[np.int64])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b5723c41310e84318b6d436bb305b994a61b61b3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nditer.pyi
@@ -0,0 +1,51 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+nditer_obj: np.nditer
+
+assert_type(np.nditer([0, 1], flags=["c_index"]), np.nditer)
+assert_type(np.nditer([0, 1], op_flags=[["readonly", "readonly"]]), np.nditer)
+assert_type(np.nditer([0, 1], op_dtypes=np.int_), np.nditer)
+assert_type(np.nditer([0, 1], order="C", casting="no"), np.nditer)
+
+assert_type(nditer_obj.dtypes, tuple[np.dtype[Any], ...])
+assert_type(nditer_obj.finished, bool)
+assert_type(nditer_obj.has_delayed_bufalloc, bool)
+assert_type(nditer_obj.has_index, bool)
+assert_type(nditer_obj.has_multi_index, bool)
+assert_type(nditer_obj.index, int)
+assert_type(nditer_obj.iterationneedsapi, bool)
+assert_type(nditer_obj.iterindex, int)
+assert_type(nditer_obj.iterrange, tuple[int, ...])
+assert_type(nditer_obj.itersize, int)
+assert_type(nditer_obj.itviews, tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.multi_index, tuple[int, ...])
+assert_type(nditer_obj.ndim, int)
+assert_type(nditer_obj.nop, int)
+assert_type(nditer_obj.operands, tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.shape, tuple[int, ...])
+assert_type(nditer_obj.value, tuple[npt.NDArray[Any], ...])
+
+assert_type(nditer_obj.close(), None)
+assert_type(nditer_obj.copy(), np.nditer)
+assert_type(nditer_obj.debug_print(), None)
+assert_type(nditer_obj.enable_external_loop(), None)
+assert_type(nditer_obj.iternext(), bool)
+assert_type(nditer_obj.remove_axis(0), None)
+assert_type(nditer_obj.remove_multi_index(), None)
+assert_type(nditer_obj.reset(), None)
+
+assert_type(len(nditer_obj), int)
+assert_type(iter(nditer_obj), np.nditer)
+assert_type(next(nditer_obj), tuple[npt.NDArray[Any], ...])
+assert_type(nditer_obj.__copy__(), np.nditer)
+with nditer_obj as f:
+    assert_type(f, np.nditer)
+assert_type(nditer_obj[0], npt.NDArray[Any])
+assert_type(nditer_obj[:], tuple[npt.NDArray[Any], ...])
+nditer_obj[0] = 0
+nditer_obj[:] = [0, 1]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..06acbbd9ce84e41481053de8e3b0b7c402e6a296
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/nested_sequence.pyi
@@ -0,0 +1,28 @@
+from collections.abc import Sequence
+from typing import Any
+
+from numpy._typing import _NestedSequence
+
+from typing_extensions import assert_type
+
+a: Sequence[int]
+b: Sequence[Sequence[int]]
+c: Sequence[Sequence[Sequence[int]]]
+d: Sequence[Sequence[Sequence[Sequence[int]]]]
+e: Sequence[bool]
+f: tuple[int, ...]
+g: list[int]
+h: Sequence[Any]
+
+def func(a: _NestedSequence[int]) -> None:
+    ...
+
+assert_type(func(a), None)
+assert_type(func(b), None)
+assert_type(func(c), None)
+assert_type(func(d), None)
+assert_type(func(e), None)
+assert_type(func(f), None)
+assert_type(func(g), None)
+assert_type(func(h), None)
+assert_type(func(range(15)), None)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d4c47b665ca50d5d5ecd503bcaeaea717bdc889b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/npyio.pyi
@@ -0,0 +1,85 @@
+import re
+import zipfile
+import pathlib
+from typing import IO, Any
+from collections.abc import Mapping
+
+import numpy.typing as npt
+import numpy as np
+from numpy.lib._npyio_impl import BagObj
+
+from typing_extensions import assert_type
+
+str_path: str
+pathlib_path: pathlib.Path
+str_file: IO[str]
+bytes_file: IO[bytes]
+
+npz_file: np.lib.npyio.NpzFile
+
+AR_i8: npt.NDArray[np.int64]
+AR_LIKE_f8: list[float]
+
+class BytesWriter:
+    def write(self, data: bytes) -> None: ...
+
+class BytesReader:
+    def read(self, n: int = ...) -> bytes: ...
+    def seek(self, offset: int, whence: int = ...) -> int: ...
+
+bytes_writer: BytesWriter
+bytes_reader: BytesReader
+
+assert_type(npz_file.zip, zipfile.ZipFile)
+assert_type(npz_file.fid, None | IO[str])
+assert_type(npz_file.files, list[str])
+assert_type(npz_file.allow_pickle, bool)
+assert_type(npz_file.pickle_kwargs, None | Mapping[str, Any])
+assert_type(npz_file.f, BagObj[np.lib.npyio.NpzFile])
+assert_type(npz_file["test"], npt.NDArray[Any])
+assert_type(len(npz_file), int)
+with npz_file as f:
+    assert_type(f, np.lib.npyio.NpzFile)
+
+assert_type(np.load(bytes_file), Any)
+assert_type(np.load(pathlib_path, allow_pickle=True), Any)
+assert_type(np.load(str_path, encoding="bytes"), Any)
+assert_type(np.load(bytes_reader), Any)
+
+assert_type(np.save(bytes_file, AR_LIKE_f8), None)
+assert_type(np.save(pathlib_path, AR_i8, allow_pickle=True), None)
+assert_type(np.save(str_path, AR_LIKE_f8), None)
+assert_type(np.save(bytes_writer, AR_LIKE_f8), None)
+
+assert_type(np.savez(bytes_file, AR_LIKE_f8), None)
+assert_type(np.savez(pathlib_path, ar1=AR_i8, ar2=AR_i8), None)
+assert_type(np.savez(str_path, AR_LIKE_f8, ar1=AR_i8), None)
+assert_type(np.savez(bytes_writer, AR_LIKE_f8, ar1=AR_i8), None)
+
+assert_type(np.savez_compressed(bytes_file, AR_LIKE_f8), None)
+assert_type(np.savez_compressed(pathlib_path, ar1=AR_i8, ar2=AR_i8), None)
+assert_type(np.savez_compressed(str_path, AR_LIKE_f8, ar1=AR_i8), None)
+assert_type(np.savez_compressed(bytes_writer, AR_LIKE_f8, ar1=AR_i8), None)
+
+assert_type(np.loadtxt(bytes_file), npt.NDArray[np.float64])
+assert_type(np.loadtxt(pathlib_path, dtype=np.str_), npt.NDArray[np.str_])
+assert_type(np.loadtxt(str_path, dtype=str, skiprows=2), npt.NDArray[Any])
+assert_type(np.loadtxt(str_file, comments="test"), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_file, comments=None), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_path, delimiter="\n"), npt.NDArray[np.float64])
+assert_type(np.loadtxt(str_path, ndmin=2), npt.NDArray[np.float64])
+assert_type(np.loadtxt(["1", "2", "3"]), npt.NDArray[np.float64])
+
+assert_type(np.fromregex(bytes_file, "test", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromregex(str_file, b"test", dtype=float), npt.NDArray[Any])
+assert_type(np.fromregex(str_path, re.compile("test"), dtype=np.str_, encoding="utf8"), npt.NDArray[np.str_])
+assert_type(np.fromregex(pathlib_path, "test", np.float64), npt.NDArray[np.float64])
+assert_type(np.fromregex(bytes_reader, "test", np.float64), npt.NDArray[np.float64])
+
+assert_type(np.genfromtxt(bytes_file), npt.NDArray[Any])
+assert_type(np.genfromtxt(pathlib_path, dtype=np.str_), npt.NDArray[np.str_])
+assert_type(np.genfromtxt(str_path, dtype=str, skip_header=2), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_file, comments="test"), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_path, delimiter="\n"), npt.NDArray[Any])
+assert_type(np.genfromtxt(str_path, ndmin=2), npt.NDArray[Any])
+assert_type(np.genfromtxt(["1", "2", "3"], ndmin=2), npt.NDArray[Any])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..90e6674a85e39a806020d5768883356c9e85bed4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numeric.pyi
@@ -0,0 +1,137 @@
+"""
+Tests for :mod:`_core.numeric`.
+
+Does not include tests which fall under ``array_constructors``.
+
+"""
+
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+class SubClass(npt.NDArray[np.int64]):
+    ...
+
+i8: np.int64
+
+AR_b: npt.NDArray[np.bool]
+AR_u8: npt.NDArray[np.uint64]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_O: npt.NDArray[np.object_]
+
+B: list[int]
+C: SubClass
+
+assert_type(np.count_nonzero(i8), int)
+assert_type(np.count_nonzero(AR_i8), int)
+assert_type(np.count_nonzero(B), int)
+assert_type(np.count_nonzero(AR_i8, keepdims=True), npt.NDArray[np.intp])
+assert_type(np.count_nonzero(AR_i8, axis=0), Any)
+
+assert_type(np.isfortran(i8), bool)
+assert_type(np.isfortran(AR_i8), bool)
+
+assert_type(np.argwhere(i8), npt.NDArray[np.intp])
+assert_type(np.argwhere(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.flatnonzero(i8), npt.NDArray[np.intp])
+assert_type(np.flatnonzero(AR_i8), npt.NDArray[np.intp])
+
+assert_type(np.correlate(B, AR_i8, mode="valid"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_i8, mode="same"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.correlate(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.correlate(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.correlate(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.correlate(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.correlate(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.convolve(B, AR_i8, mode="valid"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_i8, mode="same"), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.convolve(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.convolve(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.convolve(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.convolve(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.outer(i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_i8, out=C), SubClass)
+assert_type(np.outer(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.outer(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.outer(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.convolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.outer(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.outer(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.outer(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.tensordot(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8, axes=0), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_i8, axes=(0, 1)), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.tensordot(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.tensordot(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.tensordot(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.tensordot(AR_i8, AR_m), npt.NDArray[np.timedelta64])
+assert_type(np.tensordot(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.isscalar(i8), bool)
+assert_type(np.isscalar(AR_i8), bool)
+assert_type(np.isscalar(B), bool)
+
+assert_type(np.roll(AR_i8, 1), npt.NDArray[np.int64])
+assert_type(np.roll(AR_i8, (1, 2)), npt.NDArray[np.int64])
+assert_type(np.roll(B, 1), npt.NDArray[Any])
+
+assert_type(np.rollaxis(AR_i8, 0, 1), npt.NDArray[np.int64])
+
+assert_type(np.moveaxis(AR_i8, 0, 1), npt.NDArray[np.int64])
+assert_type(np.moveaxis(AR_i8, (0, 1), (1, 2)), npt.NDArray[np.int64])
+
+assert_type(np.cross(B, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_b, AR_u8), npt.NDArray[np.unsignedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.cross(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(np.cross(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.cross(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(np.indices([0, 1, 2]), npt.NDArray[np.int_])
+assert_type(np.indices([0, 1, 2], sparse=True), tuple[npt.NDArray[np.int_], ...])
+assert_type(np.indices([0, 1, 2], dtype=np.float64), npt.NDArray[np.float64])
+assert_type(np.indices([0, 1, 2], sparse=True, dtype=np.float64), tuple[npt.NDArray[np.float64], ...])
+assert_type(np.indices([0, 1, 2], dtype=float), npt.NDArray[Any])
+assert_type(np.indices([0, 1, 2], sparse=True, dtype=float), tuple[npt.NDArray[Any], ...])
+
+assert_type(np.binary_repr(1), str)
+
+assert_type(np.base_repr(1), str)
+
+assert_type(np.allclose(i8, AR_i8), bool)
+assert_type(np.allclose(B, AR_i8), bool)
+assert_type(np.allclose(AR_i8, AR_i8), bool)
+
+assert_type(np.isclose(i8, i8), np.bool)
+assert_type(np.isclose(i8, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isclose(B, AR_i8), npt.NDArray[np.bool])
+assert_type(np.isclose(AR_i8, AR_i8), npt.NDArray[np.bool])
+
+assert_type(np.array_equal(i8, AR_i8), bool)
+assert_type(np.array_equal(B, AR_i8), bool)
+assert_type(np.array_equal(AR_i8, AR_i8), bool)
+
+assert_type(np.array_equiv(i8, AR_i8), bool)
+assert_type(np.array_equiv(B, AR_i8), bool)
+assert_type(np.array_equiv(AR_i8, AR_i8), bool)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a8ad4e0e1f4b287245ae3d457ccf3c67b5483b10
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/numerictypes.pyi
@@ -0,0 +1,53 @@
+from typing import Literal
+from typing_extensions import assert_type
+
+import numpy as np
+
+
+assert_type(
+    np.ScalarType,
+    tuple[
+        type[int],
+        type[float],
+        type[complex],
+        type[bool],
+        type[bytes],
+        type[str],
+        type[memoryview],
+        type[np.bool],
+        type[np.csingle],
+        type[np.cdouble],
+        type[np.clongdouble],
+        type[np.half],
+        type[np.single],
+        type[np.double],
+        type[np.longdouble],
+        type[np.byte],
+        type[np.short],
+        type[np.intc],
+        type[np.long],
+        type[np.longlong],
+        type[np.timedelta64],
+        type[np.datetime64],
+        type[np.object_],
+        type[np.bytes_],
+        type[np.str_],
+        type[np.ubyte],
+        type[np.ushort],
+        type[np.uintc],
+        type[np.ulong],
+        type[np.ulonglong],
+        type[np.void],
+    ],
+)
+assert_type(np.ScalarType[0], type[int])
+assert_type(np.ScalarType[3], type[bool])
+assert_type(np.ScalarType[8], type[np.csingle])
+assert_type(np.ScalarType[10], type[np.clongdouble])
+assert_type(np.bool_(object()), np.bool)
+
+assert_type(np.typecodes["Character"], Literal["c"])
+assert_type(np.typecodes["Complex"], Literal["FDG"])
+assert_type(np.typecodes["All"], Literal["?bhilqnpBHILQNPefdgFDGSUVOMm"])
+
+assert_type(np.sctypeDict['uint8'], type[np.generic])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..40c13e646f4a31a4e1c1196ebeb953098c745b91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polybase.pyi
@@ -0,0 +1,221 @@
+from fractions import Fraction
+from collections.abc import Sequence
+from decimal import Decimal
+from typing import Any, Literal as L, TypeAlias, TypeVar
+
+import numpy as np
+import numpy.polynomial as npp
+import numpy.typing as npt
+
+from typing_extensions import assert_type, LiteralString
+
+_Ar_x: TypeAlias = npt.NDArray[np.inexact[Any] | np.object_]
+_Ar_f: TypeAlias = npt.NDArray[np.floating[Any]]
+_Ar_c: TypeAlias = npt.NDArray[np.complexfloating[Any, Any]]
+_Ar_O: TypeAlias = npt.NDArray[np.object_]
+
+_Ar_x_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.inexact[Any] | np.object_]]
+_Ar_f_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_Ar_c_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_Ar_O_n: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+_Ar_x_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.inexact[Any] | np.object_]]
+_Ar_f_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.floating[Any]]]
+_Ar_c_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.complexfloating[Any, Any]]]
+_Ar_O_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.object_]]
+
+_SCT = TypeVar("_SCT", bound=np.generic)
+_Ar_1d: TypeAlias = np.ndarray[tuple[int], np.dtype[_SCT]]
+
+_BasisName: TypeAlias = L["X"]
+
+SC_i: np.int_
+SC_i_co: int | np.int_
+SC_f: np.float64
+SC_f_co: float | np.float64 | np.int_
+SC_c: np.complex128
+SC_c_co: complex | np.complex128
+SC_O: Decimal
+
+AR_i: npt.NDArray[np.int_]
+AR_f: npt.NDArray[np.float64]
+AR_f_co: npt.NDArray[np.float64] | npt.NDArray[np.int_]
+AR_c: npt.NDArray[np.complex128]
+AR_c_co: npt.NDArray[np.complex128] |npt.NDArray[np.float64] | npt.NDArray[np.int_]
+AR_O: npt.NDArray[np.object_]
+AR_O_co: npt.NDArray[np.object_ | np.number[Any]]
+
+SQ_i: Sequence[int]
+SQ_f: Sequence[float]
+SQ_c: Sequence[complex]
+SQ_O: Sequence[Decimal]
+
+PS_poly: npp.Polynomial
+PS_cheb: npp.Chebyshev
+PS_herm: npp.Hermite
+PS_herme: npp.HermiteE
+PS_lag: npp.Laguerre
+PS_leg: npp.Legendre
+PS_all: (
+    npp.Polynomial
+    | npp.Chebyshev
+    | npp.Hermite
+    | npp.HermiteE
+    | npp.Laguerre
+    | npp.Legendre
+)
+
+# static- and classmethods
+
+assert_type(type(PS_poly).basis_name, None)
+assert_type(type(PS_cheb).basis_name, L['T'])
+assert_type(type(PS_herm).basis_name, L['H'])
+assert_type(type(PS_herme).basis_name, L['He'])
+assert_type(type(PS_lag).basis_name, L['L'])
+assert_type(type(PS_leg).basis_name, L['P'])
+
+assert_type(type(PS_all).__hash__, None)
+assert_type(type(PS_all).__array_ufunc__, None)
+assert_type(type(PS_all).maxpower, L[100])
+
+assert_type(type(PS_poly).fromroots(SC_i), npp.Polynomial)
+assert_type(type(PS_poly).fromroots(SQ_i), npp.Polynomial)
+assert_type(type(PS_poly).fromroots(AR_i), npp.Polynomial)
+assert_type(type(PS_cheb).fromroots(SC_f), npp.Chebyshev)
+assert_type(type(PS_cheb).fromroots(SQ_f), npp.Chebyshev)
+assert_type(type(PS_cheb).fromroots(AR_f_co), npp.Chebyshev)
+assert_type(type(PS_herm).fromroots(SC_c), npp.Hermite)
+assert_type(type(PS_herm).fromroots(SQ_c), npp.Hermite)
+assert_type(type(PS_herm).fromroots(AR_c_co), npp.Hermite)
+assert_type(type(PS_leg).fromroots(SC_O), npp.Legendre)
+assert_type(type(PS_leg).fromroots(SQ_O), npp.Legendre)
+assert_type(type(PS_leg).fromroots(AR_O_co), npp.Legendre)
+
+assert_type(type(PS_poly).identity(), npp.Polynomial)
+assert_type(type(PS_cheb).identity(symbol='z'), npp.Chebyshev)
+
+assert_type(type(PS_lag).basis(SC_i), npp.Laguerre)
+assert_type(type(PS_leg).basis(32, symbol='u'), npp.Legendre)
+
+assert_type(type(PS_herm).cast(PS_poly), npp.Hermite)
+assert_type(type(PS_herme).cast(PS_leg), npp.HermiteE)
+
+# attributes / properties
+
+assert_type(PS_all.coef, _Ar_x_n)
+assert_type(PS_all.domain, _Ar_x_2)
+assert_type(PS_all.window, _Ar_x_2)
+assert_type(PS_all.symbol, LiteralString)
+
+# instance methods
+
+assert_type(PS_all.has_samecoef(PS_all), bool)
+assert_type(PS_all.has_samedomain(PS_all), bool)
+assert_type(PS_all.has_samewindow(PS_all), bool)
+assert_type(PS_all.has_sametype(PS_all), bool)
+assert_type(PS_poly.has_sametype(PS_poly), bool)
+assert_type(PS_poly.has_sametype(PS_leg), bool)
+assert_type(PS_poly.has_sametype(NotADirectoryError), L[False])
+
+assert_type(PS_poly.copy(), npp.Polynomial)
+assert_type(PS_cheb.copy(), npp.Chebyshev)
+assert_type(PS_herm.copy(), npp.Hermite)
+assert_type(PS_herme.copy(), npp.HermiteE)
+assert_type(PS_lag.copy(), npp.Laguerre)
+assert_type(PS_leg.copy(), npp.Legendre)
+
+assert_type(PS_leg.cutdeg(), npp.Legendre)
+assert_type(PS_leg.trim(), npp.Legendre)
+assert_type(PS_leg.trim(tol=SC_f_co), npp.Legendre)
+assert_type(PS_leg.truncate(SC_i_co), npp.Legendre)
+
+assert_type(PS_all.convert(None, npp.Chebyshev), npp.Chebyshev)
+assert_type(PS_all.convert((0, 1), npp.Laguerre), npp.Laguerre)
+assert_type(PS_all.convert([0, 1], npp.Hermite, [-1, 1]), npp.Hermite)
+
+assert_type(PS_all.degree(), int)
+assert_type(PS_all.mapparms(), tuple[Any, Any])
+
+assert_type(PS_poly.integ(), npp.Polynomial)
+assert_type(PS_herme.integ(SC_i_co), npp.HermiteE)
+assert_type(PS_lag.integ(SC_i_co, SC_f_co), npp.Laguerre)
+assert_type(PS_poly.deriv(), npp.Polynomial)
+assert_type(PS_herm.deriv(SC_i_co), npp.Hermite)
+
+assert_type(PS_poly.roots(), _Ar_x_n)
+
+assert_type(
+    PS_poly.linspace(),
+    tuple[_Ar_1d[np.float64 | np.complex128], _Ar_1d[np.float64 | np.complex128]],
+)
+
+assert_type(
+    PS_poly.linspace(9),
+    tuple[_Ar_1d[np.float64 | np.complex128], _Ar_1d[np.float64 | np.complex128]],
+)
+
+assert_type(PS_cheb.fit(AR_c_co, AR_c_co, SC_i_co), npp.Chebyshev)
+assert_type(PS_leg.fit(AR_c_co, AR_c_co, AR_i), npp.Legendre)
+assert_type(PS_herm.fit(AR_c_co, AR_c_co, SQ_i), npp.Hermite)
+assert_type(PS_poly.fit(AR_c_co, SQ_c, SQ_i), npp.Polynomial)
+assert_type(PS_lag.fit(SQ_c, SQ_c, SQ_i, full=False), npp.Laguerre)
+assert_type(
+    PS_herme.fit(SQ_c, AR_c_co, SC_i_co, full=True),
+    tuple[npp.HermiteE, Sequence[np.inexact[Any] | np.int32]],
+)
+
+# custom operations
+
+assert_type(PS_all.__hash__, None)
+assert_type(PS_all.__array_ufunc__, None)
+
+assert_type(str(PS_all), str)
+assert_type(repr(PS_all), str)
+assert_type(format(PS_all), str)
+
+assert_type(len(PS_all), int)
+assert_type(next(iter(PS_all)), np.inexact[Any] | object)
+
+assert_type(PS_all(SC_f_co), np.float64 | np.complex128)
+assert_type(PS_all(SC_c_co), np.complex128)
+assert_type(PS_all(Decimal()), np.float64 | np.complex128)
+assert_type(PS_all(Fraction()), np.float64 | np.complex128)
+assert_type(PS_poly(SQ_f), npt.NDArray[np.float64] | npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(SQ_c), npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(SQ_O), npt.NDArray[np.object_])
+assert_type(PS_poly(AR_f), npt.NDArray[np.float64] | npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(AR_c), npt.NDArray[np.complex128] | npt.NDArray[np.object_])
+assert_type(PS_poly(AR_O), npt.NDArray[np.object_])
+assert_type(PS_all(PS_poly), npp.Polynomial)
+
+assert_type(PS_poly == PS_poly, bool)
+assert_type(PS_poly != PS_poly, bool)
+
+assert_type(-PS_poly, npp.Polynomial)
+assert_type(+PS_poly, npp.Polynomial)
+
+assert_type(PS_poly + 5, npp.Polynomial)
+assert_type(PS_poly - 5, npp.Polynomial)
+assert_type(PS_poly * 5, npp.Polynomial)
+assert_type(PS_poly / 5, npp.Polynomial)
+assert_type(PS_poly // 5, npp.Polynomial)
+assert_type(PS_poly % 5, npp.Polynomial)
+
+assert_type(PS_poly + PS_leg, npp.Polynomial)
+assert_type(PS_poly - PS_leg, npp.Polynomial)
+assert_type(PS_poly * PS_leg, npp.Polynomial)
+assert_type(PS_poly / PS_leg, npp.Polynomial)
+assert_type(PS_poly // PS_leg, npp.Polynomial)
+assert_type(PS_poly % PS_leg, npp.Polynomial)
+
+assert_type(5 + PS_poly, npp.Polynomial)
+assert_type(5 - PS_poly, npp.Polynomial)
+assert_type(5 * PS_poly, npp.Polynomial)
+assert_type(5 / PS_poly, npp.Polynomial)
+assert_type(5 // PS_poly, npp.Polynomial)
+assert_type(5 % PS_poly, npp.Polynomial)
+assert_type(divmod(PS_poly, 5), tuple[npp.Polynomial, npp.Polynomial])
+assert_type(divmod(5, PS_poly), tuple[npp.Polynomial, npp.Polynomial])
+
+assert_type(PS_poly**1, npp.Polynomial)
+assert_type(PS_poly**1.0, npp.Polynomial)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ca5852808ce7102460016a9e1fd5cad5faf781e7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_polyutils.pyi
@@ -0,0 +1,220 @@
+from collections.abc import Sequence
+from decimal import Decimal
+from fractions import Fraction
+from typing import Any, Literal as L, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+import numpy.polynomial.polyutils as pu
+from numpy.polynomial._polytypes import _Tuple2
+
+from typing_extensions import assert_type
+
+_ArrFloat1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_ArrComplex1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_ArrObject1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+_ArrFloat1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.float64]]
+_ArrComplex1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.complex128]]
+_ArrObject1D_2: TypeAlias = np.ndarray[tuple[L[2]], np.dtype[np.object_]]
+
+num_int: int
+num_float: float
+num_complex: complex
+# will result in an `object_` dtype
+num_object: Decimal | Fraction
+
+sct_int: np.int_
+sct_float: np.float64
+sct_complex: np.complex128
+sct_object: np.object_  # doesn't exist at runtime
+
+arr_int: npt.NDArray[np.int_]
+arr_float: npt.NDArray[np.float64]
+arr_complex: npt.NDArray[np.complex128]
+arr_object: npt.NDArray[np.object_]
+
+seq_num_int: Sequence[int]
+seq_num_float: Sequence[float]
+seq_num_complex: Sequence[complex]
+seq_num_object: Sequence[Decimal | Fraction]
+
+seq_sct_int: Sequence[np.int_]
+seq_sct_float: Sequence[np.float64]
+seq_sct_complex: Sequence[np.complex128]
+seq_sct_object: Sequence[np.object_]
+
+seq_arr_int: Sequence[npt.NDArray[np.int_]]
+seq_arr_float: Sequence[npt.NDArray[np.float64]]
+seq_arr_complex: Sequence[npt.NDArray[np.complex128]]
+seq_arr_object: Sequence[npt.NDArray[np.object_]]
+
+seq_seq_num_int: Sequence[Sequence[int]]
+seq_seq_num_float: Sequence[Sequence[float]]
+seq_seq_num_complex: Sequence[Sequence[complex]]
+seq_seq_num_object: Sequence[Sequence[Decimal | Fraction]]
+
+seq_seq_sct_int: Sequence[Sequence[np.int_]]
+seq_seq_sct_float: Sequence[Sequence[np.float64]]
+seq_seq_sct_complex: Sequence[Sequence[np.complex128]]
+seq_seq_sct_object: Sequence[Sequence[np.object_]]  # doesn't exist at runtime
+
+# as_series
+
+assert_type(pu.as_series(arr_int), list[_ArrFloat1D])
+assert_type(pu.as_series(arr_float), list[_ArrFloat1D])
+assert_type(pu.as_series(arr_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(arr_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_num_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_num_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_num_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_num_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_sct_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_sct_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_sct_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_sct_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_arr_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_arr_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_arr_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_arr_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_seq_num_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_num_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_num_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_seq_num_object), list[_ArrObject1D])
+
+assert_type(pu.as_series(seq_seq_sct_int), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_sct_float), list[_ArrFloat1D])
+assert_type(pu.as_series(seq_seq_sct_complex), list[_ArrComplex1D])
+assert_type(pu.as_series(seq_seq_sct_object), list[_ArrObject1D])
+
+# trimcoef
+
+assert_type(pu.trimcoef(num_int), _ArrFloat1D)
+assert_type(pu.trimcoef(num_float), _ArrFloat1D)
+assert_type(pu.trimcoef(num_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(num_object), _ArrObject1D)
+assert_type(pu.trimcoef(num_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(sct_int), _ArrFloat1D)
+assert_type(pu.trimcoef(sct_float), _ArrFloat1D)
+assert_type(pu.trimcoef(sct_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(sct_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(arr_int), _ArrFloat1D)
+assert_type(pu.trimcoef(arr_float), _ArrFloat1D)
+assert_type(pu.trimcoef(arr_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(arr_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(seq_num_int), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_num_float), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_num_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(seq_num_object), _ArrObject1D)
+
+assert_type(pu.trimcoef(seq_sct_int), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_sct_float), _ArrFloat1D)
+assert_type(pu.trimcoef(seq_sct_complex), _ArrComplex1D)
+assert_type(pu.trimcoef(seq_sct_object), _ArrObject1D)
+
+# getdomain
+
+assert_type(pu.getdomain(num_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(num_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(num_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(num_object), _ArrObject1D_2)
+assert_type(pu.getdomain(num_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(sct_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(sct_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(sct_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(sct_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(arr_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(arr_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(arr_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(arr_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(seq_num_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_num_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_num_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(seq_num_object), _ArrObject1D_2)
+
+assert_type(pu.getdomain(seq_sct_int), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_sct_float), _ArrFloat1D_2)
+assert_type(pu.getdomain(seq_sct_complex), _ArrComplex1D_2)
+assert_type(pu.getdomain(seq_sct_object), _ArrObject1D_2)
+
+# mapparms
+
+assert_type(pu.mapparms(seq_num_int, seq_num_int), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_int, seq_num_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_float, seq_num_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_num_float, seq_num_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_num_complex, seq_num_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_num_complex, seq_num_object), _Tuple2[object])
+assert_type(pu.mapparms(seq_num_object, seq_num_object), _Tuple2[object])
+
+assert_type(pu.mapparms(seq_sct_int, seq_sct_int), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(seq_sct_int, seq_sct_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(seq_sct_float, seq_sct_float), _Tuple2[float])
+assert_type(pu.mapparms(seq_sct_float, seq_sct_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_sct_complex, seq_sct_complex), _Tuple2[complex])
+assert_type(pu.mapparms(seq_sct_complex, seq_sct_object), _Tuple2[object])
+assert_type(pu.mapparms(seq_sct_object, seq_sct_object), _Tuple2[object])
+
+assert_type(pu.mapparms(arr_int, arr_int), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_int, arr_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_float, arr_float), _Tuple2[np.floating[Any]])
+assert_type(pu.mapparms(arr_float, arr_complex), _Tuple2[np.complexfloating[Any, Any]])
+assert_type(pu.mapparms(arr_complex, arr_complex), _Tuple2[np.complexfloating[Any, Any]])
+assert_type(pu.mapparms(arr_complex, arr_object), _Tuple2[object])
+assert_type(pu.mapparms(arr_object, arr_object), _Tuple2[object])
+
+# mapdomain
+
+assert_type(pu.mapdomain(num_int, seq_num_int, seq_num_int), np.floating[Any])
+assert_type(pu.mapdomain(num_int, seq_num_int, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_int, seq_num_float, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_float, seq_num_float, seq_num_float), np.floating[Any])
+assert_type(pu.mapdomain(num_float, seq_num_float, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_float, seq_num_complex, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_complex, seq_num_complex, seq_num_complex), np.complexfloating[Any, Any])
+assert_type(pu.mapdomain(num_complex, seq_num_complex, seq_num_object), object)
+assert_type(pu.mapdomain(num_complex, seq_num_object, seq_num_object), object)
+assert_type(pu.mapdomain(num_object, seq_num_object, seq_num_object), object)
+
+assert_type(pu.mapdomain(seq_num_int, seq_num_int, seq_num_int), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_int, seq_num_int, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_int, seq_num_float, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_float, seq_num_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_float, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_float, seq_num_complex, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_complex, seq_num_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_complex, seq_num_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_num_complex, seq_num_object, seq_num_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_num_object, seq_num_object, seq_num_object), _ArrObject1D)
+
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_int, seq_sct_int), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_int, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_int, seq_sct_float, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_float, seq_sct_float), _ArrFloat1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_float, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_float, seq_sct_complex, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_complex, seq_sct_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_complex, seq_sct_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_sct_complex, seq_sct_object, seq_sct_object), _ArrObject1D)
+assert_type(pu.mapdomain(seq_sct_object, seq_sct_object, seq_sct_object), _ArrObject1D)
+
+assert_type(pu.mapdomain(arr_int, arr_int, arr_int), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_int, arr_int, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_int, arr_float, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_float, arr_float, arr_float), _ArrFloat1D)
+assert_type(pu.mapdomain(arr_float, arr_float, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_float, arr_complex, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_complex, arr_complex, arr_complex), _ArrComplex1D)
+assert_type(pu.mapdomain(arr_complex, arr_complex, arr_object), _ArrObject1D)
+assert_type(pu.mapdomain(arr_complex, arr_object, arr_object), _ArrObject1D)
+assert_type(pu.mapdomain(arr_object, arr_object, arr_object), _ArrObject1D)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..80ec9c0ff56ac1905b8a97483d26f85eba02e58f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/polynomial_series.pyi
@@ -0,0 +1,140 @@
+from collections.abc import Sequence
+from typing import Any, TypeAlias
+
+import numpy as np
+import numpy.polynomial as npp
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_ArrFloat1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.floating[Any]]]
+_ArrFloat1D64: TypeAlias = np.ndarray[tuple[int], np.dtype[np.float64]]
+_ArrComplex1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complexfloating[Any, Any]]]
+_ArrComplex1D128: TypeAlias = np.ndarray[tuple[int], np.dtype[np.complex128]]
+_ArrObject1D: TypeAlias = np.ndarray[tuple[int], np.dtype[np.object_]]
+
+AR_b: npt.NDArray[np.bool]
+AR_u4: npt.NDArray[np.uint32]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+PS_poly: npp.Polynomial
+PS_cheb: npp.Chebyshev
+
+assert_type(npp.polynomial.polyroots(AR_f8), _ArrFloat1D64)
+assert_type(npp.polynomial.polyroots(AR_c16), _ArrComplex1D128)
+assert_type(npp.polynomial.polyroots(AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polyfromroots(AR_f8), _ArrFloat1D)
+assert_type(npp.polynomial.polyfromroots(AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polyfromroots(AR_O), _ArrObject1D)
+
+# assert_type(npp.polynomial.polyadd(AR_b, AR_b), NoReturn)
+assert_type(npp.polynomial.polyadd(AR_u4, AR_b), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_i8, AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_f8, AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polyadd(AR_i8, AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polyadd(AR_O, AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polymulx(AR_u4), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_i8), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_f8), _ArrFloat1D)
+assert_type(npp.polynomial.polymulx(AR_c16), _ArrComplex1D)
+assert_type(npp.polynomial.polymulx(AR_O), _ArrObject1D)
+
+assert_type(npp.polynomial.polypow(AR_u4, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_i8, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_f8, 2), _ArrFloat1D)
+assert_type(npp.polynomial.polypow(AR_c16, 2), _ArrComplex1D)
+assert_type(npp.polynomial.polypow(AR_O, 2), _ArrObject1D)
+
+# assert_type(npp.polynomial.polyder(PS_poly), npt.NDArray[np.object_])
+assert_type(npp.polynomial.polyder(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyder(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyder(AR_O, m=2), npt.NDArray[np.object_])
+
+# assert_type(npp.polynomial.polyint(PS_poly), npt.NDArray[np.object_])
+assert_type(npp.polynomial.polyint(AR_f8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyint(AR_f8, k=AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyint(AR_O, m=2), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval(AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval2d(AR_b, AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_u4, AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_i8, AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_f8, AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval2d(AR_i8, AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval2d(AR_O, AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyval3d(AR_b, AR_b, AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_u4, AR_u4, AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_i8, AR_i8, AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_f8, AR_f8, AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyval3d(AR_i8, AR_i8, AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyval3d(AR_O, AR_O, AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvalfromroots(AR_b, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_u4, AR_b), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_i8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_f8, AR_i8), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_i8, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvalfromroots(AR_O, AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander(AR_f8, 3), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander(AR_c16, 3), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander(AR_O, 3), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander2d(AR_f8, AR_f8, [4, 2]), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander2d(AR_c16, AR_c16, [4, 2]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander2d(AR_O, AR_O, [4, 2]), npt.NDArray[np.object_])
+
+assert_type(npp.polynomial.polyvander3d(AR_f8, AR_f8, AR_f8, [4, 3, 2]), npt.NDArray[np.floating[Any]])
+assert_type(npp.polynomial.polyvander3d(AR_c16, AR_c16, AR_c16, [4, 3, 2]), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(npp.polynomial.polyvander3d(AR_O, AR_O, AR_O, [4, 3, 2]), npt.NDArray[np.object_])
+
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_f8, 2),
+    npt.NDArray[np.floating[Any]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_i8, 1, full=True),
+    tuple[npt.NDArray[np.floating[Any]], Sequence[np.inexact[Any] | np.int32]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_c16, AR_f8, 2),
+    npt.NDArray[np.complexfloating[Any, Any]],
+)
+assert_type(
+    npp.polynomial.polyfit(AR_f8, AR_c16, 1, full=True)[0],
+    npt.NDArray[np.complexfloating[Any, Any]],
+)
+
+assert_type(npp.chebyshev.chebgauss(2), tuple[_ArrFloat1D64, _ArrFloat1D64])
+
+assert_type(npp.chebyshev.chebweight(AR_f8), npt.NDArray[np.float64])
+assert_type(npp.chebyshev.chebweight(AR_c16), npt.NDArray[np.complex128])
+assert_type(npp.chebyshev.chebweight(AR_O), npt.NDArray[np.object_])
+
+assert_type(npp.chebyshev.poly2cheb(AR_f8), _ArrFloat1D)
+assert_type(npp.chebyshev.poly2cheb(AR_c16), _ArrComplex1D)
+assert_type(npp.chebyshev.poly2cheb(AR_O), _ArrObject1D)
+
+assert_type(npp.chebyshev.cheb2poly(AR_f8), _ArrFloat1D)
+assert_type(npp.chebyshev.cheb2poly(AR_c16), _ArrComplex1D)
+assert_type(npp.chebyshev.cheb2poly(AR_O), _ArrObject1D)
+
+assert_type(npp.chebyshev.chebpts1(6), _ArrFloat1D64)
+assert_type(npp.chebyshev.chebpts2(6), _ArrFloat1D64)
+
+assert_type(
+    npp.chebyshev.chebinterpolate(np.tanh, 3),
+    npt.NDArray[np.float64 | np.complex128 | np.object_],
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4c1c8abd927c5950445e725f0ccefe7005cf96c2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/random.pyi
@@ -0,0 +1,1551 @@
+import threading
+from typing import Any
+from collections.abc import Sequence
+
+import numpy as np
+import numpy.typing as npt
+from numpy.random._generator import Generator
+from numpy.random._mt19937 import MT19937
+from numpy.random._pcg64 import PCG64
+from numpy.random._sfc64 import SFC64
+from numpy.random._philox import Philox
+from numpy.random.bit_generator import SeedSequence, SeedlessSeedSequence
+
+from typing_extensions import assert_type
+
+def_rng = np.random.default_rng()
+seed_seq = np.random.SeedSequence()
+mt19937 = np.random.MT19937()
+pcg64 = np.random.PCG64()
+sfc64 = np.random.SFC64()
+philox = np.random.Philox()
+seedless_seq = SeedlessSeedSequence()
+
+assert_type(def_rng, Generator)
+assert_type(mt19937, MT19937)
+assert_type(pcg64, PCG64)
+assert_type(sfc64, SFC64)
+assert_type(philox, Philox)
+assert_type(seed_seq, SeedSequence)
+assert_type(seedless_seq, SeedlessSeedSequence)
+
+mt19937_jumped = mt19937.jumped()
+mt19937_jumped3 = mt19937.jumped(3)
+mt19937_raw = mt19937.random_raw()
+mt19937_raw_arr = mt19937.random_raw(5)
+
+assert_type(mt19937_jumped, MT19937)
+assert_type(mt19937_jumped3, MT19937)
+assert_type(mt19937_raw, int)
+assert_type(mt19937_raw_arr, npt.NDArray[np.uint64])
+assert_type(mt19937.lock, threading.Lock)
+
+pcg64_jumped = pcg64.jumped()
+pcg64_jumped3 = pcg64.jumped(3)
+pcg64_adv = pcg64.advance(3)
+pcg64_raw = pcg64.random_raw()
+pcg64_raw_arr = pcg64.random_raw(5)
+
+assert_type(pcg64_jumped, PCG64)
+assert_type(pcg64_jumped3, PCG64)
+assert_type(pcg64_adv, PCG64)
+assert_type(pcg64_raw, int)
+assert_type(pcg64_raw_arr, npt.NDArray[np.uint64])
+assert_type(pcg64.lock, threading.Lock)
+
+philox_jumped = philox.jumped()
+philox_jumped3 = philox.jumped(3)
+philox_adv = philox.advance(3)
+philox_raw = philox.random_raw()
+philox_raw_arr = philox.random_raw(5)
+
+assert_type(philox_jumped, Philox)
+assert_type(philox_jumped3, Philox)
+assert_type(philox_adv, Philox)
+assert_type(philox_raw, int)
+assert_type(philox_raw_arr, npt.NDArray[np.uint64])
+assert_type(philox.lock, threading.Lock)
+
+sfc64_raw = sfc64.random_raw()
+sfc64_raw_arr = sfc64.random_raw(5)
+
+assert_type(sfc64_raw, int)
+assert_type(sfc64_raw_arr, npt.NDArray[np.uint64])
+assert_type(sfc64.lock, threading.Lock)
+
+assert_type(seed_seq.pool, npt.NDArray[np.uint32])
+assert_type(seed_seq.entropy, None | int | Sequence[int])
+assert_type(seed_seq.spawn(1), list[np.random.SeedSequence])
+assert_type(seed_seq.generate_state(8, "uint32"), npt.NDArray[np.uint32 | np.uint64])
+assert_type(seed_seq.generate_state(8, "uint64"), npt.NDArray[np.uint32 | np.uint64])
+
+
+def_gen: np.random.Generator = np.random.default_rng()
+
+D_arr_0p1: npt.NDArray[np.float64] = np.array([0.1])
+D_arr_0p5: npt.NDArray[np.float64] = np.array([0.5])
+D_arr_0p9: npt.NDArray[np.float64] = np.array([0.9])
+D_arr_1p5: npt.NDArray[np.float64] = np.array([1.5])
+I_arr_10: npt.NDArray[np.int_] = np.array([10], dtype=np.int_)
+I_arr_20: npt.NDArray[np.int_] = np.array([20], dtype=np.int_)
+D_arr_like_0p1: list[float] = [0.1]
+D_arr_like_0p5: list[float] = [0.5]
+D_arr_like_0p9: list[float] = [0.9]
+D_arr_like_1p5: list[float] = [1.5]
+I_arr_like_10: list[int] = [10]
+I_arr_like_20: list[int] = [20]
+D_2D_like: list[list[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]]
+D_2D: npt.NDArray[np.float64] = np.array(D_2D_like)
+S_out: npt.NDArray[np.float32] = np.empty(1, dtype=np.float32)
+D_out: npt.NDArray[np.float64] = np.empty(1)
+
+assert_type(def_gen.standard_normal(), float)
+assert_type(def_gen.standard_normal(dtype=np.float32), float)
+assert_type(def_gen.standard_normal(dtype="float32"), float)
+assert_type(def_gen.standard_normal(dtype="double"), float)
+assert_type(def_gen.standard_normal(dtype=np.float64), float)
+assert_type(def_gen.standard_normal(size=None), float)
+assert_type(def_gen.standard_normal(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.random(), float)
+assert_type(def_gen.random(dtype=np.float32), float)
+assert_type(def_gen.random(dtype="float32"), float)
+assert_type(def_gen.random(dtype="double"), float)
+assert_type(def_gen.random(dtype=np.float64), float)
+assert_type(def_gen.random(size=None), float)
+assert_type(def_gen.random(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.random(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_cauchy(), float)
+assert_type(def_gen.standard_cauchy(size=None), float)
+assert_type(def_gen.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_exponential(), float)
+assert_type(def_gen.standard_exponential(method="inv"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float32), float)
+assert_type(def_gen.standard_exponential(dtype="float32"), float)
+assert_type(def_gen.standard_exponential(dtype="double"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float64), float)
+assert_type(def_gen.standard_exponential(size=None), float)
+assert_type(def_gen.standard_exponential(size=None, method="inv"), float)
+assert_type(def_gen.standard_exponential(size=1, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.zipf(1.5), int)
+assert_type(def_gen.zipf(1.5, size=None), int)
+assert_type(def_gen.zipf(1.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.weibull(0.5), float)
+assert_type(def_gen.weibull(0.5, size=None), float)
+assert_type(def_gen.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_t(0.5), float)
+assert_type(def_gen.standard_t(0.5, size=None), float)
+assert_type(def_gen.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.poisson(0.5), int)
+assert_type(def_gen.poisson(0.5, size=None), int)
+assert_type(def_gen.poisson(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.power(0.5), float)
+assert_type(def_gen.power(0.5, size=None), float)
+assert_type(def_gen.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.pareto(0.5), float)
+assert_type(def_gen.pareto(0.5, size=None), float)
+assert_type(def_gen.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.chisquare(0.5), float)
+assert_type(def_gen.chisquare(0.5, size=None), float)
+assert_type(def_gen.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.exponential(0.5), float)
+assert_type(def_gen.exponential(0.5, size=None), float)
+assert_type(def_gen.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.geometric(0.5), int)
+assert_type(def_gen.geometric(0.5, size=None), int)
+assert_type(def_gen.geometric(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.logseries(0.5), int)
+assert_type(def_gen.logseries(0.5, size=None), int)
+assert_type(def_gen.logseries(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.rayleigh(0.5), float)
+assert_type(def_gen.rayleigh(0.5, size=None), float)
+assert_type(def_gen.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_gamma(0.5), float)
+assert_type(def_gen.standard_gamma(0.5, size=None), float)
+assert_type(def_gen.standard_gamma(0.5, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=None, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(0.5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64), npt.NDArray[np.float64])
+
+assert_type(def_gen.vonmises(0.5, 0.5), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=None), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.wald(0.5, 0.5), float)
+assert_type(def_gen.wald(0.5, 0.5, size=None), float)
+assert_type(def_gen.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.uniform(0.5, 0.5), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=None), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.beta(0.5, 0.5), float)
+assert_type(def_gen.beta(0.5, 0.5, size=None), float)
+assert_type(def_gen.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.f(0.5, 0.5), float)
+assert_type(def_gen.f(0.5, 0.5, size=None), float)
+assert_type(def_gen.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gamma(0.5, 0.5), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=None), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gumbel(0.5, 0.5), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=None), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.laplace(0.5, 0.5), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=None), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.logistic(0.5, 0.5), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=None), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.lognormal(0.5, 0.5), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=None), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.normal(0.5, 0.5), float)
+assert_type(def_gen.normal(0.5, 0.5, size=None), float)
+assert_type(def_gen.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.triangular(0.1, 0.5, 0.9), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.binomial(10, 0.5), int)
+assert_type(def_gen.binomial(10, 0.5, size=None), int)
+assert_type(def_gen.binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.negative_binomial(10, 0.5), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=None), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.hypergeometric(20, 20, 10), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=None), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+
+I_int64_100: npt.NDArray[np.int64] = np.array([100], dtype=np.int64)
+
+assert_type(def_gen.integers(0, 100), np.int64)
+assert_type(def_gen.integers(100), np.int64)
+assert_type(def_gen.integers([100]), npt.NDArray[np.int64])
+assert_type(def_gen.integers(0, [100]), npt.NDArray[np.int64])
+
+I_bool_low: npt.NDArray[np.bool] = np.array([0], dtype=np.bool)
+I_bool_low_like: list[int] = [0]
+I_bool_high_open: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+I_bool_high_closed: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+
+assert_type(def_gen.integers(2, dtype=bool), bool)
+assert_type(def_gen.integers(0, 2, dtype=bool), bool)
+assert_type(def_gen.integers(1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(0, 1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+
+assert_type(def_gen.integers(2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(0, 2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(0, 1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+
+I_u1_low: npt.NDArray[np.uint8] = np.array([0], dtype=np.uint8)
+I_u1_low_like: list[int] = [0]
+I_u1_high_open: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+I_u1_high_closed: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+
+assert_type(def_gen.integers(256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+
+I_u2_low: npt.NDArray[np.uint16] = np.array([0], dtype=np.uint16)
+I_u2_low_like: list[int] = [0]
+I_u2_high_open: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+I_u2_high_closed: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+
+assert_type(def_gen.integers(65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+
+I_u4_low: npt.NDArray[np.uint32] = np.array([0], dtype=np.uint32)
+I_u4_low_like: list[int] = [0]
+I_u4_high_open: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+I_u4_high_closed: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+
+assert_type(def_gen.integers(4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+
+
+assert_type(def_gen.integers(4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+
+I_u8_low: npt.NDArray[np.uint64] = np.array([0], dtype=np.uint64)
+I_u8_low_like: list[int] = [0]
+I_u8_high_open: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+I_u8_high_closed: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+
+assert_type(def_gen.integers(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+
+I_i1_low: npt.NDArray[np.int8] = np.array([-128], dtype=np.int8)
+I_i1_low_like: list[int] = [-128]
+I_i1_high_open: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+I_i1_high_closed: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+
+assert_type(def_gen.integers(128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+
+I_i2_low: npt.NDArray[np.int16] = np.array([-32768], dtype=np.int16)
+I_i2_low_like: list[int] = [-32768]
+I_i2_high_open: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+I_i2_high_closed: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+
+assert_type(def_gen.integers(32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+
+I_i4_low: npt.NDArray[np.int32] = np.array([-2147483648], dtype=np.int32)
+I_i4_low_like: list[int] = [-2147483648]
+I_i4_high_open: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+I_i4_high_closed: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+
+assert_type(def_gen.integers(2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+
+I_i8_low: npt.NDArray[np.int64] = np.array([-9223372036854775808], dtype=np.int64)
+I_i8_low_like: list[int] = [-9223372036854775808]
+I_i8_high_open: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+I_i8_high_closed: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+
+assert_type(def_gen.integers(9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+
+
+assert_type(def_gen.bit_generator, np.random.BitGenerator)
+
+assert_type(def_gen.bytes(2), bytes)
+
+assert_type(def_gen.choice(5), int)
+assert_type(def_gen.choice(5, 3), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, replace=True), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.int64])
+
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(def_gen.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2)), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2)), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7)), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count"), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals"), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(def_gen.permutation(10), npt.NDArray[np.int64])
+assert_type(def_gen.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(def_gen.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(def_gen.permutation(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1, out=D_2D), npt.NDArray[Any])
+
+assert_type(def_gen.shuffle(np.arange(10)), None)
+assert_type(def_gen.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(def_gen.shuffle(D_2D, axis=1), None)
+
+assert_type(np.random.Generator(pcg64), np.random.Generator)
+assert_type(def_gen.__str__(), str)
+assert_type(def_gen.__repr__(), str)
+assert_type(def_gen.__setstate__(dict(def_gen.bit_generator.state)), None)
+
+# RandomState
+random_st: np.random.RandomState = np.random.RandomState()
+
+assert_type(random_st.standard_normal(), float)
+assert_type(random_st.standard_normal(size=None), float)
+assert_type(random_st.standard_normal(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.random(), float)
+assert_type(random_st.random(size=None), float)
+assert_type(random_st.random(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_cauchy(), float)
+assert_type(random_st.standard_cauchy(size=None), float)
+assert_type(random_st.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_exponential(), float)
+assert_type(random_st.standard_exponential(size=None), float)
+assert_type(random_st.standard_exponential(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.zipf(1.5), int)
+assert_type(random_st.zipf(1.5, size=None), int)
+assert_type(random_st.zipf(1.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.weibull(0.5), float)
+assert_type(random_st.weibull(0.5, size=None), float)
+assert_type(random_st.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_t(0.5), float)
+assert_type(random_st.standard_t(0.5, size=None), float)
+assert_type(random_st.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.poisson(0.5), int)
+assert_type(random_st.poisson(0.5, size=None), int)
+assert_type(random_st.poisson(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.power(0.5), float)
+assert_type(random_st.power(0.5, size=None), float)
+assert_type(random_st.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.pareto(0.5), float)
+assert_type(random_st.pareto(0.5, size=None), float)
+assert_type(random_st.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.chisquare(0.5), float)
+assert_type(random_st.chisquare(0.5, size=None), float)
+assert_type(random_st.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.exponential(0.5), float)
+assert_type(random_st.exponential(0.5, size=None), float)
+assert_type(random_st.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.geometric(0.5), int)
+assert_type(random_st.geometric(0.5, size=None), int)
+assert_type(random_st.geometric(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.logseries(0.5), int)
+assert_type(random_st.logseries(0.5, size=None), int)
+assert_type(random_st.logseries(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.rayleigh(0.5), float)
+assert_type(random_st.rayleigh(0.5, size=None), float)
+assert_type(random_st.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_gamma(0.5), float)
+assert_type(random_st.standard_gamma(0.5, size=None), float)
+assert_type(random_st.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.vonmises(0.5, 0.5), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=None), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.wald(0.5, 0.5), float)
+assert_type(random_st.wald(0.5, 0.5, size=None), float)
+assert_type(random_st.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.uniform(0.5, 0.5), float)
+assert_type(random_st.uniform(0.5, 0.5, size=None), float)
+assert_type(random_st.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.beta(0.5, 0.5), float)
+assert_type(random_st.beta(0.5, 0.5, size=None), float)
+assert_type(random_st.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.f(0.5, 0.5), float)
+assert_type(random_st.f(0.5, 0.5, size=None), float)
+assert_type(random_st.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gamma(0.5, 0.5), float)
+assert_type(random_st.gamma(0.5, 0.5, size=None), float)
+assert_type(random_st.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gumbel(0.5, 0.5), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=None), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.laplace(0.5, 0.5), float)
+assert_type(random_st.laplace(0.5, 0.5, size=None), float)
+assert_type(random_st.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.logistic(0.5, 0.5), float)
+assert_type(random_st.logistic(0.5, 0.5, size=None), float)
+assert_type(random_st.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.lognormal(0.5, 0.5), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=None), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_chisquare(0.5, 0.5), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.normal(0.5, 0.5), float)
+assert_type(random_st.normal(0.5, 0.5, size=None), float)
+assert_type(random_st.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.triangular(0.1, 0.5, 0.9), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.binomial(10, 0.5), int)
+assert_type(random_st.binomial(10, 0.5, size=None), int)
+assert_type(random_st.binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.negative_binomial(10, 0.5), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=None), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.hypergeometric(20, 20, 10), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=None), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.randint(0, 100), int)
+assert_type(random_st.randint(100), int)
+assert_type(random_st.randint([100]), npt.NDArray[np.long])
+assert_type(random_st.randint(0, [100]), npt.NDArray[np.long])
+
+assert_type(random_st.randint(2, dtype=bool), bool)
+assert_type(random_st.randint(0, 2, dtype=bool), bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(0, 2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(0, 256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+
+assert_type(random_st.randint(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(128, dtype="i1"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="i1"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype="int8"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="int8"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(-128, 128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+
+assert_type(random_st.randint(9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(random_st._bit_generator, np.random.BitGenerator)
+
+assert_type(random_st.bytes(2), bytes)
+
+assert_type(random_st.choice(5), int)
+assert_type(random_st.choice(5, 3), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, replace=True), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.long])
+
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(random_st.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.long])
+
+assert_type(random_st.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(random_st.permutation(10), npt.NDArray[np.long])
+assert_type(random_st.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(random_st.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(random_st.permutation(D_2D), npt.NDArray[Any])
+
+assert_type(random_st.shuffle(np.arange(10)), None)
+assert_type(random_st.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(random_st.shuffle(D_2D), None)
+
+assert_type(np.random.RandomState(pcg64), np.random.RandomState)
+assert_type(np.random.RandomState(0), np.random.RandomState)
+assert_type(np.random.RandomState([0, 1, 2]), np.random.RandomState)
+assert_type(random_st.__str__(), str)
+assert_type(random_st.__repr__(), str)
+random_st_state = random_st.__getstate__()
+assert_type(random_st_state, dict[str, Any])
+assert_type(random_st.__setstate__(random_st_state), None)
+assert_type(random_st.seed(), None)
+assert_type(random_st.seed(1), None)
+assert_type(random_st.seed([0, 1]), None)
+random_st_get_state = random_st.get_state()
+assert_type(random_st_state, dict[str, Any])
+random_st_get_state_legacy = random_st.get_state(legacy=True)
+assert_type(random_st_get_state_legacy, dict[str, Any] | tuple[str, npt.NDArray[np.uint32], int, int, float])
+assert_type(random_st.set_state(random_st_get_state), None)
+
+assert_type(random_st.rand(), float)
+assert_type(random_st.rand(1), npt.NDArray[np.float64])
+assert_type(random_st.rand(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.randn(), float)
+assert_type(random_st.randn(1), npt.NDArray[np.float64])
+assert_type(random_st.randn(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(), float)
+assert_type(random_st.random_sample(1), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(size=(1, 2)), npt.NDArray[np.float64])
+
+assert_type(random_st.tomaxint(), int)
+assert_type(random_st.tomaxint(1), npt.NDArray[np.int64])
+assert_type(random_st.tomaxint((1,)), npt.NDArray[np.int64])
+
+assert_type(np.random.mtrand.set_bit_generator(pcg64), None)
+assert_type(np.random.mtrand.get_bit_generator(), np.random.BitGenerator)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1b88f6b46316c11a742143c3f48982733ac19744
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/rec.pyi
@@ -0,0 +1,168 @@
+import io
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_i8: npt.NDArray[np.int64]
+REC_AR_V: np.recarray[tuple[int, ...], np.dtype[np.record]]
+AR_LIST: list[npt.NDArray[np.int64]]
+
+record: np.record
+file_obj: io.BufferedIOBase
+
+assert_type(np.rec.format_parser(
+    formats=[np.float64, np.int64, np.bool],
+    names=["f8", "i8", "?"],
+    titles=None,
+    aligned=True,
+), np.rec.format_parser)
+assert_type(np.rec.format_parser.dtype, np.dtype[np.void])
+
+assert_type(record.field_a, Any)
+assert_type(record.field_b, Any)
+assert_type(record["field_a"], Any)
+assert_type(record["field_b"], Any)
+assert_type(record.pprint(), str)
+record.field_c = 5
+
+assert_type(REC_AR_V.field(0), Any)
+assert_type(REC_AR_V.field("field_a"), Any)
+assert_type(REC_AR_V.field(0, AR_i8), None)
+assert_type(REC_AR_V.field("field_a", AR_i8), None)
+assert_type(REC_AR_V["field_a"], npt.NDArray[Any])
+assert_type(REC_AR_V.field_a, Any)
+assert_type(REC_AR_V.__array_finalize__(object()), None)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        formats=[np.float64, np.int64, np.bool],
+        order="K",
+        byteorder="|",
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        dtype=[("f8", np.float64), ("i8", np.int64)],
+        strides=(5, 5),
+    ),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(np.rec.fromarrays(AR_LIST), np.recarray[Any, np.dtype[Any]])
+assert_type(
+    np.rec.fromarrays(AR_LIST, dtype=np.int64),
+    np.recarray[Any, np.dtype[Any]],
+)
+assert_type(
+    np.rec.fromarrays(
+        AR_LIST,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromrecords((1, 1.5)),
+    np.recarray[Any, np.dtype[np.record]]
+)
+
+assert_type(
+    np.rec.fromrecords(
+        [(1, 1.5)],
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromrecords(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromstring(
+        b"(1, 1.5)",
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.fromstring(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(np.rec.fromfile(
+    "test_file.txt",
+    dtype=[("i8", np.int64), ("f8", np.float64)],
+), np.recarray[Any, np.dtype[Any]])
+
+assert_type(
+    np.rec.fromfile(
+        file_obj,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(np.rec.array(AR_i8), np.recarray[Any, np.dtype[np.int64]])
+
+assert_type(
+    np.rec.array([(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)]),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(
+        [(1, 1.5)],
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        dtype=np.float64,
+        shape=(10, 3),
+    ),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"],
+        shape=(10, 3),
+    ),
+    np.recarray[Any, np.dtype[np.record]],
+)
+
+assert_type(
+    np.rec.array(file_obj, dtype=np.float64),
+    np.recarray[Any, np.dtype[Any]],
+)
+
+assert_type(
+    np.rec.array(file_obj, formats=[np.int64, np.float64], names=["i8", "f8"]),
+    np.recarray[Any, np.dtype[np.record]],
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d3070437b740a3c122ae4d73dc5b971f4421c9d0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
@@ -0,0 +1,193 @@
+from typing import Any, Literal, TypeAlias
+from typing_extensions import Unpack, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+_1: TypeAlias = Literal[1]
+
+b: np.bool
+u8: np.uint64
+i8: np.int64
+f8: np.float64
+c8: np.complex64
+c16: np.complex128
+m: np.timedelta64
+U: np.str_
+S: np.bytes_
+V: np.void
+O: np.object_  # cannot exists at runtime
+
+array_nd: np.ndarray[Any, Any]
+array_0d: np.ndarray[tuple[()], Any]
+array_2d_2x2: np.ndarray[tuple[Literal[2], Literal[2]], Any]
+
+assert_type(c8.real, np.float32)
+assert_type(c8.imag, np.float32)
+
+assert_type(c8.real.real, np.float32)
+assert_type(c8.real.imag, np.float32)
+
+assert_type(c8.itemsize, int)
+assert_type(c8.shape, tuple[()])
+assert_type(c8.strides, tuple[()])
+
+assert_type(c8.ndim, Literal[0])
+assert_type(c8.size, Literal[1])
+
+assert_type(c8.squeeze(), np.complex64)
+assert_type(c8.byteswap(), np.complex64)
+assert_type(c8.transpose(), np.complex64)
+
+assert_type(c8.dtype, np.dtype[np.complex64])
+
+assert_type(c8.real, np.float32)
+assert_type(c16.imag, np.float64)
+
+assert_type(np.str_('foo'), np.str_)
+
+assert_type(V[0], Any)
+assert_type(V["field1"], Any)
+assert_type(V[["field1", "field2"]], np.void)
+V[0] = 5
+
+# Aliases
+assert_type(np.bool_(), np.bool[Literal[False]])
+assert_type(np.byte(), np.byte)
+assert_type(np.short(), np.short)
+assert_type(np.intc(), np.intc)
+assert_type(np.intp(), np.intp)
+assert_type(np.int_(), np.int_)
+assert_type(np.long(), np.long)
+assert_type(np.longlong(), np.longlong)
+
+assert_type(np.ubyte(), np.ubyte)
+assert_type(np.ushort(), np.ushort)
+assert_type(np.uintc(), np.uintc)
+assert_type(np.uintp(), np.uintp)
+assert_type(np.uint(), np.uint)
+assert_type(np.ulong(), np.ulong)
+assert_type(np.ulonglong(), np.ulonglong)
+
+assert_type(np.half(), np.half)
+assert_type(np.single(), np.single)
+assert_type(np.double(), np.double)
+assert_type(np.longdouble(), np.longdouble)
+
+assert_type(np.csingle(), np.csingle)
+assert_type(np.cdouble(), np.cdouble)
+assert_type(np.clongdouble(), np.clongdouble)
+
+assert_type(b.item(), bool)
+assert_type(i8.item(), int)
+assert_type(u8.item(), int)
+assert_type(f8.item(), float)
+assert_type(c16.item(), complex)
+assert_type(U.item(), str)
+assert_type(S.item(), bytes)
+
+assert_type(b.tolist(), bool)
+assert_type(i8.tolist(), int)
+assert_type(u8.tolist(), int)
+assert_type(f8.tolist(), float)
+assert_type(c16.tolist(), complex)
+assert_type(U.tolist(), str)
+assert_type(S.tolist(), bytes)
+
+assert_type(b.ravel(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.ravel(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.ravel(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.ravel(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.ravel(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.ravel(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.ravel(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.flatten(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.flatten(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.flatten(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.flatten(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.flatten(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.flatten(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.flatten(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.reshape(()), np.bool)
+assert_type(i8.reshape([]), np.int64)
+assert_type(b.reshape(1), np.ndarray[tuple[_1], np.dtype[np.bool]])
+assert_type(i8.reshape(-1), np.ndarray[tuple[_1], np.dtype[np.int64]])
+assert_type(u8.reshape(1, 1), np.ndarray[tuple[_1, _1], np.dtype[np.uint64]])
+assert_type(f8.reshape(1, -1), np.ndarray[tuple[_1, _1], np.dtype[np.float64]])
+assert_type(c16.reshape(1, 1, 1), np.ndarray[tuple[_1, _1, _1], np.dtype[np.complex128]])
+assert_type(U.reshape(1, 1, 1, 1), np.ndarray[tuple[_1, _1, _1, _1], np.dtype[np.str_]])
+assert_type(
+    S.reshape(1, 1, 1, 1, 1),
+    np.ndarray[
+        # len(shape) >= 5
+        tuple[_1, _1, _1, _1, _1, Unpack[tuple[_1, ...]]],
+        np.dtype[np.bytes_],
+    ],
+)
+
+assert_type(i8.astype(float), Any)
+assert_type(i8.astype(np.float64), np.float64)
+
+assert_type(i8.view(), np.int64)
+assert_type(i8.view(np.float64), np.float64)
+assert_type(i8.view(float), Any)
+assert_type(i8.view(np.float64, np.ndarray), np.float64)
+
+assert_type(i8.getfield(float), Any)
+assert_type(i8.getfield(np.float64), np.float64)
+assert_type(i8.getfield(np.float64, 8), np.float64)
+
+assert_type(f8.as_integer_ratio(), tuple[int, int])
+assert_type(f8.is_integer(), bool)
+assert_type(f8.__trunc__(), int)
+assert_type(f8.__getformat__("float"), str)
+assert_type(f8.hex(), str)
+assert_type(np.float64.fromhex("0x0.0p+0"), np.float64)
+
+assert_type(f8.__getnewargs__(), tuple[float])
+assert_type(c16.__getnewargs__(), tuple[float, float])
+
+assert_type(i8.numerator, np.int64)
+assert_type(i8.denominator, Literal[1])
+assert_type(u8.numerator, np.uint64)
+assert_type(u8.denominator, Literal[1])
+assert_type(m.numerator, np.timedelta64)
+assert_type(m.denominator, Literal[1])
+
+assert_type(round(i8), int)
+assert_type(round(i8, 3), np.int64)
+assert_type(round(u8), int)
+assert_type(round(u8, 3), np.uint64)
+assert_type(round(f8), int)
+assert_type(round(f8, 3), np.float64)
+
+assert_type(f8.__ceil__(), int)
+assert_type(f8.__floor__(), int)
+
+assert_type(i8.is_integer(), Literal[True])
+
+assert_type(O.real, np.object_)
+assert_type(O.imag, np.object_)
+assert_type(int(O), int)
+assert_type(float(O), float)
+assert_type(complex(O), complex)
+
+# These fail fail because of a mypy __new__ bug:
+# https://github.com/python/mypy/issues/15182
+# According to the typing spec, the following statements are valid, see
+# https://typing.readthedocs.io/en/latest/spec/constructors.html#new-method
+
+# assert_type(np.object_(), None)
+# assert_type(np.object_(None), None)
+# assert_type(np.object_(array_nd), np.ndarray[Any, np.dtype[np.object_]])
+# assert_type(np.object_([]), npt.NDArray[np.object_])
+# assert_type(np.object_(()), npt.NDArray[np.object_])
+# assert_type(np.object_(range(4)), npt.NDArray[np.object_])
+# assert_type(np.object_(+42), int)
+# assert_type(np.object_(1 / 137), float)
+# assert_type(np.object_('Developers! ' * (1 << 6)), str)
+# assert_type(np.object_(object()), object)
+# assert_type(np.object_({False, True, NotADirectoryError}), set[Any])
+# assert_type(np.object_({'spam': 'food', 'ham': 'food'}), dict[str, str])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8f8d819cbcea964d9c66cc80b82c1622db2ce2a9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape.pyi
@@ -0,0 +1,15 @@
+from typing import Any, NamedTuple
+
+import numpy as np
+from typing_extensions import assert_type
+
+
+# Subtype of tuple[int, int]
+class XYGrid(NamedTuple):
+    x_axis: int
+    y_axis: int
+
+arr: np.ndarray[XYGrid, Any]
+
+# Test shape property matches shape typevar
+assert_type(arr.shape, XYGrid)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a4b4bba3f9fc68e233a4f06d80b837dea449cc0e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
@@ -0,0 +1,54 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+i8: np.int64
+f8: np.float64
+
+AR_b: npt.NDArray[np.bool]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+AR_LIKE_f8: list[float]
+
+assert_type(np.take_along_axis(AR_f8, AR_i8, axis=1), npt.NDArray[np.float64])
+assert_type(np.take_along_axis(f8, AR_i8, axis=None), npt.NDArray[np.float64])
+
+assert_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1), None)
+
+assert_type(np.expand_dims(AR_i8, 2), npt.NDArray[np.int64])
+assert_type(np.expand_dims(AR_LIKE_f8, 2), npt.NDArray[Any])
+
+assert_type(np.column_stack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.column_stack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.dstack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.dstack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.array_split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.hsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.vsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.dsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.kron(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.kron(AR_b, AR_i8), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.kron(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
+
+assert_type(np.tile(AR_i8, 5), npt.NDArray[np.int64])
+assert_type(np.tile(AR_LIKE_f8, [2, 2]), npt.NDArray[Any])
+
+assert_type(np.unstack(AR_i8, axis=0), tuple[npt.NDArray[np.int64], ...])
+assert_type(np.unstack(AR_LIKE_f8, axis=0), tuple[npt.NDArray[Any], ...])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2ce666280f64a58a4d8f96efd1bca6d4ceeaffbe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
@@ -0,0 +1,29 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE_f: list[float]
+interface_dict: dict[str, Any]
+
+assert_type(np.lib.stride_tricks.as_strided(AR_f8), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_LIKE_f), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5)), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20]), npt.NDArray[np.float64])
+
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_to(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.broadcast_to(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.broadcast_to(AR_f8, [4, 6], subok=True), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_shapes((1, 2), [3, 1], (3, 2)), tuple[int, ...])
+assert_type(np.broadcast_shapes((6, 7), (5, 6, 1), 7, (5, 1, 7)), tuple[int, ...])
+
+assert_type(np.broadcast_arrays(AR_f8, AR_f8), tuple[npt.NDArray[Any], ...])
+assert_type(np.broadcast_arrays(AR_f8, AR_LIKE_f), tuple[npt.NDArray[Any], ...])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9339456b61aea342c9f00ac0c17d794ca12e35e8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/strings.pyi
@@ -0,0 +1,193 @@
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as np_t
+
+from typing_extensions import assert_type
+from typing import TypeAlias
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+AR_T: np.ndarray[np_t._Shape, np.dtypes.StringDType]
+
+AR_T_alias: TypeAlias = np.ndarray[np_t._Shape, np.dtypes.StringDType]
+AR_TU_alias: TypeAlias = AR_T_alias | npt.NDArray[np.str_]
+
+assert_type(np.strings.equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.not_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.add(AR_U, AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.add(AR_S, AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.add(AR_T, AR_T), AR_T_alias)
+
+assert_type(np.strings.multiply(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.multiply(AR_S, [5, 4, 3]), npt.NDArray[np.bytes_])
+assert_type(np.strings.multiply(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.mod(AR_U, "test"), npt.NDArray[np.str_])
+assert_type(np.strings.mod(AR_S, "test"), npt.NDArray[np.bytes_])
+assert_type(np.strings.mod(AR_T, "test"), AR_T_alias)
+
+assert_type(np.strings.capitalize(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.capitalize(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.capitalize(AR_T), AR_T_alias)
+
+assert_type(np.strings.center(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.center(AR_S, [2, 3, 4], b"a"), npt.NDArray[np.bytes_])
+assert_type(np.strings.center(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.encode(AR_U), npt.NDArray[np.bytes_])
+assert_type(np.strings.encode(AR_T), npt.NDArray[np.bytes_])
+assert_type(np.strings.decode(AR_S), npt.NDArray[np.str_])
+
+assert_type(np.strings.expandtabs(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.expandtabs(AR_S, tabsize=4), npt.NDArray[np.bytes_])
+assert_type(np.strings.expandtabs(AR_T), AR_T_alias)
+
+assert_type(np.strings.ljust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.ljust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.ljust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.rjust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rjust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.rjust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.lstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.lstrip(AR_T), AR_T_alias)
+assert_type(np.strings.lstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.rstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.rstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.rstrip(AR_T), AR_T_alias)
+assert_type(np.strings.rstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.strip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.strip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.strip(AR_T), AR_T_alias)
+assert_type(np.strings.strip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.count(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, ["a", "b", "c"], end=9), npt.NDArray[np.int_])
+
+assert_type(np.strings.partition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.partition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.partition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.rpartition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.rpartition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rpartition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.replace(AR_U, "_", "-"), npt.NDArray[np.str_])
+assert_type(np.strings.replace(AR_S, [b"_", b""], [b"a", b"b"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.replace(AR_T, "_", "_"), AR_TU_alias)
+
+assert_type(np.strings.lower(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lower(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.lower(AR_T), AR_T_alias)
+
+assert_type(np.strings.upper(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.upper(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.upper(AR_T), AR_T_alias)
+
+assert_type(np.strings.swapcase(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.swapcase(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.swapcase(AR_T), AR_T_alias)
+
+assert_type(np.strings.title(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.title(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.title(AR_T), AR_T_alias)
+
+assert_type(np.strings.zfill(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.zfill(AR_S, [2, 3, 4]), npt.NDArray[np.bytes_])
+assert_type(np.strings.zfill(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.endswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.startswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.find(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rfind(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.index(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rindex(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.isalpha(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isalnum(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdecimal(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdecimal(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdigit(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.islower(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isnumeric(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isnumeric(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isspace(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.istitle(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isupper(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.str_len(AR_U), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_S), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_T), npt.NDArray[np.int_])
+
+assert_type(np.strings.translate(AR_U, ""), npt.NDArray[np.str_])
+assert_type(np.strings.translate(AR_S, ""), npt.NDArray[np.bytes_])
+assert_type(np.strings.translate(AR_T, ""), AR_T_alias)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..741c71f62a5bfbca0e3c8f2ed03917a46899d0ed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/testing.pyi
@@ -0,0 +1,200 @@
+import re
+import sys
+import warnings
+import types
+import unittest
+import contextlib
+from collections.abc import Callable
+from typing import Any, TypeVar
+from pathlib import Path
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+bool_obj: bool
+suppress_obj: np.testing.suppress_warnings
+FT = TypeVar("FT", bound=Callable[..., Any])
+
+def func() -> int: ...
+
+def func2(
+    x: npt.NDArray[np.number[Any]],
+    y: npt.NDArray[np.number[Any]],
+) -> npt.NDArray[np.bool]: ...
+
+assert_type(np.testing.KnownFailureException(), np.testing.KnownFailureException)
+assert_type(np.testing.IgnoreException(), np.testing.IgnoreException)
+
+assert_type(
+    np.testing.clear_and_catch_warnings(modules=[np.testing]),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(True),
+    np.testing.clear_and_catch_warnings[list[warnings.WarningMessage]],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(False),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(bool_obj),
+    np.testing.clear_and_catch_warnings,
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.class_modules,
+    tuple[types.ModuleType, ...],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.modules,
+    set[types.ModuleType],
+)
+
+with np.testing.clear_and_catch_warnings(True) as c1:
+    assert_type(c1, list[warnings.WarningMessage])
+with np.testing.clear_and_catch_warnings() as c2:
+    assert_type(c2, None)
+
+assert_type(np.testing.suppress_warnings("once"), np.testing.suppress_warnings)
+assert_type(np.testing.suppress_warnings()(func), Callable[[], int])
+assert_type(suppress_obj.filter(RuntimeWarning), None)
+assert_type(suppress_obj.record(RuntimeWarning), list[warnings.WarningMessage])
+with suppress_obj as c3:
+    assert_type(c3, np.testing.suppress_warnings)
+
+assert_type(np.testing.verbose, int)
+assert_type(np.testing.IS_PYPY, bool)
+assert_type(np.testing.HAS_REFCOUNT, bool)
+assert_type(np.testing.HAS_LAPACK64, bool)
+
+assert_type(np.testing.assert_(1, msg="test"), None)
+assert_type(np.testing.assert_(2, msg=lambda: "test"), None)
+
+if sys.platform == "win32" or sys.platform == "cygwin":
+    assert_type(np.testing.memusage(), int)
+elif sys.platform == "linux":
+    assert_type(np.testing.memusage(), None | int)
+
+assert_type(np.testing.jiffies(), int)
+
+assert_type(np.testing.build_err_msg([0, 1, 2], "test"), str)
+assert_type(np.testing.build_err_msg(range(2), "test", header="header"), str)
+assert_type(np.testing.build_err_msg(np.arange(9).reshape(3, 3), "test", verbose=False), str)
+assert_type(np.testing.build_err_msg("abc", "test", names=["x", "y"]), str)
+assert_type(np.testing.build_err_msg([1.0, 2.0], "test", precision=5), str)
+
+assert_type(np.testing.assert_equal({1}, {1}), None)
+assert_type(np.testing.assert_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_equal(1, 1.0, verbose=True), None)
+
+assert_type(np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]), None)
+
+assert_type(np.testing.assert_almost_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_almost_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0001, decimal=2), None)
+
+assert_type(np.testing.assert_approx_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_approx_equal("1", "2", err_msg="fail"), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0001, significant=2), None)
+
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, header="header"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, precision=np.int64()), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_nan=False), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_inf=True), None)
+
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, decimal=1), None)
+
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.runstring("1 + 1", {}), Any)
+assert_type(np.testing.runstring("int64() + 1", {"int64": np.int64}), Any)
+
+assert_type(np.testing.assert_string_equal("1", "1"), None)
+
+assert_type(np.testing.rundocs(), None)
+assert_type(np.testing.rundocs("test.py"), None)
+assert_type(np.testing.rundocs(Path("test.py"), raise_on_error=True), None)
+
+def func3(a: int) -> bool: ...
+
+assert_type(
+    np.testing.assert_raises(RuntimeWarning),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises(RuntimeWarning, func3, 5), None)
+
+assert_type(
+    np.testing.assert_raises_regex(RuntimeWarning, r"test"),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, b"test", func3, 5), None)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, re.compile(b"test"), func3, 5), None)
+
+class Test: ...
+
+def decorate(a: FT) -> FT:
+    return a
+
+assert_type(np.testing.decorate_methods(Test, decorate), None)
+assert_type(np.testing.decorate_methods(Test, decorate, None), None)
+assert_type(np.testing.decorate_methods(Test, decorate, "test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, b"test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, re.compile("test")), None)
+
+assert_type(np.testing.measure("for i in range(1000): np.sqrt(i**2)"), float)
+assert_type(np.testing.measure(b"for i in range(1000): np.sqrt(i**2)", times=5), float)
+
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, rtol=0.005), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, atol=1), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, equal_nan=True), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, err_msg="err"), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, verbose=False), None)
+
+assert_type(np.testing.assert_array_almost_equal_nulp(AR_i8, AR_f8, nulp=2), None)
+
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2), npt.NDArray[Any])
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32), npt.NDArray[Any])
+
+assert_type(np.testing.assert_warns(RuntimeWarning), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_warns(RuntimeWarning, func3, 5), bool)
+
+def func4(a: int, b: str) -> bool: ...
+
+assert_type(np.testing.assert_no_warnings(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_warnings(func3, 5), bool)
+assert_type(np.testing.assert_no_warnings(func4, a=1, b="test"), bool)
+assert_type(np.testing.assert_no_warnings(func4, 1, "test"), bool)
+
+assert_type(np.testing.tempdir("test_dir"), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.tempdir(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.tempdir("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.temppath("test_dir", text=True), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.temppath(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.temppath("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.assert_no_gc_cycles(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_gc_cycles(func3, 5), None)
+
+assert_type(np.testing.break_cycles(), None)
+
+assert_type(np.testing.TestCase(), unittest.case.TestCase)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2f1cd56d1e7b7b6941431a40fea1001bf5a7d7e3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
@@ -0,0 +1,152 @@
+from typing import Any, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+_SCT = TypeVar("_SCT", bound=np.generic)
+
+
+def func1(ar: npt.NDArray[_SCT], a: int) -> npt.NDArray[_SCT]:
+    pass
+
+
+def func2(ar: npt.NDArray[np.number[Any]], a: str) -> npt.NDArray[np.float64]:
+    pass
+
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint64]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+
+AR_LIKE_b: list[bool]
+AR_LIKE_c: list[complex]
+
+assert_type(np.fliplr(AR_b), npt.NDArray[np.bool])
+assert_type(np.fliplr(AR_LIKE_b), npt.NDArray[Any])
+
+assert_type(np.flipud(AR_b), npt.NDArray[np.bool])
+assert_type(np.flipud(AR_LIKE_b), npt.NDArray[Any])
+
+assert_type(np.eye(10), npt.NDArray[np.float64])
+assert_type(np.eye(10, M=20, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.eye(10, k=2, dtype=int), npt.NDArray[Any])
+
+assert_type(np.diag(AR_b), npt.NDArray[np.bool])
+assert_type(np.diag(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.diagflat(AR_b), npt.NDArray[np.bool])
+assert_type(np.diagflat(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.tri(10), npt.NDArray[np.float64])
+assert_type(np.tri(10, M=20, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(np.tri(10, k=2, dtype=int), npt.NDArray[Any])
+
+assert_type(np.tril(AR_b), npt.NDArray[np.bool])
+assert_type(np.tril(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.triu(AR_b), npt.NDArray[np.bool])
+assert_type(np.triu(AR_LIKE_b, k=0), npt.NDArray[Any])
+
+assert_type(np.vander(AR_b), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_u), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_i, N=2), npt.NDArray[np.signedinteger[Any]])
+assert_type(np.vander(AR_f, increasing=True), npt.NDArray[np.floating[Any]])
+assert_type(np.vander(AR_c), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.vander(AR_O), npt.NDArray[np.object_])
+
+assert_type(
+    np.histogram2d(AR_LIKE_c, AR_LIKE_c),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128 | np.float64],
+        npt.NDArray[np.complex128 | np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_i, AR_b),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_i),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_i, AR_f),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+        npt.NDArray[np.float64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_c, weights=AR_LIKE_b),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_f, AR_c, bins=8),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_f, bins=(8, 5)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.complex128],
+        npt.NDArray[np.complex128],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_i, bins=AR_u),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.uint64],
+        npt.NDArray[np.uint64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_c, bins=(AR_u, AR_u)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.uint64],
+        npt.NDArray[np.uint64],
+    ],
+)
+assert_type(
+    np.histogram2d(AR_c, AR_c, bins=(AR_b, 8)),
+    tuple[
+        npt.NDArray[np.float64],
+        npt.NDArray[np.bool | np.complex128],
+        npt.NDArray[np.bool | np.complex128],
+    ],
+)
+
+assert_type(np.mask_indices(10, func1), tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])
+assert_type(np.mask_indices(8, func2, "0"), tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])
+
+assert_type(np.tril_indices(10), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.tril_indices_from(AR_b), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.triu_indices(10), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
+
+assert_type(np.triu_indices_from(AR_b), tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4a7ef36e9e2658c924e78604362f550a45679d0b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
@@ -0,0 +1,79 @@
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _16Bit, _32Bit, _64Bit, _128Bit
+
+from typing_extensions import assert_type
+
+f8: np.float64
+f: float
+
+# NOTE: Avoid importing the platform specific `np.float128` type
+AR_i8: npt.NDArray[np.int64]
+AR_i4: npt.NDArray[np.int32]
+AR_f2: npt.NDArray[np.float16]
+AR_f8: npt.NDArray[np.float64]
+AR_f16: npt.NDArray[np.floating[_128Bit]]
+AR_c8: npt.NDArray[np.complex64]
+AR_c16: npt.NDArray[np.complex128]
+
+AR_LIKE_f: list[float]
+
+class ComplexObj:
+    real: slice
+    imag: slice
+
+assert_type(np.mintypecode(["f8"], typeset="qfQF"), str)
+
+assert_type(np.real(ComplexObj()), slice)
+assert_type(np.real(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real(AR_c16), npt.NDArray[np.float64])
+assert_type(np.real(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.imag(ComplexObj()), slice)
+assert_type(np.imag(AR_f8), npt.NDArray[np.float64])
+assert_type(np.imag(AR_c16), npt.NDArray[np.float64])
+assert_type(np.imag(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.iscomplex(f8), np.bool)
+assert_type(np.iscomplex(AR_f8), npt.NDArray[np.bool])
+assert_type(np.iscomplex(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.isreal(f8), np.bool)
+assert_type(np.isreal(AR_f8), npt.NDArray[np.bool])
+assert_type(np.isreal(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.iscomplexobj(f8), bool)
+assert_type(np.isrealobj(f8), bool)
+
+assert_type(np.nan_to_num(f8), np.float64)
+assert_type(np.nan_to_num(f, copy=True), Any)
+assert_type(np.nan_to_num(AR_f8, nan=1.5), npt.NDArray[np.float64])
+assert_type(np.nan_to_num(AR_LIKE_f, posinf=9999), npt.NDArray[Any])
+
+assert_type(np.real_if_close(AR_f8), npt.NDArray[np.float64])
+assert_type(
+    np.real_if_close(AR_c16),
+    npt.NDArray[np.floating[_64Bit]] | npt.NDArray[np.complexfloating[_64Bit, _64Bit]],
+)
+assert_type(np.real_if_close(AR_c8), npt.NDArray[np.float32] | npt.NDArray[np.complex64])
+assert_type(np.real_if_close(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.typename("h"), Literal["short"])
+assert_type(np.typename("B"), Literal["unsigned char"])
+assert_type(np.typename("V"), Literal["void"])
+assert_type(np.typename("S1"), Literal["character"])
+
+assert_type(np.common_type(AR_i4), type[np.floating[_64Bit]])
+assert_type(np.common_type(AR_f2), type[np.float16])
+assert_type(np.common_type(AR_f2, AR_i4), type[np.floating[_16Bit | _64Bit]])
+assert_type(np.common_type(AR_f16, AR_i4), type[np.floating[_64Bit | _128Bit]])
+assert_type(
+    np.common_type(AR_c8, AR_f2),
+    type[np.complexfloating[_16Bit | _32Bit, _16Bit | _32Bit]],
+)
+assert_type(
+    np.common_type(AR_f2, AR_c8, AR_i4),
+    type[np.complexfloating[_16Bit | _32Bit | _64Bit, _16Bit | _32Bit | _64Bit]],
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b98157d1d4515e411ade4e8957ac46844f3051e8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
@@ -0,0 +1,31 @@
+"""Typing tests for `_core._ufunc_config`."""
+
+from _typeshed import SupportsWrite
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+
+from typing_extensions import assert_type
+
+def func(a: str, b: int) -> None: ...
+
+class Write:
+    def write(self, value: str) -> None: ...
+
+assert_type(np.seterr(all=None), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(divide="ignore"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(over="warn"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(under="call"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(invalid="raise"), np._core._ufunc_config._ErrDict)
+assert_type(np.geterr(), np._core._ufunc_config._ErrDict)
+
+assert_type(np.setbufsize(4096), int)
+assert_type(np.getbufsize(), int)
+
+assert_type(np.seterrcall(func), Callable[[str, int], Any] | None | SupportsWrite[str])
+assert_type(np.seterrcall(Write()), Callable[[str, int], Any] | None | SupportsWrite[str])
+assert_type(np.geterrcall(), Callable[[str, int], Any] | None | SupportsWrite[str])
+
+assert_type(np.errstate(call=func, all="call"), np.errstate)
+assert_type(np.errstate(call=Write(), divide="log", over="log"), np.errstate)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2a0c6c65ea5da1ea26ce296dc2330e3544c7c3bd
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
@@ -0,0 +1,33 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_O: list[np.object_]
+
+AR_U: npt.NDArray[np.str_]
+
+assert_type(np.fix(AR_LIKE_b), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_u), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_i), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_f), npt.NDArray[np.floating[Any]])
+assert_type(np.fix(AR_LIKE_O), npt.NDArray[np.object_])
+assert_type(np.fix(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
+
+assert_type(np.isposinf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
+
+assert_type(np.isneginf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8d3527ac84157d931bd3063d5afe6467fa39badb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
@@ -0,0 +1,125 @@
+from typing import Literal, Any, NoReturn
+
+import numpy as np
+import numpy.typing as npt
+
+from typing_extensions import assert_type
+
+i8: np.int64
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+assert_type(np.absolute.__doc__, str)
+assert_type(np.absolute.types, list[str])
+
+assert_type(np.absolute.__name__, Literal["absolute"])
+assert_type(np.absolute.__qualname__, Literal["absolute"])
+assert_type(np.absolute.ntypes, Literal[20])
+assert_type(np.absolute.identity, None)
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nout, Literal[1])
+assert_type(np.absolute.nargs, Literal[2])
+assert_type(np.absolute.signature, None)
+assert_type(np.absolute(f8), Any)
+assert_type(np.absolute(AR_f8), npt.NDArray[Any])
+assert_type(np.absolute.at(AR_f8, AR_i8), None)
+
+assert_type(np.add.__name__, Literal["add"])
+assert_type(np.add.__qualname__, Literal["add"])
+assert_type(np.add.ntypes, Literal[22])
+assert_type(np.add.identity, Literal[0])
+assert_type(np.add.nin, Literal[2])
+assert_type(np.add.nout, Literal[1])
+assert_type(np.add.nargs, Literal[3])
+assert_type(np.add.signature, None)
+assert_type(np.add(f8, f8), Any)
+assert_type(np.add(AR_f8, f8), npt.NDArray[Any])
+assert_type(np.add.at(AR_f8, AR_i8, f8), None)
+assert_type(np.add.reduce(AR_f8, axis=0), Any)
+assert_type(np.add.accumulate(AR_f8), npt.NDArray[Any])
+assert_type(np.add.reduceat(AR_f8, AR_i8), npt.NDArray[Any])
+assert_type(np.add.outer(f8, f8), Any)
+assert_type(np.add.outer(AR_f8, f8), npt.NDArray[Any])
+
+assert_type(np.frexp.__name__, Literal["frexp"])
+assert_type(np.frexp.__qualname__, Literal["frexp"])
+assert_type(np.frexp.ntypes, Literal[4])
+assert_type(np.frexp.identity, None)
+assert_type(np.frexp.nin, Literal[1])
+assert_type(np.frexp.nout, Literal[2])
+assert_type(np.frexp.nargs, Literal[3])
+assert_type(np.frexp.signature, None)
+assert_type(np.frexp(f8), tuple[Any, Any])
+assert_type(np.frexp(AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.divmod.__name__, Literal["divmod"])
+assert_type(np.divmod.__qualname__, Literal["divmod"])
+assert_type(np.divmod.ntypes, Literal[15])
+assert_type(np.divmod.identity, None)
+assert_type(np.divmod.nin, Literal[2])
+assert_type(np.divmod.nout, Literal[2])
+assert_type(np.divmod.nargs, Literal[4])
+assert_type(np.divmod.signature, None)
+assert_type(np.divmod(f8, f8), tuple[Any, Any])
+assert_type(np.divmod(AR_f8, f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.matmul.__name__, Literal["matmul"])
+assert_type(np.matmul.__qualname__, Literal["matmul"])
+assert_type(np.matmul.ntypes, Literal[19])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul.nin, Literal[2])
+assert_type(np.matmul.nout, Literal[1])
+assert_type(np.matmul.nargs, Literal[3])
+assert_type(np.matmul.signature, Literal["(n?,k),(k,m?)->(n?,m?)"])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul(AR_f8, AR_f8), Any)
+assert_type(np.matmul(AR_f8, AR_f8, axes=[(0, 1), (0, 1), (0, 1)]), Any)
+
+assert_type(np.vecdot.__name__, Literal["vecdot"])
+assert_type(np.vecdot.__qualname__, Literal["vecdot"])
+assert_type(np.vecdot.ntypes, Literal[19])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot.nin, Literal[2])
+assert_type(np.vecdot.nout, Literal[1])
+assert_type(np.vecdot.nargs, Literal[3])
+assert_type(np.vecdot.signature, Literal["(n),(n)->()"])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot(AR_f8, AR_f8), Any)
+
+assert_type(np.bitwise_count.__name__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.__qualname__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.ntypes, Literal[11])
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count.nin, Literal[1])
+assert_type(np.bitwise_count.nout, Literal[1])
+assert_type(np.bitwise_count.nargs, Literal[2])
+assert_type(np.bitwise_count.signature, None)
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count(i8), Any)
+assert_type(np.bitwise_count(AR_i8), npt.NDArray[Any])
+
+assert_type(np.absolute.outer(), NoReturn)
+assert_type(np.frexp.outer(), NoReturn)
+assert_type(np.divmod.outer(), NoReturn)
+assert_type(np.matmul.outer(), NoReturn)
+
+assert_type(np.absolute.reduceat(), NoReturn)
+assert_type(np.frexp.reduceat(), NoReturn)
+assert_type(np.divmod.reduceat(), NoReturn)
+assert_type(np.matmul.reduceat(), NoReturn)
+
+assert_type(np.absolute.reduce(), NoReturn)
+assert_type(np.frexp.reduce(), NoReturn)
+assert_type(np.divmod.reduce(), NoReturn)
+assert_type(np.matmul.reduce(), NoReturn)
+
+assert_type(np.absolute.accumulate(), NoReturn)
+assert_type(np.frexp.accumulate(), NoReturn)
+assert_type(np.divmod.accumulate(), NoReturn)
+assert_type(np.matmul.accumulate(), NoReturn)
+
+assert_type(np.frexp.at(), NoReturn)
+assert_type(np.divmod.at(), NoReturn)
+assert_type(np.matmul.at(), NoReturn)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..9b1e23dfb081fc38213959d6a1937fc46f81daaf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
@@ -0,0 +1,11 @@
+import numpy.exceptions as ex
+
+from typing_extensions import assert_type
+
+assert_type(ex.ModuleDeprecationWarning(), ex.ModuleDeprecationWarning)
+assert_type(ex.VisibleDeprecationWarning(), ex.VisibleDeprecationWarning)
+assert_type(ex.ComplexWarning(), ex.ComplexWarning)
+assert_type(ex.RankWarning(), ex.RankWarning)
+assert_type(ex.TooHardError(), ex.TooHardError)
+assert_type(ex.AxisError("test"), ex.AxisError)
+assert_type(ex.AxisError(5, 1), ex.AxisError)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_isfile.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_isfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..e77b560f8c762b5df16bdccd6fd4193583de5c21
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_isfile.py
@@ -0,0 +1,32 @@
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+from numpy.testing import assert_
+
+ROOT = Path(np.__file__).parents[0]
+FILES = [
+    ROOT / "py.typed",
+    ROOT / "__init__.pyi",
+    ROOT / "ctypeslib.pyi",
+    ROOT / "_core" / "__init__.pyi",
+    ROOT / "f2py" / "__init__.pyi",
+    ROOT / "fft" / "__init__.pyi",
+    ROOT / "lib" / "__init__.pyi",
+    ROOT / "linalg" / "__init__.pyi",
+    ROOT / "ma" / "__init__.pyi",
+    ROOT / "matrixlib" / "__init__.pyi",
+    ROOT / "polynomial" / "__init__.pyi",
+    ROOT / "random" / "__init__.pyi",
+    ROOT / "testing" / "__init__.pyi",
+]
+if sys.version_info < (3, 12):
+    FILES += [ROOT / "distutils" / "__init__.pyi"]
+
+
+class TestIsFile:
+    def test_isfile(self):
+        """Test if all ``.pyi`` files are properly installed."""
+        for file in FILES:
+            assert_(os.path.isfile(file))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_runtime.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..c32c5db3266aff7643cc70b1e139aa17e24a26f6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_runtime.py
@@ -0,0 +1,109 @@
+"""Test the runtime usage of `numpy.typing`."""
+
+from __future__ import annotations
+
+from typing import (
+    get_type_hints,
+    Union,
+    NamedTuple,
+    get_args,
+    get_origin,
+    Any,
+)
+
+import pytest
+import numpy as np
+import numpy.typing as npt
+import numpy._typing as _npt
+
+
+class TypeTup(NamedTuple):
+    typ: type
+    args: tuple[type, ...]
+    origin: None | type
+
+
+NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray)
+
+TYPES = {
+    "ArrayLike": TypeTup(npt.ArrayLike, npt.ArrayLike.__args__, Union),
+    "DTypeLike": TypeTup(npt.DTypeLike, npt.DTypeLike.__args__, Union),
+    "NBitBase": TypeTup(npt.NBitBase, (), None),
+    "NDArray": NDArrayTup,
+}
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_args(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_args`."""
+    typ, ref = tup.typ, tup.args
+    out = get_args(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_origin(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_origin`."""
+    typ, ref = tup.typ, tup.origin
+    out = get_origin(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints`."""
+    typ = tup.typ
+
+    # Explicitly set `__annotations__` in order to circumvent the
+    # stringification performed by `from __future__ import annotations`
+    def func(a): pass
+    func.__annotations__ = {"a": typ, "return": None}
+
+    out = get_type_hints(func)
+    ref = {"a": typ, "return": type(None)}
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints_str(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints` with string-representation of types."""
+    typ_str, typ = f"npt.{name}", tup.typ
+
+    # Explicitly set `__annotations__` in order to circumvent the
+    # stringification performed by `from __future__ import annotations`
+    def func(a): pass
+    func.__annotations__ = {"a": typ_str, "return": None}
+
+    out = get_type_hints(func)
+    ref = {"a": typ, "return": type(None)}
+    assert out == ref
+
+
+def test_keys() -> None:
+    """Test that ``TYPES.keys()`` and ``numpy.typing.__all__`` are synced."""
+    keys = TYPES.keys()
+    ref = set(npt.__all__)
+    assert keys == ref
+
+
+PROTOCOLS: dict[str, tuple[type[Any], object]] = {
+    "_SupportsDType": (_npt._SupportsDType, np.int64(1)),
+    "_SupportsArray": (_npt._SupportsArray, np.arange(10)),
+    "_SupportsArrayFunc": (_npt._SupportsArrayFunc, np.arange(10)),
+    "_NestedSequence": (_npt._NestedSequence, [1]),
+}
+
+
+@pytest.mark.parametrize("cls,obj", PROTOCOLS.values(), ids=PROTOCOLS.keys())
+class TestRuntimeProtocol:
+    def test_isinstance(self, cls: type[Any], obj: object) -> None:
+        assert isinstance(obj, cls)
+        assert not isinstance(None, cls)
+
+    def test_issubclass(self, cls: type[Any], obj: object) -> None:
+        if cls is _npt._SupportsDType:
+            pytest.xfail(
+                "Protocols with non-method members don't support issubclass()"
+            )
+        assert issubclass(type(obj), cls)
+        assert not issubclass(type(None), cls)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d6f0d4df26b435675b549ac322b4688651ddf2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/numpy/typing/tests/test_typing.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+
+import importlib.util
+import os
+import re
+import shutil
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+import pytest
+from numpy.typing.mypy_plugin import _EXTENDED_PRECISION_LIST
+
+
+# Only trigger a full `mypy` run if this environment variable is set
+# Note that these tests tend to take over a minute even on a macOS M1 CPU,
+# and more than that in CI.
+RUN_MYPY = "NPY_RUN_MYPY_IN_TESTSUITE" in os.environ
+if RUN_MYPY and RUN_MYPY not in ('0', '', 'false'):
+    RUN_MYPY = True
+
+# Skips all functions in this file
+pytestmark = pytest.mark.skipif(
+    not RUN_MYPY,
+    reason="`NPY_RUN_MYPY_IN_TESTSUITE` not set"
+)
+
+
+try:
+    from mypy import api
+except ImportError:
+    NO_MYPY = True
+else:
+    NO_MYPY = False
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+    # We need this as annotation, but it's located in a private namespace.
+    # As a compromise, do *not* import it during runtime
+    from _pytest.mark.structures import ParameterSet
+
+DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
+PASS_DIR = os.path.join(DATA_DIR, "pass")
+FAIL_DIR = os.path.join(DATA_DIR, "fail")
+REVEAL_DIR = os.path.join(DATA_DIR, "reveal")
+MISC_DIR = os.path.join(DATA_DIR, "misc")
+MYPY_INI = os.path.join(DATA_DIR, "mypy.ini")
+CACHE_DIR = os.path.join(DATA_DIR, ".mypy_cache")
+
+#: A dictionary with file names as keys and lists of the mypy stdout as values.
+#: To-be populated by `run_mypy`.
+OUTPUT_MYPY: defaultdict[str, list[str]] = defaultdict(list)
+
+
+def _key_func(key: str) -> str:
+    """Split at the first occurrence of the ``:`` character.
+
+    Windows drive-letters (*e.g.* ``C:``) are ignored herein.
+    """
+    drive, tail = os.path.splitdrive(key)
+    return os.path.join(drive, tail.split(":", 1)[0])
+
+
+def _strip_filename(msg: str) -> tuple[int, str]:
+    """Strip the filename and line number from a mypy message."""
+    _, tail = os.path.splitdrive(msg)
+    _, lineno, msg = tail.split(":", 2)
+    return int(lineno), msg.strip()
+
+
+def strip_func(match: re.Match[str]) -> str:
+    """`re.sub` helper function for stripping module names."""
+    return match.groups()[1]
+
+
+@pytest.fixture(scope="module", autouse=True)
+def run_mypy() -> None:
+    """Clears the cache and run mypy before running any of the typing tests.
+
+    The mypy results are cached in `OUTPUT_MYPY` for further use.
+
+    The cache refresh can be skipped using
+
+    NUMPY_TYPING_TEST_CLEAR_CACHE=0 pytest numpy/typing/tests
+    """
+    if (
+        os.path.isdir(CACHE_DIR)
+        and bool(os.environ.get("NUMPY_TYPING_TEST_CLEAR_CACHE", True))
+    ):
+        shutil.rmtree(CACHE_DIR)
+
+    split_pattern = re.compile(r"(\s+)?\^(\~+)?")
+    for directory in (PASS_DIR, REVEAL_DIR, FAIL_DIR, MISC_DIR):
+        # Run mypy
+        stdout, stderr, exit_code = api.run([
+            "--config-file",
+            MYPY_INI,
+            "--cache-dir",
+            CACHE_DIR,
+            directory,
+        ])
+        if stderr:
+            pytest.fail(f"Unexpected mypy standard error\n\n{stderr}")
+        elif exit_code not in {0, 1}:
+            pytest.fail(f"Unexpected mypy exit code: {exit_code}\n\n{stdout}")
+
+        str_concat = ""
+        filename: str | None = None
+        for i in stdout.split("\n"):
+            if "note:" in i:
+                continue
+            if filename is None:
+                filename = _key_func(i)
+
+            str_concat += f"{i}\n"
+            if split_pattern.match(i) is not None:
+                OUTPUT_MYPY[filename].append(str_concat)
+                str_concat = ""
+                filename = None
+
+
+def get_test_cases(directory: str) -> Iterator[ParameterSet]:
+    for root, _, files in os.walk(directory):
+        for fname in files:
+            short_fname, ext = os.path.splitext(fname)
+            if ext in (".pyi", ".py"):
+                fullpath = os.path.join(root, fname)
+                yield pytest.param(fullpath, id=short_fname)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
+def test_success(path) -> None:
+    # Alias `OUTPUT_MYPY` so that it appears in the local namespace
+    output_mypy = OUTPUT_MYPY
+    if path in output_mypy:
+        msg = "Unexpected mypy output\n\n"
+        msg += "\n".join(_strip_filename(v)[1] for v in output_mypy[path])
+        raise AssertionError(msg)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(FAIL_DIR))
+def test_fail(path: str) -> None:
+    __tracebackhide__ = True
+
+    with open(path) as fin:
+        lines = fin.readlines()
+
+    errors = defaultdict(lambda: "")
+
+    output_mypy = OUTPUT_MYPY
+    assert path in output_mypy
+
+    for error_line in output_mypy[path]:
+        lineno, error_line = _strip_filename(error_line)
+        errors[lineno] += f'{error_line}\n'
+
+    for i, line in enumerate(lines):
+        lineno = i + 1
+        if (
+            line.startswith('#')
+            or (" E:" not in line and lineno not in errors)
+        ):
+            continue
+
+        target_line = lines[lineno - 1]
+        if "# E:" in target_line:
+            expression, _, marker = target_line.partition("  # E: ")
+            error = errors[lineno].strip()
+            expected_error = marker.strip()
+            _test_fail(path, expression, error, expected_error, lineno)
+        else:
+            pytest.fail(
+                f"Unexpected mypy output at line {lineno}\n\n{errors[lineno]}"
+            )
+
+
+_FAIL_MSG1 = """Extra error at line {}
+
+Expression: {}
+Extra error: {!r}
+"""
+
+_FAIL_MSG2 = """Error mismatch at line {}
+
+Expression: {}
+Expected error: {}
+Observed error: {!r}
+"""
+
+
+def _test_fail(
+    path: str,
+    expression: str,
+    error: str,
+    expected_error: None | str,
+    lineno: int,
+) -> None:
+    if expected_error is None:
+        raise AssertionError(_FAIL_MSG1.format(lineno, expression, error))
+    elif expected_error not in error:
+        raise AssertionError(_FAIL_MSG2.format(
+            lineno, expression, expected_error, error
+        ))
+
+
+_REVEAL_MSG = """Reveal mismatch at line {}
+
+{}
+"""
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR))
+def test_reveal(path: str) -> None:
+    """Validate that mypy correctly infers the return-types of
+    the expressions in `path`.
+    """
+    __tracebackhide__ = True
+
+    output_mypy = OUTPUT_MYPY
+    if path not in output_mypy:
+        return
+
+    for error_line in output_mypy[path]:
+        lineno, error_line = _strip_filename(error_line)
+        raise AssertionError(_REVEAL_MSG.format(lineno, error_line))
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
+def test_code_runs(path: str) -> None:
+    """Validate that the code in `path` properly during runtime."""
+    path_without_extension, _ = os.path.splitext(path)
+    dirname, filename = path.split(os.sep)[-2:]
+
+    spec = importlib.util.spec_from_file_location(
+        f"{dirname}.{filename}", path
+    )
+    assert spec is not None
+    assert spec.loader is not None
+
+    test_module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(test_module)
+
+
+LINENO_MAPPING = {
+    11: "uint128",
+    12: "uint256",
+    14: "int128",
+    15: "int256",
+    17: "float80",
+    18: "float96",
+    19: "float128",
+    20: "float256",
+    22: "complex160",
+    23: "complex192",
+    24: "complex256",
+    25: "complex512",
+}
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+def test_extended_precision() -> None:
+    path = os.path.join(MISC_DIR, "extended_precision.pyi")
+    output_mypy = OUTPUT_MYPY
+    assert path in output_mypy
+
+    with open(path) as f:
+        expression_list = f.readlines()
+
+    for _msg in output_mypy[path]:
+        lineno, msg = _strip_filename(_msg)
+        expression = expression_list[lineno - 1].rstrip("\n")
+
+        if LINENO_MAPPING[lineno] in _EXTENDED_PRECISION_LIST:
+            raise AssertionError(_REVEAL_MSG.format(lineno, msg))
+        elif "error" not in msg:
+            _test_fail(
+                path, expression, msg, 'Expression is of type "Any"', lineno
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c14c38b872d47370d5b0f0333ffd14df9bd17061
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/__init__.py
@@ -0,0 +1,162 @@
+"""Configure global settings and get information about the working environment."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# Machine learning module for Python
+# ==================================
+#
+# sklearn is a Python module integrating classical machine
+# learning algorithms in the tightly-knit world of scientific Python
+# packages (numpy, scipy, matplotlib).
+#
+# It aims to provide simple and efficient solutions to learning problems
+# that are accessible to everybody and reusable in various contexts:
+# machine-learning as a versatile tool for science and engineering.
+#
+# See https://scikit-learn.org for complete documentation.
+
+import importlib as _importlib
+import logging
+import os
+import random
+
+from ._config import config_context, get_config, set_config
+
+logger = logging.getLogger(__name__)
+
+
+# PEP0440 compatible formatted version, see:
+# https://www.python.org/dev/peps/pep-0440/
+#
+# Generic release markers:
+#   X.Y.0   # For first release after an increment in Y
+#   X.Y.Z   # For bugfix releases
+#
+# Admissible pre-release markers:
+#   X.Y.ZaN   # Alpha release
+#   X.Y.ZbN   # Beta release
+#   X.Y.ZrcN  # Release Candidate
+#   X.Y.Z     # Final release
+#
+# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
+# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
+#
+__version__ = "1.7.1"
+
+
+# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
+# simultaneously. This can happen for instance when calling BLAS inside a
+# prange. Setting the following environment variable allows multiple OpenMP
+# libraries to be loaded. It should not degrade performances since we manually
+# take care of potential over-subcription performance issues, in sections of
+# the code where nested OpenMP loops can happen, by dynamically reconfiguring
+# the inner OpenMP runtime to temporarily disable it while under the scope of
+# the outer OpenMP parallel section.
+os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")
+
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
+
+# `_distributor_init` allows distributors to run custom init code.
+# For instance, for the Windows wheel, this is used to pre-load the
+# vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
+# sub-folder.
+# It is necessary to do this prior to importing show_versions as the
+# later is linked to the OpenMP runtime to make it possible to introspect
+# it and importing it first would fail if the OpenMP dll cannot be found.
+from . import (  # noqa: F401 E402
+    __check_build,
+    _distributor_init,
+)
+from .base import clone  # noqa: E402
+from .utils._show_versions import show_versions  # noqa: E402
+
+_submodules = [
+    "calibration",
+    "cluster",
+    "covariance",
+    "cross_decomposition",
+    "datasets",
+    "decomposition",
+    "dummy",
+    "ensemble",
+    "exceptions",
+    "experimental",
+    "externals",
+    "feature_extraction",
+    "feature_selection",
+    "frozen",
+    "gaussian_process",
+    "inspection",
+    "isotonic",
+    "kernel_approximation",
+    "kernel_ridge",
+    "linear_model",
+    "manifold",
+    "metrics",
+    "mixture",
+    "model_selection",
+    "multiclass",
+    "multioutput",
+    "naive_bayes",
+    "neighbors",
+    "neural_network",
+    "pipeline",
+    "preprocessing",
+    "random_projection",
+    "semi_supervised",
+    "svm",
+    "tree",
+    "discriminant_analysis",
+    "impute",
+    "compose",
+]
+
+__all__ = _submodules + [
+    # Non-modules:
+    "clone",
+    "get_config",
+    "set_config",
+    "config_context",
+    "show_versions",
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    if name in _submodules:
+        return _importlib.import_module(f"sklearn.{name}")
+    else:
+        try:
+            return globals()[name]
+        except KeyError:
+            raise AttributeError(f"Module 'sklearn' has no attribute '{name}'")
+
+
+_BUILT_WITH_MESON = False
+try:
+    import sklearn._built_with_meson  # noqa: F401
+
+    _BUILT_WITH_MESON = True
+except ModuleNotFoundError:
+    pass
+
+
+def setup_module(module):
+    """Fixture for the tests to assure globally controllable seeding of RNGs"""
+
+    import numpy as np
+
+    # Check if a random seed exists in the environment, if not create one.
+    _random_seed = os.environ.get("SKLEARN_SEED", None)
+    if _random_seed is None:
+        _random_seed = np.random.uniform() * np.iinfo(np.int32).max
+    _random_seed = int(_random_seed)
+    print("I: Seeding RNGs with %r" % _random_seed)
+    np.random.seed(_random_seed)
+    random.seed(_random_seed)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_built_with_meson.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_built_with_meson.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_config.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..66d119e02d1a3992ac59b86cef9b15d4e4e5b670
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_config.py
@@ -0,0 +1,407 @@
+"""Global configuration state and functions for management"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import os
+import threading
+from contextlib import contextmanager as contextmanager
+
+_global_config = {
+    "assume_finite": bool(os.environ.get("SKLEARN_ASSUME_FINITE", False)),
+    "working_memory": int(os.environ.get("SKLEARN_WORKING_MEMORY", 1024)),
+    "print_changed_only": True,
+    "display": "diagram",
+    "pairwise_dist_chunk_size": int(
+        os.environ.get("SKLEARN_PAIRWISE_DIST_CHUNK_SIZE", 256)
+    ),
+    "enable_cython_pairwise_dist": True,
+    "array_api_dispatch": False,
+    "transform_output": "default",
+    "enable_metadata_routing": False,
+    "skip_parameter_validation": False,
+}
+_threadlocal = threading.local()
+
+
+def _get_threadlocal_config():
+    """Get a threadlocal **mutable** configuration. If the configuration
+    does not exist, copy the default global configuration."""
+    if not hasattr(_threadlocal, "global_config"):
+        _threadlocal.global_config = _global_config.copy()
+    return _threadlocal.global_config
+
+
+def get_config():
+    """Retrieve the current scikit-learn configuration.
+
+    This reflects the effective global configurations as established by default upon
+    library import, or modified via :func:`set_config` or :func:`config_context`.
+
+    Returns
+    -------
+    config : dict
+        Keys are parameter names that can be passed to :func:`set_config`.
+
+    See Also
+    --------
+    config_context : Context manager for global scikit-learn configuration.
+    set_config : Set global scikit-learn configuration.
+
+    Examples
+    --------
+    >>> import sklearn
+    >>> config = sklearn.get_config()
+    >>> config.keys()
+    dict_keys([...])
+    """
+    # Return a copy of the threadlocal configuration so that users will
+    # not be able to modify the configuration with the returned dict.
+    return _get_threadlocal_config().copy()
+
+
+def set_config(
+    assume_finite=None,
+    working_memory=None,
+    print_changed_only=None,
+    display=None,
+    pairwise_dist_chunk_size=None,
+    enable_cython_pairwise_dist=None,
+    array_api_dispatch=None,
+    transform_output=None,
+    enable_metadata_routing=None,
+    skip_parameter_validation=None,
+):
+    """Set global scikit-learn configuration.
+
+    These settings control the behaviour of scikit-learn functions during a library
+    usage session. Global configuration defaults (as described in the parameter list
+    below) take effect when scikit-learn is imported.
+
+    This function can be used to modify the global scikit-learn configuration at
+    runtime. Passing `None` as an argument (the default) leaves the corresponding
+    setting unchanged. This allows users to selectively update the global configuration
+    values without affecting the others.
+
+    .. versionadded:: 0.19
+
+    Parameters
+    ----------
+    assume_finite : bool, default=None
+        If True, validation for finiteness will be skipped,
+        saving time, but leading to potential crashes. If
+        False, validation for finiteness will be performed,
+        avoiding error. Global default: False.
+
+        .. versionadded:: 0.19
+
+    working_memory : int, default=None
+        If set, scikit-learn will attempt to limit the size of temporary arrays
+        to this number of MiB (per job when parallelised), often saving both
+        computation time and memory on expensive operations that can be
+        performed in chunks. Global default: 1024.
+
+        .. versionadded:: 0.20
+
+    print_changed_only : bool, default=None
+        If True, only the parameters that were set to non-default
+        values will be printed when printing an estimator. For example,
+        ``print(SVC())`` while True will only print 'SVC()' while the default
+        behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with
+        all the non-changed parameters. Global default: True.
+
+        .. versionadded:: 0.21
+        .. versionchanged:: 0.23
+           Global default configuration changed from False to True.
+
+    display : {'text', 'diagram'}, default=None
+        If 'diagram', estimators will be displayed as a diagram in a Jupyter
+        lab or notebook context. If 'text', estimators will be displayed as
+        text. Global default: 'diagram'.
+
+        .. versionadded:: 0.23
+
+    pairwise_dist_chunk_size : int, default=None
+        The number of row vectors per chunk for the accelerated pairwise-
+        distances reduction backend. Global default: 256 (suitable for most of
+        modern laptops' caches and architectures).
+
+        Intended for easier benchmarking and testing of scikit-learn internals.
+        End users are not expected to benefit from customizing this configuration
+        setting.
+
+        .. versionadded:: 1.1
+
+    enable_cython_pairwise_dist : bool, default=None
+        Use the accelerated pairwise-distances reduction backend when
+        possible. Global default: True.
+
+        Intended for easier benchmarking and testing of scikit-learn internals.
+        End users are not expected to benefit from customizing this configuration
+        setting.
+
+        .. versionadded:: 1.1
+
+    array_api_dispatch : bool, default=None
+        Use Array API dispatching when inputs follow the Array API standard.
+        Global default: False.
+
+        See the :ref:`User Guide ` for more details.
+
+        .. versionadded:: 1.2
+
+    transform_output : str, default=None
+        Configure output of `transform` and `fit_transform`.
+
+        See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
+        for an example on how to use the API.
+
+        - `"default"`: Default output format of a transformer
+        - `"pandas"`: DataFrame output
+        - `"polars"`: Polars output
+        - `None`: Transform configuration is unchanged
+
+        Global default: "default".
+
+        .. versionadded:: 1.2
+        .. versionadded:: 1.4
+            `"polars"` option was added.
+
+    enable_metadata_routing : bool, default=None
+        Enable metadata routing. By default this feature is disabled.
+
+        Refer to :ref:`metadata routing user guide ` for more
+        details.
+
+        - `True`: Metadata routing is enabled
+        - `False`: Metadata routing is disabled, use the old syntax.
+        - `None`: Configuration is unchanged
+
+        Global default: False.
+
+        .. versionadded:: 1.3
+
+    skip_parameter_validation : bool, default=None
+        If `True`, disable the validation of the hyper-parameters' types and values in
+        the fit method of estimators and for arguments passed to public helper
+        functions. It can save time in some situations but can lead to low level
+        crashes and exceptions with confusing error messages.
+        Global default: False.
+
+        Note that for data parameters, such as `X` and `y`, only type validation is
+        skipped but validation with `check_array` will continue to run.
+
+        .. versionadded:: 1.3
+
+    See Also
+    --------
+    config_context : Context manager for global scikit-learn configuration.
+    get_config : Retrieve current values of the global configuration.
+
+    Examples
+    --------
+    >>> from sklearn import set_config
+    >>> set_config(display='diagram')  # doctest: +SKIP
+    """
+    local_config = _get_threadlocal_config()
+
+    if assume_finite is not None:
+        local_config["assume_finite"] = assume_finite
+    if working_memory is not None:
+        local_config["working_memory"] = working_memory
+    if print_changed_only is not None:
+        local_config["print_changed_only"] = print_changed_only
+    if display is not None:
+        local_config["display"] = display
+    if pairwise_dist_chunk_size is not None:
+        local_config["pairwise_dist_chunk_size"] = pairwise_dist_chunk_size
+    if enable_cython_pairwise_dist is not None:
+        local_config["enable_cython_pairwise_dist"] = enable_cython_pairwise_dist
+    if array_api_dispatch is not None:
+        from .utils._array_api import _check_array_api_dispatch
+
+        _check_array_api_dispatch(array_api_dispatch)
+        local_config["array_api_dispatch"] = array_api_dispatch
+    if transform_output is not None:
+        local_config["transform_output"] = transform_output
+    if enable_metadata_routing is not None:
+        local_config["enable_metadata_routing"] = enable_metadata_routing
+    if skip_parameter_validation is not None:
+        local_config["skip_parameter_validation"] = skip_parameter_validation
+
+
+@contextmanager
+def config_context(
+    *,
+    assume_finite=None,
+    working_memory=None,
+    print_changed_only=None,
+    display=None,
+    pairwise_dist_chunk_size=None,
+    enable_cython_pairwise_dist=None,
+    array_api_dispatch=None,
+    transform_output=None,
+    enable_metadata_routing=None,
+    skip_parameter_validation=None,
+):
+    """Context manager to temporarily change the global scikit-learn configuration.
+
+    This context manager can be used to apply scikit-learn configuration changes within
+    the scope of the with statement. Once the context exits, the global configuration is
+    restored again.
+
+    The default global configurations (which take effect when scikit-learn is imported)
+    are defined below in the parameter list.
+
+    Parameters
+    ----------
+    assume_finite : bool, default=None
+        If True, validation for finiteness will be skipped,
+        saving time, but leading to potential crashes. If
+        False, validation for finiteness will be performed,
+        avoiding error. If None, the existing configuration won't change.
+        Global default: False.
+
+    working_memory : int, default=None
+        If set, scikit-learn will attempt to limit the size of temporary arrays
+        to this number of MiB (per job when parallelised), often saving both
+        computation time and memory on expensive operations that can be
+        performed in chunks. If None, the existing configuration won't change.
+        Global default: 1024.
+
+    print_changed_only : bool, default=None
+        If True, only the parameters that were set to non-default
+        values will be printed when printing an estimator. For example,
+        ``print(SVC())`` while True will only print 'SVC()', but would print
+        'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
+        when False. If None, the existing configuration won't change.
+        Global default: True.
+
+        .. versionchanged:: 0.23
+           Global default configuration changed from False to True.
+
+    display : {'text', 'diagram'}, default=None
+        If 'diagram', estimators will be displayed as a diagram in a Jupyter
+        lab or notebook context. If 'text', estimators will be displayed as
+        text. If None, the existing configuration won't change.
+        Global default: 'diagram'.
+
+        .. versionadded:: 0.23
+
+    pairwise_dist_chunk_size : int, default=None
+        The number of row vectors per chunk for the accelerated pairwise-
+        distances reduction backend. Global default: 256 (suitable for most of
+        modern laptops' caches and architectures).
+
+        Intended for easier benchmarking and testing of scikit-learn internals.
+        End users are not expected to benefit from customizing this configuration
+        setting.
+
+        .. versionadded:: 1.1
+
+    enable_cython_pairwise_dist : bool, default=None
+        Use the accelerated pairwise-distances reduction backend when
+        possible. Global default: True.
+
+        Intended for easier benchmarking and testing of scikit-learn internals.
+        End users are not expected to benefit from customizing this configuration
+        setting.
+
+        .. versionadded:: 1.1
+
+    array_api_dispatch : bool, default=None
+        Use Array API dispatching when inputs follow the Array API standard.
+        Global default: False.
+
+        See the :ref:`User Guide ` for more details.
+
+        .. versionadded:: 1.2
+
+    transform_output : str, default=None
+        Configure output of `transform` and `fit_transform`.
+
+        See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
+        for an example on how to use the API.
+
+        - `"default"`: Default output format of a transformer
+        - `"pandas"`: DataFrame output
+        - `"polars"`: Polars output
+        - `None`: Transform configuration is unchanged
+
+        Global default: "default".
+
+        .. versionadded:: 1.2
+        .. versionadded:: 1.4
+            `"polars"` option was added.
+
+    enable_metadata_routing : bool, default=None
+        Enable metadata routing. By default this feature is disabled.
+
+        Refer to :ref:`metadata routing user guide ` for more
+        details.
+
+        - `True`: Metadata routing is enabled
+        - `False`: Metadata routing is disabled, use the old syntax.
+        - `None`: Configuration is unchanged
+
+        Global default: False.
+
+        .. versionadded:: 1.3
+
+    skip_parameter_validation : bool, default=None
+        If `True`, disable the validation of the hyper-parameters' types and values in
+        the fit method of estimators and for arguments passed to public helper
+        functions. It can save time in some situations but can lead to low level
+        crashes and exceptions with confusing error messages.
+        Global default: False.
+
+        Note that for data parameters, such as `X` and `y`, only type validation is
+        skipped but validation with `check_array` will continue to run.
+
+        .. versionadded:: 1.3
+
+    Yields
+    ------
+    None.
+
+    See Also
+    --------
+    set_config : Set global scikit-learn configuration.
+    get_config : Retrieve current values of the global configuration.
+
+    Notes
+    -----
+    All settings, not just those presently modified, will be returned to
+    their previous values when the context manager is exited.
+
+    Examples
+    --------
+    >>> import sklearn
+    >>> from sklearn.utils.validation import assert_all_finite
+    >>> with sklearn.config_context(assume_finite=True):
+    ...     assert_all_finite([float('nan')])
+    >>> with sklearn.config_context(assume_finite=True):
+    ...     with sklearn.config_context(assume_finite=False):
+    ...         assert_all_finite([float('nan')])
+    Traceback (most recent call last):
+    ...
+    ValueError: Input contains NaN...
+    """
+    old_config = get_config()
+    set_config(
+        assume_finite=assume_finite,
+        working_memory=working_memory,
+        print_changed_only=print_changed_only,
+        display=display,
+        pairwise_dist_chunk_size=pairwise_dist_chunk_size,
+        enable_cython_pairwise_dist=enable_cython_pairwise_dist,
+        array_api_dispatch=array_api_dispatch,
+        transform_output=transform_output,
+        enable_metadata_routing=enable_metadata_routing,
+        skip_parameter_validation=skip_parameter_validation,
+    )
+
+    try:
+        yield
+    finally:
+        set_config(**old_config)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_distributor_init.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_distributor_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..d66d5d36955c1ca5debf5821e2a2f265ef1f98ed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_distributor_init.py
@@ -0,0 +1,13 @@
+"""Distributor init file
+
+Distributors: you can add custom code here to support particular distributions
+of scikit-learn.
+
+For example, this is a good place to put any checks for hardware requirements.
+
+The scikit-learn standard source distribution will not put code in this file,
+so you can safely replace this file with your own version.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_isotonic.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_isotonic.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..3dfb0421f0c190b293ce4121fc21cc68bc54f79c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_isotonic.pyx
@@ -0,0 +1,116 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# Uses the pool adjacent violators algorithm (PAVA), with the
+# enhancement of searching for the longest decreasing subsequence to
+# pool at each step.
+
+import numpy as np
+from cython cimport floating
+
+
+def _inplace_contiguous_isotonic_regression(floating[::1] y, floating[::1] w):
+    cdef:
+        Py_ssize_t n = y.shape[0], i, k
+        floating prev_y, sum_wy, sum_w
+        Py_ssize_t[::1] target = np.arange(n, dtype=np.intp)
+
+    # target describes a list of blocks.  At any time, if [i..j] (inclusive) is
+    # an active block, then target[i] := j and target[j] := i.
+
+    # For "active" indices (block starts):
+    # w[i] := sum{w_orig[j], j=[i..target[i]]}
+    # y[i] := sum{y_orig[j]*w_orig[j], j=[i..target[i]]} / w[i]
+
+    with nogil:
+        i = 0
+        while i < n:
+            k = target[i] + 1
+            if k == n:
+                break
+            if y[i] < y[k]:
+                i = k
+                continue
+            sum_wy = w[i] * y[i]
+            sum_w = w[i]
+            while True:
+                # We are within a decreasing subsequence.
+                prev_y = y[k]
+                sum_wy += w[k] * y[k]
+                sum_w += w[k]
+                k = target[k] + 1
+                if k == n or prev_y < y[k]:
+                    # Non-singleton decreasing subsequence is finished,
+                    # update first entry.
+                    y[i] = sum_wy / sum_w
+                    w[i] = sum_w
+                    target[i] = k - 1
+                    target[k - 1] = i
+                    if i > 0:
+                        # Backtrack if we can.  This makes the algorithm
+                        # single-pass and ensures O(n) complexity.
+                        i = target[i - 1]
+                    # Otherwise, restart from the same point.
+                    break
+        # Reconstruct the solution.
+        i = 0
+        while i < n:
+            k = target[i] + 1
+            y[i + 1 : k] = y[i]
+            i = k
+
+
+def _make_unique(const floating[::1] X,
+                 const floating[::1] y,
+                 const floating[::1] sample_weights):
+    """Average targets for duplicate X, drop duplicates.
+
+    Aggregates duplicate X values into a single X value where
+    the target y is a (sample_weighted) average of the individual
+    targets.
+
+    Assumes that X is ordered, so that all duplicates follow each other.
+    """
+    unique_values = len(np.unique(X))
+
+    if floating is float:
+        dtype = np.float32
+    else:
+        dtype = np.float64
+
+    cdef floating[::1] y_out = np.empty(unique_values, dtype=dtype)
+    cdef floating[::1] x_out = np.empty_like(y_out)
+    cdef floating[::1] weights_out = np.empty_like(y_out)
+
+    cdef floating current_x = X[0]
+    cdef floating current_y = 0
+    cdef floating current_weight = 0
+    cdef int i = 0
+    cdef int j
+    cdef floating x
+    cdef int n_samples = len(X)
+    cdef floating eps = np.finfo(dtype).resolution
+
+    for j in range(n_samples):
+        x = X[j]
+        if x - current_x >= eps:
+            # next unique value
+            x_out[i] = current_x
+            weights_out[i] = current_weight
+            y_out[i] = current_y / current_weight
+            i += 1
+            current_x = x
+            current_weight = sample_weights[j]
+            current_y = y[j] * sample_weights[j]
+        else:
+            current_weight += sample_weights[j]
+            current_y += y[j] * sample_weights[j]
+
+    x_out[i] = current_x
+    weights_out[i] = current_weight
+    y_out[i] = current_y / current_weight
+    return(
+        np.asarray(x_out[:i+1]),
+        np.asarray(y_out[:i+1]),
+        np.asarray(weights_out[:i+1]),
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_min_dependencies.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_min_dependencies.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac588206869144b4839496f694fd94744967f26a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/_min_dependencies.py
@@ -0,0 +1,74 @@
+"""All minimum dependencies for scikit-learn."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import argparse
+from collections import defaultdict
+
+# scipy and cython should by in sync with pyproject.toml
+NUMPY_MIN_VERSION = "1.22.0"
+SCIPY_MIN_VERSION = "1.8.0"
+JOBLIB_MIN_VERSION = "1.2.0"
+THREADPOOLCTL_MIN_VERSION = "3.1.0"
+PYTEST_MIN_VERSION = "7.1.2"
+CYTHON_MIN_VERSION = "3.0.10"
+
+
+# 'build' and 'install' is included to have structured metadata for CI.
+# It will NOT be included in setup's extras_require
+# The values are (version_spec, comma separated tags)
+dependent_packages = {
+    "numpy": (NUMPY_MIN_VERSION, "build, install"),
+    "scipy": (SCIPY_MIN_VERSION, "build, install"),
+    "joblib": (JOBLIB_MIN_VERSION, "install"),
+    "threadpoolctl": (THREADPOOLCTL_MIN_VERSION, "install"),
+    "cython": (CYTHON_MIN_VERSION, "build"),
+    "meson-python": ("0.17.1", "build"),
+    "matplotlib": ("3.5.0", "benchmark, docs, examples, tests"),
+    "scikit-image": ("0.19.0", "docs, examples, tests"),
+    "pandas": ("1.4.0", "benchmark, docs, examples, tests"),
+    "seaborn": ("0.9.0", "docs, examples"),
+    "memory_profiler": ("0.57.0", "benchmark, docs"),
+    "pytest": (PYTEST_MIN_VERSION, "tests"),
+    "pytest-cov": ("2.9.0", "tests"),
+    "ruff": ("0.11.7", "tests"),
+    "mypy": ("1.15", "tests"),
+    "pyamg": ("4.2.1", "tests"),
+    "polars": ("0.20.30", "docs, tests"),
+    "pyarrow": ("12.0.0", "tests"),
+    "sphinx": ("7.3.7", "docs"),
+    "sphinx-copybutton": ("0.5.2", "docs"),
+    "sphinx-gallery": ("0.17.1", "docs"),
+    "numpydoc": ("1.2.0", "docs, tests"),
+    "Pillow": ("8.4.0", "docs"),
+    "pooch": ("1.6.0", "docs, examples, tests"),
+    "sphinx-prompt": ("1.4.0", "docs"),
+    "sphinxext-opengraph": ("0.9.1", "docs"),
+    "plotly": ("5.14.0", "docs, examples"),
+    "sphinxcontrib-sass": ("0.3.4", "docs"),
+    "sphinx-remove-toctrees": ("1.0.0.post1", "docs"),
+    "sphinx-design": ("0.6.0", "docs"),
+    "pydata-sphinx-theme": ("0.15.3", "docs"),
+    "towncrier": ("24.8.0", "docs"),
+    # XXX: Pin conda-lock to the latest released version (needs manual update
+    # from time to time)
+    "conda-lock": ("3.0.1", "maintenance"),
+}
+
+
+# create inverse mapping for setuptools
+tag_to_packages: dict = defaultdict(list)
+for package, (min_version, extras) in dependent_packages.items():
+    for extra in extras.split(", "):
+        tag_to_packages[extra].append("{}>={}".format(package, min_version))
+
+
+# Used by CI to get the min dependencies
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Get min dependencies for a package")
+
+    parser.add_argument("package", choices=dependent_packages)
+    args = parser.parse_args()
+    min_version = dependent_packages[args.package][0]
+    print(min_version)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9308d8f1376f53bf8144acc918cac71d7f58bae
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/base.py
@@ -0,0 +1,1369 @@
+"""Base classes for all estimators and various utility functions."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import copy
+import functools
+import inspect
+import platform
+import re
+import warnings
+from collections import defaultdict
+
+import numpy as np
+
+from . import __version__
+from ._config import config_context, get_config
+from .exceptions import InconsistentVersionWarning
+from .utils._metadata_requests import _MetadataRequester, _routing_enabled
+from .utils._missing import is_scalar_nan
+from .utils._param_validation import validate_parameter_constraints
+from .utils._repr_html.base import ReprHTMLMixin, _HTMLDocumentationLinkMixin
+from .utils._repr_html.estimator import estimator_html_repr
+from .utils._repr_html.params import ParamsDict
+from .utils._set_output import _SetOutputMixin
+from .utils._tags import (
+    ClassifierTags,
+    RegressorTags,
+    Tags,
+    TargetTags,
+    TransformerTags,
+    get_tags,
+)
+from .utils.fixes import _IS_32BIT
+from .utils.validation import (
+    _check_feature_names_in,
+    _generate_get_feature_names_out,
+    _is_fitted,
+    check_array,
+    check_is_fitted,
+)
+
+
+def clone(estimator, *, safe=True):
+    """Construct a new unfitted estimator with the same parameters.
+
+    Clone does a deep copy of the model in an estimator
+    without actually copying attached data. It returns a new estimator
+    with the same parameters that has not been fitted on any data.
+
+    .. versionchanged:: 1.3
+        Delegates to `estimator.__sklearn_clone__` if the method exists.
+
+    Parameters
+    ----------
+    estimator : {list, tuple, set} of estimator instance or a single \
+            estimator instance
+        The estimator or group of estimators to be cloned.
+    safe : bool, default=True
+        If safe is False, clone will fall back to a deep copy on objects
+        that are not estimators. Ignored if `estimator.__sklearn_clone__`
+        exists.
+
+    Returns
+    -------
+    estimator : object
+        The deep copy of the input, an estimator if input is an estimator.
+
+    Notes
+    -----
+    If the estimator's `random_state` parameter is an integer (or if the
+    estimator doesn't have a `random_state` parameter), an *exact clone* is
+    returned: the clone and the original estimator will give the exact same
+    results. Otherwise, *statistical clone* is returned: the clone might
+    return different results from the original estimator. More details can be
+    found in :ref:`randomness`.
+
+    Examples
+    --------
+    >>> from sklearn.base import clone
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]]
+    >>> y = [0, 0, 1, 1]
+    >>> classifier = LogisticRegression().fit(X, y)
+    >>> cloned_classifier = clone(classifier)
+    >>> hasattr(classifier, "classes_")
+    True
+    >>> hasattr(cloned_classifier, "classes_")
+    False
+    >>> classifier is cloned_classifier
+    False
+    """
+    if hasattr(estimator, "__sklearn_clone__") and not inspect.isclass(estimator):
+        return estimator.__sklearn_clone__()
+    return _clone_parametrized(estimator, safe=safe)
+
+
+def _clone_parametrized(estimator, *, safe=True):
+    """Default implementation of clone. See :func:`sklearn.base.clone` for details."""
+
+    estimator_type = type(estimator)
+    if estimator_type is dict:
+        return {k: clone(v, safe=safe) for k, v in estimator.items()}
+    elif estimator_type in (list, tuple, set, frozenset):
+        return estimator_type([clone(e, safe=safe) for e in estimator])
+    elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
+        if not safe:
+            return copy.deepcopy(estimator)
+        else:
+            if isinstance(estimator, type):
+                raise TypeError(
+                    "Cannot clone object. "
+                    "You should provide an instance of "
+                    "scikit-learn estimator instead of a class."
+                )
+            else:
+                raise TypeError(
+                    "Cannot clone object '%s' (type %s): "
+                    "it does not seem to be a scikit-learn "
+                    "estimator as it does not implement a "
+                    "'get_params' method." % (repr(estimator), type(estimator))
+                )
+
+    klass = estimator.__class__
+    new_object_params = estimator.get_params(deep=False)
+    for name, param in new_object_params.items():
+        new_object_params[name] = clone(param, safe=False)
+
+    new_object = klass(**new_object_params)
+    try:
+        new_object._metadata_request = copy.deepcopy(estimator._metadata_request)
+    except AttributeError:
+        pass
+
+    params_set = new_object.get_params(deep=False)
+
+    # quick sanity check of the parameters of the clone
+    for name in new_object_params:
+        param1 = new_object_params[name]
+        param2 = params_set[name]
+        if param1 is not param2:
+            raise RuntimeError(
+                "Cannot clone object %s, as the constructor "
+                "either does not set or modifies parameter %s" % (estimator, name)
+            )
+
+    # _sklearn_output_config is used by `set_output` to configure the output
+    # container of an estimator.
+    if hasattr(estimator, "_sklearn_output_config"):
+        new_object._sklearn_output_config = copy.deepcopy(
+            estimator._sklearn_output_config
+        )
+    return new_object
+
+
+class BaseEstimator(ReprHTMLMixin, _HTMLDocumentationLinkMixin, _MetadataRequester):
+    """Base class for all estimators in scikit-learn.
+
+    Inheriting from this class provides default implementations of:
+
+    - setting and getting parameters used by `GridSearchCV` and friends;
+    - textual and HTML representation displayed in terminals and IDEs;
+    - estimator serialization;
+    - parameters validation;
+    - data validation;
+    - feature names validation.
+
+    Read more in the :ref:`User Guide `.
+
+
+    Notes
+    -----
+    All estimators should specify all the parameters that can be set
+    at the class level in their ``__init__`` as explicit keyword
+    arguments (no ``*args`` or ``**kwargs``).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator
+    >>> class MyEstimator(BaseEstimator):
+    ...     def __init__(self, *, param=1):
+    ...         self.param = param
+    ...     def fit(self, X, y=None):
+    ...         self.is_fitted_ = True
+    ...         return self
+    ...     def predict(self, X):
+    ...         return np.full(shape=X.shape[0], fill_value=self.param)
+    >>> estimator = MyEstimator(param=2)
+    >>> estimator.get_params()
+    {'param': 2}
+    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
+    >>> y = np.array([1, 0, 1])
+    >>> estimator.fit(X, y).predict(X)
+    array([2, 2, 2])
+    >>> estimator.set_params(param=3).fit(X, y).predict(X)
+    array([3, 3, 3])
+    """
+
+    _html_repr = estimator_html_repr
+
+    @classmethod
+    def _get_param_names(cls):
+        """Get parameter names for the estimator"""
+        # fetch the constructor or the original constructor before
+        # deprecation wrapping if any
+        init = getattr(cls.__init__, "deprecated_original", cls.__init__)
+        if init is object.__init__:
+            # No explicit constructor to introspect
+            return []
+
+        # introspect the constructor arguments to find the model parameters
+        # to represent
+        init_signature = inspect.signature(init)
+        # Consider the constructor parameters excluding 'self'
+        parameters = [
+            p
+            for p in init_signature.parameters.values()
+            if p.name != "self" and p.kind != p.VAR_KEYWORD
+        ]
+        for p in parameters:
+            if p.kind == p.VAR_POSITIONAL:
+                raise RuntimeError(
+                    "scikit-learn estimators should always "
+                    "specify their parameters in the signature"
+                    " of their __init__ (no varargs)."
+                    " %s with constructor %s doesn't "
+                    " follow this convention." % (cls, init_signature)
+                )
+        # Extract and sort argument names excluding 'self'
+        return sorted([p.name for p in parameters])
+
+    def get_params(self, deep=True):
+        """
+        Get parameters for this estimator.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : dict
+            Parameter names mapped to their values.
+        """
+        out = dict()
+        for key in self._get_param_names():
+            value = getattr(self, key)
+            if deep and hasattr(value, "get_params") and not isinstance(value, type):
+                deep_items = value.get_params().items()
+                out.update((key + "__" + k, val) for k, val in deep_items)
+            out[key] = value
+        return out
+
+    def _get_params_html(self, deep=True):
+        """
+        Get parameters for this estimator with a specific HTML representation.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : ParamsDict
+            Parameter names mapped to their values. We return a `ParamsDict`
+            dictionary, which renders a specific HTML representation in table
+            form.
+        """
+        out = self.get_params(deep=deep)
+
+        init_func = getattr(self.__init__, "deprecated_original", self.__init__)
+        init_default_params = inspect.signature(init_func).parameters
+        init_default_params = {
+            name: param.default for name, param in init_default_params.items()
+        }
+
+        def is_non_default(param_name, param_value):
+            """Finds the parameters that have been set by the user."""
+            if param_name not in init_default_params:
+                # happens if k is part of a **kwargs
+                return True
+            if init_default_params[param_name] == inspect._empty:
+                # k has no default value
+                return True
+            # avoid calling repr on nested estimators
+            if isinstance(param_value, BaseEstimator) and type(param_value) is not type(
+                init_default_params[param_name]
+            ):
+                return True
+            if not np.array_equal(
+                param_value, init_default_params[param_name]
+            ) and not (
+                is_scalar_nan(init_default_params[param_name])
+                and is_scalar_nan(param_value)
+            ):
+                return True
+
+            return False
+
+        # reorder the parameters from `self.get_params` using the `__init__`
+        # signature
+        remaining_params = [name for name in out if name not in init_default_params]
+        ordered_out = {name: out[name] for name in init_default_params if name in out}
+        ordered_out.update({name: out[name] for name in remaining_params})
+
+        non_default_ls = tuple(
+            [name for name, value in ordered_out.items() if is_non_default(name, value)]
+        )
+
+        return ParamsDict(ordered_out, non_default=non_default_ls)
+
+    def set_params(self, **params):
+        """Set the parameters of this estimator.
+
+        The method works on simple estimators as well as on nested objects
+        (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
+        parameters of the form ``__`` so that it's
+        possible to update each component of a nested object.
+
+        Parameters
+        ----------
+        **params : dict
+            Estimator parameters.
+
+        Returns
+        -------
+        self : estimator instance
+            Estimator instance.
+        """
+        if not params:
+            # Simple optimization to gain speed (inspect is slow)
+            return self
+        valid_params = self.get_params(deep=True)
+
+        nested_params = defaultdict(dict)  # grouped by prefix
+        for key, value in params.items():
+            key, delim, sub_key = key.partition("__")
+            if key not in valid_params:
+                local_valid_params = self._get_param_names()
+                raise ValueError(
+                    f"Invalid parameter {key!r} for estimator {self}. "
+                    f"Valid parameters are: {local_valid_params!r}."
+                )
+
+            if delim:
+                nested_params[key][sub_key] = value
+            else:
+                setattr(self, key, value)
+                valid_params[key] = value
+
+        for key, sub_params in nested_params.items():
+            valid_params[key].set_params(**sub_params)
+
+        return self
+
+    def __sklearn_clone__(self):
+        return _clone_parametrized(self)
+
+    def __repr__(self, N_CHAR_MAX=700):
+        # N_CHAR_MAX is the (approximate) maximum number of non-blank
+        # characters to render. We pass it as an optional parameter to ease
+        # the tests.
+
+        from .utils._pprint import _EstimatorPrettyPrinter
+
+        N_MAX_ELEMENTS_TO_SHOW = 30  # number of elements to show in sequences
+
+        # use ellipsis for sequences with a lot of elements
+        pp = _EstimatorPrettyPrinter(
+            compact=True,
+            indent=1,
+            indent_at_name=True,
+            n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
+        )
+
+        repr_ = pp.pformat(self)
+
+        # Use bruteforce ellipsis when there are a lot of non-blank characters
+        n_nonblank = len("".join(repr_.split()))
+        if n_nonblank > N_CHAR_MAX:
+            lim = N_CHAR_MAX // 2  # apprx number of chars to keep on both ends
+            regex = r"^(\s*\S){%d}" % lim
+            # The regex '^(\s*\S){%d}' % n
+            # matches from the start of the string until the nth non-blank
+            # character:
+            # - ^ matches the start of string
+            # - (pattern){n} matches n repetitions of pattern
+            # - \s*\S matches a non-blank char following zero or more blanks
+            left_lim = re.match(regex, repr_).end()
+            right_lim = re.match(regex, repr_[::-1]).end()
+
+            if "\n" in repr_[left_lim:-right_lim]:
+                # The left side and right side aren't on the same line.
+                # To avoid weird cuts, e.g.:
+                # categoric...ore',
+                # we need to start the right side with an appropriate newline
+                # character so that it renders properly as:
+                # categoric...
+                # handle_unknown='ignore',
+                # so we add [^\n]*\n which matches until the next \n
+                regex += r"[^\n]*\n"
+                right_lim = re.match(regex, repr_[::-1]).end()
+
+            ellipsis = "..."
+            if left_lim + len(ellipsis) < len(repr_) - right_lim:
+                # Only add ellipsis if it results in a shorter repr
+                repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]
+
+        return repr_
+
+    def __getstate__(self):
+        if getattr(self, "__slots__", None):
+            raise TypeError(
+                "You cannot use `__slots__` in objects inheriting from "
+                "`sklearn.base.BaseEstimator`."
+            )
+
+        try:
+            state = super().__getstate__()
+            if state is None:
+                # For Python 3.11+, empty instance (no `__slots__`,
+                # and `__dict__`) will return a state equal to `None`.
+                state = self.__dict__.copy()
+        except AttributeError:
+            # Python < 3.11
+            state = self.__dict__.copy()
+
+        if type(self).__module__.startswith("sklearn."):
+            return dict(state.items(), _sklearn_version=__version__)
+        else:
+            return state
+
+    def __setstate__(self, state):
+        if type(self).__module__.startswith("sklearn."):
+            pickle_version = state.pop("_sklearn_version", "pre-0.18")
+            if pickle_version != __version__:
+                warnings.warn(
+                    InconsistentVersionWarning(
+                        estimator_name=self.__class__.__name__,
+                        current_sklearn_version=__version__,
+                        original_sklearn_version=pickle_version,
+                    ),
+                )
+        try:
+            super().__setstate__(state)
+        except AttributeError:
+            self.__dict__.update(state)
+
+    def __sklearn_tags__(self):
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+            transformer_tags=None,
+            regressor_tags=None,
+            classifier_tags=None,
+        )
+
+    def _validate_params(self):
+        """Validate types and values of constructor parameters
+
+        The expected type and values must be defined in the `_parameter_constraints`
+        class attribute, which is a dictionary `param_name: list of constraints`. See
+        the docstring of `validate_parameter_constraints` for a description of the
+        accepted constraints.
+        """
+        validate_parameter_constraints(
+            self._parameter_constraints,
+            self.get_params(deep=False),
+            caller_name=self.__class__.__name__,
+        )
+
+
+class ClassifierMixin:
+    """Mixin class for all classifiers in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - set estimator type to `"classifier"` through the `estimator_type` tag;
+    - `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
+    - enforce that `fit` requires `y` to be passed through the `requires_y` tag,
+      which is done by setting the classifier type tag.
+
+    Read more in the :ref:`User Guide `.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, ClassifierMixin
+    >>> # Mixin classes should always be on the left-hand side for a correct MRO
+    >>> class MyEstimator(ClassifierMixin, BaseEstimator):
+    ...     def __init__(self, *, param=1):
+    ...         self.param = param
+    ...     def fit(self, X, y=None):
+    ...         self.is_fitted_ = True
+    ...         return self
+    ...     def predict(self, X):
+    ...         return np.full(shape=X.shape[0], fill_value=self.param)
+    >>> estimator = MyEstimator(param=1)
+    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
+    >>> y = np.array([1, 0, 1])
+    >>> estimator.fit(X, y).predict(X)
+    array([1, 1, 1])
+    >>> estimator.score(X, y)
+    0.66...
+    """
+
+    # TODO(1.8): Remove this attribute
+    _estimator_type = "classifier"
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.estimator_type = "classifier"
+        tags.classifier_tags = ClassifierTags()
+        tags.target_tags.required = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """
+        Return :ref:`accuracy ` on provided data and labels.
+
+        In multi-label classification, this is the subset accuracy
+        which is a harsh metric since you require for each sample that
+        each label set be correctly predicted.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True labels for `X`.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
+        """
+        from .metrics import accuracy_score
+
+        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
+
+
+class RegressorMixin:
+    """Mixin class for all regression estimators in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - set estimator type to `"regressor"` through the `estimator_type` tag;
+    - `score` method that default to :func:`~sklearn.metrics.r2_score`.
+    - enforce that `fit` requires `y` to be passed through the `requires_y` tag,
+      which is done by setting the regressor type tag.
+
+    Read more in the :ref:`User Guide `.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, RegressorMixin
+    >>> # Mixin classes should always be on the left-hand side for a correct MRO
+    >>> class MyEstimator(RegressorMixin, BaseEstimator):
+    ...     def __init__(self, *, param=1):
+    ...         self.param = param
+    ...     def fit(self, X, y=None):
+    ...         self.is_fitted_ = True
+    ...         return self
+    ...     def predict(self, X):
+    ...         return np.full(shape=X.shape[0], fill_value=self.param)
+    >>> estimator = MyEstimator(param=0)
+    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
+    >>> y = np.array([-1, 0, 1])
+    >>> estimator.fit(X, y).predict(X)
+    array([0, 0, 0])
+    >>> estimator.score(X, y)
+    0.0
+    """
+
+    # TODO(1.8): Remove this attribute
+    _estimator_type = "regressor"
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.estimator_type = "regressor"
+        tags.regressor_tags = RegressorTags()
+        tags.target_tags.required = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """Return :ref:`coefficient of determination ` on test data.
+
+        The coefficient of determination, :math:`R^2`, is defined as
+        :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual
+        sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`
+        is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.
+        The best possible score is 1.0 and it can be negative (because the
+        model can be arbitrarily worse). A constant model that always predicts
+        the expected value of `y`, disregarding the input features, would get
+        a :math:`R^2` score of 0.0.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples. For some estimators this may be a precomputed
+            kernel matrix or a list of generic objects instead with shape
+            ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
+            is the number of samples used in the fitting for the estimator.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True values for `X`.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.
+
+        Notes
+        -----
+        The :math:`R^2` score used when calling ``score`` on a regressor uses
+        ``multioutput='uniform_average'`` from version 0.23 to keep consistent
+        with default value of :func:`~sklearn.metrics.r2_score`.
+        This influences the ``score`` method of all the multioutput
+        regressors (except for
+        :class:`~sklearn.multioutput.MultiOutputRegressor`).
+        """
+
+        from .metrics import r2_score
+
+        y_pred = self.predict(X)
+        return r2_score(y, y_pred, sample_weight=sample_weight)
+
+
+class ClusterMixin:
+    """Mixin class for all cluster estimators in scikit-learn.
+
+    - set estimator type to `"clusterer"` through the `estimator_type` tag;
+    - `fit_predict` method returning the cluster labels associated to each sample.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, ClusterMixin
+    >>> class MyClusterer(ClusterMixin, BaseEstimator):
+    ...     def fit(self, X, y=None):
+    ...         self.labels_ = np.ones(shape=(len(X),), dtype=np.int64)
+    ...         return self
+    >>> X = [[1, 2], [2, 3], [3, 4]]
+    >>> MyClusterer().fit_predict(X)
+    array([1, 1, 1])
+    """
+
+    # TODO(1.8): Remove this attribute
+    _estimator_type = "clusterer"
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.estimator_type = "clusterer"
+        if tags.transformer_tags is not None:
+            tags.transformer_tags.preserves_dtype = []
+        return tags
+
+    def fit_predict(self, X, y=None, **kwargs):
+        """
+        Perform clustering on `X` and returns cluster labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        **kwargs : dict
+            Arguments to be passed to ``fit``.
+
+            .. versionadded:: 1.4
+
+        Returns
+        -------
+        labels : ndarray of shape (n_samples,), dtype=np.int64
+            Cluster labels.
+        """
+        # non-optimized default implementation; override when a better
+        # method is possible for a given clustering algorithm
+        self.fit(X, **kwargs)
+        return self.labels_
+
+
+class BiclusterMixin:
+    """Mixin class for all bicluster estimators in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - `biclusters_` property that returns the row and column indicators;
+    - `get_indices` method that returns the row and column indices of a bicluster;
+    - `get_shape` method that returns the shape of a bicluster;
+    - `get_submatrix` method that returns the submatrix corresponding to a bicluster.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, BiclusterMixin
+    >>> class DummyBiClustering(BiclusterMixin, BaseEstimator):
+    ...     def fit(self, X, y=None):
+    ...         self.rows_ = np.ones(shape=(1, X.shape[0]), dtype=bool)
+    ...         self.columns_ = np.ones(shape=(1, X.shape[1]), dtype=bool)
+    ...         return self
+    >>> X = np.array([[1, 1], [2, 1], [1, 0],
+    ...               [4, 7], [3, 5], [3, 6]])
+    >>> bicluster = DummyBiClustering().fit(X)
+    >>> hasattr(bicluster, "biclusters_")
+    True
+    >>> bicluster.get_indices(0)
+    (array([0, 1, 2, 3, 4, 5]), array([0, 1]))
+    """
+
+    @property
+    def biclusters_(self):
+        """Convenient way to get row and column indicators together.
+
+        Returns the ``rows_`` and ``columns_`` members.
+        """
+        return self.rows_, self.columns_
+
+    def get_indices(self, i):
+        """Row and column indices of the `i`'th bicluster.
+
+        Only works if ``rows_`` and ``columns_`` attributes exist.
+
+        Parameters
+        ----------
+        i : int
+            The index of the cluster.
+
+        Returns
+        -------
+        row_ind : ndarray, dtype=np.intp
+            Indices of rows in the dataset that belong to the bicluster.
+        col_ind : ndarray, dtype=np.intp
+            Indices of columns in the dataset that belong to the bicluster.
+        """
+        rows = self.rows_[i]
+        columns = self.columns_[i]
+        return np.nonzero(rows)[0], np.nonzero(columns)[0]
+
+    def get_shape(self, i):
+        """Shape of the `i`'th bicluster.
+
+        Parameters
+        ----------
+        i : int
+            The index of the cluster.
+
+        Returns
+        -------
+        n_rows : int
+            Number of rows in the bicluster.
+
+        n_cols : int
+            Number of columns in the bicluster.
+        """
+        indices = self.get_indices(i)
+        return tuple(len(i) for i in indices)
+
+    def get_submatrix(self, i, data):
+        """Return the submatrix corresponding to bicluster `i`.
+
+        Parameters
+        ----------
+        i : int
+            The index of the cluster.
+        data : array-like of shape (n_samples, n_features)
+            The data.
+
+        Returns
+        -------
+        submatrix : ndarray of shape (n_rows, n_cols)
+            The submatrix corresponding to bicluster `i`.
+
+        Notes
+        -----
+        Works with sparse matrices. Only works if ``rows_`` and
+        ``columns_`` attributes exist.
+        """
+
+        data = check_array(data, accept_sparse="csr")
+        row_ind, col_ind = self.get_indices(i)
+        return data[row_ind[:, np.newaxis], col_ind]
+
+
+class TransformerMixin(_SetOutputMixin):
+    """Mixin class for all transformers in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - a `fit_transform` method that delegates to `fit` and `transform`;
+    - a `set_output` method to output `X` as a specific container type.
+
+    If :term:`get_feature_names_out` is defined, then :class:`BaseEstimator` will
+    automatically wrap `transform` and `fit_transform` to follow the `set_output`
+    API. See the :ref:`developer_api_set_output` for details.
+
+    :class:`OneToOneFeatureMixin` and
+    :class:`ClassNamePrefixFeaturesOutMixin` are helpful mixins for
+    defining :term:`get_feature_names_out`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, TransformerMixin
+    >>> class MyTransformer(TransformerMixin, BaseEstimator):
+    ...     def __init__(self, *, param=1):
+    ...         self.param = param
+    ...     def fit(self, X, y=None):
+    ...         return self
+    ...     def transform(self, X):
+    ...         return np.full(shape=len(X), fill_value=self.param)
+    >>> transformer = MyTransformer()
+    >>> X = [[1, 2], [2, 3], [3, 4]]
+    >>> transformer.fit_transform(X)
+    array([1, 1, 1])
+    """
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.transformer_tags = TransformerTags()
+        return tags
+
+    def fit_transform(self, X, y=None, **fit_params):
+        """
+        Fit to data, then transform it.
+
+        Fits transformer to `X` and `y` with optional parameters `fit_params`
+        and returns a transformed version of `X`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input samples.
+
+        y :  array-like of shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        X_new : ndarray array of shape (n_samples, n_features_new)
+            Transformed array.
+        """
+        # non-optimized default implementation; override when a better
+        # method is possible for a given clustering algorithm
+
+        # we do not route parameters here, since consumers don't route. But
+        # since it's possible for a `transform` method to also consume
+        # metadata, we check if that's the case, and we raise a warning telling
+        # users that they should implement a custom `fit_transform` method
+        # to forward metadata to `transform` as well.
+        #
+        # For that, we calculate routing and check if anything would be routed
+        # to `transform` if we were to route them.
+        if _routing_enabled():
+            transform_params = self.get_metadata_routing().consumes(
+                method="transform", params=fit_params.keys()
+            )
+            if transform_params:
+                warnings.warn(
+                    (
+                        f"This object ({self.__class__.__name__}) has a `transform`"
+                        " method which consumes metadata, but `fit_transform` does not"
+                        " forward metadata to `transform`. Please implement a custom"
+                        " `fit_transform` method to forward metadata to `transform` as"
+                        " well. Alternatively, you can explicitly do"
+                        " `set_transform_request`and set all values to `False` to"
+                        " disable metadata routed to `transform`, if that's an option."
+                    ),
+                    UserWarning,
+                )
+
+        if y is None:
+            # fit method of arity 1 (unsupervised transformation)
+            return self.fit(X, **fit_params).transform(X)
+        else:
+            # fit method of arity 2 (supervised transformation)
+            return self.fit(X, y, **fit_params).transform(X)
+
+
+class OneToOneFeatureMixin:
+    """Provides `get_feature_names_out` for simple transformers.
+
+    This mixin assumes there's a 1-to-1 correspondence between input features
+    and output features, such as :class:`~sklearn.preprocessing.StandardScaler`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import OneToOneFeatureMixin, BaseEstimator
+    >>> class MyEstimator(OneToOneFeatureMixin, BaseEstimator):
+    ...     def fit(self, X, y=None):
+    ...         self.n_features_in_ = X.shape[1]
+    ...         return self
+    >>> X = np.array([[1, 2], [3, 4]])
+    >>> MyEstimator().fit(X).get_feature_names_out()
+    array(['x0', 'x1'], dtype=object)
+    """
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Same as input features.
+        """
+        # Note that passing attributes="n_features_in_" forces check_is_fitted
+        # to check if the attribute is present. Otherwise it will pass on
+        # stateless estimators (requires_fit=False)
+        check_is_fitted(self, attributes="n_features_in_")
+        return _check_feature_names_in(self, input_features)
+
+
+class ClassNamePrefixFeaturesOutMixin:
+    """Mixin class for transformers that generate their own names by prefixing.
+
+    This mixin is useful when the transformer needs to generate its own feature
+    names out, such as :class:`~sklearn.decomposition.PCA`. For example, if
+    :class:`~sklearn.decomposition.PCA` outputs 3 features, then the generated feature
+    names out are: `["pca0", "pca1", "pca2"]`.
+
+    This mixin assumes that a `_n_features_out` attribute is defined when the
+    transformer is fitted. `_n_features_out` is the number of output features
+    that the transformer will return in `transform` of `fit_transform`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import ClassNamePrefixFeaturesOutMixin, BaseEstimator
+    >>> class MyEstimator(ClassNamePrefixFeaturesOutMixin, BaseEstimator):
+    ...     def fit(self, X, y=None):
+    ...         self._n_features_out = X.shape[1]
+    ...         return self
+    >>> X = np.array([[1, 2], [3, 4]])
+    >>> MyEstimator().fit(X).get_feature_names_out()
+    array(['myestimator0', 'myestimator1'], dtype=object)
+    """
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        The feature names out will prefixed by the lowercased class name. For
+        example, if the transformer outputs 3 features, then the feature names
+        out are: `["class_name0", "class_name1", "class_name2"]`.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Only used to validate feature names with the names seen in `fit`.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self, "_n_features_out")
+        return _generate_get_feature_names_out(
+            self, self._n_features_out, input_features=input_features
+        )
+
+
+class DensityMixin:
+    """Mixin class for all density estimators in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - sets estimator type to `"density_estimator"` through the `estimator_type` tag;
+    - `score` method that default that do no-op.
+
+    Examples
+    --------
+    >>> from sklearn.base import DensityMixin
+    >>> class MyEstimator(DensityMixin):
+    ...     def fit(self, X, y=None):
+    ...         self.is_fitted_ = True
+    ...         return self
+    >>> estimator = MyEstimator()
+    >>> hasattr(estimator, "score")
+    True
+    """
+
+    # TODO(1.8): Remove this attribute
+    _estimator_type = "DensityEstimator"
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.estimator_type = "density_estimator"
+        return tags
+
+    def score(self, X, y=None):
+        """Return the score of the model on the data `X`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        score : float
+        """
+        pass
+
+
+class OutlierMixin:
+    """Mixin class for all outlier detection estimators in scikit-learn.
+
+    This mixin defines the following functionality:
+
+    - set estimator type to `"outlier_detector"` through the `estimator_type` tag;
+    - `fit_predict` method that default to `fit` and `predict`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.base import BaseEstimator, OutlierMixin
+    >>> class MyEstimator(OutlierMixin):
+    ...     def fit(self, X, y=None):
+    ...         self.is_fitted_ = True
+    ...         return self
+    ...     def predict(self, X):
+    ...         return np.ones(shape=len(X))
+    >>> estimator = MyEstimator()
+    >>> X = np.array([[1, 2], [2, 3], [3, 4]])
+    >>> estimator.fit_predict(X)
+    array([1., 1., 1.])
+    """
+
+    # TODO(1.8): Remove this attribute
+    _estimator_type = "outlier_detector"
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.estimator_type = "outlier_detector"
+        return tags
+
+    def fit_predict(self, X, y=None, **kwargs):
+        """Perform fit on X and returns labels for X.
+
+        Returns -1 for outliers and 1 for inliers.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        **kwargs : dict
+            Arguments to be passed to ``fit``.
+
+            .. versionadded:: 1.4
+
+        Returns
+        -------
+        y : ndarray of shape (n_samples,)
+            1 for inliers, -1 for outliers.
+        """
+        # we do not route parameters here, since consumers don't route. But
+        # since it's possible for a `predict` method to also consume
+        # metadata, we check if that's the case, and we raise a warning telling
+        # users that they should implement a custom `fit_predict` method
+        # to forward metadata to `predict` as well.
+        #
+        # For that, we calculate routing and check if anything would be routed
+        # to `predict` if we were to route them.
+        if _routing_enabled():
+            transform_params = self.get_metadata_routing().consumes(
+                method="predict", params=kwargs.keys()
+            )
+            if transform_params:
+                warnings.warn(
+                    (
+                        f"This object ({self.__class__.__name__}) has a `predict` "
+                        "method which consumes metadata, but `fit_predict` does not "
+                        "forward metadata to `predict`. Please implement a custom "
+                        "`fit_predict` method to forward metadata to `predict` as well."
+                        "Alternatively, you can explicitly do `set_predict_request`"
+                        "and set all values to `False` to disable metadata routed to "
+                        "`predict`, if that's an option."
+                    ),
+                    UserWarning,
+                )
+
+        # override for transductive outlier detectors like LocalOulierFactor
+        return self.fit(X, **kwargs).predict(X)
+
+
+class MetaEstimatorMixin:
+    """Mixin class for all meta estimators in scikit-learn.
+
+    This mixin is empty, and only exists to indicate that the estimator is a
+    meta-estimator.
+
+    .. versionchanged:: 1.6
+        The `_required_parameters` is now removed and is unnecessary since tests are
+        refactored and don't use this anymore.
+
+    Examples
+    --------
+    >>> from sklearn.base import MetaEstimatorMixin
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> class MyEstimator(MetaEstimatorMixin):
+    ...     def __init__(self, *, estimator=None):
+    ...         self.estimator = estimator
+    ...     def fit(self, X, y=None):
+    ...         if self.estimator is None:
+    ...             self.estimator_ = LogisticRegression()
+    ...         else:
+    ...             self.estimator_ = self.estimator
+    ...         return self
+    >>> X, y = load_iris(return_X_y=True)
+    >>> estimator = MyEstimator().fit(X, y)
+    >>> estimator.estimator_
+    LogisticRegression()
+    """
+
+
+class MultiOutputMixin:
+    """Mixin to mark estimators that support multioutput."""
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.target_tags.multi_output = True
+        return tags
+
+
+class _UnstableArchMixin:
+    """Mark estimators that are non-determinstic on 32bit or PowerPC"""
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.non_deterministic = _IS_32BIT or platform.machine().startswith(
+            ("ppc", "powerpc")
+        )
+        return tags
+
+
+def is_classifier(estimator):
+    """Return True if the given estimator is (probably) a classifier.
+
+    Parameters
+    ----------
+    estimator : object
+        Estimator object to test.
+
+    Returns
+    -------
+    out : bool
+        True if estimator is a classifier and False otherwise.
+
+    Examples
+    --------
+    >>> from sklearn.base import is_classifier
+    >>> from sklearn.cluster import KMeans
+    >>> from sklearn.svm import SVC, SVR
+    >>> classifier = SVC()
+    >>> regressor = SVR()
+    >>> kmeans = KMeans()
+    >>> is_classifier(classifier)
+    True
+    >>> is_classifier(regressor)
+    False
+    >>> is_classifier(kmeans)
+    False
+    """
+    # TODO(1.8): Remove this check
+    if isinstance(estimator, type):
+        warnings.warn(
+            f"passing a class to {print(inspect.stack()[0][3])} is deprecated and "
+            "will be removed in 1.8. Use an instance of the class instead.",
+            FutureWarning,
+        )
+        return getattr(estimator, "_estimator_type", None) == "classifier"
+
+    return get_tags(estimator).estimator_type == "classifier"
+
+
+def is_regressor(estimator):
+    """Return True if the given estimator is (probably) a regressor.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Estimator object to test.
+
+    Returns
+    -------
+    out : bool
+        True if estimator is a regressor and False otherwise.
+
+    Examples
+    --------
+    >>> from sklearn.base import is_regressor
+    >>> from sklearn.cluster import KMeans
+    >>> from sklearn.svm import SVC, SVR
+    >>> classifier = SVC()
+    >>> regressor = SVR()
+    >>> kmeans = KMeans()
+    >>> is_regressor(classifier)
+    False
+    >>> is_regressor(regressor)
+    True
+    >>> is_regressor(kmeans)
+    False
+    """
+    # TODO(1.8): Remove this check
+    if isinstance(estimator, type):
+        warnings.warn(
+            f"passing a class to {print(inspect.stack()[0][3])} is deprecated and "
+            "will be removed in 1.8. Use an instance of the class instead.",
+            FutureWarning,
+        )
+        return getattr(estimator, "_estimator_type", None) == "regressor"
+
+    return get_tags(estimator).estimator_type == "regressor"
+
+
+def is_clusterer(estimator):
+    """Return True if the given estimator is (probably) a clusterer.
+
+    .. versionadded:: 1.6
+
+    Parameters
+    ----------
+    estimator : object
+        Estimator object to test.
+
+    Returns
+    -------
+    out : bool
+        True if estimator is a clusterer and False otherwise.
+
+    Examples
+    --------
+    >>> from sklearn.base import is_clusterer
+    >>> from sklearn.cluster import KMeans
+    >>> from sklearn.svm import SVC, SVR
+    >>> classifier = SVC()
+    >>> regressor = SVR()
+    >>> kmeans = KMeans()
+    >>> is_clusterer(classifier)
+    False
+    >>> is_clusterer(regressor)
+    False
+    >>> is_clusterer(kmeans)
+    True
+    """
+    # TODO(1.8): Remove this check
+    if isinstance(estimator, type):
+        warnings.warn(
+            f"passing a class to {print(inspect.stack()[0][3])} is deprecated and "
+            "will be removed in 1.8. Use an instance of the class instead.",
+            FutureWarning,
+        )
+        return getattr(estimator, "_estimator_type", None) == "clusterer"
+
+    return get_tags(estimator).estimator_type == "clusterer"
+
+
+def is_outlier_detector(estimator):
+    """Return True if the given estimator is (probably) an outlier detector.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Estimator object to test.
+
+    Returns
+    -------
+    out : bool
+        True if estimator is an outlier detector and False otherwise.
+    """
+    # TODO(1.8): Remove this check
+    if isinstance(estimator, type):
+        warnings.warn(
+            f"passing a class to {print(inspect.stack()[0][3])} is deprecated and "
+            "will be removed in 1.8. Use an instance of the class instead.",
+            FutureWarning,
+        )
+        return getattr(estimator, "_estimator_type", None) == "outlier_detector"
+
+    return get_tags(estimator).estimator_type == "outlier_detector"
+
+
+def _fit_context(*, prefer_skip_nested_validation):
+    """Decorator to run the fit methods of estimators within context managers.
+
+    Parameters
+    ----------
+    prefer_skip_nested_validation : bool
+        If True, the validation of parameters of inner estimators or functions
+        called during fit will be skipped.
+
+        This is useful to avoid validating many times the parameters passed by the
+        user from the public facing API. It's also useful to avoid validating
+        parameters that we pass internally to inner functions that are guaranteed to
+        be valid by the test suite.
+
+        It should be set to True for most estimators, except for those that receive
+        non-validated objects as parameters, such as meta-estimators that are given
+        estimator objects.
+
+    Returns
+    -------
+    decorated_fit : method
+        The decorated fit method.
+    """
+
+    def decorator(fit_method):
+        @functools.wraps(fit_method)
+        def wrapper(estimator, *args, **kwargs):
+            global_skip_validation = get_config()["skip_parameter_validation"]
+
+            # we don't want to validate again for each call to partial_fit
+            partial_fit_and_fitted = (
+                fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
+            )
+
+            if not global_skip_validation and not partial_fit_and_fitted:
+                estimator._validate_params()
+
+            with config_context(
+                skip_parameter_validation=(
+                    prefer_skip_nested_validation or global_skip_validation
+                )
+            ):
+                return fit_method(estimator, *args, **kwargs)
+
+        return wrapper
+
+    return decorator
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/calibration.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/calibration.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b2bca2edfcc01bdec5ad60906061888d4fd7f8d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/calibration.py
@@ -0,0 +1,1448 @@
+"""Methods for calibrating predicted probabilities."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from inspect import signature
+from math import log
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.optimize import minimize
+from scipy.special import expit
+
+from sklearn.utils import Bunch
+
+from ._loss import HalfBinomialLoss
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    RegressorMixin,
+    _fit_context,
+    clone,
+)
+from .frozen import FrozenEstimator
+from .isotonic import IsotonicRegression
+from .model_selection import LeaveOneOut, check_cv, cross_val_predict
+from .preprocessing import LabelEncoder, label_binarize
+from .svm import LinearSVC
+from .utils import _safe_indexing, column_or_1d, get_tags, indexable
+from .utils._param_validation import (
+    HasMethods,
+    Hidden,
+    Interval,
+    StrOptions,
+    validate_params,
+)
+from .utils._plotting import _BinaryClassifierCurveDisplayMixin, _validate_style_kwargs
+from .utils._response import _get_response_values, _process_predict_proba
+from .utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _routing_enabled,
+    process_routing,
+)
+from .utils.multiclass import check_classification_targets
+from .utils.parallel import Parallel, delayed
+from .utils.validation import (
+    _check_method_params,
+    _check_pos_label_consistency,
+    _check_response_method,
+    _check_sample_weight,
+    _num_samples,
+    check_consistent_length,
+    check_is_fitted,
+)
+
+
+class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
+    """Probability calibration with isotonic regression or logistic regression.
+
+    This class uses cross-validation to both estimate the parameters of a
+    classifier and subsequently calibrate a classifier. With
+    `ensemble=True`, for each cv split it
+    fits a copy of the base estimator to the training subset, and calibrates it
+    using the testing subset. For prediction, predicted probabilities are
+    averaged across these individual calibrated classifiers. When
+    `ensemble=False`, cross-validation is used to obtain unbiased predictions,
+    via :func:`~sklearn.model_selection.cross_val_predict`, which are then
+    used for calibration. For prediction, the base estimator, trained using all
+    the data, is used. This is the prediction method implemented when
+    `probabilities=True` for :class:`~sklearn.svm.SVC` and :class:`~sklearn.svm.NuSVC`
+    estimators (see :ref:`User Guide ` for details).
+
+    Already fitted classifiers can be calibrated by wrapping the model in a
+    :class:`~sklearn.frozen.FrozenEstimator`. In this case all provided
+    data is used for calibration. The user has to take care manually that data
+    for model fitting and calibration are disjoint.
+
+    The calibration is based on the :term:`decision_function` method of the
+    `estimator` if it exists, else on :term:`predict_proba`.
+
+    Read more in the :ref:`User Guide `.
+    In order to learn more on the CalibratedClassifierCV class, see the
+    following calibration examples:
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`,
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`, and
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`.
+
+    Parameters
+    ----------
+    estimator : estimator instance, default=None
+        The classifier whose output need to be calibrated to provide more
+        accurate `predict_proba` outputs. The default classifier is
+        a :class:`~sklearn.svm.LinearSVC`.
+
+        .. versionadded:: 1.2
+
+    method : {'sigmoid', 'isotonic'}, default='sigmoid'
+        The method to use for calibration. Can be 'sigmoid' which
+        corresponds to Platt's method (i.e. a logistic regression model) or
+        'isotonic' which is a non-parametric approach. It is not advised to
+        use isotonic calibration with too few calibration samples
+        ``(<<1000)`` since it tends to overfit.
+
+    cv : int, cross-validation generator, or iterable, default=None
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+
+        - None, to use the default 5-fold cross-validation,
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`~sklearn.model_selection.StratifiedKFold` is used. If ``y`` is
+        neither binary nor multiclass, :class:`~sklearn.model_selection.KFold`
+        is used.
+
+        Refer to the :ref:`User Guide ` for the various
+        cross-validation strategies that can be used here.
+
+        .. versionchanged:: 0.22
+            ``cv`` default value if None changed from 3-fold to 5-fold.
+
+        .. versionchanged:: 1.6
+            `"prefit"` is deprecated. Use :class:`~sklearn.frozen.FrozenEstimator`
+            instead.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors.
+
+        Base estimator clones are fitted in parallel across cross-validation
+        iterations. Therefore parallelism happens only when `cv != "prefit"`.
+
+        See :term:`Glossary ` for more details.
+
+        .. versionadded:: 0.24
+
+    ensemble : bool, or "auto", default="auto"
+        Determines how the calibrator is fitted.
+
+        "auto" will use `False` if the `estimator` is a
+        :class:`~sklearn.frozen.FrozenEstimator`, and `True` otherwise.
+
+        If `True`, the `estimator` is fitted using training data, and
+        calibrated using testing data, for each `cv` fold. The final estimator
+        is an ensemble of `n_cv` fitted classifier and calibrator pairs, where
+        `n_cv` is the number of cross-validation folds. The output is the
+        average predicted probabilities of all pairs.
+
+        If `False`, `cv` is used to compute unbiased predictions, via
+        :func:`~sklearn.model_selection.cross_val_predict`, which are then
+        used for calibration. At prediction time, the classifier used is the
+        `estimator` trained on all the data.
+        Note that this method is also internally implemented  in
+        :mod:`sklearn.svm` estimators with the `probabilities=True` parameter.
+
+        .. versionadded:: 0.24
+
+        .. versionchanged:: 1.6
+            `"auto"` option is added and is the default.
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        The class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    calibrated_classifiers_ : list (len() equal to cv or 1 if `ensemble=False`)
+        The list of classifier and calibrator pairs.
+
+        - When `ensemble=True`, `n_cv` fitted `estimator` and calibrator pairs.
+          `n_cv` is the number of cross-validation folds.
+        - When `ensemble=False`, the `estimator`, fitted on all the data, and fitted
+          calibrator.
+
+        .. versionchanged:: 0.24
+            Single calibrated classifier case when `ensemble=False`.
+
+    See Also
+    --------
+    calibration_curve : Compute true and predicted probabilities
+        for a calibration curve.
+
+    References
+    ----------
+    .. [1] Obtaining calibrated probability estimates from decision trees
+           and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
+
+    .. [2] Transforming Classifier Scores into Accurate Multiclass
+           Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
+
+    .. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to
+           Regularized Likelihood Methods, J. Platt, (1999)
+
+    .. [4] Predicting Good Probabilities with Supervised Learning,
+           A. Niculescu-Mizil & R. Caruana, ICML 2005
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> from sklearn.calibration import CalibratedClassifierCV
+    >>> X, y = make_classification(n_samples=100, n_features=2,
+    ...                            n_redundant=0, random_state=42)
+    >>> base_clf = GaussianNB()
+    >>> calibrated_clf = CalibratedClassifierCV(base_clf, cv=3)
+    >>> calibrated_clf.fit(X, y)
+    CalibratedClassifierCV(...)
+    >>> len(calibrated_clf.calibrated_classifiers_)
+    3
+    >>> calibrated_clf.predict_proba(X)[:5, :]
+    array([[0.110, 0.889],
+           [0.072, 0.927],
+           [0.928, 0.072],
+           [0.928, 0.072],
+           [0.072, 0.928]])
+    >>> from sklearn.model_selection import train_test_split
+    >>> X, y = make_classification(n_samples=100, n_features=2,
+    ...                            n_redundant=0, random_state=42)
+    >>> X_train, X_calib, y_train, y_calib = train_test_split(
+    ...        X, y, random_state=42
+    ... )
+    >>> base_clf = GaussianNB()
+    >>> base_clf.fit(X_train, y_train)
+    GaussianNB()
+    >>> from sklearn.frozen import FrozenEstimator
+    >>> calibrated_clf = CalibratedClassifierCV(FrozenEstimator(base_clf))
+    >>> calibrated_clf.fit(X_calib, y_calib)
+    CalibratedClassifierCV(...)
+    >>> len(calibrated_clf.calibrated_classifiers_)
+    1
+    >>> calibrated_clf.predict_proba([[-0.5, 0.5]])
+    array([[0.936, 0.063]])
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [
+            HasMethods(["fit", "predict_proba"]),
+            HasMethods(["fit", "decision_function"]),
+            None,
+        ],
+        "method": [StrOptions({"isotonic", "sigmoid"})],
+        "cv": ["cv_object", Hidden(StrOptions({"prefit"}))],
+        "n_jobs": [Integral, None],
+        "ensemble": ["boolean", StrOptions({"auto"})],
+    }
+
+    def __init__(
+        self,
+        estimator=None,
+        *,
+        method="sigmoid",
+        cv=None,
+        n_jobs=None,
+        ensemble="auto",
+    ):
+        self.estimator = estimator
+        self.method = method
+        self.cv = cv
+        self.n_jobs = n_jobs
+        self.ensemble = ensemble
+
+    def _get_estimator(self):
+        """Resolve which estimator to return (default is LinearSVC)"""
+        if self.estimator is None:
+            # we want all classifiers that don't expose a random_state
+            # to be deterministic (and we don't want to expose this one).
+            estimator = LinearSVC(random_state=0)
+            if _routing_enabled():
+                estimator.set_fit_request(sample_weight=True)
+        else:
+            estimator = self.estimator
+
+        return estimator
+
+    @_fit_context(
+        # CalibratedClassifierCV.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, sample_weight=None, **fit_params):
+        """Fit the calibrated model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted.
+
+        **fit_params : dict
+            Parameters to pass to the `fit` method of the underlying
+            classifier.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        check_classification_targets(y)
+        X, y = indexable(X, y)
+        estimator = self._get_estimator()
+
+        _ensemble = self.ensemble
+        if _ensemble == "auto":
+            _ensemble = not isinstance(estimator, FrozenEstimator)
+
+        self.calibrated_classifiers_ = []
+        if self.cv == "prefit":
+            # TODO(1.8): Remove this code branch and cv='prefit'
+            warnings.warn(
+                "The `cv='prefit'` option is deprecated in 1.6 and will be removed in"
+                " 1.8. You can use CalibratedClassifierCV(FrozenEstimator(estimator))"
+                " instead.",
+                category=FutureWarning,
+            )
+            # `classes_` should be consistent with that of estimator
+            check_is_fitted(self.estimator, attributes=["classes_"])
+            self.classes_ = self.estimator.classes_
+
+            predictions, _ = _get_response_values(
+                estimator,
+                X,
+                response_method=["decision_function", "predict_proba"],
+            )
+            if predictions.ndim == 1:
+                # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+                predictions = predictions.reshape(-1, 1)
+
+            if sample_weight is not None:
+                # Check that the sample_weight dtype is consistent with the predictions
+                # to avoid unintentional upcasts.
+                sample_weight = _check_sample_weight(
+                    sample_weight, predictions, dtype=predictions.dtype
+                )
+
+            calibrated_classifier = _fit_calibrator(
+                estimator,
+                predictions,
+                y,
+                self.classes_,
+                self.method,
+                sample_weight,
+            )
+            self.calibrated_classifiers_.append(calibrated_classifier)
+        else:
+            # Set `classes_` using all `y`
+            label_encoder_ = LabelEncoder().fit(y)
+            self.classes_ = label_encoder_.classes_
+
+            if _routing_enabled():
+                routed_params = process_routing(
+                    self,
+                    "fit",
+                    sample_weight=sample_weight,
+                    **fit_params,
+                )
+            else:
+                # sample_weight checks
+                fit_parameters = signature(estimator.fit).parameters
+                supports_sw = "sample_weight" in fit_parameters
+                if sample_weight is not None and not supports_sw:
+                    estimator_name = type(estimator).__name__
+                    warnings.warn(
+                        f"Since {estimator_name} does not appear to accept"
+                        " sample_weight, sample weights will only be used for the"
+                        " calibration itself. This can be caused by a limitation of"
+                        " the current scikit-learn API. See the following issue for"
+                        " more details:"
+                        " https://github.com/scikit-learn/scikit-learn/issues/21134."
+                        " Be warned that the result of the calibration is likely to be"
+                        " incorrect."
+                    )
+                routed_params = Bunch()
+                routed_params.splitter = Bunch(split={})  # no routing for splitter
+                routed_params.estimator = Bunch(fit=fit_params)
+                if sample_weight is not None and supports_sw:
+                    routed_params.estimator.fit["sample_weight"] = sample_weight
+
+            # Check that each cross-validation fold can have at least one
+            # example per class
+            if isinstance(self.cv, int):
+                n_folds = self.cv
+            elif hasattr(self.cv, "n_splits"):
+                n_folds = self.cv.n_splits
+            else:
+                n_folds = None
+            if n_folds and np.any(np.unique(y, return_counts=True)[1] < n_folds):
+                raise ValueError(
+                    f"Requesting {n_folds}-fold "
+                    "cross-validation but provided less than "
+                    f"{n_folds} examples for at least one class."
+                )
+            if isinstance(self.cv, LeaveOneOut):
+                raise ValueError(
+                    "LeaveOneOut cross-validation does not allow"
+                    "all classes to be present in test splits. "
+                    "Please use a cross-validation generator that allows "
+                    "all classes to appear in every test and train split."
+                )
+            cv = check_cv(self.cv, y, classifier=True)
+
+            if _ensemble:
+                parallel = Parallel(n_jobs=self.n_jobs)
+                self.calibrated_classifiers_ = parallel(
+                    delayed(_fit_classifier_calibrator_pair)(
+                        clone(estimator),
+                        X,
+                        y,
+                        train=train,
+                        test=test,
+                        method=self.method,
+                        classes=self.classes_,
+                        sample_weight=sample_weight,
+                        fit_params=routed_params.estimator.fit,
+                    )
+                    for train, test in cv.split(X, y, **routed_params.splitter.split)
+                )
+            else:
+                this_estimator = clone(estimator)
+                method_name = _check_response_method(
+                    this_estimator,
+                    ["decision_function", "predict_proba"],
+                ).__name__
+                predictions = cross_val_predict(
+                    estimator=this_estimator,
+                    X=X,
+                    y=y,
+                    cv=cv,
+                    method=method_name,
+                    n_jobs=self.n_jobs,
+                    params=routed_params.estimator.fit,
+                )
+                if len(self.classes_) == 2:
+                    # Ensure shape (n_samples, 1) in the binary case
+                    if method_name == "predict_proba":
+                        # Select the probability column of the positive class
+                        predictions = _process_predict_proba(
+                            y_pred=predictions,
+                            target_type="binary",
+                            classes=self.classes_,
+                            pos_label=self.classes_[1],
+                        )
+                    predictions = predictions.reshape(-1, 1)
+
+                if sample_weight is not None:
+                    # Check that the sample_weight dtype is consistent with the
+                    # predictions to avoid unintentional upcasts.
+                    sample_weight = _check_sample_weight(
+                        sample_weight, predictions, dtype=predictions.dtype
+                    )
+
+                this_estimator.fit(X, y, **routed_params.estimator.fit)
+                # Note: Here we don't pass on fit_params because the supported
+                # calibrators don't support fit_params anyway
+                calibrated_classifier = _fit_calibrator(
+                    this_estimator,
+                    predictions,
+                    y,
+                    self.classes_,
+                    self.method,
+                    sample_weight,
+                )
+                self.calibrated_classifiers_.append(calibrated_classifier)
+
+        first_clf = self.calibrated_classifiers_[0].estimator
+        if hasattr(first_clf, "n_features_in_"):
+            self.n_features_in_ = first_clf.n_features_in_
+        if hasattr(first_clf, "feature_names_in_"):
+            self.feature_names_in_ = first_clf.feature_names_in_
+        return self
+
+    def predict_proba(self, X):
+        """Calibrated probabilities of classification.
+
+        This function returns calibrated probabilities of classification
+        according to each class on an array of test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict_proba`.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            The predicted probas.
+        """
+        check_is_fitted(self)
+        # Compute the arithmetic mean of the predictions of the calibrated
+        # classifiers
+        mean_proba = np.zeros((_num_samples(X), len(self.classes_)))
+        for calibrated_classifier in self.calibrated_classifiers_:
+            proba = calibrated_classifier.predict_proba(X)
+            mean_proba += proba
+
+        mean_proba /= len(self.calibrated_classifiers_)
+
+        return mean_proba
+
+    def predict(self, X):
+        """Predict the target of new samples.
+
+        The predicted class is the class that has the highest probability,
+        and can thus be different from the prediction of the uncalibrated classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict`.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,)
+            The predicted class.
+        """
+        check_is_fitted(self)
+        return self.classes_[np.argmax(self.predict_proba(X), axis=1)]
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add_self_request(self)
+            .add(
+                estimator=self._get_estimator(),
+                method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+            )
+            .add(
+                splitter=self.cv,
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
+            )
+        )
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = get_tags(self._get_estimator()).input_tags.sparse
+        return tags
+
+
+def _fit_classifier_calibrator_pair(
+    estimator,
+    X,
+    y,
+    train,
+    test,
+    method,
+    classes,
+    sample_weight=None,
+    fit_params=None,
+):
+    """Fit a classifier/calibration pair on a given train/test split.
+
+    Fit the classifier on the train set, compute its predictions on the test
+    set and use the predictions as input to fit the calibrator along with the
+    test labels.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Cloned base estimator.
+
+    X : array-like, shape (n_samples, n_features)
+        Sample data.
+
+    y : array-like, shape (n_samples,)
+        Targets.
+
+    train : ndarray, shape (n_train_indices,)
+        Indices of the training subset.
+
+    test : ndarray, shape (n_test_indices,)
+        Indices of the testing subset.
+
+    method : {'sigmoid', 'isotonic'}
+        Method to use for calibration.
+
+    classes : ndarray, shape (n_classes,)
+        The target classes.
+
+    sample_weight : array-like, default=None
+        Sample weights for `X`.
+
+    fit_params : dict, default=None
+        Parameters to pass to the `fit` method of the underlying
+        classifier.
+
+    Returns
+    -------
+    calibrated_classifier : _CalibratedClassifier instance
+    """
+    fit_params_train = _check_method_params(X, params=fit_params, indices=train)
+    X_train, y_train = _safe_indexing(X, train), _safe_indexing(y, train)
+    X_test, y_test = _safe_indexing(X, test), _safe_indexing(y, test)
+
+    estimator.fit(X_train, y_train, **fit_params_train)
+
+    predictions, _ = _get_response_values(
+        estimator,
+        X_test,
+        response_method=["decision_function", "predict_proba"],
+    )
+    if predictions.ndim == 1:
+        # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+        predictions = predictions.reshape(-1, 1)
+
+    if sample_weight is not None:
+        # Check that the sample_weight dtype is consistent with the predictions
+        # to avoid unintentional upcasts.
+        sample_weight = _check_sample_weight(sample_weight, X, dtype=predictions.dtype)
+        sw_test = _safe_indexing(sample_weight, test)
+    else:
+        sw_test = None
+    calibrated_classifier = _fit_calibrator(
+        estimator, predictions, y_test, classes, method, sample_weight=sw_test
+    )
+    return calibrated_classifier
+
+
+def _fit_calibrator(clf, predictions, y, classes, method, sample_weight=None):
+    """Fit calibrator(s) and return a `_CalibratedClassifier`
+    instance.
+
+    `n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted.
+    However, if `n_classes` equals 2, one calibrator is fitted.
+
+    Parameters
+    ----------
+    clf : estimator instance
+        Fitted classifier.
+
+    predictions : array-like, shape (n_samples, n_classes) or (n_samples, 1) \
+                    when binary.
+        Raw predictions returned by the un-calibrated base classifier.
+
+    y : array-like, shape (n_samples,)
+        The targets.
+
+    classes : ndarray, shape (n_classes,)
+        All the prediction classes.
+
+    method : {'sigmoid', 'isotonic'}
+        The method to use for calibration.
+
+    sample_weight : ndarray, shape (n_samples,), default=None
+        Sample weights. If None, then samples are equally weighted.
+
+    Returns
+    -------
+    pipeline : _CalibratedClassifier instance
+    """
+    Y = label_binarize(y, classes=classes)
+    label_encoder = LabelEncoder().fit(classes)
+    pos_class_indices = label_encoder.transform(clf.classes_)
+    calibrators = []
+    for class_idx, this_pred in zip(pos_class_indices, predictions.T):
+        if method == "isotonic":
+            calibrator = IsotonicRegression(out_of_bounds="clip")
+        else:  # "sigmoid"
+            calibrator = _SigmoidCalibration()
+        calibrator.fit(this_pred, Y[:, class_idx], sample_weight)
+        calibrators.append(calibrator)
+
+    pipeline = _CalibratedClassifier(clf, calibrators, method=method, classes=classes)
+    return pipeline
+
+
+class _CalibratedClassifier:
+    """Pipeline-like chaining a fitted classifier and its fitted calibrators.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Fitted classifier.
+
+    calibrators : list of fitted estimator instances
+        List of fitted calibrators (either 'IsotonicRegression' or
+        '_SigmoidCalibration'). The number of calibrators equals the number of
+        classes. However, if there are 2 classes, the list contains only one
+        fitted calibrator.
+
+    classes : array-like of shape (n_classes,)
+        All the prediction classes.
+
+    method : {'sigmoid', 'isotonic'}, default='sigmoid'
+        The method to use for calibration. Can be 'sigmoid' which
+        corresponds to Platt's method or 'isotonic' which is a
+        non-parametric approach based on isotonic regression.
+    """
+
+    def __init__(self, estimator, calibrators, *, classes, method="sigmoid"):
+        self.estimator = estimator
+        self.calibrators = calibrators
+        self.classes = classes
+        self.method = method
+
+    def predict_proba(self, X):
+        """Calculate calibrated probabilities.
+
+        Calculates classification calibrated probabilities
+        for each class, in a one-vs-all manner, for `X`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The sample data.
+
+        Returns
+        -------
+        proba : array, shape (n_samples, n_classes)
+            The predicted probabilities. Can be exact zeros.
+        """
+        predictions, _ = _get_response_values(
+            self.estimator,
+            X,
+            response_method=["decision_function", "predict_proba"],
+        )
+        if predictions.ndim == 1:
+            # Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
+            predictions = predictions.reshape(-1, 1)
+
+        n_classes = len(self.classes)
+
+        label_encoder = LabelEncoder().fit(self.classes)
+        pos_class_indices = label_encoder.transform(self.estimator.classes_)
+
+        proba = np.zeros((_num_samples(X), n_classes))
+        for class_idx, this_pred, calibrator in zip(
+            pos_class_indices, predictions.T, self.calibrators
+        ):
+            if n_classes == 2:
+                # When binary, `predictions` consists only of predictions for
+                # clf.classes_[1] but `pos_class_indices` = 0
+                class_idx += 1
+            proba[:, class_idx] = calibrator.predict(this_pred)
+
+        # Normalize the probabilities
+        if n_classes == 2:
+            proba[:, 0] = 1.0 - proba[:, 1]
+        else:
+            denominator = np.sum(proba, axis=1)[:, np.newaxis]
+            # In the edge case where for each class calibrator returns a null
+            # probability for a given sample, use the uniform distribution
+            # instead.
+            uniform_proba = np.full_like(proba, 1 / n_classes)
+            proba = np.divide(
+                proba, denominator, out=uniform_proba, where=denominator != 0
+            )
+
+        # Deal with cases where the predicted probability minimally exceeds 1.0
+        proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0
+
+        return proba
+
+
+# The max_abs_prediction_threshold was approximated using
+# logit(np.finfo(np.float64).eps) which is about -36
+def _sigmoid_calibration(
+    predictions, y, sample_weight=None, max_abs_prediction_threshold=30
+):
+    """Probability Calibration with sigmoid method (Platt 2000)
+
+    Parameters
+    ----------
+    predictions : ndarray of shape (n_samples,)
+        The decision function or predict proba for the samples.
+
+    y : ndarray of shape (n_samples,)
+        The targets.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights. If None, then samples are equally weighted.
+
+    Returns
+    -------
+    a : float
+        The slope.
+
+    b : float
+        The intercept.
+
+    References
+    ----------
+    Platt, "Probabilistic Outputs for Support Vector Machines"
+    """
+    predictions = column_or_1d(predictions)
+    y = column_or_1d(y)
+
+    F = predictions  # F follows Platt's notations
+
+    scale_constant = 1.0
+    max_prediction = np.max(np.abs(F))
+
+    # If the predictions have large values we scale them in order to bring
+    # them within a suitable range. This has no effect on the final
+    # (prediction) result because linear models like Logisitic Regression
+    # without a penalty are invariant to multiplying the features by a
+    # constant.
+    if max_prediction >= max_abs_prediction_threshold:
+        scale_constant = max_prediction
+        # We rescale the features in a copy: inplace rescaling could confuse
+        # the caller and make the code harder to reason about.
+        F = F / scale_constant
+
+    # Bayesian priors (see Platt end of section 2.2):
+    # It corresponds to the number of samples, taking into account the
+    # `sample_weight`.
+    mask_negative_samples = y <= 0
+    if sample_weight is not None:
+        prior0 = (sample_weight[mask_negative_samples]).sum()
+        prior1 = (sample_weight[~mask_negative_samples]).sum()
+    else:
+        prior0 = float(np.sum(mask_negative_samples))
+        prior1 = y.shape[0] - prior0
+    T = np.zeros_like(y, dtype=predictions.dtype)
+    T[y > 0] = (prior1 + 1.0) / (prior1 + 2.0)
+    T[y <= 0] = 1.0 / (prior0 + 2.0)
+
+    bin_loss = HalfBinomialLoss()
+
+    def loss_grad(AB):
+        # .astype below is needed to ensure y_true and raw_prediction have the
+        # same dtype. With result = np.float64(0) * np.array([1, 2], dtype=np.float32)
+        # - in Numpy 2, result.dtype is float64
+        # - in Numpy<2, result.dtype is float32
+        raw_prediction = -(AB[0] * F + AB[1]).astype(dtype=predictions.dtype)
+        l, g = bin_loss.loss_gradient(
+            y_true=T,
+            raw_prediction=raw_prediction,
+            sample_weight=sample_weight,
+        )
+        loss = l.sum()
+        # TODO: Remove casting to np.float64 when minimum supported SciPy is 1.11.2
+        # With SciPy >= 1.11.2, the LBFGS implementation will cast to float64
+        # https://github.com/scipy/scipy/pull/18825.
+        # Here we cast to float64 to support SciPy < 1.11.2
+        grad = np.asarray([-g @ F, -g.sum()], dtype=np.float64)
+        return loss, grad
+
+    AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))])
+
+    opt_result = minimize(
+        loss_grad,
+        AB0,
+        method="L-BFGS-B",
+        jac=True,
+        options={
+            "gtol": 1e-6,
+            "ftol": 64 * np.finfo(float).eps,
+        },
+    )
+    AB_ = opt_result.x
+
+    # The tuned multiplicative parameter is converted back to the original
+    # input feature scale. The offset parameter does not need rescaling since
+    # we did not rescale the outcome variable.
+    return AB_[0] / scale_constant, AB_[1]
+
+
+class _SigmoidCalibration(RegressorMixin, BaseEstimator):
+    """Sigmoid regression model.
+
+    Attributes
+    ----------
+    a_ : float
+        The slope.
+
+    b_ : float
+        The intercept.
+    """
+
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model using X, y as training data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples,)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Training target.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        X = column_or_1d(X)
+        y = column_or_1d(y)
+        X, y = indexable(X, y)
+
+        self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)
+        return self
+
+    def predict(self, T):
+        """Predict new data by linear interpolation.
+
+        Parameters
+        ----------
+        T : array-like of shape (n_samples,)
+            Data to predict from.
+
+        Returns
+        -------
+        T_ : ndarray of shape (n_samples,)
+            The predicted data.
+        """
+        T = column_or_1d(T)
+        return expit(-(self.a_ * T + self.b_))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_prob": ["array-like"],
+        "pos_label": [Real, str, "boolean", None],
+        "n_bins": [Interval(Integral, 1, None, closed="left")],
+        "strategy": [StrOptions({"uniform", "quantile"})],
+    },
+    prefer_skip_nested_validation=True,
+)
+def calibration_curve(
+    y_true,
+    y_prob,
+    *,
+    pos_label=None,
+    n_bins=5,
+    strategy="uniform",
+):
+    """Compute true and predicted probabilities for a calibration curve.
+
+    The method assumes the inputs come from a binary classifier, and
+    discretize the [0, 1] interval into bins.
+
+    Calibration curves may also be referred to as reliability diagrams.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True targets.
+
+    y_prob : array-like of shape (n_samples,)
+        Probabilities of the positive class.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+
+        .. versionadded:: 1.1
+
+    n_bins : int, default=5
+        Number of bins to discretize the [0, 1] interval. A bigger number
+        requires more data. Bins with no samples (i.e. without
+        corresponding values in `y_prob`) will not be returned, thus the
+        returned arrays may have less than `n_bins` values.
+
+    strategy : {'uniform', 'quantile'}, default='uniform'
+        Strategy used to define the widths of the bins.
+
+        uniform
+            The bins have identical widths.
+        quantile
+            The bins have the same number of samples and depend on `y_prob`.
+
+    Returns
+    -------
+    prob_true : ndarray of shape (n_bins,) or smaller
+        The proportion of samples whose class is the positive class, in each
+        bin (fraction of positives).
+
+    prob_pred : ndarray of shape (n_bins,) or smaller
+        The mean predicted probability in each bin.
+
+    See Also
+    --------
+    CalibrationDisplay.from_predictions : Plot calibration curve using true
+        and predicted labels.
+    CalibrationDisplay.from_estimator : Plot calibration curve using an
+        estimator and data.
+
+    References
+    ----------
+    Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
+    Probabilities With Supervised Learning, in Proceedings of the 22nd
+    International Conference on Machine Learning (ICML).
+    See section 4 (Qualitative Analysis of Predictions).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.calibration import calibration_curve
+    >>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
+    >>> y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9,  1.])
+    >>> prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3)
+    >>> prob_true
+    array([0. , 0.5, 1. ])
+    >>> prob_pred
+    array([0.2  , 0.525, 0.85 ])
+    """
+    y_true = column_or_1d(y_true)
+    y_prob = column_or_1d(y_prob)
+    check_consistent_length(y_true, y_prob)
+    pos_label = _check_pos_label_consistency(pos_label, y_true)
+
+    if y_prob.min() < 0 or y_prob.max() > 1:
+        raise ValueError("y_prob has values outside [0, 1].")
+
+    labels = np.unique(y_true)
+    if len(labels) > 2:
+        raise ValueError(
+            f"Only binary classification is supported. Provided labels {labels}."
+        )
+    y_true = y_true == pos_label
+
+    if strategy == "quantile":  # Determine bin edges by distribution of data
+        quantiles = np.linspace(0, 1, n_bins + 1)
+        bins = np.percentile(y_prob, quantiles * 100)
+    elif strategy == "uniform":
+        bins = np.linspace(0.0, 1.0, n_bins + 1)
+    else:
+        raise ValueError(
+            "Invalid entry to 'strategy' input. Strategy "
+            "must be either 'quantile' or 'uniform'."
+        )
+
+    binids = np.searchsorted(bins[1:-1], y_prob)
+
+    bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins))
+    bin_true = np.bincount(binids, weights=y_true, minlength=len(bins))
+    bin_total = np.bincount(binids, minlength=len(bins))
+
+    nonzero = bin_total != 0
+    prob_true = bin_true[nonzero] / bin_total[nonzero]
+    prob_pred = bin_sums[nonzero] / bin_total[nonzero]
+
+    return prob_true, prob_pred
+
+
+class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
+    """Calibration curve (also known as reliability diagram) visualization.
+
+    It is recommended to use
+    :func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or
+    :func:`~sklearn.calibration.CalibrationDisplay.from_predictions`
+    to create a `CalibrationDisplay`. All parameters are stored as attributes.
+
+    Read more about calibration in the :ref:`User Guide ` and
+    more about the scikit-learn visualization API in :ref:`visualizations`.
+
+    For an example on how to use the visualization, see
+    :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`.
+
+    .. versionadded:: 1.0
+
+    Parameters
+    ----------
+    prob_true : ndarray of shape (n_bins,)
+        The proportion of samples whose class is the positive class (fraction
+        of positives), in each bin.
+
+    prob_pred : ndarray of shape (n_bins,)
+        The mean predicted probability in each bin.
+
+    y_prob : ndarray of shape (n_samples,)
+        Probability estimates for the positive class, for each sample.
+
+    estimator_name : str, default=None
+        Name of estimator. If None, the estimator name is not shown.
+
+    pos_label : int, float, bool or str, default=None
+        The positive class when computing the calibration curve.
+        By default, `pos_label` is set to `estimators.classes_[1]` when using
+        `from_estimator` and set to 1 when using `from_predictions`.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    line_ : matplotlib Artist
+        Calibration curve.
+
+    ax_ : matplotlib Axes
+        Axes with calibration curve.
+
+    figure_ : matplotlib Figure
+        Figure containing the curve.
+
+    See Also
+    --------
+    calibration_curve : Compute true and predicted probabilities for a
+        calibration curve.
+    CalibrationDisplay.from_predictions : Plot calibration curve using true
+        and predicted labels.
+    CalibrationDisplay.from_estimator : Plot calibration curve using an
+        estimator and data.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> from sklearn.calibration import calibration_curve, CalibrationDisplay
+    >>> X, y = make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, random_state=0)
+    >>> clf = LogisticRegression(random_state=0)
+    >>> clf.fit(X_train, y_train)
+    LogisticRegression(random_state=0)
+    >>> y_prob = clf.predict_proba(X_test)[:, 1]
+    >>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10)
+    >>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob)
+    >>> disp.plot()
+    <...>
+    """
+
+    def __init__(
+        self, prob_true, prob_pred, y_prob, *, estimator_name=None, pos_label=None
+    ):
+        self.prob_true = prob_true
+        self.prob_pred = prob_pred
+        self.y_prob = y_prob
+        self.estimator_name = estimator_name
+        self.pos_label = pos_label
+
+    def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
+        """Plot visualization.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Parameters
+        ----------
+        ax : Matplotlib Axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        name : str, default=None
+            Name for labeling curve. If `None`, use `estimator_name` if
+            not `None`, otherwise no labeling is shown.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`
+            Object that stores computed values.
+        """
+        self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
+
+        info_pos_label = (
+            f"(Positive class: {self.pos_label})" if self.pos_label is not None else ""
+        )
+
+        default_line_kwargs = {"marker": "s", "linestyle": "-"}
+        if name is not None:
+            default_line_kwargs["label"] = name
+        line_kwargs = _validate_style_kwargs(default_line_kwargs, kwargs)
+
+        ref_line_label = "Perfectly calibrated"
+        existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1]
+        if ref_line and not existing_ref_line:
+            self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label)
+        self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0]
+
+        # We always have to show the legend for at least the reference line
+        self.ax_.legend(loc="lower right")
+
+        xlabel = f"Mean predicted probability {info_pos_label}"
+        ylabel = f"Fraction of positives {info_pos_label}"
+        self.ax_.set(xlabel=xlabel, ylabel=ylabel)
+
+        return self
+
+    @classmethod
+    def from_estimator(
+        cls,
+        estimator,
+        X,
+        y,
+        *,
+        n_bins=5,
+        strategy="uniform",
+        pos_label=None,
+        name=None,
+        ax=None,
+        ref_line=True,
+        **kwargs,
+    ):
+        """Plot calibration curve using a binary classifier and data.
+
+        A calibration curve, also known as a reliability diagram, uses inputs
+        from a binary classifier and plots the average predicted probability
+        for each bin against the fraction of positive classes, on the
+        y-axis.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Read more about calibration in the :ref:`User Guide ` and
+        more about the scikit-learn visualization API in :ref:`visualizations`.
+
+        .. versionadded:: 1.0
+
+        Parameters
+        ----------
+        estimator : estimator instance
+            Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
+            in which the last estimator is a classifier. The classifier must
+            have a :term:`predict_proba` method.
+
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input values.
+
+        y : array-like of shape (n_samples,)
+            Binary target values.
+
+        n_bins : int, default=5
+            Number of bins to discretize the [0, 1] interval into when
+            calculating the calibration curve. A bigger number requires more
+            data.
+
+        strategy : {'uniform', 'quantile'}, default='uniform'
+            Strategy used to define the widths of the bins.
+
+            - `'uniform'`: The bins have identical widths.
+            - `'quantile'`: The bins have the same number of samples and depend
+              on predicted probabilities.
+
+        pos_label : int, float, bool or str, default=None
+            The positive class when computing the calibration curve.
+            By default, `estimators.classes_[1]` is considered as the
+            positive class.
+
+            .. versionadded:: 1.1
+
+        name : str, default=None
+            Name for labeling curve. If `None`, the name of the estimator is
+            used.
+
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`.
+            Object that stores computed values.
+
+        See Also
+        --------
+        CalibrationDisplay.from_predictions : Plot calibration curve using true
+            and predicted labels.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.model_selection import train_test_split
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.calibration import CalibrationDisplay
+        >>> X, y = make_classification(random_state=0)
+        >>> X_train, X_test, y_train, y_test = train_test_split(
+        ...     X, y, random_state=0)
+        >>> clf = LogisticRegression(random_state=0)
+        >>> clf.fit(X_train, y_train)
+        LogisticRegression(random_state=0)
+        >>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
+        >>> plt.show()
+        """
+        y_prob, pos_label, name = cls._validate_and_get_response_values(
+            estimator,
+            X,
+            y,
+            response_method="predict_proba",
+            pos_label=pos_label,
+            name=name,
+        )
+
+        return cls.from_predictions(
+            y,
+            y_prob,
+            n_bins=n_bins,
+            strategy=strategy,
+            pos_label=pos_label,
+            name=name,
+            ref_line=ref_line,
+            ax=ax,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_predictions(
+        cls,
+        y_true,
+        y_prob,
+        *,
+        n_bins=5,
+        strategy="uniform",
+        pos_label=None,
+        name=None,
+        ax=None,
+        ref_line=True,
+        **kwargs,
+    ):
+        """Plot calibration curve using true labels and predicted probabilities.
+
+        Calibration curve, also known as reliability diagram, uses inputs
+        from a binary classifier and plots the average predicted probability
+        for each bin against the fraction of positive classes, on the
+        y-axis.
+
+        Extra keyword arguments will be passed to
+        :func:`matplotlib.pyplot.plot`.
+
+        Read more about calibration in the :ref:`User Guide ` and
+        more about the scikit-learn visualization API in :ref:`visualizations`.
+
+        .. versionadded:: 1.0
+
+        Parameters
+        ----------
+        y_true : array-like of shape (n_samples,)
+            True labels.
+
+        y_prob : array-like of shape (n_samples,)
+            The predicted probabilities of the positive class.
+
+        n_bins : int, default=5
+            Number of bins to discretize the [0, 1] interval into when
+            calculating the calibration curve. A bigger number requires more
+            data.
+
+        strategy : {'uniform', 'quantile'}, default='uniform'
+            Strategy used to define the widths of the bins.
+
+            - `'uniform'`: The bins have identical widths.
+            - `'quantile'`: The bins have the same number of samples and depend
+              on predicted probabilities.
+
+        pos_label : int, float, bool or str, default=None
+            The positive class when computing the calibration curve.
+            By default `pos_label` is set to 1.
+
+            .. versionadded:: 1.1
+
+        name : str, default=None
+            Name for labeling curve.
+
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        ref_line : bool, default=True
+            If `True`, plots a reference line representing a perfectly
+            calibrated classifier.
+
+        **kwargs : dict
+            Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
+
+        Returns
+        -------
+        display : :class:`~sklearn.calibration.CalibrationDisplay`.
+            Object that stores computed values.
+
+        See Also
+        --------
+        CalibrationDisplay.from_estimator : Plot calibration curve using an
+            estimator and data.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.model_selection import train_test_split
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.calibration import CalibrationDisplay
+        >>> X, y = make_classification(random_state=0)
+        >>> X_train, X_test, y_train, y_test = train_test_split(
+        ...     X, y, random_state=0)
+        >>> clf = LogisticRegression(random_state=0)
+        >>> clf.fit(X_train, y_train)
+        LogisticRegression(random_state=0)
+        >>> y_prob = clf.predict_proba(X_test)[:, 1]
+        >>> disp = CalibrationDisplay.from_predictions(y_test, y_prob)
+        >>> plt.show()
+        """
+        pos_label_validated, name = cls._validate_from_predictions_params(
+            y_true, y_prob, sample_weight=None, pos_label=pos_label, name=name
+        )
+
+        prob_true, prob_pred = calibration_curve(
+            y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label
+        )
+
+        disp = cls(
+            prob_true=prob_true,
+            prob_pred=prob_pred,
+            y_prob=y_prob,
+            estimator_name=name,
+            pos_label=pos_label_validated,
+        )
+        return disp.plot(ax=ax, ref_line=ref_line, **kwargs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/conftest.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5255ead1ffdceb3d825f51a24d0a531b1833ecf
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/conftest.py
@@ -0,0 +1,375 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import builtins
+import faulthandler
+import platform
+import sys
+from contextlib import suppress
+from functools import wraps
+from os import environ
+from unittest import SkipTest
+
+import joblib
+import numpy as np
+import pytest
+from _pytest.doctest import DoctestItem
+from threadpoolctl import threadpool_limits
+
+from sklearn import set_config
+from sklearn._min_dependencies import PYTEST_MIN_VERSION
+from sklearn.datasets import (
+    fetch_20newsgroups,
+    fetch_20newsgroups_vectorized,
+    fetch_california_housing,
+    fetch_covtype,
+    fetch_kddcup99,
+    fetch_lfw_pairs,
+    fetch_lfw_people,
+    fetch_olivetti_faces,
+    fetch_rcv1,
+    fetch_species_distributions,
+)
+from sklearn.utils._testing import get_pytest_filterwarning_lines
+from sklearn.utils.fixes import (
+    _IS_32BIT,
+    np_base_version,
+    parse_version,
+    sp_version,
+)
+
+try:
+    from scipy_doctest.conftest import dt_config
+except ModuleNotFoundError:
+    dt_config = None
+
+if parse_version(pytest.__version__) < parse_version(PYTEST_MIN_VERSION):
+    raise ImportError(
+        f"Your version of pytest is too old. Got version {pytest.__version__}, you"
+        f" should have pytest >= {PYTEST_MIN_VERSION} installed."
+    )
+
+scipy_datasets_require_network = sp_version >= parse_version("1.10")
+
+
+def raccoon_face_or_skip():
+    # SciPy >= 1.10 requires network to access to get data
+    if scipy_datasets_require_network:
+        run_network_tests = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
+        if not run_network_tests:
+            raise SkipTest("test is enabled when SKLEARN_SKIP_NETWORK_TESTS=0")
+
+        try:
+            import pooch  # noqa: F401
+        except ImportError:
+            raise SkipTest("test requires pooch to be installed")
+
+        from scipy.datasets import face
+    else:
+        from scipy.misc import face
+
+    return face(gray=True)
+
+
+dataset_fetchers = {
+    "fetch_20newsgroups_fxt": fetch_20newsgroups,
+    "fetch_20newsgroups_vectorized_fxt": fetch_20newsgroups_vectorized,
+    "fetch_california_housing_fxt": fetch_california_housing,
+    "fetch_covtype_fxt": fetch_covtype,
+    "fetch_kddcup99_fxt": fetch_kddcup99,
+    "fetch_lfw_pairs_fxt": fetch_lfw_pairs,
+    "fetch_lfw_people_fxt": fetch_lfw_people,
+    "fetch_olivetti_faces_fxt": fetch_olivetti_faces,
+    "fetch_rcv1_fxt": fetch_rcv1,
+    "fetch_species_distributions_fxt": fetch_species_distributions,
+}
+
+if scipy_datasets_require_network:
+    dataset_fetchers["raccoon_face_fxt"] = raccoon_face_or_skip
+
+_SKIP32_MARK = pytest.mark.skipif(
+    environ.get("SKLEARN_RUN_FLOAT32_TESTS", "0") != "1",
+    reason="Set SKLEARN_RUN_FLOAT32_TESTS=1 to run float32 dtype tests",
+)
+
+
+# Global fixtures
+@pytest.fixture(params=[pytest.param(np.float32, marks=_SKIP32_MARK), np.float64])
+def global_dtype(request):
+    yield request.param
+
+
+def _fetch_fixture(f):
+    """Fetch dataset (download if missing and requested by environment)."""
+    download_if_missing = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
+
+    @wraps(f)
+    def wrapped(*args, **kwargs):
+        kwargs["download_if_missing"] = download_if_missing
+        try:
+            return f(*args, **kwargs)
+        except OSError as e:
+            if str(e) != "Data not found and `download_if_missing` is False":
+                raise
+            pytest.skip("test is enabled when SKLEARN_SKIP_NETWORK_TESTS=0")
+
+    return pytest.fixture(lambda: wrapped)
+
+
+# Adds fixtures for fetching data
+fetch_20newsgroups_fxt = _fetch_fixture(fetch_20newsgroups)
+fetch_20newsgroups_vectorized_fxt = _fetch_fixture(fetch_20newsgroups_vectorized)
+fetch_california_housing_fxt = _fetch_fixture(fetch_california_housing)
+fetch_covtype_fxt = _fetch_fixture(fetch_covtype)
+fetch_kddcup99_fxt = _fetch_fixture(fetch_kddcup99)
+fetch_lfw_pairs_fxt = _fetch_fixture(fetch_lfw_pairs)
+fetch_lfw_people_fxt = _fetch_fixture(fetch_lfw_people)
+fetch_olivetti_faces_fxt = _fetch_fixture(fetch_olivetti_faces)
+fetch_rcv1_fxt = _fetch_fixture(fetch_rcv1)
+fetch_species_distributions_fxt = _fetch_fixture(fetch_species_distributions)
+raccoon_face_fxt = pytest.fixture(raccoon_face_or_skip)
+
+
+def pytest_collection_modifyitems(config, items):
+    """Called after collect is completed.
+
+    Parameters
+    ----------
+    config : pytest config
+    items : list of collected items
+    """
+    run_network_tests = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
+    skip_network = pytest.mark.skip(
+        reason="test is enabled when SKLEARN_SKIP_NETWORK_TESTS=0"
+    )
+
+    # download datasets during collection to avoid thread unsafe behavior
+    # when running pytest in parallel with pytest-xdist
+    dataset_features_set = set(dataset_fetchers)
+    datasets_to_download = set()
+
+    for item in items:
+        if isinstance(item, DoctestItem) and "fetch_" in item.name:
+            fetcher_function_name = item.name.split(".")[-1]
+            dataset_fetchers_key = f"{fetcher_function_name}_fxt"
+            dataset_to_fetch = set([dataset_fetchers_key]) & dataset_features_set
+        elif not hasattr(item, "fixturenames"):
+            continue
+        else:
+            item_fixtures = set(item.fixturenames)
+            dataset_to_fetch = item_fixtures & dataset_features_set
+
+        if not dataset_to_fetch:
+            continue
+
+        if run_network_tests:
+            datasets_to_download |= dataset_to_fetch
+        else:
+            # network tests are skipped
+            item.add_marker(skip_network)
+
+    # Only download datasets on the first worker spawned by pytest-xdist
+    # to avoid thread unsafe behavior. If pytest-xdist is not used, we still
+    # download before tests run.
+    worker_id = environ.get("PYTEST_XDIST_WORKER", "gw0")
+    if worker_id == "gw0" and run_network_tests:
+        for name in datasets_to_download:
+            with suppress(SkipTest):
+                dataset_fetchers[name]()
+
+    for item in items:
+        # Known failure on with GradientBoostingClassifier on ARM64
+        if (
+            item.name.endswith("GradientBoostingClassifier")
+            and platform.machine() == "aarch64"
+        ):
+            marker = pytest.mark.xfail(
+                reason=(
+                    "know failure. See "
+                    "https://github.com/scikit-learn/scikit-learn/issues/17797"
+                )
+            )
+            item.add_marker(marker)
+
+    skip_doctests = False
+    try:
+        import matplotlib  # noqa: F401
+    except ImportError:
+        skip_doctests = True
+        reason = "matplotlib is required to run the doctests"
+
+    if _IS_32BIT:
+        reason = "doctest are only run when the default numpy int is 64 bits."
+        skip_doctests = True
+    elif sys.platform.startswith("win32"):
+        reason = (
+            "doctests are not run for Windows because numpy arrays "
+            "repr is inconsistent across platforms."
+        )
+        skip_doctests = True
+
+    if np_base_version < parse_version("2"):
+        # TODO: configure numpy to output scalar arrays as regular Python scalars
+        # once possible to improve readability of the tests docstrings.
+        # https://numpy.org/neps/nep-0051-scalar-representation.html#implementation
+        reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2"
+        skip_doctests = True
+
+    if sp_version < parse_version("1.14"):
+        reason = "Scipy sparse matrix repr has changed in scipy 1.14"
+        skip_doctests = True
+
+    # Normally doctest has the entire module's scope. Here we set globs to an empty dict
+    # to remove the module's scope:
+    # https://docs.python.org/3/library/doctest.html#what-s-the-execution-context
+    for item in items:
+        if isinstance(item, DoctestItem):
+            item.dtest.globs = {}
+
+    if skip_doctests:
+        skip_marker = pytest.mark.skip(reason=reason)
+
+        for item in items:
+            if isinstance(item, DoctestItem):
+                # work-around an internal error with pytest if adding a skip
+                # mark to a doctest in a contextmanager, see
+                # https://github.com/pytest-dev/pytest/issues/8796 for more
+                # details.
+                if item.name != "sklearn._config.config_context":
+                    item.add_marker(skip_marker)
+    try:
+        import PIL  # noqa: F401
+
+        pillow_installed = True
+    except ImportError:
+        pillow_installed = False
+
+    if not pillow_installed:
+        skip_marker = pytest.mark.skip(reason="pillow (or PIL) not installed!")
+        for item in items:
+            if item.name in [
+                "sklearn.feature_extraction.image.PatchExtractor",
+                "sklearn.feature_extraction.image.extract_patches_2d",
+            ]:
+                item.add_marker(skip_marker)
+
+
+@pytest.fixture(scope="function")
+def pyplot():
+    """Setup and teardown fixture for matplotlib.
+
+    This fixture checks if we can import matplotlib. If not, the tests will be
+    skipped. Otherwise, we close the figures before and after running the
+    functions.
+
+    Returns
+    -------
+    pyplot : module
+        The ``matplotlib.pyplot`` module.
+    """
+    pyplot = pytest.importorskip("matplotlib.pyplot")
+    pyplot.close("all")
+    yield pyplot
+    pyplot.close("all")
+
+
+def pytest_generate_tests(metafunc):
+    """Parametrization of global_random_seed fixture
+
+    based on the SKLEARN_TESTS_GLOBAL_RANDOM_SEED environment variable.
+
+    The goal of this fixture is to prevent tests that use it to be sensitive
+    to a specific seed value while still being deterministic by default.
+
+    See the documentation for the SKLEARN_TESTS_GLOBAL_RANDOM_SEED
+    variable for instructions on how to use this fixture.
+
+    https://scikit-learn.org/dev/computing/parallelism.html#sklearn-tests-global-random-seed
+
+    """
+    # When using pytest-xdist this function is called in the xdist workers.
+    # We rely on SKLEARN_TESTS_GLOBAL_RANDOM_SEED environment variable which is
+    # set in before running pytest and is available in xdist workers since they
+    # are subprocesses.
+    RANDOM_SEED_RANGE = list(range(100))  # All seeds in [0, 99] should be valid.
+    random_seed_var = environ.get("SKLEARN_TESTS_GLOBAL_RANDOM_SEED")
+
+    default_random_seeds = [42]
+
+    if random_seed_var is None:
+        random_seeds = default_random_seeds
+    elif random_seed_var == "all":
+        random_seeds = RANDOM_SEED_RANGE
+    else:
+        if "-" in random_seed_var:
+            start, stop = random_seed_var.split("-")
+            random_seeds = list(range(int(start), int(stop) + 1))
+        else:
+            random_seeds = [int(random_seed_var)]
+
+        if min(random_seeds) < 0 or max(random_seeds) > 99:
+            raise ValueError(
+                "The value(s) of the environment variable "
+                "SKLEARN_TESTS_GLOBAL_RANDOM_SEED must be in the range [0, 99] "
+                f"(or 'all'), got: {random_seed_var}"
+            )
+
+    if "global_random_seed" in metafunc.fixturenames:
+        metafunc.parametrize("global_random_seed", random_seeds)
+
+
+def pytest_configure(config):
+    # Use matplotlib agg backend during the tests including doctests
+    try:
+        import matplotlib
+
+        matplotlib.use("agg")
+    except ImportError:
+        pass
+
+    allowed_parallelism = joblib.cpu_count(only_physical_cores=True)
+    xdist_worker_count = environ.get("PYTEST_XDIST_WORKER_COUNT")
+    if xdist_worker_count is not None:
+        # Set the number of OpenMP and BLAS threads based on the number of workers
+        # xdist is using to prevent oversubscription.
+        allowed_parallelism = max(allowed_parallelism // int(xdist_worker_count), 1)
+    threadpool_limits(allowed_parallelism)
+
+    if environ.get("SKLEARN_WARNINGS_AS_ERRORS", "0") != "0":
+        # This seems like the only way to programmatically change the config
+        # filterwarnings. This was suggested in
+        # https://github.com/pytest-dev/pytest/issues/3311#issuecomment-373177592
+        for line in get_pytest_filterwarning_lines():
+            config.addinivalue_line("filterwarnings", line)
+
+    faulthandler_timeout = int(environ.get("SKLEARN_FAULTHANDLER_TIMEOUT", "0"))
+    if faulthandler_timeout > 0:
+        faulthandler.enable()
+        faulthandler.dump_traceback_later(faulthandler_timeout, exit=True)
+
+
+@pytest.fixture
+def hide_available_pandas(monkeypatch):
+    """Pretend pandas was not installed."""
+    import_orig = builtins.__import__
+
+    def mocked_import(name, *args, **kwargs):
+        if name == "pandas":
+            raise ImportError()
+        return import_orig(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", mocked_import)
+
+
+@pytest.fixture
+def print_changed_only_false():
+    """Set `print_changed_only` to False for the duration of the test."""
+    set_config(print_changed_only=False)
+    yield
+    set_config(print_changed_only=True)  # reset to default
+
+
+if dt_config is not None:
+    # Strict mode to differentiate between 3.14 and np.float64(3.14)
+    dt_config.strict_check = True
+    # dt_config.rtol = 0.01
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/discriminant_analysis.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/discriminant_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..6df26a05a87817a1c50c10c73626c1638df09ccb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/discriminant_analysis.py
@@ -0,0 +1,1129 @@
+"""Linear and quadratic discriminant analysis."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.linalg
+from scipy import linalg
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from .covariance import empirical_covariance, ledoit_wolf, shrunk_covariance
+from .linear_model._base import LinearClassifierMixin
+from .preprocessing import StandardScaler
+from .utils._array_api import _expit, device, get_namespace, size
+from .utils._param_validation import HasMethods, Interval, StrOptions
+from .utils.extmath import softmax
+from .utils.multiclass import check_classification_targets, unique_labels
+from .utils.validation import check_is_fitted, validate_data
+
+__all__ = ["LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis"]
+
+
+def _cov(X, shrinkage=None, covariance_estimator=None):
+    """Estimate covariance matrix (using optional covariance_estimator).
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Input data.
+
+    shrinkage : {'empirical', 'auto'} or float, default=None
+        Shrinkage parameter, possible values:
+          - None or 'empirical': no shrinkage (default).
+          - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
+          - float between 0 and 1: fixed shrinkage parameter.
+
+        Shrinkage parameter is ignored if  `covariance_estimator`
+        is not None.
+
+    covariance_estimator : estimator, default=None
+        If not None, `covariance_estimator` is used to estimate
+        the covariance matrices instead of relying on the empirical
+        covariance estimator (with potential shrinkage).
+        The object should have a fit method and a ``covariance_`` attribute
+        like the estimators in :mod:`sklearn.covariance``.
+        if None the shrinkage parameter drives the estimate.
+
+        .. versionadded:: 0.24
+
+    Returns
+    -------
+    s : ndarray of shape (n_features, n_features)
+        Estimated covariance matrix.
+    """
+    if covariance_estimator is None:
+        shrinkage = "empirical" if shrinkage is None else shrinkage
+        if isinstance(shrinkage, str):
+            if shrinkage == "auto":
+                sc = StandardScaler()  # standardize features
+                X = sc.fit_transform(X)
+                s = ledoit_wolf(X)[0]
+                # rescale
+                s = sc.scale_[:, np.newaxis] * s * sc.scale_[np.newaxis, :]
+            elif shrinkage == "empirical":
+                s = empirical_covariance(X)
+        elif isinstance(shrinkage, Real):
+            s = shrunk_covariance(empirical_covariance(X), shrinkage)
+    else:
+        if shrinkage is not None and shrinkage != 0:
+            raise ValueError(
+                "covariance_estimator and shrinkage parameters "
+                "are not None. Only one of the two can be set."
+            )
+        covariance_estimator.fit(X)
+        if not hasattr(covariance_estimator, "covariance_"):
+            raise ValueError(
+                "%s does not have a covariance_ attribute"
+                % covariance_estimator.__class__.__name__
+            )
+        s = covariance_estimator.covariance_
+    return s
+
+
+def _class_means(X, y):
+    """Compute class means.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Input data.
+
+    y : array-like of shape (n_samples,) or (n_samples, n_targets)
+        Target values.
+
+    Returns
+    -------
+    means : array-like of shape (n_classes, n_features)
+        Class means.
+    """
+    xp, is_array_api_compliant = get_namespace(X)
+    classes, y = xp.unique_inverse(y)
+    means = xp.zeros((classes.shape[0], X.shape[1]), device=device(X), dtype=X.dtype)
+
+    if is_array_api_compliant:
+        for i in range(classes.shape[0]):
+            means[i, :] = xp.mean(X[y == i], axis=0)
+    else:
+        # TODO: Explore the choice of using bincount + add.at as it seems sub optimal
+        # from a performance-wise
+        cnt = np.bincount(y)
+        np.add.at(means, y, X)
+        means /= cnt[:, None]
+    return means
+
+
+def _class_cov(X, y, priors, shrinkage=None, covariance_estimator=None):
+    """Compute weighted within-class covariance matrix.
+
+    The per-class covariance are weighted by the class priors.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Input data.
+
+    y : array-like of shape (n_samples,) or (n_samples, n_targets)
+        Target values.
+
+    priors : array-like of shape (n_classes,)
+        Class priors.
+
+    shrinkage : 'auto' or float, default=None
+        Shrinkage parameter, possible values:
+          - None: no shrinkage (default).
+          - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
+          - float between 0 and 1: fixed shrinkage parameter.
+
+        Shrinkage parameter is ignored if `covariance_estimator` is not None.
+
+    covariance_estimator : estimator, default=None
+        If not None, `covariance_estimator` is used to estimate
+        the covariance matrices instead of relying the empirical
+        covariance estimator (with potential shrinkage).
+        The object should have a fit method and a ``covariance_`` attribute
+        like the estimators in sklearn.covariance.
+        If None, the shrinkage parameter drives the estimate.
+
+        .. versionadded:: 0.24
+
+    Returns
+    -------
+    cov : array-like of shape (n_features, n_features)
+        Weighted within-class covariance matrix
+    """
+    classes = np.unique(y)
+    cov = np.zeros(shape=(X.shape[1], X.shape[1]))
+    for idx, group in enumerate(classes):
+        Xg = X[y == group, :]
+        cov += priors[idx] * np.atleast_2d(_cov(Xg, shrinkage, covariance_estimator))
+    return cov
+
+
+class DiscriminantAnalysisPredictionMixin:
+    """Mixin class for QuadraticDiscriminantAnalysis and NearestCentroid."""
+
+    def decision_function(self, X):
+        """Apply decision function to an array of samples.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Array of samples (test vectors).
+
+        Returns
+        -------
+        y_scores : ndarray of shape (n_samples,) or (n_samples, n_classes)
+            Decision function values related to each class, per sample.
+            In the two-class case, the shape is `(n_samples,)`, giving the
+            log likelihood ratio of the positive class.
+        """
+        y_scores = self._decision_function(X)
+        if len(self.classes_) == 2:
+            return y_scores[:, 1] - y_scores[:, 0]
+        return y_scores
+
+    def predict(self, X):
+        """Perform classification on an array of vectors `X`.
+
+        Returns the class label for each sample.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Class label for each sample.
+        """
+        scores = self._decision_function(X)
+        return self.classes_.take(scores.argmax(axis=1))
+
+    def predict_proba(self, X):
+        """Estimate class probabilities.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        y_proba : ndarray of shape (n_samples, n_classes)
+            Probability estimate of the sample for each class in the
+            model, where classes are ordered as they are in `self.classes_`.
+        """
+        return np.exp(self.predict_log_proba(X))
+
+    def predict_log_proba(self, X):
+        """Estimate log class probabilities.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        y_log_proba : ndarray of shape (n_samples, n_classes)
+            Estimated log probabilities.
+        """
+        scores = self._decision_function(X)
+        log_likelihood = scores - scores.max(axis=1)[:, np.newaxis]
+        return log_likelihood - np.log(
+            np.exp(log_likelihood).sum(axis=1)[:, np.newaxis]
+        )
+
+
+class LinearDiscriminantAnalysis(
+    ClassNamePrefixFeaturesOutMixin,
+    LinearClassifierMixin,
+    TransformerMixin,
+    BaseEstimator,
+):
+    """Linear Discriminant Analysis.
+
+    A classifier with a linear decision boundary, generated by fitting class
+    conditional densities to the data and using Bayes' rule.
+
+    The model fits a Gaussian density to each class, assuming that all classes
+    share the same covariance matrix.
+
+    The fitted model can also be used to reduce the dimensionality of the input
+    by projecting it to the most discriminative directions, using the
+    `transform` method.
+
+    .. versionadded:: 0.17
+
+    For a comparison between
+    :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis`
+    and :class:`~sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`, see
+    :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    solver : {'svd', 'lsqr', 'eigen'}, default='svd'
+        Solver to use, possible values:
+          - 'svd': Singular value decomposition (default).
+            Does not compute the covariance matrix, therefore this solver is
+            recommended for data with a large number of features.
+          - 'lsqr': Least squares solution.
+            Can be combined with shrinkage or custom covariance estimator.
+          - 'eigen': Eigenvalue decomposition.
+            Can be combined with shrinkage or custom covariance estimator.
+
+        .. versionchanged:: 1.2
+            `solver="svd"` now has experimental Array API support. See the
+            :ref:`Array API User Guide ` for more details.
+
+    shrinkage : 'auto' or float, default=None
+        Shrinkage parameter, possible values:
+          - None: no shrinkage (default).
+          - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
+          - float between 0 and 1: fixed shrinkage parameter.
+
+        This should be left to None if `covariance_estimator` is used.
+        Note that shrinkage works only with 'lsqr' and 'eigen' solvers.
+
+        For a usage example, see
+        :ref:`sphx_glr_auto_examples_classification_plot_lda.py`.
+
+    priors : array-like of shape (n_classes,), default=None
+        The class prior probabilities. By default, the class proportions are
+        inferred from the training data.
+
+    n_components : int, default=None
+        Number of components (<= min(n_classes - 1, n_features)) for
+        dimensionality reduction. If None, will be set to
+        min(n_classes - 1, n_features). This parameter only affects the
+        `transform` method.
+
+        For a usage example, see
+        :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`.
+
+    store_covariance : bool, default=False
+        If True, explicitly compute the weighted within-class covariance
+        matrix when solver is 'svd'. The matrix is always computed
+        and stored for the other solvers.
+
+        .. versionadded:: 0.17
+
+    tol : float, default=1.0e-4
+        Absolute threshold for a singular value of X to be considered
+        significant, used to estimate the rank of X. Dimensions whose
+        singular values are non-significant are discarded. Only used if
+        solver is 'svd'.
+
+        .. versionadded:: 0.17
+
+    covariance_estimator : covariance estimator, default=None
+        If not None, `covariance_estimator` is used to estimate
+        the covariance matrices instead of relying on the empirical
+        covariance estimator (with potential shrinkage).
+        The object should have a fit method and a ``covariance_`` attribute
+        like the estimators in :mod:`sklearn.covariance`.
+        if None the shrinkage parameter drives the estimate.
+
+        This should be left to None if `shrinkage` is used.
+        Note that `covariance_estimator` works only with 'lsqr' and 'eigen'
+        solvers.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (n_features,) or (n_classes, n_features)
+        Weight vector(s).
+
+    intercept_ : ndarray of shape (n_classes,)
+        Intercept term.
+
+    covariance_ : array-like of shape (n_features, n_features)
+        Weighted within-class covariance matrix. It corresponds to
+        `sum_k prior_k * C_k` where `C_k` is the covariance matrix of the
+        samples in class `k`. The `C_k` are estimated using the (potentially
+        shrunk) biased estimator of covariance. If solver is 'svd', only
+        exists when `store_covariance` is True.
+
+    explained_variance_ratio_ : ndarray of shape (n_components,)
+        Percentage of variance explained by each of the selected components.
+        If ``n_components`` is not set then all components are stored and the
+        sum of explained variances is equal to 1.0. Only available when eigen
+        or svd solver is used.
+
+    means_ : array-like of shape (n_classes, n_features)
+        Class-wise means.
+
+    priors_ : array-like of shape (n_classes,)
+        Class priors (sum to 1).
+
+    scalings_ : array-like of shape (rank, n_classes - 1)
+        Scaling of the features in the space spanned by the class centroids.
+        Only available for 'svd' and 'eigen' solvers.
+
+    xbar_ : array-like of shape (n_features,)
+        Overall mean. Only present if solver is 'svd'.
+
+    classes_ : array-like of shape (n_classes,)
+        Unique class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    QuadraticDiscriminantAnalysis : Quadratic Discriminant Analysis.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+    >>> y = np.array([1, 1, 1, 2, 2, 2])
+    >>> clf = LinearDiscriminantAnalysis()
+    >>> clf.fit(X, y)
+    LinearDiscriminantAnalysis()
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
+    """
+
+    _parameter_constraints: dict = {
+        "solver": [StrOptions({"svd", "lsqr", "eigen"})],
+        "shrinkage": [StrOptions({"auto"}), Interval(Real, 0, 1, closed="both"), None],
+        "n_components": [Interval(Integral, 1, None, closed="left"), None],
+        "priors": ["array-like", None],
+        "store_covariance": ["boolean"],
+        "tol": [Interval(Real, 0, None, closed="left")],
+        "covariance_estimator": [HasMethods("fit"), None],
+    }
+
+    def __init__(
+        self,
+        solver="svd",
+        shrinkage=None,
+        priors=None,
+        n_components=None,
+        store_covariance=False,
+        tol=1e-4,
+        covariance_estimator=None,
+    ):
+        self.solver = solver
+        self.shrinkage = shrinkage
+        self.priors = priors
+        self.n_components = n_components
+        self.store_covariance = store_covariance  # used only in svd solver
+        self.tol = tol  # used only in svd solver
+        self.covariance_estimator = covariance_estimator
+
+    def _solve_lstsq(self, X, y, shrinkage, covariance_estimator):
+        """Least squares solver.
+
+        The least squares solver computes a straightforward solution of the
+        optimal decision rule based directly on the discriminant functions. It
+        can only be used for classification (with any covariance estimator),
+        because
+        estimation of eigenvectors is not performed. Therefore, dimensionality
+        reduction with the transform is not supported.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_classes)
+            Target values.
+
+        shrinkage : 'auto', float or None
+            Shrinkage parameter, possible values:
+              - None: no shrinkage.
+              - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
+              - float between 0 and 1: fixed shrinkage parameter.
+
+            Shrinkage parameter is ignored if  `covariance_estimator` i
+            not None
+
+        covariance_estimator : estimator, default=None
+            If not None, `covariance_estimator` is used to estimate
+            the covariance matrices instead of relying the empirical
+            covariance estimator (with potential shrinkage).
+            The object should have a fit method and a ``covariance_`` attribute
+            like the estimators in sklearn.covariance.
+            if None the shrinkage parameter drives the estimate.
+
+            .. versionadded:: 0.24
+
+        Notes
+        -----
+        This solver is based on [1]_, section 2.6.2, pp. 39-41.
+
+        References
+        ----------
+        .. [1] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
+           (Second Edition). John Wiley & Sons, Inc., New York, 2001. ISBN
+           0-471-05669-3.
+        """
+        self.means_ = _class_means(X, y)
+        self.covariance_ = _class_cov(
+            X, y, self.priors_, shrinkage, covariance_estimator
+        )
+        self.coef_ = linalg.lstsq(self.covariance_, self.means_.T)[0].T
+        self.intercept_ = -0.5 * np.diag(np.dot(self.means_, self.coef_.T)) + np.log(
+            self.priors_
+        )
+
+    def _solve_eigen(self, X, y, shrinkage, covariance_estimator):
+        """Eigenvalue solver.
+
+        The eigenvalue solver computes the optimal solution of the Rayleigh
+        coefficient (basically the ratio of between class scatter to within
+        class scatter). This solver supports both classification and
+        dimensionality reduction (with any covariance estimator).
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_targets)
+            Target values.
+
+        shrinkage : 'auto', float or None
+            Shrinkage parameter, possible values:
+              - None: no shrinkage.
+              - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
+              - float between 0 and 1: fixed shrinkage constant.
+
+            Shrinkage parameter is ignored if  `covariance_estimator` i
+            not None
+
+        covariance_estimator : estimator, default=None
+            If not None, `covariance_estimator` is used to estimate
+            the covariance matrices instead of relying the empirical
+            covariance estimator (with potential shrinkage).
+            The object should have a fit method and a ``covariance_`` attribute
+            like the estimators in sklearn.covariance.
+            if None the shrinkage parameter drives the estimate.
+
+            .. versionadded:: 0.24
+
+        Notes
+        -----
+        This solver is based on [1]_, section 3.8.3, pp. 121-124.
+
+        References
+        ----------
+        .. [1] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
+           (Second Edition). John Wiley & Sons, Inc., New York, 2001. ISBN
+           0-471-05669-3.
+        """
+        self.means_ = _class_means(X, y)
+        self.covariance_ = _class_cov(
+            X, y, self.priors_, shrinkage, covariance_estimator
+        )
+
+        Sw = self.covariance_  # within scatter
+        St = _cov(X, shrinkage, covariance_estimator)  # total scatter
+        Sb = St - Sw  # between scatter
+
+        evals, evecs = linalg.eigh(Sb, Sw)
+        self.explained_variance_ratio_ = np.sort(evals / np.sum(evals))[::-1][
+            : self._max_components
+        ]
+        evecs = evecs[:, np.argsort(evals)[::-1]]  # sort eigenvectors
+
+        self.scalings_ = evecs
+        self.coef_ = np.dot(self.means_, evecs).dot(evecs.T)
+        self.intercept_ = -0.5 * np.diag(np.dot(self.means_, self.coef_.T)) + np.log(
+            self.priors_
+        )
+
+    def _solve_svd(self, X, y):
+        """SVD solver.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_targets)
+            Target values.
+        """
+        xp, is_array_api_compliant = get_namespace(X)
+
+        if is_array_api_compliant:
+            svd = xp.linalg.svd
+        else:
+            svd = scipy.linalg.svd
+
+        n_samples, n_features = X.shape
+        n_classes = self.classes_.shape[0]
+
+        self.means_ = _class_means(X, y)
+        if self.store_covariance:
+            self.covariance_ = _class_cov(X, y, self.priors_)
+
+        Xc = []
+        for idx, group in enumerate(self.classes_):
+            Xg = X[y == group]
+            Xc.append(Xg - self.means_[idx, :])
+
+        self.xbar_ = self.priors_ @ self.means_
+
+        Xc = xp.concat(Xc, axis=0)
+
+        # 1) within (univariate) scaling by with classes std-dev
+        std = xp.std(Xc, axis=0)
+        # avoid division by zero in normalization
+        std[std == 0] = 1.0
+        fac = xp.asarray(1.0 / (n_samples - n_classes), dtype=X.dtype, device=device(X))
+
+        # 2) Within variance scaling
+        X = xp.sqrt(fac) * (Xc / std)
+        # SVD of centered (within)scaled data
+        U, S, Vt = svd(X, full_matrices=False)
+
+        rank = xp.sum(xp.astype(S > self.tol, xp.int32))
+        # Scaling of within covariance is: V' 1/S
+        scalings = (Vt[:rank, :] / std).T / S[:rank]
+        fac = 1.0 if n_classes == 1 else 1.0 / (n_classes - 1)
+
+        # 3) Between variance scaling
+        # Scale weighted centers
+        X = (
+            (xp.sqrt((n_samples * self.priors_) * fac)) * (self.means_ - self.xbar_).T
+        ).T @ scalings
+        # Centers are living in a space with n_classes-1 dim (maximum)
+        # Use SVD to find projection in the space spanned by the
+        # (n_classes) centers
+        _, S, Vt = svd(X, full_matrices=False)
+
+        if self._max_components == 0:
+            self.explained_variance_ratio_ = xp.empty((0,), dtype=S.dtype)
+        else:
+            self.explained_variance_ratio_ = (S**2 / xp.sum(S**2))[
+                : self._max_components
+            ]
+
+        rank = xp.sum(xp.astype(S > self.tol * S[0], xp.int32))
+        self.scalings_ = scalings @ Vt.T[:, :rank]
+        coef = (self.means_ - self.xbar_) @ self.scalings_
+        self.intercept_ = -0.5 * xp.sum(coef**2, axis=1) + xp.log(self.priors_)
+        self.coef_ = coef @ self.scalings_.T
+        self.intercept_ -= self.xbar_ @ self.coef_.T
+
+    @_fit_context(
+        # LinearDiscriminantAnalysis.covariance_estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y):
+        """Fit the Linear Discriminant Analysis model.
+
+        .. versionchanged:: 0.19
+            `store_covariance` and `tol` has been moved to main constructor.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        xp, _ = get_namespace(X)
+
+        X, y = validate_data(
+            self, X, y, ensure_min_samples=2, dtype=[xp.float64, xp.float32]
+        )
+        self.classes_ = unique_labels(y)
+        n_samples, _ = X.shape
+        n_classes = self.classes_.shape[0]
+
+        if n_samples == n_classes:
+            raise ValueError(
+                "The number of samples must be more than the number of classes."
+            )
+
+        if self.priors is None:  # estimate priors from sample
+            _, cnts = xp.unique_counts(y)  # non-negative ints
+            self.priors_ = xp.astype(cnts, X.dtype) / float(y.shape[0])
+        else:
+            self.priors_ = xp.asarray(self.priors, dtype=X.dtype)
+
+        if xp.any(self.priors_ < 0):
+            raise ValueError("priors must be non-negative")
+
+        if xp.abs(xp.sum(self.priors_) - 1.0) > 1e-5:
+            warnings.warn("The priors do not sum to 1. Renormalizing", UserWarning)
+            self.priors_ = self.priors_ / self.priors_.sum()
+
+        # Maximum number of components no matter what n_components is
+        # specified:
+        max_components = min(n_classes - 1, X.shape[1])
+
+        if self.n_components is None:
+            self._max_components = max_components
+        else:
+            if self.n_components > max_components:
+                raise ValueError(
+                    "n_components cannot be larger than min(n_features, n_classes - 1)."
+                )
+            self._max_components = self.n_components
+
+        if self.solver == "svd":
+            if self.shrinkage is not None:
+                raise NotImplementedError("shrinkage not supported with 'svd' solver.")
+            if self.covariance_estimator is not None:
+                raise ValueError(
+                    "covariance estimator "
+                    "is not supported "
+                    "with svd solver. Try another solver"
+                )
+            self._solve_svd(X, y)
+        elif self.solver == "lsqr":
+            self._solve_lstsq(
+                X,
+                y,
+                shrinkage=self.shrinkage,
+                covariance_estimator=self.covariance_estimator,
+            )
+        elif self.solver == "eigen":
+            self._solve_eigen(
+                X,
+                y,
+                shrinkage=self.shrinkage,
+                covariance_estimator=self.covariance_estimator,
+            )
+        if size(self.classes_) == 2:  # treat binary case as a special case
+            coef_ = xp.asarray(self.coef_[1, :] - self.coef_[0, :], dtype=X.dtype)
+            self.coef_ = xp.reshape(coef_, (1, -1))
+            intercept_ = xp.asarray(
+                self.intercept_[1] - self.intercept_[0], dtype=X.dtype
+            )
+            self.intercept_ = xp.reshape(intercept_, (1,))
+        self._n_features_out = self._max_components
+        return self
+
+    def transform(self, X):
+        """Project data to maximize class separation.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components) or \
+            (n_samples, min(rank, n_components))
+            Transformed data. In the case of the 'svd' solver, the shape
+            is (n_samples, min(rank, n_components)).
+        """
+        if self.solver == "lsqr":
+            raise NotImplementedError(
+                "transform not implemented for 'lsqr' solver (use 'svd' or 'eigen')."
+            )
+        check_is_fitted(self)
+        xp, _ = get_namespace(X)
+        X = validate_data(self, X, reset=False)
+
+        if self.solver == "svd":
+            X_new = (X - self.xbar_) @ self.scalings_
+        elif self.solver == "eigen":
+            X_new = X @ self.scalings_
+
+        return X_new[:, : self._max_components]
+
+    def predict_proba(self, X):
+        """Estimate probability.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            Estimated probabilities.
+        """
+        check_is_fitted(self)
+        xp, is_array_api_compliant = get_namespace(X)
+        decision = self.decision_function(X)
+        if size(self.classes_) == 2:
+            proba = _expit(decision, xp)
+            return xp.stack([1 - proba, proba], axis=1)
+        else:
+            return softmax(decision)
+
+    def predict_log_proba(self, X):
+        """Estimate log probability.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            Estimated log probabilities.
+        """
+        xp, _ = get_namespace(X)
+        prediction = self.predict_proba(X)
+
+        info = xp.finfo(prediction.dtype)
+        if hasattr(info, "smallest_normal"):
+            smallest_normal = info.smallest_normal
+        else:
+            # smallest_normal was introduced in NumPy 1.22
+            smallest_normal = info.tiny
+
+        prediction[prediction == 0.0] += smallest_normal
+        return xp.log(prediction)
+
+    def decision_function(self, X):
+        """Apply decision function to an array of samples.
+
+        The decision function is equal (up to a constant factor) to the
+        log-posterior of the model, i.e. `log p(y = k | x)`. In a binary
+        classification setting this instead corresponds to the difference
+        `log p(y = 1 | x) - log p(y = 0 | x)`. See :ref:`lda_qda_math`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Array of samples (test vectors).
+
+        Returns
+        -------
+        y_scores : ndarray of shape (n_samples,) or (n_samples, n_classes)
+            Decision function values related to each class, per sample.
+            In the two-class case, the shape is `(n_samples,)`, giving the
+            log likelihood ratio of the positive class.
+        """
+        # Only override for the doc
+        return super().decision_function(X)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.array_api_support = True
+        return tags
+
+
+class QuadraticDiscriminantAnalysis(
+    DiscriminantAnalysisPredictionMixin, ClassifierMixin, BaseEstimator
+):
+    """Quadratic Discriminant Analysis.
+
+    A classifier with a quadratic decision boundary, generated
+    by fitting class conditional densities to the data
+    and using Bayes' rule.
+
+    The model fits a Gaussian density to each class.
+
+    .. versionadded:: 0.17
+
+    For a comparison between
+    :class:`~sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`
+    and :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis`, see
+    :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    priors : array-like of shape (n_classes,), default=None
+        Class priors. By default, the class proportions are inferred from the
+        training data.
+
+    reg_param : float, default=0.0
+        Regularizes the per-class covariance estimates by transforming S2 as
+        ``S2 = (1 - reg_param) * S2 + reg_param * np.eye(n_features)``,
+        where S2 corresponds to the `scaling_` attribute of a given class.
+
+    store_covariance : bool, default=False
+        If True, the class covariance matrices are explicitly computed and
+        stored in the `self.covariance_` attribute.
+
+        .. versionadded:: 0.17
+
+    tol : float, default=1.0e-4
+        Absolute threshold for the covariance matrix to be considered rank
+        deficient after applying some regularization (see `reg_param`) to each
+        `Sk` where `Sk` represents covariance matrix for k-th class. This
+        parameter does not affect the predictions. It controls when a warning
+        is raised if the covariance matrix is not full rank.
+
+        .. versionadded:: 0.17
+
+    Attributes
+    ----------
+    covariance_ : list of len n_classes of ndarray \
+            of shape (n_features, n_features)
+        For each class, gives the covariance matrix estimated using the
+        samples of that class. The estimations are unbiased. Only present if
+        `store_covariance` is True.
+
+    means_ : array-like of shape (n_classes, n_features)
+        Class-wise means.
+
+    priors_ : array-like of shape (n_classes,)
+        Class priors (sum to 1).
+
+    rotations_ : list of len n_classes of ndarray of shape (n_features, n_k)
+        For each class k an array of shape (n_features, n_k), where
+        ``n_k = min(n_features, number of elements in class k)``
+        It is the rotation of the Gaussian distribution, i.e. its
+        principal axis. It corresponds to `V`, the matrix of eigenvectors
+        coming from the SVD of `Xk = U S Vt` where `Xk` is the centered
+        matrix of samples from class k.
+
+    scalings_ : list of len n_classes of ndarray of shape (n_k,)
+        For each class, contains the scaling of
+        the Gaussian distributions along its principal axes, i.e. the
+        variance in the rotated coordinate system. It corresponds to `S^2 /
+        (n_samples - 1)`, where `S` is the diagonal matrix of singular values
+        from the SVD of `Xk`, where `Xk` is the centered matrix of samples
+        from class k.
+
+    classes_ : ndarray of shape (n_classes,)
+        Unique class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    LinearDiscriminantAnalysis : Linear Discriminant Analysis.
+
+    Examples
+    --------
+    >>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+    >>> import numpy as np
+    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+    >>> y = np.array([1, 1, 1, 2, 2, 2])
+    >>> clf = QuadraticDiscriminantAnalysis()
+    >>> clf.fit(X, y)
+    QuadraticDiscriminantAnalysis()
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
+    """
+
+    _parameter_constraints: dict = {
+        "priors": ["array-like", None],
+        "reg_param": [Interval(Real, 0, 1, closed="both")],
+        "store_covariance": ["boolean"],
+        "tol": [Interval(Real, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self, *, priors=None, reg_param=0.0, store_covariance=False, tol=1.0e-4
+    ):
+        self.priors = priors
+        self.reg_param = reg_param
+        self.store_covariance = store_covariance
+        self.tol = tol
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y):
+        """Fit the model according to the given training data and parameters.
+
+        .. versionchanged:: 0.19
+            ``store_covariances`` has been moved to main constructor as
+            ``store_covariance``.
+
+        .. versionchanged:: 0.19
+            ``tol`` has been moved to main constructor.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values (integers).
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        X, y = validate_data(self, X, y)
+        check_classification_targets(y)
+        self.classes_, y = np.unique(y, return_inverse=True)
+        n_samples, n_features = X.shape
+        n_classes = len(self.classes_)
+        if n_classes < 2:
+            raise ValueError(
+                "The number of classes has to be greater than one; got %d class"
+                % (n_classes)
+            )
+        if self.priors is None:
+            self.priors_ = np.bincount(y) / float(n_samples)
+        else:
+            self.priors_ = np.array(self.priors)
+
+        cov = None
+        store_covariance = self.store_covariance
+        if store_covariance:
+            cov = []
+        means = []
+        scalings = []
+        rotations = []
+        for ind in range(n_classes):
+            Xg = X[y == ind, :]
+            meang = Xg.mean(0)
+            means.append(meang)
+            if len(Xg) == 1:
+                raise ValueError(
+                    "y has only 1 sample in class %s, covariance is ill defined."
+                    % str(self.classes_[ind])
+                )
+            Xgc = Xg - meang
+            # Xgc = U * S * V.T
+            _, S, Vt = np.linalg.svd(Xgc, full_matrices=False)
+            S2 = (S**2) / (len(Xg) - 1)
+            S2 = ((1 - self.reg_param) * S2) + self.reg_param
+            rank = np.sum(S2 > self.tol)
+            if rank < n_features:
+                warnings.warn(
+                    f"The covariance matrix of class {ind} is not full rank. "
+                    "Increasing the value of parameter `reg_param` might help"
+                    " reducing the collinearity.",
+                    linalg.LinAlgWarning,
+                )
+            if self.store_covariance or store_covariance:
+                # cov = V * (S^2 / (n-1)) * V.T
+                cov.append(np.dot(S2 * Vt.T, Vt))
+            scalings.append(S2)
+            rotations.append(Vt.T)
+        if self.store_covariance or store_covariance:
+            self.covariance_ = cov
+        self.means_ = np.asarray(means)
+        self.scalings_ = scalings
+        self.rotations_ = rotations
+        return self
+
+    def _decision_function(self, X):
+        # return log posterior, see eq (4.12) p. 110 of the ESL.
+        check_is_fitted(self)
+
+        X = validate_data(self, X, reset=False)
+        norm2 = []
+        for i in range(len(self.classes_)):
+            R = self.rotations_[i]
+            S = self.scalings_[i]
+            Xm = X - self.means_[i]
+            X2 = np.dot(Xm, R * (S ** (-0.5)))
+            norm2.append(np.sum(X2**2, axis=1))
+        norm2 = np.array(norm2).T  # shape = [len(X), n_classes]
+        u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
+        return -0.5 * (norm2 + u) + np.log(self.priors_)
+
+    def decision_function(self, X):
+        """Apply decision function to an array of samples.
+
+        The decision function is equal (up to a constant factor) to the
+        log-posterior of the model, i.e. `log p(y = k | x)`. In a binary
+        classification setting this instead corresponds to the difference
+        `log p(y = 1 | x) - log p(y = 0 | x)`. See :ref:`lda_qda_math`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Array of samples (test vectors).
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,) or (n_samples, n_classes)
+            Decision function values related to each class, per sample.
+            In the two-class case, the shape is `(n_samples,)`, giving the
+            log likelihood ratio of the positive class.
+        """
+        return super().decision_function(X)
+
+    def predict(self, X):
+        """Perform classification on an array of test vectors X.
+
+        The predicted class C for each sample in X is returned.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Vector to be scored, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,)
+            Estimated probabilities.
+        """
+        return super().predict(X)
+
+    def predict_proba(self, X):
+        """Return posterior probabilities of classification.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Array of samples/test vectors.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            Posterior probabilities of classification per class.
+        """
+        # compute the likelihood of the underlying gaussian models
+        # up to a multiplicative constant.
+        return super().predict_proba(X)
+
+    def predict_log_proba(self, X):
+        """Return log of posterior probabilities of classification.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Array of samples/test vectors.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            Posterior log-probabilities of classification per class.
+        """
+        # XXX : can do better to avoid precision overflows
+        return super().predict_log_proba(X)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/dummy.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/dummy.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d44fa2e473bba9110eb2d998eb4b7eee4d2d543
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/dummy.py
@@ -0,0 +1,704 @@
+"""Dummy estimators that implement simple rules of thumb."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MultiOutputMixin,
+    RegressorMixin,
+    _fit_context,
+)
+from .utils import check_random_state
+from .utils._param_validation import Interval, StrOptions
+from .utils.multiclass import class_distribution
+from .utils.random import _random_choice_csc
+from .utils.stats import _weighted_percentile
+from .utils.validation import (
+    _check_sample_weight,
+    _num_samples,
+    check_array,
+    check_consistent_length,
+    check_is_fitted,
+    validate_data,
+)
+
+
+class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
+    """DummyClassifier makes predictions that ignore the input features.
+
+    This classifier serves as a simple baseline to compare against other more
+    complex classifiers.
+
+    The specific behavior of the baseline is selected with the `strategy`
+    parameter.
+
+    All strategies make predictions that ignore the input feature values passed
+    as the `X` argument to `fit` and `predict`. The predictions, however,
+    typically depend on values observed in the `y` parameter passed to `fit`.
+
+    Note that the "stratified" and "uniform" strategies lead to
+    non-deterministic predictions that can be rendered deterministic by setting
+    the `random_state` parameter if needed. The other strategies are naturally
+    deterministic and, once fit, always return the same constant prediction
+    for any value of `X`.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    strategy : {"most_frequent", "prior", "stratified", "uniform", \
+            "constant"}, default="prior"
+        Strategy to use to generate predictions.
+
+        * "most_frequent": the `predict` method always returns the most
+          frequent class label in the observed `y` argument passed to `fit`.
+          The `predict_proba` method returns the matching one-hot encoded
+          vector.
+        * "prior": the `predict` method always returns the most frequent
+          class label in the observed `y` argument passed to `fit` (like
+          "most_frequent"). ``predict_proba`` always returns the empirical
+          class distribution of `y` also known as the empirical class prior
+          distribution.
+        * "stratified": the `predict_proba` method randomly samples one-hot
+          vectors from a multinomial distribution parametrized by the empirical
+          class prior probabilities.
+          The `predict` method returns the class label which got probability
+          one in the one-hot vector of `predict_proba`.
+          Each sampled row of both methods is therefore independent and
+          identically distributed.
+        * "uniform": generates predictions uniformly at random from the list
+          of unique classes observed in `y`, i.e. each class has equal
+          probability.
+        * "constant": always predicts a constant label that is provided by
+          the user. This is useful for metrics that evaluate a non-majority
+          class.
+
+          .. versionchanged:: 0.24
+             The default value of `strategy` has changed to "prior" in version
+             0.24.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the randomness to generate the predictions when
+        ``strategy='stratified'`` or ``strategy='uniform'``.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    constant : int or str or array-like of shape (n_outputs,), default=None
+        The explicit constant as predicted by the "constant" strategy. This
+        parameter is useful only for the "constant" strategy.
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,) or list of such arrays
+        Unique class labels observed in `y`. For multi-output classification
+        problems, this attribute is a list of arrays as each output has an
+        independent set of possible classes.
+
+    n_classes_ : int or list of int
+        Number of label for each output.
+
+    class_prior_ : ndarray of shape (n_classes,) or list of such arrays
+        Frequency of each class observed in `y`. For multioutput classification
+        problems, this is computed independently for each output.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X` has
+        feature names that are all strings.
+
+    n_outputs_ : int
+        Number of outputs.
+
+    sparse_output_ : bool
+        True if the array returned from predict is to be in sparse CSC format.
+        Is automatically set to True if the input `y` is passed in sparse
+        format.
+
+    See Also
+    --------
+    DummyRegressor : Regressor that makes predictions using simple rules.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.dummy import DummyClassifier
+    >>> X = np.array([-1, 1, 1, 1])
+    >>> y = np.array([0, 1, 1, 1])
+    >>> dummy_clf = DummyClassifier(strategy="most_frequent")
+    >>> dummy_clf.fit(X, y)
+    DummyClassifier(strategy='most_frequent')
+    >>> dummy_clf.predict(X)
+    array([1, 1, 1, 1])
+    >>> dummy_clf.score(X, y)
+    0.75
+    """
+
+    _parameter_constraints: dict = {
+        "strategy": [
+            StrOptions({"most_frequent", "prior", "stratified", "uniform", "constant"})
+        ],
+        "random_state": ["random_state"],
+        "constant": [Integral, str, "array-like", None],
+    }
+
+    def __init__(self, *, strategy="prior", random_state=None, constant=None):
+        self.strategy = strategy
+        self.random_state = random_state
+        self.constant = constant
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the baseline classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        validate_data(self, X, skip_check_array=True)
+
+        self._strategy = self.strategy
+
+        if self._strategy == "uniform" and sp.issparse(y):
+            y = y.toarray()
+            warnings.warn(
+                (
+                    "A local copy of the target data has been converted "
+                    "to a numpy array. Predicting on sparse target data "
+                    "with the uniform strategy would not save memory "
+                    "and would be slower."
+                ),
+                UserWarning,
+            )
+
+        self.sparse_output_ = sp.issparse(y)
+
+        if not self.sparse_output_:
+            y = np.asarray(y)
+            y = np.atleast_1d(y)
+
+        if y.ndim == 1:
+            y = np.reshape(y, (-1, 1))
+
+        self.n_outputs_ = y.shape[1]
+
+        check_consistent_length(X, y)
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        if self._strategy == "constant":
+            if self.constant is None:
+                raise ValueError(
+                    "Constant target value has to be specified "
+                    "when the constant strategy is used."
+                )
+            else:
+                constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))
+                if constant.shape[0] != self.n_outputs_:
+                    raise ValueError(
+                        "Constant target value should have shape (%d, 1)."
+                        % self.n_outputs_
+                    )
+
+        (self.classes_, self.n_classes_, self.class_prior_) = class_distribution(
+            y, sample_weight
+        )
+
+        if self._strategy == "constant":
+            for k in range(self.n_outputs_):
+                if not any(constant[k][0] == c for c in self.classes_[k]):
+                    # Checking in case of constant strategy if the constant
+                    # provided by the user is in y.
+                    err_msg = (
+                        "The constant target value must be present in "
+                        "the training data. You provided constant={}. "
+                        "Possible values are: {}.".format(
+                            self.constant, self.classes_[k].tolist()
+                        )
+                    )
+                    raise ValueError(err_msg)
+
+        if self.n_outputs_ == 1:
+            self.n_classes_ = self.n_classes_[0]
+            self.classes_ = self.classes_[0]
+            self.class_prior_ = self.class_prior_[0]
+
+        return self
+
+    def predict(self, X):
+        """Perform classification on test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        Returns
+        -------
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Predicted target values for X.
+        """
+        check_is_fitted(self)
+
+        # numpy random_state expects Python int and not long as size argument
+        # under Windows
+        n_samples = _num_samples(X)
+        rs = check_random_state(self.random_state)
+
+        n_classes_ = self.n_classes_
+        classes_ = self.classes_
+        class_prior_ = self.class_prior_
+        constant = self.constant
+        if self.n_outputs_ == 1:
+            # Get same type even for self.n_outputs_ == 1
+            n_classes_ = [n_classes_]
+            classes_ = [classes_]
+            class_prior_ = [class_prior_]
+            constant = [constant]
+        # Compute probability only once
+        if self._strategy == "stratified":
+            proba = self.predict_proba(X)
+            if self.n_outputs_ == 1:
+                proba = [proba]
+
+        if self.sparse_output_:
+            class_prob = None
+            if self._strategy in ("most_frequent", "prior"):
+                classes_ = [np.array([cp.argmax()]) for cp in class_prior_]
+
+            elif self._strategy == "stratified":
+                class_prob = class_prior_
+
+            elif self._strategy == "uniform":
+                raise ValueError(
+                    "Sparse target prediction is not "
+                    "supported with the uniform strategy"
+                )
+
+            elif self._strategy == "constant":
+                classes_ = [np.array([c]) for c in constant]
+
+            y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)
+        else:
+            if self._strategy in ("most_frequent", "prior"):
+                y = np.tile(
+                    [
+                        classes_[k][class_prior_[k].argmax()]
+                        for k in range(self.n_outputs_)
+                    ],
+                    [n_samples, 1],
+                )
+
+            elif self._strategy == "stratified":
+                y = np.vstack(
+                    [
+                        classes_[k][proba[k].argmax(axis=1)]
+                        for k in range(self.n_outputs_)
+                    ]
+                ).T
+
+            elif self._strategy == "uniform":
+                ret = [
+                    classes_[k][rs.randint(n_classes_[k], size=n_samples)]
+                    for k in range(self.n_outputs_)
+                ]
+                y = np.vstack(ret).T
+
+            elif self._strategy == "constant":
+                y = np.tile(self.constant, (n_samples, 1))
+
+            if self.n_outputs_ == 1:
+                y = np.ravel(y)
+
+        return y
+
+    def predict_proba(self, X):
+        """
+        Return probability estimates for the test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        Returns
+        -------
+        P : ndarray of shape (n_samples, n_classes) or list of such arrays
+            Returns the probability of the sample for each class in
+            the model, where classes are ordered arithmetically, for each
+            output.
+        """
+        check_is_fitted(self)
+
+        # numpy random_state expects Python int and not long as size argument
+        # under Windows
+        n_samples = _num_samples(X)
+        rs = check_random_state(self.random_state)
+
+        n_classes_ = self.n_classes_
+        classes_ = self.classes_
+        class_prior_ = self.class_prior_
+        constant = self.constant
+        if self.n_outputs_ == 1:
+            # Get same type even for self.n_outputs_ == 1
+            n_classes_ = [n_classes_]
+            classes_ = [classes_]
+            class_prior_ = [class_prior_]
+            constant = [constant]
+
+        P = []
+        for k in range(self.n_outputs_):
+            if self._strategy == "most_frequent":
+                ind = class_prior_[k].argmax()
+                out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
+                out[:, ind] = 1.0
+            elif self._strategy == "prior":
+                out = np.ones((n_samples, 1)) * class_prior_[k]
+
+            elif self._strategy == "stratified":
+                out = rs.multinomial(1, class_prior_[k], size=n_samples)
+                out = out.astype(np.float64)
+
+            elif self._strategy == "uniform":
+                out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)
+                out /= n_classes_[k]
+
+            elif self._strategy == "constant":
+                ind = np.where(classes_[k] == constant[k])
+                out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
+                out[:, ind] = 1.0
+
+            P.append(out)
+
+        if self.n_outputs_ == 1:
+            P = P[0]
+
+        return P
+
+    def predict_log_proba(self, X):
+        """
+        Return log probability estimates for the test vectors X.
+
+        Parameters
+        ----------
+        X : {array-like, object with finite length or shape}
+            Training data.
+
+        Returns
+        -------
+        P : ndarray of shape (n_samples, n_classes) or list of such arrays
+            Returns the log probability of the sample for each class in
+            the model, where classes are ordered arithmetically for each
+            output.
+        """
+        proba = self.predict_proba(X)
+        if self.n_outputs_ == 1:
+            return np.log(proba)
+        else:
+            return [np.log(p) for p in proba]
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.classifier_tags.poor_score = True
+        tags.no_validation = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """Return the mean accuracy on the given test data and labels.
+
+        In multi-label classification, this is the subset accuracy
+        which is a harsh metric since you require for each sample that
+        each label set be correctly predicted.
+
+        Parameters
+        ----------
+        X : None or array-like of shape (n_samples, n_features)
+            Test samples. Passing None as test samples gives the same result
+            as passing real test samples, since DummyClassifier
+            operates independently of the sampled observations.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True labels for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) w.r.t. y.
+        """
+        if X is None:
+            X = np.zeros(shape=(len(y), 1))
+        return super().score(X, y, sample_weight)
+
+
+class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
+    """Regressor that makes predictions using simple rules.
+
+    This regressor is useful as a simple baseline to compare with other
+    (real) regressors. Do not use it for real problems.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    strategy : {"mean", "median", "quantile", "constant"}, default="mean"
+        Strategy to use to generate predictions.
+
+        * "mean": always predicts the mean of the training set
+        * "median": always predicts the median of the training set
+        * "quantile": always predicts a specified quantile of the training set,
+          provided with the quantile parameter.
+        * "constant": always predicts a constant value that is provided by
+          the user.
+
+    constant : int or float or array-like of shape (n_outputs,), default=None
+        The explicit constant as predicted by the "constant" strategy. This
+        parameter is useful only for the "constant" strategy.
+
+    quantile : float in [0.0, 1.0], default=None
+        The quantile to predict using the "quantile" strategy. A quantile of
+        0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
+        maximum.
+
+    Attributes
+    ----------
+    constant_ : ndarray of shape (1, n_outputs)
+        Mean or median or quantile of the training targets or constant value
+        given by the user.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X` has
+        feature names that are all strings.
+
+    n_outputs_ : int
+        Number of outputs.
+
+    See Also
+    --------
+    DummyClassifier: Classifier that makes predictions using simple rules.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.dummy import DummyRegressor
+    >>> X = np.array([1.0, 2.0, 3.0, 4.0])
+    >>> y = np.array([2.0, 3.0, 5.0, 10.0])
+    >>> dummy_regr = DummyRegressor(strategy="mean")
+    >>> dummy_regr.fit(X, y)
+    DummyRegressor()
+    >>> dummy_regr.predict(X)
+    array([5., 5., 5., 5.])
+    >>> dummy_regr.score(X, y)
+    0.0
+    """
+
+    _parameter_constraints: dict = {
+        "strategy": [StrOptions({"mean", "median", "quantile", "constant"})],
+        "quantile": [Interval(Real, 0.0, 1.0, closed="both"), None],
+        "constant": [
+            Interval(Real, None, None, closed="neither"),
+            "array-like",
+            None,
+        ],
+    }
+
+    def __init__(self, *, strategy="mean", constant=None, quantile=None):
+        self.strategy = strategy
+        self.constant = constant
+        self.quantile = quantile
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the baseline regressor.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        validate_data(self, X, skip_check_array=True)
+
+        y = check_array(y, ensure_2d=False, input_name="y")
+        if len(y) == 0:
+            raise ValueError("y must not be empty.")
+
+        if y.ndim == 1:
+            y = np.reshape(y, (-1, 1))
+        self.n_outputs_ = y.shape[1]
+
+        check_consistent_length(X, y, sample_weight)
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        if self.strategy == "mean":
+            self.constant_ = np.average(y, axis=0, weights=sample_weight)
+
+        elif self.strategy == "median":
+            if sample_weight is None:
+                self.constant_ = np.median(y, axis=0)
+            else:
+                self.constant_ = [
+                    _weighted_percentile(y[:, k], sample_weight, percentile_rank=50.0)
+                    for k in range(self.n_outputs_)
+                ]
+
+        elif self.strategy == "quantile":
+            if self.quantile is None:
+                raise ValueError(
+                    "When using `strategy='quantile', you have to specify the desired "
+                    "quantile in the range [0, 1]."
+                )
+            percentile_rank = self.quantile * 100.0
+            if sample_weight is None:
+                self.constant_ = np.percentile(y, axis=0, q=percentile_rank)
+            else:
+                self.constant_ = [
+                    _weighted_percentile(
+                        y[:, k], sample_weight, percentile_rank=percentile_rank
+                    )
+                    for k in range(self.n_outputs_)
+                ]
+
+        elif self.strategy == "constant":
+            if self.constant is None:
+                raise TypeError(
+                    "Constant target value has to be specified "
+                    "when the constant strategy is used."
+                )
+
+            self.constant_ = check_array(
+                self.constant,
+                accept_sparse=["csr", "csc", "coo"],
+                ensure_2d=False,
+                ensure_min_samples=0,
+            )
+
+            if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]:
+                raise ValueError(
+                    "Constant target value should have shape (%d, 1)." % y.shape[1]
+                )
+
+        self.constant_ = np.reshape(self.constant_, (1, -1))
+        return self
+
+    def predict(self, X, return_std=False):
+        """Perform classification on test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        return_std : bool, default=False
+            Whether to return the standard deviation of posterior prediction.
+            All zeros in this case.
+
+            .. versionadded:: 0.20
+
+        Returns
+        -------
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Predicted target values for X.
+
+        y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Standard deviation of predictive distribution of query points.
+        """
+        check_is_fitted(self)
+        n_samples = _num_samples(X)
+
+        y = np.full(
+            (n_samples, self.n_outputs_),
+            self.constant_,
+            dtype=np.array(self.constant_).dtype,
+        )
+        y_std = np.zeros((n_samples, self.n_outputs_))
+
+        if self.n_outputs_ == 1:
+            y = np.ravel(y)
+            y_std = np.ravel(y_std)
+
+        return (y, y_std) if return_std else y
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.regressor_tags.poor_score = True
+        tags.no_validation = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """Return the coefficient of determination R^2 of the prediction.
+
+        The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the
+        residual sum of squares `((y_true - y_pred) ** 2).sum()` and `v` is the
+        total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best
+        possible score is 1.0 and it can be negative (because the model can be
+        arbitrarily worse). A constant model that always predicts the expected
+        value of y, disregarding the input features, would get a R^2 score of
+        0.0.
+
+        Parameters
+        ----------
+        X : None or array-like of shape (n_samples, n_features)
+            Test samples. Passing None as test samples gives the same result
+            as passing real test samples, since `DummyRegressor`
+            operates independently of the sampled observations.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True values for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            R^2 of `self.predict(X)` w.r.t. y.
+        """
+        if X is None:
+            X = np.zeros(shape=(len(y), 1))
+        return super().score(X, y, sample_weight)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/exceptions.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db5a2ff0435f4327208860aaf0dfaa2e101a250
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/exceptions.py
@@ -0,0 +1,249 @@
+"""Custom warnings and errors used across scikit-learn."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+__all__ = [
+    "ConvergenceWarning",
+    "DataConversionWarning",
+    "DataDimensionalityWarning",
+    "EfficiencyWarning",
+    "EstimatorCheckFailedWarning",
+    "FitFailedWarning",
+    "NotFittedError",
+    "PositiveSpectrumWarning",
+    "SkipTestWarning",
+    "UndefinedMetricWarning",
+    "UnsetMetadataPassedError",
+]
+
+
+class UnsetMetadataPassedError(ValueError):
+    """Exception class to raise if a metadata is passed which is not explicitly \
+        requested (metadata=True) or not requested (metadata=False).
+
+    .. versionadded:: 1.3
+
+    Parameters
+    ----------
+    message : str
+        The message
+
+    unrequested_params : dict
+        A dictionary of parameters and their values which are provided but not
+        requested.
+
+    routed_params : dict
+        A dictionary of routed parameters.
+    """
+
+    def __init__(self, *, message, unrequested_params, routed_params):
+        super().__init__(message)
+        self.unrequested_params = unrequested_params
+        self.routed_params = routed_params
+
+
+class NotFittedError(ValueError, AttributeError):
+    """Exception class to raise if estimator is used before fitting.
+
+    This class inherits from both ValueError and AttributeError to help with
+    exception handling and backward compatibility.
+
+    Examples
+    --------
+    >>> from sklearn.svm import LinearSVC
+    >>> from sklearn.exceptions import NotFittedError
+    >>> try:
+    ...     LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
+    ... except NotFittedError as e:
+    ...     print(repr(e))
+    NotFittedError("This LinearSVC instance is not fitted yet. Call 'fit' with
+    appropriate arguments before using this estimator."...)
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
+    """
+
+
+class ConvergenceWarning(UserWarning):
+    """Custom warning to capture convergence problems
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
+    """
+
+
+class DataConversionWarning(UserWarning):
+    """Warning used to notify implicit data conversions happening in the code.
+
+    This warning occurs when some input data needs to be converted or
+    interpreted in a way that may not match the user's expectations.
+
+    For example, this warning may occur when the user
+        - passes an integer array to a function which expects float input and
+          will convert the input
+        - requests a non-copying operation, but a copy is required to meet the
+          implementation's data-type expectations;
+        - passes an input whose shape can be interpreted ambiguously.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
+    """
+
+
+class DataDimensionalityWarning(UserWarning):
+    """Custom warning to notify potential issues with data dimensionality.
+
+    For example, in random projection, this warning is raised when the
+    number of components, which quantifies the dimensionality of the target
+    projection space, is higher than the number of features, which quantifies
+    the dimensionality of the original source space, to imply that the
+    dimensionality of the problem will not be reduced.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
+    """
+
+
+class EfficiencyWarning(UserWarning):
+    """Warning used to notify the user of inefficient computation.
+
+    This warning notifies the user that the efficiency may not be optimal due
+    to some reason which may be included as a part of the warning message.
+    This may be subclassed into a more specific Warning class.
+
+    .. versionadded:: 0.18
+    """
+
+
+class FitFailedWarning(RuntimeWarning):
+    """Warning class used if there is an error while fitting the estimator.
+
+    This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV
+    and the cross-validation helper function cross_val_score to warn when there
+    is an error while fitting the estimator.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.cross_validation.
+    """
+
+
+class SkipTestWarning(UserWarning):
+    """Warning class used to notify the user of a test that was skipped.
+
+    For example, one of the estimator checks requires a pandas import.
+    If the pandas package cannot be imported, the test will be skipped rather
+    than register as a failure.
+    """
+
+
+class UndefinedMetricWarning(UserWarning):
+    """Warning used when the metric is invalid
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.base.
+    """
+
+
+class PositiveSpectrumWarning(UserWarning):
+    """Warning raised when the eigenvalues of a PSD matrix have issues
+
+    This warning is typically raised by ``_check_psd_eigenvalues`` when the
+    eigenvalues of a positive semidefinite (PSD) matrix such as a gram matrix
+    (kernel) present significant negative eigenvalues, or bad conditioning i.e.
+    very small non-zero eigenvalues compared to the largest eigenvalue.
+
+    .. versionadded:: 0.22
+    """
+
+
+class InconsistentVersionWarning(UserWarning):
+    """Warning raised when an estimator is unpickled with an inconsistent version.
+
+    Parameters
+    ----------
+    estimator_name : str
+        Estimator name.
+
+    current_sklearn_version : str
+        Current scikit-learn version.
+
+    original_sklearn_version : str
+        Original scikit-learn version.
+    """
+
+    def __init__(
+        self, *, estimator_name, current_sklearn_version, original_sklearn_version
+    ):
+        self.estimator_name = estimator_name
+        self.current_sklearn_version = current_sklearn_version
+        self.original_sklearn_version = original_sklearn_version
+
+    def __str__(self):
+        return (
+            f"Trying to unpickle estimator {self.estimator_name} from version"
+            f" {self.original_sklearn_version} when "
+            f"using version {self.current_sklearn_version}. This might lead to breaking"
+            " code or "
+            "invalid results. Use at your own risk. "
+            "For more info please refer to:\n"
+            "https://scikit-learn.org/stable/model_persistence.html"
+            "#security-maintainability-limitations"
+        )
+
+
+class EstimatorCheckFailedWarning(UserWarning):
+    """Warning raised when an estimator check from the common tests fails.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        Estimator instance for which the test failed.
+
+    check_name : str
+        Name of the check that failed.
+
+    exception : Exception
+        Exception raised by the failed check.
+
+    status : str
+        Status of the check.
+
+    expected_to_fail : bool
+        Whether the check was expected to fail.
+
+    expected_to_fail_reason : str
+        Reason for the expected failure.
+    """
+
+    def __init__(
+        self,
+        *,
+        estimator,
+        check_name: str,
+        exception: Exception,
+        status: str,
+        expected_to_fail: bool,
+        expected_to_fail_reason: str,
+    ):
+        self.estimator = estimator
+        self.check_name = check_name
+        self.exception = exception
+        self.status = status
+        self.expected_to_fail = expected_to_fail
+        self.expected_to_fail_reason = expected_to_fail_reason
+
+    def __repr__(self):
+        expected_to_fail_str = (
+            f"Expected to fail: {self.expected_to_fail_reason}"
+            if self.expected_to_fail
+            else "Not expected to fail"
+        )
+        return (
+            f"Test {self.check_name} failed for estimator {self.estimator!r}.\n"
+            f"Expected to fail reason: {expected_to_fail_str}\n"
+            f"Exception: {self.exception}"
+        )
+
+    def __str__(self):
+        return self.__repr__()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/README b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/README
new file mode 100644
index 0000000000000000000000000000000000000000..eef7ba7dd652e73413dad8ed1c6096dc4066d214
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/README
@@ -0,0 +1,7 @@
+This directory contains bundled external dependencies that are updated
+every once in a while.
+
+Note for distribution packagers: if you want to remove the duplicated
+code and depend on a packaged version, we suggest that you simply do a
+symbolic link in this directory.
+
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..97cda1858d5655b4179183372d271299298c62be
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/__init__.py
@@ -0,0 +1,5 @@
+
+"""
+External, bundled dependencies.
+
+"""
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_arff.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_arff.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c9d51d0702ff5cbe70b80d405747e37a5e6cb1d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_arff.py
@@ -0,0 +1,1107 @@
+# =============================================================================
+# Federal University of Rio Grande do Sul (UFRGS)
+# Connectionist Artificial Intelligence Laboratory (LIAC)
+# Renato de Pontes Pereira - rppereira@inf.ufrgs.br
+# =============================================================================
+# Copyright (c) 2011 Renato de Pontes Pereira, renato.ppontes at gmail dot com
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# =============================================================================
+
+'''
+The liac-arff module implements functions to read and write ARFF files in
+Python. It was created in the Connectionist Artificial Intelligence Laboratory
+(LIAC), which takes place at the Federal University of Rio Grande do Sul
+(UFRGS), in Brazil.
+
+ARFF (Attribute-Relation File Format) is an file format specially created for
+describe datasets which are commonly used for machine learning experiments and
+software. This file format was created to be used in Weka, the best
+representative software for machine learning automated experiments.
+
+An ARFF file can be divided into two sections: header and data. The Header
+describes the metadata of the dataset, including a general description of the
+dataset, its name and its attributes. The source below is an example of a
+header section in a XOR dataset::
+
+    %
+    % XOR Dataset
+    %
+    % Created by Renato Pereira
+    %            rppereira@inf.ufrgs.br
+    %            http://inf.ufrgs.br/~rppereira
+    %
+    %
+    @RELATION XOR
+
+    @ATTRIBUTE input1 REAL
+    @ATTRIBUTE input2 REAL
+    @ATTRIBUTE y REAL
+
+The Data section of an ARFF file describes the observations of the dataset, in
+the case of XOR dataset::
+
+    @DATA
+    0.0,0.0,0.0
+    0.0,1.0,1.0
+    1.0,0.0,1.0
+    1.0,1.0,0.0
+    %
+    %
+    %
+
+Notice that several lines are starting with an ``%`` symbol, denoting a
+comment, thus, lines with ``%`` at the beginning will be ignored, except by the
+description part at the beginning of the file. The declarations ``@RELATION``,
+``@ATTRIBUTE``, and ``@DATA`` are all case insensitive and obligatory.
+
+For more information and details about the ARFF file description, consult
+http://www.cs.waikato.ac.nz/~ml/weka/arff.html
+
+
+ARFF Files in Python
+~~~~~~~~~~~~~~~~~~~~
+
+This module uses built-ins python objects to represent a deserialized ARFF
+file. A dictionary is used as the container of the data and metadata of ARFF,
+and have the following keys:
+
+- **description**: (OPTIONAL) a string with the description of the dataset.
+- **relation**: (OBLIGATORY) a string with the name of the dataset.
+- **attributes**: (OBLIGATORY) a list of attributes with the following
+  template::
+
+    (attribute_name, attribute_type)
+
+  the attribute_name is a string, and attribute_type must be an string
+  or a list of strings.
+- **data**: (OBLIGATORY) a list of data instances. Each data instance must be
+  a list with values, depending on the attributes.
+
+The above keys must follow the case which were described, i.e., the keys are
+case sensitive. The attribute type ``attribute_type`` must be one of these
+strings (they are not case sensitive): ``NUMERIC``, ``INTEGER``, ``REAL`` or
+``STRING``. For nominal attributes, the ``atribute_type`` must be a list of
+strings.
+
+In this format, the XOR dataset presented above can be represented as a python
+object as::
+
+    xor_dataset = {
+        'description': 'XOR Dataset',
+        'relation': 'XOR',
+        'attributes': [
+            ('input1', 'REAL'),
+            ('input2', 'REAL'),
+            ('y', 'REAL'),
+        ],
+        'data': [
+            [0.0, 0.0, 0.0],
+            [0.0, 1.0, 1.0],
+            [1.0, 0.0, 1.0],
+            [1.0, 1.0, 0.0]
+        ]
+    }
+
+
+Features
+~~~~~~~~
+
+This module provides several features, including:
+
+- Read and write ARFF files using python built-in structures, such dictionaries
+  and lists;
+- Supports `scipy.sparse.coo `_
+  and lists of dictionaries as used by SVMLight
+- Supports the following attribute types: NUMERIC, REAL, INTEGER, STRING, and
+  NOMINAL;
+- Has an interface similar to other built-in modules such as ``json``, or
+  ``zipfile``;
+- Supports read and write the descriptions of files;
+- Supports missing values and names with spaces;
+- Supports unicode values and names;
+- Fully compatible with Python 2.7+, Python 3.5+, pypy and pypy3;
+- Under `MIT License `_
+
+'''
+__author__ = 'Renato de Pontes Pereira, Matthias Feurer, Joel Nothman'
+__author_email__ = ('renato.ppontes@gmail.com, '
+                    'feurerm@informatik.uni-freiburg.de, '
+                    'joel.nothman@gmail.com')
+__version__ = '2.4.0'
+
+import re
+import csv
+from typing import TYPE_CHECKING
+from typing import Optional, List, Dict, Any, Iterator, Union, Tuple
+
+# CONSTANTS ===================================================================
+_SIMPLE_TYPES = ['NUMERIC', 'REAL', 'INTEGER', 'STRING']
+
+_TK_DESCRIPTION = '%'
+_TK_COMMENT     = '%'
+_TK_RELATION    = '@RELATION'
+_TK_ATTRIBUTE   = '@ATTRIBUTE'
+_TK_DATA        = '@DATA'
+
+_RE_RELATION     = re.compile(r'^([^\{\}%,\s]*|\".*\"|\'.*\')$', re.UNICODE)
+_RE_ATTRIBUTE    = re.compile(r'^(\".*\"|\'.*\'|[^\{\}%,\s]*)\s+(.+)$', re.UNICODE)
+_RE_QUOTE_CHARS = re.compile(r'["\'\\\s%,\000-\031]', re.UNICODE)
+_RE_ESCAPE_CHARS = re.compile(r'(?=["\'\\%])|[\n\r\t\000-\031]')
+_RE_SPARSE_LINE = re.compile(r'^\s*\{.*\}\s*$', re.UNICODE)
+_RE_NONTRIVIAL_DATA = re.compile('["\'{}\\s]', re.UNICODE)
+
+ArffDenseDataType = Iterator[List]
+ArffSparseDataType = Tuple[List, ...]
+
+
+if TYPE_CHECKING:
+    # typing_extensions is available when mypy is installed
+    from typing_extensions import TypedDict
+
+    class ArffContainerType(TypedDict):
+        description: str
+        relation: str
+        attributes: List
+        data: Union[ArffDenseDataType, ArffSparseDataType]
+
+else:
+    ArffContainerType = Dict[str, Any]
+
+
+def _build_re_values():
+    quoted_re = r'''
+                    "      # open quote followed by zero or more of:
+                    (?:
+                        (?= len(conversors):
+                    raise BadDataFormat(row)
+                # XXX: int 0 is used for implicit values, not '0'
+                values = [values[i] if i in values else 0 for i in
+                          range(len(conversors))]
+            else:
+                if len(values) != len(conversors):
+                    raise BadDataFormat(row)
+
+            yield self._decode_values(values, conversors)
+
+    @staticmethod
+    def _decode_values(values, conversors):
+        try:
+            values = [None if value is None else conversor(value)
+                      for conversor, value
+                      in zip(conversors, values)]
+        except ValueError as exc:
+            if 'float: ' in str(exc):
+                raise BadNumericalValue()
+        return values
+
+    def encode_data(self, data, attributes):
+        '''(INTERNAL) Encodes a line of data.
+
+        Data instances follow the csv format, i.e, attribute values are
+        delimited by commas. After converted from csv.
+
+        :param data: a list of values.
+        :param attributes: a list of attributes. Used to check if data is valid.
+        :return: a string with the encoded data line.
+        '''
+        current_row = 0
+
+        for inst in data:
+            if len(inst) != len(attributes):
+                raise BadObject(
+                    'Instance %d has %d attributes, expected %d' %
+                     (current_row, len(inst), len(attributes))
+                )
+
+            new_data = []
+            for value in inst:
+                if value is None or value == '' or value != value:
+                    s = '?'
+                else:
+                    s = encode_string(str(value))
+                new_data.append(s)
+
+            current_row += 1
+            yield ','.join(new_data)
+
+
+class _DataListMixin:
+    """Mixin to return a list from decode_rows instead of a generator"""
+    def decode_rows(self, stream, conversors):
+        return list(super().decode_rows(stream, conversors))
+
+
+class Data(_DataListMixin, DenseGeneratorData):
+    pass
+
+
+class COOData:
+    def decode_rows(self, stream, conversors):
+        data, rows, cols = [], [], []
+        for i, row in enumerate(stream):
+            values = _parse_values(row)
+            if not isinstance(values, dict):
+                raise BadLayout()
+            if not values:
+                continue
+            row_cols, values = zip(*sorted(values.items()))
+            try:
+                values = [value if value is None else conversors[key](value)
+                          for key, value in zip(row_cols, values)]
+            except ValueError as exc:
+                if 'float: ' in str(exc):
+                    raise BadNumericalValue()
+                raise
+            except IndexError:
+                # conversor out of range
+                raise BadDataFormat(row)
+
+            data.extend(values)
+            rows.extend([i] * len(values))
+            cols.extend(row_cols)
+
+        return data, rows, cols
+
+    def encode_data(self, data, attributes):
+        num_attributes = len(attributes)
+        new_data = []
+        current_row = 0
+
+        row = data.row
+        col = data.col
+        data = data.data
+
+        # Check if the rows are sorted
+        if not all(row[i] <= row[i + 1] for i in range(len(row) - 1)):
+            raise ValueError("liac-arff can only output COO matrices with "
+                             "sorted rows.")
+
+        for v, col, row in zip(data, col, row):
+            if row > current_row:
+                # Add empty rows if necessary
+                while current_row < row:
+                    yield " ".join(["{", ','.join(new_data), "}"])
+                    new_data = []
+                    current_row += 1
+
+            if col >= num_attributes:
+                raise BadObject(
+                    'Instance %d has at least %d attributes, expected %d' %
+                    (current_row, col + 1, num_attributes)
+                )
+
+            if v is None or v == '' or v != v:
+                s = '?'
+            else:
+                s = encode_string(str(v))
+            new_data.append("%d %s" % (col, s))
+
+        yield " ".join(["{", ','.join(new_data), "}"])
+
+class LODGeneratorData:
+    def decode_rows(self, stream, conversors):
+        for row in stream:
+            values = _parse_values(row)
+
+            if not isinstance(values, dict):
+                raise BadLayout()
+            try:
+                yield {key: None if value is None else conversors[key](value)
+                       for key, value in values.items()}
+            except ValueError as exc:
+                if 'float: ' in str(exc):
+                    raise BadNumericalValue()
+                raise
+            except IndexError:
+                # conversor out of range
+                raise BadDataFormat(row)
+
+    def encode_data(self, data, attributes):
+        current_row = 0
+
+        num_attributes = len(attributes)
+        for row in data:
+            new_data = []
+
+            if len(row) > 0 and max(row) >= num_attributes:
+                raise BadObject(
+                    'Instance %d has %d attributes, expected %d' %
+                    (current_row, max(row) + 1, num_attributes)
+                )
+
+            for col in sorted(row):
+                v = row[col]
+                if v is None or v == '' or v != v:
+                    s = '?'
+                else:
+                    s = encode_string(str(v))
+                new_data.append("%d %s" % (col, s))
+
+            current_row += 1
+            yield " ".join(["{", ','.join(new_data), "}"])
+
+class LODData(_DataListMixin, LODGeneratorData):
+    pass
+
+
+def _get_data_object_for_decoding(matrix_type):
+    if matrix_type == DENSE:
+        return Data()
+    elif matrix_type == COO:
+        return COOData()
+    elif matrix_type == LOD:
+        return LODData()
+    elif matrix_type == DENSE_GEN:
+        return DenseGeneratorData()
+    elif matrix_type == LOD_GEN:
+        return LODGeneratorData()
+    else:
+        raise ValueError("Matrix type %s not supported." % str(matrix_type))
+
+def _get_data_object_for_encoding(matrix):
+    # Probably a scipy.sparse
+    if hasattr(matrix, 'format'):
+        if matrix.format == 'coo':
+            return COOData()
+        else:
+            raise ValueError('Cannot guess matrix format!')
+    elif isinstance(matrix[0], dict):
+        return LODData()
+    else:
+        return Data()
+
+# =============================================================================
+
+# ADVANCED INTERFACE ==========================================================
+class ArffDecoder:
+    '''An ARFF decoder.'''
+
+    def __init__(self):
+        '''Constructor.'''
+        self._conversors = []
+        self._current_line = 0
+
+    def _decode_comment(self, s):
+        '''(INTERNAL) Decodes a comment line.
+
+        Comments are single line strings starting, obligatorily, with the ``%``
+        character, and can have any symbol, including whitespaces or special
+        characters.
+
+        This method must receive a normalized string, i.e., a string without
+        padding, including the "\r\n" characters.
+
+        :param s: a normalized string.
+        :return: a string with the decoded comment.
+        '''
+        res = re.sub(r'^\%( )?', '', s)
+        return res
+
+    def _decode_relation(self, s):
+        '''(INTERNAL) Decodes a relation line.
+
+        The relation declaration is a line with the format ``@RELATION
+        ``, where ``relation-name`` is a string. The string must
+        start with alphabetic character and must be quoted if the name includes
+        spaces, otherwise this method will raise a `BadRelationFormat` exception.
+
+        This method must receive a normalized string, i.e., a string without
+        padding, including the "\r\n" characters.
+
+        :param s: a normalized string.
+        :return: a string with the decoded relation name.
+        '''
+        _, v = s.split(' ', 1)
+        v = v.strip()
+
+        if not _RE_RELATION.match(v):
+            raise BadRelationFormat()
+
+        res = str(v.strip('"\''))
+        return res
+
+    def _decode_attribute(self, s):
+        '''(INTERNAL) Decodes an attribute line.
+
+        The attribute is the most complex declaration in an arff file. All
+        attributes must follow the template::
+
+             @attribute  
+
+        where ``attribute-name`` is a string, quoted if the name contains any
+        whitespace, and ``datatype`` can be:
+
+        - Numerical attributes as ``NUMERIC``, ``INTEGER`` or ``REAL``.
+        - Strings as ``STRING``.
+        - Dates (NOT IMPLEMENTED).
+        - Nominal attributes with format:
+
+            {, , , ...}
+
+        The nominal names follow the rules for the attribute names, i.e., they
+        must be quoted if the name contains whitespaces.
+
+        This method must receive a normalized string, i.e., a string without
+        padding, including the "\r\n" characters.
+
+        :param s: a normalized string.
+        :return: a tuple (ATTRIBUTE_NAME, TYPE_OR_VALUES).
+        '''
+        _, v = s.split(' ', 1)
+        v = v.strip()
+
+        # Verify the general structure of declaration
+        m = _RE_ATTRIBUTE.match(v)
+        if not m:
+            raise BadAttributeFormat()
+
+        # Extracts the raw name and type
+        name, type_ = m.groups()
+
+        # Extracts the final name
+        name = str(name.strip('"\''))
+
+        # Extracts the final type
+        if type_[:1] == "{" and type_[-1:] == "}":
+            try:
+                type_ = _parse_values(type_.strip('{} '))
+            except Exception:
+                raise BadAttributeType()
+            if isinstance(type_, dict):
+                raise BadAttributeType()
+
+        else:
+            # If not nominal, verify the type name
+            type_ = str(type_).upper()
+            if type_ not in ['NUMERIC', 'REAL', 'INTEGER', 'STRING']:
+                raise BadAttributeType()
+
+        return (name, type_)
+
+    def _decode(self, s, encode_nominal=False, matrix_type=DENSE):
+        '''Do the job the ``encode``.'''
+
+        # Make sure this method is idempotent
+        self._current_line = 0
+
+        # If string, convert to a list of lines
+        if isinstance(s, str):
+            s = s.strip('\r\n ').replace('\r\n', '\n').split('\n')
+
+        # Create the return object
+        obj: ArffContainerType = {
+            'description': '',
+            'relation': '',
+            'attributes': [],
+            'data': []
+        }
+        attribute_names = {}
+
+        # Create the data helper object
+        data = _get_data_object_for_decoding(matrix_type)
+
+        # Read all lines
+        STATE = _TK_DESCRIPTION
+        s = iter(s)
+        for row in s:
+            self._current_line += 1
+            # Ignore empty lines
+            row = row.strip(' \r\n')
+            if not row: continue
+
+            u_row = row.upper()
+
+            # DESCRIPTION -----------------------------------------------------
+            if u_row.startswith(_TK_DESCRIPTION) and STATE == _TK_DESCRIPTION:
+                obj['description'] += self._decode_comment(row) + '\n'
+            # -----------------------------------------------------------------
+
+            # RELATION --------------------------------------------------------
+            elif u_row.startswith(_TK_RELATION):
+                if STATE != _TK_DESCRIPTION:
+                    raise BadLayout()
+
+                STATE = _TK_RELATION
+                obj['relation'] = self._decode_relation(row)
+            # -----------------------------------------------------------------
+
+            # ATTRIBUTE -------------------------------------------------------
+            elif u_row.startswith(_TK_ATTRIBUTE):
+                if STATE != _TK_RELATION and STATE != _TK_ATTRIBUTE:
+                    raise BadLayout()
+
+                STATE = _TK_ATTRIBUTE
+
+                attr = self._decode_attribute(row)
+                if attr[0] in attribute_names:
+                    raise BadAttributeName(attr[0], attribute_names[attr[0]])
+                else:
+                    attribute_names[attr[0]] = self._current_line
+                obj['attributes'].append(attr)
+
+                if isinstance(attr[1], (list, tuple)):
+                    if encode_nominal:
+                        conversor = EncodedNominalConversor(attr[1])
+                    else:
+                        conversor = NominalConversor(attr[1])
+                else:
+                    CONVERSOR_MAP = {'STRING': str,
+                                     'INTEGER': lambda x: int(float(x)),
+                                     'NUMERIC': float,
+                                     'REAL': float}
+                    conversor = CONVERSOR_MAP[attr[1]]
+
+                self._conversors.append(conversor)
+            # -----------------------------------------------------------------
+
+            # DATA ------------------------------------------------------------
+            elif u_row.startswith(_TK_DATA):
+                if STATE != _TK_ATTRIBUTE:
+                    raise BadLayout()
+
+                break
+            # -----------------------------------------------------------------
+
+            # COMMENT ---------------------------------------------------------
+            elif u_row.startswith(_TK_COMMENT):
+                pass
+            # -----------------------------------------------------------------
+        else:
+            # Never found @DATA
+            raise BadLayout()
+
+        def stream():
+            for row in s:
+                self._current_line += 1
+                row = row.strip()
+                # Ignore empty lines and comment lines.
+                if row and not row.startswith(_TK_COMMENT):
+                    yield row
+
+        # Alter the data object
+        obj['data'] = data.decode_rows(stream(), self._conversors)
+        if obj['description'].endswith('\n'):
+            obj['description'] = obj['description'][:-1]
+
+        return obj
+
+    def decode(self, s, encode_nominal=False, return_type=DENSE):
+        '''Returns the Python representation of a given ARFF file.
+
+        When a file object is passed as an argument, this method reads lines
+        iteratively, avoiding to load unnecessary information to the memory.
+
+        :param s: a string or file object with the ARFF file.
+        :param encode_nominal: boolean, if True perform a label encoding
+            while reading the .arff file.
+        :param return_type: determines the data structure used to store the
+            dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
+            `arff.DENSE_GEN` or `arff.LOD_GEN`.
+            Consult the sections on `working with sparse data`_ and `loading
+            progressively`_.
+        '''
+        try:
+            return self._decode(s, encode_nominal=encode_nominal,
+                                matrix_type=return_type)
+        except ArffException as e:
+            e.line = self._current_line
+            raise e
+
+
+class ArffEncoder:
+    '''An ARFF encoder.'''
+
+    def _encode_comment(self, s=''):
+        '''(INTERNAL) Encodes a comment line.
+
+        Comments are single line strings starting, obligatorily, with the ``%``
+        character, and can have any symbol, including whitespaces or special
+        characters.
+
+        If ``s`` is None, this method will simply return an empty comment.
+
+        :param s: (OPTIONAL) string.
+        :return: a string with the encoded comment line.
+        '''
+        if s:
+            return '%s %s'%(_TK_COMMENT, s)
+        else:
+            return '%s' % _TK_COMMENT
+
+    def _encode_relation(self, name):
+        '''(INTERNAL) Decodes a relation line.
+
+        The relation declaration is a line with the format ``@RELATION
+        ``, where ``relation-name`` is a string.
+
+        :param name: a string.
+        :return: a string with the encoded relation declaration.
+        '''
+        for char in ' %{},':
+            if char in name:
+                name = '"%s"'%name
+                break
+
+        return '%s %s'%(_TK_RELATION, name)
+
+    def _encode_attribute(self, name, type_):
+        '''(INTERNAL) Encodes an attribute line.
+
+        The attribute follow the template::
+
+             @attribute  
+
+        where ``attribute-name`` is a string, and ``datatype`` can be:
+
+        - Numerical attributes as ``NUMERIC``, ``INTEGER`` or ``REAL``.
+        - Strings as ``STRING``.
+        - Dates (NOT IMPLEMENTED).
+        - Nominal attributes with format:
+
+            {, , , ...}
+
+        This method must receive a the name of the attribute and its type, if
+        the attribute type is nominal, ``type`` must be a list of values.
+
+        :param name: a string.
+        :param type_: a string or a list of string.
+        :return: a string with the encoded attribute declaration.
+        '''
+        for char in ' %{},':
+            if char in name:
+                name = '"%s"'%name
+                break
+
+        if isinstance(type_, (tuple, list)):
+            type_tmp = ['%s' % encode_string(type_k) for type_k in type_]
+            type_ = '{%s}'%(', '.join(type_tmp))
+
+        return '%s %s %s'%(_TK_ATTRIBUTE, name, type_)
+
+    def encode(self, obj):
+        '''Encodes a given object to an ARFF file.
+
+        :param obj: the object containing the ARFF information.
+        :return: the ARFF file as an string.
+        '''
+        data = [row for row in self.iter_encode(obj)]
+
+        return '\n'.join(data)
+
+    def iter_encode(self, obj):
+        '''The iterative version of `arff.ArffEncoder.encode`.
+
+        This encodes iteratively a given object and return, one-by-one, the
+        lines of the ARFF file.
+
+        :param obj: the object containing the ARFF information.
+        :return: (yields) the ARFF file as strings.
+        '''
+        # DESCRIPTION
+        if obj.get('description', None):
+            for row in obj['description'].split('\n'):
+                yield self._encode_comment(row)
+
+        # RELATION
+        if not obj.get('relation'):
+            raise BadObject('Relation name not found or with invalid value.')
+
+        yield self._encode_relation(obj['relation'])
+        yield ''
+
+        # ATTRIBUTES
+        if not obj.get('attributes'):
+            raise BadObject('Attributes not found.')
+
+        attribute_names = set()
+        for attr in obj['attributes']:
+            # Verify for bad object format
+            if not isinstance(attr, (tuple, list)) or \
+               len(attr) != 2 or \
+               not isinstance(attr[0], str):
+                raise BadObject('Invalid attribute declaration "%s"'%str(attr))
+
+            if isinstance(attr[1], str):
+                # Verify for invalid types
+                if attr[1] not in _SIMPLE_TYPES:
+                    raise BadObject('Invalid attribute type "%s"'%str(attr))
+
+            # Verify for bad object format
+            elif not isinstance(attr[1], (tuple, list)):
+                raise BadObject('Invalid attribute type "%s"'%str(attr))
+
+            # Verify attribute name is not used twice
+            if attr[0] in attribute_names:
+                raise BadObject('Trying to use attribute name "%s" for the '
+                                'second time.' % str(attr[0]))
+            else:
+                attribute_names.add(attr[0])
+
+            yield self._encode_attribute(attr[0], attr[1])
+        yield ''
+        attributes = obj['attributes']
+
+        # DATA
+        yield _TK_DATA
+        if 'data' in obj:
+            data = _get_data_object_for_encoding(obj.get('data'))
+            yield from data.encode_data(obj.get('data'), attributes)
+
+        yield ''
+
+# =============================================================================
+
+# BASIC INTERFACE =============================================================
+def load(fp, encode_nominal=False, return_type=DENSE):
+    '''Load a file-like object containing the ARFF document and convert it into
+    a Python object.
+
+    :param fp: a file-like object.
+    :param encode_nominal: boolean, if True perform a label encoding
+        while reading the .arff file.
+    :param return_type: determines the data structure used to store the
+        dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
+        `arff.DENSE_GEN` or `arff.LOD_GEN`.
+        Consult the sections on `working with sparse data`_ and `loading
+        progressively`_.
+    :return: a dictionary.
+     '''
+    decoder = ArffDecoder()
+    return decoder.decode(fp, encode_nominal=encode_nominal,
+                          return_type=return_type)
+
+def loads(s, encode_nominal=False, return_type=DENSE):
+    '''Convert a string instance containing the ARFF document into a Python
+    object.
+
+    :param s: a string object.
+    :param encode_nominal: boolean, if True perform a label encoding
+        while reading the .arff file.
+    :param return_type: determines the data structure used to store the
+        dataset. Can be one of `arff.DENSE`, `arff.COO`, `arff.LOD`,
+        `arff.DENSE_GEN` or `arff.LOD_GEN`.
+        Consult the sections on `working with sparse data`_ and `loading
+        progressively`_.
+    :return: a dictionary.
+    '''
+    decoder = ArffDecoder()
+    return decoder.decode(s, encode_nominal=encode_nominal,
+                          return_type=return_type)
+
+def dump(obj, fp):
+    '''Serialize an object representing the ARFF document to a given file-like
+    object.
+
+    :param obj: a dictionary.
+    :param fp: a file-like object.
+    '''
+    encoder = ArffEncoder()
+    generator = encoder.iter_encode(obj)
+
+    last_row = next(generator)
+    for row in generator:
+        fp.write(last_row + '\n')
+        last_row = row
+    fp.write(last_row)
+
+    return fp
+
+def dumps(obj):
+    '''Serialize an object representing the ARFF document, returning a string.
+
+    :param obj: a dictionary.
+    :return: a string with the ARFF document.
+    '''
+    encoder = ArffEncoder()
+    return encoder.encode(obj)
+# =============================================================================
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_array_api_compat_vendor.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_array_api_compat_vendor.py
new file mode 100644
index 0000000000000000000000000000000000000000..38cefd2fe6f3f51cb76caa0137eef1af927b9e45
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_array_api_compat_vendor.py
@@ -0,0 +1,5 @@
+# DO NOT RENAME THIS FILE
+# This is a hook for array_api_extra/_lib/_compat.py
+# to co-vendor array_api_compat and potentially override its functions.
+
+from .array_api_compat import *  # noqa: F403
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/__init__.py
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/_structures.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/_structures.py
new file mode 100644
index 0000000000000000000000000000000000000000..837e3a7946d70355b46606d20a4b6c0f038b0815
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/_structures.py
@@ -0,0 +1,90 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/_structures.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+class InfinityType:
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+class NegativeInfinityType:
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __ne__(self, other: object) -> bool:
+        return not isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/version.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f1e5b833699c38690679515bb788820de4168b0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_packaging/version.py
@@ -0,0 +1,535 @@
+"""Vendoered from
+https://github.com/pypa/packaging/blob/main/packaging/version.py
+"""
+# Copyright (c) Donald Stufft and individual contributors.
+# All rights reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+#     1. Redistributions of source code must retain the above copyright notice,
+#        this list of conditions and the following disclaimer.
+
+#     2. Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import collections
+import itertools
+import re
+import warnings
+from typing import Callable, Iterator, List, Optional, SupportsInt, Tuple, Union
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+__all__ = ["parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN"]
+
+InfiniteTypes = Union[InfinityType, NegativeInfinityType]
+PrePostDevType = Union[InfiniteTypes, Tuple[str, int]]
+SubLocalType = Union[InfiniteTypes, int, str]
+LocalType = Union[
+    NegativeInfinityType,
+    Tuple[
+        Union[
+            SubLocalType,
+            Tuple[SubLocalType, str],
+            Tuple[NegativeInfinityType, SubLocalType],
+        ],
+        ...,
+    ],
+]
+CmpKey = Tuple[
+    int, Tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType
+]
+LegacyCmpKey = Tuple[int, Tuple[str, ...]]
+VersionComparisonMethod = Callable[
+    [Union[CmpKey, LegacyCmpKey], Union[CmpKey, LegacyCmpKey]], bool
+]
+
+_Version = collections.namedtuple(
+    "_Version", ["epoch", "release", "dev", "pre", "post", "local"]
+)
+
+
+def parse(version: str) -> Union["LegacyVersion", "Version"]:
+    """Parse the given version from a string to an appropriate class.
+
+    Parameters
+    ----------
+    version : str
+        Version in a string format, eg. "0.9.1" or "1.2.dev0".
+
+    Returns
+    -------
+    version : :class:`Version` object or a :class:`LegacyVersion` object
+        Returned class depends on the given version: if is a valid
+        PEP 440 version or a legacy version.
+    """
+    try:
+        return Version(version)
+    except InvalidVersion:
+        return LegacyVersion(version)
+
+
+class InvalidVersion(ValueError):
+    """
+    An invalid version was found, users should refer to PEP 440.
+    """
+
+
+class _BaseVersion:
+    _key: Union[CmpKey, LegacyCmpKey]
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+class LegacyVersion(_BaseVersion):
+    def __init__(self, version: str) -> None:
+        self._version = str(version)
+        self._key = _legacy_cmpkey(self._version)
+
+        warnings.warn(
+            "Creating a LegacyVersion has been deprecated and will be "
+            "removed in the next major release",
+            DeprecationWarning,
+        )
+
+    def __str__(self) -> str:
+        return self._version
+
+    def __repr__(self) -> str:
+        return f""
+
+    @property
+    def public(self) -> str:
+        return self._version
+
+    @property
+    def base_version(self) -> str:
+        return self._version
+
+    @property
+    def epoch(self) -> int:
+        return -1
+
+    @property
+    def release(self) -> None:
+        return None
+
+    @property
+    def pre(self) -> None:
+        return None
+
+    @property
+    def post(self) -> None:
+        return None
+
+    @property
+    def dev(self) -> None:
+        return None
+
+    @property
+    def local(self) -> None:
+        return None
+
+    @property
+    def is_prerelease(self) -> bool:
+        return False
+
+    @property
+    def is_postrelease(self) -> bool:
+        return False
+
+    @property
+    def is_devrelease(self) -> bool:
+        return False
+
+
+_legacy_version_component_re = re.compile(r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE)
+
+_legacy_version_replacement_map = {
+    "pre": "c",
+    "preview": "c",
+    "-": "final-",
+    "rc": "c",
+    "dev": "@",
+}
+
+
+def _parse_version_parts(s: str) -> Iterator[str]:
+    for part in _legacy_version_component_re.split(s):
+        part = _legacy_version_replacement_map.get(part, part)
+
+        if not part or part == ".":
+            continue
+
+        if part[:1] in "0123456789":
+            # pad for numeric comparison
+            yield part.zfill(8)
+        else:
+            yield "*" + part
+
+    # ensure that alpha/beta/candidate are before final
+    yield "*final"
+
+
+def _legacy_cmpkey(version: str) -> LegacyCmpKey:
+
+    # We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch
+    # greater than or equal to 0. This will effectively put the LegacyVersion,
+    # which uses the defacto standard originally implemented by setuptools,
+    # as before all PEP 440 versions.
+    epoch = -1
+
+    # This scheme is taken from pkg_resources.parse_version setuptools prior to
+    # it's adoption of the packaging library.
+    parts: List[str] = []
+    for part in _parse_version_parts(version.lower()):
+        if part.startswith("*"):
+            # remove "-" before a prerelease tag
+            if part < "*final":
+                while parts and parts[-1] == "*final-":
+                    parts.pop()
+
+            # remove trailing zeros from each series of numeric parts
+            while parts and parts[-1] == "00000000":
+                parts.pop()
+
+        parts.append(part)
+
+    return epoch, tuple(parts)
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P[0-9]+)!)?                           # epoch
+        (?P[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P
                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+
+    def __init__(self, version: str) -> None:
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        return f""
+
+    def __str__(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        _epoch: int = self._version.epoch
+        return _epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        _release: Tuple[int, ...] = self._version.release
+        return _release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        _pre: Optional[Tuple[str, int]] = self._version.pre
+        return _pre
+
+    @property
+    def post(self) -> Optional[int]:
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: str, number: Union[str, bytes, SupportsInt]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[Tuple[SubLocalType]],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: PrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: PrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: PrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: LocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..15fc11fc81f20138388335f81cbdfca5b366e348
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/__init__.py
@@ -0,0 +1 @@
+from ._laplacian import laplacian
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/_laplacian.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/_laplacian.py
new file mode 100644
index 0000000000000000000000000000000000000000..34c816628ee736deb6ba1696a422d9a34df80266
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/_scipy/sparse/csgraph/_laplacian.py
@@ -0,0 +1,557 @@
+"""
+This file is a copy of the scipy.sparse.csgraph._laplacian module from SciPy 1.12
+
+scipy.sparse.csgraph.laplacian supports sparse arrays only starting from Scipy 1.12,
+see https://github.com/scipy/scipy/pull/19156. This vendored file can be removed as
+soon as Scipy 1.12 becomes the minimum supported version.
+
+Laplacian of a compressed-sparse graph
+"""
+
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+from scipy.sparse import issparse
+from scipy.sparse.linalg import LinearOperator
+
+
+###############################################################################
+# Graph laplacian
+def laplacian(
+    csgraph,
+    normed=False,
+    return_diag=False,
+    use_out_degree=False,
+    *,
+    copy=True,
+    form="array",
+    dtype=None,
+    symmetrized=False,
+):
+    """
+    Return the Laplacian of a directed graph.
+
+    Parameters
+    ----------
+    csgraph : array_like or sparse matrix, 2 dimensions
+        Compressed-sparse graph, with shape (N, N).
+    normed : bool, optional
+        If True, then compute symmetrically normalized Laplacian.
+        Default: False.
+    return_diag : bool, optional
+        If True, then also return an array related to vertex degrees.
+        Default: False.
+    use_out_degree : bool, optional
+        If True, then use out-degree instead of in-degree.
+        This distinction matters only if the graph is asymmetric.
+        Default: False.
+    copy : bool, optional
+        If False, then change `csgraph` in place if possible,
+        avoiding doubling the memory use.
+        Default: True, for backward compatibility.
+    form : 'array', or 'function', or 'lo'
+        Determines the format of the output Laplacian:
+
+        * 'array' is a numpy array;
+        * 'function' is a pointer to evaluating the Laplacian-vector
+          or Laplacian-matrix product;
+        * 'lo' results in the format of the `LinearOperator`.
+
+        Choosing 'function' or 'lo' always avoids doubling
+        the memory use, ignoring `copy` value.
+        Default: 'array', for backward compatibility.
+    dtype : None or one of numeric numpy dtypes, optional
+        The dtype of the output. If ``dtype=None``, the dtype of the
+        output matches the dtype of the input csgraph, except for
+        the case ``normed=True`` and integer-like csgraph, where
+        the output dtype is 'float' allowing accurate normalization,
+        but dramatically increasing the memory use.
+        Default: None, for backward compatibility.
+    symmetrized : bool, optional
+        If True, then the output Laplacian is symmetric/Hermitian.
+        The symmetrization is done by ``csgraph + csgraph.T.conj``
+        without dividing by 2 to preserve integer dtypes if possible
+        prior to the construction of the Laplacian.
+        The symmetrization will increase the memory footprint of
+        sparse matrices unless the sparsity pattern is symmetric or
+        `form` is 'function' or 'lo'.
+        Default: False, for backward compatibility.
+
+    Returns
+    -------
+    lap : ndarray, or sparse matrix, or `LinearOperator`
+        The N x N Laplacian of csgraph. It will be a NumPy array (dense)
+        if the input was dense, or a sparse matrix otherwise, or
+        the format of a function or `LinearOperator` if
+        `form` equals 'function' or 'lo', respectively.
+    diag : ndarray, optional
+        The length-N main diagonal of the Laplacian matrix.
+        For the normalized Laplacian, this is the array of square roots
+        of vertex degrees or 1 if the degree is zero.
+
+    Notes
+    -----
+    The Laplacian matrix of a graph is sometimes referred to as the
+    "Kirchhoff matrix" or just the "Laplacian", and is useful in many
+    parts of spectral graph theory.
+    In particular, the eigen-decomposition of the Laplacian can give
+    insight into many properties of the graph, e.g.,
+    is commonly used for spectral data embedding and clustering.
+
+    The constructed Laplacian doubles the memory use if ``copy=True`` and
+    ``form="array"`` which is the default.
+    Choosing ``copy=False`` has no effect unless ``form="array"``
+    or the matrix is sparse in the ``coo`` format, or dense array, except
+    for the integer input with ``normed=True`` that forces the float output.
+
+    Sparse input is reformatted into ``coo`` if ``form="array"``,
+    which is the default.
+
+    If the input adjacency matrix is not symmetric, the Laplacian is
+    also non-symmetric unless ``symmetrized=True`` is used.
+
+    Diagonal entries of the input adjacency matrix are ignored and
+    replaced with zeros for the purpose of normalization where ``normed=True``.
+    The normalization uses the inverse square roots of row-sums of the input
+    adjacency matrix, and thus may fail if the row-sums contain
+    negative or complex with a non-zero imaginary part values.
+
+    The normalization is symmetric, making the normalized Laplacian also
+    symmetric if the input csgraph was symmetric.
+
+    References
+    ----------
+    .. [1] Laplacian matrix. https://en.wikipedia.org/wiki/Laplacian_matrix
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.sparse import csgraph
+
+    Our first illustration is the symmetric graph
+
+    >>> G = np.arange(4) * np.arange(4)[:, np.newaxis]
+    >>> G
+    array([[0, 0, 0, 0],
+           [0, 1, 2, 3],
+           [0, 2, 4, 6],
+           [0, 3, 6, 9]])
+
+    and its symmetric Laplacian matrix
+
+    >>> csgraph.laplacian(G)
+    array([[ 0,  0,  0,  0],
+           [ 0,  5, -2, -3],
+           [ 0, -2,  8, -6],
+           [ 0, -3, -6,  9]])
+
+    The non-symmetric graph
+
+    >>> G = np.arange(9).reshape(3, 3)
+    >>> G
+    array([[0, 1, 2],
+           [3, 4, 5],
+           [6, 7, 8]])
+
+    has different row- and column sums, resulting in two varieties
+    of the Laplacian matrix, using an in-degree, which is the default
+
+    >>> L_in_degree = csgraph.laplacian(G)
+    >>> L_in_degree
+    array([[ 9, -1, -2],
+           [-3,  8, -5],
+           [-6, -7,  7]])
+
+    or alternatively an out-degree
+
+    >>> L_out_degree = csgraph.laplacian(G, use_out_degree=True)
+    >>> L_out_degree
+    array([[ 3, -1, -2],
+           [-3,  8, -5],
+           [-6, -7, 13]])
+
+    Constructing a symmetric Laplacian matrix, one can add the two as
+
+    >>> L_in_degree + L_out_degree.T
+    array([[ 12,  -4,  -8],
+            [ -4,  16, -12],
+            [ -8, -12,  20]])
+
+    or use the ``symmetrized=True`` option
+
+    >>> csgraph.laplacian(G, symmetrized=True)
+    array([[ 12,  -4,  -8],
+           [ -4,  16, -12],
+           [ -8, -12,  20]])
+
+    that is equivalent to symmetrizing the original graph
+
+    >>> csgraph.laplacian(G + G.T)
+    array([[ 12,  -4,  -8],
+           [ -4,  16, -12],
+           [ -8, -12,  20]])
+
+    The goal of normalization is to make the non-zero diagonal entries
+    of the Laplacian matrix to be all unit, also scaling off-diagonal
+    entries correspondingly. The normalization can be done manually, e.g.,
+
+    >>> G = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
+    >>> L, d = csgraph.laplacian(G, return_diag=True)
+    >>> L
+    array([[ 2, -1, -1],
+           [-1,  2, -1],
+           [-1, -1,  2]])
+    >>> d
+    array([2, 2, 2])
+    >>> scaling = np.sqrt(d)
+    >>> scaling
+    array([1.41421356, 1.41421356, 1.41421356])
+    >>> (1/scaling)*L*(1/scaling)
+    array([[ 1. , -0.5, -0.5],
+           [-0.5,  1. , -0.5],
+           [-0.5, -0.5,  1. ]])
+
+    Or using ``normed=True`` option
+
+    >>> L, d = csgraph.laplacian(G, return_diag=True, normed=True)
+    >>> L
+    array([[ 1. , -0.5, -0.5],
+           [-0.5,  1. , -0.5],
+           [-0.5, -0.5,  1. ]])
+
+    which now instead of the diagonal returns the scaling coefficients
+
+    >>> d
+    array([1.41421356, 1.41421356, 1.41421356])
+
+    Zero scaling coefficients are substituted with 1s, where scaling
+    has thus no effect, e.g.,
+
+    >>> G = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0]])
+    >>> G
+    array([[0, 0, 0],
+           [0, 0, 1],
+           [0, 1, 0]])
+    >>> L, d = csgraph.laplacian(G, return_diag=True, normed=True)
+    >>> L
+    array([[ 0., -0., -0.],
+           [-0.,  1., -1.],
+           [-0., -1.,  1.]])
+    >>> d
+    array([1., 1., 1.])
+
+    Only the symmetric normalization is implemented, resulting
+    in a symmetric Laplacian matrix if and only if its graph is symmetric
+    and has all non-negative degrees, like in the examples above.
+
+    The output Laplacian matrix is by default a dense array or a sparse matrix
+    inferring its shape, format, and dtype from the input graph matrix:
+
+    >>> G = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]).astype(np.float32)
+    >>> G
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]], dtype=float32)
+    >>> csgraph.laplacian(G)
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]], dtype=float32)
+
+    but can alternatively be generated matrix-free as a LinearOperator:
+
+    >>> L = csgraph.laplacian(G, form="lo")
+    >>> L
+    <3x3 _CustomLinearOperator with dtype=float32>
+    >>> L(np.eye(3))
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]])
+
+    or as a lambda-function:
+
+    >>> L = csgraph.laplacian(G, form="function")
+    >>> L
+    . at 0x0000012AE6F5A598>
+    >>> L(np.eye(3))
+    array([[ 2., -1., -1.],
+           [-1.,  2., -1.],
+           [-1., -1.,  2.]])
+
+    The Laplacian matrix is used for
+    spectral data clustering and embedding
+    as well as for spectral graph partitioning.
+    Our final example illustrates the latter
+    for a noisy directed linear graph.
+
+    >>> from scipy.sparse import diags, random
+    >>> from scipy.sparse.linalg import lobpcg
+
+    Create a directed linear graph with ``N=35`` vertices
+    using a sparse adjacency matrix ``G``:
+
+    >>> N = 35
+    >>> G = diags(np.ones(N-1), 1, format="csr")
+
+    Fix a random seed ``rng`` and add a random sparse noise to the graph ``G``:
+
+    >>> rng = np.random.default_rng()
+    >>> G += 1e-2 * random(N, N, density=0.1, random_state=rng)
+
+    Set initial approximations for eigenvectors:
+
+    >>> X = rng.random((N, 2))
+
+    The constant vector of ones is always a trivial eigenvector
+    of the non-normalized Laplacian to be filtered out:
+
+    >>> Y = np.ones((N, 1))
+
+    Alternating (1) the sign of the graph weights allows determining
+    labels for spectral max- and min- cuts in a single loop.
+    Since the graph is undirected, the option ``symmetrized=True``
+    must be used in the construction of the Laplacian.
+    The option ``normed=True`` cannot be used in (2) for the negative weights
+    here as the symmetric normalization evaluates square roots.
+    The option ``form="lo"`` in (2) is matrix-free, i.e., guarantees
+    a fixed memory footprint and read-only access to the graph.
+    Calling the eigenvalue solver ``lobpcg`` (3) computes the Fiedler vector
+    that determines the labels as the signs of its components in (5).
+    Since the sign in an eigenvector is not deterministic and can flip,
+    we fix the sign of the first component to be always +1 in (4).
+
+    >>> for cut in ["max", "min"]:
+    ...     G = -G  # 1.
+    ...     L = csgraph.laplacian(G, symmetrized=True, form="lo")  # 2.
+    ...     _, eves = lobpcg(L, X, Y=Y, largest=False, tol=1e-3)  # 3.
+    ...     eves *= np.sign(eves[0, 0])  # 4.
+    ...     print(cut + "-cut labels:\\n", 1 * (eves[:, 0]>0))  # 5.
+    max-cut labels:
+    [1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1]
+    min-cut labels:
+    [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
+
+    As anticipated for a (slightly noisy) linear graph,
+    the max-cut strips all the edges of the graph coloring all
+    odd vertices into one color and all even vertices into another one,
+    while the balanced min-cut partitions the graph
+    in the middle by deleting a single edge.
+    Both determined partitions are optimal.
+    """
+    if csgraph.ndim != 2 or csgraph.shape[0] != csgraph.shape[1]:
+        raise ValueError("csgraph must be a square matrix or array")
+
+    if normed and (
+        np.issubdtype(csgraph.dtype, np.signedinteger)
+        or np.issubdtype(csgraph.dtype, np.uint)
+    ):
+        csgraph = csgraph.astype(np.float64)
+
+    if form == "array":
+        create_lap = _laplacian_sparse if issparse(csgraph) else _laplacian_dense
+    else:
+        create_lap = (
+            _laplacian_sparse_flo if issparse(csgraph) else _laplacian_dense_flo
+        )
+
+    degree_axis = 1 if use_out_degree else 0
+
+    lap, d = create_lap(
+        csgraph,
+        normed=normed,
+        axis=degree_axis,
+        copy=copy,
+        form=form,
+        dtype=dtype,
+        symmetrized=symmetrized,
+    )
+    if return_diag:
+        return lap, d
+    return lap
+
+
+def _setdiag_dense(m, d):
+    step = len(d) + 1
+    m.flat[::step] = d
+
+
+def _laplace(m, d):
+    return lambda v: v * d[:, np.newaxis] - m @ v
+
+
+def _laplace_normed(m, d, nd):
+    laplace = _laplace(m, d)
+    return lambda v: nd[:, np.newaxis] * laplace(v * nd[:, np.newaxis])
+
+
+def _laplace_sym(m, d):
+    return (
+        lambda v: v * d[:, np.newaxis]
+        - m @ v
+        - np.transpose(np.conjugate(np.transpose(np.conjugate(v)) @ m))
+    )
+
+
+def _laplace_normed_sym(m, d, nd):
+    laplace_sym = _laplace_sym(m, d)
+    return lambda v: nd[:, np.newaxis] * laplace_sym(v * nd[:, np.newaxis])
+
+
+def _linearoperator(mv, shape, dtype):
+    return LinearOperator(matvec=mv, matmat=mv, shape=shape, dtype=dtype)
+
+
+def _laplacian_sparse_flo(graph, normed, axis, copy, form, dtype, symmetrized):
+    # The keyword argument `copy` is unused and has no effect here.
+    del copy
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    graph_sum = np.asarray(graph.sum(axis=axis)).ravel()
+    graph_diagonal = graph.diagonal()
+    diag = graph_sum - graph_diagonal
+    if symmetrized:
+        graph_sum += np.asarray(graph.sum(axis=1 - axis)).ravel()
+        diag = graph_sum - graph_diagonal - graph_diagonal
+
+    if normed:
+        isolated_node_mask = diag == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(diag))
+        if symmetrized:
+            md = _laplace_normed_sym(graph, graph_sum, 1.0 / w)
+        else:
+            md = _laplace_normed(graph, graph_sum, 1.0 / w)
+        if form == "function":
+            return md, w.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, w.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+    else:
+        if symmetrized:
+            md = _laplace_sym(graph, graph_sum)
+        else:
+            md = _laplace(graph, graph_sum)
+        if form == "function":
+            return md, diag.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, diag.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+
+
+def _laplacian_sparse(graph, normed, axis, copy, form, dtype, symmetrized):
+    # The keyword argument `form` is unused and has no effect here.
+    del form
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    needs_copy = False
+    if graph.format in ("lil", "dok"):
+        m = graph.tocoo()
+    else:
+        m = graph
+        if copy:
+            needs_copy = True
+
+    if symmetrized:
+        m += m.T.conj()
+
+    w = np.asarray(m.sum(axis=axis)).ravel() - m.diagonal()
+    if normed:
+        m = m.tocoo(copy=needs_copy)
+        isolated_node_mask = w == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(w))
+        m.data /= w[m.row]
+        m.data /= w[m.col]
+        m.data *= -1
+        m.setdiag(1 - isolated_node_mask)
+    else:
+        if m.format == "dia":
+            m = m.copy()
+        else:
+            m = m.tocoo(copy=needs_copy)
+        m.data *= -1
+        m.setdiag(w)
+
+    return m.astype(dtype, copy=False), w.astype(dtype)
+
+
+def _laplacian_dense_flo(graph, normed, axis, copy, form, dtype, symmetrized):
+    if copy:
+        m = np.array(graph)
+    else:
+        m = np.asarray(graph)
+
+    if dtype is None:
+        dtype = m.dtype
+
+    graph_sum = m.sum(axis=axis)
+    graph_diagonal = m.diagonal()
+    diag = graph_sum - graph_diagonal
+    if symmetrized:
+        graph_sum += m.sum(axis=1 - axis)
+        diag = graph_sum - graph_diagonal - graph_diagonal
+
+    if normed:
+        isolated_node_mask = diag == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(diag))
+        if symmetrized:
+            md = _laplace_normed_sym(m, graph_sum, 1.0 / w)
+        else:
+            md = _laplace_normed(m, graph_sum, 1.0 / w)
+        if form == "function":
+            return md, w.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, w.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+    else:
+        if symmetrized:
+            md = _laplace_sym(m, graph_sum)
+        else:
+            md = _laplace(m, graph_sum)
+        if form == "function":
+            return md, diag.astype(dtype, copy=False)
+        elif form == "lo":
+            m = _linearoperator(md, shape=graph.shape, dtype=dtype)
+            return m, diag.astype(dtype, copy=False)
+        else:
+            raise ValueError(f"Invalid form: {form!r}")
+
+
+def _laplacian_dense(graph, normed, axis, copy, form, dtype, symmetrized):
+    if form != "array":
+        raise ValueError(f'{form!r} must be "array"')
+
+    if dtype is None:
+        dtype = graph.dtype
+
+    if copy:
+        m = np.array(graph)
+    else:
+        m = np.asarray(graph)
+
+    if dtype is None:
+        dtype = m.dtype
+
+    if symmetrized:
+        m += m.T.conj()
+    np.fill_diagonal(m, 0)
+    w = m.sum(axis=axis)
+    if normed:
+        isolated_node_mask = w == 0
+        w = np.where(isolated_node_mask, 1, np.sqrt(w))
+        m /= w
+        m /= w[:, np.newaxis]
+        m *= -1
+        _setdiag_dense(m, 1 - isolated_node_mask)
+    else:
+        m *= -1
+        _setdiag_dense(m, w)
+
+    return m.astype(dtype, copy=False), w.astype(dtype, copy=False)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/LICENSE b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..ca9f2fee821caf1a99a27ea17e3467cccd8c73b8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Consortium for Python Data API Standards
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/README.md b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/README.md
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index 0000000000000000000000000000000000000000..a3360988cbc1cd2d9710d9b010b44300122ca6ef
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/README.md
@@ -0,0 +1 @@
+Update this directory using maint_tools/vendor_array_api_compat.sh
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..653cb40a37607e0e82b6e91d57a96bf0021a95a8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/__init__.py
@@ -0,0 +1,22 @@
+"""
+NumPy Array API compatibility library
+
+This is a small wrapper around NumPy, CuPy, JAX, sparse and others that are
+compatible with the Array API standard https://data-apis.org/array-api/latest/.
+See also NEP 47 https://numpy.org/neps/nep-0047-array-api-standard.html.
+
+Unlike array_api_strict, this is not a strict minimal implementation of the
+Array API, but rather just an extension of the main NumPy namespace with
+changes needed to be compliant with the Array API. See
+https://numpy.org/doc/stable/reference/array_api.html for a full list of
+changes. In particular, unlike array_api_strict, this package does not use a
+separate Array object, but rather just uses numpy.ndarray directly.
+
+Library authors using the Array API may wish to test against array_api_strict
+to ensure they are not using functionality outside of the standard, but prefer
+this implementation for the default when working with NumPy arrays.
+
+"""
+__version__ = '1.12.0'
+
+from .common import *  # noqa: F401, F403
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/_internal.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/_internal.py
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--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/_internal.py
@@ -0,0 +1,59 @@
+"""
+Internal helpers
+"""
+
+from collections.abc import Callable
+from functools import wraps
+from inspect import signature
+from types import ModuleType
+from typing import TypeVar
+
+_T = TypeVar("_T")
+
+
+def get_xp(xp: ModuleType) -> Callable[[Callable[..., _T]], Callable[..., _T]]:
+    """
+    Decorator to automatically replace xp with the corresponding array module.
+
+    Use like
+
+    import numpy as np
+
+    @get_xp(np)
+    def func(x, /, xp, kwarg=None):
+        return xp.func(x, kwarg=kwarg)
+
+    Note that xp must be a keyword argument and come after all non-keyword
+    arguments.
+
+    """
+
+    def inner(f: Callable[..., _T], /) -> Callable[..., _T]:
+        @wraps(f)
+        def wrapped_f(*args: object, **kwargs: object) -> object:
+            return f(*args, xp=xp, **kwargs)
+
+        sig = signature(f)
+        new_sig = sig.replace(
+            parameters=[par for i, par in sig.parameters.items() if i != "xp"]
+        )
+
+        if wrapped_f.__doc__ is None:
+            wrapped_f.__doc__ = f"""\
+Array API compatibility wrapper for {f.__name__}.
+
+See the corresponding documentation in NumPy/CuPy and/or the array API
+specification for more details.
+
+"""
+        wrapped_f.__signature__ = new_sig  # pyright: ignore[reportAttributeAccessIssue]
+        return wrapped_f  # pyright: ignore[reportReturnType]
+
+    return inner
+
+
+__all__ = ["get_xp"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/__init__.py
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@@ -0,0 +1 @@
+from ._helpers import *  # noqa: F403
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+"""
+These are functions that are just aliases of existing functions in NumPy.
+"""
+
+from __future__ import annotations
+
+import inspect
+from typing import TYPE_CHECKING, Any, NamedTuple, Optional, Sequence, cast
+
+from ._helpers import _check_device, array_namespace
+from ._helpers import device as _get_device
+from ._helpers import is_cupy_namespace as _is_cupy_namespace
+from ._typing import Array, Device, DType, Namespace
+
+if TYPE_CHECKING:
+    # TODO: import from typing (requires Python >=3.13)
+    from typing_extensions import TypeIs
+
+# These functions are modified from the NumPy versions.
+
+# Creation functions add the device keyword (which does nothing for NumPy and Dask)
+
+
+def arange(
+    start: float,
+    /,
+    stop: float | None = None,
+    step: float = 1,
+    *,
+    xp: Namespace,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.arange(start, stop=stop, step=step, dtype=dtype, **kwargs)
+
+
+def empty(
+    shape: int | tuple[int, ...],
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.empty(shape, dtype=dtype, **kwargs)
+
+
+def empty_like(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.empty_like(x, dtype=dtype, **kwargs)
+
+
+def eye(
+    n_rows: int,
+    n_cols: int | None = None,
+    /,
+    *,
+    xp: Namespace,
+    k: int = 0,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.eye(n_rows, M=n_cols, k=k, dtype=dtype, **kwargs)
+
+
+def full(
+    shape: int | tuple[int, ...],
+    fill_value: complex,
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.full(shape, fill_value, dtype=dtype, **kwargs)
+
+
+def full_like(
+    x: Array,
+    /,
+    fill_value: complex,
+    *,
+    xp: Namespace,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.full_like(x, fill_value, dtype=dtype, **kwargs)
+
+
+def linspace(
+    start: float,
+    stop: float,
+    /,
+    num: int,
+    *,
+    xp: Namespace,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    endpoint: bool = True,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.linspace(start, stop, num, dtype=dtype, endpoint=endpoint, **kwargs)
+
+
+def ones(
+    shape: int | tuple[int, ...],
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.ones(shape, dtype=dtype, **kwargs)
+
+
+def ones_like(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.ones_like(x, dtype=dtype, **kwargs)
+
+
+def zeros(
+    shape: int | tuple[int, ...],
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.zeros(shape, dtype=dtype, **kwargs)
+
+
+def zeros_like(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    _check_device(xp, device)
+    return xp.zeros_like(x, dtype=dtype, **kwargs)
+
+
+# np.unique() is split into four functions in the array API:
+# unique_all, unique_counts, unique_inverse, and unique_values (this is done
+# to remove polymorphic return types).
+
+# The functions here return namedtuples (np.unique() returns a normal
+# tuple).
+
+
+# Note that these named tuples aren't actually part of the standard namespace,
+# but I don't see any issue with exporting the names here regardless.
+class UniqueAllResult(NamedTuple):
+    values: Array
+    indices: Array
+    inverse_indices: Array
+    counts: Array
+
+
+class UniqueCountsResult(NamedTuple):
+    values: Array
+    counts: Array
+
+
+class UniqueInverseResult(NamedTuple):
+    values: Array
+    inverse_indices: Array
+
+
+def _unique_kwargs(xp: Namespace) -> dict[str, bool]:
+    # Older versions of NumPy and CuPy do not have equal_nan. Rather than
+    # trying to parse version numbers, just check if equal_nan is in the
+    # signature.
+    s = inspect.signature(xp.unique)
+    if "equal_nan" in s.parameters:
+        return {"equal_nan": False}
+    return {}
+
+
+def unique_all(x: Array, /, xp: Namespace) -> UniqueAllResult:
+    kwargs = _unique_kwargs(xp)
+    values, indices, inverse_indices, counts = xp.unique(
+        x,
+        return_counts=True,
+        return_index=True,
+        return_inverse=True,
+        **kwargs,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueAllResult(
+        values,
+        indices,
+        inverse_indices,
+        counts,
+    )
+
+
+def unique_counts(x: Array, /, xp: Namespace) -> UniqueCountsResult:
+    kwargs = _unique_kwargs(xp)
+    res = xp.unique(
+        x, return_counts=True, return_index=False, return_inverse=False, **kwargs
+    )
+
+    return UniqueCountsResult(*res)
+
+
+def unique_inverse(x: Array, /, xp: Namespace) -> UniqueInverseResult:
+    kwargs = _unique_kwargs(xp)
+    values, inverse_indices = xp.unique(
+        x,
+        return_counts=False,
+        return_index=False,
+        return_inverse=True,
+        **kwargs,
+    )
+    # xp.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueInverseResult(values, inverse_indices)
+
+
+def unique_values(x: Array, /, xp: Namespace) -> Array:
+    kwargs = _unique_kwargs(xp)
+    return xp.unique(
+        x,
+        return_counts=False,
+        return_index=False,
+        return_inverse=False,
+        **kwargs,
+    )
+
+
+# These functions have different keyword argument names
+
+
+def std(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int | tuple[int, ...] | None = None,
+    correction: float = 0.0,  # correction instead of ddof
+    keepdims: bool = False,
+    **kwargs: object,
+) -> Array:
+    return xp.std(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs)
+
+
+def var(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int | tuple[int, ...] | None = None,
+    correction: float = 0.0,  # correction instead of ddof
+    keepdims: bool = False,
+    **kwargs: object,
+) -> Array:
+    return xp.var(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs)
+
+
+# cumulative_sum is renamed from cumsum, and adds the include_initial keyword
+# argument
+
+
+def cumulative_sum(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int | None = None,
+    dtype: DType | None = None,
+    include_initial: bool = False,
+    **kwargs: object,
+) -> Array:
+    wrapped_xp = array_namespace(x)
+
+    # TODO: The standard is not clear about what should happen when x.ndim == 0.
+    if axis is None:
+        if x.ndim > 1:
+            raise ValueError(
+                "axis must be specified in cumulative_sum for more than one dimension"
+            )
+        axis = 0
+
+    res = xp.cumsum(x, axis=axis, dtype=dtype, **kwargs)
+
+    # np.cumsum does not support include_initial
+    if include_initial:
+        initial_shape = list(x.shape)
+        initial_shape[axis] = 1
+        res = xp.concatenate(
+            [
+                wrapped_xp.zeros(
+                    shape=initial_shape, dtype=res.dtype, device=_get_device(res)
+                ),
+                res,
+            ],
+            axis=axis,
+        )
+    return res
+
+
+def cumulative_prod(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int | None = None,
+    dtype: DType | None = None,
+    include_initial: bool = False,
+    **kwargs: object,
+) -> Array:
+    wrapped_xp = array_namespace(x)
+
+    if axis is None:
+        if x.ndim > 1:
+            raise ValueError(
+                "axis must be specified in cumulative_prod for more than one dimension"
+            )
+        axis = 0
+
+    res = xp.cumprod(x, axis=axis, dtype=dtype, **kwargs)
+
+    # np.cumprod does not support include_initial
+    if include_initial:
+        initial_shape = list(x.shape)
+        initial_shape[axis] = 1
+        res = xp.concatenate(
+            [
+                wrapped_xp.ones(
+                    shape=initial_shape, dtype=res.dtype, device=_get_device(res)
+                ),
+                res,
+            ],
+            axis=axis,
+        )
+    return res
+
+
+# The min and max argument names in clip are different and not optional in numpy, and type
+# promotion behavior is different.
+def clip(
+    x: Array,
+    /,
+    min: float | Array | None = None,
+    max: float | Array | None = None,
+    *,
+    xp: Namespace,
+    # TODO: np.clip has other ufunc kwargs
+    out: Array | None = None,
+) -> Array:
+    def _isscalar(a: object) -> TypeIs[int | float | None]:
+        return isinstance(a, (int, float, type(None)))
+
+    min_shape = () if _isscalar(min) else min.shape
+    max_shape = () if _isscalar(max) else max.shape
+
+    wrapped_xp = array_namespace(x)
+
+    result_shape = xp.broadcast_shapes(x.shape, min_shape, max_shape)
+
+    # np.clip does type promotion but the array API clip requires that the
+    # output have the same dtype as x. We do this instead of just downcasting
+    # the result of xp.clip() to handle some corner cases better (e.g.,
+    # avoiding uint64 -> float64 promotion).
+
+    # Note: cases where min or max overflow (integer) or round (float) in the
+    # wrong direction when downcasting to x.dtype are unspecified. This code
+    # just does whatever NumPy does when it downcasts in the assignment, but
+    # other behavior could be preferred, especially for integers. For example,
+    # this code produces:
+
+    # >>> clip(asarray(0, dtype=int8), asarray(128, dtype=int16), None)
+    # -128
+
+    # but an answer of 0 might be preferred. See
+    # https://github.com/numpy/numpy/issues/24976 for more discussion on this issue.
+
+    # At least handle the case of Python integers correctly (see
+    # https://github.com/numpy/numpy/pull/26892).
+    if wrapped_xp.isdtype(x.dtype, "integral"):
+        if type(min) is int and min <= wrapped_xp.iinfo(x.dtype).min:
+            min = None
+        if type(max) is int and max >= wrapped_xp.iinfo(x.dtype).max:
+            max = None
+
+    dev = _get_device(x)
+    if out is None:
+        out = wrapped_xp.empty(result_shape, dtype=x.dtype, device=dev)
+    assert out is not None  # workaround for a type-narrowing issue in pyright
+    out[()] = x
+
+    if min is not None:
+        a = wrapped_xp.asarray(min, dtype=x.dtype, device=dev)
+        a = xp.broadcast_to(a, result_shape)
+        ia = (out < a) | xp.isnan(a)
+        out[ia] = a[ia]
+
+    if max is not None:
+        b = wrapped_xp.asarray(max, dtype=x.dtype, device=dev)
+        b = xp.broadcast_to(b, result_shape)
+        ib = (out > b) | xp.isnan(b)
+        out[ib] = b[ib]
+
+    # Return a scalar for 0-D
+    return out[()]
+
+
+# Unlike transpose(), the axes argument to permute_dims() is required.
+def permute_dims(x: Array, /, axes: tuple[int, ...], xp: Namespace) -> Array:
+    return xp.transpose(x, axes)
+
+
+# np.reshape calls the keyword argument 'newshape' instead of 'shape'
+def reshape(
+    x: Array,
+    /,
+    shape: tuple[int, ...],
+    xp: Namespace,
+    *,
+    copy: Optional[bool] = None,
+    **kwargs: object,
+) -> Array:
+    if copy is True:
+        x = x.copy()
+    elif copy is False:
+        y = x.view()
+        y.shape = shape
+        return y
+    return xp.reshape(x, shape, **kwargs)
+
+
+# The descending keyword is new in sort and argsort, and 'kind' replaced with
+# 'stable'
+def argsort(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int = -1,
+    descending: bool = False,
+    stable: bool = True,
+    **kwargs: object,
+) -> Array:
+    # Note: this keyword argument is different, and the default is different.
+    # We set it in kwargs like this because numpy.sort uses kind='quicksort'
+    # as the default whereas cupy.sort uses kind=None.
+    if stable:
+        kwargs["kind"] = "stable"
+    if not descending:
+        res = xp.argsort(x, axis=axis, **kwargs)
+    else:
+        # As NumPy has no native descending sort, we imitate it here. Note that
+        # simply flipping the results of xp.argsort(x, ...) would not
+        # respect the relative order like it would in native descending sorts.
+        res = xp.flip(
+            xp.argsort(xp.flip(x, axis=axis), axis=axis, **kwargs),
+            axis=axis,
+        )
+        # Rely on flip()/argsort() to validate axis
+        normalised_axis = axis if axis >= 0 else x.ndim + axis
+        max_i = x.shape[normalised_axis] - 1
+        res = max_i - res
+    return res
+
+
+def sort(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int = -1,
+    descending: bool = False,
+    stable: bool = True,
+    **kwargs: object,
+) -> Array:
+    # Note: this keyword argument is different, and the default is different.
+    # We set it in kwargs like this because numpy.sort uses kind='quicksort'
+    # as the default whereas cupy.sort uses kind=None.
+    if stable:
+        kwargs["kind"] = "stable"
+    res = xp.sort(x, axis=axis, **kwargs)
+    if descending:
+        res = xp.flip(res, axis=axis)
+    return res
+
+
+# nonzero should error for zero-dimensional arrays
+def nonzero(x: Array, /, xp: Namespace, **kwargs: object) -> tuple[Array, ...]:
+    if x.ndim == 0:
+        raise ValueError("nonzero() does not support zero-dimensional arrays")
+    return xp.nonzero(x, **kwargs)
+
+
+# ceil, floor, and trunc return integers for integer inputs
+
+
+def ceil(x: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    if xp.issubdtype(x.dtype, xp.integer):
+        return x
+    return xp.ceil(x, **kwargs)
+
+
+def floor(x: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    if xp.issubdtype(x.dtype, xp.integer):
+        return x
+    return xp.floor(x, **kwargs)
+
+
+def trunc(x: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    if xp.issubdtype(x.dtype, xp.integer):
+        return x
+    return xp.trunc(x, **kwargs)
+
+
+# linear algebra functions
+
+
+def matmul(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    return xp.matmul(x1, x2, **kwargs)
+
+
+# Unlike transpose, matrix_transpose only transposes the last two axes.
+def matrix_transpose(x: Array, /, xp: Namespace) -> Array:
+    if x.ndim < 2:
+        raise ValueError("x must be at least 2-dimensional for matrix_transpose")
+    return xp.swapaxes(x, -1, -2)
+
+
+def tensordot(
+    x1: Array,
+    x2: Array,
+    /,
+    xp: Namespace,
+    *,
+    axes: int | tuple[Sequence[int], Sequence[int]] = 2,
+    **kwargs: object,
+) -> Array:
+    return xp.tensordot(x1, x2, axes=axes, **kwargs)
+
+
+def vecdot(x1: Array, x2: Array, /, xp: Namespace, *, axis: int = -1) -> Array:
+    if x1.shape[axis] != x2.shape[axis]:
+        raise ValueError("x1 and x2 must have the same size along the given axis")
+
+    if hasattr(xp, "broadcast_tensors"):
+        _broadcast = xp.broadcast_tensors
+    else:
+        _broadcast = xp.broadcast_arrays
+
+    x1_ = xp.moveaxis(x1, axis, -1)
+    x2_ = xp.moveaxis(x2, axis, -1)
+    x1_, x2_ = _broadcast(x1_, x2_)
+
+    res = xp.conj(x1_[..., None, :]) @ x2_[..., None]
+    return res[..., 0, 0]
+
+
+# isdtype is a new function in the 2022.12 array API specification.
+
+
+def isdtype(
+    dtype: DType,
+    kind: DType | str | tuple[DType | str, ...],
+    xp: Namespace,
+    *,
+    _tuple: bool = True,  # Disallow nested tuples
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    Note that outside of this function, this compat library does not yet fully
+    support complex numbers.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple) and _tuple:
+        return any(
+            isdtype(dtype, k, xp, _tuple=False)
+            for k in cast("tuple[DType | str, ...]", kind)
+        )
+    elif isinstance(kind, str):
+        if kind == "bool":
+            return dtype == xp.bool_
+        elif kind == "signed integer":
+            return xp.issubdtype(dtype, xp.signedinteger)
+        elif kind == "unsigned integer":
+            return xp.issubdtype(dtype, xp.unsignedinteger)
+        elif kind == "integral":
+            return xp.issubdtype(dtype, xp.integer)
+        elif kind == "real floating":
+            return xp.issubdtype(dtype, xp.floating)
+        elif kind == "complex floating":
+            return xp.issubdtype(dtype, xp.complexfloating)
+        elif kind == "numeric":
+            return xp.issubdtype(dtype, xp.number)
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    else:
+        # This will allow things that aren't required by the spec, like
+        # isdtype(np.float64, float) or isdtype(np.int64, 'l'). Should we be
+        # more strict here to match the type annotation? Note that the
+        # array_api_strict implementation will be very strict.
+        return dtype == kind
+
+
+# unstack is a new function in the 2023.12 array API standard
+def unstack(x: Array, /, xp: Namespace, *, axis: int = 0) -> tuple[Array, ...]:
+    if x.ndim == 0:
+        raise ValueError("Input array must be at least 1-d.")
+    return tuple(xp.moveaxis(x, axis, 0))
+
+
+# numpy 1.26 does not use the standard definition for sign on complex numbers
+
+
+def sign(x: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    if isdtype(x.dtype, "complex floating", xp=xp):
+        out = (x / xp.abs(x, **kwargs))[...]
+        # sign(0) = 0 but the above formula would give nan
+        out[x == 0j] = 0j
+    else:
+        out = xp.sign(x, **kwargs)
+    # CuPy sign() does not propagate nans. See
+    # https://github.com/data-apis/array-api-compat/issues/136
+    if _is_cupy_namespace(xp) and isdtype(x.dtype, "real floating", xp=xp):
+        out[xp.isnan(x)] = xp.nan
+    return out[()]
+
+
+def finfo(type_: DType | Array, /, xp: Namespace) -> Any:
+    # It is surprisingly difficult to recognize a dtype apart from an array.
+    # np.int64 is not the same as np.asarray(1).dtype!
+    try:
+        return xp.finfo(type_)
+    except (ValueError, TypeError):
+        return xp.finfo(type_.dtype)
+
+
+def iinfo(type_: DType | Array, /, xp: Namespace) -> Any:
+    try:
+        return xp.iinfo(type_)
+    except (ValueError, TypeError):
+        return xp.iinfo(type_.dtype)
+
+
+__all__ = [
+    "arange",
+    "empty",
+    "empty_like",
+    "eye",
+    "full",
+    "full_like",
+    "linspace",
+    "ones",
+    "ones_like",
+    "zeros",
+    "zeros_like",
+    "UniqueAllResult",
+    "UniqueCountsResult",
+    "UniqueInverseResult",
+    "unique_all",
+    "unique_counts",
+    "unique_inverse",
+    "unique_values",
+    "std",
+    "var",
+    "cumulative_sum",
+    "cumulative_prod",
+    "clip",
+    "permute_dims",
+    "reshape",
+    "argsort",
+    "sort",
+    "nonzero",
+    "ceil",
+    "floor",
+    "trunc",
+    "matmul",
+    "matrix_transpose",
+    "tensordot",
+    "vecdot",
+    "isdtype",
+    "unstack",
+    "sign",
+    "finfo",
+    "iinfo",
+]
+_all_ignore = ["inspect", "array_namespace", "NamedTuple"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_fft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..18839d37f84949ec2bf851b548c2e13b5179e04e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_fft.py
@@ -0,0 +1,213 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Literal, TypeAlias
+
+from ._typing import Array, Device, DType, Namespace
+
+_Norm: TypeAlias = Literal["backward", "ortho", "forward"]
+
+# Note: NumPy fft functions improperly upcast float32 and complex64 to
+# complex128, which is why we require wrapping them all here.
+
+def fft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.fft(x, n=n, axis=axis, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.complex64)
+    return res
+
+def ifft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.ifft(x, n=n, axis=axis, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.complex64)
+    return res
+
+def fftn(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    s: Sequence[int] | None = None,
+    axes: Sequence[int] | None = None,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.fftn(x, s=s, axes=axes, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.complex64)
+    return res
+
+def ifftn(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    s: Sequence[int] | None = None,
+    axes: Sequence[int] | None = None,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.ifftn(x, s=s, axes=axes, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.complex64)
+    return res
+
+def rfft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.rfft(x, n=n, axis=axis, norm=norm)
+    if x.dtype == xp.float32:
+        return res.astype(xp.complex64)
+    return res
+
+def irfft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.irfft(x, n=n, axis=axis, norm=norm)
+    if x.dtype == xp.complex64:
+        return res.astype(xp.float32)
+    return res
+
+def rfftn(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    s: Sequence[int] | None = None,
+    axes: Sequence[int] | None = None,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.rfftn(x, s=s, axes=axes, norm=norm)
+    if x.dtype == xp.float32:
+        return res.astype(xp.complex64)
+    return res
+
+def irfftn(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    s: Sequence[int] | None = None,
+    axes: Sequence[int] | None = None,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.irfftn(x, s=s, axes=axes, norm=norm)
+    if x.dtype == xp.complex64:
+        return res.astype(xp.float32)
+    return res
+
+def hfft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.hfft(x, n=n, axis=axis, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.float32)
+    return res
+
+def ihfft(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    n: int | None = None,
+    axis: int = -1,
+    norm: _Norm = "backward",
+) -> Array:
+    res = xp.fft.ihfft(x, n=n, axis=axis, norm=norm)
+    if x.dtype in [xp.float32, xp.complex64]:
+        return res.astype(xp.complex64)
+    return res
+
+def fftfreq(
+    n: int,
+    /,
+    xp: Namespace,
+    *,
+    d: float = 1.0,
+    dtype: DType | None = None,
+    device: Device | None = None,
+) -> Array:
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    res = xp.fft.fftfreq(n, d=d)
+    if dtype is not None:
+        return res.astype(dtype)
+    return res
+
+def rfftfreq(
+    n: int,
+    /,
+    xp: Namespace,
+    *,
+    d: float = 1.0,
+    dtype: DType | None = None,
+    device: Device | None = None,
+) -> Array:
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    res = xp.fft.rfftfreq(n, d=d)
+    if dtype is not None:
+        return res.astype(dtype)
+    return res
+
+def fftshift(
+    x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None
+) -> Array:
+    return xp.fft.fftshift(x, axes=axes)
+
+def ifftshift(
+    x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None
+) -> Array:
+    return xp.fft.ifftshift(x, axes=axes)
+
+__all__ = [
+    "fft",
+    "ifft",
+    "fftn",
+    "ifftn",
+    "rfft",
+    "irfft",
+    "rfftn",
+    "irfftn",
+    "hfft",
+    "ihfft",
+    "fftfreq",
+    "rfftfreq",
+    "fftshift",
+    "ifftshift",
+]
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_helpers.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..77175d0d1e97425567f90421e5805c64612c4b4a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_helpers.py
@@ -0,0 +1,1058 @@
+"""
+Various helper functions which are not part of the spec.
+
+Functions which start with an underscore are for internal use only but helpers
+that are in __all__ are intended as additional helper functions for use by end
+users of the compat library.
+"""
+
+from __future__ import annotations
+
+import inspect
+import math
+import sys
+import warnings
+from collections.abc import Collection, Hashable
+from functools import lru_cache
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Final,
+    Literal,
+    SupportsIndex,
+    TypeAlias,
+    TypeGuard,
+    TypeVar,
+    cast,
+    overload,
+)
+
+from ._typing import Array, Device, HasShape, Namespace, SupportsArrayNamespace
+
+if TYPE_CHECKING:
+
+    import dask.array as da
+    import jax
+    import ndonnx as ndx
+    import numpy as np
+    import numpy.typing as npt
+    import sparse  # pyright: ignore[reportMissingTypeStubs]
+    import torch
+
+    # TODO: import from typing (requires Python >=3.13)
+    from typing_extensions import TypeIs, TypeVar
+
+    _SizeT = TypeVar("_SizeT", bound = int | None)
+
+    _ZeroGradientArray: TypeAlias = npt.NDArray[np.void]
+    _CupyArray: TypeAlias = Any  # cupy has no py.typed
+
+    _ArrayApiObj: TypeAlias = (
+        npt.NDArray[Any]
+        | da.Array
+        | jax.Array
+        | ndx.Array
+        | sparse.SparseArray
+        | torch.Tensor
+        | SupportsArrayNamespace[Any]
+        | _CupyArray
+    )
+
+_API_VERSIONS_OLD: Final = frozenset({"2021.12", "2022.12", "2023.12"})
+_API_VERSIONS: Final = _API_VERSIONS_OLD | frozenset({"2024.12"})
+
+
+@lru_cache(100)
+def _issubclass_fast(cls: type, modname: str, clsname: str) -> bool:
+    try:
+        mod = sys.modules[modname]
+    except KeyError:
+        return False
+    parent_cls = getattr(mod, clsname)
+    return issubclass(cls, parent_cls)
+
+
+def _is_jax_zero_gradient_array(x: object) -> TypeGuard[_ZeroGradientArray]:
+    """Return True if `x` is a zero-gradient array.
+
+    These arrays are a design quirk of Jax that may one day be removed.
+    See https://github.com/google/jax/issues/20620.
+    """
+    # Fast exit
+    try:
+        dtype = x.dtype  # type: ignore[attr-defined]
+    except AttributeError:
+        return False
+    cls = cast(Hashable, type(dtype))
+    if not _issubclass_fast(cls, "numpy.dtypes", "VoidDType"):
+        return False
+
+    if "jax" not in sys.modules:
+        return False
+
+    import jax
+    # jax.float0 is a np.dtype([('float0', 'V')])
+    return dtype == jax.float0
+
+
+def is_numpy_array(x: object) -> TypeGuard[npt.NDArray[Any]]:
+    """
+    Return True if `x` is a NumPy array.
+
+    This function does not import NumPy if it has not already been imported
+    and is therefore cheap to use.
+
+    This also returns True for `ndarray` subclasses and NumPy scalar objects.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    # TODO: Should we reject ndarray subclasses?
+    cls = cast(Hashable, type(x))
+    return (
+        _issubclass_fast(cls, "numpy", "ndarray") 
+        or _issubclass_fast(cls, "numpy", "generic")
+    ) and not _is_jax_zero_gradient_array(x)
+
+
+def is_cupy_array(x: object) -> bool:
+    """
+    Return True if `x` is a CuPy array.
+
+    This function does not import CuPy if it has not already been imported
+    and is therefore cheap to use.
+
+    This also returns True for `cupy.ndarray` subclasses and CuPy scalar objects.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "cupy", "ndarray")
+
+
+def is_torch_array(x: object) -> TypeIs[torch.Tensor]:
+    """
+    Return True if `x` is a PyTorch tensor.
+
+    This function does not import PyTorch if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "torch", "Tensor")
+
+
+def is_ndonnx_array(x: object) -> TypeIs[ndx.Array]:
+    """
+    Return True if `x` is a ndonnx Array.
+
+    This function does not import ndonnx if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "ndonnx", "Array")
+
+
+def is_dask_array(x: object) -> TypeIs[da.Array]:
+    """
+    Return True if `x` is a dask.array Array.
+
+    This function does not import dask if it has not already been imported
+    and is therefore cheap to use.
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_jax_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "dask.array", "Array")
+
+
+def is_jax_array(x: object) -> TypeIs[jax.Array]:
+    """
+    Return True if `x` is a JAX array.
+
+    This function does not import JAX if it has not already been imported
+    and is therefore cheap to use.
+
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_pydata_sparse_array
+    """
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "jax", "Array") or _is_jax_zero_gradient_array(x)
+
+
+def is_pydata_sparse_array(x: object) -> TypeIs[sparse.SparseArray]:
+    """
+    Return True if `x` is an array from the `sparse` package.
+
+    This function does not import `sparse` if it has not already been imported
+    and is therefore cheap to use.
+
+
+    See Also
+    --------
+
+    array_namespace
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    """
+    # TODO: Account for other backends.
+    cls = cast(Hashable, type(x))
+    return _issubclass_fast(cls, "sparse", "SparseArray")
+
+
+def is_array_api_obj(x: object) -> TypeIs[_ArrayApiObj]:  # pyright: ignore[reportUnknownParameterType]
+    """
+    Return True if `x` is an array API compatible array object.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_ndonnx_array
+    is_dask_array
+    is_jax_array
+    """
+    return (
+        hasattr(x, '__array_namespace__') 
+        or _is_array_api_cls(cast(Hashable, type(x)))
+    )
+
+
+@lru_cache(100)
+def _is_array_api_cls(cls: type) -> bool:
+    return (
+        # TODO: drop support for numpy<2 which didn't have __array_namespace__
+        _issubclass_fast(cls, "numpy", "ndarray")
+        or _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "cupy", "ndarray")
+        or _issubclass_fast(cls, "torch", "Tensor")
+        or _issubclass_fast(cls, "dask.array", "Array")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+        # TODO: drop support for jax<0.4.32 which didn't have __array_namespace__
+        or _issubclass_fast(cls, "jax", "Array")
+    )
+
+
+def _compat_module_name() -> str:
+    assert __name__.endswith(".common._helpers")
+    return __name__.removesuffix(".common._helpers")
+
+
+@lru_cache(100)
+def is_numpy_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a NumPy namespace.
+
+    This includes both NumPy itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"numpy", _compat_module_name() + ".numpy"}
+
+
+@lru_cache(100)
+def is_cupy_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a CuPy namespace.
+
+    This includes both CuPy itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"cupy", _compat_module_name() + ".cupy"}
+
+
+@lru_cache(100)
+def is_torch_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a PyTorch namespace.
+
+    This includes both PyTorch itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"torch", _compat_module_name() + ".torch"}
+
+
+def is_ndonnx_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is an NDONNX namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ == "ndonnx"
+
+
+@lru_cache(100)
+def is_dask_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a Dask namespace.
+
+    This includes both ``dask.array`` itself and the version wrapped by array-api-compat.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"dask.array", _compat_module_name() + ".dask.array"}
+
+
+def is_jax_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a JAX namespace.
+
+    This includes ``jax.numpy`` and ``jax.experimental.array_api`` which existed in
+    older versions of JAX.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_pydata_sparse_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ in {"jax.numpy", "jax.experimental.array_api"}
+
+
+def is_pydata_sparse_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is a pydata/sparse namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_array_api_strict_namespace
+    """
+    return xp.__name__ == "sparse"
+
+
+def is_array_api_strict_namespace(xp: Namespace) -> bool:
+    """
+    Returns True if `xp` is an array-api-strict namespace.
+
+    See Also
+    --------
+
+    array_namespace
+    is_numpy_namespace
+    is_cupy_namespace
+    is_torch_namespace
+    is_ndonnx_namespace
+    is_dask_namespace
+    is_jax_namespace
+    is_pydata_sparse_namespace
+    """
+    return xp.__name__ == "array_api_strict"
+
+
+def _check_api_version(api_version: str | None) -> None:
+    if api_version in _API_VERSIONS_OLD:
+        warnings.warn(
+            f"The {api_version} version of the array API specification was requested but the returned namespace is actually version 2024.12"
+        )
+    elif api_version is not None and api_version not in _API_VERSIONS:
+        raise ValueError(
+            "Only the 2024.12 version of the array API specification is currently supported"
+        )
+
+
+def array_namespace(
+    *xs: Array | complex | None,
+    api_version: str | None = None,
+    use_compat: bool | None = None,
+) -> Namespace:
+    """
+    Get the array API compatible namespace for the arrays `xs`.
+
+    Parameters
+    ----------
+    xs: arrays
+        one or more arrays. xs can also be Python scalars (bool, int, float,
+        complex, or None), which are ignored.
+
+    api_version: str
+        The newest version of the spec that you need support for (currently
+        the compat library wrapped APIs support v2024.12).
+
+    use_compat: bool or None
+        If None (the default), the native namespace will be returned if it is
+        already array API compatible, otherwise a compat wrapper is used. If
+        True, the compat library wrapped library will be returned. If False,
+        the native library namespace is returned.
+
+    Returns
+    -------
+
+    out: namespace
+        The array API compatible namespace corresponding to the arrays in `xs`.
+
+    Raises
+    ------
+    TypeError
+        If `xs` contains arrays from different array libraries or contains a
+        non-array.
+
+
+    Typical usage is to pass the arguments of a function to
+    `array_namespace()` at the top of a function to get the corresponding
+    array API namespace:
+
+    .. code:: python
+
+       def your_function(x, y):
+           xp = array_api_compat.array_namespace(x, y)
+           # Now use xp as the array library namespace
+           return xp.mean(x, axis=0) + 2*xp.std(y, axis=0)
+
+
+    Wrapped array namespaces can also be imported directly. For example,
+    `array_namespace(np.array(...))` will return `array_api_compat.numpy`.
+    This function will also work for any array library not wrapped by
+    array-api-compat if it explicitly defines `__array_namespace__
+    `__
+    (the wrapped namespace is always preferred if it exists).
+
+    See Also
+    --------
+
+    is_array_api_obj
+    is_numpy_array
+    is_cupy_array
+    is_torch_array
+    is_dask_array
+    is_jax_array
+    is_pydata_sparse_array
+
+    """
+    if use_compat not in [None, True, False]:
+        raise ValueError("use_compat must be None, True, or False")
+
+    _use_compat = use_compat in [None, True]
+
+    namespaces: set[Namespace] = set()
+    for x in xs:
+        if is_numpy_array(x):
+            import numpy as np
+
+            from .. import numpy as numpy_namespace
+
+            if use_compat is True:
+                _check_api_version(api_version)
+                namespaces.add(numpy_namespace)
+            elif use_compat is False:
+                namespaces.add(np)
+            else:
+                # numpy 2.0+ have __array_namespace__, however, they are not yet fully array API
+                # compatible.
+                namespaces.add(numpy_namespace)
+        elif is_cupy_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from .. import cupy as cupy_namespace
+
+                namespaces.add(cupy_namespace)
+            else:
+                import cupy as cp  # pyright: ignore[reportMissingTypeStubs]
+
+                namespaces.add(cp)
+        elif is_torch_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from .. import torch as torch_namespace
+
+                namespaces.add(torch_namespace)
+            else:
+                import torch
+
+                namespaces.add(torch)
+        elif is_dask_array(x):
+            if _use_compat:
+                _check_api_version(api_version)
+                from ..dask import array as dask_namespace
+
+                namespaces.add(dask_namespace)
+            else:
+                import dask.array as da
+
+                namespaces.add(da)
+        elif is_jax_array(x):
+            if use_compat is True:
+                _check_api_version(api_version)
+                raise ValueError("JAX does not have an array-api-compat wrapper")
+            elif use_compat is False:
+                import jax.numpy as jnp
+            else:
+                # JAX v0.4.32 and newer implements the array API directly in jax.numpy.
+                # For older JAX versions, it is available via jax.experimental.array_api.
+                import jax.numpy
+
+                if hasattr(jax.numpy, "__array_api_version__"):
+                    jnp = jax.numpy
+                else:
+                    import jax.experimental.array_api as jnp  # pyright: ignore[reportMissingImports]
+            namespaces.add(jnp)
+        elif is_pydata_sparse_array(x):
+            if use_compat is True:
+                _check_api_version(api_version)
+                raise ValueError("`sparse` does not have an array-api-compat wrapper")
+            else:
+                import sparse  # pyright: ignore[reportMissingTypeStubs]
+            # `sparse` is already an array namespace. We do not have a wrapper
+            # submodule for it.
+            namespaces.add(sparse)
+        elif hasattr(x, "__array_namespace__"):
+            if use_compat is True:
+                raise ValueError(
+                    "The given array does not have an array-api-compat wrapper"
+                )
+            x = cast("SupportsArrayNamespace[Any]", x)
+            namespaces.add(x.__array_namespace__(api_version=api_version))
+        elif isinstance(x, (bool, int, float, complex, type(None))):
+            continue
+        else:
+            # TODO: Support Python scalars?
+            raise TypeError(f"{type(x).__name__} is not a supported array type")
+
+    if not namespaces:
+        raise TypeError("Unrecognized array input")
+
+    if len(namespaces) != 1:
+        raise TypeError(f"Multiple namespaces for array inputs: {namespaces}")
+
+    (xp,) = namespaces
+
+    return xp
+
+
+# backwards compatibility alias
+get_namespace = array_namespace
+
+
+def _check_device(bare_xp: Namespace, device: Device) -> None:  # pyright: ignore[reportUnusedFunction]
+    """
+    Validate dummy device on device-less array backends.
+
+    Notes
+    -----
+    This function is also invoked by CuPy, which does have multiple devices
+    if there are multiple GPUs available.
+    However, CuPy multi-device support is currently impossible
+    without using the global device or a context manager:
+
+    https://github.com/data-apis/array-api-compat/pull/293
+    """
+    if bare_xp is sys.modules.get("numpy"):
+        if device not in ("cpu", None):
+            raise ValueError(f"Unsupported device for NumPy: {device!r}")
+
+    elif bare_xp is sys.modules.get("dask.array"):
+        if device not in ("cpu", _DASK_DEVICE, None):
+            raise ValueError(f"Unsupported device for Dask: {device!r}")
+
+
+# Placeholder object to represent the dask device
+# when the array backend is not the CPU.
+# (since it is not easy to tell which device a dask array is on)
+class _dask_device:
+    def __repr__(self) -> Literal["DASK_DEVICE"]:
+        return "DASK_DEVICE"
+
+
+_DASK_DEVICE = _dask_device()
+
+
+# device() is not on numpy.ndarray or dask.array and to_device() is not on numpy.ndarray
+# or cupy.ndarray. They are not included in array objects of this library
+# because this library just reuses the respective ndarray classes without
+# wrapping or subclassing them. These helper functions can be used instead of
+# the wrapper functions for libraries that need to support both NumPy/CuPy and
+# other libraries that use devices.
+def device(x: _ArrayApiObj, /) -> Device:
+    """
+    Hardware device the array data resides on.
+
+    This is equivalent to `x.device` according to the `standard
+    `__.
+    This helper is included because some array libraries either do not have
+    the `device` attribute or include it with an incompatible API.
+
+    Parameters
+    ----------
+    x: array
+        array instance from an array API compatible library.
+
+    Returns
+    -------
+    out: device
+        a ``device`` object (see the `Device Support `__
+        section of the array API specification).
+
+    Notes
+    -----
+
+    For NumPy the device is always `"cpu"`. For Dask, the device is always a
+    special `DASK_DEVICE` object.
+
+    See Also
+    --------
+
+    to_device : Move array data to a different device.
+
+    """
+    if is_numpy_array(x):
+        return "cpu"
+    elif is_dask_array(x):
+        # Peek at the metadata of the Dask array to determine type
+        if is_numpy_array(x._meta):  # pyright: ignore
+            # Must be on CPU since backed by numpy
+            return "cpu"
+        return _DASK_DEVICE
+    elif is_jax_array(x):
+        # FIXME Jitted JAX arrays do not have a device attribute
+        #       https://github.com/jax-ml/jax/issues/26000
+        #       Return None in this case. Note that this workaround breaks
+        #       the standard and will result in new arrays being created on the
+        #       default device instead of the same device as the input array(s).
+        x_device = getattr(x, "device", None)
+        # Older JAX releases had .device() as a method, which has been replaced
+        # with a property in accordance with the standard.
+        if inspect.ismethod(x_device):
+            return x_device()
+        else:
+            return x_device
+    elif is_pydata_sparse_array(x):
+        # `sparse` will gain `.device`, so check for this first.
+        x_device = getattr(x, "device", None)
+        if x_device is not None:
+            return x_device
+        # Everything but DOK has this attr.
+        try:
+            inner = x.data  # pyright: ignore
+        except AttributeError:
+            return "cpu"
+        # Return the device of the constituent array
+        return device(inner)  # pyright: ignore
+    return x.device  # pyright: ignore
+
+
+# Prevent shadowing, used below
+_device = device
+
+
+# Based on cupy.array_api.Array.to_device
+def _cupy_to_device(
+    x: _CupyArray,
+    device: Device,
+    /,
+    stream: int | Any | None = None,
+) -> _CupyArray:
+    import cupy as cp
+
+    if device == "cpu":
+        # allowing us to use `to_device(x, "cpu")`
+        # is useful for portable test swapping between
+        # host and device backends
+        return x.get()
+    if not isinstance(device, cp.cuda.Device):
+        raise TypeError(f"Unsupported device type {device!r}")
+
+    if stream is None:
+        with device:
+            return cp.asarray(x)
+
+    # stream can be an int as specified in __dlpack__, or a CuPy stream
+    if isinstance(stream, int):
+        stream = cp.cuda.ExternalStream(stream)
+    elif not isinstance(stream, cp.cuda.Stream):
+        raise TypeError(f"Unsupported stream type {stream!r}")
+
+    with device, stream:
+        return cp.asarray(x)
+
+
+def _torch_to_device(
+    x: torch.Tensor,
+    device: torch.device | str | int,
+    /,
+    stream: None = None,
+) -> torch.Tensor:
+    if stream is not None:
+        raise NotImplementedError
+    return x.to(device)
+
+
+def to_device(x: Array, device: Device, /, *, stream: int | Any | None = None) -> Array:
+    """
+    Copy the array from the device on which it currently resides to the specified ``device``.
+
+    This is equivalent to `x.to_device(device, stream=stream)` according to
+    the `standard
+    `__.
+    This helper is included because some array libraries do not have the
+    `to_device` method.
+
+    Parameters
+    ----------
+
+    x: array
+        array instance from an array API compatible library.
+
+    device: device
+        a ``device`` object (see the `Device Support `__
+        section of the array API specification).
+
+    stream: int | Any | None
+        stream object to use during copy. In addition to the types supported
+        in ``array.__dlpack__``, implementations may choose to support any
+        library-specific stream object with the caveat that any code using
+        such an object would not be portable.
+
+    Returns
+    -------
+
+    out: array
+        an array with the same data and data type as ``x`` and located on the
+        specified ``device``.
+
+    Notes
+    -----
+
+    For NumPy, this function effectively does nothing since the only supported
+    device is the CPU. For CuPy, this method supports CuPy CUDA
+    :external+cupy:class:`Device ` and
+    :external+cupy:class:`Stream ` objects. For PyTorch,
+    this is the same as :external+torch:meth:`x.to(device) `
+    (the ``stream`` argument is not supported in PyTorch).
+
+    See Also
+    --------
+
+    device : Hardware device the array data resides on.
+
+    """
+    if is_numpy_array(x):
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        if device == "cpu":
+            return x
+        raise ValueError(f"Unsupported device {device!r}")
+    elif is_cupy_array(x):
+        # cupy does not yet have to_device
+        return _cupy_to_device(x, device, stream=stream)
+    elif is_torch_array(x):
+        return _torch_to_device(x, device, stream=stream)  # pyright: ignore[reportArgumentType]
+    elif is_dask_array(x):
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        # TODO: What if our array is on the GPU already?
+        if device == "cpu":
+            return x
+        raise ValueError(f"Unsupported device {device!r}")
+    elif is_jax_array(x):
+        if not hasattr(x, "__array_namespace__"):
+            # In JAX v0.4.31 and older, this import adds to_device method to x...
+            import jax.experimental.array_api  # noqa: F401  # pyright: ignore
+
+            # ... but only on eager JAX. It won't work inside jax.jit.
+            if not hasattr(x, "to_device"):
+                return x
+        return x.to_device(device, stream=stream)
+    elif is_pydata_sparse_array(x) and device == _device(x):
+        # Perform trivial check to return the same array if
+        # device is same instead of err-ing.
+        return x
+    return x.to_device(device, stream=stream)  # pyright: ignore
+
+
+@overload
+def size(x: HasShape[Collection[SupportsIndex]]) -> int: ...
+@overload
+def size(x: HasShape[Collection[None]]) -> None: ...
+@overload
+def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None: ...
+def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None:
+    """
+    Return the total number of elements of x.
+
+    This is equivalent to `x.size` according to the `standard
+    `__.
+
+    This helper is included because PyTorch defines `size` in an
+    :external+torch:meth:`incompatible way `.
+    It also fixes dask.array's behaviour which returns nan for unknown sizes, whereas
+    the standard requires None.
+    """
+    # Lazy API compliant arrays, such as ndonnx, can contain None in their shape
+    if None in x.shape:
+        return None
+    out = math.prod(cast("Collection[SupportsIndex]", x.shape))
+    # dask.array.Array.shape can contain NaN
+    return None if math.isnan(out) else out
+
+
+@lru_cache(100)
+def _is_writeable_cls(cls: type) -> bool | None:
+    if (
+        _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "jax", "Array")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+    ):
+        return False
+    if _is_array_api_cls(cls):
+        return True
+    return None
+
+
+def is_writeable_array(x: object) -> bool:
+    """
+    Return False if ``x.__setitem__`` is expected to raise; True otherwise.
+    Return False if `x` is not an array API compatible object.
+
+    Warning
+    -------
+    As there is no standard way to check if an array is writeable without actually
+    writing to it, this function blindly returns True for all unknown array types.
+    """
+    cls = cast(Hashable, type(x))
+    if _issubclass_fast(cls, "numpy", "ndarray"):
+        return cast("npt.NDArray", x).flags.writeable
+    res = _is_writeable_cls(cls)
+    if res is not None:
+        return res
+    return hasattr(x, '__array_namespace__')
+
+
+@lru_cache(100)
+def _is_lazy_cls(cls: type) -> bool | None:
+    if (
+        _issubclass_fast(cls, "numpy", "ndarray")
+        or _issubclass_fast(cls, "numpy", "generic")
+        or _issubclass_fast(cls, "cupy", "ndarray")
+        or _issubclass_fast(cls, "torch", "Tensor")
+        or _issubclass_fast(cls, "sparse", "SparseArray")
+    ):
+        return False
+    if (
+        _issubclass_fast(cls, "jax", "Array")
+        or _issubclass_fast(cls, "dask.array", "Array")
+        or _issubclass_fast(cls, "ndonnx", "Array")
+    ):
+        return True
+    return  None
+
+
+def is_lazy_array(x: object) -> bool:
+    """Return True if x is potentially a future or it may be otherwise impossible or
+    expensive to eagerly read its contents, regardless of their size, e.g. by
+    calling ``bool(x)`` or ``float(x)``.
+
+    Return False otherwise; e.g. ``bool(x)`` etc. is guaranteed to succeed and to be
+    cheap as long as the array has the right dtype and size.
+
+    Note
+    ----
+    This function errs on the side of caution for array types that may or may not be
+    lazy, e.g. JAX arrays, by always returning True for them.
+    """
+    # **JAX note:** while it is possible to determine if you're inside or outside
+    # jax.jit by testing the subclass of a jax.Array object, as well as testing bool()
+    # as we do below for unknown arrays, this is not recommended by JAX best practices.
+
+    # **Dask note:** Dask eagerly computes the graph on __bool__, __float__, and so on.
+    # This behaviour, while impossible to change without breaking backwards
+    # compatibility, is highly detrimental to performance as the whole graph will end
+    # up being computed multiple times.
+
+    # Note: skipping reclassification of JAX zero gradient arrays, as one will
+    # exclusively get them once they leave a jax.grad JIT context.
+    cls = cast(Hashable, type(x))
+    res = _is_lazy_cls(cls)
+    if res is not None:
+        return res
+
+    if not hasattr(x, "__array_namespace__"):
+        return False
+
+    # Unknown Array API compatible object. Note that this test may have dire consequences
+    # in terms of performance, e.g. for a lazy object that eagerly computes the graph
+    # on __bool__ (dask is one such example, which however is special-cased above).
+
+    # Select a single point of the array
+    s = size(cast("HasShape[Collection[SupportsIndex | None]]", x))
+    if s is None:
+        return True
+    xp = array_namespace(x)
+    if s > 1:
+        x = xp.reshape(x, (-1,))[0]
+    # Cast to dtype=bool and deal with size 0 arrays
+    x = xp.any(x)
+
+    try:
+        bool(x)
+        return False
+    # The Array API standard dictactes that __bool__ should raise TypeError if the
+    # output cannot be defined.
+    # Here we allow for it to raise arbitrary exceptions, e.g. like Dask does.
+    except Exception:
+        return True
+
+
+__all__ = [
+    "array_namespace",
+    "device",
+    "get_namespace",
+    "is_array_api_obj",
+    "is_array_api_strict_namespace",
+    "is_cupy_array",
+    "is_cupy_namespace",
+    "is_dask_array",
+    "is_dask_namespace",
+    "is_jax_array",
+    "is_jax_namespace",
+    "is_numpy_array",
+    "is_numpy_namespace",
+    "is_torch_array",
+    "is_torch_namespace",
+    "is_ndonnx_array",
+    "is_ndonnx_namespace",
+    "is_pydata_sparse_array",
+    "is_pydata_sparse_namespace",
+    "is_writeable_array",
+    "is_lazy_array",
+    "size",
+    "to_device",
+]
+
+_all_ignore = ['lru_cache', 'sys', 'math', 'inspect', 'warnings']
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ad87a1be91057a6c7ee3a4e49cfa26396809650
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_linalg.py
@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+import math
+from typing import Literal, NamedTuple, cast
+
+import numpy as np
+
+if np.__version__[0] == "2":
+    from numpy.lib.array_utils import normalize_axis_tuple
+else:
+    from numpy.core.numeric import normalize_axis_tuple
+
+from .._internal import get_xp
+from ._aliases import isdtype, matmul, matrix_transpose, tensordot, vecdot
+from ._typing import Array, DType, JustFloat, JustInt, Namespace
+
+
+# These are in the main NumPy namespace but not in numpy.linalg
+def cross(
+    x1: Array,
+    x2: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int = -1,
+    **kwargs: object,
+) -> Array:
+    return xp.cross(x1, x2, axis=axis, **kwargs)
+
+def outer(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array:
+    return xp.outer(x1, x2, **kwargs)
+
+class EighResult(NamedTuple):
+    eigenvalues: Array
+    eigenvectors: Array
+
+class QRResult(NamedTuple):
+    Q: Array
+    R: Array
+
+class SlogdetResult(NamedTuple):
+    sign: Array
+    logabsdet: Array
+
+class SVDResult(NamedTuple):
+    U: Array
+    S: Array
+    Vh: Array
+
+# These functions are the same as their NumPy counterparts except they return
+# a namedtuple.
+def eigh(x: Array, /, xp: Namespace, **kwargs: object) -> EighResult:
+    return EighResult(*xp.linalg.eigh(x, **kwargs))
+
+def qr(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    mode: Literal["reduced", "complete"] = "reduced",
+    **kwargs: object,
+) -> QRResult:
+    return QRResult(*xp.linalg.qr(x, mode=mode, **kwargs))
+
+def slogdet(x: Array, /, xp: Namespace, **kwargs: object) -> SlogdetResult:
+    return SlogdetResult(*xp.linalg.slogdet(x, **kwargs))
+
+def svd(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    full_matrices: bool = True,
+    **kwargs: object,
+) -> SVDResult:
+    return SVDResult(*xp.linalg.svd(x, full_matrices=full_matrices, **kwargs))
+
+# These functions have additional keyword arguments
+
+# The upper keyword argument is new from NumPy
+def cholesky(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    upper: bool = False,
+    **kwargs: object,
+) -> Array:
+    L = xp.linalg.cholesky(x, **kwargs)
+    if upper:
+        U = get_xp(xp)(matrix_transpose)(L)
+        if get_xp(xp)(isdtype)(U.dtype, 'complex floating'):
+            U = xp.conj(U)  # pyright: ignore[reportConstantRedefinition]
+        return U
+    return L
+
+# The rtol keyword argument of matrix_rank() and pinv() is new from NumPy.
+# Note that it has a different semantic meaning from tol and rcond.
+def matrix_rank(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    rtol: float | Array | None = None,
+    **kwargs: object,
+) -> Array:
+    # this is different from xp.linalg.matrix_rank, which supports 1
+    # dimensional arrays.
+    if x.ndim < 2:
+        raise xp.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
+    S: Array = get_xp(xp)(svdvals)(x, **kwargs)
+    if rtol is None:
+        tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * xp.finfo(S.dtype).eps
+    else:
+        # this is different from xp.linalg.matrix_rank, which does not
+        # multiply the tolerance by the largest singular value.
+        tol = S.max(axis=-1, keepdims=True)*xp.asarray(rtol)[..., xp.newaxis]
+    return xp.count_nonzero(S > tol, axis=-1)
+
+def pinv(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    rtol: float | Array | None = None,
+    **kwargs: object,
+) -> Array:
+    # this is different from xp.linalg.pinv, which does not multiply the
+    # default tolerance by max(M, N).
+    if rtol is None:
+        rtol = max(x.shape[-2:]) * xp.finfo(x.dtype).eps
+    return xp.linalg.pinv(x, rcond=rtol, **kwargs)
+
+# These functions are new in the array API spec
+
+def matrix_norm(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    keepdims: bool = False,
+    ord: Literal[1, 2, -1, -2] | JustFloat | Literal["fro", "nuc"] | None = "fro",
+) -> Array:
+    return xp.linalg.norm(x, axis=(-2, -1), keepdims=keepdims, ord=ord)
+
+# svdvals is not in NumPy (but it is in SciPy). It is equivalent to
+# xp.linalg.svd(compute_uv=False).
+def svdvals(x: Array, /, xp: Namespace) -> Array | tuple[Array, ...]:
+    return xp.linalg.svd(x, compute_uv=False)
+
+def vector_norm(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    axis: int | tuple[int, ...] | None = None,
+    keepdims: bool = False,
+    ord: JustInt | JustFloat = 2,
+) -> Array:
+    # xp.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
+    # when axis=None and the input is 2-D, so to force a vector norm, we make
+    # it so the input is 1-D (for axis=None), or reshape so that norm is done
+    # on a single dimension.
+    if axis is None:
+        # Note: xp.linalg.norm() doesn't handle 0-D arrays
+        _x = x.ravel()
+        _axis = 0
+    elif isinstance(axis, tuple):
+        # Note: The axis argument supports any number of axes, whereas
+        # xp.linalg.norm() only supports a single axis for vector norm.
+        normalized_axis = cast(
+            "tuple[int, ...]",
+            normalize_axis_tuple(axis, x.ndim),  # pyright: ignore[reportCallIssue]
+        )
+        rest = tuple(i for i in range(x.ndim) if i not in normalized_axis)
+        newshape = axis + rest
+        _x = xp.transpose(x, newshape).reshape(
+            (math.prod([x.shape[i] for i in axis]), *[x.shape[i] for i in rest]))
+        _axis = 0
+    else:
+        _x = x
+        _axis = axis
+
+    res = xp.linalg.norm(_x, axis=_axis, ord=ord)
+
+    if keepdims:
+        # We can't reuse xp.linalg.norm(keepdims) because of the reshape hacks
+        # above to avoid matrix norm logic.
+        shape = list(x.shape)
+        _axis = cast(
+            "tuple[int, ...]",
+            normalize_axis_tuple(  # pyright: ignore[reportCallIssue]
+                range(x.ndim) if axis is None else axis,
+                x.ndim,
+            ),
+        )
+        for i in _axis:
+            shape[i] = 1
+        res = xp.reshape(res, tuple(shape))
+
+    return res
+
+# xp.diagonal and xp.trace operate on the first two axes whereas these
+# operates on the last two
+
+def diagonal(x: Array, /, xp: Namespace, *, offset: int = 0, **kwargs: object) -> Array:
+    return xp.diagonal(x, offset=offset, axis1=-2, axis2=-1, **kwargs)
+
+def trace(
+    x: Array,
+    /,
+    xp: Namespace,
+    *,
+    offset: int = 0,
+    dtype: DType | None = None,
+    **kwargs: object,
+) -> Array:
+    return xp.asarray(
+        xp.trace(x, offset=offset, dtype=dtype, axis1=-2, axis2=-1, **kwargs)
+    )
+
+__all__ = ['cross', 'matmul', 'outer', 'tensordot', 'EighResult',
+           'QRResult', 'SlogdetResult', 'SVDResult', 'eigh', 'qr', 'slogdet',
+           'svd', 'cholesky', 'matrix_rank', 'pinv', 'matrix_norm',
+           'matrix_transpose', 'svdvals', 'vecdot', 'vector_norm', 'diagonal',
+           'trace']
+
+_all_ignore = ['math', 'normalize_axis_tuple', 'get_xp', 'np', 'isdtype']
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd26feeba4dffefaf38df52faf2b28c804bfa50a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/common/_typing.py
@@ -0,0 +1,192 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from types import ModuleType as Namespace
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    Protocol,
+    TypeAlias,
+    TypedDict,
+    TypeVar,
+    final,
+)
+
+if TYPE_CHECKING:
+    from _typeshed import Incomplete
+
+    SupportsBufferProtocol: TypeAlias = Incomplete
+    Array: TypeAlias = Incomplete
+    Device: TypeAlias = Incomplete
+    DType: TypeAlias = Incomplete
+else:
+    SupportsBufferProtocol = object
+    Array = object
+    Device = object
+    DType = object
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+# These "Just" types are equivalent to the `Just` type from the `optype` library,
+# apart from them not being `@runtime_checkable`.
+# - docs: https://github.com/jorenham/optype/blob/master/README.md#just
+# - code: https://github.com/jorenham/optype/blob/master/optype/_core/_just.py
+@final
+class JustInt(Protocol):
+    @property
+    def __class__(self, /) -> type[int]: ...
+    @__class__.setter
+    def __class__(self, value: type[int], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
+
+
+@final
+class JustFloat(Protocol):
+    @property
+    def __class__(self, /) -> type[float]: ...
+    @__class__.setter
+    def __class__(self, value: type[float], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
+
+
+@final
+class JustComplex(Protocol):
+    @property
+    def __class__(self, /) -> type[complex]: ...
+    @__class__.setter
+    def __class__(self, value: type[complex], /) -> None: ...  # pyright: ignore[reportIncompatibleMethodOverride]
+
+
+#
+
+
+class NestedSequence(Protocol[_T_co]):
+    def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ...
+    def __len__(self, /) -> int: ...
+
+
+class SupportsArrayNamespace(Protocol[_T_co]):
+    def __array_namespace__(self, /, *, api_version: str | None) -> _T_co: ...
+
+
+class HasShape(Protocol[_T_co]):
+    @property
+    def shape(self, /) -> _T_co: ...
+
+
+# Return type of `__array_namespace_info__.default_dtypes`
+Capabilities = TypedDict(
+    "Capabilities",
+    {
+        "boolean indexing": bool,
+        "data-dependent shapes": bool,
+        "max dimensions": int,
+    },
+)
+
+# Return type of `__array_namespace_info__.default_dtypes`
+DefaultDTypes = TypedDict(
+    "DefaultDTypes",
+    {
+        "real floating": DType,
+        "complex floating": DType,
+        "integral": DType,
+        "indexing": DType,
+    },
+)
+
+
+_DTypeKind: TypeAlias = Literal[
+    "bool",
+    "signed integer",
+    "unsigned integer",
+    "integral",
+    "real floating",
+    "complex floating",
+    "numeric",
+]
+# Type of the `kind` parameter in `__array_namespace_info__.dtypes`
+DTypeKind: TypeAlias = _DTypeKind | tuple[_DTypeKind, ...]
+
+
+# `__array_namespace_info__.dtypes(kind="bool")`
+class DTypesBool(TypedDict):
+    bool: DType
+
+
+# `__array_namespace_info__.dtypes(kind="signed integer")`
+class DTypesSigned(TypedDict):
+    int8: DType
+    int16: DType
+    int32: DType
+    int64: DType
+
+
+# `__array_namespace_info__.dtypes(kind="unsigned integer")`
+class DTypesUnsigned(TypedDict):
+    uint8: DType
+    uint16: DType
+    uint32: DType
+    uint64: DType
+
+
+# `__array_namespace_info__.dtypes(kind="integral")`
+class DTypesIntegral(DTypesSigned, DTypesUnsigned):
+    pass
+
+
+# `__array_namespace_info__.dtypes(kind="real floating")`
+class DTypesReal(TypedDict):
+    float32: DType
+    float64: DType
+
+
+# `__array_namespace_info__.dtypes(kind="complex floating")`
+class DTypesComplex(TypedDict):
+    complex64: DType
+    complex128: DType
+
+
+# `__array_namespace_info__.dtypes(kind="numeric")`
+class DTypesNumeric(DTypesIntegral, DTypesReal, DTypesComplex):
+    pass
+
+
+# `__array_namespace_info__.dtypes(kind=None)` (default)
+class DTypesAll(DTypesBool, DTypesNumeric):
+    pass
+
+
+# `__array_namespace_info__.dtypes(kind=?)` (fallback)
+DTypesAny: TypeAlias = Mapping[str, DType]
+
+
+__all__ = [
+    "Array",
+    "Capabilities",
+    "DType",
+    "DTypeKind",
+    "DTypesAny",
+    "DTypesAll",
+    "DTypesBool",
+    "DTypesNumeric",
+    "DTypesIntegral",
+    "DTypesSigned",
+    "DTypesUnsigned",
+    "DTypesReal",
+    "DTypesComplex",
+    "DefaultDTypes",
+    "Device",
+    "HasShape",
+    "Namespace",
+    "JustInt",
+    "JustFloat",
+    "JustComplex",
+    "NestedSequence",
+    "SupportsArrayNamespace",
+    "SupportsBufferProtocol",
+]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a30f95ddf12c3853626e0645c431155ef507107
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/__init__.py
@@ -0,0 +1,13 @@
+from cupy import * # noqa: F403
+
+# from cupy import * doesn't overwrite these builtin names
+from cupy import abs, max, min, round # noqa: F401
+
+# These imports may overwrite names from the import * above.
+from ._aliases import * # noqa: F403
+
+# See the comment in the numpy __init__.py
+__import__(__package__ + '.linalg')
+__import__(__package__ + '.fft')
+
+__array_api_version__ = '2024.12'
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_aliases.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..90b48f059bafad6d9016e6284d5a3fb9ca18f6d6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_aliases.py
@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+from typing import Optional
+
+import cupy as cp
+
+from ..common import _aliases, _helpers
+from ..common._typing import NestedSequence, SupportsBufferProtocol
+from .._internal import get_xp
+from ._info import __array_namespace_info__
+from ._typing import Array, Device, DType
+
+bool = cp.bool_
+
+# Basic renames
+acos = cp.arccos
+acosh = cp.arccosh
+asin = cp.arcsin
+asinh = cp.arcsinh
+atan = cp.arctan
+atan2 = cp.arctan2
+atanh = cp.arctanh
+bitwise_left_shift = cp.left_shift
+bitwise_invert = cp.invert
+bitwise_right_shift = cp.right_shift
+concat = cp.concatenate
+pow = cp.power
+
+arange = get_xp(cp)(_aliases.arange)
+empty = get_xp(cp)(_aliases.empty)
+empty_like = get_xp(cp)(_aliases.empty_like)
+eye = get_xp(cp)(_aliases.eye)
+full = get_xp(cp)(_aliases.full)
+full_like = get_xp(cp)(_aliases.full_like)
+linspace = get_xp(cp)(_aliases.linspace)
+ones = get_xp(cp)(_aliases.ones)
+ones_like = get_xp(cp)(_aliases.ones_like)
+zeros = get_xp(cp)(_aliases.zeros)
+zeros_like = get_xp(cp)(_aliases.zeros_like)
+UniqueAllResult = get_xp(cp)(_aliases.UniqueAllResult)
+UniqueCountsResult = get_xp(cp)(_aliases.UniqueCountsResult)
+UniqueInverseResult = get_xp(cp)(_aliases.UniqueInverseResult)
+unique_all = get_xp(cp)(_aliases.unique_all)
+unique_counts = get_xp(cp)(_aliases.unique_counts)
+unique_inverse = get_xp(cp)(_aliases.unique_inverse)
+unique_values = get_xp(cp)(_aliases.unique_values)
+std = get_xp(cp)(_aliases.std)
+var = get_xp(cp)(_aliases.var)
+cumulative_sum = get_xp(cp)(_aliases.cumulative_sum)
+cumulative_prod = get_xp(cp)(_aliases.cumulative_prod)
+clip = get_xp(cp)(_aliases.clip)
+permute_dims = get_xp(cp)(_aliases.permute_dims)
+reshape = get_xp(cp)(_aliases.reshape)
+argsort = get_xp(cp)(_aliases.argsort)
+sort = get_xp(cp)(_aliases.sort)
+nonzero = get_xp(cp)(_aliases.nonzero)
+ceil = get_xp(cp)(_aliases.ceil)
+floor = get_xp(cp)(_aliases.floor)
+trunc = get_xp(cp)(_aliases.trunc)
+matmul = get_xp(cp)(_aliases.matmul)
+matrix_transpose = get_xp(cp)(_aliases.matrix_transpose)
+tensordot = get_xp(cp)(_aliases.tensordot)
+sign = get_xp(cp)(_aliases.sign)
+finfo = get_xp(cp)(_aliases.finfo)
+iinfo = get_xp(cp)(_aliases.iinfo)
+
+
+# asarray also adds the copy keyword, which is not present in numpy 1.0.
+def asarray(
+    obj: (
+        Array 
+        | bool | int | float | complex 
+        | NestedSequence[bool | int | float | complex] 
+        | SupportsBufferProtocol
+    ),
+    /,
+    *,
+    dtype: Optional[DType] = None,
+    device: Optional[Device] = None,
+    copy: Optional[bool] = None,
+    **kwargs,
+) -> Array:
+    """
+    Array API compatibility wrapper for asarray().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    with cp.cuda.Device(device):
+        if copy is None:
+            return cp.asarray(obj, dtype=dtype, **kwargs)
+        else:
+            res = cp.array(obj, dtype=dtype, copy=copy, **kwargs)
+            if not copy and res is not obj:
+                raise ValueError("Unable to avoid copy while creating an array as requested")
+            return res
+
+
+def astype(
+    x: Array,
+    dtype: DType,
+    /,
+    *,
+    copy: bool = True,
+    device: Optional[Device] = None,
+) -> Array:
+    if device is None:
+        return x.astype(dtype=dtype, copy=copy)
+    out = _helpers.to_device(x.astype(dtype=dtype, copy=False), device)
+    return out.copy() if copy and out is x else out
+
+
+# cupy.count_nonzero does not have keepdims
+def count_nonzero(
+    x: Array,
+    axis=None,
+    keepdims=False
+) -> Array:
+   result = cp.count_nonzero(x, axis)
+   if keepdims:
+       if axis is None:
+            return cp.reshape(result, [1]*x.ndim)
+       return cp.expand_dims(result, axis)
+   return result
+
+
+# take_along_axis: axis defaults to -1 but in cupy (and numpy) axis is a required arg
+def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1):
+    return cp.take_along_axis(x, indices, axis=axis)
+
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(cp, 'vecdot'):
+    vecdot = cp.vecdot
+else:
+    vecdot = get_xp(cp)(_aliases.vecdot)
+
+if hasattr(cp, 'isdtype'):
+    isdtype = cp.isdtype
+else:
+    isdtype = get_xp(cp)(_aliases.isdtype)
+
+if hasattr(cp, 'unstack'):
+    unstack = cp.unstack
+else:
+    unstack = get_xp(cp)(_aliases.unstack)
+
+__all__ = _aliases.__all__ + ['__array_namespace_info__', 'asarray', 'astype',
+                              'acos', 'acosh', 'asin', 'asinh', 'atan',
+                              'atan2', 'atanh', 'bitwise_left_shift',
+                              'bitwise_invert', 'bitwise_right_shift',
+                              'bool', 'concat', 'count_nonzero', 'pow', 'sign',
+                              'take_along_axis']
+
+_all_ignore = ['cp', 'get_xp']
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..78e48a3358cf547a9a77c5ebdc95fa81c3d4e144
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_info.py
@@ -0,0 +1,336 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+from cupy import (
+    dtype,
+    cuda,
+    bool_ as bool,
+    intp,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+)
+
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for CuPy.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for CuPy.
+
+    Examples
+    --------
+    >>> info = xp.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': cupy.float64,
+     'complex floating': cupy.complex128,
+     'integral': cupy.int64,
+     'indexing': cupy.int64}
+
+    """
+
+    __module__ = 'cupy'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for CuPy.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          CuPy.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True,
+         'max dimensions': 64}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            "max dimensions": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new CuPy arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : Device
+            The default device used for new CuPy arrays.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_device()
+        Device(0)
+
+        Notes
+        -----
+        This method returns the static default device when CuPy is initialized.
+        However, the *current* device used by creation functions (``empty`` etc.)
+        can be changed globally or with a context manager.
+
+        See Also
+        --------
+        https://github.com/data-apis/array-api/issues/835
+        """
+        return cuda.Device(0)
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new CuPy arrays.
+
+        For CuPy, this always returns the following dictionary:
+
+        - **"real floating"**: ``cupy.float64``
+        - **"complex floating"**: ``cupy.complex128``
+        - **"integral"**: ``cupy.intp``
+        - **"indexing"**: ``cupy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new CuPy
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': cupy.float64,
+         'complex floating': cupy.complex128,
+         'integral': cupy.int64,
+         'indexing': cupy.int64}
+
+        """
+        # TODO: Does this depend on device?
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by CuPy.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            CuPy data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': cupy.int8,
+         'int16': cupy.int16,
+         'int32': cupy.int32,
+         'int64': cupy.int64}
+
+        """
+        # TODO: Does this depend on device?
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self):
+        """
+        The devices supported by CuPy.
+
+        Returns
+        -------
+        devices : list[Device]
+            The devices supported by CuPy.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        """
+        return [cuda.Device(i) for i in range(cuda.runtime.getDeviceCount())]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e49ca773dc553cfbc14e526c2c48054e564a12
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/_typing.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+__all__ = ["Array", "DType", "Device"]
+_all_ignore = ["cp"]
+
+from typing import TYPE_CHECKING
+
+import cupy as cp
+from cupy import ndarray as Array
+from cupy.cuda.device import Device
+
+if TYPE_CHECKING:
+    # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
+    DType = cp.dtype[
+        cp.intp
+        | cp.int8
+        | cp.int16
+        | cp.int32
+        | cp.int64
+        | cp.uint8
+        | cp.uint16
+        | cp.uint32
+        | cp.uint64
+        | cp.float32
+        | cp.float64
+        | cp.complex64
+        | cp.complex128
+        | cp.bool_
+    ]
+else:
+    DType = cp.dtype
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/fft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..307e0f7277710693063ef8c4d2cd7893275ad44a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/fft.py
@@ -0,0 +1,36 @@
+from cupy.fft import * # noqa: F403
+# cupy.fft doesn't have __all__. If it is added, replace this with
+#
+# from cupy.fft import __all__ as linalg_all
+_n = {}
+exec('from cupy.fft import *', _n)
+del _n['__builtins__']
+fft_all = list(_n)
+del _n
+
+from ..common import _fft
+from .._internal import get_xp
+
+import cupy as cp
+
+fft = get_xp(cp)(_fft.fft)
+ifft = get_xp(cp)(_fft.ifft)
+fftn = get_xp(cp)(_fft.fftn)
+ifftn = get_xp(cp)(_fft.ifftn)
+rfft = get_xp(cp)(_fft.rfft)
+irfft = get_xp(cp)(_fft.irfft)
+rfftn = get_xp(cp)(_fft.rfftn)
+irfftn = get_xp(cp)(_fft.irfftn)
+hfft = get_xp(cp)(_fft.hfft)
+ihfft = get_xp(cp)(_fft.ihfft)
+fftfreq = get_xp(cp)(_fft.fftfreq)
+rfftfreq = get_xp(cp)(_fft.rfftfreq)
+fftshift = get_xp(cp)(_fft.fftshift)
+ifftshift = get_xp(cp)(_fft.ifftshift)
+
+__all__ = fft_all + _fft.__all__
+
+del get_xp
+del cp
+del fft_all
+del _fft
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fcdd498e0073ada094a20a9ae423e01cb0f8ceb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/cupy/linalg.py
@@ -0,0 +1,49 @@
+from cupy.linalg import * # noqa: F403
+# cupy.linalg doesn't have __all__. If it is added, replace this with
+#
+# from cupy.linalg import __all__ as linalg_all
+_n = {}
+exec('from cupy.linalg import *', _n)
+del _n['__builtins__']
+linalg_all = list(_n)
+del _n
+
+from ..common import _linalg
+from .._internal import get_xp
+
+import cupy as cp
+
+# These functions are in both the main and linalg namespaces
+from ._aliases import matmul, matrix_transpose, tensordot, vecdot # noqa: F401
+
+cross = get_xp(cp)(_linalg.cross)
+outer = get_xp(cp)(_linalg.outer)
+EighResult = _linalg.EighResult
+QRResult = _linalg.QRResult
+SlogdetResult = _linalg.SlogdetResult
+SVDResult = _linalg.SVDResult
+eigh = get_xp(cp)(_linalg.eigh)
+qr = get_xp(cp)(_linalg.qr)
+slogdet = get_xp(cp)(_linalg.slogdet)
+svd = get_xp(cp)(_linalg.svd)
+cholesky = get_xp(cp)(_linalg.cholesky)
+matrix_rank = get_xp(cp)(_linalg.matrix_rank)
+pinv = get_xp(cp)(_linalg.pinv)
+matrix_norm = get_xp(cp)(_linalg.matrix_norm)
+svdvals = get_xp(cp)(_linalg.svdvals)
+diagonal = get_xp(cp)(_linalg.diagonal)
+trace = get_xp(cp)(_linalg.trace)
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(cp.linalg, 'vector_norm'):
+    vector_norm = cp.linalg.vector_norm
+else:
+    vector_norm = get_xp(cp)(_linalg.vector_norm)
+
+__all__ = linalg_all + _linalg.__all__
+
+del get_xp
+del cp
+del linalg_all
+del _linalg
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e47b9606b7744620f41e9888b7c064f9c504520
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/__init__.py
@@ -0,0 +1,12 @@
+from typing import Final
+
+from dask.array import *  # noqa: F403
+
+# These imports may overwrite names from the import * above.
+from ._aliases import *  # noqa: F403
+
+__array_api_version__: Final = "2024.12"
+
+# See the comment in the numpy __init__.py
+__import__(__package__ + '.linalg')
+__import__(__package__ + '.fft')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_aliases.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..d43881ab18f1c0e11efcbd38598a5ca285842919
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_aliases.py
@@ -0,0 +1,376 @@
+# pyright: reportPrivateUsage=false
+# pyright: reportUnknownArgumentType=false
+# pyright: reportUnknownMemberType=false
+# pyright: reportUnknownVariableType=false
+
+from __future__ import annotations
+
+from builtins import bool as py_bool
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeIs
+
+import dask.array as da
+import numpy as np
+from numpy import bool_ as bool
+from numpy import (
+    can_cast,
+    complex64,
+    complex128,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    result_type,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+
+from ..._internal import get_xp
+from ...common import _aliases, _helpers, array_namespace
+from ...common._typing import (
+    Array,
+    Device,
+    DType,
+    NestedSequence,
+    SupportsBufferProtocol,
+)
+from ._info import __array_namespace_info__
+
+isdtype = get_xp(np)(_aliases.isdtype)
+unstack = get_xp(da)(_aliases.unstack)
+
+
+# da.astype doesn't respect copy=True
+def astype(
+    x: Array,
+    dtype: DType,
+    /,
+    *,
+    copy: py_bool = True,
+    device: Device | None = None,
+) -> Array:
+    """
+    Array API compatibility wrapper for astype().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    # TODO: respect device keyword?
+    _helpers._check_device(da, device)
+
+    if not copy and dtype == x.dtype:
+        return x
+    x = x.astype(dtype)
+    return x.copy() if copy else x
+
+
+# Common aliases
+
+
+# This arange func is modified from the common one to
+# not pass stop/step as keyword arguments, which will cause
+# an error with dask
+def arange(
+    start: float,
+    /,
+    stop: float | None = None,
+    step: float = 1,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    **kwargs: object,
+) -> Array:
+    """
+    Array API compatibility wrapper for arange().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    # TODO: respect device keyword?
+    _helpers._check_device(da, device)
+
+    args: list[Any] = [start]
+    if stop is not None:
+        args.append(stop)
+    else:
+        # stop is None, so start is actually stop
+        # prepend the default value for start which is 0
+        args.insert(0, 0)
+    args.append(step)
+
+    return da.arange(*args, dtype=dtype, **kwargs)
+
+
+eye = get_xp(da)(_aliases.eye)
+linspace = get_xp(da)(_aliases.linspace)
+UniqueAllResult = get_xp(da)(_aliases.UniqueAllResult)
+UniqueCountsResult = get_xp(da)(_aliases.UniqueCountsResult)
+UniqueInverseResult = get_xp(da)(_aliases.UniqueInverseResult)
+unique_all = get_xp(da)(_aliases.unique_all)
+unique_counts = get_xp(da)(_aliases.unique_counts)
+unique_inverse = get_xp(da)(_aliases.unique_inverse)
+unique_values = get_xp(da)(_aliases.unique_values)
+permute_dims = get_xp(da)(_aliases.permute_dims)
+std = get_xp(da)(_aliases.std)
+var = get_xp(da)(_aliases.var)
+cumulative_sum = get_xp(da)(_aliases.cumulative_sum)
+cumulative_prod = get_xp(da)(_aliases.cumulative_prod)
+empty = get_xp(da)(_aliases.empty)
+empty_like = get_xp(da)(_aliases.empty_like)
+full = get_xp(da)(_aliases.full)
+full_like = get_xp(da)(_aliases.full_like)
+ones = get_xp(da)(_aliases.ones)
+ones_like = get_xp(da)(_aliases.ones_like)
+zeros = get_xp(da)(_aliases.zeros)
+zeros_like = get_xp(da)(_aliases.zeros_like)
+reshape = get_xp(da)(_aliases.reshape)
+matrix_transpose = get_xp(da)(_aliases.matrix_transpose)
+vecdot = get_xp(da)(_aliases.vecdot)
+nonzero = get_xp(da)(_aliases.nonzero)
+ceil = get_xp(np)(_aliases.ceil)
+floor = get_xp(np)(_aliases.floor)
+trunc = get_xp(np)(_aliases.trunc)
+matmul = get_xp(np)(_aliases.matmul)
+tensordot = get_xp(np)(_aliases.tensordot)
+sign = get_xp(np)(_aliases.sign)
+finfo = get_xp(np)(_aliases.finfo)
+iinfo = get_xp(np)(_aliases.iinfo)
+
+
+# asarray also adds the copy keyword, which is not present in numpy 1.0.
+def asarray(
+    obj: complex | NestedSequence[complex] | Array | SupportsBufferProtocol,
+    /,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    copy: py_bool | None = None,
+    **kwargs: object,
+) -> Array:
+    """
+    Array API compatibility wrapper for asarray().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    # TODO: respect device keyword?
+    _helpers._check_device(da, device)
+
+    if isinstance(obj, da.Array):
+        if dtype is not None and dtype != obj.dtype:
+            if copy is False:
+                raise ValueError("Unable to avoid copy when changing dtype")
+            obj = obj.astype(dtype)
+        return obj.copy() if copy else obj  # pyright: ignore[reportAttributeAccessIssue]
+
+    if copy is False:
+        raise ValueError(
+            "Unable to avoid copy when converting a non-dask object to dask"
+        )
+
+    # copy=None to be uniform across dask < 2024.12 and >= 2024.12
+    # see https://github.com/dask/dask/pull/11524/
+    obj = np.array(obj, dtype=dtype, copy=True)
+    return da.from_array(obj)
+
+
+# Element wise aliases
+from dask.array import arccos as acos
+from dask.array import arccosh as acosh
+from dask.array import arcsin as asin
+from dask.array import arcsinh as asinh
+from dask.array import arctan as atan
+from dask.array import arctan2 as atan2
+from dask.array import arctanh as atanh
+
+# Other
+from dask.array import concatenate as concat
+from dask.array import invert as bitwise_invert
+from dask.array import left_shift as bitwise_left_shift
+from dask.array import power as pow
+from dask.array import right_shift as bitwise_right_shift
+
+
+# dask.array.clip does not work unless all three arguments are provided.
+# Furthermore, the masking workaround in common._aliases.clip cannot work with
+# dask (meaning uint64 promoting to float64 is going to just be unfixed for
+# now).
+def clip(
+    x: Array,
+    /,
+    min: float | Array | None = None,
+    max: float | Array | None = None,
+) -> Array:
+    """
+    Array API compatibility wrapper for clip().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+
+    def _isscalar(a: float | Array | None, /) -> TypeIs[float | None]:
+        return a is None or isinstance(a, (int, float))
+
+    min_shape = () if _isscalar(min) else min.shape
+    max_shape = () if _isscalar(max) else max.shape
+
+    # TODO: This won't handle dask unknown shapes
+    result_shape = np.broadcast_shapes(x.shape, min_shape, max_shape)
+
+    if min is not None:
+        min = da.broadcast_to(da.asarray(min), result_shape)
+    if max is not None:
+        max = da.broadcast_to(da.asarray(max), result_shape)
+
+    if min is None and max is None:
+        return da.positive(x)
+
+    if min is None:
+        return astype(da.minimum(x, max), x.dtype)
+    if max is None:
+        return astype(da.maximum(x, min), x.dtype)
+
+    return astype(da.minimum(da.maximum(x, min), max), x.dtype)
+
+
+def _ensure_single_chunk(x: Array, axis: int) -> tuple[Array, Callable[[Array], Array]]:
+    """
+    Make sure that Array is not broken into multiple chunks along axis.
+
+    Returns
+    -------
+    x : Array
+        The input Array with a single chunk along axis.
+    restore : Callable[Array, Array]
+        function to apply to the output to rechunk it back into reasonable chunks
+    """
+    if axis < 0:
+        axis += x.ndim
+    if x.numblocks[axis] < 2:
+        return x, lambda x: x
+
+    # Break chunks on other axes in an attempt to keep chunk size low
+    x = x.rechunk({i: -1 if i == axis else "auto" for i in range(x.ndim)})
+
+    # Rather than reconstructing the original chunks, which can be a
+    # very expensive affair, just break down oversized chunks without
+    # incurring in any transfers over the network.
+    # This has the downside of a risk of overchunking if the array is
+    # then used in operations against other arrays that match the
+    # original chunking pattern.
+    return x, lambda x: x.rechunk()
+
+
+def sort(
+    x: Array,
+    /,
+    *,
+    axis: int = -1,
+    descending: py_bool = False,
+    stable: py_bool = True,
+) -> Array:
+    """
+    Array API compatibility layer around the lack of sort() in Dask.
+
+    Warnings
+    --------
+    This function temporarily rechunks the array along `axis` to a single chunk.
+    This can be extremely inefficient and can lead to out-of-memory errors.
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    x, restore = _ensure_single_chunk(x, axis)
+
+    meta_xp = array_namespace(x._meta)
+    x = da.map_blocks(
+        meta_xp.sort,
+        x,
+        axis=axis,
+        meta=x._meta,
+        dtype=x.dtype,
+        descending=descending,
+        stable=stable,
+    )
+
+    return restore(x)
+
+
+def argsort(
+    x: Array,
+    /,
+    *,
+    axis: int = -1,
+    descending: py_bool = False,
+    stable: py_bool = True,
+) -> Array:
+    """
+    Array API compatibility layer around the lack of argsort() in Dask.
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+
+    Warnings
+    --------
+    This function temporarily rechunks the array along `axis` into a single chunk.
+    This can be extremely inefficient and can lead to out-of-memory errors.
+    """
+    x, restore = _ensure_single_chunk(x, axis)
+
+    meta_xp = array_namespace(x._meta)
+    dtype = meta_xp.argsort(x._meta).dtype
+    meta = meta_xp.astype(x._meta, dtype)
+    x = da.map_blocks(
+        meta_xp.argsort,
+        x,
+        axis=axis,
+        meta=meta,
+        dtype=dtype,
+        descending=descending,
+        stable=stable,
+    )
+
+    return restore(x)
+
+
+# dask.array.count_nonzero does not have keepdims
+def count_nonzero(
+    x: Array,
+    axis: int | None = None,
+    keepdims: py_bool = False,
+) -> Array:
+    result = da.count_nonzero(x, axis)
+    if keepdims:
+        if axis is None:
+            return da.reshape(result, [1] * x.ndim)
+        return da.expand_dims(result, axis)
+    return result
+
+
+__all__ = [
+    "__array_namespace_info__",
+    "count_nonzero",
+    "bool",
+    "int8", "int16", "int32", "int64",
+    "uint8", "uint16", "uint32", "uint64",
+    "float32", "float64",
+    "complex64", "complex128",
+    "asarray", "astype", "can_cast", "result_type",
+    "pow",
+    "concat",
+    "acos", "acosh", "asin", "asinh", "atan", "atan2", "atanh",
+    "bitwise_left_shift", "bitwise_right_shift", "bitwise_invert",
+]  # fmt: skip
+__all__ += _aliases.__all__
+_all_ignore = ["array_namespace", "get_xp", "da", "np"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e4d736f99657e11b972739ceead83c18fefe1e2
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/_info.py
@@ -0,0 +1,416 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+
+# pyright: reportPrivateUsage=false
+
+from __future__ import annotations
+
+from typing import Literal as L
+from typing import TypeAlias, overload
+
+from numpy import bool_ as bool
+from numpy import (
+    complex64,
+    complex128,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    intp,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+
+from ...common._helpers import _DASK_DEVICE, _dask_device
+from ...common._typing import (
+    Capabilities,
+    DefaultDTypes,
+    DType,
+    DTypeKind,
+    DTypesAll,
+    DTypesAny,
+    DTypesBool,
+    DTypesComplex,
+    DTypesIntegral,
+    DTypesNumeric,
+    DTypesReal,
+    DTypesSigned,
+    DTypesUnsigned,
+)
+
+_Device: TypeAlias = L["cpu"] | _dask_device
+
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for Dask.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for Dask.
+
+    Examples
+    --------
+    >>> info = xp.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': dask.float64,
+     'complex floating': dask.complex128,
+     'integral': dask.int64,
+     'indexing': dask.int64}
+
+    """
+
+    __module__ = "dask.array"
+
+    def capabilities(self) -> Capabilities:
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing.
+
+          Dask support boolean indexing as long as both the index
+          and the indexed arrays have known shapes.
+          Note however that the output .shape and .size properties
+          will contain a non-compliant math.nan instead of None.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes.
+
+          Dask implements unique_values et.al.
+          Note however that the output .shape and .size properties
+          will contain a non-compliant math.nan instead of None.
+
+        - **"max dimensions"**: integer indicating the maximum number of
+          dimensions supported by the array library.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True,
+         'max dimensions': 64}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            "max dimensions": 64,
+        }
+
+    def default_device(self) -> L["cpu"]:
+        """
+        The default device used for new Dask arrays.
+
+        For Dask, this always returns ``'cpu'``.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : Device
+            The default device used for new Dask arrays.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_device()
+        'cpu'
+
+        """
+        return "cpu"
+
+    def default_dtypes(self, /, *, device: _Device | None = None) -> DefaultDTypes:
+        """
+        The default data types used for new Dask arrays.
+
+        For Dask, this always returns the following dictionary:
+
+        - **"real floating"**: ``numpy.float64``
+        - **"complex floating"**: ``numpy.complex128``
+        - **"integral"**: ``numpy.intp``
+        - **"indexing"**: ``numpy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new Dask
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': dask.float64,
+         'complex floating': dask.complex128,
+         'integral': dask.int64,
+         'indexing': dask.int64}
+
+        """
+        if device not in ["cpu", _DASK_DEVICE, None]:
+            raise ValueError(
+                f'Device not understood. Only "cpu" or _DASK_DEVICE is allowed, '
+                f"but received: {device!r}"
+            )
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: None = None
+    ) -> DTypesAll: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["bool"]
+    ) -> DTypesBool: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["signed integer"]
+    ) -> DTypesSigned: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["unsigned integer"]
+    ) -> DTypesUnsigned: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["integral"]
+    ) -> DTypesIntegral: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["real floating"]
+    ) -> DTypesReal: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["complex floating"]
+    ) -> DTypesComplex: ...
+    @overload
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: L["numeric"]
+    ) -> DTypesNumeric: ...
+    def dtypes(
+        self, /, *, device: _Device | None = None, kind: DTypeKind | None = None
+    ) -> DTypesAny:
+        """
+        The array API data types supported by Dask.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            Dask data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': dask.int8,
+         'int16': dask.int16,
+         'int32': dask.int32,
+         'int64': dask.int64}
+
+        """
+        if device not in ["cpu", _DASK_DEVICE, None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" or _DASK_DEVICE is allowed, but received:'
+                f" {device}"
+            )
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):  # type: ignore[reportUnnecessaryIsinstanceCall]
+            res: dict[str, DType] = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self) -> list[_Device]:
+        """
+        The devices supported by Dask.
+
+        For Dask, this always returns ``['cpu', DASK_DEVICE]``.
+
+        Returns
+        -------
+        devices : list[Device]
+            The devices supported by Dask.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.devices()
+        ['cpu', DASK_DEVICE]
+
+        """
+        return ["cpu", _DASK_DEVICE]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/fft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f40dffe7abd51d5b4c12d29949e70d883b1c405
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/fft.py
@@ -0,0 +1,21 @@
+from dask.array.fft import * # noqa: F403
+# dask.array.fft doesn't have __all__. If it is added, replace this with
+#
+# from dask.array.fft import __all__ as linalg_all
+_n = {}
+exec('from dask.array.fft import *', _n)
+for k in ("__builtins__", "Sequence", "annotations", "warnings"):
+    _n.pop(k, None)
+fft_all = list(_n)
+del _n, k
+
+from ...common import _fft
+from ..._internal import get_xp
+
+import dask.array as da
+
+fftfreq = get_xp(da)(_fft.fftfreq)
+rfftfreq = get_xp(da)(_fft.rfftfreq)
+
+__all__ = fft_all + ["fftfreq", "rfftfreq"]
+_all_ignore = ["da", "fft_all", "get_xp", "warnings"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..0825386ed5dc3418d06b3bbe04e7ddac5945ed82
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/dask/array/linalg.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from typing import Literal
+
+import dask.array as da
+
+# The `matmul` and `tensordot` functions are in both the main and linalg namespaces
+from dask.array import matmul, outer, tensordot
+
+# Exports
+from dask.array.linalg import *  # noqa: F403
+
+from ..._internal import get_xp
+from ...common import _linalg
+from ...common._typing import Array as _Array
+from ._aliases import matrix_transpose, vecdot
+
+# dask.array.linalg doesn't have __all__. If it is added, replace this with
+#
+# from dask.array.linalg import __all__ as linalg_all
+_n = {}
+exec('from dask.array.linalg import *', _n)
+for k in ('__builtins__', 'annotations', 'operator', 'warnings', 'Array'):
+    _n.pop(k, None)
+linalg_all = list(_n)
+del _n, k
+
+EighResult = _linalg.EighResult
+QRResult = _linalg.QRResult
+SlogdetResult = _linalg.SlogdetResult
+SVDResult = _linalg.SVDResult
+# TODO: use the QR wrapper once dask
+# supports the mode keyword on QR
+# https://github.com/dask/dask/issues/10388
+#qr = get_xp(da)(_linalg.qr)
+def qr(
+    x: _Array,
+    mode: Literal["reduced", "complete"] = "reduced",
+    **kwargs: object,
+) -> QRResult:
+    if mode != "reduced":
+        raise ValueError("dask arrays only support using mode='reduced'")
+    return QRResult(*da.linalg.qr(x, **kwargs))
+trace = get_xp(da)(_linalg.trace)
+cholesky = get_xp(da)(_linalg.cholesky)
+matrix_rank = get_xp(da)(_linalg.matrix_rank)
+matrix_norm = get_xp(da)(_linalg.matrix_norm)
+
+
+# Wrap the svd functions to not pass full_matrices to dask
+# when full_matrices=False (as that is the default behavior for dask),
+# and dask doesn't have the full_matrices keyword
+def svd(x: _Array, full_matrices: bool = True, **kwargs) -> SVDResult:
+    if full_matrices:
+        raise ValueError("full_matrics=True is not supported by dask.")
+    return da.linalg.svd(x, coerce_signs=False, **kwargs)
+
+def svdvals(x: _Array) -> _Array:
+    # TODO: can't avoid computing U or V for dask
+    _, s, _ =  svd(x)
+    return s
+
+vector_norm = get_xp(da)(_linalg.vector_norm)
+diagonal = get_xp(da)(_linalg.diagonal)
+
+__all__ = linalg_all + ["trace", "outer", "matmul", "tensordot",
+                        "matrix_transpose", "vecdot", "EighResult",
+                        "QRResult", "SlogdetResult", "SVDResult", "qr",
+                        "cholesky", "matrix_rank", "matrix_norm", "svdvals",
+                        "vector_norm", "diagonal"]
+
+_all_ignore = ['get_xp', 'da', 'linalg_all', 'warnings']
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e138f53db006566b19857bd2ef4c26ce988f020
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/__init__.py
@@ -0,0 +1,28 @@
+# ruff: noqa: PLC0414
+from typing import Final
+
+from numpy import *  # noqa: F403  # pyright: ignore[reportWildcardImportFromLibrary]
+
+# from numpy import * doesn't overwrite these builtin names
+from numpy import abs as abs
+from numpy import max as max
+from numpy import min as min
+from numpy import round as round
+
+# These imports may overwrite names from the import * above.
+from ._aliases import *  # noqa: F403
+
+# Don't know why, but we have to do an absolute import to import linalg. If we
+# instead do
+#
+# from . import linalg
+#
+# It doesn't overwrite np.linalg from above. The import is generated
+# dynamically so that the library can be vendored.
+__import__(__package__ + ".linalg")
+
+__import__(__package__ + ".fft")
+
+from .linalg import matrix_transpose, vecdot  # type: ignore[no-redef]  # noqa: F401
+
+__array_api_version__: Final = "2024.12"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/__pycache__/__init__.cpython-310.pyc b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/__pycache__/__init__.cpython-310.pyc
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_aliases.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1aee5c0df7969ba8308980d194f7ee92f307eed
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_aliases.py
@@ -0,0 +1,190 @@
+# pyright: reportPrivateUsage=false
+from __future__ import annotations
+
+from builtins import bool as py_bool
+from typing import TYPE_CHECKING, Any, Literal, TypeAlias, cast
+
+import numpy as np
+
+from .._internal import get_xp
+from ..common import _aliases, _helpers
+from ..common._typing import NestedSequence, SupportsBufferProtocol
+from ._info import __array_namespace_info__
+from ._typing import Array, Device, DType
+
+if TYPE_CHECKING:
+    from typing_extensions import Buffer, TypeIs
+
+# The values of the `_CopyMode` enum can be either `False`, `True`, or `2`:
+# https://github.com/numpy/numpy/blob/5a8a6a79d9c2fff8f07dcab5d41e14f8508d673f/numpy/_globals.pyi#L7-L10
+_Copy: TypeAlias = py_bool | Literal[2] | np._CopyMode
+
+bool = np.bool_
+
+# Basic renames
+acos = np.arccos
+acosh = np.arccosh
+asin = np.arcsin
+asinh = np.arcsinh
+atan = np.arctan
+atan2 = np.arctan2
+atanh = np.arctanh
+bitwise_left_shift = np.left_shift
+bitwise_invert = np.invert
+bitwise_right_shift = np.right_shift
+concat = np.concatenate
+pow = np.power
+
+arange = get_xp(np)(_aliases.arange)
+empty = get_xp(np)(_aliases.empty)
+empty_like = get_xp(np)(_aliases.empty_like)
+eye = get_xp(np)(_aliases.eye)
+full = get_xp(np)(_aliases.full)
+full_like = get_xp(np)(_aliases.full_like)
+linspace = get_xp(np)(_aliases.linspace)
+ones = get_xp(np)(_aliases.ones)
+ones_like = get_xp(np)(_aliases.ones_like)
+zeros = get_xp(np)(_aliases.zeros)
+zeros_like = get_xp(np)(_aliases.zeros_like)
+UniqueAllResult = get_xp(np)(_aliases.UniqueAllResult)
+UniqueCountsResult = get_xp(np)(_aliases.UniqueCountsResult)
+UniqueInverseResult = get_xp(np)(_aliases.UniqueInverseResult)
+unique_all = get_xp(np)(_aliases.unique_all)
+unique_counts = get_xp(np)(_aliases.unique_counts)
+unique_inverse = get_xp(np)(_aliases.unique_inverse)
+unique_values = get_xp(np)(_aliases.unique_values)
+std = get_xp(np)(_aliases.std)
+var = get_xp(np)(_aliases.var)
+cumulative_sum = get_xp(np)(_aliases.cumulative_sum)
+cumulative_prod = get_xp(np)(_aliases.cumulative_prod)
+clip = get_xp(np)(_aliases.clip)
+permute_dims = get_xp(np)(_aliases.permute_dims)
+reshape = get_xp(np)(_aliases.reshape)
+argsort = get_xp(np)(_aliases.argsort)
+sort = get_xp(np)(_aliases.sort)
+nonzero = get_xp(np)(_aliases.nonzero)
+ceil = get_xp(np)(_aliases.ceil)
+floor = get_xp(np)(_aliases.floor)
+trunc = get_xp(np)(_aliases.trunc)
+matmul = get_xp(np)(_aliases.matmul)
+matrix_transpose = get_xp(np)(_aliases.matrix_transpose)
+tensordot = get_xp(np)(_aliases.tensordot)
+sign = get_xp(np)(_aliases.sign)
+finfo = get_xp(np)(_aliases.finfo)
+iinfo = get_xp(np)(_aliases.iinfo)
+
+
+def _supports_buffer_protocol(obj: object) -> TypeIs[Buffer]:  # pyright: ignore[reportUnusedFunction]
+    try:
+        memoryview(obj)  # pyright: ignore[reportArgumentType]
+    except TypeError:
+        return False
+    return True
+
+
+# asarray also adds the copy keyword, which is not present in numpy 1.0.
+# asarray() is different enough between numpy, cupy, and dask, the logic
+# complicated enough that it's easier to define it separately for each module
+# rather than trying to combine everything into one function in common/
+def asarray(
+    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
+    /,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    copy: _Copy | None = None,
+    **kwargs: Any,
+) -> Array:
+    """
+    Array API compatibility wrapper for asarray().
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    _helpers._check_device(np, device)
+
+    if copy is None:
+        copy = np._CopyMode.IF_NEEDED
+    elif copy is False:
+        copy = np._CopyMode.NEVER
+    elif copy is True:
+        copy = np._CopyMode.ALWAYS
+
+    return np.array(obj, copy=copy, dtype=dtype, **kwargs)  # pyright: ignore
+
+
+def astype(
+    x: Array,
+    dtype: DType,
+    /,
+    *,
+    copy: py_bool = True,
+    device: Device | None = None,
+) -> Array:
+    _helpers._check_device(np, device)
+    return x.astype(dtype=dtype, copy=copy)
+
+
+# count_nonzero returns a python int for axis=None and keepdims=False
+# https://github.com/numpy/numpy/issues/17562
+def count_nonzero(
+    x: Array,
+    axis: int | tuple[int, ...] | None = None,
+    keepdims: py_bool = False,
+) -> Array:
+    # NOTE: this is currently incorrectly typed in numpy, but will be fixed in
+    # numpy 2.2.5 and 2.3.0: https://github.com/numpy/numpy/pull/28750
+    result = cast("Any", np.count_nonzero(x, axis=axis, keepdims=keepdims))  # pyright: ignore[reportArgumentType, reportCallIssue]
+    if axis is None and not keepdims:
+        return np.asarray(result)
+    return result
+
+
+# take_along_axis: axis defaults to -1 but in numpy axis is a required arg
+def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1):
+    return np.take_along_axis(x, indices, axis=axis)
+
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(np, "vecdot"):
+    vecdot = np.vecdot
+else:
+    vecdot = get_xp(np)(_aliases.vecdot)
+
+if hasattr(np, "isdtype"):
+    isdtype = np.isdtype
+else:
+    isdtype = get_xp(np)(_aliases.isdtype)
+
+if hasattr(np, "unstack"):
+    unstack = np.unstack
+else:
+    unstack = get_xp(np)(_aliases.unstack)
+
+__all__ = [
+    "__array_namespace_info__",
+    "asarray",
+    "astype",
+    "acos",
+    "acosh",
+    "asin",
+    "asinh",
+    "atan",
+    "atan2",
+    "atanh",
+    "bitwise_left_shift",
+    "bitwise_invert",
+    "bitwise_right_shift",
+    "bool",
+    "concat",
+    "count_nonzero",
+    "pow",
+    "take_along_axis"
+]
+__all__ += _aliases.__all__
+_all_ignore = ["np", "get_xp"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..f307f62c5d5d5d3b100de9adb8c9beb70b754cb3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_info.py
@@ -0,0 +1,366 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+from __future__ import annotations
+
+from numpy import bool_ as bool
+from numpy import (
+    complex64,
+    complex128,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    intp,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+
+from ._typing import Device, DType
+
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for NumPy.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for NumPy.
+
+    Examples
+    --------
+    >>> info = np.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': numpy.float64,
+     'complex floating': numpy.complex128,
+     'integral': numpy.int64,
+     'indexing': numpy.int64}
+
+    """
+
+    __module__ = 'numpy'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for NumPy.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          NumPy.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True,
+         'max dimensions': 64}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            "max dimensions": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new NumPy arrays.
+
+        For NumPy, this always returns ``'cpu'``.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : Device
+            The default device used for new NumPy arrays.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_device()
+        'cpu'
+
+        """
+        return "cpu"
+
+    def default_dtypes(
+        self,
+        *,
+        device: Device | None = None,
+    ) -> dict[str, dtype[intp | float64 | complex128]]:
+        """
+        The default data types used for new NumPy arrays.
+
+        For NumPy, this always returns the following dictionary:
+
+        - **"real floating"**: ``numpy.float64``
+        - **"complex floating"**: ``numpy.complex128``
+        - **"integral"**: ``numpy.intp``
+        - **"indexing"**: ``numpy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for. For NumPy, only
+            ``'cpu'`` is allowed.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new NumPy
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': numpy.float64,
+         'complex floating': numpy.complex128,
+         'integral': numpy.int64,
+         'indexing': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    def dtypes(
+        self,
+        *,
+        device: Device | None = None,
+        kind: str | tuple[str, ...] | None = None,
+    ) -> dict[str, DType]:
+        """
+        The array API data types supported by NumPy.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for. For NumPy, only ``'cpu'`` is
+            allowed.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            NumPy data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': numpy.int8,
+         'int16': numpy.int16,
+         'int32': numpy.int32,
+         'int64': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": dtype(bool)}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):
+            res: dict[str, DType] = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self) -> list[Device]:
+        """
+        The devices supported by NumPy.
+
+        For NumPy, this always returns ``['cpu']``.
+
+        Returns
+        -------
+        devices : list[Device]
+            The devices supported by NumPy.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.devices()
+        ['cpu']
+
+        """
+        return ["cpu"]
+
+
+__all__ = ["__array_namespace_info__"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e771c788bbcab3c289304790acb664df0c0b5b5a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/_typing.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Literal, TypeAlias
+
+import numpy as np
+
+Device: TypeAlias = Literal["cpu"]
+
+if TYPE_CHECKING:
+
+    # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
+    DType: TypeAlias = np.dtype[
+        np.bool_
+        | np.integer[Any]
+        | np.float32
+        | np.float64
+        | np.complex64
+        | np.complex128
+    ]
+    Array: TypeAlias = np.ndarray[Any, DType]
+else:
+    DType: TypeAlias = np.dtype
+    Array: TypeAlias = np.ndarray
+
+__all__ = ["Array", "DType", "Device"]
+_all_ignore = ["np"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/fft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..06875f00b4312c65f27c6afe62ccee00619e86ff
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/fft.py
@@ -0,0 +1,35 @@
+import numpy as np
+from numpy.fft import __all__ as fft_all
+from numpy.fft import fft2, ifft2, irfft2, rfft2
+
+from .._internal import get_xp
+from ..common import _fft
+
+fft = get_xp(np)(_fft.fft)
+ifft = get_xp(np)(_fft.ifft)
+fftn = get_xp(np)(_fft.fftn)
+ifftn = get_xp(np)(_fft.ifftn)
+rfft = get_xp(np)(_fft.rfft)
+irfft = get_xp(np)(_fft.irfft)
+rfftn = get_xp(np)(_fft.rfftn)
+irfftn = get_xp(np)(_fft.irfftn)
+hfft = get_xp(np)(_fft.hfft)
+ihfft = get_xp(np)(_fft.ihfft)
+fftfreq = get_xp(np)(_fft.fftfreq)
+rfftfreq = get_xp(np)(_fft.rfftfreq)
+fftshift = get_xp(np)(_fft.fftshift)
+ifftshift = get_xp(np)(_fft.ifftshift)
+
+
+__all__ = ["rfft2", "irfft2", "fft2", "ifft2"]
+__all__ += _fft.__all__
+
+
+def __dir__() -> list[str]:
+    return __all__
+
+
+del get_xp
+del np
+del fft_all
+del _fft
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/linalg.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/linalg.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d3e731da3fc037949cc1f4c8f0bff385aef8bdc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/numpy/linalg.py
@@ -0,0 +1,143 @@
+# pyright: reportAttributeAccessIssue=false
+# pyright: reportUnknownArgumentType=false
+# pyright: reportUnknownMemberType=false
+# pyright: reportUnknownVariableType=false
+
+from __future__ import annotations
+
+import numpy as np
+
+# intersection of `np.linalg.__all__` on numpy 1.22 and 2.2, minus `_linalg.__all__`
+from numpy.linalg import (
+    LinAlgError,
+    cond,
+    det,
+    eig,
+    eigvals,
+    eigvalsh,
+    inv,
+    lstsq,
+    matrix_power,
+    multi_dot,
+    norm,
+    tensorinv,
+    tensorsolve,
+)
+
+from .._internal import get_xp
+from ..common import _linalg
+
+# These functions are in both the main and linalg namespaces
+from ._aliases import matmul, matrix_transpose, tensordot, vecdot  # noqa: F401
+from ._typing import Array
+
+cross = get_xp(np)(_linalg.cross)
+outer = get_xp(np)(_linalg.outer)
+EighResult = _linalg.EighResult
+QRResult = _linalg.QRResult
+SlogdetResult = _linalg.SlogdetResult
+SVDResult = _linalg.SVDResult
+eigh = get_xp(np)(_linalg.eigh)
+qr = get_xp(np)(_linalg.qr)
+slogdet = get_xp(np)(_linalg.slogdet)
+svd = get_xp(np)(_linalg.svd)
+cholesky = get_xp(np)(_linalg.cholesky)
+matrix_rank = get_xp(np)(_linalg.matrix_rank)
+pinv = get_xp(np)(_linalg.pinv)
+matrix_norm = get_xp(np)(_linalg.matrix_norm)
+svdvals = get_xp(np)(_linalg.svdvals)
+diagonal = get_xp(np)(_linalg.diagonal)
+trace = get_xp(np)(_linalg.trace)
+
+# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
+# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
+# of matrices. The np.linalg.solve behavior of allowing stacks of both
+# matrices and vectors is ambiguous c.f.
+# https://github.com/numpy/numpy/issues/15349 and
+# https://github.com/data-apis/array-api/issues/285.
+
+# To workaround this, the below is the code from np.linalg.solve except
+# only calling solve1 in the exactly 1D case.
+
+
+# This code is here instead of in common because it is numpy specific. Also
+# note that CuPy's solve() does not currently support broadcasting (see
+# https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43).
+def solve(x1: Array, x2: Array, /) -> Array:
+    try:
+        from numpy.linalg._linalg import (
+            _assert_stacked_2d,
+            _assert_stacked_square,
+            _commonType,
+            _makearray,
+            _raise_linalgerror_singular,
+            isComplexType,
+        )
+    except ImportError:
+        from numpy.linalg.linalg import (
+            _assert_stacked_2d,
+            _assert_stacked_square,
+            _commonType,
+            _makearray,
+            _raise_linalgerror_singular,
+            isComplexType,
+        )
+    from numpy.linalg import _umath_linalg
+
+    x1, _ = _makearray(x1)
+    _assert_stacked_2d(x1)
+    _assert_stacked_square(x1)
+    x2, wrap = _makearray(x2)
+    t, result_t = _commonType(x1, x2)
+
+    # This part is different from np.linalg.solve
+    gufunc: np.ufunc
+    if x2.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    # This does nothing currently but is left in because it will be relevant
+    # when complex dtype support is added to the spec in 2022.
+    signature = "DD->D" if isComplexType(t) else "dd->d"
+    with np.errstate(
+        call=_raise_linalgerror_singular,
+        invalid="call",
+        over="ignore",
+        divide="ignore",
+        under="ignore",
+    ):
+        r: Array = gufunc(x1, x2, signature=signature)
+
+    return wrap(r.astype(result_t, copy=False))
+
+
+# These functions are completely new here. If the library already has them
+# (i.e., numpy 2.0), use the library version instead of our wrapper.
+if hasattr(np.linalg, "vector_norm"):
+    vector_norm = np.linalg.vector_norm
+else:
+    vector_norm = get_xp(np)(_linalg.vector_norm)
+
+
+__all__ = [
+    "LinAlgError",
+    "cond",
+    "det",
+    "eig",
+    "eigvals",
+    "eigvalsh",
+    "inv",
+    "lstsq",
+    "matrix_power",
+    "multi_dot",
+    "norm",
+    "tensorinv",
+    "tensorsolve",
+]
+__all__ += _linalg.__all__
+__all__ += ["solve", "vector_norm"]
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/py.typed b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..69fd19ce83a5672a9ee574fd30599b55adb26cb0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/__init__.py
@@ -0,0 +1,22 @@
+from torch import * # noqa: F403
+
+# Several names are not included in the above import *
+import torch
+for n in dir(torch):
+    if (n.startswith('_')
+        or n.endswith('_')
+        or 'cuda' in n
+        or 'cpu' in n
+        or 'backward' in n):
+        continue
+    exec(f"{n} = torch.{n}")
+del n
+
+# These imports may overwrite names from the import * above.
+from ._aliases import * # noqa: F403
+
+# See the comment in the numpy __init__.py
+__import__(__package__ + '.linalg')
+__import__(__package__ + '.fft')
+
+__array_api_version__ = '2024.12'
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_aliases.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_aliases.py
new file mode 100644
index 0000000000000000000000000000000000000000..de5d1a5d40eb5e17e8a935003e02bd2039a33c1a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_aliases.py
@@ -0,0 +1,855 @@
+from __future__ import annotations
+
+from functools import reduce as _reduce, wraps as _wraps
+from builtins import all as _builtin_all, any as _builtin_any
+from typing import Any, List, Optional, Sequence, Tuple, Union, Literal
+
+import torch
+
+from .._internal import get_xp
+from ..common import _aliases
+from ..common._typing import NestedSequence, SupportsBufferProtocol
+from ._info import __array_namespace_info__
+from ._typing import Array, Device, DType
+
+_int_dtypes = {
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.int64,
+}
+try:
+    # torch >=2.3
+    _int_dtypes |= {torch.uint16, torch.uint32, torch.uint64}
+except AttributeError:
+    pass
+
+
+_array_api_dtypes = {
+    torch.bool,
+    *_int_dtypes,
+    torch.float32,
+    torch.float64,
+    torch.complex64,
+    torch.complex128,
+}
+
+_promotion_table = {
+    # ints
+    (torch.int8, torch.int16): torch.int16,
+    (torch.int8, torch.int32): torch.int32,
+    (torch.int8, torch.int64): torch.int64,
+    (torch.int16, torch.int32): torch.int32,
+    (torch.int16, torch.int64): torch.int64,
+    (torch.int32, torch.int64): torch.int64,
+    # ints and uints (mixed sign)
+    (torch.uint8, torch.int8): torch.int16,
+    (torch.uint8, torch.int16): torch.int16,
+    (torch.uint8, torch.int32): torch.int32,
+    (torch.uint8, torch.int64): torch.int64,
+    # floats
+    (torch.float32, torch.float64): torch.float64,
+    # complexes
+    (torch.complex64, torch.complex128): torch.complex128,
+    # Mixed float and complex
+    (torch.float32, torch.complex64): torch.complex64,
+    (torch.float32, torch.complex128): torch.complex128,
+    (torch.float64, torch.complex64): torch.complex128,
+    (torch.float64, torch.complex128): torch.complex128,
+}
+
+_promotion_table.update({(b, a): c for (a, b), c in _promotion_table.items()})
+_promotion_table.update({(a, a): a for a in _array_api_dtypes})
+
+
+def _two_arg(f):
+    @_wraps(f)
+    def _f(x1, x2, /, **kwargs):
+        x1, x2 = _fix_promotion(x1, x2)
+        return f(x1, x2, **kwargs)
+    if _f.__doc__ is None:
+        _f.__doc__ = f"""\
+Array API compatibility wrapper for torch.{f.__name__}.
+
+See the corresponding PyTorch documentation and/or the array API specification
+for more details.
+
+"""
+    return _f
+
+def _fix_promotion(x1, x2, only_scalar=True):
+    if not isinstance(x1, torch.Tensor) or not isinstance(x2, torch.Tensor):
+        return x1, x2
+    if x1.dtype not in _array_api_dtypes or x2.dtype not in _array_api_dtypes:
+        return x1, x2
+    # If an argument is 0-D pytorch downcasts the other argument
+    if not only_scalar or x1.shape == ():
+        dtype = result_type(x1, x2)
+        x2 = x2.to(dtype)
+    if not only_scalar or x2.shape == ():
+        dtype = result_type(x1, x2)
+        x1 = x1.to(dtype)
+    return x1, x2
+
+
+_py_scalars = (bool, int, float, complex)
+
+
+def result_type(
+    *arrays_and_dtypes: Array | DType | bool | int | float | complex
+) -> DType:
+    num = len(arrays_and_dtypes)
+
+    if num == 0:
+        raise ValueError("At least one array or dtype must be provided")
+
+    elif num == 1:
+        x = arrays_and_dtypes[0]
+        if isinstance(x, torch.dtype):
+            return x
+        return x.dtype
+
+    if num == 2:
+        x, y = arrays_and_dtypes
+        return _result_type(x, y)
+
+    else:
+        # sort scalars so that they are treated last
+        scalars, others = [], []
+        for x in arrays_and_dtypes:
+            if isinstance(x, _py_scalars):
+                scalars.append(x)
+            else:
+                others.append(x)
+        if not others:
+            raise ValueError("At least one array or dtype must be provided")
+
+        # combine left-to-right
+        return _reduce(_result_type, others + scalars)
+
+
+def _result_type(
+    x: Array | DType | bool | int | float | complex,
+    y: Array | DType | bool | int | float | complex,
+) -> DType:
+    if not (isinstance(x, _py_scalars) or isinstance(y, _py_scalars)):
+        xdt = x if isinstance(x, torch.dtype) else x.dtype
+        ydt = y if isinstance(y, torch.dtype) else y.dtype
+
+        try:
+            return _promotion_table[xdt, ydt]
+        except KeyError:
+            pass
+
+    # This doesn't result_type(dtype, dtype) for non-array API dtypes
+    # because torch.result_type only accepts tensors. This does however, allow
+    # cross-kind promotion.
+    x = torch.tensor([], dtype=x) if isinstance(x, torch.dtype) else x
+    y = torch.tensor([], dtype=y) if isinstance(y, torch.dtype) else y
+    return torch.result_type(x, y)
+
+
+def can_cast(from_: Union[DType, Array], to: DType, /) -> bool:
+    if not isinstance(from_, torch.dtype):
+        from_ = from_.dtype
+    return torch.can_cast(from_, to)
+
+# Basic renames
+bitwise_invert = torch.bitwise_not
+newaxis = None
+# torch.conj sets the conjugation bit, which breaks conversion to other
+# libraries. See https://github.com/data-apis/array-api-compat/issues/173
+conj = torch.conj_physical
+
+# Two-arg elementwise functions
+# These require a wrapper to do the correct type promotion on 0-D tensors
+add = _two_arg(torch.add)
+atan2 = _two_arg(torch.atan2)
+bitwise_and = _two_arg(torch.bitwise_and)
+bitwise_left_shift = _two_arg(torch.bitwise_left_shift)
+bitwise_or = _two_arg(torch.bitwise_or)
+bitwise_right_shift = _two_arg(torch.bitwise_right_shift)
+bitwise_xor = _two_arg(torch.bitwise_xor)
+copysign = _two_arg(torch.copysign)
+divide = _two_arg(torch.divide)
+# Also a rename. torch.equal does not broadcast
+equal = _two_arg(torch.eq)
+floor_divide = _two_arg(torch.floor_divide)
+greater = _two_arg(torch.greater)
+greater_equal = _two_arg(torch.greater_equal)
+hypot = _two_arg(torch.hypot)
+less = _two_arg(torch.less)
+less_equal = _two_arg(torch.less_equal)
+logaddexp = _two_arg(torch.logaddexp)
+# logical functions are not included here because they only accept bool in the
+# spec, so type promotion is irrelevant.
+maximum = _two_arg(torch.maximum)
+minimum = _two_arg(torch.minimum)
+multiply = _two_arg(torch.multiply)
+not_equal = _two_arg(torch.not_equal)
+pow = _two_arg(torch.pow)
+remainder = _two_arg(torch.remainder)
+subtract = _two_arg(torch.subtract)
+
+
+def asarray(
+    obj: (
+    Array 
+        | bool | int | float | complex 
+        | NestedSequence[bool | int | float | complex] 
+        | SupportsBufferProtocol
+    ),
+    /,
+    *,
+    dtype: DType | None = None,
+    device: Device | None = None,
+    copy: bool | None = None,
+    **kwargs: Any,
+) -> Array:
+    # torch.asarray does not respect input->output device propagation
+    # https://github.com/pytorch/pytorch/issues/150199
+    if device is None and isinstance(obj, torch.Tensor):
+        device = obj.device
+    return torch.asarray(obj, dtype=dtype, device=device, copy=copy, **kwargs)
+
+
+# These wrappers are mostly based on the fact that pytorch uses 'dim' instead
+# of 'axis'.
+
+# torch.min and torch.max return a tuple and don't support multiple axes https://github.com/pytorch/pytorch/issues/58745
+def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    return torch.amax(x, axis, keepdims=keepdims)
+
+def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    return torch.amin(x, axis, keepdims=keepdims)
+
+clip = get_xp(torch)(_aliases.clip)
+unstack = get_xp(torch)(_aliases.unstack)
+cumulative_sum = get_xp(torch)(_aliases.cumulative_sum)
+cumulative_prod = get_xp(torch)(_aliases.cumulative_prod)
+finfo = get_xp(torch)(_aliases.finfo)
+iinfo = get_xp(torch)(_aliases.iinfo)
+
+
+# torch.sort also returns a tuple
+# https://github.com/pytorch/pytorch/issues/70921
+def sort(x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True, **kwargs) -> Array:
+    return torch.sort(x, dim=axis, descending=descending, stable=stable, **kwargs).values
+
+def _normalize_axes(axis, ndim):
+    axes = []
+    if ndim == 0 and axis:
+        # Better error message in this case
+        raise IndexError(f"Dimension out of range: {axis[0]}")
+    lower, upper = -ndim, ndim - 1
+    for a in axis:
+        if a < lower or a > upper:
+            # Match torch error message (e.g., from sum())
+            raise IndexError(f"Dimension out of range (expected to be in range of [{lower}, {upper}], but got {a}")
+        if a < 0:
+            a = a + ndim
+        if a in axes:
+            # Use IndexError instead of RuntimeError, and "axis" instead of "dim"
+            raise IndexError(f"Axis {a} appears multiple times in the list of axes")
+        axes.append(a)
+    return sorted(axes)
+
+def _axis_none_keepdims(x, ndim, keepdims):
+    # Apply keepdims when axis=None
+    # (https://github.com/pytorch/pytorch/issues/71209)
+    # Note that this is only valid for the axis=None case.
+    if keepdims:
+        for i in range(ndim):
+            x = torch.unsqueeze(x, 0)
+    return x
+
+def _reduce_multiple_axes(f, x, axis, keepdims=False, **kwargs):
+    # Some reductions don't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    axes = _normalize_axes(axis, x.ndim)
+    for a in reversed(axes):
+        x = torch.movedim(x, a, -1)
+    x = torch.flatten(x, -len(axes))
+
+    out = f(x, -1, **kwargs)
+
+    if keepdims:
+        for a in axes:
+            out = torch.unsqueeze(out, a)
+    return out
+
+
+def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array:
+    """
+    Implements `sum(..., axis=())` and `prod(..., axis=())`.
+    
+    Works around https://github.com/pytorch/pytorch/issues/29137
+    """
+    if dtype is not None:
+        return x.clone() if dtype == x.dtype else x.to(dtype)
+
+    # We can't upcast uint8 according to the spec because there is no
+    # torch.uint64, so at least upcast to int64 which is what prod does
+    # when axis=None.
+    if x.dtype in (torch.uint8, torch.int8, torch.int16, torch.int32):
+        return x.to(torch.int64)
+
+    return x.clone()
+
+
+def prod(x: Array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         dtype: Optional[DType] = None,
+         keepdims: bool = False,
+         **kwargs) -> Array:
+
+    if axis == ():
+        return _sum_prod_no_axis(x, dtype)
+    # torch.prod doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        return _reduce_multiple_axes(torch.prod, x, axis, keepdims=keepdims, dtype=dtype, **kwargs)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.prod(x, dtype=dtype, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+
+    return torch.prod(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
+
+
+def sum(x: Array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         dtype: Optional[DType] = None,
+         keepdims: bool = False,
+         **kwargs) -> Array:
+
+    if axis == ():
+        return _sum_prod_no_axis(x, dtype)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.sum(x, dtype=dtype, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+
+    return torch.sum(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
+
+def any(x: Array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        keepdims: bool = False,
+        **kwargs) -> Array:
+
+    if axis == ():
+        return x.to(torch.bool)
+    # torch.any doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        res = _reduce_multiple_axes(torch.any, x, axis, keepdims=keepdims, **kwargs)
+        return res.to(torch.bool)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.any(x, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res.to(torch.bool)
+
+    # torch.any doesn't return bool for uint8
+    return torch.any(x, axis, keepdims=keepdims).to(torch.bool)
+
+def all(x: Array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        keepdims: bool = False,
+        **kwargs) -> Array:
+
+    if axis == ():
+        return x.to(torch.bool)
+    # torch.all doesn't support multiple axes
+    # (https://github.com/pytorch/pytorch/issues/56586).
+    if isinstance(axis, tuple):
+        res = _reduce_multiple_axes(torch.all, x, axis, keepdims=keepdims, **kwargs)
+        return res.to(torch.bool)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.all(x, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res.to(torch.bool)
+
+    # torch.all doesn't return bool for uint8
+    return torch.all(x, axis, keepdims=keepdims).to(torch.bool)
+
+def mean(x: Array,
+         /,
+         *,
+         axis: Optional[Union[int, Tuple[int, ...]]] = None,
+         keepdims: bool = False,
+         **kwargs) -> Array:
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.clone(x)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.mean(x, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.mean(x, axis, keepdims=keepdims, **kwargs)
+
+def std(x: Array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        correction: Union[int, float] = 0.0,
+        keepdims: bool = False,
+        **kwargs) -> Array:
+    # Note, float correction is not supported
+    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
+    # implement it here for now.
+
+    if isinstance(correction, float):
+        _correction = int(correction)
+        if correction != _correction:
+            raise NotImplementedError("float correction in torch std() is not yet supported")
+    else:
+        _correction = correction
+
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.zeros_like(x)
+    if isinstance(axis, int):
+        axis = (axis,)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.std(x, tuple(range(x.ndim)), correction=_correction, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.std(x, axis, correction=_correction, keepdims=keepdims, **kwargs)
+
+def var(x: Array,
+        /,
+        *,
+        axis: Optional[Union[int, Tuple[int, ...]]] = None,
+        correction: Union[int, float] = 0.0,
+        keepdims: bool = False,
+        **kwargs) -> Array:
+    # Note, float correction is not supported
+    # https://github.com/pytorch/pytorch/issues/61492. We don't try to
+    # implement it here for now.
+
+    # if isinstance(correction, float):
+    #     correction = int(correction)
+
+    # https://github.com/pytorch/pytorch/issues/29137
+    if axis == ():
+        return torch.zeros_like(x)
+    if isinstance(axis, int):
+        axis = (axis,)
+    if axis is None:
+        # torch doesn't support keepdims with axis=None
+        # (https://github.com/pytorch/pytorch/issues/71209)
+        res = torch.var(x, tuple(range(x.ndim)), correction=correction, **kwargs)
+        res = _axis_none_keepdims(res, x.ndim, keepdims)
+        return res
+    return torch.var(x, axis, correction=correction, keepdims=keepdims, **kwargs)
+
+# torch.concat doesn't support dim=None
+# https://github.com/pytorch/pytorch/issues/70925
+def concat(arrays: Union[Tuple[Array, ...], List[Array]],
+           /,
+           *,
+           axis: Optional[int] = 0,
+           **kwargs) -> Array:
+    if axis is None:
+        arrays = tuple(ar.flatten() for ar in arrays)
+        axis = 0
+    return torch.concat(arrays, axis, **kwargs)
+
+# torch.squeeze only accepts int dim and doesn't require it
+# https://github.com/pytorch/pytorch/issues/70924. Support for tuple dim was
+# added at https://github.com/pytorch/pytorch/pull/89017.
+def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array:
+    if isinstance(axis, int):
+        axis = (axis,)
+    for a in axis:
+        if x.shape[a] != 1:
+            raise ValueError("squeezed dimensions must be equal to 1")
+    axes = _normalize_axes(axis, x.ndim)
+    # Remove this once pytorch 1.14 is released with the above PR #89017.
+    sequence = [a - i for i, a in enumerate(axes)]
+    for a in sequence:
+        x = torch.squeeze(x, a)
+    return x
+
+# torch.broadcast_to uses size instead of shape
+def broadcast_to(x: Array, /, shape: Tuple[int, ...], **kwargs) -> Array:
+    return torch.broadcast_to(x, shape, **kwargs)
+
+# torch.permute uses dims instead of axes
+def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
+    return torch.permute(x, axes)
+
+# The axis parameter doesn't work for flip() and roll()
+# https://github.com/pytorch/pytorch/issues/71210. Also torch.flip() doesn't
+# accept axis=None
+def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> Array:
+    if axis is None:
+        axis = tuple(range(x.ndim))
+    # torch.flip doesn't accept dim as an int but the method does
+    # https://github.com/pytorch/pytorch/issues/18095
+    return x.flip(axis, **kwargs)
+
+def roll(x: Array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> Array:
+    return torch.roll(x, shift, axis, **kwargs)
+
+def nonzero(x: Array, /, **kwargs) -> Tuple[Array, ...]:
+    if x.ndim == 0:
+        raise ValueError("nonzero() does not support zero-dimensional arrays")
+    return torch.nonzero(x, as_tuple=True, **kwargs)
+
+
+# torch uses `dim` instead of `axis`
+def diff(
+    x: Array,
+    /,
+    *,
+    axis: int = -1,
+    n: int = 1,
+    prepend: Optional[Array] = None,
+    append: Optional[Array] = None,
+) -> Array:
+    return torch.diff(x, dim=axis, n=n, prepend=prepend, append=append)
+
+
+# torch uses `dim` instead of `axis`, does not have keepdims
+def count_nonzero(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    result = torch.count_nonzero(x, dim=axis)
+    if keepdims:
+        if isinstance(axis, int):
+            return result.unsqueeze(axis)
+        elif isinstance(axis, tuple):
+            n_axis = [x.ndim + ax if ax < 0 else ax for ax in axis]
+            sh = [1 if i in n_axis else x.shape[i] for i in range(x.ndim)]
+            return torch.reshape(result, sh)
+        return _axis_none_keepdims(result, x.ndim, keepdims)
+    else:
+        return result
+
+
+# "repeat" is torch.repeat_interleave;  also the dim argument
+def repeat(x: Array, repeats: int | Array, /, *, axis: int | None = None) -> Array:
+    return torch.repeat_interleave(x, repeats, axis)
+
+
+def where(
+    condition: Array, 
+    x1: Array | bool | int | float | complex, 
+    x2: Array | bool | int | float | complex,
+    /,
+) -> Array:
+    x1, x2 = _fix_promotion(x1, x2)
+    return torch.where(condition, x1, x2)
+
+
+# torch.reshape doesn't have the copy keyword
+def reshape(x: Array,
+            /,
+            shape: Tuple[int, ...],
+            *,
+            copy: Optional[bool] = None,
+            **kwargs) -> Array:
+    if copy is not None:
+        raise NotImplementedError("torch.reshape doesn't yet support the copy keyword")
+    return torch.reshape(x, shape, **kwargs)
+
+# torch.arange doesn't support returning empty arrays
+# (https://github.com/pytorch/pytorch/issues/70915), and doesn't support some
+# keyword argument combinations
+# (https://github.com/pytorch/pytorch/issues/70914)
+def arange(start: Union[int, float],
+           /,
+           stop: Optional[Union[int, float]] = None,
+           step: Union[int, float] = 1,
+           *,
+           dtype: Optional[DType] = None,
+           device: Optional[Device] = None,
+           **kwargs) -> Array:
+    if stop is None:
+        start, stop = 0, start
+    if step > 0 and stop <= start or step < 0 and stop >= start:
+        if dtype is None:
+            if _builtin_all(isinstance(i, int) for i in [start, stop, step]):
+                dtype = torch.int64
+            else:
+                dtype = torch.float32
+        return torch.empty(0, dtype=dtype, device=device, **kwargs)
+    return torch.arange(start, stop, step, dtype=dtype, device=device, **kwargs)
+
+# torch.eye does not accept None as a default for the second argument and
+# doesn't support off-diagonals (https://github.com/pytorch/pytorch/issues/70910)
+def eye(n_rows: int,
+        n_cols: Optional[int] = None,
+        /,
+        *,
+        k: int = 0,
+        dtype: Optional[DType] = None,
+        device: Optional[Device] = None,
+        **kwargs) -> Array:
+    if n_cols is None:
+        n_cols = n_rows
+    z = torch.zeros(n_rows, n_cols, dtype=dtype, device=device, **kwargs)
+    if abs(k) <= n_rows + n_cols:
+        z.diagonal(k).fill_(1)
+    return z
+
+# torch.linspace doesn't have the endpoint parameter
+def linspace(start: Union[int, float],
+             stop: Union[int, float],
+             /,
+             num: int,
+             *,
+             dtype: Optional[DType] = None,
+             device: Optional[Device] = None,
+             endpoint: bool = True,
+             **kwargs) -> Array:
+    if not endpoint:
+        return torch.linspace(start, stop, num+1, dtype=dtype, device=device, **kwargs)[:-1]
+    return torch.linspace(start, stop, num, dtype=dtype, device=device, **kwargs)
+
+# torch.full does not accept an int size
+# https://github.com/pytorch/pytorch/issues/70906
+def full(shape: Union[int, Tuple[int, ...]],
+         fill_value: bool | int | float | complex,
+         *,
+         dtype: Optional[DType] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> Array:
+    if isinstance(shape, int):
+        shape = (shape,)
+
+    return torch.full(shape, fill_value, dtype=dtype, device=device, **kwargs)
+
+# ones, zeros, and empty do not accept shape as a keyword argument
+def ones(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[DType] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> Array:
+    return torch.ones(shape, dtype=dtype, device=device, **kwargs)
+
+def zeros(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[DType] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> Array:
+    return torch.zeros(shape, dtype=dtype, device=device, **kwargs)
+
+def empty(shape: Union[int, Tuple[int, ...]],
+         *,
+         dtype: Optional[DType] = None,
+         device: Optional[Device] = None,
+         **kwargs) -> Array:
+    return torch.empty(shape, dtype=dtype, device=device, **kwargs)
+
+# tril and triu do not call the keyword argument k
+
+def tril(x: Array, /, *, k: int = 0) -> Array:
+    return torch.tril(x, k)
+
+def triu(x: Array, /, *, k: int = 0) -> Array:
+    return torch.triu(x, k)
+
+# Functions that aren't in torch https://github.com/pytorch/pytorch/issues/58742
+def expand_dims(x: Array, /, *, axis: int = 0) -> Array:
+    return torch.unsqueeze(x, axis)
+
+
+def astype(
+    x: Array,
+    dtype: DType,
+    /,
+    *,
+    copy: bool = True,
+    device: Optional[Device] = None,
+) -> Array:
+    if device is not None:
+        return x.to(device, dtype=dtype, copy=copy)
+    return x.to(dtype=dtype, copy=copy)
+
+
+def broadcast_arrays(*arrays: Array) -> List[Array]:
+    shape = torch.broadcast_shapes(*[a.shape for a in arrays])
+    return [torch.broadcast_to(a, shape) for a in arrays]
+
+# Note that these named tuples aren't actually part of the standard namespace,
+# but I don't see any issue with exporting the names here regardless.
+from ..common._aliases import (UniqueAllResult, UniqueCountsResult,
+                               UniqueInverseResult)
+
+# https://github.com/pytorch/pytorch/issues/70920
+def unique_all(x: Array) -> UniqueAllResult:
+    # torch.unique doesn't support returning indices.
+    # https://github.com/pytorch/pytorch/issues/36748. The workaround
+    # suggested in that issue doesn't actually function correctly (it relies
+    # on non-deterministic behavior of scatter()).
+    raise NotImplementedError("unique_all() not yet implemented for pytorch (see https://github.com/pytorch/pytorch/issues/36748)")
+
+    # values, inverse_indices, counts = torch.unique(x, return_counts=True, return_inverse=True)
+    # # torch.unique incorrectly gives a 0 count for nan values.
+    # # https://github.com/pytorch/pytorch/issues/94106
+    # counts[torch.isnan(values)] = 1
+    # return UniqueAllResult(values, indices, inverse_indices, counts)
+
+def unique_counts(x: Array) -> UniqueCountsResult:
+    values, counts = torch.unique(x, return_counts=True)
+
+    # torch.unique incorrectly gives a 0 count for nan values.
+    # https://github.com/pytorch/pytorch/issues/94106
+    counts[torch.isnan(values)] = 1
+    return UniqueCountsResult(values, counts)
+
+def unique_inverse(x: Array) -> UniqueInverseResult:
+    values, inverse = torch.unique(x, return_inverse=True)
+    return UniqueInverseResult(values, inverse)
+
+def unique_values(x: Array) -> Array:
+    return torch.unique(x)
+
+def matmul(x1: Array, x2: Array, /, **kwargs) -> Array:
+    # torch.matmul doesn't type promote (but differently from _fix_promotion)
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return torch.matmul(x1, x2, **kwargs)
+
+matrix_transpose = get_xp(torch)(_aliases.matrix_transpose)
+_vecdot = get_xp(torch)(_aliases.vecdot)
+
+def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return _vecdot(x1, x2, axis=axis)
+
+# torch.tensordot uses dims instead of axes
+def tensordot(
+    x1: Array,
+    x2: Array,
+    /,
+    *, 
+    axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2, 
+    **kwargs,
+) -> Array:
+    # Note: torch.tensordot fails with integer dtypes when there is only 1
+    # element in the axis (https://github.com/pytorch/pytorch/issues/84530).
+    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
+    return torch.tensordot(x1, x2, dims=axes, **kwargs)
+
+
+def isdtype(
+    dtype: DType, kind: Union[DType, str, Tuple[Union[DType, str], ...]],
+    *, _tuple=True, # Disallow nested tuples
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    Note that outside of this function, this compat library does not yet fully
+    support complex numbers.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple) and _tuple:
+        return _builtin_any(isdtype(dtype, k, _tuple=False) for k in kind)
+    elif isinstance(kind, str):
+        if kind == 'bool':
+            return dtype == torch.bool
+        elif kind == 'signed integer':
+            return dtype in _int_dtypes and dtype.is_signed
+        elif kind == 'unsigned integer':
+            return dtype in _int_dtypes and not dtype.is_signed
+        elif kind == 'integral':
+            return dtype in _int_dtypes
+        elif kind == 'real floating':
+            return dtype.is_floating_point
+        elif kind == 'complex floating':
+            return dtype.is_complex
+        elif kind == 'numeric':
+            return isdtype(dtype, ('integral', 'real floating', 'complex floating'))
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    else:
+        return dtype == kind
+
+def take(x: Array, indices: Array, /, *, axis: Optional[int] = None, **kwargs) -> Array:
+    if axis is None:
+        if x.ndim != 1:
+            raise ValueError("axis must be specified when ndim > 1")
+        axis = 0
+    return torch.index_select(x, axis, indices, **kwargs)
+
+
+def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
+    return torch.take_along_dim(x, indices, dim=axis)
+
+
+def sign(x: Array, /) -> Array:
+    # torch sign() does not support complex numbers and does not propagate
+    # nans. See https://github.com/data-apis/array-api-compat/issues/136
+    if x.dtype.is_complex:
+        out = x/torch.abs(x)
+        # sign(0) = 0 but the above formula would give nan
+        out[x == 0+0j] = 0+0j
+        return out
+    else:
+        out = torch.sign(x)
+        if x.dtype.is_floating_point:
+            out[torch.isnan(x)] = torch.nan
+        return out
+
+
+def meshgrid(*arrays: Array, indexing: Literal['xy', 'ij'] = 'xy') -> List[Array]:
+    # enforce the default of 'xy'
+    # TODO: is the return type a list or a tuple
+    return list(torch.meshgrid(*arrays, indexing='xy'))
+
+
+__all__ = ['__array_namespace_info__', 'asarray', 'result_type', 'can_cast',
+           'permute_dims', 'bitwise_invert', 'newaxis', 'conj', 'add',
+           'atan2', 'bitwise_and', 'bitwise_left_shift', 'bitwise_or',
+           'bitwise_right_shift', 'bitwise_xor', 'copysign', 'count_nonzero',
+           'diff', 'divide',
+           'equal', 'floor_divide', 'greater', 'greater_equal', 'hypot',
+           'less', 'less_equal', 'logaddexp', 'maximum', 'minimum',
+           'multiply', 'not_equal', 'pow', 'remainder', 'subtract', 'max',
+           'min', 'clip', 'unstack', 'cumulative_sum', 'cumulative_prod', 'sort', 'prod', 'sum',
+           'any', 'all', 'mean', 'std', 'var', 'concat', 'squeeze',
+           'broadcast_to', 'flip', 'roll', 'nonzero', 'where', 'reshape',
+           'arange', 'eye', 'linspace', 'full', 'ones', 'zeros', 'empty',
+           'tril', 'triu', 'expand_dims', 'astype', 'broadcast_arrays',
+           'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult',
+           'unique_all', 'unique_counts', 'unique_inverse', 'unique_values',
+           'matmul', 'matrix_transpose', 'vecdot', 'tensordot', 'isdtype',
+           'take', 'take_along_axis', 'sign', 'finfo', 'iinfo', 'repeat', 'meshgrid']
+
+_all_ignore = ['torch', 'get_xp']
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_info.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..818e5d3702e38281a607e1d4462a0819c8594a1e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_info.py
@@ -0,0 +1,369 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+import torch
+
+from functools import cache
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for PyTorch.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for PyTorch.
+
+    Examples
+    --------
+    >>> info = xp.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': numpy.float64,
+     'complex floating': numpy.complex128,
+     'integral': numpy.int64,
+     'indexing': numpy.int64}
+
+    """
+
+    __module__ = 'torch'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for PyTorch.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          PyTorch.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True,
+         'max dimensions': 64}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            "max dimensions": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new PyTorch arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : Device
+            The default device used for new PyTorch arrays.
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_device()
+        device(type='cpu')
+
+        Notes
+        -----
+        This method returns the static default device when PyTorch is initialized.
+        However, the *current* device used by creation functions (``empty`` etc.)
+        can be changed at runtime.
+
+        See Also
+        --------
+        https://github.com/data-apis/array-api/issues/835
+        """
+        return torch.device("cpu")
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new PyTorch arrays.
+
+        Parameters
+        ----------
+        device : Device, optional
+            The device to get the default data types for.
+            Unused for PyTorch, as all devices use the same default dtypes.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new PyTorch
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': torch.float32,
+         'complex floating': torch.complex64,
+         'integral': torch.int64,
+         'indexing': torch.int64}
+
+        """
+        # Note: if the default is set to float64, the devices like MPS that
+        # don't support float64 will error. We still return the default_dtype
+        # value here because this error doesn't represent a different default
+        # per-device.
+        default_floating = torch.get_default_dtype()
+        default_complex = torch.complex64 if default_floating == torch.float32 else torch.complex128
+        default_integral = torch.int64
+        return {
+            "real floating": default_floating,
+            "complex floating": default_complex,
+            "integral": default_integral,
+            "indexing": default_integral,
+        }
+
+
+    def _dtypes(self, kind):
+        bool = torch.bool
+        int8 = torch.int8
+        int16 = torch.int16
+        int32 = torch.int32
+        int64 = torch.int64
+        uint8 = torch.uint8
+        # uint16, uint32, and uint64 are present in newer versions of pytorch,
+        # but they aren't generally supported by the array API functions, so
+        # we omit them from this function.
+        float32 = torch.float32
+        float64 = torch.float64
+        complex64 = torch.complex64
+        complex128 = torch.complex128
+
+        if kind is None:
+            return {
+                "bool": bool,
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+                "float32": float32,
+                "float64": float64,
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": uint8,
+            }
+        if kind == "integral":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+            }
+        if kind == "real floating":
+            return {
+                "float32": float32,
+                "float64": float64,
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if kind == "numeric":
+            return {
+                "int8": int8,
+                "int16": int16,
+                "int32": int32,
+                "int64": int64,
+                "uint8": uint8,
+                "float32": float32,
+                "float64": float64,
+                "complex64": complex64,
+                "complex128": complex128,
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    @cache
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by PyTorch.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : Device, optional
+            The device to get the data types for.
+            Unused for PyTorch, as all devices use the same dtypes.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            PyTorch data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': numpy.int8,
+         'int16': numpy.int16,
+         'int32': numpy.int32,
+         'int64': numpy.int64}
+
+        """
+        res = self._dtypes(kind)
+        for k, v in res.copy().items():
+            try:
+                torch.empty((0,), dtype=v, device=device)
+            except:
+                del res[k]
+        return res
+
+    @cache
+    def devices(self):
+        """
+        The devices supported by PyTorch.
+
+        Returns
+        -------
+        devices : list[Device]
+            The devices supported by PyTorch.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = xp.__array_namespace_info__()
+        >>> info.devices()
+        [device(type='cpu'), device(type='mps', index=0), device(type='meta')]
+
+        """
+        # Torch doesn't have a straightforward way to get the list of all
+        # currently supported devices. To do this, we first parse the error
+        # message of torch.device to get the list of all possible types of
+        # device:
+        try:
+            torch.device('notadevice')
+            raise AssertionError("unreachable")  # pragma: nocover
+        except RuntimeError as e:
+            # The error message is something like:
+            # "Expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: notadevice"
+            devices_names = e.args[0].split('Expected one of ')[1].split(' device type')[0].split(', ')
+
+        # Next we need to check for different indices for different devices.
+        # device(device_name, index=index) doesn't actually check if the
+        # device name or index is valid. We have to try to create a tensor
+        # with it (which is why this function is cached).
+        devices = []
+        for device_name in devices_names:
+            i = 0
+            while True:
+                try:
+                    a = torch.empty((0,), device=torch.device(device_name, index=i))
+                    if a.device in devices:
+                        break
+                    devices.append(a.device)
+                except:
+                    break
+                i += 1
+
+        return devices
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_typing.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..52670871563710d326b0a114a7a517bf87098bf0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/_typing.py
@@ -0,0 +1,3 @@
+__all__ = ["Array", "Device", "DType"]
+
+from torch import device as Device, dtype as DType, Tensor as Array
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/fft.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/fft.py
new file mode 100644
index 0000000000000000000000000000000000000000..50e6a0d0a396863568132e5ab2136078ac28649c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_compat/torch/fft.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from typing import Union, Sequence, Literal
+
+import torch
+import torch.fft
+from torch.fft import * # noqa: F403
+
+from ._typing import Array
+
+# Several torch fft functions do not map axes to dim
+
+def fftn(
+    x: Array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> Array:
+    return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def ifftn(
+    x: Array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> Array:
+    return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def rfftn(
+    x: Array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> Array:
+    return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def irfftn(
+    x: Array,
+    /,
+    *,
+    s: Sequence[int] = None,
+    axes: Sequence[int] = None,
+    norm: Literal["backward", "ortho", "forward"] = "backward",
+    **kwargs,
+) -> Array:
+    return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
+
+def fftshift(
+    x: Array,
+    /,
+    *,
+    axes: Union[int, Sequence[int]] = None,
+    **kwargs,
+) -> Array:
+    return torch.fft.fftshift(x, dim=axes, **kwargs)
+
+def ifftshift(
+    x: Array,
+    /,
+    *,
+    axes: Union[int, Sequence[int]] = None,
+    **kwargs,
+) -> Array:
+    return torch.fft.ifftshift(x, dim=axes, **kwargs)
+
+
+__all__ = torch.fft.__all__ + [
+    "fftn",
+    "ifftn",
+    "rfftn",
+    "irfftn",
+    "fftshift",
+    "ifftshift",
+]
+
+_all_ignore = ['torch']
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/LICENSE b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..45bbb9450877122a8de63039fe1bcd84397bb4db
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Consortium for Python Data API Standards
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..924c23b9351a3899dfee68bd3a185b451233f98a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/array_api_extra/__init__.py
@@ -0,0 +1,38 @@
+"""Extra array functions built on top of the array API standard."""
+
+from ._delegation import isclose, pad
+from ._lib._at import at
+from ._lib._funcs import (
+    apply_where,
+    atleast_nd,
+    broadcast_shapes,
+    cov,
+    create_diagonal,
+    expand_dims,
+    kron,
+    nunique,
+    setdiff1d,
+    sinc,
+)
+from ._lib._lazy import lazy_apply
+
+__version__ = "0.7.1"
+
+# pylint: disable=duplicate-code
+__all__ = [
+    "__version__",
+    "apply_where",
+    "at",
+    "atleast_nd",
+    "broadcast_shapes",
+    "cov",
+    "create_diagonal",
+    "expand_dims",
+    "isclose",
+    "kron",
+    "lazy_apply",
+    "nunique",
+    "pad",
+    "setdiff1d",
+    "sinc",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/conftest.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..c763d9761a438dca43e5856d6eaf9747cdeed2bb
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/externals/conftest.py
@@ -0,0 +1,6 @@
+# Do not collect any tests in externals. This is more robust than using
+# --ignore because --ignore needs a path and it is not convenient to pass in
+# the externals path (very long install-dependent path in site-packages) when
+# using --pyargs
+def pytest_ignore_collect(collection_path, config):
+    return True
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ca540b79229c87447f40eed6717fe59202885f0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/__init__.py
@@ -0,0 +1,6 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from ._frozen import FrozenEstimator
+
+__all__ = ["FrozenEstimator"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/_frozen.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/_frozen.py
new file mode 100644
index 0000000000000000000000000000000000000000..7585ea2597b5995a5e7ffcaf8f7f9b78fd676e6e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/_frozen.py
@@ -0,0 +1,166 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from copy import deepcopy
+
+from ..base import BaseEstimator
+from ..exceptions import NotFittedError
+from ..utils import get_tags
+from ..utils.metaestimators import available_if
+from ..utils.validation import check_is_fitted
+
+
+def _estimator_has(attr):
+    """Check that final_estimator has `attr`.
+
+    Used together with `available_if`.
+    """
+
+    def check(self):
+        # raise original `AttributeError` if `attr` does not exist
+        getattr(self.estimator, attr)
+        return True
+
+    return check
+
+
+class FrozenEstimator(BaseEstimator):
+    """Estimator that wraps a fitted estimator to prevent re-fitting.
+
+    This meta-estimator takes an estimator and freezes it, in the sense that calling
+    `fit` on it has no effect. `fit_predict` and `fit_transform` are also disabled.
+    All other methods are delegated to the original estimator and original estimator's
+    attributes are accessible as well.
+
+    This is particularly useful when you have a fitted or a pre-trained model as a
+    transformer in a pipeline, and you'd like `pipeline.fit` to have no effect on this
+    step.
+
+    Parameters
+    ----------
+    estimator : estimator
+        The estimator which is to be kept frozen.
+
+    See Also
+    --------
+    None: No similar entry in the scikit-learn documentation.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.frozen import FrozenEstimator
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> X, y = make_classification(random_state=0)
+    >>> clf = LogisticRegression(random_state=0).fit(X, y)
+    >>> frozen_clf = FrozenEstimator(clf)
+    >>> frozen_clf.fit(X, y)  # No-op
+    FrozenEstimator(estimator=LogisticRegression(random_state=0))
+    >>> frozen_clf.predict(X)  # Predictions from `clf.predict`
+    array(...)
+    """
+
+    def __init__(self, estimator):
+        self.estimator = estimator
+
+    @available_if(_estimator_has("__getitem__"))
+    def __getitem__(self, *args, **kwargs):
+        """__getitem__ is defined in :class:`~sklearn.pipeline.Pipeline` and \
+            :class:`~sklearn.compose.ColumnTransformer`.
+        """
+        return self.estimator.__getitem__(*args, **kwargs)
+
+    def __getattr__(self, name):
+        # `estimator`'s attributes are now accessible except `fit_predict` and
+        # `fit_transform`
+        if name in ["fit_predict", "fit_transform"]:
+            raise AttributeError(f"{name} is not available for frozen estimators.")
+        return getattr(self.estimator, name)
+
+    def __sklearn_clone__(self):
+        return self
+
+    def __sklearn_is_fitted__(self):
+        try:
+            check_is_fitted(self.estimator)
+            return True
+        except NotFittedError:
+            return False
+
+    def fit(self, X, y, *args, **kwargs):
+        """No-op.
+
+        As a frozen estimator, calling `fit` has no effect.
+
+        Parameters
+        ----------
+        X : object
+            Ignored.
+
+        y : object
+            Ignored.
+
+        *args : tuple
+            Additional positional arguments. Ignored, but present for API compatibility
+            with `self.estimator`.
+
+        **kwargs : dict
+            Additional keyword arguments. Ignored, but present for API compatibility
+            with `self.estimator`.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        check_is_fitted(self.estimator)
+        return self
+
+    def set_params(self, **kwargs):
+        """Set the parameters of this estimator.
+
+        The only valid key here is `estimator`. You cannot set the parameters of the
+        inner estimator.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Estimator parameters.
+
+        Returns
+        -------
+        self : FrozenEstimator
+            This estimator.
+        """
+        estimator = kwargs.pop("estimator", None)
+        if estimator is not None:
+            self.estimator = estimator
+        if kwargs:
+            raise ValueError(
+                "You cannot set parameters of the inner estimator in a frozen "
+                "estimator since calling `fit` has no effect. You can use "
+                "`frozenestimator.estimator.set_params` to set parameters of the inner "
+                "estimator."
+            )
+
+    def get_params(self, deep=True):
+        """Get parameters for this estimator.
+
+        Returns a `{"estimator": estimator}` dict. The parameters of the inner
+        estimator are not included.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            Ignored.
+
+        Returns
+        -------
+        params : dict
+            Parameter names mapped to their values.
+        """
+        return {"estimator": self.estimator}
+
+    def __sklearn_tags__(self):
+        tags = deepcopy(get_tags(self.estimator))
+        tags._skip_test = True
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/tests/test_frozen.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/tests/test_frozen.py
new file mode 100644
index 0000000000000000000000000000000000000000..b304d3ac0aa2c32d6b494351ef0c0d0209866b71
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/frozen/tests/test_frozen.py
@@ -0,0 +1,223 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import re
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from sklearn import config_context
+from sklearn.base import (
+    BaseEstimator,
+    clone,
+    is_classifier,
+    is_clusterer,
+    is_outlier_detector,
+    is_regressor,
+)
+from sklearn.cluster import KMeans
+from sklearn.compose import make_column_transformer
+from sklearn.datasets import make_classification, make_regression
+from sklearn.exceptions import NotFittedError, UnsetMetadataPassedError
+from sklearn.frozen import FrozenEstimator
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import RobustScaler, StandardScaler
+from sklearn.utils._testing import set_random_state
+from sklearn.utils.validation import check_is_fitted
+
+
+@pytest.fixture
+def regression_dataset():
+    return make_regression()
+
+
+@pytest.fixture
+def classification_dataset():
+    return make_classification()
+
+
+@pytest.mark.parametrize(
+    "estimator, dataset",
+    [
+        (LinearRegression(), "regression_dataset"),
+        (LogisticRegression(), "classification_dataset"),
+        (make_pipeline(StandardScaler(), LinearRegression()), "regression_dataset"),
+        (
+            make_pipeline(StandardScaler(), LogisticRegression()),
+            "classification_dataset",
+        ),
+        (StandardScaler(), "regression_dataset"),
+        (KMeans(), "regression_dataset"),
+        (LocalOutlierFactor(), "regression_dataset"),
+        (
+            make_column_transformer(
+                (StandardScaler(), [0]),
+                (RobustScaler(), [1]),
+            ),
+            "regression_dataset",
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "method",
+    ["predict", "predict_proba", "predict_log_proba", "decision_function", "transform"],
+)
+def test_frozen_methods(estimator, dataset, request, method):
+    """Test that frozen.fit doesn't do anything, and that all other methods are
+    exposed by the frozen estimator and return the same values as the estimator.
+    """
+    X, y = request.getfixturevalue(dataset)
+    set_random_state(estimator)
+    estimator.fit(X, y)
+    frozen = FrozenEstimator(estimator)
+    # this should be no-op
+    frozen.fit([[1]], [1])
+
+    if hasattr(estimator, method):
+        assert_array_equal(getattr(estimator, method)(X), getattr(frozen, method)(X))
+
+    assert is_classifier(estimator) == is_classifier(frozen)
+    assert is_regressor(estimator) == is_regressor(frozen)
+    assert is_clusterer(estimator) == is_clusterer(frozen)
+    assert is_outlier_detector(estimator) == is_outlier_detector(frozen)
+
+
+@config_context(enable_metadata_routing=True)
+def test_frozen_metadata_routing(regression_dataset):
+    """Test that metadata routing works with frozen estimators."""
+
+    class ConsumesMetadata(BaseEstimator):
+        def __init__(self, on_fit=None, on_predict=None):
+            self.on_fit = on_fit
+            self.on_predict = on_predict
+
+        def fit(self, X, y, metadata=None):
+            if self.on_fit:
+                assert metadata is not None
+            self.fitted_ = True
+            return self
+
+        def predict(self, X, metadata=None):
+            if self.on_predict:
+                assert metadata is not None
+            return np.ones(len(X))
+
+    X, y = regression_dataset
+    pipeline = make_pipeline(
+        ConsumesMetadata(on_fit=True, on_predict=True)
+        .set_fit_request(metadata=True)
+        .set_predict_request(metadata=True)
+    )
+
+    pipeline.fit(X, y, metadata="test")
+    frozen = FrozenEstimator(pipeline)
+    pipeline.predict(X, metadata="test")
+    frozen.predict(X, metadata="test")
+
+    frozen["consumesmetadata"].set_predict_request(metadata=False)
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "Pipeline.predict got unexpected argument(s) {'metadata'}, which are not "
+            "routed to any object."
+        ),
+    ):
+        frozen.predict(X, metadata="test")
+
+    frozen["consumesmetadata"].set_predict_request(metadata=None)
+    with pytest.raises(UnsetMetadataPassedError):
+        frozen.predict(X, metadata="test")
+
+
+def test_composite_fit(classification_dataset):
+    """Test that calling fit_transform and fit_predict doesn't call fit."""
+
+    class Estimator(BaseEstimator):
+        def fit(self, X, y):
+            try:
+                self._fit_counter += 1
+            except AttributeError:
+                self._fit_counter = 1
+            return self
+
+        def fit_transform(self, X, y=None):
+            # only here to test that it doesn't get called
+            ...  # pragma: no cover
+
+        def fit_predict(self, X, y=None):
+            # only here to test that it doesn't get called
+            ...  # pragma: no cover
+
+    X, y = classification_dataset
+    est = Estimator().fit(X, y)
+    frozen = FrozenEstimator(est)
+
+    with pytest.raises(AttributeError):
+        frozen.fit_predict(X, y)
+    with pytest.raises(AttributeError):
+        frozen.fit_transform(X, y)
+
+    assert frozen._fit_counter == 1
+
+
+def test_clone_frozen(regression_dataset):
+    """Test that cloning a frozen estimator keeps the frozen state."""
+    X, y = regression_dataset
+    estimator = LinearRegression().fit(X, y)
+    frozen = FrozenEstimator(estimator)
+    cloned = clone(frozen)
+    assert cloned.estimator is estimator
+
+
+def test_check_is_fitted(regression_dataset):
+    """Test that check_is_fitted works on frozen estimators."""
+    X, y = regression_dataset
+
+    estimator = LinearRegression()
+    frozen = FrozenEstimator(estimator)
+    with pytest.raises(NotFittedError):
+        check_is_fitted(frozen)
+
+    estimator = LinearRegression().fit(X, y)
+    frozen = FrozenEstimator(estimator)
+    check_is_fitted(frozen)
+
+
+def test_frozen_tags():
+    """Test that frozen estimators have the same tags as the original estimator
+    except for the skip_test tag."""
+
+    class Estimator(BaseEstimator):
+        def __sklearn_tags__(self):
+            tags = super().__sklearn_tags__()
+            tags.input_tags.categorical = True
+            return tags
+
+    estimator = Estimator()
+    frozen = FrozenEstimator(estimator)
+    frozen_tags = frozen.__sklearn_tags__()
+    estimator_tags = estimator.__sklearn_tags__()
+
+    assert frozen_tags._skip_test is True
+    assert estimator_tags._skip_test is False
+
+    assert estimator_tags.input_tags.categorical is True
+    assert frozen_tags.input_tags.categorical is True
+
+
+def test_frozen_params():
+    """Test that FrozenEstimator only exposes the estimator parameter."""
+    est = LogisticRegression()
+    frozen = FrozenEstimator(est)
+
+    with pytest.raises(ValueError, match="You cannot set parameters of the inner"):
+        frozen.set_params(estimator__C=1)
+
+    assert frozen.get_params() == {"estimator": est}
+
+    other_est = LocalOutlierFactor()
+    frozen.set_params(estimator=other_est)
+    assert frozen.get_params() == {"estimator": other_est}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/isotonic.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/isotonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f2c56ae5d13cf51e2c165f4ab433c2abba224b7
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/isotonic.py
@@ -0,0 +1,517 @@
+"""Isotonic regression for obtaining monotonic fit to data."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import math
+import warnings
+from numbers import Real
+
+import numpy as np
+from scipy import interpolate, optimize
+from scipy.stats import spearmanr
+
+from sklearn.utils import metadata_routing
+
+from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
+from .base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context
+from .utils import check_array, check_consistent_length
+from .utils._param_validation import Interval, StrOptions, validate_params
+from .utils.fixes import parse_version, sp_base_version
+from .utils.validation import _check_sample_weight, check_is_fitted
+
+__all__ = ["IsotonicRegression", "check_increasing", "isotonic_regression"]
+
+
+@validate_params(
+    {
+        "x": ["array-like"],
+        "y": ["array-like"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def check_increasing(x, y):
+    """Determine whether y is monotonically correlated with x.
+
+    y is found increasing or decreasing with respect to x based on a Spearman
+    correlation test.
+
+    Parameters
+    ----------
+    x : array-like of shape (n_samples,)
+            Training data.
+
+    y : array-like of shape (n_samples,)
+        Training target.
+
+    Returns
+    -------
+    increasing_bool : boolean
+        Whether the relationship is increasing or decreasing.
+
+    Notes
+    -----
+    The Spearman correlation coefficient is estimated from the data, and the
+    sign of the resulting estimate is used as the result.
+
+    In the event that the 95% confidence interval based on Fisher transform
+    spans zero, a warning is raised.
+
+    References
+    ----------
+    Fisher transformation. Wikipedia.
+    https://en.wikipedia.org/wiki/Fisher_transformation
+
+    Examples
+    --------
+    >>> from sklearn.isotonic import check_increasing
+    >>> x, y = [1, 2, 3, 4, 5], [2, 4, 6, 8, 10]
+    >>> check_increasing(x, y)
+    np.True_
+    >>> y = [10, 8, 6, 4, 2]
+    >>> check_increasing(x, y)
+    np.False_
+    """
+
+    # Calculate Spearman rho estimate and set return accordingly.
+    rho, _ = spearmanr(x, y)
+    increasing_bool = rho >= 0
+
+    # Run Fisher transform to get the rho CI, but handle rho=+/-1
+    if rho not in [-1.0, 1.0] and len(x) > 3:
+        F = 0.5 * math.log((1.0 + rho) / (1.0 - rho))
+        F_se = 1 / math.sqrt(len(x) - 3)
+
+        # Use a 95% CI, i.e., +/-1.96 S.E.
+        # https://en.wikipedia.org/wiki/Fisher_transformation
+        rho_0 = math.tanh(F - 1.96 * F_se)
+        rho_1 = math.tanh(F + 1.96 * F_se)
+
+        # Warn if the CI spans zero.
+        if np.sign(rho_0) != np.sign(rho_1):
+            warnings.warn(
+                "Confidence interval of the Spearman "
+                "correlation coefficient spans zero. "
+                "Determination of ``increasing`` may be "
+                "suspect."
+            )
+
+    return increasing_bool
+
+
+@validate_params(
+    {
+        "y": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "y_min": [Interval(Real, None, None, closed="both"), None],
+        "y_max": [Interval(Real, None, None, closed="both"), None],
+        "increasing": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def isotonic_regression(
+    y, *, sample_weight=None, y_min=None, y_max=None, increasing=True
+):
+    """Solve the isotonic regression model.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y : array-like of shape (n_samples,)
+        The data.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Weights on each point of the regression.
+        If None, weight is set to 1 (equal weights).
+
+    y_min : float, default=None
+        Lower bound on the lowest predicted value (the minimum value may
+        still be higher). If not set, defaults to -inf.
+
+    y_max : float, default=None
+        Upper bound on the highest predicted value (the maximum may still be
+        lower). If not set, defaults to +inf.
+
+    increasing : bool, default=True
+        Whether to compute ``y_`` is increasing (if set to True) or decreasing
+        (if set to False).
+
+    Returns
+    -------
+    y_ : ndarray of shape (n_samples,)
+        Isotonic fit of y.
+
+    References
+    ----------
+    "Active set algorithms for isotonic regression; A unifying framework"
+    by Michael J. Best and Nilotpal Chakravarti, section 3.
+
+    Examples
+    --------
+    >>> from sklearn.isotonic import isotonic_regression
+    >>> isotonic_regression([5, 3, 1, 2, 8, 10, 7, 9, 6, 4])
+    array([2.75   , 2.75   , 2.75   , 2.75   , 7.33,
+           7.33, 7.33, 7.33, 7.33, 7.33])
+    """
+    y = check_array(y, ensure_2d=False, input_name="y", dtype=[np.float64, np.float32])
+    if sp_base_version >= parse_version("1.12.0"):
+        res = optimize.isotonic_regression(
+            y=y, weights=sample_weight, increasing=increasing
+        )
+        y = np.asarray(res.x, dtype=y.dtype)
+    else:
+        # TODO: remove this branch when Scipy 1.12 is the minimum supported version
+        # Also remove _inplace_contiguous_isotonic_regression.
+        order = np.s_[:] if increasing else np.s_[::-1]
+        y = np.array(y[order], dtype=y.dtype)
+        sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype, copy=True)
+        sample_weight = np.ascontiguousarray(sample_weight[order])
+        _inplace_contiguous_isotonic_regression(y, sample_weight)
+        y = y[order]
+
+    if y_min is not None or y_max is not None:
+        # Older versions of np.clip don't accept None as a bound, so use np.inf
+        if y_min is None:
+            y_min = -np.inf
+        if y_max is None:
+            y_max = np.inf
+        np.clip(y, y_min, y_max, y)
+    return y
+
+
+class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
+    """Isotonic regression model.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    y_min : float, default=None
+        Lower bound on the lowest predicted value (the minimum value may
+        still be higher). If not set, defaults to -inf.
+
+    y_max : float, default=None
+        Upper bound on the highest predicted value (the maximum may still be
+        lower). If not set, defaults to +inf.
+
+    increasing : bool or 'auto', default=True
+        Determines whether the predictions should be constrained to increase
+        or decrease with `X`. 'auto' will decide based on the Spearman
+        correlation estimate's sign.
+
+    out_of_bounds : {'nan', 'clip', 'raise'}, default='nan'
+        Handles how `X` values outside of the training domain are handled
+        during prediction.
+
+        - 'nan', predictions will be NaN.
+        - 'clip', predictions will be set to the value corresponding to
+          the nearest train interval endpoint.
+        - 'raise', a `ValueError` is raised.
+
+    Attributes
+    ----------
+    X_min_ : float
+        Minimum value of input array `X_` for left bound.
+
+    X_max_ : float
+        Maximum value of input array `X_` for right bound.
+
+    X_thresholds_ : ndarray of shape (n_thresholds,)
+        Unique ascending `X` values used to interpolate
+        the y = f(X) monotonic function.
+
+        .. versionadded:: 0.24
+
+    y_thresholds_ : ndarray of shape (n_thresholds,)
+        De-duplicated `y` values suitable to interpolate the y = f(X)
+        monotonic function.
+
+        .. versionadded:: 0.24
+
+    f_ : function
+        The stepwise interpolating function that covers the input domain ``X``.
+
+    increasing_ : bool
+        Inferred value for ``increasing``.
+
+    See Also
+    --------
+    sklearn.linear_model.LinearRegression : Ordinary least squares Linear
+        Regression.
+    sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that
+        is a non-parametric model accepting monotonicity constraints.
+    isotonic_regression : Function to solve the isotonic regression model.
+
+    Notes
+    -----
+    Ties are broken using the secondary method from de Leeuw, 1977.
+
+    References
+    ----------
+    Isotonic Median Regression: A Linear Programming Approach
+    Nilotpal Chakravarti
+    Mathematics of Operations Research
+    Vol. 14, No. 2 (May, 1989), pp. 303-308
+
+    Isotone Optimization in R : Pool-Adjacent-Violators
+    Algorithm (PAVA) and Active Set Methods
+    de Leeuw, Hornik, Mair
+    Journal of Statistical Software 2009
+
+    Correctness of Kruskal's algorithms for monotone regression with ties
+    de Leeuw, Psychometrica, 1977
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_regression
+    >>> from sklearn.isotonic import IsotonicRegression
+    >>> X, y = make_regression(n_samples=10, n_features=1, random_state=41)
+    >>> iso_reg = IsotonicRegression().fit(X, y)
+    >>> iso_reg.predict([.1, .2])
+    array([1.8628, 3.7256])
+    """
+
+    # T should have been called X
+    __metadata_request__predict = {"T": metadata_routing.UNUSED}
+    __metadata_request__transform = {"T": metadata_routing.UNUSED}
+
+    _parameter_constraints: dict = {
+        "y_min": [Interval(Real, None, None, closed="both"), None],
+        "y_max": [Interval(Real, None, None, closed="both"), None],
+        "increasing": ["boolean", StrOptions({"auto"})],
+        "out_of_bounds": [StrOptions({"nan", "clip", "raise"})],
+    }
+
+    def __init__(self, *, y_min=None, y_max=None, increasing=True, out_of_bounds="nan"):
+        self.y_min = y_min
+        self.y_max = y_max
+        self.increasing = increasing
+        self.out_of_bounds = out_of_bounds
+
+    def _check_input_data_shape(self, X):
+        if not (X.ndim == 1 or (X.ndim == 2 and X.shape[1] == 1)):
+            msg = (
+                "Isotonic regression input X should be a 1d array or "
+                "2d array with 1 feature"
+            )
+            raise ValueError(msg)
+
+    def _build_f(self, X, y):
+        """Build the f_ interp1d function."""
+
+        bounds_error = self.out_of_bounds == "raise"
+        if len(y) == 1:
+            # single y, constant prediction
+            self.f_ = lambda x: y.repeat(x.shape)
+        else:
+            self.f_ = interpolate.interp1d(
+                X, y, kind="linear", bounds_error=bounds_error
+            )
+
+    def _build_y(self, X, y, sample_weight, trim_duplicates=True):
+        """Build the y_ IsotonicRegression."""
+        self._check_input_data_shape(X)
+        X = X.reshape(-1)  # use 1d view
+
+        # Determine increasing if auto-determination requested
+        if self.increasing == "auto":
+            self.increasing_ = check_increasing(X, y)
+        else:
+            self.increasing_ = self.increasing
+
+        # If sample_weights is passed, removed zero-weight values and clean
+        # order
+        sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
+        mask = sample_weight > 0
+        X, y, sample_weight = X[mask], y[mask], sample_weight[mask]
+
+        order = np.lexsort((y, X))
+        X, y, sample_weight = [array[order] for array in [X, y, sample_weight]]
+        unique_X, unique_y, unique_sample_weight = _make_unique(X, y, sample_weight)
+
+        X = unique_X
+        y = isotonic_regression(
+            unique_y,
+            sample_weight=unique_sample_weight,
+            y_min=self.y_min,
+            y_max=self.y_max,
+            increasing=self.increasing_,
+        )
+
+        # Handle the left and right bounds on X
+        self.X_min_, self.X_max_ = np.min(X), np.max(X)
+
+        if trim_duplicates:
+            # Remove unnecessary points for faster prediction
+            keep_data = np.ones((len(y),), dtype=bool)
+            # Aside from the 1st and last point, remove points whose y values
+            # are equal to both the point before and the point after it.
+            keep_data[1:-1] = np.logical_or(
+                np.not_equal(y[1:-1], y[:-2]), np.not_equal(y[1:-1], y[2:])
+            )
+            return X[keep_data], y[keep_data]
+        else:
+            # The ability to turn off trim_duplicates is only used to it make
+            # easier to unit test that removing duplicates in y does not have
+            # any impact the resulting interpolation function (besides
+            # prediction speed).
+            return X, y
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model using X, y as training data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples,) or (n_samples, 1)
+            Training data.
+
+            .. versionchanged:: 0.24
+               Also accepts 2d array with 1 feature.
+
+        y : array-like of shape (n_samples,)
+            Training target.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights. If set to None, all weights will be set to 1 (equal
+            weights).
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+
+        Notes
+        -----
+        X is stored for future use, as :meth:`transform` needs X to interpolate
+        new input data.
+        """
+        check_params = dict(accept_sparse=False, ensure_2d=False)
+        X = check_array(
+            X, input_name="X", dtype=[np.float64, np.float32], **check_params
+        )
+        y = check_array(y, input_name="y", dtype=X.dtype, **check_params)
+        check_consistent_length(X, y, sample_weight)
+
+        # Transform y by running the isotonic regression algorithm and
+        # transform X accordingly.
+        X, y = self._build_y(X, y, sample_weight)
+
+        # It is necessary to store the non-redundant part of the training set
+        # on the model to make it possible to support model persistence via
+        # the pickle module as the object built by scipy.interp1d is not
+        # picklable directly.
+        self.X_thresholds_, self.y_thresholds_ = X, y
+
+        # Build the interpolation function
+        self._build_f(X, y)
+        return self
+
+    def _transform(self, T):
+        """`_transform` is called by both `transform` and `predict` methods.
+
+        Since `transform` is wrapped to output arrays of specific types (e.g.
+        NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform`
+        directly.
+
+        The above behaviour could be changed in the future, if we decide to output
+        other type of arrays when calling `predict`.
+        """
+        if hasattr(self, "X_thresholds_"):
+            dtype = self.X_thresholds_.dtype
+        else:
+            dtype = np.float64
+
+        T = check_array(T, dtype=dtype, ensure_2d=False)
+
+        self._check_input_data_shape(T)
+        T = T.reshape(-1)  # use 1d view
+
+        if self.out_of_bounds == "clip":
+            T = np.clip(T, self.X_min_, self.X_max_)
+
+        res = self.f_(T)
+
+        # on scipy 0.17, interp1d up-casts to float64, so we cast back
+        res = res.astype(T.dtype)
+
+        return res
+
+    def transform(self, T):
+        """Transform new data by linear interpolation.
+
+        Parameters
+        ----------
+        T : array-like of shape (n_samples,) or (n_samples, 1)
+            Data to transform.
+
+            .. versionchanged:: 0.24
+               Also accepts 2d array with 1 feature.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The transformed data.
+        """
+        return self._transform(T)
+
+    def predict(self, T):
+        """Predict new data by linear interpolation.
+
+        Parameters
+        ----------
+        T : array-like of shape (n_samples,) or (n_samples, 1)
+            Data to transform.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Transformed data.
+        """
+        return self._transform(T)
+
+    # We implement get_feature_names_out here instead of using
+    # `ClassNamePrefixFeaturesOutMixin`` because `input_features` are ignored.
+    # `input_features` are ignored because `IsotonicRegression` accepts 1d
+    # arrays and the semantics of `feature_names_in_` are not clear for 1d arrays.
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Ignored.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            An ndarray with one string i.e. ["isotonicregression0"].
+        """
+        check_is_fitted(self, "f_")
+        class_name = self.__class__.__name__.lower()
+        return np.asarray([f"{class_name}0"], dtype=object)
+
+    def __getstate__(self):
+        """Pickle-protocol - return state of the estimator."""
+        state = super().__getstate__()
+        # remove interpolation method
+        state.pop("f_", None)
+        return state
+
+    def __setstate__(self, state):
+        """Pickle-protocol - set state of the estimator.
+
+        We need to rebuild the interpolation function.
+        """
+        super().__setstate__(state)
+        if hasattr(self, "X_thresholds_") and hasattr(self, "y_thresholds_"):
+            self._build_f(self.X_thresholds_, self.y_thresholds_)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.one_d_array = True
+        tags.input_tags.two_d_array = False
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_approximation.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_approximation.py
new file mode 100644
index 0000000000000000000000000000000000000000..02c8af755baeaa53ffe13c33371167dcc2aeeb6e
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_approximation.py
@@ -0,0 +1,1106 @@
+"""Approximate kernel feature maps based on Fourier transforms and count sketches."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+from scipy.fft import fft, ifft
+from scipy.linalg import svd
+
+from .base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from .metrics.pairwise import KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
+from .utils import check_random_state
+from .utils._param_validation import Interval, StrOptions
+from .utils.extmath import safe_sparse_dot
+from .utils.validation import (
+    _check_feature_names_in,
+    check_is_fitted,
+    validate_data,
+)
+
+
+class PolynomialCountSketch(
+    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
+):
+    """Polynomial kernel approximation via Tensor Sketch.
+
+    Implements Tensor Sketch, which approximates the feature map
+    of the polynomial kernel::
+
+        K(X, Y) = (gamma *  + coef0)^degree
+
+    by efficiently computing a Count Sketch of the outer product of a
+    vector with itself using Fast Fourier Transforms (FFT). Read more in the
+    :ref:`User Guide `.
+
+    .. versionadded:: 0.24
+
+    Parameters
+    ----------
+    gamma : float, default=1.0
+        Parameter of the polynomial kernel whose feature map
+        will be approximated.
+
+    degree : int, default=2
+        Degree of the polynomial kernel whose feature map
+        will be approximated.
+
+    coef0 : int, default=0
+        Constant term of the polynomial kernel whose feature map
+        will be approximated.
+
+    n_components : int, default=100
+        Dimensionality of the output feature space. Usually, `n_components`
+        should be greater than the number of features in input samples in
+        order to achieve good performance. The optimal score / run time
+        balance is typically achieved around `n_components` = 10 * `n_features`,
+        but this depends on the specific dataset being used.
+
+    random_state : int, RandomState instance, default=None
+        Determines random number generation for indexHash and bitHash
+        initialization. Pass an int for reproducible results across multiple
+        function calls. See :term:`Glossary `.
+
+    Attributes
+    ----------
+    indexHash_ : ndarray of shape (degree, n_features), dtype=int64
+        Array of indexes in range [0, n_components) used to represent
+        the 2-wise independent hash functions for Count Sketch computation.
+
+    bitHash_ : ndarray of shape (degree, n_features), dtype=float32
+        Array with random entries in {+1, -1}, used to represent
+        the 2-wise independent hash functions for Count Sketch computation.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.
+    Nystroem : Approximate a kernel map using a subset of the training data.
+    RBFSampler : Approximate a RBF kernel feature map using random Fourier
+        features.
+    SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel.
+    sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
+
+    Examples
+    --------
+    >>> from sklearn.kernel_approximation import PolynomialCountSketch
+    >>> from sklearn.linear_model import SGDClassifier
+    >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
+    >>> y = [0, 0, 1, 1]
+    >>> ps = PolynomialCountSketch(degree=3, random_state=1)
+    >>> X_features = ps.fit_transform(X)
+    >>> clf = SGDClassifier(max_iter=10, tol=1e-3)
+    >>> clf.fit(X_features, y)
+    SGDClassifier(max_iter=10)
+    >>> clf.score(X_features, y)
+    1.0
+
+    For a more detailed example of usage, see
+    :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py`
+    """
+
+    _parameter_constraints: dict = {
+        "gamma": [Interval(Real, 0, None, closed="left")],
+        "degree": [Interval(Integral, 1, None, closed="left")],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(
+        self, *, gamma=1.0, degree=2, coef0=0, n_components=100, random_state=None
+    ):
+        self.gamma = gamma
+        self.degree = degree
+        self.coef0 = coef0
+        self.n_components = n_components
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model with X.
+
+        Initializes the internal variables. The method needs no information
+        about the distribution of data, so we only care about n_features in X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = validate_data(self, X, accept_sparse="csc")
+        random_state = check_random_state(self.random_state)
+
+        n_features = X.shape[1]
+        if self.coef0 != 0:
+            n_features += 1
+
+        self.indexHash_ = random_state.randint(
+            0, high=self.n_components, size=(self.degree, n_features)
+        )
+
+        self.bitHash_ = random_state.choice(a=[-1, 1], size=(self.degree, n_features))
+        self._n_features_out = self.n_components
+        return self
+
+    def transform(self, X):
+        """Generate the feature map approximation for X.
+
+        Parameters
+        ----------
+        X : {array-like}, shape (n_samples, n_features)
+            New data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : array-like, shape (n_samples, n_components)
+            Returns the instance itself.
+        """
+
+        check_is_fitted(self)
+        X = validate_data(self, X, accept_sparse="csc", reset=False)
+
+        X_gamma = np.sqrt(self.gamma) * X
+
+        if sp.issparse(X_gamma) and self.coef0 != 0:
+            X_gamma = sp.hstack(
+                [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))],
+                format="csc",
+            )
+
+        elif not sp.issparse(X_gamma) and self.coef0 != 0:
+            X_gamma = np.hstack(
+                [X_gamma, np.sqrt(self.coef0) * np.ones((X_gamma.shape[0], 1))]
+            )
+
+        if X_gamma.shape[1] != self.indexHash_.shape[1]:
+            raise ValueError(
+                "Number of features of test samples does not"
+                " match that of training samples."
+            )
+
+        count_sketches = np.zeros((X_gamma.shape[0], self.degree, self.n_components))
+
+        if sp.issparse(X_gamma):
+            for j in range(X_gamma.shape[1]):
+                for d in range(self.degree):
+                    iHashIndex = self.indexHash_[d, j]
+                    iHashBit = self.bitHash_[d, j]
+                    count_sketches[:, d, iHashIndex] += (
+                        (iHashBit * X_gamma[:, [j]]).toarray().ravel()
+                    )
+
+        else:
+            for j in range(X_gamma.shape[1]):
+                for d in range(self.degree):
+                    iHashIndex = self.indexHash_[d, j]
+                    iHashBit = self.bitHash_[d, j]
+                    count_sketches[:, d, iHashIndex] += iHashBit * X_gamma[:, j]
+
+        # For each same, compute a count sketch of phi(x) using the polynomial
+        # multiplication (via FFT) of p count sketches of x.
+        count_sketches_fft = fft(count_sketches, axis=2, overwrite_x=True)
+        count_sketches_fft_prod = np.prod(count_sketches_fft, axis=1)
+        data_sketch = np.real(ifft(count_sketches_fft_prod, overwrite_x=True))
+
+        return data_sketch
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
+
+
+class RBFSampler(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Approximate a RBF kernel feature map using random Fourier features.
+
+    It implements a variant of Random Kitchen Sinks.[1]
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    gamma : 'scale' or float, default=1.0
+        Parameter of RBF kernel: exp(-gamma * x^2).
+        If ``gamma='scale'`` is passed then it uses
+        1 / (n_features * X.var()) as value of gamma.
+
+        .. versionadded:: 1.2
+           The option `"scale"` was added in 1.2.
+
+    n_components : int, default=100
+        Number of Monte Carlo samples per original feature.
+        Equals the dimensionality of the computed feature space.
+
+    random_state : int, RandomState instance or None, default=None
+        Pseudo-random number generator to control the generation of the random
+        weights and random offset when fitting the training data.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    random_offset_ : ndarray of shape (n_components,), dtype={np.float64, np.float32}
+        Random offset used to compute the projection in the `n_components`
+        dimensions of the feature space.
+
+    random_weights_ : ndarray of shape (n_features, n_components),\
+        dtype={np.float64, np.float32}
+        Random projection directions drawn from the Fourier transform
+        of the RBF kernel.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.
+    Nystroem : Approximate a kernel map using a subset of the training data.
+    PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch.
+    SkewedChi2Sampler : Approximate feature map for
+        "skewed chi-squared" kernel.
+    sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
+
+    Notes
+    -----
+    See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
+    Benjamin Recht.
+
+    [1] "Weighted Sums of Random Kitchen Sinks: Replacing
+    minimization with randomization in learning" by A. Rahimi and
+    Benjamin Recht.
+    (https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
+
+    Examples
+    --------
+    >>> from sklearn.kernel_approximation import RBFSampler
+    >>> from sklearn.linear_model import SGDClassifier
+    >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
+    >>> y = [0, 0, 1, 1]
+    >>> rbf_feature = RBFSampler(gamma=1, random_state=1)
+    >>> X_features = rbf_feature.fit_transform(X)
+    >>> clf = SGDClassifier(max_iter=5, tol=1e-3)
+    >>> clf.fit(X_features, y)
+    SGDClassifier(max_iter=5)
+    >>> clf.score(X_features, y)
+    1.0
+    """
+
+    _parameter_constraints: dict = {
+        "gamma": [
+            StrOptions({"scale"}),
+            Interval(Real, 0.0, None, closed="left"),
+        ],
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(self, *, gamma=1.0, n_components=100, random_state=None):
+        self.gamma = gamma
+        self.n_components = n_components
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model with X.
+
+        Samples random projection according to n_features.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = validate_data(self, X, accept_sparse="csr")
+        random_state = check_random_state(self.random_state)
+        n_features = X.shape[1]
+        sparse = sp.issparse(X)
+        if self.gamma == "scale":
+            # var = E[X^2] - E[X]^2 if sparse
+            X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var()
+            self._gamma = 1.0 / (n_features * X_var) if X_var != 0 else 1.0
+        else:
+            self._gamma = self.gamma
+        self.random_weights_ = (2.0 * self._gamma) ** 0.5 * random_state.normal(
+            size=(n_features, self.n_components)
+        )
+
+        self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)
+
+        if X.dtype == np.float32:
+            # Setting the data type of the fitted attribute will ensure the
+            # output data type during `transform`.
+            self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)
+            self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)
+
+        self._n_features_out = self.n_components
+        return self
+
+    def transform(self, X):
+        """Apply the approximate feature map to X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            New data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : array-like, shape (n_samples, n_components)
+            Returns the instance itself.
+        """
+        check_is_fitted(self)
+
+        X = validate_data(self, X, accept_sparse="csr", reset=False)
+        projection = safe_sparse_dot(X, self.random_weights_)
+        projection += self.random_offset_
+        np.cos(projection, projection)
+        projection *= (2.0 / self.n_components) ** 0.5
+        return projection
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.transformer_tags.preserves_dtype = ["float64", "float32"]
+        return tags
+
+
+class SkewedChi2Sampler(
+    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
+):
+    """Approximate feature map for "skewed chi-squared" kernel.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    skewedness : float, default=1.0
+        "skewedness" parameter of the kernel. Needs to be cross-validated.
+
+    n_components : int, default=100
+        Number of Monte Carlo samples per original feature.
+        Equals the dimensionality of the computed feature space.
+
+    random_state : int, RandomState instance or None, default=None
+        Pseudo-random number generator to control the generation of the random
+        weights and random offset when fitting the training data.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    random_weights_ : ndarray of shape (n_features, n_components)
+        Weight array, sampled from a secant hyperbolic distribution, which will
+        be used to linearly transform the log of the data.
+
+    random_offset_ : ndarray of shape (n_features, n_components)
+        Bias term, which will be added to the data. It is uniformly distributed
+        between 0 and 2*pi.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.
+    Nystroem : Approximate a kernel map using a subset of the training data.
+    RBFSampler : Approximate a RBF kernel feature map using random Fourier
+        features.
+    SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel.
+    sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
+    sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
+
+    References
+    ----------
+    See "Random Fourier Approximations for Skewed Multiplicative Histogram
+    Kernels" by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
+
+    Examples
+    --------
+    >>> from sklearn.kernel_approximation import SkewedChi2Sampler
+    >>> from sklearn.linear_model import SGDClassifier
+    >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
+    >>> y = [0, 0, 1, 1]
+    >>> chi2_feature = SkewedChi2Sampler(skewedness=.01,
+    ...                                  n_components=10,
+    ...                                  random_state=0)
+    >>> X_features = chi2_feature.fit_transform(X, y)
+    >>> clf = SGDClassifier(max_iter=10, tol=1e-3)
+    >>> clf.fit(X_features, y)
+    SGDClassifier(max_iter=10)
+    >>> clf.score(X_features, y)
+    1.0
+    """
+
+    _parameter_constraints: dict = {
+        "skewedness": [Interval(Real, None, None, closed="neither")],
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+    }
+
+    def __init__(self, *, skewedness=1.0, n_components=100, random_state=None):
+        self.skewedness = skewedness
+        self.n_components = n_components
+        self.random_state = random_state
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit the model with X.
+
+        Samples random projection according to n_features.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = validate_data(self, X)
+        random_state = check_random_state(self.random_state)
+        n_features = X.shape[1]
+        uniform = random_state.uniform(size=(n_features, self.n_components))
+        # transform by inverse CDF of sech
+        self.random_weights_ = 1.0 / np.pi * np.log(np.tan(np.pi / 2.0 * uniform))
+        self.random_offset_ = random_state.uniform(0, 2 * np.pi, size=self.n_components)
+
+        if X.dtype == np.float32:
+            # Setting the data type of the fitted attribute will ensure the
+            # output data type during `transform`.
+            self.random_weights_ = self.random_weights_.astype(X.dtype, copy=False)
+            self.random_offset_ = self.random_offset_.astype(X.dtype, copy=False)
+
+        self._n_features_out = self.n_components
+        return self
+
+    def transform(self, X):
+        """Apply the approximate feature map to X.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            New data, where `n_samples` is the number of samples
+            and `n_features` is the number of features. All values of X must be
+            strictly greater than "-skewedness".
+
+        Returns
+        -------
+        X_new : array-like, shape (n_samples, n_components)
+            Returns the instance itself.
+        """
+        check_is_fitted(self)
+        X = validate_data(
+            self, X, copy=True, dtype=[np.float64, np.float32], reset=False
+        )
+        if (X <= -self.skewedness).any():
+            raise ValueError("X may not contain entries smaller than -skewedness.")
+
+        X += self.skewedness
+        np.log(X, X)
+        projection = safe_sparse_dot(X, self.random_weights_)
+        projection += self.random_offset_
+        np.cos(projection, projection)
+        projection *= np.sqrt(2.0) / np.sqrt(self.n_components)
+        return projection
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.transformer_tags.preserves_dtype = ["float64", "float32"]
+        return tags
+
+
+class AdditiveChi2Sampler(TransformerMixin, BaseEstimator):
+    """Approximate feature map for additive chi2 kernel.
+
+    Uses sampling the fourier transform of the kernel characteristic
+    at regular intervals.
+
+    Since the kernel that is to be approximated is additive, the components of
+    the input vectors can be treated separately.  Each entry in the original
+    space is transformed into 2*sample_steps-1 features, where sample_steps is
+    a parameter of the method. Typical values of sample_steps include 1, 2 and
+    3.
+
+    Optimal choices for the sampling interval for certain data ranges can be
+    computed (see the reference). The default values should be reasonable.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    sample_steps : int, default=2
+        Gives the number of (complex) sampling points.
+
+    sample_interval : float, default=None
+        Sampling interval. Must be specified when sample_steps not in {1,2,3}.
+
+    Attributes
+    ----------
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
+        the chi squared kernel.
+
+    sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
+
+    sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
+        squared kernel.
+
+    Notes
+    -----
+    This estimator approximates a slightly different version of the additive
+    chi squared kernel then ``metric.additive_chi2`` computes.
+
+    This estimator is stateless and does not need to be fitted. However, we
+    recommend to call :meth:`fit_transform` instead of :meth:`transform`, as
+    parameter validation is only performed in :meth:`fit`.
+
+    References
+    ----------
+    See `"Efficient additive kernels via explicit feature maps"
+    `_
+    A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
+    2011
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.linear_model import SGDClassifier
+    >>> from sklearn.kernel_approximation import AdditiveChi2Sampler
+    >>> X, y = load_digits(return_X_y=True)
+    >>> chi2sampler = AdditiveChi2Sampler(sample_steps=2)
+    >>> X_transformed = chi2sampler.fit_transform(X, y)
+    >>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3)
+    >>> clf.fit(X_transformed, y)
+    SGDClassifier(max_iter=5, random_state=0)
+    >>> clf.score(X_transformed, y)
+    0.9499...
+    """
+
+    _parameter_constraints: dict = {
+        "sample_steps": [Interval(Integral, 1, None, closed="left")],
+        "sample_interval": [Interval(Real, 0, None, closed="left"), None],
+    }
+
+    def __init__(self, *, sample_steps=2, sample_interval=None):
+        self.sample_steps = sample_steps
+        self.sample_interval = sample_interval
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Only validates estimator's parameters.
+
+        This method allows to: (i) validate the estimator's parameters and
+        (ii) be consistent with the scikit-learn transformer API.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        Returns
+        -------
+        self : object
+            Returns the transformer.
+        """
+        X = validate_data(self, X, accept_sparse="csr", ensure_non_negative=True)
+
+        if self.sample_interval is None and self.sample_steps not in (1, 2, 3):
+            raise ValueError(
+                "If sample_steps is not in [1, 2, 3],"
+                " you need to provide sample_interval"
+            )
+
+        return self
+
+    def transform(self, X):
+        """Apply approximate feature map to X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        Returns
+        -------
+        X_new : {ndarray, sparse matrix}, \
+               shape = (n_samples, n_features * (2*sample_steps - 1))
+            Whether the return value is an array or sparse matrix depends on
+            the type of the input X.
+        """
+        X = validate_data(
+            self, X, accept_sparse="csr", reset=False, ensure_non_negative=True
+        )
+        sparse = sp.issparse(X)
+
+        if self.sample_interval is None:
+            # See figure 2 c) of "Efficient additive kernels via explicit feature maps"
+            # 
+            # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
+            # 2011
+            if self.sample_steps == 1:
+                sample_interval = 0.8
+            elif self.sample_steps == 2:
+                sample_interval = 0.5
+            elif self.sample_steps == 3:
+                sample_interval = 0.4
+            else:
+                raise ValueError(
+                    "If sample_steps is not in [1, 2, 3],"
+                    " you need to provide sample_interval"
+                )
+        else:
+            sample_interval = self.sample_interval
+
+        # zeroth component
+        # 1/cosh = sech
+        # cosh(0) = 1.0
+        transf = self._transform_sparse if sparse else self._transform_dense
+        return transf(X, self.sample_steps, sample_interval)
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Only used to validate feature names with the names seen in :meth:`fit`.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        # Note that passing attributes="n_features_in_" forces check_is_fitted
+        # to check if the attribute is present. Otherwise it will pass on this
+        # stateless estimator (requires_fit=False)
+        check_is_fitted(self, attributes="n_features_in_")
+        input_features = _check_feature_names_in(
+            self, input_features, generate_names=True
+        )
+        est_name = self.__class__.__name__.lower()
+
+        names_list = [f"{est_name}_{name}_sqrt" for name in input_features]
+
+        for j in range(1, self.sample_steps):
+            cos_names = [f"{est_name}_{name}_cos{j}" for name in input_features]
+            sin_names = [f"{est_name}_{name}_sin{j}" for name in input_features]
+            names_list.extend(cos_names + sin_names)
+
+        return np.asarray(names_list, dtype=object)
+
+    @staticmethod
+    def _transform_dense(X, sample_steps, sample_interval):
+        non_zero = X != 0.0
+        X_nz = X[non_zero]
+
+        X_step = np.zeros_like(X)
+        X_step[non_zero] = np.sqrt(X_nz * sample_interval)
+
+        X_new = [X_step]
+
+        log_step_nz = sample_interval * np.log(X_nz)
+        step_nz = 2 * X_nz * sample_interval
+
+        for j in range(1, sample_steps):
+            factor_nz = np.sqrt(step_nz / np.cosh(np.pi * j * sample_interval))
+
+            X_step = np.zeros_like(X)
+            X_step[non_zero] = factor_nz * np.cos(j * log_step_nz)
+            X_new.append(X_step)
+
+            X_step = np.zeros_like(X)
+            X_step[non_zero] = factor_nz * np.sin(j * log_step_nz)
+            X_new.append(X_step)
+
+        return np.hstack(X_new)
+
+    @staticmethod
+    def _transform_sparse(X, sample_steps, sample_interval):
+        indices = X.indices.copy()
+        indptr = X.indptr.copy()
+
+        data_step = np.sqrt(X.data * sample_interval)
+        X_step = sp.csr_matrix(
+            (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False
+        )
+        X_new = [X_step]
+
+        log_step_nz = sample_interval * np.log(X.data)
+        step_nz = 2 * X.data * sample_interval
+
+        for j in range(1, sample_steps):
+            factor_nz = np.sqrt(step_nz / np.cosh(np.pi * j * sample_interval))
+
+            data_step = factor_nz * np.cos(j * log_step_nz)
+            X_step = sp.csr_matrix(
+                (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False
+            )
+            X_new.append(X_step)
+
+            data_step = factor_nz * np.sin(j * log_step_nz)
+            X_step = sp.csr_matrix(
+                (data_step, indices, indptr), shape=X.shape, dtype=X.dtype, copy=False
+            )
+            X_new.append(X_step)
+
+        return sp.hstack(X_new)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        tags.input_tags.positive_only = True
+        tags.input_tags.sparse = True
+        return tags
+
+
+class Nystroem(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
+    """Approximate a kernel map using a subset of the training data.
+
+    Constructs an approximate feature map for an arbitrary kernel
+    using a subset of the data as basis.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    kernel : str or callable, default='rbf'
+        Kernel map to be approximated. A callable should accept two arguments
+        and the keyword arguments passed to this object as `kernel_params`, and
+        should return a floating point number.
+
+    gamma : float, default=None
+        Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
+        and sigmoid kernels. Interpretation of the default value is left to
+        the kernel; see the documentation for sklearn.metrics.pairwise.
+        Ignored by other kernels.
+
+    coef0 : float, default=None
+        Zero coefficient for polynomial and sigmoid kernels.
+        Ignored by other kernels.
+
+    degree : float, default=None
+        Degree of the polynomial kernel. Ignored by other kernels.
+
+    kernel_params : dict, default=None
+        Additional parameters (keyword arguments) for kernel function passed
+        as callable object.
+
+    n_components : int, default=100
+        Number of features to construct.
+        How many data points will be used to construct the mapping.
+
+    random_state : int, RandomState instance or None, default=None
+        Pseudo-random number generator to control the uniform sampling without
+        replacement of `n_components` of the training data to construct the
+        basis kernel.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation. This works by breaking
+        down the kernel matrix into `n_jobs` even slices and computing them in
+        parallel.
+
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    components_ : ndarray of shape (n_components, n_features)
+        Subset of training points used to construct the feature map.
+
+    component_indices_ : ndarray of shape (n_components)
+        Indices of ``components_`` in the training set.
+
+    normalization_ : ndarray of shape (n_components, n_components)
+        Normalization matrix needed for embedding.
+        Square root of the kernel matrix on ``components_``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.
+    PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch.
+    RBFSampler : Approximate a RBF kernel feature map using random Fourier
+        features.
+    SkewedChi2Sampler : Approximate feature map for "skewed chi-squared" kernel.
+    sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
+
+    References
+    ----------
+    * Williams, C.K.I. and Seeger, M.
+      "Using the Nystroem method to speed up kernel machines",
+      Advances in neural information processing systems 2001
+
+    * T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
+      "Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
+      Comparison",
+      Advances in Neural Information Processing Systems 2012
+
+    Examples
+    --------
+    >>> from sklearn import datasets, svm
+    >>> from sklearn.kernel_approximation import Nystroem
+    >>> X, y = datasets.load_digits(n_class=9, return_X_y=True)
+    >>> data = X / 16.
+    >>> clf = svm.LinearSVC()
+    >>> feature_map_nystroem = Nystroem(gamma=.2,
+    ...                                 random_state=1,
+    ...                                 n_components=300)
+    >>> data_transformed = feature_map_nystroem.fit_transform(data)
+    >>> clf.fit(data_transformed, y)
+    LinearSVC()
+    >>> clf.score(data_transformed, y)
+    0.9987...
+    """
+
+    _parameter_constraints: dict = {
+        "kernel": [
+            StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS.keys()) | {"precomputed"}),
+            callable,
+        ],
+        "gamma": [Interval(Real, 0, None, closed="left"), None],
+        "coef0": [Interval(Real, None, None, closed="neither"), None],
+        "degree": [Interval(Real, 1, None, closed="left"), None],
+        "kernel_params": [dict, None],
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+        "n_jobs": [Integral, None],
+    }
+
+    def __init__(
+        self,
+        kernel="rbf",
+        *,
+        gamma=None,
+        coef0=None,
+        degree=None,
+        kernel_params=None,
+        n_components=100,
+        random_state=None,
+        n_jobs=None,
+    ):
+        self.kernel = kernel
+        self.gamma = gamma
+        self.coef0 = coef0
+        self.degree = degree
+        self.kernel_params = kernel_params
+        self.n_components = n_components
+        self.random_state = random_state
+        self.n_jobs = n_jobs
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Fit estimator to data.
+
+        Samples a subset of training points, computes kernel
+        on these and computes normalization matrix.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training data, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
+                default=None
+            Target values (None for unsupervised transformations).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X = validate_data(self, X, accept_sparse="csr")
+        rnd = check_random_state(self.random_state)
+        n_samples = X.shape[0]
+
+        # get basis vectors
+        if self.n_components > n_samples:
+            # XXX should we just bail?
+            n_components = n_samples
+            warnings.warn(
+                "n_components > n_samples. This is not possible.\n"
+                "n_components was set to n_samples, which results"
+                " in inefficient evaluation of the full kernel."
+            )
+
+        else:
+            n_components = self.n_components
+        n_components = min(n_samples, n_components)
+        inds = rnd.permutation(n_samples)
+        basis_inds = inds[:n_components]
+        basis = X[basis_inds]
+
+        basis_kernel = pairwise_kernels(
+            basis,
+            metric=self.kernel,
+            filter_params=True,
+            n_jobs=self.n_jobs,
+            **self._get_kernel_params(),
+        )
+
+        # sqrt of kernel matrix on basis vectors
+        U, S, V = svd(basis_kernel)
+        S = np.maximum(S, 1e-12)
+        self.normalization_ = np.dot(U / np.sqrt(S), V)
+        self.components_ = basis
+        self.component_indices_ = basis_inds
+        self._n_features_out = n_components
+        return self
+
+    def transform(self, X):
+        """Apply feature map to X.
+
+        Computes an approximate feature map using the kernel
+        between some training points and X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Data to transform.
+
+        Returns
+        -------
+        X_transformed : ndarray of shape (n_samples, n_components)
+            Transformed data.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X, accept_sparse="csr", reset=False)
+
+        kernel_params = self._get_kernel_params()
+        embedded = pairwise_kernels(
+            X,
+            self.components_,
+            metric=self.kernel,
+            filter_params=True,
+            n_jobs=self.n_jobs,
+            **kernel_params,
+        )
+        return np.dot(embedded, self.normalization_.T)
+
+    def _get_kernel_params(self):
+        params = self.kernel_params
+        if params is None:
+            params = {}
+        if not callable(self.kernel) and self.kernel != "precomputed":
+            for param in KERNEL_PARAMS[self.kernel]:
+                if getattr(self, param) is not None:
+                    params[param] = getattr(self, param)
+        else:
+            if (
+                self.gamma is not None
+                or self.coef0 is not None
+                or self.degree is not None
+            ):
+                raise ValueError(
+                    "Don't pass gamma, coef0 or degree to "
+                    "Nystroem if using a callable "
+                    "or precomputed kernel"
+                )
+
+        return params
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.transformer_tags.preserves_dtype = ["float64", "float32"]
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_ridge.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_ridge.py
new file mode 100644
index 0000000000000000000000000000000000000000..29e744647acc97d3ff7493f2cfc9af4f07ce1bdc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/kernel_ridge.py
@@ -0,0 +1,240 @@
+"""Kernel ridge regression."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from numbers import Real
+
+import numpy as np
+
+from .base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context
+from .linear_model._ridge import _solve_cholesky_kernel
+from .metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
+from .utils._param_validation import Interval, StrOptions
+from .utils.validation import _check_sample_weight, check_is_fitted, validate_data
+
+
+class KernelRidge(MultiOutputMixin, RegressorMixin, BaseEstimator):
+    """Kernel ridge regression.
+
+    Kernel ridge regression (KRR) combines ridge regression (linear least
+    squares with l2-norm regularization) with the kernel trick. It thus
+    learns a linear function in the space induced by the respective kernel and
+    the data. For non-linear kernels, this corresponds to a non-linear
+    function in the original space.
+
+    The form of the model learned by KRR is identical to support vector
+    regression (SVR). However, different loss functions are used: KRR uses
+    squared error loss while support vector regression uses epsilon-insensitive
+    loss, both combined with l2 regularization. In contrast to SVR, fitting a
+    KRR model can be done in closed-form and is typically faster for
+    medium-sized datasets. On the other hand, the learned model is non-sparse
+    and thus slower than SVR, which learns a sparse model for epsilon > 0, at
+    prediction-time.
+
+    This estimator has built-in support for multi-variate regression
+    (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    alpha : float or array-like of shape (n_targets,), default=1.0
+        Regularization strength; must be a positive float. Regularization
+        improves the conditioning of the problem and reduces the variance of
+        the estimates. Larger values specify stronger regularization.
+        Alpha corresponds to ``1 / (2C)`` in other linear models such as
+        :class:`~sklearn.linear_model.LogisticRegression` or
+        :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are
+        assumed to be specific to the targets. Hence they must correspond in
+        number. See :ref:`ridge_regression` for formula.
+
+    kernel : str or callable, default="linear"
+        Kernel mapping used internally. This parameter is directly passed to
+        :class:`~sklearn.metrics.pairwise.pairwise_kernels`.
+        If `kernel` is a string, it must be one of the metrics
+        in `pairwise.PAIRWISE_KERNEL_FUNCTIONS` or "precomputed".
+        If `kernel` is "precomputed", X is assumed to be a kernel matrix.
+        Alternatively, if `kernel` is a callable function, it is called on
+        each pair of instances (rows) and the resulting value recorded. The
+        callable should take two rows from X as input and return the
+        corresponding kernel value as a single number. This means that
+        callables from :mod:`sklearn.metrics.pairwise` are not allowed, as
+        they operate on matrices, not single samples. Use the string
+        identifying the kernel instead.
+
+    gamma : float, default=None
+        Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
+        and sigmoid kernels. Interpretation of the default value is left to
+        the kernel; see the documentation for sklearn.metrics.pairwise.
+        Ignored by other kernels.
+
+    degree : float, default=3
+        Degree of the polynomial kernel. Ignored by other kernels.
+
+    coef0 : float, default=1
+        Zero coefficient for polynomial and sigmoid kernels.
+        Ignored by other kernels.
+
+    kernel_params : dict, default=None
+        Additional parameters (keyword arguments) for kernel function passed
+        as callable object.
+
+    Attributes
+    ----------
+    dual_coef_ : ndarray of shape (n_samples,) or (n_samples, n_targets)
+        Representation of weight vector(s) in kernel space
+
+    X_fit_ : {ndarray, sparse matrix} of shape (n_samples, n_features)
+        Training data, which is also required for prediction. If
+        kernel == "precomputed" this is instead the precomputed
+        training matrix, of shape (n_samples, n_samples).
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    sklearn.gaussian_process.GaussianProcessRegressor : Gaussian
+        Process regressor providing automatic kernel hyperparameters
+        tuning and predictions uncertainty.
+    sklearn.linear_model.Ridge : Linear ridge regression.
+    sklearn.linear_model.RidgeCV : Ridge regression with built-in
+        cross-validation.
+    sklearn.svm.SVR : Support Vector Regression accepting a large variety
+        of kernels.
+
+    References
+    ----------
+    * Kevin P. Murphy
+      "Machine Learning: A Probabilistic Perspective", The MIT Press
+      chapter 14.4.3, pp. 492-493
+
+    Examples
+    --------
+    >>> from sklearn.kernel_ridge import KernelRidge
+    >>> import numpy as np
+    >>> n_samples, n_features = 10, 5
+    >>> rng = np.random.RandomState(0)
+    >>> y = rng.randn(n_samples)
+    >>> X = rng.randn(n_samples, n_features)
+    >>> krr = KernelRidge(alpha=1.0)
+    >>> krr.fit(X, y)
+    KernelRidge(alpha=1.0)
+    """
+
+    _parameter_constraints: dict = {
+        "alpha": [Interval(Real, 0, None, closed="left"), "array-like"],
+        "kernel": [
+            StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS.keys()) | {"precomputed"}),
+            callable,
+        ],
+        "gamma": [Interval(Real, 0, None, closed="left"), None],
+        "degree": [Interval(Real, 0, None, closed="left")],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "kernel_params": [dict, None],
+    }
+
+    def __init__(
+        self,
+        alpha=1,
+        *,
+        kernel="linear",
+        gamma=None,
+        degree=3,
+        coef0=1,
+        kernel_params=None,
+    ):
+        self.alpha = alpha
+        self.kernel = kernel
+        self.gamma = gamma
+        self.degree = degree
+        self.coef0 = coef0
+        self.kernel_params = kernel_params
+
+    def _get_kernel(self, X, Y=None):
+        if callable(self.kernel):
+            params = self.kernel_params or {}
+        else:
+            params = {"gamma": self.gamma, "degree": self.degree, "coef0": self.coef0}
+        return pairwise_kernels(X, Y, metric=self.kernel, filter_params=True, **params)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.input_tags.pairwise = self.kernel == "precomputed"
+        return tags
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit Kernel Ridge regression model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data. If kernel == "precomputed" this is instead
+            a precomputed kernel matrix, of shape (n_samples, n_samples).
+
+        y : array-like of shape (n_samples,) or (n_samples, n_targets)
+            Target values.
+
+        sample_weight : float or array-like of shape (n_samples,), default=None
+            Individual weights for each sample, ignored if None is passed.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        # Convert data
+        X, y = validate_data(
+            self, X, y, accept_sparse=("csr", "csc"), multi_output=True, y_numeric=True
+        )
+        if sample_weight is not None and not isinstance(sample_weight, float):
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        K = self._get_kernel(X)
+        alpha = np.atleast_1d(self.alpha)
+
+        ravel = False
+        if len(y.shape) == 1:
+            y = y.reshape(-1, 1)
+            ravel = True
+
+        copy = self.kernel == "precomputed"
+        self.dual_coef_ = _solve_cholesky_kernel(K, y, alpha, sample_weight, copy)
+        if ravel:
+            self.dual_coef_ = self.dual_coef_.ravel()
+
+        self.X_fit_ = X
+
+        return self
+
+    def predict(self, X):
+        """Predict using the kernel ridge model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Samples. If kernel == "precomputed" this is instead a
+            precomputed kernel matrix, shape = [n_samples,
+            n_samples_fitted], where n_samples_fitted is the number of
+            samples used in the fitting for this estimator.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,) or (n_samples, n_targets)
+            Returns predicted values.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X, accept_sparse=("csr", "csc"), reset=False)
+        K = self._get_kernel(X, self.X_fit_)
+        return np.dot(K, self.dual_coef_)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/meson.build b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..966da14c1338b5e33cc1c2d84f26bedca35b1cde
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/meson.build
@@ -0,0 +1,273 @@
+fs = import('fs')
+
+cython_args = []
+
+# Platform detection
+is_windows = host_machine.system() == 'windows'
+is_mingw = is_windows and cc.get_id() == 'gcc'
+
+# Adapted from Scipy. mingw is untested and not officially supported. If you
+# ever bump into issues when trying to compile for mingw, please open an issue
+# in the scikit-learn issue tracker
+if is_mingw
+  # For mingw-w64, link statically against the UCRT.
+  gcc_link_args = ['-lucrt', '-static']
+  add_project_link_arguments(gcc_link_args, language: ['c', 'cpp'])
+  # Force gcc to float64 long doubles for compatibility with MSVC
+  # builds, for C only.
+  add_project_arguments('-mlong-double-64', language: 'c')
+endif
+
+# Only check build dependencies version when not cross-compiling, as running
+# Python interpreter can be tricky in cross-compilation settings. For more
+# details, see https://docs.scipy.org/doc/scipy/building/cross_compilation.html
+if not meson.is_cross_build()
+  if not py.version().version_compare('>=3.10')
+    error('scikit-learn requires Python>=3.10, got ' + py.version() + ' instead')
+  endif
+
+  cython_min_version = run_command(py, ['_min_dependencies.py', 'cython'], check: true).stdout().strip()
+  if not cython.version().version_compare('>=' + cython_min_version)
+    error('scikit-learn requires Cython>=' + cython_min_version + ', got ' + cython.version() + ' instead')
+  endif
+
+  numpy_version = run_command(py,
+    ['-c', 'import numpy; print(numpy.__version__)'], check: true).stdout().strip()
+  numpy_min_version = run_command(py, ['_min_dependencies.py', 'numpy'], check: true).stdout().strip()
+  if not numpy_version.version_compare('>=' + numpy_min_version)
+    error('scikit-learn requires numpy>=' + numpy_min_version + ', got ' + numpy_version + ' instead')
+  endif
+
+  scipy_version = run_command(py,
+    ['-c', 'import scipy; print(scipy.__version__)'], check: true).stdout().strip()
+  scipy_min_version = run_command(py, ['_min_dependencies.py', 'scipy'], check: true).stdout().strip()
+  if not scipy_version.version_compare('>=' + scipy_min_version)
+    error('scikit-learn requires scipy>=' + scipy_min_version + ', got ' + scipy_version + ' instead')
+  endif
+
+  # meson-python is required only when going through pip. Using meson directly
+  # should not check meson-python version.
+  meson_python_version_command_result = run_command(py,
+    ['-c', 'import importlib.metadata; print(importlib.metadata.version("meson-python"))'], check: false)
+  meson_python_installed = meson_python_version_command_result.returncode() == 0
+  if meson_python_installed
+    meson_python_version = meson_python_version_command_result.stdout().strip()
+    meson_python_min_version = run_command(py, ['_min_dependencies.py', 'meson-python'], check: true).stdout().strip()
+    if not meson_python_version.version_compare('>=' + meson_python_min_version)
+      error('scikit-learn requires meson-python>=' + meson_python_min_version + ', got ' + meson_python_version + ' instead')
+    endif
+  endif
+
+endif
+
+# Adapted from scipy, each project seems to have its own tweaks for this. One
+# day using dependency('numpy') will be a thing, see
+# https://github.com/mesonbuild/meson/issues/9598.
+# NumPy include directory - needed in all submodules
+# Relative paths are needed when for example a virtualenv is
+# placed inside the source tree; Meson rejects absolute paths to places inside
+# the source tree. The try-except is needed because when things are split
+# across drives on Windows, there is no relative path and an exception gets
+# raised. There may be other such cases, so add a catch-all and switch to
+# an absolute path.
+# For cross-compilation it is often not possible to run the Python interpreter
+# in order to retrieve numpy's include directory. It can be specified in the
+# cross file instead:
+#   [properties]
+#   numpy-include-dir = /abspath/to/host-pythons/site-packages/numpy/core/include
+#
+# This uses the path as is, and avoids running the interpreter.
+incdir_numpy = meson.get_external_property('numpy-include-dir', 'not-given')
+if incdir_numpy == 'not-given'
+  incdir_numpy = run_command(py,
+    [
+      '-c',
+      '''
+import os
+import numpy as np
+try:
+  incdir = os.path.relpath(np.get_include())
+except Exception:
+  incdir = np.get_include()
+print(incdir)
+'''
+    ],
+    check: true
+  ).stdout().strip()
+endif
+
+inc_np = include_directories(incdir_numpy)
+# Don't use the deprecated NumPy C API. Define this to a fixed version instead of
+# NPY_API_VERSION in order not to break compilation for released SciPy versions
+# when NumPy introduces a new deprecation.
+numpy_no_deprecated_api = ['-DNPY_NO_DEPRECATED_API=NPY_1_9_API_VERSION']
+np_dep = declare_dependency(include_directories: inc_np, compile_args: numpy_no_deprecated_api)
+
+openmp_dep = dependency('OpenMP', language: 'c', required: false)
+
+if not openmp_dep.found()
+  warn_about_missing_openmp = true
+  # On Apple Clang avoid a misleading warning if compiler variables are set.
+  # See https://github.com/scikit-learn/scikit-learn/issues/28710 for more
+  # details. This may be removed if the OpenMP detection on Apple Clang improves,
+  # see https://github.com/mesonbuild/meson/issues/7435#issuecomment-2047585466.
+  if host_machine.system() == 'darwin' and cc.get_id() == 'clang'
+    compiler_env_vars_with_openmp = run_command(py,
+      [
+        '-c',
+        '''
+import os
+
+compiler_env_vars_to_check = ["CPPFLAGS", "CFLAGS", "CXXFLAGS"]
+
+compiler_env_vars_with_openmp = [
+    var for var in compiler_env_vars_to_check if "-fopenmp" in os.getenv(var, "")]
+print(compiler_env_vars_with_openmp)
+'''], check: true).stdout().strip()
+      warn_about_missing_openmp = compiler_env_vars_with_openmp == '[]'
+  endif
+  if warn_about_missing_openmp
+    warning(
+'''
+                ***********
+                * WARNING *
+                ***********
+
+It seems that scikit-learn cannot be built with OpenMP.
+
+- Make sure you have followed the installation instructions:
+
+    https://scikit-learn.org/dev/developers/advanced_installation.html
+
+- If your compiler supports OpenMP but you still see this
+  message, please submit a bug report at:
+
+    https://github.com/scikit-learn/scikit-learn/issues
+
+- The build will continue with OpenMP-based parallelism
+  disabled. Note however that some estimators will run in
+  sequential mode instead of leveraging thread-based
+  parallelism.
+
+                    ***
+''')
+  else
+    warning(
+'''It looks like compiler environment variables were set to enable OpenMP support.
+Check the output of "import sklearn; sklearn.show_versions()" after the build
+to make sure that scikit-learn was actually built with OpenMP support.
+''')
+  endif
+endif
+
+# For now, we keep supporting SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES variable
+# (see how it is done in sklearn/_build_utils/__init__.py when building with
+# setuptools). Accessing environment variables in meson.build is discouraged,
+# so once we drop setuptools this functionality should be behind a meson option
+# or buildtype
+boundscheck = run_command(py,
+    [
+      '-c',
+      '''
+import os
+
+if os.environ.get("SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES", "0") != "0":
+    print(True)
+else:
+    print(False)
+      '''
+    ],
+    check: true
+    ).stdout().strip()
+
+cython_program = find_program(cython.cmd_array()[0])
+
+scikit_learn_cython_args = [
+  '-X language_level=3', '-X boundscheck=' + boundscheck, '-X wraparound=False',
+  '-X initializedcheck=False', '-X nonecheck=False', '-X cdivision=True',
+  '-X profile=False',
+  # Needed for cython imports across subpackages, e.g. cluster pyx that
+  # cimports metrics pxd
+  '--include-dir', meson.global_build_root(),
+]
+cython_args += scikit_learn_cython_args
+
+if cython.version().version_compare('>=3.1.0')
+  cython_shared_src = custom_target(
+    install: false,
+    output: '_cyutility.c',
+    command: [
+      cython_program, '-3', '--fast-fail',
+      '--generate-shared=' + meson.current_build_dir()/'_cyutility.c'
+    ],
+  )
+
+  py.extension_module('_cyutility',
+    cython_shared_src,
+    subdir: 'sklearn',
+    cython_args: cython_args,
+    install: true,
+  )
+
+  cython_args += ['--shared=sklearn._cyutility']
+endif
+
+cython_gen = generator(cython_program,
+  arguments : cython_args + ['@INPUT@', '--output-file', '@OUTPUT@'],
+  output : '@BASENAME@.c',
+)
+
+cython_gen_cpp = generator(cython_program,
+  arguments : cython_args + ['--cplus', '@INPUT@', '--output-file', '@OUTPUT@'],
+  output : '@BASENAME@.cpp',
+)
+
+# Write file in Meson build dir to be able to figure out from Python code
+# whether scikit-learn was built with Meson. Adapted from pandas
+# _version_meson.py.
+custom_target('write_built_with_meson_file',
+    output: '_built_with_meson.py',
+    command: [
+        py, '-c', 'with open("sklearn/_built_with_meson.py", "w") as f: f.write("")'
+    ],
+    install: true,
+    install_dir: py.get_install_dir() / 'sklearn'
+)
+
+extensions = ['_isotonic']
+
+py.extension_module(
+  '_isotonic',
+  cython_gen.process('_isotonic.pyx'),
+  cython_args: cython_args,
+  install: true,
+  subdir: 'sklearn',
+)
+
+# Need for Cython cimports across subpackages to work, i.e. avoid errors like
+# relative cimport from non-package directory is not allowed
+sklearn_root_cython_tree = [
+  fs.copyfile('__init__.py')
+]
+
+sklearn_dir = py.get_install_dir() / 'sklearn'
+
+# Subpackages are mostly in alphabetical order except to handle Cython
+# dependencies across subpackages
+subdir('__check_build')
+subdir('_loss')
+# utils needs to be early since plenty of other modules cimports utils .pxd
+subdir('utils')
+# metrics needs to be to be before cluster since cluster cimports metrics .pxd
+subdir('metrics')
+subdir('cluster')
+subdir('datasets')
+subdir('decomposition')
+subdir('ensemble')
+subdir('feature_extraction')
+subdir('linear_model')
+subdir('manifold')
+subdir('neighbors')
+subdir('preprocessing')
+subdir('svm')
+subdir('tree')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce86525acc368f681af3c1fd635fbe37ed2815c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/__init__.py
@@ -0,0 +1,181 @@
+"""Score functions, performance metrics, pairwise metrics and distance computations."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from . import cluster
+from ._classification import (
+    accuracy_score,
+    balanced_accuracy_score,
+    brier_score_loss,
+    class_likelihood_ratios,
+    classification_report,
+    cohen_kappa_score,
+    confusion_matrix,
+    d2_log_loss_score,
+    f1_score,
+    fbeta_score,
+    hamming_loss,
+    hinge_loss,
+    jaccard_score,
+    log_loss,
+    matthews_corrcoef,
+    multilabel_confusion_matrix,
+    precision_recall_fscore_support,
+    precision_score,
+    recall_score,
+    zero_one_loss,
+)
+from ._dist_metrics import DistanceMetric
+from ._plot.confusion_matrix import ConfusionMatrixDisplay
+from ._plot.det_curve import DetCurveDisplay
+from ._plot.precision_recall_curve import PrecisionRecallDisplay
+from ._plot.regression import PredictionErrorDisplay
+from ._plot.roc_curve import RocCurveDisplay
+from ._ranking import (
+    auc,
+    average_precision_score,
+    coverage_error,
+    dcg_score,
+    det_curve,
+    label_ranking_average_precision_score,
+    label_ranking_loss,
+    ndcg_score,
+    precision_recall_curve,
+    roc_auc_score,
+    roc_curve,
+    top_k_accuracy_score,
+)
+from ._regression import (
+    d2_absolute_error_score,
+    d2_pinball_score,
+    d2_tweedie_score,
+    explained_variance_score,
+    max_error,
+    mean_absolute_error,
+    mean_absolute_percentage_error,
+    mean_gamma_deviance,
+    mean_pinball_loss,
+    mean_poisson_deviance,
+    mean_squared_error,
+    mean_squared_log_error,
+    mean_tweedie_deviance,
+    median_absolute_error,
+    r2_score,
+    root_mean_squared_error,
+    root_mean_squared_log_error,
+)
+from ._scorer import check_scoring, get_scorer, get_scorer_names, make_scorer
+from .cluster import (
+    adjusted_mutual_info_score,
+    adjusted_rand_score,
+    calinski_harabasz_score,
+    completeness_score,
+    consensus_score,
+    davies_bouldin_score,
+    fowlkes_mallows_score,
+    homogeneity_completeness_v_measure,
+    homogeneity_score,
+    mutual_info_score,
+    normalized_mutual_info_score,
+    pair_confusion_matrix,
+    rand_score,
+    silhouette_samples,
+    silhouette_score,
+    v_measure_score,
+)
+from .pairwise import (
+    euclidean_distances,
+    nan_euclidean_distances,
+    pairwise_distances,
+    pairwise_distances_argmin,
+    pairwise_distances_argmin_min,
+    pairwise_distances_chunked,
+    pairwise_kernels,
+)
+
+__all__ = [
+    "ConfusionMatrixDisplay",
+    "DetCurveDisplay",
+    "DistanceMetric",
+    "PrecisionRecallDisplay",
+    "PredictionErrorDisplay",
+    "RocCurveDisplay",
+    "accuracy_score",
+    "adjusted_mutual_info_score",
+    "adjusted_rand_score",
+    "auc",
+    "average_precision_score",
+    "balanced_accuracy_score",
+    "brier_score_loss",
+    "calinski_harabasz_score",
+    "check_scoring",
+    "class_likelihood_ratios",
+    "classification_report",
+    "cluster",
+    "cohen_kappa_score",
+    "completeness_score",
+    "confusion_matrix",
+    "consensus_score",
+    "coverage_error",
+    "d2_absolute_error_score",
+    "d2_log_loss_score",
+    "d2_pinball_score",
+    "d2_tweedie_score",
+    "davies_bouldin_score",
+    "dcg_score",
+    "det_curve",
+    "euclidean_distances",
+    "explained_variance_score",
+    "f1_score",
+    "fbeta_score",
+    "fowlkes_mallows_score",
+    "get_scorer",
+    "get_scorer_names",
+    "hamming_loss",
+    "hinge_loss",
+    "homogeneity_completeness_v_measure",
+    "homogeneity_score",
+    "jaccard_score",
+    "label_ranking_average_precision_score",
+    "label_ranking_loss",
+    "log_loss",
+    "make_scorer",
+    "matthews_corrcoef",
+    "max_error",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+    "mean_gamma_deviance",
+    "mean_pinball_loss",
+    "mean_poisson_deviance",
+    "mean_squared_error",
+    "mean_squared_log_error",
+    "mean_tweedie_deviance",
+    "median_absolute_error",
+    "multilabel_confusion_matrix",
+    "mutual_info_score",
+    "nan_euclidean_distances",
+    "ndcg_score",
+    "normalized_mutual_info_score",
+    "pair_confusion_matrix",
+    "pairwise_distances",
+    "pairwise_distances_argmin",
+    "pairwise_distances_argmin_min",
+    "pairwise_distances_chunked",
+    "pairwise_kernels",
+    "precision_recall_curve",
+    "precision_recall_fscore_support",
+    "precision_score",
+    "r2_score",
+    "rand_score",
+    "recall_score",
+    "roc_auc_score",
+    "roc_curve",
+    "root_mean_squared_error",
+    "root_mean_squared_log_error",
+    "silhouette_samples",
+    "silhouette_score",
+    "top_k_accuracy_score",
+    "v_measure_score",
+    "zero_one_loss",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa4150c88a9783aee51d6bf9e89172806728c97f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_base.py
@@ -0,0 +1,193 @@
+"""
+Common code for all metrics.
+
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from itertools import combinations
+
+import numpy as np
+
+from ..utils import check_array, check_consistent_length
+from ..utils.multiclass import type_of_target
+
+
+def _average_binary_score(binary_metric, y_true, y_score, average, sample_weight=None):
+    """Average a binary metric for multilabel classification.
+
+    Parameters
+    ----------
+    y_true : array, shape = [n_samples] or [n_samples, n_classes]
+        True binary labels in binary label indicators.
+
+    y_score : array, shape = [n_samples] or [n_samples, n_classes]
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or binary decisions.
+
+    average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro'
+        If ``None``, the scores for each class are returned. Otherwise,
+        this determines the type of averaging performed on the data:
+
+        ``'micro'``:
+            Calculate metrics globally by considering each element of the label
+            indicator matrix as a label.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average, weighted
+            by support (the number of true instances for each label).
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average.
+
+        Will be ignored when ``y_true`` is binary.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    binary_metric : callable, returns shape [n_classes]
+        The binary metric function to use.
+
+    Returns
+    -------
+    score : float or array of shape [n_classes]
+        If not ``None``, average the score, else return the score for each
+        classes.
+
+    """
+    average_options = (None, "micro", "macro", "weighted", "samples")
+    if average not in average_options:
+        raise ValueError("average has to be one of {0}".format(average_options))
+
+    y_type = type_of_target(y_true)
+    if y_type not in ("binary", "multilabel-indicator"):
+        raise ValueError("{0} format is not supported".format(y_type))
+
+    if y_type == "binary":
+        return binary_metric(y_true, y_score, sample_weight=sample_weight)
+
+    check_consistent_length(y_true, y_score, sample_weight)
+    y_true = check_array(y_true)
+    y_score = check_array(y_score)
+
+    not_average_axis = 1
+    score_weight = sample_weight
+    average_weight = None
+
+    if average == "micro":
+        if score_weight is not None:
+            score_weight = np.repeat(score_weight, y_true.shape[1])
+        y_true = y_true.ravel()
+        y_score = y_score.ravel()
+
+    elif average == "weighted":
+        if score_weight is not None:
+            average_weight = np.sum(
+                np.multiply(y_true, np.reshape(score_weight, (-1, 1))), axis=0
+            )
+        else:
+            average_weight = np.sum(y_true, axis=0)
+        if np.isclose(average_weight.sum(), 0.0):
+            return 0
+
+    elif average == "samples":
+        # swap average_weight <-> score_weight
+        average_weight = score_weight
+        score_weight = None
+        not_average_axis = 0
+
+    if y_true.ndim == 1:
+        y_true = y_true.reshape((-1, 1))
+
+    if y_score.ndim == 1:
+        y_score = y_score.reshape((-1, 1))
+
+    n_classes = y_score.shape[not_average_axis]
+    score = np.zeros((n_classes,))
+    for c in range(n_classes):
+        y_true_c = y_true.take([c], axis=not_average_axis).ravel()
+        y_score_c = y_score.take([c], axis=not_average_axis).ravel()
+        score[c] = binary_metric(y_true_c, y_score_c, sample_weight=score_weight)
+
+    # Average the results
+    if average is not None:
+        if average_weight is not None:
+            # Scores with 0 weights are forced to be 0, preventing the average
+            # score from being affected by 0-weighted NaN elements.
+            average_weight = np.asarray(average_weight)
+            score[average_weight == 0] = 0
+        return float(np.average(score, weights=average_weight))
+    else:
+        return score
+
+
+def _average_multiclass_ovo_score(binary_metric, y_true, y_score, average="macro"):
+    """Average one-versus-one scores for multiclass classification.
+
+    Uses the binary metric for one-vs-one multiclass classification,
+    where the score is computed according to the Hand & Till (2001) algorithm.
+
+    Parameters
+    ----------
+    binary_metric : callable
+        The binary metric function to use that accepts the following as input:
+            y_true_target : array, shape = [n_samples_target]
+                Some sub-array of y_true for a pair of classes designated
+                positive and negative in the one-vs-one scheme.
+            y_score_target : array, shape = [n_samples_target]
+                Scores corresponding to the probability estimates
+                of a sample belonging to the designated positive class label
+
+    y_true : array-like of shape (n_samples,)
+        True multiclass labels.
+
+    y_score : array-like of shape (n_samples, n_classes)
+        Target scores corresponding to probability estimates of a sample
+        belonging to a particular class.
+
+    average : {'macro', 'weighted'}, default='macro'
+        Determines the type of averaging performed on the pairwise binary
+        metric scores:
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean. This does not take label imbalance into account. Classes
+            are assumed to be uniformly distributed.
+        ``'weighted'``:
+            Calculate metrics for each label, taking into account the
+            prevalence of the classes.
+
+    Returns
+    -------
+    score : float
+        Average of the pairwise binary metric scores.
+    """
+    check_consistent_length(y_true, y_score)
+
+    y_true_unique = np.unique(y_true)
+    n_classes = y_true_unique.shape[0]
+    n_pairs = n_classes * (n_classes - 1) // 2
+    pair_scores = np.empty(n_pairs)
+
+    is_weighted = average == "weighted"
+    prevalence = np.empty(n_pairs) if is_weighted else None
+
+    # Compute scores treating a as positive class and b as negative class,
+    # then b as positive class and a as negative class
+    for ix, (a, b) in enumerate(combinations(y_true_unique, 2)):
+        a_mask = y_true == a
+        b_mask = y_true == b
+        ab_mask = np.logical_or(a_mask, b_mask)
+
+        if is_weighted:
+            prevalence[ix] = np.average(ab_mask)
+
+        a_true = a_mask[ab_mask]
+        b_true = b_mask[ab_mask]
+
+        a_true_score = binary_metric(a_true, y_score[ab_mask, a])
+        b_true_score = binary_metric(b_true, y_score[ab_mask, b])
+        pair_scores[ix] = (a_true_score + b_true_score) / 2
+
+    return np.average(pair_scores, weights=prevalence)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..06503046790beacc11e0a40df39ec9aeb89d0cac
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py
@@ -0,0 +1,3730 @@
+"""Metrics to assess performance on classification task given class prediction.
+
+Functions named as ``*_score`` return a scalar value to maximize: the higher
+the better.
+
+Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
+the lower the better.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.sparse import coo_matrix, csr_matrix, issparse
+from scipy.special import xlogy
+
+from ..exceptions import UndefinedMetricWarning
+from ..preprocessing import LabelBinarizer, LabelEncoder
+from ..utils import (
+    assert_all_finite,
+    check_array,
+    check_consistent_length,
+    check_scalar,
+    column_or_1d,
+)
+from ..utils._array_api import (
+    _average,
+    _bincount,
+    _count_nonzero,
+    _find_matching_floating_dtype,
+    _is_numpy_namespace,
+    _max_precision_float_dtype,
+    _searchsorted,
+    _tolist,
+    _union1d,
+    get_namespace,
+    get_namespace_and_device,
+    xpx,
+)
+from ..utils._param_validation import (
+    Hidden,
+    Interval,
+    Options,
+    StrOptions,
+    validate_params,
+)
+from ..utils._unique import attach_unique
+from ..utils.extmath import _nanaverage
+from ..utils.multiclass import type_of_target, unique_labels
+from ..utils.validation import (
+    _check_pos_label_consistency,
+    _check_sample_weight,
+    _num_samples,
+)
+
+
+def _check_zero_division(zero_division):
+    if isinstance(zero_division, str) and zero_division == "warn":
+        return np.float64(0.0)
+    elif isinstance(zero_division, (int, float)) and zero_division in [0, 1]:
+        return np.float64(zero_division)
+    else:  # np.isnan(zero_division)
+        return np.nan
+
+
+def _check_targets(y_true, y_pred):
+    """Check that y_true and y_pred belong to the same classification task.
+
+    This converts multiclass or binary types to a common shape, and raises a
+    ValueError for a mix of multilabel and multiclass targets, a mix of
+    multilabel formats, for the presence of continuous-valued or multioutput
+    targets, or for targets of different lengths.
+
+    Column vectors are squeezed to 1d, while multilabel formats are returned
+    as CSR sparse label indicators.
+
+    Parameters
+    ----------
+    y_true : array-like
+
+    y_pred : array-like
+
+    Returns
+    -------
+    type_true : one of {'multilabel-indicator', 'multiclass', 'binary'}
+        The type of the true target data, as output by
+        ``utils.multiclass.type_of_target``.
+
+    y_true : array or indicator matrix
+
+    y_pred : array or indicator matrix
+    """
+    xp, _ = get_namespace(y_true, y_pred)
+    check_consistent_length(y_true, y_pred)
+    type_true = type_of_target(y_true, input_name="y_true")
+    type_pred = type_of_target(y_pred, input_name="y_pred")
+
+    y_type = {type_true, type_pred}
+    if y_type == {"binary", "multiclass"}:
+        y_type = {"multiclass"}
+
+    if len(y_type) > 1:
+        raise ValueError(
+            "Classification metrics can't handle a mix of {0} and {1} targets".format(
+                type_true, type_pred
+            )
+        )
+
+    # We can't have more than one value on y_type => The set is no more needed
+    y_type = y_type.pop()
+
+    # No metrics support "multiclass-multioutput" format
+    if y_type not in ["binary", "multiclass", "multilabel-indicator"]:
+        raise ValueError("{0} is not supported".format(y_type))
+
+    if y_type in ["binary", "multiclass"]:
+        xp, _ = get_namespace(y_true, y_pred)
+        y_true = column_or_1d(y_true)
+        y_pred = column_or_1d(y_pred)
+        if y_type == "binary":
+            try:
+                unique_values = _union1d(y_true, y_pred, xp)
+            except TypeError as e:
+                # We expect y_true and y_pred to be of the same data type.
+                # If `y_true` was provided to the classifier as strings,
+                # `y_pred` given by the classifier will also be encoded with
+                # strings. So we raise a meaningful error
+                raise TypeError(
+                    "Labels in y_true and y_pred should be of the same type. "
+                    f"Got y_true={xp.unique(y_true)} and "
+                    f"y_pred={xp.unique(y_pred)}. Make sure that the "
+                    "predictions provided by the classifier coincides with "
+                    "the true labels."
+                ) from e
+            if unique_values.shape[0] > 2:
+                y_type = "multiclass"
+
+    if y_type.startswith("multilabel"):
+        if _is_numpy_namespace(xp):
+            # XXX: do we really want to sparse-encode multilabel indicators when
+            # they are passed as a dense arrays? This is not possible for array
+            # API inputs in general hence we only do it for NumPy inputs. But even
+            # for NumPy the usefulness is questionable.
+            y_true = csr_matrix(y_true)
+            y_pred = csr_matrix(y_pred)
+        y_type = "multilabel-indicator"
+
+    return y_type, y_true, y_pred
+
+
+def _validate_multiclass_probabilistic_prediction(
+    y_true, y_prob, sample_weight, labels
+):
+    r"""Convert y_true and y_prob to shape (n_samples, n_classes)
+
+    1. Verify that y_true, y_prob, and sample_weights have the same first dim
+    2. Ensure 2 or more classes in y_true i.e. valid classification task. The
+       classes are provided by the labels argument, or inferred using y_true.
+       When inferring y_true is assumed binary if it has shape (n_samples, ).
+    3. Validate y_true, and y_prob have the same number of classes. Convert to
+       shape (n_samples, n_classes)
+
+    Parameters
+    ----------
+    y_true : array-like or label indicator matrix
+        Ground truth (correct) labels for n_samples samples.
+
+    y_prob : array-like of float, shape=(n_samples, n_classes) or (n_samples,)
+        Predicted probabilities, as returned by a classifier's
+        predict_proba method. If `y_prob.shape = (n_samples,)`
+        the probabilities provided are assumed to be that of the
+        positive class. The labels in `y_prob` are assumed to be
+        ordered lexicographically, as done by
+        :class:`preprocessing.LabelBinarizer`.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    labels : array-like, default=None
+        If not provided, labels will be inferred from y_true. If `labels`
+        is `None` and `y_prob` has shape `(n_samples,)` the labels are
+        assumed to be binary and are inferred from `y_true`.
+
+    Returns
+    -------
+    transformed_labels : array of shape (n_samples, n_classes)
+
+    y_prob : array of shape (n_samples, n_classes)
+    """
+    y_prob = check_array(
+        y_prob, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
+    )
+
+    if y_prob.max() > 1:
+        raise ValueError(f"y_prob contains values greater than 1: {y_prob.max()}")
+    if y_prob.min() < 0:
+        raise ValueError(f"y_prob contains values lower than 0: {y_prob.min()}")
+
+    check_consistent_length(y_prob, y_true, sample_weight)
+    lb = LabelBinarizer()
+
+    if labels is not None:
+        lb = lb.fit(labels)
+        # LabelBinarizer does not respect the order implied by labels, which
+        # can be misleading.
+        if not np.all(lb.classes_ == labels):
+            warnings.warn(
+                f"Labels passed were {labels}. But this function "
+                "assumes labels are ordered lexicographically. "
+                f"Pass the ordered labels={lb.classes_.tolist()} and ensure that "
+                "the columns of y_prob correspond to this ordering.",
+                UserWarning,
+            )
+        if not np.isin(y_true, labels).all():
+            undeclared_labels = set(y_true) - set(labels)
+            raise ValueError(
+                f"y_true contains values {undeclared_labels} not belonging "
+                f"to the passed labels {labels}."
+            )
+
+    else:
+        lb = lb.fit(y_true)
+
+    if len(lb.classes_) == 1:
+        if labels is None:
+            raise ValueError(
+                "y_true contains only one label ({0}). Please "
+                "provide the list of all expected class labels explicitly through the "
+                "labels argument.".format(lb.classes_[0])
+            )
+        else:
+            raise ValueError(
+                "The labels array needs to contain at least two "
+                "labels, got {0}.".format(lb.classes_)
+            )
+
+    transformed_labels = lb.transform(y_true)
+
+    if transformed_labels.shape[1] == 1:
+        transformed_labels = np.append(
+            1 - transformed_labels, transformed_labels, axis=1
+        )
+
+    # If y_prob is of single dimension, assume y_true to be binary
+    # and then check.
+    if y_prob.ndim == 1:
+        y_prob = y_prob[:, np.newaxis]
+    if y_prob.shape[1] == 1:
+        y_prob = np.append(1 - y_prob, y_prob, axis=1)
+
+    eps = np.finfo(y_prob.dtype).eps
+
+    # Make sure y_prob is normalized
+    y_prob_sum = y_prob.sum(axis=1)
+    if not np.allclose(y_prob_sum, 1, rtol=np.sqrt(eps)):
+        warnings.warn(
+            "The y_prob values do not sum to one. Make sure to pass probabilities.",
+            UserWarning,
+        )
+
+    # Check if dimensions are consistent.
+    transformed_labels = check_array(transformed_labels)
+    if len(lb.classes_) != y_prob.shape[1]:
+        if labels is None:
+            raise ValueError(
+                "y_true and y_prob contain different number of "
+                "classes: {0} vs {1}. Please provide the true "
+                "labels explicitly through the labels argument. "
+                "Classes found in "
+                "y_true: {2}".format(
+                    transformed_labels.shape[1], y_prob.shape[1], lb.classes_
+                )
+            )
+        else:
+            raise ValueError(
+                "The number of classes in labels is different "
+                "from that in y_prob. Classes found in "
+                "labels: {0}".format(lb.classes_)
+            )
+
+    return transformed_labels, y_prob
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "normalize": ["boolean"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
+    """Accuracy classification score.
+
+    In multilabel classification, this function computes subset accuracy:
+    the set of labels predicted for a sample must *exactly* match the
+    corresponding set of labels in y_true.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    normalize : bool, default=True
+        If ``False``, return the number of correctly classified samples.
+        Otherwise, return the fraction of correctly classified samples.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    score : float or int
+        If ``normalize == True``, return the fraction of correctly
+        classified samples (float), else returns the number of correctly
+        classified samples (int).
+
+        The best performance is 1 with ``normalize == True`` and the number
+        of samples with ``normalize == False``.
+
+    See Also
+    --------
+    balanced_accuracy_score : Compute the balanced accuracy to deal with
+        imbalanced datasets.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import accuracy_score
+    >>> y_pred = [0, 2, 1, 3]
+    >>> y_true = [0, 1, 2, 3]
+    >>> accuracy_score(y_true, y_pred)
+    0.5
+    >>> accuracy_score(y_true, y_pred, normalize=False)
+    2.0
+
+    In the multilabel case with binary label indicators:
+
+    >>> import numpy as np
+    >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
+    0.5
+    """
+    xp, _, device = get_namespace_and_device(y_true, y_pred, sample_weight)
+    # Compute accuracy for each possible representation
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    check_consistent_length(y_true, y_pred, sample_weight)
+
+    if y_type.startswith("multilabel"):
+        differing_labels = _count_nonzero(y_true - y_pred, xp=xp, device=device, axis=1)
+        score = xp.asarray(differing_labels == 0, device=device)
+    else:
+        score = y_true == y_pred
+
+    return float(_average(score, weights=sample_weight, normalize=normalize))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "labels": ["array-like", None],
+        "sample_weight": ["array-like", None],
+        "normalize": [StrOptions({"true", "pred", "all"}), None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def confusion_matrix(
+    y_true, y_pred, *, labels=None, sample_weight=None, normalize=None
+):
+    """Compute confusion matrix to evaluate the accuracy of a classification.
+
+    By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
+    is equal to the number of observations known to be in group :math:`i` and
+    predicted to be in group :math:`j`.
+
+    Thus in binary classification, the count of true negatives is
+    :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
+    :math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        Ground truth (correct) target values.
+
+    y_pred : array-like of shape (n_samples,)
+        Estimated targets as returned by a classifier.
+
+    labels : array-like of shape (n_classes), default=None
+        List of labels to index the matrix. This may be used to reorder
+        or select a subset of labels.
+        If ``None`` is given, those that appear at least once
+        in ``y_true`` or ``y_pred`` are used in sorted order.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+        .. versionadded:: 0.18
+
+    normalize : {'true', 'pred', 'all'}, default=None
+        Normalizes confusion matrix over the true (rows), predicted (columns)
+        conditions or all the population. If None, confusion matrix will not be
+        normalized.
+
+    Returns
+    -------
+    C : ndarray of shape (n_classes, n_classes)
+        Confusion matrix whose i-th row and j-th
+        column entry indicates the number of
+        samples with true label being i-th class
+        and predicted label being j-th class.
+
+    See Also
+    --------
+    ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
+        given an estimator, the data, and the label.
+    ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
+        given the true and predicted labels.
+    ConfusionMatrixDisplay : Confusion Matrix visualization.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Confusion matrix
+           `_
+           (Wikipedia and other references may use a different
+           convention for axes).
+
+    Examples
+    --------
+    >>> from sklearn.metrics import confusion_matrix
+    >>> y_true = [2, 0, 2, 2, 0, 1]
+    >>> y_pred = [0, 0, 2, 2, 0, 2]
+    >>> confusion_matrix(y_true, y_pred)
+    array([[2, 0, 0],
+           [0, 0, 1],
+           [1, 0, 2]])
+
+    >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
+    >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
+    >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
+    array([[2, 0, 0],
+           [0, 0, 1],
+           [1, 0, 2]])
+
+    In the binary case, we can extract true positives, etc. as follows:
+
+    >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel().tolist()
+    >>> (tn, fp, fn, tp)
+    (0, 2, 1, 1)
+    """
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    if y_type not in ("binary", "multiclass"):
+        raise ValueError("%s is not supported" % y_type)
+
+    if labels is None:
+        labels = unique_labels(y_true, y_pred)
+    else:
+        labels = np.asarray(labels)
+        n_labels = labels.size
+        if n_labels == 0:
+            raise ValueError("'labels' should contains at least one label.")
+        elif y_true.size == 0:
+            return np.zeros((n_labels, n_labels), dtype=int)
+        elif len(np.intersect1d(y_true, labels)) == 0:
+            raise ValueError("At least one label specified must be in y_true")
+
+    if sample_weight is None:
+        sample_weight = np.ones(y_true.shape[0], dtype=np.int64)
+    else:
+        sample_weight = np.asarray(sample_weight)
+
+    check_consistent_length(y_true, y_pred, sample_weight)
+
+    n_labels = labels.size
+    # If labels are not consecutive integers starting from zero, then
+    # y_true and y_pred must be converted into index form
+    need_index_conversion = not (
+        labels.dtype.kind in {"i", "u", "b"}
+        and np.all(labels == np.arange(n_labels))
+        and y_true.min() >= 0
+        and y_pred.min() >= 0
+    )
+    if need_index_conversion:
+        label_to_ind = {y: x for x, y in enumerate(labels)}
+        y_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred])
+        y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true])
+
+    # intersect y_pred, y_true with labels, eliminate items not in labels
+    ind = np.logical_and(y_pred < n_labels, y_true < n_labels)
+    if not np.all(ind):
+        y_pred = y_pred[ind]
+        y_true = y_true[ind]
+        # also eliminate weights of eliminated items
+        sample_weight = sample_weight[ind]
+
+    # Choose the accumulator dtype to always have high precision
+    if sample_weight.dtype.kind in {"i", "u", "b"}:
+        dtype = np.int64
+    else:
+        dtype = np.float64
+
+    cm = coo_matrix(
+        (sample_weight, (y_true, y_pred)),
+        shape=(n_labels, n_labels),
+        dtype=dtype,
+    ).toarray()
+
+    with np.errstate(all="ignore"):
+        if normalize == "true":
+            cm = cm / cm.sum(axis=1, keepdims=True)
+        elif normalize == "pred":
+            cm = cm / cm.sum(axis=0, keepdims=True)
+        elif normalize == "all":
+            cm = cm / cm.sum()
+        cm = np.nan_to_num(cm)
+
+    if cm.shape == (1, 1):
+        warnings.warn(
+            (
+                "A single label was found in 'y_true' and 'y_pred'. For the confusion "
+                "matrix to have the correct shape, use the 'labels' parameter to pass "
+                "all known labels."
+            ),
+            UserWarning,
+        )
+
+    return cm
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "sample_weight": ["array-like", None],
+        "labels": ["array-like", None],
+        "samplewise": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def multilabel_confusion_matrix(
+    y_true, y_pred, *, sample_weight=None, labels=None, samplewise=False
+):
+    """Compute a confusion matrix for each class or sample.
+
+    .. versionadded:: 0.21
+
+    Compute class-wise (default) or sample-wise (samplewise=True) multilabel
+    confusion matrix to evaluate the accuracy of a classification, and output
+    confusion matrices for each class or sample.
+
+    In multilabel confusion matrix :math:`MCM`, the count of true negatives
+    is :math:`MCM_{:,0,0}`, false negatives is :math:`MCM_{:,1,0}`,
+    true positives is :math:`MCM_{:,1,1}` and false positives is
+    :math:`MCM_{:,0,1}`.
+
+    Multiclass data will be treated as if binarized under a one-vs-rest
+    transformation. Returned confusion matrices will be in the order of
+    sorted unique labels in the union of (y_true, y_pred).
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
+            (n_samples,)
+        Ground truth (correct) target values.
+
+    y_pred : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
+            (n_samples,)
+        Estimated targets as returned by a classifier.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    labels : array-like of shape (n_classes,), default=None
+        A list of classes or column indices to select some (or to force
+        inclusion of classes absent from the data).
+
+    samplewise : bool, default=False
+        In the multilabel case, this calculates a confusion matrix per sample.
+
+    Returns
+    -------
+    multi_confusion : ndarray of shape (n_outputs, 2, 2)
+        A 2x2 confusion matrix corresponding to each output in the input.
+        When calculating class-wise multi_confusion (default), then
+        n_outputs = n_labels; when calculating sample-wise multi_confusion
+        (samplewise=True), n_outputs = n_samples. If ``labels`` is defined,
+        the results will be returned in the order specified in ``labels``,
+        otherwise the results will be returned in sorted order by default.
+
+    See Also
+    --------
+    confusion_matrix : Compute confusion matrix to evaluate the accuracy of a
+        classifier.
+
+    Notes
+    -----
+    The `multilabel_confusion_matrix` calculates class-wise or sample-wise
+    multilabel confusion matrices, and in multiclass tasks, labels are
+    binarized under a one-vs-rest way; while
+    :func:`~sklearn.metrics.confusion_matrix` calculates one confusion matrix
+    for confusion between every two classes.
+
+    Examples
+    --------
+    Multilabel-indicator case:
+
+    >>> import numpy as np
+    >>> from sklearn.metrics import multilabel_confusion_matrix
+    >>> y_true = np.array([[1, 0, 1],
+    ...                    [0, 1, 0]])
+    >>> y_pred = np.array([[1, 0, 0],
+    ...                    [0, 1, 1]])
+    >>> multilabel_confusion_matrix(y_true, y_pred)
+    array([[[1, 0],
+            [0, 1]],
+    
+           [[1, 0],
+            [0, 1]],
+    
+           [[0, 1],
+            [1, 0]]])
+
+    Multiclass case:
+
+    >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
+    >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
+    >>> multilabel_confusion_matrix(y_true, y_pred,
+    ...                             labels=["ant", "bird", "cat"])
+    array([[[3, 1],
+            [0, 2]],
+    
+           [[5, 0],
+            [1, 0]],
+    
+           [[2, 1],
+            [1, 2]]])
+    """
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    xp, _, device_ = get_namespace_and_device(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    if sample_weight is not None:
+        sample_weight = column_or_1d(sample_weight, device=device_)
+    check_consistent_length(y_true, y_pred, sample_weight)
+
+    if y_type not in ("binary", "multiclass", "multilabel-indicator"):
+        raise ValueError("%s is not supported" % y_type)
+
+    present_labels = unique_labels(y_true, y_pred)
+    if labels is None:
+        labels = present_labels
+        n_labels = None
+    else:
+        labels = xp.asarray(labels, device=device_)
+        n_labels = labels.shape[0]
+        labels = xp.concat(
+            [labels, xpx.setdiff1d(present_labels, labels, assume_unique=True, xp=xp)],
+            axis=-1,
+        )
+
+    if y_true.ndim == 1:
+        if samplewise:
+            raise ValueError(
+                "Samplewise metrics are not available outside of "
+                "multilabel classification."
+            )
+
+        le = LabelEncoder()
+        le.fit(labels)
+        y_true = le.transform(y_true)
+        y_pred = le.transform(y_pred)
+        sorted_labels = le.classes_
+
+        # labels are now from 0 to len(labels) - 1 -> use bincount
+        tp = y_true == y_pred
+        tp_bins = y_true[tp]
+        if sample_weight is not None:
+            tp_bins_weights = sample_weight[tp]
+        else:
+            tp_bins_weights = None
+
+        if tp_bins.shape[0]:
+            tp_sum = _bincount(
+                tp_bins, weights=tp_bins_weights, minlength=labels.shape[0], xp=xp
+            )
+        else:
+            # Pathological case
+            true_sum = pred_sum = tp_sum = xp.zeros(labels.shape[0])
+        if y_pred.shape[0]:
+            pred_sum = _bincount(
+                y_pred, weights=sample_weight, minlength=labels.shape[0], xp=xp
+            )
+        if y_true.shape[0]:
+            true_sum = _bincount(
+                y_true, weights=sample_weight, minlength=labels.shape[0], xp=xp
+            )
+
+        # Retain only selected labels
+        indices = _searchsorted(sorted_labels, labels[:n_labels], xp=xp)
+        tp_sum = xp.take(tp_sum, indices, axis=0)
+        true_sum = xp.take(true_sum, indices, axis=0)
+        pred_sum = xp.take(pred_sum, indices, axis=0)
+
+    else:
+        sum_axis = 1 if samplewise else 0
+
+        # All labels are index integers for multilabel.
+        # Select labels:
+        if labels.shape != present_labels.shape or xp.any(
+            xp.not_equal(labels, present_labels)
+        ):
+            if xp.max(labels) > xp.max(present_labels):
+                raise ValueError(
+                    "All labels must be in [0, n labels) for "
+                    "multilabel targets. "
+                    "Got %d > %d" % (xp.max(labels), xp.max(present_labels))
+                )
+            if xp.min(labels) < 0:
+                raise ValueError(
+                    "All labels must be in [0, n labels) for "
+                    "multilabel targets. "
+                    "Got %d < 0" % xp.min(labels)
+                )
+
+        if n_labels is not None:
+            y_true = y_true[:, labels[:n_labels]]
+            y_pred = y_pred[:, labels[:n_labels]]
+
+        if issparse(y_true) or issparse(y_pred):
+            true_and_pred = y_true.multiply(y_pred)
+        else:
+            true_and_pred = xp.multiply(y_true, y_pred)
+
+        # calculate weighted counts
+        tp_sum = _count_nonzero(
+            true_and_pred,
+            axis=sum_axis,
+            sample_weight=sample_weight,
+            xp=xp,
+            device=device_,
+        )
+        pred_sum = _count_nonzero(
+            y_pred,
+            axis=sum_axis,
+            sample_weight=sample_weight,
+            xp=xp,
+            device=device_,
+        )
+        true_sum = _count_nonzero(
+            y_true,
+            axis=sum_axis,
+            sample_weight=sample_weight,
+            xp=xp,
+            device=device_,
+        )
+
+    fp = pred_sum - tp_sum
+    fn = true_sum - tp_sum
+    tp = tp_sum
+
+    if sample_weight is not None and samplewise:
+        tp = xp.asarray(tp)
+        fp = xp.asarray(fp)
+        fn = xp.asarray(fn)
+        tn = sample_weight * y_true.shape[1] - tp - fp - fn
+    elif sample_weight is not None:
+        tn = xp.sum(sample_weight) - tp - fp - fn
+    elif samplewise:
+        tn = y_true.shape[1] - tp - fp - fn
+    else:
+        tn = y_true.shape[0] - tp - fp - fn
+
+    return xp.reshape(xp.stack([tn, fp, fn, tp]).T, (-1, 2, 2))
+
+
+@validate_params(
+    {
+        "y1": ["array-like"],
+        "y2": ["array-like"],
+        "labels": ["array-like", None],
+        "weights": [StrOptions({"linear", "quadratic"}), None],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def cohen_kappa_score(y1, y2, *, labels=None, weights=None, sample_weight=None):
+    r"""Compute Cohen's kappa: a statistic that measures inter-annotator agreement.
+
+    This function computes Cohen's kappa [1]_, a score that expresses the level
+    of agreement between two annotators on a classification problem. It is
+    defined as
+
+    .. math::
+        \kappa = (p_o - p_e) / (1 - p_e)
+
+    where :math:`p_o` is the empirical probability of agreement on the label
+    assigned to any sample (the observed agreement ratio), and :math:`p_e` is
+    the expected agreement when both annotators assign labels randomly.
+    :math:`p_e` is estimated using a per-annotator empirical prior over the
+    class labels [2]_.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y1 : array-like of shape (n_samples,)
+        Labels assigned by the first annotator.
+
+    y2 : array-like of shape (n_samples,)
+        Labels assigned by the second annotator. The kappa statistic is
+        symmetric, so swapping ``y1`` and ``y2`` doesn't change the value.
+
+    labels : array-like of shape (n_classes,), default=None
+        List of labels to index the matrix. This may be used to select a
+        subset of labels. If `None`, all labels that appear at least once in
+        ``y1`` or ``y2`` are used. Note that at least one label in `labels` must be
+        present in `y1`, even though this function is otherwise agnostic to the order
+        of `y1` and `y2`.
+
+    weights : {'linear', 'quadratic'}, default=None
+        Weighting type to calculate the score. `None` means not weighted;
+        "linear" means linear weighting; "quadratic" means quadratic weighting.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    kappa : float
+        The kappa statistic, which is a number between -1 and 1. The maximum
+        value means complete agreement; zero or lower means chance agreement.
+
+    References
+    ----------
+    .. [1] :doi:`J. Cohen (1960). "A coefficient of agreement for nominal scales".
+           Educational and Psychological Measurement 20(1):37-46.
+           <10.1177/001316446002000104>`
+    .. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for
+           computational linguistics". Computational Linguistics 34(4):555-596
+           `_.
+    .. [3] `Wikipedia entry for the Cohen's kappa
+            `_.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import cohen_kappa_score
+    >>> y1 = ["negative", "positive", "negative", "neutral", "positive"]
+    >>> y2 = ["negative", "positive", "negative", "neutral", "negative"]
+    >>> cohen_kappa_score(y1, y2)
+    0.6875
+    """
+    try:
+        confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight)
+    except ValueError as e:
+        if "At least one label specified must be in y_true" in str(e):
+            msg = (
+                "At least one label in `labels` must be present in `y1` (even though "
+                "`cohen_kappa_score` is otherwise agnostic to the order of `y1` and "
+                "`y2`)."
+            )
+            raise ValueError(msg) from e
+        raise
+
+    n_classes = confusion.shape[0]
+    sum0 = np.sum(confusion, axis=0)
+    sum1 = np.sum(confusion, axis=1)
+    expected = np.outer(sum0, sum1) / np.sum(sum0)
+
+    if weights is None:
+        w_mat = np.ones([n_classes, n_classes], dtype=int)
+        w_mat.flat[:: n_classes + 1] = 0
+    else:  # "linear" or "quadratic"
+        w_mat = np.zeros([n_classes, n_classes], dtype=int)
+        w_mat += np.arange(n_classes)
+        if weights == "linear":
+            w_mat = np.abs(w_mat - w_mat.T)
+        else:
+            w_mat = (w_mat - w_mat.T) ** 2
+
+    k = np.sum(w_mat * confusion) / np.sum(w_mat * expected)
+    return float(1 - k)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0, 1}),
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def jaccard_score(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    pos_label=1,
+    average="binary",
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Jaccard similarity coefficient score.
+
+    The Jaccard index [1], or Jaccard similarity coefficient, defined as
+    the size of the intersection divided by the size of the union of two label
+    sets, is used to compare set of predicted labels for a sample to the
+    corresponding set of labels in ``y_true``.
+
+    Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return the
+    Jaccard similarity coefficient for `pos_label`. If `average` is not `'binary'`,
+    `pos_label` is ignored and scores for both classes are computed, then averaged or
+    both returned (when `average=None`). Similarly, for :term:`multiclass` and
+    :term:`multilabel` targets, scores for all `labels` are either returned or
+    averaged depending on the `average` parameter. Use `labels` specify the set of
+    labels to calculate the score for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    labels : array-like of shape (n_classes,), default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', \
+            'binary'} or None, default='binary'
+        If ``None``, the scores for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average, weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : "warn", {0.0, 1.0}, default="warn"
+        Sets the value to return when there is a zero division, i.e. when there
+        there are no negative values in predictions and labels. If set to
+        "warn", this acts like 0, but a warning is also raised.
+
+        .. versionadded:: 0.24
+
+    Returns
+    -------
+    score : float or ndarray of shape (n_unique_labels,), dtype=np.float64
+        The Jaccard score. When `average` is not `None`, a single scalar is
+        returned.
+
+    See Also
+    --------
+    accuracy_score : Function for calculating the accuracy score.
+    f1_score : Function for calculating the F1 score.
+    multilabel_confusion_matrix : Function for computing a confusion matrix\
+                                  for each class or sample.
+
+    Notes
+    -----
+    :func:`jaccard_score` may be a poor metric if there are no
+    positives for some samples or classes. Jaccard is undefined if there are
+    no true or predicted labels, and our implementation will return a score
+    of 0 with a warning.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Jaccard index
+           `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import jaccard_score
+    >>> y_true = np.array([[0, 1, 1],
+    ...                    [1, 1, 0]])
+    >>> y_pred = np.array([[1, 1, 1],
+    ...                    [1, 0, 0]])
+
+    In the binary case:
+
+    >>> jaccard_score(y_true[0], y_pred[0])
+    0.6666
+
+    In the 2D comparison case (e.g. image similarity):
+
+    >>> jaccard_score(y_true, y_pred, average="micro")
+    0.6
+
+    In the multilabel case:
+
+    >>> jaccard_score(y_true, y_pred, average='samples')
+    0.5833
+    >>> jaccard_score(y_true, y_pred, average='macro')
+    0.6666
+    >>> jaccard_score(y_true, y_pred, average=None)
+    array([0.5, 0.5, 1. ])
+
+    In the multiclass case:
+
+    >>> y_pred = [0, 2, 1, 2]
+    >>> y_true = [0, 1, 2, 2]
+    >>> jaccard_score(y_true, y_pred, average=None)
+    array([1. , 0. , 0.33])
+    """
+    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
+    samplewise = average == "samples"
+    MCM = multilabel_confusion_matrix(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        labels=labels,
+        samplewise=samplewise,
+    )
+    numerator = MCM[:, 1, 1]
+    denominator = MCM[:, 1, 1] + MCM[:, 0, 1] + MCM[:, 1, 0]
+
+    xp, _, device_ = get_namespace_and_device(y_true, y_pred)
+    if average == "micro":
+        numerator = xp.asarray(xp.sum(numerator, keepdims=True), device=device_)
+        denominator = xp.asarray(xp.sum(denominator, keepdims=True), device=device_)
+
+    jaccard = _prf_divide(
+        numerator,
+        denominator,
+        "jaccard",
+        "true or predicted",
+        average,
+        ("jaccard",),
+        zero_division=zero_division,
+    )
+    if average is None:
+        return jaccard
+    if average == "weighted":
+        weights = MCM[:, 1, 0] + MCM[:, 1, 1]
+        if not xp.any(weights):
+            # numerator is 0, and warning should have already been issued
+            weights = None
+    elif average == "samples" and sample_weight is not None:
+        weights = sample_weight
+    else:
+        weights = None
+    return float(_average(jaccard, weights=weights, xp=xp))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def matthews_corrcoef(y_true, y_pred, *, sample_weight=None):
+    """Compute the Matthews correlation coefficient (MCC).
+
+    The Matthews correlation coefficient is used in machine learning as a
+    measure of the quality of binary and multiclass classifications. It takes
+    into account true and false positives and negatives and is generally
+    regarded as a balanced measure which can be used even if the classes are of
+    very different sizes. The MCC is in essence a correlation coefficient value
+    between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
+    an average random prediction and -1 an inverse prediction.  The statistic
+    is also known as the phi coefficient. [source: Wikipedia]
+
+    Binary and multiclass labels are supported.  Only in the binary case does
+    this relate to information about true and false positives and negatives.
+    See references below.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        Ground truth (correct) target values.
+
+    y_pred : array-like of shape (n_samples,)
+        Estimated targets as returned by a classifier.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+        .. versionadded:: 0.18
+
+    Returns
+    -------
+    mcc : float
+        The Matthews correlation coefficient (+1 represents a perfect
+        prediction, 0 an average random prediction and -1 and inverse
+        prediction).
+
+    References
+    ----------
+    .. [1] :doi:`Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the
+       accuracy of prediction algorithms for classification: an overview.
+       <10.1093/bioinformatics/16.5.412>`
+
+    .. [2] `Wikipedia entry for the Matthews Correlation Coefficient (phi coefficient)
+       `_.
+
+    .. [3] `Gorodkin, (2004). Comparing two K-category assignments by a
+        K-category correlation coefficient
+        `_.
+
+    .. [4] `Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN
+        Error Measures in MultiClass Prediction
+        `_.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import matthews_corrcoef
+    >>> y_true = [+1, +1, +1, -1]
+    >>> y_pred = [+1, -1, +1, +1]
+    >>> matthews_corrcoef(y_true, y_pred)
+    -0.33
+    """
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    check_consistent_length(y_true, y_pred, sample_weight)
+    if y_type not in {"binary", "multiclass"}:
+        raise ValueError("%s is not supported" % y_type)
+
+    lb = LabelEncoder()
+    lb.fit(np.hstack([y_true, y_pred]))
+    y_true = lb.transform(y_true)
+    y_pred = lb.transform(y_pred)
+
+    C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
+    t_sum = C.sum(axis=1, dtype=np.float64)
+    p_sum = C.sum(axis=0, dtype=np.float64)
+    n_correct = np.trace(C, dtype=np.float64)
+    n_samples = p_sum.sum()
+    cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
+    cov_ypyp = n_samples**2 - np.dot(p_sum, p_sum)
+    cov_ytyt = n_samples**2 - np.dot(t_sum, t_sum)
+
+    cov_ypyp_ytyt = cov_ypyp * cov_ytyt
+    if cov_ypyp_ytyt == 0:
+        return 0.0
+    else:
+        return float(cov_ytyp / np.sqrt(cov_ypyp_ytyt))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "normalize": ["boolean"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None):
+    """Zero-one classification loss.
+
+    If normalize is ``True``, return the fraction of misclassifications
+    (float), else it returns the number of misclassifications (int). The best
+    performance is 0.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    normalize : bool, default=True
+        If ``False``, return the number of misclassifications.
+        Otherwise, return the fraction of misclassifications.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    loss : float or int,
+        If ``normalize == True``, return the fraction of misclassifications
+        (float), else it returns the number of misclassifications (int).
+
+    See Also
+    --------
+    accuracy_score : Compute the accuracy score. By default, the function will
+        return the fraction of correct predictions divided by the total number
+        of predictions.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+
+    Notes
+    -----
+    In multilabel classification, the zero_one_loss function corresponds to
+    the subset zero-one loss: for each sample, the entire set of labels must be
+    correctly predicted, otherwise the loss for that sample is equal to one.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import zero_one_loss
+    >>> y_pred = [1, 2, 3, 4]
+    >>> y_true = [2, 2, 3, 4]
+    >>> zero_one_loss(y_true, y_pred)
+    0.25
+    >>> zero_one_loss(y_true, y_pred, normalize=False)
+    1.0
+
+    In the multilabel case with binary label indicators:
+
+    >>> import numpy as np
+    >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
+    0.5
+    """
+    xp, _ = get_namespace(y_true, y_pred)
+    score = accuracy_score(
+        y_true, y_pred, normalize=normalize, sample_weight=sample_weight
+    )
+
+    if normalize:
+        return 1 - score
+    else:
+        if sample_weight is not None:
+            n_samples = xp.sum(sample_weight)
+        else:
+            n_samples = _num_samples(y_true)
+        return n_samples - score
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def f1_score(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    pos_label=1,
+    average="binary",
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Compute the F1 score, also known as balanced F-score or F-measure.
+
+    The F1 score can be interpreted as a harmonic mean of the precision and
+    recall, where an F1 score reaches its best value at 1 and worst score at 0.
+    The relative contribution of precision and recall to the F1 score are
+    equal. The formula for the F1 score is:
+
+    .. math::
+        \\text{F1} = \\frac{2 * \\text{TP}}{2 * \\text{TP} + \\text{FP} + \\text{FN}}
+
+    Where :math:`\\text{TP}` is the number of true positives, :math:`\\text{FN}` is the
+    number of false negatives, and :math:`\\text{FP}` is the number of false positives.
+    F1 is by default
+    calculated as 0.0 when there are no true positives, false negatives, or
+    false positives.
+
+    Support beyond :term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return
+    F1 score for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
+    and F1 score for both classes are computed, then averaged or both returned (when
+    `average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
+    F1 score for all `labels` are either returned or averaged depending on the
+    `average` parameter. Use `labels` specify the set of labels to calculate F1 score
+    for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    labels : array-like, default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+        .. versionchanged:: 0.17
+           Parameter `labels` improved for multiclass problem.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
+            default='binary'
+        This parameter is required for multiclass/multilabel targets.
+        If ``None``, the metrics for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance; it can result in an
+            F-score that is not between precision and recall.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division, i.e. when all
+        predictions and labels are negative.
+
+        Notes:
+        - If set to "warn", this acts like 0, but a warning is also raised.
+        - If set to `np.nan`, such values will be excluded from the average.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    f1_score : float or array of float, shape = [n_unique_labels]
+        F1 score of the positive class in binary classification or weighted
+        average of the F1 scores of each class for the multiclass task.
+
+    See Also
+    --------
+    fbeta_score : Compute the F-beta score.
+    precision_recall_fscore_support : Compute the precision, recall, F-score,
+        and support.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    multilabel_confusion_matrix : Compute a confusion matrix for each class or
+        sample.
+
+    Notes
+    -----
+    When ``true positive + false positive + false negative == 0`` (i.e. a class
+    is completely absent from both ``y_true`` or ``y_pred``), f-score is
+    undefined. In such cases, by default f-score will be set to 0.0, and
+    ``UndefinedMetricWarning`` will be raised. This behavior can be modified by
+    setting the ``zero_division`` parameter.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the F1-score
+           `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import f1_score
+    >>> y_true = [0, 1, 2, 0, 1, 2]
+    >>> y_pred = [0, 2, 1, 0, 0, 1]
+    >>> f1_score(y_true, y_pred, average='macro')
+    0.267
+    >>> f1_score(y_true, y_pred, average='micro')
+    0.33
+    >>> f1_score(y_true, y_pred, average='weighted')
+    0.267
+    >>> f1_score(y_true, y_pred, average=None)
+    array([0.8, 0. , 0. ])
+
+    >>> # binary classification
+    >>> y_true_empty = [0, 0, 0, 0, 0, 0]
+    >>> y_pred_empty = [0, 0, 0, 0, 0, 0]
+    >>> f1_score(y_true_empty, y_pred_empty)
+    0.0...
+    >>> f1_score(y_true_empty, y_pred_empty, zero_division=1.0)
+    1.0...
+    >>> f1_score(y_true_empty, y_pred_empty, zero_division=np.nan)
+    nan...
+
+    >>> # multilabel classification
+    >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
+    >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
+    >>> f1_score(y_true, y_pred, average=None)
+    array([0.66666667, 1.        , 0.66666667])
+    """
+    return fbeta_score(
+        y_true,
+        y_pred,
+        beta=1,
+        labels=labels,
+        pos_label=pos_label,
+        average=average,
+        sample_weight=sample_weight,
+        zero_division=zero_division,
+    )
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "beta": [Interval(Real, 0.0, None, closed="both")],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def fbeta_score(
+    y_true,
+    y_pred,
+    *,
+    beta,
+    labels=None,
+    pos_label=1,
+    average="binary",
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Compute the F-beta score.
+
+    The F-beta score is the weighted harmonic mean of precision and recall,
+    reaching its optimal value at 1 and its worst value at 0.
+
+    The `beta` parameter represents the ratio of recall importance to
+    precision importance. `beta > 1` gives more weight to recall, while
+    `beta < 1` favors precision. For example, `beta = 2` makes recall twice
+    as important as precision, while `beta = 0.5` does the opposite.
+    Asymptotically, `beta -> +inf` considers only recall, and `beta -> 0`
+    only precision.
+
+    The formula for F-beta score is:
+
+    .. math::
+
+       F_\\beta = \\frac{(1 + \\beta^2) \\text{tp}}
+                        {(1 + \\beta^2) \\text{tp} + \\text{fp} + \\beta^2 \\text{fn}}
+
+    Where :math:`\\text{tp}` is the number of true positives, :math:`\\text{fp}` is the
+    number of false positives, and :math:`\\text{fn}` is the number of false negatives.
+
+    Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return
+    F-beta score for `pos_label`. If `average` is not `'binary'`, `pos_label` is
+    ignored and F-beta score for both classes are computed, then averaged or both
+    returned (when `average=None`). Similarly, for :term:`multiclass` and
+    :term:`multilabel` targets, F-beta score for all `labels` are either returned or
+    averaged depending on the `average` parameter. Use `labels` specify the set of
+    labels to calculate F-beta score for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    beta : float
+        Determines the weight of recall in the combined score.
+
+    labels : array-like, default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+        .. versionchanged:: 0.17
+           Parameter `labels` improved for multiclass problem.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
+            default='binary'
+        This parameter is required for multiclass/multilabel targets.
+        If ``None``, the metrics for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance; it can result in an
+            F-score that is not between precision and recall.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division, i.e. when all
+        predictions and labels are negative.
+
+        Notes:
+
+        - If set to "warn", this acts like 0, but a warning is also raised.
+        - If set to `np.nan`, such values will be excluded from the average.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    fbeta_score : float (if average is not None) or array of float, shape =\
+        [n_unique_labels]
+        F-beta score of the positive class in binary classification or weighted
+        average of the F-beta score of each class for the multiclass task.
+
+    See Also
+    --------
+    precision_recall_fscore_support : Compute the precision, recall, F-score,
+        and support.
+    multilabel_confusion_matrix : Compute a confusion matrix for each class or
+        sample.
+
+    Notes
+    -----
+    When ``true positive + false positive + false negative == 0``, f-score
+    returns 0.0 and raises ``UndefinedMetricWarning``. This behavior can be
+    modified by setting ``zero_division``.
+
+    F-beta score is not implemented as a named scorer that can be passed to
+    the `scoring` parameter of cross-validation tools directly: it requires to be
+    wrapped with :func:`make_scorer` so as to specify the value of `beta`. See
+    examples for details.
+
+    References
+    ----------
+    .. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011).
+           Modern Information Retrieval. Addison Wesley, pp. 327-328.
+
+    .. [2] `Wikipedia entry for the F1-score
+           `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import fbeta_score
+    >>> y_true = [0, 1, 2, 0, 1, 2]
+    >>> y_pred = [0, 2, 1, 0, 0, 1]
+    >>> fbeta_score(y_true, y_pred, average='macro', beta=0.5)
+    0.238
+    >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5)
+    0.33
+    >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5)
+    0.238
+    >>> fbeta_score(y_true, y_pred, average=None, beta=0.5)
+    array([0.71, 0.        , 0.        ])
+    >>> y_pred_empty = [0, 0, 0, 0, 0, 0]
+    >>> fbeta_score(
+    ...     y_true,
+    ...     y_pred_empty,
+    ...     average="macro",
+    ...     zero_division=np.nan,
+    ...     beta=0.5,
+    ... )
+    0.128
+
+    In order to use :func:`fbeta_scorer` as a scorer, a callable
+    scorer objects needs to be created first with :func:`make_scorer`,
+    passing the value for the `beta` parameter.
+
+    >>> from sklearn.metrics import fbeta_score, make_scorer
+    >>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
+    >>> from sklearn.model_selection import GridSearchCV
+    >>> from sklearn.svm import LinearSVC
+    >>> grid = GridSearchCV(
+    ...     LinearSVC(dual="auto"),
+    ...     param_grid={'C': [1, 10]},
+    ...     scoring=ftwo_scorer,
+    ...     cv=5
+    ... )
+    """
+
+    _, _, f, _ = precision_recall_fscore_support(
+        y_true,
+        y_pred,
+        beta=beta,
+        labels=labels,
+        pos_label=pos_label,
+        average=average,
+        warn_for=("f-score",),
+        sample_weight=sample_weight,
+        zero_division=zero_division,
+    )
+    return f
+
+
+def _prf_divide(
+    numerator, denominator, metric, modifier, average, warn_for, zero_division="warn"
+):
+    """Performs division and handles divide-by-zero.
+
+    On zero-division, sets the corresponding result elements equal to
+    0, 1 or np.nan (according to ``zero_division``). Plus, if
+    ``zero_division != "warn"`` raises a warning.
+
+    The metric, modifier and average arguments are used only for determining
+    an appropriate warning.
+    """
+    xp, _ = get_namespace(numerator, denominator)
+    dtype_float = _find_matching_floating_dtype(numerator, denominator, xp=xp)
+    mask = denominator == 0
+    denominator = xp.asarray(denominator, copy=True, dtype=dtype_float)
+    denominator[mask] = 1  # avoid infs/nans
+    result = xp.asarray(numerator, dtype=dtype_float) / denominator
+
+    if not xp.any(mask):
+        return result
+
+    # set those with 0 denominator to `zero_division`, and 0 when "warn"
+    zero_division_value = _check_zero_division(zero_division)
+    result[mask] = zero_division_value
+
+    # we assume the user will be removing warnings if zero_division is set
+    # to something different than "warn". If we are computing only f-score
+    # the warning will be raised only if precision and recall are ill-defined
+    if zero_division != "warn" or metric not in warn_for:
+        return result
+
+    # build appropriate warning
+    if metric in warn_for:
+        _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])
+
+    return result
+
+
+def _warn_prf(average, modifier, msg_start, result_size):
+    axis0, axis1 = "sample", "label"
+    if average == "samples":
+        axis0, axis1 = axis1, axis0
+    msg = (
+        "{0} ill-defined and being set to 0.0 {{0}} "
+        "no {1} {2}s. Use `zero_division` parameter to control"
+        " this behavior.".format(msg_start, modifier, axis0)
+    )
+    if result_size == 1:
+        msg = msg.format("due to")
+    else:
+        msg = msg.format("in {0}s with".format(axis1))
+    warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
+
+
+def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label):
+    """Validation associated with set-wise metrics.
+
+    Returns identified labels.
+    """
+    average_options = (None, "micro", "macro", "weighted", "samples")
+    if average not in average_options and average != "binary":
+        raise ValueError("average has to be one of " + str(average_options))
+
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    # Convert to Python primitive type to avoid NumPy type / Python str
+    # comparison. See https://github.com/numpy/numpy/issues/6784
+    present_labels = _tolist(unique_labels(y_true, y_pred))
+    if average == "binary":
+        if y_type == "binary":
+            if pos_label not in present_labels:
+                if len(present_labels) >= 2:
+                    raise ValueError(
+                        f"pos_label={pos_label} is not a valid label. It "
+                        f"should be one of {present_labels}"
+                    )
+            labels = [pos_label]
+        else:
+            average_options = list(average_options)
+            if y_type == "multiclass":
+                average_options.remove("samples")
+            raise ValueError(
+                "Target is %s but average='binary'. Please "
+                "choose another average setting, one of %r." % (y_type, average_options)
+            )
+    elif pos_label not in (None, 1):
+        warnings.warn(
+            "Note that pos_label (set to %r) is ignored when "
+            "average != 'binary' (got %r). You may use "
+            "labels=[pos_label] to specify a single positive class."
+            % (pos_label, average),
+            UserWarning,
+        )
+    return labels
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "beta": [Interval(Real, 0.0, None, closed="both")],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "warn_for": [list, tuple, set],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def precision_recall_fscore_support(
+    y_true,
+    y_pred,
+    *,
+    beta=1.0,
+    labels=None,
+    pos_label=1,
+    average=None,
+    warn_for=("precision", "recall", "f-score"),
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Compute precision, recall, F-measure and support for each class.
+
+    The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
+    true positives and ``fp`` the number of false positives. The precision is
+    intuitively the ability of the classifier not to label a negative sample as
+    positive.
+
+    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
+    true positives and ``fn`` the number of false negatives. The recall is
+    intuitively the ability of the classifier to find all the positive samples.
+
+    The F-beta score can be interpreted as a weighted harmonic mean of
+    the precision and recall, where an F-beta score reaches its best
+    value at 1 and worst score at 0.
+
+    The F-beta score weights recall more than precision by a factor of
+    ``beta``. ``beta == 1.0`` means recall and precision are equally important.
+
+    The support is the number of occurrences of each class in ``y_true``.
+
+    Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return
+    metrics for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
+    and metrics for both classes are computed, then averaged or both returned (when
+    `average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
+    metrics for all `labels` are either returned or averaged depending on the `average`
+    parameter. Use `labels` specify the set of labels to calculate metrics for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    beta : float, default=1.0
+        The strength of recall versus precision in the F-score.
+
+    labels : array-like, default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+        .. versionchanged:: 0.17
+           Parameter `labels` improved for multiclass problem.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
+            default='binary'
+        This parameter is required for multiclass/multilabel targets.
+        If ``None``, the metrics for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance; it can result in an
+            F-score that is not between precision and recall.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    warn_for : list, tuple or set, for internal use
+        This determines which warnings will be made in the case that this
+        function is being used to return only one of its metrics.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division:
+
+        - recall: when there are no positive labels
+        - precision: when there are no positive predictions
+        - f-score: both
+
+        Notes:
+
+        - If set to "warn", this acts like 0, but a warning is also raised.
+        - If set to `np.nan`, such values will be excluded from the average.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    precision : float (if average is not None) or array of float, shape =\
+        [n_unique_labels]
+        Precision score.
+
+    recall : float (if average is not None) or array of float, shape =\
+        [n_unique_labels]
+        Recall score.
+
+    fbeta_score : float (if average is not None) or array of float, shape =\
+        [n_unique_labels]
+        F-beta score.
+
+    support : None (if average is not None) or array of int, shape =\
+        [n_unique_labels]
+        The number of occurrences of each label in ``y_true``.
+
+    Notes
+    -----
+    When ``true positive + false positive == 0``, precision is undefined.
+    When ``true positive + false negative == 0``, recall is undefined. When
+    ``true positive + false negative + false positive == 0``, f-score is
+    undefined. In such cases, by default the metric will be set to 0, and
+    ``UndefinedMetricWarning`` will be raised. This behavior can be modified
+    with ``zero_division``.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Precision and recall
+           `_.
+
+    .. [2] `Wikipedia entry for the F1-score
+           `_.
+
+    .. [3] `Discriminative Methods for Multi-labeled Classification Advances
+           in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu
+           Godbole, Sunita Sarawagi
+           `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import precision_recall_fscore_support
+    >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
+    >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
+    >>> precision_recall_fscore_support(y_true, y_pred, average='macro')
+    (0.222, 0.333, 0.267, None)
+    >>> precision_recall_fscore_support(y_true, y_pred, average='micro')
+    (0.33, 0.33, 0.33, None)
+    >>> precision_recall_fscore_support(y_true, y_pred, average='weighted')
+    (0.222, 0.333, 0.267, None)
+
+    It is possible to compute per-label precisions, recalls, F1-scores and
+    supports instead of averaging:
+
+    >>> precision_recall_fscore_support(y_true, y_pred, average=None,
+    ... labels=['pig', 'dog', 'cat'])
+    (array([0.        , 0.        , 0.66]),
+     array([0., 0., 1.]), array([0. , 0. , 0.8]),
+     array([2, 2, 2]))
+    """
+    _check_zero_division(zero_division)
+    labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
+
+    # Calculate tp_sum, pred_sum, true_sum ###
+    samplewise = average == "samples"
+    MCM = multilabel_confusion_matrix(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        labels=labels,
+        samplewise=samplewise,
+    )
+    tp_sum = MCM[:, 1, 1]
+    pred_sum = tp_sum + MCM[:, 0, 1]
+    true_sum = tp_sum + MCM[:, 1, 0]
+
+    xp, _, device_ = get_namespace_and_device(y_true, y_pred)
+    if average == "micro":
+        tp_sum = xp.reshape(xp.sum(tp_sum), (1,))
+        pred_sum = xp.reshape(xp.sum(pred_sum), (1,))
+        true_sum = xp.reshape(xp.sum(true_sum), (1,))
+
+    # Finally, we have all our sufficient statistics. Divide! #
+    beta2 = beta**2
+
+    # Divide, and on zero-division, set scores and/or warn according to
+    # zero_division:
+    precision = _prf_divide(
+        tp_sum, pred_sum, "precision", "predicted", average, warn_for, zero_division
+    )
+    recall = _prf_divide(
+        tp_sum, true_sum, "recall", "true", average, warn_for, zero_division
+    )
+
+    if np.isposinf(beta):
+        f_score = recall
+    elif beta == 0:
+        f_score = precision
+    else:
+        # The score is defined as:
+        # score = (1 + beta**2) * precision * recall / (beta**2 * precision + recall)
+        # Therefore, we can express the score in terms of confusion matrix entries as:
+        # score = (1 + beta**2) * tp / ((1 + beta**2) * tp + beta**2 * fn + fp)
+
+        # Array api strict requires all arrays to be of the same type so we
+        # need to convert true_sum, pred_sum and tp_sum to the max supported
+        # float dtype because beta2 is a float
+        max_float_type = _max_precision_float_dtype(xp=xp, device=device_)
+        denom = beta2 * xp.astype(true_sum, max_float_type) + xp.astype(
+            pred_sum, max_float_type
+        )
+        f_score = _prf_divide(
+            (1 + beta2) * xp.astype(tp_sum, max_float_type),
+            denom,
+            "f-score",
+            "true nor predicted",
+            average,
+            warn_for,
+            zero_division,
+        )
+
+    # Average the results
+    if average == "weighted":
+        weights = true_sum
+    elif average == "samples":
+        weights = sample_weight
+    else:
+        weights = None
+
+    if average is not None:
+        precision = float(_nanaverage(precision, weights=weights))
+        recall = float(_nanaverage(recall, weights=weights))
+        f_score = float(_nanaverage(f_score, weights=weights))
+        true_sum = None  # return no support
+
+    return precision, recall, f_score, true_sum
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "sample_weight": ["array-like", None],
+        "raise_warning": ["boolean", Hidden(StrOptions({"deprecated"}))],
+        "replace_undefined_by": [
+            Options(Real, {1.0, np.nan}),
+            dict,
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def class_likelihood_ratios(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    sample_weight=None,
+    raise_warning="deprecated",
+    replace_undefined_by=np.nan,
+):
+    """Compute binary classification positive and negative likelihood ratios.
+
+    The positive likelihood ratio is `LR+ = sensitivity / (1 - specificity)`
+    where the sensitivity or recall is the ratio `tp / (tp + fn)` and the
+    specificity is `tn / (tn + fp)`. The negative likelihood ratio is `LR- = (1
+    - sensitivity) / specificity`. Here `tp` is the number of true positives,
+    `fp` the number of false positives, `tn` is the number of true negatives and
+    `fn` the number of false negatives. Both class likelihood ratios can be used
+    to obtain post-test probabilities given a pre-test probability.
+
+    `LR+` ranges from 1.0 to infinity. A `LR+` of 1.0 indicates that the probability
+    of predicting the positive class is the same for samples belonging to either
+    class; therefore, the test is useless. The greater `LR+` is, the more a
+    positive prediction is likely to be a true positive when compared with the
+    pre-test probability. A value of `LR+` lower than 1.0 is invalid as it would
+    indicate that the odds of a sample being a true positive decrease with
+    respect to the pre-test odds.
+
+    `LR-` ranges from 0.0 to 1.0. The closer it is to 0.0, the lower the probability
+    of a given sample to be a false negative. A `LR-` of 1.0 means the test is
+    useless because the odds of having the condition did not change after the
+    test. A value of `LR-` greater than 1.0 invalidates the classifier as it
+    indicates an increase in the odds of a sample belonging to the positive
+    class after being classified as negative. This is the case when the
+    classifier systematically predicts the opposite of the true label.
+
+    A typical application in medicine is to identify the positive/negative class
+    to the presence/absence of a disease, respectively; the classifier being a
+    diagnostic test; the pre-test probability of an individual having the
+    disease can be the prevalence of such disease (proportion of a particular
+    population found to be affected by a medical condition); and the post-test
+    probabilities would be the probability that the condition is truly present
+    given a positive test result.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    labels : array-like, default=None
+        List of labels to index the matrix. This may be used to select the
+        positive and negative classes with the ordering `labels=[negative_class,
+        positive_class]`. If `None` is given, those that appear at least once in
+        `y_true` or `y_pred` are used in sorted order.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    raise_warning : bool, default=True
+        Whether or not a case-specific warning message is raised when there is division
+        by zero.
+
+        .. deprecated:: 1.7
+            `raise_warning` was deprecated in version 1.7 and will be removed in 1.9,
+            when an :class:`~sklearn.exceptions.UndefinedMetricWarning` will always
+            raise in case of a division by zero.
+
+    replace_undefined_by : np.nan, 1.0, or dict, default=np.nan
+        Sets the return values for LR+ and LR- when there is a division by zero. Can
+        take the following values:
+
+        - `np.nan` to return `np.nan` for both `LR+` and `LR-`
+        - `1.0` to return the worst possible scores: `{"LR+": 1.0, "LR-": 1.0}`
+        - a dict in the format `{"LR+": value_1, "LR-": value_2}` where the values can
+          be non-negative floats, `np.inf` or `np.nan` in the range of the
+          likelihood ratios. For example, `{"LR+": 1.0, "LR-": 1.0}` can be used for
+          returning the worst scores, indicating a useless model, and `{"LR+": np.inf,
+          "LR-": 0.0}` can be used for returning the best scores, indicating a useful
+          model.
+
+        If a division by zero occurs, only the affected metric is replaced with the set
+        value; the other metric is calculated as usual.
+
+        .. versionadded:: 1.7
+
+    Returns
+    -------
+    (positive_likelihood_ratio, negative_likelihood_ratio) : tuple
+        A tuple of two floats, the first containing the positive likelihood ratio (LR+)
+        and the second the negative likelihood ratio (LR-).
+
+    Warns
+    -----
+    Raises :class:`~sklearn.exceptions.UndefinedMetricWarning` when `y_true` and
+    `y_pred` lead to the following conditions:
+
+        - The number of false positives is 0 and `raise_warning` is set to `True`
+          (default): positive likelihood ratio is undefined.
+        - The number of true negatives is 0 and `raise_warning` is set to `True`
+          (default): negative likelihood ratio is undefined.
+        - The sum of true positives and false negatives is 0 (no samples of the positive
+          class are present in `y_true`): both likelihood ratios are undefined.
+
+        For the first two cases, an undefined metric can be defined by setting the
+        `replace_undefined_by` param.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Likelihood ratios in diagnostic testing
+           `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import class_likelihood_ratios
+    >>> class_likelihood_ratios([0, 1, 0, 1, 0], [1, 1, 0, 0, 0])
+    (1.5, 0.75)
+    >>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"])
+    >>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"])
+    >>> class_likelihood_ratios(y_true, y_pred)
+    (1.33, 0.66)
+    >>> y_true = np.array(["non-zebra", "zebra", "non-zebra", "zebra", "non-zebra"])
+    >>> y_pred = np.array(["zebra", "zebra", "non-zebra", "non-zebra", "non-zebra"])
+    >>> class_likelihood_ratios(y_true, y_pred)
+    (1.5, 0.75)
+
+    To avoid ambiguities, use the notation `labels=[negative_class,
+    positive_class]`
+
+    >>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"])
+    >>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"])
+    >>> class_likelihood_ratios(y_true, y_pred, labels=["non-cat", "cat"])
+    (1.5, 0.75)
+    """
+    # TODO(1.9): When `raise_warning` is removed, the following changes need to be made:
+    # The checks for `raise_warning==True` need to be removed and we will always warn,
+    # remove `FutureWarning`, and the Warns section in the docstring should not mention
+    # `raise_warning` anymore.
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    if y_type != "binary":
+        raise ValueError(
+            "class_likelihood_ratios only supports binary classification "
+            f"problems, got targets of type: {y_type}"
+        )
+
+    msg_deprecated_param = (
+        "`raise_warning` was deprecated in version 1.7 and will be removed in 1.9. An "
+        "`UndefinedMetricWarning` will always be raised in case of a division by zero "
+        "and the value set with the `replace_undefined_by` param will be returned."
+    )
+    if raise_warning != "deprecated":
+        warnings.warn(msg_deprecated_param, FutureWarning)
+    else:
+        raise_warning = True
+
+    if replace_undefined_by == 1.0:
+        replace_undefined_by = {"LR+": 1.0, "LR-": 1.0}
+
+    if isinstance(replace_undefined_by, dict):
+        msg = (
+            "The dictionary passed as `replace_undefined_by` needs to be in the form "
+            "`{'LR+': `value_1`, 'LR-': `value_2`}` where the value for `LR+` ranges "
+            "from `1.0` to `np.inf` or is `np.nan` and the value for `LR-` ranges from "
+            f"`0.0` to `1.0` or is `np.nan`; got `{replace_undefined_by}`."
+        )
+        if ("LR+" in replace_undefined_by) and ("LR-" in replace_undefined_by):
+            try:
+                desired_lr_pos = replace_undefined_by.get("LR+", None)
+                check_scalar(
+                    desired_lr_pos,
+                    "positive_likelihood_ratio",
+                    target_type=(Real),
+                    min_val=1.0,
+                    include_boundaries="left",
+                )
+                desired_lr_neg = replace_undefined_by.get("LR-", None)
+                check_scalar(
+                    desired_lr_neg,
+                    "negative_likelihood_ratio",
+                    target_type=(Real),
+                    min_val=0.0,
+                    max_val=1.0,
+                    include_boundaries="both",
+                )
+            except Exception as e:
+                raise ValueError(msg) from e
+        else:
+            raise ValueError(msg)
+
+    cm = confusion_matrix(
+        y_true,
+        y_pred,
+        sample_weight=sample_weight,
+        labels=labels,
+    )
+
+    tn, fp, fn, tp = cm.ravel()
+    support_pos = tp + fn
+    support_neg = tn + fp
+    pos_num = tp * support_neg
+    pos_denom = fp * support_pos
+    neg_num = fn * support_neg
+    neg_denom = tn * support_pos
+
+    # if `support_pos == 0`a division by zero will occur
+    if support_pos == 0:
+        msg = (
+            "No samples of the positive class are present in `y_true`. "
+            "`positive_likelihood_ratio` and `negative_likelihood_ratio` are both set "
+            "to `np.nan`. Use the `replace_undefined_by` param to control this "
+            "behavior. To suppress this warning or turn it into an error, see Python's "
+            "`warnings` module and `warnings.catch_warnings()`."
+        )
+        warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
+        positive_likelihood_ratio = np.nan
+        negative_likelihood_ratio = np.nan
+
+    # if `fp == 0`a division by zero will occur
+    if fp == 0:
+        if raise_warning:
+            if tp == 0:
+                msg_beginning = (
+                    "No samples were predicted for the positive class and "
+                    "`positive_likelihood_ratio` is "
+                )
+            else:
+                msg_beginning = "`positive_likelihood_ratio` is ill-defined and "
+            msg_end = "set to `np.nan`. Use the `replace_undefined_by` param to "
+            "control this behavior. To suppress this warning or turn it into an error, "
+            "see Python's `warnings` module and `warnings.catch_warnings()`."
+            warnings.warn(msg_beginning + msg_end, UndefinedMetricWarning, stacklevel=2)
+        if isinstance(replace_undefined_by, float) and np.isnan(replace_undefined_by):
+            positive_likelihood_ratio = replace_undefined_by
+        else:
+            # replace_undefined_by is a dict and
+            # isinstance(replace_undefined_by.get("LR+", None), Real); this includes
+            # `np.inf` and `np.nan`
+            positive_likelihood_ratio = desired_lr_pos
+    else:
+        positive_likelihood_ratio = pos_num / pos_denom
+
+    # if `tn == 0`a division by zero will occur
+    if tn == 0:
+        if raise_warning:
+            msg = (
+                "`negative_likelihood_ratio` is ill-defined and set to `np.nan`. "
+                "Use the `replace_undefined_by` param to control this behavior. To "
+                "suppress this warning or turn it into an error, see Python's "
+                "`warnings` module and `warnings.catch_warnings()`."
+            )
+            warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
+        if isinstance(replace_undefined_by, float) and np.isnan(replace_undefined_by):
+            negative_likelihood_ratio = replace_undefined_by
+        else:
+            # replace_undefined_by is a dict and
+            # isinstance(replace_undefined_by.get("LR-", None), Real); this includes
+            # `np.nan`
+            negative_likelihood_ratio = desired_lr_neg
+    else:
+        negative_likelihood_ratio = neg_num / neg_denom
+
+    return float(positive_likelihood_ratio), float(negative_likelihood_ratio)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def precision_score(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    pos_label=1,
+    average="binary",
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Compute the precision.
+
+    The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
+    true positives and ``fp`` the number of false positives. The precision is
+    intuitively the ability of the classifier not to label as positive a sample
+    that is negative.
+
+    The best value is 1 and the worst value is 0.
+
+    Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return
+    precision for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
+    and precision for both classes are computed, then averaged or both returned (when
+    `average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
+    precision for all `labels` are either returned or averaged depending on the
+    `average` parameter. Use `labels` specify the set of labels to calculate precision
+    for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    labels : array-like, default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+        .. versionchanged:: 0.17
+           Parameter `labels` improved for multiclass problem.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
+            default='binary'
+        This parameter is required for multiclass/multilabel targets.
+        If ``None``, the metrics for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance; it can result in an
+            F-score that is not between precision and recall.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division.
+
+        Notes:
+
+        - If set to "warn", this acts like 0, but a warning is also raised.
+        - If set to `np.nan`, such values will be excluded from the average.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    precision : float (if average is not None) or array of float of shape \
+                (n_unique_labels,)
+        Precision of the positive class in binary classification or weighted
+        average of the precision of each class for the multiclass task.
+
+    See Also
+    --------
+    precision_recall_fscore_support : Compute precision, recall, F-measure and
+        support for each class.
+    recall_score :  Compute the ratio ``tp / (tp + fn)`` where ``tp`` is the
+        number of true positives and ``fn`` the number of false negatives.
+    PrecisionRecallDisplay.from_estimator : Plot precision-recall curve given
+        an estimator and some data.
+    PrecisionRecallDisplay.from_predictions : Plot precision-recall curve given
+        binary class predictions.
+    multilabel_confusion_matrix : Compute a confusion matrix for each class or
+        sample.
+
+    Notes
+    -----
+    When ``true positive + false positive == 0``, precision returns 0 and
+    raises ``UndefinedMetricWarning``. This behavior can be
+    modified with ``zero_division``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import precision_score
+    >>> y_true = [0, 1, 2, 0, 1, 2]
+    >>> y_pred = [0, 2, 1, 0, 0, 1]
+    >>> precision_score(y_true, y_pred, average='macro')
+    0.22
+    >>> precision_score(y_true, y_pred, average='micro')
+    0.33
+    >>> precision_score(y_true, y_pred, average='weighted')
+    0.22
+    >>> precision_score(y_true, y_pred, average=None)
+    array([0.66, 0.        , 0.        ])
+    >>> y_pred = [0, 0, 0, 0, 0, 0]
+    >>> precision_score(y_true, y_pred, average=None)
+    array([0.33, 0.        , 0.        ])
+    >>> precision_score(y_true, y_pred, average=None, zero_division=1)
+    array([0.33, 1.        , 1.        ])
+    >>> precision_score(y_true, y_pred, average=None, zero_division=np.nan)
+    array([0.33,        nan,        nan])
+
+    >>> # multilabel classification
+    >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
+    >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
+    >>> precision_score(y_true, y_pred, average=None)
+    array([0.5, 1. , 1. ])
+    """
+    p, _, _, _ = precision_recall_fscore_support(
+        y_true,
+        y_pred,
+        labels=labels,
+        pos_label=pos_label,
+        average=average,
+        warn_for=("precision",),
+        sample_weight=sample_weight,
+        zero_division=zero_division,
+    )
+    return p
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def recall_score(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    pos_label=1,
+    average="binary",
+    sample_weight=None,
+    zero_division="warn",
+):
+    """Compute the recall.
+
+    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
+    true positives and ``fn`` the number of false negatives. The recall is
+    intuitively the ability of the classifier to find all the positive samples.
+
+    The best value is 1 and the worst value is 0.
+
+    Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
+    and :term:`multilabel` data as a collection of binary problems, one for each
+    label. For the :term:`binary` case, setting `average='binary'` will return
+    recall for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
+    and recall for both classes are computed then averaged or both returned (when
+    `average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
+    recall for all `labels` are either returned or averaged depending on the `average`
+    parameter. Use `labels` specify the set of labels to calculate recall for.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    labels : array-like, default=None
+        The set of labels to include when `average != 'binary'`, and their
+        order if `average is None`. Labels present in the data can be
+        excluded, for example in multiclass classification to exclude a "negative
+        class". Labels not present in the data can be included and will be
+        "assigned" 0 samples. For multilabel targets, labels are column indices.
+        By default, all labels in `y_true` and `y_pred` are used in sorted order.
+
+        .. versionchanged:: 0.17
+           Parameter `labels` improved for multiclass problem.
+
+    pos_label : int, float, bool or str, default=1
+        The class to report if `average='binary'` and the data is binary,
+        otherwise this parameter is ignored.
+        For multiclass or multilabel targets, set `labels=[pos_label]` and
+        `average != 'binary'` to report metrics for one label only.
+
+    average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
+            default='binary'
+        This parameter is required for multiclass/multilabel targets.
+        If ``None``, the metrics for each class are returned. Otherwise, this
+        determines the type of averaging performed on the data:
+
+        ``'binary'``:
+            Only report results for the class specified by ``pos_label``.
+            This is applicable only if targets (``y_{true,pred}``) are binary.
+        ``'micro'``:
+            Calculate metrics globally by counting the total true positives,
+            false negatives and false positives.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average weighted
+            by support (the number of true instances for each label). This
+            alters 'macro' to account for label imbalance; it can result in an
+            F-score that is not between precision and recall. Weighted recall
+            is equal to accuracy.
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average (only
+            meaningful for multilabel classification where this differs from
+            :func:`accuracy_score`).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division.
+
+        Notes:
+
+        - If set to "warn", this acts like 0, but a warning is also raised.
+        - If set to `np.nan`, such values will be excluded from the average.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    recall : float (if average is not None) or array of float of shape \
+             (n_unique_labels,)
+        Recall of the positive class in binary classification or weighted
+        average of the recall of each class for the multiclass task.
+
+    See Also
+    --------
+    precision_recall_fscore_support : Compute precision, recall, F-measure and
+        support for each class.
+    precision_score : Compute the ratio ``tp / (tp + fp)`` where ``tp`` is the
+        number of true positives and ``fp`` the number of false positives.
+    balanced_accuracy_score : Compute balanced accuracy to deal with imbalanced
+        datasets.
+    multilabel_confusion_matrix : Compute a confusion matrix for each class or
+        sample.
+    PrecisionRecallDisplay.from_estimator : Plot precision-recall curve given
+        an estimator and some data.
+    PrecisionRecallDisplay.from_predictions : Plot precision-recall curve given
+        binary class predictions.
+
+    Notes
+    -----
+    When ``true positive + false negative == 0``, recall returns 0 and raises
+    ``UndefinedMetricWarning``. This behavior can be modified with
+    ``zero_division``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import recall_score
+    >>> y_true = [0, 1, 2, 0, 1, 2]
+    >>> y_pred = [0, 2, 1, 0, 0, 1]
+    >>> recall_score(y_true, y_pred, average='macro')
+    0.33
+    >>> recall_score(y_true, y_pred, average='micro')
+    0.33
+    >>> recall_score(y_true, y_pred, average='weighted')
+    0.33
+    >>> recall_score(y_true, y_pred, average=None)
+    array([1., 0., 0.])
+    >>> y_true = [0, 0, 0, 0, 0, 0]
+    >>> recall_score(y_true, y_pred, average=None)
+    array([0.5, 0. , 0. ])
+    >>> recall_score(y_true, y_pred, average=None, zero_division=1)
+    array([0.5, 1. , 1. ])
+    >>> recall_score(y_true, y_pred, average=None, zero_division=np.nan)
+    array([0.5, nan, nan])
+
+    >>> # multilabel classification
+    >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
+    >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
+    >>> recall_score(y_true, y_pred, average=None)
+    array([1. , 1. , 0.5])
+    """
+    _, r, _, _ = precision_recall_fscore_support(
+        y_true,
+        y_pred,
+        labels=labels,
+        pos_label=pos_label,
+        average=average,
+        warn_for=("recall",),
+        sample_weight=sample_weight,
+        zero_division=zero_division,
+    )
+    return r
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "adjusted": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=False):
+    """Compute the balanced accuracy.
+
+    The balanced accuracy in binary and multiclass classification problems to
+    deal with imbalanced datasets. It is defined as the average of recall
+    obtained on each class.
+
+    The best value is 1 and the worst value is 0 when ``adjusted=False``.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        Ground truth (correct) target values.
+
+    y_pred : array-like of shape (n_samples,)
+        Estimated targets as returned by a classifier.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    adjusted : bool, default=False
+        When true, the result is adjusted for chance, so that random
+        performance would score 0, while keeping perfect performance at a score
+        of 1.
+
+    Returns
+    -------
+    balanced_accuracy : float
+        Balanced accuracy score.
+
+    See Also
+    --------
+    average_precision_score : Compute average precision (AP) from prediction
+        scores.
+    precision_score : Compute the precision score.
+    recall_score : Compute the recall score.
+    roc_auc_score : Compute Area Under the Receiver Operating Characteristic
+        Curve (ROC AUC) from prediction scores.
+
+    Notes
+    -----
+    Some literature promotes alternative definitions of balanced accuracy. Our
+    definition is equivalent to :func:`accuracy_score` with class-balanced
+    sample weights, and shares desirable properties with the binary case.
+    See the :ref:`User Guide `.
+
+    References
+    ----------
+    .. [1] Brodersen, K.H.; Ong, C.S.; Stephan, K.E.; Buhmann, J.M. (2010).
+           The balanced accuracy and its posterior distribution.
+           Proceedings of the 20th International Conference on Pattern
+           Recognition, 3121-24.
+    .. [2] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, (2015).
+           `Fundamentals of Machine Learning for Predictive Data Analytics:
+           Algorithms, Worked Examples, and Case Studies
+           `_.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import balanced_accuracy_score
+    >>> y_true = [0, 1, 0, 0, 1, 0]
+    >>> y_pred = [0, 1, 0, 0, 0, 1]
+    >>> balanced_accuracy_score(y_true, y_pred)
+    0.625
+    """
+    C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        per_class = np.diag(C) / C.sum(axis=1)
+    if np.any(np.isnan(per_class)):
+        warnings.warn("y_pred contains classes not in y_true")
+        per_class = per_class[~np.isnan(per_class)]
+    score = np.mean(per_class)
+    if adjusted:
+        n_classes = len(per_class)
+        chance = 1 / n_classes
+        score -= chance
+        score /= 1 - chance
+    return float(score)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "target_names": ["array-like", None],
+        "sample_weight": ["array-like", None],
+        "digits": [Interval(Integral, 0, None, closed="left")],
+        "output_dict": ["boolean"],
+        "zero_division": [
+            Options(Real, {0.0, 1.0}),
+            "nan",
+            StrOptions({"warn"}),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
+def classification_report(
+    y_true,
+    y_pred,
+    *,
+    labels=None,
+    target_names=None,
+    sample_weight=None,
+    digits=2,
+    output_dict=False,
+    zero_division="warn",
+):
+    """Build a text report showing the main classification metrics.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) target values.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Estimated targets as returned by a classifier.
+
+    labels : array-like of shape (n_labels,), default=None
+        Optional list of label indices to include in the report.
+
+    target_names : array-like of shape (n_labels,), default=None
+        Optional display names matching the labels (same order).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    digits : int, default=2
+        Number of digits for formatting output floating point values.
+        When ``output_dict`` is ``True``, this will be ignored and the
+        returned values will not be rounded.
+
+    output_dict : bool, default=False
+        If True, return output as dict.
+
+        .. versionadded:: 0.20
+
+    zero_division : {"warn", 0.0, 1.0, np.nan}, default="warn"
+        Sets the value to return when there is a zero division. If set to
+        "warn", this acts as 0, but warnings are also raised.
+
+        .. versionadded:: 1.3
+           `np.nan` option was added.
+
+    Returns
+    -------
+    report : str or dict
+        Text summary of the precision, recall, F1 score for each class.
+        Dictionary returned if output_dict is True. Dictionary has the
+        following structure::
+
+            {'label 1': {'precision':0.5,
+                         'recall':1.0,
+                         'f1-score':0.67,
+                         'support':1},
+             'label 2': { ... },
+              ...
+            }
+
+        The reported averages include macro average (averaging the unweighted
+        mean per label), weighted average (averaging the support-weighted mean
+        per label), and sample average (only for multilabel classification).
+        Micro average (averaging the total true positives, false negatives and
+        false positives) is only shown for multi-label or multi-class
+        with a subset of classes, because it corresponds to accuracy
+        otherwise and would be the same for all metrics.
+        See also :func:`precision_recall_fscore_support` for more details
+        on averages.
+
+        Note that in binary classification, recall of the positive class
+        is also known as "sensitivity"; recall of the negative class is
+        "specificity".
+
+    See Also
+    --------
+    precision_recall_fscore_support: Compute precision, recall, F-measure and
+        support for each class.
+    confusion_matrix: Compute confusion matrix to evaluate the accuracy of a
+        classification.
+    multilabel_confusion_matrix: Compute a confusion matrix for each class or sample.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import classification_report
+    >>> y_true = [0, 1, 2, 2, 2]
+    >>> y_pred = [0, 0, 2, 2, 1]
+    >>> target_names = ['class 0', 'class 1', 'class 2']
+    >>> print(classification_report(y_true, y_pred, target_names=target_names))
+                  precision    recall  f1-score   support
+    
+         class 0       0.50      1.00      0.67         1
+         class 1       0.00      0.00      0.00         1
+         class 2       1.00      0.67      0.80         3
+    
+        accuracy                           0.60         5
+       macro avg       0.50      0.56      0.49         5
+    weighted avg       0.70      0.60      0.61         5
+    
+    >>> y_pred = [1, 1, 0]
+    >>> y_true = [1, 1, 1]
+    >>> print(classification_report(y_true, y_pred, labels=[1, 2, 3]))
+                  precision    recall  f1-score   support
+    
+               1       1.00      0.67      0.80         3
+               2       0.00      0.00      0.00         0
+               3       0.00      0.00      0.00         0
+    
+       micro avg       1.00      0.67      0.80         3
+       macro avg       0.33      0.22      0.27         3
+    weighted avg       1.00      0.67      0.80         3
+    
+    """
+
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+
+    if labels is None:
+        labels = unique_labels(y_true, y_pred)
+        labels_given = False
+    else:
+        labels = np.asarray(labels)
+        labels_given = True
+
+    # labelled micro average
+    micro_is_accuracy = (y_type == "multiclass" or y_type == "binary") and (
+        not labels_given or (set(labels) >= set(unique_labels(y_true, y_pred)))
+    )
+
+    if target_names is not None and len(labels) != len(target_names):
+        if labels_given:
+            warnings.warn(
+                "labels size, {0}, does not match size of target_names, {1}".format(
+                    len(labels), len(target_names)
+                )
+            )
+        else:
+            raise ValueError(
+                "Number of classes, {0}, does not match size of "
+                "target_names, {1}. Try specifying the labels "
+                "parameter".format(len(labels), len(target_names))
+            )
+    if target_names is None:
+        target_names = ["%s" % l for l in labels]
+
+    headers = ["precision", "recall", "f1-score", "support"]
+    # compute per-class results without averaging
+    p, r, f1, s = precision_recall_fscore_support(
+        y_true,
+        y_pred,
+        labels=labels,
+        average=None,
+        sample_weight=sample_weight,
+        zero_division=zero_division,
+    )
+    rows = zip(target_names, p, r, f1, s)
+
+    if y_type.startswith("multilabel"):
+        average_options = ("micro", "macro", "weighted", "samples")
+    else:
+        average_options = ("micro", "macro", "weighted")
+
+    if output_dict:
+        report_dict = {label[0]: label[1:] for label in rows}
+        for label, scores in report_dict.items():
+            report_dict[label] = dict(zip(headers, [float(i) for i in scores]))
+    else:
+        longest_last_line_heading = "weighted avg"
+        name_width = max(len(cn) for cn in target_names)
+        width = max(name_width, len(longest_last_line_heading), digits)
+        head_fmt = "{:>{width}s} " + " {:>9}" * len(headers)
+        report = head_fmt.format("", *headers, width=width)
+        report += "\n\n"
+        row_fmt = "{:>{width}s} " + " {:>9.{digits}f}" * 3 + " {:>9}\n"
+        for row in rows:
+            report += row_fmt.format(*row, width=width, digits=digits)
+        report += "\n"
+
+    # compute all applicable averages
+    for average in average_options:
+        if average.startswith("micro") and micro_is_accuracy:
+            line_heading = "accuracy"
+        else:
+            line_heading = average + " avg"
+
+        # compute averages with specified averaging method
+        avg_p, avg_r, avg_f1, _ = precision_recall_fscore_support(
+            y_true,
+            y_pred,
+            labels=labels,
+            average=average,
+            sample_weight=sample_weight,
+            zero_division=zero_division,
+        )
+        avg = [avg_p, avg_r, avg_f1, np.sum(s)]
+
+        if output_dict:
+            report_dict[line_heading] = dict(zip(headers, [float(i) for i in avg]))
+        else:
+            if line_heading == "accuracy":
+                row_fmt_accuracy = (
+                    "{:>{width}s} "
+                    + " {:>9.{digits}}" * 2
+                    + " {:>9.{digits}f}"
+                    + " {:>9}\n"
+                )
+                report += row_fmt_accuracy.format(
+                    line_heading, "", "", *avg[2:], width=width, digits=digits
+                )
+            else:
+                report += row_fmt.format(line_heading, *avg, width=width, digits=digits)
+
+    if output_dict:
+        if "accuracy" in report_dict.keys():
+            report_dict["accuracy"] = report_dict["accuracy"]["precision"]
+        return report_dict
+    else:
+        return report
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def hamming_loss(y_true, y_pred, *, sample_weight=None):
+    """Compute the average Hamming loss.
+
+    The Hamming loss is the fraction of labels that are incorrectly predicted.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : 1d array-like, or label indicator array / sparse matrix
+        Ground truth (correct) labels.
+
+    y_pred : 1d array-like, or label indicator array / sparse matrix
+        Predicted labels, as returned by a classifier.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+        .. versionadded:: 0.18
+
+    Returns
+    -------
+    loss : float or int
+        Return the average Hamming loss between element of ``y_true`` and
+        ``y_pred``.
+
+    See Also
+    --------
+    accuracy_score : Compute the accuracy score. By default, the function will
+        return the fraction of correct predictions divided by the total number
+        of predictions.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
+
+    Notes
+    -----
+    In multiclass classification, the Hamming loss corresponds to the Hamming
+    distance between ``y_true`` and ``y_pred`` which is equivalent to the
+    subset ``zero_one_loss`` function, when `normalize` parameter is set to
+    True.
+
+    In multilabel classification, the Hamming loss is different from the
+    subset zero-one loss. The zero-one loss considers the entire set of labels
+    for a given sample incorrect if it does not entirely match the true set of
+    labels. Hamming loss is more forgiving in that it penalizes only the
+    individual labels.
+
+    The Hamming loss is upperbounded by the subset zero-one loss, when
+    `normalize` parameter is set to True. It is always between 0 and 1,
+    lower being better.
+
+    References
+    ----------
+    .. [1] Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification:
+           An Overview. International Journal of Data Warehousing & Mining,
+           3(3), 1-13, July-September 2007.
+
+    .. [2] `Wikipedia entry on the Hamming distance
+           `_.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import hamming_loss
+    >>> y_pred = [1, 2, 3, 4]
+    >>> y_true = [2, 2, 3, 4]
+    >>> hamming_loss(y_true, y_pred)
+    0.25
+
+    In the multilabel case with binary label indicators:
+
+    >>> import numpy as np
+    >>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2)))
+    0.75
+    """
+    y_true, y_pred = attach_unique(y_true, y_pred)
+    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
+    check_consistent_length(y_true, y_pred, sample_weight)
+
+    xp, _, device = get_namespace_and_device(y_true, y_pred, sample_weight)
+
+    if sample_weight is None:
+        weight_average = 1.0
+    else:
+        sample_weight = xp.asarray(sample_weight, device=device)
+        weight_average = _average(sample_weight, xp=xp)
+
+    if y_type.startswith("multilabel"):
+        n_differences = _count_nonzero(
+            y_true - y_pred, xp=xp, device=device, sample_weight=sample_weight
+        )
+        return float(n_differences) / (
+            y_true.shape[0] * y_true.shape[1] * weight_average
+        )
+
+    elif y_type in ["binary", "multiclass"]:
+        return float(_average(y_true != y_pred, weights=sample_weight, normalize=True))
+    else:
+        raise ValueError("{0} is not supported".format(y_type))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "normalize": ["boolean"],
+        "sample_weight": ["array-like", None],
+        "labels": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def log_loss(y_true, y_pred, *, normalize=True, sample_weight=None, labels=None):
+    r"""Log loss, aka logistic loss or cross-entropy loss.
+
+    This is the loss function used in (multinomial) logistic regression
+    and extensions of it such as neural networks, defined as the negative
+    log-likelihood of a logistic model that returns ``y_pred`` probabilities
+    for its training data ``y_true``.
+    The log loss is only defined for two or more labels.
+    For a single sample with true label :math:`y \in \{0,1\}` and
+    a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log
+    loss is:
+
+    .. math::
+        L_{\log}(y, p) = -(y \log (p) + (1 - y) \log (1 - p))
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like or label indicator matrix
+        Ground truth (correct) labels for n_samples samples.
+
+    y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,)
+        Predicted probabilities, as returned by a classifier's
+        predict_proba method. If ``y_pred.shape = (n_samples,)``
+        the probabilities provided are assumed to be that of the
+        positive class. The labels in ``y_pred`` are assumed to be
+        ordered alphabetically, as done by
+        :class:`~sklearn.preprocessing.LabelBinarizer`.
+
+        `y_pred` values are clipped to `[eps, 1-eps]` where `eps` is the machine
+        precision for `y_pred`'s dtype.
+
+    normalize : bool, default=True
+        If true, return the mean loss per sample.
+        Otherwise, return the sum of the per-sample losses.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    labels : array-like, default=None
+        If not provided, labels will be inferred from y_true. If ``labels``
+        is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
+        assumed to be binary and are inferred from ``y_true``.
+
+        .. versionadded:: 0.18
+
+    Returns
+    -------
+    loss : float
+        Log loss, aka logistic loss or cross-entropy loss.
+
+    Notes
+    -----
+    The logarithm used is the natural logarithm (base-e).
+
+    References
+    ----------
+    C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer,
+    p. 209.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import log_loss
+    >>> log_loss(["spam", "ham", "ham", "spam"],
+    ...          [[.1, .9], [.9, .1], [.8, .2], [.35, .65]])
+    0.21616
+    """
+    transformed_labels, y_pred = _validate_multiclass_probabilistic_prediction(
+        y_true, y_pred, sample_weight, labels
+    )
+
+    # Clipping
+    eps = np.finfo(y_pred.dtype).eps
+    y_pred = np.clip(y_pred, eps, 1 - eps)
+
+    loss = -xlogy(transformed_labels, y_pred).sum(axis=1)
+
+    return float(_average(loss, weights=sample_weight, normalize=normalize))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "pred_decision": ["array-like"],
+        "labels": ["array-like", None],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None):
+    """Average hinge loss (non-regularized).
+
+    In binary class case, assuming labels in y_true are encoded with +1 and -1,
+    when a prediction mistake is made, ``margin = y_true * pred_decision`` is
+    always negative (since the signs disagree), implying ``1 - margin`` is
+    always greater than 1.  The cumulated hinge loss is therefore an upper
+    bound of the number of mistakes made by the classifier.
+
+    In multiclass case, the function expects that either all the labels are
+    included in y_true or an optional labels argument is provided which
+    contains all the labels. The multilabel margin is calculated according
+    to Crammer-Singer's method. As in the binary case, the cumulated hinge loss
+    is an upper bound of the number of mistakes made by the classifier.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True target, consisting of integers of two values. The positive label
+        must be greater than the negative label.
+
+    pred_decision : array-like of shape (n_samples,) or (n_samples, n_classes)
+        Predicted decisions, as output by decision_function (floats).
+
+    labels : array-like, default=None
+        Contains all the labels for the problem. Used in multiclass hinge loss.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    loss : float
+        Average hinge loss.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry on the Hinge loss
+           `_.
+
+    .. [2] Koby Crammer, Yoram Singer. On the Algorithmic
+           Implementation of Multiclass Kernel-based Vector
+           Machines. Journal of Machine Learning Research 2,
+           (2001), 265-292.
+
+    .. [3] `L1 AND L2 Regularization for Multiclass Hinge Loss Models
+           by Robert C. Moore, John DeNero
+           `_.
+
+    Examples
+    --------
+    >>> from sklearn import svm
+    >>> from sklearn.metrics import hinge_loss
+    >>> X = [[0], [1]]
+    >>> y = [-1, 1]
+    >>> est = svm.LinearSVC(random_state=0)
+    >>> est.fit(X, y)
+    LinearSVC(random_state=0)
+    >>> pred_decision = est.decision_function([[-2], [3], [0.5]])
+    >>> pred_decision
+    array([-2.18,  2.36,  0.09])
+    >>> hinge_loss([-1, 1, 1], pred_decision)
+    0.30
+
+    In the multiclass case:
+
+    >>> import numpy as np
+    >>> X = np.array([[0], [1], [2], [3]])
+    >>> Y = np.array([0, 1, 2, 3])
+    >>> labels = np.array([0, 1, 2, 3])
+    >>> est = svm.LinearSVC()
+    >>> est.fit(X, Y)
+    LinearSVC()
+    >>> pred_decision = est.decision_function([[-1], [2], [3]])
+    >>> y_true = [0, 2, 3]
+    >>> hinge_loss(y_true, pred_decision, labels=labels)
+    0.56
+    """
+    check_consistent_length(y_true, pred_decision, sample_weight)
+    pred_decision = check_array(pred_decision, ensure_2d=False)
+    y_true = column_or_1d(y_true)
+    y_true_unique = np.unique(labels if labels is not None else y_true)
+
+    if y_true_unique.size > 2:
+        if pred_decision.ndim <= 1:
+            raise ValueError(
+                "The shape of pred_decision cannot be 1d array"
+                "with a multiclass target. pred_decision shape "
+                "must be (n_samples, n_classes), that is "
+                f"({y_true.shape[0]}, {y_true_unique.size})."
+                f" Got: {pred_decision.shape}"
+            )
+
+        # pred_decision.ndim > 1 is true
+        if y_true_unique.size != pred_decision.shape[1]:
+            if labels is None:
+                raise ValueError(
+                    "Please include all labels in y_true "
+                    "or pass labels as third argument"
+                )
+            else:
+                raise ValueError(
+                    "The shape of pred_decision is not "
+                    "consistent with the number of classes. "
+                    "With a multiclass target, pred_decision "
+                    "shape must be "
+                    "(n_samples, n_classes), that is "
+                    f"({y_true.shape[0]}, {y_true_unique.size}). "
+                    f"Got: {pred_decision.shape}"
+                )
+        if labels is None:
+            labels = y_true_unique
+        le = LabelEncoder()
+        le.fit(labels)
+        y_true = le.transform(y_true)
+        mask = np.ones_like(pred_decision, dtype=bool)
+        mask[np.arange(y_true.shape[0]), y_true] = False
+        margin = pred_decision[~mask]
+        margin -= np.max(pred_decision[mask].reshape(y_true.shape[0], -1), axis=1)
+
+    else:
+        # Handles binary class case
+        # this code assumes that positive and negative labels
+        # are encoded as +1 and -1 respectively
+        pred_decision = column_or_1d(pred_decision)
+        pred_decision = np.ravel(pred_decision)
+
+        lbin = LabelBinarizer(neg_label=-1)
+        y_true = lbin.fit_transform(y_true)[:, 0]
+
+        try:
+            margin = y_true * pred_decision
+        except TypeError:
+            raise TypeError("pred_decision should be an array of floats.")
+
+    losses = 1 - margin
+    # The hinge_loss doesn't penalize good enough predictions.
+    np.clip(losses, 0, None, out=losses)
+    return float(np.average(losses, weights=sample_weight))
+
+
+def _validate_binary_probabilistic_prediction(y_true, y_prob, sample_weight, pos_label):
+    r"""Convert y_true and y_prob in binary classification to shape (n_samples, 2)
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True labels.
+
+    y_prob : array-like of shape (n_samples,)
+        Probabilities of the positive class.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    pos_label : int, float, bool or str, default=None
+        Label of the positive class. If None, `pos_label` will be inferred
+        in the following manner:
+
+        * if `y_true` in {-1, 1} or {0, 1}, `pos_label` defaults to 1;
+        * else if `y_true` contains string, an error will be raised and
+          `pos_label` should be explicitly specified;
+        * otherwise, `pos_label` defaults to the greater label,
+          i.e. `np.unique(y_true)[-1]`.
+
+    Returns
+    -------
+    transformed_labels : array of shape (n_samples, 2)
+
+    y_prob : array of shape (n_samples, 2)
+    """
+    # sanity checks on y_true and y_prob
+    y_true = column_or_1d(y_true)
+    y_prob = column_or_1d(y_prob)
+
+    assert_all_finite(y_true)
+    assert_all_finite(y_prob)
+
+    check_consistent_length(y_prob, y_true, sample_weight)
+
+    y_type = type_of_target(y_true, input_name="y_true")
+    if y_type != "binary":
+        raise ValueError(
+            f"The type of the target inferred from y_true is {y_type} but should be "
+            "binary according to the shape of y_prob."
+        )
+
+    if y_prob.max() > 1:
+        raise ValueError(f"y_prob contains values greater than 1: {y_prob.max()}")
+    if y_prob.min() < 0:
+        raise ValueError(f"y_prob contains values less than 0: {y_prob.min()}")
+
+    # check that pos_label is consistent with y_true
+    try:
+        pos_label = _check_pos_label_consistency(pos_label, y_true)
+    except ValueError:
+        classes = np.unique(y_true)
+        if classes.dtype.kind not in ("O", "U", "S"):
+            # for backward compatibility, if classes are not string then
+            # `pos_label` will correspond to the greater label
+            pos_label = classes[-1]
+        else:
+            raise
+
+    # convert (n_samples,) to (n_samples, 2) shape
+    y_true = np.array(y_true == pos_label, int)
+    transformed_labels = np.column_stack((1 - y_true, y_true))
+    y_prob = np.column_stack((1 - y_prob, y_prob))
+
+    return transformed_labels, y_prob
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_proba": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "labels": ["array-like", None],
+        "scale_by_half": ["boolean", StrOptions({"auto"})],
+    },
+    prefer_skip_nested_validation=True,
+)
+def brier_score_loss(
+    y_true,
+    y_proba,
+    *,
+    sample_weight=None,
+    pos_label=None,
+    labels=None,
+    scale_by_half="auto",
+):
+    r"""Compute the Brier score loss.
+
+    The smaller the Brier score loss, the better, hence the naming with "loss".
+    The Brier score measures the mean squared difference between the predicted
+    probability and the actual outcome. The Brier score is a strictly proper scoring
+    rule.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True targets.
+
+    y_proba : array-like of shape (n_samples,) or (n_samples, n_classes)
+        Predicted probabilities. If `y_proba.shape = (n_samples,)`
+        the probabilities provided are assumed to be that of the
+        positive class. If `y_proba.shape = (n_samples, n_classes)`
+        the columns in `y_proba` are assumed to correspond to the
+        labels in alphabetical order, as done by
+        :class:`~sklearn.preprocessing.LabelBinarizer`.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    pos_label : int, float, bool or str, default=None
+        Label of the positive class when `y_proba.shape = (n_samples,)`.
+        If not provided, `pos_label` will be inferred in the
+        following manner:
+
+        * if `y_true` in {-1, 1} or {0, 1}, `pos_label` defaults to 1;
+        * else if `y_true` contains string, an error will be raised and
+          `pos_label` should be explicitly specified;
+        * otherwise, `pos_label` defaults to the greater label,
+          i.e. `np.unique(y_true)[-1]`.
+
+    labels : array-like of shape (n_classes,), default=None
+        Class labels when `y_proba.shape = (n_samples, n_classes)`.
+        If not provided, labels will be inferred from `y_true`.
+
+        .. versionadded:: 1.7
+
+    scale_by_half : bool or "auto", default="auto"
+        When True, scale the Brier score by 1/2 to lie in the [0, 1] range instead
+        of the [0, 2] range. The default "auto" option implements the rescaling to
+        [0, 1] only for binary classification (as customary) but keeps the
+        original [0, 2] range for multiclass classification.
+
+        .. versionadded:: 1.7
+
+    Returns
+    -------
+    score : float
+        Brier score loss.
+
+    Notes
+    -----
+
+    For :math:`N` observations labeled from :math:`C` possible classes, the Brier
+    score is defined as:
+
+    .. math::
+        \frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}(y_{ic} - \hat{p}_{ic})^{2}
+
+    where :math:`y_{ic}` is 1 if observation `i` belongs to class `c`,
+    otherwise 0 and :math:`\hat{p}_{ic}` is the predicted probability for
+    observation `i` to belong to class `c`.
+    The Brier score then ranges between :math:`[0, 2]`.
+
+    In binary classification tasks the Brier score is usually divided by
+    two and then ranges between :math:`[0, 1]`. It can be alternatively
+    written as:
+
+    .. math::
+        \frac{1}{N}\sum_{i=1}^{N}(y_{i} - \hat{p}_{i})^{2}
+
+    where :math:`y_{i}` is the binary target and :math:`\hat{p}_{i}`
+    is the predicted probability of the positive class.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Brier score
+            `_.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import brier_score_loss
+    >>> y_true = np.array([0, 1, 1, 0])
+    >>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"])
+    >>> y_prob = np.array([0.1, 0.9, 0.8, 0.3])
+    >>> brier_score_loss(y_true, y_prob)
+    0.0375
+    >>> brier_score_loss(y_true, 1-y_prob, pos_label=0)
+    0.0375
+    >>> brier_score_loss(y_true_categorical, y_prob, pos_label="ham")
+    0.0375
+    >>> brier_score_loss(y_true, np.array(y_prob) > 0.5)
+    0.0
+    >>> brier_score_loss(y_true, y_prob, scale_by_half=False)
+    0.075
+    >>> brier_score_loss(
+    ...    ["eggs", "ham", "spam"],
+    ...    [[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.2, 0.2, 0.6]],
+    ...    labels=["eggs", "ham", "spam"]
+    ... )
+    0.146
+    """
+    y_proba = check_array(
+        y_proba, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
+    )
+
+    if y_proba.ndim == 1 or y_proba.shape[1] == 1:
+        transformed_labels, y_proba = _validate_binary_probabilistic_prediction(
+            y_true, y_proba, sample_weight, pos_label
+        )
+    else:
+        transformed_labels, y_proba = _validate_multiclass_probabilistic_prediction(
+            y_true, y_proba, sample_weight, labels
+        )
+
+    brier_score = np.average(
+        np.sum((transformed_labels - y_proba) ** 2, axis=1), weights=sample_weight
+    )
+
+    if scale_by_half == "auto":
+        scale_by_half = y_proba.ndim == 1 or y_proba.shape[1] < 3
+    if scale_by_half:
+        brier_score *= 0.5
+
+    return float(brier_score)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "labels": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def d2_log_loss_score(y_true, y_pred, *, sample_weight=None, labels=None):
+    """
+    :math:`D^2` score function, fraction of log loss explained.
+
+    Best possible score is 1.0 and it can be negative (because the model can be
+    arbitrarily worse). A model that always predicts the per-class proportions
+    of `y_true`, disregarding the input features, gets a D^2 score of 0.0.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 1.5
+
+    Parameters
+    ----------
+    y_true : array-like or label indicator matrix
+        The actuals labels for the n_samples samples.
+
+    y_pred : array-like of shape (n_samples, n_classes) or (n_samples,)
+        Predicted probabilities, as returned by a classifier's
+        predict_proba method. If ``y_pred.shape = (n_samples,)``
+        the probabilities provided are assumed to be that of the
+        positive class. The labels in ``y_pred`` are assumed to be
+        ordered alphabetically, as done by
+        :class:`~sklearn.preprocessing.LabelBinarizer`.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    labels : array-like, default=None
+        If not provided, labels will be inferred from y_true. If ``labels``
+        is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
+        assumed to be binary and are inferred from ``y_true``.
+
+    Returns
+    -------
+    d2 : float or ndarray of floats
+        The D^2 score.
+
+    Notes
+    -----
+    This is not a symmetric function.
+
+    Like R^2, D^2 score may be negative (it need not actually be the square of
+    a quantity D).
+
+    This metric is not well-defined for a single sample and will return a NaN
+    value if n_samples is less than two.
+    """
+    y_pred = check_array(y_pred, ensure_2d=False, dtype="numeric")
+    check_consistent_length(y_pred, y_true, sample_weight)
+    if _num_samples(y_pred) < 2:
+        msg = "D^2 score is not well-defined with less than two samples."
+        warnings.warn(msg, UndefinedMetricWarning)
+        return float("nan")
+
+    # log loss of the fitted model
+    numerator = log_loss(
+        y_true=y_true,
+        y_pred=y_pred,
+        normalize=False,
+        sample_weight=sample_weight,
+        labels=labels,
+    )
+
+    # Proportion of labels in the dataset
+    weights = _check_sample_weight(sample_weight, y_true)
+
+    # If labels is passed, augment y_true to ensure that all labels are represented
+    # Use 0 weight for the new samples to not affect the counts
+    y_true_, weights_ = (
+        (
+            np.concatenate([y_true, labels]),
+            np.concatenate([weights, np.zeros_like(weights, shape=len(labels))]),
+        )
+        if labels is not None
+        else (y_true, weights)
+    )
+
+    _, y_value_indices = np.unique(y_true_, return_inverse=True)
+    counts = np.bincount(y_value_indices, weights=weights_)
+    y_prob = counts / weights.sum()
+    y_pred_null = np.tile(y_prob, (len(y_true), 1))
+
+    # log loss of the null model
+    denominator = log_loss(
+        y_true=y_true,
+        y_pred=y_pred_null,
+        normalize=False,
+        sample_weight=sample_weight,
+        labels=labels,
+    )
+
+    return float(1 - (numerator / denominator))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0a249a8a9fb0a158c34c9f725891467de6041d40
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd
@@ -0,0 +1,268 @@
+from libc.math cimport sqrt, exp
+
+from ..utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
+
+cdef class DistanceMetric:
+    pass
+
+######################################################################
+# Inline distance functions
+#
+#  We use these for the default (euclidean) case so that they can be
+#  inlined.  This leads to faster computation for the most common case
+cdef inline float64_t euclidean_dist64(
+    const float64_t* x1,
+    const float64_t* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp =  (x1[j] - x2[j])
+        d += tmp * tmp
+    return sqrt(d)
+
+
+cdef inline float64_t euclidean_rdist64(
+    const float64_t* x1,
+    const float64_t* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp = (x1[j] - x2[j])
+        d += tmp * tmp
+    return d
+
+
+cdef inline float64_t euclidean_dist_to_rdist64(const float64_t dist) except -1 nogil:
+    return dist * dist
+
+
+cdef inline float64_t euclidean_rdist_to_dist64(const float64_t dist) except -1 nogil:
+    return sqrt(dist)
+
+
+######################################################################
+# DistanceMetric64 base class
+cdef class DistanceMetric64(DistanceMetric):
+    # The following attributes are required for a few of the subclasses.
+    # we must define them here so that cython's limited polymorphism will work.
+    # Because we don't expect to instantiate a lot of these objects, the
+    # extra memory overhead of this setup should not be an issue.
+    cdef float64_t p
+    cdef const float64_t[::1] vec
+    cdef const float64_t[:, ::1] mat
+    cdef intp_t size
+    cdef object func
+    cdef object kwargs
+
+    cdef float64_t dist(
+        self,
+        const float64_t* x1,
+        const float64_t* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef float64_t rdist(
+        self,
+        const float64_t* x1,
+        const float64_t* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef float64_t dist_csr(
+        self,
+        const float64_t* x1_data,
+        const int32_t* x1_indices,
+        const float64_t* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef float64_t rdist_csr(
+        self,
+        const float64_t* x1_data,
+        const int32_t* x1_indices,
+        const float64_t* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef int pdist(
+        self,
+        const float64_t[:, ::1] X,
+        float64_t[:, ::1] D,
+    ) except -1
+
+    cdef int cdist(
+        self,
+        const float64_t[:, ::1] X,
+        const float64_t[:, ::1] Y,
+        float64_t[:, ::1] D,
+    ) except -1
+
+    cdef int pdist_csr(
+        self,
+        const float64_t* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const intp_t size,
+        float64_t[:, ::1] D,
+    ) except -1 nogil
+
+    cdef int cdist_csr(
+        self,
+        const float64_t* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const float64_t* x2_data,
+        const int32_t[::1] x2_indices,
+        const int32_t[::1] x2_indptr,
+        const intp_t size,
+        float64_t[:, ::1] D,
+    ) except -1 nogil
+
+    cdef float64_t _rdist_to_dist(self, float64_t rdist) except -1 nogil
+
+    cdef float64_t _dist_to_rdist(self, float64_t dist) except -1 nogil
+
+######################################################################
+# Inline distance functions
+#
+#  We use these for the default (euclidean) case so that they can be
+#  inlined.  This leads to faster computation for the most common case
+cdef inline float64_t euclidean_dist32(
+    const float32_t* x1,
+    const float32_t* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp =  (x1[j] - x2[j])
+        d += tmp * tmp
+    return sqrt(d)
+
+
+cdef inline float64_t euclidean_rdist32(
+    const float32_t* x1,
+    const float32_t* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp = (x1[j] - x2[j])
+        d += tmp * tmp
+    return d
+
+
+cdef inline float64_t euclidean_dist_to_rdist32(const float32_t dist) except -1 nogil:
+    return dist * dist
+
+
+cdef inline float64_t euclidean_rdist_to_dist32(const float32_t dist) except -1 nogil:
+    return sqrt(dist)
+
+
+######################################################################
+# DistanceMetric32 base class
+cdef class DistanceMetric32(DistanceMetric):
+    # The following attributes are required for a few of the subclasses.
+    # we must define them here so that cython's limited polymorphism will work.
+    # Because we don't expect to instantiate a lot of these objects, the
+    # extra memory overhead of this setup should not be an issue.
+    cdef float64_t p
+    cdef const float64_t[::1] vec
+    cdef const float64_t[:, ::1] mat
+    cdef intp_t size
+    cdef object func
+    cdef object kwargs
+
+    cdef float32_t dist(
+        self,
+        const float32_t* x1,
+        const float32_t* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef float32_t rdist(
+        self,
+        const float32_t* x1,
+        const float32_t* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef float32_t dist_csr(
+        self,
+        const float32_t* x1_data,
+        const int32_t* x1_indices,
+        const float32_t* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef float32_t rdist_csr(
+        self,
+        const float32_t* x1_data,
+        const int32_t* x1_indices,
+        const float32_t* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef int pdist(
+        self,
+        const float32_t[:, ::1] X,
+        float32_t[:, ::1] D,
+    ) except -1
+
+    cdef int cdist(
+        self,
+        const float32_t[:, ::1] X,
+        const float32_t[:, ::1] Y,
+        float32_t[:, ::1] D,
+    ) except -1
+
+    cdef int pdist_csr(
+        self,
+        const float32_t* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const intp_t size,
+        float32_t[:, ::1] D,
+    ) except -1 nogil
+
+    cdef int cdist_csr(
+        self,
+        const float32_t* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const float32_t* x2_data,
+        const int32_t[::1] x2_indices,
+        const int32_t[::1] x2_indptr,
+        const intp_t size,
+        float32_t[:, ::1] D,
+    ) except -1 nogil
+
+    cdef float32_t _rdist_to_dist(self, float32_t rdist) except -1 nogil
+
+    cdef float32_t _dist_to_rdist(self, float32_t dist) except -1 nogil
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd.tp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd.tp
new file mode 100644
index 0000000000000000000000000000000000000000..313225088c776e8575bfb4cec47c1f17183fab03
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pxd.tp
@@ -0,0 +1,152 @@
+{{py:
+
+implementation_specific_values = [
+    # Values are the following ones:
+    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
+    ('64', 'float64_t', 'np.float64'),
+    ('32', 'float32_t', 'np.float32')
+]
+
+}}
+from libc.math cimport sqrt, exp
+
+from ..utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
+
+cdef class DistanceMetric:
+    pass
+
+{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
+
+######################################################################
+# Inline distance functions
+#
+#  We use these for the default (euclidean) case so that they can be
+#  inlined.  This leads to faster computation for the most common case
+cdef inline float64_t euclidean_dist{{name_suffix}}(
+    const {{INPUT_DTYPE_t}}* x1,
+    const {{INPUT_DTYPE_t}}* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp =  (x1[j] - x2[j])
+        d += tmp * tmp
+    return sqrt(d)
+
+
+cdef inline float64_t euclidean_rdist{{name_suffix}}(
+    const {{INPUT_DTYPE_t}}* x1,
+    const {{INPUT_DTYPE_t}}* x2,
+    intp_t size,
+) except -1 nogil:
+    cdef float64_t tmp, d=0
+    cdef intp_t j
+    for j in range(size):
+        tmp = (x1[j] - x2[j])
+        d += tmp * tmp
+    return d
+
+
+cdef inline float64_t euclidean_dist_to_rdist{{name_suffix}}(const {{INPUT_DTYPE_t}} dist) except -1 nogil:
+    return dist * dist
+
+
+cdef inline float64_t euclidean_rdist_to_dist{{name_suffix}}(const {{INPUT_DTYPE_t}} dist) except -1 nogil:
+    return sqrt(dist)
+
+
+######################################################################
+# DistanceMetric{{name_suffix}} base class
+cdef class DistanceMetric{{name_suffix}}(DistanceMetric):
+    # The following attributes are required for a few of the subclasses.
+    # we must define them here so that cython's limited polymorphism will work.
+    # Because we don't expect to instantiate a lot of these objects, the
+    # extra memory overhead of this setup should not be an issue.
+    cdef float64_t p
+    cdef const float64_t[::1] vec
+    cdef const float64_t[:, ::1] mat
+    cdef intp_t size
+    cdef object func
+    cdef object kwargs
+
+    cdef {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef {{INPUT_DTYPE_t}} rdist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil
+
+    cdef {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil
+
+    cdef int pdist(
+        self,
+        const {{INPUT_DTYPE_t}}[:, ::1] X,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1
+
+    cdef int cdist(
+        self,
+        const {{INPUT_DTYPE_t}}[:, ::1] X,
+        const {{INPUT_DTYPE_t}}[:, ::1] Y,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1
+
+    cdef int pdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const intp_t size,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1 nogil
+
+    cdef int cdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t[::1] x2_indices,
+        const int32_t[::1] x2_indptr,
+        const intp_t size,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1 nogil
+
+    cdef {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil
+
+    cdef {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil
+
+{{endfor}}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pyx.tp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pyx.tp
new file mode 100644
index 0000000000000000000000000000000000000000..b7d3d1f4d86a6b4817af36489d1846b74afe7e6d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_dist_metrics.pyx.tp
@@ -0,0 +1,2811 @@
+{{py:
+
+implementation_specific_values = [
+    # Values are the following ones:
+    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
+    ('64', 'float64_t', 'np.float64'),
+    ('32', 'float32_t', 'np.float32')
+]
+
+}}
+# By Jake Vanderplas (2013) 
+# written for the scikit-learn project
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+cimport numpy as cnp
+
+cnp.import_array()  # required in order to use C-API
+
+from libc.math cimport fabs, sqrt, exp, pow, cos, sin, asin
+
+from scipy.sparse import csr_matrix, issparse
+from ..utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
+from ..utils import check_array
+from ..utils.fixes import parse_version, sp_base_version
+
+cdef inline double fmax(double a, double b) noexcept nogil:
+    return max(a, b)
+
+
+######################################################################
+# newObj function
+#  this is a helper function for pickling
+def newObj(obj):
+    return obj.__new__(obj)
+
+
+BOOL_METRICS = [
+    "hamming",
+    "jaccard",
+    "dice",
+    "rogerstanimoto",
+    "russellrao",
+    "sokalsneath",
+]
+DEPRECATED_METRICS = []
+if sp_base_version < parse_version("1.17"):
+    # Deprecated in SciPy 1.15 and removed in SciPy 1.17
+    BOOL_METRICS += ["sokalmichener"]
+if sp_base_version >= parse_version("1.15"):
+    DEPRECATED_METRICS.append("sokalmichener")
+if sp_base_version < parse_version("1.11"):
+    # Deprecated in SciPy 1.9 and removed in SciPy 1.11
+    BOOL_METRICS += ["kulsinski"]
+if sp_base_version >= parse_version("1.9"):
+    DEPRECATED_METRICS.append("kulsinski")
+if sp_base_version < parse_version("1.9"):
+    # Deprecated in SciPy 1.0 and removed in SciPy 1.9
+    BOOL_METRICS += ["matching"]
+if sp_base_version >= parse_version("1.0"):
+    DEPRECATED_METRICS.append("matching")
+
+def get_valid_metric_ids(L):
+    """Given an iterable of metric class names or class identifiers,
+    return a list of metric IDs which map to those classes.
+
+    Example:
+    >>> L = get_valid_metric_ids([EuclideanDistance, 'ManhattanDistance'])
+    >>> sorted(L)
+    ['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
+    """
+    return [key for (key, val) in METRIC_MAPPING64.items()
+            if (val.__name__ in L) or (val in L)]
+
+cdef class DistanceMetric:
+    """Uniform interface for fast distance metric functions.
+
+    The `DistanceMetric` class provides a convenient way to compute pairwise distances
+    between samples. It supports various distance metrics, such as Euclidean distance,
+    Manhattan distance, and more.
+
+    The `pairwise` method can be used to compute pairwise distances between samples in
+    the input arrays. It returns a distance matrix representing the distances between
+    all pairs of samples.
+
+    The :meth:`get_metric` method allows you to retrieve a specific metric using its
+    string identifier.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import DistanceMetric
+    >>> dist = DistanceMetric.get_metric('euclidean')
+    >>> X = [[1, 2], [3, 4], [5, 6]]
+    >>> Y = [[7, 8], [9, 10]]
+    >>> dist.pairwise(X,Y)
+    array([[7.81..., 10.63...]
+           [5.65...,  8.48...]
+           [1.41...,  4.24...]])
+
+    .. rubric:: Available Metrics
+
+    The following lists the string metric identifiers and the associated
+    distance metric classes:
+
+    **Metrics intended for real-valued vector spaces:**
+
+    ==============  ====================  ========  ===============================
+    identifier      class name            args      distance function
+    --------------  --------------------  --------  -------------------------------
+    "euclidean"     EuclideanDistance     -         ``sqrt(sum((x - y)^2))``
+    "manhattan"     ManhattanDistance     -         ``sum(|x - y|)``
+    "chebyshev"     ChebyshevDistance     -         ``max(|x - y|)``
+    "minkowski"     MinkowskiDistance     p, w      ``sum(w * |x - y|^p)^(1/p)``
+    "seuclidean"    SEuclideanDistance    V         ``sqrt(sum((x - y)^2 / V))``
+    "mahalanobis"   MahalanobisDistance   V or VI   ``sqrt((x - y)' V^-1 (x - y))``
+    ==============  ====================  ========  ===============================
+
+    **Metrics intended for two-dimensional vector spaces:**  Note that the haversine
+    distance metric requires data in the form of [latitude, longitude] and both
+    inputs and outputs are in units of radians.
+
+    ============  ==================  ===============================================================
+    identifier    class name          distance function
+    ------------  ------------------  ---------------------------------------------------------------
+    "haversine"   HaversineDistance   ``2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))``
+    ============  ==================  ===============================================================
+
+
+    **Metrics intended for integer-valued vector spaces:**  Though intended
+    for integer-valued vectors, these are also valid metrics in the case of
+    real-valued vectors.
+
+    =============  ====================  ========================================
+    identifier     class name            distance function
+    -------------  --------------------  ----------------------------------------
+    "hamming"      HammingDistance       ``N_unequal(x, y) / N_tot``
+    "canberra"     CanberraDistance      ``sum(|x - y| / (|x| + |y|))``
+    "braycurtis"   BrayCurtisDistance    ``sum(|x - y|) / (sum(|x|) + sum(|y|))``
+    =============  ====================  ========================================
+
+    **Metrics intended for boolean-valued vector spaces:**  Any nonzero entry
+    is evaluated to "True".  In the listings below, the following
+    abbreviations are used:
+
+    - N: number of dimensions
+    - NTT: number of dims in which both values are True
+    - NTF: number of dims in which the first value is True, second is False
+    - NFT: number of dims in which the first value is False, second is True
+    - NFF: number of dims in which both values are False
+    - NNEQ: number of non-equal dimensions, NNEQ = NTF + NFT
+    - NNZ: number of nonzero dimensions, NNZ = NTF + NFT + NTT
+
+    =================  =======================  ===============================
+    identifier         class name               distance function
+    -----------------  -----------------------  -------------------------------
+    "jaccard"          JaccardDistance          NNEQ / NNZ
+    "matching"         MatchingDistance         NNEQ / N
+    "dice"             DiceDistance             NNEQ / (NTT + NNZ)
+    "kulsinski"        KulsinskiDistance        (NNEQ + N - NTT) / (NNEQ + N)
+    "rogerstanimoto"   RogersTanimotoDistance   2 * NNEQ / (N + NNEQ)
+    "russellrao"       RussellRaoDistance       (N - NTT) / N
+    "sokalmichener"    SokalMichenerDistance    2 * NNEQ / (N + NNEQ)
+    "sokalsneath"      SokalSneathDistance      NNEQ / (NNEQ + 0.5 * NTT)
+    =================  =======================  ===============================
+
+    **User-defined distance:**
+
+    ===========    ===============    =======
+    identifier     class name         args
+    -----------    ---------------    -------
+    "pyfunc"       PyFuncDistance     func
+    ===========    ===============    =======
+
+    Here ``func`` is a function which takes two one-dimensional numpy
+    arrays, and returns a distance.  Note that in order to be used within
+    the BallTree, the distance must be a true metric:
+    i.e. it must satisfy the following properties
+
+    1) Non-negativity: d(x, y) >= 0
+    2) Identity: d(x, y) = 0 if and only if x == y
+    3) Symmetry: d(x, y) = d(y, x)
+    4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)
+
+    Because of the Python object overhead involved in calling the python
+    function, this will be fairly slow, but it will have the same
+    scaling as other distances.
+    """
+    @classmethod
+    def get_metric(cls, metric, dtype=np.float64, **kwargs):
+        """Get the given distance metric from the string identifier.
+
+        See the docstring of DistanceMetric for a list of available metrics.
+
+        Parameters
+        ----------
+        metric : str or class name
+            The string identifier or class name of the desired distance metric.
+            See the documentation of the `DistanceMetric` class for a list of
+            available metrics.
+
+        dtype : {np.float32, np.float64}, default=np.float64
+            The data type of the input on which the metric will be applied.
+            This affects the precision of the computed distances.
+            By default, it is set to `np.float64`.
+
+        **kwargs
+            Additional keyword arguments that will be passed to the requested metric.
+            These arguments can be used to customize the behavior of the specific
+            metric.
+
+        Returns
+        -------
+        metric_obj : instance of the requested metric
+            An instance of the requested distance metric class.
+        """
+        if dtype == np.float32:
+            specialized_class = DistanceMetric32
+        elif dtype == np.float64:
+            specialized_class = DistanceMetric64
+        else:
+            raise ValueError(
+                f"Unexpected dtype {dtype} provided. Please select a dtype from"
+                " {np.float32, np.float64}"
+            )
+
+        return specialized_class.get_metric(metric, **kwargs)
+
+{{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
+
+######################################################################
+# metric mappings
+#  These map from metric id strings to class names
+METRIC_MAPPING{{name_suffix}} = {
+    'euclidean': EuclideanDistance{{name_suffix}},
+    'l2': EuclideanDistance{{name_suffix}},
+    'minkowski': MinkowskiDistance{{name_suffix}},
+    'p': MinkowskiDistance{{name_suffix}},
+    'manhattan': ManhattanDistance{{name_suffix}},
+    'cityblock': ManhattanDistance{{name_suffix}},
+    'l1': ManhattanDistance{{name_suffix}},
+    'chebyshev': ChebyshevDistance{{name_suffix}},
+    'infinity': ChebyshevDistance{{name_suffix}},
+    'seuclidean': SEuclideanDistance{{name_suffix}},
+    'mahalanobis': MahalanobisDistance{{name_suffix}},
+    'hamming': HammingDistance{{name_suffix}},
+    'canberra': CanberraDistance{{name_suffix}},
+    'braycurtis': BrayCurtisDistance{{name_suffix}},
+    'matching': MatchingDistance{{name_suffix}},
+    'jaccard': JaccardDistance{{name_suffix}},
+    'dice': DiceDistance{{name_suffix}},
+    'kulsinski': KulsinskiDistance{{name_suffix}},
+    'rogerstanimoto': RogersTanimotoDistance{{name_suffix}},
+    'russellrao': RussellRaoDistance{{name_suffix}},
+    'sokalmichener': SokalMichenerDistance{{name_suffix}},
+    'sokalsneath': SokalSneathDistance{{name_suffix}},
+    'haversine': HaversineDistance{{name_suffix}},
+    'pyfunc': PyFuncDistance{{name_suffix}},
+}
+
+cdef inline object _buffer_to_ndarray{{name_suffix}}(const {{INPUT_DTYPE_t}}* x, intp_t n):
+    # Wrap a memory buffer with an ndarray. Warning: this is not robust.
+    # In particular, if x is deallocated before the returned array goes
+    # out of scope, this could cause memory errors.  Since there is not
+    # a possibility of this for our use-case, this should be safe.
+
+    # Note: this Segfaults unless np.import_array() is called above
+    # TODO: remove the explicit cast to cnp.intp_t* when cython min version >= 3.0
+    return cnp.PyArray_SimpleNewFromData(1, &n, cnp.NPY_FLOAT64, x)
+
+
+cdef {{INPUT_DTYPE_t}} INF{{name_suffix}} = np.inf
+
+
+######################################################################
+# Distance Metric Classes
+cdef class DistanceMetric{{name_suffix}}(DistanceMetric):
+    """DistanceMetric class
+
+    This class provides a uniform interface to fast distance metric
+    functions.  The various metrics can be accessed via the :meth:`get_metric`
+    class method and the metric string identifier (see below).
+
+    Examples
+    --------
+    >>> from sklearn.metrics import DistanceMetric
+    >>> dist = DistanceMetric.get_metric('euclidean')
+    >>> X = [[0, 1, 2],
+             [3, 4, 5]]
+    >>> dist.pairwise(X)
+    array([[ 0.        ,  5.19615242],
+           [ 5.19615242,  0.        ]])
+
+    Available Metrics
+
+    The following lists the string metric identifiers and the associated
+    distance metric classes:
+
+    **Metrics intended for real-valued vector spaces:**
+
+    ==============  ====================  ========  ===============================
+    identifier      class name            args      distance function
+    --------------  --------------------  --------  -------------------------------
+    "euclidean"     EuclideanDistance     -         ``sqrt(sum((x - y)^2))``
+    "manhattan"     ManhattanDistance     -         ``sum(|x - y|)``
+    "chebyshev"     ChebyshevDistance     -         ``max(|x - y|)``
+    "minkowski"     MinkowskiDistance     p, w      ``sum(w * |x - y|^p)^(1/p)``
+    "seuclidean"    SEuclideanDistance    V         ``sqrt(sum((x - y)^2 / V))``
+    "mahalanobis"   MahalanobisDistance   V or VI   ``sqrt((x - y)' V^-1 (x - y))``
+    ==============  ====================  ========  ===============================
+
+    **Metrics intended for two-dimensional vector spaces:**  Note that the haversine
+    distance metric requires data in the form of [latitude, longitude] and both
+    inputs and outputs are in units of radians.
+
+    ============  ==================  ===============================================================
+    identifier    class name          distance function
+    ------------  ------------------  ---------------------------------------------------------------
+    "haversine"   HaversineDistance   ``2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))``
+    ============  ==================  ===============================================================
+
+
+    **Metrics intended for integer-valued vector spaces:**  Though intended
+    for integer-valued vectors, these are also valid metrics in the case of
+    real-valued vectors.
+
+    =============  ====================  ========================================
+    identifier     class name            distance function
+    -------------  --------------------  ----------------------------------------
+    "hamming"      HammingDistance       ``N_unequal(x, y) / N_tot``
+    "canberra"     CanberraDistance      ``sum(|x - y| / (|x| + |y|))``
+    "braycurtis"   BrayCurtisDistance    ``sum(|x - y|) / (sum(|x|) + sum(|y|))``
+    =============  ====================  ========================================
+
+    **Metrics intended for boolean-valued vector spaces:**  Any nonzero entry
+    is evaluated to "True".  In the listings below, the following
+    abbreviations are used:
+
+    - N: number of dimensions
+    - NTT: number of dims in which both values are True
+    - NTF: number of dims in which the first value is True, second is False
+    - NFT: number of dims in which the first value is False, second is True
+    - NFF: number of dims in which both values are False
+    - NNEQ: number of non-equal dimensions, NNEQ = NTF + NFT
+    - NNZ: number of nonzero dimensions, NNZ = NTF + NFT + NTT
+
+    =================  =======================  ===============================
+    identifier         class name               distance function
+    -----------------  -----------------------  -------------------------------
+    "jaccard"          JaccardDistance          NNEQ / NNZ
+    "matching"         MatchingDistance         NNEQ / N
+    "dice"             DiceDistance             NNEQ / (NTT + NNZ)
+    "kulsinski"        KulsinskiDistance        (NNEQ + N - NTT) / (NNEQ + N)
+    "rogerstanimoto"   RogersTanimotoDistance   2 * NNEQ / (N + NNEQ)
+    "russellrao"       RussellRaoDistance       (N - NTT) / N
+    "sokalmichener"    SokalMichenerDistance    2 * NNEQ / (N + NNEQ)
+    "sokalsneath"      SokalSneathDistance      NNEQ / (NNEQ + 0.5 * NTT)
+    =================  =======================  ===============================
+
+    **User-defined distance:**
+
+    ===========    ===============    =======
+    identifier     class name         args
+    -----------    ---------------    -------
+    "pyfunc"       PyFuncDistance     func
+    ===========    ===============    =======
+
+    Here ``func`` is a function which takes two one-dimensional numpy
+    arrays, and returns a distance.  Note that in order to be used within
+    the BallTree, the distance must be a true metric:
+    i.e. it must satisfy the following properties
+
+    1) Non-negativity: d(x, y) >= 0
+    2) Identity: d(x, y) = 0 if and only if x == y
+    3) Symmetry: d(x, y) = d(y, x)
+    4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z)
+
+    Because of the Python object overhead involved in calling the python
+    function, this will be fairly slow, but it will have the same
+    scaling as other distances.
+    """
+    def __cinit__(self):
+        self.p = 2
+        self.vec = np.zeros(1, dtype=np.float64, order='C')
+        self.mat = np.zeros((1, 1), dtype=np.float64, order='C')
+        self.size = 1
+
+    def __reduce__(self):
+        """
+        reduce method used for pickling
+        """
+        return (newObj, (self.__class__,), self.__getstate__())
+
+    def __getstate__(self):
+        """
+        get state for pickling
+        """
+        if self.__class__.__name__ == "PyFuncDistance{{name_suffix}}":
+            return (float(self.p), np.asarray(self.vec), np.asarray(self.mat), self.func, self.kwargs)
+        return (float(self.p), np.asarray(self.vec), np.asarray(self.mat))
+
+    def __setstate__(self, state):
+        """
+        set state for pickling
+        """
+        self.p = state[0]
+        self.vec = state[1]
+        self.mat = state[2]
+        if self.__class__.__name__ == "PyFuncDistance{{name_suffix}}":
+            self.func = state[3]
+            self.kwargs = state[4]
+        self.size = self.vec.shape[0]
+
+    @classmethod
+    def get_metric(cls, metric, **kwargs):
+        """Get the given distance metric from the string identifier.
+
+        See the docstring of DistanceMetric for a list of available metrics.
+
+        Parameters
+        ----------
+        metric : str or class name
+            The distance metric to use
+        **kwargs
+            additional arguments will be passed to the requested metric
+        """
+        if isinstance(metric, DistanceMetric{{name_suffix}}):
+            return metric
+
+        if callable(metric):
+            return PyFuncDistance{{name_suffix}}(metric, **kwargs)
+
+        # Map the metric string ID to the metric class
+        if isinstance(metric, type) and issubclass(metric, DistanceMetric{{name_suffix}}):
+            pass
+        else:
+            try:
+                metric = METRIC_MAPPING{{name_suffix}}[metric]
+            except:
+                raise ValueError("Unrecognized metric '%s'" % metric)
+
+        # In Minkowski special cases, return more efficient methods
+        if metric is MinkowskiDistance{{name_suffix}}:
+            p = kwargs.pop('p', 2)
+            w = kwargs.pop('w', None)
+            if p == 1 and w is None:
+                return ManhattanDistance{{name_suffix}}(**kwargs)
+            elif p == 2 and w is None:
+                return EuclideanDistance{{name_suffix}}(**kwargs)
+            elif np.isinf(p) and w is None:
+                return ChebyshevDistance{{name_suffix}}(**kwargs)
+            else:
+                return MinkowskiDistance{{name_suffix}}(p, w, **kwargs)
+        else:
+            return metric(**kwargs)
+
+    def __init__(self):
+        if self.__class__ is DistanceMetric{{name_suffix}}:
+            raise NotImplementedError("DistanceMetric{{name_suffix}} is an abstract class")
+
+    def _validate_data(self, X):
+        """Validate the input data.
+
+        This should be overridden in a base class if a specific input format
+        is required.
+        """
+        return
+
+    cdef {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        """Compute the distance between vectors x1 and x2
+
+        This should be overridden in a base class.
+        """
+        return -999
+
+    cdef {{INPUT_DTYPE_t}} rdist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        """Compute the rank-preserving surrogate distance between vectors x1 and x2.
+
+        This can optionally be overridden in a base class.
+
+        The rank-preserving surrogate distance is any measure that yields the same
+        rank as the distance, but is more efficient to compute. For example, the
+        rank-preserving surrogate distance of the Euclidean metric is the
+        squared-euclidean distance.
+        """
+        return self.dist(x1, x2, size)
+
+    cdef int pdist(
+        self,
+        const {{INPUT_DTYPE_t}}[:, ::1] X,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1:
+        """Compute the pairwise distances between points in X"""
+        cdef intp_t i1, i2
+        for i1 in range(X.shape[0]):
+            for i2 in range(i1, X.shape[0]):
+                D[i1, i2] = self.dist(&X[i1, 0], &X[i2, 0], X.shape[1])
+                D[i2, i1] = D[i1, i2]
+        return 0
+
+
+    cdef int cdist(
+        self,
+        const {{INPUT_DTYPE_t}}[:, ::1] X,
+        const {{INPUT_DTYPE_t}}[:, ::1] Y,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1:
+        """Compute the cross-pairwise distances between arrays X and Y"""
+        cdef intp_t i1, i2
+        if X.shape[1] != Y.shape[1]:
+            raise ValueError('X and Y must have the same second dimension')
+        for i1 in range(X.shape[0]):
+            for i2 in range(Y.shape[0]):
+                D[i1, i2] = self.dist(&X[i1, 0], &Y[i2, 0], X.shape[1])
+        return 0
+
+    cdef {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        """Compute the distance between vectors x1 and x2 represented
+        under the CSR format.
+
+        This must be overridden in a subclass.
+
+        Notes
+        -----
+        0. The implementation of this method in subclasses must be robust to the
+        presence of explicit zeros in the CSR representation.
+
+        1. The `data` arrays are passed using pointers to be able to support an
+        alternative representation of the CSR data structure for supporting
+        fused sparse-dense datasets pairs with minimum overhead.
+
+        See the explanations in `SparseDenseDatasetsPair.__init__`.
+
+        2. An alternative signature would be:
+
+            cdef {{INPUT_DTYPE_t}} dist_csr(
+                self,
+                const {{INPUT_DTYPE_t}}* x1_data,
+                const int32_t* x1_indices,
+                const {{INPUT_DTYPE_t}}* x2_data,
+                const int32_t* x2_indices,
+            ) except -1 nogil:
+
+        Where callers would use slicing on the original CSR data and indices
+        memoryviews:
+
+            x1_start = X1_csr.indices_ptr[i]
+            x1_end   = X1_csr.indices_ptr[i+1]
+            x2_start = X2_csr.indices_ptr[j]
+            x2_end   = X2_csr.indices_ptr[j+1]
+
+            self.dist_csr(
+                &x1_data[x1_start],
+                x1_indices[x1_start:x1_end],
+                &x2_data[x2_start],
+                x2_indices[x2_start:x2_end],
+            )
+
+        Yet, slicing on memoryview slows down execution as it takes the GIL.
+        See: https://github.com/scikit-learn/scikit-learn/issues/17299
+
+        Hence, to avoid slicing the data and indices arrays of the sparse
+        matrices containing respectively x1 and x2 (namely x{1,2}_{data,indices})
+        are passed as well as their indices pointers (namely x{1,2}_{start,end}).
+
+        3. For reference about the CSR format, see section 3.4 of
+        Saad, Y. (2003), Iterative Methods for Sparse Linear Systems, SIAM.
+        https://www-users.cse.umn.edu/~saad/IterMethBook_2ndEd.pdf
+        """
+        return -999
+
+    cdef {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        """Distance between rows of CSR matrices x1 and x2.
+
+        This can optionally be overridden in a subclass.
+
+        The rank-preserving surrogate distance is any measure that yields the same
+        rank as the distance, but is more efficient to compute. For example, the
+        rank-preserving surrogate distance of the Euclidean metric is the
+        squared-euclidean distance.
+
+        Notes
+        -----
+        The implementation of this method in subclasses must be robust to the
+        presence of explicit zeros in the CSR representation.
+
+        More information about the motives for this method signature is given
+        in the docstring of dist_csr.
+        """
+        return self.dist_csr(
+            x1_data,
+            x1_indices,
+            x2_data,
+            x2_indices,
+            x1_start,
+            x1_end,
+            x2_start,
+            x2_end,
+            size,
+        )
+
+    cdef int pdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const intp_t size,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1 nogil:
+        """Pairwise distances between rows in CSR matrix X.
+
+        Note that this implementation is twice faster than cdist_csr(X, X)
+        because it leverages the symmetry of the problem.
+        """
+        cdef:
+            intp_t i1, i2
+            intp_t n_x1 = x1_indptr.shape[0] - 1
+            intp_t x1_start, x1_end, x2_start, x2_end
+
+        for i1 in range(n_x1):
+            x1_start = x1_indptr[i1]
+            x1_end = x1_indptr[i1 + 1]
+            for i2 in range(i1, n_x1):
+                x2_start = x1_indptr[i2]
+                x2_end = x1_indptr[i2 + 1]
+                D[i1, i2] = D[i2, i1] = self.dist_csr(
+                    x1_data,
+                    &x1_indices[0],
+                    x1_data,
+                    &x1_indices[0],
+                    x1_start,
+                    x1_end,
+                    x2_start,
+                    x2_end,
+                    size,
+                )
+        return 0
+
+    cdef int cdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t[::1] x1_indices,
+        const int32_t[::1] x1_indptr,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t[::1] x2_indices,
+        const int32_t[::1] x2_indptr,
+        const intp_t size,
+        {{INPUT_DTYPE_t}}[:, ::1] D,
+    ) except -1 nogil:
+        """Compute the cross-pairwise distances between arrays X and Y
+        represented in the CSR format."""
+        cdef:
+            intp_t i1, i2
+            intp_t n_x1 = x1_indptr.shape[0] - 1
+            intp_t n_x2 = x2_indptr.shape[0] - 1
+            intp_t x1_start, x1_end, x2_start, x2_end
+
+        for i1 in range(n_x1):
+            x1_start = x1_indptr[i1]
+            x1_end = x1_indptr[i1 + 1]
+            for i2 in range(n_x2):
+                x2_start = x2_indptr[i2]
+                x2_end = x2_indptr[i2 + 1]
+
+                D[i1, i2] = self.dist_csr(
+                    x1_data,
+                    &x1_indices[0],
+                    x2_data,
+                    &x2_indices[0],
+                    x1_start,
+                    x1_end,
+                    x2_start,
+                    x2_end,
+                    size,
+                )
+        return 0
+
+    cdef {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        """Convert the rank-preserving surrogate distance to the distance"""
+        return rdist
+
+    cdef {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        """Convert the distance to the rank-preserving surrogate distance"""
+        return dist
+
+    def rdist_to_dist(self, rdist):
+        """Convert the rank-preserving surrogate distance to the distance.
+
+        The surrogate distance is any measure that yields the same rank as the
+        distance, but is more efficient to compute. For example, the
+        rank-preserving surrogate distance of the Euclidean metric is the
+        squared-euclidean distance.
+
+        Parameters
+        ----------
+        rdist : double
+            Surrogate distance.
+
+        Returns
+        -------
+        double
+            True distance.
+        """
+        return rdist
+
+    def dist_to_rdist(self, dist):
+        """Convert the true distance to the rank-preserving surrogate distance.
+
+        The surrogate distance is any measure that yields the same rank as the
+        distance, but is more efficient to compute. For example, the
+        rank-preserving surrogate distance of the Euclidean metric is the
+        squared-euclidean distance.
+
+        Parameters
+        ----------
+        dist : double
+            True distance.
+
+        Returns
+        -------
+        double
+            Surrogate distance.
+        """
+        return dist
+
+    def _pairwise_dense_dense(self, X, Y):
+        cdef const {{INPUT_DTYPE_t}}[:, ::1] Xarr
+        cdef const {{INPUT_DTYPE_t}}[:, ::1] Yarr
+        cdef {{INPUT_DTYPE_t}}[:, ::1] Darr
+
+        Xarr = np.asarray(X, dtype={{INPUT_DTYPE}}, order='C')
+        self._validate_data(Xarr)
+        if X is Y:
+            Darr = np.empty((Xarr.shape[0], Xarr.shape[0]), dtype={{INPUT_DTYPE}}, order='C')
+            self.pdist(Xarr, Darr)
+        else:
+            Yarr = np.asarray(Y, dtype={{INPUT_DTYPE}}, order='C')
+            self._validate_data(Yarr)
+            Darr = np.empty((Xarr.shape[0], Yarr.shape[0]), dtype={{INPUT_DTYPE}}, order='C')
+            self.cdist(Xarr, Yarr, Darr)
+        return np.asarray(Darr)
+
+    def _pairwise_sparse_sparse(self, X: csr_matrix , Y: csr_matrix):
+        cdef:
+            intp_t n_X, n_features
+            const {{INPUT_DTYPE_t}}[::1] X_data
+            const int32_t[::1] X_indices
+            const int32_t[::1] X_indptr
+
+            intp_t n_Y
+            const {{INPUT_DTYPE_t}}[::1] Y_data
+            const int32_t[::1] Y_indices
+            const int32_t[::1] Y_indptr
+
+            {{INPUT_DTYPE_t}}[:, ::1] Darr
+
+        X_csr = X.tocsr()
+        n_X, n_features = X_csr.shape
+        X_data = np.asarray(X_csr.data, dtype={{INPUT_DTYPE}})
+        X_indices = np.asarray(X_csr.indices, dtype=np.int32)
+        X_indptr = np.asarray(X_csr.indptr, dtype=np.int32)
+        if X is Y:
+            Darr = np.empty((n_X, n_X), dtype={{INPUT_DTYPE}}, order='C')
+            self.pdist_csr(
+                x1_data=&X_data[0],
+                x1_indices=X_indices,
+                x1_indptr=X_indptr,
+                size=n_features,
+                D=Darr,
+            )
+        else:
+            Y_csr = Y.tocsr()
+            n_Y, _ = Y_csr.shape
+            Y_data = np.asarray(Y_csr.data, dtype={{INPUT_DTYPE}})
+            Y_indices = np.asarray(Y_csr.indices, dtype=np.int32)
+            Y_indptr = np.asarray(Y_csr.indptr, dtype=np.int32)
+
+            Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
+            self.cdist_csr(
+                x1_data=&X_data[0],
+                x1_indices=X_indices,
+                x1_indptr=X_indptr,
+                x2_data=&Y_data[0],
+                x2_indices=Y_indices,
+                x2_indptr=Y_indptr,
+                size=n_features,
+                D=Darr,
+            )
+        return np.asarray(Darr)
+
+    def _pairwise_sparse_dense(self, X: csr_matrix, Y):
+        cdef:
+            intp_t n_X = X.shape[0]
+            intp_t n_features = X.shape[1]
+            const {{INPUT_DTYPE_t}}[::1] X_data = np.asarray(
+                X.data, dtype={{INPUT_DTYPE}},
+            )
+            const int32_t[::1] X_indices = np.asarray(
+                X.indices, dtype=np.int32,
+            )
+            const int32_t[::1] X_indptr = np.asarray(
+                X.indptr, dtype=np.int32,
+            )
+
+            const {{INPUT_DTYPE_t}}[:, ::1] Y_data = np.asarray(
+                Y, dtype={{INPUT_DTYPE}}, order="C",
+            )
+            intp_t n_Y = Y_data.shape[0]
+            const int32_t[::1] Y_indices = (
+                np.arange(n_features, dtype=np.int32)
+            )
+
+            {{INPUT_DTYPE_t}}[:, ::1] Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
+
+            intp_t i1, i2
+            intp_t x1_start, x1_end
+            {{INPUT_DTYPE_t}} * x2_data
+
+        with nogil:
+            # Use the exact same adaptation for CSR than in SparseDenseDatasetsPair
+            # for supporting the sparse-dense case with minimal overhead.
+            # Note: at this point this method is only a convenience method
+            # used in the tests via the DistanceMetric.pairwise method.
+            # Therefore, there is no need to attempt parallelization of those
+            # nested for-loops.
+            # Efficient parallel computation of pairwise distances can be
+            # achieved via the PairwiseDistances class instead. The latter
+            # internally calls into vector-wise distance computation from
+            # the DistanceMetric subclass while benefiting from the generic
+            # Cython/OpenMP parallelization template for the generic pairwise
+            # distance + reduction computational pattern.
+            for i1 in range(n_X):
+                x1_start = X_indptr[i1]
+                x1_end = X_indptr[i1 + 1]
+                for i2 in range(n_Y):
+                    x2_data = &Y_data[0, 0] + i2 * n_features
+
+                    Darr[i1, i2] = self.dist_csr(
+                        x1_data=&X_data[0],
+                        x1_indices=&X_indices[0],
+                        x2_data=x2_data,
+                        x2_indices=&Y_indices[0],
+                        x1_start=x1_start,
+                        x1_end=x1_end,
+                        x2_start=0,
+                        x2_end=n_features,
+                        size=n_features,
+                    )
+
+        return np.asarray(Darr)
+
+    def _pairwise_dense_sparse(self, X, Y: csr_matrix):
+        # We could have implemented this method using _pairwise_dense_sparse by
+        # swapping argument and by transposing the results, but this would
+        # have come with an extra copy to ensure C-contiguity of the result.
+        cdef:
+            intp_t n_X = X.shape[0]
+            intp_t n_features = X.shape[1]
+
+            const {{INPUT_DTYPE_t}}[:, ::1] X_data = np.asarray(
+                X, dtype={{INPUT_DTYPE}}, order="C",
+            )
+            const int32_t[::1] X_indices = np.arange(
+                n_features, dtype=np.int32,
+            )
+
+            intp_t n_Y = Y.shape[0]
+            const {{INPUT_DTYPE_t}}[::1] Y_data = np.asarray(
+                Y.data, dtype={{INPUT_DTYPE}},
+            )
+            const int32_t[::1] Y_indices = np.asarray(
+                Y.indices, dtype=np.int32,
+            )
+            const int32_t[::1] Y_indptr = np.asarray(
+                Y.indptr, dtype=np.int32,
+            )
+
+            {{INPUT_DTYPE_t}}[:, ::1] Darr = np.empty((n_X, n_Y), dtype={{INPUT_DTYPE}}, order='C')
+
+            intp_t i1, i2
+            {{INPUT_DTYPE_t}} * x1_data
+
+            intp_t x2_start, x2_end
+
+        with nogil:
+            # Use the exact same adaptation for CSR than in SparseDenseDatasetsPair
+            # for supporting the dense-sparse case with minimal overhead.
+            # Note: at this point this method is only a convenience method
+            # used in the tests via the DistanceMetric.pairwise method.
+            # Therefore, there is no need to attempt parallelization of those
+            # nested for-loops.
+            # Efficient parallel computation of pairwise distances can be
+            # achieved via the PairwiseDistances class instead. The latter
+            # internally calls into vector-wise distance computation from
+            # the DistanceMetric subclass while benefiting from the generic
+            # Cython/OpenMP parallelization template for the generic pairwise
+            # distance + reduction computational pattern.
+            for i1 in range(n_X):
+                x1_data = &X_data[0, 0] + i1 * n_features
+                for i2 in range(n_Y):
+                    x2_start = Y_indptr[i2]
+                    x2_end = Y_indptr[i2 + 1]
+
+                    Darr[i1, i2] = self.dist_csr(
+                        x1_data=x1_data,
+                        x1_indices=&X_indices[0],
+                        x2_data=&Y_data[0],
+                        x2_indices=&Y_indices[0],
+                        x1_start=0,
+                        x1_end=n_features,
+                        x2_start=x2_start,
+                        x2_end=x2_end,
+                        size=n_features,
+                    )
+
+        return np.asarray(Darr)
+
+
+    def pairwise(self, X, Y=None):
+        """Compute the pairwise distances between X and Y
+
+        This is a convenience routine for the sake of testing.  For many
+        metrics, the utilities in scipy.spatial.distance.cdist and
+        scipy.spatial.distance.pdist will be faster.
+
+        Parameters
+        ----------
+        X : ndarray or CSR matrix of shape (n_samples_X, n_features)
+            Input data.
+        Y : ndarray or CSR matrix of shape (n_samples_Y, n_features)
+            Input data.
+            If not specified, then Y=X.
+
+        Returns
+        -------
+        dist : ndarray of shape  (n_samples_X, n_samples_Y)
+            The distance matrix of pairwise distances between points in X and Y.
+        """
+        X = check_array(X, accept_sparse=['csr'])
+
+        if Y is None:
+            Y = X
+        else:
+            Y = check_array(Y, accept_sparse=['csr'])
+
+        X_is_sparse = issparse(X)
+        Y_is_sparse = issparse(Y)
+
+        if not X_is_sparse and not Y_is_sparse:
+            return self._pairwise_dense_dense(X, Y)
+
+        if X_is_sparse and Y_is_sparse:
+            return self._pairwise_sparse_sparse(X, Y)
+
+        if X_is_sparse and not Y_is_sparse:
+            return self._pairwise_sparse_dense(X, Y)
+
+        return self._pairwise_dense_sparse(X, Y)
+
+#------------------------------------------------------------
+# Euclidean Distance
+#  d = sqrt(sum(x_i^2 - y_i^2))
+cdef class EuclideanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Euclidean Distance metric
+
+    .. math::
+       D(x, y) = \sqrt{ \sum_i (x_i - y_i) ^ 2 }
+    """
+    def __init__(self):
+        self.p = 2
+
+    cdef inline {{INPUT_DTYPE_t}} dist(self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return euclidean_dist{{name_suffix}}(x1, x2, size)
+
+    cdef inline {{INPUT_DTYPE_t}} rdist(self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return euclidean_rdist{{name_suffix}}(x1, x2, size)
+
+    cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        return sqrt(rdist)
+
+    cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        return dist * dist
+
+    def rdist_to_dist(self, rdist):
+        return np.sqrt(rdist)
+
+    def dist_to_rdist(self, dist):
+        return dist ** 2
+
+    cdef inline {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+            float64_t unsquared = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                unsquared = x1_data[i1] - x2_data[i2]
+                d = d + (unsquared * unsquared)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                unsquared = x1_data[i1]
+                d = d + (unsquared * unsquared)
+                i1 = i1 + 1
+            else:
+                unsquared = x2_data[i2]
+                d = d + (unsquared * unsquared)
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                unsquared = x2_data[i2]
+                d = d + (unsquared * unsquared)
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                unsquared = x1_data[i1]
+                d = d + (unsquared * unsquared)
+                i1 = i1 + 1
+
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        return sqrt(
+            self.rdist_csr(
+            x1_data,
+            x1_indices,
+            x2_data,
+            x2_indices,
+            x1_start,
+            x1_end,
+            x2_start,
+            x2_end,
+            size,
+        ))
+
+#------------------------------------------------------------
+# SEuclidean Distance
+#  d = sqrt(sum((x_i - y_i2)^2 / v_i))
+cdef class SEuclideanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Standardized Euclidean Distance metric
+
+    .. math::
+       D(x, y) = \sqrt{ \sum_i \frac{ (x_i - y_i) ^ 2}{V_i} }
+    """
+    def __init__(self, V):
+        self.vec = np.asarray(V, dtype=np.float64)
+        self.size = self.vec.shape[0]
+        self.p = 2
+
+    def _validate_data(self, X):
+        if X.shape[1] != self.size:
+            raise ValueError('SEuclidean dist: size of V does not match')
+
+    cdef inline {{INPUT_DTYPE_t}} rdist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t tmp, d=0
+        cdef intp_t j
+        for j in range(size):
+            tmp = x1[j] - x2[j]
+            d += (tmp * tmp / self.vec[j])
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return sqrt(self.rdist(x1, x2, size))
+
+    cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        return sqrt(rdist)
+
+    cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        return dist * dist
+
+    def rdist_to_dist(self, rdist):
+        return np.sqrt(rdist)
+
+    def dist_to_rdist(self, dist):
+        return dist ** 2
+
+    cdef inline {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+            float64_t unsquared = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                unsquared = x1_data[i1] - x2_data[i2]
+                d = d + (unsquared * unsquared) / self.vec[ix1]
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                unsquared = x1_data[i1]
+                d = d + (unsquared * unsquared) / self.vec[ix1]
+                i1 = i1 + 1
+            else:
+                unsquared = x2_data[i2]
+                d = d + (unsquared * unsquared) / self.vec[ix2]
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                ix2 = x2_indices[i2]
+                unsquared = x2_data[i2]
+                d = d + (unsquared * unsquared) / self.vec[ix2]
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                ix1 = x1_indices[i1]
+                unsquared = x1_data[i1]
+                d = d + (unsquared * unsquared) / self.vec[ix1]
+                i1 = i1 + 1
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        return sqrt(
+            self.rdist_csr(
+            x1_data,
+            x1_indices,
+            x2_data,
+            x2_indices,
+            x1_start,
+            x1_end,
+            x2_start,
+            x2_end,
+            size,
+        ))
+
+#------------------------------------------------------------
+# Manhattan Distance
+#  d = sum(abs(x_i - y_i))
+cdef class ManhattanDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Manhattan/City-block Distance metric
+
+    .. math::
+       D(x, y) = \sum_i |x_i - y_i|
+    """
+    def __init__(self):
+        self.p = 1
+
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t d = 0
+        cdef intp_t j
+        for j in range(size):
+            d += fabs(x1[j] - x2[j])
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            {{INPUT_DTYPE_t}} d = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                d = d + fabs(x1_data[i1] - x2_data[i2])
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                d = d + fabs(x1_data[i1])
+                i1 = i1 + 1
+            else:
+                d = d + fabs(x2_data[i2])
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                d = d + fabs(x2_data[i2])
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                d = d + fabs(x1_data[i1])
+                i1 = i1 + 1
+
+        return d
+
+
+#------------------------------------------------------------
+# Chebyshev Distance
+#  d = max_i(abs(x_i - y_i))
+cdef class ChebyshevDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    """Chebyshev/Infinity Distance
+
+    .. math::
+       D(x, y) = max_i (|x_i - y_i|)
+
+    Examples
+    --------
+    >>> from sklearn.metrics.dist_metrics import DistanceMetric
+    >>> dist = DistanceMetric.get_metric('chebyshev')
+    >>> X = [[0, 1, 2],
+    ...      [3, 4, 5]]
+    >>> Y = [[-1, 0, 1],
+    ...      [3, 4, 5]]
+    >>> dist.pairwise(X, Y)
+    array([[1.732..., 5.196...],
+           [6.928..., 0....   ]])
+    """
+    def __init__(self):
+        self.p = INF{{name_suffix}}
+
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t d = 0
+        cdef intp_t j
+        for j in range(size):
+            d = fmax(d, fabs(x1[j] - x2[j]))
+        return d
+
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                d = fmax(d, fabs(x1_data[i1] - x2_data[i2]))
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                d = fmax(d, fabs(x1_data[i1]))
+                i1 = i1 + 1
+            else:
+                d = fmax(d, fabs(x2_data[i2]))
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                d = fmax(d, fabs(x2_data[i2]))
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                d = fmax(d, fabs(x1_data[i1]))
+                i1 = i1 + 1
+
+        return d
+
+
+#------------------------------------------------------------
+# Minkowski Distance
+cdef class MinkowskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Minkowski Distance
+
+    .. math::
+        D(x, y) = {||u-v||}_p
+
+    when w is None.
+
+    Here is the more general expanded expression for the weighted case:
+
+    .. math::
+        D(x, y) = [\sum_i w_i *|x_i - y_i|^p] ^ (1/p)
+
+    Parameters
+    ----------
+    p : float
+        The order of the p-norm of the difference (see above).
+
+        .. versionchanged:: 1.4.0
+            Minkowski distance allows `p` to be `0 0 and finite.
+    When :math:`p \in (0,1)`, it isn't a true metric but is permissible when
+    the triangular inequality isn't necessary.
+    For p = infinity, use ChebyshevDistance.
+    Note that for p=1, ManhattanDistance is more efficient, and for
+    p=2, EuclideanDistance is more efficient.
+
+    """
+    def __init__(self, p, w=None):
+        if p <= 0:
+            raise ValueError("p must be greater than 0")
+        elif np.isinf(p):
+            raise ValueError("MinkowskiDistance requires finite p. "
+                             "For p=inf, use ChebyshevDistance.")
+
+        self.p = p
+        if w is not None:
+            w_array = check_array(
+                w, ensure_2d=False, dtype=np.float64, input_name="w"
+            )
+            if (w_array < 0).any():
+                raise ValueError("w cannot contain negative weights")
+            self.vec = w_array
+            self.size = self.vec.shape[0]
+        else:
+            self.vec = np.asarray([], dtype=np.float64)
+            self.size = 0
+
+    def _validate_data(self, X):
+        if self.size > 0 and X.shape[1] != self.size:
+            raise ValueError("MinkowskiDistance: the size of w must match "
+                             f"the number of features ({X.shape[1]}). "
+                             f"Currently len(w)={self.size}.")
+
+    cdef inline {{INPUT_DTYPE_t}} rdist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t d=0
+        cdef intp_t j
+        cdef bint has_w = self.size > 0
+        if has_w:
+            for j in range(size):
+                d += (self.vec[j] * pow(fabs(x1[j] - x2[j]), self.p))
+        else:
+            for j in range(size):
+                d += (pow(fabs(x1[j] - x2[j]), self.p))
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return pow(self.rdist(x1, x2, size), 1. / self.p)
+
+    cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        return pow(rdist, 1. / self.p)
+
+    cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        return pow(dist, self.p)
+
+    def rdist_to_dist(self, rdist):
+        return rdist ** (1. / self.p)
+
+    def dist_to_rdist(self, dist):
+        return dist ** self.p
+
+    cdef inline {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+            bint has_w = self.size > 0
+
+        if has_w:
+            while i1 < x1_end and i2 < x2_end:
+                ix1 = x1_indices[i1]
+                ix2 = x2_indices[i2]
+
+                if ix1 == ix2:
+                    d = d + (self.vec[ix1] * pow(fabs(
+                        x1_data[i1] - x2_data[i2]
+                    ), self.p))
+                    i1 = i1 + 1
+                    i2 = i2 + 1
+                elif ix1 < ix2:
+                    d = d + (self.vec[ix1] * pow(fabs(x1_data[i1]), self.p))
+                    i1 = i1 + 1
+                else:
+                    d = d + (self.vec[ix2] * pow(fabs(x2_data[i2]), self.p))
+                    i2 = i2 + 1
+
+            if i1 == x1_end:
+                while i2 < x2_end:
+                    ix2 = x2_indices[i2]
+                    d = d + (self.vec[ix2] * pow(fabs(x2_data[i2]), self.p))
+                    i2 = i2 + 1
+            else:
+                while i1 < x1_end:
+                    ix1 = x1_indices[i1]
+                    d = d + (self.vec[ix1] * pow(fabs(x1_data[i1]), self.p))
+                    i1 = i1 + 1
+
+            return d
+        else:
+            while i1 < x1_end and i2 < x2_end:
+                ix1 = x1_indices[i1]
+                ix2 = x2_indices[i2]
+
+                if ix1 == ix2:
+                    d = d + (pow(fabs(
+                        x1_data[i1] - x2_data[i2]
+                    ), self.p))
+                    i1 = i1 + 1
+                    i2 = i2 + 1
+                elif ix1 < ix2:
+                    d = d + (pow(fabs(x1_data[i1]), self.p))
+                    i1 = i1 + 1
+                else:
+                    d = d + (pow(fabs(x2_data[i2]), self.p))
+                    i2 = i2 + 1
+
+            if i1 == x1_end:
+                while i2 < x2_end:
+                    d = d + (pow(fabs(x2_data[i2]), self.p))
+                    i2 = i2 + 1
+            else:
+                while i1 < x1_end:
+                    d = d + (pow(fabs(x1_data[i1]), self.p))
+                    i1 = i1 + 1
+
+            return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        return pow(
+            self.rdist_csr(
+                x1_data,
+                x1_indices,
+                x2_data,
+                x2_indices,
+                x1_start,
+                x1_end,
+                x2_start,
+                x2_end,
+                size,
+            ),
+            1 / self.p
+        )
+
+#------------------------------------------------------------
+# Mahalanobis Distance
+#  d = sqrt( (x - y)^T V^-1 (x - y) )
+cdef class MahalanobisDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    """Mahalanobis Distance
+
+    .. math::
+       D(x, y) = \sqrt{ (x - y)^T V^{-1} (x - y) }
+
+    Parameters
+    ----------
+    V : array-like
+        Symmetric positive-definite covariance matrix.
+        The inverse of this matrix will be explicitly computed.
+    VI : array-like
+        optionally specify the inverse directly.  If VI is passed,
+        then V is not referenced.
+    """
+    cdef float64_t[::1] buffer
+
+    def __init__(self, V=None, VI=None):
+        if VI is None:
+            if V is None:
+                raise ValueError("Must provide either V or VI "
+                                 "for Mahalanobis distance")
+            VI = np.linalg.inv(V)
+        if VI.ndim != 2 or VI.shape[0] != VI.shape[1]:
+            raise ValueError("V/VI must be square")
+
+        self.mat = np.asarray(VI, dtype=np.float64, order='C')
+
+        self.size = self.mat.shape[0]
+
+        # We need to create a buffer to store the vectors' coordinates' differences
+        self.buffer = np.zeros(self.size, dtype=np.float64)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self.size = self.mat.shape[0]
+        self.buffer = np.zeros(self.size, dtype=np.float64)
+
+    def _validate_data(self, X):
+        if X.shape[1] != self.size:
+            raise ValueError('Mahalanobis dist: size of V does not match')
+
+    cdef inline {{INPUT_DTYPE_t}} rdist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t tmp, d = 0
+        cdef intp_t i, j
+
+        # compute (x1 - x2).T * VI * (x1 - x2)
+        for i in range(size):
+            self.buffer[i] = x1[i] - x2[i]
+
+        for i in range(size):
+            tmp = 0
+            for j in range(size):
+                tmp += self.mat[i, j] * self.buffer[j]
+            d += tmp * self.buffer[i]
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return sqrt(self.rdist(x1, x2, size))
+
+    cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        return sqrt(rdist)
+
+    cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        return dist * dist
+
+    def rdist_to_dist(self, rdist):
+        return np.sqrt(rdist)
+
+    def dist_to_rdist(self, dist):
+        return dist ** 2
+
+    cdef inline {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t tmp, d = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                self.buffer[ix1] = x1_data[i1] - x2_data[i2]
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                self.buffer[ix1] = x1_data[i1]
+                i1 = i1 + 1
+            else:
+                self.buffer[ix2] = - x2_data[i2]
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                ix2 = x2_indices[i2]
+                self.buffer[ix2] = - x2_data[i2]
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                ix1 = x1_indices[i1]
+                self.buffer[ix1] = x1_data[i1]
+                i1 = i1 + 1
+
+        for i in range(size):
+            tmp = 0
+            for j in range(size):
+                tmp += self.mat[i, j] * self.buffer[j]
+            d += tmp * self.buffer[i]
+
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        return sqrt(
+            self.rdist_csr(
+            x1_data,
+            x1_indices,
+            x2_data,
+            x2_indices,
+            x1_start,
+            x1_end,
+            x2_start,
+            x2_end,
+            size,
+        ))
+
+#------------------------------------------------------------
+# Hamming Distance
+#  d = N_unequal(x, y) / N_tot
+cdef class HammingDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Hamming Distance
+
+    Hamming distance is meant for discrete-valued vectors, though it is
+    a valid metric for real-valued vectors.
+
+    .. math::
+       D(x, y) = \frac{1}{N} \sum_i \delta_{x_i, y_i}
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int n_unequal = 0
+        cdef intp_t j
+        for j in range(size):
+            if x1[j] != x2[j]:
+                n_unequal += 1
+        return float(n_unequal) / size
+
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                d += (x1_data[i1] != x2_data[i2])
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                d += (x1_data[i1] != 0)
+                i1 = i1 + 1
+            else:
+                d += (x2_data[i2] != 0)
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                d += (x2_data[i2] != 0)
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                d += (x1_data[i1] != 0)
+                i1 = i1 + 1
+
+        d /= size
+
+        return d
+
+
+#------------------------------------------------------------
+# Canberra Distance
+#  D(x, y) = sum[ abs(x_i - y_i) / (abs(x_i) + abs(y_i)) ]
+cdef class CanberraDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Canberra Distance
+
+    Canberra distance is meant for discrete-valued vectors, though it is
+    a valid metric for real-valued vectors.
+
+    .. math::
+       D(x, y) = \sum_i \frac{|x_i - y_i|}{|x_i| + |y_i|}
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t denom, d = 0
+        cdef intp_t j
+        for j in range(size):
+            denom = fabs(x1[j]) + fabs(x2[j])
+            if denom > 0:
+                d += fabs(x1[j] - x2[j]) / denom
+        return d
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t d = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                d += (
+                        fabs(x1_data[i1] - x2_data[i2]) /
+                        (fabs(x1_data[i1]) + fabs(x2_data[i2]))
+                )
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                d += 1.
+                i1 = i1 + 1
+            else:
+                d += 1.
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                d += 1.
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                d += 1.
+                i1 = i1 + 1
+
+        return d
+
+#------------------------------------------------------------
+# Bray-Curtis Distance
+#  D(x, y) = sum[abs(x_i - y_i)] / sum[abs(x_i) + abs(y_i)]
+cdef class BrayCurtisDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Bray-Curtis Distance
+
+    Bray-Curtis distance is meant for discrete-valued vectors, though it is
+    a valid metric for real-valued vectors.
+
+    .. math::
+       D(x, y) = \frac{\sum_i |x_i - y_i|}{\sum_i(|x_i| + |y_i|)}
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t num = 0, denom = 0
+        cdef intp_t j
+        for j in range(size):
+            num += fabs(x1[j] - x2[j])
+            denom += fabs(x1[j]) + fabs(x2[j])
+        if denom > 0:
+            return num / denom
+        else:
+            return 0.0
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t num = 0.0
+            float64_t denom = 0.0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                num += fabs(x1_data[i1] - x2_data[i2])
+                denom += fabs(x1_data[i1]) + fabs(x2_data[i2])
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                num += fabs(x1_data[i1])
+                denom += fabs(x1_data[i1])
+                i1 = i1 + 1
+            else:
+                num += fabs(x2_data[i2])
+                denom += fabs(x2_data[i2])
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                num += fabs(x1_data[i1])
+                denom += fabs(x1_data[i1])
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                num += fabs(x2_data[i2])
+                denom += fabs(x2_data[i2])
+                i1 = i1 + 1
+
+        return num / denom
+
+#------------------------------------------------------------
+# Jaccard Distance (boolean)
+#  D(x, y) = N_unequal(x, y) / N_nonzero(x, y)
+cdef class JaccardDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Jaccard Distance
+
+    Jaccard Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = (N_TF + N_FT) / (N_TT + N_TF + N_FT)
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_eq = 0, nnz = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            nnz += (tf1 or tf2)
+            n_eq += (tf1 and tf2)
+        # Based on https://github.com/scipy/scipy/pull/7373
+        # When comparing two all-zero vectors, scipy>=1.2.0 jaccard metric
+        # was changed to return 0, instead of nan.
+        if nnz == 0:
+            return 0
+        return (nnz - n_eq) * 1.0 / nnz
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_tt = 0, nnz = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                nnz += (tf1 or tf2)
+                n_tt += (tf1 and tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                nnz += tf1
+                i1 = i1 + 1
+            else:
+                nnz += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                nnz += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                nnz += tf1
+                i1 = i1 + 1
+
+        # Based on https://github.com/scipy/scipy/pull/7373
+        # When comparing two all-zero vectors, scipy>=1.2.0 jaccard metric
+        # was changed to return 0, instead of nan.
+        if nnz == 0:
+            return 0
+        return (nnz - n_tt) * 1.0 / nnz
+
+#------------------------------------------------------------
+# Matching Distance (boolean)
+#  D(x, y) = n_neq / n
+cdef class MatchingDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Matching Distance
+
+    Matching Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = (N_TF + N_FT) / N
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_neq = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_neq += (tf1 != tf2)
+        return n_neq * 1. / size
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            if ix1 == ix2:
+                tf1 = x1_data[i1] != 0
+                tf2 = x2_data[i2] != 0
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += (x1_data[i1] != 0)
+                i1 = i1 + 1
+            else:
+                n_neq += (x2_data[i2] != 0)
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                n_neq += (x2_data[i2] != 0)
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                n_neq += (x1_data[i1] != 0)
+                i1 = i1 + 1
+
+        return n_neq * 1.0 / size
+
+#------------------------------------------------------------
+# Dice Distance (boolean)
+#  D(x, y) = n_neq / (2 * ntt + n_neq)
+cdef class DiceDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Dice Distance
+
+    Dice Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = (N_TF + N_FT) / (2 * N_TT + N_TF + N_FT)
+
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_neq = 0, n_tt = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_tt += (tf1 and tf2)
+            n_neq += (tf1 != tf2)
+        return n_neq / (2.0 * n_tt + n_neq)
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_tt = 0, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_tt += (tf1 and tf2)
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += tf1
+                i1 = i1 + 1
+            else:
+                n_neq += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                n_neq += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                n_neq += tf1
+                i1 = i1 + 1
+
+        return n_neq / (2.0 * n_tt + n_neq)
+
+
+#------------------------------------------------------------
+# Kulsinski Distance (boolean)
+#  D(x, y) = (ntf + nft - ntt + n) / (n_neq + n)
+cdef class KulsinskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Kulsinski Distance
+
+    Kulsinski Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = 1 - N_TT / (N + N_TF + N_FT)
+
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_tt = 0, n_neq = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_neq += (tf1 != tf2)
+            n_tt += (tf1 and tf2)
+        return (n_neq - n_tt + size) * 1.0 / (n_neq + size)
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_tt = 0, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_tt += (tf1 and tf2)
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += tf1
+                i1 = i1 + 1
+            else:
+                n_neq += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                n_neq += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                n_neq += tf1
+                i1 = i1 + 1
+
+        return (n_neq - n_tt + size) * 1.0 / (n_neq + size)
+
+#------------------------------------------------------------
+# Rogers-Tanimoto Distance (boolean)
+#  D(x, y) = 2 * n_neq / (n + n_neq)
+cdef class RogersTanimotoDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Rogers-Tanimoto Distance
+
+    Rogers-Tanimoto Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = 2 (N_TF + N_FT) / (N + N_TF + N_FT)
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_neq = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_neq += (tf1 != tf2)
+        return (2.0 * n_neq) / (size + n_neq)
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += tf1
+                i1 = i1 + 1
+            else:
+                n_neq += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                n_neq += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                n_neq += tf1
+                i1 = i1 + 1
+
+        return (2.0 * n_neq) / (size + n_neq)
+
+#------------------------------------------------------------
+# Russell-Rao Distance (boolean)
+#  D(x, y) = (n - ntt) / n
+cdef class RussellRaoDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Russell-Rao Distance
+
+    Russell-Rao Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = (N - N_TT) / N
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_tt = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_tt += (tf1 and tf2)
+        return (size - n_tt) * 1. / size
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_tt = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_tt += (tf1 and tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                i1 = i1 + 1
+            else:
+                i2 = i2 + 1
+
+        # We don't need to go through all the longest
+        # vector because tf1 or tf2 will be false
+        # and thus n_tt won't be increased.
+
+        return (size - n_tt) * 1. / size
+
+
+
+#------------------------------------------------------------
+# Sokal-Michener Distance (boolean)
+#  D(x, y) = 2 * n_neq / (n + n_neq)
+cdef class SokalMichenerDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Sokal-Michener Distance
+
+    Sokal-Michener Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = 2 (N_TF + N_FT) / (N + N_TF + N_FT)
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_neq = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_neq += (tf1 != tf2)
+        return (2.0 * n_neq) / (size + n_neq)
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += tf1
+                i1 = i1 + 1
+            else:
+                n_neq += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                n_neq += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                n_neq += tf1
+                i1 = i1 + 1
+
+        return (2.0 * n_neq) / (size + n_neq)
+
+#------------------------------------------------------------
+# Sokal-Sneath Distance (boolean)
+#  D(x, y) = n_neq / (0.5 * n_tt + n_neq)
+cdef class SokalSneathDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    r"""Sokal-Sneath Distance
+
+    Sokal-Sneath Distance is a dissimilarity measure for boolean-valued
+    vectors. All nonzero entries will be treated as True, zero entries will
+    be treated as False.
+
+        D(x, y) = (N_TF + N_FT) / (N_TT / 2 + N_FT + N_TF)
+    """
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef int tf1, tf2, n_tt = 0, n_neq = 0
+        cdef intp_t j
+        for j in range(size):
+            tf1 = x1[j] != 0
+            tf2 = x2[j] != 0
+            n_neq += (tf1 != tf2)
+            n_tt += (tf1 and tf2)
+        return n_neq / (0.5 * n_tt + n_neq)
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            intp_t tf1, tf2, n_tt = 0, n_neq = 0
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            tf1 = x1_data[i1] != 0
+            tf2 = x2_data[i2] != 0
+
+            if ix1 == ix2:
+                n_tt += (tf1 and tf2)
+                n_neq += (tf1 != tf2)
+                i1 = i1 + 1
+                i2 = i2 + 1
+            elif ix1 < ix2:
+                n_neq += tf1
+                i1 = i1 + 1
+            else:
+                n_neq += tf2
+                i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                tf2 = x2_data[i2] != 0
+                n_neq += tf2
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                tf1 = x1_data[i1] != 0
+                n_neq += tf1
+                i1 = i1 + 1
+
+        return n_neq / (0.5 * n_tt + n_neq)
+
+
+#------------------------------------------------------------
+# Haversine Distance (2 dimensional)
+#  D(x, y) = 2 arcsin{sqrt[sin^2 ((x1 - y1) / 2)
+#                          + cos(x1) cos(y1) sin^2 ((x2 - y2) / 2)]}
+cdef class HaversineDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    """Haversine (Spherical) Distance
+
+    The Haversine distance is the angular distance between two points on
+    the surface of a sphere.  The first distance of each point is assumed
+    to be the latitude, the second is the longitude, given in radians.
+    The dimension of the points must be 2:
+
+    D(x, y) = 2 arcsin[sqrt{sin^2((x1 - y1) / 2) + cos(x1)cos(y1)sin^2((x2 - y2) / 2)}]
+
+    """
+
+    def _validate_data(self, X):
+        if X.shape[1] != 2:
+            raise ValueError("Haversine distance only valid "
+                             "in 2 dimensions")
+
+    cdef inline {{INPUT_DTYPE_t}} rdist(self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        cdef float64_t sin_0 = sin(0.5 * ((x1[0]) - (x2[0])))
+        cdef float64_t sin_1 = sin(0.5 * ((x1[1]) - (x2[1])))
+        return (sin_0 * sin_0 + cos(x1[0]) * cos(x2[0]) * sin_1 * sin_1)
+
+    cdef inline {{INPUT_DTYPE_t}} dist(self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return 2 * asin(sqrt(self.rdist(x1, x2, size)))
+
+    cdef inline {{INPUT_DTYPE_t}} _rdist_to_dist(self, {{INPUT_DTYPE_t}} rdist) except -1 nogil:
+        return 2 * asin(sqrt(rdist))
+
+    cdef inline {{INPUT_DTYPE_t}} _dist_to_rdist(self, {{INPUT_DTYPE_t}} dist) except -1 nogil:
+        cdef float64_t tmp = sin(0.5 *  dist)
+        return tmp * tmp
+
+    def rdist_to_dist(self, rdist):
+        return 2 * np.arcsin(np.sqrt(rdist))
+
+    def dist_to_rdist(self, dist):
+        tmp = np.sin(0.5 * dist)
+        return tmp * tmp
+
+    cdef inline {{INPUT_DTYPE_t}} dist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+        return 2 * asin(sqrt(self.rdist_csr(
+            x1_data,
+            x1_indices,
+            x2_data,
+            x2_indices,
+            x1_start,
+            x1_end,
+            x2_start,
+            x2_end,
+            size,
+        )))
+
+    cdef inline {{INPUT_DTYPE_t}} rdist_csr(
+        self,
+        const {{INPUT_DTYPE_t}}* x1_data,
+        const int32_t* x1_indices,
+        const {{INPUT_DTYPE_t}}* x2_data,
+        const int32_t* x2_indices,
+        const int32_t x1_start,
+        const int32_t x1_end,
+        const int32_t x2_start,
+        const int32_t x2_end,
+        const intp_t size,
+    ) except -1 nogil:
+
+        cdef:
+            intp_t ix1, ix2
+            intp_t i1 = x1_start
+            intp_t i2 = x2_start
+
+            float64_t x1_0 = 0
+            float64_t x1_1 = 0
+            float64_t x2_0 = 0
+            float64_t x2_1 = 0
+            float64_t sin_0
+            float64_t sin_1
+
+        while i1 < x1_end and i2 < x2_end:
+            ix1 = x1_indices[i1]
+            ix2 = x2_indices[i2]
+
+            # Find the components in the 2D vectors to work with
+            x1_component = ix1 if (x1_start == 0) else ix1 % x1_start
+            x2_component = ix2 if (x2_start == 0) else ix2 % x2_start
+
+            if x1_component == 0:
+                x1_0 = x1_data[i1]
+            else:
+                x1_1 = x1_data[i1]
+
+            if x2_component == 0:
+                x2_0 = x2_data[i2]
+            else:
+                x2_1 = x2_data[i2]
+
+            i1 = i1 + 1
+            i2 = i2 + 1
+
+        if i1 == x1_end:
+            while i2 < x2_end:
+                ix2 = x2_indices[i2]
+                x2_component = ix2 if (x2_start == 0) else ix2 % x2_start
+                if x2_component == 0:
+                    x2_0 = x2_data[i2]
+                else:
+                    x2_1 = x2_data[i2]
+                i2 = i2 + 1
+        else:
+            while i1 < x1_end:
+                ix1 = x1_indices[i1]
+                x1_component = ix1 if (x1_start == 0) else ix1 % x1_start
+                if x1_component == 0:
+                    x1_0 = x1_data[i1]
+                else:
+                    x1_1 = x1_data[i1]
+                i1 = i1 + 1
+
+        sin_0 = sin(0.5 * (x1_0 - x2_0))
+        sin_1 = sin(0.5 * (x1_1 - x2_1))
+
+        return (sin_0 * sin_0 + cos(x1_0) * cos(x2_0) * sin_1 * sin_1)
+
+#------------------------------------------------------------
+# User-defined distance
+#
+cdef class PyFuncDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
+    """PyFunc Distance
+
+    A user-defined distance
+
+    Parameters
+    ----------
+    func : function
+        func should take two numpy arrays as input, and return a distance.
+    """
+    def __init__(self, func, **kwargs):
+        self.func = func
+        self.kwargs = kwargs
+
+    # in cython < 0.26, GIL was required to be acquired during definition of
+    # the function and inside the body of the function. This behaviour is not
+    # allowed in cython >= 0.26 since it is a redundant GIL acquisition. The
+    # only way to be back compatible is to inherit `dist` from the base class
+    # without GIL and called an inline `_dist` which acquire GIL.
+    cdef inline {{INPUT_DTYPE_t}} dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 nogil:
+        return self._dist(x1, x2, size)
+
+    cdef inline {{INPUT_DTYPE_t}} _dist(
+        self,
+        const {{INPUT_DTYPE_t}}* x1,
+        const {{INPUT_DTYPE_t}}* x2,
+        intp_t size,
+    ) except -1 with gil:
+        cdef:
+            object x1arr = _buffer_to_ndarray{{name_suffix}}(x1, size)
+            object x2arr = _buffer_to_ndarray{{name_suffix}}(x2, size)
+        d = self.func(x1arr, x2arr, **self.kwargs)
+        try:
+            # Cython generates code here that results in a TypeError
+            # if d is the wrong type.
+            return d
+        except TypeError:
+            raise TypeError("Custom distance function must accept two "
+                            "vectors and return a float.")
+
+{{endfor}}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_pairwise_fast.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_pairwise_fast.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..bf4ded09b2610eef7949cd56e5270b77cb2ce4db
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_pairwise_fast.pyx
@@ -0,0 +1,107 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from cython cimport floating
+from cython.parallel cimport prange
+from libc.math cimport fabs
+
+from ..utils._typedefs cimport intp_t
+
+from ..utils._openmp_helpers import _openmp_effective_n_threads
+
+
+def _chi2_kernel_fast(floating[:, :] X,
+                      floating[:, :] Y,
+                      floating[:, :] result):
+    cdef intp_t i, j, k
+    cdef intp_t n_samples_X = X.shape[0]
+    cdef intp_t n_samples_Y = Y.shape[0]
+    cdef intp_t n_features = X.shape[1]
+    cdef double res, nom, denom
+
+    with nogil:
+        for i in range(n_samples_X):
+            for j in range(n_samples_Y):
+                res = 0
+                for k in range(n_features):
+                    denom = (X[i, k] - Y[j, k])
+                    nom = (X[i, k] + Y[j, k])
+                    if nom != 0:
+                        res += denom * denom / nom
+                result[i, j] = -res
+
+
+def _sparse_manhattan(
+    const floating[::1] X_data,
+    const int[:] X_indices,
+    const int[:] X_indptr,
+    const floating[::1] Y_data,
+    const int[:] Y_indices,
+    const int[:] Y_indptr,
+    double[:, ::1] D,
+):
+    """Pairwise L1 distances for CSR matrices.
+
+    Usage:
+    >>> D = np.zeros(X.shape[0], Y.shape[0])
+    >>> _sparse_manhattan(X.data, X.indices, X.indptr,
+    ...                   Y.data, Y.indices, Y.indptr,
+    ...                   D)
+    """
+    cdef intp_t px, py, i, j, ix, iy
+    cdef double d = 0.0
+
+    cdef int m = D.shape[0]
+    cdef int n = D.shape[1]
+
+    cdef int X_indptr_end = 0
+    cdef int Y_indptr_end = 0
+
+    cdef int num_threads = _openmp_effective_n_threads()
+
+    # We scan the matrices row by row.
+    # Given row px in X and row py in Y, we find the positions (i and j
+    # respectively), in .indices where the indices for the two rows start.
+    # If the indices (ix and iy) are the same, the corresponding data values
+    # are processed and the cursors i and j are advanced.
+    # If not, the lowest index is considered. Its associated data value is
+    # processed and its cursor is advanced.
+    # We proceed like this until one of the cursors hits the end for its row.
+    # Then we process all remaining data values in the other row.
+
+    # Below the avoidance of inplace operators is intentional.
+    # When prange is used, the inplace operator has a special meaning, i.e. it
+    # signals a "reduction"
+
+    for px in prange(m, nogil=True, num_threads=num_threads):
+        X_indptr_end = X_indptr[px + 1]
+        for py in range(n):
+            Y_indptr_end = Y_indptr[py + 1]
+            i = X_indptr[px]
+            j = Y_indptr[py]
+            d = 0.0
+            while i < X_indptr_end and j < Y_indptr_end:
+                ix = X_indices[i]
+                iy = Y_indices[j]
+
+                if ix == iy:
+                    d = d + fabs(X_data[i] - Y_data[j])
+                    i = i + 1
+                    j = j + 1
+                elif ix < iy:
+                    d = d + fabs(X_data[i])
+                    i = i + 1
+                else:
+                    d = d + fabs(Y_data[j])
+                    j = j + 1
+
+            if i == X_indptr_end:
+                while j < Y_indptr_end:
+                    d = d + fabs(Y_data[j])
+                    j = j + 1
+            else:
+                while i < X_indptr_end:
+                    d = d + fabs(X_data[i])
+                    i = i + 1
+
+            D[px, py] = d
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_ranking.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_ranking.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d0e5211c236c703676923a65bfe5df75affef96
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/_ranking.py
@@ -0,0 +1,2077 @@
+"""Metrics to assess performance on classification task given scores.
+
+Functions named as ``*_score`` return a scalar value to maximize: the higher
+the better.
+
+Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
+the lower the better.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from functools import partial
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.integrate import trapezoid
+from scipy.sparse import csr_matrix, issparse
+from scipy.stats import rankdata
+
+from ..exceptions import UndefinedMetricWarning
+from ..preprocessing import label_binarize
+from ..utils import (
+    assert_all_finite,
+    check_array,
+    check_consistent_length,
+    column_or_1d,
+)
+from ..utils._encode import _encode, _unique
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.extmath import stable_cumsum
+from ..utils.multiclass import type_of_target
+from ..utils.sparsefuncs import count_nonzero
+from ..utils.validation import _check_pos_label_consistency, _check_sample_weight
+from ._base import _average_binary_score, _average_multiclass_ovo_score
+
+
+@validate_params(
+    {"x": ["array-like"], "y": ["array-like"]},
+    prefer_skip_nested_validation=True,
+)
+def auc(x, y):
+    """Compute Area Under the Curve (AUC) using the trapezoidal rule.
+
+    This is a general function, given points on a curve.  For computing the
+    area under the ROC-curve, see :func:`roc_auc_score`.  For an alternative
+    way to summarize a precision-recall curve, see
+    :func:`average_precision_score`.
+
+    Parameters
+    ----------
+    x : array-like of shape (n,)
+        X coordinates. These must be either monotonic increasing or monotonic
+        decreasing.
+    y : array-like of shape (n,)
+        Y coordinates.
+
+    Returns
+    -------
+    auc : float
+        Area Under the Curve.
+
+    See Also
+    --------
+    roc_auc_score : Compute the area under the ROC curve.
+    average_precision_score : Compute average precision from prediction scores.
+    precision_recall_curve : Compute precision-recall pairs for different
+        probability thresholds.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn import metrics
+    >>> y_true = np.array([1, 1, 2, 2])
+    >>> y_score = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score, pos_label=2)
+    >>> metrics.auc(fpr, tpr)
+    0.75
+    """
+    check_consistent_length(x, y)
+    x = column_or_1d(x)
+    y = column_or_1d(y)
+
+    if x.shape[0] < 2:
+        raise ValueError(
+            "At least 2 points are needed to compute area under curve, but x.shape = %s"
+            % x.shape
+        )
+
+    direction = 1
+    dx = np.diff(x)
+    if np.any(dx < 0):
+        if np.all(dx <= 0):
+            direction = -1
+        else:
+            raise ValueError("x is neither increasing nor decreasing : {}.".format(x))
+
+    area = direction * trapezoid(y, x)
+    if isinstance(area, np.memmap):
+        # Reductions such as .sum used internally in trapezoid do not return a
+        # scalar by default for numpy.memmap instances contrary to
+        # regular numpy.ndarray instances.
+        area = area.dtype.type(area)
+    return float(area)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "average": [StrOptions({"micro", "samples", "weighted", "macro"}), None],
+        "pos_label": [Real, str, "boolean"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def average_precision_score(
+    y_true, y_score, *, average="macro", pos_label=1, sample_weight=None
+):
+    """Compute average precision (AP) from prediction scores.
+
+    AP summarizes a precision-recall curve as the weighted mean of precisions
+    achieved at each threshold, with the increase in recall from the previous
+    threshold used as the weight:
+
+    .. math::
+        \\text{AP} = \\sum_n (R_n - R_{n-1}) P_n
+
+    where :math:`P_n` and :math:`R_n` are the precision and recall at the nth
+    threshold [1]_. This implementation is not interpolated and is different
+    from computing the area under the precision-recall curve with the
+    trapezoidal rule, which uses linear interpolation and can be too
+    optimistic.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
+        True binary labels or binary label indicators.
+
+    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by :term:`decision_function` on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    average : {'micro', 'samples', 'weighted', 'macro'} or None, \
+            default='macro'
+        If ``None``, the scores for each class are returned. Otherwise,
+        this determines the type of averaging performed on the data:
+
+        ``'micro'``:
+            Calculate metrics globally by considering each element of the label
+            indicator matrix as a label.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average, weighted
+            by support (the number of true instances for each label).
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average.
+
+        Will be ignored when ``y_true`` is binary.
+
+    pos_label : int, float, bool or str, default=1
+        The label of the positive class. Only applied to binary ``y_true``.
+        For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    average_precision : float
+        Average precision score.
+
+    See Also
+    --------
+    roc_auc_score : Compute the area under the ROC curve.
+    precision_recall_curve : Compute precision-recall pairs for different
+        probability thresholds.
+    PrecisionRecallDisplay.from_estimator : Plot the precision recall curve
+        using an estimator and data.
+    PrecisionRecallDisplay.from_predictions : Plot the precision recall curve
+        using true and predicted labels.
+
+    Notes
+    -----
+    .. versionchanged:: 0.19
+      Instead of linearly interpolating between operating points, precisions
+      are weighted by the change in recall since the last operating point.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Average precision
+           `_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import average_precision_score
+    >>> y_true = np.array([0, 0, 1, 1])
+    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> average_precision_score(y_true, y_scores)
+    0.83
+    >>> y_true = np.array([0, 0, 1, 1, 2, 2])
+    >>> y_scores = np.array([
+    ...     [0.7, 0.2, 0.1],
+    ...     [0.4, 0.3, 0.3],
+    ...     [0.1, 0.8, 0.1],
+    ...     [0.2, 0.3, 0.5],
+    ...     [0.4, 0.4, 0.2],
+    ...     [0.1, 0.2, 0.7],
+    ... ])
+    >>> average_precision_score(y_true, y_scores)
+    0.77
+    """
+
+    def _binary_uninterpolated_average_precision(
+        y_true, y_score, pos_label=1, sample_weight=None
+    ):
+        precision, recall, _ = precision_recall_curve(
+            y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
+        )
+        # Return the step function integral
+        # The following works because the last entry of precision is
+        # guaranteed to be 1, as returned by precision_recall_curve.
+        # Due to numerical error, we can get `-0.0` and we therefore clip it.
+        return float(max(0.0, -np.sum(np.diff(recall) * np.array(precision)[:-1])))
+
+    y_type = type_of_target(y_true, input_name="y_true")
+
+    # Convert to Python primitive type to avoid NumPy type / Python str
+    # comparison. See https://github.com/numpy/numpy/issues/6784
+    present_labels = np.unique(y_true).tolist()
+
+    if y_type == "binary":
+        if len(present_labels) == 2 and pos_label not in present_labels:
+            raise ValueError(
+                f"pos_label={pos_label} is not a valid label. It should be "
+                f"one of {present_labels}"
+            )
+
+    elif y_type == "multilabel-indicator" and pos_label != 1:
+        raise ValueError(
+            "Parameter pos_label is fixed to 1 for multilabel-indicator y_true. "
+            "Do not set pos_label or set pos_label to 1."
+        )
+
+    elif y_type == "multiclass":
+        if pos_label != 1:
+            raise ValueError(
+                "Parameter pos_label is fixed to 1 for multiclass y_true. "
+                "Do not set pos_label or set pos_label to 1."
+            )
+        y_true = label_binarize(y_true, classes=present_labels)
+
+    average_precision = partial(
+        _binary_uninterpolated_average_precision, pos_label=pos_label
+    )
+    return _average_binary_score(
+        average_precision, y_true, y_score, average, sample_weight=sample_weight
+    )
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "pos_label": [Real, str, "boolean", None],
+        "sample_weight": ["array-like", None],
+        "drop_intermediate": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def det_curve(
+    y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=False
+):
+    """Compute Detection Error Tradeoff (DET) for different probability thresholds.
+
+    .. note::
+       This metric is used for evaluation of ranking and error tradeoffs of
+       a binary classification task.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.24
+
+    .. versionchanged:: 1.7
+       An arbitrary threshold at infinity is added to represent a classifier
+       that always predicts the negative class, i.e. `fpr=0` and `fnr=1`, unless
+       `fpr=0` is already reached at a finite threshold.
+
+    Parameters
+    ----------
+    y_true : ndarray of shape (n_samples,)
+        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
+        pos_label should be explicitly given.
+
+    y_score : ndarray of shape of (n_samples,)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by "decision_function" on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+        When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
+        ``pos_label`` is set to 1, otherwise an error will be raised.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    drop_intermediate : bool, default=False
+        Whether to drop thresholds where true positives (tp) do not change from
+        the previous or subsequent threshold. All points with the same tp value
+        have the same `fnr` and thus same y coordinate.
+
+        .. versionadded:: 1.7
+
+    Returns
+    -------
+    fpr : ndarray of shape (n_thresholds,)
+        False positive rate (FPR) such that element i is the false positive
+        rate of predictions with score >= thresholds[i]. This is occasionally
+        referred to as false acceptance probability or fall-out.
+
+    fnr : ndarray of shape (n_thresholds,)
+        False negative rate (FNR) such that element i is the false negative
+        rate of predictions with score >= thresholds[i]. This is occasionally
+        referred to as false rejection or miss rate.
+
+    thresholds : ndarray of shape (n_thresholds,)
+        Decreasing thresholds on the decision function (either `predict_proba`
+        or `decision_function`) used to compute FPR and FNR.
+
+        .. versionchanged:: 1.7
+           An arbitrary threshold at infinity is added for the case `fpr=0`
+           and `fnr=1`.
+
+    See Also
+    --------
+    DetCurveDisplay.from_estimator : Plot DET curve given an estimator and
+        some data.
+    DetCurveDisplay.from_predictions : Plot DET curve given the true and
+        predicted labels.
+    DetCurveDisplay : DET curve visualization.
+    roc_curve : Compute Receiver operating characteristic (ROC) curve.
+    precision_recall_curve : Compute precision-recall curve.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import det_curve
+    >>> y_true = np.array([0, 0, 1, 1])
+    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fpr, fnr, thresholds = det_curve(y_true, y_scores)
+    >>> fpr
+    array([0.5, 0.5, 0. ])
+    >>> fnr
+    array([0. , 0.5, 0.5])
+    >>> thresholds
+    array([0.35, 0.4 , 0.8 ])
+    """
+    fps, tps, thresholds = _binary_clf_curve(
+        y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
+    )
+
+    # add a threshold at inf where the clf always predicts the negative class
+    # i.e. tps = fps = 0
+    tps = np.concatenate(([0], tps))
+    fps = np.concatenate(([0], fps))
+    thresholds = np.concatenate(([np.inf], thresholds))
+
+    if drop_intermediate and len(fps) > 2:
+        # Drop thresholds where true positives (tp) do not change from the
+        # previous or subsequent threshold. As tp + fn, is fixed for a dataset,
+        # this means the false negative rate (fnr) remains constant while the
+        # false positive rate (fpr) changes, producing horizontal line segments
+        # in the transformed (normal deviate) scale. These intermediate points
+        # can be dropped to create lighter DET curve plots.
+        optimal_idxs = np.where(
+            np.concatenate(
+                [[True], np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])), [True]]
+            )
+        )[0]
+        fps = fps[optimal_idxs]
+        tps = tps[optimal_idxs]
+        thresholds = thresholds[optimal_idxs]
+
+    if len(np.unique(y_true)) != 2:
+        raise ValueError(
+            "Only one class is present in y_true. Detection error "
+            "tradeoff curve is not defined in that case."
+        )
+
+    fns = tps[-1] - tps
+    p_count = tps[-1]
+    n_count = fps[-1]
+
+    # start with false positives zero, which may be at a finite threshold
+    first_ind = (
+        fps.searchsorted(fps[0], side="right") - 1
+        if fps.searchsorted(fps[0], side="right") > 0
+        else None
+    )
+    # stop with false negatives zero
+    last_ind = tps.searchsorted(tps[-1]) + 1
+    sl = slice(first_ind, last_ind)
+
+    # reverse the output such that list of false positives is decreasing
+    return (fps[sl][::-1] / n_count, fns[sl][::-1] / p_count, thresholds[sl][::-1])
+
+
+def _binary_roc_auc_score(y_true, y_score, sample_weight=None, max_fpr=None):
+    """Binary roc auc score."""
+    if len(np.unique(y_true)) != 2:
+        warnings.warn(
+            (
+                "Only one class is present in y_true. ROC AUC score "
+                "is not defined in that case."
+            ),
+            UndefinedMetricWarning,
+        )
+        return np.nan
+
+    fpr, tpr, _ = roc_curve(y_true, y_score, sample_weight=sample_weight)
+    if max_fpr is None or max_fpr == 1:
+        return auc(fpr, tpr)
+    if max_fpr <= 0 or max_fpr > 1:
+        raise ValueError("Expected max_fpr in range (0, 1], got: %r" % max_fpr)
+
+    # Add a single point at max_fpr by linear interpolation
+    stop = np.searchsorted(fpr, max_fpr, "right")
+    x_interp = [fpr[stop - 1], fpr[stop]]
+    y_interp = [tpr[stop - 1], tpr[stop]]
+    tpr = np.append(tpr[:stop], np.interp(max_fpr, x_interp, y_interp))
+    fpr = np.append(fpr[:stop], max_fpr)
+    partial_auc = auc(fpr, tpr)
+
+    # McClish correction: standardize result to be 0.5 if non-discriminant
+    # and 1 if maximal
+    min_area = 0.5 * max_fpr**2
+    max_area = max_fpr
+    return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "average": [StrOptions({"micro", "macro", "samples", "weighted"}), None],
+        "sample_weight": ["array-like", None],
+        "max_fpr": [Interval(Real, 0.0, 1, closed="right"), None],
+        "multi_class": [StrOptions({"raise", "ovr", "ovo"})],
+        "labels": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def roc_auc_score(
+    y_true,
+    y_score,
+    *,
+    average="macro",
+    sample_weight=None,
+    max_fpr=None,
+    multi_class="raise",
+    labels=None,
+):
+    """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
+    from prediction scores.
+
+    Note: this implementation can be used with binary, multiclass and
+    multilabel classification, but some restrictions apply (see Parameters).
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
+        True labels or binary label indicators. The binary and multiclass cases
+        expect labels with shape (n_samples,) while the multilabel case expects
+        binary label indicators with shape (n_samples, n_classes).
+
+    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
+        Target scores.
+
+        * In the binary case, it corresponds to an array of shape
+          `(n_samples,)`. Both probability estimates and non-thresholded
+          decision values can be provided. The probability estimates correspond
+          to the **probability of the class with the greater label**,
+          i.e. `estimator.classes_[1]` and thus
+          `estimator.predict_proba(X, y)[:, 1]`. The decision values
+          corresponds to the output of `estimator.decision_function(X, y)`.
+          See more information in the :ref:`User guide `;
+        * In the multiclass case, it corresponds to an array of shape
+          `(n_samples, n_classes)` of probability estimates provided by the
+          `predict_proba` method. The probability estimates **must**
+          sum to 1 across the possible classes. In addition, the order of the
+          class scores must correspond to the order of ``labels``,
+          if provided, or else to the numerical or lexicographical order of
+          the labels in ``y_true``. See more information in the
+          :ref:`User guide `;
+        * In the multilabel case, it corresponds to an array of shape
+          `(n_samples, n_classes)`. Probability estimates are provided by the
+          `predict_proba` method and the non-thresholded decision values by
+          the `decision_function` method. The probability estimates correspond
+          to the **probability of the class with the greater label for each
+          output** of the classifier. See more information in the
+          :ref:`User guide `.
+
+    average : {'micro', 'macro', 'samples', 'weighted'} or None, \
+            default='macro'
+        If ``None``, the scores for each class are returned.
+        Otherwise, this determines the type of averaging performed on the data.
+        Note: multiclass ROC AUC currently only handles the 'macro' and
+        'weighted' averages. For multiclass targets, `average=None` is only
+        implemented for `multi_class='ovr'` and `average='micro'` is only
+        implemented for `multi_class='ovr'`.
+
+        ``'micro'``:
+            Calculate metrics globally by considering each element of the label
+            indicator matrix as a label.
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean.  This does not take label imbalance into account.
+        ``'weighted'``:
+            Calculate metrics for each label, and find their average, weighted
+            by support (the number of true instances for each label).
+        ``'samples'``:
+            Calculate metrics for each instance, and find their average.
+
+        Will be ignored when ``y_true`` is binary.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    max_fpr : float > 0 and <= 1, default=None
+        If not ``None``, the standardized partial AUC [2]_ over the range
+        [0, max_fpr] is returned. For the multiclass case, ``max_fpr``,
+        should be either equal to ``None`` or ``1.0`` as AUC ROC partial
+        computation currently is not supported for multiclass.
+
+    multi_class : {'raise', 'ovr', 'ovo'}, default='raise'
+        Only used for multiclass targets. Determines the type of configuration
+        to use. The default value raises an error, so either
+        ``'ovr'`` or ``'ovo'`` must be passed explicitly.
+
+        ``'ovr'``:
+            Stands for One-vs-rest. Computes the AUC of each class
+            against the rest [3]_ [4]_. This
+            treats the multiclass case in the same way as the multilabel case.
+            Sensitive to class imbalance even when ``average == 'macro'``,
+            because class imbalance affects the composition of each of the
+            'rest' groupings.
+        ``'ovo'``:
+            Stands for One-vs-one. Computes the average AUC of all
+            possible pairwise combinations of classes [5]_.
+            Insensitive to class imbalance when
+            ``average == 'macro'``.
+
+    labels : array-like of shape (n_classes,), default=None
+        Only used for multiclass targets. List of labels that index the
+        classes in ``y_score``. If ``None``, the numerical or lexicographical
+        order of the labels in ``y_true`` is used.
+
+    Returns
+    -------
+    auc : float
+        Area Under the Curve score.
+
+    See Also
+    --------
+    average_precision_score : Area under the precision-recall curve.
+    roc_curve : Compute Receiver operating characteristic (ROC) curve.
+    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
+        (ROC) curve given an estimator and some data.
+    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
+        (ROC) curve given the true and predicted values.
+
+    Notes
+    -----
+    The Gini Coefficient is a summary measure of the ranking ability of binary
+    classifiers. It is expressed using the area under of the ROC as follows:
+
+    G = 2 * AUC - 1
+
+    Where G is the Gini coefficient and AUC is the ROC-AUC score. This normalisation
+    will ensure that random guessing will yield a score of 0 in expectation, and it is
+    upper bounded by 1.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Receiver operating characteristic
+            `_
+
+    .. [2] `Analyzing a portion of the ROC curve. McClish, 1989
+            `_
+
+    .. [3] Provost, F., Domingos, P. (2000). Well-trained PETs: Improving
+           probability estimation trees (Section 6.2), CeDER Working Paper
+           #IS-00-04, Stern School of Business, New York University.
+
+    .. [4] `Fawcett, T. (2006). An introduction to ROC analysis. Pattern
+            Recognition Letters, 27(8), 861-874.
+            `_
+
+    .. [5] `Hand, D.J., Till, R.J. (2001). A Simple Generalisation of the Area
+            Under the ROC Curve for Multiple Class Classification Problems.
+            Machine Learning, 45(2), 171-186.
+            `_
+    .. [6] `Wikipedia entry for the Gini coefficient
+            `_
+
+    Examples
+    --------
+    Binary case:
+
+    >>> from sklearn.datasets import load_breast_cancer
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> from sklearn.metrics import roc_auc_score
+    >>> X, y = load_breast_cancer(return_X_y=True)
+    >>> clf = LogisticRegression(solver="newton-cholesky", random_state=0).fit(X, y)
+    >>> roc_auc_score(y, clf.predict_proba(X)[:, 1])
+    0.99
+    >>> roc_auc_score(y, clf.decision_function(X))
+    0.99
+
+    Multiclass case:
+
+    >>> from sklearn.datasets import load_iris
+    >>> X, y = load_iris(return_X_y=True)
+    >>> clf = LogisticRegression(solver="newton-cholesky").fit(X, y)
+    >>> roc_auc_score(y, clf.predict_proba(X), multi_class='ovr')
+    0.99
+
+    Multilabel case:
+
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_multilabel_classification
+    >>> from sklearn.multioutput import MultiOutputClassifier
+    >>> X, y = make_multilabel_classification(random_state=0)
+    >>> clf = MultiOutputClassifier(clf).fit(X, y)
+    >>> # get a list of n_output containing probability arrays of shape
+    >>> # (n_samples, n_classes)
+    >>> y_score = clf.predict_proba(X)
+    >>> # extract the positive columns for each output
+    >>> y_score = np.transpose([score[:, 1] for score in y_score])
+    >>> roc_auc_score(y, y_score, average=None)
+    array([0.828, 0.852, 0.94, 0.869, 0.95])
+    >>> from sklearn.linear_model import RidgeClassifierCV
+    >>> clf = RidgeClassifierCV().fit(X, y)
+    >>> roc_auc_score(y, clf.decision_function(X), average=None)
+    array([0.82, 0.847, 0.93, 0.872, 0.944])
+    """
+
+    y_type = type_of_target(y_true, input_name="y_true")
+    y_true = check_array(y_true, ensure_2d=False, dtype=None)
+    y_score = check_array(y_score, ensure_2d=False)
+
+    if y_type == "multiclass" or (
+        y_type == "binary" and y_score.ndim == 2 and y_score.shape[1] > 2
+    ):
+        # do not support partial ROC computation for multiclass
+        if max_fpr is not None and max_fpr != 1.0:
+            raise ValueError(
+                "Partial AUC computation not available in "
+                "multiclass setting, 'max_fpr' must be"
+                " set to `None`, received `max_fpr={0}` "
+                "instead".format(max_fpr)
+            )
+        if multi_class == "raise":
+            raise ValueError("multi_class must be in ('ovo', 'ovr')")
+        return _multiclass_roc_auc_score(
+            y_true, y_score, labels, multi_class, average, sample_weight
+        )
+    elif y_type == "binary":
+        labels = np.unique(y_true)
+        y_true = label_binarize(y_true, classes=labels)[:, 0]
+        return _average_binary_score(
+            partial(_binary_roc_auc_score, max_fpr=max_fpr),
+            y_true,
+            y_score,
+            average,
+            sample_weight=sample_weight,
+        )
+    else:  # multilabel-indicator
+        return _average_binary_score(
+            partial(_binary_roc_auc_score, max_fpr=max_fpr),
+            y_true,
+            y_score,
+            average,
+            sample_weight=sample_weight,
+        )
+
+
+def _multiclass_roc_auc_score(
+    y_true, y_score, labels, multi_class, average, sample_weight
+):
+    """Multiclass roc auc score.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True multiclass labels.
+
+    y_score : array-like of shape (n_samples, n_classes)
+        Target scores corresponding to probability estimates of a sample
+        belonging to a particular class
+
+    labels : array-like of shape (n_classes,) or None
+        List of labels to index ``y_score`` used for multiclass. If ``None``,
+        the lexical order of ``y_true`` is used to index ``y_score``.
+
+    multi_class : {'ovr', 'ovo'}
+        Determines the type of multiclass configuration to use.
+        ``'ovr'``:
+            Calculate metrics for the multiclass case using the one-vs-rest
+            approach.
+        ``'ovo'``:
+            Calculate metrics for the multiclass case using the one-vs-one
+            approach.
+
+    average : {'micro', 'macro', 'weighted'}
+        Determines the type of averaging performed on the pairwise binary
+        metric scores
+        ``'micro'``:
+            Calculate metrics for the binarized-raveled classes. Only supported
+            for `multi_class='ovr'`.
+
+        .. versionadded:: 1.2
+
+        ``'macro'``:
+            Calculate metrics for each label, and find their unweighted
+            mean. This does not take label imbalance into account. Classes
+            are assumed to be uniformly distributed.
+        ``'weighted'``:
+            Calculate metrics for each label, taking into account the
+            prevalence of the classes.
+
+    sample_weight : array-like of shape (n_samples,) or None
+        Sample weights.
+
+    """
+    # validation of the input y_score
+    if not np.allclose(1, y_score.sum(axis=1)):
+        raise ValueError(
+            "Target scores need to be probabilities for multiclass "
+            "roc_auc, i.e. they should sum up to 1.0 over classes"
+        )
+
+    # validation for multiclass parameter specifications
+    average_options = ("macro", "weighted", None)
+    if multi_class == "ovr":
+        average_options = ("micro",) + average_options
+    if average not in average_options:
+        raise ValueError(
+            "average must be one of {0} for multiclass problems".format(average_options)
+        )
+
+    multiclass_options = ("ovo", "ovr")
+    if multi_class not in multiclass_options:
+        raise ValueError(
+            "multi_class='{0}' is not supported "
+            "for multiclass ROC AUC, multi_class must be "
+            "in {1}".format(multi_class, multiclass_options)
+        )
+
+    if average is None and multi_class == "ovo":
+        raise NotImplementedError(
+            "average=None is not implemented for multi_class='ovo'."
+        )
+
+    if labels is not None:
+        labels = column_or_1d(labels)
+        classes = _unique(labels)
+        if len(classes) != len(labels):
+            raise ValueError("Parameter 'labels' must be unique")
+        if not np.array_equal(classes, labels):
+            raise ValueError("Parameter 'labels' must be ordered")
+        if len(classes) != y_score.shape[1]:
+            raise ValueError(
+                "Number of given labels, {0}, not equal to the number "
+                "of columns in 'y_score', {1}".format(len(classes), y_score.shape[1])
+            )
+        if len(np.setdiff1d(y_true, classes)):
+            raise ValueError("'y_true' contains labels not in parameter 'labels'")
+    else:
+        classes = _unique(y_true)
+        if len(classes) != y_score.shape[1]:
+            raise ValueError(
+                "Number of classes in y_true not equal to the number of "
+                "columns in 'y_score'"
+            )
+
+    if multi_class == "ovo":
+        if sample_weight is not None:
+            raise ValueError(
+                "sample_weight is not supported "
+                "for multiclass one-vs-one ROC AUC, "
+                "'sample_weight' must be None in this case."
+            )
+        y_true_encoded = _encode(y_true, uniques=classes)
+        # Hand & Till (2001) implementation (ovo)
+        return _average_multiclass_ovo_score(
+            _binary_roc_auc_score, y_true_encoded, y_score, average=average
+        )
+    else:
+        # ovr is same as multi-label
+        y_true_multilabel = label_binarize(y_true, classes=classes)
+        return _average_binary_score(
+            _binary_roc_auc_score,
+            y_true_multilabel,
+            y_score,
+            average,
+            sample_weight=sample_weight,
+        )
+
+
+def _binary_clf_curve(y_true, y_score, pos_label=None, sample_weight=None):
+    """Calculate true and false positives per binary classification threshold.
+
+    Parameters
+    ----------
+    y_true : ndarray of shape (n_samples,)
+        True targets of binary classification.
+
+    y_score : ndarray of shape (n_samples,)
+        Estimated probabilities or output of a decision function.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    fps : ndarray of shape (n_thresholds,)
+        A count of false positives, at index i being the number of negative
+        samples assigned a score >= thresholds[i]. The total number of
+        negative samples is equal to fps[-1] (thus true negatives are given by
+        fps[-1] - fps).
+
+    tps : ndarray of shape (n_thresholds,)
+        An increasing count of true positives, at index i being the number
+        of positive samples assigned a score >= thresholds[i]. The total
+        number of positive samples is equal to tps[-1] (thus false negatives
+        are given by tps[-1] - tps).
+
+    thresholds : ndarray of shape (n_thresholds,)
+        Decreasing score values.
+    """
+    # Check to make sure y_true is valid
+    y_type = type_of_target(y_true, input_name="y_true")
+    if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
+        raise ValueError("{0} format is not supported".format(y_type))
+
+    check_consistent_length(y_true, y_score, sample_weight)
+    y_true = column_or_1d(y_true)
+    y_score = column_or_1d(y_score)
+    assert_all_finite(y_true)
+    assert_all_finite(y_score)
+
+    # Filter out zero-weighted samples, as they should not impact the result
+    if sample_weight is not None:
+        sample_weight = column_or_1d(sample_weight)
+        sample_weight = _check_sample_weight(sample_weight, y_true)
+        nonzero_weight_mask = sample_weight != 0
+        y_true = y_true[nonzero_weight_mask]
+        y_score = y_score[nonzero_weight_mask]
+        sample_weight = sample_weight[nonzero_weight_mask]
+
+    pos_label = _check_pos_label_consistency(pos_label, y_true)
+
+    # make y_true a boolean vector
+    y_true = y_true == pos_label
+
+    # sort scores and corresponding truth values
+    desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1]
+    y_score = y_score[desc_score_indices]
+    y_true = y_true[desc_score_indices]
+    if sample_weight is not None:
+        weight = sample_weight[desc_score_indices]
+    else:
+        weight = 1.0
+
+    # y_score typically has many tied values. Here we extract
+    # the indices associated with the distinct values. We also
+    # concatenate a value for the end of the curve.
+    distinct_value_indices = np.where(np.diff(y_score))[0]
+    threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1]
+
+    # accumulate the true positives with decreasing threshold
+    tps = stable_cumsum(y_true * weight)[threshold_idxs]
+    if sample_weight is not None:
+        # express fps as a cumsum to ensure fps is increasing even in
+        # the presence of floating point errors
+        fps = stable_cumsum((1 - y_true) * weight)[threshold_idxs]
+    else:
+        fps = 1 + threshold_idxs - tps
+    return fps, tps, y_score[threshold_idxs]
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "pos_label": [Real, str, "boolean", None],
+        "sample_weight": ["array-like", None],
+        "drop_intermediate": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def precision_recall_curve(
+    y_true,
+    y_score,
+    *,
+    pos_label=None,
+    sample_weight=None,
+    drop_intermediate=False,
+):
+    """Compute precision-recall pairs for different probability thresholds.
+
+    Note: this implementation is restricted to the binary classification task.
+
+    The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
+    true positives and ``fp`` the number of false positives. The precision is
+    intuitively the ability of the classifier not to label as positive a sample
+    that is negative.
+
+    The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
+    true positives and ``fn`` the number of false negatives. The recall is
+    intuitively the ability of the classifier to find all the positive samples.
+
+    The last precision and recall values are 1. and 0. respectively and do not
+    have a corresponding threshold. This ensures that the graph starts on the
+    y axis.
+
+    The first precision and recall values are precision=class balance and recall=1.0
+    which corresponds to a classifier that always predicts the positive class.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
+        pos_label should be explicitly given.
+
+    y_score : array-like of shape (n_samples,)
+        Target scores, can either be probability estimates of the positive
+        class, or non-thresholded measure of decisions (as returned by
+        `decision_function` on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+        When ``pos_label=None``, if y_true is in {-1, 1} or {0, 1},
+        ``pos_label`` is set to 1, otherwise an error will be raised.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    drop_intermediate : bool, default=False
+        Whether to drop some suboptimal thresholds which would not appear
+        on a plotted precision-recall curve. This is useful in order to create
+        lighter precision-recall curves.
+
+        .. versionadded:: 1.3
+
+    Returns
+    -------
+    precision : ndarray of shape (n_thresholds + 1,)
+        Precision values such that element i is the precision of
+        predictions with score >= thresholds[i] and the last element is 1.
+
+    recall : ndarray of shape (n_thresholds + 1,)
+        Decreasing recall values such that element i is the recall of
+        predictions with score >= thresholds[i] and the last element is 0.
+
+    thresholds : ndarray of shape (n_thresholds,)
+        Increasing thresholds on the decision function used to compute
+        precision and recall where `n_thresholds = len(np.unique(y_score))`.
+
+    See Also
+    --------
+    PrecisionRecallDisplay.from_estimator : Plot Precision Recall Curve given
+        a binary classifier.
+    PrecisionRecallDisplay.from_predictions : Plot Precision Recall Curve
+        using predictions from a binary classifier.
+    average_precision_score : Compute average precision from prediction scores.
+    det_curve: Compute error rates for different probability thresholds.
+    roc_curve : Compute Receiver operating characteristic (ROC) curve.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import precision_recall_curve
+    >>> y_true = np.array([0, 0, 1, 1])
+    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> precision, recall, thresholds = precision_recall_curve(
+    ...     y_true, y_scores)
+    >>> precision
+    array([0.5       , 0.66666667, 0.5       , 1.        , 1.        ])
+    >>> recall
+    array([1. , 1. , 0.5, 0.5, 0. ])
+    >>> thresholds
+    array([0.1 , 0.35, 0.4 , 0.8 ])
+    """
+    fps, tps, thresholds = _binary_clf_curve(
+        y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
+    )
+
+    if drop_intermediate and len(fps) > 2:
+        # Drop thresholds corresponding to points where true positives (tps)
+        # do not change from the previous or subsequent point. This will keep
+        # only the first and last point for each tps value. All points
+        # with the same tps value have the same recall and thus x coordinate.
+        # They appear as a vertical line on the plot.
+        optimal_idxs = np.where(
+            np.concatenate(
+                [[True], np.logical_or(np.diff(tps[:-1]), np.diff(tps[1:])), [True]]
+            )
+        )[0]
+        fps = fps[optimal_idxs]
+        tps = tps[optimal_idxs]
+        thresholds = thresholds[optimal_idxs]
+
+    ps = tps + fps
+    # Initialize the result array with zeros to make sure that precision[ps == 0]
+    # does not contain uninitialized values.
+    precision = np.zeros_like(tps)
+    np.divide(tps, ps, out=precision, where=(ps != 0))
+
+    # When no positive label in y_true, recall is set to 1 for all thresholds
+    # tps[-1] == 0 <=> y_true == all negative labels
+    if tps[-1] == 0:
+        warnings.warn(
+            "No positive class found in y_true, "
+            "recall is set to one for all thresholds."
+        )
+        recall = np.ones_like(tps)
+    else:
+        recall = tps / tps[-1]
+
+    # reverse the outputs so recall is decreasing
+    sl = slice(None, None, -1)
+    return np.hstack((precision[sl], 1)), np.hstack((recall[sl], 0)), thresholds[sl]
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "pos_label": [Real, str, "boolean", None],
+        "sample_weight": ["array-like", None],
+        "drop_intermediate": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def roc_curve(
+    y_true, y_score, *, pos_label=None, sample_weight=None, drop_intermediate=True
+):
+    """Compute Receiver operating characteristic (ROC).
+
+    Note: this implementation is restricted to the binary classification task.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
+        pos_label should be explicitly given.
+
+    y_score : array-like of shape (n_samples,)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by "decision_function" on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class.
+        When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
+        ``pos_label`` is set to 1, otherwise an error will be raised.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    drop_intermediate : bool, default=True
+        Whether to drop thresholds where the resulting point is collinear with
+        its neighbors in ROC space. This has no effect on the ROC AUC or visual
+        shape of the curve, but reduces the number of plotted points.
+
+        .. versionadded:: 0.17
+           parameter *drop_intermediate*.
+
+    Returns
+    -------
+    fpr : ndarray of shape (>2,)
+        Increasing false positive rates such that element i is the false
+        positive rate of predictions with score >= `thresholds[i]`.
+
+    tpr : ndarray of shape (>2,)
+        Increasing true positive rates such that element `i` is the true
+        positive rate of predictions with score >= `thresholds[i]`.
+
+    thresholds : ndarray of shape (n_thresholds,)
+        Decreasing thresholds on the decision function used to compute
+        fpr and tpr. The first threshold is set to `np.inf`.
+
+        .. versionchanged:: 1.3
+           An arbitrary threshold at infinity (stored in `thresholds[0]`) is
+           added to represent a classifier that always predicts the negative
+           class, i.e. `fpr=0` and `tpr=0`.
+
+    See Also
+    --------
+    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
+        (ROC) curve given an estimator and some data.
+    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
+        (ROC) curve given the true and predicted values.
+    det_curve: Compute error rates for different probability thresholds.
+    roc_auc_score : Compute the area under the ROC curve.
+
+    Notes
+    -----
+    Since the thresholds are sorted from low to high values, they
+    are reversed upon returning them to ensure they correspond to both ``fpr``
+    and ``tpr``, which are sorted in reversed order during their calculation.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Receiver operating characteristic
+            `_
+
+    .. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
+           Letters, 2006, 27(8):861-874.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn import metrics
+    >>> y = np.array([1, 1, 2, 2])
+    >>> scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2)
+    >>> fpr
+    array([0. , 0. , 0.5, 0.5, 1. ])
+    >>> tpr
+    array([0. , 0.5, 0.5, 1. , 1. ])
+    >>> thresholds
+    array([ inf, 0.8 , 0.4 , 0.35, 0.1 ])
+    """
+    fps, tps, thresholds = _binary_clf_curve(
+        y_true, y_score, pos_label=pos_label, sample_weight=sample_weight
+    )
+
+    # Attempt to drop thresholds corresponding to points in between and
+    # collinear with other points. These are always suboptimal and do not
+    # appear on a plotted ROC curve (and thus do not affect the AUC).
+    # Here np.diff(_, 2) is used as a "second derivative" to tell if there
+    # is a corner at the point. Both fps and tps must be tested to handle
+    # thresholds with multiple data points (which are combined in
+    # _binary_clf_curve). This keeps all cases where the point should be kept,
+    # but does not drop more complicated cases like fps = [1, 3, 7],
+    # tps = [1, 2, 4]; there is no harm in keeping too many thresholds.
+    if drop_intermediate and len(fps) > 2:
+        optimal_idxs = np.where(
+            np.r_[True, np.logical_or(np.diff(fps, 2), np.diff(tps, 2)), True]
+        )[0]
+        fps = fps[optimal_idxs]
+        tps = tps[optimal_idxs]
+        thresholds = thresholds[optimal_idxs]
+
+    # Add an extra threshold position
+    # to make sure that the curve starts at (0, 0)
+    tps = np.r_[0, tps]
+    fps = np.r_[0, fps]
+    # get dtype of `y_score` even if it is an array-like
+    thresholds = np.r_[np.inf, thresholds]
+
+    if fps[-1] <= 0:
+        warnings.warn(
+            "No negative samples in y_true, false positive value should be meaningless",
+            UndefinedMetricWarning,
+        )
+        fpr = np.repeat(np.nan, fps.shape)
+    else:
+        fpr = fps / fps[-1]
+
+    if tps[-1] <= 0:
+        warnings.warn(
+            "No positive samples in y_true, true positive value should be meaningless",
+            UndefinedMetricWarning,
+        )
+        tpr = np.repeat(np.nan, tps.shape)
+    else:
+        tpr = tps / tps[-1]
+
+    return fpr, tpr, thresholds
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_score": ["array-like"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def label_ranking_average_precision_score(y_true, y_score, *, sample_weight=None):
+    """Compute ranking-based average precision.
+
+    Label ranking average precision (LRAP) is the average over each ground
+    truth label assigned to each sample, of the ratio of true vs. total
+    labels with lower score.
+
+    This metric is used in multilabel ranking problem, where the goal
+    is to give better rank to the labels associated to each sample.
+
+    The obtained score is always strictly greater than 0 and
+    the best value is 1.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
+        True binary labels in binary indicator format.
+
+    y_score : array-like of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by "decision_function" on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+        .. versionadded:: 0.20
+
+    Returns
+    -------
+    score : float
+        Ranking-based average precision score.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import label_ranking_average_precision_score
+    >>> y_true = np.array([[1, 0, 0], [0, 0, 1]])
+    >>> y_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1]])
+    >>> label_ranking_average_precision_score(y_true, y_score)
+    0.416
+    """
+    check_consistent_length(y_true, y_score, sample_weight)
+    y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr")
+    y_score = check_array(y_score, ensure_2d=False)
+
+    if y_true.shape != y_score.shape:
+        raise ValueError("y_true and y_score have different shape")
+
+    # Handle badly formatted array and the degenerate case with one label
+    y_type = type_of_target(y_true, input_name="y_true")
+    if y_type != "multilabel-indicator" and not (
+        y_type == "binary" and y_true.ndim == 2
+    ):
+        raise ValueError("{0} format is not supported".format(y_type))
+
+    if not issparse(y_true):
+        y_true = csr_matrix(y_true)
+
+    y_score = -y_score
+
+    n_samples, n_labels = y_true.shape
+
+    out = 0.0
+    for i, (start, stop) in enumerate(zip(y_true.indptr, y_true.indptr[1:])):
+        relevant = y_true.indices[start:stop]
+
+        if relevant.size == 0 or relevant.size == n_labels:
+            # If all labels are relevant or unrelevant, the score is also
+            # equal to 1. The label ranking has no meaning.
+            aux = 1.0
+        else:
+            scores_i = y_score[i]
+            rank = rankdata(scores_i, "max")[relevant]
+            L = rankdata(scores_i[relevant], "max")
+            aux = (L / rank).mean()
+
+        if sample_weight is not None:
+            aux = aux * sample_weight[i]
+        out += aux
+
+    if sample_weight is None:
+        out /= n_samples
+    else:
+        out /= np.sum(sample_weight)
+
+    return float(out)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def coverage_error(y_true, y_score, *, sample_weight=None):
+    """Coverage error measure.
+
+    Compute how far we need to go through the ranked scores to cover all
+    true labels. The best value is equal to the average number
+    of labels in ``y_true`` per sample.
+
+    Ties in ``y_scores`` are broken by giving maximal rank that would have
+    been assigned to all tied values.
+
+    Note: Our implementation's score is 1 greater than the one given in
+    Tsoumakas et al., 2010. This extends it to handle the degenerate case
+    in which an instance has 0 true labels.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples, n_labels)
+        True binary labels in binary indicator format.
+
+    y_score : array-like of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by "decision_function" on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    coverage_error : float
+        The coverage error.
+
+    References
+    ----------
+    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
+           Mining multi-label data. In Data mining and knowledge discovery
+           handbook (pp. 667-685). Springer US.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import coverage_error
+    >>> y_true = [[1, 0, 0], [0, 1, 1]]
+    >>> y_score = [[1, 0, 0], [0, 1, 1]]
+    >>> coverage_error(y_true, y_score)
+    1.5
+    """
+    y_true = check_array(y_true, ensure_2d=True)
+    y_score = check_array(y_score, ensure_2d=True)
+    check_consistent_length(y_true, y_score, sample_weight)
+
+    y_type = type_of_target(y_true, input_name="y_true")
+    if y_type != "multilabel-indicator":
+        raise ValueError("{0} format is not supported".format(y_type))
+
+    if y_true.shape != y_score.shape:
+        raise ValueError("y_true and y_score have different shape")
+
+    y_score_mask = np.ma.masked_array(y_score, mask=np.logical_not(y_true))
+    y_min_relevant = y_score_mask.min(axis=1).reshape((-1, 1))
+    coverage = (y_score >= y_min_relevant).sum(axis=1)
+    coverage = coverage.filled(0)
+
+    return float(np.average(coverage, weights=sample_weight))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_score": ["array-like"],
+        "sample_weight": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def label_ranking_loss(y_true, y_score, *, sample_weight=None):
+    """Compute Ranking loss measure.
+
+    Compute the average number of label pairs that are incorrectly ordered
+    given y_score weighted by the size of the label set and the number of
+    labels not in the label set.
+
+    This is similar to the error set size, but weighted by the number of
+    relevant and irrelevant labels. The best performance is achieved with
+    a ranking loss of zero.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.17
+       A function *label_ranking_loss*
+
+    Parameters
+    ----------
+    y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
+        True binary labels in binary indicator format.
+
+    y_score : array-like of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates of the positive
+        class, confidence values, or non-thresholded measure of decisions
+        (as returned by "decision_function" on some classifiers).
+        For :term:`decision_function` scores, values greater than or equal to
+        zero should indicate the positive class.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    Returns
+    -------
+    loss : float
+        Average number of label pairs that are incorrectly ordered given
+        y_score weighted by the size of the label set and the number of labels not
+        in the label set.
+
+    References
+    ----------
+    .. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
+           Mining multi-label data. In Data mining and knowledge discovery
+           handbook (pp. 667-685). Springer US.
+
+    Examples
+    --------
+    >>> from sklearn.metrics import label_ranking_loss
+    >>> y_true = [[1, 0, 0], [0, 0, 1]]
+    >>> y_score = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
+    >>> label_ranking_loss(y_true, y_score)
+    0.75
+    """
+    y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr")
+    y_score = check_array(y_score, ensure_2d=False)
+    check_consistent_length(y_true, y_score, sample_weight)
+
+    y_type = type_of_target(y_true, input_name="y_true")
+    if y_type not in ("multilabel-indicator",):
+        raise ValueError("{0} format is not supported".format(y_type))
+
+    if y_true.shape != y_score.shape:
+        raise ValueError("y_true and y_score have different shape")
+
+    n_samples, n_labels = y_true.shape
+
+    y_true = csr_matrix(y_true)
+
+    loss = np.zeros(n_samples)
+    for i, (start, stop) in enumerate(zip(y_true.indptr, y_true.indptr[1:])):
+        # Sort and bin the label scores
+        unique_scores, unique_inverse = np.unique(y_score[i], return_inverse=True)
+        true_at_reversed_rank = np.bincount(
+            unique_inverse[y_true.indices[start:stop]], minlength=len(unique_scores)
+        )
+        all_at_reversed_rank = np.bincount(unique_inverse, minlength=len(unique_scores))
+        false_at_reversed_rank = all_at_reversed_rank - true_at_reversed_rank
+
+        # if the scores are ordered, it's possible to count the number of
+        # incorrectly ordered paires in linear time by cumulatively counting
+        # how many false labels of a given score have a score higher than the
+        # accumulated true labels with lower score.
+        loss[i] = np.dot(true_at_reversed_rank.cumsum(), false_at_reversed_rank)
+
+    n_positives = count_nonzero(y_true, axis=1)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        loss /= (n_labels - n_positives) * n_positives
+
+    # When there is no positive or no negative labels, those values should
+    # be consider as correct, i.e. the ranking doesn't matter.
+    loss[np.logical_or(n_positives == 0, n_positives == n_labels)] = 0.0
+
+    return float(np.average(loss, weights=sample_weight))
+
+
+def _dcg_sample_scores(y_true, y_score, k=None, log_base=2, ignore_ties=False):
+    """Compute Discounted Cumulative Gain.
+
+    Sum the true scores ranked in the order induced by the predicted scores,
+    after applying a logarithmic discount.
+
+    This ranking metric yields a high value if true labels are ranked high by
+    ``y_score``.
+
+    Parameters
+    ----------
+    y_true : ndarray of shape (n_samples, n_labels)
+        True targets of multilabel classification, or true scores of entities
+        to be ranked.
+
+    y_score : ndarray of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates, confidence values,
+        or non-thresholded measure of decisions (as returned by
+        "decision_function" on some classifiers).
+
+    k : int, default=None
+        Only consider the highest k scores in the ranking. If `None`, use all
+        outputs.
+
+    log_base : float, default=2
+        Base of the logarithm used for the discount. A low value means a
+        sharper discount (top results are more important).
+
+    ignore_ties : bool, default=False
+        Assume that there are no ties in y_score (which is likely to be the
+        case if y_score is continuous) for efficiency gains.
+
+    Returns
+    -------
+    discounted_cumulative_gain : ndarray of shape (n_samples,)
+        The DCG score for each sample.
+
+    See Also
+    --------
+    ndcg_score : The Discounted Cumulative Gain divided by the Ideal Discounted
+        Cumulative Gain (the DCG obtained for a perfect ranking), in order to
+        have a score between 0 and 1.
+    """
+    discount = 1 / (np.log(np.arange(y_true.shape[1]) + 2) / np.log(log_base))
+    if k is not None:
+        discount[k:] = 0
+    if ignore_ties:
+        ranking = np.argsort(y_score)[:, ::-1]
+        ranked = y_true[np.arange(ranking.shape[0])[:, np.newaxis], ranking]
+        cumulative_gains = discount.dot(ranked.T)
+    else:
+        discount_cumsum = np.cumsum(discount)
+        cumulative_gains = [
+            _tie_averaged_dcg(y_t, y_s, discount_cumsum)
+            for y_t, y_s in zip(y_true, y_score)
+        ]
+        cumulative_gains = np.asarray(cumulative_gains)
+    return cumulative_gains
+
+
+def _tie_averaged_dcg(y_true, y_score, discount_cumsum):
+    """
+    Compute DCG by averaging over possible permutations of ties.
+
+    The gain (`y_true`) of an index falling inside a tied group (in the order
+    induced by `y_score`) is replaced by the average gain within this group.
+    The discounted gain for a tied group is then the average `y_true` within
+    this group times the sum of discounts of the corresponding ranks.
+
+    This amounts to averaging scores for all possible orderings of the tied
+    groups.
+
+    (note in the case of dcg@k the discount is 0 after index k)
+
+    Parameters
+    ----------
+    y_true : ndarray
+        The true relevance scores.
+
+    y_score : ndarray
+        Predicted scores.
+
+    discount_cumsum : ndarray
+        Precomputed cumulative sum of the discounts.
+
+    Returns
+    -------
+    discounted_cumulative_gain : float
+        The discounted cumulative gain.
+
+    References
+    ----------
+    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
+    performance measures efficiently in the presence of tied scores. In
+    European conference on information retrieval (pp. 414-421). Springer,
+    Berlin, Heidelberg.
+    """
+    _, inv, counts = np.unique(-y_score, return_inverse=True, return_counts=True)
+    ranked = np.zeros(len(counts))
+    np.add.at(ranked, inv, y_true)
+    ranked /= counts
+    groups = np.cumsum(counts) - 1
+    discount_sums = np.empty(len(counts))
+    discount_sums[0] = discount_cumsum[groups[0]]
+    discount_sums[1:] = np.diff(discount_cumsum[groups])
+    return (ranked * discount_sums).sum()
+
+
+def _check_dcg_target_type(y_true):
+    y_type = type_of_target(y_true, input_name="y_true")
+    supported_fmt = (
+        "multilabel-indicator",
+        "continuous-multioutput",
+        "multiclass-multioutput",
+    )
+    if y_type not in supported_fmt:
+        raise ValueError(
+            "Only {} formats are supported. Got {} instead".format(
+                supported_fmt, y_type
+            )
+        )
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "k": [Interval(Integral, 1, None, closed="left"), None],
+        "log_base": [Interval(Real, 0.0, None, closed="neither")],
+        "sample_weight": ["array-like", None],
+        "ignore_ties": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def dcg_score(
+    y_true, y_score, *, k=None, log_base=2, sample_weight=None, ignore_ties=False
+):
+    """Compute Discounted Cumulative Gain.
+
+    Sum the true scores ranked in the order induced by the predicted scores,
+    after applying a logarithmic discount.
+
+    This ranking metric yields a high value if true labels are ranked high by
+    ``y_score``.
+
+    Usually the Normalized Discounted Cumulative Gain (NDCG, computed by
+    ndcg_score) is preferred.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples, n_labels)
+        True targets of multilabel classification, or true scores of entities
+        to be ranked.
+
+    y_score : array-like of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates, confidence values,
+        or non-thresholded measure of decisions (as returned by
+        "decision_function" on some classifiers).
+
+    k : int, default=None
+        Only consider the highest k scores in the ranking. If None, use all
+        outputs.
+
+    log_base : float, default=2
+        Base of the logarithm used for the discount. A low value means a
+        sharper discount (top results are more important).
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights. If `None`, all samples are given the same weight.
+
+    ignore_ties : bool, default=False
+        Assume that there are no ties in y_score (which is likely to be the
+        case if y_score is continuous) for efficiency gains.
+
+    Returns
+    -------
+    discounted_cumulative_gain : float
+        The averaged sample DCG scores.
+
+    See Also
+    --------
+    ndcg_score : The Discounted Cumulative Gain divided by the Ideal Discounted
+        Cumulative Gain (the DCG obtained for a perfect ranking), in order to
+        have a score between 0 and 1.
+
+    References
+    ----------
+    `Wikipedia entry for Discounted Cumulative Gain
+    `_.
+
+    Jarvelin, K., & Kekalainen, J. (2002).
+    Cumulated gain-based evaluation of IR techniques. ACM Transactions on
+    Information Systems (TOIS), 20(4), 422-446.
+
+    Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).
+    A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th
+    Annual Conference on Learning Theory (COLT 2013).
+
+    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
+    performance measures efficiently in the presence of tied scores. In
+    European conference on information retrieval (pp. 414-421). Springer,
+    Berlin, Heidelberg.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import dcg_score
+    >>> # we have ground-truth relevance of some answers to a query:
+    >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
+    >>> # we predict scores for the answers
+    >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
+    >>> dcg_score(true_relevance, scores)
+    9.49
+    >>> # we can set k to truncate the sum; only top k answers contribute
+    >>> dcg_score(true_relevance, scores, k=2)
+    5.63
+    >>> # now we have some ties in our prediction
+    >>> scores = np.asarray([[1, 0, 0, 0, 1]])
+    >>> # by default ties are averaged, so here we get the average true
+    >>> # relevance of our top predictions: (10 + 5) / 2 = 7.5
+    >>> dcg_score(true_relevance, scores, k=1)
+    7.5
+    >>> # we can choose to ignore ties for faster results, but only
+    >>> # if we know there aren't ties in our scores, otherwise we get
+    >>> # wrong results:
+    >>> dcg_score(true_relevance,
+    ...           scores, k=1, ignore_ties=True)
+    5.0
+    """
+    y_true = check_array(y_true, ensure_2d=False)
+    y_score = check_array(y_score, ensure_2d=False)
+    check_consistent_length(y_true, y_score, sample_weight)
+    _check_dcg_target_type(y_true)
+    return float(
+        np.average(
+            _dcg_sample_scores(
+                y_true, y_score, k=k, log_base=log_base, ignore_ties=ignore_ties
+            ),
+            weights=sample_weight,
+        )
+    )
+
+
+def _ndcg_sample_scores(y_true, y_score, k=None, ignore_ties=False):
+    """Compute Normalized Discounted Cumulative Gain.
+
+    Sum the true scores ranked in the order induced by the predicted scores,
+    after applying a logarithmic discount. Then divide by the best possible
+    score (Ideal DCG, obtained for a perfect ranking) to obtain a score between
+    0 and 1.
+
+    This ranking metric yields a high value if true labels are ranked high by
+    ``y_score``.
+
+    Parameters
+    ----------
+    y_true : ndarray of shape (n_samples, n_labels)
+        True targets of multilabel classification, or true scores of entities
+        to be ranked.
+
+    y_score : ndarray of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates, confidence values,
+        or non-thresholded measure of decisions (as returned by
+        "decision_function" on some classifiers).
+
+    k : int, default=None
+        Only consider the highest k scores in the ranking. If None, use all
+        outputs.
+
+    ignore_ties : bool, default=False
+        Assume that there are no ties in y_score (which is likely to be the
+        case if y_score is continuous) for efficiency gains.
+
+    Returns
+    -------
+    normalized_discounted_cumulative_gain : ndarray of shape (n_samples,)
+        The NDCG score for each sample (float in [0., 1.]).
+
+    See Also
+    --------
+    dcg_score : Discounted Cumulative Gain (not normalized).
+
+    """
+    gain = _dcg_sample_scores(y_true, y_score, k, ignore_ties=ignore_ties)
+    # Here we use the order induced by y_true so we can ignore ties since
+    # the gain associated to tied indices is the same (permuting ties doesn't
+    # change the value of the re-ordered y_true)
+    normalizing_gain = _dcg_sample_scores(y_true, y_true, k, ignore_ties=True)
+    all_irrelevant = normalizing_gain == 0
+    gain[all_irrelevant] = 0
+    gain[~all_irrelevant] /= normalizing_gain[~all_irrelevant]
+    return gain
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "k": [Interval(Integral, 1, None, closed="left"), None],
+        "sample_weight": ["array-like", None],
+        "ignore_ties": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False):
+    """Compute Normalized Discounted Cumulative Gain.
+
+    Sum the true scores ranked in the order induced by the predicted scores,
+    after applying a logarithmic discount. Then divide by the best possible
+    score (Ideal DCG, obtained for a perfect ranking) to obtain a score between
+    0 and 1.
+
+    This ranking metric returns a high value if true labels are ranked high by
+    ``y_score``.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples, n_labels)
+        True targets of multilabel classification, or true scores of entities
+        to be ranked. Negative values in `y_true` may result in an output
+        that is not between 0 and 1.
+
+    y_score : array-like of shape (n_samples, n_labels)
+        Target scores, can either be probability estimates, confidence values,
+        or non-thresholded measure of decisions (as returned by
+        "decision_function" on some classifiers).
+
+    k : int, default=None
+        Only consider the highest k scores in the ranking. If `None`, use all
+        outputs.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights. If `None`, all samples are given the same weight.
+
+    ignore_ties : bool, default=False
+        Assume that there are no ties in y_score (which is likely to be the
+        case if y_score is continuous) for efficiency gains.
+
+    Returns
+    -------
+    normalized_discounted_cumulative_gain : float in [0., 1.]
+        The averaged NDCG scores for all samples.
+
+    See Also
+    --------
+    dcg_score : Discounted Cumulative Gain (not normalized).
+
+    References
+    ----------
+    `Wikipedia entry for Discounted Cumulative Gain
+    `_
+
+    Jarvelin, K., & Kekalainen, J. (2002).
+    Cumulated gain-based evaluation of IR techniques. ACM Transactions on
+    Information Systems (TOIS), 20(4), 422-446.
+
+    Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).
+    A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th
+    Annual Conference on Learning Theory (COLT 2013)
+
+    McSherry, F., & Najork, M. (2008, March). Computing information retrieval
+    performance measures efficiently in the presence of tied scores. In
+    European conference on information retrieval (pp. 414-421). Springer,
+    Berlin, Heidelberg.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import ndcg_score
+    >>> # we have ground-truth relevance of some answers to a query:
+    >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
+    >>> # we predict some scores (relevance) for the answers
+    >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
+    >>> ndcg_score(true_relevance, scores)
+    0.69
+    >>> scores = np.asarray([[.05, 1.1, 1., .5, .0]])
+    >>> ndcg_score(true_relevance, scores)
+    0.49
+    >>> # we can set k to truncate the sum; only top k answers contribute.
+    >>> ndcg_score(true_relevance, scores, k=4)
+    0.35
+    >>> # the normalization takes k into account so a perfect answer
+    >>> # would still get 1.0
+    >>> ndcg_score(true_relevance, true_relevance, k=4)
+    1.0...
+    >>> # now we have some ties in our prediction
+    >>> scores = np.asarray([[1, 0, 0, 0, 1]])
+    >>> # by default ties are averaged, so here we get the average (normalized)
+    >>> # true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75
+    >>> ndcg_score(true_relevance, scores, k=1)
+    0.75
+    >>> # we can choose to ignore ties for faster results, but only
+    >>> # if we know there aren't ties in our scores, otherwise we get
+    >>> # wrong results:
+    >>> ndcg_score(true_relevance,
+    ...           scores, k=1, ignore_ties=True)
+    0.5...
+    """
+    y_true = check_array(y_true, ensure_2d=False)
+    y_score = check_array(y_score, ensure_2d=False)
+    check_consistent_length(y_true, y_score, sample_weight)
+
+    if y_true.min() < 0:
+        raise ValueError("ndcg_score should not be used on negative y_true values.")
+    if y_true.ndim > 1 and y_true.shape[1] <= 1:
+        raise ValueError(
+            "Computing NDCG is only meaningful when there is more than 1 document. "
+            f"Got {y_true.shape[1]} instead."
+        )
+    _check_dcg_target_type(y_true)
+    gain = _ndcg_sample_scores(y_true, y_score, k=k, ignore_ties=ignore_ties)
+    return float(np.average(gain, weights=sample_weight))
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "k": [Interval(Integral, 1, None, closed="left")],
+        "normalize": ["boolean"],
+        "sample_weight": ["array-like", None],
+        "labels": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def top_k_accuracy_score(
+    y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None
+):
+    """Top-k Accuracy classification score.
+
+    This metric computes the number of times where the correct label is among
+    the top `k` labels predicted (ranked by predicted scores). Note that the
+    multilabel case isn't covered here.
+
+    Read more in the :ref:`User Guide `
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples,)
+        True labels.
+
+    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
+        Target scores. These can be either probability estimates or
+        non-thresholded decision values (as returned by
+        :term:`decision_function` on some classifiers).
+        The binary case expects scores with shape (n_samples,) while the
+        multiclass case expects scores with shape (n_samples, n_classes).
+        In the multiclass case, the order of the class scores must
+        correspond to the order of ``labels``, if provided, or else to
+        the numerical or lexicographical order of the labels in ``y_true``.
+        If ``y_true`` does not contain all the labels, ``labels`` must be
+        provided.
+
+    k : int, default=2
+        Number of most likely outcomes considered to find the correct label.
+
+    normalize : bool, default=True
+        If `True`, return the fraction of correctly classified samples.
+        Otherwise, return the number of correctly classified samples.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights. If `None`, all samples are given the same weight.
+
+    labels : array-like of shape (n_classes,), default=None
+        Multiclass only. List of labels that index the classes in ``y_score``.
+        If ``None``, the numerical or lexicographical order of the labels in
+        ``y_true`` is used. If ``y_true`` does not contain all the labels,
+        ``labels`` must be provided.
+
+    Returns
+    -------
+    score : float
+        The top-k accuracy score. The best performance is 1 with
+        `normalize == True` and the number of samples with
+        `normalize == False`.
+
+    See Also
+    --------
+    accuracy_score : Compute the accuracy score. By default, the function will
+        return the fraction of correct predictions divided by the total number
+        of predictions.
+
+    Notes
+    -----
+    In cases where two or more labels are assigned equal predicted scores,
+    the labels with the highest indices will be chosen first. This might
+    impact the result if the correct label falls after the threshold because
+    of that.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import top_k_accuracy_score
+    >>> y_true = np.array([0, 1, 2, 2])
+    >>> y_score = np.array([[0.5, 0.2, 0.2],  # 0 is in top 2
+    ...                     [0.3, 0.4, 0.2],  # 1 is in top 2
+    ...                     [0.2, 0.4, 0.3],  # 2 is in top 2
+    ...                     [0.7, 0.2, 0.1]]) # 2 isn't in top 2
+    >>> top_k_accuracy_score(y_true, y_score, k=2)
+    0.75
+    >>> # Not normalizing gives the number of "correctly" classified samples
+    >>> top_k_accuracy_score(y_true, y_score, k=2, normalize=False)
+    3.0
+    """
+    y_true = check_array(y_true, ensure_2d=False, dtype=None)
+    y_true = column_or_1d(y_true)
+    y_type = type_of_target(y_true, input_name="y_true")
+    if y_type == "binary" and labels is not None and len(labels) > 2:
+        y_type = "multiclass"
+    if y_type not in {"binary", "multiclass"}:
+        raise ValueError(
+            f"y type must be 'binary' or 'multiclass', got '{y_type}' instead."
+        )
+    y_score = check_array(y_score, ensure_2d=False)
+    if y_type == "binary":
+        if y_score.ndim == 2 and y_score.shape[1] != 1:
+            raise ValueError(
+                "`y_true` is binary while y_score is 2d with"
+                f" {y_score.shape[1]} classes. If `y_true` does not contain all the"
+                " labels, `labels` must be provided."
+            )
+        y_score = column_or_1d(y_score)
+
+    check_consistent_length(y_true, y_score, sample_weight)
+    y_score_n_classes = y_score.shape[1] if y_score.ndim == 2 else 2
+
+    if labels is None:
+        classes = _unique(y_true)
+        n_classes = len(classes)
+
+        if n_classes != y_score_n_classes:
+            raise ValueError(
+                f"Number of classes in 'y_true' ({n_classes}) not equal "
+                f"to the number of classes in 'y_score' ({y_score_n_classes})."
+                "You can provide a list of all known classes by assigning it "
+                "to the `labels` parameter."
+            )
+    else:
+        labels = column_or_1d(labels)
+        classes = _unique(labels)
+        n_labels = len(labels)
+        n_classes = len(classes)
+
+        if n_classes != n_labels:
+            raise ValueError("Parameter 'labels' must be unique.")
+
+        if not np.array_equal(classes, labels):
+            raise ValueError("Parameter 'labels' must be ordered.")
+
+        if n_classes != y_score_n_classes:
+            raise ValueError(
+                f"Number of given labels ({n_classes}) not equal to the "
+                f"number of classes in 'y_score' ({y_score_n_classes})."
+            )
+
+        if len(np.setdiff1d(y_true, classes)):
+            raise ValueError("'y_true' contains labels not in parameter 'labels'.")
+
+    if k >= n_classes:
+        warnings.warn(
+            (
+                f"'k' ({k}) greater than or equal to 'n_classes' ({n_classes}) "
+                "will result in a perfect score and is therefore meaningless."
+            ),
+            UndefinedMetricWarning,
+        )
+
+    y_true_encoded = _encode(y_true, uniques=classes)
+
+    if y_type == "binary":
+        if k == 1:
+            threshold = 0.5 if y_score.min() >= 0 and y_score.max() <= 1 else 0
+            y_pred = (y_score > threshold).astype(np.int64)
+            hits = y_pred == y_true_encoded
+        else:
+            hits = np.ones_like(y_score, dtype=np.bool_)
+    elif y_type == "multiclass":
+        sorted_pred = np.argsort(y_score, axis=1, kind="mergesort")[:, ::-1]
+        hits = (y_true_encoded == sorted_pred[:, :k].T).any(axis=0)
+
+    if normalize:
+        return float(np.average(hits, weights=sample_weight))
+    elif sample_weight is None:
+        return float(np.sum(hits))
+    else:
+        return float(np.dot(hits, sample_weight))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/meson.build b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..f0f9894cc6f59a9500a1598c9c9a94d5d6f58429
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/meson.build
@@ -0,0 +1,49 @@
+# Metrics is cimported from other subpackages so this is needed for the cimport
+# to work
+metrics_cython_tree = [
+  fs.copyfile('__init__.py')
+]
+# Some metrics code cimports code from utils, we may as well copy all the necessary files
+metrics_cython_tree += utils_cython_tree
+
+_dist_metrics_pxd = custom_target(
+  '_dist_metrics_pxd',
+  output: '_dist_metrics.pxd',
+  input: '_dist_metrics.pxd.tp',
+  command: [tempita, '@INPUT@', '-o', '@OUTDIR@'],
+  # Need to install the generated pxd because it is needed in other subpackages
+  # Cython code, e.g. sklearn.cluster
+  install_dir: sklearn_dir / 'metrics',
+  install: true,
+)
+metrics_cython_tree += [_dist_metrics_pxd]
+
+_dist_metrics_pyx = custom_target(
+  '_dist_metrics_pyx',
+  output: '_dist_metrics.pyx',
+  input: '_dist_metrics.pyx.tp',
+  command: [tempita, '@INPUT@', '-o', '@OUTDIR@'],
+  # TODO in principle this should go in py.exension_module below. This is
+  # temporary work-around for dependency issue with .pyx.tp files. For more
+  # details, see https://github.com/mesonbuild/meson/issues/13212
+  depends: metrics_cython_tree,
+)
+
+_dist_metrics = py.extension_module(
+  '_dist_metrics',
+  cython_gen.process(_dist_metrics_pyx),
+  dependencies: [np_dep],
+  subdir: 'sklearn/metrics',
+  install: true
+)
+
+py.extension_module(
+  '_pairwise_fast',
+  [cython_gen.process('_pairwise_fast.pyx'), metrics_cython_tree],
+  dependencies: [openmp_dep],
+  subdir: 'sklearn/metrics',
+  install: true
+)
+
+subdir('_pairwise_distances_reduction')
+subdir('cluster')
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_pairwise.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_pairwise.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c1ba4b2f7d5280235ed2038ac2bd933db4b701d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_pairwise.py
@@ -0,0 +1,1683 @@
+import warnings
+from types import GeneratorType
+
+import numpy as np
+import pytest
+from numpy import linalg
+from scipy.sparse import issparse
+from scipy.spatial.distance import (
+    cdist,
+    cityblock,
+    cosine,
+    minkowski,
+    pdist,
+    squareform,
+)
+
+from sklearn import config_context
+from sklearn.exceptions import DataConversionWarning
+from sklearn.metrics.pairwise import (
+    PAIRED_DISTANCES,
+    PAIRWISE_BOOLEAN_FUNCTIONS,
+    PAIRWISE_DISTANCE_FUNCTIONS,
+    PAIRWISE_KERNEL_FUNCTIONS,
+    _euclidean_distances_upcast,
+    additive_chi2_kernel,
+    check_paired_arrays,
+    check_pairwise_arrays,
+    chi2_kernel,
+    cosine_distances,
+    cosine_similarity,
+    euclidean_distances,
+    haversine_distances,
+    laplacian_kernel,
+    linear_kernel,
+    manhattan_distances,
+    nan_euclidean_distances,
+    paired_cosine_distances,
+    paired_distances,
+    paired_euclidean_distances,
+    paired_manhattan_distances,
+    pairwise_distances,
+    pairwise_distances_argmin,
+    pairwise_distances_argmin_min,
+    pairwise_distances_chunked,
+    pairwise_kernels,
+    polynomial_kernel,
+    rbf_kernel,
+    sigmoid_kernel,
+)
+from sklearn.preprocessing import normalize
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_equal,
+    ignore_warnings,
+)
+from sklearn.utils.fixes import (
+    BSR_CONTAINERS,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+    DOK_CONTAINERS,
+)
+from sklearn.utils.parallel import Parallel, delayed
+
+
+def test_pairwise_distances_for_dense_data(global_dtype):
+    # Test the pairwise_distance helper function.
+    rng = np.random.RandomState(0)
+
+    # Euclidean distance should be equivalent to calling the function.
+    X = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+    S = pairwise_distances(X, metric="euclidean")
+    S2 = euclidean_distances(X)
+    assert_allclose(S, S2)
+    assert S.dtype == S2.dtype == global_dtype
+
+    # Euclidean distance, with Y != X.
+    Y = rng.random_sample((2, 4)).astype(global_dtype, copy=False)
+    S = pairwise_distances(X, Y, metric="euclidean")
+    S2 = euclidean_distances(X, Y)
+    assert_allclose(S, S2)
+    assert S.dtype == S2.dtype == global_dtype
+
+    # Check to ensure NaNs work with pairwise_distances.
+    X_masked = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+    Y_masked = rng.random_sample((2, 4)).astype(global_dtype, copy=False)
+    X_masked[0, 0] = np.nan
+    Y_masked[0, 0] = np.nan
+    S_masked = pairwise_distances(X_masked, Y_masked, metric="nan_euclidean")
+    S2_masked = nan_euclidean_distances(X_masked, Y_masked)
+    assert_allclose(S_masked, S2_masked)
+    assert S_masked.dtype == S2_masked.dtype == global_dtype
+
+    # Test with tuples as X and Y
+    X_tuples = tuple([tuple([v for v in row]) for row in X])
+    Y_tuples = tuple([tuple([v for v in row]) for row in Y])
+    S2 = pairwise_distances(X_tuples, Y_tuples, metric="euclidean")
+    assert_allclose(S, S2)
+    assert S.dtype == S2.dtype == global_dtype
+
+    # Test haversine distance
+    # The data should be valid latitude and longitude
+    # haversine converts to float64 currently so we don't check dtypes.
+    X = rng.random_sample((5, 2)).astype(global_dtype, copy=False)
+    X[:, 0] = (X[:, 0] - 0.5) * 2 * np.pi / 2
+    X[:, 1] = (X[:, 1] - 0.5) * 2 * np.pi
+    S = pairwise_distances(X, metric="haversine")
+    S2 = haversine_distances(X)
+    assert_allclose(S, S2)
+
+    # Test haversine distance, with Y != X
+    Y = rng.random_sample((2, 2)).astype(global_dtype, copy=False)
+    Y[:, 0] = (Y[:, 0] - 0.5) * 2 * np.pi / 2
+    Y[:, 1] = (Y[:, 1] - 0.5) * 2 * np.pi
+    S = pairwise_distances(X, Y, metric="haversine")
+    S2 = haversine_distances(X, Y)
+    assert_allclose(S, S2)
+
+    # "cityblock" uses scikit-learn metric, cityblock (function) is
+    # scipy.spatial.
+    # The metric functions from scipy converts to float64 so we don't check the dtypes.
+    S = pairwise_distances(X, metric="cityblock")
+    S2 = pairwise_distances(X, metric=cityblock)
+    assert S.shape[0] == S.shape[1]
+    assert S.shape[0] == X.shape[0]
+    assert_allclose(S, S2)
+
+    # The manhattan metric should be equivalent to cityblock.
+    S = pairwise_distances(X, Y, metric="manhattan")
+    S2 = pairwise_distances(X, Y, metric=cityblock)
+    assert S.shape[0] == X.shape[0]
+    assert S.shape[1] == Y.shape[0]
+    assert_allclose(S, S2)
+
+    # Test cosine as a string metric versus cosine callable
+    # The string "cosine" uses sklearn.metric,
+    # while the function cosine is scipy.spatial
+    S = pairwise_distances(X, Y, metric="cosine")
+    S2 = pairwise_distances(X, Y, metric=cosine)
+    assert S.shape[0] == X.shape[0]
+    assert S.shape[1] == Y.shape[0]
+    assert_allclose(S, S2)
+
+
+@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+@pytest.mark.parametrize("bsr_container", BSR_CONTAINERS)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_pairwise_distances_for_sparse_data(
+    coo_container, csc_container, bsr_container, csr_container, global_dtype
+):
+    # Test the pairwise_distance helper function.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+    Y = rng.random_sample((2, 4)).astype(global_dtype, copy=False)
+
+    # Test with sparse X and Y,
+    # currently only supported for Euclidean, L1 and cosine.
+    X_sparse = csr_container(X)
+    Y_sparse = csr_container(Y)
+
+    S = pairwise_distances(X_sparse, Y_sparse, metric="euclidean")
+    S2 = euclidean_distances(X_sparse, Y_sparse)
+    assert_allclose(S, S2)
+    assert S.dtype == S2.dtype == global_dtype
+
+    S = pairwise_distances(X_sparse, Y_sparse, metric="cosine")
+    S2 = cosine_distances(X_sparse, Y_sparse)
+    assert_allclose(S, S2)
+    assert S.dtype == S2.dtype == global_dtype
+
+    S = pairwise_distances(X_sparse, csc_container(Y), metric="manhattan")
+    S2 = manhattan_distances(bsr_container(X), coo_container(Y))
+    assert_allclose(S, S2)
+    if global_dtype == np.float64:
+        assert S.dtype == S2.dtype == global_dtype
+    else:
+        # TODO Fix manhattan_distances to preserve dtype.
+        # currently pairwise_distances uses manhattan_distances but converts the result
+        # back to the input dtype
+        with pytest.raises(AssertionError):
+            assert S.dtype == S2.dtype == global_dtype
+
+    S2 = manhattan_distances(X, Y)
+    assert_allclose(S, S2)
+    if global_dtype == np.float64:
+        assert S.dtype == S2.dtype == global_dtype
+    else:
+        # TODO Fix manhattan_distances to preserve dtype.
+        # currently pairwise_distances uses manhattan_distances but converts the result
+        # back to the input dtype
+        with pytest.raises(AssertionError):
+            assert S.dtype == S2.dtype == global_dtype
+
+    # Test with scipy.spatial.distance metric, with a kwd
+    kwds = {"p": 2.0}
+    S = pairwise_distances(X, Y, metric="minkowski", **kwds)
+    S2 = pairwise_distances(X, Y, metric=minkowski, **kwds)
+    assert_allclose(S, S2)
+
+    # same with Y = None
+    kwds = {"p": 2.0}
+    S = pairwise_distances(X, metric="minkowski", **kwds)
+    S2 = pairwise_distances(X, metric=minkowski, **kwds)
+    assert_allclose(S, S2)
+
+    # Test that scipy distance metrics throw an error if sparse matrix given
+    with pytest.raises(TypeError):
+        pairwise_distances(X_sparse, metric="minkowski")
+    with pytest.raises(TypeError):
+        pairwise_distances(X, Y_sparse, metric="minkowski")
+
+
+# Some scipy metrics are deprecated (depending on the scipy version) but we
+# still want to test them.
+@ignore_warnings(category=DeprecationWarning)
+@pytest.mark.parametrize("metric", PAIRWISE_BOOLEAN_FUNCTIONS)
+def test_pairwise_boolean_distance(metric):
+    # test that we convert to boolean arrays for boolean distances
+    rng = np.random.RandomState(0)
+    X = rng.randn(5, 4)
+    Y = X.copy()
+    Y[0, 0] = 1 - Y[0, 0]
+
+    # ignore conversion to boolean in pairwise_distances
+    with ignore_warnings(category=DataConversionWarning):
+        for Z in [Y, None]:
+            res = pairwise_distances(X, Z, metric=metric)
+            np.nan_to_num(res, nan=0, posinf=0, neginf=0, copy=False)
+            assert np.sum(res != 0) == 0
+
+    # non-boolean arrays are converted to boolean for boolean
+    # distance metrics with a data conversion warning
+    msg = "Data was converted to boolean for metric %s" % metric
+    with pytest.warns(DataConversionWarning, match=msg):
+        pairwise_distances(X, metric=metric)
+
+    # Check that the warning is raised if X is boolean by Y is not boolean:
+    with pytest.warns(DataConversionWarning, match=msg):
+        pairwise_distances(X.astype(bool), Y=Y, metric=metric)
+
+    # Check that no warning is raised if X is already boolean and Y is None:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DataConversionWarning)
+        pairwise_distances(X.astype(bool), metric=metric)
+
+
+def test_no_data_conversion_warning():
+    # No warnings issued if metric is not a boolean distance function
+    rng = np.random.RandomState(0)
+    X = rng.randn(5, 4)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DataConversionWarning)
+        pairwise_distances(X, metric="minkowski")
+
+
+@pytest.mark.parametrize("func", [pairwise_distances, pairwise_kernels])
+def test_pairwise_precomputed(func):
+    # Test correct shape
+    with pytest.raises(ValueError, match=".* shape .*"):
+        func(np.zeros((5, 3)), metric="precomputed")
+    # with two args
+    with pytest.raises(ValueError, match=".* shape .*"):
+        func(np.zeros((5, 3)), np.zeros((4, 4)), metric="precomputed")
+    # even if shape[1] agrees (although thus second arg is spurious)
+    with pytest.raises(ValueError, match=".* shape .*"):
+        func(np.zeros((5, 3)), np.zeros((4, 3)), metric="precomputed")
+
+    # Test not copied (if appropriate dtype)
+    S = np.zeros((5, 5))
+    S2 = func(S, metric="precomputed")
+    assert S is S2
+    # with two args
+    S = np.zeros((5, 3))
+    S2 = func(S, np.zeros((3, 3)), metric="precomputed")
+    assert S is S2
+
+    # Test always returns float dtype
+    S = func(np.array([[1]], dtype="int"), metric="precomputed")
+    assert "f" == S.dtype.kind
+
+    # Test converts list to array-like
+    S = func([[1.0]], metric="precomputed")
+    assert isinstance(S, np.ndarray)
+
+
+def test_pairwise_precomputed_non_negative():
+    # Test non-negative values
+    with pytest.raises(ValueError, match=".* non-negative values.*"):
+        pairwise_distances(np.full((5, 5), -1), metric="precomputed")
+
+
+_minkowski_kwds = {"w": np.arange(1, 5).astype("double", copy=False), "p": 1}
+
+
+def callable_rbf_kernel(x, y, **kwds):
+    # Callable version of pairwise.rbf_kernel.
+    K = rbf_kernel(np.atleast_2d(x), np.atleast_2d(y), **kwds)
+    # unpack the output since this is a scalar packed in a 0-dim array
+    return K.item()
+
+
+@pytest.mark.parametrize(
+    "func, metric, kwds",
+    [
+        (pairwise_distances, "euclidean", {}),
+        (
+            pairwise_distances,
+            minkowski,
+            _minkowski_kwds,
+        ),
+        (
+            pairwise_distances,
+            "minkowski",
+            _minkowski_kwds,
+        ),
+        (pairwise_kernels, "polynomial", {"degree": 1}),
+        (pairwise_kernels, callable_rbf_kernel, {"gamma": 0.1}),
+    ],
+)
+@pytest.mark.parametrize("dtype", [np.float64, np.float32, int])
+def test_pairwise_parallel(func, metric, kwds, dtype):
+    rng = np.random.RandomState(0)
+    X = np.array(5 * rng.random_sample((5, 4)), dtype=dtype)
+    Y = np.array(5 * rng.random_sample((3, 4)), dtype=dtype)
+
+    S = func(X, metric=metric, n_jobs=1, **kwds)
+    S2 = func(X, metric=metric, n_jobs=2, **kwds)
+    assert_allclose(S, S2)
+
+    S = func(X, Y, metric=metric, n_jobs=1, **kwds)
+    S2 = func(X, Y, metric=metric, n_jobs=2, **kwds)
+    assert_allclose(S, S2)
+
+
+def test_pairwise_callable_nonstrict_metric():
+    # paired_distances should allow callable metric where metric(x, x) != 0
+    # Knowing that the callable is a strict metric would allow the diagonal to
+    # be left uncalculated and set to 0.
+    assert pairwise_distances([[1.0]], metric=lambda x, y: 5)[0, 0] == 5
+
+
+# Test with all metrics that should be in PAIRWISE_KERNEL_FUNCTIONS.
+@pytest.mark.parametrize(
+    "metric",
+    ["rbf", "laplacian", "sigmoid", "polynomial", "linear", "chi2", "additive_chi2"],
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_pairwise_kernels(metric, csr_container):
+    # Test the pairwise_kernels helper function.
+
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((2, 4))
+    function = PAIRWISE_KERNEL_FUNCTIONS[metric]
+    # Test with Y=None
+    K1 = pairwise_kernels(X, metric=metric)
+    K2 = function(X)
+    assert_allclose(K1, K2)
+    # Test with Y=Y
+    K1 = pairwise_kernels(X, Y=Y, metric=metric)
+    K2 = function(X, Y=Y)
+    assert_allclose(K1, K2)
+    # Test with tuples as X and Y
+    X_tuples = tuple([tuple([v for v in row]) for row in X])
+    Y_tuples = tuple([tuple([v for v in row]) for row in Y])
+    K2 = pairwise_kernels(X_tuples, Y_tuples, metric=metric)
+    assert_allclose(K1, K2)
+
+    # Test with sparse X and Y
+    X_sparse = csr_container(X)
+    Y_sparse = csr_container(Y)
+    if metric in ["chi2", "additive_chi2"]:
+        # these don't support sparse matrices yet
+        return
+    K1 = pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
+    assert_allclose(K1, K2)
+
+
+def test_pairwise_kernels_callable():
+    # Test the pairwise_kernels helper function
+    # with a callable function, with given keywords.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((2, 4))
+
+    metric = callable_rbf_kernel
+    kwds = {"gamma": 0.1}
+    K1 = pairwise_kernels(X, Y=Y, metric=metric, **kwds)
+    K2 = rbf_kernel(X, Y=Y, **kwds)
+    assert_allclose(K1, K2)
+
+    # callable function, X=Y
+    K1 = pairwise_kernels(X, Y=X, metric=metric, **kwds)
+    K2 = rbf_kernel(X, Y=X, **kwds)
+    assert_allclose(K1, K2)
+
+
+def test_pairwise_kernels_filter_param():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((2, 4))
+    K = rbf_kernel(X, Y, gamma=0.1)
+    params = {"gamma": 0.1, "blabla": ":)"}
+    K2 = pairwise_kernels(X, Y, metric="rbf", filter_params=True, **params)
+    assert_allclose(K, K2)
+
+    with pytest.raises(TypeError):
+        pairwise_kernels(X, Y, metric="rbf", **params)
+
+
+@pytest.mark.parametrize("metric, func", PAIRED_DISTANCES.items())
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_paired_distances(metric, func, csr_container):
+    # Test the pairwise_distance helper function.
+    rng = np.random.RandomState(0)
+    # Euclidean distance should be equivalent to calling the function.
+    X = rng.random_sample((5, 4))
+    # Euclidean distance, with Y != X.
+    Y = rng.random_sample((5, 4))
+
+    S = paired_distances(X, Y, metric=metric)
+    S2 = func(X, Y)
+    assert_allclose(S, S2)
+    S3 = func(csr_container(X), csr_container(Y))
+    assert_allclose(S, S3)
+    if metric in PAIRWISE_DISTANCE_FUNCTIONS:
+        # Check the pairwise_distances implementation
+        # gives the same value
+        distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
+        distances = np.diag(distances)
+        assert_allclose(distances, S)
+
+
+def test_paired_distances_callable(global_dtype):
+    # Test the paired_distance helper function
+    # with the callable implementation
+    rng = np.random.RandomState(0)
+    # Euclidean distance should be equivalent to calling the function.
+    X = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+    # Euclidean distance, with Y != X.
+    Y = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+
+    S = paired_distances(X, Y, metric="manhattan")
+    S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))
+    assert_allclose(S, S2)
+
+    # Test that a value error is raised when the lengths of X and Y should not
+    # differ
+    Y = rng.random_sample((3, 4))
+    with pytest.raises(ValueError):
+        paired_distances(X, Y)
+
+
+@pytest.mark.parametrize("dok_container", DOK_CONTAINERS)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_pairwise_distances_argmin_min(dok_container, csr_container, global_dtype):
+    # Check pairwise minimum distances computation for any metric
+    X = np.asarray([[0], [1]], dtype=global_dtype)
+    Y = np.asarray([[-2], [3]], dtype=global_dtype)
+
+    Xsp = dok_container(X)
+    Ysp = csr_container(Y, dtype=global_dtype)
+
+    expected_idx = [0, 1]
+    expected_vals = [2, 2]
+    expected_vals_sq = [4, 4]
+
+    # euclidean metric
+    idx, vals = pairwise_distances_argmin_min(X, Y, metric="euclidean")
+    idx2 = pairwise_distances_argmin(X, Y, metric="euclidean")
+    assert_allclose(idx, expected_idx)
+    assert_allclose(idx2, expected_idx)
+    assert_allclose(vals, expected_vals)
+    # sparse matrix case
+    idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="euclidean")
+    idxsp2 = pairwise_distances_argmin(Xsp, Ysp, metric="euclidean")
+    assert_allclose(idxsp, expected_idx)
+    assert_allclose(idxsp2, expected_idx)
+    assert_allclose(valssp, expected_vals)
+    # We don't want np.matrix here
+    assert type(idxsp) == np.ndarray
+    assert type(valssp) == np.ndarray
+
+    # Squared Euclidean metric
+    idx, vals = pairwise_distances_argmin_min(X, Y, metric="sqeuclidean")
+    idx2, vals2 = pairwise_distances_argmin_min(
+        X, Y, metric="euclidean", metric_kwargs={"squared": True}
+    )
+    idx3 = pairwise_distances_argmin(X, Y, metric="sqeuclidean")
+    idx4 = pairwise_distances_argmin(
+        X, Y, metric="euclidean", metric_kwargs={"squared": True}
+    )
+
+    assert_allclose(vals, expected_vals_sq)
+    assert_allclose(vals2, expected_vals_sq)
+
+    assert_allclose(idx, expected_idx)
+    assert_allclose(idx2, expected_idx)
+    assert_allclose(idx3, expected_idx)
+    assert_allclose(idx4, expected_idx)
+
+    # Non-euclidean scikit-learn metric
+    idx, vals = pairwise_distances_argmin_min(X, Y, metric="manhattan")
+    idx2 = pairwise_distances_argmin(X, Y, metric="manhattan")
+    assert_allclose(idx, expected_idx)
+    assert_allclose(idx2, expected_idx)
+    assert_allclose(vals, expected_vals)
+    # sparse matrix case
+    idxsp, valssp = pairwise_distances_argmin_min(Xsp, Ysp, metric="manhattan")
+    idxsp2 = pairwise_distances_argmin(Xsp, Ysp, metric="manhattan")
+    assert_allclose(idxsp, expected_idx)
+    assert_allclose(idxsp2, expected_idx)
+    assert_allclose(valssp, expected_vals)
+
+    # Non-euclidean Scipy distance (callable)
+    idx, vals = pairwise_distances_argmin_min(
+        X, Y, metric=minkowski, metric_kwargs={"p": 2}
+    )
+    assert_allclose(idx, expected_idx)
+    assert_allclose(vals, expected_vals)
+
+    # Non-euclidean Scipy distance (string)
+    idx, vals = pairwise_distances_argmin_min(
+        X, Y, metric="minkowski", metric_kwargs={"p": 2}
+    )
+    assert_allclose(idx, expected_idx)
+    assert_allclose(vals, expected_vals)
+
+    # Compare with naive implementation
+    rng = np.random.RandomState(0)
+    X = rng.randn(97, 149)
+    Y = rng.randn(111, 149)
+
+    dist = pairwise_distances(X, Y, metric="manhattan")
+    dist_orig_ind = dist.argmin(axis=0)
+    dist_orig_val = dist[dist_orig_ind, range(len(dist_orig_ind))]
+
+    dist_chunked_ind, dist_chunked_val = pairwise_distances_argmin_min(
+        X, Y, axis=0, metric="manhattan"
+    )
+    assert_allclose(dist_orig_ind, dist_chunked_ind, rtol=1e-7)
+    assert_allclose(dist_orig_val, dist_chunked_val, rtol=1e-7)
+
+    # Changing the axis and permuting datasets must give the same results
+    argmin_0, dist_0 = pairwise_distances_argmin_min(X, Y, axis=0)
+    argmin_1, dist_1 = pairwise_distances_argmin_min(Y, X, axis=1)
+
+    assert_allclose(dist_0, dist_1)
+    assert_array_equal(argmin_0, argmin_1)
+
+    argmin_0, dist_0 = pairwise_distances_argmin_min(X, X, axis=0)
+    argmin_1, dist_1 = pairwise_distances_argmin_min(X, X, axis=1)
+
+    assert_allclose(dist_0, dist_1)
+    assert_array_equal(argmin_0, argmin_1)
+
+    # Changing the axis and permuting datasets must give the same results
+    argmin_0 = pairwise_distances_argmin(X, Y, axis=0)
+    argmin_1 = pairwise_distances_argmin(Y, X, axis=1)
+
+    assert_array_equal(argmin_0, argmin_1)
+
+    argmin_0 = pairwise_distances_argmin(X, X, axis=0)
+    argmin_1 = pairwise_distances_argmin(X, X, axis=1)
+
+    assert_array_equal(argmin_0, argmin_1)
+
+    # F-contiguous arrays must be supported and must return identical results.
+    argmin_C_contiguous = pairwise_distances_argmin(X, Y)
+    argmin_F_contiguous = pairwise_distances_argmin(
+        np.asfortranarray(X), np.asfortranarray(Y)
+    )
+
+    assert_array_equal(argmin_C_contiguous, argmin_F_contiguous)
+
+
+def _reduce_func(dist, start):
+    return dist[:, :100]
+
+
+def test_pairwise_distances_chunked_reduce(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((400, 4)).astype(global_dtype, copy=False)
+    # Reduced Euclidean distance
+    S = pairwise_distances(X)[:, :100]
+    S_chunks = pairwise_distances_chunked(
+        X, None, reduce_func=_reduce_func, working_memory=2**-16
+    )
+    assert isinstance(S_chunks, GeneratorType)
+    S_chunks = list(S_chunks)
+    assert len(S_chunks) > 1
+    assert S_chunks[0].dtype == X.dtype
+
+    # atol is for diagonal where S is explicitly zeroed on the diagonal
+    assert_allclose(np.vstack(S_chunks), S, atol=1e-7)
+
+
+def test_pairwise_distances_chunked_reduce_none(global_dtype):
+    # check that the reduce func is allowed to return None
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((10, 4)).astype(global_dtype, copy=False)
+    S_chunks = pairwise_distances_chunked(
+        X, None, reduce_func=lambda dist, start: None, working_memory=2**-16
+    )
+    assert isinstance(S_chunks, GeneratorType)
+    S_chunks = list(S_chunks)
+    assert len(S_chunks) > 1
+    assert all(chunk is None for chunk in S_chunks)
+
+
+@pytest.mark.parametrize(
+    "good_reduce",
+    [
+        lambda D, start: list(D),
+        lambda D, start: np.array(D),
+        lambda D, start: (list(D), list(D)),
+    ]
+    + [
+        lambda D, start, scipy_csr_type=scipy_csr_type: scipy_csr_type(D)
+        for scipy_csr_type in CSR_CONTAINERS
+    ]
+    + [
+        lambda D, start, scipy_dok_type=scipy_dok_type: (
+            scipy_dok_type(D),
+            np.array(D),
+            list(D),
+        )
+        for scipy_dok_type in DOK_CONTAINERS
+    ],
+)
+def test_pairwise_distances_chunked_reduce_valid(good_reduce):
+    X = np.arange(10).reshape(-1, 1)
+    S_chunks = pairwise_distances_chunked(
+        X, None, reduce_func=good_reduce, working_memory=64
+    )
+    next(S_chunks)
+
+
+@pytest.mark.parametrize(
+    ("bad_reduce", "err_type", "message"),
+    [
+        (
+            lambda D, s: np.concatenate([D, D[-1:]]),
+            ValueError,
+            r"length 11\..* input: 10\.",
+        ),
+        (
+            lambda D, s: (D, np.concatenate([D, D[-1:]])),
+            ValueError,
+            r"length \(10, 11\)\..* input: 10\.",
+        ),
+        (lambda D, s: (D[:9], D), ValueError, r"length \(9, 10\)\..* input: 10\."),
+        (
+            lambda D, s: 7,
+            TypeError,
+            r"returned 7\. Expected sequence\(s\) of length 10\.",
+        ),
+        (
+            lambda D, s: (7, 8),
+            TypeError,
+            r"returned \(7, 8\)\. Expected sequence\(s\) of length 10\.",
+        ),
+        (
+            lambda D, s: (np.arange(10), 9),
+            TypeError,
+            r", 9\)\. Expected sequence\(s\) of length 10\.",
+        ),
+    ],
+)
+def test_pairwise_distances_chunked_reduce_invalid(
+    global_dtype, bad_reduce, err_type, message
+):
+    X = np.arange(10).reshape(-1, 1).astype(global_dtype, copy=False)
+    S_chunks = pairwise_distances_chunked(
+        X, None, reduce_func=bad_reduce, working_memory=64
+    )
+    with pytest.raises(err_type, match=message):
+        next(S_chunks)
+
+
+def check_pairwise_distances_chunked(X, Y, working_memory, metric="euclidean"):
+    gen = pairwise_distances_chunked(X, Y, working_memory=working_memory, metric=metric)
+    assert isinstance(gen, GeneratorType)
+    blockwise_distances = list(gen)
+    Y = X if Y is None else Y
+    min_block_mib = len(Y) * 8 * 2**-20
+
+    for block in blockwise_distances:
+        memory_used = block.nbytes
+        assert memory_used <= max(working_memory, min_block_mib) * 2**20
+
+    blockwise_distances = np.vstack(blockwise_distances)
+    S = pairwise_distances(X, Y, metric=metric)
+    assert_allclose(blockwise_distances, S, atol=1e-7)
+
+
+@pytest.mark.parametrize("metric", ("euclidean", "l2", "sqeuclidean"))
+def test_pairwise_distances_chunked_diagonal(metric, global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.normal(size=(1000, 10), scale=1e10).astype(global_dtype, copy=False)
+    chunks = list(pairwise_distances_chunked(X, working_memory=1, metric=metric))
+    assert len(chunks) > 1
+    assert_allclose(np.diag(np.vstack(chunks)), 0, rtol=1e-10)
+
+
+@pytest.mark.parametrize("metric", ("euclidean", "l2", "sqeuclidean"))
+def test_parallel_pairwise_distances_diagonal(metric, global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.normal(size=(1000, 10), scale=1e10).astype(global_dtype, copy=False)
+    distances = pairwise_distances(X, metric=metric, n_jobs=2)
+    assert_allclose(np.diag(distances), 0, atol=1e-10)
+
+
+@pytest.mark.filterwarnings("ignore:Could not adhere to working_memory config")
+def test_pairwise_distances_chunked(global_dtype):
+    # Test the pairwise_distance helper function.
+    rng = np.random.RandomState(0)
+    # Euclidean distance should be equivalent to calling the function.
+    X = rng.random_sample((200, 4)).astype(global_dtype, copy=False)
+    check_pairwise_distances_chunked(X, None, working_memory=1, metric="euclidean")
+    # Test small amounts of memory
+    for power in range(-16, 0):
+        check_pairwise_distances_chunked(
+            X, None, working_memory=2**power, metric="euclidean"
+        )
+    # X as list
+    check_pairwise_distances_chunked(
+        X.tolist(), None, working_memory=1, metric="euclidean"
+    )
+    # Euclidean distance, with Y != X.
+    Y = rng.random_sample((100, 4)).astype(global_dtype, copy=False)
+    check_pairwise_distances_chunked(X, Y, working_memory=1, metric="euclidean")
+    check_pairwise_distances_chunked(
+        X.tolist(), Y.tolist(), working_memory=1, metric="euclidean"
+    )
+    # absurdly large working_memory
+    check_pairwise_distances_chunked(X, Y, working_memory=10000, metric="euclidean")
+    # "cityblock" uses scikit-learn metric, cityblock (function) is
+    # scipy.spatial.
+    check_pairwise_distances_chunked(X, Y, working_memory=1, metric="cityblock")
+
+    # Test precomputed returns all at once
+    D = pairwise_distances(X)
+    gen = pairwise_distances_chunked(D, working_memory=2**-16, metric="precomputed")
+    assert isinstance(gen, GeneratorType)
+    assert next(gen) is D
+    with pytest.raises(StopIteration):
+        next(gen)
+
+
+@pytest.mark.parametrize(
+    "x_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+@pytest.mark.parametrize(
+    "y_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances_known_result(x_array_constr, y_array_constr):
+    # Check the pairwise Euclidean distances computation on known result
+    X = x_array_constr([[0]])
+    Y = y_array_constr([[1], [2]])
+    D = euclidean_distances(X, Y)
+    assert_allclose(D, [[1.0, 2.0]])
+
+
+@pytest.mark.parametrize(
+    "y_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances_with_norms(global_dtype, y_array_constr):
+    # check that we still get the right answers with {X,Y}_norm_squared
+    # and that we get a wrong answer with wrong {X,Y}_norm_squared
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((10, 10)).astype(global_dtype, copy=False)
+    Y = rng.random_sample((20, 10)).astype(global_dtype, copy=False)
+
+    # norms will only be used if their dtype is float64
+    X_norm_sq = (X.astype(np.float64) ** 2).sum(axis=1).reshape(1, -1)
+    Y_norm_sq = (Y.astype(np.float64) ** 2).sum(axis=1).reshape(1, -1)
+
+    Y = y_array_constr(Y)
+
+    D1 = euclidean_distances(X, Y)
+    D2 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq)
+    D3 = euclidean_distances(X, Y, Y_norm_squared=Y_norm_sq)
+    D4 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq, Y_norm_squared=Y_norm_sq)
+    assert_allclose(D2, D1)
+    assert_allclose(D3, D1)
+    assert_allclose(D4, D1)
+
+    # check we get the wrong answer with wrong {X,Y}_norm_squared
+    wrong_D = euclidean_distances(
+        X,
+        Y,
+        X_norm_squared=np.zeros_like(X_norm_sq),
+        Y_norm_squared=np.zeros_like(Y_norm_sq),
+    )
+    with pytest.raises(AssertionError):
+        assert_allclose(wrong_D, D1)
+
+
+@pytest.mark.parametrize("symmetric", [True, False])
+def test_euclidean_distances_float32_norms(global_random_seed, symmetric):
+    # Non-regression test for #27621
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((10, 10))
+    Y = X if symmetric else rng.random_sample((20, 10))
+    X_norm_sq = (X.astype(np.float32) ** 2).sum(axis=1).reshape(1, -1)
+    Y_norm_sq = (Y.astype(np.float32) ** 2).sum(axis=1).reshape(1, -1)
+    D1 = euclidean_distances(X, Y)
+    D2 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq)
+    D3 = euclidean_distances(X, Y, Y_norm_squared=Y_norm_sq)
+    D4 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq, Y_norm_squared=Y_norm_sq)
+    assert_allclose(D2, D1)
+    assert_allclose(D3, D1)
+    assert_allclose(D4, D1)
+
+
+def test_euclidean_distances_norm_shapes():
+    # Check all accepted shapes for the norms or appropriate error messages.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((10, 10))
+    Y = rng.random_sample((20, 10))
+
+    X_norm_squared = (X**2).sum(axis=1)
+    Y_norm_squared = (Y**2).sum(axis=1)
+
+    D1 = euclidean_distances(
+        X, Y, X_norm_squared=X_norm_squared, Y_norm_squared=Y_norm_squared
+    )
+    D2 = euclidean_distances(
+        X,
+        Y,
+        X_norm_squared=X_norm_squared.reshape(-1, 1),
+        Y_norm_squared=Y_norm_squared.reshape(-1, 1),
+    )
+    D3 = euclidean_distances(
+        X,
+        Y,
+        X_norm_squared=X_norm_squared.reshape(1, -1),
+        Y_norm_squared=Y_norm_squared.reshape(1, -1),
+    )
+
+    assert_allclose(D2, D1)
+    assert_allclose(D3, D1)
+
+    with pytest.raises(ValueError, match="Incompatible dimensions for X"):
+        euclidean_distances(X, Y, X_norm_squared=X_norm_squared[:5])
+    with pytest.raises(ValueError, match="Incompatible dimensions for Y"):
+        euclidean_distances(X, Y, Y_norm_squared=Y_norm_squared[:5])
+
+
+@pytest.mark.parametrize(
+    "x_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+@pytest.mark.parametrize(
+    "y_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances(global_dtype, x_array_constr, y_array_constr):
+    # check that euclidean distances gives same result as scipy cdist
+    # when X and Y != X are provided
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    X[X < 0.8] = 0
+    Y = rng.random_sample((10, 10)).astype(global_dtype, copy=False)
+    Y[Y < 0.8] = 0
+
+    expected = cdist(X, Y)
+
+    X = x_array_constr(X)
+    Y = y_array_constr(Y)
+    distances = euclidean_distances(X, Y)
+
+    # the default rtol=1e-7 is too close to the float32 precision
+    # and fails due to rounding errors.
+    assert_allclose(distances, expected, rtol=1e-6)
+    assert distances.dtype == global_dtype
+
+
+@pytest.mark.parametrize(
+    "x_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances_sym(global_dtype, x_array_constr):
+    # check that euclidean distances gives same result as scipy pdist
+    # when only X is provided
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    X[X < 0.8] = 0
+
+    expected = squareform(pdist(X))
+
+    X = x_array_constr(X)
+    distances = euclidean_distances(X)
+
+    # the default rtol=1e-7 is too close to the float32 precision
+    # and fails due to rounding errors.
+    assert_allclose(distances, expected, rtol=1e-6)
+    assert distances.dtype == global_dtype
+
+
+@pytest.mark.parametrize("batch_size", [None, 5, 7, 101])
+@pytest.mark.parametrize(
+    "x_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+@pytest.mark.parametrize(
+    "y_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances_upcast(batch_size, x_array_constr, y_array_constr):
+    # check batches handling when Y != X (#13910)
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(np.float32)
+    X[X < 0.8] = 0
+    Y = rng.random_sample((10, 10)).astype(np.float32)
+    Y[Y < 0.8] = 0
+
+    expected = cdist(X, Y)
+
+    X = x_array_constr(X)
+    Y = y_array_constr(Y)
+    distances = _euclidean_distances_upcast(X, Y=Y, batch_size=batch_size)
+    distances = np.sqrt(np.maximum(distances, 0))
+
+    # the default rtol=1e-7 is too close to the float32 precision
+    # and fails due to rounding errors.
+    assert_allclose(distances, expected, rtol=1e-6)
+
+
+@pytest.mark.parametrize("batch_size", [None, 5, 7, 101])
+@pytest.mark.parametrize(
+    "x_array_constr",
+    [np.array] + CSR_CONTAINERS,
+    ids=["dense"] + [container.__name__ for container in CSR_CONTAINERS],
+)
+def test_euclidean_distances_upcast_sym(batch_size, x_array_constr):
+    # check batches handling when X is Y (#13910)
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(np.float32)
+    X[X < 0.8] = 0
+
+    expected = squareform(pdist(X))
+
+    X = x_array_constr(X)
+    distances = _euclidean_distances_upcast(X, Y=X, batch_size=batch_size)
+    distances = np.sqrt(np.maximum(distances, 0))
+
+    # the default rtol=1e-7 is too close to the float32 precision
+    # and fails due to rounding errors.
+    assert_allclose(distances, expected, rtol=1e-6)
+
+
+@pytest.mark.parametrize(
+    "dtype, eps, rtol",
+    [
+        (np.float32, 1e-4, 1e-5),
+        pytest.param(
+            np.float64,
+            1e-8,
+            0.99,
+            marks=pytest.mark.xfail(reason="failing due to lack of precision"),
+        ),
+    ],
+)
+@pytest.mark.parametrize("dim", [1, 1000000])
+def test_euclidean_distances_extreme_values(dtype, eps, rtol, dim):
+    # check that euclidean distances is correct with float32 input thanks to
+    # upcasting. On float64 there are still precision issues.
+    X = np.array([[1.0] * dim], dtype=dtype)
+    Y = np.array([[1.0 + eps] * dim], dtype=dtype)
+
+    distances = euclidean_distances(X, Y)
+    expected = cdist(X, Y)
+
+    assert_allclose(distances, expected, rtol=1e-5)
+
+
+@pytest.mark.parametrize("squared", [True, False])
+def test_nan_euclidean_distances_equal_to_euclidean_distance(squared):
+    # with no nan values
+    rng = np.random.RandomState(1337)
+    X = rng.randn(3, 4)
+    Y = rng.randn(4, 4)
+
+    normal_distance = euclidean_distances(X, Y=Y, squared=squared)
+    nan_distance = nan_euclidean_distances(X, Y=Y, squared=squared)
+    assert_allclose(normal_distance, nan_distance)
+
+
+@pytest.mark.parametrize("X", [np.array([[np.inf, 0]]), np.array([[0, -np.inf]])])
+@pytest.mark.parametrize("Y", [np.array([[np.inf, 0]]), np.array([[0, -np.inf]]), None])
+def test_nan_euclidean_distances_infinite_values(X, Y):
+    with pytest.raises(ValueError) as excinfo:
+        nan_euclidean_distances(X, Y=Y)
+
+    exp_msg = "Input contains infinity or a value too large for dtype('float64')."
+    assert exp_msg == str(excinfo.value)
+
+
+@pytest.mark.parametrize(
+    "X, X_diag, missing_value",
+    [
+        (np.array([[0, 1], [1, 0]]), np.sqrt(2), np.nan),
+        (np.array([[0, 1], [1, np.nan]]), np.sqrt(2), np.nan),
+        (np.array([[np.nan, 1], [1, np.nan]]), np.nan, np.nan),
+        (np.array([[np.nan, 1], [np.nan, 0]]), np.sqrt(2), np.nan),
+        (np.array([[0, np.nan], [1, np.nan]]), np.sqrt(2), np.nan),
+        (np.array([[0, 1], [1, 0]]), np.sqrt(2), -1),
+        (np.array([[0, 1], [1, -1]]), np.sqrt(2), -1),
+        (np.array([[-1, 1], [1, -1]]), np.nan, -1),
+        (np.array([[-1, 1], [-1, 0]]), np.sqrt(2), -1),
+        (np.array([[0, -1], [1, -1]]), np.sqrt(2), -1),
+    ],
+)
+def test_nan_euclidean_distances_2x2(X, X_diag, missing_value):
+    exp_dist = np.array([[0.0, X_diag], [X_diag, 0]])
+
+    dist = nan_euclidean_distances(X, missing_values=missing_value)
+    assert_allclose(exp_dist, dist)
+
+    dist_sq = nan_euclidean_distances(X, squared=True, missing_values=missing_value)
+    assert_allclose(exp_dist**2, dist_sq)
+
+    dist_two = nan_euclidean_distances(X, X, missing_values=missing_value)
+    assert_allclose(exp_dist, dist_two)
+
+    dist_two_copy = nan_euclidean_distances(X, X.copy(), missing_values=missing_value)
+    assert_allclose(exp_dist, dist_two_copy)
+
+
+@pytest.mark.parametrize("missing_value", [np.nan, -1])
+def test_nan_euclidean_distances_complete_nan(missing_value):
+    X = np.array([[missing_value, missing_value], [0, 1]])
+
+    exp_dist = np.array([[np.nan, np.nan], [np.nan, 0]])
+
+    dist = nan_euclidean_distances(X, missing_values=missing_value)
+    assert_allclose(exp_dist, dist)
+
+    dist = nan_euclidean_distances(X, X.copy(), missing_values=missing_value)
+    assert_allclose(exp_dist, dist)
+
+
+@pytest.mark.parametrize("missing_value", [np.nan, -1])
+def test_nan_euclidean_distances_not_trival(missing_value):
+    X = np.array(
+        [
+            [1.0, missing_value, 3.0, 4.0, 2.0],
+            [missing_value, 4.0, 6.0, 1.0, missing_value],
+            [3.0, missing_value, missing_value, missing_value, 1.0],
+        ]
+    )
+
+    Y = np.array(
+        [
+            [missing_value, 7.0, 7.0, missing_value, 2.0],
+            [missing_value, missing_value, 5.0, 4.0, 7.0],
+            [missing_value, missing_value, missing_value, 4.0, 5.0],
+        ]
+    )
+
+    # Check for symmetry
+    D1 = nan_euclidean_distances(X, Y, missing_values=missing_value)
+    D2 = nan_euclidean_distances(Y, X, missing_values=missing_value)
+
+    assert_almost_equal(D1, D2.T)
+
+    # Check with explicit formula and squared=True
+    assert_allclose(
+        nan_euclidean_distances(
+            X[:1], Y[:1], squared=True, missing_values=missing_value
+        ),
+        [[5.0 / 2.0 * ((7 - 3) ** 2 + (2 - 2) ** 2)]],
+    )
+
+    # Check with explicit formula and squared=False
+    assert_allclose(
+        nan_euclidean_distances(
+            X[1:2], Y[1:2], squared=False, missing_values=missing_value
+        ),
+        [[np.sqrt(5.0 / 2.0 * ((6 - 5) ** 2 + (1 - 4) ** 2))]],
+    )
+
+    # Check when Y = X is explicitly passed
+    D3 = nan_euclidean_distances(X, missing_values=missing_value)
+    D4 = nan_euclidean_distances(X, X, missing_values=missing_value)
+    D5 = nan_euclidean_distances(X, X.copy(), missing_values=missing_value)
+    assert_allclose(D3, D4)
+    assert_allclose(D4, D5)
+
+    # Check copy = True against copy = False
+    D6 = nan_euclidean_distances(X, Y, copy=True)
+    D7 = nan_euclidean_distances(X, Y, copy=False)
+    assert_allclose(D6, D7)
+
+
+@pytest.mark.parametrize("missing_value", [np.nan, -1])
+def test_nan_euclidean_distances_one_feature_match_positive(missing_value):
+    # First feature is the only feature that is non-nan and in both
+    # samples. The result of `nan_euclidean_distances` with squared=True
+    # should be non-negative. The non-squared version should all be close to 0.
+    X = np.array(
+        [
+            [-122.27, 648.0, missing_value, 37.85],
+            [-122.27, missing_value, 2.34701493, missing_value],
+        ]
+    )
+
+    dist_squared = nan_euclidean_distances(
+        X, missing_values=missing_value, squared=True
+    )
+    assert np.all(dist_squared >= 0)
+
+    dist = nan_euclidean_distances(X, missing_values=missing_value, squared=False)
+    assert_allclose(dist, 0.0)
+
+
+def test_cosine_distances():
+    # Check the pairwise Cosine distances computation
+    rng = np.random.RandomState(1337)
+    x = np.abs(rng.rand(910))
+    XA = np.vstack([x, x])
+    D = cosine_distances(XA)
+    assert_allclose(D, [[0.0, 0.0], [0.0, 0.0]], atol=1e-10)
+    # check that all elements are in [0, 2]
+    assert np.all(D >= 0.0)
+    assert np.all(D <= 2.0)
+    # check that diagonal elements are equal to 0
+    assert_allclose(D[np.diag_indices_from(D)], [0.0, 0.0])
+
+    XB = np.vstack([x, -x])
+    D2 = cosine_distances(XB)
+    # check that all elements are in [0, 2]
+    assert np.all(D2 >= 0.0)
+    assert np.all(D2 <= 2.0)
+    # check that diagonal elements are equal to 0 and non diagonal to 2
+    assert_allclose(D2, [[0.0, 2.0], [2.0, 0.0]])
+
+    # check large random matrix
+    X = np.abs(rng.rand(1000, 5000))
+    D = cosine_distances(X)
+    # check that diagonal elements are equal to 0
+    assert_allclose(D[np.diag_indices_from(D)], [0.0] * D.shape[0])
+    assert np.all(D >= 0.0)
+    assert np.all(D <= 2.0)
+
+
+def test_haversine_distances():
+    # Check haversine distance with distances computation
+    def slow_haversine_distances(x, y):
+        diff_lat = y[0] - x[0]
+        diff_lon = y[1] - x[1]
+        a = np.sin(diff_lat / 2) ** 2 + (
+            np.cos(x[0]) * np.cos(y[0]) * np.sin(diff_lon / 2) ** 2
+        )
+        c = 2 * np.arcsin(np.sqrt(a))
+        return c
+
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 2))
+    Y = rng.random_sample((10, 2))
+    D1 = np.array([[slow_haversine_distances(x, y) for y in Y] for x in X])
+    D2 = haversine_distances(X, Y)
+    assert_allclose(D1, D2)
+    # Test haversine distance does not accept X where n_feature != 2
+    X = rng.random_sample((10, 3))
+    err_msg = "Haversine distance only valid in 2 dimensions"
+    with pytest.raises(ValueError, match=err_msg):
+        haversine_distances(X)
+
+
+# Paired distances
+
+
+def test_paired_euclidean_distances():
+    # Check the paired Euclidean distances computation
+    X = [[0], [0]]
+    Y = [[1], [2]]
+    D = paired_euclidean_distances(X, Y)
+    assert_allclose(D, [1.0, 2.0])
+
+
+def test_paired_manhattan_distances():
+    # Check the paired manhattan distances computation
+    X = [[0], [0]]
+    Y = [[1], [2]]
+    D = paired_manhattan_distances(X, Y)
+    assert_allclose(D, [1.0, 2.0])
+
+
+def test_paired_cosine_distances():
+    # Check the paired manhattan distances computation
+    X = [[0], [0]]
+    Y = [[1], [2]]
+    D = paired_cosine_distances(X, Y)
+    assert_allclose(D, [0.5, 0.5])
+
+
+def test_chi_square_kernel():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((10, 4))
+    K_add = additive_chi2_kernel(X, Y)
+    gamma = 0.1
+    K = chi2_kernel(X, Y, gamma=gamma)
+    assert K.dtype == float
+    for i, x in enumerate(X):
+        for j, y in enumerate(Y):
+            chi2 = -np.sum((x - y) ** 2 / (x + y))
+            chi2_exp = np.exp(gamma * chi2)
+            assert_almost_equal(K_add[i, j], chi2)
+            assert_almost_equal(K[i, j], chi2_exp)
+
+    # check diagonal is ones for data with itself
+    K = chi2_kernel(Y)
+    assert_array_equal(np.diag(K), 1)
+    # check off-diagonal is < 1 but > 0:
+    assert np.all(K > 0)
+    assert np.all(K - np.diag(np.diag(K)) < 1)
+    # check that float32 is preserved
+    X = rng.random_sample((5, 4)).astype(np.float32)
+    Y = rng.random_sample((10, 4)).astype(np.float32)
+    K = chi2_kernel(X, Y)
+    assert K.dtype == np.float32
+
+    # check integer type gets converted,
+    # check that zeros are handled
+    X = rng.random_sample((10, 4)).astype(np.int32)
+    K = chi2_kernel(X, X)
+    assert np.isfinite(K).all()
+    assert K.dtype == float
+
+    # check that kernel of similar things is greater than dissimilar ones
+    X = [[0.3, 0.7], [1.0, 0]]
+    Y = [[0, 1], [0.9, 0.1]]
+    K = chi2_kernel(X, Y)
+    assert K[0, 0] > K[0, 1]
+    assert K[1, 1] > K[1, 0]
+
+    # test negative input
+    with pytest.raises(ValueError):
+        chi2_kernel([[0, -1]])
+    with pytest.raises(ValueError):
+        chi2_kernel([[0, -1]], [[-1, -1]])
+    with pytest.raises(ValueError):
+        chi2_kernel([[0, 1]], [[-1, -1]])
+
+    # different n_features in X and Y
+    with pytest.raises(ValueError):
+        chi2_kernel([[0, 1]], [[0.2, 0.2, 0.6]])
+
+
+@pytest.mark.parametrize(
+    "kernel",
+    (
+        linear_kernel,
+        polynomial_kernel,
+        rbf_kernel,
+        laplacian_kernel,
+        sigmoid_kernel,
+        cosine_similarity,
+    ),
+)
+def test_kernel_symmetry(kernel):
+    # Valid kernels should be symmetric
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    K = kernel(X, X)
+    assert_allclose(K, K.T, 15)
+
+
+@pytest.mark.parametrize(
+    "kernel",
+    (
+        linear_kernel,
+        polynomial_kernel,
+        rbf_kernel,
+        laplacian_kernel,
+        sigmoid_kernel,
+        cosine_similarity,
+    ),
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_kernel_sparse(kernel, csr_container):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    X_sparse = csr_container(X)
+    K = kernel(X, X)
+    K2 = kernel(X_sparse, X_sparse)
+    assert_allclose(K, K2)
+
+
+def test_linear_kernel():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    K = linear_kernel(X, X)
+    # the diagonal elements of a linear kernel are their squared norm
+    assert_allclose(K.flat[::6], [linalg.norm(x) ** 2 for x in X])
+
+
+def test_rbf_kernel():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    K = rbf_kernel(X, X)
+    # the diagonal elements of a rbf kernel are 1
+    assert_allclose(K.flat[::6], np.ones(5))
+
+
+def test_laplacian_kernel():
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    K = laplacian_kernel(X, X)
+    # the diagonal elements of a laplacian kernel are 1
+    assert_allclose(np.diag(K), np.ones(5))
+
+    # off-diagonal elements are < 1 but > 0:
+    assert np.all(K > 0)
+    assert np.all(K - np.diag(np.diag(K)) < 1)
+
+
+@pytest.mark.parametrize(
+    "metric, pairwise_func",
+    [("linear", linear_kernel), ("cosine", cosine_similarity)],
+)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_pairwise_similarity_sparse_output(metric, pairwise_func, csr_container):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((3, 4))
+    Xcsr = csr_container(X)
+    Ycsr = csr_container(Y)
+
+    # should be sparse
+    K1 = pairwise_func(Xcsr, Ycsr, dense_output=False)
+    assert issparse(K1)
+
+    # should be dense, and equal to K1
+    K2 = pairwise_func(X, Y, dense_output=True)
+    assert not issparse(K2)
+    assert_allclose(K1.toarray(), K2)
+
+    # show the kernel output equal to the sparse.toarray()
+    K3 = pairwise_kernels(X, Y=Y, metric=metric)
+    assert_allclose(K1.toarray(), K3)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_cosine_similarity(csr_container):
+    # Test the cosine_similarity.
+
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((5, 4))
+    Y = rng.random_sample((3, 4))
+    Xcsr = csr_container(X)
+    Ycsr = csr_container(Y)
+
+    for X_, Y_ in ((X, None), (X, Y), (Xcsr, None), (Xcsr, Ycsr)):
+        # Test that the cosine is kernel is equal to a linear kernel when data
+        # has been previously normalized by L2-norm.
+        K1 = pairwise_kernels(X_, Y=Y_, metric="cosine")
+        X_ = normalize(X_)
+        if Y_ is not None:
+            Y_ = normalize(Y_)
+        K2 = pairwise_kernels(X_, Y=Y_, metric="linear")
+        assert_allclose(K1, K2)
+
+
+def test_check_dense_matrices():
+    # Ensure that pairwise array check works for dense matrices.
+    # Check that if XB is None, XB is returned as reference to XA
+    XA = np.resize(np.arange(40), (5, 8))
+    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
+    assert XA_checked is XB_checked
+    assert_array_equal(XA, XA_checked)
+
+
+def test_check_XB_returned():
+    # Ensure that if XA and XB are given correctly, they return as equal.
+    # Check that if XB is not None, it is returned equal.
+    # Note that the second dimension of XB is the same as XA.
+    XA = np.resize(np.arange(40), (5, 8))
+    XB = np.resize(np.arange(32), (4, 8))
+    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
+    assert_array_equal(XA, XA_checked)
+    assert_array_equal(XB, XB_checked)
+
+    XB = np.resize(np.arange(40), (5, 8))
+    XA_checked, XB_checked = check_paired_arrays(XA, XB)
+    assert_array_equal(XA, XA_checked)
+    assert_array_equal(XB, XB_checked)
+
+
+def test_check_different_dimensions():
+    # Ensure an error is raised if the dimensions are different.
+    XA = np.resize(np.arange(45), (5, 9))
+    XB = np.resize(np.arange(32), (4, 8))
+    with pytest.raises(ValueError):
+        check_pairwise_arrays(XA, XB)
+
+    XB = np.resize(np.arange(4 * 9), (4, 9))
+    with pytest.raises(ValueError):
+        check_paired_arrays(XA, XB)
+
+
+def test_check_invalid_dimensions():
+    # Ensure an error is raised on 1D input arrays.
+    # The modified tests are not 1D. In the old test, the array was internally
+    # converted to 2D anyways
+    XA = np.arange(45).reshape(9, 5)
+    XB = np.arange(32).reshape(4, 8)
+    with pytest.raises(ValueError):
+        check_pairwise_arrays(XA, XB)
+    XA = np.arange(45).reshape(9, 5)
+    XB = np.arange(32).reshape(4, 8)
+    with pytest.raises(ValueError):
+        check_pairwise_arrays(XA, XB)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_check_sparse_arrays(csr_container):
+    # Ensures that checks return valid sparse matrices.
+    rng = np.random.RandomState(0)
+    XA = rng.random_sample((5, 4))
+    XA_sparse = csr_container(XA)
+    XB = rng.random_sample((5, 4))
+    XB_sparse = csr_container(XB)
+    XA_checked, XB_checked = check_pairwise_arrays(XA_sparse, XB_sparse)
+    # compare their difference because testing csr matrices for
+    # equality with '==' does not work as expected.
+    assert issparse(XA_checked)
+    assert abs(XA_sparse - XA_checked).sum() == 0
+    assert issparse(XB_checked)
+    assert abs(XB_sparse - XB_checked).sum() == 0
+
+    XA_checked, XA_2_checked = check_pairwise_arrays(XA_sparse, XA_sparse)
+    assert issparse(XA_checked)
+    assert abs(XA_sparse - XA_checked).sum() == 0
+    assert issparse(XA_2_checked)
+    assert abs(XA_2_checked - XA_checked).sum() == 0
+
+
+def tuplify(X):
+    # Turns a numpy matrix (any n-dimensional array) into tuples.
+    s = X.shape
+    if len(s) > 1:
+        # Tuplify each sub-array in the input.
+        return tuple(tuplify(row) for row in X)
+    else:
+        # Single dimension input, just return tuple of contents.
+        return tuple(r for r in X)
+
+
+def test_check_tuple_input():
+    # Ensures that checks return valid tuples.
+    rng = np.random.RandomState(0)
+    XA = rng.random_sample((5, 4))
+    XA_tuples = tuplify(XA)
+    XB = rng.random_sample((5, 4))
+    XB_tuples = tuplify(XB)
+    XA_checked, XB_checked = check_pairwise_arrays(XA_tuples, XB_tuples)
+    assert_array_equal(XA_tuples, XA_checked)
+    assert_array_equal(XB_tuples, XB_checked)
+
+
+def test_check_preserve_type():
+    # Ensures that type float32 is preserved.
+    XA = np.resize(np.arange(40), (5, 8)).astype(np.float32)
+    XB = np.resize(np.arange(40), (5, 8)).astype(np.float32)
+
+    XA_checked, XB_checked = check_pairwise_arrays(XA, None)
+    assert XA_checked.dtype == np.float32
+
+    # both float32
+    XA_checked, XB_checked = check_pairwise_arrays(XA, XB)
+    assert XA_checked.dtype == np.float32
+    assert XB_checked.dtype == np.float32
+
+    # mismatched A
+    XA_checked, XB_checked = check_pairwise_arrays(XA.astype(float), XB)
+    assert XA_checked.dtype == float
+    assert XB_checked.dtype == float
+
+    # mismatched B
+    XA_checked, XB_checked = check_pairwise_arrays(XA, XB.astype(float))
+    assert XA_checked.dtype == float
+    assert XB_checked.dtype == float
+
+
+@pytest.mark.parametrize("n_jobs", [1, 2])
+@pytest.mark.parametrize("metric", ["seuclidean", "mahalanobis"])
+@pytest.mark.parametrize(
+    "dist_function", [pairwise_distances, pairwise_distances_chunked]
+)
+def test_pairwise_distances_data_derived_params(n_jobs, metric, dist_function):
+    # check that pairwise_distances give the same result in sequential and
+    # parallel, when metric has data-derived parameters.
+    with config_context(working_memory=0.1):  # to have more than 1 chunk
+        rng = np.random.RandomState(0)
+        X = rng.random_sample((100, 10))
+
+        expected_dist = squareform(pdist(X, metric=metric))
+        dist = np.vstack(tuple(dist_function(X, metric=metric, n_jobs=n_jobs)))
+
+        assert_allclose(dist, expected_dist)
+
+
+@pytest.mark.parametrize("metric", ["seuclidean", "mahalanobis"])
+def test_pairwise_distances_data_derived_params_error(metric):
+    # check that pairwise_distances raises an error when Y is passed but
+    # metric has data-derived params that are not provided by the user.
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10))
+    Y = rng.random_sample((100, 10))
+
+    with pytest.raises(
+        ValueError,
+        match=rf"The '(V|VI)' parameter is required for the {metric} metric",
+    ):
+        pairwise_distances(X, Y, metric=metric)
+
+
+@pytest.mark.parametrize(
+    "metric",
+    [
+        "braycurtis",
+        "canberra",
+        "chebyshev",
+        "correlation",
+        "hamming",
+        "mahalanobis",
+        "minkowski",
+        "seuclidean",
+        "sqeuclidean",
+        "cityblock",
+        "cosine",
+        "euclidean",
+    ],
+)
+@pytest.mark.parametrize("y_is_x", [True, False], ids=["Y is X", "Y is not X"])
+def test_numeric_pairwise_distances_datatypes(metric, global_dtype, y_is_x):
+    # Check that pairwise distances gives the same result as pdist and cdist
+    # regardless of input datatype when using any scipy metric for comparing
+    # numeric vectors
+    #
+    # This test is necessary because pairwise_distances used to throw an
+    # error when using metric='seuclidean' and the input data was not
+    # of type np.float64 (#15730)
+
+    rng = np.random.RandomState(0)
+
+    X = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+
+    params = {}
+    if y_is_x:
+        Y = X
+        expected_dist = squareform(pdist(X, metric=metric))
+    else:
+        Y = rng.random_sample((5, 4)).astype(global_dtype, copy=False)
+        expected_dist = cdist(X, Y, metric=metric)
+        # precompute parameters for seuclidean & mahalanobis when x is not y
+        if metric == "seuclidean":
+            params = {"V": np.var(np.vstack([X, Y]), axis=0, ddof=1, dtype=np.float64)}
+        elif metric == "mahalanobis":
+            params = {"VI": np.linalg.inv(np.cov(np.vstack([X, Y]).T)).T}
+
+    dist = pairwise_distances(X, Y, metric=metric, **params)
+
+    assert_allclose(dist, expected_dist)
+
+
+@pytest.mark.parametrize(
+    "pairwise_distances_func",
+    [pairwise_distances, pairwise_distances_argmin, pairwise_distances_argmin_min],
+)
+def test_nan_euclidean_support(pairwise_distances_func):
+    """Check that `nan_euclidean` is lenient with `nan` values."""
+
+    X = [[0, 1], [1, np.nan], [2, 3], [3, 5]]
+    output = pairwise_distances_func(X, X, metric="nan_euclidean")
+
+    assert not np.isnan(output).any()
+
+
+def test_nan_euclidean_constant_input_argmin():
+    """Check that the behavior of constant input is the same in the case of
+    full of nan vector and full of zero vector.
+    """
+
+    X_nan = [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]]
+    argmin_nan = pairwise_distances_argmin(X_nan, X_nan, metric="nan_euclidean")
+
+    X_const = [[0, 0], [0, 0], [0, 0]]
+    argmin_const = pairwise_distances_argmin(X_const, X_const, metric="nan_euclidean")
+
+    assert_allclose(argmin_nan, argmin_const)
+
+
+@pytest.mark.parametrize(
+    "X,Y,expected_distance",
+    [
+        (
+            ["a", "ab", "abc"],
+            None,
+            [[0.0, 1.0, 2.0], [1.0, 0.0, 1.0], [2.0, 1.0, 0.0]],
+        ),
+        (
+            ["a", "ab", "abc"],
+            ["a", "ab"],
+            [[0.0, 1.0], [1.0, 0.0], [2.0, 1.0]],
+        ),
+    ],
+)
+def test_pairwise_dist_custom_metric_for_string(X, Y, expected_distance):
+    """Check pairwise_distances with lists of strings as input."""
+
+    def dummy_string_similarity(x, y):
+        return np.abs(len(x) - len(y))
+
+    actual_distance = pairwise_distances(X=X, Y=Y, metric=dummy_string_similarity)
+    assert_allclose(actual_distance, expected_distance)
+
+
+def test_pairwise_dist_custom_metric_for_bool():
+    """Check that pairwise_distances does not convert boolean input to float
+    when using a custom metric.
+    """
+
+    def dummy_bool_dist(v1, v2):
+        # dummy distance func using `&` and thus relying on the input data being boolean
+        return 1 - (v1 & v2).sum() / (v1 | v2).sum()
+
+    X = np.array([[1, 0, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]], dtype=bool)
+
+    expected_distance = np.array(
+        [
+            [0.0, 0.5, 0.75],
+            [0.5, 0.0, 0.5],
+            [0.75, 0.5, 0.0],
+        ]
+    )
+
+    actual_distance = pairwise_distances(X=X, metric=dummy_bool_dist)
+    assert_allclose(actual_distance, expected_distance)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_manhattan_readonly_dataset(csr_container):
+    # Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/7981
+    matrices1 = [csr_container(np.ones((5, 5)))]
+    matrices2 = [csr_container(np.ones((5, 5)))]
+    # Joblib memory maps datasets which makes them read-only.
+    # The following call was reporting as failing in #7981, but this must pass.
+    Parallel(n_jobs=2, max_nbytes=0)(
+        delayed(manhattan_distances)(m1, m2) for m1, m2 in zip(matrices1, matrices2)
+    )
+
+
+# TODO(1.8): remove
+def test_force_all_finite_rename_warning():
+    X = np.random.uniform(size=(10, 10))
+    Y = np.random.uniform(size=(10, 10))
+
+    msg = "'force_all_finite' was renamed to 'ensure_all_finite'"
+
+    with pytest.warns(FutureWarning, match=msg):
+        check_pairwise_arrays(X, Y, force_all_finite=True)
+
+    with pytest.warns(FutureWarning, match=msg):
+        pairwise_distances(X, Y, force_all_finite=True)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_ranking.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_ranking.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d740249f8aba4d5a87ecd2d6a16087557335d9a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_ranking.py
@@ -0,0 +1,2270 @@
+import math
+import re
+
+import numpy as np
+import pytest
+from scipy import stats
+
+from sklearn import datasets, svm
+from sklearn.datasets import make_multilabel_classification
+from sklearn.exceptions import UndefinedMetricWarning
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    accuracy_score,
+    auc,
+    average_precision_score,
+    coverage_error,
+    dcg_score,
+    det_curve,
+    label_ranking_average_precision_score,
+    label_ranking_loss,
+    ndcg_score,
+    precision_recall_curve,
+    roc_auc_score,
+    roc_curve,
+    top_k_accuracy_score,
+)
+from sklearn.metrics._ranking import _dcg_sample_scores, _ndcg_sample_scores
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import label_binarize
+from sklearn.random_projection import _sparse_random_matrix
+from sklearn.utils._testing import (
+    _convert_container,
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+from sklearn.utils.extmath import softmax
+from sklearn.utils.fixes import CSR_CONTAINERS
+from sklearn.utils.validation import (
+    check_array,
+    check_consistent_length,
+    check_random_state,
+)
+
+###############################################################################
+# Utilities for testing
+
+CURVE_FUNCS = [
+    det_curve,
+    precision_recall_curve,
+    roc_curve,
+]
+
+
+def make_prediction(dataset=None, binary=False):
+    """Make some classification predictions on a toy dataset using a SVC
+
+    If binary is True restrict to a binary classification problem instead of a
+    multiclass classification problem
+    """
+
+    if dataset is None:
+        # import some data to play with
+        dataset = datasets.load_iris()
+
+    X = dataset.data
+    y = dataset.target
+
+    if binary:
+        # restrict to a binary classification task
+        X, y = X[y < 2], y[y < 2]
+
+    n_samples, n_features = X.shape
+    p = np.arange(n_samples)
+
+    rng = check_random_state(37)
+    rng.shuffle(p)
+    X, y = X[p], y[p]
+    half = int(n_samples / 2)
+
+    # add noisy features to make the problem harder and avoid perfect results
+    rng = np.random.RandomState(0)
+    X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
+
+    # run classifier, get class probabilities and label predictions
+    clf = svm.SVC(kernel="linear", probability=True, random_state=0)
+    y_score = clf.fit(X[:half], y[:half]).predict_proba(X[half:])
+
+    if binary:
+        # only interested in probabilities of the positive case
+        # XXX: do we really want a special API for the binary case?
+        y_score = y_score[:, 1]
+
+    y_pred = clf.predict(X[half:])
+    y_true = y[half:]
+    return y_true, y_pred, y_score
+
+
+###############################################################################
+# Tests
+
+
+def _auc(y_true, y_score):
+    """Alternative implementation to check for correctness of
+    `roc_auc_score`."""
+    pos_label = np.unique(y_true)[1]
+
+    # Count the number of times positive samples are correctly ranked above
+    # negative samples.
+    pos = y_score[y_true == pos_label]
+    neg = y_score[y_true != pos_label]
+    diff_matrix = pos.reshape(1, -1) - neg.reshape(-1, 1)
+    n_correct = np.sum(diff_matrix > 0)
+
+    return n_correct / float(len(pos) * len(neg))
+
+
+def _average_precision(y_true, y_score):
+    """Alternative implementation to check for correctness of
+    `average_precision_score`.
+
+    Note that this implementation fails on some edge cases.
+    For example, for constant predictions e.g. [0.5, 0.5, 0.5],
+    y_true = [1, 0, 0] returns an average precision of 0.33...
+    but y_true = [0, 0, 1] returns 1.0.
+    """
+    pos_label = np.unique(y_true)[1]
+    n_pos = np.sum(y_true == pos_label)
+    order = np.argsort(y_score)[::-1]
+    y_score = y_score[order]
+    y_true = y_true[order]
+
+    score = 0
+    for i in range(len(y_score)):
+        if y_true[i] == pos_label:
+            # Compute precision up to document i
+            # i.e, percentage of relevant documents up to document i.
+            prec = 0
+            for j in range(0, i + 1):
+                if y_true[j] == pos_label:
+                    prec += 1.0
+            prec /= i + 1.0
+            score += prec
+
+    return score / n_pos
+
+
+def _average_precision_slow(y_true, y_score):
+    """A second alternative implementation of average precision that closely
+    follows the Wikipedia article's definition (see References). This should
+    give identical results as `average_precision_score` for all inputs.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for the Average precision
+       `_
+    """
+    precision, recall, threshold = precision_recall_curve(y_true, y_score)
+    precision = list(reversed(precision))
+    recall = list(reversed(recall))
+    average_precision = 0
+    for i in range(1, len(precision)):
+        average_precision += precision[i] * (recall[i] - recall[i - 1])
+    return average_precision
+
+
+def _partial_roc_auc_score(y_true, y_predict, max_fpr):
+    """Alternative implementation to check for correctness of `roc_auc_score`
+    with `max_fpr` set.
+    """
+
+    def _partial_roc(y_true, y_predict, max_fpr):
+        fpr, tpr, _ = roc_curve(y_true, y_predict)
+        new_fpr = fpr[fpr <= max_fpr]
+        new_fpr = np.append(new_fpr, max_fpr)
+        new_tpr = tpr[fpr <= max_fpr]
+        idx_out = np.argmax(fpr > max_fpr)
+        idx_in = idx_out - 1
+        x_interp = [fpr[idx_in], fpr[idx_out]]
+        y_interp = [tpr[idx_in], tpr[idx_out]]
+        new_tpr = np.append(new_tpr, np.interp(max_fpr, x_interp, y_interp))
+        return (new_fpr, new_tpr)
+
+    new_fpr, new_tpr = _partial_roc(y_true, y_predict, max_fpr)
+    partial_auc = auc(new_fpr, new_tpr)
+
+    # Formula (5) from McClish 1989
+    fpr1 = 0
+    fpr2 = max_fpr
+    min_area = 0.5 * (fpr2 - fpr1) * (fpr2 + fpr1)
+    max_area = fpr2 - fpr1
+    return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
+
+
+@pytest.mark.parametrize("drop", [True, False])
+def test_roc_curve(drop):
+    # Test Area under Receiver Operating Characteristic (ROC) curve
+    y_true, _, y_score = make_prediction(binary=True)
+    expected_auc = _auc(y_true, y_score)
+
+    fpr, tpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=drop)
+    roc_auc = auc(fpr, tpr)
+    assert_array_almost_equal(roc_auc, expected_auc, decimal=2)
+    assert_almost_equal(roc_auc, roc_auc_score(y_true, y_score))
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+
+def test_roc_curve_end_points():
+    # Make sure that roc_curve returns a curve start at 0 and ending and
+    # 1 even in corner cases
+    rng = np.random.RandomState(0)
+    y_true = np.array([0] * 50 + [1] * 50)
+    y_pred = rng.randint(3, size=100)
+    fpr, tpr, thr = roc_curve(y_true, y_pred, drop_intermediate=True)
+    assert fpr[0] == 0
+    assert fpr[-1] == 1
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thr.shape
+
+
+def test_roc_returns_consistency():
+    # Test whether the returned threshold matches up with tpr
+    # make small toy dataset
+    y_true, _, y_score = make_prediction(binary=True)
+    fpr, tpr, thresholds = roc_curve(y_true, y_score)
+
+    # use the given thresholds to determine the tpr
+    tpr_correct = []
+    for t in thresholds:
+        tp = np.sum((y_score >= t) & y_true)
+        p = np.sum(y_true)
+        tpr_correct.append(1.0 * tp / p)
+
+    # compare tpr and tpr_correct to see if the thresholds' order was correct
+    assert_array_almost_equal(tpr, tpr_correct, decimal=2)
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+
+def test_roc_curve_multi():
+    # roc_curve not applicable for multi-class problems
+    y_true, _, y_score = make_prediction(binary=False)
+
+    with pytest.raises(ValueError):
+        roc_curve(y_true, y_score)
+
+
+def test_roc_curve_confidence():
+    # roc_curve for confidence scores
+    y_true, _, y_score = make_prediction(binary=True)
+
+    fpr, tpr, thresholds = roc_curve(y_true, y_score - 0.5)
+    roc_auc = auc(fpr, tpr)
+    assert_array_almost_equal(roc_auc, 0.90, decimal=2)
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+
+def test_roc_curve_hard():
+    # roc_curve for hard decisions
+    y_true, pred, y_score = make_prediction(binary=True)
+
+    # always predict one
+    trivial_pred = np.ones(y_true.shape)
+    fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
+    roc_auc = auc(fpr, tpr)
+    assert_array_almost_equal(roc_auc, 0.50, decimal=2)
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+    # always predict zero
+    trivial_pred = np.zeros(y_true.shape)
+    fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
+    roc_auc = auc(fpr, tpr)
+    assert_array_almost_equal(roc_auc, 0.50, decimal=2)
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+    # hard decisions
+    fpr, tpr, thresholds = roc_curve(y_true, pred)
+    roc_auc = auc(fpr, tpr)
+    assert_array_almost_equal(roc_auc, 0.78, decimal=2)
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+
+def test_roc_curve_one_label():
+    y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    y_pred = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
+    # assert there are warnings
+    expected_message = (
+        "No negative samples in y_true, false positive value should be meaningless"
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        fpr, tpr, thresholds = roc_curve(y_true, y_pred)
+
+    # all true labels, all fpr should be nan
+    assert_array_equal(fpr, np.full(len(thresholds), np.nan))
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+    # assert there are warnings
+    expected_message = (
+        "No positive samples in y_true, true positive value should be meaningless"
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        fpr, tpr, thresholds = roc_curve([1 - x for x in y_true], y_pred)
+    # all negative labels, all tpr should be nan
+    assert_array_equal(tpr, np.full(len(thresholds), np.nan))
+    assert fpr.shape == tpr.shape
+    assert fpr.shape == thresholds.shape
+
+
+def test_roc_curve_toydata():
+    # Binary classification
+    y_true = [0, 1]
+    y_score = [0, 1]
+    tpr, fpr, _ = roc_curve(y_true, y_score)
+    roc_auc = roc_auc_score(y_true, y_score)
+    assert_array_almost_equal(tpr, [0, 0, 1])
+    assert_array_almost_equal(fpr, [0, 1, 1])
+    assert_almost_equal(roc_auc, 1.0)
+
+    y_true = [0, 1]
+    y_score = [1, 0]
+    tpr, fpr, _ = roc_curve(y_true, y_score)
+    roc_auc = roc_auc_score(y_true, y_score)
+    assert_array_almost_equal(tpr, [0, 1, 1])
+    assert_array_almost_equal(fpr, [0, 0, 1])
+    assert_almost_equal(roc_auc, 0.0)
+
+    y_true = [1, 0]
+    y_score = [1, 1]
+    tpr, fpr, _ = roc_curve(y_true, y_score)
+    roc_auc = roc_auc_score(y_true, y_score)
+    assert_array_almost_equal(tpr, [0, 1])
+    assert_array_almost_equal(fpr, [0, 1])
+    assert_almost_equal(roc_auc, 0.5)
+
+    y_true = [1, 0]
+    y_score = [1, 0]
+    tpr, fpr, _ = roc_curve(y_true, y_score)
+    roc_auc = roc_auc_score(y_true, y_score)
+    assert_array_almost_equal(tpr, [0, 0, 1])
+    assert_array_almost_equal(fpr, [0, 1, 1])
+    assert_almost_equal(roc_auc, 1.0)
+
+    y_true = [1, 0]
+    y_score = [0.5, 0.5]
+    tpr, fpr, _ = roc_curve(y_true, y_score)
+    roc_auc = roc_auc_score(y_true, y_score)
+    assert_array_almost_equal(tpr, [0, 1])
+    assert_array_almost_equal(fpr, [0, 1])
+    assert_almost_equal(roc_auc, 0.5)
+
+    # case with no positive samples
+    y_true = [0, 0]
+    y_score = [0.25, 0.75]
+    # assert UndefinedMetricWarning because of no positive sample in y_true
+    expected_message = (
+        "No positive samples in y_true, true positive value should be meaningless"
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        tpr, fpr, _ = roc_curve(y_true, y_score)
+    assert_array_almost_equal(tpr, [0.0, 0.5, 1.0])
+    assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan])
+    expected_message = (
+        "Only one class is present in y_true. "
+        "ROC AUC score is not defined in that case."
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        auc = roc_auc_score(y_true, y_score)
+    assert math.isnan(auc)
+
+    # case with no negative samples
+    y_true = [1, 1]
+    y_score = [0.25, 0.75]
+    # assert UndefinedMetricWarning because of no negative sample in y_true
+    expected_message = (
+        "No negative samples in y_true, false positive value should be meaningless"
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        tpr, fpr, _ = roc_curve(y_true, y_score)
+    assert_array_almost_equal(tpr, [np.nan, np.nan, np.nan])
+    assert_array_almost_equal(fpr, [0.0, 0.5, 1.0])
+    expected_message = (
+        "Only one class is present in y_true. "
+        "ROC AUC score is not defined in that case."
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        auc = roc_auc_score(y_true, y_score)
+    assert math.isnan(auc)
+
+    # Multi-label classification task
+    y_true = np.array([[0, 1], [0, 1]])
+    y_score = np.array([[0, 1], [0, 1]])
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        roc_auc_score(y_true, y_score, average="macro")
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        roc_auc_score(y_true, y_score, average="weighted")
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 1.0)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 1.0)
+
+    y_true = np.array([[0, 1], [0, 1]])
+    y_score = np.array([[0, 1], [1, 0]])
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        roc_auc_score(y_true, y_score, average="macro")
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        roc_auc_score(y_true, y_score, average="weighted")
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0.5)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0.5)
+
+    y_true = np.array([[1, 0], [0, 1]])
+    y_score = np.array([[0, 1], [1, 0]])
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), 0)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), 0)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0)
+
+    y_true = np.array([[1, 0], [0, 1]])
+    y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), 0.5)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), 0.5)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0.5)
+    assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0.5)
+
+
+def test_roc_curve_drop_intermediate():
+    # Test that drop_intermediate drops the correct thresholds
+    y_true = [0, 0, 0, 0, 1, 1]
+    y_score = [0.0, 0.2, 0.5, 0.6, 0.7, 1.0]
+    tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
+    assert_array_almost_equal(thresholds, [np.inf, 1.0, 0.7, 0.0])
+
+    # Test dropping thresholds with repeating scores
+    y_true = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
+    y_score = [0.0, 0.1, 0.6, 0.6, 0.7, 0.8, 0.9, 0.6, 0.7, 0.8, 0.9, 0.9, 1.0]
+    tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
+    assert_array_almost_equal(thresholds, [np.inf, 1.0, 0.9, 0.7, 0.6, 0.0])
+
+
+def test_roc_curve_fpr_tpr_increasing():
+    # Ensure that fpr and tpr returned by roc_curve are increasing.
+    # Construct an edge case with float y_score and sample_weight
+    # when some adjacent values of fpr and tpr are actually the same.
+    y_true = [0, 0, 1, 1, 1]
+    y_score = [0.1, 0.7, 0.3, 0.4, 0.5]
+    sample_weight = np.repeat(0.2, 5)
+    fpr, tpr, _ = roc_curve(y_true, y_score, sample_weight=sample_weight)
+    assert (np.diff(fpr) < 0).sum() == 0
+    assert (np.diff(tpr) < 0).sum() == 0
+
+
+def test_auc():
+    # Test Area Under Curve (AUC) computation
+    x = [0, 1]
+    y = [0, 1]
+    assert_array_almost_equal(auc(x, y), 0.5)
+    x = [1, 0]
+    y = [0, 1]
+    assert_array_almost_equal(auc(x, y), 0.5)
+    x = [1, 0, 0]
+    y = [0, 1, 1]
+    assert_array_almost_equal(auc(x, y), 0.5)
+    x = [0, 1]
+    y = [1, 1]
+    assert_array_almost_equal(auc(x, y), 1)
+    x = [0, 0.5, 1]
+    y = [0, 0.5, 1]
+    assert_array_almost_equal(auc(x, y), 0.5)
+
+
+def test_auc_errors():
+    # Incompatible shapes
+    with pytest.raises(ValueError):
+        auc([0.0, 0.5, 1.0], [0.1, 0.2])
+
+    # Too few x values
+    with pytest.raises(ValueError):
+        auc([0.0], [0.1])
+
+    # x is not in order
+    x = [2, 1, 3, 4]
+    y = [5, 6, 7, 8]
+    error_message = "x is neither increasing nor decreasing : {}".format(np.array(x))
+    with pytest.raises(ValueError, match=re.escape(error_message)):
+        auc(x, y)
+
+
+@pytest.mark.parametrize(
+    "y_true, labels",
+    [
+        (np.array([0, 1, 0, 2]), [0, 1, 2]),
+        (np.array([0, 1, 0, 2]), None),
+        (["a", "b", "a", "c"], ["a", "b", "c"]),
+        (["a", "b", "a", "c"], None),
+    ],
+)
+def test_multiclass_ovo_roc_auc_toydata(y_true, labels):
+    # Tests the one-vs-one multiclass ROC AUC algorithm
+    # on a small example, representative of an expected use case.
+    y_scores = np.array(
+        [[0.1, 0.8, 0.1], [0.3, 0.4, 0.3], [0.35, 0.5, 0.15], [0, 0.2, 0.8]]
+    )
+
+    # Used to compute the expected output.
+    # Consider labels 0 and 1:
+    # positive label is 0, negative label is 1
+    score_01 = roc_auc_score([1, 0, 1], [0.1, 0.3, 0.35])
+    # positive label is 1, negative label is 0
+    score_10 = roc_auc_score([0, 1, 0], [0.8, 0.4, 0.5])
+    average_score_01 = (score_01 + score_10) / 2
+
+    # Consider labels 0 and 2:
+    score_02 = roc_auc_score([1, 1, 0], [0.1, 0.35, 0])
+    score_20 = roc_auc_score([0, 0, 1], [0.1, 0.15, 0.8])
+    average_score_02 = (score_02 + score_20) / 2
+
+    # Consider labels 1 and 2:
+    score_12 = roc_auc_score([1, 0], [0.4, 0.2])
+    score_21 = roc_auc_score([0, 1], [0.3, 0.8])
+    average_score_12 = (score_12 + score_21) / 2
+
+    # Unweighted, one-vs-one multiclass ROC AUC algorithm
+    ovo_unweighted_score = (average_score_01 + average_score_02 + average_score_12) / 3
+    assert_almost_equal(
+        roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo"),
+        ovo_unweighted_score,
+    )
+
+    # Weighted, one-vs-one multiclass ROC AUC algorithm
+    # Each term is weighted by the prevalence for the positive label.
+    pair_scores = [average_score_01, average_score_02, average_score_12]
+    prevalence = [0.75, 0.75, 0.50]
+    ovo_weighted_score = np.average(pair_scores, weights=prevalence)
+    assert_almost_equal(
+        roc_auc_score(
+            y_true, y_scores, labels=labels, multi_class="ovo", average="weighted"
+        ),
+        ovo_weighted_score,
+    )
+
+    # Check that average=None raises NotImplemented error
+    error_message = "average=None is not implemented for multi_class='ovo'."
+    with pytest.raises(NotImplementedError, match=error_message):
+        roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo", average=None)
+
+
+@pytest.mark.parametrize(
+    "y_true, labels",
+    [
+        (np.array([0, 2, 0, 2]), [0, 1, 2]),
+        (np.array(["a", "d", "a", "d"]), ["a", "b", "d"]),
+    ],
+)
+def test_multiclass_ovo_roc_auc_toydata_binary(y_true, labels):
+    # Tests the one-vs-one multiclass ROC AUC algorithm for binary y_true
+    #
+    # on a small example, representative of an expected use case.
+    y_scores = np.array(
+        [[0.2, 0.0, 0.8], [0.6, 0.0, 0.4], [0.55, 0.0, 0.45], [0.4, 0.0, 0.6]]
+    )
+
+    # Used to compute the expected output.
+    # Consider labels 0 and 1:
+    # positive label is 0, negative label is 1
+    score_01 = roc_auc_score([1, 0, 1, 0], [0.2, 0.6, 0.55, 0.4])
+    # positive label is 1, negative label is 0
+    score_10 = roc_auc_score([0, 1, 0, 1], [0.8, 0.4, 0.45, 0.6])
+    ovo_score = (score_01 + score_10) / 2
+
+    assert_almost_equal(
+        roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo"), ovo_score
+    )
+
+    # Weighted, one-vs-one multiclass ROC AUC algorithm
+    assert_almost_equal(
+        roc_auc_score(
+            y_true, y_scores, labels=labels, multi_class="ovo", average="weighted"
+        ),
+        ovo_score,
+    )
+
+
+@pytest.mark.parametrize(
+    "y_true, labels",
+    [
+        (np.array([0, 1, 2, 2]), None),
+        (["a", "b", "c", "c"], None),
+        ([0, 1, 2, 2], [0, 1, 2]),
+        (["a", "b", "c", "c"], ["a", "b", "c"]),
+    ],
+)
+def test_multiclass_ovr_roc_auc_toydata(y_true, labels):
+    # Tests the unweighted, one-vs-rest multiclass ROC AUC algorithm
+    # on a small example, representative of an expected use case.
+    y_scores = np.array(
+        [[1.0, 0.0, 0.0], [0.1, 0.5, 0.4], [0.1, 0.1, 0.8], [0.3, 0.3, 0.4]]
+    )
+    # Compute the expected result by individually computing the 'one-vs-rest'
+    # ROC AUC scores for classes 0, 1, and 2.
+    out_0 = roc_auc_score([1, 0, 0, 0], y_scores[:, 0])
+    out_1 = roc_auc_score([0, 1, 0, 0], y_scores[:, 1])
+    out_2 = roc_auc_score([0, 0, 1, 1], y_scores[:, 2])
+    assert_almost_equal(
+        roc_auc_score(y_true, y_scores, multi_class="ovr", labels=labels, average=None),
+        [out_0, out_1, out_2],
+    )
+
+    # Compute unweighted results (default behaviour is average="macro")
+    result_unweighted = (out_0 + out_1 + out_2) / 3.0
+    assert_almost_equal(
+        roc_auc_score(y_true, y_scores, multi_class="ovr", labels=labels),
+        result_unweighted,
+    )
+
+    # Tests the weighted, one-vs-rest multiclass ROC AUC algorithm
+    # on the same input (Provost & Domingos, 2000)
+    result_weighted = out_0 * 0.25 + out_1 * 0.25 + out_2 * 0.5
+    assert_almost_equal(
+        roc_auc_score(
+            y_true, y_scores, multi_class="ovr", labels=labels, average="weighted"
+        ),
+        result_weighted,
+    )
+
+
+@pytest.mark.parametrize(
+    "multi_class, average",
+    [
+        ("ovr", "macro"),
+        ("ovr", "micro"),
+        ("ovo", "macro"),
+    ],
+)
+def test_perfect_imperfect_chance_multiclass_roc_auc(multi_class, average):
+    y_true = np.array([3, 1, 2, 0])
+
+    # Perfect classifier (from a ranking point of view) has roc_auc_score = 1.0
+    y_perfect = [
+        [0.0, 0.0, 0.0, 1.0],
+        [0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, 1.0, 0.0],
+        [0.75, 0.05, 0.05, 0.15],
+    ]
+    assert_almost_equal(
+        roc_auc_score(y_true, y_perfect, multi_class=multi_class, average=average),
+        1.0,
+    )
+
+    # Imperfect classifier has roc_auc_score < 1.0
+    y_imperfect = [
+        [0.0, 0.0, 0.0, 1.0],
+        [0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, 1.0, 0.0],
+        [0.0, 0.0, 0.0, 1.0],
+    ]
+    assert (
+        roc_auc_score(y_true, y_imperfect, multi_class=multi_class, average=average)
+        < 1.0
+    )
+
+    # Chance level classifier has roc_auc_score = 5.0
+    y_chance = 0.25 * np.ones((4, 4))
+    assert roc_auc_score(
+        y_true, y_chance, multi_class=multi_class, average=average
+    ) == pytest.approx(0.5)
+
+
+def test_micro_averaged_ovr_roc_auc(global_random_seed):
+    seed = global_random_seed
+    # Let's generate a set of random predictions and matching true labels such
+    # that the predictions are not perfect. To make the problem more interesting,
+    # we use an imbalanced class distribution (by using different parameters
+    # in the Dirichlet prior (conjugate prior of the multinomial distribution).
+    y_pred = stats.dirichlet.rvs([2.0, 1.0, 0.5], size=1000, random_state=seed)
+    y_true = np.asarray(
+        [
+            stats.multinomial.rvs(n=1, p=y_pred_i, random_state=seed).argmax()
+            for y_pred_i in y_pred
+        ]
+    )
+    y_onehot = label_binarize(y_true, classes=[0, 1, 2])
+    fpr, tpr, _ = roc_curve(y_onehot.ravel(), y_pred.ravel())
+    roc_auc_by_hand = auc(fpr, tpr)
+    roc_auc_auto = roc_auc_score(y_true, y_pred, multi_class="ovr", average="micro")
+    assert roc_auc_by_hand == pytest.approx(roc_auc_auto)
+
+
+@pytest.mark.parametrize(
+    "msg, y_true, labels",
+    [
+        ("Parameter 'labels' must be unique", np.array([0, 1, 2, 2]), [0, 2, 0]),
+        (
+            "Parameter 'labels' must be unique",
+            np.array(["a", "b", "c", "c"]),
+            ["a", "a", "b"],
+        ),
+        (
+            (
+                "Number of classes in y_true not equal to the number of columns "
+                "in 'y_score'"
+            ),
+            np.array([0, 2, 0, 2]),
+            None,
+        ),
+        (
+            "Parameter 'labels' must be ordered",
+            np.array(["a", "b", "c", "c"]),
+            ["a", "c", "b"],
+        ),
+        (
+            (
+                "Number of given labels, 2, not equal to the number of columns in "
+                "'y_score', 3"
+            ),
+            np.array([0, 1, 2, 2]),
+            [0, 1],
+        ),
+        (
+            (
+                "Number of given labels, 2, not equal to the number of columns in "
+                "'y_score', 3"
+            ),
+            np.array(["a", "b", "c", "c"]),
+            ["a", "b"],
+        ),
+        (
+            (
+                "Number of given labels, 4, not equal to the number of columns in "
+                "'y_score', 3"
+            ),
+            np.array([0, 1, 2, 2]),
+            [0, 1, 2, 3],
+        ),
+        (
+            (
+                "Number of given labels, 4, not equal to the number of columns in "
+                "'y_score', 3"
+            ),
+            np.array(["a", "b", "c", "c"]),
+            ["a", "b", "c", "d"],
+        ),
+        (
+            "'y_true' contains labels not in parameter 'labels'",
+            np.array(["a", "b", "c", "e"]),
+            ["a", "b", "c"],
+        ),
+        (
+            "'y_true' contains labels not in parameter 'labels'",
+            np.array(["a", "b", "c", "d"]),
+            ["a", "b", "c"],
+        ),
+        (
+            "'y_true' contains labels not in parameter 'labels'",
+            np.array([0, 1, 2, 3]),
+            [0, 1, 2],
+        ),
+    ],
+)
+@pytest.mark.parametrize("multi_class", ["ovo", "ovr"])
+def test_roc_auc_score_multiclass_labels_error(msg, y_true, labels, multi_class):
+    y_scores = np.array(
+        [[0.1, 0.8, 0.1], [0.3, 0.4, 0.3], [0.35, 0.5, 0.15], [0, 0.2, 0.8]]
+    )
+
+    with pytest.raises(ValueError, match=msg):
+        roc_auc_score(y_true, y_scores, labels=labels, multi_class=multi_class)
+
+
+@pytest.mark.parametrize(
+    "msg, kwargs",
+    [
+        (
+            (
+                r"average must be one of \('macro', 'weighted', None\) for "
+                r"multiclass problems"
+            ),
+            {"average": "samples", "multi_class": "ovo"},
+        ),
+        (
+            (
+                r"average must be one of \('micro', 'macro', 'weighted', None\) for "
+                r"multiclass problems"
+            ),
+            {"average": "samples", "multi_class": "ovr"},
+        ),
+        (
+            (
+                r"sample_weight is not supported for multiclass one-vs-one "
+                r"ROC AUC, 'sample_weight' must be None in this case"
+            ),
+            {"multi_class": "ovo", "sample_weight": []},
+        ),
+        (
+            (
+                r"Partial AUC computation not available in multiclass setting, "
+                r"'max_fpr' must be set to `None`, received `max_fpr=0.5` "
+                r"instead"
+            ),
+            {"multi_class": "ovo", "max_fpr": 0.5},
+        ),
+        (r"multi_class must be in \('ovo', 'ovr'\)", {}),
+    ],
+)
+def test_roc_auc_score_multiclass_error(msg, kwargs):
+    # Test that roc_auc_score function returns an error when trying
+    # to compute multiclass AUC for parameters where an output
+    # is not defined.
+    rng = check_random_state(404)
+    y_score = rng.rand(20, 3)
+    y_prob = softmax(y_score)
+    y_true = rng.randint(0, 3, size=20)
+    with pytest.raises(ValueError, match=msg):
+        roc_auc_score(y_true, y_prob, **kwargs)
+
+
+def test_auc_score_non_binary_class():
+    # Test that roc_auc_score function returns an error when trying
+    # to compute AUC for non-binary class values.
+    rng = check_random_state(404)
+    y_pred = rng.rand(10)
+    # y_true contains only one class value
+    y_true = np.zeros(10, dtype="int")
+    warn_message = (
+        "Only one class is present in y_true. "
+        "ROC AUC score is not defined in that case."
+    )
+    with pytest.warns(UndefinedMetricWarning, match=warn_message):
+        roc_auc_score(y_true, y_pred)
+    y_true = np.ones(10, dtype="int")
+    with pytest.warns(UndefinedMetricWarning, match=warn_message):
+        roc_auc_score(y_true, y_pred)
+    y_true = np.full(10, -1, dtype="int")
+    with pytest.warns(UndefinedMetricWarning, match=warn_message):
+        roc_auc_score(y_true, y_pred)
+
+
+@pytest.mark.parametrize("curve_func", CURVE_FUNCS)
+def test_binary_clf_curve_multiclass_error(curve_func):
+    rng = check_random_state(404)
+    y_true = rng.randint(0, 3, size=10)
+    y_pred = rng.rand(10)
+    msg = "multiclass format is not supported"
+    with pytest.raises(ValueError, match=msg):
+        curve_func(y_true, y_pred)
+
+
+@pytest.mark.parametrize("curve_func", CURVE_FUNCS)
+def test_binary_clf_curve_implicit_pos_label(curve_func):
+    # Check that using string class labels raises an informative
+    # error for any supported string dtype:
+    msg = (
+        "y_true takes value in {'a', 'b'} and pos_label is "
+        "not specified: either make y_true take "
+        "value in {0, 1} or {-1, 1} or pass pos_label "
+        "explicitly."
+    )
+    with pytest.raises(ValueError, match=msg):
+        curve_func(np.array(["a", "b"], dtype="= 0 and y_score.max() <= 1 else 0
+    y_pred = (y_score > threshold).astype(np.int64) if k == 1 else y_true
+
+    score = top_k_accuracy_score(y_true, y_score, k=k)
+    score_acc = accuracy_score(y_true, y_pred)
+
+    assert score == score_acc == pytest.approx(true_score)
+
+
+@pytest.mark.parametrize(
+    "y_true, true_score, labels",
+    [
+        (np.array([0, 1, 1, 2]), 0.75, [0, 1, 2, 3]),
+        (np.array([0, 1, 1, 1]), 0.5, [0, 1, 2, 3]),
+        (np.array([1, 1, 1, 1]), 0.5, [0, 1, 2, 3]),
+        (np.array(["a", "e", "e", "a"]), 0.75, ["a", "b", "d", "e"]),
+    ],
+)
+@pytest.mark.parametrize("labels_as_ndarray", [True, False])
+def test_top_k_accuracy_score_multiclass_with_labels(
+    y_true, true_score, labels, labels_as_ndarray
+):
+    """Test when labels and y_score are multiclass."""
+    if labels_as_ndarray:
+        labels = np.asarray(labels)
+    y_score = np.array(
+        [
+            [0.4, 0.3, 0.2, 0.1],
+            [0.1, 0.3, 0.4, 0.2],
+            [0.4, 0.1, 0.2, 0.3],
+            [0.3, 0.2, 0.4, 0.1],
+        ]
+    )
+
+    score = top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
+    assert score == pytest.approx(true_score)
+
+
+def test_top_k_accuracy_score_increasing():
+    # Make sure increasing k leads to a higher score
+    X, y = datasets.make_classification(
+        n_classes=10, n_samples=1000, n_informative=10, random_state=0
+    )
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    clf = LogisticRegression(random_state=0)
+    clf.fit(X_train, y_train)
+
+    for X, y in zip((X_train, X_test), (y_train, y_test)):
+        scores = [
+            top_k_accuracy_score(y, clf.predict_proba(X), k=k) for k in range(2, 10)
+        ]
+
+        assert np.all(np.diff(scores) > 0)
+
+
+@pytest.mark.parametrize(
+    "y_true, k, true_score",
+    [
+        ([0, 1, 2, 3], 1, 0.25),
+        ([0, 1, 2, 3], 2, 0.5),
+        ([0, 1, 2, 3], 3, 1),
+    ],
+)
+def test_top_k_accuracy_score_ties(y_true, k, true_score):
+    # Make sure highest indices labels are chosen first in case of ties
+    y_score = np.array(
+        [
+            [5, 5, 7, 0],
+            [1, 5, 5, 5],
+            [0, 0, 3, 3],
+            [1, 1, 1, 1],
+        ]
+    )
+    assert top_k_accuracy_score(y_true, y_score, k=k) == pytest.approx(true_score)
+
+
+@pytest.mark.parametrize(
+    "y_true, k",
+    [
+        ([0, 1, 2, 3], 4),
+        ([0, 1, 2, 3], 5),
+    ],
+)
+def test_top_k_accuracy_score_warning(y_true, k):
+    y_score = np.array(
+        [
+            [0.4, 0.3, 0.2, 0.1],
+            [0.1, 0.4, 0.3, 0.2],
+            [0.2, 0.1, 0.4, 0.3],
+            [0.3, 0.2, 0.1, 0.4],
+        ]
+    )
+    expected_message = (
+        r"'k' \(\d+\) greater than or equal to 'n_classes' \(\d+\) will result in a "
+        "perfect score and is therefore meaningless."
+    )
+    with pytest.warns(UndefinedMetricWarning, match=expected_message):
+        score = top_k_accuracy_score(y_true, y_score, k=k)
+    assert score == 1
+
+
+@pytest.mark.parametrize(
+    "y_true, y_score, labels, msg",
+    [
+        (
+            [0, 0.57, 1, 2],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            None,
+            "y type must be 'binary' or 'multiclass', got 'continuous'",
+        ),
+        (
+            [0, 1, 2, 3],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            None,
+            r"Number of classes in 'y_true' \(4\) not equal to the number of "
+            r"classes in 'y_score' \(3\).",
+        ),
+        (
+            ["c", "c", "a", "b"],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            ["a", "b", "c", "c"],
+            "Parameter 'labels' must be unique.",
+        ),
+        (
+            ["c", "c", "a", "b"],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            ["a", "c", "b"],
+            "Parameter 'labels' must be ordered.",
+        ),
+        (
+            [0, 0, 1, 2],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            [0, 1, 2, 3],
+            r"Number of given labels \(4\) not equal to the number of classes in "
+            r"'y_score' \(3\).",
+        ),
+        (
+            [0, 0, 1, 2],
+            [
+                [0.2, 0.1, 0.7],
+                [0.4, 0.3, 0.3],
+                [0.3, 0.4, 0.3],
+                [0.4, 0.5, 0.1],
+            ],
+            [0, 1, 3],
+            "'y_true' contains labels not in parameter 'labels'.",
+        ),
+        (
+            [0, 1],
+            [[0.5, 0.2, 0.2], [0.3, 0.4, 0.2]],
+            None,
+            (
+                "`y_true` is binary while y_score is 2d with 3 classes. If"
+                " `y_true` does not contain all the labels, `labels` must be provided"
+            ),
+        ),
+    ],
+)
+def test_top_k_accuracy_score_error(y_true, y_score, labels, msg):
+    with pytest.raises(ValueError, match=msg):
+        top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_label_ranking_avg_precision_score_should_allow_csr_matrix_for_y_true_input(
+    csr_container,
+):
+    # Test that label_ranking_avg_precision_score accept sparse y_true.
+    # Non-regression test for #22575
+    y_true = csr_container([[1, 0, 0], [0, 0, 1]])
+    y_score = np.array([[0.5, 0.9, 0.6], [0, 0, 1]])
+    result = label_ranking_average_precision_score(y_true, y_score)
+    assert result == pytest.approx(2 / 3)
+
+
+@pytest.mark.parametrize(
+    "metric", [average_precision_score, det_curve, precision_recall_curve, roc_curve]
+)
+@pytest.mark.parametrize(
+    "classes", [(False, True), (0, 1), (0.0, 1.0), ("zero", "one")]
+)
+def test_ranking_metric_pos_label_types(metric, classes):
+    """Check that the metric works with different types of `pos_label`.
+
+    We can expect `pos_label` to be a bool, an integer, a float, a string.
+    No error should be raised for those types.
+    """
+    rng = np.random.RandomState(42)
+    n_samples, pos_label = 10, classes[-1]
+    y_true = rng.choice(classes, size=n_samples, replace=True)
+    y_proba = rng.rand(n_samples)
+    result = metric(y_true, y_proba, pos_label=pos_label)
+    if isinstance(result, float):
+        assert not np.isnan(result)
+    else:
+        metric_1, metric_2, thresholds = result
+        assert not np.isnan(metric_1).any()
+        assert not np.isnan(metric_2).any()
+        assert not np.isnan(thresholds).any()
+
+
+def test_roc_curve_with_probablity_estimates(global_random_seed):
+    """Check that thresholds do not exceed 1.0 when `y_score` is a probability
+    estimate.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/26193
+    """
+    rng = np.random.RandomState(global_random_seed)
+    y_true = rng.randint(0, 2, size=10)
+    y_score = rng.rand(10)
+    _, _, thresholds = roc_curve(y_true, y_score)
+    assert np.isinf(thresholds[0])
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_regression.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..396ae5d0ffae143e333f14861dc839931326a030
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_regression.py
@@ -0,0 +1,636 @@
+from itertools import product
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+from scipy import optimize
+from scipy.special import factorial, xlogy
+
+from sklearn.dummy import DummyRegressor
+from sklearn.exceptions import UndefinedMetricWarning
+from sklearn.metrics import (
+    d2_absolute_error_score,
+    d2_pinball_score,
+    d2_tweedie_score,
+    explained_variance_score,
+    make_scorer,
+    max_error,
+    mean_absolute_error,
+    mean_absolute_percentage_error,
+    mean_pinball_loss,
+    mean_squared_error,
+    mean_squared_log_error,
+    mean_tweedie_deviance,
+    median_absolute_error,
+    r2_score,
+    root_mean_squared_error,
+    root_mean_squared_log_error,
+)
+from sklearn.metrics._regression import _check_reg_targets
+from sklearn.model_selection import GridSearchCV
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+
+
+def test_regression_metrics(n_samples=50):
+    y_true = np.arange(n_samples)
+    y_pred = y_true + 1
+    y_pred_2 = y_true - 1
+
+    assert_almost_equal(mean_squared_error(y_true, y_pred), 1.0)
+    assert_almost_equal(
+        mean_squared_log_error(y_true, y_pred),
+        mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred)),
+    )
+    assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.0)
+    assert_almost_equal(mean_pinball_loss(y_true, y_pred), 0.5)
+    assert_almost_equal(mean_pinball_loss(y_true, y_pred_2), 0.5)
+    assert_almost_equal(mean_pinball_loss(y_true, y_pred, alpha=0.4), 0.6)
+    assert_almost_equal(mean_pinball_loss(y_true, y_pred_2, alpha=0.4), 0.4)
+    assert_almost_equal(median_absolute_error(y_true, y_pred), 1.0)
+    mape = mean_absolute_percentage_error(y_true, y_pred)
+    assert np.isfinite(mape)
+    assert mape > 1e6
+    assert_almost_equal(max_error(y_true, y_pred), 1.0)
+    assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
+    assert_almost_equal(r2_score(y_true, y_pred, force_finite=False), 0.995, 2)
+    assert_almost_equal(explained_variance_score(y_true, y_pred), 1.0)
+    assert_almost_equal(
+        explained_variance_score(y_true, y_pred, force_finite=False), 1.0
+    )
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=0),
+        mean_squared_error(y_true, y_pred),
+    )
+    assert_almost_equal(
+        d2_tweedie_score(y_true, y_pred, power=0), r2_score(y_true, y_pred)
+    )
+    dev_median = np.abs(y_true - np.median(y_true)).sum()
+    assert_array_almost_equal(
+        d2_absolute_error_score(y_true, y_pred),
+        1 - np.abs(y_true - y_pred).sum() / dev_median,
+    )
+    alpha = 0.2
+    pinball_loss = lambda y_true, y_pred, alpha: alpha * np.maximum(
+        y_true - y_pred, 0
+    ) + (1 - alpha) * np.maximum(y_pred - y_true, 0)
+    y_quantile = np.percentile(y_true, q=alpha * 100)
+    assert_almost_equal(
+        d2_pinball_score(y_true, y_pred, alpha=alpha),
+        1
+        - pinball_loss(y_true, y_pred, alpha).sum()
+        / pinball_loss(y_true, y_quantile, alpha).sum(),
+    )
+    assert_almost_equal(
+        d2_absolute_error_score(y_true, y_pred),
+        d2_pinball_score(y_true, y_pred, alpha=0.5),
+    )
+
+    # Tweedie deviance needs positive y_pred, except for p=0,
+    # p>=2 needs positive y_true
+    # results evaluated by sympy
+    y_true = np.arange(1, 1 + n_samples)
+    y_pred = 2 * y_true
+    n = n_samples
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=-1),
+        5 / 12 * n * (n**2 + 2 * n + 1),
+    )
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=1), (n + 1) * (1 - np.log(2))
+    )
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=2), 2 * np.log(2) - 1
+    )
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=3 / 2),
+        ((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum(),
+    )
+    assert_almost_equal(
+        mean_tweedie_deviance(y_true, y_pred, power=3), np.sum(1 / y_true) / (4 * n)
+    )
+
+    dev_mean = 2 * np.mean(xlogy(y_true, 2 * y_true / (n + 1)))
+    assert_almost_equal(
+        d2_tweedie_score(y_true, y_pred, power=1),
+        1 - (n + 1) * (1 - np.log(2)) / dev_mean,
+    )
+
+    dev_mean = 2 * np.log((n + 1) / 2) - 2 / n * np.log(factorial(n))
+    assert_almost_equal(
+        d2_tweedie_score(y_true, y_pred, power=2), 1 - (2 * np.log(2) - 1) / dev_mean
+    )
+
+
+def test_root_mean_squared_error_multioutput_raw_value():
+    # non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/pull/16323
+    mse = mean_squared_error([[1]], [[10]], multioutput="raw_values")
+    rmse = root_mean_squared_error([[1]], [[10]], multioutput="raw_values")
+    assert np.sqrt(mse) == pytest.approx(rmse)
+
+
+def test_multioutput_regression():
+    y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
+    y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
+
+    error = mean_squared_error(y_true, y_pred)
+    assert_almost_equal(error, (1.0 / 3 + 2.0 / 3 + 2.0 / 3) / 4.0)
+
+    error = root_mean_squared_error(y_true, y_pred)
+    assert_almost_equal(error, 0.454, decimal=2)
+
+    error = mean_squared_log_error(y_true, y_pred)
+    assert_almost_equal(error, 0.200, decimal=2)
+
+    error = root_mean_squared_log_error(y_true, y_pred)
+    assert_almost_equal(error, 0.315, decimal=2)
+
+    # mean_absolute_error and mean_squared_error are equal because
+    # it is a binary problem.
+    error = mean_absolute_error(y_true, y_pred)
+    assert_almost_equal(error, (1.0 + 2.0 / 3) / 4.0)
+
+    error = mean_pinball_loss(y_true, y_pred)
+    assert_almost_equal(error, (1.0 + 2.0 / 3) / 8.0)
+
+    error = np.around(mean_absolute_percentage_error(y_true, y_pred), decimals=2)
+    assert np.isfinite(error)
+    assert error > 1e6
+    error = median_absolute_error(y_true, y_pred)
+    assert_almost_equal(error, (1.0 + 1.0) / 4.0)
+
+    error = r2_score(y_true, y_pred, multioutput="variance_weighted")
+    assert_almost_equal(error, 1.0 - 5.0 / 2)
+    error = r2_score(y_true, y_pred, multioutput="uniform_average")
+    assert_almost_equal(error, -0.875)
+
+    score = d2_pinball_score(y_true, y_pred, alpha=0.5, multioutput="raw_values")
+    raw_expected_score = [
+        1
+        - np.abs(y_true[:, i] - y_pred[:, i]).sum()
+        / np.abs(y_true[:, i] - np.median(y_true[:, i])).sum()
+        for i in range(y_true.shape[1])
+    ]
+    # in the last case, the denominator vanishes and hence we get nan,
+    # but since the numerator vanishes as well the expected score is 1.0
+    raw_expected_score = np.where(np.isnan(raw_expected_score), 1, raw_expected_score)
+    assert_array_almost_equal(score, raw_expected_score)
+
+    score = d2_pinball_score(y_true, y_pred, alpha=0.5, multioutput="uniform_average")
+    assert_almost_equal(score, raw_expected_score.mean())
+    # constant `y_true` with force_finite=True leads to 1. or 0.
+    yc = [5.0, 5.0]
+    error = r2_score(yc, [5.0, 5.0], multioutput="variance_weighted")
+    assert_almost_equal(error, 1.0)
+    error = r2_score(yc, [5.0, 5.1], multioutput="variance_weighted")
+    assert_almost_equal(error, 0.0)
+
+    # Setting force_finite=False results in the nan for 4th output propagating
+    error = r2_score(
+        y_true, y_pred, multioutput="variance_weighted", force_finite=False
+    )
+    assert_almost_equal(error, np.nan)
+    error = r2_score(y_true, y_pred, multioutput="uniform_average", force_finite=False)
+    assert_almost_equal(error, np.nan)
+
+    # Dropping the 4th output to check `force_finite=False` for nominal
+    y_true = y_true[:, :-1]
+    y_pred = y_pred[:, :-1]
+    error = r2_score(y_true, y_pred, multioutput="variance_weighted")
+    error2 = r2_score(
+        y_true, y_pred, multioutput="variance_weighted", force_finite=False
+    )
+    assert_almost_equal(error, error2)
+    error = r2_score(y_true, y_pred, multioutput="uniform_average")
+    error2 = r2_score(y_true, y_pred, multioutput="uniform_average", force_finite=False)
+    assert_almost_equal(error, error2)
+
+    # constant `y_true` with force_finite=False leads to NaN or -Inf.
+    error = r2_score(
+        yc, [5.0, 5.0], multioutput="variance_weighted", force_finite=False
+    )
+    assert_almost_equal(error, np.nan)
+    error = r2_score(
+        yc, [5.0, 6.0], multioutput="variance_weighted", force_finite=False
+    )
+    assert_almost_equal(error, -np.inf)
+
+
+def test_regression_metrics_at_limits():
+    # Single-sample case
+    # Note: for r2 and d2_tweedie see also test_regression_single_sample
+    assert_almost_equal(mean_squared_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(root_mean_squared_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(mean_squared_log_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(mean_absolute_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(mean_pinball_loss([0.0], [0.0]), 0.0)
+    assert_almost_equal(mean_absolute_percentage_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(median_absolute_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(max_error([0.0], [0.0]), 0.0)
+    assert_almost_equal(explained_variance_score([0.0], [0.0]), 1.0)
+
+    # Perfect cases
+    assert_almost_equal(r2_score([0.0, 1], [0.0, 1]), 1.0)
+    assert_almost_equal(d2_pinball_score([0.0, 1], [0.0, 1]), 1.0)
+
+    # Non-finite cases
+    # R² and explained variance have a fix by default for non-finite cases
+    for s in (r2_score, explained_variance_score):
+        assert_almost_equal(s([0, 0], [1, -1]), 0.0)
+        assert_almost_equal(s([0, 0], [1, -1], force_finite=False), -np.inf)
+        assert_almost_equal(s([1, 1], [1, 1]), 1.0)
+        assert_almost_equal(s([1, 1], [1, 1], force_finite=False), np.nan)
+    msg = (
+        "Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+    with pytest.raises(ValueError, match=msg):
+        mean_squared_log_error([-1.0], [-1.0])
+    msg = (
+        "Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+    with pytest.raises(ValueError, match=msg):
+        mean_squared_log_error([1.0, 2.0, 3.0], [1.0, -2.0, 3.0])
+    msg = (
+        "Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+    with pytest.raises(ValueError, match=msg):
+        mean_squared_log_error([1.0, -2.0, 3.0], [1.0, 2.0, 3.0])
+    msg = (
+        "Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+    with pytest.raises(ValueError, match=msg):
+        root_mean_squared_log_error([1.0, -2.0, 3.0], [1.0, 2.0, 3.0])
+    msg = (
+        "Root Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+
+    # Tweedie deviance error
+    power = -1.2
+    assert_allclose(
+        mean_tweedie_deviance([0], [1.0], power=power), 2 / (2 - power), rtol=1e-3
+    )
+    msg = "can only be used on strictly positive y_pred."
+    with pytest.raises(ValueError, match=msg):
+        mean_tweedie_deviance([0.0], [0.0], power=power)
+    with pytest.raises(ValueError, match=msg):
+        d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
+
+    assert_almost_equal(mean_tweedie_deviance([0.0], [0.0], power=0), 0.0, 2)
+
+    power = 1.0
+    msg = "only be used on non-negative y and strictly positive y_pred."
+    with pytest.raises(ValueError, match=msg):
+        mean_tweedie_deviance([0.0], [0.0], power=power)
+    with pytest.raises(ValueError, match=msg):
+        d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
+
+    power = 1.5
+    assert_allclose(mean_tweedie_deviance([0.0], [1.0], power=power), 2 / (2 - power))
+    msg = "only be used on non-negative y and strictly positive y_pred."
+    with pytest.raises(ValueError, match=msg):
+        mean_tweedie_deviance([0.0], [0.0], power=power)
+    with pytest.raises(ValueError, match=msg):
+        d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
+
+    power = 2.0
+    assert_allclose(mean_tweedie_deviance([1.0], [1.0], power=power), 0.00, atol=1e-8)
+    msg = "can only be used on strictly positive y and y_pred."
+    with pytest.raises(ValueError, match=msg):
+        mean_tweedie_deviance([0.0], [0.0], power=power)
+    with pytest.raises(ValueError, match=msg):
+        d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
+
+    power = 3.0
+    assert_allclose(mean_tweedie_deviance([1.0], [1.0], power=power), 0.00, atol=1e-8)
+    msg = "can only be used on strictly positive y and y_pred."
+    with pytest.raises(ValueError, match=msg):
+        mean_tweedie_deviance([0.0], [0.0], power=power)
+    with pytest.raises(ValueError, match=msg):
+        d2_tweedie_score([0.0] * 2, [0.0] * 2, power=power)
+
+
+def test__check_reg_targets():
+    # All of length 3
+    EXAMPLES = [
+        ("continuous", [1, 2, 3], 1),
+        ("continuous", [[1], [2], [3]], 1),
+        ("continuous-multioutput", [[1, 1], [2, 2], [3, 1]], 2),
+        ("continuous-multioutput", [[5, 1], [4, 2], [3, 1]], 2),
+        ("continuous-multioutput", [[1, 3, 4], [2, 2, 2], [3, 1, 1]], 3),
+    ]
+
+    for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES, repeat=2):
+        if type1 == type2 and n_out1 == n_out2:
+            y_type, y_check1, y_check2, _, _ = _check_reg_targets(
+                y1, y2, sample_weight=None, multioutput=None
+            )
+            assert type1 == y_type
+            if type1 == "continuous":
+                assert_array_equal(y_check1, np.reshape(y1, (-1, 1)))
+                assert_array_equal(y_check2, np.reshape(y2, (-1, 1)))
+            else:
+                assert_array_equal(y_check1, y1)
+                assert_array_equal(y_check2, y2)
+        else:
+            with pytest.raises(ValueError):
+                _check_reg_targets(y1, y2, sample_weight=None, multioutput=None)
+
+
+def test__check_reg_targets_exception():
+    invalid_multioutput = "this_value_is_not_valid"
+    expected_message = (
+        "Allowed 'multioutput' string values are.+You provided multioutput={!r}".format(
+            invalid_multioutput
+        )
+    )
+    with pytest.raises(ValueError, match=expected_message):
+        _check_reg_targets([1, 2, 3], [[1], [2], [3]], None, invalid_multioutput)
+
+
+def test_regression_multioutput_array():
+    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
+    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
+
+    mse = mean_squared_error(y_true, y_pred, multioutput="raw_values")
+    mae = mean_absolute_error(y_true, y_pred, multioutput="raw_values")
+
+    pbl = mean_pinball_loss(y_true, y_pred, multioutput="raw_values")
+    mape = mean_absolute_percentage_error(y_true, y_pred, multioutput="raw_values")
+    r = r2_score(y_true, y_pred, multioutput="raw_values")
+    evs = explained_variance_score(y_true, y_pred, multioutput="raw_values")
+    d2ps = d2_pinball_score(y_true, y_pred, alpha=0.5, multioutput="raw_values")
+    evs2 = explained_variance_score(
+        y_true, y_pred, multioutput="raw_values", force_finite=False
+    )
+
+    assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
+    assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
+    assert_array_almost_equal(pbl, [0.25 / 2, 0.625 / 2], decimal=2)
+    assert_array_almost_equal(mape, [0.0778, 0.2262], decimal=2)
+    assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
+    assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)
+    assert_array_almost_equal(d2ps, [0.833, 0.722], decimal=2)
+    assert_array_almost_equal(evs2, [0.95, 0.93], decimal=2)
+
+    # mean_absolute_error and mean_squared_error are equal because
+    # it is a binary problem.
+    y_true = [[0, 0]] * 4
+    y_pred = [[1, 1]] * 4
+    mse = mean_squared_error(y_true, y_pred, multioutput="raw_values")
+    mae = mean_absolute_error(y_true, y_pred, multioutput="raw_values")
+    pbl = mean_pinball_loss(y_true, y_pred, multioutput="raw_values")
+    r = r2_score(y_true, y_pred, multioutput="raw_values")
+    d2ps = d2_pinball_score(y_true, y_pred, multioutput="raw_values")
+    assert_array_almost_equal(mse, [1.0, 1.0], decimal=2)
+    assert_array_almost_equal(mae, [1.0, 1.0], decimal=2)
+    assert_array_almost_equal(pbl, [0.5, 0.5], decimal=2)
+    assert_array_almost_equal(r, [0.0, 0.0], decimal=2)
+    assert_array_almost_equal(d2ps, [0.0, 0.0], decimal=2)
+
+    r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput="raw_values")
+    assert_array_almost_equal(r, [0, -3.5], decimal=2)
+    assert np.mean(r) == r2_score(
+        [[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput="uniform_average"
+    )
+    evs = explained_variance_score(
+        [[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput="raw_values"
+    )
+    assert_array_almost_equal(evs, [0, -1.25], decimal=2)
+    evs2 = explained_variance_score(
+        [[0, -1], [0, 1]],
+        [[2, 2], [1, 1]],
+        multioutput="raw_values",
+        force_finite=False,
+    )
+    assert_array_almost_equal(evs2, [-np.inf, -1.25], decimal=2)
+
+    # Checking for the condition in which both numerator and denominator is
+    # zero.
+    y_true = [[1, 3], [1, 2]]
+    y_pred = [[1, 4], [1, 1]]
+    r2 = r2_score(y_true, y_pred, multioutput="raw_values")
+    assert_array_almost_equal(r2, [1.0, -3.0], decimal=2)
+    assert np.mean(r2) == r2_score(y_true, y_pred, multioutput="uniform_average")
+    r22 = r2_score(y_true, y_pred, multioutput="raw_values", force_finite=False)
+    assert_array_almost_equal(r22, [np.nan, -3.0], decimal=2)
+    assert_almost_equal(
+        np.mean(r22),
+        r2_score(y_true, y_pred, multioutput="uniform_average", force_finite=False),
+    )
+
+    evs = explained_variance_score(y_true, y_pred, multioutput="raw_values")
+    assert_array_almost_equal(evs, [1.0, -3.0], decimal=2)
+    assert np.mean(evs) == explained_variance_score(y_true, y_pred)
+    d2ps = d2_pinball_score(y_true, y_pred, alpha=0.5, multioutput="raw_values")
+    assert_array_almost_equal(d2ps, [1.0, -1.0], decimal=2)
+    evs2 = explained_variance_score(
+        y_true, y_pred, multioutput="raw_values", force_finite=False
+    )
+    assert_array_almost_equal(evs2, [np.nan, -3.0], decimal=2)
+    assert_almost_equal(
+        np.mean(evs2), explained_variance_score(y_true, y_pred, force_finite=False)
+    )
+
+    # Handling msle separately as it does not accept negative inputs.
+    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
+    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
+    msle = mean_squared_log_error(y_true, y_pred, multioutput="raw_values")
+    msle2 = mean_squared_error(
+        np.log(1 + y_true), np.log(1 + y_pred), multioutput="raw_values"
+    )
+    assert_array_almost_equal(msle, msle2, decimal=2)
+
+
+def test_regression_custom_weights():
+    y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
+    y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
+
+    msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
+    rmsew = root_mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
+    maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
+    mapew = mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.4, 0.6])
+    rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
+    evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])
+    d2psw = d2_pinball_score(y_true, y_pred, alpha=0.5, multioutput=[0.4, 0.6])
+    evsw2 = explained_variance_score(
+        y_true, y_pred, multioutput=[0.4, 0.6], force_finite=False
+    )
+
+    assert_almost_equal(msew, 0.39, decimal=2)
+    assert_almost_equal(rmsew, 0.59, decimal=2)
+    assert_almost_equal(maew, 0.475, decimal=3)
+    assert_almost_equal(mapew, 0.1668, decimal=2)
+    assert_almost_equal(rw, 0.94, decimal=2)
+    assert_almost_equal(evsw, 0.94, decimal=2)
+    assert_almost_equal(d2psw, 0.766, decimal=2)
+    assert_almost_equal(evsw2, 0.94, decimal=2)
+
+    # Handling msle separately as it does not accept negative inputs.
+    y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
+    y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
+    msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
+    msle2 = mean_squared_error(
+        np.log(1 + y_true), np.log(1 + y_pred), multioutput=[0.3, 0.7]
+    )
+    assert_almost_equal(msle, msle2, decimal=2)
+
+
+@pytest.mark.parametrize("metric", [r2_score, d2_tweedie_score, d2_pinball_score])
+def test_regression_single_sample(metric):
+    y_true = [0]
+    y_pred = [1]
+    warning_msg = "not well-defined with less than two samples."
+
+    # Trigger the warning
+    with pytest.warns(UndefinedMetricWarning, match=warning_msg):
+        score = metric(y_true, y_pred)
+        assert np.isnan(score)
+
+
+def test_tweedie_deviance_continuity(global_random_seed):
+    n_samples = 100
+
+    rng = np.random.RandomState(global_random_seed)
+
+    y_true = rng.rand(n_samples) + 0.1
+    y_pred = rng.rand(n_samples) + 0.1
+
+    assert_allclose(
+        mean_tweedie_deviance(y_true, y_pred, power=0 - 1e-10),
+        mean_tweedie_deviance(y_true, y_pred, power=0),
+    )
+
+    # Ws we get closer to the limit, with 1e-12 difference the
+    # tolerance to pass the below check increases. There are likely
+    # numerical precision issues on the edges of different definition
+    # regions.
+    assert_allclose(
+        mean_tweedie_deviance(y_true, y_pred, power=1 + 1e-10),
+        mean_tweedie_deviance(y_true, y_pred, power=1),
+        rtol=1e-5,
+    )
+
+    assert_allclose(
+        mean_tweedie_deviance(y_true, y_pred, power=2 - 1e-10),
+        mean_tweedie_deviance(y_true, y_pred, power=2),
+        rtol=1e-5,
+    )
+
+    assert_allclose(
+        mean_tweedie_deviance(y_true, y_pred, power=2 + 1e-10),
+        mean_tweedie_deviance(y_true, y_pred, power=2),
+        rtol=1e-5,
+    )
+
+
+def test_mean_absolute_percentage_error(global_random_seed):
+    random_number_generator = np.random.RandomState(global_random_seed)
+    y_true = random_number_generator.exponential(size=100)
+    y_pred = 1.2 * y_true
+    assert mean_absolute_percentage_error(y_true, y_pred) == pytest.approx(0.2)
+
+
+@pytest.mark.parametrize(
+    "distribution", ["normal", "lognormal", "exponential", "uniform"]
+)
+@pytest.mark.parametrize("target_quantile", [0.05, 0.5, 0.75])
+def test_mean_pinball_loss_on_constant_predictions(
+    distribution, target_quantile, global_random_seed
+):
+    if not hasattr(np, "quantile"):
+        pytest.skip(
+            "This test requires a more recent version of numpy "
+            "with support for np.quantile."
+        )
+
+    # Check that the pinball loss is minimized by the empirical quantile.
+    n_samples = 3000
+    rng = np.random.RandomState(global_random_seed)
+    data = getattr(rng, distribution)(size=n_samples)
+
+    # Compute the best possible pinball loss for any constant predictor:
+    best_pred = np.quantile(data, target_quantile)
+    best_constant_pred = np.full(n_samples, fill_value=best_pred)
+    best_pbl = mean_pinball_loss(data, best_constant_pred, alpha=target_quantile)
+
+    # Evaluate the loss on a grid of quantiles
+    candidate_predictions = np.quantile(data, np.linspace(0, 1, 100))
+    for pred in candidate_predictions:
+        # Compute the pinball loss of a constant predictor:
+        constant_pred = np.full(n_samples, fill_value=pred)
+        pbl = mean_pinball_loss(data, constant_pred, alpha=target_quantile)
+
+        # Check that the loss of this constant predictor is greater or equal
+        # than the loss of using the optimal quantile (up to machine
+        # precision):
+        assert pbl >= best_pbl - np.finfo(np.float64).eps
+
+        # Check that the value of the pinball loss matches the analytical
+        # formula.
+        expected_pbl = (pred - data[data < pred]).sum() * (1 - target_quantile) + (
+            data[data >= pred] - pred
+        ).sum() * target_quantile
+        expected_pbl /= n_samples
+        assert_almost_equal(expected_pbl, pbl)
+
+    # Check that we can actually recover the target_quantile by minimizing the
+    # pinball loss w.r.t. the constant prediction quantile.
+    def objective_func(x):
+        constant_pred = np.full(n_samples, fill_value=x)
+        return mean_pinball_loss(data, constant_pred, alpha=target_quantile)
+
+    result = optimize.minimize(objective_func, data.mean())
+    assert result.success
+    # The minimum is not unique with limited data, hence the large tolerance.
+    # For the normal distribution and the 0.5 quantile, the expected result is close to
+    # 0, hence the additional use of absolute tolerance.
+    assert_allclose(result.x, best_pred, rtol=1e-1, atol=1e-3)
+    assert result.fun == pytest.approx(best_pbl)
+
+
+def test_dummy_quantile_parameter_tuning(global_random_seed):
+    # Integration test to check that it is possible to use the pinball loss to
+    # tune the hyperparameter of a quantile regressor. This is conceptually
+    # similar to the previous test but using the scikit-learn estimator and
+    # scoring API instead.
+    n_samples = 1000
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.normal(size=(n_samples, 5))  # Ignored
+    y = rng.exponential(size=n_samples)
+
+    all_quantiles = [0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95]
+    for alpha in all_quantiles:
+        neg_mean_pinball_loss = make_scorer(
+            mean_pinball_loss,
+            alpha=alpha,
+            greater_is_better=False,
+        )
+        regressor = DummyRegressor(strategy="quantile", quantile=0.25)
+        grid_search = GridSearchCV(
+            regressor,
+            param_grid=dict(quantile=all_quantiles),
+            scoring=neg_mean_pinball_loss,
+        ).fit(X, y)
+
+        assert grid_search.best_params_["quantile"] == pytest.approx(alpha)
+
+
+def test_pinball_loss_relation_with_mae(global_random_seed):
+    # Test that mean_pinball loss with alpha=0.5 if half of mean absolute error
+    rng = np.random.RandomState(global_random_seed)
+    n = 100
+    y_true = rng.normal(size=n)
+    y_pred = y_true.copy() + rng.uniform(n)
+    assert (
+        mean_absolute_error(y_true, y_pred)
+        == mean_pinball_loss(y_true, y_pred, alpha=0.5) * 2
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_score_objects.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_score_objects.py
new file mode 100644
index 0000000000000000000000000000000000000000..672ed8ae7eecc593e0aa02e76e7158c9f01e67e4
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/metrics/tests/test_score_objects.py
@@ -0,0 +1,1665 @@
+import numbers
+import pickle
+import warnings
+from copy import deepcopy
+from functools import partial
+
+import joblib
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+
+from sklearn import config_context
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.cluster import KMeans
+from sklearn.datasets import (
+    load_diabetes,
+    make_blobs,
+    make_classification,
+    make_multilabel_classification,
+    make_regression,
+)
+from sklearn.linear_model import LogisticRegression, Perceptron, Ridge
+from sklearn.metrics import (
+    accuracy_score,
+    average_precision_score,
+    balanced_accuracy_score,
+    brier_score_loss,
+    check_scoring,
+    f1_score,
+    fbeta_score,
+    get_scorer,
+    get_scorer_names,
+    jaccard_score,
+    log_loss,
+    make_scorer,
+    matthews_corrcoef,
+    precision_score,
+    r2_score,
+    recall_score,
+    roc_auc_score,
+    top_k_accuracy_score,
+)
+from sklearn.metrics import cluster as cluster_module
+from sklearn.metrics._scorer import (
+    _check_multimetric_scoring,
+    _CurveScorer,
+    _MultimetricScorer,
+    _PassthroughScorer,
+    _Scorer,
+)
+from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import LinearSVC
+from sklearn.tests.metadata_routing_common import (
+    assert_request_is_empty,
+)
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_equal,
+    ignore_warnings,
+)
+from sklearn.utils.metadata_routing import MetadataRouter, MethodMapping
+
+REGRESSION_SCORERS = [
+    "d2_absolute_error_score",
+    "explained_variance",
+    "r2",
+    "neg_mean_absolute_error",
+    "neg_mean_squared_error",
+    "neg_mean_absolute_percentage_error",
+    "neg_mean_squared_log_error",
+    "neg_median_absolute_error",
+    "neg_root_mean_squared_error",
+    "neg_root_mean_squared_log_error",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+    "mean_squared_error",
+    "median_absolute_error",
+    "neg_max_error",
+    "neg_mean_poisson_deviance",
+    "neg_mean_gamma_deviance",
+]
+
+CLF_SCORERS = [
+    "accuracy",
+    "balanced_accuracy",
+    "top_k_accuracy",
+    "f1",
+    "f1_weighted",
+    "f1_macro",
+    "f1_micro",
+    "roc_auc",
+    "average_precision",
+    "precision",
+    "precision_weighted",
+    "precision_macro",
+    "precision_micro",
+    "recall",
+    "recall_weighted",
+    "recall_macro",
+    "recall_micro",
+    "neg_log_loss",
+    "neg_brier_score",
+    "jaccard",
+    "jaccard_weighted",
+    "jaccard_macro",
+    "jaccard_micro",
+    "roc_auc_ovr",
+    "roc_auc_ovo",
+    "roc_auc_ovr_weighted",
+    "roc_auc_ovo_weighted",
+    "matthews_corrcoef",
+    "positive_likelihood_ratio",
+    "neg_negative_likelihood_ratio",
+]
+
+# All supervised cluster scorers (They behave like classification metric)
+CLUSTER_SCORERS = [
+    "adjusted_rand_score",
+    "rand_score",
+    "homogeneity_score",
+    "completeness_score",
+    "v_measure_score",
+    "mutual_info_score",
+    "adjusted_mutual_info_score",
+    "normalized_mutual_info_score",
+    "fowlkes_mallows_score",
+]
+
+MULTILABEL_ONLY_SCORERS = [
+    "precision_samples",
+    "recall_samples",
+    "f1_samples",
+    "jaccard_samples",
+]
+
+REQUIRE_POSITIVE_Y_SCORERS = ["neg_mean_poisson_deviance", "neg_mean_gamma_deviance"]
+
+
+def _require_positive_y(y):
+    """Make targets strictly positive"""
+    offset = abs(y.min()) + 1
+    y = y + offset
+    return y
+
+
+def _make_estimators(X_train, y_train, y_ml_train):
+    # Make estimators that make sense to test various scoring methods
+    sensible_regr = DecisionTreeRegressor(random_state=0)
+    # some of the regressions scorers require strictly positive input.
+    sensible_regr.fit(X_train, _require_positive_y(y_train))
+    sensible_clf = DecisionTreeClassifier(random_state=0)
+    sensible_clf.fit(X_train, y_train)
+    sensible_ml_clf = DecisionTreeClassifier(random_state=0)
+    sensible_ml_clf.fit(X_train, y_ml_train)
+    return dict(
+        [(name, sensible_regr) for name in REGRESSION_SCORERS]
+        + [(name, sensible_clf) for name in CLF_SCORERS]
+        + [(name, sensible_clf) for name in CLUSTER_SCORERS]
+        + [(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS]
+    )
+
+
+@pytest.fixture(scope="module")
+def memmap_data_and_estimators(tmp_path_factory):
+    temp_folder = tmp_path_factory.mktemp("sklearn_test_score_objects")
+    X, y = make_classification(n_samples=30, n_features=5, random_state=0)
+    _, y_ml = make_multilabel_classification(n_samples=X.shape[0], random_state=0)
+    filename = temp_folder / "test_data.pkl"
+    joblib.dump((X, y, y_ml), filename)
+    X_mm, y_mm, y_ml_mm = joblib.load(filename, mmap_mode="r")
+    estimators = _make_estimators(X_mm, y_mm, y_ml_mm)
+
+    yield X_mm, y_mm, y_ml_mm, estimators
+
+
+class EstimatorWithFit(BaseEstimator):
+    """Dummy estimator to test scoring validators"""
+
+    def fit(self, X, y):
+        return self
+
+
+class EstimatorWithFitAndScore(BaseEstimator):
+    """Dummy estimator to test scoring validators"""
+
+    def fit(self, X, y):
+        return self
+
+    def score(self, X, y):
+        return 1.0
+
+
+class EstimatorWithFitAndPredict(BaseEstimator):
+    """Dummy estimator to test scoring validators"""
+
+    def fit(self, X, y):
+        self.y = y
+        return self
+
+    def predict(self, X):
+        return self.y
+
+
+class DummyScorer:
+    """Dummy scorer that always returns 1."""
+
+    def __call__(self, est, X, y):
+        return 1
+
+
+def test_all_scorers_repr():
+    # Test that all scorers have a working repr
+    for name in get_scorer_names():
+        repr(get_scorer(name))
+
+
+def check_scoring_validator_for_single_metric_usecases(scoring_validator):
+    # Test all branches of single metric usecases
+    estimator = EstimatorWithFitAndScore()
+    estimator.fit([[1]], [1])
+    scorer = scoring_validator(estimator)
+    assert isinstance(scorer, _PassthroughScorer)
+    assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0)
+
+    estimator = EstimatorWithFitAndPredict()
+    estimator.fit([[1]], [1])
+    pattern = (
+        r"If no scoring is specified, the estimator passed should have"
+        r" a 'score' method\. The estimator .* does not\."
+    )
+    with pytest.raises(TypeError, match=pattern):
+        scoring_validator(estimator)
+
+    scorer = scoring_validator(estimator, scoring="accuracy")
+    assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0)
+
+    estimator = EstimatorWithFit()
+    scorer = scoring_validator(estimator, scoring="accuracy")
+    assert isinstance(scorer, _Scorer)
+    assert scorer._response_method == "predict"
+
+    # Test the allow_none parameter for check_scoring alone
+    if scoring_validator is check_scoring:
+        estimator = EstimatorWithFit()
+        scorer = scoring_validator(estimator, allow_none=True)
+        assert scorer is None
+
+
+@pytest.mark.parametrize(
+    "scoring",
+    (
+        ("accuracy",),
+        ["precision"],
+        {"acc": "accuracy", "precision": "precision"},
+        ("accuracy", "precision"),
+        ["precision", "accuracy"],
+        {
+            "accuracy": make_scorer(accuracy_score),
+            "precision": make_scorer(precision_score),
+        },
+    ),
+    ids=[
+        "single_tuple",
+        "single_list",
+        "dict_str",
+        "multi_tuple",
+        "multi_list",
+        "dict_callable",
+    ],
+)
+def test_check_scoring_and_check_multimetric_scoring(scoring):
+    check_scoring_validator_for_single_metric_usecases(check_scoring)
+    # To make sure the check_scoring is correctly applied to the constituent
+    # scorers
+
+    estimator = LinearSVC(random_state=0)
+    estimator.fit([[1], [2], [3]], [1, 1, 0])
+
+    scorers = _check_multimetric_scoring(estimator, scoring)
+    assert isinstance(scorers, dict)
+    assert sorted(scorers.keys()) == sorted(list(scoring))
+    assert all([isinstance(scorer, _Scorer) for scorer in list(scorers.values())])
+    assert all(scorer._response_method == "predict" for scorer in scorers.values())
+
+    if "acc" in scoring:
+        assert_almost_equal(
+            scorers["acc"](estimator, [[1], [2], [3]], [1, 0, 0]), 2.0 / 3.0
+        )
+    if "accuracy" in scoring:
+        assert_almost_equal(
+            scorers["accuracy"](estimator, [[1], [2], [3]], [1, 0, 0]), 2.0 / 3.0
+        )
+    if "precision" in scoring:
+        assert_almost_equal(
+            scorers["precision"](estimator, [[1], [2], [3]], [1, 0, 0]), 0.5
+        )
+
+
+@pytest.mark.parametrize(
+    "scoring, msg",
+    [
+        (
+            (make_scorer(precision_score), make_scorer(accuracy_score)),
+            "One or more of the elements were callables",
+        ),
+        ([5], "Non-string types were found"),
+        ((make_scorer(precision_score),), "One or more of the elements were callables"),
+        ((), "Empty list was given"),
+        (("f1", "f1"), "Duplicate elements were found"),
+        ({4: "accuracy"}, "Non-string types were found in the keys"),
+        ({}, "An empty dict was passed"),
+    ],
+    ids=[
+        "tuple of callables",
+        "list of int",
+        "tuple of one callable",
+        "empty tuple",
+        "non-unique str",
+        "non-string key dict",
+        "empty dict",
+    ],
+)
+def test_check_scoring_and_check_multimetric_scoring_errors(scoring, msg):
+    # Make sure it raises errors when scoring parameter is not valid.
+    # More weird corner cases are tested at test_validation.py
+    estimator = EstimatorWithFitAndPredict()
+    estimator.fit([[1]], [1])
+
+    with pytest.raises(ValueError, match=msg):
+        _check_multimetric_scoring(estimator, scoring=scoring)
+
+
+def test_check_scoring_gridsearchcv():
+    # test that check_scoring works on GridSearchCV and pipeline.
+    # slightly redundant non-regression test.
+
+    grid = GridSearchCV(LinearSVC(), param_grid={"C": [0.1, 1]}, cv=3)
+    scorer = check_scoring(grid, scoring="f1")
+    assert isinstance(scorer, _Scorer)
+    assert scorer._response_method == "predict"
+
+    pipe = make_pipeline(LinearSVC())
+    scorer = check_scoring(pipe, scoring="f1")
+    assert isinstance(scorer, _Scorer)
+    assert scorer._response_method == "predict"
+
+    # check that cross_val_score definitely calls the scorer
+    # and doesn't make any assumptions about the estimator apart from having a
+    # fit.
+    scores = cross_val_score(
+        EstimatorWithFit(), [[1], [2], [3]], [1, 0, 1], scoring=DummyScorer(), cv=3
+    )
+    assert_array_equal(scores, 1)
+
+
+@pytest.mark.parametrize(
+    "scorer_name, metric",
+    [
+        ("f1", f1_score),
+        ("f1_weighted", partial(f1_score, average="weighted")),
+        ("f1_macro", partial(f1_score, average="macro")),
+        ("f1_micro", partial(f1_score, average="micro")),
+        ("precision", precision_score),
+        ("precision_weighted", partial(precision_score, average="weighted")),
+        ("precision_macro", partial(precision_score, average="macro")),
+        ("precision_micro", partial(precision_score, average="micro")),
+        ("recall", recall_score),
+        ("recall_weighted", partial(recall_score, average="weighted")),
+        ("recall_macro", partial(recall_score, average="macro")),
+        ("recall_micro", partial(recall_score, average="micro")),
+        ("jaccard", jaccard_score),
+        ("jaccard_weighted", partial(jaccard_score, average="weighted")),
+        ("jaccard_macro", partial(jaccard_score, average="macro")),
+        ("jaccard_micro", partial(jaccard_score, average="micro")),
+        ("top_k_accuracy", top_k_accuracy_score),
+        ("matthews_corrcoef", matthews_corrcoef),
+    ],
+)
+def test_classification_binary_scores(scorer_name, metric):
+    # check consistency between score and scorer for scores supporting
+    # binary classification.
+    X, y = make_blobs(random_state=0, centers=2)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LinearSVC(random_state=0)
+    clf.fit(X_train, y_train)
+
+    score = get_scorer(scorer_name)(clf, X_test, y_test)
+    expected_score = metric(y_test, clf.predict(X_test))
+    assert_almost_equal(score, expected_score)
+
+
+@pytest.mark.parametrize(
+    "scorer_name, metric",
+    [
+        ("accuracy", accuracy_score),
+        ("balanced_accuracy", balanced_accuracy_score),
+        ("f1_weighted", partial(f1_score, average="weighted")),
+        ("f1_macro", partial(f1_score, average="macro")),
+        ("f1_micro", partial(f1_score, average="micro")),
+        ("precision_weighted", partial(precision_score, average="weighted")),
+        ("precision_macro", partial(precision_score, average="macro")),
+        ("precision_micro", partial(precision_score, average="micro")),
+        ("recall_weighted", partial(recall_score, average="weighted")),
+        ("recall_macro", partial(recall_score, average="macro")),
+        ("recall_micro", partial(recall_score, average="micro")),
+        ("jaccard_weighted", partial(jaccard_score, average="weighted")),
+        ("jaccard_macro", partial(jaccard_score, average="macro")),
+        ("jaccard_micro", partial(jaccard_score, average="micro")),
+    ],
+)
+def test_classification_multiclass_scores(scorer_name, metric):
+    # check consistency between score and scorer for scores supporting
+    # multiclass classification.
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=30, random_state=0
+    )
+
+    # use `stratify` = y to ensure train and test sets capture all classes
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, random_state=0, stratify=y
+    )
+
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(X_train, y_train)
+    score = get_scorer(scorer_name)(clf, X_test, y_test)
+    expected_score = metric(y_test, clf.predict(X_test))
+    assert score == pytest.approx(expected_score)
+
+
+def test_custom_scorer_pickling():
+    # test that custom scorer can be pickled
+    X, y = make_blobs(random_state=0, centers=2)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LinearSVC(random_state=0)
+    clf.fit(X_train, y_train)
+
+    scorer = make_scorer(fbeta_score, beta=2)
+    score1 = scorer(clf, X_test, y_test)
+    unpickled_scorer = pickle.loads(pickle.dumps(scorer))
+    score2 = unpickled_scorer(clf, X_test, y_test)
+    assert score1 == pytest.approx(score2)
+
+    # smoke test the repr:
+    repr(fbeta_score)
+
+
+def test_regression_scorers():
+    # Test regression scorers.
+    diabetes = load_diabetes()
+    X, y = diabetes.data, diabetes.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = Ridge()
+    clf.fit(X_train, y_train)
+    score1 = get_scorer("r2")(clf, X_test, y_test)
+    score2 = r2_score(y_test, clf.predict(X_test))
+    assert_almost_equal(score1, score2)
+
+
+def test_thresholded_scorers():
+    # Test scorers that take thresholds.
+    X, y = make_blobs(random_state=0, centers=2)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LogisticRegression(random_state=0)
+    clf.fit(X_train, y_train)
+    score1 = get_scorer("roc_auc")(clf, X_test, y_test)
+    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
+    score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
+    assert_almost_equal(score1, score2)
+    assert_almost_equal(score1, score3)
+
+    logscore = get_scorer("neg_log_loss")(clf, X_test, y_test)
+    logloss = log_loss(y_test, clf.predict_proba(X_test))
+    assert_almost_equal(-logscore, logloss)
+
+    # same for an estimator without decision_function
+    clf = DecisionTreeClassifier()
+    clf.fit(X_train, y_train)
+    score1 = get_scorer("roc_auc")(clf, X_test, y_test)
+    score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
+    assert_almost_equal(score1, score2)
+
+    # test with a regressor (no decision_function)
+    reg = DecisionTreeRegressor()
+    reg.fit(X_train, y_train)
+    err_msg = "DecisionTreeRegressor has none of the following attributes"
+    with pytest.raises(AttributeError, match=err_msg):
+        get_scorer("roc_auc")(reg, X_test, y_test)
+
+    # Test that an exception is raised on more than two classes
+    X, y = make_blobs(random_state=0, centers=3)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf.fit(X_train, y_train)
+    with pytest.raises(ValueError, match="multi_class must be in \\('ovo', 'ovr'\\)"):
+        get_scorer("roc_auc")(clf, X_test, y_test)
+
+    # test error is raised with a single class present in model
+    # (predict_proba shape is not suitable for binary auc)
+    X, y = make_blobs(random_state=0, centers=2)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = DecisionTreeClassifier()
+    clf.fit(X_train, np.zeros_like(y_train))
+    with pytest.raises(ValueError, match="need classifier with two classes"):
+        get_scorer("roc_auc")(clf, X_test, y_test)
+
+    # for proba scorers
+    with pytest.raises(ValueError, match="need classifier with two classes"):
+        get_scorer("neg_log_loss")(clf, X_test, y_test)
+
+
+def test_thresholded_scorers_multilabel_indicator_data():
+    # Test that the scorer work with multilabel-indicator format
+    # for multilabel and multi-output multi-class classifier
+    X, y = make_multilabel_classification(allow_unlabeled=False, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    # Multi-output multi-class predict_proba
+    clf = DecisionTreeClassifier()
+    clf.fit(X_train, y_train)
+    y_proba = clf.predict_proba(X_test)
+    score1 = get_scorer("roc_auc")(clf, X_test, y_test)
+    score2 = roc_auc_score(y_test, np.vstack([p[:, -1] for p in y_proba]).T)
+    assert_almost_equal(score1, score2)
+
+    # Multilabel predict_proba
+    clf = OneVsRestClassifier(DecisionTreeClassifier())
+    clf.fit(X_train, y_train)
+    score1 = get_scorer("roc_auc")(clf, X_test, y_test)
+    score2 = roc_auc_score(y_test, clf.predict_proba(X_test))
+    assert_almost_equal(score1, score2)
+
+    # Multilabel decision function
+    clf = OneVsRestClassifier(LinearSVC(random_state=0))
+    clf.fit(X_train, y_train)
+    score1 = get_scorer("roc_auc")(clf, X_test, y_test)
+    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
+    assert_almost_equal(score1, score2)
+
+
+def test_supervised_cluster_scorers():
+    # Test clustering scorers against gold standard labeling.
+    X, y = make_blobs(random_state=0, centers=2)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    km = KMeans(n_clusters=3, n_init="auto")
+    km.fit(X_train)
+    for name in CLUSTER_SCORERS:
+        score1 = get_scorer(name)(km, X_test, y_test)
+        score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
+        assert_almost_equal(score1, score2)
+
+
+def test_raises_on_score_list():
+    # Test that when a list of scores is returned, we raise proper errors.
+    X, y = make_blobs(random_state=0)
+    f1_scorer_no_average = make_scorer(f1_score, average=None)
+    clf = DecisionTreeClassifier()
+    with pytest.raises(ValueError):
+        cross_val_score(clf, X, y, scoring=f1_scorer_no_average)
+    grid_search = GridSearchCV(
+        clf, scoring=f1_scorer_no_average, param_grid={"max_depth": [1, 2]}
+    )
+    with pytest.raises(ValueError):
+        grid_search.fit(X, y)
+
+
+def test_classification_scorer_sample_weight():
+    # Test that classification scorers support sample_weight or raise sensible
+    # errors
+
+    # Unlike the metrics invariance test, in the scorer case it's harder
+    # to ensure that, on the classifier output, weighted and unweighted
+    # scores really should be unequal.
+    X, y = make_classification(random_state=0)
+    _, y_ml = make_multilabel_classification(n_samples=X.shape[0], random_state=0)
+    split = train_test_split(X, y, y_ml, random_state=0)
+    X_train, X_test, y_train, y_test, y_ml_train, y_ml_test = split
+
+    sample_weight = np.ones_like(y_test)
+    sample_weight[:10] = 0
+
+    # get sensible estimators for each metric
+    estimator = _make_estimators(X_train, y_train, y_ml_train)
+
+    for name in get_scorer_names():
+        scorer = get_scorer(name)
+        if name in REGRESSION_SCORERS:
+            # skip the regression scores
+            continue
+        if name == "top_k_accuracy":
+            # in the binary case k > 1 will always lead to a perfect score
+            scorer._kwargs = {"k": 1}
+        if name in MULTILABEL_ONLY_SCORERS:
+            target = y_ml_test
+        else:
+            target = y_test
+        try:
+            weighted = scorer(
+                estimator[name], X_test, target, sample_weight=sample_weight
+            )
+            ignored = scorer(estimator[name], X_test[10:], target[10:])
+            unweighted = scorer(estimator[name], X_test, target)
+            # this should not raise. sample_weight should be ignored if None.
+            _ = scorer(estimator[name], X_test[:10], target[:10], sample_weight=None)
+            assert weighted != unweighted, (
+                f"scorer {name} behaves identically when called with "
+                f"sample weights: {weighted} vs {unweighted}"
+            )
+            assert_almost_equal(
+                weighted,
+                ignored,
+                err_msg=(
+                    f"scorer {name} behaves differently "
+                    "when ignoring samples and setting "
+                    f"sample_weight to 0: {weighted} vs {ignored}"
+                ),
+            )
+
+        except TypeError as e:
+            assert "sample_weight" in str(e), (
+                f"scorer {name} raises unhelpful exception when called "
+                f"with sample weights: {e}"
+            )
+
+
+def test_regression_scorer_sample_weight():
+    # Test that regression scorers support sample_weight or raise sensible
+    # errors
+
+    # Odd number of test samples req for neg_median_absolute_error
+    X, y = make_regression(n_samples=101, n_features=20, random_state=0)
+    y = _require_positive_y(y)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    sample_weight = np.ones_like(y_test)
+    # Odd number req for neg_median_absolute_error
+    sample_weight[:11] = 0
+
+    reg = DecisionTreeRegressor(random_state=0)
+    reg.fit(X_train, y_train)
+
+    for name in get_scorer_names():
+        scorer = get_scorer(name)
+        if name not in REGRESSION_SCORERS:
+            # skip classification scorers
+            continue
+        try:
+            weighted = scorer(reg, X_test, y_test, sample_weight=sample_weight)
+            ignored = scorer(reg, X_test[11:], y_test[11:])
+            unweighted = scorer(reg, X_test, y_test)
+            assert weighted != unweighted, (
+                f"scorer {name} behaves identically when called with "
+                f"sample weights: {weighted} vs {unweighted}"
+            )
+            assert_almost_equal(
+                weighted,
+                ignored,
+                err_msg=(
+                    f"scorer {name} behaves differently "
+                    "when ignoring samples and setting "
+                    f"sample_weight to 0: {weighted} vs {ignored}"
+                ),
+            )
+
+        except TypeError as e:
+            assert "sample_weight" in str(e), (
+                f"scorer {name} raises unhelpful exception when called "
+                f"with sample weights: {e}"
+            )
+
+
+@pytest.mark.parametrize("name", get_scorer_names())
+def test_scorer_memmap_input(name, memmap_data_and_estimators):
+    # Non-regression test for #6147: some score functions would
+    # return singleton memmap when computed on memmap data instead of scalar
+    # float values.
+    X_mm, y_mm, y_ml_mm, estimators = memmap_data_and_estimators
+
+    if name in REQUIRE_POSITIVE_Y_SCORERS:
+        y_mm_1 = _require_positive_y(y_mm)
+        y_ml_mm_1 = _require_positive_y(y_ml_mm)
+    else:
+        y_mm_1, y_ml_mm_1 = y_mm, y_ml_mm
+
+    # UndefinedMetricWarning for P / R scores
+    with ignore_warnings():
+        scorer, estimator = get_scorer(name), estimators[name]
+        if name in MULTILABEL_ONLY_SCORERS:
+            score = scorer(estimator, X_mm, y_ml_mm_1)
+        else:
+            score = scorer(estimator, X_mm, y_mm_1)
+        assert isinstance(score, numbers.Number), name
+
+
+def test_scoring_is_not_metric():
+    with pytest.raises(ValueError, match="make_scorer"):
+        check_scoring(LogisticRegression(), scoring=f1_score)
+    with pytest.raises(ValueError, match="make_scorer"):
+        check_scoring(LogisticRegression(), scoring=roc_auc_score)
+    with pytest.raises(ValueError, match="make_scorer"):
+        check_scoring(Ridge(), scoring=r2_score)
+    with pytest.raises(ValueError, match="make_scorer"):
+        check_scoring(KMeans(), scoring=cluster_module.adjusted_rand_score)
+    with pytest.raises(ValueError, match="make_scorer"):
+        check_scoring(KMeans(), scoring=cluster_module.rand_score)
+
+
+def test_deprecated_scorer():
+    X, y = make_regression(n_samples=10, n_features=1, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    reg = DecisionTreeRegressor()
+    reg.fit(X_train, y_train)
+    deprecated_scorer = get_scorer("max_error")
+    with pytest.warns(DeprecationWarning):
+        deprecated_scorer(reg, X_test, y_test)
+
+
+@pytest.mark.parametrize(
+    (
+        "scorers,expected_predict_count,"
+        "expected_predict_proba_count,expected_decision_func_count"
+    ),
+    [
+        (
+            {
+                "a1": "accuracy",
+                "a2": "accuracy",
+                "ll1": "neg_log_loss",
+                "ll2": "neg_log_loss",
+                "ra1": "roc_auc",
+                "ra2": "roc_auc",
+            },
+            1,
+            1,
+            1,
+        ),
+        (["roc_auc", "accuracy"], 1, 0, 1),
+        (["neg_log_loss", "accuracy"], 1, 1, 0),
+    ],
+)
+def test_multimetric_scorer_calls_method_once(
+    scorers,
+    expected_predict_count,
+    expected_predict_proba_count,
+    expected_decision_func_count,
+):
+    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
+    pos_proba = np.random.rand(X.shape[0])
+    proba = np.c_[1 - pos_proba, pos_proba]
+
+    class MyClassifier(ClassifierMixin, BaseEstimator):
+        def __init__(self):
+            self._expected_predict_count = 0
+            self._expected_predict_proba_count = 0
+            self._expected_decision_function_count = 0
+
+        def fit(self, X, y):
+            self.classes_ = np.unique(y)
+            return self
+
+        def predict(self, X):
+            self._expected_predict_count += 1
+            return y
+
+        def predict_proba(self, X):
+            self._expected_predict_proba_count += 1
+            return proba
+
+        def decision_function(self, X):
+            self._expected_decision_function_count += 1
+            return pos_proba
+
+    mock_est = MyClassifier().fit(X, y)
+    scorer_dict = _check_multimetric_scoring(LogisticRegression(), scorers)
+    multi_scorer = _MultimetricScorer(scorers=scorer_dict)
+    results = multi_scorer(mock_est, X, y)
+
+    assert set(scorers) == set(results)  # compare dict keys
+
+    assert mock_est._expected_predict_count == expected_predict_count
+    assert mock_est._expected_predict_proba_count == expected_predict_proba_count
+    assert mock_est._expected_decision_function_count == expected_decision_func_count
+
+
+@pytest.mark.parametrize(
+    "scorers",
+    [
+        (["roc_auc", "neg_log_loss"]),
+        (
+            {
+                "roc_auc": make_scorer(
+                    roc_auc_score,
+                    response_method=["predict_proba", "decision_function"],
+                ),
+                "neg_log_loss": make_scorer(log_loss, response_method="predict_proba"),
+            }
+        ),
+    ],
+)
+def test_multimetric_scorer_calls_method_once_classifier_no_decision(scorers):
+    predict_proba_call_cnt = 0
+
+    class MockKNeighborsClassifier(KNeighborsClassifier):
+        def predict_proba(self, X):
+            nonlocal predict_proba_call_cnt
+            predict_proba_call_cnt += 1
+            return super().predict_proba(X)
+
+    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
+
+    # no decision function
+    clf = MockKNeighborsClassifier(n_neighbors=1)
+    clf.fit(X, y)
+
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    scorer = _MultimetricScorer(scorers=scorer_dict)
+    scorer(clf, X, y)
+
+    assert predict_proba_call_cnt == 1
+
+
+def test_multimetric_scorer_calls_method_once_regressor_threshold():
+    predict_called_cnt = 0
+
+    class MockDecisionTreeRegressor(DecisionTreeRegressor):
+        def predict(self, X):
+            nonlocal predict_called_cnt
+            predict_called_cnt += 1
+            return super().predict(X)
+
+    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])
+
+    # no decision function
+    clf = MockDecisionTreeRegressor()
+    clf.fit(X, y)
+
+    scorers = {"neg_mse": "neg_mean_squared_error", "r2": "r2"}
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    scorer = _MultimetricScorer(scorers=scorer_dict)
+    scorer(clf, X, y)
+
+    assert predict_called_cnt == 1
+
+
+def test_multimetric_scorer_sanity_check():
+    # scoring dictionary returned is the same as calling each scorer separately
+    scorers = {
+        "a1": "accuracy",
+        "a2": "accuracy",
+        "ll1": "neg_log_loss",
+        "ll2": "neg_log_loss",
+        "ra1": "roc_auc",
+        "ra2": "roc_auc",
+    }
+
+    X, y = make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier()
+    clf.fit(X, y)
+
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    multi_scorer = _MultimetricScorer(scorers=scorer_dict)
+
+    result = multi_scorer(clf, X, y)
+
+    separate_scores = {
+        name: get_scorer(name)(clf, X, y)
+        for name in ["accuracy", "neg_log_loss", "roc_auc"]
+    }
+
+    for key, value in result.items():
+        score_name = scorers[key]
+        assert_allclose(value, separate_scores[score_name])
+
+
+@pytest.mark.parametrize("raise_exc", [True, False])
+def test_multimetric_scorer_exception_handling(raise_exc):
+    """Check that the calling of the `_MultimetricScorer` returns
+    exception messages in the result dict for the failing scorers
+    in case of `raise_exc` is `False` and if `raise_exc` is `True`,
+    then the proper exception is raised.
+    """
+    scorers = {
+        "failing_1": "neg_mean_squared_log_error",
+        "non_failing": "neg_median_absolute_error",
+        "failing_2": "neg_mean_squared_log_error",
+    }
+
+    X, y = make_classification(
+        n_samples=50, n_features=2, n_redundant=0, random_state=0
+    )
+    # neg_mean_squared_log_error fails if y contains values less than or equal to -1
+    y *= -1
+
+    clf = DecisionTreeClassifier().fit(X, y)
+
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    multi_scorer = _MultimetricScorer(scorers=scorer_dict, raise_exc=raise_exc)
+
+    error_msg = (
+        "Mean Squared Logarithmic Error cannot be used when "
+        "targets contain values less than or equal to -1."
+    )
+
+    if raise_exc:
+        with pytest.raises(ValueError, match=error_msg):
+            multi_scorer(clf, X, y)
+    else:
+        result = multi_scorer(clf, X, y)
+
+        exception_message_1 = result["failing_1"]
+        score = result["non_failing"]
+        exception_message_2 = result["failing_2"]
+
+        assert isinstance(exception_message_1, str) and error_msg in exception_message_1
+        assert isinstance(score, float)
+        assert isinstance(exception_message_2, str) and error_msg in exception_message_2
+
+
+@pytest.mark.parametrize(
+    "scorer_name, metric",
+    [
+        ("roc_auc_ovr", partial(roc_auc_score, multi_class="ovr")),
+        ("roc_auc_ovo", partial(roc_auc_score, multi_class="ovo")),
+        (
+            "roc_auc_ovr_weighted",
+            partial(roc_auc_score, multi_class="ovr", average="weighted"),
+        ),
+        (
+            "roc_auc_ovo_weighted",
+            partial(roc_auc_score, multi_class="ovo", average="weighted"),
+        ),
+    ],
+)
+def test_multiclass_roc_proba_scorer(scorer_name, metric):
+    scorer = get_scorer(scorer_name)
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = LogisticRegression().fit(X, y)
+    y_proba = lr.predict_proba(X)
+    expected_score = metric(y, y_proba)
+
+    assert scorer(lr, X, y) == pytest.approx(expected_score)
+
+
+def test_multiclass_roc_proba_scorer_label():
+    scorer = make_scorer(
+        roc_auc_score,
+        multi_class="ovo",
+        labels=[0, 1, 2],
+        response_method="predict_proba",
+    )
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = LogisticRegression().fit(X, y)
+    y_proba = lr.predict_proba(X)
+
+    y_binary = y == 0
+    expected_score = roc_auc_score(
+        y_binary, y_proba, multi_class="ovo", labels=[0, 1, 2]
+    )
+
+    assert scorer(lr, X, y_binary) == pytest.approx(expected_score)
+
+
+@pytest.mark.parametrize(
+    "scorer_name",
+    ["roc_auc_ovr", "roc_auc_ovo", "roc_auc_ovr_weighted", "roc_auc_ovo_weighted"],
+)
+def test_multiclass_roc_no_proba_scorer_errors(scorer_name):
+    # Perceptron has no predict_proba
+    scorer = get_scorer(scorer_name)
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = Perceptron().fit(X, y)
+    msg = "Perceptron has none of the following attributes: predict_proba."
+    with pytest.raises(AttributeError, match=msg):
+        scorer(lr, X, y)
+
+
+@pytest.fixture
+def string_labeled_classification_problem():
+    """Train a classifier on binary problem with string target.
+
+    The classifier is trained on a binary classification problem where the
+    minority class of interest has a string label that is intentionally not the
+    greatest class label using the lexicographic order. In this case, "cancer"
+    is the positive label, and `classifier.classes_` is
+    `["cancer", "not cancer"]`.
+
+    In addition, the dataset is imbalanced to better identify problems when
+    using non-symmetric performance metrics such as f1-score, average precision
+    and so on.
+
+    Returns
+    -------
+    classifier : estimator object
+        Trained classifier on the binary problem.
+    X_test : ndarray of shape (n_samples, n_features)
+        Data to be used as testing set in tests.
+    y_test : ndarray of shape (n_samples,), dtype=object
+        Binary target where labels are strings.
+    y_pred : ndarray of shape (n_samples,), dtype=object
+        Prediction of `classifier` when predicting for `X_test`.
+    y_pred_proba : ndarray of shape (n_samples, 2), dtype=np.float64
+        Probabilities of `classifier` when predicting for `X_test`.
+    y_pred_decision : ndarray of shape (n_samples,), dtype=np.float64
+        Decision function values of `classifier` when predicting on `X_test`.
+    """
+    from sklearn.datasets import load_breast_cancer
+    from sklearn.utils import shuffle
+
+    X, y = load_breast_cancer(return_X_y=True)
+    # create an highly imbalanced classification task
+    idx_positive = np.flatnonzero(y == 1)
+    idx_negative = np.flatnonzero(y == 0)
+    idx_selected = np.hstack([idx_negative, idx_positive[:25]])
+    X, y = X[idx_selected], y[idx_selected]
+    X, y = shuffle(X, y, random_state=42)
+    # only use 2 features to make the problem even harder
+    X = X[:, :2]
+    y = np.array(["cancer" if c == 1 else "not cancer" for c in y], dtype=object)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X,
+        y,
+        stratify=y,
+        random_state=0,
+    )
+    classifier = LogisticRegression().fit(X_train, y_train)
+    y_pred = classifier.predict(X_test)
+    y_pred_proba = classifier.predict_proba(X_test)
+    y_pred_decision = classifier.decision_function(X_test)
+
+    return classifier, X_test, y_test, y_pred, y_pred_proba, y_pred_decision
+
+
+def test_average_precision_pos_label(string_labeled_classification_problem):
+    # check that _Scorer will lead to the right score when passing
+    # `pos_label`. Currently, only `average_precision_score` is defined to
+    # be such a scorer.
+    (
+        clf,
+        X_test,
+        y_test,
+        _,
+        y_pred_proba,
+        y_pred_decision,
+    ) = string_labeled_classification_problem
+
+    pos_label = "cancer"
+    # we need to select the positive column or reverse the decision values
+    y_pred_proba = y_pred_proba[:, 0]
+    y_pred_decision = y_pred_decision * -1
+    assert clf.classes_[0] == pos_label
+
+    # check that when calling the scoring function, probability estimates and
+    # decision values lead to the same results
+    ap_proba = average_precision_score(y_test, y_pred_proba, pos_label=pos_label)
+    ap_decision_function = average_precision_score(
+        y_test, y_pred_decision, pos_label=pos_label
+    )
+    assert ap_proba == pytest.approx(ap_decision_function)
+
+    # create a scorer which would require to pass a `pos_label`
+    # check that it fails if `pos_label` is not provided
+    average_precision_scorer = make_scorer(
+        average_precision_score,
+        response_method=("decision_function", "predict_proba"),
+    )
+    err_msg = "pos_label=1 is not a valid label. It should be one of "
+    with pytest.raises(ValueError, match=err_msg):
+        average_precision_scorer(clf, X_test, y_test)
+
+    # otherwise, the scorer should give the same results than calling the
+    # scoring function
+    average_precision_scorer = make_scorer(
+        average_precision_score,
+        response_method=("decision_function", "predict_proba"),
+        pos_label=pos_label,
+    )
+    ap_scorer = average_precision_scorer(clf, X_test, y_test)
+
+    assert ap_scorer == pytest.approx(ap_proba)
+
+    # The above scorer call is using `clf.decision_function`. We will force
+    # it to use `clf.predict_proba`.
+    clf_without_predict_proba = deepcopy(clf)
+
+    def _predict_proba(self, X):
+        raise NotImplementedError
+
+    clf_without_predict_proba.predict_proba = partial(
+        _predict_proba, clf_without_predict_proba
+    )
+    # sanity check
+    with pytest.raises(NotImplementedError):
+        clf_without_predict_proba.predict_proba(X_test)
+
+    ap_scorer = average_precision_scorer(clf_without_predict_proba, X_test, y_test)
+    assert ap_scorer == pytest.approx(ap_proba)
+
+
+def test_brier_score_loss_pos_label(string_labeled_classification_problem):
+    # check that _Scorer leads to the right score when `pos_label` is
+    # provided. Currently only the `brier_score_loss` is defined to be such
+    # a scorer.
+    clf, X_test, y_test, _, y_pred_proba, _ = string_labeled_classification_problem
+
+    pos_label = "cancer"
+    assert clf.classes_[0] == pos_label
+
+    # brier score loss is symmetric
+    brier_pos_cancer = brier_score_loss(y_test, y_pred_proba[:, 0], pos_label="cancer")
+    brier_pos_not_cancer = brier_score_loss(
+        y_test, y_pred_proba[:, 1], pos_label="not cancer"
+    )
+    assert brier_pos_cancer == pytest.approx(brier_pos_not_cancer)
+
+    brier_scorer = make_scorer(
+        brier_score_loss,
+        response_method="predict_proba",
+        pos_label=pos_label,
+    )
+    assert brier_scorer(clf, X_test, y_test) == pytest.approx(brier_pos_cancer)
+
+
+@pytest.mark.parametrize(
+    "score_func", [f1_score, precision_score, recall_score, jaccard_score]
+)
+def test_non_symmetric_metric_pos_label(
+    score_func, string_labeled_classification_problem
+):
+    # check that _Scorer leads to the right score when `pos_label` is
+    # provided. We check for all possible metric supported.
+    # Note: At some point we may end up having "scorer tags".
+    clf, X_test, y_test, y_pred, _, _ = string_labeled_classification_problem
+
+    pos_label = "cancer"
+    assert clf.classes_[0] == pos_label
+
+    score_pos_cancer = score_func(y_test, y_pred, pos_label="cancer")
+    score_pos_not_cancer = score_func(y_test, y_pred, pos_label="not cancer")
+
+    assert score_pos_cancer != pytest.approx(score_pos_not_cancer)
+
+    scorer = make_scorer(score_func, pos_label=pos_label)
+    assert scorer(clf, X_test, y_test) == pytest.approx(score_pos_cancer)
+
+
+@pytest.mark.parametrize(
+    "scorer",
+    [
+        make_scorer(
+            average_precision_score,
+            response_method=("decision_function", "predict_proba"),
+            pos_label="xxx",
+        ),
+        make_scorer(brier_score_loss, response_method="predict_proba", pos_label="xxx"),
+        make_scorer(f1_score, pos_label="xxx"),
+    ],
+    ids=["non-thresholded scorer", "probability scorer", "thresholded scorer"],
+)
+def test_scorer_select_proba_error(scorer):
+    # check that we raise the proper error when passing an unknown
+    # pos_label
+    X, y = make_classification(
+        n_classes=2, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = LogisticRegression().fit(X, y)
+    assert scorer._kwargs["pos_label"] not in np.unique(y).tolist()
+
+    err_msg = "is not a valid label"
+    with pytest.raises(ValueError, match=err_msg):
+        scorer(lr, X, y)
+
+
+def test_get_scorer_return_copy():
+    # test that get_scorer returns a copy
+    assert get_scorer("roc_auc") is not get_scorer("roc_auc")
+
+
+def test_scorer_no_op_multiclass_select_proba():
+    # check that calling a _Scorer on a multiclass problem do not raise
+    # even if `y_true` would be binary during the scoring.
+    # `_select_proba_binary` should not be called in this case.
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = LogisticRegression().fit(X, y)
+
+    mask_last_class = y == lr.classes_[-1]
+    X_test, y_test = X[~mask_last_class], y[~mask_last_class]
+    assert_array_equal(np.unique(y_test), lr.classes_[:-1])
+
+    scorer = make_scorer(
+        roc_auc_score,
+        response_method="predict_proba",
+        multi_class="ovo",
+        labels=lr.classes_,
+    )
+    scorer(lr, X_test, y_test)
+
+
+@pytest.mark.parametrize("name", get_scorer_names())
+def test_scorer_set_score_request_raises(name):
+    """Test that set_score_request is only available when feature flag is on."""
+    # Make sure they expose the routing methods.
+    scorer = get_scorer(name)
+    with pytest.raises(RuntimeError, match="This method is only available"):
+        scorer.set_score_request()
+
+
+@pytest.mark.parametrize("name", get_scorer_names(), ids=get_scorer_names())
+@config_context(enable_metadata_routing=True)
+def test_scorer_metadata_request(name):
+    """Testing metadata requests for scorers.
+
+    This test checks many small things in a large test, to reduce the
+    boilerplate required for each section.
+    """
+    # Make sure they expose the routing methods.
+    scorer = get_scorer(name)
+    assert hasattr(scorer, "set_score_request")
+    assert hasattr(scorer, "get_metadata_routing")
+
+    # Check that by default no metadata is requested.
+    assert_request_is_empty(scorer.get_metadata_routing())
+
+    weighted_scorer = scorer.set_score_request(sample_weight=True)
+    # set_score_request should mutate the instance, rather than returning a
+    # new instance
+    assert weighted_scorer is scorer
+
+    # make sure the scorer doesn't request anything on methods other than
+    # `score`, and that the requested value on `score` is correct.
+    assert_request_is_empty(weighted_scorer.get_metadata_routing(), exclude="score")
+    assert (
+        weighted_scorer.get_metadata_routing().score.requests["sample_weight"] is True
+    )
+
+    # make sure putting the scorer in a router doesn't request anything by
+    # default
+    router = MetadataRouter(owner="test").add(
+        scorer=get_scorer(name),
+        method_mapping=MethodMapping().add(caller="score", callee="score"),
+    )
+    # make sure `sample_weight` is refused if passed.
+    with pytest.raises(TypeError, match="got unexpected argument"):
+        router.validate_metadata(params={"sample_weight": 1}, method="score")
+    # make sure `sample_weight` is not routed even if passed.
+    routed_params = router.route_params(params={"sample_weight": 1}, caller="score")
+    assert not routed_params.scorer.score
+
+    # make sure putting weighted_scorer in a router requests sample_weight
+    router = MetadataRouter(owner="test").add(
+        scorer=weighted_scorer,
+        method_mapping=MethodMapping().add(caller="score", callee="score"),
+    )
+    router.validate_metadata(params={"sample_weight": 1}, method="score")
+    routed_params = router.route_params(params={"sample_weight": 1}, caller="score")
+    assert list(routed_params.scorer.score.keys()) == ["sample_weight"]
+
+
+@config_context(enable_metadata_routing=True)
+def test_metadata_kwarg_conflict():
+    """This test makes sure the right warning is raised if the user passes
+    some metadata both as a constructor to make_scorer, and during __call__.
+    """
+    X, y = make_classification(
+        n_classes=3, n_informative=3, n_samples=20, random_state=0
+    )
+    lr = LogisticRegression().fit(X, y)
+
+    scorer = make_scorer(
+        roc_auc_score,
+        response_method="predict_proba",
+        multi_class="ovo",
+        labels=lr.classes_,
+    )
+    with pytest.warns(UserWarning, match="already set as kwargs"):
+        scorer.set_score_request(labels=True)
+
+    with pytest.warns(UserWarning, match="There is an overlap"):
+        scorer(lr, X, y, labels=lr.classes_)
+
+
+@config_context(enable_metadata_routing=True)
+def test_PassthroughScorer_set_score_request():
+    """Test that _PassthroughScorer.set_score_request adds the correct metadata request
+    on itself and doesn't change its estimator's routing."""
+    est = LogisticRegression().set_score_request(sample_weight="estimator_weights")
+    # make a `_PassthroughScorer` with `check_scoring`:
+    scorer = check_scoring(est, None)
+    assert (
+        scorer.get_metadata_routing().score.requests["sample_weight"]
+        == "estimator_weights"
+    )
+
+    scorer.set_score_request(sample_weight="scorer_weights")
+    assert (
+        scorer.get_metadata_routing().score.requests["sample_weight"]
+        == "scorer_weights"
+    )
+
+    # making sure changing the passthrough object doesn't affect the estimator.
+    assert (
+        est.get_metadata_routing().score.requests["sample_weight"]
+        == "estimator_weights"
+    )
+
+
+def test_PassthroughScorer_set_score_request_raises_without_routing_enabled():
+    """Test that _PassthroughScorer.set_score_request raises if metadata routing is
+    disabled."""
+    scorer = check_scoring(LogisticRegression(), None)
+    msg = "This method is only available when metadata routing is enabled."
+
+    with pytest.raises(RuntimeError, match=msg):
+        scorer.set_score_request(sample_weight="my_weights")
+
+
+@config_context(enable_metadata_routing=True)
+def test_multimetric_scoring_metadata_routing():
+    # Test that _MultimetricScorer properly routes metadata.
+    def score1(y_true, y_pred):
+        return 1
+
+    def score2(y_true, y_pred, sample_weight="test"):
+        # make sure sample_weight is not passed
+        assert sample_weight == "test"
+        return 1
+
+    def score3(y_true, y_pred, sample_weight=None):
+        # make sure sample_weight is passed
+        assert sample_weight is not None
+        return 1
+
+    scorers = {
+        "score1": make_scorer(score1),
+        "score2": make_scorer(score2).set_score_request(sample_weight=False),
+        "score3": make_scorer(score3).set_score_request(sample_weight=True),
+    }
+
+    X, y = make_classification(
+        n_samples=50, n_features=2, n_redundant=0, random_state=0
+    )
+
+    clf = DecisionTreeClassifier().fit(X, y)
+
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    multi_scorer = _MultimetricScorer(scorers=scorer_dict)
+    # This passes since routing is done.
+    multi_scorer(clf, X, y, sample_weight=1)
+
+
+@config_context(enable_metadata_routing=False)
+def test_multimetric_scoring_kwargs():
+    # Test that _MultimetricScorer correctly forwards kwargs
+    # to the scorers when metadata routing is disabled.
+    # `sample_weight` is only forwarded to the scorers that accept it.
+    # Other arguments are forwarded to all scorers.
+    def score1(y_true, y_pred, common_arg=None):
+        # make sure common_arg is passed
+        assert common_arg is not None
+        return 1
+
+    def score2(y_true, y_pred, common_arg=None, sample_weight=None):
+        # make sure common_arg is passed
+        assert common_arg is not None
+        # make sure sample_weight is passed
+        assert sample_weight is not None
+        return 1
+
+    scorers = {
+        "score1": make_scorer(score1),
+        "score2": make_scorer(score2),
+    }
+
+    X, y = make_classification(
+        n_samples=50, n_features=2, n_redundant=0, random_state=0
+    )
+
+    clf = DecisionTreeClassifier().fit(X, y)
+
+    scorer_dict = _check_multimetric_scoring(clf, scorers)
+    multi_scorer = _MultimetricScorer(scorers=scorer_dict)
+    multi_scorer(clf, X, y, common_arg=1, sample_weight=1)
+
+
+def test_kwargs_without_metadata_routing_error():
+    # Test that kwargs are not supported in scorers if metadata routing is not
+    # enabled.
+    # TODO: remove when enable_metadata_routing is deprecated
+    def score(y_true, y_pred, param=None):
+        return 1  # pragma: no cover
+
+    X, y = make_classification(
+        n_samples=50, n_features=2, n_redundant=0, random_state=0
+    )
+
+    clf = DecisionTreeClassifier().fit(X, y)
+    scorer = make_scorer(score)
+    with config_context(enable_metadata_routing=False):
+        with pytest.raises(
+            ValueError, match="is only supported if enable_metadata_routing=True"
+        ):
+            scorer(clf, X, y, param="blah")
+
+
+def test_get_scorer_multilabel_indicator():
+    """Check that our scorer deal with multi-label indicator matrices.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/26817
+    """
+    X, Y = make_multilabel_classification(n_samples=72, n_classes=3, random_state=0)
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=0)
+
+    estimator = KNeighborsClassifier().fit(X_train, Y_train)
+
+    score = get_scorer("average_precision")(estimator, X_test, Y_test)
+    assert score > 0.8
+
+
+@pytest.mark.parametrize(
+    "scorer, expected_repr",
+    [
+        (
+            get_scorer("accuracy"),
+            "make_scorer(accuracy_score, response_method='predict')",
+        ),
+        (
+            get_scorer("neg_log_loss"),
+            (
+                "make_scorer(log_loss, greater_is_better=False,"
+                " response_method='predict_proba')"
+            ),
+        ),
+        (
+            get_scorer("roc_auc"),
+            (
+                "make_scorer(roc_auc_score, response_method="
+                "('decision_function', 'predict_proba'))"
+            ),
+        ),
+        (
+            make_scorer(fbeta_score, beta=2),
+            "make_scorer(fbeta_score, response_method='predict', beta=2)",
+        ),
+    ],
+)
+def test_make_scorer_repr(scorer, expected_repr):
+    """Check the representation of the scorer."""
+    assert repr(scorer) == expected_repr
+
+
+@pytest.mark.parametrize("pass_estimator", [True, False])
+def test_get_scorer_multimetric(pass_estimator):
+    """Check that check_scoring is compatible with multi-metric configurations."""
+    X, y = make_classification(n_samples=150, n_features=10, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LogisticRegression(random_state=0)
+
+    if pass_estimator:
+        check_scoring_ = check_scoring
+    else:
+        check_scoring_ = partial(check_scoring, clf)
+
+    clf.fit(X_train, y_train)
+
+    y_pred = clf.predict(X_test)
+    y_proba = clf.predict_proba(X_test)
+
+    expected_results = {
+        "r2": r2_score(y_test, y_pred),
+        "roc_auc": roc_auc_score(y_test, y_proba[:, 1]),
+        "accuracy": accuracy_score(y_test, y_pred),
+    }
+
+    for container in [set, list, tuple]:
+        scoring = check_scoring_(scoring=container(["r2", "roc_auc", "accuracy"]))
+        result = scoring(clf, X_test, y_test)
+
+        assert result.keys() == expected_results.keys()
+        for name in result:
+            assert result[name] == pytest.approx(expected_results[name])
+
+    def double_accuracy(y_true, y_pred):
+        return 2 * accuracy_score(y_true, y_pred)
+
+    custom_scorer = make_scorer(double_accuracy, response_method="predict")
+
+    # dict with different names
+    dict_scoring = check_scoring_(
+        scoring={
+            "my_r2": "r2",
+            "my_roc_auc": "roc_auc",
+            "double_accuracy": custom_scorer,
+        }
+    )
+    dict_result = dict_scoring(clf, X_test, y_test)
+    assert len(dict_result) == 3
+    assert dict_result["my_r2"] == pytest.approx(expected_results["r2"])
+    assert dict_result["my_roc_auc"] == pytest.approx(expected_results["roc_auc"])
+    assert dict_result["double_accuracy"] == pytest.approx(
+        2 * expected_results["accuracy"]
+    )
+
+
+def test_multimetric_scorer_repr():
+    """Check repr for multimetric scorer"""
+    multi_metric_scorer = check_scoring(scoring=["accuracy", "r2"])
+
+    assert str(multi_metric_scorer) == 'MultiMetricScorer("accuracy", "r2")'
+
+
+def test_check_scoring_multimetric_raise_exc():
+    """Test that check_scoring returns error code for a subset of scorers in
+    multimetric scoring if raise_exc=False and raises otherwise."""
+
+    def raising_scorer(estimator, X, y):
+        raise ValueError("That doesn't work.")
+
+    X, y = make_classification(n_samples=150, n_features=10, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LogisticRegression().fit(X_train, y_train)
+
+    # "raising_scorer" is raising ValueError and should return an string representation
+    # of the error of the last scorer:
+    scoring = {
+        "accuracy": make_scorer(accuracy_score),
+        "raising_scorer": raising_scorer,
+    }
+    scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=False)
+    scores = scoring_call(clf, X_test, y_test)
+    assert "That doesn't work." in scores["raising_scorer"]
+
+    # should raise an error
+    scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=True)
+    err_msg = "That doesn't work."
+    with pytest.raises(ValueError, match=err_msg):
+        scores = scoring_call(clf, X_test, y_test)
+
+
+@pytest.mark.parametrize("enable_metadata_routing", [True, False])
+def test_metadata_routing_multimetric_metadata_routing(enable_metadata_routing):
+    """Test multimetric scorer works with and without metadata routing enabled when
+    there is no actual metadata to pass.
+
+    Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/28256
+    """
+    X, y = make_classification(n_samples=50, n_features=10, random_state=0)
+    estimator = EstimatorWithFitAndPredict().fit(X, y)
+
+    multimetric_scorer = _MultimetricScorer(scorers={"acc": get_scorer("accuracy")})
+    with config_context(enable_metadata_routing=enable_metadata_routing):
+        multimetric_scorer(estimator, X, y)
+
+
+def test_curve_scorer():
+    """Check the behaviour of the `_CurveScorer` class."""
+    X, y = make_classification(random_state=0)
+    estimator = LogisticRegression().fit(X, y)
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    assert thresholds.shape == scores.shape
+    # check that the thresholds are probabilities with extreme values close to 0 and 1.
+    # they are not exactly 0 and 1 because they are the extremum of the
+    # `estimator.predict_proba(X)` values.
+    assert 0 <= thresholds.min() <= 0.01
+    assert 0.99 <= thresholds.max() <= 1
+    # balanced accuracy should be between 0.5 and 1 when it is not adjusted
+    assert 0.5 <= scores.min() <= 1
+
+    # check that passing kwargs to the scorer works
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"adjusted": True},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    # balanced accuracy should be between 0.5 and 1 when it is not adjusted
+    assert 0 <= scores.min() <= 0.5
+
+    # check that we can inverse the sign of the score when dealing with `neg_*` scorer
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=-1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"adjusted": True},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    assert all(scores <= 0)
+
+
+def test_curve_scorer_pos_label(global_random_seed):
+    """Check that we propagate properly the `pos_label` parameter to the scorer."""
+    n_samples = 30
+    X, y = make_classification(
+        n_samples=n_samples, weights=[0.9, 0.1], random_state=global_random_seed
+    )
+    estimator = LogisticRegression().fit(X, y)
+
+    curve_scorer = _CurveScorer(
+        recall_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"pos_label": 1},
+    )
+    scores_pos_label_1, thresholds_pos_label_1 = curve_scorer(estimator, X, y)
+
+    curve_scorer = _CurveScorer(
+        recall_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"pos_label": 0},
+    )
+    scores_pos_label_0, thresholds_pos_label_0 = curve_scorer(estimator, X, y)
+
+    # Since `pos_label` is forwarded to the curve_scorer, the thresholds are not equal.
+    assert not (thresholds_pos_label_1 == thresholds_pos_label_0).all()
+    # The min-max range for the thresholds is defined by the probabilities of the
+    # `pos_label` class (the column of `predict_proba`).
+    y_pred = estimator.predict_proba(X)
+    assert thresholds_pos_label_0.min() == pytest.approx(y_pred.min(axis=0)[0])
+    assert thresholds_pos_label_0.max() == pytest.approx(y_pred.max(axis=0)[0])
+    assert thresholds_pos_label_1.min() == pytest.approx(y_pred.min(axis=0)[1])
+    assert thresholds_pos_label_1.max() == pytest.approx(y_pred.max(axis=0)[1])
+
+    # The recall cannot be negative and `pos_label=1` should have a higher recall
+    # since there is less samples to be considered.
+    assert 0.0 < scores_pos_label_0.min() < scores_pos_label_1.min()
+    assert scores_pos_label_0.max() == pytest.approx(1.0)
+    assert scores_pos_label_1.max() == pytest.approx(1.0)
+
+
+# TODO(1.8): remove
+def test_make_scorer_reponse_method_default_warning():
+    with pytest.warns(FutureWarning, match="response_method=None is deprecated"):
+        make_scorer(accuracy_score, response_method=None)
+
+    # No warning is raised if response_method is left to its default value
+    # because the future default value has the same effect as the current one.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", FutureWarning)
+        make_scorer(accuracy_score)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c27263a0ed74381a7c8dad4d6488eba570eb49b8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/__init__.py
@@ -0,0 +1,9 @@
+"""Mixture modeling algorithms."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from ._bayesian_mixture import BayesianGaussianMixture
+from ._gaussian_mixture import GaussianMixture
+
+__all__ = ["BayesianGaussianMixture", "GaussianMixture"]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..f66344a2847533629f52ddb10a4e819144cc8cfe
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_base.py
@@ -0,0 +1,571 @@
+"""Base class for mixture models."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from numbers import Integral, Real
+from time import time
+
+import numpy as np
+from scipy.special import logsumexp
+
+from .. import cluster
+from ..base import BaseEstimator, DensityMixin, _fit_context
+from ..cluster import kmeans_plusplus
+from ..exceptions import ConvergenceWarning
+from ..utils import check_random_state
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.validation import check_is_fitted, validate_data
+
+
+def _check_shape(param, param_shape, name):
+    """Validate the shape of the input parameter 'param'.
+
+    Parameters
+    ----------
+    param : array
+
+    param_shape : tuple
+
+    name : str
+    """
+    param = np.array(param)
+    if param.shape != param_shape:
+        raise ValueError(
+            "The parameter '%s' should have the shape of %s, but got %s"
+            % (name, param_shape, param.shape)
+        )
+
+
+class BaseMixture(DensityMixin, BaseEstimator, metaclass=ABCMeta):
+    """Base class for mixture models.
+
+    This abstract class specifies an interface for all mixture classes and
+    provides basic common methods for mixture models.
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "tol": [Interval(Real, 0.0, None, closed="left")],
+        "reg_covar": [Interval(Real, 0.0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "n_init": [Interval(Integral, 1, None, closed="left")],
+        "init_params": [
+            StrOptions({"kmeans", "random", "random_from_data", "k-means++"})
+        ],
+        "random_state": ["random_state"],
+        "warm_start": ["boolean"],
+        "verbose": ["verbose"],
+        "verbose_interval": [Interval(Integral, 1, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        n_components,
+        tol,
+        reg_covar,
+        max_iter,
+        n_init,
+        init_params,
+        random_state,
+        warm_start,
+        verbose,
+        verbose_interval,
+    ):
+        self.n_components = n_components
+        self.tol = tol
+        self.reg_covar = reg_covar
+        self.max_iter = max_iter
+        self.n_init = n_init
+        self.init_params = init_params
+        self.random_state = random_state
+        self.warm_start = warm_start
+        self.verbose = verbose
+        self.verbose_interval = verbose_interval
+
+    @abstractmethod
+    def _check_parameters(self, X):
+        """Check initial parameters of the derived class.
+
+        Parameters
+        ----------
+        X : array-like of shape  (n_samples, n_features)
+        """
+        pass
+
+    def _initialize_parameters(self, X, random_state):
+        """Initialize the model parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape  (n_samples, n_features)
+
+        random_state : RandomState
+            A random number generator instance that controls the random seed
+            used for the method chosen to initialize the parameters.
+        """
+        n_samples, _ = X.shape
+
+        if self.init_params == "kmeans":
+            resp = np.zeros((n_samples, self.n_components), dtype=X.dtype)
+            label = (
+                cluster.KMeans(
+                    n_clusters=self.n_components, n_init=1, random_state=random_state
+                )
+                .fit(X)
+                .labels_
+            )
+            resp[np.arange(n_samples), label] = 1
+        elif self.init_params == "random":
+            resp = np.asarray(
+                random_state.uniform(size=(n_samples, self.n_components)), dtype=X.dtype
+            )
+            resp /= resp.sum(axis=1)[:, np.newaxis]
+        elif self.init_params == "random_from_data":
+            resp = np.zeros((n_samples, self.n_components), dtype=X.dtype)
+            indices = random_state.choice(
+                n_samples, size=self.n_components, replace=False
+            )
+            resp[indices, np.arange(self.n_components)] = 1
+        elif self.init_params == "k-means++":
+            resp = np.zeros((n_samples, self.n_components), dtype=X.dtype)
+            _, indices = kmeans_plusplus(
+                X,
+                self.n_components,
+                random_state=random_state,
+            )
+            resp[indices, np.arange(self.n_components)] = 1
+
+        self._initialize(X, resp)
+
+    @abstractmethod
+    def _initialize(self, X, resp):
+        """Initialize the model parameters of the derived class.
+
+        Parameters
+        ----------
+        X : array-like of shape  (n_samples, n_features)
+
+        resp : array-like of shape (n_samples, n_components)
+        """
+        pass
+
+    def fit(self, X, y=None):
+        """Estimate model parameters with the EM algorithm.
+
+        The method fits the model ``n_init`` times and sets the parameters with
+        which the model has the largest likelihood or lower bound. Within each
+        trial, the method iterates between E-step and M-step for ``max_iter``
+        times until the change of likelihood or lower bound is less than
+        ``tol``, otherwise, a ``ConvergenceWarning`` is raised.
+        If ``warm_start`` is ``True``, then ``n_init`` is ignored and a single
+        initialization is performed upon the first call. Upon consecutive
+        calls, training starts where it left off.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            The fitted mixture.
+        """
+        # parameters are validated in fit_predict
+        self.fit_predict(X, y)
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit_predict(self, X, y=None):
+        """Estimate model parameters using X and predict the labels for X.
+
+        The method fits the model n_init times and sets the parameters with
+        which the model has the largest likelihood or lower bound. Within each
+        trial, the method iterates between E-step and M-step for `max_iter`
+        times until the change of likelihood or lower bound is less than
+        `tol`, otherwise, a :class:`~sklearn.exceptions.ConvergenceWarning` is
+        raised. After fitting, it predicts the most probable label for the
+        input data points.
+
+        .. versionadded:: 0.20
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        labels : array, shape (n_samples,)
+            Component labels.
+        """
+        X = validate_data(self, X, dtype=[np.float64, np.float32], ensure_min_samples=2)
+        if X.shape[0] < self.n_components:
+            raise ValueError(
+                "Expected n_samples >= n_components "
+                f"but got n_components = {self.n_components}, "
+                f"n_samples = {X.shape[0]}"
+            )
+        self._check_parameters(X)
+
+        # if we enable warm_start, we will have a unique initialisation
+        do_init = not (self.warm_start and hasattr(self, "converged_"))
+        n_init = self.n_init if do_init else 1
+
+        max_lower_bound = -np.inf
+        best_lower_bounds = []
+        self.converged_ = False
+
+        random_state = check_random_state(self.random_state)
+
+        n_samples, _ = X.shape
+        for init in range(n_init):
+            self._print_verbose_msg_init_beg(init)
+
+            if do_init:
+                self._initialize_parameters(X, random_state)
+
+            lower_bound = -np.inf if do_init else self.lower_bound_
+            current_lower_bounds = []
+
+            if self.max_iter == 0:
+                best_params = self._get_parameters()
+                best_n_iter = 0
+            else:
+                converged = False
+                for n_iter in range(1, self.max_iter + 1):
+                    prev_lower_bound = lower_bound
+
+                    log_prob_norm, log_resp = self._e_step(X)
+                    self._m_step(X, log_resp)
+                    lower_bound = self._compute_lower_bound(log_resp, log_prob_norm)
+                    current_lower_bounds.append(lower_bound)
+
+                    change = lower_bound - prev_lower_bound
+                    self._print_verbose_msg_iter_end(n_iter, change)
+
+                    if abs(change) < self.tol:
+                        converged = True
+                        break
+
+                self._print_verbose_msg_init_end(lower_bound, converged)
+
+                if lower_bound > max_lower_bound or max_lower_bound == -np.inf:
+                    max_lower_bound = lower_bound
+                    best_params = self._get_parameters()
+                    best_n_iter = n_iter
+                    best_lower_bounds = current_lower_bounds
+                    self.converged_ = converged
+
+        # Should only warn about convergence if max_iter > 0, otherwise
+        # the user is assumed to have used 0-iters initialization
+        # to get the initial means.
+        if not self.converged_ and self.max_iter > 0:
+            warnings.warn(
+                (
+                    "Best performing initialization did not converge. "
+                    "Try different init parameters, or increase max_iter, "
+                    "tol, or check for degenerate data."
+                ),
+                ConvergenceWarning,
+            )
+
+        self._set_parameters(best_params)
+        self.n_iter_ = best_n_iter
+        self.lower_bound_ = max_lower_bound
+        self.lower_bounds_ = best_lower_bounds
+
+        # Always do a final e-step to guarantee that the labels returned by
+        # fit_predict(X) are always consistent with fit(X).predict(X)
+        # for any value of max_iter and tol (and any random_state).
+        _, log_resp = self._e_step(X)
+
+        return log_resp.argmax(axis=1)
+
+    def _e_step(self, X):
+        """E step.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        Returns
+        -------
+        log_prob_norm : float
+            Mean of the logarithms of the probabilities of each sample in X
+
+        log_responsibility : array, shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of each sample in X.
+        """
+        log_prob_norm, log_resp = self._estimate_log_prob_resp(X)
+        return np.mean(log_prob_norm), log_resp
+
+    @abstractmethod
+    def _m_step(self, X, log_resp):
+        """M step.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        log_resp : array-like of shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of each sample in X.
+        """
+        pass
+
+    @abstractmethod
+    def _get_parameters(self):
+        pass
+
+    @abstractmethod
+    def _set_parameters(self, params):
+        pass
+
+    def score_samples(self, X):
+        """Compute the log-likelihood of each sample.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        Returns
+        -------
+        log_prob : array, shape (n_samples,)
+            Log-likelihood of each sample in `X` under the current model.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X, reset=False)
+
+        return logsumexp(self._estimate_weighted_log_prob(X), axis=1)
+
+    def score(self, X, y=None):
+        """Compute the per-sample average log-likelihood of the given data X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_dimensions)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        log_likelihood : float
+            Log-likelihood of `X` under the Gaussian mixture model.
+        """
+        return self.score_samples(X).mean()
+
+    def predict(self, X):
+        """Predict the labels for the data samples in X using trained model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        Returns
+        -------
+        labels : array, shape (n_samples,)
+            Component labels.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X, reset=False)
+        return self._estimate_weighted_log_prob(X).argmax(axis=1)
+
+    def predict_proba(self, X):
+        """Evaluate the components' density for each sample.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            List of n_features-dimensional data points. Each row
+            corresponds to a single data point.
+
+        Returns
+        -------
+        resp : array, shape (n_samples, n_components)
+            Density of each Gaussian component for each sample in X.
+        """
+        check_is_fitted(self)
+        X = validate_data(self, X, reset=False)
+        _, log_resp = self._estimate_log_prob_resp(X)
+        return np.exp(log_resp)
+
+    def sample(self, n_samples=1):
+        """Generate random samples from the fitted Gaussian distribution.
+
+        Parameters
+        ----------
+        n_samples : int, default=1
+            Number of samples to generate.
+
+        Returns
+        -------
+        X : array, shape (n_samples, n_features)
+            Randomly generated sample.
+
+        y : array, shape (nsamples,)
+            Component labels.
+        """
+        check_is_fitted(self)
+
+        if n_samples < 1:
+            raise ValueError(
+                "Invalid value for 'n_samples': %d . The sampling requires at "
+                "least one sample." % (self.n_components)
+            )
+
+        _, n_features = self.means_.shape
+        rng = check_random_state(self.random_state)
+        n_samples_comp = rng.multinomial(n_samples, self.weights_)
+
+        if self.covariance_type == "full":
+            X = np.vstack(
+                [
+                    rng.multivariate_normal(mean, covariance, int(sample))
+                    for (mean, covariance, sample) in zip(
+                        self.means_, self.covariances_, n_samples_comp
+                    )
+                ]
+            )
+        elif self.covariance_type == "tied":
+            X = np.vstack(
+                [
+                    rng.multivariate_normal(mean, self.covariances_, int(sample))
+                    for (mean, sample) in zip(self.means_, n_samples_comp)
+                ]
+            )
+        else:
+            X = np.vstack(
+                [
+                    mean
+                    + rng.standard_normal(size=(sample, n_features))
+                    * np.sqrt(covariance)
+                    for (mean, covariance, sample) in zip(
+                        self.means_, self.covariances_, n_samples_comp
+                    )
+                ]
+            )
+
+        y = np.concatenate(
+            [np.full(sample, j, dtype=int) for j, sample in enumerate(n_samples_comp)]
+        )
+
+        return (X, y)
+
+    def _estimate_weighted_log_prob(self, X):
+        """Estimate the weighted log-probabilities, log P(X | Z) + log weights.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        Returns
+        -------
+        weighted_log_prob : array, shape (n_samples, n_component)
+        """
+        return self._estimate_log_prob(X) + self._estimate_log_weights()
+
+    @abstractmethod
+    def _estimate_log_weights(self):
+        """Estimate log-weights in EM algorithm, E[ log pi ] in VB algorithm.
+
+        Returns
+        -------
+        log_weight : array, shape (n_components, )
+        """
+        pass
+
+    @abstractmethod
+    def _estimate_log_prob(self, X):
+        """Estimate the log-probabilities log P(X | Z).
+
+        Compute the log-probabilities per each component for each sample.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        Returns
+        -------
+        log_prob : array, shape (n_samples, n_component)
+        """
+        pass
+
+    def _estimate_log_prob_resp(self, X):
+        """Estimate log probabilities and responsibilities for each sample.
+
+        Compute the log probabilities, weighted log probabilities per
+        component and responsibilities for each sample in X with respect to
+        the current state of the model.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        Returns
+        -------
+        log_prob_norm : array, shape (n_samples,)
+            log p(X)
+
+        log_responsibilities : array, shape (n_samples, n_components)
+            logarithm of the responsibilities
+        """
+        weighted_log_prob = self._estimate_weighted_log_prob(X)
+        log_prob_norm = logsumexp(weighted_log_prob, axis=1)
+        with np.errstate(under="ignore"):
+            # ignore underflow
+            log_resp = weighted_log_prob - log_prob_norm[:, np.newaxis]
+        return log_prob_norm, log_resp
+
+    def _print_verbose_msg_init_beg(self, n_init):
+        """Print verbose message on initialization."""
+        if self.verbose == 1:
+            print("Initialization %d" % n_init)
+        elif self.verbose >= 2:
+            print("Initialization %d" % n_init)
+            self._init_prev_time = time()
+            self._iter_prev_time = self._init_prev_time
+
+    def _print_verbose_msg_iter_end(self, n_iter, diff_ll):
+        """Print verbose message on initialization."""
+        if n_iter % self.verbose_interval == 0:
+            if self.verbose == 1:
+                print("  Iteration %d" % n_iter)
+            elif self.verbose >= 2:
+                cur_time = time()
+                print(
+                    "  Iteration %d\t time lapse %.5fs\t ll change %.5f"
+                    % (n_iter, cur_time - self._iter_prev_time, diff_ll)
+                )
+                self._iter_prev_time = cur_time
+
+    def _print_verbose_msg_init_end(self, lb, init_has_converged):
+        """Print verbose message on the end of iteration."""
+        converged_msg = "converged" if init_has_converged else "did not converge"
+        if self.verbose == 1:
+            print(f"Initialization {converged_msg}.")
+        elif self.verbose >= 2:
+            t = time() - self._init_prev_time
+            print(
+                f"Initialization {converged_msg}. time lapse {t:.5f}s\t lower bound"
+                f" {lb:.5f}."
+            )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_bayesian_mixture.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_bayesian_mixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..57220186faf61694f0945a276bc60254ba861bd5
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_bayesian_mixture.py
@@ -0,0 +1,891 @@
+"""Bayesian Gaussian Mixture Model."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import math
+from numbers import Real
+
+import numpy as np
+from scipy.special import betaln, digamma, gammaln
+
+from ..utils import check_array
+from ..utils._param_validation import Interval, StrOptions
+from ._base import BaseMixture, _check_shape
+from ._gaussian_mixture import (
+    _check_precision_matrix,
+    _check_precision_positivity,
+    _compute_log_det_cholesky,
+    _compute_precision_cholesky,
+    _estimate_gaussian_parameters,
+    _estimate_log_gaussian_prob,
+)
+
+
+def _log_dirichlet_norm(dirichlet_concentration):
+    """Compute the log of the Dirichlet distribution normalization term.
+
+    Parameters
+    ----------
+    dirichlet_concentration : array-like of shape (n_samples,)
+        The parameters values of the Dirichlet distribution.
+
+    Returns
+    -------
+    log_dirichlet_norm : float
+        The log normalization of the Dirichlet distribution.
+    """
+    return gammaln(np.sum(dirichlet_concentration)) - np.sum(
+        gammaln(dirichlet_concentration)
+    )
+
+
+def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
+    """Compute the log of the Wishart distribution normalization term.
+
+    Parameters
+    ----------
+    degrees_of_freedom : array-like of shape (n_components,)
+        The number of degrees of freedom on the covariance Wishart
+        distributions.
+
+    log_det_precision_chol : array-like of shape (n_components,)
+         The determinant of the precision matrix for each component.
+
+    n_features : int
+        The number of features.
+
+    Return
+    ------
+    log_wishart_norm : array-like of shape (n_components,)
+        The log normalization of the Wishart distribution.
+    """
+    # To simplify the computation we have removed the np.log(np.pi) term
+    return -(
+        degrees_of_freedom * log_det_precisions_chol
+        + degrees_of_freedom * n_features * 0.5 * math.log(2.0)
+        + np.sum(
+            gammaln(0.5 * (degrees_of_freedom - np.arange(n_features)[:, np.newaxis])),
+            0,
+        )
+    )
+
+
+class BayesianGaussianMixture(BaseMixture):
+    """Variational Bayesian estimation of a Gaussian mixture.
+
+    This class allows to infer an approximate posterior distribution over the
+    parameters of a Gaussian mixture distribution. The effective number of
+    components can be inferred from the data.
+
+    This class implements two types of prior for the weights distribution: a
+    finite mixture model with Dirichlet distribution and an infinite mixture
+    model with the Dirichlet Process. In practice Dirichlet Process inference
+    algorithm is approximated and uses a truncated distribution with a fixed
+    maximum number of components (called the Stick-breaking representation).
+    The number of components actually used almost always depends on the data.
+
+    .. versionadded:: 0.18
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    n_components : int, default=1
+        The number of mixture components. Depending on the data and the value
+        of the `weight_concentration_prior` the model can decide to not use
+        all the components by setting some component `weights_` to values very
+        close to zero. The number of effective components is therefore smaller
+        than n_components.
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}, default='full'
+        String describing the type of covariance parameters to use.
+        Must be one of:
+
+        - 'full' (each component has its own general covariance matrix),
+        - 'tied' (all components share the same general covariance matrix),
+        - 'diag' (each component has its own diagonal covariance matrix),
+        - 'spherical' (each component has its own single variance).
+
+    tol : float, default=1e-3
+        The convergence threshold. EM iterations will stop when the
+        lower bound average gain on the likelihood (of the training data with
+        respect to the model) is below this threshold.
+
+    reg_covar : float, default=1e-6
+        Non-negative regularization added to the diagonal of covariance.
+        Allows to assure that the covariance matrices are all positive.
+
+    max_iter : int, default=100
+        The number of EM iterations to perform.
+
+    n_init : int, default=1
+        The number of initializations to perform. The result with the highest
+        lower bound value on the likelihood is kept.
+
+    init_params : {'kmeans', 'k-means++', 'random', 'random_from_data'}, \
+    default='kmeans'
+        The method used to initialize the weights, the means and the
+        covariances. String must be one of:
+
+        - 'kmeans': responsibilities are initialized using kmeans.
+        - 'k-means++': use the k-means++ method to initialize.
+        - 'random': responsibilities are initialized randomly.
+        - 'random_from_data': initial means are randomly selected data points.
+
+        .. versionchanged:: v1.1
+            `init_params` now accepts 'random_from_data' and 'k-means++' as
+            initialization methods.
+
+    weight_concentration_prior_type : {'dirichlet_process', 'dirichlet_distribution'}, \
+            default='dirichlet_process'
+        String describing the type of the weight concentration prior.
+
+    weight_concentration_prior : float or None, default=None
+        The dirichlet concentration of each component on the weight
+        distribution (Dirichlet). This is commonly called gamma in the
+        literature. The higher concentration puts more mass in
+        the center and will lead to more components being active, while a lower
+        concentration parameter will lead to more mass at the edge of the
+        mixture weights simplex. The value of the parameter must be greater
+        than 0. If it is None, it's set to ``1. / n_components``.
+
+    mean_precision_prior : float or None, default=None
+        The precision prior on the mean distribution (Gaussian).
+        Controls the extent of where means can be placed. Larger
+        values concentrate the cluster means around `mean_prior`.
+        The value of the parameter must be greater than 0.
+        If it is None, it is set to 1.
+
+    mean_prior : array-like, shape (n_features,), default=None
+        The prior on the mean distribution (Gaussian).
+        If it is None, it is set to the mean of X.
+
+    degrees_of_freedom_prior : float or None, default=None
+        The prior of the number of degrees of freedom on the covariance
+        distributions (Wishart). If it is None, it's set to `n_features`.
+
+    covariance_prior : float or array-like, default=None
+        The prior on the covariance distribution (Wishart).
+        If it is None, the emiprical covariance prior is initialized using the
+        covariance of X. The shape depends on `covariance_type`::
+
+                (n_features, n_features) if 'full',
+                (n_features, n_features) if 'tied',
+                (n_features)             if 'diag',
+                float                    if 'spherical'
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the random seed given to the method chosen to initialize the
+        parameters (see `init_params`).
+        In addition, it controls the generation of random samples from the
+        fitted distribution (see the method `sample`).
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    warm_start : bool, default=False
+        If 'warm_start' is True, the solution of the last fitting is used as
+        initialization for the next call of fit(). This can speed up
+        convergence when fit is called several times on similar problems.
+        See :term:`the Glossary `.
+
+    verbose : int, default=0
+        Enable verbose output. If 1 then it prints the current
+        initialization and each iteration step. If greater than 1 then
+        it prints also the log probability and the time needed
+        for each step.
+
+    verbose_interval : int, default=10
+        Number of iteration done before the next print.
+
+    Attributes
+    ----------
+    weights_ : array-like of shape (n_components,)
+        The weights of each mixture components.
+
+    means_ : array-like of shape (n_components, n_features)
+        The mean of each mixture component.
+
+    covariances_ : array-like
+        The covariance of each mixture component.
+        The shape depends on `covariance_type`::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    precisions_ : array-like
+        The precision matrices for each component in the mixture. A precision
+        matrix is the inverse of a covariance matrix. A covariance matrix is
+        symmetric positive definite so the mixture of Gaussian can be
+        equivalently parameterized by the precision matrices. Storing the
+        precision matrices instead of the covariance matrices makes it more
+        efficient to compute the log-likelihood of new samples at test time.
+        The shape depends on ``covariance_type``::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    precisions_cholesky_ : array-like
+        The cholesky decomposition of the precision matrices of each mixture
+        component. A precision matrix is the inverse of a covariance matrix.
+        A covariance matrix is symmetric positive definite so the mixture of
+        Gaussian can be equivalently parameterized by the precision matrices.
+        Storing the precision matrices instead of the covariance matrices makes
+        it more efficient to compute the log-likelihood of new samples at test
+        time. The shape depends on ``covariance_type``::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    converged_ : bool
+        True when convergence of the best fit of inference was reached, False otherwise.
+
+    n_iter_ : int
+        Number of step used by the best fit of inference to reach the
+        convergence.
+
+    lower_bound_ : float
+        Lower bound value on the model evidence (of the training data) of the
+        best fit of inference.
+
+    lower_bounds_ : array-like of shape (`n_iter_`,)
+        The list of lower bound values on the model evidence from each iteration
+        of the best fit of inference.
+
+    weight_concentration_prior_ : tuple or float
+        The dirichlet concentration of each component on the weight
+        distribution (Dirichlet). The type depends on
+        ``weight_concentration_prior_type``::
+
+            (float, float) if 'dirichlet_process' (Beta parameters),
+            float          if 'dirichlet_distribution' (Dirichlet parameters).
+
+        The higher concentration puts more mass in
+        the center and will lead to more components being active, while a lower
+        concentration parameter will lead to more mass at the edge of the
+        simplex.
+
+    weight_concentration_ : array-like of shape (n_components,)
+        The dirichlet concentration of each component on the weight
+        distribution (Dirichlet).
+
+    mean_precision_prior_ : float
+        The precision prior on the mean distribution (Gaussian).
+        Controls the extent of where means can be placed.
+        Larger values concentrate the cluster means around `mean_prior`.
+        If mean_precision_prior is set to None, `mean_precision_prior_` is set
+        to 1.
+
+    mean_precision_ : array-like of shape (n_components,)
+        The precision of each components on the mean distribution (Gaussian).
+
+    mean_prior_ : array-like of shape (n_features,)
+        The prior on the mean distribution (Gaussian).
+
+    degrees_of_freedom_prior_ : float
+        The prior of the number of degrees of freedom on the covariance
+        distributions (Wishart).
+
+    degrees_of_freedom_ : array-like of shape (n_components,)
+        The number of degrees of freedom of each components in the model.
+
+    covariance_prior_ : float or array-like
+        The prior on the covariance distribution (Wishart).
+        The shape depends on `covariance_type`::
+
+            (n_features, n_features) if 'full',
+            (n_features, n_features) if 'tied',
+            (n_features)             if 'diag',
+            float                    if 'spherical'
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    GaussianMixture : Finite Gaussian mixture fit with EM.
+
+    References
+    ----------
+
+    .. [1] `Bishop, Christopher M. (2006). "Pattern recognition and machine
+       learning". Vol. 4 No. 4. New York: Springer.
+       `_
+
+    .. [2] `Hagai Attias. (2000). "A Variational Bayesian Framework for
+       Graphical Models". In Advances in Neural Information Processing
+       Systems 12.
+       `_
+
+    .. [3] `Blei, David M. and Michael I. Jordan. (2006). "Variational
+       inference for Dirichlet process mixtures". Bayesian analysis 1.1
+       `_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.mixture import BayesianGaussianMixture
+    >>> X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
+    >>> bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
+    >>> bgm.means_
+    array([[2.49 , 2.29],
+           [8.45, 4.52 ]])
+    >>> bgm.predict([[0, 0], [9, 3]])
+    array([0, 1])
+    """
+
+    _parameter_constraints: dict = {
+        **BaseMixture._parameter_constraints,
+        "covariance_type": [StrOptions({"spherical", "tied", "diag", "full"})],
+        "weight_concentration_prior_type": [
+            StrOptions({"dirichlet_process", "dirichlet_distribution"})
+        ],
+        "weight_concentration_prior": [
+            None,
+            Interval(Real, 0.0, None, closed="neither"),
+        ],
+        "mean_precision_prior": [None, Interval(Real, 0.0, None, closed="neither")],
+        "mean_prior": [None, "array-like"],
+        "degrees_of_freedom_prior": [None, Interval(Real, 0.0, None, closed="neither")],
+        "covariance_prior": [
+            None,
+            "array-like",
+            Interval(Real, 0.0, None, closed="neither"),
+        ],
+    }
+
+    def __init__(
+        self,
+        *,
+        n_components=1,
+        covariance_type="full",
+        tol=1e-3,
+        reg_covar=1e-6,
+        max_iter=100,
+        n_init=1,
+        init_params="kmeans",
+        weight_concentration_prior_type="dirichlet_process",
+        weight_concentration_prior=None,
+        mean_precision_prior=None,
+        mean_prior=None,
+        degrees_of_freedom_prior=None,
+        covariance_prior=None,
+        random_state=None,
+        warm_start=False,
+        verbose=0,
+        verbose_interval=10,
+    ):
+        super().__init__(
+            n_components=n_components,
+            tol=tol,
+            reg_covar=reg_covar,
+            max_iter=max_iter,
+            n_init=n_init,
+            init_params=init_params,
+            random_state=random_state,
+            warm_start=warm_start,
+            verbose=verbose,
+            verbose_interval=verbose_interval,
+        )
+
+        self.covariance_type = covariance_type
+        self.weight_concentration_prior_type = weight_concentration_prior_type
+        self.weight_concentration_prior = weight_concentration_prior
+        self.mean_precision_prior = mean_precision_prior
+        self.mean_prior = mean_prior
+        self.degrees_of_freedom_prior = degrees_of_freedom_prior
+        self.covariance_prior = covariance_prior
+
+    def _check_parameters(self, X):
+        """Check that the parameters are well defined.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        """
+        self._check_weights_parameters()
+        self._check_means_parameters(X)
+        self._check_precision_parameters(X)
+        self._checkcovariance_prior_parameter(X)
+
+    def _check_weights_parameters(self):
+        """Check the parameter of the Dirichlet distribution."""
+        if self.weight_concentration_prior is None:
+            self.weight_concentration_prior_ = 1.0 / self.n_components
+        else:
+            self.weight_concentration_prior_ = self.weight_concentration_prior
+
+    def _check_means_parameters(self, X):
+        """Check the parameters of the Gaussian distribution.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        """
+        _, n_features = X.shape
+
+        if self.mean_precision_prior is None:
+            self.mean_precision_prior_ = 1.0
+        else:
+            self.mean_precision_prior_ = self.mean_precision_prior
+
+        if self.mean_prior is None:
+            self.mean_prior_ = X.mean(axis=0)
+        else:
+            self.mean_prior_ = check_array(
+                self.mean_prior, dtype=[np.float64, np.float32], ensure_2d=False
+            )
+            _check_shape(self.mean_prior_, (n_features,), "means")
+
+    def _check_precision_parameters(self, X):
+        """Check the prior parameters of the precision distribution.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        """
+        _, n_features = X.shape
+
+        if self.degrees_of_freedom_prior is None:
+            self.degrees_of_freedom_prior_ = n_features
+        elif self.degrees_of_freedom_prior > n_features - 1.0:
+            self.degrees_of_freedom_prior_ = self.degrees_of_freedom_prior
+        else:
+            raise ValueError(
+                "The parameter 'degrees_of_freedom_prior' "
+                "should be greater than %d, but got %.3f."
+                % (n_features - 1, self.degrees_of_freedom_prior)
+            )
+
+    def _checkcovariance_prior_parameter(self, X):
+        """Check the `covariance_prior_`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        """
+        _, n_features = X.shape
+
+        if self.covariance_prior is None:
+            self.covariance_prior_ = {
+                "full": np.atleast_2d(np.cov(X.T)),
+                "tied": np.atleast_2d(np.cov(X.T)),
+                "diag": np.var(X, axis=0, ddof=1),
+                "spherical": np.var(X, axis=0, ddof=1).mean(),
+            }[self.covariance_type]
+
+        elif self.covariance_type in ["full", "tied"]:
+            self.covariance_prior_ = check_array(
+                self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False
+            )
+            _check_shape(
+                self.covariance_prior_,
+                (n_features, n_features),
+                "%s covariance_prior" % self.covariance_type,
+            )
+            _check_precision_matrix(self.covariance_prior_, self.covariance_type)
+        elif self.covariance_type == "diag":
+            self.covariance_prior_ = check_array(
+                self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False
+            )
+            _check_shape(
+                self.covariance_prior_,
+                (n_features,),
+                "%s covariance_prior" % self.covariance_type,
+            )
+            _check_precision_positivity(self.covariance_prior_, self.covariance_type)
+        # spherical case
+        else:
+            self.covariance_prior_ = self.covariance_prior
+
+    def _initialize(self, X, resp):
+        """Initialization of the mixture parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        resp : array-like of shape (n_samples, n_components)
+        """
+        nk, xk, sk = _estimate_gaussian_parameters(
+            X, resp, self.reg_covar, self.covariance_type
+        )
+
+        self._estimate_weights(nk)
+        self._estimate_means(nk, xk)
+        self._estimate_precisions(nk, xk, sk)
+
+    def _estimate_weights(self, nk):
+        """Estimate the parameters of the Dirichlet distribution.
+
+        Parameters
+        ----------
+        nk : array-like of shape (n_components,)
+        """
+        if self.weight_concentration_prior_type == "dirichlet_process":
+            # For dirichlet process weight_concentration will be a tuple
+            # containing the two parameters of the beta distribution
+            self.weight_concentration_ = (
+                1.0 + nk,
+                (
+                    self.weight_concentration_prior_
+                    + np.hstack((np.cumsum(nk[::-1])[-2::-1], 0))
+                ),
+            )
+        else:
+            # case Variational Gaussian mixture with dirichlet distribution
+            self.weight_concentration_ = self.weight_concentration_prior_ + nk
+
+    def _estimate_means(self, nk, xk):
+        """Estimate the parameters of the Gaussian distribution.
+
+        Parameters
+        ----------
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+        """
+        self.mean_precision_ = self.mean_precision_prior_ + nk
+        self.means_ = (
+            self.mean_precision_prior_ * self.mean_prior_ + nk[:, np.newaxis] * xk
+        ) / self.mean_precision_[:, np.newaxis]
+
+    def _estimate_precisions(self, nk, xk, sk):
+        """Estimate the precisions parameters of the precision distribution.
+
+        Parameters
+        ----------
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+
+        sk : array-like
+            The shape depends of `covariance_type`:
+            'full' : (n_components, n_features, n_features)
+            'tied' : (n_features, n_features)
+            'diag' : (n_components, n_features)
+            'spherical' : (n_components,)
+        """
+        {
+            "full": self._estimate_wishart_full,
+            "tied": self._estimate_wishart_tied,
+            "diag": self._estimate_wishart_diag,
+            "spherical": self._estimate_wishart_spherical,
+        }[self.covariance_type](nk, xk, sk)
+
+        self.precisions_cholesky_ = _compute_precision_cholesky(
+            self.covariances_, self.covariance_type
+        )
+
+    def _estimate_wishart_full(self, nk, xk, sk):
+        """Estimate the full Wishart distribution parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+
+        sk : array-like of shape (n_components, n_features, n_features)
+        """
+        _, n_features = xk.shape
+
+        # Warning : in some Bishop book, there is a typo on the formula 10.63
+        # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` is
+        # the correct formula
+        self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
+
+        self.covariances_ = np.empty((self.n_components, n_features, n_features))
+
+        for k in range(self.n_components):
+            diff = xk[k] - self.mean_prior_
+            self.covariances_[k] = (
+                self.covariance_prior_
+                + nk[k] * sk[k]
+                + nk[k]
+                * self.mean_precision_prior_
+                / self.mean_precision_[k]
+                * np.outer(diff, diff)
+            )
+
+        # Contrary to the original bishop book, we normalize the covariances
+        self.covariances_ /= self.degrees_of_freedom_[:, np.newaxis, np.newaxis]
+
+    def _estimate_wishart_tied(self, nk, xk, sk):
+        """Estimate the tied Wishart distribution parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+
+        sk : array-like of shape (n_features, n_features)
+        """
+        _, n_features = xk.shape
+
+        # Warning : in some Bishop book, there is a typo on the formula 10.63
+        # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
+        # is the correct formula
+        self.degrees_of_freedom_ = (
+            self.degrees_of_freedom_prior_ + nk.sum() / self.n_components
+        )
+
+        diff = xk - self.mean_prior_
+        self.covariances_ = (
+            self.covariance_prior_
+            + sk * nk.sum() / self.n_components
+            + self.mean_precision_prior_
+            / self.n_components
+            * np.dot((nk / self.mean_precision_) * diff.T, diff)
+        )
+
+        # Contrary to the original bishop book, we normalize the covariances
+        self.covariances_ /= self.degrees_of_freedom_
+
+    def _estimate_wishart_diag(self, nk, xk, sk):
+        """Estimate the diag Wishart distribution parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+
+        sk : array-like of shape (n_components, n_features)
+        """
+        _, n_features = xk.shape
+
+        # Warning : in some Bishop book, there is a typo on the formula 10.63
+        # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
+        # is the correct formula
+        self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
+
+        diff = xk - self.mean_prior_
+        self.covariances_ = self.covariance_prior_ + nk[:, np.newaxis] * (
+            sk
+            + (self.mean_precision_prior_ / self.mean_precision_)[:, np.newaxis]
+            * np.square(diff)
+        )
+
+        # Contrary to the original bishop book, we normalize the covariances
+        self.covariances_ /= self.degrees_of_freedom_[:, np.newaxis]
+
+    def _estimate_wishart_spherical(self, nk, xk, sk):
+        """Estimate the spherical Wishart distribution parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        nk : array-like of shape (n_components,)
+
+        xk : array-like of shape (n_components, n_features)
+
+        sk : array-like of shape (n_components,)
+        """
+        _, n_features = xk.shape
+
+        # Warning : in some Bishop book, there is a typo on the formula 10.63
+        # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
+        # is the correct formula
+        self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
+
+        diff = xk - self.mean_prior_
+        self.covariances_ = self.covariance_prior_ + nk * (
+            sk
+            + self.mean_precision_prior_
+            / self.mean_precision_
+            * np.mean(np.square(diff), 1)
+        )
+
+        # Contrary to the original bishop book, we normalize the covariances
+        self.covariances_ /= self.degrees_of_freedom_
+
+    def _m_step(self, X, log_resp):
+        """M step.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        log_resp : array-like of shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of each sample in X.
+        """
+        n_samples, _ = X.shape
+
+        nk, xk, sk = _estimate_gaussian_parameters(
+            X, np.exp(log_resp), self.reg_covar, self.covariance_type
+        )
+        self._estimate_weights(nk)
+        self._estimate_means(nk, xk)
+        self._estimate_precisions(nk, xk, sk)
+
+    def _estimate_log_weights(self):
+        if self.weight_concentration_prior_type == "dirichlet_process":
+            digamma_sum = digamma(
+                self.weight_concentration_[0] + self.weight_concentration_[1]
+            )
+            digamma_a = digamma(self.weight_concentration_[0])
+            digamma_b = digamma(self.weight_concentration_[1])
+            return (
+                digamma_a
+                - digamma_sum
+                + np.hstack((0, np.cumsum(digamma_b - digamma_sum)[:-1]))
+            )
+        else:
+            # case Variational Gaussian mixture with dirichlet distribution
+            return digamma(self.weight_concentration_) - digamma(
+                np.sum(self.weight_concentration_)
+            )
+
+    def _estimate_log_prob(self, X):
+        _, n_features = X.shape
+        # We remove `n_features * np.log(self.degrees_of_freedom_)` because
+        # the precision matrix is normalized
+        log_gauss = _estimate_log_gaussian_prob(
+            X, self.means_, self.precisions_cholesky_, self.covariance_type
+        ) - 0.5 * n_features * np.log(self.degrees_of_freedom_)
+
+        log_lambda = n_features * np.log(2.0) + np.sum(
+            digamma(
+                0.5
+                * (self.degrees_of_freedom_ - np.arange(0, n_features)[:, np.newaxis])
+            ),
+            0,
+        )
+
+        return log_gauss + 0.5 * (log_lambda - n_features / self.mean_precision_)
+
+    def _compute_lower_bound(self, log_resp, log_prob_norm):
+        """Estimate the lower bound of the model.
+
+        The lower bound on the likelihood (of the training data with respect to
+        the model) is used to detect the convergence and has to increase at
+        each iteration.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        log_resp : array, shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of each sample in X.
+
+        log_prob_norm : float
+            Logarithm of the probability of each sample in X.
+
+        Returns
+        -------
+        lower_bound : float
+        """
+        # Contrary to the original formula, we have done some simplification
+        # and removed all the constant terms.
+        (n_features,) = self.mean_prior_.shape
+
+        # We removed `.5 * n_features * np.log(self.degrees_of_freedom_)`
+        # because the precision matrix is normalized.
+        log_det_precisions_chol = _compute_log_det_cholesky(
+            self.precisions_cholesky_, self.covariance_type, n_features
+        ) - 0.5 * n_features * np.log(self.degrees_of_freedom_)
+
+        if self.covariance_type == "tied":
+            log_wishart = self.n_components * np.float64(
+                _log_wishart_norm(
+                    self.degrees_of_freedom_, log_det_precisions_chol, n_features
+                )
+            )
+        else:
+            log_wishart = np.sum(
+                _log_wishart_norm(
+                    self.degrees_of_freedom_, log_det_precisions_chol, n_features
+                )
+            )
+
+        if self.weight_concentration_prior_type == "dirichlet_process":
+            log_norm_weight = -np.sum(
+                betaln(self.weight_concentration_[0], self.weight_concentration_[1])
+            )
+        else:
+            log_norm_weight = _log_dirichlet_norm(self.weight_concentration_)
+
+        return (
+            -np.sum(np.exp(log_resp) * log_resp)
+            - log_wishart
+            - log_norm_weight
+            - 0.5 * n_features * np.sum(np.log(self.mean_precision_))
+        )
+
+    def _get_parameters(self):
+        return (
+            self.weight_concentration_,
+            self.mean_precision_,
+            self.means_,
+            self.degrees_of_freedom_,
+            self.covariances_,
+            self.precisions_cholesky_,
+        )
+
+    def _set_parameters(self, params):
+        (
+            self.weight_concentration_,
+            self.mean_precision_,
+            self.means_,
+            self.degrees_of_freedom_,
+            self.covariances_,
+            self.precisions_cholesky_,
+        ) = params
+
+        # Weights computation
+        if self.weight_concentration_prior_type == "dirichlet_process":
+            weight_dirichlet_sum = (
+                self.weight_concentration_[0] + self.weight_concentration_[1]
+            )
+            tmp = self.weight_concentration_[1] / weight_dirichlet_sum
+            self.weights_ = (
+                self.weight_concentration_[0]
+                / weight_dirichlet_sum
+                * np.hstack((1, np.cumprod(tmp[:-1])))
+            )
+            self.weights_ /= np.sum(self.weights_)
+        else:
+            self.weights_ = self.weight_concentration_ / np.sum(
+                self.weight_concentration_
+            )
+
+        # Precisions matrices computation
+        if self.covariance_type == "full":
+            self.precisions_ = np.array(
+                [
+                    np.dot(prec_chol, prec_chol.T)
+                    for prec_chol in self.precisions_cholesky_
+                ]
+            )
+
+        elif self.covariance_type == "tied":
+            self.precisions_ = np.dot(
+                self.precisions_cholesky_, self.precisions_cholesky_.T
+            )
+        else:
+            self.precisions_ = self.precisions_cholesky_**2
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_gaussian_mixture.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_gaussian_mixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bdd3a0d68c81c73bcf6d606cf09bdd52aff66c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/_gaussian_mixture.py
@@ -0,0 +1,934 @@
+"""Gaussian Mixture Model."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+from scipy import linalg
+
+from ..utils import check_array
+from ..utils._param_validation import StrOptions
+from ..utils.extmath import row_norms
+from ._base import BaseMixture, _check_shape
+
+###############################################################################
+# Gaussian mixture shape checkers used by the GaussianMixture class
+
+
+def _check_weights(weights, n_components):
+    """Check the user provided 'weights'.
+
+    Parameters
+    ----------
+    weights : array-like of shape (n_components,)
+        The proportions of components of each mixture.
+
+    n_components : int
+        Number of components.
+
+    Returns
+    -------
+    weights : array, shape (n_components,)
+    """
+    weights = check_array(weights, dtype=[np.float64, np.float32], ensure_2d=False)
+    _check_shape(weights, (n_components,), "weights")
+
+    # check range
+    if any(np.less(weights, 0.0)) or any(np.greater(weights, 1.0)):
+        raise ValueError(
+            "The parameter 'weights' should be in the range "
+            "[0, 1], but got max value %.5f, min value %.5f"
+            % (np.min(weights), np.max(weights))
+        )
+
+    # check normalization
+    atol = 1e-6 if weights.dtype == np.float32 else 1e-8
+    if not np.allclose(np.abs(1.0 - np.sum(weights)), 0.0, atol=atol):
+        raise ValueError(
+            "The parameter 'weights' should be normalized, but got sum(weights) = %.5f"
+            % np.sum(weights)
+        )
+    return weights
+
+
+def _check_means(means, n_components, n_features):
+    """Validate the provided 'means'.
+
+    Parameters
+    ----------
+    means : array-like of shape (n_components, n_features)
+        The centers of the current components.
+
+    n_components : int
+        Number of components.
+
+    n_features : int
+        Number of features.
+
+    Returns
+    -------
+    means : array, (n_components, n_features)
+    """
+    means = check_array(means, dtype=[np.float64, np.float32], ensure_2d=False)
+    _check_shape(means, (n_components, n_features), "means")
+    return means
+
+
+def _check_precision_positivity(precision, covariance_type):
+    """Check a precision vector is positive-definite."""
+    if np.any(np.less_equal(precision, 0.0)):
+        raise ValueError("'%s precision' should be positive" % covariance_type)
+
+
+def _check_precision_matrix(precision, covariance_type):
+    """Check a precision matrix is symmetric and positive-definite."""
+    if not (
+        np.allclose(precision, precision.T) and np.all(linalg.eigvalsh(precision) > 0.0)
+    ):
+        raise ValueError(
+            "'%s precision' should be symmetric, positive-definite" % covariance_type
+        )
+
+
+def _check_precisions_full(precisions, covariance_type):
+    """Check the precision matrices are symmetric and positive-definite."""
+    for prec in precisions:
+        _check_precision_matrix(prec, covariance_type)
+
+
+def _check_precisions(precisions, covariance_type, n_components, n_features):
+    """Validate user provided precisions.
+
+    Parameters
+    ----------
+    precisions : array-like
+        'full' : shape of (n_components, n_features, n_features)
+        'tied' : shape of (n_features, n_features)
+        'diag' : shape of (n_components, n_features)
+        'spherical' : shape of (n_components,)
+
+    covariance_type : str
+
+    n_components : int
+        Number of components.
+
+    n_features : int
+        Number of features.
+
+    Returns
+    -------
+    precisions : array
+    """
+    precisions = check_array(
+        precisions,
+        dtype=[np.float64, np.float32],
+        ensure_2d=False,
+        allow_nd=covariance_type == "full",
+    )
+
+    precisions_shape = {
+        "full": (n_components, n_features, n_features),
+        "tied": (n_features, n_features),
+        "diag": (n_components, n_features),
+        "spherical": (n_components,),
+    }
+    _check_shape(
+        precisions, precisions_shape[covariance_type], "%s precision" % covariance_type
+    )
+
+    _check_precisions = {
+        "full": _check_precisions_full,
+        "tied": _check_precision_matrix,
+        "diag": _check_precision_positivity,
+        "spherical": _check_precision_positivity,
+    }
+    _check_precisions[covariance_type](precisions, covariance_type)
+    return precisions
+
+
+###############################################################################
+# Gaussian mixture parameters estimators (used by the M-Step)
+
+
+def _estimate_gaussian_covariances_full(resp, X, nk, means, reg_covar):
+    """Estimate the full covariance matrices.
+
+    Parameters
+    ----------
+    resp : array-like of shape (n_samples, n_components)
+
+    X : array-like of shape (n_samples, n_features)
+
+    nk : array-like of shape (n_components,)
+
+    means : array-like of shape (n_components, n_features)
+
+    reg_covar : float
+
+    Returns
+    -------
+    covariances : array, shape (n_components, n_features, n_features)
+        The covariance matrix of the current components.
+    """
+    n_components, n_features = means.shape
+    covariances = np.empty((n_components, n_features, n_features), dtype=X.dtype)
+    for k in range(n_components):
+        diff = X - means[k]
+        covariances[k] = np.dot(resp[:, k] * diff.T, diff) / nk[k]
+        covariances[k].flat[:: n_features + 1] += reg_covar
+    return covariances
+
+
+def _estimate_gaussian_covariances_tied(resp, X, nk, means, reg_covar):
+    """Estimate the tied covariance matrix.
+
+    Parameters
+    ----------
+    resp : array-like of shape (n_samples, n_components)
+
+    X : array-like of shape (n_samples, n_features)
+
+    nk : array-like of shape (n_components,)
+
+    means : array-like of shape (n_components, n_features)
+
+    reg_covar : float
+
+    Returns
+    -------
+    covariance : array, shape (n_features, n_features)
+        The tied covariance matrix of the components.
+    """
+    avg_X2 = np.dot(X.T, X)
+    avg_means2 = np.dot(nk * means.T, means)
+    covariance = avg_X2 - avg_means2
+    covariance /= nk.sum()
+    covariance.flat[:: len(covariance) + 1] += reg_covar
+    return covariance
+
+
+def _estimate_gaussian_covariances_diag(resp, X, nk, means, reg_covar):
+    """Estimate the diagonal covariance vectors.
+
+    Parameters
+    ----------
+    responsibilities : array-like of shape (n_samples, n_components)
+
+    X : array-like of shape (n_samples, n_features)
+
+    nk : array-like of shape (n_components,)
+
+    means : array-like of shape (n_components, n_features)
+
+    reg_covar : float
+
+    Returns
+    -------
+    covariances : array, shape (n_components, n_features)
+        The covariance vector of the current components.
+    """
+    avg_X2 = np.dot(resp.T, X * X) / nk[:, np.newaxis]
+    avg_means2 = means**2
+    return avg_X2 - avg_means2 + reg_covar
+
+
+def _estimate_gaussian_covariances_spherical(resp, X, nk, means, reg_covar):
+    """Estimate the spherical variance values.
+
+    Parameters
+    ----------
+    responsibilities : array-like of shape (n_samples, n_components)
+
+    X : array-like of shape (n_samples, n_features)
+
+    nk : array-like of shape (n_components,)
+
+    means : array-like of shape (n_components, n_features)
+
+    reg_covar : float
+
+    Returns
+    -------
+    variances : array, shape (n_components,)
+        The variance values of each components.
+    """
+    return _estimate_gaussian_covariances_diag(resp, X, nk, means, reg_covar).mean(1)
+
+
+def _estimate_gaussian_parameters(X, resp, reg_covar, covariance_type):
+    """Estimate the Gaussian distribution parameters.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        The input data array.
+
+    resp : array-like of shape (n_samples, n_components)
+        The responsibilities for each data sample in X.
+
+    reg_covar : float
+        The regularization added to the diagonal of the covariance matrices.
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}
+        The type of precision matrices.
+
+    Returns
+    -------
+    nk : array-like of shape (n_components,)
+        The numbers of data samples in the current components.
+
+    means : array-like of shape (n_components, n_features)
+        The centers of the current components.
+
+    covariances : array-like
+        The covariance matrix of the current components.
+        The shape depends of the covariance_type.
+    """
+    nk = resp.sum(axis=0) + 10 * np.finfo(resp.dtype).eps
+    means = np.dot(resp.T, X) / nk[:, np.newaxis]
+    covariances = {
+        "full": _estimate_gaussian_covariances_full,
+        "tied": _estimate_gaussian_covariances_tied,
+        "diag": _estimate_gaussian_covariances_diag,
+        "spherical": _estimate_gaussian_covariances_spherical,
+    }[covariance_type](resp, X, nk, means, reg_covar)
+    return nk, means, covariances
+
+
+def _compute_precision_cholesky(covariances, covariance_type):
+    """Compute the Cholesky decomposition of the precisions.
+
+    Parameters
+    ----------
+    covariances : array-like
+        The covariance matrix of the current components.
+        The shape depends of the covariance_type.
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}
+        The type of precision matrices.
+
+    Returns
+    -------
+    precisions_cholesky : array-like
+        The cholesky decomposition of sample precisions of the current
+        components. The shape depends of the covariance_type.
+    """
+    estimate_precision_error_message = (
+        "Fitting the mixture model failed because some components have "
+        "ill-defined empirical covariance (for instance caused by singleton "
+        "or collapsed samples). Try to decrease the number of components, "
+        "increase reg_covar, or scale the input data."
+    )
+    dtype = covariances.dtype
+    if dtype == np.float32:
+        estimate_precision_error_message += (
+            " The numerical accuracy can also be improved by passing float64"
+            " data instead of float32."
+        )
+
+    if covariance_type == "full":
+        n_components, n_features, _ = covariances.shape
+        precisions_chol = np.empty((n_components, n_features, n_features), dtype=dtype)
+        for k, covariance in enumerate(covariances):
+            try:
+                cov_chol = linalg.cholesky(covariance, lower=True)
+            except linalg.LinAlgError:
+                raise ValueError(estimate_precision_error_message)
+            precisions_chol[k] = linalg.solve_triangular(
+                cov_chol, np.eye(n_features, dtype=dtype), lower=True
+            ).T
+    elif covariance_type == "tied":
+        _, n_features = covariances.shape
+        try:
+            cov_chol = linalg.cholesky(covariances, lower=True)
+        except linalg.LinAlgError:
+            raise ValueError(estimate_precision_error_message)
+        precisions_chol = linalg.solve_triangular(
+            cov_chol, np.eye(n_features, dtype=dtype), lower=True
+        ).T
+    else:
+        if np.any(np.less_equal(covariances, 0.0)):
+            raise ValueError(estimate_precision_error_message)
+        precisions_chol = 1.0 / np.sqrt(covariances)
+    return precisions_chol
+
+
+def _flipudlr(array):
+    """Reverse the rows and columns of an array."""
+    return np.flipud(np.fliplr(array))
+
+
+def _compute_precision_cholesky_from_precisions(precisions, covariance_type):
+    r"""Compute the Cholesky decomposition of precisions using precisions themselves.
+
+    As implemented in :func:`_compute_precision_cholesky`, the `precisions_cholesky_` is
+    an upper-triangular matrix for each Gaussian component, which can be expressed as
+    the $UU^T$ factorization of the precision matrix for each Gaussian component, where
+    $U$ is an upper-triangular matrix.
+
+    In order to use the Cholesky decomposition to get $UU^T$, the precision matrix
+    $\Lambda$ needs to be permutated such that its rows and columns are reversed, which
+    can be done by applying a similarity transformation with an exchange matrix $J$,
+    where the 1 elements reside on the anti-diagonal and all other elements are 0. In
+    particular, the Cholesky decomposition of the transformed precision matrix is
+    $J\Lambda J=LL^T$, where $L$ is a lower-triangular matrix. Because $\Lambda=UU^T$
+    and $J=J^{-1}=J^T$, the `precisions_cholesky_` for each Gaussian component can be
+    expressed as $JLJ$.
+
+    Refer to #26415 for details.
+
+    Parameters
+    ----------
+    precisions : array-like
+        The precision matrix of the current components.
+        The shape depends on the covariance_type.
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}
+        The type of precision matrices.
+
+    Returns
+    -------
+    precisions_cholesky : array-like
+        The cholesky decomposition of sample precisions of the current
+        components. The shape depends on the covariance_type.
+    """
+    if covariance_type == "full":
+        precisions_cholesky = np.array(
+            [
+                _flipudlr(linalg.cholesky(_flipudlr(precision), lower=True))
+                for precision in precisions
+            ]
+        )
+    elif covariance_type == "tied":
+        precisions_cholesky = _flipudlr(
+            linalg.cholesky(_flipudlr(precisions), lower=True)
+        )
+    else:
+        precisions_cholesky = np.sqrt(precisions)
+    return precisions_cholesky
+
+
+###############################################################################
+# Gaussian mixture probability estimators
+def _compute_log_det_cholesky(matrix_chol, covariance_type, n_features):
+    """Compute the log-det of the cholesky decomposition of matrices.
+
+    Parameters
+    ----------
+    matrix_chol : array-like
+        Cholesky decompositions of the matrices.
+        'full' : shape of (n_components, n_features, n_features)
+        'tied' : shape of (n_features, n_features)
+        'diag' : shape of (n_components, n_features)
+        'spherical' : shape of (n_components,)
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}
+
+    n_features : int
+        Number of features.
+
+    Returns
+    -------
+    log_det_precision_chol : array-like of shape (n_components,)
+        The determinant of the precision matrix for each component.
+    """
+    if covariance_type == "full":
+        n_components, _, _ = matrix_chol.shape
+        log_det_chol = np.sum(
+            np.log(matrix_chol.reshape(n_components, -1)[:, :: n_features + 1]), axis=1
+        )
+
+    elif covariance_type == "tied":
+        log_det_chol = np.sum(np.log(np.diag(matrix_chol)))
+
+    elif covariance_type == "diag":
+        log_det_chol = np.sum(np.log(matrix_chol), axis=1)
+
+    else:
+        log_det_chol = n_features * np.log(matrix_chol)
+
+    return log_det_chol
+
+
+def _estimate_log_gaussian_prob(X, means, precisions_chol, covariance_type):
+    """Estimate the log Gaussian probability.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+
+    means : array-like of shape (n_components, n_features)
+
+    precisions_chol : array-like
+        Cholesky decompositions of the precision matrices.
+        'full' : shape of (n_components, n_features, n_features)
+        'tied' : shape of (n_features, n_features)
+        'diag' : shape of (n_components, n_features)
+        'spherical' : shape of (n_components,)
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}
+
+    Returns
+    -------
+    log_prob : array, shape (n_samples, n_components)
+    """
+    n_samples, n_features = X.shape
+    n_components, _ = means.shape
+    # The determinant of the precision matrix from the Cholesky decomposition
+    # corresponds to the negative half of the determinant of the full precision
+    # matrix.
+    # In short: det(precision_chol) = - det(precision) / 2
+    log_det = _compute_log_det_cholesky(precisions_chol, covariance_type, n_features)
+
+    if covariance_type == "full":
+        log_prob = np.empty((n_samples, n_components), dtype=X.dtype)
+        for k, (mu, prec_chol) in enumerate(zip(means, precisions_chol)):
+            y = np.dot(X, prec_chol) - np.dot(mu, prec_chol)
+            log_prob[:, k] = np.sum(np.square(y), axis=1)
+
+    elif covariance_type == "tied":
+        log_prob = np.empty((n_samples, n_components), dtype=X.dtype)
+        for k, mu in enumerate(means):
+            y = np.dot(X, precisions_chol) - np.dot(mu, precisions_chol)
+            log_prob[:, k] = np.sum(np.square(y), axis=1)
+
+    elif covariance_type == "diag":
+        precisions = precisions_chol**2
+        log_prob = (
+            np.sum((means**2 * precisions), 1)
+            - 2.0 * np.dot(X, (means * precisions).T)
+            + np.dot(X**2, precisions.T)
+        )
+
+    elif covariance_type == "spherical":
+        precisions = precisions_chol**2
+        log_prob = (
+            np.sum(means**2, 1) * precisions
+            - 2 * np.dot(X, means.T * precisions)
+            + np.outer(row_norms(X, squared=True), precisions)
+        )
+    # Since we are using the precision of the Cholesky decomposition,
+    # `- 0.5 * log_det_precision` becomes `+ log_det_precision_chol`
+    return -0.5 * (n_features * np.log(2 * np.pi).astype(X.dtype) + log_prob) + log_det
+
+
+class GaussianMixture(BaseMixture):
+    """Gaussian Mixture.
+
+    Representation of a Gaussian mixture model probability distribution.
+    This class allows to estimate the parameters of a Gaussian mixture
+    distribution.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.18
+
+    Parameters
+    ----------
+    n_components : int, default=1
+        The number of mixture components.
+
+    covariance_type : {'full', 'tied', 'diag', 'spherical'}, default='full'
+        String describing the type of covariance parameters to use.
+        Must be one of:
+
+        - 'full': each component has its own general covariance matrix.
+        - 'tied': all components share the same general covariance matrix.
+        - 'diag': each component has its own diagonal covariance matrix.
+        - 'spherical': each component has its own single variance.
+
+        For an example of using `covariance_type`, refer to
+        :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py`.
+
+    tol : float, default=1e-3
+        The convergence threshold. EM iterations will stop when the
+        lower bound average gain is below this threshold.
+
+    reg_covar : float, default=1e-6
+        Non-negative regularization added to the diagonal of covariance.
+        Allows to assure that the covariance matrices are all positive.
+
+    max_iter : int, default=100
+        The number of EM iterations to perform.
+
+    n_init : int, default=1
+        The number of initializations to perform. The best results are kept.
+
+    init_params : {'kmeans', 'k-means++', 'random', 'random_from_data'}, \
+    default='kmeans'
+        The method used to initialize the weights, the means and the
+        precisions.
+        String must be one of:
+
+        - 'kmeans' : responsibilities are initialized using kmeans.
+        - 'k-means++' : use the k-means++ method to initialize.
+        - 'random' : responsibilities are initialized randomly.
+        - 'random_from_data' : initial means are randomly selected data points.
+
+        .. versionchanged:: v1.1
+            `init_params` now accepts 'random_from_data' and 'k-means++' as
+            initialization methods.
+
+    weights_init : array-like of shape (n_components, ), default=None
+        The user-provided initial weights.
+        If it is None, weights are initialized using the `init_params` method.
+
+    means_init : array-like of shape (n_components, n_features), default=None
+        The user-provided initial means,
+        If it is None, means are initialized using the `init_params` method.
+
+    precisions_init : array-like, default=None
+        The user-provided initial precisions (inverse of the covariance
+        matrices).
+        If it is None, precisions are initialized using the 'init_params'
+        method.
+        The shape depends on 'covariance_type'::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the random seed given to the method chosen to initialize the
+        parameters (see `init_params`).
+        In addition, it controls the generation of random samples from the
+        fitted distribution (see the method `sample`).
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    warm_start : bool, default=False
+        If 'warm_start' is True, the solution of the last fitting is used as
+        initialization for the next call of fit(). This can speed up
+        convergence when fit is called several times on similar problems.
+        In that case, 'n_init' is ignored and only a single initialization
+        occurs upon the first call.
+        See :term:`the Glossary `.
+
+    verbose : int, default=0
+        Enable verbose output. If 1 then it prints the current
+        initialization and each iteration step. If greater than 1 then
+        it prints also the log probability and the time needed
+        for each step.
+
+    verbose_interval : int, default=10
+        Number of iteration done before the next print.
+
+    Attributes
+    ----------
+    weights_ : array-like of shape (n_components,)
+        The weights of each mixture components.
+
+    means_ : array-like of shape (n_components, n_features)
+        The mean of each mixture component.
+
+    covariances_ : array-like
+        The covariance of each mixture component.
+        The shape depends on `covariance_type`::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+        For an example of using covariances, refer to
+        :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py`.
+
+    precisions_ : array-like
+        The precision matrices for each component in the mixture. A precision
+        matrix is the inverse of a covariance matrix. A covariance matrix is
+        symmetric positive definite so the mixture of Gaussian can be
+        equivalently parameterized by the precision matrices. Storing the
+        precision matrices instead of the covariance matrices makes it more
+        efficient to compute the log-likelihood of new samples at test time.
+        The shape depends on `covariance_type`::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    precisions_cholesky_ : array-like
+        The cholesky decomposition of the precision matrices of each mixture
+        component. A precision matrix is the inverse of a covariance matrix.
+        A covariance matrix is symmetric positive definite so the mixture of
+        Gaussian can be equivalently parameterized by the precision matrices.
+        Storing the precision matrices instead of the covariance matrices makes
+        it more efficient to compute the log-likelihood of new samples at test
+        time. The shape depends on `covariance_type`::
+
+            (n_components,)                        if 'spherical',
+            (n_features, n_features)               if 'tied',
+            (n_components, n_features)             if 'diag',
+            (n_components, n_features, n_features) if 'full'
+
+    converged_ : bool
+        True when convergence of the best fit of EM was reached, False otherwise.
+
+    n_iter_ : int
+        Number of step used by the best fit of EM to reach the convergence.
+
+    lower_bound_ : float
+        Lower bound value on the log-likelihood (of the training data with
+        respect to the model) of the best fit of EM.
+
+    lower_bounds_ : array-like of shape (`n_iter_`,)
+        The list of lower bound values on the log-likelihood from each
+        iteration of the best fit of EM.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    BayesianGaussianMixture : Gaussian mixture model fit with a variational
+        inference.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.mixture import GaussianMixture
+    >>> X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
+    >>> gm = GaussianMixture(n_components=2, random_state=0).fit(X)
+    >>> gm.means_
+    array([[10.,  2.],
+           [ 1.,  2.]])
+    >>> gm.predict([[0, 0], [12, 3]])
+    array([1, 0])
+
+    For a comparison of Gaussian Mixture with other clustering algorithms, see
+    :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`
+    """
+
+    _parameter_constraints: dict = {
+        **BaseMixture._parameter_constraints,
+        "covariance_type": [StrOptions({"full", "tied", "diag", "spherical"})],
+        "weights_init": ["array-like", None],
+        "means_init": ["array-like", None],
+        "precisions_init": ["array-like", None],
+    }
+
+    def __init__(
+        self,
+        n_components=1,
+        *,
+        covariance_type="full",
+        tol=1e-3,
+        reg_covar=1e-6,
+        max_iter=100,
+        n_init=1,
+        init_params="kmeans",
+        weights_init=None,
+        means_init=None,
+        precisions_init=None,
+        random_state=None,
+        warm_start=False,
+        verbose=0,
+        verbose_interval=10,
+    ):
+        super().__init__(
+            n_components=n_components,
+            tol=tol,
+            reg_covar=reg_covar,
+            max_iter=max_iter,
+            n_init=n_init,
+            init_params=init_params,
+            random_state=random_state,
+            warm_start=warm_start,
+            verbose=verbose,
+            verbose_interval=verbose_interval,
+        )
+
+        self.covariance_type = covariance_type
+        self.weights_init = weights_init
+        self.means_init = means_init
+        self.precisions_init = precisions_init
+
+    def _check_parameters(self, X):
+        """Check the Gaussian mixture parameters are well defined."""
+        _, n_features = X.shape
+
+        if self.weights_init is not None:
+            self.weights_init = _check_weights(self.weights_init, self.n_components)
+
+        if self.means_init is not None:
+            self.means_init = _check_means(
+                self.means_init, self.n_components, n_features
+            )
+
+        if self.precisions_init is not None:
+            self.precisions_init = _check_precisions(
+                self.precisions_init,
+                self.covariance_type,
+                self.n_components,
+                n_features,
+            )
+
+    def _initialize_parameters(self, X, random_state):
+        # If all the initial parameters are all provided, then there is no need to run
+        # the initialization.
+        compute_resp = (
+            self.weights_init is None
+            or self.means_init is None
+            or self.precisions_init is None
+        )
+        if compute_resp:
+            super()._initialize_parameters(X, random_state)
+        else:
+            self._initialize(X, None)
+
+    def _initialize(self, X, resp):
+        """Initialization of the Gaussian mixture parameters.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        resp : array-like of shape (n_samples, n_components)
+        """
+        n_samples, _ = X.shape
+        weights, means, covariances = None, None, None
+        if resp is not None:
+            weights, means, covariances = _estimate_gaussian_parameters(
+                X, resp, self.reg_covar, self.covariance_type
+            )
+            if self.weights_init is None:
+                weights /= n_samples
+
+        self.weights_ = weights if self.weights_init is None else self.weights_init
+        self.means_ = means if self.means_init is None else self.means_init
+
+        if self.precisions_init is None:
+            self.covariances_ = covariances
+            self.precisions_cholesky_ = _compute_precision_cholesky(
+                covariances, self.covariance_type
+            )
+        else:
+            self.precisions_cholesky_ = _compute_precision_cholesky_from_precisions(
+                self.precisions_init, self.covariance_type
+            )
+
+    def _m_step(self, X, log_resp):
+        """M step.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        log_resp : array-like of shape (n_samples, n_components)
+            Logarithm of the posterior probabilities (or responsibilities) of
+            the point of each sample in X.
+        """
+        self.weights_, self.means_, self.covariances_ = _estimate_gaussian_parameters(
+            X, np.exp(log_resp), self.reg_covar, self.covariance_type
+        )
+        self.weights_ /= self.weights_.sum()
+        self.precisions_cholesky_ = _compute_precision_cholesky(
+            self.covariances_, self.covariance_type
+        )
+
+    def _estimate_log_prob(self, X):
+        return _estimate_log_gaussian_prob(
+            X, self.means_, self.precisions_cholesky_, self.covariance_type
+        )
+
+    def _estimate_log_weights(self):
+        return np.log(self.weights_)
+
+    def _compute_lower_bound(self, _, log_prob_norm):
+        return log_prob_norm
+
+    def _get_parameters(self):
+        return (
+            self.weights_,
+            self.means_,
+            self.covariances_,
+            self.precisions_cholesky_,
+        )
+
+    def _set_parameters(self, params):
+        (
+            self.weights_,
+            self.means_,
+            self.covariances_,
+            self.precisions_cholesky_,
+        ) = params
+
+        # Attributes computation
+        _, n_features = self.means_.shape
+
+        dtype = self.precisions_cholesky_.dtype
+        if self.covariance_type == "full":
+            self.precisions_ = np.empty_like(self.precisions_cholesky_)
+            for k, prec_chol in enumerate(self.precisions_cholesky_):
+                self.precisions_[k] = np.dot(prec_chol, prec_chol.T)
+
+        elif self.covariance_type == "tied":
+            self.precisions_ = np.dot(
+                self.precisions_cholesky_, self.precisions_cholesky_.T
+            )
+        else:
+            self.precisions_ = self.precisions_cholesky_**2
+
+    def _n_parameters(self):
+        """Return the number of free parameters in the model."""
+        _, n_features = self.means_.shape
+        if self.covariance_type == "full":
+            cov_params = self.n_components * n_features * (n_features + 1) / 2.0
+        elif self.covariance_type == "diag":
+            cov_params = self.n_components * n_features
+        elif self.covariance_type == "tied":
+            cov_params = n_features * (n_features + 1) / 2.0
+        elif self.covariance_type == "spherical":
+            cov_params = self.n_components
+        mean_params = n_features * self.n_components
+        return int(cov_params + mean_params + self.n_components - 1)
+
+    def bic(self, X):
+        """Bayesian information criterion for the current model on the input X.
+
+        You can refer to this :ref:`mathematical section ` for more
+        details regarding the formulation of the BIC used.
+
+        For an example of GMM selection using `bic` information criterion,
+        refer to :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py`.
+
+        Parameters
+        ----------
+        X : array of shape (n_samples, n_dimensions)
+            The input samples.
+
+        Returns
+        -------
+        bic : float
+            The lower the better.
+        """
+        return -2 * self.score(X) * X.shape[0] + self._n_parameters() * np.log(
+            X.shape[0]
+        )
+
+    def aic(self, X):
+        """Akaike information criterion for the current model on the input X.
+
+        You can refer to this :ref:`mathematical section ` for more
+        details regarding the formulation of the AIC used.
+
+        Parameters
+        ----------
+        X : array of shape (n_samples, n_dimensions)
+            The input samples.
+
+        Returns
+        -------
+        aic : float
+            The lower the better.
+        """
+        return -2 * self.score(X) * X.shape[0] + 2 * self._n_parameters()
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_bayesian_mixture.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_bayesian_mixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..d36543903cb87b07ea1a1c75b9a69aa63bd7dbff
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_bayesian_mixture.py
@@ -0,0 +1,464 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import copy
+
+import numpy as np
+import pytest
+from scipy.special import gammaln
+
+from sklearn.exceptions import NotFittedError
+from sklearn.metrics.cluster import adjusted_rand_score
+from sklearn.mixture import BayesianGaussianMixture
+from sklearn.mixture._bayesian_mixture import _log_dirichlet_norm, _log_wishart_norm
+from sklearn.mixture.tests.test_gaussian_mixture import RandomData
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_equal,
+)
+
+COVARIANCE_TYPE = ["full", "tied", "diag", "spherical"]
+PRIOR_TYPE = ["dirichlet_process", "dirichlet_distribution"]
+
+
+def test_log_dirichlet_norm():
+    rng = np.random.RandomState(0)
+
+    weight_concentration = rng.rand(2)
+    expected_norm = gammaln(np.sum(weight_concentration)) - np.sum(
+        gammaln(weight_concentration)
+    )
+    predected_norm = _log_dirichlet_norm(weight_concentration)
+
+    assert_almost_equal(expected_norm, predected_norm)
+
+
+def test_log_wishart_norm():
+    rng = np.random.RandomState(0)
+
+    n_components, n_features = 5, 2
+    degrees_of_freedom = np.abs(rng.rand(n_components)) + 1.0
+    log_det_precisions_chol = n_features * np.log(range(2, 2 + n_components))
+
+    expected_norm = np.empty(5)
+    for k, (degrees_of_freedom_k, log_det_k) in enumerate(
+        zip(degrees_of_freedom, log_det_precisions_chol)
+    ):
+        expected_norm[k] = -(
+            degrees_of_freedom_k * (log_det_k + 0.5 * n_features * np.log(2.0))
+            + np.sum(
+                gammaln(
+                    0.5
+                    * (degrees_of_freedom_k - np.arange(0, n_features)[:, np.newaxis])
+                ),
+                0,
+            )
+        ).item()
+    predected_norm = _log_wishart_norm(
+        degrees_of_freedom, log_det_precisions_chol, n_features
+    )
+
+    assert_almost_equal(expected_norm, predected_norm)
+
+
+def test_bayesian_mixture_weights_prior_initialisation():
+    rng = np.random.RandomState(0)
+    n_samples, n_components, n_features = 10, 5, 2
+    X = rng.rand(n_samples, n_features)
+
+    # Check correct init for a given value of weight_concentration_prior
+    weight_concentration_prior = rng.rand()
+    bgmm = BayesianGaussianMixture(
+        weight_concentration_prior=weight_concentration_prior, random_state=rng
+    ).fit(X)
+    assert_almost_equal(weight_concentration_prior, bgmm.weight_concentration_prior_)
+
+    # Check correct init for the default value of weight_concentration_prior
+    bgmm = BayesianGaussianMixture(n_components=n_components, random_state=rng).fit(X)
+    assert_almost_equal(1.0 / n_components, bgmm.weight_concentration_prior_)
+
+
+def test_bayesian_mixture_mean_prior_initialisation():
+    rng = np.random.RandomState(0)
+    n_samples, n_components, n_features = 10, 3, 2
+    X = rng.rand(n_samples, n_features)
+
+    # Check correct init for a given value of mean_precision_prior
+    mean_precision_prior = rng.rand()
+    bgmm = BayesianGaussianMixture(
+        mean_precision_prior=mean_precision_prior, random_state=rng
+    ).fit(X)
+    assert_almost_equal(mean_precision_prior, bgmm.mean_precision_prior_)
+
+    # Check correct init for the default value of mean_precision_prior
+    bgmm = BayesianGaussianMixture(random_state=rng).fit(X)
+    assert_almost_equal(1.0, bgmm.mean_precision_prior_)
+
+    # Check correct init for a given value of mean_prior
+    mean_prior = rng.rand(n_features)
+    bgmm = BayesianGaussianMixture(
+        n_components=n_components, mean_prior=mean_prior, random_state=rng
+    ).fit(X)
+    assert_almost_equal(mean_prior, bgmm.mean_prior_)
+
+    # Check correct init for the default value of bemean_priorta
+    bgmm = BayesianGaussianMixture(n_components=n_components, random_state=rng).fit(X)
+    assert_almost_equal(X.mean(axis=0), bgmm.mean_prior_)
+
+
+def test_bayesian_mixture_precisions_prior_initialisation():
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 10, 2
+    X = rng.rand(n_samples, n_features)
+
+    # Check raise message for a bad value of degrees_of_freedom_prior
+    bad_degrees_of_freedom_prior_ = n_features - 1.0
+    bgmm = BayesianGaussianMixture(
+        degrees_of_freedom_prior=bad_degrees_of_freedom_prior_, random_state=rng
+    )
+    msg = (
+        "The parameter 'degrees_of_freedom_prior' should be greater than"
+        f" {n_features - 1}, but got {bad_degrees_of_freedom_prior_:.3f}."
+    )
+    with pytest.raises(ValueError, match=msg):
+        bgmm.fit(X)
+
+    # Check correct init for a given value of degrees_of_freedom_prior
+    degrees_of_freedom_prior = rng.rand() + n_features - 1.0
+    bgmm = BayesianGaussianMixture(
+        degrees_of_freedom_prior=degrees_of_freedom_prior, random_state=rng
+    ).fit(X)
+    assert_almost_equal(degrees_of_freedom_prior, bgmm.degrees_of_freedom_prior_)
+
+    # Check correct init for the default value of degrees_of_freedom_prior
+    degrees_of_freedom_prior_default = n_features
+    bgmm = BayesianGaussianMixture(
+        degrees_of_freedom_prior=degrees_of_freedom_prior_default, random_state=rng
+    ).fit(X)
+    assert_almost_equal(
+        degrees_of_freedom_prior_default, bgmm.degrees_of_freedom_prior_
+    )
+
+    # Check correct init for a given value of covariance_prior
+    covariance_prior = {
+        "full": np.cov(X.T, bias=1) + 10,
+        "tied": np.cov(X.T, bias=1) + 5,
+        "diag": np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
+        "spherical": rng.rand(),
+    }
+
+    bgmm = BayesianGaussianMixture(random_state=rng)
+    for cov_type in ["full", "tied", "diag", "spherical"]:
+        bgmm.covariance_type = cov_type
+        bgmm.covariance_prior = covariance_prior[cov_type]
+        bgmm.fit(X)
+        assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)
+
+    # Check correct init for the default value of covariance_prior
+    covariance_prior_default = {
+        "full": np.atleast_2d(np.cov(X.T)),
+        "tied": np.atleast_2d(np.cov(X.T)),
+        "diag": np.var(X, axis=0, ddof=1),
+        "spherical": np.var(X, axis=0, ddof=1).mean(),
+    }
+
+    bgmm = BayesianGaussianMixture(random_state=0)
+    for cov_type in ["full", "tied", "diag", "spherical"]:
+        bgmm.covariance_type = cov_type
+        bgmm.fit(X)
+        assert_almost_equal(covariance_prior_default[cov_type], bgmm.covariance_prior_)
+
+
+def test_bayesian_mixture_check_is_fitted():
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 10, 2
+
+    # Check raise message
+    bgmm = BayesianGaussianMixture(random_state=rng)
+    X = rng.rand(n_samples, n_features)
+
+    msg = "This BayesianGaussianMixture instance is not fitted yet."
+    with pytest.raises(ValueError, match=msg):
+        bgmm.score(X)
+
+
+def test_bayesian_mixture_weights():
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 10, 2
+
+    X = rng.rand(n_samples, n_features)
+
+    # Case Dirichlet distribution for the weight concentration prior type
+    bgmm = BayesianGaussianMixture(
+        weight_concentration_prior_type="dirichlet_distribution",
+        n_components=3,
+        random_state=rng,
+    ).fit(X)
+
+    expected_weights = bgmm.weight_concentration_ / np.sum(bgmm.weight_concentration_)
+    assert_almost_equal(expected_weights, bgmm.weights_)
+    assert_almost_equal(np.sum(bgmm.weights_), 1.0)
+
+    # Case Dirichlet process for the weight concentration prior type
+    dpgmm = BayesianGaussianMixture(
+        weight_concentration_prior_type="dirichlet_process",
+        n_components=3,
+        random_state=rng,
+    ).fit(X)
+    weight_dirichlet_sum = (
+        dpgmm.weight_concentration_[0] + dpgmm.weight_concentration_[1]
+    )
+    tmp = dpgmm.weight_concentration_[1] / weight_dirichlet_sum
+    expected_weights = (
+        dpgmm.weight_concentration_[0]
+        / weight_dirichlet_sum
+        * np.hstack((1, np.cumprod(tmp[:-1])))
+    )
+    expected_weights /= np.sum(expected_weights)
+    assert_almost_equal(expected_weights, dpgmm.weights_)
+    assert_almost_equal(np.sum(dpgmm.weights_), 1.0)
+
+
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+def test_monotonic_likelihood():
+    # We check that each step of the each step of variational inference without
+    # regularization improve monotonically the training set of the bound
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=20)
+    n_components = rand_data.n_components
+
+    for prior_type in PRIOR_TYPE:
+        for covar_type in COVARIANCE_TYPE:
+            X = rand_data.X[covar_type]
+            bgmm = BayesianGaussianMixture(
+                weight_concentration_prior_type=prior_type,
+                n_components=2 * n_components,
+                covariance_type=covar_type,
+                warm_start=True,
+                max_iter=1,
+                random_state=rng,
+                tol=1e-3,
+            )
+            current_lower_bound = -np.inf
+            # Do one training iteration at a time so we can make sure that the
+            # training log likelihood increases after each iteration.
+            for _ in range(600):
+                prev_lower_bound = current_lower_bound
+                current_lower_bound = bgmm.fit(X).lower_bound_
+                assert current_lower_bound >= prev_lower_bound
+
+                if bgmm.converged_:
+                    break
+            assert bgmm.converged_
+
+
+def test_compare_covar_type():
+    # We can compare the 'full' precision with the other cov_type if we apply
+    # 1 iter of the M-step (done during _initialize_parameters).
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7)
+    X = rand_data.X["full"]
+    n_components = rand_data.n_components
+
+    for prior_type in PRIOR_TYPE:
+        # Computation of the full_covariance
+        bgmm = BayesianGaussianMixture(
+            weight_concentration_prior_type=prior_type,
+            n_components=2 * n_components,
+            covariance_type="full",
+            max_iter=1,
+            random_state=0,
+            tol=1e-7,
+        )
+        bgmm._check_parameters(X)
+        bgmm._initialize_parameters(X, np.random.RandomState(0))
+        full_covariances = (
+            bgmm.covariances_ * bgmm.degrees_of_freedom_[:, np.newaxis, np.newaxis]
+        )
+
+        # Check tied_covariance = mean(full_covariances, 0)
+        bgmm = BayesianGaussianMixture(
+            weight_concentration_prior_type=prior_type,
+            n_components=2 * n_components,
+            covariance_type="tied",
+            max_iter=1,
+            random_state=0,
+            tol=1e-7,
+        )
+        bgmm._check_parameters(X)
+        bgmm._initialize_parameters(X, np.random.RandomState(0))
+
+        tied_covariance = bgmm.covariances_ * bgmm.degrees_of_freedom_
+        assert_almost_equal(tied_covariance, np.mean(full_covariances, 0))
+
+        # Check diag_covariance = diag(full_covariances)
+        bgmm = BayesianGaussianMixture(
+            weight_concentration_prior_type=prior_type,
+            n_components=2 * n_components,
+            covariance_type="diag",
+            max_iter=1,
+            random_state=0,
+            tol=1e-7,
+        )
+        bgmm._check_parameters(X)
+        bgmm._initialize_parameters(X, np.random.RandomState(0))
+
+        diag_covariances = bgmm.covariances_ * bgmm.degrees_of_freedom_[:, np.newaxis]
+        assert_almost_equal(
+            diag_covariances, np.array([np.diag(cov) for cov in full_covariances])
+        )
+
+        # Check spherical_covariance = np.mean(diag_covariances, 0)
+        bgmm = BayesianGaussianMixture(
+            weight_concentration_prior_type=prior_type,
+            n_components=2 * n_components,
+            covariance_type="spherical",
+            max_iter=1,
+            random_state=0,
+            tol=1e-7,
+        )
+        bgmm._check_parameters(X)
+        bgmm._initialize_parameters(X, np.random.RandomState(0))
+
+        spherical_covariances = bgmm.covariances_ * bgmm.degrees_of_freedom_
+        assert_almost_equal(spherical_covariances, np.mean(diag_covariances, 1))
+
+
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+def test_check_covariance_precision():
+    # We check that the dot product of the covariance and the precision
+    # matrices is identity.
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7)
+    n_components, n_features = 2 * rand_data.n_components, 2
+
+    # Computation of the full_covariance
+    bgmm = BayesianGaussianMixture(
+        n_components=n_components, max_iter=100, random_state=rng, tol=1e-3, reg_covar=0
+    )
+    for covar_type in COVARIANCE_TYPE:
+        bgmm.covariance_type = covar_type
+        bgmm.fit(rand_data.X[covar_type])
+
+        if covar_type == "full":
+            for covar, precision in zip(bgmm.covariances_, bgmm.precisions_):
+                assert_almost_equal(np.dot(covar, precision), np.eye(n_features))
+        elif covar_type == "tied":
+            assert_almost_equal(
+                np.dot(bgmm.covariances_, bgmm.precisions_), np.eye(n_features)
+            )
+
+        elif covar_type == "diag":
+            assert_almost_equal(
+                bgmm.covariances_ * bgmm.precisions_,
+                np.ones((n_components, n_features)),
+            )
+
+        else:
+            assert_almost_equal(
+                bgmm.covariances_ * bgmm.precisions_, np.ones(n_components)
+            )
+
+
+def test_invariant_translation():
+    # We check here that adding a constant in the data change correctly the
+    # parameters of the mixture
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=100)
+    n_components = 2 * rand_data.n_components
+
+    for prior_type in PRIOR_TYPE:
+        for covar_type in COVARIANCE_TYPE:
+            X = rand_data.X[covar_type]
+            bgmm1 = BayesianGaussianMixture(
+                weight_concentration_prior_type=prior_type,
+                n_components=n_components,
+                max_iter=100,
+                random_state=0,
+                tol=1e-3,
+                reg_covar=0,
+            ).fit(X)
+            bgmm2 = BayesianGaussianMixture(
+                weight_concentration_prior_type=prior_type,
+                n_components=n_components,
+                max_iter=100,
+                random_state=0,
+                tol=1e-3,
+                reg_covar=0,
+            ).fit(X + 100)
+
+            assert_almost_equal(bgmm1.means_, bgmm2.means_ - 100)
+            assert_almost_equal(bgmm1.weights_, bgmm2.weights_)
+            assert_almost_equal(bgmm1.covariances_, bgmm2.covariances_)
+
+
+@pytest.mark.filterwarnings("ignore:.*did not converge.*")
+@pytest.mark.parametrize(
+    "seed, max_iter, tol",
+    [
+        (0, 2, 1e-7),  # strict non-convergence
+        (1, 2, 1e-1),  # loose non-convergence
+        (3, 300, 1e-7),  # strict convergence
+        (4, 300, 1e-1),  # loose convergence
+    ],
+)
+def test_bayesian_mixture_fit_predict(seed, max_iter, tol):
+    rng = np.random.RandomState(seed)
+    rand_data = RandomData(rng, n_samples=50, scale=7)
+    n_components = 2 * rand_data.n_components
+
+    for covar_type in COVARIANCE_TYPE:
+        bgmm1 = BayesianGaussianMixture(
+            n_components=n_components,
+            max_iter=max_iter,
+            random_state=rng,
+            tol=tol,
+            reg_covar=0,
+        )
+        bgmm1.covariance_type = covar_type
+        bgmm2 = copy.deepcopy(bgmm1)
+        X = rand_data.X[covar_type]
+
+        Y_pred1 = bgmm1.fit(X).predict(X)
+        Y_pred2 = bgmm2.fit_predict(X)
+        assert_array_equal(Y_pred1, Y_pred2)
+
+
+def test_bayesian_mixture_fit_predict_n_init():
+    # Check that fit_predict is equivalent to fit.predict, when n_init > 1
+    X = np.random.RandomState(0).randn(50, 5)
+    gm = BayesianGaussianMixture(n_components=5, n_init=10, random_state=0)
+    y_pred1 = gm.fit_predict(X)
+    y_pred2 = gm.predict(X)
+    assert_array_equal(y_pred1, y_pred2)
+
+
+def test_bayesian_mixture_predict_predict_proba():
+    # this is the same test as test_gaussian_mixture_predict_predict_proba()
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    for prior_type in PRIOR_TYPE:
+        for covar_type in COVARIANCE_TYPE:
+            X = rand_data.X[covar_type]
+            Y = rand_data.Y
+            bgmm = BayesianGaussianMixture(
+                n_components=rand_data.n_components,
+                random_state=rng,
+                weight_concentration_prior_type=prior_type,
+                covariance_type=covar_type,
+            )
+
+            # Check a warning message arrive if we don't do fit
+            msg = (
+                "This BayesianGaussianMixture instance is not fitted yet. "
+                "Call 'fit' with appropriate arguments before using this "
+                "estimator."
+            )
+            with pytest.raises(NotFittedError, match=msg):
+                bgmm.predict(X)
+
+            bgmm.fit(X)
+            Y_pred = bgmm.predict(X)
+            Y_pred_proba = bgmm.predict_proba(X).argmax(axis=1)
+            assert_array_equal(Y_pred, Y_pred_proba)
+            assert adjusted_rand_score(Y, Y_pred) >= 0.95
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_gaussian_mixture.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_gaussian_mixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..488a2ab147e8362eede842f64f4787fda47b9159
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_gaussian_mixture.py
@@ -0,0 +1,1473 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import copy
+import itertools
+import re
+import sys
+import warnings
+from io import StringIO
+from unittest.mock import Mock
+
+import numpy as np
+import pytest
+from scipy import linalg, stats
+
+import sklearn
+from sklearn.cluster import KMeans
+from sklearn.covariance import EmpiricalCovariance
+from sklearn.datasets import make_spd_matrix
+from sklearn.exceptions import ConvergenceWarning, NotFittedError
+from sklearn.metrics.cluster import adjusted_rand_score
+from sklearn.mixture import GaussianMixture
+from sklearn.mixture._gaussian_mixture import (
+    _compute_log_det_cholesky,
+    _compute_precision_cholesky,
+    _estimate_gaussian_covariances_diag,
+    _estimate_gaussian_covariances_full,
+    _estimate_gaussian_covariances_spherical,
+    _estimate_gaussian_covariances_tied,
+    _estimate_gaussian_parameters,
+)
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+from sklearn.utils.extmath import fast_logdet
+
+COVARIANCE_TYPE = ["full", "tied", "diag", "spherical"]
+
+
+def generate_data(
+    n_samples, n_features, weights, means, precisions, covariance_type, dtype=np.float64
+):
+    rng = np.random.RandomState(0)
+
+    X = []
+    if covariance_type == "spherical":
+        for _, (w, m, c) in enumerate(zip(weights, means, precisions["spherical"])):
+            X.append(
+                rng.multivariate_normal(
+                    m, c * np.eye(n_features), int(np.round(w * n_samples))
+                ).astype(dtype)
+            )
+    if covariance_type == "diag":
+        for _, (w, m, c) in enumerate(zip(weights, means, precisions["diag"])):
+            X.append(
+                rng.multivariate_normal(
+                    m, np.diag(c), int(np.round(w * n_samples))
+                ).astype(dtype)
+            )
+    if covariance_type == "tied":
+        for _, (w, m) in enumerate(zip(weights, means)):
+            X.append(
+                rng.multivariate_normal(
+                    m, precisions["tied"], int(np.round(w * n_samples))
+                ).astype(dtype)
+            )
+    if covariance_type == "full":
+        for _, (w, m, c) in enumerate(zip(weights, means, precisions["full"])):
+            X.append(
+                rng.multivariate_normal(m, c, int(np.round(w * n_samples))).astype(
+                    dtype
+                )
+            )
+
+    X = np.vstack(X)
+    return X
+
+
+class RandomData:
+    def __init__(
+        self,
+        rng,
+        n_samples=200,
+        n_components=2,
+        n_features=2,
+        scale=50,
+        dtype=np.float64,
+    ):
+        self.n_samples = n_samples
+        self.n_components = n_components
+        self.n_features = n_features
+
+        self.weights = rng.rand(n_components).astype(dtype)
+        self.weights = self.weights.astype(dtype) / self.weights.sum()
+        self.means = rng.rand(n_components, n_features).astype(dtype) * scale
+        self.covariances = {
+            "spherical": 0.5 + rng.rand(n_components).astype(dtype),
+            "diag": (0.5 + rng.rand(n_components, n_features).astype(dtype)) ** 2,
+            "tied": make_spd_matrix(n_features, random_state=rng).astype(dtype),
+            "full": np.array(
+                [
+                    make_spd_matrix(n_features, random_state=rng).astype(dtype) * 0.5
+                    for _ in range(n_components)
+                ]
+            ),
+        }
+        self.precisions = {
+            "spherical": 1.0 / self.covariances["spherical"],
+            "diag": 1.0 / self.covariances["diag"],
+            "tied": linalg.inv(self.covariances["tied"]),
+            "full": np.array(
+                [linalg.inv(covariance) for covariance in self.covariances["full"]]
+            ),
+        }
+
+        self.X = dict(
+            zip(
+                COVARIANCE_TYPE,
+                [
+                    generate_data(
+                        n_samples,
+                        n_features,
+                        self.weights,
+                        self.means,
+                        self.covariances,
+                        covar_type,
+                        dtype=dtype,
+                    )
+                    for covar_type in COVARIANCE_TYPE
+                ],
+            )
+        )
+        self.Y = np.hstack(
+            [
+                np.full(int(np.round(w * n_samples)), k, dtype=int)
+                for k, w in enumerate(self.weights)
+            ]
+        )
+
+
+def test_gaussian_mixture_attributes():
+    # test bad parameters
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 2)
+
+    # test good parameters
+    n_components, tol, n_init, max_iter, reg_covar = 2, 1e-4, 3, 30, 1e-1
+    covariance_type, init_params = "full", "random"
+    gmm = GaussianMixture(
+        n_components=n_components,
+        tol=tol,
+        n_init=n_init,
+        max_iter=max_iter,
+        reg_covar=reg_covar,
+        covariance_type=covariance_type,
+        init_params=init_params,
+    ).fit(X)
+
+    assert gmm.n_components == n_components
+    assert gmm.covariance_type == covariance_type
+    assert gmm.tol == tol
+    assert gmm.reg_covar == reg_covar
+    assert gmm.max_iter == max_iter
+    assert gmm.n_init == n_init
+    assert gmm.init_params == init_params
+
+
+def test_check_weights():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+
+    n_components = rand_data.n_components
+    X = rand_data.X["full"]
+
+    g = GaussianMixture(n_components=n_components)
+
+    # Check bad shape
+    weights_bad_shape = rng.rand(n_components, 1)
+    g.weights_init = weights_bad_shape
+    msg = re.escape(
+        "The parameter 'weights' should have the shape of "
+        f"({n_components},), but got {weights_bad_shape.shape}"
+    )
+    with pytest.raises(ValueError, match=msg):
+        g.fit(X)
+
+    # Check bad range
+    weights_bad_range = rng.rand(n_components) + 1
+    g.weights_init = weights_bad_range
+    msg = re.escape(
+        "The parameter 'weights' should be in the range [0, 1], but got"
+        f" max value {np.min(weights_bad_range):.5f}, "
+        f"min value {np.max(weights_bad_range):.5f}"
+    )
+    with pytest.raises(ValueError, match=msg):
+        g.fit(X)
+
+    # Check bad normalization
+    weights_bad_norm = rng.rand(n_components)
+    weights_bad_norm = weights_bad_norm / (weights_bad_norm.sum() + 1)
+    g.weights_init = weights_bad_norm
+    msg = re.escape(
+        "The parameter 'weights' should be normalized, "
+        f"but got sum(weights) = {np.sum(weights_bad_norm):.5f}"
+    )
+    with pytest.raises(ValueError, match=msg):
+        g.fit(X)
+
+    # Check good weights matrix
+    weights = rand_data.weights
+    g = GaussianMixture(weights_init=weights, n_components=n_components)
+    g.fit(X)
+    assert_array_equal(weights, g.weights_init)
+
+
+def test_check_means():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+
+    n_components, n_features = rand_data.n_components, rand_data.n_features
+    X = rand_data.X["full"]
+
+    g = GaussianMixture(n_components=n_components)
+
+    # Check means bad shape
+    means_bad_shape = rng.rand(n_components + 1, n_features)
+    g.means_init = means_bad_shape
+    msg = "The parameter 'means' should have the shape of "
+    with pytest.raises(ValueError, match=msg):
+        g.fit(X)
+
+    # Check good means matrix
+    means = rand_data.means
+    g.means_init = means
+    g.fit(X)
+    assert_array_equal(means, g.means_init)
+
+
+def test_check_precisions():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+
+    n_components, n_features = rand_data.n_components, rand_data.n_features
+
+    # Define the bad precisions for each covariance_type
+    precisions_bad_shape = {
+        "full": np.ones((n_components + 1, n_features, n_features)),
+        "tied": np.ones((n_features + 1, n_features + 1)),
+        "diag": np.ones((n_components + 1, n_features)),
+        "spherical": np.ones((n_components + 1)),
+    }
+
+    # Define not positive-definite precisions
+    precisions_not_pos = np.ones((n_components, n_features, n_features))
+    precisions_not_pos[0] = np.eye(n_features)
+    precisions_not_pos[0, 0, 0] = -1.0
+
+    precisions_not_positive = {
+        "full": precisions_not_pos,
+        "tied": precisions_not_pos[0],
+        "diag": np.full((n_components, n_features), -1.0),
+        "spherical": np.full(n_components, -1.0),
+    }
+
+    not_positive_errors = {
+        "full": "symmetric, positive-definite",
+        "tied": "symmetric, positive-definite",
+        "diag": "positive",
+        "spherical": "positive",
+    }
+
+    for covar_type in COVARIANCE_TYPE:
+        X = RandomData(rng).X[covar_type]
+        g = GaussianMixture(
+            n_components=n_components, covariance_type=covar_type, random_state=rng
+        )
+
+        # Check precisions with bad shapes
+        g.precisions_init = precisions_bad_shape[covar_type]
+        msg = f"The parameter '{covar_type} precision' should have the shape of"
+        with pytest.raises(ValueError, match=msg):
+            g.fit(X)
+
+        # Check not positive precisions
+        g.precisions_init = precisions_not_positive[covar_type]
+        msg = f"'{covar_type} precision' should be {not_positive_errors[covar_type]}"
+        with pytest.raises(ValueError, match=msg):
+            g.fit(X)
+
+        # Check the correct init of precisions_init
+        g.precisions_init = rand_data.precisions[covar_type]
+        g.fit(X)
+        assert_array_equal(rand_data.precisions[covar_type], g.precisions_init)
+
+
+def test_suffstat_sk_full():
+    # compare the precision matrix compute from the
+    # EmpiricalCovariance.covariance fitted on X*sqrt(resp)
+    # with _sufficient_sk_full, n_components=1
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 500, 2
+
+    # special case 1, assuming data is "centered"
+    X = rng.rand(n_samples, n_features)
+    resp = rng.rand(n_samples, 1)
+    X_resp = np.sqrt(resp) * X
+    nk = np.array([n_samples])
+    xk = np.zeros((1, n_features))
+    covars_pred = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
+    ecov = EmpiricalCovariance(assume_centered=True)
+    ecov.fit(X_resp)
+    assert_almost_equal(ecov.error_norm(covars_pred[0], norm="frobenius"), 0)
+    assert_almost_equal(ecov.error_norm(covars_pred[0], norm="spectral"), 0)
+
+    # check the precision computation
+    precs_chol_pred = _compute_precision_cholesky(covars_pred, "full")
+    precs_pred = np.array([np.dot(prec, prec.T) for prec in precs_chol_pred])
+    precs_est = np.array([linalg.inv(cov) for cov in covars_pred])
+    assert_array_almost_equal(precs_est, precs_pred)
+
+    # special case 2, assuming resp are all ones
+    resp = np.ones((n_samples, 1))
+    nk = np.array([n_samples])
+    xk = X.mean(axis=0).reshape((1, -1))
+    covars_pred = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
+    ecov = EmpiricalCovariance(assume_centered=False)
+    ecov.fit(X)
+    assert_almost_equal(ecov.error_norm(covars_pred[0], norm="frobenius"), 0)
+    assert_almost_equal(ecov.error_norm(covars_pred[0], norm="spectral"), 0)
+
+    # check the precision computation
+    precs_chol_pred = _compute_precision_cholesky(covars_pred, "full")
+    precs_pred = np.array([np.dot(prec, prec.T) for prec in precs_chol_pred])
+    precs_est = np.array([linalg.inv(cov) for cov in covars_pred])
+    assert_array_almost_equal(precs_est, precs_pred)
+
+
+def test_suffstat_sk_tied():
+    # use equation Nk * Sk / N = S_tied
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 500, 2, 2
+
+    resp = rng.rand(n_samples, n_components)
+    resp = resp / resp.sum(axis=1)[:, np.newaxis]
+    X = rng.rand(n_samples, n_features)
+    nk = resp.sum(axis=0)
+    xk = np.dot(resp.T, X) / nk[:, np.newaxis]
+
+    covars_pred_full = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
+    covars_pred_full = (
+        np.sum(nk[:, np.newaxis, np.newaxis] * covars_pred_full, 0) / n_samples
+    )
+
+    covars_pred_tied = _estimate_gaussian_covariances_tied(resp, X, nk, xk, 0)
+
+    ecov = EmpiricalCovariance()
+    ecov.covariance_ = covars_pred_full
+    assert_almost_equal(ecov.error_norm(covars_pred_tied, norm="frobenius"), 0)
+    assert_almost_equal(ecov.error_norm(covars_pred_tied, norm="spectral"), 0)
+
+    # check the precision computation
+    precs_chol_pred = _compute_precision_cholesky(covars_pred_tied, "tied")
+    precs_pred = np.dot(precs_chol_pred, precs_chol_pred.T)
+    precs_est = linalg.inv(covars_pred_tied)
+    assert_array_almost_equal(precs_est, precs_pred)
+
+
+def test_suffstat_sk_diag():
+    # test against 'full' case
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 500, 2, 2
+
+    resp = rng.rand(n_samples, n_components)
+    resp = resp / resp.sum(axis=1)[:, np.newaxis]
+    X = rng.rand(n_samples, n_features)
+    nk = resp.sum(axis=0)
+    xk = np.dot(resp.T, X) / nk[:, np.newaxis]
+    covars_pred_full = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
+    covars_pred_diag = _estimate_gaussian_covariances_diag(resp, X, nk, xk, 0)
+
+    ecov = EmpiricalCovariance()
+    for cov_full, cov_diag in zip(covars_pred_full, covars_pred_diag):
+        ecov.covariance_ = np.diag(np.diag(cov_full))
+        cov_diag = np.diag(cov_diag)
+        assert_almost_equal(ecov.error_norm(cov_diag, norm="frobenius"), 0)
+        assert_almost_equal(ecov.error_norm(cov_diag, norm="spectral"), 0)
+
+    # check the precision computation
+    precs_chol_pred = _compute_precision_cholesky(covars_pred_diag, "diag")
+    assert_almost_equal(covars_pred_diag, 1.0 / precs_chol_pred**2)
+
+
+def test_gaussian_suffstat_sk_spherical(global_dtype):
+    # computing spherical covariance equals to the variance of one-dimension
+    # data after flattening, n_components=1
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 500, 2
+
+    X = rng.rand(n_samples, n_features).astype(global_dtype)
+    X = X - X.mean()
+    resp = np.ones((n_samples, 1), dtype=global_dtype)
+    nk = np.array([n_samples], dtype=global_dtype)
+    xk = X.mean()
+    covars_pred_spherical = _estimate_gaussian_covariances_spherical(resp, X, nk, xk, 0)
+    covars_pred_spherical2 = np.dot(X.flatten().T, X.flatten()) / (
+        n_features * n_samples
+    )
+    assert_almost_equal(covars_pred_spherical, covars_pred_spherical2)
+    assert covars_pred_spherical.dtype == global_dtype
+
+    # check the precision computation
+    precs_chol_pred = _compute_precision_cholesky(covars_pred_spherical, "spherical")
+    assert_almost_equal(covars_pred_spherical, 1.0 / precs_chol_pred**2)
+    assert precs_chol_pred.dtype == global_dtype
+
+
+def test_compute_log_det_cholesky(global_dtype):
+    n_features = 2
+    rand_data = RandomData(np.random.RandomState(0), dtype=global_dtype)
+
+    for covar_type in COVARIANCE_TYPE:
+        covariance = rand_data.covariances[covar_type]
+
+        if covar_type == "full":
+            predected_det = np.array([linalg.det(cov) for cov in covariance])
+        elif covar_type == "tied":
+            predected_det = linalg.det(covariance)
+        elif covar_type == "diag":
+            predected_det = np.array([np.prod(cov) for cov in covariance])
+        elif covar_type == "spherical":
+            predected_det = covariance**n_features
+
+        # We compute the cholesky decomposition of the covariance matrix
+        assert covariance.dtype == global_dtype
+        expected_det = _compute_log_det_cholesky(
+            _compute_precision_cholesky(covariance, covar_type),
+            covar_type,
+            n_features=n_features,
+        )
+        assert_array_almost_equal(expected_det, -0.5 * np.log(predected_det))
+        assert expected_det.dtype == global_dtype
+
+
+def _naive_lmvnpdf_diag(X, means, covars):
+    resp = np.empty((len(X), len(means)))
+    stds = np.sqrt(covars)
+    for i, (mean, std) in enumerate(zip(means, stds)):
+        resp[:, i] = stats.norm.logpdf(X, mean, std).sum(axis=1)
+    return resp
+
+
+def test_gaussian_mixture_log_probabilities():
+    from sklearn.mixture._gaussian_mixture import _estimate_log_gaussian_prob
+
+    # test against with _naive_lmvnpdf_diag
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    n_samples = 500
+    n_features = rand_data.n_features
+    n_components = rand_data.n_components
+
+    means = rand_data.means
+    covars_diag = rng.rand(n_components, n_features)
+    X = rng.rand(n_samples, n_features)
+    log_prob_naive = _naive_lmvnpdf_diag(X, means, covars_diag)
+
+    # full covariances
+    precs_full = np.array([np.diag(1.0 / np.sqrt(x)) for x in covars_diag])
+
+    log_prob = _estimate_log_gaussian_prob(X, means, precs_full, "full")
+    assert_array_almost_equal(log_prob, log_prob_naive)
+
+    # diag covariances
+    precs_chol_diag = 1.0 / np.sqrt(covars_diag)
+    log_prob = _estimate_log_gaussian_prob(X, means, precs_chol_diag, "diag")
+    assert_array_almost_equal(log_prob, log_prob_naive)
+
+    # tied
+    covars_tied = np.array([x for x in covars_diag]).mean(axis=0)
+    precs_tied = np.diag(np.sqrt(1.0 / covars_tied))
+
+    log_prob_naive = _naive_lmvnpdf_diag(X, means, [covars_tied] * n_components)
+    log_prob = _estimate_log_gaussian_prob(X, means, precs_tied, "tied")
+
+    assert_array_almost_equal(log_prob, log_prob_naive)
+
+    # spherical
+    covars_spherical = covars_diag.mean(axis=1)
+    precs_spherical = 1.0 / np.sqrt(covars_diag.mean(axis=1))
+    log_prob_naive = _naive_lmvnpdf_diag(
+        X, means, [[k] * n_features for k in covars_spherical]
+    )
+    log_prob = _estimate_log_gaussian_prob(X, means, precs_spherical, "spherical")
+    assert_array_almost_equal(log_prob, log_prob_naive)
+
+
+# skip tests on weighted_log_probabilities, log_weights
+
+
+def test_gaussian_mixture_estimate_log_prob_resp():
+    # test whether responsibilities are normalized
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=5)
+    n_samples = rand_data.n_samples
+    n_features = rand_data.n_features
+    n_components = rand_data.n_components
+
+    X = rng.rand(n_samples, n_features)
+    for covar_type in COVARIANCE_TYPE:
+        weights = rand_data.weights
+        means = rand_data.means
+        precisions = rand_data.precisions[covar_type]
+        g = GaussianMixture(
+            n_components=n_components,
+            random_state=rng,
+            weights_init=weights,
+            means_init=means,
+            precisions_init=precisions,
+            covariance_type=covar_type,
+        )
+        g.fit(X)
+        resp = g.predict_proba(X)
+        assert_array_almost_equal(resp.sum(axis=1), np.ones(n_samples))
+        assert_array_equal(g.weights_init, weights)
+        assert_array_equal(g.means_init, means)
+        assert_array_equal(g.precisions_init, precisions)
+
+
+def test_gaussian_mixture_predict_predict_proba():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        Y = rand_data.Y
+        g = GaussianMixture(
+            n_components=rand_data.n_components,
+            random_state=rng,
+            weights_init=rand_data.weights,
+            means_init=rand_data.means,
+            precisions_init=rand_data.precisions[covar_type],
+            covariance_type=covar_type,
+        )
+
+        # Check a warning message arrive if we don't do fit
+        msg = (
+            "This GaussianMixture instance is not fitted yet. Call 'fit' "
+            "with appropriate arguments before using this estimator."
+        )
+        with pytest.raises(NotFittedError, match=msg):
+            g.predict(X)
+
+        g.fit(X)
+        Y_pred = g.predict(X)
+        Y_pred_proba = g.predict_proba(X).argmax(axis=1)
+        assert_array_equal(Y_pred, Y_pred_proba)
+        assert adjusted_rand_score(Y, Y_pred) > 0.95
+
+
+@pytest.mark.filterwarnings("ignore:.*did not converge.*")
+@pytest.mark.parametrize(
+    "seed, max_iter, tol",
+    [
+        (0, 2, 1e-7),  # strict non-convergence
+        (1, 2, 1e-1),  # loose non-convergence
+        (3, 300, 1e-7),  # strict convergence
+        (4, 300, 1e-1),  # loose convergence
+    ],
+)
+def test_gaussian_mixture_fit_predict(seed, max_iter, tol, global_dtype):
+    rng = np.random.RandomState(seed)
+    rand_data = RandomData(rng, dtype=global_dtype)
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        Y = rand_data.Y
+        g = GaussianMixture(
+            n_components=rand_data.n_components,
+            random_state=rng,
+            weights_init=rand_data.weights,
+            means_init=rand_data.means,
+            precisions_init=rand_data.precisions[covar_type],
+            covariance_type=covar_type,
+            max_iter=max_iter,
+            tol=tol,
+        )
+
+        # check if fit_predict(X) is equivalent to fit(X).predict(X)
+        f = copy.deepcopy(g)
+        Y_pred1 = f.fit(X).predict(X)
+        Y_pred2 = g.fit_predict(X)
+        assert_array_equal(Y_pred1, Y_pred2)
+        assert adjusted_rand_score(Y, Y_pred2) > 0.95
+        assert g.means_.dtype == global_dtype
+        assert g.weights_.dtype == global_dtype
+        assert g.precisions_.dtype == global_dtype
+
+
+def test_gaussian_mixture_fit_predict_n_init():
+    # Check that fit_predict is equivalent to fit.predict, when n_init > 1
+    X = np.random.RandomState(0).randn(1000, 5)
+    gm = GaussianMixture(n_components=5, n_init=5, random_state=0)
+    y_pred1 = gm.fit_predict(X)
+    y_pred2 = gm.predict(X)
+    assert_array_equal(y_pred1, y_pred2)
+
+
+def test_gaussian_mixture_fit(global_dtype):
+    # recover the ground truth
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, dtype=global_dtype)
+    n_features = rand_data.n_features
+    n_components = rand_data.n_components
+
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        g = GaussianMixture(
+            n_components=n_components,
+            n_init=20,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+        )
+        g.fit(X)
+
+        # needs more data to pass the test with rtol=1e-7
+        assert_allclose(
+            np.sort(g.weights_), np.sort(rand_data.weights), rtol=0.1, atol=1e-2
+        )
+
+        arg_idx1 = g.means_[:, 0].argsort()
+        arg_idx2 = rand_data.means[:, 0].argsort()
+        assert_allclose(
+            g.means_[arg_idx1], rand_data.means[arg_idx2], rtol=0.1, atol=1e-2
+        )
+
+        if covar_type == "full":
+            prec_pred = g.precisions_
+            prec_test = rand_data.precisions["full"]
+        elif covar_type == "tied":
+            prec_pred = np.array([g.precisions_] * n_components)
+            prec_test = np.array([rand_data.precisions["tied"]] * n_components)
+        elif covar_type == "spherical":
+            prec_pred = np.array([np.eye(n_features) * c for c in g.precisions_])
+            prec_test = np.array(
+                [np.eye(n_features) * c for c in rand_data.precisions["spherical"]]
+            )
+        elif covar_type == "diag":
+            prec_pred = np.array([np.diag(d) for d in g.precisions_])
+            prec_test = np.array([np.diag(d) for d in rand_data.precisions["diag"]])
+
+        arg_idx1 = np.trace(prec_pred, axis1=1, axis2=2).argsort()
+        arg_idx2 = np.trace(prec_test, axis1=1, axis2=2).argsort()
+        for k, h in zip(arg_idx1, arg_idx2):
+            ecov = EmpiricalCovariance()
+            ecov.covariance_ = prec_test[h]
+            # the accuracy depends on the number of data and randomness, rng
+            assert_allclose(ecov.error_norm(prec_pred[k]), 0, atol=0.15)
+
+        assert g.means_.dtype == global_dtype
+        assert g.covariances_.dtype == global_dtype
+        assert g.precisions_.dtype == global_dtype
+
+
+def test_gaussian_mixture_fit_best_params():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    n_components = rand_data.n_components
+    n_init = 10
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        g = GaussianMixture(
+            n_components=n_components,
+            n_init=1,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+        )
+        ll = []
+        for _ in range(n_init):
+            g.fit(X)
+            ll.append(g.score(X))
+        ll = np.array(ll)
+        g_best = GaussianMixture(
+            n_components=n_components,
+            n_init=n_init,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+        )
+        g_best.fit(X)
+        assert_almost_equal(ll.min(), g_best.score(X))
+
+
+def test_gaussian_mixture_fit_convergence_warning():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=1)
+    n_components = rand_data.n_components
+    max_iter = 1
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        g = GaussianMixture(
+            n_components=n_components,
+            n_init=1,
+            max_iter=max_iter,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+        )
+        msg = (
+            "Best performing initialization did not converge. "
+            "Try different init parameters, or increase max_iter, "
+            "tol, or check for degenerate data."
+        )
+        with pytest.warns(ConvergenceWarning, match=msg):
+            g.fit(X)
+
+
+def test_multiple_init():
+    # Test that multiple inits does not much worse than a single one
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 50, 5, 2
+    X = rng.randn(n_samples, n_features)
+    for cv_type in COVARIANCE_TYPE:
+        train1 = (
+            GaussianMixture(
+                n_components=n_components, covariance_type=cv_type, random_state=0
+            )
+            .fit(X)
+            .score(X)
+        )
+        train2 = (
+            GaussianMixture(
+                n_components=n_components,
+                covariance_type=cv_type,
+                random_state=0,
+                n_init=5,
+            )
+            .fit(X)
+            .score(X)
+        )
+        assert train2 >= train1
+
+
+def test_gaussian_mixture_n_parameters():
+    # Test that the right number of parameters is estimated
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 50, 5, 2
+    X = rng.randn(n_samples, n_features)
+    n_params = {"spherical": 13, "diag": 21, "tied": 26, "full": 41}
+    for cv_type in COVARIANCE_TYPE:
+        g = GaussianMixture(
+            n_components=n_components, covariance_type=cv_type, random_state=rng
+        ).fit(X)
+        assert g._n_parameters() == n_params[cv_type]
+
+
+def test_bic_1d_1component():
+    # Test all of the covariance_types return the same BIC score for
+    # 1-dimensional, 1 component fits.
+    rng = np.random.RandomState(0)
+    n_samples, n_dim, n_components = 100, 1, 1
+    X = rng.randn(n_samples, n_dim)
+    bic_full = (
+        GaussianMixture(
+            n_components=n_components, covariance_type="full", random_state=rng
+        )
+        .fit(X)
+        .bic(X)
+    )
+    for covariance_type in ["tied", "diag", "spherical"]:
+        bic = (
+            GaussianMixture(
+                n_components=n_components,
+                covariance_type=covariance_type,
+                random_state=rng,
+            )
+            .fit(X)
+            .bic(X)
+        )
+        assert_almost_equal(bic_full, bic)
+
+
+def test_gaussian_mixture_aic_bic():
+    # Test the aic and bic criteria
+    rng = np.random.RandomState(0)
+    n_samples, n_features, n_components = 50, 3, 2
+    X = rng.randn(n_samples, n_features)
+    # standard gaussian entropy
+    sgh = 0.5 * (
+        fast_logdet(np.cov(X.T, bias=1)) + n_features * (1 + np.log(2 * np.pi))
+    )
+    for cv_type in COVARIANCE_TYPE:
+        g = GaussianMixture(
+            n_components=n_components,
+            covariance_type=cv_type,
+            random_state=rng,
+            max_iter=200,
+        )
+        g.fit(X)
+        aic = 2 * n_samples * sgh + 2 * g._n_parameters()
+        bic = 2 * n_samples * sgh + np.log(n_samples) * g._n_parameters()
+        bound = n_features / np.sqrt(n_samples)
+        assert (g.aic(X) - aic) / n_samples < bound
+        assert (g.bic(X) - bic) / n_samples < bound
+
+
+def test_gaussian_mixture_verbose():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    n_components = rand_data.n_components
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        g = GaussianMixture(
+            n_components=n_components,
+            n_init=1,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+            verbose=1,
+        )
+        h = GaussianMixture(
+            n_components=n_components,
+            n_init=1,
+            reg_covar=0,
+            random_state=rng,
+            covariance_type=covar_type,
+            verbose=2,
+        )
+        old_stdout = sys.stdout
+        sys.stdout = StringIO()
+        try:
+            g.fit(X)
+            h.fit(X)
+        finally:
+            sys.stdout = old_stdout
+
+
+@pytest.mark.filterwarnings("ignore:.*did not converge.*")
+@pytest.mark.parametrize("seed", (0, 1, 2))
+def test_warm_start(seed):
+    random_state = seed
+    rng = np.random.RandomState(random_state)
+    n_samples, n_features, n_components = 500, 2, 2
+    X = rng.rand(n_samples, n_features)
+
+    # Assert the warm_start give the same result for the same number of iter
+    g = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        max_iter=2,
+        reg_covar=0,
+        random_state=random_state,
+        warm_start=False,
+    )
+    h = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        max_iter=1,
+        reg_covar=0,
+        random_state=random_state,
+        warm_start=True,
+    )
+
+    g.fit(X)
+    score1 = h.fit(X).score(X)
+    score2 = h.fit(X).score(X)
+
+    assert_almost_equal(g.weights_, h.weights_)
+    assert_almost_equal(g.means_, h.means_)
+    assert_almost_equal(g.precisions_, h.precisions_)
+    assert score2 > score1
+
+    # Assert that by using warm_start we can converge to a good solution
+    g = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        max_iter=5,
+        reg_covar=0,
+        random_state=random_state,
+        warm_start=False,
+        tol=1e-6,
+    )
+    h = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        max_iter=5,
+        reg_covar=0,
+        random_state=random_state,
+        warm_start=True,
+        tol=1e-6,
+    )
+
+    g.fit(X)
+    assert not g.converged_
+
+    h.fit(X)
+    # depending on the data there is large variability in the number of
+    # refit necessary to converge due to the complete randomness of the
+    # data
+    for _ in range(1000):
+        h.fit(X)
+        if h.converged_:
+            break
+    assert h.converged_
+
+
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+def test_convergence_detected_with_warm_start():
+    # We check that convergence is detected when warm_start=True
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng)
+    n_components = rand_data.n_components
+    X = rand_data.X["full"]
+
+    for max_iter in (1, 2, 50):
+        gmm = GaussianMixture(
+            n_components=n_components,
+            warm_start=True,
+            max_iter=max_iter,
+            random_state=rng,
+        )
+        for _ in range(100):
+            gmm.fit(X)
+            if gmm.converged_:
+                break
+        assert gmm.converged_
+        assert max_iter >= gmm.n_iter_
+
+
+def test_score(global_dtype):
+    covar_type = "full"
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7, dtype=global_dtype)
+    n_components = rand_data.n_components
+    X = rand_data.X[covar_type]
+    assert X.dtype == global_dtype
+
+    # Check the error message if we don't call fit
+    gmm1 = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        max_iter=1,
+        reg_covar=0,
+        random_state=rng,
+        covariance_type=covar_type,
+    )
+    msg = (
+        "This GaussianMixture instance is not fitted yet. Call 'fit' with "
+        "appropriate arguments before using this estimator."
+    )
+    with pytest.raises(NotFittedError, match=msg):
+        gmm1.score(X)
+
+    # Check score value
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", ConvergenceWarning)
+        gmm1.fit(X)
+
+    assert gmm1.means_.dtype == global_dtype
+    assert gmm1.covariances_.dtype == global_dtype
+
+    gmm_score = gmm1.score(X)
+    gmm_score_proba = gmm1.score_samples(X).mean()
+    assert_almost_equal(gmm_score, gmm_score_proba)
+    assert gmm_score_proba.dtype == global_dtype
+
+    # Check if the score increase
+    gmm2 = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        reg_covar=0,
+        random_state=rng,
+        covariance_type=covar_type,
+    ).fit(X)
+    assert gmm2.score(X) > gmm1.score(X)
+
+
+def test_score_samples():
+    covar_type = "full"
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7)
+    n_components = rand_data.n_components
+    X = rand_data.X[covar_type]
+
+    # Check the error message if we don't call fit
+    gmm = GaussianMixture(
+        n_components=n_components,
+        n_init=1,
+        reg_covar=0,
+        random_state=rng,
+        covariance_type=covar_type,
+    )
+    msg = (
+        "This GaussianMixture instance is not fitted yet. Call 'fit' with "
+        "appropriate arguments before using this estimator."
+    )
+    with pytest.raises(NotFittedError, match=msg):
+        gmm.score_samples(X)
+
+    gmm_score_samples = gmm.fit(X).score_samples(X)
+    assert gmm_score_samples.shape[0] == rand_data.n_samples
+
+
+def test_monotonic_likelihood():
+    # We check that each step of the EM without regularization improve
+    # monotonically the training set likelihood
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7)
+    n_components = rand_data.n_components
+
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+        gmm = GaussianMixture(
+            n_components=n_components,
+            covariance_type=covar_type,
+            reg_covar=0,
+            warm_start=True,
+            max_iter=1,
+            random_state=rng,
+            tol=1e-7,
+        )
+        current_log_likelihood = -np.inf
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            # Do one training iteration at a time so we can make sure that the
+            # training log likelihood increases after each iteration.
+            for _ in range(600):
+                prev_log_likelihood = current_log_likelihood
+                current_log_likelihood = gmm.fit(X).score(X)
+                assert current_log_likelihood >= prev_log_likelihood
+
+                if gmm.converged_:
+                    break
+
+            assert gmm.converged_
+
+
+def test_regularisation():
+    # We train the GaussianMixture on degenerate data by defining two clusters
+    # of a 0 covariance.
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 10, 5
+
+    X = np.vstack(
+        (np.ones((n_samples // 2, n_features)), np.zeros((n_samples // 2, n_features)))
+    )
+
+    for covar_type in COVARIANCE_TYPE:
+        gmm = GaussianMixture(
+            n_components=n_samples,
+            reg_covar=0,
+            covariance_type=covar_type,
+            random_state=rng,
+        )
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            msg = re.escape(
+                "Fitting the mixture model failed because some components have"
+                " ill-defined empirical covariance (for instance caused by "
+                "singleton or collapsed samples). Try to decrease the number "
+                "of components, increase reg_covar, or scale the input data."
+            )
+            with pytest.raises(ValueError, match=msg):
+                gmm.fit(X)
+
+            gmm.set_params(reg_covar=1e-6).fit(X)
+
+
+@pytest.mark.parametrize("covar_type", COVARIANCE_TYPE)
+def test_fitted_precision_covariance_concistency(covar_type, global_dtype):
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7, dtype=global_dtype)
+    n_components = rand_data.n_components
+
+    X = rand_data.X[covar_type]
+    gmm = GaussianMixture(
+        n_components=n_components,
+        covariance_type=covar_type,
+        random_state=rng,
+        n_init=5,
+    )
+    gmm.fit(X)
+    assert gmm.precisions_.dtype == global_dtype
+    assert gmm.covariances_.dtype == global_dtype
+    if covar_type == "full":
+        for prec, covar in zip(gmm.precisions_, gmm.covariances_):
+            assert_array_almost_equal(linalg.inv(prec), covar)
+    elif covar_type == "tied":
+        assert_array_almost_equal(linalg.inv(gmm.precisions_), gmm.covariances_)
+    else:
+        assert_array_almost_equal(gmm.precisions_, 1.0 / gmm.covariances_)
+
+
+def test_sample():
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=7, n_components=3)
+    n_features, n_components = rand_data.n_features, rand_data.n_components
+
+    for covar_type in COVARIANCE_TYPE:
+        X = rand_data.X[covar_type]
+
+        gmm = GaussianMixture(
+            n_components=n_components, covariance_type=covar_type, random_state=rng
+        )
+        # To sample we need that GaussianMixture is fitted
+        msg = "This GaussianMixture instance is not fitted"
+        with pytest.raises(NotFittedError, match=msg):
+            gmm.sample(0)
+        gmm.fit(X)
+
+        msg = "Invalid value for 'n_samples'"
+        with pytest.raises(ValueError, match=msg):
+            gmm.sample(0)
+
+        # Just to make sure the class samples correctly
+        n_samples = 20000
+        X_s, y_s = gmm.sample(n_samples)
+
+        for k in range(n_components):
+            if covar_type == "full":
+                assert_array_almost_equal(
+                    gmm.covariances_[k], np.cov(X_s[y_s == k].T), decimal=1
+                )
+            elif covar_type == "tied":
+                assert_array_almost_equal(
+                    gmm.covariances_, np.cov(X_s[y_s == k].T), decimal=1
+                )
+            elif covar_type == "diag":
+                assert_array_almost_equal(
+                    gmm.covariances_[k], np.diag(np.cov(X_s[y_s == k].T)), decimal=1
+                )
+            else:
+                assert_array_almost_equal(
+                    gmm.covariances_[k],
+                    np.var(X_s[y_s == k] - gmm.means_[k]),
+                    decimal=1,
+                )
+
+        means_s = np.array([np.mean(X_s[y_s == k], 0) for k in range(n_components)])
+        assert_array_almost_equal(gmm.means_, means_s, decimal=1)
+
+        # Check shapes of sampled data, see
+        # https://github.com/scikit-learn/scikit-learn/issues/7701
+        assert X_s.shape == (n_samples, n_features)
+
+        for sample_size in range(1, 100):
+            X_s, _ = gmm.sample(sample_size)
+            assert X_s.shape == (sample_size, n_features)
+
+
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+def test_init():
+    # We check that by increasing the n_init number we have a better solution
+    for random_state in range(15):
+        rand_data = RandomData(
+            np.random.RandomState(random_state), n_samples=50, scale=1
+        )
+        n_components = rand_data.n_components
+        X = rand_data.X["full"]
+
+        gmm1 = GaussianMixture(
+            n_components=n_components, n_init=1, max_iter=1, random_state=random_state
+        ).fit(X)
+        gmm2 = GaussianMixture(
+            n_components=n_components, n_init=10, max_iter=1, random_state=random_state
+        ).fit(X)
+
+        assert gmm2.lower_bound_ >= gmm1.lower_bound_
+
+
+def test_gaussian_mixture_setting_best_params():
+    """`GaussianMixture`'s best_parameters, `n_iter_` and `lower_bound_`
+    must be set appropriately in the case of divergence.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/18216
+    """
+    rnd = np.random.RandomState(0)
+    n_samples = 30
+    X = rnd.uniform(size=(n_samples, 3))
+
+    # following initialization parameters were found to lead to divergence
+    means_init = np.array(
+        [
+            [0.670637869618158, 0.21038256107384043, 0.12892629765485303],
+            [0.09394051075844147, 0.5759464955561779, 0.929296197576212],
+            [0.5033230372781258, 0.9569852381759425, 0.08654043447295741],
+            [0.18578301420435747, 0.5531158970919143, 0.19388943970532435],
+            [0.4548589928173794, 0.35182513658825276, 0.568146063202464],
+            [0.609279894978321, 0.7929063819678847, 0.9620097270828052],
+        ]
+    )
+    precisions_init = np.array(
+        [
+            999999.999604483,
+            999999.9990869573,
+            553.7603944542167,
+            204.78596008931834,
+            15.867423501783637,
+            85.4595728389735,
+        ]
+    )
+    weights_init = [
+        0.03333333333333341,
+        0.03333333333333341,
+        0.06666666666666674,
+        0.06666666666666674,
+        0.7000000000000001,
+        0.10000000000000007,
+    ]
+
+    gmm = GaussianMixture(
+        covariance_type="spherical",
+        reg_covar=0,
+        means_init=means_init,
+        weights_init=weights_init,
+        random_state=rnd,
+        n_components=len(weights_init),
+        precisions_init=precisions_init,
+        max_iter=1,
+    )
+    # ensure that no error is thrown during fit
+    gmm.fit(X)
+
+    # check that the fit did not converge
+    assert not gmm.converged_
+
+    # check that parameters are set for gmm
+    for attr in [
+        "weights_",
+        "means_",
+        "covariances_",
+        "precisions_cholesky_",
+        "n_iter_",
+        "lower_bound_",
+        "lower_bounds_",
+    ]:
+        assert hasattr(gmm, attr)
+
+
+@pytest.mark.parametrize(
+    "init_params", ["random", "random_from_data", "k-means++", "kmeans"]
+)
+def test_init_means_not_duplicated(init_params, global_random_seed):
+    # Check that all initialisations provide not duplicated starting means
+    rng = np.random.RandomState(global_random_seed)
+    rand_data = RandomData(rng, scale=5)
+    n_components = rand_data.n_components
+    X = rand_data.X["full"]
+
+    gmm = GaussianMixture(
+        n_components=n_components, init_params=init_params, random_state=rng, max_iter=0
+    )
+    gmm.fit(X)
+
+    means = gmm.means_
+    for i_mean, j_mean in itertools.combinations(means, r=2):
+        assert not np.allclose(i_mean, j_mean)
+
+
+@pytest.mark.parametrize(
+    "init_params", ["random", "random_from_data", "k-means++", "kmeans"]
+)
+def test_means_for_all_inits(init_params, global_random_seed, global_dtype):
+    # Check fitted means properties for all initializations
+    rng = np.random.RandomState(global_random_seed)
+    rand_data = RandomData(rng, scale=5, dtype=global_dtype)
+    n_components = rand_data.n_components
+    X = rand_data.X["full"]
+
+    gmm = GaussianMixture(
+        n_components=n_components, init_params=init_params, random_state=rng
+    )
+    gmm.fit(X)
+
+    assert gmm.means_.shape == (n_components, X.shape[1])
+    assert np.all(X.min(axis=0) <= gmm.means_)
+    assert np.all(gmm.means_ <= X.max(axis=0))
+    assert gmm.converged_
+    assert gmm.means_.dtype == global_dtype
+    assert gmm.covariances_.dtype == global_dtype
+    assert gmm.weights_.dtype == global_dtype
+
+
+def test_max_iter_zero():
+    # Check that max_iter=0 returns initialisation as expected
+    # Pick arbitrary initial means and check equal to max_iter=0
+    rng = np.random.RandomState(0)
+    rand_data = RandomData(rng, scale=5)
+    n_components = rand_data.n_components
+    X = rand_data.X["full"]
+    means_init = [[20, 30], [30, 25]]
+    gmm = GaussianMixture(
+        n_components=n_components,
+        random_state=rng,
+        means_init=means_init,
+        tol=1e-06,
+        max_iter=0,
+    )
+    gmm.fit(X)
+
+    assert_allclose(gmm.means_, means_init)
+
+
+def test_gaussian_mixture_precisions_init_diag(global_dtype):
+    """Check that we properly initialize `precision_cholesky_` when we manually
+    provide the precision matrix.
+
+    In this regard, we check the consistency between estimating the precision
+    matrix and providing the same precision matrix as initialization. It should
+    lead to the same results with the same number of iterations.
+
+    If the initialization is wrong then the number of iterations will increase.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/16944
+    """
+    # generate a toy dataset
+    n_samples = 300
+    rng = np.random.RandomState(0)
+    shifted_gaussian = rng.randn(n_samples, 2) + np.array([20, 20])
+    C = np.array([[0.0, -0.7], [3.5, 0.7]])
+    stretched_gaussian = np.dot(rng.randn(n_samples, 2), C)
+    X = np.vstack([shifted_gaussian, stretched_gaussian]).astype(global_dtype)
+
+    # common parameters to check the consistency of precision initialization
+    n_components, covariance_type, reg_covar, random_state = 2, "diag", 1e-6, 0
+
+    # execute the manual initialization to compute the precision matrix:
+    # - run KMeans to have an initial guess
+    # - estimate the covariance
+    # - compute the precision matrix from the estimated covariance
+    resp = np.zeros((X.shape[0], n_components)).astype(global_dtype)
+    label = (
+        KMeans(n_clusters=n_components, n_init=1, random_state=random_state)
+        .fit(X)
+        .labels_
+    )
+    resp[np.arange(X.shape[0]), label] = 1
+    _, _, covariance = _estimate_gaussian_parameters(
+        X, resp, reg_covar=reg_covar, covariance_type=covariance_type
+    )
+    assert covariance.dtype == global_dtype
+    precisions_init = 1 / covariance
+
+    gm_with_init = GaussianMixture(
+        n_components=n_components,
+        covariance_type=covariance_type,
+        reg_covar=reg_covar,
+        precisions_init=precisions_init,
+        random_state=random_state,
+    ).fit(X)
+    assert gm_with_init.means_.dtype == global_dtype
+    assert gm_with_init.covariances_.dtype == global_dtype
+    assert gm_with_init.precisions_cholesky_.dtype == global_dtype
+
+    gm_without_init = GaussianMixture(
+        n_components=n_components,
+        covariance_type=covariance_type,
+        reg_covar=reg_covar,
+        random_state=random_state,
+    ).fit(X)
+    assert gm_without_init.means_.dtype == global_dtype
+    assert gm_without_init.covariances_.dtype == global_dtype
+    assert gm_without_init.precisions_cholesky_.dtype == global_dtype
+
+    assert gm_without_init.n_iter_ == gm_with_init.n_iter_
+    assert_allclose(
+        gm_with_init.precisions_cholesky_, gm_without_init.precisions_cholesky_
+    )
+
+
+def _generate_data(seed, n_samples, n_features, n_components, dtype=np.float64):
+    """Randomly generate samples and responsibilities."""
+    rs = np.random.RandomState(seed)
+    X = rs.random_sample((n_samples, n_features)).astype(dtype)
+    resp = rs.random_sample((n_samples, n_components)).astype(dtype)
+    resp /= resp.sum(axis=1)[:, np.newaxis]
+    return X, resp
+
+
+def _calculate_precisions(X, resp, covariance_type):
+    """Calculate precision matrix of X and its Cholesky decomposition
+    for the given covariance type.
+    """
+    reg_covar = 1e-6
+    weights, means, covariances = _estimate_gaussian_parameters(
+        X, resp, reg_covar, covariance_type
+    )
+    precisions_cholesky = _compute_precision_cholesky(covariances, covariance_type)
+
+    _, n_components = resp.shape
+    # Instantiate a `GaussianMixture` model in order to use its
+    # `_set_parameters` method to return the `precisions_` and
+    #  `precisions_cholesky_` from matching the `covariance_type`
+    # provided.
+    gmm = GaussianMixture(n_components=n_components, covariance_type=covariance_type)
+    params = (weights, means, covariances, precisions_cholesky)
+    gmm._set_parameters(params)
+    return gmm.precisions_, gmm.precisions_cholesky_
+
+
+@pytest.mark.parametrize("covariance_type", COVARIANCE_TYPE)
+def test_gaussian_mixture_precisions_init(
+    covariance_type, global_random_seed, global_dtype
+):
+    """Non-regression test for #26415."""
+
+    X, resp = _generate_data(
+        seed=global_random_seed,
+        n_samples=100,
+        n_features=3,
+        n_components=4,
+        dtype=global_dtype,
+    )
+
+    precisions_init, desired_precisions_cholesky = _calculate_precisions(
+        X, resp, covariance_type
+    )
+    assert precisions_init.dtype == global_dtype
+    assert desired_precisions_cholesky.dtype == global_dtype
+
+    gmm = GaussianMixture(
+        covariance_type=covariance_type, precisions_init=precisions_init
+    )
+    gmm._initialize(X, resp)
+    actual_precisions_cholesky = gmm.precisions_cholesky_
+    assert_allclose(actual_precisions_cholesky, desired_precisions_cholesky)
+
+
+def test_gaussian_mixture_single_component_stable():
+    """
+    Non-regression test for #23032 ensuring 1-component GM works on only a
+    few samples.
+    """
+    rng = np.random.RandomState(0)
+    X = rng.multivariate_normal(np.zeros(2), np.identity(2), size=3)
+    gm = GaussianMixture(n_components=1)
+    gm.fit(X).sample()
+
+
+def test_gaussian_mixture_all_init_does_not_estimate_gaussian_parameters(
+    monkeypatch,
+    global_random_seed,
+):
+    """When all init parameters are provided, the Gaussian parameters
+    are not estimated.
+
+    Non-regression test for gh-26015.
+    """
+
+    mock = Mock(side_effect=_estimate_gaussian_parameters)
+    monkeypatch.setattr(
+        sklearn.mixture._gaussian_mixture, "_estimate_gaussian_parameters", mock
+    )
+
+    rng = np.random.RandomState(global_random_seed)
+    rand_data = RandomData(rng)
+
+    gm = GaussianMixture(
+        n_components=rand_data.n_components,
+        weights_init=rand_data.weights,
+        means_init=rand_data.means,
+        precisions_init=rand_data.precisions["full"],
+        random_state=rng,
+    )
+    gm.fit(rand_data.X["full"])
+    # The initial gaussian parameters are not estimated. They are estimated for every
+    # m_step.
+    assert mock.call_count == gm.n_iter_
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_mixture.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_mixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c98d150f06a8c7685d24c083e2ed2866f17c8ca
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/mixture/tests/test_mixture.py
@@ -0,0 +1,30 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+import pytest
+
+from sklearn.mixture import BayesianGaussianMixture, GaussianMixture
+
+
+@pytest.mark.parametrize("estimator", [GaussianMixture(), BayesianGaussianMixture()])
+def test_gaussian_mixture_n_iter(estimator):
+    # check that n_iter is the number of iteration performed.
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 5)
+    max_iter = 1
+    estimator.set_params(max_iter=max_iter)
+    estimator.fit(X)
+    assert estimator.n_iter_ == max_iter
+
+
+@pytest.mark.parametrize("estimator", [GaussianMixture(), BayesianGaussianMixture()])
+def test_mixture_n_components_greater_than_n_samples_error(estimator):
+    """Check error when n_components <= n_samples"""
+    rng = np.random.RandomState(0)
+    X = rng.rand(10, 5)
+    estimator.set_params(n_components=12)
+
+    msg = "Expected n_samples >= n_components"
+    with pytest.raises(ValueError, match=msg):
+        estimator.fit(X)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multiclass.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multiclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4208e0f542c77d68c6a6d87d3b409c85e6850e6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multiclass.py
@@ -0,0 +1,1287 @@
+"""Multiclass learning algorithms.
+
+- one-vs-the-rest / one-vs-all
+- one-vs-one
+- error correcting output codes
+
+The estimators provided in this module are meta-estimators: they require a base
+estimator to be provided in their constructor. For example, it is possible to
+use these estimators to turn a binary classifier or a regressor into a
+multiclass classifier. It is also possible to use these estimators with
+multiclass estimators in the hope that their accuracy or runtime performance
+improves.
+
+All classifiers in scikit-learn implement multiclass classification; you
+only need to use this module if you want to experiment with custom multiclass
+strategies.
+
+The one-vs-the-rest meta-classifier also implements a `predict_proba` method,
+so long as such a method is implemented by the base classifier. This method
+returns probabilities of class membership in both the single label and
+multilabel case.  Note that in the multilabel case, probabilities are the
+marginal probability that a given sample falls in the given class. As such, in
+the multilabel case the sum of these probabilities over all possible labels
+for a given sample *will not* sum to unity, as they do in the single label
+case.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import array
+import itertools
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    MultiOutputMixin,
+    _fit_context,
+    clone,
+    is_classifier,
+    is_regressor,
+)
+from .metrics.pairwise import pairwise_distances_argmin
+from .preprocessing import LabelBinarizer
+from .utils import check_random_state
+from .utils._param_validation import HasMethods, Interval
+from .utils._tags import get_tags
+from .utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    process_routing,
+)
+from .utils.metaestimators import _safe_split, available_if
+from .utils.multiclass import (
+    _check_partial_fit_first_call,
+    _ovr_decision_function,
+    check_classification_targets,
+)
+from .utils.parallel import Parallel, delayed
+from .utils.validation import (
+    _check_method_params,
+    _num_samples,
+    check_is_fitted,
+    validate_data,
+)
+
+__all__ = [
+    "OneVsOneClassifier",
+    "OneVsRestClassifier",
+    "OutputCodeClassifier",
+]
+
+
+def _fit_binary(estimator, X, y, fit_params, classes=None):
+    """Fit a single binary estimator."""
+    unique_y = np.unique(y)
+    if len(unique_y) == 1:
+        if classes is not None:
+            if y[0] == -1:
+                c = 0
+            else:
+                c = y[0]
+            warnings.warn(
+                "Label %s is present in all training examples." % str(classes[c])
+            )
+        estimator = _ConstantPredictor().fit(X, unique_y)
+    else:
+        estimator = clone(estimator)
+        estimator.fit(X, y, **fit_params)
+    return estimator
+
+
+def _partial_fit_binary(estimator, X, y, partial_fit_params):
+    """Partially fit a single binary estimator."""
+    estimator.partial_fit(X, y, classes=np.array((0, 1)), **partial_fit_params)
+    return estimator
+
+
+def _predict_binary(estimator, X):
+    """Make predictions using a single binary estimator."""
+    if is_regressor(estimator):
+        return estimator.predict(X)
+    try:
+        score = np.ravel(estimator.decision_function(X))
+    except (AttributeError, NotImplementedError):
+        # probabilities of the positive class
+        score = estimator.predict_proba(X)[:, 1]
+    return score
+
+
+def _threshold_for_binary_predict(estimator):
+    """Threshold for predictions from binary estimator."""
+    if hasattr(estimator, "decision_function") and is_classifier(estimator):
+        return 0.0
+    else:
+        # predict_proba threshold
+        return 0.5
+
+
+class _ConstantPredictor(BaseEstimator):
+    """Helper predictor to be used when only one class is present."""
+
+    def fit(self, X, y):
+        check_params = dict(
+            ensure_all_finite=False, dtype=None, ensure_2d=False, accept_sparse=True
+        )
+        validate_data(
+            self, X, y, reset=True, validate_separately=(check_params, check_params)
+        )
+        self.y_ = y
+        return self
+
+    def predict(self, X):
+        check_is_fitted(self)
+        validate_data(
+            self,
+            X,
+            ensure_all_finite=False,
+            dtype=None,
+            accept_sparse=True,
+            ensure_2d=False,
+            reset=False,
+        )
+
+        return np.repeat(self.y_, _num_samples(X))
+
+    def decision_function(self, X):
+        check_is_fitted(self)
+        validate_data(
+            self,
+            X,
+            ensure_all_finite=False,
+            dtype=None,
+            accept_sparse=True,
+            ensure_2d=False,
+            reset=False,
+        )
+
+        return np.repeat(self.y_, _num_samples(X))
+
+    def predict_proba(self, X):
+        check_is_fitted(self)
+        validate_data(
+            self,
+            X,
+            ensure_all_finite=False,
+            dtype=None,
+            accept_sparse=True,
+            ensure_2d=False,
+            reset=False,
+        )
+        y_ = self.y_.astype(np.float64)
+        return np.repeat([np.hstack([1 - y_, y_])], _num_samples(X), axis=0)
+
+
+def _estimators_has(attr):
+    """Check if self.estimator or self.estimators_[0] has attr.
+
+    If `self.estimators_[0]` has the attr, then its safe to assume that other
+    estimators have it too. We raise the original `AttributeError` if `attr`
+    does not exist. This function is used together with `available_if`.
+    """
+
+    def check(self):
+        if hasattr(self, "estimators_"):
+            getattr(self.estimators_[0], attr)
+        else:
+            getattr(self.estimator, attr)
+
+        return True
+
+    return check
+
+
+class OneVsRestClassifier(
+    MultiOutputMixin,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    BaseEstimator,
+):
+    """One-vs-the-rest (OvR) multiclass strategy.
+
+    Also known as one-vs-all, this strategy consists in fitting one classifier
+    per class. For each classifier, the class is fitted against all the other
+    classes. In addition to its computational efficiency (only `n_classes`
+    classifiers are needed), one advantage of this approach is its
+    interpretability. Since each class is represented by one and one classifier
+    only, it is possible to gain knowledge about the class by inspecting its
+    corresponding classifier. This is the most commonly used strategy for
+    multiclass classification and is a fair default choice.
+
+    OneVsRestClassifier can also be used for multilabel classification. To use
+    this feature, provide an indicator matrix for the target `y` when calling
+    `.fit`. In other words, the target labels should be formatted as a 2D
+    binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j
+    in sample i. This estimator uses the binary relevance method to perform
+    multilabel classification, which involves training one binary classifier
+    independently for each label.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        A regressor or a classifier that implements :term:`fit`.
+        When a classifier is passed, :term:`decision_function` will be used
+        in priority and it will fallback to :term:`predict_proba` if it is not
+        available.
+        When a regressor is passed, :term:`predict` is used.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation: the `n_classes`
+        one-vs-rest problems are computed in parallel.
+
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+        .. versionchanged:: 0.20
+           `n_jobs` default changed from 1 to None
+
+    verbose : int, default=0
+        The verbosity level, if non zero, progress messages are printed.
+        Below 50, the output is sent to stderr. Otherwise, the output is sent
+        to stdout. The frequency of the messages increases with the verbosity
+        level, reporting all iterations at 10. See :class:`joblib.Parallel` for
+        more details.
+
+        .. versionadded:: 1.1
+
+    Attributes
+    ----------
+    estimators_ : list of `n_classes` estimators
+        Estimators used for predictions.
+
+    classes_ : array, shape = [`n_classes`]
+        Class labels.
+
+    n_classes_ : int
+        Number of classes.
+
+    label_binarizer_ : LabelBinarizer object
+        Object used to transform multiclass labels to binary labels and
+        vice-versa.
+
+    multilabel_ : boolean
+        Whether a OneVsRestClassifier is a multilabel classifier.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    OneVsOneClassifier : One-vs-one multiclass strategy.
+    OutputCodeClassifier : (Error-Correcting) Output-Code multiclass strategy.
+    sklearn.multioutput.MultiOutputClassifier : Alternate way of extending an
+        estimator for multilabel classification.
+    sklearn.preprocessing.MultiLabelBinarizer : Transform iterable of iterables
+        to binary indicator matrix.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.multiclass import OneVsRestClassifier
+    >>> from sklearn.svm import SVC
+    >>> X = np.array([
+    ...     [10, 10],
+    ...     [8, 10],
+    ...     [-5, 5.5],
+    ...     [-5.4, 5.5],
+    ...     [-20, -20],
+    ...     [-15, -20]
+    ... ])
+    >>> y = np.array([0, 0, 1, 1, 2, 2])
+    >>> clf = OneVsRestClassifier(SVC()).fit(X, y)
+    >>> clf.predict([[-19, -20], [9, 9], [-5, 5]])
+    array([2, 0, 1])
+    """
+
+    _parameter_constraints = {
+        "estimator": [HasMethods(["fit"])],
+        "n_jobs": [Integral, None],
+        "verbose": ["verbose"],
+    }
+
+    def __init__(self, estimator, *, n_jobs=None, verbose=0):
+        self.estimator = estimator
+        self.n_jobs = n_jobs
+        self.verbose = verbose
+
+    @_fit_context(
+        # OneVsRestClassifier.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, **fit_params):
+        """Fit underlying estimators.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)
+            Multi-class targets. An indicator matrix turns on multilabel
+            classification.
+
+        **fit_params : dict
+            Parameters passed to the ``estimator.fit`` method of each
+            sub-estimator.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        self : object
+            Instance of fitted estimator.
+        """
+        _raise_for_params(fit_params, self, "fit")
+
+        routed_params = process_routing(
+            self,
+            "fit",
+            **fit_params,
+        )
+        # A sparse LabelBinarizer, with sparse_output=True, has been shown to
+        # outperform or match a dense label binarizer in all cases and has also
+        # resulted in less or equal memory consumption in the fit_ovr function
+        # overall.
+        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+        Y = self.label_binarizer_.fit_transform(y)
+        Y = Y.tocsc()
+        self.classes_ = self.label_binarizer_.classes_
+        columns = (col.toarray().ravel() for col in Y.T)
+        # In cases where individual estimators are very fast to train setting
+        # n_jobs > 1 in can results in slower performance due to the overhead
+        # of spawning threads.  See joblib issue #112.
+        self.estimators_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+            delayed(_fit_binary)(
+                self.estimator,
+                X,
+                column,
+                fit_params=routed_params.estimator.fit,
+                classes=[
+                    "not %s" % self.label_binarizer_.classes_[i],
+                    self.label_binarizer_.classes_[i],
+                ],
+            )
+            for i, column in enumerate(columns)
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return self
+
+    @available_if(_estimators_has("partial_fit"))
+    @_fit_context(
+        # OneVsRestClassifier.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def partial_fit(self, X, y, classes=None, **partial_fit_params):
+        """Partially fit underlying estimators.
+
+        Should be used when memory is inefficient to train all data.
+        Chunks of data can be passed in several iterations.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)
+            Multi-class targets. An indicator matrix turns on multilabel
+            classification.
+
+        classes : array, shape (n_classes, )
+            Classes across all calls to partial_fit.
+            Can be obtained via `np.unique(y_all)`, where y_all is the
+            target vector of the entire dataset.
+            This argument is only required in the first call of partial_fit
+            and can be omitted in the subsequent calls.
+
+        **partial_fit_params : dict
+            Parameters passed to the ``estimator.partial_fit`` method of each
+            sub-estimator.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        self : object
+            Instance of partially fitted estimator.
+        """
+        _raise_for_params(partial_fit_params, self, "partial_fit")
+
+        routed_params = process_routing(
+            self,
+            "partial_fit",
+            **partial_fit_params,
+        )
+
+        if _check_partial_fit_first_call(self, classes):
+            self.estimators_ = [clone(self.estimator) for _ in range(self.n_classes_)]
+
+            # A sparse LabelBinarizer, with sparse_output=True, has been
+            # shown to outperform or match a dense label binarizer in all
+            # cases and has also resulted in less or equal memory consumption
+            # in the fit_ovr function overall.
+            self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+            self.label_binarizer_.fit(self.classes_)
+
+        if len(np.setdiff1d(y, self.classes_)):
+            raise ValueError(
+                (
+                    "Mini-batch contains {0} while classes " + "must be subset of {1}"
+                ).format(np.unique(y), self.classes_)
+            )
+
+        Y = self.label_binarizer_.transform(y)
+        Y = Y.tocsc()
+        columns = (col.toarray().ravel() for col in Y.T)
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_partial_fit_binary)(
+                estimator,
+                X,
+                column,
+                partial_fit_params=routed_params.estimator.partial_fit,
+            )
+            for estimator, column in zip(self.estimators_, columns)
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+
+        return self
+
+    def predict(self, X):
+        """Predict multi-class targets using underlying estimators.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        Returns
+        -------
+        y : {array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)
+            Predicted multi-class targets.
+        """
+        check_is_fitted(self)
+
+        n_samples = _num_samples(X)
+        if self.label_binarizer_.y_type_ == "multiclass":
+            maxima = np.empty(n_samples, dtype=float)
+            maxima.fill(-np.inf)
+            argmaxima = np.zeros(n_samples, dtype=int)
+            for i, e in enumerate(self.estimators_):
+                pred = _predict_binary(e, X)
+                np.maximum(maxima, pred, out=maxima)
+                argmaxima[maxima == pred] = i
+            return self.classes_[argmaxima]
+        else:
+            thresh = _threshold_for_binary_predict(self.estimators_[0])
+            indices = array.array("i")
+            indptr = array.array("i", [0])
+            for e in self.estimators_:
+                indices.extend(np.where(_predict_binary(e, X) > thresh)[0])
+                indptr.append(len(indices))
+            data = np.ones(len(indices), dtype=int)
+            indicator = sp.csc_matrix(
+                (data, indices, indptr), shape=(n_samples, len(self.estimators_))
+            )
+            return self.label_binarizer_.inverse_transform(indicator)
+
+    @available_if(_estimators_has("predict_proba"))
+    def predict_proba(self, X):
+        """Probability estimates.
+
+        The returned estimates for all classes are ordered by label of classes.
+
+        Note that in the multilabel case, each sample can have any number of
+        labels. This returns the marginal probability that the given sample has
+        the label in question. For example, it is entirely consistent that two
+        labels both have a 90% probability of applying to a given sample.
+
+        In the single label multiclass case, the rows of the returned matrix
+        sum to 1.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        T : array-like of shape (n_samples, n_classes)
+            Returns the probability of the sample for each class in the model,
+            where classes are ordered as they are in `self.classes_`.
+        """
+        check_is_fitted(self)
+        # Y[i, j] gives the probability that sample i has the label j.
+        # In the multi-label case, these are not disjoint.
+        Y = np.array([e.predict_proba(X)[:, 1] for e in self.estimators_]).T
+
+        if len(self.estimators_) == 1:
+            # Only one estimator, but we still want to return probabilities
+            # for two classes.
+            Y = np.concatenate(((1 - Y), Y), axis=1)
+
+        if not self.multilabel_:
+            # Then, (nonzero) sample probability distributions should be normalized.
+            row_sums = np.sum(Y, axis=1)[:, np.newaxis]
+            np.divide(Y, row_sums, out=Y, where=row_sums != 0)
+
+        return Y
+
+    @available_if(_estimators_has("decision_function"))
+    def decision_function(self, X):
+        """Decision function for the OneVsRestClassifier.
+
+        Return the distance of each sample from the decision boundary for each
+        class. This can only be used with estimators which implement the
+        `decision_function` method.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        T : array-like of shape (n_samples, n_classes) or (n_samples,) for \
+            binary classification.
+            Result of calling `decision_function` on the final estimator.
+
+            .. versionchanged:: 0.19
+                output shape changed to ``(n_samples,)`` to conform to
+                scikit-learn conventions for binary classification.
+        """
+        check_is_fitted(self)
+        if len(self.estimators_) == 1:
+            return self.estimators_[0].decision_function(X)
+        return np.array(
+            [est.decision_function(X).ravel() for est in self.estimators_]
+        ).T
+
+    @property
+    def multilabel_(self):
+        """Whether this is a multilabel classifier."""
+        return self.label_binarizer_.y_type_.startswith("multilabel")
+
+    @property
+    def n_classes_(self):
+        """Number of classes."""
+        return len(self.classes_)
+
+    def __sklearn_tags__(self):
+        """Indicate if wrapped estimator is using a precomputed Gram matrix"""
+        tags = super().__sklearn_tags__()
+        tags.input_tags.pairwise = get_tags(self.estimator).input_tags.pairwise
+        tags.input_tags.sparse = get_tags(self.estimator).input_tags.sparse
+        return tags
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.4
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add_self_request(self)
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="partial_fit", callee="partial_fit"),
+            )
+        )
+        return router
+
+
+def _fit_ovo_binary(estimator, X, y, i, j, fit_params):
+    """Fit a single binary estimator (one-vs-one)."""
+    cond = np.logical_or(y == i, y == j)
+    y = y[cond]
+    y_binary = np.empty(y.shape, int)
+    y_binary[y == i] = 0
+    y_binary[y == j] = 1
+    indcond = np.arange(_num_samples(X))[cond]
+
+    fit_params_subset = _check_method_params(X, params=fit_params, indices=indcond)
+    return (
+        _fit_binary(
+            estimator,
+            _safe_split(estimator, X, None, indices=indcond)[0],
+            y_binary,
+            fit_params=fit_params_subset,
+            classes=[i, j],
+        ),
+        indcond,
+    )
+
+
+def _partial_fit_ovo_binary(estimator, X, y, i, j, partial_fit_params):
+    """Partially fit a single binary estimator(one-vs-one)."""
+
+    cond = np.logical_or(y == i, y == j)
+    y = y[cond]
+    if len(y) != 0:
+        y_binary = np.zeros_like(y)
+        y_binary[y == j] = 1
+        partial_fit_params_subset = _check_method_params(
+            X, params=partial_fit_params, indices=cond
+        )
+        return _partial_fit_binary(
+            estimator, X[cond], y_binary, partial_fit_params=partial_fit_params_subset
+        )
+    return estimator
+
+
+class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
+    """One-vs-one multiclass strategy.
+
+    This strategy consists in fitting one classifier per class pair.
+    At prediction time, the class which received the most votes is selected.
+    Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
+    this method is usually slower than one-vs-the-rest, due to its
+    O(n_classes^2) complexity. However, this method may be advantageous for
+    algorithms such as kernel algorithms which don't scale well with
+    `n_samples`. This is because each individual learning problem only involves
+    a small subset of the data whereas, with one-vs-the-rest, the complete
+    dataset is used `n_classes` times.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        A regressor or a classifier that implements :term:`fit`.
+        When a classifier is passed, :term:`decision_function` will be used
+        in priority and it will fallback to :term:`predict_proba` if it is not
+        available.
+        When a regressor is passed, :term:`predict` is used.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation: the `n_classes * (
+        n_classes - 1) / 2` OVO problems are computed in parallel.
+
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+    Attributes
+    ----------
+    estimators_ : list of ``n_classes * (n_classes - 1) / 2`` estimators
+        Estimators used for predictions.
+
+    classes_ : numpy array of shape [n_classes]
+        Array containing labels.
+
+    n_classes_ : int
+        Number of classes.
+
+    pairwise_indices_ : list, length = ``len(estimators_)``, or ``None``
+        Indices of samples used when training the estimators.
+        ``None`` when ``estimator``'s `pairwise` tag is False.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    OneVsRestClassifier : One-vs-all multiclass strategy.
+    OutputCodeClassifier : (Error-Correcting) Output-Code multiclass strategy.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.multiclass import OneVsOneClassifier
+    >>> from sklearn.svm import LinearSVC
+    >>> X, y = load_iris(return_X_y=True)
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, test_size=0.33, shuffle=True, random_state=0)
+    >>> clf = OneVsOneClassifier(
+    ...     LinearSVC(random_state=0)).fit(X_train, y_train)
+    >>> clf.predict(X_test[:10])
+    array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1])
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [HasMethods(["fit"])],
+        "n_jobs": [Integral, None],
+    }
+
+    def __init__(self, estimator, *, n_jobs=None):
+        self.estimator = estimator
+        self.n_jobs = n_jobs
+
+    @_fit_context(
+        # OneVsOneClassifier.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, **fit_params):
+        """Fit underlying estimators.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        y : array-like of shape (n_samples,)
+            Multi-class targets.
+
+        **fit_params : dict
+            Parameters passed to the ``estimator.fit`` method of each
+            sub-estimator.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        self : object
+            The fitted underlying estimator.
+        """
+        _raise_for_params(fit_params, self, "fit")
+
+        routed_params = process_routing(
+            self,
+            "fit",
+            **fit_params,
+        )
+
+        # We need to validate the data because we do a safe_indexing later.
+        X, y = validate_data(
+            self, X, y, accept_sparse=["csr", "csc"], ensure_all_finite=False
+        )
+        check_classification_targets(y)
+
+        self.classes_ = np.unique(y)
+        if len(self.classes_) == 1:
+            raise ValueError(
+                "OneVsOneClassifier can not be fit when only one class is present."
+            )
+        n_classes = self.classes_.shape[0]
+        estimators_indices = list(
+            zip(
+                *(
+                    Parallel(n_jobs=self.n_jobs)(
+                        delayed(_fit_ovo_binary)(
+                            self.estimator,
+                            X,
+                            y,
+                            self.classes_[i],
+                            self.classes_[j],
+                            fit_params=routed_params.estimator.fit,
+                        )
+                        for i in range(n_classes)
+                        for j in range(i + 1, n_classes)
+                    )
+                )
+            )
+        )
+
+        self.estimators_ = estimators_indices[0]
+
+        pairwise = self.__sklearn_tags__().input_tags.pairwise
+        self.pairwise_indices_ = estimators_indices[1] if pairwise else None
+
+        return self
+
+    @available_if(_estimators_has("partial_fit"))
+    @_fit_context(
+        # OneVsOneClassifier.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def partial_fit(self, X, y, classes=None, **partial_fit_params):
+        """Partially fit underlying estimators.
+
+        Should be used when memory is inefficient to train all data. Chunks
+        of data can be passed in several iteration, where the first call
+        should have an array of all target variables.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix) of shape (n_samples, n_features)
+            Data.
+
+        y : array-like of shape (n_samples,)
+            Multi-class targets.
+
+        classes : array, shape (n_classes, )
+            Classes across all calls to partial_fit.
+            Can be obtained via `np.unique(y_all)`, where y_all is the
+            target vector of the entire dataset.
+            This argument is only required in the first call of partial_fit
+            and can be omitted in the subsequent calls.
+
+        **partial_fit_params : dict
+            Parameters passed to the ``estimator.partial_fit`` method of each
+            sub-estimator.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        self : object
+            The partially fitted underlying estimator.
+        """
+        _raise_for_params(partial_fit_params, self, "partial_fit")
+
+        routed_params = process_routing(
+            self,
+            "partial_fit",
+            **partial_fit_params,
+        )
+
+        first_call = _check_partial_fit_first_call(self, classes)
+        if first_call:
+            self.estimators_ = [
+                clone(self.estimator)
+                for _ in range(self.n_classes_ * (self.n_classes_ - 1) // 2)
+            ]
+
+        if len(np.setdiff1d(y, self.classes_)):
+            raise ValueError(
+                "Mini-batch contains {0} while it must be subset of {1}".format(
+                    np.unique(y), self.classes_
+                )
+            )
+
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse=["csr", "csc"],
+            ensure_all_finite=False,
+            reset=first_call,
+        )
+        check_classification_targets(y)
+        combinations = itertools.combinations(range(self.n_classes_), 2)
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_partial_fit_ovo_binary)(
+                estimator,
+                X,
+                y,
+                self.classes_[i],
+                self.classes_[j],
+                partial_fit_params=routed_params.estimator.partial_fit,
+            )
+            for estimator, (i, j) in zip(self.estimators_, (combinations))
+        )
+
+        self.pairwise_indices_ = None
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+
+        return self
+
+    def predict(self, X):
+        """Estimate the best class label for each sample in X.
+
+        This is implemented as ``argmax(decision_function(X), axis=1)`` which
+        will return the label of the class with most votes by estimators
+        predicting the outcome of a decision for each possible class pair.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        Returns
+        -------
+        y : numpy array of shape [n_samples]
+            Predicted multi-class targets.
+        """
+        Y = self.decision_function(X)
+        if self.n_classes_ == 2:
+            thresh = _threshold_for_binary_predict(self.estimators_[0])
+            return self.classes_[(Y > thresh).astype(int)]
+        return self.classes_[Y.argmax(axis=1)]
+
+    def decision_function(self, X):
+        """Decision function for the OneVsOneClassifier.
+
+        The decision values for the samples are computed by adding the
+        normalized sum of pair-wise classification confidence levels to the
+        votes in order to disambiguate between the decision values when the
+        votes for all the classes are equal leading to a tie.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        Y : array-like of shape (n_samples, n_classes) or (n_samples,)
+            Result of calling `decision_function` on the final estimator.
+
+            .. versionchanged:: 0.19
+                output shape changed to ``(n_samples,)`` to conform to
+                scikit-learn conventions for binary classification.
+        """
+        check_is_fitted(self)
+        X = validate_data(
+            self,
+            X,
+            accept_sparse=True,
+            ensure_all_finite=False,
+            reset=False,
+        )
+
+        indices = self.pairwise_indices_
+        if indices is None:
+            Xs = [X] * len(self.estimators_)
+        else:
+            Xs = [X[:, idx] for idx in indices]
+
+        predictions = np.vstack(
+            [est.predict(Xi) for est, Xi in zip(self.estimators_, Xs)]
+        ).T
+        confidences = np.vstack(
+            [_predict_binary(est, Xi) for est, Xi in zip(self.estimators_, Xs)]
+        ).T
+        Y = _ovr_decision_function(predictions, confidences, len(self.classes_))
+        if self.n_classes_ == 2:
+            return Y[:, 1]
+        return Y
+
+    @property
+    def n_classes_(self):
+        """Number of classes."""
+        return len(self.classes_)
+
+    def __sklearn_tags__(self):
+        """Indicate if wrapped estimator is using a precomputed Gram matrix"""
+        tags = super().__sklearn_tags__()
+        tags.input_tags.pairwise = get_tags(self.estimator).input_tags.pairwise
+        tags.input_tags.sparse = get_tags(self.estimator).input_tags.sparse
+        return tags
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.4
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add_self_request(self)
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="partial_fit", callee="partial_fit"),
+            )
+        )
+        return router
+
+
+class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
+    """(Error-Correcting) Output-Code multiclass strategy.
+
+    Output-code based strategies consist in representing each class with a
+    binary code (an array of 0s and 1s). At fitting time, one binary
+    classifier per bit in the code book is fitted.  At prediction time, the
+    classifiers are used to project new points in the class space and the class
+    closest to the points is chosen. The main advantage of these strategies is
+    that the number of classifiers used can be controlled by the user, either
+    for compressing the model (0 < `code_size` < 1) or for making the model more
+    robust to errors (`code_size` > 1). See the documentation for more details.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        An estimator object implementing :term:`fit` and one of
+        :term:`decision_function` or :term:`predict_proba`.
+
+    code_size : float, default=1.5
+        Percentage of the number of classes to be used to create the code book.
+        A number between 0 and 1 will require fewer classifiers than
+        one-vs-the-rest. A number greater than 1 will require more classifiers
+        than one-vs-the-rest.
+
+    random_state : int, RandomState instance, default=None
+        The generator used to initialize the codebook.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    n_jobs : int, default=None
+        The number of jobs to use for the computation: the multiclass problems
+        are computed in parallel.
+
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+    Attributes
+    ----------
+    estimators_ : list of `int(n_classes * code_size)` estimators
+        Estimators used for predictions.
+
+    classes_ : ndarray of shape (n_classes,)
+        Array containing labels.
+
+    code_book_ : ndarray of shape (n_classes, `len(estimators_)`)
+        Binary array containing the code of each class.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    OneVsRestClassifier : One-vs-all multiclass strategy.
+    OneVsOneClassifier : One-vs-one multiclass strategy.
+
+    References
+    ----------
+
+    .. [1] "Solving multiclass learning problems via error-correcting output
+       codes",
+       Dietterich T., Bakiri G.,
+       Journal of Artificial Intelligence Research 2,
+       1995.
+
+    .. [2] "The error coding method and PICTs",
+       James G., Hastie T.,
+       Journal of Computational and Graphical statistics 7,
+       1998.
+
+    .. [3] "The Elements of Statistical Learning",
+       Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
+       2008.
+
+    Examples
+    --------
+    >>> from sklearn.multiclass import OutputCodeClassifier
+    >>> from sklearn.ensemble import RandomForestClassifier
+    >>> from sklearn.datasets import make_classification
+    >>> X, y = make_classification(n_samples=100, n_features=4,
+    ...                            n_informative=2, n_redundant=0,
+    ...                            random_state=0, shuffle=False)
+    >>> clf = OutputCodeClassifier(
+    ...     estimator=RandomForestClassifier(random_state=0),
+    ...     random_state=0).fit(X, y)
+    >>> clf.predict([[0, 0, 0, 0]])
+    array([1])
+    """
+
+    _parameter_constraints: dict = {
+        "estimator": [
+            HasMethods(["fit", "decision_function"]),
+            HasMethods(["fit", "predict_proba"]),
+        ],
+        "code_size": [Interval(Real, 0.0, None, closed="neither")],
+        "random_state": ["random_state"],
+        "n_jobs": [Integral, None],
+    }
+
+    def __init__(self, estimator, *, code_size=1.5, random_state=None, n_jobs=None):
+        self.estimator = estimator
+        self.code_size = code_size
+        self.random_state = random_state
+        self.n_jobs = n_jobs
+
+    @_fit_context(
+        # OutputCodeClassifier.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, **fit_params):
+        """Fit underlying estimators.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        y : array-like of shape (n_samples,)
+            Multi-class targets.
+
+        **fit_params : dict
+            Parameters passed to the ``estimator.fit`` method of each
+            sub-estimator.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance of self.
+        """
+        _raise_for_params(fit_params, self, "fit")
+
+        routed_params = process_routing(
+            self,
+            "fit",
+            **fit_params,
+        )
+
+        y = validate_data(self, X="no_validation", y=y)
+
+        random_state = check_random_state(self.random_state)
+        check_classification_targets(y)
+
+        self.classes_ = np.unique(y)
+        n_classes = self.classes_.shape[0]
+        if n_classes == 0:
+            raise ValueError(
+                "OutputCodeClassifier can not be fit when no class is present."
+            )
+        n_estimators = int(n_classes * self.code_size)
+
+        # FIXME: there are more elaborate methods than generating the codebook
+        # randomly.
+        self.code_book_ = random_state.uniform(size=(n_classes, n_estimators))
+        self.code_book_[self.code_book_ > 0.5] = 1.0
+
+        if hasattr(self.estimator, "decision_function"):
+            self.code_book_[self.code_book_ != 1] = -1.0
+        else:
+            self.code_book_[self.code_book_ != 1] = 0.0
+
+        classes_index = {c: i for i, c in enumerate(self.classes_)}
+
+        Y = np.array(
+            [self.code_book_[classes_index[y[i]]] for i in range(_num_samples(y))],
+            dtype=int,
+        )
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_fit_binary)(
+                self.estimator, X, Y[:, i], fit_params=routed_params.estimator.fit
+            )
+            for i in range(Y.shape[1])
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return self
+
+    def predict(self, X):
+        """Predict multi-class targets using underlying estimators.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Data.
+
+        Returns
+        -------
+        y : ndarray of shape (n_samples,)
+            Predicted multi-class targets.
+        """
+        check_is_fitted(self)
+        # ArgKmin only accepts C-contiguous array. The aggregated predictions need to be
+        # transposed. We therefore create a F-contiguous array to avoid a copy and have
+        # a C-contiguous array after the transpose operation.
+        Y = np.array(
+            [_predict_binary(e, X) for e in self.estimators_],
+            order="F",
+            dtype=np.float64,
+        ).T
+        pred = pairwise_distances_argmin(Y, self.code_book_, metric="euclidean")
+        return self.classes_[pred]
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.4
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self.estimator,
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+        )
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = get_tags(self.estimator).input_tags.sparse
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multioutput.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multioutput.py
new file mode 100644
index 0000000000000000000000000000000000000000..08b0c95c945586490ffa39e6b01c94515248cfb8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/multioutput.py
@@ -0,0 +1,1328 @@
+"""Multioutput regression and classification.
+
+The estimators provided in this module are meta-estimators: they require
+a base estimator to be provided in their constructor. The meta-estimator
+extends single output estimators to multioutput estimators.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from numbers import Integral
+
+import numpy as np
+import scipy.sparse as sp
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    RegressorMixin,
+    _fit_context,
+    clone,
+    is_classifier,
+)
+from .model_selection import cross_val_predict
+from .utils import Bunch, check_random_state, get_tags
+from .utils._param_validation import (
+    HasMethods,
+    Hidden,
+    StrOptions,
+)
+from .utils._response import _get_response_values
+from .utils._user_interface import _print_elapsed_time
+from .utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    _routing_enabled,
+    process_routing,
+)
+from .utils.metaestimators import available_if
+from .utils.multiclass import check_classification_targets
+from .utils.parallel import Parallel, delayed
+from .utils.validation import (
+    _check_method_params,
+    _check_response_method,
+    check_is_fitted,
+    has_fit_parameter,
+    validate_data,
+)
+
+__all__ = [
+    "ClassifierChain",
+    "MultiOutputClassifier",
+    "MultiOutputRegressor",
+    "RegressorChain",
+]
+
+
+def _fit_estimator(estimator, X, y, sample_weight=None, **fit_params):
+    estimator = clone(estimator)
+    if sample_weight is not None:
+        estimator.fit(X, y, sample_weight=sample_weight, **fit_params)
+    else:
+        estimator.fit(X, y, **fit_params)
+    return estimator
+
+
+def _partial_fit_estimator(
+    estimator, X, y, classes=None, partial_fit_params=None, first_time=True
+):
+    partial_fit_params = {} if partial_fit_params is None else partial_fit_params
+    if first_time:
+        estimator = clone(estimator)
+
+    if classes is not None:
+        estimator.partial_fit(X, y, classes=classes, **partial_fit_params)
+    else:
+        estimator.partial_fit(X, y, **partial_fit_params)
+    return estimator
+
+
+def _available_if_estimator_has(attr):
+    """Return a function to check if the sub-estimator(s) has(have) `attr`.
+
+    Helper for Chain implementations.
+    """
+
+    def _check(self):
+        if hasattr(self, "estimators_"):
+            return all(hasattr(est, attr) for est in self.estimators_)
+
+        if hasattr(self.estimator, attr):
+            return True
+
+        return False
+
+    return available_if(_check)
+
+
+class _MultiOutputEstimator(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
+    _parameter_constraints: dict = {
+        "estimator": [HasMethods(["fit", "predict"])],
+        "n_jobs": [Integral, None],
+    }
+
+    @abstractmethod
+    def __init__(self, estimator, *, n_jobs=None):
+        self.estimator = estimator
+        self.n_jobs = n_jobs
+
+    @_available_if_estimator_has("partial_fit")
+    @_fit_context(
+        # MultiOutput*.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def partial_fit(self, X, y, classes=None, sample_weight=None, **partial_fit_params):
+        """Incrementally fit a separate model for each class output.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets.
+
+        classes : list of ndarray of shape (n_outputs,), default=None
+            Each array is unique classes for one output in str/int.
+            Can be obtained via
+            ``[np.unique(y[:, i]) for i in range(y.shape[1])]``, where `y`
+            is the target matrix of the entire dataset.
+            This argument is required for the first call to partial_fit
+            and can be omitted in the subsequent calls.
+            Note that `y` doesn't need to contain all labels in `classes`.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying regressor supports sample
+            weights.
+
+        **partial_fit_params : dict of str -> object
+            Parameters passed to the ``estimator.partial_fit`` method of each
+            sub-estimator.
+
+            Only available if `enable_metadata_routing=True`. See the
+            :ref:`User Guide `.
+
+            .. versionadded:: 1.3
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        _raise_for_params(partial_fit_params, self, "partial_fit")
+
+        first_time = not hasattr(self, "estimators_")
+
+        y = validate_data(self, X="no_validation", y=y, multi_output=True)
+
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for "
+                "multi-output regression but has only one."
+            )
+
+        if _routing_enabled():
+            if sample_weight is not None:
+                partial_fit_params["sample_weight"] = sample_weight
+            routed_params = process_routing(
+                self,
+                "partial_fit",
+                **partial_fit_params,
+            )
+        else:
+            if sample_weight is not None and not has_fit_parameter(
+                self.estimator, "sample_weight"
+            ):
+                raise ValueError(
+                    "Underlying estimator does not support sample weights."
+                )
+
+            if sample_weight is not None:
+                routed_params = Bunch(
+                    estimator=Bunch(partial_fit=Bunch(sample_weight=sample_weight))
+                )
+            else:
+                routed_params = Bunch(estimator=Bunch(partial_fit=Bunch()))
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_partial_fit_estimator)(
+                self.estimators_[i] if not first_time else self.estimator,
+                X,
+                y[:, i],
+                classes[i] if classes is not None else None,
+                partial_fit_params=routed_params.estimator.partial_fit,
+                first_time=first_time,
+            )
+            for i in range(y.shape[1])
+        )
+
+        if first_time and hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if first_time and hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return self
+
+    @_fit_context(
+        # MultiOutput*.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, sample_weight=None, **fit_params):
+        """Fit the model to data, separately for each output variable.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets. An indicator matrix turns on multilabel
+            estimation.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying regressor supports sample
+            weights.
+
+        **fit_params : dict of string -> object
+            Parameters passed to the ``estimator.fit`` method of each step.
+
+            .. versionadded:: 0.23
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        if not hasattr(self.estimator, "fit"):
+            raise ValueError("The base estimator should implement a fit method")
+
+        y = validate_data(self, X="no_validation", y=y, multi_output=True)
+
+        if is_classifier(self):
+            check_classification_targets(y)
+
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for "
+                "multi-output regression but has only one."
+            )
+
+        if _routing_enabled():
+            if sample_weight is not None:
+                fit_params["sample_weight"] = sample_weight
+            routed_params = process_routing(
+                self,
+                "fit",
+                **fit_params,
+            )
+        else:
+            if sample_weight is not None and not has_fit_parameter(
+                self.estimator, "sample_weight"
+            ):
+                raise ValueError(
+                    "Underlying estimator does not support sample weights."
+                )
+
+            fit_params_validated = _check_method_params(X, params=fit_params)
+            routed_params = Bunch(estimator=Bunch(fit=fit_params_validated))
+            if sample_weight is not None:
+                routed_params.estimator.fit["sample_weight"] = sample_weight
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_fit_estimator)(
+                self.estimator, X, y[:, i], **routed_params.estimator.fit
+            )
+            for i in range(y.shape[1])
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return self
+
+    def predict(self, X):
+        """Predict multi-output variable using model for each target variable.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets predicted across multiple predictors.
+            Note: Separate models are generated for each predictor.
+        """
+        check_is_fitted(self)
+        if not hasattr(self.estimators_[0], "predict"):
+            raise ValueError("The base estimator should implement a predict method")
+
+        y = Parallel(n_jobs=self.n_jobs)(
+            delayed(e.predict)(X) for e in self.estimators_
+        )
+
+        return np.asarray(y).T
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = get_tags(self.estimator).input_tags.sparse
+        tags.target_tags.single_output = False
+        tags.target_tags.multi_output = True
+        return tags
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.3
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self.estimator,
+            method_mapping=MethodMapping()
+            .add(caller="partial_fit", callee="partial_fit")
+            .add(caller="fit", callee="fit"),
+        )
+        return router
+
+
+class MultiOutputRegressor(RegressorMixin, _MultiOutputEstimator):
+    """Multi target regression.
+
+    This strategy consists of fitting one regressor per target. This is a
+    simple strategy for extending regressors that do not natively support
+    multi-target regression.
+
+    .. versionadded:: 0.18
+
+    Parameters
+    ----------
+    estimator : estimator object
+        An estimator object implementing :term:`fit` and :term:`predict`.
+
+    n_jobs : int or None, optional (default=None)
+        The number of jobs to run in parallel.
+        :meth:`fit`, :meth:`predict` and :meth:`partial_fit` (if supported
+        by the passed estimator) will be parallelized for each target.
+
+        When individual estimators are fast to train or predict,
+        using ``n_jobs > 1`` can result in slower performance due
+        to the parallelism overhead.
+
+        ``None`` means `1` unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all available processes / threads.
+        See :term:`Glossary ` for more details.
+
+        .. versionchanged:: 0.20
+            `n_jobs` default changed from `1` to `None`.
+
+    Attributes
+    ----------
+    estimators_ : list of ``n_output`` estimators
+        Estimators used for predictions.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying `estimator` exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimators expose such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    RegressorChain : A multi-label model that arranges regressions into a
+        chain.
+    MultiOutputClassifier : Classifies each output independently rather than
+        chaining.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import load_linnerud
+    >>> from sklearn.multioutput import MultiOutputRegressor
+    >>> from sklearn.linear_model import Ridge
+    >>> X, y = load_linnerud(return_X_y=True)
+    >>> regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
+    >>> regr.predict(X[[0]])
+    array([[176, 35.1, 57.1]])
+    """
+
+    def __init__(self, estimator, *, n_jobs=None):
+        super().__init__(estimator, n_jobs=n_jobs)
+
+    @_available_if_estimator_has("partial_fit")
+    def partial_fit(self, X, y, sample_weight=None, **partial_fit_params):
+        """Incrementally fit the model to data, for each output variable.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying regressor supports sample
+            weights.
+
+        **partial_fit_params : dict of str -> object
+            Parameters passed to the ``estimator.partial_fit`` method of each
+            sub-estimator.
+
+            Only available if `enable_metadata_routing=True`. See the
+            :ref:`User Guide `.
+
+            .. versionadded:: 1.3
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        super().partial_fit(X, y, sample_weight=sample_weight, **partial_fit_params)
+
+
+class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):
+    """Multi target classification.
+
+    This strategy consists of fitting one classifier per target. This is a
+    simple strategy for extending classifiers that do not natively support
+    multi-target classification.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        An estimator object implementing :term:`fit` and :term:`predict`.
+        A :term:`predict_proba` method will be exposed only if `estimator` implements
+        it.
+
+    n_jobs : int or None, optional (default=None)
+        The number of jobs to run in parallel.
+        :meth:`fit`, :meth:`predict` and :meth:`partial_fit` (if supported
+        by the passed estimator) will be parallelized for each target.
+
+        When individual estimators are fast to train or predict,
+        using ``n_jobs > 1`` can result in slower performance due
+        to the parallelism overhead.
+
+        ``None`` means `1` unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all available processes / threads.
+        See :term:`Glossary ` for more details.
+
+        .. versionchanged:: 0.20
+            `n_jobs` default changed from `1` to `None`.
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        Class labels.
+
+    estimators_ : list of ``n_output`` estimators
+        Estimators used for predictions.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying `estimator` exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimators expose such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    ClassifierChain : A multi-label model that arranges binary classifiers
+        into a chain.
+    MultiOutputRegressor : Fits one regressor per target variable.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.datasets import make_multilabel_classification
+    >>> from sklearn.multioutput import MultiOutputClassifier
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
+    >>> clf = MultiOutputClassifier(LogisticRegression()).fit(X, y)
+    >>> clf.predict(X[-2:])
+    array([[1, 1, 1],
+           [1, 0, 1]])
+    """
+
+    def __init__(self, estimator, *, n_jobs=None):
+        super().__init__(estimator, n_jobs=n_jobs)
+
+    def fit(self, X, Y, sample_weight=None, **fit_params):
+        """Fit the model to data matrix X and targets Y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Y : array-like of shape (n_samples, n_classes)
+            The target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying classifier supports sample
+            weights.
+
+        **fit_params : dict of string -> object
+            Parameters passed to the ``estimator.fit`` method of each step.
+
+            .. versionadded:: 0.23
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        super().fit(X, Y, sample_weight=sample_weight, **fit_params)
+        self.classes_ = [estimator.classes_ for estimator in self.estimators_]
+        return self
+
+    def _check_predict_proba(self):
+        if hasattr(self, "estimators_"):
+            # raise an AttributeError if `predict_proba` does not exist for
+            # each estimator
+            [getattr(est, "predict_proba") for est in self.estimators_]
+            return True
+        # raise an AttributeError if `predict_proba` does not exist for the
+        # unfitted estimator
+        getattr(self.estimator, "predict_proba")
+        return True
+
+    @available_if(_check_predict_proba)
+    def predict_proba(self, X):
+        """Return prediction probabilities for each class of each output.
+
+        This method will raise a ``ValueError`` if any of the
+        estimators do not have ``predict_proba``.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        p : array of shape (n_samples, n_classes), or a list of n_outputs \
+                such arrays if n_outputs > 1.
+            The class probabilities of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+
+            .. versionchanged:: 0.19
+                This function now returns a list of arrays where the length of
+                the list is ``n_outputs``, and each array is (``n_samples``,
+                ``n_classes``) for that particular output.
+        """
+        check_is_fitted(self)
+        results = [estimator.predict_proba(X) for estimator in self.estimators_]
+        return results
+
+    def score(self, X, y):
+        """Return the mean accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+
+        y : array-like of shape (n_samples, n_outputs)
+            True values for X.
+
+        Returns
+        -------
+        scores : float
+            Mean accuracy of predicted target versus true target.
+        """
+        check_is_fitted(self)
+        n_outputs_ = len(self.estimators_)
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for "
+                "multi target classification but has only one"
+            )
+        if y.shape[1] != n_outputs_:
+            raise ValueError(
+                "The number of outputs of Y for fit {0} and"
+                " score {1} should be same".format(n_outputs_, y.shape[1])
+            )
+        y_pred = self.predict(X)
+        return np.mean(np.all(y == y_pred, axis=1))
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # FIXME
+        tags._skip_test = True
+        return tags
+
+
+def _available_if_base_estimator_has(attr):
+    """Return a function to check if `base_estimator` or `estimators_` has `attr`.
+
+    Helper for Chain implementations.
+    """
+
+    def _check(self):
+        return hasattr(self._get_estimator(), attr) or all(
+            hasattr(est, attr) for est in self.estimators_
+        )
+
+    return available_if(_check)
+
+
+class _BaseChain(BaseEstimator, metaclass=ABCMeta):
+    _parameter_constraints: dict = {
+        "base_estimator": [
+            HasMethods(["fit", "predict"]),
+            StrOptions({"deprecated"}),
+        ],
+        "estimator": [
+            HasMethods(["fit", "predict"]),
+            Hidden(None),
+        ],
+        "order": ["array-like", StrOptions({"random"}), None],
+        "cv": ["cv_object", StrOptions({"prefit"})],
+        "random_state": ["random_state"],
+        "verbose": ["boolean"],
+    }
+
+    # TODO(1.9): Remove base_estimator
+    def __init__(
+        self,
+        estimator=None,
+        *,
+        order=None,
+        cv=None,
+        random_state=None,
+        verbose=False,
+        base_estimator="deprecated",
+    ):
+        self.estimator = estimator
+        self.base_estimator = base_estimator
+        self.order = order
+        self.cv = cv
+        self.random_state = random_state
+        self.verbose = verbose
+
+    # TODO(1.8): This is a temporary getter method to validate input wrt deprecation.
+    # It was only included to avoid relying on the presence of self.estimator_
+    def _get_estimator(self):
+        """Get and validate estimator."""
+
+        if self.estimator is not None and (self.base_estimator != "deprecated"):
+            raise ValueError(
+                "Both `estimator` and `base_estimator` are provided. You should only"
+                " pass `estimator`. `base_estimator` as a parameter is deprecated in"
+                " version 1.7, and will be removed in version 1.9."
+            )
+
+        if self.base_estimator != "deprecated":
+            warning_msg = (
+                "`base_estimator` as an argument was deprecated in 1.7 and will be"
+                " removed in 1.9. Use `estimator` instead."
+            )
+            warnings.warn(warning_msg, FutureWarning)
+            return self.base_estimator
+        else:
+            return self.estimator
+
+    def _log_message(self, *, estimator_idx, n_estimators, processing_msg):
+        if not self.verbose:
+            return None
+        return f"({estimator_idx} of {n_estimators}) {processing_msg}"
+
+    def _get_predictions(self, X, *, output_method):
+        """Get predictions for each model in the chain."""
+        check_is_fitted(self)
+        X = validate_data(self, X, accept_sparse=True, reset=False)
+        Y_output_chain = np.zeros((X.shape[0], len(self.estimators_)))
+        Y_feature_chain = np.zeros((X.shape[0], len(self.estimators_)))
+
+        # `RegressorChain` does not have a `chain_method_` parameter so we
+        # default to "predict"
+        chain_method = getattr(self, "chain_method_", "predict")
+        hstack = sp.hstack if sp.issparse(X) else np.hstack
+        for chain_idx, estimator in enumerate(self.estimators_):
+            previous_predictions = Y_feature_chain[:, :chain_idx]
+            # if `X` is a scipy sparse dok_array, we convert it to a sparse
+            # coo_array format before hstacking, it's faster; see
+            # https://github.com/scipy/scipy/issues/20060#issuecomment-1937007039:
+            if sp.issparse(X) and not sp.isspmatrix(X) and X.format == "dok":
+                X = sp.coo_array(X)
+            X_aug = hstack((X, previous_predictions))
+
+            feature_predictions, _ = _get_response_values(
+                estimator,
+                X_aug,
+                response_method=chain_method,
+            )
+            Y_feature_chain[:, chain_idx] = feature_predictions
+
+            output_predictions, _ = _get_response_values(
+                estimator,
+                X_aug,
+                response_method=output_method,
+            )
+            Y_output_chain[:, chain_idx] = output_predictions
+
+        inv_order = np.empty_like(self.order_)
+        inv_order[self.order_] = np.arange(len(self.order_))
+        Y_output = Y_output_chain[:, inv_order]
+
+        return Y_output
+
+    @abstractmethod
+    def fit(self, X, Y, **fit_params):
+        """Fit the model to data matrix X and targets Y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Y : array-like of shape (n_samples, n_classes)
+            The target values.
+
+        **fit_params : dict of string -> object
+            Parameters passed to the `fit` method of each step.
+
+            .. versionadded:: 0.23
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        X, Y = validate_data(self, X, Y, multi_output=True, accept_sparse=True)
+
+        random_state = check_random_state(self.random_state)
+        self.order_ = self.order
+        if isinstance(self.order_, tuple):
+            self.order_ = np.array(self.order_)
+
+        if self.order_ is None:
+            self.order_ = np.array(range(Y.shape[1]))
+        elif isinstance(self.order_, str):
+            if self.order_ == "random":
+                self.order_ = random_state.permutation(Y.shape[1])
+        elif sorted(self.order_) != list(range(Y.shape[1])):
+            raise ValueError("invalid order")
+
+        self.estimators_ = [clone(self._get_estimator()) for _ in range(Y.shape[1])]
+
+        if self.cv is None:
+            Y_pred_chain = Y[:, self.order_]
+            if sp.issparse(X):
+                X_aug = sp.hstack((X, Y_pred_chain), format="lil")
+                X_aug = X_aug.tocsr()
+            else:
+                X_aug = np.hstack((X, Y_pred_chain))
+
+        elif sp.issparse(X):
+            # TODO: remove this condition check when the minimum supported scipy version
+            # doesn't support sparse matrices anymore
+            if not sp.isspmatrix(X):
+                # if `X` is a scipy sparse dok_array, we convert it to a sparse
+                # coo_array format before hstacking, it's faster; see
+                # https://github.com/scipy/scipy/issues/20060#issuecomment-1937007039:
+                if X.format == "dok":
+                    X = sp.coo_array(X)
+                # in case that `X` is a sparse array we create `Y_pred_chain` as a
+                # sparse array format:
+                Y_pred_chain = sp.coo_array((X.shape[0], Y.shape[1]))
+            else:
+                Y_pred_chain = sp.coo_matrix((X.shape[0], Y.shape[1]))
+            X_aug = sp.hstack((X, Y_pred_chain), format="lil")
+
+        else:
+            Y_pred_chain = np.zeros((X.shape[0], Y.shape[1]))
+            X_aug = np.hstack((X, Y_pred_chain))
+
+        del Y_pred_chain
+
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit", **fit_params)
+        else:
+            routed_params = Bunch(estimator=Bunch(fit=fit_params))
+
+        if hasattr(self, "chain_method"):
+            chain_method = _check_response_method(
+                self._get_estimator(),
+                self.chain_method,
+            ).__name__
+            self.chain_method_ = chain_method
+        else:
+            # `RegressorChain` does not have a `chain_method` parameter
+            chain_method = "predict"
+
+        for chain_idx, estimator in enumerate(self.estimators_):
+            message = self._log_message(
+                estimator_idx=chain_idx + 1,
+                n_estimators=len(self.estimators_),
+                processing_msg=f"Processing order {self.order_[chain_idx]}",
+            )
+            y = Y[:, self.order_[chain_idx]]
+            with _print_elapsed_time("Chain", message):
+                estimator.fit(
+                    X_aug[:, : (X.shape[1] + chain_idx)],
+                    y,
+                    **routed_params.estimator.fit,
+                )
+
+            if self.cv is not None and chain_idx < len(self.estimators_) - 1:
+                col_idx = X.shape[1] + chain_idx
+                cv_result = cross_val_predict(
+                    self._get_estimator(),
+                    X_aug[:, :col_idx],
+                    y=y,
+                    cv=self.cv,
+                    method=chain_method,
+                )
+                # `predict_proba` output is 2D, we use only output for classes[-1]
+                if cv_result.ndim > 1:
+                    cv_result = cv_result[:, 1]
+                if sp.issparse(X_aug):
+                    X_aug[:, col_idx] = np.expand_dims(cv_result, 1)
+                else:
+                    X_aug[:, col_idx] = cv_result
+
+        return self
+
+    def predict(self, X):
+        """Predict on the data matrix X using the ClassifierChain model.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        Y_pred : array-like of shape (n_samples, n_classes)
+            The predicted values.
+        """
+        return self._get_predictions(X, output_method="predict")
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = get_tags(self._get_estimator()).input_tags.sparse
+        return tags
+
+
+class ClassifierChain(MetaEstimatorMixin, ClassifierMixin, _BaseChain):
+    """A multi-label model that arranges binary classifiers into a chain.
+
+    Each model makes a prediction in the order specified by the chain using
+    all of the available features provided to the model plus the predictions
+    of models that are earlier in the chain.
+
+    For an example of how to use ``ClassifierChain`` and benefit from its
+    ensemble, see
+    :ref:`ClassifierChain on a yeast dataset
+    ` example.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.19
+
+    Parameters
+    ----------
+    estimator : estimator
+        The base estimator from which the classifier chain is built.
+
+    order : array-like of shape (n_outputs,) or 'random', default=None
+        If `None`, the order will be determined by the order of columns in
+        the label matrix Y.::
+
+            order = [0, 1, 2, ..., Y.shape[1] - 1]
+
+        The order of the chain can be explicitly set by providing a list of
+        integers. For example, for a chain of length 5.::
+
+            order = [1, 3, 2, 4, 0]
+
+        means that the first model in the chain will make predictions for
+        column 1 in the Y matrix, the second model will make predictions
+        for column 3, etc.
+
+        If order is `random` a random ordering will be used.
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines whether to use cross validated predictions or true
+        labels for the results of previous estimators in the chain.
+        Possible inputs for cv are:
+
+        - None, to use true labels when fitting,
+        - integer, to specify the number of folds in a (Stratified)KFold,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+    chain_method : {'predict', 'predict_proba', 'predict_log_proba', \
+            'decision_function'} or list of such str's, default='predict'
+
+        Prediction method to be used by estimators in the chain for
+        the 'prediction' features of previous estimators in the chain.
+
+        - if `str`, name of the method;
+        - if a list of `str`, provides the method names in order of
+          preference. The method used corresponds to the first method in
+          the list that is implemented by `base_estimator`.
+
+        .. versionadded:: 1.5
+
+    random_state : int, RandomState instance or None, optional (default=None)
+        If ``order='random'``, determines random number generation for the
+        chain order.
+        In addition, it controls the random seed given at each `base_estimator`
+        at each chaining iteration. Thus, it is only used when `base_estimator`
+        exposes a `random_state`.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    verbose : bool, default=False
+        If True, chain progress is output as each model is completed.
+
+        .. versionadded:: 1.2
+
+    base_estimator : estimator, default="deprecated"
+        Use `estimator` instead.
+
+        .. deprecated:: 1.7
+            `base_estimator` is deprecated and will be removed in 1.9.
+            Use `estimator` instead.
+
+    Attributes
+    ----------
+    classes_ : list
+        A list of arrays of length ``len(estimators_)`` containing the
+        class labels for each estimator in the chain.
+
+    estimators_ : list
+        A list of clones of base_estimator.
+
+    order_ : list
+        The order of labels in the classifier chain.
+
+    chain_method_ : str
+        Prediction method used by estimators in the chain for the prediction
+        features.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying `base_estimator` exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    RegressorChain : Equivalent for regression.
+    MultiOutputClassifier : Classifies each output independently rather than
+        chaining.
+
+    References
+    ----------
+    Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, "Classifier
+    Chains for Multi-label Classification", 2009.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_multilabel_classification
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.multioutput import ClassifierChain
+    >>> X, Y = make_multilabel_classification(
+    ...    n_samples=12, n_classes=3, random_state=0
+    ... )
+    >>> X_train, X_test, Y_train, Y_test = train_test_split(
+    ...    X, Y, random_state=0
+    ... )
+    >>> base_lr = LogisticRegression(solver='lbfgs', random_state=0)
+    >>> chain = ClassifierChain(base_lr, order='random', random_state=0)
+    >>> chain.fit(X_train, Y_train).predict(X_test)
+    array([[1., 1., 0.],
+           [1., 0., 0.],
+           [0., 1., 0.]])
+    >>> chain.predict_proba(X_test)
+    array([[0.8387, 0.9431, 0.4576],
+           [0.8878, 0.3684, 0.2640],
+           [0.0321, 0.9935, 0.0626]])
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseChain._parameter_constraints,
+        "chain_method": [
+            list,
+            tuple,
+            StrOptions(
+                {"predict", "predict_proba", "predict_log_proba", "decision_function"}
+            ),
+        ],
+    }
+
+    # TODO(1.9): Remove base_estimator from __init__
+    def __init__(
+        self,
+        estimator=None,
+        *,
+        order=None,
+        cv=None,
+        chain_method="predict",
+        random_state=None,
+        verbose=False,
+        base_estimator="deprecated",
+    ):
+        super().__init__(
+            estimator,
+            order=order,
+            cv=cv,
+            random_state=random_state,
+            verbose=verbose,
+            base_estimator=base_estimator,
+        )
+        self.chain_method = chain_method
+
+    @_fit_context(
+        # ClassifierChain.base_estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, Y, **fit_params):
+        """Fit the model to data matrix X and targets Y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Y : array-like of shape (n_samples, n_classes)
+            The target values.
+
+        **fit_params : dict of string -> object
+            Parameters passed to the `fit` method of each step.
+
+            Only available if `enable_metadata_routing=True`. See the
+            :ref:`User Guide `.
+
+            .. versionadded:: 1.3
+
+        Returns
+        -------
+        self : object
+            Class instance.
+        """
+        _raise_for_params(fit_params, self, "fit")
+
+        super().fit(X, Y, **fit_params)
+        self.classes_ = [estimator.classes_ for estimator in self.estimators_]
+        return self
+
+    @_available_if_base_estimator_has("predict_proba")
+    def predict_proba(self, X):
+        """Predict probability estimates.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        Y_prob : array-like of shape (n_samples, n_classes)
+            The predicted probabilities.
+        """
+        return self._get_predictions(X, output_method="predict_proba")
+
+    def predict_log_proba(self, X):
+        """Predict logarithm of probability estimates.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        Y_log_prob : array-like of shape (n_samples, n_classes)
+            The predicted logarithm of the probabilities.
+        """
+        return np.log(self.predict_proba(X))
+
+    @_available_if_base_estimator_has("decision_function")
+    def decision_function(self, X):
+        """Evaluate the decision_function of the models in the chain.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input data.
+
+        Returns
+        -------
+        Y_decision : array-like of shape (n_samples, n_classes)
+            Returns the decision function of the sample for each model
+            in the chain.
+        """
+        return self._get_predictions(X, output_method="decision_function")
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.3
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self._get_estimator(),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+        )
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # FIXME
+        tags._skip_test = True
+        tags.target_tags.single_output = False
+        tags.target_tags.multi_output = True
+        return tags
+
+
+class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
+    """A multi-label model that arranges regressions into a chain.
+
+    Each model makes a prediction in the order specified by the chain using
+    all of the available features provided to the model plus the predictions
+    of models that are earlier in the chain.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    estimator : estimator
+        The base estimator from which the regressor chain is built.
+
+    order : array-like of shape (n_outputs,) or 'random', default=None
+        If `None`, the order will be determined by the order of columns in
+        the label matrix Y.::
+
+            order = [0, 1, 2, ..., Y.shape[1] - 1]
+
+        The order of the chain can be explicitly set by providing a list of
+        integers. For example, for a chain of length 5.::
+
+            order = [1, 3, 2, 4, 0]
+
+        means that the first model in the chain will make predictions for
+        column 1 in the Y matrix, the second model will make predictions
+        for column 3, etc.
+
+        If order is 'random' a random ordering will be used.
+
+    cv : int, cross-validation generator or an iterable, default=None
+        Determines whether to use cross validated predictions or true
+        labels for the results of previous estimators in the chain.
+        Possible inputs for cv are:
+
+        - None, to use true labels when fitting,
+        - integer, to specify the number of folds in a (Stratified)KFold,
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+
+    random_state : int, RandomState instance or None, optional (default=None)
+        If ``order='random'``, determines random number generation for the
+        chain order.
+        In addition, it controls the random seed given at each `base_estimator`
+        at each chaining iteration. Thus, it is only used when `base_estimator`
+        exposes a `random_state`.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    verbose : bool, default=False
+        If True, chain progress is output as each model is completed.
+
+        .. versionadded:: 1.2
+
+    base_estimator : estimator, default="deprecated"
+        Use `estimator` instead.
+
+        .. deprecated:: 1.7
+            `base_estimator` is deprecated and will be removed in 1.9.
+            Use `estimator` instead.
+
+    Attributes
+    ----------
+    estimators_ : list
+        A list of clones of base_estimator.
+
+    order_ : list
+        The order of labels in the classifier chain.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying `base_estimator` exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    ClassifierChain : Equivalent for classification.
+    MultiOutputRegressor : Learns each output independently rather than
+        chaining.
+
+    Examples
+    --------
+    >>> from sklearn.multioutput import RegressorChain
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> logreg = LogisticRegression(solver='lbfgs')
+    >>> X, Y = [[1, 0], [0, 1], [1, 1]], [[0, 2], [1, 1], [2, 0]]
+    >>> chain = RegressorChain(logreg, order=[0, 1]).fit(X, Y)
+    >>> chain.predict(X)
+    array([[0., 2.],
+           [1., 1.],
+           [2., 0.]])
+    """
+
+    @_fit_context(
+        # RegressorChain.base_estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, Y, **fit_params):
+        """Fit the model to data matrix X and targets Y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+
+        Y : array-like of shape (n_samples, n_classes)
+            The target values.
+
+        **fit_params : dict of string -> object
+            Parameters passed to the `fit` method at each step
+            of the regressor chain.
+
+            .. versionadded:: 0.23
+
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+        super().fit(X, Y, **fit_params)
+        return self
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.3
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self._get_estimator(),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+        )
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.target_tags.single_output = False
+        tags.target_tags.multi_output = True
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/naive_bayes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/naive_bayes.py
new file mode 100644
index 0000000000000000000000000000000000000000..31a1b87af2916e352a55b68307ba395c9193ce70
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/naive_bayes.py
@@ -0,0 +1,1540 @@
+"""Naive Bayes algorithms.
+
+These are supervised learning methods based on applying Bayes' theorem with strong
+(naive) feature independence assumptions.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.special import logsumexp
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    _fit_context,
+)
+from .preprocessing import LabelBinarizer, binarize, label_binarize
+from .utils._param_validation import Interval
+from .utils.extmath import safe_sparse_dot
+from .utils.multiclass import _check_partial_fit_first_call
+from .utils.validation import (
+    _check_n_features,
+    _check_sample_weight,
+    check_is_fitted,
+    check_non_negative,
+    validate_data,
+)
+
+__all__ = [
+    "BernoulliNB",
+    "CategoricalNB",
+    "ComplementNB",
+    "GaussianNB",
+    "MultinomialNB",
+]
+
+
+class _BaseNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):
+    """Abstract base class for naive Bayes estimators"""
+
+    @abstractmethod
+    def _joint_log_likelihood(self, X):
+        """Compute the unnormalized posterior log probability of X
+
+        I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of
+        shape (n_samples, n_classes).
+
+        Public methods predict, predict_proba, predict_log_proba, and
+        predict_joint_log_proba pass the input through _check_X before handing it
+        over to _joint_log_likelihood. The term "joint log likelihood" is used
+        interchangibly with "joint log probability".
+        """
+
+    @abstractmethod
+    def _check_X(self, X):
+        """To be overridden in subclasses with the actual checks.
+
+        Only used in predict* methods.
+        """
+
+    def predict_joint_log_proba(self, X):
+        """Return joint log probability estimates for the test vector X.
+
+        For each row x of X and class y, the joint log probability is given by
+        ``log P(x, y) = log P(y) + log P(x|y),``
+        where ``log P(y)`` is the class prior probability and ``log P(x|y)`` is
+        the class-conditional probability.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples, n_classes)
+            Returns the joint log-probability of the samples for each class in
+            the model. The columns correspond to the classes in sorted
+            order, as they appear in the attribute :term:`classes_`.
+        """
+        check_is_fitted(self)
+        X = self._check_X(X)
+        return self._joint_log_likelihood(X)
+
+    def predict(self, X):
+        """
+        Perform classification on an array of test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Returns
+        -------
+        C : ndarray of shape (n_samples,)
+            Predicted target values for X.
+        """
+        check_is_fitted(self)
+        X = self._check_X(X)
+        jll = self._joint_log_likelihood(X)
+        return self.classes_[np.argmax(jll, axis=1)]
+
+    def predict_log_proba(self, X):
+        """
+        Return log-probability estimates for the test vector X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Returns
+        -------
+        C : array-like of shape (n_samples, n_classes)
+            Returns the log-probability of the samples for each class in
+            the model. The columns correspond to the classes in sorted
+            order, as they appear in the attribute :term:`classes_`.
+        """
+        check_is_fitted(self)
+        X = self._check_X(X)
+        jll = self._joint_log_likelihood(X)
+        # normalize by P(x) = P(f_1, ..., f_n)
+        log_prob_x = logsumexp(jll, axis=1)
+        return jll - np.atleast_2d(log_prob_x).T
+
+    def predict_proba(self, X):
+        """
+        Return probability estimates for the test vector X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Returns
+        -------
+        C : array-like of shape (n_samples, n_classes)
+            Returns the probability of the samples for each class in
+            the model. The columns correspond to the classes in sorted
+            order, as they appear in the attribute :term:`classes_`.
+        """
+        return np.exp(self.predict_log_proba(X))
+
+
+class GaussianNB(_BaseNB):
+    """
+    Gaussian Naive Bayes (GaussianNB).
+
+    Can perform online updates to model parameters via :meth:`partial_fit`.
+    For details on algorithm used to update feature means and variance online,
+    see `Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque
+    `_.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    priors : array-like of shape (n_classes,), default=None
+        Prior probabilities of the classes. If specified, the priors are not
+        adjusted according to the data.
+
+    var_smoothing : float, default=1e-9
+        Portion of the largest variance of all features that is added to
+        variances for calculation stability.
+
+        .. versionadded:: 0.20
+
+    Attributes
+    ----------
+    class_count_ : ndarray of shape (n_classes,)
+        number of training samples observed in each class.
+
+    class_prior_ : ndarray of shape (n_classes,)
+        probability of each class.
+
+    classes_ : ndarray of shape (n_classes,)
+        class labels known to the classifier.
+
+    epsilon_ : float
+        absolute additive value to variances.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    var_ : ndarray of shape (n_classes, n_features)
+        Variance of each feature per class.
+
+        .. versionadded:: 1.0
+
+    theta_ : ndarray of shape (n_classes, n_features)
+        mean of each feature per class.
+
+    See Also
+    --------
+    BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models.
+    CategoricalNB : Naive Bayes classifier for categorical features.
+    ComplementNB : Complement Naive Bayes classifier.
+    MultinomialNB : Naive Bayes classifier for multinomial models.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+    >>> Y = np.array([1, 1, 1, 2, 2, 2])
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> clf = GaussianNB()
+    >>> clf.fit(X, Y)
+    GaussianNB()
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
+    >>> clf_pf = GaussianNB()
+    >>> clf_pf.partial_fit(X, Y, np.unique(Y))
+    GaussianNB()
+    >>> print(clf_pf.predict([[-0.8, -1]]))
+    [1]
+    """
+
+    _parameter_constraints: dict = {
+        "priors": ["array-like", None],
+        "var_smoothing": [Interval(Real, 0, None, closed="left")],
+    }
+
+    def __init__(self, *, priors=None, var_smoothing=1e-9):
+        self.priors = priors
+        self.var_smoothing = var_smoothing
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit Gaussian Naive Bayes according to X, y.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+            .. versionadded:: 0.17
+               Gaussian Naive Bayes supports fitting with *sample_weight*.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        y = validate_data(self, y=y)
+        return self._partial_fit(
+            X, y, np.unique(y), _refit=True, sample_weight=sample_weight
+        )
+
+    def _check_X(self, X):
+        """Validate X, used only in predict* methods."""
+        return validate_data(self, X, reset=False)
+
+    @staticmethod
+    def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
+        """Compute online update of Gaussian mean and variance.
+
+        Given starting sample count, mean, and variance, a new set of
+        points X, and optionally sample weights, return the updated mean and
+        variance. (NB - each dimension (column) in X is treated as independent
+        -- you get variance, not covariance).
+
+        Can take scalar mean and variance, or vector mean and variance to
+        simultaneously update a number of independent Gaussians.
+
+        See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
+
+        http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
+
+        Parameters
+        ----------
+        n_past : int
+            Number of samples represented in old mean and variance. If sample
+            weights were given, this should contain the sum of sample
+            weights represented in old mean and variance.
+
+        mu : array-like of shape (number of Gaussians,)
+            Means for Gaussians in original set.
+
+        var : array-like of shape (number of Gaussians,)
+            Variances for Gaussians in original set.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        total_mu : array-like of shape (number of Gaussians,)
+            Updated mean for each Gaussian over the combined set.
+
+        total_var : array-like of shape (number of Gaussians,)
+            Updated variance for each Gaussian over the combined set.
+        """
+        if X.shape[0] == 0:
+            return mu, var
+
+        # Compute (potentially weighted) mean and variance of new datapoints
+        if sample_weight is not None:
+            n_new = float(sample_weight.sum())
+            if np.isclose(n_new, 0.0):
+                return mu, var
+            new_mu = np.average(X, axis=0, weights=sample_weight)
+            new_var = np.average((X - new_mu) ** 2, axis=0, weights=sample_weight)
+        else:
+            n_new = X.shape[0]
+            new_var = np.var(X, axis=0)
+            new_mu = np.mean(X, axis=0)
+
+        if n_past == 0:
+            return new_mu, new_var
+
+        n_total = float(n_past + n_new)
+
+        # Combine mean of old and new data, taking into consideration
+        # (weighted) number of observations
+        total_mu = (n_new * new_mu + n_past * mu) / n_total
+
+        # Combine variance of old and new data, taking into consideration
+        # (weighted) number of observations. This is achieved by combining
+        # the sum-of-squared-differences (ssd)
+        old_ssd = n_past * var
+        new_ssd = n_new * new_var
+        total_ssd = old_ssd + new_ssd + (n_new * n_past / n_total) * (mu - new_mu) ** 2
+        total_var = total_ssd / n_total
+
+        return total_mu, total_var
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y, classes=None, sample_weight=None):
+        """Incremental fit on a batch of samples.
+
+        This method is expected to be called several times consecutively
+        on different chunks of a dataset so as to implement out-of-core
+        or online learning.
+
+        This is especially useful when the whole dataset is too big to fit in
+        memory at once.
+
+        This method has some performance and numerical stability overhead,
+        hence it is better to call partial_fit on chunks of data that are
+        as large as possible (as long as fitting in the memory budget) to
+        hide the overhead.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        classes : array-like of shape (n_classes,), default=None
+            List of all the classes that can possibly appear in the y vector.
+
+            Must be provided at the first call to partial_fit, can be omitted
+            in subsequent calls.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+            .. versionadded:: 0.17
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        return self._partial_fit(
+            X, y, classes, _refit=False, sample_weight=sample_weight
+        )
+
+    def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None):
+        """Actual implementation of Gaussian NB fitting.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        classes : array-like of shape (n_classes,), default=None
+            List of all the classes that can possibly appear in the y vector.
+
+            Must be provided at the first call to partial_fit, can be omitted
+            in subsequent calls.
+
+        _refit : bool, default=False
+            If true, act as though this were the first time we called
+            _partial_fit (ie, throw away any past fitting and start over).
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        self : object
+        """
+        if _refit:
+            self.classes_ = None
+
+        first_call = _check_partial_fit_first_call(self, classes)
+        X, y = validate_data(self, X, y, reset=first_call)
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        # If the ratio of data variance between dimensions is too small, it
+        # will cause numerical errors. To address this, we artificially
+        # boost the variance by epsilon, a small fraction of the standard
+        # deviation of the largest dimension.
+        self.epsilon_ = self.var_smoothing * np.var(X, axis=0).max()
+
+        if first_call:
+            # This is the first call to partial_fit:
+            # initialize various cumulative counters
+            n_features = X.shape[1]
+            n_classes = len(self.classes_)
+            self.theta_ = np.zeros((n_classes, n_features))
+            self.var_ = np.zeros((n_classes, n_features))
+
+            self.class_count_ = np.zeros(n_classes, dtype=np.float64)
+
+            # Initialise the class prior
+            # Take into account the priors
+            if self.priors is not None:
+                priors = np.asarray(self.priors)
+                # Check that the provided prior matches the number of classes
+                if len(priors) != n_classes:
+                    raise ValueError("Number of priors must match number of classes.")
+                # Check that the sum is 1
+                if not np.isclose(priors.sum(), 1.0):
+                    raise ValueError("The sum of the priors should be 1.")
+                # Check that the priors are non-negative
+                if (priors < 0).any():
+                    raise ValueError("Priors must be non-negative.")
+                self.class_prior_ = priors
+            else:
+                # Initialize the priors to zeros for each class
+                self.class_prior_ = np.zeros(len(self.classes_), dtype=np.float64)
+        else:
+            if X.shape[1] != self.theta_.shape[1]:
+                msg = "Number of features %d does not match previous data %d."
+                raise ValueError(msg % (X.shape[1], self.theta_.shape[1]))
+            # Put epsilon back in each time
+            self.var_[:, :] -= self.epsilon_
+
+        classes = self.classes_
+
+        unique_y = np.unique(y)
+        unique_y_in_classes = np.isin(unique_y, classes)
+
+        if not np.all(unique_y_in_classes):
+            raise ValueError(
+                "The target label(s) %s in y do not exist in the initial classes %s"
+                % (unique_y[~unique_y_in_classes], classes)
+            )
+
+        for y_i in unique_y:
+            i = classes.searchsorted(y_i)
+            X_i = X[y == y_i, :]
+
+            if sample_weight is not None:
+                sw_i = sample_weight[y == y_i]
+                N_i = sw_i.sum()
+            else:
+                sw_i = None
+                N_i = X_i.shape[0]
+
+            new_theta, new_sigma = self._update_mean_variance(
+                self.class_count_[i], self.theta_[i, :], self.var_[i, :], X_i, sw_i
+            )
+
+            self.theta_[i, :] = new_theta
+            self.var_[i, :] = new_sigma
+            self.class_count_[i] += N_i
+
+        self.var_[:, :] += self.epsilon_
+
+        # Update if only no priors is provided
+        if self.priors is None:
+            # Empirical prior, with sample_weight taken into account
+            self.class_prior_ = self.class_count_ / self.class_count_.sum()
+
+        return self
+
+    def _joint_log_likelihood(self, X):
+        joint_log_likelihood = []
+        for i in range(np.size(self.classes_)):
+            jointi = np.log(self.class_prior_[i])
+            n_ij = -0.5 * np.sum(np.log(2.0 * np.pi * self.var_[i, :]))
+            n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) / (self.var_[i, :]), 1)
+            joint_log_likelihood.append(jointi + n_ij)
+
+        joint_log_likelihood = np.array(joint_log_likelihood).T
+        return joint_log_likelihood
+
+
+class _BaseDiscreteNB(_BaseNB):
+    """Abstract base class for naive Bayes on discrete/categorical data
+
+    Any estimator based on this class should provide:
+
+    __init__
+    _joint_log_likelihood(X) as per _BaseNB
+    _update_feature_log_prob(alpha)
+    _count(X, Y)
+    """
+
+    _parameter_constraints: dict = {
+        "alpha": [Interval(Real, 0, None, closed="left"), "array-like"],
+        "fit_prior": ["boolean"],
+        "class_prior": ["array-like", None],
+        "force_alpha": ["boolean"],
+    }
+
+    def __init__(self, alpha=1.0, fit_prior=True, class_prior=None, force_alpha=True):
+        self.alpha = alpha
+        self.fit_prior = fit_prior
+        self.class_prior = class_prior
+        self.force_alpha = force_alpha
+
+    @abstractmethod
+    def _count(self, X, Y):
+        """Update counts that are used to calculate probabilities.
+
+        The counts make up a sufficient statistic extracted from the data.
+        Accordingly, this method is called each time `fit` or `partial_fit`
+        update the model. `class_count_` and `feature_count_` must be updated
+        here along with any model specific counts.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            The input samples.
+        Y : ndarray of shape (n_samples, n_classes)
+            Binarized class labels.
+        """
+
+    @abstractmethod
+    def _update_feature_log_prob(self, alpha):
+        """Update feature log probabilities based on counts.
+
+        This method is called each time `fit` or `partial_fit` update the
+        model.
+
+        Parameters
+        ----------
+        alpha : float
+            smoothing parameter. See :meth:`_check_alpha`.
+        """
+
+    def _check_X(self, X):
+        """Validate X, used only in predict* methods."""
+        return validate_data(self, X, accept_sparse="csr", reset=False)
+
+    def _check_X_y(self, X, y, reset=True):
+        """Validate X and y in fit methods."""
+        return validate_data(self, X, y, accept_sparse="csr", reset=reset)
+
+    def _update_class_log_prior(self, class_prior=None):
+        """Update class log priors.
+
+        The class log priors are based on `class_prior`, class count or the
+        number of classes. This method is called each time `fit` or
+        `partial_fit` update the model.
+        """
+        n_classes = len(self.classes_)
+        if class_prior is not None:
+            if len(class_prior) != n_classes:
+                raise ValueError("Number of priors must match number of classes.")
+            self.class_log_prior_ = np.log(class_prior)
+        elif self.fit_prior:
+            with warnings.catch_warnings():
+                # silence the warning when count is 0 because class was not yet
+                # observed
+                warnings.simplefilter("ignore", RuntimeWarning)
+                log_class_count = np.log(self.class_count_)
+
+            # empirical prior, with sample_weight taken into account
+            self.class_log_prior_ = log_class_count - np.log(self.class_count_.sum())
+        else:
+            self.class_log_prior_ = np.full(n_classes, -np.log(n_classes))
+
+    def _check_alpha(self):
+        alpha = (
+            np.asarray(self.alpha) if not isinstance(self.alpha, Real) else self.alpha
+        )
+        alpha_min = np.min(alpha)
+        if isinstance(alpha, np.ndarray):
+            if not alpha.shape[0] == self.n_features_in_:
+                raise ValueError(
+                    "When alpha is an array, it should contains `n_features`. "
+                    f"Got {alpha.shape[0]} elements instead of {self.n_features_in_}."
+                )
+            # check that all alpha are positive
+            if alpha_min < 0:
+                raise ValueError("All values in alpha must be greater than 0.")
+        alpha_lower_bound = 1e-10
+        if alpha_min < alpha_lower_bound and not self.force_alpha:
+            warnings.warn(
+                "alpha too small will result in numeric errors, setting alpha ="
+                f" {alpha_lower_bound:.1e}. Use `force_alpha=True` to keep alpha"
+                " unchanged."
+            )
+            return np.maximum(alpha, alpha_lower_bound)
+        return alpha
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def partial_fit(self, X, y, classes=None, sample_weight=None):
+        """Incremental fit on a batch of samples.
+
+        This method is expected to be called several times consecutively
+        on different chunks of a dataset so as to implement out-of-core
+        or online learning.
+
+        This is especially useful when the whole dataset is too big to fit in
+        memory at once.
+
+        This method has some performance overhead hence it is better to call
+        partial_fit on chunks of data that are as large as possible
+        (as long as fitting in the memory budget) to hide the overhead.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        classes : array-like of shape (n_classes,), default=None
+            List of all the classes that can possibly appear in the y vector.
+
+            Must be provided at the first call to partial_fit, can be omitted
+            in subsequent calls.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        first_call = not hasattr(self, "classes_")
+
+        X, y = self._check_X_y(X, y, reset=first_call)
+        _, n_features = X.shape
+
+        if _check_partial_fit_first_call(self, classes):
+            # This is the first call to partial_fit:
+            # initialize various cumulative counters
+            n_classes = len(classes)
+            self._init_counters(n_classes, n_features)
+
+        Y = label_binarize(y, classes=self.classes_)
+        if Y.shape[1] == 1:
+            if len(self.classes_) == 2:
+                Y = np.concatenate((1 - Y, Y), axis=1)
+            else:  # degenerate case: just one class
+                Y = np.ones_like(Y)
+
+        if X.shape[0] != Y.shape[0]:
+            msg = "X.shape[0]=%d and y.shape[0]=%d are incompatible."
+            raise ValueError(msg % (X.shape[0], y.shape[0]))
+
+        # label_binarize() returns arrays with dtype=np.int64.
+        # We convert it to np.float64 to support sample_weight consistently
+        Y = Y.astype(np.float64, copy=False)
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+            sample_weight = np.atleast_2d(sample_weight)
+            Y *= sample_weight.T
+
+        class_prior = self.class_prior
+
+        # Count raw events from data before updating the class log prior
+        # and feature log probas
+        self._count(X, Y)
+
+        # XXX: OPTIM: we could introduce a public finalization method to
+        # be called by the user explicitly just once after several consecutive
+        # calls to partial_fit and prior any call to predict[_[log_]proba]
+        # to avoid computing the smooth log probas at each call to partial fit
+        alpha = self._check_alpha()
+        self._update_feature_log_prob(alpha)
+        self._update_class_log_prior(class_prior=class_prior)
+        return self
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit Naive Bayes classifier according to X, y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        X, y = self._check_X_y(X, y)
+        _, n_features = X.shape
+
+        labelbin = LabelBinarizer()
+        Y = labelbin.fit_transform(y)
+        self.classes_ = labelbin.classes_
+        if Y.shape[1] == 1:
+            if len(self.classes_) == 2:
+                Y = np.concatenate((1 - Y, Y), axis=1)
+            else:  # degenerate case: just one class
+                Y = np.ones_like(Y)
+
+        # LabelBinarizer().fit_transform() returns arrays with dtype=np.int64.
+        # We convert it to np.float64 to support sample_weight consistently;
+        # this means we also don't have to cast X to floating point
+        if sample_weight is not None:
+            Y = Y.astype(np.float64, copy=False)
+            sample_weight = _check_sample_weight(sample_weight, X)
+            sample_weight = np.atleast_2d(sample_weight)
+            Y *= sample_weight.T
+
+        class_prior = self.class_prior
+
+        # Count raw events from data before updating the class log prior
+        # and feature log probas
+        n_classes = Y.shape[1]
+        self._init_counters(n_classes, n_features)
+        self._count(X, Y)
+        alpha = self._check_alpha()
+        self._update_feature_log_prob(alpha)
+        self._update_class_log_prior(class_prior=class_prior)
+        return self
+
+    def _init_counters(self, n_classes, n_features):
+        self.class_count_ = np.zeros(n_classes, dtype=np.float64)
+        self.feature_count_ = np.zeros((n_classes, n_features), dtype=np.float64)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.classifier_tags.poor_score = True
+        return tags
+
+
+class MultinomialNB(_BaseDiscreteNB):
+    """
+    Naive Bayes classifier for multinomial models.
+
+    The multinomial Naive Bayes classifier is suitable for classification with
+    discrete features (e.g., word counts for text classification). The
+    multinomial distribution normally requires integer feature counts. However,
+    in practice, fractional counts such as tf-idf may also work.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    alpha : float or array-like of shape (n_features,), default=1.0
+        Additive (Laplace/Lidstone) smoothing parameter
+        (set alpha=0 and force_alpha=True, for no smoothing).
+
+    force_alpha : bool, default=True
+        If False and alpha is less than 1e-10, it will set alpha to
+        1e-10. If True, alpha will remain unchanged. This may cause
+        numerical errors if alpha is too close to 0.
+
+        .. versionadded:: 1.2
+        .. versionchanged:: 1.4
+           The default value of `force_alpha` changed to `True`.
+
+    fit_prior : bool, default=True
+        Whether to learn class prior probabilities or not.
+        If false, a uniform prior will be used.
+
+    class_prior : array-like of shape (n_classes,), default=None
+        Prior probabilities of the classes. If specified, the priors are not
+        adjusted according to the data.
+
+    Attributes
+    ----------
+    class_count_ : ndarray of shape (n_classes,)
+        Number of samples encountered for each class during fitting. This
+        value is weighted by the sample weight when provided.
+
+    class_log_prior_ : ndarray of shape (n_classes,)
+        Smoothed empirical log probability for each class.
+
+    classes_ : ndarray of shape (n_classes,)
+        Class labels known to the classifier
+
+    feature_count_ : ndarray of shape (n_classes, n_features)
+        Number of samples encountered for each (class, feature)
+        during fitting. This value is weighted by the sample weight when
+        provided.
+
+    feature_log_prob_ : ndarray of shape (n_classes, n_features)
+        Empirical log probability of features
+        given a class, ``P(x_i|y)``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models.
+    CategoricalNB : Naive Bayes classifier for categorical features.
+    ComplementNB : Complement Naive Bayes classifier.
+    GaussianNB : Gaussian Naive Bayes.
+
+    References
+    ----------
+    C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
+    Information Retrieval. Cambridge University Press, pp. 234-265.
+    https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> y = np.array([1, 2, 3, 4, 5, 6])
+    >>> from sklearn.naive_bayes import MultinomialNB
+    >>> clf = MultinomialNB()
+    >>> clf.fit(X, y)
+    MultinomialNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
+    """
+
+    def __init__(
+        self, *, alpha=1.0, force_alpha=True, fit_prior=True, class_prior=None
+    ):
+        super().__init__(
+            alpha=alpha,
+            fit_prior=fit_prior,
+            class_prior=class_prior,
+            force_alpha=force_alpha,
+        )
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.positive_only = True
+        return tags
+
+    def _count(self, X, Y):
+        """Count and smooth feature occurrences."""
+        check_non_negative(X, "MultinomialNB (input X)")
+        self.feature_count_ += safe_sparse_dot(Y.T, X)
+        self.class_count_ += Y.sum(axis=0)
+
+    def _update_feature_log_prob(self, alpha):
+        """Apply smoothing to raw counts and recompute log probabilities"""
+        smoothed_fc = self.feature_count_ + alpha
+        smoothed_cc = smoothed_fc.sum(axis=1)
+
+        self.feature_log_prob_ = np.log(smoothed_fc) - np.log(
+            smoothed_cc.reshape(-1, 1)
+        )
+
+    def _joint_log_likelihood(self, X):
+        """Calculate the posterior log probability of the samples X"""
+        return safe_sparse_dot(X, self.feature_log_prob_.T) + self.class_log_prior_
+
+
+class ComplementNB(_BaseDiscreteNB):
+    """The Complement Naive Bayes classifier described in Rennie et al. (2003).
+
+    The Complement Naive Bayes classifier was designed to correct the "severe
+    assumptions" made by the standard Multinomial Naive Bayes classifier. It is
+    particularly suited for imbalanced data sets.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    alpha : float or array-like of shape (n_features,), default=1.0
+        Additive (Laplace/Lidstone) smoothing parameter
+        (set alpha=0 and force_alpha=True, for no smoothing).
+
+    force_alpha : bool, default=True
+        If False and alpha is less than 1e-10, it will set alpha to
+        1e-10. If True, alpha will remain unchanged. This may cause
+        numerical errors if alpha is too close to 0.
+
+        .. versionadded:: 1.2
+        .. versionchanged:: 1.4
+           The default value of `force_alpha` changed to `True`.
+
+    fit_prior : bool, default=True
+        Only used in edge case with a single class in the training set.
+
+    class_prior : array-like of shape (n_classes,), default=None
+        Prior probabilities of the classes. Not used.
+
+    norm : bool, default=False
+        Whether or not a second normalization of the weights is performed. The
+        default behavior mirrors the implementations found in Mahout and Weka,
+        which do not follow the full algorithm described in Table 9 of the
+        paper.
+
+    Attributes
+    ----------
+    class_count_ : ndarray of shape (n_classes,)
+        Number of samples encountered for each class during fitting. This
+        value is weighted by the sample weight when provided.
+
+    class_log_prior_ : ndarray of shape (n_classes,)
+        Smoothed empirical log probability for each class. Only used in edge
+        case with a single class in the training set.
+
+    classes_ : ndarray of shape (n_classes,)
+        Class labels known to the classifier
+
+    feature_all_ : ndarray of shape (n_features,)
+        Number of samples encountered for each feature during fitting. This
+        value is weighted by the sample weight when provided.
+
+    feature_count_ : ndarray of shape (n_classes, n_features)
+        Number of samples encountered for each (class, feature) during fitting.
+        This value is weighted by the sample weight when provided.
+
+    feature_log_prob_ : ndarray of shape (n_classes, n_features)
+        Empirical weights for class complements.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models.
+    CategoricalNB : Naive Bayes classifier for categorical features.
+    GaussianNB : Gaussian Naive Bayes.
+    MultinomialNB : Naive Bayes classifier for multinomial models.
+
+    References
+    ----------
+    Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003).
+    Tackling the poor assumptions of naive bayes text classifiers. In ICML
+    (Vol. 3, pp. 616-623).
+    https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> y = np.array([1, 2, 3, 4, 5, 6])
+    >>> from sklearn.naive_bayes import ComplementNB
+    >>> clf = ComplementNB()
+    >>> clf.fit(X, y)
+    ComplementNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseDiscreteNB._parameter_constraints,
+        "norm": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        *,
+        alpha=1.0,
+        force_alpha=True,
+        fit_prior=True,
+        class_prior=None,
+        norm=False,
+    ):
+        super().__init__(
+            alpha=alpha,
+            force_alpha=force_alpha,
+            fit_prior=fit_prior,
+            class_prior=class_prior,
+        )
+        self.norm = norm
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.positive_only = True
+        return tags
+
+    def _count(self, X, Y):
+        """Count feature occurrences."""
+        check_non_negative(X, "ComplementNB (input X)")
+        self.feature_count_ += safe_sparse_dot(Y.T, X)
+        self.class_count_ += Y.sum(axis=0)
+        self.feature_all_ = self.feature_count_.sum(axis=0)
+
+    def _update_feature_log_prob(self, alpha):
+        """Apply smoothing to raw counts and compute the weights."""
+        comp_count = self.feature_all_ + alpha - self.feature_count_
+        logged = np.log(comp_count / comp_count.sum(axis=1, keepdims=True))
+        # _BaseNB.predict uses argmax, but ComplementNB operates with argmin.
+        if self.norm:
+            summed = logged.sum(axis=1, keepdims=True)
+            feature_log_prob = logged / summed
+        else:
+            feature_log_prob = -logged
+        self.feature_log_prob_ = feature_log_prob
+
+    def _joint_log_likelihood(self, X):
+        """Calculate the class scores for the samples in X."""
+        jll = safe_sparse_dot(X, self.feature_log_prob_.T)
+        if len(self.classes_) == 1:
+            jll += self.class_log_prior_
+        return jll
+
+
+class BernoulliNB(_BaseDiscreteNB):
+    """Naive Bayes classifier for multivariate Bernoulli models.
+
+    Like MultinomialNB, this classifier is suitable for discrete data. The
+    difference is that while MultinomialNB works with occurrence counts,
+    BernoulliNB is designed for binary/boolean features.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    alpha : float or array-like of shape (n_features,), default=1.0
+        Additive (Laplace/Lidstone) smoothing parameter
+        (set alpha=0 and force_alpha=True, for no smoothing).
+
+    force_alpha : bool, default=True
+        If False and alpha is less than 1e-10, it will set alpha to
+        1e-10. If True, alpha will remain unchanged. This may cause
+        numerical errors if alpha is too close to 0.
+
+        .. versionadded:: 1.2
+        .. versionchanged:: 1.4
+           The default value of `force_alpha` changed to `True`.
+
+    binarize : float or None, default=0.0
+        Threshold for binarizing (mapping to booleans) of sample features.
+        If None, input is presumed to already consist of binary vectors.
+
+    fit_prior : bool, default=True
+        Whether to learn class prior probabilities or not.
+        If false, a uniform prior will be used.
+
+    class_prior : array-like of shape (n_classes,), default=None
+        Prior probabilities of the classes. If specified, the priors are not
+        adjusted according to the data.
+
+    Attributes
+    ----------
+    class_count_ : ndarray of shape (n_classes,)
+        Number of samples encountered for each class during fitting. This
+        value is weighted by the sample weight when provided.
+
+    class_log_prior_ : ndarray of shape (n_classes,)
+        Log probability of each class (smoothed).
+
+    classes_ : ndarray of shape (n_classes,)
+        Class labels known to the classifier
+
+    feature_count_ : ndarray of shape (n_classes, n_features)
+        Number of samples encountered for each (class, feature)
+        during fitting. This value is weighted by the sample weight when
+        provided.
+
+    feature_log_prob_ : ndarray of shape (n_classes, n_features)
+        Empirical log probability of features given a class, P(x_i|y).
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    CategoricalNB : Naive Bayes classifier for categorical features.
+    ComplementNB : The Complement Naive Bayes classifier
+        described in Rennie et al. (2003).
+    GaussianNB : Gaussian Naive Bayes (GaussianNB).
+    MultinomialNB : Naive Bayes classifier for multinomial models.
+
+    References
+    ----------
+    C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
+    Information Retrieval. Cambridge University Press, pp. 234-265.
+    https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
+
+    A. McCallum and K. Nigam (1998). A comparison of event models for naive
+    Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for
+    Text Categorization, pp. 41-48.
+
+    V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
+    naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> Y = np.array([1, 2, 3, 4, 4, 5])
+    >>> from sklearn.naive_bayes import BernoulliNB
+    >>> clf = BernoulliNB()
+    >>> clf.fit(X, Y)
+    BernoulliNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseDiscreteNB._parameter_constraints,
+        "binarize": [None, Interval(Real, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        *,
+        alpha=1.0,
+        force_alpha=True,
+        binarize=0.0,
+        fit_prior=True,
+        class_prior=None,
+    ):
+        super().__init__(
+            alpha=alpha,
+            fit_prior=fit_prior,
+            class_prior=class_prior,
+            force_alpha=force_alpha,
+        )
+        self.binarize = binarize
+
+    def _check_X(self, X):
+        """Validate X, used only in predict* methods."""
+        X = super()._check_X(X)
+        if self.binarize is not None:
+            X = binarize(X, threshold=self.binarize)
+        return X
+
+    def _check_X_y(self, X, y, reset=True):
+        X, y = super()._check_X_y(X, y, reset=reset)
+        if self.binarize is not None:
+            X = binarize(X, threshold=self.binarize)
+        return X, y
+
+    def _count(self, X, Y):
+        """Count and smooth feature occurrences."""
+        self.feature_count_ += safe_sparse_dot(Y.T, X)
+        self.class_count_ += Y.sum(axis=0)
+
+    def _update_feature_log_prob(self, alpha):
+        """Apply smoothing to raw counts and recompute log probabilities"""
+        smoothed_fc = self.feature_count_ + alpha
+        smoothed_cc = self.class_count_ + alpha * 2
+
+        self.feature_log_prob_ = np.log(smoothed_fc) - np.log(
+            smoothed_cc.reshape(-1, 1)
+        )
+
+    def _joint_log_likelihood(self, X):
+        """Calculate the posterior log probability of the samples X"""
+        n_features = self.feature_log_prob_.shape[1]
+        n_features_X = X.shape[1]
+
+        if n_features_X != n_features:
+            raise ValueError(
+                "Expected input with %d features, got %d instead"
+                % (n_features, n_features_X)
+            )
+
+        neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+        # Compute  neg_prob · (1 - X).T  as  ∑neg_prob - X · neg_prob
+        jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T)
+        jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+
+        return jll
+
+
+class CategoricalNB(_BaseDiscreteNB):
+    """Naive Bayes classifier for categorical features.
+
+    The categorical Naive Bayes classifier is suitable for classification with
+    discrete features that are categorically distributed. The categories of
+    each feature are drawn from a categorical distribution.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    alpha : float, default=1.0
+        Additive (Laplace/Lidstone) smoothing parameter
+        (set alpha=0 and force_alpha=True, for no smoothing).
+
+    force_alpha : bool, default=True
+        If False and alpha is less than 1e-10, it will set alpha to
+        1e-10. If True, alpha will remain unchanged. This may cause
+        numerical errors if alpha is too close to 0.
+
+        .. versionadded:: 1.2
+        .. versionchanged:: 1.4
+           The default value of `force_alpha` changed to `True`.
+
+    fit_prior : bool, default=True
+        Whether to learn class prior probabilities or not.
+        If false, a uniform prior will be used.
+
+    class_prior : array-like of shape (n_classes,), default=None
+        Prior probabilities of the classes. If specified, the priors are not
+        adjusted according to the data.
+
+    min_categories : int or array-like of shape (n_features,), default=None
+        Minimum number of categories per feature.
+
+        - integer: Sets the minimum number of categories per feature to
+          `n_categories` for each features.
+        - array-like: shape (n_features,) where `n_categories[i]` holds the
+          minimum number of categories for the ith column of the input.
+        - None (default): Determines the number of categories automatically
+          from the training data.
+
+        .. versionadded:: 0.24
+
+    Attributes
+    ----------
+    category_count_ : list of arrays of shape (n_features,)
+        Holds arrays of shape (n_classes, n_categories of respective feature)
+        for each feature. Each array provides the number of samples
+        encountered for each class and category of the specific feature.
+
+    class_count_ : ndarray of shape (n_classes,)
+        Number of samples encountered for each class during fitting. This
+        value is weighted by the sample weight when provided.
+
+    class_log_prior_ : ndarray of shape (n_classes,)
+        Smoothed empirical log probability for each class.
+
+    classes_ : ndarray of shape (n_classes,)
+        Class labels known to the classifier
+
+    feature_log_prob_ : list of arrays of shape (n_features,)
+        Holds arrays of shape (n_classes, n_categories of respective feature)
+        for each feature. Each array provides the empirical log probability
+        of categories given the respective feature and class, ``P(x_i|y)``.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_categories_ : ndarray of shape (n_features,), dtype=np.int64
+        Number of categories for each feature. This value is
+        inferred from the data or set by the minimum number of categories.
+
+        .. versionadded:: 0.24
+
+    See Also
+    --------
+    BernoulliNB : Naive Bayes classifier for multivariate Bernoulli models.
+    ComplementNB : Complement Naive Bayes classifier.
+    GaussianNB : Gaussian Naive Bayes.
+    MultinomialNB : Naive Bayes classifier for multinomial models.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> y = np.array([1, 2, 3, 4, 5, 6])
+    >>> from sklearn.naive_bayes import CategoricalNB
+    >>> clf = CategoricalNB()
+    >>> clf.fit(X, y)
+    CategoricalNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
+    """
+
+    _parameter_constraints: dict = {
+        **_BaseDiscreteNB._parameter_constraints,
+        "min_categories": [
+            None,
+            "array-like",
+            Interval(Integral, 1, None, closed="left"),
+        ],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        *,
+        alpha=1.0,
+        force_alpha=True,
+        fit_prior=True,
+        class_prior=None,
+        min_categories=None,
+    ):
+        super().__init__(
+            alpha=alpha,
+            force_alpha=force_alpha,
+            fit_prior=fit_prior,
+            class_prior=class_prior,
+        )
+        self.min_categories = min_categories
+
+    def fit(self, X, y, sample_weight=None):
+        """Fit Naive Bayes classifier according to X, y.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features. Here, each feature of X is
+            assumed to be from a different categorical distribution.
+            It is further assumed that all categories of each feature are
+            represented by the numbers 0, ..., n - 1, where n refers to the
+            total number of categories for the given feature. This can, for
+            instance, be achieved with the help of OrdinalEncoder.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        return super().fit(X, y, sample_weight=sample_weight)
+
+    def partial_fit(self, X, y, classes=None, sample_weight=None):
+        """Incremental fit on a batch of samples.
+
+        This method is expected to be called several times consecutively
+        on different chunks of a dataset so as to implement out-of-core
+        or online learning.
+
+        This is especially useful when the whole dataset is too big to fit in
+        memory at once.
+
+        This method has some performance overhead hence it is better to call
+        partial_fit on chunks of data that are as large as possible
+        (as long as fitting in the memory budget) to hide the overhead.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features. Here, each feature of X is
+            assumed to be from a different categorical distribution.
+            It is further assumed that all categories of each feature are
+            represented by the numbers 0, ..., n - 1, where n refers to the
+            total number of categories for the given feature. This can, for
+            instance, be achieved with the help of OrdinalEncoder.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        classes : array-like of shape (n_classes,), default=None
+            List of all the classes that can possibly appear in the y vector.
+
+            Must be provided at the first call to partial_fit, can be omitted
+            in subsequent calls.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Weights applied to individual samples (1. for unweighted).
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        return super().partial_fit(X, y, classes, sample_weight=sample_weight)
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.categorical = True
+        tags.input_tags.sparse = False
+        tags.input_tags.positive_only = True
+        return tags
+
+    def _check_X(self, X):
+        """Validate X, used only in predict* methods."""
+        X = validate_data(
+            self,
+            X,
+            dtype="int",
+            accept_sparse=False,
+            ensure_all_finite=True,
+            reset=False,
+        )
+        check_non_negative(X, "CategoricalNB (input X)")
+        return X
+
+    def _check_X_y(self, X, y, reset=True):
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            dtype="int",
+            accept_sparse=False,
+            ensure_all_finite=True,
+            reset=reset,
+        )
+        check_non_negative(X, "CategoricalNB (input X)")
+        return X, y
+
+    def _init_counters(self, n_classes, n_features):
+        self.class_count_ = np.zeros(n_classes, dtype=np.float64)
+        self.category_count_ = [np.zeros((n_classes, 0)) for _ in range(n_features)]
+
+    @staticmethod
+    def _validate_n_categories(X, min_categories):
+        # rely on max for n_categories categories are encoded between 0...n-1
+        n_categories_X = X.max(axis=0) + 1
+        min_categories_ = np.array(min_categories)
+        if min_categories is not None:
+            if not np.issubdtype(min_categories_.dtype, np.signedinteger):
+                raise ValueError(
+                    "'min_categories' should have integral type. Got "
+                    f"{min_categories_.dtype} instead."
+                )
+            n_categories_ = np.maximum(n_categories_X, min_categories_, dtype=np.int64)
+            if n_categories_.shape != n_categories_X.shape:
+                raise ValueError(
+                    f"'min_categories' should have shape ({X.shape[1]},"
+                    ") when an array-like is provided. Got"
+                    f" {min_categories_.shape} instead."
+                )
+            return n_categories_
+        else:
+            return n_categories_X
+
+    def _count(self, X, Y):
+        def _update_cat_count_dims(cat_count, highest_feature):
+            diff = highest_feature + 1 - cat_count.shape[1]
+            if diff > 0:
+                # we append a column full of zeros for each new category
+                return np.pad(cat_count, [(0, 0), (0, diff)], "constant")
+            return cat_count
+
+        def _update_cat_count(X_feature, Y, cat_count, n_classes):
+            for j in range(n_classes):
+                mask = Y[:, j].astype(bool)
+                if Y.dtype.type == np.int64:
+                    weights = None
+                else:
+                    weights = Y[mask, j]
+                counts = np.bincount(X_feature[mask], weights=weights)
+                indices = np.nonzero(counts)[0]
+                cat_count[j, indices] += counts[indices]
+
+        self.class_count_ += Y.sum(axis=0)
+        self.n_categories_ = self._validate_n_categories(X, self.min_categories)
+        for i in range(self.n_features_in_):
+            X_feature = X[:, i]
+            self.category_count_[i] = _update_cat_count_dims(
+                self.category_count_[i], self.n_categories_[i] - 1
+            )
+            _update_cat_count(
+                X_feature, Y, self.category_count_[i], self.class_count_.shape[0]
+            )
+
+    def _update_feature_log_prob(self, alpha):
+        feature_log_prob = []
+        for i in range(self.n_features_in_):
+            smoothed_cat_count = self.category_count_[i] + alpha
+            smoothed_class_count = smoothed_cat_count.sum(axis=1)
+            feature_log_prob.append(
+                np.log(smoothed_cat_count) - np.log(smoothed_class_count.reshape(-1, 1))
+            )
+        self.feature_log_prob_ = feature_log_prob
+
+    def _joint_log_likelihood(self, X):
+        _check_n_features(self, X, reset=False)
+        jll = np.zeros((X.shape[0], self.class_count_.shape[0]))
+        for i in range(self.n_features_in_):
+            indices = X[:, i]
+            jll += self.feature_log_prob_[i][:, indices].T
+        total_ll = jll + self.class_log_prior_
+        return total_ll
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/pipeline.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..dddf33668903b224a873d44d45a0cf8f6b7a1f91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/pipeline.py
@@ -0,0 +1,2188 @@
+"""Utilities to build a composite estimator as a chain of transforms and estimators."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from collections import Counter, defaultdict
+from contextlib import contextmanager
+from copy import deepcopy
+from itertools import chain, islice
+
+import numpy as np
+from scipy import sparse
+
+from .base import TransformerMixin, _fit_context, clone
+from .exceptions import NotFittedError
+from .preprocessing import FunctionTransformer
+from .utils import Bunch
+from .utils._metadata_requests import METHODS
+from .utils._param_validation import HasMethods, Hidden
+from .utils._repr_html.estimator import _VisualBlock
+from .utils._set_output import (
+    _get_container_adapter,
+    _safe_set_output,
+)
+from .utils._tags import get_tags
+from .utils._user_interface import _print_elapsed_time
+from .utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    _routing_enabled,
+    get_routing_for_object,
+    process_routing,
+)
+from .utils.metaestimators import _BaseComposition, available_if
+from .utils.parallel import Parallel, delayed
+from .utils.validation import check_is_fitted, check_memory
+
+__all__ = ["FeatureUnion", "Pipeline", "make_pipeline", "make_union"]
+
+
+@contextmanager
+def _raise_or_warn_if_not_fitted(estimator):
+    """A context manager to make sure a NotFittedError is raised, if a sub-estimator
+    raises the error.
+
+    Otherwise, we raise a warning if the pipeline is not fitted, with the deprecation.
+
+    TODO(1.8): remove this context manager and replace with check_is_fitted.
+    """
+    try:
+        yield
+    except NotFittedError as exc:
+        raise NotFittedError("Pipeline is not fitted yet.") from exc
+
+    # we only get here if the above didn't raise
+    try:
+        check_is_fitted(estimator)
+    except NotFittedError:
+        warnings.warn(
+            "This Pipeline instance is not fitted yet. Call 'fit' with "
+            "appropriate arguments before using other methods such as transform, "
+            "predict, etc. This will raise an error in 1.8 instead of the current "
+            "warning.",
+            FutureWarning,
+        )
+
+
+def _final_estimator_has(attr):
+    """Check that final_estimator has `attr`.
+
+    Used together with `available_if` in `Pipeline`."""
+
+    def check(self):
+        # raise original `AttributeError` if `attr` does not exist
+        getattr(self._final_estimator, attr)
+        return True
+
+    return check
+
+
+def _cached_transform(
+    sub_pipeline, *, cache, param_name, param_value, transform_params
+):
+    """Transform a parameter value using a sub-pipeline and cache the result.
+
+    Parameters
+    ----------
+    sub_pipeline : Pipeline
+        The sub-pipeline to be used for transformation.
+    cache : dict
+        The cache dictionary to store the transformed values.
+    param_name : str
+        The name of the parameter to be transformed.
+    param_value : object
+        The value of the parameter to be transformed.
+    transform_params : dict
+        The metadata to be used for transformation. This passed to the
+        `transform` method of the sub-pipeline.
+
+    Returns
+    -------
+    transformed_value : object
+        The transformed value of the parameter.
+    """
+    if param_name not in cache:
+        # If the parameter is a tuple, transform each element of the
+        # tuple. This is needed to support the pattern present in
+        # `lightgbm` and `xgboost` where users can pass multiple
+        # validation sets.
+        if isinstance(param_value, tuple):
+            cache[param_name] = tuple(
+                sub_pipeline.transform(element, **transform_params)
+                for element in param_value
+            )
+        else:
+            cache[param_name] = sub_pipeline.transform(param_value, **transform_params)
+
+    return cache[param_name]
+
+
+class Pipeline(_BaseComposition):
+    """
+    A sequence of data transformers with an optional final predictor.
+
+    `Pipeline` allows you to sequentially apply a list of transformers to
+    preprocess the data and, if desired, conclude the sequence with a final
+    :term:`predictor` for predictive modeling.
+
+    Intermediate steps of the pipeline must be transformers, that is, they
+    must implement `fit` and `transform` methods.
+    The final :term:`estimator` only needs to implement `fit`.
+    The transformers in the pipeline can be cached using ``memory`` argument.
+
+    The purpose of the pipeline is to assemble several steps that can be
+    cross-validated together while setting different parameters. For this, it
+    enables setting parameters of the various steps using their names and the
+    parameter name separated by a `'__'`, as in the example below. A step's
+    estimator may be replaced entirely by setting the parameter with its name
+    to another estimator, or a transformer removed by setting it to
+    `'passthrough'` or `None`.
+
+    For an example use case of `Pipeline` combined with
+    :class:`~sklearn.model_selection.GridSearchCV`, refer to
+    :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The
+    example :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` shows how
+    to grid search on a pipeline using `'__'` as a separator in the parameter names.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.5
+
+    Parameters
+    ----------
+    steps : list of tuples
+        List of (name of step, estimator) tuples that are to be chained in
+        sequential order. To be compatible with the scikit-learn API, all steps
+        must define `fit`. All non-last steps must also define `transform`. See
+        :ref:`Combining Estimators ` for more details.
+
+    transform_input : list of str, default=None
+        The names of the :term:`metadata` parameters that should be transformed by the
+        pipeline before passing it to the step consuming it.
+
+        This enables transforming some input arguments to ``fit`` (other than ``X``)
+        to be transformed by the steps of the pipeline up to the step which requires
+        them. Requirement is defined via :ref:`metadata routing `.
+        For instance, this can be used to pass a validation set through the pipeline.
+
+        You can only set this if metadata routing is enabled, which you
+        can enable using ``sklearn.set_config(enable_metadata_routing=True)``.
+
+        .. versionadded:: 1.6
+
+    memory : str or object with the joblib.Memory interface, default=None
+        Used to cache the fitted transformers of the pipeline. The last step
+        will never be cached, even if it is a transformer. By default, no
+        caching is performed. If a string is given, it is the path to the
+        caching directory. Enabling caching triggers a clone of the transformers
+        before fitting. Therefore, the transformer instance given to the
+        pipeline cannot be inspected directly. Use the attribute ``named_steps``
+        or ``steps`` to inspect estimators within the pipeline. Caching the
+        transformers is advantageous when fitting is time consuming. See
+        :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`
+        for an example on how to enable caching.
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each step will be printed as it
+        is completed.
+
+    Attributes
+    ----------
+    named_steps : :class:`~sklearn.utils.Bunch`
+        Dictionary-like object, with the following attributes.
+        Read-only attribute to access any step parameter by user given name.
+        Keys are step names and values are steps parameters.
+
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels. Only exist if the last step of the pipeline is a
+        classifier.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying first estimator in `steps` exposes such an attribute
+        when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    make_pipeline : Convenience function for simplified pipeline construction.
+
+    Examples
+    --------
+    >>> from sklearn.svm import SVC
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.pipeline import Pipeline
+    >>> X, y = make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
+    ...                                                     random_state=0)
+    >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
+    >>> # The pipeline can be used as any other estimator
+    >>> # and avoids leaking the test set into the train set
+    >>> pipe.fit(X_train, y_train).score(X_test, y_test)
+    0.88
+    >>> # An estimator's parameter can be set using '__' syntax
+    >>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test)
+    0.76
+    """
+
+    # BaseEstimator interface
+    _parameter_constraints: dict = {
+        "steps": [list, Hidden(tuple)],
+        "transform_input": [list, None],
+        "memory": [None, str, HasMethods(["cache"])],
+        "verbose": ["boolean"],
+    }
+
+    def __init__(self, steps, *, transform_input=None, memory=None, verbose=False):
+        self.steps = steps
+        self.transform_input = transform_input
+        self.memory = memory
+        self.verbose = verbose
+
+    def set_output(self, *, transform=None):
+        """Set the output container when `"transform"` and `"fit_transform"` are called.
+
+        Calling `set_output` will set the output of all estimators in `steps`.
+
+        Parameters
+        ----------
+        transform : {"default", "pandas", "polars"}, default=None
+            Configure output of `transform` and `fit_transform`.
+
+            - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
+            - `"polars"`: Polars output
+            - `None`: Transform configuration is unchanged
+
+            .. versionadded:: 1.4
+                `"polars"` option was added.
+
+        Returns
+        -------
+        self : estimator instance
+            Estimator instance.
+        """
+        for _, _, step in self._iter():
+            _safe_set_output(step, transform=transform)
+        return self
+
+    def get_params(self, deep=True):
+        """Get parameters for this estimator.
+
+        Returns the parameters given in the constructor as well as the
+        estimators contained within the `steps` of the `Pipeline`.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : mapping of string to any
+            Parameter names mapped to their values.
+        """
+        return self._get_params("steps", deep=deep)
+
+    def set_params(self, **kwargs):
+        """Set the parameters of this estimator.
+
+        Valid parameter keys can be listed with ``get_params()``. Note that
+        you can directly set the parameters of the estimators contained in
+        `steps`.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Parameters of this estimator or parameters of estimators contained
+            in `steps`. Parameters of the steps may be set using its name and
+            the parameter name separated by a '__'.
+
+        Returns
+        -------
+        self : object
+            Pipeline class instance.
+        """
+        self._set_params("steps", **kwargs)
+        return self
+
+    def _validate_steps(self):
+        if not self.steps:
+            raise ValueError("The pipeline is empty. Please add steps.")
+        names, estimators = zip(*self.steps)
+
+        # validate names
+        self._validate_names(names)
+
+        # validate estimators
+        transformers = estimators[:-1]
+        estimator = estimators[-1]
+
+        for t in transformers:
+            if t is None or t == "passthrough":
+                continue
+            if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
+                t, "transform"
+            ):
+                raise TypeError(
+                    "All intermediate steps should be "
+                    "transformers and implement fit and transform "
+                    "or be the string 'passthrough' "
+                    "'%s' (type %s) doesn't" % (t, type(t))
+                )
+
+        # We allow last estimator to be None as an identity transformation
+        if (
+            estimator is not None
+            and estimator != "passthrough"
+            and not hasattr(estimator, "fit")
+        ):
+            raise TypeError(
+                "Last step of Pipeline should implement fit "
+                "or be the string 'passthrough'. "
+                "'%s' (type %s) doesn't" % (estimator, type(estimator))
+            )
+
+    def _iter(self, with_final=True, filter_passthrough=True):
+        """
+        Generate (idx, (name, trans)) tuples from self.steps
+
+        When filter_passthrough is True, 'passthrough' and None transformers
+        are filtered out.
+        """
+        stop = len(self.steps)
+        if not with_final:
+            stop -= 1
+
+        for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)):
+            if not filter_passthrough:
+                yield idx, name, trans
+            elif trans is not None and trans != "passthrough":
+                yield idx, name, trans
+
+    def __len__(self):
+        """
+        Returns the length of the Pipeline
+        """
+        return len(self.steps)
+
+    def __getitem__(self, ind):
+        """Returns a sub-pipeline or a single estimator in the pipeline
+
+        Indexing with an integer will return an estimator; using a slice
+        returns another Pipeline instance which copies a slice of this
+        Pipeline. This copy is shallow: modifying (or fitting) estimators in
+        the sub-pipeline will affect the larger pipeline and vice-versa.
+        However, replacing a value in `step` will not affect a copy.
+
+        See
+        :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
+        for an example of how to use slicing to inspect part of a pipeline.
+        """
+        if isinstance(ind, slice):
+            if ind.step not in (1, None):
+                raise ValueError("Pipeline slicing only supports a step of 1")
+            return self.__class__(
+                self.steps[ind], memory=self.memory, verbose=self.verbose
+            )
+        try:
+            name, est = self.steps[ind]
+        except TypeError:
+            # Not an int, try get step by name
+            return self.named_steps[ind]
+        return est
+
+    # TODO(1.8): Remove this property
+    @property
+    def _estimator_type(self):
+        """Return the estimator type of the last step in the pipeline."""
+
+        if not self.steps:
+            return None
+
+        return self.steps[-1][1]._estimator_type
+
+    @property
+    def named_steps(self):
+        """Access the steps by name.
+
+        Read-only attribute to access any step by given name.
+        Keys are steps names and values are the steps objects."""
+        # Use Bunch object to improve autocomplete
+        return Bunch(**dict(self.steps))
+
+    @property
+    def _final_estimator(self):
+        try:
+            estimator = self.steps[-1][1]
+            return "passthrough" if estimator is None else estimator
+        except (ValueError, AttributeError, TypeError):
+            # This condition happens when a call to a method is first calling
+            # `_available_if` and `fit` did not validate `steps` yet. We
+            # return `None` and an `InvalidParameterError` will be raised
+            # right after.
+            return None
+
+    def _log_message(self, step_idx):
+        if not self.verbose:
+            return None
+        name, _ = self.steps[step_idx]
+
+        return "(step %d of %d) Processing %s" % (step_idx + 1, len(self.steps), name)
+
+    def _check_method_params(self, method, props, **kwargs):
+        if _routing_enabled():
+            routed_params = process_routing(self, method, **props, **kwargs)
+            return routed_params
+        else:
+            fit_params_steps = Bunch(
+                **{
+                    name: Bunch(**{method: {} for method in METHODS})
+                    for name, step in self.steps
+                    if step is not None
+                }
+            )
+            for pname, pval in props.items():
+                if "__" not in pname:
+                    raise ValueError(
+                        "Pipeline.fit does not accept the {} parameter. "
+                        "You can pass parameters to specific steps of your "
+                        "pipeline using the stepname__parameter format, e.g. "
+                        "`Pipeline.fit(X, y, logisticregression__sample_weight"
+                        "=sample_weight)`.".format(pname)
+                    )
+                step, param = pname.split("__", 1)
+                fit_params_steps[step]["fit"][param] = pval
+                # without metadata routing, fit_transform and fit_predict
+                # get all the same params and pass it to the last fit.
+                fit_params_steps[step]["fit_transform"][param] = pval
+                fit_params_steps[step]["fit_predict"][param] = pval
+            return fit_params_steps
+
+    def _get_metadata_for_step(self, *, step_idx, step_params, all_params):
+        """Get params (metadata) for step `name`.
+
+        This transforms the metadata up to this step if required, which is
+        indicated by the `transform_input` parameter.
+
+        If a param in `step_params` is included in the `transform_input` list,
+        it will be transformed.
+
+        Parameters
+        ----------
+        step_idx : int
+            Index of the step in the pipeline.
+
+        step_params : dict
+            Parameters specific to the step. These are routed parameters, e.g.
+            `routed_params[name]`. If a parameter name here is included in the
+            `pipeline.transform_input`, then it will be transformed. Note that
+            these parameters are *after* routing, so the aliases are already
+            resolved.
+
+        all_params : dict
+            All parameters passed by the user. Here this is used to call
+            `transform` on the slice of the pipeline itself.
+
+        Returns
+        -------
+        dict
+            Parameters to be passed to the step. The ones which should be
+            transformed are transformed.
+        """
+        if (
+            self.transform_input is None
+            or not all_params
+            or not step_params
+            or step_idx == 0
+        ):
+            # we only need to process step_params if transform_input is set
+            # and metadata is given by the user.
+            return step_params
+
+        sub_pipeline = self[:step_idx]
+        sub_metadata_routing = get_routing_for_object(sub_pipeline)
+        # here we get the metadata required by sub_pipeline.transform
+        transform_params = {
+            key: value
+            for key, value in all_params.items()
+            if key
+            in sub_metadata_routing.consumes(
+                method="transform", params=all_params.keys()
+            )
+        }
+        transformed_params = dict()  # this is to be returned
+        transformed_cache = dict()  # used to transform each param once
+        # `step_params` is the output of `process_routing`, so it has a dict for each
+        # method (e.g. fit, transform, predict), which are the args to be passed to
+        # those methods. We need to transform the parameters which are in the
+        # `transform_input`, before returning these dicts.
+        for method, method_params in step_params.items():
+            transformed_params[method] = Bunch()
+            for param_name, param_value in method_params.items():
+                # An example of `(param_name, param_value)` is
+                # `('sample_weight', array([0.5, 0.5, ...]))`
+                if param_name in self.transform_input:
+                    # This parameter now needs to be transformed by the sub_pipeline, to
+                    # this step. We cache these computations to avoid repeating them.
+                    transformed_params[method][param_name] = _cached_transform(
+                        sub_pipeline,
+                        cache=transformed_cache,
+                        param_name=param_name,
+                        param_value=param_value,
+                        transform_params=transform_params,
+                    )
+                else:
+                    transformed_params[method][param_name] = param_value
+        return transformed_params
+
+    # Estimator interface
+
+    def _fit(self, X, y=None, routed_params=None, raw_params=None):
+        """Fit the pipeline except the last step.
+
+        routed_params is the output of `process_routing`
+        raw_params is the parameters passed by the user, used when `transform_input`
+            is set by the user, to transform metadata using a sub-pipeline.
+        """
+        # shallow copy of steps - this should really be steps_
+        self.steps = list(self.steps)
+        self._validate_steps()
+        # Setup the memory
+        memory = check_memory(self.memory)
+
+        fit_transform_one_cached = memory.cache(_fit_transform_one)
+
+        for step_idx, name, transformer in self._iter(
+            with_final=False, filter_passthrough=False
+        ):
+            if transformer is None or transformer == "passthrough":
+                with _print_elapsed_time("Pipeline", self._log_message(step_idx)):
+                    continue
+
+            if hasattr(memory, "location") and memory.location is None:
+                # we do not clone when caching is disabled to
+                # preserve backward compatibility
+                cloned_transformer = transformer
+            else:
+                cloned_transformer = clone(transformer)
+            # Fit or load from cache the current transformer
+            step_params = self._get_metadata_for_step(
+                step_idx=step_idx,
+                step_params=routed_params[name],
+                all_params=raw_params,
+            )
+
+            X, fitted_transformer = fit_transform_one_cached(
+                cloned_transformer,
+                X,
+                y,
+                weight=None,
+                message_clsname="Pipeline",
+                message=self._log_message(step_idx),
+                params=step_params,
+            )
+            # Replace the transformer of the step with the fitted
+            # transformer. This is necessary when loading the transformer
+            # from the cache.
+            self.steps[step_idx] = (name, fitted_transformer)
+        return X
+
+    @_fit_context(
+        # estimators in Pipeline.steps are not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y=None, **params):
+        """Fit the model.
+
+        Fit all the transformers one after the other and sequentially transform the
+        data. Finally, fit the transformed data using the final estimator.
+
+        Parameters
+        ----------
+        X : iterable
+            Training data. Must fulfill input requirements of first step of the
+            pipeline.
+
+        y : iterable, default=None
+            Training targets. Must fulfill label requirements for all steps of
+            the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters passed to the
+              ``fit`` method of each step, where each parameter name is prefixed such
+              that parameter ``p`` for step ``s`` has key ``s__p``.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True` is set via
+                :func:`~sklearn.set_config`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+        Returns
+        -------
+        self : object
+            Pipeline with fitted steps.
+        """
+        if not _routing_enabled() and self.transform_input is not None:
+            raise ValueError(
+                "The `transform_input` parameter can only be set if metadata "
+                "routing is enabled. You can enable metadata routing using "
+                "`sklearn.set_config(enable_metadata_routing=True)`."
+            )
+
+        routed_params = self._check_method_params(method="fit", props=params)
+        Xt = self._fit(X, y, routed_params, raw_params=params)
+        with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
+            if self._final_estimator != "passthrough":
+                last_step_params = self._get_metadata_for_step(
+                    step_idx=len(self) - 1,
+                    step_params=routed_params[self.steps[-1][0]],
+                    all_params=params,
+                )
+                self._final_estimator.fit(Xt, y, **last_step_params["fit"])
+
+        return self
+
+    def _can_fit_transform(self):
+        return (
+            self._final_estimator == "passthrough"
+            or hasattr(self._final_estimator, "transform")
+            or hasattr(self._final_estimator, "fit_transform")
+        )
+
+    @available_if(_can_fit_transform)
+    @_fit_context(
+        # estimators in Pipeline.steps are not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit_transform(self, X, y=None, **params):
+        """Fit the model and transform with the final estimator.
+
+        Fit all the transformers one after the other and sequentially transform
+        the data. Only valid if the final estimator either implements
+        `fit_transform` or `fit` and `transform`.
+
+        Parameters
+        ----------
+        X : iterable
+            Training data. Must fulfill input requirements of first step of the
+            pipeline.
+
+        y : iterable, default=None
+            Training targets. Must fulfill label requirements for all steps of
+            the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters passed to the
+              ``fit`` method of each step, where each parameter name is prefixed such
+              that parameter ``p`` for step ``s`` has key ``s__p``.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+        Returns
+        -------
+        Xt : ndarray of shape (n_samples, n_transformed_features)
+            Transformed samples.
+        """
+        routed_params = self._check_method_params(method="fit_transform", props=params)
+        Xt = self._fit(X, y, routed_params)
+
+        last_step = self._final_estimator
+        with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
+            if last_step == "passthrough":
+                return Xt
+            last_step_params = self._get_metadata_for_step(
+                step_idx=len(self) - 1,
+                step_params=routed_params[self.steps[-1][0]],
+                all_params=params,
+            )
+            if hasattr(last_step, "fit_transform"):
+                return last_step.fit_transform(
+                    Xt, y, **last_step_params["fit_transform"]
+                )
+            else:
+                return last_step.fit(Xt, y, **last_step_params["fit"]).transform(
+                    Xt, **last_step_params["transform"]
+                )
+
+    @available_if(_final_estimator_has("predict"))
+    def predict(self, X, **params):
+        """Transform the data, and apply `predict` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls `predict`
+        method. Only valid if the final estimator implements `predict`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters to the
+              ``predict`` called at the end of all transformations in the pipeline.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionadded:: 0.20
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True` is set via
+                :func:`~sklearn.set_config`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+            Note that while this may be used to return uncertainties from some
+            models with ``return_std`` or ``return_cov``, uncertainties that are
+            generated by the transformations in the pipeline are not propagated
+            to the final estimator.
+
+        Returns
+        -------
+        y_pred : ndarray
+            Result of calling `predict` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            Xt = X
+
+            if not _routing_enabled():
+                for _, name, transform in self._iter(with_final=False):
+                    Xt = transform.transform(Xt)
+                return self.steps[-1][1].predict(Xt, **params)
+
+            # metadata routing enabled
+            routed_params = process_routing(self, "predict", **params)
+            for _, name, transform in self._iter(with_final=False):
+                Xt = transform.transform(Xt, **routed_params[name].transform)
+            return self.steps[-1][1].predict(
+                Xt, **routed_params[self.steps[-1][0]].predict
+            )
+
+    @available_if(_final_estimator_has("fit_predict"))
+    @_fit_context(
+        # estimators in Pipeline.steps are not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit_predict(self, X, y=None, **params):
+        """Transform the data, and apply `fit_predict` with the final estimator.
+
+        Call `fit_transform` of each transformer in the pipeline. The
+        transformed data are finally passed to the final estimator that calls
+        `fit_predict` method. Only valid if the final estimator implements
+        `fit_predict`.
+
+        Parameters
+        ----------
+        X : iterable
+            Training data. Must fulfill input requirements of first step of
+            the pipeline.
+
+        y : iterable, default=None
+            Training targets. Must fulfill label requirements for all steps
+            of the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters to the
+              ``predict`` called at the end of all transformations in the pipeline.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionadded:: 0.20
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+            Note that while this may be used to return uncertainties from some
+            models with ``return_std`` or ``return_cov``, uncertainties that are
+            generated by the transformations in the pipeline are not propagated
+            to the final estimator.
+
+        Returns
+        -------
+        y_pred : ndarray
+            Result of calling `fit_predict` on the final estimator.
+        """
+        routed_params = self._check_method_params(method="fit_predict", props=params)
+        Xt = self._fit(X, y, routed_params)
+
+        params_last_step = routed_params[self.steps[-1][0]]
+        with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
+            y_pred = self.steps[-1][1].fit_predict(
+                Xt, y, **params_last_step.get("fit_predict", {})
+            )
+        return y_pred
+
+    @available_if(_final_estimator_has("predict_proba"))
+    def predict_proba(self, X, **params):
+        """Transform the data, and apply `predict_proba` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `predict_proba` method. Only valid if the final estimator implements
+        `predict_proba`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters to the
+              `predict_proba` called at the end of all transformations in the pipeline.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionadded:: 0.20
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+        Returns
+        -------
+        y_proba : ndarray of shape (n_samples, n_classes)
+            Result of calling `predict_proba` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            Xt = X
+
+            if not _routing_enabled():
+                for _, name, transform in self._iter(with_final=False):
+                    Xt = transform.transform(Xt)
+                return self.steps[-1][1].predict_proba(Xt, **params)
+
+            # metadata routing enabled
+            routed_params = process_routing(self, "predict_proba", **params)
+            for _, name, transform in self._iter(with_final=False):
+                Xt = transform.transform(Xt, **routed_params[name].transform)
+            return self.steps[-1][1].predict_proba(
+                Xt, **routed_params[self.steps[-1][0]].predict_proba
+            )
+
+    @available_if(_final_estimator_has("decision_function"))
+    def decision_function(self, X, **params):
+        """Transform the data, and apply `decision_function` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `decision_function` method. Only valid if the final estimator
+        implements `decision_function`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        **params : dict of string -> object
+            Parameters requested and accepted by steps. Each step must have
+            requested certain metadata for these parameters to be forwarded to
+            them.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        y_score : ndarray of shape (n_samples, n_classes)
+            Result of calling `decision_function` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            _raise_for_params(params, self, "decision_function")
+
+            # not branching here since params is only available if
+            # enable_metadata_routing=True
+            routed_params = process_routing(self, "decision_function", **params)
+
+            Xt = X
+            for _, name, transform in self._iter(with_final=False):
+                Xt = transform.transform(
+                    Xt, **routed_params.get(name, {}).get("transform", {})
+                )
+            return self.steps[-1][1].decision_function(
+                Xt,
+                **routed_params.get(self.steps[-1][0], {}).get("decision_function", {}),
+            )
+
+    @available_if(_final_estimator_has("score_samples"))
+    def score_samples(self, X):
+        """Transform the data, and apply `score_samples` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `score_samples` method. Only valid if the final estimator implements
+        `score_samples`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        Returns
+        -------
+        y_score : ndarray of shape (n_samples,)
+            Result of calling `score_samples` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            Xt = X
+            for _, _, transformer in self._iter(with_final=False):
+                Xt = transformer.transform(Xt)
+            return self.steps[-1][1].score_samples(Xt)
+
+    @available_if(_final_estimator_has("predict_log_proba"))
+    def predict_log_proba(self, X, **params):
+        """Transform the data, and apply `predict_log_proba` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `predict_log_proba` method. Only valid if the final estimator
+        implements `predict_log_proba`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        **params : dict of str -> object
+            - If `enable_metadata_routing=False` (default): Parameters to the
+              `predict_log_proba` called at the end of all transformations in the
+              pipeline.
+
+            - If `enable_metadata_routing=True`: Parameters requested and accepted by
+              steps. Each step must have requested certain metadata for these parameters
+              to be forwarded to them.
+
+            .. versionadded:: 0.20
+
+            .. versionchanged:: 1.4
+                Parameters are now passed to the ``transform`` method of the
+                intermediate steps as well, if requested, and if
+                `enable_metadata_routing=True`.
+
+            See :ref:`Metadata Routing User Guide ` for more
+            details.
+
+        Returns
+        -------
+        y_log_proba : ndarray of shape (n_samples, n_classes)
+            Result of calling `predict_log_proba` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            Xt = X
+
+            if not _routing_enabled():
+                for _, name, transform in self._iter(with_final=False):
+                    Xt = transform.transform(Xt)
+                return self.steps[-1][1].predict_log_proba(Xt, **params)
+
+            # metadata routing enabled
+            routed_params = process_routing(self, "predict_log_proba", **params)
+            for _, name, transform in self._iter(with_final=False):
+                Xt = transform.transform(Xt, **routed_params[name].transform)
+            return self.steps[-1][1].predict_log_proba(
+                Xt, **routed_params[self.steps[-1][0]].predict_log_proba
+            )
+
+    def _can_transform(self):
+        return self._final_estimator == "passthrough" or hasattr(
+            self._final_estimator, "transform"
+        )
+
+    @available_if(_can_transform)
+    def transform(self, X, **params):
+        """Transform the data, and apply `transform` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `transform` method. Only valid if the final estimator
+        implements `transform`.
+
+        This also works where final estimator is `None` in which case all prior
+        transformations are applied.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to transform. Must fulfill input requirements of first step
+            of the pipeline.
+
+        **params : dict of str -> object
+            Parameters requested and accepted by steps. Each step must have
+            requested certain metadata for these parameters to be forwarded to
+            them.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        Xt : ndarray of shape (n_samples, n_transformed_features)
+            Transformed data.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            _raise_for_params(params, self, "transform")
+
+            # not branching here since params is only available if
+            # enable_metadata_routing=True
+            routed_params = process_routing(self, "transform", **params)
+            Xt = X
+            for _, name, transform in self._iter():
+                Xt = transform.transform(Xt, **routed_params[name].transform)
+            return Xt
+
+    def _can_inverse_transform(self):
+        return all(hasattr(t, "inverse_transform") for _, _, t in self._iter())
+
+    @available_if(_can_inverse_transform)
+    def inverse_transform(self, X, **params):
+        """Apply `inverse_transform` for each step in a reverse order.
+
+        All estimators in the pipeline must support `inverse_transform`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_transformed_features)
+            Data samples, where ``n_samples`` is the number of samples and
+            ``n_features`` is the number of features. Must fulfill
+            input requirements of last step of pipeline's
+            ``inverse_transform`` method.
+
+        **params : dict of str -> object
+            Parameters requested and accepted by steps. Each step must have
+            requested certain metadata for these parameters to be forwarded to
+            them.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        X_original : ndarray of shape (n_samples, n_features)
+            Inverse transformed data, that is, data in the original feature
+            space.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            _raise_for_params(params, self, "inverse_transform")
+
+            # we don't have to branch here, since params is only non-empty if
+            # enable_metadata_routing=True.
+            routed_params = process_routing(self, "inverse_transform", **params)
+            reverse_iter = reversed(list(self._iter()))
+            for _, name, transform in reverse_iter:
+                X = transform.inverse_transform(
+                    X, **routed_params[name].inverse_transform
+                )
+            return X
+
+    @available_if(_final_estimator_has("score"))
+    def score(self, X, y=None, sample_weight=None, **params):
+        """Transform the data, and apply `score` with the final estimator.
+
+        Call `transform` of each transformer in the pipeline. The transformed
+        data are finally passed to the final estimator that calls
+        `score` method. Only valid if the final estimator implements `score`.
+
+        Parameters
+        ----------
+        X : iterable
+            Data to predict on. Must fulfill input requirements of first step
+            of the pipeline.
+
+        y : iterable, default=None
+            Targets used for scoring. Must fulfill label requirements for all
+            steps of the pipeline.
+
+        sample_weight : array-like, default=None
+            If not None, this argument is passed as ``sample_weight`` keyword
+            argument to the ``score`` method of the final estimator.
+
+        **params : dict of str -> object
+            Parameters requested and accepted by steps. Each step must have
+            requested certain metadata for these parameters to be forwarded to
+            them.
+
+            .. versionadded:: 1.4
+                Only available if `enable_metadata_routing=True`. See
+                :ref:`Metadata Routing User Guide ` for more
+                details.
+
+        Returns
+        -------
+        score : float
+            Result of calling `score` on the final estimator.
+        """
+        # TODO(1.8): Remove the context manager and use check_is_fitted(self)
+        with _raise_or_warn_if_not_fitted(self):
+            Xt = X
+            if not _routing_enabled():
+                for _, name, transform in self._iter(with_final=False):
+                    Xt = transform.transform(Xt)
+                score_params = {}
+                if sample_weight is not None:
+                    score_params["sample_weight"] = sample_weight
+                return self.steps[-1][1].score(Xt, y, **score_params)
+
+            # metadata routing is enabled.
+            routed_params = process_routing(
+                self, "score", sample_weight=sample_weight, **params
+            )
+
+            Xt = X
+            for _, name, transform in self._iter(with_final=False):
+                Xt = transform.transform(Xt, **routed_params[name].transform)
+            return self.steps[-1][1].score(
+                Xt, y, **routed_params[self.steps[-1][0]].score
+            )
+
+    @property
+    def classes_(self):
+        """The classes labels. Only exist if the last step is a classifier."""
+        return self.steps[-1][1].classes_
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+
+        if not self.steps:
+            return tags
+
+        try:
+            if self.steps[0][1] is not None and self.steps[0][1] != "passthrough":
+                tags.input_tags.pairwise = get_tags(
+                    self.steps[0][1]
+                ).input_tags.pairwise
+            # WARNING: the sparse tag can be incorrect.
+            # Some Pipelines accepting sparse data are wrongly tagged sparse=False.
+            # For example Pipeline([PCA(), estimator]) accepts sparse data
+            # even if the estimator doesn't as PCA outputs a dense array.
+            tags.input_tags.sparse = all(
+                get_tags(step).input_tags.sparse
+                for name, step in self.steps
+                if step is not None and step != "passthrough"
+            )
+        except (ValueError, AttributeError, TypeError):
+            # This happens when the `steps` is not a list of (name, estimator)
+            # tuples and `fit` is not called yet to validate the steps.
+            pass
+
+        try:
+            if self.steps[-1][1] is not None and self.steps[-1][1] != "passthrough":
+                last_step_tags = get_tags(self.steps[-1][1])
+                tags.estimator_type = last_step_tags.estimator_type
+                tags.target_tags.multi_output = last_step_tags.target_tags.multi_output
+                tags.classifier_tags = deepcopy(last_step_tags.classifier_tags)
+                tags.regressor_tags = deepcopy(last_step_tags.regressor_tags)
+                tags.transformer_tags = deepcopy(last_step_tags.transformer_tags)
+        except (ValueError, AttributeError, TypeError):
+            # This happens when the `steps` is not a list of (name, estimator)
+            # tuples and `fit` is not called yet to validate the steps.
+            pass
+
+        return tags
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Transform input features using the pipeline.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        feature_names_out = input_features
+        for _, name, transform in self._iter():
+            if not hasattr(transform, "get_feature_names_out"):
+                raise AttributeError(
+                    "Estimator {} does not provide get_feature_names_out. "
+                    "Did you mean to call pipeline[:-1].get_feature_names_out"
+                    "()?".format(name)
+                )
+            feature_names_out = transform.get_feature_names_out(feature_names_out)
+        return feature_names_out
+
+    @property
+    def n_features_in_(self):
+        """Number of features seen during first step `fit` method."""
+        # delegate to first step (which will call check_is_fitted)
+        return self.steps[0][1].n_features_in_
+
+    @property
+    def feature_names_in_(self):
+        """Names of features seen during first step `fit` method."""
+        # delegate to first step (which will call check_is_fitted)
+        return self.steps[0][1].feature_names_in_
+
+    def __sklearn_is_fitted__(self):
+        """Indicate whether pipeline has been fit.
+
+        This is done by checking whether the last non-`passthrough` step of the
+        pipeline is fitted.
+
+        An empty pipeline is considered fitted.
+        """
+        # First find the last step that is not 'passthrough'
+        last_step = None
+        for _, estimator in reversed(self.steps):
+            if estimator != "passthrough":
+                last_step = estimator
+                break
+
+        if last_step is None:
+            # All steps are 'passthrough', so the pipeline is considered fitted
+            return True
+
+        try:
+            # check if the last step of the pipeline is fitted
+            # we only check the last step since if the last step is fit, it
+            # means the previous steps should also be fit. This is faster than
+            # checking if every step of the pipeline is fit.
+            check_is_fitted(last_step)
+            return True
+        except NotFittedError:
+            return False
+
+    def _sk_visual_block_(self):
+        _, estimators = zip(*self.steps)
+
+        def _get_name(name, est):
+            if est is None or est == "passthrough":
+                return f"{name}: passthrough"
+            # Is an estimator
+            return f"{name}: {est.__class__.__name__}"
+
+        names = [_get_name(name, est) for name, est in self.steps]
+        name_details = [str(est) for est in estimators]
+        return _VisualBlock(
+            "serial",
+            estimators,
+            names=names,
+            name_details=name_details,
+            dash_wrapped=False,
+        )
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__)
+
+        # first we add all steps except the last one
+        for _, name, trans in self._iter(with_final=False, filter_passthrough=True):
+            method_mapping = MethodMapping()
+            # fit, fit_predict, and fit_transform call fit_transform if it
+            # exists, or else fit and transform
+            if hasattr(trans, "fit_transform"):
+                (
+                    method_mapping.add(caller="fit", callee="fit_transform")
+                    .add(caller="fit_transform", callee="fit_transform")
+                    .add(caller="fit_predict", callee="fit_transform")
+                )
+            else:
+                (
+                    method_mapping.add(caller="fit", callee="fit")
+                    .add(caller="fit", callee="transform")
+                    .add(caller="fit_transform", callee="fit")
+                    .add(caller="fit_transform", callee="transform")
+                    .add(caller="fit_predict", callee="fit")
+                    .add(caller="fit_predict", callee="transform")
+                )
+
+            (
+                method_mapping.add(caller="predict", callee="transform")
+                .add(caller="predict", callee="transform")
+                .add(caller="predict_proba", callee="transform")
+                .add(caller="decision_function", callee="transform")
+                .add(caller="predict_log_proba", callee="transform")
+                .add(caller="transform", callee="transform")
+                .add(caller="inverse_transform", callee="inverse_transform")
+                .add(caller="score", callee="transform")
+            )
+
+            router.add(method_mapping=method_mapping, **{name: trans})
+
+        final_name, final_est = self.steps[-1]
+        if final_est is None or final_est == "passthrough":
+            return router
+
+        # then we add the last step
+        method_mapping = MethodMapping()
+        if hasattr(final_est, "fit_transform"):
+            method_mapping.add(caller="fit_transform", callee="fit_transform")
+        else:
+            method_mapping.add(caller="fit", callee="fit").add(
+                caller="fit", callee="transform"
+            )
+        (
+            method_mapping.add(caller="fit", callee="fit")
+            .add(caller="predict", callee="predict")
+            .add(caller="fit_predict", callee="fit_predict")
+            .add(caller="predict_proba", callee="predict_proba")
+            .add(caller="decision_function", callee="decision_function")
+            .add(caller="predict_log_proba", callee="predict_log_proba")
+            .add(caller="transform", callee="transform")
+            .add(caller="inverse_transform", callee="inverse_transform")
+            .add(caller="score", callee="score")
+        )
+
+        router.add(method_mapping=method_mapping, **{final_name: final_est})
+        return router
+
+
+def _name_estimators(estimators):
+    """Generate names for estimators."""
+
+    names = [
+        estimator if isinstance(estimator, str) else type(estimator).__name__.lower()
+        for estimator in estimators
+    ]
+    namecount = defaultdict(int)
+    for est, name in zip(estimators, names):
+        namecount[name] += 1
+
+    for k, v in list(namecount.items()):
+        if v == 1:
+            del namecount[k]
+
+    for i in reversed(range(len(estimators))):
+        name = names[i]
+        if name in namecount:
+            names[i] += "-%d" % namecount[name]
+            namecount[name] -= 1
+
+    return list(zip(names, estimators))
+
+
+def make_pipeline(*steps, memory=None, transform_input=None, verbose=False):
+    """Construct a :class:`Pipeline` from the given estimators.
+
+    This is a shorthand for the :class:`Pipeline` constructor; it does not
+    require, and does not permit, naming the estimators. Instead, their names
+    will be set to the lowercase of their types automatically.
+
+    Parameters
+    ----------
+    *steps : list of Estimator objects
+        List of the scikit-learn estimators that are chained together.
+
+    memory : str or object with the joblib.Memory interface, default=None
+        Used to cache the fitted transformers of the pipeline. The last step
+        will never be cached, even if it is a transformer. By default, no
+        caching is performed. If a string is given, it is the path to the
+        caching directory. Enabling caching triggers a clone of the transformers
+        before fitting. Therefore, the transformer instance given to the
+        pipeline cannot be inspected directly. Use the attribute ``named_steps``
+        or ``steps`` to inspect estimators within the pipeline. Caching the
+        transformers is advantageous when fitting is time consuming.
+
+    transform_input : list of str, default=None
+        This enables transforming some input arguments to ``fit`` (other than ``X``)
+        to be transformed by the steps of the pipeline up to the step which requires
+        them. Requirement is defined via :ref:`metadata routing `.
+        This can be used to pass a validation set through the pipeline for instance.
+
+        You can only set this if metadata routing is enabled, which you
+        can enable using ``sklearn.set_config(enable_metadata_routing=True)``.
+
+        .. versionadded:: 1.6
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each step will be printed as it
+        is completed.
+
+    Returns
+    -------
+    p : Pipeline
+        Returns a scikit-learn :class:`Pipeline` object.
+
+    See Also
+    --------
+    Pipeline : Class for creating a pipeline of transforms with a final
+        estimator.
+
+    Examples
+    --------
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.pipeline import make_pipeline
+    >>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('gaussiannb', GaussianNB())])
+    """
+    return Pipeline(
+        _name_estimators(steps),
+        transform_input=transform_input,
+        memory=memory,
+        verbose=verbose,
+    )
+
+
+def _transform_one(transformer, X, y, weight, params):
+    """Call transform and apply weight to output.
+
+    Parameters
+    ----------
+    transformer : estimator
+        Estimator to be used for transformation.
+
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Input data to be transformed.
+
+    y : ndarray of shape (n_samples,)
+        Ignored.
+
+    weight : float
+        Weight to be applied to the output of the transformation.
+
+    params : dict
+        Parameters to be passed to the transformer's ``transform`` method.
+
+        This should be of the form ``process_routing()["step_name"]``.
+    """
+    res = transformer.transform(X, **params.transform)
+    # if we have a weight for this transformer, multiply output
+    if weight is None:
+        return res
+    return res * weight
+
+
+def _fit_transform_one(
+    transformer, X, y, weight, message_clsname="", message=None, params=None
+):
+    """
+    Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned
+    with the fitted transformer. If ``weight`` is not ``None``, the result will
+    be multiplied by ``weight``.
+
+    ``params`` needs to be of the form ``process_routing()["step_name"]``.
+    """
+    params = params or {}
+    with _print_elapsed_time(message_clsname, message):
+        if hasattr(transformer, "fit_transform"):
+            res = transformer.fit_transform(X, y, **params.get("fit_transform", {}))
+        else:
+            res = transformer.fit(X, y, **params.get("fit", {})).transform(
+                X, **params.get("transform", {})
+            )
+
+    if weight is None:
+        return res, transformer
+    return res * weight, transformer
+
+
+def _fit_one(transformer, X, y, weight, message_clsname="", message=None, params=None):
+    """
+    Fits ``transformer`` to ``X`` and ``y``.
+    """
+    with _print_elapsed_time(message_clsname, message):
+        return transformer.fit(X, y, **params["fit"])
+
+
+class FeatureUnion(TransformerMixin, _BaseComposition):
+    """Concatenates results of multiple transformer objects.
+
+    This estimator applies a list of transformer objects in parallel to the
+    input data, then concatenates the results. This is useful to combine
+    several feature extraction mechanisms into a single transformer.
+
+    Parameters of the transformers may be set using its name and the parameter
+    name separated by a '__'. A transformer may be replaced entirely by
+    setting the parameter with its name to another transformer, removed by
+    setting to 'drop' or disabled by setting to 'passthrough' (features are
+    passed without transformation).
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    transformer_list : list of (str, transformer) tuples
+        List of transformer objects to be applied to the data. The first
+        half of each tuple is the name of the transformer. The transformer can
+        be 'drop' for it to be ignored or can be 'passthrough' for features to
+        be passed unchanged.
+
+        .. versionadded:: 1.1
+           Added the option `"passthrough"`.
+
+        .. versionchanged:: 0.22
+           Deprecated `None` as a transformer in favor of 'drop'.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+        .. versionchanged:: v0.20
+           `n_jobs` default changed from 1 to None
+
+    transformer_weights : dict, default=None
+        Multiplicative weights for features per transformer.
+        Keys are transformer names, values the weights.
+        Raises ValueError if key not present in ``transformer_list``.
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each transformer will be
+        printed as it is completed.
+
+    verbose_feature_names_out : bool, default=True
+        If True, :meth:`get_feature_names_out` will prefix all feature names
+        with the name of the transformer that generated that feature.
+        If False, :meth:`get_feature_names_out` will not prefix any feature
+        names and will error if feature names are not unique.
+
+        .. versionadded:: 1.5
+
+    Attributes
+    ----------
+    named_transformers : :class:`~sklearn.utils.Bunch`
+        Dictionary-like object, with the following attributes.
+        Read-only attribute to access any transformer parameter by user
+        given name. Keys are transformer names and values are
+        transformer parameters.
+
+        .. versionadded:: 1.2
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying first transformer in `transformer_list` exposes such an
+        attribute when fit.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when
+        `X` has feature names that are all strings.
+
+        .. versionadded:: 1.3
+
+    See Also
+    --------
+    make_union : Convenience function for simplified feature union
+        construction.
+
+    Examples
+    --------
+    >>> from sklearn.pipeline import FeatureUnion
+    >>> from sklearn.decomposition import PCA, TruncatedSVD
+    >>> union = FeatureUnion([("pca", PCA(n_components=1)),
+    ...                       ("svd", TruncatedSVD(n_components=2))])
+    >>> X = [[0., 1., 3], [2., 2., 5]]
+    >>> union.fit_transform(X)
+    array([[-1.5       ,  3.04, -0.872],
+           [ 1.5       ,  5.72,  0.463]])
+    >>> # An estimator's parameter can be set using '__' syntax
+    >>> union.set_params(svd__n_components=1).fit_transform(X)
+    array([[-1.5       ,  3.04],
+           [ 1.5       ,  5.72]])
+
+    For a more detailed example of usage, see
+    :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`.
+    """
+
+    def __init__(
+        self,
+        transformer_list,
+        *,
+        n_jobs=None,
+        transformer_weights=None,
+        verbose=False,
+        verbose_feature_names_out=True,
+    ):
+        self.transformer_list = transformer_list
+        self.n_jobs = n_jobs
+        self.transformer_weights = transformer_weights
+        self.verbose = verbose
+        self.verbose_feature_names_out = verbose_feature_names_out
+
+    def set_output(self, *, transform=None):
+        """Set the output container when `"transform"` and `"fit_transform"` are called.
+
+        `set_output` will set the output of all estimators in `transformer_list`.
+
+        Parameters
+        ----------
+        transform : {"default", "pandas", "polars"}, default=None
+            Configure output of `transform` and `fit_transform`.
+
+            - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
+            - `"polars"`: Polars output
+            - `None`: Transform configuration is unchanged
+
+        Returns
+        -------
+        self : estimator instance
+            Estimator instance.
+        """
+        super().set_output(transform=transform)
+        for _, step, _ in self._iter():
+            _safe_set_output(step, transform=transform)
+        return self
+
+    @property
+    def named_transformers(self):
+        # Use Bunch object to improve autocomplete
+        return Bunch(**dict(self.transformer_list))
+
+    def get_params(self, deep=True):
+        """Get parameters for this estimator.
+
+        Returns the parameters given in the constructor as well as the
+        estimators contained within the `transformer_list` of the
+        `FeatureUnion`.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : mapping of string to any
+            Parameter names mapped to their values.
+        """
+        return self._get_params("transformer_list", deep=deep)
+
+    def set_params(self, **kwargs):
+        """Set the parameters of this estimator.
+
+        Valid parameter keys can be listed with ``get_params()``. Note that
+        you can directly set the parameters of the estimators contained in
+        `transformer_list`.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Parameters of this estimator or parameters of estimators contained
+            in `transform_list`. Parameters of the transformers may be set
+            using its name and the parameter name separated by a '__'.
+
+        Returns
+        -------
+        self : object
+            FeatureUnion class instance.
+        """
+        self._set_params("transformer_list", **kwargs)
+        return self
+
+    def _validate_transformers(self):
+        names, transformers = zip(*self.transformer_list)
+
+        # validate names
+        self._validate_names(names)
+
+        # validate estimators
+        for t in transformers:
+            if t in ("drop", "passthrough"):
+                continue
+            if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
+                t, "transform"
+            ):
+                raise TypeError(
+                    "All estimators should implement fit and "
+                    "transform. '%s' (type %s) doesn't" % (t, type(t))
+                )
+
+    def _validate_transformer_weights(self):
+        if not self.transformer_weights:
+            return
+
+        transformer_names = set(name for name, _ in self.transformer_list)
+        for name in self.transformer_weights:
+            if name not in transformer_names:
+                raise ValueError(
+                    f'Attempting to weight transformer "{name}", '
+                    "but it is not present in transformer_list."
+                )
+
+    def _iter(self):
+        """
+        Generate (name, trans, weight) tuples excluding None and
+        'drop' transformers.
+        """
+
+        get_weight = (self.transformer_weights or {}).get
+
+        for name, trans in self.transformer_list:
+            if trans == "drop":
+                continue
+            if trans == "passthrough":
+                trans = FunctionTransformer(feature_names_out="one-to-one")
+            yield (name, trans, get_weight(name))
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        # List of tuples (name, feature_names_out)
+        transformer_with_feature_names_out = []
+        for name, trans, _ in self._iter():
+            if not hasattr(trans, "get_feature_names_out"):
+                raise AttributeError(
+                    "Transformer %s (type %s) does not provide get_feature_names_out."
+                    % (str(name), type(trans).__name__)
+                )
+            feature_names_out = trans.get_feature_names_out(input_features)
+            transformer_with_feature_names_out.append((name, feature_names_out))
+
+        return self._add_prefix_for_feature_names_out(
+            transformer_with_feature_names_out
+        )
+
+    def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):
+        """Add prefix for feature names out that includes the transformer names.
+
+        Parameters
+        ----------
+        transformer_with_feature_names_out : list of tuples of (str, array-like of str)
+            The tuple consistent of the transformer's name and its feature names out.
+
+        Returns
+        -------
+        feature_names_out : ndarray of shape (n_features,), dtype=str
+            Transformed feature names.
+        """
+        if self.verbose_feature_names_out:
+            # Prefix the feature names out with the transformers name
+            names = list(
+                chain.from_iterable(
+                    (f"{name}__{i}" for i in feature_names_out)
+                    for name, feature_names_out in transformer_with_feature_names_out
+                )
+            )
+            return np.asarray(names, dtype=object)
+
+        # verbose_feature_names_out is False
+        # Check that names are all unique without a prefix
+        feature_names_count = Counter(
+            chain.from_iterable(s for _, s in transformer_with_feature_names_out)
+        )
+        top_6_overlap = [
+            name for name, count in feature_names_count.most_common(6) if count > 1
+        ]
+        top_6_overlap.sort()
+        if top_6_overlap:
+            if len(top_6_overlap) == 6:
+                # There are more than 5 overlapping names, we only show the 5
+                # of the feature names
+                names_repr = str(top_6_overlap[:5])[:-1] + ", ...]"
+            else:
+                names_repr = str(top_6_overlap)
+            raise ValueError(
+                f"Output feature names: {names_repr} are not unique. Please set "
+                "verbose_feature_names_out=True to add prefixes to feature names"
+            )
+
+        return np.concatenate(
+            [name for _, name in transformer_with_feature_names_out],
+        )
+
+    def fit(self, X, y=None, **fit_params):
+        """Fit all transformers using X.
+
+        Parameters
+        ----------
+        X : iterable or array-like, depending on transformers
+            Input data, used to fit transformers.
+
+        y : array-like of shape (n_samples, n_outputs), default=None
+            Targets for supervised learning.
+
+        **fit_params : dict, default=None
+            - If `enable_metadata_routing=False` (default):
+              Parameters directly passed to the `fit` methods of the
+              sub-transformers.
+
+            - If `enable_metadata_routing=True`:
+              Parameters safely routed to the `fit` methods of the
+              sub-transformers. See :ref:`Metadata Routing User Guide
+              ` for more details.
+
+            .. versionchanged:: 1.5
+                `**fit_params` can be routed via metadata routing API.
+
+        Returns
+        -------
+        self : object
+            FeatureUnion class instance.
+        """
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit", **fit_params)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            routed_params = Bunch()
+            for name, _ in self.transformer_list:
+                routed_params[name] = Bunch(fit={})
+                routed_params[name].fit = fit_params
+
+        transformers = self._parallel_func(X, y, _fit_one, routed_params)
+
+        if not transformers:
+            # All transformers are None
+            return self
+
+        self._update_transformer_list(transformers)
+        return self
+
+    def fit_transform(self, X, y=None, **params):
+        """Fit all transformers, transform the data and concatenate results.
+
+        Parameters
+        ----------
+        X : iterable or array-like, depending on transformers
+            Input data to be transformed.
+
+        y : array-like of shape (n_samples, n_outputs), default=None
+            Targets for supervised learning.
+
+        **params : dict, default=None
+            - If `enable_metadata_routing=False` (default):
+              Parameters directly passed to the `fit` methods of the
+              sub-transformers.
+
+            - If `enable_metadata_routing=True`:
+              Parameters safely routed to the `fit` methods of the
+              sub-transformers. See :ref:`Metadata Routing User Guide
+              ` for more details.
+
+            .. versionchanged:: 1.5
+                `**params` can now be routed via metadata routing API.
+
+        Returns
+        -------
+        X_t : array-like or sparse matrix of \
+                shape (n_samples, sum_n_components)
+            The `hstack` of results of transformers. `sum_n_components` is the
+            sum of `n_components` (output dimension) over transformers.
+        """
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit_transform", **params)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            routed_params = Bunch()
+            for name, obj in self.transformer_list:
+                if hasattr(obj, "fit_transform"):
+                    routed_params[name] = Bunch(fit_transform={})
+                    routed_params[name].fit_transform = params
+                else:
+                    routed_params[name] = Bunch(fit={})
+                    routed_params[name] = Bunch(transform={})
+                    routed_params[name].fit = params
+
+        results = self._parallel_func(X, y, _fit_transform_one, routed_params)
+        if not results:
+            # All transformers are None
+            return np.zeros((X.shape[0], 0))
+
+        Xs, transformers = zip(*results)
+        self._update_transformer_list(transformers)
+
+        return self._hstack(Xs)
+
+    def _log_message(self, name, idx, total):
+        if not self.verbose:
+            return None
+        return "(step %d of %d) Processing %s" % (idx, total, name)
+
+    def _parallel_func(self, X, y, func, routed_params):
+        """Runs func in parallel on X and y"""
+        self.transformer_list = list(self.transformer_list)
+        self._validate_transformers()
+        self._validate_transformer_weights()
+        transformers = list(self._iter())
+
+        return Parallel(n_jobs=self.n_jobs)(
+            delayed(func)(
+                transformer,
+                X,
+                y,
+                weight,
+                message_clsname="FeatureUnion",
+                message=self._log_message(name, idx, len(transformers)),
+                params=routed_params[name],
+            )
+            for idx, (name, transformer, weight) in enumerate(transformers, 1)
+        )
+
+    def transform(self, X, **params):
+        """Transform X separately by each transformer, concatenate results.
+
+        Parameters
+        ----------
+        X : iterable or array-like, depending on transformers
+            Input data to be transformed.
+
+        **params : dict, default=None
+
+            Parameters routed to the `transform` method of the sub-transformers via the
+            metadata routing API. See :ref:`Metadata Routing User Guide
+            ` for more details.
+
+            .. versionadded:: 1.5
+
+        Returns
+        -------
+        X_t : array-like or sparse matrix of shape (n_samples, sum_n_components)
+            The `hstack` of results of transformers. `sum_n_components` is the
+            sum of `n_components` (output dimension) over transformers.
+        """
+        _raise_for_params(params, self, "transform")
+
+        if _routing_enabled():
+            routed_params = process_routing(self, "transform", **params)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            routed_params = Bunch()
+            for name, _ in self.transformer_list:
+                routed_params[name] = Bunch(transform={})
+
+        Xs = Parallel(n_jobs=self.n_jobs)(
+            delayed(_transform_one)(trans, X, None, weight, params=routed_params[name])
+            for name, trans, weight in self._iter()
+        )
+        if not Xs:
+            # All transformers are None
+            return np.zeros((X.shape[0], 0))
+
+        return self._hstack(Xs)
+
+    def _hstack(self, Xs):
+        adapter = _get_container_adapter("transform", self)
+        if adapter and all(adapter.is_supported_container(X) for X in Xs):
+            return adapter.hstack(Xs)
+
+        if any(sparse.issparse(f) for f in Xs):
+            Xs = sparse.hstack(Xs).tocsr()
+        else:
+            Xs = np.hstack(Xs)
+        return Xs
+
+    def _update_transformer_list(self, transformers):
+        transformers = iter(transformers)
+        self.transformer_list[:] = [
+            (name, old if old == "drop" else next(transformers))
+            for name, old in self.transformer_list
+        ]
+
+    @property
+    def n_features_in_(self):
+        """Number of features seen during :term:`fit`."""
+
+        # X is passed to all transformers so we just delegate to the first one
+        return self.transformer_list[0][1].n_features_in_
+
+    @property
+    def feature_names_in_(self):
+        """Names of features seen during :term:`fit`."""
+        # X is passed to all transformers -- delegate to the first one
+        return self.transformer_list[0][1].feature_names_in_
+
+    def __sklearn_is_fitted__(self):
+        # Delegate whether feature union was fitted
+        for _, transformer, _ in self._iter():
+            check_is_fitted(transformer)
+        return True
+
+    def _sk_visual_block_(self):
+        names, transformers = zip(*self.transformer_list)
+        return _VisualBlock("parallel", transformers, names=names)
+
+    def __getitem__(self, name):
+        """Return transformer with name."""
+        if not isinstance(name, str):
+            raise KeyError("Only string keys are supported")
+        return self.named_transformers[name]
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide ` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.5
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__)
+
+        for name, transformer in self.transformer_list:
+            router.add(
+                **{name: transformer},
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="fit_transform", callee="fit_transform")
+                .add(caller="fit_transform", callee="fit")
+                .add(caller="fit_transform", callee="transform")
+                .add(caller="transform", callee="transform"),
+            )
+
+        return router
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        try:
+            tags.input_tags.sparse = all(
+                get_tags(trans).input_tags.sparse
+                for name, trans in self.transformer_list
+                if trans not in {"passthrough", "drop"}
+            )
+        except Exception:
+            # If `transformer_list` does not comply with our API (list of tuples)
+            # then it will fail. In this case, we assume that `sparse` is False
+            # but the parameter validation will raise an error during `fit`.
+            pass  # pragma: no cover
+        return tags
+
+
+def make_union(
+    *transformers, n_jobs=None, verbose=False, verbose_feature_names_out=True
+):
+    """Construct a :class:`FeatureUnion` from the given transformers.
+
+    This is a shorthand for the :class:`FeatureUnion` constructor; it does not
+    require, and does not permit, naming the transformers. Instead, they will
+    be given names automatically based on their types. It also does not allow
+    weighting.
+
+    Parameters
+    ----------
+    *transformers : list of estimators
+        One or more estimators.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary `
+        for more details.
+
+        .. versionchanged:: v0.20
+           `n_jobs` default changed from 1 to None.
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each transformer will be
+        printed as it is completed.
+
+    verbose_feature_names_out : bool, default=True
+        If True, the feature names generated by `get_feature_names_out` will
+        include prefixes derived from the transformer names.
+
+    Returns
+    -------
+    f : FeatureUnion
+        A :class:`FeatureUnion` object for concatenating the results of multiple
+        transformer objects.
+
+    See Also
+    --------
+    FeatureUnion : Class for concatenating the results of multiple transformer
+        objects.
+
+    Examples
+    --------
+    >>> from sklearn.decomposition import PCA, TruncatedSVD
+    >>> from sklearn.pipeline import make_union
+    >>> make_union(PCA(), TruncatedSVD())
+     FeatureUnion(transformer_list=[('pca', PCA()),
+                                   ('truncatedsvd', TruncatedSVD())])
+    """
+    return FeatureUnion(
+        _name_estimators(transformers),
+        n_jobs=n_jobs,
+        verbose=verbose,
+        verbose_feature_names_out=verbose_feature_names_out,
+    )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/random_projection.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/random_projection.py
new file mode 100644
index 0000000000000000000000000000000000000000..f98b11365dd3bcfc54eae62515e040ed5d237466
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/random_projection.py
@@ -0,0 +1,824 @@
+"""Random projection transformers.
+
+Random projections are a simple and computationally efficient way to
+reduce the dimensionality of the data by trading a controlled amount
+of accuracy (as additional variance) for faster processing times and
+smaller model sizes.
+
+The dimensions and distribution of random projections matrices are
+controlled so as to preserve the pairwise distances between any two
+samples of the dataset.
+
+The main theoretical result behind the efficiency of random projection is the
+`Johnson-Lindenstrauss lemma (quoting Wikipedia)
+`_:
+
+  In mathematics, the Johnson-Lindenstrauss lemma is a result
+  concerning low-distortion embeddings of points from high-dimensional
+  into low-dimensional Euclidean space. The lemma states that a small set
+  of points in a high-dimensional space can be embedded into a space of
+  much lower dimension in such a way that distances between the points are
+  nearly preserved. The map used for the embedding is at least Lipschitz,
+  and can even be taken to be an orthogonal projection.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+from scipy import linalg
+
+from .base import (
+    BaseEstimator,
+    ClassNamePrefixFeaturesOutMixin,
+    TransformerMixin,
+    _fit_context,
+)
+from .exceptions import DataDimensionalityWarning
+from .utils import check_random_state
+from .utils._param_validation import Interval, StrOptions, validate_params
+from .utils.extmath import safe_sparse_dot
+from .utils.random import sample_without_replacement
+from .utils.validation import check_array, check_is_fitted, validate_data
+
+__all__ = [
+    "GaussianRandomProjection",
+    "SparseRandomProjection",
+    "johnson_lindenstrauss_min_dim",
+]
+
+
+@validate_params(
+    {
+        "n_samples": ["array-like", Interval(Real, 1, None, closed="left")],
+        "eps": ["array-like", Interval(Real, 0, 1, closed="neither")],
+    },
+    prefer_skip_nested_validation=True,
+)
+def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1):
+    """Find a 'safe' number of components to randomly project to.
+
+    The distortion introduced by a random projection `p` only changes the
+    distance between two points by a factor (1 +- eps) in a euclidean space
+    with good probability. The projection `p` is an eps-embedding as defined
+    by:
+
+    .. code-block:: text
+
+      (1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2
+
+    Where u and v are any rows taken from a dataset of shape (n_samples,
+    n_features), eps is in ]0, 1[ and p is a projection by a random Gaussian
+    N(0, 1) matrix of shape (n_components, n_features) (or a sparse
+    Achlioptas matrix).
+
+    The minimum number of components to guarantee the eps-embedding is
+    given by:
+
+    .. code-block:: text
+
+      n_components >= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3)
+
+    Note that the number of dimensions is independent of the original
+    number of features but instead depends on the size of the dataset:
+    the larger the dataset, the higher is the minimal dimensionality of
+    an eps-embedding.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    n_samples : int or array-like of int
+        Number of samples that should be an integer greater than 0. If an array
+        is given, it will compute a safe number of components array-wise.
+
+    eps : float or array-like of shape (n_components,), dtype=float, \
+            default=0.1
+        Maximum distortion rate in the range (0, 1) as defined by the
+        Johnson-Lindenstrauss lemma. If an array is given, it will compute a
+        safe number of components array-wise.
+
+    Returns
+    -------
+    n_components : int or ndarray of int
+        The minimal number of components to guarantee with good probability
+        an eps-embedding with n_samples.
+
+    References
+    ----------
+
+    .. [1] https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
+
+    .. [2] `Sanjoy Dasgupta and Anupam Gupta, 1999,
+           "An elementary proof of the Johnson-Lindenstrauss Lemma."
+           `_
+
+    Examples
+    --------
+    >>> from sklearn.random_projection import johnson_lindenstrauss_min_dim
+    >>> johnson_lindenstrauss_min_dim(1e6, eps=0.5)
+    np.int64(663)
+
+    >>> johnson_lindenstrauss_min_dim(1e6, eps=[0.5, 0.1, 0.01])
+    array([    663,   11841, 1112658])
+
+    >>> johnson_lindenstrauss_min_dim([1e4, 1e5, 1e6], eps=0.1)
+    array([ 7894,  9868, 11841])
+    """
+    eps = np.asarray(eps)
+    n_samples = np.asarray(n_samples)
+
+    if np.any(eps <= 0.0) or np.any(eps >= 1):
+        raise ValueError("The JL bound is defined for eps in ]0, 1[, got %r" % eps)
+
+    if np.any(n_samples <= 0):
+        raise ValueError(
+            "The JL bound is defined for n_samples greater than zero, got %r"
+            % n_samples
+        )
+
+    denominator = (eps**2 / 2) - (eps**3 / 3)
+    return (4 * np.log(n_samples) / denominator).astype(np.int64)
+
+
+def _check_density(density, n_features):
+    """Factorize density check according to Li et al."""
+    if density == "auto":
+        density = 1 / np.sqrt(n_features)
+
+    elif density <= 0 or density > 1:
+        raise ValueError("Expected density in range ]0, 1], got: %r" % density)
+    return density
+
+
+def _check_input_size(n_components, n_features):
+    """Factorize argument checking for random matrix generation."""
+    if n_components <= 0:
+        raise ValueError(
+            "n_components must be strictly positive, got %d" % n_components
+        )
+    if n_features <= 0:
+        raise ValueError("n_features must be strictly positive, got %d" % n_features)
+
+
+def _gaussian_random_matrix(n_components, n_features, random_state=None):
+    """Generate a dense Gaussian random matrix.
+
+    The components of the random matrix are drawn from
+
+        N(0, 1.0 / n_components).
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    n_components : int,
+        Dimensionality of the target projection space.
+
+    n_features : int,
+        Dimensionality of the original source space.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generator used to generate the matrix
+        at fit time.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Returns
+    -------
+    components : ndarray of shape (n_components, n_features)
+        The generated Gaussian random matrix.
+
+    See Also
+    --------
+    GaussianRandomProjection
+    """
+    _check_input_size(n_components, n_features)
+    rng = check_random_state(random_state)
+    components = rng.normal(
+        loc=0.0, scale=1.0 / np.sqrt(n_components), size=(n_components, n_features)
+    )
+    return components
+
+
+def _sparse_random_matrix(n_components, n_features, density="auto", random_state=None):
+    """Generalized Achlioptas random sparse matrix for random projection.
+
+    Setting density to 1 / 3 will yield the original matrix by Dimitris
+    Achlioptas while setting a lower value will yield the generalization
+    by Ping Li et al.
+
+    If we note :math:`s = 1 / density`, the components of the random matrix are
+    drawn from:
+
+      - -sqrt(s) / sqrt(n_components)   with probability 1 / 2s
+      -  0                              with probability 1 - 1 / s
+      - +sqrt(s) / sqrt(n_components)   with probability 1 / 2s
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    n_components : int,
+        Dimensionality of the target projection space.
+
+    n_features : int,
+        Dimensionality of the original source space.
+
+    density : float or 'auto', default='auto'
+        Ratio of non-zero component in the random projection matrix in the
+        range `(0, 1]`
+
+        If density = 'auto', the value is set to the minimum density
+        as recommended by Ping Li et al.: 1 / sqrt(n_features).
+
+        Use density = 1 / 3.0 if you want to reproduce the results from
+        Achlioptas, 2001.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generator used to generate the matrix
+        at fit time.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Returns
+    -------
+    components : {ndarray, sparse matrix} of shape (n_components, n_features)
+        The generated Gaussian random matrix. Sparse matrix will be of CSR
+        format.
+
+    See Also
+    --------
+    SparseRandomProjection
+
+    References
+    ----------
+
+    .. [1] Ping Li, T. Hastie and K. W. Church, 2006,
+           "Very Sparse Random Projections".
+           https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
+
+    .. [2] D. Achlioptas, 2001, "Database-friendly random projections",
+           https://cgi.di.uoa.gr/~optas/papers/jl.pdf
+
+    """
+    _check_input_size(n_components, n_features)
+    density = _check_density(density, n_features)
+    rng = check_random_state(random_state)
+
+    if density == 1:
+        # skip index generation if totally dense
+        components = rng.binomial(1, 0.5, (n_components, n_features)) * 2 - 1
+        return 1 / np.sqrt(n_components) * components
+
+    else:
+        # Generate location of non zero elements
+        indices = []
+        offset = 0
+        indptr = [offset]
+        for _ in range(n_components):
+            # find the indices of the non-zero components for row i
+            n_nonzero_i = rng.binomial(n_features, density)
+            indices_i = sample_without_replacement(
+                n_features, n_nonzero_i, random_state=rng
+            )
+            indices.append(indices_i)
+            offset += n_nonzero_i
+            indptr.append(offset)
+
+        indices = np.concatenate(indices)
+
+        # Among non zero components the probability of the sign is 50%/50%
+        data = rng.binomial(1, 0.5, size=np.size(indices)) * 2 - 1
+
+        # build the CSR structure by concatenating the rows
+        components = sp.csr_matrix(
+            (data, indices, indptr), shape=(n_components, n_features)
+        )
+
+        return np.sqrt(1 / density) / np.sqrt(n_components) * components
+
+
+class BaseRandomProjection(
+    ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator, metaclass=ABCMeta
+):
+    """Base class for random projections.
+
+    Warning: This class should not be used directly.
+    Use derived classes instead.
+    """
+
+    _parameter_constraints: dict = {
+        "n_components": [
+            Interval(Integral, 1, None, closed="left"),
+            StrOptions({"auto"}),
+        ],
+        "eps": [Interval(Real, 0, None, closed="neither")],
+        "compute_inverse_components": ["boolean"],
+        "random_state": ["random_state"],
+    }
+
+    @abstractmethod
+    def __init__(
+        self,
+        n_components="auto",
+        *,
+        eps=0.1,
+        compute_inverse_components=False,
+        random_state=None,
+    ):
+        self.n_components = n_components
+        self.eps = eps
+        self.compute_inverse_components = compute_inverse_components
+        self.random_state = random_state
+
+    @abstractmethod
+    def _make_random_matrix(self, n_components, n_features):
+        """Generate the random projection matrix.
+
+        Parameters
+        ----------
+        n_components : int,
+            Dimensionality of the target projection space.
+
+        n_features : int,
+            Dimensionality of the original source space.
+
+        Returns
+        -------
+        components : {ndarray, sparse matrix} of shape (n_components, n_features)
+            The generated random matrix. Sparse matrix will be of CSR format.
+
+        """
+
+    def _compute_inverse_components(self):
+        """Compute the pseudo-inverse of the (densified) components."""
+        components = self.components_
+        if sp.issparse(components):
+            components = components.toarray()
+        return linalg.pinv(components, check_finite=False)
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y=None):
+        """Generate a sparse random projection matrix.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            Training set: only the shape is used to find optimal random
+            matrix dimensions based on the theory referenced in the
+            afore mentioned papers.
+
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            BaseRandomProjection class instance.
+        """
+        X = validate_data(
+            self, X, accept_sparse=["csr", "csc"], dtype=[np.float64, np.float32]
+        )
+
+        n_samples, n_features = X.shape
+
+        if self.n_components == "auto":
+            self.n_components_ = johnson_lindenstrauss_min_dim(
+                n_samples=n_samples, eps=self.eps
+            )
+
+            if self.n_components_ <= 0:
+                raise ValueError(
+                    "eps=%f and n_samples=%d lead to a target dimension of "
+                    "%d which is invalid" % (self.eps, n_samples, self.n_components_)
+                )
+
+            elif self.n_components_ > n_features:
+                raise ValueError(
+                    "eps=%f and n_samples=%d lead to a target dimension of "
+                    "%d which is larger than the original space with "
+                    "n_features=%d"
+                    % (self.eps, n_samples, self.n_components_, n_features)
+                )
+        else:
+            if self.n_components > n_features:
+                warnings.warn(
+                    "The number of components is higher than the number of"
+                    " features: n_features < n_components (%s < %s)."
+                    "The dimensionality of the problem will not be reduced."
+                    % (n_features, self.n_components),
+                    DataDimensionalityWarning,
+                )
+
+            self.n_components_ = self.n_components
+
+        # Generate a projection matrix of size [n_components, n_features]
+        self.components_ = self._make_random_matrix(
+            self.n_components_, n_features
+        ).astype(X.dtype, copy=False)
+
+        if self.compute_inverse_components:
+            self.inverse_components_ = self._compute_inverse_components()
+
+        # Required by ClassNamePrefixFeaturesOutMixin.get_feature_names_out.
+        self._n_features_out = self.n_components
+
+        return self
+
+    def inverse_transform(self, X):
+        """Project data back to its original space.
+
+        Returns an array X_original whose transform would be X. Note that even
+        if X is sparse, X_original is dense: this may use a lot of RAM.
+
+        If `compute_inverse_components` is False, the inverse of the components is
+        computed during each call to `inverse_transform` which can be costly.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_components)
+            Data to be transformed back.
+
+        Returns
+        -------
+        X_original : ndarray of shape (n_samples, n_features)
+            Reconstructed data.
+        """
+        check_is_fitted(self)
+
+        X = check_array(X, dtype=[np.float64, np.float32], accept_sparse=("csr", "csc"))
+
+        if self.compute_inverse_components:
+            return X @ self.inverse_components_.T
+
+        inverse_components = self._compute_inverse_components()
+        return X @ inverse_components.T
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.transformer_tags.preserves_dtype = ["float64", "float32"]
+        tags.input_tags.sparse = True
+        return tags
+
+
+class GaussianRandomProjection(BaseRandomProjection):
+    """Reduce dimensionality through Gaussian random projection.
+
+    The components of the random matrix are drawn from N(0, 1 / n_components).
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    n_components : int or 'auto', default='auto'
+        Dimensionality of the target projection space.
+
+        n_components can be automatically adjusted according to the
+        number of samples in the dataset and the bound given by the
+        Johnson-Lindenstrauss lemma. In that case the quality of the
+        embedding is controlled by the ``eps`` parameter.
+
+        It should be noted that Johnson-Lindenstrauss lemma can yield
+        very conservative estimated of the required number of components
+        as it makes no assumption on the structure of the dataset.
+
+    eps : float, default=0.1
+        Parameter to control the quality of the embedding according to
+        the Johnson-Lindenstrauss lemma when `n_components` is set to
+        'auto'. The value should be strictly positive.
+
+        Smaller values lead to better embedding and higher number of
+        dimensions (n_components) in the target projection space.
+
+    compute_inverse_components : bool, default=False
+        Learn the inverse transform by computing the pseudo-inverse of the
+        components during fit. Note that computing the pseudo-inverse does not
+        scale well to large matrices.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generator used to generate the
+        projection matrix at fit time.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    n_components_ : int
+        Concrete number of components computed when n_components="auto".
+
+    components_ : ndarray of shape (n_components, n_features)
+        Random matrix used for the projection.
+
+    inverse_components_ : ndarray of shape (n_features, n_components)
+        Pseudo-inverse of the components, only computed if
+        `compute_inverse_components` is True.
+
+        .. versionadded:: 1.1
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    SparseRandomProjection : Reduce dimensionality through sparse
+        random projection.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.random_projection import GaussianRandomProjection
+    >>> rng = np.random.RandomState(42)
+    >>> X = rng.rand(25, 3000)
+    >>> transformer = GaussianRandomProjection(random_state=rng)
+    >>> X_new = transformer.fit_transform(X)
+    >>> X_new.shape
+    (25, 2759)
+    """
+
+    def __init__(
+        self,
+        n_components="auto",
+        *,
+        eps=0.1,
+        compute_inverse_components=False,
+        random_state=None,
+    ):
+        super().__init__(
+            n_components=n_components,
+            eps=eps,
+            compute_inverse_components=compute_inverse_components,
+            random_state=random_state,
+        )
+
+    def _make_random_matrix(self, n_components, n_features):
+        """Generate the random projection matrix.
+
+        Parameters
+        ----------
+        n_components : int,
+            Dimensionality of the target projection space.
+
+        n_features : int,
+            Dimensionality of the original source space.
+
+        Returns
+        -------
+        components : ndarray of shape (n_components, n_features)
+            The generated random matrix.
+        """
+        random_state = check_random_state(self.random_state)
+        return _gaussian_random_matrix(
+            n_components, n_features, random_state=random_state
+        )
+
+    def transform(self, X):
+        """Project the data by using matrix product with the random matrix.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            The input data to project into a smaller dimensional space.
+
+        Returns
+        -------
+        X_new : ndarray of shape (n_samples, n_components)
+            Projected array.
+        """
+        check_is_fitted(self)
+        X = validate_data(
+            self,
+            X,
+            accept_sparse=["csr", "csc"],
+            reset=False,
+            dtype=[np.float64, np.float32],
+        )
+
+        return X @ self.components_.T
+
+
+class SparseRandomProjection(BaseRandomProjection):
+    """Reduce dimensionality through sparse random projection.
+
+    Sparse random matrix is an alternative to dense random
+    projection matrix that guarantees similar embedding quality while being
+    much more memory efficient and allowing faster computation of the
+    projected data.
+
+    If we note `s = 1 / density` the components of the random matrix are
+    drawn from:
+
+    .. code-block:: text
+
+      -sqrt(s) / sqrt(n_components)   with probability 1 / 2s
+       0                              with probability 1 - 1 / s
+      +sqrt(s) / sqrt(n_components)   with probability 1 / 2s
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    n_components : int or 'auto', default='auto'
+        Dimensionality of the target projection space.
+
+        n_components can be automatically adjusted according to the
+        number of samples in the dataset and the bound given by the
+        Johnson-Lindenstrauss lemma. In that case the quality of the
+        embedding is controlled by the ``eps`` parameter.
+
+        It should be noted that Johnson-Lindenstrauss lemma can yield
+        very conservative estimated of the required number of components
+        as it makes no assumption on the structure of the dataset.
+
+    density : float or 'auto', default='auto'
+        Ratio in the range (0, 1] of non-zero component in the random
+        projection matrix.
+
+        If density = 'auto', the value is set to the minimum density
+        as recommended by Ping Li et al.: 1 / sqrt(n_features).
+
+        Use density = 1 / 3.0 if you want to reproduce the results from
+        Achlioptas, 2001.
+
+    eps : float, default=0.1
+        Parameter to control the quality of the embedding according to
+        the Johnson-Lindenstrauss lemma when n_components is set to
+        'auto'. This value should be strictly positive.
+
+        Smaller values lead to better embedding and higher number of
+        dimensions (n_components) in the target projection space.
+
+    dense_output : bool, default=False
+        If True, ensure that the output of the random projection is a
+        dense numpy array even if the input and random projection matrix
+        are both sparse. In practice, if the number of components is
+        small the number of zero components in the projected data will
+        be very small and it will be more CPU and memory efficient to
+        use a dense representation.
+
+        If False, the projected data uses a sparse representation if
+        the input is sparse.
+
+    compute_inverse_components : bool, default=False
+        Learn the inverse transform by computing the pseudo-inverse of the
+        components during fit. Note that the pseudo-inverse is always a dense
+        array, even if the training data was sparse. This means that it might be
+        necessary to call `inverse_transform` on a small batch of samples at a
+        time to avoid exhausting the available memory on the host. Moreover,
+        computing the pseudo-inverse does not scale well to large matrices.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generator used to generate the
+        projection matrix at fit time.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    n_components_ : int
+        Concrete number of components computed when n_components="auto".
+
+    components_ : sparse matrix of shape (n_components, n_features)
+        Random matrix used for the projection. Sparse matrix will be of CSR
+        format.
+
+    inverse_components_ : ndarray of shape (n_features, n_components)
+        Pseudo-inverse of the components, only computed if
+        `compute_inverse_components` is True.
+
+        .. versionadded:: 1.1
+
+    density_ : float in range 0.0 - 1.0
+        Concrete density computed from when density = "auto".
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    See Also
+    --------
+    GaussianRandomProjection : Reduce dimensionality through Gaussian
+        random projection.
+
+    References
+    ----------
+
+    .. [1] Ping Li, T. Hastie and K. W. Church, 2006,
+           "Very Sparse Random Projections".
+           https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
+
+    .. [2] D. Achlioptas, 2001, "Database-friendly random projections",
+           https://cgi.di.uoa.gr/~optas/papers/jl.pdf
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.random_projection import SparseRandomProjection
+    >>> rng = np.random.RandomState(42)
+    >>> X = rng.rand(25, 3000)
+    >>> transformer = SparseRandomProjection(random_state=rng)
+    >>> X_new = transformer.fit_transform(X)
+    >>> X_new.shape
+    (25, 2759)
+    >>> # very few components are non-zero
+    >>> np.mean(transformer.components_ != 0)
+    np.float64(0.0182)
+    """
+
+    _parameter_constraints: dict = {
+        **BaseRandomProjection._parameter_constraints,
+        "density": [Interval(Real, 0.0, 1.0, closed="right"), StrOptions({"auto"})],
+        "dense_output": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        n_components="auto",
+        *,
+        density="auto",
+        eps=0.1,
+        dense_output=False,
+        compute_inverse_components=False,
+        random_state=None,
+    ):
+        super().__init__(
+            n_components=n_components,
+            eps=eps,
+            compute_inverse_components=compute_inverse_components,
+            random_state=random_state,
+        )
+
+        self.dense_output = dense_output
+        self.density = density
+
+    def _make_random_matrix(self, n_components, n_features):
+        """Generate the random projection matrix
+
+        Parameters
+        ----------
+        n_components : int
+            Dimensionality of the target projection space.
+
+        n_features : int
+            Dimensionality of the original source space.
+
+        Returns
+        -------
+        components : sparse matrix of shape (n_components, n_features)
+            The generated random matrix in CSR format.
+
+        """
+        random_state = check_random_state(self.random_state)
+        self.density_ = _check_density(self.density, n_features)
+        return _sparse_random_matrix(
+            n_components, n_features, density=self.density_, random_state=random_state
+        )
+
+    def transform(self, X):
+        """Project the data by using matrix product with the random matrix.
+
+        Parameters
+        ----------
+        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
+            The input data to project into a smaller dimensional space.
+
+        Returns
+        -------
+        X_new : {ndarray, sparse matrix} of shape (n_samples, n_components)
+            Projected array. It is a sparse matrix only when the input is sparse and
+            `dense_output = False`.
+        """
+        check_is_fitted(self)
+        X = validate_data(
+            self,
+            X,
+            accept_sparse=["csr", "csc"],
+            reset=False,
+            dtype=[np.float64, np.float32],
+        )
+
+        return safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a039d2e15abddf5aaca8faad462b1b951ec6e18a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/__init__.py
@@ -0,0 +1,21 @@
+"""Support vector machine algorithms."""
+
+# See http://scikit-learn.sourceforge.net/modules/svm.html for complete
+# documentation.
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from ._bounds import l1_min_c
+from ._classes import SVC, SVR, LinearSVC, LinearSVR, NuSVC, NuSVR, OneClassSVM
+
+__all__ = [
+    "SVC",
+    "SVR",
+    "LinearSVC",
+    "LinearSVR",
+    "NuSVC",
+    "NuSVR",
+    "OneClassSVM",
+    "l1_min_c",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_base.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..db295e4e877b50e7dff639de4dd6bb98c95d7b91
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_base.py
@@ -0,0 +1,1262 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from abc import ABCMeta, abstractmethod
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+
+from ..base import BaseEstimator, ClassifierMixin, _fit_context
+from ..exceptions import ConvergenceWarning, NotFittedError
+from ..preprocessing import LabelEncoder
+from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.extmath import safe_sparse_dot
+from ..utils.metaestimators import available_if
+from ..utils.multiclass import _ovr_decision_function, check_classification_targets
+from ..utils.validation import (
+    _check_large_sparse,
+    _check_sample_weight,
+    _num_samples,
+    check_consistent_length,
+    check_is_fitted,
+    validate_data,
+)
+from . import _liblinear as liblinear  # type: ignore[attr-defined]
+
+# mypy error: error: Module 'sklearn.svm' has no attribute '_libsvm'
+# (and same for other imports)
+from . import _libsvm as libsvm  # type: ignore[attr-defined]
+from . import _libsvm_sparse as libsvm_sparse  # type: ignore[attr-defined]
+
+LIBSVM_IMPL = ["c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr"]
+
+
+def _one_vs_one_coef(dual_coef, n_support, support_vectors):
+    """Generate primal coefficients from dual coefficients
+    for the one-vs-one multi class LibSVM in the case
+    of a linear kernel."""
+
+    # get 1vs1 weights for all n*(n-1) classifiers.
+    # this is somewhat messy.
+    # shape of dual_coef_ is nSV * (n_classes -1)
+    # see docs for details
+    n_class = dual_coef.shape[0] + 1
+
+    # XXX we could do preallocation of coef but
+    # would have to take care in the sparse case
+    coef = []
+    sv_locs = np.cumsum(np.hstack([[0], n_support]))
+    for class1 in range(n_class):
+        # SVs for class1:
+        sv1 = support_vectors[sv_locs[class1] : sv_locs[class1 + 1], :]
+        for class2 in range(class1 + 1, n_class):
+            # SVs for class1:
+            sv2 = support_vectors[sv_locs[class2] : sv_locs[class2 + 1], :]
+
+            # dual coef for class1 SVs:
+            alpha1 = dual_coef[class2 - 1, sv_locs[class1] : sv_locs[class1 + 1]]
+            # dual coef for class2 SVs:
+            alpha2 = dual_coef[class1, sv_locs[class2] : sv_locs[class2 + 1]]
+            # build weight for class1 vs class2
+
+            coef.append(safe_sparse_dot(alpha1, sv1) + safe_sparse_dot(alpha2, sv2))
+    return coef
+
+
+class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
+    """Base class for estimators that use libsvm as backing library.
+
+    This implements support vector machine classification and regression.
+
+    Parameter documentation is in the derived `SVC` class.
+    """
+
+    _parameter_constraints: dict = {
+        "kernel": [
+            StrOptions({"linear", "poly", "rbf", "sigmoid", "precomputed"}),
+            callable,
+        ],
+        "degree": [Interval(Integral, 0, None, closed="left")],
+        "gamma": [
+            StrOptions({"scale", "auto"}),
+            Interval(Real, 0.0, None, closed="left"),
+        ],
+        "coef0": [Interval(Real, None, None, closed="neither")],
+        "tol": [Interval(Real, 0.0, None, closed="neither")],
+        "C": [Interval(Real, 0.0, None, closed="right")],
+        "nu": [Interval(Real, 0.0, 1.0, closed="right")],
+        "epsilon": [Interval(Real, 0.0, None, closed="left")],
+        "shrinking": ["boolean"],
+        "probability": ["boolean"],
+        "cache_size": [Interval(Real, 0, None, closed="neither")],
+        "class_weight": [StrOptions({"balanced"}), dict, None],
+        "verbose": ["verbose"],
+        "max_iter": [Interval(Integral, -1, None, closed="left")],
+        "random_state": ["random_state"],
+    }
+
+    # The order of these must match the integer values in LibSVM.
+    # XXX These are actually the same in the dense case. Need to factor
+    # this out.
+    _sparse_kernels = ["linear", "poly", "rbf", "sigmoid", "precomputed"]
+
+    @abstractmethod
+    def __init__(
+        self,
+        kernel,
+        degree,
+        gamma,
+        coef0,
+        tol,
+        C,
+        nu,
+        epsilon,
+        shrinking,
+        probability,
+        cache_size,
+        class_weight,
+        verbose,
+        max_iter,
+        random_state,
+    ):
+        if self._impl not in LIBSVM_IMPL:
+            raise ValueError(
+                "impl should be one of %s, %s was given" % (LIBSVM_IMPL, self._impl)
+            )
+
+        self.kernel = kernel
+        self.degree = degree
+        self.gamma = gamma
+        self.coef0 = coef0
+        self.tol = tol
+        self.C = C
+        self.nu = nu
+        self.epsilon = epsilon
+        self.shrinking = shrinking
+        self.probability = probability
+        self.cache_size = cache_size
+        self.class_weight = class_weight
+        self.verbose = verbose
+        self.max_iter = max_iter
+        self.random_state = random_state
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # Used by cross_val_score.
+        tags.input_tags.pairwise = self.kernel == "precomputed"
+        tags.input_tags.sparse = self.kernel != "precomputed"
+        return tags
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the SVM model according to the given training data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features) \
+                or (n_samples, n_samples)
+            Training vectors, where `n_samples` is the number of samples
+            and `n_features` is the number of features.
+            For kernel="precomputed", the expected shape of X is
+            (n_samples, n_samples).
+
+        y : array-like of shape (n_samples,)
+            Target values (class labels in classification, real numbers in
+            regression).
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Per-sample weights. Rescale C per sample. Higher weights
+            force the classifier to put more emphasis on these points.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+
+        Notes
+        -----
+        If X and y are not C-ordered and contiguous arrays of np.float64 and
+        X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
+
+        If X is a dense array, then the other methods will not support sparse
+        matrices as input.
+        """
+        rnd = check_random_state(self.random_state)
+
+        sparse = sp.issparse(X)
+        if sparse and self.kernel == "precomputed":
+            raise TypeError("Sparse precomputed kernels are not supported.")
+        self._sparse = sparse and not callable(self.kernel)
+
+        if callable(self.kernel):
+            check_consistent_length(X, y)
+        else:
+            X, y = validate_data(
+                self,
+                X,
+                y,
+                dtype=np.float64,
+                order="C",
+                accept_sparse="csr",
+                accept_large_sparse=False,
+            )
+
+        y = self._validate_targets(y)
+
+        sample_weight = np.asarray(
+            [] if sample_weight is None else sample_weight, dtype=np.float64
+        )
+        solver_type = LIBSVM_IMPL.index(self._impl)
+
+        # input validation
+        n_samples = _num_samples(X)
+        if solver_type != 2 and n_samples != y.shape[0]:
+            raise ValueError(
+                "X and y have incompatible shapes.\n"
+                + "X has %s samples, but y has %s." % (n_samples, y.shape[0])
+            )
+
+        if self.kernel == "precomputed" and n_samples != X.shape[1]:
+            raise ValueError(
+                "Precomputed matrix must be a square matrix."
+                " Input is a {}x{} matrix.".format(X.shape[0], X.shape[1])
+            )
+
+        if sample_weight.shape[0] > 0 and sample_weight.shape[0] != n_samples:
+            raise ValueError(
+                "sample_weight and X have incompatible shapes: "
+                "%r vs %r\n"
+                "Note: Sparse matrices cannot be indexed w/"
+                "boolean masks (use `indices=True` in CV)."
+                % (sample_weight.shape, X.shape)
+            )
+
+        kernel = "precomputed" if callable(self.kernel) else self.kernel
+
+        if kernel == "precomputed":
+            # unused but needs to be a float for cython code that ignores
+            # it anyway
+            self._gamma = 0.0
+        elif isinstance(self.gamma, str):
+            if self.gamma == "scale":
+                # var = E[X^2] - E[X]^2 if sparse
+                X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var()
+                self._gamma = 1.0 / (X.shape[1] * X_var) if X_var != 0 else 1.0
+            elif self.gamma == "auto":
+                self._gamma = 1.0 / X.shape[1]
+        elif isinstance(self.gamma, Real):
+            self._gamma = self.gamma
+
+        fit = self._sparse_fit if self._sparse else self._dense_fit
+        if self.verbose:
+            print("[LibSVM]", end="")
+
+        seed = rnd.randint(np.iinfo("i").max)
+        fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)
+        # see comment on the other call to np.iinfo in this file
+
+        self.shape_fit_ = X.shape if hasattr(X, "shape") else (n_samples,)
+
+        # In binary case, we need to flip the sign of coef, intercept and
+        # decision function. Use self._intercept_ and self._dual_coef_
+        # internally.
+        self._intercept_ = self.intercept_.copy()
+        self._dual_coef_ = self.dual_coef_
+        if self._impl in ["c_svc", "nu_svc"] and len(self.classes_) == 2:
+            self.intercept_ *= -1
+            self.dual_coef_ = -self.dual_coef_
+
+        dual_coef = self._dual_coef_.data if self._sparse else self._dual_coef_
+        intercept_finiteness = np.isfinite(self._intercept_).all()
+        dual_coef_finiteness = np.isfinite(dual_coef).all()
+        if not (intercept_finiteness and dual_coef_finiteness):
+            raise ValueError(
+                "The dual coefficients or intercepts are not finite."
+                " The input data may contain large values and need to be"
+                " preprocessed."
+            )
+
+        # Since, in the case of SVC and NuSVC, the number of models optimized by
+        # libSVM could be greater than one (depending on the input), `n_iter_`
+        # stores an ndarray.
+        # For the other sub-classes (SVR, NuSVR, and OneClassSVM), the number of
+        # models optimized by libSVM is always one, so `n_iter_` stores an
+        # integer.
+        if self._impl in ["c_svc", "nu_svc"]:
+            self.n_iter_ = self._num_iter
+        else:
+            self.n_iter_ = self._num_iter.item()
+
+        return self
+
+    def _validate_targets(self, y):
+        """Validation of y and class_weight.
+
+        Default implementation for SVR and one-class; overridden in BaseSVC.
+        """
+        return column_or_1d(y, warn=True).astype(np.float64, copy=False)
+
+    def _warn_from_fit_status(self):
+        assert self.fit_status_ in (0, 1)
+        if self.fit_status_ == 1:
+            warnings.warn(
+                "Solver terminated early (max_iter=%i)."
+                "  Consider pre-processing your data with"
+                " StandardScaler or MinMaxScaler." % self.max_iter,
+                ConvergenceWarning,
+            )
+
+    def _dense_fit(self, X, y, sample_weight, solver_type, kernel, random_seed):
+        if callable(self.kernel):
+            # you must store a reference to X to compute the kernel in predict
+            # TODO: add keyword copy to copy on demand
+            self.__Xfit = X
+            X = self._compute_kernel(X)
+
+            if X.shape[0] != X.shape[1]:
+                raise ValueError("X.shape[0] should be equal to X.shape[1]")
+
+        libsvm.set_verbosity_wrap(self.verbose)
+
+        # we don't pass **self.get_params() to allow subclasses to
+        # add other parameters to __init__
+        (
+            self.support_,
+            self.support_vectors_,
+            self._n_support,
+            self.dual_coef_,
+            self.intercept_,
+            self._probA,
+            self._probB,
+            self.fit_status_,
+            self._num_iter,
+        ) = libsvm.fit(
+            X,
+            y,
+            svm_type=solver_type,
+            sample_weight=sample_weight,
+            class_weight=getattr(self, "class_weight_", np.empty(0)),
+            kernel=kernel,
+            C=self.C,
+            nu=self.nu,
+            probability=self.probability,
+            degree=self.degree,
+            shrinking=self.shrinking,
+            tol=self.tol,
+            cache_size=self.cache_size,
+            coef0=self.coef0,
+            gamma=self._gamma,
+            epsilon=self.epsilon,
+            max_iter=self.max_iter,
+            random_seed=random_seed,
+        )
+
+        self._warn_from_fit_status()
+
+    def _sparse_fit(self, X, y, sample_weight, solver_type, kernel, random_seed):
+        X.data = np.asarray(X.data, dtype=np.float64, order="C")
+        X.sort_indices()
+
+        kernel_type = self._sparse_kernels.index(kernel)
+
+        libsvm_sparse.set_verbosity_wrap(self.verbose)
+
+        (
+            self.support_,
+            self.support_vectors_,
+            dual_coef_data,
+            self.intercept_,
+            self._n_support,
+            self._probA,
+            self._probB,
+            self.fit_status_,
+            self._num_iter,
+        ) = libsvm_sparse.libsvm_sparse_train(
+            X.shape[1],
+            X.data,
+            X.indices,
+            X.indptr,
+            y,
+            solver_type,
+            kernel_type,
+            self.degree,
+            self._gamma,
+            self.coef0,
+            self.tol,
+            self.C,
+            getattr(self, "class_weight_", np.empty(0)),
+            sample_weight,
+            self.nu,
+            self.cache_size,
+            self.epsilon,
+            int(self.shrinking),
+            int(self.probability),
+            self.max_iter,
+            random_seed,
+        )
+
+        self._warn_from_fit_status()
+
+        if hasattr(self, "classes_"):
+            n_class = len(self.classes_) - 1
+        else:  # regression
+            n_class = 1
+        n_SV = self.support_vectors_.shape[0]
+
+        dual_coef_indices = np.tile(np.arange(n_SV), n_class)
+        if not n_SV:
+            self.dual_coef_ = sp.csr_matrix([])
+        else:
+            dual_coef_indptr = np.arange(
+                0, dual_coef_indices.size + 1, dual_coef_indices.size / n_class
+            )
+            self.dual_coef_ = sp.csr_matrix(
+                (dual_coef_data, dual_coef_indices, dual_coef_indptr), (n_class, n_SV)
+            )
+
+    def predict(self, X):
+        """Perform regression on samples in X.
+
+        For an one-class model, +1 (inlier) or -1 (outlier) is returned.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted values.
+        """
+        X = self._validate_for_predict(X)
+        predict = self._sparse_predict if self._sparse else self._dense_predict
+        return predict(X)
+
+    def _dense_predict(self, X):
+        X = self._compute_kernel(X)
+        if X.ndim == 1:
+            X = check_array(X, order="C", accept_large_sparse=False)
+
+        kernel = self.kernel
+        if callable(self.kernel):
+            kernel = "precomputed"
+            if X.shape[1] != self.shape_fit_[0]:
+                raise ValueError(
+                    "X.shape[1] = %d should be equal to %d, "
+                    "the number of samples at training time"
+                    % (X.shape[1], self.shape_fit_[0])
+                )
+
+        svm_type = LIBSVM_IMPL.index(self._impl)
+
+        return libsvm.predict(
+            X,
+            self.support_,
+            self.support_vectors_,
+            self._n_support,
+            self._dual_coef_,
+            self._intercept_,
+            self._probA,
+            self._probB,
+            svm_type=svm_type,
+            kernel=kernel,
+            degree=self.degree,
+            coef0=self.coef0,
+            gamma=self._gamma,
+            cache_size=self.cache_size,
+        )
+
+    def _sparse_predict(self, X):
+        # Precondition: X is a csr_matrix of dtype np.float64.
+        kernel = self.kernel
+        if callable(kernel):
+            kernel = "precomputed"
+
+        kernel_type = self._sparse_kernels.index(kernel)
+
+        C = 0.0  # C is not useful here
+
+        return libsvm_sparse.libsvm_sparse_predict(
+            X.data,
+            X.indices,
+            X.indptr,
+            self.support_vectors_.data,
+            self.support_vectors_.indices,
+            self.support_vectors_.indptr,
+            self._dual_coef_.data,
+            self._intercept_,
+            LIBSVM_IMPL.index(self._impl),
+            kernel_type,
+            self.degree,
+            self._gamma,
+            self.coef0,
+            self.tol,
+            C,
+            getattr(self, "class_weight_", np.empty(0)),
+            self.nu,
+            self.epsilon,
+            self.shrinking,
+            self.probability,
+            self._n_support,
+            self._probA,
+            self._probB,
+        )
+
+    def _compute_kernel(self, X):
+        """Return the data transformed by a callable kernel"""
+        if callable(self.kernel):
+            # in the case of precomputed kernel given as a function, we
+            # have to compute explicitly the kernel matrix
+            kernel = self.kernel(X, self.__Xfit)
+            if sp.issparse(kernel):
+                kernel = kernel.toarray()
+            X = np.asarray(kernel, dtype=np.float64, order="C")
+        return X
+
+    def _decision_function(self, X):
+        """Evaluates the decision function for the samples in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+
+        Returns
+        -------
+        X : array-like of shape (n_samples, n_class * (n_class-1) / 2)
+            Returns the decision function of the sample for each class
+            in the model.
+        """
+        # NOTE: _validate_for_predict contains check for is_fitted
+        # hence must be placed before any other attributes are used.
+        X = self._validate_for_predict(X)
+        X = self._compute_kernel(X)
+
+        if self._sparse:
+            dec_func = self._sparse_decision_function(X)
+        else:
+            dec_func = self._dense_decision_function(X)
+
+        # In binary case, we need to flip the sign of coef, intercept and
+        # decision function.
+        if self._impl in ["c_svc", "nu_svc"] and len(self.classes_) == 2:
+            return -dec_func.ravel()
+
+        return dec_func
+
+    def _dense_decision_function(self, X):
+        X = check_array(X, dtype=np.float64, order="C", accept_large_sparse=False)
+
+        kernel = self.kernel
+        if callable(kernel):
+            kernel = "precomputed"
+
+        return libsvm.decision_function(
+            X,
+            self.support_,
+            self.support_vectors_,
+            self._n_support,
+            self._dual_coef_,
+            self._intercept_,
+            self._probA,
+            self._probB,
+            svm_type=LIBSVM_IMPL.index(self._impl),
+            kernel=kernel,
+            degree=self.degree,
+            cache_size=self.cache_size,
+            coef0=self.coef0,
+            gamma=self._gamma,
+        )
+
+    def _sparse_decision_function(self, X):
+        X.data = np.asarray(X.data, dtype=np.float64, order="C")
+
+        kernel = self.kernel
+        if hasattr(kernel, "__call__"):
+            kernel = "precomputed"
+
+        kernel_type = self._sparse_kernels.index(kernel)
+
+        return libsvm_sparse.libsvm_sparse_decision_function(
+            X.data,
+            X.indices,
+            X.indptr,
+            self.support_vectors_.data,
+            self.support_vectors_.indices,
+            self.support_vectors_.indptr,
+            self._dual_coef_.data,
+            self._intercept_,
+            LIBSVM_IMPL.index(self._impl),
+            kernel_type,
+            self.degree,
+            self._gamma,
+            self.coef0,
+            self.tol,
+            self.C,
+            getattr(self, "class_weight_", np.empty(0)),
+            self.nu,
+            self.epsilon,
+            self.shrinking,
+            self.probability,
+            self._n_support,
+            self._probA,
+            self._probB,
+        )
+
+    def _validate_for_predict(self, X):
+        check_is_fitted(self)
+
+        if not callable(self.kernel):
+            X = validate_data(
+                self,
+                X,
+                accept_sparse="csr",
+                dtype=np.float64,
+                order="C",
+                accept_large_sparse=False,
+                reset=False,
+            )
+
+        if self._sparse and not sp.issparse(X):
+            X = sp.csr_matrix(X)
+        if self._sparse:
+            X.sort_indices()
+
+        if sp.issparse(X) and not self._sparse and not callable(self.kernel):
+            raise ValueError(
+                "cannot use sparse input in %r trained on dense data"
+                % type(self).__name__
+            )
+
+        if self.kernel == "precomputed":
+            if X.shape[1] != self.shape_fit_[0]:
+                raise ValueError(
+                    "X.shape[1] = %d should be equal to %d, "
+                    "the number of samples at training time"
+                    % (X.shape[1], self.shape_fit_[0])
+                )
+        # Fixes https://nvd.nist.gov/vuln/detail/CVE-2020-28975
+        # Check that _n_support is consistent with support_vectors
+        sv = self.support_vectors_
+        if not self._sparse and sv.size > 0 and self.n_support_.sum() != sv.shape[0]:
+            raise ValueError(
+                f"The internal representation of {self.__class__.__name__} was altered"
+            )
+        return X
+
+    @property
+    def coef_(self):
+        """Weights assigned to the features when `kernel="linear"`.
+
+        Returns
+        -------
+        ndarray of shape (n_features, n_classes)
+        """
+        if self.kernel != "linear":
+            raise AttributeError("coef_ is only available when using a linear kernel")
+
+        coef = self._get_coef()
+
+        # coef_ being a read-only property, it's better to mark the value as
+        # immutable to avoid hiding potential bugs for the unsuspecting user.
+        if sp.issparse(coef):
+            # sparse matrix do not have global flags
+            coef.data.flags.writeable = False
+        else:
+            # regular dense array
+            coef.flags.writeable = False
+        return coef
+
+    def _get_coef(self):
+        return safe_sparse_dot(self._dual_coef_, self.support_vectors_)
+
+    @property
+    def n_support_(self):
+        """Number of support vectors for each class."""
+        try:
+            check_is_fitted(self)
+        except NotFittedError:
+            raise AttributeError
+
+        svm_type = LIBSVM_IMPL.index(self._impl)
+        if svm_type in (0, 1):
+            return self._n_support
+        else:
+            # SVR and OneClass
+            # _n_support has size 2, we make it size 1
+            return np.array([self._n_support[0]])
+
+
+class BaseSVC(ClassifierMixin, BaseLibSVM, metaclass=ABCMeta):
+    """ABC for LibSVM-based classifiers."""
+
+    _parameter_constraints: dict = {
+        **BaseLibSVM._parameter_constraints,
+        "decision_function_shape": [StrOptions({"ovr", "ovo"})],
+        "break_ties": ["boolean"],
+    }
+    for unused_param in ["epsilon", "nu"]:
+        _parameter_constraints.pop(unused_param)
+
+    @abstractmethod
+    def __init__(
+        self,
+        kernel,
+        degree,
+        gamma,
+        coef0,
+        tol,
+        C,
+        nu,
+        shrinking,
+        probability,
+        cache_size,
+        class_weight,
+        verbose,
+        max_iter,
+        decision_function_shape,
+        random_state,
+        break_ties,
+    ):
+        self.decision_function_shape = decision_function_shape
+        self.break_ties = break_ties
+        super().__init__(
+            kernel=kernel,
+            degree=degree,
+            gamma=gamma,
+            coef0=coef0,
+            tol=tol,
+            C=C,
+            nu=nu,
+            epsilon=0.0,
+            shrinking=shrinking,
+            probability=probability,
+            cache_size=cache_size,
+            class_weight=class_weight,
+            verbose=verbose,
+            max_iter=max_iter,
+            random_state=random_state,
+        )
+
+    def _validate_targets(self, y):
+        y_ = column_or_1d(y, warn=True)
+        check_classification_targets(y)
+        cls, y = np.unique(y_, return_inverse=True)
+        self.class_weight_ = compute_class_weight(self.class_weight, classes=cls, y=y_)
+        if len(cls) < 2:
+            raise ValueError(
+                "The number of classes has to be greater than one; got %d class"
+                % len(cls)
+            )
+
+        self.classes_ = cls
+
+        return np.asarray(y, dtype=np.float64, order="C")
+
+    def decision_function(self, X):
+        """Evaluate the decision function for the samples in X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The input samples.
+
+        Returns
+        -------
+        X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
+            Returns the decision function of the sample for each class
+            in the model.
+            If decision_function_shape='ovr', the shape is (n_samples,
+            n_classes).
+
+        Notes
+        -----
+        If decision_function_shape='ovo', the function values are proportional
+        to the distance of the samples X to the separating hyperplane. If the
+        exact distances are required, divide the function values by the norm of
+        the weight vector (``coef_``). See also `this question
+        `_ for further details.
+        If decision_function_shape='ovr', the decision function is a monotonic
+        transformation of ovo decision function.
+        """
+        dec = self._decision_function(X)
+        if self.decision_function_shape == "ovr" and len(self.classes_) > 2:
+            return _ovr_decision_function(dec < 0, -dec, len(self.classes_))
+        return dec
+
+    def predict(self, X):
+        """Perform classification on samples in X.
+
+        For an one-class model, +1 or -1 is returned.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
+                (n_samples_test, n_samples_train)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Class labels for samples in X.
+        """
+        check_is_fitted(self)
+        if self.break_ties and self.decision_function_shape == "ovo":
+            raise ValueError(
+                "break_ties must be False when decision_function_shape is 'ovo'"
+            )
+
+        if (
+            self.break_ties
+            and self.decision_function_shape == "ovr"
+            and len(self.classes_) > 2
+        ):
+            y = np.argmax(self.decision_function(X), axis=1)
+        else:
+            y = super().predict(X)
+        return self.classes_.take(np.asarray(y, dtype=np.intp))
+
+    # Hacky way of getting predict_proba to raise an AttributeError when
+    # probability=False using properties. Do not use this in new code; when
+    # probabilities are not available depending on a setting, introduce two
+    # estimators.
+    def _check_proba(self):
+        if not self.probability:
+            raise AttributeError(
+                "predict_proba is not available when probability=False"
+            )
+        if self._impl not in ("c_svc", "nu_svc"):
+            raise AttributeError("predict_proba only implemented for SVC and NuSVC")
+        return True
+
+    @available_if(_check_proba)
+    def predict_proba(self, X):
+        """Compute probabilities of possible outcomes for samples in X.
+
+        The model needs to have probability information computed at training
+        time: fit with attribute `probability` set to True.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+        T : ndarray of shape (n_samples, n_classes)
+            Returns the probability of the sample for each class in
+            the model. The columns correspond to the classes in sorted
+            order, as they appear in the attribute :term:`classes_`.
+
+        Notes
+        -----
+        The probability model is created using cross validation, so
+        the results can be slightly different than those obtained by
+        predict. Also, it will produce meaningless results on very small
+        datasets.
+        """
+        X = self._validate_for_predict(X)
+        if self.probA_.size == 0 or self.probB_.size == 0:
+            raise NotFittedError(
+                "predict_proba is not available when fitted with probability=False"
+            )
+        pred_proba = (
+            self._sparse_predict_proba if self._sparse else self._dense_predict_proba
+        )
+        return pred_proba(X)
+
+    @available_if(_check_proba)
+    def predict_log_proba(self, X):
+        """Compute log probabilities of possible outcomes for samples in X.
+
+        The model need to have probability information computed at training
+        time: fit with attribute `probability` set to True.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features) or \
+                (n_samples_test, n_samples_train)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+        T : ndarray of shape (n_samples, n_classes)
+            Returns the log-probabilities of the sample for each class in
+            the model. The columns correspond to the classes in sorted
+            order, as they appear in the attribute :term:`classes_`.
+
+        Notes
+        -----
+        The probability model is created using cross validation, so
+        the results can be slightly different than those obtained by
+        predict. Also, it will produce meaningless results on very small
+        datasets.
+        """
+        return np.log(self.predict_proba(X))
+
+    def _dense_predict_proba(self, X):
+        X = self._compute_kernel(X)
+
+        kernel = self.kernel
+        if callable(kernel):
+            kernel = "precomputed"
+
+        svm_type = LIBSVM_IMPL.index(self._impl)
+        pprob = libsvm.predict_proba(
+            X,
+            self.support_,
+            self.support_vectors_,
+            self._n_support,
+            self._dual_coef_,
+            self._intercept_,
+            self._probA,
+            self._probB,
+            svm_type=svm_type,
+            kernel=kernel,
+            degree=self.degree,
+            cache_size=self.cache_size,
+            coef0=self.coef0,
+            gamma=self._gamma,
+        )
+
+        return pprob
+
+    def _sparse_predict_proba(self, X):
+        X.data = np.asarray(X.data, dtype=np.float64, order="C")
+
+        kernel = self.kernel
+        if callable(kernel):
+            kernel = "precomputed"
+
+        kernel_type = self._sparse_kernels.index(kernel)
+
+        return libsvm_sparse.libsvm_sparse_predict_proba(
+            X.data,
+            X.indices,
+            X.indptr,
+            self.support_vectors_.data,
+            self.support_vectors_.indices,
+            self.support_vectors_.indptr,
+            self._dual_coef_.data,
+            self._intercept_,
+            LIBSVM_IMPL.index(self._impl),
+            kernel_type,
+            self.degree,
+            self._gamma,
+            self.coef0,
+            self.tol,
+            self.C,
+            getattr(self, "class_weight_", np.empty(0)),
+            self.nu,
+            self.epsilon,
+            self.shrinking,
+            self.probability,
+            self._n_support,
+            self._probA,
+            self._probB,
+        )
+
+    def _get_coef(self):
+        if self.dual_coef_.shape[0] == 1:
+            # binary classifier
+            coef = safe_sparse_dot(self.dual_coef_, self.support_vectors_)
+        else:
+            # 1vs1 classifier
+            coef = _one_vs_one_coef(
+                self.dual_coef_, self._n_support, self.support_vectors_
+            )
+            if sp.issparse(coef[0]):
+                coef = sp.vstack(coef).tocsr()
+            else:
+                coef = np.vstack(coef)
+
+        return coef
+
+    @property
+    def probA_(self):
+        """Parameter learned in Platt scaling when `probability=True`.
+
+        Returns
+        -------
+        ndarray of shape  (n_classes * (n_classes - 1) / 2)
+        """
+        return self._probA
+
+    @property
+    def probB_(self):
+        """Parameter learned in Platt scaling when `probability=True`.
+
+        Returns
+        -------
+        ndarray of shape  (n_classes * (n_classes - 1) / 2)
+        """
+        return self._probB
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = self.kernel != "precomputed"
+        return tags
+
+
+def _get_liblinear_solver_type(multi_class, penalty, loss, dual):
+    """Find the liblinear magic number for the solver.
+
+    This number depends on the values of the following attributes:
+      - multi_class
+      - penalty
+      - loss
+      - dual
+
+    The same number is also internally used by LibLinear to determine
+    which solver to use.
+    """
+    # nested dicts containing level 1: available loss functions,
+    # level2: available penalties for the given loss function,
+    # level3: whether the dual solver is available for the specified
+    # combination of loss function and penalty
+    _solver_type_dict = {
+        "logistic_regression": {"l1": {False: 6}, "l2": {False: 0, True: 7}},
+        "hinge": {"l2": {True: 3}},
+        "squared_hinge": {"l1": {False: 5}, "l2": {False: 2, True: 1}},
+        "epsilon_insensitive": {"l2": {True: 13}},
+        "squared_epsilon_insensitive": {"l2": {False: 11, True: 12}},
+        "crammer_singer": 4,
+    }
+
+    if multi_class == "crammer_singer":
+        return _solver_type_dict[multi_class]
+    elif multi_class != "ovr":
+        raise ValueError(
+            "`multi_class` must be one of `ovr`, `crammer_singer`, got %r" % multi_class
+        )
+
+    _solver_pen = _solver_type_dict.get(loss, None)
+    if _solver_pen is None:
+        error_string = "loss='%s' is not supported" % loss
+    else:
+        _solver_dual = _solver_pen.get(penalty, None)
+        if _solver_dual is None:
+            error_string = (
+                "The combination of penalty='%s' and loss='%s' is not supported"
+                % (penalty, loss)
+            )
+        else:
+            solver_num = _solver_dual.get(dual, None)
+            if solver_num is None:
+                error_string = (
+                    "The combination of penalty='%s' and "
+                    "loss='%s' are not supported when dual=%s" % (penalty, loss, dual)
+                )
+            else:
+                return solver_num
+    raise ValueError(
+        "Unsupported set of arguments: %s, Parameters: penalty=%r, loss=%r, dual=%r"
+        % (error_string, penalty, loss, dual)
+    )
+
+
+def _fit_liblinear(
+    X,
+    y,
+    C,
+    fit_intercept,
+    intercept_scaling,
+    class_weight,
+    penalty,
+    dual,
+    verbose,
+    max_iter,
+    tol,
+    random_state=None,
+    multi_class="ovr",
+    loss="logistic_regression",
+    epsilon=0.1,
+    sample_weight=None,
+):
+    """Used by Logistic Regression (and CV) and LinearSVC/LinearSVR.
+
+    Preprocessing is done in this function before supplying it to liblinear.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Training vector, where `n_samples` is the number of samples and
+        `n_features` is the number of features.
+
+    y : array-like of shape (n_samples,)
+        Target vector relative to X
+
+    C : float
+        Inverse of cross-validation parameter. The lower the C, the higher
+        the penalization.
+
+    fit_intercept : bool
+        Whether or not to fit an intercept. If set to True, the feature vector
+        is extended to include an intercept term: ``[x_1, ..., x_n, 1]``, where
+        1 corresponds to the intercept. If set to False, no intercept will be
+        used in calculations (i.e. data is expected to be already centered).
+
+    intercept_scaling : float
+        Liblinear internally penalizes the intercept, treating it like any
+        other term in the feature vector. To reduce the impact of the
+        regularization on the intercept, the `intercept_scaling` parameter can
+        be set to a value greater than 1; the higher the value of
+        `intercept_scaling`, the lower the impact of regularization on it.
+        Then, the weights become `[w_x_1, ..., w_x_n,
+        w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent
+        the feature weights and the intercept weight is scaled by
+        `intercept_scaling`. This scaling allows the intercept term to have a
+        different regularization behavior compared to the other features.
+
+    class_weight : dict or 'balanced', default=None
+        Weights associated with classes in the form ``{class_label: weight}``.
+        If not given, all classes are supposed to have weight one. For
+        multi-output problems, a list of dicts can be provided in the same
+        order as the columns of y.
+
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``
+
+    penalty : {'l1', 'l2'}
+        The norm of the penalty used in regularization.
+
+    dual : bool
+        Dual or primal formulation,
+
+    verbose : int
+        Set verbose to any positive number for verbosity.
+
+    max_iter : int
+        Number of iterations.
+
+    tol : float
+        Stopping condition.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generation for shuffling the data.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    multi_class : {'ovr', 'crammer_singer'}, default='ovr'
+        `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer`
+        optimizes a joint objective over all classes.
+        While `crammer_singer` is interesting from an theoretical perspective
+        as it is consistent it is seldom used in practice and rarely leads to
+        better accuracy and is more expensive to compute.
+        If `crammer_singer` is chosen, the options loss, penalty and dual will
+        be ignored.
+
+    loss : {'logistic_regression', 'hinge', 'squared_hinge', \
+            'epsilon_insensitive', 'squared_epsilon_insensitive}, \
+            default='logistic_regression'
+        The loss function used to fit the model.
+
+    epsilon : float, default=0.1
+        Epsilon parameter in the epsilon-insensitive loss function. Note
+        that the value of this parameter depends on the scale of the target
+        variable y. If unsure, set epsilon=0.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Weights assigned to each sample.
+
+    Returns
+    -------
+    coef_ : ndarray of shape (n_features, n_features + 1)
+        The coefficient vector got by minimizing the objective function.
+
+    intercept_ : float
+        The intercept term added to the vector.
+
+    n_iter_ : array of int
+        Number of iterations run across for each class.
+    """
+    if loss not in ["epsilon_insensitive", "squared_epsilon_insensitive"]:
+        enc = LabelEncoder()
+        y_ind = enc.fit_transform(y)
+        classes_ = enc.classes_
+        if len(classes_) < 2:
+            raise ValueError(
+                "This solver needs samples of at least 2 classes"
+                " in the data, but the data contains only one"
+                " class: %r" % classes_[0]
+            )
+        class_weight_ = compute_class_weight(
+            class_weight, classes=classes_, y=y, sample_weight=sample_weight
+        )
+    else:
+        class_weight_ = np.empty(0, dtype=np.float64)
+        y_ind = y
+    liblinear.set_verbosity_wrap(verbose)
+    rnd = check_random_state(random_state)
+    if verbose:
+        print("[LibLinear]", end="")
+
+    # LinearSVC breaks when intercept_scaling is <= 0
+    bias = -1.0
+    if fit_intercept:
+        if intercept_scaling <= 0:
+            raise ValueError(
+                "Intercept scaling is %r but needs to be greater "
+                "than 0. To disable fitting an intercept,"
+                " set fit_intercept=False." % intercept_scaling
+            )
+        else:
+            bias = intercept_scaling
+
+    libsvm.set_verbosity_wrap(verbose)
+    libsvm_sparse.set_verbosity_wrap(verbose)
+    liblinear.set_verbosity_wrap(verbose)
+
+    # Liblinear doesn't support 64bit sparse matrix indices yet
+    if sp.issparse(X):
+        _check_large_sparse(X)
+
+    # LibLinear wants targets as doubles, even for classification
+    y_ind = np.asarray(y_ind, dtype=np.float64).ravel()
+    y_ind = np.require(y_ind, requirements="W")
+
+    sample_weight = _check_sample_weight(sample_weight, X, dtype=np.float64)
+
+    solver_type = _get_liblinear_solver_type(multi_class, penalty, loss, dual)
+    raw_coef_, n_iter_ = liblinear.train_wrap(
+        X,
+        y_ind,
+        sp.issparse(X),
+        solver_type,
+        tol,
+        bias,
+        C,
+        class_weight_,
+        max_iter,
+        rnd.randint(np.iinfo("i").max),
+        epsilon,
+        sample_weight,
+    )
+    # Regarding rnd.randint(..) in the above signature:
+    # seed for srand in range [0..INT_MAX); due to limitations in Numpy
+    # on 32-bit platforms, we can't get to the UINT_MAX limit that
+    # srand supports
+    n_iter_max = max(n_iter_)
+    if n_iter_max >= max_iter:
+        warnings.warn(
+            "Liblinear failed to converge, increase the number of iterations.",
+            ConvergenceWarning,
+        )
+
+    if fit_intercept:
+        coef_ = raw_coef_[:, :-1]
+        intercept_ = intercept_scaling * raw_coef_[:, -1]
+    else:
+        coef_ = raw_coef_
+        intercept_ = 0.0
+
+    return coef_, intercept_, n_iter_
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_bounds.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_bounds.py
new file mode 100644
index 0000000000000000000000000000000000000000..44923cb12976776507a9dc02502424832158391c
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_bounds.py
@@ -0,0 +1,98 @@
+"""Determination of parameter bounds"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from numbers import Real
+
+import numpy as np
+
+from ..preprocessing import LabelBinarizer
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.extmath import safe_sparse_dot
+from ..utils.validation import check_array, check_consistent_length
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "loss": [StrOptions({"squared_hinge", "log"})],
+        "fit_intercept": ["boolean"],
+        "intercept_scaling": [Interval(Real, 0, None, closed="neither")],
+    },
+    prefer_skip_nested_validation=True,
+)
+def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scaling=1.0):
+    """Return the lowest bound for `C`.
+
+    The lower bound for `C` is computed such that for `C` in `(l1_min_C, infinity)`
+    the model is guaranteed not to be empty. This applies to l1 penalized
+    classifiers, such as :class:`sklearn.svm.LinearSVC` with penalty='l1' and
+    :class:`sklearn.linear_model.LogisticRegression` with penalty='l1'.
+
+    This value is valid if `class_weight` parameter in `fit()` is not set.
+
+    For an example of how to use this function, see
+    :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Training vector, where `n_samples` is the number of samples and
+        `n_features` is the number of features.
+
+    y : array-like of shape (n_samples,)
+        Target vector relative to X.
+
+    loss : {'squared_hinge', 'log'}, default='squared_hinge'
+        Specifies the loss function.
+        With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
+        With 'log' it is the loss of logistic regression models.
+
+    fit_intercept : bool, default=True
+        Specifies if the intercept should be fitted by the model.
+        It must match the fit() method parameter.
+
+    intercept_scaling : float, default=1.0
+        When fit_intercept is True, instance vector x becomes
+        [x, intercept_scaling],
+        i.e. a "synthetic" feature with constant value equals to
+        intercept_scaling is appended to the instance vector.
+        It must match the fit() method parameter.
+
+    Returns
+    -------
+    l1_min_c : float
+        Minimum value for C.
+
+    Examples
+    --------
+    >>> from sklearn.svm import l1_min_c
+    >>> from sklearn.datasets import make_classification
+    >>> X, y = make_classification(n_samples=100, n_features=20, random_state=42)
+    >>> print(f"{l1_min_c(X, y, loss='squared_hinge', fit_intercept=True):.4f}")
+    0.0044
+    """
+
+    X = check_array(X, accept_sparse="csc")
+    check_consistent_length(X, y)
+
+    Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
+    # maximum absolute value over classes and features
+    den = np.max(np.abs(safe_sparse_dot(Y, X)))
+    if fit_intercept:
+        bias = np.full(
+            (np.size(y), 1), intercept_scaling, dtype=np.array(intercept_scaling).dtype
+        )
+        den = max(den, abs(np.dot(Y, bias)).max())
+
+    if den == 0.0:
+        raise ValueError(
+            "Ill-posed l1_min_c calculation: l1 will always "
+            "select zero coefficients for this data"
+        )
+    if loss == "squared_hinge":
+        return 0.5 / den
+    else:  # loss == 'log':
+        return 2.0 / den
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..277da42893eaff6737f32fea006e719a2f00e4d0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py
@@ -0,0 +1,1789 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from numbers import Integral, Real
+
+import numpy as np
+
+from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
+from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
+from ..utils._param_validation import Interval, StrOptions
+from ..utils.multiclass import check_classification_targets
+from ..utils.validation import _num_samples, validate_data
+from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
+
+
+def _validate_dual_parameter(dual, loss, penalty, multi_class, X):
+    """Helper function to assign the value of dual parameter."""
+    if dual == "auto":
+        if X.shape[0] < X.shape[1]:
+            try:
+                _get_liblinear_solver_type(multi_class, penalty, loss, True)
+                return True
+            except ValueError:  # dual not supported for the combination
+                return False
+        else:
+            try:
+                _get_liblinear_solver_type(multi_class, penalty, loss, False)
+                return False
+            except ValueError:  # primal not supported by the combination
+                return True
+    else:
+        return dual
+
+
+class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
+    """Linear Support Vector Classification.
+
+    Similar to SVC with parameter kernel='linear', but implemented in terms of
+    liblinear rather than libsvm, so it has more flexibility in the choice of
+    penalties and loss functions and should scale better to large numbers of
+    samples.
+
+    The main differences between :class:`~sklearn.svm.LinearSVC` and
+    :class:`~sklearn.svm.SVC` lie in the loss function used by default, and in
+    the handling of intercept regularization between those two implementations.
+
+    This class supports both dense and sparse input and the multiclass support
+    is handled according to a one-vs-the-rest scheme.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    penalty : {'l1', 'l2'}, default='l2'
+        Specifies the norm used in the penalization. The 'l2'
+        penalty is the standard used in SVC. The 'l1' leads to ``coef_``
+        vectors that are sparse.
+
+    loss : {'hinge', 'squared_hinge'}, default='squared_hinge'
+        Specifies the loss function. 'hinge' is the standard SVM loss
+        (used e.g. by the SVC class) while 'squared_hinge' is the
+        square of the hinge loss. The combination of ``penalty='l1'``
+        and ``loss='hinge'`` is not supported.
+
+    dual : "auto" or bool, default="auto"
+        Select the algorithm to either solve the dual or primal
+        optimization problem. Prefer dual=False when n_samples > n_features.
+        `dual="auto"` will choose the value of the parameter automatically,
+        based on the values of `n_samples`, `n_features`, `loss`, `multi_class`
+        and `penalty`. If `n_samples` < `n_features` and optimizer supports
+        chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,
+        otherwise it will be set to False.
+
+        .. versionchanged:: 1.3
+           The `"auto"` option is added in version 1.3 and will be the default
+           in version 1.5.
+
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+
+    C : float, default=1.0
+        Regularization parameter. The strength of the regularization is
+        inversely proportional to C. Must be strictly positive.
+        For an intuitive visualization of the effects of scaling
+        the regularization parameter C, see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.
+
+    multi_class : {'ovr', 'crammer_singer'}, default='ovr'
+        Determines the multi-class strategy if `y` contains more than
+        two classes.
+        ``"ovr"`` trains n_classes one-vs-rest classifiers, while
+        ``"crammer_singer"`` optimizes a joint objective over all classes.
+        While `crammer_singer` is interesting from a theoretical perspective
+        as it is consistent, it is seldom used in practice as it rarely leads
+        to better accuracy and is more expensive to compute.
+        If ``"crammer_singer"`` is chosen, the options loss, penalty and dual
+        will be ignored.
+
+    fit_intercept : bool, default=True
+        Whether or not to fit an intercept. If set to True, the feature vector
+        is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where
+        1 corresponds to the intercept. If set to False, no intercept will be
+        used in calculations (i.e. data is expected to be already centered).
+
+    intercept_scaling : float, default=1.0
+        When `fit_intercept` is True, the instance vector x becomes ``[x_1,
+        ..., x_n, intercept_scaling]``, i.e. a "synthetic" feature with a
+        constant value equal to `intercept_scaling` is appended to the instance
+        vector. The intercept becomes intercept_scaling * synthetic feature
+        weight. Note that liblinear internally penalizes the intercept,
+        treating it like any other term in the feature vector. To reduce the
+        impact of the regularization on the intercept, the `intercept_scaling`
+        parameter can be set to a value greater than 1; the higher the value of
+        `intercept_scaling`, the lower the impact of regularization on it.
+        Then, the weights become `[w_x_1, ..., w_x_n,
+        w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent
+        the feature weights and the intercept weight is scaled by
+        `intercept_scaling`. This scaling allows the intercept term to have a
+        different regularization behavior compared to the other features.
+
+    class_weight : dict or 'balanced', default=None
+        Set the parameter C of class i to ``class_weight[i]*C`` for
+        SVC. If not given, all classes are supposed to have
+        weight one.
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``.
+
+    verbose : int, default=0
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in liblinear that, if enabled, may not work
+        properly in a multithreaded context.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generation for shuffling the data for
+        the dual coordinate descent (if ``dual=True``). When ``dual=False`` the
+        underlying implementation of :class:`LinearSVC` is not random and
+        ``random_state`` has no effect on the results.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    max_iter : int, default=1000
+        The maximum number of iterations to be run.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (1, n_features) if n_classes == 2 \
+            else (n_classes, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem).
+
+        ``coef_`` is a readonly property derived from ``raw_coef_`` that
+        follows the internal memory layout of liblinear.
+
+    intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
+        Constants in decision function.
+
+    classes_ : ndarray of shape (n_classes,)
+        The unique classes labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Maximum number of iterations run across all classes.
+
+    See Also
+    --------
+    SVC : Implementation of Support Vector Machine classifier using libsvm:
+        the kernel can be non-linear but its SMO algorithm does not
+        scale to large number of samples as LinearSVC does.
+
+        Furthermore SVC multi-class mode is implemented using one
+        vs one scheme while LinearSVC uses one vs the rest. It is
+        possible to implement one vs the rest with SVC by using the
+        :class:`~sklearn.multiclass.OneVsRestClassifier` wrapper.
+
+        Finally SVC can fit dense data without memory copy if the input
+        is C-contiguous. Sparse data will still incur memory copy though.
+
+    sklearn.linear_model.SGDClassifier : SGDClassifier can optimize the same
+        cost function as LinearSVC
+        by adjusting the penalty and loss parameters. In addition it requires
+        less memory, allows incremental (online) learning, and implements
+        various loss functions and regularization regimes.
+
+    Notes
+    -----
+    The underlying C implementation uses a random number generator to
+    select features when fitting the model. It is thus not uncommon
+    to have slightly different results for the same input data. If
+    that happens, try with a smaller ``tol`` parameter.
+
+    The underlying implementation, liblinear, uses a sparse internal
+    representation for the data that will incur a memory copy.
+
+    Predict output may not match that of standalone liblinear in certain
+    cases. See :ref:`differences from liblinear `
+    in the narrative documentation.
+
+    References
+    ----------
+    `LIBLINEAR: A Library for Large Linear Classification
+    `__
+
+    Examples
+    --------
+    >>> from sklearn.svm import LinearSVC
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.datasets import make_classification
+    >>> X, y = make_classification(n_features=4, random_state=0)
+    >>> clf = make_pipeline(StandardScaler(),
+    ...                     LinearSVC(random_state=0, tol=1e-5))
+    >>> clf.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('linearsvc', LinearSVC(random_state=0, tol=1e-05))])
+
+    >>> print(clf.named_steps['linearsvc'].coef_)
+    [[0.141   0.526 0.679 0.493]]
+
+    >>> print(clf.named_steps['linearsvc'].intercept_)
+    [0.1693]
+    >>> print(clf.predict([[0, 0, 0, 0]]))
+    [1]
+    """
+
+    _parameter_constraints: dict = {
+        "penalty": [StrOptions({"l1", "l2"})],
+        "loss": [StrOptions({"hinge", "squared_hinge"})],
+        "dual": ["boolean", StrOptions({"auto"})],
+        "tol": [Interval(Real, 0.0, None, closed="neither")],
+        "C": [Interval(Real, 0.0, None, closed="neither")],
+        "multi_class": [StrOptions({"ovr", "crammer_singer"})],
+        "fit_intercept": ["boolean"],
+        "intercept_scaling": [Interval(Real, 0, None, closed="neither")],
+        "class_weight": [None, dict, StrOptions({"balanced"})],
+        "verbose": ["verbose"],
+        "random_state": ["random_state"],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        penalty="l2",
+        loss="squared_hinge",
+        *,
+        dual="auto",
+        tol=1e-4,
+        C=1.0,
+        multi_class="ovr",
+        fit_intercept=True,
+        intercept_scaling=1,
+        class_weight=None,
+        verbose=0,
+        random_state=None,
+        max_iter=1000,
+    ):
+        self.dual = dual
+        self.tol = tol
+        self.C = C
+        self.multi_class = multi_class
+        self.fit_intercept = fit_intercept
+        self.intercept_scaling = intercept_scaling
+        self.class_weight = class_weight
+        self.verbose = verbose
+        self.random_state = random_state
+        self.max_iter = max_iter
+        self.penalty = penalty
+        self.loss = loss
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target vector relative to X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Array of weights that are assigned to individual
+            samples. If not provided,
+            then each sample is given unit weight.
+
+            .. versionadded:: 0.18
+
+        Returns
+        -------
+        self : object
+            An instance of the estimator.
+        """
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse="csr",
+            dtype=np.float64,
+            order="C",
+            accept_large_sparse=False,
+        )
+        check_classification_targets(y)
+        self.classes_ = np.unique(y)
+
+        _dual = _validate_dual_parameter(
+            self.dual, self.loss, self.penalty, self.multi_class, X
+        )
+
+        self.coef_, self.intercept_, n_iter_ = _fit_liblinear(
+            X,
+            y,
+            self.C,
+            self.fit_intercept,
+            self.intercept_scaling,
+            self.class_weight,
+            self.penalty,
+            _dual,
+            self.verbose,
+            self.max_iter,
+            self.tol,
+            self.random_state,
+            self.multi_class,
+            self.loss,
+            sample_weight=sample_weight,
+        )
+        # Backward compatibility: _fit_liblinear is used both by LinearSVC/R
+        # and LogisticRegression but LogisticRegression sets a structured
+        # `n_iter_` attribute with information about the underlying OvR fits
+        # while LinearSVC/R only reports the maximum value.
+        self.n_iter_ = n_iter_.max().item()
+
+        if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
+            self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
+            if self.fit_intercept:
+                intercept = self.intercept_[1] - self.intercept_[0]
+                self.intercept_ = np.array([intercept])
+
+        return self
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
+
+
+class LinearSVR(RegressorMixin, LinearModel):
+    """Linear Support Vector Regression.
+
+    Similar to SVR with parameter kernel='linear', but implemented in terms of
+    liblinear rather than libsvm, so it has more flexibility in the choice of
+    penalties and loss functions and should scale better to large numbers of
+    samples.
+
+    The main differences between :class:`~sklearn.svm.LinearSVR` and
+    :class:`~sklearn.svm.SVR` lie in the loss function used by default, and in
+    the handling of intercept regularization between those two implementations.
+
+    This class supports both dense and sparse input.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.16
+
+    Parameters
+    ----------
+    epsilon : float, default=0.0
+        Epsilon parameter in the epsilon-insensitive loss function. Note
+        that the value of this parameter depends on the scale of the target
+        variable y. If unsure, set ``epsilon=0``.
+
+    tol : float, default=1e-4
+        Tolerance for stopping criteria.
+
+    C : float, default=1.0
+        Regularization parameter. The strength of the regularization is
+        inversely proportional to C. Must be strictly positive.
+
+    loss : {'epsilon_insensitive', 'squared_epsilon_insensitive'}, \
+            default='epsilon_insensitive'
+        Specifies the loss function. The epsilon-insensitive loss
+        (standard SVR) is the L1 loss, while the squared epsilon-insensitive
+        loss ('squared_epsilon_insensitive') is the L2 loss.
+
+    fit_intercept : bool, default=True
+        Whether or not to fit an intercept. If set to True, the feature vector
+        is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where
+        1 corresponds to the intercept. If set to False, no intercept will be
+        used in calculations (i.e. data is expected to be already centered).
+
+    intercept_scaling : float, default=1.0
+        When `fit_intercept` is True, the instance vector x becomes `[x_1, ...,
+        x_n, intercept_scaling]`, i.e. a "synthetic" feature with a constant
+        value equal to `intercept_scaling` is appended to the instance vector.
+        The intercept becomes intercept_scaling * synthetic feature weight.
+        Note that liblinear internally penalizes the intercept, treating it
+        like any other term in the feature vector. To reduce the impact of the
+        regularization on the intercept, the `intercept_scaling` parameter can
+        be set to a value greater than 1; the higher the value of
+        `intercept_scaling`, the lower the impact of regularization on it.
+        Then, the weights become `[w_x_1, ..., w_x_n,
+        w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent
+        the feature weights and the intercept weight is scaled by
+        `intercept_scaling`. This scaling allows the intercept term to have a
+        different regularization behavior compared to the other features.
+
+    dual : "auto" or bool, default="auto"
+        Select the algorithm to either solve the dual or primal
+        optimization problem. Prefer dual=False when n_samples > n_features.
+        `dual="auto"` will choose the value of the parameter automatically,
+        based on the values of `n_samples`, `n_features` and `loss`. If
+        `n_samples` < `n_features` and optimizer supports chosen `loss`,
+        then dual will be set to True, otherwise it will be set to False.
+
+        .. versionchanged:: 1.3
+           The `"auto"` option is added in version 1.3 and will be the default
+           in version 1.5.
+
+    verbose : int, default=0
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in liblinear that, if enabled, may not work
+        properly in a multithreaded context.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generation for shuffling the data.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    max_iter : int, default=1000
+        The maximum number of iterations to be run.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (n_features) if n_classes == 2 \
+            else (n_classes, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem).
+
+        `coef_` is a readonly property derived from `raw_coef_` that
+        follows the internal memory layout of liblinear.
+
+    intercept_ : ndarray of shape (1) if n_classes == 2 else (n_classes)
+        Constants in decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Maximum number of iterations run across all classes.
+
+    See Also
+    --------
+    LinearSVC : Implementation of Support Vector Machine classifier using the
+        same library as this class (liblinear).
+
+    SVR : Implementation of Support Vector Machine regression using libsvm:
+        the kernel can be non-linear but its SMO algorithm does not scale to
+        large number of samples as :class:`~sklearn.svm.LinearSVR` does.
+
+    sklearn.linear_model.SGDRegressor : SGDRegressor can optimize the same cost
+        function as LinearSVR
+        by adjusting the penalty and loss parameters. In addition it requires
+        less memory, allows incremental (online) learning, and implements
+        various loss functions and regularization regimes.
+
+    Examples
+    --------
+    >>> from sklearn.svm import LinearSVR
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(n_features=4, random_state=0)
+    >>> regr = make_pipeline(StandardScaler(),
+    ...                      LinearSVR(random_state=0, tol=1e-5))
+    >>> regr.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('linearsvr', LinearSVR(random_state=0, tol=1e-05))])
+
+    >>> print(regr.named_steps['linearsvr'].coef_)
+    [18.582 27.023 44.357 64.522]
+    >>> print(regr.named_steps['linearsvr'].intercept_)
+    [-4.]
+    >>> print(regr.predict([[0, 0, 0, 0]]))
+    [-2.384]
+    """
+
+    _parameter_constraints: dict = {
+        "epsilon": [Real],
+        "tol": [Interval(Real, 0.0, None, closed="neither")],
+        "C": [Interval(Real, 0.0, None, closed="neither")],
+        "loss": [StrOptions({"epsilon_insensitive", "squared_epsilon_insensitive"})],
+        "fit_intercept": ["boolean"],
+        "intercept_scaling": [Interval(Real, 0, None, closed="neither")],
+        "dual": ["boolean", StrOptions({"auto"})],
+        "verbose": ["verbose"],
+        "random_state": ["random_state"],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        *,
+        epsilon=0.0,
+        tol=1e-4,
+        C=1.0,
+        loss="epsilon_insensitive",
+        fit_intercept=True,
+        intercept_scaling=1.0,
+        dual="auto",
+        verbose=0,
+        random_state=None,
+        max_iter=1000,
+    ):
+        self.tol = tol
+        self.C = C
+        self.epsilon = epsilon
+        self.fit_intercept = fit_intercept
+        self.intercept_scaling = intercept_scaling
+        self.verbose = verbose
+        self.random_state = random_state
+        self.max_iter = max_iter
+        self.dual = dual
+        self.loss = loss
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like of shape (n_samples,)
+            Target vector relative to X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Array of weights that are assigned to individual
+            samples. If not provided,
+            then each sample is given unit weight.
+
+            .. versionadded:: 0.18
+
+        Returns
+        -------
+        self : object
+            An instance of the estimator.
+        """
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            accept_sparse="csr",
+            dtype=np.float64,
+            order="C",
+            accept_large_sparse=False,
+        )
+        penalty = "l2"  # SVR only accepts l2 penalty
+
+        _dual = _validate_dual_parameter(self.dual, self.loss, penalty, "ovr", X)
+
+        self.coef_, self.intercept_, n_iter_ = _fit_liblinear(
+            X,
+            y,
+            self.C,
+            self.fit_intercept,
+            self.intercept_scaling,
+            None,
+            penalty,
+            _dual,
+            self.verbose,
+            self.max_iter,
+            self.tol,
+            self.random_state,
+            loss=self.loss,
+            epsilon=self.epsilon,
+            sample_weight=sample_weight,
+        )
+        self.coef_ = self.coef_.ravel()
+        # Backward compatibility: _fit_liblinear is used both by LinearSVC/R
+        # and LogisticRegression but LogisticRegression sets a structured
+        # `n_iter_` attribute with information about the underlying OvR fits
+        # while LinearSVC/R only reports the maximum value.
+        self.n_iter_ = n_iter_.max().item()
+
+        return self
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
+
+
+class SVC(BaseSVC):
+    """C-Support Vector Classification.
+
+    The implementation is based on libsvm. The fit time scales at least
+    quadratically with the number of samples and may be impractical
+    beyond tens of thousands of samples. For large datasets
+    consider using :class:`~sklearn.svm.LinearSVC` or
+    :class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a
+    :class:`~sklearn.kernel_approximation.Nystroem` transformer or
+    other :ref:`kernel_approximation`.
+
+    The multiclass support is handled according to a one-vs-one scheme.
+
+    For details on the precise mathematical formulation of the provided
+    kernel functions and how `gamma`, `coef0` and `degree` affect each
+    other, see the corresponding section in the narrative documentation:
+    :ref:`svm_kernels`.
+
+    To learn how to tune SVC's hyperparameters, see the following example:
+    :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    C : float, default=1.0
+        Regularization parameter. The strength of the regularization is
+        inversely proportional to C. Must be strictly positive. The penalty
+        is a squared l2 penalty. For an intuitive visualization of the effects
+        of scaling the regularization parameter C, see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.
+
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,  \
+        default='rbf'
+        Specifies the kernel type to be used in the algorithm. If
+        none is given, 'rbf' will be used. If a callable is given it is used to
+        pre-compute the kernel matrix from data matrices; that matrix should be
+        an array of shape ``(n_samples, n_samples)``. For an intuitive
+        visualization of different kernel types see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.
+
+    degree : int, default=3
+        Degree of the polynomial kernel function ('poly').
+        Must be non-negative. Ignored by all other kernels.
+
+    gamma : {'scale', 'auto'} or float, default='scale'
+        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
+
+        - if ``gamma='scale'`` (default) is passed then it uses
+          1 / (n_features * X.var()) as value of gamma,
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
+
+        .. versionchanged:: 0.22
+           The default value of ``gamma`` changed from 'auto' to 'scale'.
+
+    coef0 : float, default=0.0
+        Independent term in kernel function.
+        It is only significant in 'poly' and 'sigmoid'.
+
+    shrinking : bool, default=True
+        Whether to use the shrinking heuristic.
+        See the :ref:`User Guide `.
+
+    probability : bool, default=False
+        Whether to enable probability estimates. This must be enabled prior
+        to calling `fit`, will slow down that method as it internally uses
+        5-fold cross-validation, and `predict_proba` may be inconsistent with
+        `predict`. Read more in the :ref:`User Guide `.
+
+    tol : float, default=1e-3
+        Tolerance for stopping criterion.
+
+    cache_size : float, default=200
+        Specify the size of the kernel cache (in MB).
+
+    class_weight : dict or 'balanced', default=None
+        Set the parameter C of class i to class_weight[i]*C for
+        SVC. If not given, all classes are supposed to have
+        weight one.
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``.
+
+    verbose : bool, default=False
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in libsvm that, if enabled, may not work
+        properly in a multithreaded context.
+
+    max_iter : int, default=-1
+        Hard limit on iterations within solver, or -1 for no limit.
+
+    decision_function_shape : {'ovo', 'ovr'}, default='ovr'
+        Whether to return a one-vs-rest ('ovr') decision function of shape
+        (n_samples, n_classes) as all other classifiers, or the original
+        one-vs-one ('ovo') decision function of libsvm which has shape
+        (n_samples, n_classes * (n_classes - 1) / 2). However, note that
+        internally, one-vs-one ('ovo') is always used as a multi-class strategy
+        to train models; an ovr matrix is only constructed from the ovo matrix.
+        The parameter is ignored for binary classification.
+
+        .. versionchanged:: 0.19
+            decision_function_shape is 'ovr' by default.
+
+        .. versionadded:: 0.17
+           *decision_function_shape='ovr'* is recommended.
+
+        .. versionchanged:: 0.17
+           Deprecated *decision_function_shape='ovo' and None*.
+
+    break_ties : bool, default=False
+        If true, ``decision_function_shape='ovr'``, and number of classes > 2,
+        :term:`predict` will break ties according to the confidence values of
+        :term:`decision_function`; otherwise the first class among the tied
+        classes is returned. Please note that breaking ties comes at a
+        relatively high computational cost compared to a simple predict. See
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an
+        example of its usage with ``decision_function_shape='ovr'``.
+
+        .. versionadded:: 0.22
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generation for shuffling the data for
+        probability estimates. Ignored when `probability` is False.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    class_weight_ : ndarray of shape (n_classes,)
+        Multipliers of parameter C for each class.
+        Computed based on the ``class_weight`` parameter.
+
+    classes_ : ndarray of shape (n_classes,)
+        The classes labels.
+
+    coef_ : ndarray of shape (n_classes * (n_classes - 1) / 2, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem). This is only available in the case of a linear kernel.
+
+        `coef_` is a readonly property derived from `dual_coef_` and
+        `support_vectors_`.
+
+    dual_coef_ : ndarray of shape (n_classes -1, n_SV)
+        Dual coefficients of the support vector in the decision
+        function (see :ref:`sgd_mathematical_formulation`), multiplied by
+        their targets.
+        For multiclass, coefficient for all 1-vs-1 classifiers.
+        The layout of the coefficients in the multiclass case is somewhat
+        non-trivial. See the :ref:`multi-class section of the User Guide
+        ` for details.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 otherwise (will raise warning)
+
+    intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
+        Constants in decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : ndarray of shape (n_classes * (n_classes - 1) // 2,)
+        Number of iterations run by the optimization routine to fit the model.
+        The shape of this attribute depends on the number of models optimized
+        which in turn depends on the number of classes.
+
+        .. versionadded:: 1.1
+
+    support_ : ndarray of shape (n_SV)
+        Indices of support vectors.
+
+    support_vectors_ : ndarray of shape (n_SV, n_features)
+        Support vectors. An empty array if kernel is precomputed.
+
+    n_support_ : ndarray of shape (n_classes,), dtype=int32
+        Number of support vectors for each class.
+
+    probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
+    probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
+        If `probability=True`, it corresponds to the parameters learned in
+        Platt scaling to produce probability estimates from decision values.
+        If `probability=False`, it's an empty array. Platt scaling uses the
+        logistic function
+        ``1 / (1 + exp(decision_value * probA_ + probB_))``
+        where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
+        more information on the multiclass case and training procedure see
+        section 8 of [1]_.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
+    See Also
+    --------
+    SVR : Support Vector Machine for Regression implemented using libsvm.
+
+    LinearSVC : Scalable Linear Support Vector Machine for classification
+        implemented using liblinear. Check the See Also section of
+        LinearSVC for more comparison element.
+
+    References
+    ----------
+    .. [1] `LIBSVM: A Library for Support Vector Machines
+        `_
+
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        `_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
+    >>> y = np.array([1, 1, 2, 2])
+    >>> from sklearn.svm import SVC
+    >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
+    >>> clf.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('svc', SVC(gamma='auto'))])
+
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
+
+    For a comparison of the SVC with other classifiers see:
+    :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`.
+    """
+
+    _impl = "c_svc"
+
+    def __init__(
+        self,
+        *,
+        C=1.0,
+        kernel="rbf",
+        degree=3,
+        gamma="scale",
+        coef0=0.0,
+        shrinking=True,
+        probability=False,
+        tol=1e-3,
+        cache_size=200,
+        class_weight=None,
+        verbose=False,
+        max_iter=-1,
+        decision_function_shape="ovr",
+        break_ties=False,
+        random_state=None,
+    ):
+        super().__init__(
+            kernel=kernel,
+            degree=degree,
+            gamma=gamma,
+            coef0=coef0,
+            tol=tol,
+            C=C,
+            nu=0.0,
+            shrinking=shrinking,
+            probability=probability,
+            cache_size=cache_size,
+            class_weight=class_weight,
+            verbose=verbose,
+            max_iter=max_iter,
+            decision_function_shape=decision_function_shape,
+            break_ties=break_ties,
+            random_state=random_state,
+        )
+
+
+class NuSVC(BaseSVC):
+    """Nu-Support Vector Classification.
+
+    Similar to SVC but uses a parameter to control the number of support
+    vectors.
+
+    The implementation is based on libsvm.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    nu : float, default=0.5
+        An upper bound on the fraction of margin errors (see :ref:`User Guide
+        `) and a lower bound of the fraction of support vectors.
+        Should be in the interval (0, 1].
+
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,  \
+        default='rbf'
+        Specifies the kernel type to be used in the algorithm.
+        If none is given, 'rbf' will be used. If a callable is given it is
+        used to precompute the kernel matrix. For an intuitive
+        visualization of different kernel types see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.
+
+    degree : int, default=3
+        Degree of the polynomial kernel function ('poly').
+        Must be non-negative. Ignored by all other kernels.
+
+    gamma : {'scale', 'auto'} or float, default='scale'
+        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
+
+        - if ``gamma='scale'`` (default) is passed then it uses
+          1 / (n_features * X.var()) as value of gamma,
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
+
+        .. versionchanged:: 0.22
+           The default value of ``gamma`` changed from 'auto' to 'scale'.
+
+    coef0 : float, default=0.0
+        Independent term in kernel function.
+        It is only significant in 'poly' and 'sigmoid'.
+
+    shrinking : bool, default=True
+        Whether to use the shrinking heuristic.
+        See the :ref:`User Guide `.
+
+    probability : bool, default=False
+        Whether to enable probability estimates. This must be enabled prior
+        to calling `fit`, will slow down that method as it internally uses
+        5-fold cross-validation, and `predict_proba` may be inconsistent with
+        `predict`. Read more in the :ref:`User Guide `.
+
+    tol : float, default=1e-3
+        Tolerance for stopping criterion.
+
+    cache_size : float, default=200
+        Specify the size of the kernel cache (in MB).
+
+    class_weight : {dict, 'balanced'}, default=None
+        Set the parameter C of class i to class_weight[i]*C for
+        SVC. If not given, all classes are supposed to have
+        weight one. The "balanced" mode uses the values of y to automatically
+        adjust weights inversely proportional to class frequencies as
+        ``n_samples / (n_classes * np.bincount(y))``.
+
+    verbose : bool, default=False
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in libsvm that, if enabled, may not work
+        properly in a multithreaded context.
+
+    max_iter : int, default=-1
+        Hard limit on iterations within solver, or -1 for no limit.
+
+    decision_function_shape : {'ovo', 'ovr'}, default='ovr'
+        Whether to return a one-vs-rest ('ovr') decision function of shape
+        (n_samples, n_classes) as all other classifiers, or the original
+        one-vs-one ('ovo') decision function of libsvm which has shape
+        (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one
+        ('ovo') is always used as multi-class strategy. The parameter is
+        ignored for binary classification.
+
+        .. versionchanged:: 0.19
+            decision_function_shape is 'ovr' by default.
+
+        .. versionadded:: 0.17
+           *decision_function_shape='ovr'* is recommended.
+
+        .. versionchanged:: 0.17
+           Deprecated *decision_function_shape='ovo' and None*.
+
+    break_ties : bool, default=False
+        If true, ``decision_function_shape='ovr'``, and number of classes > 2,
+        :term:`predict` will break ties according to the confidence values of
+        :term:`decision_function`; otherwise the first class among the tied
+        classes is returned. Please note that breaking ties comes at a
+        relatively high computational cost compared to a simple predict.
+        See :ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an
+        example of its usage with ``decision_function_shape='ovr'``.
+
+        .. versionadded:: 0.22
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the pseudo random number generation for shuffling the data for
+        probability estimates. Ignored when `probability` is False.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    Attributes
+    ----------
+    class_weight_ : ndarray of shape (n_classes,)
+        Multipliers of parameter C of each class.
+        Computed based on the ``class_weight`` parameter.
+
+    classes_ : ndarray of shape (n_classes,)
+        The unique classes labels.
+
+    coef_ : ndarray of shape (n_classes * (n_classes -1) / 2, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem). This is only available in the case of a linear kernel.
+
+        `coef_` is readonly property derived from `dual_coef_` and
+        `support_vectors_`.
+
+    dual_coef_ : ndarray of shape (n_classes - 1, n_SV)
+        Dual coefficients of the support vector in the decision
+        function (see :ref:`sgd_mathematical_formulation`), multiplied by
+        their targets.
+        For multiclass, coefficient for all 1-vs-1 classifiers.
+        The layout of the coefficients in the multiclass case is somewhat
+        non-trivial. See the :ref:`multi-class section of the User Guide
+        ` for details.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 if the algorithm did not converge.
+
+    intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
+        Constants in decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : ndarray of shape (n_classes * (n_classes - 1) // 2,)
+        Number of iterations run by the optimization routine to fit the model.
+        The shape of this attribute depends on the number of models optimized
+        which in turn depends on the number of classes.
+
+        .. versionadded:: 1.1
+
+    support_ : ndarray of shape (n_SV,)
+        Indices of support vectors.
+
+    support_vectors_ : ndarray of shape (n_SV, n_features)
+        Support vectors.
+
+    n_support_ : ndarray of shape (n_classes,), dtype=int32
+        Number of support vectors for each class.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 if the algorithm did not converge.
+
+    probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
+
+    probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
+        If `probability=True`, it corresponds to the parameters learned in
+        Platt scaling to produce probability estimates from decision values.
+        If `probability=False`, it's an empty array. Platt scaling uses the
+        logistic function
+        ``1 / (1 + exp(decision_value * probA_ + probB_))``
+        where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
+        more information on the multiclass case and training procedure see
+        section 8 of [1]_.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
+    See Also
+    --------
+    SVC : Support Vector Machine for classification using libsvm.
+
+    LinearSVC : Scalable linear Support Vector Machine for classification using
+        liblinear.
+
+    References
+    ----------
+    .. [1] `LIBSVM: A Library for Support Vector Machines
+        `_
+
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        `_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
+    >>> y = np.array([1, 1, 2, 2])
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.svm import NuSVC
+    >>> clf = make_pipeline(StandardScaler(), NuSVC())
+    >>> clf.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
+    """
+
+    _impl = "nu_svc"
+
+    _parameter_constraints: dict = {
+        **BaseSVC._parameter_constraints,
+        "nu": [Interval(Real, 0.0, 1.0, closed="right")],
+    }
+    _parameter_constraints.pop("C")
+
+    def __init__(
+        self,
+        *,
+        nu=0.5,
+        kernel="rbf",
+        degree=3,
+        gamma="scale",
+        coef0=0.0,
+        shrinking=True,
+        probability=False,
+        tol=1e-3,
+        cache_size=200,
+        class_weight=None,
+        verbose=False,
+        max_iter=-1,
+        decision_function_shape="ovr",
+        break_ties=False,
+        random_state=None,
+    ):
+        super().__init__(
+            kernel=kernel,
+            degree=degree,
+            gamma=gamma,
+            coef0=coef0,
+            tol=tol,
+            C=0.0,
+            nu=nu,
+            shrinking=shrinking,
+            probability=probability,
+            cache_size=cache_size,
+            class_weight=class_weight,
+            verbose=verbose,
+            max_iter=max_iter,
+            decision_function_shape=decision_function_shape,
+            break_ties=break_ties,
+            random_state=random_state,
+        )
+
+
+class SVR(RegressorMixin, BaseLibSVM):
+    """Epsilon-Support Vector Regression.
+
+    The free parameters in the model are C and epsilon.
+
+    The implementation is based on libsvm. The fit time complexity
+    is more than quadratic with the number of samples which makes it hard
+    to scale to datasets with more than a couple of 10000 samples. For large
+    datasets consider using :class:`~sklearn.svm.LinearSVR` or
+    :class:`~sklearn.linear_model.SGDRegressor` instead, possibly after a
+    :class:`~sklearn.kernel_approximation.Nystroem` transformer or
+    other :ref:`kernel_approximation`.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,  \
+        default='rbf'
+         Specifies the kernel type to be used in the algorithm.
+         If none is given, 'rbf' will be used. If a callable is given it is
+         used to precompute the kernel matrix.
+         For an intuitive visualization of different kernel types
+         see :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`
+
+    degree : int, default=3
+        Degree of the polynomial kernel function ('poly').
+        Must be non-negative. Ignored by all other kernels.
+
+    gamma : {'scale', 'auto'} or float, default='scale'
+        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
+
+        - if ``gamma='scale'`` (default) is passed then it uses
+          1 / (n_features * X.var()) as value of gamma,
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
+
+        .. versionchanged:: 0.22
+           The default value of ``gamma`` changed from 'auto' to 'scale'.
+
+    coef0 : float, default=0.0
+        Independent term in kernel function.
+        It is only significant in 'poly' and 'sigmoid'.
+
+    tol : float, default=1e-3
+        Tolerance for stopping criterion.
+
+    C : float, default=1.0
+        Regularization parameter. The strength of the regularization is
+        inversely proportional to C. Must be strictly positive.
+        The penalty is a squared l2. For an intuitive visualization of the
+        effects of scaling the regularization parameter C, see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.
+
+    epsilon : float, default=0.1
+         Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
+         within which no penalty is associated in the training loss function
+         with points predicted within a distance epsilon from the actual
+         value. Must be non-negative.
+
+    shrinking : bool, default=True
+        Whether to use the shrinking heuristic.
+        See the :ref:`User Guide `.
+
+    cache_size : float, default=200
+        Specify the size of the kernel cache (in MB).
+
+    verbose : bool, default=False
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in libsvm that, if enabled, may not work
+        properly in a multithreaded context.
+
+    max_iter : int, default=-1
+        Hard limit on iterations within solver, or -1 for no limit.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (1, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem). This is only available in the case of a linear kernel.
+
+        `coef_` is readonly property derived from `dual_coef_` and
+        `support_vectors_`.
+
+    dual_coef_ : ndarray of shape (1, n_SV)
+        Coefficients of the support vector in the decision function.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 otherwise (will raise warning)
+
+    intercept_ : ndarray of shape (1,)
+        Constants in decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
+        .. versionadded:: 1.1
+
+    n_support_ : ndarray of shape (1,), dtype=int32
+        Number of support vectors.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
+    support_ : ndarray of shape (n_SV,)
+        Indices of support vectors.
+
+    support_vectors_ : ndarray of shape (n_SV, n_features)
+        Support vectors.
+
+    See Also
+    --------
+    NuSVR : Support Vector Machine for regression implemented using libsvm
+        using a parameter to control the number of support vectors.
+
+    LinearSVR : Scalable Linear Support Vector Machine for regression
+        implemented using liblinear.
+
+    References
+    ----------
+    .. [1] `LIBSVM: A Library for Support Vector Machines
+        `_
+
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        `_
+
+    Examples
+    --------
+    >>> from sklearn.svm import SVR
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> import numpy as np
+    >>> n_samples, n_features = 10, 5
+    >>> rng = np.random.RandomState(0)
+    >>> y = rng.randn(n_samples)
+    >>> X = rng.randn(n_samples, n_features)
+    >>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
+    >>> regr.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('svr', SVR(epsilon=0.2))])
+    """
+
+    _impl = "epsilon_svr"
+
+    _parameter_constraints: dict = {**BaseLibSVM._parameter_constraints}
+    for unused_param in ["class_weight", "nu", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
+    def __init__(
+        self,
+        *,
+        kernel="rbf",
+        degree=3,
+        gamma="scale",
+        coef0=0.0,
+        tol=1e-3,
+        C=1.0,
+        epsilon=0.1,
+        shrinking=True,
+        cache_size=200,
+        verbose=False,
+        max_iter=-1,
+    ):
+        super().__init__(
+            kernel=kernel,
+            degree=degree,
+            gamma=gamma,
+            coef0=coef0,
+            tol=tol,
+            C=C,
+            nu=0.0,
+            epsilon=epsilon,
+            verbose=verbose,
+            shrinking=shrinking,
+            probability=False,
+            cache_size=cache_size,
+            class_weight=None,
+            max_iter=max_iter,
+            random_state=None,
+        )
+
+
+class NuSVR(RegressorMixin, BaseLibSVM):
+    """Nu Support Vector Regression.
+
+    Similar to NuSVC, for regression, uses a parameter nu to control
+    the number of support vectors. However, unlike NuSVC, where nu
+    replaces C, here nu replaces the parameter epsilon of epsilon-SVR.
+
+    The implementation is based on libsvm.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    nu : float, default=0.5
+        An upper bound on the fraction of training errors and a lower bound of
+        the fraction of support vectors. Should be in the interval (0, 1].  By
+        default 0.5 will be taken.
+
+    C : float, default=1.0
+        Penalty parameter C of the error term. For an intuitive visualization
+        of the effects of scaling the regularization parameter C, see
+        :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.
+
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,  \
+        default='rbf'
+         Specifies the kernel type to be used in the algorithm.
+         If none is given, 'rbf' will be used. If a callable is given it is
+         used to precompute the kernel matrix.
+         For an intuitive visualization of different kernel types see
+         See :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`
+
+    degree : int, default=3
+        Degree of the polynomial kernel function ('poly').
+        Must be non-negative. Ignored by all other kernels.
+
+    gamma : {'scale', 'auto'} or float, default='scale'
+        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
+
+        - if ``gamma='scale'`` (default) is passed then it uses
+          1 / (n_features * X.var()) as value of gamma,
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
+
+        .. versionchanged:: 0.22
+           The default value of ``gamma`` changed from 'auto' to 'scale'.
+
+    coef0 : float, default=0.0
+        Independent term in kernel function.
+        It is only significant in 'poly' and 'sigmoid'.
+
+    shrinking : bool, default=True
+        Whether to use the shrinking heuristic.
+        See the :ref:`User Guide `.
+
+    tol : float, default=1e-3
+        Tolerance for stopping criterion.
+
+    cache_size : float, default=200
+        Specify the size of the kernel cache (in MB).
+
+    verbose : bool, default=False
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in libsvm that, if enabled, may not work
+        properly in a multithreaded context.
+
+    max_iter : int, default=-1
+        Hard limit on iterations within solver, or -1 for no limit.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (1, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem). This is only available in the case of a linear kernel.
+
+        `coef_` is readonly property derived from `dual_coef_` and
+        `support_vectors_`.
+
+    dual_coef_ : ndarray of shape (1, n_SV)
+        Coefficients of the support vector in the decision function.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 otherwise (will raise warning)
+
+    intercept_ : ndarray of shape (1,)
+        Constants in decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
+        .. versionadded:: 1.1
+
+    n_support_ : ndarray of shape (1,), dtype=int32
+        Number of support vectors.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
+    support_ : ndarray of shape (n_SV,)
+        Indices of support vectors.
+
+    support_vectors_ : ndarray of shape (n_SV, n_features)
+        Support vectors.
+
+    See Also
+    --------
+    NuSVC : Support Vector Machine for classification implemented with libsvm
+        with a parameter to control the number of support vectors.
+
+    SVR : Epsilon Support Vector Machine for regression implemented with
+        libsvm.
+
+    References
+    ----------
+    .. [1] `LIBSVM: A Library for Support Vector Machines
+        `_
+
+    .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
+        Machines and Comparisons to Regularized Likelihood Methods"
+        `_
+
+    Examples
+    --------
+    >>> from sklearn.svm import NuSVR
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> import numpy as np
+    >>> n_samples, n_features = 10, 5
+    >>> np.random.seed(0)
+    >>> y = np.random.randn(n_samples)
+    >>> X = np.random.randn(n_samples, n_features)
+    >>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
+    >>> regr.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('nusvr', NuSVR(nu=0.1))])
+    """
+
+    _impl = "nu_svr"
+
+    _parameter_constraints: dict = {**BaseLibSVM._parameter_constraints}
+    for unused_param in ["class_weight", "epsilon", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
+    def __init__(
+        self,
+        *,
+        nu=0.5,
+        C=1.0,
+        kernel="rbf",
+        degree=3,
+        gamma="scale",
+        coef0=0.0,
+        shrinking=True,
+        tol=1e-3,
+        cache_size=200,
+        verbose=False,
+        max_iter=-1,
+    ):
+        super().__init__(
+            kernel=kernel,
+            degree=degree,
+            gamma=gamma,
+            coef0=coef0,
+            tol=tol,
+            C=C,
+            nu=nu,
+            epsilon=0.0,
+            shrinking=shrinking,
+            probability=False,
+            cache_size=cache_size,
+            class_weight=None,
+            verbose=verbose,
+            max_iter=max_iter,
+            random_state=None,
+        )
+
+
+class OneClassSVM(OutlierMixin, BaseLibSVM):
+    """Unsupervised Outlier Detection.
+
+    Estimate the support of a high-dimensional distribution.
+
+    The implementation is based on libsvm.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,  \
+        default='rbf'
+         Specifies the kernel type to be used in the algorithm.
+         If none is given, 'rbf' will be used. If a callable is given it is
+         used to precompute the kernel matrix.
+
+    degree : int, default=3
+        Degree of the polynomial kernel function ('poly').
+        Must be non-negative. Ignored by all other kernels.
+
+    gamma : {'scale', 'auto'} or float, default='scale'
+        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
+
+        - if ``gamma='scale'`` (default) is passed then it uses
+          1 / (n_features * X.var()) as value of gamma,
+        - if 'auto', uses 1 / n_features
+        - if float, must be non-negative.
+
+        .. versionchanged:: 0.22
+           The default value of ``gamma`` changed from 'auto' to 'scale'.
+
+    coef0 : float, default=0.0
+        Independent term in kernel function.
+        It is only significant in 'poly' and 'sigmoid'.
+
+    tol : float, default=1e-3
+        Tolerance for stopping criterion.
+
+    nu : float, default=0.5
+        An upper bound on the fraction of training
+        errors and a lower bound of the fraction of support
+        vectors. Should be in the interval (0, 1]. By default 0.5
+        will be taken.
+
+    shrinking : bool, default=True
+        Whether to use the shrinking heuristic.
+        See the :ref:`User Guide `.
+
+    cache_size : float, default=200
+        Specify the size of the kernel cache (in MB).
+
+    verbose : bool, default=False
+        Enable verbose output. Note that this setting takes advantage of a
+        per-process runtime setting in libsvm that, if enabled, may not work
+        properly in a multithreaded context.
+
+    max_iter : int, default=-1
+        Hard limit on iterations within solver, or -1 for no limit.
+
+    Attributes
+    ----------
+    coef_ : ndarray of shape (1, n_features)
+        Weights assigned to the features (coefficients in the primal
+        problem). This is only available in the case of a linear kernel.
+
+        `coef_` is readonly property derived from `dual_coef_` and
+        `support_vectors_`.
+
+    dual_coef_ : ndarray of shape (1, n_SV)
+        Coefficients of the support vectors in the decision function.
+
+    fit_status_ : int
+        0 if correctly fitted, 1 otherwise (will raise warning)
+
+    intercept_ : ndarray of shape (1,)
+        Constant in the decision function.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations run by the optimization routine to fit the model.
+
+        .. versionadded:: 1.1
+
+    n_support_ : ndarray of shape (n_classes,), dtype=int32
+        Number of support vectors for each class.
+
+    offset_ : float
+        Offset used to define the decision function from the raw scores.
+        We have the relation: decision_function = score_samples - `offset_`.
+        The offset is the opposite of `intercept_` and is provided for
+        consistency with other outlier detection algorithms.
+
+        .. versionadded:: 0.20
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
+    support_ : ndarray of shape (n_SV,)
+        Indices of support vectors.
+
+    support_vectors_ : ndarray of shape (n_SV, n_features)
+        Support vectors.
+
+    See Also
+    --------
+    sklearn.linear_model.SGDOneClassSVM : Solves linear One-Class SVM using
+        Stochastic Gradient Descent.
+    sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection using
+        Local Outlier Factor (LOF).
+    sklearn.ensemble.IsolationForest : Isolation Forest Algorithm.
+
+    Examples
+    --------
+    >>> from sklearn.svm import OneClassSVM
+    >>> X = [[0], [0.44], [0.45], [0.46], [1]]
+    >>> clf = OneClassSVM(gamma='auto').fit(X)
+    >>> clf.predict(X)
+    array([-1,  1,  1,  1, -1])
+    >>> clf.score_samples(X)
+    array([1.7798, 2.0547, 2.0556, 2.0561, 1.7332])
+
+    For a more extended example,
+    see :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
+    """
+
+    _impl = "one_class"
+
+    _parameter_constraints: dict = {**BaseLibSVM._parameter_constraints}
+    for unused_param in ["C", "class_weight", "epsilon", "probability", "random_state"]:
+        _parameter_constraints.pop(unused_param)
+
+    def __init__(
+        self,
+        *,
+        kernel="rbf",
+        degree=3,
+        gamma="scale",
+        coef0=0.0,
+        tol=1e-3,
+        nu=0.5,
+        shrinking=True,
+        cache_size=200,
+        verbose=False,
+        max_iter=-1,
+    ):
+        super().__init__(
+            kernel,
+            degree,
+            gamma,
+            coef0,
+            tol,
+            0.0,
+            nu,
+            0.0,
+            shrinking,
+            False,
+            cache_size,
+            None,
+            verbose,
+            max_iter,
+            random_state=None,
+        )
+
+    def fit(self, X, y=None, sample_weight=None):
+        """Detect the soft boundary of the set of samples X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Set of samples, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Per-sample weights. Rescale C per sample. Higher weights
+            force the classifier to put more emphasis on these points.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+
+        Notes
+        -----
+        If X is not a C-ordered contiguous array it is copied.
+        """
+        super().fit(X, np.ones(_num_samples(X)), sample_weight=sample_weight)
+        self.offset_ = -self._intercept_
+        return self
+
+    def decision_function(self, X):
+        """Signed distance to the separating hyperplane.
+
+        Signed distance is positive for an inlier and negative for an outlier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        dec : ndarray of shape (n_samples,)
+            Returns the decision function of the samples.
+        """
+        dec = self._decision_function(X).ravel()
+        return dec
+
+    def score_samples(self, X):
+        """Raw scoring function of the samples.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            The data matrix.
+
+        Returns
+        -------
+        score_samples : ndarray of shape (n_samples,)
+            Returns the (unshifted) scoring function of the samples.
+        """
+        return self.decision_function(X) + self.offset_
+
+    def predict(self, X):
+        """Perform classification on samples in X.
+
+        For a one-class model, +1 or -1 is returned.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
+                (n_samples_test, n_samples_train)
+            For kernel="precomputed", the expected shape of X is
+            (n_samples_test, n_samples_train).
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Class labels for samples in X.
+        """
+        y = super().predict(X)
+        return np.asarray(y, dtype=np.intp)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_liblinear.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_liblinear.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..6d5347e746384d34876ca1d569204afa3573ac76
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_liblinear.pyx
@@ -0,0 +1,147 @@
+"""
+Wrapper for liblinear
+
+Author: fabian.pedregosa@inria.fr
+"""
+
+import  numpy as np
+
+from ..utils._cython_blas cimport _dot, _axpy, _scal, _nrm2
+from ..utils._typedefs cimport float32_t, float64_t, int32_t
+
+include "_liblinear.pxi"
+
+
+def train_wrap(
+    object X,
+    const float64_t[::1] Y,
+    bint is_sparse,
+    int solver_type,
+    double eps,
+    double bias,
+    double C,
+    const float64_t[:] class_weight,
+    int max_iter,
+    unsigned random_seed,
+    double epsilon,
+    const float64_t[::1] sample_weight
+):
+    cdef parameter *param
+    cdef problem *problem
+    cdef model *model
+    cdef char_const_ptr error_msg
+    cdef int len_w
+    cdef bint X_has_type_float64 = X.dtype == np.float64
+    cdef char * X_data_bytes_ptr
+    cdef const float64_t[::1] X_data_64
+    cdef const float32_t[::1] X_data_32
+    cdef const int32_t[::1] X_indices
+    cdef const int32_t[::1] X_indptr
+
+    if is_sparse:
+        X_indices = X.indices
+        X_indptr = X.indptr
+        if X_has_type_float64:
+            X_data_64 = X.data
+            X_data_bytes_ptr =  &X_data_64[0]
+        else:
+            X_data_32 = X.data
+            X_data_bytes_ptr =  &X_data_32[0]
+
+        problem = csr_set_problem(
+            X_data_bytes_ptr,
+            X_has_type_float64,
+             &X_indices[0],
+             &X_indptr[0],
+            (X.shape[0]),
+            (X.shape[1]),
+            (X.nnz),
+            bias,
+             &sample_weight[0],
+             &Y[0]
+        )
+    else:
+        X_as_1d_array = X.reshape(-1)
+        if X_has_type_float64:
+            X_data_64 = X_as_1d_array
+            X_data_bytes_ptr =  &X_data_64[0]
+        else:
+            X_data_32 = X_as_1d_array
+            X_data_bytes_ptr =  &X_data_32[0]
+
+        problem = set_problem(
+            X_data_bytes_ptr,
+            X_has_type_float64,
+            (X.shape[0]),
+            (X.shape[1]),
+            (np.count_nonzero(X)),
+            bias,
+             &sample_weight[0],
+             &Y[0]
+        )
+
+    cdef int32_t[::1] class_weight_label = np.arange(class_weight.shape[0], dtype=np.intc)
+    param = set_parameter(
+        solver_type,
+        eps,
+        C,
+        class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        max_iter,
+        random_seed,
+        epsilon
+    )
+
+    error_msg = check_parameter(problem, param)
+    if error_msg:
+        free_problem(problem)
+        free_parameter(param)
+        raise ValueError(error_msg)
+
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    blas_functions.axpy = _axpy[double]
+    blas_functions.scal = _scal[double]
+    blas_functions.nrm2 = _nrm2[double]
+
+    # early return
+    with nogil:
+        model = train(problem, param, &blas_functions)
+
+    # FREE
+    free_problem(problem)
+    free_parameter(param)
+    # destroy_param(param)  don't call this or it will destroy class_weight_label and class_weight
+
+    # coef matrix holder created as fortran since that's what's used in liblinear
+    cdef float64_t[::1, :] w
+    cdef int nr_class = get_nr_class(model)
+
+    cdef int labels_ = nr_class
+    if nr_class == 2:
+        labels_ = 1
+    cdef int32_t[::1] n_iter = np.zeros(labels_, dtype=np.intc)
+    get_n_iter(model,  &n_iter[0])
+
+    cdef int nr_feature = get_nr_feature(model)
+    if bias > 0:
+        nr_feature = nr_feature + 1
+    if nr_class == 2 and solver_type != 4:  # solver is not Crammer-Singer
+        w = np.empty((1, nr_feature), order='F')
+        copy_w(&w[0, 0], model, nr_feature)
+    else:
+        len_w = (nr_class) * nr_feature
+        w = np.empty((nr_class, nr_feature), order='F')
+        copy_w(&w[0, 0], model, len_w)
+
+    free_and_destroy_model(&model)
+
+    return w.base, n_iter.base
+
+
+def set_verbosity_wrap(int verbosity):
+    """
+    Control verbosity of libsvm library
+    """
+    set_verbosity(verbosity)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pxi b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..74ddfd66c538e712e95ba183bcf34695f5b85a14
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pxi
@@ -0,0 +1,75 @@
+################################################################################
+# Includes
+from ..utils._typedefs cimport intp_t
+
+cdef extern from "_svm_cython_blas_helpers.h":
+    ctypedef double (*dot_func)(int, const double*, int, const double*, int)
+    cdef struct BlasFunctions:
+        dot_func dot
+
+
+cdef extern from "svm.h":
+    cdef struct svm_node
+    cdef struct svm_model
+    cdef struct svm_parameter:
+        int svm_type
+        int kernel_type
+        int degree    # for poly
+        double gamma  # for poly/rbf/sigmoid
+        double coef0  # for poly/sigmoid
+
+        # these are for training only
+        double cache_size  # in MB
+        double eps         # stopping criteria
+        double C           # for C_SVC, EPSILON_SVR and NU_SVR
+        int nr_weight      # for C_SVC
+        int *weight_label  # for C_SVC
+        double* weight     # for C_SVC
+        double nu          # for NU_SVC, ONE_CLASS, and NU_SVR
+        double p           # for EPSILON_SVR
+        int shrinking      # use the shrinking heuristics
+        int probability    # do probability estimates
+        int max_iter       # ceiling on Solver runtime
+        int random_seed    # seed for random generator in probability estimation
+
+    cdef struct svm_problem:
+        int l
+        double *y
+        svm_node *x
+        double *W  # instance weights
+
+    char *svm_check_parameter(svm_problem *, svm_parameter *)
+    svm_model *svm_train(svm_problem *, svm_parameter *, int *, BlasFunctions *) nogil
+    void svm_free_and_destroy_model(svm_model** model_ptr_ptr)
+    void svm_cross_validation(svm_problem *, svm_parameter *, int nr_fold, double *target, BlasFunctions *) nogil
+
+
+cdef extern from "libsvm_helper.c":
+    # this file contains methods for accessing libsvm 'hidden' fields
+    svm_node **dense_to_sparse (char *, intp_t *)
+    void set_parameter (svm_parameter *, int , int , int , double, double ,
+                        double , double , double , double,
+                        double, int, int, int, char *, char *, int,
+                        int)
+    void set_problem (svm_problem *, char *, char *, char *, intp_t *, int)
+
+    svm_model *set_model (svm_parameter *, int, char *, intp_t *,
+                          char *, intp_t *, intp_t *, char *,
+                          char *, char *, char *, char *)
+
+    void copy_sv_coef   (char *, svm_model *)
+    void copy_n_iter  (char *, svm_model *)
+    void copy_intercept (char *, svm_model *, intp_t *)
+    void copy_SV        (char *, svm_model *, intp_t *)
+    int copy_support (char *data, svm_model *model)
+    int copy_predict (char *, svm_model *, intp_t *, char *, BlasFunctions *) nogil
+    int copy_predict_proba (char *, svm_model *, intp_t *, char *, BlasFunctions *) nogil
+    int copy_predict_values(char *, svm_model *, intp_t *, char *, int, BlasFunctions *) nogil
+    void copy_nSV     (char *, svm_model *)
+    void copy_probA   (char *, svm_model *, intp_t *)
+    void copy_probB   (char *, svm_model *, intp_t *)
+    intp_t  get_l  (svm_model *)
+    intp_t  get_nr (svm_model *)
+    int  free_problem   (svm_problem *)
+    int  free_model     (svm_model *)
+    void set_verbosity(int)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..be0a0826c3736469fdafbf5f42bff39d1205a6ec
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm.pyx
@@ -0,0 +1,917 @@
+"""
+Binding for libsvm_skl
+----------------------
+
+These are the bindings for libsvm_skl, which is a fork of libsvm[1]
+that adds to libsvm some capabilities, like index of support vectors
+and efficient representation of dense matrices.
+
+These are low-level routines, but can be used for flexibility or
+performance reasons. See sklearn.svm for a higher-level API.
+
+Low-level memory management is done in libsvm_helper.c. If we happen
+to run out of memory a MemoryError will be raised. In practice this is
+not very helpful since high chances are malloc fails inside svm.cpp,
+where no sort of memory checks are done.
+
+[1] https://www.csie.ntu.edu.tw/~cjlin/libsvm/
+
+Notes
+-----
+The signature mode='c' is somewhat superficial, since we already
+check that arrays are C-contiguous in svm.py
+
+Authors
+-------
+2010: Fabian Pedregosa 
+      Gael Varoquaux 
+"""
+
+import  numpy as np
+from libc.stdlib cimport free
+from ..utils._cython_blas cimport _dot
+from ..utils._typedefs cimport float64_t, int32_t, intp_t
+
+include "_libsvm.pxi"
+
+cdef extern from *:
+    ctypedef struct svm_parameter:
+        pass
+
+
+################################################################################
+# Internal variables
+LIBSVM_KERNEL_TYPES = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
+
+
+################################################################################
+# Wrapper functions
+
+def fit(
+    const float64_t[:, ::1] X,
+    const float64_t[::1] Y,
+    int svm_type=0,
+    kernel='rbf',
+    int degree=3,
+    double gamma=0.1,
+    double coef0=0.0,
+    double tol=1e-3,
+    double C=1.0,
+    double nu=0.5,
+    double epsilon=0.1,
+    const float64_t[::1] class_weight=np.empty(0),
+    const float64_t[::1] sample_weight=np.empty(0),
+    int shrinking=1,
+    int probability=0,
+    double cache_size=100.,
+    int max_iter=-1,
+    int random_seed=0,
+):
+    """
+    Train the model using libsvm (low-level method)
+
+    Parameters
+    ----------
+    X : array-like, dtype=float64 of shape (n_samples, n_features)
+
+    Y : array, dtype=float64 of shape (n_samples,)
+        target vector
+
+    svm_type : {0, 1, 2, 3, 4}, default=0
+        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
+        respectively.
+
+    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
+        Kernel to use in the model: linear, polynomial, RBF, sigmoid
+        or precomputed.
+
+    degree : int32, default=3
+        Degree of the polynomial kernel (only relevant if kernel is
+        set to polynomial).
+
+    gamma : float64, default=0.1
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels.
+
+    coef0 : float64, default=0
+        Independent parameter in poly/sigmoid kernel.
+
+    tol : float64, default=1e-3
+        Numeric stopping criterion (WRITEME).
+
+    C : float64, default=1
+        C parameter in C-Support Vector Classification.
+
+    nu : float64, default=0.5
+        An upper bound on the fraction of training errors and a lower bound of
+        the fraction of support vectors. Should be in the interval (0, 1].
+
+    epsilon : double, default=0.1
+        Epsilon parameter in the epsilon-insensitive loss function.
+
+    class_weight : array, dtype=float64, shape (n_classes,), \
+            default=np.empty(0)
+        Set the parameter C of class i to class_weight[i]*C for
+        SVC. If not given, all classes are supposed to have
+        weight one.
+
+    sample_weight : array, dtype=float64, shape (n_samples,), \
+            default=np.empty(0)
+        Weights assigned to each sample.
+
+    shrinking : int, default=1
+        Whether to use the shrinking heuristic.
+
+    probability : int, default=0
+        Whether to enable probability estimates.
+
+    cache_size : float64, default=100
+        Cache size for gram matrix columns (in megabytes).
+
+    max_iter : int (-1 for no limit), default=-1
+        Stop solver after this many iterations regardless of accuracy
+        (XXX Currently there is no API to know whether this kicked in.)
+
+    random_seed : int, default=0
+        Seed for the random number generator used for probability estimates.
+
+    Returns
+    -------
+    support : array of shape (n_support,)
+        Index of support vectors.
+
+    support_vectors : array of shape (n_support, n_features)
+        Support vectors (equivalent to X[support]). Will return an
+        empty array in the case of precomputed kernel.
+
+    n_class_SV : array of shape (n_class,)
+        Number of support vectors in each class.
+
+    sv_coef : array of shape (n_class-1, n_support)
+        Coefficients of support vectors in decision function.
+
+    intercept : array of shape (n_class*(n_class-1)/2,)
+        Intercept in decision function.
+
+    probA, probB : array of shape (n_class*(n_class-1)/2,)
+        Probability estimates, empty array for probability=False.
+
+    n_iter : ndarray of shape (max(1, (n_class * (n_class - 1) // 2)),)
+        Number of iterations run by the optimization routine to fit the model.
+    """
+
+    cdef svm_parameter param
+    cdef svm_problem problem
+    cdef svm_model *model
+    cdef const char *error_msg
+    cdef intp_t SV_len
+
+    if len(sample_weight) == 0:
+        sample_weight = np.ones(X.shape[0], dtype=np.float64)
+    else:
+        assert sample_weight.shape[0] == X.shape[0], (
+            f"sample_weight and X have incompatible shapes: sample_weight has "
+            f"{sample_weight.shape[0]} samples while X has {X.shape[0]}"
+        )
+
+    kernel_index = LIBSVM_KERNEL_TYPES.index(kernel)
+    set_problem(
+        &problem,
+         &X[0, 0],
+         &Y[0],
+         &sample_weight[0],
+         X.shape,
+        kernel_index,
+    )
+    if problem.x == NULL:
+        raise MemoryError("Seems we've run out of memory")
+    cdef int32_t[::1] class_weight_label = np.arange(
+        class_weight.shape[0], dtype=np.int32
+    )
+    set_parameter(
+        ¶m,
+        svm_type,
+        kernel_index,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        cache_size,
+        C,
+        tol,
+        epsilon,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        max_iter,
+        random_seed,
+    )
+
+    error_msg = svm_check_parameter(&problem, ¶m)
+    if error_msg:
+        # for SVR: epsilon is called p in libsvm
+        error_repl = error_msg.decode('utf-8').replace("p < 0", "epsilon < 0")
+        raise ValueError(error_repl)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    # this does the real work
+    cdef int fit_status = 0
+    with nogil:
+        model = svm_train(&problem, ¶m, &fit_status, &blas_functions)
+
+    # from here until the end, we just copy the data returned by
+    # svm_train
+    SV_len = get_l(model)
+    n_class = get_nr(model)
+
+    cdef int[::1] n_iter = np.empty(max(1, n_class * (n_class - 1) // 2), dtype=np.intc)
+    copy_n_iter( &n_iter[0], model)
+
+    cdef float64_t[:, ::1] sv_coef = np.empty((n_class-1, SV_len), dtype=np.float64)
+    copy_sv_coef( &sv_coef[0, 0] if sv_coef.size > 0 else NULL, model)
+
+    # the intercept is just model.rho but with sign changed
+    cdef float64_t[::1] intercept = np.empty(
+        int((n_class*(n_class-1))/2), dtype=np.float64
+    )
+    copy_intercept( &intercept[0], model,  intercept.shape)
+
+    cdef int32_t[::1] support = np.empty(SV_len, dtype=np.int32)
+    copy_support( &support[0] if support.size > 0 else NULL, model)
+
+    # copy model.SV
+    cdef float64_t[:, ::1] support_vectors
+    if kernel_index == 4:
+        # precomputed kernel
+        support_vectors = np.empty((0, 0), dtype=np.float64)
+    else:
+        support_vectors = np.empty((SV_len, X.shape[1]), dtype=np.float64)
+        copy_SV(
+             &support_vectors[0, 0] if support_vectors.size > 0 else NULL,
+            model,
+             support_vectors.shape,
+        )
+
+    cdef int32_t[::1] n_class_SV
+    if svm_type == 0 or svm_type == 1:
+        n_class_SV = np.empty(n_class, dtype=np.int32)
+        copy_nSV( &n_class_SV[0] if n_class_SV.size > 0 else NULL, model)
+    else:
+        # OneClass and SVR are considered to have 2 classes
+        n_class_SV = np.array([SV_len, SV_len], dtype=np.int32)
+
+    cdef float64_t[::1] probA
+    cdef float64_t[::1] probB
+    if probability != 0:
+        if svm_type < 2:  # SVC and NuSVC
+            probA = np.empty(int(n_class*(n_class-1)/2), dtype=np.float64)
+            probB = np.empty(int(n_class*(n_class-1)/2), dtype=np.float64)
+            copy_probB( &probB[0], model,  probB.shape)
+        else:
+            probA = np.empty(1, dtype=np.float64)
+            probB = np.empty(0, dtype=np.float64)
+        copy_probA( &probA[0], model,  probA.shape)
+    else:
+        probA = np.empty(0, dtype=np.float64)
+        probB = np.empty(0, dtype=np.float64)
+
+    svm_free_and_destroy_model(&model)
+    free(problem.x)
+
+    return (
+        support.base,
+        support_vectors.base,
+        n_class_SV.base,
+        sv_coef.base,
+        intercept.base,
+        probA.base,
+        probB.base,
+        fit_status,
+        n_iter.base,
+    )
+
+
+cdef void set_predict_params(
+    svm_parameter *param,
+    int svm_type,
+    kernel,
+    int degree,
+    double gamma,
+    double coef0,
+    double cache_size,
+    int probability,
+    int nr_weight,
+    char *weight_label,
+    char *weight,
+) except *:
+    """Fill param with prediction time-only parameters."""
+
+    # training-time only parameters
+    cdef double C = 0.0
+    cdef double epsilon = 0.1
+    cdef int max_iter = 0
+    cdef double nu = 0.5
+    cdef int shrinking = 0
+    cdef double tol = 0.1
+    cdef int random_seed = -1
+
+    kernel_index = LIBSVM_KERNEL_TYPES.index(kernel)
+
+    set_parameter(
+        param,
+        svm_type,
+        kernel_index,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        cache_size,
+        C,
+        tol,
+        epsilon,
+        shrinking,
+        probability,
+        nr_weight,
+        weight_label,
+        weight,
+        max_iter,
+        random_seed,
+    )
+
+
+def predict(
+    const float64_t[:, ::1] X,
+    const int32_t[::1] support,
+    const float64_t[:, ::1] SV,
+    const int32_t[::1] nSV,
+    const float64_t[:, ::1] sv_coef,
+    const float64_t[::1] intercept,
+    const float64_t[::1] probA=np.empty(0),
+    const float64_t[::1] probB=np.empty(0),
+    int svm_type=0,
+    kernel='rbf',
+    int degree=3,
+    double gamma=0.1,
+    double coef0=0.0,
+    const float64_t[::1] class_weight=np.empty(0),
+    const float64_t[::1] sample_weight=np.empty(0),
+    double cache_size=100.0,
+):
+    """
+    Predict target values of X given a model (low-level method)
+
+    Parameters
+    ----------
+    X : array-like, dtype=float of shape (n_samples, n_features)
+
+    support : array of shape (n_support,)
+        Index of support vectors in training set.
+
+    SV : array of shape (n_support, n_features)
+        Support vectors.
+
+    nSV : array of shape (n_class,)
+        Number of support vectors in each class.
+
+    sv_coef : array of shape (n_class-1, n_support)
+        Coefficients of support vectors in decision function.
+
+    intercept : array of shape (n_class*(n_class-1)/2)
+        Intercept in decision function.
+
+    probA, probB : array of shape (n_class*(n_class-1)/2,)
+        Probability estimates.
+
+    svm_type : {0, 1, 2, 3, 4}, default=0
+        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
+        respectively.
+
+    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
+        Kernel to use in the model: linear, polynomial, RBF, sigmoid
+        or precomputed.
+
+    degree : int32, default=3
+        Degree of the polynomial kernel (only relevant if kernel is
+        set to polynomial).
+
+    gamma : float64, default=0.1
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels.
+
+    coef0 : float64, default=0.0
+        Independent parameter in poly/sigmoid kernel.
+
+    Returns
+    -------
+    dec_values : array
+        Predicted values.
+    """
+    cdef float64_t[::1] dec_values
+    cdef svm_parameter param
+    cdef svm_model *model
+    cdef int rv
+
+    cdef int32_t[::1] class_weight_label = np.arange(
+        class_weight.shape[0], dtype=np.int32
+    )
+
+    set_predict_params(
+        ¶m,
+        svm_type,
+        kernel,
+        degree,
+        gamma,
+        coef0,
+        cache_size,
+        0,
+        class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+    )
+    model = set_model(
+        ¶m,
+         nSV.shape[0],
+         &SV[0, 0] if SV.size > 0 else NULL,
+         SV.shape,
+         &support[0] if support.size > 0 else NULL,
+         support.shape,
+         sv_coef.strides,
+         &sv_coef[0, 0] if sv_coef.size > 0 else NULL,
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    # TODO: use check_model
+    try:
+        dec_values = np.empty(X.shape[0])
+        with nogil:
+            rv = copy_predict(
+                 &X[0, 0],
+                model,
+                 X.shape,
+                 &dec_values[0],
+                &blas_functions,
+            )
+        if rv < 0:
+            raise MemoryError("We've run out of memory")
+    finally:
+        free_model(model)
+
+    return dec_values.base
+
+
+def predict_proba(
+    const float64_t[:, ::1] X,
+    const int32_t[::1] support,
+    const float64_t[:, ::1] SV,
+    const int32_t[::1] nSV,
+    float64_t[:, ::1] sv_coef,
+    float64_t[::1] intercept,
+    float64_t[::1] probA=np.empty(0),
+    float64_t[::1] probB=np.empty(0),
+    int svm_type=0,
+    kernel='rbf',
+    int degree=3,
+    double gamma=0.1,
+    double coef0=0.0,
+    float64_t[::1] class_weight=np.empty(0),
+    float64_t[::1] sample_weight=np.empty(0),
+    double cache_size=100.0,
+):
+    """
+    Predict probabilities
+
+    svm_model stores all parameters needed to predict a given value.
+
+    For speed, all real work is done at the C level in function
+    copy_predict (libsvm_helper.c).
+
+    We have to reconstruct model and parameters to make sure we stay
+    in sync with the python object.
+
+    See sklearn.svm.predict for a complete list of parameters.
+
+    Parameters
+    ----------
+    X : array-like, dtype=float of shape (n_samples, n_features)
+
+    support : array of shape (n_support,)
+        Index of support vectors in training set.
+
+    SV : array of shape (n_support, n_features)
+        Support vectors.
+
+    nSV : array of shape (n_class,)
+        Number of support vectors in each class.
+
+    sv_coef : array of shape (n_class-1, n_support)
+        Coefficients of support vectors in decision function.
+
+    intercept : array of shape (n_class*(n_class-1)/2,)
+        Intercept in decision function.
+
+    probA, probB : array of shape (n_class*(n_class-1)/2,)
+        Probability estimates.
+
+    svm_type : {0, 1, 2, 3, 4}, default=0
+        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
+        respectively.
+
+    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
+        Kernel to use in the model: linear, polynomial, RBF, sigmoid
+        or precomputed.
+
+    degree : int32, default=3
+        Degree of the polynomial kernel (only relevant if kernel is
+        set to polynomial).
+
+    gamma : float64, default=0.1
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels.
+
+    coef0 : float64, default=0.0
+        Independent parameter in poly/sigmoid kernel.
+
+    Returns
+    -------
+    dec_values : array
+        Predicted values.
+    """
+    cdef float64_t[:, ::1] dec_values
+    cdef svm_parameter param
+    cdef svm_model *model
+    cdef int32_t[::1] class_weight_label = np.arange(
+        class_weight.shape[0], dtype=np.int32
+    )
+    cdef int rv
+
+    set_predict_params(
+        ¶m,
+        svm_type,
+        kernel,
+        degree,
+        gamma,
+        coef0,
+        cache_size,
+        1,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+    )
+    model = set_model(
+        ¶m,
+         nSV.shape[0],
+         &SV[0, 0] if SV.size > 0 else NULL,
+         SV.shape,
+         &support[0],
+         support.shape,
+         sv_coef.strides,
+         &sv_coef[0, 0],
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+
+    cdef intp_t n_class = get_nr(model)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    try:
+        dec_values = np.empty((X.shape[0], n_class), dtype=np.float64)
+        with nogil:
+            rv = copy_predict_proba(
+                 &X[0, 0],
+                model,
+                 X.shape,
+                 &dec_values[0, 0],
+                &blas_functions,
+            )
+        if rv < 0:
+            raise MemoryError("We've run out of memory")
+    finally:
+        free_model(model)
+
+    return dec_values.base
+
+
+def decision_function(
+    const float64_t[:, ::1] X,
+    const int32_t[::1] support,
+    const float64_t[:, ::1] SV,
+    const int32_t[::1] nSV,
+    const float64_t[:, ::1] sv_coef,
+    const float64_t[::1] intercept,
+    const float64_t[::1] probA=np.empty(0),
+    const float64_t[::1] probB=np.empty(0),
+    int svm_type=0,
+    kernel='rbf',
+    int degree=3,
+    double gamma=0.1,
+    double coef0=0.0,
+    const float64_t[::1] class_weight=np.empty(0),
+    const float64_t[::1] sample_weight=np.empty(0),
+    double cache_size=100.0,
+):
+    """
+    Predict margin (libsvm name for this is predict_values)
+
+    We have to reconstruct model and parameters to make sure we stay
+    in sync with the python object.
+
+    Parameters
+    ----------
+    X : array-like, dtype=float, size=[n_samples, n_features]
+
+    support : array, shape=[n_support]
+        Index of support vectors in training set.
+
+    SV : array, shape=[n_support, n_features]
+        Support vectors.
+
+    nSV : array, shape=[n_class]
+        Number of support vectors in each class.
+
+    sv_coef : array, shape=[n_class-1, n_support]
+        Coefficients of support vectors in decision function.
+
+    intercept : array, shape=[n_class*(n_class-1)/2]
+        Intercept in decision function.
+
+    probA, probB : array, shape=[n_class*(n_class-1)/2]
+        Probability estimates.
+
+    svm_type : {0, 1, 2, 3, 4}, optional
+        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
+        respectively. 0 by default.
+
+    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, optional
+        Kernel to use in the model: linear, polynomial, RBF, sigmoid
+        or precomputed. 'rbf' by default.
+
+    degree : int32, optional
+        Degree of the polynomial kernel (only relevant if kernel is
+        set to polynomial), 3 by default.
+
+    gamma : float64, optional
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels. 0.1 by default.
+
+    coef0 : float64, optional
+        Independent parameter in poly/sigmoid kernel. 0 by default.
+
+    Returns
+    -------
+    dec_values : array
+        Predicted values.
+    """
+    cdef float64_t[:, ::1] dec_values
+    cdef svm_parameter param
+    cdef svm_model *model
+    cdef intp_t n_class
+
+    cdef int32_t[::1] class_weight_label = np.arange(
+        class_weight.shape[0], dtype=np.int32
+    )
+
+    cdef int rv
+
+    set_predict_params(
+        ¶m,
+        svm_type,
+        kernel,
+        degree,
+        gamma,
+        coef0,
+        cache_size,
+        0,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+    )
+
+    model = set_model(
+        ¶m,
+         nSV.shape[0],
+         &SV[0, 0] if SV.size > 0 else NULL,
+         SV.shape,
+         &support[0],
+         support.shape,
+         sv_coef.strides,
+         &sv_coef[0, 0],
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+
+    if svm_type > 1:
+        n_class = 1
+    else:
+        n_class = get_nr(model)
+        n_class = n_class * (n_class - 1) // 2
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    try:
+        dec_values = np.empty((X.shape[0], n_class), dtype=np.float64)
+        with nogil:
+            rv = copy_predict_values(
+                 &X[0, 0],
+                model,
+                 X.shape,
+                 &dec_values[0, 0],
+                n_class,
+                &blas_functions,
+            )
+        if rv < 0:
+            raise MemoryError("We've run out of memory")
+    finally:
+        free_model(model)
+
+    return dec_values.base
+
+
+def cross_validation(
+    const float64_t[:, ::1] X,
+    const float64_t[::1] Y,
+    int n_fold,
+    int svm_type=0,
+    kernel='rbf',
+    int degree=3,
+    double gamma=0.1,
+    double coef0=0.0,
+    double tol=1e-3,
+    double C=1.0,
+    double nu=0.5,
+    double epsilon=0.1,
+    float64_t[::1] class_weight=np.empty(0),
+    float64_t[::1] sample_weight=np.empty(0),
+    int shrinking=0,
+    int probability=0,
+    double cache_size=100.0,
+    int max_iter=-1,
+    int random_seed=0,
+):
+    """
+    Binding of the cross-validation routine (low-level routine)
+
+    Parameters
+    ----------
+
+    X : array-like, dtype=float of shape (n_samples, n_features)
+
+    Y : array, dtype=float of shape (n_samples,)
+        target vector
+
+    n_fold : int32
+        Number of folds for cross validation.
+
+    svm_type : {0, 1, 2, 3, 4}, default=0
+        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
+        respectively.
+
+    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default='rbf'
+        Kernel to use in the model: linear, polynomial, RBF, sigmoid
+        or precomputed.
+
+    degree : int32, default=3
+        Degree of the polynomial kernel (only relevant if kernel is
+        set to polynomial).
+
+    gamma : float64, default=0.1
+        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
+        kernels.
+
+    coef0 : float64, default=0.0
+        Independent parameter in poly/sigmoid kernel.
+
+    tol : float64, default=1e-3
+        Numeric stopping criterion (WRITEME).
+
+    C : float64, default=1
+        C parameter in C-Support Vector Classification.
+
+    nu : float64, default=0.5
+        An upper bound on the fraction of training errors and a lower bound of
+        the fraction of support vectors. Should be in the interval (0, 1].
+
+    epsilon : double, default=0.1
+        Epsilon parameter in the epsilon-insensitive loss function.
+
+    class_weight : array, dtype=float64, shape (n_classes,), \
+            default=np.empty(0)
+        Set the parameter C of class i to class_weight[i]*C for
+        SVC. If not given, all classes are supposed to have
+        weight one.
+
+    sample_weight : array, dtype=float64, shape (n_samples,), \
+            default=np.empty(0)
+        Weights assigned to each sample.
+
+    shrinking : int, default=1
+        Whether to use the shrinking heuristic.
+
+    probability : int, default=0
+        Whether to enable probability estimates.
+
+    cache_size : float64, default=100
+        Cache size for gram matrix columns (in megabytes).
+
+    max_iter : int (-1 for no limit), default=-1
+        Stop solver after this many iterations regardless of accuracy
+        (XXX Currently there is no API to know whether this kicked in.)
+
+    random_seed : int, default=0
+        Seed for the random number generator used for probability estimates.
+
+    Returns
+    -------
+    target : array, float
+
+    """
+
+    cdef svm_parameter param
+    cdef svm_problem problem
+    cdef const char *error_msg
+
+    if len(sample_weight) == 0:
+        sample_weight = np.ones(X.shape[0], dtype=np.float64)
+    else:
+        assert sample_weight.shape[0] == X.shape[0], (
+            f"sample_weight and X have incompatible shapes: sample_weight has "
+            f"{sample_weight.shape[0]} samples while X has {X.shape[0]}"
+        )
+
+    if X.shape[0] < n_fold:
+        raise ValueError("Number of samples is less than number of folds")
+
+    # set problem
+    kernel_index = LIBSVM_KERNEL_TYPES.index(kernel)
+    set_problem(
+        &problem,
+         &X[0, 0],
+         &Y[0],
+         &sample_weight[0] if sample_weight.size > 0 else NULL,
+         X.shape,
+        kernel_index,
+    )
+    if problem.x == NULL:
+        raise MemoryError("Seems we've run out of memory")
+    cdef int32_t[::1] class_weight_label = np.arange(
+        class_weight.shape[0], dtype=np.int32
+    )
+
+    # set parameters
+    set_parameter(
+        ¶m,
+        svm_type,
+        kernel_index,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        cache_size,
+        C,
+        tol,
+        tol,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        max_iter,
+        random_seed,
+    )
+
+    error_msg = svm_check_parameter(&problem, ¶m)
+    if error_msg:
+        raise ValueError(error_msg)
+
+    cdef float64_t[::1] target
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    try:
+        target = np.empty((X.shape[0]), dtype=np.float64)
+        with nogil:
+            svm_cross_validation(
+                &problem,
+                ¶m,
+                n_fold,
+                 &target[0],
+                &blas_functions,
+            )
+    finally:
+        free(problem.x)
+
+    return target.base
+
+
+def set_verbosity_wrap(int verbosity):
+    """
+    Control verbosity of libsvm library
+    """
+    set_verbosity(verbosity)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm_sparse.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm_sparse.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..529758061d299f095bbe3834d85e3f10e475c537
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_libsvm_sparse.pyx
@@ -0,0 +1,550 @@
+import  numpy as np
+from scipy import sparse
+from ..utils._cython_blas cimport _dot
+from ..utils._typedefs cimport float64_t, int32_t, intp_t
+
+cdef extern from *:
+    ctypedef char* const_char_p "const char*"
+
+################################################################################
+# Includes
+
+cdef extern from "_svm_cython_blas_helpers.h":
+    ctypedef double (*dot_func)(int, const double*, int, const double*, int)
+    cdef struct BlasFunctions:
+        dot_func dot
+
+cdef extern from "svm.h":
+    cdef struct svm_csr_node
+    cdef struct svm_csr_model
+    cdef struct svm_parameter
+    cdef struct svm_csr_problem
+    char *svm_csr_check_parameter(svm_csr_problem *, svm_parameter *)
+    svm_csr_model *svm_csr_train(svm_csr_problem *, svm_parameter *, int *, BlasFunctions *) nogil
+    void svm_csr_free_and_destroy_model(svm_csr_model** model_ptr_ptr)
+
+cdef extern from "libsvm_sparse_helper.c":
+    # this file contains methods for accessing libsvm 'hidden' fields
+    svm_csr_problem * csr_set_problem (
+        char *, intp_t *, char *, intp_t *, char *, char *, char *, int)
+    svm_csr_model *csr_set_model(svm_parameter *param, int nr_class,
+                                 char *SV_data, intp_t *SV_indices_dims,
+                                 char *SV_indices, intp_t *SV_intptr_dims,
+                                 char *SV_intptr,
+                                 char *sv_coef, char *rho, char *nSV,
+                                 char *probA, char *probB)
+    svm_parameter *set_parameter (int , int , int , double, double ,
+                                  double , double , double , double,
+                                  double, int, int, int, char *, char *, int,
+                                  int)
+    void copy_sv_coef   (char *, svm_csr_model *)
+    void copy_n_iter  (char *, svm_csr_model *)
+    void copy_support   (char *, svm_csr_model *)
+    void copy_intercept (char *, svm_csr_model *, intp_t *)
+    int copy_predict (char *, svm_csr_model *, intp_t *, char *, BlasFunctions *)
+    int csr_copy_predict_values (intp_t *data_size, char *data, intp_t *index_size,
+                                 char *index, intp_t *intptr_size, char *size,
+                                 svm_csr_model *model, char *dec_values, int nr_class, BlasFunctions *)
+    int csr_copy_predict (intp_t *data_size, char *data, intp_t *index_size,
+                          char *index, intp_t *intptr_size, char *size,
+                          svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
+    int csr_copy_predict_proba (intp_t *data_size, char *data, intp_t *index_size,
+                                char *index, intp_t *intptr_size, char *size,
+                                svm_csr_model *model, char *dec_values, BlasFunctions *) nogil
+
+    int  copy_predict_values(char *, svm_csr_model *, intp_t *, char *, int, BlasFunctions *)
+    int  csr_copy_SV (char *values, intp_t *n_indices,
+                      char *indices, intp_t *n_indptr, char *indptr,
+                      svm_csr_model *model, int n_features)
+    intp_t get_nonzero_SV (svm_csr_model *)
+    void copy_nSV     (char *, svm_csr_model *)
+    void copy_probA   (char *, svm_csr_model *, intp_t *)
+    void copy_probB   (char *, svm_csr_model *, intp_t *)
+    intp_t  get_l  (svm_csr_model *)
+    intp_t  get_nr (svm_csr_model *)
+    int  free_problem   (svm_csr_problem *)
+    int  free_model     (svm_csr_model *)
+    int  free_param     (svm_parameter *)
+    int free_model_SV(svm_csr_model *model)
+    void set_verbosity(int)
+
+
+def libsvm_sparse_train (int n_features,
+                         const float64_t[::1] values,
+                         const int32_t[::1] indices,
+                         const int32_t[::1] indptr,
+                         const float64_t[::1] Y,
+                         int svm_type, int kernel_type, int degree, double gamma,
+                         double coef0, double eps, double C,
+                         const float64_t[::1] class_weight,
+                         const float64_t[::1] sample_weight,
+                         double nu, double cache_size, double p, int
+                         shrinking, int probability, int max_iter,
+                         int random_seed):
+    """
+    Wrap svm_train from libsvm using a scipy.sparse.csr matrix
+
+    Work in progress.
+
+    Parameters
+    ----------
+    n_features : number of features.
+        XXX: can we retrieve this from any other parameter ?
+
+    X : array-like, dtype=float, size=[N, D]
+
+    Y : array, dtype=float, size=[N]
+        target vector
+
+    ...
+
+    Notes
+    -------------------
+    See sklearn.svm.predict for a complete list of parameters.
+
+    """
+
+    cdef svm_parameter *param
+    cdef svm_csr_problem *problem
+    cdef svm_csr_model *model
+    cdef const_char_p error_msg
+
+    if len(sample_weight) == 0:
+        sample_weight = np.ones(Y.shape[0], dtype=np.float64)
+    else:
+        assert sample_weight.shape[0] == indptr.shape[0] - 1, \
+               "sample_weight and X have incompatible shapes: " + \
+               "sample_weight has %s samples while X has %s" % \
+               (sample_weight.shape[0], indptr.shape[0] - 1)
+
+    # we should never end up here with a precomputed kernel matrix,
+    # as this is always dense.
+    assert(kernel_type != 4)
+
+    # set libsvm problem
+    problem = csr_set_problem(
+         &values[0],
+         indices.shape,
+         &indices[0],
+         indptr.shape,
+         &indptr[0],
+         &Y[0],
+         &sample_weight[0],
+        kernel_type,
+    )
+
+    cdef int32_t[::1] \
+        class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
+
+    # set parameters
+    param = set_parameter(
+        svm_type,
+        kernel_type,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        cache_size,
+        C,
+        eps,
+        p,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL, max_iter,
+        random_seed,
+    )
+
+    # check parameters
+    if (param == NULL or problem == NULL):
+        raise MemoryError("Seems we've run out of memory")
+    error_msg = svm_csr_check_parameter(problem, param)
+    if error_msg:
+        free_problem(problem)
+        free_param(param)
+        raise ValueError(error_msg)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    # call svm_train, this does the real work
+    cdef int fit_status = 0
+    with nogil:
+        model = svm_csr_train(problem, param, &fit_status, &blas_functions)
+
+    cdef intp_t SV_len = get_l(model)
+    cdef intp_t n_class = get_nr(model)
+
+    cdef int[::1] n_iter
+    n_iter = np.empty(max(1, n_class * (n_class - 1) // 2), dtype=np.intc)
+    copy_n_iter( &n_iter[0], model)
+
+    # copy model.sv_coef
+    # we create a new array instead of resizing, otherwise
+    # it would not erase previous information
+    cdef float64_t[::1] sv_coef_data
+    sv_coef_data = np.empty((n_class-1)*SV_len, dtype=np.float64)
+    copy_sv_coef ( &sv_coef_data[0] if sv_coef_data.size > 0 else NULL, model)
+
+    cdef int32_t[::1] support
+    support = np.empty(SV_len, dtype=np.int32)
+    copy_support( &support[0] if support.size > 0 else NULL, model)
+
+    # copy model.rho into the intercept
+    # the intercept is just model.rho but with sign changed
+    cdef float64_t[::1]intercept
+    intercept = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
+    copy_intercept ( &intercept[0], model,  intercept.shape)
+
+    # copy model.SV
+    # we erase any previous information in SV
+    # TODO: custom kernel
+    cdef intp_t nonzero_SV
+    nonzero_SV = get_nonzero_SV (model)
+
+    cdef float64_t[::1] SV_data
+    cdef int32_t[::1] SV_indices, SV_indptr
+    SV_data = np.empty(nonzero_SV, dtype=np.float64)
+    SV_indices = np.empty(nonzero_SV, dtype=np.int32)
+    SV_indptr = np.empty(SV_len + 1, dtype=np.int32)
+    csr_copy_SV(
+         &SV_data[0] if SV_data.size > 0 else NULL,
+         SV_indices.shape,
+         &SV_indices[0] if SV_indices.size > 0 else NULL,
+         SV_indptr.shape,
+         &SV_indptr[0] if SV_indptr.size > 0 else NULL,
+        model,
+        n_features,
+    )
+    support_vectors_ = sparse.csr_matrix(
+        (SV_data, SV_indices, SV_indptr), (SV_len, n_features)
+    )
+
+    # copy model.nSV
+    # TODO: do only in classification
+    cdef int32_t[::1]n_class_SV
+    n_class_SV = np.empty(n_class, dtype=np.int32)
+    copy_nSV( &n_class_SV[0], model)
+
+    # # copy probabilities
+    cdef float64_t[::1] probA, probB
+    if probability != 0:
+        if svm_type < 2:  # SVC and NuSVC
+            probA = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
+            probB = np.empty(n_class*(n_class-1)//2, dtype=np.float64)
+            copy_probB( &probB[0], model,  probB.shape)
+        else:
+            probA = np.empty(1, dtype=np.float64)
+            probB = np.empty(0, dtype=np.float64)
+        copy_probA( &probA[0], model,  probA.shape)
+    else:
+        probA = np.empty(0, dtype=np.float64)
+        probB = np.empty(0, dtype=np.float64)
+
+    svm_csr_free_and_destroy_model (&model)
+    free_problem(problem)
+    free_param(param)
+
+    return (
+        support.base,
+        support_vectors_,
+        sv_coef_data.base,
+        intercept.base,
+        n_class_SV.base,
+        probA.base,
+        probB.base,
+        fit_status,
+        n_iter.base,
+    )
+
+
+def libsvm_sparse_predict (const float64_t[::1] T_data,
+                           const int32_t[::1] T_indices,
+                           const int32_t[::1] T_indptr,
+                           const float64_t[::1] SV_data,
+                           const int32_t[::1] SV_indices,
+                           const int32_t[::1] SV_indptr,
+                           const float64_t[::1] sv_coef,
+                           const float64_t[::1]
+                           intercept, int svm_type, int kernel_type, int
+                           degree, double gamma, double coef0, double
+                           eps, double C,
+                           const float64_t[:] class_weight,
+                           double nu, double p, int
+                           shrinking, int probability,
+                           const int32_t[::1] nSV,
+                           const float64_t[::1] probA,
+                           const float64_t[::1] probB):
+    """
+    Predict values T given a model.
+
+    For speed, all real work is done at the C level in function
+    copy_predict (libsvm_helper.c).
+
+    We have to reconstruct model and parameters to make sure we stay
+    in sync with the python object.
+
+    See sklearn.svm.predict for a complete list of parameters.
+
+    Parameters
+    ----------
+    X : array-like, dtype=float
+    Y : array
+        target vector
+
+    Returns
+    -------
+    dec_values : array
+        predicted values.
+    """
+    cdef float64_t[::1] dec_values
+    cdef svm_parameter *param
+    cdef svm_csr_model *model
+    cdef int32_t[::1] \
+        class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
+    cdef int rv
+    param = set_parameter(
+        svm_type,
+        kernel_type,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        100.0,  # cache size has no effect on predict
+        C,
+        eps,
+        p,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        -1,
+        -1,  # random seed has no effect on predict either
+    )
+
+    model = csr_set_model(
+        param,  nSV.shape[0],
+         &SV_data[0] if SV_data.size > 0 else NULL,
+        SV_indices.shape,
+         &SV_indices[0] if SV_indices.size > 0 else NULL,
+         SV_indptr.shape,
+         &SV_indptr[0] if SV_indptr.size > 0 else NULL,
+         &sv_coef[0] if sv_coef.size > 0 else NULL,
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+    # TODO: use check_model
+    dec_values = np.empty(T_indptr.shape[0]-1)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    with nogil:
+        rv = csr_copy_predict(
+             T_data.shape,
+             &T_data[0],
+             T_indices.shape,
+             &T_indices[0],
+             T_indptr.shape,
+             &T_indptr[0],
+            model,
+             &dec_values[0],
+            &blas_functions,
+        )
+    if rv < 0:
+        raise MemoryError("We've run out of memory")
+    # free model and param
+    free_model_SV(model)
+    free_model(model)
+    free_param(param)
+    return dec_values.base
+
+
+def libsvm_sparse_predict_proba(
+    const float64_t[::1] T_data,
+    const int32_t[::1] T_indices,
+    const int32_t[::1] T_indptr,
+    const float64_t[::1] SV_data,
+    const int32_t[::1] SV_indices,
+    const int32_t[::1] SV_indptr,
+    const float64_t[::1] sv_coef,
+    const float64_t[::1]
+    intercept, int svm_type, int kernel_type, int
+    degree, double gamma, double coef0, double
+    eps, double C,
+    const float64_t[:] class_weight,
+    double nu, double p, int shrinking, int probability,
+    const int32_t[::1] nSV,
+    const float64_t[::1] probA,
+    const float64_t[::1] probB,
+):
+    """
+    Predict values T given a model.
+    """
+    cdef float64_t[:, ::1] dec_values
+    cdef svm_parameter *param
+    cdef svm_csr_model *model
+    cdef int32_t[::1] \
+        class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
+    param = set_parameter(
+        svm_type,
+        kernel_type,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        100.0,  # cache size has no effect on predict
+        C,
+        eps,
+        p,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        -1,
+        -1,  # random seed has no effect on predict either
+    )
+
+    model = csr_set_model(
+        param,
+         nSV.shape[0],
+         &SV_data[0] if SV_data.size > 0 else NULL,
+         SV_indices.shape,
+         &SV_indices[0] if SV_indices.size > 0 else NULL,
+         SV_indptr.shape,
+         &SV_indptr[0] if SV_indptr.size > 0 else NULL,
+         &sv_coef[0] if sv_coef.size > 0 else NULL,
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+    # TODO: use check_model
+    cdef intp_t n_class = get_nr(model)
+    cdef int rv
+    dec_values = np.empty((T_indptr.shape[0]-1, n_class), dtype=np.float64)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    with nogil:
+        rv = csr_copy_predict_proba(
+             T_data.shape,
+             &T_data[0],
+             T_indices.shape,
+             &T_indices[0],
+             T_indptr.shape,
+             &T_indptr[0],
+            model,
+             &dec_values[0, 0],
+            &blas_functions,
+        )
+    if rv < 0:
+        raise MemoryError("We've run out of memory")
+    # free model and param
+    free_model_SV(model)
+    free_model(model)
+    free_param(param)
+    return dec_values.base
+
+
+def libsvm_sparse_decision_function(
+    const float64_t[::1] T_data,
+    const int32_t[::1] T_indices,
+    const int32_t[::1] T_indptr,
+    const float64_t[::1] SV_data,
+    const int32_t[::1] SV_indices,
+    const int32_t[::1] SV_indptr,
+    const float64_t[::1] sv_coef,
+    const float64_t[::1]
+    intercept, int svm_type, int kernel_type, int
+    degree, double gamma, double coef0, double
+    eps, double C,
+    const float64_t[:] class_weight,
+    double nu, double p, int shrinking, int probability,
+    const int32_t[::1] nSV,
+    const float64_t[::1] probA,
+    const float64_t[::1] probB,
+):
+    """
+    Predict margin (libsvm name for this is predict_values)
+
+    We have to reconstruct model and parameters to make sure we stay
+    in sync with the python object.
+    """
+    cdef float64_t[:, ::1] dec_values
+    cdef svm_parameter *param
+    cdef intp_t n_class
+
+    cdef svm_csr_model *model
+    cdef int32_t[::1] \
+        class_weight_label = np.arange(class_weight.shape[0], dtype=np.int32)
+    param = set_parameter(
+        svm_type,
+        kernel_type,
+        degree,
+        gamma,
+        coef0,
+        nu,
+        100.0,  # cache size has no effect on predict
+        C,
+        eps,
+        p,
+        shrinking,
+        probability,
+         class_weight.shape[0],
+         &class_weight_label[0] if class_weight_label.size > 0 else NULL,
+         &class_weight[0] if class_weight.size > 0 else NULL,
+        -1,
+        -1,
+    )
+
+    model = csr_set_model(
+        param,
+         nSV.shape[0],
+         &SV_data[0] if SV_data.size > 0 else NULL,
+         SV_indices.shape,
+         &SV_indices[0] if SV_indices.size > 0 else NULL,
+         SV_indptr.shape,
+         &SV_indptr[0] if SV_indptr.size > 0 else NULL,
+         &sv_coef[0] if sv_coef.size > 0 else NULL,
+         &intercept[0],
+         &nSV[0],
+         &probA[0] if probA.size > 0 else NULL,
+         &probB[0] if probB.size > 0 else NULL,
+    )
+
+    if svm_type > 1:
+        n_class = 1
+    else:
+        n_class = get_nr(model)
+        n_class = n_class * (n_class - 1) // 2
+
+    dec_values = np.empty((T_indptr.shape[0] - 1, n_class), dtype=np.float64)
+    cdef BlasFunctions blas_functions
+    blas_functions.dot = _dot[double]
+    if csr_copy_predict_values(
+             T_data.shape,
+             &T_data[0],
+             T_indices.shape,
+             &T_indices[0],
+             T_indptr.shape,
+             &T_indptr[0],
+            model,
+             &dec_values[0, 0],
+            n_class,
+            &blas_functions,
+    ) < 0:
+        raise MemoryError("We've run out of memory")
+    # free model and param
+    free_model_SV(model)
+    free_model(model)
+    free_param(param)
+
+    return dec_values.base
+
+
+def set_verbosity_wrap(int verbosity):
+    """
+    Control verbosity of libsvm library
+    """
+    set_verbosity(verbosity)
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diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_newrand.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_newrand.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..af543ed73286a06bfb0053807bc8b8c39bfc53c0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/_newrand.pyx
@@ -0,0 +1,13 @@
+"""Wrapper for newrand.h"""
+
+cdef extern from "newrand.h":
+    void set_seed(unsigned int)
+    unsigned int bounded_rand_int(unsigned int)
+
+
+def set_seed_wrap(unsigned int custom_seed):
+    set_seed(custom_seed)
+
+
+def bounded_rand_int_wrap(unsigned int range_):
+    return bounded_rand_int(range_)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/meson.build b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..6232d747d1feb220eb4656396314d7caddac9c52
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/meson.build
@@ -0,0 +1,48 @@
+newrand_include = include_directories('src/newrand')
+libsvm_include = include_directories('src/libsvm')
+liblinear_include = include_directories('src/liblinear')
+
+_newrand = py.extension_module(
+  '_newrand',
+  cython_gen_cpp.process('_newrand.pyx'),
+  include_directories: [newrand_include],
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+libsvm_skl = static_library(
+  'libsvm-skl',
+  ['src/libsvm/libsvm_template.cpp'],
+)
+
+py.extension_module(
+  '_libsvm',
+  [cython_gen.process('_libsvm.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, libsvm_include],
+  link_with: libsvm_skl,
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+py.extension_module(
+  '_libsvm_sparse',
+  [cython_gen.process('_libsvm_sparse.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, libsvm_include],
+  link_with: libsvm_skl,
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+liblinear_skl = static_library(
+  'liblinear-skl',
+  ['src/liblinear/linear.cpp', 'src/liblinear/tron.cpp'],
+)
+
+py.extension_module(
+  '_liblinear',
+  [cython_gen.process('_liblinear.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, liblinear_include],
+  link_with: [liblinear_skl],
+  subdir: 'sklearn/svm',
+  install: true
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/COPYRIGHT b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/COPYRIGHT
new file mode 100644
index 0000000000000000000000000000000000000000..94371bb4cfd3a117775792c38e8354e62c46dc8f
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/COPYRIGHT
@@ -0,0 +1,31 @@
+
+Copyright (c) 2007-2014 The LIBLINEAR Project.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3. Neither name of copyright holders nor the names of its contributors
+may be used to endorse or promote products derived from this software
+without specific prior written permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/_cython_blas_helpers.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/_cython_blas_helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..bdec1a2f99eb9c0cd57f4e588e9b277ab5f93a6a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/_cython_blas_helpers.h
@@ -0,0 +1,16 @@
+#ifndef _CYTHON_BLAS_HELPERS_H
+#define _CYTHON_BLAS_HELPERS_H
+
+typedef double (*dot_func)(int, const double*, int, const double*, int);
+typedef void (*axpy_func)(int, double, const double*, int, double*, int);
+typedef void (*scal_func)(int, double, const double*, int);
+typedef double (*nrm2_func)(int, const double*, int);
+
+typedef struct BlasFunctions{
+    dot_func dot;
+    axpy_func axpy;
+    scal_func scal;
+    nrm2_func nrm2;
+} BlasFunctions;
+
+#endif
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/liblinear_helper.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/liblinear_helper.c
new file mode 100644
index 0000000000000000000000000000000000000000..b66f08413e11b6af16d72a35d1e8e85a5addfd43
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/liblinear_helper.c
@@ -0,0 +1,236 @@
+#include 
+#define PY_SSIZE_T_CLEAN
+#include 
+#include "linear.h"
+
+
+/*
+ * Convert matrix to sparse representation suitable for liblinear. x is
+ * expected to be an array of length n_samples*n_features.
+ *
+ * Whether the matrix is densely or sparsely populated, the fastest way to
+ * convert it to liblinear's sparse format is to calculate the amount of memory
+ * needed and allocate a single big block.
+ *
+ * Special care must be taken with indices, since liblinear indices start at 1
+ * and not at 0.
+ *
+ * If bias is > 0, we append an item at the end.
+ */
+static struct feature_node **dense_to_sparse(char *x, int double_precision,
+        int n_samples, int n_features, int n_nonzero, double bias)
+{
+    float *x32 = (float *)x;
+    double *x64 = (double *)x;
+    struct feature_node **sparse;
+    int i, j;                           /* number of nonzero elements in row i */
+    struct feature_node *T;             /* pointer to the top of the stack */
+    int have_bias = (bias > 0);
+
+    sparse = malloc (n_samples * sizeof(struct feature_node *));
+    if (sparse == NULL)
+        return NULL;
+
+    n_nonzero += (have_bias+1) * n_samples;
+    T = malloc (n_nonzero * sizeof(struct feature_node));
+    if (T == NULL) {
+        free(sparse);
+        return NULL;
+    }
+
+    for (i=0; ivalue = *x64;
+                    T->index = j;
+                    ++ T;
+                }
+                ++ x64; /* go to next element */
+            } else {
+                if (*x32 != 0) {
+                    T->value = *x32;
+                    T->index = j;
+                    ++ T;
+                }
+                ++ x32; /* go to next element */
+            }
+        }
+
+        /* set bias element */
+        if (have_bias) {
+                T->value = bias;
+                T->index = j;
+                ++ T;
+            }
+
+        /* set sentinel */
+        T->index = -1;
+        ++ T;
+    }
+
+    return sparse;
+}
+
+
+/*
+ * Convert scipy.sparse.csr to liblinear's sparse data structure
+ */
+static struct feature_node **csr_to_sparse(char *x, int double_precision,
+        int *indices, int *indptr, int n_samples, int n_features, int n_nonzero,
+        double bias)
+{
+    float *x32 = (float *)x;
+    double *x64 = (double *)x;
+    struct feature_node **sparse;
+    int i, j=0, k=0, n;
+    struct feature_node *T;
+    int have_bias = (bias > 0);
+
+    sparse = malloc (n_samples * sizeof(struct feature_node *));
+    if (sparse == NULL)
+        return NULL;
+
+    n_nonzero += (have_bias+1) * n_samples;
+    T = malloc (n_nonzero * sizeof(struct feature_node));
+    if (T == NULL) {
+        free(sparse);
+        return NULL;
+    }
+
+    for (i=0; ivalue = double_precision ? x64[k] : x32[k];
+            T->index = indices[k] + 1; /* liblinear uses 1-based indexing */
+            ++T;
+            ++k;
+        }
+
+        if (have_bias) {
+            T->value = bias;
+            T->index = n_features + 1;
+            ++T;
+            ++j;
+        }
+
+        /* set sentinel */
+        T->index = -1;
+        ++T;
+    }
+
+    return sparse;
+}
+
+struct problem * set_problem(char *X, int double_precision_X, int n_samples,
+        int n_features, int n_nonzero, double bias, char* sample_weight,
+        char *Y)
+{
+    struct problem *problem;
+    /* not performant but simple */
+    problem = malloc(sizeof(struct problem));
+    if (problem == NULL) return NULL;
+    problem->l = n_samples;
+    problem->n = n_features + (bias > 0);
+    problem->y = (double *) Y;
+    problem->W = (double *) sample_weight;
+    problem->x = dense_to_sparse(X, double_precision_X, n_samples, n_features,
+                        n_nonzero, bias);
+    problem->bias = bias;
+
+    if (problem->x == NULL) {
+        free(problem);
+        return NULL;
+    }
+
+    return problem;
+}
+
+struct problem * csr_set_problem (char *X, int double_precision_X,
+        char *indices, char *indptr, int n_samples, int n_features,
+        int n_nonzero, double bias, char *sample_weight, char *Y)
+{
+    struct problem *problem;
+    problem = malloc (sizeof (struct problem));
+    if (problem == NULL) return NULL;
+    problem->l = n_samples;
+    problem->n = n_features + (bias > 0);
+    problem->y = (double *) Y;
+    problem->W = (double *) sample_weight;
+    problem->x = csr_to_sparse(X, double_precision_X, (int *) indices,
+                        (int *) indptr, n_samples, n_features, n_nonzero, bias);
+    problem->bias = bias;
+
+    if (problem->x == NULL) {
+        free(problem);
+        return NULL;
+    }
+
+    return problem;
+}
+
+
+/* Create a parameter struct with and return it */
+struct parameter *set_parameter(int solver_type, double eps, double C,
+                                Py_ssize_t nr_weight, char *weight_label,
+                                char *weight, int max_iter, unsigned seed,
+                                double epsilon)
+{
+    struct parameter *param = malloc(sizeof(struct parameter));
+    if (param == NULL)
+        return NULL;
+
+    set_seed(seed);
+    param->solver_type = solver_type;
+    param->eps = eps;
+    param->C = C;
+    param->p = epsilon;  // epsilon for epsilon-SVR
+    param->nr_weight = (int) nr_weight;
+    param->weight_label = (int *) weight_label;
+    param->weight = (double *) weight;
+    param->max_iter = max_iter;
+    return param;
+}
+
+void copy_w(void *data, struct model *model, int len)
+{
+    memcpy(data, model->w, len * sizeof(double));
+}
+
+double get_bias(struct model *model)
+{
+    return model->bias;
+}
+
+void free_problem(struct problem *problem)
+{
+    free(problem->x[0]);
+    free(problem->x);
+    free(problem);
+}
+
+void free_parameter(struct parameter *param)
+{
+    free(param);
+}
+
+/* rely on built-in facility to control verbose output */
+static void print_null(const char *s) {}
+
+static void print_string_stdout(const char *s)
+{
+    fputs(s ,stdout);
+    fflush(stdout);
+}
+
+/* provide convenience wrapper */
+void set_verbosity(int verbosity_flag){
+    if (verbosity_flag)
+        set_print_string_function(&print_string_stdout);
+    else
+        set_print_string_function(&print_null);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..63648adbe2947de03449580f060a795fd4eb3cb6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.cpp
@@ -0,0 +1,3075 @@
+/*
+   Modified 2011:
+
+   - Make labels sorted in group_classes, Dan Yamins.
+
+   Modified 2012:
+
+   - Changes roles of +1 and -1 to match scikit API, Andreas Mueller
+        See issue 546: https://github.com/scikit-learn/scikit-learn/pull/546
+   - Also changed roles for pairwise class weights, Andreas Mueller
+        See issue 1491: https://github.com/scikit-learn/scikit-learn/pull/1491
+
+   Modified 2014:
+
+   - Remove the hard-coded value of max_iter (1000), that allows max_iter
+     to be passed as a parameter from the classes LogisticRegression and
+     LinearSVC, Manoj Kumar
+   - Added function get_n_iter that exposes the number of iterations.
+        See issue 3499: https://github.com/scikit-learn/scikit-learn/issues/3499
+        See pull 3501: https://github.com/scikit-learn/scikit-learn/pull/3501
+
+   Modified 2015:
+   - Patched liblinear for sample_weights - Manoj Kumar
+     See https://github.com/scikit-learn/scikit-learn/pull/5274
+
+   Modified 2020:
+   - Improved random number generator by using a mersenne twister + tweaked
+     lemire postprocessor. This fixed a convergence issue on windows targets.
+     Sylvain Marie, Schneider Electric
+     See 
+
+ */
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include "linear.h"
+#include "tron.h"
+#include 
+#include 
+#include "../newrand/newrand.h"
+
+typedef signed char schar;
+template  static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
+#ifndef min
+template  static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; }
+#endif
+template  static inline void clone(T*& dst, S* src, int n)
+{
+	dst = new T[n];
+	memcpy((void *)dst,(void *)src,sizeof(T)*n);
+}
+#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
+#define INF HUGE_VAL
+
+static void print_string_stdout(const char *s)
+{
+	fputs(s,stdout);
+	fflush(stdout);
+}
+
+static void (*liblinear_print_string) (const char *) = &print_string_stdout;
+
+#if 1
+static void info(const char *fmt,...)
+{
+	char buf[BUFSIZ];
+	va_list ap;
+	va_start(ap,fmt);
+	vsprintf(buf,fmt,ap);
+	va_end(ap);
+	(*liblinear_print_string)(buf);
+}
+#else
+static void info(const char *fmt,...) {}
+#endif
+
+class l2r_lr_fun: public function
+{
+public:
+	l2r_lr_fun(const problem *prob, double *C);
+	~l2r_lr_fun();
+
+	double fun(double *w);
+	void grad(double *w, double *g);
+	void Hv(double *s, double *Hs);
+
+	int get_nr_variable(void);
+
+private:
+	void Xv(double *v, double *Xv);
+	void XTv(double *v, double *XTv);
+
+	double *C;
+	double *z;
+	double *D;
+	const problem *prob;
+};
+
+l2r_lr_fun::l2r_lr_fun(const problem *prob, double *C)
+{
+	int l=prob->l;
+
+	this->prob = prob;
+
+	z = new double[l];
+	D = new double[l];
+	this->C = C;
+}
+
+l2r_lr_fun::~l2r_lr_fun()
+{
+	delete[] z;
+	delete[] D;
+}
+
+
+double l2r_lr_fun::fun(double *w)
+{
+	int i;
+	double f=0;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+
+	Xv(w, z);
+
+	for(i=0;i= 0)
+			f += C[i]*log(1 + exp(-yz));
+		else
+			f += C[i]*(-yz+log(1 + exp(yz)));
+	}
+
+	return(f);
+}
+
+void l2r_lr_fun::grad(double *w, double *g)
+{
+	int i;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+
+	for(i=0;in;
+}
+
+void l2r_lr_fun::Hv(double *s, double *Hs)
+{
+	int i;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+	double *wa = new double[l];
+
+	Xv(s, wa);
+	for(i=0;il;
+	feature_node **x=prob->x;
+
+	for(i=0;iindex!=-1)
+		{
+			Xv[i]+=v[s->index-1]*s->value;
+			s++;
+		}
+	}
+}
+
+void l2r_lr_fun::XTv(double *v, double *XTv)
+{
+	int i;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+	feature_node **x=prob->x;
+
+	for(i=0;iindex!=-1)
+		{
+			XTv[s->index-1]+=v[i]*s->value;
+			s++;
+		}
+	}
+}
+
+class l2r_l2_svc_fun: public function
+{
+public:
+	l2r_l2_svc_fun(const problem *prob, double *C);
+	~l2r_l2_svc_fun();
+
+	double fun(double *w);
+	void grad(double *w, double *g);
+	void Hv(double *s, double *Hs);
+
+	int get_nr_variable(void);
+
+protected:
+	void Xv(double *v, double *Xv);
+	void subXv(double *v, double *Xv);
+	void subXTv(double *v, double *XTv);
+
+	double *C;
+	double *z;
+	double *D;
+	int *I;
+	int sizeI;
+	const problem *prob;
+};
+
+l2r_l2_svc_fun::l2r_l2_svc_fun(const problem *prob, double *C)
+{
+	int l=prob->l;
+
+	this->prob = prob;
+
+	z = new double[l];
+	D = new double[l];
+	I = new int[l];
+	this->C = C;
+}
+
+l2r_l2_svc_fun::~l2r_l2_svc_fun()
+{
+	delete[] z;
+	delete[] D;
+	delete[] I;
+}
+
+double l2r_l2_svc_fun::fun(double *w)
+{
+	int i;
+	double f=0;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+
+	Xv(w, z);
+
+	for(i=0;i 0)
+			f += C[i]*d*d;
+	}
+
+	return(f);
+}
+
+void l2r_l2_svc_fun::grad(double *w, double *g)
+{
+	int i;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+
+	sizeI = 0;
+	for (i=0;in;
+}
+
+void l2r_l2_svc_fun::Hv(double *s, double *Hs)
+{
+	int i;
+	int w_size=get_nr_variable();
+	double *wa = new double[sizeI];
+
+	subXv(s, wa);
+	for(i=0;il;
+	feature_node **x=prob->x;
+
+	for(i=0;iindex!=-1)
+		{
+			Xv[i]+=v[s->index-1]*s->value;
+			s++;
+		}
+	}
+}
+
+void l2r_l2_svc_fun::subXv(double *v, double *Xv)
+{
+	int i;
+	feature_node **x=prob->x;
+
+	for(i=0;iindex!=-1)
+		{
+			Xv[i]+=v[s->index-1]*s->value;
+			s++;
+		}
+	}
+}
+
+void l2r_l2_svc_fun::subXTv(double *v, double *XTv)
+{
+	int i;
+	int w_size=get_nr_variable();
+	feature_node **x=prob->x;
+
+	for(i=0;iindex!=-1)
+		{
+			XTv[s->index-1]+=v[i]*s->value;
+			s++;
+		}
+	}
+}
+
+class l2r_l2_svr_fun: public l2r_l2_svc_fun
+{
+public:
+	l2r_l2_svr_fun(const problem *prob, double *C, double p);
+
+	double fun(double *w);
+	void grad(double *w, double *g);
+
+private:
+	double p;
+};
+
+l2r_l2_svr_fun::l2r_l2_svr_fun(const problem *prob, double *C, double p):
+	l2r_l2_svc_fun(prob, C)
+{
+	this->p = p;
+}
+
+double l2r_l2_svr_fun::fun(double *w)
+{
+	int i;
+	double f=0;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+	double d;
+
+	Xv(w, z);
+
+	for(i=0;i p)
+			f += C[i]*(d-p)*(d-p);
+	}
+
+	return(f);
+}
+
+void l2r_l2_svr_fun::grad(double *w, double *g)
+{
+	int i;
+	double *y=prob->y;
+	int l=prob->l;
+	int w_size=get_nr_variable();
+	double d;
+
+	sizeI = 0;
+	for(i=0;i p)
+		{
+			z[sizeI] = C[i]*(d-p);
+			I[sizeI] = i;
+			sizeI++;
+		}
+
+	}
+	subXTv(z, g);
+
+	for(i=0;iw_size = prob->n;
+	this->l = prob->l;
+	this->nr_class = nr_class;
+	this->eps = eps;
+	this->max_iter = max_iter;
+	this->prob = prob;
+	this->B = new double[nr_class];
+	this->G = new double[nr_class];
+	this->C = new double[prob->l];
+	for(int i = 0; i < prob->l; i++)
+		this->C[i] = prob->W[i] * weighted_C[(int)prob->y[i]];
+}
+
+Solver_MCSVM_CS::~Solver_MCSVM_CS()
+{
+	delete[] B;
+	delete[] G;
+	delete[] C;
+}
+
+int compare_double(const void *a, const void *b)
+{
+	if(*(double *)a > *(double *)b)
+		return -1;
+	if(*(double *)a < *(double *)b)
+		return 1;
+	return 0;
+}
+
+void Solver_MCSVM_CS::solve_sub_problem(double A_i, int yi, double C_yi, int active_i, double *alpha_new)
+{
+	int r;
+	double *D;
+
+	clone(D, B, active_i);
+	if(yi < active_i)
+		D[yi] += A_i*C_yi;
+	qsort(D, active_i, sizeof(double), compare_double);
+
+	double beta = D[0] - A_i*C_yi;
+	for(r=1;ry[i] == m
+	// alpha[i*nr_class+m] <= 0 if prob->y[i] != m
+	// If initial alpha isn't zero, uncomment the for loop below to initialize w
+	for(i=0;ix[i];
+		QD[i] = 0;
+		while(xi->index != -1)
+		{
+			double val = xi->value;
+			QD[i] += val*val;
+
+			// Uncomment the for loop if initial alpha isn't zero
+			// for(m=0; mindex-1)*nr_class+m] += alpha[i*nr_class+m]*val;
+			xi++;
+		}
+		active_size_i[i] = nr_class;
+		y_index[i] = (int)prob->y[i];
+		index[i] = i;
+	}
+
+	while(iter < max_iter)
+	{
+		double stopping = -INF;
+		for(i=0;i 0)
+			{
+				for(m=0;mx[i];
+				while(xi->index!= -1)
+				{
+					double *w_i = &w[(xi->index-1)*nr_class];
+					for(m=0;mvalue);
+					xi++;
+				}
+
+				double minG = INF;
+				double maxG = -INF;
+				for(m=0;m maxG)
+						maxG = G[m];
+				}
+				if(y_index[i] < active_size_i[i])
+					if(alpha_i[(int) prob->y[i]] < C[GETI(i)] && G[y_index[i]] < minG)
+						minG = G[y_index[i]];
+
+				for(m=0;mm)
+						{
+							if(!be_shrunk(i, active_size_i[i], y_index[i],
+											alpha_i[alpha_index_i[active_size_i[i]]], minG))
+							{
+								swap(alpha_index_i[m], alpha_index_i[active_size_i[i]]);
+								swap(G[m], G[active_size_i[i]]);
+								if(y_index[i] == active_size_i[i])
+									y_index[i] = m;
+								else if(y_index[i] == m)
+									y_index[i] = active_size_i[i];
+								break;
+							}
+							active_size_i[i]--;
+						}
+					}
+				}
+
+				if(active_size_i[i] <= 1)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+
+				if(maxG-minG <= 1e-12)
+					continue;
+				else
+					stopping = max(maxG - minG, stopping);
+
+				for(m=0;m= 1e-12)
+					{
+						d_ind[nz_d] = alpha_index_i[m];
+						d_val[nz_d] = d;
+						nz_d++;
+					}
+				}
+
+				xi = prob->x[i];
+				while(xi->index != -1)
+				{
+					double *w_i = &w[(xi->index-1)*nr_class];
+					for(m=0;mvalue;
+					xi++;
+				}
+			}
+		}
+
+		iter++;
+		if(iter % 10 == 0)
+		{
+			info(".");
+		}
+
+		if(stopping < eps_shrink)
+		{
+			if(stopping < eps && start_from_all == true)
+				break;
+			else
+			{
+				active_size = l;
+				for(i=0;i= max_iter)
+		info("\nWARNING: reaching max number of iterations\n");
+
+	// calculate objective value
+	double v = 0;
+	int nSV = 0;
+	for(i=0;i 0)
+			nSV++;
+	}
+	for(i=0;iy[i]];
+	info("Objective value = %lf\n",v);
+	info("nSV = %d\n",nSV);
+
+	delete [] alpha;
+	delete [] alpha_new;
+	delete [] index;
+	delete [] QD;
+	delete [] d_ind;
+	delete [] d_val;
+	delete [] alpha_index;
+	delete [] y_index;
+	delete [] active_size_i;
+	return iter;
+}
+
+// A coordinate descent algorithm for
+// L1-loss and L2-loss SVM dual problems
+//
+//  min_\alpha  0.5(\alpha^T (Q + D)\alpha) - e^T \alpha,
+//    s.t.      0 <= \alpha_i <= upper_bound_i,
+//
+//  where Qij = yi yj xi^T xj and
+//  D is a diagonal matrix
+//
+// In L1-SVM case:
+// 		upper_bound_i = Cp if y_i = 1
+// 		upper_bound_i = Cn if y_i = -1
+// 		D_ii = 0
+// In L2-SVM case:
+// 		upper_bound_i = INF
+// 		D_ii = 1/(2*Cp)	if y_i = 1
+// 		D_ii = 1/(2*Cn)	if y_i = -1
+//
+// Given:
+// x, y, Cp, Cn
+// eps is the stopping tolerance
+//
+// solution will be put in w
+//
+// See Algorithm 3 of Hsieh et al., ICML 2008
+
+#undef GETI
+#define GETI(i) (i)
+// To support weights for instances, use GETI(i) (i)
+
+static int solve_l2r_l1l2_svc(
+	const problem *prob, double *w, double eps,
+	double Cp, double Cn, int solver_type, int max_iter)
+{
+	int l = prob->l;
+	int w_size = prob->n;
+	int i, s, iter = 0;
+	double C, d, G;
+	double *QD = new double[l];
+	int *index = new int[l];
+	double *alpha = new double[l];
+	schar *y = new schar[l];
+	int active_size = l;
+
+	// PG: projected gradient, for shrinking and stopping
+	double PG;
+	double PGmax_old = INF;
+	double PGmin_old = -INF;
+	double PGmax_new, PGmin_new;
+
+	// default solver_type: L2R_L2LOSS_SVC_DUAL
+	double *diag = new double[l];
+	double *upper_bound = new double[l];
+	double *C_ = new double[l];
+	for(i=0; iy[i]>0)
+			C_[i] = prob->W[i] * Cp;
+		else
+			C_[i] = prob->W[i] * Cn;
+		diag[i] = 0.5/C_[i];
+		upper_bound[i] = INF;
+	}
+	if(solver_type == L2R_L1LOSS_SVC_DUAL)
+	{
+		for(i=0; iy[i] > 0)
+		{
+			y[i] = +1;
+		}
+		else
+		{
+			y[i] = -1;
+		}
+	}
+
+	// Initial alpha can be set here. Note that
+	// 0 <= alpha[i] <= upper_bound[GETI(i)]
+	for(i=0; ix[i];
+		while (xi->index != -1)
+		{
+			double val = xi->value;
+			QD[i] += val*val;
+			w[xi->index-1] += y[i]*alpha[i]*val;
+			xi++;
+		}
+		index[i] = i;
+	}
+
+	while (iter < max_iter)
+	{
+		PGmax_new = -INF;
+		PGmin_new = INF;
+
+		for (i=0; ix[i];
+			while(xi->index!= -1)
+			{
+				G += w[xi->index-1]*(xi->value);
+				xi++;
+			}
+			G = G*yi-1;
+
+			C = upper_bound[GETI(i)];
+			G += alpha[i]*diag[GETI(i)];
+
+			PG = 0;
+			if (alpha[i] == 0)
+			{
+				if (G > PGmax_old)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+				else if (G < 0)
+					PG = G;
+			}
+			else if (alpha[i] == C)
+			{
+				if (G < PGmin_old)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+				else if (G > 0)
+					PG = G;
+			}
+			else
+				PG = G;
+
+			PGmax_new = max(PGmax_new, PG);
+			PGmin_new = min(PGmin_new, PG);
+
+			if(fabs(PG) > 1.0e-12)
+			{
+				double alpha_old = alpha[i];
+				alpha[i] = min(max(alpha[i] - G/QD[i], 0.0), C);
+				d = (alpha[i] - alpha_old)*yi;
+				xi = prob->x[i];
+				while (xi->index != -1)
+				{
+					w[xi->index-1] += d*xi->value;
+					xi++;
+				}
+			}
+		}
+
+		iter++;
+		if(iter % 10 == 0)
+			info(".");
+
+		if(PGmax_new - PGmin_new <= eps)
+		{
+			if(active_size == l)
+				break;
+			else
+			{
+				active_size = l;
+				info("*");
+				PGmax_old = INF;
+				PGmin_old = -INF;
+				continue;
+			}
+		}
+		PGmax_old = PGmax_new;
+		PGmin_old = PGmin_new;
+		if (PGmax_old <= 0)
+			PGmax_old = INF;
+		if (PGmin_old >= 0)
+			PGmin_old = -INF;
+	}
+
+	info("\noptimization finished, #iter = %d\n",iter);
+	if (iter >= max_iter)
+		info("\nWARNING: reaching max number of iterations\nUsing -s 2 may be faster (also see FAQ)\n\n");
+
+	// calculate objective value
+
+	double v = 0;
+	int nSV = 0;
+	for(i=0; i 0)
+			++nSV;
+	}
+	info("Objective value = %lf\n",v/2);
+	info("nSV = %d\n",nSV);
+
+	delete [] QD;
+	delete [] alpha;
+	delete [] y;
+	delete [] index;
+	delete [] diag;
+	delete [] upper_bound;
+	delete [] C_;
+	return iter;
+}
+
+
+// A coordinate descent algorithm for
+// L1-loss and L2-loss epsilon-SVR dual problem
+//
+//  min_\beta  0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i,
+//    s.t.      -upper_bound_i <= \beta_i <= upper_bound_i,
+//
+//  where Qij = xi^T xj and
+//  D is a diagonal matrix
+//
+// In L1-SVM case:
+// 		upper_bound_i = C
+// 		lambda_i = 0
+// In L2-SVM case:
+// 		upper_bound_i = INF
+// 		lambda_i = 1/(2*C)
+//
+// Given:
+// x, y, p, C
+// eps is the stopping tolerance
+//
+// solution will be put in w
+//
+// See Algorithm 4 of Ho and Lin, 2012
+
+#undef GETI
+#define GETI(i) (i)
+// To support weights for instances, use GETI(i) (i)
+
+static int solve_l2r_l1l2_svr(
+	const problem *prob, double *w, const parameter *param,
+	int solver_type, int max_iter)
+{
+	int l = prob->l;
+	double C = param->C;
+	double p = param->p;
+	int w_size = prob->n;
+	double eps = param->eps;
+	int i, s, iter = 0;
+	int active_size = l;
+	int *index = new int[l];
+
+	double d, G, H;
+	double Gmax_old = INF;
+	double Gmax_new, Gnorm1_new;
+	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
+	double *beta = new double[l];
+	double *QD = new double[l];
+	double *y = prob->y;
+
+	// L2R_L2LOSS_SVR_DUAL
+	double *lambda = new double[l];
+	double *upper_bound = new double[l];
+	double *C_ = new double[l];
+	for (i=0; iW[i] * C;
+		lambda[i] = 0.5/C_[i];
+		upper_bound[i] = INF;
+	}
+	if(solver_type == L2R_L1LOSS_SVR_DUAL)
+	{
+		for (i=0; ix[i];
+		while(xi->index != -1)
+		{
+			double val = xi->value;
+			QD[i] += val*val;
+			w[xi->index-1] += beta[i]*val;
+			xi++;
+		}
+
+		index[i] = i;
+	}
+
+
+	while(iter < max_iter)
+	{
+		Gmax_new = 0;
+		Gnorm1_new = 0;
+
+		for(i=0; ix[i];
+			while(xi->index != -1)
+			{
+				int ind = xi->index-1;
+				double val = xi->value;
+				G += val*w[ind];
+				xi++;
+			}
+
+			double Gp = G+p;
+			double Gn = G-p;
+			double violation = 0;
+			if(beta[i] == 0)
+			{
+				if(Gp < 0)
+					violation = -Gp;
+				else if(Gn > 0)
+					violation = Gn;
+				else if(Gp>Gmax_old && Gn<-Gmax_old)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+			}
+			else if(beta[i] >= upper_bound[GETI(i)])
+			{
+				if(Gp > 0)
+					violation = Gp;
+				else if(Gp < -Gmax_old)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+			}
+			else if(beta[i] <= -upper_bound[GETI(i)])
+			{
+				if(Gn < 0)
+					violation = -Gn;
+				else if(Gn > Gmax_old)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+			}
+			else if(beta[i] > 0)
+				violation = fabs(Gp);
+			else
+				violation = fabs(Gn);
+
+			Gmax_new = max(Gmax_new, violation);
+			Gnorm1_new += violation;
+
+			// obtain Newton direction d
+			if(Gp < H*beta[i])
+				d = -Gp/H;
+			else if(Gn > H*beta[i])
+				d = -Gn/H;
+			else
+				d = -beta[i];
+
+			if(fabs(d) < 1.0e-12)
+				continue;
+
+			double beta_old = beta[i];
+			beta[i] = min(max(beta[i]+d, -upper_bound[GETI(i)]), upper_bound[GETI(i)]);
+			d = beta[i]-beta_old;
+
+			if(d != 0)
+			{
+				xi = prob->x[i];
+				while(xi->index != -1)
+				{
+					w[xi->index-1] += d*xi->value;
+					xi++;
+				}
+			}
+		}
+
+		if(iter == 0)
+			Gnorm1_init = Gnorm1_new;
+		iter++;
+		if(iter % 10 == 0)
+			info(".");
+
+		if(Gnorm1_new <= eps*Gnorm1_init)
+		{
+			if(active_size == l)
+				break;
+			else
+			{
+				active_size = l;
+				info("*");
+				Gmax_old = INF;
+				continue;
+			}
+		}
+
+		Gmax_old = Gmax_new;
+	}
+
+	info("\noptimization finished, #iter = %d\n", iter);
+	if(iter >= max_iter)
+		info("\nWARNING: reaching max number of iterations\nUsing -s 11 may be faster\n\n");
+
+	// calculate objective value
+	double v = 0;
+	int nSV = 0;
+	for(i=0; il;
+	int w_size = prob->n;
+	int i, s, iter = 0;
+	double *xTx = new double[l];
+	int *index = new int[l];
+	double *alpha = new double[2*l]; // store alpha and C - alpha
+	schar *y = new schar[l];
+	int max_inner_iter = 100; // for inner Newton
+	double innereps = 1e-2;
+	double innereps_min = min(1e-8, eps);
+	double *upper_bound = new double [l];
+
+	for(i=0; iy[i] > 0)
+		{
+			upper_bound[i] = prob->W[i] * Cp;
+			y[i] = +1;
+		}
+		else
+		{
+			upper_bound[i] = prob->W[i] * Cn;
+			y[i] = -1;
+		}
+	}
+
+	// Initial alpha can be set here. Note that
+	// 0 < alpha[i] < upper_bound[GETI(i)]
+	// alpha[2*i] + alpha[2*i+1] = upper_bound[GETI(i)]
+	for(i=0; ix[i];
+		while (xi->index != -1)
+		{
+			double val = xi->value;
+			xTx[i] += val*val;
+			w[xi->index-1] += y[i]*alpha[2*i]*val;
+			xi++;
+		}
+		index[i] = i;
+	}
+
+	while (iter < max_iter)
+	{
+		for (i=0; ix[i];
+			while (xi->index != -1)
+			{
+				ywTx += w[xi->index-1]*xi->value;
+				xi++;
+			}
+			ywTx *= y[i];
+			double a = xisq, b = ywTx;
+
+			// Decide to minimize g_1(z) or g_2(z)
+			int ind1 = 2*i, ind2 = 2*i+1, sign = 1;
+			if(0.5*a*(alpha[ind2]-alpha[ind1])+b < 0)
+			{
+				ind1 = 2*i+1;
+				ind2 = 2*i;
+				sign = -1;
+			}
+
+			//  g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*b(z-alpha_old)
+			double alpha_old = alpha[ind1];
+			double z = alpha_old;
+			if(C - z < 0.5 * C)
+				z = 0.1*z;
+			double gp = a*(z-alpha_old)+sign*b+log(z/(C-z));
+			Gmax = max(Gmax, fabs(gp));
+
+			// Newton method on the sub-problem
+			const double eta = 0.1; // xi in the paper
+			int inner_iter = 0;
+			while (inner_iter <= max_inner_iter)
+			{
+				if(fabs(gp) < innereps)
+					break;
+				double gpp = a + C/(C-z)/z;
+				double tmpz = z - gp/gpp;
+				if(tmpz <= 0)
+					z *= eta;
+				else // tmpz in (0, C)
+					z = tmpz;
+				gp = a*(z-alpha_old)+sign*b+log(z/(C-z));
+				newton_iter++;
+				inner_iter++;
+			}
+
+			if(inner_iter > 0) // update w
+			{
+				alpha[ind1] = z;
+				alpha[ind2] = C-z;
+				xi = prob->x[i];
+				while (xi->index != -1)
+				{
+					w[xi->index-1] += sign*(z-alpha_old)*yi*xi->value;
+					xi++;
+				}
+			}
+		}
+
+		iter++;
+		if(iter % 10 == 0)
+			info(".");
+
+		if(Gmax < eps)
+			break;
+
+		if(newton_iter <= l/10)
+			innereps = max(innereps_min, 0.1*innereps);
+
+	}
+
+	info("\noptimization finished, #iter = %d\n",iter);
+	if (iter >= max_iter)
+		info("\nWARNING: reaching max number of iterations\nUsing -s 0 may be faster (also see FAQ)\n\n");
+
+	// calculate objective value
+
+	double v = 0;
+	for(i=0; il;
+	int w_size = prob_col->n;
+	int j, s, iter = 0;
+	int active_size = w_size;
+	int max_num_linesearch = 20;
+
+	double sigma = 0.01;
+	double d, G_loss, G, H;
+	double Gmax_old = INF;
+	double Gmax_new, Gnorm1_new;
+	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
+	double d_old, d_diff;
+	double loss_old, loss_new;
+	double appxcond, cond;
+
+	int *index = new int[w_size];
+	schar *y = new schar[l];
+	double *b = new double[l]; // b = 1-ywTx
+	double *xj_sq = new double[w_size];
+	feature_node *x;
+
+	double *C = new double[l];
+
+	// Initial w can be set here.
+	for(j=0; jy[j] > 0)
+		{
+			y[j] = 1;
+			C[j] = prob_col->W[j] * Cp;
+		}
+		else
+		{
+			y[j] = -1;
+			C[j] = prob_col->W[j] * Cn;
+		}
+	}
+	for(j=0; jx[j];
+		while(x->index != -1)
+		{
+			int ind = x->index-1;
+			x->value *= y[ind]; // x->value stores yi*xij
+			double val = x->value;
+			b[ind] -= w[j]*val;
+			xj_sq[j] += C[GETI(ind)]*val*val;
+			x++;
+		}
+	}
+
+	while(iter < max_iter)
+	{
+		Gmax_new = 0;
+		Gnorm1_new = 0;
+
+		for(j=0; jx[j];
+			while(x->index != -1)
+			{
+				int ind = x->index-1;
+				if(b[ind] > 0)
+				{
+					double val = x->value;
+					double tmp = C[GETI(ind)]*val;
+					G_loss -= tmp*b[ind];
+					H += tmp*val;
+				}
+				x++;
+			}
+			G_loss *= 2;
+
+			G = G_loss;
+			H *= 2;
+			H = max(H, 1e-12);
+
+			double Gp = G+1;
+			double Gn = G-1;
+			double violation = 0;
+			if(w[j] == 0)
+			{
+				if(Gp < 0)
+					violation = -Gp;
+				else if(Gn > 0)
+					violation = Gn;
+				else if(Gp>Gmax_old/l && Gn<-Gmax_old/l)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+			}
+			else if(w[j] > 0)
+				violation = fabs(Gp);
+			else
+				violation = fabs(Gn);
+
+			Gmax_new = max(Gmax_new, violation);
+			Gnorm1_new += violation;
+
+			// obtain Newton direction d
+			if(Gp < H*w[j])
+				d = -Gp/H;
+			else if(Gn > H*w[j])
+				d = -Gn/H;
+			else
+				d = -w[j];
+
+			if(fabs(d) < 1.0e-12)
+				continue;
+
+			double delta = fabs(w[j]+d)-fabs(w[j]) + G*d;
+			d_old = 0;
+			int num_linesearch;
+			for(num_linesearch=0; num_linesearch < max_num_linesearch; num_linesearch++)
+			{
+				d_diff = d_old - d;
+				cond = fabs(w[j]+d)-fabs(w[j]) - sigma*delta;
+
+				appxcond = xj_sq[j]*d*d + G_loss*d + cond;
+				if(appxcond <= 0)
+				{
+					x = prob_col->x[j];
+					while(x->index != -1)
+					{
+						b[x->index-1] += d_diff*x->value;
+						x++;
+					}
+					break;
+				}
+
+				if(num_linesearch == 0)
+				{
+					loss_old = 0;
+					loss_new = 0;
+					x = prob_col->x[j];
+					while(x->index != -1)
+					{
+						int ind = x->index-1;
+						if(b[ind] > 0)
+							loss_old += C[GETI(ind)]*b[ind]*b[ind];
+						double b_new = b[ind] + d_diff*x->value;
+						b[ind] = b_new;
+						if(b_new > 0)
+							loss_new += C[GETI(ind)]*b_new*b_new;
+						x++;
+					}
+				}
+				else
+				{
+					loss_new = 0;
+					x = prob_col->x[j];
+					while(x->index != -1)
+					{
+						int ind = x->index-1;
+						double b_new = b[ind] + d_diff*x->value;
+						b[ind] = b_new;
+						if(b_new > 0)
+							loss_new += C[GETI(ind)]*b_new*b_new;
+						x++;
+					}
+				}
+
+				cond = cond + loss_new - loss_old;
+				if(cond <= 0)
+					break;
+				else
+				{
+					d_old = d;
+					d *= 0.5;
+					delta *= 0.5;
+				}
+			}
+
+			w[j] += d;
+
+			// recompute b[] if line search takes too many steps
+			if(num_linesearch >= max_num_linesearch)
+			{
+				info("#");
+				for(int i=0; ix[i];
+					while(x->index != -1)
+					{
+						b[x->index-1] -= w[i]*x->value;
+						x++;
+					}
+				}
+			}
+		}
+
+		if(iter == 0)
+			Gnorm1_init = Gnorm1_new;
+		iter++;
+		if(iter % 10 == 0)
+			info(".");
+
+		if(Gnorm1_new <= eps*Gnorm1_init)
+		{
+			if(active_size == w_size)
+				break;
+			else
+			{
+				active_size = w_size;
+				info("*");
+				Gmax_old = INF;
+				continue;
+			}
+		}
+
+		Gmax_old = Gmax_new;
+	}
+
+	info("\noptimization finished, #iter = %d\n", iter);
+	if(iter >= max_iter)
+		info("\nWARNING: reaching max number of iterations\n");
+
+	// calculate objective value
+
+	double v = 0;
+	int nnz = 0;
+	for(j=0; jx[j];
+		while(x->index != -1)
+		{
+			x->value *= prob_col->y[x->index-1]; // restore x->value
+			x++;
+		}
+		if(w[j] != 0)
+		{
+			v += fabs(w[j]);
+			nnz++;
+		}
+	}
+	for(j=0; j 0)
+			v += C[GETI(j)]*b[j]*b[j];
+
+	info("Objective value = %lf\n", v);
+	info("#nonzeros/#features = %d/%d\n", nnz, w_size);
+
+	delete [] index;
+	delete [] y;
+	delete [] b;
+	delete [] xj_sq;
+	delete [] C;
+	return iter;
+}
+
+// A coordinate descent algorithm for
+// L1-regularized logistic regression problems
+//
+//  min_w \sum |wj| + C \sum log(1+exp(-yi w^T xi)),
+//
+// Given:
+// x, y, Cp, Cn
+// eps is the stopping tolerance
+//
+// solution will be put in w
+//
+// See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008)
+
+#undef GETI
+#define GETI(i) (i)
+// To support weights for instances, use GETI(i) (i)
+
+static int solve_l1r_lr(
+	const problem *prob_col, double *w, double eps,
+	double Cp, double Cn, int max_newton_iter)
+{
+	int l = prob_col->l;
+	int w_size = prob_col->n;
+	int j, s, newton_iter=0, iter=0;
+	int max_iter = 1000;
+	int max_num_linesearch = 20;
+	int active_size;
+	int QP_active_size;
+	int QP_no_change = 0;
+
+	double nu = 1e-12;
+	double inner_eps = 1;
+	double sigma = 0.01;
+	double w_norm, w_norm_new;
+	double z, G, H;
+	double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration
+	double Gmax_old = INF;
+	double Gmax_new, Gnorm1_new;
+	double QP_Gmax_old = INF;
+	double QP_Gmax_new, QP_Gnorm1_new;
+	double delta, negsum_xTd, cond;
+
+	int *index = new int[w_size];
+	schar *y = new schar[l];
+	double *Hdiag = new double[w_size];
+	double *Grad = new double[w_size];
+	double *wpd = new double[w_size];
+	double *xjneg_sum = new double[w_size];
+	double *xTd = new double[l];
+	double *exp_wTx = new double[l];
+	double *exp_wTx_new = new double[l];
+	double *tau = new double[l];
+	double *D = new double[l];
+	feature_node *x;
+
+	double *C = new double[l];
+
+	// Initial w can be set here.
+	for(j=0; jy[j] > 0)
+		{
+			y[j] = 1;
+			C[j] = prob_col->W[j] * Cp;
+		}
+		else
+		{
+			y[j] = -1;
+			C[j] = prob_col->W[j] * Cn;
+		}
+
+		exp_wTx[j] = 0;
+	}
+
+	w_norm = 0;
+	for(j=0; jx[j];
+		while(x->index != -1)
+		{
+			int ind = x->index-1;
+			double val = x->value;
+			exp_wTx[ind] += w[j]*val;
+			if(y[ind] == -1)
+				xjneg_sum[j] += C[GETI(ind)]*val;
+			x++;
+		}
+	}
+	for(j=0; jx[j];
+			while(x->index != -1)
+			{
+				int ind = x->index-1;
+				Hdiag[j] += x->value*x->value*D[ind];
+				tmp += x->value*tau[ind];
+				x++;
+			}
+			Grad[j] = -tmp + xjneg_sum[j];
+
+			double Gp = Grad[j]+1;
+			double Gn = Grad[j]-1;
+			double violation = 0;
+			if(w[j] == 0)
+			{
+				if(Gp < 0)
+					violation = -Gp;
+				else if(Gn > 0)
+					violation = Gn;
+				//outer-level shrinking
+				else if(Gp>Gmax_old/l && Gn<-Gmax_old/l)
+				{
+					active_size--;
+					swap(index[s], index[active_size]);
+					s--;
+					continue;
+				}
+			}
+			else if(w[j] > 0)
+				violation = fabs(Gp);
+			else
+				violation = fabs(Gn);
+
+			Gmax_new = max(Gmax_new, violation);
+			Gnorm1_new += violation;
+		}
+
+		if(newton_iter == 0)
+			Gnorm1_init = Gnorm1_new;
+
+		// Break outer-loop if the accumulated violation is small.
+		// Also break if no update in QP inner-loop ten times in a row.
+		if(Gnorm1_new <= eps*Gnorm1_init || QP_no_change >= 10)
+			break;
+
+		QP_no_change++;
+
+		iter = 0;
+		QP_Gmax_old = INF;
+		QP_active_size = active_size;
+
+		for(int i=0; ix[j];
+				G = Grad[j] + (wpd[j]-w[j])*nu;
+				while(x->index != -1)
+				{
+					int ind = x->index-1;
+					G += x->value*D[ind]*xTd[ind];
+					x++;
+				}
+
+				double Gp = G+1;
+				double Gn = G-1;
+				double violation = 0;
+				if(wpd[j] == 0)
+				{
+					if(Gp < 0)
+						violation = -Gp;
+					else if(Gn > 0)
+						violation = Gn;
+					//inner-level shrinking
+					else if(Gp>QP_Gmax_old/l && Gn<-QP_Gmax_old/l)
+					{
+						QP_active_size--;
+						swap(index[s], index[QP_active_size]);
+						s--;
+						continue;
+					}
+				}
+				else if(wpd[j] > 0)
+					violation = fabs(Gp);
+				else
+					violation = fabs(Gn);
+
+				// obtain solution of one-variable problem
+				if(Gp < H*wpd[j])
+					z = -Gp/H;
+				else if(Gn > H*wpd[j])
+					z = -Gn/H;
+				else
+					z = -wpd[j];
+
+				if(fabs(z) < 1.0e-12)
+					continue;
+				z = min(max(z,-10.0),10.0);
+
+				QP_no_change = 0;
+				QP_Gmax_new = max(QP_Gmax_new, violation);
+				QP_Gnorm1_new += violation;
+
+				wpd[j] += z;
+
+				x = prob_col->x[j];
+				while(x->index != -1)
+				{
+					int ind = x->index-1;
+					xTd[ind] += x->value*z;
+					x++;
+				}
+			}
+
+			iter++;
+
+			if(QP_Gnorm1_new <= inner_eps*Gnorm1_init)
+			{
+				//inner stopping
+				if(QP_active_size == active_size)
+					break;
+				//active set reactivation
+				else
+				{
+					QP_active_size = active_size;
+					QP_Gmax_old = INF;
+					continue;
+				}
+			}
+
+			QP_Gmax_old = QP_Gmax_new;
+		}
+
+		if(iter >= max_iter)
+			info("WARNING: reaching max number of inner iterations\n");
+
+		delta = 0;
+		w_norm_new = 0;
+		for(j=0; j= max_num_linesearch)
+		{
+			for(int i=0; ix[i];
+				while(x->index != -1)
+				{
+					exp_wTx[x->index-1] += w[i]*x->value;
+					x++;
+				}
+			}
+
+			for(int i=0; i= max_newton_iter)
+		info("WARNING: reaching max number of iterations\n");
+
+	// calculate objective value
+
+	double v = 0;
+	int nnz = 0;
+	for(j=0; jl;
+	int n = prob->n;
+	size_t nnz = 0;
+	size_t *col_ptr = new size_t [n+1];
+	feature_node *x_space;
+	prob_col->l = l;
+	prob_col->n = n;
+	prob_col->y = new double[l];
+	prob_col->x = new feature_node*[n];
+	prob_col->W = new double[l];
+
+	for(i=0; iy[i] = prob->y[i];
+		prob_col->W[i] = prob->W[i];
+	}
+
+	for(i=0; ix[i];
+		while(x->index != -1)
+		{
+			nnz++;
+			col_ptr[x->index]++;
+			x++;
+		}
+	}
+	for(i=1; ix[i] = &x_space[col_ptr[i]];
+
+	for(i=0; ix[i];
+		while(x->index != -1)
+		{
+			int ind = x->index-1;
+			x_space[col_ptr[ind]].index = i+1; // starts from 1
+			x_space[col_ptr[ind]].value = x->value;
+			col_ptr[ind]++;
+			x++;
+		}
+	}
+	for(i=0; il;
+	int max_nr_class = 16;
+	int nr_class = 0;
+	int *label = Malloc(int,max_nr_class);
+	int *count = Malloc(int,max_nr_class);
+	int *data_label = Malloc(int,l);
+	int i;
+
+	for(i=0;iy[i];
+		int j;
+		for(j=0;j=0 && label[i] > this_label)
+                {
+                        label[i+1] = label[i];
+                        count[i+1] = count[i];
+                        i--;
+                }
+                label[i+1] = this_label;
+                count[i+1] = this_count;
+        }
+
+        for (i=0; i y[i];
+                while(this_label != label[j])
+                {
+                        j++;
+                }
+                data_label[i] = j;
+
+        }
+
+        /* END MOD */
+
+#if 0
+	//
+	// Labels are ordered by their first occurrence in the training set.
+	// However, for two-class sets with -1/+1 labels and -1 appears first,
+	// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
+	//
+	if (nr_class == 2 && label[0] == -1 && label[1] == 1)
+	{
+		swap(label[0],label[1]);
+		swap(count[0],count[1]);
+		for(i=0;ieps;
+	int max_iter=param->max_iter;
+	int pos = 0;
+	int neg = 0;
+	int n_iter = -1;
+	for(int i=0;il;i++)
+		if(prob->y[i] > 0)
+			pos++;
+	neg = prob->l - pos;
+
+	double primal_solver_tol = eps*max(min(pos,neg), 1)/prob->l;
+
+	function *fun_obj=NULL;
+	switch(param->solver_type)
+	{
+		case L2R_LR:
+		{
+			double *C = new double[prob->l];
+			for(int i = 0; i < prob->l; i++)
+			{
+				if(prob->y[i] > 0)
+					C[i] = prob->W[i] * Cp;
+				else
+					C[i] = prob->W[i] * Cn;
+			}
+
+			fun_obj=new l2r_lr_fun(prob, C);
+			TRON tron_obj(fun_obj, primal_solver_tol, max_iter, blas_functions);
+			tron_obj.set_print_string(liblinear_print_string);
+			n_iter=tron_obj.tron(w);
+			delete fun_obj;
+			delete[] C;
+			break;
+		}
+		case L2R_L2LOSS_SVC:
+		{
+			double *C = new double[prob->l];
+			for(int i = 0; i < prob->l; i++)
+			{
+				if(prob->y[i] > 0)
+					C[i] = prob->W[i] * Cp;
+				else
+					C[i] = prob->W[i] * Cn;
+			}
+			fun_obj=new l2r_l2_svc_fun(prob, C);
+			TRON tron_obj(fun_obj, primal_solver_tol, max_iter, blas_functions);
+			tron_obj.set_print_string(liblinear_print_string);
+			n_iter=tron_obj.tron(w);
+			delete fun_obj;
+			delete[] C;
+			break;
+		}
+		case L2R_L2LOSS_SVC_DUAL:
+			n_iter=solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L2LOSS_SVC_DUAL, max_iter);
+			break;
+		case L2R_L1LOSS_SVC_DUAL:
+			n_iter=solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, L2R_L1LOSS_SVC_DUAL, max_iter);
+			break;
+		case L1R_L2LOSS_SVC:
+		{
+			problem prob_col;
+			feature_node *x_space = NULL;
+			transpose(prob, &x_space ,&prob_col);
+			n_iter=solve_l1r_l2_svc(&prob_col, w, primal_solver_tol, Cp, Cn, max_iter);
+			delete [] prob_col.y;
+			delete [] prob_col.x;
+			delete [] prob_col.W;
+			delete [] x_space;
+			break;
+		}
+		case L1R_LR:
+		{
+			problem prob_col;
+			feature_node *x_space = NULL;
+			transpose(prob, &x_space ,&prob_col);
+			n_iter=solve_l1r_lr(&prob_col, w, primal_solver_tol, Cp, Cn, max_iter);
+			delete [] prob_col.y;
+			delete [] prob_col.x;
+			delete [] prob_col.W;
+			delete [] x_space;
+			break;
+		}
+		case L2R_LR_DUAL:
+			n_iter=solve_l2r_lr_dual(prob, w, eps, Cp, Cn, max_iter);
+			break;
+		case L2R_L2LOSS_SVR:
+		{
+			double *C = new double[prob->l];
+			for(int i = 0; i < prob->l; i++)
+				C[i] = prob->W[i] * param->C;
+
+			fun_obj=new l2r_l2_svr_fun(prob, C, param->p);
+			TRON tron_obj(fun_obj, param->eps, max_iter, blas_functions);
+			tron_obj.set_print_string(liblinear_print_string);
+			n_iter=tron_obj.tron(w);
+			delete fun_obj;
+			delete[] C;
+			break;
+
+		}
+		case L2R_L1LOSS_SVR_DUAL:
+			n_iter=solve_l2r_l1l2_svr(prob, w, param, L2R_L1LOSS_SVR_DUAL, max_iter);
+			break;
+		case L2R_L2LOSS_SVR_DUAL:
+			n_iter=solve_l2r_l1l2_svr(prob, w, param, L2R_L2LOSS_SVR_DUAL, max_iter);
+			break;
+		default:
+			fprintf(stderr, "ERROR: unknown solver_type\n");
+			break;
+	}
+	return n_iter;
+}
+
+//
+// Remove zero weighed data as libsvm and some liblinear solvers require C > 0.
+//
+static void remove_zero_weight(problem *newprob, const problem *prob)
+{
+	int i;
+	int l = 0;
+	for(i=0;il;i++)
+		if(prob->W[i] > 0) l++;
+	*newprob = *prob;
+	newprob->l = l;
+	newprob->x = Malloc(feature_node*,l);
+	newprob->y = Malloc(double,l);
+	newprob->W = Malloc(double,l);
+
+	int j = 0;
+	for(i=0;il;i++)
+		if(prob->W[i] > 0)
+		{
+			newprob->x[j] = prob->x[i];
+			newprob->y[j] = prob->y[i];
+			newprob->W[j] = prob->W[i];
+			j++;
+		}
+}
+
+//
+// Interface functions
+//
+model* train(const problem *prob, const parameter *param, BlasFunctions *blas_functions)
+{
+	problem newprob;
+	remove_zero_weight(&newprob, prob);
+	prob = &newprob;
+	int i,j;
+	int l = prob->l;
+	int n = prob->n;
+	int w_size = prob->n;
+	model *model_ = Malloc(model,1);
+
+	if(prob->bias>=0)
+		model_->nr_feature=n-1;
+	else
+		model_->nr_feature=n;
+	model_->param = *param;
+	model_->bias = prob->bias;
+
+	if(check_regression_model(model_))
+	{
+		model_->w = Malloc(double, w_size);
+		model_->n_iter = Malloc(int, 1);
+		model_->nr_class = 2;
+		model_->label = NULL;
+		model_->n_iter[0] =train_one(prob, param, &model_->w[0], 0, 0, blas_functions);
+	}
+	else
+	{
+		int nr_class;
+		int *label = NULL;
+		int *start = NULL;
+		int *count = NULL;
+		int *perm = Malloc(int,l);
+
+		// group training data of the same class
+		group_classes(prob,&nr_class,&label,&start,&count,perm);
+
+		model_->nr_class=nr_class;
+		model_->label = Malloc(int,nr_class);
+		for(i=0;ilabel[i] = label[i];
+
+		// calculate weighted C
+		double *weighted_C = Malloc(double, nr_class);
+		for(i=0;iC;
+		for(i=0;inr_weight;i++)
+		{
+			for(j=0;jweight_label[i] == label[j])
+					break;
+			if(j == nr_class)
+				fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
+			else
+				weighted_C[j] *= param->weight[i];
+		}
+
+		// constructing the subproblem
+		feature_node **x = Malloc(feature_node *,l);
+		for(i=0;ix[perm[i]];
+
+		int k;
+		problem sub_prob;
+		sub_prob.l = l;
+		sub_prob.n = n;
+		sub_prob.x = Malloc(feature_node *,sub_prob.l);
+		sub_prob.y = Malloc(double,sub_prob.l);
+		sub_prob.W = Malloc(double,sub_prob.l);
+		for(k=0; kW[perm[k]];
+		}
+
+		// multi-class svm by Crammer and Singer
+		if(param->solver_type == MCSVM_CS)
+		{
+			model_->w=Malloc(double, n*nr_class);
+			model_->n_iter=Malloc(int, 1);
+			for(i=0;ieps);
+			model_->n_iter[0]=Solver.Solve(model_->w);
+		}
+		else
+		{
+			if(nr_class == 2)
+			{
+				model_->w=Malloc(double, w_size);
+				model_->n_iter=Malloc(int, 1);
+				int e0 = start[0]+count[0];
+				k=0;
+				for(; kn_iter[0]=train_one(&sub_prob, param, &model_->w[0], weighted_C[1], weighted_C[0], blas_functions);
+			}
+			else
+			{
+				model_->w=Malloc(double, w_size*nr_class);
+				double *w=Malloc(double, w_size);
+				model_->n_iter=Malloc(int, nr_class);
+				for(i=0;in_iter[i]=train_one(&sub_prob, param, w, weighted_C[i], param->C, blas_functions);
+
+					for(int j=0;jw[j*nr_class+i] = w[j];
+				}
+				free(w);
+			}
+
+		}
+
+		free(x);
+		free(label);
+		free(start);
+		free(count);
+		free(perm);
+		free(sub_prob.x);
+		free(sub_prob.y);
+		free(sub_prob.W);
+		free(weighted_C);
+		free(newprob.x);
+		free(newprob.y);
+		free(newprob.W);
+	}
+	return model_;
+}
+
+#if 0
+void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target)
+{
+	int i;
+	int *fold_start;
+	int l = prob->l;
+	int *perm = Malloc(int,l);
+	if (nr_fold > l)
+	{
+		nr_fold = l;
+		fprintf(stderr,"WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n");
+	}
+	fold_start = Malloc(int,nr_fold+1);
+	for(i=0;ibias;
+		subprob.n = prob->n;
+		subprob.l = l-(end-begin);
+		subprob.x = Malloc(struct feature_node*,subprob.l);
+		subprob.y = Malloc(double,subprob.l);
+
+		k=0;
+		for(j=0;jx[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			++k;
+		}
+		for(j=end;jx[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			++k;
+		}
+		struct model *submodel = train(&subprob,param);
+		for(j=begin;jx[perm[j]]);
+		free_and_destroy_model(&submodel);
+		free(subprob.x);
+		free(subprob.y);
+	}
+	free(fold_start);
+	free(perm);
+}
+
+double predict_values(const struct model *model_, const struct feature_node *x, double *dec_values)
+{
+	int idx;
+	int n;
+	if(model_->bias>=0)
+		n=model_->nr_feature+1;
+	else
+		n=model_->nr_feature;
+	double *w=model_->w;
+	int nr_class=model_->nr_class;
+	int i;
+	int nr_w;
+	if(nr_class==2 && model_->param.solver_type != MCSVM_CS)
+		nr_w = 1;
+	else
+		nr_w = nr_class;
+
+	const feature_node *lx=x;
+	for(i=0;iindex)!=-1; lx++)
+	{
+		// the dimension of testing data may exceed that of training
+		if(idx<=n)
+			for(i=0;ivalue;
+	}
+
+	if(nr_class==2)
+	{
+		if(check_regression_model(model_))
+			return dec_values[0];
+		else
+			return (dec_values[0]>0)?model_->label[0]:model_->label[1];
+	}
+	else
+	{
+		int dec_max_idx = 0;
+		for(i=1;i dec_values[dec_max_idx])
+				dec_max_idx = i;
+		}
+		return model_->label[dec_max_idx];
+	}
+}
+
+double predict(const model *model_, const feature_node *x)
+{
+	double *dec_values = Malloc(double, model_->nr_class);
+	double label=predict_values(model_, x, dec_values);
+	free(dec_values);
+	return label;
+}
+
+double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates)
+{
+	if(check_probability_model(model_))
+	{
+		int i;
+		int nr_class=model_->nr_class;
+		int nr_w;
+		if(nr_class==2)
+			nr_w = 1;
+		else
+			nr_w = nr_class;
+
+		double label=predict_values(model_, x, prob_estimates);
+		for(i=0;inr_feature;
+	int n;
+	const parameter& param = model_->param;
+
+	if(model_->bias>=0)
+		n=nr_feature+1;
+	else
+		n=nr_feature;
+	int w_size = n;
+	FILE *fp = fopen(model_file_name,"w");
+	if(fp==NULL) return -1;
+
+	char *old_locale = strdup(setlocale(LC_ALL, NULL));
+	setlocale(LC_ALL, "C");
+
+	int nr_w;
+	if(model_->nr_class==2 && model_->param.solver_type != MCSVM_CS)
+		nr_w=1;
+	else
+		nr_w=model_->nr_class;
+
+	fprintf(fp, "solver_type %s\n", solver_type_table[param.solver_type]);
+	fprintf(fp, "nr_class %d\n", model_->nr_class);
+
+	if(model_->label)
+	{
+		fprintf(fp, "label");
+		for(i=0; inr_class; i++)
+			fprintf(fp, " %d", model_->label[i]);
+		fprintf(fp, "\n");
+	}
+
+	fprintf(fp, "nr_feature %d\n", nr_feature);
+
+	fprintf(fp, "bias %.16g\n", model_->bias);
+
+	fprintf(fp, "w\n");
+	for(i=0; iw[i*nr_w+j]);
+		fprintf(fp, "\n");
+	}
+
+	setlocale(LC_ALL, old_locale);
+	free(old_locale);
+
+	if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
+	else return 0;
+}
+
+struct model *load_model(const char *model_file_name)
+{
+	FILE *fp = fopen(model_file_name,"r");
+	if(fp==NULL) return NULL;
+
+	int i;
+	int nr_feature;
+	int n;
+	int nr_class;
+	double bias;
+	model *model_ = Malloc(model,1);
+	parameter& param = model_->param;
+
+	model_->label = NULL;
+
+	char *old_locale = strdup(setlocale(LC_ALL, NULL));
+	setlocale(LC_ALL, "C");
+
+	char cmd[81];
+	while(1)
+	{
+		fscanf(fp,"%80s",cmd);
+		if(strcmp(cmd,"solver_type")==0)
+		{
+			fscanf(fp,"%80s",cmd);
+			int i;
+			for(i=0;solver_type_table[i];i++)
+			{
+				if(strcmp(solver_type_table[i],cmd)==0)
+				{
+					param.solver_type=i;
+					break;
+				}
+			}
+			if(solver_type_table[i] == NULL)
+			{
+				fprintf(stderr,"unknown solver type.\n");
+
+				setlocale(LC_ALL, old_locale);
+				free(model_->label);
+				free(model_);
+				free(old_locale);
+				return NULL;
+			}
+		}
+		else if(strcmp(cmd,"nr_class")==0)
+		{
+			fscanf(fp,"%d",&nr_class);
+			model_->nr_class=nr_class;
+		}
+		else if(strcmp(cmd,"nr_feature")==0)
+		{
+			fscanf(fp,"%d",&nr_feature);
+			model_->nr_feature=nr_feature;
+		}
+		else if(strcmp(cmd,"bias")==0)
+		{
+			fscanf(fp,"%lf",&bias);
+			model_->bias=bias;
+		}
+		else if(strcmp(cmd,"w")==0)
+		{
+			break;
+		}
+		else if(strcmp(cmd,"label")==0)
+		{
+			int nr_class = model_->nr_class;
+			model_->label = Malloc(int,nr_class);
+			for(int i=0;ilabel[i]);
+		}
+		else
+		{
+			fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
+			setlocale(LC_ALL, old_locale);
+			free(model_->label);
+			free(model_);
+			free(old_locale);
+			return NULL;
+		}
+	}
+
+	nr_feature=model_->nr_feature;
+	if(model_->bias>=0)
+		n=nr_feature+1;
+	else
+		n=nr_feature;
+	int w_size = n;
+	int nr_w;
+	if(nr_class==2 && param.solver_type != MCSVM_CS)
+		nr_w = 1;
+	else
+		nr_w = nr_class;
+
+	model_->w=Malloc(double, w_size*nr_w);
+	for(i=0; iw[i*nr_w+j]);
+		fscanf(fp, "\n");
+	}
+
+	setlocale(LC_ALL, old_locale);
+	free(old_locale);
+
+	if (ferror(fp) != 0 || fclose(fp) != 0) return NULL;
+
+	return model_;
+}
+#endif
+
+int get_nr_feature(const model *model_)
+{
+	return model_->nr_feature;
+}
+
+int get_nr_class(const model *model_)
+{
+	return model_->nr_class;
+}
+
+void get_labels(const model *model_, int* label)
+{
+	if (model_->label != NULL)
+		for(int i=0;inr_class;i++)
+			label[i] = model_->label[i];
+}
+
+void get_n_iter(const model *model_, int* n_iter)
+{
+    int labels;
+    labels = model_->nr_class;
+    if (labels == 2)
+        labels = 1;
+
+    if (model_->n_iter != NULL)
+        for(int i=0;in_iter[i];
+}
+
+#if 0
+// use inline here for better performance (around 20% faster than the non-inline one)
+static inline double get_w_value(const struct model *model_, int idx, int label_idx)
+{
+	int nr_class = model_->nr_class;
+	int solver_type = model_->param.solver_type;
+	const double *w = model_->w;
+
+	if(idx < 0 || idx > model_->nr_feature)
+		return 0;
+	if(check_regression_model(model_))
+		return w[idx];
+	else
+	{
+		if(label_idx < 0 || label_idx >= nr_class)
+			return 0;
+		if(nr_class == 2 && solver_type != MCSVM_CS)
+		{
+			if(label_idx == 0)
+				return w[idx];
+			else
+				return -w[idx];
+		}
+		else
+			return w[idx*nr_class+label_idx];
+	}
+}
+
+// feat_idx: starting from 1 to nr_feature
+// label_idx: starting from 0 to nr_class-1 for classification models;
+//            for regression models, label_idx is ignored.
+double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx)
+{
+	if(feat_idx > model_->nr_feature)
+		return 0;
+	return get_w_value(model_, feat_idx-1, label_idx);
+}
+
+double get_decfun_bias(const struct model *model_, int label_idx)
+{
+	int bias_idx = model_->nr_feature;
+	double bias = model_->bias;
+	if(bias <= 0)
+		return 0;
+	else
+		return bias*get_w_value(model_, bias_idx, label_idx);
+}
+#endif
+
+void free_model_content(struct model *model_ptr)
+{
+	if(model_ptr->w != NULL)
+		free(model_ptr->w);
+	if(model_ptr->label != NULL)
+		free(model_ptr->label);
+	if(model_ptr->n_iter != NULL)
+	    free(model_ptr->n_iter);
+}
+
+void free_and_destroy_model(struct model **model_ptr_ptr)
+{
+	struct model *model_ptr = *model_ptr_ptr;
+	if(model_ptr != NULL)
+	{
+		free_model_content(model_ptr);
+		free(model_ptr);
+	}
+}
+
+void destroy_param(parameter* param)
+{
+	if(param->weight_label != NULL)
+		free(param->weight_label);
+	if(param->weight != NULL)
+		free(param->weight);
+}
+
+const char *check_parameter(const problem *prob, const parameter *param)
+{
+	if(param->eps <= 0)
+		return "eps <= 0";
+
+	if(param->C <= 0)
+		return "C <= 0";
+
+	if(param->p < 0)
+		return "p < 0";
+
+	if(param->solver_type != L2R_LR
+		&& param->solver_type != L2R_L2LOSS_SVC_DUAL
+		&& param->solver_type != L2R_L2LOSS_SVC
+		&& param->solver_type != L2R_L1LOSS_SVC_DUAL
+		&& param->solver_type != MCSVM_CS
+		&& param->solver_type != L1R_L2LOSS_SVC
+		&& param->solver_type != L1R_LR
+		&& param->solver_type != L2R_LR_DUAL
+		&& param->solver_type != L2R_L2LOSS_SVR
+		&& param->solver_type != L2R_L2LOSS_SVR_DUAL
+		&& param->solver_type != L2R_L1LOSS_SVR_DUAL)
+		return "unknown solver type";
+
+	return NULL;
+}
+
+#if 0
+int check_probability_model(const struct model *model_)
+{
+	return (model_->param.solver_type==L2R_LR ||
+			model_->param.solver_type==L2R_LR_DUAL ||
+			model_->param.solver_type==L1R_LR);
+}
+#endif
+
+int check_regression_model(const struct model *model_)
+{
+	return (model_->param.solver_type==L2R_L2LOSS_SVR ||
+			model_->param.solver_type==L2R_L1LOSS_SVR_DUAL ||
+			model_->param.solver_type==L2R_L2LOSS_SVR_DUAL);
+}
+
+void set_print_string_function(void (*print_func)(const char*))
+{
+	if (print_func == NULL)
+		liblinear_print_string = &print_string_stdout;
+	else
+		liblinear_print_string = print_func;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.h
new file mode 100644
index 0000000000000000000000000000000000000000..1dfc1c0ed014943bc797cd89689237761f41568b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/linear.h
@@ -0,0 +1,86 @@
+#ifndef _LIBLINEAR_H
+#define _LIBLINEAR_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include "_cython_blas_helpers.h"
+
+struct feature_node
+{
+	int index;
+	double value;
+};
+
+struct problem
+{
+	int l, n;
+	double *y;
+	struct feature_node **x;
+	double bias;            /* < 0 if no bias term */
+	double *W;
+};
+
+enum { L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR = 11, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL }; /* solver_type */
+
+struct parameter
+{
+	int solver_type;
+
+	/* these are for training only */
+	double eps;	        /* stopping criteria */
+	double C;
+	int nr_weight;
+	int *weight_label;
+	double* weight;
+	int max_iter;
+	double p;
+};
+
+struct model
+{
+	struct parameter param;
+	int nr_class;		/* number of classes */
+	int nr_feature;
+	double *w;
+	int *label;		/* label of each class */
+	double bias;
+	int *n_iter;    /* no. of iterations of each class */
+};
+
+void set_seed(unsigned seed);
+
+struct model* train(const struct problem *prob, const struct parameter *param, BlasFunctions *blas_functions);
+void cross_validation(const struct problem *prob, const struct parameter *param, int nr_fold, double *target);
+
+double predict_values(const struct model *model_, const struct feature_node *x, double* dec_values);
+double predict(const struct model *model_, const struct feature_node *x);
+double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);
+
+int save_model(const char *model_file_name, const struct model *model_);
+struct model *load_model(const char *model_file_name);
+
+int get_nr_feature(const struct model *model_);
+int get_nr_class(const struct model *model_);
+void get_labels(const struct model *model_, int* label);
+void get_n_iter(const struct model *model_, int* n_iter);
+#if 0
+double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx);
+double get_decfun_bias(const struct model *model_, int label_idx);
+#endif
+
+void free_model_content(struct model *model_ptr);
+void free_and_destroy_model(struct model **model_ptr_ptr);
+void destroy_param(struct parameter *param);
+
+const char *check_parameter(const struct problem *prob, const struct parameter *param);
+int check_probability_model(const struct model *model);
+int check_regression_model(const struct model *model);
+void set_print_string_function(void (*print_func) (const char*));
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _LIBLINEAR_H */
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..168a62ca47a2f4850508f6a0130eee3b8bd09194
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.cpp
@@ -0,0 +1,223 @@
+#include 
+#include 
+#include 
+#include 
+#include "tron.h"
+
+#ifndef min
+template  static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; }
+#endif
+
+static void default_print(const char *buf)
+{
+	fputs(buf,stdout);
+	fflush(stdout);
+}
+
+void TRON::info(const char *fmt,...)
+{
+	char buf[BUFSIZ];
+	va_list ap;
+	va_start(ap,fmt);
+	vsprintf(buf,fmt,ap);
+	va_end(ap);
+	(*tron_print_string)(buf);
+}
+
+TRON::TRON(const function *fun_obj, double eps, int max_iter, BlasFunctions *blas)
+{
+	this->fun_obj=const_cast(fun_obj);
+	this->eps=eps;
+	this->max_iter=max_iter;
+	this->blas=blas;
+	tron_print_string = default_print;
+}
+
+TRON::~TRON()
+{
+}
+
+int TRON::tron(double *w)
+{
+	// Parameters for updating the iterates.
+	double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75;
+
+	// Parameters for updating the trust region size delta.
+	double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4;
+
+	int n = fun_obj->get_nr_variable();
+	int i, cg_iter;
+	double delta, snorm;
+	double alpha, f, fnew, prered, actred, gs;
+	int search = 1, iter = 1, inc = 1;
+	double *s = new double[n];
+	double *r = new double[n];
+	double *w_new = new double[n];
+	double *g = new double[n];
+
+	for (i=0; ifun(w);
+	fun_obj->grad(w, g);
+	delta = blas->nrm2(n, g, inc);
+	double gnorm1 = delta;
+	double gnorm = gnorm1;
+
+	if (gnorm <= eps*gnorm1)
+		search = 0;
+
+	iter = 1;
+
+	while (iter <= max_iter && search)
+	{
+		cg_iter = trcg(delta, g, s, r);
+
+		memcpy(w_new, w, sizeof(double)*n);
+		blas->axpy(n, 1.0, s, inc, w_new, inc);
+
+		gs = blas->dot(n, g, inc, s, inc);
+		prered = -0.5*(gs - blas->dot(n, s, inc, r, inc));
+		fnew = fun_obj->fun(w_new);
+
+		// Compute the actual reduction.
+		actred = f - fnew;
+
+		// On the first iteration, adjust the initial step bound.
+		snorm = blas->nrm2(n, s, inc);
+		if (iter == 1)
+			delta = min(delta, snorm);
+
+		// Compute prediction alpha*snorm of the step.
+		if (fnew - f - gs <= 0)
+			alpha = sigma3;
+		else
+			alpha = max(sigma1, -0.5*(gs/(fnew - f - gs)));
+
+		// Update the trust region bound according to the ratio of actual to predicted reduction.
+		if (actred < eta0*prered)
+			delta = min(max(alpha, sigma1)*snorm, sigma2*delta);
+		else if (actred < eta1*prered)
+			delta = max(sigma1*delta, min(alpha*snorm, sigma2*delta));
+		else if (actred < eta2*prered)
+			delta = max(sigma1*delta, min(alpha*snorm, sigma3*delta));
+		else
+			delta = max(delta, min(alpha*snorm, sigma3*delta));
+
+		info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d\n", iter, actred, prered, delta, f, gnorm, cg_iter);
+
+		if (actred > eta0*prered)
+		{
+			iter++;
+			memcpy(w, w_new, sizeof(double)*n);
+			f = fnew;
+			fun_obj->grad(w, g);
+
+			gnorm = blas->nrm2(n, g, inc);
+			if (gnorm <= eps*gnorm1)
+				break;
+		}
+		if (f < -1.0e+32)
+		{
+			info("WARNING: f < -1.0e+32\n");
+			break;
+		}
+		if (fabs(actred) <= 0 && prered <= 0)
+		{
+			info("WARNING: actred and prered <= 0\n");
+			break;
+		}
+		if (fabs(actred) <= 1.0e-12*fabs(f) &&
+		    fabs(prered) <= 1.0e-12*fabs(f))
+		{
+			info("WARNING: actred and prered too small\n");
+			break;
+		}
+	}
+
+	delete[] g;
+	delete[] r;
+	delete[] w_new;
+	delete[] s;
+	return --iter;
+}
+
+int TRON::trcg(double delta, double *g, double *s, double *r)
+{
+	int i, inc = 1;
+	int n = fun_obj->get_nr_variable();
+	double *d = new double[n];
+	double *Hd = new double[n];
+	double rTr, rnewTrnew, alpha, beta, cgtol;
+
+	for (i=0; inrm2(n, g, inc);
+
+	int cg_iter = 0;
+	rTr = blas->dot(n, r, inc, r, inc);
+	while (1)
+	{
+		if (blas->nrm2(n, r, inc) <= cgtol)
+			break;
+		cg_iter++;
+		fun_obj->Hv(d, Hd);
+
+		alpha = rTr / blas->dot(n, d, inc, Hd, inc);
+		blas->axpy(n, alpha, d, inc, s, inc);
+		if (blas->nrm2(n, s, inc) > delta)
+		{
+			info("cg reaches trust region boundary\n");
+			alpha = -alpha;
+			blas->axpy(n, alpha, d, inc, s, inc);
+
+			double std = blas->dot(n, s, inc, d, inc);
+			double sts = blas->dot(n, s, inc, s, inc);
+			double dtd = blas->dot(n, d, inc, d, inc);
+			double dsq = delta*delta;
+			double rad = sqrt(std*std + dtd*(dsq-sts));
+			if (std >= 0)
+				alpha = (dsq - sts)/(std + rad);
+			else
+				alpha = (rad - std)/dtd;
+			blas->axpy(n, alpha, d, inc, s, inc);
+			alpha = -alpha;
+			blas->axpy(n, alpha, Hd, inc, r, inc);
+			break;
+		}
+		alpha = -alpha;
+		blas->axpy(n, alpha, Hd, inc, r, inc);
+		rnewTrnew = blas->dot(n, r, inc, r, inc);
+		beta = rnewTrnew/rTr;
+		blas->scal(n, beta, d, inc);
+		blas->axpy(n, 1.0, r, inc, d, inc);
+		rTr = rnewTrnew;
+	}
+
+	delete[] d;
+	delete[] Hd;
+
+	return(cg_iter);
+}
+
+double TRON::norm_inf(int n, double *x)
+{
+	double dmax = fabs(x[0]);
+	for (int i=1; i= dmax)
+			dmax = fabs(x[i]);
+	return(dmax);
+}
+
+void TRON::set_print_string(void (*print_string) (const char *buf))
+{
+	tron_print_string = print_string;
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.h
new file mode 100644
index 0000000000000000000000000000000000000000..735304ed16b6fc28c5900d2be2f41f47a32ccc9a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/liblinear/tron.h
@@ -0,0 +1,37 @@
+#ifndef _TRON_H
+#define _TRON_H
+
+#include "_cython_blas_helpers.h"
+
+class function
+{
+public:
+	virtual double fun(double *w) = 0 ;
+	virtual void grad(double *w, double *g) = 0 ;
+	virtual void Hv(double *s, double *Hs) = 0 ;
+
+	virtual int get_nr_variable(void) = 0 ;
+	virtual ~function(void){}
+};
+
+class TRON
+{
+public:
+	TRON(const function *fun_obj, double eps = 0.1, int max_iter = 1000, BlasFunctions *blas = 0);
+	~TRON();
+
+	int tron(double *w);
+	void set_print_string(void (*i_print) (const char *buf));
+
+private:
+	int trcg(double delta, double *g, double *s, double *r);
+	double norm_inf(int n, double *x);
+
+	double eps;
+	int max_iter;
+	function *fun_obj;
+	BlasFunctions *blas;
+	void info(const char *fmt,...);
+	void (*tron_print_string)(const char *buf);
+};
+#endif
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/LIBSVM_CHANGES b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/LIBSVM_CHANGES
new file mode 100644
index 0000000000000000000000000000000000000000..663550b8ddd6fa905d3cec6e02be50faa43859c3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/LIBSVM_CHANGES
@@ -0,0 +1,11 @@
+Changes to Libsvm
+
+This is here mainly as checklist for incorporation of new versions of libsvm.
+
+  * Add copyright to files svm.cpp and svm.h
+  * Add random_seed support and call to srand in fit function
+  * Improved random number generator (fix on windows, enhancement on other
+    platforms). See 
+  * invoke scipy blas api for svm kernel function to improve performance with speedup rate of 1.5X to 2X for dense data only. See 
+  * Expose the number of iterations run in optimization. See 
+The changes made with respect to upstream are detailed in the heading of svm.cpp
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/_svm_cython_blas_helpers.h b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/_svm_cython_blas_helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..2548c7844d267ec631102ae1f44e48cab2b0a729
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/_svm_cython_blas_helpers.h
@@ -0,0 +1,9 @@
+#ifndef _SVM_CYTHON_BLAS_HELPERS_H
+#define _SVM_CYTHON_BLAS_HELPERS_H
+
+typedef double (*dot_func)(int, const double*, int, const double*, int);
+typedef struct BlasFunctions{
+    dot_func dot;
+} BlasFunctions;
+
+#endif
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_helper.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_helper.c
new file mode 100644
index 0000000000000000000000000000000000000000..b87b52a6fbdc244df315c6f03f80b3321c852fdc
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_helper.c
@@ -0,0 +1,425 @@
+#include 
+#define PY_SSIZE_T_CLEAN
+#include 
+#include "svm.h"
+#include "_svm_cython_blas_helpers.h"
+
+
+#ifndef MAX
+    #define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#endif
+
+
+/*
+ * Some helper methods for libsvm bindings.
+ *
+ * We need to access from python some parameters stored in svm_model
+ * but libsvm does not expose this structure, so we define it here
+ * along some utilities to convert from numpy arrays.
+ *
+ * Authors: The scikit-learn developers
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ */
+
+
+/*
+ * Convert matrix to sparse representation suitable for libsvm. x is
+ * expected to be an array of length nrow*ncol.
+ *
+ * Typically the matrix will be dense, so we speed up the routine for
+ * this case. We create a temporary array temp that collects non-zero
+ * elements and after we just memcpy that to the proper array.
+ *
+ * Special care must be taken with indinces, since libsvm indices start
+ * at 1 and not at 0.
+ *
+ * Strictly speaking, the C standard does not require that structs are
+ * contiguous, but in practice its a reasonable assumption.
+ *
+ */
+struct svm_node *dense_to_libsvm (double *x, Py_ssize_t *dims)
+{
+    struct svm_node *node;
+    Py_ssize_t len_row = dims[1];
+    double *tx = x;
+    int i;
+
+    node = malloc (dims[0] * sizeof(struct svm_node));
+
+    if (node == NULL) return NULL;
+    for (i=0; isvm_type = svm_type;
+    param->kernel_type = kernel_type;
+    param->degree = degree;
+    param->coef0 = coef0;
+    param->nu = nu;
+    param->cache_size = cache_size;
+    param->C = C;
+    param->eps = eps;
+    param->p = p;
+    param->shrinking = shrinking;
+    param->probability = probability;
+    param->nr_weight = nr_weight;
+    param->weight_label = (int *) weight_label;
+    param->weight = (double *) weight;
+    param->gamma = gamma;
+    param->max_iter = max_iter;
+    param->random_seed = random_seed;
+}
+
+/*
+ * Fill an svm_problem struct. problem->x will be malloc'd.
+ */
+void set_problem(struct svm_problem *problem, char *X, char *Y, char *sample_weight, Py_ssize_t *dims, int kernel_type)
+{
+    if (problem == NULL) return;
+    problem->l = (int) dims[0]; /* number of samples */
+    problem->y = (double *) Y;
+    problem->x = dense_to_libsvm((double *) X, dims); /* implicit call to malloc */
+    problem->W = (double *) sample_weight;
+}
+
+/*
+ * Create and return an instance of svm_model.
+ *
+ * The copy of model->sv_coef should be straightforward, but
+ * unfortunately to represent a matrix numpy and libsvm use different
+ * approaches, so it requires some iteration.
+ *
+ * Possible issue: on 64 bits, the number of columns that numpy can
+ * store is a long, but libsvm enforces this number (model->l) to be
+ * an int, so we might have numpy matrices that do not fit into libsvm's
+ * data structure.
+ *
+ */
+struct svm_model *set_model(struct svm_parameter *param, int nr_class,
+                            char *SV, Py_ssize_t *SV_dims,
+                            char *support, Py_ssize_t *support_dims,
+                            Py_ssize_t *sv_coef_strides,
+                            char *sv_coef, char *rho, char *nSV,
+                            char *probA, char *probB)
+{
+    struct svm_model *model;
+    double *dsv_coef = (double *) sv_coef;
+    int i, m;
+
+    m = nr_class * (nr_class-1)/2;
+
+    if ((model = malloc(sizeof(struct svm_model))) == NULL)
+        goto model_error;
+    if ((model->nSV = malloc(nr_class * sizeof(int))) == NULL)
+        goto nsv_error;
+    if ((model->label = malloc(nr_class * sizeof(int))) == NULL)
+        goto label_error;
+    if ((model->sv_coef = malloc((nr_class-1)*sizeof(double *))) == NULL)
+        goto sv_coef_error;
+    if ((model->rho = malloc( m * sizeof(double))) == NULL)
+        goto rho_error;
+
+    // This is only allocated in dynamic memory while training.
+    model->n_iter = NULL;
+
+    model->nr_class = nr_class;
+    model->param = *param;
+    model->l = (int) support_dims[0];
+
+    if (param->kernel_type == PRECOMPUTED) {
+        if ((model->SV = malloc ((model->l) * sizeof(struct svm_node))) == NULL)
+            goto SV_error;
+        for (i=0; il; ++i) {
+            model->SV[i].ind = ((int *) support)[i];
+            model->SV[i].values = NULL;
+        }
+    } else {
+        model->SV = dense_to_libsvm((double *) SV, SV_dims);
+    }
+    /*
+     * regression and one-class does not use nSV, label.
+     * TODO: does this provoke memory leaks (we just malloc'ed them)?
+     */
+    if (param->svm_type < 2) {
+        memcpy(model->nSV, nSV,     model->nr_class * sizeof(int));
+        for(i=0; i < model->nr_class; i++)
+            model->label[i] = i;
+    }
+
+    for (i=0; i < model->nr_class-1; i++) {
+        model->sv_coef[i] = dsv_coef + i*(model->l);
+    }
+
+    for (i=0; irho)[i] = -((double *) rho)[i];
+    }
+
+    /*
+     * just to avoid segfaults, these features are not wrapped but
+     * svm_destroy_model will try to free them.
+     */
+
+    if (param->probability) {
+        if ((model->probA = malloc(m * sizeof(double))) == NULL)
+            goto probA_error;
+        memcpy(model->probA, probA, m * sizeof(double));
+        if ((model->probB = malloc(m * sizeof(double))) == NULL)
+            goto probB_error;
+        memcpy(model->probB, probB, m * sizeof(double));
+    } else {
+        model->probA = NULL;
+        model->probB = NULL;
+    }
+
+    /* We'll free SV ourselves */
+    model->free_sv = 0;
+    return model;
+
+probB_error:
+    free(model->probA);
+probA_error:
+    free(model->SV);
+SV_error:
+    free(model->rho);
+rho_error:
+    free(model->sv_coef);
+sv_coef_error:
+    free(model->label);
+label_error:
+    free(model->nSV);
+nsv_error:
+    free(model);
+model_error:
+    return NULL;
+}
+
+
+
+/*
+ * Get the number of support vectors in a model.
+ */
+Py_ssize_t get_l(struct svm_model *model)
+{
+    return (Py_ssize_t) model->l;
+}
+
+/*
+ * Get the number of classes in a model, = 2 in regression/one class
+ * svm.
+ */
+Py_ssize_t get_nr(struct svm_model *model)
+{
+    return (Py_ssize_t) model->nr_class;
+}
+
+/*
+ * Get the number of iterations run in optimization
+ */
+void copy_n_iter(char *data, struct svm_model *model)
+{
+    const int n_models = MAX(1, model->nr_class * (model->nr_class-1) / 2);
+    memcpy(data, model->n_iter, n_models * sizeof(int));
+}
+
+/*
+ * Some helpers to convert from libsvm sparse data structures
+ * model->sv_coef is a double **, whereas data is just a double *,
+ * so we have to do some stupid copying.
+ */
+void copy_sv_coef(char *data, struct svm_model *model)
+{
+    int i, len = model->nr_class-1;
+    double *temp = (double *) data;
+    for(i=0; isv_coef[i], sizeof(double) * model->l);
+        temp += model->l;
+    }
+}
+
+void copy_intercept(char *data, struct svm_model *model, Py_ssize_t *dims)
+{
+    /* intercept = -rho */
+    Py_ssize_t i, n = dims[0];
+    double t, *ddata = (double *) data;
+    for (i=0; irho[i];
+        /* we do this to avoid ugly -0.0 */
+        *ddata = (t != 0) ? -t : 0;
+        ++ddata;
+    }
+}
+
+/*
+ * This is a bit more complex since SV are stored as sparse
+ * structures, so we have to do the conversion on the fly and also
+ * iterate fast over data.
+ */
+void copy_SV(char *data, struct svm_model *model, Py_ssize_t *dims)
+{
+    int i, n = model->l;
+    double *tdata = (double *) data;
+    int dim = model->SV[0].dim;
+    for (i=0; iSV[i].values, dim * sizeof(double));
+        tdata += dim;
+    }
+}
+
+void copy_support (char *data, struct svm_model *model)
+{
+    memcpy (data, model->sv_ind, (model->l) * sizeof(int));
+}
+
+/*
+ * copy svm_model.nSV, an array with the number of SV for each class
+ * will be NULL in the case of SVR, OneClass
+ */
+void copy_nSV(char *data, struct svm_model *model)
+{
+    if (model->label == NULL) return;
+    memcpy(data, model->nSV, model->nr_class * sizeof(int));
+}
+
+void copy_probA(char *data, struct svm_model *model, Py_ssize_t * dims)
+{
+    memcpy(data, model->probA, dims[0] * sizeof(double));
+}
+
+void copy_probB(char *data, struct svm_model *model, Py_ssize_t * dims)
+{
+    memcpy(data, model->probB, dims[0] * sizeof(double));
+}
+
+/*
+ * Predict using model.
+ *
+ *  It will return -1 if we run out of memory.
+ */
+int copy_predict(char *predict, struct svm_model *model, Py_ssize_t *predict_dims,
+                 char *dec_values, BlasFunctions *blas_functions)
+{
+    double *t = (double *) dec_values;
+    struct svm_node *predict_nodes;
+    Py_ssize_t i;
+
+    predict_nodes = dense_to_libsvm((double *) predict, predict_dims);
+
+    if (predict_nodes == NULL)
+        return -1;
+    for(i=0; inr_class;
+    predict_nodes = dense_to_libsvm((double *) predict, predict_dims);
+    if (predict_nodes == NULL)
+        return -1;
+    for(i=0; iSV);
+
+    /* We don't free sv_ind and n_iter, since we did not create them in
+       set_model */
+    /* free(model->sv_ind);
+     * free(model->n_iter);
+     */
+    free(model->sv_coef);
+    free(model->rho);
+    free(model->label);
+    free(model->probA);
+    free(model->probB);
+    free(model->nSV);
+    free(model);
+
+    return 0;
+}
+
+int free_param(struct svm_parameter *param)
+{
+    if (param == NULL) return -1;
+    free(param);
+    return 0;
+}
+
+
+/* borrowed from original libsvm code */
+static void print_null(const char *s) {}
+
+static void print_string_stdout(const char *s)
+{
+	fputs(s,stdout);
+	fflush(stdout);
+}
+
+/* provide convenience wrapper */
+void set_verbosity(int verbosity_flag){
+	if (verbosity_flag)
+		svm_set_print_string_function(&print_string_stdout);
+	else
+		svm_set_print_string_function(&print_null);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_sparse_helper.c b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_sparse_helper.c
new file mode 100644
index 0000000000000000000000000000000000000000..0ba153647cb8c158de24cb41e69fad90f44b1fc8
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_sparse_helper.c
@@ -0,0 +1,472 @@
+#include 
+#define PY_SSIZE_T_CLEAN
+#include 
+#include "svm.h"
+#include "_svm_cython_blas_helpers.h"
+
+
+#ifndef MAX
+    #define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#endif
+
+
+/*
+ * Convert scipy.sparse.csr to libsvm's sparse data structure
+ */
+struct svm_csr_node **csr_to_libsvm (double *values, int* indices, int* indptr, int n_samples)
+{
+    struct svm_csr_node **sparse, *temp;
+    int i, j=0, k=0, n;
+    sparse = malloc (n_samples * sizeof(struct svm_csr_node *));
+
+    if (sparse == NULL)
+        return NULL;
+
+    for (i=0; isvm_type = svm_type;
+    param->kernel_type = kernel_type;
+    param->degree = degree;
+    param->coef0 = coef0;
+    param->nu = nu;
+    param->cache_size = cache_size;
+    param->C = C;
+    param->eps = eps;
+    param->p = p;
+    param->shrinking = shrinking;
+    param->probability = probability;
+    param->nr_weight = nr_weight;
+    param->weight_label = (int *) weight_label;
+    param->weight = (double *) weight;
+    param->gamma = gamma;
+    param->max_iter = max_iter;
+    param->random_seed = random_seed;
+    return param;
+}
+
+
+/*
+ * Create and return a svm_csr_problem struct from a scipy.sparse.csr matrix. It is
+ * up to the user to free resulting structure.
+ *
+ * TODO: precomputed kernel.
+ */
+struct svm_csr_problem * csr_set_problem (char *values, Py_ssize_t *n_indices,
+		char *indices, Py_ssize_t *n_indptr, char *indptr, char *Y,
+                char *sample_weight, int kernel_type) {
+
+    struct svm_csr_problem *problem;
+    problem = malloc (sizeof (struct svm_csr_problem));
+    if (problem == NULL) return NULL;
+    problem->l = (int) n_indptr[0] - 1;
+    problem->y = (double *) Y;
+    problem->x = csr_to_libsvm((double *) values, (int *) indices,
+                               (int *) indptr, problem->l);
+    /* should be removed once we implement weighted samples */
+    problem->W = (double *) sample_weight;
+
+    if (problem->x == NULL) {
+        free(problem);
+        return NULL;
+    }
+    return problem;
+}
+
+
+struct svm_csr_model *csr_set_model(struct svm_parameter *param, int nr_class,
+                            char *SV_data, Py_ssize_t *SV_indices_dims,
+                            char *SV_indices, Py_ssize_t *SV_indptr_dims,
+                            char *SV_intptr,
+                            char *sv_coef, char *rho, char *nSV,
+                            char *probA, char *probB)
+{
+    struct svm_csr_model *model;
+    double *dsv_coef = (double *) sv_coef;
+    int i, m;
+
+    m = nr_class * (nr_class-1)/2;
+
+    if ((model = malloc(sizeof(struct svm_csr_model))) == NULL)
+        goto model_error;
+    if ((model->nSV = malloc(nr_class * sizeof(int))) == NULL)
+        goto nsv_error;
+    if ((model->label = malloc(nr_class * sizeof(int))) == NULL)
+        goto label_error;
+    if ((model->sv_coef = malloc((nr_class-1)*sizeof(double *))) == NULL)
+        goto sv_coef_error;
+    if ((model->rho = malloc( m * sizeof(double))) == NULL)
+        goto rho_error;
+
+    // This is only allocated in dynamic memory while training.
+    model->n_iter = NULL;
+
+    /* in the case of precomputed kernels we do not use
+       dense_to_precomputed because we don't want the leading 0. As
+       indices start at 1 (not at 0) this will work */
+    model->l = (int) SV_indptr_dims[0] - 1;
+    model->SV = csr_to_libsvm((double *) SV_data, (int *) SV_indices,
+                              (int *) SV_intptr, model->l);
+    model->nr_class = nr_class;
+    model->param = *param;
+
+    /*
+     * regression and one-class does not use nSV, label.
+     */
+    if (param->svm_type < 2) {
+        memcpy(model->nSV,   nSV,   model->nr_class * sizeof(int));
+        for(i=0; i < model->nr_class; i++)
+            model->label[i] = i;
+    }
+
+    for (i=0; i < model->nr_class-1; i++) {
+        /*
+         * We cannot squash all this mallocs in a single call since
+         * svm_destroy_model will free each element of the array.
+         */
+        if ((model->sv_coef[i] = malloc((model->l) * sizeof(double))) == NULL) {
+            int j;
+            for (j=0; jsv_coef[j]);
+            goto sv_coef_i_error;
+        }
+        memcpy(model->sv_coef[i], dsv_coef, (model->l) * sizeof(double));
+        dsv_coef += model->l;
+    }
+
+    for (i=0; irho)[i] = -((double *) rho)[i];
+    }
+
+    /*
+     * just to avoid segfaults, these features are not wrapped but
+     * svm_destroy_model will try to free them.
+     */
+
+    if (param->probability) {
+        if ((model->probA = malloc(m * sizeof(double))) == NULL)
+            goto probA_error;
+        memcpy(model->probA, probA, m * sizeof(double));
+        if ((model->probB = malloc(m * sizeof(double))) == NULL)
+            goto probB_error;
+        memcpy(model->probB, probB, m * sizeof(double));
+    } else {
+        model->probA = NULL;
+        model->probB = NULL;
+    }
+
+    /* We'll free SV ourselves */
+    model->free_sv = 0;
+    return model;
+
+probB_error:
+    free(model->probA);
+probA_error:
+    for (i=0; i < model->nr_class-1; i++)
+        free(model->sv_coef[i]);
+sv_coef_i_error:
+    free(model->rho);
+rho_error:
+    free(model->sv_coef);
+sv_coef_error:
+    free(model->label);
+label_error:
+    free(model->nSV);
+nsv_error:
+    free(model);
+model_error:
+    return NULL;
+}
+
+
+/*
+ * Copy support vectors into a scipy.sparse.csr matrix
+ */
+int csr_copy_SV (char *data, Py_ssize_t *n_indices,
+		char *indices, Py_ssize_t *n_indptr, char *indptr,
+		struct svm_csr_model *model, int n_features)
+{
+	int i, j, k=0, index;
+	double *dvalues = (double *) data;
+	int *iindices = (int *) indices;
+	int *iindptr  = (int *) indptr;
+	iindptr[0] = 0;
+	for (i=0; il; ++i) { /* iterate over support vectors */
+		index = model->SV[i][0].index;
+        for(j=0; index >=0 ; ++j) {
+        	iindices[k] = index - 1;
+            dvalues[k] = model->SV[i][j].value;
+            index = model->SV[i][j+1].index;
+            ++k;
+        }
+        iindptr[i+1] = k;
+	}
+
+	return 0;
+}
+
+/* get number of nonzero coefficients in support vectors */
+Py_ssize_t get_nonzero_SV (struct svm_csr_model *model) {
+	int i, j;
+	Py_ssize_t count=0;
+	for (i=0; il; ++i) {
+		j = 0;
+		while (model->SV[i][j].index != -1) {
+			++j;
+			++count;
+		}
+	}
+	return count;
+}
+
+
+/*
+ * Predict using a model, where data is expected to be encoded into a csr matrix.
+ */
+int csr_copy_predict (Py_ssize_t *data_size, char *data, Py_ssize_t *index_size,
+		char *index, Py_ssize_t *intptr_size, char *intptr, struct svm_csr_model *model,
+		char *dec_values, BlasFunctions *blas_functions) {
+    double *t = (double *) dec_values;
+    struct svm_csr_node **predict_nodes;
+    Py_ssize_t i;
+
+    predict_nodes = csr_to_libsvm((double *) data, (int *) index,
+                                  (int *) intptr, intptr_size[0]-1);
+
+    if (predict_nodes == NULL)
+        return -1;
+    for(i=0; i < intptr_size[0] - 1; ++i) {
+        *t = svm_csr_predict(model, predict_nodes[i], blas_functions);
+        free(predict_nodes[i]);
+        ++t;
+    }
+    free(predict_nodes);
+    return 0;
+}
+
+int csr_copy_predict_values (Py_ssize_t *data_size, char *data, Py_ssize_t *index_size,
+                char *index, Py_ssize_t *intptr_size, char *intptr, struct svm_csr_model *model,
+                char *dec_values, int nr_class, BlasFunctions *blas_functions) {
+    struct svm_csr_node **predict_nodes;
+    Py_ssize_t i;
+
+    predict_nodes = csr_to_libsvm((double *) data, (int *) index,
+                                  (int *) intptr, intptr_size[0]-1);
+
+    if (predict_nodes == NULL)
+        return -1;
+    for(i=0; i < intptr_size[0] - 1; ++i) {
+        svm_csr_predict_values(model, predict_nodes[i],
+                               ((double *) dec_values) + i*nr_class,
+			       blas_functions);
+        free(predict_nodes[i]);
+    }
+    free(predict_nodes);
+
+    return 0;
+}
+
+int csr_copy_predict_proba (Py_ssize_t *data_size, char *data, Py_ssize_t *index_size,
+		char *index, Py_ssize_t *intptr_size, char *intptr, struct svm_csr_model *model,
+		char *dec_values, BlasFunctions *blas_functions) {
+
+    struct svm_csr_node **predict_nodes;
+    Py_ssize_t i;
+    int m = model->nr_class;
+
+    predict_nodes = csr_to_libsvm((double *) data, (int *) index,
+                                  (int *) intptr, intptr_size[0]-1);
+
+    if (predict_nodes == NULL)
+        return -1;
+    for(i=0; i < intptr_size[0] - 1; ++i) {
+        svm_csr_predict_probability(
+		model, predict_nodes[i], ((double *) dec_values) + i*m, blas_functions);
+        free(predict_nodes[i]);
+    }
+    free(predict_nodes);
+    return 0;
+}
+
+
+Py_ssize_t get_nr(struct svm_csr_model *model)
+{
+    return (Py_ssize_t) model->nr_class;
+}
+
+void copy_intercept(char *data, struct svm_csr_model *model, Py_ssize_t *dims)
+{
+    /* intercept = -rho */
+    Py_ssize_t i, n = dims[0];
+    double t, *ddata = (double *) data;
+    for (i=0; irho[i];
+        /* we do this to avoid ugly -0.0 */
+        *ddata = (t != 0) ? -t : 0;
+        ++ddata;
+    }
+}
+
+void copy_support (char *data, struct svm_csr_model *model)
+{
+    memcpy (data, model->sv_ind, (model->l) * sizeof(int));
+}
+
+/*
+ * Some helpers to convert from libsvm sparse data structures
+ * model->sv_coef is a double **, whereas data is just a double *,
+ * so we have to do some stupid copying.
+ */
+void copy_sv_coef(char *data, struct svm_csr_model *model)
+{
+    int i, len = model->nr_class-1;
+    double *temp = (double *) data;
+    for(i=0; isv_coef[i], sizeof(double) * model->l);
+        temp += model->l;
+    }
+}
+
+/*
+ * Get the number of iterations run in optimization
+ */
+void copy_n_iter(char *data, struct svm_csr_model *model)
+{
+    const int n_models = MAX(1, model->nr_class * (model->nr_class-1) / 2);
+    memcpy(data, model->n_iter, n_models * sizeof(int));
+}
+
+/*
+ * Get the number of support vectors in a model.
+ */
+Py_ssize_t get_l(struct svm_csr_model *model)
+{
+    return (Py_ssize_t) model->l;
+}
+
+void copy_nSV(char *data, struct svm_csr_model *model)
+{
+    if (model->label == NULL) return;
+    memcpy(data, model->nSV, model->nr_class * sizeof(int));
+}
+
+/*
+ * same as above with model->label
+ * TODO: merge in the cython layer
+ */
+void copy_label(char *data, struct svm_csr_model *model)
+{
+    if (model->label == NULL) return;
+    memcpy(data, model->label, model->nr_class * sizeof(int));
+}
+
+void copy_probA(char *data, struct svm_csr_model *model, Py_ssize_t * dims)
+{
+    memcpy(data, model->probA, dims[0] * sizeof(double));
+}
+
+void copy_probB(char *data, struct svm_csr_model *model, Py_ssize_t * dims)
+{
+    memcpy(data, model->probB, dims[0] * sizeof(double));
+}
+
+
+/*
+ * Some free routines. Some of them are nontrivial since a lot of
+ * sharing happens across objects (they *must* be called in the
+ * correct order)
+ */
+int free_problem(struct svm_csr_problem *problem)
+{
+    int i;
+    if (problem == NULL) return -1;
+    for (i=0; il; ++i)
+        free (problem->x[i]);
+    free (problem->x);
+    free (problem);
+    return 0;
+}
+
+int free_model(struct svm_csr_model *model)
+{
+    /* like svm_free_and_destroy_model, but does not free sv_coef[i] */
+    /* We don't free n_iter, since we did not create them in set_model. */
+    if (model == NULL) return -1;
+    free(model->SV);
+    free(model->sv_coef);
+    free(model->rho);
+    free(model->label);
+    free(model->probA);
+    free(model->probB);
+    free(model->nSV);
+    free(model);
+
+    return 0;
+}
+
+int free_param(struct svm_parameter *param)
+{
+    if (param == NULL) return -1;
+    free(param);
+    return 0;
+}
+
+
+int free_model_SV(struct svm_csr_model *model)
+{
+    int i;
+    for (i=model->l-1; i>=0; --i) free(model->SV[i]);
+    /* svn_destroy_model frees model->SV */
+    for (i=0; i < model->nr_class-1 ; ++i) free(model->sv_coef[i]);
+    /* svn_destroy_model frees model->sv_coef */
+    return 0;
+}
+
+
+/* borrowed from original libsvm code */
+static void print_null(const char *s) {}
+
+static void print_string_stdout(const char *s)
+{
+	fputs(s,stdout);
+	fflush(stdout);
+}
+
+/* provide convenience wrapper */
+void set_verbosity(int verbosity_flag){
+	if (verbosity_flag)
+		svm_set_print_string_function(&print_string_stdout);
+	else
+		svm_set_print_string_function(&print_null);
+}
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_template.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_template.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8f6dbd0dfd9ecd81bdd79c74a19d7299e179389d
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/libsvm_template.cpp
@@ -0,0 +1,8 @@
+
+/* this is a hack to generate libsvm with both sparse and dense
+   methods in the same binary*/
+
+#define _DENSE_REP
+#include "svm.cpp"
+#undef _DENSE_REP
+#include "svm.cpp"
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/svm.cpp b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/svm.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a6f191d6616c968e4e2a31e24a23536da329d873
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/src/libsvm/svm.cpp
@@ -0,0 +1,3187 @@
+/*
+Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3. Neither name of copyright holders nor the names of its contributors
+may be used to endorse or promote products derived from this software
+without specific prior written permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+
+/*
+   Modified 2010:
+
+   - Support for dense data by Ming-Fang Weng
+
+   - Return indices for support vectors, Fabian Pedregosa
+     
+
+   - Fixes to avoid name collision, Fabian Pedregosa
+
+   - Add support for instance weights, Fabian Pedregosa based on work
+     by Ming-Wei Chang, Hsuan-Tien Lin, Ming-Hen Tsai, Chia-Hua Ho and
+     Hsiang-Fu Yu,
+     .
+
+   - Make labels sorted in svm_group_classes, Fabian Pedregosa.
+
+   Modified 2020:
+
+   - Improved random number generator by using a mersenne twister + tweaked
+     lemire postprocessor. This fixed a convergence issue on windows targets.
+     Sylvain Marie, Schneider Electric
+     see 
+
+   Modified 2021:
+
+   - Exposed number of iterations run in optimization, Juan Martín Loyola.
+     See 
+ */
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include "svm.h"
+#include "_svm_cython_blas_helpers.h"
+#include "../newrand/newrand.h"
+
+
+#ifndef _LIBSVM_CPP
+typedef float Qfloat;
+typedef signed char schar;
+#ifndef min
+template  static inline T min(T x,T y) { return (x static inline T max(T x,T y) { return (x>y)?x:y; }
+#endif
+template  static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
+template  static inline void clone(T*& dst, S* src, int n)
+{
+	dst = new T[n];
+	memcpy((void *)dst,(void *)src,sizeof(T)*n);
+}
+static inline double powi(double base, int times)
+{
+	double tmp = base, ret = 1.0;
+
+	for(int t=times; t>0; t/=2)
+	{
+		if(t%2==1) ret*=tmp;
+		tmp = tmp * tmp;
+	}
+	return ret;
+}
+#define INF HUGE_VAL
+#define TAU 1e-12
+#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
+
+static void print_string_stdout(const char *s)
+{
+	fputs(s,stdout);
+	fflush(stdout);
+}
+static void (*svm_print_string) (const char *) = &print_string_stdout;
+
+static void info(const char *fmt,...)
+{
+	char buf[BUFSIZ];
+	va_list ap;
+	va_start(ap,fmt);
+	vsprintf(buf,fmt,ap);
+	va_end(ap);
+	(*svm_print_string)(buf);
+}
+#endif
+#define _LIBSVM_CPP
+
+
+/* yeah, this is ugly.  It helps us to have unique names for both sparse
+and dense versions of this library */
+#ifdef _DENSE_REP
+  #ifdef PREFIX
+    #undef PREFIX
+  #endif
+  #ifdef NAMESPACE
+    #undef NAMESPACE
+  #endif
+  #define PREFIX(name) svm_##name
+  #define NAMESPACE svm
+  namespace svm {
+#else
+  /* sparse representation */
+  #ifdef PREFIX
+    #undef PREFIX
+  #endif
+  #ifdef NAMESPACE
+    #undef NAMESPACE
+  #endif
+  #define PREFIX(name) svm_csr_##name
+  #define NAMESPACE svm_csr
+  namespace svm_csr {
+#endif
+
+
+//
+// Kernel Cache
+//
+// l is the number of total data items
+// size is the cache size limit in bytes
+//
+class Cache
+{
+public:
+	Cache(int l,long int size);
+	~Cache();
+
+	// request data [0,len)
+	// return some position p where [p,len) need to be filled
+	// (p >= len if nothing needs to be filled)
+	int get_data(const int index, Qfloat **data, int len);
+	void swap_index(int i, int j);
+private:
+	int l;
+	long int size;
+	struct head_t
+	{
+		head_t *prev, *next;	// a circular list
+		Qfloat *data;
+		int len;		// data[0,len) is cached in this entry
+	};
+
+	head_t *head;
+	head_t lru_head;
+	void lru_delete(head_t *h);
+	void lru_insert(head_t *h);
+};
+
+Cache::Cache(int l_,long int size_):l(l_),size(size_)
+{
+	head = (head_t *)calloc(l,sizeof(head_t));	// initialized to 0
+	size /= sizeof(Qfloat);
+	size -= l * sizeof(head_t) / sizeof(Qfloat);
+	size = max(size, 2 * (long int) l);	// cache must be large enough for two columns
+	lru_head.next = lru_head.prev = &lru_head;
+}
+
+Cache::~Cache()
+{
+	for(head_t *h = lru_head.next; h != &lru_head; h=h->next)
+		free(h->data);
+	free(head);
+}
+
+void Cache::lru_delete(head_t *h)
+{
+	// delete from current location
+	h->prev->next = h->next;
+	h->next->prev = h->prev;
+}
+
+void Cache::lru_insert(head_t *h)
+{
+	// insert to last position
+	h->next = &lru_head;
+	h->prev = lru_head.prev;
+	h->prev->next = h;
+	h->next->prev = h;
+}
+
+int Cache::get_data(const int index, Qfloat **data, int len)
+{
+	head_t *h = &head[index];
+	if(h->len) lru_delete(h);
+	int more = len - h->len;
+
+	if(more > 0)
+	{
+		// free old space
+		while(size < more)
+		{
+			head_t *old = lru_head.next;
+			lru_delete(old);
+			free(old->data);
+			size += old->len;
+			old->data = 0;
+			old->len = 0;
+		}
+
+		// allocate new space
+		h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len);
+		size -= more;
+		swap(h->len,len);
+	}
+
+	lru_insert(h);
+	*data = h->data;
+	return len;
+}
+
+void Cache::swap_index(int i, int j)
+{
+	if(i==j) return;
+
+	if(head[i].len) lru_delete(&head[i]);
+	if(head[j].len) lru_delete(&head[j]);
+	swap(head[i].data,head[j].data);
+	swap(head[i].len,head[j].len);
+	if(head[i].len) lru_insert(&head[i]);
+	if(head[j].len) lru_insert(&head[j]);
+
+	if(i>j) swap(i,j);
+	for(head_t *h = lru_head.next; h!=&lru_head; h=h->next)
+	{
+		if(h->len > i)
+		{
+			if(h->len > j)
+				swap(h->data[i],h->data[j]);
+			else
+			{
+				// give up
+				lru_delete(h);
+				free(h->data);
+				size += h->len;
+				h->data = 0;
+				h->len = 0;
+			}
+		}
+	}
+}
+
+//
+// Kernel evaluation
+//
+// the static method k_function is for doing single kernel evaluation
+// the constructor of Kernel prepares to calculate the l*l kernel matrix
+// the member function get_Q is for getting one column from the Q Matrix
+//
+class QMatrix {
+public:
+	virtual Qfloat *get_Q(int column, int len) const = 0;
+	virtual double *get_QD() const = 0;
+	virtual void swap_index(int i, int j) const = 0;
+	virtual ~QMatrix() {}
+};
+
+class Kernel: public QMatrix {
+public:
+#ifdef _DENSE_REP
+	Kernel(int l, PREFIX(node) * x, const svm_parameter& param, BlasFunctions *blas_functions);
+#else
+	Kernel(int l, PREFIX(node) * const * x, const svm_parameter& param, BlasFunctions *blas_functions);
+#endif
+	virtual ~Kernel();
+
+	static double k_function(const PREFIX(node) *x, const PREFIX(node) *y,
+				 const svm_parameter& param, BlasFunctions *blas_functions);
+	virtual Qfloat *get_Q(int column, int len) const = 0;
+	virtual double *get_QD() const = 0;
+	virtual void swap_index(int i, int j) const	// no so const...
+	{
+		swap(x[i],x[j]);
+		if(x_square) swap(x_square[i],x_square[j]);
+	}
+protected:
+
+	double (Kernel::*kernel_function)(int i, int j) const;
+
+private:
+#ifdef _DENSE_REP
+	PREFIX(node) *x;
+#else
+	const PREFIX(node) **x;
+#endif
+	double *x_square;
+	// scipy blas pointer
+	BlasFunctions *m_blas;
+
+	// svm_parameter
+	const int kernel_type;
+	const int degree;
+	const double gamma;
+	const double coef0;
+
+	static double dot(const PREFIX(node) *px, const PREFIX(node) *py, BlasFunctions *blas_functions);
+#ifdef _DENSE_REP
+	static double dot(const PREFIX(node) &px, const PREFIX(node) &py, BlasFunctions *blas_functions);
+#endif
+
+	double kernel_linear(int i, int j) const
+	{
+		return dot(x[i],x[j],m_blas);
+	}
+	double kernel_poly(int i, int j) const
+	{
+		return powi(gamma*dot(x[i],x[j],m_blas)+coef0,degree);
+	}
+	double kernel_rbf(int i, int j) const
+	{
+		return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j],m_blas)));
+	}
+	double kernel_sigmoid(int i, int j) const
+	{
+		return tanh(gamma*dot(x[i],x[j],m_blas)+coef0);
+	}
+	double kernel_precomputed(int i, int j) const
+	{
+#ifdef _DENSE_REP
+		return (x+i)->values[x[j].ind];
+#else
+		return x[i][(int)(x[j][0].value)].value;
+#endif
+	}
+};
+
+#ifdef _DENSE_REP
+Kernel::Kernel(int l, PREFIX(node) * x_, const svm_parameter& param, BlasFunctions *blas_functions)
+#else
+Kernel::Kernel(int l, PREFIX(node) * const * x_, const svm_parameter& param, BlasFunctions *blas_functions)
+#endif
+:kernel_type(param.kernel_type), degree(param.degree),
+ gamma(param.gamma), coef0(param.coef0)
+{
+	m_blas = blas_functions;
+	switch(kernel_type)
+	{
+		case LINEAR:
+			kernel_function = &Kernel::kernel_linear;
+			break;
+		case POLY:
+			kernel_function = &Kernel::kernel_poly;
+			break;
+		case RBF:
+			kernel_function = &Kernel::kernel_rbf;
+			break;
+		case SIGMOID:
+			kernel_function = &Kernel::kernel_sigmoid;
+			break;
+		case PRECOMPUTED:
+			kernel_function = &Kernel::kernel_precomputed;
+			break;
+	}
+
+	clone(x,x_,l);
+
+	if(kernel_type == RBF)
+	{
+		x_square = new double[l];
+		for(int i=0;idim, py->dim);
+	sum = blas_functions->dot(dim, px->values, 1, py->values, 1);
+	return sum;
+}
+
+double Kernel::dot(const PREFIX(node) &px, const PREFIX(node) &py, BlasFunctions *blas_functions)
+{
+	double sum = 0;
+
+	int dim = min(px.dim, py.dim);
+	sum = blas_functions->dot(dim, px.values, 1, py.values, 1);
+	return sum;
+}
+#else
+double Kernel::dot(const PREFIX(node) *px, const PREFIX(node) *py, BlasFunctions *blas_functions)
+{
+	double sum = 0;
+	while(px->index != -1 && py->index != -1)
+	{
+		if(px->index == py->index)
+		{
+			sum += px->value * py->value;
+			++px;
+			++py;
+		}
+		else
+		{
+			if(px->index > py->index)
+				++py;
+			else
+				++px;
+		}
+	}
+	return sum;
+}
+#endif
+
+double Kernel::k_function(const PREFIX(node) *x, const PREFIX(node) *y,
+			  const svm_parameter& param, BlasFunctions *blas_functions)
+{
+	switch(param.kernel_type)
+	{
+		case LINEAR:
+			return dot(x,y,blas_functions);
+		case POLY:
+			return powi(param.gamma*dot(x,y,blas_functions)+param.coef0,param.degree);
+		case RBF:
+		{
+			double sum = 0;
+#ifdef _DENSE_REP
+			int dim = min(x->dim, y->dim), i;
+			double* m_array = (double*)malloc(sizeof(double)*dim);
+			for (i = 0; i < dim; i++)
+			{
+				m_array[i] = x->values[i] - y->values[i];
+			}
+			sum = blas_functions->dot(dim, m_array, 1, m_array, 1);
+			free(m_array);
+			for (; i < x->dim; i++)
+				sum += x->values[i] * x->values[i];
+			for (; i < y->dim; i++)
+				sum += y->values[i] * y->values[i];
+#else
+			while(x->index != -1 && y->index !=-1)
+			{
+				if(x->index == y->index)
+				{
+					double d = x->value - y->value;
+					sum += d*d;
+					++x;
+					++y;
+				}
+				else
+				{
+					if(x->index > y->index)
+					{
+						sum += y->value * y->value;
+						++y;
+					}
+					else
+					{
+						sum += x->value * x->value;
+						++x;
+					}
+				}
+			}
+
+			while(x->index != -1)
+			{
+				sum += x->value * x->value;
+				++x;
+			}
+
+			while(y->index != -1)
+			{
+				sum += y->value * y->value;
+				++y;
+			}
+#endif
+			return exp(-param.gamma*sum);
+		}
+		case SIGMOID:
+			return tanh(param.gamma*dot(x,y,blas_functions)+param.coef0);
+		case PRECOMPUTED:  //x: test (validation), y: SV
+                    {
+#ifdef _DENSE_REP
+			return x->values[y->ind];
+#else
+			return x[(int)(y->value)].value;
+#endif
+                    }
+		default:
+			return 0;  // Unreachable
+	}
+}
+// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
+// Solves:
+//
+//	min 0.5(\alpha^T Q \alpha) + p^T \alpha
+//
+//		y^T \alpha = \delta
+//		y_i = +1 or -1
+//		0 <= alpha_i <= Cp for y_i = 1
+//		0 <= alpha_i <= Cn for y_i = -1
+//
+// Given:
+//
+//	Q, p, y, Cp, Cn, and an initial feasible point \alpha
+//	l is the size of vectors and matrices
+//	eps is the stopping tolerance
+//
+// solution will be put in \alpha, objective value will be put in obj
+//
+
+class Solver {
+public:
+	Solver() {};
+	virtual ~Solver() {};
+
+	struct SolutionInfo {
+		double obj;
+		double rho;
+                double *upper_bound;
+		double r;	// for Solver_NU
+                bool solve_timed_out;
+		int n_iter;
+	};
+
+	void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
+		   double *alpha_, const double *C_, double eps,
+		   SolutionInfo* si, int shrinking, int max_iter);
+protected:
+	int active_size;
+	schar *y;
+	double *G;		// gradient of objective function
+	enum { LOWER_BOUND, UPPER_BOUND, FREE };
+	char *alpha_status;	// LOWER_BOUND, UPPER_BOUND, FREE
+	double *alpha;
+	const QMatrix *Q;
+	const double *QD;
+	double eps;
+	double Cp,Cn;
+        double *C;
+	double *p;
+	int *active_set;
+	double *G_bar;		// gradient, if we treat free variables as 0
+	int l;
+	bool unshrink;	// XXX
+
+	double get_C(int i)
+	{
+		return C[i];
+	}
+	void update_alpha_status(int i)
+	{
+		if(alpha[i] >= get_C(i))
+			alpha_status[i] = UPPER_BOUND;
+		else if(alpha[i] <= 0)
+			alpha_status[i] = LOWER_BOUND;
+		else alpha_status[i] = FREE;
+	}
+	bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
+	bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
+	bool is_free(int i) { return alpha_status[i] == FREE; }
+	void swap_index(int i, int j);
+	void reconstruct_gradient();
+	virtual int select_working_set(int &i, int &j);
+	virtual double calculate_rho();
+	virtual void do_shrinking();
+private:
+	bool be_shrunk(int i, double Gmax1, double Gmax2);
+};
+
+void Solver::swap_index(int i, int j)
+{
+	Q->swap_index(i,j);
+	swap(y[i],y[j]);
+	swap(G[i],G[j]);
+	swap(alpha_status[i],alpha_status[j]);
+	swap(alpha[i],alpha[j]);
+	swap(p[i],p[j]);
+	swap(active_set[i],active_set[j]);
+	swap(G_bar[i],G_bar[j]);
+        swap(C[i], C[j]);
+}
+
+void Solver::reconstruct_gradient()
+{
+	// reconstruct inactive elements of G from G_bar and free variables
+
+	if(active_size == l) return;
+
+	int i,j;
+	int nr_free = 0;
+
+	for(j=active_size;j 2*active_size*(l-active_size))
+	{
+		for(i=active_size;iget_Q(i,active_size);
+			for(j=0;jget_Q(i,l);
+				double alpha_i = alpha[i];
+				for(j=active_size;jl = l;
+	this->Q = &Q;
+	QD=Q.get_QD();
+	clone(p, p_,l);
+	clone(y, y_,l);
+	clone(alpha,alpha_,l);
+        clone(C, C_, l);
+	this->eps = eps;
+	unshrink = false;
+        si->solve_timed_out = false;
+
+	// initialize alpha_status
+	{
+		alpha_status = new char[l];
+		for(int i=0;i= max_iter)) {
+                    info("WARN: libsvm Solver reached max_iter");
+                    si->solve_timed_out = true;
+                    break;
+                }
+
+		// show progress and do shrinking
+
+		if(--counter == 0)
+		{
+			counter = min(l,1000);
+			if(shrinking) do_shrinking();
+			info(".");
+		}
+
+		int i,j;
+		if(select_working_set(i,j)!=0)
+		{
+			// reconstruct the whole gradient
+			reconstruct_gradient();
+			// reset active set size and check
+			active_size = l;
+			info("*");
+			if(select_working_set(i,j)!=0)
+				break;
+			else
+				counter = 1;	// do shrinking next iteration
+		}
+
+		++iter;
+
+		// update alpha[i] and alpha[j], handle bounds carefully
+
+		const Qfloat *Q_i = Q.get_Q(i,active_size);
+		const Qfloat *Q_j = Q.get_Q(j,active_size);
+
+		double C_i = get_C(i);
+		double C_j = get_C(j);
+
+		double old_alpha_i = alpha[i];
+		double old_alpha_j = alpha[j];
+
+		if(y[i]!=y[j])
+		{
+			double quad_coef = QD[i]+QD[j]+2*Q_i[j];
+			if (quad_coef <= 0)
+				quad_coef = TAU;
+			double delta = (-G[i]-G[j])/quad_coef;
+			double diff = alpha[i] - alpha[j];
+			alpha[i] += delta;
+			alpha[j] += delta;
+
+			if(diff > 0)
+			{
+				if(alpha[j] < 0)
+				{
+					alpha[j] = 0;
+					alpha[i] = diff;
+				}
+			}
+			else
+			{
+				if(alpha[i] < 0)
+				{
+					alpha[i] = 0;
+					alpha[j] = -diff;
+				}
+			}
+			if(diff > C_i - C_j)
+			{
+				if(alpha[i] > C_i)
+				{
+					alpha[i] = C_i;
+					alpha[j] = C_i - diff;
+				}
+			}
+			else
+			{
+				if(alpha[j] > C_j)
+				{
+					alpha[j] = C_j;
+					alpha[i] = C_j + diff;
+				}
+			}
+		}
+		else
+		{
+			double quad_coef = QD[i]+QD[j]-2*Q_i[j];
+			if (quad_coef <= 0)
+				quad_coef = TAU;
+			double delta = (G[i]-G[j])/quad_coef;
+			double sum = alpha[i] + alpha[j];
+			alpha[i] -= delta;
+			alpha[j] += delta;
+
+			if(sum > C_i)
+			{
+				if(alpha[i] > C_i)
+				{
+					alpha[i] = C_i;
+					alpha[j] = sum - C_i;
+				}
+			}
+			else
+			{
+				if(alpha[j] < 0)
+				{
+					alpha[j] = 0;
+					alpha[i] = sum;
+				}
+			}
+			if(sum > C_j)
+			{
+				if(alpha[j] > C_j)
+				{
+					alpha[j] = C_j;
+					alpha[i] = sum - C_j;
+				}
+			}
+			else
+			{
+				if(alpha[i] < 0)
+				{
+					alpha[i] = 0;
+					alpha[j] = sum;
+				}
+			}
+		}
+
+		// update G
+
+		double delta_alpha_i = alpha[i] - old_alpha_i;
+		double delta_alpha_j = alpha[j] - old_alpha_j;
+
+		for(int k=0;krho = calculate_rho();
+
+	// calculate objective value
+	{
+		double v = 0;
+		int i;
+		for(i=0;iobj = v/2;
+	}
+
+	// put back the solution
+	{
+		for(int i=0;iupper_bound[i] = C[i];
+
+	// store number of iterations
+	si->n_iter = iter;
+
+	info("\noptimization finished, #iter = %d\n",iter);
+
+	delete[] p;
+	delete[] y;
+	delete[] alpha;
+	delete[] alpha_status;
+	delete[] active_set;
+	delete[] G;
+	delete[] G_bar;
+	delete[] C;
+}
+
+// return 1 if already optimal, return 0 otherwise
+int Solver::select_working_set(int &out_i, int &out_j)
+{
+	// return i,j such that
+	// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
+	// j: minimizes the decrease of obj value
+	//    (if quadratic coefficient <= 0, replace it with tau)
+	//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
+
+	double Gmax = -INF;
+	double Gmax2 = -INF;
+	int Gmax_idx = -1;
+	int Gmin_idx = -1;
+	double obj_diff_min = INF;
+
+	for(int t=0;t= Gmax)
+				{
+					Gmax = -G[t];
+					Gmax_idx = t;
+				}
+		}
+		else
+		{
+			if(!is_lower_bound(t))
+				if(G[t] >= Gmax)
+				{
+					Gmax = G[t];
+					Gmax_idx = t;
+				}
+		}
+
+	int i = Gmax_idx;
+	const Qfloat *Q_i = NULL;
+	if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1
+		Q_i = Q->get_Q(i,active_size);
+
+	for(int j=0;j= Gmax2)
+					Gmax2 = G[j];
+				if (grad_diff > 0)
+				{
+					double obj_diff;
+					double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
+					if (quad_coef > 0)
+						obj_diff = -(grad_diff*grad_diff)/quad_coef;
+					else
+						obj_diff = -(grad_diff*grad_diff)/TAU;
+
+					if (obj_diff <= obj_diff_min)
+					{
+						Gmin_idx=j;
+						obj_diff_min = obj_diff;
+					}
+				}
+			}
+		}
+		else
+		{
+			if (!is_upper_bound(j))
+			{
+				double grad_diff= Gmax-G[j];
+				if (-G[j] >= Gmax2)
+					Gmax2 = -G[j];
+				if (grad_diff > 0)
+				{
+					double obj_diff;
+					double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
+					if (quad_coef > 0)
+						obj_diff = -(grad_diff*grad_diff)/quad_coef;
+					else
+						obj_diff = -(grad_diff*grad_diff)/TAU;
+
+					if (obj_diff <= obj_diff_min)
+					{
+						Gmin_idx=j;
+						obj_diff_min = obj_diff;
+					}
+				}
+			}
+		}
+	}
+
+	if(Gmax+Gmax2 < eps || Gmin_idx == -1)
+		return 1;
+
+	out_i = Gmax_idx;
+	out_j = Gmin_idx;
+	return 0;
+}
+
+bool Solver::be_shrunk(int i, double Gmax1, double Gmax2)
+{
+	if(is_upper_bound(i))
+	{
+		if(y[i]==+1)
+			return(-G[i] > Gmax1);
+		else
+			return(-G[i] > Gmax2);
+	}
+	else if(is_lower_bound(i))
+	{
+		if(y[i]==+1)
+			return(G[i] > Gmax2);
+		else
+			return(G[i] > Gmax1);
+	}
+	else
+		return(false);
+}
+
+void Solver::do_shrinking()
+{
+	int i;
+	double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
+	double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }
+
+	// find maximal violating pair first
+	for(i=0;i= Gmax1)
+					Gmax1 = -G[i];
+			}
+			if(!is_lower_bound(i))
+			{
+				if(G[i] >= Gmax2)
+					Gmax2 = G[i];
+			}
+		}
+		else
+		{
+			if(!is_upper_bound(i))
+			{
+				if(-G[i] >= Gmax2)
+					Gmax2 = -G[i];
+			}
+			if(!is_lower_bound(i))
+			{
+				if(G[i] >= Gmax1)
+					Gmax1 = G[i];
+			}
+		}
+	}
+
+	if(unshrink == false && Gmax1 + Gmax2 <= eps*10)
+	{
+		unshrink = true;
+		reconstruct_gradient();
+		active_size = l;
+		info("*");
+	}
+
+	for(i=0;i i)
+			{
+				if (!be_shrunk(active_size, Gmax1, Gmax2))
+				{
+					swap_index(i,active_size);
+					break;
+				}
+				active_size--;
+			}
+		}
+}
+
+double Solver::calculate_rho()
+{
+	double r;
+	int nr_free = 0;
+	double ub = INF, lb = -INF, sum_free = 0;
+	for(int i=0;i0)
+		r = sum_free/nr_free;
+	else
+		r = (ub+lb)/2;
+
+	return r;
+}
+
+//
+// Solver for nu-svm classification and regression
+//
+// additional constraint: e^T \alpha = constant
+//
+class Solver_NU : public Solver
+{
+public:
+	Solver_NU() {}
+	void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
+		   double *alpha, const double *C_, double eps,
+		   SolutionInfo* si, int shrinking, int max_iter)
+	{
+		this->si = si;
+		Solver::Solve(l,Q,p,y,alpha,C_,eps,si,shrinking,max_iter);
+	}
+private:
+	SolutionInfo *si;
+	int select_working_set(int &i, int &j);
+	double calculate_rho();
+	bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4);
+	void do_shrinking();
+};
+
+// return 1 if already optimal, return 0 otherwise
+int Solver_NU::select_working_set(int &out_i, int &out_j)
+{
+	// return i,j such that y_i = y_j and
+	// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
+	// j: minimizes the decrease of obj value
+	//    (if quadratic coefficient <= 0, replace it with tau)
+	//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
+
+	double Gmaxp = -INF;
+	double Gmaxp2 = -INF;
+	int Gmaxp_idx = -1;
+
+	double Gmaxn = -INF;
+	double Gmaxn2 = -INF;
+	int Gmaxn_idx = -1;
+
+	int Gmin_idx = -1;
+	double obj_diff_min = INF;
+
+	for(int t=0;t= Gmaxp)
+				{
+					Gmaxp = -G[t];
+					Gmaxp_idx = t;
+				}
+		}
+		else
+		{
+			if(!is_lower_bound(t))
+				if(G[t] >= Gmaxn)
+				{
+					Gmaxn = G[t];
+					Gmaxn_idx = t;
+				}
+		}
+
+	int ip = Gmaxp_idx;
+	int in = Gmaxn_idx;
+	const Qfloat *Q_ip = NULL;
+	const Qfloat *Q_in = NULL;
+	if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1
+		Q_ip = Q->get_Q(ip,active_size);
+	if(in != -1)
+		Q_in = Q->get_Q(in,active_size);
+
+	for(int j=0;j= Gmaxp2)
+					Gmaxp2 = G[j];
+				if (grad_diff > 0)
+				{
+					double obj_diff;
+					double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
+					if (quad_coef > 0)
+						obj_diff = -(grad_diff*grad_diff)/quad_coef;
+					else
+						obj_diff = -(grad_diff*grad_diff)/TAU;
+
+					if (obj_diff <= obj_diff_min)
+					{
+						Gmin_idx=j;
+						obj_diff_min = obj_diff;
+					}
+				}
+			}
+		}
+		else
+		{
+			if (!is_upper_bound(j))
+			{
+				double grad_diff=Gmaxn-G[j];
+				if (-G[j] >= Gmaxn2)
+					Gmaxn2 = -G[j];
+				if (grad_diff > 0)
+				{
+					double obj_diff;
+					double quad_coef = QD[in]+QD[j]-2*Q_in[j];
+					if (quad_coef > 0)
+						obj_diff = -(grad_diff*grad_diff)/quad_coef;
+					else
+						obj_diff = -(grad_diff*grad_diff)/TAU;
+
+					if (obj_diff <= obj_diff_min)
+					{
+						Gmin_idx=j;
+						obj_diff_min = obj_diff;
+					}
+				}
+			}
+		}
+	}
+
+	if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps || Gmin_idx == -1)
+		return 1;
+
+	if (y[Gmin_idx] == +1)
+		out_i = Gmaxp_idx;
+	else
+		out_i = Gmaxn_idx;
+	out_j = Gmin_idx;
+
+	return 0;
+}
+
+bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
+{
+	if(is_upper_bound(i))
+	{
+		if(y[i]==+1)
+			return(-G[i] > Gmax1);
+		else
+			return(-G[i] > Gmax4);
+	}
+	else if(is_lower_bound(i))
+	{
+		if(y[i]==+1)
+			return(G[i] > Gmax2);
+		else
+			return(G[i] > Gmax3);
+	}
+	else
+		return(false);
+}
+
+void Solver_NU::do_shrinking()
+{
+	double Gmax1 = -INF;	// max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
+	double Gmax2 = -INF;	// max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
+	double Gmax3 = -INF;	// max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
+	double Gmax4 = -INF;	// max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
+
+	// find maximal violating pair first
+	int i;
+	for(i=0;i Gmax1) Gmax1 = -G[i];
+			}
+			else	if(-G[i] > Gmax4) Gmax4 = -G[i];
+		}
+		if(!is_lower_bound(i))
+		{
+			if(y[i]==+1)
+			{
+				if(G[i] > Gmax2) Gmax2 = G[i];
+			}
+			else	if(G[i] > Gmax3) Gmax3 = G[i];
+		}
+	}
+
+	if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10)
+	{
+		unshrink = true;
+		reconstruct_gradient();
+		active_size = l;
+	}
+
+	for(i=0;i i)
+			{
+				if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
+				{
+					swap_index(i,active_size);
+					break;
+				}
+				active_size--;
+			}
+		}
+}
+
+double Solver_NU::calculate_rho()
+{
+	int nr_free1 = 0,nr_free2 = 0;
+	double ub1 = INF, ub2 = INF;
+	double lb1 = -INF, lb2 = -INF;
+	double sum_free1 = 0, sum_free2 = 0;
+
+	for(int i=0;i 0)
+		r1 = sum_free1/nr_free1;
+	else
+		r1 = (ub1+lb1)/2;
+
+	if(nr_free2 > 0)
+		r2 = sum_free2/nr_free2;
+	else
+		r2 = (ub2+lb2)/2;
+
+	si->r = (r1+r2)/2;
+	return (r1-r2)/2;
+}
+
+//
+// Q matrices for various formulations
+//
+class SVC_Q: public Kernel
+{
+public:
+	SVC_Q(const PREFIX(problem)& prob, const svm_parameter& param, const schar *y_, BlasFunctions *blas_functions)
+	:Kernel(prob.l, prob.x, param, blas_functions)
+	{
+		clone(y,y_,prob.l);
+		cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
+		QD = new double[prob.l];
+		for(int i=0;i*kernel_function)(i,i);
+	}
+
+	Qfloat *get_Q(int i, int len) const
+	{
+		Qfloat *data;
+		int start, j;
+		if((start = cache->get_data(i,&data,len)) < len)
+		{
+			for(j=start;j*kernel_function)(i,j));
+		}
+		return data;
+	}
+
+	double *get_QD() const
+	{
+		return QD;
+	}
+
+	void swap_index(int i, int j) const
+	{
+		cache->swap_index(i,j);
+		Kernel::swap_index(i,j);
+		swap(y[i],y[j]);
+		swap(QD[i],QD[j]);
+	}
+
+	~SVC_Q()
+	{
+		delete[] y;
+		delete cache;
+		delete[] QD;
+	}
+private:
+	schar *y;
+	Cache *cache;
+	double *QD;
+};
+
+class ONE_CLASS_Q: public Kernel
+{
+public:
+	ONE_CLASS_Q(const PREFIX(problem)& prob, const svm_parameter& param, BlasFunctions *blas_functions)
+	:Kernel(prob.l, prob.x, param, blas_functions)
+	{
+		cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
+		QD = new double[prob.l];
+		for(int i=0;i*kernel_function)(i,i);
+	}
+
+	Qfloat *get_Q(int i, int len) const
+	{
+		Qfloat *data;
+		int start, j;
+		if((start = cache->get_data(i,&data,len)) < len)
+		{
+			for(j=start;j*kernel_function)(i,j);
+		}
+		return data;
+	}
+
+	double *get_QD() const
+	{
+		return QD;
+	}
+
+	void swap_index(int i, int j) const
+	{
+		cache->swap_index(i,j);
+		Kernel::swap_index(i,j);
+		swap(QD[i],QD[j]);
+	}
+
+	~ONE_CLASS_Q()
+	{
+		delete cache;
+		delete[] QD;
+	}
+private:
+	Cache *cache;
+	double *QD;
+};
+
+class SVR_Q: public Kernel
+{
+public:
+	SVR_Q(const PREFIX(problem)& prob, const svm_parameter& param, BlasFunctions *blas_functions)
+	:Kernel(prob.l, prob.x, param, blas_functions)
+	{
+		l = prob.l;
+		cache = new Cache(l,(long int)(param.cache_size*(1<<20)));
+		QD = new double[2*l];
+		sign = new schar[2*l];
+		index = new int[2*l];
+		for(int k=0;k*kernel_function)(k,k);
+			QD[k+l] = QD[k];
+		}
+		buffer[0] = new Qfloat[2*l];
+		buffer[1] = new Qfloat[2*l];
+		next_buffer = 0;
+	}
+
+	void swap_index(int i, int j) const
+	{
+		swap(sign[i],sign[j]);
+		swap(index[i],index[j]);
+		swap(QD[i],QD[j]);
+	}
+
+	Qfloat *get_Q(int i, int len) const
+	{
+		Qfloat *data;
+		int j, real_i = index[i];
+		if(cache->get_data(real_i,&data,l) < l)
+		{
+			for(j=0;j*kernel_function)(real_i,j);
+		}
+
+		// reorder and copy
+		Qfloat *buf = buffer[next_buffer];
+		next_buffer = 1 - next_buffer;
+		schar si = sign[i];
+		for(j=0;jl;
+	double *minus_ones = new double[l];
+	schar *y = new schar[l];
+        double *C = new double[l];
+
+	int i;
+
+	for(i=0;iy[i] > 0)
+		{
+			y[i] = +1;
+			C[i] = prob->W[i]*Cp;
+		}
+		else
+		{
+			y[i] = -1;
+			C[i] = prob->W[i]*Cn;
+		}
+	}
+
+	Solver s;
+	s.Solve(l, SVC_Q(*prob,*param,y, blas_functions), minus_ones, y,
+		alpha, C, param->eps, si, param->shrinking,
+                param->max_iter);
+
+        /*
+	double sum_alpha=0;
+	for(i=0;il));
+        */
+
+	for(i=0;il;
+	double nu = param->nu;
+
+	schar *y = new schar[l];
+        double *C = new double[l];
+
+	for(i=0;iy[i]>0)
+			y[i] = +1;
+		else
+			y[i] = -1;
+
+		C[i] = prob->W[i];
+	}
+
+	double nu_l = 0;
+	for(i=0;ieps, si,  param->shrinking, param->max_iter);
+	double r = si->r;
+
+	info("C = %f\n",1/r);
+
+	for(i=0;iupper_bound[i] /= r;
+        }
+
+	si->rho /= r;
+	si->obj /= (r*r);
+
+        delete[] C;
+	delete[] y;
+	delete[] zeros;
+}
+
+static void solve_one_class(
+	const PREFIX(problem) *prob, const svm_parameter *param,
+	double *alpha, Solver::SolutionInfo* si, BlasFunctions *blas_functions)
+{
+	int l = prob->l;
+	double *zeros = new double[l];
+	schar *ones = new schar[l];
+	double *C = new double[l];
+	int i;
+
+	double nu_l = 0;
+
+	for(i=0;iW[i];
+		nu_l += C[i] * param->nu;
+	}
+
+	i = 0;
+	while(nu_l > 0)
+	{
+		alpha[i] = min(C[i],nu_l);
+		nu_l -= alpha[i];
+		++i;
+	}
+	for(;ieps, si, param->shrinking, param->max_iter);
+
+        delete[] C;
+	delete[] zeros;
+	delete[] ones;
+}
+
+static void solve_epsilon_svr(
+	const PREFIX(problem) *prob, const svm_parameter *param,
+	double *alpha, Solver::SolutionInfo* si, BlasFunctions *blas_functions)
+{
+	int l = prob->l;
+	double *alpha2 = new double[2*l];
+	double *linear_term = new double[2*l];
+	schar *y = new schar[2*l];
+        double *C = new double[2*l];
+        int i;
+
+	for(i=0;ip - prob->y[i];
+		y[i] = 1;
+                C[i] = prob->W[i]*param->C;
+
+		alpha2[i+l] = 0;
+		linear_term[i+l] = param->p + prob->y[i];
+		y[i+l] = -1;
+                C[i+l] = prob->W[i]*param->C;
+	}
+
+	Solver s;
+	s.Solve(2*l, SVR_Q(*prob,*param,blas_functions), linear_term, y,
+		alpha2, C, param->eps, si, param->shrinking, param->max_iter);
+
+	double sum_alpha = 0;
+	for(i=0;il;
+	double *C = new double[2*l];
+	double *alpha2 = new double[2*l];
+	double *linear_term = new double[2*l];
+	schar *y = new schar[2*l];
+	int i;
+
+	double sum = 0;
+	for(i=0;iW[i]*param->C;
+		sum += C[i] * param->nu;
+	}
+	sum /= 2;
+
+	for(i=0;iy[i];
+		y[i] = 1;
+
+		linear_term[i+l] = prob->y[i];
+		y[i+l] = -1;
+	}
+
+	Solver_NU s;
+	s.Solve(2*l, SVR_Q(*prob,*param,blas_functions), linear_term, y,
+		alpha2, C, param->eps, si, param->shrinking, param->max_iter);
+
+	info("epsilon = %f\n",-si->r);
+
+	for(i=0;il);
+	Solver::SolutionInfo si;
+	switch(param->svm_type)
+	{
+ 		case C_SVC:
+			si.upper_bound = Malloc(double,prob->l);
+ 			solve_c_svc(prob,param,alpha,&si,Cp,Cn,blas_functions);
+ 			break;
+ 		case NU_SVC:
+			si.upper_bound = Malloc(double,prob->l);
+ 			solve_nu_svc(prob,param,alpha,&si,blas_functions);
+ 			break;
+ 		case ONE_CLASS:
+			si.upper_bound = Malloc(double,prob->l);
+ 			solve_one_class(prob,param,alpha,&si,blas_functions);
+ 			break;
+ 		case EPSILON_SVR:
+			si.upper_bound = Malloc(double,2*prob->l);
+ 			solve_epsilon_svr(prob,param,alpha,&si,blas_functions);
+ 			break;
+ 		case NU_SVR:
+			si.upper_bound = Malloc(double,2*prob->l);
+ 			solve_nu_svr(prob,param,alpha,&si,blas_functions);
+ 			break;
+	}
+
+        *status |= si.solve_timed_out;
+
+	info("obj = %f, rho = %f\n",si.obj,si.rho);
+
+	// output SVs
+
+	int nSV = 0;
+	int nBSV = 0;
+	for(int i=0;il;i++)
+	{
+		if(fabs(alpha[i]) > 0)
+		{
+			++nSV;
+			if(prob->y[i] > 0)
+			{
+				if(fabs(alpha[i]) >= si.upper_bound[i])
+					++nBSV;
+			}
+			else
+			{
+				if(fabs(alpha[i]) >= si.upper_bound[i])
+					++nBSV;
+			}
+		}
+	}
+
+        free(si.upper_bound);
+
+	info("nSV = %d, nBSV = %d\n",nSV,nBSV);
+
+	decision_function f;
+	f.alpha = alpha;
+	f.rho = si.rho;
+	f.n_iter = si.n_iter;
+	return f;
+}
+
+// Platt's binary SVM Probabilistic Output: an improvement from Lin et al.
+static void sigmoid_train(
+	int l, const double *dec_values, const double *labels,
+	double& A, double& B)
+{
+	double prior1=0, prior0 = 0;
+	int i;
+
+	for (i=0;i 0) prior1+=1;
+		else prior0+=1;
+
+	int max_iter=100;	// Maximal number of iterations
+	double min_step=1e-10;	// Minimal step taken in line search
+	double sigma=1e-12;	// For numerically strict PD of Hessian
+	double eps=1e-5;
+	double hiTarget=(prior1+1.0)/(prior1+2.0);
+	double loTarget=1/(prior0+2.0);
+	double *t=Malloc(double,l);
+	double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
+	double newA,newB,newf,d1,d2;
+	int iter;
+
+	// Initial Point and Initial Fun Value
+	A=0.0; B=log((prior0+1.0)/(prior1+1.0));
+	double fval = 0.0;
+
+	for (i=0;i0) t[i]=hiTarget;
+		else t[i]=loTarget;
+		fApB = dec_values[i]*A+B;
+		if (fApB>=0)
+			fval += t[i]*fApB + log(1+exp(-fApB));
+		else
+			fval += (t[i] - 1)*fApB +log(1+exp(fApB));
+	}
+	for (iter=0;iter= 0)
+			{
+				p=exp(-fApB)/(1.0+exp(-fApB));
+				q=1.0/(1.0+exp(-fApB));
+			}
+			else
+			{
+				p=1.0/(1.0+exp(fApB));
+				q=exp(fApB)/(1.0+exp(fApB));
+			}
+			d2=p*q;
+			h11+=dec_values[i]*dec_values[i]*d2;
+			h22+=d2;
+			h21+=dec_values[i]*d2;
+			d1=t[i]-p;
+			g1+=dec_values[i]*d1;
+			g2+=d1;
+		}
+
+		// Stopping Criteria
+		if (fabs(g1)= min_step)
+		{
+			newA = A + stepsize * dA;
+			newB = B + stepsize * dB;
+
+			// New function value
+			newf = 0.0;
+			for (i=0;i= 0)
+					newf += t[i]*fApB + log(1+exp(-fApB));
+				else
+					newf += (t[i] - 1)*fApB +log(1+exp(fApB));
+			}
+			// Check sufficient decrease
+			if (newf=max_iter)
+		info("Reaching maximal iterations in two-class probability estimates\n");
+	free(t);
+}
+
+static double sigmoid_predict(double decision_value, double A, double B)
+{
+	double fApB = decision_value*A+B;
+	// 1-p used later; avoid catastrophic cancellation
+	if (fApB >= 0)
+		return exp(-fApB)/(1.0+exp(-fApB));
+	else
+		return 1.0/(1+exp(fApB)) ;
+}
+
+// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
+static void multiclass_probability(int k, double **r, double *p)
+{
+	int t,j;
+	int iter = 0, max_iter=max(100,k);
+	double **Q=Malloc(double *,k);
+	double *Qp=Malloc(double,k);
+	double pQp, eps=0.005/k;
+
+	for (t=0;tmax_error)
+				max_error=error;
+		}
+		if (max_error=max_iter)
+		info("Exceeds max_iter in multiclass_prob\n");
+	for(t=0;tl);
+	double *dec_values = Malloc(double,prob->l);
+
+	// random shuffle
+	for(i=0;il;i++) perm[i]=i;
+	for(i=0;il;i++)
+	{
+		int j = i+bounded_rand_int(prob->l-i);
+		swap(perm[i],perm[j]);
+	}
+	for(i=0;il/nr_fold;
+		int end = (i+1)*prob->l/nr_fold;
+		int j,k;
+		struct PREFIX(problem) subprob;
+
+		subprob.l = prob->l-(end-begin);
+#ifdef _DENSE_REP
+		subprob.x = Malloc(struct PREFIX(node),subprob.l);
+#else
+		subprob.x = Malloc(struct PREFIX(node)*,subprob.l);
+#endif
+		subprob.y = Malloc(double,subprob.l);
+                subprob.W = Malloc(double,subprob.l);
+
+		k=0;
+		for(j=0;jx[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			subprob.W[k] = prob->W[perm[j]];
+			++k;
+		}
+		for(j=end;jl;j++)
+		{
+			subprob.x[k] = prob->x[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			subprob.W[k] = prob->W[perm[j]];
+			++k;
+		}
+		int p_count=0,n_count=0;
+		for(j=0;j0)
+				p_count++;
+			else
+				n_count++;
+
+		if(p_count==0 && n_count==0)
+			for(j=begin;j 0 && n_count == 0)
+			for(j=begin;j 0)
+			for(j=begin;jx+perm[j]),&(dec_values[perm[j]]), blas_functions);
+#else
+				PREFIX(predict_values)(submodel,prob->x[perm[j]],&(dec_values[perm[j]]), blas_functions);
+#endif
+				// ensure +1 -1 order; reason not using CV subroutine
+				dec_values[perm[j]] *= submodel->label[0];
+			}
+			PREFIX(free_and_destroy_model)(&submodel);
+			PREFIX(destroy_param)(&subparam);
+		}
+		free(subprob.x);
+		free(subprob.y);
+                free(subprob.W);
+	}
+	sigmoid_train(prob->l,dec_values,prob->y,probA,probB);
+	free(dec_values);
+	free(perm);
+}
+
+// Return parameter of a Laplace distribution
+static double svm_svr_probability(
+	const PREFIX(problem) *prob, const svm_parameter *param, BlasFunctions *blas_functions)
+{
+	int i;
+	int nr_fold = 5;
+	double *ymv = Malloc(double,prob->l);
+	double mae = 0;
+
+	svm_parameter newparam = *param;
+	newparam.probability = 0;
+    newparam.random_seed = -1; // This is called from train, which already sets
+                               // the seed.
+	PREFIX(cross_validation)(prob,&newparam,nr_fold,ymv, blas_functions);
+	for(i=0;il;i++)
+	{
+		ymv[i]=prob->y[i]-ymv[i];
+		mae += fabs(ymv[i]);
+	}
+	mae /= prob->l;
+	double std=sqrt(2*mae*mae);
+	int count=0;
+	mae=0;
+	for(i=0;il;i++)
+		if (fabs(ymv[i]) > 5*std)
+			count=count+1;
+		else
+			mae+=fabs(ymv[i]);
+	mae /= (prob->l-count);
+	info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae);
+	free(ymv);
+	return mae;
+}
+
+
+
+// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
+// perm, length l, must be allocated before calling this subroutine
+static void svm_group_classes(const PREFIX(problem) *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
+{
+	int l = prob->l;
+	int max_nr_class = 16;
+	int nr_class = 0;
+	int *label = Malloc(int,max_nr_class);
+	int *count = Malloc(int,max_nr_class);
+	int *data_label = Malloc(int,l);
+	int i, j, this_label, this_count;
+
+	for(i=0;iy[i];
+		for(j=0;j=0 && label[i] > this_label)
+                {
+                        label[i+1] = label[i];
+                        count[i+1] = count[i];
+                        i--;
+                }
+                label[i+1] = this_label;
+                count[i+1] = this_count;
+        }
+
+        for (i=0; iy[i];
+                while(this_label != label[j]){
+                        j ++;
+                }
+                data_label[i] = j;
+        }
+
+	int *start = Malloc(int,nr_class);
+	start[0] = 0;
+	for(i=1;i 0.
+//
+static void remove_zero_weight(PREFIX(problem) *newprob, const PREFIX(problem) *prob)
+{
+	int i;
+	int l = 0;
+	for(i=0;il;i++)
+		if(prob->W[i] > 0) l++;
+	*newprob = *prob;
+	newprob->l = l;
+#ifdef _DENSE_REP
+	newprob->x = Malloc(PREFIX(node),l);
+#else
+      	newprob->x = Malloc(PREFIX(node) *,l);
+#endif
+	newprob->y = Malloc(double,l);
+	newprob->W = Malloc(double,l);
+
+	int j = 0;
+	for(i=0;il;i++)
+		if(prob->W[i] > 0)
+		{
+			newprob->x[j] = prob->x[i];
+			newprob->y[j] = prob->y[i];
+			newprob->W[j] = prob->W[i];
+			j++;
+		}
+}
+
+//
+// Interface functions
+//
+PREFIX(model) *PREFIX(train)(const PREFIX(problem) *prob, const svm_parameter *param,
+        int *status, BlasFunctions *blas_functions)
+{
+	PREFIX(problem) newprob;
+	remove_zero_weight(&newprob, prob);
+	prob = &newprob;
+
+	PREFIX(model) *model = Malloc(PREFIX(model),1);
+	model->param = *param;
+	model->free_sv = 0;	// XXX
+
+    if(param->random_seed >= 0)
+    {
+        set_seed(param->random_seed);
+    }
+
+	if(param->svm_type == ONE_CLASS ||
+	   param->svm_type == EPSILON_SVR ||
+	   param->svm_type == NU_SVR)
+	{
+		// regression or one-class-svm
+		model->nr_class = 2;
+		model->label = NULL;
+		model->nSV = NULL;
+		model->probA = NULL; model->probB = NULL;
+		model->sv_coef = Malloc(double *,1);
+
+		if(param->probability &&
+		   (param->svm_type == EPSILON_SVR ||
+		    param->svm_type == NU_SVR))
+		{
+			model->probA = Malloc(double,1);
+			model->probA[0] = NAMESPACE::svm_svr_probability(prob,param,blas_functions);
+		}
+
+                NAMESPACE::decision_function f = NAMESPACE::svm_train_one(prob,param,0,0, status,blas_functions);
+		model->rho = Malloc(double,1);
+		model->rho[0] = f.rho;
+		model->n_iter = Malloc(int,1);
+		model->n_iter[0] = f.n_iter;
+
+		int nSV = 0;
+		int i;
+		for(i=0;il;i++)
+			if(fabs(f.alpha[i]) > 0) ++nSV;
+		model->l = nSV;
+#ifdef _DENSE_REP
+		model->SV = Malloc(PREFIX(node),nSV);
+#else
+		model->SV = Malloc(PREFIX(node) *,nSV);
+#endif
+                model->sv_ind = Malloc(int, nSV);
+		model->sv_coef[0] = Malloc(double, nSV);
+		int j = 0;
+		for(i=0;il;i++)
+			if(fabs(f.alpha[i]) > 0)
+			{
+				model->SV[j] = prob->x[i];
+                                model->sv_ind[j] = i;
+				model->sv_coef[0][j] = f.alpha[i];
+				++j;
+			}
+
+		free(f.alpha);
+	}
+	else
+	{
+		// classification
+		int l = prob->l;
+		int nr_class;
+		int *label = NULL;
+		int *start = NULL;
+		int *count = NULL;
+		int *perm = Malloc(int,l);
+
+		// group training data of the same class
+                NAMESPACE::svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
+#ifdef _DENSE_REP
+		PREFIX(node) *x = Malloc(PREFIX(node),l);
+#else
+		PREFIX(node) **x = Malloc(PREFIX(node) *,l);
+#endif
+                double *W = Malloc(double, l);
+
+		int i;
+		for(i=0;ix[perm[i]];
+			W[i] = prob->W[perm[i]];
+                }
+
+		// calculate weighted C
+
+		double *weighted_C = Malloc(double, nr_class);
+		for(i=0;iC;
+		for(i=0;inr_weight;i++)
+		{
+			int j;
+			for(j=0;jweight_label[i] == label[j])
+					break;
+			if(j == nr_class)
+				fprintf(stderr,"warning: class label %d specified in weight is not found\n", param->weight_label[i]);
+			else
+				weighted_C[j] *= param->weight[i];
+		}
+
+		// train k*(k-1)/2 models
+
+		bool *nonzero = Malloc(bool,l);
+		for(i=0;iprobability)
+		{
+			probA=Malloc(double,nr_class*(nr_class-1)/2);
+			probB=Malloc(double,nr_class*(nr_class-1)/2);
+		}
+
+		int p = 0;
+		for(i=0;iprobability)
+                                    NAMESPACE::svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p], status, blas_functions);
+
+				f[p] = NAMESPACE::svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j], status, blas_functions);
+				for(k=0;k 0)
+						nonzero[si+k] = true;
+				for(k=0;k 0)
+						nonzero[sj+k] = true;
+				free(sub_prob.x);
+				free(sub_prob.y);
+                                free(sub_prob.W);
+				++p;
+			}
+
+		// build output
+
+		model->nr_class = nr_class;
+
+		model->label = Malloc(int,nr_class);
+		for(i=0;ilabel[i] = label[i];
+
+		model->rho = Malloc(double,nr_class*(nr_class-1)/2);
+		model->n_iter = Malloc(int,nr_class*(nr_class-1)/2);
+		for(i=0;irho[i] = f[i].rho;
+			model->n_iter[i] = f[i].n_iter;
+		}
+
+		if(param->probability)
+		{
+			model->probA = Malloc(double,nr_class*(nr_class-1)/2);
+			model->probB = Malloc(double,nr_class*(nr_class-1)/2);
+			for(i=0;iprobA[i] = probA[i];
+				model->probB[i] = probB[i];
+			}
+		}
+		else
+		{
+			model->probA=NULL;
+			model->probB=NULL;
+		}
+
+		int total_sv = 0;
+		int *nz_count = Malloc(int,nr_class);
+		model->nSV = Malloc(int,nr_class);
+		for(i=0;inSV[i] = nSV;
+			nz_count[i] = nSV;
+		}
+
+                info("Total nSV = %d\n",total_sv);
+
+		model->l = total_sv;
+                model->sv_ind = Malloc(int, total_sv);
+#ifdef _DENSE_REP
+		model->SV = Malloc(PREFIX(node),total_sv);
+#else
+		model->SV = Malloc(PREFIX(node) *,total_sv);
+#endif
+		p = 0;
+		for(i=0;iSV[p] = x[i];
+                                model->sv_ind[p] = perm[i];
+                                ++p;
+                        }
+                }
+
+		int *nz_start = Malloc(int,nr_class);
+		nz_start[0] = 0;
+		for(i=1;isv_coef = Malloc(double *,nr_class-1);
+		for(i=0;isv_coef[i] = Malloc(double,total_sv);
+
+		p = 0;
+		for(i=0;isv_coef[j-1][q++] = f[p].alpha[k];
+				q = nz_start[j];
+				for(k=0;ksv_coef[i][q++] = f[p].alpha[ci+k];
+				++p;
+			}
+
+		free(label);
+		free(probA);
+		free(probB);
+		free(count);
+		free(perm);
+		free(start);
+                free(W);
+		free(x);
+		free(weighted_C);
+		free(nonzero);
+		for(i=0;il;
+	int *perm = Malloc(int,l);
+	int nr_class;
+    if(param->random_seed >= 0)
+    {
+        set_seed(param->random_seed);
+    }
+
+	// stratified cv may not give leave-one-out rate
+	// Each class to l folds -> some folds may have zero elements
+	if((param->svm_type == C_SVC ||
+	    param->svm_type == NU_SVC) && nr_fold < l)
+	{
+		int *start = NULL;
+		int *label = NULL;
+		int *count = NULL;
+                NAMESPACE::svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
+
+		// random shuffle and then data grouped by fold using the array perm
+		int *fold_count = Malloc(int,nr_fold);
+		int c;
+		int *index = Malloc(int,l);
+		for(i=0;ix[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			subprob.W[k] = prob->W[perm[j]];
+			++k;
+		}
+		for(j=end;jx[perm[j]];
+			subprob.y[k] = prob->y[perm[j]];
+			subprob.W[k] = prob->W[perm[j]];
+			++k;
+		}
+                int dummy_status = 0; // IGNORES TIMEOUT ERRORS
+		struct PREFIX(model) *submodel = PREFIX(train)(&subprob,param, &dummy_status, blas_functions);
+		if(param->probability &&
+		   (param->svm_type == C_SVC || param->svm_type == NU_SVC))
+		{
+			double *prob_estimates=Malloc(double, PREFIX(get_nr_class)(submodel));
+			for(j=begin;jx + perm[j]),prob_estimates, blas_functions);
+#else
+                                target[perm[j]] = PREFIX(predict_probability)(submodel,prob->x[perm[j]],prob_estimates, blas_functions);
+#endif
+			free(prob_estimates);
+		}
+		else
+			for(j=begin;jx+perm[j],blas_functions);
+#else
+                target[perm[j]] = PREFIX(predict)(submodel,prob->x[perm[j]],blas_functions);
+#endif
+		PREFIX(free_and_destroy_model)(&submodel);
+		free(subprob.x);
+		free(subprob.y);
+                free(subprob.W);
+	}
+	free(fold_start);
+	free(perm);
+}
+
+
+int PREFIX(get_svm_type)(const PREFIX(model) *model)
+{
+	return model->param.svm_type;
+}
+
+int PREFIX(get_nr_class)(const PREFIX(model) *model)
+{
+	return model->nr_class;
+}
+
+void PREFIX(get_labels)(const PREFIX(model) *model, int* label)
+{
+	if (model->label != NULL)
+		for(int i=0;inr_class;i++)
+			label[i] = model->label[i];
+}
+
+double PREFIX(get_svr_probability)(const PREFIX(model) *model)
+{
+	if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
+	    model->probA!=NULL)
+		return model->probA[0];
+	else
+	{
+		fprintf(stderr,"Model doesn't contain information for SVR probability inference\n");
+		return 0;
+	}
+}
+
+double PREFIX(predict_values)(const PREFIX(model) *model, const PREFIX(node) *x, double* dec_values, BlasFunctions *blas_functions)
+{
+	int i;
+	if(model->param.svm_type == ONE_CLASS ||
+	   model->param.svm_type == EPSILON_SVR ||
+	   model->param.svm_type == NU_SVR)
+	{
+		double *sv_coef = model->sv_coef[0];
+		double sum = 0;
+
+		for(i=0;il;i++)
+#ifdef _DENSE_REP
+                    sum += sv_coef[i] * NAMESPACE::Kernel::k_function(x,model->SV+i,model->param,blas_functions);
+#else
+                sum += sv_coef[i] * NAMESPACE::Kernel::k_function(x,model->SV[i],model->param,blas_functions);
+#endif
+		sum -= model->rho[0];
+		*dec_values = sum;
+
+		if(model->param.svm_type == ONE_CLASS)
+			return (sum>0)?1:-1;
+		else
+			return sum;
+	}
+	else
+	{
+		int nr_class = model->nr_class;
+		int l = model->l;
+
+		double *kvalue = Malloc(double,l);
+		for(i=0;iSV+i,model->param,blas_functions);
+#else
+                kvalue[i] = NAMESPACE::Kernel::k_function(x,model->SV[i],model->param,blas_functions);
+#endif
+
+		int *start = Malloc(int,nr_class);
+		start[0] = 0;
+		for(i=1;inSV[i-1];
+
+		int *vote = Malloc(int,nr_class);
+		for(i=0;inSV[i];
+				int cj = model->nSV[j];
+
+				int k;
+				double *coef1 = model->sv_coef[j-1];
+				double *coef2 = model->sv_coef[i];
+				for(k=0;krho[p];
+				dec_values[p] = sum;
+
+				if(dec_values[p] > 0)
+					++vote[i];
+				else
+					++vote[j];
+				p++;
+			}
+
+		int vote_max_idx = 0;
+		for(i=1;i vote[vote_max_idx])
+				vote_max_idx = i;
+
+		free(kvalue);
+		free(start);
+		free(vote);
+		return model->label[vote_max_idx];
+	}
+}
+
+double PREFIX(predict)(const PREFIX(model) *model, const PREFIX(node) *x, BlasFunctions *blas_functions)
+{
+	int nr_class = model->nr_class;
+	double *dec_values;
+	if(model->param.svm_type == ONE_CLASS ||
+	   model->param.svm_type == EPSILON_SVR ||
+	   model->param.svm_type == NU_SVR)
+		dec_values = Malloc(double, 1);
+	else
+		dec_values = Malloc(double, nr_class*(nr_class-1)/2);
+	double pred_result = PREFIX(predict_values)(model, x, dec_values, blas_functions);
+	free(dec_values);
+	return pred_result;
+}
+
+double PREFIX(predict_probability)(
+	const PREFIX(model) *model, const PREFIX(node) *x, double *prob_estimates, BlasFunctions *blas_functions)
+{
+	if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
+	    model->probA!=NULL && model->probB!=NULL)
+	{
+		int i;
+		int nr_class = model->nr_class;
+		double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);
+		PREFIX(predict_values)(model, x, dec_values, blas_functions);
+
+		double min_prob=1e-7;
+		double **pairwise_prob=Malloc(double *,nr_class);
+		for(i=0;iprobA[k],model->probB[k]),min_prob),1-min_prob);
+				pairwise_prob[j][i]=1-pairwise_prob[i][j];
+				k++;
+			}
+                NAMESPACE::multiclass_probability(nr_class,pairwise_prob,prob_estimates);
+
+		int prob_max_idx = 0;
+		for(i=1;i prob_estimates[prob_max_idx])
+				prob_max_idx = i;
+		for(i=0;ilabel[prob_max_idx];
+	}
+	else
+		return PREFIX(predict)(model, x, blas_functions);
+}
+
+
+void PREFIX(free_model_content)(PREFIX(model)* model_ptr)
+{
+	if(model_ptr->free_sv && model_ptr->l > 0 && model_ptr->SV != NULL)
+#ifdef _DENSE_REP
+		for (int i = 0; i < model_ptr->l; i++)
+			free(model_ptr->SV[i].values);
+#else
+		free((void *)(model_ptr->SV[0]));
+#endif
+
+	if(model_ptr->sv_coef)
+	{
+		for(int i=0;inr_class-1;i++)
+			free(model_ptr->sv_coef[i]);
+	}
+
+	free(model_ptr->SV);
+	model_ptr->SV = NULL;
+
+	free(model_ptr->sv_coef);
+	model_ptr->sv_coef = NULL;
+
+	free(model_ptr->sv_ind);
+	model_ptr->sv_ind = NULL;
+
+	free(model_ptr->rho);
+	model_ptr->rho = NULL;
+
+	free(model_ptr->label);
+	model_ptr->label= NULL;
+
+	free(model_ptr->probA);
+	model_ptr->probA = NULL;
+
+	free(model_ptr->probB);
+	model_ptr->probB= NULL;
+
+	free(model_ptr->nSV);
+	model_ptr->nSV = NULL;
+
+	free(model_ptr->n_iter);
+	model_ptr->n_iter = NULL;
+}
+
+void PREFIX(free_and_destroy_model)(PREFIX(model)** model_ptr_ptr)
+{
+	if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL)
+	{
+		PREFIX(free_model_content)(*model_ptr_ptr);
+		free(*model_ptr_ptr);
+		*model_ptr_ptr = NULL;
+	}
+}
+
+void PREFIX(destroy_param)(svm_parameter* param)
+{
+	free(param->weight_label);
+	free(param->weight);
+}
+
+const char *PREFIX(check_parameter)(const PREFIX(problem) *prob, const svm_parameter *param)
+{
+	// svm_type
+
+	int svm_type = param->svm_type;
+	if(svm_type != C_SVC &&
+	   svm_type != NU_SVC &&
+	   svm_type != ONE_CLASS &&
+	   svm_type != EPSILON_SVR &&
+	   svm_type != NU_SVR)
+		return "unknown svm type";
+
+	// kernel_type, degree
+
+	int kernel_type = param->kernel_type;
+	if(kernel_type != LINEAR &&
+	   kernel_type != POLY &&
+	   kernel_type != RBF &&
+	   kernel_type != SIGMOID &&
+	   kernel_type != PRECOMPUTED)
+		return "unknown kernel type";
+
+	if(param->gamma < 0)
+		return "gamma < 0";
+
+	if(param->degree < 0)
+		return "degree of polynomial kernel < 0";
+
+	// cache_size,eps,C,nu,p,shrinking
+
+	if(param->cache_size <= 0)
+		return "cache_size <= 0";
+
+	if(param->eps <= 0)
+		return "eps <= 0";
+
+	if(svm_type == C_SVC ||
+	   svm_type == EPSILON_SVR ||
+	   svm_type == NU_SVR)
+		if(param->C <= 0)
+			return "C <= 0";
+
+	if(svm_type == NU_SVC ||
+	   svm_type == ONE_CLASS ||
+	   svm_type == NU_SVR)
+		if(param->nu <= 0 || param->nu > 1)
+			return "nu <= 0 or nu > 1";
+
+	if(svm_type == EPSILON_SVR)
+		if(param->p < 0)
+			return "p < 0";
+
+	if(param->shrinking != 0 &&
+	   param->shrinking != 1)
+		return "shrinking != 0 and shrinking != 1";
+
+	if(param->probability != 0 &&
+	   param->probability != 1)
+		return "probability != 0 and probability != 1";
+
+	if(param->probability == 1 &&
+	   svm_type == ONE_CLASS)
+		return "one-class SVM probability output not supported yet";
+
+
+	// check whether nu-svc is feasible
+
+	if(svm_type == NU_SVC)
+	{
+		int l = prob->l;
+		int max_nr_class = 16;
+		int nr_class = 0;
+		int *label = Malloc(int,max_nr_class);
+		double *count = Malloc(double,max_nr_class);
+
+		int i;
+		for(i=0;iy[i];
+			int j;
+			for(j=0;jW[i];
+					break;
+				}
+			if(j == nr_class)
+			{
+				if(nr_class == max_nr_class)
+				{
+					max_nr_class *= 2;
+					label = (int *)realloc(label,max_nr_class*sizeof(int));
+					count = (double *)realloc(count,max_nr_class*sizeof(double));
+
+				}
+				label[nr_class] = this_label;
+				count[nr_class] = prob->W[i];
+				++nr_class;
+			}
+		}
+
+		for(i=0;inu*(n1+n2)/2 > min(n1,n2))
+				{
+					free(label);
+					free(count);
+					return "specified nu is infeasible";
+				}
+			}
+		}
+		free(label);
+		free(count);
+	}
+
+	if(svm_type == C_SVC ||
+	   svm_type == EPSILON_SVR ||
+	   svm_type == NU_SVR ||
+	   svm_type == ONE_CLASS)
+	{
+		PREFIX(problem) newprob;
+		// filter samples with negative and null weights
+		remove_zero_weight(&newprob, prob);
+
+		// all samples were removed
+		if(newprob.l == 0) {
+			free(newprob.x);
+			free(newprob.y);
+			free(newprob.W);
+			return "Invalid input - all samples have zero or negative weights.";
+		}
+		else if(prob->l != newprob.l &&
+		        svm_type == C_SVC)
+		{
+			bool only_one_label = true;
+			int first_label = newprob.y[0];
+			for(int i=1;i
+ */
+#ifndef _NEWRAND_H
+#define _NEWRAND_H
+
+#ifdef __cplusplus
+#include   // needed for cython to generate a .cpp file from newrand.h
+extern "C" {
+#endif
+
+// Scikit-Learn-specific random number generator replacing `rand()` originally
+// used in LibSVM / LibLinear, to ensure the same behaviour on windows-linux,
+// with increased speed
+// - (1) Init a `mt_rand` object
+std::mt19937 mt_rand(std::mt19937::default_seed);
+
+// - (2) public `set_seed()` function that should be used instead of `srand()` to set a new seed.
+void set_seed(unsigned custom_seed) {
+    mt_rand.seed(custom_seed);
+}
+
+// - (3) New internal `bounded_rand_int` function, used instead of rand() everywhere.
+inline uint32_t bounded_rand_int(uint32_t range) {
+    // "LibSVM / LibLinear Original way" - make a 31bit positive
+    // random number and use modulo to make it fit in the range
+    // return abs( (int)mt_rand()) % range;
+
+    // "Better way": tweaked Lemire post-processor
+    // from http://www.pcg-random.org/posts/bounded-rands.html
+    uint32_t x = mt_rand();
+    uint64_t m = uint64_t(x) * uint64_t(range);
+    uint32_t l = uint32_t(m);
+    if (l < range) {
+        uint32_t t = -range;
+        if (t >= range) {
+            t -= range;
+            if (t >= range)
+                t %= range;
+        }
+        while (l < t) {
+            x = mt_rand();
+            m = uint64_t(x) * uint64_t(range);
+            l = uint32_t(m);
+        }
+    }
+    return m >> 32;
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _NEWRAND_H */
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_bounds.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_bounds.py
new file mode 100644
index 0000000000000000000000000000000000000000..af7e8cfb1159d1c7520d4b506015727c80391cad
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_bounds.py
@@ -0,0 +1,147 @@
+import numpy as np
+import pytest
+from scipy import stats
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import LinearSVC
+from sklearn.svm._bounds import l1_min_c
+from sklearn.svm._newrand import bounded_rand_int_wrap, set_seed_wrap
+from sklearn.utils.fixes import CSR_CONTAINERS
+
+dense_X = [[-1, 0], [0, 1], [1, 1], [1, 1]]
+
+Y1 = [0, 1, 1, 1]
+Y2 = [2, 1, 0, 0]
+
+
+# TODO(1.8): remove filterwarnings after the deprecation of liblinear multiclass
+#            and maybe remove LogisticRegression from this test
+@pytest.mark.filterwarnings(
+    "ignore:.*'liblinear' solver for multiclass classification is deprecated.*"
+)
+@pytest.mark.parametrize("X_container", CSR_CONTAINERS + [np.array])
+@pytest.mark.parametrize("loss", ["squared_hinge", "log"])
+@pytest.mark.parametrize("Y_label", ["two-classes", "multi-class"])
+@pytest.mark.parametrize("intercept_label", ["no-intercept", "fit-intercept"])
+def test_l1_min_c(X_container, loss, Y_label, intercept_label):
+    Ys = {"two-classes": Y1, "multi-class": Y2}
+    intercepts = {
+        "no-intercept": {"fit_intercept": False},
+        "fit-intercept": {"fit_intercept": True, "intercept_scaling": 10},
+    }
+
+    X = X_container(dense_X)
+    Y = Ys[Y_label]
+    intercept_params = intercepts[intercept_label]
+    check_l1_min_c(X, Y, loss, **intercept_params)
+
+
+def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=1.0):
+    min_c = l1_min_c(
+        X,
+        y,
+        loss=loss,
+        fit_intercept=fit_intercept,
+        intercept_scaling=intercept_scaling,
+    )
+
+    clf = {
+        "log": LogisticRegression(penalty="l1", solver="liblinear"),
+        "squared_hinge": LinearSVC(loss="squared_hinge", penalty="l1", dual=False),
+    }[loss]
+
+    clf.fit_intercept = fit_intercept
+    clf.intercept_scaling = intercept_scaling
+
+    clf.C = min_c
+    clf.fit(X, y)
+    assert (np.asarray(clf.coef_) == 0).all()
+    assert (np.asarray(clf.intercept_) == 0).all()
+
+    clf.C = min_c * 1.01
+    clf.fit(X, y)
+    assert (np.asarray(clf.coef_) != 0).any() or (np.asarray(clf.intercept_) != 0).any()
+
+
+def test_ill_posed_min_c():
+    X = [[0, 0], [0, 0]]
+    y = [0, 1]
+    with pytest.raises(ValueError):
+        l1_min_c(X, y)
+
+
+_MAX_UNSIGNED_INT = 4294967295
+
+
+def test_newrand_default():
+    """Test that bounded_rand_int_wrap without seeding respects the range
+
+    Note this test should pass either if executed alone, or in conjunctions
+    with other tests that call set_seed explicit in any order: it checks
+    invariants on the RNG instead of specific values.
+    """
+    generated = [bounded_rand_int_wrap(100) for _ in range(10)]
+    assert all(0 <= x < 100 for x in generated)
+    assert not all(x == generated[0] for x in generated)
+
+
+@pytest.mark.parametrize("seed, expected", [(0, 54), (_MAX_UNSIGNED_INT, 9)])
+def test_newrand_set_seed(seed, expected):
+    """Test that `set_seed` produces deterministic results"""
+    set_seed_wrap(seed)
+    generated = bounded_rand_int_wrap(100)
+    assert generated == expected
+
+
+@pytest.mark.parametrize("seed", [-1, _MAX_UNSIGNED_INT + 1])
+def test_newrand_set_seed_overflow(seed):
+    """Test that `set_seed_wrap` is defined for unsigned 32bits ints"""
+    with pytest.raises(OverflowError):
+        set_seed_wrap(seed)
+
+
+@pytest.mark.parametrize("range_, n_pts", [(_MAX_UNSIGNED_INT, 10000), (100, 25)])
+def test_newrand_bounded_rand_int(range_, n_pts):
+    """Test that `bounded_rand_int` follows a uniform distribution"""
+    # XXX: this test is very seed sensitive: either it is wrong (too strict?)
+    # or the wrapped RNG is not uniform enough, at least on some platforms.
+    set_seed_wrap(42)
+    n_iter = 100
+    ks_pvals = []
+    uniform_dist = stats.uniform(loc=0, scale=range_)
+    # perform multiple samplings to make chance of outlier sampling negligible
+    for _ in range(n_iter):
+        # Deterministic random sampling
+        sample = [bounded_rand_int_wrap(range_) for _ in range(n_pts)]
+        res = stats.kstest(sample, uniform_dist.cdf)
+        ks_pvals.append(res.pvalue)
+    # Null hypothesis = samples come from an uniform distribution.
+    # Under the null hypothesis, p-values should be uniformly distributed
+    # and not concentrated on low values
+    # (this may seem counter-intuitive but is backed by multiple refs)
+    # So we can do two checks:
+
+    # (1) check uniformity of p-values
+    uniform_p_vals_dist = stats.uniform(loc=0, scale=1)
+    res_pvals = stats.kstest(ks_pvals, uniform_p_vals_dist.cdf)
+    assert res_pvals.pvalue > 0.05, (
+        "Null hypothesis rejected: generated random numbers are not uniform."
+        " Details: the (meta) p-value of the test of uniform distribution"
+        f" of p-values is {res_pvals.pvalue} which is not > 0.05"
+    )
+
+    # (2) (safety belt) check that 90% of p-values are above 0.05
+    min_10pct_pval = np.percentile(ks_pvals, q=10)
+    # lower 10th quantile pvalue <= 0.05 means that the test rejects the
+    # null hypothesis that the sample came from the uniform distribution
+    assert min_10pct_pval > 0.05, (
+        "Null hypothesis rejected: generated random numbers are not uniform. "
+        f"Details: lower 10th quantile p-value of {min_10pct_pval} not > 0.05."
+    )
+
+
+@pytest.mark.parametrize("range_", [-1, _MAX_UNSIGNED_INT + 1])
+def test_newrand_bounded_rand_int_limits(range_):
+    """Test that `bounded_rand_int_wrap` is defined for unsigned 32bits ints"""
+    with pytest.raises(OverflowError):
+        bounded_rand_int_wrap(range_)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_sparse.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e22c86a66cd8b5625f100990e441675c7f62e34
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_sparse.py
@@ -0,0 +1,496 @@
+import numpy as np
+import pytest
+from scipy import sparse
+
+from sklearn import base, datasets, linear_model, svm
+from sklearn.datasets import load_digits, make_blobs, make_classification
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.svm.tests import test_svm
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_array_almost_equal,
+    assert_array_equal,
+    ignore_warnings,
+    skip_if_32bit,
+)
+from sklearn.utils.extmath import safe_sparse_dot
+from sklearn.utils.fixes import (
+    CSR_CONTAINERS,
+    DOK_CONTAINERS,
+    LIL_CONTAINERS,
+)
+
+# test sample 1
+X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
+Y = [1, 1, 1, 2, 2, 2]
+T = np.array([[-1, -1], [2, 2], [3, 2]])
+true_result = [1, 2, 2]
+
+# test sample 2
+X2 = np.array(
+    [
+        [0, 0, 0],
+        [1, 1, 1],
+        [2, 0, 0],
+        [0, 0, 2],
+        [3, 3, 3],
+    ]
+)
+Y2 = [1, 2, 2, 2, 3]
+T2 = np.array([[-1, -1, -1], [1, 1, 1], [2, 2, 2]])
+true_result2 = [1, 2, 3]
+
+iris = datasets.load_iris()
+rng = np.random.RandomState(0)
+perm = rng.permutation(iris.target.size)
+iris.data = iris.data[perm]
+iris.target = iris.target[perm]
+
+X_blobs, y_blobs = make_blobs(n_samples=100, centers=10, random_state=0)
+
+
+def check_svm_model_equal(dense_svm, X_train, y_train, X_test):
+    # Use the original svm model for dense fit and clone an exactly same
+    # svm model for sparse fit
+    sparse_svm = base.clone(dense_svm)
+
+    dense_svm.fit(X_train.toarray(), y_train)
+    if sparse.issparse(X_test):
+        X_test_dense = X_test.toarray()
+    else:
+        X_test_dense = X_test
+    sparse_svm.fit(X_train, y_train)
+    assert sparse.issparse(sparse_svm.support_vectors_)
+    assert sparse.issparse(sparse_svm.dual_coef_)
+    assert_allclose(dense_svm.support_vectors_, sparse_svm.support_vectors_.toarray())
+    assert_allclose(dense_svm.dual_coef_, sparse_svm.dual_coef_.toarray())
+    if dense_svm.kernel == "linear":
+        assert sparse.issparse(sparse_svm.coef_)
+        assert_array_almost_equal(dense_svm.coef_, sparse_svm.coef_.toarray())
+    assert_allclose(dense_svm.support_, sparse_svm.support_)
+    assert_allclose(dense_svm.predict(X_test_dense), sparse_svm.predict(X_test))
+
+    assert_array_almost_equal(
+        dense_svm.decision_function(X_test_dense), sparse_svm.decision_function(X_test)
+    )
+    assert_array_almost_equal(
+        dense_svm.decision_function(X_test_dense),
+        sparse_svm.decision_function(X_test_dense),
+    )
+    if isinstance(dense_svm, svm.OneClassSVM):
+        msg = "cannot use sparse input in 'OneClassSVM' trained on dense data"
+    else:
+        assert_array_almost_equal(
+            dense_svm.predict_proba(X_test_dense),
+            sparse_svm.predict_proba(X_test),
+            decimal=4,
+        )
+        msg = "cannot use sparse input in 'SVC' trained on dense data"
+    if sparse.issparse(X_test):
+        with pytest.raises(ValueError, match=msg):
+            dense_svm.predict(X_test)
+
+
+@skip_if_32bit
+@pytest.mark.parametrize(
+    "X_train, y_train, X_test",
+    [
+        [X, Y, T],
+        [X2, Y2, T2],
+        [X_blobs[:80], y_blobs[:80], X_blobs[80:]],
+        [iris.data, iris.target, iris.data],
+    ],
+)
+@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
+@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
+def test_svc(X_train, y_train, X_test, kernel, sparse_container):
+    """Check that sparse SVC gives the same result as SVC."""
+    X_train = sparse_container(X_train)
+
+    clf = svm.SVC(
+        gamma=1,
+        kernel=kernel,
+        probability=True,
+        random_state=0,
+        decision_function_shape="ovo",
+    )
+    check_svm_model_equal(clf, X_train, y_train, X_test)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_unsorted_indices(csr_container):
+    # test that the result with sorted and unsorted indices in csr is the same
+    # we use a subset of digits as iris, blobs or make_classification didn't
+    # show the problem
+    X, y = load_digits(return_X_y=True)
+    X_test = csr_container(X[50:100])
+    X, y = X[:50], y[:50]
+    tols = dict(rtol=1e-12, atol=1e-14)
+
+    X_sparse = csr_container(X)
+    coef_dense = (
+        svm.SVC(kernel="linear", probability=True, random_state=0).fit(X, y).coef_
+    )
+    sparse_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
+        X_sparse, y
+    )
+    coef_sorted = sparse_svc.coef_
+    # make sure dense and sparse SVM give the same result
+    assert_allclose(coef_dense, coef_sorted.toarray(), **tols)
+
+    # reverse each row's indices
+    def scramble_indices(X):
+        new_data = []
+        new_indices = []
+        for i in range(1, len(X.indptr)):
+            row_slice = slice(*X.indptr[i - 1 : i + 1])
+            new_data.extend(X.data[row_slice][::-1])
+            new_indices.extend(X.indices[row_slice][::-1])
+        return csr_container((new_data, new_indices, X.indptr), shape=X.shape)
+
+    X_sparse_unsorted = scramble_indices(X_sparse)
+    X_test_unsorted = scramble_indices(X_test)
+
+    assert not X_sparse_unsorted.has_sorted_indices
+    assert not X_test_unsorted.has_sorted_indices
+
+    unsorted_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
+        X_sparse_unsorted, y
+    )
+    coef_unsorted = unsorted_svc.coef_
+    # make sure unsorted indices give same result
+    assert_allclose(coef_unsorted.toarray(), coef_sorted.toarray(), **tols)
+    assert_allclose(
+        sparse_svc.predict_proba(X_test_unsorted),
+        sparse_svc.predict_proba(X_test),
+        **tols,
+    )
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_svc_with_custom_kernel(lil_container):
+    def kfunc(x, y):
+        return safe_sparse_dot(x, y.T)
+
+    X_sp = lil_container(X)
+    clf_lin = svm.SVC(kernel="linear").fit(X_sp, Y)
+    clf_mylin = svm.SVC(kernel=kfunc).fit(X_sp, Y)
+    assert_array_equal(clf_lin.predict(X_sp), clf_mylin.predict(X_sp))
+
+
+@skip_if_32bit
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf"])
+def test_svc_iris(csr_container, kernel):
+    # Test the sparse SVC with the iris dataset
+    iris_data_sp = csr_container(iris.data)
+
+    sp_clf = svm.SVC(kernel=kernel).fit(iris_data_sp, iris.target)
+    clf = svm.SVC(kernel=kernel).fit(iris.data, iris.target)
+
+    assert_allclose(clf.support_vectors_, sp_clf.support_vectors_.toarray())
+    assert_allclose(clf.dual_coef_, sp_clf.dual_coef_.toarray())
+    assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))
+    if kernel == "linear":
+        assert_allclose(clf.coef_, sp_clf.coef_.toarray())
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_decision_function(csr_container):
+    # Test decision_function
+
+    # Sanity check, test that decision_function implemented in python
+    # returns the same as the one in libsvm
+
+    # multi class:
+    iris_data_sp = csr_container(iris.data)
+    svc = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo")
+    clf = svc.fit(iris_data_sp, iris.target)
+
+    dec = safe_sparse_dot(iris_data_sp, clf.coef_.T) + clf.intercept_
+
+    assert_allclose(dec, clf.decision_function(iris_data_sp))
+
+    # binary:
+    clf.fit(X, Y)
+    dec = np.dot(X, clf.coef_.T) + clf.intercept_
+    prediction = clf.predict(X)
+    assert_allclose(dec.ravel(), clf.decision_function(X))
+    assert_allclose(
+        prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int).ravel()]
+    )
+    expected = np.array([-1.0, -0.66, -1.0, 0.66, 1.0, 1.0])
+    assert_array_almost_equal(clf.decision_function(X), expected, decimal=2)
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_error(lil_container):
+    # Test that it gives proper exception on deficient input
+    clf = svm.SVC()
+    X_sp = lil_container(X)
+
+    Y2 = Y[:-1]  # wrong dimensions for labels
+    with pytest.raises(ValueError):
+        clf.fit(X_sp, Y2)
+
+    clf.fit(X_sp, Y)
+    assert_array_equal(clf.predict(T), true_result)
+
+
+@pytest.mark.parametrize(
+    "lil_container, dok_container", zip(LIL_CONTAINERS, DOK_CONTAINERS)
+)
+def test_linearsvc(lil_container, dok_container):
+    # Similar to test_SVC
+    X_sp = lil_container(X)
+    X2_sp = dok_container(X2)
+
+    clf = svm.LinearSVC(random_state=0).fit(X, Y)
+    sp_clf = svm.LinearSVC(random_state=0).fit(X_sp, Y)
+
+    assert sp_clf.fit_intercept
+
+    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
+    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
+
+    assert_allclose(clf.predict(X), sp_clf.predict(X_sp))
+
+    clf.fit(X2, Y2)
+    sp_clf.fit(X2_sp, Y2)
+
+    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=4)
+    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=4)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_linearsvc_iris(csr_container):
+    # Test the sparse LinearSVC with the iris dataset
+    iris_data_sp = csr_container(iris.data)
+
+    sp_clf = svm.LinearSVC(random_state=0).fit(iris_data_sp, iris.target)
+    clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)
+
+    assert clf.fit_intercept == sp_clf.fit_intercept
+
+    assert_array_almost_equal(clf.coef_, sp_clf.coef_, decimal=1)
+    assert_array_almost_equal(clf.intercept_, sp_clf.intercept_, decimal=1)
+    assert_allclose(clf.predict(iris.data), sp_clf.predict(iris_data_sp))
+
+    # check decision_function
+    pred = np.argmax(sp_clf.decision_function(iris_data_sp), axis=1)
+    assert_allclose(pred, clf.predict(iris.data))
+
+    # sparsify the coefficients on both models and check that they still
+    # produce the same results
+    clf.sparsify()
+    assert_array_equal(pred, clf.predict(iris_data_sp))
+    sp_clf.sparsify()
+    assert_array_equal(pred, sp_clf.predict(iris_data_sp))
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_weight(csr_container):
+    # Test class weights
+    X_, y_ = make_classification(
+        n_samples=200, n_features=100, weights=[0.833, 0.167], random_state=0
+    )
+
+    X_ = csr_container(X_)
+    for clf in (
+        linear_model.LogisticRegression(),
+        svm.LinearSVC(random_state=0),
+        svm.SVC(),
+    ):
+        clf.set_params(class_weight={0: 5})
+        clf.fit(X_[:180], y_[:180])
+        y_pred = clf.predict(X_[180:])
+        assert np.sum(y_pred == y_[180:]) >= 11
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_sample_weights(lil_container):
+    # Test weights on individual samples
+    X_sp = lil_container(X)
+
+    clf = svm.SVC()
+    clf.fit(X_sp, Y)
+    assert_array_equal(clf.predict([X[2]]), [1.0])
+
+    sample_weight = [0.1] * 3 + [10] * 3
+    clf.fit(X_sp, Y, sample_weight=sample_weight)
+    assert_array_equal(clf.predict([X[2]]), [2.0])
+
+
+def test_sparse_liblinear_intercept_handling():
+    # Test that sparse liblinear honours intercept_scaling param
+    test_svm.test_dense_liblinear_intercept_handling(svm.LinearSVC)
+
+
+@pytest.mark.parametrize(
+    "X_train, y_train, X_test",
+    [
+        [X, None, T],
+        [X2, None, T2],
+        [X_blobs[:80], None, X_blobs[80:]],
+        [iris.data, None, iris.data],
+    ],
+)
+@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
+@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
+@skip_if_32bit
+def test_sparse_oneclasssvm(X_train, y_train, X_test, kernel, sparse_container):
+    # Check that sparse OneClassSVM gives the same result as dense OneClassSVM
+    X_train = sparse_container(X_train)
+
+    clf = svm.OneClassSVM(gamma=1, kernel=kernel)
+    check_svm_model_equal(clf, X_train, y_train, X_test)
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_realdata(csr_container):
+    # Test on a subset from the 20newsgroups dataset.
+    # This catches some bugs if input is not correctly converted into
+    # sparse format or weights are not correctly initialized.
+    data = np.array([0.03771744, 0.1003567, 0.01174647, 0.027069])
+
+    # SVC does not support large sparse, so we specify int32 indices
+    # In this case, `csr_matrix` automatically uses int32 regardless of the dtypes of
+    # `indices` and `indptr` but `csr_array` may or may not use the same dtype as
+    # `indices` and `indptr`, which would be int64 if not specified
+    indices = np.array([6, 5, 35, 31], dtype=np.int32)
+    indptr = np.array([0] * 8 + [1] * 32 + [2] * 38 + [4] * 3, dtype=np.int32)
+
+    X = csr_container((data, indices, indptr))
+    y = np.array(
+        [
+            1.0,
+            0.0,
+            2.0,
+            2.0,
+            1.0,
+            1.0,
+            1.0,
+            2.0,
+            2.0,
+            0.0,
+            1.0,
+            2.0,
+            2.0,
+            0.0,
+            2.0,
+            0.0,
+            3.0,
+            0.0,
+            3.0,
+            0.0,
+            1.0,
+            1.0,
+            3.0,
+            2.0,
+            3.0,
+            2.0,
+            0.0,
+            3.0,
+            1.0,
+            0.0,
+            2.0,
+            1.0,
+            2.0,
+            0.0,
+            1.0,
+            0.0,
+            2.0,
+            3.0,
+            1.0,
+            3.0,
+            0.0,
+            1.0,
+            0.0,
+            0.0,
+            2.0,
+            0.0,
+            1.0,
+            2.0,
+            2.0,
+            2.0,
+            3.0,
+            2.0,
+            0.0,
+            3.0,
+            2.0,
+            1.0,
+            2.0,
+            3.0,
+            2.0,
+            2.0,
+            0.0,
+            1.0,
+            0.0,
+            1.0,
+            2.0,
+            3.0,
+            0.0,
+            0.0,
+            2.0,
+            2.0,
+            1.0,
+            3.0,
+            1.0,
+            1.0,
+            0.0,
+            1.0,
+            2.0,
+            1.0,
+            1.0,
+            3.0,
+        ]
+    )
+
+    clf = svm.SVC(kernel="linear").fit(X.toarray(), y)
+    sp_clf = svm.SVC(kernel="linear").fit(X.tocoo(), y)
+
+    assert_array_equal(clf.support_vectors_, sp_clf.support_vectors_.toarray())
+    assert_array_equal(clf.dual_coef_, sp_clf.dual_coef_.toarray())
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_sparse_svc_clone_with_callable_kernel(lil_container):
+    # Test that the "dense_fit" is called even though we use sparse input
+    # meaning that everything works fine.
+    a = svm.SVC(C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0)
+    b = base.clone(a)
+
+    X_sp = lil_container(X)
+    b.fit(X_sp, Y)
+    pred = b.predict(X_sp)
+    b.predict_proba(X_sp)
+
+    dense_svm = svm.SVC(
+        C=1, kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0
+    )
+    pred_dense = dense_svm.fit(X, Y).predict(X)
+    assert_array_equal(pred_dense, pred)
+    # b.decision_function(X_sp)  # XXX : should be supported
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_timeout(lil_container):
+    sp = svm.SVC(
+        C=1, kernel=lambda x, y: x @ y.T, probability=True, random_state=0, max_iter=1
+    )
+    warning_msg = (
+        r"Solver terminated early \(max_iter=1\).  Consider pre-processing "
+        r"your data with StandardScaler or MinMaxScaler."
+    )
+    with pytest.warns(ConvergenceWarning, match=warning_msg):
+        sp.fit(lil_container(X), Y)
+
+
+def test_consistent_proba():
+    a = svm.SVC(probability=True, max_iter=1, random_state=0)
+    with ignore_warnings(category=ConvergenceWarning):
+        proba_1 = a.fit(X, Y).predict_proba(X)
+    a = svm.SVC(probability=True, max_iter=1, random_state=0)
+    with ignore_warnings(category=ConvergenceWarning):
+        proba_2 = a.fit(X, Y).predict_proba(X)
+    assert_allclose(proba_1, proba_2)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_svm.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_svm.py
new file mode 100644
index 0000000000000000000000000000000000000000..62396451e736d02fffce21dd1f7219eba2614199
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/svm/tests/test_svm.py
@@ -0,0 +1,1440 @@
+"""
+Testing for Support Vector Machine module (sklearn.svm)
+
+TODO: remove hard coded numerical results when possible
+"""
+
+import numpy as np
+import pytest
+from numpy.testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+)
+
+from sklearn import base, datasets, linear_model, metrics, svm
+from sklearn.datasets import make_blobs, make_classification, make_regression
+from sklearn.exceptions import (
+    ConvergenceWarning,
+    NotFittedError,
+)
+from sklearn.metrics import f1_score
+from sklearn.metrics.pairwise import rbf_kernel
+from sklearn.model_selection import train_test_split
+from sklearn.multiclass import OneVsRestClassifier
+
+# mypy error: Module 'sklearn.svm' has no attribute '_libsvm'
+from sklearn.svm import (  # type: ignore[attr-defined]
+    SVR,
+    LinearSVC,
+    LinearSVR,
+    NuSVR,
+    OneClassSVM,
+    _libsvm,
+)
+from sklearn.svm._classes import _validate_dual_parameter
+from sklearn.utils import check_random_state, shuffle
+from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS, LIL_CONTAINERS
+from sklearn.utils.validation import _num_samples
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+Y = [1, 1, 1, 2, 2, 2]
+T = [[-1, -1], [2, 2], [3, 2]]
+true_result = [1, 2, 2]
+
+# also load the iris dataset
+iris = datasets.load_iris()
+rng = check_random_state(42)
+perm = rng.permutation(iris.target.size)
+iris.data = iris.data[perm]
+iris.target = iris.target[perm]
+
+
+def test_libsvm_parameters():
+    # Test parameters on classes that make use of libsvm.
+    clf = svm.SVC(kernel="linear").fit(X, Y)
+    assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
+    assert_array_equal(clf.support_, [1, 3])
+    assert_array_equal(clf.support_vectors_, (X[1], X[3]))
+    assert_array_equal(clf.intercept_, [0.0])
+    assert_array_equal(clf.predict(X), Y)
+
+
+def test_libsvm_iris():
+    # Check consistency on dataset iris.
+
+    # shuffle the dataset so that labels are not ordered
+    for k in ("linear", "rbf"):
+        clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
+        assert np.mean(clf.predict(iris.data) == iris.target) > 0.9
+        assert hasattr(clf, "coef_") == (k == "linear")
+
+    assert_array_equal(clf.classes_, np.sort(clf.classes_))
+
+    # check also the low-level API
+    # We unpack the values to create a dictionary with some of the return values
+    # from Libsvm's fit.
+    (
+        libsvm_support,
+        libsvm_support_vectors,
+        libsvm_n_class_SV,
+        libsvm_sv_coef,
+        libsvm_intercept,
+        libsvm_probA,
+        libsvm_probB,
+        # libsvm_fit_status and libsvm_n_iter won't be used below.
+        libsvm_fit_status,
+        libsvm_n_iter,
+    ) = _libsvm.fit(iris.data, iris.target.astype(np.float64))
+
+    model_params = {
+        "support": libsvm_support,
+        "SV": libsvm_support_vectors,
+        "nSV": libsvm_n_class_SV,
+        "sv_coef": libsvm_sv_coef,
+        "intercept": libsvm_intercept,
+        "probA": libsvm_probA,
+        "probB": libsvm_probB,
+    }
+    pred = _libsvm.predict(iris.data, **model_params)
+    assert np.mean(pred == iris.target) > 0.95
+
+    # We unpack the values to create a dictionary with some of the return values
+    # from Libsvm's fit.
+    (
+        libsvm_support,
+        libsvm_support_vectors,
+        libsvm_n_class_SV,
+        libsvm_sv_coef,
+        libsvm_intercept,
+        libsvm_probA,
+        libsvm_probB,
+        # libsvm_fit_status and libsvm_n_iter won't be used below.
+        libsvm_fit_status,
+        libsvm_n_iter,
+    ) = _libsvm.fit(iris.data, iris.target.astype(np.float64), kernel="linear")
+
+    model_params = {
+        "support": libsvm_support,
+        "SV": libsvm_support_vectors,
+        "nSV": libsvm_n_class_SV,
+        "sv_coef": libsvm_sv_coef,
+        "intercept": libsvm_intercept,
+        "probA": libsvm_probA,
+        "probB": libsvm_probB,
+    }
+    pred = _libsvm.predict(iris.data, **model_params, kernel="linear")
+    assert np.mean(pred == iris.target) > 0.95
+
+    pred = _libsvm.cross_validation(
+        iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
+    )
+    assert np.mean(pred == iris.target) > 0.95
+
+    # If random_seed >= 0, the libsvm rng is seeded (by calling `srand`), hence
+    # we should get deterministic results (assuming that there is no other
+    # thread calling this wrapper calling `srand` concurrently).
+    pred2 = _libsvm.cross_validation(
+        iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
+    )
+    assert_array_equal(pred, pred2)
+
+
+def test_precomputed():
+    # SVC with a precomputed kernel.
+    # We test it with a toy dataset and with iris.
+    clf = svm.SVC(kernel="precomputed")
+    # Gram matrix for train data (square matrix)
+    # (we use just a linear kernel)
+    K = np.dot(X, np.array(X).T)
+    clf.fit(K, Y)
+    # Gram matrix for test data (rectangular matrix)
+    KT = np.dot(T, np.array(X).T)
+    pred = clf.predict(KT)
+    with pytest.raises(ValueError):
+        clf.predict(KT.T)
+
+    assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
+    assert_array_equal(clf.support_, [1, 3])
+    assert_array_equal(clf.intercept_, [0])
+    assert_array_almost_equal(clf.support_, [1, 3])
+    assert_array_equal(pred, true_result)
+
+    # Gram matrix for test data but compute KT[i,j]
+    # for support vectors j only.
+    KT = np.zeros_like(KT)
+    for i in range(len(T)):
+        for j in clf.support_:
+            KT[i, j] = np.dot(T[i], X[j])
+
+    pred = clf.predict(KT)
+    assert_array_equal(pred, true_result)
+
+    # same as before, but using a callable function instead of the kernel
+    # matrix. kernel is just a linear kernel
+
+    def kfunc(x, y):
+        return np.dot(x, y.T)
+
+    clf = svm.SVC(kernel=kfunc)
+    clf.fit(np.array(X), Y)
+    pred = clf.predict(T)
+
+    assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
+    assert_array_equal(clf.intercept_, [0])
+    assert_array_almost_equal(clf.support_, [1, 3])
+    assert_array_equal(pred, true_result)
+
+    # test a precomputed kernel with the iris dataset
+    # and check parameters against a linear SVC
+    clf = svm.SVC(kernel="precomputed")
+    clf2 = svm.SVC(kernel="linear")
+    K = np.dot(iris.data, iris.data.T)
+    clf.fit(K, iris.target)
+    clf2.fit(iris.data, iris.target)
+    pred = clf.predict(K)
+    assert_array_almost_equal(clf.support_, clf2.support_)
+    assert_array_almost_equal(clf.dual_coef_, clf2.dual_coef_)
+    assert_array_almost_equal(clf.intercept_, clf2.intercept_)
+    assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
+
+    # Gram matrix for test data but compute KT[i,j]
+    # for support vectors j only.
+    K = np.zeros_like(K)
+    for i in range(len(iris.data)):
+        for j in clf.support_:
+            K[i, j] = np.dot(iris.data[i], iris.data[j])
+
+    pred = clf.predict(K)
+    assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
+
+    clf = svm.SVC(kernel=kfunc)
+    clf.fit(iris.data, iris.target)
+    assert_almost_equal(np.mean(pred == iris.target), 0.99, decimal=2)
+
+
+def test_svr():
+    # Test Support Vector Regression
+
+    diabetes = datasets.load_diabetes()
+    for clf in (
+        svm.NuSVR(kernel="linear", nu=0.4, C=1.0),
+        svm.NuSVR(kernel="linear", nu=0.4, C=10.0),
+        svm.SVR(kernel="linear", C=10.0),
+        svm.LinearSVR(C=10.0),
+        svm.LinearSVR(C=10.0),
+    ):
+        clf.fit(diabetes.data, diabetes.target)
+        assert clf.score(diabetes.data, diabetes.target) > 0.02
+
+    # non-regression test; previously, BaseLibSVM would check that
+    # len(np.unique(y)) < 2, which must only be done for SVC
+    svm.SVR().fit(diabetes.data, np.ones(len(diabetes.data)))
+    svm.LinearSVR().fit(diabetes.data, np.ones(len(diabetes.data)))
+
+
+def test_linearsvr():
+    # check that SVR(kernel='linear') and LinearSVC() give
+    # comparable results
+    diabetes = datasets.load_diabetes()
+    lsvr = svm.LinearSVR(C=1e3).fit(diabetes.data, diabetes.target)
+    score1 = lsvr.score(diabetes.data, diabetes.target)
+
+    svr = svm.SVR(kernel="linear", C=1e3).fit(diabetes.data, diabetes.target)
+    score2 = svr.score(diabetes.data, diabetes.target)
+
+    assert_allclose(np.linalg.norm(lsvr.coef_), np.linalg.norm(svr.coef_), 1, 0.0001)
+    assert_almost_equal(score1, score2, 2)
+
+
+def test_linearsvr_fit_sampleweight():
+    # check correct result when sample_weight is 1
+    # check that SVR(kernel='linear') and LinearSVC() give
+    # comparable results
+    diabetes = datasets.load_diabetes()
+    n_samples = len(diabetes.target)
+    unit_weight = np.ones(n_samples)
+    lsvr = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
+        diabetes.data, diabetes.target, sample_weight=unit_weight
+    )
+    score1 = lsvr.score(diabetes.data, diabetes.target)
+
+    lsvr_no_weight = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
+        diabetes.data, diabetes.target
+    )
+    score2 = lsvr_no_weight.score(diabetes.data, diabetes.target)
+
+    assert_allclose(
+        np.linalg.norm(lsvr.coef_), np.linalg.norm(lsvr_no_weight.coef_), 1, 0.0001
+    )
+    assert_almost_equal(score1, score2, 2)
+
+    # check that fit(X)  = fit([X1, X2, X3], sample_weight = [n1, n2, n3]) where
+    # X = X1 repeated n1 times, X2 repeated n2 times and so forth
+    random_state = check_random_state(0)
+    random_weight = random_state.randint(0, 10, n_samples)
+    lsvr_unflat = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
+        diabetes.data, diabetes.target, sample_weight=random_weight
+    )
+    score3 = lsvr_unflat.score(
+        diabetes.data, diabetes.target, sample_weight=random_weight
+    )
+
+    X_flat = np.repeat(diabetes.data, random_weight, axis=0)
+    y_flat = np.repeat(diabetes.target, random_weight, axis=0)
+    lsvr_flat = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(X_flat, y_flat)
+    score4 = lsvr_flat.score(X_flat, y_flat)
+
+    assert_almost_equal(score3, score4, 2)
+
+
+def test_svr_errors():
+    X = [[0.0], [1.0]]
+    y = [0.0, 0.5]
+
+    # Bad kernel
+    clf = svm.SVR(kernel=lambda x, y: np.array([[1.0]]))
+    clf.fit(X, y)
+    with pytest.raises(ValueError):
+        clf.predict(X)
+
+
+def test_oneclass():
+    # Test OneClassSVM
+    clf = svm.OneClassSVM()
+    clf.fit(X)
+    pred = clf.predict(T)
+
+    assert_array_equal(pred, [1, -1, -1])
+    assert pred.dtype == np.dtype("intp")
+    assert_array_almost_equal(clf.intercept_, [-1.218], decimal=3)
+    assert_array_almost_equal(clf.dual_coef_, [[0.750, 0.750, 0.750, 0.750]], decimal=3)
+    with pytest.raises(AttributeError):
+        (lambda: clf.coef_)()
+
+
+def test_oneclass_decision_function():
+    # Test OneClassSVM decision function
+    clf = svm.OneClassSVM()
+    rnd = check_random_state(2)
+
+    # Generate train data
+    X = 0.3 * rnd.randn(100, 2)
+    X_train = np.r_[X + 2, X - 2]
+
+    # Generate some regular novel observations
+    X = 0.3 * rnd.randn(20, 2)
+    X_test = np.r_[X + 2, X - 2]
+    # Generate some abnormal novel observations
+    X_outliers = rnd.uniform(low=-4, high=4, size=(20, 2))
+
+    # fit the model
+    clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
+    clf.fit(X_train)
+
+    # predict things
+    y_pred_test = clf.predict(X_test)
+    assert np.mean(y_pred_test == 1) > 0.9
+    y_pred_outliers = clf.predict(X_outliers)
+    assert np.mean(y_pred_outliers == -1) > 0.9
+    dec_func_test = clf.decision_function(X_test)
+    assert_array_equal((dec_func_test > 0).ravel(), y_pred_test == 1)
+    dec_func_outliers = clf.decision_function(X_outliers)
+    assert_array_equal((dec_func_outliers > 0).ravel(), y_pred_outliers == 1)
+
+
+def test_oneclass_score_samples():
+    X_train = [[1, 1], [1, 2], [2, 1]]
+    clf = svm.OneClassSVM(gamma=1).fit(X_train)
+    assert_array_equal(
+        clf.score_samples([[2.0, 2.0]]),
+        clf.decision_function([[2.0, 2.0]]) + clf.offset_,
+    )
+
+
+def test_tweak_params():
+    # Make sure some tweaking of parameters works.
+    # We change clf.dual_coef_ at run time and expect .predict() to change
+    # accordingly. Notice that this is not trivial since it involves a lot
+    # of C/Python copying in the libsvm bindings.
+    # The success of this test ensures that the mapping between libsvm and
+    # the python classifier is complete.
+    clf = svm.SVC(kernel="linear", C=1.0)
+    clf.fit(X, Y)
+    assert_array_equal(clf.dual_coef_, [[-0.25, 0.25]])
+    assert_array_equal(clf.predict([[-0.1, -0.1]]), [1])
+    clf._dual_coef_ = np.array([[0.0, 1.0]])
+    assert_array_equal(clf.predict([[-0.1, -0.1]]), [2])
+
+
+def test_probability():
+    # Predict probabilities using SVC
+    # This uses cross validation, so we use a slightly bigger testing set.
+
+    for clf in (
+        svm.SVC(probability=True, random_state=0, C=1.0),
+        svm.NuSVC(probability=True, random_state=0),
+    ):
+        clf.fit(iris.data, iris.target)
+
+        prob_predict = clf.predict_proba(iris.data)
+        assert_array_almost_equal(np.sum(prob_predict, 1), np.ones(iris.data.shape[0]))
+        assert np.mean(np.argmax(prob_predict, 1) == clf.predict(iris.data)) > 0.9
+
+        assert_almost_equal(
+            clf.predict_proba(iris.data), np.exp(clf.predict_log_proba(iris.data)), 8
+        )
+
+
+def test_decision_function():
+    # Test decision_function
+    # Sanity check, test that decision_function implemented in python
+    # returns the same as the one in libsvm
+    # multi class:
+    clf = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo").fit(
+        iris.data, iris.target
+    )
+
+    dec = np.dot(iris.data, clf.coef_.T) + clf.intercept_
+
+    assert_array_almost_equal(dec, clf.decision_function(iris.data))
+
+    # binary:
+    clf.fit(X, Y)
+    dec = np.dot(X, clf.coef_.T) + clf.intercept_
+    prediction = clf.predict(X)
+    assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
+    assert_array_almost_equal(
+        prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int)]
+    )
+    expected = np.array([-1.0, -0.66, -1.0, 0.66, 1.0, 1.0])
+    assert_array_almost_equal(clf.decision_function(X), expected, 2)
+
+    # kernel binary:
+    clf = svm.SVC(kernel="rbf", gamma=1, decision_function_shape="ovo")
+    clf.fit(X, Y)
+
+    rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
+    dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
+    assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
+
+
+@pytest.mark.parametrize("SVM", (svm.SVC, svm.NuSVC))
+def test_decision_function_shape(SVM):
+    # check that decision_function_shape='ovr' or 'ovo' gives
+    # correct shape and is consistent with predict
+
+    clf = SVM(kernel="linear", decision_function_shape="ovr").fit(
+        iris.data, iris.target
+    )
+    dec = clf.decision_function(iris.data)
+    assert dec.shape == (len(iris.data), 3)
+    assert_array_equal(clf.predict(iris.data), np.argmax(dec, axis=1))
+
+    # with five classes:
+    X, y = make_blobs(n_samples=80, centers=5, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    clf = SVM(kernel="linear", decision_function_shape="ovr").fit(X_train, y_train)
+    dec = clf.decision_function(X_test)
+    assert dec.shape == (len(X_test), 5)
+    assert_array_equal(clf.predict(X_test), np.argmax(dec, axis=1))
+
+    # check shape of ovo_decition_function=True
+    clf = SVM(kernel="linear", decision_function_shape="ovo").fit(X_train, y_train)
+    dec = clf.decision_function(X_train)
+    assert dec.shape == (len(X_train), 10)
+
+
+def test_svr_predict():
+    # Test SVR's decision_function
+    # Sanity check, test that predict implemented in python
+    # returns the same as the one in libsvm
+
+    X = iris.data
+    y = iris.target
+
+    # linear kernel
+    reg = svm.SVR(kernel="linear", C=0.1).fit(X, y)
+
+    dec = np.dot(X, reg.coef_.T) + reg.intercept_
+    assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
+
+    # rbf kernel
+    reg = svm.SVR(kernel="rbf", gamma=1).fit(X, y)
+
+    rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma)
+    dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_
+    assert_array_almost_equal(dec.ravel(), reg.predict(X).ravel())
+
+
+def test_weight():
+    # Test class weights
+    clf = svm.SVC(class_weight={1: 0.1})
+    # we give a small weights to class 1
+    clf.fit(X, Y)
+    # so all predicted values belong to class 2
+    assert_array_almost_equal(clf.predict(X), [2] * 6)
+
+    X_, y_ = make_classification(
+        n_samples=200, n_features=10, weights=[0.833, 0.167], random_state=2
+    )
+
+    for clf in (
+        linear_model.LogisticRegression(),
+        svm.LinearSVC(random_state=0),
+        svm.SVC(),
+    ):
+        clf.set_params(class_weight={0: 0.1, 1: 10})
+        clf.fit(X_[:100], y_[:100])
+        y_pred = clf.predict(X_[100:])
+        assert f1_score(y_[100:], y_pred) > 0.3
+
+
+@pytest.mark.parametrize("estimator", [svm.SVC(C=1e-2), svm.NuSVC()])
+def test_svm_classifier_sided_sample_weight(estimator):
+    # fit a linear SVM and check that giving more weight to opposed samples
+    # in the space will flip the decision toward these samples.
+    X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
+    estimator.set_params(kernel="linear")
+
+    # check that with unit weights, a sample is supposed to be predicted on
+    # the boundary
+    sample_weight = [1] * 6
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.decision_function([[-1.0, 1.0]])
+    assert y_pred == pytest.approx(0)
+
+    # give more weights to opposed samples
+    sample_weight = [10.0, 0.1, 0.1, 0.1, 0.1, 10]
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.decision_function([[-1.0, 1.0]])
+    assert y_pred < 0
+
+    sample_weight = [1.0, 0.1, 10.0, 10.0, 0.1, 0.1]
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.decision_function([[-1.0, 1.0]])
+    assert y_pred > 0
+
+
+@pytest.mark.parametrize("estimator", [svm.SVR(C=1e-2), svm.NuSVR(C=1e-2)])
+def test_svm_regressor_sided_sample_weight(estimator):
+    # similar test to test_svm_classifier_sided_sample_weight but for
+    # SVM regressors
+    X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
+    estimator.set_params(kernel="linear")
+
+    # check that with unit weights, a sample is supposed to be predicted on
+    # the boundary
+    sample_weight = [1] * 6
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.predict([[-1.0, 1.0]])
+    assert y_pred == pytest.approx(1.5)
+
+    # give more weights to opposed samples
+    sample_weight = [10.0, 0.1, 0.1, 0.1, 0.1, 10]
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.predict([[-1.0, 1.0]])
+    assert y_pred < 1.5
+
+    sample_weight = [1.0, 0.1, 10.0, 10.0, 0.1, 0.1]
+    estimator.fit(X, Y, sample_weight=sample_weight)
+    y_pred = estimator.predict([[-1.0, 1.0]])
+    assert y_pred > 1.5
+
+
+def test_svm_equivalence_sample_weight_C():
+    # test that rescaling all samples is the same as changing C
+    clf = svm.SVC()
+    clf.fit(X, Y)
+    dual_coef_no_weight = clf.dual_coef_
+    clf.set_params(C=100)
+    clf.fit(X, Y, sample_weight=np.repeat(0.01, len(X)))
+    assert_allclose(dual_coef_no_weight, clf.dual_coef_)
+
+
+@pytest.mark.parametrize(
+    "Estimator, err_msg",
+    [
+        (svm.SVC, "Invalid input - all samples have zero or negative weights."),
+        (svm.NuSVC, "(negative dimensions are not allowed|nu is infeasible)"),
+        (svm.SVR, "Invalid input - all samples have zero or negative weights."),
+        (svm.NuSVR, "Invalid input - all samples have zero or negative weights."),
+        (svm.OneClassSVM, "Invalid input - all samples have zero or negative weights."),
+    ],
+    ids=["SVC", "NuSVC", "SVR", "NuSVR", "OneClassSVM"],
+)
+@pytest.mark.parametrize(
+    "sample_weight",
+    [[0] * len(Y), [-0.3] * len(Y)],
+    ids=["weights-are-zero", "weights-are-negative"],
+)
+def test_negative_sample_weights_mask_all_samples(Estimator, err_msg, sample_weight):
+    est = Estimator(kernel="linear")
+    with pytest.raises(ValueError, match=err_msg):
+        est.fit(X, Y, sample_weight=sample_weight)
+
+
+@pytest.mark.parametrize(
+    "Classifier, err_msg",
+    [
+        (
+            svm.SVC,
+            (
+                "Invalid input - all samples with positive weights belong to the same"
+                " class"
+            ),
+        ),
+        (svm.NuSVC, "specified nu is infeasible"),
+    ],
+    ids=["SVC", "NuSVC"],
+)
+@pytest.mark.parametrize(
+    "sample_weight",
+    [[0, -0.5, 0, 1, 1, 1], [1, 1, 1, 0, -0.1, -0.3]],
+    ids=["mask-label-1", "mask-label-2"],
+)
+def test_negative_weights_svc_leave_just_one_label(Classifier, err_msg, sample_weight):
+    clf = Classifier(kernel="linear")
+    with pytest.raises(ValueError, match=err_msg):
+        clf.fit(X, Y, sample_weight=sample_weight)
+
+
+@pytest.mark.parametrize(
+    "Classifier, model",
+    [
+        (svm.SVC, {"when-left": [0.3998, 0.4], "when-right": [0.4, 0.3999]}),
+        (svm.NuSVC, {"when-left": [0.3333, 0.3333], "when-right": [0.3333, 0.3333]}),
+    ],
+    ids=["SVC", "NuSVC"],
+)
+@pytest.mark.parametrize(
+    "sample_weight, mask_side",
+    [([1, -0.5, 1, 1, 1, 1], "when-left"), ([1, 1, 1, 0, 1, 1], "when-right")],
+    ids=["partial-mask-label-1", "partial-mask-label-2"],
+)
+def test_negative_weights_svc_leave_two_labels(
+    Classifier, model, sample_weight, mask_side
+):
+    clf = Classifier(kernel="linear")
+    clf.fit(X, Y, sample_weight=sample_weight)
+    assert_allclose(clf.coef_, [model[mask_side]], rtol=1e-3)
+
+
+@pytest.mark.parametrize(
+    "Estimator", [svm.SVC, svm.NuSVC, svm.NuSVR], ids=["SVC", "NuSVC", "NuSVR"]
+)
+@pytest.mark.parametrize(
+    "sample_weight",
+    [[1, -0.5, 1, 1, 1, 1], [1, 1, 1, 0, 1, 1]],
+    ids=["partial-mask-label-1", "partial-mask-label-2"],
+)
+def test_negative_weight_equal_coeffs(Estimator, sample_weight):
+    # model generates equal coefficients
+    est = Estimator(kernel="linear")
+    est.fit(X, Y, sample_weight=sample_weight)
+    coef = np.abs(est.coef_).ravel()
+    assert coef[0] == pytest.approx(coef[1], rel=1e-3)
+
+
+def test_auto_weight():
+    # Test class weights for imbalanced data
+    from sklearn.linear_model import LogisticRegression
+
+    # We take as dataset the two-dimensional projection of iris so
+    # that it is not separable and remove half of predictors from
+    # class 1.
+    # We add one to the targets as a non-regression test:
+    # class_weight="balanced"
+    # used to work only when the labels where a range [0..K).
+    from sklearn.utils import compute_class_weight
+
+    X, y = iris.data[:, :2], iris.target + 1
+    unbalanced = np.delete(np.arange(y.size), np.where(y > 2)[0][::2])
+
+    classes = np.unique(y[unbalanced])
+    class_weights = compute_class_weight("balanced", classes=classes, y=y[unbalanced])
+    assert np.argmax(class_weights) == 2
+
+    for clf in (
+        svm.SVC(kernel="linear"),
+        svm.LinearSVC(random_state=0),
+        LogisticRegression(),
+    ):
+        # check that score is better when class='balanced' is set.
+        y_pred = clf.fit(X[unbalanced], y[unbalanced]).predict(X)
+        clf.set_params(class_weight="balanced")
+        y_pred_balanced = clf.fit(
+            X[unbalanced],
+            y[unbalanced],
+        ).predict(X)
+        assert metrics.f1_score(y, y_pred, average="macro") <= metrics.f1_score(
+            y, y_pred_balanced, average="macro"
+        )
+
+
+@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)
+def test_bad_input(lil_container):
+    # Test dimensions for labels
+    Y2 = Y[:-1]  # wrong dimensions for labels
+    with pytest.raises(ValueError):
+        svm.SVC().fit(X, Y2)
+
+    # Test with arrays that are non-contiguous.
+    for clf in (svm.SVC(), svm.LinearSVC(random_state=0)):
+        Xf = np.asfortranarray(X)
+        assert not Xf.flags["C_CONTIGUOUS"]
+        yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T)
+        yf = yf[:, -1]
+        assert not yf.flags["F_CONTIGUOUS"]
+        assert not yf.flags["C_CONTIGUOUS"]
+        clf.fit(Xf, yf)
+        assert_array_equal(clf.predict(T), true_result)
+
+    # error for precomputed kernelsx
+    clf = svm.SVC(kernel="precomputed")
+    with pytest.raises(ValueError):
+        clf.fit(X, Y)
+
+    # predict with sparse input when trained with dense
+    clf = svm.SVC().fit(X, Y)
+    with pytest.raises(ValueError):
+        clf.predict(lil_container(X))
+
+    Xt = np.array(X).T
+    clf.fit(np.dot(X, Xt), Y)
+    with pytest.raises(ValueError):
+        clf.predict(X)
+
+    clf = svm.SVC()
+    clf.fit(X, Y)
+    with pytest.raises(ValueError):
+        clf.predict(Xt)
+
+
+def test_svc_nonfinite_params():
+    # Check SVC throws ValueError when dealing with non-finite parameter values
+    rng = np.random.RandomState(0)
+    n_samples = 10
+    fmax = np.finfo(np.float64).max
+    X = fmax * rng.uniform(size=(n_samples, 2))
+    y = rng.randint(0, 2, size=n_samples)
+
+    clf = svm.SVC()
+    msg = "The dual coefficients or intercepts are not finite"
+    with pytest.raises(ValueError, match=msg):
+        clf.fit(X, y)
+
+
+def test_unicode_kernel():
+    # Test that a unicode kernel name does not cause a TypeError
+    clf = svm.SVC(kernel="linear", probability=True)
+    clf.fit(X, Y)
+    clf.predict_proba(T)
+    _libsvm.cross_validation(
+        iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
+    )
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_precomputed(csr_container):
+    clf = svm.SVC(kernel="precomputed")
+    sparse_gram = csr_container([[1, 0], [0, 1]])
+    with pytest.raises(TypeError, match="Sparse precomputed"):
+        clf.fit(sparse_gram, [0, 1])
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_sparse_fit_support_vectors_empty(csr_container):
+    # Regression test for #14893
+    X_train = csr_container([[0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]])
+    y_train = np.array([0.04, 0.04, 0.10, 0.16])
+    model = svm.SVR(kernel="linear")
+    model.fit(X_train, y_train)
+    assert not model.support_vectors_.data.size
+    assert not model.dual_coef_.data.size
+
+
+@pytest.mark.parametrize("loss", ["hinge", "squared_hinge"])
+@pytest.mark.parametrize("penalty", ["l1", "l2"])
+@pytest.mark.parametrize("dual", [True, False])
+def test_linearsvc_parameters(loss, penalty, dual):
+    # Test possible parameter combinations in LinearSVC
+    # Generate list of possible parameter combinations
+    X, y = make_classification(n_samples=5, n_features=5, random_state=0)
+
+    clf = svm.LinearSVC(penalty=penalty, loss=loss, dual=dual, random_state=0)
+    if (
+        (loss, penalty) == ("hinge", "l1")
+        or (loss, penalty, dual) == ("hinge", "l2", False)
+        or (penalty, dual) == ("l1", True)
+    ):
+        with pytest.raises(
+            ValueError,
+            match="Unsupported set of arguments.*penalty='%s.*loss='%s.*dual=%s"
+            % (penalty, loss, dual),
+        ):
+            clf.fit(X, y)
+    else:
+        clf.fit(X, y)
+
+
+def test_linearsvc():
+    # Test basic routines using LinearSVC
+    clf = svm.LinearSVC(random_state=0).fit(X, Y)
+
+    # by default should have intercept
+    assert clf.fit_intercept
+
+    assert_array_equal(clf.predict(T), true_result)
+    assert_array_almost_equal(clf.intercept_, [0], decimal=3)
+
+    # the same with l1 penalty
+    clf = svm.LinearSVC(
+        penalty="l1", loss="squared_hinge", dual=False, random_state=0
+    ).fit(X, Y)
+    assert_array_equal(clf.predict(T), true_result)
+
+    # l2 penalty with dual formulation
+    clf = svm.LinearSVC(penalty="l2", dual=True, random_state=0).fit(X, Y)
+    assert_array_equal(clf.predict(T), true_result)
+
+    # l2 penalty, l1 loss
+    clf = svm.LinearSVC(penalty="l2", loss="hinge", dual=True, random_state=0)
+    clf.fit(X, Y)
+    assert_array_equal(clf.predict(T), true_result)
+
+    # test also decision function
+    dec = clf.decision_function(T)
+    res = (dec > 0).astype(int) + 1
+    assert_array_equal(res, true_result)
+
+
+def test_linearsvc_crammer_singer():
+    # Test LinearSVC with crammer_singer multi-class svm
+    ovr_clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target)
+    cs_clf = svm.LinearSVC(multi_class="crammer_singer", random_state=0)
+    cs_clf.fit(iris.data, iris.target)
+
+    # similar prediction for ovr and crammer-singer:
+    assert (ovr_clf.predict(iris.data) == cs_clf.predict(iris.data)).mean() > 0.9
+
+    # classifiers shouldn't be the same
+    assert (ovr_clf.coef_ != cs_clf.coef_).all()
+
+    # test decision function
+    assert_array_equal(
+        cs_clf.predict(iris.data),
+        np.argmax(cs_clf.decision_function(iris.data), axis=1),
+    )
+    dec_func = np.dot(iris.data, cs_clf.coef_.T) + cs_clf.intercept_
+    assert_array_almost_equal(dec_func, cs_clf.decision_function(iris.data))
+
+
+def test_linearsvc_fit_sampleweight():
+    # check correct result when sample_weight is 1
+    n_samples = len(X)
+    unit_weight = np.ones(n_samples)
+    clf = svm.LinearSVC(random_state=0).fit(X, Y)
+    clf_unitweight = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
+        X, Y, sample_weight=unit_weight
+    )
+
+    # check if same as sample_weight=None
+    assert_array_equal(clf_unitweight.predict(T), clf.predict(T))
+    assert_allclose(clf.coef_, clf_unitweight.coef_, 1, 0.0001)
+
+    # check that fit(X)  = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
+    # X = X1 repeated n1 times, X2 repeated n2 times and so forth
+
+    random_state = check_random_state(0)
+    random_weight = random_state.randint(0, 10, n_samples)
+    lsvc_unflat = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
+        X, Y, sample_weight=random_weight
+    )
+
+    pred1 = lsvc_unflat.predict(T)
+
+    X_flat = np.repeat(X, random_weight, axis=0)
+    y_flat = np.repeat(Y, random_weight, axis=0)
+    lsvc_flat = svm.LinearSVC(random_state=0, tol=1e-12, max_iter=1000).fit(
+        X_flat, y_flat
+    )
+    pred2 = lsvc_flat.predict(T)
+
+    assert_array_equal(pred1, pred2)
+    assert_allclose(lsvc_unflat.coef_, lsvc_flat.coef_, 1, 0.0001)
+
+
+def test_crammer_singer_binary():
+    # Test Crammer-Singer formulation in the binary case
+    X, y = make_classification(n_classes=2, random_state=0)
+
+    for fit_intercept in (True, False):
+        acc = (
+            svm.LinearSVC(
+                fit_intercept=fit_intercept,
+                multi_class="crammer_singer",
+                random_state=0,
+            )
+            .fit(X, y)
+            .score(X, y)
+        )
+        assert acc > 0.9
+
+
+def test_linearsvc_iris():
+    # Test that LinearSVC gives plausible predictions on the iris dataset
+    # Also, test symbolic class names (classes_).
+    target = iris.target_names[iris.target]
+    clf = svm.LinearSVC(random_state=0).fit(iris.data, target)
+    assert set(clf.classes_) == set(iris.target_names)
+    assert np.mean(clf.predict(iris.data) == target) > 0.8
+
+    dec = clf.decision_function(iris.data)
+    pred = iris.target_names[np.argmax(dec, 1)]
+    assert_array_equal(pred, clf.predict(iris.data))
+
+
+def test_dense_liblinear_intercept_handling(classifier=svm.LinearSVC):
+    # Test that dense liblinear honours intercept_scaling param
+    X = [[2, 1], [3, 1], [1, 3], [2, 3]]
+    y = [0, 0, 1, 1]
+    clf = classifier(
+        fit_intercept=True,
+        penalty="l1",
+        loss="squared_hinge",
+        dual=False,
+        C=4,
+        tol=1e-7,
+        random_state=0,
+    )
+    assert clf.intercept_scaling == 1, clf.intercept_scaling
+    assert clf.fit_intercept
+
+    # when intercept_scaling is low the intercept value is highly "penalized"
+    # by regularization
+    clf.intercept_scaling = 1
+    clf.fit(X, y)
+    assert_almost_equal(clf.intercept_, 0, decimal=5)
+
+    # when intercept_scaling is sufficiently high, the intercept value
+    # is not affected by regularization
+    clf.intercept_scaling = 100
+    clf.fit(X, y)
+    intercept1 = clf.intercept_
+    assert intercept1 < -1
+
+    # when intercept_scaling is sufficiently high, the intercept value
+    # doesn't depend on intercept_scaling value
+    clf.intercept_scaling = 1000
+    clf.fit(X, y)
+    intercept2 = clf.intercept_
+    assert_array_almost_equal(intercept1, intercept2, decimal=2)
+
+
+def test_liblinear_set_coef():
+    # multi-class case
+    clf = svm.LinearSVC().fit(iris.data, iris.target)
+    values = clf.decision_function(iris.data)
+    clf.coef_ = clf.coef_.copy()
+    clf.intercept_ = clf.intercept_.copy()
+    values2 = clf.decision_function(iris.data)
+    assert_array_almost_equal(values, values2)
+
+    # binary-class case
+    X = [[2, 1], [3, 1], [1, 3], [2, 3]]
+    y = [0, 0, 1, 1]
+
+    clf = svm.LinearSVC().fit(X, y)
+    values = clf.decision_function(X)
+    clf.coef_ = clf.coef_.copy()
+    clf.intercept_ = clf.intercept_.copy()
+    values2 = clf.decision_function(X)
+    assert_array_equal(values, values2)
+
+
+def test_immutable_coef_property():
+    # Check that primal coef modification are not silently ignored
+    svms = [
+        svm.SVC(kernel="linear").fit(iris.data, iris.target),
+        svm.NuSVC(kernel="linear").fit(iris.data, iris.target),
+        svm.SVR(kernel="linear").fit(iris.data, iris.target),
+        svm.NuSVR(kernel="linear").fit(iris.data, iris.target),
+        svm.OneClassSVM(kernel="linear").fit(iris.data),
+    ]
+    for clf in svms:
+        with pytest.raises(AttributeError):
+            clf.__setattr__("coef_", np.arange(3))
+        with pytest.raises((RuntimeError, ValueError)):
+            clf.coef_.__setitem__((0, 0), 0)
+
+
+def test_linearsvc_verbose():
+    # stdout: redirect
+    import os
+
+    stdout = os.dup(1)  # save original stdout
+    os.dup2(os.pipe()[1], 1)  # replace it
+
+    # actual call
+    clf = svm.LinearSVC(verbose=1)
+    clf.fit(X, Y)
+
+    # stdout: restore
+    os.dup2(stdout, 1)  # restore original stdout
+
+
+def test_svc_clone_with_callable_kernel():
+    # create SVM with callable linear kernel, check that results are the same
+    # as with built-in linear kernel
+    svm_callable = svm.SVC(
+        kernel=lambda x, y: np.dot(x, y.T),
+        probability=True,
+        random_state=0,
+        decision_function_shape="ovr",
+    )
+    # clone for checking clonability with lambda functions..
+    svm_cloned = base.clone(svm_callable)
+    svm_cloned.fit(iris.data, iris.target)
+
+    svm_builtin = svm.SVC(
+        kernel="linear", probability=True, random_state=0, decision_function_shape="ovr"
+    )
+    svm_builtin.fit(iris.data, iris.target)
+
+    assert_array_almost_equal(svm_cloned.dual_coef_, svm_builtin.dual_coef_)
+    assert_array_almost_equal(svm_cloned.intercept_, svm_builtin.intercept_)
+    assert_array_equal(svm_cloned.predict(iris.data), svm_builtin.predict(iris.data))
+
+    assert_array_almost_equal(
+        svm_cloned.predict_proba(iris.data),
+        svm_builtin.predict_proba(iris.data),
+        decimal=4,
+    )
+    assert_array_almost_equal(
+        svm_cloned.decision_function(iris.data),
+        svm_builtin.decision_function(iris.data),
+    )
+
+
+def test_svc_bad_kernel():
+    svc = svm.SVC(kernel=lambda x, y: x)
+    with pytest.raises(ValueError):
+        svc.fit(X, Y)
+
+
+def test_libsvm_convergence_warnings():
+    a = svm.SVC(
+        kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0, max_iter=2
+    )
+    warning_msg = (
+        r"Solver terminated early \(max_iter=2\).  Consider pre-processing "
+        r"your data with StandardScaler or MinMaxScaler."
+    )
+    with pytest.warns(ConvergenceWarning, match=warning_msg):
+        a.fit(np.array(X), Y)
+    assert np.all(a.n_iter_ == 2)
+
+
+def test_unfitted():
+    X = "foo!"  # input validation not required when SVM not fitted
+
+    clf = svm.SVC()
+    with pytest.raises(Exception, match=r".*\bSVC\b.*\bnot\b.*\bfitted\b"):
+        clf.predict(X)
+
+    clf = svm.NuSVR()
+    with pytest.raises(Exception, match=r".*\bNuSVR\b.*\bnot\b.*\bfitted\b"):
+        clf.predict(X)
+
+
+# ignore convergence warnings from max_iter=1
+@pytest.mark.filterwarnings("ignore::sklearn.exceptions.ConvergenceWarning")
+def test_consistent_proba():
+    a = svm.SVC(probability=True, max_iter=1, random_state=0)
+    proba_1 = a.fit(X, Y).predict_proba(X)
+    a = svm.SVC(probability=True, max_iter=1, random_state=0)
+    proba_2 = a.fit(X, Y).predict_proba(X)
+    assert_array_almost_equal(proba_1, proba_2)
+
+
+def test_linear_svm_convergence_warnings():
+    # Test that warnings are raised if model does not converge
+
+    lsvc = svm.LinearSVC(random_state=0, max_iter=2)
+    warning_msg = "Liblinear failed to converge, increase the number of iterations."
+    with pytest.warns(ConvergenceWarning, match=warning_msg):
+        lsvc.fit(X, Y)
+    # Check that we have an n_iter_ attribute with int type as opposed to a
+    # numpy array or an np.int32 so as to match the docstring.
+    assert isinstance(lsvc.n_iter_, int)
+    assert lsvc.n_iter_ == 2
+
+    lsvr = svm.LinearSVR(random_state=0, max_iter=2)
+    with pytest.warns(ConvergenceWarning, match=warning_msg):
+        lsvr.fit(iris.data, iris.target)
+    assert isinstance(lsvr.n_iter_, int)
+    assert lsvr.n_iter_ == 2
+
+
+def test_svr_coef_sign():
+    # Test that SVR(kernel="linear") has coef_ with the right sign.
+    # Non-regression test for #2933.
+    X = np.random.RandomState(21).randn(10, 3)
+    y = np.random.RandomState(12).randn(10)
+
+    for svr in [
+        svm.SVR(kernel="linear"),
+        svm.NuSVR(kernel="linear"),
+        svm.LinearSVR(),
+    ]:
+        svr.fit(X, y)
+        assert_array_almost_equal(
+            svr.predict(X), np.dot(X, svr.coef_.ravel()) + svr.intercept_
+        )
+
+
+def test_lsvc_intercept_scaling_zero():
+    # Test that intercept_scaling is ignored when fit_intercept is False
+
+    lsvc = svm.LinearSVC(fit_intercept=False)
+    lsvc.fit(X, Y)
+    assert lsvc.intercept_ == 0.0
+
+
+def test_hasattr_predict_proba():
+    # Method must be (un)available before or after fit, switched by
+    # `probability` param
+
+    G = svm.SVC(probability=True)
+    assert hasattr(G, "predict_proba")
+    G.fit(iris.data, iris.target)
+    assert hasattr(G, "predict_proba")
+
+    G = svm.SVC(probability=False)
+    assert not hasattr(G, "predict_proba")
+    G.fit(iris.data, iris.target)
+    assert not hasattr(G, "predict_proba")
+
+    # Switching to `probability=True` after fitting should make
+    # predict_proba available, but calling it must not work:
+    G.probability = True
+    assert hasattr(G, "predict_proba")
+    msg = "predict_proba is not available when fitted with probability=False"
+
+    with pytest.raises(NotFittedError, match=msg):
+        G.predict_proba(iris.data)
+
+
+def test_decision_function_shape_two_class():
+    for n_classes in [2, 3]:
+        X, y = make_blobs(centers=n_classes, random_state=0)
+        for estimator in [svm.SVC, svm.NuSVC]:
+            clf = OneVsRestClassifier(estimator(decision_function_shape="ovr")).fit(
+                X, y
+            )
+            assert len(clf.predict(X)) == len(y)
+
+
+def test_ovr_decision_function():
+    # One point from each quadrant represents one class
+    X_train = np.array([[1, 1], [-1, 1], [-1, -1], [1, -1]])
+    y_train = [0, 1, 2, 3]
+
+    # First point is closer to the decision boundaries than the second point
+    base_points = np.array([[5, 5], [10, 10]])
+
+    # For all the quadrants (classes)
+    X_test = np.vstack(
+        (
+            base_points * [1, 1],  # Q1
+            base_points * [-1, 1],  # Q2
+            base_points * [-1, -1],  # Q3
+            base_points * [1, -1],  # Q4
+        )
+    )
+
+    y_test = [0] * 2 + [1] * 2 + [2] * 2 + [3] * 2
+
+    clf = svm.SVC(kernel="linear", decision_function_shape="ovr")
+    clf.fit(X_train, y_train)
+
+    y_pred = clf.predict(X_test)
+
+    # Test if the prediction is the same as y
+    assert_array_equal(y_pred, y_test)
+
+    deci_val = clf.decision_function(X_test)
+
+    # Assert that the predicted class has the maximum value
+    assert_array_equal(np.argmax(deci_val, axis=1), y_pred)
+
+    # Get decision value at test points for the predicted class
+    pred_class_deci_val = deci_val[range(8), y_pred].reshape((4, 2))
+
+    # Assert pred_class_deci_val > 0 here
+    assert np.min(pred_class_deci_val) > 0.0
+
+    # Test if the first point has lower decision value on every quadrant
+    # compared to the second point
+    assert np.all(pred_class_deci_val[:, 0] < pred_class_deci_val[:, 1])
+
+
+@pytest.mark.parametrize("SVCClass", [svm.SVC, svm.NuSVC])
+def test_svc_invalid_break_ties_param(SVCClass):
+    X, y = make_blobs(random_state=42)
+
+    svm = SVCClass(
+        kernel="linear", decision_function_shape="ovo", break_ties=True, random_state=42
+    ).fit(X, y)
+
+    with pytest.raises(ValueError, match="break_ties must be False"):
+        svm.predict(y)
+
+
+@pytest.mark.parametrize("SVCClass", [svm.SVC, svm.NuSVC])
+def test_svc_ovr_tie_breaking(SVCClass):
+    """Test if predict breaks ties in OVR mode.
+    Related issue: https://github.com/scikit-learn/scikit-learn/issues/8277
+    """
+    if SVCClass.__name__ == "NuSVC" and _IS_32BIT:
+        # XXX: known failure to be investigated. Either the code needs to be
+        # fixed or the test itself might need to be made less sensitive to
+        # random changes in test data and rounding errors more generally.
+        # https://github.com/scikit-learn/scikit-learn/issues/29633
+        pytest.xfail("Failing test on 32bit OS")
+
+    X, y = make_blobs(random_state=0, n_samples=20, n_features=2)
+
+    xs = np.linspace(X[:, 0].min(), X[:, 0].max(), 100)
+    ys = np.linspace(X[:, 1].min(), X[:, 1].max(), 100)
+    xx, yy = np.meshgrid(xs, ys)
+
+    common_params = dict(
+        kernel="rbf", gamma=1e6, random_state=42, decision_function_shape="ovr"
+    )
+    svm = SVCClass(
+        break_ties=False,
+        **common_params,
+    ).fit(X, y)
+    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
+    dv = svm.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    assert not np.all(pred == np.argmax(dv, axis=1))
+
+    svm = SVCClass(
+        break_ties=True,
+        **common_params,
+    ).fit(X, y)
+    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
+    dv = svm.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    assert np.all(pred == np.argmax(dv, axis=1))
+
+
+def test_gamma_scale():
+    X, y = [[0.0], [1.0]], [0, 1]
+
+    clf = svm.SVC()
+    clf.fit(X, y)
+    assert_almost_equal(clf._gamma, 4)
+
+
+@pytest.mark.parametrize(
+    "SVM, params",
+    [
+        (LinearSVC, {"penalty": "l1", "loss": "squared_hinge", "dual": False}),
+        (LinearSVC, {"penalty": "l2", "loss": "squared_hinge", "dual": True}),
+        (LinearSVC, {"penalty": "l2", "loss": "squared_hinge", "dual": False}),
+        (LinearSVC, {"penalty": "l2", "loss": "hinge", "dual": True}),
+        (LinearSVR, {"loss": "epsilon_insensitive", "dual": True}),
+        (LinearSVR, {"loss": "squared_epsilon_insensitive", "dual": True}),
+        (LinearSVR, {"loss": "squared_epsilon_insensitive", "dual": True}),
+    ],
+)
+def test_linearsvm_liblinear_sample_weight(SVM, params):
+    X = np.array(
+        [
+            [1, 3],
+            [1, 3],
+            [1, 3],
+            [1, 3],
+            [2, 1],
+            [2, 1],
+            [2, 1],
+            [2, 1],
+            [3, 3],
+            [3, 3],
+            [3, 3],
+            [3, 3],
+            [4, 1],
+            [4, 1],
+            [4, 1],
+            [4, 1],
+        ],
+        dtype=np.dtype("float"),
+    )
+    y = np.array(
+        [1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2], dtype=np.dtype("int")
+    )
+
+    X2 = np.vstack([X, X])
+    y2 = np.hstack([y, 3 - y])
+    sample_weight = np.ones(shape=len(y) * 2)
+    sample_weight[len(y) :] = 0
+    X2, y2, sample_weight = shuffle(X2, y2, sample_weight, random_state=0)
+
+    base_estimator = SVM(random_state=42)
+    base_estimator.set_params(**params)
+    base_estimator.set_params(tol=1e-12, max_iter=1000)
+    est_no_weight = base.clone(base_estimator).fit(X, y)
+    est_with_weight = base.clone(base_estimator).fit(
+        X2, y2, sample_weight=sample_weight
+    )
+
+    for method in ("predict", "decision_function"):
+        if hasattr(base_estimator, method):
+            X_est_no_weight = getattr(est_no_weight, method)(X)
+            X_est_with_weight = getattr(est_with_weight, method)(X)
+            assert_allclose(X_est_no_weight, X_est_with_weight)
+
+
+@pytest.mark.parametrize("Klass", (OneClassSVM, SVR, NuSVR))
+def test_n_support(Klass):
+    # Make n_support is correct for oneclass and SVR (used to be
+    # non-initialized)
+    # this is a non regression test for issue #14774
+    X = np.array([[0], [0.44], [0.45], [0.46], [1]])
+    y = np.arange(X.shape[0])
+    est = Klass()
+    assert not hasattr(est, "n_support_")
+    est.fit(X, y)
+    assert est.n_support_[0] == est.support_vectors_.shape[0]
+    assert est.n_support_.size == 1
+
+
+@pytest.mark.parametrize("Estimator", [svm.SVC, svm.SVR])
+def test_custom_kernel_not_array_input(Estimator):
+    """Test using a custom kernel that is not fed with array-like for floats"""
+    data = ["A A", "A", "B", "B B", "A B"]
+    X = np.array([[2, 0], [1, 0], [0, 1], [0, 2], [1, 1]])  # count encoding
+    y = np.array([1, 1, 2, 2, 1])
+
+    def string_kernel(X1, X2):
+        assert isinstance(X1[0], str)
+        n_samples1 = _num_samples(X1)
+        n_samples2 = _num_samples(X2)
+        K = np.zeros((n_samples1, n_samples2))
+        for ii in range(n_samples1):
+            for jj in range(ii, n_samples2):
+                K[ii, jj] = X1[ii].count("A") * X2[jj].count("A")
+                K[ii, jj] += X1[ii].count("B") * X2[jj].count("B")
+                K[jj, ii] = K[ii, jj]
+        return K
+
+    K = string_kernel(data, data)
+    assert_array_equal(np.dot(X, X.T), K)
+
+    svc1 = Estimator(kernel=string_kernel).fit(data, y)
+    svc2 = Estimator(kernel="linear").fit(X, y)
+    svc3 = Estimator(kernel="precomputed").fit(K, y)
+
+    assert svc1.score(data, y) == svc3.score(K, y)
+    assert svc1.score(data, y) == svc2.score(X, y)
+    if hasattr(svc1, "decision_function"):  # classifier
+        assert_allclose(svc1.decision_function(data), svc2.decision_function(X))
+        assert_allclose(svc1.decision_function(data), svc3.decision_function(K))
+        assert_array_equal(svc1.predict(data), svc2.predict(X))
+        assert_array_equal(svc1.predict(data), svc3.predict(K))
+    else:  # regressor
+        assert_allclose(svc1.predict(data), svc2.predict(X))
+        assert_allclose(svc1.predict(data), svc3.predict(K))
+
+
+def test_svc_raises_error_internal_representation():
+    """Check that SVC raises error when internal representation is altered.
+
+    Non-regression test for #18891 and https://nvd.nist.gov/vuln/detail/CVE-2020-28975
+    """
+    clf = svm.SVC(kernel="linear").fit(X, Y)
+    clf._n_support[0] = 1000000
+
+    msg = "The internal representation of SVC was altered"
+    with pytest.raises(ValueError, match=msg):
+        clf.predict(X)
+
+
+@pytest.mark.parametrize(
+    "estimator, expected_n_iter_type",
+    [
+        (svm.SVC, np.ndarray),
+        (svm.NuSVC, np.ndarray),
+        (svm.SVR, int),
+        (svm.NuSVR, int),
+        (svm.OneClassSVM, int),
+    ],
+)
+@pytest.mark.parametrize(
+    "dataset",
+    [
+        make_classification(n_classes=2, n_informative=2, random_state=0),
+        make_classification(n_classes=3, n_informative=3, random_state=0),
+        make_classification(n_classes=4, n_informative=4, random_state=0),
+    ],
+)
+def test_n_iter_libsvm(estimator, expected_n_iter_type, dataset):
+    # Check that the type of n_iter_ is correct for the classes that inherit
+    # from BaseSVC.
+    # Note that for SVC, and NuSVC this is an ndarray; while for SVR, NuSVR, and
+    # OneClassSVM, it is an int.
+    # For SVC and NuSVC also check the shape of n_iter_.
+    X, y = dataset
+    n_iter = estimator(kernel="linear").fit(X, y).n_iter_
+    assert type(n_iter) == expected_n_iter_type
+    if estimator in [svm.SVC, svm.NuSVC]:
+        n_classes = len(np.unique(y))
+        assert n_iter.shape == (n_classes * (n_classes - 1) // 2,)
+
+
+@pytest.mark.parametrize("loss", ["squared_hinge", "squared_epsilon_insensitive"])
+def test_dual_auto(loss):
+    # OvR, L2, N > M (6,2)
+    dual = _validate_dual_parameter("auto", loss, "l2", "ovr", np.asarray(X))
+    assert dual is False
+    # OvR, L2, N < M (2,6)
+    dual = _validate_dual_parameter("auto", loss, "l2", "ovr", np.asarray(X).T)
+    assert dual is True
+
+
+def test_dual_auto_edge_cases():
+    # Hinge, OvR, L2, N > M (6,2)
+    dual = _validate_dual_parameter("auto", "hinge", "l2", "ovr", np.asarray(X))
+    assert dual is True  # only supports True
+    dual = _validate_dual_parameter(
+        "auto", "epsilon_insensitive", "l2", "ovr", np.asarray(X)
+    )
+    assert dual is True  # only supports True
+    # SqHinge, OvR, L1, N < M (2,6)
+    dual = _validate_dual_parameter(
+        "auto", "squared_hinge", "l1", "ovr", np.asarray(X).T
+    )
+    assert dual is False  # only supports False
+
+
+@pytest.mark.parametrize(
+    "Estimator, make_dataset",
+    [(svm.SVC, make_classification), (svm.SVR, make_regression)],
+)
+@pytest.mark.parametrize("C_inf", [np.inf, float("inf")])
+def test_svm_with_infinite_C(Estimator, make_dataset, C_inf, global_random_seed):
+    """Check that we can pass `C=inf` that is equivalent to a very large C value.
+
+    Non-regression test for
+    https://github.com/scikit-learn/scikit-learn/issues/29772
+    """
+    X, y = make_dataset(random_state=global_random_seed)
+    estimator_C_inf = Estimator(C=C_inf).fit(X, y)
+    estimator_C_large = Estimator(C=1e10).fit(X, y)
+
+    assert_allclose(estimator_C_large.predict(X), estimator_C_inf.predict(X))
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4b03b66eb6e57cb3d7fc66bed6e0d191f75e9d0
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/__init__.py
@@ -0,0 +1,24 @@
+"""Decision tree based models for classification and regression."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from ._classes import (
+    BaseDecisionTree,
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    ExtraTreeClassifier,
+    ExtraTreeRegressor,
+)
+from ._export import export_graphviz, export_text, plot_tree
+
+__all__ = [
+    "BaseDecisionTree",
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "ExtraTreeClassifier",
+    "ExtraTreeRegressor",
+    "export_graphviz",
+    "export_text",
+    "plot_tree",
+]
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_classes.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8536ccf0d6f6bbd07d069c67a9f004bc29ca67f3
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_classes.py
@@ -0,0 +1,1997 @@
+"""
+This module gathers tree-based methods, including decision, regression and
+randomized trees. Single and multi-output problems are both handled.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import copy
+import numbers
+from abc import ABCMeta, abstractmethod
+from math import ceil
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.sparse import issparse
+
+from sklearn.utils import metadata_routing
+
+from ..base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MultiOutputMixin,
+    RegressorMixin,
+    _fit_context,
+    clone,
+    is_classifier,
+)
+from ..utils import Bunch, check_random_state, compute_sample_weight
+from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
+from ..utils.multiclass import check_classification_targets
+from ..utils.validation import (
+    _assert_all_finite_element_wise,
+    _check_n_features,
+    _check_sample_weight,
+    assert_all_finite,
+    check_is_fitted,
+    validate_data,
+)
+from . import _criterion, _splitter, _tree
+from ._criterion import Criterion
+from ._splitter import Splitter
+from ._tree import (
+    BestFirstTreeBuilder,
+    DepthFirstTreeBuilder,
+    Tree,
+    _build_pruned_tree_ccp,
+    ccp_pruning_path,
+)
+from ._utils import _any_isnan_axis0
+
+__all__ = [
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "ExtraTreeClassifier",
+    "ExtraTreeRegressor",
+]
+
+
+# =============================================================================
+# Types and constants
+# =============================================================================
+
+DTYPE = _tree.DTYPE
+DOUBLE = _tree.DOUBLE
+
+CRITERIA_CLF = {
+    "gini": _criterion.Gini,
+    "log_loss": _criterion.Entropy,
+    "entropy": _criterion.Entropy,
+}
+CRITERIA_REG = {
+    "squared_error": _criterion.MSE,
+    "friedman_mse": _criterion.FriedmanMSE,
+    "absolute_error": _criterion.MAE,
+    "poisson": _criterion.Poisson,
+}
+
+DENSE_SPLITTERS = {"best": _splitter.BestSplitter, "random": _splitter.RandomSplitter}
+
+SPARSE_SPLITTERS = {
+    "best": _splitter.BestSparseSplitter,
+    "random": _splitter.RandomSparseSplitter,
+}
+
+# =============================================================================
+# Base decision tree
+# =============================================================================
+
+
+class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
+    """Base class for decision trees.
+
+    Warning: This class should not be used directly.
+    Use derived classes instead.
+    """
+
+    # "check_input" is used for optimisation and isn't something to be passed
+    # around in a pipeline.
+    __metadata_request__predict = {"check_input": metadata_routing.UNUSED}
+
+    _parameter_constraints: dict = {
+        "splitter": [StrOptions({"best", "random"})],
+        "max_depth": [Interval(Integral, 1, None, closed="left"), None],
+        "min_samples_split": [
+            Interval(Integral, 2, None, closed="left"),
+            Interval(RealNotInt, 0.0, 1.0, closed="right"),
+        ],
+        "min_samples_leaf": [
+            Interval(Integral, 1, None, closed="left"),
+            Interval(RealNotInt, 0.0, 1.0, closed="neither"),
+        ],
+        "min_weight_fraction_leaf": [Interval(Real, 0.0, 0.5, closed="both")],
+        "max_features": [
+            Interval(Integral, 1, None, closed="left"),
+            Interval(RealNotInt, 0.0, 1.0, closed="right"),
+            StrOptions({"sqrt", "log2"}),
+            None,
+        ],
+        "random_state": ["random_state"],
+        "max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None],
+        "min_impurity_decrease": [Interval(Real, 0.0, None, closed="left")],
+        "ccp_alpha": [Interval(Real, 0.0, None, closed="left")],
+        "monotonic_cst": ["array-like", None],
+    }
+
+    @abstractmethod
+    def __init__(
+        self,
+        *,
+        criterion,
+        splitter,
+        max_depth,
+        min_samples_split,
+        min_samples_leaf,
+        min_weight_fraction_leaf,
+        max_features,
+        max_leaf_nodes,
+        random_state,
+        min_impurity_decrease,
+        class_weight=None,
+        ccp_alpha=0.0,
+        monotonic_cst=None,
+    ):
+        self.criterion = criterion
+        self.splitter = splitter
+        self.max_depth = max_depth
+        self.min_samples_split = min_samples_split
+        self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
+        self.max_features = max_features
+        self.max_leaf_nodes = max_leaf_nodes
+        self.random_state = random_state
+        self.min_impurity_decrease = min_impurity_decrease
+        self.class_weight = class_weight
+        self.ccp_alpha = ccp_alpha
+        self.monotonic_cst = monotonic_cst
+
+    def get_depth(self):
+        """Return the depth of the decision tree.
+
+        The depth of a tree is the maximum distance between the root
+        and any leaf.
+
+        Returns
+        -------
+        self.tree_.max_depth : int
+            The maximum depth of the tree.
+        """
+        check_is_fitted(self)
+        return self.tree_.max_depth
+
+    def get_n_leaves(self):
+        """Return the number of leaves of the decision tree.
+
+        Returns
+        -------
+        self.tree_.n_leaves : int
+            Number of leaves.
+        """
+        check_is_fitted(self)
+        return self.tree_.n_leaves
+
+    def _support_missing_values(self, X):
+        return (
+            not issparse(X)
+            and self.__sklearn_tags__().input_tags.allow_nan
+            and self.monotonic_cst is None
+        )
+
+    def _compute_missing_values_in_feature_mask(self, X, estimator_name=None):
+        """Return boolean mask denoting if there are missing values for each feature.
+
+        This method also ensures that X is finite.
+
+        Parameter
+        ---------
+        X : array-like of shape (n_samples, n_features), dtype=DOUBLE
+            Input data.
+
+        estimator_name : str or None, default=None
+            Name to use when raising an error. Defaults to the class name.
+
+        Returns
+        -------
+        missing_values_in_feature_mask : ndarray of shape (n_features,), or None
+            Missing value mask. If missing values are not supported or there
+            are no missing values, return None.
+        """
+        estimator_name = estimator_name or self.__class__.__name__
+        common_kwargs = dict(estimator_name=estimator_name, input_name="X")
+
+        if not self._support_missing_values(X):
+            assert_all_finite(X, **common_kwargs)
+            return None
+
+        with np.errstate(over="ignore"):
+            overall_sum = np.sum(X)
+
+        if not np.isfinite(overall_sum):
+            # Raise a ValueError in case of the presence of an infinite element.
+            _assert_all_finite_element_wise(X, xp=np, allow_nan=True, **common_kwargs)
+
+        # If the sum is not nan, then there are no missing values
+        if not np.isnan(overall_sum):
+            return None
+
+        missing_values_in_feature_mask = _any_isnan_axis0(X)
+        return missing_values_in_feature_mask
+
+    def _fit(
+        self,
+        X,
+        y,
+        sample_weight=None,
+        check_input=True,
+        missing_values_in_feature_mask=None,
+    ):
+        random_state = check_random_state(self.random_state)
+
+        if check_input:
+            # Need to validate separately here.
+            # We can't pass multi_output=True because that would allow y to be
+            # csr.
+
+            # _compute_missing_values_in_feature_mask will check for finite values and
+            # compute the missing mask if the tree supports missing values
+            check_X_params = dict(
+                dtype=DTYPE, accept_sparse="csc", ensure_all_finite=False
+            )
+            check_y_params = dict(ensure_2d=False, dtype=None)
+            X, y = validate_data(
+                self, X, y, validate_separately=(check_X_params, check_y_params)
+            )
+
+            missing_values_in_feature_mask = (
+                self._compute_missing_values_in_feature_mask(X)
+            )
+            if issparse(X):
+                X.sort_indices()
+
+                if X.indices.dtype != np.intc or X.indptr.dtype != np.intc:
+                    raise ValueError(
+                        "No support for np.int64 index based sparse matrices"
+                    )
+
+            if self.criterion == "poisson":
+                if np.any(y < 0):
+                    raise ValueError(
+                        "Some value(s) of y are negative which is"
+                        " not allowed for Poisson regression."
+                    )
+                if np.sum(y) <= 0:
+                    raise ValueError(
+                        "Sum of y is not positive which is "
+                        "necessary for Poisson regression."
+                    )
+
+        # Determine output settings
+        n_samples, self.n_features_in_ = X.shape
+        is_classification = is_classifier(self)
+
+        y = np.atleast_1d(y)
+        expanded_class_weight = None
+
+        if y.ndim == 1:
+            # reshape is necessary to preserve the data contiguity against vs
+            # [:, np.newaxis] that does not.
+            y = np.reshape(y, (-1, 1))
+
+        self.n_outputs_ = y.shape[1]
+
+        if is_classification:
+            check_classification_targets(y)
+            y = np.copy(y)
+
+            self.classes_ = []
+            self.n_classes_ = []
+
+            if self.class_weight is not None:
+                y_original = np.copy(y)
+
+            y_encoded = np.zeros(y.shape, dtype=int)
+            for k in range(self.n_outputs_):
+                classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True)
+                self.classes_.append(classes_k)
+                self.n_classes_.append(classes_k.shape[0])
+            y = y_encoded
+
+            if self.class_weight is not None:
+                expanded_class_weight = compute_sample_weight(
+                    self.class_weight, y_original
+                )
+
+            self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
+
+        if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
+            y = np.ascontiguousarray(y, dtype=DOUBLE)
+
+        max_depth = np.iinfo(np.int32).max if self.max_depth is None else self.max_depth
+
+        if isinstance(self.min_samples_leaf, numbers.Integral):
+            min_samples_leaf = self.min_samples_leaf
+        else:  # float
+            min_samples_leaf = ceil(self.min_samples_leaf * n_samples)
+
+        if isinstance(self.min_samples_split, numbers.Integral):
+            min_samples_split = self.min_samples_split
+        else:  # float
+            min_samples_split = ceil(self.min_samples_split * n_samples)
+            min_samples_split = max(2, min_samples_split)
+
+        min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
+
+        if isinstance(self.max_features, str):
+            if self.max_features == "sqrt":
+                max_features = max(1, int(np.sqrt(self.n_features_in_)))
+            elif self.max_features == "log2":
+                max_features = max(1, int(np.log2(self.n_features_in_)))
+        elif self.max_features is None:
+            max_features = self.n_features_in_
+        elif isinstance(self.max_features, numbers.Integral):
+            max_features = self.max_features
+        else:  # float
+            if self.max_features > 0.0:
+                max_features = max(1, int(self.max_features * self.n_features_in_))
+            else:
+                max_features = 0
+
+        self.max_features_ = max_features
+
+        max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes
+
+        if len(y) != n_samples:
+            raise ValueError(
+                "Number of labels=%d does not match number of samples=%d"
+                % (len(y), n_samples)
+            )
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X, dtype=DOUBLE)
+
+        if expanded_class_weight is not None:
+            if sample_weight is not None:
+                sample_weight = sample_weight * expanded_class_weight
+            else:
+                sample_weight = expanded_class_weight
+
+        # Set min_weight_leaf from min_weight_fraction_leaf
+        if sample_weight is None:
+            min_weight_leaf = self.min_weight_fraction_leaf * n_samples
+        else:
+            min_weight_leaf = self.min_weight_fraction_leaf * np.sum(sample_weight)
+
+        # Build tree
+        criterion = self.criterion
+        if not isinstance(criterion, Criterion):
+            if is_classification:
+                criterion = CRITERIA_CLF[self.criterion](
+                    self.n_outputs_, self.n_classes_
+                )
+            else:
+                criterion = CRITERIA_REG[self.criterion](self.n_outputs_, n_samples)
+        else:
+            # Make a deepcopy in case the criterion has mutable attributes that
+            # might be shared and modified concurrently during parallel fitting
+            criterion = copy.deepcopy(criterion)
+
+        SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS
+
+        splitter = self.splitter
+        if self.monotonic_cst is None:
+            monotonic_cst = None
+        else:
+            if self.n_outputs_ > 1:
+                raise ValueError(
+                    "Monotonicity constraints are not supported with multiple outputs."
+                )
+            # Check to correct monotonicity constraint' specification,
+            # by applying element-wise logical conjunction
+            # Note: we do not cast `np.asarray(self.monotonic_cst, dtype=np.int8)`
+            # straight away here so as to generate error messages for invalid
+            # values using the original values prior to any dtype related conversion.
+            monotonic_cst = np.asarray(self.monotonic_cst)
+            if monotonic_cst.shape[0] != X.shape[1]:
+                raise ValueError(
+                    "monotonic_cst has shape {} but the input data "
+                    "X has {} features.".format(monotonic_cst.shape[0], X.shape[1])
+                )
+            valid_constraints = np.isin(monotonic_cst, (-1, 0, 1))
+            if not np.all(valid_constraints):
+                unique_constaints_value = np.unique(monotonic_cst)
+                raise ValueError(
+                    "monotonic_cst must be None or an array-like of -1, 0 or 1, but"
+                    f" got {unique_constaints_value}"
+                )
+            monotonic_cst = np.asarray(monotonic_cst, dtype=np.int8)
+            if is_classifier(self):
+                if self.n_classes_[0] > 2:
+                    raise ValueError(
+                        "Monotonicity constraints are not supported with multiclass "
+                        "classification"
+                    )
+                # Binary classification trees are built by constraining probabilities
+                # of the *negative class* in order to make the implementation similar
+                # to regression trees.
+                # Since self.monotonic_cst encodes constraints on probabilities of the
+                # *positive class*, all signs must be flipped.
+                monotonic_cst *= -1
+
+        if not isinstance(self.splitter, Splitter):
+            splitter = SPLITTERS[self.splitter](
+                criterion,
+                self.max_features_,
+                min_samples_leaf,
+                min_weight_leaf,
+                random_state,
+                monotonic_cst,
+            )
+
+        if is_classifier(self):
+            self.tree_ = Tree(self.n_features_in_, self.n_classes_, self.n_outputs_)
+        else:
+            self.tree_ = Tree(
+                self.n_features_in_,
+                # TODO: tree shouldn't need this in this case
+                np.array([1] * self.n_outputs_, dtype=np.intp),
+                self.n_outputs_,
+            )
+
+        # Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
+        if max_leaf_nodes < 0:
+            builder = DepthFirstTreeBuilder(
+                splitter,
+                min_samples_split,
+                min_samples_leaf,
+                min_weight_leaf,
+                max_depth,
+                self.min_impurity_decrease,
+            )
+        else:
+            builder = BestFirstTreeBuilder(
+                splitter,
+                min_samples_split,
+                min_samples_leaf,
+                min_weight_leaf,
+                max_depth,
+                max_leaf_nodes,
+                self.min_impurity_decrease,
+            )
+
+        builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
+
+        if self.n_outputs_ == 1 and is_classifier(self):
+            self.n_classes_ = self.n_classes_[0]
+            self.classes_ = self.classes_[0]
+
+        self._prune_tree()
+
+        return self
+
+    def _validate_X_predict(self, X, check_input):
+        """Validate the training data on predict (probabilities)."""
+        if check_input:
+            if self._support_missing_values(X):
+                ensure_all_finite = "allow-nan"
+            else:
+                ensure_all_finite = True
+            X = validate_data(
+                self,
+                X,
+                dtype=DTYPE,
+                accept_sparse="csr",
+                reset=False,
+                ensure_all_finite=ensure_all_finite,
+            )
+            if issparse(X) and (
+                X.indices.dtype != np.intc or X.indptr.dtype != np.intc
+            ):
+                raise ValueError("No support for np.int64 index based sparse matrices")
+        else:
+            # The number of features is checked regardless of `check_input`
+            _check_n_features(self, X, reset=False)
+        return X
+
+    def predict(self, X, check_input=True):
+        """Predict class or regression value for X.
+
+        For a classification model, the predicted class for each sample in X is
+        returned. For a regression model, the predicted value based on X is
+        returned.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            The predicted classes, or the predict values.
+        """
+        check_is_fitted(self)
+        X = self._validate_X_predict(X, check_input)
+        proba = self.tree_.predict(X)
+        n_samples = X.shape[0]
+
+        # Classification
+        if is_classifier(self):
+            if self.n_outputs_ == 1:
+                return self.classes_.take(np.argmax(proba, axis=1), axis=0)
+
+            else:
+                class_type = self.classes_[0].dtype
+                predictions = np.zeros((n_samples, self.n_outputs_), dtype=class_type)
+                for k in range(self.n_outputs_):
+                    predictions[:, k] = self.classes_[k].take(
+                        np.argmax(proba[:, k], axis=1), axis=0
+                    )
+
+                return predictions
+
+        # Regression
+        else:
+            if self.n_outputs_ == 1:
+                return proba[:, 0]
+
+            else:
+                return proba[:, :, 0]
+
+    def apply(self, X, check_input=True):
+        """Return the index of the leaf that each sample is predicted as.
+
+        .. versionadded:: 0.17
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        X_leaves : array-like of shape (n_samples,)
+            For each datapoint x in X, return the index of the leaf x
+            ends up in. Leaves are numbered within
+            ``[0; self.tree_.node_count)``, possibly with gaps in the
+            numbering.
+        """
+        check_is_fitted(self)
+        X = self._validate_X_predict(X, check_input)
+        return self.tree_.apply(X)
+
+    def decision_path(self, X, check_input=True):
+        """Return the decision path in the tree.
+
+        .. versionadded:: 0.18
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        indicator : sparse matrix of shape (n_samples, n_nodes)
+            Return a node indicator CSR matrix where non zero elements
+            indicates that the samples goes through the nodes.
+        """
+        X = self._validate_X_predict(X, check_input)
+        return self.tree_.decision_path(X)
+
+    def _prune_tree(self):
+        """Prune tree using Minimal Cost-Complexity Pruning."""
+        check_is_fitted(self)
+
+        if self.ccp_alpha == 0.0:
+            return
+
+        # build pruned tree
+        if is_classifier(self):
+            n_classes = np.atleast_1d(self.n_classes_)
+            pruned_tree = Tree(self.n_features_in_, n_classes, self.n_outputs_)
+        else:
+            pruned_tree = Tree(
+                self.n_features_in_,
+                # TODO: the tree shouldn't need this param
+                np.array([1] * self.n_outputs_, dtype=np.intp),
+                self.n_outputs_,
+            )
+        _build_pruned_tree_ccp(pruned_tree, self.tree_, self.ccp_alpha)
+
+        self.tree_ = pruned_tree
+
+    def cost_complexity_pruning_path(self, X, y, sample_weight=None):
+        """Compute the pruning path during Minimal Cost-Complexity Pruning.
+
+        See :ref:`minimal_cost_complexity_pruning` for details on the pruning
+        process.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csc_matrix``.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            The target values (class labels) as integers or strings.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted. Splits
+            that would create child nodes with net zero or negative weight are
+            ignored while searching for a split in each node. Splits are also
+            ignored if they would result in any single class carrying a
+            negative weight in either child node.
+
+        Returns
+        -------
+        ccp_path : :class:`~sklearn.utils.Bunch`
+            Dictionary-like object, with the following attributes.
+
+            ccp_alphas : ndarray
+                Effective alphas of subtree during pruning.
+
+            impurities : ndarray
+                Sum of the impurities of the subtree leaves for the
+                corresponding alpha value in ``ccp_alphas``.
+        """
+        est = clone(self).set_params(ccp_alpha=0.0)
+        est.fit(X, y, sample_weight=sample_weight)
+        return Bunch(**ccp_pruning_path(est.tree_))
+
+    @property
+    def feature_importances_(self):
+        """Return the feature importances.
+
+        The importance of a feature is computed as the (normalized) total
+        reduction of the criterion brought by that feature.
+        It is also known as the Gini importance.
+
+        Warning: impurity-based feature importances can be misleading for
+        high cardinality features (many unique values). See
+        :func:`sklearn.inspection.permutation_importance` as an alternative.
+
+        Returns
+        -------
+        feature_importances_ : ndarray of shape (n_features,)
+            Normalized total reduction of criteria by feature
+            (Gini importance).
+        """
+        check_is_fitted(self)
+
+        return self.tree_.compute_feature_importances()
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
+
+
+# =============================================================================
+# Public estimators
+# =============================================================================
+
+
+class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
+    """A decision tree classifier.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    criterion : {"gini", "entropy", "log_loss"}, default="gini"
+        The function to measure the quality of a split. Supported criteria are
+        "gini" for the Gini impurity and "log_loss" and "entropy" both for the
+        Shannon information gain, see :ref:`tree_mathematical_formulation`.
+
+    splitter : {"best", "random"}, default="best"
+        The strategy used to choose the split at each node. Supported
+        strategies are "best" to choose the best split and "random" to choose
+        the best random split.
+
+    max_depth : int, default=None
+        The maximum depth of the tree. If None, then nodes are expanded until
+        all leaves are pure or until all leaves contain less than
+        min_samples_split samples.
+
+    min_samples_split : int or float, default=2
+        The minimum number of samples required to split an internal node:
+
+        - If int, then consider `min_samples_split` as the minimum number.
+        - If float, then `min_samples_split` is a fraction and
+          `ceil(min_samples_split * n_samples)` are the minimum
+          number of samples for each split.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_samples_leaf : int or float, default=1
+        The minimum number of samples required to be at a leaf node.
+        A split point at any depth will only be considered if it leaves at
+        least ``min_samples_leaf`` training samples in each of the left and
+        right branches.  This may have the effect of smoothing the model,
+        especially in regression.
+
+        - If int, then consider `min_samples_leaf` as the minimum number.
+        - If float, then `min_samples_leaf` is a fraction and
+          `ceil(min_samples_leaf * n_samples)` are the minimum
+          number of samples for each node.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_weight_fraction_leaf : float, default=0.0
+        The minimum weighted fraction of the sum total of weights (of all
+        the input samples) required to be at a leaf node. Samples have
+        equal weight when sample_weight is not provided.
+
+    max_features : int, float or {"sqrt", "log2"}, default=None
+        The number of features to consider when looking for the best split:
+
+        - If int, then consider `max_features` features at each split.
+        - If float, then `max_features` is a fraction and
+          `max(1, int(max_features * n_features_in_))` features are considered at
+          each split.
+        - If "sqrt", then `max_features=sqrt(n_features)`.
+        - If "log2", then `max_features=log2(n_features)`.
+        - If None, then `max_features=n_features`.
+
+        .. note::
+
+            The search for a split does not stop until at least one
+            valid partition of the node samples is found, even if it requires to
+            effectively inspect more than ``max_features`` features.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the randomness of the estimator. The features are always
+        randomly permuted at each split, even if ``splitter`` is set to
+        ``"best"``. When ``max_features < n_features``, the algorithm will
+        select ``max_features`` at random at each split before finding the best
+        split among them. But the best found split may vary across different
+        runs, even if ``max_features=n_features``. That is the case, if the
+        improvement of the criterion is identical for several splits and one
+        split has to be selected at random. To obtain a deterministic behaviour
+        during fitting, ``random_state`` has to be fixed to an integer.
+        See :term:`Glossary ` for details.
+
+    max_leaf_nodes : int, default=None
+        Grow a tree with ``max_leaf_nodes`` in best-first fashion.
+        Best nodes are defined as relative reduction in impurity.
+        If None then unlimited number of leaf nodes.
+
+    min_impurity_decrease : float, default=0.0
+        A node will be split if this split induces a decrease of the impurity
+        greater than or equal to this value.
+
+        The weighted impurity decrease equation is the following::
+
+            N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)
+
+        where ``N`` is the total number of samples, ``N_t`` is the number of
+        samples at the current node, ``N_t_L`` is the number of samples in the
+        left child, and ``N_t_R`` is the number of samples in the right child.
+
+        ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
+        if ``sample_weight`` is passed.
+
+        .. versionadded:: 0.19
+
+    class_weight : dict, list of dict or "balanced", default=None
+        Weights associated with classes in the form ``{class_label: weight}``.
+        If None, all classes are supposed to have weight one. For
+        multi-output problems, a list of dicts can be provided in the same
+        order as the columns of y.
+
+        Note that for multioutput (including multilabel) weights should be
+        defined for each class of every column in its own dict. For example,
+        for four-class multilabel classification weights should be
+        [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
+        [{1:1}, {2:5}, {3:1}, {4:1}].
+
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``
+
+        For multi-output, the weights of each column of y will be multiplied.
+
+        Note that these weights will be multiplied with sample_weight (passed
+        through the fit method) if sample_weight is specified.
+
+    ccp_alpha : non-negative float, default=0.0
+        Complexity parameter used for Minimal Cost-Complexity Pruning. The
+        subtree with the largest cost complexity that is smaller than
+        ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
+        :ref:`minimal_cost_complexity_pruning` for details. See
+        :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
+        for an example of such pruning.
+
+        .. versionadded:: 0.22
+
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonicity constraint to enforce on each feature.
+          - 1: monotonic increase
+          - 0: no constraint
+          - -1: monotonic decrease
+
+        If monotonic_cst is None, no constraints are applied.
+
+        Monotonicity constraints are not supported for:
+          - multiclass classifications (i.e. when `n_classes > 2`),
+          - multioutput classifications (i.e. when `n_outputs_ > 1`),
+          - classifications trained on data with missing values.
+
+        The constraints hold over the probability of the positive class.
+
+        Read more in the :ref:`User Guide `.
+
+        .. versionadded:: 1.4
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,) or list of ndarray
+        The classes labels (single output problem),
+        or a list of arrays of class labels (multi-output problem).
+
+    feature_importances_ : ndarray of shape (n_features,)
+        The impurity-based feature importances.
+        The higher, the more important the feature.
+        The importance of a feature is computed as the (normalized)
+        total reduction of the criterion brought by that feature.  It is also
+        known as the Gini importance [4]_.
+
+        Warning: impurity-based feature importances can be misleading for
+        high cardinality features (many unique values). See
+        :func:`sklearn.inspection.permutation_importance` as an alternative.
+
+    max_features_ : int
+        The inferred value of max_features.
+
+    n_classes_ : int or list of int
+        The number of classes (for single output problems),
+        or a list containing the number of classes for each
+        output (for multi-output problems).
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_outputs_ : int
+        The number of outputs when ``fit`` is performed.
+
+    tree_ : Tree instance
+        The underlying Tree object. Please refer to
+        ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+        :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+        for basic usage of these attributes.
+
+    See Also
+    --------
+    DecisionTreeRegressor : A decision tree regressor.
+
+    Notes
+    -----
+    The default values for the parameters controlling the size of the trees
+    (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
+    unpruned trees which can potentially be very large on some data sets. To
+    reduce memory consumption, the complexity and size of the trees should be
+    controlled by setting those parameter values.
+
+    The :meth:`predict` method operates using the :func:`numpy.argmax`
+    function on the outputs of :meth:`predict_proba`. This means that in
+    case the highest predicted probabilities are tied, the classifier will
+    predict the tied class with the lowest index in :term:`classes_`.
+
+    References
+    ----------
+
+    .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
+
+    .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
+           and Regression Trees", Wadsworth, Belmont, CA, 1984.
+
+    .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
+           Learning", Springer, 2009.
+
+    .. [4] L. Breiman, and A. Cutler, "Random Forests",
+           https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.model_selection import cross_val_score
+    >>> from sklearn.tree import DecisionTreeClassifier
+    >>> clf = DecisionTreeClassifier(random_state=0)
+    >>> iris = load_iris()
+    >>> cross_val_score(clf, iris.data, iris.target, cv=10)
+    ...                             # doctest: +SKIP
+    ...
+    array([ 1.     ,  0.93,  0.86,  0.93,  0.93,
+            0.93,  0.93,  1.     ,  0.93,  1.      ])
+    """
+
+    # "check_input" is used for optimisation and isn't something to be passed
+    # around in a pipeline.
+    __metadata_request__predict_proba = {"check_input": metadata_routing.UNUSED}
+    __metadata_request__fit = {"check_input": metadata_routing.UNUSED}
+
+    _parameter_constraints: dict = {
+        **BaseDecisionTree._parameter_constraints,
+        "criterion": [StrOptions({"gini", "entropy", "log_loss"}), Hidden(Criterion)],
+        "class_weight": [dict, list, StrOptions({"balanced"}), None],
+    }
+
+    def __init__(
+        self,
+        *,
+        criterion="gini",
+        splitter="best",
+        max_depth=None,
+        min_samples_split=2,
+        min_samples_leaf=1,
+        min_weight_fraction_leaf=0.0,
+        max_features=None,
+        random_state=None,
+        max_leaf_nodes=None,
+        min_impurity_decrease=0.0,
+        class_weight=None,
+        ccp_alpha=0.0,
+        monotonic_cst=None,
+    ):
+        super().__init__(
+            criterion=criterion,
+            splitter=splitter,
+            max_depth=max_depth,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            min_weight_fraction_leaf=min_weight_fraction_leaf,
+            max_features=max_features,
+            max_leaf_nodes=max_leaf_nodes,
+            class_weight=class_weight,
+            random_state=random_state,
+            min_impurity_decrease=min_impurity_decrease,
+            monotonic_cst=monotonic_cst,
+            ccp_alpha=ccp_alpha,
+        )
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None, check_input=True):
+        """Build a decision tree classifier from the training set (X, y).
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csc_matrix``.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            The target values (class labels) as integers or strings.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted. Splits
+            that would create child nodes with net zero or negative weight are
+            ignored while searching for a split in each node. Splits are also
+            ignored if they would result in any single class carrying a
+            negative weight in either child node.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        self : DecisionTreeClassifier
+            Fitted estimator.
+        """
+
+        super()._fit(
+            X,
+            y,
+            sample_weight=sample_weight,
+            check_input=check_input,
+        )
+        return self
+
+    def predict_proba(self, X, check_input=True):
+        """Predict class probabilities of the input samples X.
+
+        The predicted class probability is the fraction of samples of the same
+        class in a leaf.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \
+            such arrays if n_outputs > 1
+            The class probabilities of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+        """
+        check_is_fitted(self)
+        X = self._validate_X_predict(X, check_input)
+        proba = self.tree_.predict(X)
+
+        if self.n_outputs_ == 1:
+            return proba[:, : self.n_classes_]
+        else:
+            all_proba = []
+            for k in range(self.n_outputs_):
+                proba_k = proba[:, k, : self.n_classes_[k]]
+                all_proba.append(proba_k)
+            return all_proba
+
+    def predict_log_proba(self, X):
+        """Predict class log-probabilities of the input samples X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csr_matrix``.
+
+        Returns
+        -------
+        proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \
+            such arrays if n_outputs > 1
+            The class log-probabilities of the input samples. The order of the
+            classes corresponds to that in the attribute :term:`classes_`.
+        """
+        proba = self.predict_proba(X)
+
+        if self.n_outputs_ == 1:
+            return np.log(proba)
+
+        else:
+            for k in range(self.n_outputs_):
+                proba[k] = np.log(proba[k])
+
+            return proba
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # XXX: nan is only support for dense arrays, but we set this for common test to
+        # pass, specifically: check_estimators_nan_inf
+        allow_nan = self.splitter in ("best", "random") and self.criterion in {
+            "gini",
+            "log_loss",
+            "entropy",
+        }
+        tags.classifier_tags.multi_label = True
+        tags.input_tags.allow_nan = allow_nan
+        return tags
+
+
+class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
+    """A decision tree regressor.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    criterion : {"squared_error", "friedman_mse", "absolute_error", \
+            "poisson"}, default="squared_error"
+        The function to measure the quality of a split. Supported criteria
+        are "squared_error" for the mean squared error, which is equal to
+        variance reduction as feature selection criterion and minimizes the L2
+        loss using the mean of each terminal node, "friedman_mse", which uses
+        mean squared error with Friedman's improvement score for potential
+        splits, "absolute_error" for the mean absolute error, which minimizes
+        the L1 loss using the median of each terminal node, and "poisson" which
+        uses reduction in the half mean Poisson deviance to find splits.
+
+        .. versionadded:: 0.18
+           Mean Absolute Error (MAE) criterion.
+
+        .. versionadded:: 0.24
+            Poisson deviance criterion.
+
+    splitter : {"best", "random"}, default="best"
+        The strategy used to choose the split at each node. Supported
+        strategies are "best" to choose the best split and "random" to choose
+        the best random split.
+
+    max_depth : int, default=None
+        The maximum depth of the tree. If None, then nodes are expanded until
+        all leaves are pure or until all leaves contain less than
+        min_samples_split samples.
+
+        For an example of how ``max_depth`` influences the model, see
+        :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`.
+
+    min_samples_split : int or float, default=2
+        The minimum number of samples required to split an internal node:
+
+        - If int, then consider `min_samples_split` as the minimum number.
+        - If float, then `min_samples_split` is a fraction and
+          `ceil(min_samples_split * n_samples)` are the minimum
+          number of samples for each split.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_samples_leaf : int or float, default=1
+        The minimum number of samples required to be at a leaf node.
+        A split point at any depth will only be considered if it leaves at
+        least ``min_samples_leaf`` training samples in each of the left and
+        right branches.  This may have the effect of smoothing the model,
+        especially in regression.
+
+        - If int, then consider `min_samples_leaf` as the minimum number.
+        - If float, then `min_samples_leaf` is a fraction and
+          `ceil(min_samples_leaf * n_samples)` are the minimum
+          number of samples for each node.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_weight_fraction_leaf : float, default=0.0
+        The minimum weighted fraction of the sum total of weights (of all
+        the input samples) required to be at a leaf node. Samples have
+        equal weight when sample_weight is not provided.
+
+    max_features : int, float or {"sqrt", "log2"}, default=None
+        The number of features to consider when looking for the best split:
+
+        - If int, then consider `max_features` features at each split.
+        - If float, then `max_features` is a fraction and
+          `max(1, int(max_features * n_features_in_))` features are considered at each
+          split.
+        - If "sqrt", then `max_features=sqrt(n_features)`.
+        - If "log2", then `max_features=log2(n_features)`.
+        - If None, then `max_features=n_features`.
+
+        Note: the search for a split does not stop until at least one
+        valid partition of the node samples is found, even if it requires to
+        effectively inspect more than ``max_features`` features.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the randomness of the estimator. The features are always
+        randomly permuted at each split, even if ``splitter`` is set to
+        ``"best"``. When ``max_features < n_features``, the algorithm will
+        select ``max_features`` at random at each split before finding the best
+        split among them. But the best found split may vary across different
+        runs, even if ``max_features=n_features``. That is the case, if the
+        improvement of the criterion is identical for several splits and one
+        split has to be selected at random. To obtain a deterministic behaviour
+        during fitting, ``random_state`` has to be fixed to an integer.
+        See :term:`Glossary ` for details.
+
+    max_leaf_nodes : int, default=None
+        Grow a tree with ``max_leaf_nodes`` in best-first fashion.
+        Best nodes are defined as relative reduction in impurity.
+        If None then unlimited number of leaf nodes.
+
+    min_impurity_decrease : float, default=0.0
+        A node will be split if this split induces a decrease of the impurity
+        greater than or equal to this value.
+
+        The weighted impurity decrease equation is the following::
+
+            N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)
+
+        where ``N`` is the total number of samples, ``N_t`` is the number of
+        samples at the current node, ``N_t_L`` is the number of samples in the
+        left child, and ``N_t_R`` is the number of samples in the right child.
+
+        ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
+        if ``sample_weight`` is passed.
+
+        .. versionadded:: 0.19
+
+    ccp_alpha : non-negative float, default=0.0
+        Complexity parameter used for Minimal Cost-Complexity Pruning. The
+        subtree with the largest cost complexity that is smaller than
+        ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
+        :ref:`minimal_cost_complexity_pruning` for details. See
+        :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
+        for an example of such pruning.
+
+        .. versionadded:: 0.22
+
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonicity constraint to enforce on each feature.
+          - 1: monotonic increase
+          - 0: no constraint
+          - -1: monotonic decrease
+
+        If monotonic_cst is None, no constraints are applied.
+
+        Monotonicity constraints are not supported for:
+          - multioutput regressions (i.e. when `n_outputs_ > 1`),
+          - regressions trained on data with missing values.
+
+        Read more in the :ref:`User Guide `.
+
+        .. versionadded:: 1.4
+
+    Attributes
+    ----------
+    feature_importances_ : ndarray of shape (n_features,)
+        The feature importances.
+        The higher, the more important the feature.
+        The importance of a feature is computed as the
+        (normalized) total reduction of the criterion brought
+        by that feature. It is also known as the Gini importance [4]_.
+
+        Warning: impurity-based feature importances can be misleading for
+        high cardinality features (many unique values). See
+        :func:`sklearn.inspection.permutation_importance` as an alternative.
+
+    max_features_ : int
+        The inferred value of max_features.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_outputs_ : int
+        The number of outputs when ``fit`` is performed.
+
+    tree_ : Tree instance
+        The underlying Tree object. Please refer to
+        ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+        :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+        for basic usage of these attributes.
+
+    See Also
+    --------
+    DecisionTreeClassifier : A decision tree classifier.
+
+    Notes
+    -----
+    The default values for the parameters controlling the size of the trees
+    (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
+    unpruned trees which can potentially be very large on some data sets. To
+    reduce memory consumption, the complexity and size of the trees should be
+    controlled by setting those parameter values.
+
+    References
+    ----------
+
+    .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
+
+    .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
+           and Regression Trees", Wadsworth, Belmont, CA, 1984.
+
+    .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
+           Learning", Springer, 2009.
+
+    .. [4] L. Breiman, and A. Cutler, "Random Forests",
+           https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_diabetes
+    >>> from sklearn.model_selection import cross_val_score
+    >>> from sklearn.tree import DecisionTreeRegressor
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> regressor = DecisionTreeRegressor(random_state=0)
+    >>> cross_val_score(regressor, X, y, cv=10)
+    ...                    # doctest: +SKIP
+    ...
+    array([-0.39, -0.46,  0.02,  0.06, -0.50,
+           0.16,  0.11, -0.73, -0.30, -0.00])
+    """
+
+    # "check_input" is used for optimisation and isn't something to be passed
+    # around in a pipeline.
+    __metadata_request__fit = {"check_input": metadata_routing.UNUSED}
+
+    _parameter_constraints: dict = {
+        **BaseDecisionTree._parameter_constraints,
+        "criterion": [
+            StrOptions({"squared_error", "friedman_mse", "absolute_error", "poisson"}),
+            Hidden(Criterion),
+        ],
+    }
+
+    def __init__(
+        self,
+        *,
+        criterion="squared_error",
+        splitter="best",
+        max_depth=None,
+        min_samples_split=2,
+        min_samples_leaf=1,
+        min_weight_fraction_leaf=0.0,
+        max_features=None,
+        random_state=None,
+        max_leaf_nodes=None,
+        min_impurity_decrease=0.0,
+        ccp_alpha=0.0,
+        monotonic_cst=None,
+    ):
+        super().__init__(
+            criterion=criterion,
+            splitter=splitter,
+            max_depth=max_depth,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            min_weight_fraction_leaf=min_weight_fraction_leaf,
+            max_features=max_features,
+            max_leaf_nodes=max_leaf_nodes,
+            random_state=random_state,
+            min_impurity_decrease=min_impurity_decrease,
+            ccp_alpha=ccp_alpha,
+            monotonic_cst=monotonic_cst,
+        )
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None, check_input=True):
+        """Build a decision tree regressor from the training set (X, y).
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples. Internally, it will be converted to
+            ``dtype=np.float32`` and if a sparse matrix is provided
+            to a sparse ``csc_matrix``.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            The target values (real numbers). Use ``dtype=np.float64`` and
+            ``order='C'`` for maximum efficiency.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If None, then samples are equally weighted. Splits
+            that would create child nodes with net zero or negative weight are
+            ignored while searching for a split in each node.
+
+        check_input : bool, default=True
+            Allow to bypass several input checking.
+            Don't use this parameter unless you know what you're doing.
+
+        Returns
+        -------
+        self : DecisionTreeRegressor
+            Fitted estimator.
+        """
+
+        super()._fit(
+            X,
+            y,
+            sample_weight=sample_weight,
+            check_input=check_input,
+        )
+        return self
+
+    def _compute_partial_dependence_recursion(self, grid, target_features):
+        """Fast partial dependence computation.
+
+        Parameters
+        ----------
+        grid : ndarray of shape (n_samples, n_target_features), dtype=np.float32
+            The grid points on which the partial dependence should be
+            evaluated.
+        target_features : ndarray of shape (n_target_features), dtype=np.intp
+            The set of target features for which the partial dependence
+            should be evaluated.
+
+        Returns
+        -------
+        averaged_predictions : ndarray of shape (n_samples,), dtype=np.float64
+            The value of the partial dependence function on each grid point.
+        """
+        grid = np.asarray(grid, dtype=DTYPE, order="C")
+        averaged_predictions = np.zeros(
+            shape=grid.shape[0], dtype=np.float64, order="C"
+        )
+        target_features = np.asarray(target_features, dtype=np.intp, order="C")
+
+        self.tree_.compute_partial_dependence(
+            grid, target_features, averaged_predictions
+        )
+        return averaged_predictions
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # XXX: nan is only support for dense arrays, but we set this for common test to
+        # pass, specifically: check_estimators_nan_inf
+        allow_nan = self.splitter in ("best", "random") and self.criterion in {
+            "squared_error",
+            "friedman_mse",
+            "poisson",
+        }
+        tags.input_tags.allow_nan = allow_nan
+        return tags
+
+
+class ExtraTreeClassifier(DecisionTreeClassifier):
+    """An extremely randomized tree classifier.
+
+    Extra-trees differ from classic decision trees in the way they are built.
+    When looking for the best split to separate the samples of a node into two
+    groups, random splits are drawn for each of the `max_features` randomly
+    selected features and the best split among those is chosen. When
+    `max_features` is set 1, this amounts to building a totally random
+    decision tree.
+
+    Warning: Extra-trees should only be used within ensemble methods.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    criterion : {"gini", "entropy", "log_loss"}, default="gini"
+        The function to measure the quality of a split. Supported criteria are
+        "gini" for the Gini impurity and "log_loss" and "entropy" both for the
+        Shannon information gain, see :ref:`tree_mathematical_formulation`.
+
+    splitter : {"random", "best"}, default="random"
+        The strategy used to choose the split at each node. Supported
+        strategies are "best" to choose the best split and "random" to choose
+        the best random split.
+
+    max_depth : int, default=None
+        The maximum depth of the tree. If None, then nodes are expanded until
+        all leaves are pure or until all leaves contain less than
+        min_samples_split samples.
+
+    min_samples_split : int or float, default=2
+        The minimum number of samples required to split an internal node:
+
+        - If int, then consider `min_samples_split` as the minimum number.
+        - If float, then `min_samples_split` is a fraction and
+          `ceil(min_samples_split * n_samples)` are the minimum
+          number of samples for each split.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_samples_leaf : int or float, default=1
+        The minimum number of samples required to be at a leaf node.
+        A split point at any depth will only be considered if it leaves at
+        least ``min_samples_leaf`` training samples in each of the left and
+        right branches.  This may have the effect of smoothing the model,
+        especially in regression.
+
+        - If int, then consider `min_samples_leaf` as the minimum number.
+        - If float, then `min_samples_leaf` is a fraction and
+          `ceil(min_samples_leaf * n_samples)` are the minimum
+          number of samples for each node.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_weight_fraction_leaf : float, default=0.0
+        The minimum weighted fraction of the sum total of weights (of all
+        the input samples) required to be at a leaf node. Samples have
+        equal weight when sample_weight is not provided.
+
+    max_features : int, float, {"sqrt", "log2"} or None, default="sqrt"
+        The number of features to consider when looking for the best split:
+
+        - If int, then consider `max_features` features at each split.
+        - If float, then `max_features` is a fraction and
+          `max(1, int(max_features * n_features_in_))` features are considered at
+          each split.
+        - If "sqrt", then `max_features=sqrt(n_features)`.
+        - If "log2", then `max_features=log2(n_features)`.
+        - If None, then `max_features=n_features`.
+
+        .. versionchanged:: 1.1
+            The default of `max_features` changed from `"auto"` to `"sqrt"`.
+
+        Note: the search for a split does not stop until at least one
+        valid partition of the node samples is found, even if it requires to
+        effectively inspect more than ``max_features`` features.
+
+    random_state : int, RandomState instance or None, default=None
+        Used to pick randomly the `max_features` used at each split.
+        See :term:`Glossary ` for details.
+
+    max_leaf_nodes : int, default=None
+        Grow a tree with ``max_leaf_nodes`` in best-first fashion.
+        Best nodes are defined as relative reduction in impurity.
+        If None then unlimited number of leaf nodes.
+
+    min_impurity_decrease : float, default=0.0
+        A node will be split if this split induces a decrease of the impurity
+        greater than or equal to this value.
+
+        The weighted impurity decrease equation is the following::
+
+            N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)
+
+        where ``N`` is the total number of samples, ``N_t`` is the number of
+        samples at the current node, ``N_t_L`` is the number of samples in the
+        left child, and ``N_t_R`` is the number of samples in the right child.
+
+        ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
+        if ``sample_weight`` is passed.
+
+        .. versionadded:: 0.19
+
+    class_weight : dict, list of dict or "balanced", default=None
+        Weights associated with classes in the form ``{class_label: weight}``.
+        If None, all classes are supposed to have weight one. For
+        multi-output problems, a list of dicts can be provided in the same
+        order as the columns of y.
+
+        Note that for multioutput (including multilabel) weights should be
+        defined for each class of every column in its own dict. For example,
+        for four-class multilabel classification weights should be
+        [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
+        [{1:1}, {2:5}, {3:1}, {4:1}].
+
+        The "balanced" mode uses the values of y to automatically adjust
+        weights inversely proportional to class frequencies in the input data
+        as ``n_samples / (n_classes * np.bincount(y))``
+
+        For multi-output, the weights of each column of y will be multiplied.
+
+        Note that these weights will be multiplied with sample_weight (passed
+        through the fit method) if sample_weight is specified.
+
+    ccp_alpha : non-negative float, default=0.0
+        Complexity parameter used for Minimal Cost-Complexity Pruning. The
+        subtree with the largest cost complexity that is smaller than
+        ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
+        :ref:`minimal_cost_complexity_pruning` for details. See
+        :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
+        for an example of such pruning.
+
+        .. versionadded:: 0.22
+
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonicity constraint to enforce on each feature.
+          - 1: monotonic increase
+          - 0: no constraint
+          - -1: monotonic decrease
+
+        If monotonic_cst is None, no constraints are applied.
+
+        Monotonicity constraints are not supported for:
+          - multiclass classifications (i.e. when `n_classes > 2`),
+          - multioutput classifications (i.e. when `n_outputs_ > 1`),
+          - classifications trained on data with missing values.
+
+        The constraints hold over the probability of the positive class.
+
+        Read more in the :ref:`User Guide `.
+
+        .. versionadded:: 1.4
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,) or list of ndarray
+        The classes labels (single output problem),
+        or a list of arrays of class labels (multi-output problem).
+
+    max_features_ : int
+        The inferred value of max_features.
+
+    n_classes_ : int or list of int
+        The number of classes (for single output problems),
+        or a list containing the number of classes for each
+        output (for multi-output problems).
+
+    feature_importances_ : ndarray of shape (n_features,)
+        The impurity-based feature importances.
+        The higher, the more important the feature.
+        The importance of a feature is computed as the (normalized)
+        total reduction of the criterion brought by that feature.  It is also
+        known as the Gini importance.
+
+        Warning: impurity-based feature importances can be misleading for
+        high cardinality features (many unique values). See
+        :func:`sklearn.inspection.permutation_importance` as an alternative.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_outputs_ : int
+        The number of outputs when ``fit`` is performed.
+
+    tree_ : Tree instance
+        The underlying Tree object. Please refer to
+        ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+        :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+        for basic usage of these attributes.
+
+    See Also
+    --------
+    ExtraTreeRegressor : An extremely randomized tree regressor.
+    sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier.
+    sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor.
+    sklearn.ensemble.RandomForestClassifier : A random forest classifier.
+    sklearn.ensemble.RandomForestRegressor : A random forest regressor.
+    sklearn.ensemble.RandomTreesEmbedding : An ensemble of
+        totally random trees.
+
+    Notes
+    -----
+    The default values for the parameters controlling the size of the trees
+    (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
+    unpruned trees which can potentially be very large on some data sets. To
+    reduce memory consumption, the complexity and size of the trees should be
+    controlled by setting those parameter values.
+
+    References
+    ----------
+
+    .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
+           Machine Learning, 63(1), 3-42, 2006.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.ensemble import BaggingClassifier
+    >>> from sklearn.tree import ExtraTreeClassifier
+    >>> X, y = load_iris(return_X_y=True)
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...    X, y, random_state=0)
+    >>> extra_tree = ExtraTreeClassifier(random_state=0)
+    >>> cls = BaggingClassifier(extra_tree, random_state=0).fit(
+    ...    X_train, y_train)
+    >>> cls.score(X_test, y_test)
+    0.8947
+    """
+
+    def __init__(
+        self,
+        *,
+        criterion="gini",
+        splitter="random",
+        max_depth=None,
+        min_samples_split=2,
+        min_samples_leaf=1,
+        min_weight_fraction_leaf=0.0,
+        max_features="sqrt",
+        random_state=None,
+        max_leaf_nodes=None,
+        min_impurity_decrease=0.0,
+        class_weight=None,
+        ccp_alpha=0.0,
+        monotonic_cst=None,
+    ):
+        super().__init__(
+            criterion=criterion,
+            splitter=splitter,
+            max_depth=max_depth,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            min_weight_fraction_leaf=min_weight_fraction_leaf,
+            max_features=max_features,
+            max_leaf_nodes=max_leaf_nodes,
+            class_weight=class_weight,
+            min_impurity_decrease=min_impurity_decrease,
+            random_state=random_state,
+            ccp_alpha=ccp_alpha,
+            monotonic_cst=monotonic_cst,
+        )
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # XXX: nan is only supported for dense arrays, but we set this for the
+        # common test to pass, specifically: check_estimators_nan_inf
+        allow_nan = self.splitter == "random" and self.criterion in {
+            "gini",
+            "log_loss",
+            "entropy",
+        }
+        tags.classifier_tags.multi_label = True
+        tags.input_tags.allow_nan = allow_nan
+        return tags
+
+
+class ExtraTreeRegressor(DecisionTreeRegressor):
+    """An extremely randomized tree regressor.
+
+    Extra-trees differ from classic decision trees in the way they are built.
+    When looking for the best split to separate the samples of a node into two
+    groups, random splits are drawn for each of the `max_features` randomly
+    selected features and the best split among those is chosen. When
+    `max_features` is set 1, this amounts to building a totally random
+    decision tree.
+
+    Warning: Extra-trees should only be used within ensemble methods.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    criterion : {"squared_error", "friedman_mse", "absolute_error", "poisson"}, \
+            default="squared_error"
+        The function to measure the quality of a split. Supported criteria
+        are "squared_error" for the mean squared error, which is equal to
+        variance reduction as feature selection criterion and minimizes the L2
+        loss using the mean of each terminal node, "friedman_mse", which uses
+        mean squared error with Friedman's improvement score for potential
+        splits, "absolute_error" for the mean absolute error, which minimizes
+        the L1 loss using the median of each terminal node, and "poisson" which
+        uses reduction in Poisson deviance to find splits.
+
+        .. versionadded:: 0.18
+           Mean Absolute Error (MAE) criterion.
+
+        .. versionadded:: 0.24
+            Poisson deviance criterion.
+
+    splitter : {"random", "best"}, default="random"
+        The strategy used to choose the split at each node. Supported
+        strategies are "best" to choose the best split and "random" to choose
+        the best random split.
+
+    max_depth : int, default=None
+        The maximum depth of the tree. If None, then nodes are expanded until
+        all leaves are pure or until all leaves contain less than
+        min_samples_split samples.
+
+    min_samples_split : int or float, default=2
+        The minimum number of samples required to split an internal node:
+
+        - If int, then consider `min_samples_split` as the minimum number.
+        - If float, then `min_samples_split` is a fraction and
+          `ceil(min_samples_split * n_samples)` are the minimum
+          number of samples for each split.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_samples_leaf : int or float, default=1
+        The minimum number of samples required to be at a leaf node.
+        A split point at any depth will only be considered if it leaves at
+        least ``min_samples_leaf`` training samples in each of the left and
+        right branches.  This may have the effect of smoothing the model,
+        especially in regression.
+
+        - If int, then consider `min_samples_leaf` as the minimum number.
+        - If float, then `min_samples_leaf` is a fraction and
+          `ceil(min_samples_leaf * n_samples)` are the minimum
+          number of samples for each node.
+
+        .. versionchanged:: 0.18
+           Added float values for fractions.
+
+    min_weight_fraction_leaf : float, default=0.0
+        The minimum weighted fraction of the sum total of weights (of all
+        the input samples) required to be at a leaf node. Samples have
+        equal weight when sample_weight is not provided.
+
+    max_features : int, float, {"sqrt", "log2"} or None, default=1.0
+        The number of features to consider when looking for the best split:
+
+        - If int, then consider `max_features` features at each split.
+        - If float, then `max_features` is a fraction and
+          `max(1, int(max_features * n_features_in_))` features are considered at each
+          split.
+        - If "sqrt", then `max_features=sqrt(n_features)`.
+        - If "log2", then `max_features=log2(n_features)`.
+        - If None, then `max_features=n_features`.
+
+        .. versionchanged:: 1.1
+            The default of `max_features` changed from `"auto"` to `1.0`.
+
+        Note: the search for a split does not stop until at least one
+        valid partition of the node samples is found, even if it requires to
+        effectively inspect more than ``max_features`` features.
+
+    random_state : int, RandomState instance or None, default=None
+        Used to pick randomly the `max_features` used at each split.
+        See :term:`Glossary ` for details.
+
+    min_impurity_decrease : float, default=0.0
+        A node will be split if this split induces a decrease of the impurity
+        greater than or equal to this value.
+
+        The weighted impurity decrease equation is the following::
+
+            N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)
+
+        where ``N`` is the total number of samples, ``N_t`` is the number of
+        samples at the current node, ``N_t_L`` is the number of samples in the
+        left child, and ``N_t_R`` is the number of samples in the right child.
+
+        ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
+        if ``sample_weight`` is passed.
+
+        .. versionadded:: 0.19
+
+    max_leaf_nodes : int, default=None
+        Grow a tree with ``max_leaf_nodes`` in best-first fashion.
+        Best nodes are defined as relative reduction in impurity.
+        If None then unlimited number of leaf nodes.
+
+    ccp_alpha : non-negative float, default=0.0
+        Complexity parameter used for Minimal Cost-Complexity Pruning. The
+        subtree with the largest cost complexity that is smaller than
+        ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
+        :ref:`minimal_cost_complexity_pruning` for details. See
+        :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
+        for an example of such pruning.
+
+        .. versionadded:: 0.22
+
+    monotonic_cst : array-like of int of shape (n_features), default=None
+        Indicates the monotonicity constraint to enforce on each feature.
+          - 1: monotonic increase
+          - 0: no constraint
+          - -1: monotonic decrease
+
+        If monotonic_cst is None, no constraints are applied.
+
+        Monotonicity constraints are not supported for:
+          - multioutput regressions (i.e. when `n_outputs_ > 1`),
+          - regressions trained on data with missing values.
+
+        Read more in the :ref:`User Guide `.
+
+        .. versionadded:: 1.4
+
+    Attributes
+    ----------
+    max_features_ : int
+        The inferred value of max_features.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    feature_importances_ : ndarray of shape (n_features,)
+        Return impurity-based feature importances (the higher, the more
+        important the feature).
+
+        Warning: impurity-based feature importances can be misleading for
+        high cardinality features (many unique values). See
+        :func:`sklearn.inspection.permutation_importance` as an alternative.
+
+    n_outputs_ : int
+        The number of outputs when ``fit`` is performed.
+
+    tree_ : Tree instance
+        The underlying Tree object. Please refer to
+        ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+        :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+        for basic usage of these attributes.
+
+    See Also
+    --------
+    ExtraTreeClassifier : An extremely randomized tree classifier.
+    sklearn.ensemble.ExtraTreesClassifier : An extra-trees classifier.
+    sklearn.ensemble.ExtraTreesRegressor : An extra-trees regressor.
+
+    Notes
+    -----
+    The default values for the parameters controlling the size of the trees
+    (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
+    unpruned trees which can potentially be very large on some data sets. To
+    reduce memory consumption, the complexity and size of the trees should be
+    controlled by setting those parameter values.
+
+    References
+    ----------
+
+    .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
+           Machine Learning, 63(1), 3-42, 2006.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_diabetes
+    >>> from sklearn.model_selection import train_test_split
+    >>> from sklearn.ensemble import BaggingRegressor
+    >>> from sklearn.tree import ExtraTreeRegressor
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, random_state=0)
+    >>> extra_tree = ExtraTreeRegressor(random_state=0)
+    >>> reg = BaggingRegressor(extra_tree, random_state=0).fit(
+    ...     X_train, y_train)
+    >>> reg.score(X_test, y_test)
+    0.33
+    """
+
+    def __init__(
+        self,
+        *,
+        criterion="squared_error",
+        splitter="random",
+        max_depth=None,
+        min_samples_split=2,
+        min_samples_leaf=1,
+        min_weight_fraction_leaf=0.0,
+        max_features=1.0,
+        random_state=None,
+        min_impurity_decrease=0.0,
+        max_leaf_nodes=None,
+        ccp_alpha=0.0,
+        monotonic_cst=None,
+    ):
+        super().__init__(
+            criterion=criterion,
+            splitter=splitter,
+            max_depth=max_depth,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            min_weight_fraction_leaf=min_weight_fraction_leaf,
+            max_features=max_features,
+            max_leaf_nodes=max_leaf_nodes,
+            min_impurity_decrease=min_impurity_decrease,
+            random_state=random_state,
+            ccp_alpha=ccp_alpha,
+            monotonic_cst=monotonic_cst,
+        )
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        # XXX: nan is only supported for dense arrays, but we set this for the
+        # common test to pass, specifically: check_estimators_nan_inf
+        allow_nan = self.splitter == "random" and self.criterion in {
+            "squared_error",
+            "friedman_mse",
+            "poisson",
+        }
+        tags.input_tags.allow_nan = allow_nan
+        return tags
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..84d2e800d6a87506852b7b21c8006db5b9ec7e99
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pxd
@@ -0,0 +1,109 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _criterion.pyx for implementation details.
+from ..utils._typedefs cimport float64_t, int8_t, intp_t
+
+
+cdef class Criterion:
+    # The criterion computes the impurity of a node and the reduction of
+    # impurity of a split on that node. It also computes the output statistics
+    # such as the mean in regression and class probabilities in classification.
+
+    # Internal structures
+    cdef const float64_t[:, ::1] y         # Values of y
+    cdef const float64_t[:] sample_weight  # Sample weights
+
+    cdef const intp_t[:] sample_indices    # Sample indices in X, y
+    cdef intp_t start                      # samples[start:pos] are the samples in the left node
+    cdef intp_t pos                        # samples[pos:end] are the samples in the right node
+    cdef intp_t end
+    cdef intp_t n_missing                  # Number of missing values for the feature being evaluated
+    cdef bint missing_go_to_left           # Whether missing values go to the left node
+
+    cdef intp_t n_outputs                  # Number of outputs
+    cdef intp_t n_samples                  # Number of samples
+    cdef intp_t n_node_samples             # Number of samples in the node (end-start)
+    cdef float64_t weighted_n_samples         # Weighted number of samples (in total)
+    cdef float64_t weighted_n_node_samples    # Weighted number of samples in the node
+    cdef float64_t weighted_n_left            # Weighted number of samples in the left node
+    cdef float64_t weighted_n_right           # Weighted number of samples in the right node
+    cdef float64_t weighted_n_missing         # Weighted number of samples that are missing
+
+    # The criterion object is maintained such that left and right collected
+    # statistics correspond to samples[start:pos] and samples[pos:end].
+
+    # Methods
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end
+    ) except -1 nogil
+    cdef void init_sum_missing(self)
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil
+    cdef int reset(self) except -1 nogil
+    cdef int reverse_reset(self) except -1 nogil
+    cdef int update(self, intp_t new_pos) except -1 nogil
+    cdef float64_t node_impurity(self) noexcept nogil
+    cdef void children_impurity(
+        self,
+        float64_t* impurity_left,
+        float64_t* impurity_right
+    ) noexcept nogil
+    cdef void node_value(
+        self,
+        float64_t* dest
+    ) noexcept nogil
+    cdef void clip_node_value(
+        self,
+        float64_t* dest,
+        float64_t lower_bound,
+        float64_t upper_bound
+    ) noexcept nogil
+    cdef float64_t middle_value(self) noexcept nogil
+    cdef float64_t impurity_improvement(
+        self,
+        float64_t impurity_parent,
+        float64_t impurity_left,
+        float64_t impurity_right
+    ) noexcept nogil
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil
+    cdef bint check_monotonicity(
+            self,
+            int8_t monotonic_cst,
+            float64_t lower_bound,
+            float64_t upper_bound,
+    ) noexcept nogil
+    cdef inline bint _check_monotonicity(
+            self,
+            int8_t monotonic_cst,
+            float64_t lower_bound,
+            float64_t upper_bound,
+            float64_t sum_left,
+            float64_t sum_right,
+    ) noexcept nogil
+
+cdef class ClassificationCriterion(Criterion):
+    """Abstract criterion for classification."""
+
+    cdef intp_t[::1] n_classes
+    cdef intp_t max_n_classes
+
+    cdef float64_t[:, ::1] sum_total    # The sum of the weighted count of each label.
+    cdef float64_t[:, ::1] sum_left     # Same as above, but for the left side of the split
+    cdef float64_t[:, ::1] sum_right    # Same as above, but for the right side of the split
+    cdef float64_t[:, ::1] sum_missing  # Same as above, but for missing values in X
+
+cdef class RegressionCriterion(Criterion):
+    """Abstract regression criterion."""
+
+    cdef float64_t sq_sum_total
+
+    cdef float64_t[::1] sum_total    # The sum of w*y.
+    cdef float64_t[::1] sum_left     # Same as above, but for the left side of the split
+    cdef float64_t[::1] sum_right    # Same as above, but for the right side of the split
+    cdef float64_t[::1] sum_missing  # Same as above, but for missing values in X
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..9f3db83399569e2789894de4697a11e2e5c17c10
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_criterion.pyx
@@ -0,0 +1,1697 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from libc.string cimport memcpy
+from libc.string cimport memset
+from libc.math cimport fabs, INFINITY
+
+import numpy as np
+cimport numpy as cnp
+cnp.import_array()
+
+from scipy.special.cython_special cimport xlogy
+
+from ._utils cimport log
+from ._utils cimport WeightedMedianCalculator
+
+# EPSILON is used in the Poisson criterion
+cdef float64_t EPSILON = 10 * np.finfo('double').eps
+
+cdef class Criterion:
+    """Interface for impurity criteria.
+
+    This object stores methods on how to calculate how good a split is using
+    different metrics.
+    """
+    def __getstate__(self):
+        return {}
+
+    def __setstate__(self, d):
+        pass
+
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end,
+    ) except -1 nogil:
+        """Placeholder for a method which will initialize the criterion.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+
+        Parameters
+        ----------
+        y : ndarray, dtype=float64_t
+            y is a buffer that can store values for n_outputs target variables
+            stored as a Cython memoryview.
+        sample_weight : ndarray, dtype=float64_t
+            The weight of each sample stored as a Cython memoryview.
+        weighted_n_samples : float64_t
+            The total weight of the samples being considered
+        sample_indices : ndarray, dtype=intp_t
+            A mask on the samples. Indices of the samples in X and y we want to use,
+            where sample_indices[start:end] correspond to the samples in this node.
+        start : intp_t
+            The first sample to be used on this node
+        end : intp_t
+            The last sample used on this node
+
+        """
+        pass
+
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
+        """Initialize sum_missing if there are missing values.
+
+        This method assumes that caller placed the missing samples in
+        self.sample_indices[-n_missing:]
+
+        Parameters
+        ----------
+        n_missing: intp_t
+            Number of missing values for specific feature.
+        """
+        pass
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the criterion at pos=start.
+
+        This method must be implemented by the subclass.
+        """
+        pass
+
+    cdef int reverse_reset(self) except -1 nogil:
+        """Reset the criterion at pos=end.
+
+        This method must be implemented by the subclass.
+        """
+        pass
+
+    cdef int update(self, intp_t new_pos) except -1 nogil:
+        """Updated statistics by moving sample_indices[pos:new_pos] to the left child.
+
+        This updates the collected statistics by moving sample_indices[pos:new_pos]
+        from the right child to the left child. It must be implemented by
+        the subclass.
+
+        Parameters
+        ----------
+        new_pos : intp_t
+            New starting index position of the sample_indices in the right child
+        """
+        pass
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Placeholder for calculating the impurity of the node.
+
+        Placeholder for a method which will evaluate the impurity of
+        the current node, i.e. the impurity of sample_indices[start:end]. This is the
+        primary function of the criterion class. The smaller the impurity the
+        better.
+        """
+        pass
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        """Placeholder for calculating the impurity of children.
+
+        Placeholder for a method which evaluates the impurity in
+        children nodes, i.e. the impurity of sample_indices[start:pos] + the impurity
+        of sample_indices[pos:end].
+
+        Parameters
+        ----------
+        impurity_left : float64_t pointer
+            The memory address where the impurity of the left child should be
+            stored.
+        impurity_right : float64_t pointer
+            The memory address where the impurity of the right child should be
+            stored
+        """
+        pass
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil:
+        """Placeholder for storing the node value.
+
+        Placeholder for a method which will compute the node value
+        of sample_indices[start:end] and save the value into dest.
+
+        Parameters
+        ----------
+        dest : float64_t pointer
+            The memory address where the node value should be stored.
+        """
+        pass
+
+    cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
+        pass
+
+    cdef float64_t middle_value(self) noexcept nogil:
+        """Compute the middle value of a split for monotonicity constraints
+
+        This method is implemented in ClassificationCriterion and RegressionCriterion.
+        """
+        pass
+
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
+        """Compute a proxy of the impurity reduction.
+
+        This method is used to speed up the search for the best split.
+        It is a proxy quantity such that the split that maximizes this value
+        also maximizes the impurity improvement. It neglects all constant terms
+        of the impurity decrease for a given split.
+
+        The absolute impurity improvement is only computed by the
+        impurity_improvement method once the best split has been found.
+        """
+        cdef float64_t impurity_left
+        cdef float64_t impurity_right
+        self.children_impurity(&impurity_left, &impurity_right)
+
+        return (- self.weighted_n_right * impurity_right
+                - self.weighted_n_left * impurity_left)
+
+    cdef float64_t impurity_improvement(self, float64_t impurity_parent,
+                                        float64_t impurity_left,
+                                        float64_t impurity_right) noexcept nogil:
+        """Compute the improvement in impurity.
+
+        This method computes the improvement in impurity when a split occurs.
+        The weighted impurity improvement equation is the following:
+
+            N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)
+
+        where N is the total number of samples, N_t is the number of samples
+        at the current node, N_t_L is the number of samples in the left child,
+        and N_t_R is the number of samples in the right child,
+
+        Parameters
+        ----------
+        impurity_parent : float64_t
+            The initial impurity of the parent node before the split
+
+        impurity_left : float64_t
+            The impurity of the left child
+
+        impurity_right : float64_t
+            The impurity of the right child
+
+        Return
+        ------
+        float64_t : improvement in impurity after the split occurs
+        """
+        return ((self.weighted_n_node_samples / self.weighted_n_samples) *
+                (impurity_parent - (self.weighted_n_right /
+                                    self.weighted_n_node_samples * impurity_right)
+                                 - (self.weighted_n_left /
+                                    self.weighted_n_node_samples * impurity_left)))
+
+    cdef bint check_monotonicity(
+        self,
+        cnp.int8_t monotonic_cst,
+        float64_t lower_bound,
+        float64_t upper_bound,
+    ) noexcept nogil:
+        pass
+
+    cdef inline bint _check_monotonicity(
+        self,
+        cnp.int8_t monotonic_cst,
+        float64_t lower_bound,
+        float64_t upper_bound,
+        float64_t value_left,
+        float64_t value_right,
+    ) noexcept nogil:
+        cdef:
+            bint check_lower_bound = (
+                (value_left >= lower_bound) &
+                (value_right >= lower_bound)
+            )
+            bint check_upper_bound = (
+                (value_left <= upper_bound) &
+                (value_right <= upper_bound)
+            )
+            bint check_monotonic_cst = (
+                (value_left - value_right) * monotonic_cst <= 0
+            )
+        return check_lower_bound & check_upper_bound & check_monotonic_cst
+
+    cdef void init_sum_missing(self):
+        """Init sum_missing to hold sums for missing values."""
+
+cdef inline void _move_sums_classification(
+    ClassificationCriterion criterion,
+    float64_t[:, ::1] sum_1,
+    float64_t[:, ::1] sum_2,
+    float64_t* weighted_n_1,
+    float64_t* weighted_n_2,
+    bint put_missing_in_1,
+) noexcept nogil:
+    """Distribute sum_total and sum_missing into sum_1 and sum_2.
+
+    If there are missing values and:
+    - put_missing_in_1 is True, then missing values to go sum_1. Specifically:
+        sum_1 = sum_missing
+        sum_2 = sum_total - sum_missing
+
+    - put_missing_in_1 is False, then missing values go to sum_2. Specifically:
+        sum_1 = 0
+        sum_2 = sum_total
+    """
+    cdef intp_t k, c, n_bytes
+    if criterion.n_missing != 0 and put_missing_in_1:
+        for k in range(criterion.n_outputs):
+            n_bytes = criterion.n_classes[k] * sizeof(float64_t)
+            memcpy(&sum_1[k, 0], &criterion.sum_missing[k, 0], n_bytes)
+
+        for k in range(criterion.n_outputs):
+            for c in range(criterion.n_classes[k]):
+                sum_2[k, c] = criterion.sum_total[k, c] - criterion.sum_missing[k, c]
+
+        weighted_n_1[0] = criterion.weighted_n_missing
+        weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
+    else:
+        # Assigning sum_2 = sum_total for all outputs.
+        for k in range(criterion.n_outputs):
+            n_bytes = criterion.n_classes[k] * sizeof(float64_t)
+            memset(&sum_1[k, 0], 0, n_bytes)
+            memcpy(&sum_2[k, 0], &criterion.sum_total[k, 0], n_bytes)
+
+        weighted_n_1[0] = 0.0
+        weighted_n_2[0] = criterion.weighted_n_node_samples
+
+
+cdef class ClassificationCriterion(Criterion):
+    """Abstract criterion for classification."""
+
+    def __cinit__(self, intp_t n_outputs,
+                  cnp.ndarray[intp_t, ndim=1] n_classes):
+        """Initialize attributes for this criterion.
+
+        Parameters
+        ----------
+        n_outputs : intp_t
+            The number of targets, the dimensionality of the prediction
+        n_classes : numpy.ndarray, dtype=intp_t
+            The number of unique classes in each target
+        """
+        self.start = 0
+        self.pos = 0
+        self.end = 0
+        self.missing_go_to_left = 0
+
+        self.n_outputs = n_outputs
+        self.n_samples = 0
+        self.n_node_samples = 0
+        self.weighted_n_node_samples = 0.0
+        self.weighted_n_left = 0.0
+        self.weighted_n_right = 0.0
+        self.weighted_n_missing = 0.0
+
+        self.n_classes = np.empty(n_outputs, dtype=np.intp)
+
+        cdef intp_t k = 0
+        cdef intp_t max_n_classes = 0
+
+        # For each target, set the number of unique classes in that target,
+        # and also compute the maximal stride of all targets
+        for k in range(n_outputs):
+            self.n_classes[k] = n_classes[k]
+
+            if n_classes[k] > max_n_classes:
+                max_n_classes = n_classes[k]
+
+        self.max_n_classes = max_n_classes
+
+        # Count labels for each output
+        self.sum_total = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
+        self.sum_left = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
+        self.sum_right = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
+
+    def __reduce__(self):
+        return (type(self),
+                (self.n_outputs, np.asarray(self.n_classes)), self.__getstate__())
+
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end
+    ) except -1 nogil:
+        """Initialize the criterion.
+
+        This initializes the criterion at node sample_indices[start:end] and children
+        sample_indices[start:start] and sample_indices[start:end].
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+
+        Parameters
+        ----------
+        y : ndarray, dtype=float64_t
+            The target stored as a buffer for memory efficiency.
+        sample_weight : ndarray, dtype=float64_t
+            The weight of each sample stored as a Cython memoryview.
+        weighted_n_samples : float64_t
+            The total weight of all samples
+        sample_indices : ndarray, dtype=intp_t
+            A mask on the samples. Indices of the samples in X and y we want to use,
+            where sample_indices[start:end] correspond to the samples in this node.
+        start : intp_t
+            The first sample to use in the mask
+        end : intp_t
+            The last sample to use in the mask
+        """
+        self.y = y
+        self.sample_weight = sample_weight
+        self.sample_indices = sample_indices
+        self.start = start
+        self.end = end
+        self.n_node_samples = end - start
+        self.weighted_n_samples = weighted_n_samples
+        self.weighted_n_node_samples = 0.0
+
+        cdef intp_t i
+        cdef intp_t p
+        cdef intp_t k
+        cdef intp_t c
+        cdef float64_t w = 1.0
+
+        for k in range(self.n_outputs):
+            memset(&self.sum_total[k, 0], 0, self.n_classes[k] * sizeof(float64_t))
+
+        for p in range(start, end):
+            i = sample_indices[p]
+
+            # w is originally set to be 1.0, meaning that if no sample weights
+            # are given, the default weight of each sample is 1.0.
+            if sample_weight is not None:
+                w = sample_weight[i]
+
+            # Count weighted class frequency for each target
+            for k in range(self.n_outputs):
+                c =  self.y[i, k]
+                self.sum_total[k, c] += w
+
+            self.weighted_n_node_samples += w
+
+        # Reset to pos=start
+        self.reset()
+        return 0
+
+    cdef void init_sum_missing(self):
+        """Init sum_missing to hold sums for missing values."""
+        self.sum_missing = np.zeros((self.n_outputs, self.max_n_classes), dtype=np.float64)
+
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
+        """Initialize sum_missing if there are missing values.
+
+        This method assumes that caller placed the missing samples in
+        self.sample_indices[-n_missing:]
+        """
+        cdef intp_t i, p, k, c
+        cdef float64_t w = 1.0
+
+        self.n_missing = n_missing
+        if n_missing == 0:
+            return
+
+        memset(&self.sum_missing[0, 0], 0, self.max_n_classes * self.n_outputs * sizeof(float64_t))
+
+        self.weighted_n_missing = 0.0
+
+        # The missing samples are assumed to be in self.sample_indices[-n_missing:]
+        for p in range(self.end - n_missing, self.end):
+            i = self.sample_indices[p]
+            if self.sample_weight is not None:
+                w = self.sample_weight[i]
+
+            for k in range(self.n_outputs):
+                c =  self.y[i, k]
+                self.sum_missing[k, c] += w
+
+            self.weighted_n_missing += w
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the criterion at pos=start.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        self.pos = self.start
+        _move_sums_classification(
+            self,
+            self.sum_left,
+            self.sum_right,
+            &self.weighted_n_left,
+            &self.weighted_n_right,
+            self.missing_go_to_left,
+        )
+        return 0
+
+    cdef int reverse_reset(self) except -1 nogil:
+        """Reset the criterion at pos=end.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        self.pos = self.end
+        _move_sums_classification(
+            self,
+            self.sum_right,
+            self.sum_left,
+            &self.weighted_n_right,
+            &self.weighted_n_left,
+            not self.missing_go_to_left
+        )
+        return 0
+
+    cdef int update(self, intp_t new_pos) except -1 nogil:
+        """Updated statistics by moving sample_indices[pos:new_pos] to the left child.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+
+        Parameters
+        ----------
+        new_pos : intp_t
+            The new ending position for which to move sample_indices from the right
+            child to the left child.
+        """
+        cdef intp_t pos = self.pos
+        # The missing samples are assumed to be in
+        # self.sample_indices[-self.n_missing:] that is
+        # self.sample_indices[end_non_missing:self.end].
+        cdef intp_t end_non_missing = self.end - self.n_missing
+
+        cdef const intp_t[:] sample_indices = self.sample_indices
+        cdef const float64_t[:] sample_weight = self.sample_weight
+
+        cdef intp_t i
+        cdef intp_t p
+        cdef intp_t k
+        cdef intp_t c
+        cdef float64_t w = 1.0
+
+        # Update statistics up to new_pos
+        #
+        # Given that
+        #   sum_left[x] +  sum_right[x] = sum_total[x]
+        # and that sum_total is known, we are going to update
+        # sum_left from the direction that require the least amount
+        # of computations, i.e. from pos to new_pos or from end to new_po.
+        if (new_pos - pos) <= (end_non_missing - new_pos):
+            for p in range(pos, new_pos):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    self.sum_left[k,  self.y[i, k]] += w
+
+                self.weighted_n_left += w
+
+        else:
+            self.reverse_reset()
+
+            for p in range(end_non_missing - 1, new_pos - 1, -1):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    self.sum_left[k,  self.y[i, k]] -= w
+
+                self.weighted_n_left -= w
+
+        # Update right part statistics
+        self.weighted_n_right = self.weighted_n_node_samples - self.weighted_n_left
+        for k in range(self.n_outputs):
+            for c in range(self.n_classes[k]):
+                self.sum_right[k, c] = self.sum_total[k, c] - self.sum_left[k, c]
+
+        self.pos = new_pos
+        return 0
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        pass
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        pass
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil:
+        """Compute the node value of sample_indices[start:end] and save it into dest.
+
+        Parameters
+        ----------
+        dest : float64_t pointer
+            The memory address which we will save the node value into.
+        """
+        cdef intp_t k, c
+
+        for k in range(self.n_outputs):
+            for c in range(self.n_classes[k]):
+                dest[c] = self.sum_total[k, c] / self.weighted_n_node_samples
+            dest += self.max_n_classes
+
+    cdef inline void clip_node_value(
+        self, float64_t * dest, float64_t lower_bound, float64_t upper_bound
+    ) noexcept nogil:
+        """Clip the values in dest such that predicted probabilities stay between
+        `lower_bound` and `upper_bound` when monotonic constraints are enforced.
+        Note that monotonicity constraints are only supported for:
+        - single-output trees and
+        - binary classifications.
+        """
+        if dest[0] < lower_bound:
+            dest[0] = lower_bound
+        elif dest[0] > upper_bound:
+            dest[0] = upper_bound
+
+        # Values for binary classification must sum to 1.
+        dest[1] = 1 - dest[0]
+
+    cdef inline float64_t middle_value(self) noexcept nogil:
+        """Compute the middle value of a split for monotonicity constraints as the simple average
+        of the left and right children values.
+
+        Note that monotonicity constraints are only supported for:
+        - single-output trees and
+        - binary classifications.
+        """
+        return (
+            (self.sum_left[0, 0] / (2 * self.weighted_n_left)) +
+            (self.sum_right[0, 0] / (2 * self.weighted_n_right))
+        )
+
+    cdef inline bint check_monotonicity(
+        self,
+        cnp.int8_t monotonic_cst,
+        float64_t lower_bound,
+        float64_t upper_bound,
+    ) noexcept nogil:
+        """Check monotonicity constraint is satisfied at the current classification split"""
+        cdef:
+            float64_t value_left = self.sum_left[0][0] / self.weighted_n_left
+            float64_t value_right = self.sum_right[0][0] / self.weighted_n_right
+
+        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
+
+
+cdef class Entropy(ClassificationCriterion):
+    r"""Cross Entropy impurity criterion.
+
+    This handles cases where the target is a classification taking values
+    0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
+    then let
+
+        count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k)
+
+    be the proportion of class k observations in node m.
+
+    The cross-entropy is then defined as
+
+        cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k)
+    """
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Evaluate the impurity of the current node.
+
+        Evaluate the cross-entropy criterion as impurity of the current node,
+        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
+        better.
+        """
+        cdef float64_t entropy = 0.0
+        cdef float64_t count_k
+        cdef intp_t k
+        cdef intp_t c
+
+        for k in range(self.n_outputs):
+            for c in range(self.n_classes[k]):
+                count_k = self.sum_total[k, c]
+                if count_k > 0.0:
+                    count_k /= self.weighted_n_node_samples
+                    entropy -= count_k * log(count_k)
+
+        return entropy / self.n_outputs
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        """Evaluate the impurity in children nodes.
+
+        i.e. the impurity of the left child (sample_indices[start:pos]) and the
+        impurity the right child (sample_indices[pos:end]).
+
+        Parameters
+        ----------
+        impurity_left : float64_t pointer
+            The memory address to save the impurity of the left node
+        impurity_right : float64_t pointer
+            The memory address to save the impurity of the right node
+        """
+        cdef float64_t entropy_left = 0.0
+        cdef float64_t entropy_right = 0.0
+        cdef float64_t count_k
+        cdef intp_t k
+        cdef intp_t c
+
+        for k in range(self.n_outputs):
+            for c in range(self.n_classes[k]):
+                count_k = self.sum_left[k, c]
+                if count_k > 0.0:
+                    count_k /= self.weighted_n_left
+                    entropy_left -= count_k * log(count_k)
+
+                count_k = self.sum_right[k, c]
+                if count_k > 0.0:
+                    count_k /= self.weighted_n_right
+                    entropy_right -= count_k * log(count_k)
+
+        impurity_left[0] = entropy_left / self.n_outputs
+        impurity_right[0] = entropy_right / self.n_outputs
+
+
+cdef class Gini(ClassificationCriterion):
+    r"""Gini Index impurity criterion.
+
+    This handles cases where the target is a classification taking values
+    0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
+    then let
+
+        count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k)
+
+    be the proportion of class k observations in node m.
+
+    The Gini Index is then defined as:
+
+        index = \sum_{k=0}^{K-1} count_k (1 - count_k)
+              = 1 - \sum_{k=0}^{K-1} count_k ** 2
+    """
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Evaluate the impurity of the current node.
+
+        Evaluate the Gini criterion as impurity of the current node,
+        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
+        better.
+        """
+        cdef float64_t gini = 0.0
+        cdef float64_t sq_count
+        cdef float64_t count_k
+        cdef intp_t k
+        cdef intp_t c
+
+        for k in range(self.n_outputs):
+            sq_count = 0.0
+
+            for c in range(self.n_classes[k]):
+                count_k = self.sum_total[k, c]
+                sq_count += count_k * count_k
+
+            gini += 1.0 - sq_count / (self.weighted_n_node_samples *
+                                      self.weighted_n_node_samples)
+
+        return gini / self.n_outputs
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        """Evaluate the impurity in children nodes.
+
+        i.e. the impurity of the left child (sample_indices[start:pos]) and the
+        impurity the right child (sample_indices[pos:end]) using the Gini index.
+
+        Parameters
+        ----------
+        impurity_left : float64_t pointer
+            The memory address to save the impurity of the left node to
+        impurity_right : float64_t pointer
+            The memory address to save the impurity of the right node to
+        """
+        cdef float64_t gini_left = 0.0
+        cdef float64_t gini_right = 0.0
+        cdef float64_t sq_count_left
+        cdef float64_t sq_count_right
+        cdef float64_t count_k
+        cdef intp_t k
+        cdef intp_t c
+
+        for k in range(self.n_outputs):
+            sq_count_left = 0.0
+            sq_count_right = 0.0
+
+            for c in range(self.n_classes[k]):
+                count_k = self.sum_left[k, c]
+                sq_count_left += count_k * count_k
+
+                count_k = self.sum_right[k, c]
+                sq_count_right += count_k * count_k
+
+            gini_left += 1.0 - sq_count_left / (self.weighted_n_left *
+                                                self.weighted_n_left)
+
+            gini_right += 1.0 - sq_count_right / (self.weighted_n_right *
+                                                  self.weighted_n_right)
+
+        impurity_left[0] = gini_left / self.n_outputs
+        impurity_right[0] = gini_right / self.n_outputs
+
+
+cdef inline void _move_sums_regression(
+    RegressionCriterion criterion,
+    float64_t[::1] sum_1,
+    float64_t[::1] sum_2,
+    float64_t* weighted_n_1,
+    float64_t* weighted_n_2,
+    bint put_missing_in_1,
+) noexcept nogil:
+    """Distribute sum_total and sum_missing into sum_1 and sum_2.
+
+    If there are missing values and:
+    - put_missing_in_1 is True, then missing values to go sum_1. Specifically:
+        sum_1 = sum_missing
+        sum_2 = sum_total - sum_missing
+
+    - put_missing_in_1 is False, then missing values go to sum_2. Specifically:
+        sum_1 = 0
+        sum_2 = sum_total
+    """
+    cdef:
+        intp_t i
+        intp_t n_bytes = criterion.n_outputs * sizeof(float64_t)
+        bint has_missing = criterion.n_missing != 0
+
+    if has_missing and put_missing_in_1:
+        memcpy(&sum_1[0], &criterion.sum_missing[0], n_bytes)
+        for i in range(criterion.n_outputs):
+            sum_2[i] = criterion.sum_total[i] - criterion.sum_missing[i]
+        weighted_n_1[0] = criterion.weighted_n_missing
+        weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
+    else:
+        memset(&sum_1[0], 0, n_bytes)
+        # Assigning sum_2 = sum_total for all outputs.
+        memcpy(&sum_2[0], &criterion.sum_total[0], n_bytes)
+        weighted_n_1[0] = 0.0
+        weighted_n_2[0] = criterion.weighted_n_node_samples
+
+
+cdef class RegressionCriterion(Criterion):
+    r"""Abstract regression criterion.
+
+    This handles cases where the target is a continuous value, and is
+    evaluated by computing the variance of the target values left and right
+    of the split point. The computation takes linear time with `n_samples`
+    by using ::
+
+        var = \sum_i^n (y_i - y_bar) ** 2
+            = (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2
+    """
+
+    def __cinit__(self, intp_t n_outputs, intp_t n_samples):
+        """Initialize parameters for this criterion.
+
+        Parameters
+        ----------
+        n_outputs : intp_t
+            The number of targets to be predicted
+
+        n_samples : intp_t
+            The total number of samples to fit on
+        """
+        # Default values
+        self.start = 0
+        self.pos = 0
+        self.end = 0
+
+        self.n_outputs = n_outputs
+        self.n_samples = n_samples
+        self.n_node_samples = 0
+        self.weighted_n_node_samples = 0.0
+        self.weighted_n_left = 0.0
+        self.weighted_n_right = 0.0
+        self.weighted_n_missing = 0.0
+
+        self.sq_sum_total = 0.0
+
+        self.sum_total = np.zeros(n_outputs, dtype=np.float64)
+        self.sum_left = np.zeros(n_outputs, dtype=np.float64)
+        self.sum_right = np.zeros(n_outputs, dtype=np.float64)
+
+    def __reduce__(self):
+        return (type(self), (self.n_outputs, self.n_samples), self.__getstate__())
+
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end,
+    ) except -1 nogil:
+        """Initialize the criterion.
+
+        This initializes the criterion at node sample_indices[start:end] and children
+        sample_indices[start:start] and sample_indices[start:end].
+        """
+        # Initialize fields
+        self.y = y
+        self.sample_weight = sample_weight
+        self.sample_indices = sample_indices
+        self.start = start
+        self.end = end
+        self.n_node_samples = end - start
+        self.weighted_n_samples = weighted_n_samples
+        self.weighted_n_node_samples = 0.
+
+        cdef intp_t i
+        cdef intp_t p
+        cdef intp_t k
+        cdef float64_t y_ik
+        cdef float64_t w_y_ik
+        cdef float64_t w = 1.0
+        self.sq_sum_total = 0.0
+        memset(&self.sum_total[0], 0, self.n_outputs * sizeof(float64_t))
+
+        for p in range(start, end):
+            i = sample_indices[p]
+
+            if sample_weight is not None:
+                w = sample_weight[i]
+
+            for k in range(self.n_outputs):
+                y_ik = self.y[i, k]
+                w_y_ik = w * y_ik
+                self.sum_total[k] += w_y_ik
+                self.sq_sum_total += w_y_ik * y_ik
+
+            self.weighted_n_node_samples += w
+
+        # Reset to pos=start
+        self.reset()
+        return 0
+
+    cdef void init_sum_missing(self):
+        """Init sum_missing to hold sums for missing values."""
+        self.sum_missing = np.zeros(self.n_outputs, dtype=np.float64)
+
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
+        """Initialize sum_missing if there are missing values.
+
+        This method assumes that caller placed the missing samples in
+        self.sample_indices[-n_missing:]
+        """
+        cdef intp_t i, p, k
+        cdef float64_t y_ik
+        cdef float64_t w_y_ik
+        cdef float64_t w = 1.0
+
+        self.n_missing = n_missing
+        if n_missing == 0:
+            return
+
+        memset(&self.sum_missing[0], 0, self.n_outputs * sizeof(float64_t))
+
+        self.weighted_n_missing = 0.0
+
+        # The missing samples are assumed to be in self.sample_indices[-n_missing:]
+        for p in range(self.end - n_missing, self.end):
+            i = self.sample_indices[p]
+            if self.sample_weight is not None:
+                w = self.sample_weight[i]
+
+            for k in range(self.n_outputs):
+                y_ik = self.y[i, k]
+                w_y_ik = w * y_ik
+                self.sum_missing[k] += w_y_ik
+
+            self.weighted_n_missing += w
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the criterion at pos=start."""
+        self.pos = self.start
+        _move_sums_regression(
+            self,
+            self.sum_left,
+            self.sum_right,
+            &self.weighted_n_left,
+            &self.weighted_n_right,
+            self.missing_go_to_left
+        )
+        return 0
+
+    cdef int reverse_reset(self) except -1 nogil:
+        """Reset the criterion at pos=end."""
+        self.pos = self.end
+        _move_sums_regression(
+            self,
+            self.sum_right,
+            self.sum_left,
+            &self.weighted_n_right,
+            &self.weighted_n_left,
+            not self.missing_go_to_left
+        )
+        return 0
+
+    cdef int update(self, intp_t new_pos) except -1 nogil:
+        """Updated statistics by moving sample_indices[pos:new_pos] to the left."""
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+
+        cdef intp_t pos = self.pos
+
+        # The missing samples are assumed to be in
+        # self.sample_indices[-self.n_missing:] that is
+        # self.sample_indices[end_non_missing:self.end].
+        cdef intp_t end_non_missing = self.end - self.n_missing
+        cdef intp_t i
+        cdef intp_t p
+        cdef intp_t k
+        cdef float64_t w = 1.0
+
+        # Update statistics up to new_pos
+        #
+        # Given that
+        #           sum_left[x] +  sum_right[x] = sum_total[x]
+        # and that sum_total is known, we are going to update
+        # sum_left from the direction that require the least amount
+        # of computations, i.e. from pos to new_pos or from end to new_pos.
+        if (new_pos - pos) <= (end_non_missing - new_pos):
+            for p in range(pos, new_pos):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    self.sum_left[k] += w * self.y[i, k]
+
+                self.weighted_n_left += w
+        else:
+            self.reverse_reset()
+
+            for p in range(end_non_missing - 1, new_pos - 1, -1):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    self.sum_left[k] -= w * self.y[i, k]
+
+                self.weighted_n_left -= w
+
+        self.weighted_n_right = (self.weighted_n_node_samples -
+                                 self.weighted_n_left)
+        for k in range(self.n_outputs):
+            self.sum_right[k] = self.sum_total[k] - self.sum_left[k]
+
+        self.pos = new_pos
+        return 0
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        pass
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        pass
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil:
+        """Compute the node value of sample_indices[start:end] into dest."""
+        cdef intp_t k
+
+        for k in range(self.n_outputs):
+            dest[k] = self.sum_total[k] / self.weighted_n_node_samples
+
+    cdef inline void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
+        """Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
+        if dest[0] < lower_bound:
+            dest[0] = lower_bound
+        elif dest[0] > upper_bound:
+            dest[0] = upper_bound
+
+    cdef float64_t middle_value(self) noexcept nogil:
+        """Compute the middle value of a split for monotonicity constraints as the simple average
+        of the left and right children values.
+
+        Monotonicity constraints are only supported for single-output trees we can safely assume
+        n_outputs == 1.
+        """
+        return (
+            (self.sum_left[0] / (2 * self.weighted_n_left)) +
+            (self.sum_right[0] / (2 * self.weighted_n_right))
+        )
+
+    cdef bint check_monotonicity(
+        self,
+        cnp.int8_t monotonic_cst,
+        float64_t lower_bound,
+        float64_t upper_bound,
+    ) noexcept nogil:
+        """Check monotonicity constraint is satisfied at the current regression split"""
+        cdef:
+            float64_t value_left = self.sum_left[0] / self.weighted_n_left
+            float64_t value_right = self.sum_right[0] / self.weighted_n_right
+
+        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
+
+cdef class MSE(RegressionCriterion):
+    """Mean squared error impurity criterion.
+
+        MSE = var_left + var_right
+    """
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Evaluate the impurity of the current node.
+
+        Evaluate the MSE criterion as impurity of the current node,
+        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
+        better.
+        """
+        cdef float64_t impurity
+        cdef intp_t k
+
+        impurity = self.sq_sum_total / self.weighted_n_node_samples
+        for k in range(self.n_outputs):
+            impurity -= (self.sum_total[k] / self.weighted_n_node_samples)**2.0
+
+        return impurity / self.n_outputs
+
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
+        """Compute a proxy of the impurity reduction.
+
+        This method is used to speed up the search for the best split.
+        It is a proxy quantity such that the split that maximizes this value
+        also maximizes the impurity improvement. It neglects all constant terms
+        of the impurity decrease for a given split.
+
+        The absolute impurity improvement is only computed by the
+        impurity_improvement method once the best split has been found.
+
+        The MSE proxy is derived from
+
+            sum_{i left}(y_i - y_pred_L)^2 + sum_{i right}(y_i - y_pred_R)^2
+            = sum(y_i^2) - n_L * mean_{i left}(y_i)^2 - n_R * mean_{i right}(y_i)^2
+
+        Neglecting constant terms, this gives:
+
+            - 1/n_L * sum_{i left}(y_i)^2 - 1/n_R * sum_{i right}(y_i)^2
+        """
+        cdef intp_t k
+        cdef float64_t proxy_impurity_left = 0.0
+        cdef float64_t proxy_impurity_right = 0.0
+
+        for k in range(self.n_outputs):
+            proxy_impurity_left += self.sum_left[k] * self.sum_left[k]
+            proxy_impurity_right += self.sum_right[k] * self.sum_right[k]
+
+        return (proxy_impurity_left / self.weighted_n_left +
+                proxy_impurity_right / self.weighted_n_right)
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        """Evaluate the impurity in children nodes.
+
+        i.e. the impurity of the left child (sample_indices[start:pos]) and the
+        impurity the right child (sample_indices[pos:end]).
+        """
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+        cdef intp_t pos = self.pos
+        cdef intp_t start = self.start
+
+        cdef float64_t y_ik
+
+        cdef float64_t sq_sum_left = 0.0
+        cdef float64_t sq_sum_right
+
+        cdef intp_t i
+        cdef intp_t p
+        cdef intp_t k
+        cdef float64_t w = 1.0
+
+        cdef intp_t end_non_missing
+
+        for p in range(start, pos):
+            i = sample_indices[p]
+
+            if sample_weight is not None:
+                w = sample_weight[i]
+
+            for k in range(self.n_outputs):
+                y_ik = self.y[i, k]
+                sq_sum_left += w * y_ik * y_ik
+
+        if self.missing_go_to_left:
+            # add up the impact of these missing values on the left child
+            # statistics.
+            # Note: this only impacts the square sum as the sum
+            # is modified elsewhere.
+            end_non_missing = self.end - self.n_missing
+
+            for p in range(end_non_missing, self.end):
+                i = sample_indices[p]
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    y_ik = self.y[i, k]
+                    sq_sum_left += w * y_ik * y_ik
+
+        sq_sum_right = self.sq_sum_total - sq_sum_left
+
+        impurity_left[0] = sq_sum_left / self.weighted_n_left
+        impurity_right[0] = sq_sum_right / self.weighted_n_right
+
+        for k in range(self.n_outputs):
+            impurity_left[0] -= (self.sum_left[k] / self.weighted_n_left) ** 2.0
+            impurity_right[0] -= (self.sum_right[k] / self.weighted_n_right) ** 2.0
+
+        impurity_left[0] /= self.n_outputs
+        impurity_right[0] /= self.n_outputs
+
+
+cdef class MAE(RegressionCriterion):
+    r"""Mean absolute error impurity criterion.
+
+       MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
+       value and f_i is the predicted value."""
+
+    cdef cnp.ndarray left_child
+    cdef cnp.ndarray right_child
+    cdef void** left_child_ptr
+    cdef void** right_child_ptr
+    cdef float64_t[::1] node_medians
+
+    def __cinit__(self, intp_t n_outputs, intp_t n_samples):
+        """Initialize parameters for this criterion.
+
+        Parameters
+        ----------
+        n_outputs : intp_t
+            The number of targets to be predicted
+
+        n_samples : intp_t
+            The total number of samples to fit on
+        """
+        # Default values
+        self.start = 0
+        self.pos = 0
+        self.end = 0
+
+        self.n_outputs = n_outputs
+        self.n_samples = n_samples
+        self.n_node_samples = 0
+        self.weighted_n_node_samples = 0.0
+        self.weighted_n_left = 0.0
+        self.weighted_n_right = 0.0
+
+        self.node_medians = np.zeros(n_outputs, dtype=np.float64)
+
+        self.left_child = np.empty(n_outputs, dtype='object')
+        self.right_child = np.empty(n_outputs, dtype='object')
+        # initialize WeightedMedianCalculators
+        for k in range(n_outputs):
+            self.left_child[k] = WeightedMedianCalculator(n_samples)
+            self.right_child[k] = WeightedMedianCalculator(n_samples)
+
+        self.left_child_ptr =  cnp.PyArray_DATA(self.left_child)
+        self.right_child_ptr =  cnp.PyArray_DATA(self.right_child)
+
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end,
+    ) except -1 nogil:
+        """Initialize the criterion.
+
+        This initializes the criterion at node sample_indices[start:end] and children
+        sample_indices[start:start] and sample_indices[start:end].
+        """
+        cdef intp_t i, p, k
+        cdef float64_t w = 1.0
+
+        # Initialize fields
+        self.y = y
+        self.sample_weight = sample_weight
+        self.sample_indices = sample_indices
+        self.start = start
+        self.end = end
+        self.n_node_samples = end - start
+        self.weighted_n_samples = weighted_n_samples
+        self.weighted_n_node_samples = 0.
+
+        cdef void** left_child = self.left_child_ptr
+        cdef void** right_child = self.right_child_ptr
+
+        for k in range(self.n_outputs):
+            ( left_child[k]).reset()
+            ( right_child[k]).reset()
+
+        for p in range(start, end):
+            i = sample_indices[p]
+
+            if sample_weight is not None:
+                w = sample_weight[i]
+
+            for k in range(self.n_outputs):
+                # push method ends up calling safe_realloc, hence `except -1`
+                # push all values to the right side,
+                # since pos = start initially anyway
+                ( right_child[k]).push(self.y[i, k], w)
+
+            self.weighted_n_node_samples += w
+        # calculate the node medians
+        for k in range(self.n_outputs):
+            self.node_medians[k] = ( right_child[k]).get_median()
+
+        # Reset to pos=start
+        self.reset()
+        return 0
+
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
+        """Raise error if n_missing != 0."""
+        if n_missing == 0:
+            return
+        with gil:
+            raise ValueError("missing values is not supported for MAE.")
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the criterion at pos=start.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        cdef intp_t i, k
+        cdef float64_t value
+        cdef float64_t weight
+
+        cdef void** left_child = self.left_child_ptr
+        cdef void** right_child = self.right_child_ptr
+
+        self.weighted_n_left = 0.0
+        self.weighted_n_right = self.weighted_n_node_samples
+        self.pos = self.start
+
+        # reset the WeightedMedianCalculators, left should have no
+        # elements and right should have all elements.
+
+        for k in range(self.n_outputs):
+            # if left has no elements, it's already reset
+            for i in range(( left_child[k]).size()):
+                # remove everything from left and put it into right
+                ( left_child[k]).pop(&value,
+                                                               &weight)
+                # push method ends up calling safe_realloc, hence `except -1`
+                ( right_child[k]).push(value,
+                                                                 weight)
+        return 0
+
+    cdef int reverse_reset(self) except -1 nogil:
+        """Reset the criterion at pos=end.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        self.weighted_n_right = 0.0
+        self.weighted_n_left = self.weighted_n_node_samples
+        self.pos = self.end
+
+        cdef float64_t value
+        cdef float64_t weight
+        cdef void** left_child = self.left_child_ptr
+        cdef void** right_child = self.right_child_ptr
+
+        # reverse reset the WeightedMedianCalculators, right should have no
+        # elements and left should have all elements.
+        for k in range(self.n_outputs):
+            # if right has no elements, it's already reset
+            for i in range(( right_child[k]).size()):
+                # remove everything from right and put it into left
+                ( right_child[k]).pop(&value,
+                                                                &weight)
+                # push method ends up calling safe_realloc, hence `except -1`
+                ( left_child[k]).push(value,
+                                                                weight)
+        return 0
+
+    cdef int update(self, intp_t new_pos) except -1 nogil:
+        """Updated statistics by moving sample_indices[pos:new_pos] to the left.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+
+        cdef void** left_child = self.left_child_ptr
+        cdef void** right_child = self.right_child_ptr
+
+        cdef intp_t pos = self.pos
+        cdef intp_t end = self.end
+        cdef intp_t i, p, k
+        cdef float64_t w = 1.0
+
+        # Update statistics up to new_pos
+        #
+        # We are going to update right_child and left_child
+        # from the direction that require the least amount of
+        # computations, i.e. from pos to new_pos or from end to new_pos.
+        if (new_pos - pos) <= (end - new_pos):
+            for p in range(pos, new_pos):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    # remove y_ik and its weight w from right and add to left
+                    ( right_child[k]).remove(self.y[i, k], w)
+                    # push method ends up calling safe_realloc, hence except -1
+                    ( left_child[k]).push(self.y[i, k], w)
+
+                self.weighted_n_left += w
+        else:
+            self.reverse_reset()
+
+            for p in range(end - 1, new_pos - 1, -1):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                for k in range(self.n_outputs):
+                    # remove y_ik and its weight w from left and add to right
+                    ( left_child[k]).remove(self.y[i, k], w)
+                    ( right_child[k]).push(self.y[i, k], w)
+
+                self.weighted_n_left -= w
+
+        self.weighted_n_right = (self.weighted_n_node_samples -
+                                 self.weighted_n_left)
+        self.pos = new_pos
+        return 0
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil:
+        """Computes the node value of sample_indices[start:end] into dest."""
+        cdef intp_t k
+        for k in range(self.n_outputs):
+            dest[k] =  self.node_medians[k]
+
+    cdef inline float64_t middle_value(self) noexcept nogil:
+        """Compute the middle value of a split for monotonicity constraints as the simple average
+        of the left and right children values.
+
+        Monotonicity constraints are only supported for single-output trees we can safely assume
+        n_outputs == 1.
+        """
+        return (
+                ( self.left_child_ptr[0]).get_median() +
+                ( self.right_child_ptr[0]).get_median()
+        ) / 2
+
+    cdef inline bint check_monotonicity(
+        self,
+        cnp.int8_t monotonic_cst,
+        float64_t lower_bound,
+        float64_t upper_bound,
+    ) noexcept nogil:
+        """Check monotonicity constraint is satisfied at the current regression split"""
+        cdef:
+            float64_t value_left = ( self.left_child_ptr[0]).get_median()
+            float64_t value_right = ( self.right_child_ptr[0]).get_median()
+
+        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Evaluate the impurity of the current node.
+
+        Evaluate the MAE criterion as impurity of the current node,
+        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
+        better.
+        """
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+        cdef intp_t i, p, k
+        cdef float64_t w = 1.0
+        cdef float64_t impurity = 0.0
+
+        for k in range(self.n_outputs):
+            for p in range(self.start, self.end):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                impurity += fabs(self.y[i, k] - self.node_medians[k]) * w
+
+        return impurity / (self.weighted_n_node_samples * self.n_outputs)
+
+    cdef void children_impurity(self, float64_t* p_impurity_left,
+                                float64_t* p_impurity_right) noexcept nogil:
+        """Evaluate the impurity in children nodes.
+
+        i.e. the impurity of the left child (sample_indices[start:pos]) and the
+        impurity the right child (sample_indices[pos:end]).
+        """
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+
+        cdef intp_t start = self.start
+        cdef intp_t pos = self.pos
+        cdef intp_t end = self.end
+
+        cdef intp_t i, p, k
+        cdef float64_t median
+        cdef float64_t w = 1.0
+        cdef float64_t impurity_left = 0.0
+        cdef float64_t impurity_right = 0.0
+
+        cdef void** left_child = self.left_child_ptr
+        cdef void** right_child = self.right_child_ptr
+
+        for k in range(self.n_outputs):
+            median = ( left_child[k]).get_median()
+            for p in range(start, pos):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                impurity_left += fabs(self.y[i, k] - median) * w
+        p_impurity_left[0] = impurity_left / (self.weighted_n_left *
+                                              self.n_outputs)
+
+        for k in range(self.n_outputs):
+            median = ( right_child[k]).get_median()
+            for p in range(pos, end):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                impurity_right += fabs(self.y[i, k] - median) * w
+        p_impurity_right[0] = impurity_right / (self.weighted_n_right *
+                                                self.n_outputs)
+
+
+cdef class FriedmanMSE(MSE):
+    """Mean squared error impurity criterion with improvement score by Friedman.
+
+    Uses the formula (35) in Friedman's original Gradient Boosting paper:
+
+        diff = mean_left - mean_right
+        improvement = n_left * n_right * diff^2 / (n_left + n_right)
+    """
+
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
+        """Compute a proxy of the impurity reduction.
+
+        This method is used to speed up the search for the best split.
+        It is a proxy quantity such that the split that maximizes this value
+        also maximizes the impurity improvement. It neglects all constant terms
+        of the impurity decrease for a given split.
+
+        The absolute impurity improvement is only computed by the
+        impurity_improvement method once the best split has been found.
+        """
+        cdef float64_t total_sum_left = 0.0
+        cdef float64_t total_sum_right = 0.0
+
+        cdef intp_t k
+        cdef float64_t diff = 0.0
+
+        for k in range(self.n_outputs):
+            total_sum_left += self.sum_left[k]
+            total_sum_right += self.sum_right[k]
+
+        diff = (self.weighted_n_right * total_sum_left -
+                self.weighted_n_left * total_sum_right)
+
+        return diff * diff / (self.weighted_n_left * self.weighted_n_right)
+
+    cdef float64_t impurity_improvement(self, float64_t impurity_parent, float64_t
+                                        impurity_left, float64_t impurity_right) noexcept nogil:
+        # Note: none of the arguments are used here
+        cdef float64_t total_sum_left = 0.0
+        cdef float64_t total_sum_right = 0.0
+
+        cdef intp_t k
+        cdef float64_t diff = 0.0
+
+        for k in range(self.n_outputs):
+            total_sum_left += self.sum_left[k]
+            total_sum_right += self.sum_right[k]
+
+        diff = (self.weighted_n_right * total_sum_left -
+                self.weighted_n_left * total_sum_right) / self.n_outputs
+
+        return (diff * diff / (self.weighted_n_left * self.weighted_n_right *
+                               self.weighted_n_node_samples))
+
+
+cdef class Poisson(RegressionCriterion):
+    """Half Poisson deviance as impurity criterion.
+
+    Poisson deviance = 2/n * sum(y_true * log(y_true/y_pred) + y_pred - y_true)
+
+    Note that the deviance is >= 0, and since we have `y_pred = mean(y_true)`
+    at the leaves, one always has `sum(y_pred - y_true) = 0`. It remains the
+    implemented impurity (factor 2 is skipped):
+        1/n * sum(y_true * log(y_true/y_pred)
+    """
+    # FIXME in 1.0:
+    # min_impurity_split with default = 0 forces us to use a non-negative
+    # impurity like the Poisson deviance. Without this restriction, one could
+    # throw away the 'constant' term sum(y_true * log(y_true)) and just use
+    # Poisson loss = - 1/n * sum(y_true * log(y_pred))
+    #              = - 1/n * sum(y_true * log(mean(y_true))
+    #              = - mean(y_true) * log(mean(y_true))
+    # With this trick (used in proxy_impurity_improvement()), as for MSE,
+    # children_impurity would only need to go over left xor right split, not
+    # both. This could be faster.
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Evaluate the impurity of the current node.
+
+        Evaluate the Poisson criterion as impurity of the current node,
+        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
+        better.
+        """
+        return self.poisson_loss(self.start, self.end, self.sum_total,
+                                 self.weighted_n_node_samples)
+
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
+        """Compute a proxy of the impurity reduction.
+
+        This method is used to speed up the search for the best split.
+        It is a proxy quantity such that the split that maximizes this value
+        also maximizes the impurity improvement. It neglects all constant terms
+        of the impurity decrease for a given split.
+
+        The absolute impurity improvement is only computed by the
+        impurity_improvement method once the best split has been found.
+
+        The Poisson proxy is derived from:
+
+              sum_{i left }(y_i * log(y_i / y_pred_L))
+            + sum_{i right}(y_i * log(y_i / y_pred_R))
+            = sum(y_i * log(y_i) - n_L * mean_{i left}(y_i) * log(mean_{i left}(y_i))
+                                 - n_R * mean_{i right}(y_i) * log(mean_{i right}(y_i))
+
+        Neglecting constant terms, this gives
+
+            - sum{i left }(y_i) * log(mean{i left}(y_i))
+            - sum{i right}(y_i) * log(mean{i right}(y_i))
+        """
+        cdef intp_t k
+        cdef float64_t proxy_impurity_left = 0.0
+        cdef float64_t proxy_impurity_right = 0.0
+        cdef float64_t y_mean_left = 0.
+        cdef float64_t y_mean_right = 0.
+
+        for k in range(self.n_outputs):
+            if (self.sum_left[k] <= EPSILON) or (self.sum_right[k] <= EPSILON):
+                # Poisson loss does not allow non-positive predictions. We
+                # therefore forbid splits that have child nodes with
+                # sum(y_i) <= 0.
+                # Since sum_right = sum_total - sum_left, it can lead to
+                # floating point rounding error and will not give zero. Thus,
+                # we relax the above comparison to sum(y_i) <= EPSILON.
+                return -INFINITY
+            else:
+                y_mean_left = self.sum_left[k] / self.weighted_n_left
+                y_mean_right = self.sum_right[k] / self.weighted_n_right
+                proxy_impurity_left -= self.sum_left[k] * log(y_mean_left)
+                proxy_impurity_right -= self.sum_right[k] * log(y_mean_right)
+
+        return - proxy_impurity_left - proxy_impurity_right
+
+    cdef void children_impurity(self, float64_t* impurity_left,
+                                float64_t* impurity_right) noexcept nogil:
+        """Evaluate the impurity in children nodes.
+
+        i.e. the impurity of the left child (sample_indices[start:pos]) and the
+        impurity of the right child (sample_indices[pos:end]) for Poisson.
+        """
+        cdef intp_t start = self.start
+        cdef intp_t pos = self.pos
+        cdef intp_t end = self.end
+
+        impurity_left[0] = self.poisson_loss(start, pos, self.sum_left,
+                                             self.weighted_n_left)
+
+        impurity_right[0] = self.poisson_loss(pos, end, self.sum_right,
+                                              self.weighted_n_right)
+
+    cdef inline float64_t poisson_loss(
+        self,
+        intp_t start,
+        intp_t end,
+        const float64_t[::1] y_sum,
+        float64_t weight_sum
+    ) noexcept nogil:
+        """Helper function to compute Poisson loss (~deviance) of a given node.
+        """
+        cdef const float64_t[:, ::1] y = self.y
+        cdef const float64_t[:] sample_weight = self.sample_weight
+        cdef const intp_t[:] sample_indices = self.sample_indices
+
+        cdef float64_t y_mean = 0.
+        cdef float64_t poisson_loss = 0.
+        cdef float64_t w = 1.0
+        cdef intp_t i, k, p
+        cdef intp_t n_outputs = self.n_outputs
+
+        for k in range(n_outputs):
+            if y_sum[k] <= EPSILON:
+                # y_sum could be computed from the subtraction
+                # sum_right = sum_total - sum_left leading to a potential
+                # floating point rounding error.
+                # Thus, we relax the comparison y_sum <= 0 to
+                # y_sum <= EPSILON.
+                return INFINITY
+
+            y_mean = y_sum[k] / weight_sum
+
+            for p in range(start, end):
+                i = sample_indices[p]
+
+                if sample_weight is not None:
+                    w = sample_weight[i]
+
+                poisson_loss += w * xlogy(y[i, k], y[i, k] / y_mean)
+        return poisson_loss / (weight_sum * n_outputs)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_export.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_export.py
new file mode 100644
index 0000000000000000000000000000000000000000..6726d0c67bfb135aaa54392bb1f89fabe3b996ff
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_export.py
@@ -0,0 +1,1167 @@
+"""
+This module defines export functions for decision trees.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from collections.abc import Iterable
+from io import StringIO
+from numbers import Integral
+
+import numpy as np
+
+from ..base import is_classifier
+from ..utils._param_validation import HasMethods, Interval, StrOptions, validate_params
+from ..utils.validation import check_array, check_is_fitted
+from . import DecisionTreeClassifier, DecisionTreeRegressor, _criterion, _tree
+from ._reingold_tilford import Tree, buchheim
+
+
+def _color_brew(n):
+    """Generate n colors with equally spaced hues.
+
+    Parameters
+    ----------
+    n : int
+        The number of colors required.
+
+    Returns
+    -------
+    color_list : list, length n
+        List of n tuples of form (R, G, B) being the components of each color.
+    """
+    color_list = []
+
+    # Initialize saturation & value; calculate chroma & value shift
+    s, v = 0.75, 0.9
+    c = s * v
+    m = v - c
+
+    for h in np.arange(25, 385, 360.0 / n).astype(int):
+        # Calculate some intermediate values
+        h_bar = h / 60.0
+        x = c * (1 - abs((h_bar % 2) - 1))
+        # Initialize RGB with same hue & chroma as our color
+        rgb = [
+            (c, x, 0),
+            (x, c, 0),
+            (0, c, x),
+            (0, x, c),
+            (x, 0, c),
+            (c, 0, x),
+            (c, x, 0),
+        ]
+        r, g, b = rgb[int(h_bar)]
+        # Shift the initial RGB values to match value and store
+        rgb = [(int(255 * (r + m))), (int(255 * (g + m))), (int(255 * (b + m)))]
+        color_list.append(rgb)
+
+    return color_list
+
+
+class Sentinel:
+    def __repr__(self):
+        return '"tree.dot"'
+
+
+SENTINEL = Sentinel()
+
+
+@validate_params(
+    {
+        "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
+        "max_depth": [Interval(Integral, 0, None, closed="left"), None],
+        "feature_names": ["array-like", None],
+        "class_names": ["array-like", "boolean", None],
+        "label": [StrOptions({"all", "root", "none"})],
+        "filled": ["boolean"],
+        "impurity": ["boolean"],
+        "node_ids": ["boolean"],
+        "proportion": ["boolean"],
+        "rounded": ["boolean"],
+        "precision": [Interval(Integral, 0, None, closed="left"), None],
+        "ax": "no_validation",  # delegate validation to matplotlib
+        "fontsize": [Interval(Integral, 0, None, closed="left"), None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def plot_tree(
+    decision_tree,
+    *,
+    max_depth=None,
+    feature_names=None,
+    class_names=None,
+    label="all",
+    filled=False,
+    impurity=True,
+    node_ids=False,
+    proportion=False,
+    rounded=False,
+    precision=3,
+    ax=None,
+    fontsize=None,
+):
+    """Plot a decision tree.
+
+    The sample counts that are shown are weighted with any sample_weights that
+    might be present.
+
+    The visualization is fit automatically to the size of the axis.
+    Use the ``figsize`` or ``dpi`` arguments of ``plt.figure``  to control
+    the size of the rendering.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.21
+
+    Parameters
+    ----------
+    decision_tree : decision tree regressor or classifier
+        The decision tree to be plotted.
+
+    max_depth : int, default=None
+        The maximum depth of the representation. If None, the tree is fully
+        generated.
+
+    feature_names : array-like of str, default=None
+        Names of each of the features.
+        If None, generic names will be used ("x[0]", "x[1]", ...).
+
+    class_names : array-like of str or True, default=None
+        Names of each of the target classes in ascending numerical order.
+        Only relevant for classification and not supported for multi-output.
+        If ``True``, shows a symbolic representation of the class name.
+
+    label : {'all', 'root', 'none'}, default='all'
+        Whether to show informative labels for impurity, etc.
+        Options include 'all' to show at every node, 'root' to show only at
+        the top root node, or 'none' to not show at any node.
+
+    filled : bool, default=False
+        When set to ``True``, paint nodes to indicate majority class for
+        classification, extremity of values for regression, or purity of node
+        for multi-output.
+
+    impurity : bool, default=True
+        When set to ``True``, show the impurity at each node.
+
+    node_ids : bool, default=False
+        When set to ``True``, show the ID number on each node.
+
+    proportion : bool, default=False
+        When set to ``True``, change the display of 'values' and/or 'samples'
+        to be proportions and percentages respectively.
+
+    rounded : bool, default=False
+        When set to ``True``, draw node boxes with rounded corners and use
+        Helvetica fonts instead of Times-Roman.
+
+    precision : int, default=3
+        Number of digits of precision for floating point in the values of
+        impurity, threshold and value attributes of each node.
+
+    ax : matplotlib axis, default=None
+        Axes to plot to. If None, use current axis. Any previous content
+        is cleared.
+
+    fontsize : int, default=None
+        Size of text font. If None, determined automatically to fit figure.
+
+    Returns
+    -------
+    annotations : list of artists
+        List containing the artists for the annotation boxes making up the
+        tree.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn import tree
+
+    >>> clf = tree.DecisionTreeClassifier(random_state=0)
+    >>> iris = load_iris()
+
+    >>> clf = clf.fit(iris.data, iris.target)
+    >>> tree.plot_tree(clf)
+    [...]
+    """
+
+    check_is_fitted(decision_tree)
+
+    exporter = _MPLTreeExporter(
+        max_depth=max_depth,
+        feature_names=feature_names,
+        class_names=class_names,
+        label=label,
+        filled=filled,
+        impurity=impurity,
+        node_ids=node_ids,
+        proportion=proportion,
+        rounded=rounded,
+        precision=precision,
+        fontsize=fontsize,
+    )
+    return exporter.export(decision_tree, ax=ax)
+
+
+class _BaseTreeExporter:
+    def __init__(
+        self,
+        max_depth=None,
+        feature_names=None,
+        class_names=None,
+        label="all",
+        filled=False,
+        impurity=True,
+        node_ids=False,
+        proportion=False,
+        rounded=False,
+        precision=3,
+        fontsize=None,
+    ):
+        self.max_depth = max_depth
+        self.feature_names = feature_names
+        self.class_names = class_names
+        self.label = label
+        self.filled = filled
+        self.impurity = impurity
+        self.node_ids = node_ids
+        self.proportion = proportion
+        self.rounded = rounded
+        self.precision = precision
+        self.fontsize = fontsize
+
+    def get_color(self, value):
+        # Find the appropriate color & intensity for a node
+        if self.colors["bounds"] is None:
+            # Classification tree
+            color = list(self.colors["rgb"][np.argmax(value)])
+            sorted_values = sorted(value, reverse=True)
+            if len(sorted_values) == 1:
+                alpha = 0.0
+            else:
+                alpha = (sorted_values[0] - sorted_values[1]) / (1 - sorted_values[1])
+        else:
+            # Regression tree or multi-output
+            color = list(self.colors["rgb"][0])
+            alpha = (value - self.colors["bounds"][0]) / (
+                self.colors["bounds"][1] - self.colors["bounds"][0]
+            )
+        # compute the color as alpha against white
+        color = [int(round(alpha * c + (1 - alpha) * 255, 0)) for c in color]
+        # Return html color code in #RRGGBB format
+        return "#%2x%2x%2x" % tuple(color)
+
+    def get_fill_color(self, tree, node_id):
+        # Fetch appropriate color for node
+        if "rgb" not in self.colors:
+            # Initialize colors and bounds if required
+            self.colors["rgb"] = _color_brew(tree.n_classes[0])
+            if tree.n_outputs != 1:
+                # Find max and min impurities for multi-output
+                # The next line uses -max(impurity) instead of min(-impurity)
+                # and -min(impurity) instead of max(-impurity) on purpose, in
+                # order to avoid what looks like an issue with SIMD on non
+                # memory aligned arrays on 32bit OS. For more details see
+                # https://github.com/scikit-learn/scikit-learn/issues/27506.
+                self.colors["bounds"] = (-np.max(tree.impurity), -np.min(tree.impurity))
+            elif tree.n_classes[0] == 1 and len(np.unique(tree.value)) != 1:
+                # Find max and min values in leaf nodes for regression
+                self.colors["bounds"] = (np.min(tree.value), np.max(tree.value))
+        if tree.n_outputs == 1:
+            node_val = tree.value[node_id][0, :]
+            if (
+                tree.n_classes[0] == 1
+                and isinstance(node_val, Iterable)
+                and self.colors["bounds"] is not None
+            ):
+                # Unpack the float only for the regression tree case.
+                # Classification tree requires an Iterable in `get_color`.
+                node_val = node_val.item()
+        else:
+            # If multi-output color node by impurity
+            node_val = -tree.impurity[node_id]
+        return self.get_color(node_val)
+
+    def node_to_str(self, tree, node_id, criterion):
+        # Generate the node content string
+        if tree.n_outputs == 1:
+            value = tree.value[node_id][0, :]
+        else:
+            value = tree.value[node_id]
+
+        # Should labels be shown?
+        labels = (self.label == "root" and node_id == 0) or self.label == "all"
+
+        characters = self.characters
+        node_string = characters[-1]
+
+        # Write node ID
+        if self.node_ids:
+            if labels:
+                node_string += "node "
+            node_string += characters[0] + str(node_id) + characters[4]
+
+        # Write decision criteria
+        if tree.children_left[node_id] != _tree.TREE_LEAF:
+            # Always write node decision criteria, except for leaves
+            if self.feature_names is not None:
+                feature = self.feature_names[tree.feature[node_id]]
+                feature = self.str_escape(feature)
+            else:
+                feature = "x%s%s%s" % (
+                    characters[1],
+                    tree.feature[node_id],
+                    characters[2],
+                )
+            node_string += "%s %s %s%s" % (
+                feature,
+                characters[3],
+                round(tree.threshold[node_id], self.precision),
+                characters[4],
+            )
+
+        # Write impurity
+        if self.impurity:
+            if isinstance(criterion, _criterion.FriedmanMSE):
+                criterion = "friedman_mse"
+            elif isinstance(criterion, _criterion.MSE) or criterion == "squared_error":
+                criterion = "squared_error"
+            elif not isinstance(criterion, str):
+                criterion = "impurity"
+            if labels:
+                node_string += "%s = " % criterion
+            node_string += (
+                str(round(tree.impurity[node_id], self.precision)) + characters[4]
+            )
+
+        # Write node sample count
+        if labels:
+            node_string += "samples = "
+        if self.proportion:
+            percent = (
+                100.0 * tree.n_node_samples[node_id] / float(tree.n_node_samples[0])
+            )
+            node_string += str(round(percent, 1)) + "%" + characters[4]
+        else:
+            node_string += str(tree.n_node_samples[node_id]) + characters[4]
+
+        # Write node class distribution / regression value
+        if not self.proportion and tree.n_classes[0] != 1:
+            # For classification this will show the proportion of samples
+            value = value * tree.weighted_n_node_samples[node_id]
+        if labels:
+            node_string += "value = "
+        if tree.n_classes[0] == 1:
+            # Regression
+            value_text = np.around(value, self.precision)
+        elif self.proportion:
+            # Classification
+            value_text = np.around(value, self.precision)
+        elif np.all(np.equal(np.mod(value, 1), 0)):
+            # Classification without floating-point weights
+            value_text = value.astype(int)
+        else:
+            # Classification with floating-point weights
+            value_text = np.around(value, self.precision)
+        # Strip whitespace
+        value_text = str(value_text.astype("S32")).replace("b'", "'")
+        value_text = value_text.replace("' '", ", ").replace("'", "")
+        if tree.n_classes[0] == 1 and tree.n_outputs == 1:
+            value_text = value_text.replace("[", "").replace("]", "")
+        value_text = value_text.replace("\n ", characters[4])
+        node_string += value_text + characters[4]
+
+        # Write node majority class
+        if (
+            self.class_names is not None
+            and tree.n_classes[0] != 1
+            and tree.n_outputs == 1
+        ):
+            # Only done for single-output classification trees
+            if labels:
+                node_string += "class = "
+            if self.class_names is not True:
+                class_name = self.class_names[np.argmax(value)]
+                class_name = self.str_escape(class_name)
+            else:
+                class_name = "y%s%s%s" % (
+                    characters[1],
+                    np.argmax(value),
+                    characters[2],
+                )
+            node_string += class_name
+
+        # Clean up any trailing newlines
+        if node_string.endswith(characters[4]):
+            node_string = node_string[: -len(characters[4])]
+
+        return node_string + characters[5]
+
+    def str_escape(self, string):
+        return string
+
+
+class _DOTTreeExporter(_BaseTreeExporter):
+    def __init__(
+        self,
+        out_file=SENTINEL,
+        max_depth=None,
+        feature_names=None,
+        class_names=None,
+        label="all",
+        filled=False,
+        leaves_parallel=False,
+        impurity=True,
+        node_ids=False,
+        proportion=False,
+        rotate=False,
+        rounded=False,
+        special_characters=False,
+        precision=3,
+        fontname="helvetica",
+    ):
+        super().__init__(
+            max_depth=max_depth,
+            feature_names=feature_names,
+            class_names=class_names,
+            label=label,
+            filled=filled,
+            impurity=impurity,
+            node_ids=node_ids,
+            proportion=proportion,
+            rounded=rounded,
+            precision=precision,
+        )
+        self.leaves_parallel = leaves_parallel
+        self.out_file = out_file
+        self.special_characters = special_characters
+        self.fontname = fontname
+        self.rotate = rotate
+
+        # PostScript compatibility for special characters
+        if special_characters:
+            self.characters = ["#", "", "", "≤", "
", ">", "<"]
+        else:
+            self.characters = ["#", "[", "]", "<=", "\\n", '"', '"']
+
+        # The depth of each node for plotting with 'leaf' option
+        self.ranks = {"leaves": []}
+        # The colors to render each node with
+        self.colors = {"bounds": None}
+
+    def export(self, decision_tree):
+        # Check length of feature_names before getting into the tree node
+        # Raise error if length of feature_names does not match
+        # n_features_in_ in the decision_tree
+        if self.feature_names is not None:
+            if len(self.feature_names) != decision_tree.n_features_in_:
+                raise ValueError(
+                    "Length of feature_names, %d does not match number of features, %d"
+                    % (len(self.feature_names), decision_tree.n_features_in_)
+                )
+        # each part writes to out_file
+        self.head()
+        # Now recurse the tree and add node & edge attributes
+        if isinstance(decision_tree, _tree.Tree):
+            self.recurse(decision_tree, 0, criterion="impurity")
+        else:
+            self.recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion)
+
+        self.tail()
+
+    def tail(self):
+        # If required, draw leaf nodes at same depth as each other
+        if self.leaves_parallel:
+            for rank in sorted(self.ranks):
+                self.out_file.write(
+                    "{rank=same ; " + "; ".join(r for r in self.ranks[rank]) + "} ;\n"
+                )
+        self.out_file.write("}")
+
+    def head(self):
+        self.out_file.write("digraph Tree {\n")
+
+        # Specify node aesthetics
+        self.out_file.write("node [shape=box")
+        rounded_filled = []
+        if self.filled:
+            rounded_filled.append("filled")
+        if self.rounded:
+            rounded_filled.append("rounded")
+        if len(rounded_filled) > 0:
+            self.out_file.write(
+                ', style="%s", color="black"' % ", ".join(rounded_filled)
+            )
+
+        self.out_file.write(', fontname="%s"' % self.fontname)
+        self.out_file.write("] ;\n")
+
+        # Specify graph & edge aesthetics
+        if self.leaves_parallel:
+            self.out_file.write("graph [ranksep=equally, splines=polyline] ;\n")
+
+        self.out_file.write('edge [fontname="%s"] ;\n' % self.fontname)
+
+        if self.rotate:
+            self.out_file.write("rankdir=LR ;\n")
+
+    def recurse(self, tree, node_id, criterion, parent=None, depth=0):
+        if node_id == _tree.TREE_LEAF:
+            raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF)
+
+        left_child = tree.children_left[node_id]
+        right_child = tree.children_right[node_id]
+
+        # Add node with description
+        if self.max_depth is None or depth <= self.max_depth:
+            # Collect ranks for 'leaf' option in plot_options
+            if left_child == _tree.TREE_LEAF:
+                self.ranks["leaves"].append(str(node_id))
+            elif str(depth) not in self.ranks:
+                self.ranks[str(depth)] = [str(node_id)]
+            else:
+                self.ranks[str(depth)].append(str(node_id))
+
+            self.out_file.write(
+                "%d [label=%s" % (node_id, self.node_to_str(tree, node_id, criterion))
+            )
+
+            if self.filled:
+                self.out_file.write(
+                    ', fillcolor="%s"' % self.get_fill_color(tree, node_id)
+                )
+            self.out_file.write("] ;\n")
+
+            if parent is not None:
+                # Add edge to parent
+                self.out_file.write("%d -> %d" % (parent, node_id))
+                if parent == 0:
+                    # Draw True/False labels if parent is root node
+                    angles = np.array([45, -45]) * ((self.rotate - 0.5) * -2)
+                    self.out_file.write(" [labeldistance=2.5, labelangle=")
+                    if node_id == 1:
+                        self.out_file.write('%d, headlabel="True"]' % angles[0])
+                    else:
+                        self.out_file.write('%d, headlabel="False"]' % angles[1])
+                self.out_file.write(" ;\n")
+
+            if left_child != _tree.TREE_LEAF:
+                self.recurse(
+                    tree,
+                    left_child,
+                    criterion=criterion,
+                    parent=node_id,
+                    depth=depth + 1,
+                )
+                self.recurse(
+                    tree,
+                    right_child,
+                    criterion=criterion,
+                    parent=node_id,
+                    depth=depth + 1,
+                )
+
+        else:
+            self.ranks["leaves"].append(str(node_id))
+
+            self.out_file.write('%d [label="(...)"' % node_id)
+            if self.filled:
+                # color cropped nodes grey
+                self.out_file.write(', fillcolor="#C0C0C0"')
+            self.out_file.write("] ;\n" % node_id)
+
+            if parent is not None:
+                # Add edge to parent
+                self.out_file.write("%d -> %d ;\n" % (parent, node_id))
+
+    def str_escape(self, string):
+        # override default escaping for graphviz
+        return string.replace('"', r"\"")
+
+
+class _MPLTreeExporter(_BaseTreeExporter):
+    def __init__(
+        self,
+        max_depth=None,
+        feature_names=None,
+        class_names=None,
+        label="all",
+        filled=False,
+        impurity=True,
+        node_ids=False,
+        proportion=False,
+        rounded=False,
+        precision=3,
+        fontsize=None,
+    ):
+        super().__init__(
+            max_depth=max_depth,
+            feature_names=feature_names,
+            class_names=class_names,
+            label=label,
+            filled=filled,
+            impurity=impurity,
+            node_ids=node_ids,
+            proportion=proportion,
+            rounded=rounded,
+            precision=precision,
+        )
+        self.fontsize = fontsize
+
+        # The depth of each node for plotting with 'leaf' option
+        self.ranks = {"leaves": []}
+        # The colors to render each node with
+        self.colors = {"bounds": None}
+
+        self.characters = ["#", "[", "]", "<=", "\n", "", ""]
+        self.bbox_args = dict()
+        if self.rounded:
+            self.bbox_args["boxstyle"] = "round"
+
+        self.arrow_args = dict(arrowstyle="<-")
+
+    def _make_tree(self, node_id, et, criterion, depth=0):
+        # traverses _tree.Tree recursively, builds intermediate
+        # "_reingold_tilford.Tree" object
+        name = self.node_to_str(et, node_id, criterion=criterion)
+        if et.children_left[node_id] != _tree.TREE_LEAF and (
+            self.max_depth is None or depth <= self.max_depth
+        ):
+            children = [
+                self._make_tree(
+                    et.children_left[node_id], et, criterion, depth=depth + 1
+                ),
+                self._make_tree(
+                    et.children_right[node_id], et, criterion, depth=depth + 1
+                ),
+            ]
+        else:
+            return Tree(name, node_id)
+        return Tree(name, node_id, *children)
+
+    def export(self, decision_tree, ax=None):
+        import matplotlib.pyplot as plt
+        from matplotlib.text import Annotation
+
+        if ax is None:
+            ax = plt.gca()
+        ax.clear()
+        ax.set_axis_off()
+        my_tree = self._make_tree(0, decision_tree.tree_, decision_tree.criterion)
+        draw_tree = buchheim(my_tree)
+
+        # important to make sure we're still
+        # inside the axis after drawing the box
+        # this makes sense because the width of a box
+        # is about the same as the distance between boxes
+        max_x, max_y = draw_tree.max_extents() + 1
+        ax_width = ax.get_window_extent().width
+        ax_height = ax.get_window_extent().height
+
+        scale_x = ax_width / max_x
+        scale_y = ax_height / max_y
+        self.recurse(draw_tree, decision_tree.tree_, ax, max_x, max_y)
+
+        anns = [ann for ann in ax.get_children() if isinstance(ann, Annotation)]
+
+        # update sizes of all bboxes
+        renderer = ax.figure.canvas.get_renderer()
+
+        for ann in anns:
+            ann.update_bbox_position_size(renderer)
+
+        if self.fontsize is None:
+            # get figure to data transform
+            # adjust fontsize to avoid overlap
+            # get max box width and height
+            extents = [
+                bbox_patch.get_window_extent()
+                for ann in anns
+                if (bbox_patch := ann.get_bbox_patch()) is not None
+            ]
+            max_width = max([extent.width for extent in extents])
+            max_height = max([extent.height for extent in extents])
+            # width should be around scale_x in axis coordinates
+            size = anns[0].get_fontsize() * min(
+                scale_x / max_width, scale_y / max_height
+            )
+            for ann in anns:
+                ann.set_fontsize(size)
+
+        return anns
+
+    def recurse(self, node, tree, ax, max_x, max_y, depth=0):
+        import matplotlib.pyplot as plt
+
+        # kwargs for annotations without a bounding box
+        common_kwargs = dict(
+            zorder=100 - 10 * depth,
+            xycoords="axes fraction",
+        )
+        if self.fontsize is not None:
+            common_kwargs["fontsize"] = self.fontsize
+
+        # kwargs for annotations with a bounding box
+        kwargs = dict(
+            ha="center",
+            va="center",
+            bbox=self.bbox_args.copy(),
+            arrowprops=self.arrow_args.copy(),
+            **common_kwargs,
+        )
+        kwargs["arrowprops"]["edgecolor"] = plt.rcParams["text.color"]
+
+        # offset things by .5 to center them in plot
+        xy = ((node.x + 0.5) / max_x, (max_y - node.y - 0.5) / max_y)
+
+        if self.max_depth is None or depth <= self.max_depth:
+            if self.filled:
+                kwargs["bbox"]["fc"] = self.get_fill_color(tree, node.tree.node_id)
+            else:
+                kwargs["bbox"]["fc"] = ax.get_facecolor()
+
+            if node.parent is None:
+                # root
+                ax.annotate(node.tree.label, xy, **kwargs)
+            else:
+                xy_parent = (
+                    (node.parent.x + 0.5) / max_x,
+                    (max_y - node.parent.y - 0.5) / max_y,
+                )
+                ax.annotate(node.tree.label, xy_parent, xy, **kwargs)
+
+                # Draw True/False labels if parent is root node
+                if node.parent.parent is None:
+                    # Adjust the position for the text to be slightly above the arrow
+                    text_pos = (
+                        (xy_parent[0] + xy[0]) / 2,
+                        (xy_parent[1] + xy[1]) / 2,
+                    )
+                    # Annotate the arrow with the edge label to indicate the child
+                    # where the sample-split condition is satisfied
+                    if node.parent.left() == node:
+                        label_text, label_ha = ("True  ", "right")
+                    else:
+                        label_text, label_ha = ("  False", "left")
+                    ax.annotate(label_text, text_pos, ha=label_ha, **common_kwargs)
+            for child in node.children:
+                self.recurse(child, tree, ax, max_x, max_y, depth=depth + 1)
+
+        else:
+            xy_parent = (
+                (node.parent.x + 0.5) / max_x,
+                (max_y - node.parent.y - 0.5) / max_y,
+            )
+            kwargs["bbox"]["fc"] = "grey"
+            ax.annotate("\n  (...)  \n", xy_parent, xy, **kwargs)
+
+
+@validate_params(
+    {
+        "decision_tree": "no_validation",
+        "out_file": [str, None, HasMethods("write")],
+        "max_depth": [Interval(Integral, 0, None, closed="left"), None],
+        "feature_names": ["array-like", None],
+        "class_names": ["array-like", "boolean", None],
+        "label": [StrOptions({"all", "root", "none"})],
+        "filled": ["boolean"],
+        "leaves_parallel": ["boolean"],
+        "impurity": ["boolean"],
+        "node_ids": ["boolean"],
+        "proportion": ["boolean"],
+        "rotate": ["boolean"],
+        "rounded": ["boolean"],
+        "special_characters": ["boolean"],
+        "precision": [Interval(Integral, 0, None, closed="left"), None],
+        "fontname": [str],
+    },
+    prefer_skip_nested_validation=True,
+)
+def export_graphviz(
+    decision_tree,
+    out_file=None,
+    *,
+    max_depth=None,
+    feature_names=None,
+    class_names=None,
+    label="all",
+    filled=False,
+    leaves_parallel=False,
+    impurity=True,
+    node_ids=False,
+    proportion=False,
+    rotate=False,
+    rounded=False,
+    special_characters=False,
+    precision=3,
+    fontname="helvetica",
+):
+    """Export a decision tree in DOT format.
+
+    This function generates a GraphViz representation of the decision tree,
+    which is then written into `out_file`. Once exported, graphical renderings
+    can be generated using, for example::
+
+        $ dot -Tps tree.dot -o tree.ps      (PostScript format)
+        $ dot -Tpng tree.dot -o tree.png    (PNG format)
+
+    The sample counts that are shown are weighted with any sample_weights that
+    might be present.
+
+    Read more in the :ref:`User Guide `.
+
+    Parameters
+    ----------
+    decision_tree : object
+        The decision tree estimator to be exported to GraphViz.
+
+    out_file : object or str, default=None
+        Handle or name of the output file. If ``None``, the result is
+        returned as a string.
+
+        .. versionchanged:: 0.20
+            Default of out_file changed from "tree.dot" to None.
+
+    max_depth : int, default=None
+        The maximum depth of the representation. If None, the tree is fully
+        generated.
+
+    feature_names : array-like of shape (n_features,), default=None
+        An array containing the feature names.
+        If None, generic names will be used ("x[0]", "x[1]", ...).
+
+    class_names : array-like of shape (n_classes,) or bool, default=None
+        Names of each of the target classes in ascending numerical order.
+        Only relevant for classification and not supported for multi-output.
+        If ``True``, shows a symbolic representation of the class name.
+
+    label : {'all', 'root', 'none'}, default='all'
+        Whether to show informative labels for impurity, etc.
+        Options include 'all' to show at every node, 'root' to show only at
+        the top root node, or 'none' to not show at any node.
+
+    filled : bool, default=False
+        When set to ``True``, paint nodes to indicate majority class for
+        classification, extremity of values for regression, or purity of node
+        for multi-output.
+
+    leaves_parallel : bool, default=False
+        When set to ``True``, draw all leaf nodes at the bottom of the tree.
+
+    impurity : bool, default=True
+        When set to ``True``, show the impurity at each node.
+
+    node_ids : bool, default=False
+        When set to ``True``, show the ID number on each node.
+
+    proportion : bool, default=False
+        When set to ``True``, change the display of 'values' and/or 'samples'
+        to be proportions and percentages respectively.
+
+    rotate : bool, default=False
+        When set to ``True``, orient tree left to right rather than top-down.
+
+    rounded : bool, default=False
+        When set to ``True``, draw node boxes with rounded corners.
+
+    special_characters : bool, default=False
+        When set to ``False``, ignore special characters for PostScript
+        compatibility.
+
+    precision : int, default=3
+        Number of digits of precision for floating point in the values of
+        impurity, threshold and value attributes of each node.
+
+    fontname : str, default='helvetica'
+        Name of font used to render text.
+
+    Returns
+    -------
+    dot_data : str
+        String representation of the input tree in GraphViz dot format.
+        Only returned if ``out_file`` is None.
+
+        .. versionadded:: 0.18
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn import tree
+
+    >>> clf = tree.DecisionTreeClassifier()
+    >>> iris = load_iris()
+
+    >>> clf = clf.fit(iris.data, iris.target)
+    >>> tree.export_graphviz(clf)
+    'digraph Tree {...
+    """
+    if feature_names is not None:
+        feature_names = check_array(
+            feature_names, ensure_2d=False, dtype=None, ensure_min_samples=0
+        )
+    if class_names is not None and not isinstance(class_names, bool):
+        class_names = check_array(
+            class_names, ensure_2d=False, dtype=None, ensure_min_samples=0
+        )
+
+    check_is_fitted(decision_tree)
+    own_file = False
+    return_string = False
+    try:
+        if isinstance(out_file, str):
+            out_file = open(out_file, "w", encoding="utf-8")
+            own_file = True
+
+        if out_file is None:
+            return_string = True
+            out_file = StringIO()
+
+        exporter = _DOTTreeExporter(
+            out_file=out_file,
+            max_depth=max_depth,
+            feature_names=feature_names,
+            class_names=class_names,
+            label=label,
+            filled=filled,
+            leaves_parallel=leaves_parallel,
+            impurity=impurity,
+            node_ids=node_ids,
+            proportion=proportion,
+            rotate=rotate,
+            rounded=rounded,
+            special_characters=special_characters,
+            precision=precision,
+            fontname=fontname,
+        )
+        exporter.export(decision_tree)
+
+        if return_string:
+            return exporter.out_file.getvalue()
+
+    finally:
+        if own_file:
+            out_file.close()
+
+
+def _compute_depth(tree, node):
+    """
+    Returns the depth of the subtree rooted in node.
+    """
+
+    def compute_depth_(
+        current_node, current_depth, children_left, children_right, depths
+    ):
+        depths += [current_depth]
+        left = children_left[current_node]
+        right = children_right[current_node]
+        if left != -1 and right != -1:
+            compute_depth_(
+                left, current_depth + 1, children_left, children_right, depths
+            )
+            compute_depth_(
+                right, current_depth + 1, children_left, children_right, depths
+            )
+
+    depths = []
+    compute_depth_(node, 1, tree.children_left, tree.children_right, depths)
+    return max(depths)
+
+
+@validate_params(
+    {
+        "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
+        "feature_names": ["array-like", None],
+        "class_names": ["array-like", None],
+        "max_depth": [Interval(Integral, 0, None, closed="left"), None],
+        "spacing": [Interval(Integral, 1, None, closed="left"), None],
+        "decimals": [Interval(Integral, 0, None, closed="left"), None],
+        "show_weights": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def export_text(
+    decision_tree,
+    *,
+    feature_names=None,
+    class_names=None,
+    max_depth=10,
+    spacing=3,
+    decimals=2,
+    show_weights=False,
+):
+    """Build a text report showing the rules of a decision tree.
+
+    Note that backwards compatibility may not be supported.
+
+    Parameters
+    ----------
+    decision_tree : object
+        The decision tree estimator to be exported.
+        It can be an instance of
+        DecisionTreeClassifier or DecisionTreeRegressor.
+
+    feature_names : array-like of shape (n_features,), default=None
+        An array containing the feature names.
+        If None generic names will be used ("feature_0", "feature_1", ...).
+
+    class_names : array-like of shape (n_classes,), default=None
+        Names of each of the target classes in ascending numerical order.
+        Only relevant for classification and not supported for multi-output.
+
+        - if `None`, the class names are delegated to `decision_tree.classes_`;
+        - otherwise, `class_names` will be used as class names instead of
+          `decision_tree.classes_`. The length of `class_names` must match
+          the length of `decision_tree.classes_`.
+
+        .. versionadded:: 1.3
+
+    max_depth : int, default=10
+        Only the first max_depth levels of the tree are exported.
+        Truncated branches will be marked with "...".
+
+    spacing : int, default=3
+        Number of spaces between edges. The higher it is, the wider the result.
+
+    decimals : int, default=2
+        Number of decimal digits to display.
+
+    show_weights : bool, default=False
+        If true the classification weights will be exported on each leaf.
+        The classification weights are the number of samples each class.
+
+    Returns
+    -------
+    report : str
+        Text summary of all the rules in the decision tree.
+
+    Examples
+    --------
+
+    >>> from sklearn.datasets import load_iris
+    >>> from sklearn.tree import DecisionTreeClassifier
+    >>> from sklearn.tree import export_text
+    >>> iris = load_iris()
+    >>> X = iris['data']
+    >>> y = iris['target']
+    >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
+    >>> decision_tree = decision_tree.fit(X, y)
+    >>> r = export_text(decision_tree, feature_names=iris['feature_names'])
+    >>> print(r)
+    |--- petal width (cm) <= 0.80
+    |   |--- class: 0
+    |--- petal width (cm) >  0.80
+    |   |--- petal width (cm) <= 1.75
+    |   |   |--- class: 1
+    |   |--- petal width (cm) >  1.75
+    |   |   |--- class: 2
+    """
+    if feature_names is not None:
+        feature_names = check_array(
+            feature_names, ensure_2d=False, dtype=None, ensure_min_samples=0
+        )
+    if class_names is not None:
+        class_names = check_array(
+            class_names, ensure_2d=False, dtype=None, ensure_min_samples=0
+        )
+
+    check_is_fitted(decision_tree)
+    tree_ = decision_tree.tree_
+    if is_classifier(decision_tree):
+        if class_names is None:
+            class_names = decision_tree.classes_
+        elif len(class_names) != len(decision_tree.classes_):
+            raise ValueError(
+                "When `class_names` is an array, it should contain as"
+                " many items as `decision_tree.classes_`. Got"
+                f" {len(class_names)} while the tree was fitted with"
+                f" {len(decision_tree.classes_)} classes."
+            )
+    right_child_fmt = "{} {} <= {}\n"
+    left_child_fmt = "{} {} >  {}\n"
+    truncation_fmt = "{} {}\n"
+
+    if feature_names is not None and len(feature_names) != tree_.n_features:
+        raise ValueError(
+            "feature_names must contain %d elements, got %d"
+            % (tree_.n_features, len(feature_names))
+        )
+
+    if isinstance(decision_tree, DecisionTreeClassifier):
+        value_fmt = "{}{} weights: {}\n"
+        if not show_weights:
+            value_fmt = "{}{}{}\n"
+    else:
+        value_fmt = "{}{} value: {}\n"
+
+    if feature_names is not None:
+        feature_names_ = [
+            feature_names[i] if i != _tree.TREE_UNDEFINED else None
+            for i in tree_.feature
+        ]
+    else:
+        feature_names_ = ["feature_{}".format(i) for i in tree_.feature]
+
+    export_text.report = ""
+
+    def _add_leaf(value, weighted_n_node_samples, class_name, indent):
+        val = ""
+        if isinstance(decision_tree, DecisionTreeClassifier):
+            if show_weights:
+                val = [
+                    "{1:.{0}f}, ".format(decimals, v * weighted_n_node_samples)
+                    for v in value
+                ]
+                val = "[" + "".join(val)[:-2] + "]"
+                weighted_n_node_samples
+            val += " class: " + str(class_name)
+        else:
+            val = ["{1:.{0}f}, ".format(decimals, v) for v in value]
+            val = "[" + "".join(val)[:-2] + "]"
+        export_text.report += value_fmt.format(indent, "", val)
+
+    def print_tree_recurse(node, depth):
+        indent = ("|" + (" " * spacing)) * depth
+        indent = indent[:-spacing] + "-" * spacing
+
+        value = None
+        if tree_.n_outputs == 1:
+            value = tree_.value[node][0]
+        else:
+            value = tree_.value[node].T[0]
+        class_name = np.argmax(value)
+
+        if tree_.n_classes[0] != 1 and tree_.n_outputs == 1:
+            class_name = class_names[class_name]
+
+        weighted_n_node_samples = tree_.weighted_n_node_samples[node]
+
+        if depth <= max_depth + 1:
+            info_fmt = ""
+            info_fmt_left = info_fmt
+            info_fmt_right = info_fmt
+
+            if tree_.feature[node] != _tree.TREE_UNDEFINED:
+                name = feature_names_[node]
+                threshold = tree_.threshold[node]
+                threshold = "{1:.{0}f}".format(decimals, threshold)
+                export_text.report += right_child_fmt.format(indent, name, threshold)
+                export_text.report += info_fmt_left
+                print_tree_recurse(tree_.children_left[node], depth + 1)
+
+                export_text.report += left_child_fmt.format(indent, name, threshold)
+                export_text.report += info_fmt_right
+                print_tree_recurse(tree_.children_right[node], depth + 1)
+            else:  # leaf
+                _add_leaf(value, weighted_n_node_samples, class_name, indent)
+        else:
+            subtree_depth = _compute_depth(tree_, node)
+            if subtree_depth == 1:
+                _add_leaf(value, weighted_n_node_samples, class_name, indent)
+            else:
+                trunc_report = "truncated branch of depth %d" % subtree_depth
+                export_text.report += truncation_fmt.format(indent, trunc_report)
+
+    print_tree_recurse(0, 1)
+    return export_text.report
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..fd41dec2e62c7c09714cfe13cd0bae674470c06b
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pxd
@@ -0,0 +1,178 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _partitioner.pyx for details.
+
+from ..utils._typedefs cimport (
+    float32_t, float64_t, int8_t, int32_t, intp_t, uint8_t, uint32_t
+)
+from ._splitter cimport SplitRecord
+
+
+# Mitigate precision differences between 32 bit and 64 bit
+cdef float32_t FEATURE_THRESHOLD = 1e-7
+
+
+# We provide here the abstract interface for a Partitioner that would be
+# theoretically shared between the Dense and Sparse partitioners. However,
+# we leave it commented out for now as it is not used in the current
+# implementation due to the performance hit from vtable lookups when using
+# inheritance based polymorphism. It is left here for future reference.
+#
+# Note: Instead, in `_splitter.pyx`, we define a fused type that can be used
+# to represent both the dense and sparse partitioners.
+#
+# cdef class BasePartitioner:
+#     cdef intp_t[::1] samples
+#     cdef float32_t[::1] feature_values
+#     cdef intp_t start
+#     cdef intp_t end
+#     cdef intp_t n_missing
+#     cdef const uint8_t[::1] missing_values_in_feature_mask
+
+#     cdef void sort_samples_and_feature_values(
+#         self, intp_t current_feature
+#     ) noexcept nogil
+#     cdef void init_node_split(
+#         self,
+#         intp_t start,
+#         intp_t end
+#     ) noexcept nogil
+#     cdef void find_min_max(
+#         self,
+#         intp_t current_feature,
+#         float32_t* min_feature_value_out,
+#         float32_t* max_feature_value_out,
+#     ) noexcept nogil
+#     cdef void next_p(
+#         self,
+#         intp_t* p_prev,
+#         intp_t* p
+#     ) noexcept nogil
+#     cdef intp_t partition_samples(
+#         self,
+#         float64_t current_threshold
+#     ) noexcept nogil
+#     cdef void partition_samples_final(
+#         self,
+#         intp_t best_pos,
+#         float64_t best_threshold,
+#         intp_t best_feature,
+#         intp_t n_missing,
+#     ) noexcept nogil
+
+
+cdef class DensePartitioner:
+    """Partitioner specialized for dense data.
+
+    Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+    """
+    cdef const float32_t[:, :] X
+    cdef intp_t[::1] samples
+    cdef float32_t[::1] feature_values
+    cdef intp_t start
+    cdef intp_t end
+    cdef intp_t n_missing
+    cdef const uint8_t[::1] missing_values_in_feature_mask
+
+    cdef void sort_samples_and_feature_values(
+        self, intp_t current_feature
+    ) noexcept nogil
+    cdef void init_node_split(
+        self,
+        intp_t start,
+        intp_t end
+    ) noexcept nogil
+    cdef void find_min_max(
+        self,
+        intp_t current_feature,
+        float32_t* min_feature_value_out,
+        float32_t* max_feature_value_out,
+    ) noexcept nogil
+    cdef void next_p(
+        self,
+        intp_t* p_prev,
+        intp_t* p
+    ) noexcept nogil
+    cdef intp_t partition_samples(
+        self,
+        float64_t current_threshold
+    ) noexcept nogil
+    cdef void partition_samples_final(
+        self,
+        intp_t best_pos,
+        float64_t best_threshold,
+        intp_t best_feature,
+        intp_t n_missing,
+    ) noexcept nogil
+
+
+cdef class SparsePartitioner:
+    """Partitioner specialized for sparse CSC data.
+
+    Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+    """
+    cdef const float32_t[::1] X_data
+    cdef const int32_t[::1] X_indices
+    cdef const int32_t[::1] X_indptr
+    cdef intp_t n_total_samples
+    cdef intp_t[::1] index_to_samples
+    cdef intp_t[::1] sorted_samples
+    cdef intp_t start_positive
+    cdef intp_t end_negative
+    cdef bint is_samples_sorted
+
+    cdef intp_t[::1] samples
+    cdef float32_t[::1] feature_values
+    cdef intp_t start
+    cdef intp_t end
+    cdef intp_t n_missing
+    cdef const uint8_t[::1] missing_values_in_feature_mask
+
+    cdef void sort_samples_and_feature_values(
+        self, intp_t current_feature
+    ) noexcept nogil
+    cdef void init_node_split(
+        self,
+        intp_t start,
+        intp_t end
+    ) noexcept nogil
+    cdef void find_min_max(
+        self,
+        intp_t current_feature,
+        float32_t* min_feature_value_out,
+        float32_t* max_feature_value_out,
+    ) noexcept nogil
+    cdef void next_p(
+        self,
+        intp_t* p_prev,
+        intp_t* p
+    ) noexcept nogil
+    cdef intp_t partition_samples(
+        self,
+        float64_t current_threshold
+    ) noexcept nogil
+    cdef void partition_samples_final(
+        self,
+        intp_t best_pos,
+        float64_t best_threshold,
+        intp_t best_feature,
+        intp_t n_missing,
+    ) noexcept nogil
+
+    cdef void extract_nnz(
+        self,
+        intp_t feature
+    ) noexcept nogil
+    cdef intp_t _partition(
+        self,
+        float64_t threshold,
+        intp_t zero_pos
+    ) noexcept nogil
+
+
+cdef void shift_missing_values_to_left_if_required(
+    SplitRecord* best,
+    intp_t[::1] samples,
+    intp_t end,
+) noexcept nogil
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..7c342ed3a7d6b807619900de2ebba6bdf7deabb9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_partitioner.pyx
@@ -0,0 +1,821 @@
+"""Partition samples in the construction of a tree.
+
+This module contains the algorithms for moving sample indices to
+the left and right child node given a split determined by the
+splitting algorithm in `_splitter.pyx`.
+
+Partitioning is done in a way that is efficient for both dense data,
+and sparse data stored in a Compressed Sparse Column (CSC) format.
+"""
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from cython cimport final
+from libc.math cimport isnan, log2
+from libc.stdlib cimport qsort
+from libc.string cimport memcpy
+
+import numpy as np
+from scipy.sparse import issparse
+
+
+# Constant to switch between algorithm non zero value extract algorithm
+# in SparsePartitioner
+cdef float32_t EXTRACT_NNZ_SWITCH = 0.1
+
+# Allow for 32 bit float comparisons
+cdef float32_t INFINITY_32t = np.inf
+
+
+@final
+cdef class DensePartitioner:
+    """Partitioner specialized for dense data.
+
+    Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+    """
+    def __init__(
+        self,
+        const float32_t[:, :] X,
+        intp_t[::1] samples,
+        float32_t[::1] feature_values,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ):
+        self.X = X
+        self.samples = samples
+        self.feature_values = feature_values
+        self.missing_values_in_feature_mask = missing_values_in_feature_mask
+
+    cdef inline void init_node_split(self, intp_t start, intp_t end) noexcept nogil:
+        """Initialize splitter at the beginning of node_split."""
+        self.start = start
+        self.end = end
+        self.n_missing = 0
+
+    cdef inline void sort_samples_and_feature_values(
+        self, intp_t current_feature
+    ) noexcept nogil:
+        """Simultaneously sort based on the feature_values.
+
+        Missing values are stored at the end of feature_values.
+        The number of missing values observed in feature_values is stored
+        in self.n_missing.
+        """
+        cdef:
+            intp_t i, current_end
+            float32_t[::1] feature_values = self.feature_values
+            const float32_t[:, :] X = self.X
+            intp_t[::1] samples = self.samples
+            intp_t n_missing = 0
+            const uint8_t[::1] missing_values_in_feature_mask = self.missing_values_in_feature_mask
+
+        # Sort samples along that feature; by copying the values into an array and
+        # sorting the array in a manner which utilizes the cache more effectively.
+        if missing_values_in_feature_mask is not None and missing_values_in_feature_mask[current_feature]:
+            i, current_end = self.start, self.end - 1
+            # Missing values are placed at the end and do not participate in the sorting.
+            while i <= current_end:
+                # Finds the right-most value that is not missing so that
+                # it can be swapped with missing values at its left.
+                if isnan(X[samples[current_end], current_feature]):
+                    n_missing += 1
+                    current_end -= 1
+                    continue
+
+                # X[samples[current_end], current_feature] is a non-missing value
+                if isnan(X[samples[i], current_feature]):
+                    samples[i], samples[current_end] = samples[current_end], samples[i]
+                    n_missing += 1
+                    current_end -= 1
+
+                feature_values[i] = X[samples[i], current_feature]
+                i += 1
+        else:
+            # When there are no missing values, we only need to copy the data into
+            # feature_values
+            for i in range(self.start, self.end):
+                feature_values[i] = X[samples[i], current_feature]
+
+        sort(&feature_values[self.start], &samples[self.start], self.end - self.start - n_missing)
+        self.n_missing = n_missing
+
+    cdef inline void find_min_max(
+        self,
+        intp_t current_feature,
+        float32_t* min_feature_value_out,
+        float32_t* max_feature_value_out,
+    ) noexcept nogil:
+        """Find the minimum and maximum value for current_feature.
+
+        Missing values are stored at the end of feature_values. The number of missing
+        values observed in feature_values is stored in self.n_missing.
+        """
+        cdef:
+            intp_t p, current_end
+            float32_t current_feature_value
+            const float32_t[:, :] X = self.X
+            intp_t[::1] samples = self.samples
+            float32_t min_feature_value = INFINITY_32t
+            float32_t max_feature_value = -INFINITY_32t
+            float32_t[::1] feature_values = self.feature_values
+            intp_t n_missing = 0
+            const uint8_t[::1] missing_values_in_feature_mask = self.missing_values_in_feature_mask
+
+        # We are copying the values into an array and finding min/max of the array in
+        # a manner which utilizes the cache more effectively. We need to also count
+        # the number of missing-values there are.
+        if missing_values_in_feature_mask is not None and missing_values_in_feature_mask[current_feature]:
+            p, current_end = self.start, self.end - 1
+            # Missing values are placed at the end and do not participate in the
+            # min/max calculation.
+            while p <= current_end:
+                # Finds the right-most value that is not missing so that
+                # it can be swapped with missing values towards its left.
+                if isnan(X[samples[current_end], current_feature]):
+                    n_missing += 1
+                    current_end -= 1
+                    continue
+
+                # X[samples[current_end], current_feature] is a non-missing value
+                if isnan(X[samples[p], current_feature]):
+                    samples[p], samples[current_end] = samples[current_end], samples[p]
+                    n_missing += 1
+                    current_end -= 1
+
+                current_feature_value = X[samples[p], current_feature]
+                feature_values[p] = current_feature_value
+                if current_feature_value < min_feature_value:
+                    min_feature_value = current_feature_value
+                elif current_feature_value > max_feature_value:
+                    max_feature_value = current_feature_value
+                p += 1
+        else:
+            min_feature_value = X[samples[self.start], current_feature]
+            max_feature_value = min_feature_value
+
+            feature_values[self.start] = min_feature_value
+            for p in range(self.start + 1, self.end):
+                current_feature_value = X[samples[p], current_feature]
+                feature_values[p] = current_feature_value
+
+                if current_feature_value < min_feature_value:
+                    min_feature_value = current_feature_value
+                elif current_feature_value > max_feature_value:
+                    max_feature_value = current_feature_value
+
+        min_feature_value_out[0] = min_feature_value
+        max_feature_value_out[0] = max_feature_value
+        self.n_missing = n_missing
+
+    cdef inline void next_p(self, intp_t* p_prev, intp_t* p) noexcept nogil:
+        """Compute the next p_prev and p for iterating over feature values.
+
+        The missing values are not included when iterating through the feature values.
+        """
+        cdef:
+            float32_t[::1] feature_values = self.feature_values
+            intp_t end_non_missing = self.end - self.n_missing
+
+        while (
+            p[0] + 1 < end_non_missing and
+            feature_values[p[0] + 1] <= feature_values[p[0]] + FEATURE_THRESHOLD
+        ):
+            p[0] += 1
+
+        p_prev[0] = p[0]
+
+        # By adding 1, we have
+        # (feature_values[p] >= end) or (feature_values[p] > feature_values[p - 1])
+        p[0] += 1
+
+    cdef inline intp_t partition_samples(
+        self,
+        float64_t current_threshold
+    ) noexcept nogil:
+        """Partition samples for feature_values at the current_threshold."""
+        cdef:
+            intp_t p = self.start
+            intp_t partition_end = self.end - self.n_missing
+            intp_t[::1] samples = self.samples
+            float32_t[::1] feature_values = self.feature_values
+
+        while p < partition_end:
+            if feature_values[p] <= current_threshold:
+                p += 1
+            else:
+                partition_end -= 1
+
+                feature_values[p], feature_values[partition_end] = (
+                    feature_values[partition_end], feature_values[p]
+                )
+                samples[p], samples[partition_end] = samples[partition_end], samples[p]
+
+        return partition_end
+
+    cdef inline void partition_samples_final(
+        self,
+        intp_t best_pos,
+        float64_t best_threshold,
+        intp_t best_feature,
+        intp_t best_n_missing,
+    ) noexcept nogil:
+        """Partition samples for X at the best_threshold and best_feature.
+
+        If missing values are present, this method partitions `samples`
+        so that the `best_n_missing` missing values' indices are in the
+        right-most end of `samples`, that is `samples[end_non_missing:end]`.
+        """
+        cdef:
+            # Local invariance: start <= p <= partition_end <= end
+            intp_t start = self.start
+            intp_t p = start
+            intp_t end = self.end - 1
+            intp_t partition_end = end - best_n_missing
+            intp_t[::1] samples = self.samples
+            const float32_t[:, :] X = self.X
+            float32_t current_value
+
+        if best_n_missing != 0:
+            # Move samples with missing values to the end while partitioning the
+            # non-missing samples
+            while p < partition_end:
+                # Keep samples with missing values at the end
+                if isnan(X[samples[end], best_feature]):
+                    end -= 1
+                    continue
+
+                # Swap sample with missing values with the sample at the end
+                current_value = X[samples[p], best_feature]
+                if isnan(current_value):
+                    samples[p], samples[end] = samples[end], samples[p]
+                    end -= 1
+
+                    # The swapped sample at the end is always a non-missing value, so
+                    # we can continue the algorithm without checking for missingness.
+                    current_value = X[samples[p], best_feature]
+
+                # Partition the non-missing samples
+                if current_value <= best_threshold:
+                    p += 1
+                else:
+                    samples[p], samples[partition_end] = samples[partition_end], samples[p]
+                    partition_end -= 1
+        else:
+            # Partitioning routine when there are no missing values
+            while p < partition_end:
+                if X[samples[p], best_feature] <= best_threshold:
+                    p += 1
+                else:
+                    samples[p], samples[partition_end] = samples[partition_end], samples[p]
+                    partition_end -= 1
+
+
+@final
+cdef class SparsePartitioner:
+    """Partitioner specialized for sparse CSC data.
+
+    Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+    """
+    def __init__(
+        self,
+        object X,
+        intp_t[::1] samples,
+        intp_t n_samples,
+        float32_t[::1] feature_values,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ):
+        if not (issparse(X) and X.format == "csc"):
+            raise ValueError("X should be in csc format")
+
+        self.samples = samples
+        self.feature_values = feature_values
+
+        # Initialize X
+        cdef intp_t n_total_samples = X.shape[0]
+
+        self.X_data = X.data
+        self.X_indices = X.indices
+        self.X_indptr = X.indptr
+        self.n_total_samples = n_total_samples
+
+        # Initialize auxiliary array used to perform split
+        self.index_to_samples = np.full(n_total_samples, fill_value=-1, dtype=np.intp)
+        self.sorted_samples = np.empty(n_samples, dtype=np.intp)
+
+        cdef intp_t p
+        for p in range(n_samples):
+            self.index_to_samples[samples[p]] = p
+
+        self.missing_values_in_feature_mask = missing_values_in_feature_mask
+
+    cdef inline void init_node_split(self, intp_t start, intp_t end) noexcept nogil:
+        """Initialize splitter at the beginning of node_split."""
+        self.start = start
+        self.end = end
+        self.is_samples_sorted = 0
+        self.n_missing = 0
+
+    cdef inline void sort_samples_and_feature_values(
+        self,
+        intp_t current_feature
+    ) noexcept nogil:
+        """Simultaneously sort based on the feature_values."""
+        cdef:
+            float32_t[::1] feature_values = self.feature_values
+            intp_t[::1] index_to_samples = self.index_to_samples
+            intp_t[::1] samples = self.samples
+
+        self.extract_nnz(current_feature)
+        # Sort the positive and negative parts of `feature_values`
+        sort(&feature_values[self.start], &samples[self.start], self.end_negative - self.start)
+        if self.start_positive < self.end:
+            sort(
+                &feature_values[self.start_positive],
+                &samples[self.start_positive],
+                self.end - self.start_positive
+            )
+
+        # Update index_to_samples to take into account the sort
+        for p in range(self.start, self.end_negative):
+            index_to_samples[samples[p]] = p
+        for p in range(self.start_positive, self.end):
+            index_to_samples[samples[p]] = p
+
+        # Add one or two zeros in feature_values, if there is any
+        if self.end_negative < self.start_positive:
+            self.start_positive -= 1
+            feature_values[self.start_positive] = 0.
+
+            if self.end_negative != self.start_positive:
+                feature_values[self.end_negative] = 0.
+                self.end_negative += 1
+
+        # XXX: When sparse supports missing values, this should be set to the
+        # number of missing values for current_feature
+        self.n_missing = 0
+
+    cdef inline void find_min_max(
+        self,
+        intp_t current_feature,
+        float32_t* min_feature_value_out,
+        float32_t* max_feature_value_out,
+    ) noexcept nogil:
+        """Find the minimum and maximum value for current_feature."""
+        cdef:
+            intp_t p
+            float32_t current_feature_value, min_feature_value, max_feature_value
+            float32_t[::1] feature_values = self.feature_values
+
+        self.extract_nnz(current_feature)
+
+        if self.end_negative != self.start_positive:
+            # There is a zero
+            min_feature_value = 0
+            max_feature_value = 0
+        else:
+            min_feature_value = feature_values[self.start]
+            max_feature_value = min_feature_value
+
+        # Find min, max in feature_values[start:end_negative]
+        for p in range(self.start, self.end_negative):
+            current_feature_value = feature_values[p]
+
+            if current_feature_value < min_feature_value:
+                min_feature_value = current_feature_value
+            elif current_feature_value > max_feature_value:
+                max_feature_value = current_feature_value
+
+        # Update min, max given feature_values[start_positive:end]
+        for p in range(self.start_positive, self.end):
+            current_feature_value = feature_values[p]
+
+            if current_feature_value < min_feature_value:
+                min_feature_value = current_feature_value
+            elif current_feature_value > max_feature_value:
+                max_feature_value = current_feature_value
+
+        min_feature_value_out[0] = min_feature_value
+        max_feature_value_out[0] = max_feature_value
+
+    cdef inline void next_p(self, intp_t* p_prev, intp_t* p) noexcept nogil:
+        """Compute the next p_prev and p for iterating over feature values."""
+        cdef:
+            intp_t p_next
+            float32_t[::1] feature_values = self.feature_values
+
+        if p[0] + 1 != self.end_negative:
+            p_next = p[0] + 1
+        else:
+            p_next = self.start_positive
+
+        while (p_next < self.end and
+                feature_values[p_next] <= feature_values[p[0]] + FEATURE_THRESHOLD):
+            p[0] = p_next
+            if p[0] + 1 != self.end_negative:
+                p_next = p[0] + 1
+            else:
+                p_next = self.start_positive
+
+        p_prev[0] = p[0]
+        p[0] = p_next
+
+    cdef inline intp_t partition_samples(
+        self,
+        float64_t current_threshold
+    ) noexcept nogil:
+        """Partition samples for feature_values at the current_threshold."""
+        return self._partition(current_threshold, self.start_positive)
+
+    cdef inline void partition_samples_final(
+        self,
+        intp_t best_pos,
+        float64_t best_threshold,
+        intp_t best_feature,
+        intp_t n_missing,
+    ) noexcept nogil:
+        """Partition samples for X at the best_threshold and best_feature."""
+        self.extract_nnz(best_feature)
+        self._partition(best_threshold, best_pos)
+
+    cdef inline intp_t _partition(self, float64_t threshold, intp_t zero_pos) noexcept nogil:
+        """Partition samples[start:end] based on threshold."""
+        cdef:
+            intp_t p, partition_end
+            intp_t[::1] index_to_samples = self.index_to_samples
+            float32_t[::1] feature_values = self.feature_values
+            intp_t[::1] samples = self.samples
+
+        if threshold < 0.:
+            p = self.start
+            partition_end = self.end_negative
+        elif threshold > 0.:
+            p = self.start_positive
+            partition_end = self.end
+        else:
+            # Data are already split
+            return zero_pos
+
+        while p < partition_end:
+            if feature_values[p] <= threshold:
+                p += 1
+
+            else:
+                partition_end -= 1
+
+                feature_values[p], feature_values[partition_end] = (
+                    feature_values[partition_end], feature_values[p]
+                )
+                sparse_swap(index_to_samples, samples, p, partition_end)
+
+        return partition_end
+
+    cdef inline void extract_nnz(self, intp_t feature) noexcept nogil:
+        """Extract and partition values for a given feature.
+
+        The extracted values are partitioned between negative values
+        feature_values[start:end_negative[0]] and positive values
+        feature_values[start_positive[0]:end].
+        The samples and index_to_samples are modified according to this
+        partition.
+
+        The extraction corresponds to the intersection between the arrays
+        X_indices[indptr_start:indptr_end] and samples[start:end].
+        This is done efficiently using either an index_to_samples based approach
+        or binary search based approach.
+
+        Parameters
+        ----------
+        feature : intp_t,
+            Index of the feature we want to extract non zero value.
+        """
+        cdef intp_t[::1] samples = self.samples
+        cdef float32_t[::1] feature_values = self.feature_values
+        cdef intp_t indptr_start = self.X_indptr[feature],
+        cdef intp_t indptr_end = self.X_indptr[feature + 1]
+        cdef intp_t n_indices = (indptr_end - indptr_start)
+        cdef intp_t n_samples = self.end - self.start
+        cdef intp_t[::1] index_to_samples = self.index_to_samples
+        cdef intp_t[::1] sorted_samples = self.sorted_samples
+        cdef const int32_t[::1] X_indices = self.X_indices
+        cdef const float32_t[::1] X_data = self.X_data
+
+        # Use binary search if n_samples * log(n_indices) <
+        # n_indices and index_to_samples approach otherwise.
+        # O(n_samples * log(n_indices)) is the running time of binary
+        # search and O(n_indices) is the running time of index_to_samples
+        # approach.
+        if ((1 - self.is_samples_sorted) * n_samples * log2(n_samples) +
+                n_samples * log2(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
+            extract_nnz_binary_search(X_indices, X_data,
+                                      indptr_start, indptr_end,
+                                      samples, self.start, self.end,
+                                      index_to_samples,
+                                      feature_values,
+                                      &self.end_negative, &self.start_positive,
+                                      sorted_samples, &self.is_samples_sorted)
+
+        # Using an index to samples  technique to extract non zero values
+        # index_to_samples is a mapping from X_indices to samples
+        else:
+            extract_nnz_index_to_samples(X_indices, X_data,
+                                         indptr_start, indptr_end,
+                                         samples, self.start, self.end,
+                                         index_to_samples,
+                                         feature_values,
+                                         &self.end_negative, &self.start_positive)
+
+
+cdef int compare_SIZE_t(const void* a, const void* b) noexcept nogil:
+    """Comparison function for sort.
+
+    This must return an `int` as it is used by stdlib's qsort, which expects
+    an `int` return value.
+    """
+    return ((a)[0] - (b)[0])
+
+
+cdef inline void binary_search(const int32_t[::1] sorted_array,
+                               int32_t start, int32_t end,
+                               intp_t value, intp_t* index,
+                               int32_t* new_start) noexcept nogil:
+    """Return the index of value in the sorted array.
+
+    If not found, return -1. new_start is the last pivot + 1
+    """
+    cdef int32_t pivot
+    index[0] = -1
+    while start < end:
+        pivot = start + (end - start) / 2
+
+        if sorted_array[pivot] == value:
+            index[0] = pivot
+            start = pivot + 1
+            break
+
+        if sorted_array[pivot] < value:
+            start = pivot + 1
+        else:
+            end = pivot
+    new_start[0] = start
+
+
+cdef inline void extract_nnz_index_to_samples(const int32_t[::1] X_indices,
+                                              const float32_t[::1] X_data,
+                                              int32_t indptr_start,
+                                              int32_t indptr_end,
+                                              intp_t[::1] samples,
+                                              intp_t start,
+                                              intp_t end,
+                                              intp_t[::1] index_to_samples,
+                                              float32_t[::1] feature_values,
+                                              intp_t* end_negative,
+                                              intp_t* start_positive) noexcept nogil:
+    """Extract and partition values for a feature using index_to_samples.
+
+    Complexity is O(indptr_end - indptr_start).
+    """
+    cdef int32_t k
+    cdef intp_t index
+    cdef intp_t end_negative_ = start
+    cdef intp_t start_positive_ = end
+
+    for k in range(indptr_start, indptr_end):
+        if start <= index_to_samples[X_indices[k]] < end:
+            if X_data[k] > 0:
+                start_positive_ -= 1
+                feature_values[start_positive_] = X_data[k]
+                index = index_to_samples[X_indices[k]]
+                sparse_swap(index_to_samples, samples, index, start_positive_)
+
+            elif X_data[k] < 0:
+                feature_values[end_negative_] = X_data[k]
+                index = index_to_samples[X_indices[k]]
+                sparse_swap(index_to_samples, samples, index, end_negative_)
+                end_negative_ += 1
+
+    # Returned values
+    end_negative[0] = end_negative_
+    start_positive[0] = start_positive_
+
+
+cdef inline void extract_nnz_binary_search(const int32_t[::1] X_indices,
+                                           const float32_t[::1] X_data,
+                                           int32_t indptr_start,
+                                           int32_t indptr_end,
+                                           intp_t[::1] samples,
+                                           intp_t start,
+                                           intp_t end,
+                                           intp_t[::1] index_to_samples,
+                                           float32_t[::1] feature_values,
+                                           intp_t* end_negative,
+                                           intp_t* start_positive,
+                                           intp_t[::1] sorted_samples,
+                                           bint* is_samples_sorted) noexcept nogil:
+    """Extract and partition values for a given feature using binary search.
+
+    If n_samples = end - start and n_indices = indptr_end - indptr_start,
+    the complexity is
+
+        O((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
+          n_samples * log(n_indices)).
+    """
+    cdef intp_t n_samples
+
+    if not is_samples_sorted[0]:
+        n_samples = end - start
+        memcpy(&sorted_samples[start], &samples[start],
+               n_samples * sizeof(intp_t))
+        qsort(&sorted_samples[start], n_samples, sizeof(intp_t),
+              compare_SIZE_t)
+        is_samples_sorted[0] = 1
+
+    while (indptr_start < indptr_end and
+           sorted_samples[start] > X_indices[indptr_start]):
+        indptr_start += 1
+
+    while (indptr_start < indptr_end and
+           sorted_samples[end - 1] < X_indices[indptr_end - 1]):
+        indptr_end -= 1
+
+    cdef intp_t p = start
+    cdef intp_t index
+    cdef intp_t k
+    cdef intp_t end_negative_ = start
+    cdef intp_t start_positive_ = end
+
+    while (p < end and indptr_start < indptr_end):
+        # Find index of sorted_samples[p] in X_indices
+        binary_search(X_indices, indptr_start, indptr_end,
+                      sorted_samples[p], &k, &indptr_start)
+
+        if k != -1:
+            # If k != -1, we have found a non zero value
+
+            if X_data[k] > 0:
+                start_positive_ -= 1
+                feature_values[start_positive_] = X_data[k]
+                index = index_to_samples[X_indices[k]]
+                sparse_swap(index_to_samples, samples, index, start_positive_)
+
+            elif X_data[k] < 0:
+                feature_values[end_negative_] = X_data[k]
+                index = index_to_samples[X_indices[k]]
+                sparse_swap(index_to_samples, samples, index, end_negative_)
+                end_negative_ += 1
+        p += 1
+
+    # Returned values
+    end_negative[0] = end_negative_
+    start_positive[0] = start_positive_
+
+
+cdef inline void sparse_swap(intp_t[::1] index_to_samples, intp_t[::1] samples,
+                             intp_t pos_1, intp_t pos_2) noexcept nogil:
+    """Swap sample pos_1 and pos_2 preserving sparse invariant."""
+    samples[pos_1], samples[pos_2] = samples[pos_2], samples[pos_1]
+    index_to_samples[samples[pos_1]] = pos_1
+    index_to_samples[samples[pos_2]] = pos_2
+
+
+cdef inline void shift_missing_values_to_left_if_required(
+    SplitRecord* best,
+    intp_t[::1] samples,
+    intp_t end,
+) noexcept nogil:
+    """Shift missing value sample indices to the left of the split if required.
+
+    Note: this should always be called at the very end because it will
+    move samples around, thereby affecting the criterion.
+    This affects the computation of the children impurity, which affects
+    the computation of the next node.
+    """
+    cdef intp_t i, p, current_end
+    # The partitioner partitions the data such that the missing values are in
+    # samples[-n_missing:] for the criterion to consume. If the missing values
+    # are going to the right node, then the missing values are already in the
+    # correct position. If the missing values go left, then we move the missing
+    # values to samples[best.pos:best.pos+n_missing] and update `best.pos`.
+    if best.n_missing > 0 and best.missing_go_to_left:
+        for p in range(best.n_missing):
+            i = best.pos + p
+            current_end = end - 1 - p
+            samples[i], samples[current_end] = samples[current_end], samples[i]
+        best.pos += best.n_missing
+
+
+def _py_sort(float32_t[::1] feature_values, intp_t[::1] samples, intp_t n):
+    """Used for testing sort."""
+    sort(&feature_values[0], &samples[0], n)
+
+
+# Sort n-element arrays pointed to by feature_values and samples, simultaneously,
+# by the values in feature_values. Algorithm: Introsort (Musser, SP&E, 1997).
+cdef inline void sort(float32_t* feature_values, intp_t* samples, intp_t n) noexcept nogil:
+    if n == 0:
+        return
+    cdef intp_t maxd = 2 * log2(n)
+    introsort(feature_values, samples, n, maxd)
+
+
+cdef inline void swap(float32_t* feature_values, intp_t* samples,
+                      intp_t i, intp_t j) noexcept nogil:
+    # Helper for sort
+    feature_values[i], feature_values[j] = feature_values[j], feature_values[i]
+    samples[i], samples[j] = samples[j], samples[i]
+
+
+cdef inline float32_t median3(float32_t* feature_values, intp_t n) noexcept nogil:
+    # Median of three pivot selection, after Bentley and McIlroy (1993).
+    # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
+    cdef float32_t a = feature_values[0], b = feature_values[n / 2], c = feature_values[n - 1]
+    if a < b:
+        if b < c:
+            return b
+        elif a < c:
+            return c
+        else:
+            return a
+    elif b < c:
+        if a < c:
+            return a
+        else:
+            return c
+    else:
+        return b
+
+
+# Introsort with median of 3 pivot selection and 3-way partition function
+# (robust to repeated elements, e.g. lots of zero features).
+cdef void introsort(float32_t* feature_values, intp_t *samples,
+                    intp_t n, intp_t maxd) noexcept nogil:
+    cdef float32_t pivot
+    cdef intp_t i, l, r
+
+    while n > 1:
+        if maxd <= 0:   # max depth limit exceeded ("gone quadratic")
+            heapsort(feature_values, samples, n)
+            return
+        maxd -= 1
+
+        pivot = median3(feature_values, n)
+
+        # Three-way partition.
+        i = l = 0
+        r = n
+        while i < r:
+            if feature_values[i] < pivot:
+                swap(feature_values, samples, i, l)
+                i += 1
+                l += 1
+            elif feature_values[i] > pivot:
+                r -= 1
+                swap(feature_values, samples, i, r)
+            else:
+                i += 1
+
+        introsort(feature_values, samples, l, maxd)
+        feature_values += r
+        samples += r
+        n -= r
+
+
+cdef inline void sift_down(float32_t* feature_values, intp_t* samples,
+                           intp_t start, intp_t end) noexcept nogil:
+    # Restore heap order in feature_values[start:end] by moving the max element to start.
+    cdef intp_t child, maxind, root
+
+    root = start
+    while True:
+        child = root * 2 + 1
+
+        # find max of root, left child, right child
+        maxind = root
+        if child < end and feature_values[maxind] < feature_values[child]:
+            maxind = child
+        if child + 1 < end and feature_values[maxind] < feature_values[child + 1]:
+            maxind = child + 1
+
+        if maxind == root:
+            break
+        else:
+            swap(feature_values, samples, root, maxind)
+            root = maxind
+
+
+cdef void heapsort(float32_t* feature_values, intp_t* samples, intp_t n) noexcept nogil:
+    cdef intp_t start, end
+
+    # heapify
+    start = (n - 2) / 2
+    end = n
+    while True:
+        sift_down(feature_values, samples, start, end)
+        if start == 0:
+            break
+        start -= 1
+
+    # sort by shrinking the heap, putting the max element immediately after it
+    end = n - 1
+    while end > 0:
+        swap(feature_values, samples, 0, end)
+        sift_down(feature_values, samples, 0, end)
+        end = end - 1
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_reingold_tilford.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_reingold_tilford.py
new file mode 100644
index 0000000000000000000000000000000000000000..deb4d84f6d324bc640ce8f37d5e9bfbc599816b9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_reingold_tilford.py
@@ -0,0 +1,188 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+
+
+class DrawTree:
+    def __init__(self, tree, parent=None, depth=0, number=1):
+        self.x = -1.0
+        self.y = depth
+        self.tree = tree
+        self.children = [
+            DrawTree(c, self, depth + 1, i + 1) for i, c in enumerate(tree.children)
+        ]
+        self.parent = parent
+        self.thread = None
+        self.mod = 0
+        self.ancestor = self
+        self.change = self.shift = 0
+        self._lmost_sibling = None
+        # this is the number of the node in its group of siblings 1..n
+        self.number = number
+
+    def left(self):
+        return self.thread or (len(self.children) and self.children[0])
+
+    def right(self):
+        return self.thread or (len(self.children) and self.children[-1])
+
+    def lbrother(self):
+        n = None
+        if self.parent:
+            for node in self.parent.children:
+                if node == self:
+                    return n
+                else:
+                    n = node
+        return n
+
+    def get_lmost_sibling(self):
+        if not self._lmost_sibling and self.parent and self != self.parent.children[0]:
+            self._lmost_sibling = self.parent.children[0]
+        return self._lmost_sibling
+
+    lmost_sibling = property(get_lmost_sibling)
+
+    def __str__(self):
+        return "%s: x=%s mod=%s" % (self.tree, self.x, self.mod)
+
+    def __repr__(self):
+        return self.__str__()
+
+    def max_extents(self):
+        extents = [c.max_extents() for c in self.children]
+        extents.append((self.x, self.y))
+        return np.max(extents, axis=0)
+
+
+def buchheim(tree):
+    dt = first_walk(DrawTree(tree))
+    min = second_walk(dt)
+    if min < 0:
+        third_walk(dt, -min)
+    return dt
+
+
+def third_walk(tree, n):
+    tree.x += n
+    for c in tree.children:
+        third_walk(c, n)
+
+
+def first_walk(v, distance=1.0):
+    if len(v.children) == 0:
+        if v.lmost_sibling:
+            v.x = v.lbrother().x + distance
+        else:
+            v.x = 0.0
+    else:
+        default_ancestor = v.children[0]
+        for w in v.children:
+            first_walk(w)
+            default_ancestor = apportion(w, default_ancestor, distance)
+        # print("finished v =", v.tree, "children")
+        execute_shifts(v)
+
+        midpoint = (v.children[0].x + v.children[-1].x) / 2
+
+        w = v.lbrother()
+        if w:
+            v.x = w.x + distance
+            v.mod = v.x - midpoint
+        else:
+            v.x = midpoint
+    return v
+
+
+def apportion(v, default_ancestor, distance):
+    w = v.lbrother()
+    if w is not None:
+        # in buchheim notation:
+        # i == inner; o == outer; r == right; l == left; r = +; l = -
+        vir = vor = v
+        vil = w
+        vol = v.lmost_sibling
+        sir = sor = v.mod
+        sil = vil.mod
+        sol = vol.mod
+        while vil.right() and vir.left():
+            vil = vil.right()
+            vir = vir.left()
+            vol = vol.left()
+            vor = vor.right()
+            vor.ancestor = v
+            shift = (vil.x + sil) - (vir.x + sir) + distance
+            if shift > 0:
+                move_subtree(ancestor(vil, v, default_ancestor), v, shift)
+                sir = sir + shift
+                sor = sor + shift
+            sil += vil.mod
+            sir += vir.mod
+            sol += vol.mod
+            sor += vor.mod
+        if vil.right() and not vor.right():
+            vor.thread = vil.right()
+            vor.mod += sil - sor
+        else:
+            if vir.left() and not vol.left():
+                vol.thread = vir.left()
+                vol.mod += sir - sol
+            default_ancestor = v
+    return default_ancestor
+
+
+def move_subtree(wl, wr, shift):
+    subtrees = wr.number - wl.number
+    # print(wl.tree, "is conflicted with", wr.tree, 'moving', subtrees,
+    # 'shift', shift)
+    # print wl, wr, wr.number, wl.number, shift, subtrees, shift/subtrees
+    wr.change -= shift / subtrees
+    wr.shift += shift
+    wl.change += shift / subtrees
+    wr.x += shift
+    wr.mod += shift
+
+
+def execute_shifts(v):
+    shift = change = 0
+    for w in v.children[::-1]:
+        # print("shift:", w, shift, w.change)
+        w.x += shift
+        w.mod += shift
+        change += w.change
+        shift += w.shift + change
+
+
+def ancestor(vil, v, default_ancestor):
+    # the relevant text is at the bottom of page 7 of
+    # "Improving Walker's Algorithm to Run in Linear Time" by Buchheim et al,
+    # (2002)
+    # https://citeseerx.ist.psu.edu/doc_view/pid/1f41c3c2a4880dc49238e46d555f16d28da2940d
+    if vil.ancestor in v.parent.children:
+        return vil.ancestor
+    else:
+        return default_ancestor
+
+
+def second_walk(v, m=0, depth=0, min=None):
+    v.x += m
+    v.y = depth
+
+    if min is None or v.x < min:
+        min = v.x
+
+    for w in v.children:
+        min = second_walk(w, m + v.mod, depth + 1, min)
+
+    return min
+
+
+class Tree:
+    def __init__(self, label="", node_id=-1, *children):
+        self.label = label
+        self.node_id = node_id
+        if children:
+            self.children = children
+        else:
+            self.children = []
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..42c6c6d935a9cf3382db5bd5ec6a2ce833a6adb6
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pxd
@@ -0,0 +1,106 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _splitter.pyx for details.
+
+from ..utils._typedefs cimport (
+    float32_t, float64_t, int8_t, int32_t, intp_t, uint8_t, uint32_t
+)
+from ._criterion cimport Criterion
+from ._tree cimport ParentInfo
+
+
+cdef struct SplitRecord:
+    # Data to track sample split
+    intp_t feature         # Which feature to split on.
+    intp_t pos             # Split samples array at the given position,
+    #                      # i.e. count of samples below threshold for feature.
+    #                      # pos is >= end if the node is a leaf.
+    float64_t threshold       # Threshold to split at.
+    float64_t improvement     # Impurity improvement given parent node.
+    float64_t impurity_left   # Impurity of the left split.
+    float64_t impurity_right  # Impurity of the right split.
+    float64_t lower_bound     # Lower bound on value of both children for monotonicity
+    float64_t upper_bound     # Upper bound on value of both children for monotonicity
+    uint8_t missing_go_to_left  # Controls if missing values go to the left node.
+    intp_t n_missing            # Number of missing values for the feature being split on
+
+cdef class Splitter:
+    # The splitter searches in the input space for a feature and a threshold
+    # to split the samples samples[start:end].
+    #
+    # The impurity computations are delegated to a criterion object.
+
+    # Internal structures
+    cdef public Criterion criterion      # Impurity criterion
+    cdef public intp_t max_features      # Number of features to test
+    cdef public intp_t min_samples_leaf  # Min samples in a leaf
+    cdef public float64_t min_weight_leaf   # Minimum weight in a leaf
+
+    cdef object random_state             # Random state
+    cdef uint32_t rand_r_state           # sklearn_rand_r random number state
+
+    cdef intp_t[::1] samples             # Sample indices in X, y
+    cdef intp_t n_samples                # X.shape[0]
+    cdef float64_t weighted_n_samples       # Weighted number of samples
+    cdef intp_t[::1] features            # Feature indices in X
+    cdef intp_t[::1] constant_features   # Constant features indices
+    cdef intp_t n_features               # X.shape[1]
+    cdef float32_t[::1] feature_values   # temp. array holding feature values
+
+    cdef intp_t start                    # Start position for the current node
+    cdef intp_t end                      # End position for the current node
+
+    cdef const float64_t[:, ::1] y
+    # Monotonicity constraints for each feature.
+    # The encoding is as follows:
+    #   -1: monotonic decrease
+    #    0: no constraint
+    #   +1: monotonic increase
+    cdef const int8_t[:] monotonic_cst
+    cdef bint with_monotonic_cst
+    cdef const float64_t[:] sample_weight
+
+    # The samples vector `samples` is maintained by the Splitter object such
+    # that the samples contained in a node are contiguous. With this setting,
+    # `node_split` reorganizes the node samples `samples[start:end]` in two
+    # subsets `samples[start:pos]` and `samples[pos:end]`.
+
+    # The 1-d  `features` array of size n_features contains the features
+    # indices and allows fast sampling without replacement of features.
+
+    # The 1-d `constant_features` array of size n_features holds in
+    # `constant_features[:n_constant_features]` the feature ids with
+    # constant values for all the samples that reached a specific node.
+    # The value `n_constant_features` is given by the parent node to its
+    # child nodes.  The content of the range `[n_constant_features:]` is left
+    # undefined, but preallocated for performance reasons
+    # This allows optimization with depth-based tree building.
+
+    # Methods
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1
+
+    cdef int node_reset(
+        self,
+        intp_t start,
+        intp_t end,
+        float64_t* weighted_n_node_samples
+    ) except -1 nogil
+
+    cdef int node_split(
+        self,
+        ParentInfo* parent,
+        SplitRecord* split,
+    ) except -1 nogil
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil
+
+    cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil
+
+    cdef float64_t node_impurity(self) noexcept nogil
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..b557a4d1c6300c3a79e8260e331c27df6c062426
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_splitter.pyx
@@ -0,0 +1,901 @@
+"""Splitting algorithms in the construction of a tree.
+
+This module contains the main splitting algorithms for constructing a tree.
+Splitting is concerned with finding the optimal partition of the data into
+two groups. The impurity of the groups is minimized, and the impurity is measured
+by some criterion, which is typically the Gini impurity or the entropy. Criterion
+are implemented in the ``_criterion`` module.
+
+Splitting evaluates a subset of features (defined by `max_features` also
+known as mtry in the literature). The module supports two primary types
+of splitting strategies:
+
+- Best Split: A greedy approach to find the optimal split. This method
+  ensures that the best possible split is chosen by examining various
+  thresholds for each candidate feature.
+- Random Split: A stochastic approach that selects a split randomly
+  from a subset of the best splits. This method is faster but does
+  not guarantee the optimal split.
+"""
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from libc.string cimport memcpy
+
+from ..utils._typedefs cimport int8_t
+from ._criterion cimport Criterion
+from ._partitioner cimport (
+    FEATURE_THRESHOLD, DensePartitioner, SparsePartitioner,
+    shift_missing_values_to_left_if_required
+)
+from ._utils cimport RAND_R_MAX, rand_int, rand_uniform
+
+import numpy as np
+
+# Introduce a fused-class to make it possible to share the split implementation
+# between the dense and sparse cases in the node_split_best and node_split_random
+# functions. The alternative would have been to use inheritance-based polymorphism
+# but it would have resulted in a ~10% overall tree fitting performance
+# degradation caused by the overhead frequent virtual method lookups.
+ctypedef fused Partitioner:
+    DensePartitioner
+    SparsePartitioner
+
+
+cdef float64_t INFINITY = np.inf
+
+
+cdef inline void _init_split(SplitRecord* self, intp_t start_pos) noexcept nogil:
+    self.impurity_left = INFINITY
+    self.impurity_right = INFINITY
+    self.pos = start_pos
+    self.feature = 0
+    self.threshold = 0.
+    self.improvement = -INFINITY
+    self.missing_go_to_left = False
+    self.n_missing = 0
+
+cdef class Splitter:
+    """Abstract splitter class.
+
+    Splitters are called by tree builders to find the best splits on both
+    sparse and dense data, one split at a time.
+    """
+
+    def __cinit__(
+        self,
+        Criterion criterion,
+        intp_t max_features,
+        intp_t min_samples_leaf,
+        float64_t min_weight_leaf,
+        object random_state,
+        const int8_t[:] monotonic_cst,
+    ):
+        """
+        Parameters
+        ----------
+        criterion : Criterion
+            The criterion to measure the quality of a split.
+
+        max_features : intp_t
+            The maximal number of randomly selected features which can be
+            considered for a split.
+
+        min_samples_leaf : intp_t
+            The minimal number of samples each leaf can have, where splits
+            which would result in having less samples in a leaf are not
+            considered.
+
+        min_weight_leaf : float64_t
+            The minimal weight each leaf can have, where the weight is the sum
+            of the weights of each sample in it.
+
+        random_state : object
+            The user inputted random state to be used for pseudo-randomness
+
+        monotonic_cst : const int8_t[:]
+            Monotonicity constraints
+
+        """
+
+        self.criterion = criterion
+
+        self.n_samples = 0
+        self.n_features = 0
+
+        self.max_features = max_features
+        self.min_samples_leaf = min_samples_leaf
+        self.min_weight_leaf = min_weight_leaf
+        self.random_state = random_state
+        self.monotonic_cst = monotonic_cst
+        self.with_monotonic_cst = monotonic_cst is not None
+
+    def __getstate__(self):
+        return {}
+
+    def __setstate__(self, d):
+        pass
+
+    def __reduce__(self):
+        return (type(self), (self.criterion,
+                             self.max_features,
+                             self.min_samples_leaf,
+                             self.min_weight_leaf,
+                             self.random_state,
+                             self.monotonic_cst), self.__getstate__())
+
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1:
+        """Initialize the splitter.
+
+        Take in the input data X, the target Y, and optional sample weights.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+
+        Parameters
+        ----------
+        X : object
+            This contains the inputs. Usually it is a 2d numpy array.
+
+        y : ndarray, dtype=float64_t
+            This is the vector of targets, or true labels, for the samples represented
+            as a Cython memoryview.
+
+        sample_weight : ndarray, dtype=float64_t
+            The weights of the samples, where higher weighted samples are fit
+            closer than lower weight samples. If not provided, all samples
+            are assumed to have uniform weight. This is represented
+            as a Cython memoryview.
+
+        has_missing : bool
+            At least one missing values is in X.
+        """
+
+        self.rand_r_state = self.random_state.randint(0, RAND_R_MAX)
+        cdef intp_t n_samples = X.shape[0]
+
+        # Create a new array which will be used to store nonzero
+        # samples from the feature of interest
+        self.samples = np.empty(n_samples, dtype=np.intp)
+        cdef intp_t[::1] samples = self.samples
+
+        cdef intp_t i, j
+        cdef float64_t weighted_n_samples = 0.0
+        j = 0
+
+        for i in range(n_samples):
+            # Only work with positively weighted samples
+            if sample_weight is None or sample_weight[i] != 0.0:
+                samples[j] = i
+                j += 1
+
+            if sample_weight is not None:
+                weighted_n_samples += sample_weight[i]
+            else:
+                weighted_n_samples += 1.0
+
+        # Number of samples is number of positively weighted samples
+        self.n_samples = j
+        self.weighted_n_samples = weighted_n_samples
+
+        cdef intp_t n_features = X.shape[1]
+        self.features = np.arange(n_features, dtype=np.intp)
+        self.n_features = n_features
+
+        self.feature_values = np.empty(n_samples, dtype=np.float32)
+        self.constant_features = np.empty(n_features, dtype=np.intp)
+
+        self.y = y
+
+        self.sample_weight = sample_weight
+        if missing_values_in_feature_mask is not None:
+            self.criterion.init_sum_missing()
+        return 0
+
+    cdef int node_reset(
+        self,
+        intp_t start,
+        intp_t end,
+        float64_t* weighted_n_node_samples
+    ) except -1 nogil:
+        """Reset splitter on node samples[start:end].
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+
+        Parameters
+        ----------
+        start : intp_t
+            The index of the first sample to consider
+        end : intp_t
+            The index of the last sample to consider
+        weighted_n_node_samples : ndarray, dtype=float64_t pointer
+            The total weight of those samples
+        """
+
+        self.start = start
+        self.end = end
+
+        self.criterion.init(
+            self.y,
+            self.sample_weight,
+            self.weighted_n_samples,
+            self.samples,
+            start,
+            end
+        )
+
+        weighted_n_node_samples[0] = self.criterion.weighted_n_node_samples
+        return 0
+
+    cdef int node_split(
+        self,
+        ParentInfo* parent_record,
+        SplitRecord* split,
+    ) except -1 nogil:
+
+        """Find the best split on node samples[start:end].
+
+        This is a placeholder method. The majority of computation will be done
+        here.
+
+        It should return -1 upon errors.
+        """
+
+        pass
+
+    cdef void node_value(self, float64_t* dest) noexcept nogil:
+        """Copy the value of node samples[start:end] into dest."""
+
+        self.criterion.node_value(dest)
+
+    cdef inline void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
+        """Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
+
+        self.criterion.clip_node_value(dest, lower_bound, upper_bound)
+
+    cdef float64_t node_impurity(self) noexcept nogil:
+        """Return the impurity of the current node."""
+
+        return self.criterion.node_impurity()
+
+
+cdef inline int node_split_best(
+    Splitter splitter,
+    Partitioner partitioner,
+    Criterion criterion,
+    SplitRecord* split,
+    ParentInfo* parent_record,
+) except -1 nogil:
+    """Find the best split on node samples[start:end]
+
+    Returns -1 in case of failure to allocate memory (and raise MemoryError)
+    or 0 otherwise.
+    """
+    cdef const int8_t[:] monotonic_cst = splitter.monotonic_cst
+    cdef bint with_monotonic_cst = splitter.with_monotonic_cst
+
+    # Find the best split
+    cdef intp_t start = splitter.start
+    cdef intp_t end = splitter.end
+    cdef intp_t end_non_missing
+    cdef intp_t n_missing = 0
+    cdef bint has_missing = 0
+    cdef intp_t n_searches
+    cdef intp_t n_left, n_right
+    cdef bint missing_go_to_left
+
+    cdef intp_t[::1] samples = splitter.samples
+    cdef intp_t[::1] features = splitter.features
+    cdef intp_t[::1] constant_features = splitter.constant_features
+    cdef intp_t n_features = splitter.n_features
+
+    cdef float32_t[::1] feature_values = splitter.feature_values
+    cdef intp_t max_features = splitter.max_features
+    cdef intp_t min_samples_leaf = splitter.min_samples_leaf
+    cdef float64_t min_weight_leaf = splitter.min_weight_leaf
+    cdef uint32_t* random_state = &splitter.rand_r_state
+
+    cdef SplitRecord best_split, current_split
+    cdef float64_t current_proxy_improvement = -INFINITY
+    cdef float64_t best_proxy_improvement = -INFINITY
+
+    cdef float64_t impurity = parent_record.impurity
+    cdef float64_t lower_bound = parent_record.lower_bound
+    cdef float64_t upper_bound = parent_record.upper_bound
+
+    cdef intp_t f_i = n_features
+    cdef intp_t f_j
+    cdef intp_t p
+    cdef intp_t p_prev
+
+    cdef intp_t n_visited_features = 0
+    # Number of features discovered to be constant during the split search
+    cdef intp_t n_found_constants = 0
+    # Number of features known to be constant and drawn without replacement
+    cdef intp_t n_drawn_constants = 0
+    cdef intp_t n_known_constants = parent_record.n_constant_features
+    # n_total_constants = n_known_constants + n_found_constants
+    cdef intp_t n_total_constants = n_known_constants
+
+    _init_split(&best_split, end)
+
+    partitioner.init_node_split(start, end)
+
+    # Sample up to max_features without replacement using a
+    # Fisher-Yates-based algorithm (using the local variables `f_i` and
+    # `f_j` to compute a permutation of the `features` array).
+    #
+    # Skip the CPU intensive evaluation of the impurity criterion for
+    # features that were already detected as constant (hence not suitable
+    # for good splitting) by ancestor nodes and save the information on
+    # newly discovered constant features to spare computation on descendant
+    # nodes.
+    while (f_i > n_total_constants and  # Stop early if remaining features
+                                        # are constant
+            (n_visited_features < max_features or
+             # At least one drawn features must be non constant
+             n_visited_features <= n_found_constants + n_drawn_constants)):
+
+        n_visited_features += 1
+
+        # Loop invariant: elements of features in
+        # - [:n_drawn_constant[ holds drawn and known constant features;
+        # - [n_drawn_constant:n_known_constant[ holds known constant
+        #   features that haven't been drawn yet;
+        # - [n_known_constant:n_total_constant[ holds newly found constant
+        #   features;
+        # - [n_total_constant:f_i[ holds features that haven't been drawn
+        #   yet and aren't constant apriori.
+        # - [f_i:n_features[ holds features that have been drawn
+        #   and aren't constant.
+
+        # Draw a feature at random
+        f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
+                       random_state)
+
+        if f_j < n_known_constants:
+            # f_j in the interval [n_drawn_constants, n_known_constants[
+            features[n_drawn_constants], features[f_j] = features[f_j], features[n_drawn_constants]
+
+            n_drawn_constants += 1
+            continue
+
+        # f_j in the interval [n_known_constants, f_i - n_found_constants[
+        f_j += n_found_constants
+        # f_j in the interval [n_total_constants, f_i[
+        current_split.feature = features[f_j]
+        partitioner.sort_samples_and_feature_values(current_split.feature)
+        n_missing = partitioner.n_missing
+        end_non_missing = end - n_missing
+
+        if (
+            # All values for this feature are missing, or
+            end_non_missing == start or
+            # This feature is considered constant (max - min <= FEATURE_THRESHOLD)
+            feature_values[end_non_missing - 1] <= feature_values[start] + FEATURE_THRESHOLD
+        ):
+            # We consider this feature constant in this case.
+            # Since finding a split among constant feature is not valuable,
+            # we do not consider this feature for splitting.
+            features[f_j], features[n_total_constants] = features[n_total_constants], features[f_j]
+
+            n_found_constants += 1
+            n_total_constants += 1
+            continue
+
+        f_i -= 1
+        features[f_i], features[f_j] = features[f_j], features[f_i]
+        has_missing = n_missing != 0
+        criterion.init_missing(n_missing)  # initialize even when n_missing == 0
+
+        # Evaluate all splits
+
+        # If there are missing values, then we search twice for the most optimal split.
+        # The first search will have all the missing values going to the right node.
+        # The second search will have all the missing values going to the left node.
+        # If there are no missing values, then we search only once for the most
+        # optimal split.
+        n_searches = 2 if has_missing else 1
+
+        for i in range(n_searches):
+            missing_go_to_left = i == 1
+            criterion.missing_go_to_left = missing_go_to_left
+            criterion.reset()
+
+            p = start
+
+            while p < end_non_missing:
+                partitioner.next_p(&p_prev, &p)
+
+                if p >= end_non_missing:
+                    continue
+
+                if missing_go_to_left:
+                    n_left = p - start + n_missing
+                    n_right = end_non_missing - p
+                else:
+                    n_left = p - start
+                    n_right = end_non_missing - p + n_missing
+
+                # Reject if min_samples_leaf is not guaranteed
+                if n_left < min_samples_leaf or n_right < min_samples_leaf:
+                    continue
+
+                current_split.pos = p
+                criterion.update(current_split.pos)
+
+                # Reject if monotonicity constraints are not satisfied
+                if (
+                    with_monotonic_cst and
+                    monotonic_cst[current_split.feature] != 0 and
+                    not criterion.check_monotonicity(
+                        monotonic_cst[current_split.feature],
+                        lower_bound,
+                        upper_bound,
+                    )
+                ):
+                    continue
+
+                # Reject if min_weight_leaf is not satisfied
+                if ((criterion.weighted_n_left < min_weight_leaf) or
+                        (criterion.weighted_n_right < min_weight_leaf)):
+                    continue
+
+                current_proxy_improvement = criterion.proxy_impurity_improvement()
+
+                if current_proxy_improvement > best_proxy_improvement:
+                    best_proxy_improvement = current_proxy_improvement
+                    # sum of halves is used to avoid infinite value
+                    current_split.threshold = (
+                        feature_values[p_prev] / 2.0 + feature_values[p] / 2.0
+                    )
+
+                    if (
+                        current_split.threshold == feature_values[p] or
+                        current_split.threshold == INFINITY or
+                        current_split.threshold == -INFINITY
+                    ):
+                        current_split.threshold = feature_values[p_prev]
+
+                    current_split.n_missing = n_missing
+
+                    # if there are no missing values in the training data, during
+                    # test time, we send missing values to the branch that contains
+                    # the most samples during training time.
+                    if n_missing == 0:
+                        current_split.missing_go_to_left = n_left > n_right
+                    else:
+                        current_split.missing_go_to_left = missing_go_to_left
+
+                    best_split = current_split  # copy
+
+        # Evaluate when there are missing values and all missing values goes
+        # to the right node and non-missing values goes to the left node.
+        if has_missing:
+            n_left, n_right = end - start - n_missing, n_missing
+            p = end - n_missing
+            missing_go_to_left = 0
+
+            if not (n_left < min_samples_leaf or n_right < min_samples_leaf):
+                criterion.missing_go_to_left = missing_go_to_left
+                criterion.update(p)
+
+                if not ((criterion.weighted_n_left < min_weight_leaf) or
+                        (criterion.weighted_n_right < min_weight_leaf)):
+                    current_proxy_improvement = criterion.proxy_impurity_improvement()
+
+                    if current_proxy_improvement > best_proxy_improvement:
+                        best_proxy_improvement = current_proxy_improvement
+                        current_split.threshold = INFINITY
+                        current_split.missing_go_to_left = missing_go_to_left
+                        current_split.n_missing = n_missing
+                        current_split.pos = p
+                        best_split = current_split
+
+    # Reorganize into samples[start:best_split.pos] + samples[best_split.pos:end]
+    if best_split.pos < end:
+        partitioner.partition_samples_final(
+            best_split.pos,
+            best_split.threshold,
+            best_split.feature,
+            best_split.n_missing
+        )
+        criterion.init_missing(best_split.n_missing)
+        criterion.missing_go_to_left = best_split.missing_go_to_left
+
+        criterion.reset()
+        criterion.update(best_split.pos)
+        criterion.children_impurity(
+            &best_split.impurity_left, &best_split.impurity_right
+        )
+        best_split.improvement = criterion.impurity_improvement(
+            impurity,
+            best_split.impurity_left,
+            best_split.impurity_right
+        )
+
+        shift_missing_values_to_left_if_required(&best_split, samples, end)
+
+    # Respect invariant for constant features: the original order of
+    # element in features[:n_known_constants] must be preserved for sibling
+    # and child nodes
+    memcpy(&features[0], &constant_features[0], sizeof(intp_t) * n_known_constants)
+
+    # Copy newly found constant features
+    memcpy(&constant_features[n_known_constants],
+           &features[n_known_constants],
+           sizeof(intp_t) * n_found_constants)
+
+    # Return values
+    parent_record.n_constant_features = n_total_constants
+    split[0] = best_split
+    return 0
+
+
+cdef inline int node_split_random(
+    Splitter splitter,
+    Partitioner partitioner,
+    Criterion criterion,
+    SplitRecord* split,
+    ParentInfo* parent_record,
+) except -1 nogil:
+    """Find the best random split on node samples[start:end]
+
+    Returns -1 in case of failure to allocate memory (and raise MemoryError)
+    or 0 otherwise.
+    """
+    cdef const int8_t[:] monotonic_cst = splitter.monotonic_cst
+    cdef bint with_monotonic_cst = splitter.with_monotonic_cst
+
+    # Draw random splits and pick the best
+    cdef intp_t start = splitter.start
+    cdef intp_t end = splitter.end
+    cdef intp_t end_non_missing
+    cdef intp_t n_missing = 0
+    cdef bint has_missing = 0
+    cdef intp_t n_left, n_right
+    cdef bint missing_go_to_left
+
+    cdef intp_t[::1] samples = splitter.samples
+    cdef intp_t[::1] features = splitter.features
+    cdef intp_t[::1] constant_features = splitter.constant_features
+    cdef intp_t n_features = splitter.n_features
+
+    cdef intp_t max_features = splitter.max_features
+    cdef intp_t min_samples_leaf = splitter.min_samples_leaf
+    cdef float64_t min_weight_leaf = splitter.min_weight_leaf
+    cdef uint32_t* random_state = &splitter.rand_r_state
+
+    cdef SplitRecord best_split, current_split
+    cdef float64_t current_proxy_improvement = - INFINITY
+    cdef float64_t best_proxy_improvement = - INFINITY
+
+    cdef float64_t impurity = parent_record.impurity
+    cdef float64_t lower_bound = parent_record.lower_bound
+    cdef float64_t upper_bound = parent_record.upper_bound
+
+    cdef intp_t f_i = n_features
+    cdef intp_t f_j
+    # Number of features discovered to be constant during the split search
+    cdef intp_t n_found_constants = 0
+    # Number of features known to be constant and drawn without replacement
+    cdef intp_t n_drawn_constants = 0
+    cdef intp_t n_known_constants = parent_record.n_constant_features
+    # n_total_constants = n_known_constants + n_found_constants
+    cdef intp_t n_total_constants = n_known_constants
+    cdef intp_t n_visited_features = 0
+    cdef float32_t min_feature_value
+    cdef float32_t max_feature_value
+
+    _init_split(&best_split, end)
+
+    partitioner.init_node_split(start, end)
+
+    # Sample up to max_features without replacement using a
+    # Fisher-Yates-based algorithm (using the local variables `f_i` and
+    # `f_j` to compute a permutation of the `features` array).
+    #
+    # Skip the CPU intensive evaluation of the impurity criterion for
+    # features that were already detected as constant (hence not suitable
+    # for good splitting) by ancestor nodes and save the information on
+    # newly discovered constant features to spare computation on descendant
+    # nodes.
+    while (f_i > n_total_constants and  # Stop early if remaining features
+                                        # are constant
+            (n_visited_features < max_features or
+             # At least one drawn features must be non constant
+             n_visited_features <= n_found_constants + n_drawn_constants)):
+        n_visited_features += 1
+
+        # Loop invariant: elements of features in
+        # - [:n_drawn_constant[ holds drawn and known constant features;
+        # - [n_drawn_constant:n_known_constant[ holds known constant
+        #   features that haven't been drawn yet;
+        # - [n_known_constant:n_total_constant[ holds newly found constant
+        #   features;
+        # - [n_total_constant:f_i[ holds features that haven't been drawn
+        #   yet and aren't constant apriori.
+        # - [f_i:n_features[ holds features that have been drawn
+        #   and aren't constant.
+
+        # Draw a feature at random
+        f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
+                       random_state)
+
+        if f_j < n_known_constants:
+            # f_j in the interval [n_drawn_constants, n_known_constants[
+            features[n_drawn_constants], features[f_j] = features[f_j], features[n_drawn_constants]
+            n_drawn_constants += 1
+            continue
+
+        # f_j in the interval [n_known_constants, f_i - n_found_constants[
+        f_j += n_found_constants
+        # f_j in the interval [n_total_constants, f_i[
+
+        current_split.feature = features[f_j]
+
+        # Find min, max as we will randomly select a threshold between them
+        partitioner.find_min_max(
+            current_split.feature, &min_feature_value, &max_feature_value
+        )
+        n_missing = partitioner.n_missing
+        end_non_missing = end - n_missing
+
+        if (
+            # All values for this feature are missing, or
+            end_non_missing == start or
+            # This feature is considered constant (max - min <= FEATURE_THRESHOLD)
+            max_feature_value <= min_feature_value + FEATURE_THRESHOLD
+        ):
+            # We consider this feature constant in this case.
+            # Since finding a split with a constant feature is not valuable,
+            # we do not consider this feature for splitting.
+            features[f_j], features[n_total_constants] = features[n_total_constants], current_split.feature
+
+            n_found_constants += 1
+            n_total_constants += 1
+            continue
+
+        f_i -= 1
+        features[f_i], features[f_j] = features[f_j], features[f_i]
+        has_missing = n_missing != 0
+        criterion.init_missing(n_missing)
+
+        # Draw a random threshold
+        current_split.threshold = rand_uniform(
+            min_feature_value,
+            max_feature_value,
+            random_state,
+        )
+
+        if has_missing:
+            # If there are missing values, then we randomly make all missing
+            # values go to the right or left.
+            #
+            # Note: compared to the BestSplitter, we do not evaluate the
+            # edge case where all the missing values go to the right node
+            # and the non-missing values go to the left node. This is because
+            # this would indicate a threshold outside of the observed range
+            # of the feature. However, it is not clear how much probability weight should
+            # be given to this edge case.
+            missing_go_to_left = rand_int(0, 2, random_state)
+        else:
+            missing_go_to_left = 0
+        criterion.missing_go_to_left = missing_go_to_left
+
+        if current_split.threshold == max_feature_value:
+            current_split.threshold = min_feature_value
+
+        # Partition
+        current_split.pos = partitioner.partition_samples(
+            current_split.threshold
+        )
+
+        if missing_go_to_left:
+            n_left = current_split.pos - start + n_missing
+            n_right = end_non_missing - current_split.pos
+        else:
+            n_left = current_split.pos - start
+            n_right = end_non_missing - current_split.pos + n_missing
+
+        # Reject if min_samples_leaf is not guaranteed
+        if n_left < min_samples_leaf or n_right < min_samples_leaf:
+            continue
+
+        # Evaluate split
+        # At this point, the criterion has a view into the samples that was partitioned
+        # by the partitioner. The criterion will use the partition to evaluating the split.
+        criterion.reset()
+        criterion.update(current_split.pos)
+
+        # Reject if min_weight_leaf is not satisfied
+        if ((criterion.weighted_n_left < min_weight_leaf) or
+                (criterion.weighted_n_right < min_weight_leaf)):
+            continue
+
+        # Reject if monotonicity constraints are not satisfied
+        if (
+                with_monotonic_cst and
+                monotonic_cst[current_split.feature] != 0 and
+                not criterion.check_monotonicity(
+                    monotonic_cst[current_split.feature],
+                    lower_bound,
+                    upper_bound,
+                )
+        ):
+            continue
+
+        current_proxy_improvement = criterion.proxy_impurity_improvement()
+
+        if current_proxy_improvement > best_proxy_improvement:
+            current_split.n_missing = n_missing
+
+            # if there are no missing values in the training data, during
+            # test time, we send missing values to the branch that contains
+            # the most samples during training time.
+            if has_missing:
+                current_split.missing_go_to_left = missing_go_to_left
+            else:
+                current_split.missing_go_to_left = n_left > n_right
+
+            best_proxy_improvement = current_proxy_improvement
+            best_split = current_split  # copy
+
+    # Reorganize into samples[start:best.pos] + samples[best.pos:end]
+    if best_split.pos < end:
+        if current_split.feature != best_split.feature:
+            partitioner.partition_samples_final(
+                best_split.pos,
+                best_split.threshold,
+                best_split.feature,
+                best_split.n_missing
+            )
+        criterion.init_missing(best_split.n_missing)
+        criterion.missing_go_to_left = best_split.missing_go_to_left
+
+        criterion.reset()
+        criterion.update(best_split.pos)
+        criterion.children_impurity(
+            &best_split.impurity_left, &best_split.impurity_right
+        )
+        best_split.improvement = criterion.impurity_improvement(
+            impurity,
+            best_split.impurity_left,
+            best_split.impurity_right
+        )
+
+        shift_missing_values_to_left_if_required(&best_split, samples, end)
+
+    # Respect invariant for constant features: the original order of
+    # element in features[:n_known_constants] must be preserved for sibling
+    # and child nodes
+    memcpy(&features[0], &constant_features[0], sizeof(intp_t) * n_known_constants)
+
+    # Copy newly found constant features
+    memcpy(&constant_features[n_known_constants],
+           &features[n_known_constants],
+           sizeof(intp_t) * n_found_constants)
+
+    # Return values
+    parent_record.n_constant_features = n_total_constants
+    split[0] = best_split
+    return 0
+
+
+cdef class BestSplitter(Splitter):
+    """Splitter for finding the best split on dense data."""
+    cdef DensePartitioner partitioner
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1:
+        Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
+        self.partitioner = DensePartitioner(
+            X, self.samples, self.feature_values, missing_values_in_feature_mask
+        )
+
+    cdef int node_split(
+            self,
+            ParentInfo* parent_record,
+            SplitRecord* split,
+    ) except -1 nogil:
+        return node_split_best(
+            self,
+            self.partitioner,
+            self.criterion,
+            split,
+            parent_record,
+        )
+
+cdef class BestSparseSplitter(Splitter):
+    """Splitter for finding the best split, using the sparse data."""
+    cdef SparsePartitioner partitioner
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1:
+        Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
+        self.partitioner = SparsePartitioner(
+            X, self.samples, self.n_samples, self.feature_values, missing_values_in_feature_mask
+        )
+
+    cdef int node_split(
+            self,
+            ParentInfo* parent_record,
+            SplitRecord* split,
+    ) except -1 nogil:
+        return node_split_best(
+            self,
+            self.partitioner,
+            self.criterion,
+            split,
+            parent_record,
+        )
+
+cdef class RandomSplitter(Splitter):
+    """Splitter for finding the best random split on dense data."""
+    cdef DensePartitioner partitioner
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1:
+        Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
+        self.partitioner = DensePartitioner(
+            X, self.samples, self.feature_values, missing_values_in_feature_mask
+        )
+
+    cdef int node_split(
+            self,
+            ParentInfo* parent_record,
+            SplitRecord* split,
+    ) except -1 nogil:
+        return node_split_random(
+            self,
+            self.partitioner,
+            self.criterion,
+            split,
+            parent_record,
+        )
+
+cdef class RandomSparseSplitter(Splitter):
+    """Splitter for finding the best random split, using the sparse data."""
+    cdef SparsePartitioner partitioner
+    cdef int init(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        const uint8_t[::1] missing_values_in_feature_mask,
+    ) except -1:
+        Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
+        self.partitioner = SparsePartitioner(
+            X, self.samples, self.n_samples, self.feature_values, missing_values_in_feature_mask
+        )
+    cdef int node_split(
+            self,
+            ParentInfo* parent_record,
+            SplitRecord* split,
+    ) except -1 nogil:
+        return node_split_random(
+            self,
+            self.partitioner,
+            self.criterion,
+            split,
+            parent_record,
+        )
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..2cadca4564a87f8d4b5212d665cf53fbe41cb974
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pxd
@@ -0,0 +1,133 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _tree.pyx for details.
+
+import numpy as np
+cimport numpy as cnp
+
+from ..utils._typedefs cimport float32_t, float64_t, intp_t, int32_t, uint8_t, uint32_t
+
+from ._splitter cimport Splitter
+from ._splitter cimport SplitRecord
+
+cdef struct Node:
+    # Base storage structure for the nodes in a Tree object
+
+    intp_t left_child                    # id of the left child of the node
+    intp_t right_child                   # id of the right child of the node
+    intp_t feature                       # Feature used for splitting the node
+    float64_t threshold                  # Threshold value at the node
+    float64_t impurity                   # Impurity of the node (i.e., the value of the criterion)
+    intp_t n_node_samples                # Number of samples at the node
+    float64_t weighted_n_node_samples    # Weighted number of samples at the node
+    uint8_t missing_go_to_left     # Whether features have missing values
+
+
+cdef struct ParentInfo:
+    # Structure to store information about the parent of a node
+    # This is passed to the splitter, to provide information about the previous split
+
+    float64_t lower_bound           # the lower bound of the parent's impurity
+    float64_t upper_bound           # the upper bound of the parent's impurity
+    float64_t impurity              # the impurity of the parent
+    intp_t n_constant_features      # the number of constant features found in parent
+
+cdef class Tree:
+    # The Tree object is a binary tree structure constructed by the
+    # TreeBuilder. The tree structure is used for predictions and
+    # feature importances.
+
+    # Input/Output layout
+    cdef public intp_t n_features        # Number of features in X
+    cdef intp_t* n_classes               # Number of classes in y[:, k]
+    cdef public intp_t n_outputs         # Number of outputs in y
+    cdef public intp_t max_n_classes     # max(n_classes)
+
+    # Inner structures: values are stored separately from node structure,
+    # since size is determined at runtime.
+    cdef public intp_t max_depth         # Max depth of the tree
+    cdef public intp_t node_count        # Counter for node IDs
+    cdef public intp_t capacity          # Capacity of tree, in terms of nodes
+    cdef Node* nodes                     # Array of nodes
+    cdef float64_t* value                # (capacity, n_outputs, max_n_classes) array of values
+    cdef intp_t value_stride             # = n_outputs * max_n_classes
+
+    # Methods
+    cdef intp_t _add_node(self, intp_t parent, bint is_left, bint is_leaf,
+                          intp_t feature, float64_t threshold, float64_t impurity,
+                          intp_t n_node_samples,
+                          float64_t weighted_n_node_samples,
+                          uint8_t missing_go_to_left) except -1 nogil
+    cdef int _resize(self, intp_t capacity) except -1 nogil
+    cdef int _resize_c(self, intp_t capacity=*) except -1 nogil
+
+    cdef cnp.ndarray _get_value_ndarray(self)
+    cdef cnp.ndarray _get_node_ndarray(self)
+
+    cpdef cnp.ndarray predict(self, object X)
+
+    cpdef cnp.ndarray apply(self, object X)
+    cdef cnp.ndarray _apply_dense(self, object X)
+    cdef cnp.ndarray _apply_sparse_csr(self, object X)
+
+    cpdef object decision_path(self, object X)
+    cdef object _decision_path_dense(self, object X)
+    cdef object _decision_path_sparse_csr(self, object X)
+
+    cpdef compute_node_depths(self)
+    cpdef compute_feature_importances(self, normalize=*)
+
+
+# =============================================================================
+# Tree builder
+# =============================================================================
+
+cdef class TreeBuilder:
+    # The TreeBuilder recursively builds a Tree object from training samples,
+    # using a Splitter object for splitting internal nodes and assigning
+    # values to leaves.
+    #
+    # This class controls the various stopping criteria and the node splitting
+    # evaluation order, e.g. depth-first or best-first.
+
+    cdef Splitter splitter              # Splitting algorithm
+
+    cdef intp_t min_samples_split       # Minimum number of samples in an internal node
+    cdef intp_t min_samples_leaf        # Minimum number of samples in a leaf
+    cdef float64_t min_weight_leaf         # Minimum weight in a leaf
+    cdef intp_t max_depth               # Maximal tree depth
+    cdef float64_t min_impurity_decrease   # Impurity threshold for early stopping
+
+    cpdef build(
+        self,
+        Tree tree,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight=*,
+        const uint8_t[::1] missing_values_in_feature_mask=*,
+    )
+
+    cdef _check_input(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+    )
+
+
+# =============================================================================
+# Tree pruning
+# =============================================================================
+
+# The private function allows any external caller to prune the tree and return
+# a new tree with the pruned nodes. The pruned tree is a new tree object.
+#
+# .. warning:: this function is not backwards compatible and may change without
+#              notice.
+cdef void _build_pruned_tree(
+    Tree tree,  # OUT
+    Tree orig_tree,
+    const uint8_t[:] leaves_in_subtree,
+    intp_t capacity
+)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..9d0b2854c3ba0bfb79035943652fa9b20f7784f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_tree.pyx
@@ -0,0 +1,1989 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from cpython cimport Py_INCREF, PyObject, PyTypeObject
+
+from libc.stdlib cimport free
+from libc.string cimport memcpy
+from libc.string cimport memset
+from libc.stdint cimport INTPTR_MAX
+from libc.math cimport isnan
+from libcpp.vector cimport vector
+from libcpp.algorithm cimport pop_heap
+from libcpp.algorithm cimport push_heap
+from libcpp.stack cimport stack
+from libcpp cimport bool
+
+import struct
+
+import numpy as np
+cimport numpy as cnp
+cnp.import_array()
+
+from scipy.sparse import issparse
+from scipy.sparse import csr_matrix
+
+from ._utils cimport safe_realloc
+from ._utils cimport sizet_ptr_to_ndarray
+
+cdef extern from "numpy/arrayobject.h":
+    object PyArray_NewFromDescr(PyTypeObject* subtype, cnp.dtype descr,
+                                int nd, cnp.npy_intp* dims,
+                                cnp.npy_intp* strides,
+                                void* data, int flags, object obj)
+    int PyArray_SetBaseObject(cnp.ndarray arr, PyObject* obj)
+
+# =============================================================================
+# Types and constants
+# =============================================================================
+
+from numpy import float32 as DTYPE
+from numpy import float64 as DOUBLE
+
+cdef float64_t INFINITY = np.inf
+cdef float64_t EPSILON = np.finfo('double').eps
+
+# Some handy constants (BestFirstTreeBuilder)
+cdef bint IS_FIRST = 1
+cdef bint IS_NOT_FIRST = 0
+cdef bint IS_LEFT = 1
+cdef bint IS_NOT_LEFT = 0
+
+TREE_LEAF = -1
+TREE_UNDEFINED = -2
+cdef intp_t _TREE_LEAF = TREE_LEAF
+cdef intp_t _TREE_UNDEFINED = TREE_UNDEFINED
+
+# Build the corresponding numpy dtype for Node.
+# This works by casting `dummy` to an array of Node of length 1, which numpy
+# can construct a `dtype`-object for. See https://stackoverflow.com/q/62448946
+# for a more detailed explanation.
+cdef Node dummy
+NODE_DTYPE = np.asarray((&dummy)).dtype
+
+cdef inline void _init_parent_record(ParentInfo* record) noexcept nogil:
+    record.n_constant_features = 0
+    record.impurity = INFINITY
+    record.lower_bound = -INFINITY
+    record.upper_bound = INFINITY
+
+# =============================================================================
+# TreeBuilder
+# =============================================================================
+
+cdef class TreeBuilder:
+    """Interface for different tree building strategies."""
+
+    cpdef build(
+        self,
+        Tree tree,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight=None,
+        const uint8_t[::1] missing_values_in_feature_mask=None,
+    ):
+        """Build a decision tree from the training set (X, y)."""
+        pass
+
+    cdef inline _check_input(
+        self,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+    ):
+        """Check input dtype, layout and format"""
+        if issparse(X):
+            X = X.tocsc()
+            X.sort_indices()
+
+            if X.data.dtype != DTYPE:
+                X.data = np.ascontiguousarray(X.data, dtype=DTYPE)
+
+            if X.indices.dtype != np.int32 or X.indptr.dtype != np.int32:
+                raise ValueError("No support for np.int64 index based "
+                                 "sparse matrices")
+
+        elif X.dtype != DTYPE:
+            # since we have to copy we will make it fortran for efficiency
+            X = np.asfortranarray(X, dtype=DTYPE)
+
+        if sample_weight is not None and not sample_weight.base.flags.contiguous:
+            sample_weight = np.asarray(sample_weight, dtype=DOUBLE, order="C")
+
+        return X, y, sample_weight
+
+# Depth first builder ---------------------------------------------------------
+# A record on the stack for depth-first tree growing
+cdef struct StackRecord:
+    intp_t start
+    intp_t end
+    intp_t depth
+    intp_t parent
+    bint is_left
+    float64_t impurity
+    intp_t n_constant_features
+    float64_t lower_bound
+    float64_t upper_bound
+
+cdef class DepthFirstTreeBuilder(TreeBuilder):
+    """Build a decision tree in depth-first fashion."""
+
+    def __cinit__(self, Splitter splitter, intp_t min_samples_split,
+                  intp_t min_samples_leaf, float64_t min_weight_leaf,
+                  intp_t max_depth, float64_t min_impurity_decrease):
+        self.splitter = splitter
+        self.min_samples_split = min_samples_split
+        self.min_samples_leaf = min_samples_leaf
+        self.min_weight_leaf = min_weight_leaf
+        self.max_depth = max_depth
+        self.min_impurity_decrease = min_impurity_decrease
+
+    cpdef build(
+        self,
+        Tree tree,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight=None,
+        const uint8_t[::1] missing_values_in_feature_mask=None,
+    ):
+        """Build a decision tree from the training set (X, y)."""
+
+        # check input
+        X, y, sample_weight = self._check_input(X, y, sample_weight)
+
+        # Initial capacity
+        cdef intp_t init_capacity
+
+        if tree.max_depth <= 10:
+            init_capacity =  (2 ** (tree.max_depth + 1)) - 1
+        else:
+            init_capacity = 2047
+
+        tree._resize(init_capacity)
+
+        # Parameters
+        cdef Splitter splitter = self.splitter
+        cdef intp_t max_depth = self.max_depth
+        cdef intp_t min_samples_leaf = self.min_samples_leaf
+        cdef float64_t min_weight_leaf = self.min_weight_leaf
+        cdef intp_t min_samples_split = self.min_samples_split
+        cdef float64_t min_impurity_decrease = self.min_impurity_decrease
+
+        # Recursive partition (without actual recursion)
+        splitter.init(X, y, sample_weight, missing_values_in_feature_mask)
+
+        cdef intp_t start
+        cdef intp_t end
+        cdef intp_t depth
+        cdef intp_t parent
+        cdef bint is_left
+        cdef intp_t n_node_samples = splitter.n_samples
+        cdef float64_t weighted_n_node_samples
+        cdef SplitRecord split
+        cdef intp_t node_id
+
+        cdef float64_t middle_value
+        cdef float64_t left_child_min
+        cdef float64_t left_child_max
+        cdef float64_t right_child_min
+        cdef float64_t right_child_max
+        cdef bint is_leaf
+        cdef bint first = 1
+        cdef intp_t max_depth_seen = -1
+        cdef int rc = 0
+
+        cdef stack[StackRecord] builder_stack
+        cdef StackRecord stack_record
+
+        cdef ParentInfo parent_record
+        _init_parent_record(&parent_record)
+
+        with nogil:
+            # push root node onto stack
+            builder_stack.push({
+                "start": 0,
+                "end": n_node_samples,
+                "depth": 0,
+                "parent": _TREE_UNDEFINED,
+                "is_left": 0,
+                "impurity": INFINITY,
+                "n_constant_features": 0,
+                "lower_bound": -INFINITY,
+                "upper_bound": INFINITY,
+            })
+
+            while not builder_stack.empty():
+                stack_record = builder_stack.top()
+                builder_stack.pop()
+
+                start = stack_record.start
+                end = stack_record.end
+                depth = stack_record.depth
+                parent = stack_record.parent
+                is_left = stack_record.is_left
+                parent_record.impurity = stack_record.impurity
+                parent_record.n_constant_features = stack_record.n_constant_features
+                parent_record.lower_bound = stack_record.lower_bound
+                parent_record.upper_bound = stack_record.upper_bound
+
+                n_node_samples = end - start
+                splitter.node_reset(start, end, &weighted_n_node_samples)
+
+                is_leaf = (depth >= max_depth or
+                           n_node_samples < min_samples_split or
+                           n_node_samples < 2 * min_samples_leaf or
+                           weighted_n_node_samples < 2 * min_weight_leaf)
+
+                if first:
+                    parent_record.impurity = splitter.node_impurity()
+                    first = 0
+
+                # impurity == 0 with tolerance due to rounding errors
+                is_leaf = is_leaf or parent_record.impurity <= EPSILON
+
+                if not is_leaf:
+                    splitter.node_split(
+                        &parent_record,
+                        &split,
+                    )
+                    # If EPSILON=0 in the below comparison, float precision
+                    # issues stop splitting, producing trees that are
+                    # dissimilar to v0.18
+                    is_leaf = (is_leaf or split.pos >= end or
+                               (split.improvement + EPSILON <
+                                min_impurity_decrease))
+
+                node_id = tree._add_node(parent, is_left, is_leaf, split.feature,
+                                         split.threshold, parent_record.impurity,
+                                         n_node_samples, weighted_n_node_samples,
+                                         split.missing_go_to_left)
+
+                if node_id == INTPTR_MAX:
+                    rc = -1
+                    break
+
+                # Store value for all nodes, to facilitate tree/model
+                # inspection and interpretation
+                splitter.node_value(tree.value + node_id * tree.value_stride)
+                if splitter.with_monotonic_cst:
+                    splitter.clip_node_value(tree.value + node_id * tree.value_stride, parent_record.lower_bound, parent_record.upper_bound)
+
+                if not is_leaf:
+                    if (
+                        not splitter.with_monotonic_cst or
+                        splitter.monotonic_cst[split.feature] == 0
+                    ):
+                        # Split on a feature with no monotonicity constraint
+
+                        # Current bounds must always be propagated to both children.
+                        # If a monotonic constraint is active, bounds are used in
+                        # node value clipping.
+                        left_child_min = right_child_min = parent_record.lower_bound
+                        left_child_max = right_child_max = parent_record.upper_bound
+                    elif splitter.monotonic_cst[split.feature] == 1:
+                        # Split on a feature with monotonic increase constraint
+                        left_child_min = parent_record.lower_bound
+                        right_child_max = parent_record.upper_bound
+
+                        # Lower bound for right child and upper bound for left child
+                        # are set to the same value.
+                        middle_value = splitter.criterion.middle_value()
+                        right_child_min = middle_value
+                        left_child_max = middle_value
+                    else:  # i.e. splitter.monotonic_cst[split.feature] == -1
+                        # Split on a feature with monotonic decrease constraint
+                        right_child_min = parent_record.lower_bound
+                        left_child_max = parent_record.upper_bound
+
+                        # Lower bound for left child and upper bound for right child
+                        # are set to the same value.
+                        middle_value = splitter.criterion.middle_value()
+                        left_child_min = middle_value
+                        right_child_max = middle_value
+
+                    # Push right child on stack
+                    builder_stack.push({
+                        "start": split.pos,
+                        "end": end,
+                        "depth": depth + 1,
+                        "parent": node_id,
+                        "is_left": 0,
+                        "impurity": split.impurity_right,
+                        "n_constant_features": parent_record.n_constant_features,
+                        "lower_bound": right_child_min,
+                        "upper_bound": right_child_max,
+                    })
+
+                    # Push left child on stack
+                    builder_stack.push({
+                        "start": start,
+                        "end": split.pos,
+                        "depth": depth + 1,
+                        "parent": node_id,
+                        "is_left": 1,
+                        "impurity": split.impurity_left,
+                        "n_constant_features": parent_record.n_constant_features,
+                        "lower_bound": left_child_min,
+                        "upper_bound": left_child_max,
+                    })
+
+                if depth > max_depth_seen:
+                    max_depth_seen = depth
+
+            if rc >= 0:
+                rc = tree._resize_c(tree.node_count)
+
+            if rc >= 0:
+                tree.max_depth = max_depth_seen
+        if rc == -1:
+            raise MemoryError()
+
+
+# Best first builder ----------------------------------------------------------
+cdef struct FrontierRecord:
+    # Record of information of a Node, the frontier for a split. Those records are
+    # maintained in a heap to access the Node with the best improvement in impurity,
+    # allowing growing trees greedily on this improvement.
+    intp_t node_id
+    intp_t start
+    intp_t end
+    intp_t pos
+    intp_t depth
+    bint is_leaf
+    float64_t impurity
+    float64_t impurity_left
+    float64_t impurity_right
+    float64_t improvement
+    float64_t lower_bound
+    float64_t upper_bound
+    float64_t middle_value
+
+cdef inline bool _compare_records(
+    const FrontierRecord& left,
+    const FrontierRecord& right,
+):
+    return left.improvement < right.improvement
+
+cdef inline void _add_to_frontier(
+    FrontierRecord rec,
+    vector[FrontierRecord]& frontier,
+) noexcept nogil:
+    """Adds record `rec` to the priority queue `frontier`."""
+    frontier.push_back(rec)
+    push_heap(frontier.begin(), frontier.end(), &_compare_records)
+
+
+cdef class BestFirstTreeBuilder(TreeBuilder):
+    """Build a decision tree in best-first fashion.
+
+    The best node to expand is given by the node at the frontier that has the
+    highest impurity improvement.
+    """
+    cdef intp_t max_leaf_nodes
+
+    def __cinit__(self, Splitter splitter, intp_t min_samples_split,
+                  intp_t min_samples_leaf,  min_weight_leaf,
+                  intp_t max_depth, intp_t max_leaf_nodes,
+                  float64_t min_impurity_decrease):
+        self.splitter = splitter
+        self.min_samples_split = min_samples_split
+        self.min_samples_leaf = min_samples_leaf
+        self.min_weight_leaf = min_weight_leaf
+        self.max_depth = max_depth
+        self.max_leaf_nodes = max_leaf_nodes
+        self.min_impurity_decrease = min_impurity_decrease
+
+    cpdef build(
+        self,
+        Tree tree,
+        object X,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight=None,
+        const uint8_t[::1] missing_values_in_feature_mask=None,
+    ):
+        """Build a decision tree from the training set (X, y)."""
+
+        # check input
+        X, y, sample_weight = self._check_input(X, y, sample_weight)
+
+        # Parameters
+        cdef Splitter splitter = self.splitter
+        cdef intp_t max_leaf_nodes = self.max_leaf_nodes
+
+        # Recursive partition (without actual recursion)
+        splitter.init(X, y, sample_weight, missing_values_in_feature_mask)
+
+        cdef vector[FrontierRecord] frontier
+        cdef FrontierRecord record
+        cdef FrontierRecord split_node_left
+        cdef FrontierRecord split_node_right
+        cdef float64_t left_child_min
+        cdef float64_t left_child_max
+        cdef float64_t right_child_min
+        cdef float64_t right_child_max
+
+        cdef intp_t n_node_samples = splitter.n_samples
+        cdef intp_t max_split_nodes = max_leaf_nodes - 1
+        cdef bint is_leaf
+        cdef intp_t max_depth_seen = -1
+        cdef int rc = 0
+        cdef Node* node
+
+        cdef ParentInfo parent_record
+        _init_parent_record(&parent_record)
+
+        # Initial capacity
+        cdef intp_t init_capacity = max_split_nodes + max_leaf_nodes
+        tree._resize(init_capacity)
+
+        with nogil:
+            # add root to frontier
+            rc = self._add_split_node(
+                splitter=splitter,
+                tree=tree,
+                start=0,
+                end=n_node_samples,
+                is_first=IS_FIRST,
+                is_left=IS_LEFT,
+                parent=NULL,
+                depth=0,
+                parent_record=&parent_record,
+                res=&split_node_left,
+            )
+            if rc >= 0:
+                _add_to_frontier(split_node_left, frontier)
+
+            while not frontier.empty():
+                pop_heap(frontier.begin(), frontier.end(), &_compare_records)
+                record = frontier.back()
+                frontier.pop_back()
+
+                node = &tree.nodes[record.node_id]
+                is_leaf = (record.is_leaf or max_split_nodes <= 0)
+
+                if is_leaf:
+                    # Node is not expandable; set node as leaf
+                    node.left_child = _TREE_LEAF
+                    node.right_child = _TREE_LEAF
+                    node.feature = _TREE_UNDEFINED
+                    node.threshold = _TREE_UNDEFINED
+
+                else:
+                    # Node is expandable
+
+                    if (
+                        not splitter.with_monotonic_cst or
+                        splitter.monotonic_cst[node.feature] == 0
+                    ):
+                        # Split on a feature with no monotonicity constraint
+
+                        # Current bounds must always be propagated to both children.
+                        # If a monotonic constraint is active, bounds are used in
+                        # node value clipping.
+                        left_child_min = right_child_min = record.lower_bound
+                        left_child_max = right_child_max = record.upper_bound
+                    elif splitter.monotonic_cst[node.feature] == 1:
+                        # Split on a feature with monotonic increase constraint
+                        left_child_min = record.lower_bound
+                        right_child_max = record.upper_bound
+
+                        # Lower bound for right child and upper bound for left child
+                        # are set to the same value.
+                        right_child_min = record.middle_value
+                        left_child_max = record.middle_value
+                    else:  # i.e. splitter.monotonic_cst[split.feature] == -1
+                        # Split on a feature with monotonic decrease constraint
+                        right_child_min = record.lower_bound
+                        left_child_max = record.upper_bound
+
+                        # Lower bound for left child and upper bound for right child
+                        # are set to the same value.
+                        left_child_min = record.middle_value
+                        right_child_max = record.middle_value
+
+                    # Decrement number of split nodes available
+                    max_split_nodes -= 1
+
+                    # Compute left split node
+                    parent_record.lower_bound = left_child_min
+                    parent_record.upper_bound = left_child_max
+                    parent_record.impurity = record.impurity_left
+                    rc = self._add_split_node(
+                        splitter=splitter,
+                        tree=tree,
+                        start=record.start,
+                        end=record.pos,
+                        is_first=IS_NOT_FIRST,
+                        is_left=IS_LEFT,
+                        parent=node,
+                        depth=record.depth + 1,
+                        parent_record=&parent_record,
+                        res=&split_node_left,
+                    )
+                    if rc == -1:
+                        break
+
+                    # tree.nodes may have changed
+                    node = &tree.nodes[record.node_id]
+
+                    # Compute right split node
+                    parent_record.lower_bound = right_child_min
+                    parent_record.upper_bound = right_child_max
+                    parent_record.impurity = record.impurity_right
+                    rc = self._add_split_node(
+                        splitter=splitter,
+                        tree=tree,
+                        start=record.pos,
+                        end=record.end,
+                        is_first=IS_NOT_FIRST,
+                        is_left=IS_NOT_LEFT,
+                        parent=node,
+                        depth=record.depth + 1,
+                        parent_record=&parent_record,
+                        res=&split_node_right,
+                    )
+                    if rc == -1:
+                        break
+
+                    # Add nodes to queue
+                    _add_to_frontier(split_node_left, frontier)
+                    _add_to_frontier(split_node_right, frontier)
+
+                if record.depth > max_depth_seen:
+                    max_depth_seen = record.depth
+
+            if rc >= 0:
+                rc = tree._resize_c(tree.node_count)
+
+            if rc >= 0:
+                tree.max_depth = max_depth_seen
+
+        if rc == -1:
+            raise MemoryError()
+
+    cdef inline int _add_split_node(
+        self,
+        Splitter splitter,
+        Tree tree,
+        intp_t start,
+        intp_t end,
+        bint is_first,
+        bint is_left,
+        Node* parent,
+        intp_t depth,
+        ParentInfo* parent_record,
+        FrontierRecord* res
+    ) except -1 nogil:
+        """Adds node w/ partition ``[start, end)`` to the frontier. """
+        cdef SplitRecord split
+        cdef intp_t node_id
+        cdef intp_t n_node_samples
+        cdef float64_t min_impurity_decrease = self.min_impurity_decrease
+        cdef float64_t weighted_n_node_samples
+        cdef bint is_leaf
+
+        splitter.node_reset(start, end, &weighted_n_node_samples)
+
+        # reset n_constant_features for this specific split before beginning split search
+        parent_record.n_constant_features = 0
+
+        if is_first:
+            parent_record.impurity = splitter.node_impurity()
+
+        n_node_samples = end - start
+        is_leaf = (depth >= self.max_depth or
+                   n_node_samples < self.min_samples_split or
+                   n_node_samples < 2 * self.min_samples_leaf or
+                   weighted_n_node_samples < 2 * self.min_weight_leaf or
+                   parent_record.impurity <= EPSILON  # impurity == 0 with tolerance
+                   )
+
+        if not is_leaf:
+            splitter.node_split(
+                parent_record,
+                &split,
+            )
+            # If EPSILON=0 in the below comparison, float precision issues stop
+            # splitting early, producing trees that are dissimilar to v0.18
+            is_leaf = (is_leaf or split.pos >= end or
+                       split.improvement + EPSILON < min_impurity_decrease)
+
+        node_id = tree._add_node(parent - tree.nodes
+                                 if parent != NULL
+                                 else _TREE_UNDEFINED,
+                                 is_left, is_leaf,
+                                 split.feature, split.threshold, parent_record.impurity,
+                                 n_node_samples, weighted_n_node_samples,
+                                 split.missing_go_to_left)
+        if node_id == INTPTR_MAX:
+            return -1
+
+        # compute values also for split nodes (might become leafs later).
+        splitter.node_value(tree.value + node_id * tree.value_stride)
+        if splitter.with_monotonic_cst:
+            splitter.clip_node_value(tree.value + node_id * tree.value_stride, parent_record.lower_bound, parent_record.upper_bound)
+
+        res.node_id = node_id
+        res.start = start
+        res.end = end
+        res.depth = depth
+        res.impurity = parent_record.impurity
+        res.lower_bound = parent_record.lower_bound
+        res.upper_bound = parent_record.upper_bound
+        res.middle_value = splitter.criterion.middle_value()
+
+        if not is_leaf:
+            # is split node
+            res.pos = split.pos
+            res.is_leaf = 0
+            res.improvement = split.improvement
+            res.impurity_left = split.impurity_left
+            res.impurity_right = split.impurity_right
+
+        else:
+            # is leaf => 0 improvement
+            res.pos = end
+            res.is_leaf = 1
+            res.improvement = 0.0
+            res.impurity_left = parent_record.impurity
+            res.impurity_right = parent_record.impurity
+
+        return 0
+
+
+# =============================================================================
+# Tree
+# =============================================================================
+
+cdef class Tree:
+    """Array-based representation of a binary decision tree.
+
+    The binary tree is represented as a number of parallel arrays. The i-th
+    element of each array holds information about the node `i`. Node 0 is the
+    tree's root. You can find a detailed description of all arrays in
+    `_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split
+    nodes, resp. In this case the values of nodes of the other type are
+    arbitrary!
+
+    Attributes
+    ----------
+    node_count : intp_t
+        The number of nodes (internal nodes + leaves) in the tree.
+
+    capacity : intp_t
+        The current capacity (i.e., size) of the arrays, which is at least as
+        great as `node_count`.
+
+    max_depth : intp_t
+        The depth of the tree, i.e. the maximum depth of its leaves.
+
+    children_left : array of intp_t, shape [node_count]
+        children_left[i] holds the node id of the left child of node i.
+        For leaves, children_left[i] == TREE_LEAF. Otherwise,
+        children_left[i] > i. This child handles the case where
+        X[:, feature[i]] <= threshold[i].
+
+    children_right : array of intp_t, shape [node_count]
+        children_right[i] holds the node id of the right child of node i.
+        For leaves, children_right[i] == TREE_LEAF. Otherwise,
+        children_right[i] > i. This child handles the case where
+        X[:, feature[i]] > threshold[i].
+
+    n_leaves : intp_t
+        Number of leaves in the tree.
+
+    feature : array of intp_t, shape [node_count]
+        feature[i] holds the feature to split on, for the internal node i.
+
+    threshold : array of float64_t, shape [node_count]
+        threshold[i] holds the threshold for the internal node i.
+
+    value : array of float64_t, shape [node_count, n_outputs, max_n_classes]
+        Contains the constant prediction value of each node.
+
+    impurity : array of float64_t, shape [node_count]
+        impurity[i] holds the impurity (i.e., the value of the splitting
+        criterion) at node i.
+
+    n_node_samples : array of intp_t, shape [node_count]
+        n_node_samples[i] holds the number of training samples reaching node i.
+
+    weighted_n_node_samples : array of float64_t, shape [node_count]
+        weighted_n_node_samples[i] holds the weighted number of training samples
+        reaching node i.
+
+    missing_go_to_left : array of bool, shape [node_count]
+        missing_go_to_left[i] holds a bool indicating whether or not there were
+        missing values at node i.
+    """
+    # Wrap for outside world.
+    # WARNING: these reference the current `nodes` and `value` buffers, which
+    # must not be freed by a subsequent memory allocation.
+    # (i.e. through `_resize` or `__setstate__`)
+    @property
+    def n_classes(self):
+        return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs)
+
+    @property
+    def children_left(self):
+        return self._get_node_ndarray()['left_child'][:self.node_count]
+
+    @property
+    def children_right(self):
+        return self._get_node_ndarray()['right_child'][:self.node_count]
+
+    @property
+    def n_leaves(self):
+        return np.sum(np.logical_and(
+            self.children_left == -1,
+            self.children_right == -1))
+
+    @property
+    def feature(self):
+        return self._get_node_ndarray()['feature'][:self.node_count]
+
+    @property
+    def threshold(self):
+        return self._get_node_ndarray()['threshold'][:self.node_count]
+
+    @property
+    def impurity(self):
+        return self._get_node_ndarray()['impurity'][:self.node_count]
+
+    @property
+    def n_node_samples(self):
+        return self._get_node_ndarray()['n_node_samples'][:self.node_count]
+
+    @property
+    def weighted_n_node_samples(self):
+        return self._get_node_ndarray()['weighted_n_node_samples'][:self.node_count]
+
+    @property
+    def missing_go_to_left(self):
+        return self._get_node_ndarray()['missing_go_to_left'][:self.node_count]
+
+    @property
+    def value(self):
+        return self._get_value_ndarray()[:self.node_count]
+
+    # TODO: Convert n_classes to cython.integral memory view once
+    #  https://github.com/cython/cython/issues/5243 is fixed
+    def __cinit__(self, intp_t n_features, cnp.ndarray n_classes, intp_t n_outputs):
+        """Constructor."""
+        cdef intp_t dummy = 0
+        size_t_dtype = np.array(dummy).dtype
+
+        n_classes = _check_n_classes(n_classes, size_t_dtype)
+
+        # Input/Output layout
+        self.n_features = n_features
+        self.n_outputs = n_outputs
+        self.n_classes = NULL
+        safe_realloc(&self.n_classes, n_outputs)
+
+        self.max_n_classes = np.max(n_classes)
+        self.value_stride = n_outputs * self.max_n_classes
+
+        cdef intp_t k
+        for k in range(n_outputs):
+            self.n_classes[k] = n_classes[k]
+
+        # Inner structures
+        self.max_depth = 0
+        self.node_count = 0
+        self.capacity = 0
+        self.value = NULL
+        self.nodes = NULL
+
+    def __dealloc__(self):
+        """Destructor."""
+        # Free all inner structures
+        free(self.n_classes)
+        free(self.value)
+        free(self.nodes)
+
+    def __reduce__(self):
+        """Reduce re-implementation, for pickling."""
+        return (Tree, (self.n_features,
+                       sizet_ptr_to_ndarray(self.n_classes, self.n_outputs),
+                       self.n_outputs), self.__getstate__())
+
+    def __getstate__(self):
+        """Getstate re-implementation, for pickling."""
+        d = {}
+        # capacity is inferred during the __setstate__ using nodes
+        d["max_depth"] = self.max_depth
+        d["node_count"] = self.node_count
+        d["nodes"] = self._get_node_ndarray()
+        d["values"] = self._get_value_ndarray()
+        return d
+
+    def __setstate__(self, d):
+        """Setstate re-implementation, for unpickling."""
+        self.max_depth = d["max_depth"]
+        self.node_count = d["node_count"]
+
+        if 'nodes' not in d:
+            raise ValueError('You have loaded Tree version which '
+                             'cannot be imported')
+
+        node_ndarray = d['nodes']
+        value_ndarray = d['values']
+
+        value_shape = (node_ndarray.shape[0], self.n_outputs,
+                       self.max_n_classes)
+
+        node_ndarray = _check_node_ndarray(node_ndarray, expected_dtype=NODE_DTYPE)
+        value_ndarray = _check_value_ndarray(
+            value_ndarray,
+            expected_dtype=np.dtype(np.float64),
+            expected_shape=value_shape
+        )
+
+        self.capacity = node_ndarray.shape[0]
+        if self._resize_c(self.capacity) != 0:
+            raise MemoryError("resizing tree to %d" % self.capacity)
+
+        memcpy(self.nodes, cnp.PyArray_DATA(node_ndarray),
+               self.capacity * sizeof(Node))
+        memcpy(self.value, cnp.PyArray_DATA(value_ndarray),
+               self.capacity * self.value_stride * sizeof(float64_t))
+
+    cdef int _resize(self, intp_t capacity) except -1 nogil:
+        """Resize all inner arrays to `capacity`, if `capacity` == -1, then
+           double the size of the inner arrays.
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        if self._resize_c(capacity) != 0:
+            # Acquire gil only if we need to raise
+            with gil:
+                raise MemoryError()
+
+    cdef int _resize_c(self, intp_t capacity=INTPTR_MAX) except -1 nogil:
+        """Guts of _resize
+
+        Returns -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        if capacity == self.capacity and self.nodes != NULL:
+            return 0
+
+        if capacity == INTPTR_MAX:
+            if self.capacity == 0:
+                capacity = 3  # default initial value
+            else:
+                capacity = 2 * self.capacity
+
+        safe_realloc(&self.nodes, capacity)
+        safe_realloc(&self.value, capacity * self.value_stride)
+
+        if capacity > self.capacity:
+            # value memory is initialised to 0 to enable classifier argmax
+            memset((self.value + self.capacity * self.value_stride), 0,
+                   (capacity - self.capacity) * self.value_stride *
+                   sizeof(float64_t))
+            # node memory is initialised to 0 to ensure deterministic pickle (padding in Node struct)
+            memset((self.nodes + self.capacity), 0, (capacity - self.capacity) * sizeof(Node))
+
+        # if capacity smaller than node_count, adjust the counter
+        if capacity < self.node_count:
+            self.node_count = capacity
+
+        self.capacity = capacity
+        return 0
+
+    cdef intp_t _add_node(self, intp_t parent, bint is_left, bint is_leaf,
+                          intp_t feature, float64_t threshold, float64_t impurity,
+                          intp_t n_node_samples,
+                          float64_t weighted_n_node_samples,
+                          uint8_t missing_go_to_left) except -1 nogil:
+        """Add a node to the tree.
+
+        The new node registers itself as the child of its parent.
+
+        Returns (size_t)(-1) on error.
+        """
+        cdef intp_t node_id = self.node_count
+
+        if node_id >= self.capacity:
+            if self._resize_c() != 0:
+                return INTPTR_MAX
+
+        cdef Node* node = &self.nodes[node_id]
+        node.impurity = impurity
+        node.n_node_samples = n_node_samples
+        node.weighted_n_node_samples = weighted_n_node_samples
+
+        if parent != _TREE_UNDEFINED:
+            if is_left:
+                self.nodes[parent].left_child = node_id
+            else:
+                self.nodes[parent].right_child = node_id
+
+        if is_leaf:
+            node.left_child = _TREE_LEAF
+            node.right_child = _TREE_LEAF
+            node.feature = _TREE_UNDEFINED
+            node.threshold = _TREE_UNDEFINED
+
+        else:
+            # left_child and right_child will be set later
+            node.feature = feature
+            node.threshold = threshold
+            node.missing_go_to_left = missing_go_to_left
+
+        self.node_count += 1
+
+        return node_id
+
+    cpdef cnp.ndarray predict(self, object X):
+        """Predict target for X."""
+        out = self._get_value_ndarray().take(self.apply(X), axis=0,
+                                             mode='clip')
+        if self.n_outputs == 1:
+            out = out.reshape(X.shape[0], self.max_n_classes)
+        return out
+
+    cpdef cnp.ndarray apply(self, object X):
+        """Finds the terminal region (=leaf node) for each sample in X."""
+        if issparse(X):
+            return self._apply_sparse_csr(X)
+        else:
+            return self._apply_dense(X)
+
+    cdef inline cnp.ndarray _apply_dense(self, object X):
+        """Finds the terminal region (=leaf node) for each sample in X."""
+
+        # Check input
+        if not isinstance(X, np.ndarray):
+            raise ValueError("X should be in np.ndarray format, got %s"
+                             % type(X))
+
+        if X.dtype != DTYPE:
+            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
+
+        # Extract input
+        cdef const float32_t[:, :] X_ndarray = X
+        cdef intp_t n_samples = X.shape[0]
+        cdef float32_t X_i_node_feature
+
+        # Initialize output
+        cdef intp_t[:] out = np.zeros(n_samples, dtype=np.intp)
+
+        # Initialize auxiliary data-structure
+        cdef Node* node = NULL
+        cdef intp_t i = 0
+
+        with nogil:
+            for i in range(n_samples):
+                node = self.nodes
+                # While node not a leaf
+                while node.left_child != _TREE_LEAF:
+                    X_i_node_feature = X_ndarray[i, node.feature]
+                    # ... and node.right_child != _TREE_LEAF:
+                    if isnan(X_i_node_feature):
+                        if node.missing_go_to_left:
+                            node = &self.nodes[node.left_child]
+                        else:
+                            node = &self.nodes[node.right_child]
+                    elif X_i_node_feature <= node.threshold:
+                        node = &self.nodes[node.left_child]
+                    else:
+                        node = &self.nodes[node.right_child]
+
+                out[i] = (node - self.nodes)  # node offset
+
+        return np.asarray(out)
+
+    cdef inline cnp.ndarray _apply_sparse_csr(self, object X):
+        """Finds the terminal region (=leaf node) for each sample in sparse X.
+        """
+        # Check input
+        if not (issparse(X) and X.format == 'csr'):
+            raise ValueError("X should be in csr_matrix format, got %s"
+                             % type(X))
+
+        if X.dtype != DTYPE:
+            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
+
+        # Extract input
+        cdef const float32_t[:] X_data = X.data
+        cdef const int32_t[:] X_indices = X.indices
+        cdef const int32_t[:] X_indptr = X.indptr
+
+        cdef intp_t n_samples = X.shape[0]
+        cdef intp_t n_features = X.shape[1]
+
+        # Initialize output
+        cdef intp_t[:] out = np.zeros(n_samples, dtype=np.intp)
+
+        # Initialize auxiliary data-structure
+        cdef float32_t feature_value = 0.
+        cdef Node* node = NULL
+        cdef float32_t* X_sample = NULL
+        cdef intp_t i = 0
+        cdef int32_t k = 0
+
+        # feature_to_sample as a data structure records the last seen sample
+        # for each feature; functionally, it is an efficient way to identify
+        # which features are nonzero in the present sample.
+        cdef intp_t* feature_to_sample = NULL
+
+        safe_realloc(&X_sample, n_features)
+        safe_realloc(&feature_to_sample, n_features)
+
+        with nogil:
+            memset(feature_to_sample, -1, n_features * sizeof(intp_t))
+
+            for i in range(n_samples):
+                node = self.nodes
+
+                for k in range(X_indptr[i], X_indptr[i + 1]):
+                    feature_to_sample[X_indices[k]] = i
+                    X_sample[X_indices[k]] = X_data[k]
+
+                # While node not a leaf
+                while node.left_child != _TREE_LEAF:
+                    # ... and node.right_child != _TREE_LEAF:
+                    if feature_to_sample[node.feature] == i:
+                        feature_value = X_sample[node.feature]
+
+                    else:
+                        feature_value = 0.
+
+                    if feature_value <= node.threshold:
+                        node = &self.nodes[node.left_child]
+                    else:
+                        node = &self.nodes[node.right_child]
+
+                out[i] = (node - self.nodes)  # node offset
+
+            # Free auxiliary arrays
+            free(X_sample)
+            free(feature_to_sample)
+
+        return np.asarray(out)
+
+    cpdef object decision_path(self, object X):
+        """Finds the decision path (=node) for each sample in X."""
+        if issparse(X):
+            return self._decision_path_sparse_csr(X)
+        else:
+            return self._decision_path_dense(X)
+
+    cdef inline object _decision_path_dense(self, object X):
+        """Finds the decision path (=node) for each sample in X."""
+
+        # Check input
+        if not isinstance(X, np.ndarray):
+            raise ValueError("X should be in np.ndarray format, got %s"
+                             % type(X))
+
+        if X.dtype != DTYPE:
+            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
+
+        # Extract input
+        cdef const float32_t[:, :] X_ndarray = X
+        cdef intp_t n_samples = X.shape[0]
+
+        # Initialize output
+        cdef intp_t[:] indptr = np.zeros(n_samples + 1, dtype=np.intp)
+        cdef intp_t[:] indices = np.zeros(
+            n_samples * (1 + self.max_depth), dtype=np.intp
+        )
+
+        # Initialize auxiliary data-structure
+        cdef Node* node = NULL
+        cdef intp_t i = 0
+
+        with nogil:
+            for i in range(n_samples):
+                node = self.nodes
+                indptr[i + 1] = indptr[i]
+
+                # Add all external nodes
+                while node.left_child != _TREE_LEAF:
+                    # ... and node.right_child != _TREE_LEAF:
+                    indices[indptr[i + 1]] = (node - self.nodes)
+                    indptr[i + 1] += 1
+
+                    if X_ndarray[i, node.feature] <= node.threshold:
+                        node = &self.nodes[node.left_child]
+                    else:
+                        node = &self.nodes[node.right_child]
+
+                # Add the leave node
+                indices[indptr[i + 1]] = (node - self.nodes)
+                indptr[i + 1] += 1
+
+        indices = indices[:indptr[n_samples]]
+        cdef intp_t[:] data = np.ones(shape=len(indices), dtype=np.intp)
+        out = csr_matrix((data, indices, indptr),
+                         shape=(n_samples, self.node_count))
+
+        return out
+
+    cdef inline object _decision_path_sparse_csr(self, object X):
+        """Finds the decision path (=node) for each sample in X."""
+
+        # Check input
+        if not (issparse(X) and X.format == "csr"):
+            raise ValueError("X should be in csr_matrix format, got %s"
+                             % type(X))
+
+        if X.dtype != DTYPE:
+            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
+
+        # Extract input
+        cdef const float32_t[:] X_data = X.data
+        cdef const int32_t[:] X_indices = X.indices
+        cdef const int32_t[:] X_indptr = X.indptr
+
+        cdef intp_t n_samples = X.shape[0]
+        cdef intp_t n_features = X.shape[1]
+
+        # Initialize output
+        cdef intp_t[:] indptr = np.zeros(n_samples + 1, dtype=np.intp)
+        cdef intp_t[:] indices = np.zeros(
+            n_samples * (1 + self.max_depth), dtype=np.intp
+        )
+
+        # Initialize auxiliary data-structure
+        cdef float32_t feature_value = 0.
+        cdef Node* node = NULL
+        cdef float32_t* X_sample = NULL
+        cdef intp_t i = 0
+        cdef int32_t k = 0
+
+        # feature_to_sample as a data structure records the last seen sample
+        # for each feature; functionally, it is an efficient way to identify
+        # which features are nonzero in the present sample.
+        cdef intp_t* feature_to_sample = NULL
+
+        safe_realloc(&X_sample, n_features)
+        safe_realloc(&feature_to_sample, n_features)
+
+        with nogil:
+            memset(feature_to_sample, -1, n_features * sizeof(intp_t))
+
+            for i in range(n_samples):
+                node = self.nodes
+                indptr[i + 1] = indptr[i]
+
+                for k in range(X_indptr[i], X_indptr[i + 1]):
+                    feature_to_sample[X_indices[k]] = i
+                    X_sample[X_indices[k]] = X_data[k]
+
+                # While node not a leaf
+                while node.left_child != _TREE_LEAF:
+                    # ... and node.right_child != _TREE_LEAF:
+
+                    indices[indptr[i + 1]] = (node - self.nodes)
+                    indptr[i + 1] += 1
+
+                    if feature_to_sample[node.feature] == i:
+                        feature_value = X_sample[node.feature]
+
+                    else:
+                        feature_value = 0.
+
+                    if feature_value <= node.threshold:
+                        node = &self.nodes[node.left_child]
+                    else:
+                        node = &self.nodes[node.right_child]
+
+                # Add the leave node
+                indices[indptr[i + 1]] = (node - self.nodes)
+                indptr[i + 1] += 1
+
+            # Free auxiliary arrays
+            free(X_sample)
+            free(feature_to_sample)
+
+        indices = indices[:indptr[n_samples]]
+        cdef intp_t[:] data = np.ones(shape=len(indices), dtype=np.intp)
+        out = csr_matrix((data, indices, indptr),
+                         shape=(n_samples, self.node_count))
+
+        return out
+
+    cpdef compute_node_depths(self):
+        """Compute the depth of each node in a tree.
+
+        .. versionadded:: 1.3
+
+        Returns
+        -------
+        depths : ndarray of shape (self.node_count,), dtype=np.int64
+            The depth of each node in the tree.
+        """
+        cdef:
+            cnp.int64_t[::1] depths = np.empty(self.node_count, dtype=np.int64)
+            cnp.npy_intp[:] children_left = self.children_left
+            cnp.npy_intp[:] children_right = self.children_right
+            cnp.npy_intp node_id
+            cnp.npy_intp node_count = self.node_count
+            cnp.int64_t depth
+
+        depths[0] = 1  # init root node
+        for node_id in range(node_count):
+            if children_left[node_id] != _TREE_LEAF:
+                depth = depths[node_id] + 1
+                depths[children_left[node_id]] = depth
+                depths[children_right[node_id]] = depth
+
+        return depths.base
+
+    cpdef compute_feature_importances(self, normalize=True):
+        """Computes the importance of each feature (aka variable)."""
+        cdef Node* left
+        cdef Node* right
+        cdef Node* nodes = self.nodes
+        cdef Node* node = nodes
+        cdef Node* end_node = node + self.node_count
+
+        cdef float64_t normalizer = 0.
+
+        cdef cnp.float64_t[:] importances = np.zeros(self.n_features)
+
+        with nogil:
+            while node != end_node:
+                if node.left_child != _TREE_LEAF:
+                    # ... and node.right_child != _TREE_LEAF:
+                    left = &nodes[node.left_child]
+                    right = &nodes[node.right_child]
+
+                    importances[node.feature] += (
+                        node.weighted_n_node_samples * node.impurity -
+                        left.weighted_n_node_samples * left.impurity -
+                        right.weighted_n_node_samples * right.impurity)
+                node += 1
+
+        for i in range(self.n_features):
+            importances[i] /= nodes[0].weighted_n_node_samples
+
+        if normalize:
+            normalizer = np.sum(importances)
+
+            if normalizer > 0.0:
+                # Avoid dividing by zero (e.g., when root is pure)
+                for i in range(self.n_features):
+                    importances[i] /= normalizer
+
+        return np.asarray(importances)
+
+    cdef cnp.ndarray _get_value_ndarray(self):
+        """Wraps value as a 3-d NumPy array.
+
+        The array keeps a reference to this Tree, which manages the underlying
+        memory.
+        """
+        cdef cnp.npy_intp shape[3]
+        shape[0] =  self.node_count
+        shape[1] =  self.n_outputs
+        shape[2] =  self.max_n_classes
+        cdef cnp.ndarray arr
+        arr = cnp.PyArray_SimpleNewFromData(3, shape, cnp.NPY_DOUBLE, self.value)
+        Py_INCREF(self)
+        if PyArray_SetBaseObject(arr,  self) < 0:
+            raise ValueError("Can't initialize array.")
+        return arr
+
+    cdef cnp.ndarray _get_node_ndarray(self):
+        """Wraps nodes as a NumPy struct array.
+
+        The array keeps a reference to this Tree, which manages the underlying
+        memory. Individual fields are publicly accessible as properties of the
+        Tree.
+        """
+        cdef cnp.npy_intp shape[1]
+        shape[0] =  self.node_count
+        cdef cnp.npy_intp strides[1]
+        strides[0] = sizeof(Node)
+        cdef cnp.ndarray arr
+        Py_INCREF(NODE_DTYPE)
+        arr = PyArray_NewFromDescr( cnp.ndarray,
+                                    NODE_DTYPE, 1, shape,
+                                   strides,  self.nodes,
+                                   cnp.NPY_ARRAY_DEFAULT, None)
+        Py_INCREF(self)
+        if PyArray_SetBaseObject(arr,  self) < 0:
+            raise ValueError("Can't initialize array.")
+        return arr
+
+    def compute_partial_dependence(self, float32_t[:, ::1] X,
+                                   const intp_t[::1] target_features,
+                                   float64_t[::1] out):
+        """Partial dependence of the response on the ``target_feature`` set.
+
+        For each sample in ``X`` a tree traversal is performed.
+        Each traversal starts from the root with weight 1.0.
+
+        At each non-leaf node that splits on a target feature, either
+        the left child or the right child is visited based on the feature
+        value of the current sample, and the weight is not modified.
+        At each non-leaf node that splits on a complementary feature,
+        both children are visited and the weight is multiplied by the fraction
+        of training samples which went to each child.
+
+        At each leaf, the value of the node is multiplied by the current
+        weight (weights sum to 1 for all visited terminal nodes).
+
+        Parameters
+        ----------
+        X : view on 2d ndarray, shape (n_samples, n_target_features)
+            The grid points on which the partial dependence should be
+            evaluated.
+        target_features : view on 1d ndarray, shape (n_target_features)
+            The set of target features for which the partial dependence
+            should be evaluated.
+        out : view on 1d ndarray, shape (n_samples)
+            The value of the partial dependence function on each grid
+            point.
+        """
+        cdef:
+            float64_t[::1] weight_stack = np.zeros(self.node_count,
+                                                   dtype=np.float64)
+            intp_t[::1] node_idx_stack = np.zeros(self.node_count,
+                                                  dtype=np.intp)
+            intp_t sample_idx
+            intp_t feature_idx
+            intp_t stack_size
+            float64_t left_sample_frac
+            float64_t current_weight
+            float64_t total_weight  # used for sanity check only
+            Node *current_node  # use a pointer to avoid copying attributes
+            intp_t current_node_idx
+            bint is_target_feature
+            intp_t _TREE_LEAF = TREE_LEAF  # to avoid python interactions
+
+        for sample_idx in range(X.shape[0]):
+            # init stacks for current sample
+            stack_size = 1
+            node_idx_stack[0] = 0  # root node
+            weight_stack[0] = 1  # all the samples are in the root node
+            total_weight = 0
+
+            while stack_size > 0:
+                # pop the stack
+                stack_size -= 1
+                current_node_idx = node_idx_stack[stack_size]
+                current_node = &self.nodes[current_node_idx]
+
+                if current_node.left_child == _TREE_LEAF:
+                    # leaf node
+                    out[sample_idx] += (weight_stack[stack_size] *
+                                        self.value[current_node_idx])
+                    total_weight += weight_stack[stack_size]
+                else:
+                    # non-leaf node
+
+                    # determine if the split feature is a target feature
+                    is_target_feature = False
+                    for feature_idx in range(target_features.shape[0]):
+                        if target_features[feature_idx] == current_node.feature:
+                            is_target_feature = True
+                            break
+
+                    if is_target_feature:
+                        # In this case, we push left or right child on stack
+                        if X[sample_idx, feature_idx] <= current_node.threshold:
+                            node_idx_stack[stack_size] = current_node.left_child
+                        else:
+                            node_idx_stack[stack_size] = current_node.right_child
+                        stack_size += 1
+                    else:
+                        # In this case, we push both children onto the stack,
+                        # and give a weight proportional to the number of
+                        # samples going through each branch.
+
+                        # push left child
+                        node_idx_stack[stack_size] = current_node.left_child
+                        left_sample_frac = (
+                            self.nodes[current_node.left_child].weighted_n_node_samples /
+                            current_node.weighted_n_node_samples)
+                        current_weight = weight_stack[stack_size]
+                        weight_stack[stack_size] = current_weight * left_sample_frac
+                        stack_size += 1
+
+                        # push right child
+                        node_idx_stack[stack_size] = current_node.right_child
+                        weight_stack[stack_size] = (
+                            current_weight * (1 - left_sample_frac))
+                        stack_size += 1
+
+            # Sanity check. Should never happen.
+            if not (0.999 < total_weight < 1.001):
+                raise ValueError("Total weight should be 1.0 but was %.9f" %
+                                 total_weight)
+
+
+def _check_n_classes(n_classes, expected_dtype):
+    if n_classes.ndim != 1:
+        raise ValueError(
+            f"Wrong dimensions for n_classes from the pickle: "
+            f"expected 1, got {n_classes.ndim}"
+        )
+
+    if n_classes.dtype == expected_dtype:
+        return n_classes
+
+    # Handles both different endianness and different bitness
+    if n_classes.dtype.kind == "i" and n_classes.dtype.itemsize in [4, 8]:
+        return n_classes.astype(expected_dtype, casting="same_kind")
+
+    raise ValueError(
+        "n_classes from the pickle has an incompatible dtype:\n"
+        f"- expected: {expected_dtype}\n"
+        f"- got:      {n_classes.dtype}"
+    )
+
+
+def _check_value_ndarray(value_ndarray, expected_dtype, expected_shape):
+    if value_ndarray.shape != expected_shape:
+        raise ValueError(
+            "Wrong shape for value array from the pickle: "
+            f"expected {expected_shape}, got {value_ndarray.shape}"
+        )
+
+    if not value_ndarray.flags.c_contiguous:
+        raise ValueError(
+            "value array from the pickle should be a C-contiguous array"
+        )
+
+    if value_ndarray.dtype == expected_dtype:
+        return value_ndarray
+
+    # Handles different endianness
+    if value_ndarray.dtype.str.endswith('f8'):
+        return value_ndarray.astype(expected_dtype, casting='equiv')
+
+    raise ValueError(
+        "value array from the pickle has an incompatible dtype:\n"
+        f"- expected: {expected_dtype}\n"
+        f"- got:      {value_ndarray.dtype}"
+    )
+
+
+def _dtype_to_dict(dtype):
+    return {name: dt.str for name, (dt, *rest) in dtype.fields.items()}
+
+
+def _dtype_dict_with_modified_bitness(dtype_dict):
+    # field names in Node struct with intp_t types (see sklearn/tree/_tree.pxd)
+    indexing_field_names = ["left_child", "right_child", "feature", "n_node_samples"]
+
+    expected_dtype_size = str(struct.calcsize("P"))
+    allowed_dtype_size = "8" if expected_dtype_size == "4" else "4"
+
+    allowed_dtype_dict = dtype_dict.copy()
+    for name in indexing_field_names:
+        allowed_dtype_dict[name] = allowed_dtype_dict[name].replace(
+            expected_dtype_size, allowed_dtype_size
+        )
+
+    return allowed_dtype_dict
+
+
+def _all_compatible_dtype_dicts(dtype):
+    # The Cython code for decision trees uses platform-specific intp_t
+    # typed indexing fields that correspond to either i4 or i8 dtypes for
+    # the matching fields in the numpy array depending on the bitness of
+    # the platform (32 bit or 64 bit respectively).
+    #
+    # We need to cast the indexing fields of the NODE_DTYPE-dtyped array at
+    # pickle load time to enable cross-bitness deployment scenarios. We
+    # typically want to make it possible to run the expensive fit method of
+    # a tree estimator on a 64 bit server platform, pickle the estimator
+    # for deployment and run the predict method of a low power 32 bit edge
+    # platform.
+    #
+    # A similar thing happens for endianness, the machine where the pickle was
+    # saved can have a different endianness than the machine where the pickle
+    # is loaded
+
+    dtype_dict = _dtype_to_dict(dtype)
+    dtype_dict_with_modified_bitness = _dtype_dict_with_modified_bitness(dtype_dict)
+    dtype_dict_with_modified_endianness = _dtype_to_dict(dtype.newbyteorder())
+    dtype_dict_with_modified_bitness_and_endianness = _dtype_dict_with_modified_bitness(
+        dtype_dict_with_modified_endianness
+    )
+
+    return [
+        dtype_dict,
+        dtype_dict_with_modified_bitness,
+        dtype_dict_with_modified_endianness,
+        dtype_dict_with_modified_bitness_and_endianness,
+    ]
+
+
+def _check_node_ndarray(node_ndarray, expected_dtype):
+    if node_ndarray.ndim != 1:
+        raise ValueError(
+            "Wrong dimensions for node array from the pickle: "
+            f"expected 1, got {node_ndarray.ndim}"
+        )
+
+    if not node_ndarray.flags.c_contiguous:
+        raise ValueError(
+            "node array from the pickle should be a C-contiguous array"
+        )
+
+    node_ndarray_dtype = node_ndarray.dtype
+    if node_ndarray_dtype == expected_dtype:
+        return node_ndarray
+
+    node_ndarray_dtype_dict = _dtype_to_dict(node_ndarray_dtype)
+    all_compatible_dtype_dicts = _all_compatible_dtype_dicts(expected_dtype)
+
+    if node_ndarray_dtype_dict not in all_compatible_dtype_dicts:
+        raise ValueError(
+            "node array from the pickle has an incompatible dtype:\n"
+            f"- expected: {expected_dtype}\n"
+            f"- got     : {node_ndarray_dtype}"
+        )
+
+    return node_ndarray.astype(expected_dtype, casting="same_kind")
+
+
+# =============================================================================
+# Build Pruned Tree
+# =============================================================================
+
+
+cdef class _CCPPruneController:
+    """Base class used by build_pruned_tree_ccp and ccp_pruning_path
+    to control pruning.
+    """
+    cdef bint stop_pruning(self, float64_t effective_alpha) noexcept nogil:
+        """Return 1 to stop pruning and 0 to continue pruning"""
+        return 0
+
+    cdef void save_metrics(self, float64_t effective_alpha,
+                           float64_t subtree_impurities) noexcept nogil:
+        """Save metrics when pruning"""
+        pass
+
+    cdef void after_pruning(self, uint8_t[:] in_subtree) noexcept nogil:
+        """Called after pruning"""
+        pass
+
+
+cdef class _AlphaPruner(_CCPPruneController):
+    """Use alpha to control when to stop pruning."""
+    cdef float64_t ccp_alpha
+    cdef intp_t capacity
+
+    def __cinit__(self, float64_t ccp_alpha):
+        self.ccp_alpha = ccp_alpha
+        self.capacity = 0
+
+    cdef bint stop_pruning(self, float64_t effective_alpha) noexcept nogil:
+        # The subtree on the previous iteration has the greatest ccp_alpha
+        # less than or equal to self.ccp_alpha
+        return self.ccp_alpha < effective_alpha
+
+    cdef void after_pruning(self, uint8_t[:] in_subtree) noexcept nogil:
+        """Updates the number of leaves in subtree"""
+        for i in range(in_subtree.shape[0]):
+            if in_subtree[i]:
+                self.capacity += 1
+
+
+cdef class _PathFinder(_CCPPruneController):
+    """Record metrics used to return the cost complexity path."""
+    cdef float64_t[:] ccp_alphas
+    cdef float64_t[:] impurities
+    cdef uint32_t count
+
+    def __cinit__(self,  intp_t node_count):
+        self.ccp_alphas = np.zeros(shape=(node_count), dtype=np.float64)
+        self.impurities = np.zeros(shape=(node_count), dtype=np.float64)
+        self.count = 0
+
+    cdef void save_metrics(self,
+                           float64_t effective_alpha,
+                           float64_t subtree_impurities) noexcept nogil:
+        self.ccp_alphas[self.count] = effective_alpha
+        self.impurities[self.count] = subtree_impurities
+        self.count += 1
+
+
+cdef struct CostComplexityPruningRecord:
+    intp_t node_idx
+    intp_t parent
+
+cdef _cost_complexity_prune(uint8_t[:] leaves_in_subtree,  # OUT
+                            Tree orig_tree,
+                            _CCPPruneController controller):
+    """Perform cost complexity pruning.
+
+    This function takes an already grown tree, `orig_tree` and outputs a
+    boolean mask `leaves_in_subtree` which are the leaves in the pruned tree.
+    During the pruning process, the controller is passed the effective alpha and
+    the subtree impurities. Furthermore, the controller signals when to stop
+    pruning.
+
+    Parameters
+    ----------
+    leaves_in_subtree : uint8_t[:]
+        Output for leaves of subtree
+    orig_tree : Tree
+        Original tree
+    ccp_controller : _CCPPruneController
+        Cost complexity controller
+    """
+
+    cdef:
+        intp_t i
+        intp_t n_nodes = orig_tree.node_count
+        # prior probability using weighted samples
+        float64_t[:] weighted_n_node_samples = orig_tree.weighted_n_node_samples
+        float64_t total_sum_weights = weighted_n_node_samples[0]
+        float64_t[:] impurity = orig_tree.impurity
+        # weighted impurity of each node
+        float64_t[:] r_node = np.empty(shape=n_nodes, dtype=np.float64)
+
+        intp_t[:] child_l = orig_tree.children_left
+        intp_t[:] child_r = orig_tree.children_right
+        intp_t[:] parent = np.zeros(shape=n_nodes, dtype=np.intp)
+
+        stack[CostComplexityPruningRecord] ccp_stack
+        CostComplexityPruningRecord stack_record
+        intp_t node_idx
+        stack[intp_t] node_indices_stack
+
+        intp_t[:] n_leaves = np.zeros(shape=n_nodes, dtype=np.intp)
+        float64_t[:] r_branch = np.zeros(shape=n_nodes, dtype=np.float64)
+        float64_t current_r
+        intp_t leaf_idx
+        intp_t parent_idx
+
+        # candidate nodes that can be pruned
+        uint8_t[:] candidate_nodes = np.zeros(shape=n_nodes, dtype=np.uint8)
+        # nodes in subtree
+        uint8_t[:] in_subtree = np.ones(shape=n_nodes, dtype=np.uint8)
+        intp_t pruned_branch_node_idx
+        float64_t subtree_alpha
+        float64_t effective_alpha
+        intp_t n_pruned_leaves
+        float64_t r_diff
+        float64_t max_float64 = np.finfo(np.float64).max
+
+    # find parent node ids and leaves
+    with nogil:
+
+        for i in range(r_node.shape[0]):
+            r_node[i] = (
+                weighted_n_node_samples[i] * impurity[i] / total_sum_weights)
+
+        # Push the root node
+        ccp_stack.push({"node_idx": 0, "parent": _TREE_UNDEFINED})
+
+        while not ccp_stack.empty():
+            stack_record = ccp_stack.top()
+            ccp_stack.pop()
+
+            node_idx = stack_record.node_idx
+            parent[node_idx] = stack_record.parent
+
+            if child_l[node_idx] == _TREE_LEAF:
+                # ... and child_r[node_idx] == _TREE_LEAF:
+                leaves_in_subtree[node_idx] = 1
+            else:
+                ccp_stack.push({"node_idx": child_l[node_idx], "parent": node_idx})
+                ccp_stack.push({"node_idx": child_r[node_idx], "parent": node_idx})
+
+        # computes number of leaves in all branches and the overall impurity of
+        # the branch. The overall impurity is the sum of r_node in its leaves.
+        for leaf_idx in range(leaves_in_subtree.shape[0]):
+            if not leaves_in_subtree[leaf_idx]:
+                continue
+            r_branch[leaf_idx] = r_node[leaf_idx]
+
+            # bubble up values to ancestor nodes
+            current_r = r_node[leaf_idx]
+            while leaf_idx != 0:
+                parent_idx = parent[leaf_idx]
+                r_branch[parent_idx] += current_r
+                n_leaves[parent_idx] += 1
+                leaf_idx = parent_idx
+
+        for i in range(leaves_in_subtree.shape[0]):
+            candidate_nodes[i] = not leaves_in_subtree[i]
+
+        # save metrics before pruning
+        controller.save_metrics(0.0, r_branch[0])
+
+        # while root node is not a leaf
+        while candidate_nodes[0]:
+
+            # computes ccp_alpha for subtrees and finds the minimal alpha
+            effective_alpha = max_float64
+            for i in range(n_nodes):
+                if not candidate_nodes[i]:
+                    continue
+                subtree_alpha = (r_node[i] - r_branch[i]) / (n_leaves[i] - 1)
+                if subtree_alpha < effective_alpha:
+                    effective_alpha = subtree_alpha
+                    pruned_branch_node_idx = i
+
+            if controller.stop_pruning(effective_alpha):
+                break
+
+            node_indices_stack.push(pruned_branch_node_idx)
+
+            # descendants of branch are not in subtree
+            while not node_indices_stack.empty():
+                node_idx = node_indices_stack.top()
+                node_indices_stack.pop()
+
+                if not in_subtree[node_idx]:
+                    continue  # branch has already been marked for pruning
+                candidate_nodes[node_idx] = 0
+                leaves_in_subtree[node_idx] = 0
+                in_subtree[node_idx] = 0
+
+                if child_l[node_idx] != _TREE_LEAF:
+                    # ... and child_r[node_idx] != _TREE_LEAF:
+                    node_indices_stack.push(child_l[node_idx])
+                    node_indices_stack.push(child_r[node_idx])
+            leaves_in_subtree[pruned_branch_node_idx] = 1
+            in_subtree[pruned_branch_node_idx] = 1
+
+            # updates number of leaves
+            n_pruned_leaves = n_leaves[pruned_branch_node_idx] - 1
+            n_leaves[pruned_branch_node_idx] = 0
+
+            # computes the increase in r_branch to bubble up
+            r_diff = r_node[pruned_branch_node_idx] - r_branch[pruned_branch_node_idx]
+            r_branch[pruned_branch_node_idx] = r_node[pruned_branch_node_idx]
+
+            # bubble up values to ancestors
+            node_idx = parent[pruned_branch_node_idx]
+            while node_idx != _TREE_UNDEFINED:
+                n_leaves[node_idx] -= n_pruned_leaves
+                r_branch[node_idx] += r_diff
+                node_idx = parent[node_idx]
+
+            controller.save_metrics(effective_alpha, r_branch[0])
+
+        controller.after_pruning(in_subtree)
+
+
+def _build_pruned_tree_ccp(
+    Tree tree,  # OUT
+    Tree orig_tree,
+    float64_t ccp_alpha
+):
+    """Build a pruned tree from the original tree using cost complexity
+    pruning.
+
+    The values and nodes from the original tree are copied into the pruned
+    tree.
+
+    Parameters
+    ----------
+    tree : Tree
+        Location to place the pruned tree
+    orig_tree : Tree
+        Original tree
+    ccp_alpha : positive float64_t
+        Complexity parameter. The subtree with the largest cost complexity
+        that is smaller than ``ccp_alpha`` will be chosen. By default,
+        no pruning is performed.
+    """
+
+    cdef:
+        intp_t n_nodes = orig_tree.node_count
+        uint8_t[:] leaves_in_subtree = np.zeros(
+            shape=n_nodes, dtype=np.uint8)
+
+    pruning_controller = _AlphaPruner(ccp_alpha=ccp_alpha)
+
+    _cost_complexity_prune(leaves_in_subtree, orig_tree, pruning_controller)
+
+    _build_pruned_tree(tree, orig_tree, leaves_in_subtree,
+                       pruning_controller.capacity)
+
+
+def ccp_pruning_path(Tree orig_tree):
+    """Computes the cost complexity pruning path.
+
+    Parameters
+    ----------
+    tree : Tree
+        Original tree.
+
+    Returns
+    -------
+    path_info : dict
+        Information about pruning path with attributes:
+
+        ccp_alphas : ndarray
+            Effective alphas of subtree during pruning.
+
+        impurities : ndarray
+            Sum of the impurities of the subtree leaves for the
+            corresponding alpha value in ``ccp_alphas``.
+    """
+    cdef:
+        uint8_t[:] leaves_in_subtree = np.zeros(
+            shape=orig_tree.node_count, dtype=np.uint8)
+
+    path_finder = _PathFinder(orig_tree.node_count)
+
+    _cost_complexity_prune(leaves_in_subtree, orig_tree, path_finder)
+
+    cdef:
+        uint32_t total_items = path_finder.count
+        float64_t[:] ccp_alphas = np.empty(shape=total_items, dtype=np.float64)
+        float64_t[:] impurities = np.empty(shape=total_items, dtype=np.float64)
+        uint32_t count = 0
+
+    while count < total_items:
+        ccp_alphas[count] = path_finder.ccp_alphas[count]
+        impurities[count] = path_finder.impurities[count]
+        count += 1
+
+    return {
+        'ccp_alphas': np.asarray(ccp_alphas),
+        'impurities': np.asarray(impurities),
+    }
+
+
+cdef struct BuildPrunedRecord:
+    intp_t start
+    intp_t depth
+    intp_t parent
+    bint is_left
+
+cdef void _build_pruned_tree(
+    Tree tree,  # OUT
+    Tree orig_tree,
+    const uint8_t[:] leaves_in_subtree,
+    intp_t capacity
+):
+    """Build a pruned tree.
+
+    Build a pruned tree from the original tree by transforming the nodes in
+    ``leaves_in_subtree`` into leaves.
+
+    Parameters
+    ----------
+    tree : Tree
+        Location to place the pruned tree
+    orig_tree : Tree
+        Original tree
+    leaves_in_subtree : uint8_t memoryview, shape=(node_count, )
+        Boolean mask for leaves to include in subtree
+    capacity : intp_t
+        Number of nodes to initially allocate in pruned tree
+    """
+    tree._resize(capacity)
+
+    cdef:
+        intp_t orig_node_id
+        intp_t new_node_id
+        intp_t depth
+        intp_t parent
+        bint is_left
+        bint is_leaf
+
+        # value_stride for original tree and new tree are the same
+        intp_t value_stride = orig_tree.value_stride
+        intp_t max_depth_seen = -1
+        int rc = 0
+        Node* node
+        float64_t* orig_value_ptr
+        float64_t* new_value_ptr
+
+        stack[BuildPrunedRecord] prune_stack
+        BuildPrunedRecord stack_record
+
+    with nogil:
+        # push root node onto stack
+        prune_stack.push({"start": 0, "depth": 0, "parent": _TREE_UNDEFINED, "is_left": 0})
+
+        while not prune_stack.empty():
+            stack_record = prune_stack.top()
+            prune_stack.pop()
+
+            orig_node_id = stack_record.start
+            depth = stack_record.depth
+            parent = stack_record.parent
+            is_left = stack_record.is_left
+
+            is_leaf = leaves_in_subtree[orig_node_id]
+            node = &orig_tree.nodes[orig_node_id]
+
+            # protect against an infinite loop as a runtime error, when leaves_in_subtree
+            # are improperly set where a node is not marked as a leaf, but is a node
+            # in the original tree. Thus, it violates the assumption that the node
+            # is a leaf in the pruned tree, or has a descendant that will be pruned.
+            if (not is_leaf and node.left_child == _TREE_LEAF
+                    and node.right_child == _TREE_LEAF):
+                rc = -2
+                break
+
+            new_node_id = tree._add_node(
+                parent, is_left, is_leaf, node.feature, node.threshold,
+                node.impurity, node.n_node_samples,
+                node.weighted_n_node_samples, node.missing_go_to_left)
+
+            if new_node_id == INTPTR_MAX:
+                rc = -1
+                break
+
+            # copy value from original tree to new tree
+            orig_value_ptr = orig_tree.value + value_stride * orig_node_id
+            new_value_ptr = tree.value + value_stride * new_node_id
+            memcpy(new_value_ptr, orig_value_ptr, sizeof(float64_t) * value_stride)
+
+            if not is_leaf:
+                # Push right child on stack
+                prune_stack.push({"start": node.right_child, "depth": depth + 1,
+                                  "parent": new_node_id, "is_left": 0})
+                # push left child on stack
+                prune_stack.push({"start": node.left_child, "depth": depth + 1,
+                                  "parent": new_node_id, "is_left": 1})
+
+            if depth > max_depth_seen:
+                max_depth_seen = depth
+
+        if rc >= 0:
+            tree.max_depth = max_depth_seen
+    if rc == -1:
+        raise MemoryError("pruning tree")
+    elif rc == -2:
+        raise ValueError(
+            "Node has reached a leaf in the original tree, but is not "
+            "marked as a leaf in the leaves_in_subtree mask."
+        )
+
+
+def _build_pruned_tree_py(Tree tree, Tree orig_tree, const uint8_t[:] leaves_in_subtree):
+    """Build a pruned tree.
+
+    Build a pruned tree from the original tree by transforming the nodes in
+    ``leaves_in_subtree`` into leaves.
+
+    Parameters
+    ----------
+    tree : Tree
+        Location to place the pruned tree
+    orig_tree : Tree
+        Original tree
+    leaves_in_subtree : uint8_t ndarray, shape=(node_count, )
+        Boolean mask for leaves to include in subtree. The array must have
+        the same size as the number of nodes in the original tree.
+    """
+    if leaves_in_subtree.shape[0] != orig_tree.node_count:
+        raise ValueError(
+            f"The length of leaves_in_subtree {len(leaves_in_subtree)} must be "
+            f"equal to the number of nodes in the original tree {orig_tree.node_count}."
+        )
+
+    _build_pruned_tree(tree, orig_tree, leaves_in_subtree, orig_tree.node_count)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pxd b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..bc1d7668187d77087b8ad82beef81b012c849a51
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pxd
@@ -0,0 +1,100 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _utils.pyx for details.
+
+cimport numpy as cnp
+from ._tree cimport Node
+from ..neighbors._quad_tree cimport Cell
+from ..utils._typedefs cimport float32_t, float64_t, intp_t, uint8_t, int32_t, uint32_t
+
+
+cdef enum:
+    # Max value for our rand_r replacement (near the bottom).
+    # We don't use RAND_MAX because it's different across platforms and
+    # particularly tiny on Windows/MSVC.
+    # It corresponds to the maximum representable value for
+    # 32-bit signed integers (i.e. 2^31 - 1).
+    RAND_R_MAX = 2147483647
+
+
+# safe_realloc(&p, n) resizes the allocation of p to n * sizeof(*p) bytes or
+# raises a MemoryError. It never calls free, since that's __dealloc__'s job.
+#   cdef float32_t *p = NULL
+#   safe_realloc(&p, n)
+# is equivalent to p = malloc(n * sizeof(*p)) with error checking.
+ctypedef fused realloc_ptr:
+    # Add pointer types here as needed.
+    (float32_t*)
+    (intp_t*)
+    (uint8_t*)
+    (WeightedPQueueRecord*)
+    (float64_t*)
+    (float64_t**)
+    (Node*)
+    (Cell*)
+    (Node**)
+
+cdef int safe_realloc(realloc_ptr* p, size_t nelems) except -1 nogil
+
+
+cdef cnp.ndarray sizet_ptr_to_ndarray(intp_t* data, intp_t size)
+
+
+cdef intp_t rand_int(intp_t low, intp_t high,
+                     uint32_t* random_state) noexcept nogil
+
+
+cdef float64_t rand_uniform(float64_t low, float64_t high,
+                            uint32_t* random_state) noexcept nogil
+
+
+cdef float64_t log(float64_t x) noexcept nogil
+
+# =============================================================================
+# WeightedPQueue data structure
+# =============================================================================
+
+# A record stored in the WeightedPQueue
+cdef struct WeightedPQueueRecord:
+    float64_t data
+    float64_t weight
+
+cdef class WeightedPQueue:
+    cdef intp_t capacity
+    cdef intp_t array_ptr
+    cdef WeightedPQueueRecord* array_
+
+    cdef bint is_empty(self) noexcept nogil
+    cdef int reset(self) except -1 nogil
+    cdef intp_t size(self) noexcept nogil
+    cdef int push(self, float64_t data, float64_t weight) except -1 nogil
+    cdef int remove(self, float64_t data, float64_t weight) noexcept nogil
+    cdef int pop(self, float64_t* data, float64_t* weight) noexcept nogil
+    cdef int peek(self, float64_t* data, float64_t* weight) noexcept nogil
+    cdef float64_t get_weight_from_index(self, intp_t index) noexcept nogil
+    cdef float64_t get_value_from_index(self, intp_t index) noexcept nogil
+
+
+# =============================================================================
+# WeightedMedianCalculator data structure
+# =============================================================================
+
+cdef class WeightedMedianCalculator:
+    cdef intp_t initial_capacity
+    cdef WeightedPQueue samples
+    cdef float64_t total_weight
+    cdef intp_t k
+    cdef float64_t sum_w_0_k  # represents sum(weights[0:k]) = w[0] + w[1] + ... + w[k-1]
+    cdef intp_t size(self) noexcept nogil
+    cdef int push(self, float64_t data, float64_t weight) except -1 nogil
+    cdef int reset(self) except -1 nogil
+    cdef int update_median_parameters_post_push(
+        self, float64_t data, float64_t weight,
+        float64_t original_median) noexcept nogil
+    cdef int remove(self, float64_t data, float64_t weight) noexcept nogil
+    cdef int pop(self, float64_t* data, float64_t* weight) noexcept nogil
+    cdef int update_median_parameters_post_remove(
+        self, float64_t data, float64_t weight,
+        float64_t original_median) noexcept nogil
+    cdef float64_t get_median(self) noexcept nogil
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pyx b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..c5e936ae48eb14b39553e187166440469947403a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/_utils.pyx
@@ -0,0 +1,460 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from libc.stdlib cimport free
+from libc.stdlib cimport realloc
+from libc.math cimport log as ln
+from libc.math cimport isnan
+
+import numpy as np
+cimport numpy as cnp
+cnp.import_array()
+
+from ..utils._random cimport our_rand_r
+
+# =============================================================================
+# Helper functions
+# =============================================================================
+
+cdef int safe_realloc(realloc_ptr* p, size_t nelems) except -1 nogil:
+    # sizeof(realloc_ptr[0]) would be more like idiomatic C, but causes Cython
+    # 0.20.1 to crash.
+    cdef size_t nbytes = nelems * sizeof(p[0][0])
+    if nbytes / sizeof(p[0][0]) != nelems:
+        # Overflow in the multiplication
+        raise MemoryError(f"could not allocate ({nelems} * {sizeof(p[0][0])}) bytes")
+
+    cdef realloc_ptr tmp = realloc(p[0], nbytes)
+    if tmp == NULL:
+        raise MemoryError(f"could not allocate {nbytes} bytes")
+
+    p[0] = tmp
+    return 0
+
+
+def _realloc_test():
+    # Helper for tests. Tries to allocate (-1) / 2 * sizeof(size_t)
+    # bytes, which will always overflow.
+    cdef intp_t* p = NULL
+    safe_realloc(&p, (-1) / 2)
+    if p != NULL:
+        free(p)
+        assert False
+
+
+cdef inline cnp.ndarray sizet_ptr_to_ndarray(intp_t* data, intp_t size):
+    """Return copied data as 1D numpy array of intp's."""
+    cdef cnp.npy_intp shape[1]
+    shape[0] =  size
+    return cnp.PyArray_SimpleNewFromData(1, shape, cnp.NPY_INTP, data).copy()
+
+
+cdef inline intp_t rand_int(intp_t low, intp_t high,
+                            uint32_t* random_state) noexcept nogil:
+    """Generate a random integer in [low; end)."""
+    return low + our_rand_r(random_state) % (high - low)
+
+
+cdef inline float64_t rand_uniform(float64_t low, float64_t high,
+                                   uint32_t* random_state) noexcept nogil:
+    """Generate a random float64_t in [low; high)."""
+    return ((high - low) *  our_rand_r(random_state) /
+             RAND_R_MAX) + low
+
+
+cdef inline float64_t log(float64_t x) noexcept nogil:
+    return ln(x) / ln(2.0)
+
+# =============================================================================
+# WeightedPQueue data structure
+# =============================================================================
+
+cdef class WeightedPQueue:
+    """A priority queue class, always sorted in increasing order.
+
+    Attributes
+    ----------
+    capacity : intp_t
+        The capacity of the priority queue.
+
+    array_ptr : intp_t
+        The water mark of the priority queue; the priority queue grows from
+        left to right in the array ``array_``. ``array_ptr`` is always
+        less than ``capacity``.
+
+    array_ : WeightedPQueueRecord*
+        The array of priority queue records. The minimum element is on the
+        left at index 0, and the maximum element is on the right at index
+        ``array_ptr-1``.
+    """
+
+    def __cinit__(self, intp_t capacity):
+        self.capacity = capacity
+        self.array_ptr = 0
+        safe_realloc(&self.array_, capacity)
+
+    def __dealloc__(self):
+        free(self.array_)
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the WeightedPQueue to its state at construction
+
+        Return -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        self.array_ptr = 0
+        # Since safe_realloc can raise MemoryError, use `except -1`
+        safe_realloc(&self.array_, self.capacity)
+        return 0
+
+    cdef bint is_empty(self) noexcept nogil:
+        return self.array_ptr <= 0
+
+    cdef intp_t size(self) noexcept nogil:
+        return self.array_ptr
+
+    cdef int push(self, float64_t data, float64_t weight) except -1 nogil:
+        """Push record on the array.
+
+        Return -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        cdef intp_t array_ptr = self.array_ptr
+        cdef WeightedPQueueRecord* array = NULL
+        cdef intp_t i
+
+        # Resize if capacity not sufficient
+        if array_ptr >= self.capacity:
+            self.capacity *= 2
+            # Since safe_realloc can raise MemoryError, use `except -1`
+            safe_realloc(&self.array_, self.capacity)
+
+        # Put element as last element of array
+        array = self.array_
+        array[array_ptr].data = data
+        array[array_ptr].weight = weight
+
+        # bubble last element up according until it is sorted
+        # in ascending order
+        i = array_ptr
+        while(i != 0 and array[i].data < array[i-1].data):
+            array[i], array[i-1] = array[i-1], array[i]
+            i -= 1
+
+        # Increase element count
+        self.array_ptr = array_ptr + 1
+        return 0
+
+    cdef int remove(self, float64_t data, float64_t weight) noexcept nogil:
+        """Remove a specific value/weight record from the array.
+        Returns 0 if successful, -1 if record not found."""
+        cdef intp_t array_ptr = self.array_ptr
+        cdef WeightedPQueueRecord* array = self.array_
+        cdef intp_t idx_to_remove = -1
+        cdef intp_t i
+
+        if array_ptr <= 0:
+            return -1
+
+        # find element to remove
+        for i in range(array_ptr):
+            if array[i].data == data and array[i].weight == weight:
+                idx_to_remove = i
+                break
+
+        if idx_to_remove == -1:
+            return -1
+
+        # shift the elements after the removed element
+        # to the left.
+        for i in range(idx_to_remove, array_ptr-1):
+            array[i] = array[i+1]
+
+        self.array_ptr = array_ptr - 1
+        return 0
+
+    cdef int pop(self, float64_t* data, float64_t* weight) noexcept nogil:
+        """Remove the top (minimum) element from array.
+        Returns 0 if successful, -1 if nothing to remove."""
+        cdef intp_t array_ptr = self.array_ptr
+        cdef WeightedPQueueRecord* array = self.array_
+        cdef intp_t i
+
+        if array_ptr <= 0:
+            return -1
+
+        data[0] = array[0].data
+        weight[0] = array[0].weight
+
+        # shift the elements after the removed element
+        # to the left.
+        for i in range(0, array_ptr-1):
+            array[i] = array[i+1]
+
+        self.array_ptr = array_ptr - 1
+        return 0
+
+    cdef int peek(self, float64_t* data, float64_t* weight) noexcept nogil:
+        """Write the top element from array to a pointer.
+        Returns 0 if successful, -1 if nothing to write."""
+        cdef WeightedPQueueRecord* array = self.array_
+        if self.array_ptr <= 0:
+            return -1
+        # Take first value
+        data[0] = array[0].data
+        weight[0] = array[0].weight
+        return 0
+
+    cdef float64_t get_weight_from_index(self, intp_t index) noexcept nogil:
+        """Given an index between [0,self.current_capacity], access
+        the appropriate heap and return the requested weight"""
+        cdef WeightedPQueueRecord* array = self.array_
+
+        # get weight at index
+        return array[index].weight
+
+    cdef float64_t get_value_from_index(self, intp_t index) noexcept nogil:
+        """Given an index between [0,self.current_capacity], access
+        the appropriate heap and return the requested value"""
+        cdef WeightedPQueueRecord* array = self.array_
+
+        # get value at index
+        return array[index].data
+
+# =============================================================================
+# WeightedMedianCalculator data structure
+# =============================================================================
+
+cdef class WeightedMedianCalculator:
+    """A class to handle calculation of the weighted median from streams of
+    data. To do so, it maintains a parameter ``k`` such that the sum of the
+    weights in the range [0,k) is greater than or equal to half of the total
+    weight. By minimizing the value of ``k`` that fulfills this constraint,
+    calculating the median is done by either taking the value of the sample
+    at index ``k-1`` of ``samples`` (samples[k-1].data) or the average of
+    the samples at index ``k-1`` and ``k`` of ``samples``
+    ((samples[k-1] + samples[k]) / 2).
+
+    Attributes
+    ----------
+    initial_capacity : intp_t
+        The initial capacity of the WeightedMedianCalculator.
+
+    samples : WeightedPQueue
+        Holds the samples (consisting of values and their weights) used in the
+        weighted median calculation.
+
+    total_weight : float64_t
+        The sum of the weights of items in ``samples``. Represents the total
+        weight of all samples used in the median calculation.
+
+    k : intp_t
+        Index used to calculate the median.
+
+    sum_w_0_k : float64_t
+        The sum of the weights from samples[0:k]. Used in the weighted
+        median calculation; minimizing the value of ``k`` such that
+        ``sum_w_0_k`` >= ``total_weight / 2`` provides a mechanism for
+        calculating the median in constant time.
+
+    """
+
+    def __cinit__(self, intp_t initial_capacity):
+        self.initial_capacity = initial_capacity
+        self.samples = WeightedPQueue(initial_capacity)
+        self.total_weight = 0
+        self.k = 0
+        self.sum_w_0_k = 0
+
+    cdef intp_t size(self) noexcept nogil:
+        """Return the number of samples in the
+        WeightedMedianCalculator"""
+        return self.samples.size()
+
+    cdef int reset(self) except -1 nogil:
+        """Reset the WeightedMedianCalculator to its state at construction
+
+        Return -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        # samples.reset (WeightedPQueue.reset) uses safe_realloc, hence
+        # except -1
+        self.samples.reset()
+        self.total_weight = 0
+        self.k = 0
+        self.sum_w_0_k = 0
+        return 0
+
+    cdef int push(self, float64_t data, float64_t weight) except -1 nogil:
+        """Push a value and its associated weight to the WeightedMedianCalculator
+
+        Return -1 in case of failure to allocate memory (and raise MemoryError)
+        or 0 otherwise.
+        """
+        cdef int return_value
+        cdef float64_t original_median = 0.0
+
+        if self.size() != 0:
+            original_median = self.get_median()
+        # samples.push (WeightedPQueue.push) uses safe_realloc, hence except -1
+        return_value = self.samples.push(data, weight)
+        self.update_median_parameters_post_push(data, weight,
+                                                original_median)
+        return return_value
+
+    cdef int update_median_parameters_post_push(
+            self, float64_t data, float64_t weight,
+            float64_t original_median) noexcept nogil:
+        """Update the parameters used in the median calculation,
+        namely `k` and `sum_w_0_k` after an insertion"""
+
+        # trivial case of one element.
+        if self.size() == 1:
+            self.k = 1
+            self.total_weight = weight
+            self.sum_w_0_k = self.total_weight
+            return 0
+
+        # get the original weighted median
+        self.total_weight += weight
+
+        if data < original_median:
+            # inserting below the median, so increment k and
+            # then update self.sum_w_0_k accordingly by adding
+            # the weight that was added.
+            self.k += 1
+            # update sum_w_0_k by adding the weight added
+            self.sum_w_0_k += weight
+
+            # minimize k such that sum(W[0:k]) >= total_weight / 2
+            # minimum value of k is 1
+            while(self.k > 1 and ((self.sum_w_0_k -
+                                   self.samples.get_weight_from_index(self.k-1))
+                                  >= self.total_weight / 2.0)):
+                self.k -= 1
+                self.sum_w_0_k -= self.samples.get_weight_from_index(self.k)
+            return 0
+
+        if data >= original_median:
+            # inserting above or at the median
+            # minimize k such that sum(W[0:k]) >= total_weight / 2
+            while(self.k < self.samples.size() and
+                  (self.sum_w_0_k < self.total_weight / 2.0)):
+                self.k += 1
+                self.sum_w_0_k += self.samples.get_weight_from_index(self.k-1)
+            return 0
+
+    cdef int remove(self, float64_t data, float64_t weight) noexcept nogil:
+        """Remove a value from the MedianHeap, removing it
+        from consideration in the median calculation
+        """
+        cdef int return_value
+        cdef float64_t original_median = 0.0
+
+        if self.size() != 0:
+            original_median = self.get_median()
+
+        return_value = self.samples.remove(data, weight)
+        self.update_median_parameters_post_remove(data, weight,
+                                                  original_median)
+        return return_value
+
+    cdef int pop(self, float64_t* data, float64_t* weight) noexcept nogil:
+        """Pop a value from the MedianHeap, starting from the
+        left and moving to the right.
+        """
+        cdef int return_value
+        cdef float64_t original_median = 0.0
+
+        if self.size() != 0:
+            original_median = self.get_median()
+
+        # no elements to pop
+        if self.samples.size() == 0:
+            return -1
+
+        return_value = self.samples.pop(data, weight)
+        self.update_median_parameters_post_remove(data[0],
+                                                  weight[0],
+                                                  original_median)
+        return return_value
+
+    cdef int update_median_parameters_post_remove(
+            self, float64_t data, float64_t weight,
+            float64_t original_median) noexcept nogil:
+        """Update the parameters used in the median calculation,
+        namely `k` and `sum_w_0_k` after a removal"""
+        # reset parameters because it there are no elements
+        if self.samples.size() == 0:
+            self.k = 0
+            self.total_weight = 0
+            self.sum_w_0_k = 0
+            return 0
+
+        # trivial case of one element.
+        if self.samples.size() == 1:
+            self.k = 1
+            self.total_weight -= weight
+            self.sum_w_0_k = self.total_weight
+            return 0
+
+        # get the current weighted median
+        self.total_weight -= weight
+
+        if data < original_median:
+            # removing below the median, so decrement k and
+            # then update self.sum_w_0_k accordingly by subtracting
+            # the removed weight
+
+            self.k -= 1
+            # update sum_w_0_k by removing the weight at index k
+            self.sum_w_0_k -= weight
+
+            # minimize k such that sum(W[0:k]) >= total_weight / 2
+            # by incrementing k and updating sum_w_0_k accordingly
+            # until the condition is met.
+            while(self.k < self.samples.size() and
+                  (self.sum_w_0_k < self.total_weight / 2.0)):
+                self.k += 1
+                self.sum_w_0_k += self.samples.get_weight_from_index(self.k-1)
+            return 0
+
+        if data >= original_median:
+            # removing above the median
+            # minimize k such that sum(W[0:k]) >= total_weight / 2
+            while(self.k > 1 and ((self.sum_w_0_k -
+                                   self.samples.get_weight_from_index(self.k-1))
+                                  >= self.total_weight / 2.0)):
+                self.k -= 1
+                self.sum_w_0_k -= self.samples.get_weight_from_index(self.k)
+            return 0
+
+    cdef float64_t get_median(self) noexcept nogil:
+        """Write the median to a pointer, taking into account
+        sample weights."""
+        if self.sum_w_0_k == (self.total_weight / 2.0):
+            # split median
+            return (self.samples.get_value_from_index(self.k) +
+                    self.samples.get_value_from_index(self.k-1)) / 2.0
+        if self.sum_w_0_k > (self.total_weight / 2.0):
+            # whole median
+            return self.samples.get_value_from_index(self.k-1)
+
+
+def _any_isnan_axis0(const float32_t[:, :] X):
+    """Same as np.any(np.isnan(X), axis=0)"""
+    cdef:
+        intp_t i, j
+        intp_t n_samples = X.shape[0]
+        intp_t n_features = X.shape[1]
+        uint8_t[::1] isnan_out = np.zeros(X.shape[1], dtype=np.bool_)
+
+    with nogil:
+        for i in range(n_samples):
+            for j in range(n_features):
+                if isnan_out[j]:
+                    continue
+                if isnan(X[i, j]):
+                    isnan_out[j] = True
+                    break
+    return np.asarray(isnan_out)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/meson.build b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..87345a1e344bf52095419d40cc0ada947a238396
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/meson.build
@@ -0,0 +1,28 @@
+tree_extension_metadata = {
+  '_tree':
+    {'sources': [cython_gen_cpp.process('_tree.pyx')],
+     'override_options': ['optimization=3']},
+  '_splitter':
+    {'sources': [cython_gen.process('_splitter.pyx')],
+     'override_options': ['optimization=3']},
+  '_partitioner':
+    {'sources': [cython_gen.process('_partitioner.pyx')],
+     'override_options': ['optimization=3']},
+  '_criterion':
+    {'sources': [cython_gen.process('_criterion.pyx')],
+     'override_options': ['optimization=3']},
+  '_utils':
+    {'sources': [cython_gen.process('_utils.pyx')],
+     'override_options': ['optimization=3']},
+}
+
+foreach ext_name, ext_dict : tree_extension_metadata
+  py.extension_module(
+    ext_name,
+    [ext_dict.get('sources'), utils_cython_tree],
+    dependencies: [np_dep],
+    override_options : ext_dict.get('override_options', []),
+    subdir: 'sklearn/tree',
+    install: true
+  )
+endforeach
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/__init__.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_export.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_export.py
new file mode 100644
index 0000000000000000000000000000000000000000..d05e657072b170901d880b74757d1483b38049f1
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_export.py
@@ -0,0 +1,630 @@
+"""
+Testing for export functions of decision trees (sklearn.tree.export).
+"""
+
+from io import StringIO
+from re import finditer, search
+from textwrap import dedent
+
+import numpy as np
+import pytest
+from numpy.random import RandomState
+
+from sklearn.base import is_classifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.exceptions import NotFittedError
+from sklearn.tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    export_graphviz,
+    export_text,
+    plot_tree,
+)
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+y2 = [[-1, 1], [-1, 1], [-1, 1], [1, 2], [1, 2], [1, 3]]
+w = [1, 1, 1, 0.5, 0.5, 0.5]
+y_degraded = [1, 1, 1, 1, 1, 1]
+
+
+def test_graphviz_toy():
+    # Check correctness of export_graphviz
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    contents1 = export_graphviz(clf, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+    assert contents1 == contents2
+
+    # Test with feature_names
+    contents1 = export_graphviz(
+        clf, feature_names=["feature0", "feature1"], out_file=None
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with feature_names (escaped)
+    contents1 = export_graphviz(
+        clf, feature_names=['feature"0"', 'feature"1"'], out_file=None
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="feature\\"0\\" <= 0.0\\n'
+        "gini = 0.5\\nsamples = 6\\n"
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with class_names
+    contents1 = export_graphviz(clf, class_names=["yes", "no"], out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = yes"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
+        'class = yes"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
+        'class = no"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with class_names (escaped)
+    contents1 = export_graphviz(clf, class_names=['"yes"', '"no"'], out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = \\"yes\\""] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
+        'class = \\"yes\\""] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
+        'class = \\"no\\""] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test plot_options
+    contents1 = export_graphviz(
+        clf,
+        filled=True,
+        impurity=False,
+        proportion=True,
+        special_characters=True,
+        rounded=True,
+        out_file=None,
+        fontname="sans",
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled, rounded", color="black", '
+        'fontname="sans"] ;\n'
+        'edge [fontname="sans"] ;\n'
+        "0 [label=0 ≤ 0.0
samples = 100.0%
"
+        'value = [0.5, 0.5]>, fillcolor="#ffffff"] ;\n'
+        "1 [label=value = [1.0, 0.0]>, "
+        'fillcolor="#e58139"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        "2 [label=value = [0.0, 1.0]>, "
+        'fillcolor="#399de5"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test max_depth
+    contents1 = export_graphviz(clf, max_depth=0, class_names=True, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = y[0]"] ;\n'
+        '1 [label="(...)"] ;\n'
+        "0 -> 1 ;\n"
+        '2 [label="(...)"] ;\n'
+        "0 -> 2 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test max_depth with plot_options
+    contents1 = export_graphviz(
+        clf, max_depth=0, filled=True, out_file=None, node_ids=True
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="node #0\\nx[0] <= 0.0\\ngini = 0.5\\n'
+        'samples = 6\\nvalue = [3, 3]", fillcolor="#ffffff"] ;\n'
+        '1 [label="(...)", fillcolor="#C0C0C0"] ;\n'
+        "0 -> 1 ;\n"
+        '2 [label="(...)", fillcolor="#C0C0C0"] ;\n'
+        "0 -> 2 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test multi-output with weighted samples
+    clf = DecisionTreeClassifier(
+        max_depth=2, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf = clf.fit(X, y2, sample_weight=w)
+
+    contents1 = export_graphviz(clf, filled=True, impurity=False, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\nsamples = 6\\n'
+        "value = [[3.0, 1.5, 0.0]\\n"
+        '[3.0, 1.0, 0.5]]", fillcolor="#ffffff"] ;\n'
+        '1 [label="samples = 3\\nvalue = [[3, 0, 0]\\n'
+        '[3, 0, 0]]", fillcolor="#e58139"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="x[0] <= 1.5\\nsamples = 3\\n'
+        "value = [[0.0, 1.5, 0.0]\\n"
+        '[0.0, 1.0, 0.5]]", fillcolor="#f1bd97"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        '3 [label="samples = 2\\nvalue = [[0, 1, 0]\\n'
+        '[0, 1, 0]]", fillcolor="#e58139"] ;\n'
+        "2 -> 3 ;\n"
+        '4 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n'
+        '[0.0, 0.0, 0.5]]", fillcolor="#e58139"] ;\n'
+        "2 -> 4 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test regression output with plot_options
+    clf = DecisionTreeRegressor(
+        max_depth=3, min_samples_split=2, criterion="squared_error", random_state=2
+    )
+    clf.fit(X, y)
+
+    contents1 = export_graphviz(
+        clf,
+        filled=True,
+        leaves_parallel=True,
+        out_file=None,
+        rotate=True,
+        rounded=True,
+        fontname="sans",
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled, rounded", color="black", '
+        'fontname="sans"] ;\n'
+        "graph [ranksep=equally, splines=polyline] ;\n"
+        'edge [fontname="sans"] ;\n'
+        "rankdir=LR ;\n"
+        '0 [label="x[0] <= 0.0\\nsquared_error = 1.0\\nsamples = 6\\n'
+        'value = 0.0", fillcolor="#f2c09c"] ;\n'
+        '1 [label="squared_error = 0.0\\nsamples = 3\\'
+        'nvalue = -1.0", '
+        'fillcolor="#ffffff"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="True"] ;\n'
+        '2 [label="squared_error = 0.0\\nsamples = 3\\nvalue = 1.0", '
+        'fillcolor="#e58139"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=45, "
+        'headlabel="False"] ;\n'
+        "{rank=same ; 0} ;\n"
+        "{rank=same ; 1; 2} ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test classifier with degraded learning set
+    clf = DecisionTreeClassifier(max_depth=3)
+    clf.fit(X, y_degraded)
+
+    contents1 = export_graphviz(clf, filled=True, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="gini = 0.0\\nsamples = 6\\nvalue = 6.0", '
+        'fillcolor="#ffffff"] ;\n'
+        "}"
+    )
+
+
+@pytest.mark.parametrize("constructor", [list, np.array])
+def test_graphviz_feature_class_names_array_support(constructor):
+    # Check that export_graphviz treats feature names
+    # and class names correctly and supports arrays
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test with feature_names
+    contents1 = export_graphviz(
+        clf, feature_names=constructor(["feature0", "feature1"]), out_file=None
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with class_names
+    contents1 = export_graphviz(
+        clf, class_names=constructor(["yes", "no"]), out_file=None
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = yes"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
+        'class = yes"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
+        'class = no"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+
+def test_graphviz_errors():
+    # Check for errors of export_graphviz
+    clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
+
+    # Check not-fitted decision tree error
+    out = StringIO()
+    with pytest.raises(NotFittedError):
+        export_graphviz(clf, out)
+
+    clf.fit(X, y)
+
+    # Check if it errors when length of feature_names
+    # mismatches with number of features
+    message = "Length of feature_names, 1 does not match number of features, 2"
+    with pytest.raises(ValueError, match=message):
+        export_graphviz(clf, None, feature_names=["a"])
+
+    message = "Length of feature_names, 3 does not match number of features, 2"
+    with pytest.raises(ValueError, match=message):
+        export_graphviz(clf, None, feature_names=["a", "b", "c"])
+
+    # Check error when argument is not an estimator
+    message = "is not an estimator instance"
+    with pytest.raises(TypeError, match=message):
+        export_graphviz(clf.fit(X, y).tree_)
+
+    # Check class_names error
+    out = StringIO()
+    with pytest.raises(IndexError):
+        export_graphviz(clf, out, class_names=[])
+
+
+def test_friedman_mse_in_graphviz():
+    clf = DecisionTreeRegressor(criterion="friedman_mse", random_state=0)
+    clf.fit(X, y)
+    dot_data = StringIO()
+    export_graphviz(clf, out_file=dot_data)
+
+    clf = GradientBoostingClassifier(n_estimators=2, random_state=0)
+    clf.fit(X, y)
+    for estimator in clf.estimators_:
+        export_graphviz(estimator[0], out_file=dot_data)
+
+    for finding in finditer(r"\[.*?samples.*?\]", dot_data.getvalue()):
+        assert "friedman_mse" in finding.group()
+
+
+def test_precision():
+    rng_reg = RandomState(2)
+    rng_clf = RandomState(8)
+    for X, y, clf in zip(
+        (rng_reg.random_sample((5, 2)), rng_clf.random_sample((1000, 4))),
+        (rng_reg.random_sample((5,)), rng_clf.randint(2, size=(1000,))),
+        (
+            DecisionTreeRegressor(
+                criterion="friedman_mse", random_state=0, max_depth=1
+            ),
+            DecisionTreeClassifier(max_depth=1, random_state=0),
+        ),
+    ):
+        clf.fit(X, y)
+        for precision in (4, 3):
+            dot_data = export_graphviz(
+                clf, out_file=None, precision=precision, proportion=True
+            )
+
+            # With the current random state, the impurity and the threshold
+            # will have the number of precision set in the export_graphviz
+            # function. We will check the number of precision with a strict
+            # equality. The value reported will have only 2 precision and
+            # therefore, only a less equal comparison will be done.
+
+            # check value
+            for finding in finditer(r"value = \d+\.\d+", dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) <= precision + 1
+            # check impurity
+            if is_classifier(clf):
+                pattern = r"gini = \d+\.\d+"
+            else:
+                pattern = r"friedman_mse = \d+\.\d+"
+
+            # check impurity
+            for finding in finditer(pattern, dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
+            # check threshold
+            for finding in finditer(r"<= \d+\.\d+", dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
+
+
+def test_export_text_errors():
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+    err_msg = "feature_names must contain 2 elements, got 1"
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, feature_names=["a"])
+    err_msg = (
+        "When `class_names` is an array, it should contain as"
+        " many items as `decision_tree.classes_`. Got 1 while"
+        " the tree was fitted with 2 classes."
+    )
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, class_names=["a"])
+
+
+def test_export_text():
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: -1
+    |--- feature_1 >  0.00
+    |   |--- class: 1
+    """
+    ).lstrip()
+
+    assert export_text(clf) == expected_report
+    # testing that leaves at level 1 are not truncated
+    assert export_text(clf, max_depth=0) == expected_report
+    # testing that the rest of the tree is truncated
+    assert export_text(clf, max_depth=10) == expected_report
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- weights: [3.00, 0.00] class: -1
+    |--- feature_1 >  0.00
+    |   |--- weights: [0.00, 3.00] class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, show_weights=True) == expected_report
+
+    expected_report = dedent(
+        """
+    |- feature_1 <= 0.00
+    | |- class: -1
+    |- feature_1 >  0.00
+    | |- class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, spacing=1) == expected_report
+
+    X_l = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, 1]]
+    y_l = [-1, -1, -1, 1, 1, 1, 2]
+    clf = DecisionTreeClassifier(max_depth=4, random_state=0)
+    clf.fit(X_l, y_l)
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: -1
+    |--- feature_1 >  0.00
+    |   |--- truncated branch of depth 2
+    """
+    ).lstrip()
+    assert export_text(clf, max_depth=0) == expected_report
+
+    X_mo = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+    y_mo = [[-1, -1], [-1, -1], [-1, -1], [1, 1], [1, 1], [1, 1]]
+
+    reg = DecisionTreeRegressor(max_depth=2, random_state=0)
+    reg.fit(X_mo, y_mo)
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.0
+    |   |--- value: [-1.0, -1.0]
+    |--- feature_1 >  0.0
+    |   |--- value: [1.0, 1.0]
+    """
+    ).lstrip()
+    assert export_text(reg, decimals=1) == expected_report
+    assert export_text(reg, decimals=1, show_weights=True) == expected_report
+
+    X_single = [[-2], [-1], [-1], [1], [1], [2]]
+    reg = DecisionTreeRegressor(max_depth=2, random_state=0)
+    reg.fit(X_single, y_mo)
+
+    expected_report = dedent(
+        """
+    |--- first <= 0.0
+    |   |--- value: [-1.0, -1.0]
+    |--- first >  0.0
+    |   |--- value: [1.0, 1.0]
+    """
+    ).lstrip()
+    assert export_text(reg, decimals=1, feature_names=["first"]) == expected_report
+    assert (
+        export_text(reg, decimals=1, show_weights=True, feature_names=["first"])
+        == expected_report
+    )
+
+
+@pytest.mark.parametrize("constructor", [list, np.array])
+def test_export_text_feature_class_names_array_support(constructor):
+    # Check that export_graphviz treats feature names
+    # and class names correctly and supports arrays
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+
+    expected_report = dedent(
+        """
+    |--- b <= 0.00
+    |   |--- class: -1
+    |--- b >  0.00
+    |   |--- class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, feature_names=constructor(["a", "b"])) == expected_report
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: cat
+    |--- feature_1 >  0.00
+    |   |--- class: dog
+    """
+    ).lstrip()
+    assert export_text(clf, class_names=constructor(["cat", "dog"])) == expected_report
+
+
+def test_plot_tree_entropy(pyplot):
+    # mostly smoke tests
+    # Check correctness of export_graphviz for criterion = entropy
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="entropy", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    feature_names = ["first feat", "sepal_width"]
+    nodes = plot_tree(clf, feature_names=feature_names)
+    assert len(nodes) == 5
+    assert (
+        nodes[0].get_text()
+        == "first feat <= 0.0\nentropy = 1.0\nsamples = 6\nvalue = [3, 3]"
+    )
+    assert nodes[1].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [3, 0]"
+    assert nodes[2].get_text() == "True  "
+    assert nodes[3].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [0, 3]"
+    assert nodes[4].get_text() == "  False"
+
+
+@pytest.mark.parametrize("fontsize", [None, 10, 20])
+def test_plot_tree_gini(pyplot, fontsize):
+    # mostly smoke tests
+    # Check correctness of export_graphviz for criterion = gini
+    clf = DecisionTreeClassifier(
+        max_depth=3,
+        min_samples_split=2,
+        criterion="gini",
+        random_state=2,
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    feature_names = ["first feat", "sepal_width"]
+    nodes = plot_tree(clf, feature_names=feature_names, fontsize=fontsize)
+    assert len(nodes) == 5
+    if fontsize is not None:
+        assert all(node.get_fontsize() == fontsize for node in nodes)
+    assert (
+        nodes[0].get_text()
+        == "first feat <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]"
+    )
+    assert nodes[1].get_text() == "gini = 0.0\nsamples = 3\nvalue = [3, 0]"
+    assert nodes[2].get_text() == "True  "
+    assert nodes[3].get_text() == "gini = 0.0\nsamples = 3\nvalue = [0, 3]"
+    assert nodes[4].get_text() == "  False"
+
+
+def test_not_fitted_tree(pyplot):
+    # Testing if not fitted tree throws the correct error
+    clf = DecisionTreeRegressor()
+    with pytest.raises(NotFittedError):
+        plot_tree(clf)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_monotonic_tree.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_monotonic_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfe39720df2240c8647be5d8b4666882fc33d520
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_monotonic_tree.py
@@ -0,0 +1,512 @@
+import numpy as np
+import pytest
+
+from sklearn.datasets import make_classification, make_regression
+from sklearn.ensemble import (
+    ExtraTreesClassifier,
+    ExtraTreesRegressor,
+    RandomForestClassifier,
+    RandomForestRegressor,
+)
+from sklearn.tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    ExtraTreeClassifier,
+    ExtraTreeRegressor,
+)
+from sklearn.utils._testing import assert_allclose
+from sklearn.utils.fixes import CSC_CONTAINERS
+
+TREE_CLASSIFIER_CLASSES = [DecisionTreeClassifier, ExtraTreeClassifier]
+TREE_REGRESSOR_CLASSES = [DecisionTreeRegressor, ExtraTreeRegressor]
+TREE_BASED_CLASSIFIER_CLASSES = TREE_CLASSIFIER_CLASSES + [
+    RandomForestClassifier,
+    ExtraTreesClassifier,
+]
+TREE_BASED_REGRESSOR_CLASSES = TREE_REGRESSOR_CLASSES + [
+    RandomForestRegressor,
+    ExtraTreesRegressor,
+]
+
+
+@pytest.mark.parametrize("TreeClassifier", TREE_BASED_CLASSIFIER_CLASSES)
+@pytest.mark.parametrize("depth_first_builder", (True, False))
+@pytest.mark.parametrize("sparse_splitter", (True, False))
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_monotonic_constraints_classifications(
+    TreeClassifier,
+    depth_first_builder,
+    sparse_splitter,
+    global_random_seed,
+    csc_container,
+):
+    n_samples = 1000
+    n_samples_train = 900
+    X, y = make_classification(
+        n_samples=n_samples,
+        n_classes=2,
+        n_features=5,
+        n_informative=5,
+        n_redundant=0,
+        random_state=global_random_seed,
+    )
+    X_train, y_train = X[:n_samples_train], y[:n_samples_train]
+    X_test, _ = X[n_samples_train:], y[n_samples_train:]
+
+    X_test_0incr, X_test_0decr = np.copy(X_test), np.copy(X_test)
+    X_test_1incr, X_test_1decr = np.copy(X_test), np.copy(X_test)
+    X_test_0incr[:, 0] += 10
+    X_test_0decr[:, 0] -= 10
+    X_test_1incr[:, 1] += 10
+    X_test_1decr[:, 1] -= 10
+    monotonic_cst = np.zeros(X.shape[1])
+    monotonic_cst[0] = 1
+    monotonic_cst[1] = -1
+
+    if depth_first_builder:
+        est = TreeClassifier(max_depth=None, monotonic_cst=monotonic_cst)
+    else:
+        est = TreeClassifier(
+            max_depth=None,
+            monotonic_cst=monotonic_cst,
+            max_leaf_nodes=n_samples_train,
+        )
+    if hasattr(est, "random_state"):
+        est.set_params(**{"random_state": global_random_seed})
+    if hasattr(est, "n_estimators"):
+        est.set_params(**{"n_estimators": 5})
+    if sparse_splitter:
+        X_train = csc_container(X_train)
+    est.fit(X_train, y_train)
+    proba_test = est.predict_proba(X_test)
+
+    assert np.logical_and(proba_test >= 0.0, proba_test <= 1.0).all(), (
+        "Probability should always be in [0, 1] range."
+    )
+    assert_allclose(proba_test.sum(axis=1), 1.0)
+
+    # Monotonic increase constraint, it applies to the positive class
+    assert np.all(est.predict_proba(X_test_0incr)[:, 1] >= proba_test[:, 1])
+    assert np.all(est.predict_proba(X_test_0decr)[:, 1] <= proba_test[:, 1])
+
+    # Monotonic decrease constraint, it applies to the positive class
+    assert np.all(est.predict_proba(X_test_1incr)[:, 1] <= proba_test[:, 1])
+    assert np.all(est.predict_proba(X_test_1decr)[:, 1] >= proba_test[:, 1])
+
+
+@pytest.mark.parametrize("TreeRegressor", TREE_BASED_REGRESSOR_CLASSES)
+@pytest.mark.parametrize("depth_first_builder", (True, False))
+@pytest.mark.parametrize("sparse_splitter", (True, False))
+@pytest.mark.parametrize("criterion", ("absolute_error", "squared_error"))
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_monotonic_constraints_regressions(
+    TreeRegressor,
+    depth_first_builder,
+    sparse_splitter,
+    criterion,
+    global_random_seed,
+    csc_container,
+):
+    n_samples = 1000
+    n_samples_train = 900
+    # Build a regression task using 5 informative features
+    X, y = make_regression(
+        n_samples=n_samples,
+        n_features=5,
+        n_informative=5,
+        random_state=global_random_seed,
+    )
+    train = np.arange(n_samples_train)
+    test = np.arange(n_samples_train, n_samples)
+    X_train = X[train]
+    y_train = y[train]
+    X_test = np.copy(X[test])
+    X_test_incr = np.copy(X_test)
+    X_test_decr = np.copy(X_test)
+    X_test_incr[:, 0] += 10
+    X_test_decr[:, 1] += 10
+    monotonic_cst = np.zeros(X.shape[1])
+    monotonic_cst[0] = 1
+    monotonic_cst[1] = -1
+
+    if depth_first_builder:
+        est = TreeRegressor(
+            max_depth=None,
+            monotonic_cst=monotonic_cst,
+            criterion=criterion,
+        )
+    else:
+        est = TreeRegressor(
+            max_depth=8,
+            monotonic_cst=monotonic_cst,
+            criterion=criterion,
+            max_leaf_nodes=n_samples_train,
+        )
+    if hasattr(est, "random_state"):
+        est.set_params(random_state=global_random_seed)
+    if hasattr(est, "n_estimators"):
+        est.set_params(**{"n_estimators": 5})
+    if sparse_splitter:
+        X_train = csc_container(X_train)
+    est.fit(X_train, y_train)
+    y = est.predict(X_test)
+    # Monotonic increase constraint
+    y_incr = est.predict(X_test_incr)
+    # y_incr should always be greater than y
+    assert np.all(y_incr >= y)
+
+    # Monotonic decrease constraint
+    y_decr = est.predict(X_test_decr)
+    # y_decr should always be lower than y
+    assert np.all(y_decr <= y)
+
+
+@pytest.mark.parametrize("TreeClassifier", TREE_BASED_CLASSIFIER_CLASSES)
+def test_multiclass_raises(TreeClassifier):
+    X, y = make_classification(
+        n_samples=100, n_features=5, n_classes=3, n_informative=3, random_state=0
+    )
+    y[0] = 0
+    monotonic_cst = np.zeros(X.shape[1])
+    monotonic_cst[0] = -1
+    monotonic_cst[1] = 1
+    est = TreeClassifier(max_depth=None, monotonic_cst=monotonic_cst, random_state=0)
+
+    msg = "Monotonicity constraints are not supported with multiclass classification"
+    with pytest.raises(ValueError, match=msg):
+        est.fit(X, y)
+
+
+@pytest.mark.parametrize("TreeClassifier", TREE_BASED_CLASSIFIER_CLASSES)
+def test_multiple_output_raises(TreeClassifier):
+    X = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]
+    y = [[1, 0, 1, 0, 1], [1, 0, 1, 0, 1]]
+
+    est = TreeClassifier(
+        max_depth=None, monotonic_cst=np.array([-1, 1]), random_state=0
+    )
+    msg = "Monotonicity constraints are not supported with multiple output"
+    with pytest.raises(ValueError, match=msg):
+        est.fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "Tree",
+    [
+        DecisionTreeClassifier,
+        DecisionTreeRegressor,
+        ExtraTreeClassifier,
+        ExtraTreeRegressor,
+    ],
+)
+def test_missing_values_raises(Tree):
+    X, y = make_classification(
+        n_samples=100, n_features=5, n_classes=2, n_informative=3, random_state=0
+    )
+    X[0, 0] = np.nan
+    monotonic_cst = np.zeros(X.shape[1])
+    monotonic_cst[0] = 1
+    est = Tree(max_depth=None, monotonic_cst=monotonic_cst, random_state=0)
+
+    msg = "Input X contains NaN"
+    with pytest.raises(ValueError, match=msg):
+        est.fit(X, y)
+
+
+@pytest.mark.parametrize("TreeClassifier", TREE_BASED_CLASSIFIER_CLASSES)
+def test_bad_monotonic_cst_raises(TreeClassifier):
+    X = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
+    y = [1, 0, 1, 0, 1]
+
+    msg = "monotonic_cst has shape 3 but the input data X has 2 features."
+    est = TreeClassifier(
+        max_depth=None, monotonic_cst=np.array([-1, 1, 0]), random_state=0
+    )
+    with pytest.raises(ValueError, match=msg):
+        est.fit(X, y)
+
+    msg = "monotonic_cst must be None or an array-like of -1, 0 or 1."
+    est = TreeClassifier(
+        max_depth=None, monotonic_cst=np.array([-2, 2]), random_state=0
+    )
+    with pytest.raises(ValueError, match=msg):
+        est.fit(X, y)
+
+    est = TreeClassifier(
+        max_depth=None, monotonic_cst=np.array([-1, 0.8]), random_state=0
+    )
+    with pytest.raises(ValueError, match=msg + "(.*)0.8]"):
+        est.fit(X, y)
+
+
+def assert_1d_reg_tree_children_monotonic_bounded(tree_, monotonic_sign):
+    values = tree_.value
+    for i in range(tree_.node_count):
+        if tree_.children_left[i] > i and tree_.children_right[i] > i:
+            # Check monotonicity on children
+            i_left = tree_.children_left[i]
+            i_right = tree_.children_right[i]
+            if monotonic_sign == 1:
+                assert values[i_left] <= values[i_right]
+            elif monotonic_sign == -1:
+                assert values[i_left] >= values[i_right]
+            val_middle = (values[i_left] + values[i_right]) / 2
+            # Check bounds on grand-children, filtering out leaf nodes
+            if tree_.feature[i_left] >= 0:
+                i_left_right = tree_.children_right[i_left]
+                if monotonic_sign == 1:
+                    assert values[i_left_right] <= val_middle
+                elif monotonic_sign == -1:
+                    assert values[i_left_right] >= val_middle
+            if tree_.feature[i_right] >= 0:
+                i_right_left = tree_.children_left[i_right]
+                if monotonic_sign == 1:
+                    assert val_middle <= values[i_right_left]
+                elif monotonic_sign == -1:
+                    assert val_middle >= values[i_right_left]
+
+
+def test_assert_1d_reg_tree_children_monotonic_bounded():
+    X = np.linspace(-1, 1, 7).reshape(-1, 1)
+    y = np.sin(2 * np.pi * X.ravel())
+
+    reg = DecisionTreeRegressor(max_depth=None, random_state=0).fit(X, y)
+
+    with pytest.raises(AssertionError):
+        assert_1d_reg_tree_children_monotonic_bounded(reg.tree_, 1)
+
+    with pytest.raises(AssertionError):
+        assert_1d_reg_tree_children_monotonic_bounded(reg.tree_, -1)
+
+
+def assert_1d_reg_monotonic(clf, monotonic_sign, min_x, max_x, n_steps):
+    X_grid = np.linspace(min_x, max_x, n_steps).reshape(-1, 1)
+    y_pred_grid = clf.predict(X_grid)
+    if monotonic_sign == 1:
+        assert (np.diff(y_pred_grid) >= 0.0).all()
+    elif monotonic_sign == -1:
+        assert (np.diff(y_pred_grid) <= 0.0).all()
+
+
+@pytest.mark.parametrize("TreeRegressor", TREE_REGRESSOR_CLASSES)
+def test_1d_opposite_monotonicity_cst_data(TreeRegressor):
+    # Check that positive monotonic data with negative monotonic constraint
+    # yield constant predictions, equal to the average of target values
+    X = np.linspace(-2, 2, 10).reshape(-1, 1)
+    y = X.ravel()
+    clf = TreeRegressor(monotonic_cst=[-1])
+    clf.fit(X, y)
+    assert clf.tree_.node_count == 1
+    assert clf.tree_.value[0] == 0.0
+
+    # Swap monotonicity
+    clf = TreeRegressor(monotonic_cst=[1])
+    clf.fit(X, -y)
+    assert clf.tree_.node_count == 1
+    assert clf.tree_.value[0] == 0.0
+
+
+@pytest.mark.parametrize("TreeRegressor", TREE_REGRESSOR_CLASSES)
+@pytest.mark.parametrize("monotonic_sign", (-1, 1))
+@pytest.mark.parametrize("depth_first_builder", (True, False))
+@pytest.mark.parametrize("criterion", ("absolute_error", "squared_error"))
+def test_1d_tree_nodes_values(
+    TreeRegressor, monotonic_sign, depth_first_builder, criterion, global_random_seed
+):
+    # Adaptation from test_nodes_values in test_monotonic_constraints.py
+    # in sklearn.ensemble._hist_gradient_boosting
+    # Build a single tree with only one feature, and make sure the node
+    # values respect the monotonicity constraints.
+
+    # Considering the following tree with a monotonic +1 constraint, we
+    # should have:
+    #
+    #       root
+    #      /    \
+    #     a      b
+    #    / \    / \
+    #   c   d  e   f
+    #
+    #        a <=  root  <= b
+    # c <= d <= (a + b) / 2 <= e <= f
+
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    n_features = 1
+    X = rng.rand(n_samples, n_features)
+    y = rng.rand(n_samples)
+
+    if depth_first_builder:
+        # No max_leaf_nodes, default depth first tree builder
+        clf = TreeRegressor(
+            monotonic_cst=[monotonic_sign],
+            criterion=criterion,
+            random_state=global_random_seed,
+        )
+    else:
+        # max_leaf_nodes triggers best first tree builder
+        clf = TreeRegressor(
+            monotonic_cst=[monotonic_sign],
+            max_leaf_nodes=n_samples,
+            criterion=criterion,
+            random_state=global_random_seed,
+        )
+    clf.fit(X, y)
+
+    assert_1d_reg_tree_children_monotonic_bounded(clf.tree_, monotonic_sign)
+    assert_1d_reg_monotonic(clf, monotonic_sign, np.min(X), np.max(X), 100)
+
+
+def assert_nd_reg_tree_children_monotonic_bounded(tree_, monotonic_cst):
+    upper_bound = np.full(tree_.node_count, np.inf)
+    lower_bound = np.full(tree_.node_count, -np.inf)
+    for i in range(tree_.node_count):
+        feature = tree_.feature[i]
+        node_value = tree_.value[i][0][0]  # unpack value from nx1x1 array
+        # While building the tree, the computed middle value is slightly
+        # different from the average of the siblings values, because
+        # sum_right / weighted_n_right
+        # is slightly different from the value of the right sibling.
+        # This can cause a discrepancy up to numerical noise when clipping,
+        # which is resolved by comparing with some loss of precision.
+        assert np.float32(node_value) <= np.float32(upper_bound[i])
+        assert np.float32(node_value) >= np.float32(lower_bound[i])
+
+        if feature < 0:
+            # Leaf: nothing to do
+            continue
+
+        # Split node: check and update bounds for the children.
+        i_left = tree_.children_left[i]
+        i_right = tree_.children_right[i]
+        # unpack value from nx1x1 array
+        middle_value = (tree_.value[i_left][0][0] + tree_.value[i_right][0][0]) / 2
+
+        if monotonic_cst[feature] == 0:
+            # Feature without monotonicity constraint: propagate bounds
+            # down the tree to both children.
+            # Otherwise, with 2 features and a monotonic increase constraint
+            # (encoded by +1) on feature 0, the following tree can be accepted,
+            # although it does not respect the monotonic increase constraint:
+            #
+            #                      X[0] <= 0
+            #                      value = 100
+            #                     /            \
+            #          X[0] <= -1                X[1] <= 0
+            #          value = 50                value = 150
+            #        /            \             /            \
+            #    leaf           leaf           leaf          leaf
+            #    value = 25     value = 75     value = 50    value = 250
+
+            lower_bound[i_left] = lower_bound[i]
+            upper_bound[i_left] = upper_bound[i]
+            lower_bound[i_right] = lower_bound[i]
+            upper_bound[i_right] = upper_bound[i]
+
+        elif monotonic_cst[feature] == 1:
+            # Feature with constraint: check monotonicity
+            assert tree_.value[i_left] <= tree_.value[i_right]
+
+            # Propagate bounds down the tree to both children.
+            lower_bound[i_left] = lower_bound[i]
+            upper_bound[i_left] = middle_value
+            lower_bound[i_right] = middle_value
+            upper_bound[i_right] = upper_bound[i]
+
+        elif monotonic_cst[feature] == -1:
+            # Feature with constraint: check monotonicity
+            assert tree_.value[i_left] >= tree_.value[i_right]
+
+            # Update and propagate bounds down the tree to both children.
+            lower_bound[i_left] = middle_value
+            upper_bound[i_left] = upper_bound[i]
+            lower_bound[i_right] = lower_bound[i]
+            upper_bound[i_right] = middle_value
+
+        else:  # pragma: no cover
+            raise ValueError(f"monotonic_cst[{feature}]={monotonic_cst[feature]}")
+
+
+def test_assert_nd_reg_tree_children_monotonic_bounded():
+    # Check that assert_nd_reg_tree_children_monotonic_bounded can detect
+    # non-monotonic tree predictions.
+    X = np.linspace(0, 2 * np.pi, 30).reshape(-1, 1)
+    y = np.sin(X).ravel()
+    reg = DecisionTreeRegressor(max_depth=None, random_state=0).fit(X, y)
+
+    with pytest.raises(AssertionError):
+        assert_nd_reg_tree_children_monotonic_bounded(reg.tree_, [1])
+
+    with pytest.raises(AssertionError):
+        assert_nd_reg_tree_children_monotonic_bounded(reg.tree_, [-1])
+
+    assert_nd_reg_tree_children_monotonic_bounded(reg.tree_, [0])
+
+    # Check that assert_nd_reg_tree_children_monotonic_bounded raises
+    # when the data (and therefore the model) is naturally monotonic in the
+    # opposite direction.
+    X = np.linspace(-5, 5, 5).reshape(-1, 1)
+    y = X.ravel() ** 3
+    reg = DecisionTreeRegressor(max_depth=None, random_state=0).fit(X, y)
+
+    with pytest.raises(AssertionError):
+        assert_nd_reg_tree_children_monotonic_bounded(reg.tree_, [-1])
+
+    # For completeness, check that the converse holds when swapping the sign.
+    reg = DecisionTreeRegressor(max_depth=None, random_state=0).fit(X, -y)
+
+    with pytest.raises(AssertionError):
+        assert_nd_reg_tree_children_monotonic_bounded(reg.tree_, [1])
+
+
+@pytest.mark.parametrize("TreeRegressor", TREE_REGRESSOR_CLASSES)
+@pytest.mark.parametrize("monotonic_sign", (-1, 1))
+@pytest.mark.parametrize("depth_first_builder", (True, False))
+@pytest.mark.parametrize("criterion", ("absolute_error", "squared_error"))
+def test_nd_tree_nodes_values(
+    TreeRegressor, monotonic_sign, depth_first_builder, criterion, global_random_seed
+):
+    # Build tree with several features, and make sure the nodes
+    # values respect the monotonicity constraints.
+
+    # Considering the following tree with a monotonic increase constraint on X[0],
+    # we should have:
+    #
+    #            root
+    #           X[0]<=t
+    #          /       \
+    #         a         b
+    #     X[0]<=u   X[1]<=v
+    #    /       \   /     \
+    #   c        d  e       f
+    #
+    # i)   a <= root <= b
+    # ii)  c <= a <= d <= (a+b)/2
+    # iii) (a+b)/2 <= min(e,f)
+    # For iii) we check that each node value is within the proper lower and
+    # upper bounds.
+
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    n_features = 2
+    monotonic_cst = [monotonic_sign, 0]
+    X = rng.rand(n_samples, n_features)
+    y = rng.rand(n_samples)
+
+    if depth_first_builder:
+        # No max_leaf_nodes, default depth first tree builder
+        clf = TreeRegressor(
+            monotonic_cst=monotonic_cst,
+            criterion=criterion,
+            random_state=global_random_seed,
+        )
+    else:
+        # max_leaf_nodes triggers best first tree builder
+        clf = TreeRegressor(
+            monotonic_cst=monotonic_cst,
+            max_leaf_nodes=n_samples,
+            criterion=criterion,
+            random_state=global_random_seed,
+        )
+    clf.fit(X, y)
+    assert_nd_reg_tree_children_monotonic_bounded(clf.tree_, monotonic_cst)
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_reingold_tilford.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_reingold_tilford.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf0ce3ce2cffc2792db28858bff69acb8eb4d45a
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_reingold_tilford.py
@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+
+from sklearn.tree._reingold_tilford import Tree, buchheim
+
+simple_tree = Tree("", 0, Tree("", 1), Tree("", 2))
+
+bigger_tree = Tree(
+    "",
+    0,
+    Tree(
+        "",
+        1,
+        Tree("", 3),
+        Tree("", 4, Tree("", 7), Tree("", 8)),
+    ),
+    Tree("", 2, Tree("", 5), Tree("", 6)),
+)
+
+
+@pytest.mark.parametrize("tree, n_nodes", [(simple_tree, 3), (bigger_tree, 9)])
+def test_buchheim(tree, n_nodes):
+    def walk_tree(draw_tree):
+        res = [(draw_tree.x, draw_tree.y)]
+        for child in draw_tree.children:
+            # parents higher than children:
+            assert child.y == draw_tree.y + 1
+            res.extend(walk_tree(child))
+        if len(draw_tree.children):
+            # these trees are always binary
+            # parents are centered above children
+            assert (
+                draw_tree.x == (draw_tree.children[0].x + draw_tree.children[1].x) / 2
+            )
+        return res
+
+    layout = buchheim(tree)
+    coordinates = walk_tree(layout)
+    assert len(coordinates) == n_nodes
+    # test that x values are unique per depth / level
+    # we could also do it quicker using defaultdicts..
+    depth = 0
+    while True:
+        x_at_this_depth = [node[0] for node in coordinates if node[1] == depth]
+        if not x_at_this_depth:
+            # reached all leafs
+            break
+        assert len(np.unique(x_at_this_depth)) == len(x_at_this_depth)
+        depth += 1
diff --git a/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_tree.py b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..790ebdcea11279bf46a1f0e90ab08b5c890f71f9
--- /dev/null
+++ b/Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/sklearn/tree/tests/test_tree.py
@@ -0,0 +1,2839 @@
+"""
+Testing for the tree module (sklearn.tree).
+"""
+
+import copy
+import copyreg
+import io
+import pickle
+import re
+import struct
+from itertools import chain, pairwise, product
+
+import joblib
+import numpy as np
+import pytest
+from joblib.numpy_pickle import NumpyPickler
+from numpy.testing import assert_allclose
+
+from sklearn import clone, datasets, tree
+from sklearn.dummy import DummyRegressor
+from sklearn.exceptions import NotFittedError
+from sklearn.impute import SimpleImputer
+from sklearn.metrics import accuracy_score, mean_poisson_deviance, mean_squared_error
+from sklearn.model_selection import cross_val_score, train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.random_projection import _sparse_random_matrix
+from sklearn.tree import (
+    DecisionTreeClassifier,
+    DecisionTreeRegressor,
+    ExtraTreeClassifier,
+    ExtraTreeRegressor,
+)
+from sklearn.tree._classes import (
+    CRITERIA_CLF,
+    CRITERIA_REG,
+    DENSE_SPLITTERS,
+    SPARSE_SPLITTERS,
+)
+from sklearn.tree._partitioner import _py_sort
+from sklearn.tree._tree import (
+    NODE_DTYPE,
+    TREE_LEAF,
+    TREE_UNDEFINED,
+    _build_pruned_tree_py,
+    _check_n_classes,
+    _check_node_ndarray,
+    _check_value_ndarray,
+)
+from sklearn.tree._tree import Tree as CythonTree
+from sklearn.utils import compute_sample_weight
+from sklearn.utils._testing import (
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    create_memmap_backed_data,
+    ignore_warnings,
+    skip_if_32bit,
+)
+from sklearn.utils.fixes import (
+    _IS_32BIT,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+)
+from sklearn.utils.validation import check_random_state
+
+CLF_CRITERIONS = ("gini", "log_loss")
+REG_CRITERIONS = ("squared_error", "absolute_error", "friedman_mse", "poisson")
+
+CLF_TREES = {
+    "DecisionTreeClassifier": DecisionTreeClassifier,
+    "ExtraTreeClassifier": ExtraTreeClassifier,
+}
+
+REG_TREES = {
+    "DecisionTreeRegressor": DecisionTreeRegressor,
+    "ExtraTreeRegressor": ExtraTreeRegressor,
+}
+
+ALL_TREES: dict = dict()
+ALL_TREES.update(CLF_TREES)
+ALL_TREES.update(REG_TREES)
+
+SPARSE_TREES = [
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "ExtraTreeClassifier",
+    "ExtraTreeRegressor",
+]
+
+
+X_small = np.array(
+    [
+        [0, 0, 4, 0, 0, 0, 1, -14, 0, -4, 0, 0, 0, 0],
+        [0, 0, 5, 3, 0, -4, 0, 0, 1, -5, 0.2, 0, 4, 1],
+        [-1, -1, 0, 0, -4.5, 0, 0, 2.1, 1, 0, 0, -4.5, 0, 1],
+        [-1, -1, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 1],
+        [-1, -1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1],
+        [-1, -2, 0, 4, -3, 10, 4, 0, -3.2, 0, 4, 3, -4, 1],
+        [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -3, 1],
+        [2.11, 0, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1],
+        [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0, 0, -2, 1],
+        [2.11, 8, -6, -0.5, 0, 11, 0, 0, -3.2, 6, 0.5, 0, -1, 0],
+        [2, 8, 5, 1, 0.5, -4, 10, 0, 1, -5, 3, 0, 2, 0],
+        [2, 0, 1, 1, 1, -1, 1, 0, 0, -2, 3, 0, 1, 0],
+        [2, 0, 1, 2, 3, -1, 10, 2, 0, -1, 1, 2, 2, 0],
+        [1, 1, 0, 2, 2, -1, 1, 2, 0, -5, 1, 2, 3, 0],
+        [3, 1, 0, 3, 0, -4, 10, 0, 1, -5, 3, 0, 3, 1],
+        [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 0.5, 0, -3, 1],
+        [2.11, 8, -6, -0.5, 0, 1, 0, 0, -3.2, 6, 1.5, 1, -1, -1],
+        [2.11, 8, -6, -0.5, 0, 10, 0, 0, -3.2, 6, 0.5, 0, -1, -1],
+        [2, 0, 5, 1, 0.5, -2, 10, 0, 1, -5, 3, 1, 0, -1],
+        [2, 0, 1, 1, 1, -2, 1, 0, 0, -2, 0, 0, 0, 1],
+        [2, 1, 1, 1, 2, -1, 10, 2, 0, -1, 0, 2, 1, 1],
+        [1, 1, 0, 0, 1, -3, 1, 2, 0, -5, 1, 2, 1, 1],
+        [3, 1, 0, 1, 0, -4, 1, 0, 1, -2, 0, 0, 1, 0],
+    ]
+)
+
+y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
+y_small_reg = [
+    1.0,
+    2.1,
+    1.2,
+    0.05,
+    10,
+    2.4,
+    3.1,
+    1.01,
+    0.01,
+    2.98,
+    3.1,
+    1.1,
+    0.0,
+    1.2,
+    2,
+    11,
+    0,
+    0,
+    4.5,
+    0.201,
+    1.06,
+    0.9,
+    0,
+]
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+T = [[-1, -1], [2, 2], [3, 2]]
+true_result = [-1, 1, 1]
+
+# also load the iris dataset
+# and randomly permute it
+iris = datasets.load_iris()
+rng = np.random.RandomState(1)
+perm = rng.permutation(iris.target.size)
+iris.data = iris.data[perm]
+iris.target = iris.target[perm]
+
+# also load the diabetes dataset
+# and randomly permute it
+diabetes = datasets.load_diabetes()
+perm = rng.permutation(diabetes.target.size)
+diabetes.data = diabetes.data[perm]
+diabetes.target = diabetes.target[perm]
+
+digits = datasets.load_digits()
+perm = rng.permutation(digits.target.size)
+digits.data = digits.data[perm]
+digits.target = digits.target[perm]
+
+random_state = check_random_state(0)
+X_multilabel, y_multilabel = datasets.make_multilabel_classification(
+    random_state=0, n_samples=30, n_features=10
+)
+
+# NB: despite their names X_sparse_* are numpy arrays (and not sparse matrices)
+X_sparse_pos = random_state.uniform(size=(20, 5))
+X_sparse_pos[X_sparse_pos <= 0.8] = 0.0
+y_random = random_state.randint(0, 4, size=(20,))
+X_sparse_mix = _sparse_random_matrix(20, 10, density=0.25, random_state=0).toarray()
+
+
+DATASETS = {
+    "iris": {"X": iris.data, "y": iris.target},
+    "diabetes": {"X": diabetes.data, "y": diabetes.target},
+    "digits": {"X": digits.data, "y": digits.target},
+    "toy": {"X": X, "y": y},
+    "clf_small": {"X": X_small, "y": y_small},
+    "reg_small": {"X": X_small, "y": y_small_reg},
+    "multilabel": {"X": X_multilabel, "y": y_multilabel},
+    "sparse-pos": {"X": X_sparse_pos, "y": y_random},
+    "sparse-neg": {"X": -X_sparse_pos, "y": y_random},
+    "sparse-mix": {"X": X_sparse_mix, "y": y_random},
+    "zeros": {"X": np.zeros((20, 3)), "y": y_random},
+}
+
+
+def assert_tree_equal(d, s, message):
+    assert s.node_count == d.node_count, (
+        "{0}: inequal number of node ({1} != {2})".format(
+            message, s.node_count, d.node_count
+        )
+    )
+
+    assert_array_equal(
+        d.children_right, s.children_right, message + ": inequal children_right"
+    )
+    assert_array_equal(
+        d.children_left, s.children_left, message + ": inequal children_left"
+    )
+
+    external = d.children_right == TREE_LEAF
+    internal = np.logical_not(external)
+
+    assert_array_equal(
+        d.feature[internal], s.feature[internal], message + ": inequal features"
+    )
+    assert_array_equal(
+        d.threshold[internal], s.threshold[internal], message + ": inequal threshold"
+    )
+    assert_array_equal(
+        d.n_node_samples.sum(),
+        s.n_node_samples.sum(),
+        message + ": inequal sum(n_node_samples)",
+    )
+    assert_array_equal(
+        d.n_node_samples, s.n_node_samples, message + ": inequal n_node_samples"
+    )
+
+    assert_almost_equal(d.impurity, s.impurity, err_msg=message + ": inequal impurity")
+
+    assert_array_almost_equal(
+        d.value[external], s.value[external], err_msg=message + ": inequal value"
+    )
+
+
+def test_classification_toy():
+    # Check classification on a toy dataset.
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+        clf = Tree(max_features=1, random_state=1)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+
+def test_weighted_classification_toy():
+    # Check classification on a weighted toy dataset.
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+
+        clf.fit(X, y, sample_weight=np.ones(len(X)))
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+        clf.fit(X, y, sample_weight=np.full(len(X), 0.5))
+        assert_array_equal(clf.predict(T), true_result, "Failed with {0}".format(name))
+
+
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+@pytest.mark.parametrize("criterion", REG_CRITERIONS)
+def test_regression_toy(Tree, criterion):
+    # Check regression on a toy dataset.
+    if criterion == "poisson":
+        # make target positive while not touching the original y and
+        # true_result
+        a = np.abs(np.min(y)) + 1
+        y_train = np.array(y) + a
+        y_test = np.array(true_result) + a
+    else:
+        y_train = y
+        y_test = true_result
+
+    reg = Tree(criterion=criterion, random_state=1)
+    reg.fit(X, y_train)
+    assert_allclose(reg.predict(T), y_test)
+
+    clf = Tree(criterion=criterion, max_features=1, random_state=1)
+    clf.fit(X, y_train)
+    assert_allclose(reg.predict(T), y_test)
+
+
+def test_xor():
+    # Check on a XOR problem
+    y = np.zeros((10, 10))
+    y[:5, :5] = 1
+    y[5:, 5:] = 1
+
+    gridx, gridy = np.indices(y.shape)
+
+    X = np.vstack([gridx.ravel(), gridy.ravel()]).T
+    y = y.ravel()
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+        clf.fit(X, y)
+        assert clf.score(X, y) == 1.0, "Failed with {0}".format(name)
+
+        clf = Tree(random_state=0, max_features=1)
+        clf.fit(X, y)
+        assert clf.score(X, y) == 1.0, "Failed with {0}".format(name)
+
+
+def test_iris():
+    # Check consistency on dataset iris.
+    for (name, Tree), criterion in product(CLF_TREES.items(), CLF_CRITERIONS):
+        clf = Tree(criterion=criterion, random_state=0)
+        clf.fit(iris.data, iris.target)
+        score = accuracy_score(clf.predict(iris.data), iris.target)
+        assert score > 0.9, "Failed with {0}, criterion = {1} and score = {2}".format(
+            name, criterion, score
+        )
+
+        clf = Tree(criterion=criterion, max_features=2, random_state=0)
+        clf.fit(iris.data, iris.target)
+        score = accuracy_score(clf.predict(iris.data), iris.target)
+        assert score > 0.5, "Failed with {0}, criterion = {1} and score = {2}".format(
+            name, criterion, score
+        )
+
+
+@pytest.mark.parametrize("name, Tree", REG_TREES.items())
+@pytest.mark.parametrize("criterion", REG_CRITERIONS)
+def test_diabetes_overfit(name, Tree, criterion):
+    # check consistency of overfitted trees on the diabetes dataset
+    # since the trees will overfit, we expect an MSE of 0
+    reg = Tree(criterion=criterion, random_state=0)
+    reg.fit(diabetes.data, diabetes.target)
+    score = mean_squared_error(diabetes.target, reg.predict(diabetes.data))
+    assert score == pytest.approx(0), (
+        f"Failed with {name}, criterion = {criterion} and score = {score}"
+    )
+
+
+@skip_if_32bit
+@pytest.mark.parametrize("name, Tree", REG_TREES.items())
+@pytest.mark.parametrize(
+    "criterion, max_depth, metric, max_loss",
+    [
+        ("squared_error", 15, mean_squared_error, 60),
+        ("absolute_error", 20, mean_squared_error, 60),
+        ("friedman_mse", 15, mean_squared_error, 60),
+        ("poisson", 15, mean_poisson_deviance, 30),
+    ],
+)
+def test_diabetes_underfit(name, Tree, criterion, max_depth, metric, max_loss):
+    # check consistency of trees when the depth and the number of features are
+    # limited
+
+    reg = Tree(criterion=criterion, max_depth=max_depth, max_features=6, random_state=0)
+    reg.fit(diabetes.data, diabetes.target)
+    loss = metric(diabetes.target, reg.predict(diabetes.data))
+    assert 0 < loss < max_loss
+
+
+def test_probability():
+    # Predict probabilities using DecisionTreeClassifier.
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(max_depth=1, max_features=1, random_state=42)
+        clf.fit(iris.data, iris.target)
+
+        prob_predict = clf.predict_proba(iris.data)
+        assert_array_almost_equal(
+            np.sum(prob_predict, 1),
+            np.ones(iris.data.shape[0]),
+            err_msg="Failed with {0}".format(name),
+        )
+        assert_array_equal(
+            np.argmax(prob_predict, 1),
+            clf.predict(iris.data),
+            err_msg="Failed with {0}".format(name),
+        )
+        assert_almost_equal(
+            clf.predict_proba(iris.data),
+            np.exp(clf.predict_log_proba(iris.data)),
+            8,
+            err_msg="Failed with {0}".format(name),
+        )
+
+
+def test_arrayrepr():
+    # Check the array representation.
+    # Check resize
+    X = np.arange(10000)[:, np.newaxis]
+    y = np.arange(10000)
+
+    for name, Tree in REG_TREES.items():
+        reg = Tree(max_depth=None, random_state=0)
+        reg.fit(X, y)
+
+
+def test_pure_set():
+    # Check when y is pure.
+    X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+    y = [1, 1, 1, 1, 1, 1]
+
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, y)
+        assert_array_equal(clf.predict(X), y, err_msg="Failed with {0}".format(name))
+
+    for name, TreeRegressor in REG_TREES.items():
+        reg = TreeRegressor(random_state=0)
+        reg.fit(X, y)
+        assert_almost_equal(reg.predict(X), y, err_msg="Failed with {0}".format(name))
+
+
+def test_numerical_stability():
+    # Check numerical stability.
+    X = np.array(
+        [
+            [152.08097839, 140.40744019, 129.75102234, 159.90493774],
+            [142.50700378, 135.81935120, 117.82884979, 162.75781250],
+            [127.28772736, 140.40744019, 129.75102234, 159.90493774],
+            [132.37025452, 143.71923828, 138.35694885, 157.84558105],
+            [103.10237122, 143.71928406, 138.35696411, 157.84559631],
+            [127.71276855, 143.71923828, 138.35694885, 157.84558105],
+            [120.91514587, 140.40744019, 129.75102234, 159.90493774],
+        ]
+    )
+
+    y = np.array([1.0, 0.70209277, 0.53896582, 0.0, 0.90914464, 0.48026916, 0.49622521])
+
+    with np.errstate(all="raise"):
+        for name, Tree in REG_TREES.items():
+            reg = Tree(random_state=0)
+            reg.fit(X, y)
+            reg.fit(X, -y)
+            reg.fit(-X, y)
+            reg.fit(-X, -y)
+
+
+def test_importances():
+    # Check variable importances.
+    X, y = datasets.make_classification(
+        n_samples=5000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        shuffle=False,
+        random_state=0,
+    )
+
+    for name, Tree in CLF_TREES.items():
+        clf = Tree(random_state=0)
+
+        clf.fit(X, y)
+        importances = clf.feature_importances_
+        n_important = np.sum(importances > 0.1)
+
+        assert importances.shape[0] == 10, "Failed with {0}".format(name)
+        assert n_important == 3, "Failed with {0}".format(name)
+
+    # Check on iris that importances are the same for all builders
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(iris.data, iris.target)
+    clf2 = DecisionTreeClassifier(random_state=0, max_leaf_nodes=len(iris.data))
+    clf2.fit(iris.data, iris.target)
+
+    assert_array_equal(clf.feature_importances_, clf2.feature_importances_)
+
+
+def test_importances_raises():
+    # Check if variable importance before fit raises ValueError.
+    clf = DecisionTreeClassifier()
+    with pytest.raises(ValueError):
+        getattr(clf, "feature_importances_")
+
+
+def test_importances_gini_equal_squared_error():
+    # Check that gini is equivalent to squared_error for binary output variable
+
+    X, y = datasets.make_classification(
+        n_samples=2000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        shuffle=False,
+        random_state=0,
+    )
+
+    # The gini index and the mean square error (variance) might differ due
+    # to numerical instability. Since those instabilities mainly occurs at
+    # high tree depth, we restrict this maximal depth.
+    clf = DecisionTreeClassifier(criterion="gini", max_depth=5, random_state=0).fit(
+        X, y
+    )
+    reg = DecisionTreeRegressor(
+        criterion="squared_error", max_depth=5, random_state=0
+    ).fit(X, y)
+
+    assert_almost_equal(clf.feature_importances_, reg.feature_importances_)
+    assert_array_equal(clf.tree_.feature, reg.tree_.feature)
+    assert_array_equal(clf.tree_.children_left, reg.tree_.children_left)
+    assert_array_equal(clf.tree_.children_right, reg.tree_.children_right)
+    assert_array_equal(clf.tree_.n_node_samples, reg.tree_.n_node_samples)
+
+
+def test_max_features():
+    # Check max_features.
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_features="sqrt")
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(np.sqrt(iris.data.shape[1]))
+
+        est = TreeEstimator(max_features="log2")
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(np.log2(iris.data.shape[1]))
+
+        est = TreeEstimator(max_features=1)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 1
+
+        est = TreeEstimator(max_features=3)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 3
+
+        est = TreeEstimator(max_features=0.01)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == 1
+
+        est = TreeEstimator(max_features=0.5)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == int(0.5 * iris.data.shape[1])
+
+        est = TreeEstimator(max_features=1.0)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == iris.data.shape[1]
+
+        est = TreeEstimator(max_features=None)
+        est.fit(iris.data, iris.target)
+        assert est.max_features_ == iris.data.shape[1]
+
+
+def test_error():
+    # Test that it gives proper exception on deficient input.
+    for name, TreeEstimator in CLF_TREES.items():
+        # predict before fit
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.predict_proba(X)
+
+        est.fit(X, y)
+        X2 = [[-2, -1, 1]]  # wrong feature shape for sample
+        with pytest.raises(ValueError):
+            est.predict_proba(X2)
+
+        # Wrong dimensions
+        est = TreeEstimator()
+        y2 = y[:-1]
+        with pytest.raises(ValueError):
+            est.fit(X, y2)
+
+        # Test with arrays that are non-contiguous.
+        Xf = np.asfortranarray(X)
+        est = TreeEstimator()
+        est.fit(Xf, y)
+        assert_almost_equal(est.predict(T), true_result)
+
+        # predict before fitting
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.predict(T)
+
+        # predict on vector with different dims
+        est.fit(X, y)
+        t = np.asarray(T)
+        with pytest.raises(ValueError):
+            est.predict(t[:, 1:])
+
+        # wrong sample shape
+        Xt = np.array(X).T
+
+        est = TreeEstimator()
+        est.fit(np.dot(X, Xt), y)
+        with pytest.raises(ValueError):
+            est.predict(X)
+        with pytest.raises(ValueError):
+            est.apply(X)
+
+        clf = TreeEstimator()
+        clf.fit(X, y)
+        with pytest.raises(ValueError):
+            clf.predict(Xt)
+        with pytest.raises(ValueError):
+            clf.apply(Xt)
+
+        # apply before fitting
+        est = TreeEstimator()
+        with pytest.raises(NotFittedError):
+            est.apply(T)
+
+    # non positive target for Poisson splitting Criterion
+    est = DecisionTreeRegressor(criterion="poisson")
+    with pytest.raises(ValueError, match="y is not positive.*Poisson"):
+        est.fit([[0, 1, 2]], [0, 0, 0])
+    with pytest.raises(ValueError, match="Some.*y are negative.*Poisson"):
+        est.fit([[0, 1, 2]], [5, -0.1, 2])
+
+
+def test_min_samples_split():
+    """Test min_samples_split parameter"""
+    X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE)
+    y = iris.target
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # test for integer parameter
+        est = TreeEstimator(
+            min_samples_split=10, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        # count samples on nodes, -1 means it is a leaf
+        node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1]
+
+        assert np.min(node_samples) > 9, "Failed with {0}".format(name)
+
+        # test for float parameter
+        est = TreeEstimator(
+            min_samples_split=0.2, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        # count samples on nodes, -1 means it is a leaf
+        node_samples = est.tree_.n_node_samples[est.tree_.children_left != -1]
+
+        assert np.min(node_samples) > 9, "Failed with {0}".format(name)
+
+
+def test_min_samples_leaf():
+    # Test if leaves contain more than leaf_count training examples
+    X = np.asfortranarray(iris.data, dtype=tree._tree.DTYPE)
+    y = iris.target
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # test integer parameter
+        est = TreeEstimator(
+            min_samples_leaf=5, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        out = est.tree_.apply(X)
+        node_counts = np.bincount(out)
+        # drop inner nodes
+        leaf_count = node_counts[node_counts != 0]
+        assert np.min(leaf_count) > 4, "Failed with {0}".format(name)
+
+        # test float parameter
+        est = TreeEstimator(
+            min_samples_leaf=0.1, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+        out = est.tree_.apply(X)
+        node_counts = np.bincount(out)
+        # drop inner nodes
+        leaf_count = node_counts[node_counts != 0]
+        assert np.min(leaf_count) > 4, "Failed with {0}".format(name)
+
+
+def check_min_weight_fraction_leaf(name, datasets, sparse_container=None):
+    """Test if leaves contain at least min_weight_fraction_leaf of the
+    training set"""
+    X = DATASETS[datasets]["X"].astype(np.float32)
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = DATASETS[datasets]["y"]
+
+    weights = rng.rand(X.shape[0])
+    total_weight = np.sum(weights)
+
+    TreeEstimator = ALL_TREES[name]
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)):
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y, sample_weight=weights)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out, weights=weights)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf, (
+            "Failed with {0} min_weight_fraction_leaf={1}".format(
+                name, est.min_weight_fraction_leaf
+            )
+        )
+
+    # test case with no weights passed in
+    total_weight = X.shape[0]
+
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 6)):
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac, max_leaf_nodes=max_leaf_nodes, random_state=0
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= total_weight * est.min_weight_fraction_leaf, (
+            "Failed with {0} min_weight_fraction_leaf={1}".format(
+                name, est.min_weight_fraction_leaf
+            )
+        )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_min_weight_fraction_leaf_on_dense_input(name):
+    check_min_weight_fraction_leaf(name, "iris")
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_min_weight_fraction_leaf_on_sparse_input(name, csc_container):
+    check_min_weight_fraction_leaf(name, "multilabel", sparse_container=csc_container)
+
+
+def check_min_weight_fraction_leaf_with_min_samples_leaf(
+    name, datasets, sparse_container=None
+):
+    """Test the interaction between min_weight_fraction_leaf and
+    min_samples_leaf when sample_weights is not provided in fit."""
+    X = DATASETS[datasets]["X"].astype(np.float32)
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = DATASETS[datasets]["y"]
+
+    total_weight = X.shape[0]
+    TreeEstimator = ALL_TREES[name]
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)):
+        # test integer min_samples_leaf
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac,
+            max_leaf_nodes=max_leaf_nodes,
+            min_samples_leaf=5,
+            random_state=0,
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= max(
+            (total_weight * est.min_weight_fraction_leaf), 5
+        ), "Failed with {0} min_weight_fraction_leaf={1}, min_samples_leaf={2}".format(
+            name, est.min_weight_fraction_leaf, est.min_samples_leaf
+        )
+    for max_leaf_nodes, frac in product((None, 1000), np.linspace(0, 0.5, 3)):
+        # test float min_samples_leaf
+        est = TreeEstimator(
+            min_weight_fraction_leaf=frac,
+            max_leaf_nodes=max_leaf_nodes,
+            min_samples_leaf=0.1,
+            random_state=0,
+        )
+        est.fit(X, y)
+
+        if sparse_container is not None:
+            out = est.tree_.apply(X.tocsr())
+        else:
+            out = est.tree_.apply(X)
+
+        node_weights = np.bincount(out)
+        # drop inner nodes
+        leaf_weights = node_weights[node_weights != 0]
+        assert np.min(leaf_weights) >= max(
+            (total_weight * est.min_weight_fraction_leaf),
+            (total_weight * est.min_samples_leaf),
+        ), "Failed with {0} min_weight_fraction_leaf={1}, min_samples_leaf={2}".format(
+            name, est.min_weight_fraction_leaf, est.min_samples_leaf
+        )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_min_weight_fraction_leaf_with_min_samples_leaf_on_dense_input(name):
+    check_min_weight_fraction_leaf_with_min_samples_leaf(name, "iris")
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input(
+    name, csc_container
+):
+    check_min_weight_fraction_leaf_with_min_samples_leaf(
+        name, "multilabel", sparse_container=csc_container
+    )
+
+
+def test_min_impurity_decrease(global_random_seed):
+    # test if min_impurity_decrease ensure that a split is made only if
+    # if the impurity decrease is at least that value
+    X, y = datasets.make_classification(n_samples=100, random_state=global_random_seed)
+
+    # test both DepthFirstTreeBuilder and BestFirstTreeBuilder
+    # by setting max_leaf_nodes
+    for max_leaf_nodes, name in product((None, 1000), ALL_TREES.keys()):
+        TreeEstimator = ALL_TREES[name]
+
+        # Check default value of min_impurity_decrease, 1e-7
+        est1 = TreeEstimator(max_leaf_nodes=max_leaf_nodes, random_state=0)
+        # Check with explicit value of 0.05
+        est2 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.05, random_state=0
+        )
+        # Check with a much lower value of 0.0001
+        est3 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.0001, random_state=0
+        )
+        # Check with a much lower value of 0.1
+        est4 = TreeEstimator(
+            max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=0.1, random_state=0
+        )
+
+        for est, expected_decrease in (
+            (est1, 1e-7),
+            (est2, 0.05),
+            (est3, 0.0001),
+            (est4, 0.1),
+        ):
+            assert est.min_impurity_decrease <= expected_decrease, (
+                "Failed, min_impurity_decrease = {0} > {1}".format(
+                    est.min_impurity_decrease, expected_decrease
+                )
+            )
+            est.fit(X, y)
+            for node in range(est.tree_.node_count):
+                # If current node is a not leaf node, check if the split was
+                # justified w.r.t the min_impurity_decrease
+                if est.tree_.children_left[node] != TREE_LEAF:
+                    imp_parent = est.tree_.impurity[node]
+                    wtd_n_node = est.tree_.weighted_n_node_samples[node]
+
+                    left = est.tree_.children_left[node]
+                    wtd_n_left = est.tree_.weighted_n_node_samples[left]
+                    imp_left = est.tree_.impurity[left]
+                    wtd_imp_left = wtd_n_left * imp_left
+
+                    right = est.tree_.children_right[node]
+                    wtd_n_right = est.tree_.weighted_n_node_samples[right]
+                    imp_right = est.tree_.impurity[right]
+                    wtd_imp_right = wtd_n_right * imp_right
+
+                    wtd_avg_left_right_imp = wtd_imp_right + wtd_imp_left
+                    wtd_avg_left_right_imp /= wtd_n_node
+
+                    fractional_node_weight = (
+                        est.tree_.weighted_n_node_samples[node] / X.shape[0]
+                    )
+
+                    actual_decrease = fractional_node_weight * (
+                        imp_parent - wtd_avg_left_right_imp
+                    )
+
+                    assert actual_decrease >= expected_decrease, (
+                        "Failed with {0} expected min_impurity_decrease={1}".format(
+                            actual_decrease, expected_decrease
+                        )
+                    )
+
+
+def test_pickle():
+    """Test pickling preserves Tree properties and performance."""
+    for name, TreeEstimator in ALL_TREES.items():
+        if "Classifier" in name:
+            X, y = iris.data, iris.target
+        else:
+            X, y = diabetes.data, diabetes.target
+
+        est = TreeEstimator(random_state=0)
+        est.fit(X, y)
+        score = est.score(X, y)
+
+        # test that all class properties are maintained
+        attributes = [
+            "max_depth",
+            "node_count",
+            "capacity",
+            "n_classes",
+            "children_left",
+            "children_right",
+            "n_leaves",
+            "feature",
+            "threshold",
+            "impurity",
+            "n_node_samples",
+            "weighted_n_node_samples",
+            "value",
+        ]
+        fitted_attribute = {
+            attribute: getattr(est.tree_, attribute) for attribute in attributes
+        }
+
+        serialized_object = pickle.dumps(est)
+        est2 = pickle.loads(serialized_object)
+        assert type(est2) == est.__class__
+
+        score2 = est2.score(X, y)
+        assert score == score2, (
+            "Failed to generate same score  after pickling with {0}".format(name)
+        )
+        for attribute in fitted_attribute:
+            assert_array_equal(
+                getattr(est2.tree_, attribute),
+                fitted_attribute[attribute],
+                err_msg=(
+                    f"Failed to generate same attribute {attribute} after pickling with"
+                    f" {name}"
+                ),
+            )
+
+
+def test_multioutput():
+    # Check estimators on multi-output problems.
+    X = [
+        [-2, -1],
+        [-1, -1],
+        [-1, -2],
+        [1, 1],
+        [1, 2],
+        [2, 1],
+        [-2, 1],
+        [-1, 1],
+        [-1, 2],
+        [2, -1],
+        [1, -1],
+        [1, -2],
+    ]
+
+    y = [
+        [-1, 0],
+        [-1, 0],
+        [-1, 0],
+        [1, 1],
+        [1, 1],
+        [1, 1],
+        [-1, 2],
+        [-1, 2],
+        [-1, 2],
+        [1, 3],
+        [1, 3],
+        [1, 3],
+    ]
+
+    T = [[-1, -1], [1, 1], [-1, 1], [1, -1]]
+    y_true = [[-1, 0], [1, 1], [-1, 2], [1, 3]]
+
+    # toy classification problem
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        y_hat = clf.fit(X, y).predict(T)
+        assert_array_equal(y_hat, y_true)
+        assert y_hat.shape == (4, 2)
+
+        proba = clf.predict_proba(T)
+        assert len(proba) == 2
+        assert proba[0].shape == (4, 2)
+        assert proba[1].shape == (4, 4)
+
+        log_proba = clf.predict_log_proba(T)
+        assert len(log_proba) == 2
+        assert log_proba[0].shape == (4, 2)
+        assert log_proba[1].shape == (4, 4)
+
+    # toy regression problem
+    for name, TreeRegressor in REG_TREES.items():
+        reg = TreeRegressor(random_state=0)
+        y_hat = reg.fit(X, y).predict(T)
+        assert_almost_equal(y_hat, y_true)
+        assert y_hat.shape == (4, 2)
+
+
+def test_classes_shape():
+    # Test that n_classes_ and classes_ have proper shape.
+    for name, TreeClassifier in CLF_TREES.items():
+        # Classification, single output
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, y)
+
+        assert clf.n_classes_ == 2
+        assert_array_equal(clf.classes_, [-1, 1])
+
+        # Classification, multi-output
+        _y = np.vstack((y, np.array(y) * 2)).T
+        clf = TreeClassifier(random_state=0)
+        clf.fit(X, _y)
+        assert len(clf.n_classes_) == 2
+        assert len(clf.classes_) == 2
+        assert_array_equal(clf.n_classes_, [2, 2])
+        assert_array_equal(clf.classes_, [[-1, 1], [-2, 2]])
+
+
+def test_unbalanced_iris():
+    # Check class rebalancing.
+    unbalanced_X = iris.data[:125]
+    unbalanced_y = iris.target[:125]
+    sample_weight = compute_sample_weight("balanced", unbalanced_y)
+
+    for name, TreeClassifier in CLF_TREES.items():
+        clf = TreeClassifier(random_state=0)
+        clf.fit(unbalanced_X, unbalanced_y, sample_weight=sample_weight)
+        assert_almost_equal(clf.predict(unbalanced_X), unbalanced_y)
+
+
+def test_memory_layout():
+    # Check that it works no matter the memory layout
+    for (name, TreeEstimator), dtype in product(
+        ALL_TREES.items(), [np.float64, np.float32]
+    ):
+        est = TreeEstimator(random_state=0)
+
+        # Nothing
+        X = np.asarray(iris.data, dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # C-order
+        X = np.asarray(iris.data, order="C", dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # F-order
+        X = np.asarray(iris.data, order="F", dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # Contiguous
+        X = np.ascontiguousarray(iris.data, dtype=dtype)
+        y = iris.target
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # csr
+        for csr_container in CSR_CONTAINERS:
+            X = csr_container(iris.data, dtype=dtype)
+            y = iris.target
+            assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # csc
+        for csc_container in CSC_CONTAINERS:
+            X = csc_container(iris.data, dtype=dtype)
+            y = iris.target
+            assert_array_equal(est.fit(X, y).predict(X), y)
+
+        # Strided
+        X = np.asarray(iris.data[::3], dtype=dtype)
+        y = iris.target[::3]
+        assert_array_equal(est.fit(X, y).predict(X), y)
+
+
+def test_sample_weight():
+    # Check sample weighting.
+    # Test that zero-weighted samples are not taken into account
+    X = np.arange(100)[:, np.newaxis]
+    y = np.ones(100)
+    y[:50] = 0.0
+
+    sample_weight = np.ones(100)
+    sample_weight[y == 0] = 0.0
+
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert_array_equal(clf.predict(X), np.ones(100))
+
+    # Test that low weighted samples are not taken into account at low depth
+    X = np.arange(200)[:, np.newaxis]
+    y = np.zeros(200)
+    y[50:100] = 1
+    y[100:200] = 2
+    X[100:200, 0] = 200
+
+    sample_weight = np.ones(200)
+
+    sample_weight[y == 2] = 0.51  # Samples of class '2' are still weightier
+    clf = DecisionTreeClassifier(max_depth=1, random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert clf.tree_.threshold[0] == 149.5
+
+    sample_weight[y == 2] = 0.5  # Samples of class '2' are no longer weightier
+    clf = DecisionTreeClassifier(max_depth=1, random_state=0)
+    clf.fit(X, y, sample_weight=sample_weight)
+    assert clf.tree_.threshold[0] == 49.5  # Threshold should have moved
+
+    # Test that sample weighting is the same as having duplicates
+    X = iris.data
+    y = iris.target
+
+    duplicates = rng.randint(0, X.shape[0], 100)
+
+    clf = DecisionTreeClassifier(random_state=1)
+    clf.fit(X[duplicates], y[duplicates])
+
+    sample_weight = np.bincount(duplicates, minlength=X.shape[0])
+    clf2 = DecisionTreeClassifier(random_state=1)
+    clf2.fit(X, y, sample_weight=sample_weight)
+
+    internal = clf.tree_.children_left != tree._tree.TREE_LEAF
+    assert_array_almost_equal(
+        clf.tree_.threshold[internal], clf2.tree_.threshold[internal]
+    )
+
+
+def test_sample_weight_invalid():
+    # Check sample weighting raises errors.
+    X = np.arange(100)[:, np.newaxis]
+    y = np.ones(100)
+    y[:50] = 0.0
+
+    clf = DecisionTreeClassifier(random_state=0)
+
+    sample_weight = np.random.rand(100, 1)
+    with pytest.raises(ValueError):
+        clf.fit(X, y, sample_weight=sample_weight)
+
+    sample_weight = np.array(0)
+
+    expected_err = re.escape(
+        (
+            "Input should have at least 1 dimension i.e. satisfy "
+            "`len(x.shape) > 0`, got scalar `array(0.)` instead."
+        )
+    )
+    with pytest.raises(TypeError, match=expected_err):
+        clf.fit(X, y, sample_weight=sample_weight)
+
+
+@pytest.mark.parametrize("name", CLF_TREES)
+def test_class_weights(name):
+    # Test that class_weights resemble sample_weights behavior.
+    TreeClassifier = CLF_TREES[name]
+
+    # Iris is balanced, so no effect expected for using 'balanced' weights
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target)
+    clf2 = TreeClassifier(class_weight="balanced", random_state=0)
+    clf2.fit(iris.data, iris.target)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+    # Make a multi-output problem with three copies of Iris
+    iris_multi = np.vstack((iris.target, iris.target, iris.target)).T
+    # Create user-defined weights that should balance over the outputs
+    clf3 = TreeClassifier(
+        class_weight=[
+            {0: 2.0, 1: 2.0, 2: 1.0},
+            {0: 2.0, 1: 1.0, 2: 2.0},
+            {0: 1.0, 1: 2.0, 2: 2.0},
+        ],
+        random_state=0,
+    )
+    clf3.fit(iris.data, iris_multi)
+    assert_almost_equal(clf2.feature_importances_, clf3.feature_importances_)
+    # Check against multi-output "auto" which should also have no effect
+    clf4 = TreeClassifier(class_weight="balanced", random_state=0)
+    clf4.fit(iris.data, iris_multi)
+    assert_almost_equal(clf3.feature_importances_, clf4.feature_importances_)
+
+    # Inflate importance of class 1, check against user-defined weights
+    sample_weight = np.ones(iris.target.shape)
+    sample_weight[iris.target == 1] *= 100
+    class_weight = {0: 1.0, 1: 100.0, 2: 1.0}
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target, sample_weight)
+    clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
+    clf2.fit(iris.data, iris.target)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+    # Check that sample_weight and class_weight are multiplicative
+    clf1 = TreeClassifier(random_state=0)
+    clf1.fit(iris.data, iris.target, sample_weight**2)
+    clf2 = TreeClassifier(class_weight=class_weight, random_state=0)
+    clf2.fit(iris.data, iris.target, sample_weight)
+    assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
+
+
+@pytest.mark.parametrize("name", CLF_TREES)
+def test_class_weight_errors(name):
+    # Test if class_weight raises errors and warnings when expected.
+    TreeClassifier = CLF_TREES[name]
+    _y = np.vstack((y, np.array(y) * 2)).T
+
+    # Incorrect length list for multi-output
+    clf = TreeClassifier(class_weight=[{-1: 0.5, 1: 1.0}], random_state=0)
+    err_msg = "number of elements in class_weight should match number of outputs."
+    with pytest.raises(ValueError, match=err_msg):
+        clf.fit(X, _y)
+
+
+def test_max_leaf_nodes():
+    # Test greedy trees with max_depth + 1 leafs.
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    k = 4
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_depth=None, max_leaf_nodes=k + 1).fit(X, y)
+        assert est.get_n_leaves() == k + 1
+
+
+def test_max_leaf_nodes_max_depth():
+    # Test precedence of max_leaf_nodes over max_depth.
+    X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
+    k = 4
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
+        assert est.get_depth() == 1
+
+
+def test_arrays_persist():
+    # Ensure property arrays' memory stays alive when tree disappears
+    # non-regression for #2726
+    for attr in [
+        "n_classes",
+        "value",
+        "children_left",
+        "children_right",
+        "threshold",
+        "impurity",
+        "feature",
+        "n_node_samples",
+    ]:
+        value = getattr(DecisionTreeClassifier().fit([[0], [1]], [0, 1]).tree_, attr)
+        # if pointing to freed memory, contents may be arbitrary
+        assert -3 <= value.flat[0] < 3, "Array points to arbitrary memory"
+
+
+def test_only_constant_features():
+    random_state = check_random_state(0)
+    X = np.zeros((10, 20))
+    y = random_state.randint(0, 2, (10,))
+    for name, TreeEstimator in ALL_TREES.items():
+        est = TreeEstimator(random_state=0)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 0
+
+
+def test_behaviour_constant_feature_after_splits():
+    X = np.transpose(
+        np.vstack(([[0, 0, 0, 0, 0, 1, 2, 4, 5, 6, 7]], np.zeros((4, 11))))
+    )
+    y = [0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3]
+    for name, TreeEstimator in ALL_TREES.items():
+        # do not check extra random trees
+        if "ExtraTree" not in name:
+            est = TreeEstimator(random_state=0, max_features=1)
+            est.fit(X, y)
+            assert est.tree_.max_depth == 2
+            assert est.tree_.node_count == 5
+
+
+def test_with_only_one_non_constant_features():
+    X = np.hstack([np.array([[1.0], [1.0], [0.0], [0.0]]), np.zeros((4, 1000))])
+
+    y = np.array([0.0, 1.0, 0.0, 1.0])
+    for name, TreeEstimator in CLF_TREES.items():
+        est = TreeEstimator(random_state=0, max_features=1)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 1
+        assert_array_equal(est.predict_proba(X), np.full((4, 2), 0.5))
+
+    for name, TreeEstimator in REG_TREES.items():
+        est = TreeEstimator(random_state=0, max_features=1)
+        est.fit(X, y)
+        assert est.tree_.max_depth == 1
+        assert_array_equal(est.predict(X), np.full((4,), 0.5))
+
+
+def test_big_input():
+    # Test if the warning for too large inputs is appropriate.
+    X = np.repeat(10**40.0, 4).astype(np.float64).reshape(-1, 1)
+    clf = DecisionTreeClassifier()
+    with pytest.raises(ValueError, match="float32"):
+        clf.fit(X, [0, 1, 0, 1])
+
+
+def test_realloc():
+    from sklearn.tree._utils import _realloc_test
+
+    with pytest.raises(MemoryError):
+        _realloc_test()
+
+
+def test_huge_allocations():
+    n_bits = 8 * struct.calcsize("P")
+
+    X = np.random.randn(10, 2)
+    y = np.random.randint(0, 2, 10)
+
+    # Sanity check: we cannot request more memory than the size of the address
+    # space. Currently raises OverflowError.
+    huge = 2 ** (n_bits + 1)
+    clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge)
+    with pytest.raises(Exception):
+        clf.fit(X, y)
+
+    # Non-regression test: MemoryError used to be dropped by Cython
+    # because of missing "except *".
+    huge = 2 ** (n_bits - 1) - 1
+    clf = DecisionTreeClassifier(splitter="best", max_leaf_nodes=huge)
+    with pytest.raises(MemoryError):
+        clf.fit(X, y)
+
+
+def check_sparse_input(tree, dataset, max_depth=None):
+    TreeEstimator = ALL_TREES[tree]
+    X = DATASETS[dataset]["X"]
+    y = DATASETS[dataset]["y"]
+
+    # Gain testing time
+    if dataset in ["digits", "diabetes"]:
+        n_samples = X.shape[0] // 5
+        X = X[:n_samples]
+        y = y[:n_samples]
+
+    for sparse_container in COO_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS:
+        X_sparse = sparse_container(X)
+
+        # Check the default (depth first search)
+        d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
+        s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
+
+        assert_tree_equal(
+            d.tree_,
+            s.tree_,
+            "{0} with dense and sparse format gave different trees".format(tree),
+        )
+
+        y_pred = d.predict(X)
+        if tree in CLF_TREES:
+            y_proba = d.predict_proba(X)
+            y_log_proba = d.predict_log_proba(X)
+
+        for sparse_container_test in COO_CONTAINERS + CSR_CONTAINERS + CSC_CONTAINERS:
+            X_sparse_test = sparse_container_test(X_sparse, dtype=np.float32)
+
+            assert_array_almost_equal(s.predict(X_sparse_test), y_pred)
+
+            if tree in CLF_TREES:
+                assert_array_almost_equal(s.predict_proba(X_sparse_test), y_proba)
+                assert_array_almost_equal(
+                    s.predict_log_proba(X_sparse_test), y_log_proba
+                )
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize(
+    "dataset",
+    (
+        "clf_small",
+        "toy",
+        "digits",
+        "multilabel",
+        "sparse-pos",
+        "sparse-neg",
+        "sparse-mix",
+        "zeros",
+    ),
+)
+def test_sparse_input(tree_type, dataset):
+    max_depth = 3 if dataset == "digits" else None
+    check_sparse_input(tree_type, dataset, max_depth)
+
+
+@pytest.mark.parametrize("tree_type", sorted(set(SPARSE_TREES).intersection(REG_TREES)))
+@pytest.mark.parametrize("dataset", ["diabetes", "reg_small"])
+def test_sparse_input_reg_trees(tree_type, dataset):
+    # Due to numerical instability of MSE and too strict test, we limit the
+    # maximal depth
+    check_sparse_input(tree_type, dataset, 2)
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"])
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_sparse_parameters(tree_type, dataset, csc_container):
+    TreeEstimator = ALL_TREES[tree_type]
+    X = DATASETS[dataset]["X"]
+    X_sparse = csc_container(X)
+    y = DATASETS[dataset]["y"]
+
+    # Check max_features
+    d = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X, y)
+    s = TreeEstimator(random_state=0, max_features=1, max_depth=2).fit(X_sparse, y)
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check min_samples_split
+    d = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(X, y)
+    s = TreeEstimator(random_state=0, max_features=1, min_samples_split=10).fit(
+        X_sparse, y
+    )
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check min_samples_leaf
+    d = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(X, y)
+    s = TreeEstimator(random_state=0, min_samples_leaf=X_sparse.shape[0] // 2).fit(
+        X_sparse, y
+    )
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+    # Check best-first search
+    d = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X, y)
+    s = TreeEstimator(random_state=0, max_leaf_nodes=3).fit(X_sparse, y)
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+
+@pytest.mark.parametrize(
+    "tree_type, criterion",
+    list(product([tree for tree in SPARSE_TREES if tree in REG_TREES], REG_CRITERIONS))
+    + list(
+        product([tree for tree in SPARSE_TREES if tree in CLF_TREES], CLF_CRITERIONS)
+    ),
+)
+@pytest.mark.parametrize("dataset", ["sparse-pos", "sparse-neg", "sparse-mix", "zeros"])
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_sparse_criteria(tree_type, dataset, csc_container, criterion):
+    TreeEstimator = ALL_TREES[tree_type]
+    X = DATASETS[dataset]["X"]
+    X_sparse = csc_container(X)
+    y = DATASETS[dataset]["y"]
+
+    d = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X, y)
+    s = TreeEstimator(random_state=0, max_depth=3, criterion=criterion).fit(X_sparse, y)
+
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree_type),
+    )
+    assert_array_almost_equal(s.predict(X), d.predict(X))
+
+
+@pytest.mark.parametrize("tree_type", SPARSE_TREES)
+@pytest.mark.parametrize(
+    "csc_container,csr_container", zip(CSC_CONTAINERS, CSR_CONTAINERS)
+)
+def test_explicit_sparse_zeros(tree_type, csc_container, csr_container):
+    TreeEstimator = ALL_TREES[tree_type]
+    max_depth = 3
+    n_features = 10
+
+    # n_samples set n_feature to ease construction of a simultaneous
+    # construction of a csr and csc matrix
+    n_samples = n_features
+    samples = np.arange(n_samples)
+
+    # Generate X, y
+    random_state = check_random_state(0)
+    indices = []
+    data = []
+    offset = 0
+    indptr = [offset]
+    for i in range(n_features):
+        n_nonzero_i = random_state.binomial(n_samples, 0.5)
+        indices_i = random_state.permutation(samples)[:n_nonzero_i]
+        indices.append(indices_i)
+        data_i = random_state.binomial(3, 0.5, size=(n_nonzero_i,)) - 1
+        data.append(data_i)
+        offset += n_nonzero_i
+        indptr.append(offset)
+
+    indices = np.concatenate(indices).astype(np.int32)
+    indptr = np.array(indptr, dtype=np.int32)
+    data = np.array(np.concatenate(data), dtype=np.float32)
+    X_sparse = csc_container((data, indices, indptr), shape=(n_samples, n_features))
+    X = X_sparse.toarray()
+    X_sparse_test = csr_container(
+        (data, indices, indptr), shape=(n_samples, n_features)
+    )
+    X_test = X_sparse_test.toarray()
+    y = random_state.randint(0, 3, size=(n_samples,))
+
+    # Ensure that X_sparse_test owns its data, indices and indptr array
+    X_sparse_test = X_sparse_test.copy()
+
+    # Ensure that we have explicit zeros
+    assert (X_sparse.data == 0.0).sum() > 0
+    assert (X_sparse_test.data == 0.0).sum() > 0
+
+    # Perform the comparison
+    d = TreeEstimator(random_state=0, max_depth=max_depth).fit(X, y)
+    s = TreeEstimator(random_state=0, max_depth=max_depth).fit(X_sparse, y)
+
+    assert_tree_equal(
+        d.tree_,
+        s.tree_,
+        "{0} with dense and sparse format gave different trees".format(tree),
+    )
+
+    Xs = (X_test, X_sparse_test)
+    for X1, X2 in product(Xs, Xs):
+        assert_array_almost_equal(s.tree_.apply(X1), d.tree_.apply(X2))
+        assert_array_almost_equal(s.apply(X1), d.apply(X2))
+        assert_array_almost_equal(s.apply(X1), s.tree_.apply(X1))
+
+        assert_array_almost_equal(
+            s.tree_.decision_path(X1).toarray(), d.tree_.decision_path(X2).toarray()
+        )
+        assert_array_almost_equal(
+            s.decision_path(X1).toarray(), d.decision_path(X2).toarray()
+        )
+        assert_array_almost_equal(
+            s.decision_path(X1).toarray(), s.tree_.decision_path(X1).toarray()
+        )
+
+        assert_array_almost_equal(s.predict(X1), d.predict(X2))
+
+        if tree in CLF_TREES:
+            assert_array_almost_equal(s.predict_proba(X1), d.predict_proba(X2))
+
+
+def check_raise_error_on_1d_input(name):
+    TreeEstimator = ALL_TREES[name]
+
+    X = iris.data[:, 0].ravel()
+    X_2d = iris.data[:, 0].reshape((-1, 1))
+    y = iris.target
+
+    with pytest.raises(ValueError):
+        TreeEstimator(random_state=0).fit(X, y)
+
+    est = TreeEstimator(random_state=0)
+    est.fit(X_2d, y)
+    with pytest.raises(ValueError):
+        est.predict([X])
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_1d_input(name):
+    with ignore_warnings():
+        check_raise_error_on_1d_input(name)
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
+def test_min_weight_leaf_split_level(name, sparse_container):
+    TreeEstimator = ALL_TREES[name]
+
+    X = np.array([[0], [0], [0], [0], [1]])
+    y = [0, 0, 0, 0, 1]
+    sample_weight = [0.2, 0.2, 0.2, 0.2, 0.2]
+    if sparse_container is not None:
+        X = sparse_container(X)
+
+    est = TreeEstimator(random_state=0)
+    est.fit(X, y, sample_weight=sample_weight)
+    assert est.tree_.max_depth == 1
+
+    est = TreeEstimator(random_state=0, min_weight_fraction_leaf=0.4)
+    est.fit(X, y, sample_weight=sample_weight)
+    assert est.tree_.max_depth == 0
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_public_apply_all_trees(name):
+    X_small32 = X_small.astype(tree._tree.DTYPE, copy=False)
+
+    est = ALL_TREES[name]()
+    est.fit(X_small, y_small)
+    assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32))
+
+
+@pytest.mark.parametrize("name", SPARSE_TREES)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_public_apply_sparse_trees(name, csr_container):
+    X_small32 = csr_container(X_small.astype(tree._tree.DTYPE, copy=False))
+
+    est = ALL_TREES[name]()
+    est.fit(X_small, y_small)
+    assert_array_equal(est.apply(X_small), est.tree_.apply(X_small32))
+
+
+def test_decision_path_hardcoded():
+    X = iris.data
+    y = iris.target
+    est = DecisionTreeClassifier(random_state=0, max_depth=1).fit(X, y)
+    node_indicator = est.decision_path(X[:2]).toarray()
+    assert_array_equal(node_indicator, [[1, 1, 0], [1, 0, 1]])
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+def test_decision_path(name):
+    X = iris.data
+    y = iris.target
+    n_samples = X.shape[0]
+
+    TreeEstimator = ALL_TREES[name]
+    est = TreeEstimator(random_state=0, max_depth=2)
+    est.fit(X, y)
+
+    node_indicator_csr = est.decision_path(X)
+    node_indicator = node_indicator_csr.toarray()
+    assert node_indicator.shape == (n_samples, est.tree_.node_count)
+
+    # Assert that leaves index are correct
+    leaves = est.apply(X)
+    leave_indicator = [node_indicator[i, j] for i, j in enumerate(leaves)]
+    assert_array_almost_equal(leave_indicator, np.ones(shape=n_samples))
+
+    # Ensure only one leave node per sample
+    all_leaves = est.tree_.children_left == TREE_LEAF
+    assert_array_almost_equal(
+        np.dot(node_indicator, all_leaves), np.ones(shape=n_samples)
+    )
+
+    # Ensure max depth is consistent with sum of indicator
+    max_depth = node_indicator.sum(axis=1).max()
+    assert est.tree_.max_depth <= max_depth
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_no_sparse_y_support(name, csr_container):
+    # Currently we don't support sparse y
+    X, y = X_multilabel, csr_container(y_multilabel)
+    TreeEstimator = ALL_TREES[name]
+    with pytest.raises(TypeError):
+        TreeEstimator(random_state=0).fit(X, y)
+
+
+def test_mae():
+    """Check MAE criterion produces correct results on small toy dataset:
+
+    ------------------
+    | X | y | weight |
+    ------------------
+    | 3 | 3 |  0.1   |
+    | 5 | 3 |  0.3   |
+    | 8 | 4 |  1.0   |
+    | 3 | 6 |  0.6   |
+    | 5 | 7 |  0.3   |
+    ------------------
+    |sum wt:|  2.3   |
+    ------------------
+
+    Because we are dealing with sample weights, we cannot find the median by
+    simply choosing/averaging the centre value(s), instead we consider the
+    median where 50% of the cumulative weight is found (in a y sorted data set)
+    . Therefore with regards to this test data, the cumulative weight is >= 50%
+    when y = 4.  Therefore:
+    Median = 4
+
+    For all the samples, we can get the total error by summing:
+    Absolute(Median - y) * weight
+
+    I.e., total error = (Absolute(4 - 3) * 0.1)
+                      + (Absolute(4 - 3) * 0.3)
+                      + (Absolute(4 - 4) * 1.0)
+                      + (Absolute(4 - 6) * 0.6)
+                      + (Absolute(4 - 7) * 0.3)
+                      = 2.5
+
+    Impurity = Total error / total weight
+             = 2.5 / 2.3
+             = 1.08695652173913
+             ------------------
+
+    From this root node, the next best split is between X values of 3 and 5.
+    Thus, we have left and right child nodes:
+
+    LEFT                    RIGHT
+    ------------------      ------------------
+    | X | y | weight |      | X | y | weight |
+    ------------------      ------------------
+    | 3 | 3 |  0.1   |      | 5 | 3 |  0.3   |
+    | 3 | 6 |  0.6   |      | 8 | 4 |  1.0   |
+    ------------------      | 5 | 7 |  0.3   |
+    |sum wt:|  0.7   |      ------------------
+    ------------------      |sum wt:|  1.6   |
+                            ------------------
+
+    Impurity is found in the same way:
+    Left node Median = 6
+    Total error = (Absolute(6 - 3) * 0.1)
+                + (Absolute(6 - 6) * 0.6)
+                = 0.3
+
+    Left Impurity = Total error / total weight
+            = 0.3 / 0.7
+            = 0.428571428571429
+            -------------------
+
+    Likewise for Right node:
+    Right node Median = 4
+    Total error = (Absolute(4 - 3) * 0.3)
+                + (Absolute(4 - 4) * 1.0)
+                + (Absolute(4 - 7) * 0.3)
+                = 1.2
+
+    Right Impurity = Total error / total weight
+            = 1.2 / 1.6
+            = 0.75
+            ------
+    """
+    dt_mae = DecisionTreeRegressor(
+        random_state=0, criterion="absolute_error", max_leaf_nodes=2
+    )
+
+    # Test MAE where sample weights are non-uniform (as illustrated above):
+    dt_mae.fit(
+        X=[[3], [5], [3], [8], [5]],
+        y=[6, 7, 3, 4, 3],
+        sample_weight=[0.6, 0.3, 0.1, 1.0, 0.3],
+    )
+    assert_allclose(dt_mae.tree_.impurity, [2.5 / 2.3, 0.3 / 0.7, 1.2 / 1.6])
+    assert_array_equal(dt_mae.tree_.value.flat, [4.0, 6.0, 4.0])
+
+    # Test MAE where all sample weights are uniform:
+    dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3], sample_weight=np.ones(5))
+    assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0])
+    assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0])
+
+    # Test MAE where a `sample_weight` is not explicitly provided.
+    # This is equivalent to providing uniform sample weights, though
+    # the internal logic is different:
+    dt_mae.fit(X=[[3], [5], [3], [8], [5]], y=[6, 7, 3, 4, 3])
+    assert_array_equal(dt_mae.tree_.impurity, [1.4, 1.5, 4.0 / 3.0])
+    assert_array_equal(dt_mae.tree_.value.flat, [4, 4.5, 4.0])
+
+
+def test_criterion_copy():
+    # Let's check whether copy of our criterion has the same type
+    # and properties as original
+    n_outputs = 3
+    n_classes = np.arange(3, dtype=np.intp)
+    n_samples = 100
+
+    def _pickle_copy(obj):
+        return pickle.loads(pickle.dumps(obj))
+
+    for copy_func in [copy.copy, copy.deepcopy, _pickle_copy]:
+        for _, typename in CRITERIA_CLF.items():
+            criteria = typename(n_outputs, n_classes)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_classes_), _ = result
+            assert typename == typename_
+            assert n_outputs == n_outputs_
+            assert_array_equal(n_classes, n_classes_)
+
+        for _, typename in CRITERIA_REG.items():
+            criteria = typename(n_outputs, n_samples)
+            result = copy_func(criteria).__reduce__()
+            typename_, (n_outputs_, n_samples_), _ = result
+            assert typename == typename_
+            assert n_outputs == n_outputs_
+            assert n_samples == n_samples_
+
+
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
+def test_empty_leaf_infinite_threshold(sparse_container):
+    # try to make empty leaf by using near infinite value.
+    data = np.random.RandomState(0).randn(100, 11) * 2e38
+    data = np.nan_to_num(data.astype("float32"))
+    X = data[:, :-1]
+    if sparse_container is not None:
+        X = sparse_container(X)
+    y = data[:, -1]
+
+    tree = DecisionTreeRegressor(random_state=0).fit(X, y)
+    terminal_regions = tree.apply(X)
+    left_leaf = set(np.where(tree.tree_.children_left == TREE_LEAF)[0])
+    empty_leaf = left_leaf.difference(terminal_regions)
+    infinite_threshold = np.where(~np.isfinite(tree.tree_.threshold))[0]
+    assert len(infinite_threshold) == 0
+    assert len(empty_leaf) == 0
+
+
+@pytest.mark.parametrize(
+    "dataset", sorted(set(DATASETS.keys()) - {"reg_small", "diabetes"})
+)
+@pytest.mark.parametrize("tree_cls", [DecisionTreeClassifier, ExtraTreeClassifier])
+def test_prune_tree_classifier_are_subtrees(dataset, tree_cls):
+    dataset = DATASETS[dataset]
+    X, y = dataset["X"], dataset["y"]
+    est = tree_cls(max_leaf_nodes=20, random_state=0)
+    info = est.cost_complexity_pruning_path(X, y)
+
+    pruning_path = info.ccp_alphas
+    impurities = info.impurities
+    assert np.all(np.diff(pruning_path) >= 0)
+    assert np.all(np.diff(impurities) >= 0)
+
+    assert_pruning_creates_subtree(tree_cls, X, y, pruning_path)
+
+
+@pytest.mark.parametrize("dataset", DATASETS.keys())
+@pytest.mark.parametrize("tree_cls", [DecisionTreeRegressor, ExtraTreeRegressor])
+def test_prune_tree_regression_are_subtrees(dataset, tree_cls):
+    dataset = DATASETS[dataset]
+    X, y = dataset["X"], dataset["y"]
+
+    est = tree_cls(max_leaf_nodes=20, random_state=0)
+    info = est.cost_complexity_pruning_path(X, y)
+
+    pruning_path = info.ccp_alphas
+    impurities = info.impurities
+    assert np.all(np.diff(pruning_path) >= 0)
+    assert np.all(np.diff(impurities) >= 0)
+
+    assert_pruning_creates_subtree(tree_cls, X, y, pruning_path)
+
+
+def test_prune_single_node_tree():
+    # single node tree
+    clf1 = DecisionTreeClassifier(random_state=0)
+    clf1.fit([[0], [1]], [0, 0])
+
+    # pruned single node tree
+    clf2 = DecisionTreeClassifier(random_state=0, ccp_alpha=10)
+    clf2.fit([[0], [1]], [0, 0])
+
+    assert_is_subtree(clf1.tree_, clf2.tree_)
+
+
+def assert_pruning_creates_subtree(estimator_cls, X, y, pruning_path):
+    # generate trees with increasing alphas
+    estimators = []
+    for ccp_alpha in pruning_path:
+        est = estimator_cls(max_leaf_nodes=20, ccp_alpha=ccp_alpha, random_state=0).fit(
+            X, y
+        )
+        estimators.append(est)
+
+    # A pruned tree must be a subtree of the previous tree (which had a
+    # smaller ccp_alpha)
+    for prev_est, next_est in pairwise(estimators):
+        assert_is_subtree(prev_est.tree_, next_est.tree_)
+
+
+def assert_is_subtree(tree, subtree):
+    assert tree.node_count >= subtree.node_count
+    assert tree.max_depth >= subtree.max_depth
+
+    tree_c_left = tree.children_left
+    tree_c_right = tree.children_right
+    subtree_c_left = subtree.children_left
+    subtree_c_right = subtree.children_right
+
+    stack = [(0, 0)]
+    while stack:
+        tree_node_idx, subtree_node_idx = stack.pop()
+        assert_array_almost_equal(
+            tree.value[tree_node_idx], subtree.value[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.impurity[tree_node_idx], subtree.impurity[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.n_node_samples[tree_node_idx], subtree.n_node_samples[subtree_node_idx]
+        )
+        assert_almost_equal(
+            tree.weighted_n_node_samples[tree_node_idx],
+            subtree.weighted_n_node_samples[subtree_node_idx],
+        )
+
+        if subtree_c_left[subtree_node_idx] == subtree_c_right[subtree_node_idx]:
+            # is a leaf
+            assert_almost_equal(TREE_UNDEFINED, subtree.threshold[subtree_node_idx])
+        else:
+            # not a leaf
+            assert_almost_equal(
+                tree.threshold[tree_node_idx], subtree.threshold[subtree_node_idx]
+            )
+            stack.append((tree_c_left[tree_node_idx], subtree_c_left[subtree_node_idx]))
+            stack.append(
+                (tree_c_right[tree_node_idx], subtree_c_right[subtree_node_idx])
+            )
+
+
+@pytest.mark.parametrize("name", ALL_TREES)
+@pytest.mark.parametrize("splitter", ["best", "random"])
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS + CSR_CONTAINERS)
+def test_apply_path_readonly_all_trees(name, splitter, sparse_container):
+    dataset = DATASETS["clf_small"]
+    X_small = dataset["X"].astype(tree._tree.DTYPE, copy=False)
+    if sparse_container is None:
+        X_readonly = create_memmap_backed_data(X_small)
+    else:
+        X_readonly = sparse_container(dataset["X"])
+
+        X_readonly.data = np.array(X_readonly.data, dtype=tree._tree.DTYPE)
+        (
+            X_readonly.data,
+            X_readonly.indices,
+            X_readonly.indptr,
+        ) = create_memmap_backed_data(
+            (X_readonly.data, X_readonly.indices, X_readonly.indptr)
+        )
+
+    y_readonly = create_memmap_backed_data(np.array(y_small, dtype=tree._tree.DTYPE))
+    est = ALL_TREES[name](splitter=splitter)
+    est.fit(X_readonly, y_readonly)
+    assert_array_equal(est.predict(X_readonly), est.predict(X_small))
+    assert_array_equal(
+        est.decision_path(X_readonly).todense(), est.decision_path(X_small).todense()
+    )
+
+
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse", "poisson"])
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+def test_balance_property(criterion, Tree):
+    # Test that sum(y_pred)=sum(y_true) on training set.
+    # This works if the mean is predicted (should even be true for each leaf).
+    # MAE predicts the median and is therefore excluded from this test.
+
+    # Choose a training set with non-negative targets (for poisson)
+    X, y = diabetes.data, diabetes.target
+    reg = Tree(criterion=criterion)
+    reg.fit(X, y)
+    assert np.sum(reg.predict(X)) == pytest.approx(np.sum(y))
+
+
+@pytest.mark.parametrize("seed", range(3))
+def test_poisson_zero_nodes(seed):
+    # Test that sum(y)=0 and therefore y_pred=0 is forbidden on nodes.
+    X = [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 2], [1, 2], [1, 3]]
+    y = [0, 0, 0, 0, 1, 2, 3, 4]
+    # Note that X[:, 0] == 0 is a 100% indicator for y == 0. The tree can
+    # easily learn that:
+    reg = DecisionTreeRegressor(criterion="squared_error", random_state=seed)
+    reg.fit(X, y)
+    assert np.amin(reg.predict(X)) == 0
+    # whereas Poisson must predict strictly positive numbers
+    reg = DecisionTreeRegressor(criterion="poisson", random_state=seed)
+    reg.fit(X, y)
+    assert np.all(reg.predict(X) > 0)
+
+    # Test additional dataset where something could go wrong.
+    n_features = 10
+    X, y = datasets.make_regression(
+        effective_rank=n_features * 2 // 3,
+        tail_strength=0.6,
+        n_samples=1_000,
+        n_features=n_features,
+        n_informative=n_features * 2 // 3,
+        random_state=seed,
+    )
+    # some excess zeros
+    y[(-1 < y) & (y < 0)] = 0
+    # make sure the target is positive
+    y = np.abs(y)
+    reg = DecisionTreeRegressor(criterion="poisson", random_state=seed)
+    reg.fit(X, y)
+    assert np.all(reg.predict(X) > 0)
+
+
+def test_poisson_vs_mse():
+    # For a Poisson distributed target, Poisson loss should give better results
+    # than squared error measured in Poisson deviance as metric.
+    # We have a similar test, test_poisson(), in
+    # sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+    rng = np.random.RandomState(42)
+    n_train, n_test, n_features = 500, 500, 10
+    X = datasets.make_low_rank_matrix(
+        n_samples=n_train + n_test, n_features=n_features, random_state=rng
+    )
+    # We create a log-linear Poisson model and downscale coef as it will get
+    # exponentiated.
+    coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
+    y = rng.poisson(lam=np.exp(X @ coef))
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=n_test, random_state=rng
+    )
+    # We prevent some overfitting by setting min_samples_split=10.
+    tree_poi = DecisionTreeRegressor(
+        criterion="poisson", min_samples_split=10, random_state=rng
+    )
+    tree_mse = DecisionTreeRegressor(
+        criterion="squared_error", min_samples_split=10, random_state=rng
+    )
+
+    tree_poi.fit(X_train, y_train)
+    tree_mse.fit(X_train, y_train)
+    dummy = DummyRegressor(strategy="mean").fit(X_train, y_train)
+
+    for X, y, val in [(X_train, y_train, "train"), (X_test, y_test, "test")]:
+        metric_poi = mean_poisson_deviance(y, tree_poi.predict(X))
+        # squared_error might produce non-positive predictions => clip
+        metric_mse = mean_poisson_deviance(y, np.clip(tree_mse.predict(X), 1e-15, None))
+        metric_dummy = mean_poisson_deviance(y, dummy.predict(X))
+        # As squared_error might correctly predict 0 in train set, its train
+        # score can be better than Poisson. This is no longer the case for the
+        # test set.
+        if val == "test":
+            assert metric_poi < 0.5 * metric_mse
+        assert metric_poi < 0.75 * metric_dummy
+
+
+@pytest.mark.parametrize("Tree", [DecisionTreeClassifier, ExtraTreeClassifier])
+@pytest.mark.parametrize("n_classes", [2, 4])
+def test_criterion_entropy_same_as_log_loss(Tree, n_classes):
+    """Test that criterion=entropy gives same as log_loss."""
+    n_samples, n_features = 50, 5
+    X, y = datasets.make_classification(
+        n_classes=n_classes,
+        n_samples=n_samples,
+        n_features=n_features,
+        n_informative=n_features,
+        n_redundant=0,
+        random_state=42,
+    )
+    tree_log_loss = Tree(criterion="log_loss", random_state=43).fit(X, y)
+    tree_entropy = Tree(criterion="entropy", random_state=43).fit(X, y)
+
+    assert_tree_equal(
+        tree_log_loss.tree_,
+        tree_entropy.tree_,
+        f"{Tree!r} with criterion 'entropy' and 'log_loss' gave different trees.",
+    )
+    assert_allclose(tree_log_loss.predict(X), tree_entropy.predict(X))
+
+
+def test_different_endianness_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def reduce_ndarray(arr):
+        return arr.byteswap().view(arr.dtype.newbyteorder()).__reduce__()
+
+    def get_pickle_non_native_endianness():
+        f = io.BytesIO()
+        p = pickle.Pickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[np.ndarray] = reduce_ndarray
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = pickle.load(get_pickle_non_native_endianness())
+    new_score = new_clf.score(X, y)
+    assert np.isclose(score, new_score)
+
+
+def test_different_endianness_joblib_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    class NonNativeEndiannessNumpyPickler(NumpyPickler):
+        def save(self, obj):
+            if isinstance(obj, np.ndarray):
+                obj = obj.byteswap().view(obj.dtype.newbyteorder())
+            super().save(obj)
+
+    def get_joblib_pickle_non_native_endianness():
+        f = io.BytesIO()
+        p = NonNativeEndiannessNumpyPickler(f)
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = joblib.load(get_joblib_pickle_non_native_endianness())
+    new_score = new_clf.score(X, y)
+    assert np.isclose(score, new_score)
+
+
+def get_different_bitness_node_ndarray(node_ndarray):
+    new_dtype_for_indexing_fields = np.int64 if _IS_32BIT else np.int32
+
+    # field names in Node struct with SIZE_t types (see sklearn/tree/_tree.pxd)
+    indexing_field_names = ["left_child", "right_child", "feature", "n_node_samples"]
+
+    new_dtype_dict = {
+        name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()
+    }
+    for name in indexing_field_names:
+        new_dtype_dict[name] = new_dtype_for_indexing_fields
+
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+    return node_ndarray.astype(new_dtype, casting="same_kind")
+
+
+def get_different_alignment_node_ndarray(node_ndarray):
+    new_dtype_dict = {
+        name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()
+    }
+    offsets = [offset for dtype, offset in node_ndarray.dtype.fields.values()]
+    shifted_offsets = [8 + offset for offset in offsets]
+
+    new_dtype = np.dtype(
+        {
+            "names": list(new_dtype_dict.keys()),
+            "formats": list(new_dtype_dict.values()),
+            "offsets": shifted_offsets,
+        }
+    )
+    return node_ndarray.astype(new_dtype, casting="same_kind")
+
+
+def reduce_tree_with_different_bitness(tree):
+    new_dtype = np.int64 if _IS_32BIT else np.int32
+    tree_cls, (n_features, n_classes, n_outputs), state = tree.__reduce__()
+    new_n_classes = n_classes.astype(new_dtype, casting="same_kind")
+
+    new_state = state.copy()
+    new_state["nodes"] = get_different_bitness_node_ndarray(new_state["nodes"])
+
+    return (tree_cls, (n_features, new_n_classes, n_outputs), new_state)
+
+
+def test_different_bitness_pickle():
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def pickle_dump_with_different_bitness():
+        f = io.BytesIO()
+        p = pickle.Pickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[CythonTree] = reduce_tree_with_different_bitness
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = pickle.load(pickle_dump_with_different_bitness())
+    new_score = new_clf.score(X, y)
+    assert score == pytest.approx(new_score)
+
+
+def test_different_bitness_joblib_pickle():
+    # Make sure that a platform specific pickle generated on a 64 bit
+    # platform can be converted at pickle load time into an estimator
+    # with Cython code that works with the host's native integer precision
+    # to index nodes in the tree data structure when the host is a 32 bit
+    # platform (and vice versa).
+    X, y = datasets.make_classification(random_state=0)
+
+    clf = DecisionTreeClassifier(random_state=0, max_depth=3)
+    clf.fit(X, y)
+    score = clf.score(X, y)
+
+    def joblib_dump_with_different_bitness():
+        f = io.BytesIO()
+        p = NumpyPickler(f)
+        p.dispatch_table = copyreg.dispatch_table.copy()
+        p.dispatch_table[CythonTree] = reduce_tree_with_different_bitness
+
+        p.dump(clf)
+        f.seek(0)
+        return f
+
+    new_clf = joblib.load(joblib_dump_with_different_bitness())
+    new_score = new_clf.score(X, y)
+    assert score == pytest.approx(new_score)
+
+
+def test_check_n_classes():
+    expected_dtype = np.dtype(np.int32) if _IS_32BIT else np.dtype(np.int64)
+    allowed_dtypes = [np.dtype(np.int32), np.dtype(np.int64)]
+    allowed_dtypes += [dt.newbyteorder() for dt in allowed_dtypes]
+
+    n_classes = np.array([0, 1], dtype=expected_dtype)
+    for dt in allowed_dtypes:
+        _check_n_classes(n_classes.astype(dt), expected_dtype)
+
+    with pytest.raises(ValueError, match="Wrong dimensions.+n_classes"):
+        wrong_dim_n_classes = np.array([[0, 1]], dtype=expected_dtype)
+        _check_n_classes(wrong_dim_n_classes, expected_dtype)
+
+    with pytest.raises(ValueError, match="n_classes.+incompatible dtype"):
+        wrong_dtype_n_classes = n_classes.astype(np.float64)
+        _check_n_classes(wrong_dtype_n_classes, expected_dtype)
+
+
+def test_check_value_ndarray():
+    expected_dtype = np.dtype(np.float64)
+    expected_shape = (5, 1, 2)
+    value_ndarray = np.zeros(expected_shape, dtype=expected_dtype)
+
+    allowed_dtypes = [expected_dtype, expected_dtype.newbyteorder()]
+
+    for dt in allowed_dtypes:
+        _check_value_ndarray(
+            value_ndarray, expected_dtype=dt, expected_shape=expected_shape
+        )
+
+    with pytest.raises(ValueError, match="Wrong shape.+value array"):
+        _check_value_ndarray(
+            value_ndarray, expected_dtype=expected_dtype, expected_shape=(1, 2)
+        )
+
+    for problematic_arr in [value_ndarray[:, :, :1], np.asfortranarray(value_ndarray)]:
+        with pytest.raises(ValueError, match="value array.+C-contiguous"):
+            _check_value_ndarray(
+                problematic_arr,
+                expected_dtype=expected_dtype,
+                expected_shape=problematic_arr.shape,
+            )
+
+    with pytest.raises(ValueError, match="value array.+incompatible dtype"):
+        _check_value_ndarray(
+            value_ndarray.astype(np.float32),
+            expected_dtype=expected_dtype,
+            expected_shape=expected_shape,
+        )
+
+
+def test_check_node_ndarray():
+    expected_dtype = NODE_DTYPE
+
+    node_ndarray = np.zeros((5,), dtype=expected_dtype)
+
+    valid_node_ndarrays = [
+        node_ndarray,
+        get_different_bitness_node_ndarray(node_ndarray),
+        get_different_alignment_node_ndarray(node_ndarray),
+    ]
+    valid_node_ndarrays += [
+        arr.astype(arr.dtype.newbyteorder()) for arr in valid_node_ndarrays
+    ]
+
+    for arr in valid_node_ndarrays:
+        _check_node_ndarray(node_ndarray, expected_dtype=expected_dtype)
+
+    with pytest.raises(ValueError, match="Wrong dimensions.+node array"):
+        problematic_node_ndarray = np.zeros((5, 2), dtype=expected_dtype)
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    with pytest.raises(ValueError, match="node array.+C-contiguous"):
+        problematic_node_ndarray = node_ndarray[::2]
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    dtype_dict = {name: dtype for name, (dtype, _) in node_ndarray.dtype.fields.items()}
+
+    # array with wrong 'threshold' field dtype (int64 rather than float64)
+    new_dtype_dict = dtype_dict.copy()
+    new_dtype_dict["threshold"] = np.int64
+
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+    problematic_node_ndarray = node_ndarray.astype(new_dtype)
+
+    with pytest.raises(ValueError, match="node array.+incompatible dtype"):
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+    # array with wrong 'left_child' field dtype (float64 rather than int64 or int32)
+    new_dtype_dict = dtype_dict.copy()
+    new_dtype_dict["left_child"] = np.float64
+    new_dtype = np.dtype(
+        {"names": list(new_dtype_dict.keys()), "formats": list(new_dtype_dict.values())}
+    )
+
+    problematic_node_ndarray = node_ndarray.astype(new_dtype)
+
+    with pytest.raises(ValueError, match="node array.+incompatible dtype"):
+        _check_node_ndarray(problematic_node_ndarray, expected_dtype=expected_dtype)
+
+
+@pytest.mark.parametrize(
+    "Splitter", chain(DENSE_SPLITTERS.values(), SPARSE_SPLITTERS.values())
+)
+def test_splitter_serializable(Splitter):
+    """Check that splitters are serializable."""
+    rng = np.random.RandomState(42)
+    max_features = 10
+    n_outputs, n_classes = 2, np.array([3, 2], dtype=np.intp)
+
+    criterion = CRITERIA_CLF["gini"](n_outputs, n_classes)
+    splitter = Splitter(criterion, max_features, 5, 0.5, rng, monotonic_cst=None)
+    splitter_serialize = pickle.dumps(splitter)
+
+    splitter_back = pickle.loads(splitter_serialize)
+    assert splitter_back.max_features == max_features
+    assert isinstance(splitter_back, Splitter)
+
+
+def test_tree_deserialization_from_read_only_buffer(tmpdir):
+    """Check that Trees can be deserialized with read only buffers.
+
+    Non-regression test for gh-25584.
+    """
+    pickle_path = str(tmpdir.join("clf.joblib"))
+    clf = DecisionTreeClassifier(random_state=0)
+    clf.fit(X_small, y_small)
+
+    joblib.dump(clf, pickle_path)
+    loaded_clf = joblib.load(pickle_path, mmap_mode="r")
+
+    assert_tree_equal(
+        loaded_clf.tree_,
+        clf.tree_,
+        "The trees of the original and loaded classifiers are not equal.",
+    )
+
+
+@pytest.mark.parametrize("Tree", ALL_TREES.values())
+def test_min_sample_split_1_error(Tree):
+    """Check that an error is raised when min_sample_split=1.
+
+    non-regression test for issue gh-25481.
+    """
+    X = np.array([[0, 0], [1, 1]])
+    y = np.array([0, 1])
+
+    # min_samples_split=1.0 is valid
+    Tree(min_samples_split=1.0).fit(X, y)
+
+    # min_samples_split=1 is invalid
+    tree = Tree(min_samples_split=1)
+    msg = (
+        r"'min_samples_split' .* must be an int in the range \[2, inf\) "
+        r"or a float in the range \(0.0, 1.0\]"
+    )
+    with pytest.raises(ValueError, match=msg):
+        tree.fit(X, y)
+
+
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_missing_values_best_splitter_on_equal_nodes_no_missing(criterion):
+    """Check missing values goes to correct node during predictions."""
+    X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T
+    y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6])
+
+    dtc = DecisionTreeRegressor(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X, y)
+
+    # Goes to right node because it has the most data points
+    y_pred = dtc.predict([[np.nan]])
+    assert_allclose(y_pred, [np.mean(y[-5:])])
+
+    # equal number of elements in both nodes
+    X_equal = X[:-1]
+    y_equal = y[:-1]
+
+    dtc = DecisionTreeRegressor(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X_equal, y_equal)
+
+    # Goes to right node because the implementation sets:
+    # missing_go_to_left = n_left > n_right, which is False
+    y_pred = dtc.predict([[np.nan]])
+    assert_allclose(y_pred, [np.mean(y_equal[-4:])])
+
+
+@pytest.mark.parametrize("seed", range(3))
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_missing_values_random_splitter_on_equal_nodes_no_missing(criterion, seed):
+    """Check missing values go to the correct node during predictions for ExtraTree.
+
+    Since ETC use random splits, we use different seeds to verify that the
+    left/right node is chosen correctly when the splits occur.
+    """
+    X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T
+    y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6])
+
+    etr = ExtraTreeRegressor(random_state=seed, max_depth=1, criterion=criterion)
+    etr.fit(X, y)
+
+    # Get the left and right children of the root node
+    left_child = etr.tree_.children_left[0]
+    right_child = etr.tree_.children_right[0]
+
+    # Get the number of samples for the left and right children
+    left_samples = etr.tree_.weighted_n_node_samples[left_child]
+    right_samples = etr.tree_.weighted_n_node_samples[right_child]
+    went_left = left_samples > right_samples
+
+    # predictions
+    y_pred_left = etr.tree_.value[left_child][0]
+    y_pred_right = etr.tree_.value[right_child][0]
+
+    # Goes to node with the most data points
+    y_pred = etr.predict([[np.nan]])
+    if went_left:
+        assert_allclose(y_pred_left, y_pred)
+    else:
+        assert_allclose(y_pred_right, y_pred)
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_three_classes(criterion):
+    """Test when missing values are uniquely present in a class among 3 classes."""
+    missing_values_class = 0
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 8, 9, 11, 12]]).T
+    y = np.array([missing_values_class] * 4 + [1] * 4 + [2] * 4)
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 3, 12]]).T
+    y_nan_pred = dtc.predict(X_test)
+    # Missing values necessarily are associated to the observed class.
+    assert_array_equal(y_nan_pred, [missing_values_class, 1, 2])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_to_left(criterion):
+    """Missing values spanning only one class at fit-time must make missing
+    values at predict-time be classified has belonging to this class."""
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 4, 5]]).T
+    y = np.array([0] * 4 + [1] * 6)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 5, np.nan]]).T
+    y_pred = dtc.predict(X_test)
+
+    assert_array_equal(y_pred, [0, 1, 0])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_to_right(criterion):
+    """Missing values and non-missing values sharing one class at fit-time
+    must make missing values at predict-time be classified has belonging
+    to this class."""
+    X = np.array([[np.nan] * 4 + [0, 1, 2, 3, 4, 5]]).T
+    y = np.array([1] * 4 + [0] * 4 + [1] * 2)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=2, criterion=criterion)
+    dtc.fit(X, y)
+
+    X_test = np.array([[np.nan, 1.2, 4.8]]).T
+    y_pred = dtc.predict(X_test)
+
+    assert_array_equal(y_pred, [1, 0, 1])
+
+
+@pytest.mark.parametrize("criterion", ["entropy", "gini"])
+def test_missing_values_best_splitter_missing_both_classes_has_nan(criterion):
+    """Check behavior of missing value when there is one missing value in each class."""
+    X = np.array([[1, 2, 3, 5, np.nan, 10, 20, 30, 60, np.nan]]).T
+    y = np.array([0] * 5 + [1] * 5)
+
+    dtc = DecisionTreeClassifier(random_state=42, max_depth=1, criterion=criterion)
+    dtc.fit(X, y)
+    X_test = np.array([[np.nan, 2.3, 34.2]]).T
+    y_pred = dtc.predict(X_test)
+
+    # Missing value goes to the class at the right (here 1) because the implementation
+    # searches right first.
+    assert_array_equal(y_pred, [1, 0, 1])
+
+
+@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
+@pytest.mark.parametrize(
+    "tree",
+    [
+        DecisionTreeRegressor(criterion="absolute_error"),
+        ExtraTreeRegressor(criterion="absolute_error"),
+    ],
+)
+def test_missing_value_errors(sparse_container, tree):
+    """Check unsupported configurations for missing values."""
+
+    X = np.array([[1, 2, 3, 5, np.nan, 10, 20, 30, 60, np.nan]]).T
+    y = np.array([0] * 5 + [1] * 5)
+
+    if sparse_container is not None:
+        X = sparse_container(X)
+
+    with pytest.raises(ValueError, match="Input X contains NaN"):
+        tree.fit(X, y)
+
+
+@pytest.mark.parametrize("Tree", REG_TREES.values())
+def test_missing_values_poisson(Tree):
+    """Smoke test for poisson regression and missing values."""
+    X, y = diabetes.data.copy(), diabetes.target
+
+    # Set some values missing
+    X[::5, 0] = np.nan
+    X[::6, -1] = np.nan
+
+    reg = Tree(criterion="poisson", random_state=42)
+    reg.fit(X, y)
+
+    y_pred = reg.predict(X)
+    assert (y_pred >= 0.0).all()
+
+
+def make_friedman1_classification(*args, **kwargs):
+    X, y = datasets.make_friedman1(*args, **kwargs)
+    y = y > 14
+    return X, y
+
+
+@pytest.mark.parametrize(
+    "make_data, Tree, tolerance",
+    [
+        # Due to the sine link between X and y, we expect the native handling of
+        # missing values to always be better than the naive mean imputation in the
+        # regression case.
+        #
+        # Due to randomness in ExtraTree, we expect the native handling of missing
+        # values to be sometimes better than the naive mean imputation, but not always
+        (datasets.make_friedman1, DecisionTreeRegressor, 0),
+        (datasets.make_friedman1, ExtraTreeRegressor, 0.07),
+        (make_friedman1_classification, DecisionTreeClassifier, 0.03),
+        (make_friedman1_classification, ExtraTreeClassifier, 0.12),
+    ],
+)
+@pytest.mark.parametrize("sample_weight_train", [None, "ones"])
+def test_missing_values_is_resilience(
+    make_data, Tree, sample_weight_train, global_random_seed, tolerance
+):
+    """Check that trees can deal with missing values have decent performance."""
+    n_samples, n_features = 5_000, 10
+    X, y = make_data(
+        n_samples=n_samples,
+        n_features=n_features,
+        noise=1.0,
+        random_state=global_random_seed,
+    )
+
+    X_missing = X.copy()
+    rng = np.random.RandomState(global_random_seed)
+    X_missing[rng.choice([False, True], size=X.shape, p=[0.9, 0.1])] = np.nan
+    X_missing_train, X_missing_test, y_train, y_test = train_test_split(
+        X_missing, y, random_state=global_random_seed
+    )
+    if sample_weight_train == "ones":
+        sample_weight = np.ones(X_missing_train.shape[0])
+    else:
+        sample_weight = None
+
+    # max_depth is used to avoid overfitting and also improve the runtime
+    # of the test.
+    max_depth = 10
+    native_tree = Tree(max_depth=max_depth, random_state=global_random_seed)
+    native_tree.fit(X_missing_train, y_train, sample_weight=sample_weight)
+    score_native_tree = native_tree.score(X_missing_test, y_test)
+
+    tree_with_imputer = make_pipeline(
+        SimpleImputer(), Tree(max_depth=max_depth, random_state=global_random_seed)
+    )
+    tree_with_imputer.fit(X_missing_train, y_train)
+    score_tree_with_imputer = tree_with_imputer.score(X_missing_test, y_test)
+
+    assert score_native_tree + tolerance > score_tree_with_imputer, (
+        f"{score_native_tree=} + {tolerance} should be strictly greater than"
+        f" {score_tree_with_imputer}"
+    )
+
+
+# A single ExtraTree will randomly send missing values down the left, or right child,
+# and therefore will not necessarily have the same performance as the greedy
+# handling of missing values.
+@pytest.mark.parametrize("Tree, expected_score", zip(CLF_TREES.values(), [0.85, 0.53]))
+def test_missing_value_is_predictive(Tree, expected_score, global_random_seed):
+    """Check the tree learns when only the missing value is predictive."""
+    rng = np.random.RandomState(0)
+    n_samples = 500
+
+    X = rng.standard_normal(size=(n_samples, 20))
+    y = np.concatenate([np.zeros(n_samples // 2), np.ones(n_samples // 2)])
+    # y = rng.randint(0, high=2, size=n_samples)
+
+    # Create a predictive feature using `y` and with some noise
+    X_random_mask = rng.choice([False, True], size=n_samples, p=[0.95, 0.05])
+    y_mask = y.copy().astype(bool)
+    y_mask[X_random_mask] = ~y_mask[X_random_mask]
+
+    X_predictive = rng.standard_normal(size=n_samples)
+    X_predictive[y_mask] = np.nan
+
+    X[:, 5] = X_predictive
+
+    tree = Tree(random_state=global_random_seed)
+
+    # Check that the tree can learn the predictive feature
+    # over an average of cross-validation fits.
+    tree_cv_score = cross_val_score(tree, X, y, cv=5).mean()
+    assert tree_cv_score >= expected_score, (
+        f"Expected CV score: {expected_score} but got {tree_cv_score}"
+    )
+
+
+@pytest.mark.parametrize(
+    "make_data, Tree",
+    [
+        (datasets.make_regression, DecisionTreeRegressor),
+        (datasets.make_classification, DecisionTreeClassifier),
+    ],
+)
+def test_sample_weight_non_uniform(make_data, Tree):
+    """Check sample weight is correctly handled with missing values."""
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 1000, 10
+    X, y = make_data(n_samples=n_samples, n_features=n_features, random_state=rng)
+
+    # Create dataset with missing values
+    X[rng.choice([False, True], size=X.shape, p=[0.9, 0.1])] = np.nan
+
+    # Zero sample weight is the same as removing the sample
+    sample_weight = np.ones(X.shape[0])
+    sample_weight[::2] = 0.0
+
+    tree_with_sw = Tree(random_state=0)
+    tree_with_sw.fit(X, y, sample_weight=sample_weight)
+
+    tree_samples_removed = Tree(random_state=0)
+    tree_samples_removed.fit(X[1::2, :], y[1::2])
+
+    assert_allclose(tree_samples_removed.predict(X), tree_with_sw.predict(X))
+
+
+def test_deterministic_pickle():
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/27268
+    # Uninitialised memory would lead to the two pickle strings being different.
+    tree1 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+    tree2 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+
+    pickle1 = pickle.dumps(tree1)
+    pickle2 = pickle.dumps(tree2)
+
+    assert pickle1 == pickle2
+
+
+@pytest.mark.parametrize("Tree", [DecisionTreeRegressor, ExtraTreeRegressor])
+@pytest.mark.parametrize(
+    "X",
+    [
+        # missing values will go left for greedy splits
+        np.array([np.nan, 2, np.nan, 4, 5, 6]),
+        np.array([np.nan, np.nan, 3, 4, 5, 6]),
+        # missing values will go right for greedy splits
+        np.array([1, 2, 3, 4, np.nan, np.nan]),
+        np.array([1, 2, 3, np.nan, 6, np.nan]),
+    ],
+)
+@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"])
+def test_regression_tree_missing_values_toy(Tree, X, criterion):
+    """Check that we properly handle missing values in regression trees using a toy
+    dataset.
+
+    The regression targeted by this test was that we were not reinitializing the
+    criterion when it comes to the number of missing values. Therefore, the value
+    of the critetion (i.e. MSE) was completely wrong.
+
+    This test check that the MSE is null when there is a single sample in the leaf.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28254
+    https://github.com/scikit-learn/scikit-learn/issues/28316
+    """
+    X = X.reshape(-1, 1)
+    y = np.arange(6)
+
+    tree = Tree(criterion=criterion, random_state=0).fit(X, y)
+    tree_ref = clone(tree).fit(y.reshape(-1, 1), y)
+
+    impurity = tree.tree_.impurity
+    assert all(impurity >= 0), impurity.min()  # MSE should always be positive
+
+    # Check the impurity match after the first split
+    assert_allclose(tree.tree_.impurity[:2], tree_ref.tree_.impurity[:2])
+
+    # Find the leaves with a single sample where the MSE should be 0
+    leaves_idx = np.flatnonzero(
+        (tree.tree_.children_left == -1) & (tree.tree_.n_node_samples == 1)
+    )
+    assert_allclose(tree.tree_.impurity[leaves_idx], 0.0)
+
+
+def test_regression_extra_tree_missing_values_toy(global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 100
+    X = np.arange(n_samples, dtype=np.float64).reshape(-1, 1)
+    X[-20:, :] = np.nan
+    rng.shuffle(X)
+    y = np.arange(n_samples)
+
+    tree = ExtraTreeRegressor(random_state=global_random_seed, max_depth=5).fit(X, y)
+
+    impurity = tree.tree_.impurity
+    assert all(impurity >= 0), impurity  # MSE should always be positive
+
+
+def test_classification_tree_missing_values_toy():
+    """Check that we properly handle missing values in classification trees using a toy
+    dataset.
+
+    The test is more involved because we use a case where we detected a regression
+    in a random forest. We therefore define the seed and bootstrap indices to detect
+    one of the non-frequent regression.
+
+    Here, we check that the impurity is null or positive in the leaves.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/28254
+    """
+    X, y = datasets.load_iris(return_X_y=True)
+
+    rng = np.random.RandomState(42)
+    X_missing = X.copy()
+    mask = rng.binomial(
+        n=np.ones(shape=(1, 4), dtype=np.int32), p=X[:, [2]] / 8
+    ).astype(bool)
+    X_missing[mask] = np.nan
+    X_train, _, y_train, _ = train_test_split(X_missing, y, random_state=13)
+
+    # fmt: off
+    # no black reformatting for this specific array
+    indices = np.array([
+        2, 81, 39, 97, 91, 38, 46, 31, 101, 13, 89, 82, 100, 42, 69, 27, 81, 16, 73, 74,
+        51, 47, 107, 17, 75, 110, 20, 15, 104, 57, 26, 15, 75, 79, 35, 77, 90, 51, 46,
+        13, 94, 91, 23, 8, 93, 93, 73, 77, 12, 13, 74, 109, 110, 24, 10, 23, 104, 27,
+        92, 52, 20, 109, 8, 8, 28, 27, 35, 12, 12, 7, 43, 0, 30, 31, 78, 12, 24, 105,
+        50, 0, 73, 12, 102, 105, 13, 31, 1, 69, 11, 32, 75, 90, 106, 94, 60, 56, 35, 17,
+        62, 85, 81, 39, 80, 16, 63, 6, 80, 84, 3, 3, 76, 78
+    ], dtype=np.int32)
+    # fmt: on
+
+    tree = DecisionTreeClassifier(
+        max_depth=3, max_features="sqrt", random_state=1857819720
+    )
+    tree.fit(X_train[indices], y_train[indices])
+    assert all(tree.tree_.impurity >= 0)
+
+    leaves_idx = np.flatnonzero(
+        (tree.tree_.children_left == -1) & (tree.tree_.n_node_samples == 1)
+    )
+    assert_allclose(tree.tree_.impurity[leaves_idx], 0.0)
+
+
+def test_build_pruned_tree_py():
+    """Test pruning a tree with the Python caller of the Cythonized prune tree."""
+    tree = DecisionTreeClassifier(random_state=0, max_depth=1)
+    tree.fit(iris.data, iris.target)
+
+    n_classes = np.atleast_1d(tree.n_classes_)
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+
+    # only keep the root note
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[0] = 1
+    _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+
+    assert tree.tree_.node_count == 3
+    assert pruned_tree.node_count == 1
+    with pytest.raises(AssertionError):
+        assert_array_equal(tree.tree_.value, pruned_tree.value)
+    assert_array_equal(tree.tree_.value[0], pruned_tree.value[0])
+
+    # now keep all the leaves
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[1:] = 1
+
+    # Prune the tree
+    _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+    assert tree.tree_.node_count == 3
+    assert pruned_tree.node_count == 3, pruned_tree.node_count
+    assert_array_equal(tree.tree_.value, pruned_tree.value)
+
+
+def test_build_pruned_tree_infinite_loop():
+    """Test pruning a tree does not result in an infinite loop."""
+
+    # Create a tree with root and two children
+    tree = DecisionTreeClassifier(random_state=0, max_depth=1)
+    tree.fit(iris.data, iris.target)
+    n_classes = np.atleast_1d(tree.n_classes_)
+    pruned_tree = CythonTree(tree.n_features_in_, n_classes, tree.n_outputs_)
+
+    # only keeping one child as a leaf results in an improper tree
+    leave_in_subtree = np.zeros(tree.tree_.node_count, dtype=np.uint8)
+    leave_in_subtree[1] = 1
+    with pytest.raises(
+        ValueError, match="Node has reached a leaf in the original tree"
+    ):
+        _build_pruned_tree_py(pruned_tree, tree.tree_, leave_in_subtree)
+
+
+def test_sort_log2_build():
+    """Non-regression test for gh-30554.
+
+    Using log2 and log in sort correctly sorts feature_values, but the tie breaking is
+    different which can results in placing samples in a different order.
+    """
+    rng = np.random.default_rng(75)
+    some = rng.normal(loc=0.0, scale=10.0, size=10).astype(np.float32)
+    feature_values = np.concatenate([some] * 5)
+    samples = np.arange(50, dtype=np.intp)
+    _py_sort(feature_values, samples, 50)
+    # fmt: off
+    # no black reformatting for this specific array
+    expected_samples = [
+        0, 40, 30, 20, 10, 29, 39, 19, 49,  9, 45, 15, 35,  5, 25, 11, 31,
+        41,  1, 21, 22, 12,  2, 42, 32, 23, 13, 43,  3, 33,  6, 36, 46, 16,
+        26,  4, 14, 24, 34, 44, 27, 47,  7, 37, 17,  8, 38, 48, 28, 18
+    ]
+    # fmt: on
+    assert_array_equal(samples, expected_samples)